A Theoretically Grounded Model to Reduce the Risk of Knowledge Loss in Organisations: An Energy Company Evaluation

Parboteeah, Paul; Jackson, Thomas W.; Wilkinson, Nicola

doi:10.1002/kpm.1502

Cited by 4 publications

(9 citation statements)

References 12 publications

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“…Knowledge emerges from the tacit knowledge of employees, with written procedures documenting best practices and lessons learned from past experiences (Levy, 2011; Sitlington and Marshall, 2011; Parise et al. , 2006; Agarwal and Islam, 2015; Parboteeah et al. , 2016).…”

Section: Resultsmentioning

confidence: 99%

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Managing knowledge loss: a systematic literature review and future research directions

Daghfous

Amer²,

Belkhodja

et al. 2023

JEIM

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PurposeJob market shifts, such as workforce mobility and aging societies, cause the exit of knowledgeable personnel from organizations. The ensuing knowledge loss (K-loss) has broad negative effects. This study analyzes the knowledge management literature on K-loss published from 2000 to 2021 and identifies fruitful directions for future research.Design/methodology/approachThe authors conduct a systematic literature review of 74 peer-reviewed articles published between 2000 and 2021. These articles were retrieved from ProQuest Central, Science Direct, EBSCOhost and Emerald databases. The analysis utilizes Jesson et al.’s (2011) six principles: field mapping, comprehensive search, quality assessment, data extraction, synthesis and write-up.FindingsThree sub-topics emerge from the systematic literature review: K-loss drivers, positive and negative impacts of K-loss and mitigation strategies. Over half of the literature addresses mitigation strategies and provides solutions for K-loss already in progress, rather than proposing preventive measures.Research limitations/implicationsThis study has limitations related to the time span covered. Moreover, it focuses on articles published in refereed journals. Therefore, important contributions from conference papers, books and professional reports were excluded.Originality/valueThis research comprehensively synthesizes the K-loss literature and proposes future avenues of research to address under-investigated areas and potentially lead to theoretical and empirical advancements in the field. This study also provides suggestions for improving managerial practices.

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Section: Resultsmentioning

confidence: 99%

“…, 2020), which facilitates a culture of knowledge-sharing and transfer (Durst and Wilhelm, 2012; Joe et al. , 2013) through communities of practice, team building, cross-functional teams and informal social events (De Long and Davenport, 2003; Parboteeah et al. , 2016; Bratianu and Leon, 2015).…”

Section: Resultsmentioning

confidence: 99%

Managing knowledge loss: a systematic literature review and future research directions

Daghfous

Amer²,

Belkhodja

et al. 2023

JEIM

View full text Add to dashboard Cite

show abstract

“…Nonetheless, it is possible that the use of KTMs at the wrong level (i.e. at a collective or social level vs. individual level), notably IT-based ones, may lead to decreased knowledge sharing and potential KL (Parboteeah et al , 2016). Consequently, it is important to understand the types of KTMs that facilitate KR and mitigate KL.…”

Section: Theoretical Foundationsmentioning

confidence: 99%

Organizational knowledge retention and knowledge loss

Levallet

Chan

2019

JKM

101

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Purpose This paper aims to examine the effectiveness of organizational information technology (IT)-based and non-IT-based knowledge transfer mechanisms (KTMs) for the retention of different types of knowledge from mobile experts. It differentiates among four types of knowledge loss (KL), namely, conscious knowledge (i.e. individual explicit knowledge that can be codified); codified knowledge (i.e. explicit knowledge captured at the social level); automatic knowledge (i.e. implicit individual knowledge); and collective knowledge (i.e. implicit knowledge embedded in the organization). Design/methodology/approach A research framework connecting the organizational knowledge retention (KR) cycle to KL is developed and an exploratory analysis is conducted using data from two case studies in the Canadian federal public service. Findings are confirmed using a third government agency. Findings Without the right processes in place for organizational knowledge retrieval and reuse, the KR cycle is not complete, leading to KL. The lack of available social KTMs for the conversion of individual to social objectified knowledge leads to KL. KTMs shortcomings increase the risk of automatic and objectified KL. Research limitations/implications Exploratory results demonstrate that KL does not always equate to lack of KR. Implementing knowledge-specific organizational KTMs is important to encourage the retention of individual knowledge at the social level. Propositions and a framework are developed for future research. Practical implications Mobile experts hold valuable knowledge at high risk of being lost by organizations. This paper provides managers with a set of guidelines to develop a knowledge-specific strategy focused on KTMs that increase KR and mitigate KL. Originality/value This paper challenges the assumption that KL only results from poor retention and studies both retention and loss to identify additional types of unintentional loss that occur when individual knowledge is not converted to social knowledge.

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“…In the public or private R&D organizations, the interaction of research and technical people with clients during the development of a product and even after its introduction to market is a non-ending process that allows a build-up of tacit knowledge and improves the creation of ideas. Nevertheless, according to Parboteeah et al, (2016) there is a risk of losing knowledge within an organization that occurs when an employee leaves, taking all knowledge with him. Therefore, a strategy needs to be implemented to ensure that knowledge remains within the team.…”

Section: Case Studies In Business and Managementmentioning

confidence: 99%

“…Cit. ) Also, Parboteeah et al, (2016) proposed models for including the risk of knowledge sharing and knowledge loss in the KM process.…”

Section: Management Of Knowledge Energymentioning

confidence: 99%

Knowledge Energy, Recurrent Learning and the Transformation of an R&D Institution in a Mexican Public University

Vega-González

Zanella²,

Bruce³

2018

CSBM

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When University R&D institutions perform projects to create technology solutions, they produce knowledge and learn. The basic idea proposed in this article is that one of the fuels that produce such a dynamic process is the organization’s knowledge energy. When it flows in multidisciplinary projects within and outside an organization's borders, it also produces recurrent learning along each technology prototype improvement. Through the years this dynamic is capable of producing substantial transformations and empower R&D institutions, as we found in the transformation of the Applied Sciences and Technological Development Center into the Applied Sciences and Technology Institute of the National Autonomous University in México. This is a limited case study.

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A Theoretically Grounded Model to Reduce the Risk of Knowledge Loss in Organisations: An Energy Company Evaluation

Cited by 4 publications

References 12 publications

Managing knowledge loss: a systematic literature review and future research directions

Managing knowledge loss: a systematic literature review and future research directions

Organizational knowledge retention and knowledge loss

Knowledge Energy, Recurrent Learning and the Transformation of an R&D Institution in a Mexican Public University

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