Segmenting knowledge management (KM) practitioners and its relationship to performance variation – some empirical evidence

Purpose – This paper aims at identifying segments among knowledge management (KM) practitioners and analyzes whether performance varies across these segments. The field of KM aims at enhancing performance through knowledge practitioners. However, not all KM implementations have similar outcomes. Design/methodology/approach – A literature review is conducted through secondary sources. Based on the themes identified for research, qualitative research through a focus group discussion (FGD) and personal interviews is used to explore the themes. This is used to develop a conceptual KM framework. An instrument is developed which is tested for validity and reliability. The instrument is administered to respondents and 313 responses are obtained. Convenience sampling is used to select the respondents. Further, k-means cluster is used to identify segments among KM practitioners. A one-way analysis of variance (ANOVA) test is conducted to determine if the average scores of KM constructs varied between the three clusters. Further, ANOVA test is also used to analyze whether organizational and financial performance scores vary between the three clusters. Post hoc test is used to determine the extent of variation between cluster pairs. Findings – The results show that the sample comprises three segments which were subsequently labeled as active, partly and passive KM practitioners. It was found that active KM practitioners scored highest on various KM constructs, passive KM practitioners scored the least and partly KM practitioners had scores in between the two. One-way ANOVA results showed that the average scores of KM constructs varied significantly between the three clusters. The results show that a significant difference is found in organizational as well as financial performance between any two cluster pairs. Research limitations/implications – The sample comprises 313 respondents, of which around 65 per cent are from services industry and 67 per cent from private sector. A higher representation from public sector and manufacturing industry would have made the comparison more meaningful. The findings are based on data collected from India, and therefore, the results may not be generalizable to all economies. Practical implications – The three clusters identified from the sample data may help organizations who have initiated the KM process to benchmark themselves with the obtained clusters and identify the trust areas important to their KM initiative. Originality/value – The study builds upon both qualitative methodology through FGD and personal interviews and quantitative methodology through questionnaire and surveys. This comprehensive coverage of KM constructs and identification of respondent clusters is insightful. It also provides researchers useful means to enhance performance through KM within clusters.