DEVELOPMENT OF AN EMPIRICAL KNOWLEDGE MANAGEMENT FRAMEWORK FOR PROFESSIONAL VIRTUAL COMMUNITY IN KNOWLEDGE-INTENSIVE SERVICE INDUSTRIES

2011 ◽  
Keyword(s):  
Knowledge Management ◽  
Virtual Community ◽  
Service Industries ◽  
Empirical Knowledge ◽  
Management Framework ◽  
Knowledge Intensive

The Influence of Collaborative Web on Knowledge Management, Organizational Structure and Culture in Knowledge-Intensive Companies

2011 ◽  
pp. 184-201 ◽  
Author(s):  
Kathrin Kirchner ◽  
Mladen Cudanov
Keyword(s):  
Social Networks ◽  
Knowledge Management ◽  
Organizational Structure ◽  
Knowledge Sharing ◽  
Virtual Community ◽  
Project Teams ◽  
Management Change ◽  
Community Orientation ◽  
Web Tools ◽  
Knowledge Intensive

Knowledge-intensive companies are quickly changing, involving many people working in different activities. Knowledge in such companies is diverse and its proportions immense and steadily growing. The distribution of knowledge across project teams, communities of practice, and individuals is therefore an important factor. With collaborative Web, tools like wikis, blogs, or social networks are used for collaboration and knowledge sharing. In this chapter, we question what influence these tools have on knowledge management, organizational structure, and culture of knowledge-intensive companies. As a result of our interviews and surveys done in Serbia, we found that with collaborative Web, organizational structure, culture, and knowledge management change is perceived among employees and that employee’s loyalty changes from company orientation toward virtual community orientation.

Operational Knowledge Management

2011 ◽  
pp. 1237-1249 ◽  
Author(s):  
Fons Wijnhoven
Keyword(s):  
Knowledge Management ◽  
Operations Management ◽  
Main Question ◽  
Human Capabilities ◽  
Business Economics ◽  
Management Framework ◽  
Knowledge Processes ◽  
Management Processes ◽  
Organization Analysis ◽  
Knowledge Intensive

The differences between the paradigms of knowledge management (KM) and operations management are huge. Whereas KM is rooted in the disciplines of human relations, sociology, organization analysis, and strategic management, the operations management paradigm finds its roots in industrial engineering, business economics, and information systems. These differences result in poor acceptance of KM ideas in operations management and vice versa. Several approaches to this problem are possible. For instance, one may state that the operations management paradigm is irrelevant for knowledge management. This is incorrect, because besides of the traditional person-oriented knowledge management processes, modern knowledge intensive firms use reengineered knowledge processes intensively (e.g., Hansen, Nohria, & Tierney, 1999). An alternative approach may be to forget about the KM paradigm and only use the operations management paradigm. This is wrong again, because most industrial enterprises compete on the development and exploitation of their expertise and human capabilities (Hamel & Prahalad, 1994; Quinn, 1992). Consequently, if knowledge management is relevant and if operations management is not irrelevant, then the main question is how to translate knowledge management issues into an operations management framework. I provide a conceptual framework for such a knowledge operations management (KOM) perspective.

Toward a Knowledge Management Framework for Auditing Processes

2017 ◽  
Vol 8 (3) ◽  
pp. 45-67 ◽  
Author(s):  
Loan Nguyen ◽  
Youji Kohda
Keyword(s):  
Knowledge Management ◽  
Theoretical Model ◽  
Qualitative Methodology ◽  
Experimental Studies ◽  
Management Framework ◽  
Documentary Analysis ◽  
Management Processes ◽  
Assurance Services ◽  
Knowledge Intensive

We aimed at discovering how auditors working in an auditing firm managed their knowledge-related processes, and then built a theoretical model for the knowledge management of professional knowledge-intensive services like auditing. We conducted a case study in an auditing firm in Vietnam by employing a qualitative methodology in this research by using twenty in-depth interviews, observations, and documentary analysis. A literature review revealed that auditing research has been developed through various approaches ranging from experimental studies to information processing and experience-focused and knowledge-related interests. However, there has not been much empirical research that explains how knowledge is created during an auditing process. We conducted an empirical case study in this research that provided useful insights into constructing a theoretical model of knowledge management processes in auditing. Because the theoretical model consisted of three phases of collecting data, analyzing data (thereby turning them into information), and synthesizing information into knowledge, we called it the collect-analyze-synthesize (CAS) model. The model was used to visualize the auditing process as a spiral with many iterative CAS processes. Wisdom in the CAS model is defined as high levels of accumulated knowledge and the ability to exercise professional judgments attained from long-term experience. Wisdom is retained by members in an auditing firm and drives the auditing process. The significance of this study was inherent in three main areas: providing scholarly extensions of the literature by suggesting a knowledge management framework for auditing processes, helping auditors and auditing firms with their roles, and ensuring better assurance services for society.

Operational Knowledge Management

2011 ◽  
pp. 2829-2842
Author(s):  
Fons Wijnhoven
Keyword(s):  
Knowledge Management ◽  
Operations Management ◽  
Main Question ◽  
Human Capabilities ◽  
Business Economics ◽  
Management Framework ◽  
Knowledge Processes ◽  
Management Processes ◽  
Organization Analysis ◽  
Knowledge Intensive

The differences between the paradigms of knowledge management (KM) and operations management are huge. Whereas KM is rooted in the disciplines of human relations, sociology, organization analysis, and strategic management, the operations management paradigm finds its roots in industrial engineering, business economics, and information systems. These differences result in poor acceptance of KM ideas in operations management and vice versa. Several approaches to this problem are possible. For instance, one may state that the operations management paradigm is irrelevant for knowledge management. This is incorrect, because besides of the traditional person-oriented knowledge management processes, modern knowledge intensive firms use reengineered knowledge processes intensively (e.g., Hansen, Nohria, & Tierney, 1999). An alternative approach may be to forget about the KM paradigm and only use the operations management paradigm. This is wrong again, because most industrial enterprises compete on the development and exploitation of their expertise and human capabilities (Hamel & Prahalad, 1994; Quinn, 1992). Consequently, if knowledge management is relevant and if operations management is not irrelevant, then the main question is how to translate knowledge management issues into an operations management framework. I provide a conceptual framework for such a knowledge operations management (KOM) perspective.

Operational Knowledge Management

2011 ◽  
pp. 703-712
Author(s):  
Fons Wijnhoven
Keyword(s):  
Knowledge Management ◽  
Operations Management ◽  
Main Question ◽  
Human Capabilities ◽  
Business Economics ◽  
Management Framework ◽  
Knowledge Processes ◽  
Management Processes ◽  
Organization Analysis ◽  
Knowledge Intensive

The differences between the paradigms of knowledge management (KM) and operations management are huge. Whereas KM is rooted in the disciplines of human relations, sociology, organization analysis, and strategic management, the operations management paradigm finds its roots in industrial engineering, business economics, and information systems. These differences result in poor acceptance of KM ideas in operations management and vice versa. Several approaches to this problem are possible. For instance, one may state that the operations management paradigm is irrelevant for knowledge management. This is incorrect, because besides of the traditional person-oriented knowledge management processes, modern knowledge intensive firms use reengineered knowledge processes intensively (e.g., Hansen, Nohria, & Tierney, 1999). An alternative approach may be to forget about the KM paradigm and only use the operations management paradigm. This is wrong again, because most industrial enterprises compete on the development and exploitation of their expertise and human capabilities (Hamel & Prahalad, 1994; Quinn, 1992). Consequently, if knowledge management is relevant and if operations management is not irrelevant, then the main question is how to translate knowledge management issues into an operations management framework. I provide a conceptual framework for such a knowledge operations management (KOM) perspective.

High-tech business in crisis conditions: development trends of large, medium and small enterprises

2020 ◽  
Vol 18 (4) ◽  
pp. 48-58
Author(s):  
Vladislav V. Spitsyn ◽  
Alexander A. Mikhal'chuk ◽  
Anastasia A. Bulykina ◽  
Svetlana N. Popova ◽  
Irina E. Nikulina
Keyword(s):  
Small Businesses ◽  
Negative Impact ◽  
Service Industries ◽  
High Tech ◽  
Small Enterprises ◽  
Mathematical Methods ◽  
Crisis Period ◽  
Knowledge Intensive ◽  
Crisis Conditions ◽  
Medium And Small Enterprises

Leading world countries view innovative development and high-tech business as an opportunity to overcome economic stagnation and decline in economic growth. One of the modern trends in the analysis of high-tech development is the study of high-tech knowledge-intensive service industries and their development in times of crisis. The purpose of the paper is to identify patterns of development of large, medium and small enterprises in high-tech service industries in Russia during periods of crisis. Economic and economic-mathematical methods of analysis are applied to the formed samples of enterprises. The research period is 2013-2017. The financial indicators of enterprises were adjusted for the level of accumulated inflation in relation to 2013. According to results, large and medium-sized enterprises showed insignificant or weak significant positive dynamics of revenue during all years of the crisis period. The crisis period did not lead to a decrease in the revenue of these groups of enterprises. The acute phase of the crisis (2014-2015) had a pronounced negative impact on the group of small enterprises in all studied industries, but they successfully recovered in 2016-2017 and reached the pre-crisis level of revenue. The total revenue by industries and groups of enterprises in 2017 became higher than in 2013, and its growth rates were significant for many groups of enterprises, which indicates a successful overcoming of the crisis period and signs of growth in high-tech service industries. Our study shows the need for state support for small businesses in high-tech service industries in crisis conditions, and identifies the possibilities of adaptation of enterprises in these industries to an unfavorable external environment. Our results may be useful for the purposes of government stimulation of economic development in the current environment.

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

2017 ◽  
Vol 21 (3) ◽  
pp. 623-639 ◽  
Author(s):  
Tingting Zhang ◽  
William Yu Chung Wang ◽  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Stock Market ◽  
Value Chain ◽  
Stock Prices ◽  
Economic Value ◽  
Information Value ◽  
Publicly Traded ◽  
Content Type ◽  
Knowledge Intensive

Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

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