Cognitive biases and time stress in team decision making

Lehner, Paul E.; Seyed-Solorforough, M.-M.; OʼConnor, Michael; Sak, S.; Mullin, Theresa

doi:10.1109/3468.618269

Cited by 69 publications

(24 citation statements)

References 9 publications

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“…For example, a role-playing survey study conducted by Carnegie Mellon University researchers [11] examined the relationship between demographics and phishing susceptibility for 1,001 online survey respondents. They found that participants between the ages of 18 and 25 were more susceptible to phishing than other age groups (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), 46-55, and older than 56) [11]. On the other hand, a phishing experiment found no evidence for age-related patterns in phishing susceptibility for students at different undergraduate levels (freshman, sophomore, junior, senior) [8]; note that the age range in this study is more restrictive than that in [11].…”

Section: A Demographic Factorsmentioning

confidence: 99%

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Analysis of Unintentional Insider Threats Deriving from Social Engineering Exploits

Greitzer¹,

Strozer

Cohen

et al. 2014

2014 IEEE Security and Privacy Workshops

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Abstract-Organizations often suffer harm from individuals who bear no malice against them but whose actions unintentionally expose the organizations to risk-the unintentional insider threat (UIT). In this paper we examine UIT cases that derive from social engineering exploits. We report on our efforts to collect and analyze data from UIT social engineering incidents to identify possible behavioral and technical patterns and to inform future research and development of UIT mitigation strategies.

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Section: A Demographic Factorsmentioning

confidence: 99%

“…Heavy or prolonged workload and constant time pressure may be correlated with higher task error rates; time pressure negatively impacts even well-trained individuals [31].…”

Section: Human Factorsmentioning

confidence: 99%

Analysis of Unintentional Insider Threats Deriving from Social Engineering Exploits

Greitzer¹,

Strozer

Cohen

et al. 2014

2014 IEEE Security and Privacy Workshops

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“…The decisions are in cascade and there is evidence that individuals tend to rely on previous responses regardless of previous response success [34]. It is for this reason that when a trader loses in one decision, this can negatively affect the next decision.…”

Section: The Influence Of Stress In Decision Makingmentioning

confidence: 99%

A Sensor Technology Survey for a Stress-Aware Trading Process

Fernández

Augusto

Seepold

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

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Abstract-The role of the global economy is fundamentally important to our daily lives. The stock markets reflect the state of the economy on a daily basis. Traders are the workers within the stock markets who deal with numbers, statistics, company analysis, news and many other factors which influence the economy in real time. However, whilst making significant decisions within their workplace, traders must also deal with their own emotions. In fact, traders have one of the most stressful professional occupations. This survey merges current knowledge about stress effects and sensor technology by reviewing, comparing, and highlighting relevant existing research and commercial products that are available on the market. This assessment is made in order to establish how sensor technology can support traders to avoid poor decision making during the trading process. The purpose of this article is: 1) to review the studies about the impact of stress on the decision making process and on biological stress parameters that are applied in sensor design; 2) to compare different ways to measure stress by using sensors currently available in the market according to basic biometric principles under trading context; and 3) to suggest new directions in the use of sensor technology in stock markets.

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“…Time stress results when demands are perceived to be disproportionately high in relation to the time allotted for completion (Kinicki & Vecchio, 1994) and greatly hinders decision processes (Lehner, Seyed-Solorforough, O'Connor, Sak, & Mullin, 1997). Time stress has a divergent magnitude of impact on performance, depending on individual differences (Revelle, Amaral, & Turriff, 1976).…”

Section: Factors Influencing Decisions In Health Carementioning

confidence: 99%

Decision Making on the Labor and Delivery Unit: An Investigation of Influencing Factors

et al. 2017

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Objective: The aim of this study was to describe the relationship between negative affect (NA), decision-making style, time stress, and decision quality in health care.Background: Health care providers must often make swift, high-stakes decisions. Influencing factors of the decisionmaking process in this context have been understudied.Method: Within a sample of labor and delivery nurses, physicians, and allied personnel, we used self-report measures to examine the impact of trait factors, including NA, decision-making style, and perceived time stress, on decision quality in a situational judgment test (Study 1). In Study 2, we observed the influence of state NA, state decision-making style, state time stress, and their relationship with decision quality on real clinical decisions.Results: In Study 1, we found that trait NA significantly predicted avoidant decision-making style. Furthermore, those who were higher on trait time stress and trait avoidant decision-making style exhibited poorer decisions. In Study 2, we observed associations between state NA with state avoidant and analytical decision-making styles. We also observed that these decision-making styles, when considered in tandem with time stress, were influential in predicting clinical decision quality.Conclusion: NA predicts some decision-making styles, and decision-making style can affect decision quality under time stress. This is particularly true for state factors.Application: Individual differences, such as affect and decision-making style, should be considered during selection. Training to reduce time stress perceptions should be provided.

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Cognitive biases and time stress in team decision making

Cited by 69 publications

References 9 publications

Analysis of Unintentional Insider Threats Deriving from Social Engineering Exploits

Analysis of Unintentional Insider Threats Deriving from Social Engineering Exploits

A Sensor Technology Survey for a Stress-Aware Trading Process

Decision Making on the Labor and Delivery Unit: An Investigation of Influencing Factors

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