Knowledge Audit (KA) is a systematic way of determining the level of critical knowledge in an organization and its entities. It discovers, analyzes, measures and evaluates the organization’s knowledge needs, the existing knowledge assets, knowledge gaps, the flow of knowledge within the organization and the hindrances for the flow. It plays a crucial role in developing Knowledge Management (KM) strategy by examining the health of an organization’s knowledge. According to Chong and Chong (2009), business strategy, organizational structure, KM team, knowledge map and KA form the five preliminary success factors for effective KM in an organization. There are several techniques to conduct a KA and most of them involve identifying the organization’s knowledge needs, drawing up a knowledge inventory, analyzing knowledge flows, creating a knowledge map, and checking for any other knowledge gaps. In the first paper, “A Methodology for Documenting Key Knowledge Through the Application of Knowledge Audit Techniques: The Case of a Mexican Pulp Company”, the authors, Alonso Perez-Soltero, Rosario Alvarez-Quijada, Mario Barcelo-Valenzuela and Andres Diaz-Valladares, have proposed a methodology based on the application of KA tools to structure and document the process of development of new products, by assuring quality in the finished products under ISO 9001-2008 model. The methodology was built on three major steps: (1) Auditing knowledge to develop new products; (2) Identifying problems in product development and solving them; and (3) Documentation of results based on ISO 9001-2008. To validate their methodology, the authors implemented the same in a Northwest Mexican company dealing with design and production of all kinds of molded pulp packaging. With the success achieved, the authors have expressed their confidence in the application of the proposed model to organizations that are developing new products or systems.

According to Ron Young (2010), there are four major dimensions of KM: (1) Personal KM; (2) Team KM; (3) Organizational KM; and (4) Inter-organizational KM. In the second paper, “Knowledge Dimensions to Monitor Knowledge Growth in Service Sector”, the author, Deepa Ittimani Tholath, has investigated the status of monitoring knowledge growth in the service sector, focusing on banking. The author has analyzed nine KM dimensions—nature of service, role of workers, location of knowledge, nature of problem, natural organizational type, suitability for automation, ease of transfer, feasible product variety and quality control.

The sample consisting of 400 respondents, covering both the employees and customers of banks, was categorized into four groups. Some common factors relating to knowledge growth of the banks were reported with slight variations from one category to other category of banks. Finally, it is suggested that while installing a KM system in a typical service industry, various factors that are to be monitored include: nature of the service and suitability for automation along with two complementary factors of quality control and nature of problem solving.

According to Warfield (1974) and Sage (1977), Interpretive Structual Modeling (ISM) is an interactive learning process, wherein a set of different elements which are directly and indirectly related are structured into a comprehensive systematic model. In the third paper, “Modeling of Knowledge Management Technologies: An ISM Approach”, the authors, A K Singh, M D Singh and B P Sharma, begin their study with identification of 24 KM Technologies (KMTs) from a literature review, as basic facilitators to enhance knowledge among the employees of the industries. They use the ISM methodology to develop a hierarchy of these KMTs according to their driving power. Based on the opinion of experts regarding the relationship existing between different pairs of KMTs, a Structural Self-Interaction Matrix (SSIM) is developed. SSIM is then converted into an initial reachability matrix by transforming the information of each cell of SSIM into binary digits. Finally, MICMAC analysis, which is a cross impact matrix multiplication applied to classification, is applied to analyze the driving power and dependence power of KMTs in order to identify the key KMTs that drive the system into various categories. This results in the classification of KMTs into four categories—autonomous, linkage, dependent and driver. Managers are recommended to make decisions and strategies keeping in view that those KMTs possessing higher driving power in the ISM model need to be enabled on a priority basis, followed by others.

Search Engine Marketing (SEM) is a type of Internet marketing to promote websites by increasing their visibility in Search Engine Results Pages (SERPs) through optimization and advertising. SEM consists of a number of different skills, including Search Engine Optimization (SEO), Pay Per Click (PPC), etc. SEM uses SEO to adjust or rewrite website content so as to achieve a higher ranking SERPs or use PPC listings. According to Osama Fayyad et al. (1996), knowledge discovery is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. In the last paper, “Search Engine Marketing: Does the Knowledge Discovery Process Help Online Retailers?”, the author, Tapan K Panda, has examined the advantages and uses of SEM as a User-led Knowledge Discovery Process (UKDP) for online retailers. The author has developed hypotheses to be tested to get a holistic view, and tested the key constructs selected for measurement for their validity and reliability with the help of Cronbach’s alpha and Confirmatory Factor Analysis (CFA) with a sample size of 103 online shoppers. The focus is on the analysis of knowledge discovery process and customer preferences of page location of the search pages. It is reported that the positioning of advertisements in SERPs plays a key role in generating traffic for the website. PPC framework is found to be more effective than SEO framework.

-- Nasina Jigeesh
Consulting Editor