Formulation of Strategic Priorities for Smart Factory Implementation Through Importance–Performance Map Analysis (IPMA) in a SEM-PLS-Based TAM–UTAUT Integration Model

Abstract

The success of Smart Factory implementation is determined not only by satisfying statistical significance requirements between constructs, but also by an organization's ability to set well-targeted improvement priorities. Many technology acceptance studies stop at the hypothesis-testing stage without translating those findings into actionable managerial direction. This study aims to formulate strategic priorities for improving the acceptance and use of the Smart Factory 4.0 system in railway manufacturing using Importance–Performance Map Analysis (IPMA) built upon an integrated Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) model. Data were collected through a structured questionnaire administered to 60 active users on the Bogie Fabrication and Finishing lines, then analyzed using Partial Least Squares-based Structural Equation Modeling (SEM-PLS) as the basis for calculating the importance value (total effect) and performance value (rescaled mean score) of each construct with respect to Actual Use. The IPMA results show that Facilitating Conditions occupies Quadrant I (high importance, suboptimal performance), making it the top priority for improvement, while Behavioral Intention and Perceived Usefulness fall into Quadrant II (high importance, high performance) and thus need to be maintained as key strengths. Perceived Ease of Use and Social Influence contribute indirectly through the mediation of Behavioral Intention, while demographic variables (age, experience, and gender) show low levels of importance and/or performance and are therefore not the focus of short-term intervention. Based on this mapping, the study formulates five tiered strategic recommendations that can be directly adopted by management, covering network infrastructure strengthening, provision of production-environment-resistant devices, rapid technical assistance mechanisms, microlearning-based training, and the use of social influence through digital change agents. The main contribution of this study lies in providing a data-based diagnostic instrument that guides measurable organizational resource allocation, rather than merely confirming causal relationships between constructs.