We didn't invent the risks.
Science did. We made them visible.
Every score in CogniSource traces to peer-reviewed research in cognitive psychology, occupational health, project management, queueing theory, reliability engineering, information theory, and organizational behavior. 40 verified citations, every one checked against its source.
CogniSource calculates six Key Risk Indicators (KRIs), each grounded in a validated research framework drawn from engineering reliability, queueing theory, information theory, stress physiology, cognitive psychology, occupational health, and operations research.
SPOF Risk draws on engineering series-system reliability theory (Birolini, 2017) and the peer-reviewed evidence on how concurrent multi-project ownership creates cascade risk under blocking conditions (Zika-Viktorsson et al., 2006). Capacity Overrun applies queueing theory to knowledge work throughput (Kingman, 1961; Reinertsen, 2009): as utilisation approaches its sustainable limit, expected wait time grows non-linearly. Small increases in load near the ceiling produce disproportionate downstream delays. Dependency Debt uses reliability engineering’s hazard-rate model (Weibull, 1951; Vacanti, 2015) to score blocked task age risk: blocked items carry an accelerating hazard rate as they age, aggregated so that each additional unresolved dependency compounds the total rather than adding to it linearly. Context Switching draws on three research streams: information-theoretic attention fragmentation (Shannon, 1948), cognitive attention residue from incomplete-task switching (Leroy, 2009), and the empirical meeting-interruption literature (Rubinstein et al., 2001; Mark et al., 2005). Burnout Velocity uses the allostatic load accumulation model (McEwen & Stellar, 1993; McEwen, 1998): stress carryover builds across loaded periods and decays with recovery, bounded by the JDR framework’s identification of insufficient recovery as the primary structural burnout predictor (Demerouti et al., 2001; Bakker & Demerouti, 2017). Recovery Buffer applies time-discounted capacity analysis (Bellman, 1957): near-term available headroom is weighted more heavily than distant capacity.
The unplanned work component is additionally grounded in research on how digital communication norms create structural urgency: Barley, Meyerson & Grodal (2011) on email as a source of overload, and Mazmanian, Orlikowski & Yates (2013) on how collective connectivity norms compound individual urgency. Full citations below.
To see how these signals appear inside the product, visit the Features page.
CogniSource is built on published science, and we are transparent about what that means in practice. Four principles guide how we use the research.
1. Validated behavioral signals, no hardware required
CogniSource works from the signals you already create: task counts, deadline proximity, meeting density, interruption frequency, and effort size. These are validated correlates of cognitive load and burnout risk, confirmed across decades of peer-reviewed research. They do not require wearables, biometric sensors, or clinical consent. The model gives you actionable insight from data you generate every day, without adding any new instrumentation to your work.
2. Calibrated Pressure Index, continuously improving
The KRIs combine into the Pressure Index using the current evidence in the workspace and calibrated weights for each signal. CogniSource looks for both patterns: multiple risks building together, and any single risk serious enough to need attention. After enough daily history exists, the model can compare current pressure with the workspace's usual pattern. That history helps tune the score, while the current workload evidence remains the deciding signal. The model preserves the research-backed direction of each KRI while avoiding false precision before large-scale outcome data exists.
3. Simple input by design
Effort sizing uses three intuitive levels (S/M/L) rather than precise hour estimates, because the friction of time-logging is the leading cause of tool abandonment in productivity research. The trade-off is intentional: a tool you use every day with simple inputs outperforms a theoretically more precise tool you stop using after a week. CogniSource is designed to stay out of your way. The model accounts for input simplicity internally, using principled estimation techniques grounded in statistics and operations research.
4. Burnout Velocity is a trend signal, not a clinical verdict
The Burnout Velocity score reflects accumulated stress carryover relative to a research-grounded threshold. It is most powerful as a personal trend indicator: a score rising over consecutive weeks is the signal to act on, before the situation becomes harder to reverse. It is not a clinical burnout probability estimate and should not replace professional occupational health guidance. CogniSource tells you when the pattern is forming; what you do with that information is yours to decide.
The Invisible Load
Unplanned tasks, arriving via Teams, email, colleague requests, and management directives, carry embedded urgency norms that amplify their cognitive cost beyond the task itself. Research confirms that digital communication creates structural urgency, not just personal preference.
Barley, S. R., Meyerson, D. E., & Grodal, S. (2011). E-mail as a source and symbol of stress. Organization Science, 22(4), 887–906. https://doi.org/10.1287/orsc.1100.0573
Mazmanian, M., Orlikowski, W. J., & Yates, J. (2013). The autonomy paradox: The implications of mobile email devices for knowledge professionals. Organization Science, 24(5), 1337–1357. https://doi.org/10.1287/orsc.1120.0806
Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate. IT Revolution Press. High performers: 21% unplanned work/rework. Low performers: 27%.
Mark, G., Iqbal, S. T., Czerwinski, M., & Johns, P. (2014). Bored Mondays and focused afternoons: The rhythm of attention and online activity in the workplace. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14) (pp. 3025–3034). ACM Press. https://doi.org/10.1145/2556288.2557204
SPOF Risk: Concurrent Project Overload
Managing too many active projects simultaneously creates single-point-of-failure risk. When one project blocks, cascades follow across all concurrent work. CogniSource scores SPOF using a Bus Factor × Impact model grounded in engineering series-system reliability theory (Birolini, 2017): for each solo-owned project, failure probability is weighted by linked effort and deadline proximity. Risks are aggregated across all exposed projects using the series-system model, so that adding one more solo-owned project always increases the total risk, never resets it. The peer-reviewed basis for why solo ownership creates cascade risk is established by Zika-Viktorsson et al. (2006).
Zika-Viktorsson, A., Sundström, P., & Engwall, M. (2006). Project overload: An exploratory study of work and management in multi-project settings. International Journal of Project Management, 24(5), 385–394. https://doi.org/10.1016/j.ijproman.2006.02.010
Goldratt, E. M., & Cox, J. (1992). The Goal: A Process of Ongoing Improvement (2nd revised ed.). North River Press.
Project Management Institute. (2017). Pulse of the Profession: Success Rates Rise. PMI.
Birolini, A. (2017). Reliability Engineering: Theory and Practice (8th ed.). Springer. ISBN: 9783662564745. Series-system reliability model: the failure probability of a system increases multiplicatively as more critical-path components are added, regardless of their individual reliability.
Hubbard, D. W. (2014). How to Measure Anything: Finding the Value of Intangibles in Business (3rd ed.). John Wiley & Sons. ISBN: 9781118539279. Framework for quantifying the probability and impact of intangible risks, applied here to weighting Bus Factor exposure by effort and deadline proximity.
Cosentino, C. (2015). Agile Metrics in Action: Measuring and Enhancing the Performance of Agile Teams. Manning Publications. Bus factor as a team risk metric: the number of people whose loss would stall a project.
Capacity Overrun: Working Memory Limits
Sustainable work capacity has a measurable threshold. Beyond it, quality and throughput degrade non-linearly. CogniSource grounds Capacity Overrun in Kingman’s foundational queueing theory work (1961; Reinertsen, 2009): as utilisation approaches its sustainable limit, expected wait time grows non-linearly. Small increases in load near that ceiling produce disproportionate downstream delays. That is why adding one more task to an already-loaded week can collapse throughput rather than merely slowing it down. Cognitive science further establishes the 3–5 item working memory limit as the empirical ceiling for healthy parallel task load (Miller, 1956; Cowan, 2001).
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–185. https://doi.org/10.1017/S0140525X01003922
Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
Goldratt, E. M. (1997). Critical Chain. North River Press.
Kingman, J. F. C. (1961). The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902–904. https://doi.org/10.1017/S0305004100035793. Foundational derivation of the relationship between utilisation and queue wait time: as utilisation approaches its limit, expected wait time grows non-linearly and disproportionately. The mathematical basis for CogniSource’s Capacity Overrun score.
Reinertsen, D. G. (2009). The Principles of Product Development Flow: Second Generation Lean Product Development. Celeritas Publishing. ISBN: 9781935401001. Applies Kingman’s queueing formula to knowledge work: high utilisation is the primary cause of long queues, not high workload per se. Capacity management is queue management.
Hopp, W. J., & Spearman, M. L. (2011). Factory Physics (3rd ed.). Waveland Press. ISBN: 9781577667391. The Variability Law: increasing variability always degrades performance. Applied here to the arrival variability and service variability components of queueing theory, where unplanned work ratio drives arrival variability.
Dependency Debt: Blocked Task Aging
Blocked tasks that age without resolution compound risk non-linearly. CogniSource scores Dependency Debt using the Weibull hazard function (Weibull, 1951; Vacanti, 2015): blocked items accumulate risk as a function of age, with an accelerating hazard rate: the older an unresolved block, the faster its risk grows. Pending items carry a constant hazard rate. Individual item risks are aggregated using the series-system model, so that each additional unresolved dependency compounds the total rather than adding to it linearly. DORA research further establishes that lead time (how long work remains unresolved) is a primary predictor of delivery performance (Forsgren, Humble & Kim, 2018).
Goldratt, E. M. (1997). Critical Chain. North River Press.
Poppendieck, M., & Poppendieck, T. (2003). Lean Software Development: An Agile Toolkit. Addison-Wesley. ISBN: 9780321150783.
Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps. IT Revolution Press. ISBN: 9781942788331. Based on large-scale annual State of DevOps surveys; not peer-reviewed.
Kim, G., Behr, K., & Spafford, G. (2013). The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win. IT Revolution Press. ISBN: 9780988262591.
Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, 18(3), 293–297. The Weibull distribution models failure probability as a function of age with a flexible shape parameter: a shape parameter greater than 1 produces an accelerating hazard rate (risk grows faster as age increases); a parameter of 1 produces a constant hazard rate. Originally developed for engineering component reliability; applied here to model blocked task aging risk.
Vacanti, D. S. (2015). Actionable Agile Metrics for Predictability: An Introduction. ActionableAgile Press. Application of the Weibull distribution to knowledge work cycle time, establishing the empirical basis for using a shape parameter to model accelerating vs. constant failure rates in blocked work items.
Context Switching Cost
CogniSource scores Context Switching using three independently validated components: Shannon entropy (1948) measuring attention fragmentation across active projects: the more evenly attention is distributed across projects, the higher the entropy; Leroy’s (2009) attention residue decay function, which models how incomplete-task switches leave involuntary cognitive residue that degrades performance on the next task; and a meeting fragmentation proxy for structural interruption load. The three components are combined according to their relative weight in the research literature. The underlying cognitive cost research is extensive. Switching between concurrent projects carries a measurable cognitive cost that activates automatically and immediately, not after a threshold is crossed. Research identifies two distinct mechanisms. The first is the task-switch cost itself: every context switch triggers mandatory goal-shifting and rule-activation processes taking milliseconds to seconds per event, compounding across the day (Rubinstein, Meyer & Evans, 2001). The second is attention residue (Leroy, 2009): switching away from an unfinished task leaves involuntary residual cognitive processing about that task in working memory, actively degrading performance on whatever you switch to. There is no conscious override: the residue is automatic and immediate. Mark's 2005 field study (CHI 2005, n=24 office knowledge workers) found workers took approximately 25 minutes to return to an original task after interruption, passing through 2.3 other activities: a behavioral measure of displacement, not a cognitive refocus time. Mark, Gudith & Klocke (2008) found that interrupted workers completed tasks faster but under significantly more stress and effort: the short-term cost of interruptions is strain accumulation, not throughput loss. Note: the widely-cited "23 minutes to refocus" figure traces to a 2008 journalist interview with Gloria Mark, not to any peer-reviewed paper. The closest published figure is ~25 minutes from CHI 2005, measuring elapsed time-to-return including 2.3 intervening tasks.
Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 763–797. https://doi.org/10.1037/0096-1523.27.4.763
González, V. M., & Mark, G. (2004). "Constant, constant, multi-tasking craziness": Managing multiple working spheres. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '04) (pp. 113–120). ACM Press. https://doi.org/10.1145/985692.985707
Mark, G., Gonzalez, V., & Harris, J. (2005). No task left behind? Examining the nature of fragmented work. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '05) (pp. 321–330). ACM Press. https://doi.org/10.1145/1054972.1055017
Mark, G., Gudith, D., & Klocke, U. (2008). The cost of interrupted work: More speed and stress. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '08) (pp. 107–110). ACM Press. https://doi.org/10.1145/1357054.1357072
Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583–15587. https://doi.org/10.1073/pnas.0903620106
American Psychological Association. (2006, March 20). Multitasking: Switching costs. APA Science Directorate. https://www.apa.org/research/action/multitask
Leroy, S. (2009). Why is it so hard to do my work? The challenge of attention residue when switching between work tasks. Organizational Behavior and Human Decision Processes, 109(2), 168–181. https://doi.org/10.1016/j.obhdp.2009.04.002
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x. Shannon entropy is the information-theoretic measure of disorder across a probability distribution. Applied here to measure attention fragmentation: when effort is evenly spread across many projects, entropy is maximized and attentional efficiency is lowest.
Weinberg, G. M. (1992). Quality Software Management, Vol. 1: Systems Thinking (p. 284). Dorset House. Framed as a practitioner rule-of-thumb, not scientific evidence.
Burnout Velocity: Recovery Debt and the Streak Threshold
Burnout Velocity tracks recovery debt: the accumulation of high-load weeks without a decompression point. The research is clear: burnout is not caused by hard work, it is caused by the absence of recovery. The Maslach Burnout Inventory and the Job Demands-Resources model both identify consecutive overload without recovery as the primary predictor of chronic strain. Burnout is formally classified by the WHO as an occupational phenomenon (ICD-11, 2019). CogniSource scores Burnout Velocity using an allostatic load accumulation model (McEwen & Stellar, 1993; McEwen, 1998): stress carryover builds across consecutive high-load periods with exponential decay. Recent weeks matter more than distant ones, but no week is fully forgotten until recovery occurs. The score captures the non-linear nature of burnout onset: early overload weeks contribute less than later weeks when the system has exhausted its recovery capacity. Allostatic load theory establishes the physiological basis: the body’s ability to adapt to stress is finite and degrades with sustained activation, producing cumulative damage even after each individual stressor has resolved.
Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99–113. https://doi.org/10.1002/job.4030020205
World Health Organization. (2019). Burn-out an "occupational phenomenon": International Classification of Diseases. ICD-11 code QD85. https://www.who.int/news/item/28-05-2019-burn-out-an-occupational-phenomenon-international-classification-of-diseases
Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499
Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual Review of Psychology, 52, 397–422. https://doi.org/10.1146/annurev.psych.52.1.397
Schaufeli, W. B., Leiter, M. P., & Maslach, C. (2009). Burnout: 35 years of research and practice. Career Development International, 14(3), 204–220. https://doi.org/10.1108/13620430910966406
McEwen, B. S., & Stellar, E. (1993). Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine, 153(18), 2093–2101. https://doi.org/10.1001/archinte.1993.00410180039004. Original formulation of allostatic load theory: the cumulative physiological cost of chronic stress adaptation. The body’s compensatory responses, active over time, degrade its own regulatory capacity.
McEwen, B. S. (1998). Protective and damaging effects of stress mediators. New England Journal of Medicine, 338(3), 171–179. https://doi.org/10.1056/NEJM199801153380307. Establishes the accumulator model of stress: each stressor adds to a running physiological load that decays only during genuine recovery. Provides the neurobiological basis for exponential decay-rate modeling of stress carryover across consecutive high-load periods.
Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056. Updated formulation of the JD-R model: personal resources buffer the effect of job demands on burnout; their depletion over time drives the transition from engagement to burnout. Supports the decay-factor formulation of stress carryover.
Recovery Buffer: Concrete Absorb Capacity for the Next Four Weeks
Recovery Buffer answers one question: if an unplanned task lands this week, does it have somewhere to go without displacing something already committed? CogniSource translates forward schedule density (loaded versus light weeks across the next 4 weeks) into a concrete absorb capacity using time-discounted headroom. Near-term light weeks count more than distant ones, following Bellman’s (1957) principle that the present value of future slack decays with temporal distance. A light week available next week matters more than a light week available in four weeks, because the near-term week is the one that absorbs the next unplanned task. This approach also draws on slack theory and the Theory of Constraints (Goldratt): systems without slack cannot respond to the unexpected without cost. The buffer score updates in real time as tasks are closed, deadlines move, or meetings are added or removed.
Goldratt, E. M. (1997). Critical Chain. North River Press. Organisations operating above 80% utilisation have no capacity buffer to absorb variability. Systems without slack fail non-linearly when unplanned work arrives.
Mark, G., Gudith, D., & Klocke, U. (2008). The cost of interrupted work: More speed and stress. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 107–110. https://doi.org/10.1145/1357054.1357072
Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499 Insufficient recovery space is identified as a primary structural predictor of burnout onset, distinct from acute overload.
Bellman, R. (1957). Dynamic Programming. Princeton University Press. Foundational formulation of temporal discounting: the present value of a future state decreases as a function of the time horizon, expressed through a discount factor applied recursively. Applied here to weight near-term slack capacity more heavily than distant slack in the Recovery Buffer score.
All 40 verified citations
Verified by independent research review. All DOIs and publishers confirmed. Citations marked [PRACTITIONER] are not peer-reviewed but are industry-standard references.
Goldratt, E. M., & Cox, J. (1992). The Goal: A Process of Ongoing Improvement (2nd revised ed.). North River Press.
Goldratt, E. M. (1997). Critical Chain. North River Press.
Weinberg, G. M. (1992). Quality Software Management, Vol. 1: Systems Thinking (p. 284). Dorset House.
Kim, G., Behr, K., & Spafford, G. (2013). The Phoenix Project. IT Revolution Press. ISBN: 9780988262591.
Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps. IT Revolution Press. ISBN: 9781942788331.
Poppendieck, M., & Poppendieck, T. (2003). Lean Software Development: An Agile Toolkit. Addison-Wesley. ISBN: 9780321150783.
Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
Reinertsen, D. G. (2009). The Principles of Product Development Flow: Second Generation Lean Product Development. Celeritas Publishing. ISBN: 9781935401001.
Hopp, W. J., & Spearman, M. L. (2011). Factory Physics (3rd ed.). Waveland Press. ISBN: 9781577667391.
Vacanti, D. S. (2015). Actionable Agile Metrics for Predictability: An Introduction. ActionableAgile Press.
Birolini, A. (2017). Reliability Engineering: Theory and Practice (8th ed.). Springer. ISBN: 9783662564745.
Hubbard, D. W. (2014). How to Measure Anything: Finding the Value of Intangibles in Business (3rd ed.). John Wiley & Sons. ISBN: 9781118539279.
Bellman, R. (1957). Dynamic Programming. Princeton University Press.
Cosentino, C. (2015). Agile Metrics in Action: Measuring and Enhancing the Performance of Agile Teams. Manning Publications.
Miller, G. A. (1956). The magical number seven, plus or minus two. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99–113. https://doi.org/10.1002/job.4030020205
Cowan, N. (2001). The magical number 4 in short-term memory. Behavioral and Brain Sciences, 24(1), 87–185. https://doi.org/10.1017/S0140525X01003922
Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499
Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual Review of Psychology, 52, 397–422. https://doi.org/10.1146/annurev.psych.52.1.397
Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 763–797. https://doi.org/10.1037/0096-1523.27.4.763
Zika-Viktorsson, A., Sundström, P., & Engwall, M. (2006). Project overload: An exploratory study of work and management in multi-project settings. International Journal of Project Management, 24(5), 385–394. https://doi.org/10.1016/j.ijproman.2006.02.010
Schaufeli, W. B., Leiter, M. P., & Maslach, C. (2009). Burnout: 35 years of research and practice. Career Development International, 14(3), 204–220. https://doi.org/10.1108/13620430910966406
Barley, S. R., Meyerson, D. E., & Grodal, S. (2011). E-mail as a source and symbol of stress. Organization Science, 22(4), 887–906. https://doi.org/10.1287/orsc.1100.0573
Mazmanian, M., Orlikowski, W. J., & Yates, J. (2013). The autonomy paradox. Organization Science, 24(5), 1337–1357. https://doi.org/10.1287/orsc.1120.0806
Leroy, S. (2009). Why is it so hard to do my work? The challenge of attention residue when switching between work tasks. Organizational Behavior and Human Decision Processes, 109(2), 168–181. https://doi.org/10.1016/j.obhdp.2009.04.002
Kingman, J. F. C. (1961). The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902–904. https://doi.org/10.1017/S0305004100035793
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, 18(3), 293–297.
McEwen, B. S., & Stellar, E. (1993). Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine, 153(18), 2093–2101. https://doi.org/10.1001/archinte.1993.00410180039004
McEwen, B. S. (1998). Protective and damaging effects of stress mediators. New England Journal of Medicine, 338(3), 171–179. https://doi.org/10.1056/NEJM199801153380307
Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056
González, V. M., & Mark, G. (2004). "Constant, constant, multi-tasking craziness." In Proceedings CHI '04 (pp. 113–120). ACM Press. https://doi.org/10.1145/985692.985707
Mark, G., Gonzalez, V., & Harris, J. (2005). No task left behind? In Proceedings CHI '05 (pp. 321–330). ACM Press. https://doi.org/10.1145/1054972.1055017
Mark, G., Gudith, D., & Klocke, U. (2008). The cost of interrupted work. In Proceedings CHI '08 (pp. 107–110). ACM Press. https://doi.org/10.1145/1357054.1357072
Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. PNAS, 106(37), 15583–15587. https://doi.org/10.1073/pnas.0903620106
Mark, G., Iqbal, S. T., Czerwinski, M., & Johns, P. (2014). Bored Mondays and focused afternoons. In Proceedings CHI '14 (pp. 3025–3034). ACM Press. https://doi.org/10.1145/2556288.2557204
World Health Organization. (2019). Burn-out an "occupational phenomenon": International Classification of Diseases. ICD-11 code QD85. https://www.who.int/news/item/28-05-2019-burn-out-an-occupational-phenomenon-international-classification-of-diseases
American Psychological Association. (2006). Multitasking: Switching costs. APA Science Directorate. https://www.apa.org/research/action/multitask
Project Management Institute. (2017). Pulse of the Profession: Success Rates Rise. PMI. https://www.pmi.org/-/media/pmi/documents/public/pdf/learning/thought-leadership/pulse/pulse-of-the-profession-2017.pdf
Spira, J. B., & Feintuch, J. B. (2005). The Cost of Not Paying Attention: How Interruptions Impact Knowledge Worker Productivity. Basex.