Research report: richness versus parsimony in modeling technology adoption decisions--understanding merchant adoption of a smart card-based payment system.

The Technology Acceptance Model (TAM) has received considerable research attention in the IS field over the past decade, placing an emphasis on the roles played by perceived ease-of-use and perceived usefulness in influencing technology adoption decisions. Meanwhile, alternative sets of antecedents to adoption have received less attention. In this paper, sets of antecedent constructs drawn from both TAM and the Perceived Characteristics of Innovating (PCI) inventory are tested and subsequently compared with one another. The comparison is done in the context of a large-scale market trial of a smart card-based electronic payment system being evaluated by a group of retailers and merchants. The PCI set of antecedents explains substantially more variance than does TAM, while also providing managers with more detailed information regarding the antecedents driving technology innovation adoption.

(TAM; PCI; Adoption; Managers; Perceptions; Attitudes; Intentions; Field Study; High Technology; Smart Cards)

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Over the past decade, researchers within the information systems (IS) community have sought to conceptualize, empirically validate, and extend various models of individual-level information technology adoption and usage. These models have generally attempted to use key antecedent attitudinal constructs drawn from established psychological theories to predict new IS technology adoption. For example, the Technology Acceptance Model (TAM) proposed by Davis et al. (1989) to explain IS adoption in a variety of contexts incorporates Fishbein and Ajzen's Theory of Reasoned Action (1975) as its theoretical foundation.

TAM has become one of the most widely applied individual-level technology adoption models in the IS literature. Several alternative models of technology adoption have been proposed in an attempt to overcome the limitations of TAM by incorporating additional constructs suggested by theories other than the Theory of Reasoned Action (TRA). For example, Mathieson (1991) proposed a model of technology adoption premised on Ajzen's (1991) Theory of Planned Behavior that expands TAM to include two additional constructs. Other efforts have sought to develop measures of actual system acceptance as opposed to intended usage (Szajna 1996), and to identify important additional antecedent constructs that also underlie the technology adoption decision, such as computer self-efficacy (Campeau and Higgins 1995) and the role of prior experience (Taylor and Todd 1995a).

Although these conceptual and empirical advances have helped IS researchers to better understand the antecedents to technology adoption, at least two broad concerns remain. First, TAM is often employed because of its parsimony and robustness, allowing the user to explain considerable variance while using only two antecedents (perceived usefulness and perceived ease-of-use). However, although parsimony is an important consideration, individual responses to new technologies are likely to differ depending on the context within which they are encountered. Complete understanding of adoption behavior requires a model that captures the richness of the adoption process across many different contexts.

Second, much of the existing IS adoption literature has focused on the adoption of new behaviors, such as the usage of a personal computer or a particular software package. In these studies, subjects are typically asked to assess innovations that are described across a limited range of possible benefits, and to make adoption decisions that involve minimal acquisition costs. Fewer field-based tests of technology adoption models have been undertaken, although their number is growing (for one exception in a marketing context, see Taylor and Todd 1995b). This latter stream of research is important because individuals and firms making decisions in the field must augment their concerns about price/performance issues with more qualitative assessments of image and visibility. Furthermore, the costs of adoption are typically quite large in these settings. Thus, to assess the generalizability of the proposed technology adoption models, it is important to study their application across both experimental and field-based settings.