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

Keywords:

M-commerce Adoption, Trust, Perceived Usefulness, Self-efﬁcacy, Normative Inﬂuence,Decomposed TPB.

Abstract:

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.

1 INTRODUCTION

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

357

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.

2 BACKGROUND

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,

2001).

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

358

Behavioral

Beliefs and

Evaluation

Control

Beliefs and

Perveived

Power

Normative

Beliefs and

Motivation

to Comply

Perceived

Behavioural

Control

Attitude

Subjective

Norms

Intention

Behaviour

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

adoption.

3 PROPOSED MODEL

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

359

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.,

2013):

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-

dividual.

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

360

Self

Efficacy

Normative

Influence

Perceived

Enjoyment

PEOU

Trust

Perceived

Behavioural

Control

Attitude towards

adopting

m−Commerce

Intention to

adopt

m−commerce

Personal

Subjective

Norm

Innovativeness

Figure 2: Proposed research model based on Decomposed Theory of Planned Behavior.

the next section, we present the details of our data

analysis.

4 DATA ANALYSIS

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

enj-2

items) (Davis et al., 1992), attitude (3 items) (Tay-

lor and Todd, 1995), normative beliefs (Norm

bel-

Application of Decomposed Theory of Planned Behavior for M-commerce Adoption in India

361

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

item/variable.

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-

ity.

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

362

Table 1: Fit Indices of the Proposed Model.

Measure Recommended

Criteria

Measurement

Model

Structural

Model

Source

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

(NFI)

> 0.9 0.882 0.839 (Hair et al., 2006)

Comparative ﬁt Index

(CFI)

> 0.9 0.963 0.928 (Bagozzi and Yi, 1988)

Tucker Lewis Index

(TLI)

> 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

363

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-

ings

Intentions

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

Attitude

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

Trust

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

PEOU

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

m-commerce.

0.807 0.651

People who are important to me would think that I should use

m-commerce.

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

PBC

Using m-commerce to purchase products/services is within my

control.

0.802 0.643

PBC

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

364

Self

Efficacy

Normative

Influence

Perceived

Enjoyment

PEOU

Trust

Intention to

adopt

m−commerce

Personal

Subjective

Norm

H11

0.725***

(7.439)

0.570***

(6.430)

0.282***

(4.124)

0.145**

(2.070)

0.186***

(2.801)

0.262***

(3.735)

.537***

(5.982)

(2.322)

0.180**

0.220**

(2.462)

0.422***

(4.393)

Attitude towards

adopting

m−Commerce

Perceived

Behavioural

Control

(R2=0.56)

(R2 = 0.29)

(R2=0.52)

(R2=0.60)

(4.90)

0.361***

H10

Innovativeness

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.

5 DISCUSSION

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-

niﬁcant.

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

365

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.

6 CONCLUSION

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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