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1 – 10 of 56Fadhilah Aman and Khairul Huda Yusof
This article investigates the determinants of knowledge management system (KMS) adoption, specifically with reference to Malaysian organizations across various industries.
Abstract
Purpose
This article investigates the determinants of knowledge management system (KMS) adoption, specifically with reference to Malaysian organizations across various industries.
Design/methodology/approach
The structural equation modelling approach using PLS technique was utilized to analyze the hypotheses developed, based on the survey data from 830 respondents comprised of information technology or knowledge management managers in Malaysian organizations from various industries.
Findings
Knowledge management (KM) enabling processes, perceived usefulness of KMS, knowledge sharing culture, knowledge taxonomy, and policy and procedure for KMS work, display significant positive effects on the KMS adoption level, with KM enabling processes having the strongest significant positive influence. Meanwhile, incentive and reward, management commitment, and KMS perceived ease of use possess no significant direct effect. However, management commitment was found to have an indirect effect on the KMS adoption level, where its effect is mediated by knowledge sharing culture.
Practical implications
This article outlines several managerial implications for enhancing the adoption of KMS, which include establishing appropriate KM enabling processes, identifying pertinent information to be preserved, shared, and reuse, and generating initiatives to instil a culture of knowledge sharing.
Originality/value
The empirical findings support the relevancy of the technology acceptance model (TAM) construct of perceived usefulness in KMS adoption context and advances the understanding that knowledge sharing culture is a highly influential factor for this construct. This study enriches and extends technology acceptance theory by presenting a more holistic model for KMS adoption with the integration of influencing factors that are inherent to organization.
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Deden Sumirat Hidayat, Winaring Suryo Satuti, Dana Indra Sensuse, Damayanti Elisabeth and Lintang Matahari Hasani
Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these…
Abstract
Purpose
Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.
Design/methodology/approach
This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.
Findings
The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.
Originality/value
This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.
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Muhammad Ashraf Fauzi, Biswajeet Pradhan, Noraina Mazuin Sapuan and Ratih Dyah Kusumastuti
The purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through…
Abstract
Purpose
The purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through loss of life and damage to properties. KM has been shown to dampen the impact of the disaster on the utilization of knowledge among agencies involved and the local communities impacted by disasters.
Design/methodology/approach
Through a bibliometric methodology (co-citation, bibliographic coupling and co-word analysis), this study presents significant themes in the past, current and future predictions on the role of KM in disaster management. In this review paper, 437 publications were retrieved from the Web of Science and analyzed through VOSviewer software to visualize and explore the knowledge map on the subject domain.
Findings
Findings suggest that the significant themes derived are centralized to disaster preparedness during disaster and disaster postrecovery. This review presents a state-of-art bibliometric analysis of the crucial role of KM in building networks and interconnection among relevant players and stakeholders involved in disaster management.
Research limitations/implications
The main implication of this study is how the authorities, stakeholders and local community can integrate the KM system within the three stages of disasters and the crucial role of technologies and social media in facilitating disaster management.
Originality/value
To the best of the authors’ knowledge, this is the first study to present a bibliometric analysis in mapping KM’s past, present and future trends in disaster management.
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Assunta Di Vaio, Badar Latif, Nuwan Gunarathne, Manjul Gupta and Idiano D'Adamo
In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management…
Abstract
Purpose
In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.
Design/methodology/approach
Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.
Findings
The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.
Research limitations/implications
The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.
Practical implications
This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.
Originality/value
This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.
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Mojtaba Rezaei, Cemil Gündüz, Nizar Ghamgui, Marco Pironti and Tomas Kliestik
This study aims to examine the impact of the COVID-19 pandemic on knowledge-sharing drivers in small- and medium-sized family firms within the restaurant and fast-food industry…
Abstract
Purpose
This study aims to examine the impact of the COVID-19 pandemic on knowledge-sharing drivers in small- and medium-sized family firms within the restaurant and fast-food industry. The pandemic has led to significant changes in business culture and consumer behaviour, accelerating digital transformation, disruptions in global supply chains and emerging new business opportunities. These changes have also influenced knowledge sharing (KS) and its underlying drivers.
Design/methodology/approach
To address the research objectives, a two-phase study was conducted. In the first phase, an exploratory analysis using the Delphi method was used to identify the essential drivers and factors of KS in family businesses (FBs). This phase aimed to establish a conceptual model for the study. In the second phase, confirmatory factor analysis was conducted to analyse the impact of the COVID-19 pandemic on the identified knowledge-sharing drivers. The study examined both the pre-pandemic and post-pandemic periods to capture the shifts in attitudes towards KS.
Findings
The findings indicate a significant shift in attitudes towards knowledge-sharing drivers. Before the pandemic, organisational drivers played a central role in KS. However, after the emergence of the pandemic, technological drivers became more prominent. This shift highlights the impact of the COVID-19 pandemic on KS within FB.
Originality/value
The research contributes to understanding knowledge-sharing in the context of FBs and sheds light on the specific effects of the COVID-19 pandemic on knowledge-sharing drivers. The insights gained from this study can inform strategies and practices aimed at enhancing KS in similar organisational settings.
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This study aims to test the relationship between employee exit and knowledge retention. The study also tests the moderating role of organizational structure on the relationship…
Abstract
Purpose
This study aims to test the relationship between employee exit and knowledge retention. The study also tests the moderating role of organizational structure on the relationship between employee exit and knowledge retention.
Design/methodology/approach
A purposive sample of 310 in India was used. The hypotheses were tested using the exploratory factor analysis (EFA), structural equation modeling and moderating analysis using SmartPLS.
Findings
The results showed that employee exit positively affects knowledge retention. Moreover, the organizational structure does not moderate the relationship between employee exit and knowledge retention. Two factors were identified through the EFA, of which knowledge-based systems were found to be the most important, followed by management support.
Originality/value
The study attempts to test the relationship between employee exit and knowledge retention and also develops and validates the multidimensional measure of knowledge retention.
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Muzzammil Wasim Syed, Huaming Song and Muhammad Junaid
Drawing upon information processing theory (IPT) and natural resource-based view (NRBV), this study analyses the role of social media technologies (SMT) on internal and external…
Abstract
Purpose
Drawing upon information processing theory (IPT) and natural resource-based view (NRBV), this study analyses the role of social media technologies (SMT) on internal and external environmental collaboration and green innovation (green product, process and managerial innovation).
Design/methodology/approach
This study took in-depth empirical research by developing a survey questionnaire to identify the relationship between SMTs, environmental collaboration and green innovation. The respondents of the questionnaire were supply chain professionals working in the manufacturing industry of Pakistan. The survey collected 475 responses, which were tested through PLS-SEM using Smart-PLS.
Findings
The study results indicate that SMTs positively influence both internal and external environmental collaboration. Furthermore, internal environmental collaboration (IEC) fosters green products and green managerial innovation. In contrast, external environmental collaboration (EEC) fosters green processes and green managerial innovation. This study has also tested the mediation of IEC and EEC, which shows that both IEC and EEC mediate all the relationships except green process and green product innovation. The results also revealed that innovation capabilities moderate the relationship between environmental collaboration and green innovation.
Research limitations/implications
Though this study has various practical implications, it is not free of limitations. First, the data were collected from Pakistan, and the results may only be compared with other developing countries. Second, few social media platforms have been considered, but they are increasing in numbers and could be used in upcoming studies. Third, green innovation in the context of products, processes and management is considered, but the concept is evolving, and its other indicators can be taken in upcoming studies.
Practical implications
This study addresses the implication of SMTs, environmental collaboration, innovation capabilities and green innovation, which are helpful for managers and policymakers to design policies.
Originality/value
This study provides the seminal operationalization of SMTs in environmental collaboration and green innovation. This study emphasizes innovation capabilities that firms should adopt.
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Jianhua Zhang, Liangchen Li, Fredrick Ahenkora Boamah, Dandan Wen, Jiake Li and Dandan Guo
Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of…
Abstract
Purpose
Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.
Design/methodology/approach
The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.
Findings
The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.
Practical implications
This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.
Social implications
The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.
Originality/value
This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.
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Rafal Kusa, Marcin Suder, Joanna Duda, Wojciech Czakon and David Juárez-Varón
This study investigates the impact of entrepreneurial orientation (EO) and knowledge management (KM) on firm performance (PERF), as well as the mediating role of KM in the EO–PERF…
Abstract
Purpose
This study investigates the impact of entrepreneurial orientation (EO) and knowledge management (KM) on firm performance (PERF), as well as the mediating role of KM in the EO–PERF (EO-PERF relationship). In particular, this study aims to explain the impact of KM on the relationship between the EO dimensions and PERF; dimensions are risk-taking (RT), innovativeness (IN) and proactiveness (PR).
Design/methodology/approach
This study uses structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) methodologies to explore target relationships. The sample consists of 150 small furniture manufacturers operating in Poland (out of 1,480 in the population).
Findings
The study findings show that KM partially mediates the IN–PERF relationship. Furthermore, fsQCA reveals that KM accompanied by IN is a core condition that leads to PERF. Moreover, the absence of KM (accompanied by the absence of RT and IN) leads to the absence of PERF. In addition, the results show that all the variables examined (RT, IN, PR and KM) positively impact PERF.
Originality/value
This study explores the role of KM in the context of EO and its impact on PERF in the low-tech industry. The study uses simultaneously two methodologies that represent different approaches in the search for the expected relationships. The findings reveal that KM mediates the EO-PERF relationship.
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Ting-Cheng Lee and Min-Ren Yan
The purpose of this study is to discuss how organizations can drive organizational performance through human capital (HC) investment through systematic thinking.
Abstract
Purpose
The purpose of this study is to discuss how organizations can drive organizational performance through human capital (HC) investment through systematic thinking.
Design/methodology/approach
This study analyzes three companies from various industries, adopts systems thinking and uses three leading indicators from the balanced scorecard framework to explore the effects of strategic orientations for HC on innovation ecosystems and organizational performance.
Findings
In terms of academic contributions, this study broadly verifies the innovation ecosystem model for organizations and reveals that customer-oriented, internal process-oriented and innovation learning-oriented HC strategies reinforce the pathways in organizational innovation ecosystems, thereby enriching the literature on innovation ecosystems.
Practical implications
In terms of practical contributions, this study provides a novel HC-based perspective on developmental dynamics and details the relationships among each aspect of the innovation ecosystem and HC strategies.
Originality/value
The proposed architecture and strategic frameworks provide a reference for corporations to implement strategic orientations of HC, drive operations in organizational innovation ecosystems and improve organizational performance.
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