Reproduced with permission of the copyright owner. Further re.docx

Reproduced with permission of the copyright owner. Further
reproduction prohibited without permission.
ICT and Business in the New Economy: Globalization and
Attitudes Towards eCommerce
Sagi, John;Carayannis, Elias;Dasgupta, Subhashish;Thomas,
Gary
Journal of Global Information Management; Jul-Sep 2004; 12,
3; ProQuest Central
pg. 44

Assignment 1
Questions:
a) Discuss why you chose to be an accounting major and what
type of career you hope to have.
b) Discuss whether you believe accounting disclosures
(financial statements, 10-k’s, etc.) should be mandated by the
federal government in the United States for publicly listed
companies.
Answers for questions a and b together should be no greater
than one page double spaced times new romans 12 point font
with standard margins.
TEM Journal. Volume 7, Issue 1, Pages 118-127, ISSN 2217-
8309, DOI: 10.18421/TEM71-14, February 2018.
118
TEM Journal – Volume 7 / Number 1 / 2018.

Drivers and Barriers to Online Shopping in
a Newly Digitalized Society
M. Shakaib Akram
College of Business Administration, King Saud University,
Riyadh, KSA
Abstract – Despite the massive penetration of
internet in the developed and the developing world, e-
commerce is still struggling in most of the developing
and emerging economies. In this context, this study
investigates why the customers, in developing
countries, do not prefer online shopping for apparel
despite the several benefits such as convenience,
control, variety and enjoyment being offered by this
mode of shopping. Moreover, the study assesses the
boundary conditions under which consumers’
perceived risk diminishes online shopping benefits.
With data from prospective online customers in an
emerging economy, the proposed concept is tested
using SmartPLS 3.0 based SEM approach. The results
indicate a significant positive effect of online shopping
benefits on consumers’ purchase intention for online
shopping. In addition, the relationship between online
shopping benefits and purchase intention is contingent
on the consumers’ level of perceived risk in the digital
environment. The paper concludes with a discussion on
the managerial and the theoretical implications.

Keywords – convenience, control, variety,
enjoyment, online shopping, risk
DOI: 10.18421/TEM71-14
https://dx.doi.org/10.18421/TEM71-14
Corresponding author: M. Shakaib Akram,
College of Business Administration, King Saud University,
Riyadh, KSA
Email: [email protected]
Received: 25 September 2017.
Accepted: 26 December 2017.
Published: 23 February 2018.
© 2018 M. Shakaib Akram; published by
UIKTEN. This work is licensed under the Creative
Commons Attribution-NonCommercial-NoDerivs 3.0
License.
The article is published with Open Access
at www.temjournal.com
1. Introduction
In the past decade, internet has penetrated into our
daily life and has become an essential medium of
communication both for individuals and for

businesses. This has helped companies to
communicate with their customers electronically and
sell their products and services online. However,
many e-commerce surveys reflect that although
companies are trying their best to satisfy their
customers yet a large number of customers are
reluctant to purchase online due to their concerns
about privacy and security of their online
transactions. This is especially relevant to the
developing countries where digital commerce is
gradually emerging.
E-commerce offers some benefits to the
consumers, as compared to traditional commerce, in
terms of convenience, a variety of products, greater
control over their buying and entertaining
experience. Past research shows a positive impact of
e-commerce benefits in developing consumers’
favorable attitude toward online shopping and also in
enhancing their likelihood for online buying various
products and services [6,7,11,18]. So, in this research
perceived benefits of online shopping, namely:
Perceived Convenience, Perceived Control,
Perceived Variety and Perceived Enjoyment are
incorporated as the drivers of online shopping.
As the perceived risk may vary across various
products and services and also on the basis of
customers’ personal profile, so this study is restricted
to the examination of drivers and barriers to online
shopping of apparels. Further, commonly for
apparels, a physical examination for such products is
greatly desired and thus offers a greater perceived
risk [1]. Apparels offer to be a suitable product
category for this study, as we want to assess the
conditions under which the impact of perceived

benefits of online shopping strengthens or diminishes
consumers’ purchase intention from online websites.
https://dx.doi.org/10.18421/TEM71-14
http://www.temjournal.com/
8309, DOI: 10.18421/TEM71-14, February 2018.
119
Many surveys have reported that most internet
surfers are not making any online transaction because
they are concerned about internet security [17]. The
customers making online transaction have
apprehension about passing along their credit card
numbers and other confidential information on the
internet. Internet fraud is one of the major factors
causing growing concern in the minds of online
customers. Consumers’ personal risk profile (i.e.,
being risk-take or risk-averse) also plays a key role in
their online shopping decisions. Generally, it is
observed that “risk neutral consumers are more likely
than risk-averse consumers to consummate a
purchase transaction when faced with buying a
product (or service) with uncertain outcomes or
possible loss” [15]. Therefore, those consumers
having higher risk perception in the online channel
may avoid or delay their buying decision through this
channel.
Previous studies have identified perceived risk as a

key factor in customers’ participation in e-commerce,
while others have highlighted the role of perceived
risk as an antecedent to the willingness to be profiled
online. This research contributes to the literature by
examining the role of consumers’ risk profile on the
relationship between online shopping benefits and
purchase intention. The proposed conceptual model
has been empirically tested with data from
prospective online shoppers in an emerging
economy.
The rest of the paper is organized as follows: the
next section is dedicated to theoretical background
and hypotheses development, the methodology is
discussed in the third section, the following section is
devoted to analysis and the results and the last
section concludes the paper with the discussion and
the implications for academicians and practitioners.
2. Theoretical background and hypotheses
This section is divided into three parts, i.e., online
shopping benefits, perceived risk, and demographics.
In the following section, the perceived benefit of the
online shopping has been discussed and substantiated
by literature.
2.1. Perceived Convenience (CNV)
Literature has mainly focused on the service
convenience in the context of traditional stores [18],
but this study focuses on the website convenience
aspect. Jiang et al. (2013) proposed five dimensions
of perceived convenience in an online shopping
context including access, search, evaluation,

transaction, and possession/post-purchase.
Customers’ CNV is considered one of the major
motivators for online shopping environment [7] due
to several reasons such as time-saving, avoiding
crowds, flexibility, 24/7 availability, etc.
These days, people do not have enough free time
to go to the market and stand in the long queues.
Therefore, online shopping websites have provided
them with an alternative for conveniently conducting
their purchasing needs according to their flexible
schedule.
Unlike the brick and mortar stores, an online
store’s website plays a crucial role in forming
customers’ pleasant experience. In fact, an online
store website acts as an environment element
throughout the shopping process and is directly
linked to customers’ perceived convenience [8].
Customers’ effortless experience will help them form
a positive attitude towards the online website, and
their likelihood of engaging in online store will
increase. Website quality characteristics such as easy
to navigate, easy to search, easy to transact and easy
to order also contribute to customers’ convenience
[14].
Based on the above arguments, it is expected that
consumers’ perceiving online channel to be
convenient will have a favorable attitude toward this
channel. Hence, it is hypothesized:
H1: Perceived convenience of shopping from an e-
retailer positively impacts customers’ purchase
intention.

2.2. Perceived Control (CNT)
According to [2], perceived behavioral control is
an individual’s belief of “perceived ease or difficulty
of performing behavior” (Ajzen, 1991, p.188). In
Theory of Planned Behavior (TPB), perceived
control is proposed as a part of individuals’ beliefs
affecting their intention which consequently results
in their actual behavior. Thus, perceived behavioral
control depicts consumer’s perception of their control
over their actions. In the context of online shopping
perceived control is the level of control that the
customers perceived in the online buying process
[11].
Moreover, the Unified Theory of Acceptance and
Use of Technology (UTAUT), [24] argued that
facilitating conditions capture the essence of the
TBP’s construct perceived control over behavior.
This means that the facilitating conditions such as
availability of resources, ease of use, ability to search
and customization of the products help increase the
degree of perceived control on online transactions.
Therefore, in case of online shopping customers’
greater control over their shopping experience helps
them develop a favorable attitude toward e-
commerce.
Generally, online stores provide customers with a
large number of products and services as compared
8309, DOI: 10.18421/TEM71-14, February 2018.

120
to their traditional brick and mortar counterparts. In
addition, customers can quickly navigate into the
catalog and thus feel more control over their product
selection. High-quality online shopping websites
provide many options such as product search,
selection, customization and so on. Through all their
online shopping experience, customers have a lot of
flexibility, and they are in full control of their
decision. This control builds their positive attitude
and enhances their likelihood of engaging in online
shopping. Literature has also supported the influence
of perceived control for online shopping [11,19,25].
Martin et al. (2015) reasoned that ease of use and
customization are major drivers of perceived control
that eventually leads to customers’ satisfaction and
repurchase intentions.
From the above discussion, we can conclude that
perceived control is an important factor in
determining information systems usage intention and
adoption. Therefore, we argue that customers’
perceived control would positively contribute toward
their online purchase decision, and this leads to the
following hypothesis:
H2: Customers’ perceived control on their online
shopping positively impacts their purchase intention.
2.3. Perceived Variety (VRT)
Product variety mostly signifies the depth and
breadth of product collection. A large product

assortment of online stores allows customers’ greater
choices and more comparisons [7]. This helps them
develop a favorable attitude toward the online store
and consequently their likelihood to purchase online
increases. With not much location/space constraints,
online stores, generally, may offer a greater product
choice to their customers as compared to the
traditional stores. This is because these do not face
certain limitation as faced by a traditional outlet such
as an expensive prime location, limited shelf space
and these may offer as many products as these can
and so provide a greater choice of products to the
consumers. Chang (2011), through her study
conducted in Taiwan, examined the impact of
product categorization on product variety and found
that participants with more product subcategories
perceived greater product variety on the website and
they showed favorable attitude toward e-commerce.
Therefore, a greater product/service assortment or
product/service variety of online store may positively
contribute to form favorable customer evaluations of
this channel.
Literature signifies that it’s not the actual rather the
perceived product variety that influences consumer
behavior [6]. Therefore, online shopping website,
taking advantage of the technology, can categorize
and portray the products in a way that gives an
impression of a greater assortment.
Product assortment or variety is generally
addressed in literature from traditional shopping
environment, but its benefits from online shopping
perspective are not systematically addressed [12].
Therefore, this study is an attempt to examine how

product assortment may affect consumers’ decision-
making in the digital environment. Specifically, this
study assesses the influence of perceived variety on
the consumers’ purchase intention for online
shopping in an environment where e-commerce is at
an initial stage of its development and e-commerce is
an emerging trend. Thus, our hypothesis is:
H3: Product variety at an e-retailer has a positive
impact on customers’ purchase intention.
2.4. Perceived Enjoyment (ENJ)
Perceived enjoyment refers to “the extent to which
the activity of using a specific system is perceived to
be enjoyable in its own right, aside from any
performance consequences resulting from system
use” (Venkatesh, 2000, p.351) [23]. Thus in case of
online shopping perceived enjoyment will be the
consumers’ enjoyable experience of using e-
commerce website to explore and buy products
online. The interactive nature of online shopping is a
source of entertainment for some online consumers.
As there is no external intervention, this allows
consumers to focus on buying their preferred
products in their own way. Online merchants can
decrease consumers’ risk perceptions and increase
their trust by providing them with an entertaining
environment, an environment where consumers can
make their buying decisions in a playful manner.
Online shopping can do so with the help of a high-
quality interactive website.
Literature has established perceived enjoyment’s
role in intrinsic motivation to describe information
system’s adoption [9,11,22]. Thus, considering the

importance of perceived enjoyment as a critical
factor in forming consumers’ decision making in an
e-commerce environment, the next hypothesis is:
H4: Perceived enjoyment of shopping from an e-
retailer positively impacts customers’ purchase
intention.
2.5. Perceived Risk (PR)
The concept of perceived risk was introduced in
the late 60’s by Bauer as “the likelihood of
unfavorable outcomes, and consequences” of one’s
actions [3–5]. Forsythe and Shi (2003), defined
perceived risk in online shopping to be a subjective
evaluation of expected loss due to online shopping.
So in terms of online shopping, perceived risk will be
8309, DOI: 10.18421/TEM71-14, February 2018.
121
any potential uncertain negative outcome from their
online interaction.
The consumers, making an online transaction, may
be reluctant to purchase on the web because the sense
of risk may be far more as compared to the
traditional mode of shopping, where he can touch,
feel, and even try the product before finally
purchasing it. While purchasing online, a consumer
has to provide personal information and even

confidential credit card information. After providing
the necessary information, the consumer can only
hope that the transaction will be processed
completely, accurately and on time.
Historically, perceived risk is considered a barrier
towards online shopping. The negative impact of
consumers’ risk is linked to lower purchase
intentions. Literature highlighted that consumer’s
higher risk perceptions in the online channel leads to
their lesser chances of using this channel (Lim,
2003). Depending on consumers’ personal profile
and the product/service characteristics, their risk
perceptions may vary. Thus, the impact of perceived
risk may also be different for different consumers.
This study extends beyond exploring simple, direct
effects of perceived risk on purchase likelihood and
examines how consumers’ higher/lower risk profile
may play a moderating role in the relationship
between online shopping benefits and purchase
intentions. Though many types of perceived risk have
been presented in the literature [1,17] yet this study
focuses on the moderating role of overall risk due to
the online channel. Therefore, we hypothesize:
H5: Consumers’ perceived risk moderates the
relationship between online shopping benefits
(convenience, control, variety, and enjoyment) and
their purchase intention such that the relationship is
stronger (weaker) for lower (higher) risk levels.
2.6. Demographics

Consumers’ risk perception as well as their attitude
and purchase intention through online channels may
vary depending upon their profile. For instance,
customers’ demographics such as gender, age,
education, experience, may play a significant role in
their decision to use or not to use the online channels.
Thus, we include customers’ demographics as
covariates in the model (see Figure 1).
8309, DOI: 10.18421/TEM71-14, February 2018.
122
3. Method
3.1. Sample Profile
An online survey is used to collect data from
potential users of online shopping websites in Saudi
Arabia. At the beginning of the survey, the
respondents were asked about their recent purchase
of apparels online. Only those respondents were
allowed to participate in the survey who answered
yes to the screening question. The online survey
resulted in 284 responses. After dropping the
incomplete or invalid responses, 260 were retained
for the final analysis. For detailed respondents’
demographic profile see Table-1:

3.2. Instrument
To retain the essence of the original scales and face
validity, most of the scales in this study have been
adapted from the well-established literature. Multiple
items have been used to measure each latent
construct in the conceptual model. The scale to
measure perceived convenience and perceived
variety is adapted from [7]. The perceived risk is
measured using the scale adapted from Chakraborty
et al. (2016). The perceived enjoyment and the
purchase intention are measured by the scales
adapted from [21]. The scale for the perceived
control has been adapted from [11] and [10].
4. Analysis
After initial screening of the data, the structural
equation modeling (SEM) served the purpose of data
analysis. The psychometric properties of the scale
and the hypotheses have been tested using Partial
Least Squares based Structural Equation Modeling
(PLS-SEM) method by SmartPLS 3.2. There are four
independent, one dependent and one moderating
variable in the conceptual model. So, first of all, the
reliability and validity of these latent constructs are
assessed. The scale reliability is examined in terms of
internal consistency (Cronbach Alpha’s) and
composite reliability (CR). The coefficients’ (α)
values range from 0.78 to 0.89 while the CR values
range from 0.87 to 0.93. Table 2 shows that all these
values are above the minimum threshold of 0.7 [20],

thus indicating the high reliability of the used scales.
After setting up the scale reliability, it is assessed
for convergent and discernment validity. According
to [13], a scale should explain at least 50% of the
variance to meet the convergent validity requirement.
Statistically, to attain convergent validity, each
construct should have an average variance extracted
(AVE) value above 0.50. This condition is met for
each latent construct (Table 2), thus, proving
convergent validity. Finally, to assess the
discriminant validity, we compared the AVE values
for relevant shared variance [13]. Table 2 confirms
that the diagonal values (square root of AVE) for
each construct is significantly greater than off-
diagonal values (correlation with other constructs),
thus establishing discriminant validity.
4.1. Direct Effects
In Hypotheses 1 to 4, it is argued that the
dimensions (i.e., convenience, control, variety, and
enjoyment) of online shopping benefits positively
affect consumers’ purchase intention. PLS-SEM
8309, DOI: 10.18421/TEM71-14, February 2018.
123
results indicate a significant effect of each of these
four perceived benefits (convenience, control,
variety, and enjoyment) on PI (Table 3). Out of these

benefits, the perceived convenience of the online
channel has emerged as the strongest predictor of
consumers PI from this channel. The results also
confirm a strong negative impact of consumers’ risk
perceptions on their PI.
Broadly, our results confirm that consumers’
perception of convenience, control, variety, and
enjoyment of the online channel positively contribute
to their decision to engage in online shopping.
Therefore, the online store managers need to design
their store in a way that their customers have a sense
of convenience, control, variety, and enjoyment
while they are on their website.
Table 3: Direct Effects
As far as the consumers’ demographics are
concerned, we did not find a significant impact of
any of the demographic variables except past online
shopping experience. This suggests that the
consumers having a great experience with the online
retailers are more likely to engage in online buying in
future as well. Table 3 summarizes the results.
4.2. Moderating Effects
In H5, we hypothesize that the relationship
between the online shopping benefits (convenience,
control, variety, and enjoyment) and the PI is
moderated by customers’ risk profile and this
relationship is stronger (weaker) for lower (higher)
risk levels. As both the predicting and the moderating
variables are continuous, so we use product indicator

method in SmartPLS 3.2 to calculate interaction
effects.
Table 4: Moderating Effects
Relationship Estimates T-Values P-Values
PR*CNV -> PI -0.16 2.70 0.01
PR*CNT -> PI -0.20 3.35 0.00
PR*VRT -> PI -0.24 5.45 0.00
PR*ENJ -> PI -0.07 0.97 0.33
CNV: Perceived Convenience (CNV), CNT: Perceived Control
(CNT),
VRT: Perceived Variety (VRT), ENJ: Perceived Enjoyment
(ENJ), PR:
Perceived Risk, PI: Purchase Intention
The interaction effect of PR with each of the
predictors (convenience, control and variety and
enjoyment) turned out to be significant (Table 4).
Figure 2-4 also reflect these moderating effects or
higher and lower PR. The slopes of the line
presenting the impact of each of the variables
(convenience, control, and variety) on PI is stronger
for the lower values of PR and weaker for higher
values of PR (Figure 2-4).
However, we do not find any impact of the
interaction term between PR and enjoyment. Thus
enjoyment does not moderate the effect of the PR on
the PI. Figure 5 also depicts this fact as there is no
significant difference between the slope of the two
lines for the relationship between enjoyment and PI
for higher and lower PR values.

The empirical results affirm that the impact of
online shopping benefits on PI is dampened
(strengthened) for higher (lower) levels of PR values.
This implies that customers’ shopping decision from
the electronic channel is contingent on their risk
profile (i.e., high vs. low perceived risk).
Figure 2: Moderating effect or CNV
8309, DOI: 10.18421/TEM71-14, February 2018.
124
Figure 3: Moderating effect or CNT
Figure 4: Moderating effect of VRT
Figure 5: Moderating effect of ENJ
5. Discussion and Conclusions

In the perspective of the emerging online shopping
trends in the developing economies, this paper
investigates the role of the customers’ perceived
benefits on their purchase decision through online
online shopping are identified and their impact is
empirically tested on the PI. In addition, the paper
also extends the literature by studying the role of
perceived risk in a developing country context where
online stores are at an emerging stage. Though we
find that penetration of internet and social media is
quite high yet people are not much comfortable to
purchase products online, especially the apparels.
Empirical results demonstrate a significant effect
of consumers’ perceived benefits from online stores
on their PI from such stores. Our results endorse that
perception about potential benefits of the online
channel enhances consumers’ likelihood to engage in
online shopping activity.
Perceived convenience has emerged as the most
dominant variable, among the others, influencing PI
from online stores. Our research is also in line with
the literature which shows that consumers’ perceived
convenience in online vendor positively influences
their attitude and purchase intention. For instance,
Jiang et al. (2013) argued that convenience is one of
the major motivators behind consumers’ online
purchase intention. Similarly, [16] demonstrated
perceived convenience and perceived enjoyment to
be the key determinants of mobile shopping.
The empirical results assert the positive impact of
perceived control on customers’ online purchase

intention. This result shows that individuals’
perceived control over their buying process boosts
their confidence and they feel more involved and
independent. Unlike traditional brick and mortar
stores’ environment, the consumers have no
dependence on the store assistance; rather, if
required, they may take independent opinion from
other consumers on the internet. In this way,
consumers are in control of their shopping, and they
may avoid upselling and cross-selling from the store
employees. This finding is also in line with the past
research. For instance, Elwalda et al. (2016)
proposed that consumers’ level of control on their
online shopping activity helps in developing a
positive attitude toward online stores.
Product variety at online stores has emerged to be
another significant benefit of online shopping.
Generally, customers feel comparatively more
product/service options and variety on the online
stores than the traditional ones. This argument is true
to some extent; as online stores don’t face limitation
such as location. They can also take advantage of
8309, DOI: 10.18421/TEM71-14, February 2018.
125
technology to present even small number of products
in different ways. This gives an impression of a
greater product assortment to the consumers. This
finding is also in line with the literature [7,12]

Finally, perceived enjoyment is also found to be
significantly affecting customers PI from online
stores. This suggests that the sense of playfulness and
enjoyment of online channel helps consumers
develop a favorable attitude toward online stores.
Thus, online retailers need to incorporate interesting
features into their website design that consumers do
not feel boring during their visit to the online store.
This finding is consistent with the past research such
as [11] and [22].
Apart from empirically assessing the impact of
online shopping benefits including convenience,
control, variety, and enjoyment this study contributes
to the literature by extending the current
understanding of direct effects of perceived risk to
incorporating it as a moderator on online shopping
empirical basis for the varying impact of high/low
perceived risks on
relationship. This exhibits that though perceived
benefits may help in developing consumers’
favorable attitude yet their risk levels in the online
channel may dampen or strengthen their final
decision. Therefore, the str
relationship may vary depending upon the level of
perceived risk. In addition, the impact of
convenience, control, variety and enjoyment on PI
from online stores increases (decreases) for the
consumers with lower (higher) level of perceived risk
in the online channel.
For the role of individual’s demographics, in their
PI from an online store, we did not find any
significant role of gender, age or education but past

experience has emerged to be significantly associated
with PI. Nonetheless, care must be taken with respect
to the impact of gender, age and education on
purchase intention as in our sample majority of the
subjects were male between the age of 20 to 30 years
having a college degree. Therefore, more evenly
distributed sample may provide better insights into
the role of age, gender and education in forming
consumers’ purchase intention for online stores.
5.1. Theoretical and Managerial Implications
The findings of this study will be of interest to
both academicians and online website managers. For
the academicians, this research addresses an
important aspect of consumers’ online shopping
intention and highlights its facilitators and barriers in
the context of an emerging economy. Specifically,
the study reflects on the moderating role of perceived
risk in this phenomenon. Managers of online
shopping websites can employ these findings in
developing strategies to leverage on the highlighted
benefits and minimize consumers’ risk perceptions.
In this age of social media, it is difficult for the e-
commerce companies to control the information. Past
research shows that information asymmetry can
diminish consumers’ perceived trust and escalate
their perceived risks [5], so e-commerce firms need
to regularly update their consumers about potential
online threats and carefully design strategies to
counter this issue of asymmetric information.
Customers should be provided with the community
page where they can exchange their experience with
other consumers. This will help consumers in

building their trust in the online shopping website
and reduce any risk perceptions. For the community
pages, the company may use its own website or can
also take advantage of famous social networking
platforms such as Facebook and Twitter. In addition,
based on the consumers’ feedback and discussions on
the community platforms, the company can provide
answers to the frequently asked questions on their
website. This will not only solve consumers’
problems quickly but also reduce company’s
incoming calls/queries; this will help the company
save the time of its employees.
In this time of smart devices, consumers can easily
search for similar products/services and can compare
prices from the local and the global competitors.
Therefore, attracting not only new consumers but
also their retention is an important issue in this
information age. Thus, online website managers need
to continuously ensure that their consumers are
satisfied with their offerings and are having the
requisite convenience, control, variety, and
enjoyment through their online marketplace.
The study suggests that in order to mitigate
consumers’ concerns regarding online payments
through their credit card they may be offered various
options such as pay on delivery, pay through ATM,
etc.
5.2. Limitations and Future Research
Though the study offers interesting insights and
explains a significant variance in the dependent
variable, yet there are a number of limitations leading
to future research prospects. First, the study relied on

a data from only one emerging economy so its
generalizability may be limited due to differing
economic and technological conditions in other
emerging economies in particular and globally in
general. Second, most of the subjects in this study,
are male having a university education and in the age
range of 20 to 30 years. Further research may address
this issue by relying on more rigorous and
heterogeneous sample. Third, the KSA is one of the
8309, DOI: 10.18421/TEM71-14, February 2018.
126
leading countries in information and communication
adoption, future research may test the proposed
model in other countries to see its generalizability.
Fourth, the study has focused only on apparels but
the consumer’s risks may vary for different product
categories or even for various services, so future
research may take into account moderating effects of
perceived risk for other product categories such as
electronics, books. Fifth, this study has relied on the
overall risk but there may be different types of
perceived risks in online shopping such as financial,
psychological, time, security, etc. It will be an
interesting future research opportunity to see how
each of these dimensions of perceived risk moderates
the relationship between online shopping benefits
and purchase intention.
Acknowledgements

The researcher would like to thank the Deanship of
Scientific Research at King Saud University
represented by the Research Centre at College of
Business Administration for supporting this research
financially.
References
[1] Aghekyan-Simonian, M., Forsythe, S., Kwon, W. S.,
& Chattaraman, V. (2012). The role of product brand
image and online store image on perceived risks and
online purchase intentions for apparel. Journal of
Retailing and Consumer Services, 19(3), 325-331.
[2] Ajzen, I. (1991). The theory of planned
behavior. Organizational behavior and human
decision processes, 50(2), 179-211.
[3] Akram, M. S., & Malik, A. (2012, June). Evaluating
citizens' readiness to embrace e-government services.
In Proceedings of the 13th Annual International
Conference on digital government research (pp. 58-
67). ACM.
[4] Bauer, R. A. (1960). Consumer behavior as risk
taking. In Proceedings of the 43rd National
Conference of the American Marketing Assocation,
June 15, 16, 17, Chicago, Illinois, 1960. American
Marketing Association.
[5] Chakraborty, R., Lee, J., Bagchi-Sen, S., Upadhyaya,
S., & Rao, H. R. (2016). Online shopping intention in
the context of data breach in online retail stores: An

examination of older and younger adults. Decision
Support Systems, 83, 47-56.
[6] Chang, C. (2011). The effect of the number of product
subcategories on perceived variety and shopping
experience in an online store. Journal of Interactive
Marketing, 25(3), 159-168.
[7] Clemes, M. D., Gan, C., & Zhang, J. (2014). An
empirical analysis of online shopping adoption in
Beijing, China. Journal of Retailing and Consumer
Services, 21(3), 364-375.
[8] Davari, A., Iyer, P., & Rokonuzzaman, M. (2016).
Identifying the determinants of online retail
patronage: A perceived-risk perspective. Journal of
Retailing and Consumer Services, 33, 186-193.
[9] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R.
(1992). Extrinsic and intrinsic motivation to use
computers in the workplace. Journal of applied social
psychology, 22(14), 1111-1132.
[10] Van Dolen, W. M., Dabholkar, P. A., & De Ruyter, K.
(2007). Satisfaction with online commercial group
chat: the influence of perceived technology attributes,
chat group characteristics, and advisor communication
style. Journal of retailing, 83(3), 339-358.
[11] Elwalda, A., Lü, K., & Ali, M. (2016). Perceived
derived attributes of online customer
reviews. Computers in Human Behavior, 56, 306-319.
[12] Emrich, O., Paul, M., & Rudolph, T. (2015).
Shopping benefits of multichannel assortment

integration and the moderating role of retailer
type. Journal of Retailing, 91(2), 326-342.
[13] Fornell, C., & Larcker, D. F. (1981). Evaluating
structural equation models with unobservable
variables and measurement error. Journal of
marketing research, 39-50.
[14] Forsythe, S. M., & Shi, B. (2003). Consumer
patronage and risk perceptions in Internet
shopping. journal of Business research, 56(11), 867-
875.
[15] Gupta, A., Su, B. C., & Walter, Z. (2004). Risk profile
and consumer shopping behavior in electronic and
traditional channels. Decision Support Systems, 38(3),
347-367.
[16] Gupta, A., & Arora, N. (2017). Understanding
determinants and barriers of mobile shopping
adoption using behavioral reasoning theory. Journal
of Retailing and Consumer Services, 36, 1-7.
[17] Hassan, A. M., Kunz, M. B., Pearson, A. W., &
Mohamed, F. A. (2006). Conceptualization and
measurement of perceived risk in online
shopping. Marketing Management Journal, 16(1),
138–147.
[18] Jiang, L., Yang, Z., & Jun, M. (2013). Measuring
consumer perceptions of online shopping
convenience. Journal of Service Management, 24(2),
191-214.
[19] Martin, J., Mortimer, G., & Andrews, L. (2015). Re-
examining online customer experience to include

purchase frequency and perceived risk. Journal of
retailing and consumer services, 25, 81-95.
[20] Jum C. Nunnally. (1978). Psychometric Theory (2nd
ed.). Mcgraw-Hill College, New York.
[21] Pappas, I. O., Kourouthanassis, P. E., Giannakos, M.
N., & Lekakos, G. (2017). The interplay of online
shopping motivations and experiential factors on
personalized e-commerce: A complexity theory
approach. Telematics and Informatics, 34(5), 730-
742.
[22] Rouibah, K., Lowry, P. B., & Hwang, Y. (2016). The
effects of perceived enjoyment and perceived risks on
trust formation and intentions to use online payment
systems: New perspectives from an Arab
country. Electronic Commerce Research and
Applications, 19, 33-43.
8309, DOI: 10.18421/TEM71-14, February 2018.
127
[23] Venkatesh, V. (2000). Determinants of perceived ease
of use: Integrating control, intrinsic motivation, and
emotion into the technology acceptance
model. Information systems research, 11(4), 342-365.
[24] Venkatesh, V., Morris, M. G., Davis, G. B., & Davis,
F. D. (2003). User acceptance of information
technology: Toward a unified view. MIS quarterly,

425-478.
[25] Yang, Q., Pang, C., Liu, L., Yen, D. C., & Tarn, J. M.
(2015). Exploring consumer perceived risk and trust
for online payments: An empirical study in China’s
younger generation. Computers in Human
Behavior, 50, 9-24.
Copyright of TEM Journal is the property of UIKTEN-
Association for Information
Communication Technology Education & Science and its
content may not be copied or
emailed to multiple sites or posted to a listserv without the
copyright holder's express written
permission. However, users may print, download, or email
articles for individual use.
International Journal of Productivity and Performance
Management
A structural analysis of the enablers of u-commerce
proliferation in a developing
economy
Mohd. Nishat Faisal, Habibullah Khan,
Article information:
To cite this document:
Mohd. Nishat Faisal, Habibullah Khan, (2016) "A structural
analysis of the enablers of u-commerce

proliferation in a developing economy", International Journal of
Productivity and Performance
Management, Vol. 65 Issue: 7, pp.925-946,
https://doi.org/10.1108/IJPPM-10-2014-0162
Permanent link to this document:
Downloaded on: 30 August 2018, At: 18:03 (PT)
References: this document contains references to 109 other
documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 419 times
since 2016*
Users who downloaded this article also downloaded:
(2016),"E-commerce performance in hospitality and tourism",
International Journal of Contemporary
Hospitality Management, Vol. 28 Iss 9 pp. 2052-2079 <a
href="https://doi.org/10.1108/
IJCHM-05-2015-0247">https://doi.org/10.1108/IJCHM-05-
2015-0247</a>
(2016),"Online privacy and security concerns of consumers",
Information and Computer Security, Vol.
24 Iss 4 pp. 348-371 <a href="https://doi.org/10.1108/ICS-05-
2015-0020">https://doi.org/10.1108/
ICS-05-2015-0020</a>
Access to this document was granted through an Emerald
subscription provided by emerald-
srm:552352 []
For Authors
If you would like to write for this, or any other Emerald
publication, then please use our Emerald
for Authors service information about how to choose which
publication to write for and submission

guidelines are available for all. Please visit
www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to
the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350
books and book series volumes, as
well as providing an extensive range of online products and
additional customer resources and
services.
Emerald is both COUNTER 4 and TRANSFER compliant. The
organization is a partner of the
Committee on Publication Ethics (COPE) and also works with
Portico and the LOCKSS initiative for
digital archive preservation.
*Related content and download information correct at time of
download.
D
ow
nl
oa
de
d
by
W
al
de

n
U
ni
ve
rs
it
y
A
t
18
:0
3
30
A
ug
us
t
20
18
(
P
T
)

A structural analysis of the
enablers of u-commerce
proliferation in a
developing economy
Mohd. Nishat Faisal
Department of Management and Marketing,
College of Business and Economics, Qatar University, Doha,
Qatar, and
Habibullah Khan
Department of Accounting and Information Systems,
College of Business and Economics, Qatar University, Doha,
Qatar
Abstract
Purpose – U-commerce is an emerging paradigm transcending
traditional e-commerce boundaries.
The purpose of this paper is to highlight those issues that
deserve attention in developing successful
u-commerce models.
Design/methodology/approach – The interpretive structural
model technique is adopted to construct
a hierarchical structure, and the impact matrix cross-reference
multiplication applied to a classification
(MIC-MAC) approach is employed to analyze the effect and
dependence among these factors.
Findings – The research shows that there exists a group of
enablers having a high driving power and
low dependence requiring maximum attention and of strategic
importance, while another group

consists of those variables that have high dependence and are
the resultant actions.
Practical implications – Organizations that plans to develop a u-
commerce model would be
benefited from this study. They can understand the difference
between the independent and dependent
variables and their mutual relationships. This would help them
to prioritize their budget and
implement suitable strategies to cater to key variables so as to
exploit the benefits of u-commerce.
Social implications – Most of the GCC countries have very
similar business environment. This
research can easily be adapted to other GCC nations thereby
saving the duplication of time,
efforts and money.
Originality/value – This research was conducted in a developing
economy in a GCC country which is
very fast adopter of new technology. The findings of this study
would serve as a guide to the
businesses who are migrating to a u-commerce model in future.
Keywords Qatar, Interpretive structural model, U-commerce
Paper type Research paper
1. Introduction
The spread of modern wireless technology has opened new
vistas in commerce. The last
decade has witnessed many such interventions in almost all
sectors, which have caused
increase in the customer reach as well as profits. One such
intervention is ubiquitous
computing (Weiser, 1991) is the integration of information
processing in the form of
miniature sensors, cheap microchips and wireless networks into
everyday objects and
activities. The term ubiquitous suggests that small devices will
be so pervasive in

everyday objects that we will not realize them (Serrano and
Botia, 2013) while these International Journal ofProductivity
and Performance
Management
Vol. 65 No. 7, 2016
pp. 925-946
© Emerald Group Publishing Limited
1741-0401
DOI 10.1108/IJPPM-10-2014-0162
Received 22 October 2014
Revised 6 July 2015
Accepted 7 July 2015
The current issue and full text archive of this journal is
available on Emerald Insight at:
www.emeraldinsight.com/1741-0401.htm
The authors would like to express their sincere gratitude toward
the anonymous reviewer(s) and
Editors, John Heap and Dr Thomas F. Burgess for their
insightful comments which have
significantly improved the quality of the final paper.
925
Enablers of
u-commerce
proliferation
D
ow

nl
oa
de
d
by
W
al
de
n
U
ni
ve
rs
it
y
A
t
18
:0
3
30
A
ug
us

t
20
18
(
P
T
)
developments would affect all spheres of human activity
(Langheinrich, 2010). It is
expected that the applications of ubiquitous computing would
pervade all spheres of life:
smart homes, energy efficiency, road safety, work productivity,
health monitoring and
assistance, transportation, education, etc. (Aarts and
Encarnaçao, 2005; Cook et al., 2009).
Ubiquitous computing technologies enable both digital and
physical objects to be
integrated together for the advancement of existing electronic or
mobile commerce into the
next phase that is often referred to as ubiquitous (u-) commerce
(Shi et al., 2012).
U-commerce can be defined as “the integration of e-commerce,
by electronically
identifying physical products, m-commerce, by allowing users
to shop anywhere and
anytime, and ubiquitous computing, by allowing users to shop
intelligently and intuitively
with the help of a smart environment” (Franco et al., 2011, p.

237). According to Roussos
et al. (2003), u-commerce is intimately related to e-commerce
and m-commerce, employing
the infrastructure and the expertise of both. Junglas and Watson
(2003) view u-commerce
as a conceptual extension of e-commerce and m-commerce.
They also identify four main
constructs of u-commerce: ubiquity, uniqueness, universality
and unison. U-commerce is
expected to open new vistas of services that would not only
change the way of access and
use of information, rather it would facilitate the emergence of a
whole new paradigm of
services (Sanchez-Pi and Molina, 2010). Major characteristics
of u-commerce are
(Galanxhi-Janaqi and Nah, 2004):
• customization of information based on variables such as time,
place, preference,
and even weather and traffic conditions; and
• pervasiveness of the devices which are always connected to
the internet via
wireless networks or satellites.
Context is a central key in ubiquitous commerce (Coutaz et al.,
2005). This content
delivery can be adapted to the unique context of the person, the
time, the place, the
network and can act in unison in order to support smarter and
more intelligent delivery
(Russell et al., 2005). Companies can utilize u-commerce for
developing effective
relationships with their customers and provide them with
innovative services
(Kim et al., 2009; Sheng et al., 2008). What makes the u-

commerce model different from
the existing e-commerce models is the context awareness and
intelligent applications.
The technology has the ability to help the business understand
the customer as well as
its environment thus assisting business to develop and present
the customer with
innovative products and services (Wang and Wu, 2014).
Qatar is an oil rich nation with a very fast developing economy.
Though the country
has a very high per capita income, its economy is very much
like an emerging nation.
Efforts to diversify the economy and reduce reliance on the
energy sector have been
only moderately successful. The oil and gas industries still
contribute about half of
GDP. The recent fall in oil prices did have an impact on the
economy. The government
is trying to reduce dependence on oil and gas and diversify the
economy by positioning
Qatar as a logistics and financial hub. Most of the large
companies in Qatar are typical
manufacturing companies though there is an impetus to develop
the knowledge-based
economy. These traits characterize it as a developing economy
much like any other
Asian country. However, the country is considered as an early
adopter of new
technology solutions. This has led the businesses to implement
new solutions to
facilitate growth. In this context, u-commerce opens new
opportunities for retail,
healthcare, travel, education, etc. According to Galanxhi-Janaqi
and Nah (2004), one of
the key issues to accelerate the growth of u-commerce adoption

is to develop a plan for
926
IJPPM
65,7
D
ow
nl
oa
de
d
by
W
al
de
n
U
ni
ve
rs
it
y
A
t
18

:0
3
30
A
ug
us
t
20
18
(
P
T
)
u-commerce’s path. This requires an understanding of the
variables that impact
u-commerce adoption and their contextual relationships. To
achieve this, interpretive
structural modeling (ISM) is an effective modeling technique to
ascertain relationships
among the variables that characterize a problem or an issue
(Warfield, 1974; Sage,
1977). In this paper, factors that can affect the proliferation of
u-commerce are modeled
using ISM and then also categorized depending on their driving
power and dependence.
Based on detailed discussions with experts from retail,

healthcare, transportation, IT
and academia, several variables were identified. Then a critical
analysis was carried
out which led to the combining of a few variables and deletion
of others as they did not
find enough support from the literature. Considering the final
list of ten enablers that
can affect u-commerce, the major objectives of this paper can
be stated as:
• to identify and rank the enablers of u-commerce; and
• to understand the relationships among identified enablers
using ISM.
Following this introduction, the remainder of the paper is
organized as follows. Section 2
provides a background to u-commerce. Section 4 describes
various critical success
factors or enablers related to u-commerce. Section 4 describes
the ISM methodology and
its application to develop a model of enablers of u-commerce.
Finally, Section 5 presents
the discussion of the results.
2. Background
U-commerce, being a new phenomenon in the field of
commerce, provides unique
advantages to the customers – transaction anytime, anywhere
and with anyone
(Sabati et al., 2010). This can be perceived as an extension of e-
commerce, which is a popular
way for doing transactions on the internet (Mannan, 2013), and
m-commerce where mobile
devices and supporting networks facilitate the transaction
(Schwiderski-Groshe and

Knopse, 2002). The concept of u-commerce is to provide and
extend the electronic mode of
operations beyond the conventional personal computers and the
other audio visual aids like
television to a much further horizon immaterial of location,
time and infrastructure. Core
factors identified as components of u-commerce have the
characteristics of uninterrupted
availability with respect to power and battery operated devices,
customization, and
provides individual identity (Wen and Mahatanankoon, 2004).
The multidimensional aspect of u-commerce facilitate
consumers to utilize the
technology in a much more personalized fashion in a custom
made application formats
(Sheng et al., 2008). This addresses both transactional and non-
transactional parameters.
Providing a significant feature of entertainment in u-commerce
applications captures the
customer’s attention to use u-commerce in a user-friendly and
value-adding approach
(Anckar et al., 2003). Balasubramanian et al. (2002) stated that
depending on the nature of
the work, u-commerce applications are classified into two types:
content-delivery-related
and transaction-related. The first one is aimed at reporting,
notification and consultation
and the second one at data entry, promotions, and purchasing.
Some researchers argue
that u-commerce is a new paradigm that extends e-commerce by
integrating wireless,
television, voice and silent commerce (Galanxhi-Janaqi and
Nah, 2004). A comparison of
traditional, e-, m- and u-commerce is provided in Table I.

By the end of 2014 mobile commerce sales in USA was forecast
to touch $70 billion
dollars and is expected to reach a figure of $173 billion by the
end of 2018 (Candrlic,
2014). Juniper Research (2014) was expecting a rise of 40
percent in mobile payments in
2014, which would reach $504 billion. These figures are an
indication of the changing
927
Enablers of
u-commerce
proliferation
D
ow
nl
oa
de
d
by
W
al
de
n
U
ni
ve

rs
it
y
A
t
18
:0
3
30
A
ug
us
t
20
18
(
P
T
)
trends of doing business in which u-commerce is perceived as a
succeeding wave to
earlier technologies (Russell et al., 2005). However, there are
also some studies
that present a different picture about the acceptance of u-

commerce. Mallat and
Tuunainen (2005) expressed that in spite of the potential
benefits of u-commerce, the
anticipated growth was not there in the market. This was
because of the stringent
setbacks in the process of embracing the technology. Therefore,
identifying the
enablers will help to overcome the limitations and develop plans
to embrace the new
models in a seamless fashion.
3. Enablers of u-commerce
The proliferation of u-commerce that would benefit the user and
the provider can be
improved if the supporting variables and their relationships are
delineated. In the
proposed ISM, to identify u-commerce enablers, and to establish
mutual relationship,
brainstorming sessions were conducted with experts. These
experts were from
academia and industry. In the beginning 17 experts were
identified on the basis of
their expertise. As Qatar is not a big country it is not possible to
find many experts.
These experts were first contacted through e-mail and phone to
invite them
to participate in the research. Four of these experts expressed
their inability to
Characteristic Traditional Commerce E-commerce M-commerce
U-commerce
Approach to
customer
Face to face approach

of selling products/
services
Online selling or
buying products/
services
Selling or
buying using
mobile mode of
communication
Using online and mobile
mode of selling and
buying
Device None Desktop or
laptops
Mobile devices
but primarily,
mobile phones
Combination of various
devices, including
“nontraditional” devices
such as everyday objects
Portability Physical location is
required to complete
trade
Laptops or
desktops are
normally
required. normal

portability
Customer’s
device can be
taken almost
anywhere like
cell phone
Good portability of
devices like cell phone
and laptops
Identification
of Customer
Personal identification
or organizational
identification are
required to complete
transactions
Need to identify
to complete
transactions
M-commerce
service can
identify who
customer is
U-commerce has both
options. Customers can
be identifying
automatically or can
introduce themselves

Payment
method
Cash, credit card Digital money Digital money,
mobile credit
Digital money
Reachability Customer are not
reachable all the time
Customer will be
reachable when
they are online
Almost all the
time customer
are reachable
Customers are reachable
anytime anywhere
Accessibility Customer cannot
contact business at
anytime from
anywhere
Mostly customer
cannot contact
business at
anytime from
anywhere
Customer can
contact
business at

anytime from
anywhere
Customer can contact
business at anytime from
anywhere
Source: Authors
Table I.
A comparative
analysis of
traditional, e-, m-
and u-commerce
928
IJPPM
65,7
D
ow
nl
oa
de
d
by
W
al
de
n

U
ni
ve
rs
it
y
A
t
18
:0
3
30
A
ug
us
t
20
18
(
P
T
)

participate citing their busy schedule and two were dropped as
they had less than
ten years of work experience in the industry. Thus, finally out
of 11 experts, seven
were from industry and four were from the academia. Out of the
seven experts from
industry, three were working as IT managers, two as CIO’s, one
as head of
networking, and one as head of IT security. The four experts
from academia had
research interests in the areas of e-commerce, IT security,
privacy, and e-government.
The experts from academia had more than ten years of post-PhD
research experience,
all of them had public Google scholar accounts and they had h-
indexes in range of
15-27. This provided us with confidence that these experts were
well researched in
their domain and the outcome from their discussion could be
used with confidence to
develop our model.
At an initial meeting, literature related to u-commerce was
circulated among
the experts. This literature was based on the comprehensive
literature review done
by the authors. Within a period of two weeks, a brainstorming
session was organized to
identify the variables. Unfortunately, three more experts from
the industry and
one from academia dropped out due to some emergency
appointments in their
organizations. Thus, the workshop/brainstorming session was
conducted with seven
experts. In all, 14 variables/enablers were identified in this
session. The number

was reduced to ten as some variables overlapped, for example,
interoperability
and compatibility. The literature related to these ten variables
was circulated among
the experts.
A week after the initial meeting, a second session was organized
to establish the
relationship among the variables. Before this session, the
opinions of individual experts
were collected regarding the contextual relationships among the
variables. Also before
the session, the authors compiled the responses and highlighted
those relationships
where major differences were found. In the second
brainstorming session, relationships
among all variables were established. In cases of disagreement,
the authors took the
lead to work out a consensus among the experts. Thus, ten
enablers and their
contextual relationships were developed in these brainstorming
sessions, which were
further utilized to develop the ISM model. These enablers are
discussed in the
paragraphs below with a final summary presented in Table II.
3.1 Security
Security considerations are very important for successful u-
commerce applications as it
might become the bottleneck as in case of e-Commerce
development (Gerber and Von
Solms, 2001). A single security breach may result in irreparable
damage to firms in
terms of corporate liability, loss of credibility and reduced
revenues (Cavusoglu et al.,
2015). U-commerce brings forth the possibility of a vast number

of new applications on
the internet that would connect devices, systems, services and
even smart objects, with
a variety of protocols, domains and applications. These changes
make would make it
difficult to anticipate and quantify the information security risk
(Pfleeger and Caputo,
2012). With so many possibilities for using user information,
suitable information
security training is an imperative to improve users’ awareness
that leads to secure
behavior (Safaa et al., 2015). Training courses, workshops,
formal presentations,
internet pages, e-mails, screen savers, posters, pens, games and
meetings are among the
ways that experts can improve the knowledge of users’
information security
(Albrechtsen and Hovden, 2010). Further, security issues like
legal security, physical
security and managerial security should be take into
consideration to increase the
whole security (Zhang et al., 2012).
929
Enablers of
u-commerce
proliferation
D
ow
nl
oa
de

d
by
W
al
de
n
U
ni
ve
rs
it
y
A
t
18
:0
3
30
A
ug
us
t
20
18

(
P
T
)
S.
No.
Enablers of
u-commerce Supported By Comments
1. Security Teo et al. (2005), Koenig-Lewis et al.
(2010), Mattila (2003), Harma and
Dubey (2009), Yu (2012), Liou (2008),
Ketkar et al. (2012) and Amin (2008)
Security can be considered as a state of
being free from any sort of threat or
danger. The need of the solutions with
the value added features like security
and customer data confidentiality for
better returns to u-commerce
2. Compatibility/
interoperability
Koenig-Lewis et al. (2010), Mattila
(2003), Wu and Wang (2005), Lu and Su
(2009), Balaji et al. (2013) and Khraim
et al. (2011)
Compatibility is considered as ability of

one computer or device or software to
work with each other. Compatibility is
one of the main concerns while adopting
or selecting any new technology like u-
commerce. It is stated by the diffusion of
innovation model that the degree of
novelty in the service or product would
be estimated by its compatibility
3. Ease of access Lu and Su (2009), Balaji et al. (2013) and
Ketkar et al. (2012)
Ease of access considered as sending
and receiving information from different
locations, without any problem. It
presents the relevant and specific
choices to the consumers at the specific
location and time in order to make
transactions, irrespective of the present
location and the location required
Ease of access is one of the primary
factors for the consumers of
u-commerce to adopt these technologies
4. Flexibility of
time
Anckar et al. (2003), Fraunholz and
Unnithan (2005), Carlsson and Walden
(2002), Mattila (2003), Suoranta et al.
(2005) and Kim et al. (2010)
Flexibility of time can be understood as
variable work schedule. It is against
traditional working hours to complete
certain task. U-commerce is providing

chance to customers to complete their
transactions under flextime option.
There are no certain working hours to
complete these u-commerce
transactions
5. Lower
transaction
cost
Mattila (2003), Suoranta et al. (2005),
Ketkar et al. (2012), Harma and Dubey
(2009) and Yu (2012)
Cost benefit trend plays a significant
role in customer’s perception of utility
and usage of technology. Proper
understanding of costs can make them
realize the benefits of adopting
u-commerce
6. Convenience
and ease of use
Mattila (2003), Suoranta et al. (2005),
Shen et al. (2010), Harma and Dubey
(2009), Ketkar et al. (2012), Luo et al.
(2010), Gu et al. (2009), Amin (2008) and
Kim et al. (2010)
Customer belief in accepting new
technology depends on his perceived
usefulness. Perceived ease of use not
only helps in understanding the
thought process of customer as an
enabler of u-commerce, but also

explains the variation in the user
intentions
(continued)
Table II.
Enablers of
U-commerce
930
IJPPM
65,7
D
ow
nl
oa
de
d
by
W
al
de
n
U
ni
ve
rs

it
y
A
t
18
:0
3
30
A
ug
us
t
20
18
(
P
T
)
3.2 Compatibility/interoperability
Innovation diffusion model (Rogers, 1995), identifies
“compatibility” as a critical factor
in consumer adoption decision and defines it as “the degree to
which an innovation is
perceived as consistent with the existing values, past

experiences, and needs of
potential adopters” (Yang, 2005). Detlor et al. (2013) in their
research affirmed that
compatibility is the degree to which an innovation is seen to be
compatible with
existing values, beliefs, experiences and needs of adopters. As
u-commerce needs many
applications to work cohesively, a relevant aspect for
environments with multiple
independent systems is interoperability. Interoperability is the
ability of two or more
systems or components to exchange information and use the
information that has been
exchanged (Geraci, 1991). It is widely believed that the
establishment of interoperability
of the information systems of a firm with the ones of other
cooperating firms
(e.g. customers, suppliers and business partners) can generate
significant business
value (Loukis and Charalabidis, 2013). According to Jardim-
Goncalves et al. (2012),
interoperability is a key enabler for unlocking the full potential
of organizations,
processes and systems enabling seamless cooperation among
organizations in all
stages of development and production of goods and services,
reducing barriers to
S.
No.
Enablers of
u-commerce Supported By Comments
7. Privacy Koenig-Lewis et al. (2010), Amin (2008)
and Efraimidis et al. (2009)

Personal information is always
sensitive; it is the responsibility
of the service providers to
enhance the security capabilities in this
emerging technology era.
This enhances the trust on the
privacy provided
8. Saving time
and efforts
Mattila (2003), Suoranta et al. (2005)
and Ketkar et al. (2012)
Saving time and efforts can be
understood as less time and energy
required by individual to complete
certain task. U-commerce can help
people to complete their transactions in
less time as compared to traditional
manual transactions
9. Perceived
usefulness
Wei et al. (2009), Wen and
Mahatanankoon (2004), Koenig-Lewis
et al. (2010), Wu and Wang (2005), Lu
and Su (2009), Balaji et al. (2013), Yang
(2005), Zhou (2011), Yu (2012), Khraim
et al. (2011), Luo et al. (2010), Gu et al.
(2009), Amin (2008) and Kim et al. (2010)
Perceived usefulness can be
understood as a belief of a person to

enhance his or her performance, after
the use of a certain system. Estimating
the perceived usefulness and the
interest of the individual to perform
online transaction using u-commerce
can be a good enabler
10. Technology
innovation
Anckar et al. (2003), Carlsson and
Walden (2002), Wen and
Mahatanankoon (2004) and Zhang et al.
(2009)
Technology innovation is
considered as finding a better way of
doing things with the support of
technology. Overall it can be viewed as
a technology which can provide better
and new solution to meet the
requirement. U-commerce is giving
technological innovation to the current
traditional markets Table II.
931
Enablers of
u-commerce
proliferation
D
ow
nl
oa

de
d
by
W
al
de
n
U
ni
ve
rs
it
y
A
t
18
:0
3
30
A
ug
us
t
20

18
(
P
T
)
communication and fostering a new networked business culture
leading to the growth
of u-commerce. Study of Lin (2011), investigating the effect of
innovation attributes and
knowledge-based trust in online banking, has highlighted the
importance of
compatibility in a u-commerce system.
3.3 Ease of access
Ease of access is the degree to which the consumer believes that
accessing the internet
through any mobile will be free of any effort and will yield
hassle- free transactions
(Lu and Su, 2009). People will no longer be constrained by time
or place in accessing
e-commerce activities. Rather, u-commerce could be accessed in
a manner that may
eliminate some of the labor of life’s activities (Mahatanankoon
et al., 2005). It is one of
the primary factors driving the consumers of u-commerce to
adopt these technologies
(Sharma and Lijuan, 2014). It enables the users to seek
location-specific information
through global positioning systems technology (Zhang et al.,
2010). Delivering

personalized information through devices like mobiles
empowers the customers’ to
adopt a much user-friendly approach in embracing u-commerce
(Zhou, 2011).
It presents the relevant and specific choices to the consumers at
the specific location
and time in order to make transactions, irrespective of the
present location and the
location required (Mahatanankoon et al., 2005).
3.4 Flexibility of time
Keen and Mackintosh (2001) highlighted that among all other
factors flexibility of time
is the most important benefit of the concept of commerce in
online transactions. Time
flexibility is found to be the most acceptable factor for u-
commerce (Teo et al., 2005).
Many researchers discussed that with the help of improved
methods, this can be a vital
factor that influences customers and enables them to adopt u-
commerce (Carlsson and
Walden, 2002; Gu et al., 2009).
3.5 Lower transaction cost
A transaction is a process by which a good or service is
transferred across a
technologically separable interface and the cost involved with
such transaction-related
activities represent transaction cost (Chen et al., 2006).
McEachern (2000) argued that
the transaction costs are the costs of time and information
required to carry out market
exchange. Transaction costs occur in all steps of a consumer’s
purchase decision: need
recognition, search, alternative evaluation, purchase and
outcome. To acquire products,

or resources, customers go through a resource lifecycle that
includes several stages,
each with associated costs: establishing and specifying
requirements, identifying the
source, ordering, paying for, acquiring and testing, integrating,
updating, monitoring
and maintaining, and retiring the product (Chircu and Mahajan,
2006). The major
sources of value creation for a firm are obtained by cost
reduction on account of
efficiencies in the management of transaction costs. Hence the
transaction cost is
considered as critical in order to understand and interpret the
value creation
proposition of a u-commerce application (Andoh-Baidoo et al.,
2012). Cost benefit plays
a significant role in customer’s perception of utility and usage
of technology. According
to Zhang et al. (2010), the reason for high number of users for
SMS and WAP is due to
the user-friendly technology which is inexpensive. Chen and
Hitt (2002) opines that a
proper understanding of costs handling and weighing can make
them realize the
benefits of adopting u-commerce technology.
932
IJPPM
65,7
D
ow
nl
oa

18
(
P
T
)
3.6 Convenience and ease of use
Perceived ease of use can be described as the degree to which a
person believes that
using a particular system is free of effort (Saadé and Bahli,
2005). For any emerging
IT/IS, perceived ease of use is an important determinant of
users’ intention to accept
and usage behavior (Venkatesh, 1999; Agarwal and Karahanna,
2000; Henderson and
Divett, 2003). Perceived ease of use not only helps in
understanding the thought process
of customer as an enabler of u-commerce, but also explains the
variation in the user
intentions (Khraim et al., 2011; Tsiaousis and Giaglis, 2014). It
is stated by Kim et al.
(2010) that perceived ease of use, being influenced by the
innovativeness, influences
individual decision making attitude.
3.7 Privacy
Privacy is a strategic issue that deserves great attention from
both scholars and
practitioners because customer information is used in a variety
of business processes and
can be used in response to competitive pressures (Wang and

Wu, 2014). Online privacy
concerns among the general public originated with the rise of
database systems in the
1980s and the internet in the 1990s (Baek, 2014). On the
internet, people’s online activities
can be traced, stored, saved and even traded to unknown third
parties (Lessig, 2002)
thereby making individuals worried about engaging in e-
commerce (Belanger et al., 2002).
Today, collecting information related to individual customer
preferences and choices is a
competitive necessity for organizations (Lee et al., 2011). This
is due to saturated markets
and intense competition thereby forcing the organizations to use
consumers’ personal
information to develop better marketing strategies (Schwaig et
al., 2013). The threat of the
accidental or deliberate dissemination and use/reuse of personal
information for
unauthorized purposes is a critical impediment to u-service
development and adoption
(Ryan, 2011). Many surveys have revealed that for consumers
of mobile or e-services,
privacy is a key concern (Miltgen and Smith, 2015). These
concerns are more prominent
for u-commerce as u-commerce applications are more pervasive
and ubiquitous. Based on
a study done in Singapore, Yang (2005) opined that privacy is
important throughout the
world for the success of u-commerce.
3.8 Saving of time and effort
Ubiquity of u-commerce facilitates providers to reach their
customers anywhere,
anytime while consumers can obtain information whenever, and
wherever they want

(Chong, 2013) thereby saving time and effort of both groups.
The characteristics of the
customer such as saving time and efforts, zeal toward new
technologies and flair for
novelty are part of personality construct (Keen and Mackintosh
2001). Consumer
approaches differs from person to person. Apart from the above,
the socio-economic
factors also play a vital role in nurturing such attitudes of the
customer that form the
base for accepting the technology of u-commerce (Liou, 2008).
3.9 Perceived usefulness
Prior research indicates that perceived usefulness is an
important indicator for
technology acceptance (Bhattacherjee and Premkumar, 2004;
Venkatesh and Davis,
2000). Mawhinney and Lederer (1990) state that user
satisfaction is strongly related to
the perceived usefulness of the technology-based system. Wei et
al. (2009) found that
perceived usefulness plays an important role in influencing a
user’s decision to
adopt mobile internet activities and m-commerce. Similarly,
consumers would adopt
933
Enablers of
u-commerce
proliferation
D
ow
nl

oa
de
d
by
W
al
de
n
U
ni
ve
rs
it
y
A
t
18
:0
3
30
A
ug
us
t

20
18
(
P
T
)
u-commerce applications only when they perceive it to be useful
as compared to
existing e-commerce applications. The technology acceptance
model identified the role
of perceived usefulness and perceived ease of use as the prime
enablers for adopting
u-commerce (Anckar et al., 2003; Lee and Chang 2013).
Bhattacherjee (2002) added that
by estimating the perceived usefulness, the interest of the
individual to perform online
transaction using u-commerce can be revealed.
3.10 Technology innovation
Today’s consumer can be considered an active information
seeker and they use the
information to adopt new ideas (Lu et al., 2005). Liou (2008)
says that in the process of
enabling the u-commerce, right from conceiving to completion,
every player has a
significant role. In particular, the technological development
should always accommodate
better interface with the customer. Wu and Wang (2005)
concluded from their study
that on the one hand u-commerce users get awareness of the

technological development
while on the other, user interface problems are being nullified
by the service providing
organizations. This is the reason for the rapid spread of u-
commerce.
4. Building the ISM model
4.1 ISM
ISM as a modeling technique has gained popularity as it
provides a digraph model
which makes it easier to understand the implicit relationships
among various variables.
ISM has been applied by a number of researchers in various
fields like m-commerce
(Khan et al., 2015), green supply chain management (Diabat and
Kannan, 2011), supply
chain agility (Agarwal et al., 2007), transparency in food supply
chain (Faisal, 2015),
e-government (Faisal and Rahman, 2008). In ISM, identification
of the variables and the
type of relationships among them is defined by a group to
develop the hierarchical
structure (Bolaños et al., 2005).
ISM model allows the managers to prioritize resources of the
firm accordingly in
managing the issue at hand. Models developed using ISM
technique facilitates effective
planning, scheduling, monitoring and control, thereby
improving the effectiveness of
the strategic process (Faisal, 2010). ISM has the strength that it
can be either used as
group learning process, or individually. Various steps involved
in the ISM methodology
can be summarized as (Faisal and Al-Esmael, 2014; Joshi et al.,
2009):

• Variables that are relevant to the problem or issues are
identified by exhaustive
literature review, opinion of experts or survey.
• Brainstorming is carried out to arrive at contextual
relationships among the
variables leading to the development of Structural Self-
Interaction Matrix (SSIM).
• Initial and final reachability matrices are developed from the
SSIM keeping in
view the transitive links. Transitive links are investigated by
applying that if a
variable X impacts Y and Y impacts Z, then X necessarily has
an impact on Z.
• Based on the relationships as deducted in the reachability
matrix, directed graph
(DIGRAPH) is drawn, and transitive links are removed.
• The resultant digraph is converted into an ISM, by replacing
element nodes with
statements.
• ISM model is reviewed to check for conceptual inconsistency,
and the necessary
modifications are made.
934
IJPPM
65,7
D
ow

t
20
18
(
P
T
)
4.2 SSIM
For analyzing the enablers of u-commerce, a contextual
relationship of the “positive
impact” type is considered. The relationship between any two
enablers (i and j) and
the direction of this relationship is developed for all the
variables. This would lead to
the development of SSIM. Four symbols as shown in Table III
are used to denote the
direction of relationship between the enablers (i and j).
Using the above analogies, Table IV depicts the existence and
nature of
relationships among the ten enablers of u-commerce.
4.3 Reachability matrix
The SSIM as shown in Table IV is transformed into a binary
matrix, called the initial
reachability matrix (Table V), by substituting V, A, X, O by 1
and 0 as per the rules
mentioned in Table VI.

Nature of relationship Symbol
i positively impact j V
j positively impact i A
i and j positively impact each other X
i and j are unrelated O
Table III.
Nature of
relationship and
the symbol
10 9 8 7 6 5 4 3 2
1. Security V O V V O O O V O
2. Compatibility/interoperability A O V O X V X X
3. Ease of access A V V A X V X
4. Flexibility of time A V V A X V
5. Lower transaction cost O O A O A
6. Convenience and ease of use A O V O
7. Privacy O V O
8. Saving of time and effort A V
9. Perceived usefulness A
10. Technology innovation
Table IV.
Structural
self-interaction
matrix (SSIM)
1 2 3 4 5 6 7 8 9 10
1. Security 1 0 1 0 0 0 1 1 0 1
2. Compatibility/interoperability 0 1 1 1 1 1 0 1 0 0

3. Ease of access 0 1 1 1 1 1 0 1 1 0
4. Flexibility of time 0 1 1 1 1 1 0 1 1 0
5. Lower transaction cost 0 0 0 0 1 0 0 0 0 0
6. Convenience and ease of use 0 1 1 1 1 1 0 1 0 0
7. Privacy 0 0 1 1 0 0 1 0 1 0
8. Saving of time and effort 0 0 0 0 1 0 0 1 1 0
9. Perceived usefulness 0 0 0 0 0 0 0 0 1 0
10. Technology innovation 0 1 1 1 0 1 0 1 1 1
Table V.
Initial reachability
matrix
935
Enablers of
u-commerce
proliferation
D
ow
nl
oa
de
d
by
W
al
de
n

Next step is to explore the transitive links exist among the
variables. Though in
Table IV several entries are O, indicating that there exist no
direct relationships among
these variables and thus the corresponding entries in the initial
reachability matrix is
0 in both the column and the row. But in reality when we apply
the transitive impact
rule several entries might change. For example, in the SSIM
(Table IV) there is no direct
relationship between enabler 1 and enabler 5, thus in the initial
reachability matrix the
cell entry ( p15) is 0. But on examining the transitive links in
SSIM, it was found that
enabler 1 impacts enabler 8 and enabler 8 impacts enabler 5.
Hence according to step 4
of the ISM methodology, it can be inferred that enabler 1 has an
impact on enabler 5.
Thus in final reachability matrix (shown in Table VII) the cell
entry ( p15) is 1. Several
other entries (marked with an * in Table VII) were similarly
changed.
Table VII which is the final reachability matrix also provides
the driving power and
the dependence of each enabler which are the sum of entries
across row and column for
each enabler. Driving power indicates the total number of
enablers (including self)
which an enabler can positively impact. Dependence of an
enabler is the total number of
enablers (including self) which may be positively impacting it.
4.4 Level partitions
Final reachability matrix as shown in Table VII is utilized to
develop the reachability

set and antecedent set for each enabler. The reachability set can
be found by
examining the row of the reachability matrix while antecedent
set consists of all the
elements found in the column of each variable (Warfield, 1974).
Further, an
intersection of these two sets is also developed. Once this is
completed for all the
elements, an analysis is done to find out the element for which
the entries of the
intersection set and the reachability are identical. This
element(s) would
(i, j) entry in SSIM (i, j) entry (j, i) entry
V 1 0
A 0 1
X 1 1
O 0 0
Table VI.
Rules for
transforming SSIM
into reachability
matrix
1 2 3 4 5 6 7 8 9 10 Driving power
1. Security 1 1* 1 1* 1* 1* 1 1 1* 1 10
2. Compatibility/interoperability 0 1 1 1 1 1 0 1 1* 0 7
3. Ease of access 0 1 1 1 1 1 0 1 1 0 7
4. Flexibility of time 0 1 1 1 1 1 0 1 1 0 7
5. Lower transaction cost 0 0 0 0 1 0 0 0 0 0 1
6. Convenience and ease of use 0 1 1 1 1 1 0 1 1* 0 7
7. Privacy 0 1* 1 1 1* 1* 1 1* 1 0 8
8. Saving of time and effort 0 0 0 0 1 0 0 1 1 0 3

9. Perceived usefulness 0 0 0 0 0 0 0 0 1 0 1
10. Technology innovation 0 1 1 1 1* 1 0 1 1 1 8
Dependence 1 7 7 7 9 7 2 8 9 2
Note: *Indicates a transitive link
Table VII.
Final reachability
matrix
936
IJPPM
65,7
D
ow
nl
oa
de
d
by
W
al
de
n
U
ni
ve

rs
it
y
A
t
18
:0
3
30
A
ug
us
t
20
18
(
P
T
)
be considered as the topmost element(s) in the hierarchy. This
element would then
be removed from the reachability set and the antecedent set of
all the remaining
elements. This iterative process is continued till the levels of all

the variables under
study are identified (Tables VIII and IX).
4.5 Building the ISM-based model
From the literature review it is clear that u-commerce
proliferation may be affected by a
number of variables and thus in place of considering their
individual affect it would be
helpful if the relationship among these variables are presented
in a form of a model.
To facilitate this understating ISM emerges as a preferred
methodology. ISM is capable
of representing implicit relationships in a well-defined
structure. A digraph is
developed utilizing the entries in Table V, after removal of
transitive links the ISM
model emerges as shown in Figure 1.
4.6 MIC-MAC analysis
MIC-MAC (Matrice d’Impact Croisés – Multiplication
Appliqueé à un Classement or
Matrix of Cross-Impact – Multiplications Applied to
Classification) analysis (Godet,
1986, 1987), is a methodology to classify the enablers into four
clusters (Diabat and
Kannan, 2011). “Autonomous category” of variables are those
that are weak on driver
power and dependence. In contrast to these “connecting
variables” are strong on both
of these dimensions. This indicates that they are influenced by
lower level variables
and affects the variables higher in the hierarchy. Those
variables that exhibit high
dependence and very low driving power can be called
“dependent enablers.” They can

Enabler pi Reachability set R( pi) Antecedent set A( pi)
Intersection set R( pi) ∩ A( pi) Level
1 1,2,3,4,5,6,7,8,9,10 1 1
2 2,3,4,5,6,8,9 1,2,3,4,6,7,10 2,3,4,6
3 2,3,4,5,6,8,9 1,2,3,4,6,7,10 2,3,4,6
4 2,3,4,5,6,8,9 1,2,3,4,6,7,10 2,3,4,6
5 5 1,2,3,4,5,6,7,8,10 5 I
6 2,3,4,5,6,8,9 1,2,3,4,6,7,10 2,3,4,6
7 2,3,4,5,6,7,8,9 1,7 7
8 5,8,9 1,2,3,4,6,7,8,10 8
9 9 1,2,3,4,6,7,8,9,10 9 I
10 2,3,4,5,6,8,9,10 1,10 10
Table VIII.
Iteration i
Enabler pi Reachability set R( pi) Antecedent set A( pi)
Intersection set R( pi) ∩ A( pi) Level
1 1,2,3,4,6,7,8,10 1 1 V
2 2,3,4,6,8 1,2,3,4,6,7,10 2,3,4,6 III
3 2,3,4,6,8 1,2,3,4,6,7,10 2,3,4,6 III
4 2,3,4,6,8 1,2,3,4,6,7,10 2,3,4,6 III
6 2,3,4,6,8 1,2,3,4,6,7,10 2,3,4,6 III
7 2,3,4,6,7,8 1,7 7 IV
8 8 1,2,3,4,6,7,8,10 8 II
10 2,3,4,6,8,10 1,10 10 IV
Table IX.
Iteration ii-iv
937
Enablers of
u-commerce

proliferation
D
ow
nl
oa
de
d
by
W
al
de
n
U
ni
ve
rs
it
y
A
t
18
:0
3
30

A
ug
us
t
20
18
(
P
T
)
be thought of as the resultant action of all the lower level
variables. Lastly, those
variables that rank very high on driving power dimension are
known as “strategic
variables.” A driving power and dependence diagram is
constructed using Table V as
shown in Figure 2.
Flexibility
of Time
Compatibility/
Interoperability
Technology
Innovation
Convenience and

Ease of use
Security
Privacy
Saving of time and effort
Lower Transaction
Cost
Perceived
Usefulness
Ease of Access
Figure 1.
ISM-based model
for the enablers of
u-commerce
10 1
9
8 7 IV III
7 2, 3
4, 6
6
5
4

3 I 8 II
2
1 5
1 2 3 4 5 6 7 8 9 10
Dependence
D
ri
ve
r
P
o
w
e
r
Figure 2.
Driver power and
dependence diagram
938
IJPPM
65,7
D
ow
nl

20
18
(
P
T
)
5. Discussion
The driver power-dependence diagram shown in Figure 2 helps
to classify various
enablers of u-commerce in a developing economy. It is found
that none of the enablers
have a low driving power and low dependence and thus it can be
inferred that all the
variables are important and the management need to consider
them all if they really
want to have a successful u-commerce model. In the next cluster
we have variables like
privacy, trust, and security. These variables have high driving
power and low
dependence which indicates their importance in the whole
model. These variables are
most important for u-commerce to be adopted by the consumers.
U-commerce is
generally supported by open platforms which are very dynamic
and distributed
thereby increasing the security concerns of the customers. Also
due to concerns of
these systems falling prey to security breaches, customers might
be apprehensive
about the loss of their private data. The model explicitly

highlights this concern of the
customers as these two variables form the base of the model and
any threat of the
leakage of and use/reuse of personal information for
unauthorized purposes is a critical
barrier to u-service development and adoption (Ryan, 2011).
Among this cluster
security emerges as the enabler with the highest driver power
indicating
that appropriate enforcement of security protection is vital for
wider acceptance of
u-commerce systems (Shi et al., 2012). Robust security systems
expedite the process of
innovation in technology leading organizations to invest in new
technologies. Further,
u-commerce technology platform has emerged as today’s
prominent computing
paradigm as a result of advances in related technologies,
especially, wireless, mobile
and sensor technologies coupled with the dissemination of these
technologies in prices
affordable by the masses (Cayci et al., 2013).
The second cluster is of connecting variables and consists of
variables like flexibility
of time, convenience and ease of use, ease of access and
compatibility/interoperability.
These factors form a connection among the lower and upper
level variables in the
model. These variables are the ones which are influenced by
lower level variables and
in turn impact other variables in the model. All of these
variables would help in the
saving of time and effort by the consumer of u-commerce
services.

The last cluster consists of variables such as lower transaction
cost and perceived
usefulness. These variables have high dependence indicating
that they are the resultant
actions. U-commerce provides customers the opportunity to be
connected seamlessly in
context-aware networks, allowing personalized services to be
delivered in a timely manner
(Kim et al., 2009). This would ultimately result in lower cost
and saving of time and effort
for the end customer and improvement in the perception of the
usefulness of u-commerce
services. Though perceived usefulness is critical for the
adoption of u-commerce model of
business by customers, the model presented in this paper
indicates that perceived
usefulness cannot be improved independently rather it requires
working on other lower
level variables which in turn have an impact on this variable.
U-commerce is based on the emerging paradigm of ubiquitous
computing which
is thought to impact the quality of life in a positive manner and
augment the capabilities
of humans by providing an integrated framework of computers,
humans and objects
(Fano and Gershman, 2002). But ubiquitous computing and u-
commerce are still in the
early stages of development (Martínez-Torres et al., 2015) and
thus the ISM model
developed in this paper helps to provide an understanding of
mutual relationships
among the variables. The model delineates those aspects that
need attention from the
strategists to make u-commerce ventures successful and provide
better customer value

by improving customer satisfaction and developing sustainable
relationships.
939
Enablers of
u-commerce
proliferation
D
ow
nl
oa
de
d
by
W
al
de
n
U
ni
ve
rs
it
y
A

t
18
:0
3
30
A
ug
us
t
20
18
(
P
T
)
Though there are privacy and security concerns, there is
research that motivates the
provider of u-commerce as it is expected that in lieu of benefits
provided by an
organization customers are willing to share their personal
information (Wang and Wu,
2014). A classification of people on the basis of privacy
concerns categorizes them into
three groups: privacy fundamentalists, privacy pragmatists and
unconcerned customers.
Most customers fall in privacy pragmatists category, those who

are likely to assess the
potential benefits and privacy risks of providing their
information before deciding
whether to disclose it (Kobsa, 2007; Angst and Agarwal, 2009).
Thus, organizations need
to present to the customers the potential benefits of u-commerce
and devise suitable
strategies to solicit customer data and use it effectively to gain
competitive advantage.
By eliminating specific time and position to collect customer
information, in future it is
expected that u-commerce would provide new opportunities for
businesses and would
emerge as the key to gather relevant customer information to
improve their service (Wang
and Wu, 2014). It is hoped that the result of this research may
be of benefit to
organizations in retail, healthcare, and logistics among others
that are intending to
migrate to a u-commerce model in future. It will help the
managers in three ways:
(1) develop suitable strategies to cater those factors that are
most critical for the
successful u-commerce venture;
(2) understand interrelationships among the factors to prioritize
time and resources
for implementing u-commerce applications; and
(3) developing a u-commerce strategy for all the players in the
value chain.
6. Limitations and scope for future research
Similar to other research, the present study also has several

limitations. First, the ISM
model’s accuracy is dependent on the decision makers’
knowledge about topic and thus
may have an element of bias due to the emerging nature of the
issue. Second, the model
developed in the study has not been statistically validated. Co-
variance-based
structural equation modeling (SEM) approaches can be used to
test the validity of the
model developed through ISM approach. These models can be
tested using AMOS or
LISREL software to further examine the relationships. In case it
is not possible to
collect a large amount of data as required by the co-variance-
based SEM, partial least
squares-based model using the software PLF-Graph can be
applied to test the validity
of the model derived through ISM. Future work may consider
developing a similar
model for other GCC countries that have a very similar
economic environment to Qatar.
Furthermore, ISM does not provide any quantitative information
about the linked
variables. Thus, in future work a graph theoretic approach can
be applied to develop a
quantitative value for the system.
References
Aarts, E. and Encarnacao, J.L. (2005), “Into ambient
intelligence”, in Aarts, E. and Encarnacao, J.L.
(Eds), True Visions: The Emergence of Ambient Intelligence,
Springer, Berlin and
Heidelberg, pp. 1-16.
Agarwal, A., Shankar, R. and Tiwari, M.K. (2007), “Modeling

agility of supply chain”, Industrial
Marketing Management, Vol. 36 No. 4, pp. 443-457.
Agarwal, R. and Karahanna, E. (2000), “Time flies when you’re
having fun: cognitive absorption
and beliefs about information technology usage”, MIS
Quarterly, Vol. 24 No. 4, pp. 665-694.
940
IJPPM
65,7
D
ow
nl
oa
de
d
by
W
al
de
n
U
ni
ve
rs
it

y
A
t
18
:0
3
30
A
ug
us
t
20
18
(
P
T
)
http://www.emeraldinsight.com/action/showLinks?doi=10.1108
%2FIJPPM-10-2014-
0162&crossref=10.1016%2Fj.indmarman.2005.12.004&isi=0002
46590500005&citationId=p_2
%2FIJPPM-10-2014-
0162&crossref=10.1016%2Fj.indmarman.2005.12.004&isi=0002
46590500005&citationId=p_2

%2FIJPPM-10-2014-
0162&crossref=10.2307%2F3250951&isi=000173923600006&ci
tationId=p_3
Albrechtsen, E. and Hovden, J. (2010), “Improving information
security awareness and behaviour
through dialogue, participation and collective reflection. An
intervention study”, Computer
& Security, Vol. 29 No. 4, pp. 432-445.
Amin, H. (2008), “Factors affecting the intentions of customers
in Malaysia to use mobile phone
credit cards”, Management Research News, Vol. 31 No. 7, pp.
493-503.
Anckar, B., Carlsson, C. and Walden, P. (2003), “Factors
affecting consumer adoption decisions
and intents in ubiquitous commerce: empirical insights”,
Proceedings of 16th Bled
e-Commerce Conference, Slovenia, pp. 886-902.
Andoh-Baidoo, F.K., Osei-Bryson, K.M. and Amoako-Gyampah,
K. (2012), “Effects of firm and IT
characteristics on the value of e-commerce initiatives: an
inductive theoretical framework”,
Information Systems Frontiers, Vol. 14 No. 2, pp. 237-259.
Angst, C.M. and Agarwal, R. (2009), “Adoption of electronic
health records in the presence of
privacy concerns: the elaboration likelihood model and
individual persuasion”, MIS
Baek, Y.M. (2014), “Solving the privacy paradox: a counter-
argument experimental approach”,

Computers in Human Behavior, Vol. 38, pp. 33-42.
Balaji, K.V.A., Karnataka, S.J.C.E., Jha, B. and Rajiv, R.
(2013), “Factors affecting purchase
intention on mobile shopping websites in India”, Proceedings of
International Conference
on Management and Information Systems, pp. 231-241.
Balasubramanian, S., Peterson, R.A. and Jarvenpaa, S.L. (2002),
“Exploring the implications of
M-commerce for markets and marketing”, Journal of the
Academy of Marketing Science,
Vol. 30 No. 4, pp. 348-361.
Belanger, F., Hiller, J.S. and Smith, W.J. (2002),
“Trustworthiness in electronic commerce: the role
of privacy, security, and site Attributes”, Journal of Strategic
Information Systems, Vol. 11
No. 3, pp. 245-270.
Bhattacherjee, A. (2002), “Individual trust in online firms: scale
development and initial test”,
Journal of Management and Information Systems, Vol. 19 No.
1, pp. 211-241.
Bhattacherjee, A. and Premkumar, G. (2004), “Understanding
changes in beliefs and attitude
towards information technology usage: a theoretical model and
longitudinal test”, MIS
Bolanos, R., Fontela, E., Nenclares, A. and Paster, P. (2005),
“Using interpretive structural
modeling in strategic decision making groups”, Management
Decision, Vol. 43 No. 6,
pp. 877-895.

Candrlic, G. (2014), “E-commerce trends 2014-2018 – slower
growth but still a huge opportunity”,
ecommerce, news, security, website speed, Globaldots, London,
May 31, available at:
www.globaldots.com/e-commerce-trends-2014-2018-slower-
growth-still-huge-opportunity/
(accessed July 6, 2014).
Carlsson, C. and Walden, P. (2002), “Further quests for value-
added products & services in
ubiquitous commerce”, Proceedings of the ECIS 2002 – The
European Conference on
Information Systems, Gdańsk, June 6-8.
Cavusoglu, H., Cavusoglu, H., Son, J.Y. and Benbasat, I.
(2015), “Institutional pressures in security
management: direct and indirect influences on organizational
investment in information
security control resources”, Information & Management, Vol.
52 No. 4, pp. 385-400.
Cayci, A., Menasalvas, E., Saygin, Y. and Eibe, S. (2013),
“Self-configuring data mining for
ubiquitous computing”, Information Sciences, Vol. 246, pp. 83-
99.
Chen, M.S., Chang, H.J., Huang, C.W. and Liao, C.N. (2006),
“Channel coordination and transaction
cost: a game-theoretic analysis”, Industrial Marketing
Management, Vol. 35 No. 2,
pp. 178-190.
941
Enablers of

u-commerce
proliferation
D
ow
nl
oa
de
d
by
W
al
de
n
U
ni
ve
rs
it
y
A
t
18
:0
3

Reproduced with permission of the copyright owner. Further re.docx

Reproduced with permission of the copyright owner. Further re.docx

Recommended

Recommended

More Related Content

Similar to Reproduced with permission of the copyright owner. Further re.docx

Similar to Reproduced with permission of the copyright owner. Further re.docx (20)

More from kellet1

More from kellet1 (20)

Recently uploaded

Recently uploaded (20)

Reproduced with permission of the copyright owner. Further re.docx