Predicting Information Technology Adoption in Small Businesses: An Extension of the Technology Acceptance Model

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ABSTRACT

Studies that examine information technology (IT) adoption in small businesses are relatively scarce. Of those studies, a few have used the Technology Acceptance Model (TAM) to predict IT adoption in a small businesses environment. Even so, the TAM still fails to explain much of the variance in computer usage. The mental model literature suggests that providing users with a diagram of how a particular technology works may be an enhancement over the current version of the TAM. In the current paper, a revised model that incorporates mental models is empirically tested. Future research aspirations are also discussed.

INTRODUCTION

There are only a handful of studies that examine information technology (IT) adoption in small businesses. This is quite surprising given that smaller firms far outnumber larger ones and contribute significantly to the economy. According to the U.S. Small Business Administration (2009), small businesses are responsible for creating many new jobs and innovations as well as contributing close to half of the U.S. GDP (1).

Moreover, research suggests that small companies face unique IT issues, such as reliance on external IT expertise (Thong, Yap & Raman, 1996). Thus, the implication is that many studies that look at IT adoption may not be applicable to small businesses. Further, the studies that have examined IT adoption in small firms all imply that an important determinant is attitude toward the technology (Riemenschneider, Harrison, & Mykytyn, Jr., 2003; Caldeira & Ward, 2003; Mirchandani & Motwani, 2001).

Attitude toward technology is an integral component of the Technology Acceptance Model (TAM). Specifically, the TAM predicts that a user's attitude toward a particular technology ultimately affects whether or not they accept that technology. In fact, the TAM has already been used to study IT adoption in small businesses (e.g., Dembla, Palvia & Krishnan, 2007; Riemenschneider, Harrison, & Mykytyn, Jr., 2003).

However, by itself, the ??? only explains about 40% of the variance in computer usage, suggesting that additional factors may help explain IT acceptance (Legris, Ingham & Collerette, 2003). Thus, the purpose of this paper is twofold. Obviously, one goal is to improve upon the TAM by proposing a revised model that incorporates elements from the mental model literature. Most importantly, however, the goal is to offer a model that ultimately better explains IT adoption in a small business environment.

The rest of the paper is laid out as follows. First, a review of the ??? and mental model literature is provided followed by the revised model. Next, the revised model is empirically tested. Finally, limitations and future aspirations are discussed.

TECHNOLOGY ACCEPTANCE MODEL

The original form of the Technology Acceptance Model (???) (Davis, 1989; Davis, Bagozzi & Warshaw, 1989) is derived from the Theory of Reasoned Action (TRA), a commonly used theory from social psychology (Fishbein & Ajzen, 1975) (Figure 1). The TRA can be described as a conceptual framework that predicts whether or not an individual performs a certain behavior based on their behavioral intention (BI) to perform that behavior (16). Further, one's BI is determined by the individual's attitude (A) and subjective norm (SN) with respect to the behavior, where A is determined by one's beliefs and evaluations of the consequences related to that behavior (16). SN is determined by the individual's perception that one's referents have opinions about whether or not to perform the behavior and by the individual's motivation to comply with those referent opinions (16).

The TAM also asserts that one's behavior is determined by their intention to perform that behavior. The TAM, however, is specifically adapted to model users' acceptance of information systems (Davis, Bagozzi & Warshaw, 1989) (Figure 2). ??? posits that computer users' usage behavior is indirectly determined by two particular beliefs, perceived usefulness (U) and perceived ease of use (EOU) (985). …

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