From smart phones to smart watches, GPS tracking, autonomous driving vehicles, Bluetooth headsets, and online teleconferencing, technology is becoming increasingly integrated into daily life and basic human functioning. With the amount of interfacing we do with technology growing rapidly and exponentially, it has become easy for companies to track and collect data related to our behavior. Never before have we had the ability to collect and analyze such quantities of complex data about what people are doing and when they are doing it.
The recent leak that exposed the U.S. National Security Agency (NSA) for their global surveillance practices (i.e., collecting and storing phone and email records en masse) sparked quite a controversy regarding the invasion of privacy. It became quickly apparent that people were uncomfortable with the idea that their government could and was watching them so closely. This demonstrated that so-called “Big Data” and “data mining” practices do pose certain questions, not only for governments, but also for organizations. Namely, what effect does electronic monitoring have, if any, on those from whom the data are collected, specifically employees? Do they care? Under what circumstances might employees care? If they care, does it actually have an organizational impact?
Industrial-organizational psychology research can and, actually, has answered some of these questions. This research has uncovered some intriguing patterns about employee attitudes and behavior regarding organizational data mining practices, and the subsequent organizational impact of these behaviors and attitudes. Researchers have looked at the issue within the context of five questions.
- Does giving employees a sense of control over the quantity and kinds of data being collected have an impact.
- Does the job relevance of data collected matter?
- Does the intensity (quantity and persistence) of the data mining effort impact employees or organizations?
- Does the source and form of feedback generated from the data being gathered matter?
- Are there individual differences that predict who might care, and who might not?
SENSE OF CONTROL: This focuses on people’s ability to control when data is being collected about them and how it is used. One study showed that allowing employees to delay or prevent data collection led to higher task performance. Another study showed that when employees perceived they had control over the information gathering and handling, they felt more motivated to support the organization through voluntary behaviors like going out of the way to help a team member or putting in extra effort to achieve organizational goals. Employees who could control how they were measured also had higher performance for tasks requiring creativity. A third study demonstrated that when employees felt less control over the data collection, they had lower job satisfaction, and lower perceptions of organizational fairness. Further, when electronic data gathering was present, they had lower task performance, especially for complex tasks, unless they were given control over when and how much data was collected.
JOB RELEVANCE: This focuses on whether the data being collected is directly related to the person’s job. Research has shown that employees care strongly about how relevant the information being collected is to the job. One study showed that if the monitoring was related to job activities, and they were allowed input into the process, they had reduced feelings of invasion of privacy, which led to higher beliefs about fairness with regard to organizational policies and procedures.
INTENSITY: This focuses on how much, and how often data is being collected. Two studies showed that employee well-being decreased as their perception of data mining intensity increased. This relationship was even stronger if job autonomy and supervisor support was low., Perhaps not surprisingly, people don’t like to be measured, particularly when they don’t feel supported by their manager. One of these studies showed that perceived intensity was related to emotional exhaustion, while less job control and supervisory support were associated with depression and reduced job satisfaction.5
SOURCE AND FORM OF FEEDBACK: This focuses on whether data collected on employees is shared back with the employees themselves. Ideally, data collected on employees will be used to provide constructive feedback to employees so that they can improve their performance. This feedback can be generated and delivered automatically by computer software, or by a person’s supervisor. Research has shown that employees prefer the latter. When the feedback comes from a supervisor, employees judge the electronic monitoring as being fairer. The form of the feedback has been shown to matter as well. Studies show that if employees perceive the purpose of monitoring is developmental, rather than punitive, they have higher well-being,5 an increased sense of fairness, job satisfaction, and organizational commitment (i.e., less likely to leave). Another study demonstrated that when the feedback generated from electronic monitoring is constructive, employees had a higher sense of fairness, which in turn led to increased performance and job satisfaction.6
INDIVIDUAL DIFFERENCES: This focuses on whether some people react differently to electronic data monitoring than others. Not a lot of research exists to answer questions about how people react differently to electronic monitoring and data mining, although one study showed that employees with a high orientation toward ethical values were much more apprehensive about electronic monitoring practices.
So what does all this mean? Research shows that employees don’t like large scale, intense data mining programs. Such programs may lead to decreased job satisfaction, well-being, organizational commitment, attitudes about fairness, and performance by providing a sense of invasion of privacy. However, there are certain conditions where electronic monitoring and data mining can actually increase motivation, job satisfaction, commitment, and performance. Based on these findings, the following five recommendations can be offered for successful employee data mining:
- Make a plan: Know why you’re collecting the data. Have a plan and make sure it is relevant to individual, team, and organizational success. In other words, don’t collect data for data’s sake.
- Be transparent: Show all organizational members exactly what kinds of data are being collected, during which times, what you plan to do with the information, and how it impacts them.
- Offer control: Give employees a sense of control by allowing them some flexibility with regard to the kinds of information you gather, when you gather it, and what you do with it.
- Give positive feedback: Be constructive with feedback generated from electronic monitoring. Focus on employee development, not punitive action, and let the feedback come from a supervisor, not a machine.
- Consider individual differences: Understand that electronic monitoring practices may affect some employees more negatively than others (e.g., older vs. younger workers, personality, ethnicity, gender, organizational tenure, etc.). Be prepared to make appropriate accommodations if you find that it is negatively affecting some employees.
 Stanton, J. M., & Barnes-Farrell, J. L. (1996). Effects of electronic performance monitoring on personal control, task satisfaction, and task performance. Journal of Applied Psychology, 81(6), 738.
 Alge, B. J., Ballinger, G. A., Tangirala, S., & Oakley, J. L. (2006). Information privacy in organizations: empowering creative and extrarole performance. Journal of Applied Psychology, 91(1), 221.
 Douthitt, E. A., & Aiello, J. R. (2001). The role of participation and control in the effects of computer monitoring on fairness perceptions, task satisfaction, and performance. Journal of Applied Psychology,86(5), 867.
 Alge, B. J. (2001). Effects of computer surveillance on perceptions of privacy and procedural justice.Journal of Applied Psychology, 86(4), 797.
 Holman, D., Chissick, C., & Totterdell, P. (2002). The effects of performance monitoring on emotional labor and well-being in call centers. Motivation and Emotion, 26(1), 57-81.
 Alder, G. S., & Ambrose, M. L. (2005). An examination of the effect of computerized performance monitoring feedback on monitoring fairness, performance, and satisfaction. Organizational Behavior and Human Decision Processes, 97(2), 161-177.
 Wells, D. L., Moorman, R. H., & Werner, J. M. (2007). The impact of the perceived purpose of electronic performance monitoring on an array of attitudinal variables. Human Resource Development Quarterly, 18(1), 121.
 Alder, G. S., Schminke, M., Noel, T. W., & Kuenzi, M. (2008). Employee reactions to internet monitoring: The moderating role of ethical orientation. Journal of Business Ethics, 80(3), 481-498.
If a job paid contractors and full-time employees the exact same annual wage it would fall precisely on the diagonal line in Figure 1. Jobs below the diagonal line pay contractors less than full-time employees. Jobs above the line pay contractors more. The jobs in Figure 1 largely fall into two general clusters. On the lower left are what might be called “general skill” jobs, with a broad talent pool available. These positions typically do not require specialized education or training, and are often open to workers with little prior job experience. Thirty percent of the jobs in this sample fell into this category. Forty two percent of jobs fell in the upper right cluster and could be called “highly specialized” assignments. These often require an undergraduate degree or beyond, advanced certification and/or extensive prior job experience. There is typically a limited talent pool for these in-demand roles, and contracted rates were higher on average than that of full-time employees.
The data in Figure 1 provides an interesting answer to the question “is the gig economy good for employees?” At least in terms of compensation, it depends on employee job qualifications and the kind of contract work they are pursuing. The gig economy appears to be financially beneficial for employees in many high skilled jobs. Given the data in Figure 1, it is not surprising that the percentage of the workforce made up of external talent is expected to grow, and that highly specialized work of the future will increasingly be done by experts who are increasingly opting-in to flexible work.
For employers, the question to ask is not whether the gig economy is good for workers – that is a decision employees must make for themselves. The question for organizations is how can they prepare for a future workforce that encompasses both full-time and external workers. The key to success in this new world of work will be having tools and methods that allow companies to identifying opportunities to gain competitive advantage by targeting the right combination of resources, in the right location, at the right time.
I would like to thank my colleague Geoff Jordan for providing the data analysis and several of the citations reported in this post