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Big Data, Little Individual: Considering the Human Side of Big Data

Published online by Cambridge University Press:  17 December 2015

Michael N. Karim,
Jon C. Willford and
Tara S. Behrend
Michael N. Karim*
Affiliation:
Fors Marsh Group, LLC, Arlington, Virginia
Jon C. Willford
Affiliation:
Department of Organizational Sciences and Communication, The George Washington University
Tara S. Behrend
Affiliation:
Department of Organizational Sciences and Communication, The George Washington University
*
Correspondence concerning this article should be addressed to Michael N. Karim, Fors Marsh Group, LLC, 1010 North Glebe Road, Number 510, Arlington, VA 22201. E-mail: mkarim@forsmarshgroup.com
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Extract

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.

Information

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 8 , Issue 4 , December 2015, pp. 527 - 533
Copyright
Copyright © Society for Industrial and Organizational Psychology 2015 

Guzzo, Fink, King, Tonidandel, and Landis (Reference Guzzo, Fink, King, Tonidandel and Landis2015) 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.

Introduction to Electronic Performance Monitoring

Big data includes data that have been obtained via the automatic collection of a variety of worker behaviors. As the New York Times puts it, “every e-mail, instant message, phone call, line of written code and mouse-click leaves a digital signal” (Lohr, Reference Lohr2013) that may be further analyzed to provide organizations with insight into employee productivity.

However, the collection of these data is not unique to initiatives that are assigned the big data label; over the past 30 years our field has explored these issues under the label of EPM. EPM reflects the electronic and automatic collection of performance data across a variety of contexts (Stanton, Reference Stanton2000). For example, EPM has been used in call centers to automatically collect call statistics for performance management (Wells, Moorman, & Werner, Reference Wells, Moorman and Werner2007); in computer-based training to identify profiles of effective and ineffective learning strategies, provide learners with detailed feedback regarding their performance, or provide management with evaluative information on learner performance (Karim & Behrend, Reference Karim, Behrend, Young and Beier2015; Thompson, Sebastianelli, & Murray, Reference Thompson, Sebastianelli and Murray2009; Watson et al., Reference Watson, Thompson, Rudolph, Whelan, Behrend and Gissel2013); and in online testing to log keystrokes, voiceprints, or webcams to identify patterns of cheating (Foster, Reference Foster2009).

As such, EPM is not inherently tied to one purpose, context, or technology, and EPM instead reflects the simple action of collecting mass data about employees. For example, in selection, EPM can take on the role of identifying and detecting cheating (Foster, Reference Foster2009; Karim, Kaminsky, & Behrend, Reference Karim, Kaminsky and Behrend2014); in training, it can take on either a developmental role that provides learners feedback or an evaluative one that provides supervisors with detailed reports on learner behaviors (Karim & Behrend, Reference Karim, Behrend, Young and Beier2015; Watson et al., Reference Watson, Thompson, Rudolph, Whelan, Behrend and Gissel2013); and in performance management, it can take on an evaluative role to identify counterproductive or destructive work behaviors (e.g., Stanton, Reference Stanton2000). The technologies used to collect these data are similarly diverse and can include biometric sensors, keystroke logging, GPS devices, or even audio or visual captures.

Beyond examples from research, EPM continues to be found in different contexts in organizations, in many ways expanding its presence as the ability of technology to capture human behavior expands. Mobile devices and relatively inexpensive monitoring technologies now allow employers to collect employee data far beyond the scope of work computers and on-site video surveillance. Companies in the trucking industry now are able to monitor not just the location of their drivers but also the drivers’ speed and braking patterns and when and for how long the drivers take breaks (Ante & Weber, Reference Ante and Weber2013; Bowman, Reference Bowman2014). In addition, wearable technologies such as the Fitbit, a health monitoring bracelet, are being used to track employee activity and health for the purpose of incentivizing corporate wellness programs (McGregor, Reference McGregor2014). Each of these applications of EPM has implications for employees that need to be considered and falls under the umbrella of the label of big data.

In this sense, EPM is big data—the fundamental principle of mass automatic collection of employee data is the same. The big data analyses discussed by Guzzo et al. inherently rely on the massive amounts of data produced and accumulated by EPM systems. Therefore, it is essential that we consider what we already know about EPM when discussing big data rather than treat it as something new entirely.

Impact of EPM on Individuals

Research on EPM has considered a number of individual-level outcomes, most prominently performance or employee attitudes and reactions. Performance-focused EPM research has suggested a social facilitation effect wherein EPM improves individual task performance on lower-skilled tasks and decreases task performance on more complex tasks (Aiello & Kolb, Reference Aiello and Kolb1995; Aiello & Svec, Reference Aiello and Svec1993). In a longitudinal study of call-center employees, Bhave (Reference Bhave2014) found that frequent use of EPM by supervisors improved task performance appraisal metrics; it was also associated with increased organizational citizenship behaviors and was not related to counterproductive work behaviors. This could be in part because EPM is often associated with increased feedback (Alder & Ambrose, Reference Alder and Ambrose2005) that may be viewed by employees as indicative of areas to focus effort (Stanton & Julian, Reference Stanton and Julian2002). Thus, tracking a particular behavior may signify the importance of this behavior to employees and result in employees redirecting effort to that task.

In terms of attitudes and reactions, the most comprehensive framework of EPM's impact is detailed by Stanton (Reference Stanton2000). Subsequent research further exploring Stanton's framework has indicated that reactions to EPM are closely linked to perceptions of organizational justice, fairness, and not surprisingly, privacy (Alder & Ambrose, Reference Alder and Ambrose2005; McNall & Roch, Reference McNall and Roch2009; Stanton, Reference Stanton2000). Although Guzzo et al. discuss privacy in terms of its structural elements (i.e., how to build it and maintain it), they do not discuss the importance of perceived violation of privacy, one of the more substantial outcomes of EPM. Indeed, EPM is often associated with increased feelings of privacy invasion. In our own work, we found that the use of webcams during cognitive ability testing resulted in testers feeling as if their privacy was invaded and withdrawing from the study (Karim et al., Reference Karim, Kaminsky and Behrend2014). Others’ research supports this finding and links EPM to other negative attitudinal outcomes, such as decreased satisfaction and personal control (McNall & Roch, Reference McNall and Roch2007; Stanton & Barnes-Farrell, Reference Stanton and Barnes-Farrell1996). As such, it is myopic to consider privacy only in terms of data security. Instead, privacy concern should be viewed as an individual-level perception that is in part driven by individual and situational characteristics (Stone, Gueutal, Gardner, & McClure, Reference Stone, Gueutal, Gardner and McClure1983) and that may ultimately function independently of actual structural privacy.

EPM research has indicated, however, that the negative impact of EPM on attitudes can be mitigated with proper use and implementation. For example, EPM used for developmental (i.e., nonevaluative) purposes is generally viewed more favorably than EPM used for evaluation (Wells et al., Reference Wells, Moorman and Werner2007). Further, privacy concerns can be reduced by focusing monitoring only on relevant work-related behaviors and by allowing participation from employees in determining how and when these behaviors are monitored (Alder, Reference Alder2001; Alge, Reference Alge2001; McNall & Stanton, Reference McNall and Stanton2011; Stanton & Barnes-Farrell, Reference Stanton and Barnes-Farrell1996). In fact, explicit knowledge of monitoring may itself be an important factor in mitigating concerns over EPM as uncertainty over what is being monitored, when these behaviors are being tracked, and who is viewing the collected information can result in increased anxiety (Stanton, Reference Stanton2000). Our research has found that those who are uncertain about whether they are being monitored have the strongest negative reactions to the use of EPM, even compared with those who are pervasively monitored (Willford, Howard, Cox, Badger, & Behrend, Reference Willford, Howard, Cox, Badger and Behrend2015). As such, designing monitoring systems with employees in mind and being transparent about the use of these systems may alleviate the negative impact of automatic data collection on employee attitudes and behaviors.

What Is Unique About the Big Data Context?

As we hope is clear by now, one of the sources of big data within organizations is the automatic collection of employee data via EPM. Although we can use over 30 years of research on EPM to inform our field's discussion on big data, it is also important to consider the manner in which modern big data analyses may differ from traditional research on EPM. We believe that two factors differentiate existing EPM research and the new context of big data, and therefore these factors can affect the validity of inferences drawn to this new context: the level of decision making and the context in which data are being collected. Each of these is explained further below.

Level

EPM is often used for evaluation or development at the individual level (Wells et al., Reference Wells, Moorman and Werner2007). Modern uses of EPM for big data analyses don't necessarily fit into either of these categories because their outcome may or may not be tied to a particular individual or group of individuals. Instead, the examples discussed by Guzzo et al. seem to serve more of a process improvement perspective (e.g., improving selection procedures). Although EPM has been used to improve processes in the past, literature examining EPM has traditionally focused on its use as a developmental or evaluative system, and this purpose may have played a role in the outcomes discussed above. As such, we believe that further research on the outcome level of mass data collection is a necessary part of an individual-level perspective, and the purpose of data collection may affect the applicability of the conclusions drawn above.

Context

Modern technology organizations have been the driving force behind the human resource (HR) analytics movement (e.g., Google, Facebook); many of these organizations use a similar approach to understand their consumers. As such, employees of these companies may have different expectations regarding data collection, privacy, and the importance of data-driven decisions. Our own and others’ research has shown that individual differences in privacy expectations and values are an important factor in monitoring acceptance. Given the role and importance of individual differences and organizational culture described above, as well as the importance of information privacy values and beliefs in shaping individuals’ satisfaction with technology (Stone et al., Reference Stone, Gueutal, Gardner and McClure1983), we believe that context, which shapes expectations, is a second important difference to consider between existing EPM research and the current discussion on big data.

A Call to Action

Ultimately, industrial–organizational psychologists are well positioned to lead the collection, analysis, interpretation, and implementation of big data for personnel decisions in organizations. The HR analytics movement is a prime example of these successes. However, we are uniquely qualified to go beyond this role and study the neglected individual-level perspective in discussions surrounding big data. For example, even a recent report from the President's Council on Advisors of Science and Technology eschewed the psychological and individual-level impact of privacy concerns stemming from big data collection (Executive Office of the President, President's Council of Advisors on Science and Technology, 2014). Not only are we well positioned to contribute in this manner but it is also our professional obligation (American Psychological Association, 2010).

We must not forget that, much like soylent green, big data is made from people. As psychologists, our obligation is to consider not only organizational- and professional-level issues surrounding big data but also individual-level issues. Although big data may differ in a number of ways from extant research on EPM, some of which we have tried to enumerate above, we need not start from scratch on this issue. We firmly believe that questions about big data can dive deeper than simply asking how to do it, as Guzzo et al. have done, to ask what happens to people if we do it.

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