Measuring the reliability of knowledge management

Hesamamiri, Roozbeh; Mazdeh, Mohammad Mahdavi; Jafari, Mostafa

doi:10.1108/ap-04-2013-0029

Cited by 8 publications

(7 citation statements)

References 53 publications

(55 reference statements)

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“…MTTR is a measure of time period that is required for the equipment to get back to normal service status when it fails, also known as "Down Time". MTTR is calculated as shown in the following equations [16], [17].…”

Section: Mean Time To Repair (Mttr)mentioning

confidence: 99%

“…Reliability is defined as "the capability of apparatus to perform; satisfactorily, the essential function under specified situations through a specified period of time", Ireson et al [17]. Reliability can also be defined as "the probability that the apparatus is operating without failure in the time period t" as shown in the following equation [17], [18].…”

Section: Reliability (R (T))mentioning

confidence: 99%

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Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions

Al‐Bossly¹

2020

Preprint

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Reliability analysis techniques are customary standard tools that are used for evaluating the performance of different equipment and devices in order to minimize their downtime. To predict the reliability, life data from a sample that is satisfactorily representative of the equipment should be fitted to the suitable statistical distribution. The parameterized distribution may be used to estimate essential characteristics such as failure rate; and probability at a precise time, as well as system reliability. In the current study, Weibull++/ALTA software package is used as a novel tool to fit the available data set to estimate the best fitted probability density function (PDF) using Maximum Likelihood (MLE) for parameter estimation. The determined distributions are then assessed using goodness-of-fit test to define how well it fits the available data set. There are multiple methods for determining goodness-of-fit. Weibull distributions and their special cases’ parameters have an effect on life times.

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Section: Mean Time To Repair (Mttr)mentioning

confidence: 99%

Section: Reliability (R (T))mentioning

confidence: 99%

Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions

Al‐Bossly¹

2020

Preprint

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“…The latest literature claims that the key to successful KM is to create a cognitive infrastructure that enables simultaneous adaptive learning and provides an organizational reliability infrastructure through the identification and management of unwanted, unanticipated, and unexplained failures in KM's required capabilities (Mahdavi Mazdeh and Hesamamiri, 2014). In order to assess the ability of an organization to keep KM-required capabilities from unexpected, unwanted, or unmanaged failures, Hesamamiri et al (2013) developed a KM reliability theory and a measurement instrument that involves the dimensions of KM reliability, including preoccupation with failure in KM, sensitivity to KM operations, commitment to KM resilience, and deference to expertise. Based on the KM reliability theory, a perfect KM initiative is one that achieves KM objectives without any failure during a pre-defined duration.…”

Section: Introductionmentioning

confidence: 99%

“…III shows a CET for the combination of multiple causal effects on infrastructure capability from three causes including technological, structural, and cultural Category/process Proposed dimensions Category and proposed dimensions of process capabilities are adapted fromKing et al (2008). Adapted fromHesamamiri et al (2013) …”

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confidence: 99%

Knowledge management reliability assessment: an empirical investigation

Hesamamiri

Mazdeh

Jafari

et al. 2015

Aslib Journal of Information Management

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Purpose – 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. Design/methodology/approach – 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. Findings – 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. Practical implications – 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. Originality/value – 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.

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“…Based on a knowledge-based view of the organization, a firm's competitive advantage depends on its ability to continuously organize, integrate, and apply knowledge into value-creating decisions (Spender, 1996;Grant, 2000). Furthermore, the reliable knowledge integration requires cooperation, interaction, and knowledge sharing among individuals (Mahdavi and Hesamamiri, 2014;Jafari et al, 2011;Hesamamiri et al, 2013). Accordingly, while different types of formal and informal interactions are fundamental to knowledge integration and sharing in organizations, the concept of knowledge governance is discussed by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Organizational groupings and performance in project-based organizations

Bourouni

Noori

Jafari

2014

Aslib Journal of Information Management

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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.

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Measuring the reliability of knowledge management

Cited by 8 publications

References 53 publications

Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions

Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions

Knowledge management reliability assessment: an empirical investigation

Organizational groupings and performance in project-based organizations

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