Gauging the Technology Acceptance of Manufacturing Employees: A New Measure for Pre-Implementation

Kristen Haynes (), Gregory Harris, Mark C. Schall, Jia Liu and Jerry Davis
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Kristen Haynes: Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA
Gregory Harris: Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA
Mark C. Schall: Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA
Jia Liu: Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA
Jerry Davis: Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA

Sustainability, 2024, vol. 16, issue 12, 1-19

Abstract: Recent technological advances are bringing about the digitalization of manufacturing, enabled by introducing and integrating new and improved technologies into existing processes and activities. Integrating advanced technologies into the workplace can have a positive effect on manufacturing efficiency and competitiveness, as well as sustainability and environmental impact. Employee acceptance of these new technologies is critical for manufacturing organizations to achieve these goals. Unfortunately, a notable deficiency of tools to assess the readiness of an employee work group or organization to accept a new technology exists. The objective of this study was to develop and validate a new tool for gauging employee technology acceptance in a pre-implementation decision context known as the Technology Acceptance in a Manufacturing Environment (TAME). Statistical validation measures were conducted on survey responses from 823 respondents across seven locations of one large organization. The results indicate that TAME is appropriate for assessing readiness for technology acceptance among manufacturing workers with little to no training or knowledge of the technology being considered for implementation (R 2 = 86%). TAME can facilitate the organizational assessment of employee perception of new technologies before implementation, increasing the chances of a successful launch. This research results in the first known application of technology acceptance models in a pre-implementation context in a manufacturing environment.

Keywords: pre-implementation; technology acceptance; smart manufacturing; sustainable manufacturing; UTAUT; ORIC; augmented reality; employee acceptance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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