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Building a Model of Technology Preference: The Case of Channel Choices
Author(s) -
Muthitcharoen Achita Mi,
Palvia Prashant C.,
Grover Varun
Publication year - 2011
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.2010.00306.x
Subject(s) - preference , theory of reasoned action , context (archaeology) , computer science , technology acceptance model , knowledge management , theory of planned behavior , channel (broadcasting) , selection (genetic algorithm) , marketing , usability , psychology , business , microeconomics , control (management) , human–computer interaction , economics , artificial intelligence , social psychology , paleontology , computer network , biology
Intention theories, such as the Theory of Reasoned Action, the Theory of Planned Behavior, and the Technology Acceptance Model (TAM), have been widely adopted to explain information system usage. These theories, however, do not explicitly consider the availability of alternative systems that users may have access to and may have a preference for. Recent calls for advancing knowledge in technology acceptance have included the examination of selection among competing channels and extending the investigation beyond adoption of a single technology. In this study, we provide a theoretical extension to the TAM by integrating preferential decision knowledge to its constructs. The concept of Attitude‐Based Preference and Attribute‐Based Preference are introduced to produce a new intention model, namely the Model of Technology Preference (MTP). MTP was validated in the context of alternative behaviors in adopting two service channels: one a technology‐based online store and the other a traditional brick‐and‐mortar store. A sample of 320 responses was used to run a structural equation model. Empirical results show that MTP is a powerful predictor of alternative behaviors. Furthermore, in the context of service channel selection, incorporating preferential decision knowledge into intention models can be used to develop successful business strategies.