Hindawi
Mobile Information Systems
Volume 2019, Article ID 8968264, 9 pages
https://doi.org/10.1155/2019/8968264
Research Article
Chronotypes’ Task-Technology Fit for Search and Purchase in
Omnichannel Context
Syed Waqar Haider , Guijun Zhuang, Hammad bin Azam Hashmi, and Shahid Ali
School of Management, Xi’an Jiaotong University, Xi’an 710049, China
Correspondence should be addressed to Syed Waqar Haider; snow_berg2001@yahoo.com
Received 16 August 2018; Revised 17 December 2018; Accepted 7 February 2019; Published 25 March 2019
Academic Editor: Amon Rapp
Copyright © 2019 Syed Waqar Haider et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Advances in technology have led to a spurt in tech-savvy consumers displaying increasingly complex behavior. In the past,
consumers concluded their search and purchases at a single physical store. Nowadays, however, they possess a number of digital
devices (desktops and/or mobiles) through which they can switch channels seamlessly to search for information and make a
purchase. There are very few studies that investigate desktop and mobile channels separately; this is perhaps the first study that
examines the effect of chronotypes (evening- and morning-type individuals) on a sample of Chinese university students using
online and mobile channels in the omnichannel process. The results from a sample of 311 digital shoppers (desktops and/or
mobiles) confirm that the mobile channel offers greater task-technology fit to evening-type respondents and desktop channels
present better task-technology fit to morning-type respondents to engage in the omnichannel process. Furthermore, this study
discussed contributions and insights for managers to develop an effective omnichannel strategy.
1. Introduction
In recent times, consumers’ shopping behavior has been
changing because they have gone through an evolutionary
process, moving from brick and mortar models and multichannel to omnichannel [1]. In this regards, the authors
have examined the shoppers’ behavior across multilevel
channels in terms of channel choice, adoption, and usage
[2, 3]. Furthermore, studies have analyzed the important
drivers behind channel choice and usage which include
channel attributes, marketing, psychological, and social
factors [2–4]. However, it has been observed that retailing is
shifting from the multichannel approach to omnichannel
approach because of the introduction of advanced online
channels and new digital channels like mobile channels.
Consequently, there is a growing number of research studies
which investigate mobile commerce, mobile marketing, and
social commerce [5–8]. Therefore, researchers are now
trying to examine the link of customers’ shopping behavior
with omnichannel [9]. The omnichannel behavior refers to
the use of both digital and physical channels, offering
consumers a seamless shopping experience and enabling
them to switch channels almost effortlessly [10]. It provides
customers the freedom to shop anytime and anywhere,
thereby eliminating barriers between channels [11]. Now, it
is normal for customers to shop by using digital devices. For
example, in the United States alone, 49% of retail sales take
place via digital devices (mobile and online) before or during
the shopping journey [12]. Moreover, the literature focuses
primarily on the choice between offline and online channels
for different decision-making stages of shopping [1, 13]. But
a new form can be seen in omnichannel with the advancement in technology; customers are now using a
growing range of devices for shopping such as tablets, iPads,
and smartphones along with computers. Consequently, just
as offline and online channels have been segregated, similarly, computers and portable devices (primarily tablets and
smartphones) can be separated as they possess evidence-rich
differences in terms of portability, screen size, capacity, ease
of use, and personality [14, 15]. These two categories of
devices also differ in the search and purchase tasks performance, as mobiles offer instant, time-saving, location-
2
based contents and image and voice search capability,
whereas desktops offer more detail-oriented information,
better evaluation, and richer contents. Taking this change,
the research distinguishing shopping behavior on desktop/
online (hereinafter both terms used interchangeably) and
mobile channels remains sparse. Exceptions include the
study which is conducted by Holmes et al. [16] in which they
explore consumers’ use of mobile channels in the decisionmaking process and state the differences in the use of online,
offline, and mobile channels, proving that preferences in
channel attributes vary between online and mobile
customers.
In this regard, there is task-technology fit theory “TTF”
introduced by Goodhue and Thompson [17]. The TTF explains optimal choice of technology to perform a task.
Moreover, TTF mostly has been investigated from task and
technology characteristics point of view [18, 19], but how
characteristics of the individual who performs the task effect
this fit has been under explored [20, 21]. So question arises
that how customer’s individual differences can influence the
choice between mobile and online omnichannel to perform
search and purchase task? Because it is evident from the
literature that channel attributes (digital or otherwise) shape
customer’s choice of the channel at different stages of the
decision-making process [22, 23]. Hence, current research
attempts to gain insight into omnichannel behavior using a
different angle, i.e., incorporating individuals’ characteristics
and chronotypes (circadian rhythms). The circadian rhythm
or chronotype refers to differences in individuals’ sleep
patterns and time management, which have an impact on
their behavior. The literature has confirmed that different
chronotypes (evening or morning) exhibit different behaviors and personalities [24]. Morning types prefer to wake
up early and complete the tasks in the morning hours. But
evening types are used to wake up late and prefer to complete
their activities in the evening hours [25]. Initially, the
chronotype construct has been studied in biological [26] and
psychological fields [27]. However, the concept of chronotype has gained less focus in the field of marketing and
business. Exceptions include Hornik and Miniero [28] who
investigated chronotypes in relation to waiting time, service
evaluation, and ad recall performance and Gullo et al. [29]
who studied time of the day in relation to variety seeking
behavior. To the best of the authors’ knowledge, this is the
first study to investigate chronotypes in the omnichannel
context. Therefore, this study aims at addressing the important question that how chronotype (morning vs evening
types) can influence the choice among the mobile and online
channels to perform search and purchase tasks in omnichannel shopping process.
The main gaps filled by this study are as follows. First,
prior studies have focused primarily on multichannel
marketing while there are very few which investigate
omnichannel marketing. While most studies deal with the
omnichannel process in terms of offline and online channels
(PC), this study incorporates mobile channels as well. In
comparison with prior studies, this study examines the two
most significant stages of the decision-making process—search and purchase [30]. In particular, this work
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investigates the collective use of digital and offline channels.
Second, this study analyzes how personal constructs that
contribute to consumer behavior shape the way consumers
are involved in the omnichannel process. Third, by analyzing
chronotype variable in the omnichannel process, this study
provides a deeper understanding of omnichannel consumer
behavior, which will be likely to grab the attention of academicians and businesses alike in future research. Finally,
this study explores, how chronotype affect task (search and
purchase)-technology (mobile/desktop) fit in omnichannel
process.
Furthermore, while prior studies regard omnichannel
engagement from the perspective of consumers in the West,
this study investigates the omnichannel process in the
Chinese context. According to a report by Borak [31], 80% of
online shoppers are between 18 and 35 years old. According
to Team [32], in the second quarter of 2017, in China, online
B2C sales were about US$ 132.6 billion. As most businesses
are struggling to develop an omnichannel strategy for mobile
consumers, this study will help both academicians and
marketers understand the ever-growing base of omnichannel consumers.
2. Literature Review
2.1. Chronotypes. Time is the only finite asset that one
cannot get more of, regardless of money and status. Hence,
there are several disciplines, notably biology and psychology,
which have given much focus on investigating individual
differences in terms of time and sleep management and their
impact on behaviors. Circadian typology (chronotype or
diurnal preferences) consists of three styles of chronotypes,
morning types or “larks;” evening types or “owls;” and
neither type. Owls have a propensity to sleep late and wake
up late; they are more energetic during the evenings and
have uneven sleep-wake schedules. In contrast, the larks
have a tendency to wake up early and like to complete tasks
in the morning. Furthermore, many authors have shown
that chronotype has a significant influence on several variables like depression, well-being, personality, and academic
achievements [33, 34]. They also exhibit differences in terms
of work engagement, educational performance, and so on.
Studies have shown that evening types are more addicted to
the Internet, computer game, and mobile phone [35, 36].
According to Bartel et al. [37], engaging in information and
communications technologies such as the Internet, video
games, laptops, and phones is linked to later bedtimes.
Further, owls keep their devices on for a longer time [38]. All
these attributes are connected to individual differences such
as chronotypes, which could have an influence on consumer
behavior in device choice for search and purchase purpose.
In their research, Hornik and Miniero [28] discovered that
subjects showed a greater ability for ad recall during their
peak hours rather than their off-peak hours. In their study on
users’ behavior on Facebook, Horzum and Demirhan [25]
confirmed that chronotypes affect attitude. In sum, chronotypes have been studied in relation to many technology
and personality behavior-related variables, but it has received less attention in the marketing field. Furthermore, it
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can be interesting and worthy to investigate the link of
chronotypes with task-technology fit in the omnichannel
process.
2.2. Omnichannel. In the recent past, shoppers used to visit
physical stores to search for information and make a purchase simultaneously. Today, consumers are well equipped
with an ample range of digital devices. These devices are
multipurpose in nature and enable information searching
and sharing, comparisons between products, finding the best
offers, and making purchases through the preferred channel
[39]. Now, offline and online shopping is a common practice
among consumers. Moreover, as the number of channels
and touch points (mobile devices, mobile and laptop apps,
and websites) is growing, marketers are presented with
considerable challenges to understand consumer behavior in
such a dynamic environment. This new phase of digital
shopping in which consumers use and switch between
different channels seamlessly is called the “omnichannel”
process [10, 11]. Omnichannel has been gaining popularity
among researchers, for example Kazancoglu and Aydin [40]
have explored consumer behavior and purchase intention in
omnichannel, Blom et al. [9] unfolded that how purchase
behavior is shaped by omnichannel based promotion and
studied omnichannel and willingness to pay, JuanedaAyensa et al.[11] discovered relationship among technology acceptance and purchase intention in omnichannel
context, and Rodrı́guez-Torrico et al. [41] investigated how
personality and individual differences affect the channel
choice of omnichannel shoppers. The omnichannel differs
from multichannel on the bases of integration of channels,
service consistency, and switching ease [1]. The shoppers
may search for a product via their mobile phones, compare it
on their laptops, evaluate it in the physical store, and finally,
make a purchase via their mobile phone. This behavior varies
among consumers as every individual is unique and has a
different personality and preference. Therefore, this research
tends to disentangle the chronotype and omnichannel relation for search and purchase by digital consumers.
2.3. Theoretical Underpinning. Goodhue and Thompson [17]
introduced task-technology fit (TTF) theory, which explains
the optimal choice of technology to perform a task. It has
been widely used by a large number of research studies
including to explain the location-based services and user
mobility [18], online purchase intention [42], and wireless
technology adoption [19]. Moreover, it has been mostly
investigated from task and technology characteristics point
of view, which implies that task characteristics and technology characteristics determine fitness level between tasktechnology, but how the characteristics of the individual who
performs the task affect this fit has been underexplored [21].
The present research shed the light on TTF through this
underexplored angle in omnichannel context that how individuals’ chronotype affect the device preference to perform
search and purchase tasks in e-commerce.
3
3. Research Hypotheses
The patterns of mobile usage among evening and morning
types are different from each other. Demirhan et al. [43]
reported that evening types are more technology oriented
and they are more prone to mobile phone usage. In contrast,
morning types have more focus on traditional media and are
less inclined towards the latest technology [44]. Moreover,
Bartel et al. [37] showed that individuals who are more
inclined towards mobile usage have late night sleeping
habits. And these habits can be prolonged because emissions
from mobile phone can delay the circadian rhythm [45].
These individual differences in terms of mobile usage can
also influence and shape the consumer omnichannel behavior. It has been examined that benefits offered by mobile
devices in terms of size, mobility, and continuous Internet
connectivity make it an ideal choice for evening-type consumers as they can search for information, receive instant
product recommendations, and make quick purchases
anytime and anywhere [15, 46]. Furthermore, mobile search
and purchase tasks performance differ from that of online/
desktop, as mobiles offer instant, time-saving, locationbased contents and image and voice search capability.
Hence, it can be assumed that evening types have more
tendency towards the use of mobile channel during
searching the information of the product and even purchase
the product. Or there is a possibility that evening types have
more tendency towards the use of the mobile channel to get
promotional benefits from the company and perform purchase in physical store.
The prior studies have determined that evening chronotypes are more impulsive personalities [47, 48]. And it has
been confirmed that impulsivity is associated with the
greater use of smartphones wherein consumers can search
for information and make purchases immediately [49].
Today, the digital shopper carries his/her mobile device
everywhere and at all times owing to its portability, and
mobile marketing signals are designed to boost impulse
purchases [50]. The tendency for impulse shopping is
stimulus-driven and impulse buyers are expected to respond
favorably to these stimuli [51]. Consequently, these stimuli
enhance the feeling of urgency among consumers, resulting
in this channel attracting more evening-type chronotypes.
Keeping in view the above theoretical support, it is positive
that
H1. Evening-type chronotypes tend to use more mobile
channels than morning-type chronotypes to engage in
the omnichannel process.
H1o. Evening-type chronotypes do not use more mobile
channels than morning-type chronotypes to engage in
the omnichannel process.
H2. Evening-type chronotypes tend to use more mobile
channels than online channels to engage in the
omnichannel process.
H2o. Evening-type chronotypes do not use more mobile
channels than online channels to engage in the
omnichannel process.
4
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Furthermore, online omnichannel consumers may lean
towards contemplative behaviors. Morning types (larks) are
more thought-guided than evening types (owls) [27] and are
expected to consider the consequences of their behavior
[52]. Larks are associated with consciousness [53, 54], which
refers to being goal-directed, careful, and less impulsive.
Larks tend to be well organized and detailed information
seekers. Moreover, larks rely more on the need for touch to
reduce uncertainty and become confident about their
judgments. In addition to device attributes (a larger display,
high resolution, easy interface, more capacity, and better
connection), online channels offer greater access to information [55]. Consumers are confident that computers
offer better information and evaluation facilities [56] than
smartphones. Individuals use computers mostly in their
offices and homes for prolonged periods and display more
contemplative behaviors in making a purchase. Smartphone
screens are too small for consumers to navigate, search, and
evaluate information easily and arrive at a decision [15].
Realizing that haptic information for morning-type consumers can enhance confidence in their judgment [57],
online channels offer more detailed information and easier
navigation than the mobile one to make the decision-making
process easier. Consequently, morning-type shoppers prefer
online channels over mobile channels. Therefore,
H3. Morning-type chronotypes tend to use more online
channels than evening-type chronotypes to engage in
the omnichannel process.
H3o. Morning-type chronotypes do not use more
online channels than evening-type chronotypes to
engage in the omnichannel process.
H4. Morning-type chronotypes tend to use more online
channels than mobile channels to engage in the
omnichannel process.
H4o. Morning-type chronotypes do not use more
online channels than mobile channels to engage in the
omnichannel process.
4. Methodology
4.1. Sample and Data Collection. The sample consists of
university students from Beijing (Tsinghua University) and
the ancient capital of Xi’an (Xi’an Jiaotong University). The
sample is based on the argument of Borak [31] that the
Chinese B2C e-commerce retail industry comprises consumers aged 16 to 35 who conduct 85.5% of online shopping
and majority university students are tech-savvy and fall
under this age category. Furthermore, students in the capital
territory (current and ancient) universities come from almost all parts of the country. Taobao, JingDong (JD), and
vip.com have over 1 billion monthly active users [31].
Taobao and jd.com are the biggest e-commerce platforms in
China. It is remarkable that in the second quarter of 2017,
the online B2C retail market surpassed 860 billion yuan
(US$ 132.4 billion), an increase of 32% compared to the
same time period in 2016 [31]. Therefore, university students
were selected as respondents.
The data were collected through a survey from 30th Oct
2017 to 1st Feb 2018 using the convenience sampling method
as sample was easily accessible in university premises. In the
beginning, the questionnaire was translated into Chinese with
the help of a Chinese-English linguistic expert and subsequently distributed to 17 students and three professors for
their feedback and recommendations. Necessary changes
were made before distributing it to the actual sample. A total
of 505 online questionnaires were distributed via WeChat
(WeChat is a Chinese multipurpose social app with an active
user base of over 1 billion in 2018 and is considered the app
for all types of communication because of its wide range of
functions and platforms; besides social connections, it can be
used for payments, shopping, educational purposes, and so
on.) QR code, of which 410 were returned and 311 were found
appropriate for further analysis. After scanning the QR code,
respondents were directed to the questionnaire which was
linked with “wjx.cn” database, a data collection service widely
used in China for research purposes. The questionnaires’ QR
code were distributed till 15th of January 2018 and to make
sure that respondents have ample time to scan and respond to
QR code, the link to online questionnaire remained active till
1st February 2018. A 5-yuan red packet (money gifted through
a WeChat transfer) was given to the respondents to encourage
them to complete the survey. It is noteworthy that respondents could submit the questionnaire and receive a
coupon from any given IP address only once. This helped us
to reduce respondents’ bias.
4.2. Operationalization of Variables. In the first part of the
survey, respondents were asked about their e-commerce
platform and experience in using these platforms in years,
age, gender, and education level. The second part of the
survey comprises the chronotype and omnichannel process.
These variables were adopted from the literature and all of
them are reliable according to prior studies. To measure the
chronotype, the composite scale of morningness (CSM) was
adopted with typologies (morning, evening, neither) based
on the 10th and 90th percentiles [58, 59]. The scale has 13
Likert scale items (with 3 items on 5-point and 10 items on 4point scale) with minimum score of 13 to 55 maximum. The
cutoff points for CSM scores in our study were 28 and 38,
respectively. While scores lower than 28 denote more eveningness, scores higher than 38 denote more morningness.
The dependent variable of our study is the omnichannel
process adopted from [41]. The omnichannel process has
four items, namely, mobile omnichannel process (a: search
offline and purchase mobile; b: search mobile and purchase
offline) and online omnichannel process (c: search offline
and purchase online; d: search online and purchase offline)
ranked on 5 point Likert scale (1 strongly disagree to 5:
strongly agree), that show how consumers engage in
omnichannel behavior.
5. Data Analysis and Results
Table 1 presents respondents’ profile information. The
sample is almost equally divided into male and female, and
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5
Table 1: Respondents’ profile.
Demographics
Gender
Age in years
Educational level
E-commerce platform
Experience in using ecommerce platforms
Frequency
percentage (%)
Male
48.23
Female
51.77
18–23
45.98
24–29
28.94
30–35
11.58
36 and above
13.5
Undergraduate
48.55
Masters’
37.94
degree
PhD
13.5
Taobao
28.62
Jingdong
5.79
Both
65.59
1 year
9
2 years
12.86
Above 2 years
78.14
Characteristics
the majority are bachelors’ and masters’ students using both
e-commerce platforms (Taobao and Jingdong).
The separate confirmatory factor analysis (CFA) is done
to determine the factor structure of chronotype scale and
omnichannel scale. The results related to CFA of chronotype
scale show that model has good fit with χ2/df � 2.03 (χ 2/
df < 3), RMSEA � 0.058 (RMSEA < 0.08), CFI � 0.936
(CFI > 0.90), and NNFI � 0.919 (NNFI > 0.90). The factor
loadings range between 0.428 and 0.642. The value of
Cronbach alpha is 0.799. These findings show that reliability
and validity of chronotype scale are ensured. Moreover,
results related to CFA of omnichannel scale show that model
has good fit with χ 2/df � 2.11 (χ2/df < 3), RMSEA � 0.060
(RMSEA < 0.08), CFI � 0.95 (CFI > 0.90), and NNFI � 0.91
(NNFI > 0.90). The factor loadings range between 0.538 and
0.704. The value of Cronbach alpha is 0.718. Hence, reliability and validity of omnichannel scale are also satisfied.
In order to test study hypotheses, univariate analysis is
conducted with the help of SPSS 20. For that purpose, the
variable of chronotype is characterized into two groups
(evening type and morning type) based on cutoff scores [58].
According to 10th and 90th percentiles, evening type accounts for 40.24% (n � 165), neither accounts for 24.14%
(n � 99), and morning type accounts for 35.62% (n � 146).
Furthermore, independent sample t-test is employed to
analyze the mean differences among the groups. Results
show that the mean value of evening type is 2.84 which is
significantly different from the mean value of morning type
(3.77) with (t > 2, p < 0.05). After determination of mean
differences among the morning and evening groups, these
factors are tested with dependent variables of the study.
Tables 2 and 3 show that evening types are more frequent in
using mobile channels (mean � 3.95, SD � 0.56) as compared
to morning types (mean � 2.71, SD � 0.78) (F > 5, p < 0.05),
hence confirming H1 and rejecting H1o.
Furthermore, Tables 4 and 5 show that morning types are
more frequent in using online channels (mean � 3.85,
SD � 0.61) as compared to evening types (mean � 2.97,
SD � 0.87) (F > 5, p < 0.05), hence confirming H3 and
rejecting H3o.
Pairwise comparison (Table 6) shows that the mean
value of evening types is greater in the mobile omnichannel
process as compared to morning types (mean difference � 1.23, p < 0.05) and lesser in the online omnichannel
process as compared to morning types (mean
difference � −0.874, p < 0.05), hence confirming H2 and
rejecting H2o. Moreover, pairwise comparison table reveals
that mean value of morning types is greater in online
omnichannel process as compared to evening types (mean
difference � 0.874, p < 0.05) and lesser in mobile omnichannel process as compared to evening types (mean
differences � −1.23, p < 0.05), hence confirming H4 and
rejecting H4o.
Finally, a summary of hypotheses is given in Table 7.
6. Discussion
Currently, tech-savvy consumers possess a number of digital
devices, notably mobiles and computers to make purchase
decisions, which allows them more control and access to the
ever-growing electronic commerce world. They use digital
devices to collect and disseminate information at every stage
of the purchase cycle. This study has incorporated interesting variable chronotype, which denotes individual
differences based on their sleep-wake cycle. The results show
that evening-type chronotypes tend to use mobile channel
more as compared to morning-type chronotypes during
omnichannel process. Similar findings are given in [41],
which reported that buyers who are high in impulsivity
prefer mobile omnichannel as compared to those who are
low in impulsivity. And it has been shown that evening types
are impulsive in nature. Moreover, research studies show
that evening types are more technology oriented and
addicted to mobile phone, Internet, and computer games.
Furthermore, search and purchase tasks differ from each
other on mobiles and desktops, as mobile offers instant,
time-saving, location-based, customized contents and image
and voice search capability which offer greater tasktechnology fit for evening-type consumers. The second
findings determine that morning types tend to use online or
desktop omnichannel more as compared to evening types.
Previous research studies suggest that morning types have
more focus on detail information and are less risk takers
[25, 27, 43]. Therefore, they may choose desktop channels as
best source to make buying decisions. This research contributes to the literature in several ways. First, chronotypes
are relevant to consumer behavior and have been analyzed
for the first time in the omnichannel context. Second, these
variables affect channel usage in the omnichannel process at
the search and purchase stages and determine channel
preference. The findings provide the foundation to conclude
that evening types prefer mobile devices and morning types
prefer online channels in the omnichannel process. These
findings confirm that individual characteristics influence the
task-technology fit. The benefits offered by mobile devices in
terms of size, mobility, and continuous Internet connectivity
make it an ideal choice for evening-type consumers, as they
6
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Table 2: Descriptive statistics on mobile omnichannel frequency use.
Evening
Morning
Total
N
Mean
Std. deviation
Std. error
165
146
311
3.9467
2.7123
3.3672
0.56315
0.78748
0.91571
0.04384
0.06517
0.05193
95% confidence interval for mean
Lower bound
Upper bound
3.8601
4.0332
2.5835
2.8411
3.2650
3.4694
Minimum
Maximum
2.50
1.00
1.00
5.00
4.50
5.00
Table 3: ANOVA results on mobile channel frequency according to chronotypes (evening vs morning).
Mobile channel
Sum of squares
118.017
141.928
259.945
Between groups
Within groups
Total
df
1
309
310
Mean square
118.017
0.459
F
256.941
Sig.
0.000
Table 4: Descriptive statistics on online omnichannel frequency use.
Morning
Evening
Total
N
Mean
Std. deviation
Std. error
146
165
311
3.8493
2.9758
3.3859
0.61412
0.87095
0.87657
0.05082
0.06780
0.04971
95% confidence interval for mean
Lower bound
Upper bound
3.7489
3.9498
2.8419
3.1096
3.2880
3.4837
Minimum
Maximum
2.00
1.00
1.00
5.00
5.00
5.00
Table 5: ANOVA results on online channel frequency according to chronotypes (evening vs morning).
Online channel
Between groups
Within groups
Total
Sum of squares
59.110
179.088
238.198
df
1
309
310
Mean square
59.110
0.580
F
101.989
Sig.
0.000
Table 6: Pairwise comparisons.
Dependent variable
Mobile
Online
(I) chronotype
(J) chronotype
Mean difference (I − J)
Std. error
Sig.b
Evening
Morning
Evening
Morning
Morning
Evening
Morning
Evening
1.234
−1.234
−0.874
0.874
0.077
0.077
0.086
0.086
0.000
0.000
0.000
0.000
95% confidence interval for
differenceb
Lower bound Upper bound
1.083
1.386
−1.386
−1.083
−1.044
−0.703
0.703
1.044
Based on estimated marginal means. The mean difference is significant at the 0.05 level. bAdjustment for multiple comparisons: least significant difference
(equivalent to no adjustments).
Table 7: Summary of hypothesis testing.
Hypothesis no.
H1
H2
H3
H4
Relationship
Evening types ⟶ mobile omnichannel > online
omnichannel
Evening types > morning types ⟶ mobile
omnichannel
Morning types ⟶ online omnichannel > mobile
omnichannel
Morning types > evening types ⟶ online
omnichannel
can search for information, receive instant recommendations for products, and make quick purchases anytime and
anywhere [15, 46]. In the Chinese context, a growing
Results
Accepted
Accepted
Accepted
Accepted
number of mobile phone users, improved lifestyles, trustworthy e-commerce platforms, and, most importantly,
WeChat payment and discount coupons on purchases via
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mobile devices are among the justifications for such
behavior.
Computers and laptops offer exclusive benefits for online
omnichannel consumers. Morning-type individuals are
good planners, detailed information seekers, and rely on
more haptic information to make their decisions. They show
contemplative behavior in shopping. The purchasing process
is also easier for them as they have access to HD videos,
images, and reviews on a large screen, which satisfy their
decision-making needs [56]. There is another explanation
why consumers use laptops to look for information and
make comparisons and subsequently visit the brick-andmortar store to make the final decision; they need to touch
and examine the product before or after a purchase. Furthermore, they may evaluate a product in the store to satisfy
their information needs and experience a real-time shopping
experience [30]. Therefore, online/desktop channels offer
better task-technology fit to morning type shoppers. Finally,
through the outcomes of this study, it can be concluded that
chronotypes affect the omnichannel process engagement
significantly. Evening-type individuals prefer mobile channels over online channels and morning-type individuals
favor online channels over mobile channels.
7. Managerial Implications
Today, the omnichannel process consumer uses many
channels and devices simultaneously during his/her purchase journey. The divide between digital and physical
channels does not appear to be that important as tech-savvy
consumers access physical and digital channels interchangeably and expect uniformity in offerings. This notion suggests that businesses need to develop an
omnichannel strategy that can cater to the needs of modern
consumers and create a uniform experience across all
channels. Failing to do so can annoy shoppers and force
them to switch to other options easily as they have seamless
access to the retail world. Furthermore, as mobile omnichannel consumers are more the evening type and impulsive
in nature, they want a quick purchase process. They are more
active and access their mobile devices late at night, and
therefore companies should design strategies that provide
them with a quick and easy shopping experience. To do so,
marketers need to develop an app and/or website that is easy
to check-in, check-out, and navigate. The information and
product display size and quality must be appropriate. As
evening-type consumers are late night mobile users, companies can offer them impulsive nature products on mobile
channels to receive a favorable response. Providing Wi-Fi
facilities in stores can yield positive responses from such
consumers as they can connect their mobile devices to carry
out multipurpose tasks and thus arrive at a decision. Offering discount coupons on purchase via mobile channels
can also attract the traffic. Furthermore, mobile-based location marketing is a growing trend and evening types are
risk takers and innovation seekers, so they are more open to
share their location and information which can help businesses to target them effectively Moreover, morning types
have risk prevention focus; they have less tendency to share
7
their location and private information; thus, marketers
should make strategy accordingly to target them. Another
insight for marketers is that evening types face more healthrelated issues, including insomnia, obesity, and addiction to
gaming, so gym businesses and fitness consultants can target
such persons over mobile channel to yield favorable response. Furthermore, mobile marketing is a growing trend
and if marketers know their potential customers and their
channel preference, then they can tailor their marketing
signals (SMS, MMS, and Websites) accordingly.
Data Availability
This research is part of a series of research studies conducted
by a group of researchers, and few parallel studies are still in
progress; the data used to support the findings of this study
are currently under embargo, while the research findings are
commercialized. Requests for data after 12 months of
publication of this article will be considered by the corresponding author with mutual consultation of related group
members and researchers.
Additional Points
This study, like others, has its limitations too. For instance, in
the Chinese context, mobile channels have a greater use than
online channels in general, contrary to prior studies conducted in the West state. Therefore, further research in different countries and cultures can be interesting. Furthermore,
while this study investigates the omnichannel process in
general, future studies can consider investigating different
product categories. While this study used the internal aspects
of consumers, future studies can investigate external dimensions such as channel attributes. As chronotypes have
been classified into morning- and evening-type consumers
only, future studies can include the “neither type” too for a
deeper understanding of consumer psychology.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
References
[1] P. C. Verhoef, P. K. Kannan, and J. J. Inman, “From multichannel retailing to omni-channel retailing,” Journal of Retailing, vol. 91, no. 2, pp. 174–181, 2015.
[2] R. Venkatesan, V. Kumar, and N. Ravishanker, “Multichannel
shopping: causes and consequences,” Journal of Marketing,
vol. 71, no. 2, pp. 114–132, 2007.
[3] A. Ansari, C. F. Mela, and S. A. Neslin, “Customer channel
migration,” Journal of Marketing Research, vol. 45, no. 1,
pp. 60–76, 2008.
[4] U. M. Dholakia, B. E. Kahn, R. Reeves, A. Rindfleisch,
D. Stewart, and E. Taylor, “Consumer behavior in a multichannel, multimedia retailing environment,” Journal of Interactive Marketing, vol. 24, no. 2, pp. 86–95, 2010.
[5] Y. H. D. Chou, T. Y. D. Li, and C. T. B. Ho, “Factors
influencing the adoption of mobile commerce in Taiwan,”
International Journal of Mobile Communications, vol. 16,
no. 2, pp. 117–134, 2018.
8
[6] J. Heo and C. W. Chang, “Factors influencing intention to
accept location-based mobile advertising among young mobile user segments: a social exchange perspective,” International Journal of Mobile Communications, vol. 16, no. 6,
pp. 607–623, 2018.
[7] C. W. Hsu and C. C. Yeh, “Understanding the critical factors
for successful M-commerce adoption,” International Journal
of Mobile Communications, vol. 16, no. 1, pp. 50–62, 2018.
[8] A. Waheed and J. Yang, “The impact of mobile marketing on
online consumer buying behaviour: empirical evidence from
Pakistan,” International Journal of Mobile Communications,
vol. 16, no. 6, pp. 624–643, 2018.
[9] A. Blom, F. Lange, and R. L. Hess Jr., “Omnichannel-based
promotions’ effects on purchase behavior and brand image,”
Journal of Retailing and Consumer Services, vol. 39, pp. 286–
295, 2017.
[10] C. Lazaris and A. Vrechopoulos, “From multi-channel to
“omnichannel” retailing: review of the literature and calls for
research,” in Proceedings of 2nd International Conference on
Contemporary Marketing Issues (ICCMI), pp. 18–20, Athens,
Greece, June 2014.
[11] E. Juaneda-Ayensa, A. Mosquera, and Y. Sierra Murillo,
“Omnichannel customer behavior: key drivers of technology
acceptance and use and their effects on purchase intention,”
Frontiers in Psychology, vol. 7, p. 1117, 2016.
[12] Deloitte, Navigating the New Digital Divide. A Global Summary of Findings from Nine Countries on Digital Influence in
Retail, Deloitte, London, UK, 2016, https://www2.deloitte.
com/global/en/pages/consumer-business/articles/gx-globaldigital-divide-retail.html.
[13] A. Hübner, J. Wollenburg, and A. Holzapfel, “Retail logistics
in the transition from multi-channel to omni-channel,” International Journal of Physical Distribution & Logistics
Management, vol. 46, no. 6-7, pp. 562–583, 2016.
[14] K. Yang and H. Y. Kim, “Mobile shopping motivation: an
application of multiple discriminant analysis,” International
Journal of Retail & Distribution Management, vol. 40, no. 10,
pp. 778–789, 2012.
[15] L. Gao, K. A. Waechter, and X. Bai, “Understanding consumers’ continuance intention towards mobile purchase: a
theoretical framework and empirical study-a case of China,”
Computers in Human Behavior, vol. 53, pp. 249–262, 2015.
[16] A. Holmes, A. Byrne, and J. Rowley, “Mobile shopping behaviour: insights into attitudes, shopping process involvement
and location,” International Journal of Retail & Distribution
Management, vol. 42, no. 1, pp. 25–39, 2013.
[17] D. L. Goodhue and R. L. Thompson, “Task-technology fit and
individual performance,” MIS Quarterly, vol. 19, no. 2,
pp. 213–236, 1995.
[18] J. Gebauer and Y. Tang, “Applying the theory of tasktechnology fit to mobile technology: the role of user mobility,” International Journal of Mobile Communications, vol. 6,
no. 3, pp. 321–344, 2008.
[19] D. C. Yen, C.-S. Wu, F.-F. Cheng, and Y.-W. Huang, “Determinants of users’ intention to adopt wireless technology: an
empirical study by integrating TTF with TAM,” Computers in
Human Behavior, vol. 26, no. 5, pp. 906–915, 2010.
[20] C.-C. Lee, H. K. Cheng, and H.-H. Cheng, “An empirical
study of mobile commerce in insurance industry: tasktechnology fit and individual differences,” Decision Support
Systems, vol. 43, no. 1, pp. 95–110, 2007.
[21] E. M. Overby and B. Konsynski, Task-Technology Fit and
Process Virtualization Theory: An Integrated Model and
Empirical Test, 2010.
Mobile Information Systems
[22] P. C. Verhoef, S. A. Neslin, and B. Vroomen, “Multichannel
customer management: understanding the research-shopper
phenomenon,” International Journal of Research in Marketing, vol. 24, no. 2, pp. 129–148, 2007.
[23] S. Gensler, P. C. Verhoef, and M. Böhm, “Understanding
consumers’ multichannel choices across the different stages of
the buying process,” Marketing Letters, vol. 23, no. 4,
pp. 987–1003, 2012.
[24] V. Natale, A. Alzani, and P. Cicogna, “Cognitive efficiency and
circadian typologies: a diurnal study,” Personality and Individual differences, vol. 35, no. 5, pp. 1089–1105, 2003.
[25] M. B. Horzum and E. Demirhan, “The role of chronotype on
Facebook usage aims and attitudes towards Facebook and its
features,” Computers in Human Behavior, vol. 73, pp. 125–131,
2017.
[26] A. B. Reddy and J. S. O’Neill, “Healthy clocks, healthy body,
healthy mind,” Trends in Cell Biology, vol. 20, no. 1, pp. 36–44,
2010.
[27] J. F. Dı́az-Morales, “Morning and evening-types: exploring
their personality styles,” Personality and Individual Differences, vol. 43, no. 4, pp. 769–778, 2007.
[28] J. Hornik and G. Miniero, “Synchrony effects on customers’
responses and behaviors,” International Journal of Research in
Marketing, vol. 26, no. 1, pp. 34–40, 2009.
[29] K. Gullo, J. Berger, J. Etkin, and B. Bollinger, “Does time of
day affect variety-seeking?,” Journal of Consumer Research,
2019.
[30] M. T. Elliott, F. Q. Fu, and P. S. Speck, “Information search
and purchase patterns in a multichannel service industry,”
Services Marketing Quarterly, vol. 33, no. 4, pp. 292–310, 2012.
[31] M. Borak, “Data shows 2017 was the year of e-commerce apps:
report,” 2018, https://technode.com/2018/01/31/2017-ecommerce-apps/.
[32] C. Team, “Mobile share exceeded 80% in China online
shopping market in Q2 2017,” 2017, https://www.
chinainternetwatch.com/22461/mobile-shopping-q2-2017/
#ixzz5BaiNfMZD.
[33] S. Rouis, M. Limayem, and E. Salehi-Sangari, Impact of
Facebook Usage on Students’ Academic Achievement: Role of
Self-Regulation and Trust, 2011.
[34] E. C. Tandoc, P. Ferrucci, and M. Duffy, “Facebook use, envy,
and depression among college students: is facebooking depressing?,” Computers in Human Behavior, vol. 43, pp. 139–
146, 2015.
[35] C. Randler, M. B. Horzum, and C. Vollmer, “Internet addiction and its relationship to chronotype and personality in a
Turkish University student sample,” Social Science Computer
Review, vol. 32, no. 4, pp. 484–495, 2014.
[36] C. Vollmer, C. Randler, M. B. Horzum, and T. Ayas,
“Computer game addiction in adolescents and its relationship
to chronotype and personality,” Sage Open, vol. 4, article
2158244013518054, 2014.
[37] K. A. Bartel, M. Gradisar, and P. Williamson, “Protective and
risk factors for adolescent sleep: a meta-analytic review,” Sleep
Medicine Reviews, vol. 21, pp. 72–85, 2015.
[38] S. Kauderer and C. Randler, “Differences in time use among
chronotypes in adolescents,” Biological Rhythm Research,
vol. 44, no. 4, pp. 601–608, 2013.
[39] A. Rapp, T. L. Baker, D. G. Bachrach, J. Ogilvie, and
L. S. Beitelspacher, “Perceived customer showrooming behavior
and the effect on retail salesperson self-efficacy and performance,” Journal of Retailing, vol. 91, no. 2, pp. 358–369, 2015.
[40] I. Kazancoglu and H. Aydin, “An investigation of consumers’
purchase intentions towards omni-channel shopping,”
Mobile Information Systems
[41]
[42]
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
[51]
[52]
[53]
[54]
[55]
[56]
[57]
[58]
International Journal of Retail & Distribution Management,
vol. 46, no. 10, pp. 959–976, 2018.
P. Rodrı́guez-Torrico, R. San José Cabezudo, and S. SanMartı́n, “Tell me what they are like and I will tell you where
they buy. An analysis of omnichannel consumer behavior,”
Computers in Human Behavior, vol. 68, pp. 465–471, 2017.
Y.-S. Chen and S. Y. B. Huang, “The effect of task-technology
fit on purchase intention: the moderating role of perceived
risks,” Journal of Risk Research, vol. 20, no. 11, pp. 1418–1438,
2017.
E. Demirhan, C. Randler, and M. B. Horzum, “Is problematic
mobile phone use explained by chronotype and personality?,”
Chronobiology International, vol. 33, no. 7, pp. 821–831, 2016.
G. Nimrod, “Early birds and night owls: differences in media
preferences, usages, and environments,” International Journal
of Communication, vol. 9, p. 21, 2015.
S. J. Crowley, E. Van Reen, M. K. Lebourgeois et al., “A
longitudinal assessment of sleep timing, circadian phase, and
phase angle of entrainment across human adolescence,” PLoS
One, vol. 9, no. 11, Article ID e112199, 2014.
R. J.-H. Wang, E. C. Malthouse, and L. Krishnamurthi, “On
the go: how mobile shopping affects customer purchase behavior,” Journal of Retailing, vol. 91, no. 2, pp. 217–234, 2015.
G. M. Cavallera and S. Giudici, “Morningness and eveningness personality: a survey in literature from 1995 up till 2006,”
Personality and Individual Differences, vol. 44, no. 1, pp. 3–21,
2008.
A. Adan, V. Natale, H. Caci, and G. Prat, “Relationship between circadian typology and functional and dysfunctional
impulsivity,” Chronobiology International, vol. 27, no. 3,
pp. 606–619, 2010.
J. Drennan and G. Mort, Examination of the Influence of
Personal Attributes on Consumer Use on m-Services, 2003.
S. San-Martin and B. López-Catalán, “How can a mobile
vendor get satisfied customers?,” Industrial Management &
Data Systems, vol. 113, no. 3, pp. 156–170, 2013.
D. W. Rook and R. J. Fisher, “Normative influences on impulsive buying behavior,” Journal of Consumer Research,
vol. 22, pp. 305–313, 1995.
K. Nowack and E. Van Der Meer, “Are larks future-oriented
and owls present-oriented? Age- and sex-related shifts in
chronotype-time perspective associations,” Chronobiology
International, vol. 30, no. 10, pp. 1240–1250, 2013.
A. L. Hogben, J. Ellis, S. N. Archer, and M. Von Schantz,
“Conscientiousness is a predictor of diurnal preference,”
Chronobiology International, vol. 24, no. 6, pp. 1249–1254,
2007.
L. Tonetti, M. Fabbri, and V. Natale, “Relationship between
circadian typology and big five personality domains,” Chronobiology International, vol. 26, no. 2, pp. 337–347, 2009.
A. A. Ozok and J. Wei, “An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices: results from a college student
population,” Electronic Commerce Research, vol. 10, no. 2,
pp. 111–137, 2010.
T. J. Strader and A. Inapudi, “User perceptions of mobile and
online channels for purchasing financial products,” Journal of
Internet Commerce, vol. 3, no. 1, pp. 91–101, 2004.
J. Peck and T. L. Childers, “To have and to hold: the influence
of haptic information on product judgments,” Journal of
Marketing, vol. 67, no. 2, pp. 35–48, 2003.
C. S. Smith, C. Reilly, and K. Midkiff, “Evaluation of three
circadian rhythm questionnaires with suggestions for an
9
improved measure of morningness,” Journal of Applied psychology, vol. 74, no. 5, pp. 728–738, 1989.
[59] I. Önder, Ş. Beşoluk, and M. B. Horzum, “Psychometric
properties of the Turkish version of the composite scale of
morningness,” Spanish Journal of Psychology, vol. 16, 2013.
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