Response time (RT) and accuracy are highly dependent (Pachella, 1974). Typically, more efficient information processing results in both decreases in RT and increases in accuracy. However, in any response process, an optimal balance of speed and accuracy can also be considered by the responder on the basis of the priorities of the current situation. The mutual dependence of RT and accuracy is evident when more emphasis on accuracy results in increases in RTs and when, conversely, more emphasis on speed (i.e., shorter RTs) causes accuracy to decrease. As was noted by Wickelgren (1977), the study of RTs in conjunction with accuracy (i.e., the whole speed-accuracy trade-off [SAT] function) has the potential to provide much greater knowledge about information processing dynamics than can the study of either the RT or accuracy aspects of performance separately.Switching between tasks results in costs in terms of both RT and accuracy (Monsell, 2003). A review of the influential studies in the task-switching area reveals that the issue of SATs and how corresponding speed-accuracy manipulations can affect task-switching performance has rarely been directly examined. One exception is Gopher, Armony, and Greenshpan (2000), whose major interest in SATs, however, involved a comparison of the costs associated with switching between tasks and the costs associated with switching between speed and accuracy strategy emphases. Another exception is Lien, Ruthruff, Remington, and Johnston (2005), involving time deadlines, which were utilized by Lien et al. in order to provide their participants with the strongest possible motivation for optimal performance. Studying SATsTrading off accuracy for speed or vice versa can be expected in presumably any task. Systematic experimental work on the phenomenon began in the 1950s and '60s (for a discussion of this work, see Wickelgren, 1977). The SAT paradigm in cognitive psychology addresses the inverse relationship between speed and accuracy (or, correspondingly, the positive relation between RTs and accuracy) by examining the process of information accumulation through time. In this way, an experimenter can observe how different methods of manipulation of speed emphasis result in changes in both the RTs and the accuracy of the responses. Under speed pressure, more incorrect responses are expected, and SAT curves can be derived to specify how much time is needed to achieve a particular accuracy level. This procedure was initially used by a few researchers in the 1970s (e.g., Reed, 1973Reed, , 1976) to study cognitive processing.One important method through which speed-accuracy performance can be explicitly manipulated is by using response signals. Within such paradigms, the participants' stimulus processing is interrupted at different times after stimulus presentation (e.g., Boldini, Russo, & Avons, 2004;Boldini, Russo, Punia, & Avons, 2007;Corbett & Wickelgren, 1978;Dosher, 1981Dosher, , 1982Göthe & Oberauer, 2008;Kumar, Rakitin, Nambisan, Habeck, & Stern, 2008;Lohman, 1986;McElree & Dosher,...
Background: Since employing talent management as a system to identify, recruit, train, promote, and retain talented faculty members to optimize the capabilities of the university to achieve goals seems necessary, the current study aimed to identify the talent management components of faculty members. Methods: This qualitative study was conducted on the directors and faculty members of Isfahan University, Isfahan University of Medical Sciences, and Isfahan University of Technology, Iran. A purposive sampling method was used and semi-structured interviews conducted to collect data. All interviews were recorded, transcribed, and transferred to a computer using MAXQDA software. Following this, the data were coded, categorized, and interpreted, and then analyzed using a content analysis method. Results: After interviewing 18 university directors and faculty members, data pooling, data saturation, and data replication were performed. Accordingly, 107 codes were extracted from the obtained data and categorized into seven major classes. These were Defining and identifying talent needs, discovering talent sources, Attracting talents, Developing the potential abilities of talents, Strategically deploying talents, Retaining talents, and evaluation and alignment of talent management activities. Conclusions: Based on the results, 7 main classes and 20 subclasses of talent management components of faculty members were identified. These components are provided in a conceptual model. The evaluation and alignment of talent management activities are the main factors supporting talent management. Furthermore, dynamic coherence and integrity in identifying, discovering, attracting, developing, retaining, and effectively using talent, and the alignment of such features with the general strategy of the university were considered the strengths of the suggested conceptual model.
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