The Technology Acceptance Model E-Commerce Extension: A Conceptual Framework

Procedia Economics and Finance 26 ( 2015 ) 1000 – 1006
2212-5671 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of Academic World Research and Education Center
doi: 10.1016/S2212-5671(15)00922-3
ScienceDirect
Available online at www.sciencedirect.com
4th World Conference on Business, Economics and Management, WCBEM
The Technology Acceptance Model E-Commerce Extension: A
Conceptual Framework
Rima Fayad a *, David Paper b
aAssistant Professor, Lebanese University, IUT, Saida, Lebanon
bProfessor, Utah State University, Logan, UT, USA
Abstract
Electronic-commerce has become an important channel for conducting business. Researchers as well as market executives are
trying to better understand online consumer behavior. One model used by researchers to understand behavior in the information
systems field in general is the technology acceptance model (TAM). The TAM variables are perceived usefulness, perceived ease
of use, and intentions. In this study, we suggest the extension of the TAM for its application in the E-commerce field. The
original TAM will be extended, by adding four predictor variables. The four predictor variables are process satisfaction, outcome
satisfaction, expectations, and E-commerce use. In addition, the TAM will be extended by measuring actual behavior as opposed
to measuring intentions as a substitute for actual behavior in previous TAM application studies. We suggest measuring actual use
variable in terms of four criterion variables, namely, purchase, access number, access total time, and access average time. The
extended TAM is expected to better explain actual behavior in E-commerce environments than the original TAM.
© 2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of Academic World Research and Education Center.
Keywords: Technology Acceptance Model; User Satisfaction; Process Satisfaction; Outcome Satisfaction; Intentions; Actual Behavior;
Behavioral Expectations; E-commerce
* Rima Fayad. Tel.: [phone]
E-mail address: [e-mail]
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of Academic World Research and Education Center

1001Rima Fayad and David Paper / Procedia Economics and Finance 26 ( 2015 ) 1000 – 1006
1. Introduction
Electronic-commerce (E-commerce) is defined as all aspects of business and market processes enabled by the
Internet. E-commerce is rapidly becoming a viable means of conducting business, as evidenced by the tremendous
amounts of money spent online. The United States online retail sales are estimated to reach $278.9 billion in 2015 as
reported by Forrester Research (Mulpuru, 2011). As a result, the economic impact of E-commerce is increasing
exponentially. Web based companies, Net Enabled Organizations (NEO), and researchers are still trying to
understand and predict online consumer behavior; therefore, research in this area is needed.
Information systems (IS) researchers have explored online consumer behavior in terms of online shopping
adoption (Bhattacherjee, 2001; Gefen, Karahanna, & Straub, 2003b; Gefen & Straub, 2000; Koch, Toker, & Brulez,
2011; Koufaris, 2002). The most widely referenced adoption model in IS research is Davis’s (1989) technology
acceptance model (TAM) (Gefen & Straub, 2000). The TAM is an adaptation of the theory of reasoned action
(TRA) (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975) for predicting IS adoption (Davis, Bagozzi, & Warshaw,
1989). The TAM has two elements, perceived usefulness (PU) and perceived ease of use (PEOU), that are correlated
with the decision to adopt a new technology (Davis, 1989). Although designed to explain new technology adoption,
not specifically E-commerce behavior, researchers have recently used the TAM to explore Internet consumer
behavior (Bhattacherjee, 2001; Gefen et al., 2003; Gefen & Straub, 2000; Koch, et al., 2011; Koufaris, 2002).
We argue that the TAM in its current form cannot be used to fully explain online consumer behavior as E-
commerce adoption is considerably different from new technology adoption in an organization. One difference is
that the decision to buy online is voluntary, while the decision to use new software in an organization is typically
mandated by organizational policy. Also, shopping online is one choice among alternatives (e.g. shopping in a
conventional store) for the shopper, while more often than not there is no choice among different software or
systems mandated by an organization. Although the use of the TAM, as it was originally conceived, is not likely to
lead to a full explanation of online consumer behavior, an E-commerce specific, extended TAM may prove useful in
explaining such behavior. Hence, there is a need to extend the TAM to serve as an E-commerce adoption model.
2. Theoretical Background
Before developing our model we examined the published body of knowledge about the topic. Our review and
evaluation of the literature are presented in the following section. Based on that evaluation and grounded in the
literature, our new TAM extending variables are identified.
2.1. The Theory of Reasoned Action
In order to develop an extended model of the TAM with solid conceptual foundations, we need to fully
understand its antecedents. The TAM’s major antecedent is the TRA (Ajzen & Fishbein, 1980; Fishbein & Ajzen,
1975). The TRA (Fig.1) is a model developed to predict human behavior in general. Two main elements, attitude
towards a behavior and subjective norm, are identified as determinants of behavior (Fishbein & Ajzen, 1975). An
attitude towards a behavior is “an individual’s positive or negative feelings (evaluative affect) about performing the
target behavior” (p. 216). A subjective norm is “the person’s perception that most people who are important to him
think he should or should not perform the behavior in question” (p. 302). A person’s attitude towards a behavior is
determined by that person’s beliefs about that behavior. In addition, a person’s subjective norm towards a behavior
is determined by that person’s normative beliefs about that attitude (Fishbein & Ajzen, 1975).
Researchers using the TRA as a behavioral intention model should be able to predict the performance of any
voluntary act, unless intent changes between assessment and performance of that behavior. Researchers should also
be able to predict whether a behavior will occur. However, choice among alternative behaviors was not included
(Fishbein & Ajzen, 1975).
People have different sets of beliefs about each behavior. As such, researchers developing behavioral adoption
models have to generate the behavior’s related belief set. Moreover, the performance of a certain behavior might
lead to new beliefs, which might influence the attitude and, thus, performance (Fishbein & Ajzen, 1975).

1002 Rima Fayad and David Paper / Procedia Economics and Finance 26 ( 2015 ) 1000 – 1006
Fig. 1. The Theory of Reasoned Action
A meta-analysis of past TRA research was conducted by Sheppard, Hartwick, and Warshaw (1988) to investigate
the relationship between intention to perform a behavior and the actual behavior. The research reports on the TRA
were published in the Journal of Consumer Research, the Journal of Marketing, the Journal of Marketing Research,
Advances in Consumer Research, the Journal of Personality and Social Psychology, the Journal of Experimental
Social Psychology, the Journal of Social Psychology, the Journal of Applied Social Psychology, and the Journal of
Applied Psychology prior to 1987. Studies were rejected if the authors had failed to measure all the variables in the
TRA, or did not include bivariate/multivariate correlation, or did not use measures that corresponded with the
behavior/intention that was studied. Sheppard and his associates reported that the TRA was effective in predicting
different behaviors (e.g., study a few hours, go to a weekend job, or write a letter). The frequency-weighted-average
correlation was 0.53 for the intention/behavior relationship, and 0.66 for the (attitudes and subjective
norm)/intention. They also reported that behavior was predicted using the TRA even in situations that fell outside
the boundary conditions set for the model (e.g., behavior involving an explicit choice among alternatives) (Sheppard
et al.). Thus, the robustness of the TRA was established.
2.2. The Technology Acceptance Model
The TRA provided the theoretical framework used by Davis (1989) to study technology adoption behavior. A
belief set for adopting technology was generated by Davis in consistence with Fishbein and Ajzen’s (1975)
recommendation. The belief set consisted of two elements, perceived usefulness (PU) and perceived ease of use
(PEOU). Davis (1989) defined PU as “the degree to which a person believes that using a particular [information]
system would enhance his or her job performance” (p. 320), and PEOU as “the degree to which a person believes
that using a particular [information] system would be free of effort” (p. 320). A visual representation of the elements
in the TAM is presented in Fig. 2.