The combinative effect of electronic open networks and closed interpersonal networks on knowledge creation in academic communities

Sherif, Karma; Munasinghe, Methsika; Sharma, C.B.

doi:10.1108/03055721211227291

Cited by 6 publications

(6 citation statements)

References 61 publications

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“…Hassanian et al (2015) stated that the dynamism of three social factor sub-domainseducators' positive comorbidity, students' positive comorbidity and colleagues' positive comorbiditysupported KC. Similarly, Sherif et al (2012) indicated that open electronic networks and closed interpersonal networks in universities played a significant role in acquiring and simulating new knowledge, as well as in communicating and utilizing that knowledge.…”

Section: Organizational Factorsmentioning

confidence: 99%

Factors that enable knowledge creation in higher education: a structural model

Thani

Mirkamali

2018

DTA

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Purpose Knowledge is recognized as a valuable asset and universities are in search of a new strategy that allows them to build their knowledge and experience. To achieve this goal, it seems essential to find the factors associated with knowledge creation (KC) in universities. There is currently no comprehensive model that delineates the relationships between personal, institutional and support-related factors of KC. The purpose of this paper is to gain a better understanding of the factors that affect KC in higher education institutions. Design/methodology/approach This is an explanatory mixed methods approach that consists of qualitative and quantitative stages. In the qualitative phase, 14 authorities on KC were interviewed and the data yielded were subjected to content analysis. A model and hypotheses were then formulated and a questionnaire was developed to test these. The questionnaire was submitted to faculty members of Tehran University. Questionnaire data were was analyzed using structural equation and partial least squares with the aid of SmartPLS. Findings The results showed three main categories of KC factors: institutional, personal and support. A total of 19 sub-factors were identified within these main categories. According to the results, social capital (path coefficient=0.84) had the strongest correlation with the institutional; basic skills for KC (path coefficient=0.92) had the strongest correlation with the personal, and information and library resources (path coefficient=0.95) had the highest correlation with the support aspect of KC. Originality/value The study uses a multidimensional approach to test the effect of factors on KC, and can contribute to organizations (especially universities) through developing a more comprehensive model of KC. This research may lead to guidelines for universities, using Tehran University as a case study, which give more attention to the main factors of KC and improve and develop the KC process.

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Section: Organizational Factorsmentioning

confidence: 99%

Factors that enable knowledge creation in higher education: a structural model

Thani

Mirkamali

2018

DTA

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“…For Yoo et al (2011), absorptive capacity enables relevant knowledge to be identified and ensures the flexibility necessary to integrate a variety of knowledge types and create innovative ways of thinking. Absorptive capacity is also best developed in open networks because acquisition and assimilation are facilitated by the structural and cognitive dimensions of social capital (Sherif et al, 2012). Thus, it is suggested that absorptive capacity plays a mediating role between the knowledge-collection and donation processes and organizational performance; thus, the following hypotheses are proposed:…”

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confidence: 99%

Organizational performance through the donation and collection of interorganizational knowledge

Nodari

Oliveira

Maçada

2016

VINE Journal of Information and Knowledge Management Systems

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Purpose – This paper aims to provide empirical evidence to support the relationship between interorganizational knowledge sharing, absorptive capacity and organizational performance, and proposes that interorganizational knowledge sharing is composed of two processes: knowledge donation and collection. Design/methodology/approach – A quantitative methodology is adopted to examine the proposed relationship between interorganizational knowledge sharing, absorptive capacity and organizational performance. The study uses survey data from 269 companies in Brazil. Structural equation modeling is applied to test the stated hypotheses and the model. Findings – The empirical findings indicate that interorganizational knowledge sharing is composed of the donation and collection of knowledge. Interorganizational knowledge collection is found to have a positive effect on interorganizational knowledge donation, while collection is found to have a positive effect, mediated by absorptive capacity, on organizational performance. Research limitations/implications – One limitation of this research was the predominant participation of smaller companies. Another is that the data were only collected from Brazilian companies. Moreover, an instrument to measure these constructs was proposed and validated to enable future research to be conducted into the process of interorganizational knowledge sharing and its components: knowledge donation and knowledge collection. Practical implications – Managers can enhance organizational performance by developing both the donation and collection of knowledge. Knowledge donation is particularly important because, in addition to its impact on absorptive capacity and organizational performance, it contributes to the development of knowledge collection, which is also indirectly related to performance. Originality value – The donation and collection of knowledge were validated as components of the interorganizational knowledge-sharing process, and the relationship between these processes and organizational performance is mediated by the absorptive capacity of the organization.

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“…Transactive memory bridges personal knowing to collaborative memory knowledge and takes the object of knowledge and creates shared, interactive dynamic knowing (Maruta, 2012, pp. 37-40; Sherif et al , 2012, pp. 278-279 and pp.…”

Section: Discussion Of the Concepts For Applying Skills And Meta-skills To Manage Complexitymentioning

confidence: 99%

Organisational climate change is here: memory curators in the digital age

McFadzean

2017

VJIKMS

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Purpose This paper aims to describe two themes of information and knowledge management in building corporate memory through curation in complex systems. The first theme describes the skillsets of new memory curators: curation; appraisal; strategist and manager. The second theme describes four concepts that support information management in complex systems: David Snowden’s just-in-time process; Polanyi’s personal knowing; Wenger’s transactive memory system; and David Snowden’s ASHEN database schema. Design/methodology/approach Academic journals and professional publications were analysed for educational requirements for information professionals in complex adaptive systems. Findings The skills described should be readily applied and useful in a complex adaptive system with the four concepts described. The four concepts displayed features indicating each separate concept could be aligned and integrated with the other concepts to create an information sharing model based on synergy between reasoning and computing. Research limitations/implications Research is needed into the capability and potential of folksonomies using recordkeeping metadata and archival appraisal to support peer production information and communication systems. Originality/value The author has not found any research that links archival appraisal, user-generated metadata tagging, folksonomies and transactive memory systems governance policy to support digital online, co-innovation peer production.

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The combinative effect of electronic open networks and closed interpersonal networks on knowledge creation in academic communities

Cited by 6 publications

References 61 publications

Factors that enable knowledge creation in higher education: a structural model

Factors that enable knowledge creation in higher education: a structural model

Organizational performance through the donation and collection of interorganizational knowledge

Organisational climate change is here: memory curators in the digital age

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