Skip to main content

When more is too much

Introduction

Thomas Gilbert, in his behavior engineering model (BEM), considers tools a critical environmental support for performance improvement (the instrumentation category). Is there a point, however, at which digital tools compromise rather than improve worker performance? This research report may provide you with a new way of thinking about the technology-driven environmental support for performance improvement.

Article

Karr-Wisniewski, P,. & Lu, Y. (2010). When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity. Computers in Human Behavior, 26(5), 1061-1072. doi: http://dx.doi.org/10.1016/j.chb.2010.03.008

Background

White-collar workers, also known as knowledge workers, comprise the bulk of the workforce in the United States. Much of their work is conducted with the help of computerized devices, such as desktops, laptops, tablets, and mobile devices.  Information technology (IT) departments systematically upgrade hardware and software with the intent of further enhancing the capabilities and productivity of the workforce. Research literature suggests that the additional technology improves productivity only up to a point, after which productivity gains level off and actually decline!

The bulk of existing research deals with the linear relationship between IT investments and productivity gains among knowledge workers. The researchers in this study examined the extent to which other, human factors play a role in the relationship between technology use and productivity. They proposed and tested a theoretical model called *technology overload* by linking the effects of cognitive load theory, bounded rationality, and human interruption theory with system feature overload, information overload, and communications overload, respectively. They also considered the role of technology dependence on the proposed model.

Research

The researchers engaged in a series of three studies to validate their model. In the first study, they used previous qualitative research to develop and pre-test a scale measurement for technology overload and its three distinct dimensions:

  1. system feature overload (where the given technology is too complex for the assigned task),
  2. information overload (where an individual is presented with more information than s/he has the time or capability to process), and
  3. communications overload (where a third party interrupts the work of the individual via email, text messaging, etc.).

Items aligned to each of those dimensions were created and subject to established empirical processes (ranking and Q-sort) to establish initial construct validity and reliability. A total of 19 items across all three dimensions were tested and subsequently revised based on results.

In the second study, a web-based survey of 111 knowledge workers from a wide-range of industries was conducted to further validate the 19 items and the model. Of the survey participants, 50% were female, 74% were between the ages of 25 and 50, and 77% had at least a four-year degree. Participants responded to the 19 items with a 9-point Likert scale (1-Strongly Disagree, 9-Strongly Agree).  Survey results were subjected to regression analysis to identify multivariate outliers. A confirmatory factor analysis (CFA) was used to validate the construct of technology overload. An exploratory factor analysis (EFA) was also run to determine interdependencies across the three dimensions.

The third study used the validated model and measurement instrument to explore how technology overload impacts knowledge worker productivity. Four additional items capturing the dependent variable of interest (worker productivity) were added to the set of 19 items aligned to technology overload dimensions; another four items tied to technology dependence were added as well. The same statistical rigor was used to validate the worker productivity and technology dependence constructs as was used to validate the technology overload construct.

Findings and Implications

This discussion focuses on the findings from the third study. A simple bivariate correlation analysis showed that technology overload was significantly negatively correlated with knowledge worker productivity (r = – 0.205, p < .05).  Of the three dimensions assessed, *communication overload* had the most significant negative correlation to knowledge worker productivity; that is to say, as perceived communication overload increased, perceived worker productivity decreased. The negative correlation for the other two dimensions was not as strong. The data also showed, predictably, that knowledge workers who do not rely heavily on technology to fulfill their job responsibilities are less likely to be negatively impacted by technology overload than workers with a high level of technology dependence.

Information technology can be leveraged to confer productivity gains, but can become counterproductive when technology usage surpasses optimal levels. Organizations whose workers rely heavily on IT can use the survey instrument to tailor IT tools for users in order to mitigate potential negative effects of technology overload; direct reports can submit executive summaries to supervisors rather than detailed electronic reports in order to reduce information overload, or Blackberry usage could be limited to prevent communication overload. The objective is to find ways to arrive at the optimal level of IT usage to maximize knowledge worker productivity.

Questions for OPWL-N Members

To what extent does your job rely on information technology? How has that dependence affected your productivity or the productivity of your team or department? Which of the three dimensions of technology overload (system feature overload, information overload, and communication overload) seems to have the greatest impact on your job performance? When analyzing performance problems and causes using Gilbert’s BEM, should you now pay attention to both ends of the spectrum (lacking and overloaded)?

Workplace Oriented Research Central (WORC)
Prepared by OPWL Graduate Assistant, Susan Virgilio
Directed by OPWL Professor, Yonnie Chyung
Posted on February 6, 2013