A Decadal Survey of the Social and Behavioral Sciences A Research Agenda for Advancing Intelligence Analysis (2019) / Chapter Skim
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8 Strengthening the Analytic Workforce for Future Challenges
8 Strengthening the Analytic Workforce for Future Challenges
Pages 253-286
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From page 253... ...
In a decade or less, for example, analysts may have the capacity to obtain sophisticated analysis of a months-long narrative stream on social media sites, compare it with activities from that period identified through geospatial imaging, and develop a graphical representation of the intersections between the two -- as part of a day's work. To leverage these opportunities, the analytic workforce will need new skills: developments in such areas as network science, complex systems models, statistics, and data analytics of all kinds will likely add new methods and tools to the analyst's toolbox.1 In areas in which intelligence analysts are expert -- qualitative analysis of text and narrative, for example -- new developments such as improved quantitative methods for text analysis, 1Many of the challenges facing analysts are documented in the National Research Council (2011)
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From page 254... ...
As in any large organization, the agencies of the IC pay attention to means of identifying, recruiting, and selecting individuals likely to excel as intelligence analysts; providing training and using other means to develop their skills and abilities; obtaining optimal performance from the workforce; and retaining effective employees. Researchers in the fields of industrial
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Applying this work for the IC context, however, requires translational research on the precise applications of well-established findings in the unique context of intelligence analysis. This chapter provides a review of the state of the foundational research in industrial-organizational psychology and human resource development that is relevant to the evolving needs of the IC.
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. This work has provided the basis for approaches with a strong record of effectiveness in predicting job performance and improving the quality of the selected workforce.
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Digital trace data captured passively from the workforce's use of social media and enterprise social media channels can also be used to assess individuals' social capital, based on their position in the network. The result has been a growing interest in relational analytics (or the interplay or interactions among people)
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Recognizing the limitations of definitions of job performance, researchers have recently focused on developing more nuanced understanding of what characterizes effective performance. Once viewed as an undifferentiated phenomenon, performance is best understood in terms of various components that contribute to an employee's effectiveness.
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For example, although 2007 Intelligence Community Directive (ICD) Number 203 set standards regarding the production and evaluation of intelligence analysis and analytical products in order to improve the critical thinking and writing skills of analysts across the IC (Miles, 2016)
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What is the value to the organization of, say, a 20 percent increase in the number of high-performing employees? Utility analysis is an approach used by industrial-organizational psychologists and human resources specialists to systematically balance multiple possible benefits and costs.
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Researchers studying selection have recently focused on troubling findings that are difficult to reconcile with expectations. For example, elaborate job simulations have been developed for use in evaluating job candidates, yet a meta-analytic comparison found that a brief multiple-choice test of cognitive ability had greater predictive power than a lengthy (e.g., 1- to 2-day)
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Finally, sharing of successes and failures in the development of selection systems would have obvious benefits for all the agencies. RETAINING EFFECTIVE ANALYSTS The research literature on employee turnover and retention is vast and nuanced.
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, who showed that turnover is not reliably predicted by such individual characteristics as cognitive ability, education, training, tenure, or demographic factors, but is predicted by variables from the work context. These authors found, for example, that employees were less likely to leave their jobs when they reported more job satisfaction, supervisory satisfaction, coworker satisfaction, role clarity, participation, and organizational commitment.
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An Unfolding Model of Turnover Decisions Research on employee turnover underwent a transformation in response to a highly influential paper by Lee and Mitchell (1994) describing an "unfolding" model of turnover.
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. The authors also posited that embeddedness helps predict both turnover intentions and voluntary turnover, even after accounting for the predictive power of job attitudes, job alternatives, and job search behaviors.
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found that coworkers' job search behaviors predicted individual turnover above and beyond individual job attitudes, coworker job attitudes, and other individual- and group-level predictors. In terms of the unfolding model, seeing coworkers engage in job search activities could be viewed as a "shock" that initiates an individual's own evaluation of present and alternative opportunities.
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Less turnover also is seen in units with stronger perceptions of cohesiveness and teamwork, management and leadership quality, satisfaction, commitment, climate and culture, and justice and fairness. Retaining Effective Analysts: Implications for the Intelligence Community Keeping employees in the organization once they have joined is a focus of the retention literature, but a related important issue is whether the pool of recruits persists through the application process or drops out along the way.
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The IC could benefit from translational research to assess the application of these findings for the analytic workforce. DEVELOPING SKILLS THROUGH FORMAL TRAINING AND INFORMAL LEARNING Further developing existing knowledge and skills and acquiring new ones are key objectives for most workers, including intelligence analysts.
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A considerable body of research documents the importance of this factor with respect to formal training, and while the same might be hypothesized for autonomous learning, more empirical research on this linkage is needed. Other key organizational factors identified by Tannenbaum and colleagues (2010)
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. Other research delves more deeply into motivational processes underlying autonomous learning.
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Autonomous learning is important for the work of the intelligence analyst. In a demanding job, there is tension between the need to allocate effort to meeting current task demands and a desire to invest in new knowledge and skill.
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, have also been used to evaluate cognitive workload. 7Mental workload that is too high or too low can cause the human–system performance to decline, so workload assessments are conducted on existing systems and during the design phases of new systems, such as those described in Chapter 7.
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. Cognitive workload is directly related to human agents' vigilance and their cognitive fatigue in work environments.
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. This line of research suggests that enhancement of attentional performance in the absence of sleep deprivation or other fatigue-inducing factors can be effective, but additional research is needed to examine the long-term effectiveness of noninvasive brain stimulation as a strategy for mitigating decreased task vigilance and cognitive fatigue.
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From page 275... ...
.9 When designing complex human–machine systems as a mitigation strategy for cognitive fatigue, then, it is important to evaluate both human and machine abilities and limitations, as well as the appropriate levels of 9Indual-task situations, low levels of automation lead to improved performance, and intermediate levels of automation lead to improved situational awareness on the primary task. But when a greater percentage of the primary task is automated, the human agent is able to shift resources, which leads to improved performance on the secondary task.
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Some of the approaches mentioned here have been studied extensively for safety. For example, transcranial brain stimulation has been the subject of numerous safety reviews that have shown no serious adverse events in tens of thousands of patients and subjects across varying health spectrums (Antal et al., 2017; Bikson et al., 2016)
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The research highlighted in this chapter includes some ideas and approaches that could be integrated immediately into the intelligence analysis workforce, as well as others that will be increasingly applicable as they evolve. The findings, methods, and tools described in this chapter can be used to strengthen the analyst workforce and prepare it to meet emerging security challenges, make optimal use of available technologies, and collaborate with others who are instrumental to analytic success (including machines)
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To strengthen its capacity to select individuals well suited to work as an intelligence analyst, the Intelligence Community (IC) would benefit from • regularly updating its assessment of the facets of the analyst's job performance that are of greatest value to the IC and the attributes most useful for selection of personnel for intelligence analysis roles; • having the capacity to measure a broad range of attributes for use in selecting individuals who possess those attributes; and • evaluating the predictive power and potential ethical implications of such assessment devices as digital games, gleaning information
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careful consideration of safety and ethical issues related to their use. CONCLUSION 8-5: To fully benefit from research findings relevant to the development of an optimal analytic workforce, the Intelligence Community (IC) would need to invest in research and evaluation to guide their application in the context of intelligence analysis.
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Brain Stimulation, 9(5)
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. Turnover contagion: How coworkers' job embeddedness and job search behaviors influence quitting.
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intelligence analysis? International Journal of Intelligence and Counterintelligence, 28(4)
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. Workforce Development and Intelligence Analysis for National Security Purposes: Proceedings of a Workshop.
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. Effects of realistic job previews on multiple organizational outcomes: A meta-analysis.
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Noe (Eds.) , Autonomous Learning in the Workplace (pp.
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