Skip to main content
Log in

Influence of network-based structural and power diversity on research performance

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

With an increase in studies of actors’ collaborative activities on their performance very few have examined the role of the structural influence of actors on their cohorts’ performance. In this study, we argue that the collaborative process involves social influence and social capital embedded within relationships and network structures amongst direct partner. We examine whether such influence is directly associated with an actor’s productivity and performance. In particular, we aim to understand how structural position of an actor’s cohorts and their structural and power diversity in a network influence the productivity and performance. Network-based centrality measures are used to evaluate the structural position. To investigate how partners of an actor in a collaboration network can influence their productivity and/or performance, we evaluate their structural diversity in the network by measuring the variance of their centrality measures. We evaluate if the variance of centrality measures of partners of a scholar influence their productivity or performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Abbasi, A. (2013). h-Type hybrid centrality measures for weighted networks. Scientometrics, 96(2), 633–640.

    Article  Google Scholar 

  • Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594–607.

    Article  Google Scholar 

  • Abbasi, A., Altmann, J., & Hwang, J. (2010). Evaluating scholars based on their academic collaboration activities: Two indices, the RC-index and the CC-index, for quantifying collaboration activities of researchers and scientific communities. Scientometrics, 83(1), 1–13.

    Article  Google Scholar 

  • Abbasi, A., & Hossain, L. (2013). Hybrid centrality measures for binary and weighted networks. In R. Menezes, A. Evsukoff, & M. C. González (Eds.), Complex networks (Vol. 424, pp. 1–7). Berlin: Springer.

    Chapter  Google Scholar 

  • Abbasi, A., & Jaafari, A. (2013). Research impact and scholars’ geographical diversity. Journal of Informetrics, 7(3), 683–692.

    Article  Google Scholar 

  • Abbasi, A., Wigand, R., & Hossain, L. (2014). Measuring social capital and its influence on individual performance. Library & Information Science Research, 36(1), 66–73.

    Article  Google Scholar 

  • Abramo, G., & D’Angelo, C. A. (2016). A farewell to the MNCS and like size-independent indicators. Journal of Informetrics, 10(2), 646–651.

    Article  Google Scholar 

  • Barabási, A.-L. (2013). Network science. Philosophical Transactions of the Royal Society A, 371, 20120375.

    Article  Google Scholar 

  • Bavelas, A. (1950). Communication patterns in task-oriented groups. Journal of the Acoustical Society of America, 22, 725–730.

    Article  Google Scholar 

  • Borgatti, S. (1995). Centrality and AIDS. Connections, 18(1), 112–114.

    Google Scholar 

  • Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., & Suri, S. (2008). Feedback effects between similarity and social influence in online communities. New York: ACM.

    Book  Google Scholar 

  • Demsetz, H. (1991). The theory of the firm revisited. In O. E. Williamson, S. G. Winter, & R. H. Coase (Eds.), The nature of the firm: Origins, evolution, and development (pp. 159–179). New York: Oxford University Press.

    Google Scholar 

  • Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.

    Article  MathSciNet  Google Scholar 

  • Freeman, L. C. (1980). The gatekeeper, pair-dependency and structural centrality. Quality & Quantity, 14(4), 585–592.

    Article  Google Scholar 

  • Friedkin, N. E. (1998). A structural theory of social influence. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Haque, A., & Ginsparg, P. (2009). Positional effects on citation and readership in arXiv. Journal of the American Society for Information Science and Technology, 60(11), 2203–2218.

    Article  Google Scholar 

  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the USA, 102(46), 16569–16572.

    Article  MATH  Google Scholar 

  • Imran, M., Elbassuoni, S. M., Castillo, C., Diaz, F., & Meier, P. (2013). Extracting information nuggets from disaster-related messages in social media. Baden-Baden: ISCRAM.

    Google Scholar 

  • Jalili, M., Orouskhani, Y., Asgari, M., Alipourfard, N., & Perc, M. (2017). Link prediction in multilayer online social networks. Royal Society Open Science, 4(2), 160863.

    Article  MathSciNet  Google Scholar 

  • Jalili, M., & Perc, M. (2017). Information cascades in complex networks. Journal of Complex Networks, 5(5), 665–693.

    MathSciNet  Google Scholar 

  • Javari, A., & Jalili, M. (2015). A probabilistic model to resolve diversity-accuracy challenge of recommendation systems. Knowledge and Information Systems, 44, 609–627.

    Article  Google Scholar 

  • Jiang, Y. (2008). Locating active actors in the scientific collaboration communities based on interaction topology analyses. Scientometrics, 74(3), 471–482.

    Article  Google Scholar 

  • Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A, 390, 1150–1170.

    Article  Google Scholar 

  • Peng, S., Wang, G., & Xie, D. (2017). Social influence analysis in social networking big data: Opportunities and challenges. IEEE Network, 31(1), 11–17.

    Article  Google Scholar 

  • Saarela, M., Kärkkäinen, T., Lahtonen, T., & Rossi, T. (2016). Expert-based versus citation-based ranking of scholarly and scientific publication channels. Journal of Informetrics, 10(3), 693–718.

    Article  Google Scholar 

  • Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31(4), 581–603.

    Article  MathSciNet  MATH  Google Scholar 

  • Scott, J. (1991). Social network analysis: A handbook. Beverley Hills: Sage.

    Google Scholar 

  • Shahriari, M., & Jalili, M. (2014). Ranking nodes in signed social networks. Social Network Analysis and Mining, 4(1), 1–12.

    Article  Google Scholar 

  • Strang, D. (2000). Review: A structural theory of social influence. JSTOR, 45, 162–164.

    Google Scholar 

  • Takeda, H., Truex III, D., & Cuellar, M. (2010). Evaluating scholarly influence through social network analysis: The next step in evaluating scholarly influence. In AMCIS 2010 proceedings (Vol. 573).

  • Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365–391.

    Article  Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge, NY: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology, 60(10), 2107–2118.

    Article  Google Scholar 

  • Zhang, C., Lu, T., Chen, S., & Zhang, C. (2017). Integrating ego, homophily, and structural factors to measure user influence in online community. IEEE Transactions on Professional Communication, 60(3), 292–305.

    Article  Google Scholar 

  • Zhuge, H., & Zhang, J. (2010). Topological centrality and its e-science applications. Journal of the American Society for Information Science and Technology, 61(9), 1824–1841.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Korean MSIT (Ministry of Science and ICT) under the ITRC support program (IITP-2017-2016-0-00312) supervised by the IITP.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abolghasem Sadeghi-Niaraki.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abbasi, A., Jalili, M. & Sadeghi-Niaraki, A. Influence of network-based structural and power diversity on research performance. Scientometrics 117, 579–590 (2018). https://doi.org/10.1007/s11192-018-2879-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-018-2879-3

Keywords

Navigation