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Intelligence Analysis: Behavioral and Social Scientific Foundations 12 Workforce Effectiveness: Acquiring Human Resources and Developing Human Capital Steve W. J. Kozlowski Among the many important ingredients in the complex alchemy of organizational effectiveness is a capable, highly motivated, and adaptive workforce. To accomplish mission objectives, organizations must navigate the complexities, uncertainties, and dynamics of their external environments, outperforming and counteracting competitors and adversaries, by being better, faster, or more innovative. They must build a uniquely capable workforce, then leverage its special talents. This is accomplished by developing a strategy to meet mission objectives, and aligning the internal organization with respect to leadership, administrative structure, work processes (i.e., technology), and human resource management (HRM) practices to support strategy execution. In that sense, acquiring and building an effective workforce is predicated on providing the organization with unique capabilities, enabling it to meet strategic objectives, and simultaneously making it difficult for adversaries to be successful. The purpose of this chapter is to describe behavioral science theory and research findings from organizational psychology and human resource management that underpin the acquisition of human resources and development of human capital, both of which are essential for creating a capable, innovative, and adaptive workforce. I will begin by providing a brief overview of the shifting strategic landscape faced by the intelligence community (IC) and implications of this shift for IC strategy and internal alignment. I will then discuss strategic HRM, which describes how the workforce can be aligned to help accomplish IC strategic objectives, and I will present a strategic HRM architecture for acquiring human resources and developing human capital. I will then describe in detail specific clusters of HRM
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Intelligence Analysis: Behavioral and Social Scientific Foundations practices that implement strategic HRM: recruitment and selection, training and development, performance management and incentives, and work design and teamwork. Finally, I will close with research issues relevant to sustaining employee development, collaboration, and organizational learning for the long haul. STRATEGIC ALIGNMENT The IC as an Organization Some readers are likely to assert that the IC is not like other organizations and that behavioral science knowledge about the effective functioning of business organizations is not relevant to the IC because it is so uniquely different. I will not make the claim that the IC is exactly like other organizations in all ways, but I will claim that it is quite similar to nearly any other organization in many important ways. With respect to differences, Zegart (this volume, Chapter 13) identifies some key factors that make public institutions and the IC less sensitive to the adaptive pressures that commercial firms face. That is, the benefits of competition for adaptation are limited because survival within the IC is less of an issue; IC agencies do not compete directly. Rather, they are aligned to serve unique customer needs (Fingar, this volume, Chapter 1) and, thus, the IC is arrayed more as a loosely coupled divisional structure than a set of centralized units competing in the same environmental niche (Galbraith, 1972). In that sense, the basic mechanisms of organizational alignment—external and internal—apply equally well or well enough to the IC so that theory and research findings from organizational science are relevant. This chapter is intended to summarize lessons from research on organizational effectiveness that can be applied to improving workforce development and organizational learning in the IC. The Strategic Environment and IC Strategy As described by Fingar (this volume, Chapter 1), the strategic environment of the IC has shifted dramatically in the post-Soviet Union era. Following the end of World War II, the IC had been arrayed to assess and counteract a large, militarily capable, state actor and its many coaligned proxy states. Although many uncertainties were inherent in the strategic balance between the United States and the Union of Soviet Socialist Republics, there was also a high degree of stability in the nature of the relationship, the intentions of key actors, and their likely means of action.
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Intelligence Analysis: Behavioral and Social Scientific Foundations Stability calls for an organizational strategy that exploits what is known, with internal alignments relying on tight structural control. The previous strategic environment of the IC has shifted dramatically. As described by the National Intelligence Strategy (NIS): The United States faces a complex and rapidly changing national security environment in which nation-states, highly capable non-state actors, and other transnational forces will continue to compete with and challenge U.S. national interests. Adversaries are likely to use asymmetric means and technology (either new or applied in a novel way) to counter U.S. interests at home and abroad. (Office of the Director of National Intelligence, 2009a, p. 3) Environmental turbulence calls for an organizational strategy based on exploration and innovation. This strategic shift requires an internal alignment that enables unique capabilities to be acquired, developed, and leveraged to promote flexibility, agility, and adaptability. Indeed, the NIS specifies two overarching “Enterprise Goals” focused on internal alignment that are designed to help it accomplish its “Mission Goals” (i.e., external alignment) (Office of the Director of National Intelligence, 2009a, p. 9): Deliver balanced and improving capabilities that leverage the diversity of the community’s unique competencies and evolve to support new missions and operating concepts. Operate as a single integrated team, employing collaborative teams that leverage the full range of IC capabilities to meet the requirements of our users, from the President to deployed military units. With the NIS as a point of departure, I now turn to how the behavioral science literature on strategic HRM and HRM practices can be instrumental in achieving these IC strategic goals. Implications for Strategic Alignment The dominant conceptualization of organizations is that they are systems of interacting elements at multiple levels of analysis (i.e., individuals, teams, subsystems, and the organization); open to environmental inputs (e.g., resources and stakeholders; competitors and adversaries); and purposeful as they seek to accomplish goals, maintain balance between external environmental demands and internal structure, and adapt to their environmental niche (Katz and Kahn, 1966).
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Intelligence Analysis: Behavioral and Social Scientific Foundations Macro-Level: The Environment–Organization Interface Organizations seek alignment with their external environment. They pursue a mission that exploits an environmental niche—to accomplish goals by providing products or services that are supported by customers and stakeholders. Competitors seek to exploit the same niche and to gain advantage. For the IC, “competitors” are adversaries to U.S. national interests in the form of nations, nonstate actors, and their intelligence operations. Thus, senior leaders craft a strategy to accomplish mission goals, with the intent of being superior relative to competitors. In general, strategy is designed to exploit environmental stability through control and efficiency (defender), create environmental turbulence through flexibility and innovation (prospector), or achieve a balance of both strategic orientations (analyzer) (Miles et al., 1978). From a contingency perspective, different strategic orientations need different internal alignments. A defender strategy requires routine, wellknown core technologies (i.e., product or service delivery systems) and tight bureaucratic structures to achieve control and efficiency. A prospector strategy needs reconfigurable technologies and a discretionary, organic structure to achieve flexibility and innovation. An analyzer strategy needs to manage and balance both forms of technology-structure fit. Looking at the IC with limited insight from the outside, the IC strategy appears to conform roughly to the analyzer archetype, although the exact balance of exploitation and exploration is difficult to characterize. The reason this macro perspective is important is because strategic alignment has implications for HRM, meaning the types of human resources the firm seeks—the knowledge, skills, abilities, and other characteristics, or KSAOs (e.g., personality, interests, and values), of its people—and the management approach used to lead, develop, and motivate the workforce (Miles et al., 1978). In general, a defender strategy uses an authoritative management approach (i.e., directive), an analyzer strategy is more participative (i.e., seeks employee input, but maintains control), and a prospector strategy encourages employee empowerment (i.e., shifts discretion to employees and teams to fuel innovation). This is an early conceptualization and, as I will discuss later, it is evolving. However, it illustrates the important connections among organizational strategy, internal alignment, and the link to HRM. Meso-Level: Workgroups and Teams The macro-level is important for shaping the internal organization—that is, the way the workforce experiences the implications of technology systems, administrative structures, and leadership approaches. However,
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Intelligence Analysis: Behavioral and Social Scientific Foundations employees do not experience such factors directly. Rather, it is the direct experience with their job, their connection to coworkers in a workflow (which may be tightly or only loosely coupled) and in social groups, and the relationship enacted with their leader that characterizes their primary experience of the organization. Thus, although the macro context is important for constraining and shaping the nature of the proximal context, the meso-level is what employees experience directly (Indik, 1968). The work unit, the workgroup, or the team is where people “live” in the organization. The meso-level sits at the juncture between the organization as a broad entity and the individual in isolation. It is “where the rubber meets the road” in organizational behavior (Kozlowski and Bell, 2003; Kozlowski and Ilgen, 2006). In addition, over the past two decades, organizations worldwide have shifted the structure of work from individual jobs in a functional structure to team-based structures (Devine et al., 1999). This shift has many drivers, including increased problem complexity, demands for rapid decision making, and the need for adaptability in turbulent environments. The advantages of work teams is that they can bring diverse and specific expertise to bear on problems; team members can back each other up, catch errors, and correct them; and they can flexibly adapt to the emergent needs of the problem situation (Kozlowski et al., 1999; Marks et al., 2001; LePine et al., 2008). Teams enable collective, “macro cognition” to be applied to high-stakes, challenging, and critical problems (Fiore et al., 2010). Micro-Level: Individuals and Their Capabilities At the micro-level, we focus on the capabilities that individuals bring to the organization, including their knowledge, skills, abilities, and other characteristics (Ployhart, 2011). A simplistic but useful heuristic is to view human performance as resulting from a combination of ability and motivation (Campbell et al., 1993). KSAOs encompass both ability (“can do”) and motivational (“will do”) factors (Cronbach, 1970). Motivation is also shaped by meso-level factors (e.g., effective leadership, supportive peers, engaging work). Thus, at a fundamental level, the organizational design target is one of achieving external and internal alignment. Workforce effectiveness is a product of selecting the right mix of individuals, based on their KSAOs, to create a pool of human resources consistent with the organization’s strategic alignment, then to invest in human capital by developing and motivating the workforce so the organization can accomplish its mission more effectively than its competitors.
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Intelligence Analysis: Behavioral and Social Scientific Foundations STRATEGIC HUMAN RESOURCE MANAGEMENT Systemic Fit Perspective Until the early 1980s, HRM was regarded as an important functional area in organizations, but not as a critical aspect of organizational strategy. The “strategic alignment and adaptation” perspective advanced by Miles et al. (1978), which I highlighted previously, began to bring HRM practices more directly into the strategic equation, with HRM as an integral support for organizational strategy. Snow and Snell (2011) characterize this early view as a systemic fit perspective that focused on aligning HRM policies and practices with strategy. Strategy was a deliberate effort to maintain organizational fit with a dynamic external environment and to align internal systems, including HRM, to execute the strategy well. In a systemic fit perspective, HRM is strategy driven. This orientation is a basic foundation for effective HRM design. Strategic Capabilities Perspective More recent work has begun to explore how HRM can create sustained competitive advantage by building organizational capabilities. The strategic capabilities perspective is future oriented and focused on fostering learning, motivation, and innovation. This shifts the view from one of just having the right pool of human resources to one of also being able to build human capital by investing in the development of the workforce to create unique capabilities. Key talent pools are identified and targeted for specific human capital investments (Boudreau and Ramstad, 2005, 2007). Human capital propels strategy formulation (Snow and Snell, 2011); it allows novel strategies to be developed based on the unique capabilities of organizational members. If such capabilities are difficult to imitate and hard for adversaries to replicate, and if they cannot be substituted by other resources, they provide a foundation for long-term competitive advantage (Barney and Wright, 1998; Ployhart, 2006, 2011). With respect to the IC, the lesson is to recruit and select the right people to acquire a pool of high-quality human resources and then to develop, motivate, and integrate that talent to create unique capabilities for the IC. IC Workforce Strategy Previously I described the strategic environment of the IC and highlighted its two internally oriented enterprise goals documented in the NIS (Office of the Director of National Intelligence, 2009a, Sec 1:16). Those two enterprise goals are intended to be implemented by six more specific
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Intelligence Analysis: Behavioral and Social Scientific Foundations “enterprise objectives (EOs).” EO 6: Develop the Workforce is directly relevant to the current discussion. Actions specified to meet EO 6 include (1) build a diverse and balanced workforce, (2) enhance professional development, (3) cultivate relevant expertise, (4) support an entrepreneurial ethos, (5) deploy integrated agile teams, and (6) build a culture of leadership excellence. The material that follows describes research-based applications that can enable this HRM strategy for the IC workforce to be accomplished. AN ARCHITECTURE FOR STRATEGIC HUMAN RESOURCE MANAGEMENT Individual Differences People differ from one another on a wide range of characteristics. Individuals differ on demographic features (e.g., age, sex, race), abilities (e.g., cognitive, physical), and preferences (e.g., personality, values). The focus from a human resources perspective is on differences in KSAOs (e.g., personality, interests, and values) that are linked to differences in, for example, educational attainment, vocational preferences, job performance, and career success. At the most basic level, KSAOs are individual differences that contribute to job performance. At the aggregate level, the collection of KSAOs across the workforce comprises an organization’s human resource pool. Stable and Malleable Individual Differences KSAOs can be divided into those that are stable and those that are malleable. Stable KSAOs include factors such as cognitive ability, personality, and values that are relatively enduring across the span of adult development. Malleable KSAOs include factors such as domain knowledge, job-specific skills, and motivational characteristics. For example, cognitive ability, which is a generalized predictor of learning and performance effectiveness and has a high genetic component, is very stable across a person’s career (Lyons et al., 2009), whereas domain knowledge and job-specific skills accrue over time through experience and training. Over lengthy periods of experience, very high levels of domain-specific expertise develop (Charness and Tuffiash, 2008). Importantly, stable KSAOs influence malleable KSAOs. In particular, individuals with higher cognitive ability gain more from experience than those with less cognitive ability. For example, researchers have shown that individuals with higher cognitive ability have steeper trajectories of career success, as indexed by salary growth, relative to those with lower cognitive ability. Factors that accounted for their increasingly greater success over time include: they sought more training,
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Intelligence Analysis: Behavioral and Social Scientific Foundations gravitated to more complex jobs, and pursued higher status occupations (Judge et al., 2010). They invested in their human resource endowment, gained human capital, and were able to leverage it at an increasing rate over time. Human Resources and Human Capital This distinction between stable and malleable KSAOs is important because it underpins a way of conceptualizing the distinction and relationship between human resources and human capital. This conceptual distinction links back to the systemic fit and strategic capabilities perspectives and, thus, sketches a basic architecture for the mechanisms of achieving strategic HRM. This architecture is illustrated in Figure 12-1. Stable KSAOs cannot be changed; they are human resource endowments. They are generic in that they are applicable to a wide range of jobs, situations, and organizations. In general, we know that individuals who have high cognitive ability (Schmidt and Hunter, 2004) and a conscientious personality profile (Barrick and Mount, 1991) perform at a higher level across a wide range of jobs. In that sense, those endowments are valuable in the broad labor market and allow individuals who possess them to seek the highest pay-off in organizational fit. Thus, organizations have to invest to recruit and select the best candidates with high-valued KSAOs. Those investments yield an aggregate pool of human resources. From a systemic fit perspective, strategic HRM should target selection of individuals with KSAO profiles that are consistent with the existing organizational strategy. The value of the resource pool for the organization is that positive effects manifest quickly in the form of performance effectiveness. Moreover, from a strategic capabilities perspective, efforts to maximize the quality of the resource pool have the potential, with additional investments, to develop human capital. Malleable KSAOs are targets for human capital investments. Although they are influenced by stable individual differences, their value to the organization can be enhanced by targeted development. From an organizational perspective, the more job specific, unique, difficult to replicate, and nonsubstitutable the knowledge and skills are that are developed, the better the organization fares (Barney and Wright, 1998; Ployhart, 2006, 2011). Why? Because investments in general knowledge or skills are valuable in the broader labor market, whereas specific skills are not as easily marketed by the individual, poached by other organizations, or imitated. Thus, for example, investing in job-specific training makes more sense for an organization because it can be applied immediately and is difficult for an individual to market elsewhere, whereas an investment in, say, an advanced
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Intelligence Analysis: Behavioral and Social Scientific Foundations FIGURE 12-1 Knowledge, skills, abilities, and other characteristics (KSAOs): Human resources and human capital. degree is valuable in many different jobs and organizations.1 More importantly, from a strategic capabilities perspective, the goal is to create human capital that is valuable, unique, and difficult for other organizations to replicate and that can be leveraged to create competitive advantage. With respect to the IC, application of this approach would create unique analytic capabilities, and mechanisms to link analysts collaboratively, to gain advantage over adversaries. HUMAN RESOURCE MANAGEMENT PRACTICES Translating Strategic HRM into Action Human resources and human capital provide a basis for understanding the differences in resource endowments and capabilities that in aggregate distinguish organizations competing in a particular environmental niche. At the firm level, one can liken them to aggregate individual abilities or “can do” characteristics. They are necessary, but not sufficient. What is also needed is motivation among employees to engage in human capital 1 This is not to say that encouraging advanced education is always poor HRM policy. I merely illustrate that human capital investment implications must be carefully considered with respect to strategic HRM goals. Under the right set of assumptions and constraints, a policy supporting advanced degrees may yield strategic advantage.
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Intelligence Analysis: Behavioral and Social Scientific Foundations development and to collectively apply their KSAOs for the benefit of the organization. HRM practices are designed to enhance an organizational workforce’s ability to perform and/or their motivation to do so (Becker and Huselid, 1998; Delery and Shaw, 2001). Many of these practices—such as recruitment, selection, training, performance management, compensation, and work design—have been used for quite some time, but only within the past few decades have researchers engaged in concerted efforts to empirically link HRM practices to indicators of organizational effectiveness. This link provides the means to implement the strategic HRM architecture. Early research in this area examined individual practices. For example, Holzer (1987) showed that investments in more extensive recruiting efforts were associated with organizational productivity. Terpstra and Rozell (1993) reported positive relations between specific selection practices and organizational performance. McEvoy and Cascio (1985) showed that job enrichment reduced employee turnover (which is associated with organizational productivity) (Brown and Medoff, 1978), and Gerhart and Milkovich (1992) reported that incentive compensation plans were positively related to productivity. An early meta-analysis reported that training, goal setting, and sociotechnical systems were positively associated with productivity (Guzzo et al., 1985). This early research provided recognition that HRM practices were linked to firm effectiveness. These HRM practices were labeled high-performance work practices by the U.S. Department of Labor (1993), and they use a variety of other names, including high-involvement, high-commitment, and high-performance work systems. The next generation of research advances has been aimed at resolving two primary limitations. First, the early research efforts tended to examine single practices, whereas strategic HRM theory suggests that “bundles” of aligned practices (MacDuffie, 1995) or particular combinations of practices (Youndt et al., 1996) work in synergistic fashion. Second, the methodology of the early research was less than ideal because the designs were typically cross-sectional (i.e., all data collected simultaneously), thereby yielding causal ambiguity, and the data were often self-reported (i.e., a manager was the sole data source), yielding concerns about response biases that could artificially inflate the observed relations (Huselid, 1995). Subsequent research has sought to address these limitations, solidify the linkage between HRM practices and organizational effectiveness (Delery and Doty, 1996; Hatch and Dyer, 2004; Huselid, 1995; Koch and McGrath, 1996; MacDuffie, 1995), and resolve causal ambiguity (Ployhart et al., 2009; Wright et al., 2005; Van Iddekinge et al., 2009). For example, Delery and Doty (1996) showed that HRM practices were associated with profits for a sample of banks, and MacDuffie (1995) found positive associations between HRM practice bundles with
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Intelligence Analysis: Behavioral and Social Scientific Foundations productivity and quality in a sample of automobile assembly plants. Although research by Wright et al. (2005) concluded that a causal linkage between HRM practices and organizational effectiveness is ambiguous, Van Iddekinge et al. (2009) showed that the implementation of selection and training at the unit level was positively predictive of future unit performance (see also Ployhart et al., 2009). In the ensuing years, research has developed and several qualitative reviews have concluded that HRM practices positively influence organizational performance (Becker and Huselid, 1998; Lepak et al., 2006; Wright and Boswell, 2002). More recently, the empirical foundation became sufficient to enable a meta-analytic review of the relationship between HRM practices and organizational effectiveness.2 Combs et al. (2006) cumulated findings from 92 studies that examined HRM practice relationships across 19,319 organizations. They reported a corrected overall correlation between HRM practices and indicators of organizational effectiveness of .20, which was significantly stronger for bundles (rc = .28) than for individual practices (rc = .14). Although a relationship of .20 might not appear to be very large, it is statistically and practically significant; increasing HRM practices by one standard deviation increases firm performance by 20 percent of a standard deviation. As the authors note, “In this sample, a one standard deviation increase in the use of HRM practices translates, on average, to a 4.6 percentage-point increase in gross return on assets from 5.1 to 9.7 and a 4.4 percentage-point decrease in turnover from 18.4 to 14 percent. Thus, HRM practices’ impact on organizational performance is not only statistically significant, but managerially relevant” (Combs et al., 2006, p. 518). Moreover, a recent meta-analysis of 66 primary studies (68 samples with 12,163 observations) found that the positive relationship between human capital and firm performance was significantly stronger (rc = .14) when the measures of human capital were form specific rather than general (Crook et al., in press), a key point made in this chapter. Although there is a need to improve methodological rigor and to refine understanding of the mechanisms that account for these relations (Becker and Huselid, 2006; Ostroff and Bowen, 2000), there is a sufficient basis to conclude that HRM practices are a viable means to implement strategic HRM, develop the workforce, and enhance organizational effectiveness. 2 A meta-analysis quantitatively cumulates indicators of relationship or effect size, correcting for statistical artifacts (e.g., measurement error), and reporting an estimate of the “true” magnitude of the relationship in question.
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Intelligence Analysis: Behavioral and Social Scientific Foundations pool. Recruitment is not a methodology, but a set of practices. Sometimes the practices are “traditional” (e.g., there are pathways for new hires from prior institutions—such as the military or candidates who already possess a security clearance—that yield good candidates for the IC). Although the use of current employees to target potential recruits can help identify specialized talent, it can also yield restrictions on the diversity of the applicant pool. Such practices merit scrutiny and should be supplemented by more pathways to improve diversity in the pool of applicant KSAOs. Training is a well-developed methodology. The primary challenges are at the intersection with the work context: Will the skills transfer? Will they be supported? Will they influence organizational performance? Ensuring that the context is aligned to support training is critical. Moreover, alignment will also prompt development. Kozlowski et al. (2009) describe this alignment between formal and informal learning, across levels of the organizational system, and consistent with organizational strategy as an “infrastructure” to promote a learning organization. Performance management comprises a set of well-supported techniques for developing skills and improving performance. Its effectiveness is in the implementation, and a critical element is how well performance is measured. If performance measurement does not capture desired behaviors, the system will be seriously flawed. This is the linchpin. The importance of the measurement issue is compounded by the use of financial incentives. For example, if rewards place an emphasis on the quantity of analytic products produced (because it is easy to count), quality may suffer. You will get what you pay for, so make sure it is exactly what you want. Finally, work design is well supported, and tools are available to analyze and implement work design changes. We know a lot about team effectiveness. Teams are not a panacea, and the general advice is not to form teams to perform jobs that an individual can perform alone (Steiner, 1972). On the other hand, for problem-solving tasks in which performance is enhanced by diverse expertise, multiple perspectives, and collaboration, teams are a viable HRM practice. On the horizon, virtual teams, network-centric problem solving, and self-organizing communities of practice represent a peek at exciting, technology-fueled, and team-enabled learning organizations of the future. These forms of work and organizational design are emergent, with little systematic research, and this is an obvious and important research target. The key is to make all these elements work in concert. A Broader Research Question: The IC as a Learning Organization The systems character of organizations, their multilevel structures, and their need to adapt to dynamic, often unpredictable, environmental
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Intelligence Analysis: Behavioral and Social Scientific Foundations shifts has placed the concept of organizational learning central to understanding organizational behavior and effectiveness (Cyert and March, 1963; Fiol and Lyles, 1985; March and Simon, 1958). The problem is that in organizational behavior—a domain with more than its fair share of fuzzy concepts—organizational learning is among the fuzziest because it encompasses nearly everything, including formal and informal mechanisms; processes and outcomes; and a wide range of phenomena at multiple levels, including learning, development, leadership, and culture (Fiol and Lyles, 1985). Recent theoretical work intended to make the concept more tractable for research and application developed an infrastructure for organizational learning based on three primary features: (1) alignment of informal and formal learning mechanisms, (2) specification of different developmental targets and outcomes at different levels of the system, and (3) alignment of the multilevel system around strategic imperatives (Kozlowski et al., 2009). One key assumption in this approach is that learning is inherently a psychological phenomenon at the individual level. Thus, the theory is built around the construction of an aligned system that fosters learning at the individual level and promotes its emergence as a collective phenomenon. It conceptualizes organizational learning as a bottom-up process. Organizational change, a challenging endeavor fraught with failure (Zegart, this volume, Chapter 13), is a management initiated, top-down process. From a complexity theory perspective, long-term, lasting change in multilevel systems occurs via bottom-up emergent processes (Kozlowski and Klein, 2000). What I sketch above is theoretical. Simulated data support some basic mechanisms of emergence, but no empirical foundation has been well developed. There are case-based exemplars as organizations implement tools designed to promote learning as an emergent process of change. Interestingly, the IC has already embarked on analyses and initial interventions consistent with a bottom-up approach to foster organizational learning. The IC is a set of units with divisions under the umbrella of the U.S. government. The units are “analytic boutiques” (Fingar, this volume, Chapter 1) attached to the unique sensibilities and needs of different customers. This arrangement provides much more flexibility than a centralized structure (Galbraith, 1972), but it also promotes information silos (Zegart, this volume, Chapter 13). The big challenge is to retain the flexibility of this distributed architecture, while breaking down barriers that impede collaboration. That means capitalizing on the HRM practices reviewed previously and building an infrastructure to promote organizational learning. So, for example, the IC has developed performance standards (i.e., competencies) and qualification standards for positions across agencies. It has systematically identified the content of expertise across IC units, providing a map of the distribution
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Intelligence Analysis: Behavioral and Social Scientific Foundations and location of key knowledge (Fingar, this volume, Chapter 1). It has inventoried intelligence analyst skills in the Analytic Resources Catalog, which represents the KSAO capability pool (Fingar, this volume, Chapter 1). These acts provide some basic actions needed to target desired human resources, locate key talent, and identify human capital to be developed. This is a good start. Moreover, it has implemented Intellipedia, a secure wiki site to share information and catalog intelligence (Andrus, 2005), and A-Space, a web-enabled networking tool to promote collaborative problem solving (Dixon, 2009). These tools enable bottom-up, self-organizing forms of learning, dynamic team networks, adaptation, and system evolution. Good tools will survive and thrive.8 Poor ones will die from disuse. The IC has shown a willingness to try new approaches, experiment, and see what works. Improving intelligence analysis will require more than the use of mathematically based decision-making tools and techniques. Such tools will help improve and reduce variance in some aspects of individual decision effectiveness. That is a good start, but it is not enough. Improving intelligence analysis requires harnessing the workforce as a collective. It requires integration and networking mechanisms to link disparate expertise spread across the IC architecture, foster collaborative learning and information amplification, and provide process feedback and peer input to advance critical thinking. It requires crafting the IC into a learning organization. This is an extraordinary opportunity to research the emergence of collaborative networks, to map them, and to develop a living model of organizational learning in the IC. REFERENCES Aguinis, H. 2007. Performance management. Upper Saddle River, NJ: Pearson Prentice Hall. Allen, T. D. 2007. Mentoring relationships from the perspective of the mentor. In B. R. Ragins and K. E. Kram, eds., The handbook of mentoring: Theory, research and practice (pp. 123–147). Thousand Oaks, CA: Sage. Allen, T. D., L. T. Eby, M. L. Poteet, E. Lentz, and L. Lima. 2004. Career benefits associated with mentoring protégés: A meta-analysis. Journal of Applied Psychology 89(1):127–136. Andrus, D. C. 2005. The wiki and the blog: Toward a complex adaptive intelligence community. Studies in Intelligence 49(3). Available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=755904 [accessed June 2010]. Baldwin, T. T., and J. K. Ford. 1988. Transfer of training: A review and directions for future research. Personnel Psychology 41(1):63–105. Barber, A. E. 1998. Recruitment employees. Thousand Oaks, CA: Sage. 8 A briefing by the Office of the Director of National Intelligence to the National Research Council, August 18, 2009, indicated that in approximately one year of operation, of 12,800 analysts, A-Space had acquired about 11,000 voluntarily registered users, with 30 percent of those users contributing to A-Space. That indicates a high implementation rate relative to typical implementations of new technology in organizations.
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