New short scale to measure workers’ attitudes toward the implementation of cooperative robots in industrial work settings: Instrument development and exploration of attitude structure

Abstract

Technological innovations are oftentimes accompanied by organizational change processes. A new context-specific questionnaire was developed as a diagnostic tool to measure workers’ attitudes toward mobile manufacturing robots to provide a basis for managerial decisions and interventions – the “Attitudes toward Cooperative Industrial Robots Questionnaire” (ACIR-Q). Two samples, an online sample of 355 German manufacturing workers and a field sample of 201 workers from 4 local manufacturing companies were collected. For a large item pool, exploratory and confirmatory factor analysis was used to identify the attitudinal factor structure. Data showed a combined affective-behavioral factor and two cognitive factors on task-related and social-related beliefs. Based on this, the 12-item short scale ACIR-Q was derived using ant colony optimization. As attitudes can also be interpreted as networks of evaluative responses, network analysis was used for further insights. The small-world network structure (high clustering and connectivity) allows to hold complex attitudes and centrality measures indicate the most influential evaluative responses. Additionally, we explored relationships between workers attitudes and interpersonal variables (perceived competence, perceived control, and general self-efficacy), as well as organizational variables (trust in management, support climate, job insecurity and job characteristics). Based on the results, practical implications are suggested to improve workers’ attitudes.

Development and Validation of a Basic Psychological Needs Scale for Technology Use

“…As Cronbach (1971) notes, construct validation is an ever extending and thus never-ending process. This article describes a successful start of such a validation process, of which there are still far too few in the literature on HCI (Leichtmann et al, 2022(Leichtmann et al, , 2023. It is thus a first step toward achieving more reliable measurements and thus more robust results in user studies.…”