The Learning Curve of IT Knowledge Workers in a Computing Call Center.

Author List: Kim, Youngsoo; Krishnan, Ramayya; Argote, Linda;
Information Systems Research, 2012, Volume 23, Issue 3, Page 887-902.
We analyze learning and knowledge transfer in a computing call center. The information technology (IT) technical services provided by call centers are characterized by constant changes in relevant knowledge and a wide variety of support requests. Under this IT problem-solving context, we analyze the learning curve relationship between problem-solving experience and performance enhancement. Based on data collected from a university computing call center consisting of different types of consultants, our empirical findings indicate that (a) the learning effect-as measured by the reduction of average resolution time-occurs with experience, (b) knowledge transfer within a group occurs among lower-level consultants utilizing application-level knowledge (as opposed to technical-level knowledge), and (c) knowledge transfers across IT problem types. These estimates of learning and knowledge transfer contribute to the development of an empirically grounded understanding of IT knowledge workers' learning behavior. The results also have implications for operational decisions about the staffing and problem-solving strategy of call centers.
Keywords: computing call center; IT problem type; knowledge classification; knowledge transfer; learning curves
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#144 0.243 knowledge transfer management technology creation organizational process tacit research study organization processes work organizations implications practice explicit models consultants transfers
#248 0.116 computing end-user center support euc centers management provided users user services organizations end satisfaction applications article ibm step field policies
#182 0.114 percent sales average economic growth increasing total using number million percentage evidence analyze approximately does business flow annual book daily
#31 0.101 problem problems solution solving problem-solving solutions reasoning heuristic theorizing rules solve general generating complex example formulation heuristics effective given finding
#93 0.086 performance results study impact research influence effects data higher efficiency effect significantly findings impacts empirical significant suggest outcomes better positive
#187 0.085 learning model optimal rate hand domain effort increasing curve result experts explicit strategies estimate acquire learn referral observational skills activities
#0 0.073 information types different type sources analysis develop used behavior specific conditions consider improve using alternative understanding data available main target