Using social network analysis to measure transactive memory systems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kylie King ◽  
Tracy Sweet
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Memory Systems ◽  
Transactive Memory ◽  
Transactive Memory Systems ◽  
Content Type ◽  
Network Measures ◽  
Information Work ◽  
Network Instrument

Purpose This study aims to explore how social networks could be used in the measurement of transactive memory systems (TMS) or other team constructs and provide motivation for future analyses of TMS measurement. Design/methodology/approach TMSs describe the structures and processes that teams use to share information, work together and accomplish shared goals. This paper proposes the use of social network analysis in measuring TMS. This is accomplished by describing the creation and administration of a TMS network instrument and evaluating the relation of the proposed network measures, previous measures of TMS and performance. Findings Findings include that proposed network measures perform similarly to previously proposed, frequently used measures of TMS. Originality/value To the best of the authors’ knowledge, this is among the first papers to propose network measures for the evaluation of TMS.

SCM research leadership: the ranked agents and their networks

2019 ◽  
Vol 24 (6) ◽  
pp. 821-854 ◽  
Author(s):  
Sunil Babbar ◽  
Xenophon Koufteros ◽  
Ravi S. Behara ◽  
Christina W.Y. Wong
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Supply Chain Management ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Chain Management ◽  
Content Type ◽  
Network Measures ◽  
The World

Purpose This study aims to examine publications of supply chain management (SCM) researchers from across the world and maps the leadership role of authors and institutions based on how prolific they are in publishing and on network measures of centrality while accounting for the quality of the outlets that they publish in. It aims to inform stakeholders on who the leading SCM scholars are, their primary areas of SCM research, their publication profiles and the nature of their networks. It also identifies and informs on the leading SCM research institutions of the world and where leadership in specific areas of SCM research is emerging from. Design/methodology/approach Based on SCM papers appearing in a set of seven leading journals over the 15-year period of 2001-2015, publication scores and social network analysis measures of total degree centrality and Bonacich power centrality are used to identify the highest ranked agents in SCM research overall, as well as in some specific areas of SCM research. Social network analysis is also used to examine the nature and scope of the networks of the ranked agents and where leadership in SCM research is emerging from. Findings Authors and institutions from the USA and UK are found to dominate much of the rankings in SCM research both by publication score and social network analysis measures of centrality. In examining the networks of the very top authors and institutions of the world, their networks are found to be more inward-looking (country-centric) than outward-looking (globally dispersed). Further, researchers in Europe and Asia alike are found to exhibit significant continental inclinations in their network formations with researchers in Europe displaying greater propensity to collaborate with their European-based counterparts and researchers in Asia with their Asian-based counterparts. Also, from among the journals, Supply Chain Management: An International Journal is found to exhibit a far more expansive global reach than any of the other journals. Research limitations/implications The journal set used in this study, though representative of high-quality SCM research outlets, is not exhaustive of all potential outlets that publish SCM research. Further, the measure of quality that this study assigns to the various publications is based solely on a publication score that accounts for the quality of the journals, as rated by Association of Business Schools that the papers appear in and nothing else. Practical implications By informing the community of stakeholders of SCM research about the top-ranked SCM authors, institutions and countries of the world, the nature of their networks, as well as what the primary areas of SCM research of the leading authors in the world are, this research provides stakeholders, including managers, researchers and students, information that is helpful to them not only because of the insights it provides but also for the gauging of potential for embedding themselves in specific networks, engaging in collaborative research with the leading agents or pursuing educational opportunities with them. Originality/value This research is the first of its kind to identify and rank the top SCM authors and institutions from across the world using a representative set of seven leading SCM and primary OM journals based on publication scores and social network measures of centrality. The research is also the first of its kind to identify and rank the top authors and institutions within specific areas of SCM research and to identify future research opportunities relating to aspects of collaboration and networking in research endeavors.

Social network analysis applications in sustainable construction and built environment management: a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nandun Madhusanka Hewa Welege ◽  
Wei Pan ◽  
Mohan Kumaraswamy
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Built Environment ◽  
Methodological Approach ◽  
Relevant Literature ◽  
Sustainable Construction ◽  
Centrality Measures ◽  
Environment Management ◽  
Content Type

PurposeApplications of social network analysis (SNA) are evidently popular amongst scholars for mapping stakeholder and other relational networks in improving the sustainability of construction activities and the resulting built environment. Nevertheless, the literature reveals a lack of thorough understanding of optimal SNA applications in this field. Therefore, this paper aims to convey a comprehensive critical review of past applications of SNA in this field.Design/methodology/approach95 relevant journal papers were initially identified from the “Web of Science” database and a bibliometric analysis was carried out using the “VOS Viewer” software. The subsequent in-depth review of the SNA methods, focussed on 24 specifically relevant papers selected from these aforesaid 95 papers.FindingsA significant growth of publications in this field was identified after 2014, especially related to topics on stakeholder management. “Journal of Cleaner Production”, “International Journal of Project Management” and “Sustainability” were identified as the most productive sources in this field, with the majority of publications from China. Interviews and questionnaires were the popular data collection methods while SNA “Centrality” measures were utilised in over 70% of the studies. Furthermore, potential areas were noted, to improve the mapping and thereby provide useful information to managers who could influence relevant networks and consequentially better sustainability outcomes, including those enhanced by collaborative networks.Originality/valueCloser collaboration has been found to help enhance sustainability in construction and built environment, hence attracting research interest amongst scholars on how best to enable this. SNA is established as a significant methodological approach to analysing interrelationships and collaborative potential in general. In a pioneering application here, this paper initiates the drawing together of findings from relevant literature to provide useful insights for future researchers to comprehensively identify, compare and contrast the applications of SNA techniques in construction and built environment management from a sustainability viewpoint.

The evolution of the intellectual structure of strategic management between 1980 and 2019

2020 ◽  
Vol 13 (4) ◽  
pp. 503-534
Author(s):  
Mehmet Ali Köseoğlu ◽  
John Parnell
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Strategic Management ◽  
Citation Analysis ◽  
Intellectual Structure ◽  
Future Directions ◽  
Content Type ◽  
The Social ◽  
Economic Foundation

PurposeThe authors evaluate the evolution of the intellectual structure of strategic management (SM) by employing a document co-citation analysis through a network analysis for academic citations in articles published in the Strategic Management Journal (SMJ).Design/methodology/approachThe authors employed the co-citation analysis through the social network analysis.FindingsThe authors outlined the evolution of the academic foundations of the structure and emphasized several domains. The economic foundation of SM research with macro and micro perspectives has generated a solid knowledge stock in the literature. Industrial organization (IO) psychology has also been another dominant foundation. Its robust development and extension in the literature have focused on cognitive issues in actors' behaviors as a behavioral foundation of SM. Methodological issues in SM research have become dominant between 2004 and 2011, but their influence has been inconsistent. The authors concluded by recommending future directions to increase maturity in the SM research domain.Originality/valueThis is the first paper to elucidate the intellectual structure of SM by adopting the co-citation analysis through the social network analysis.

scholarly journals Network ties and transactive memory systems: leadership as an enabler

2017 ◽  
Vol 38 (1) ◽  
pp. 56-73 ◽  
Author(s):  
Dainelis Cabeza Pulles ◽  
Francisco Javier LLorens Montes ◽  
Leopoldo Gutierrez-Gutierrrez
Keyword(s):  
Linear Regression Analysis ◽  
Memory Systems ◽  
Multiple Linear Regression Analysis ◽  
Transactive Memory ◽  
Moderating Effect ◽  
Three Dimensions ◽  
Network Ties ◽  
Cross Sectional ◽  
Transactive Memory Systems ◽  
Content Type

Purpose The purpose of this paper is to study the relationship between network ties (NT) and transactive memory systems (TMS), observed through three dimensions – specialization (TMSS), credibility (TMSCR), and coordination (TMSCO) – in the presence of leadership (LDR) as a moderating variable, in university research-and-development (R&D) groups. Design/methodology/approach The data are composed of 257 university R&D groups. To confirm the hypotheses, the authors use multiple linear regression analysis with a moderating effect. Findings The conclusions show that the relationships between NT and two of the three dimensions of TMS (TMSCR and TMSCO) are significant when LDR is included as a moderating variable. Although the effect of TMSS is positive, it is not significant. Including the interaction element enables better explanation of two of the dimensions of TMS in the sector analyzed. Thus, LDR is perfectly applicable to the university R&D environment. Research limitations/implications This research has several limitations that suggest further possibilities for empirical research. The limitations include the cross-sectional nature of the research and the judgment of a single manager as the basis of the perception analyzed for each group. Practical implications The authors provide several implications for R&D practitioners. The results of this study could be validated in other universities in other geographic areas, enabling better generalization and applicability of the results. The results described may serve as a guide for group leaders of university R&D. This research helps us to see the importance of LDR in forming internal research networks that help researchers to perform common projects in order to obtain better results in the group. Thus, the groups provided better results to society. Originality/value No studies have tested the moderating effect of LDR in university R&D empirically. The results provide information to fill this gap and demonstrate the applicability of LDR as a key element in the organization, improvement, and cohesion of R&D groups.

FinTech innovation and knowledge flows in Hong Kong’s financial sector: a social network analysis approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anson Au
Keyword(s):  
Hong Kong ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Commercial Banks ◽  
Financial Sector ◽  
Knowledge Flows ◽  
Foreign Firms ◽  
Content Type ◽  
Network Position

Purpose This paper aims to examine how financial technology (FinTech) knowledge from foreign firms flows into and among elite commercial banks in Hong Kong’s financial sector to drive innovation. Design/methodology/approach Using social network analysis and regression analysis on a novel database of patents held by Hong Kong’s elite commercial banks, this paper examines the relationships between network position and FinTech knowledge flow. Findings This paper finds four untold patterns of innovation and inequality in Hong Kong’s financial sector: only three banks are responsible for all the FinTech knowledge entering Hong Kong; most foreign FinTech comes from the USA through Hong Kong and Shanghai Banking Corporation, whereas most FinTech from China enters through Fubon Bank and Development Bank of Singapore; older banks and banks with more connections to firms inside Asia are more likely to import FinTech; the most beneficial sources of FinTech for a bank’s network position are firms from outside Asia. Originality/value Despite the well-documented volumes of cross-border and cross-continental movement of financial institutions in Hong Kong, there is little work on the knowledge flows that underwrite this mobility. This paper addresses this gap by using FinTech knowledge flows to map the distribution of innovation, network position and competitive advantage in Hong Kong’s financial sector.

Social network analysis and data visualization of MIS international collaboration in Taiwan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yung-Ting Chuang ◽  
Yi-Hsi Chen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Collaboration ◽  
Research Productivity ◽  
Research Collaboration ◽  
Clustering Algorithms ◽  
The United States ◽  
Content Type ◽  
Collaboration Patterns

PurposeThe purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.Design/methodology/approachThe authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.FindingsThe authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.Originality/valueThis study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chun Chang ◽  
Kuan-Ting Lai ◽  
Seng-Cho T. Chou ◽  
Wei-Chuan Chiang ◽  
Yuan-Chen Lin
Keyword(s):  
Social Network ◽  
Random Walk ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Walks ◽  
Interpersonal Relationships ◽  
Interaction Network ◽  
Data Set ◽  
Content Type ◽  
Structural Equivalence

PurposeTelecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.Design/methodology/approachThe authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.FindingsThe authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.Research limitations/implicationsThe study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.Originality/valueThis paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

Relationship conflict, task conflict and teams’ transactive memory systems

2019 ◽  
Vol 34 (3) ◽  
pp. 626-640
Author(s):  
Jason M. Riley ◽  
William A. Ellegood
Keyword(s):  
Experiential Learning ◽  
Team Performance ◽  
Operations Management ◽  
Memory Systems ◽  
Transactive Memory ◽  
Task Conflict ◽  
Relationship Conflict ◽  
Conflict Task ◽  
Transactive Memory Systems ◽  
Content Type

Purpose The purpose of this paper is to understand how task conflict and relationship conflict influence teams’ transactive memory systems (TMS) and by extension team performance. Design/methodology/approach Leveraging experiential learning theory and a popular operations management simulation tool, survey data from 341 students, who worked on 117 simulation teams, are collected. To examine the present hypotheses bootstrapping analysis and SPSS were used. Findings Both task and relationship conflict can significantly diminish TMS development, which in turn, inhibits team performance. Thus, when teams disagree on how to approach a task, conflict could diminish TMS formation. In addition, when one team member has a personal conflict with one or more members that it further amplifies the influence of task conflict. To address the negative influence of both task and relationship conflict, teams should develop processes to better utilize members’ specialized knowledge and work together in a coordinated manner. Research limitations/implications The research adds to the literature by articulating the mediating influence that relationship conflict has on task conflict. Furthermore, it highlights how teams can develop TMS as a means to improve team performance when using simulation tools as a teaching device. Originality/value This work broadens our understanding of the conditions under which educators can teach students about teams and teamwork capabilities. In addition, the authors expand the use of simulations as an experiential learning tool.

Obstacles and opportunities for networked practice: a social network analysis of an inter-organizational STEM ecosystem

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Hwa Liou ◽  
Alan J. Daly
Keyword(s):  
Higher Education ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Geographic Region ◽  
Central Position ◽  
Content Type ◽  
Related Information ◽  
Stem Pipeline ◽  
And Mathematics

PurposeThis study responds to major administrative and policy priorities to support science, technology, engineering and mathematics (STEM) education by investigating a multi-sector ecosystem of regional organizations that support a STEM pipeline for education and careers.Design/methodology/approachWe use social network analysis to investigate an entire region within a geographic region of California which included 316 organizations that represent different stakeholder groups, including educational institutions (school districts, schools and higher education), government, private companies, museums, libraries and multiple community-based organizations. This STEM ecosystem reflects a systems-level analysis of a region from a unique social network perspective.FindingsResults indicate that organizations have a surface-level access to STEM-related information, but the deeper and more intense relationship which involves strategic collaboration is limited. Further, interactions around information and collaboration between organizations were purportedly in part to be about education, rarely included PK-12 schools and district as central actors in the ecosystem. In addition, while institutions of higher education occupy a central position in connecting and bridging organizations within the ecosystem, higher education's connectivity to the PK-12 education sector is relatively limited in terms of building research and practice partnerships.Originality/valueThis research has implications for how regional-level complex systems are analyzed, led and catalyzed and further reflects the need to intentionally attend to the growth of STEM networks.

Social network analysis: organizational implications in tourism management

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marco Valeri ◽  
Rodolfo Baggio
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Single Case ◽  
Effective Interaction ◽  
Organizational Structures ◽  
Review Literature ◽  
Future Research ◽  
Economic Sectors ◽  
Content Type

Purpose The purpose of this paper is to provide an overview of how quantitative analysis methods have been and can be used to improve the competitiveness of tourism destination. The focus of the study is social network analysis (SNA). Design/methodology/approach The research methodology is qualitative and consists of the review literature relevant to this thesis. This methodology is necessary to give an account of the methods and the techniques adopted for the data collection used in other economic sectors. Findings SNA is needed to analyze the creation and configuration of communities of practice within destination and to identify possible barriers to effective interaction. Essentially, it is a complex adaptive socio-economic system. It shares many (if not all) of the characteristics usually associated with such entities, namely, non-linear relationships among the components, self-organization and emergence of organizational structures, robustness to external shocks. Research limitations/implications The most important limit of this paper is that all the results presented here do not concern a single case study. Future research studies will provide a larger number of cases and examples to give the necessary validation to the findings presented here. Practical implications This paper provides a view into the network of relationships that may give tourism organization managers a strong leverage to improve the flow of information and to target opportunities where this flow may have the most impact on regulatory or business activities. Originality/value SNA can help to detect actual expertise and consequently project the potential losses deriving from an inefficient flow of knowledge. In addition, the authors will be able to define roles in the organizational networks and make an evaluation of informal organizational structures over the formal ones. Traditional organizational theories lack a concrete correspondence with mathematical studies and in this respect the authors sought to identify a correspondence.

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