Big Data at Work

The Data Science Revolution and Organizational Psychology

Scott Tonidandel, Eden B. King, Jose M. Cortina

• Publisher: Routledge
• Year: 2015
• Print ISBN: 9781848725812
• eBook ISBN: 9781317702696
• Topic: Psychology
• Subtopic: Applied Psychology
• Index: Psychology

The amount of data in our world has been exploding, and analyzing large data sets—so called big data—will become a key basis of competition in business. Statisticians and researchers will be updating their analytic approaches, methods and research to meet the demands created by the availability of big data. The goal of this book is to show how advances in data science have the ability to fundamentally influence and improve organizational science and practice. This book is primarily designed for researchers and advanced undergraduate and graduate students in psychology, management and statistics.

1

Building Understanding of the Data Science Revolution and I-O Psychology

Eden B. King, Scott Tonidandel, Jose M. Cortina, and Alexis A. Fink

“The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus …”

—James Manyika et al., McKinsey Global Institute (2011)

“Work-force science, in short, is what happens when big data meets H.R… . In the past, studies of worker behavior were typically based on observing a few hundred people at most. Today, studies can include thousands or hundreds of thousands of workers, an exponential leap ahead.”

—Steve Lohr, New York Times (2013)

“We’re seeing a revolution in measurement, and it will revolutionize organizational economics and personnel economics.”

—Erik Brynjolfsson, MIT (2013)

These quotes exemplify the profuse conversations about the intersection of big data and business in newspapers, blogs, and popular books. Markedly absent from this discourse is the voice of industrial-organizational (I-O) psychologists. This book, purposefully and permanently, brings I-O psychologists into this important discussion.

It is our view that I-O psychologists are poised at an opportune moment in history to leverage our knowledge of people, work, and quantitative methods to serve as ambassadors, interpreters, and translators between computer scientists and business clients. We are uniquely trained to help decipher and make sense of business-related data patterns from the lens of psychological science. We further argue that organizational psychologists may also be uniquely suited to address questions of privacy, ethics, and “dustbowl empiricism” that emerge in discussions of big data. Thus, this volume strives to accomplish two important goals: (1) to review critical issues in collecting, analyzing, communicating, and theorizing about big data, and (2) to ignite rigorous scholarship on big data in organizations.

To fulfill these objectives, this book is organized into two primary sections. The first section deals with technical and methodological aspects of big data (e.g., collecting, analyzing, warehousing, integrating, and visualizing) and the second addresses topical content areas where big data might be well positioned to contribute to paths of future inquiry (e.g., selection, teamwork, and diversity). Here we set the stage for these ideas by introducing a general definition of big data and generating ideas about opportunities for its integration in I-O psychology. We also describe potential practical and conceptual challenges that are brought about by big data. In the context of these descriptions, we foreshadow the chapters that follow; we briefly summarize the ways in which each chapter responds to the practical, conceptual, and substantive challenges and opportunities of big data. Altogether, these chapters will describe how advances in data science have the ability to fundamentally influence and improve organizational science and practice.

What Are Big Data?

Big data can be understood with regard to three primary characteristics (the “three V’s”; Laney, 2001): (1) volume—large number of data points, (2) velocity—both the throughput of the data (amount being added constantly) and the latency in using this information, and (3) variety—multiple sources of data being integrated. Organizational psychologists may encounter data that fulfill all three of these key factors, but our interpretation is broader—big data in organizational sciences might not necessarily include all three of these characteristics. Moreover, we don’t believe that any particular amount of each V defines big data. Rather, data become big data when the different V’s force you to think about and interact with your data differently. For example, the most central component of big data in most peoples’ minds is volume. But there is no single sample size that qualifies as big data. The volume of data that we might deal with would most likely not reach the level of computer science applications (hundreds of terabytes), but high volume instead might be data sets that overwhelm commonly available computing resources and require nontraditional analytic procedures. Similarly, the actual sample size itself might not be very large, but there is big data volume because data for a large number of variables are being collected for each individual (think moment by moment performance or location data) that can’t be analyzed using traditional data reduction techniques. Or the volume of the data could be manageable, but the high velocity of the data forces us to abandon our theories and methods that were developed for more static studies. In each of these instances, we enter the realm of big data because the situation created by the V’s requires us to reconsider our science, to apply new theories and methods, and to ask new and different questions of the data that were not previously possible. Further, the emergence of these statistical and data management approaches allows us to apply new methods to old problems and potentially gain additional insight. That is, we may find novel and useful insights by applying new techniques to data sets that could be analyzed using standard methods—thus expanding overall the universe of insights we can bring to bear on the world of work.

What Are the Emerging Opportunities for Science and Practice?

What could it mean for the study and practice of organizational psychology if we had access to varied and dynamic data? How can we apply new analytic strategies to understand workplace dynamics in more nuanced ways? What could we learn and how could we enhance organizational effectiveness and employee wellness? This is the world of big data, which represents an opportunity to build our science and expand the impact of our discipline. Here we hope to ignite interest in this topic by brainstorming about the major areas of scholarship and practice in organizational psychology that could be explored, expanded, and impacted though big data. Testing of models in these areas—a small sample of which are listed in Table 1 and discussed in the second section of the book—might be facilitated through new data and techniques.

Emerging Tools and Potential Applications

In this section, we describe several new tools and sources of data that can be leveraged to build big data and organizational science: sociometric sensors, social media data and sentiment analyses, microexpression analyses, and psychophysiological measures. This is not an exhaustive list, but rather a preliminary set of data sources that (especially in combination) might offer new insights into I-O psychology. These and other tools, through complementary inductive and deductive approaches, allow new questions and ideas to be generated.

Table 1.1
Areas of opportunity at the intersection of big data and I-O psychology

Area	Example New Topics
Selection	- Targeted recruitment (targeted marketing efforts) - Limitless biodata to increase validity and inspire new theory
Training	- Genuinely adaptive training environments - Individually tailored training experiences - Objective indicators of transfer of training
Performance Management	- Visualization of individual, team, and organizational performance over time - Real-time, continuous monitoring and feedback
Leadership	- Neural networks of effective leaders - Live assessment of leadership behaviors for succession planning - Modeling of leader-member exchange relationships for leader development - Data-driven decision making
Teams	- Identification of effective communication and coordination patterns across team types - Tracking and visualizing team development over time - Deducing effective physical space designs for teams that vary with regard to type and interdependence
Occupational Health	- Preventative identification of health or safety risk factors or behaviors - Adaptive gamification to motivate employee health behavior
Work-Family	- Genuine time-use studies with intervention designs - Exploration of policies, practices, signals, and traditions that comprise family-friendly cultures
Diversity	- Assessing inclusion through geospatial shapes of communication, coordination, and friendship networks - Identifying barriers through sentiment or microexpression analysis of intergroup communication
Future Horizons	- The influence of global or community factors/events on employees and organizations

Sociometric sensors. Sociometric sensors are wearable technology that can collect a wide range of information automatically from users and individuals around them. These devices exploit the fact that many people are already comfortable with wearable electronics, such as cell phones, digital watches, pedometers, and the emerging device category around personal biometrics such as Fitbit and Google Glass. These devices have a number of benefits over traditional observational data collection methods and can replace costly human observation, which is susceptible to subjective biases and memory errors. A variety of highly accurate data can be available such as nonlinguistic social signals (e.g., interest, excitement, influence) and relative location monitoring. Indeed, such sensors are being used to investigate a host of phenomena in organizational behavior. For example, activity and number of team interactions have been shown to be related to creativity (Tripathi & Burleson, 2012). Similarly, Olguín-Olguín and Pentland (2010) found that activity level and interaction patterns as measured by sociometric sensors predicted success by teams in an entrepreneurship competition. These same sensors have also been used in field studies to measure inter-team collaboration patterns as well as integration processes in multicultural teams (Kim, McFee, Olguín, Waber, & Pentland, 2012). In their chapter on teamwork, Kozlowski Chao, Chang, and Fernandez describe the initial stages of team-based research that leverages this kind of technology to assess team process dynamics.

Social media data, text analysis, and sentiment analyses. An obvious area of focus in the world of big data is social media. Social media include websites and applications that enable users to create and share content or participate in social networking. The content of this electronic communication is a treasure trove of psychologically relevant information about people, their relationships, and their behavior. Analyses might involve simply tracking patterns of viewing or clicking, time spent in different virtual spaces, or social network patterns such as who is interacting with whom. According to IBM (2015), social media analytics are designed to “help organizations understand and act upon the social media impact of their products, services, markets, campaigns, employees and partners.” For employers, of course, social media can take on a different importance. Social media activity can give clues to employee engagement and warn of exit behavior. Recruiters can and do review social media information to find and vet candidates. Reviews on sites like Glassdoor affect employment branding and thereby influence the recruiting process of organizations.

Social media may be particularly informative through the lens of sentiment analyses. Sentiment analyses go beyond simple counts of frequency of clicks to analyze the content of what is spoken or written. For example, sentiment analysis could be used to examine the positive or negative content of tweets or to analyze an email to determine whether its author is happy, frustrated, or sad. More sophisticated forms of sentiment analyses use deep learning models to represent full sentences and capture the contexts around which particular language is used. The potential of big data sentiment analyses on business-relevant constructs is further evidenced by the chapter on Twitter analysis (Hernandez, Newman, & Jeon), which develops and applies a word count dictionary representing job satisfaction to a Twitter feed of over one million tweets per day. This is an exciting area, with emerging firms applying real-time methodologies like natural language processing that can recognize sarcasm and emotional nuances (e.g., Kanjoya) and connecting specific text strings and properties to outcomes (e.g., Textio). The leading players in this space are combining sophisticated algorithms with powerful computing and elegant visualization, and they are driving specific, measurable business outcomes.

Microexpression analyses. Another exciting tool with a range of potential applications involves microexpression analyses. Microexpressions can be understood as representations of brief and unconscious reactions to stimuli that cannot be masked but can be detected through careful observation (Ekman, 2009). The original facial action coding system was first published in 1978 and involved intensive ongoing training procedures and coding schemes. Technology has advanced to the point that these codings can be programmed and used to automatically assess genuine reactions and responses to stimuli (Shreve, Godavarthy, Goldgof, & Sarkar, 2011). This advancement has clear applications for law enforcement (i.e., detection of lies, hostility, and dangerous demeanor), but being able to objectively assess genuine human emotion can also be useful to phenomena relevant to I-O such as selection, decision-making, and leadership (see Barsade, Ramarajan, & Westen, 2009). The ability to link microexpression measurement to specific organizational stimuli may lead to insights into effective manager behavior and change management strategies, or to new approaches to measuring and managing employee engagement.

Neuro/psychophysiological tools. A wide set of tools has been developed to detect subtle changes in physiological reactions to stimuli such as brain activation, heart rate, and hormonal variation. This might include EEGs, blood pressure or heart rate monitors, automatic hormone testers, and FMRI imaging (see Becker, Cropanzano, & Sanfey, 2011). This emerging set of technologies has much to tell us about the inner workings of the brain as well as indicators of health and fitness that have relevance for occupational health psychology. These are not new but are growing in production, shrinking in size and cost, and increasing in their potential applications for understanding workplace dynamics. The rise in personal biometrics systems opens the door to scalable data capture and enables fascinating new analyses. As offerings in this segment evolve to include biometric measurements and connectivity to smartphones and other devices, new opportunities emerge for real-time linkage analyses.

Novel Questions Enabled by Big Data

Perhaps the most highly touted advantage of big data is that it will allow us to answer old questions in more comprehensive ways. We must not forget, however, that big data may also allow (or force) us to reconceptualize phenomena that we have been studying for decades. Consider the findings of Ilies, Scott, and Judge (2006) that citizenship varies substantially within person, and that this variance can be explained in part by job attitudes (again, within person). Regardless of whether one considers the experience sampling approaches used in their study to be big...

Table of contents

• Cover Page
• Halftitle Page
• Series Page
• Series Page
• Series Page
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Foreword by Richard Klimoski
• Chapter 1 Building Understanding of the Data Science Revolution and I-O Psychology
• PART I Big Issues for Big Data Methods
• PART II Big Ideas for Big Data in Organization
• Contributors
• Index