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DOI: 10.4018/JGIM.20211101.oa27
Volume 29 • Issue 6 • November-December 2021
This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License
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Changqin Yin, School of Management, Huazhong University of Science and Technology, Wuhan, China
Huimin Ma, School of Management, Huazhong University of Science and Technology, Wuhan, China
Qian Chen, School of Management, Huazhong University of Science and Technology, Wuhan, China
Yeming Gong, EMLYON Business School, Écully, France
Xiaobing Shu, School of Public Administration, Central China Normal University, Hubei, China
While the perceived waiting time can undermine user evaluation and cause application abandonment,
there is little scientific research on waiting in mobile applications. This paper incorporates three
mobile interactivity features (ubiquitous connectivity, active control, and responsiveness) into the
model and examines the mediating role of cognitive absorption and the moderating role of perceived
procedural justice between these features and perceived waiting time in a short-waiting application.
The researchers empirically examine the model using data from 468 users of the ride-sharing mobile
application. The results reveal that mobile interactivity can directly and indirectly (via cognitive
absorption) lead to more tolerance in perceived waiting time. The findings elicit several implications
for theories and practice.
Cognitive Absorption, Mobile Application, Mobile Interactivity, Perceived Procedural Justice, Perceived Waiting
Time
Today, wait time reduction is a global challenge in ride-sharing mobile applications (Luo et al., 2015;
Peng et al., 2020). However, there is little scientific research on understanding and managing mobile
waiting. Ride-sharing applications are booming around the world. As of December 2018, China has
190 million ride-sharing users, and the market is still expanding (Peng et al., 2020). In Germany,
“Mitfahrgelenheit”, one of the largest ridesharing websites, offers nearly 900 thousand ride-sharing
options online in real time. In 2017, Moscow had one sharing car for every 5,000 residents, while
Washington led with one sharing car for every 692 residents, and it is still spreading around the
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world. Thereby, reducing wait time for ride-sharing applications has become a global marketing and
e-commerce problem.
Perceived waiting time is critical in short-waiting applications, such as ride-sharing applications.
Public data show that the arrival time of their services is usually within an hour, and the majority of
the application users wait less than 15 minutes (Ai De Chu Xing, 2018). Because of the short arrival
time of the services, customers may constantly check the application’s interface and keep an eye
on the time of delivery (Kondrateva et al., 2020). For example, when customers use a ride-sharing
application to take a taxi, from the time they click on the interface of the application to the beginning
of the service, they may constantly check their mobile phones and keep an eye on when they purchased
services will arrive. In this case, users keep watching the screen and it is easy to notice and record
the passage of waiting time, the longer they wait the less satisfaction they feel and is more likely
to lead user’s application abandonment behavior (Voorhees et al., 2009; Khedhaouria et al., 2016).
So, the management of waiting time in short-waiting applications is important. However, previous
studies ignore this point.
Waiting is a ubiquitous and inseparable part of people’s life and service experience, which
negatively influences people’s overall evaluations of products, services, and stores (Voorhees et al.,
2009; Al-Otaibi et al., 2018). It has been confirmed that waiting can bring pressure and dissatisfaction
to customers (Hamidi & Moradi, 2017; Li & Chen, 2019). The actual waiting time exists objectively
in the transaction, while perceived waiting time is the subjective judgment of the customer. Previous
studies mainly focus on how to reduce the actual waiting time of customers, such as opening more
cash registers during peak hours, hiring more employees during peak season, and queuing to provide
services (e.g., Dennis & Taylor, 2006; Li & Chen, 2019). In some cases, it is difficult to reduce the
actual waiting time, such as during the peak of consumer spikes in consumption. Sometimes, the
decrease in actual waiting time does not affect satisfaction, but the perception of waiting time does
(Thompson et al., 1996).
Scholars have found that perceived waiting time may be more useful and more controllable.
Perceived waiting time is more suitable in measuring the length of waiting time and has a greater
impact on satisfaction than the actual waiting time (Rose, 2005). Thus, the previous researchers
propose various solutions to reduce perceived waiting time, such as adding various fillers, text, music,
animation, and games on the website’s interactive interface, which have been proved to significantly
reduce the perception of the wait (Lee et al., 2012; Oh & Sundar, 2015; Colleen, 2015). However,
the current research on the influence of interactive interface on perceived waiting time focuses on
websites (Mou et al., 2020), and there is a lack of research on mobile services.
Compare with the website interactive interface, the mobile interactive interface has distinctive
attributes. First, it allows the user to contact the interactive interface anytime and anywhere through
the mobile device. This is the first feature of mobile interactivity: ubiquitous connectivity. Second,
when using a mobile application, a mobile device is convenient for the user to actively control
the information search and acquisition in the application at any time. This is the second feature of
mobile interactivity: active control. Finally, mobile applications’ dialogue technology with people
is becoming more and more humanized, and the response speed of the applications is getting faster
and faster. This is the third feature of mobile interactivity: responsiveness. Therefore, the research
results of waiting time in websites cannot be directly used in terms of mobile waiting. Although the
role of web interactivity in reducing user-perceived waiting time has been confirmed by scholars, the
impact of mobile interactivity on user-perceived waiting time is still to be verified.
Moreover, previous research focuses on the direct impact of technology and psychology on
perceived waiting time. Few scholars study the interactivity as an environmental stimulus that affects
the user’s psychology. The Stimulus-Organism-Response model (Animesh & Pinsonneault, 2011)
provides a complete framework for environmental stimuli to influence the user’s psychological
experience. As a stimulus in this framework, there is little relevant research on whether the three main
features of mobile interactivity (Ubiquitous connectivity, active control, responsiveness) will affect
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the users’ psychological experience and thus affect users’ evaluation of waiting time. In the previous
studies, scholars have shown that perceived procedural justice can affect customers’ cognitive state
and psychological experience, and the effects of waiting time will be attenuated when waiting time
procedures are perceived more equitable in the service recovery area (Voorhees and Baker, 2009).
However, the important role of perceived procedural justice has not been fully studied in mobile
waiting. Thus, to fill the research gaps above, this study answers the two questions:
(1) Does mobile interactivity reduce the user’s perception of waiting time? If so, what internal
mechanism leads to a decrease in perceived waiting time?
(2) Does perceived procedural justice play an important role in mobile waiting?
To address the research questions, the researchers develop a research model based on the Stimulus-
Organism-Response framework and propose hypotheses by focusing on the flow theory and justice
theory. The flow theory and justice theory will explain the psychological process of the effect of
mobile interactivity on perceived waiting time. The researchers then collect samples from customers
of ride-sharing applications to examine hypotheses. The results show that the three features of mobile
interactivity have a significant negative effect on perceived waiting time, and cognitive absorption
plays a mediating role. Further, perceived procedural justice positively moderates the relationship
between cognitive absorption and perceived waiting time.
The contributions are as follows: (1) This paper fills the gap of mobile service waiting time
research from the perspective of mobile application system designs. (2) The researchers extend the
research field of the justice theory to the mobile service area and find the key factors that affect the
function of flow theory in mobile services. (3) The researchers explore the influence of the internal
mechanism of short-waiting application system designs on user-perceived waiting time.
Although the S-O-R model originates from environmental psychology, it was introduced into the
retailing environment as early as 1994. Baker (1994) conceptualizes the stimuli as environmental
cues in the retail environment, which include surrounding conditions, functional/esthetic design
factors, and social factors. Researches of the S-O-R model in the retailing context show that the
stimulus of the retailing environment affects the internal state of the customers, which in turn drives
their behavior (Animesh et al., 2011). When used in the online commerce environment, the stimuli
refer to the design features of the interface that the customers interact with (Eroglu & Machleit,
2010). The inner states represent the emotional and cognitive states of customers, containing their
perceptions, experiences, and evaluations of the service (Jiang et al., 2010). The responses refer to
customer attitude or behavior, such as purchase intention, product evaluation, service evaluation, and
satisfaction (Sautter & Hyman, 2004).
The researchers use the S-O-R framework as the theoretical framework of our research for two
reasons. (1) The S-O-R framework has been widely used as a theoretical framework in the online
commerce environment to explain how website characteristics affect customer attitudes and behaviors
(Animesh & Pinsonneault, 2011). (2) The previous research shows that the S-O-R framework provides
a concise and clear framework to examine the effects of technological features as environmental
stimuli on user intention and behavior (Luqman et al., 2017). In our research, ride-sharing mobile
applications are artifacts with unique mobile interactive technological features, and these features
surround customers and stimulate their intentions and behaviors. So the researchers apply the S-O-R
framework to ride-sharing applications, to identify customer’s psychological experience under the
mobile interactive features and the consequence of such experience.
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A stimulus is something that provokes action (Bagozzi, 1986). Baker (1994) conceptualized the stimuli
as environmental cues in the retail environment. According to Baker (1994), environmental cues
include surrounding conditions, functional/esthetic design factors, and social factors. The customers’
attitude toward the website and their behaviors can be predicted by studying the stimulating factors
on them (Baker, 1994; Changchit, 2018; Hong, 2019). In our article, mobile interactivity is taken
to represent the “stimuli” because it is one of the functions design factors, which will affect the
customer’s intention and behavior. Though there is no uniform definition of interactivity in academia,
it is generally agreed that interactivity is the technical ability to create an interactive intermediary
environment for people and machines based on communication technology, in which people and
machines can communicate and exchange information (Kiousis, 2002).
Website interactivity mainly consists of three concepts: control, communication, and
responsiveness. Control means the customer’s degree of free control of the software platform, and it
is related to the individual’s contextual perception ability (Fortin, 2005). Communication refers to
the individual’s participation and the participatory process (Liu & Shrum, 2002). Responsiveness
refers to the synchronicity and connectivity of the software platform (Deighton & Kornfeld, 2008).
The mobile application’s significant feature of interactivity is mobility. Besides, there are four
important features of mobile interactivity (Lee, 2005): connectedness, personalization, ubiquitous
connectivity, and contextual offer. Connectedness emphasizes on providing consumers with
unprecedented fluid interactivity. Personalization focuses mainly on creating customer profiles and
developing interactivity relationships to meet individual needs. Ubiquitous connectivity is related
to consumers, who get information or mobile services anytime and anywhere. The contextual offer
focuses on the extent to which marketers provide customers with the best information or services
related to their backgrounds based on their personal and time information (Yang & Lee, 2017).
Ubiquitous connectivity contains features of communication, synchronicity, connectivity,
mobility, and contextual offer (Yang & Lee, 2017). It not only highlights the characteristics of mobile
interactivity but also inherits and develops the interactivity features of websites. Whether it is mobile
interactivity or website interactivity, users want to take the initiative to control, and this includes
personalized features, which means that users will control the interface according to their personality
and habits. Thus, the researchers argue that the second dimension of mobile interactivity is active
control. After the active control, more attention is paid to the speed and efficiency of the interface
response, so the researchers presume that mobile interactivity has three dimensions: ubiquitous
connectivity, active control, and responsiveness.
The ride-sharing application is an artifact with unique mobile technological features, the customer
interacts with the application system through the mobile interactive features and forms an assessment
of these features. Ubiquitous connectivity, active control, and responsiveness are the three features
of mobile interactivity, they capture various aspects of a customer’s interactions with the mobile
interactive interface that include customer and the technology factor. They do not only represent the
mobile technology that supports interactions between customers and technology, but also the stimuli
that can affect customers’ intentions and behaviors.
The S-O-R model shows the impact of environmental stimuli on customer attitudes or behaviors,
through the customer’s experience of psychological feelings (Kamboj & Sarmah, 2018). In the
online environment, the customer’s experience of feelings is the process of psychological activities
of customers after being stimulated by the technological features, which they interact with (Eroglu
& Machleit, 2010).
The psychological activities refer to the individual’s cognitive and emotional systems, containing
an individual’s perceptions, cognitive networks (Jiang et al., 2010). The customers’ psychological
process in an online environment similar to the flow theory (Jiang et al., 2010). Cognitive absorption
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is developed from the theory of flow, and it describes a state of deep participation in software (Lin,
2009). Bozoglan and Demirer (2014) suggest that cognitive absorption is relevant to the intrinsic
motivation of the user environment of information technology. It is a crucial cause of IT acceptance
behavior and is often used to study virtual communities (Shoham & Brenčič, 2004). Hence, cognitive
absorption is closely related to Internet use and can be used to explain the internal state of customers
in the usage of mobile applications. Cognitive absorption has two dimensions: temporal dissociation
and focused immersion.
(1) Temporal dissociation is considered to be “the unable to record the passage of time when
participating in the interaction (Tan et al., 2015. p747),” in mobile commerce, which refers to an
individual’s inability to notice the passing of time while deeply involved in the mobile interactive
interfaces (Agarwal & Karahanna, 2000).
(2) Focused immersion is defined as the degree to which an individual participates in a task or object
(Hess et al., 2006), referring to a fully engaging experience, in which other attentional needs
are largely ignored (Agarwal & Karahanna, 2000). It is a highly engaging experience, in which
people tend to ignore other disturbances at the time, indicating that an individual’s full attention
resources are concentrated on a specific task, thereby reducing other cognitive burdens.
In an online environment, customers expose to various technological features (Zha et al., 2014), which
affect their intentions and behaviors. In our research, the perceived waiting time is the customers’
reaction after interacting with the mobile ride-sharing application, and it can be predicted by the
technological features, such as ubiquitous connectivity, active control, and responsiveness.
Perceive waiting time refers to consumers’ subjective judgment of the elapsed time (Shchytko,
2018). The customers’ perception of the wait time changes with the external environment and
individual demand factors (Krauser, 2015; Cao et al., 2019). Since personal factors are beyond the
control of the business, it is only possible to control the situational factors to influence the perceived
waiting time (Lee & Chen, 2019), and customers can reduce it by introducing environmental stimuli
(Krauser, 2015). The more stimuli, the faster the perceived time pass. Under the background of
mobile commerce, consumers judge and evaluate the perception of waiting time under the dual
influence of mobile interactivity and personal reasons. In a short-waiting application, the length of
the perceived waiting time is an important evaluation of customers after they are interacting with the
interactive interface (Thompson et al., 1996), which is influenced by the interacting features. With
the technological features customers are feeling less stressed about the waiting, thus perceive the time
be shorter (Zakay, 1989; Shchytko, 2018). Hence, the researchers take the perceived waiting time as
a response to the customer after interacting with mobile application interfaces.
The research model is shown in Figure 1.
In the short-waiting application context, consumers interact with mobile commerce platforms
through the interactivity of the interface of mobile devices. These mobile interactive features may
affect consumers’ psychological processes and lead to different attitudes and behaviors (Jiang et
al., 2010). Therefore, the interactivity of the mobile application interface can be considered as
environmental stimuli felt by consumers during the mobile commerce process (Liu & Shrum, 2009).
Mobile interactivity allows users to mobilize their perception, movement, and cognitive abilities by
interacting with the content provided by the interface (Alalwan, 2018), bringing a closer distance
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between the customer and the device. It provides a natural, real, and easy-to-operate interface that
makes content more accessible to users when browsing (Oh & Sundar, 2015), and the user experience
more heartwarming (Luo et al., 2016). Hence, mobile interactivity can create more positive attitudes
and higher cognitive absorption (Oh & Sundar, 2015).
The ubiquitous connectivity is the convenience and pleasure that the device carrying the mobile
application itself brings to the customer, which is the feeling that the computer can’t bring. To a
large extent, convenient, fast, and ubiquitous connectivity is the magic weapon that makes a mobile
application stand out. Mobile applications move with mobile devices, and customers can use mobile
applications no matter when and where they are. Therefore, the mobile device itself is an attraction,
and combine with mobile applications that can work on any mobile device, it is convenient and fast,
and naturally attracts the attention of customers. When customers use mobile applications, they can
control and interact with the application interface anytime, anywhere. Ubiquitous connectivity will
shift the user’s attention from the waiting time to interacting with the mobile interface to create a
sense of focused immersion (Oh & Sundar, 2015), creating a deeper customer involvement. When
interacting with a mobile application, a customer may forget to pay attention to the time as they focus
on the interactive interface, thus producing a sense of temporal dissociation, which is consistent with
the competition for attention theory (Kahneman et al., 1973) and resource-allocation theory (Lee et
al., 2017). One’s energy is limited. When energy is occupied by one thing, it is difficult to concentrate
on another thing. Therefore, the researchers hypothesize:
H1a: Perceived ubiquitous connectivity will positively affect temporal dissociation.
H1b: Perceived ubiquitous connectivity will positively affect focused immersion.
Active control involves the customer’s perception of the degree of control over the mobile
application interface. For example, the customer can choose the interface according to his or her
preference, block the information that is not like, and the system will record the customer’s choice,
and push the information of interest to the customer when the interface is opened next time. The
intrinsic motivation of the flow theory holds that individuals pursue what they do for their inner
satisfaction. When customers perceive a high degree of control over the mobile application interface,
they will feel a sense of satisfaction and feel integrate with the interactive interface, and this feeling
will stimulate the customer’s psychological experience and generate a kind of cognitive involvement,
just like people who are immersed in mobile games. In this situation, customers are not interested in
Figure 1. Conceptual model
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other things at all. In such a state of self-satisfaction (Faiola, 2013), customers will feel empowered
and controlled and in harmony with their surroundings (Tan & Lee, 2015). At this time, people will
be immersed in the interactive interface, thus losing their sense of time (Csikszentmihalyi, 1988) and
thereby creating a sense of temporal dissociation. Therefore, the researchers hypothesize:
H2a: Perceived active control will positively affect temporal dissociation.
H2b: Perceived active control will positively affect focused immersion.
As for the responsiveness, the response of the platform is related to whether it can attract
customers’ attention. When using the application, the customer has questions or needs to consult,
can have a way to talk to the interface system, and the interface system can communicate with the
customer in a timely and correct way. For example, the customer temporarily replaces the boarding
address, and after the interface system inputs the information, the interface can respond to the customer
and replace the address for the first time. When the customer triggers the alarm system, the interface
system can respond and give the customer help for the first time. Customers may give a negative
evaluation in the long process of waiting for the response of the platform and turn their attention
elsewhere. However, when a platform responds to customers’ instructions in a timely, synchronous,
and with a quick manner, customers’ attention will be attracted by the interactive interface, and they
will be immersed in conversations with the interface, thus reducing their attention to the waiting time
and creating a sense of temporal dissociation, which is consistent with competition for attention theory
(Oh & Sundar, 2015). A customer’s limited attention resources are assigned to non-time information
processing and cognitive timers, reducing the ability of customers to track time. Therefore, the
researchers hypothesize:
H3a: Perceived responsiveness will positively affect temporal dissociation.
H3b: Perceived responsiveness will positively affect focused immersion.
Temporal dissociation is the inability to notice the time. Researchers find that distractors will shift the
customers’ attention from the wait, inducing temporal dissociation, and thereby reducing the perceived
waiting time (Shchytko, 2018). The greater it is caused by the distractors, the more it negatively affects
customers’ perception of waiting time in an online environment (Luo et al., 2015). It is consistent
with the resource-allocation theory (Zakay et al., 2010). Since the customer’s attention is limited,
adding other non-temporal visual or auditory stimuli during the waiting can distract their attention
and make customers unable to make a correct perception of the passage of time. In the context of
short-waiting mobile applications, customer’s attention is occupied by the interactive interface, and
they may be unable to register the passage of time and perceive time goes by quickly, which will
result in a reduction of the perceived waiting time (Lee et al., 2017). The direct negative effect of
temporal dissociation on the perception of waiting time has been shown by Lee and Chen (2012).
Therefore, the researchers hypothesize:
H4a: Temporal dissociation negatively affects perceived waiting time.
Focused immersion is a high involvement experience, in which people are usually unable to
notice other disturbances at the interaction (Luo & Wang, 2015). Some teenagers’ online gaming
addictions are the typical example: with focus immersion they can forgets the time (Wan & Chiou,
2006). In online commerce environment, when customers are highly involved in interacting with the
interface, they will forget themselves and lose their sense of time and themselves (Csikszentmihalyi,
1988). They get completely immersed in the interaction, thus ignoring other things around them,
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including time, and thereby reducing the perceived waiting time. Lee et al. (2012) research the online
waiting problem and conclude that focused immersion negatively affects the perception of the wait.
The prior research revealed that there would be a main effect of immersion on the perception of time,
and the customers’ prospective duration judgment will decrease as the level of immersion increase
(Ledbetter, 2016). Lee et al. (2017) use temporal information and distractor to distract customers’
focused attention on waiting, proving the negative influence of focused immersion on the perception
of waiting time once again. Therefore, the researchers hypothesize:
H4b: Focused immersion negatively affects perceived waiting time.
The mediating role of cognitive absorption. (1) Customers interact with the interactive interface,
and their intention will be occupied by technological features, such as ubiquitous connectivity, active
control, and responsiveness. Then fewer pulses pass through the cognitive counter, and they may
be unable to record the passing of time and perceive a highly time distortion (Lee et al., 2017), that
is, a sense of temporal dissociation. They may further perceive that time passed very quickly, thus
reducing the perceived waiting time. (2) It is the same as the game users who experience higher levels
of game immersion estimate perceive time to be lower (Wan & Chiou, 2006). When the customer
interacts with a short-waiting application’ interface, they will feel a highly interacting immersion.
That is the feeling of focused immersion, which will make the perceived waiting time shorter (Li
& Yuen, 2015). From what we have discussed above, the three interactive features will influence
customers’ cognitive absorption, and cognitive absorption will reduce the perceived waiting time.
Therefore, the researchers hypothesize:
H5a: Ubiquitous connectivity indirectly affects perceived waiting time through the mediation of
temporal dissociation and focused immersion.
H5b: Active control indirectly affects perceived waiting time through the mediation of temporal
dissociation and focused immersion.
H5c: Responsiveness indirectly affects perceived waiting time through the mediation of temporal
dissociation and focused immersion.
Perceived procedural justice refers to the customer’s sense of fairness on the rules and procedures
used by the platform in dealing with the problem (Chou et al., 2016), as well as the evaluation of the
procedures and systems that determine customer outcomes (Luo, 2007; Gohary et al., 2016). Previous
studies show that procedural justice in online commerce is particularly significant when awakening
cognitive reactions because service companies or sales representatives and customers typically do
not have the chance to meet in reality (Voorhees et al., 2009). A customer’s justice perception as a
specific belief determines the psychological responses of customers’ overall evaluation (Chih et al.,
2017). Temporal dissociation and focused immersion are two kinds of cognitive states that customers
display after interacting with a short-waiting application when wait. These cognitive states are affected
by fair perception. When customers feel a highly fair procedure, they are more inclined to give a
good evaluation (Choi et al., 2016). In this research, the overall evaluation is the perception of time.
Besides, social justice theory suggests that when a long wait is combined with an unfair procedure,
customers’ dissatisfaction increases, and then causing a multiplicative increase in negative evaluations
of the wait (Voorhees et al., 2009). In this situation, even a short wait can make them feel long. But
when customers feel that the procedures of wait are fair, they will feel waits are not so difficult to
accept (Voorhees et al., 2009). This view is consistent with the appraisal theory. Appraisal theory
holds that the customer consciously evaluates threats when they in a waiting situation if the waiting
is evaluated as fair, they will not perceive much threat to them and will not negatively evaluate the
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waiting (Lazarus, 1999; Voorhees et al., 2009). However, when customers perceive an unfair waiting
procedure, the assessment of threats in the waiting will increase and the customer will distress and
the perceived waiting time will increase accordingly. Customers believe they are treated more fairly
when they perceive the provider follows a company formal procedure (Goodwin, 1990). In the context
of waiting for a ride-sharing application when this wait properly complies with the ride-sharing
application platform procedures, the customer will also consider themselves to be treated fairly.
Here, the moderate function of perceived procedural justice plays a role. Therefore, the researchers
hypothesize:
H6a: Perceived procedural justice acts as a moderator between temporal dissociation and perceived
waiting time.
H6b: Perceived procedural justice acts as a moderator between focused immersion and perceived
waiting time.
The Likert seven-point scale was used to compile the questionnaire and obtain samples through the
questionnaire survey of users of mobile ride-sharing applications. The indicators used to measure
mobile interactivity were adapted from Lee (2005), Yang and Lee (2017), Liu (2003), and Gao (2010).
The indicators used to measure cognitive absorption were adapted based on Saadé and Bahli (2005)
and Tan (2015), and the indicators used to evaluate perceived procedural justice were adapted based
on del Rio-Lanza (2009). Finally, the indicators used to evaluate perceived waiting time were adapted
based on Voorhees et al. (2009). Measurement items are seen in Table 3.
All scales used are mature scales, and two doctoral students with high English proficiency in
the field were invited to translate the English scale into Chinese, then translate it from Chinese into
English, repeat the operation, repeatedly correct the ambiguous words, modify the scale according
to the practical characteristics and conditions of the ride-sharing application and revise it. According
to the characteristics of users of the mobile ride-sharing application, gender, age, and education level
were selected as control variables (Lee et al., 2017).
First, we selected users who had used ride-sharing applications. Second, we set up reverse
questionnaires to test the effect of respondents’ responses. Besides, we motivated the respondents
with cash and small gifts to answer questions. Finally, although the questionnaires all adapted from
mature scales, the researchers still pretested before the formal survey.
The researchers collected questionnaires offline in the pretest. 100 questionnaires were randomly
distributed to ride-sharing application users, and 92 questionnaires were collected. Nine invalid
questionnaires were eliminated for the reasons either that questionnaires are incomplete, or that
respondents are not the users of ride-sharing applications. The reliability and validity of the 81
questionnaires were tested. In the pretest, the KMO value of mobile interactivity, cognitive absorption,
perceived waiting time, and perceived procedural justice are 0.859, 0.746, 0.836, 0.822, respectively,
all of which are larger than 0.5, and the probability of concomitant ball test is 0 (Nunnally, 1978),
indicating that the results of this investigation are suitable for factor analysis. The factor load of each
item of the variables is more than 0.5 (Chin, 1997), and there is no significant cross load problem.
Therefore, the scale has a good validity structure. Then the researchers conducted a formal survey.
In the formal survey, 600 questionnaires were randomly sent out among ride-sharing application
users online and offline. We motivated online respondents with cash and offline respondents with
small gifts offline to answer the questions. Our ANOVA test showed no significant difference between
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online and offline data. To mitigate the risk of homologous errors, the survey was conducted three
times, two months apart, for a total of four months, and 569 were eventually recovered, with a response
rate of 94.8%. Then, we eliminated the invalid questionnaires, including the questionnaires which
are not filled by ride-sharing applications users, the questionnaires with missing data. Finally, 468
effective data were screened out. The users of ride-sharing applications are mainly young people
under 35 years old, accounting for 98.5%. Among the respondents of the questionnaire, 86.7% were
the users of the Didi and Uber taxi application, followed by the Quick taxi 8.93% and Shenzhou
special car 2.68%. Demographics of the research samples are in Table 1.
Finally, to avoid the threat of the common method bias, Harman’s single factor test was
implemented to examine it. The most significant factor in the seven factors extracted from the data
accounted for only 36.398%. So the common method bias is not a threat to this study.
In this research (see in Table 3), the Cronbach’s alpha values of the constructs are above 0.8, the
composite reliability (CR) of the constructs is between 0.80 and 0.95, all above the recommended
level of 0.7, thus indicating that our scale has good reliability. The validity of our scale shown in Table
3, all the standardized factor loadings were higher than 0.7 (p < 0.001), and all the AVE values were
higher than 0.6, implied a good convergent validity. Then the discriminant validity was evaluated
by comparing the AVE and the correlation between a variable and other variables. Table 2 shows
Table 1. Demographics of the research samples (N=468)
Demographic
profile Categories Frequency Percent (%)
Gender
Male 170 36.3
Female 298 63.7
Age
<25 173 36.9
25-35 285 61.6
35-45 8 1.1
>45 2 0.4
Education
Junior college and below 88 18.75
Undergraduate 125 26.79
Master graduate student 242 51.79
The doctor and above 13 2.68
Service application
Didi and Uber 406 86.7
No.1 special car 0 0
EasyGo 5 0.89
Ttyongche 5 0.89
A quick taxi 42 8.93
Shenzhou special car 14 2.68
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that the square root of the AVE for each construct is larger than the correlations, thus indicating that
discriminant validity is acceptable.
A structural model established in AMOS (23.0), the results as shown in Figure 2 and Table 4. The
path coefficient between ubiquitous connectivity and temporal dissociation (β = 0.243, p < 0.001)
supports H1a. The path coefficient between active control and temporal dissociation (β = 0.163, p
< 0.01) and the path coefficient between responsiveness and temporal dissociation (β = 0.095, p
< 0.05) support H2a and H3a. Next, the path coefficient between ubiquitous connectivity, active
control, responsiveness, and focused immersion is (β = 0.161, p < 0.01), (β = 0.338, p < 0.001), (β
= 0.354, p < 0.001), so H1b, H2b, and H3b are supported. Finally, the effect of temporal dissociation
on perceived waiting time (β = -0.141, p < 0.01) and the focused immersion on perceived waiting
time (β = -0.125, p < 0.01) were both found to have a significant negative effect, thus supporting H3a
and H3b. The explained variances (R2) for perceived waiting time were 67%. The model fit indices,
including χ2/df (2.733), RMSEA (0.061), CFI (0.955); TLI (0.945); AGFI (0.890); NFI (0.931), and
IFI (0.955), implied good explanatory power (Lee, 2012).
Then, Table 5 shows that the direct effect of ubiquitous connectivity on perceived waiting time (β
= -0.594, T = 10.906, p < 0.001) is significant, but when the indirect effect of temporal dissociation
and focused immersion are introduced (β = -0.314, T = 5.657, p < 0.001), the direct effect is reduced,
suggesting temporal dissociation (β = -0.190, T = 3.84, p < 0.001) and focused immersion (β =
-0.404, T = 7.93, p < 0.001) having a mediated effect. Next, Sobel (1982) test is used to further test
the mediated effect. The results indicate that temporal dissociation (Z = 7.59, p < 0.001) and focused
immersion (Z = 7.58, p < 0.001) have a significant mediated effect. The bootstrap 95% confidence
intervals for temporal dissociation (-0.206 to -0.078) and focused immersion (-0.277 to -0.131) do
not contain zero, so the indirect effect exists. Thus, H5a is verified.
Next, Table 5 shows that the direct effect of active control on perceived waiting time (β =
-0.623, T = 10.806, p < 0.001) is significant, but as the indirect effect of temporal dissociation and
focused immersion are introduced (β = -0.330, T = 5.52, p < 0.001), the direct effect is reduced,
which suggests that temporal dissociation (β = -0.219, T = 4.56, p < 0.001) and focused immersion
(β = -0.357, T = 6.62, p < 0.001) have a mediated effect. The Sobel (1982) test was used to further
examine the mediated effect. The outcomes indicate that temporal dissociation (Z = 6.86, p < 0.001)
and focused immersion (Z = 5.26, p < 0.001) have a mediated effect. The bootstrap 95% confidence
Table 2. Results of correlation and discriminant validity testing
UC AC RS TD FI PJ PW
UC 0.806
AC 0.467** 0.775
RS 0.419** 0.477** 0.933
TD 0.395** 0.351** 0.328** 0.794
FI 0.415** 0.472** 0.528** 0.384** 0.825
PJ 0.096* 0.074 0.054 0.011 0.092* 0.979
PW -0.532** -0.538** -0.656** -0.440** -0.548** -0.228** 0.825
Note:1. ***p<0.001, **p<0.01, *p<0.05;
2.UC: Ubiquitous connectivity; AC: Active control; RS: Responsiveness; TD: Temporal dissociation; FI: Focused
immersion; PJ: Perceived procedural justice; PW: Perceived waiting time.
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Table 3. Measurement model evaluation result
Construct Indicators loading AVE CR Alpha
Ubiquitous
Connectivity
I can access this ride-sharing application anytime for the
necessary information and service 0.767
0.65 0.84 0.878
I can access this ride-sharing application anywhere for
the necessary information and service 0.793
I can use this ride-sharing application “anywhere”,
“anytime” at the point of need 0.802
This ride-sharing application enables me to order taxi
services anywhere at any time 0.726
Active control
I felt that I had a lot of control over my visiting
experiences at this ride-sharing application 0.767
0.60 0.83 0.902
While I was on the ride-sharing application, I could
choose freely what I wanted to see 0.793
While using the ride-sharing application, I had no control
over what I can do on the ride-sharing application
platform
0.802
While using the ride-sharing application, my current
actions will be determined by the kind of experiences I
got in the past
0.726
Responsiveness
This ride-sharing application could respond to my
specific questions quickly 0.929
0.87 0.89 0.930
This ride-sharing application could respond to my
specific questions relevantly 0.936
Temporal
dissociation
Time appears to go by very quickly when I am using the
ride-sharing application 0.748
0.63 0.80 0.834Time flies when I am using the ride-sharing application 0.839
Sometimes I lose track of time when I am using the ride-
sharing application 0.787
Focused immersion
When I am using the ride-sharing application I can block
out most other distraction 0.855
0.68 0.81 0.865
While using the ride-sharing application, I am absorbed
in what I am doing 0.821
While using the ride-sharing application, I am immersed
in the service I am performing 0.807
Perceived
procedural justice
I think my problem in the ride-sharing application was
resolved in the right way 0.991
0.96 0.95 0.990
I think this ride-sharing application has good policies
and practices for dealing with problems 0.989
Despite the trouble caused by the problem, this ride-
sharing application was able to adequately respond to me 0.973
This ride-sharing application proved flexible in solving
the problem 0.968
Perceived waiting
time
Short 1 2 3 4 5 6 7 Long 0.812
0.68 0.88 0.894
Unacceptable 1 2 3 4 5 6 7 Acceptable 0.876
Brief 1 2 3 4 5 6 7 Lengthy 0.814
Reasonable 1 2 3 4 5 6 7 Unreasonable 0.787
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Figure 2. Results of the research model tests Note:***p < 0.001, **p < 0.01, *p < 0.05
Table 4. Standardized parameter estimates
Structural path Path analysis T value Result
H1a: Ubiquitous connectivity ---> Temporal dissociation 0.243*** 5.192 Supported
H2a: Active control ---> Temporal dissociation 0.163*** 2.617 Supported
H3a: Responsiveness ---> Temporal dissociation 0.095* 2.36 Supported
H1b: Ubiquitous connectivity ---> Focused immersion 0.161** 2.879 Supported
H2b: Active control ---> Focused immersion 0.338*** 4.355 Supported
H3b: Responsiveness ---> Focused immersion 0.354*** 7.000 Supported
H4a: Temporal dissociation ---> Perceived waiting time -0.141** 2.847 Supported
H4b: Focused immersion ---> Perceived waiting time -0.125** 3.065 Supported
Note:***p < 0.001, **p < 0.01, *p < 0.05
Table 5. Results of mediation test
Constructs Path coefficient
Mediation
existence
Independent
variable (X)
Mediator
(M)
Dependent
variable
(Y)
X—Y X—M
X+M—Y
X—Y M—Y
UC
TD
PW -0.594***
0.485***
-0.314***
-0.190***
Partial
FI 0.466*** -0.404***
AC
TD
PW -0.623***
0.433***
-0.330***
-0.219***
Partial
FI 0.557*** -0.357***
RS
TD
PW -0.740***
0.384***
-0.526***
-0.205***
Partial
FI 0.589*** -0.230***
Note:(1) ***p < 0.001, **p < 0.01, *p < 0.05;
(2) UC: Ubiquitous connectivity; AC: Active control; RS: Responsiveness; TD: Temporal dissociation; FI: Focused
immersion; PW: Perceived waiting time.
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intervals for temporal dissociation (-0.193 to -0.066) and focused immersion (-0.312 to -0.148) do
not contain zero, so there is an indirect effect. Therefore, H5b is verified.
Furthermore, the direct effect of responsiveness on perceived waiting time (β = -0.740, T =
15.28, p < 0.001) is significant, but when the indirect effect of temporal dissociation and focused
immersion are introduced (β = -0.526, T = 10.29, p < 0.001), the direct effect is reduced, suggesting
that temporal dissociation (β = -0.205, T = 4.64, p < 0.001) and focused immersion (β = -0.230,
T = 4.63, p < 0.001) have a mediated effect. The Sobel (1982) test is used to further examine the
mediated effect. The results indicate that temporal dissociation (Z = 9.39, p < 0.001) and focused
immersion (Z = 6.04, p < 0.001) have a mediated effect. The bootstrap 95% confidence intervals for
temporal dissociation (-0.154 to -0.049) and focused immersion (-0.256 to -0.102) do not contain
zero. Hence, H5c is also supported.
Finally, the moderate influence of procedural justice on the relationship between temporal
dissociation and perceived waiting time is significant (β = 0.012, p < 0.05), showing that H6a is
supported. The moderating effect is shown in Figure 3(left). The moderate influence of procedural
justice on the relationship between focused immersion and perceived waiting time is also significant (β
= 0.017, p < 0.001), wherein H6b is supported. The moderating effect is shown in Figure 3 (below).
Despite being an important part of online waiting, the existing literature rarely mentions the mobile
waiting left a research gap in the information system literature and e-commerce. To fill the gap, the
researchers using the flow theory and justice theory in the framework of the Stimulus-Organism-
Response paradigm to investigate the influence of mobile interactivity on mobile waiting. The results
provide initial evidence that mobile interactivity can directly and indirectly (via cognitive absorption)
affects the customer’s perceived waiting time and these effects are moderated by perceived procedural
justice.
Specifically, the research first demonstrates that mobile interactivity can directly reduce the
perceived waiting time. Ubiquitous connectivity, active control, and responsiveness do not only
add value to customer’s wait but also engage customers to the wait, and thereby reducing perceived
waiting. Second, the researchers demonstrate how mobile interactivity influence perceived time
through cognitive absorption. Specifically, the customers will experience a significant sense of
temporal dissociation and focused immersion when interacting with the interactive interface and
Figure 3. The moderation effect Note: TD: Temporal dissociation; FI: Focused immersion; PJ: Perceived procedural justice.
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reducing perceived waiting. Third, the results also confirm that perceived procedural justice is a
crucial moderate factor in manage mobile waiting.
The theoretical implications as follows:
First, the rapid development of global mobile information technology promotes the emergence
of mobile commerce and mobile applications, and the research on the waiting problem of mobile
applications not only fills the gap of wait management in mobile commerce but also makes a certain
contribution to the further development of global mobile information technology and the prosperity
of mobile commerce. (1) This paper contributes to the information system literature development by
presenting a valuable tool - mobile interactive system designs, as a way to manage the customer’s
perceived waiting time in the mobile application. (2) This paper contributes to the m-commerce
theoretical development by providing a strong theoretical explanation for the internal mechanism of
the influence of mobile interactivity on perceived waiting time.
Second, by systematically examining online wait in a mobile commerce environment, the
researchers extend the theoretical boundaries of online wait research from e-commerce to m-commerce.
Although many scholars study waiting and make great contributions to e-commerce (Weinberg, 2000;
Dennis & Taylor, 2006), it has not been fully studied in the m-commerce. The researchers develop
and test a model to manage the perceived waiting time in the mobile application environment, and
make significant progress in deepening the m-commerce wait time literature.
Third, this research introduces cognitive absorption, justice, and flow theory into the mobile
commerce environment. It not only expands the application scenarios of these theories, enriches
the theoretical literature, but also helps solve the waiting problem of ride-sharing applications and
promote the prosperity development of mobile commerce. Specifically: (1) We expand the cognitive
absorption research boundary from the website to mobile application and enrich the cognitive
absorption theoretical advancement by confirming its new antecedents (ubiquitous connectivity,
active control, and responsiveness). (2) This study extends the research boundary and enriches the
literature of justice theory by introducing it into the mobile commerce environment. (3) This study
deeply analyzes the key factors that affect the function of flow theory in waiting in mobile commerce
is cognitive absorption, which mainly refers to temporal dissociation and focused immersion.
The practical implications are as follows:
First, the results of this study on mobile ride-sharing applications are not only applicable to China,
but also have the potentials to be applied to a range of countries because there is a waiting problem
for ride-sharing application in many countries. Such as Yandex Taxi in Russia, Uber, Sidecar, Zipcar,
and Lyft in the United States, Blablacar in France, and Ola in India. Waiting is also a problem in these
ride-sharing applications, the results are also applicable to these ride-sharing applications. Therefore,
the role of the interactive interface in reducing perceived waiting time is also applicable to the mobile
commerce environment and information systems of other countries in the world.
Second, the results confirm that mobile interactive technological features can directly reduce the
customers’ perceived waiting time. Therefore, the researchers propose: (1) Developers of short-waiting
application systems should pass the full range of equipment testing, ensure that customers can use
the application unimpeded in different operating systems and different types of mobile devices, and
ensure the connectivity of different interactivity interfaces. (2) Designers of application’s interactive
systems should fully consider the customer’s active control, ensure that the information display of
interactive interface is more comprehensive and the information classification is more accurate and
specific, and enable customers to have better control of the information search and acquisition process,
thereby increasing their attention on the interactive interface to reducing their perceived waiting time.
(3) Managers of the mobile service platform should notice the responsiveness. The responsiveness
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involves speed and correctness. Developers of mobile short-waiting applications should design a
program that responds instantly and correctly to the user’s instructions. Ensuring the synchronization
of the user’s input and the response received from the platform to divert customers’ attention and
reducing their perceived waiting time.
Third, this study finds that perceived procedural justice can positively moderate the relationship
between cognitive absorption and perceived waiting time. This finds suggests to the practitioners and
service managers of short-waiting applications, a fair procedure should be placed in the system in
addition to interactive considerations. Specifically, service managers should explain the priorities of
the wait for all the customers and make sure that all of the waiting is fair. The service managers can
appropriately disclose the operating rules of the platform, such as appropriately disclosing the price,
the order of the peak period, the authority of different levels of users, and more. Letting customers
perceive procedural justice and reduce perceived waiting time.
Although this study provides some theoretical and practical implications, it has some limitations:
this study collected data from ride-sharing application users, so they may not fully represent other
short-waiting applications. Furthermore, the current research focuses on the characteristics of the
interactivity features in short-waiting applications. Therefore, it may not be suitable for long-waiting
applications.
The researchers also discuss future research directions: First, it is interesting to study mobile
waiting based on the specific characteristics of different industries, such as takeout applications,
daily fresh applications. Second, it is interesting to choose different research methods to study
mobile waiting, such as case study, experimental study, and mixed-method approach with combining
qualitative and quantitative researches. Third, researchers could study the impact of interactive
interfaces on perceived waiting time before or after waiting. For example, does the influence of
interactive interfaces on perceived waiting time change after customers exiting the interface?
In summary, the researchers reveal the significant influences of mobile interactivity on perceived
waiting time, and also confirm the important role of cognitive absorption and perceived procedural
justice. The researchers provide a basis for a theoretical model of mobile waits and are conducive to
the extension of the waiting theory. This research and also help to the analysis of the system interactive
placements design factors and the understanding of the user’s psychological factors in waiting time.
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Volume 29 • Issue 6 • November-December 2021
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Changqin Yin is a Ph.D. candidate of Management Information Systems at the School of Management, Huazhong
University of Science and Technology. Her areas of interest are focused on information management, customer
behavior, and human resource management.
Huimin Ma (PhD) is a professor of Management Science and Information Management and an executive deputy
director of the E-Commerce Center, Huazhong University of Science and Technology. He has presided over and
participated in the research of the National Natural Science Foundation of China and the National Science and
Technology Support Project. He has presided over dozens of enterprise and government information network
design and construction. He has published more than 70 papers in important journals and academic conferences
and applied for 4 software works.
Qian Chen is a postdoctoral researcher of Management Information Systems at School of Management, Huazhong
University of Science and Technology. She has published a paper in the International Journal of Information
Management.
Yeming Gong is a Professor of Management Science at EMLYON Business School, Lyon, France. He is Director
for Business Intelligence Center. He is PI for 3 national scientific grants. He holds a Ph.D. of Management Science
from Rotterdam School of Management, Erasmus University, Netherlands, an MSc from INSEAD, France, and MSc
of Management Information Systems in HUST, China. He was a post-doc researcher at the University of Chicago,
USA. He was a visiting professor at Cornell University, USA. Prof. Yeming Gong studies Management Science
and Information Management. He has published two books in Erasmus and Springer. He published 46 articles in
journals like International Journal of Information Management, IEEE Transactions on Engineering Management,
Production and Operations Management, IIE Transaction, European Journal of Operational Research, International
Journal of Production Economics, International Journal of Production Research, and Management Decision. Prof.
Gong received “2010 the Best Paper” from IIE, and “Erasmus Scholarship for Teaching” from European Union.
Xiaobing Shu (PhD) is a professor of Human Resource Management at the School of Public Administration,
Central China Normal University. He was engaged in postdoctoral research in the business administration of the
Renmin University of China in 2008, and he is a member of the Chinese society of social psychology and Beijing
psychological society. At the same time, he also is a Vice president of the Hubei society of human resource
management and a director of the human resource research center of central China normal university, academic
leader of master’s degree in enterprise management.
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Yang, S., & Lee, Y. J. (2017). The Dimensions of M-Interactivity and Their Impacts in the Mobile Commerce
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Advances in Psychology, 59, 365-397.
Zha, X., Yang, H., Yan, Y., Liu, K., & Huang, C. (2018). Exploring the effect of social media information quality,
source credibility and reputation on informational fit-to-task: Moderating role of focused immersion. Computers
in Human Behavior, 79, 227–237. doi:10.1016/j.chb.2017.10.038
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