Data analytics-driven innovation: UTAUT model perspectives for advancing healthcare social work

Abstract

In today’s dynamic and increasingly complex healthcare landscape, leveraging data analytics in decision-making processes has become indispensable for fostering innovation and enabling more effective, data-driven approaches to healthcare delivery. This study explores the factors influencing the adoption of data analytics techniques in healthcare social work practice. It is guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model and is aligned with key principles of open innovation dynamics. The analysis, conducted through binary logistic regression, revealed that facilitating conditions, effort expectancy, and self-efficacy are the most influential predictors of healthcare social workers’ intention to adopt data analytics. This underscores the critical role of organizational support, perceived ease of use, and individual confidence in fostering the adoption of innovative data-driven practices within healthcare social work. The results also indicate that social influence has a limited impact on shaping adoption decisions. The study’s findings have significant practical implications for healthcare decision-makers, social workers, and other stakeholders aiming to advance service delivery and improve patient outcomes through innovative data-driven solutions.

Keywords

• Data analytics
• Innovative healthcare delivery
• Social work
• UTAUT
• Logistic regression