The success of Knowledge Management (KM) activities depend not only on people, but also on the organization. The people-related barriers to KM include: culture, time, tacit knowledge and trust, value identification, language and preferential sharing; whereas, the organizational-related barriers include strategy alignment, reward and recognition, allocation of resources, top management support, organizational structure, staff turnover, organizational culture, unidirectional KM, competition and the power of management. According to McDermott and O’Dell (2001), even if systems are equipped with the state-of-the-art technology and are designed to handle vast amounts of knowledge, if the organization is not focused on making a success of KM, it would most probably fail. Rego et al. (2003) showed how researchers from several research centers of a Portuguese university perceive the facilitators and barriers to KM by considering three domains as knowledge gathering, creation, and diffusion. They showed that although technology is an important facilitator, it is people and their interactions that create knowledge and promote knowledge flow. Within people and organization, there have also been certain facilitators to KM. The people related facilitators include culture, dual commitment, and perception changes; and the organization related facilitators include business alignment, structural changes, and organizational culture.

In the paper “Barriers and Facilitators to Knowledge Management: Evidence from Selected Indian Universities”, the authors, Renu Vashisth, Ravinder Kumar, and Abhijeet Chandra, examined the facilitation of knowledge flow and knowledge creation in the Indian university system and attempted to identify the barriers and facilitators to the KM process in the university departments and research centers in India. Three aspects of barriers and facilitators were considered in this study: individual, socio-organizational, and technological. According to the scope of literature reviewed earlier in this paper, the authors hypothesize that the main barriers and facilitators are perceived to be rooted in the individual and socio-organizational processes. It was observed that universities and research centers in India were understudied. The survey findings indicate that any measures adopted for improving the process of KM in university departments and research centers are bound to fail if workforce idiosyncrasies are not considered. In their study, the participating researchers are found not perceiving the technological issues as significant barriers, as such barriers are mentioned by few respondents. In consonance with earlier studies, their findings state that researchers are more concerned about the ‘soft’ aspects of KM than the ‘hard’ ones. The findings are consistent with many of the earlier studies. Based on their observations and findings, they also made several suggestions which have managerial implications.
Some overlap is apparent between KM and Intellectual Capital (IC), but the relationship is far from simple and clearly justifies exploration. Early attempts like Wiig (1997) perceived KM as the implementation, as IC promotes values, i.e., acknowledgment, reporting assets, etc. According to Sullivan (2000), KM can be treated as value creation in all its aspects, whereas IC or ICM as value extraction (measurement, accountability, explicability, etc.). According to Gil Ariely (2003), it is possible to consider IC as the ‘knowledge’ phase in accounting.

V Kavida and Sivakoumar N in their paper, “The Relevance of Intellectual Capital in the Indian Information Technology Industry”, attempted to decipher IC related information from the publicly available data, i.e., stock market related data bases. They found that among the variables, those which significantly influence the market value are the R&D expenses when treated as investments. They also found that the influence of profit was insignificant, even though it was generally viewed that profits influence the market value. The relevance of IC in the Indian IT industry was also explored.

In the paper, “Intellectual Capital Disclosure Quality: Lessons from Selected Scandinavian Countries”, the authors, Norman Mohd Saleh, Mohamat Sabri Hassan, Romlah Jaffar and Zaleha Abdul Shukor, explored the implementation issues related to IC disclosure to be used as inputs for policy makers in order to improve transparent and accountable reporting with respect to IC. Their study analyzed and summarized the experience of companies and people involved in IC disclosure project, particularly in the Scandinavian countries. According to them, with the increase in shareholders activism, capital market players and other users of annual reports are no longer satisfied with the disclosed information in the annual report, and the trend also shows that the management of companies tends to disclose more information through alternative communication channels other than the annual report to users. They concluded that it is imperative for companies as well as the respective regulatory authorities in Malaysia and other countries to realize the importance of IC reporting.

The House of Quality (HOQ) is a part of the Quality Function Deployment (QFD) techniques used in the Design for Six Sigma (DFSS) and the Failure Mode and Effects Analysis (FMEA), which is part of the Design for Reliability (DFR) process. Ertugrul Karsak et al. (2002) says that QFD starts with the HOQ, which is a planning matrix translating the customer needs into measurable Product Technical Requirements (PTRs). HOQ tool is useful to capture, quantify, categorize, and prioritize the ‘Voice of the Customer’ for planning the activities required to augment and improve a particular product or service.

Nikhil Chandra Shil and Bhagaban Das, in their paper, “Product Planning Through HOQ: An Algorithm”, presented an algorithm to complete all of the required steps of HOQ development with a certain objective to help in calculating the three matrices for Absolute Weight of Customer Requirement (AWCR), Absolute and Relative Weights for Technical descriptors (AWTD and RWTD), which will ultimately help to identify the prioritized customer requirements and technical descriptors, once ranked in the order of respective matrix values. They interpreted that if this algorithm is used for developing an intelligent agent, the design of HOQ will be simple in changing situations.

-- Nasina Jigeesh
Consulting Editor