Emerging trends in data analytics and knowledge management job market: extending KSA framework. Issue 4 (15th November 2018)

• Record Type: Journal Article
• Authors: Chang, Hsia-Ching; Wang, Chen-Ya; Hawamdeh, Suliman
• Is Part Of: Journal of knowledge management. Volume 23:Issue 4(2019)
• Page Start: 664
• Page End: 686
• Publication Date: 2018-11-15
• Subjects: Big Data -- Analytics -- Competencies -- Curriculum development -- Data science -- Knowledge management; Knowledge management -- Periodicals; 658.4
• DOI: 10.1108/JKM-02-2018-0088
• Languages: English
• ISSNs: 1367-3270

Abstract

Abstract : Purpose: This paper aims to investigate emerging trends in data analytics and knowledge management (KM) job market by using the knowledge, skills and abilities (KSA) framework. The findings from the study provide insights into curriculum development and academic program design. Design/methodology/approach: This study traced and retrieved job ads on LinkedIn to understand how data analytics and KM interplay in terms of job functions, knowledge, skills and abilities required for jobs, as well as career progression. Conducting content analysis using text analytics and multiple correspondence analysis, this paper extends the framework of KSA proposed by Cegielski and Jones‐Farmer to the field of data analytics and KM. Findings: Using content analysis, the study analyzes the requisite KSA that connect analytics to KM from the job demand perspective. While Kruskal–Wallis tests assist in examining the relationships between different types of KSA and company's characteristics, multiple correspondence analysis (MCA) aids in reducing dimensions and representing the KSA data points in two-dimensional space to identify potential associations between levels of categorical variables. The results from the Kruskal–Wallis tests indicate a significant relationship between job experience levels and KSA. The MCA diagrams illustrate key distinctions between hard and soft skills in data across different experience levels. Practical implications: The practical implications of the study are two-fold. First, the extended KSA framework can guide KM professionals with their career planning toward data analytics. Second, the findings can inform academic institutions with regard to broadening and refining their data analytics or KM curricula. Originality/value: This paper is one of the first studies to investigate the connection between data analytics and KM from the job demand perspective. It contributes to the ongoing discussion and provides insights into curriculum development and academic program design.