Purpose – This study aims to empirically evaluate the impact of interorganizational groupings on corporate performance in project-based organizations. Design/methodology/approach – The study develops and tests a theoretical model whereby groupings include project team, community of practice (CoP), community of interest (CoI), and knowledge network (KN). Organizational performance is supported on financial, process, internal, and cultural aspects. Based on a questionnaire, data was obtained from a sample of 142 companies in North America. The measurement model was tested and confirmed by using structural equation modeling (SEM). Findings – The results confirm the positive effects of knowledge networks and communities of interest. However, the impact of project teams turned out to be negative, and communities of practice were not shown to affect corporate performance. Additionally, the results underscore the importance of organizational networks in creating conditions favorable to a firm's success. Practical implications – It was verified that knowledge networks and communities of interest affect the measures of organizational performance, including financial, process, internal, and cultural performance. This is useful for researchers and executives looking for appropriate outcomes through the implementation of knowledge management initiatives. Furthermore, this study provides a starting point for further research on the role of inter- and intraorganizational networks in project-based organizations. Originality/value – This study claims that a key to performance for project-based organizations is development and management of organizational networks in the form of knowledge networks and communities of interest.
Purpose -The objective of this paper is to propose a holistic dynamic model for understanding the behavior of a complex and internet-based kind of knowledge market by considering both social and economic interactions. Design/methodology/approach -A system dynamics (SD) model is formulated in this study to investigate the dynamic characteristics of complex interactions in a fee-based online question & answer (Q&A) knowledge market. The proposed model considers the dynamic, non-linear, asymmetric, and reciprocal relationships between its components, and allows the study of the evolution of the market under assumed conditions. Findings -Some illustrative results show that: this market is very sensitive to the prices that the customers choose; low-priced questions are as important as high-priced ones; gradually increasing experts' proportion of a question's price reduces customer satisfaction and experts' reputation; and training programs for experts result in higher customer satisfaction and researchers' reputation. Furthermore, three types of customers are identified and discussed.Practical implications -This model can be used to change, manage, and control this market and also helps to design new similar markets. In addition, the proposed model helps to observe the behavior of a market under one or more policies before applying to the real world. Social implications -Since GA was shut down in 2006, the implications of this research serve as a strategic tool (strategic evaluation software) for understanding and examining the effects of policies for many existing similar Q&A business models. Furthermore, the SD approach can provide new insights into the field of online Q&A knowledge markets and overcome traditional econometric treatment of data for understanding the dynamic behavior of these markets. Originality/value -Understanding the complex social and economic behavior of Q&A markets is one of the most important concerns for academics and practitioners in the areas of online markets' management. The paper shows how SD can provide attractive insights into the field of online fee-based knowledge markets based on a qualitative and quantitative modeling. However, the background literature lacks a holistic view of these kinds of markets.
Purpose – The purpose of this paper is to develop a novel hybrid multi-criteria decision-making (MCDM) model to help organizations select their knowledge-based strategy effectively. Knowledge management (KM) initiatives are often started with the selection of a strategy, which is a critical decision for a successful KM implementation. Design/methodology/approach – KM initiatives are often started with the selection of a strategy, which is a critical decision for a successful KM implementation. Thus, the aim of this paper is to develop a novel hybrid MCDM model to help organizations select their knowledge-based strategy effectively. Findings – Results illustrate that the proposed model is efficient to consider the complex interactions among criteria and provides a consistent decision with less pair-wise comparisons. Furthermore, a case study indicates that a “codification versus tacitness” strategy is preferred over other strategies considering nine main domain criteria. Originality/value – The contribution of this paper is threefold: it addresses the gaps in KM literature on the effective and efficient assessment of KM strategy selection; it provides a comprehensive and systematic framework that combines analytic network process (ANP) and consistent fuzzy preference relations (CFPR) to assess KM implementation strategy; and it illustrates a real-world study to exhibit the applicability of the proposed approach and the efficacy of the framework.
Purpose – Customer support has always been considered a competitive advantage in many industries. In recent years, firms have begun to provide customers with a high-quality service experience, in order to attract more customers and achieve higher customer satisfaction. Although customer service and satisfaction have been discussed by other researchers, to the knowledge, there has been no dynamic and intelligent way to model and optimize customer support systems for product and service providers. The purpose of this paper is to develop a modeling method for customer support optimization. Design/methodology/approach – In this study, a system dynamics (SD) model has been formulated to investigate the dynamic characteristics of customer support in an IT service provider. The proposed simulation model considers the dynamic, non-linear, and asymmetric interactions among its components, and allows study of the behavior of the customer support system under controlled conditions. Furthermore, a particle swarm optimization method was developed to investigate the proper combination of parameters and strategy development of the support center. Findings – This paper proposes a novel modeling, simulation, and optimization approach for complex customer support systems of information and communications technology (ICT) service providers. This method helps managers improve their customer support systems. Moreover, the simulation results of the case study show that ICT service providers can gain benefit by managing their customer service dynamically over time using the proposed artificial intelligent multi-parameter modeling and optimization method. Research limitations/implications – The proposed holistic modeling approach and multi-parameter optimization method will greatly help managers and researchers understand the factors influencing customer support. Moreover, it facilitates the process of making new improvement strategies based on provided insights. Originality/value – The paper shows how SD simulation and multi-parameter optimization can provide insights into the field of customer support. However, the existing literature lacks a holistic view of these kinds of simulation systems, as well as a multi-parameter optimization method for SD methodology.
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