Job Stress and Individual Creativity: A Screen-Based Partial Least Squares Structural Equation Modeling Protocol for AI-Enabled Workplaces

Summary

This research presents a screen-based survey-to-partial least squares structural equation modeling (PLS-SEM) protocol for testing mediation and moderation, conducting common-method diagnostics, interpreting simple slopes, and assessing predictive relevance in AI-embedded workplace research.

Abstract

The increasing use of artificial intelligence (AI) in organizations is reshaping employees’ job demands, leadership perceptions, and opportunities for creativity. Drawing on the Job Demands–Resources (JD–R) model and Social Learning Theory, this study examines whether employees’ perceptions of ethical leadership mediate the relationship between job stress and individual creativity, and whether organizational AI embeddedness amplifies this relationship. Using partial least squares structural equation modeling (PLS-SEM) with 5,000 bootstrap resamples, the findings show that job stress negatively influences ethical leadership perception, while ethical leadership perception positively affects creativity. Job stress was not directly related to creativity; instead, ethical leadership perception served as a significant indirect pathway. In addition, organizational AI embeddedness strengthened the relationship between perceptions of ethical leadership and creativity. These findings suggest that creativity in AI-enabled workplaces is shaped less by technology itself and more by organizational psychological mechanisms. Organizations seeking to enhance creativity during digital transformation should therefore manage job stress and foster ethical leadership environments, rather than focusing solely on AI implementation. This protocol uses the empirical model as a worked example for demonstrating a reproducible survey-to-PLS-SEM workflow.

Introduction

Artificial intelligence (AI) has transformed how employees do their work, understand the nature of their job requirements, and identify opportunities for creativity, as organizations begin to adopt AI for decision support, automation, and coordination^1,2,3,4. AI-enabled capabilities can increase the scope of what information is accessible, and support experimentation, but in and of themselves fail to encourage people to behave creatively^5,6. Instead, employees’ creative behavio....

Protocol

The research process followed all ethical protocols for research with human participants. All participant information was collected anonymously via online survey technology and digital informed-consent procedures. No information identifying any participant was collected, and no video of any participant was recorded. All participants were presented with a digital informed-consent screen prior to reaching the survey. The screen explained the study's academic purpose, that participation was voluntary, that responses would be kept confidential, and that the data would be used for research purposes only. Participants who did not actively acknowledge informed consent were r....

Results

Assessing the measurement model

The measurement model was subsequently used to assess construct reliability and validity. Reliability, convergent validity, item loadings, and collinearity diagnostics are presented in Table 3. Cronbach's alpha values ranging from 0.877 to 0.932 exceed the cutoff of 0.70. The estimates of composite reliability were acceptable, with CR (ρ_a) ranging from 0.880 to 0.959 and CR (ρ_c) ranging from 0.911 to 0.945. The AVE was substantially above the .......

Discussion

The protocol presents a transferable survey-based method for understanding how psychological demands, leadership perceptions, and AI-connected contextual conditions affect employee outcomes in digitally transformed work contexts^1,2,4. Its main contribution is not a filmed manipulation, but a traceable analytic workflow for online recruitment, consent, and screening, measuring constructs of interest, preparing data, specifying a .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
ChatGPT (AI Language Model)	OpenAI	N/A	Used only for language editing, grammar checking, and structural refinement during manuscript preparation. The tool was not used for data collection, statistical analysis, or interpretation of the empirical results. The authors reviewed, edited, and approved the final manuscript and take full responsibility for its content. URL: https://chatgpt.com/
Prolific (Online Participant Recruitment Platform)	Prolific Academic Ltd.	N/A	Online platform used to recruit survey participants for the worked example. Participants were screened using predefined eligibility criteria, including full-time employment and relevance to the target workplace population. URL: https://www.prolific.com/
Qualtrics (Online Survey Platform)	Qualtrics, LLC	N/A	Web-based survey platform used for questionnaire design, digital informed consent, screening, construct-block presentation, response-data collection, and de-identified dataset export. Measurement items were administered using a 7-point Likert-type scale ranging from 1 = strongly disagree to 7 = strongly agree. URL: https://www.qualtrics.com/
SmartPLS 4.1 (PLS-SEM Software)	SmartPLS GmbH	Version 4.1	Used for partial least squares structural equation modeling (PLS-SEM), including model specification, PLS algorithm estimation, measurement-model assessment, collinearity diagnostics, bootstrapping with 5,000 resamples, specific indirect-effect testing, interaction-term estimation, effect decomposition, and PLSpredict assessment. URL: https://smartpls.com/