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Journal of Human Resources in Hospitality & Tourism
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Preparing Hospitality Organizations for Self-
Service Technology
Joseph D. Lema
To cite this article: Joseph D. Lema (2009) Preparing Hospitality Organizations for Self-
Service Technology, Journal of Human Resources in Hospitality & Tourism, 8:2, 153-169, DOI:
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Journal of Human Resources in Hospitality & Tourism, 8:153–169, 2009
Copyright © Taylor & Francis Group, LLC
ISSN: 1533-2845 print / 1533-2853 online
DOI: 10.1080/15332840802269791
Preparing Hospitality Organizations
for Self-Service Technology
JOSEPH D. LEMA
Hospitality Management, Drexel University, Philadelphia, PA
Self-service technology is rapidly changing the hospitality indus-
try, providing new opportunities for the delivery of services and
options for customers. Preparing to implement effective self-service
technology delivery programs requires a workforce that can rapidly
adapt to change. Understanding factors that influence employee
readiness to engage in and support self-directed processes are an
important consideration when implementing self-service technol-
ogy. The results of the linear regression model in this study indicate
that generalized self-efficacy and the self-directed learning readi-
ness of employees in the hospitality industry are significantly related
variables. While self-efficacy was the most highly correlated vari-
able to the self-directed learning readiness of hospitality employees,
future studies should consider other characteristics that may influ-
ence self-direction. As self-service technology continues to rapidly
expand in all areas of the hospitality industry, opportunities and
challenges exist for both employees and customers.
KEYWORDS Self-service technology, hospitality, self-directed
learning.
INTRODUCTION
Encompassing areas of food and beverage, lodging, and entertainment, the
hospitality industry is one of the largest and fastest growing industries in the
world, with an enormous amount of human capital investment in a diverse
range of jobs. It is estimated that by the year 2014 the hospitality industry
will employ more than 14,693 million workers in the United States (U.S.
Department of Labor, 2005). With an industry that has one of the largest
Address correspondence to Joseph D. Lema, PhD, Assistant Professor, Hospitality Man-
agement, Drexel University, 3001 Market St., Philadelphia, PA 19104. E-mail: jdl42@drexel.edu
153
154 J. D. Lema
capital labor expenditures in the global economy, the need to focus on the
foundation of the hospitality workforce includes an examination of employee
development (Erdly & Chatterjee, 2003).
The rapid development of self-service technology is significantly influ-
encing hospitality organizations by providing new opportunities and chal-
lenges for customers and employees. A Time magazine article reported on
the popularity of self-service, with “U.S. customers spending $128 billion at
self-service kiosks last year, an 80 percent jump from the year before, and by
2007 it could hit $1.3 trillion” (Kiviat, 2004, p. 101). To maximize learning ca-
pacity, self-directed learning readiness is a required strategy to consider with
the self-service concept. Self-service allows users greater control over their
experience, just as self-directed learning emphasizes the learner’s personal
control over his or her own learning experience (Long, 2000).
The skill level and adaptability of employees to initiate change in their
jobs are factors in the competitiveness of a hospitality organization. With
greater emphasis on productivity and accountability for individual perfor-
mance, the responsibility for employees to rapidly adapt to change is moving
from an organizational perspective to that of self-directedness. As the hospi-
tality industry continues to consolidate, these organizations are experiencing
mergers and acquisitions as rapidly as many individual employees change
jobs and careers.
Technology in hospitality organizations has provided much of the suc-
cess over past challenges with self-service strategies, although human cap-
ital investment is required to match the personal characteristics necessary
to maximize the full potential of self-service benefits. A recent article in
The Economist reported, “Self-service is now doing for the service sector
what mass production once did for manufacturing, automating processes
and significantly reducing costs” (You’re Hired, 2004, p. 21). With self-
service kiosks in hotels, restaurants, and airports, self-service options are
becoming a part of everyday life. The explosion of online learning in ed-
ucation and business is another example; the result of rapid increases in
technology. Although the capacity of self-service strategies is limited by
the competency of the user, self-directed learning is at the center of the
self-service concept. The purpose of this study was to examine how self-
efficacy and selected demographic variables (position, gender, and ethnic-
ity) relate to the self-directed learning readiness of employees in hospitality
organizations.
Examining whether significant relationships exist among the variables of
self-efficacy, demographics, and the self-directed learning readiness of em-
ployees in the hospitality industry will have an impact on the productivity
and competitiveness of the hospitality industry workforce. Connecting these
theoretical relationships in the dynamic and diverse environment of the hos-
pitality industry will help link learning theories with emerging practical ap-
plications. Examining self-efficacy and demographic variables that influence
Preparing Hospitality Organizations for Self-Service Technology 155
the self-directed learning readiness of employees provides valuable insights
into employee learning strategies and human resource development.
As hospitality organizations are required to remain competitive by im-
plementing technologies that affect the learning environment of employees,
the responsibility for self-directed learning has increased, along with technol-
ogy. Characteristics that embrace self-directed learning and those that act as
a barrier to self-directed learning need to be examined in order to determine
the readiness of an employee to adopt self-directed learning (Long, 1991b).
As reported by senior executives within the hospitality industry, some of the
overarching issues for organizations include retention, education, training,
and recruitment. In addition, positive solutions are needed to help transition
employees and adopt strategies that initiate continuous change efficiently in
targeted high-growth areas such as the hospitality industry (U.S. Department
of Labor, 2005). Peter Senge (1990), known for his “learning organization”
theory, reported in his widely referenced book, The Fifth Discipline, that the
new learning organization comprises workers who can adapt to change with
transformation through motivated self-directed learning and critical thinking.
While technology is fueling the growth of vast amounts of available in-
formation, employees are being challenged to apply relevant information in
the context of their own work situation. As technology continues to rapidly
advance in the hospitality industry and society, it is becoming extremely
important to have a highly capable workforce. Developing a competitive
workforce is important in a global economy, and it is essential in highly
competitive areas such as the hospitality industry. While technology has
fueled much of the growth in delivering new services and options that cus-
tomers are demanding, a well-prepared and dynamic workforce is required
to match the rapidly changing levels of technology.
Motivating employees to initiate change requires preparation and prac-
tice. Preparing an employee to be self-directed involves consideration of
assumptions that follow self-directed learning methods. An understanding of
the theoretical research of the variables that influence self-directed learning
readiness will allow a hospitality organization to create effective, efficient
programs and practices that maximize the talents of its employees. This
unique workforce may have a much greater responsibility to meet the di-
verse and ever-demanding needs of customers.
Organizations and employees that promote self-directed learning readi-
ness will help prepare their employees to participate in self-directed work
teams and support a learning organizational strategy. One of the challenges
of this study is to investigate the relationships that selected variables have
on self-directed learning readiness. Developing a broader knowledge base
of self-directed learning readiness with selected variables may not only ben-
efit a people-based business that is highly concentrated in employing and
serving people, but other researchers and practitioners may benefit from the
unique social learning environment that the hospitality industry has to offer.
156 J. D. Lema
LITERATURE REVIEW
Self-directed learning has been a high-interest topic in the fields of busi-
ness and education for more than a decade (Mezirow, 1985). Definitions of
self-directed learning share a number of unique and similar perspectives.
Although there are a number of unifying elements in defining self-directed
learning, there is also a concerning ambiguity in precise definitions among
the research (Oddi, 1987). Research into self-directed learning, according
to Long (1991a), has developed over the past decade in both quantity and
quality. Self-directed learning theory may have received greater attention
than the practical aspect, which remains underdeveloped and has not re-
ceived the same attention. Brookfield (1984) describes the process of self-
directed learning as lacking a full appreciation for the impact of a skillful
instructor and may fail to appreciate the social influence of subgroups in
the surrounding community or environment. Part of the confusion with the
self-directed learning term may be linked to learning as an internal change
process and education as an external change process that facilitates internal
change (Brockett & Hiemstra, 1991). Rather than moving away from self-
directed learning, Brockett and Hiemstra advocate expanding the concept
through continued development as a central theme and conceptual frame-
work for adult learning.
Self-directedness is a characteristic of adult learning that is closely asso-
ciated with self-directed learning and includes a level of decision making and
personal control throughout the learning experience. Tough (1979) regards
self-directed learning as a form of adult learning that includes the ability
to plan and guide the learning process. Adults, according to Tough, have
a desire to learn by drawing upon personal autonomy, self-worth, and ac-
knowledgment of life experiences. Providing adults the opportunity to direct
and plan their own learning builds self-directedness that supports a growing
number of adult learning theories (Knowles, 1984/1998; Long, 1991b; Tough,
1979).
Self-Efficacy
Self-efficacy is the belief in one’s own capability to initiate control over
situations in an organized process (Bandura, 1994). Expectations of self-
efficacy involve psychological procedures which when analyzed may be
distinguished as two expectations of efficacy and outcome (Bandura, 1977).
“An efficacy expectation is the conviction that one can successfully execute
the behavior required to produce the outcomes. An outcome expectation
is defined as a person’s estimate that a given behavior will lead to certain
outcomes” (Bandura, 1977, p. 79). The differentiation between efficacy and
outcome expectations is linked to the learner’s belief that a course of action
Preparing Hospitality Organizations for Self-Service Technology 157
may produce certain outcomes, but the learner questions whether he or she
can actually perform those actions.
Bandura (1977) argues that the strength of conviction of the learner’s
own belief in effectiveness may determine whether the learner will pursue
changing or challenging situations. Learners may fear and avoid challeng-
ing situations when their belief is that they will not be able to handle the
problem. Conversely, Bandura explains, learners may behave with confi-
dence when they judge themselves to be capable of successfully handling
situations that would have otherwise been threatening to them.
Self-efficacy theory is based on two types of expectations, mentioned
earlier as efficacy expectations and outcome expectations, along with the
characteristics, behavior, and behavioral outcomes of the person (Bandura,
1986). Efficacy expectation (self-efficacy) is the person’s confidence in his
or her ability to produce the behavior, while the outcome expectation re-
sults from the behavior based on a person’s belief about the outcome. Self-
efficacy may be a more accurate predictor of performance since outcome
expectations are dependent upon self-efficacy (Bandura, 1986). Employees,
for example, may be more motivated to perform behaviors that they believe
will produce desired outcomes.
Using self-efficacy as a predictor, Bandura (1986) explains, is important
in understanding how people function in terms of the choices they make
(selection processes), effort (time and persistence), motivation (initiation),
thought patterns (cognitive processes), and emotional reactions (affective
processes) to various situations. The main sources of information that in-
fluence beliefs in self-efficacy include experience of mastery, observation,
verbal persuasion, and physiological information (Bandura, 1986, 1997).
One of the most influential sources of information on self-efficacy is
experience of mastery (Bandura, 1986). According to Bandura, success and
failure attributes are important sources of information for developing self-
efficacy. Successful experiences help enhance self-efficacy with a feeling of
mastery and control, while repeated failure decreases self-efficacy over time
(van der Bijl & Shortridge-Baggett, 2002). When a learner has developed a
strong self-efficacy, explains van der Bijl and Shortridge-Baggett, the effect of
one failure may not have much influence since the effects of failure follow
a total pattern of experiences, although the timing of the moment in the
learning process may vary in the power of the effect. If failure takes place in
the early stages of the learning process, for instance, the greater will be its
negative impact on self-efficacy (van der Bijl and Shortridge-Baggett, 2002).
Bandura (1986) describes a hierarchy in the sources of information for
self-efficacy and categorizes them as direct and indirect sources of informa-
tion. Experience of mastery, for example, is one of the most powerful sources
of information, as a person experiences success or failure immediately based
on direct information. The other information sources include observation of
others, verbal persuasion, and physiological information based on indirect
158 J. D. Lema
sources of information. Indirect sources of information may not be nearly
as powerful in terms of information for self-efficacy as the cognitive pro-
cess associated with critically reflective patterns of direct earlier experiences.
Other sources that influence self-efficacy include personality traits (Strecher,
DeVellis, Becker, & Rosenstock, 1986) such as self-esteem, locus of control,
self-confidence, and hardiness (Coppel, 1980), and environmental factors
such as expectations and support of others (Bandura, 1986).
In a further theoretical analysis of sources that influence self-efficacy,
Gist and Mitchell (1992) suggest that experiences of mastery, observation,
verbal persuasion, and physiological information contribute through a vari-
ety of internal and external information cues. Internal information cues relate
to an individual’s knowledge or skills and the person’s effectiveness in us-
ing these skills through various strategies. An individual’s self-efficacy can
be determined by an internal assessment (adequate, inferior, or superior) of
abilities when performing at various task levels. Judgments about expected
performance when engaged in a task can be influenced by mood, health, or
degree of arousal, whether positive (excited) or negative (fearful). External
information cues relate to the characteristics of the task itself, such as com-
plexity, number of components, parts, sequence, uncertainty, and steps. The
resources and interdependence required to successfully complete the task
can also influence the estimated level of self-efficacy.
Examinations of self-efficacy, Bandura (1997) suggests, often require as-
sessments that an individual makes in terms of the variability in influencing
determinants, previously described as experience of mastery, observation,
verbal persuasion, physiological information, and others. The level of vari-
ability may provide sources of information that range from low to high,
immediate or over longer periods, stable or unpredictable. Acquiring knowl-
edge, for instance, is one factor that may have an immediate effect on an
individual, whereas other factors such as ability may change after longer
periods of time. Bandura also argues that immediate variability in a factor
may result in greater perceived control over those factors that are relatively
stable and require longer periods of time.
Bandura (1986) emphasizes that one important element to consider
with self-efficacy is the perception of control. Some factors involve personal
control (e.g., effort), while other factors are controlled by someone else (e.g.,
facilitator). The perception that the causes of performance are uncontrollable
may result in lower levels of variability, resistance to change, and a lower
level of self-efficacy. Bandura claims that analysis and understanding of the
individual and task is necessary to enhance self-efficacy.
According to Bandura (1994), self-efficacy involves the belief that peo-
ple have in their personal capabilities the ability establish personal stan-
dards. Since personal standards may be modified by the environment or
demographic characteristics (position, gender, ethnicity), beliefs in personal
capabilities may influence possible discrepancies between capabilities and
Preparing Hospitality Organizations for Self-Service Technology 159
self-generated standards. Based on his social cognitive theory, Bandura’s re-
ciprocal process of self-efficacy has a primary objective of enhancing learning
skills and self-directedness in individuals (Bandura, 1994; Kitson, Lekan, &
Guglielmino, 1995).
Self-efficacy theories have created a framework for understanding el-
ements related to self-directed learning. Brockett and Hiemstra (1991), for
example, developed a two-component model referred to as the Personal
Responsibility Orientation (PRO) that supports personal responsibility and
individual ownership of the learner’s thoughts and actions or learner self-
direction. The other component consists of self-directed learning that em-
phasizes the relationship between the learner and facilitator. The PRO model
suggests that self-efficacy is central to understanding self-direction in regards
to employee learning. The model also suggests that employees are capable
of taking a proactive approach to learning and, when given the opportunity
to be self-directed, there is the potential to maximize benefits for both the
employee and organization.
Another model based on the situational nature of the learner and facili-
tator was developed by Grow (1991) called the Staged Self-Directed Learning
Model (SSDL). This model assumes that the self-directedness of the learner is
based on situational processes. Grow explains that learners progress through
stages of self-direction that may either increase or decrease depending upon
the situational circumstances. Furthermore, Grow argues, depending on the
facilitator’s approach, learning may be supported or hindered in the process.
The SSDL model may help to indicate whether a facilitator’s style aligns with
the learner’s self-directed learning readiness.
METHODOLOGY
The convenience sample consisted of employees who work in hospitality
organizations, with approximately 216 employees participating. Data col-
lection occurred at three participating hospitality organizations during April
2006. The participating organizations offer a diverse workforce, with food
and beverage, lodging, and entertainment operations representing significant
areas of the hospitality organization. A facilitator administered the survey to
the employees who volunteered for the study in the participating organi-
zations’ business facilities. Completion time for the survey was 30 minutes.
Participants were presented with an informed consent form before they par-
ticipated in the study which clearly stated the voluntary nature of participa-
tion, the ability to withdraw from the survey at any time, and confidentiality
of the participants’ identities.
A survey integrating the Oddi Continuing Learning Inventory (OCLI)
and Generalized Self-Efficacy Scale (GSE) was presented to the participants
as a five-page instrument consisting of 49 questions. The OCLI and GSE
160 J. D. Lema
instruments uniquely complement each other in their generalizability to pro-
vide a stable comprehensive indicator of relationships rather than measuring
a narrowly defined activity that could be the result of a brief occurrence.
Rather than measuring a specific task, the OCLI and GSE measure overall
job-related activities. The GSE instrument was used to assess self-beliefs of
personal capabilities of the employee (Jerusalem & Schwarzer, 1993). The
OCLI instrument, developed as a doctoral dissertation by Oddi (1984), was
administered to assess employees’ levels of self-directed learning readiness.
The relationships among GSE scores and OCLI scores were examined.
The OCLI survey is one of the most widely reliable and validated in-
struments used for the measurement of readiness for self-directed learning
(Brockett & Hiemstra, 1991). The OCLI survey measures the level of self-
directed learning readiness of adults. With a reported Cronbach’s alpha of.88
and retest reliability of r = .89, the OCLI is an adequately reliable instrument
for this study (Oddi, 1984).
Validation of the OCLI instrument was conducted by Oddi, Ellis, and
Altman-Roberson (1990) to examine the relationship of the survey constructs
and behavioral characteristics that indicate self-directed learning readiness.
Three theories were developed to describe the affective, motivational, and
cognitive attributes of the self-directed learner. The proactive drive versus
reactive drive, commitment to learning versus apathy to learning, and cogni-
tive openness versus defensiveness were reported by Oddi et al. (1990) to be
the three constructs that emerged. Factor analysis reported by Oddi (1984)
indicated that OCLI items contained self-confidence, autonomous learning,
and learning with the participation of others. When items were loaded on a
general factor analysis, reading avidity and self-regulation emerged as sub-
sidiary factors. No factor was related to cognitive openness in the analysis,
since scores failed to correlate with the adult intelligence factor. When scores
failed to correlate with adult intelligence, discriminate validity was provided.
The two other dimensions that Oddi describes as reading avidity and ability
to be self-regulating positively correlated with self-confidence, participation,
and endurance. These results indicate that the total OCLI score can be used
to provide a reliable and valid measure for the construct of self-directed
learning readiness.
The generalizability of the OCLI, detailed in a follow-up study by Six
(1987), reported that factor analysis across different populations suggested
that the factors identified by Oddi (1984) in the development of the OCLI
instrument were not unique to the sample. The factor analysis indicated that
the factors derived by Oddi did not break up under different study condi-
tions to form new factors and, as a result, remained stable across different
studies (Six, 1987). Validation of the factor match, Six argued, demonstrates
the generality of the instrument across different populations. Respondents
circled an answer from a 7-point Likert scale ranging from 1 (strongly agree)
to 7 (strongly disagree) to best describe their behavior. The total self-directed
Preparing Hospitality Organizations for Self-Service Technology 161
learning readiness score from the survey was used in the statistical proce-
dures as recommended in the literature (Brockett & Hiemstra, 1991; Oddi,
1984).
The GSE consists of a scale designed to measure the generalized self-
efficacy or the employees’ belief in their ability to perform their job. Jerusalem
and Schwarzer developed the GSE in 1980, and it has been used with
thousands of participants in 27 language versions throughout the world
(Jerusalem & Schwarzer, 1993). The stability of the GSE has been reported in
a number of longitudinal studies along with validation of the instrument in
similar occupational and educational environments (Jerusalem & Schwarzer,
1993; Pasveer, 1998; Schwarzer, BaBler, Kwiatek, Schroder, & Zhang, 1997;
Schwarzer & Born, 1997; Schwarzer, Mueller, & Greenglass, 1999; Schwarzer
& Schroder, 1997). The GSE reports a Cronbach’s alpha of.75, with a retest
reliability (after 1 year) of r = .67 and a stability coefficient (after 2 years)
of r = .75 (Jerusalem & Schwarzer, 1993). The participant was required to
circle the correct response from a 4-point scale ranging from 1 (not at all
true) to 4 (exactly true). The overall score of the instrument was calculated
by totaling the response score to the appropriate questions. Scoring of the
instrument was in accordance with the guidelines provided by the authors
(Jerusalem & Schwarzer, 1993).
FINDINGS
Descriptive data for the total sample include position, gender, and ethnicity.
The sample consisted of individuals working as supervisors (34%, n = 71)
and nonsupervisors (66%, n = 141). In addition, the sample contained 52%
females (n = 111) and 48% males (n = 101). Ethnicity included 14% African
American (n = 30), 2% American Indian (n = 5), 17% Asian (n = 35),
45% Caucasian (n = 96), 13% Hispanic (n = 27), and 9% Pacific Islander
(n = 19). Due to missing responses on the OCLI and GSE, four participants
were eliminated from the study. Three participants inaccurately completed
responses on the OCLI scale and one participant failed to complete responses
on the GSE scale. Consequently the OCLI and GSE scales could not be
sufficiently scored for these participants. A total of 212 participants provided
an adequate sample size for the number of predictors used in the stepwise
multiple linear regression model.
The OCLI variable consisted of a 7-point scale, with a lower number
(24 being the lowest possible score) indicating less self-directed learning
readiness and a higher number (168 being the highest possible score) repre-
senting greater self-directed learning readiness. Scores for the OCLI ranged
from a low score of 51 to a high score of 154 with a mean score of 107. The
range of OCLI scores in this research were consistent with other research
in the area of self-directed learning readiness. The mean OCLI score in this
162 J. D. Lema
study (107), however, was lower than the OCLI mean score (128) in a study
on practicing nurses (Oddi, 1987).
The GSE is measured on a 4-point scale, with a lower number indicating
less generalized self-efficacy and a higher number representing greater gen-
eralized self-efficacy. Scores for the GSE ranged from a low score of 12 to a
high score of 40 with a mean score of 30. Industry experience was measured
in months of hospitality industry work experience. Position was effect coded
into the classifications of Supervisor and Nonsupervisor. Gender was effect
coded into the categories of Male and Female. Finally, ethnicity was effect
coded into the classifications of African American, American Indian, Asian,
Caucasian, Hispanic, and Pacific Islander.
Overall Stepwise Multiple Linear Regression Model
A correlation matrix is presented in Table 1 to report the relationships be-
tween the OCLI and GSE, along with selected demographic variables. The
GSE variable reported the strongest correlation with OCLI scores (r = .840)
when compared to position, gender, and ethnicity. Position was negatively
correlated with OCLI scores, indicating that supervisors had higher OCLI
scores and nonsupervisors had lower OCLI scores. All variables reported a
statistically significant correlation with OCLI scores at or below the.05 alpha
level.
Considering the number of predictors and exploratory nature of the
study, stepwise multiple linear regression was selected for the statistical anal-
ysis. Only significant predictors remain in the stepwise multiple linear regres-
sion model by the retesting of the predictor variables at each step (Mertler
& Vannatta, 2005). The final stepwise multiple linear regression model con-
sisted of four predictor variables, including generalized self-efficacy, position,
gender, and ethnicity.
Data inspection did not locate any outliers, therefore no cases
were deleted from the analysis. Evaluations of linearity, Kolmogorov-
Smirnov tests of normality, homoscedasticity, and multicollinearity showed
that the assumptions were within the range of tolerance. Four of the
TABLE 1 Pearson Correlation Matrix (N = 212)
OCLI GSE Position Gender Ethnicity
OCLI —
GSE .840∗ —
Position −.405∗ −.414 —
Gender .310∗ .089 .024 —
Ethnicity .129∗ .006 −.105 .082 —
Note. ∗p < .05, one-tailed.
Preparing Hospitality Organizations for Self-Service Technology 163
variables—generalized self-efficacy, position, gender, and ethnicity—entered
into the overall model, R2 = .81, R2
adj = .81, F(4, 206) = 176.48, p = .001.
The final stepwise multiple linear regression model accounted for 81.1% of
the variance in OCLI scores.
Hypotheses Testing
Hypothesis 1: There is a significant relationship between self-directed learn-
ing readiness and self-efficacy of employees in the hospitality industry.
The results of the stepwise multiple linear regression model indicated
that the self-efficacy variable makes a statistically significant contribution to
the self-directed learning readiness of employees in the hospitality indus-
try. The strength of the correlation (r = .840) between self-efficacy and
self-directed learning readiness indicated that higher self-efficacy scores are
associated with higher self-directed learning readiness scores. Furthermore,
self-efficacy reported the strongest correlation among all of the tested vari-
ables to the self-directed learning readiness of employees.
Hypothesis 2: There is a significant relationship between self-directed learn-
ing readiness and selected demographic variables (position, gender, and
ethnicity) of employees in the hospitality industry.
The stepwise multiple linear regression model reported that position
had a statistically significant relationship to self-directed learning readiness
of employees in the hospitality industry. The strength of the relationship
was (r = −.405), indicating that supervisors have higher self-directed learn-
ing readiness scores than nonsupervisors. Position was the most strongly
correlated variable to self-directed learning readiness of the demographic
variables. Gender also reported a statistically significant relationship to the
self-directed learning readiness of employees in the hospitality industry and
entered into the stepwise multiple linear regression model. The strength of
the relationship (r = .310) indicated that females had higher self-directed
learning scores that males. Finally, ethnicity entered into the stepwise multi-
ple linear regression model and had a statistically significant relationship to
the self-directed learning readiness of employees in the hospitality industry.
The strength of the relationship (r = .129), although weak, indicated that
Asians had higher self-directed learning readiness scores.
DISCUSSION
Self-Efficacy
The findings of this study provided both expected and unexpected results in
view of previous studies. Self-efficacy, as expected, significantly correlated
164 J. D. Lema
with the self-directed learning readiness of employees in the study, yet the
strength of the relationship compared to the other variables was surprisingly
higher. Self-efficacy, as described by Bandura (1994), refers to the belief
in one’s own capability to initiate control over situations in an organized
process. The strong, significant relationship between self-efficacy and self-
directed learning readiness supports Bandura’s (1997) notion that learners
will pursue challenging situations when they have a belief that their capa-
bilities to handle situations will produce positive outcomes. An employee’s
motivation to participate in self-directed learning activities comes from the
belief that they are capable of succeeding in handling those particular
situations.
The strength of the relationship between self-efficacy and self-directed
learning readiness was discovered to be stronger than personal character-
istics and demographic variables. In examining the four dynamics Bandura
(1977) describes as performance accomplishments, experience, verbal per-
suasion, and emotional arousal, the importance of building positive experi-
ences is also evident in the significance of the strength in the relationship
between self-efficacy and the self-directed learning readiness of employees
in this study. Since experience of mastery is one of the most important
sources of information for determining levels of self-efficacy, positive expe-
riences with self-directed learning opportunities may reciprocally indicate a
greater readiness to engage in self-directed activities and higher levels of
self-efficacy. Lower levels of self-efficacy imply lower levels of self-directed
learning readiness based on the results of this study. The quality of the expe-
riences, however, may not be determined from the findings of this research.
Demographic Variables
Position was found to be significantly correlated to the self-directed learning
readiness of employees in this study. In addition, position entered into the
final stepwise multiple linear regression model. Individuals in supervisory
positions showed higher levels of self-directed learning readiness than those
in nonsupervisory positions. Although the significant results of this variable
are consistent with the findings of another study (Roberts, 1986) involving a
Hong Kong telephone company, other researchers have indicated mixed re-
sults in examining position relative to self-directed learning readiness (Brock-
ett & Hiemstra, 1991). Oddi (1987) also reports that further examination of
the variable “position” is recommended. Supervisor positions in the hospital-
ity industry typically require greater leadership and critical decision-making
responsibilities than nonsupervisors. The results of this study imply that su-
pervisors have a greater self-directed learning readiness than nonsupervisors
and, although statistically significant, the correlation is not nearly as strong
as the self-efficacy variable.
Preparing Hospitality Organizations for Self-Service Technology 165
Gender was significantly correlated with self-directed learning readiness.
Furthermore, gender entered into the final stepwise multiple linear regression
model. Coinciding with a previous study by Guglielmino and Guglielmino
(as cited in Brockett & Hiemstra, 1991), females reported higher overall levels
of self-directed learning readiness scores than males. Although their study
reported a significant relationship between gender and self-directed learning
readiness scores, the difference was narrow. The results of this study similarly
reported a narrow correlational significance between self-directed learning
readiness scores relative to the three other variables of interest, including
self-efficacy, position, and ethnicity.
The results of ethnicity indicated a significant relationship with self-
directed learning readiness scores. In addition, ethnicity entered into the
final stepwise multiple linear regression model, indicating possible predictive
capabilities in determining self-directed learning readiness for employees.
Ethnicity has shown inconclusive results in a number of self-directed learning
readiness studies in another field, nursing, as reported by Oddi (1987). The
correlation between ethnicity and self-directed learning readiness is relatively
weak in comparison to the other variables that were tested in this study.
CONCLUSION
The role of self-efficacy in relation to self-directed learning readiness will
require careful consideration. An increase in the level of self-directed learning
readiness of employees will need to coincide with strategies that enhance
self-efficacy. As Bandura (1977) argues, experience of mastery is one of
the most significant information sources relative to self-efficacy. In view of
Bandura’s theory, providing focused facilitation to complement self-directed
processes may help to provide positive and successful experiences with
self-service technologies and increased self-efficacy.
Bandura’s (1986) self-efficacy theory also emphasizes that environmental
factors can impact levels of self-efficacy. The social element of the hospital-
ity industry provides unique situations where concentrations of employees
and customers interact in dynamic environments. Levels of support may
change in an instant for employees and customers, and social support may
be instrumental in enhancing levels of self-efficacy. Organizational cultures
that create an environment in which hospitality among employees and cus-
tomers is an essential priority will help to positively influence self-efficacy
and self-directed learning readiness strategies.
Organizations that are able to incorporate self-directed learning con-
cepts into their self-service processes may benefit by delivering successful
programs to their employees and customers. With further understanding in
the differences of self-directed learning readiness, measurement, and bench-
marking procedures among participants, hospitality organizations have an
166 J. D. Lema
opportunity to gain competitive advantages. Recognizing self-directed learn-
ing as a dynamic process that varies among different groups of individuals
based on their unique characteristics may help provide successful self-service
programs for hospitality organizations.
Employee self-efficacy should be considered when implementing self-
directed learning processes. Examining the self-directed learning readiness
of employees will help to determine at what level employees are able to
successfully engage in self-directed processes. The importance of building
strong levels of self-efficacy relative to the self-directed learning readiness of
employees is evident in the results of this study.
Self-efficacy may be easily enhanced in situations that offer immedi-
ate personal benefits to the learner. An employee who, for example, is a
novice user of technology may be highly motivated to pursue a learning
opportunity that will provide an immediate impact on his or her life, such as
having unlimited access to self-service benefit options. An organization that
can facilitate personal rewarding experiences for their employees also have
an opportunity to create positive experiences that may increase self-efficacy
and advance technological skill levels. One of the concerns that Hu, Nelson,
Braunlich, & Hsieh (2003) explain in their research on technology-related
training is that participants need to be more self-motivated in training ac-
tivities in order to use the technological capabilities to the fullest extent.
Providing learning opportunities through activities that have an immediate
personal interest and impact on employees may be one possible motivational
strategy. Furthermore, in view of rapid technological developments, offer-
ing employees incentives to purchase personal computers for their homes
may provide other opportunities for employees to gain experience with
self-service applications. Providing employees with the opportunity to gain
self-service technology experience can begin with initiatives that are of per-
sonal interest to employees, such as self-service benefits enrollment, payroll
transactions, and other personnel-related activities.
In moving beyond the misconceptions and misunderstandings of self-
directed learning, Brocket et al. (2000) propose that by identifying new lines
of inquiry into self-directed learning readiness, opportunities exist to fully
expand the potential of self-directed learning. Oddi (1987) argues that op-
portunities exist to move beyond self-directed learning as a self-instructional
process to examine self-directed learning in terms of cognitive, motivational,
and affective characteristics and personalities of self-directed learners. Houle
(1961) suggests, for example, the essence of self-directed learning is the
inquiring mind that approaches life with openness to discovery. Houle ar-
gues that outstanding continuing learners have this attribute of personality
(inquiry) to initiate learning. Although Oddi advocates that various modes
of learning should not fail to be recognized, linking personality charac-
teristics to self-directed learners offers substantial benefits to understand-
ing the self-directed learning readiness of learners. Self-efficacy, being the
Preparing Hospitality Organizations for Self-Service Technology 167
predominantly significant variable relative to the self-directed learning readi-
ness of employees as examined in this study, similarly supports Oddi’s at-
tempt to identify significant relationships related to self-directed learning
processes.
Since the total OCLI and GSE scores are the most highly recommended
and valid reporting statistics, factor analysis within the scales will not be
used in further interpretation of the results due to a less stable level of
reliability. Furthermore, another limitation of this study is in regards to the
GSE instrument as a measure of generalized self-efficacy and not a measure of
self-efficacy of a particular task, therefore limiting the analysis to generalized
results. The results from this study did not attempt to provide cause-and-
effect relationships among the variables. While offering suggestions for future
research investigations to refine self-directed learning, careful consideration
should be given to the operational definition of variables. The complexity
of examining aspects related to self-directed learning may appear to be
narrow in some circumstances, yet broad in others. Inquiry into self-directed
learning, however, should continue to experiment with variables that will
provide significant relationships and predictive capabilities with self-directed
learning instruments to measure participants’ levels of readiness in both
educational and occupational environments.
Opportunities exist to further develop instruments that measure self-
directed learning readiness. Both Oddi and Guglielmino have significantly
contributed to advancing self-directed learning theories with the develop-
ment of their self-directed learning readiness instruments. As technology
continues to rapidly shape society and the hospitality industry, development
of new instruments that build on the framework of existing self-directed
learning readiness instruments will help to provide greater understanding of
emerging self-directed learning processes.
Self-directed learning research should continue to benefit the hospi-
tality industry as self-service technologies become part of daily operations.
Hospitality organizations that are able to gain competitive advantages from
self-service processes will need support in developing strategies that can
provide the best experiences for their employees and customers.
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Journal of Hospitality & Tourism Research, Vol. XX, No. X, Month 201X, 1 –35
DOI: https://doi.org/10.1177/1096348020946383
© The Author(s) 2020
1
How To Build a BeTTer roBoT . . .
For Quick-Service reSTauranTS
dina Marie v. Zemke
Ball State University
Jason Tang
carola raab
Jungsun kim
University of Nevada, Las Vegas
Hospitality firms are exploring opportunities to incorporate innovative technologies,
such as robotics, into their operations. This qualitative study used focus groups to
investigate diner perspectives on issues related to using robot technology in quick-
service restaurant (QSR) operations. QSR guests have major concerns regarding
the societal impact of robotics entering the realm of QSR operations; the cleanliness
and food safety of robot technology; and communication quality, especially voice
recognition, from both native and nonnative English speakers. Participants also
offered opinions about the functionality and physical appearance of robots, the value
of the “human touch,” and devised creative solutions for deploying this technology.
Surprisingly, few differences in attitudes and perceptions were found between age
groups, and the participants were highly ambivalent about the technology. Future
research may consider further exploration of robot applications in other restaurant
segments, using quantitative methods with a larger sampl
e.
Keywords: restaurants; robotics; qualitative; service encounter; smart technology;
customer perception
inTroducTion
The use of technology in hospitality businesses has evolved over time. It
originated in information management, where handwritten orders and folios
were replaced by point-of-sale systems. The computers used to operate these
systems also evolved from punch-card entry mainframes to desktop computers,
laptops, pads, and smartphones. For the past 40 years, the evolution in technol-
ogy has focused on data entry and data management, and much hospitality
946383 JHTXXX10.1177/1096348020946383JOURNAL OF HOSPITALITY & TOURISM RESEARCHZemke et al. / BeTTer roBoT For QUICK-serVICe resTAUrANTs
research-article2020
Authors’ Note: The research team gratefully acknowledges the support of the William F. Harrah
College of Hospitality in conducting this study. Jason Tang is now affiliated with Mount Royal
University, Calgary, Canada.
http://crossmark.crossref.org/dialog/?doi=10.1177%2F1096348020946383&domain=pdf&date_stamp=2020-08-05
2 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
research has focused on how to facilitate acceptance and usage of this technol-
ogy, but not necessarily how to influence its creation and presentation.
Robotic technology to perform specific physical tasks has recently emerged
as an option for hospitality businesses. Integrating robots into hospitality opera-
tions has become more feasible due to decreasing robotic equipment costs.
Currently, the majority of robots used in the hospitality industry are technologies
initially developed for other industries, such as automobile and food manufac-
turing, which have been modified from their original functions to perform their
tasks in a hospitality setting. Examples include the work performed by robotic
vacuums (now presented as housekeeping robots), information displays (now
made mobile on a rolling platform), and robotic manufacturing assembly arms
(which now assemble pizzas and cocktails).
The genesis of this study was the “Fight for $15” movement in the United
States which focused heavily on hourly service jobs, such as those in franchised
quick-service restaurants (QSRs). The minimum hourly wage would increase to
at least $15 per hour, and many restaurant operators cautiously suggested that
they may explore robotics as an alternative to absorbing these increased labor
costs. The most vocal proponent for exploration was Andy Puzder, the former
CEO for CKE Enterprises (which owns the Carl’s Jr. and Hardees restaurant
brands). While he was not threatening to replace employees with robots, he
forthrightly stated that all restaurant companies may need to consider this tech-
nology to remain competitive.
The media’s attention to this issue raised the public’s awareness of robotics,
and there is evidence of increased incorporation of robotic technology in produc-
tion operations. Most of the advances in “robotics” are simply information pads
or kiosks for order taking, which do not move or perform a physical task and, as
such, do not qualify as robots; however, a few restaurant companies now use
actual robotics. For example, the Cali-Burger chain has deployed its “Flippy”
hamburger-making robot in its stores. In July 2018, McDonald’s opened its first
“all-robot” restaurant in Phoenix, Arizona (which actually is not 100% robot-
staffed; humans are required to ensure that the robots are functioning properly).
The current study explored the QSR customer’s perceptions of the use of
robotic technology in the QSR industry. Rather than using preexisting measure-
ment instruments, which may have limited the range of the guest’s perceptions,
this zero-based qualitative study used a focus group technique to elicit this infor-
mation from QSR patrons. The participants offered their thoughts on issues that
included what robots should or should not do, what they should or should not
look like, and the benefits or hazards that could be posed by using robots in
QSRs. The objective of this qualitative study was to reveal a broad range of
positive and negative perceptions of this technology that will inform the devel-
opment of an instrument to gain perceptions from a broader swath of the popula-
tion. This study’s results establish a fertile ground for future academic study as
well as inform industry practitioners’ efforts to design and implement these tech-
nologies into their operations.
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 3
liTeraTure review
defining robotics
International Organization for Standardization (ISO) standard 8373:2012 pro-
vides definitions of common robot terminology, officially defining a robot as “an
actuated mechanism programmable in two or more axes with a degree of auton-
omy, moving within its environment, to perform intended tasks” (International
Organization for Standardization, 2012). Robots can be segmented into two pri-
mary types, industrial and service. The International Federation of Robotics
(2016) defines a service robot as one that autonomously performs useful tasks for
humans or equipment outside an industrial automation application without human
intervention. Robots are subsequently categorized as personal service robots or
professional service robots. Personal service robots are utilized in a noncommer-
cial setting and include examples such as automated wheelchairs and personal
mobility assistive robots, while professional service robots are utilized for com-
mercial tasks, such as to make deliveries or for cleaning. In addition, professional
service robots require a human operator to start, monitor, and stop the robot’s
operation.
The physical appearance of robots. Past research into the physical appear-
ance of robots is rooted in anthropomorphism, or how nonhuman creatures or
objects can mimic the appearance of humans, and the process of disambigua-
tion, which proposes a model that explains the human response to anthropomor-
phic presentations of nonhuman objects and images.
Anthropomorphism. Anthropomorphism has been broadly defined as the
attribution of human character and behavior to nonhuman entities (Bartneck
et al., 2007). Examples of anthropomorphic representations include toys, adver-
tising images, video game characters, and robotic devices that bear some resem-
blance to a human form and human behavior. In the context of robotics,
anthropomorphism refers to the degree to which a robot resembles a human
through visual cues, movements, and communications (Murphy, Gretzel, et al.,
2017). These elements of anthropomorphism are critical when evaluating how
robots are used in a service context, as they assist in predicting the degree to
which guests perceive robots to express moral care, concern, responsibility,
trust, and social influence during social interactions (Waytz et al., 2014). These
elements are particularly critical when examining human-robot interactions
(Murphy, Hofacker, et al., 2017).
Companies have successfully incorporated anthropomorphism to encour-
age brand attachment and loyalty (Veer, 2013). As a result, Murphy, Gretzel,
et al. (2017) proposed a new construct, anthropomorphism loyalty, which
could be applied to human robot interactions. Anthropomorphism loyalty sug-
gests that anthropomorphism could potentially create a level of attachment
and loyalty previously unattainable with inanimate digital objects. By exten-
sion, anthropomorphism loyalty could be applied to animated physical objects,
such as robots.
4 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
Disambiguation and the “uncanny valley hypothesis.” Disambiguation is a
cognitive system of category processing through which humans make sense of,
or remove the ambiguity posed by, an ambiguous image or object. It is a natural
response that humans employ to make sense of language, images, or other situ-
ations that are unclear. The results of this process can range from acceptance/
positive affect through repulsion/negative affect. Mori (1970, 2012) proposed
the “uncanny valley hypothesis” to explain the relationship between an image’s
human likeness and shinwa-kan. Here, human likeness is an example of anthro-
pomorphism, the attribution of human form, character or behavior to nonhuman
entities (Bartneck et al., 2007; Epley et al., 2008). Mori’s use of the Japanese
term shinwa-kan was translated into English as familiarity (MacDorman, 2005;
MacDorman & Ishiguro, 2006). However, as the concept of the uncanny valley
grew in popularity, shinwa-kan has also been translated into other English terms,
including likeability (Bartneck et al., 2007), affinity (Bartneck et al., 2007;
MacDorman & Entezari, 2015; Mori, 2012), emotional response (Blow et al.,
2006), and pleasantness (Seyama & Nagayama, 2007).
Mori (1970, 2012) posited that the likability of nonhuman entities increases
as anthropomorphism increases. However, when anthropomorphism reaches a
point where human replicas appear nearly, but not exactly, like human beings,
feelings of distress, eeriness, and repulsion are elicited (Blow et al., 2006;
MacDorman, 2005) and likability drastically drops. It then rapidly recovers to
surpass the previous peak in likability when the nonhuman entity becomes virtu-
ally indistinguishable from a human (Bartneck et al., 2007). Notwithstanding
the increased attention that the uncanny valley has recently received, there has
been sparse empirical support for this concept (Bartneck et al., 2007; Blow
et al., 2006). The limited evidence substantiating the scale and pace of growth,
decline, and subsequent recovery of likability, relative to increasing anthropo-
morphism, constrains the utility of this hypothesis.
robotic Technology in Service encounters
The quality of the face-to-face interaction between the service provider and
the customer is critical to service organizations, as it may exclusively determine
customer satisfaction and repurchase intentions (Solomon et al., 1985). Past
researchers have evaluated the quality of these interactions in terms of rapport
(Gremler & Gwinner, 2000; Hennig-Thurau et al., 2006) and how well custom-
ers have enjoyed interactions with service providers (Gremler & Gwinner, 2000;
Rafaeli et al., 2017). An implied requirement for effective service and positive
rapport during social interactions is a display of positive emotions from service
providers (Rafaeli et al., 2017). If the human customer service representative is
replaced by a digital or robotic version, the quality of reciprocal interactions
between the robot and the customer needs to be considered. Researchers have
examined the role of technology in service encounters, finding that customer
perceptions of such service situations vary depending on the presence, or
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 5
absence, of employee rapport (Giebelhausen et al., 2014). Early studies of
human–computer interactions have stressed the importance of building rapport
between humans and nonhuman entities (Picard, 1997, 2003). In certain service
situations, technology effectively enhanced social interactions between service
providers and customers. A study of robot deployment in a shopping mall found
that robots need to be friendly for customers to feel comfortable during interac-
tions (Kanda et al., 2009).
Other researchers have focused on the differences in group perceptions and
attitudes toward robotics. Most research in this area has focused on health care
robotics for consumer use (often a mobile screen) or on young children’s percep-
tions of robots. Conventional wisdom suggests that younger people will have
more positive perceptions and will be more accepting of robotics than older
people; however, little research has explored this and the conventional wisdom
may be fallible. For example, I. H. Kuo et al. (2009) found little difference
between middle-aged and elderly people in their perceptions and acceptance of
a health care robot, although they did find that males are more likely to accept
robots than are females. Another study of owners of household robotic vacuum
cleaners found no significant differences between age or gender groups, although
the researchers did find that, over time, the perception and acceptance of the
robot improved (Fink et al., 2011).
More recent research has found that the proper integration of smart technol-
ogy can complement human service providers, eliminating the need to trade-off
between service efficiency and effectiveness (Marinova et al., 2017). Smart
technology has been defined as tools consisting of information, software, and
hardware to facilitate learning from service encounters to coproduce value.
Marinova et al. (2017) proposed that learning, facilitated by smart technology,
enables both service providers and customers to retain knowledge and enhance
service encounters in real time. However, past smart technology research has
focused on information systems, but has not investigated robots as a form of
smart technology within the context of a service encounter.
Robotics in the hospitality industry. Hospitality businesses are operated using
a significant number of entry-level employees, often staffed with part-time and
seasonal employees (Baum, 2006). The rapid advancements in robotic technol-
ogy are expected to assist operators in mitigating difficulties related to seasonal
and entry-level employment, while maximizing labor utilization (C. M. Kuo
et al., 2017). Researchers have proposed the benefits of using robots as an alter-
native to low-skilled employees, since robots are easier to train, are capable of
providing more reliable and consistent levels of service, and do not lose interest
in mundane and repetitive tasks (C. M. Kuo et al., 2017; Qureshi & Sajjad,
2017). However, the restaurant industry is expected to remain highly labor-
intensive, regardless of the amount of technology implemented (National
Restaurant Association [NRA], 2019). The U.S. labor force participation rate is
expected to increase modestly in the decade between 2020 and 2030 (NRA,
2019). The biggest gains in labor force participation are expected to be adults
6 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
aged 65 years and older, while teenagers entering the workforce are expected to
decline (Abrams & Gebeloff, 2018). Therefore, the QSR industry must address
the potential for a chronic shortage of its traditional entry-level employees.
An exploratory qualitative investigation of robots in hospitality found that
hoteliers value obtaining customer feedback while the guest is still physically
present on the property, specifically using robots to collect guest feedback
(Chung & Cakmak, 2018). One participant in Chung and Cakmak’s study envi-
sioned that robots could increase direct guest feedback, as the robot would act as
an effective neutral agent between the guest and the hotel. To avoid interper-
sonal confrontation or hurting an employee’s feelings, guests may be reluctant
to share negative experiences directly with employees; therefore, the robot
might provide a neutral avenue to share these experiences. The hotel industry
has begun to incorporate service robots into some locations. The best-known
examples at this time are the Botlr delivery robot and the Pepper robotic con-
cierge. Little data has been published about the actual cost savings or operational
benefits of using one of these devices, although there is a great deal of publicity
in the popular and trade press about the novelty and delight that these robots
generate among hotel guests.
If a robot’s ultimate objective is to serve people through providing informa-
tion or assisting with physical tasks (Zalama et al., 2014), research on how hos-
pitality guests feel about robots is necessary. One recent study has attempted to
identify critical components for human–robot interactions in a hospitality set-
ting. Tussyadiah and Park (2018) recommended that the design of hotel service
robots that appear to be more humanoid must emphasize the design of the “face”
of the robot (vs. the body). They also found that robots that are currently designed
as “delivery” robots, which do not look humanoid, should emphasize the intel-
ligence displayed by the robot.
Robotics in restaurants. Most of the extant research on robots in hospitality
has been conducted in a hotel setting, focusing on concierge and delivery robots.
In contrast, very little published research has been conducted that examines the
application of robotics to the restaurant industry. A few studies have explored
the benefits to restaurateurs when deploying robots (C. M. Kuo et al., 2017;
Zalama et al., 2014), including financial benefits such as reduced labor and
training costs and operational benefits such as improved quality control and
consistency.
The restaurant industry is experimenting with automation in an attempt to
provide novelty for customers and to cut costs. For example, the Flippy ham-
burger-cooking robot costs approximately $60,000 to purchase, plus the cost to
maintain the machine (Bishop, 2019). However, many industrial service robots,
such as robotic vacuums and robotic assembly arms, are leased. Purchasing a
commercial robotic vacuum will cost between $7,000 and $15,000, but they are
often leased at $4 to $6 per hour of operating time; the manufacturer or distribu-
tor is responsible for all maintenance on the device (Ackerman, 2014). The
actual costs and financial benefits are not published, as they are proprietary
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 7
information for the manufacturer or dealer. However, if the hourly wage rate at
a base cost of $15.00/hour plus fringe benefits is compared with an hourly lease
rate of $6.00/hour for a commercial robotic vacuum, it is likely that cost effi-
ciencies are available as long as the technology works as promised. Costs for
these devices is expected to continue to decline.
As the cost of robotics has decreased, some restaurants have already substi-
tuted humans with robots for certain jobs (Bowen & Morosan, 2018). A study of
the use of robots in restaurants found adoption in the front-of-house (FOH) in
the form of tablets, for order taking and payment, and back-of-house (BOH)
robots acting as chefs and engaging in cleaning functions (Ivanov et al., 2017).
Robots can potentially cook hundreds of different dishes (Yu et al., 2012), with
some QSRs currently using robots to prepare and cook hamburgers, limiting the
human participation in the process to simply providing the finishing touches
(Graham, 2018).
While the operational and financial benefits of using robotics in restaurants
have received some attention, scarce research has been conducted that explores
the guest’s perspectives on the benefits of a restaurant using robotics. There is
scant evidence for research that examines the guest’s perspectives on the service
process, the quality of the service, communication experiences while interacting
with the technology, and broader societal implications. The current study
explores how the use of robotics might benefit the customer or detract from his
or her experience.
Purpose of the Study
This qualitative study explored the positive and negative aspects of using
robotics in QSRs, from the guest’s perspective. Rather than using well-estab-
lished quantitative measurement items, this qualitative technique used a phe-
nomenological approach to explore the restaurant guest’s perspectives on using
robotics without placing pre-determined boundaries on the range of the partici-
pants’ responses. This zero-based approach (Creswell, 2015) was intended to
elicit the top-of-mind impressions, concerns, advantages, and disadvantages of
incorporating robotic technology in QSRs. Much of the extant literature on
robotics, in both the popular press and scholarly hospitality publications, has
focused on their use in hotels; less attention has been paid to their deployment in
restaurants.
Very few “mom and pop” restaurants have the financial foundation to incor-
porate robotics into their operations at this time and are not likely to be a signifi-
cant source of technology innovation in the near future (NRA, 2019). However,
the larger brands/chains are well-positioned to support incorporating robotics
into their operations (NRA, 2019). Restaurants, and QSRs in particular, are
exemplified by several operating characteristics that provide a fertile environ-
ment for deploying robotics. First, QSR employees are engaged in highly repeti-
tive tasks, which have been identified as an ideal opportunity for robotic
8 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
adaptation, as “more facets of cooking will be organized to be readable by arti-
ficial intelligence” (NRA, 2019, slide 39). Most QSRs offer a highly standard-
ized product and generate high-volume production (Byrd, 2015), which are also
ideal attributes for the efficiencies offered by robotic technology. The QSR
industry experiences a very high employee turnover rate (Abrams & Gebeloff,
2018), which has been identified as a factor that enhances the feasibility of
deploying robotic technology. Moreover, when an economy experiences periods
of low unemployment, a labor shortage often occurs which leads to even higher
rates of employee turnover (Patton, 2019), further increasing the attractiveness
of robotic technology. Evidence also shows that QSRs are increasing their
hours of operation, where many locations now operate 24 hours/day, increasing
their staffing needs in an already tight market. Finally, there is a well-docu-
mented social and political movement in the United States to increase the mini-
mum wage rate (“the fight for $15”) along with increased efforts to unionize
QSR employees. An environment of rising wages provides a compelling ratio-
nale for QSR owners to consider incorporating robotics into their operations
(McFarland, 2016).
MeTHod
This study used a phenomenological approach to explore QSR customers’
perceptions and attitudes regarding the use of robotics in these restaurants.
Specifically, this is an exploratory sequential mixed methods study; in this
article, the results of the qualitative phase of the process are presented.
Exploratory sequential studies are typically designed as follows: (1) the
research question is formed; (2) qualitative data are collected to answer “how”
or “what” questions (but not “why” questions, which require quantitative
data); (3) the qualitative data are analyzed to uncover major themes; (4) the
themes are used to develop questions that can be used as variables/measures in
a quantitative technique; (5) quantitative data are collected and analyzed to
establish reliability and validity; and (6) the validated data are examined to
determine if the original questions can be explained (Creswell, 2015). The cur-
rent article covers Steps 1 through 3, the qualitative phase, with recommenda-
tions for the quantitative phase.
The customers’ perceptions and attitudes were obtained through a series of
three focus groups (10 participants per group) that were held in a large metro-
politan area in the Southwestern United States. The focus group discussion for-
mat elicits not only the participants’ initial thoughts on the discussion topic but
also provides a free-flowing discussion that uncovers new ideas and perspec-
tives, changes opinions, uncovers conditional opinions, and permits the
researcher to uncover “deeper truths” related to particularly emotional responses
(Mariampolski, 2001). Ultimately, the technique’s results should reveal multiple
aspects of a phenomenon, which then permits identification of specific themes
for deeper exploration.
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 9
Participant recruitment and Sample characteristics
Study participants were recruited through on-campus recruiting at a local
university, as well as through intercepts at a shopping mall approximately one
mile away from the university. Individuals 18 years of age or older who self-
identified as eating at a QSR (a.k.a., “fast food restaurant”) at least once during
the previous month were eligible to participate in the study.
Participant demographic data were not formally collected, as the intent of this
qualitative phase of a larger mixed methods exploratory study was only to iden-
tify the phenomena surrounding the use of robotics, rather than to attempt to
generalize the results to the greater population (Creswell, 2015; Weber, 1990).
Subsequent phases of this study will use the qualitative data to develop a quan-
titative measurement instrument that will be used to collect data from a larger
sample that will permit generalizability to a larger population.
The first focus group consisted of participants who were recruited through
the mall intercepts; these participants ranged in age from 25 to 70 years. The
second and third groups consisted of college students who ranged in age between
20 and 29 years, with two students who self-describe as “nontraditional” (i.e.,
older than 30 years) students. The student participants were invited to participate
in this study at the end of a class period; none of the students were current stu-
dents of any member of the research team. All participants were recruited with
the understanding that they would receive a $50 Amazon gift card at the end of
the focus group session in consideration of their time.
Of the 30 participants, 16 were female and 14 were male. A total of 20 under-
graduate students, 2 graduate students, and 8 nonstudents participated in the
study. Twelve participants were categorized as “older,” which here means 29
years of age or older, and the remaining 18 students were younger than 29 years.
Creswell (2015) recommends a sample size of between 3 and 10 participants for
a phenomenological study, and this sample size exceeds that recommendation.
Focus group recruitment often requires overrecruitment of participants, since it
is common for some of the recruits to fail to show up for the focus group. In
addition, while the intent of the study was to examine restaurant customer per-
ceptions of robots, many of the participants reported during the focus group
discussions that they either currently or had previously worked in the foodser-
vice industry. This is typical of the U.S. population, where nearly 50% of all
adult Americans have at one time or another worked in the foodservice industry
(NRA, 2019; The Aspen Institute, 2013).
data collection
The focus groups were held in May 2018. Each focus group lasted between 60
and 75 minutes in meeting rooms on the university’s campus, providing a neutral
(nonrestaurant) environment. The groups were led by an experienced moderator,
using a semistructured interview protocol, which is displayed in Supplement
10 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
Figure 1 (available online). Interestingly, all study participants reported being
familiar with robots used in hospitality industry settings, and nearly half of the
participants report having first-hand experience with them.
The focus groups were also observed by members of the research team, who
took notes of the proceedings for future reference. The discussions lasted until
data saturation was reached, where each topic was exhausted and further dis-
cussion would not yield additional insight (Miller & Fredericks, 1999). The
discussions were digitally-recorded (audio only), and were later professionally
transcribed. On completion of the focus group session, each participant was
provided a $50 Amazon gift card, as promised at the time of recruitment.
analySiS and reSulTS
The focus group transcripts were analyzed manually using content analysis,
via a grounded approach involving a series of immersion/crystallization cycles
(Miller & Fredericks, 1999). This emergent, inductive process follows a cyclical
approach where the data are reviewed, the researcher mulls over it, the categori-
zation of data is reviewed and revised, and finally a set of initial codes or themes
emerges (Saldaña, 2016). The research team then coded the transcripts for rela-
tionships with each theme, followed by valence analysis (a form of sentiment
analysis) to assess the focus group participants’ sense of whether the use of
robotics in QSRs is positive, negative, or both. A summary of the results of the
analysis is presented, and a full description of the results is provided in the next
section. A flowchart illustrating the steps in this process is provided in Figure 1.
Stage 1: identifying Themes
First, four trained raters used a team coding approach (Saldaña, 2016). Each
team member read through the transcripts independently numerous times to
identify the overarching themes. Each team member created a set of written
theme descriptions. The team then met to discuss the themes to clarify and
generally reach agreement. Each description was transferred to a slip of paper
which was color coded to reflect the team member who generated the descrip-
tion; the team members organized each individual slip of paper into cohesive
groups on a sheet of flipchart paper (Supplement Figure 2, available online).
This method yielded the following 19 themes: customer experience, robot
tasks—BOH, robot tasks—FOH, physical appearance, human touch, labor
force impact, communication, novelty, benefits—efficiency, benefits—quality
control, cost control, safety—robot movement, safety—sanitation, safety—
food, safety—robot sanitation, motivations to visit or repatronize, trust/distrust,
flexibility, and societal change.
In the next step, a random sample of 30 fragments (statements) from the tran-
scripts was entered into a spreadsheet, where each comment was assigned a case
number and occupied its own row. The fragments were randomized to avoid
order bias. The rating was a simple binary code, where the fragment received a
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 11
“1” if it related to the theme, and a “0” if it did not relate to the theme. Each rater
then independently rated each fragment on whether it was related to each of the
19 themes. The results were analyzed to determine if the raters generally agreed
on each fragment’s relationship with each theme (Weber, 1990). Where there
was disagreement, the raters then discussed their rationale for their rating of the
item. Furthermore, the discussion led to the raters agreeing that one additional
theme had emerged—Service Speed. The final set of 20 themes and their descrip-
tions is shown in Table 1.
Frequency analysis. Once all team members completed their evaluations of
the transcripts, the results were combined and frequency analysis was conducted
using Excel. Cases that at least two out of the four team members coded as being
related to the relevant theme were included. A total of 406 speech fragments
were analyzed for their relationship to each of the 20 themes. The results of this
analysis are shown in Table 2.
When the analysis yields a large number of themes, Creswell (2015) recom-
mends focusing subsequent analysis on a smaller subset of themes, usually lim-
ited to five or six themes. The coding team discussed the results of the frequency
analysis, and reached consensus on selecting nine themes for subsequent analy-
sis (Table 3). However, the nine selected were not automatically drawn from the
themes with the highest frequencies. Two themes—Customer Experience and
Benefits—were rejected for further analysis and the team identified three sub-
themes related to Safety that were combined into one overarching theme, as
explained next.
Figure 1
Flow chart of Methods
12 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
Table 1
initial Themes identified Through content analysis
Theme Description
Customer experience Elements of the customer experience.
Robot tasks—BOH Tasks that robots might perform in the back-of-house
(BOH) areas, such as food preparation or cleaning the
kitchen.
Robot tasks—FOH Tasks that robots might perform in the front-of-house
(FOH) areas, such as delivering food to guests or
cleaning tables.
Physical appearance The physical attributes of the robot, such as shape,
material, finishes, and attachments.
Human touch The special quality that humans bring to the process,
service or product.
Labor force impact The impact that robots would have on labor; most often in
terms of reducing or changing employment.
Communication The experience that participants have with communicating
with robots or humans; often language-based.
Novelty The unique qualities that a robot would bring to a
restaurant; often related to desire to visit restaurant at
least once to “check out the robot.”
Benefits—efficiency The benefits that robots would bring in terms of efficiency,
such as reducing waste, reducing incorrect orders, and
so on.
Benefits—quality
control
The benefits that robots would bring in terms of
maintaining consistent quality standards.
Cost control Generally, the use of robotics to save money, particularly
in terms of labor costs.
Safety—robot
movement
The safety of the robot’s movement; particularly in terms of
movement of the entire unit, robotic arms, and whether
or not these pose a threat to safety.
Safety—sanitation This refers to the sanitation of the restaurant itself.
Safety—food This refers to food sanitation and safety.
Safety—robot
sanitation
This refers to how well the robot itself can be cleaned and
sanitized.
Motivations to visit or
repatronize
Related to the “novelty” theme, but more broadly
expressed as being motivated (or not) to visit to satisfy
curiosity, get more accurate order, receive faster service,
and to not have to interact with a human.
Service speed Mentioned if robotics were likely to increase the speed of
service, although occasionally the robot may slow the
service.
Trust/distrust Whether or not the participant trusted robotics to perform
properly or deliver anticipated results.
Flexibility Whether a robot could perform different tasks or if it could
be easily reprogrammed.
Societal change The effect of robotics on greater societal trends.
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 13
Table 2
Frequencies
Themea Total Percentage
Robot tasks—BOH 262 13.0
Customer experience 252 12.5
Robot tasks—FOH 226 11.2
Physical appearance 170 8.4
Human touch 140 6.9
Labor force impact 110 5.4
Communication 108 5.3
Novelty 106 5.2
Safety—food 94 4.7
Benefits—efficiency 85 4.2
Cost control 70 3.5
Safety—robot movement 69 3.4
Safety—sanitation 53 2.6
Service speed 52 2.6
Motivations to visit or repatronize 50 2.5
Benefits—quality control 46 2.3
Safety—robot sanitation 43 2.1
Trust/distrust 32 1.6
Flexibility 27 1.3
Societal change 26 1.3
Total frequencies 2,021
Note. A total of 406 fragments were analyzed.
aFragments with 2 or more rater scores.
Table 3
Mean valence for nine Themes
One-Sample Statistics
N M SD SE
Communication 99 0.0253 1.10345 .11090
Human touch 164 −0.3999 1.48123 .11566
Labor impact 158 −0.5105 1.74421 .13876
Novelty 66 1.2412 1.23041 .15145
Physical appearance 87 −0.0326 0.91370 .09796
Restaurant safety 69 −0.2041 1.49970 .18054
Robot safety 406 −0.0782 0.41225 .02046
Task—BOH 216 0.6323 1.14168 .07768
Task—FOH 211 0.4364 1.21583 .08370
14 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
While the theme that received the most mentions was Customer Experience,
it was rejected for further analysis. This construct was the focus of the study and
the term was used heavily in participant recruitment and as prompts for the focus
group discussion. In addition, a visual inspection of the data revealed that
Customer Experience almost always corresponded to a more explicit theme
(e.g., a comment was rated as being related to both Customer Experience and
Robot—FOH Task). Thus, since the use of Customer Experience almost cer-
tainly influenced the frequency of participant references to it, the team set
Customer Experience aside and did not use it for further analysis.
The team also reviewed the two Benefits subthemes of efficiency and quality
control. The Benefits constructs were removed from further analysis. The first
reason for removal was because virtually every comment regarding Benefits was
highly positive (as benefits would naturally be), and no additional insight was
offered by the participants. For example, when asked about the benefits of robot-
ics, the responses were usually limited to one or two words—efficiency and/or
quality control; the participants did not elaborate and a mere mention of the
word “efficiency” did not yield deeper insight. The second reason for removing
Benefits from further analysis was that the focus group discussion included fre-
quent prompting regarding the benefits of robotics. Similar to the rational for
removing Customer Experience, the prompting generated frequent mentions of
the benefits overall, but the resulting comments did not offer deeper insight.
Finally, the Safety theme was initially broken into three subthemes; Safety-
food, Safety-robot movement, and Safety-sanitation. However, while each Safety
subtheme yielded rated fragments, the frequencies were relatively low in num-
ber. As Weber (1990) and Saldaña (2016) discuss, using human coders, particu-
larly when they are also present during data collection, will “impose the reality
of the investigator on the text” (Weber, 1990, p. 37). Each of the coding team
members had attended one or more of the focus group sessions. The team mem-
bers reviewed their notes taken while observing the focus groups, including
recording nonverbal communications. Their recollections showed that the focus
group participants exhibited high intensity and emotion when discussing the
Safety themes, despite the fact that each of Safety’s subthemes yielded relatively
low frequencies of fragments. Ultimately, the team combined two subthemes—
Safety-sanitation and Safety-food, thus creating a new theme labeled Restaurant
Safety. The robot movement subtheme was not related to food safety or sanita-
tion, and was thus relabeled Robot Safety, describing the robot’s safety (from
harm) in the environment and the safeness/unsafeness of the robot’s movements
in the environment.
Stage 2: valence analysis
The nine themes identified in the previous step—Communication, Human
Touch, Labor Impact, Novelty, Physical Appearance, Restaurant Safety, Robot
Safety, Task—BOH, and Task—FOH, were then explored using valence analysis.
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 15
Valence analysis is a technique that is used to determine the level of positive or
negative attitudes associated with a construct or, here, a speech fragment
(Colombetti, 2005). This study used a form of magnitude coding (Saldaña,
2016) to determine the level of overall positiveness or negativeness attached to
each fragment.
An initial coding scheme ranging from −3 to +3, where a −3 was very nega-
tive, a +3 was very positive, and a 0 was neutral, was used (Barrett, 2005;
Colombetti, 2005; Mazurenko et al., 2015). This permitted the research team to
assess the relative positiveness, negativeness, or neutrality of each fragment.
The goal was to triangulate the raters’ results. A trial sample was again con-
ducted to clarify the coding process. After reviewing the coding trial’s results,
the research team determined that further clarification was not required. The full
set of randomized transcript fragments was then analyzed for valence. Each
fragment was entered into an Excel spreadsheet where each column contained
one of the nine themes. Each team member read each fragment and if the frag-
ment was related to a theme, the coder entered his or her valence rating. If the
fragment was not related to the theme, the coder left the cell blank. The mean
valence for each theme was calculated, as presented in Table 3.
Once each team member completed his/her valence coding, the results were
combined into a master sheet in Excel, then transferred to SPSS. The valence
coding for each theme was then analyzed for interrater agreement across all four
raters, using intraclass correlation analysis (Shrout & Fleiss, 1979). Intraclass
correlation is an appropriate reliability test because each “target,” or transcript
fragment, is analyzed by a fixed set of judges (the four raters, and not a random
sample of a larger population of raters), thus creating a fixed effect. Here, the
individual valence ratings of each fragment for each judge were entered into the
analysis. The results are displayed for Cronbach’s alpha, which is an appropriate
measure for inter-rater reliability in this situation (Shrout & Fleiss, 1979).
Intraclass correlation is shown for single (individual rater) measures as well as
for the average rating across all four judges. The results for interrater agreement
correlations are displayed in Table 4.
For each theme, the single measure intraclass correlation was lower than the
correlation for the average measures. This was expected, where there was more
variability for a single rater, but when all raters’ values were combined, some of
the variability was smoothed out and the correlations were higher. The
Cronbach’s alpha values were between .70 and .80 for five of the themes, con-
sidered “good”; all were statistically significant (p < .01). Two additional
themes had Cronbach’s alpha values of .665 and .691; these do not reach the
“good” level, but both are statistically significant (p < .01). The remaining two
themes—Novelty and Human Touch—had low Cronbach’s alpha values of .304
and .339, respectively; neither was statistically significant. Although these
results indicate that there was not perfect agreement between the raters’ assess-
ments of valence of all themes, the process was successful overall in obtaining a
reasonable level of agreement. As illustrated by the small number of degrees of
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18 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
freedom, the limited and fixed number of raters, combined with a relatively
small set of fragments that aligned with many of the themes, could result in a
rough but relatively robust assessment of reliability for qualitative items.
Stage 3: interaction Between Frequency and valence
Strictly relying on either frequency or valence provides limited insight
(Colombetti, 2005). For example, some fragments that received frequent theme
associations yield common sense results (e.g., a discussion topic of “customer
experience” yielded numerous statements related to the customer’s experience).
Alternatively, some fragments may receive a highly positive or negative valence
value, but are related to topics that are infrequently mentioned. The comment
may simply be an outlier, or it may suggest that the issue merits further discus-
sion. Each of the nine themes is discussed in detail in the next section of this
article.
diScuSSion
This study elicited diner perspectives on a variety of issues related to the use
of robotic technology in QSR operations. Major findings of this study indicated
that guests have concerns regarding the societal impact of robotics entering the
realm of QSR operations; the cleanliness and food safety of robotic technology;
and the quality of communication, especially voice recognition, from both native
and non-native English speakers. Diners also voiced compelling ideas about the
functionality and physical appearance of robots; expressed strong bipolar com-
ments relating to the value of the “human touch” in fast food restaurants; and
offered creative solutions for the deployment of this technology.
communication
The Communication theme received numerous mentions (99) with a slightly
positive valence of 0.0253. The focus group participants generally approved of
using robots for basic communication functions, such as touchpads or kiosks for
placing orders. However, these devices do not constitute robotics, as they do not
perform physical tasks. The participants also discussed verbally interacting with
devices. As the nearly neutral valence shows, the participants were divided in
their thoughts on communication with robots. Some mentioned it as a positive,
as a way to avoid having to interact with other people. As one participant (male,
mid-60s) stated,
I have a problem hearing, like in a crowded restaurant . . . yesterday, at [restaurant
name], I tried to put in [an order]. First of all, I come up and she goes “gibberish.”
“Ah, I’m sorry, what?” “Ah, welcome to [restaurant]. Can I help you today?” Well,
you know, I mean, she had to slow down and say it louder. And then . . . I told her
my order and then she said something else that I couldn’t hear . . . so personally,
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 19
I’d rather not interact with people. If . . . a robot could take my order the first time
and make it faster . . . and smoother, I would be interested in that. Easier.
Another positive aspect of Communication was the potential to offer communi-
cation options in virtually any language. One participant (female, early-20s)
suggested that the potential capability of robots to understand foreign languages
could be a major advantage when communicating with customers, “But what if
it knows all the languages? Like what if it knows your natural tongue?”
On the other hand, participants also viewed their past communication expe-
riences in a negative light, primarily driven by deficiencies in the current
technology. The main concern was related to deficiencies in voice recognition
technology. While some of the participants who were non-native English speak-
ers expressed frustration with not being understood by English language-only
systems, voice recognition was a concern for both the nonnative and the native
English speakers. For example, one participant (male, mid-20s) stated, “ . . . but
to be fair, Siri doesn’t understand me when I speak, like, Californian.” There was
consensus among participants that the technology should not be incorporated
until the communication technology is perfected, as deficiencies in the technol-
ogy will lead to user frustration and decreased customer satisfaction.
Human Touch
Strong bipolar sentiments arose regarding the value of providing the “human
touch” versus not wanting to interact with humans in a QSR setting. In this con-
text, the “human touch” refers to the special quality that humans bring to the
process, service, or product. One participant (female, early-20s) indicated that
most robots she has encountered in service situations are missing the human
touch: “And a lot of the complaints that they’ll get, no matter where they are in
the world . . . a lot of people feel like it’s too impersonal. And I know it’s a robot,
but it looks like me, and I don’t like that.”
Some participants were in favor of freely implementing robots throughout
the BOH areas. As one participant (male, mid-60s) commented, “They should
do everything, no humans in there,” although one area of caution involved con-
cerns about robots handling knives. On the other hand, others mentioned the
importance of having their meals prepared by human cooks and that customer
complaints, or “escalations,” should be handled by humans, not by robots.
Participants also indicated that they would not miss the human touch in a
QSR but required human touch in other restaurant segments. As one participant
(male, late-60s) stated,
I have no objection to it, and I’m just talking about fast food, I think if I went to a
bar, I’d rather talk to a human bartender because usually you get to have a
conversation with them and that’s a big, a good part about the experience. So I’m
not sure a robot could do that. Maybe it can.
20 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
Others stated that customers would miss the human interaction, “Eventually
people will feel like its missing the part of the connection, people-to-people.”
As the participants actively deliberated the value and role of the “human
touch” in QSRs, they offered creative solutions for the use of information tech-
nology, such as facial recognition for regular customers. For example, one par-
ticipant (male, mid-30s) suggested that the robot could greet the customer at the
door and say,
“Hi, [name], how are you today? How was work today? Are you gonna come in
and have your Number 1 (combination meal)?” . . . that would really be amazing.
Like, have face recognition software. They could just, “Hi, [name], do you want
your Number 1? You want your fries, right?” And you’re like, “Yeah.” By the time
you get to the counter, it’s already waiting for you.”
Another participant (male, mid-60s) piggybacked on this comment by adding,
“It should take your order in the parking lot, so when you walk in the door, it’s
already at the table.”
labor and Societal impacts
One theme—Societal Change—was mentioned relatively infrequently and
was not selected for valence analysis. Another theme—Labor Impact—was
mentioned frequently and displayed a negative valence. Both themes are dis-
cussed here because they are closely related from a conceptual basis. The par-
ticipants suggested an inevitability about the deployment of robotic technology.
Participants pointed out that QSRs are the first employer for many people,
leading to concerns regarding adverse societal effects arising from the use of
robots. Many participants were concerned that an increased presence of robotic
technology in QSRs will lead to a decline in the employment of young adults
as well as to the subsequent negative effects on society. For example, one par-
ticipant (male, mid-20s) voiced the following concern, “(I) do not like the
idea, simply because it’s going to hurt the economy as we reduce the basic
labor force.” Another participant (male, mid-60s) proposed that “It’s a bit of a
philosophical issue. And that is, are we replacing humans for automation?”
While some participants held negative sentiments regarding this chain of
effects, others acknowledged that such changes in society are not uncommon
and that over time, major changes are inevitable. One participant (female,
early-20s) stated,
And actually, even if we accommodate robotic systems at fast food restaurants, we
aren’t really taking jobs from people because it’s like, energy is not created but it’s
only transformed into different forms of energy. It’s the same with jobs. There will
be some other jobs (that will) appear and those will be available to the workers at
fast food restaurants.
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 21
novelty
Generally speaking, focus group participants had very positive impressions
of the ability of the Novelty of robotic technology to entice guests to visit the
location at least once, although they were unsure whether the robotics would
sufficiently overcome average food or service to entice them to return to the
restaurant a second time. This is consistent with past examples of restaurant
concepts that provided a highly unique experience but suffered from the reputa-
tion that the guest would visit once because of the “experience” but would not
return because the food was too expensive and/or the food quality or service
provided a poor value overall.
Physical appearance
The Physical Appearance theme was mentioned frequently but had a very
slightly negative valence. Participants stressed that robots should not appear
menacing or scary (e.g., “ . . . there’s a lot of people that are scared of clowns”),
but rather held strong preferences for connecting a robot’s physical appearance
to its intended functionality. Examples include “I don’t think they should look
humanoid. I think that’d be weird” (female, late-20s), and “ . . . they should look
like they have a purpose . . . designed for a specific purpose that they’re intended
to do . . . utilitarian” (male, mid-20s). As one participant (male, late-60s) stated
in particular, “I don’t like the humanoid ones very much. I think if they’re robots,
they should be good robots and not try to be humans.” These sentiments are
explained, in part, by disambiguation. Participants in this study had a preference
for robots to appear plain, dull, and uninteresting rather than take the form of a
humanoid, corresponding to the section of the likeability graph that appears just
prior to the uncanny valley (Mori, 1970). Functionally, the robots should have
“no sharp edges,” “smooth surfaces,” “ability to switch tools,” and should
appear “not tacky.”
restaurant Safety
Strong concerns about restaurant cleanliness and food safety arose through-
out the discussions. Several participants commented on the benefits of using
robotics to improve food safety, citing the opportunity to have robots that can
check food temperature, have sensors to check the quality of the ingredients,
and to remove some of the risk of human contamination. Interestingly, multi-
ple participants in all three groups mentioned the benefit of not worrying about
a robot spitting in the food. For example, one participant (female, early-20s)
stated that
Like, for example, I know some people—I’m not saying all people do that, when
there’s a personal feeling involved, some people mess the food up that’s supposed
22 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
to go to the customer. Like, for example, they’ll spit on the food and no one will
know. (The) customer will not know, but if you have (a) robot that cooks for
people, you will probably not . . . have that kind of issue.
However, many participants also raised concerns about robots creating food
safety problems. In particular, the participants were concerned a robot that pre-
pares food items not being able to identify if food ingredients were safe for
consumption. One participant (female, early-20s) expressed this concern: “ . . .
some of the food gets, like, bad and then the robot couldn’t recognize that and
just cook it. And it’s like there’s mold on it and they don’t know, cook it.” There
were also concerns about the integrity of the robot’s components and ensuring
that pieces of the robot did not contaminate the food product. For example, one
participant (male, mid-60s) described his past experiences with finding foreign
objects in his food, although he then stated that “I’d rather have a nut or bolt (in
the food) than, say, a couple of cockroaches that I once found in a restaurant (in
town).”
Many of the comments focused on the ability to keep the robot clean to avoid
contaminating food and service items. For example, one participant (male, mid-
30s) stated, “They should have rust prevention. If a robot is a dishwasher, do not
let it rust. Well, the robots have to be cleaned. There’s gonna be build-ups.”
Therefore, if robots are in direct contact with food, it should be possible to clean
and disinfect the robots properly. One participant also mentioned her concerns
about air-borne oil and grease from the grill and deep fryers. The airborne oils
settle on all surfaces and can be difficult to clean, according to one participant
(female, late-20s):
. . . like inside the machine somehow. It’ll fly in there and it’ll somehow get to,
like, anywhere (in) the whole robot. And what if they’re not getting cleaned, like,
for the whole week? Am I gonna eat the fries or burger . . . after they touched it? I
don’t know how long that oil’s been there, staying on the body. And when it’s
working in a kitchen, it’s always hot. . . . And the oil might melt on them and just
drop back to the food.
There are some measures already available in foodservice industry to ensure
the hygienic design of such robots. For example, “wash-down robots” feature a
sanitary design with a smooth surface to prevent foreign substances from
remaining on the robot arm, and they can be easily sanitized during the antisep-
tic cleaning process. They can resist harsh chemicals and intense water pressure,
making it easy to maintain proper cleanliness for food and other applications
that require wash down capabilities (Edelbrock, 2012). Some manufacturers
have opted for protective coatings such as epoxide; and others have made their
robots entirely out of stainless steel that does not react with cleaning agents,
acids, or alkalis. It is also suggested that the lubricants used on these robots be
food-grade certified (NSF H1).
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 23
robot Safety
The potential dangers posed by using robotics, either from the perspective of
the robot physically harming people or being harmed by people, was discussed.
A major concern focused on whether robots should wield sharp objects, particu-
larly knives. Another concern was whether the robot should be mobile, either
moving through the space or having robotic arms that move. As one participant
(male, early-20s) explained,
Participant: I don’t think the robot should be moving around in the kitchen.
Moderator: Why should they not move around the kitchen?
Participant: Sometimes it’s chaos in the kitchen . . . it’s always chaos in the
kitchen, so people moving around with knives and stuff. So, we don’t want
to trip over a robot.
One participant (female, late-50s) was concerned about the robot running
into guests: “I’d be afraid they’d run over somebody in the front of the house.”
Another participant (female, mid-20s) stated,
. . . if they’re small or if they’re big, somebody will find something to complain
about. Or to sue them about. Like, “oh, this robot . . . ,” “I tripped over this robot
because it was small,” or “I ran into this robot and busted my head open because it
was too big.” I just feel like . . . if it’s moving around, it will be an issue some way
or another.
Another participant suggested that robots should only operate in a designated
area to avoid liability problems, or not a “free range robot” (male, early-30s).
One recommendation for what amounts to a robotic “train” to deliver food or
other items was mentioned in each group. A description from a participant
(female, early-20s) is as follows:
The thing is not touching the food and it’s on its own train (track) . . . so they won’t
. . . fly out or something. So basically, in the front of house . . . they can move and
won’t . . . injure other people. But if it’s a host robot and it’s running (around the
dining room), then that might (lead to injury).
Concerns for the robot’s safety were also expressed. As one participant (male,
mid-20s) stated, “ . . . I guarantee you, my brother, who’s younger than me,
would totally mess with that (robot), and push it to its limits.”
Tasks—BoH
The participants identified numerous BOH tasks that robots could perform.
First, participants expressed strong support for tasks that humans do not like to
do, such as dishwashing. Participants in all three focus groups repeated that no
24 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
one likes to work in the dishwashing area, due to the exposure to hot water, heat,
humidity, and food waste. Robots could perform this function, to the point where
one participant suggested that the dishwasher be a robot. While current dish-
washers are machines (and may be perceived as robotic devices), the dishwash-
ing robot could load itself, clean and sanitize the service items, and then move
through the BOH area to put the items away.
Food preparation. Participants also strongly supported the idea of using
robots that perform basic food preparation such as cutting vegetables, cutting
potatoes for French fries, and making dough. They also supported robots per-
forming basic cooking tasks, such as hamburger patties and French fries. The
robots could have sensors that check temperature and doneness, or sensors that
provide a visual examination of the product and possibly sample air quality
around the food item to test if it was still fresh and/or safe to use. However,
several participants then suggested that the final finish work should be per-
formed by a human, as explained by one participant (male, mid-20s) who stated,
From a guest perspective, I don’t like the idea of my food being—I don’t mind it
being prepped, so like vegetable cutting, dough making, all of that. Perfectly okay
with that. You’re saving money, you’re saving steps. I don’t like the idea of my
food after that point being made by a robot. I like the human element. I like
knowing that someone put effort into my food.
Inventory. Other participants suggested using robots to manage inventory, illus-
trated by the following exchange between two younger participants:
C (female, late-20s): Inventory actually would probably be a good (idea). . . .
If you can program one that’s complex enough to manage inventory, you
lose the human lying elements and you keep consistency, which is
helpful.
M (male, mid-20s): Yeah, there’s an algorithm about how much in a box
equates to what, just by looking at it.
C: Yeah. Or looking at barcodes. So it scans which product it is. Kinda like
what they do in big warehouses.
However, the participants did not believe that robots should perform tasks
requiring fine attention to detail, such as finishing plating a meal—although
this task is less common in QSRs than in fast-casual or more formal dining situ-
ations. The participants were also more likely to prefer a human to complete
these tasks if the guest interacts with the cooking staff; the belief is that human
cooks know the customer and can better customize an order the way the guest
likes.
Integrating robots into existing equipment. One participant suggested that
maintaining safe temperatures for food product and in coolers could be
improved by incorporating a robot that could move materials in and out of a
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 25
cooler without opening doors. The benefit was described by a participant (male,
mid-20s),
. . . robotic service to grab ingredients from the fridge where the employee doesn’t
have to go through a door, let all that air escape. That should be awesome, because
you would save time and save electricity as well.
This recommendation would work well for restaurant companies that desire
to enhance their corporate social responsibility initiatives, since it will serve to
reduce the environmental footprint. It will also reduce energy costs, which could
defray the cost of implementing the robotic technology.
Other tasks. Numerous additional tasks that would be useful in the BOH
areas included equipment maintenance, such as “changing out oils in fryers,
sharpening knives, sanding down cutting boards. . . . And those are the things
that everyone I’ve ever worked with hate doing, because they are very disruptive
to your traditional schedule,” according to one participant (male, mid-20s).
Other tasks included forming burger patties, scraping the grill between uses, and
general kitchen sanitation.
Tasks—FoH
The participants frequently suggested that robotics would be appropriate for
FOH tasks such as order-taking, food delivery, and FOH sanitation.
Order-taking. While discussing the opportunities for robots to take food
orders, with face-to-face or on the telephone, numerous participants mentioned
their concerns about the quality of communication. Strong negative sentiments
focused on the accuracy and efficiency of voice recognition technology, as dis-
cussed earlier in the Communication theme section.
Finally, while the participants did acknowledge the utility of using robots in
order-taking, they unanimously agreed that if a conflict with a guest arises, the
situation should be resolved through human intervention. In part, the partici-
pants repeated their concerns regarding the current deficiencies in voice recog-
nition technology. However, they also focused on their beliefs that until robotic
technology can better interpret nonverbal prompts, the use of a robot during a
service conflict/escalation would only exacerbate the problem. Regardless of the
attractiveness of the physical appearance of the robotic device, the tension that
forms when a customer is dissatisfied may erode any initial acceptance of the
robotic technology and could exacerbate a negative service experience. Human
intervention will be necessary to manage the subtleties of conflict resolution
with unhappy customers.
Food delivery. Participants also suggested using robotics for food delivery
systems, such as delivering food to a table located next to a wall by sending the
food out on a track, similar to a train. The participants preferred that robots not
move freely around the restaurant dining room, due to the potential for collisions
26 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
with guests. The participants were also concerned about the potential guests to
“tamper with” the robot, such as the risk that small children or immature adults
might “mess with it,” as suggested by one participant (male, mid-30s). These
creative solutions primarily focused on increasing efficiency of QSRs to subse-
quently enhance the guest experience.
Interestingly, some of these suggestions have already been implemented in
the foodservice industry. For example, Alibaba recently opened a restaurant in
the Hema supermarket in Shanghai, China, which uses a mobile app, QR codes,
and robots to provide a high-tech dining experience without losing the “human
touch.” Customers interact with employees when they select fresh seafood in the
supermarket. At the entrance of the restaurant, the app informs customers where
to sit, and the app can be used for customers to order and pay for meals. Human
cooks prepare the food and place it inside small pod-like robots. Then the robots
travel along tracks to deliver the food directly to customers.
FOH sanitation. The participants also offered thoughts on how robots could
be used to clean the FOH areas during service periods. Most of the suggestions
involved using robots to clear tables and sweep/mop the floor. However, while
these functions come with attendant risks, the participants were very concerned
about the potential risks for a robotic device moving through the dining area and
creating a trip/fall hazard for customers. A way to incorporate robotics in the
FOH areas may include limiting the robot’s activities to one portion of the din-
ing area at a time or providing a dining room layout that permits adequate space
between tables that will allow traffic circulation for both robots and humans.
Security. The focus group participants almost unanimously agreed that
robotic devices should not be used for security functions in QSRs. While secu-
rity robots are increasingly common in larger public spaces, such as airports, this
study’s participants thought a security robot “would freak people out,” accord-
ing to a younger female participant and an older male participant. There would
likely also be highly negative reactions from members of the public, who may
have privacy concerns.
unexpected results
Some unexpected results arose during the focus group discussions. The research
team expected differences in perceptions and expectations of robot deployment in
QSRs across age groups. Surprisingly, all participants, regardless of age, exhibited
approximately equal parts of enthusiasm and skepticism with respect to robotic
technology. A large percentage of participants with past or current restaurant work
experience added an unexpected dimension to the discussions, as participants
were able to relate how the inclusion of robots into QSR operations would affect
both employees and guests. Last, there was a high level of resignation about the
inevitability of QSRs incorporating robots. This finding is similar to the accept-
ability of routine societal change. Participants felt that the incorporation of robotic
technology is a question of when, rather than a question of if.
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 27
contributions of the study
This study’s results contribute to the extant literature by enhancing the cur-
rent body of knowledge regarding multiple facets of the QSR customer’s per-
ceptions of both FOH and BOH applications of robotic technology. In addition,
this study revealed several factors that robotics manufacturers and QSR opera-
tors should consider when designing and/or implementing this technology.
While the study focused on the QSR industry, many of the positive and negative
attitudes can easily translate to the potential incorporation of robotic technology
in other service industries.
contributions to the extant literature
This study makes three contributions to the extant literature. First, the current
study adds to previous research by addressing restaurant customers’ points of
view. Previous research concentrated mainly on the benefits realized by opera-
tors when using robots. Since a robot’s ultimate task is to serve people, the study
greatly contributed by revealing crucial customer concerns. This study also
enhances research on the interaction of smart technology and human service
providers (Marinova et al., 2017) by revealing how robots can complement ser-
vice providers and improve the customer experience in QSRs.
Extant published research has suggested that further research is needed in the
area of developing and incorporating robotic technologies in the service industry
(Tung & Law, 2017). The current study begins this work in the context of the
QSR industry to determine the future directions for these technologies.
Furthermore, the current study advanced Murphy, Gretzel, et al.’s (2017) and
Murphy, Hofacker, et al.’s (2017) research by significantly enhancing an under-
standing of the customer’s acceptance of robots in the hospitality industry.
This study’s results also augment Veer’s (2013) and Murphy, Gretzel, et al.’s
(2017) research on anthropomorphism loyalty, by providing detailed customer
perception information of the advantages and disadvantages of using robots in
QSRs, therefore suggesting how to potentially create successful robot human
interactions. The results also support previous research (Blow et al., 2006;
MacDorman, 2005; Mori, 1970, 2012), which found that when anthropomor-
phism reaches a point where human replicas appear nearly like human beings,
feelings of distress are experienced and likability drastically drops. On the other
hand, as the nonhuman entity becomes exactly humanlike (Bartneck et al.,
2007), likability drastically increases. Participants in this study expressed the
same sentiments, explaining their belief that the current state-of-the-art technol-
ogy cannot provide a humanlike machine that will overcome the feelings of
creepiness or eeriness.
Moreover, this study enhances previous research by Giebelhausen et al.
(2014), who found that successful implementation of smart technology depends
on maintaining an appropriate employee–customer rapport and finding the right
balance between technology and human employees. Little prior research
28 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
examined the need to experience or to provide the “human touch,” or desire for
interaction, in hospitality (Ko, 2017) and for building rapport between the cus-
tomer and the service provider (Gremler & Gwinner, 2000; Ivanov et al., 2017;
Sutton & Rafaeli, 1988). This study’s participants expressed concerns about the
lack of “human touch,” revealing it as an important theme that merits further
exploration. The NRA forecasts that a “Backlash against automation of all kinds
could create a “return to artisanal” movement—predicated on humans being the
center of all parts of the food and beverage process” (NRA, 2019, slide 39).
While it is unlikely that most QSR brands will create significantly “artisanal”
experiences, they should remain aware of this potential preference among their
customers.
Finally, the study enhances previous research by revealing 20 major themes
of customers’ perceptions of the use of robotics in restaurants. These themes
lend themselves to further investigation using quantitative techniques to poten-
tially yield new constructs of interest in this area, thus enhancing the literature
in this field.
Managerial implications
The study also revealed important customer concerns regarding the use of
robots in QSRs that owners and/or operators should acknowledge, as the adop-
tion of this technology requires significant capital investment. Restaurant cus-
tomers and health code agencies demand that restaurants serve food that is safe
for human consumption. This study’s participants expressed deep concerns
about potential problems related to cleaning and sanitizing the robots to avoid
food contamination. They are also concerned about a robot’s inability to detect
unsafe or undesirable food ingredients through visual inspection or detecting
bad odors, functions which are currently served by trained employees. While all
consumers expect food that is fit for consumption, the significant portion of the
population that has worked in the foodservice industry heightens their aware-
ness of restaurant sanitation protocols. Participants suggested that if a robot
comes into direct contact with food, the company that develops or uses the robot
needs to pay high attention to sanitation and cleanliness. QSR brand managers
and their franchisees need to make sure that robots selected for use in the restau-
rant are designed for easy sanitization and ensure that all lubricants are of food-
grade quality. QSR brand managers and restaurant managers should also actively
educate customers about food safety and the sanitation processes used with the
robots. Providing this education should help alleviate concerns about the robots
and contaminated food product. In fact, it could be an opportunity to emphasize
the enhanced food quality and safety that a robotic device can offer versus the
risks posed by human failure.
This study also revealed which attributes of physical appearances of robots
are acceptable by QSR customers. For example, respondents rejected the notion
of robots that are somewhat human in appearance and stated that they would
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 29
only feel comfortable with humanlike robots that are identical to humans. Until
that technology is perfected, manufacturers and managers should avoid human-
like appearances and should instead create devices that more clearly resemble
the function they are designed to perform. If used in the FOH to interact with
guests, the robots should also not rely on voice communication until the technol-
ogy is perfected.
Next, while many studies point to the efficiencies gained through robotics,
the experiences reported by this study’s participants suggest that future research
is required to measure how well these technologies work in QSRs. For example,
participants mentioned how slowly a pizza-making robot operates versus the
participant’s own ability to complete preparation of a pizza in a fraction of the
time. Similarly, the research team visited a bar that features two bartending
robots, which are industrial robotic arms that have been adapted to make cock-
tails. The research team members include experienced bartenders; they are able
to prepare drinks much faster than the bartending robots can. Custom-designed
robotics may be more effective at this time than robotic technology adapted
from other industries.
QSR managers can also use the information provided in this study to create suc-
cessful human–robot interactions and use the successful implementation of robots
to encourage brand attachment and loyalty previously unattainable. For example,
managers can create an environment that provides a synergy of novelty and enter-
tainment, efficiency of food and beverage production, and service excellence.
Surprisingly, this study revealed very few generational differences in the per-
ceptions of or attitudes toward robotics in the restaurant industry, with approxi-
mately equal levels of enthusiasm and skepticism about robotic technology.
Nevertheless, despite few generational differences, managers still need to under-
stand their guests’ demographics to detect any differences in perceptions and
attitudes that may not have been evident in this study.
Finally, managers should emphasize that the use of robots in the restaurant
industry can complement human service providers (Marinova et al., 2017). This
study’s participants were concerned about the potential reduction of employ-
ment and its effects on society, along with the potential lack of “human touch”
in customer interactions. QSR managers should explain that savings in labor
costs will translate into stable or reduced prices for customers. They could also
emphasize the labor opportunities created by robots in their restaurants in order
to counteract such concerns. The NRA (2019) proposed that a future job cate-
gory in the restaurant industry is the “food engineer.” Operators also need to
ensure that employees who work side-by-side with the robots have excellent
service management skills so that the core product—food and beverage—is
delivered with that still-essential human touch. In addition, the use of robots
may have the potential to raise the quality of life of employees and customers if
robots are doing a good job serving both groups. This may also allow employees
to train for jobs that require more advanced skills by leaving menial tasks to
robots, therefore advancing society as a whole.
30 JOURNAL OF HOSPITALITY & TOURISM RESEARCH
limitations
It should be noted that the qualitative method used in this study cannot render
data or insights that are generalizable across the population, nor are the data suit-
able for analysis of reliability or validity. The research team’s interpretation of
the results could have been influenced by their first-hand observations of the
focus groups and their own experiences in the foodservice and greater hospital-
ity industry. The very nature of qualitative research introduces a level of subjec-
tivity into the analysis and interpretation of the results, as Weber (1990) suggests.
In addition, a high percentage of focus group participants had either past or
current work experience in the restaurant industry. While each participant quali-
fied for focus group participation by being a current QSR customer, their knowl-
edge of the restaurant industry itself may have introduced bias into their
perceptions.
Next, the study’s participants were solicited via mall intercepts and on-cam-
pus recruitment at a local university; thus, the study’s findings may be influ-
enced by the effects of self-selection bias, as the individuals who agreed to
participate in focus groups may share implicit characteristics that might not be
representative of the general population.
Suggestions for Future research
Future research could use the themes and insights gleaned from this study to
create a quantitative instrument that could be used to obtain data from a broader
population to gain deeper, more objective insight into customer perceptions
about using robotics in QSRs, full-service restaurants, and throughout the hospi-
tality industry. Numerous mentions of the desire for human interaction or to
provide or experience the “human touch” suggested that researchers should fur-
ther examine constructs related to the desire for interaction with humans (Ko,
2017). The results of this study also pointed to further refinement of the indus-
try’s understanding of design, maintenance, sanitation, and high-functioning
interactions between robots and humans, particularly in BOH operations.
concluding SuMMary
To date, most robotic devices used in the hospitality industry are machines
that were developed for other industries, which were then altered to fit a hospi-
tality task. Today’s restaurant industry owners and managers face economic and
labor availability challenges that may make incorporating robotic technology
into their operations attractive. The most logical sector of the restaurant industry
to deploy this technology is the QSR segment. This study sought to understand
QSR customers’ perspectives on using robotics in the QSR industry to identify
positive and negative aspects of the technology, including how/where robots can
be most appropriately used, how they should not be used, what they should or
should not look like, and other societal, safety, and cost-related concerns.
Zemke et al. / BETTER ROBOT FOR QUICK-SERVICE RESTAURANTS 31
The study used focus groups consisting of QSR customers to elicit this infor-
mation. Key findings of the study indicate that the study’s participants believe
that robots are coming to the QSR industry, whether society is ready for them or
not. Many participants also wistfully discussed the potential loss of the “human
touch” in the restaurant industry, indicating that they still thought it was critical
to maintain in some way.
Smart robotics developers and the companies deploying this technology
should ensure that the robot, whether it is for the FOH or BOH, will not merely
be a robot developed for another industry that is slightly modified to “fit” in a
restaurant setting. The robotics should be designed with aesthetics suitable to the
audience; this study’s participants had a strong preference for robots that look
like machines and not like humans at this time. The robots must operate safely
and must be able to be properly sanitized. Furthermore, the participants were not
only restaurant customers, but many of them have also worked in the restaurant
industry—similar to a large percentage of adult Americans—and could easily
visualize what might go wrong. Current restaurant industry workers should be
heavily involved in the design and deployment, partly because of their current
knowledge of the industry, but also because they will be the people who will still
continue to provide the important “human touch” going into the future.
orcid id
Dina Marie V. Zemke https://orcid.org/0000-0002-0766-2328
SuPPleMenTal MaTerial
Supplemental material for this article is available online.
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submitted July 15, 2020
Accepted April 3, 2020
refereed Anonymously
dina Marie V. Zemke, PhD (e-mail: dvzemke@bsu.edu), is an associate professor of
residential property management at Department of Applied Business Studies, Miller
College of Business, Ball State University. Jason Tang, PhD (e-mail: jason.tang@
pm.me), is a senior lecturer at Department of Accounting and Finance, Faculty of
Business and Communication Studies, Mount Royal University. Carola raab, PhD
(email: carola.raab@unlv.edu), is a professor at Department of Food and Beverage,
Meetings, and Event Management, William F. Harrah College of Hospitality, University
of Nevada, Las Vegas. Jungsun Kim, PhD (email: sunny.kim@unlv.edu), is an associate
professor at the Department of Resort, Gaming and Golf Management, William F. Harrah
College of Hospitality, University of Nevada, Las Vegas.
https://doi.org/10.1108/01439911211217107
mailto:dvzemke@bsu.edu
mailto:jason.tang@pm.me
mailto:jason.tang@pm.me
mailto:carola.raab@unlv.edu
mailto:sunny.kim@unlv.edu
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