The Online Social Networks Analysis: State of the Art

Kassiri, Asmae El; Belouadha, Fatima-Zahra

doi:10.36478/ijscomp.2019.33.43

Cited by 1 publication

(3 citation statements)

References 79 publications

(89 reference statements)

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“…In a network diagram, SNA can be categorized according to the nodes and the nature of the connections (Cross & Parker, 2004). The metrics of the network were determined as "degree of centrality," "eigenvector centrality," and "clustering coefficient," drawing on Batura (2012); Kassiri and Belouadha (2020); and Zaphiris and Pfeil (2007). The type of individual and their centrality in the networks was categorized (Friemel, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…SNA is also critical in determining how a problem can be solved, how an organization works, and how individuals can achieve their objectives (Aydin, 2018). SNA can be applied on a broader scope to analyze kinship structure, social mobility, scientific citations, contacts among members of nonstandard groups, corporate power, terrorism, international trade exploitation, class structure, and many other areas (Durland & Fredericks, 2005;Curran & Curran, 2014;Aydin, 2018;Christidis, 2020) Thomas N. Friemel (2017) has also noted that SNA encompasses a large set of methodological, statistical, and theoretical approaches developed to analyze relational data with broader purposes, for instance regarding talent management (Whelan et al 2010), types of leadership (Hoppe & Reinelt, 2010;Molano & Polo, 2015;Aydin, 2018), social media and emailbased networks (Kassiri & Belouadha, 2020;Himelboim, 2017;Waters, 2014;Christidis, 2020), data mining (Deng & Mao, 2013), telecommunication (Al-Molhem et al 2019); platforms of application (Adnan et al, 2019), and application in business sectors (Putritamara et al, 2020;Altuntas & Gök, 2020;Massey, 2016;Valente et al, 2015). Furthermore, Kassiri and Belouadha (2020) have noted that as with conventional maps that describe the geographical distance between cities, the distance and thickness of graphic relations in an SNA study illustrate the relationships between objects or individuals spatially and in terms of intensity.…”

Section: The Of Social Network Analysis In Managing Knowledge a Liter...mentioning

confidence: 99%

“…Degree of centrality, eigenvector centrality, and clustering coefficients are determined as the metrics that map the information flow in the organization. In contrast, the individuals in the respective networks are categorized as "central connectors," "boundary-spanners," "information brokers," and "peripheral players" respectively, as defined by Aydin (2018); Durland and Fredericks (2005); Hoppe and Reinelt (2010); Kassiri and Belouadha (2020);and Scott (2000). Considering the type and centrality of key knowledge workers in these networks, the results of the study are relevant for the future development of potential employees at their respective positions.…”

Section: Introductionmentioning

confidence: 99%

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The Role of Managers in R&D Organizational Networks. Are They Knowledge Workers or Bureaucrats?

Setiawan¹,

Lestari²

2022

ajtm

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Abstract. In today’s competition-driven world, competitive advantage is the only way for an organization to survive. There are various ways to gain a competitive edge through the application of knowledge, technology, and science. Indonesian public research and development (R&D) faces numerous challenges, one of which is researchers’ low productivity in terms of international journal publications. To boost research productivity and performance, efforts must be made to streamline knowledge flows within public R&D institutions. Additionally, data from dynamic information flows in networks can be used to evaluate bureaucratic effectiveness and identify potential knowledge talents or actors within R&D organizations. The purpose of this study is to determine the role of managers at all levels in a public R&D organization in coordinating information flow through knowledge networks and bureaucratic relationships. The social network analysis (SNA) technique was used to map managers’ role in aligning knowledge flows in an Indonesian public R&D organization. Individuals were classified according to their type and centrality in the network, and network metrics such as “degree of centrality,” “eigenvector centrality,” and “clustering coefficient” were determined. It was found that the organization has some issues with knowledge flows and power distribution. The two distinct knowledge flow patterns, one for knowledge-seeking and the other for administrative knowledge, do not appear to be synchronized. The director of the R&D center is assumed to be less influential than the top manager in terms of knowledge and bureaucratic influence. At the managerial level, clique tendency is lower, while it is higher at the nonmanagerial level. Such centralization and cliques are dangerous because managing knowledge cannot be handled by a single person in a knowledge-intensive organization such as an R&D institution. Additionally, it was discovered that middle managers are critical for knowledge-seeking purposes, whereas administrative managers dominate the bureaucratic role. The research implications are dependent on deciphering the complexities of knowledge flows among employees and the variables that influence the knowledge-transfer process within an organization. This issue will exacerbate the problem by decreasing productivity and increasing reliance on a small number of people. Additional research is required to generalize the findings to other Indonesian R&D institutions. Keywords: Bureaucracy, knowledge management, social network analysis

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Section: Methodsmentioning

confidence: 99%