Application of Decomposed Theory of Planned Behavior for
M-commerce Adoption in India
Neeraj Gangwal
and Veena Bansal
NURBS 3D, IIT Kanpur, Kanpur-208016, India
IME Department, IIT Kanpur, Kanpur-208016, India
M-commerce Adoption, Trust, Perceived Usefulness, Self-efficacy, Normative Influence,Decomposed TPB.
Mobile commerce (m-commerce) is the latest version of electronic commerce or e-commerce. M-commerce
is in early stages and its associated customer behavior is not well understood. In this paper, we examine the
decomposed theory of planned behavior in the context of M-commerce. We examined the roles of trust, per-
ceived usefulness, perceived ease of use and perceived enjoyment in determining the attitude towards adoption
of m-commerce. We also tested the relationship between normative influence and subjective norms as well as
the relationship between self-efficacy and perceived behavioral control. Based on the theory of planned be-
havior, we hypothesize that attitude, subjective norms, personal innovation and perceived behavioral control
have positive impact on a person’s intentions to adopt m-commerce. We conducted a survey and received 212
responses. We used structural equation modeling for data analysis. Our model was able to explain 60% of
the observed variance. Out of 11 hypotheses, 8 were significant at p < 0.01 and the remaining 3 are signifi-
cant at p < 0.05. Our results show that trust (m-commerce vendor), perceived usefulness (user), self efficacy
(technology) and the normative influence (society) are the most important factors for m-commerce adoption
in India.
Mobile commerce (m-commerce) is the latest ver-
sion of electronic commerce or e-commerce. From
a seller’s perspective, m-commerce facilitates per-
sonalization through location information and iden-
tification of the user (Zhang et al., 2012). The M-
commerce market has great potential due to the rapid
advancement of communication technology and the
increasing popularity of smart phones, notepads and
palmtops. India is the third largest country in the
world in terms of Internet users (20% of its popula-
tion) after China and the United States. India has had
low penetration of landline phones, desktop comput-
ers and laptops. However, sale of smart phones in
India is growing by 200% annually and is expected
to reach $19 billion by 2019. Accordingly, major In-
dian companies in the e-retail space have moved to
m-commerce. Every sector including banking, retail,
government and healthcare are also moving towards
m-commerce. The Indian government has also taken
a big step towards digital India. In particular, the m-
wallet market is estimated to grow from $7 million to
$28 million by 2019 and that includes operations sim-
ilar to money transfer, banking transactions, ticketing,
bill payment.
As m-commerce is still in early stages, its as-
sociated customer behavior is not well understood.
There are many factors that are important for the
growth of m-commerce like security, privacy, useful-
ness, ease of use, trust, enjoyment, etc. We can ad-
dress these issues based on observations, experience
and intuitions without using or considering a theo-
retical model. Lately, many theoretical models have
been built to understand the factors that lead to the
adoption of information technology or systems at the
individual level (micro-perspective) and at the level
of the society (macro-perspective). M-commerce is
largely an information system when viewed from a
customer’s perspective. Adoption of m-commerce by
a customer is a decision at the individual level that
is influenced by social norms. We can utilize micro-
level theoretical models to study the adoption of m-
commerce by individuals. The technology acceptance
model (Davis et al., 1989), the theory of interper-
sonal behavior (Triandis, 1979), the theory of rea-
soned action (Ajzen and Fishbein, 1977), the theory
of planned behavior (Ajzen and Madden, 1986) and
the unified theory of acceptance and use of technol-
ogy (UTAUT) (Venkatesh et al., 2003) are the popu-
Gangwal, N. and Bansal, V.
Application of Decomposed Theory of Planned Behavior for M-commerce Adoption in India.
In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 2, pages 357-367
ISBN: 978-989-758-187-8
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
lar micro-level theoretical models.
In this paper, we have made an attempt to iden-
tify constructs and understand their relationships for
m-commerce adoption in India. The behavior of In-
dian customers may not be very different from that of
the customers elsewhere and reports on adoption of
m-commerce in the Indian context are few (Vaithi-
anathan, 2010). However, it is important to delineate
any factors that may affect customer behavior in In-
dia. A study of shopping orientation of Indian shop-
pers identified that price sensitivity is not the prime
reason for on-line shopping (Gehrt et al., 2012). Ac-
curate information about products, warranties, com-
plaint management and certified websites are impor-
tant factors for on-line shoppers in India (Kiran et al.,
2009), whereas privacy seems to be less important
(Gupta et al., 2010).
We discuss related work in the next section. In
section 3, we discuss our proposed model and our hy-
potheses. We present our findings in section 4 fol-
lowed by discussion in section 5.
Customer characteristics have been viewed as sig-
nificant predictors in determining behavioral out-
comes (Davis et al., 1989), (Karahanna et al., 1999),
(Bagozzi and Yi, 1988). Theory of reasoned action
(TRA) was developed (Ajzen and Madden, 1986) to
predict and understand an individual’s behavior. Ac-
cording to the theory of reasoned action (TRA), a per-
son’s intention to perform a task is determined by his
attitude and subjective norm. A person’s attitude to-
wards a behavior is determined by his belief that a
particular behavior leads to a particular outcome and
his evaluation of the outcome. Subjective norms are
determined by the person’s perception of what others
around him believe that he should do. The intention
is the immediate antecedent of the behavior. Inten-
tions capture the motivational factors that influence a
behavior, showing how hard people are willing to try,
and how far they are willing to go in order to per-
form the behavior (Ajzen, 1991). The stronger a per-
son’s intentions, the greater his will to perform the
behavior. Consequently, the likelihood of perform-
ing the behavior increases. The relationship between
intention and behavior will hold if the target, action,
context and time (TACT) elements are identical and
appropriate measurement procedures have been em-
ployed (Ajzen and Fishbein, 1977). The determinants
of attitude (represented as A) and subjective norms
(represented as SN) are known as behavioral and nor-
mative beliefs. A behavioral belief refers to an in-
dividual’s subjective probability that a behavior will
lead to a certain consequence. Normative beliefs refer
to the likelihood that important referent individuals or
groups would approve or disapprove of the behavior.
A fair amount of research has been done to pre-
dict human behavior assuming that human behavior is
rational and motivation-based. The decision for per-
forming or not performing a behavior is based on the
information available. Attitude is formed on the ba-
sis of three general classes of information: affective
information, cognitive information and behavioral in-
formation. Affective information refers to how the
person feels towards the subject, cognitive informa-
tion refers to what a person thinks about the subject,
and behavioral information comes from the past and
future behavioral intentions in relation to the target.
The cognitive component or information processing
approach is used in attitude formation in both the the-
ories of reasoned action and planned behavior (Ajzen
and Madden, 1986). The theory of reasoned action
has been applied to many situations to predict and
understand human behavior (e.g. unethical behav-
ior, user acceptance of information systems, voting
behavior, etc.). The theory of reasoned action has
strong predictive power when behavior is under vo-
litional control.
The theory of planned behavior (TPB) (Ajzen,
1991) is an extension of the theory of reasoned action
that incorporates a third construct namely perceived
behavioral control (PBC) (shown in Figure 1). Per-
ceived behavioral control is defined as a persons per-
ception of the ease of carrying out a specific behav-
ior. TPB accounts for the non-volitional control over
behavior. TPB has been the basis for several stud-
ies of Internet purchasing behavior (George, 2004).
Most behaviors are influenced by internal or external
non-motivational factors. Internal factors are ability,
skills and knowledge while time, money, availability
of resources and cooperation from other people are
external factors (Ajzen and Madden, 1986). It is
possible that non-availability of resources or ability
may preclude a person from performing a behavior,
despite having strong intentions to do so. These non-
motivational factors are also responsible for the actual
control over a behavior. In TPB, behavioral intention
is an immediate antecedent of behavior and is deter-
mined by attitude (A), subjective norms (SN) and per-
ceived behavioral control (PBC). Meta-analysis stud-
ies show that TPB has better prediction profiles than
the theory of reasoned action (Armitage and Conner,
The Technology Acceptance Model (TAM)
(Davis et al., 1989) tries to explain an individuals be-
havior towards the use of an Information system (IS)
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
Beliefs and
Beliefs and
Beliefs and
to Comply
Theory of Reasoned Behavour
Theory of Planned Behaviour
Figure 1: Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB).
through two beliefs: perceived usefulness and the per-
ceived ease of use. Perceived usefulness (PU) is a
user’s belief that using a particular system will en-
hance his job performance, and perceived ease of use
(PEOU) is the perceived effort required to learn and
use the system. TAM has been predominantly used to
study user acceptance of various information systems
by users. M-commerce involves the use of informa-
tion systems that run on mobile devices, therefore the
constructs used in TAM seem appropriate to under-
stand the adoption of m-commerce. The more a cus-
tomer feels that using m-commerce will be useful and
effortless (PEOU), the more they will use it (Gefen
et al., 2003). But these two constructs alone cannot
capture the adoption of m-commerce because trust
and perceived enjoyment also play important roles.
It has been shown that the decomposed theory of
planned behavior has more predictive power than the
theory of planned behavior (Taylor and Todd, 1995).
We have used the decomposed TPB model for under-
standing customer behavior driving the adoption of
m-commerce in India. A decomposed model provides
several advantages over a unidimensional belief struc-
ture, because as it has been shown that belief is not
a monolithic structure. Decomposition also provides
flexibility in finding a stable set of beliefs that can be
applied across a variety of settings (Taylor and Todd,
1995). Our proposed decomposed model is presented
in Figure 2. Antecedents of intentions are attitude,
subjective norm and perceived behavioral control and
each of these are further decomposed. Behavioral
beliefs are decomposed into trust, perceived useful-
ness, the perceived ease of use and perceived enjoy-
ment. Normative belief is decomposed into influence
exerted by friends, batch-mates and family. Control
belief is decomposed into self-efficacy. We will also
test the effect of personal innovativeness on the inten-
tions mediated by the attitude towards m-commerce
Our model is primarily based on the theory of
reasoned action (TRA), the technology acceptance
model (TAM) and the theory of planned behavior
(TPB). We also include additional constructs that are
not a part of TRA, TAM and TPB but that have
been explored in the literature. Our proposed de-
composed model is presented in Figure 2. We have
used 3 antecedents of intention directly from TPB for
our research model viz., attitude, subjective norms
and perceived behavioral control (Liao and Shi,
2009) (Pavlou and Fygenson, 2006). Intention is a
construct that has been accepted at a micro-level for
adopting IT (Venkatesh et al., 2003), (Gefen et al.,
2003). Attitude, subjective norm and perceived be-
havioral control have also been accepted to play a role
in IT adoption at an individual level (Ajzen and Mad-
den, 1986), (Davis et al., 1989). Hence we propose
the following 3 hypotheses:
H1: Attitude has a positive influence on behav-
Application of Decomposed Theory of Planned Behavior for M-commerce Adoption in India
ioral intentions for adopting m-commerce. H7:
Subjective norm has a positive influence on the behav-
ioral intentions for adopting m-commerce. H9:
Perceived behavioral control has a positive influence
on the intention to adopt m-commerce.
3.1 Decomposition of Attitude
We decomposed attitude into trust, perceived useful-
ness, perceived ease of use and perceived enjoyment.
The reason of including trust is that in an m-
commerce purchase, there is a physical distance be-
tween the buyer and the seller, and the product is
not present physically. Trust affects the attitude
positively, which in turn affects customer intentions
(McKnight and Chervany, 2001), (Chow and Holden,
1997), (Jarvenpaa et al., 2003), (Sahney et al., 2013).
Hence, we propose the following hypothesis.
H2: Trust has a positive influence on the attitude
towards adopting m-commerce.
Perceived usefulness (PU) captures a user’s belief
that using a particular system will enhance his or her
job performance. In the context of m-commerce, PU
can be defined as the degree by which a customer be-
lieves that engaging in mobile shopping will improve
his effectiveness. PEOU captures the effort involved
in learning to use the system. A system will only be
used if its usefulness outweighs the effort involved in
learning to use it. In terms of m-commerce, it can be
defined as purchasing a product using mobile would
be free of effort. Based on these observations, the fol-
lowing hypotheses have been proposed (Malik et al.,
H3: Perceived usefulness has a positive influence
on the attitude towards adopting m-commerce.
H4: Perceived ease of use has a positive influence
on the attitude towards adopting m-commerce.
Perceived enjoyment (Davis et al., 1992) is the in-
dividual belief that technology is fun to use. In recent
years, researchers have been using perceived enjoy-
ment as a major construct in their m-commerce adop-
tion models (Agrebi and Jallais, 2015). Hence, we
propose the following hypothesis:
H5: Perceived enjoyment is positively related to
the attitude towards the adoption of m-commerce.
3.2 Decomposition of Subjective Norm
The theory of Reasoned Action (TRA) posits that
a person’s normative beliefs affect the subjective
norm(SN)- defined as the influence important others
have on acceptance decision. The important others
may be the family members, relatives, friends, class-
mates and colleagues. If social expectations are that
a person should engage in a behavior, then the indi-
vidual is more likely to do so. There is a positive re-
lationship between subjective norm and intention for
a behavior (Karahanna et al., 1999). Hence, we pro-
pose the following hypothesis:
H6: What important others think about adopting
m-commerce influences the subjective norm of an in-
3.3 Decomposition of Control Beliefs
Control belief is decomposed into self-efficacy (Tay-
lor and Todd, 1995), (Limayem et al., 2000), (Ajzen,
2002). Self-efficacy is an important antecedent
of PBC. In terms of m-commerce, if an individ-
ual is more self-confident and engages in mobile
shopping activities, then he/she feels more positive
about control over his/her on-line purchasing behav-
ior. This self-efficacy fuels further engagement with
m-commerce (George, 2004). We include a hypothe-
sis for the effect of PBC on behavioral intention from
the TPB model as follows:
H8: Self-efficacy has a positive influence on per-
ceived behavioral control for adopting m-commerce.
3.4 Effect of Personal Innovativeness on
Intention is Mediated by Attitude
Personal innovativeness affects individual attitude to-
wards performing a behavior. Many information sys-
tem studies show that innovativeness has a direct
and positive influence on attitude and intentions (Li-
mayem et al., 2000). This construct has been of great
importance in innovation diffusion and in marketing.
This construct has also been used in the domain of
information technology (Agarwal and Prasad, 1998).
Personal innovativeness is a personality trait which is
characteristically adapted by people (Limayem et al.,
2000). Innovative people tend to adopt mobile shop-
ping more easily making it an important construct for
our model.
For the purpose of our study, we considered both
direct and indirect effects of personal innovativeness
on the intentions of adopting m-commerce. The indi-
rect effect is mediated by the attitude. We propose the
following hypotheses
H10: Personal innovativeness has a positive influ-
ence on the attitude towards adopting m-commerce.
H11: Personal innovativeness has a positive influ-
ence on the intention of adopting m-commerce.
We have not included behavior in our model. We
rely on existing studies that have established a strong
link between the intention to perform a behavior and
the actual behavior (Ajzen and Madden, 1986). In
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
Attitude towards
Intention to
Figure 2: Proposed research model based on Decomposed Theory of Planned Behavior.
the next section, we present the details of our data
To test our research model, we conducted a survey
with multi-attribute scales that refer to the different
variables. The growth of m-commerce will be driven
by the young adult population as they are more tech-
nology savvy compared to other age groups. There-
fore, we collected data from students an academic in-
stitute in northern India where students from all over
the country come to study. The empirical data was
collected through an on line form designed using the
free service provided by Google forms. The hyperlink
of the survey form was sent to participants through
email and the link was also posted on various on line
groups existing on social media sites like Facebook
and google. The members of these social groups are
the registered students of the institute. Students vol-
untarily participated in the survey.
The data was collected in the month-end of April,
2015. We received a total of 212 responses.
The collected data was analyzed and used to ac-
cept or reject our hypotheses. Since we are using the
TPB framework, we define each behavior in terms
of the TACT principle (Ajzen, 1991). The target
for our study is m-commerce adoption, the action is
using m-commerce for purchasing products and ser-
vices, the context is the required environment (e.g.
data connectivity, Internet standards, a high resolu-
tion mobile screen to view items) and the time-frame
is a time window of 1 year. All questions were rated
on a seven-point Likert Scale where 1 means that the
user strongly disagrees with the given statement and
7 means that the user strongly agrees.
Our proposed model have 26 items describing 11
constructs: trust (3 items) (McKnight and Chervany,
2001), perceived usefulness (PU-2 items) (Gefen
et al., 2003), perceived ease of use (PEOU-2 items)
(Gefen et al., 2003), perceived enjoyment (Per
items) (Davis et al., 1992), attitude (3 items) (Tay-
lor and Todd, 1995), normative beliefs (Norm
Application of Decomposed Theory of Planned Behavior for M-commerce Adoption in India
3 items) (Taylor and Todd, 1995), subjective norm
(SN-2 items) (Taylor and Todd, 1995), self-efficacy(2
items) (Taylor and Todd, 1995), perceived behavioral
control (PBC-3 items) (Taylor and Todd, 1995), Per-
sonal innovativeness (2 items) (Hurt et al., 1977) and
intentions (2 items) (Agarwal and Prasad, 1999) to
adopt the m-commerce.
4.1 Sample Description
The majority of respondents in our survey were men
(75.5%). Most of the respondents were enrolled in
the postgraduate programs (64.6%) and aged between
25 to 30 years (47.2%). The respondents aged be-
tween 20 to 25 years constitute 44.3% and between
30 to 35 years constitute 7.5%. Respondents who
were enrolled in undergraduate program constitute
29.2% and 6.1% students are enrolled in doctoral pro-
gram. Nearly all respondents (97.2%) had the wire-
less gadgets like mobile phones, notepad, that support
features required for m-commerce (data connectivity,
Internet standards, a high resolution screen to view
items). Respondents who had used m-commerce a
couple of times in the previous year constitute 45.3%
while 38.7% respondents had used it monthly. A
small percentage of respondents (4.7%) had used m-
commerce on a weekly basis.
4.2 Measurement Model
To evaluate the measurements of our research model,
we used the confirmatory factor analysis (CFA) to test
the reliability and validity of the constructs.
Each item in the belief construct (trust, PU,
PEOU, Per enj, normative beliefs, self efficacy) had
corresponding evaluation item. For analysis, each be-
lief item score was multiplied by its corresponding
evaluation item score to get the overall score for that
The CFA was performed using the AMOS soft-
ware, v21. Before conducting the CFA test, we cal-
culated the Kaiser-Meyen-Olkin (KMO) measure of
sampling adequacy and the Bartletts test of sphericity
to check the suitability of the data for factor analysis.
The value of KMO was 0.771 and the Bartletts test
of sphericity was significant at p = .000 (p value <
0.05 is recommended) (Hair et al., 2006) indicating
that the data were suitable for the CFA.
There are three types of measurements for the
model fit: the measure of absolute model fit, the mea-
sure of incremental model fit and the measure of par-
simonious model fit (Hair et al., 2006). From the
results of CFA, we observed that the measurement
model test presents a good fit between the data and
the model. The chi square value for our model is
305.155 with degree of freedom (df) 220 and signif-
icant at p value < 0.001. The degree of freedom
is high and comparable to the chi-square value and
the p-value is below the recommended level of 0.05-
these are the good indicators for a model fit. The val-
ues of RMSEA (0.043) and GFI (0.901) give indica-
tions towards a perfect model fit. This is because the
upper limit for RMSEA is 0.08 and the lower limit
for GFI is 0.9 for a perfect model fit. The AGFI is
0.854, CFI is 0.963, NFI is 0.882 (not much less than
required value 0.9) and TLI is 0.949. Overall we can
say that our model fit is good and acceptable. Table 1
presents various indices of measurement model with
their recommended value for a model fit.
We assessed the reliability and convergent va-
lidity of the scales by evaluating the cronbachs al-
pha, composite reliability (CR) and the average vari-
ance extracted (AVE) (Bagozzi and Yi, 1988). Cron-
bachs alpha and composite reliability statistics must
be greater than 0.7 and the average variance extracted
(AVE) must be above 0.5. Composite reliability (CR)
for all constructs of our measurement model is higher
than 0.7 and the AVE is greater than the recommended
level of 0.5. We calculated the CR and AVE val-
ues using an excel plugin that uses the correlation ta-
ble and standardized regression weight tables an in-
puts. These tables were generated in AMOS after per-
forming the CFA. The difference between the CR and
Cronbachs alpha is that the former enumerates the ac-
tual factor loadings of each item instead of assuming
that each item is equally loaded in the composite load
determination as in case of the latter.
Convergent validity of the model can also be
evaluated by examining the factor loadings of each
item and their respective squared multiple correla-
tions that were generated after performing CFA. For
our measurement model, the standardized factor load-
ings were above the level of 0.5 and most of them are
above 0.7, in most cases the squared multiple corre-
lation was also very high as shown in table 3. Due
to space limitations, these values are not shown here.
Factor loadings above 0.5, are consider significant
(Hair et al., 2006). Therefore, we can say that all mea-
sures have strong and adequate reliability. To exam-
ine the discriminant validity of the constructs we used
AVE, by comparing the squared of correlations be-
tween the constructs and AVE for the construct (For-
nell and Larcker, 1981). As shown in Table 2, the
square root of AVE scores (diagonal element in corre-
lation table) is greater than the correlations among the
constructs, hence establishing the dis criminant valid-
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
Table 1: Fit Indices of the Proposed Model.
Measure Recommended
Chi-square/df < 3.0 1.387 1.647 (Hair et al., 2006)
Goodness of fit (GFI) > 0.9 0.901 0.871 (Bagozzi and Yi, 1988)
Adjusted goodness of
fit (AGFI)
> 0.8 0.854 0.834 (Bagozzi and Yi, 1988)
Normed fit index
> 0.9 0.882 0.839 (Hair et al., 2006)
Comparative fit Index
> 0.9 0.963 0.928 (Bagozzi and Yi, 1988)
Tucker Lewis Index
> 0.9 0.949 0.915 (Hair et al., 2006)
RMSEA < 0.05 0.043 .055 (Bagozzi and Yi, 1988)
Table 2: Correlation matrix (diagonal elements represents the square root of the AVE); (1): PBC, (2): Trust, (3): Attitude, (4):
PU, (5): PEOU, (6): Per
Enj, (7): Norm Bel, (8): Self Efficacy, (9): Per Inno, (10): Sub Norm, (11): Intentions.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
(1) 0.784
(2) -0.003 0.760
(3) 0.193 0.497 0.715
(4) 0.230 0.296 0.635 0.826
(5) 0.157 0.267 0.557 0.466 0.765
(6) 0.092 0.269 0.507 0.453 0.405 0.893
(7) 0.232 0.047 0.176 0.033 0.029 0.076 0.830
(8) 0.747 0.197 0.356 0.416 0.198 0.109 0.060 0.816
(9) 0.157 -0.064 0.192 0.158 0.049 0.109 0.012 0.148 0.811
(10) 0.318 0.137 0.344 0.305 0.119 0.201 0.531 0.359 0.062 0.768
(11) 0.368 0.318 0.715 0.537 0.303 0.313 0.226 0.396 0.403 0.452 0.866
4.3 Structural Model
We examined the similar fit indices for the struc-
tural model. Chi-square value was 416.71 with de-
gree of freedom (df) 253 and significance at a p
value < 0.001. Other fit indices are also shown in Ta-
ble 1. Almost all fit indices (Chi-square/df = 1.647,
AGFI=0.834, CFI= 0.928, TLI= 0.915 and RMSEA=
.055) satisfied the recommended criteria as suggested
in previous literature except GFI (0.871), and NFI
(0.839). But these values did not vary too far from
the required level of 0.9 and approached the recom-
mended level. Hence our structural model adequately
fits the data.
After examining the measurement fit of our pro-
posed structural model we proceed to check the sig-
nificance of each hypothesized path. All properties of
the casual paths,including path coefficients, squared
multiple correlation (R
), significance level and t-
value are shown in figure 3.
Our proposed model explains 60% of the variance
in intentions for adopting m-commerce, which pro-
vides good explanatory power. All hypotheses except
(H4, H5, and H10) were significant at p < 0.01; H4,
H5, H10 were significant at p < 0.05. We used the
notations β and γ for the standardized direct and indi-
rect path coefficients. The effect of trust (p value<
0.001, γ = 0.361, t = 4.90), perceived usefulness (p
value < 0.001, γ = 0.422, t = 4.393), perceived ease
of use (p value < 0.05, γ = 0.220,t = 2.462) and
perceivedenjoyment (p value< 0.05, γ = 0.180, t =
2.322) on attitude towards adopting m-commerce
were significant and positive. This supports H2, H3,
H4 and H5- factors that positively influence the atti-
tude towards adopting m-commerce. Attitude corre-
lates positively with behavioral intentions to adopt m-
commerce (p value < 0.001, β = 0.570,t = 6.430)
indicating that H1 is supported.
The effect of normative influence (p value <
0.001, γ = 0.537, t = 5.892) on the subjective norm
was positive and significant supporting our H6 hy-
pothesis. The effect of subjective norms on the inten-
tions to adopt m-commerce (p value < 0.001, β =
0.262, t = 3.735) was significant and positivesupport-
ing the H7 hypothesis. The effect of self-efficacy
Application of Decomposed Theory of Planned Behavior for M-commerce Adoption in India
Table 3: Reliability and Convergent Validity of the Measurement Scale; CR stands for composite reliability and AVE for
average variance extracted.
Factor Variable Std. fac-
tor load-
Assuming that I have access to m-commerce, I intend to use it. 0.788 0.622
I intend to increase my use of m-commerce in the future. 0.937 0.878
M-commerce would be a good idea. 0.747 0.558
M-commerce would be a wise idea. 0.773 0.597
Using m-commerce would be a pleasant experience. 0.615 0.378
Based on my experience, I know that vendors selling prod-
ucts/services using m-commerce are honest.
0.512 0.262
Based on my experience, I know that vendors selling prod-
ucts/services using m-commerce are opportunistic.
0.962 0.925
Based on my experience, I know that vendors selling prod-
ucts/services using m-commerce care about their customers.
0.738 0.545
M-commerce would enhance my effectiveness. 0.816 0.666
M-commerce is useful for me. 0.835 0.696
M-commerce cannot be used easily. 0.723 0.523
Learning how to use m-commerce will be easy for me. 0.804 0.646
Perceived Enj
I would have fun in using m-commerce. 0.959 0.919
I would find m-commerce to be enjoyable. 0.822 0.675
Personal Inno
I am generally cautious about accepting new ideas. 0.695 0.483
I am challenged by ambiguities and unsolved problems. 0.913 0.834
Subjective Norms
People who influence my behavior would think that I should use
0.807 0.651
People who are important to me would think that I should use
0.727 0.529
Normative Beliefs
My parents would think that I should use m-commerce. 0.919 0.844
My batch-mates would think that I should use m-commerce. 0.745 0.555
My friends would think that I should use m-commerce. 0.816 0.665
Perceived Behavioral Control
Using m-commerce to purchase products/services is within my
0.802 0.643
Given the resources, opportunities and knowledge it takes to use
mobile shopping, it would be easy for me to use the system.
0.765 0.586
Self Efficacy
I would feel comfortable in using m-commerce on my own. 0.963 0.927
I would be able to use m-commerce even if there was no one
around to show me how to.
0.636 0.405
(p value < 0.001, γ = 0.725, t = 7.439) on the per-
ceived behavioral control was positive and signifi-
cant supporting H8. H9 hypothesis was supported
by significant and positive effect of perceived behav-
ioral control on the intentions to adopt m-commerce
(p value < 0.001, β = 0.186,t = 2.801). The ef-
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
Intention to
Attitude towards
(R2 = 0.29)
Figure 3: Proposed research model with path coefficients specified next to the links and t-value shown in parenthesis; p <
0.001and p < 0.05.
fect of the personal innovativeness (p value <
0.001, β = 0.282, t = 4.124) on the intentions to adopt
m-commerce was significant and positive, thus sup-
porting H11 hypothesis. The effects of personal in-
novativeness are partially mediated by attitude. The
effect of personal innovativeness on the attitude to-
wards adopting m-commerce (p value < 0.05, γ =
0.145, t = 2.070) was also significant and positive,
supporting H10. Therefore, both direct and indirect
effects were significant.
The aim of our study was to identify factors that mo-
tivate Indian customers to adopt m-commerce. We
used the decomposed TPB to determine the impact
of attitudinal, normative and control beliefs. We also
tested the effect of personal innovativeness on attitude
mediated intentions. The effect of personal innova-
tiveness on intentions to adopt m-commerce was sig-
Among the 4 significant antecedents of behav-
ioral intentions to adopt m-commerce, attitude is a
strong predictor of behavioral intentions. The behav-
ioral beliefs (trust, perceived usefulness, perceived
ease of use, and perceived enjoyment) were all sig-
nificant showing that they influence a persons atti-
tude towards adopting m-commerce. All these fac-
tors together explained the 56% of the variation in
the attitude. Among these predictors, perceived use-
fulness (with the highest path loading of 0.422) was
the strongest predictor of the attitude towards the m-
commerce. It suggests that people use m-commerce
if and only if they find it useful such that it increases
their effectiveness in their everyday life. The second
important factor that determines the attitude of cus-
tomers is trust (path loading of 0.361). If vendors
or suppliers are not honest, callous towards their cus-
tomers and opportunistic, it is difficult for them to at-
Application of Decomposed Theory of Planned Behavior for M-commerce Adoption in India
tract new customers or retain existing ones.
Perceived ease of use is also a significant deter-
minant of the attitude towards adopting m-commerce
showing that in addition to trust and perceived use-
fulness, customers also prefer m-commerce applica-
tions that provide easy to use services and require lit-
tle effort to learn. Perceived enjoyment is significant
but has low path loading as compared with other de-
terminants. This may be because there are very few
people who enjoy using m-commerce services. Con-
sequently, m-commerce vendors should design their
applications or services that are easy to use and en-
joyable. Subjective norm and perceived behavioral
control are also significant determinants of behavioral
intentions. This means that ones decision to adopt
m-commerce may be influenced by related opinions
of people in ones social circle. The effect of nor-
mative beliefs on the subjective norm is significant
which means that individuals give importance to opin-
ions of their parents, friends and colleagues, which in
turn, influences their decision to perform the behavior.
Normative beliefs explain the 29% variation in the
subjective norm. People with sufficient knowledge
and resources also tend to use the m-commerce more
as compared to people those with less resources and
knowledge. Self-efficacy explains the 52% of varia-
tion in perceived behavioral control, which is a good
sign for the efficiency of our model. One limitation
of our study is that our sample included only young
students, hence our results cannot be generalized to
all age groups.
To summarize, in order to encourage people to use
m-commerce, it is necessary to project the usefulness
of m-commerce. The interface of applications should
be designed in an elegant way to preserve ease and en-
joyment of use. Targeting people with innovativeness
is useful because they are the first adopters of new
technology and spread the words about m-commerce.
In addition, people are affected by choices made by
those in their social circle; therefore selective adver-
tising through social media may be helpful. If peo-
ple haveaccess to resources required for m-commerce
(high speed data connectivity, mobile phone), they are
more willing to engage in mobile shopping and hence
adopt m-commerce.
The penetration rate of mobile phones is very high
in India but the adoption rate of m-commerce is very
low. In this study, we made an attempt to understand
this situation. We examined the decomposed theory
of planned behavior in the context of m-commerce
with added behavioral beliefs (trust, perceived use-
fulness, perceived ease of use and perceived en-
joyment), normative belief (normative influence of
family, friends and peers) and control beliefs (self-
efficacy). We also probed the direct and indirect ef-
fects of personal innovativeness on behavioral inten-
tions. We examined how these factors affect both atti-
tude and intentions for adopting m-commerce. In our
study, perceived usefulness, trust and perceived ease
of use turned out to be more weighted factors. This
implies that in order to increase their customer base,
m-commerce vendors need to demonstrate the useful-
ness of m-commerce, design mobile applications with
easy and enjoyable interfaces, and invest in market-
ing to gain the trust of customers trust and finally at-
tempt to meet customer expectations. However, there
are additional factors that affect use of m-commerce
directly or indirectly . We recommend incorporat-
ing factors like perceived financial risk, network se-
curity, perceived quality of products and services, and
the perceived cost to further study the adoption of m-
commerce . Currently, the most popular m-commerce
products and services are related to financial services,
such services can be developed and used widely in
other domains like health, retail and education.
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