To date, empirical investigations into whether nepotism, specifically, or social connection preference (SCP), generally, is positive or negative within the realm of organizational scholarship has been limited. Indeed, most of the early discussions in this field on the subject have focused on previous work done outside the organizational area (i.e., Bellow, 2003). A recent Society for Industrial and Organizational Psychology Organizational Frontier Series book on the subject has brought some initial attention to the subject by approaching it from multiple domains within the organizational literature (Jones, 2012). Jones and Stout (2015) have highlighted the possible advantages and disadvantages of SCPs and have taken the stand that SCPs might in fact provide more benefits than drawbacks, particularly given that formalized policies may lead to unfair discrimination.

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.

Landers and Behrend (2015) are the most recent in a long line of researchers who have suggested that online samples generated from sources such as Amazon's Mechanical Turk (MTurk) are as good as or potentially even better than the typical samples found in psychology studies. It is important that the authors caution that researchers and reviewers need to carefully reflect on the goals of research when evaluating the appropriateness of samples. However, although they argue that certain types of samples should not be dismissed out of hand, they note that there is only scant evidence demonstrating that online sources can provide usable data for organizational research and that there is a need for further research evaluating the validity of these new sources of data. Because the target article does not directly address the potential problems with such samples, we will review what is known about collecting online data (with a particular focus on MTurk) and illustrate some potential problems using data derived from such sources.

Belief in generational differences is nothing new. In ancient times, generational differences were seen as a threat to productivity, and efforts were made to change them. Bertman (1976) writes, “as early as the twenty-fifth century BC, the older generation in authoritarian Egypt had to ‘work at it’ to mold the younger generation in its own image.” Yet, Costanza and Finkelstein (2015) point out that “there is minimal empirical evidence actually supporting generationally based differences” and “no sufficient explanation for why such differences should even exist.” One explanation for the prevailing belief in generation-based differences is systematic biases in social perception.

The focal article by Jones and Stout (2015) has revealed just how much there is for industrial and organizational (I-O) psychologists to try to unpack in the implications of nepotism for organizations and employees, particularly in relation to selection and development practices. In this brief commentary, we try to make two contributions to this state of affairs. First, we discuss the importance of disentangling different types of nepotistic and social connection preference (SCP) effects in context because these differences may in turn implicate distinct processes and effects that shape employee outcomes. We do this in part by drawing on findings from some of our own data on nepotistic hiring within a Caribbean coast guard organization (Rajpaul-Baptiste & Calvard, 2012). Second, we argue that for nepotism and SCP to be considered more fully and fruitfully as topics for I-O research and practice, these topics need to be integrated and consolidated more thoroughly along with existing work on diversity management, cross-cultural psychology, organizational discourses, organizational contexts, institutional logics, and social network approaches. We believe this is likely to produce more theoretical, methodological, empirical, and practical coherence across emerging I-O research in this area, without leading researchers and practitioners to reinvent the wheel or misguidedly rely on cultural stereotypes about nepotism and cronyism.

I agree with Costanza and Finkelstein (2015) that it is futile to further invest in the study of generational differences in the work context due to a lack of appropriate theory and methods. The key problem with the generations concept is that splitting continuous variables such as age or time into a few discrete units involves arbitrary cutoffs and atheoretical groupings of individuals (e.g., stating that all people born between the early 1960s and early 1980s belong to Generation X). As noted by methodologists, this procedure leads to a loss of information about individuals and reduced statistical power (MacCallum, Zhang, Preacher, & Rucker, 2002). Due to these conceptual and methodological limitations, I regard it as very difficult if not impossible to develop a “comprehensive theory of generations” (Costanza & Finkelstein, p. 20) and to rigorously examine generational differences at work in empirical studies.

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.

In their focal article, Landers and Behrend (2015) propose to reevaluate the legitimacy of using the so-called convenience samples (e.g., crowdsourcing, online panels, and student samples) as compared with traditional organizational samples in industrial–organizational (I-O) psychology research. They suggest that such sampling strategies should not be judged as inappropriate per se but that decisions to accept or reject such samples must be empirically or theoretically justified. I concur with Landers and Behrend's call for a more nuanced view on convenience samples. More precisely, I suggest that we should not “throw the baby out with the bathwater” but rather carefully and empirically examine the advantages and risks associated with using each sampling strategy before classifying it as suitable or not.

The focal article by Landers and Behrend (2015) makes the case that samples collected on microtask websites like Amazon's Mechanical Turk (MTurk) are inherently no better or worse than traditional samples of convenience from university students or organizations. We wholeheartedly agree. However, having successfully used MTurk and other online sources for data collection, we feel that the focal article was insufficient regarding the caution required in identifying inattentive respondents and the problems that can arise if such individuals are not removed from the dataset. Although we focus on MTurk, similar issues arise for most “low-stakes” assessments, including student samples, which seem to be increasingly collected online.

Jones and Stout (2015) have shed much needed light on an organizational reality that industrial and organizational psychologists have unfortunately not paid much attention to: nepotism and cronyism (or what Jones and Stout have called social connection preference; SCP). Jones and Stout (2015) have made a good case (a) that SCP is pervasive, (b) that there are good reasons to believe that policies (and beliefs) against SCP are frequently counterproductive, (c) that SCP involves compelling moral dilemmas, and (d) that there are workable solutions to dealing with these moral dilemmas. I would like to offer a few observations about some additional issues involved in SCP: bias against SCP; SCP and cooperation; and nepotism, altruism, and personnel decisions criteria.

The topic of generational differences in the workplace has been immensely popular over the past decade, spawning a large number of academic publications and a far greater number of consulting reports, popular press books, magazine articles, media reports, blogs, and infographics. Indeed, a new industry of consultants and public speakers seems to have emerged primarily to capitalize on the popularity of this topic. As Costanza and Finkelstein (2015) note, the research on this “hot topic” has often seemed opportunistic, lacking rigor and depth. The relative ease of cutting existing cross-sectional data by age and calling it a generation study has tempted researchers to hop on the bandwagon, resulting in a large number of empirical studies with nearly identical literature reviews that overrely on popular press and opinion-based literature. There has been a lamentable tendency toward blind empiricism with little or no connection to theory, as has been stated elsewhere (Lyons & Kuron, 2014; Parry & Urwin, 2011).

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.

We agree with and expand on the points made by Costanza and Finkelstein (2015) regarding the definition of “generation” and its measurement, the lack of theory in this area; the harmful effects of propagating generationally based differences to organizations, society, and individuals; and the future directions to make this line of research more applicable to organizations and the workplace. Examining age differences at work through the lens of well-established within-person changes in physical ability (e.g., Maertens, Putter, Chen, Diehl, & Huang, 2012), cognition (e.g., Schaie, 1994), motivation (e.g., Kooij, De Lange, Jansen, Kanfer, & Dikkers, 2011), personality (e.g., Roberts, Walton, & Viechtbauer, 2006), and lifespan development theories (e.g., Baltes & Baltes, 1990; Carstensen, Isaacowitz, & Charles, 1999) would likely be more productive than using loose, relatively atheoretical generational stereotypes to understand age differences at work.

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.

Jones and Stout (2015) have made one claim that I would like to correct: There is substantial quantitative (and observational) research on the workplace and organizational performance effects of nepotism and cronyism. That these authors have missed this research is understandable; the research is not in traditional industrial and organizational (I-O) psychology publications (although some of it does appear in journals from the related field of organizational behavior). Nevertheless, this work is systematic and rigorous, and the work provides strong evidence to support the experience-based perceptions of practitioners that nepotism and cronyism damage employees and their supervisors and produces poorer organizational performance. I welcome the opportunity that Jones and Stout (2015) have provided to briefly introduce my colleagues in I-O psychology to this literature.

Landers and Behrend (2015) question organizational researchers’ stubborn reliance on sample source to infer the validity of research findings, and they challenge the arbitrary distinctions researchers often make between sample sources. Unconditional favoritism toward particular sampling strategies (e.g., organizational samples) can restrict choices in methodology, which in turn may limit opportunities to answer certain research questions. Landers and Behrend (2015) contend that no sampling strategy is inherently superior (or inferior), and therefore, all types of samples warrant careful consideration before any validity-related conclusions can be made. Despite sound arguments, the focal article focuses its consideration on external validity and deemphasizes the potential influence of sample source on internal validity. Agreeing with the position that no samples are the “gold standard” in organizational research and practice, we focus on insufficient effort responding (IER; Huang, Curran, Keeney, Poposki, & DeShon, 2012) as a threat to internal validity across sample sources.

Costanza and Finkelstein (2015) have justly argued that cross-sectional operationalizations of generational groups represent a confound that constrains the ability to unequivocally separate the effects of age and period. A related statistical argument against this practice bears consideration as well. Namely, cross-sectional operationalizations of generations have the potential to unduly inflate type two-error rates when compared with the analysis of simple age effects. This is a problem because true age effects can be erroneously ignored in studies where age is artificially split into assumed generational groups. Indeed, the argument against artificially bifurcating continuous data is not new (e.g., Cohen, 1983), however past attempts to make inferences about generational effects in cross-sectional designs present an opportunity to investigate the particularly insidious nature of this practice and its implications. To demonstrate the problem at hand, let us consider a brief empirical example by virtue of a simulation study.

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.

Landers and Behrend (2015) make a good argument that more consideration should be given to sampling strategies in light of the specific research question prior to data collection and that nonorganizational samples should not be automatically dismissed by journal editors and reviewers. Yet, the authors only briefly mention one particular issue that is also relevant to the validity of our research findings—participant motivation. Researchers should seek to better understand why individuals choose to participate in a study and what may be motivating the levels of effort they put forth in participating. Two critical questions include Are participants who they say they are (e.g., working adults)? And, are participants paying attention to the study instructions and questions and participating with effort? In this response, I expand on issues related to participant motivation and apply them to the sampling strategies discussed by Landers and Behrend (2015). I also provide suggestions for ways researchers may address these issues.

It is often said that the federal government needs to be more like the private sector (Brooks, 2013; Campbell, 2011; Harvey, 2012; Miller, 2013; Neal, 2013; Schnurer, 2013). However, after reading Jones and Stout's (2015) summary of private-sector antinepotism policies, we wonder whether the opposite is true when it comes to nepotism and cronyism: Should the private sector be more like the federal government? According to Jones and Stout, many organizations have antinepotism policies that prohibit the employment of the friends and family of current employees. We believe that these policies might go too far, and we propose that the policies used by the federal government could serve as a model for private-sector organizations. We also think that the federal government can shed some light on why organizations want to prohibit nepotism and cronyism.