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The adoption of software measures: a technology acceptance model (TAM) perspective
Wallace L., Sheetz S. Information and Management51 (2):249-259,2014.Type:Article
Date Reviewed: Jul 23 2014

Wallace and Sheetz present their research to explain and predict who will adopt and begin to use a software measure utilizing an extension of the technology acceptance model (TAM) that they developed and tested. The authors clearly define the problem, resulting in their research question: “What factors influence an individual’s acceptance of a software measure?”

Because software lacks a strong theoretical foundation, making it difficult for organizations to understand what makes a software measure practical or useful, the authors believe that it is necessary to develop a model to explain and predict an individual’s adoption of software measures. To accomplish this, they turned to a proven and tested theory: TAM.

Their thesis revolves around the expectation that when individuals involved in systems development perceive a particular software measure to be useful, they will be more likely to use the measure. In addition, they further believe that if developers perceive that a measure is easy to use, they are more likely to adopt it. To prove their theory, they identified five sub-dimensions of perceived usefulness or what they called the “desirable properties of software measures.” According to the authors, these desirable properties of software measures are:

  • Language independence: the degree to which computation of the measure does not depend on the programming language used;
  • Prescriptiveness: the ability of the software measure to not only diagnose, but recommend or suggest solutions;
  • Validity: the degree to which the software measure has measurement validity and the degree to which it has been empirically tested and supported;
  • Life cycle applicability: the degree to which the measure can be applied to products and processes throughout the software development life cycle;
  • Ease of use: the degree to which the measure is easy to use.

The authors contend that measures that have higher levels of these desirable properties will be perceived as more useful, and I agree. Their reasons are convincing and are supported by sufficient credible evidence. The fit statistics and scale reliabilities obtained for the proposed measurement model are all very close to or “higher than the recommended minimum values of .90 ... thus supporting the reliability and validity of the [model’s] instrument.”

I expected the conclusion to be much stronger in asserting the authors’ findings since both perceived usefulness and perceived ease of use constructs had direct, positive effects on measured use, the dependent variable, supporting the authors’ hypotheses, and perceived ease of use also had a “direct, positive effect on perceived usefulness,” also in support of the hypotheses. In addition, each “of the path coefficients associated with the relationships between the latent constructs were significant at p < .001,” except the path from perceived ease of use to use, which was significant at p < .01. All in all, this is good research that is presented very well.

Reviewer:  Boniface Nwugwo Review #: CR142538 (1410-0903)
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Software Management (K.6.3 )
 
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