Performance management

Performance management as a content area contains several concepts that are core to I-O psychology including (but not limited to) goal setting, feedback, developing employees (including coaching), evaluating performance, and rewarding performance. Each of the elements has received considerable attention from scholars and researchers. Extensive, in-depth reviews have been offered by Aguinis (Reference Aguinis2013) and Smither and London (Reference Smither and London2009, Reference Smither, London and Kozlowski2012). Despite the abundant research, two problems remain. First, far too many managers believe that performance management refers only to performance evaluation/appraisal. Such a view leads people to think of performance management problems as once-a-year events where managers sometimes provide biased evaluations while they struggle to deliver useful feedback. Second, performance management processes can be time consuming, and their value is often questioned (Pulakos, Hanson, Arad, & Moye, Reference Pulakos, Hanson, Arad and Moye2015). Much of the dissatisfaction with performance management tends to center on performance evaluations. This makes sense in light of the fact that nearly a century of research has shown that ratings can be biased by the rater's motivations (including political factors), as well as cognitive errors associated with observing, storing, and recalling information about employee performance, and that these problems cannot be helped, but not completely remedied, by rater training or introducing new rating formats or scales (for a review, see Smither & London, Reference Smither, London and Kozlowski2012).

Making big headlines, several companies, such as Adobe, Gap, and Cargill, have attempted to tackle these problems in their recent decisions to eliminate performance ratings. Instead of annual ratings, these companies ask their managers to provide ongoing feedback (“check-ins”) to their employees throughout the year. Managers are also granted enormous discretion about how to allocate salary increases among employees. Pulakos et al. (Reference Pulakos, Hanson, Arad and Moye2015) have correctly noted the history of “vicious cycles of reinventing PM processes only to achieve disappointing results” (p. 52). In this context, abandoning ratings could appear to be a reasonable next step. On the other hand, eliminating ratings, expecting managers to provide ongoing feedback, and enabling managers to allocate salary increases as they see fit might simply be another vicious cycle in reinventing the performance management process. Is it reasonable to expect managers who could not competently facilitate an annual review of employee performance to now become skillful providers of ongoing feedback and coaching to their employees? In the absence of ratings, will the size of an employee's salary increase become the new “rating” of the employee's performance? If we couldn't trust managers to provide fair and unbiased ratings, can we trust them to provide a fair and unbiased distribution of salary increases? As corporate training appears to be declining (Cappelli & Tavis, Reference Cappelli and Tavis2016), will managers receive the help they need to make these new approaches to performance management successful? Without ratings, what cues do employees use to determine how they stand in the company relative to their peers?

At this point, we do not know the answers to these questions. But a very large body of theory and research is available to provide guidance about enhancing feedback and coaching (e.g., Alvero, Bucklin, & Austin, Reference Alvero, Bucklin and Austin2001; Cannon & Edmondson, Reference Cannon and Edmondson2001; Edmondson, Reference Edmondson1999; Graham, Wedman, & Garvin-Kester, Reference Graham, Wedman and Garvin-Kester1993; Hackman & Wageman, Reference Hackman and Wageman2005; Heslin, VandeWalle, & Latham, Reference Heslin, Vandewalle and Latham2006; Ilgen & Davis, Reference Ilgen and Davis2000; Kluger & DeNisi, Reference Kluger and DeNisi1996; Payne, Youngcourt, & Beaubien, Reference Payne, Youngcourt and Beaubien2007; Steelman, Levy, & Snell, Reference Steelman, Levy and Snell2004; Whitaker, Dahling, & Levy, Reference Whitaker, Dahling and Levy2007). The emphasis on ongoing feedback and coaching (rather than annual ratings) will likely fail if those who implement it do not leverage this vast body of knowledge. Sometimes, guidance can be found in established research that does not, at first glance, seem closely related to the issue at hand. For example, employee reactions to managers allocating salary increases in the absence of an annual review might be predicted by research on distributive, procedural, interpersonal, and informational justice (Colquitt, Conlon, Wesson, Porter, & Ng, Reference Colquitt, Conlon, Wesson, Porter and Ng2001; Colquitt et al., Reference Colquitt, Scott, Rodell, Long, Zapata, Conlon and Wesson2013; Whitman, Caleo, Carpenter, Horner, & Bernerth, Reference Whitman, Caleo, Carpenter, Horner and Bernerth2012), and the extensive justice literature can provide a useful conceptual framework for practitioners implementing this approach. Also, the extensive research literature on bias in human judgment will likely apply to managers who have to allocate salary increases in the absence of an annual review. That is, the same cognitive and motivational factors that lead to bias and error in ratings will likely lead to bias and error in salary allocations.

The bottom line is that there has been a wave of companies changing their PM process, incorporating radically new design elements (e.g., not using ratings), not because they are grounded in a solid base of longstanding research but because they are following an emerging trend (Cappelli & Tavis, Reference Cappelli and Tavis2016). The good news is that I-O psychology has been part of the debate (Adler et al., Reference Adler, Campion, Colquitt and Grubb2016). The not-so-good news is that as a field we do not seem to have a consolidated point of view, which further fuels organizational experimentation.

Selection and assessment

Personnel selection and testing is arguably an area where I-O psychologists have been most involved for over a century. We could even go so far as to say that modern selection and assessment as we know it today was one of the few areas of our field that we invented, if not heavily shaped. Our field is still very much involved in the area today. A search of the last nine SIOP conferences (2008–2016) revealed that testing and assessment content areas were one of the most prominent topics in the conference, second only to leadership (620 to 629 sessions). Clearly, this is an area in which I-O psychology has deep expertise and one that tends to distinguish our field from others. In fact, it has become synonymous with SIOP's brand image (Rose, McCune, Spencer, Rupprecht, & Drogan, Reference Rose, McCune, Spencer, Rupprecht and Drogan2013).

Obtaining and maintaining this distinction has come about in a large part because of the heavy research focus we maintain in this area. We continue to publish profoundly on this topic, have journals largely dedicated to it (e.g., International Journal of Selection and Assessment, Personnel Psychology Journal), and have local professional groups focused on it (e.g., International Personnel Assessment Council). Without a doubt, these efforts have improved the practice. For example, activity has increased both in academic research as well as in practice over the use of personality assessment in selection contexts (Scroggins, Thomas, & Morris, Reference Scroggins, Thomas and Morris2009). Applicant reaction and social justice research have helped us understand the altitudinal implications of applying assessment tools, procedures, and decision rules in selection contexts (e.g., Bauer, McCarthy, Anderson, Truxillo, & Salgado, Reference Bauer, McCarthy, Anderson, Truxillo and Salgado2012). Ample research on structured over unstructured interviews (e.g., Campion, Palmer, & Campion, Reference Campion, Palmer and Campion1997) has resulted in most companies utilizing behaviorally based interview techniques and adding structure to the scoring and decision-making processes. SIOP has also been actively involved in working with the Equal Employment Opportunity Commission (EEOC) to explore the possibility, scope, and implications of updating the Uniform Guidelines on Employee Selection Procedures (1978).

Our field has attempted to push the application of known tools and methods in assessment and selection (e.g., Hollenbeck, Reference Hollenbeck2009; Morelli, Mahan, & Illingworth, Reference Morelli, Mahan and Illingworth2014). However, relative to other players in the space, we would characterize these attempts as conservative if not late to the conversation. In our view, much of the research over the past decade has been focused on deepening our existing knowledge (Quadrant 1) rather than on exploring how we can apply our knowledge in new areas (Quadrant 3) or understanding how several emerging technologies and concepts may fit into our existing lexicon (Quadrant 4). A variety of radical approaches is emerging within the selection and assessment area that potentially disrupt our current conception of selection and hiring. For example, as noted in Rotolo and Church (Reference Rotolo and Church2015), several small start-up companies—often without I-O psychologists to guide their practice—are developing “web scraping” algorithms to compile candidate profiles from their digital footprints. These big data approaches may be criterion based, but there is little that we have seen that abides by the Uniform Guidelines on Employee Selection Procedures (EEOC, 1978) or the Principles for the Validation and Use of Personnel Selection Procedures (SIOP, 2003).

These technologies are perhaps most notable because, if done properly and effectively (which we assume will be just a matter of time), they could virtually eliminate the need for assessment and testing. The field of artificial intelligence including machine learning, deep learning, and sentiment analysis will pave the way to a more passive approach to data collection and assessment. In other words, instead of asking candidates to take personality inventories and situational judgment tests, we will be able to determine their personality, skills and capabilities, and cognitive ability through the candidate's continuous interactions with the technology around them—for example, laptops, tablets, mobile devices, wearables. For example, IBM is already training its Watson computing platform to analyze an individual's personality just by reading their his or her online writing (e.g., social media posts, blog entries, etc.).

However, the research focused on the reliability and validity of these new approaches is still in the early stages (e.g., Levashina, Hartwell, Morgeson, & Campion, Reference Levashina, Hartwell, Morgeson and Campion2014), and few I-O psychologists are talking about the implications of these innovations on our field, on organizations, and on society. Although Chamorro-Premuzic et al.’s (Reference Chamorro-Premuzic, Winsborough, Sherman and Hogan2016) focal article in this journal on “new talent signals” stirred a robust dialogue on the topic, we believe that we need to continue such dialogues on these future-focused topics.

High-potential identification

I-O psychologists have been focused on the methods, tools, and processes of individual assessment for decades, and there are volumes of resources available to guide both research and practice in this area (e.g., Jeanneret & Silzer, Reference Jeanneret, Silzer, Jeanneret and Silzer1998; Scott & Reynolds, Reference Scott and Reynolds2010; Stamoulis, Reference Stamoulis2009; Thornton, Hollenbeck, & Johnson, Reference Thornton, Hollenbeck, Johnson, Farr and Tippins2010). It has only been relatively recently, however, that an emphasis on identifying high potentials as a unique classification of talent, or as a talent pool, in the applied I-O psychology literature has emerged as one of the hottest areas of applied research and practice (Church & Rotolo, Reference Church and Rotolo2013; Silzer & Church, Reference Silzer and Church2009, Reference Silzer, Church, Silzer and Dowell2010). This has in many ways been in tandem with the rise in popularity of TM itself in the field of I-O psychology. For many human resource professionals, the ability to identify high-potential talent is the “holy grail” (and sometimes the sum total) of what a successful talent management process should look like in their organizations (Church, Reference Church2015).

This simple logic is at the core of every talent management strategy, that is, the need to identify the best possible talent for (accelerated) development and succession planning. On the external side of the buy versus build equation (Cappelli, Reference Cappelli2008), this is more about selection and perhaps effective on-boarding (Adler & Stomski, Reference Adler, Stomski, Silzer and Dowell2010) than anything else. On the internal side, it's about having tools and processes in place that assist line managers and HR leaders in segmenting talent into different pools that enable the differential distribution of development opportunities. Whether the approaches are based on simple (yet relatively limited) heuristics such as “two level jumps” or flawed measures such as past performance ratings (both of which are the top two most commonly used methods based on benchmark research; Church, Rotolo, Ginther, and Levine, Reference Church, Rotolo, Ginther and Levine2015; Silzer & Church, Reference Silzer, Church, Silzer and Dowell2010), assessment tools such as those based on personality theory (e.g., Hogan & Kaiser, Reference Hogan, Kaiser, Scott and Reynolds2010), cognitive abilities tests (Schmidt & Hunter, Reference Schmidt and Hunter2004), or more comprehensive multitrait multimethod models (e.g., Silzer, Church, Rotolo, & Scott, Reference Silzer, Church, Rotolo and Scott2016), organizations are increasingly looking for data-based answers. In many ways, it is about identifying and focusing on the “few” versus the “many,” and that distinction about sums up the entire difference between developing high potential employees in TM applications versus broader audiences in general (Church, Reference Church2013, Reference Church2014).

Unfortunately, not everyone is equally good at spotting future potential in people and some are downright horrible at it, even with effective I-O assessment tools in place. Although many large organizations have well established TM systems and processes geared at identifying and moving high-potential talent through their different roles to accelerate their development from early career to the C-Suite (e.g., Carey & Ogden, Reference Carey, Ogden, Berger and Berger2004; Church & Waclawski, Reference Church, Waclawski, Silzer and Dowell2010; Effron & Ort, Reference Effron and Ort2010; Grubs, Reference Grubs, Berger and Berger2004; Ruddy & Anand, Reference Ruddy, Anand, Silzer and Dowell2010; Silzer & Dowell, Reference Silzer and Dowell2010), and even though the concept of learning from experiences has been around for decades (e.g., Lombardo & Eichinger, Reference Lombardo and Eichinger2000; McCall, Lombardo, & Morrison, Reference McCall, Lombardo and Morrison1988), companies remain poor at both aspects. In their recent benchmark study of high-potential programs at large established organizations, Church et al. (Reference Church, Rotolo, Ginther and Levine2015) reported that 53% rated themselves a “3” in the maturity model, representing some degree of consistent implementation and executive engagement but lacking transparency, and another 23% rated themselves as either inconsistent or simply reactive overall. Only 8% reported being fully business integrated. This is despite the fact that 70% of the same companies are currently using assessments to inform their high-potential processes (Church & Rotolo, Reference Church and Rotolo2013; Church et al., Reference Church, Rotolo, Ginther and Levine2015).

All of this makes high-potentials a perfect example of the AIO emerging category described above. It is an incredibly hot topic in organizations today. Line managers have been talking the language potential for decades and think they know everything there is to know. There are entire sub-functions inside organizations dedicated to conducting high-potential and executive assessments (Church & Levine, Reference Church and Levine2017). Yet theory and research have only just begun to catch up. As a result, senior clients want the “silver bullet” here as well. They are looking for the cheapest, most efficient, yet effective and valid (and not to mention perfect and confirmatory of all of their own personal talent decisions) “assessment tool” that will work miracles. Perhaps most recently, we are seeing a trend toward those who believe that big data applications will finally solve this equation using complex algorithms based on web scraping and email mining similar to Chamorro-Premuzic et al.’s (Reference Chamorro-Premuzic, Winsborough, Sherman and Hogan2016) talent signals concept. Although we have our concerns about making decisions with limited data in some of these areas (Church & Silzer, Reference Church and Silzer2016), the bottom line is if we as I-O psychology professionals do not get there in time, others will.

This is not to say there has not been progress. Comprehensive frameworks such as the Leadership Potential BluePrint (Church & Silzer, Reference Church and Silzer2014; Silzer & Church, Reference Silzer and Church2009; Silzer et al., Reference Silzer, Church, Rotolo and Scott2016) and related models (MacRae & Furnham, Reference MacRae and Furnham2014), as well as others offered by consulting firms (e.g., Aon-Hewitt, 2013; Hewitt, 2003; 2008; Paese, Smith, & Byham, Reference Paese, Smith and Byham2016) have begun to emerge that are helping to guide practice. Unfortunately, the field has yet to agree on a single core model, and aside from a handful of academics and practitioners, the work is largely being driven by consulting firms. Although there has always been tension between the consulting (for profit) side of I-O psychology and the academic side when it comes to knowledge creation, this concerns us if it serves to either slow down progress or results in ill-conceived tools and “bolt-on” constructs (AIO) in order to avoid overlap with others already in market.

At the 2006 annual SIOP conference, for example, there were only four sessions with talent management and two with high-potentials in the titles in the entire program (suffice to say that none of these were core topics for submission content either). All four of the TM submissions were submitted by internal practitioners, and one of these was featured in TIP (Church Reference Church2006) and resulted in both the Silzer and Dowell (Reference Silzer and Dowell2010) Professional Practice series book as well as the second highly successful Leading Edge Consortium several years later. There was also at least one workshop we are aware of on high-potentials offered in 2009 (Parasher & McDaniel cited by Silzer & Church, Reference Silzer and Church2009) and several since. Interestingly, siop.org indicates that at the 2017 conference, 11 years later, these topics had not grown in popularity. Once again, we had only four sessions on TM and four on high-potentials. The two topics are still entirely missing from the list of content areas offered by SIOP. This is fascinating yet disappointing at the same time to us. It is clearly an emerging topic area (or one that is fully here already) and needs our attention before other types of consulting practitioners take over completely.

Engagement, culture, and other employee surveys

If you attend SIOP, or any conference for that matter, then it is no secret that engagement is and has been a “hot” topic for over two decades now. Employee engagement is a term that was originally introduced by William Kahn in a 1990 Academy of Management Journal article titled “Psychological Conditions of Personal Engagement and Disengagement at Work.” He described personal engagement and personal disengagement as “behaviors by which people bring in or leave out their personal selves during work role performances” (Kahn, Reference Kahn1990). According to Kahn, engagement is a psychological construct that has to do with the expression of one self physically, cognitively, and emotionally in the workplace. Kahn developed the concept from the work of Maslow (Reference Maslow1954) and Alderfer (Reference Alderfer1972) by following the idea that people need self-expression and self-employment in their work lives. This idea that people's responses to organizational life stem from self-in-role behaviors as well as group membership were adapted from the fields of psychology (Freud, Reference Freud1922), sociology (Goffman, Reference Goffman1961), and group theory (Bion, Reference Bion1959).

Over the years, academic research into the construct of engagement grew and new models and frameworks emerged. In a groundbreaking study, Schaufeli, Salanova, Gonzalez-Roma, and Bakker (2002) described engagement as the “opposite” of burnout and broadened the scope of traditional research into burnout, finding support for the construct in a variety of professions. They defined engagement as “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption” (Schaufeli et al., Reference Schaufeli, Salanova, González-Romá and Bakker2002). It had also previously been described in the academic literature as being characterized by “energy, involvement, and efficacy” (Maslach & Leiter, Reference Maslach and Leiter1997).

Seeing the merits of employee engagement in organizations, academics and practitioners alike have joined the discussion over the past 25 plus years, and new models and frameworks have been introduced to attempt to better predict employee engagement. Moreover, there have been considerable efforts made to link engagement to organizational outcomes such as employee performance and even business performance, thus demonstrating the “business case” of engagement to organizational leaders. With this growing interest in engagement over the years by both scientists and practitioners alike, the division between these two groups expanded with literature and practice becoming grossly misaligned. This has led to competing, inconsistent, and incorrect interpretations and explanations of the construct (Macey & Schneider, Reference Macey and Schneider2008; Newman & Harrison, Reference Newman and Harrison2008), even to the extent of being called overrated and a potential nuisance to organizations (Church, Reference Church2016; Van Rooy, Whitman, Hart, & Caleo, Reference Van Rooy, Whitman, Hart and Caleo2011).

At the same time, organizations’ enthusiasm around engagement has led to it being used as a label for everything, and unfortunately, this lack of clarity around exactly what we're measuring in these various so-called engagement surveys means that we are really comparing apples to oranges rather than using one consistent framework. Given that organizations love to “benchmark” their survey scores against other companies’ scores both within and outside their industry, a practice that dates back to origins of employee surveys at a core methodology with the formation of consortia such as the Mayflower Group (Johnson, Reference Johnson and Kraut1996), the comparison between organizations is problematic because most are using different measures of the construct. Even the Mayflower Group itself several years ago spent significant time and energy working to align a set of shared survey engagement items (and vacillated between the Macey and Schneider behavioral approach and the more common attitudinal frameworks). Furthermore, in order to benchmark within organizations, there will likely be resistance to changing a poor measure of engagement due to the fact that historical comparisons are lost. Taken together, it is difficult to really make comparisons both within and between organizations given the current state of engagement surveys; however, organizations continue to do so despite this grounding (Church, Reference Church2016).

Ultimately, engagement research is by far one of the most influential areas where I-O psychologists can have an impact in an organization. Survey research after all blends our deep training into theoretical constructs with strong measurement and data analysis techniques as well as organization development principles from an action planning perspective. Therefore, this is an area where we can and should continue to demonstrate our knowledge. However, as practitioners, we must strike a balance. Recently, I-O psychologists have begun to fall out of love with engagement and have begun to introduce concepts of organizational health and organizational well-being (Bersin, Reference Bersin2014). As satisfying as it is to see the field continue to evolve its thinking in these areas, we must remember that it can be difficult for organizations to keep up with the latest research. Try explaining to C-Suite leaders that they should no longer be concerned with employee engagement! In order for I-O psychologists to truly add value, academics and practitioners must build bridges, not only to introduce the most scientifically sound, compelling research that is relevant to organizational leaders but also to teach new entrants to the field early on how to convey and explain obscure constructs in a meaningful way. Without this, organizations will be sold work in the form of the latest survey or “pulse check.” Inevitably when these programs do not deliver on what was promised, the organization may turn its back on survey research altogether.

Learning agility

Today's organizational environments are characterized by dynamic, constantly changing objectives around strategies aimed at succeeding in dynamic, rapidly shifting markets. Employees are being asked to adopt new technologies, continuously improve processes, and take on roles without clearly defined responsibilities. In this context, it is no wonder that the concept of learning agility has come into the spotlight as a primary enabler of leadership capability and an early indicator of leadership potential (Lombardo & Eichinger, Reference Lombardo and Eichinger2000; Silzer & Church, Reference Silzer and Church2009). Organizations are incorporating the construct into their selection and development programs, and the term is fast becoming embedded into the lexicon of TM. Yet, there is still much debate about what learning agility is as a construct (e.g., De Meuse, Guangrong, & Hallenbeck, Reference De Meuse, Guangrong and Hallenbeck2010; DeRue, Ashford, & Myers, Reference DeRue, Ashford and Myers2012), and thus it is a good example of the expanding category of AIO.

Learning agility was defined by the early pioneers, Lombardo and Eichinger (Reference Lombardo and Eichinger2000), as “the willingness and ability to learn new competencies in order to perform under first-time, tough, or different conditions” (p. 323). They conceptualized four dimensions: people agility, results agility, mental agility, and change agility. Although efforts have been made to improve upon the definition (DeRue et al., Reference DeRue, Ashford and Myers2012), others have noted that the construct is merely a reframing of learning ability, or the ability to learn (Arun, Coyle, & Hauenstein, Reference Arun, Coyle and Hauenstein2012). For example, DeRue et al. (Reference DeRue, Ashford and Myers2012) proposed a refined definition of learning agility as “the ability to come up to speed quickly in one's understanding of a situation, and move across ideas flexibly in service of learning both within and across experiences” (p. 262). Some (e.g., Wang & Beier, Reference Wang and Beier2012) see this as problematic, as it does not add anything unique from general mental ability or g. Carroll (Reference Carroll1993) for example defines g as “the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience” (p. 68).

The thrust of this conceptual debate in the field, however, is whether learning agility is unique because it implies a motivational element of willingness to learn (e.g., goal orientation, openness to experience) in addition to the ability component, or whether such motivational factors are antecedents to more core elements like processing speed and processing flexibility, none of which are new concepts. Either way, what is interesting about this particular debate is that the argument seems less about the relevance of the construct itself (whether new or not) and more about where the definitional boundary lines are drawn.

Although the debate rages on, studies have repeatedly shown that the ability to learn from experience is what differentiates successful executives from unsuccessful ones (Charan, Drotter, & Noel, Reference Charan, Drotter and Noel2001; Goldsmith, Reference Goldsmith2007; McCall, Reference McCall1998). In their original research, Lombardo and Eichinger (Reference Lombardo and Eichinger2000) found significant relationships between learning agility and supervisory ratings of performance and potential (R² = 0.30, p < .001). Connolly and Viswesvaran (Reference Connolly and Viswesvaran2002) examined learning agility among law enforcement officers from 26 organizations in the United States. They found that learning agility predicted supervisory ratings of job performance and promotability beyond what was explained by IQ and personality. More recently, Dries, Vantilborgh, and Pepermans (Reference Dries, Vantilborgh and Pepermans2012) found that learning agility was found to be a better predictor of being identified as a high potential than job performance.

Thus, despite ongoing construct debate, there continues to be ample evidence that learning agility is important to organizations despite the construct debate. How much incremental value does it add over and above other variables, and/or can it be used alone as the sole predictor as some consulting firms would suggest? For example, in a survey of 80 companies known for their talent management practices, Church et al. (Reference Church, Rotolo, Ginther and Levine2015) found that over 50% of them included learning agility (or ability) in their assessments of high potentials and senior leaders. This was higher than other seemingly important constructs such as resilience, executive presence, and even cognitive skills.

Relatedly, and perhaps more troubling, is the fact that the definition continues to morph as the construct debate continues. Thus, although the concept is an increasing part of competency models and high potential frameworks in organizations, many adhere to the older four-factor model (mental agility, people agility, change agility, and results agility), whereas others use a consultant's proprietary definition, and others create their own in-house definitions. Most troubling is that the newer concepts emerging in the marketplace (and owned by consulting firms) appear to be complex, comprehensive, and more resembling of leadership competencies rather than a targeted concept around learning from experiences as originally conceived. The net effect of this in our view is that it only serves to raise concern among internal line and HR communities when they see more and more complex frameworks describing what they thought was something simple and quite predictive. We suspect that these efforts at greater complexity are being driven by I-O forces at least in part to support marketplace differentiation. Thus, we are in effect enabling our own AIO challenges.

In sum, regardless of where the boundary lines are drawn around the construct, there is little debate that learning agility is an area that has a lot of pull from organizations and is being applied in areas such as senior leadership assessment and high potential identification to solve important organizational needs (Church & Rotolo, Reference Church and Rotolo2013). The challenge in preventing it from taking Path 4 (fading into oblivion) versus Path 3 (embedded into TM best practices) is ensuring the construct becomes well defined, well researched, and consistently measured and reported back to organizations.

Millennials entering the workforce

A recent focal article and a rich set of commentaries in this journal addressed the question of whether Millennials comprise a unique generation in the workforce, with their set of unique needs and consequent set of unique challenges to organizations (Costanza & Finkelstein, Reference Costanza and Finkelstein2015). Although there are differences in select work-related variables, the empirical research on this question is largely cross-sectional in design, and generational membership is of course confounded with age, historical and cultural experiences, and other cohort effects (e.g., Costanza, Badger, Fraser, Severt, & Gade, Reference Costanza, Badger, Fraser, Severt and Gade2012. The thrust of most of the discussion on this question in our field has been on whether differences in motives, interests, work values, attitudes, and so on that purportedly distinguish Millennials—however that generation is bracketed—are due to age, career stage, maturation, organizational level, or other factors—a question that is, in any practical sense, unanswerable. It is, sadly enough, way too late to start a study of the roots of current generational effects in the workplace; the opportunity to initiate those studies occurred soon after World War II with the birth of the Boomers.

It has nonetheless become virtually a leap of faith that organizations must do a better job of understanding and adapting to the unique needs of Millennials in recruitment, assimilation, performance management, reward, promotional, and a variety of other talent management practices. Interestingly this has been the mantra for some time in the business world (Zemke, Raines, & Filipczak, Reference Zemke, Raines and Filipczak2000), as it has its origins in marketing research as well. Organizations have embraced the notion that Millennials are special in ways that older generations in the work place who now manage Millennials are challenged to understand, presumably because these older generations were so very different when they were young. An example of such a prescriptive how-to guide by Espinoza, Ukleja, and Rusch (Reference Espinoza, Ukleja and Rusch2010) highlights the competencies claimed to be especially required for managers to effectively manage Millennials. However, a review of these competencies in fact reveals that they simply reflect what we know generally about the importance of openness and adaptability, sociability, conscientiousness (Judge, Bono, Ilies, & Gerhardt, Reference Judge, Bono, Ilies and Gerhardt2002), leader–member connectedness (Gerstner & Day, Reference Gerstner and Day1997), and other well-established predictors of effective leadership.

The error in I-O psychology and TM practice, then, is in framing the task of managing Millennial employees as a unique, never-before-seen challenge rather than simply the challenge of all leaders: to accurately assess the unique attributes of the people they lead and adapt their leadership behaviors accordingly. To that extent, TM practices that are applied uniformly to a set of diverse employees on the basis of those employees having been born in the same decade may actually be harmful to efforts to effectively manage that talent. As Costanza and Finkelstein (Reference Costanza and Finkelstein2015) have argued, managing individual talent based on generational stereotypes is a questionable practice, much as it would be if TM practices were tailored to apply uniformly to members of other cultural or social classifications based on ethnicity, religion, or national origin. Treating Millennials as a never-before-seen phenomenon also raises doubts about the value for contemporary talent management practice of the accumulated behavioral research and theory conducted over the decades on prior generations of young employees. Further, the evidence is far from clear that Boomers or Generation Xers had materially different work-related attitudes and expectations when they were at the age Millennials are today, despite what popular books might suggest (e.g., Meister & Willyerd, Reference Meister and Willyerd2010; Zemke et al., Reference Zemke, Raines and Filipczak2000). There is also no evidence that not having shared particular historical or cultural experience at a particular point in life-span development is a material barrier to people understanding, adapting to, and forging productive work relationships with each other. Finally, we should all be relieved that the accumulated understanding of behavior at work built over the decades, including research conducted years ago on prior generations, does not have to be tossed into the trash: People—even our own generation of Millennials—remain people. Thus, in this example, this emerging practice is more likely headed down Path 4 toward the empirical dust bin.

Big data

Big data is an emerging phenomenon within HR (Bersin, Reference Bersin2013), yet it still seems to be on the frontier of the I-O psychology landscape. Big data is commonly referred to as the use of extremely large data sets to reveal patterns, trends, and associations, and is characterized by the volume, variety, velocity, and veracity of data (McAfee & Brynjolfsson, Reference McAfee and Brynjolfsson2012). It is not uncommon to find workforce analytics, labor analytics, or strategic workforce planning teams within HR today, as well as analytic functionality bundled in software applications like SAP SuccessFactors and IBM. Although some I-O psychologists are employed in this space, it is more common that these teams are composed of statisticians, mathematicians, and economists.

Recently, SIOP's Executive Board commissioned an ad hoc committee to establish a set of recommendations for the field to “raise awareness and provide direction with regard to issues and complications uniquely associated with the advent of big data,” which resulted in a focal article this journal (Guzzo, Fink, King, Tonidandel, & Landis, Reference Guzzo, Fink, King, Tonidandel and Landis2015). As most of the 11 commentaries to the article noted, this was a step in the right direction but did not go far enough (Rotolo & Church, Reference Rotolo and Church2015). Certainly, SIOP is in catch-up mode, with the 2016 Leading Edge Consortium focused on the topic and several new books recently published or on the horizon (e.g., Tonidandel, King, & Cortina, Reference Tonidandel, King and Cortina2015). In our view, software companies, not I-O psychologists, are starting to control the dialogue in this space, making promises about the power of their analytical software suites that simply are not delivered. The problem is that I-O psychologists who know how data analysis works are frequently not involved in these conversations.

Yet, the concept of big data has been around since the late 1990s (Lohr, Reference Lohr2013). Around the same time, Rucci, Kirn, and Quinn (Reference Rucci, Kirn and Quinn1998) highlighted the techniques that I-O psychologists had used for decades, demonstrating that employee attitudes can empirically predict (“key drivers”) key business results. Many of the statistical techniques used in big data applications (e.g., association rule learning, classification tree analysis, social network analysis) have grounding in multivariate statistics—a topic that is core to I-O psychology graduate programs. Thus, in our view, I-O psychology should be playing a much bigger role in the usage and application of big data in HR. In fact, one could argue that we are going in the opposite direction or at least getting in our own way. For example, Cucina, Walmsley, Gast, Martin, and Curtin (Reference Cucina, Walmsley, Gast, Martin and Curtin2017) called for an end to the use of survey key driver analysis because of a handful of methodological issues.

Thus, it begs the question: Why haven't I-O psychologists been at the forefront of the big data movement? Our field is in a prime position to make the most significant impact in this area for the following reasons:

1. (a) Our grounding in psychology means that we understand the theory behind the data; this helps us drive what questions to ask as well as guide the interpretations and insights from the analyses. We often see, for example, those outside of our field drawing inferences out of spurious and non-job-relevant relationships (e.g., that high potentials drive red cars, therefore should be part of an algorithm to predict potential).

2. (b) Our expertise in research methods allows us to understand the statistical issues with big data. For example, big data, by its nature, brings with it issues of statistical significance testing and requires a bigger focus on effect sizes. Other fields may not carry this appreciation with them.

3. (c) We have significant history in this space. For example, those of us who are focused on survey research (e.g., employee engagement research) have been conducting for decades multivariate analyses to identify “key drivers” of desired outcomes. Similarly, those of us in employee selection research have used “big data” to identify relevant predictors of job success criteria for validation purposes.

If we do not work to provide good science with big data, then we allow for potentially inappropriate conclusions to be made (like red cars). We learn in our graduate training the ethics of using large samples, and so we know how this field should be taking shape. We owe it our business clients to be part of, if not shape, the growth of this emerging field.

Neuroscience

The areas of neuroscience and mindfulness have also received considerable attention in the media and popular press over the past few years. In fact, it seems that society at large is embracing these concepts, as evidenced by Fortune 100 organizations staffing roles such as “chief mindfulness officer” and reputable universities such as MIT offering courses on “neuroscience and leadership.” In contrast, a scan of SIOP's annual conference over the last five years yielded only 11 entries on mindfulness and four entries related to neuroscience. Why is there a lack of attention by I-O psychologists on these topics, particularly when we are most poised to influence the discussion, if not play a leading role in guiding it?

One answer could be that our attention is on more evidenced-based research than the grandiose claims that neuroscience researchers are making. For example, in a non-peer-reviewed article, Rock (Reference Rock2008) states:

Although the neurobiology of threat and reward is well understood in general, it is a leap to presume that performance reviews routinely generate status threats and therefore lower performance. This is a good example of taking a germ of truth and extrapolating the implications of studies conducted in other domains to specific work events in the absence of any scientific evidence showing that neurobiological fight-or-flight responses routinely accompany performance reviews. In addition, the suggestion that feedback typically “leads to decreased performance” is inconsistent with meta-analytic findings which have shown that, on average, the effect of feedback is positive (d = .41; Kluger & DeNisi, Reference Kluger and DeNisi1996). As I-O practitioners we need to be ready to help our line leaders and HR professionals understand what the research says versus what some of the more intriguing, yet unsubstantiated, claims might say.

Relatedly, another potential reason why these areas have not been on our collective radar is that we simply do not believe they are offering anything that we did not already know. Although advances in neuroscience, for example, have allowed us to better understand exactly what is happening in our brains when we perform a given activity, they often fail to provide any unique insights as to how to leverage this knowledge. In short, there is nothing typically offered from these perspectives in the context of what organizations should do with the information from a practices, processes, or intervention perspective. Any guidance that is offered, typically, is something that has already been settled in the research literature for quite some time. In the Rock (Reference Rock2008) example above, for example, the practice of having employees evaluate their own behavior, which was the recommended action to decrease the reported status threat, has been around for more than 40 years and was part of the transition from performance appraisal to performance management in organizations.

In sum, we believe that our field can learn from the attention that organizations are paying to the neuroscientists. They have come to the party with immediate credibility given their credentials and use hard science to explain relationships between complex phenomena such as brain activity and behavior. They remind us that our stakeholders want to know not only how we propose to solve their issues, and the extent to which our solutions will have impact, but also why our solutions will work (including, at least to some degree, the theoretical or psychological underpinnings). What is new in this emerging field is how our brains react to common workplace phenomena. We would do well to consider this area as we discuss new constructs and phenomena.