Maximizing the Potential of Contemporary Workplace Monitoring: Techno‐Cultural Developments, Transactive Memory, and Management Planning

• Author: David E. Cantor
• DOI: http://doi.org/10.1111/jbl.12115
• Published date: 01 March 2016
• Date: 01 March 2016

Maximizing the Potential of Contemporary Workplace Monitoring:

Techno-Cultural Developments, Transactive Memory, and

Management Planning

David E. Cantor

Iowa State University

Nonintrusive data collection and analysis technologies are increasingly being used to monitor worker behavior in the global workplace. This

essay explores the factors that can affect the extent to which management, work teams, and even individuals can benefit from real-time

data monitoring of worker productivity, coordination, and performance. Leveraging organizational information processing and transactive mem-

ory systems theories, I develop a theoretical framework for how access to real-time data can impact team coordination activities and how the

implementation of work monitoring technology and analytics might be best approached. Last, I present a set of future research opportunities that

supply chain scholars should pursue to examine how the real-time monitoring of work affects team performance.

Keywords: real-time supply chain data; worker and team monitoring; worker behavior

INTRODUCTION

How do workplace monitoring technologies affect a worker’s

motivation to improve team-level coordination and performance?

How can supply chain management (SCM) professionals and

academics walk the fine line between collecting and analyzing

real-time employee productivity data and not overly intruding

into an employee’s workplace privacy?

The purpose of this essay, then, is to explore the factors that

can affect the extent to which management, work teams, and

even individuals can benefit from real-time monitoring of worker

productivity, coordination, and performance. Indeed, the nature of

worker discomfort with (and acceptance of) workplace monitor-

ing is not static. It changes in step with the development of the

workplace environment and prevailing social norms. In no small

part due to technological advances, a great deal has changed both

within and outside the operations of firms over the past decade.

Even then, the potential to leverage the power of information

technology (IT) tools to greatly enhance the coordination of

work and overall supply chain productivity was becoming

salient.

Today, organizations are implementing technology solutions

that can be used to collect, monitor, and analyze how employees

coordinate daily supply chain activities. For example, several

companies have implemented warehouse management software

(WMS) to monitor the movement of inventory into and out of

the warehouse to improve the coordination of warehouse opera-

tions. WMS software contains transactional data that can be ana-

lyzed in the future for business process improvement purposes

using business intelligence tools (TecSys 2016). Thus, managers

can leverage these and related technologies to implement man-

ageable changes to the design, structure, and coordination of

work in distribution centers and warehouses. In the supply chain

context, there are many other opportunities to improve how

workers pick items in the warehouse, load/unload an intermodal

trailer, manage inventory, and make improved forecasts and pro-

curement decisions (Autry and Daugherty 2003; Eroglu and Kne-

meyer 2010). Improvements to these and many other supply

chain areas are critically dependent on the availability of worker

productivity data. Worker productivity data are, of course, lar-

gely available in enterprise databases, enterprise email systems,

and telecommunication networks. It is now commonplace for

enterprise systems to maintain individual-attributable data on sev-

eral key worker performance indicators, including worker

throughput time, error rates, employee absenteeism, access times

and durations, etc.

From an analytical perspective, it is important to increase our

understanding on how the massive amount of worker productiv-

ity data can be used to reveal opportunities for improvement in

the supply chain (Waller and Fawcett 2013). From a policy per-

spective, the greatest strength in leveraging data about an

employee’s behavior is not to focus on identifying “social loa-

fers”but, rather, to identify the conditions under which employ-

ees are more likely to work more efficiently and effectively (e.g.,

extent of collaboration among team members, ability to complete

organizational tasks efficiently, performance of tasks at optimal

times of the employee’s circadian rhythms, etc.). Organizations

can leverage and analyze performance data to design and execute

workplace and supply chain productivity improvements.

Fortunately, the advancement of nonintrusive data collection

and analysis technologies has reduced a traditional concern

regarding work monitoring (Rabinovich and Cheon 2011). Prior

concerns that workers will react negatively to management’s

desire to collect data on individual activity have diminished in

recent years. Worker privacy concerns have become less of an

issue because of changes in societal norms in which individuals

have become accustomed to monitored activity. Individuals are

more accepting of the possible value of information provided by

online retailers (e.g., Amazon), trip advisors (e.g., Booking.com),

and even biometric health monitoring devices (e.g., FitBit). An

entire market for explicitly and voluntarily tracking one’s own

Corresponding author:

David E. Cantor, Department of Supply Chain and Information

Systems, College of Business, Iowa State University, 2340 Gerdin

Business Building, Ames, IA 50011, USA; E-mail: dcantor@iastate.

edu

Journal of Business Logistics, 2016, 37(1): 18–25 doi: 10.1111/jbl.12115

© Council of Supply Chain Management Professionals