In the span of less than a single generation, knowledge workers have gained unprecedented access to continuous informal learning opportunities through interactive technology. This article summarizes a 2011 research study that I conducted, which examined the phenomena of ubiquitous technology resources and the systemic mediating effect on learning and performance in a social setting. Another way of looking at the problem is that we are more connected than at any point in the history of civilization. Yet there is vast inconsistency in the way technology is perceived and used by knowledge workers in the same cultural setting, tasked with the same activities and performance goals.

For purposes of this discussion, technology refers to Web 2.0 services used for informal (self-initiated) learning and performance support. These services are increasingly being deployed at the time and place of need through the steady proliferation of smartphones and tablets. In fact, more than 50 percent of web content is now being accessed via mobile devices.

Study background

The research study followed a mixed-methods approach, with the majority of the data obtained through one-on-one interviews and the remainder via survey. The participants in the study were knowledge workers in a multinational company. Twenty-five participants took part in the study and were all part of U.S.-based operations in two locations. The distribution was 45 percent female and 55 percent male, and the majority of the participants were between 36 and 45 years old.

The study design was based on the activity theory, which basically says that all activity is directed toward an object. The object embodies the meaning, motives, and purpose of a collective activity system. An activity theory model was constructed to provide a view of the mediating effect of technology on collaborative learning and performance in a bounded system, by examining a set of mediators for technology-enabled activity that coexist within the system.

So, for instance, the relationship between a worker and an activity (for example, the way someone completes a task) is mediated by the tools at hand, in addition to the workers ability and inclination to make use of them. The relationship between the worker and the community or group that she is a part of is mediated by the rules that are imposed by the group setting (for example, contracts, standards, regulations, policies, and the like). A third example in this type of system is the relationship between the community and the activity or object, which is mediated by the division of labor.

In total, there were five separate mediators of activity identified in the study that are interlinked in a system that determines the level of collaborative learning and performance (in other words, measurable results) that can occur within a defined group of knowledge workers. The mediators of activity identified in this theoretical model used for the study are tools, rules, division of labor, cultural and social setting, and self-perception of role.

A second dimension of the study was differences in behavioral intention of knowledge workers toward technology. Specifically, it was shown that two or more knowledge workers in the same job, with the same tools, and the same performance objective, tend to exhibit different behaviors affecting activity. Moreover, the same individuals described different behavioral intention related to technology usage affecting informal learning and performance, based on the social setting. As an example of this, several participants interviewed perceived a need to maintain separate social learning networks for use at work (i.e., LinkedIn) versus outside of work (i.e., Facebook). Yet this was not reflected in the behavioral patterns that were reported. To account for this variability, a set of categorical themes affecting behavioral intention were tested, statistically validated, and subsequently referenced against the set of mediators provided in the activity theory model described above. The categorical themes shown to affect behavioral intention, listed in order of strength of effect, highest to lowest, are:

  • Performance expectancy. What will this interactive technology do for me?
  • Implicit social influence. How are my peers using this technology?
  • Explicit social influence. Do those in positions of authority expect me to use this technology?
  • Effort expectancy. Does the benefit of this technology exceed the effort required to learn how to use it?
  • Facilitating conditions. Is there infrastructure support for this technology in the environment in which I’d be using it?

The empirical data was then organized to show which factors affecting behavioral intention are present within each of the mediators of activity,and the research findings for each of the mediators of activity were summarized as follows.

Tools. Interactive technology tools to enable social learning and collaboration are being adopted by knowledge workers on their own initiative, rather than employees waiting for them to become available through the company.


Rules. There are few formal rules for mediating the relationship between knowledge workers and communities, yet informal rules exist within communities.

Division of labor. The division of labor was observed to have a negative effect on collaboration and knowledge sharing between functional groups.

Cultural and social setting. Cultural and social setting have a mediating effect on the relationship of rules to performance activity.

Role perception. Personal perception of role has a mediating effect on a knowledge worker’s motivation to use interactive technology tools for self-directed informal learning activities to achieve a performance outcome.

The conclusions reached in this study are consistent with the social learning initiatives recommended by Tony Bingham and Marcia Conner in their 2010 book, The New Social Learning: A Guide to Transforming Organizations Through Social Media. Several of their recommendations for building personal learning networks were advocated as policy recommendations in this study.

Further research is needed to understand and document the mediating effect of Web 2.0 interactive technologies and emerging technologies, such as Semantic Web 3.0, on informal learning in other organizations and learning institutions, providing broader insight to practice and policy recommendations.

© 2012 ASTD, Alexandria, VA. All rights reserved.