Integrating TTF and UTAUT to Explain the Pharmacists’ Adopting Intentions of Telepharmacy

Author Information

Meng-Chi Liu, National Kaohsiung First University of Science and Technology, Taiwan
Ching-Chang Lee, National Kaohsiung First University of Science and Technology, Taiwan
Ching-Hsiang Chang, Chang Jung Christian University, Taiwan

Abstract

Pharmaceutical care involves the process through which a pharmacist cooperates with a patient and other professionals in implementing a therapeutic plan for the quality use of medicines. However, traditional pharmacy were limited by the shortage of the pharmacists and capabilities of communication, the quality pharmaceutical care was not provided effectively. Previous studies have pointed out the implementation of telepharmacy have potential benefits for improving pharmacy service coverage regardless of where the pharmacist is located. The success of this program depends on the aggressive usages by the pharmacists. Therefore, it’s noteworthy to investigate the determinants of pharmacists’ intention to adopt telepharmacy in their clinical practice. By integrating the task-technology fit theory (TTF) and the unified theory of acceptance and usage of technology (UTAUT), this research proposed a telepharmacy adoption model including six constructs for the pharmacists. We conducted a questionnaire survey from 225 pharmacists in Taiwan and used partial least squares (PLS) via SmartPLS to test our hypotheses of the proposed model. Our results show that both performance expectancy and effort expectancy directly affect the adopting intention of telepharmacy, but the task technology fit will not. However, the antecedent factors of performance expectancy including task technology fit and technology characteristics have significant effect on pharmacists’ intentions indirectly. This study offers a new perspective and a strategic guide for health care industries on how to promote pharmacists to implement telepharmacy and provide quality pharmaceutical care in their community.

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• Category: Computational Social Science

Posted by James Alexander Gordon

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