Techniques for the identification of organizational knowledge management requirements

Purpose: This paper aims to explore the requirements of organizational knowledge management initiatives using requirements engineering techniques, identifying the optimal techniques configuration and serving as a management tool for knowledge engineers. Design/methodology/approach: The method is selection attributes. Knowledge management enablers are characterized and mapped with the coverage capabilities of requirements engineering techniques, using the attributes of the elicited object and a box-plot analysis. The information is gathered from 280 references, 32 companies and 16 experts in requirements engineering. Findings: Requirements of organizational knowledge management initiatives are got optimally by combining interviews, use cases, scenarios, laddering and focus group techniques. The requirements of structure and processes are more complex to identify, while culture requirements are the best covered. Research limitations/implications: Knowledge management enablers are analyzed according to the current studies and comprehension of engineering techniques. Practical implications: Knowledge engineers need to consider the coverage capabilities of engineering techniques to design an optimal requirement identification and meet the objectives of organizational knowledge acquisition initiatives. Requirement engineers can improve the requirements identification by a staged selection process. Social implications: The requirements of knowledge management initiatives that impact the community can be identified and traced to ensure the knowledge objectives. Requirements related to culture and people, like shared values, beliefs, and behaviors, are also considered. Originality/value: To the best of the authors’ knowledge, this is the first study about formal requirement identification of knowledge management initiatives in the organizational context, providing the optimal configuration. A novel staged process is proposed for requirements engineering techniques selection, analyzing the enablers at component level and identifying the attributes associated with the elicited object.