The Measurement of Task Performance as Criteria in Selection Research

Borman, Walter C.; Bryant, Rebecca H.; Dorio, Jay M.

doi:10.4324/9781315690193-20

Cited by 11 publications

(13 citation statements)

References 1 publication

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“…Measures of the same latent construct should converge, as should variables that are conceptually linked by theory. The domain of job performance itself is broad, encompassing a wide range of work‐related behaviors and outcomes (Borman, Bryant, & Dorio, ; Gatewood, Field, & Barrick, ; Pulakos & O'Leary, ). It entails not only performance‐related behaviors (e.g., task performance, organizational citizenship behaviors, counterproductive work behaviors), but also outcomes associated with those behaviors.…”

Section: Measurement Using Narrative Commentsmentioning

confidence: 99%

Quantifying with words: An investigation of the validity of narrative‐derived performance scores

Speer

2018

Personnel Psychology

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Performance appraisal research has focused almost entirely on traditional numerical ratings despite narrative text comments regularly being collected within appraisals. This study investigated the theory and utility of leveraging narrative comments to better understand employee performance. Narrative sentiment scores were derived using text mining on a large sample of narrative comments. These scores were then applied to an independent set of 2 years of performance data. It was assumed that narrative comments would reflect true performance variance that overlaps with traditional ratings, but also that they would capture incremental variance due to increases in total information and a reduction in rater‐motivated biases in contexts in which narrative data were not explicitly linked to administrative outcomes. The derived narrative scores were reliable across years, converged with traditional numerical ratings and explained incremental variance in future performance outcomes (performance ratings, involuntary turnover, promotions, and pay increases). Collectively, this study highlights how narratives can enhance performance measurement and demonstrates how these data can be economically scored in applied settings.

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Section: Measurement Using Narrative Commentsmentioning

confidence: 99%

Quantifying with words: An investigation of the validity of narrative‐derived performance scores

Speer

2018

Personnel Psychology

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“…Nowadays, there is a consensus that the job performance domain includes at least two dimensions: task and contextual performance (Aguinis, 2007;Moscoso, Salgado, & Anderson, 2017;Salgado & Cabal, 2011). Task performance is defined as the proficiency with which employees perform the core technical activities that the job description includes (Borman, Bryant, & Dorio, 2010). Contextual performance refers to the contributions of the employee, that go beyond the technical obligations of the work, and that impact on the organizational, social, and psychological environment, helping to accomplish organizational goals (Borman, Penner, Allen, & Motowidlo, 2001;Dorsey, Cortina, & Luchman, 2010;Hoffman & Dilchert, 2012).…”

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confidence: 99%

The Five-Factor model and job performance in low complexity jobs: A quantitative synthesis

Lado

Alonso

2017

Rev. trabajo y las organizaciones

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This article presents the results of four primary studies that investigated the degree to which the Big Five personality dimensions predict job performance in occupations with a low level of job complexity. Job performance was assessed as overall job performance (OJP), task performance (TP), and contextual performance (CP). The results showed that conscientiousness and emotional stability proved to be predictors of the three performance measures. In addition, extroversion was a relevant predictor of OJP and TP, and agreeableness was a predictor of CP. Implications for the theory and practice of job performance and personnel selection are discussed.

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“…Additionally, the performance indicators including relatively objective criterion measures, such as job knowledge tests and production rates, the dimensionality of job performance, and validity estimates against task performance (e.g., ability, personality, etc.) need to be considered [45].…”

Section: Limitations and Future Directionmentioning

confidence: 99%

Validation of Trade-Off in Human–Automation Interaction: An Empirical Study of Contrasting Office Automation Effects on Task Performance and Workload

et al. 2020

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Automation aims to improve the task performance and the safety of human operators. The success of automation can be facilitated with well-designed human-automation interaction (HAI), which includes the consideration of a trade-off between the benefits of reliable automation and the cost of Failed automation. This study evaluated four different types of HAIs in order to validate the automation trade-off, and HAI types were configured by the levels and the statuses of office automation. The levels of automation were determined by information amount (i.e., Low and High), and the statues were decided by automation function (i.e., Routine and Failed). Task performance including task completion time and accuracy and subjective workload of participants were measured in the evaluation of the HAIs. Relatively better task performance (short task completion time and high accuracy) were presented in the High level in Routine automation, while no significant effects of automation level were reported in Failed automation. The subjective workload by the National Aeronautics and Space Administration (NASA) Task Load Index (TLX) showed higher workload in High and Failed automation than Low and Failed automation. The type of sub-functions and the task classification can be estimated as major causes of automation trade-off, and dissimilar results between empirical and subjective measures need to be considered in the design of effective HAI. the amount of automation autonomy and human physical and cognitive activity [5,6]. Sheridan and Verplank [7] suggested 10 LOA as the basis of the classic human-machine task allocation principle: at a higher level, automation can execute decision-making tasks without the aid of human operators, at a lower level, it can execute an option within the operators' controls, and at a further lower level, it may be simply performed by the human operators. Kaber and Endsley [6] proposed that automation could be classified according to the information-processing stages: perceiving the status of the system variables or scanning displays (i.e., monitoring), formulation of task-processing plans (i.e., generating), deciding on an optimal plan (i.e., selecting), and the control actions at an interface (i.e., implementing). Elaborating on this classification, Parasuraman et. al. [4] presented a four-stage model of automation to support the design of human-automation interactions (HAI) in complex systems: information acquisition, information analysis, decision and action selection, and action implementation. This classification aids the formulation of specific function allocation schemes for automation. The combination of the LOA with information processing stages led to the development of a concept known as the degree of automation (DOA) [3]. Each function in a model or an information processing stage, either by the human or machine (or some combination thereof), is responsible for the effects of automation on the task performance.Under routine conditions or at higher LOA or DOA, human operators simply supervi...

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The Measurement of Task Performance as Criteria in Selection Research

Cited by 11 publications

References 1 publication

Quantifying with words: An investigation of the validity of narrative‐derived performance scores

Quantifying with words: An investigation of the validity of narrative‐derived performance scores

The Five-Factor model and job performance in low complexity jobs: A quantitative synthesis

Validation of Trade-Off in Human–Automation Interaction: An Empirical Study of Contrasting Office Automation Effects on Task Performance and Workload

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