Internal Consistency (Cronbach's α) for a Two-Wave Technostress Study in Manufacturing

605 employees at baseline and 450 at a four-week follow-up participated in a panel study across five automated manufacturing plants. The dataset, created by Umut Elbir and last updated in April 2026, contains Cronbach's alpha values for assessing the internal consistency of scales measuring technostress, mental health, burnout, engagement, and safety outcomes. It supports a study investigating the role of job resources and psychosocial safety climate as protective factors against technostress.

Use Cases

• Assessing scale reliability for technostress and mental health constructs based on the described two-wave panel design.
• Analyzing the buffering effects of job resources like supervisory ICT support and digital literacy based on the study's moderation analyses.
• Investigating the role of psychosocial safety climate (PSC) at crew and organizational levels on engagement and safety behaviors as described in the multilevel framework.

Strengths

• Data is derived from a two-wave panel study with 605 baseline and 450 follow-up participants, providing longitudinal insights.
• Study design spans five automated manufacturing plants, offering a multi-site perspective.
• Dataset is licensed under CC-BY-4.0, facilitating open reuse and sharing.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating it likely contains summary statistics (like Cronbach's alpha) rather than raw survey responses.
• Description metadata is limited; actual data quality and completeness require manual inspection after download.

Provenance

Source

Umut Elbir via figshare.

Collection Method

Collected via surveys from employees in five automated manufacturing plants using a two-wave panel design.

Time Range

The study involved a baseline measurement and a four-week follow-up; specific years are not provided.

Freshness

Last updated 2026-04-03 17:39:37; freshness should be verified.

Geography

Data collected from automated manufacturing plants; specific country or region is not stated.