Abstract
Purpose - The field of KM aims at enhancing performance through knowledge practitioners. However, not all KM implementations have similar outcomes. This paper looks at identifying segments among KM practitioners and analyze whether performance varies across these segments.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 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 of three segments which was 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 while Partly KM practitioners had scores in between the two. One-way ANOVA results showed that the average scores of KM constructs varied significantly between three clusters. The results show that a significant difference is found on Organizational as well as Financial Performance between any two cluster pairs.Research limitations/implications - The sample comprises of 313 respondents, out of which around 65 percent are from services industry and 67 percent 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 thrust areas important to their KM initiative.Originality/value - The study builds upon both qualitative methodology through FGD and personal interviews and quantitative methodology though 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.
Purpose - The field of KM aims at enhancing performance through knowledge practitioners. However, not all KM implementations have similar outcomes. This paper looks at identifying segments among KM practitioners and analyze whether performance varies across these segments.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 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 of three segments which was 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 while Partly KM practitioners had scores in between the two. One-way ANOVA results showed that the average scores of KM constructs varied significantly between three clusters. The results show that a significant difference is found on Organizational as well as Financial Performance between any two cluster pairs.Research limitations/implications - The sample comprises of 313 respondents, out of which around 65 percent are from services industry and 67 percent 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 thrust areas important to their KM initiative.Originality/value - The study builds upon both qualitative methodology through FGD and personal interviews and quantitative methodology though 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.