Incremental Experts: How Much Knowledge Does a Team Need?

Incremental Experts: How Much Knowledge Does a Team Need?

Ronald D. Freeze (Emporia State University, USA), Sharath Sasidharan (Emporia State University, USA) and Peggy L. Lane (Emporia State University, USA)
Copyright: © 2012 |Pages: 21
DOI: 10.4018/jkm.2012070104
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Experts are often viewed as individuals with a vast storehouse of knowledge beyond the normal participants in a domain. In reality, the expert may have just enough additional knowledge beyond those they interface with to propel their team to success. This research explores the interplay between the accumulation of knowledge as facilitated by individual and team network structures and prior skill sets necessary to successfully participate in a business simulation emulating the cash-to-cash cycle of a manufacturing company. Students participate in simulated organizations that compete against one another in an introductory and extended setting, the latter being the more complex market environment. Comparisons within and between teams across simulations indicate that minor background differences in specific participant ability and associated network structures can make significant differences in simulation standings.
Article Preview

Introduction

Corporate success, as measured by market standing and net profits, is an organizational goal. That success is dependent on employees’ abilities to adapt and integrate an increasingly complex dissemination of information flowing from systems in which they are not fully conversant. With the rapid change in information systems and technologies, employees must constantly adapt to new applications, functionalities, and workflows (Ragu-Nathan et al., 2008). Given the continuous adoption of new information technology (IT), the ever changing IT workforce, and the knowledge workers IT supports, an organization is always on a learning curve (Sethi et al., 1999). To cope with the increased array of system features and information sources, individual workers often rely on co-workers to provide snippets of knowledge that allow a more rapid utilization of complex systems. Especially in ERP system implementation, an individual’s coworkers are important sources of help in overcoming knowledge barriers constraining use (Sykes et al., 2009). While these co-workers may not be considered system “experts,” the incremental system knowledge they have may be “just enough” to propel their organization to success. In such a dynamic, information rich, knowledge intensive environment, identifying the expert just ahead can provide a significant advantage in the pursuit of corporate success.

Organizations are constantly monitoring the knowledge needs of their employees and providing opportunities to improve the skills of their workers. However, in many cases, learning to use a new system entails a knowledge transfer process across users with different levels of skills (Sykes et al., 2009). Organizations cannot successfully provide training opportunities that fully meet the needs of all knowledge workers. Users thus face knowledge barriers to system use even after a system’s formal organizational adoption (Fichman & Kemerer, 1999). To alleviate this on-the-job training scenario, large organizations have made use of current system users with significant prior skills in both organizational processes and information system use. These “power users” are often identified and trained by the organization in order to facilitate the success of new system implementation (Jones & Price, 2004). This research presents an initial step of studying the interactions within organizationally oriented teams. We take two perspectives when assessing the organizational team: an organizational learning perspective and a social network perspective. From an organizational learning perspective, individual team members each own a skill set that potentially contributes differently to their organization’s success. During the course of the study, individuals learn by participating in the cognitive, experiential, and scanning processes. The study settings simulate two different organizationally complex environments and teams are considered to be in an early developmental stage of their organization. The teams’ net profit measures their organizational effectiveness. The social network perspective provides a view of the knowledge exchanges that occur within the teams during the course of the study. Individual team members have a centrality score developed along with a team centralization score. These scores reflect the efficiency of knowledge exchanges between individual team members and within the team as a whole. More efficient knowledge exchanges can lead to greater operational efficiency within the team, resulting in organizational success.

The following outlines the remainder of this study. We first review the organizational learning (OL) and social network (SN) perspectives related to the current study. The organizational simulation setup follows along with how aspects of the simulation relate to the OL and SN perspectives. Simulation results, individual and team knowledge scores, and team SN scores are reported along with the outcome of the study. A discussion and conclusions based on the outcome of the study complete the article layout.

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 13: 4 Issues (2017): 3 Released, 1 Forthcoming
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing