Mobile Shopping Adoption: Research Insights
Ms. Asha Sahu1, Dr. G.
K. Deshmukh2, *
1,2Institute of Management, Pt.
Ravishankar Shukla University, Raipur
*Corresponding Author:
gkd16@yahoo.co.in
[Received: 08 July 2020; Accepted: 18 September
2020; Published Online: 12 February 2021]
Abstract: Mobile shopping is the new attraction
among the customers and retailers. High tech life, smart connectivity, advanced
mobile phones and busy lifestyles have paved the way of mobile shopping
adoption. Mobile commerce is progression of e-commerce and mobile shopping is
considered as a subset of m-commerce. Mobile shopping has created opportunity
for retailers as well customers and has developed new means of marketing. In a
past few decades’ mobile commerce industry has seen tremendous growth and
mobile shopping is most desired applications of m-commerce. Though there is
huge potential of mobile shopping but yet a long way is to be travelled in the
emerging economies. Mobile shopping is at infancy stage in many developing
nations and hence researchers can contribute more towards the literature of
mobile shopping. This paper is an attempt to throw some light on mobile
shopping adoption studies.
Keywords: Mobile
Shopping, M-shopping, M-commerce, Mobile shopping adoption, Technology
acceptance
Introduction
The marketing model all
around the globe is changing gradually and hence Retailers have reorganized
their way of reaching out to their customers. Marketing have been
revolutionised from product based marketing to the relationship marketing and
from traditional marketing to digitised marketing. Not only MNCs but small
retailers have also shown significant evolution in their way of doing business.
They have improved their sales channels and have upgraded themselves with
changing trend and lifestyles of the customers. Marketers have identified
potential of mobile facilities for reaching out to their customers in speedy
and timely manner.Due to innovative offerings of mobile devices and
multi-functionality features utilisation of mobile devices such as: Smart phones
and tablets have seen exponential growth and have created opportunities for
technological advancements which are ultimately leading to more convenient and
efficient way of living (Groß, 2015a; Chen, 2013).
Mobile commerce is an igniting
alternative sales channel and is of huge potential in today’s era.Mobile
Commercerefers to all sort of monetary transactions, directly or indirectly
carried out with the use of wireless telecommunication network (Barnes, 2002).
Mobile commerce is defined as transactions done through mobile or
internet using mobile devices (Chong, 2013).Mobile commerce, Mobile
shopping, Mobile banking etc. are an interesting topic and has drawn attention
of academicians and industrialists since past few decades. Mobile shopping is
the most popularservice offered by mobile commerce;it is contemporary consumer
buying practices. With the passage of time, dependability of customers on smart
phones has increased and hence the trust to use mobile devices for very
purposes like shopping, banking, payments, financial services etc. has also
gradually developed among the users.Mobile
shopping allows companies to get connected to the customers’ directly;
personalised information, push notifications, new product information, creating
social media networks by recommendations are the attractiveness for the
companies which motivates them to invest in creating their own mobiles apps
finally leading to enhanced customer services (Victoria and Helen, 2013) Ease
of browsing, convenience, speedy services are the significant reasons behind
consumers’ preferences for Mobile applications. Ubiquity
(anytime,anywhere), Accessibility, Convenience, Localization, Instant
connectivity “always on”, Time sensitivity(real time), Personalization are the
advantages of mobile shopping as compared to e-commerce services. Emergence of mobile shopping applications has established virtual
outlets which have empoweredcustomers to access varieties of products and
services at their finger tips. Due to these new modes of servicing to customers
organizations have reduced the cost of Infrastructure, man power and other
expenses, whereas customers have unboxed products, services,
brands and companies without leaving home hassle free. The aim of this paper is
to (i) throw light on the existing literature on mobile shopping adoption (ii)
to discuss the theories and models used for mobile shopping adoption and to
project future research directions in the context of mobile shopping adoption.
Material and Methods
Data Source
The data was collected
from secondary sources. Search for journal articles related with mobile
shopping adoption was made from search engines – Google and Google Scholar and
from Research Gate.
Review Process
Literature review of
articles were done while following methodology suggested by Armstrong et al.
(2012) and Turner et al. (2013). It was done in three steps, in step one
articles were collected that were published during 2008 to 2020 on mobile
shopping adoption purposively. In step two, articles were screened on the basis
of objectives and relevance total 13 articles were selected for the purpose of
review. In step three, selected articles were arranged in tabular form on the
basis of context, country, sample size, factors identified, statistical
techniques used and main findings.
Literature Analysis
There are various researches on mobile shopping
which have been conducted so far in different parts of world; however, still
there is a gap in the mobile shopping literaturewhen it comes to
consumerbehaviour perspective (Faqih and Jaradat, 2015).Mobile shopping apps
acts as an interface between the retailers and customers. It allows consumers
to gather information about the product, price, and placeallowing them to have
update about the various promotional campaigns. Mobile shopping gives
opportunity to shop without time and space constraints. No physical assistance
of sales person is required and varieties of goods are available at finger
tips. They just have to browse the apps for shopping, chose the product and
delivery of products is ensured at their home or at nearby stores (Chen 2018).
Gupta and Arora (2017) posited that mobile shopping adoption is significantly
determined by consumers’ price saving orientation.Investigating the factors affecting mobile
shopping adoption is the most frequently discussed topics as per comprehensive
literature review (Dai and Palvia, 2009; Zhang et al., 2012). Previous studies
have mainly employed technology driven factors like perceivedusefulness,
perceived ease of use, interactivity and relative advantage which are derived
from traditional models such as the technology acceptance modelof Davis
(1989), innovation diffusion theoryof Rogers (1983) and the unified
theory of acceptance and usage of technology of Venkatesh et al.
(2003)however researchers have suggested to examine the factors which study the
non-technological aspects and have insisted to explore role ofpromotion and
barrier factors for deep understanding
of mobile shopping adoption (Gerpott and Thomas,2014; Zhang et al.,
2012).Mobile shopping offers shopping on the go, anywhere and anytime shopping (Hung
et al., 2012; Lu and Su, 2009), convenience, personalization and renders new & advanced services to
customers.
Theories and Models of Mobile Shopping
Adoption
Researchers have viewed Mobile shopping as
technological innovation and have therefore deployed various technology
acceptance models for studying the customers’ mobile shopping adoptions such as
Diffusion of Innovation Theory explains the stages of adoption and the
classified the types of adopters, he had also presented the five
factors(Relative advantage, Compatibility, Complexity, Triability and
Observability) which influences the adopter categories.(Rogers, 1983);theory of
reasoned action (TRA) which postulates that individual behaviour is an outcome
of his\her behavioural intentions which is again determined by an individual’s
attitude towards certain phenomenon. As per TRA attitude and subjective norm
put indirect impact on behaviour (Fishbein and Ajzen, 1975), the social
cognitive theory deals with the social influence on behaviour (Bandura, 1986),
the technology acceptance model (TAM) is the most important theory posited to
understand the determinants of information technology use and acceptance (Davis,
1989), the theory of planned behaviour (TPB) generally talks about the rational
decision making of an individual, it is considered as an extension of TRA
integrating Perceived behavioural control (Ajzen, 1991), the model of PC
utilisation (Thompson and Higgins,1991), the model combining TAM and the TPB
(Taylor and Todd, 1995), the diffusion of innovation (DOI) theory (Rogers,
1995), extended TAM (Venkatesh and Davis, 2000) articulated Perceived
usefulness as the strongest determinant of system use. Unified theory of
acceptance and use of technology (UTAUT) builds understanding on user intention
to use Information system and usage behaviour (Venkatesh et al., 2003). Table
number 1, presents brief summary of the reviewed articles with respect to
context, country, sample size, factors identified by researchers in their
study, statistical techniques used to analyse data, major findings. Table 2,
presents future research suggestions extracted from reviewed papers.
Table:
1 Review of Literature in Brief (2008-2020)
|
Researchers
|
Contexts
|
Country
|
Sample size
|
Factors identified
|
Statistical techniques
|
Findings
|
|
Manzano et al.(2008)
|
M-shopping acceptance
|
Spain
|
470
|
Personality factors, affinity to mobile
telephones, compatibility and
innovativeness
|
Structural Equation Modelling (SEM)
|
Affinity to mobile telephones,
compatibility and innovativeness directly and positively influences the
intention to engage in M-shopping.
|
|
Yang (2010)
|
M shopping services adoption
|
USA
|
400
|
Performance expectancy (PE), social influence
(SI) , and facilitating conditions (FC)
|
SEM
|
Hedonic performance expectancy emerged as
significant predictor of US consumers’ intentions to use mobile shopping
services.
|
|
Yang and Kim(2012)
|
M shopping Motivation
|
USA
|
400
|
idea, efficiency, adventure, and gratification
motivations
|
Multiple discriminant analysis
|
Idea and efficiency were found to be the
major determinants of m-shopping motivation
|
|
Yang(2012)
|
M shopping adoption
|
USA
|
400
|
Self-efficacy, Usage experience, technology innovativeness
|
SEM
|
Consumers’ technology traits moderates
perceived usefulness (PU), perceived enjoyment, perceived behavioural control
(PBC), and subjective norm (SN)
|
|
Wong et al.(2012)
|
M shopping adoption
|
Malaysia
|
142
|
Perceived Ease of Use
(PEOU), PU,
perceived risk (PR), (SN) and personal innovativeness (PI)
|
Multiple regression analysis (MRA)
|
PU,
PEOU and SN determines m shopping adoption.
|
|
Hahn and kim (2013)
|
M shopping intention
|
US
|
504
|
Peer influence, text messaging behavior
|
T-test
|
Peer influence perception positively
influences both fashion/brand interest as well as text messaging behavior via
mobile devices which forms m-shopping intention.
|
|
Yang & Forney(2013)
|
M shopping adoption
|
America
|
400
|
Technology anxiety (TA),
FC, SI
|
SEM
|
FC influences PE and TA. Consumers with a
high level of TA rely more on SI for mobile shopping than consumers with a
low level of TA.
|
|
Agrebi and Jallais(2015)
|
M shopping acceptance
|
France
|
400
|
Perceived Ease of Use,
PU, Perceived Enjoyment, Satisfaction
|
multi-group analysis
|
Perceived enjoyment and satisfaction plays
important role in determining customers’ intention towards use of smart
phones for mobile shopping.
|
|
Gupta and Arora(2017)
|
Mobile shopping attitude and Intentions
|
India
|
237
|
Motivators: Price& Saving, Variety /
Choice, convenience.
Inhibitors: SE, consumer anxiety and
relative advantage.
|
SEM
|
Price& savingfound as motivators and SE
found as inhibitors of mobile shopping adoption.
|
|
Madan and Yadav(2017)
|
M shopping adoption
|
India
|
304
|
Hedonic motivation(HM),
Perceived Critical mass(PCM), PR, FC, PBC, Cost, PI
|
SEM
|
BI was
observed in mobile shopping adoption however PRS(Perceived Regulatory
support) was found to be statistically insignificant in determining
BI(Behavioural Intention)
|
|
Natrajan et al.(2017)
|
M shopping adoption
|
India
|
675
|
PR, PU, PEOU, perceived enjoyment.
Satisfaction and PI
|
SEM
|
Device type and age found to moderate
mobile shopping applications’ intention to use.
|
|
Ghazali et al.(2018)
|
M shopping adoption
|
Malaysia
|
453
|
SN, PEOU, PU, PBC, PI, attitudes, Trust
|
SEM
|
PEOU, PU, and attitudes, PBC, trust and PI,
significantly influences m shopping intention except SN.
|
|
Phong et al.(2018)
|
M shopping adoption
|
Vietnam
|
208
|
Trust, SE, PR, perceived cost
|
SEM
|
Trust and SE acts as promotional factors
whereas PR and perceived cost acts as Barrier factors
|
(Source: Authors own)
Table:
2 Future Research Suggestions from Literature Review
|
Researchers
|
Future Research
Directions
|
|
Gupta and Arora(2017)
|
(i)Examining the moderating role of
demographic factors, experience with technology,personal innovativeness,
perceived risk and risk taking behaviour on mobile shopping adoption can be
of interesting research in future
moreover
(ii)Cross cultural differences in mobile
shopping adoptionand
(iii)actual use behaviour of mobile
shopping adoption can be studied.
|
|
Natrajan et al.(2017)
|
(i)A longitudinal study
can be conducted to analyze the constructs
(ii)Combined effect of
perceived risk and consumer trust on use mobile shopping applications
intentions can be investigated.
|
|
Yang and Kim(2012)
|
(i)Categorisation of mobile shopping
services characteristics as per customers’ motivation will an important
aspect to study for better understanding of mobile shoppers and their
lifestyle differences.
|
|
Manzano et al.(2009)
|
(i)Future researchers may incorporate
antecedents of affinity to mobile phones and innovativeness.
|
|
Phong et al.(2018)
|
(i)Antecedences of trust and risk can be
integrated to TRA for future studies
(ii)Intention toadopt is a self-reported
variable can be used in future studies.
(iii) Actual usage of m-shopping should be
studied.
|
|
Ghazali et al.(2018)
|
(i)Causal relationships between intentions
and use can be studied.
(ii)Consumer can be further classified on
the basis of moderating factors suchas age, gender and experiences in future
studies.
(iii)Consumers’ adoption intentions can be
studied with additional constructs such as compatibility, accessibility and
convenience.
(iv) Apart from analysing individual
characteristics, technology, system, and experience flow perspectives can
also be interesting research topics.
(v)Shoppers’ perceptionsof M-shopping
regarding continuous purchasing intentions, and preferred types of
productacquisitions through M-shopping should be explored.
|
|
Wong et al.(2012)
|
(i)A comparative
study on consumers’ mobile shopping intention across different age groups can
be executed.
(ii) For better
generalization respondents from different education qualifications can be
considered
(iii)Longitudinal
research can be conducted using the demographic variables.
|
|
Yang (2010)
|
(i)It will be interesting to examine
moderating roles of gender and age in the adoption of mobile shopping
services and moderating effect of the level of mobile shopping experience on
using mobile shopping services.
(ii)Future research can also examine
motivators and inhibitors of mobile shopping services
|
|
Yang(2012)
|
(i)Future studies can incorporate perceived
determinants like perceived barriers, perceived financial risks that may act
as a barrier can be helpful in identifying factors of mobile shopping that
need to be taken care of for better mobile shopping services.
|
|
Agrebi and Jallais(2015)
|
Distinguishing theexperiencedand
inexperiencedpurchasers, people who are highly likely and
thosewhoarehighlyunlikelytomakepurchasesorthose
whomakeonlinepurchasesandthosewhodonot can give better picture of mobile
shopping adoption.
|
|
Hahn and Kim (2013)
|
(i)Social media networks can be further
explored in future studies to find out how different types of mobile tools
can be used to promote products to consumers.
(ii)A cross cultural study with a larger
and more diverse population of young adult consumers can be conducted.
(iii)and importance of the social and/or financial
impacts that mobile technology and devices over economy can also be examined
|
(Source: Authors own)
Implications and
Recommendations
Mobile shopping is an amazing subset of mobile
commerce services with revolutionary prospects for retail industry. It empowers
the customers with magical box opening to variety of choices. Despite huge
future potential of mobile shopping applications developing nations have not
adopted m-shopping applications as complete shopping platform. Still Marketers
have only been able to tap upper strata of the society and remote areas are yet
to be explored. Though mobile applications are convenient and hassle free but
personal touch andshopping sentiments of going out and having fun acts as low
turnarounds in mobile shopping industry.This paper presents the glimpse of
antecedents of m-shopping adoption identified so far by the researchers which
will help both the academicians and the industrialists to explore the higher
possibilities of mobile shopping business. It will help the marketers to deploy
marketing strategies and considering the determinants for further improvements
while developing features of mobile shopping apps. It will also help them in
customizing as well as personalizing the products and services with respect to
area of operation and personality traits of an individual. It will also add in
brainstorming of new researchers in the mobile shopping adoption contexts. This
paper comprehends the literature and provides the information to the marketers
for improving their business process, awareness campaigns, and application
development. It will build their understanding of mobile shopping consumers’
buying behaviour. Social networks should be utilised for exploring the unique
potential of mobile services, M shopping can act as an intermediary between the
consumption process which can be more flexible, responsive, efficient more
personalised and speedy.
Limitations
Unlike other studies this study also has
some limitations. This study reviews only empirical papers on mobile shopping
adoption published during year 2008-20 which were published in English
language. Empirical articles were explored from search engines –
Google and Google Scholar, Researchgate only, so there
might be possibility of missing some important articles on mobile shopping
adoption
Conclusion and Future
Scope of Research
Traditional technology adoption models need not hold
true with the changes taking place. Cultural, Social, Economical factors varies
from place to place hence traditional theories and models are need to be
modified. Mobile shopping is more personalized than other mobile commerce
services hence Subjective norm plays no significant influence on adoption of
M–shopping.Mobile shopping is an advancement of online shopping hence it can provide
hassle free, convenient, localised, personalized and mobile services to the
customers.Future researchers may conduct research comparing urban and rural
customers’ mobile shopping behaviour. Influence of Personality and Technology
traits on mobile shopping adoption in the contexts of emerging economies can be
studied. This review was restricted to the mobile
shopping adoption. Factors that are unexplored till date can be future
prospects of study. Future researchers conduct study to identify post adoption
satisfaction on the part of customers. Future researchers may focus on finding
similarity and differences in rural and urban customers with respect to mobile
shopping adoption. They may also explore non-adoption behaviour with respect to
mobile shopping.
Ethical Clearance
There is no physical,
social and psychological risk attached with this study. The authors have done
the review of the already published papers which were found appropriate by the
respective journals.
Declaration of Conflicting
Interests
The
authors declared no potential conflicts of interest with respect to the
research, authorship and/or publication of this article.
References
Agrebi S and Jallais J. Explain the intention
to use smartphones for mobile shopping. Journal of Retailing and Consumer
Services.2015; 22(1), 16-23.
Ajzen I. The theory of planned behaviour”,
Organizational Behavior and Human Decision Processes. 1991; 50 (2): 179-211.
Armstrong
S.J., Cools E., & Sadler-Smith E. Role of cognitive styles in business and
management: Reviewing 40 years of research. International Journal of Management
Reviews, (2012); 14(3):238-262. https://doi.org/10.1111/j.1468-2370.2011.00315.x
Bandura, A. Social Foundations of Thought and
Action: A Social Cognitive Theory. Prentice-Hall, NY. 1986.
Barnes SJ. The mobile commerce value chain:
analysis and future developments. International Journal of Information
Management.2002; 22(2): 91–108.
Chen LY. The quality of mobile shopping
system and its impact on purchase intention and performance. International Journal of
Management Information Technology. 2013; 5 (2):23–32.
Chen YM, Hsub TH and Lub YJ. Impact of flow
on mobile shopping intention.Journal of Retailing and Consumer Services.
2018; 41: 281-287. Available from: http://dx.doi.org/
10.1016/j.jretconser. 2017.04.004
Chong AYL. Predicting m-commerce adoption
determinants: a neural network approach. Expert Systems with Applications. 2013; 40 (2):523–530.
Dai H and
Palvia PC. Mobile commerce
adoption in China and the United States: a cross-cultural study. The DATA BASE
Advances in Information Systems. 2009; 40 (4):43–61.
Available from: https://libres.uncg.edu/ir/uncg/f/P_Palvia_Mobile_2009.pdf
Davis F. Perceived usefulness, perceived ease
of use, and user acceptance of information technology.MIS Quarterly. 1989; 13
(3): 319-340.
Faqih KM and Jaradat MIRM. Assessing the
moderating effect of gender differences and individualism-collectivism at
individual-level on the adoption of mobile commerce technology: TAM3
perspective. Journal of Retailing and Consumer Services.2015; 22(1) 37-52.
Fishbein
M and Ajzen I. Belief. Attitude, Intention and Behavior: An Introduction
to Theory and Research .Addison-Wesley, Reading, MA.1975; 6.
Gerpott TJ
and Thomas S. Empirical research on mobile Internet usage: a
meta-analysis ofthe literature. Telecommunications Policy. 2014; 38(3):
291-310.
Ghazali EM, Mutum DS, Chong JH and Nguyen B.
Do consumers want mobile commerce? A closer look at M-shopping and technology
adoption in Malaysia. Asia Pacific Journal of Marketing and Logistics. 2018;
30(4): 1064-1084. Available from: https://doi.org/10.1108/APJML-05-2017-0093
Groß M. Exploring the acceptance of
technology for mobile shopping: an empirical investigation among Smartphone
users. The International Review of Retail
Distribution and Consumer Research .
2015a;25 (3): 215–235.
Gupta A and Arora N. Understanding
determinants and barriers of mobile shopping adoption using behavioral
reasoning theory. Journal of Retailing and Consumer Services. 2017; 36 (1):1–7.
Hahn KHY and Kim J. Salient Antecedents of
Mobile Shopping Intentions: MediaDependency, Fashion/Brand Interest and Peer
Influence. Journal of Global Fashion Marketing. 2013;4 (4): 225–246.
Hung
MC, Yang ST and Hsieh TC. An examination of the determinants of
mobileshopping continuance. International Journal of Electronic Business
Management. 2012;10 (1): 29–37.
Lu HP and Su PYJ. Factors affecting purchase
intention on mobile shopping web sites. Internet Research. 2009; 19(4):442-458.
Madan K and Yadav R. Understanding and
predicting antecedents of mobile shopping adoption: a developing country
perspective.
Asia Pacific Journal of Marketing and Logistics. 2018; 30(1), 139-162.
Available from: URL: https://doi.org/10.1108/APJML-02-2017-0023
Manzano JA, Mafe CR and Blas SS. Exploring
individual personality factors as drivers of M-shopping Acceptance. Industrial
Management & Data Systems. 2009; 109 (6): 739-757.
Natarajan T,
Balasubramanian SA, Kasilingam DL. Understanding the intention to use
mobile shopping applications and its influence on price sensitivity. Journal of
Retailing and Consumer Service. 2017; 37 (1):8–22.
Phong ND, Khoi NH and Le ANH. Factors
affecting mobile Shopping: a Vietnamese perspective. Journal of Asian Business
and Economic Studies.2018; 25( 2):186-205.
Rogers EM and Shoemaker FF. Diffusion of
innovation: a cross-cultural approach. The Free Press, New York. 1983.
Taylor S and Todd PA. Understanding
information technology usage: a test of competing models. Information Systems
Research. 1995; 6(2):144-176.
Thompson
R.L, Higgins CA, and Howell JM. Influence of experience on personal computer
utilization: toward a conceptual model of utilization.MIS Quarterly.1991;
15(1):124-43.
Turner
Neil, Swart Juani, & Maylor Harvey. Mechanisms for managing ambidexterity:
A review and research agenda.International Journal of Management Reviews. 2013;
15(3): 317-332. https://doi.org/10.1111/j.1468-2370.2012.00343.x
Venkatesh V, Davis FD. A theoretical
extension of the technology acceptance model: Four longitudinal field studies.
Management Science.2000; 46 (2):
186–204.
Venkatesh V, Morris MG, Davis GB and Davis
FD. User acceptance of information technology: toward a unified view. MIS
Quarterly. 2003; 27(3): 425-478. Available from: URL:http://www.jstor.org/stable/30036540
Victoria M and Helen M., Marketing design
elements of mobile fashion retail apps, Journal of Fashion Marketing
Management. 2013; 17 (1): 115-134.
Wong CH, Lee HS, Lim YH, Chua BH and Tan GWH. Predicting the consumers’ intention to adopt mobile shopping: an
emerging market perspective. International Journal of Network and Mobile Technologies. 2012; 3 (3):24–39.
Yang K
and Forney JC. The moderating role of consumer technology anxiety inmobile
shopping adoption: differential effects of facilitating conditions and social
influences. Journal of Electronic Commerce Resarch. 2013; 14 (4): 334–347.
Yang K and
Kim HY. Mobile shopping motivation: an application of multiple
discriminant analysis. International Journal of Retail & Distribution.
2012; 40(10), 778-789. Available from: URL: http://dx.doi.org/10.1108/09590551211263182
Yang K. Consumer technology traits in
determining mobile shopping adoption: anapplication of the extended theory of
planned behavior. Journal of Retailing and Consumer Service. 2012;19(5):484–491.
Yang K. Determinants of US consumer mobile
shopping services adoption: Implications for designing mobile shopping
services, Journal of Consumer Marketing. 2010; 27(3): 262-270.
Zhang L, Zhu J and Liu Q. A meta-analysis of
mobile commerce adoption and themoderating effect of culture. Computers in
Human Behavior. 2012;28 (5):1902–1911.