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The goal of focal articles in Industrial and Organizational Psychology: Perspectives on Science and Practice is to present new ideas or different takes on existing ideas and stimulate a conversation in the form of comment articles that extend the arguments in the focal article or that present new ideas stimulated by those articles. The two focal articles in this issue stimulated a wide range of reactions and a good deal of constructive input.
The world is awash in data. Data is being created and stored at ever-increasing rates through a variety of new methods and technologies. Data is accumulating in all sorts of accessible places. Much of that data is of great interest to industrial–organizational (I-O) psychologists, often in ways never anticipated by those who develop technologies and processes that generate and store that data. I-O psychologists also generate data in the course of research and practice in ways that, especially if joined with data originating from other sources, create giant datasets. This abundance of data—variables, measurements, observations, facts—can be used to inform a vast number of issues in research and practice. This is the new “big data” world, and beyond opportunities, this new world also presents challenges and potential hazards.
As discussed in Guzzo, Fink, King, Tonidandel, and Landis's (2015) focal article, big data is more than a passing trend in business analytics. The plethora of information available presents a host of interesting challenges and opportunities for industrial and organizational (I-O) psychology. When utilizing big data sources to make organizational decisions, our field has a considerable amount to offer in the form of advice on how big data metrics are derived and used and on the potential threats to validity that their use presents. We’ve all heard the axiom, “garbage in, garbage out,” and that applies regardless of whether the scale is a small wastebasket or a dump truck.
Guzzo, Fink, King, Tonidandel, and Landis's (2015) focal article was intended to be not a set of standards but instead “stepping stones” to “raise awareness and provide direction” (p. 492) to our field for working with big data. We believe that the work done by the authors successfully achieved those objectives, and we encourage the Society for Industrial and Organizational Psychology (SIOP) Executive Committee to keep advancing this work toward greater clarity and guidance. Having clear alignment among members of our field and guidelines for handling nebulous issues such as big data is an important aspect of the scientist–practitioner model.
One important issue not highlighted by Guzzo, Fink, King, Tonidandel, and Landis (2015) is that simply establishing construct validity will be significantly more challenging with big data than ever before. One needs to only look as far as the other social sciences analyzing big data (e.g., communications, economics, industrial engineering) to observe the difficulty of making valid claims as to what measured variables substantively “mean.” This presents a significant hurdle in the application of big data to organizational research questions because of the critical importance of demonstrating validity in the organizational sciences as highlighted by Guzzo et al.
Guzzo, Fink, King, Tonidandel, and Landis (2015) provide a clear overview of the implications of conducting research using big data. One element we believe was overlooked, however, was an individual-level perspective on big data; that is, what impact does this sort of data collection have on the individuals being studied? As psychologists, the ethics and impact of big data collection from workers should be at the forefront of our minds. In this reply, we use years of research on electronic monitoring and tracking to provide evidence that an individual-level perspective is an essential part of the discussion surrounding industrial–organizational psychology and big data. Specifically, we examine electronic performance monitoring (EPM) literature to identify how the widespread, pervasive collection of employee data affects employees’ attitudes and behaviors.
Businesses, governments, universities, hospitals, law enforcement agencies, and other organizations are increasingly collecting and analyzing data to inform decision making. This “big data” movement has benefited from the contributions of a number of academic disciplines, including mathematics, statistics, and computer science. The technical advances involved in big data have grown exponentially in recent years, thus contributing to its growing use by organizations, the experience of which contributes to further refinements and so forth. This cycle of technical advancement is likely to continue into the foreseeable future.
Big data is becoming a buzzword in today's corporate language and lay discussions. From individually targeting advertising based on previous consumer behavior or Internet searches to debates by Congress concerning National Security Agency (NSA) access to phone metadata, the era of big data has arrived. Thus, the Guzzo, Fink, King, Tonidandel, and Landis (2015) discussion of the challenges (e.g., confidentiality, informed consent) that big data projects present to industrial and organizational (I-O) psychologists is timely. If the hype associated with these techniques is warranted, then our field has a clear imperative to debate the ethics and best practices surrounding use of these techniques. We believe that Guzzo et al. have done our field a service by starting this discussion.
Guzzo, Fink, King, Tonidandel, and Landis (2015) review important issues—privacy, informed consent, and data/data analysis integrity—that are critical logistical considerations in any program of research with human subjects, including studies utilizing big data. Overall, we agree with the cautionary sentiment conveyed throughout the focal article; industrial and organizational (I-O) psychology researchers and practitioners should not assume that big data is a panacea, and many of our established disciplinary approaches for ensuring ethical and accurate research are applicable—or modifiable—in big data contexts. However, we believe that the conversation about big data in I-O psychology is broader than that reviewed by Guzzo et al., and we would like to further elaborate on the focal article. We present this commentary from our perspective as junior scholars entering the field at a critical time—a time when I-O psychology is becoming increasingly intertwined with big data science.
Over the past 25 years, industrial and organizational (I-O) psychologists have made great strides forward in the area of teams research. They have developed and tested meso-level theories that explain and predict the behavior of individuals in teams and teams operating within and across organizations. The continued contributions of I-O psychologists to theory and research on teams require us to address the challenges—several of which were well described in the focal article (Guzzo, Fink, King, Tonidandel, & Landis, 2015)—and embrace the opportunities that are being ushered in by big and broad data streams (Hendler, 2013). We suggest that a principal unique value add of the I-O psychologist to the basic scientific endeavor of understanding small teams comes in the form of theory—theories that explain why, when, how, and to what end individuals form relationships needed for teams to function in unison toward the accomplishment of collective goals. Some have argued that the big data revolution means “the end of theory,” suggesting petabyte data render theoretical models obsolete (Anderson, 2008). On the contrary, we submit that big-data enabled social science holds the promise of rapid progress in social science theory, particularly in the area of teams.
In this response to Guzzo, Fink, King, Tonidandel, and Landis (2015), we suggest industrial–organizational (I-O) psychologists join business analysts, data scientists, statisticians, mathematicians, and economists in creating the vanguard of expertise as we acclimate to the reality of analytics in the world of big data. We enthusiastically accept their invitation to share our perspective that extends the discussion in three key areas of the focal article—that is, big data sources, logistic and analytic challenges, and data privacy and informed consent on a global scale. In the subsequent sections, we share our thoughts on these critical elements for advancing I-O psychology's role in leveraging and adding value from big data.
The focal article (Guzzo, Fink, King, Tonidandel, & Landis, 2015) sought to “raise awareness and provide direction with regard to issues and complications uniquely associated with the advent of big data,” (p. 492), and we commend their success in offering Society for Industrial and Organizational Psychology (SIOP) members a solid foundation and resources on which to draw. Our aim here is to extend their position, particularly to drive the conversation toward concrete recommendations for how industrial and organizational psychologists (I-Os) working in industry can apply the principles set forth in the focal article in our day-to-day work, specifically around the issue of avoiding ethical missteps in this new landscape.
The “big data” movement is forcing many fields to establish best practices for the collection, analysis, and application of big data, and the field of industrial–organizational (I-O) psychology is not exempt from this disruptive influence. Over the last several years, I-O scientists and practitioners have grappled with questions related to the definition, application, and interpretation of big data (e.g., Doverspike, 2013; Maurath, 2014; Morrison & Abraham, 2015; Poeppelman, Blacksmith, & Yang, 2013). The focal article by Guzzo, Fink, King, Tonidandel, and Landis (2015) continues this discussion and represents one of the first attempts to establish a formal set of recommendations for working with big data in ways that are consistent with I-O psychology's professional guidelines and ethics requirements.
In recent years the concept of mindfulness has become increasingly popular, and with good reason. A growing body of research indicates that mindfulness provides a number of physical, psychological, and even performance benefits. As a result, some organizations have started offering mindfulness programs to their employees. But despite growing interest, mindfulness has received little attention from the industrial–organizational community. In this article, we provide an overview of what mindfulness is, where the concept came from, how it has been utilized and studied to date, and what its application in the work setting is. We also propose new directions for researchers and practitioners.
For industrial and organizational psychologists who are unfamiliar with the mindfulness literature, Hyland, Lee, and Mills (2015) nicely introduce the concept by highlighting key findings from prior studies. Although their review focuses on the many benefits of mindfulness, we believe that mindfulness research should address certain questions that will help us understand whether mindfulness interventions result in a cost-effective positive return on investment. In alignment with the perspective of evidence-based practice (Briner & Rousseau, 2011; Pfeffer & Sutton, 2006), we call for a holistic evaluation of mindfulness, including a consideration of when or how unintended side effects emerge. Importantly, we discuss the potential mechanisms by which mindfulness generates valued outcomes (e.g., performance and collective psychological climate) and the need for more sophisticated research to isolate these causal effects. We also consider how the judicious use of utility analytics (e.g., cost effectiveness and return on investment) might help demonstrate the value of mindfulness interventions while also acknowledging questions of causality that must be addressed for such value to be experienced. We close by clarifying that we have the intention of promoting research to further evidence-based practices. There are organizations that have already begun providing mindfulness meditation interventions, and it is our hope that our commentary will help practitioners in these settings to consider the evidence suggesting that there may be unknown nuances regarding mindfulness practice. Ultimately, we believe that mindfulness is an important burgeoning area of research deserving of more scholarly attention.
Mindfulness—present moment attention and awareness (Brown & Ryan, 2003)—is commonly proposed as a productive state of consciousness in the workplace. Unfortunately, being mindful at every moment of the workday is fairly uncommon. Research suggests that people engage in mind wandering—a lack of attention to and awareness of the present (Smallwood & Schooler, 2006)—for the majority of their day (in every task except making love; Killingsworth & Gilbert, 2010). Further, there is another state of consciousness called flow—an intense sense of concentration and control over activities—that has also been linked to workplace performance (Nakamura & Csikszentmihalyi, 2002). Interestingly, whereas mindfulness facilitates higher performance by being aware of external stimuli, flow enables higher performance by doing the opposite—blocking out external stimuli. These findings suggest that mindfulness is neither the most common psychological state nor the only productive psychological state for the workplace.
The focal article by Hyland, Lee, and Mills (2015) ends with several important questions and suggestions for future research. Although the review opens new avenues of investigation for industrial and organizational (I-O) psychologists, the treatment of two questions may leave readers with the impression that research in these areas is nonexistent. Specifically, the authors posed the following inquiries: (a) Is mindfulness good for everyone (across personality and culture), and (b) is it appropriate to introduce mindfulness into the workplace? As a result, our commentary delves deeper into the current literature to investigate these questions, examining who is best served by mindfulness interventions (i.e., the relationship between personality traits and outcomes) and how cultural factors can facilitate success—or failure—of mindfulness programs. Following this examination, we address the question of whether mindfulness is a suitable workplace intervention and caution against a one-size-fits-all approach that may fail to target specific organizational and employee needs. In so doing, this commentary furthers the goal of the focal article, in which the authors expressed a hope for the I-O community to develop “a more comprehensive understanding of what we know—and what we still need to learn—about mindfulness at work” (Hyland et al., 2015, p. 578).
Hyland, Lee, and Mills (2015) asserted that the many benefits of mindfulness practices have been underutilized and understudied at work. We agree with the focal article's stance that more research is needed on mindfulness at work. We extend this argument to include a request that future research pays attention to the mechanisms responsible for the effects of mindfulness at work. In this commentary, we (a) briefly discuss the practical importance of understanding the mechanisms by which mindfulness practices lead to positive outcomes, (b) outline the mediating mechanisms proposed by the leading theoretical model of mindfulness effects and how those mediators apply to work, and (c) argue that more rigorous, empirical research is needed to understand the mechanisms through which mindfulness practices lead to positive work outcomes.
The concept of mindfulness has become the topic of heated debates among scholars and practitioners alike. Hyland, Lee, and Mills's (2015) focal article has an ambitious goal: distilling how mindfulness fits into workplace research and practice. This is laudable, and we are pleased that the authors are providing a review of the many ways in which mindfulness may benefit employees and organizations. Unfortunately, the authors fall short of their aspiration to produce a comprehensive overview of the link between workplace mindfulness and performance. We outline three points that we find may have helped the authors achieve their main objective.
A common theme throughout the focal article (Hyland, Lee, & Mills, 2015) suggests that mindfulness is associated with a range of benefits, but the phenomenon itself is not well defined and conceptualized. Throughout the research area, mindfulness has been defined as a trait, as a skill, and, most commonly, as a state. In order to advance a productive research area, conceptual clarity is needed to further distinguish between these aspects of mindfulness. In this commentary, I will (a) provide a distinction between mindfulness as a state and the skill of entering a mindful state, (b) outline the implications of skillful mindfulness for working memory capacity (WMC) and job performance, and (c) discuss the conflicting hypotheses of state mindfulness and self-regulation.