Measuring Technostress in Corporate Culture: Insights from the 10-K Annual Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Editor,

Thank you for the opportunity to review this manuscript, “Measuring Technostress in Corporate Culture: Insights from the 10-K Annual Reports.” The paper addresses a timely and relevant issue: as firms intensify digital transformation (e.g., cloud, AI, automation), technology-related strain becomes a material organizational risk with potential performance implications. Conceptually, the manuscript is anchored in the technostress literature and follows the established five-dimension structure (techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty), while adding a sixth dimension—techno-risks—to capture cybersecurity-related anxiety and threats (e.g., breaches, hacks, unauthorized access).  regressions linking technostress dimensions to firm productivity. Methodologically, the approach is promising and potentially impactful for researchers who want to move beyond self-reported technostress proxies. The dataset—2,532 10-K annual reports associated with S&P 500 firms—supports the use of automated text analysis, and the attempt to build a structured dictionary-based measure (with references to factor analysis used to refine key terms into 42 clues) adds rigor beyond an ad hoc keyword list. 

At the same time, several areas require clarification and tightening before the work can be considered fully reliable and replicable. The biggest issue is measurement transparency. Dictionary-based content analysis can be powerful, but it is also sensitive to design choices. The manuscript would benefit substantially from a clearer, audit-ready description of how the 42 key clues were derived, how the factor analysis was conducted (data used, extraction/rotation choices, loading thresholds, handling of cross-loadings), and how the final scoring was computed at the report level. It is currently difficult to assess whether the resulting index captures technostress specifically, rather than broader “technology risk and change” language, especially because 10-K disclosures are shaped by legal/regulatory incentives and may reflect risk management rhetoric as much as organizational strain. Closely related, the paper should explain how it handled common text-analysis challenges in filings—negation (e.g., “we do not expect…”), boilerplate risk-factor language reused across years, and context drift where the same term can signal different meanings depending on section and sentence structure.

The validation strategy is directionally appropriate (frequencies followed by productivity regressions), but the manuscript should be more explicit about what constitutes “validation” here. Word frequency counts mostly demonstrate that the dictionary appears in the corpus; they do not, on their own, establish that the measure captures technostress rather than disclosure intensity or risk verbosity. The regression evidence is more compelling, particularly the finding that several dimensions—especially techno-risks, insecurity, uncertainty, and invasion—are negatively associated with productivity, and that the effects accumulate over time. However, because productivity and risk-factor disclosures may be jointly influenced by underlying firm conditions (e.g., operational shocks, strategic restructuring, regulatory scrutiny), readers will likely ask for stronger discussion (and ideally additional tests) addressing endogeneity, reverse causality, and omitted variables. Even within a fixed-effects framework, it would help to see robustness checks using alternative productivity proxies, alternative scaling of technostress (e.g., per total words, per section, or TF–IDF style normalization), and sensitivity to excluding “Risk Factors” sections where cybersecurity language is especially concentrated.

the paper would be strengthened by a clearer reconciliation of sample statements (S&P 500 firms vs. the number of reports), time period coverage, and any exclusion criteria (e.g., missing filings, financial firms, filing amendments). A concise table that lists each technostress dimension with its associated keywords/bigrams, example 10-K excerpts, and the scoring rule would greatly improve transparency and would make the contribution easier to reuse.

All the best

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

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Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Editor,

Thank you for sharing this revised version of the manuscript. I would like to thank the authors for being constructive and addressing all the point raised in the last round of review. I am satisfied with level of quality of the paper and no further changes are required at this stage. 

All the Best