Improving IT assessment with IT artifact affordance perception priming

• Accounting professionals play a variety of IS roles in adoption initiatives and rapidly changing IS controls drive the need to adopt new technologies. Assessments of IT adoption are, therefore, important. However, individual assessments of risks, controls and user attitudes towards adoption may be less accurate due to variations in IT perception predisposition.
• Using well known and representative constructs and items from the technology acceptance model (TAM), we demonstrate that IT perception predisposition systematically influences predictive power in a study of 246 business students who used SharePoint.
• We employ content priming as an intervention strategy to ‘nudge’ respondents towards a more balanced perspective (e.g. artifact focused individuals to be more organizationally aware). The results demonstrate that such treatments can result in improved assessments.
• The findings imply that considering IT perception preference can help accounting professionals better assess risks, more accurately identify reportable control weaknesses, and more confidently establish the reliability of financial systems.

Accurately assessing organizational information technology (IT) is important for accounting professionals, but also difficult. Both auditors and the professionals from whom they gather data are expected to make nuanced judgments regarding the adequacy and effectiveness of controls that protect key systems. IT artifacts (policies, procedures, and systems) are assessed in an audit because they “afford” relevant action possibilities but perception preferences shade the results of even systematic and well-tested assessment tools. This study of 246 business students makes two important contributions. First we demonstrate that a tendency to focus on either artifact or organizational imperative systematically reduces the power of well-regarded IT measurements. Second, we demonstrate that priming is an effective intervention strategy to increase the predictive power of constructs from the familiar technology acceptance model (TAM).