2
This assignment seeks to examine the environmental impacts to the work environment and workplace culture. This project is created to encourage good research and writing skills that are needed for assignments.
Your manager overheard you telling a colleague that you are taking a graduate-level organizational theory class at UMGC. He remembers his college course on organizational behavior and realizes that he found the knowledge he learned extremely useful over the years. Lately, he has felt that he has been overwhelmed with changes that require him and his employees to quickly respond to situations that he cannot always solve effectively. Experience has taught him that professional development is a lifelong endeavor. To that end he would like you to select some articles for him to read on the following topics: different work environments such as teams, virtual work environments and people; also how structure and workplace culture can influence management practices.
Instructions for this assignment:
Step 1: Using OneSearch in the UMGC online library, conduct a search for at least 3 scholarly articles that address work environments and workplace culture, and influences on management. The articles need to be current - published within the past five years.
Step 2: Create an annotated bibliography for each article. Guidance on writing an an
annotated bibliography can be found here
. The articles should be summarized in 2-3 paragraphs as the format suggests.Step 3: Then, aggregately analyze and explain the articles for the way in which internal and external factors cause change in workplace behavior. This section should comprise 2 paragraphs using multiple course readings and research to support how different work environments such as teams, virtual work environments and people influence the workplace and/or how the structure and workplace culture can influence management practices.
Step 4: Conclude the paper with a two to three paragraph examination of the selected articles and the impact to managing effectively. Be sure to clearly explain your reasoning for the selection and provide supporting examples and rationale from class readings related to the modern organization.
INSTRUCTIONS ON HOW TO PREPARE THE SUBMISSION:
Check the instructions to make sure ALL elements of the assignment have been covered.
Students are expected to use a variety as well as multiple course readings and research to support ideas, reasoning, and conclusions.
Follow APA style guidelines to ensure that your final submission includes a Title Page, Introduction, Conclusion, In-Text Citations and a Reference List. Consider using the
Online Guide to Writing and Research
or the Tutoring assistance - link above under Academic SupportParaphrase and do not use direct quotation marks. Check the UMGC Writing Center's
Guide to Paraphrasing
.This means you do not use more than four consecutive words from a source document, put a passage from a source document into your own words, and attribute the passage to the source document. Provide the page or paragraph number. Note that a reference within a reference list cannot exist without an associated in-text citation and vice versa.The analysis should begin with an introductory paragraph that clearly and succinctly explains what you intend to cover in the body of the paper.
The analysis should end with a conclusion/summary paragraph that clearly and succinctly explains what you covered in the body of the paper.
Third-person writing is required. Third-person means that there are no words such as “I, me, my, we, or us” (first-person writing), nor is there use of “you or your” (second-person writing). If uncertain how to write in the third person, view this link:
https://www.quickanddirtytips.com/education/grammar/first-second-and-third-person
Contractions are not used in business writing, so the expectation is that students do NOT use contractions in the assignment.
The paper must be double-spaced with 1-inch margins with 12 pt. font.
Submit the final project into the appropriate assignment submission folder.
NOTE: All submitted work is to be your original work. You may not use any work from another student, the Internet or an online clearinghouse. You are expected to understand the Academic Dishonesty and Plagiarism Policy and know that it is your responsibility to learn about instructor and general academic expectations with regard to proper citation of sources as specified in the APA Publication Manual, 7th Ed. (Students are held accountable for in-text citations and an associated reference list only).
Journalof Business and Behavioral Sciences
Vol 33, No 2; Fall 2021
11
DIVERSITY, EQUITY, AND INCLUSION POLICIES:
ARE ORGANIZATIONS TRULY COMMITTED TO A
WORKPLACE CULTURE SHIFT?
Bernadette
Baum
National UniversityABSTRACT
This paper proceeds from the premise that true change can only be realized after first
coming to terms with harsh realities. The murder of George Floyd in 2020 sent shock
waves throughout our collective conscience resulting in a racial reckoning unlike
any other in modern history. Calls for change throughout Corporate America had
organizations pledging millions of dollars toward the cause of racial justice. But
now, over one year later, has there been a significant change in workplace equality
following heightened awareness to diversity, equity, and inclusion policies in
organizations, or have we settled back into the status quo? This paper will examine
obstacles to achieving the level of workplace culture shift needed to claim a spot as
a true EEO employer. While generally addressing all legally protected
classifications, the paper will specifically focus on racial discrimination in the
workplace by exploring root causes of racism through a human behavioral lens.
Historical research and legal case studies have shown that racism can be found in all
areas of society and racial discrimination in the workplace has existed for numerous
decades, however, the Black Lives Matter movement and social unrest of 2020 have
found a platform at a time when all aspects of the issue are converging, thereby
making the time ripe for changes in legislation and challenging employers to
reimagine workplace policies on diversity, equity, and inclusion.
Key words: DE&I policies, diversity, equity, inclusion, systemic racism
INTRODUCTION
An unprecedented year in our nation, 2020 will claim a spot in history for a
convergence of high-profile events concerning civil rights issues beneath a backdrop
of a world-wide pandemic. On May 25, 2020, George Floyd, a 46-year-old black
man, was murdered in Minneapolis, Minnesota by a white police officer, Derek
Chauvin, while being arrested on suspicion of using a counterfeit $20 bill. The
following day, excruciatingly explicit videos made by witnesses and security
cameras went viral, striking a nerve in most everyone who watched them due to the
callous disregard for human life exhibited by the police officer. Floyd’s murder led
to world-wide protests against police brutality, police racism, and lack of police
accountability (Hill, et al., 2020). The event launched a modern-day civil rights
movement, re-energizing the Black Lives Matter movement, and mirroring the Civil
Rights movement of the 1960s. The movement resonated with millions of citizens
Baum12
of all races, creeds, and ages who either identified with the stories being reported
from people who had experienced similar treatment, or who had never experienced
such treatment but were struck with horror at how such actions could have
transpired. Despite being in the height of a pandemic, the horrific event propelled
citizens into action as they took to the streets in protest and participated in the
ongoing conversations on the internet. Statistics reveal that between May 26 and
June 7, 2020, the #BlackLivesMatter hashtag had been used roughly 47.8 million
times on Twitter – an average of 3.7 million times per day (Anderson, et al., 2020).
In response to demands for change from anti-racism advocacy groups, new
legislation continues to be passed in several states, as well as police reform bills.
President Biden’s passage of Juneteenth as a Federal holiday acknowledged historic
roots of racism (Pruitt-Young, 2021). Corporate America nationwide rose to the
challenge by pledging millions of dollars toward diversity, equity, and inclusion
(DE&I) programs and professing promises to do better. But have those promises
been kept? Or, has the momentum waned and the initiatives moved down the priority
list? Research shows that even the most genuine of efforts has met with challenges
and obstacles to creating the paradigm shift necessary to achieve positive change in
the area of equality in the workplace. Despite promises, companies are still behind.
The number of companies with a Chief Diversity Officer (CDO) has increased only
marginally in recent years, from 47 percent in 2018 to 52 percent as of February
2021. Many leaders in this space are realizing that pioneering this emerging field is
more challenging than expected and are quickly getting burned out (Gurchiek,
2021).
Through a reminder of key historical events in the history of the United States, this
paper analyzes not only the legal, but socio-psychological impacts of systemic
racism to determine the underlying reasons racial discrimination continues to occur
in the 21st century workplace. A starting point is to understand that history is not
repeating itself, rather, just resurfacing. Acknowledging the fact that racism has
never been uprooted - a consequence of not facing harsh truths – is a step in the
direction toward healing. The discussion will lead to an awareness of the challenges
faced in moving forward as well as highlight obstacles to implementing DE&I
workplace policies. New methods of training to comport with current updates in the
law will be explored with a focus on creating a culture of equality as a means of
fostering a diverse, equitable, and inclusive environment for all employees.
CHANGES IN DIVERSITY POLICIES
It is important to understand the expanding definitions of terms from former
diversity policies to current diversity, equity, and inclusion policies in workplace
settings, both from a legal and sociopsychological view.
Diversity. The basic definition of diversity is the differences between individuals,
based on any attribute, that may lead to the perception that another person is different
from the self (SHRM.org). From a legal policy perspective, considerations of
Journal of Business and Behavioral Sciences
13
disparate treatment, disparate impact, and stereotyping, among others, are reflected
in policymaking.
Disparate Treatment. Disparate treatment is defined as treating a similarly situated
employee differently because of prohibited Title VII or other employment
discrimination law factors.
Disparate Impact. Disparate impact refers to a deleterious effect of a facially
neutral policy on a Title VII group.
Stereotyping. Stereotyping is a standardized conception held in common by
members of a group. Assumptions are made based on such conceptions that do not
factually represent all members of a group.
According to Title VII of the Civil Rights Act of 1964, it shall be unlawful
employment practice for an employer –
(1) to fail or refuse to hire or to discharge any individual, or otherwise to discriminate against any individual with respect to his compensation, terms, conditions, or
privileges of employment, because of such individual’s race, color, national origin,
sex, or religion. [Title VII of the Civil Rights Act of 1964, as amended, 42 U.S.C. §
2000e-2(a).]
If we stop here, with the above law and theories of law in place, a person of color
may be hired for a position and not be exposed to any adverse actions by the
employer. But that same person of color may not be treated equitably or experience
inclusion in workplace groups in the same way as their similarly situated colleagues
experience equity and inclusion. The expanded DE&I arena is necessary to
holistically address systemic racist and sexist behaviors and implicit biases that have
become commonplace in the work environment in order to remedy toxic cultures in
the workplace.
Equity. A relative form of equality (equal treatment of individuals and groups) that
takes into consideration the needs and characteristics of the individuals, the context
of the situation, and circumstances that result in disparate outcomes
(SHRM.org).
Example. People of color represented in the highest levels of organizational
leadership nationwide is an abysmal number. Black people occupy only 3.2% of the
senior leadership roles at large companies in the United States and just 0.8 of all
Fortune 500 CEO positions (Brooks, K. J., 2019).
Pay equity is another example of an ongoing workplace issue with its roots based in
discrimination. Gender pay disparity continues to exist with women earning 82 cents
on every dollar that a similarly situated male earns, excepting black females who
earn 64 cents on every dollar, and Hispanic females earning 57 cents on every dollar
Baum14
of their similarly situated white males (Spiggle, T. 2021; AAUW 2021; Payscale
2021).Intersectionality. Experiencing more than one type of discrimination at a time, e.g.,
that of being black and female. Intersectionality adversely impacts various
populations of protected classes, illustrating the higher probability of discriminatory
behaviors involving, for example, racism and sexism occurring at the same time.
Inclusion. The extent to which individuals can access information and resources,
are involved in work groups, have the ability to influence decision-making
processes, and can contribute fully and effectively to an organization. “Inclusion” is
also defined as the fulfillment of needs for belongingness and uniqueness. According
to Optimal Distinctiveness Theory, employees’ needs of belongingness and
uniqueness must be met in order for employees to feel included. To feel included,
the unique characteristic of an employee must be valued within a group; more
importantly, though, this uniqueness the person brings to the group must be allowed
and encouraged to remain. Inclusive culture exists in the workplace when an
organizational environment allows people with multiple backgrounds, mindsets, and
ways of thinking to work effectively together and to perform to their highest
potential to achieve organizational objectives based on sound principles
(SHRM.org).Example. It is important to note that workplace protections from sexual orientation
and gender identity discrimination did not come to the federal arena until June 2020.
Before that time, while members of the LBGTQ+ community were protected from
workplace discrimination and harassment under some state laws, they were not
protected under the federal statute of Title VII of the Civil Rights Act of 1964 and,
as such, some members of the LQBTQ+ community were still shrouded in fear of
revealing how they identify regarding affinity orientation and gender identity.
Weaving DE&I policies into the fabric of the core federal workplace discrimination
statutes - Title VII of the Civil Rights Act of 1964; the Age Discrimination in
Employment Act (ADEA) of 1967; and the Americans with Disabilities Act (ADA)
of 1990 – while rolling out training and development policies is a full-circle
approach to the personal and professional development of employees and sends a
message from leadership that the company is committed to achieving and
maintaining a workplace culture of diversity, equity, and inclusion.
A LOOK AT THEN TO NOW
Beyond the legal and political arena, cries for equality were coming from all
segments of society during the social unrest of the 1960s. In pop culture, for
example, the Beatles did their part in helping to fight racism in the United States
when they refused to perform to a segregated audience in Jacksonville, Florida in
1964 (BBC News, 2011). Much of the music of the time reflected the need and
Journal of Business and Behavioral Sciences15
demand for change to address to inequities against Black Americans and women,
among other protected classes. It is no surprise that equal rights movements found a
voice through music, as “music bypasses the brain and resonates straight into the
heart where transformative change happens (Berson, 2020).
When Ruth Bader Ginsburg argued her first sex discrimination case in front of the
United States Supreme Court in 1973 in the case of Frontiero v. Richardson, she
cited abolitionist Sarah Grimké during her oral argument saying, “I ask no favor for
my sex. All I ask of our brethren is that they take their feet off our necks.” The
symbolism highlighting oppression against individuals based on their sex or race
was a testament to the fact that not much had changed since the turn of the 20th
century.
Fast forward to the 21st century and RBG’s statement could not be more figuratively
and literally relevant as when George Floyd was murdered by a knee to his neck.
Forcing society to look, once again, at how far we have come – or not come - in over
four centuries, revealed that old wounds continue to resurface because the necessary
work has not been done to eradicate systemic racism in our society.
Racism and sexism are intertwined and can only be uprooted at the same time
(Steinem, 2015). When examining the plight of people of color and women
throughout history, the same forms of oppression exist rooted in superiority and
patriarchal ideologies. The impact of racism and sexism is far reaching, affecting
every aspect of life from access to education, medical services, housing, and job
opportunities, among other areas, as illustrated below.
▪ According to the Brookings Institution, Black college graduates have higher debt loads, on average, than White college graduates. Black debt rises over time. White
debt diminishes. Upon graduation, the average Black graduate owes $23,400 vs. the
White graduate’s $16,000. Four years later, the gap triples. Even at the top end of
the income spectrum. Black students have higher student loans ($4,643, on average)
than White students ($3,835), and Black parents take out larger loans to help pay for
college - $3,303 vs. $1,903 (Brown, 2021).
▪ A county-level empirical analysis of structural racism and COVID-19 in the USA revealed that Black Americans as a community have experienced a long and well
documented history of exploitation and racial discrimination that has in turn
manifested in the form of persistent health disparities and preventable deaths (Bin
Shin, et al. 2021).
▪ In the first quarter of 2020, the Census Bureau reported that black households had the lowest homeownership rate at 44%, nearly 30 percentage points behind white
households. Racial discriminatory practices prevented people of color from
accumulating wealth through homeownership (Williams, 2020).
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▪ Sixty percent of employed Americans have experienced or witnessed discrimination at work on the grounds of race, gender, age, or LGBT identity (Srikanth, 2020). A
Gallup poll released in January 2021 found that of the roughly 2,000 Black
employees surveyed, 24% reported being discriminated against in their jobs in the
past year (Williams, 2021).
Specifically, regarding race discrimination in the workplace, a survey conducted by
the Society for Human Resource Management (SHRM) of 1,275 people in the U.S.
found that 49% of Black HR Professionals think that race-based discrimination
exists in their workplace, but only 13% of White HR Professionals agree. The same
survey found that 35% of Black workers say that such discrimination is part of their
workplace, while only 7% of White workers say that this is the case (Gurchiek,
2020). The findings from the report, The Journey to Equity and Inclusion suggest
that while workers agree that racial discrimination exists, there is a vast difference
in perception of how widespread the problem is, indicating a need for more
awareness and understanding of workplace racial inequality.
The Black Lives Matter movement heightened the need for employers to improve
their efforts toward workplace diversity policies. Efforts range across the spectrum
from employers outwardly advocating for change because it is the popular thing to
do in this climate but have no intention of walking the talk, to employers making
genuine efforts toward a paradigm shift in workplace culture but are finding the
challenge overwhelming.
OBSTACLES IN MOVING FORWARD
To engage in meaningful professional growth, a foundation of personal growth must
be present. Individuals lacking in this foundation may pose a major obstacle to
successful DE&I trainings because not every employee is in the same space with
regard to their level of personal growth and emotional intelligence. Every person is
shaped in some degree by their upbringing, whether cultural, religious, societal, or
combinations of all or more influences. Implicit biases and prejudices harbored
within are carried forward to the workplace. Individuals who do not possess a mature
level of emotional intelligence, may engage in acts that can be interpreted as racist
or sexist without realizing the impact of their actions on other individuals. As such,
some organizations may need to move forward in the DE&I space at a very basic
level.
A starting point would include examining the root causes of racism as a threshold
foundation. A look back in history reveals the scourge of slavery and its impact on
society over centuries and how the burden has plagued our nation, along with the
guilt of those actions weighing heavily on our collective conscience. Superiority
ideologies passed down from generation to generation are at the base in the
formation of racial prejudice. Without exposure to diversity and the plight of people
of color in society in general and in the workplace specifically, individuals cannot
Journal of Business and Behavioral Sciences17
gain the pertinent information or develop the necessary empathy to address such
issues and begin to remedy them.
Natural human behavior seeks to avoid these painful memories. At times it is easier
to live in denial. Further, when racist actions of violence and discrimination enter
our stream of consciousness, a human impulse is to excuse them away as not being
a problem anymore or, worse, not our problem. But intellectually we know and are
reminded by Dr. Martin Luther King, Jr. that, “Injustice anywhere is a threat to
justice everywhere” (King, Jr., 1963).
Egregious manifestations of racism and sexism are found by uncovering significant
events which have been expunged from history, leaving people unaware of the
perpetuation of racist and sexist actions passed down from generation to generation.
For example, until recently, most textbooks did not include historic events related to
racism such as the Tulsa Race Massacre of 1921. Before 60 Minutes ran a segment
last year of the Tulsa Race Massacre, much of the population had never heard of this
or other atrocities committed against African American communities.
Similarly, most textbooks omitted the participation of African American woman in
the United States space race of the 1960s. Neither had much of the population been
aware of the number of black women mathematicians and engineers instrumental in
sending a man into space 1965 until the appropriately titled movie Hidden Figures
hit the box office. Without this knowledge, a large segment of our society was left
uneducated as to the contributions to science made by African American women.
As if to indicate that if such events are excluded or erased or never spoken of, then
they must not have happened is at the root of oppression. This lack of accountability
has kept Black Americans and all people of color held back over centuries.
Moreover, the release of liability for the heinous crimes committed in the Tulsa
Massacre, for example, and atrocious coverup speaks to the enormity of moral
turpitude surrounding such events. Failure to be held liable through our justice
system, and failure to provide reparations for the victims is an example of the
citizenship plurality that our country was built on. It is rooted in our education and
criminal justice system, and systematically woven into popular culture.
Facing the harsh truths of racism and sexism requires a deep dive into the root causes
of such behaviors. Such exercises are not pleasant and can unearth our own implicit
biases and prejudices in a way that can cause us to examine our entire life beginning
with our familial upbringing and cultural influences and how such influences have
impacted every aspect of our life. Unless and until we do this work, we cannot move
forward. With truth comes change. Change is difficult, uncomfortable, uncertain,
and disruptive to our daily routine. Remaining in the status quo is simpler,
comfortable, secure, and orderly. The truth dismantles the status quo. It forces us to
face our own failings and challenges us to do better every day. But facing the truth
is not easy. It is easier to stay the same and continue to bury the truth down to the
bottom of our list of priorities to handle. As James Baldwin said, “Not everything
that is faced can be changed; but nothing can be changed until it is faced” (Baldwin,
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1962). While the task to achieving workplace equality may seem enormous, even
the smallest efforts toward equality are meaningful and a step in the direction of
creating positive change.
TRAINING METHODS REIMAGINED
Far too often and for far too long, diversity training methods have been compliance-
based, with a view to mitigating an employer’s exposure to legal liability. Most
training is perceived by employees as a mandatory task that takes time away from
their job duties and deadlines. Many employers are resentful for having to expend
resources to remain in compliance with labor and employment laws. Check-the-box
training - listening to video lectures in isolation, answering questions, passing the
test, receiving a certificate of completion – are the norm. After completion of the
training video, the employee does not have to think about diversity issues again for
another year or more. The employer, in turn, can check the box that the company
has satisfied the requirements of the law, thereby fulfilling legal compliance
responsibilities or be ready for any audit that may be conducted by an EEO agency.
The company has the necessary documentation to prove that the employees have
been trained in workplace diversity laws.
Learning about the elements of the law, however, is quite different from learning
how societal norms impact the behaviors of employees and leaders of an
organization. What has been missing in diversity training is a holistic approach to
the issues of diversity, equity, and inclusion. Tapping into the perceived culture of
the company can provide vital information that can be utilized to create necessary
interventions and preventive measures to restore the health of the entire organization
and all its employees.
STEPS FOR IMMEDIATE ACTION
Conduct a Climate Survey. As with any healthy relationship, the employer-
employee relationship should be built on a foundation of trust and respect. The
original definition of trust is alliance. If the HR Director is professing that the
company is an Equal Employment Opportunity (EEO) organization while individual
leaders of the company are overtly or covertly discriminating against employees,
engaging in retaliatory actions, or condoning such behaviors by inaction, employees
will know that the company’s “zero tolerance” policy is simply a façade, designed
to shield the organization from legal scrutiny. The policy then plays out as a false
commitment, and employees will realize that the leadership of the company is not
concerned about fairness, employee wellness, or maintaining a workplace free from
discrimination. The breakdown of trust will result in disillusionment and low
morale. If trust is lost, the employer-employee relationship shifts from cooperative
and collaborative to isolated and adversarial. Climate surveys can be very useful in
gauging the morale of employees, especially if employees are not inclined to be
forthcoming about problems based on distrust, fear of reprisals, or the existence of
a hostile atmosphere. An organizational development consultant can prepare and
Journal of Business and Behavioral Sciences19
administer the surveys independently and in a neutral environment. The results
should be shared with the entire organization along with concrete plans to address
critical issues and shortcomings.
Perform and Internal Pay Audit. Conduct a voluntary pay audit to proactively
assess any racial or gender-related disparities in compensation. Do not wait until a
complaint is filed or an EEO commission notifies the company of an audit.
Depending on the results of the audit, make immediate pay adjustments accordingly.
For example, if the audit reveals a 10% gender pay gap for similarly situated
employees in certain positions, then make a 10% adjustment to the adversely
affected group. This proactive approach will signal to employees that the company
is genuinely concerned about issues of inequity and is making a good faith effort to
initiate remedial actions.
Adjust Recruitment Policies. Findings from a report released in September 2021
based on an online survey of 1,115 North American organizational leaders
conducted in April and May 2021 revealed: Seventy four percent of all respondents
track the diversity of new hires; Sixty-four percent track the diversity of individuals
they recruit (SHRM.org, 2021). Tracking recruitment and selection data is critical
to a company’s DE&I commitments.
STEPS FOR ONGOING ACTION
Onboarding 1-month Class. A new employee’s perception of an organization is
formed in the first few weeks of employment. Conducting an onboarding training
session on Diversity, Equity, and Inclusion in a one-month-long format will be a
testament to the new employees that the DE&I statements professed in the
company’s mission and vision are in fact practiced in the workplace. While the class
is held over a period of one month, the time spent each week is only two hours for a
total of eight hours over the period of the month. Typically, diversity training is
approximately eight hours, but held in one session. The purpose of spreading the
time over a period of one month is to optimize the learning process by allowing time
for necessary reflections on the sensitive topics. The format and examples of
exercises are illustrated below:
Case Studies in a Group Setting. The time is ripe for meaningful, engaging
exercises in a group setting. Similar to taking an employment law class, case studies
should be utilized in a classroom format, to include group breakout sessions with a
subject matter expert facilitating the process. Time should be allowed for journal
reflections, along with voluntary sharing to enrich the learning process. Bystander
intervention could be incorporated into the case studies to illustrate in group sessions
how each person can find their voice and be given the tools to speak up.
Exercise – Reflection Papers. We all harbor implicit biases and prejudices carried
over from our upbringing, culture, and life experiences. In order to be able to
progress professionally, we must first work on our personal development. Facing
our fears and recording them in honest reflections is not an easy task. But when
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given as homework to do personally in a quiet environment, profound revelations
may occur. Then, later in a safe workplace workshop setting, employees may feel
the desire to share and by so doing become enlightened when hearing about
experiences of coworkers.
#1. Write a 2-Page Personal Reflection on the following topic:
What Do You Believe to Be the Root Cause of Racism?
#2. Write a 2-Page Personal Reflection on the following topic:
What Do You Believe to Be the Root Cause of Sexism?
Exercise – Cages. Examine the following excerpt of Oppression by Dr. Marilyn
Frye. Write your reflections in your journal.
Looking at discrimination issues is like looking at a wire birdcage. Look at the wires
closely and you can’t see why a bird can’t just fly around it. But look at it from
further away and you see that the wire you are viewing is only one of many
interconnected wires that form an impenetrable cage that keeps the bird in place.
With discrimination, each little piece may not seem very significant, but put them
together and they form a different existence for one group than another, which keeps
the group from progressing like those without the barriers.
Exercise – Stereotyping. Stereotyping weaves its divisive thread through all areas
of discrimination, sewing its seeds of superiority ideologies, the roots of which run
deep and perpetuate from generation to generation. Assumptions based on protective
classifications can create a deleterious impact on such groups.
Watch the video below: The Look
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.youtube.com_watch-
3Fv-
3DaC7lbdD1hq0&d=DwICAg&c=qwHaVVscXk_NBWd7DQFk0g&r=2GilTHcC
sqRmEHjaWl4fSA&m=pedHAzJXyJO8GjDrCLnK2LXVq7L-
cIoJpYYN6VN4gCE&s=kcAOBZmUvFNU_RtFa-sC7kMGqD3J5kpO-
Yd6e6Hu5nQ&e=
Discuss the observations you made while watching the video. How did you feel
while making your observations? What parts of the video, if any, stood out to you?
Were you surprised by the ending?
STEPS FOR LONG-TERM ACTION
EQ Training. HR professionals can utilize training methodologies associated with
emotional intelligence concepts to orient and train supervisors and non-supervisory
employees. Determining the format and venue of the training depends upon the size
Journal of Business and Behavioral Sciences21
of the group to be trained and the type of training to be administered. The
communication exercises can be rolled out in a “train the trainer” format for
leadership and top management first, then to all employees.
This new approach to training will produce a paradigm shift in workplace dynamics.
The process demands a significantly longer expenditure of time and effort than what
is required by law, but the preventive measures have considerable value that extend
beyond monetary benefits. The importance of additional time spent on meaningful
engagement cannot be overstated. The improvement to the company’s culture
through relationship-building exercises designed to foster authentic communication
will go a long way toward creating an environment of trust and respect. Once a
community of trust and respect is built, all the members of the community by their
behavior will set the tone for what is acceptable, and not acceptable, conduct.
CONCLUSION
DE&I efforts should not end once workers are hired. Leadership must regularly
monitor all related metrics and utilize the information implement change toward
continuous improvement. In order to fully realize a shift in workplace culture
surrounding diversity, equity, and inclusion, strong commitments by leadership at
the top levels must be evident and genuine. While there may be a long road ahead to
complete eradication of workplace discrimination and inequality, continuing the
conversation is imperative to effecting positive change.
REFERENCES
AAUW 2021 Update. The Simple Truth About the Gender Pay Gap.
Americans with Disabilities Act of 1990, ¶ 602, § 102.
Anderson, M., Barthel, M., Perrin, A., & Vogels, E. (2020) #BlackLivesMatter
surges on Twitter after George Floyd’s death. Pewresearch.org, June 10, 2020.
BBC News, (2021). The Beatles Banned Segregated Audiences, Contract Shows.
18 September 2021.
Bennett-Alexander, D., and Hartman, L. (2019). Employment Law for Business,
(9th ed.). NY: McGraw-Hill. ISBN 978-0-07-802379-8.
Berson, G. Z. (2020). Olivia on the Record: A Radical Experiment in Women’s
Music. Aunt Lute Books, 2020.
Bin Tan, S., DeSouza, P., Raifman, M. (2021). Structural Racism and COVID-19 in
the USA. Journal of Racial and Ethnic Health Disparities, Jan. 11, 2021.
Baum22
Brooks, K. J. (2019). Why So Many Black Business Professionals are Missing from
the C-suite. Moneywatch, CBS News, December 10, 2019.
Brown, D. A., (2021). College Isn’t the Solution for the Racial Wealth Gap. It’s Part of the
Problem. The Washington Post, April 9, 2021.
Connor, T., Fitzpatrick, S., & Abou-Sabe, K. Silent no more: Inside the USA Gymnastics
sex abuse scandal: Did USA Gymnastics try to silence McKayla, Aly and Maggie?
nbcnews.com, April 23, 2018.
Equal Employment Opportunity Commission. (1980). Guidelines on discrimination
because of sex. Federal Register, 45, 74676-74677.
Fitzgerald, L. F. (2003). Sexual harassment and social justice: Reflections on the distance
yet to go. American Psychologist, 58, 915-924.
Gurchiek, K. (2021). Are You Keeping Your DE&I Commitments? SHRM.Online, May
10, 2021.
Gurchiek, K. (2020). SHRM Research Finds Need for More Awareness, Understanding of
Racial Inequality. SHRM HR News, August 3, 2020.
Hill, E., Tiefenthäler, A., Triebert, C., Jordan, D., Willis, H., & Stein, R. (2020). How
George Floyd Was Killed in Police Custody. The New York Times, May 31, 2020.
Leskinen, E., Cortina, L., & Kabat, D. (2011). Gender harassment: Broadening our
understanding of sex-based harassment at work. Law & Human Behavior, 35, 25-
39.
Maddux, K. (2009). Women’s Suffrage and the Media. Feminist Media Studies, Vol. 9,
Issue1, pp.73-94, February 23, 2009.
McGee, S., & Moore, H., Women’s rights and their money: a timeline from Cleopatra to
Lilly Ledbetter. The Guardian, August 11, 2014.
Payscale, 2021. The State of the Gender Pay Gap in 2021, March 24, 2021.
Pruitt-Young, S., Slavery Didn’t End on Juneteenth: What You Should Know About This
Important Day. npr.org. June 17, 2021.
Shetterly, M. L. (2016). Hidden Figures: The Untold True Story of Four African American
Women Who Helped Launch Our Nation Into Space. William Morrow and
Company, 2016.
Journal of Business and Behavioral Sciences23
Sojo, V. E., Wood, R. E., & Genat, A. E. (2016). Harmful workplace experiences and
women’s occupational well-being: A meta-analysis. Psychology of Women’s
Quarterly, 40, 10-40.
Spiggle, T. (2021). The Gender Pay Gap: Why It’s Still Here. Forbes. May 26, 2021.
Srikanth, A. (2020). 60 Percent of Americans have seen or been victim of discrimination at
work, new study finds. The Hill, February 11, 2020.
Steinem, G. (2015). Gloria Steinem, My Life on the Road. Random House Publishing
Group, October 27, 2015.
Stoll, Ira. (1993). Ginsburg Blasts Harvard Law. The Harvard Crimson. July 23, 1993.
Turner, N. (2013). 10 Things That American Women Could Not Do Before the 1970s. Ms.
Magazine. May 28, 2013.
Williams, D. (2020). A Look at Housing Inequality and Racism in the U.S. Forbes.com.
June 3, 2020.
Williams, J. (2021). Gallup: One in four Black, Hispanic employees report workplace
discrimination. The Hill, January 12, 2021.
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GlobalBusiness and Management Research: An International Journal
Vol. 13, No. 4 (2021)
299
The Relationship between Human Resource
Practices and Employee Retention at a Private
University:
Work Environment as a Moderator
Shereen Noranee1, Rohana Mat Som2*, Nur Atiqah Adam3, Rozilah Abdul Aziz4 and Shafiq
Shahruddin5 1234
Universiti Teknologi MARA, Puncak Alam Campus, Selangor, Malaysia 5 Universiti Teknologi MARA, Arau Campus, Perlis, Malaysia
Email Address: rohana536@uitm.edu.my
*Corresponding author
Abstract
In present day, many organizations are bearing employees’ turnover fortune, where
organizations have to spend more to hire new people and provide training to them. For
employers, knowing how to preserve employees to remain within the organization is wiser
than to prevent them from quitting jobs. The objective of this study is to examine the
relationship between human resource practices and employee retention, with work
environment acts as a moderating variable. Respondents collected were among a private
university academic and non-academic staff and the sample size was 178 respondents.
Multiple hierarchical regression analysis was used to analyse the relationships among
variables. The results showed that supervisor support and compensation had significant
relationship with employee retention, while training did not. Interestingly, supervisor
support was found to have a negative relationship with employee retention. Meanwhile,
compensation was found to have the highest impact on employee retention. However, work
environment as the moderating variable, was found not affecting the relationship between
human resource practices and employee retention. The significant of this study would be to
assist the management to identify the human resource practices that would increase the
retention level of an employee such as training development, supervisor support and
compensation. In addition, by implementing good human resource practices, the
organization can attract and retain their employees.
Keywords: Employee retention, human resource practices, work environment
Introduction
Workers are the most essential asset of any organization. Workers might leave to other
organizations due to frustration when they cannot fully utilize their full potential or being
valued in the current workplace (Kakar, Raziq, & Khan, 2017). One of the ways that can be
done in order to reduce the possibility of employees in moving out of the organization is
through the implementation of human resource practices. An efficient human resource
practices can lower the level of employee intention to leave, thus will increase retention level.
However, it is not easy to retain an employee especially the competent one to stay loyal with
the organization and losses of such talent may give huge disadvantage to the
organization.
This is because when a worker leaves his/her current company, he/she will carry out with
him/her all the data about the organization, customers, project and previous history, very
often to competitors (Mahadi, Woo, Baskaran, & Yadi, 2020; Crosby-Hardin, 2020; Haider,
Rasli, Akhtar, Yusoff, Malik, Aamir, Arif, Naveed, & Tariq, 2015).
Studies have proven that in order to gain the competitive advantage in today’s highly volatile
mailto:rohana536@uitm.edu.myGlobal Business and Management Research: An International Journal
Vol. 13, No. 4 (2021)300
business environment, it is a must to have an effective human resource practices in the
organization as the practices play a significant role in retaining employees. Good HR
practices can help in minimizing employee turnover (Kloutsiniotis & Mihail, 2017; Zahoor,
Ijaz and Muzammil, 2015). To support this, human resource practices have been highlighted
as among of the determinants that influence employee decision to stay in their current
organization (Aburumman, Salleh, Omar, & Abadi, 2020; Ozkan, Elci, Erdilek Karabay,
Kitapci, & Garip, 2020; Kosi, Opoku-Danso, & Ofori, 2015).
Problem Statement
By having a good retention level, it can give a lot of advantages to the organization, for
example, reduce hiring and training cost. It also can improve employee morale where
employees who stay longer become more comfortable, less stressed and develop better
working relationships with their co-workers. Thus, this can give positive impact on the whole
organization. However, not all organizations in the industries secure high retention levels
among their employees.
The scenario of low retention level exists at one of private universities in Selangor (which
after this will be named as Universiti XYZ). Based on a personal interview with the
representative of Human Resource Department, the turnover rate at Universiti XYZ was
6.39% (Personal Interview, 12 June 2019). Several other staff were also interviewed where
they said they had the intention to leave if they got much more attractive offer from other
organizations that can fulfil their needs (Personal Interview, 12 June 2019). This shows that
Universiti XYZ was facing an issue regarding employee retention. Besides, according to
previous studies, there is least study regarding the influence of human practices on retention
that focuses on academic institutions. Most of the studies focused at other sectors such as
health care (Park & Min, 2020), tourism (Islam, Jantan, Yusoff, Chong, & Hossain, 2020),
employee age generation (Younas & Waseem Bari (2020), bank (Kakar, Raziq & Khan,
2017; Ldama & Bazza, 2015) and telecommunication (Zahoor, Ijaz & Muzammil, 2015;
Haider, et al., 2015). Due to that, the researchers were keen to do a study at the university by
focusing on three elements of human resource practices which are training development,
supervisor support, and compensation.
Even though training development, supervisor support and compensation are perceived as
among of important practices in retaining employees, there is another variable that has the
ability to moderate the relationship between training development, supervisor support and
employee retention which is work environment (Bibi, Ahmad, & Majid, 2018). This is due to
the empirical results on the effects work environment on employee retention appear mixed
(Naz, Li, Nisar, Khan, Ahmad, & Anwar, 2020; Yusliza, Faezah, Noor, Ramayah, Tanveer,
& Fawehinmi, 2020; Frimayasa, 2021; Luengalongkot, Chim, & Hongwiset, 2020). Based on
the contradictory findings of prior studies, the work environment is incorporated as a
moderator for the relationship between training and development, supervisor support, and
employee retention in the current study.
Literature Review
Employee Retention
Retaining employees plays an important role in any business firm because employees’ skills
and knowledge are central to companies’ ability to be economically competitive (Kryscynski,
Coff, & Campbell, 2021; Kossivi, Xu, & Kalgora, 2016). Employee retention is very
important in all business firms and therefore, having employee retention strategies will
increase the chance of long-term employees. Employee retention is essential in most
organizations. By having human resource practices putting in place, it would enhance the
Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)301
company’s profitability such as increased productivity level due to having satisfied
employees who are happy to work in such an organization. An increment in employee
retention will result in reduction of recruitment with that reason saving the business firm time
and expenses in recruitment and training (Köchling & Wehner, 2020; Wane, 2016).
Human Resource Practices
In order to retain employees in the organization, one of the ways that can be done by Human
Resource practitioners is through the implementation of human resource practices. HRM
practices can be defined as the strategies and policies executed by an organization to ensure
employees work productively to accomplish the organizational objectives and goals. In this
current competitive environment, human resource management practices play a significant
part in retaining employees, the most important asset of the organization (Malik, Baig, &
Manzoor, 2020; Leghari, Suleman, Leghari, & Aslam, 2014). According various studies,
some of human resource practices used in the organizations include supervisor support
(Abeysekera, 2007; Begum & Mohamed, 2016; Liew, Rahman, Patah & Rahman, 2016),
training development (Bibi Ahmad & Majid, 2018; Johari, Yean, Adnan, Yahya & Ahmad,
2012; Imna & Hassan, 2015) and compensation (Haider et al., 2015; Othman & Lembang,
2017; Hosain, 2016).
Training and Development
Based on a study conducted by Nguyen and Duong (2020), it shows that there was a strong
positive relationship between training and development element on employee retention. The
findings further state that employees believe that training and developing comprehensive
technical skills and professional skills which will be the best way to attract, remain, and
retain employees. Ahmad (2013) had done a study on the impact of training on employee
retention. The result of the finding shows that training had a positive relationship with
employee retention. It shows that training practices can influence employees’ decision to stay
loyal with their current organization. As specified by Othman and Lembang (2017), training
development had a significant positive relation with employees’ intention to stay. This result
shows that employees in the organization perceived training development as one of the ways
to enhance their knowledge and skills. Therefore, the first hypothesis for this study would be:
H1: There is a relationship between training development and
employee retention.
Supervisor Support
According to Khan, Abass, Khan, and Ahmad (2020), and Iqbal, Hongyun, Akhtar, Ahmad,
and Ankomah, (2020), there is a positive relationship between supervisor support and
employee retention. In line with a study conducted by Zafar (2015), it shows that there is
significant positive relationship between supervisor support and employee retention. It shows
that employee at the organization viewed supervisor support as one of the reasons they would
stay remained working in the organization. The result demonstrates that employee reward
alone did not result in employee’s job satisfaction, yet supervisor support was one of the key
factors that influenced employee decision to stay in their current organization. Furthermore,
previous findings done by Nasir and Mahmood (2016) shows that supervisor support had a
positive, significant relationship with employee retention. The result illustrates that the higher
the level of support provided by supervisor in the organization, the higher the employee
retention level. It shows that employees at organizations considered supervisor support as one
of the factors that could influence their intention to stay at the current organization. Hence,
H2 would be:
Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)302
H2: There is a relationship between supervisor support and employee retention.
Compensation
Murtiningsih (2020) stated that compensation has a relationship with employee retention.
Another study done by Olaimat and Awwad (2017) found that compensation had a significant
positive relationship on employee retention where the employees view compensation as one
of the factors that can increase their retention level. When an employee feels satisfied with
the compensation being provided by the organization, there will be a high possibility they
will stay in the company. Another author named Francis (2014) had done a study on human
resource management practices and employee retention. One of variable included was
compensation. The author finding showed that compensation had a positive impact on
employee’s retention where employee at the organization considers compensation as a tool in
influencing their decision to stay at their current workplace. In other words, monetary value
can affect employees’ behavior and long-term employment. However, both author’s result
contradicts with a study done by Hossain (2016) on the impact of best HRM practices on
retaining employees. One of variable included in his study was compensation and he found
that compensation has negative relationship with employee retention. It shows that
compensation does not influence employee’s decision at the organization whether to stay or
move out. Therefore, it is hypothesized that:
H3: There is a relationship between compensation and employee retention.
Work Environment as a ModeratorWorkplace environment tends to have positive or negative impact on certain work outcomes
such as intention to stay, commitment and involvement (Ollukkaran & Gunaseelan, 2012).
When workplace is perceived to be unsatisfactory to the employees, the probability of them
to resign will increase (Markey, Ravenswood, & Webber, 2012). There are few studies done
on human resource practices such as supervisor support, compensation (Sanjeevkumar,
2012), and training development (Othman & Lembang, 2017) on employee retention and the
result for the independent variable and the dependent variable positively correlated with
employee’s intention to stay. However, there is another variable that can moderate the
relationship between human resource practices and employee retention such as work
environment. This can be further seemed by viewing recent research study done by Bibi,
Ahmad and Majid (2018). They had done a study on two kinds of human resource practices
which are training development and supervisor support on employee retention, work
environment act as moderating variable. The result of their study shows that work
environment moderated the relationship between training development and employee
retention. Similarly goes to supervisor support where work environment moderated the
relationship between supervisor support and employee retention. Thus, it can be seen that
there is another variable can moderate the relationship between independent variable and
dependent variable. Therefore, the final hypothesis would be:
H4: Work environment moderates the relationship between human resource practices and
employee retention. Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)303
Figure 1: Conceptual framework of relationship between human resource practices and
employee retention which moderated by work environment.
Figure 1 shows the proposed conceptual framework for human resource practices and
employee retention in selected private universities at Shah Alam. For training development
and supervisor support taken from Bibi, Ahmad and Majid (2018) while compensation is
taken from Bibi, Pangil and Johari (2016). The moderating variable is taken from Juneau,
Anchorage and Kodiak (2008) and dependent variable is taken from Bibi, Ahmad and Majid
(2018).
Methodology
This study was conducted to investigate the relationship between human resource practices
(i.e., training development, supervisor support, and compensation), and employee retention
with work environment as a moderator. The researcher wanted to identify the relationship
between several human resource practices on retention among the employees. Thus, this
study is correlation in nature. As claimed by Salkind (2014), correlational research uses a
numerical index called the correlation coefficient as a measure of the strength of the
relationship between the variables.
The population for this study refers to the staffs at Universiti XYZ which is situated in
Selangor. The population of staff (academic and non-academic) at this university was 333.
The sampling technique used was convenience sampling which refers to the collection of
information from members of the population who are conveniently available to provide it
(Kumar, Talib, & Ramayah, 2013). The sample size in this study was 178. The instrument
that will be used for this study is a survey questionnaire. For training development, the
questionnaire adapted from Tonui Cherono Beatrice (2017), compensation (Mburu, 2015),
supervisor support and employee retention (Bibi, Ahmad, & Majid, 2018), and work
environment (Juneau, Anchorage, & Kodiak, 2008).
Findings
For demographic profile, out 178 of total questionnaire received by the researchers, 34.8%
are male respondents, while 65.2% are female respondents. 45.5% were in the age range of
36 years old and above. Then, followed by 31- 35 years old with 28.7%. 70.8% were married
and 75% of the respondents were Master holders. Finally, 43.8% of the respondents have
worked in the organization more than 10 years.
Regression analysis in used to measure how many percent of dependent variables can be
explained by the independent variable. Table 1 illustrates the result of regression analysis of
Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)304
three independent variables; training development, supervisor support and compensation
regressed against employee retention.
Table 1: Results for Regression Analysis
Model Unstandardized Coefficients
Standardized
Coefficients Sig
B Std. Error Beta
(Constant) 1.797 .304 .000
Training Development -.061 .074 -.041 .411
Supervisor Support -.162 .076 -.101 .036
Compensation .743 .040 .880 .000
F-value
131.532
Sig .000
Adjusted R2 .690
R2 .695
Table 1 shows that the R2 of 0.695 which implies that all the independent variables (training
development, supervisor support, compensation) explained 69.5 percent of the variance in
dependent variable (employee retention). 30.5 percent of the variance in employee retention
was not explained by training development, supervisor support and compensation in this
study. This indicates that there are other independent variables that are not included in this
study and could further strengthen the regression equation.
For this study, two factors were found to be significant which were supervisor support and
compensation. The result for supervisor support variable is 0.036 (3.6%), which is below the
5% significant level. However, for standardized coefficients beta on supervisor support the
result was -0.101 which indicated that as supervisor support increases by one standard
deviation, employee retention decreases by -0.101 of a standard deviation. Hence, explain that
supervisor support negatively related to employee retention. Compensation variable has a p-
value of 0.000 (0%). Thus, shows it is below the 5% significant level. For standardized
coefficients beta on compensation, the result was 0.880 which indicated that as compensation
increases by one standard deviation, employee retention increases by 0.880 of a standard
deviation. This shows that compensation positively related to employee retention. Even
though two variables were found to be significant but the other one which was training
development found to be insignificant. It is because the p-value for training development is
0.411 (41.1%), which is above the 5% significant level. Hence, explain that training
development not related to employee retention.
Next, to test the moderating effect of work environment, hierarchical regression analysis was
used. Hierarchical regression analysis is used to measure how many percent dependent
variable can be explained by the independent variable and the other variables such as
moderating variable. In this study, the researcher wanted to examine the extent of moderating
variable (work environment) moderate the relationship between human resource practices
(training development, supervisor support, compensation) and employee retention. Based on
the results (Table 2), there is no significant interaction of work environment between human
resource
practices and employee retention. The results for interaction between human resource
practices and work environment is B=1.064 (p>.05). It can be concluded that the work
environment did not moderate the relationship between human resource practices and
employee retention.The result for hierarchical regression analysis is shown as below.
Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)305
Table 2: Results for Moderating Effect Dependent Variable: Employee Retention
Model 1 Model 2 Model 3
Independent Variable:
Human Resource Practices0.667** 0.653** -0.062
Moderating Variable:
Work Environment
0.041** -0.528
Interaction Effect:
Human Resource Practices x Work Environment
1.064
R2
0.445
0.446
0.458
Change in R2 0.445 0.001 0.011
Change in F 140.226 0.454 3.664
Table 2 shows the results for hierarchical regression analysis for employee retention. Model 1
in the table above reflects the direct relationship between human resource practices and
employee retention. For Model 2, it reflects the extent of additional variance explained
when the moderating variable is included in the regression model. For Model 3, it
highlights the interaction of moderating variable with independent variables and their
relationship with the dependent variable. R Square is the variance in the dependent
variable (employee retention) which can be predicted from the independent variable. From
the findings, model 1 indicates that 44.5% of the variance in the dependent variable
(employee retention). Model 2 shows 44.6% additional 1% higher than variance in Model
1 and for Model 3 indicates 45.8% additional 12% higher than Model 2. Based on the
results, there is no significant interaction of work environment between human resource
practices and employee retention. The results for interaction between human resourcepractices and work environment is B= 1.064, (p>.05). It can be concluded that the work
environment did not moderate the relationship between human resource practices and employee retention.Discussion
Based on the finding, it is shown that there is no relationship between training development
and employee retention. In other words, it can be said that training development does not
have any impact on retention of employees at Universiti XYZ as the regression result was
insignificant. The result is supported by a study done by Chris-Madu (2020). The author
claims that investing in employees’ training and development without a good compensation
package is not effective enough in attaining higher retention. The result is consistent with the
previous research done by Omoikhudu (2017). She stated that in her study that training
development has no significant impact on employee retention. Another research done by
Liew, Rahman, Patah and Rahman (2016), the result of their studies indicated that training
development does not have significant effect on employee’s intention to stay in the
organization. In the case of Universiti XYZ, training development does not really influence
employee retention due to the organization itself does not sponsor regularly their employees
to attend training programs. Plus, the organization is less committed on training development
of its employees where the employees do not get enough training. When this scenario
happened, it is quite difficult for the employees to relate training development with their
retention level as they themselves were less involved with the training development.
It is shown that there is negative, significant and moderate relationship between supervisor
support and employee retention. In other words, it can be said that the higher the supervisor
support, the lower the level of employee retention. The result of this study aligned with study
done by Cho, Johanson and Guchait (2009) where there is no increment in employees’ intent
Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)306
to stay even though support from supervisor is high. In the case of Universiti XYZ, most
employees perceived that the more the supervisor helps them develop employee career plan,
the lesser they want to retain in the organization. This situation may happen due to them not
really see their future in the organization. Other than that, the negative relationship may
happen due to the supervisor may do management by objectives (MBO) with employees,
however the supervisor might not monitor them periodically. The supervisor may not fulfill
his promises by giving related resources that are needed during the MBO session. Apart from
that, even though supervisor did give ongoing feedback but the feedback might not really
effective in helping employees on their works. Feedback can be called constructive if it’s
clear, specific and actionable. When supervisor gives vague feedback, most probably, it
cannot give any good help to the employees on their work in which this can give some
influence on employees’ decision whether to stay or move out of the organization (Harter &
Adkins, 2015).
Based on the finding, it is shown that there is positive, significant and strong relationship
between compensation and employee retention. In other words, it can be said that
compensation does give some impact on employee retention at Universiti XYZ. One of the
reasons employees feel satisfied is the number of annual leave given by the organization.
Other than that, Universiti XYZ’s employees believed that they would retain because they
were proud of their hard work was rewarded by the organization. Apart from that, the
employees would stay because they also satisfied with the non-monetary reward that is given
to them. From these, it can be seen that compensation variable can influence employee
decision to stay in the organization. If employees receive a good compensation system, there
is more tendency the employee would stay loyal to the organization. This result can be
supported by research done by Olaimat and Awwad (2017) where the author indicated that
there is a positive, significant relationship between compensation and employee retention.
Employee retention levels can be increased when employee feel happy and satisfied with the
compensation package being offered and given by the organization. In addition, Ghazali,
Nasyuki and Yi (2011) had come out with a conclusion that compensation is able to influence
the employee’s decision to stay and work in the organization. The more satisfied the
employee with the compensation system, the higher the employee’s intention to stay. Apart
from that, a good reward system can help to boost employee morale to stay committed in the
company (Liew, Rahman, Patah, & Rahman, 2016).
The results of this study revealed that there is no moderating effect of work environment on
the relationship between human resource practices and employee retention. In other word, it
can be said that work environment does not give any effect on the relationship between
human resource practices and employee retention. The finding is supported by a study done
by Islam at el. (2020). This rejected the expected result that work environment would have
some ability to moderate the relationship between human resource practices and employee
retention. In the case of Universiti XYZ, the result shows that employees at the organization
do not view work environment as one of factor that can influence their retention level. This
indicates that probably any kind of environment that they might face either good or bad, it did
not affect the relationship between human resource practices and employee retention at the
organization.Recommendations and Conclusion
The results of the study show that employees at Universiti XYZ stay at the organization
because of the motivation in themselves and they feel satisfied about their job but they might
leave if they keep experiencing dissatisfaction on their work. Therefore, as a human resource
manager at the organization, the manager should come out with a strategy to ensure
Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)307
employees retain at the organization by looking into motivation-hygiene factor that is suitable
to be implemented among the employees. As noted by Herzberg (2017), in order to apply the
theory, a two-stage approach is needed to motivate the employees. For the first stage, the
management should focus on employee job dissatisfaction. Management should ensure that
the wages being given to the employees are competitive, create and support a culture of
respect and dignity, build job status by providing meaningful work for all positions, provide
supportive, effective and non-intrusive supervision and provide job security. All of these
actions can help in eliminating job dissatisfaction among the employees and human resource
manager should tackle this issue first as there is no point trying to motivate people when the
issue still exists in the organization.
Next, the second stage is to create situations for job satisfaction. Herzberg stated that in order
to create satisfaction the first thing to do is to address the motivating factors related to works
and called this “job enrichment”. His reason was that each work activity ought to be analyzed
to decide how it could be improved and more satisfying to the individual who does the work.
Some of the things that should be considered such as giving opportunities for achievement
and advancement through internal promotions, recognizing employee’s contributions, giving
each team member as much responsibility as possible and also creating work that matches
employee’s skills and abilities and rewarding.
Not only that, the management at Universiti XYZ should improve more on their
compensation package as it can give some effect on employee intent to stay. Furthermore,
management should check and ensure that supervisor or manager has the right credibility to
become a good leader in order to lead their own team members effectively. This is because a
good supervisor or manager can provide help whenever needed by employees, thus it can
influence their intention to remain in the organization.
As the result from this study shown that training development does not have any relationship
with employee retention, the researcher would like to suggest future researcher to study on
other variables apart from training development for example, recruitment and selection,
human resource planning, performance appraisal, performance management and do the study
at the same place. As the case for moderator variable, the researcher would like to suggest
other variables that may moderate the relationship between human resource practices and
employee retention. For supervisor support, the researcher suggested that future researcher to
use leadership style such as transactional leadership style that employees may want their
supervisor to equip with. At the organization, the employees possessed positive attitude
where they believe in themselves. Therefore, the researcher would like to suggest future
researchers to use self-efficacy as the moderating variable on relationship between
compensation and employee retention. Self- efficacy might be the best moderating variable to
the relationship between compensation and employee retention because employees seen
proud that their hard work is rewarded. Plus, the employees at Universiti XYZ value non-
monetary reward for example annual leave.
To conclude, this study was conducted with the intention to investigate factors that might
influence employee retention. HR practice dimensions are the predictors such as training and
development, supervisor support and compensation. The results indicate that factors such as
supervisor and compensation are related with employee retention, however training
development was not significantly related. For moderating variable, there is no moderating
effect of work environment on the relationship between human resource practices and
employee retention. Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)308
References
Abeysekera, R. (2007). The impact of human resource management practices on marketing
executive turnover of leasing companies in Sri Lanka. Contemporary Management
Research, 233-252.
Aburumman, O., Salleh, A., Omar, K., & Abadi, M. (2020). The impact of human resource
management practices and career satisfaction on employee’s turnover intention.
Management Science Letters, 10(3), 641-652.
Aguenza, B. B., & Som, A. P. (2012). Motivational factors of employee retention and
engagement in organizations. International Journal of Advances in Management and
Economics, 88-95.
Ahmad, U. (2013). Impact of training on employee retention. Case Business School, 1-14.
Alias, N. E., Rohmanan, N. H., Ismail, S., Koe, W. L., & Othman, R. (2018). Factors
Influencing Turnover Intention in a Malaysian Manufacturing Company. KnE Social
Sciences, 771-787.
Begum, F. W., & Mohamed, D. M. (2016). The role of HRM practices in gaining employee
affective commitment in manufacturing industries, Tiruchirappalli. International
Journal of Management, 15-22.
Bibi, P., Ahmad, A., & Majid, A. H. (2018). The impact of training and development and
supervisor support on employee retention in academic institutions in Pakistan: The
moderating role of work environment. Gadjah Mada International Journal of Business,
113-131.
Billah, M. M. (2009). Human resource management practices and employee turnover
intention to leave: A Study on commercial banks in Bangladesh. AMDIB Management
Review, 1: 23-42.
Cho, S., Johanson, M. M., & Guchait, P. (2009). Employees intent to leave: A comparison of
determinants of intent to leave versus intent to stay. International Journal of Hospitality
Management, 28(3), 374-381.
Chris-Madu, A. (2020). Exploring training and development as a motivational tool for
sustaining employee retention within the Irish retail industry. (Doctoral dissertation,
Dublin, National College of Ireland).
Crosby-Hardin, Z. M. (2020). The Impact of Small Business Leadership on Employee
Turnover. (PhD Dissertations, Walden University).
Foday, A. (2014). Perceived relationship between career development and employee
retention at deloitte Kenya. Kenya.
Francis, A. U. (2014). Human resource management practices and employee retention in
Nigeria’s manufacturing industries. International Journal of Scientific & Engineering
Research, 1741-1754.
Ghazali, H., Nasyuki, N. M., Yi, O. X., & Ishak, M. B. (2011). Human Resource Practices
and Employees’ Intention to Stay In The Kuala Lumpur Hotel Industry.
Haider, M., Rasli, A., Akhtar, C. S., Yusoff, R. b., Malik, O. M., Aamir, A., . . . Tariq, F.
(2015). The impact of human resource practices on employee retention in the telecom
sector. International Journal of Economics and Financial Issues, 63-69.
Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)309
Harter, J., & Adkins, A. (2015). Engaged employees less likely to have health problems.
Pridobljeno dne, 3, 2015.
Herzberg, F. (2017). Motivation to work. Routledge.
Hosain, M. S. (2016). Impact of best hrm practices on retaining the best employees: A study
on selected bangladeshi firms. Asian Journal of Social Sciences and Management
Studies, 108-114.
Hosain, M. S. (2016). Impact of best HRM practices on retaining the best employees: A study
on selected Bangladeshi firms. Asian Journal of Social Sciences and Management
Studies, 3(2), 108-114.
Imna, M., & Hassan, Z. (2015). Influence of human resource management practices on
employee retention in Maldives retail industry. International Journal of Accounting &
Business Management, 50-80.
Iqbal, S., Hongyun, T., Akhtar, S., Ahmad, U., & Ankomah, F. N. (2020). Impacts of
supervisor support on turnover intentions: Mediating role of job satisfaction. Asian
Journal of Education and Social Studies, 1-9.
Islam, M. A., Jantan, A. H., Yusoff, Y. M., Chong, C. W., & Hossain, M. S. (2020). Green
Human Resource Management (GHRM) practices and millennial employees’ turnover
intentions in tourism industry in Malaysia: Moderating role of work
environment. Global Business Review, 0972150920907000.
Johari, J., Yean, T. F., Adnan, Z., Yahya, K. K., & Ahmad, M. N. (2012). Promoting
employee intention to stay: do human resource management practices matter?
International Journal of Economics and Management, 396-416.
Kakar, P., Raziq, A., & Khan, F. (2017). The impact of human resource management
practices on employee retention a case of banking sector in Quetta Baluchistan. Journal
of Management Info, 5-11.
Khan, H. G. A., Abass, Z., Khan, M. J., & Ahmad, I. (2020). Enhancing employee retention
in supervisory support context: Role of person-organizations fit and career
commitment. NICE Research Journal, 24-46.
Kloutsiniotis, P. V., & Mihail, D. M. (2017). Linking innovative human resource practices,
employee attitudes and intention to leave in healthcare services. Employee Relations,
39(1), 34-53
Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: A systematic review
of discrimination and fairness by algorithmic decision-making in the context of HR
recruitment and HR development. Business Research, 1-54.
Kosi, I., Opoku-Danso, A., & Ofori, A. A. (2015). HRM practices and retention: An
empirical study of senior staff of university of cape coast. International Journal of
Technology Enhancement and Emerging Engineering Research, 62-68.
Kossivi, B., Xu, M., & Kalgora, B. (2016). Study on determining factors of employee
retention. Open Journal of Social Sciences, 261-268.
Kryscynski, D., Coff, R., & Campbell, B. (2021). Charting a path between firm-specific
incentives and human capital-based competitive advantage. Strategic Management
Journal, 42(2), 386-412.
Kumar, M., Talib, S. A., & Ramayah, T. (2013). Business Research Methods. Oxford Fajar
Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)310
Sdn. Bhd.
Ldama, J., & Bazza, M. I. (2015). Effect of training and development on employees’
retention in selected commercial banks in Adamawa State, Nigeria. European Journal
of Business and Management, 96-101.
Leghari, J. M., Suleman, U., Leghari, I. B., & Aslam, S. (2014). Role of HRM practices in
performance of organization and employee retention. European Journal of Business
and Management, 219-223.
Liew, J. W., Rahman, R. H., Patah, S. A., & Rahman, A. A. (2016). The relationship between
HRM strategies and intention to stay. Journal of Advanced Research in Social and
Behavioural Sciences, 90-98.
Mahadi, N., Woo, N. M., Baskaran, S., & Yadi, A. (2020). Determinant factors for employee
Retention: Should I stay? International Journal of Academic Research in Business and
Social Sciences, 10(4).
Malik, E., Baig, S. A., & Manzoor, U. (2020). Effect of HR Practices on Employee
Retention: The Role of Perceived Supervisor Support. Journal of Public Value and
Administrative Insight, 3(1), 1-7.
Markey, R., Ravenswood, K., & Webber, D. J. (2012). The impact of the quality of the work
environment on employees’ intention to quit. Economics Working Paper Series, 1-35.
Mburu, A. W. (2015). Effects of human resource management practices on employee
retention in deposit-taking microfinance institutions (MFIs) in Kenya (Doctoral
dissertation, University of Nairobi).
Mensah, R. D. (2014). Effect of human resource management practices on retention of
employees in the banking industry in Accra, Ghana.
Murtiningsih, R. S. (2020). The impact of compensation, training and development, and
organizational culture on job satisfaction and employee retention. Indonesian
Management and Accounting Research, 19(1), 33-50.
Nasir, S. Z., & Mahmood, N. (2016). HRM practices for employee retention: An analysis of
pakistani. European Journal of Business and Management, 96-97.
Nguyen, C., & Duong, A. (2020). The impact of training and development, job satisfaction
and job performance on young employee retention. International Journal of Future
Generation Communication and Networking, 13(3), 373-386.
Olaimat, D., & Awwad, B. (2017). Exploring the relationship between human resource
practices and employee retention in the house of representatives in Jordan.
International Journal of Business and Social Science, 178-185.
Ollukkaran, B. A., & Gunaseelan, R. (2012). A study on the impact of work environment on
employee performance. Namex International Journal of Management Research, 71-85.
Omoikhudu, J. (2017). The impact of training and development on employee retention in the
retail sector, an examination of the role of coaching in a leading retailer in Dublin,
Republic of Ireland. a case study of Mark and Spencer’s Store Dublin. Dublin.
Othman, S. Z., & Lembang, S. A. (2017). What attract gen y to stay in organization? HR
practices, organizational support or leadership style. International Review of
Global Business and Management Research: An International Journal Vol. 13, No. 4 (2021)311
Management and Marketing, 1-9.
Ozkan, A. H., Elci, M., Erdilek Karabay, M., Kitapci, H., & Garip, C. (2020). Antecedents of
turnover intention: a meta-analysis study in the United States. Economics and
Management, 23(1), 93–110.
Park, J., & Min, H. K. (2020). Turnover intention in the hospitality industry: A meta-analysis.
International Journal of Hospitality Management, 90, 102599.
Salkind, N. J. (2014). Exploring research (8th ed.). England: Pearson.
Sanjeevkumar, V. (2012). A study on employee's intention to stay in public companies,
Kedah, Malaysia. ZENITH International Journal of Business Economics &
Management Research, 91-101.
Senevirathna, G. A. (2017). Factors affecting employee retention.
Tonui, C. B. (2017). Human Resource Management Practices on Employee Retention
(Doctoral dissertation, University of Nairobi).
Wane, K. (2016). Effect of career development programs on employee retention in
international nongovernmental organizations in Kenya. Kenya.
Younas, M., & Waseem Bari, M. (2020). The relationship between talent management
practices and retention of generation ‘Y’ employees: mediating role of competency
development. Economic research-Ekonomska istraživanja, 33(1), 1330-1353.
Zafar, A. (2015). The consequences of supervisory support on employee retention in
Rawalpindi, Pakistan. Developing Country Studies, 23-30.
Zahoor, A., Ijaz, S., & Muzammil, T. (2015). Impact of human resource management
practices on employee retention in telecom sector of Pakistan. Journal of Resources
Development and Management, 22-31.
Naz, S., Li, C., Nisar, Q. A., Khan, M. A. S., Ahmad, N., & Anwar, F. (2020). A study in the
relationship between supportive work environment and employee retention: Role of
organizational commitment and person–organization fit as mediators. SAGE
Open, 10(2), 2158244020924694.
Yusliza, M. Y., Faezah, J. N., Noor, N. M. M., Ramayah, T., Tanveer, M. I., & Fawehinmi,
O. (2020). Effects of supportive work environment on employee retention: The
mediating role of person–organisation fit. Industrial and Commercial Training.
Frimayasa, A. (2021). Effect of compensation, career development and work environment on
employee retention (Study on Employees of PT Telkom Witel Tangerang
BSD). Journal of Research in Business, Economics, and Education, 3(1), 1715-1730.
Luengalongkot, C. C. P., Chim, C., & Hongwiset, S. (2020). Factors affecting employee
retention at Tira Thai Public Company Limited, Samut Prakan, Thailand. ARU
Research Journal Humanities and Social Sciences, 7(1), 81-88.
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International Journal of Information Management 59 (2021) 10234
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Research Article
Assimilation of business intelligence: The effect of external pressures and top leaders commitment during pandemic crisis
Akriti Chaubey a,*, Chandan Kumar Sahoo b
a School of Management, National Institute of Technology Rourkela, Rourkela, 769008, India b Human Resource Management, School of Management, National Institute of Technology Rourkela, Rourkela, 769008, India
A R T I C L E I N F O
Keywords: Business intelligence Institutional theory Business intelligence assimilation Leadership COVID-19
A B S T R A C T
The business intelligence (BI) has been often touted as a game-changer especially during the pandemic crisis. Although most managers are familiar with BI and agree that, it should be operationalized across their organi- zations. The BI is not well assimilated throughout adopting organizations. Rooted in institutional and upper echelon theories, this study proposes a theoretical model aimed toward explaining BI assimilation. We surveyed 174 respondents occupying leadership positions from174 auto-components manufacturing firms in India to gather data. The findings suggest that normative and mimetic (but not coercive) factors significantly influence top leader’s commitment to the BI initiatives. We found that the commitment of the top leaders influences the assimilation of BI via acceptance and routinization. Our study is an attempt to address the previous research calls related to BI assimilation. The findings of the study inform the information management scholars via theory- based research on phenomena related to post-adoption BI diffusion during a pandemic crisis. Practitioners can utilize the results of our study to design their policies that help assimilate BI such that forecasted benefits can be fully realized during an uncertain time.
1. Introduction
“Necessity has been the mother of invention in the response to the COVID- 19 pandemic, triggering many an innovation, often without the luxury of time to test these makeshift solutions to pressing problems. But there is much to be learned from times of crisis for times of plenty” (Harris, Bhatti, Buckley, & Sharma, 2020, p. 814)
The pandemic due to COVID-19 has seriously affected the small and medium enterprises (Dwivedi et al., 2020; Ivanov & Dolgui, 2020; Papadopoulos, Baltas, & Balta, 2020; Remko, 2020). Many organisations have significantly exploited the business intelligence (BI) capability to stay afloat in this unprecedented time (Kummitha, 2020; Queiroz, Tal- lon, Sharma, & Coltman, 2018; Ranjan & Foropon, 2021). It is well understood that BI plays an important role in improving business per- formance (Dwivedi et al., 2021; Koh & Gunasekaran, 2006; Pramanik, Mondal, & Haldar, 2020). In a recent report published by Sisence (The State of BI and Business Analytics Report, 2020) has highlighted sig- nificant rise in the use of BI and analytics in response to COVID-19 crisis (Queiroz, Ivanov, Dolgui, & Wamba, 2020). Although there are numerous BI success stories reported in the academic literature (Olszak,
2016), there remain many skeptics who often criticize the role and impact of BI (see, Božič & Dimovski, 2019) during pandemic crisis (Lee & Trimi, 2020). Although, the failure stories of the BI has gathered significant attention from the academic community (Tian et al., 2015) and in many instances, predicted benefits of BI are not realized (Aud- zeyeva & Hudson, 2016). Furthermore, BI is often inconsistently oper- ationalized across different contexts (see, Chen & Lin, 2020) and is often implemented based on prescriptive and not participative assumptions. Despite of rich body of literature on BI, the existing literature has largely remained silent on how BI is assimilated across the organisation (Elba- shir, Collier, & Davern, 2008; Fosso Wamba & Queiroz, 2020).
While there is a rich body of literature on factors influencing the success of BI implementation (Ramakrishnan, Jones, & Sidorova, 2012; Wang, 2014; López-Robles et al., 2019), studies aimed toward explain- ing BI assimilation are limited (Ahmad & Hossain, 2018; Shao, 2019). The previous studies have noted that the adoption and implementation, are often considered as the foundation of the diffusion of any techno- logical innovation. In any organization (Dubey et al., 2018; Hazen, Overstreet, & Cegielski, 2012), and the full benefits may not be well realized by the organization until and unless the technological
* Corresponding author. E-mail addresses: akritichaubey25@gmail.com (A. Chaubey), sahooc@nitrkl.ac.in (C.K. Sahoo).
Contents lists available at ScienceDirect
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journal homepage: www.elsevier.com/locate/ijinfomgt
https://doi.org/10.1016/j.ijinfomgt.2021.102344 Received 30 November 2020; Received in revised form 5 March 2021; Accepted 5 March 2021
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2innovation is fully assimilated (Dubey et al., 2018; Dwivedi, Rana, Jeyaraj, Clement, & Williams, 2019; Hazen et al., 2012; Williams, Dwivedi, Lal, & Schwarz, 2009). Based on Purvis, Sambamurthy, and Zmud (2001) and Hazen et al. (2012) definitions, we define BI assimi- lation as the extent to which BI philosophy diffuses across organizational processes and activities. Hence, the key objective of BI post-implementation activities is to assimilate the philosophy and practices across business routines such that organization achieve maximum benefits of BI implementation (Nam, Lee, & Lee, 2019). Moreover, how organization assimilate during pandemic crisis is not well understood. The purpose of this study is to investigate the means through which BI is assimilated throughout organizations during pandemic crisis. To address our research objective, we posit two guiding research questions as:
RQ1: What are the antecedents of BI assimilation? RQ2: How can firms assimilate BI across their organizations during
pandemic crisis? Kar and Dwivedi (2020) argued in favour of building theory that may
help organization to understand how the use of big data analytics and business intelligence capability may enhance performance during un- certain environment. Drawing on institutional theory (DiMaggio & Powell, 1983) and upper echelon theory (Hambrick & Mason, 1984), we develop a theoretical model to explain how the external institutional forces and the top leader’s commitment influence BI assimilation within an organization. Extending the findings of Liang, Saraf, Hu, and Xue (2007) and Nam et al. (2019), we submit that top leader’s commitment plays a pivotal role in channelizing the external institutional pressures into BI assimilation. Furthermore, we extend the work of Wang (2014) and Ain, Vaia, DeLone, and Waheed (2019) by studying assimilation instead of adoption or implementation. Hazen et al. (2012) have attempted to explain the journey from adoption to assimilation using two intermediary stages, namely acceptance and routinization.
Following previous arguments we assume the role of external pres- sures (Liang et al., 2007) and top leader’s (internal human agents) play significant roles in the acceptance, routinization and assimilation of BI, we submit that the role of contextual assimilation factors remains largely unexplored. We therefore propose a BI assimilation framework for pandemic crisis, grounded in organizational theories, that offers two unique contributions to the literature (Pan & Zhang, 2020). Firstly, we examine BI assimilation using two organizational theories (i.e. institu- tional theory and upper echelon theory). Secondly, we investigate to what extent top leader’s commitment mediates the relationship between institutional pressures and BI acceptance. This research thus provides a new perspective on BI assimilation.
The remainder of the article is organized as follows. In the next section, we discuss the theoretical framework and research hypotheses. Second section focuses on the development of our research model and hypotheses. Third section focuses on the research method. In this sec- tion, we discuss our questionnaire development, sampling design and data collection strategy. In the fourth section, we present our data analysis and results. In the fifth section, we present our discussion sec- tion based on our research findings. In this section, we have further discussed our contributions to the theory. In the same section, we further discuss our findings in context to the practice. We further outlined our limitations of our study and further noted future research directions. Finally, we concluded our study.
2. Research model and hypotheses
Our research model is grounded in extant literature. The foundation of the model is comprised of two elements, namely, institutional theory and upper echelon theory. Kauppi (2013) suggests that “…operations management (OM) researchers and practitioners tend to view their work in terms of the logic of rational efficiency, which has been questioned by organizational theorists arguing that rational action is always embedded in a social context…” (p. 1318). Hence, institutional theory may provide an
alternative perspective to examine the complexity of BI assimilation (BI-ASM).
Liang et al. (2007) developed a model to explain the assimilation of ERP using institutional theory. Our model attempts to extend Liang et al. (2007) work by examining BI assimilation. Furthermore, consistent with the work of Dubey et al. (2018), top leaders commitment is proposed to translate external forces (institutional pressures) into desired assimila- tion of BI. In our study, we draw from the extensive literature on insti- tutional theory (see, Oliver, 1997; Delmas & Toffel, 2004; Colwell & Joshi, 2013; Greenwood, Hinings, & Whetten, 2014; Dubey, Gunase- karan, Childe, & Papadopoulos, 2019) to develop a research model that identifies the antecedents of BI assimilation. In doing so, we seek to address our guiding research questions. Our research model (Fig. 1) is grounded in the proposition that institutional forces affect organiza- tional behaviour after being mediated by the leaders. Based on previous arguments, we have presented our research hypotheses. In the next subsections, we further discuss these hypotheses.
2.1. Institutional theory and BI assimilation
Zhu, Kraemer, and Xu (2006) advocate for innovation assimilation, noting that regulatory environment plays an important role. Liang et al. (2007) further found that institutional pressures significantly affect assimilation of ERP. Chinese firms comprise the setting of the study conducted by Liang and colleagues, suggesting that the role of legiti- macy in developing countries can help explain assimilation. To this end, Li et al. (2008) argued that ERP implementation can be successful if it is preceded by a BI focus. Drawing on these studies, we adapt the assimi- lation concept for the BI literature.
Dubey et al. (2018) research sought to explain TQM assimilation using three institutional factors and top management commitment. In our current study, we attempt to examine BI assimilation using institu- tional pressures to offer deeper insight into post-adoption processes. Singh, Power, and Chuong (2011) suggest that theory-based explanation enhances understanding and appreciation for standards, and provides clarity on how standards benefits organizations In comparison to other organization theories such as resource dependence theory (Singh et al., 2011) and contingency theory (Sila, 2007). Dubey et al. (2018, p.2992) argue that “the institutional theory posits that structural and behavioural changes in the organization are driven less by competition and the desire for efficiency, but more by the need of organizational legitimacy” (c.f DiMaggio & Powell, 1983). DiMaggio and Powell (1983) argue that the desire of the organization to align their business strategies in the line of the stakeholder’s expectations (i.e. legitimacy), the organization often embrace institutional logic. We can also refer the process of seeking legitimacy via embracing institutional logic as ‘institutional isomorphism’ (see, DiMaggio & Powell, 1983; Liang et al., 2007; Kauppi, 2013; Lin, Luo, & Luo, 2020; Zuo, Ma, & Yu, 2020). The institutional isomorphism occurs via three stages: coercive pressure, which refers to the external pressures resulting from government or any regulatory bodies or ex- pectations from cultural expectations of the community or any profes- sional associations (Liang et al., 2007). In an attempt to negate the pressures arising from external agencies or bodies, organization develop “coercive isomorphism” (Dubey et al., 2018). Normative pressures arise from professionalization, which is defined by DiMaggio and Powell (1983) as “…the collective struggle of members of an occupation to define the conditions and methods of their work, to control the production of the future member professionals, and to establish a cognitive base and legitimisation for their occupational autonomy.” Organizational researchers have noted that employee sharing similar traits (and hence normative isomorphism) which, is often developed via professional education and training (see DiMaggio & Powell, 1983; Liang et al., 2007; Heugens & Lander, 2009; Dubey et al., 2018; Dubey, Gunasekaran, Childe, Blome, & Papado- poulos, 2019; Zuo et al., 2020). Mimetic pressures refer to mimicking actions of organizations with respect to their competitors. This is often done because of environmental uncertainty, such as when new
A. Chaubey and C.K. Sahoo
International Journal of Information Management 59 (2021) 1023443
technology is not well understood, organizations have struggled to explain any degree of uncertainties or there is poor alignment of vision, mission and goals in organizations. In such cases, organizations develop mimetic isomorphism (DiMaggio & Powell, 1983). In sum, institutional theory can offer interesting perspective to understand BI assimilation.
2.1.1. Coercive pressures (CP) Liu, Ke, Wei, Gu, and Chen (2010) argued that institutional pressures
is considered as an important driver particularly in context to the adoption of innovation. Management scholars have increasingly argued that the pressures resulting from the government and other bodies, are transmitted via operational channels, affect the organization predispo- sition towards adoption of technology (Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019; Liang et al., 2007; Liu et al., 2010; Zhu et al., 2006). It has been shown that coercive pressures have significant influence on the adoption of ERP (Liang et al., 2007) and TQM (Dubey et al., 2018). Following previous studies we believe that coercive pressures plays a significant role in the assimilation process (see, Liang et al., 2007; Dubey et al., 2018). The existing studies on assimilation suggest that coercive pres- sures often arises from local government policies or regulatory author- ities or expectations from local bodies and community, may have direct or indirect impact on assimilation (see, Liang et al., 2007; Dubey et al., 2018). Extending this argument to BI assimilation, we hypothesise as follows:
H1. The coercive pressure has positive and significant effect on top leader’s commitment.
2.1.2. Normative pressures (NP) Following institutional logic, we argue that the institutional envi-
ronment shape the working behaviour of the individuals and the orga- nizations (DiMaggio & Powell, 1983; Liu et al., 2010; Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019). Following Liu et al. (2010, p. 374), Normative pressures (NP) refer to the “pressures that stem from collective expectations within particular organizational contexts of what constitutes appropriate, and thus legitimate, behaviour”. NP penetrate via channels of professional affiliations as well as the popularity generated by confer- ences hosted by professional bodies (Liang et al., 2007). Lowry, Zhang, Zhou, and Fu (2010) argue that normative isomorphism play an important role in the diffusion of new technology innovation in any organization. These arguments are consistent with the Zhu et al. (2006) findings. Moreover, Dubey et al. (2018) in one of their studies have found that normative pressures play a significant role in case TQM assimilation. Hence, we believe that normative pressures have signifi- cant influence during assimilation stage. Thus, we hypothesize it as:
H2. The normative pressure has positive and significant effect on top leader’s commitment.
2.1.3. Mimetic pressures (MP) DiMaggio and Powell (1983, p. 151) argue, “not all institutional
isomorphism, however, derives from coercive authority. Uncertainty is also a powerful force that encourages imitation. When organizational technologies are poorly understood, when goals are ambiguous, or when the environment creates symbolic uncertainty, organizations may model themselves on other organizations”. When organization faces highly uncertain and ambiguous, the role of leadership is highly critical in shaping organizational strategies (Schoemaker, Heaton, & Teece, 2018; Yang, Huang, & Wu, 2019). Schoemaker et al. (2018) argue that in highly uncertain environment, the leaders instead of focusing on executing the plan, the leaders prepare the organization to quickly adapt to the rapid changes with the help of technology. Prior studies have found that mimetic pressures influence management commitment (Liang et al., 2007; Zuo et al., 2020). The MP arise from the tendency of organizations to mimic other organizations. Previous studies (see, Liang et al., 2007; Dubey et al., 2018, 2019a; Lin et al., 2020; Zuo et al., 2020) have noted that lack of clarity regarding the outcomes of the programs whether it is referring to ERP adoption or adoption of TQM related practices, organizations mimic other organizations within the similar industry. The COVID-19 crisis has caused significant disruption and the continuous lockdown has create high degree of uncertainties (Bryce, Ring, Ashby, & Wardman, 2020; Pan, Cui, & Qian, 2020). Hence, we believe that mimetic pressures have significant influence during assim- ilation stage. Thus, we hypothesize it as:
H3. The mimetic pressure has positive and significant effect on top leader’s commitment.
2.2. Top leaders commitment and BI assimilation (BI-ASM)
Institutional theory predicts institutional isomorphism but in reality, organizations exhibit diversity with respect to benefits from BI imple- mentation or manifest different levels of BI assimilation under similar institutional environments. The organizational theorist have often argued that institutional logic; often fail to explain the mechanism of the translation of the external pressures in shaping organizational internal policies (Colwell & Joshi, 2013; Kostova & Roth, 2002). Hence, following Colwell and Joshi (2013) and Liang et al. (2007) arguments we, argue that role of human agent help address the limitations of the institutional theory. Thus, we propose that top leaders commitment towards BI may help translate external pressures into BI assimilation. Top leaders are expected to share the vision and mission of their orga- nization with their employees (Dubey, Gunasekaran, Bryde, Dwivedi, & Papadopoulos, 2020; Dubey et al., 2018; Dubey, Bryde et al., 2020). They not only motivate their team members but also provide adequate resources that may help assimilate BI (Dubey et al., 2018; Liang et al., 2007), thereby creating a BI culture which is conductive for employees’ involvement. Overstreet, Hazen, Skipper, and Hanna (2014) that servant leadership theory can assist in providing deeper insights into organiza- tional commitment, which indirectly leads to business performance result. Hence, we hypothesize based on previous arguments as:
H4. The top leader’s commitment has positive and significant effect on the BI acceptance;
Fig. 1. BI Assimilation Model.
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 102344 4Hazen et al. (2012) have suggested ‘acceptance’ and ‘routinization’ as two preceding activities that help assimilation. Acceptance has attracted significant attention from management scholars (see, Davis, 1989; Zhu et al., 2006; Hazen et al., 2012; Ahmad & Hossain, 2018). Acceptance (ACP) in context to BI can be defined as how well organi- zations constituents receive BI. Hazen et al. (2012) have argued that once organizational constituents have accepted an innovation like BI as a guiding philosophy, then it begins the process of being routinized within organizations. Based on Dubey et al. (2018, p. 2993) arguments we define “BI routinization as the permanent adjustment of the organiza- tions’ governance systems to account for BI”. Based on previous studies on information management and technology innovation (see, Jarvenpaa & Ives, 1991; Purvis et al., 2001; Liang et al., 2007) we posit that the top leader’s commitment may contribute to the BI assimilation via accep- tance. Thus, we hypothesize:
H5. The BI acceptance has positive and significant effect on BI routinization;
Following Zhu et al. (2006), we argue that the routinization is one of the stages involved between adoption of the BI and the assimilation of the BI. Following Zmud and Apple (1992, p. 149) we understand routinization as “the permanent adjustment of an organization’s governance system to account for the incorporation of a technology”. Routinization refers to the organizational ability to put a procedure in the place that evaluates the equipment turnover procedures to assure that the orga- nization is prepared for the dynamic changes in the environment. This is consistent with the innovation literature. Zhu et al. (2006) have found that the routinization has a significant effect on the assimilation of the technology. Ahmad and Hossain (2018) arguments is in consistent with the Zhu et al. (2006) and the Hazen et al. (2012). In pretext of pandemic crisis, the technology is quickly evolving to keep the pace with the rapid changing environments. In such case the effective routinization pro- cedure in the organization play a significant role in the assimilation of the BI (Laato, Islam, Islam, & Whelan, 2020). Based on the preceding discussions, we hypothesize it as:
H6. The BI routinization has positive and significant effect on BI assimilation.
In our studies, we have controlled the size of the organization and time since the BI has been adopted in their organization (Brown & Kaewkitipong, 2009; Dubey et al., 2018; Liang et al., 2007). Liang et al. (2007) argue that large organizations are more resilient towards hur- dles, which tend to slow down the assimilation process. Furthermore, decision-making is quite faster in smaller in comparison to large size organizations. Hence, we believe that size of the organizations may have significant influence on findings. Time since adoption has been noted in several diffusion studies (see, Liang et al., 2007; Dubey et al., 2018) as an important variable. Liang et al. (2007) argue that time is a significant predictor of diffusion-related phenomenon like assimilation. Thus, this variable reflects the assimilation learning curve (see, Fichman, 2001).
3. Research method
3.1. Construct operationalization and measurement
In our study, we have followed Churchill (1979) suggestions to improve the reliability and validity of our study via following two-stage process. Firstly, we have undertaken an extensive review of literature to draw our construct and their measurement. Secondly, we have inter- viewed twelve managers who have extensive years of experience in the BI assimilation. We used qualitative content analysis to validate our multi-item constructs. In response to the previous calls of management scholars (Flynn, Huo, & Zhao, 2010; Malhotra & Grover, 1998; Rossiter, 2008; Mithas, Ramasubbu, & Sambamurthy, 2011; Schryen, 2013; Fawcett et al., 2014; Schilke, 2014; Dubey, Gunasekaran et al., 2020; Dubey, Bryde et al., 2020), we argue that qualitative content analysis is a
useful method to validate the borrowed multi-item construct with real-life practices. We further gathered secondary data related to our samples selected for our study. In a way we attempted to overcome the limitations of our literature review and secondly, we have further tried to reduce the negative effects of the common method bias resulting from single source of data (see, Chin, Thatcher, & Wright, 2012; Fawcett et al., 2014; Iyengar, Sweeney, & Montealegre, 2015).
We finally arrived to the list of items for each construct based on our review of the literature and on a pre-test exercise, which we carried out with the panel of eighteen identified experts to avoid any ambiguity of the measurement items (see Appendix A). The measures listed in the Appendix A were measured using Likert scale with the anchors ranging between: 1 (strongly disagree) to 5 (strongly agree).
3.2. Sample and data collection
We chose auto component manufacturing sector in India due to two main reasons: firstly, Indian auto component- manufacturing sector has experienced significant dip in the operating margin in comparison to the last year performance due to the pandemic crisis. Secondly, the Indian auto component-manufacturing sector has made significant investment in the BI capability to improve the competitiveness (McKinsey & Com- pany, 2020). Hence, we believe that the data gathered from Indian auto-component manufacturing sector using survey based tool will be highly useful for testing our research hypotheses.
We distributed our questionnaire via e-mail to the 532 auto- components manufacturing firms located in the western and the southern regions. We gathered the organisations detail from the data- base of the The Automotive Components Manufacturers Association of India (ACMA) and further validated the details via Dun & Bradstreet (see, Dubey et al., 2018). Following previous studies using survey based approach (see, Dwivedi, Kapoor, Williams, & Williams, 2013; Chen, Preston, & Swink, 2015; Dubey et al., 2018; Srinivasan & Swink, 2018; Dubey, Gunasekaran, Childe, Blome et al., 2019), was adopted following Dillman’s (2011) suggestions. We followed up with the respondents after one weeks to seek whether they have received an e-mail containing the introduction letter and the questionnaire. Firstly, we received questionnaire from 110 respondents. After several follow up calls, we again sent packages to non-respondents after two weeks and 64 ques- tionnaires were subsequently returned for an overall response rate of 32.71 %. In sum, 174 completed questionnaires were received (see Table 1). We acknowledge that our association with the ACMA has played an important role in gathering quality data.
3.3. Non-response bias
We note that data gathered using survey-based instrument at one point of time might suffer from non-response bias (Fawcett et al., 2014; Dubey et al., 2018, 2019a). In this study, we have used two approaches to examine non-response bias in our collected data. Firstly, we adopted traditional method [i.e., wave-analysis (Armstrong & Overton, 1977)] to test non-response bias. The comparisons between early and late re- sponses showed no statistical differences at p < 0.05, indicating that non-response bias is not a potential issue in our study. Secondly, following Wagner and Kemmerling (2010) arguments we have
Table 1 Demographic Profile of the Respondents.
Title Number %
CEO 9 5.17 CIO 33 18.97 Finance & Accounting Manager 15 8.62 Supply Chain Manager 67 38.51 Human Resource Manager 12 6.90 Customer Relationship Manager 23 13.22 Sales Manager 15 8.62
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 1023445
compared the demographics of the respondents to demographic infor- mation from non-respondents via Dun & Bradstreet and observed no inconsistencies.
4. Data analyses and results
Before deciding on our modelling technique, we first performed an assumptions test on our indicators (see, Fawcett et al., 2014, p.13). Based on Eckstein, Goellner, Blome, and Henke (2015), we tested as- sumptions related to constant variance, outliers, and normality. We examined residual plots, rankits plot of residuals, and measures of skewness and kurtosis. Based on Cohen (2008), we used Mahalanobis distance to detect outliers. The maximum absolute values of skewness and kurtosis were found to be 1.26 and 2.29, respectively (see Appendix B). Based on Curran, West, and Finch (1996), we these values are well within recommended limits (univariate skewness < 2, kurtosis < 7). In sum, we found that the typical assumptions required for inference-based statistics were met.
4.1. Measurement model
We have performed confirmatory factor analysis (CFA) following Fornell and Larcker (1981) to examine the: (a) construct validity [indi- vidual factor loadings, scale composite reliability (SCR) and average vari- ance extracted (AVE)]. In our study we found that the individual factor loadings were greater than 0.5, the SCRs were calculated to be greater than 0.7, and the AVE for each construct was greater than 0.5 (Chin, 1998) (see, Table 2). (b) Next we have performed discriminant validity test (see, Table 3). The Table 3 represent a matrix that contain the correlations between paired constructs, and the leading diagonal of the matrix shows the square root of the AVE of each construct. All measures indicate adequate discriminant validity (Fornell & Larcker, 1981). Model fit indices indicated acceptable fit of the data to the measurement model [Normed Chi-Square = 1.9, which is less than 2 as recommended by Carmines, McIver, Bohrnstedt, and Borgatta (1981) and Hu and Bentler (1999) pointed that the threshold value of normed chi-square is 0.09].
4.2. Common method bias
The data gathered using single respondent questionnaire might suf- fer from common-method bias (CMB) that may affect our statistical re- sults (Podsakoff & Organ, 1986). To reduce the negative effects of the CMB, we performed Harman’s one factor test (see, Podsakoff, MacK- enzie, Lee, & Podsakoff, 2003) to examine whether a single latent factor would account for all the theoretical constructs (Dubey et al., 2018). The exploratory factor analysis yielded seven factors parsimonious structure. The single factor does not explain more than 13.95 % percent of total 51.03 percent of total variance. Hence common method bias is likely not a significant threat to the findings. Dubey, Gunasekaran, Childe, Blome et al. (2019), Dubey, Gunasekaran, Childe, Papadopoulos et al. (2019) noted that the Harman’s one factor test is a traditional approach and may not be sufficient to test CMB. We followed Malhotra, Kim, and Patil (2006) suggestions via performing CFA loading all items on a single factor, and further examine the fit indices. The single factor in this case is equivalent to “one factor”, which indicates the existence of bias resulting from data collection from a single source (Srinivasan & Swink, 2018). The fit for the one factor model is not adequate [RMSEA = 0.313; NNFI = 0.094; CFI = 0.231 and SRMR = 0.513] and chi-square change with respect to the hypothesized model is highly significant (p < 0.000). Finally, we tested for CMB using the correlation marker technique (Lindell & Whitney, 2001). We assumed an unrelated variable to delineate out the correlations caused by CMB. In addition, we computed the significances of the correlations based on Lindell and Whitney (2001) suggestions (Srinivasan & Swink, 2018; Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019). We observed minimal differences between adjusted and unadjusted correlations. Following the results based on three methods, we argue that the potential effects of the CMB is not significant. How- ever, we caution readers that in future an attempt should be made to collect data using instrument designed for multi-respondents (see, Ketokivi & Schroeder, 2004).
4.3. Endogeneity test
Following Guide and Ketokivi (2015) arguments, we adopted some measures to correct the endogeneity (see, Liu, Wei, Ke, Wei, & Hua, 2016). According to Guide and Ketokivi (2015, p. v), “when arguing that the variance of X gives rise to the variance of Y (causally or otherwise), we expect to see a plausible argument that the direction is indeed from X to Y, not vice versa, or perhaps caused by an omitted variable. Measurement error can also cause an endogeneity problem: if X and Y have a common measurement error source, X will unavoidably correlate with the error term of Y. Finally, sample selection bias may lead to problems very similar to that of endoge- neity”. Hence, we understand that we cannot eliminate the endogeneity problem due to our research design. However, we adopted some mea- sures to correct it in our model that may lead to inconsistent and biased outcomes (Liu et al., 2016). We performed two-stage least squares regression analysis with the questionnaire variables (Bellamy, Ghosh, & Hora, 2014; Liu et al., 2016). To conduct two-stage least squares regression analysis, we identified size of the organization as the poten- tial instrumental variable as it does not have significant effect on the BI assimilation. Following Bellamy et al. (2014) we regressed TLC on all instrumental variables and the control variables at the first stage. We observed that the R2 value increased significantly in comparison to the model with only control variables. This indicates that the organization size can be effectively assumed as an instrumental value for TLC in our study. Next, we conducted Durbin-Wu-Hausman post-estimation test for endogeneity (Davidson & MacKinnon, 1993). In this test we performed augmented test on the TLC by adding the error term that we obtained in the first stage while performing two-stage least squares regression test. The path coefficients of the error term of TLC were insignificantly related to the BI assimilation. This establishes that the endogeneity associated with the TLC is insignificant in our study. Thus, we can argue
Table 2 Loadings of Indicator Variables (Scale Composite Reliability and Average Vari- ance Extracted).
Constructs Measures Factor Loading Variance Error SCR AVE
CP CP-BI1 0.73 0.54 0.46 0.76 0.51 CP-BI2 0.70 0.49 0.51 CP-BI3 0.72 0.52 0.48
NP NP-BI1 0.87 0.76 0.24 0.85 0.65 NP-BI2 0.78 0.60 0.40 NP-BI3 0.76 0.58 0.42
MP
MP-BI1 0.83 0.69 0.31 0.89 0.67 MP-BI2 0.82 0.67 0.33 MP-BI3 0.82 0.67 0.33 MP-BI4 0.81 0.65 0.35
TLC
TLC2 0.79 0.62 0.38 0.83 0.50 TLC3 0.68 0.46 0.54 TLC4 0.53 0.28 0.72 TLC5 0.75 0.56 0.44 TLC6 0.75 0.57 0.43
BI-ASM ASM-BI1 0.69 0.48 0.52 0.75 0.50 ASM-BI2 0.61 0.37 0.63 ASM-BI3 0.81 0.65 0.35
ACP ACP-BI2 0.80 0.64 0.36 0.84 0.72 ACP-BI3 0.89 0.79 0.21
RO
RO-BI1 0.80 0.64 0.36 0.94 0.74 RO-BI2 0.79 0.62 0.38 RO-BI4 0.86 0.74 0.26 RO-BI5 0.90 0.81 0.19 RO-BI6 0.91 0.83 0.17 RO-BI7 0.89 0.79 0.21
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 1023446
that the TLC is an exogeneous variable and not the endogeneous. Similarly, we examined in case of CP, NP and MP we found the path coefficients of the error term of CP, NP and MP were insignificantly related to the TLC.
4.4. Hypotheses testing
Although we considered structural equation modelling approaches (Wendorf, 2002), we concluded that the hierarchical regression modelling is the favourable approach in this study in consideration of both the estimation function and the parsimony of presentation. Test results are presented in Table 4. Our hypothesis H2 (NP→TLC) is sup- ported (β = 0.17, p = 0.01). This result of our study is consistent with the previous studies (Dubey et al., 2018; Liang et al., 2007). Next, we found support for H3 (MP→TLC) (β = 0.71; p = 0.00). We can argue that the MP has a positive and significant effect on the TLC. These findings of our study are consistent with the previous studies in context to ERP assim- ilation (Liang et al., 2007) and the TQM assimilation (Dubey et al., 2018). Similarly, we found support for H4 (TLC→ACP) (β = 0.11; p = 0.02), H5 (ACP→RO) (β = 0.56; p = 0.00) and H6 (RO→BI-ASM) (β = 0.25; p = 0.00). These results are consistent with the Zhu et al. (2006) and Dubey et al. (2018) findings. We observed that the size of the or- ganization has no significant effect on the BI-ASM. We conclude that during pandemic resulting from COVID-19, the importance of the BI has been realized by all kind of organizations irrespective of their size. Although, we assumed that larger organization have access to more resources. However, the COVID-19 crisis has played an important role in bridging the wide gaps that existed pre-COVID-19 crisis.
Based on hypothesis testing, we found that in the case of BI assimi- lation, the top leader’s commitment may not be significantly influenced by coercive pressure, as the first research hypothesis is not supported (β= -0.11; p = 0.23). This result provides a clear insight into the particular situation that has forced the organizations to use innovative technologies to improve their business operations and decision-making abilities. The COVID-19 crisis has forced organizations to adapt to the new norms. Hence, during pandemic, the organizations have increas- ingly invested in innovative technologies to maintain their competitive advantage.
5. Discussion
In this study, we have posited two guiding research questions and five research hypotheses suggesting that the institutional pressures under the mediating effect of the top leader’s commitment influence the assimilation of the BI. More specifically, building on institutional theory and upper echelon theory, we developed our research model (see Fig. 1) to address our research questions (RQ1 and RQ2). By addressing RQ1, our study attempts to bridge, the existing research gaps. To date, the collective influence of institutional pressure on assimilation of BI has not been studied (Liang et al., 2007; Lin et al., 2020; Teo, Wei, & Benbasat, 2003; Zuo et al., 2020). Addressing this gap is important given that types of pressure (coercive, mimetic and normative) are ‘not always empirically distinct’ (DiMaggio & Powell, 1983, p. 150). Our findings therefore contributes to this literature in two ways. Firstly, we provide the liter- ature with survey based reflective measures of institutional pressures that capture coercive, mimetic and normative pressures. Secondly, the previous literature has noted the limitations of institutional theory in explaining the extent to which companies within the same institutional field (i.e. industry) actually adopt the technological innovations (Col- well & Joshi, 2013; Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019; Greenwood & Hinings, 1996; Liang et al., 2007; Oliver, 1997; Zuo et al., 2020). To address these limitations, we have incorporated the role of top leader’s commitment within the institutional theory framework (Greenwood & Hinings, 1996; Liang et al., 2007). To date, however, few studies have explored this new extension. Our study closes this gap.
In an attempt to address our RQ2, we develop and empirically test a theoretically grounded model that confirms the collective influence institutional pressures may have on top leadership commitment for the BI assimilation. Specifically, we show how top leader’s commitment to the BI assimilation can mediate the relationship between institutional pressures and BI assimilation. By doing so, we provide some of the first empirical evidence to support the inclusion of intraorganizational dy- namics (see, Greenwood & Hinings, 1996; Colwell & Joshi, 2013), within institutional theory, as an approach for understanding why or- ganizations demonstrate differential behaviour in context to the adop- tion of BI tools. Moreover our study building on previous arguments, attempted to explain BI assimilation using institutional theory and upper echelon theory and extend the BI work of Nam et al. (2019) during pandemic crisis. In this way our study, provides theoretical explanation to pandemic effect on organizational responsiveness towards BI tools assimilation (Pan & Zhang, 2020; Papadopoulos et al., 2020). Dubey et al. (2019) used institutional theory to explain the motives of organi- zations when adopting big data analytics, whereas Dubey et al. (2018) explained the impact of contextual factors on organizational perfor- mance using institutional theory. However, both studies did not focus on post-implementation phases, which was the focus of this study. Overall, we can argue that mimetic and normative pressures affect top man- agement commitment, which subsequently affects acceptance, routini- zation, and assimilation of BI. However, we found that coercive pressures were not positively related to top leader’s commitment (β= -0.11; p > 0.1). This result is in contrast to the existing literature
Table 3 Correlations among major constructs.
(Note: The leading diagonal of matrix represented in grey shade is square root of average variance extracted).
Table 4 Overview of Hypotheses Test.
Hypothesis βvalues Direction of β t- statistics
P value Conclusion
H1: CP→TLC − 0.11 reverse − 1.21 0.23 not- supported
H2:NP→TLC 0.17 positive 2.15 0.01 supported H3:MP→TLC 0.71 positive 14.9 0.00 supported H4: TLC→ACP 0.11 positive 2.31 0.02 supported H5: ACP→RO 0.56 positive 16.71 0.00 supported H6:RO→BI-
ASM 0.25 positive 3.86 0.00 supported
[Notes: CP, coercive pressures; MP, mimetic pressures; NP, normative pressures; TLC, top-leadership commitment; ACP, acceptance; RO, routinization; BI-ASM- business intelligence assimilation; BI, business intelligence].
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 1023447
exploring the role of institutional pressures and the statistically signifi- cant positive influence of coercive pressures on top managers’ posture towards the adoption of activities related to the improvement of orga- nizational processes or recovery of the environment in which firms are embedded such as reverse logistics (Ye, Zhao, Prahinski, & Li, 2013), green supply chain management practices (Zhu, Sarkis, & Lai, 2013), and supply chain management systems (Liu et al., 2010; Zhang & Dhaliwal, 2009) and technologies (Bhakoo & Choi, 2013; Liu et al., 2010; Saldanha, Mello, Knemeyer, & Vijayaraghavan, 2015). In a way, we present that how our study extend and contradicts previous studies focusing on adoption or assimilation of the technology. Our study in comparison to other studies we found that in our case we note that CP has no significant influence on TLC which contradict the Liang et al. (2007) and Dubey et al. (2018) research findings. Moreover, our study extend the previous studies (see, Teo et al., 2003; Lin et al., 2020 and Zuo et al., 2020) by examining the role of TLC in translating institutional pressures to shape the technology assimilation strategy. Hence, we believe that our statistical results paint an interesting picture of asso- ciations and complementarities among the external pressures, top management leader’s and BI diffusion stages (i.e., acceptance, routini- zation and assimilation) in pandemic crisis resulted from COVID-19. Collectively, these results have implications for the practitioners, as well as provide some new research questions in this field of research.
5.1. Theoretical contributions
There is a rich body of literature focusing on the role of institutional forces on the adoption of technology (Dubey, Gunasekaran, Childe, Blome et al., 2019; Liang et al., 2007; Lin et al., 2020; Pennings & Harianto, 1992; Teo et al., 2003). Liang et al. (2007) in context to ERP assimilation attempted to explain the role of top management commit- ment in translating the external pressures into the ERP assimilation. In a way, Liang et al. (2007) attempted to address the limitations of the previous work (see, Pennings & Harianto, 1992; Teo et al., 2003) by including top management commitment as mediating construct. How- ever, Liang et al. (2007) remained silent on the stages involved in the assimilation. Dubey et al. (2018) in one of their studies have tried to explain three stages of assimilation in context to the TQM philosophy. However, in the case of technological innovation, the existing works have largely remained silent. Our study findings enriches BI-Assimilation research via examining the role of institutional factors effects on organization intention to adopt the BI and embrace it completely across all the functional departments of the organization. Here, we attempted to develop a theory that explain the assimilation of the BI in an organization during unprecedented time. The existing literature offers rich discussion on the factors that influences the adop- tion of the BI. However, most of the studies have examined using resource based perspective or organizational information processing perspective. However, the literature on BI has remained silent on how the external pressures drive the organization to assimilate the BI as their organizational philosophy. Although, in context to ERP and TQM assimilation, the scholars have offered explanation grounded in the institutional theory. This study, thus heeds calls for theory-focused data driven study that provides in depth understanding of how BI assimila- tion occurs during pandemic crisis. Hence, we argue that our study contribution to the BI literature is threefold. Firstly, we attempted to provide an operational definition of BI assimilation and develop and statistically validate a model that examines the effect of coercive, mimetic and normative pressures on top leader’s commitment to BI assimilation. Hence, we argue that our study contributes to the theo- retical boundaries of BI assimilation in uncertain time. Secondly, we integrate institutional theory and upper echelon theory to explain BI assimilation in terms of a three-stage post-implementation process (i.e. acceptance, routinization and assimilation). In a way argue that the TLC help translate the pressures resulting from the competitors and the ex- pectations of organization to provide better results during pandemic
crisis. Thirdly, we extend prior literature (see, Nam et al., 2019; Chen & Lin, 2020) by integrating Liang et al. (2007) and Hazen et al. (2012) studies to model BI assimilation during pandemic crisis. In a way we attempted to address research calls of some information management scholars in the wake of pandemic crisis (see, Papadopoulos et al., 2020; Dwivedi et al., 2020; Pan & Zhang, 2020). We can argue that our find- ings help understand how institutional theory and upper echelon theory provides a better understanding of the assimilation of BI during an un- certain time. These findings of our study answer the antagonists who often criticized the institutional theory and their inability to influence the organizational policies.
5.2. Managerial implications
The pandemic crisis resulting from COVID-19 has transformed the lives of citizens and organizations way of doing business. The pandemic has triggered the humanity to find innovative ways of doing business to keep the sinking economy afloat. Although, we often blame COVID-19 and pandemic for current crisis. However, the pandemic has offered significant insight into our hidden problems that has plagued our world economy. The pandemic has exposed our weakness and reflected our capabilities to deal with such health crisis. The power of emerging technology has been understood during the pandemic crisis to fight against the disruptions caused by the pandemic crisis (Dwivedi et al., 2020; Ivanov, 2020). The BI tools have not only to help organizations to reduce the spread of the virus; it has helped enhance the performance of the organizations. BI has changed the overall business strategies of the organization. In recent times, the BI tools have played a significant role in building trust and collaboration among the various stakeholders. The majority of these BI tools extensively rely on data analytics to promote better communication between organizational stakeholders. Despite, availability of BI tools the organizations have struggled to optimally utilize these BI tools in an effective and efficient way. Hence, our find- ings clearly suggest practitioners who consider investments in BI care- fully evaluate: (1) how organizational policies are aligned with the external pressures; (2) to what extent the top leaders of the organization are familiar with the effects of the external pressures on the diffusion of BI. For example, during pandemic resulting from the COVID-19, BI and data analytics initiatives are proving to be a boon in disguise for many organizations. It helps organizations to sense and adapt to the disrup- tions caused by the pandemic. It enables organizations to develop new products as well as help protect their own employees without affecting their business propositions. Hence, we can argue that our findings can be used as guidance to managers and consultants who are involved in BI implementation. The mediating role of TLC in BI assimilation clearly suggests that top leaders plays a critical role in the BI assimilation process. For instance, as the organization struggle to adapt to the un- precedented crisis resulting from the COVID-19, top leaders are encouraging their BI teams to develop new solutions at a faster rate and to make allowances for needs that are changing rapidly to a high degree of uncertainties. Moreover, via this study, we have realized that the most important lesson that organizations are learning about the BI during a pandemic is that it will have a little effect within organizations still insisting on top-down decision making. Instead, the organizations using BI to the greatest effect are those that have a culture of the delegation of authority, where employees are empowered to make data-driven de- cisions without the need to wait for their superiors to approve it. We found how the acceptance of BI among an organization’s constituents further helps to align organization’s governance systems to ultimately usher in BI assimilation. The finding that institutional forces (apart from coercive forces) influence BI assimilation is quite interesting for man- agers and consultants. Traditionally, managers focus on implementation more so than post-implementation phases. Thus, a large percentage of organizations typically report BI failures due to a lack of understanding of assimilation processes. Hence, the study findings can help managers to focus on each of the intermediary steps that lead to assimilation of BI.
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 1023448
Attending to the acceptance and routinization aspects of BI is important for the eventual assimilation of BI. Further, we believe that institutional pressures (normative and mimetic), if properly translated by top man- agers who are committed towards BI assimilation, can be very useful for those companies that have reported losses due to failure to reap benefits from their investments in BI implementation and would like to further investigate the reasons behind this failure in order to re-launch or re-energize BI efforts. Finally, our study offers some useful tips to the managers who are unable to exploit BI to minimize the disruptions caused by the pandemic resulting from COVID-19 and may serve as a useful guidance for the managers to deal with future crisis. Despite the significant success, the remaining flaws of the organizations that prevent the organization from achieving maximum benefits from their BI ini- tiatives are becoming known. That offers a unique opportunity to fix them finally. With that in mind, here are the top three lessons that businesses are learning about BI amidst the pandemic.
5.3. Limitations and further research direction
Drawing on institutional theory and upper echelon theory, and literature on BI and elements of innovation diffusion, we developed and tested our theoretical model using data from quality managers at 174 auto-component manufacturers during pandemic crisis stage. Our study has some limitations that should be noted. Firstly, we have tested our theoretical model using data gathered from the auto-components manufacturing sector. However, there may be variations in terms of practices between manufacturing sectors. Hence, future research can examine this model across sectors. Secondly, to test our framework we used cross-sectional, single-source data. Future research can employ longitudinal methods to test for causality in the model. In addition, future research examining outcomes of assimilation is especially encouraged. Our study is based on a single country and single industry data, which may limit the generalizability of our study. Hence, in order to reduce the variability induced by the industry differences, we pur- posely chose the Indian auto-component manufacturing industry (see, Liu et al., 2010). To minimize the biases resulting from personal dif- ferences due to the background, we identified respondents of similar backgrounds who had obtained training from a similar kind of institu- tion. Although we believe that our data collection strategy may have helped the internal validity of our study, this may limit the external validity of the study. Moreover, this study has been conducted to capture the managers response in context to pandemic crisis resulted from the COVID-19.Thus, we believe that the findings of our study should be cautiously evaluated in context to other settings. Moreover, our comparative analysis of the results show that in context to China the role of CP is highly significant. Similarly, in the context Indian automotive industry, the role of CP is insignificant. These differential results can be better explained using national culture theory. For instance, Prakash and Majumdar (2021) investigated how national culture plays an important
role in content creation in the context of a social media platform. Similarly, George et al. (2018) and Gupta and Gupta (2019) have advocated in favor of the influence of national culture on shaping organizational strategies. We believe that our study could be extended by examining the moderating effect of national culture dimensions on the paths joining institutional pressures and TLC. We also encourage future research using multiple case study, ethnographic, and action research methods to build more comprehensive theory to explain the BI assimilation.
6. Conclusions
The study examines the role of external pressures and top leaders commitment in BI diffusion process. Informed by information manage- ment and organizational theories we have conceptualized a theoretical model. To validate our theoretical model and test our research hy- potheses, we have gathered data from Indian auto component manufacturing sector to understand how external pressures and the top leaders have played a significant role in BI assimilation during pandemic crisis, which has affected the business worldwide. Despite of the poor operating margin, the sector has learnt a new way to deal such with such unprecedented time via investing in BI and exploiting them in an appropriate way. We hope our findings and limitations of our study provide enough food for thought.
Authors comment
The first author (Mrs Akriti Chaubey, who is a Doctoral Scholar at School of Management, National Institute of Technology Rourkela) has contributed in the manuscript through the following ways:
1 Conceptualized the theoretical model via extensive literature review; 2 Formulated research hypotheses; 3 Developed a structured questionnaire; 4 Carried out data collection; 5 Performed Data Analysis 6 Drafted the manuscript
The second author (Dr Chandan Kumar Sahoo, who is Professor at the School of Management, National Institute of Technology Rourkela) has contributed in the manuscript through the following ways:
1 Provided in-depth inputs during theoretical framing; 2 Offered significant inputs related to data analyses and the selection
of appropriate statistical tools; 3 Helped during proof editing; 4 Helped during discussion section writing (i.e., contributions to
theory).
Appendix A. Constructs and Items
Construct Relevant Literature Items
Coercive Pressures (CP- BI)
Dubey et al. (2018) 1 The local authority want our organization to use BI during pandemic crisis (CP-BI1). 2 The professional associations expect our organization to use BI during pandemic crisis (CP-BI2). 3 The consumers of our organization expect our organization to adopt BI during pandemic crisis (CP-BI3).
Normative Pressures (NP-BI)
Dubey et al. (2018) 1 The extent to which your channel partners have adopted BI during pandemic crisis (NP-BI1). 2 The extent to which first and second tier suppliers of your organization have adopted BI during pandemic crisis (NP-
BI2). 3 The extent to which the professional societies promotion schemes have influenced your organization to adopt BI
during pandemic crisis (NP-BI3). Mimetic Pressures (MP-
BI) Dubey et al. (2018) 1 Our main business rival has gained significant business advantage with the adoption of BI during pandemic crisis (MP-
BI1) 2 The use of BI is well received by other competitors in our industry during pandemic crisis (MP-BI2). 3 The customers of our organization have appreciated the use of BI during pandemic crisis (MP-BI3).
(continued on next page)
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 1023449
(continued )
Construct Relevant Literature Items
4 The suppliers of our organization have appreciated the use of BI during pandemic crisis (MP4). Top leader’s
Commitment (TLC) Liang et al. (2007); Dubey et al. (2018)
1 Our organization top leaders believe that the BI has potential to enhance the business performance of our organization during pandemic crisis (TLC1).
2 Our organization top leaders believe that the use of BI will enhance business opportunities during pandemic crisis (TLC2).
3 Our organization top leaders have formulated a strategy for the use of BI during pandemic crisis (TLC3). 4 Our organization top leaders share the BI vision with all stakeholders during pandemic crisis (including you) (TLC4). 5 Our organization top leaders established the performance metrics to monitor the BI project during pandemic crisis
(TLC5). 6 Our organization top leaders recognizes the contribution of the partners engaged in BI project during pandemic crisis
(TLC6). Acceptance (ACP-BI) Hazen et al. (2012); Dubey
et al. (2018) 1 1To what extent you believe that BI enhance my job performance during pandemic crisis (ACP-BI1). 2 To what extent you and your colleagues associate with the BI during pandemic crisis (ACP-BI2). 3 To what extent the infrastructure support the innovation during pandemic crisis (ACP-BI3).
Routinization (RO-BI) Hazen et al. (2012), Dubey et al. (2018)
1 To what extent in your organization procedures are defined for replacement of tangible resources necessary to support BI during pandemic crisis (RO-BI1).
2 To what extent in your organization a separate budget has been created to support BI during pandemic crisis (RO-BI2). 3 Our organization have a dedicated team to support BI during pandemic crisis (RO-BI3). 4 Our organization have defined organizational procedures for procurement of necessary items during pandemic crisis
(RO-BI4). 5 Our organization hire and retain qualified people to support BI during pandemic crisis (RO-BI5). 6 To what extent my organization offers opportunities for initial and /or recurring training regarding the BI during
pandemic crisis (RO-BI6). 7 To what extent in my organization a person familiar with the BI have been promoted into higher positions of greater
authority such that they support the innovation further especially during pandemic crisis (RO-BI7). BI Assimilation (ASM-BI) Liang et al. (2007); Dubey
et al. (2018) 1 To what extent your organization has exploited the BI tools in every department (%) during pandemic crisis (ASM-
BI1). 2 To what extent all the functional departments in your organization used BI tool during the pandemic crisis (ASM-BI2). 3 To what extent your organization use BI tools in each functional department as indicated by you:
a) Business operations b) Management practices c) Decision making (ASM-BI3).
Appendix B. Skewness (top) and exc. kurtosis (bottom) coefficients
CP NP MP TLC BI-ASM ACP RO OS
− 0.488 − 0.634 − 0.286 − 0.453 − 0.862 − 0.289 − 1.202 1.618 0.584 0.858 0.104 − 0.073 − 0.205 − 0.606 1.307 2.183
References
Ahmad, A., & Hossain, M. A. (2018). Assimilation of business intelligence systems: The mediating role of organizational knowledge culture. Conference on e-Business, e- Services and e-Society (pp. 480–491). Cham: Springer.
Ain, N., Vaia, G., DeLone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success–A systematic literature review. Decision Support Systems, 125, Article 113113.
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.
Audzeyeva, A., & Hudson, R. (2016). How to get the most from a business intelligence application during the post implementation phase? Deep structure transformation at a UK retail bank. European Journal of Information Systems, 25(1), 29–46.
Bellamy, M. A., Ghosh, S., & Hora, M. (2014). The influence of supply network structure on firm innovation. Journal of Operations Management, 32(6), 357–373.
Bhakoo, V., & Choi, T. (2013). The iron cage exposed: Institutional pressures and heterogeneity across the healthcare supply chain. Journal of Operations Management, 31(6), 432–449.
Božič, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role of absorptive capacity. International Journal of Information Management, 46, 93–103.
Brown, D. H., & Kaewkitipong, L. (2009). Relative size and complexity: e-business use in small and medium sized tourism enterprises in Thailand. Journal of Enterprise Information Management, 22(1/2), 212–231.
Bryce, C., Ring, P., Ashby, S., & Wardman, J. K. (2020). Resilience in the face of uncertainty: Early lessons from the COVID-19 pandemic. Journal of Risk Research, 23 (7–8), 880–887.
Carmines, E. G., McIver, J. P., Bohrnstedt, G. W., & Borgatta, E. F. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. Social measurement: Current issues (pp. 65–115), 80.
Chen, Y., & Lin, Z. (2020). Business intelligence capabilities and firm performance: A study in China. International Journal of Information Management, Article 102232.
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39.
Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modelling. MIS Quarterly, 22(1), Vii–Xvii.
Chin, W. W., Thatcher, J. B., & Wright, R. T. (2012). Assessing common method bias: Problems with the ULMC technique. MIS Quarterly, 36(3), 1003–1019.
Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73.
Cohen, B. H. (2008). Explaining psychological statistics. John Wiley & Sons. Colwell, S. R., & Joshi, A. W. (2013). Corporate ecological responsiveness: Antecedent
effects of institutional pressure and top management commitment and their impact on organizational performance. Business Strategy and the Environment, 22(2), 73–91.
Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29.
Davidson, R., & MacKinnon, J. G. (1993). Estimation and inference in Econometrics. Oxford: Oxford University Press.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Delmas, M., & Toffel, M. W. (2004). Stakeholders and environmental management practices: an institutional framework. Business Strategy and the Environment, 13(4), 209–222.
Dillman, D. A. (2011). Mail and Internet surveys: The tailored design method–2007 update with new internet, visual, and mixed-mode guide. John Wiley & Sons.
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Hazen, B. T., & Roubaud, D. (2018). Examining top management commitment to TQM diffusion using institutional and upper echelon theories. International Journal of Production Research, 56(8), 2988–3006.
A. Chaubey and C.K. Sahoo http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0005 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0005 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0005 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0010
http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0010 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0010 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0015 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0015 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0020 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0020 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0020 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0025 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0025 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0030 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0030 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0030 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0035 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0035 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0035 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0040 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0040 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0040 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0045 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0045 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0045 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0050 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0050 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0050 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0055 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0055 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0060 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0060 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0060 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0065 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0065 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0070 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0070 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0075 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0075 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0080 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0085 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0085 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0085 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0090 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0090 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0090 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0095 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0095 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0100 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0100 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0105 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0105 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0105 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0110 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0110 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0115 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0115 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0115 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0120 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0120 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0120 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0120 International Journal of Information Management 59 (2021) 102344 10Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30 (2), 341–361.
Dubey, R., Gunasekaran, A., Childe, S. J., & Papadopoulos, T. (2019). Supplier relationship management for circular economy: Influence of external pressures and top management commitment. Management Decision, 57(4), 767–790.
Dubey, R., Bryde, D. J., Foropon, C., Tiwari, M., Dwivedi, Y., & Schiffling, S. (2020). An investigation of information alignment and collaboration as complements to supply chain agility in humanitarian supply chain. International Journal of Production Research, 1–20. https://doi.org/10.1080/00207543.2020.1865583.
Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58 (11), 3381–3398.
Dwivedi, Y. K., Kapoor, K. K., Williams, M. D., & Williams, J. (2013). RFID systems in libraries: An empirical examination of factors affecting system use and user satisfaction. International Journal of Information Management, 33(2), 367–377.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re- examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Galanos, V. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994.
Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., … Raman, R. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International Journal of Information Management, 55, Article 102211.
Eckstein, D., Goellner, M., Blome, C., & Henke, M. (2015). The performance impact of supply chain agility and supply chain adaptability: The moderating effect of product complexity. International Journal of Production Research, 53(10), 3028–3046.
Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135–153.
Fawcett, S. E., Waller, M. A., Miller, J. W., Schwieterman, M. A., Hazen, B. T., & Overstreet, R. E. (2014). A trail guide to publishing success: tips on writing influential conceptual, qualitative, and survey research. Journal of Business Logistics, 35(1), 1–16.
Fichman, R. G. (2001). The role of aggregation in the measurement of IT-related organizational innovation. MIS Quarterly, 25(4), 427–455.
Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(3), 39–50.
Fosso Wamba, S., & Queiroz, M. M. (2020). Industry 4.0 and the supply chain digitalisation: A blockchain diffusion perspective. Production Planning and Control, 1–18. https://doi.org/10.1080/09537287.2020.1810756.
George, J. F., Gupta, M., Giordano, G., Mills, A. M., Tennant, V. M., & Lewis, C. C. (2018). The effects of communication media and culture on deception detection accuracy. MIS Quarterly, 42(2), 551–575.
Greenwood, R., & Hinings, C. R. (1996). Understanding radical organizational change: Bringing together the old and the new institutionalism. The Academy of Management Review, 21(4), 1022–1054.
Greenwood, R., Hinings, C. R., & Whetten, D. (2014). Rethinking institutions and organizations. Journal of Management Studies, 51(7), 1206–1220.
Guide, V. D. R., & Ketokivi, M. (2015). Notes from the Editors: Redefining some methodological criteria for the journal. Journal of Operations Management, (37), v–viii.
Gupta, M., & Gupta, S. (2019). Influence of national cultures on operations management and supply chain management practices—A research agenda. Production and Operations Management, 28(11), 2681–2698.
Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. The Academy of Management Review, 9(2), 193–206.
Harris, M., Bhatti, Y., Buckley, J., & Sharma, D. (2020). Fast and frugal innovations in response to the COVID-19 pandemic. Nature Medicine, 26, 814–817.
Hazen, B. T., Overstreet, R. E., & Cegielski, C. G. (2012). Supply chain innovation diffusion: Going beyond adoption. The International Journal of Logistics Management, 23(1), 119–134.
Heugens, P. P., & Lander, M. W. (2009). Structure! Agency!(and other quarrels): A meta- analysis of institutional theories of organization. The Academy of Management Journal, 52(1), 61–85.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling A Multidisciplinary Journal, 6(1), 1–55.
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E Logistics and Transportation Review, 136, Article 101922.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915.
Iyengar, K., Sweeney, J. R., & Montealegre, R. (2015). Information technology use as a learning mechanism: The impact of IT use on knowledge transfer effectiveness, absorptive capacity, and franchisee performance. MIS Quarterly, 39(3), 615–642.
Jarvenpaa, S. L., & Ives, B. (1991). Executive involvement and participation in the management of information technology. MIS Quarterly, 205–227.
Kar, A. K., & Dwivedi, Y. K. (2020). Theory building with big data-driven research–Moving away from the “What” towards the “Why”. International Journal of Information Management, 54, Article 102205.
Kauppi, K. (2013). Extending the use of institutional theory in operations and supply chain management research. International Journal of Operations & Production Management, 33(10), 1318–1345.
Ketokivi, M. A., & Schroeder, R. G. (2004). Perceptual measures of performance: fact or fiction? Journal of Operations Management, 22(3), 247–264.
Koh, S. L., & Gunasekaran, A. (2006). A knowledge management approach for managing uncertainty in manufacturing. Industrial Management & Data Systems, 106(4), 439–459.
Kostova, T., & Roth, K. (2002). Adoption of an organizational practice by subsidiaries of multinational corporations: Institutional and relational effects. The Academy of Management Journal, 45(1), 215–233.
Kummitha, R. K. R. (2020). Smart technologies for fighting pandemics: The techno-and human-driven approaches in controlling the virus transmission. Government Information Quarterly, Article 101481.
Laato, S., Islam, A. N., Islam, M. N., & Whelan, E. (2020). What drives unverified information sharing and cyberchondria during the COVID-19 pandemic? European Journal of Information Systems, 29(3), 288–305.
Lee, S. M., & Trimi, S. (2020). Convergence innovation in the digital age and in the COVID-19 pandemic crisis. Journal of Business Research, 123, 14–22.
Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management. MIS Quarterly, 31(1), 59–87.
Lin, J., Luo, Z., & Luo, X. (2020). Understanding the roles of institutional pressures and organizational innovativeness in contextualized transformation toward e-business: Evidence from agricultural firms. International Journal of Information Management, 51, Article 102025.
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. The Journal of Applied Psychology, 86(1), 114–121.
Liu, H., Ke, W., Wei, K. K., Gu, J., & Chen, H. (2010). The role of institutional pressures and organizational culture in the firm’s intention to adopt internet-enabled supply chain management systems. Journal of Operations Management, 28(5), 372–384.
Liu, H., Wei, S., Ke, W., Wei, K. K., & Hua, Z. (2016). The configuration between supply chain integration and information technology competency: A resource orchestration perspective. Journal of Operations Management, 44, 13–29.
López-Robles, J. R., Otegi-Olaso, J. R., Gómez, I. P., & Cobo, M. J. (2019). 30 years of intelligence models in management and business: A bibliometric review. International Journal of Information Management, 48, 22–38.
Lowry, P. B., Zhang, D., Zhou, L., & Fu, X. (2010). Effects of culture, social presence, and group composition on trust in technology-supported decision-making groups. Information Systems Journal, 20(3), 297–315.
Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: From constructs to theory. Journal of Operations Management, 16(4), 407–425.
Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.
McKinsey & Company. (2020). Shaping the new normal: India’s auto component industry. September, Available at: https://www.mckinsey.com/-/media/McKinsey/Featured %20Insights/India/Shaping%20the%20new%20normal/Shaping%20the%20new% 20normal_report (Date of Access: 16th November, 2020).
Mithas, S., Ramasubbu, N., & Sambamurthy, V. (2011). How information management capability influences firm performance. MIS Quarterly, 35(1), 237–256.
Nam, D., Lee, J., & Lee, H. (2019). Business analytics adoption process: An innovation diffusion perspective. International Journal of Information Management, 49, 411–423.
Oliver, C. (1997). Sustainable competitive advantage: Combining institutional and resource-based views. Strategic Management Journal, 18(9), 697–713.
Olszak, C. M. (2016). Toward better understanding and use of Business Intelligence in organizations. Information Systems Management, 33(2), 105–123.
Overstreet, R. E., Hazen, B. T., Skipper, J. B., & Hanna, J. B. (2014). Bridging the gap between strategy and performance: Using leadership style to enable structural elements. Journal of Business Logistics, 35(2), 136–149.
Pan, S. L., Cui, M., & Qian, J. (2020). Information resource orchestration during the COVID-19 pandemic: A study of community lockdowns in China. International Journal of Information Management, 54, Article 102143.
Pan, S. L., & Zhang, S. (2020). From fighting COVID-19 pandemic to tackling sustainable development goals: An opportunity for responsible information systems research. International Journal of Information Management, 55, Article 102196.
Papadopoulos, T., Baltas, K. N., & Balta, M. E. (2020). The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. International Journal of Information Management, 55, Article 102192.
Pennings, J. M., & Harianto, F. (1992). The diffusion of technological innovation in the commercial banking industry. Strategic Management Journal, 13(1), 29–46.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903.
A. Chaubey and C.K. Sahoo http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0125 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0125 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0125 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0125 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0130 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0130 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0130 https://doi.org/10.1080/00207543.2020.1865583 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0140 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0140 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0140 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0140 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0145 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0145 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0145 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0150 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0150 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http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0175 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0175 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0175 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0180 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0180 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0185 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0185 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0185 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0190 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0190 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0190 https://doi.org/10.1080/09537287.2020.1810756 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0200 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0200 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0200 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0205 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0205 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http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0240 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0240 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0240 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0245 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0245 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0245 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0250 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0250 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0250 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0250 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0255 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0255 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0255 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0260 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0260 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0260 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http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0295 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0295 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0295 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0300 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0300 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0300 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0305 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0305 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0310 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0310 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0310 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0315 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0315 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0315 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0315 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0320 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0320 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0325 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0325 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0325 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0330 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0330 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0330 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0335 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0335 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0335 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0340 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0340 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0340 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0345 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0345 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0350 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0350 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0350 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0355 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0355 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0355 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0355 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0360 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0360 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0365 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0365 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0370 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0370 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0375 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0375 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0380 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0380 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0380 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0385 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0385 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0385 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0390 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0390 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0390 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0395 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0395 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0395 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0400 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0400 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0405 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0405 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0410 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0410 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0410 International Journal of Information Management 59 (2021) 10234411
Prakash, C. D., & Majumdar, A. (2021). Analyzing the role of national culture on content creation and user engagement on Twitter: The case of Indian Premier League cricket franchises. International Journal of Information Management, 57, Article 102268.
Pramanik, D., Mondal, S. C., & Haldar, A. (2020). A framework for managing uncertainty in information system project selection: An intelligent fuzzy approach. International Journal of Management Science and Engineering Management, 15(1), 70–78.
Purvis, R. L., Sambamurthy, V., & Zmud, R. W. (2001). The assimilation of knowledge platforms in organizations: An empirical investigation. Organization Science, 12(2), 117–135.
Queiroz, M., Tallon, P. P., Sharma, R., & Coltman, T. (2018). The role of IT application orchestration capability in improving agility and performance. The Journal of Strategic Information Systems, 27(1), 4–21.
Queiroz, M. M., Ivanov, D., Dolgui, A., & Wamba, S. F. (2020). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 1–38. https://doi.org/10.1007/s10479-020-03685-7.
Ramakrishnan, T., Jones, M. C., & Sidorova, A. (2012). Factors influencing business intelligence (BI) data collection strategies: An empirical investigation. Decision Support Systems, 52(2), 486–496.
Ranjan, J., & Foropon, C. (2021). Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, Article 102231.
Remko, V. H. (2020). Research opportunities for a more resilient post-COVID-19 supply chain–closing the gap between research findings and industry practice. International Journal of Operations & Production Management, 40(4), 341–355.
Rossiter, J. R. (2008). Content validity of measures of abstract constructs in management and organizational research. British Journal of Management, 19(4), 380–388.
Saldanha, J. P., Mello, J. E., Knemeyer, A. M., & Vijayaraghavan, T. A. S. (2015). Implementing supply chain technologies in emerging markets: An institutional theory perspective. The Journal of Supply Chain Management, 51(1), 5–26.
Schilke, O. (2014). On the contingent value of dynamic capabilities for competitive advantage: The nonlinear moderating effect of environmental dynamism. Strategic Management Journal, 35(2), 179–203.
Schoemaker, P. J., Heaton, S., & Teece, D. (2018). Innovation, dynamic capabilities, and leadership. California Management Review, 61(1), 15–42.
Schryen, G. (2013). Revisiting IS business value research: What we already know, what we still need to know, and how we can get there. European Journal of Information Systems, 22(2), 139–169.
Shao, Z. (2019). Interaction effect of strategic leadership behaviors and organizational culture on IS-Business strategic alignment and Enterprise Systems assimilation. International Journal of Information Management, 44, 96–108.
Sila, I. (2007). Examining the effects of contextual factors on TQM and performance through the lens of organizational theories: An empirical study. Journal of Operations Management, 25(1), 83–109.
Singh, P. J., Power, D., & Chuong, S. C. (2011). A resource dependence theory perspective of ISO 9000 in managing organizational environment. Journal of Operations Management, 29(1-2), 49–64.
Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849–1867.
Teo, H. H., Wei, K. K., & Benbasat, I. (2003). Predicting intention to adopt interorganizational linkages: An institutional perspective. MIS Quarterly, 27(1), 19–49.
The State of BI and Business Analytics Report (2020). https://www.sisense.com/en-gb/ whitepapers/state-of-bi-analytics-report-2020/ (Date of access 13th October, 2020).
Tian, X., Chiong, R., Martin, B., Stockdale, R., Arefin, M. S., Hoque, M. R., … Bao, Y. (2015). The impact of business intelligence on organization’s effectiveness: An empirical study. Journal of Systems and Information Technology, 17(3), 263–285.
Wagner, S. M., & Kemmerling, R. (2010). Handling nonresponse in logistics research. Journal of Business Logistics, 31(2), 357–381.
Wang, H. C. (2014). Distinguishing the adoption of business intelligence systems from their implementation: The role of managers’ personality profiles. Behaviour & Information Technology, 33(10), 1082–1092.
Wendorf, C. A. (2002). Comparisons of structural equation modeling and hierarchical linear modeling approaches to couples’ data. Structural Equation Modeling, 9(1), 126–140.
Williams, M. D., Dwivedi, Y. K., Lal, B., & Schwarz, A. (2009). Contemporary trends and issues in IT adoption and diffusion research. Journal of Information Technology, 24(1), 1–10.
Yang, F., Huang, X., & Wu, L. (2019). Experiencing meaningfulness climate in teams: How spiritual leadership enhances team effectiveness when facing uncertain tasks. Human Resource Management, 58(2), 155–168.
Ye, F., Zhao, X., Prahinski, C., & Li, Y. (2013). The impact of institutional pressures, top managers’ posture and reverse logistics on performance—Evidence from China. International Journal of Production Economics, 143, 132–143.
Zhang, C., & Dhaliwal, J. (2009). An investigation of resource-based and institutional theoretic factors in technology adoption for operations and supply chain management. International Journal of Production Economics, 120(1), 252–269.
Zhu, K., Kraemer, K. L., & Xu, S. (2006). The process of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business. Management Science, 52(10), 1557–1576.
Zhu, Q., Sarkis, J., & Lai, K.-H. (2013). Institutional-based antecedents and performance outcomes of internal and external green supply chain management practices. Journal of Purchasing and Supply Management, 19(2), 106–117.
Zmud, R. W., & Apple, L. E. (1992). Measuring technology incorporation/infusion. The Journal of Product Innovation Management, 9(2), 148–155.
Zuo, M., Ma, D., & Yu, Y. (2020). Contextual determinants of IT governance mechanism formulation for senior care services in local governments. International Journal of Information Management, 53, Article 102125.
A. Chaubey and C.K. Sahoo http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0415 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0415 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0415 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0420 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0420 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0420 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0425 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0425 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0425 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0430 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0430 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0430 https://doi.org/10.1007/s10479-020-03685-7 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0440 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0440 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0440 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0445 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0445 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0445 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0450 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0450 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0450 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0455 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0455 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0460 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0460 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0460 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0465 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0465 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0465 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0470 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0470 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0475 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0475 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0475 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0480 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0480 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0480 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0485 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0485 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0485 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0490 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0490 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0490 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0495 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0495 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0495 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0500 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0500 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0500 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0510 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0510 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0510 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0515 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0515 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0520 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0520 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0520 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0525 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0525 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0525 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0530 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0530 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0530 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0535 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0535 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0535 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0540 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0540 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0540 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0545 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0545 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0545 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0550 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0550 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0550 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0555 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0555 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0555 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0560 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0560 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0565 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0565 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0565- Assimilation of business intelligence: The effect of external pressures and top leaders commitment during pandemic crisis 1 Introduction 2 Research model and hypotheses 2.1 Institutional theory and BI assimilation 2.1.1 Coercive pressures (CP) 2.1.2 Normative pressures (NP) 2.1.3 Mimetic pressures (MP) 2.2 Top leaders commitment and BI assimilation (BI-ASM) 3 Research method 3.1 Construct operationalization and measurement 3.2 Sample and data collection 3.3 Non-response bias 4 Data analyses and results 4.1 Measurement model 4.2 Common method bias 4.3 Endogeneity test 4.4 Hypotheses testing 5 Discussion 5.1 Theoretical contributions 5.2 Managerial implications 5.3 Limitations and further research direction 6 Conclusions Authors comment Appendix A Constructs and Items Appendix B Skewness (top) and exc. kurtosis (bottom) coefficients References
International Journal of Information Management 59 (2021) 10234
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Available online 11 March 2021 0268-4012/© 2021 Elsevier Ltd. All rights reserved.
Research Article
Assimilation of business intelligence: The effect of external pressures and top leaders commitment during pandemic crisis
Akriti Chaubey a,*, Chandan Kumar Sahoo b
a School of Management, National Institute of Technology Rourkela, Rourkela, 769008, India b Human Resource Management, School of Management, National Institute of Technology Rourkela, Rourkela, 769008, India
A R T I C L E I N F O
Keywords: Business intelligence Institutional theory Business intelligence assimilation Leadership COVID-19
A B S T R A C T
The business intelligence (BI) has been often touted as a game-changer especially during the pandemic crisis. Although most managers are familiar with BI and agree that, it should be operationalized across their organi- zations. The BI is not well assimilated throughout adopting organizations. Rooted in institutional and upper echelon theories, this study proposes a theoretical model aimed toward explaining BI assimilation. We surveyed 174 respondents occupying leadership positions from174 auto-components manufacturing firms in India to gather data. The findings suggest that normative and mimetic (but not coercive) factors significantly influence top leader’s commitment to the BI initiatives. We found that the commitment of the top leaders influences the assimilation of BI via acceptance and routinization. Our study is an attempt to address the previous research calls related to BI assimilation. The findings of the study inform the information management scholars via theory- based research on phenomena related to post-adoption BI diffusion during a pandemic crisis. Practitioners can utilize the results of our study to design their policies that help assimilate BI such that forecasted benefits can be fully realized during an uncertain time.
1. Introduction
“Necessity has been the mother of invention in the response to the COVID- 19 pandemic, triggering many an innovation, often without the luxury of time to test these makeshift solutions to pressing problems. But there is much to be learned from times of crisis for times of plenty” (Harris, Bhatti, Buckley, & Sharma, 2020, p. 814)
The pandemic due to COVID-19 has seriously affected the small and medium enterprises (Dwivedi et al., 2020; Ivanov & Dolgui, 2020; Papadopoulos, Baltas, & Balta, 2020; Remko, 2020). Many organisations have significantly exploited the business intelligence (BI) capability to stay afloat in this unprecedented time (Kummitha, 2020; Queiroz, Tal- lon, Sharma, & Coltman, 2018; Ranjan & Foropon, 2021). It is well understood that BI plays an important role in improving business per- formance (Dwivedi et al., 2021; Koh & Gunasekaran, 2006; Pramanik, Mondal, & Haldar, 2020). In a recent report published by Sisence (The State of BI and Business Analytics Report, 2020) has highlighted sig- nificant rise in the use of BI and analytics in response to COVID-19 crisis (Queiroz, Ivanov, Dolgui, & Wamba, 2020). Although there are numerous BI success stories reported in the academic literature (Olszak,
2016), there remain many skeptics who often criticize the role and impact of BI (see, Božič & Dimovski, 2019) during pandemic crisis (Lee & Trimi, 2020). Although, the failure stories of the BI has gathered significant attention from the academic community (Tian et al., 2015) and in many instances, predicted benefits of BI are not realized (Aud- zeyeva & Hudson, 2016). Furthermore, BI is often inconsistently oper- ationalized across different contexts (see, Chen & Lin, 2020) and is often implemented based on prescriptive and not participative assumptions. Despite of rich body of literature on BI, the existing literature has largely remained silent on how BI is assimilated across the organisation (Elba- shir, Collier, & Davern, 2008; Fosso Wamba & Queiroz, 2020).
While there is a rich body of literature on factors influencing the success of BI implementation (Ramakrishnan, Jones, & Sidorova, 2012; Wang, 2014; López-Robles et al., 2019), studies aimed toward explain- ing BI assimilation are limited (Ahmad & Hossain, 2018; Shao, 2019). The previous studies have noted that the adoption and implementation, are often considered as the foundation of the diffusion of any techno- logical innovation. In any organization (Dubey et al., 2018; Hazen, Overstreet, & Cegielski, 2012), and the full benefits may not be well realized by the organization until and unless the technological
* Corresponding author. E-mail addresses: akritichaubey25@gmail.com (A. Chaubey), sahooc@nitrkl.ac.in (C.K. Sahoo).
Contents lists available at ScienceDirect
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https://doi.org/10.1016/j.ijinfomgt.2021.102344 Received 30 November 2020; Received in revised form 5 March 2021; Accepted 5 March 2021
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2innovation is fully assimilated (Dubey et al., 2018; Dwivedi, Rana, Jeyaraj, Clement, & Williams, 2019; Hazen et al., 2012; Williams, Dwivedi, Lal, & Schwarz, 2009). Based on Purvis, Sambamurthy, and Zmud (2001) and Hazen et al. (2012) definitions, we define BI assimi- lation as the extent to which BI philosophy diffuses across organizational processes and activities. Hence, the key objective of BI post-implementation activities is to assimilate the philosophy and practices across business routines such that organization achieve maximum benefits of BI implementation (Nam, Lee, & Lee, 2019). Moreover, how organization assimilate during pandemic crisis is not well understood. The purpose of this study is to investigate the means through which BI is assimilated throughout organizations during pandemic crisis. To address our research objective, we posit two guiding research questions as:
RQ1: What are the antecedents of BI assimilation? RQ2: How can firms assimilate BI across their organizations during
pandemic crisis? Kar and Dwivedi (2020) argued in favour of building theory that may
help organization to understand how the use of big data analytics and business intelligence capability may enhance performance during un- certain environment. Drawing on institutional theory (DiMaggio & Powell, 1983) and upper echelon theory (Hambrick & Mason, 1984), we develop a theoretical model to explain how the external institutional forces and the top leader’s commitment influence BI assimilation within an organization. Extending the findings of Liang, Saraf, Hu, and Xue (2007) and Nam et al. (2019), we submit that top leader’s commitment plays a pivotal role in channelizing the external institutional pressures into BI assimilation. Furthermore, we extend the work of Wang (2014) and Ain, Vaia, DeLone, and Waheed (2019) by studying assimilation instead of adoption or implementation. Hazen et al. (2012) have attempted to explain the journey from adoption to assimilation using two intermediary stages, namely acceptance and routinization.
Following previous arguments we assume the role of external pres- sures (Liang et al., 2007) and top leader’s (internal human agents) play significant roles in the acceptance, routinization and assimilation of BI, we submit that the role of contextual assimilation factors remains largely unexplored. We therefore propose a BI assimilation framework for pandemic crisis, grounded in organizational theories, that offers two unique contributions to the literature (Pan & Zhang, 2020). Firstly, we examine BI assimilation using two organizational theories (i.e. institu- tional theory and upper echelon theory). Secondly, we investigate to what extent top leader’s commitment mediates the relationship between institutional pressures and BI acceptance. This research thus provides a new perspective on BI assimilation.
The remainder of the article is organized as follows. In the next section, we discuss the theoretical framework and research hypotheses. Second section focuses on the development of our research model and hypotheses. Third section focuses on the research method. In this sec- tion, we discuss our questionnaire development, sampling design and data collection strategy. In the fourth section, we present our data analysis and results. In the fifth section, we present our discussion sec- tion based on our research findings. In this section, we have further discussed our contributions to the theory. In the same section, we further discuss our findings in context to the practice. We further outlined our limitations of our study and further noted future research directions. Finally, we concluded our study.
2. Research model and hypotheses
Our research model is grounded in extant literature. The foundation of the model is comprised of two elements, namely, institutional theory and upper echelon theory. Kauppi (2013) suggests that “…operations management (OM) researchers and practitioners tend to view their work in terms of the logic of rational efficiency, which has been questioned by organizational theorists arguing that rational action is always embedded in a social context…” (p. 1318). Hence, institutional theory may provide an
alternative perspective to examine the complexity of BI assimilation (BI-ASM).
Liang et al. (2007) developed a model to explain the assimilation of ERP using institutional theory. Our model attempts to extend Liang et al. (2007) work by examining BI assimilation. Furthermore, consistent with the work of Dubey et al. (2018), top leaders commitment is proposed to translate external forces (institutional pressures) into desired assimila- tion of BI. In our study, we draw from the extensive literature on insti- tutional theory (see, Oliver, 1997; Delmas & Toffel, 2004; Colwell & Joshi, 2013; Greenwood, Hinings, & Whetten, 2014; Dubey, Gunase- karan, Childe, & Papadopoulos, 2019) to develop a research model that identifies the antecedents of BI assimilation. In doing so, we seek to address our guiding research questions. Our research model (Fig. 1) is grounded in the proposition that institutional forces affect organiza- tional behaviour after being mediated by the leaders. Based on previous arguments, we have presented our research hypotheses. In the next subsections, we further discuss these hypotheses.
2.1. Institutional theory and BI assimilation
Zhu, Kraemer, and Xu (2006) advocate for innovation assimilation, noting that regulatory environment plays an important role. Liang et al. (2007) further found that institutional pressures significantly affect assimilation of ERP. Chinese firms comprise the setting of the study conducted by Liang and colleagues, suggesting that the role of legiti- macy in developing countries can help explain assimilation. To this end, Li et al. (2008) argued that ERP implementation can be successful if it is preceded by a BI focus. Drawing on these studies, we adapt the assimi- lation concept for the BI literature.
Dubey et al. (2018) research sought to explain TQM assimilation using three institutional factors and top management commitment. In our current study, we attempt to examine BI assimilation using institu- tional pressures to offer deeper insight into post-adoption processes. Singh, Power, and Chuong (2011) suggest that theory-based explanation enhances understanding and appreciation for standards, and provides clarity on how standards benefits organizations In comparison to other organization theories such as resource dependence theory (Singh et al., 2011) and contingency theory (Sila, 2007). Dubey et al. (2018, p.2992) argue that “the institutional theory posits that structural and behavioural changes in the organization are driven less by competition and the desire for efficiency, but more by the need of organizational legitimacy” (c.f DiMaggio & Powell, 1983). DiMaggio and Powell (1983) argue that the desire of the organization to align their business strategies in the line of the stakeholder’s expectations (i.e. legitimacy), the organization often embrace institutional logic. We can also refer the process of seeking legitimacy via embracing institutional logic as ‘institutional isomorphism’ (see, DiMaggio & Powell, 1983; Liang et al., 2007; Kauppi, 2013; Lin, Luo, & Luo, 2020; Zuo, Ma, & Yu, 2020). The institutional isomorphism occurs via three stages: coercive pressure, which refers to the external pressures resulting from government or any regulatory bodies or ex- pectations from cultural expectations of the community or any profes- sional associations (Liang et al., 2007). In an attempt to negate the pressures arising from external agencies or bodies, organization develop “coercive isomorphism” (Dubey et al., 2018). Normative pressures arise from professionalization, which is defined by DiMaggio and Powell (1983) as “…the collective struggle of members of an occupation to define the conditions and methods of their work, to control the production of the future member professionals, and to establish a cognitive base and legitimisation for their occupational autonomy.” Organizational researchers have noted that employee sharing similar traits (and hence normative isomorphism) which, is often developed via professional education and training (see DiMaggio & Powell, 1983; Liang et al., 2007; Heugens & Lander, 2009; Dubey et al., 2018; Dubey, Gunasekaran, Childe, Blome, & Papado- poulos, 2019; Zuo et al., 2020). Mimetic pressures refer to mimicking actions of organizations with respect to their competitors. This is often done because of environmental uncertainty, such as when new
A. Chaubey and C.K. Sahoo
International Journal of Information Management 59 (2021) 1023443
technology is not well understood, organizations have struggled to explain any degree of uncertainties or there is poor alignment of vision, mission and goals in organizations. In such cases, organizations develop mimetic isomorphism (DiMaggio & Powell, 1983). In sum, institutional theory can offer interesting perspective to understand BI assimilation.
2.1.1. Coercive pressures (CP) Liu, Ke, Wei, Gu, and Chen (2010) argued that institutional pressures
is considered as an important driver particularly in context to the adoption of innovation. Management scholars have increasingly argued that the pressures resulting from the government and other bodies, are transmitted via operational channels, affect the organization predispo- sition towards adoption of technology (Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019; Liang et al., 2007; Liu et al., 2010; Zhu et al., 2006). It has been shown that coercive pressures have significant influence on the adoption of ERP (Liang et al., 2007) and TQM (Dubey et al., 2018). Following previous studies we believe that coercive pressures plays a significant role in the assimilation process (see, Liang et al., 2007; Dubey et al., 2018). The existing studies on assimilation suggest that coercive pres- sures often arises from local government policies or regulatory author- ities or expectations from local bodies and community, may have direct or indirect impact on assimilation (see, Liang et al., 2007; Dubey et al., 2018). Extending this argument to BI assimilation, we hypothesise as follows:
H1. The coercive pressure has positive and significant effect on top leader’s commitment.
2.1.2. Normative pressures (NP) Following institutional logic, we argue that the institutional envi-
ronment shape the working behaviour of the individuals and the orga- nizations (DiMaggio & Powell, 1983; Liu et al., 2010; Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019). Following Liu et al. (2010, p. 374), Normative pressures (NP) refer to the “pressures that stem from collective expectations within particular organizational contexts of what constitutes appropriate, and thus legitimate, behaviour”. NP penetrate via channels of professional affiliations as well as the popularity generated by confer- ences hosted by professional bodies (Liang et al., 2007). Lowry, Zhang, Zhou, and Fu (2010) argue that normative isomorphism play an important role in the diffusion of new technology innovation in any organization. These arguments are consistent with the Zhu et al. (2006) findings. Moreover, Dubey et al. (2018) in one of their studies have found that normative pressures play a significant role in case TQM assimilation. Hence, we believe that normative pressures have signifi- cant influence during assimilation stage. Thus, we hypothesize it as:
H2. The normative pressure has positive and significant effect on top leader’s commitment.
2.1.3. Mimetic pressures (MP) DiMaggio and Powell (1983, p. 151) argue, “not all institutional
isomorphism, however, derives from coercive authority. Uncertainty is also a powerful force that encourages imitation. When organizational technologies are poorly understood, when goals are ambiguous, or when the environment creates symbolic uncertainty, organizations may model themselves on other organizations”. When organization faces highly uncertain and ambiguous, the role of leadership is highly critical in shaping organizational strategies (Schoemaker, Heaton, & Teece, 2018; Yang, Huang, & Wu, 2019). Schoemaker et al. (2018) argue that in highly uncertain environment, the leaders instead of focusing on executing the plan, the leaders prepare the organization to quickly adapt to the rapid changes with the help of technology. Prior studies have found that mimetic pressures influence management commitment (Liang et al., 2007; Zuo et al., 2020). The MP arise from the tendency of organizations to mimic other organizations. Previous studies (see, Liang et al., 2007; Dubey et al., 2018, 2019a; Lin et al., 2020; Zuo et al., 2020) have noted that lack of clarity regarding the outcomes of the programs whether it is referring to ERP adoption or adoption of TQM related practices, organizations mimic other organizations within the similar industry. The COVID-19 crisis has caused significant disruption and the continuous lockdown has create high degree of uncertainties (Bryce, Ring, Ashby, & Wardman, 2020; Pan, Cui, & Qian, 2020). Hence, we believe that mimetic pressures have significant influence during assim- ilation stage. Thus, we hypothesize it as:
H3. The mimetic pressure has positive and significant effect on top leader’s commitment.
2.2. Top leaders commitment and BI assimilation (BI-ASM)
Institutional theory predicts institutional isomorphism but in reality, organizations exhibit diversity with respect to benefits from BI imple- mentation or manifest different levels of BI assimilation under similar institutional environments. The organizational theorist have often argued that institutional logic; often fail to explain the mechanism of the translation of the external pressures in shaping organizational internal policies (Colwell & Joshi, 2013; Kostova & Roth, 2002). Hence, following Colwell and Joshi (2013) and Liang et al. (2007) arguments we, argue that role of human agent help address the limitations of the institutional theory. Thus, we propose that top leaders commitment towards BI may help translate external pressures into BI assimilation. Top leaders are expected to share the vision and mission of their orga- nization with their employees (Dubey, Gunasekaran, Bryde, Dwivedi, & Papadopoulos, 2020; Dubey et al., 2018; Dubey, Bryde et al., 2020). They not only motivate their team members but also provide adequate resources that may help assimilate BI (Dubey et al., 2018; Liang et al., 2007), thereby creating a BI culture which is conductive for employees’ involvement. Overstreet, Hazen, Skipper, and Hanna (2014) that servant leadership theory can assist in providing deeper insights into organiza- tional commitment, which indirectly leads to business performance result. Hence, we hypothesize based on previous arguments as:
H4. The top leader’s commitment has positive and significant effect on the BI acceptance;
Fig. 1. BI Assimilation Model.
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 102344 4Hazen et al. (2012) have suggested ‘acceptance’ and ‘routinization’ as two preceding activities that help assimilation. Acceptance has attracted significant attention from management scholars (see, Davis, 1989; Zhu et al., 2006; Hazen et al., 2012; Ahmad & Hossain, 2018). Acceptance (ACP) in context to BI can be defined as how well organi- zations constituents receive BI. Hazen et al. (2012) have argued that once organizational constituents have accepted an innovation like BI as a guiding philosophy, then it begins the process of being routinized within organizations. Based on Dubey et al. (2018, p. 2993) arguments we define “BI routinization as the permanent adjustment of the organiza- tions’ governance systems to account for BI”. Based on previous studies on information management and technology innovation (see, Jarvenpaa & Ives, 1991; Purvis et al., 2001; Liang et al., 2007) we posit that the top leader’s commitment may contribute to the BI assimilation via accep- tance. Thus, we hypothesize:
H5. The BI acceptance has positive and significant effect on BI routinization;
Following Zhu et al. (2006), we argue that the routinization is one of the stages involved between adoption of the BI and the assimilation of the BI. Following Zmud and Apple (1992, p. 149) we understand routinization as “the permanent adjustment of an organization’s governance system to account for the incorporation of a technology”. Routinization refers to the organizational ability to put a procedure in the place that evaluates the equipment turnover procedures to assure that the orga- nization is prepared for the dynamic changes in the environment. This is consistent with the innovation literature. Zhu et al. (2006) have found that the routinization has a significant effect on the assimilation of the technology. Ahmad and Hossain (2018) arguments is in consistent with the Zhu et al. (2006) and the Hazen et al. (2012). In pretext of pandemic crisis, the technology is quickly evolving to keep the pace with the rapid changing environments. In such case the effective routinization pro- cedure in the organization play a significant role in the assimilation of the BI (Laato, Islam, Islam, & Whelan, 2020). Based on the preceding discussions, we hypothesize it as:
H6. The BI routinization has positive and significant effect on BI assimilation.
In our studies, we have controlled the size of the organization and time since the BI has been adopted in their organization (Brown & Kaewkitipong, 2009; Dubey et al., 2018; Liang et al., 2007). Liang et al. (2007) argue that large organizations are more resilient towards hur- dles, which tend to slow down the assimilation process. Furthermore, decision-making is quite faster in smaller in comparison to large size organizations. Hence, we believe that size of the organizations may have significant influence on findings. Time since adoption has been noted in several diffusion studies (see, Liang et al., 2007; Dubey et al., 2018) as an important variable. Liang et al. (2007) argue that time is a significant predictor of diffusion-related phenomenon like assimilation. Thus, this variable reflects the assimilation learning curve (see, Fichman, 2001).
3. Research method
3.1. Construct operationalization and measurement
In our study, we have followed Churchill (1979) suggestions to improve the reliability and validity of our study via following two-stage process. Firstly, we have undertaken an extensive review of literature to draw our construct and their measurement. Secondly, we have inter- viewed twelve managers who have extensive years of experience in the BI assimilation. We used qualitative content analysis to validate our multi-item constructs. In response to the previous calls of management scholars (Flynn, Huo, & Zhao, 2010; Malhotra & Grover, 1998; Rossiter, 2008; Mithas, Ramasubbu, & Sambamurthy, 2011; Schryen, 2013; Fawcett et al., 2014; Schilke, 2014; Dubey, Gunasekaran et al., 2020; Dubey, Bryde et al., 2020), we argue that qualitative content analysis is a
useful method to validate the borrowed multi-item construct with real-life practices. We further gathered secondary data related to our samples selected for our study. In a way we attempted to overcome the limitations of our literature review and secondly, we have further tried to reduce the negative effects of the common method bias resulting from single source of data (see, Chin, Thatcher, & Wright, 2012; Fawcett et al., 2014; Iyengar, Sweeney, & Montealegre, 2015).
We finally arrived to the list of items for each construct based on our review of the literature and on a pre-test exercise, which we carried out with the panel of eighteen identified experts to avoid any ambiguity of the measurement items (see Appendix A). The measures listed in the Appendix A were measured using Likert scale with the anchors ranging between: 1 (strongly disagree) to 5 (strongly agree).
3.2. Sample and data collection
We chose auto component manufacturing sector in India due to two main reasons: firstly, Indian auto component- manufacturing sector has experienced significant dip in the operating margin in comparison to the last year performance due to the pandemic crisis. Secondly, the Indian auto component-manufacturing sector has made significant investment in the BI capability to improve the competitiveness (McKinsey & Com- pany, 2020). Hence, we believe that the data gathered from Indian auto-component manufacturing sector using survey based tool will be highly useful for testing our research hypotheses.
We distributed our questionnaire via e-mail to the 532 auto- components manufacturing firms located in the western and the southern regions. We gathered the organisations detail from the data- base of the The Automotive Components Manufacturers Association of India (ACMA) and further validated the details via Dun & Bradstreet (see, Dubey et al., 2018). Following previous studies using survey based approach (see, Dwivedi, Kapoor, Williams, & Williams, 2013; Chen, Preston, & Swink, 2015; Dubey et al., 2018; Srinivasan & Swink, 2018; Dubey, Gunasekaran, Childe, Blome et al., 2019), was adopted following Dillman’s (2011) suggestions. We followed up with the respondents after one weeks to seek whether they have received an e-mail containing the introduction letter and the questionnaire. Firstly, we received questionnaire from 110 respondents. After several follow up calls, we again sent packages to non-respondents after two weeks and 64 ques- tionnaires were subsequently returned for an overall response rate of 32.71 %. In sum, 174 completed questionnaires were received (see Table 1). We acknowledge that our association with the ACMA has played an important role in gathering quality data.
3.3. Non-response bias
We note that data gathered using survey-based instrument at one point of time might suffer from non-response bias (Fawcett et al., 2014; Dubey et al., 2018, 2019a). In this study, we have used two approaches to examine non-response bias in our collected data. Firstly, we adopted traditional method [i.e., wave-analysis (Armstrong & Overton, 1977)] to test non-response bias. The comparisons between early and late re- sponses showed no statistical differences at p < 0.05, indicating that non-response bias is not a potential issue in our study. Secondly, following Wagner and Kemmerling (2010) arguments we have
Table 1 Demographic Profile of the Respondents.
Title Number %
CEO 9 5.17 CIO 33 18.97 Finance & Accounting Manager 15 8.62 Supply Chain Manager 67 38.51 Human Resource Manager 12 6.90 Customer Relationship Manager 23 13.22 Sales Manager 15 8.62
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compared the demographics of the respondents to demographic infor- mation from non-respondents via Dun & Bradstreet and observed no inconsistencies.
4. Data analyses and results
Before deciding on our modelling technique, we first performed an assumptions test on our indicators (see, Fawcett et al., 2014, p.13). Based on Eckstein, Goellner, Blome, and Henke (2015), we tested as- sumptions related to constant variance, outliers, and normality. We examined residual plots, rankits plot of residuals, and measures of skewness and kurtosis. Based on Cohen (2008), we used Mahalanobis distance to detect outliers. The maximum absolute values of skewness and kurtosis were found to be 1.26 and 2.29, respectively (see Appendix B). Based on Curran, West, and Finch (1996), we these values are well within recommended limits (univariate skewness < 2, kurtosis < 7). In sum, we found that the typical assumptions required for inference-based statistics were met.
4.1. Measurement model
We have performed confirmatory factor analysis (CFA) following Fornell and Larcker (1981) to examine the: (a) construct validity [indi- vidual factor loadings, scale composite reliability (SCR) and average vari- ance extracted (AVE)]. In our study we found that the individual factor loadings were greater than 0.5, the SCRs were calculated to be greater than 0.7, and the AVE for each construct was greater than 0.5 (Chin, 1998) (see, Table 2). (b) Next we have performed discriminant validity test (see, Table 3). The Table 3 represent a matrix that contain the correlations between paired constructs, and the leading diagonal of the matrix shows the square root of the AVE of each construct. All measures indicate adequate discriminant validity (Fornell & Larcker, 1981). Model fit indices indicated acceptable fit of the data to the measurement model [Normed Chi-Square = 1.9, which is less than 2 as recommended by Carmines, McIver, Bohrnstedt, and Borgatta (1981) and Hu and Bentler (1999) pointed that the threshold value of normed chi-square is 0.09].
4.2. Common method bias
The data gathered using single respondent questionnaire might suf- fer from common-method bias (CMB) that may affect our statistical re- sults (Podsakoff & Organ, 1986). To reduce the negative effects of the CMB, we performed Harman’s one factor test (see, Podsakoff, MacK- enzie, Lee, & Podsakoff, 2003) to examine whether a single latent factor would account for all the theoretical constructs (Dubey et al., 2018). The exploratory factor analysis yielded seven factors parsimonious structure. The single factor does not explain more than 13.95 % percent of total 51.03 percent of total variance. Hence common method bias is likely not a significant threat to the findings. Dubey, Gunasekaran, Childe, Blome et al. (2019), Dubey, Gunasekaran, Childe, Papadopoulos et al. (2019) noted that the Harman’s one factor test is a traditional approach and may not be sufficient to test CMB. We followed Malhotra, Kim, and Patil (2006) suggestions via performing CFA loading all items on a single factor, and further examine the fit indices. The single factor in this case is equivalent to “one factor”, which indicates the existence of bias resulting from data collection from a single source (Srinivasan & Swink, 2018). The fit for the one factor model is not adequate [RMSEA = 0.313; NNFI = 0.094; CFI = 0.231 and SRMR = 0.513] and chi-square change with respect to the hypothesized model is highly significant (p < 0.000). Finally, we tested for CMB using the correlation marker technique (Lindell & Whitney, 2001). We assumed an unrelated variable to delineate out the correlations caused by CMB. In addition, we computed the significances of the correlations based on Lindell and Whitney (2001) suggestions (Srinivasan & Swink, 2018; Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019). We observed minimal differences between adjusted and unadjusted correlations. Following the results based on three methods, we argue that the potential effects of the CMB is not significant. How- ever, we caution readers that in future an attempt should be made to collect data using instrument designed for multi-respondents (see, Ketokivi & Schroeder, 2004).
4.3. Endogeneity test
Following Guide and Ketokivi (2015) arguments, we adopted some measures to correct the endogeneity (see, Liu, Wei, Ke, Wei, & Hua, 2016). According to Guide and Ketokivi (2015, p. v), “when arguing that the variance of X gives rise to the variance of Y (causally or otherwise), we expect to see a plausible argument that the direction is indeed from X to Y, not vice versa, or perhaps caused by an omitted variable. Measurement error can also cause an endogeneity problem: if X and Y have a common measurement error source, X will unavoidably correlate with the error term of Y. Finally, sample selection bias may lead to problems very similar to that of endoge- neity”. Hence, we understand that we cannot eliminate the endogeneity problem due to our research design. However, we adopted some mea- sures to correct it in our model that may lead to inconsistent and biased outcomes (Liu et al., 2016). We performed two-stage least squares regression analysis with the questionnaire variables (Bellamy, Ghosh, & Hora, 2014; Liu et al., 2016). To conduct two-stage least squares regression analysis, we identified size of the organization as the poten- tial instrumental variable as it does not have significant effect on the BI assimilation. Following Bellamy et al. (2014) we regressed TLC on all instrumental variables and the control variables at the first stage. We observed that the R2 value increased significantly in comparison to the model with only control variables. This indicates that the organization size can be effectively assumed as an instrumental value for TLC in our study. Next, we conducted Durbin-Wu-Hausman post-estimation test for endogeneity (Davidson & MacKinnon, 1993). In this test we performed augmented test on the TLC by adding the error term that we obtained in the first stage while performing two-stage least squares regression test. The path coefficients of the error term of TLC were insignificantly related to the BI assimilation. This establishes that the endogeneity associated with the TLC is insignificant in our study. Thus, we can argue
Table 2 Loadings of Indicator Variables (Scale Composite Reliability and Average Vari- ance Extracted).
Constructs Measures Factor Loading Variance Error SCR AVE
CP CP-BI1 0.73 0.54 0.46 0.76 0.51 CP-BI2 0.70 0.49 0.51 CP-BI3 0.72 0.52 0.48
NP NP-BI1 0.87 0.76 0.24 0.85 0.65 NP-BI2 0.78 0.60 0.40 NP-BI3 0.76 0.58 0.42
MP
MP-BI1 0.83 0.69 0.31 0.89 0.67 MP-BI2 0.82 0.67 0.33 MP-BI3 0.82 0.67 0.33 MP-BI4 0.81 0.65 0.35
TLC
TLC2 0.79 0.62 0.38 0.83 0.50 TLC3 0.68 0.46 0.54 TLC4 0.53 0.28 0.72 TLC5 0.75 0.56 0.44 TLC6 0.75 0.57 0.43
BI-ASM ASM-BI1 0.69 0.48 0.52 0.75 0.50 ASM-BI2 0.61 0.37 0.63 ASM-BI3 0.81 0.65 0.35
ACP ACP-BI2 0.80 0.64 0.36 0.84 0.72 ACP-BI3 0.89 0.79 0.21
RO
RO-BI1 0.80 0.64 0.36 0.94 0.74 RO-BI2 0.79 0.62 0.38 RO-BI4 0.86 0.74 0.26 RO-BI5 0.90 0.81 0.19 RO-BI6 0.91 0.83 0.17 RO-BI7 0.89 0.79 0.21
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 1023446
that the TLC is an exogeneous variable and not the endogeneous. Similarly, we examined in case of CP, NP and MP we found the path coefficients of the error term of CP, NP and MP were insignificantly related to the TLC.
4.4. Hypotheses testing
Although we considered structural equation modelling approaches (Wendorf, 2002), we concluded that the hierarchical regression modelling is the favourable approach in this study in consideration of both the estimation function and the parsimony of presentation. Test results are presented in Table 4. Our hypothesis H2 (NP→TLC) is sup- ported (β = 0.17, p = 0.01). This result of our study is consistent with the previous studies (Dubey et al., 2018; Liang et al., 2007). Next, we found support for H3 (MP→TLC) (β = 0.71; p = 0.00). We can argue that the MP has a positive and significant effect on the TLC. These findings of our study are consistent with the previous studies in context to ERP assim- ilation (Liang et al., 2007) and the TQM assimilation (Dubey et al., 2018). Similarly, we found support for H4 (TLC→ACP) (β = 0.11; p = 0.02), H5 (ACP→RO) (β = 0.56; p = 0.00) and H6 (RO→BI-ASM) (β = 0.25; p = 0.00). These results are consistent with the Zhu et al. (2006) and Dubey et al. (2018) findings. We observed that the size of the or- ganization has no significant effect on the BI-ASM. We conclude that during pandemic resulting from COVID-19, the importance of the BI has been realized by all kind of organizations irrespective of their size. Although, we assumed that larger organization have access to more resources. However, the COVID-19 crisis has played an important role in bridging the wide gaps that existed pre-COVID-19 crisis.
Based on hypothesis testing, we found that in the case of BI assimi- lation, the top leader’s commitment may not be significantly influenced by coercive pressure, as the first research hypothesis is not supported (β= -0.11; p = 0.23). This result provides a clear insight into the particular situation that has forced the organizations to use innovative technologies to improve their business operations and decision-making abilities. The COVID-19 crisis has forced organizations to adapt to the new norms. Hence, during pandemic, the organizations have increas- ingly invested in innovative technologies to maintain their competitive advantage.
5. Discussion
In this study, we have posited two guiding research questions and five research hypotheses suggesting that the institutional pressures under the mediating effect of the top leader’s commitment influence the assimilation of the BI. More specifically, building on institutional theory and upper echelon theory, we developed our research model (see Fig. 1) to address our research questions (RQ1 and RQ2). By addressing RQ1, our study attempts to bridge, the existing research gaps. To date, the collective influence of institutional pressure on assimilation of BI has not been studied (Liang et al., 2007; Lin et al., 2020; Teo, Wei, & Benbasat, 2003; Zuo et al., 2020). Addressing this gap is important given that types of pressure (coercive, mimetic and normative) are ‘not always empirically distinct’ (DiMaggio & Powell, 1983, p. 150). Our findings therefore contributes to this literature in two ways. Firstly, we provide the liter- ature with survey based reflective measures of institutional pressures that capture coercive, mimetic and normative pressures. Secondly, the previous literature has noted the limitations of institutional theory in explaining the extent to which companies within the same institutional field (i.e. industry) actually adopt the technological innovations (Col- well & Joshi, 2013; Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019; Greenwood & Hinings, 1996; Liang et al., 2007; Oliver, 1997; Zuo et al., 2020). To address these limitations, we have incorporated the role of top leader’s commitment within the institutional theory framework (Greenwood & Hinings, 1996; Liang et al., 2007). To date, however, few studies have explored this new extension. Our study closes this gap.
In an attempt to address our RQ2, we develop and empirically test a theoretically grounded model that confirms the collective influence institutional pressures may have on top leadership commitment for the BI assimilation. Specifically, we show how top leader’s commitment to the BI assimilation can mediate the relationship between institutional pressures and BI assimilation. By doing so, we provide some of the first empirical evidence to support the inclusion of intraorganizational dy- namics (see, Greenwood & Hinings, 1996; Colwell & Joshi, 2013), within institutional theory, as an approach for understanding why or- ganizations demonstrate differential behaviour in context to the adop- tion of BI tools. Moreover our study building on previous arguments, attempted to explain BI assimilation using institutional theory and upper echelon theory and extend the BI work of Nam et al. (2019) during pandemic crisis. In this way our study, provides theoretical explanation to pandemic effect on organizational responsiveness towards BI tools assimilation (Pan & Zhang, 2020; Papadopoulos et al., 2020). Dubey et al. (2019) used institutional theory to explain the motives of organi- zations when adopting big data analytics, whereas Dubey et al. (2018) explained the impact of contextual factors on organizational perfor- mance using institutional theory. However, both studies did not focus on post-implementation phases, which was the focus of this study. Overall, we can argue that mimetic and normative pressures affect top man- agement commitment, which subsequently affects acceptance, routini- zation, and assimilation of BI. However, we found that coercive pressures were not positively related to top leader’s commitment (β= -0.11; p > 0.1). This result is in contrast to the existing literature
Table 3 Correlations among major constructs.
(Note: The leading diagonal of matrix represented in grey shade is square root of average variance extracted).
Table 4 Overview of Hypotheses Test.
Hypothesis βvalues Direction of β t- statistics
P value Conclusion
H1: CP→TLC − 0.11 reverse − 1.21 0.23 not- supported
H2:NP→TLC 0.17 positive 2.15 0.01 supported H3:MP→TLC 0.71 positive 14.9 0.00 supported H4: TLC→ACP 0.11 positive 2.31 0.02 supported H5: ACP→RO 0.56 positive 16.71 0.00 supported H6:RO→BI-
ASM 0.25 positive 3.86 0.00 supported
[Notes: CP, coercive pressures; MP, mimetic pressures; NP, normative pressures; TLC, top-leadership commitment; ACP, acceptance; RO, routinization; BI-ASM- business intelligence assimilation; BI, business intelligence].
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 1023447
exploring the role of institutional pressures and the statistically signifi- cant positive influence of coercive pressures on top managers’ posture towards the adoption of activities related to the improvement of orga- nizational processes or recovery of the environment in which firms are embedded such as reverse logistics (Ye, Zhao, Prahinski, & Li, 2013), green supply chain management practices (Zhu, Sarkis, & Lai, 2013), and supply chain management systems (Liu et al., 2010; Zhang & Dhaliwal, 2009) and technologies (Bhakoo & Choi, 2013; Liu et al., 2010; Saldanha, Mello, Knemeyer, & Vijayaraghavan, 2015). In a way, we present that how our study extend and contradicts previous studies focusing on adoption or assimilation of the technology. Our study in comparison to other studies we found that in our case we note that CP has no significant influence on TLC which contradict the Liang et al. (2007) and Dubey et al. (2018) research findings. Moreover, our study extend the previous studies (see, Teo et al., 2003; Lin et al., 2020 and Zuo et al., 2020) by examining the role of TLC in translating institutional pressures to shape the technology assimilation strategy. Hence, we believe that our statistical results paint an interesting picture of asso- ciations and complementarities among the external pressures, top management leader’s and BI diffusion stages (i.e., acceptance, routini- zation and assimilation) in pandemic crisis resulted from COVID-19. Collectively, these results have implications for the practitioners, as well as provide some new research questions in this field of research.
5.1. Theoretical contributions
There is a rich body of literature focusing on the role of institutional forces on the adoption of technology (Dubey, Gunasekaran, Childe, Blome et al., 2019; Liang et al., 2007; Lin et al., 2020; Pennings & Harianto, 1992; Teo et al., 2003). Liang et al. (2007) in context to ERP assimilation attempted to explain the role of top management commit- ment in translating the external pressures into the ERP assimilation. In a way, Liang et al. (2007) attempted to address the limitations of the previous work (see, Pennings & Harianto, 1992; Teo et al., 2003) by including top management commitment as mediating construct. How- ever, Liang et al. (2007) remained silent on the stages involved in the assimilation. Dubey et al. (2018) in one of their studies have tried to explain three stages of assimilation in context to the TQM philosophy. However, in the case of technological innovation, the existing works have largely remained silent. Our study findings enriches BI-Assimilation research via examining the role of institutional factors effects on organization intention to adopt the BI and embrace it completely across all the functional departments of the organization. Here, we attempted to develop a theory that explain the assimilation of the BI in an organization during unprecedented time. The existing literature offers rich discussion on the factors that influences the adop- tion of the BI. However, most of the studies have examined using resource based perspective or organizational information processing perspective. However, the literature on BI has remained silent on how the external pressures drive the organization to assimilate the BI as their organizational philosophy. Although, in context to ERP and TQM assimilation, the scholars have offered explanation grounded in the institutional theory. This study, thus heeds calls for theory-focused data driven study that provides in depth understanding of how BI assimila- tion occurs during pandemic crisis. Hence, we argue that our study contribution to the BI literature is threefold. Firstly, we attempted to provide an operational definition of BI assimilation and develop and statistically validate a model that examines the effect of coercive, mimetic and normative pressures on top leader’s commitment to BI assimilation. Hence, we argue that our study contributes to the theo- retical boundaries of BI assimilation in uncertain time. Secondly, we integrate institutional theory and upper echelon theory to explain BI assimilation in terms of a three-stage post-implementation process (i.e. acceptance, routinization and assimilation). In a way argue that the TLC help translate the pressures resulting from the competitors and the ex- pectations of organization to provide better results during pandemic
crisis. Thirdly, we extend prior literature (see, Nam et al., 2019; Chen & Lin, 2020) by integrating Liang et al. (2007) and Hazen et al. (2012) studies to model BI assimilation during pandemic crisis. In a way we attempted to address research calls of some information management scholars in the wake of pandemic crisis (see, Papadopoulos et al., 2020; Dwivedi et al., 2020; Pan & Zhang, 2020). We can argue that our find- ings help understand how institutional theory and upper echelon theory provides a better understanding of the assimilation of BI during an un- certain time. These findings of our study answer the antagonists who often criticized the institutional theory and their inability to influence the organizational policies.
5.2. Managerial implications
The pandemic crisis resulting from COVID-19 has transformed the lives of citizens and organizations way of doing business. The pandemic has triggered the humanity to find innovative ways of doing business to keep the sinking economy afloat. Although, we often blame COVID-19 and pandemic for current crisis. However, the pandemic has offered significant insight into our hidden problems that has plagued our world economy. The pandemic has exposed our weakness and reflected our capabilities to deal with such health crisis. The power of emerging technology has been understood during the pandemic crisis to fight against the disruptions caused by the pandemic crisis (Dwivedi et al., 2020; Ivanov, 2020). The BI tools have not only to help organizations to reduce the spread of the virus; it has helped enhance the performance of the organizations. BI has changed the overall business strategies of the organization. In recent times, the BI tools have played a significant role in building trust and collaboration among the various stakeholders. The majority of these BI tools extensively rely on data analytics to promote better communication between organizational stakeholders. Despite, availability of BI tools the organizations have struggled to optimally utilize these BI tools in an effective and efficient way. Hence, our find- ings clearly suggest practitioners who consider investments in BI care- fully evaluate: (1) how organizational policies are aligned with the external pressures; (2) to what extent the top leaders of the organization are familiar with the effects of the external pressures on the diffusion of BI. For example, during pandemic resulting from the COVID-19, BI and data analytics initiatives are proving to be a boon in disguise for many organizations. It helps organizations to sense and adapt to the disrup- tions caused by the pandemic. It enables organizations to develop new products as well as help protect their own employees without affecting their business propositions. Hence, we can argue that our findings can be used as guidance to managers and consultants who are involved in BI implementation. The mediating role of TLC in BI assimilation clearly suggests that top leaders plays a critical role in the BI assimilation process. For instance, as the organization struggle to adapt to the un- precedented crisis resulting from the COVID-19, top leaders are encouraging their BI teams to develop new solutions at a faster rate and to make allowances for needs that are changing rapidly to a high degree of uncertainties. Moreover, via this study, we have realized that the most important lesson that organizations are learning about the BI during a pandemic is that it will have a little effect within organizations still insisting on top-down decision making. Instead, the organizations using BI to the greatest effect are those that have a culture of the delegation of authority, where employees are empowered to make data-driven de- cisions without the need to wait for their superiors to approve it. We found how the acceptance of BI among an organization’s constituents further helps to align organization’s governance systems to ultimately usher in BI assimilation. The finding that institutional forces (apart from coercive forces) influence BI assimilation is quite interesting for man- agers and consultants. Traditionally, managers focus on implementation more so than post-implementation phases. Thus, a large percentage of organizations typically report BI failures due to a lack of understanding of assimilation processes. Hence, the study findings can help managers to focus on each of the intermediary steps that lead to assimilation of BI.
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 1023448
Attending to the acceptance and routinization aspects of BI is important for the eventual assimilation of BI. Further, we believe that institutional pressures (normative and mimetic), if properly translated by top man- agers who are committed towards BI assimilation, can be very useful for those companies that have reported losses due to failure to reap benefits from their investments in BI implementation and would like to further investigate the reasons behind this failure in order to re-launch or re-energize BI efforts. Finally, our study offers some useful tips to the managers who are unable to exploit BI to minimize the disruptions caused by the pandemic resulting from COVID-19 and may serve as a useful guidance for the managers to deal with future crisis. Despite the significant success, the remaining flaws of the organizations that prevent the organization from achieving maximum benefits from their BI ini- tiatives are becoming known. That offers a unique opportunity to fix them finally. With that in mind, here are the top three lessons that businesses are learning about BI amidst the pandemic.
5.3. Limitations and further research direction
Drawing on institutional theory and upper echelon theory, and literature on BI and elements of innovation diffusion, we developed and tested our theoretical model using data from quality managers at 174 auto-component manufacturers during pandemic crisis stage. Our study has some limitations that should be noted. Firstly, we have tested our theoretical model using data gathered from the auto-components manufacturing sector. However, there may be variations in terms of practices between manufacturing sectors. Hence, future research can examine this model across sectors. Secondly, to test our framework we used cross-sectional, single-source data. Future research can employ longitudinal methods to test for causality in the model. In addition, future research examining outcomes of assimilation is especially encouraged. Our study is based on a single country and single industry data, which may limit the generalizability of our study. Hence, in order to reduce the variability induced by the industry differences, we pur- posely chose the Indian auto-component manufacturing industry (see, Liu et al., 2010). To minimize the biases resulting from personal dif- ferences due to the background, we identified respondents of similar backgrounds who had obtained training from a similar kind of institu- tion. Although we believe that our data collection strategy may have helped the internal validity of our study, this may limit the external validity of the study. Moreover, this study has been conducted to capture the managers response in context to pandemic crisis resulted from the COVID-19.Thus, we believe that the findings of our study should be cautiously evaluated in context to other settings. Moreover, our comparative analysis of the results show that in context to China the role of CP is highly significant. Similarly, in the context Indian automotive industry, the role of CP is insignificant. These differential results can be better explained using national culture theory. For instance, Prakash and Majumdar (2021) investigated how national culture plays an important
role in content creation in the context of a social media platform. Similarly, George et al. (2018) and Gupta and Gupta (2019) have advocated in favor of the influence of national culture on shaping organizational strategies. We believe that our study could be extended by examining the moderating effect of national culture dimensions on the paths joining institutional pressures and TLC. We also encourage future research using multiple case study, ethnographic, and action research methods to build more comprehensive theory to explain the BI assimilation.
6. Conclusions
The study examines the role of external pressures and top leaders commitment in BI diffusion process. Informed by information manage- ment and organizational theories we have conceptualized a theoretical model. To validate our theoretical model and test our research hy- potheses, we have gathered data from Indian auto component manufacturing sector to understand how external pressures and the top leaders have played a significant role in BI assimilation during pandemic crisis, which has affected the business worldwide. Despite of the poor operating margin, the sector has learnt a new way to deal such with such unprecedented time via investing in BI and exploiting them in an appropriate way. We hope our findings and limitations of our study provide enough food for thought.
Authors comment
The first author (Mrs Akriti Chaubey, who is a Doctoral Scholar at School of Management, National Institute of Technology Rourkela) has contributed in the manuscript through the following ways:
1 Conceptualized the theoretical model via extensive literature review; 2 Formulated research hypotheses; 3 Developed a structured questionnaire; 4 Carried out data collection; 5 Performed Data Analysis 6 Drafted the manuscript
The second author (Dr Chandan Kumar Sahoo, who is Professor at the School of Management, National Institute of Technology Rourkela) has contributed in the manuscript through the following ways:
1 Provided in-depth inputs during theoretical framing; 2 Offered significant inputs related to data analyses and the selection
of appropriate statistical tools; 3 Helped during proof editing; 4 Helped during discussion section writing (i.e., contributions to
theory).
Appendix A. Constructs and Items
Construct Relevant Literature Items
Coercive Pressures (CP- BI)
Dubey et al. (2018) 1 The local authority want our organization to use BI during pandemic crisis (CP-BI1). 2 The professional associations expect our organization to use BI during pandemic crisis (CP-BI2). 3 The consumers of our organization expect our organization to adopt BI during pandemic crisis (CP-BI3).
Normative Pressures (NP-BI)
Dubey et al. (2018) 1 The extent to which your channel partners have adopted BI during pandemic crisis (NP-BI1). 2 The extent to which first and second tier suppliers of your organization have adopted BI during pandemic crisis (NP-
BI2). 3 The extent to which the professional societies promotion schemes have influenced your organization to adopt BI
during pandemic crisis (NP-BI3). Mimetic Pressures (MP-
BI) Dubey et al. (2018) 1 Our main business rival has gained significant business advantage with the adoption of BI during pandemic crisis (MP-
BI1) 2 The use of BI is well received by other competitors in our industry during pandemic crisis (MP-BI2). 3 The customers of our organization have appreciated the use of BI during pandemic crisis (MP-BI3).
(continued on next page)
A. Chaubey and C.K. Sahoo International Journal of Information Management 59 (2021) 1023449
(continued )
Construct Relevant Literature Items
4 The suppliers of our organization have appreciated the use of BI during pandemic crisis (MP4). Top leader’s
Commitment (TLC) Liang et al. (2007); Dubey et al. (2018)
1 Our organization top leaders believe that the BI has potential to enhance the business performance of our organization during pandemic crisis (TLC1).
2 Our organization top leaders believe that the use of BI will enhance business opportunities during pandemic crisis (TLC2).
3 Our organization top leaders have formulated a strategy for the use of BI during pandemic crisis (TLC3). 4 Our organization top leaders share the BI vision with all stakeholders during pandemic crisis (including you) (TLC4). 5 Our organization top leaders established the performance metrics to monitor the BI project during pandemic crisis
(TLC5). 6 Our organization top leaders recognizes the contribution of the partners engaged in BI project during pandemic crisis
(TLC6). Acceptance (ACP-BI) Hazen et al. (2012); Dubey
et al. (2018) 1 1To what extent you believe that BI enhance my job performance during pandemic crisis (ACP-BI1). 2 To what extent you and your colleagues associate with the BI during pandemic crisis (ACP-BI2). 3 To what extent the infrastructure support the innovation during pandemic crisis (ACP-BI3).
Routinization (RO-BI) Hazen et al. (2012), Dubey et al. (2018)
1 To what extent in your organization procedures are defined for replacement of tangible resources necessary to support BI during pandemic crisis (RO-BI1).
2 To what extent in your organization a separate budget has been created to support BI during pandemic crisis (RO-BI2). 3 Our organization have a dedicated team to support BI during pandemic crisis (RO-BI3). 4 Our organization have defined organizational procedures for procurement of necessary items during pandemic crisis
(RO-BI4). 5 Our organization hire and retain qualified people to support BI during pandemic crisis (RO-BI5). 6 To what extent my organization offers opportunities for initial and /or recurring training regarding the BI during
pandemic crisis (RO-BI6). 7 To what extent in my organization a person familiar with the BI have been promoted into higher positions of greater
authority such that they support the innovation further especially during pandemic crisis (RO-BI7). BI Assimilation (ASM-BI) Liang et al. (2007); Dubey
et al. (2018) 1 To what extent your organization has exploited the BI tools in every department (%) during pandemic crisis (ASM-
BI1). 2 To what extent all the functional departments in your organization used BI tool during the pandemic crisis (ASM-BI2). 3 To what extent your organization use BI tools in each functional department as indicated by you:
a) Business operations b) Management practices c) Decision making (ASM-BI3).
Appendix B. Skewness (top) and exc. kurtosis (bottom) coefficients
CP NP MP TLC BI-ASM ACP RO OS
− 0.488 − 0.634 − 0.286 − 0.453 − 0.862 − 0.289 − 1.202 1.618 0.584 0.858 0.104 − 0.073 − 0.205 − 0.606 1.307 2.183
References
Ahmad, A., & Hossain, M. A. (2018). Assimilation of business intelligence systems: The mediating role of organizational knowledge culture. Conference on e-Business, e- Services and e-Society (pp. 480–491). Cham: Springer.
Ain, N., Vaia, G., DeLone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success–A systematic literature review. Decision Support Systems, 125, Article 113113.
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.
Audzeyeva, A., & Hudson, R. (2016). How to get the most from a business intelligence application during the post implementation phase? Deep structure transformation at a UK retail bank. European Journal of Information Systems, 25(1), 29–46.
Bellamy, M. A., Ghosh, S., & Hora, M. (2014). The influence of supply network structure on firm innovation. Journal of Operations Management, 32(6), 357–373.
Bhakoo, V., & Choi, T. (2013). The iron cage exposed: Institutional pressures and heterogeneity across the healthcare supply chain. Journal of Operations Management, 31(6), 432–449.
Božič, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role of absorptive capacity. International Journal of Information Management, 46, 93–103.
Brown, D. H., & Kaewkitipong, L. (2009). Relative size and complexity: e-business use in small and medium sized tourism enterprises in Thailand. Journal of Enterprise Information Management, 22(1/2), 212–231.
Bryce, C., Ring, P., Ashby, S., & Wardman, J. K. (2020). Resilience in the face of uncertainty: Early lessons from the COVID-19 pandemic. Journal of Risk Research, 23 (7–8), 880–887.
Carmines, E. G., McIver, J. P., Bohrnstedt, G. W., & Borgatta, E. F. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. Social measurement: Current issues (pp. 65–115), 80.
Chen, Y., & Lin, Z. (2020). Business intelligence capabilities and firm performance: A study in China. International Journal of Information Management, Article 102232.
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39.
Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modelling. MIS Quarterly, 22(1), Vii–Xvii.
Chin, W. W., Thatcher, J. B., & Wright, R. T. (2012). Assessing common method bias: Problems with the ULMC technique. MIS Quarterly, 36(3), 1003–1019.
Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73.
Cohen, B. H. (2008). Explaining psychological statistics. John Wiley & Sons. Colwell, S. R., & Joshi, A. W. (2013). Corporate ecological responsiveness: Antecedent
effects of institutional pressure and top management commitment and their impact on organizational performance. Business Strategy and the Environment, 22(2), 73–91.
Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29.
Davidson, R., & MacKinnon, J. G. (1993). Estimation and inference in Econometrics. Oxford: Oxford University Press.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Delmas, M., & Toffel, M. W. (2004). Stakeholders and environmental management practices: an institutional framework. Business Strategy and the Environment, 13(4), 209–222.
Dillman, D. A. (2011). Mail and Internet surveys: The tailored design method–2007 update with new internet, visual, and mixed-mode guide. John Wiley & Sons.
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Hazen, B. T., & Roubaud, D. (2018). Examining top management commitment to TQM diffusion using institutional and upper echelon theories. International Journal of Production Research, 56(8), 2988–3006.
A. Chaubey and C.K. Sahoo http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0005 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0005 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0005 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0010
http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0010 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0010 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0015 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0015 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0020 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0020 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0020 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0025 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0025 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0030 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0030 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0030 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0035 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0035 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0035 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0040 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0040 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0040 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0045 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0045 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0045 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0050 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0050 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0050 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0055 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0055 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0060 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0060 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0060 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0065 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0065 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0070 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0070 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0075 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0075 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0080 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0085 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0085 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0085 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0090 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0090 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0090 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0095 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0095 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0100 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0100 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0105 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0105 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0105 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0110 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0110 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0115 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0115 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0115 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0120 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0120 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0120 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0120 International Journal of Information Management 59 (2021) 102344 10Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30 (2), 341–361.
Dubey, R., Gunasekaran, A., Childe, S. J., & Papadopoulos, T. (2019). Supplier relationship management for circular economy: Influence of external pressures and top management commitment. Management Decision, 57(4), 767–790.
Dubey, R., Bryde, D. J., Foropon, C., Tiwari, M., Dwivedi, Y., & Schiffling, S. (2020). An investigation of information alignment and collaboration as complements to supply chain agility in humanitarian supply chain. International Journal of Production Research, 1–20. https://doi.org/10.1080/00207543.2020.1865583.
Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58 (11), 3381–3398.
Dwivedi, Y. K., Kapoor, K. K., Williams, M. D., & Williams, J. (2013). RFID systems in libraries: An empirical examination of factors affecting system use and user satisfaction. International Journal of Information Management, 33(2), 367–377.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re- examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Galanos, V. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994.
Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., … Raman, R. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International Journal of Information Management, 55, Article 102211.
Eckstein, D., Goellner, M., Blome, C., & Henke, M. (2015). The performance impact of supply chain agility and supply chain adaptability: The moderating effect of product complexity. International Journal of Production Research, 53(10), 3028–3046.
Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135–153.
Fawcett, S. E., Waller, M. A., Miller, J. W., Schwieterman, M. A., Hazen, B. T., & Overstreet, R. E. (2014). A trail guide to publishing success: tips on writing influential conceptual, qualitative, and survey research. Journal of Business Logistics, 35(1), 1–16.
Fichman, R. G. (2001). The role of aggregation in the measurement of IT-related organizational innovation. MIS Quarterly, 25(4), 427–455.
Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(3), 39–50.
Fosso Wamba, S., & Queiroz, M. M. (2020). Industry 4.0 and the supply chain digitalisation: A blockchain diffusion perspective. Production Planning and Control, 1–18. https://doi.org/10.1080/09537287.2020.1810756.
George, J. F., Gupta, M., Giordano, G., Mills, A. M., Tennant, V. M., & Lewis, C. C. (2018). The effects of communication media and culture on deception detection accuracy. MIS Quarterly, 42(2), 551–575.
Greenwood, R., & Hinings, C. R. (1996). Understanding radical organizational change: Bringing together the old and the new institutionalism. The Academy of Management Review, 21(4), 1022–1054.
Greenwood, R., Hinings, C. R., & Whetten, D. (2014). Rethinking institutions and organizations. Journal of Management Studies, 51(7), 1206–1220.
Guide, V. D. R., & Ketokivi, M. (2015). Notes from the Editors: Redefining some methodological criteria for the journal. Journal of Operations Management, (37), v–viii.
Gupta, M., & Gupta, S. (2019). Influence of national cultures on operations management and supply chain management practices—A research agenda. Production and Operations Management, 28(11), 2681–2698.
Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. The Academy of Management Review, 9(2), 193–206.
Harris, M., Bhatti, Y., Buckley, J., & Sharma, D. (2020). Fast and frugal innovations in response to the COVID-19 pandemic. Nature Medicine, 26, 814–817.
Hazen, B. T., Overstreet, R. E., & Cegielski, C. G. (2012). Supply chain innovation diffusion: Going beyond adoption. The International Journal of Logistics Management, 23(1), 119–134.
Heugens, P. P., & Lander, M. W. (2009). Structure! Agency!(and other quarrels): A meta- analysis of institutional theories of organization. The Academy of Management Journal, 52(1), 61–85.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling A Multidisciplinary Journal, 6(1), 1–55.
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E Logistics and Transportation Review, 136, Article 101922.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915.
Iyengar, K., Sweeney, J. R., & Montealegre, R. (2015). Information technology use as a learning mechanism: The impact of IT use on knowledge transfer effectiveness, absorptive capacity, and franchisee performance. MIS Quarterly, 39(3), 615–642.
Jarvenpaa, S. L., & Ives, B. (1991). Executive involvement and participation in the management of information technology. MIS Quarterly, 205–227.
Kar, A. K., & Dwivedi, Y. K. (2020). Theory building with big data-driven research–Moving away from the “What” towards the “Why”. International Journal of Information Management, 54, Article 102205.
Kauppi, K. (2013). Extending the use of institutional theory in operations and supply chain management research. International Journal of Operations & Production Management, 33(10), 1318–1345.
Ketokivi, M. A., & Schroeder, R. G. (2004). Perceptual measures of performance: fact or fiction? Journal of Operations Management, 22(3), 247–264.
Koh, S. L., & Gunasekaran, A. (2006). A knowledge management approach for managing uncertainty in manufacturing. Industrial Management & Data Systems, 106(4), 439–459.
Kostova, T., & Roth, K. (2002). Adoption of an organizational practice by subsidiaries of multinational corporations: Institutional and relational effects. The Academy of Management Journal, 45(1), 215–233.
Kummitha, R. K. R. (2020). Smart technologies for fighting pandemics: The techno-and human-driven approaches in controlling the virus transmission. Government Information Quarterly, Article 101481.
Laato, S., Islam, A. N., Islam, M. N., & Whelan, E. (2020). What drives unverified information sharing and cyberchondria during the COVID-19 pandemic? European Journal of Information Systems, 29(3), 288–305.
Lee, S. M., & Trimi, S. (2020). Convergence innovation in the digital age and in the COVID-19 pandemic crisis. Journal of Business Research, 123, 14–22.
Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management. MIS Quarterly, 31(1), 59–87.
Lin, J., Luo, Z., & Luo, X. (2020). Understanding the roles of institutional pressures and organizational innovativeness in contextualized transformation toward e-business: Evidence from agricultural firms. International Journal of Information Management, 51, Article 102025.
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. The Journal of Applied Psychology, 86(1), 114–121.
Liu, H., Ke, W., Wei, K. K., Gu, J., & Chen, H. (2010). The role of institutional pressures and organizational culture in the firm’s intention to adopt internet-enabled supply chain management systems. Journal of Operations Management, 28(5), 372–384.
Liu, H., Wei, S., Ke, W., Wei, K. K., & Hua, Z. (2016). The configuration between supply chain integration and information technology competency: A resource orchestration perspective. Journal of Operations Management, 44, 13–29.
López-Robles, J. R., Otegi-Olaso, J. R., Gómez, I. P., & Cobo, M. J. (2019). 30 years of intelligence models in management and business: A bibliometric review. International Journal of Information Management, 48, 22–38.
Lowry, P. B., Zhang, D., Zhou, L., & Fu, X. (2010). Effects of culture, social presence, and group composition on trust in technology-supported decision-making groups. Information Systems Journal, 20(3), 297–315.
Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: From constructs to theory. Journal of Operations Management, 16(4), 407–425.
Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.
McKinsey & Company. (2020). Shaping the new normal: India’s auto component industry. September, Available at: https://www.mckinsey.com/-/media/McKinsey/Featured %20Insights/India/Shaping%20the%20new%20normal/Shaping%20the%20new% 20normal_report (Date of Access: 16th November, 2020).
Mithas, S., Ramasubbu, N., & Sambamurthy, V. (2011). How information management capability influences firm performance. MIS Quarterly, 35(1), 237–256.
Nam, D., Lee, J., & Lee, H. (2019). Business analytics adoption process: An innovation diffusion perspective. International Journal of Information Management, 49, 411–423.
Oliver, C. (1997). Sustainable competitive advantage: Combining institutional and resource-based views. Strategic Management Journal, 18(9), 697–713.
Olszak, C. M. (2016). Toward better understanding and use of Business Intelligence in organizations. Information Systems Management, 33(2), 105–123.
Overstreet, R. E., Hazen, B. T., Skipper, J. B., & Hanna, J. B. (2014). Bridging the gap between strategy and performance: Using leadership style to enable structural elements. Journal of Business Logistics, 35(2), 136–149.
Pan, S. L., Cui, M., & Qian, J. (2020). Information resource orchestration during the COVID-19 pandemic: A study of community lockdowns in China. International Journal of Information Management, 54, Article 102143.
Pan, S. L., & Zhang, S. (2020). From fighting COVID-19 pandemic to tackling sustainable development goals: An opportunity for responsible information systems research. International Journal of Information Management, 55, Article 102196.
Papadopoulos, T., Baltas, K. N., & Balta, M. E. (2020). The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. International Journal of Information Management, 55, Article 102192.
Pennings, J. M., & Harianto, F. (1992). The diffusion of technological innovation in the commercial banking industry. Strategic Management Journal, 13(1), 29–46.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903.
A. Chaubey and C.K. Sahoo http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0125 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0125 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0125 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0125 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0130 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0130 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0130 https://doi.org/10.1080/00207543.2020.1865583 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0140 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0140 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0140 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0140 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0145 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0145 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0145 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0150 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0150 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0150 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0155 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0155 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0155 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0155 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0160 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0160 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0160 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0160 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0165 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0165 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0165 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0170 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0170 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0170 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0175 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0175 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0175 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0175 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0180 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0180 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0185 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0185 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0185 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0190 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0190 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0190 https://doi.org/10.1080/09537287.2020.1810756 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0200 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0200 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0200 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0205 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0205 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0205 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0210 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0210 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0215 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0215 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0215 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0220 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0220 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0220 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0225 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0225 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0230 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0230 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0235 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0235 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0235 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0240 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0240 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0240 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0245 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0245 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0245 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0250 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0250 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0250 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0250 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0255 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0255 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0255 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0260 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0260 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0260 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0265 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0265 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0270 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0270 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0270 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0275 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0275 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0275 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0280 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0280 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0285 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0285 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0285 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0290 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0290 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0290 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0295 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0295 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0295 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0300 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0300 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0300 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0305 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0305 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0310 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0310 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0310 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0315 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0315 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0315 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0315 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0320 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0320 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0325 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0325 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0325 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0330 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0330 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0330 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0335 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0335 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0335 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0340 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0340 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0340 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0345 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0345 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0350 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0350 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0350 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0355 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0355 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0355 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0355 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0360 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0360 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0365 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0365 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0370 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0370 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0375 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0375 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0380 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0380 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0380 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0385 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0385 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0385 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0390 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0390 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0390 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0395 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0395 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0395 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0400 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0400 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0405 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0405 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0410 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0410 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0410 International Journal of Information Management 59 (2021) 10234411
Prakash, C. D., & Majumdar, A. (2021). Analyzing the role of national culture on content creation and user engagement on Twitter: The case of Indian Premier League cricket franchises. International Journal of Information Management, 57, Article 102268.
Pramanik, D., Mondal, S. C., & Haldar, A. (2020). A framework for managing uncertainty in information system project selection: An intelligent fuzzy approach. International Journal of Management Science and Engineering Management, 15(1), 70–78.
Purvis, R. L., Sambamurthy, V., & Zmud, R. W. (2001). The assimilation of knowledge platforms in organizations: An empirical investigation. Organization Science, 12(2), 117–135.
Queiroz, M., Tallon, P. P., Sharma, R., & Coltman, T. (2018). The role of IT application orchestration capability in improving agility and performance. The Journal of Strategic Information Systems, 27(1), 4–21.
Queiroz, M. M., Ivanov, D., Dolgui, A., & Wamba, S. F. (2020). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 1–38. https://doi.org/10.1007/s10479-020-03685-7.
Ramakrishnan, T., Jones, M. C., & Sidorova, A. (2012). Factors influencing business intelligence (BI) data collection strategies: An empirical investigation. Decision Support Systems, 52(2), 486–496.
Ranjan, J., & Foropon, C. (2021). Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, Article 102231.
Remko, V. H. (2020). Research opportunities for a more resilient post-COVID-19 supply chain–closing the gap between research findings and industry practice. International Journal of Operations & Production Management, 40(4), 341–355.
Rossiter, J. R. (2008). Content validity of measures of abstract constructs in management and organizational research. British Journal of Management, 19(4), 380–388.
Saldanha, J. P., Mello, J. E., Knemeyer, A. M., & Vijayaraghavan, T. A. S. (2015). Implementing supply chain technologies in emerging markets: An institutional theory perspective. The Journal of Supply Chain Management, 51(1), 5–26.
Schilke, O. (2014). On the contingent value of dynamic capabilities for competitive advantage: The nonlinear moderating effect of environmental dynamism. Strategic Management Journal, 35(2), 179–203.
Schoemaker, P. J., Heaton, S., & Teece, D. (2018). Innovation, dynamic capabilities, and leadership. California Management Review, 61(1), 15–42.
Schryen, G. (2013). Revisiting IS business value research: What we already know, what we still need to know, and how we can get there. European Journal of Information Systems, 22(2), 139–169.
Shao, Z. (2019). Interaction effect of strategic leadership behaviors and organizational culture on IS-Business strategic alignment and Enterprise Systems assimilation. International Journal of Information Management, 44, 96–108.
Sila, I. (2007). Examining the effects of contextual factors on TQM and performance through the lens of organizational theories: An empirical study. Journal of Operations Management, 25(1), 83–109.
Singh, P. J., Power, D., & Chuong, S. C. (2011). A resource dependence theory perspective of ISO 9000 in managing organizational environment. Journal of Operations Management, 29(1-2), 49–64.
Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849–1867.
Teo, H. H., Wei, K. K., & Benbasat, I. (2003). Predicting intention to adopt interorganizational linkages: An institutional perspective. MIS Quarterly, 27(1), 19–49.
The State of BI and Business Analytics Report (2020). https://www.sisense.com/en-gb/ whitepapers/state-of-bi-analytics-report-2020/ (Date of access 13th October, 2020).
Tian, X., Chiong, R., Martin, B., Stockdale, R., Arefin, M. S., Hoque, M. R., … Bao, Y. (2015). The impact of business intelligence on organization’s effectiveness: An empirical study. Journal of Systems and Information Technology, 17(3), 263–285.
Wagner, S. M., & Kemmerling, R. (2010). Handling nonresponse in logistics research. Journal of Business Logistics, 31(2), 357–381.
Wang, H. C. (2014). Distinguishing the adoption of business intelligence systems from their implementation: The role of managers’ personality profiles. Behaviour & Information Technology, 33(10), 1082–1092.
Wendorf, C. A. (2002). Comparisons of structural equation modeling and hierarchical linear modeling approaches to couples’ data. Structural Equation Modeling, 9(1), 126–140.
Williams, M. D., Dwivedi, Y. K., Lal, B., & Schwarz, A. (2009). Contemporary trends and issues in IT adoption and diffusion research. Journal of Information Technology, 24(1), 1–10.
Yang, F., Huang, X., & Wu, L. (2019). Experiencing meaningfulness climate in teams: How spiritual leadership enhances team effectiveness when facing uncertain tasks. Human Resource Management, 58(2), 155–168.
Ye, F., Zhao, X., Prahinski, C., & Li, Y. (2013). The impact of institutional pressures, top managers’ posture and reverse logistics on performance—Evidence from China. International Journal of Production Economics, 143, 132–143.
Zhang, C., & Dhaliwal, J. (2009). An investigation of resource-based and institutional theoretic factors in technology adoption for operations and supply chain management. International Journal of Production Economics, 120(1), 252–269.
Zhu, K., Kraemer, K. L., & Xu, S. (2006). The process of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business. Management Science, 52(10), 1557–1576.
Zhu, Q., Sarkis, J., & Lai, K.-H. (2013). Institutional-based antecedents and performance outcomes of internal and external green supply chain management practices. Journal of Purchasing and Supply Management, 19(2), 106–117.
Zmud, R. W., & Apple, L. E. (1992). Measuring technology incorporation/infusion. The Journal of Product Innovation Management, 9(2), 148–155.
Zuo, M., Ma, D., & Yu, Y. (2020). Contextual determinants of IT governance mechanism formulation for senior care services in local governments. International Journal of Information Management, 53, Article 102125.
A. Chaubey and C.K. Sahoo http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0415 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0415 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0415 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0420 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0420 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0420 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0425 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0425 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0425 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0430 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0430 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0430 https://doi.org/10.1007/s10479-020-03685-7 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0440 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0440 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0440 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0445 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http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0500 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0510 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0510 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0510 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0515 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0515 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0520 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0520 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0520 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0525 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0525 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0525 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0530 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0530 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0530 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0535 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0535 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0535 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0540 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0540 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0540 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0545 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0545 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0545 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0550 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0550 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0550 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0555 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0555 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0555 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0560 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0560 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0565 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0565 http://refhub.elsevier.com/S0268-4012(21)00037-2/sbref0565- Assimilation of business intelligence: The effect of external pressures and top leaders commitment during pandemic crisis 1 Introduction 2 Research model and hypotheses 2.1 Institutional theory and BI assimilation 2.1.1 Coercive pressures (CP) 2.1.2 Normative pressures (NP) 2.1.3 Mimetic pressures (MP) 2.2 Top leaders commitment and BI assimilation (BI-ASM) 3 Research method 3.1 Construct operationalization and measurement 3.2 Sample and data collection 3.3 Non-response bias 4 Data analyses and results 4.1 Measurement model 4.2 Common method bias 4.3 Endogeneity test 4.4 Hypotheses testing 5 Discussion 5.1 Theoretical contributions 5.2 Managerial implications 5.3 Limitations and further research direction 6 Conclusions Authors comment Appendix A Constructs and Items Appendix B Skewness (top) and exc. kurtosis (bottom) coefficients References
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