Week 5 Reading Summary (HK)

Kang, K., Hahn, J., & De, P. (2017). Learning Effects of Domain, Technology, and Customer Knowledge in Information Systems Development: An Empirical Study. Information Systems Research, 28(4), 797-811.

Though there have been a myriad of developments and improvements to programming languages, development methods and tools, and formal training in information systems and technologies, information systems developments (ISDs) are still plagued by performance concerns including failing to adhere to schedules and timetables as well as budgets. Recent research has proposed and explored how capitalizing on learning (or experience) curves could improve ISDs through the theory of transfer of learning. Effectively, ISD projects are difficult due to their disparate tasks, teams, and complexity that vary across projects. To the extent that these projects capitalize on existing knowledge or experience amongst team members, they will be better able to meet performance deadlines and expectations. Using archival data outlining 497 ISD projects that included 2,393 unique employees at a prominent global IT services company from 2005 to 2007, Kang, Hahn, and De (2017) were able to explore how ISD project teams are able to learn and transfer knowledge from prior to new projects and under which conditions learning effects are stronger or weaker.

The theory behind Kang et al.’s (2017) study highlights how employees are able to accrue learning effects through repeated experience of identical elements across tasks (i.e., the environmental perspective). Results indicated that ISD project teams’ experience from prior projects benefits performance (operationalized as development effort and scheduled delay) on subsequent projects when there is an overlap in knowledge or experience. Moreover, ISD project teams can have domain (worked in the domain before), technology (worked with same technology), and customer experience (having dealt with the customers before). These three experience areas are substitutive and become either stronger or weaker depending on the projects’ team and task complexities. For example, the technology experiences a project team may have are less beneficial (i.e., becomes weaker) when the project has high technological complexity.