A Parallel Mediation Analysis of Behavioral Constructs Linking Intention-to-Use Technology to Performance Outcomes Among Pharmaceutical Executives

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Managers in organizations are at a crossroads in understanding how behavioral intention to use technology influences employee performance. In the technology acceptance model, behavioral intention is a precursor to actual system use. However, there is inadequate information on how this intention indirectly impacts performance among employees in pharmaceutical organizations. Intuitively, behavioral mechanisms (job satisfaction, conscientiousness, self-efficacy, perceived relevance) should transform technology use into desired outcomes and serve as explanatory links between behavioral intention and technology performance. Using the theoretical lens of the technology acceptance model, reasoned action, and contingency theories, this study explored the mechanisms linking behavioral intention to technology performance among pharmaceutical executives. A web-based survey of a random sample of 282 pharmaceutical executives was conducted using a self-administered Google Forms questionnaire. Data was analyzed using variance-based structural equation modeling in ADANCO software, controlling for job-based demographics. All independent variables in the model significantly explained the variance in the dependent variable, technology performance. Perceived relevance was the only substantial linkage between behavioral intention and technology performance. The study confirmed that perceived relevance is an important mediator between behavioral intention and technology performance. To enhance performance, managers should adopt strategies that enhance job satisfaction, conscientiousness, self-efficacy, and perceived relevance. Ensuring appropriate technology deployment, adequate employee education, and targeted training are crucial for enhancing these mediating effects. The study contributes to the existing literature by recommending the inclusion of perceived relevance as a mediating link in the technology acceptance model.