Key references

1 · The theory

• Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3). The origin of the claim this site keeps returning to: people protect what they value, and losing a resource hurts more than gaining an equivalent one helps.
• Hobfoll, S. E., Halbesleben, J., Neveu, J.-P., & Westman, M. (2018). Conservation of resources in the organizational context. Annual Review of Organizational Psychology and Organizational Behavior, 5. The modern statement of loss and gain spirals and of resource caravans traveling through passageways — the vocabulary behind the spiral explorer and the “which way out” widget.
• Halbesleben, J. R. B., Neveu, J.-P., Paustian-Underdahl, S. C., & Westman, M. (2014). Getting to the “COR”. Journal of Management, 40(5). The critical review that asks what counts as a resource — useful before deciding which of the markers on this site your organization should measure.

2 · The data and the profile method

• Scherpenzeel, A., & Das, M. (2011). True longitudinal and probability-based internet panels: Evidence from the Netherlands. In M. Das, P. Ester, & L. Kaczmirek (Eds.), Social and behavioral research and the internet (pp. 77–104). Routledge. LISS panel (Centerdata). How the LISS panel is recruited and run. Access to the de-identified files is by data-user agreement; nothing respondent-level appears on this site.
• Monnot, M. J. (2026). Afloat or adrift: A longitudinal study of latent personality profiles and future-of-work skills across midlife [Preprint]. PsyArXiv. The U.S. midlife study that produced the four reference profiles and the recovery-versus-disengagement contrast this paper imports and tests in a Dutch panel.
• Asselmann, E., Klimstra, T. A., & Denissen, J. J. A. (2021). Unraveling resilience: Personality predicts exposure and reaction to stressful life events. Personality Science, 2. Prior profile work in LISS itself, which retained a two-class resilient versus nonresilient solution and linked membership to reactions to unemployment and disability.
• Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide. Journal of Vocational Behavior, 120. The multi-criterion retention guidance the paper follows when it keeps four profiles despite information criteria that kept asking for more.
• Nylund-Gibson, K., Garber, A. C., Carter, D. B., et al. (2023). Ten frequently asked questions about latent transition analysis. Psychological Methods, 28(2). Why “who moves, and where” is a different estimand from “how much changes” — the distinction behind reading destination rather than prevalence.

3 · AI-related stressors at work

• Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA). Journal of Management & Organization, 24(2). The appraisal construct: employees who see their jobs as replaceable report lower commitment and higher turnover intention. The 2020 displacement-risk items sit in this tradition.
• Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1). What algorithmic management is — machine pacing, digital output registration, customer-rating appraisal — and why it is a control story, not only a workload story.
• Tarafdar, M., Cooper, C. L., & Stich, J.-F. (2019). The technostress trifecta. Information Systems Journal, 29(1). Distinguishes threat-based techno-distress from challenge-based techno-eustress — the reason technology self-efficacy ran in the opposite direction from the other three stressors.
• Ravid, D. M., White, J. C., Tomczak, D. L., Miles, A. F., & Behrend, T. S. (2023). A meta-analysis of the effects of electronic performance monitoring on work outcomes. Personnel Psychology, 76(1). Monitoring is associated with higher stress and no performance gain — the meta-analytic backdrop for the digital-monitoring items.
• Shoss, M. K. (2017). Job insecurity: An integrative review and agenda for future research. Journal of Management, 43(6). Displacement threat as a core job-insecurity stressor; the review that situates perceived replaceability inside a mature literature.

4 · Measuring exposure to AI

• Felten, E., Raj, M., & Seamans, R. (2021). Occupational, industry, and geographic exposure to artificial intelligence. Strategic Management Journal, 42(12). The AI Occupational Exposure (AIOE) index, crosswalked here from U.S. SOC to ISCO-08 codes. It passed the pre-trend falsification check in this panel — and still predicted almost nothing about well-being.
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114. The automatability probabilities that failed the pre-trend check here, which is why the paper warns against pooling exposure metrics interchangeably.
• Webb, M. (2020). The impact of artificial intelligence on the labor market [Working paper]. Stanford University. Task-level overlap with AI patents, showing exposure reaches high-skilled work; the third exposure score merged for the domain-dissociation test.
• Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6). The labor-market evidence that makes displacement threat a reasonable thing for an employee to appraise.

5 · The within-person method

• Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1). Why a random intercept is needed before cross-lags can be read as within-person: the model behind the spiral explorer.
• Hakanen, J. J., Schaufeli, W. B., & Ahola, K. (2008). The job demands–resources model: A three-year cross-lagged study of burnout, depression, commitment, and work engagement. Work & Stress, 22(3). The precedent for reading reciprocal cross-lags between resources and strain as evidence about gain and loss processes.
• Usami, S., Murayama, K., & Hamaker, E. L. (2019). A unified framework of longitudinal models to examine reciprocal relations. Psychological Methods, 24(5). How the random-intercept model, the structured-residual growth model, and the detrending sensitivity relate — the map the paper uses to choose among them.
• Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling, 21(3). The classification-error logic behind the soft-weighted and pseudo-class sensitivities that each destination odds ratio had to survive.

6 · Pricing distress

• Stewart, W. F., Ricci, J. A., Chee, E., Hahn, S. R., & Morganstein, D. (2003). Cost of lost productive work time among US workers with depression. JAMA, 289(23). The lost-productive-time construct and the human-capital convention (hours valued at the wage) behind the +65 hours and +€999 figures.
• Koopmanschap, M. A., Rutten, F. F. H., van Ineveld, B. M., & van Roijen, L. (1995). The friction cost method for measuring indirect costs of disease. Journal of Health Economics, 14(2). The Dutch correction that caps absence costs at the period needed to restore production — the variant that stayed significant when the human-capital estimate did not.
• Hassard, J., Teoh, K. R. H., Visockaite, G., Dewe, P., & Cox, T. (2018). The cost of work-related stress to society: A systematic review. Journal of Occupational Health Psychology, 23(1). Why the costs of strain are dominated by productivity loss rather than turnover — consistent with finding lost time and quit intent, but not realized exits, in this panel.
• Boushey, H., & Glynn, S. J. (2012). There are significant business costs to replacing employees. Center for American Progress. Source of the 21%-of-salary replacement-cost multiplier used to translate quit intent into a scenario euro figure.