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As a way of assessing the ability of organizations to discover and manage unexpected failures in organizational capabilities of knowledge management (KM), this study aims to develop a measurement instrument that involves the five reliability dimensions of preoccupation with failure, reluctance to simplify interpretations, sensitivity to operations, commitment to resilience, and deference to expertise.

To generate measurement items, previous research related to organizational reliability, high reliability theory, mindfulness, and required organizational capabilities of KM was reviewed. The measurement instrument was then verified in terms of reliability and validity, empirically using data from 240 companies in North America. Internal consistency of measurements, measurement item reliability, and construct reliability were examined to ensure the reliability of the instrument. Based on confirmatory factor analysis using structural equation modelling, construct validity was also tested.

The reliability evaluation instrument for KM suggested in this study was constructed with four dimensions, preoccupation with failure in KM, sensitivity to KM operations, commitment to resilience in KM, and deference to expertise. The related measurement items were also identified.

This instrument is useful for researchers and executives looking for appropriate outcomes through the implementation of KM initiatives. Furthermore, the study provides a starting point for further research on KM reliability.

To date, while many of the KM success or failure studies have relied on developing success factors or organizational capability requirements, few studies have been conducted to identify evaluation measures that can assess the cognitive infrastructure that enables simultaneous adaptive learning and provides organizational reliability infrastructure through the management of unwanted, unanticipated, and unexplainable failures in KM-required capabilities.

A perfect knowledge management (KM) initiative is one that achieves its objectives without any failure during a pre-defined period. However, KM implementation is not perfect in every organization as it requires substantial changes in organizational infrastructures, including culture, structure, and technology. Therefore, the purpose of this paper is to propose a model for assessing the reliability of KM to help organizations evaluate their ability to implement KM successfully by identifying key reliability variables, modeling the complex interaction structure among variables, and determining the probability of failure for each KM capability.

In this study, relevant variables are identified by a thorough analysis of related references in literature. In order to determine the compound structure of complicated interactions among variables, a group-based approach is utilized. Based on the combined cognitive maps, a cognitive network is constructed as a framework for graphically representing the logical relationships between variables and capturing the uncertainty in the dependency among these variables using conditional probabilities. The applicability of the proposed approach and the efficacy of the model was verified and validated with data from a banking institution.

Results show that KM reliability can be defined by the degree to which required KM capabilities, including infrastructure and process capabilities, have the ability to perform as intended in a certain organizational environment. Furthermore, it is demonstrated that reliability assessment of KM through a hybrid approach of fuzzy cognitive map and Bayesian network is possible and useful.

The proposed reliability assessment model facilitates the process of understanding why and how failures occur in KM. Moreover, the proposed approach evaluates the probability of success for each variable as well as for the entire KM initiative. Therefore, it can provide insight for managers and executives into the degree of reliability for their existing KM and prevention of failures in vital factors through necessary actions.

The suggested approach to KM reliability assessment is a novel method that provides powerful arguments for a more holistic view of KM reliability factors, which is crucial for the successful implementation of KM.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Although the topic of knowledge management (KM) failure has emerged over the past several years, no specific theory has been proposed about the ability of an organization to discover and manage unexpected failures in the organizational capabilities of KM. Thus, the main aim of this paper is to develop a theory of KM reliability by taking into account the availability of existing theory of high reliability for organizations. Furthermore, this study aims to empirically evaluate the impact of a reliable KM on organizational performance by developing a reliability measurement instrument.

The study develops and tests a theoretical framework whereby the reliable KM is supported on its reliability aspects and organizational performance on its financial, process, and internal aspects. Based on a questionnaire, data were obtained from a sample of 254 companies in North America. The measurement model was tested and confirmed by using structural equation modeling (SEM).

The results show that the reliable KM has a multi-dimensional structure as described by the proposed theoretical framework. Additionally, the results underscore the importance of KM reliability in creating conditions favorable for a firm's success.

It was verified that the reliable KM affects the measures of organizational performance, including financial, process, and internal performance. This is useful for researchers and executives looking for appropriate outcomes through the implementation of KM initiatives. Furthermore, this study provides a starting point for further research on KM reliability.

This study claims that a key to successful KM is to create a cognitive infrastructure that enables simultaneous adaptive learning and provides an organizational reliability infrastructure through the management of unwanted, unanticipated, and unexplainable failures in the KM's required capabilities.