Perceived Knowledge Update
The critical issue pertains to the assessment of how user perceptions about knowledge of a new system evolve over time. Prior studies on IS acceptance have relied on TAM and ECM to understand the change in user perceptions. They have highlighted the belief change process as the core theme and proposed that a better understanding of how user beliefs evolve from the pre-usage to the post-adoption stage is critical (Kim & Malhotra, 2005). TAM-related studies show that the influence of perceived usefulness on intention to use persists at the post-adoption stage (Venkatesh & Davis, 2000). ECM delves deeper into the belief change process and proposes confirmation as an intermediate process between pre-usage and post-adoption perceptions regarding IS (Bhattacherjee & Premkumar, 2004). In the context of learning the confirmation can be assessed by evaluating the difference between individual’s knowledge improvement perceptions. In fact conformation happens when people’s perceptions on their post-knowledge have been improved considerably compared to their prior training/usage knowledge perceptions. In other words, after the users gain firsthand experience by using ERPsim, their succeeding knowledge perceptions are revised to achieve better alignment between initial expectations, formed by their levels of effort, and beliefs after the actual use experience.
In the same vein, Kim and Malhotra (2005) suggested that the belief change process may be established through a sequential updating mechanism, which is grounded on the premise that a user’s perceptions are updated in the context of prior perceptions. The sequential updating mechanism proposes that post-adoption perceptions are a function of pre-adoption perceptions. The basic beliefs are updated based on new information that is now available.
The literature review highlights two important gaps. First, special attention needs to be given to user knowledge perceptions regarding IS features that further determine intentions of users. Most prior studies focused on aggregate usefulness perceptions. The approach proposed in the study offers a more granular assessment of perceived knowledge as an influential determinant of individual learning behaviors in the context of ERPsim. Second, limited research focuses on how users’ perceptions change over time as they gain experience in utilizing a system. Therefore, by defining perceived knowledge update as the difference between pre-training and post-training knowledge levels of a learner, we attempt to investigate the mechanism, which leads from perceived knowledge update to repeated learning behaviors.