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Managing Knowledge Management

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Knowledge Management Systems (KMSs) have an important role to play in organizations, yet many of these systems fail to achieve their intended outcomes in terms of job performance and job satisfaction. It may be that employees do not know how to use the systems properly: KMSs are usually complex and incorporate a variety of technologies and features, making them challenging to use; it is likely that staff get the majority of their work done with a small number of the system’s features.

Research in the field is limited, and against this backdrop, Xiaojun Zhang and Viswanath Venkatesh have sought to identify the key KMSs features, using a combination of literature review and qualitative study. They compared the effects of the magnitude of the use of these key features on job performance and job satisfaction.

The KMSs in question is a commercial product mainly used to facilitate organizational learning by capturing and disseminating knowledge. It incorporates many features, and employees mostly use it for knowledge sharing; for example, using group support systems and intranets with blogs and wiki to share knowledge. The research team conducted interviews with 35 employees from seven business units of a large organization in the finance industry. The interviewees were first asked about the KMSs features, and later filled out a survey rating the extent to which they agreed that each feature helped them fulfill tasks.

“Post”, “rate”, “comment” and “search” came out as the key features. Among the pertinent comments made by interviewees on their reasons for selecting the most useful features were:
“Some people mentioned using one of the methods from the discussion [on import data from a spreadsheet] greatly reduced the time it took them to complete key tasks.”
“If people know others are going to rate their postings, they are more likely to put in things of better quality.”
“I like to make comments… such as using affirmative or positive comments to encourage colleagues to share knowledge.”
“I enjoy searching for information on the system. Some is truly useful in resolving problems.”

Using a social network perspective, Zhang and Venkatesh identified help-seeking and help-providing ties as important drivers of the use of the key KMSs features and job outcomes.
They built a nomological network around the four key features and theorized how the use of these features affected job performance and job satisfaction. They discovered that while the ties were positively related to the main features, post and search were not significantly related to job satisfaction, whereas comment and rate were not significantly related to job performance.

Post and search features facilitate knowledge exchange among employees, and contribute positively to job performance. Comment and rate fulfil employees’ needs for defining and representing themselves in a social context. These features allow employees to express themselves to feel better about themselves. When staff have opportunities to express themselves and get acknowledged by coworkers for what they have contributed to others’ work, they are likely to feel content with their jobs.

Although they argued that employees were likely to be satisfied with their jobs when they used the post feature to express ideas and thoughts, making postings or contributing knowledge can take a lot of time, and employees may only want to do it to facilitate the completion of tasks but may not actually enjoy doing it. The study also argued that employees were likely to be satisfied when they used the search feature to find what they need, but using this feature effectively may require additional learning and effort they may not be motivated to undertake.

Organizations need to think about how to foster an environment or culture that makes employees feel more comfortable in using these features. The success of large-scale collaborative systems such as KMSs is not simply dependent on technology factors; social factors, such as peer support, also play a critical role. Overall, the study’s nomological network around the use of key KMSs features can be leveraged for future work on KMSs implementations, collaborative systems and job outcomes.

Xiaojun Zhang

Xiaojun Zhang is an assistant professor of Hong Kong University of Science and Technology and was also an assistant professor of HEC Montreal.

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