Lined Up? Examining a “Waiting Line” Effect in Technology-Enabled Restaurant Menu Ordering

Abstract

This research examines the impact of a waiting line on menu ordering behavior when interacting with self-service technology (SST), along with the underlying mechanisms and an intervention strategy drawing on attribution theory. We conducted three experimental studies to simulate a real-life event of menu ordering in a quick-service or fast-casual restaurant. According to the results, the presence of a waiting line can lead to time-pressured menu ordering behavior, especially when interacting with SSTs (vs. human staff). Further, we verified that customers’ perceptions of responsibility for service outcomes explain this SST-conditioned effect. Our findings also suggest a line design strategy that can mitigate the negative consequences of the “waiting line” effect in SST-enabled menu ordering. This research provides valuable insights into the unintended consequences of waiting lines and offers practical strategies for minimizing negative outcomes associated with SST-mediated services.

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https://doi.org/10.1177/10963480231211741
Journal of Hospitality & Tourism Research
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Research Article
Introduction
The hospitality industry has witnessed a vital role of tech-
nologies in enhancing customer experiences. A prominent
facet of such technological advancements is self-service
technology (hereafter SST), defined as “technological inter-
faces that enable customers to produce a service independent
of direct service employee involvement” (Meuter et al.,
2000, p. 50). Forecasted data indicate a remarkable growth in
the market value of SST, projected to increase from $1.71
trillion in 2020 to $6.25 trillion by 2028 (Global View
Research, 2021). In recent years, SSTs have also been widely
adopted in restaurants, primarily facilitating menu ordering
processes and transforming traditional human-to-human
interactions into technology-driven engagements. This trend
is particularly attributed to a growing number of quick-ser-
vice (e.g., McDonald’s, Wendy’s) and fast-casual (e.g.,
Applebee’s, Chipotle, Shake Shack) restaurants that have
increasingly implemented self-service kiosks to streamline
menu ordering processes and cope with the rising labor
shortages (Gagnon, 2022; Tillster, 2019). A noteworthy
example is McDonald’s, which has implemented menu order
kiosks in over 1,000 U.S. stores, with a strategic objective of
offering self-service options in all company-owned locations
(Johnson, 2018).
Waiting line (i.e., queue) management is an indispensable
aspect of the hospitality industry, facilitating a smooth opera-
tion of service processes (Durrande-Moreau, 1999).
Certainly, queues are commonplace in various hospitality
settings, including hotels, theme parks, and airports, where
customers often wait in line for services such as check-in/-
out, accessing attractions/services, and boarding flights.
Notably, in the restaurant industry, the surge in the popularity
of fast-casual and quick-service segments has increased the
prevalence of customers waiting in lines while placing menu
orders (Technomic, 2019). This circumstance has drawn sig-
nificant attention to the potential influence of social factors
on the ordering experience of focal customers (Hanks et al.,
1211741JHTXXX10.1177/10963480231211741JOURNAL OF HOSPITALITY & TOURISM RESEARCHLee, Lu / Examining a “Waiting Line” Eect in Technology-Enabled Menu Restaurant Ordering
research-article2023
Corresponding Author:
Wangoo Lee, Fox School of Business and School of Sport, Tourism and Hospitality Management, Temple University, 1810 13th Street, Philadelphia, PA
19122, USA. E-mail: tun47670@temple.edu
Lined Up? Examining a “Waiting Line”
Effect in Technology-Enabled Restaurant
Menu Ordering
Wangoo Lee1 and Lu Lu2
1Fox School of Business and School of Sport, Tourism and Hospitality Management, Temple University, Philadelphia, PA, USA
2School of Sport, Tourism and Hospitality Management, Temple University, Philadelphia, PA, USA
Abstract
This research examines the impact of a waiting line on menu ordering behavior when interacting with self-service
technology (SST), along with the underlying mechanisms and an intervention strategy drawing on attribution theory.
We conducted three experimental studies to simulate a real-life event of menu ordering in a quick-service or fast-
casual restaurant. According to the results, the presence of a waiting line can lead to time-pressured menu ordering
behavior, especially when interacting with SSTs (vs. human staff). Further, we verified that customers’ perceptions
of responsibility for service outcomes explain this SST-conditioned effect. Our findings also suggest a line design
strategy that can mitigate the negative consequences of the “waiting line” effect in SST-enabled menu ordering. This
research provides valuable insights into the unintended consequences of waiting lines and offers practical strategies for
minimizing negative outcomes associated with SST-mediated services.
Keywords
waiting line, queue, self-service technology, menu ordering, attribution theory

2 Journal of Hospitality & Tourism Research 00(0)
2016). Perhaps, the most influential “others” are often those
in close physical proximity, such as fellow customers stand-
ing in the waiting line (Otterbring, 2023). As an anecdotal
instance from industry practice, Wendy’s executed a simula-
tion study, wherein select participants displayed visible signs
of anxiety in the presence of co-queuing patrons; some indi-
viduals even turned around to count the number of people
behind them and gestured for others to pass by, seeking more
time to consider their options (Jargon, 2018).
As the phenomenon becomes acknowledged, a couple of
questions remain unanswered: If customers recognize others
waiting behind, would this lead them to feel pressured while
ordering menu items? How would this felt pressure influence
their menu ordering behavior? These questions are relevant
to the hospitality industry, which values customer experience
as the core of business success (Gursoy, 2018). A smooth and
pleasant menu ordering experience is integral to dining out
while experiencing pressure during this process can signifi-
cantly impact customer behavior, including the speed of
decision-making, food expenditures, and the selection of
menu items (e.g., familiar vs. novel dishes; Hyun et al., 2016;
Kim et al., 2018). Along with this notion, our research sug-
gests that restaurant customers adhere to the social norm of
not interfering with the utility of others while in a waiting
line (Fisk et al., 2010). Consequently, the focal customer
may experience psychological pressures to make quick deci-
sions when ordering menu items due to the presence of a
waiting line behind them, leading to time-pressured ordering
behavior, such as ordering familiar items without adequate
consideration and exploration of the entire menu.
Despite businesses’ premonition that a waiting line can
cause unease to customers, the behavioral implications of
such queues have been overlooked by hospitality scholars.
This literature gap has become more evident in recent years,
primarily due to the increasing adoption of SSTs in restau-
rant interactions. Our research posits that the rising usage of
SST accentuates the impact of waiting lines on customers’
pressured behavior. Customers who opt for SSTs, instead of
engaging with human staff, might be particularly susceptible
to this “waiting line” effect due to the elevated sense of con-
trol they experience over the ordering process (Fishman &
Husman, 2017). The greater sense of control is likely to
heighten customers’ felt responsibility for the speed of their
transactions and the potentially extended waits experienced
by others, consequently leading to pressured ordering behav-
ior. As such, an empirical investigation of waiting lines and
their potential impact on menu ordering behavior warrants
significant attention. In this research, we examine the effect
of waiting lines associated with SSTs via the lens of attribu-
tion theory (Weiner, 1985).
The present research seeks to investigate the impact of
waiting lines on customer menu ordering behavior when
interacting with SSTs. In particular, the study aims to (1)
examine a “waiting line” effect on menu ordering behavior
while accounting for two service interfaces (SST vs. human
staff); (2) identify the underlying mechanisms of the pro-
posed effect; and (3) explore the effectiveness of a line-
design intervention in mitigating the “waiting line” effect.
Our research contributes to the existing literature by shed-
ding light on the unintended consequences of having waiting
lines in SST-mediated services (e.g., menu ordering) and the
theoretical account for the proposed effect. The findings also
provide valuable managerial insights into customer ordering
behavior resulting from SST-mediated services and suggest
practical strategies for restaurants to manage customers’
food ordering experience. The findings also highlight the
importance of effective line design in minimizing the nega-
tive effects of waiting lines.
Literature Review
Waiting Lines (Queues)
Waiting lines, also known as queues, have received mount-
ing attention historically as they have become a significant
factor in making informed business decisions regarding
resource allocation. Organizations are keen to minimize
operational costs, improve productivity, and enhance effi-
ciency by optimizing queues. Effective queue management
can positively impact businesses’ revenue generation and
cost reduction by, for example, reducing idle time for
employees, utilizing resources to their full potential, and
maximizing customer turnover rates (Errecart, 2023). Hence,
early literature has been predominantly centered around
operational advancements in queue management (Durrande-
Moreau, 1999; INFORMS History and Traditions Committee,
n.d.; P. Jones & Peppiatt, 1996; Penttinen, 1999). As queue-
ing theory began in the early 20th century and has evolved, a
significant body of research has been dedicated to develop-
ing mathematical models, equations, and algorithms to fore-
cast and regulate production and customer flow across
industries. For instance, some waiting line policies have
been proposed in several sectors to minimize wait times. The
first-come-first-served (FCFS) approach, where customers
with the most extended wait times are attended to first, or the
last-come-first-served (LCFS) approach, where customers
with the shortest wait times are prioritized, may be selected
based on circumstances to reduce the overall wait time
(Penttinen, 1999). Alternatively, the shortest-job-next (SJN)
rule may be employed, prioritizing customers with the
smaller transaction sizes (e.g., checkout counters reserved
for handling small purchases).
Queues are ubiquitous in the service industry, where cus-
tomer experiences are integral to the delivery process
(Durrande-Moreau, 1999). In service operations, proficiently
managed waiting lines can be perceived as buffer invento-
ries, instrumental in facilitating seamless operational activi-
ties. However, unlike in manufacturing, customers have their
own perceptions regarding waits and queues, which may
affect their service consumption and subsequent behavior.

Lee, Lu / Examining a “Waiting Line” Effect in Technology-Enabled Menu Restaurant Ordering 3
The existence or length of the waiting line can serve as a
consideration or a cost factor for customers deciding on ser-
vice consumption (Kokkinou & Cranage, 2015; Wang et al.,
2012). Many customers perceive the waiting experience as
unpleasant, which has been a challenge for service providers
(P. Jones & Peppiatt, 1996).
Furthermore, customers’ perceptions of wait time can
often differ from the actual duration, influenced by various
internal or external factors such as anxiety, uncertainty, and
perceived unfairness (Dickson et al., 2005; Maister, 1985).
This highlights the subjective nature of customers’ waiting
experience and emphasizes the importance of managing not
only the objective waiting time but also the customers’ per-
ceptions. In certain situations, however, making consumers
wait can signal service quality or attractiveness, increasing
satisfaction and purchase intentions (Giebelhausen et al.,
2011; Kostecki, 1996). The mixed findings have underscored
the intricacy of managing customers’ waiting experiences as
an integral part of service consumption.
Most queuing theories and models are based on the eco-
nomic perspective, where economic agents make rational,
albeit bounded, decisions. In service settings, when con-
sumption involves other customers in a shared environment,
waiting lines are also regarded as “social systems” whereby
decision-making is not solely based on self-interest but is
also influenced by social factors and the presence of others
(Mann, 1969; Ülkü et al., 2022). For example, queue jump-
ers violate social norms, causing resistance and moral out-
rage, even if the additional person does not significantly
increase waiting times or reduce service quality (Schmitt et
al., 1992). Also, first-come-first-served (FCFS) is widely
accepted as socially agreed to be fair, although other queue
policies could be more efficient (e.g., prioritizing VIPs;
Mcguire & Kimes, 2006). More recently, Ülkü et al. (2022)
have found that consumers are more likely to speed up their
transactions and service consumptions when others are wait-
ing in line, at the sacrifice of their own consumption experi-
ence. Rather than assuming consumers are solely
self-interested, we believe that the “waiting line” induced
behaviors should be understood via a social lens to maintain
fairness and justice. In line with this stream of queuing stud-
ies, our research follows the social account of queues and
examines consumers’ menu ordering behavior as a function
of the “waiting line.” We propose this effect within an impor-
tant operating territory (i.e., SST) that significantly occupies
the restaurant and service industries.
Restaurant SST
The hospitality industry has heavily invested in service
technologies to drive innovation and provide more accu-
rate, efficient, and consistent services (Gummerus et al.,
2019; Park et al., 2023). SSTs represent a particular type of
service technology that allows customers to self-produce
services without interaction with service employees
(Meuter et al., 2000). SSTs have been implemented by
many businesses as a means of addressing operational inef-
ficiencies, such as high labor costs, high employee turn-
over, and inconsistency/bottlenecks in service deliveries
(Shin & Perdue, 2019). With the hospitality industry repre-
senting an icon of the experience economy, SSTs have also
offered pleasant and engaging experiences by incorporating
interactive elements or gamifying monotonous service pro-
cesses (Ahn & Seo, 2018; Niels & Zagel, 2018). The
demand for contactless services has further prompted the
industry to continue to implement SSTs (Wheeler, 2020). A
considerable body of literature has supported this trend of
SST proliferation, which investigated numerous factors
influencing customer adoption of SSTs; among these fac-
tors are customer readiness (Shim et al., 2020), customer
innovativeness, perceived risk (Jeon et al., 2020), custom-
ization option (Ahn & Seo, 2018; Xu et al., 2022), and
entertainment (Xu et al., 2022).
In restaurant service, SSTs have gained prominence as
efficient tools for menu ordering and facilitating purchases.
The advent of digital interfaces, such as iPads equipped with
digital menus, has led to the widespread adoption of tabletop
or tableside touchscreen monitors in numerous casual dining
establishments (Ahn & Seo, 2018). Restaurant SST interac-
tion process diverges from traditional human-mediated menu
ordering as it eliminates the need for customers to engage
with human employees. Due to the lack of employee involve-
ment, this shift to SST interactions places a greater emphasis
on active, effective customer participation in the service
delivery process for positive customer experiences (Meuter
et al., 2000). Accordingly, by engaging with SSTs, customers
are empowered to independently navigate the menu, place
orders, and conduct payment transactions, consequently
cocreating core products and experiences with the business
(Kelly et al., 2017). A prevailing trend among customers is
their inclination for increased control over service experi-
ences (Davis et al., 2011). However, these environments,
devoid of employee presence, reinforce the customer’s
responsibility to ensure enhanced service quality, value, and
satisfaction (Antwi et al., 2021; Kelly et al., 2017).
In the context of SSTs, where human employees play lim-
ited roles in shaping the customer experience, it is imperative
to consider the impact of other customers on the focal cus-
tomers. For instance, Kelly et al. (2017) investigated the
implementation of SSTs at airports and highlighted instances
where certain individuals assisted fellow customers who
faced challenges with digital check-in services. Nevertheless,
customer-to-customer interactions in SST contexts may not
always result in favorable outcomes and experiences.
Extensive research on SSTs has emphasized the significance
of the presence of other individuals as a critical situational
factor influencing customer anxiety and discomfort when
surrounded by others (e.g., López-Bonilla & López-Bonilla,
2015; Nam & Kim, 2022; Shim et al., 2020). Therefore,
given the need to examine the influence of others in SST

4 Journal of Hospitality & Tourism Research 00(0)
contexts, the present research aims to investigate an essential
external situational factor, namely the presence of a waiting
line in the process of menu ordering via restaurant SSTs.
Attribution Theory
In the current research, we use attribution theory as the theo-
retical framework which conceptualizes how people inter-
pret events’ occurrence (Martinko & Mackey, 2019). It is not
one singular theory but rather a set of theories and concepts
(e.g., the covariation model, correspondent inference theory,
locus of control) which collectively address inquiries con-
cerning the reasons behind—and comprehension of—event
occurrences (or behaviors). Attribution theory is grounded in
the assumption that the causes of certain events are attributed
to either internal factors (e.g., ability, effort) or external fac-
tors (e.g., task difficulty, luck) that can facilitate or impede
outcomes (Heider, 1958). Accordingly, attributions encom-
pass a dual-locus dimension, comprising internal attribu-
tions, which stem from individual characteristics or personal
dispositions, and external attributions, which arise from situ-
ational or environmental factors (Weiner, 1985).
The literature on attribution theory has attempted to dis-
cern antecedents of attribution to understand how individu-
als form judgments and whether they attribute certain
outcomes to themselves or external situations (e.g., E. E.
Jones & Davis, 1965; Kelley, 1973). Per this theory, the
degree of control over an event holds significant impor-
tance in evaluating the attribution paths because it demon-
strates an individual’s mastery of the situation and drives
subsequent behavior (Inesi et al., 2011). Individuals pos-
sessing a high level of control are more inclined to engage
in internal attribution, assigning outcomes primarily to
their actions. Conversely, when individuals perceive lim-
ited control over environmental conditions, they attribute
outcomes to external factors, diminishing their roles’ sig-
nificance (Fishman & Husman, 2017). In the business
world, service technologies have the potential to influence
user control by offering specific features (e.g., customiza-
tion options, self-service functions) that enhance certain
aspects of controllability.
Frequently, consumers rely on heuristics to make judg-
ments, leading to potential errors and biases in the attribution
process (Muschetto & Siegel, 2021). One prominent bias is
the fundamental attribution error, wherein individuals tend to
underestimate the impact of situational factors while overes-
timating the influence of dispositional characteristics when
assessing others’ behavior (Ross, 1977). Conversely, indi-
viduals tend to emphasize external attributions more when
evaluating their own behavior. Another noteworthy bias is
the self-serving bias, whereby individuals attribute success
(or positive outcomes) to personal traits while attributing
failure (or negative outcomes) to external circumstances
(Buss, 1978). However, within the realm of service technol-
ogy, scholars have uncovered an intriguing phenomenon
known as the “reversed” self-serving bias, where positive
(vs. negative) outcomes of service technologies are more
likely to be attributed externally rather than internally
(Jörling et al., 2019).
The “Waiting Line” Effect on Time-Pressured
Menu Ordering Behavior
Previously, the economically rational approach has been
adopted to understand customer perceptions about waiting
lines (i.e., queues; Ülkü et al., 2022). This rationale priori-
tizes self-interest in consumption utility through material or
experiential gains. However, waiting lines are also viewed as
social systems; thus, customers consider social norms when
making decisions (Mann, 1969; Schmitt et al., 1992).
Likewise, social pressure arises in the situation where a cus-
tomer browses and orders menu items while many customers
are waiting behind. There is a cross-cultural consensus that
consumers assume moral obligations not to interfere with
others’ utility while consuming products/services (Fisk et al.,
2010). When working with SSTs to place menu orders, hold-
ing a waiting line may lead the focal customer to feel socially
concerned about damaging other customers’ service experi-
ence. In a similar vein, Chatterjee (2020) suggests that ser-
vice times should not be longer for those waiting behind
because all customers are socially expected to wait equally.
In real-world settings, menu ordering is often susceptible to
social pressure so that customers conform to social norms in
that they need to order promptly (Kim et al., 2018). An
occurrence like this disqualifies the economically rational
approach to understanding consumers’ behavior since pursu-
ing self-interest could collide with others’ benefits in a shared
social system.
Therefore, we suggest that the presence of a waiting line
enables time-pressured menu ordering behaviors: (1) reduced
menu order time, (2) reduced purchase amount, and (3) like-
lihood to purchase familiar items, characterizing a “waiting
line” effect. Restaurant customers would shorten ordering
time under time pressure, which is both intuitive and empiri-
cally evidenced (Kim et al., 2018; Ülkü et al., 2022). Menu
browsing is a form of information retrieval as well as recre-
ation (Bloch et al., 1989). If customers are under pressure to
make a quick decision, they are less likely to take time to
explore new items or take chances on novel dishes. In previ-
ous research, menu browsing has been linked to hedonism
and impulsive purchase behavior, such as purchasing new
items or spending more money than usual (Hyun et al.,
2016). The pressure to complete a transaction quickly may
prevent customers from browsing the menu and exploring
more food options. Hence, we hypothesize:
Hypothesis 1: During menu ordering, the presence of
other customers in the waiting line reduces (a) menu order
time and (b) total purchase amount, but increases (c)
familiar menu purchases.

Lee, Lu / Examining a “Waiting Line” Effect in Technology-Enabled Menu Restaurant Ordering 5
Hypothesis 2: The “waiting line” effect on (a) menu order
time and (b) total purchase amount, and increases (c)
familiar menu purchases, is mediated by perceived time
pressure.
SST-Conditioned “Waiting Line” Effect
In this research, we propose that the impact of the “waiting
line” effect is more pronounced when customers place
menu orders through SSTs (vs. human staff). Drawing on
attribution theory, individuals tend to attribute outcomes to
external factors when they lack control over certain events,
thereby diminishing their roles’ perceived influence
(Fishman & Husman, 2017). Within the context of restau-
rant services, customers typically attribute service out-
comes (e.g., success/failure) to service providers who
possess a higher degree of control over the service delivery
process (Inesi et al., 2011). However, using SSTs in restau-
rants grants customers greater control over the menu order-
ing process as they can self-manage their own service
experiences (Jörling et al., 2019; Meuter et al., 2000). This
dynamic results in customers functioning as co-producers
of the service experience with shared autonomy and control
over service outcomes (Wu et al., 2023). Underpinned by
attribution theory, customers who interact with SSTs (vs.
human staff) are more likely to engage in internal (vs.
external) attributions for service outcomes, thus feeling
more responsible for the service experience of others who
share the same waiting line. For this reason, customers
could be pressured to make prompt decisions or opt for
familiar items in order to streamline the process and shorten
their ordering time, so as to minimize the negative influ-
ence on others’ experience. This alteration of ordering
behavior could potentially limit overall expenditures due to
less time devoted to exploring alternative options.
A counterargument can be made that since the self-serving
bias (Buss, 1978) posits that individuals tend to attribute fail-
ure or negative outcomes to external factors, customers inter-
acting with SSTs may still attribute long waits of others
externally to the restaurant management or even to the self-
service machine itself. However, empirical evidence from a
service technology context suggests a “reversed” self-serving
bias phenomenon, wherein consumers tend to engage in inter-
nal attribution, even when they experience negative outcomes
(Jörling et al., 2019). This phenomenon warrants further
exploration, particularly in the context of SSTs. Hence, we
propose:
Hypothesis 3: The “waiting line” effect on (a) menu
ordering time, (b) total purchase amount, and (c) familiar
menu purchases, mediated by time pressure, is stronger
when customers interact with SSTs (vs. human staff).
According to attribution theory, previous research has
suggested that individuals tend to attribute outcomes
to internal factors when they possess greater control over
specific events; this tendency leads individuals to assume a
higher level of responsibility for their roles (Fishman &
Husman, 2017; Inesi et al., 2011). In light of previous litera-
ture, our research endeavors to extend these findings by pro-
posing that interacting with SSTs (vs. human staff) can
reinforce the proposed “waiting line’ effect (Hypothesis 3).
This effect arises due to the internal attributions elicited by
the high level of control that customers experience during the
coproduction process of SST-enabled menu ordering (Jörling
et al., 2019; Wu et al., 2023). The focal customers, therefore,
perceive greater responsibility towards their fellow custom-
ers who share the same waiting line. Consequently, the
degree of perceived responsibility plays a vital role in
accounting for the “waiting line” effect during SST-enabled
menu ordering. If our theoretical framework holds to this
mechanism, the introduction of intervention strategies aimed
at reducing customers’ perceived responsibility has the
potential to weaken the impact of the “waiting line” effect.
Based on this premise, we further propose:
Hypothesis 4: When placing menu orders via SSTs,
releasing customers’ perceived responsibility can mitigate
the “waiting line” effect on (a) menu order time, (b) menu
purchase amount, and (c) familiar menu purchases.
Following the above mentioned conceptualization and
attribution theory, line design strategies may significantly
influence the “waiting line” effect. The two most common
ways to design lines are allocating (1) a “single” line or (2)
“multiple” lines in front of multiple SSTs/machines
(Al-Kadhimi et al., 2021; Kokkinou & Cranage, 2015).
That is, either one line feeds a designated ordering machine,
or one line simultaneously feeds multiple ordering
machines. When joining a single waiting line that feeds
multiple machines (i.e., SSTs), each customer facing mul-
tiple machine shares the responsibility of making the next
customer wait before they can be served. This shared
responsibility can attenuate the negative outcomes of the
waiting line effect. The literature suggests that the number
of people waiting behind would not impact to the “waiting
line” effect as the physical proximity decreases with each
additional individual (Otterbring, 2023). Hence, we
propose:
Hypothesis 5: When placing menu orders via SSTs, lines
designed as one line feeding multiple (vs. one) machines
can attenuate the “waiting line” effect on (a) menu order
time, (b) menu purchase amount, and (c) familiar menu
purchases.
This research examines the proposed hypotheses via
three studies. The following conceptual framework (Figure
1) is an overview of the three studies and the proposed
effects.

6 Journal of Hospitality & Tourism Research 00(0)
Study 1: The “Waiting Line” Effect
Accentuated by SST
The primary objective of Study 1 was to investigate the
potential influence of the presence of other customers in the
waiting line on menu ordering behaviors (i.e., menu order
time, purchase amount, and familiar menu purchase) while
also seeking to substantiate the underlying mechanism
responsible for such effect (i.e., time pressure). Additionally,
the study sought to examine the role of service interface
(SST vs. human staff) as a conditioning factor contributing to
the observed effects of this waiting line phenomenon. Hence,
Study 1 aimed to test Hypotheses 1, 2, and 3. Two types of
restaurants were included in the study—fast-casual and
quick-service establishments—where SSTs are most preva-
lently employed in their operations to augment the generaliz-
ability of the findings.
Research Design and Procedure
U.S. adults who have visited fast-casual and quick-service
restaurants in the past year were invited to participate.
Participants were recruited from an online consumer panel
platform (i.e., Prolific; www.prolific.co). We included atten-
tion check questions to ensure attentiveness while complet-
ing the experiment. We conducted a power analysis (via
G*Power) to determine the appropriate sample size for Study
1. To achieve sufficient power that exceeds 0.8, we recruited
a sample size greater than 128 for a 2 × 2 × 2 design with six
covariates. Upon excluding one response that did not pass
attention checks, a total of 200 respondents were retained for
data analysis (63% females, with an average age of
36.95 years, 69% Caucasian, 58% married, and 66.5% hold-
ing a bachelor’s degree or higher).
We carried out a 2 (waiting line: presence vs. absence) × 2
(types of service interface: SST vs. human-based) × 2
(restaurant type: fast-casual vs. quick-service restaurants)
between-subjects experiment. Participants were randomly
assigned to eight factorial groups. Apart from being subjected
to manipulations of the waiting line effect and service interface
types, participants were allocated to either fast-casual or quick-
service restaurants as the experiment context. These two seg-
ments were chosen as they are considered the dominant
segments where SSTs are commonly implemented in the res-
taurant industry (Tillster, 2019). The context itself was not a
variable of concern; instead, this deliberate step was to enhance
the real-world representation so that the findings of the waiting
line effect were not limited to any particular segment.
Subsequently, participants rated their current hunger level
and familiarity with restaurant SSTs, which were included as
control variables. They were then instructed to read a scenario
that simulated the experience of menu ordering either from a
fast-casual or quick-service restaurant; each scenario speci-
fied the presence/absence of a waiting line, depending on the
groups. Once participants completed reading the scenario,
they answered several survey questions concerning manipu-
lation checks, a control variable that measures fear of nega-
tive evaluation from the service provider (Leary, 1983), and
research variables using a 7-point scale. Research indicates
that technology-powered services are seen as less judgmental
than humans (Holthöwer & van Doorn, 2023). Likewise,
studies show that diners may alter their ordering behavior in
the presence of human employees due to the fear of being
judged on their choices (e.g., Hanks et al., 2016). Therefore,
we included fear of negative evaluation from the service pro-
vider as a control variable to rule out potential confounding
effects. We asked participants to indicate their perception of
time pressure with three items (e.g., “My menu order deci-
sion-making was rushed,” α = .96; Ashley & Noble, 2014),
intentions to reduce menu order time (α = .98), menu pur-
chase amount (α = .97), and intentions to purchase familiar
menu items (α = .97), respectively, with three items.
Figure 1. Conceptual Framework and Study Overview.

Lee, Lu / Examining a “Waiting Line” Effect in Technology-Enabled Menu Restaurant Ordering 7
Manipulation and Realism Check
Participants assigned to the “presence of waiting line” groups
reported higher agreement that customers were waiting
behind the focal customer compared to the “absence of wait-
ing line” groups (Mpresence = 6.66, Mabsence = 1.53, t = 34.97,
p < .001). For the types of the service interface, participants
assigned to the “SST” (vs. “human-based”) condition reported
greater agreement that they placed orders through the SST
(Msst = 6.70, Mhuman = 1.85, t = 29.28, p < .001) whereas those
assigned to the “human-based” (vs. “SST”) condition reported
greater agreement placing orders with a cashier (Msst = 1.32,
Mhuman = 6.32, t = 31.97, p < .001). Participants reported that
the scenario was highly realistic (M = 6.34), that it was easy to
project themselves in the scenario (M = 6.40), and that it was
easy to imagine the scenario happening in real life (M = 6.40).
Thus, the manipulation and realism checks were successful.
Results
A series of one-way ANCOVA was conducted. Participants’
hunger level, familiarity with SST, fear of negative evalua-
tion, age, gender, and income were included as control vari-
ables. The results revealed a significant “waiting line” effect;
when a waiting line was present, participants reported higher
intentions to make a quick decision (Mpresence = 5.13,
Mabsence = 3.47, F1,192 = 71.59, p < .001), to spend less money
(Mpresence = 2.52, Mabsence = 2.93, F1,192 = 4.16, p < .05), and
that they were more likely to purchase familiar items
(Mpresence = 5.88, Mabsence = 5.53, F1,192 = 6.89, p < .01). Hence,
Hypothesis 1 was supported.
Subsequently, a series of mediation analyses were con-
ducted to examine the underlying mechanism of the “waiting
line” effect via PROCESS modeling (Model 4, with 10,000
resamples). The results revealed indirect effects of the pres-
ence of a waiting line on three behavioral intentions—menu
order time (1.01, 95% CI [0.67, 1.40]), menu purchase
amount (−.87, 95% CI [−1.17, −0.53]), and familiar menu
purchase (.31, 95% CI [0.10, 0.52])—mediated by perceived
time pressure. Moderated mediation analyses were con-
ducted using Model 7 (10,000 resamples) to further explore
the phenomenon. The results showed a stronger indirect
“waiting line” effect on menu order time (.34, 95% CI [0.02,
0.70]) and familiar menu purchase (.11, 95% CI [0.01, 0.24])
when participants placed orders through SSTs (vs. human
staff). The indirect “waiting line” effect on menu purchase
amount was not significantly different between SST and
human-based interactions. The results supported Hypothesis
2 and partially supported Hypothesis 3.
Study 2: Intervention Message to
Reduce Perceived Responsibility
Study 1 revealed a conditional “waiting line” effect on menu
order behavioral outcomes—the effect was more pronounced
when interacting with SSTs (vs. human staff). Per attribution
theory, one limitation of Study 1 was that it did not examine
the responsibility attribution account in the event of interact-
ing with SSTs due to the subsequent internal attribution
(Jörling et al., 2019). As conceptualized previously, customers
tend to feel responsible for service outcomes when interacting
with SSTs, including others’ long waits. This ultimately leads
them to feel obligated to order quickly or choose familiar
items to simplify the decision process. In Study 2, we manipu-
lated perceptions of responsibility by displaying a “pop-up
message” as an intervention strategy, indicating that the res-
taurant management takes full responsibility for any delays in
service experience during menu orders. If the responsibility
theorization holds, the “pop-up message” will alleviate con-
sumers’ perceived responsibility and lessen the “waiting line”
effects. Hence, Study 2 aimed to test Hypotheses 1 and 4.
Further, Study 1 examined the “waiting line” effect by
assessing menu ordering behaviors (i.e., dependent vari-
ables) using survey-based measurements at the intention
level. To bridge this intention-behavior gap, Study 2
employed a simulation experiment that approximated a real-
life event in a hypothetical fast-casual restaurant. Unlike
Study 1, we could directly observe participants’ behavior
during the simulated menu ordering event, with recorded
measures including the duration of order completion, total
expenditure, and the ratio of familiar items relative to the
total number of items purchased. These recorded measures
were subsequently used as dependent variables.
Research Design and Procedure
Based on the power analysis (via G*Power), Study 2 needed
a sample size greater than 128 to yield a statistical power
exceeding 0.8 for a 2 × 2 ANCOVA with six covariates. We
used the same criteria and survey platform as Study 1.
Following the exclusion of 14 responses that failed to meet
the attention check and demonstrated inadequate participa-
tion in the menu order simulations (e.g., inability to provide
the correct simulation ending code), we retained 254 U.S.
adult respondents (49% females, with an average age of
39.28 years, 72% Caucasian, 51% never married, and 55.1%
holding a bachelor’s degree or higher) for data analysis.
The experiment used a 2 (waiting line: presence vs.
absence) × 2 (message intervention: presence vs. absence)
between-subjects design. Participants were randomly
assigned to four combinatory groups and rated their present
hunger level and familiarity with restaurant SSTs.
Subsequently, participants were presented with a list of menu
items generated explicitly for a hypothetical fast-casual res-
taurant, which served as the basis for our simulation experi-
ment. Participants were then required to identify menu items
they found familiar among the options provided. Before
starting the web simulation, participants were asked to read a
scenario of ordering menu items in a fast-casual restaurant
that specified the presence or absence of a waiting line. The

8 Journal of Hospitality & Tourism Research 00(0)
simulation was a website developed for this research, mim-
icking the user interface of a self-service kiosk through
which participants could browse categories (e.g., sand-
wiches, pizzas, salads) of food and beverage items (see
Appendix 1 for a screenshot of the website). The simulation
site allowed participants to browse and order the menu items
virtually. Before starting browsing items, participants
assigned to the intervention message group received a “pop-
up message” indicating that the restaurant takes full respon-
sibility for any delays in service and customer experience
during menu orders (see Appendix 2 for a “pop message”
intervention). All participants then completed their menu
orders on the simulation site.
In Study 2, we directly measured dependent variables
from the participants’ behaviors. For menu ordering time, we
measured the time each participant spent (in seconds). For
the menu purchase amount, we recorded how much each par-
ticipant spent on their orders (in U.S. dollars). For familiar
menu purchases, participants were asked to indicate their
familiarity with all menu items used in the simulation before
participating in this study. Then, the rate of familiar item pur-
chase (%) was determined by dividing the number of famil-
iar items purchased by the total number of items purchased.
Manipulation and Realism Check
Participants assigned to the “presence of waiting line” groups
(vs. “absence of waiting line” groups) exhibited a greater
inclination to agree that others were waiting behind the focal
customer (Mpresence = 6.55, Mabsence = 1.09, t = 77.55, p < .001).
Participants exposed to an intervention message (vs. without
the message) reported a higher level of agreement that the
restaurant takes responsibility for outcomes associated with
the menu order process (Mpresence = 6.45, Mabsence = 1.80,
t = 30.94, p < .001) and a lower level of agreement that they
felt responsible for the outcomes (Mpresence = 3.51,
Mabsence = 5.56, t = 9.33, p < .001). In addition, participants
reported that the scenario was highly realistic (M = 6.08), it
was easy to project themselves in the scenario (M = 6.30),
and it was easy to imagine the scenario happening in real life
(M = 6.24). Thus, the manipulation and realism checks were
successful.
Results
The results of a series of two-way ANCOVA confirmed the
“waiting line” effect when a menu order was placed via
SSTs. When participants were informed of a waiting line (vs.
without a waiting line), they ended up spending less time
placing an order (Mpresence = 92.15, Mabsence = 108.29,
F1,246 = 3.80, p < .1), spending less money (Mpresence = 15.41,
Mabsence = 17.92, F1,246 = 6.72, p < .05), and purchasing more
familiar items (Mpresence = 67.69, Mabsence = 54.01, F1,246 = 6.51,
p < .05), therefore supporting Hypothesis 1.
Further, the presence/absence of an intervention message
significantly moderated the waiting line effect on menu order
time (F1,244 = 4.35, p < .05), menu purchase amount
(F1,244 = 4.37, p < .05), and familiar menu purchase
(F1,244 = 3.88, p < .05). In instances where participants did
not receive a “pop-up message,” they reported shorter menu
order time, lower purchase amount, and that they purchased
a greater amount of familiar items when there was a waiting
line (vs. no line) behind them. However, for participants who
received the intervention message, the “waiting line” effect
on these behavioral outcomes was significantly attenuated
(Figure 2). Hence, Hypothesis 4 was supported.
Study 2 provided further evidence that refuted an alterna-
tive explanation for the perceived pleasure derived from the
intervention message. The effect of the intervention message
could be attributed to a pleasurable experience rather than
the sense of responsibility evoked, which might have been
influenced by visual appeals in the message and/or the per-
ceived sincerity of the management. The existing literature
suggests that pleasure is an important precursor of impulsive
buying (Iyer et al., 2020). Hence, the intervention message
may have elicited pleasure among customers, subsequently
leading to impulsive orders (i.e., higher expenditures, unfa-
miliar purchases). If this alternative explanation were true,
the groups with the intervention message (vs. without the
message) would have reported a higher level of pleasure. To
address this, we ran an additional ANCOVA, incorporating
three survey items (e.g., “I experienced a sense of pleasure
during the menu ordering process,” α = .96) as indicators of
perceived pleasure. The results revealed no statistically sig-
nificant difference in reported pleasure between participants
exposed to the intervention message and those without,
negating the alternative explanation associating feeling plea-
sure with the message.
Study 3: Mitigating the “Waiting Line”
Effect Via Line Design
Studies 1 and 2 collectively suggested that the “waiting line”
effect was particularly concerning when using SSTs. Study 2
also evidenced the importance of reducing perceived respon-
sibility through testing an intervention strategy. Along the
same line, we conducted Study 3 to suggest a managerial
strategy to reduce the “waiting line” effect through line
design, thus testing Hypotheses 1 and 5.
Research Design and Procedure
The power analysis (i.e., G*Power) indicated that Study 3
would require at least 158 participants for a statistical
power exceeding 0.8 for a 2 × 3 ANCOVA with six covari-
ates. This study also employed the same criteria and sur-
vey platform as the previous two studies. Upon excluding
five responses that did not pass attention checks, 371 U.S.

Lee, Lu / Examining a “Waiting Line” Effect in Technology-Enabled Menu Restaurant Ordering 9
adult respondents (59% males, with an average age of
37 years, 71% Caucasian, 55% never married, and 55%
holding a bachelor’s degree or higher) for data analysis
were retained.
Study 3 compared the “waiting line” effects across three
different line designs—a single line for a single machine ver-
sus a single line for multiple machines versus multiple lines
for multiple machines—influencing the “waiting line”
effects. A “single line for a single machine” design was
treated as a baseline. We used a 2 (waiting line: presence vs.
absence) × 3 (line designs: a single line for a single machine
vs. a single line for multiple machines vs. multiple lines for
multiple machines) between-subjects design. Participants
were instructed to read a scenario and imagine themselves
being in a menu ordering experience in a fast-casual restau-
rant. The scenario included verbal descriptions and pictorial
stimuli regarding the presence/absence of a waiting line and
three different line designs. Once participants completed the
experiment, they were asked to indicate responses to survey
measures as in Study 1.
Figure 2. The Moderating Effect of the Intervention Message (Study 2).

10 Journal of Hospitality & Tourism Research 00(0)
Manipulation and Realism Check
Participants assigned to the “presence of a waiting line”
groups reported a higher level of agreement on the exis-
tence of a waiting line compared to the “absence of a wait-
ing line” group (Mpresence = 6.34, Mabsence = 1.39, t = 41.42,
p < .001). Participants in the two “multiple machines”
groups reported that there were multiple self-service kiosk
machines unlike those in the “single machine” group
(MsingleLsingleM = 1.57, MsingleLmultiM = 6.45, MmultiLmultiM = 6.50,
F = 829.10, p < .001). Also, the “a single line for a single
machine” and “multiple lines for multiple machines”
groups showed stronger agreement that each machine had a
line of customers, compared to the “a single line for multi-
ple machines” group (MsingleLsingleM = 5.43, MsingleLmultiM = 2.59,
MmultiLmultiM = 5.80, F = 86.54, p < .001). Participants
reported that the scenario was highly realistic (M = 5.88), it
was easy to project themselves in the scenario (M = 6.16),
and it was easy to imagine the scenario happening in real
life (M = 6.22). Thus, the manipulation and realism checks
were successful.
Results
A series of two-way ANCOVA was conducted. Consistent
with Studies 1 and 2, participants who had customers in the
waiting line (vs. no waiting line) reported higher intentions
to make a quick decision (Mpresence = 6.47, Mabsence = 3.68,
F1,363 = 131.13, p < .001), to spend less money (Mpresence = 3.09,
Mabsence = 3.79, F1,363 = 6.72, p < .05), and to purchase familiar
items (Mpresence = 2.96, Mabsence = 3.95, F1,363 = 22.68, p < .001),
supporting Hypothesis 1.
Further, line design significantly moderated the “waiting
line” effect on menu order time (F1,359 = 3.50, p < .05), menu
purchase amount (F1,359 = 3.71, p < .05), and familiar menu
purchase (F1,359 = 2.99, p < .1). When there was a waiting
line behind the focal customer, participants from groups of
“single line for multiple machines” (vs. “single line for sin-
gle machine” and “multiple lines for multiple machines”)
reported longer menu ordering time, higher purchase
amounts, and less familiar purchases (see Figure 3). Hence,
Hypothesis 5 was supported.
This study included the two varying levels of line
design—one line feeding one machine (baseline) and mul-
tiple lines feeding multiple machines—as a robustness test,
as they are conceptually identical. The consistent findings
between these two groups have attested to the methodologi-
cal rigor.
General Discussion
This research investigates the impact of waiting lines on menu
ordering behaviors, explicitly examining how this effect is
intensified in SST (vs. human-based) interactions. Additionally,
we elucidate the underlying mechanisms responsible for this
effect and propose a managerial strategy. We conducted three
studies, employing measures of both behaviors (Study 2) and
intentions (Studies 1 and 3) to achieve the research objectives.
Our findings indicate that waiting lines influence cus-
tomer decision-making, resulting in quicker choices, reduced
spending, and a greater likelihood of ordering familiar menu
items. The study is among the first examination of the impact
of waiting lines on menu ordering behaviors and the valida-
tion of its underlying mechanism. While waiting lines have
been a significant concern of operations research, this study
rebalances the literature toward the service marketing aspect
by drawing on behavioral metrics. As a result, we extend the
work of Ülkü et al. (2022)—which focused solely on the
effect of waiting lines on service time—by exploring addi-
tional behavioral outcomes such as menu purchase amount
and familiar menu purchase.
Our findings regarding the “waiting line” effect support the
perspective that a queue should be regarded as a social system
that reflects social norms (Mann, 1969). The extant literature on
queues proposes reducing service time when customers are in a
waiting line (Chatterjee, 2020; Kim et al., 2018; Ülkü et al.,
2022). Our study, in concurrence with these studies, also dem-
onstrates that customers can exhibit socially pressured behav-
iors that are not necessarily in their best interest as a result of the
“waiting line” effect. However, our observations differ from
those reported by Koo and Fishbach (2010) in the context of a
cafeteria. Their study demonstrates that expenditures are higher
when many others are waiting behind, as opposed to when no
one is in line. We attribute this discrepancy to the business con-
text. In their cafeteria setting, food items such as salads, sand-
wiches, and soda were displayed on shelves along the waiting
line, allowing customers to explore and decide what to purchase
while waiting. As a result, time pressure resulting from waiting
in line would not have influenced how customers placed their
orders at the cashier.
In this research, we further enlighten the mechanism that
accounts for the “waiting line” effect for SST-mediated ser-
vices. Our first study reveals that customers exhibit pressured
menu ordering behavior more profoundly when waiting lines
are present during SST (vs. human-based) interactions. Two
subsequent studies suggest that mitigating perceived responsi-
bility for the service experience of others, either through an
intervention message or by adopting a line design strategy, can
effectively weaken the “waiting line” effect in SST-enabled
services. Consumers perceive a heightened sense of control
when interacting with SSTs in lieu of service employees (e.g.,
Meuter et al., 2000). Such a perception creates an internal attri-
bution. These findings are consistent with the findings of
Jörling et al. (2019), which propose a “reversed” self-serving
bias: when customers engage with service technologies, they
consider even negative outcomes, such as service failure, as
their responsibility. To this end, we reinforce the rationale of
consumers’ tendency toward internal attribution for technol-
ogy-enabled services.

Lee, Lu / Examining a “Waiting Line” Effect in Technology-Enabled Menu Restaurant Ordering 11
In addition, the effectiveness of using line design to miti-
gate the negative impacts associated with the “waiting line”
effect has enriched the existing queuing literature. Previous
research has highlighted the advantages of utilizing a single
line to serve multiple machines in order to ensure fairness
and expediency (e.g., Norman, 2009). Adding to this existing
endeavor, our research contributes value to the field by offer-
ing experimental evidence of how line design can effectively
tackle unintended consequences while implementing tech-
nology-based services (e.g., Chan et al., 2022).
Theoretical Implications
This research makes several significant contributions to the
existing body of knowledge. First, it advances the hospitality
marketing and service technology literature by investigating
the influence of waiting lines on restaurant menu ordering
behavior. We examine various aspects, such as menu order
time, purchase amount, and familiar menu choices. We also
delve into the underlying mechanisms of the “waiting line”
effect by exploring the roles of time pressure and perceived
responsibility in explaining pressured ordering behaviors.
Further, we highlight the significance of service interface
type by comparing interactions between human staff and
SSTs. Our findings align with attribution theory, indicating
that self-service options empower customers with greater
control and responsibility for service outcomes. Consequently,
this research offers fresh insights into the role of SSTs in rein-
forcing the suggested “waiting lines” effect.
Figure 3. The Moderating Effect of Line Design (Study 3).

12 Journal of Hospitality & Tourism Research 00(0)
Our research adds to the burgeoning literature on queuing
that extends the mathematical operational perspective.
Although some previous research has examined the impact of
waiting lines on consumer-perceived service usage time, this
area is primarily occupied by behavioral operations research
(e.g., Chatterjee, 2020; Kim et al., 2018; Ülkü et al., 2022).
By adopting a service marketing lens, this research delves
into consumers’ menu ordering behavior that is influenced by
the presence of a waiting line. The perceptions of others wait-
ing in line behind oneself become an integral part of the over-
all service encounter, significantly affecting consumption and
spending behavior. Therefore, our research provides valuable
insights moving beyond the predominant focus on reducing
the objective wait times or optimizing queue configurations
highlighted in the operational research literature (Durrande-
Moreau, 1999; P. Jones & Peppiatt, 1996; Penttinen, 1999).
As businesses strive for service excellence and design more
effective and customer-centric queuing systems, queuing
research shall introduce diversified theoretical lenses consid-
ering both service marketing and operational strategies. By
doing so, we can provide a comprehensive understanding of
the phenomenon and offer more valuable insights for busi-
nesses to optimize their operations while meeting customer
expectations. Our research thus broadens the scope to encom-
pass customer experiences, highlighting the crucial conse-
quences of customer-perceived time pressure and
responsibility induced by waiting lines.
In addition, our study emphasizes a social system per-
spective to comprehend queuing behavior. In service set-
tings, waiting lines create a shared space where social
norms, rules, and obligations exist and persist (Mann, 1969;
Schmitt et al., 1992). Our study suggests that restaurant cus-
tomers can experience social pressure when ordering in
front of a waiting line, thereby altering behavior to be other-
oriented, such as reducing time and amount spent on an
order and choosing familiar items. According to our find-
ings, specific line designs can alleviate the perception of
responsibility by influencing the social dynamics among
customers sharing the same space, thus preventing time-
pressured ordering behavior. Recent research has also shed
light on the social implications of waiting in lines. In par-
ticular, Chan et al. (2022) suggest that line design can allevi-
ate anticipated technology embarrassment, influencing
consumers’ willingness to use service technologies. To this
end, our study has contributed to the ongoing discourse in
queuing literature by highlighting the importance of social
dynamics in a waiting line and the impact on consumer
behavior.
Our research also answers the call for exploration of other
customers’ impact on SST-enabled services. Previous literature
has stressed the importance of exploring customer-to-customer
interactions in cocreating service value (e.g., Nicholls, 2011),
considering their influence on service experience and customer
loyalty (e.g., Lin et al., 2020). The SST context entails a great
deal of customer-to-customer interactions, either direct or indi-
rect, as employees have a limited role in creating service
experiences. This is particularly apparent in hospitality settings
where experiences are derived from interacting with “people”
(Gursoy, 2018). However, certain types of interactions with
other customers may rather co-destruct the service experiences
by causing the focal customers to feel anxious or uncomfortable
in SST contexts (López-Bonilla & López-Bonilla, 2015; Nam
& Kim, 2022; Shim et al., 2020). As such, this research adds
empirical evidence that dissects other customers’ impact on
SST-enabled services.
Managerial Implications
Our study provides managerial insights into restaurant man-
agement. The SST-conditioned “waiting line” effect provides
timely marketing intelligence for businesses that adopt SSTs
for customer services. For instance, fast-casual and quick-
service restaurants are leading segments that have heavily
implemented service technologies with a rapid growth rate
(Technomic, 2019). Many restaurants are replacing human
service employees with technologies to reduce operational
costs (Shin & Perdue, 2019), prevent inconsistency in ser-
vice deliveries (Gummerus et al., 2019), and meet the high
demand for contactless services (Wheeler, 2020). Our find-
ings suggest that restaurants should address the “waiting
line” effect, especially for self-service counters (e.g., order-
ing food via SSTs). When pressured by a waiting line, cus-
tomers tend to spend less, rush their orders, and forgo the
opportunity to explore the menu. Although fast turnovers
make self-service machines more operationally efficient,
businesses risk losing market interest if new products con-
stantly fail to generate sufficient appeal. Revenue from
newly developed products is critical to a restaurant’s annual
income (Hyun & Han, 2012). Therefore, to increase aware-
ness under pressure, restaurants can revamp their digital
menu to allow new items to easily fall on customers’ radars.
For example, promotional messages (e.g., “You must try our
house special!”) or graphic designs/icons to spotlight new or
signature dishes can quickly grab consumer attention. Also,
implementing a display screen that showcases the menu
items to customers in the queue can facilitate their decision-
making process by allowing them to preselect their desired
orders prior to reaching the kiosk machines.
According to our results, restaurants may seek strategies to
fundamentally alleviate customers’ perceptions that they are
responsible for others’ service experience because of using
SSTs. Our results suggest an important line design strategy—
“single line for multiple self-service machines”—to minimize
the “waiting line” effect on menu ordering behaviors.
Businesses with multiple self-service machines on-premises
may consider implementing the proposed recommendation in
queue management if their physical establishments and ser-
vice circumstances permit them to operate the suggested line
design. In addition, to alleviate the pressure on customers who
are placing an order, restaurants may provide various forms of
entertainment or distractions to keep other customers in the
waiting line engaged and occupied. For instance, Slutty Vegan,

Lee, Lu / Examining a “Waiting Line” Effect in Technology-Enabled Menu Restaurant Ordering 13
a vegan restaurant chain, has successfully transformed their
waiting lines into an entertaining party-like atmosphere, com-
plete with music and food, with staff members appearing in
the lines to enhance the guest experience (Gregory, 2021).
However, businesses should carefully consider the suitability
of the entertaining materials for their target audience and
brand image. For instance, a classic dining establishment may
offer reading materials, whereas a family-oriented restaurant
may offer mini-board games or coloring books for younger
customers. Digital entertaining tools, such as tablets or TVs,
may also be made available by restaurants. In another way,
restaurants can also use virtual queue systems by developing a
dedicated ordering app for the business or utilizing an existing
platform (e.g., Yelp Waitlist). Unlike a physical queue, a vir-
tual queue is anonymous and invisibly functioning and there-
fore does not provide any salient clues about the long queue of
customers. Thus, restaurants should expect a reduced “waiting
line” effect with virtual queue systems.
Limitations and Future Research
This research has several limitations. First, we only examine
the waiting line effect as a matter of line presence (vs.
absence). How the proposed effect operates as a function of
the number of customers remains unknown. Future research
should consider testing the pattern of this effect (e.g., linear,
curvilinear) by taking into account the number of people in
the waiting line and its subsequent influence on the focal
customers’ time-pressured ordering behavior. Further, in this
research, we did not consider the physical distance between
the focal customer and customers in the waiting line (i.e.,
other customers). Physical proximity can be a critical factor
of mutual influence (Otterbring, 2023). Therefore, future
research may investigate whether a near (vs. far) distance
can exacerbate (vs. diminish) the waiting line effect.
Next, although our research recommends a line design strat-
egy (i.e., “single line for multiple self-service machines”) to
mitigate the perceived negative impact of waiting lines, it is
important to note that this approach primarily influences cus-
tomers’ perceptions rather than optimizing operational logis-
tics. Businesses should consider implementing customized line
design strategies in different establishments, including branches
of the same restaurant brand, by considering physical facilities
and service conditions. Future research could deepen the inves-
tigation via techniques often utilized in operational research
and management science (e.g., mathematical modeling, opti-
mization techniques, and computer simulation) to continue to
explore a wide range of line design strategies that factor in cus-
tomer volume, servicescape, restaurant size, and layout. This
comprehensive investigation aims to enhance both customer
experience and operational efficiency in queue management.
By understanding the nuances and trade-offs associated with
different line design strategies, businesses can develop tailored
queue management approaches that suit the operating environ-
ments and customer expectations.
Lastly, we emphasize line design to alleviate the “waiting
line” effect. Managers may also control the effect by introduc-
ing line distractors to ease tension. For example, installing a
widescreen with fun and appealing videos might be an option to
manage the waiting experience. Scholars could investigate
whether the focal customer’s perceived time pressure can be
alleviated when others’ waiting experience is fun. Investigating
these new methods will provide richer insights into the “waiting
line” research in the restaurant SST context.
Appendix 1. A Screenshot of the Menu Order Simulation Webpage (Study 2).
Note. For Study 2, a simulated website that approximates an experience of menu browsing and ordering using an SST was designed to provide
participants. This is just a screenshot of one page of the simulated site.

14 Journal of Hospitality & Tourism Research 00(0)
Appendix 2. An Intervention Message (Study 2).
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
work was supported by the 24th Young Scholars Interdisciplinary
Forum seed funding, Temple University, Philadelphia PA.
ORCID iDs
Wangoo Lee https://orcid.org/0000-0002-9903-6999
Lu Lu https://orcid.org/0000-0001-9852-3560
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2021.1890627
Submitted January 18, 2023
Accepted September 22, 2023
Refereed Anonymously
Author Biographies
Wangoo Lee is a PhD Candidate at Fox School of Business,
Temple University. Wangoo’s research focuses on tech-mediated
services and experiences, and theory testing/building with neces-
sary causality and necessary condition analysis.
Lu Lu, PhD, is an Associate Professor at the School of Sport,
Tourism and Hospitality Management at Temple University. Dr.
Lu’s research mainly focuses on service interactions and marketing,
and food and beverage decision-making.