Causal Relationships between Perceived Enjoyment and Perceived Ease of Use: An Alternative Approach
Author(s) -
Heshan Sun,
Ping Zhang
Publication year - 2006
Publication title -
journal of the association for information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.877
H-Index - 78
ISSN - 1536-9323
DOI - 10.17705/1jais.00100
Subject(s) - causal model , ambiguity , nomological network , psychology , usability , antecedent (behavioral psychology) , computer science , cognitive psychology , structural equation modeling , social psychology , human–computer interaction , machine learning , mathematics , statistics , programming language
Identifying causal relationships is an important aspect of scientific inquiry. Causal relationships help us to infer, predict, and plan. This research investigates the causal relationships between two constructs, perceived enjoyment (PE) and perceived ease of use (PEOU), within the nomological net of user technology acceptance. PE has been theorized and empirically validated as either an antecedent or a consequence of PEOU. We believe that there are two reasons that account for this ambiguity the conceptual coupling of PE and PEOU and the limitations of covariance-based statistical methods. Accordingly, we approach this inconsistency by providing more theoretical reasoning and employing an alternative statistical method, namely Cohen’s path analysis. Specifically, as suggested by previous research on the difference between utilitarian and hedonic systems, we propose the conditional dominance of causal directions. Empirical results from two studies using different technologies and user samples support the theoretical claim that the PEAEPEOU causal direction outweighs the PEOUAEPE direction for utilitarian systems. There are both theoretical and the methodical contributions of this research. The approach applied in this research can be generalized to study causal relationships between conceptually coupled variables, which otherwise may be overlooked by confirmatory methods. We encourage researchers to pay attention to causal directions in addition to causal connectedness.
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