Skip to main content
Log in

Does prestige dimension influence the interdisciplinary performance of scientific entities in knowledge flow? Evidence from the e-government field

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

It has long been understood that knowledge flow can be divided into knowledge integration and knowledge diffusion and can be investigated by interdisciplinary scientific research (IDR) approaches. The literature describes some quantitative approaches for measuring interdisciplinary research, and all of them belong to a popularity dimension. Previous work failed to address the problem of evaluating interdisciplinary research in a prestige dimension. However, in this study, the authors introduce an extended IDR measure that combines the P-Rank algorithm with the traditional IDR approaches to promote the current IDR approaches from the popularity dimension to the prestige dimension. This extended measure explores the prestige dimension of papers and considers the subsequent contribution of papers of different prestige devoted to the knowledge flow in which they are embedded. An experiment regarding the e-government field demonstrates that the interdisciplinary performance of some papers is overestimated under traditional IDR approaches and that the performance would be more reasonable under an extended IDR measure that considers the prestige dimension. We expect that the extended IDR measure can identify the different contributions of papers of different prestige with regard to their interdisciplinary performance and then reevaluate their contributions to the knowledge flow in which they are embedded.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 665–694.

    Google Scholar 

  • Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391.

    Google Scholar 

  • Bandura, A. (1978). Self-efficacy: Toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy, 1(4), 139–161.

    Google Scholar 

  • Bélanger, F., & Carter, L. (2008). Trust and risk in e-government adoption. The Journal of Strategic Information Systems, 17(2), 165–176.

    Google Scholar 

  • Bélanger, F., & Carter, L. (2012). Digitizing government interactions with constituents: An historical review of e-government research in information systems. Journal of the Association for Information Systems, 13(5), 363–394.

    Google Scholar 

  • Belcher, B. M., Rasmussen, K. E., Kemshaw, M. R., & Zornes, D. A. (2016). Defining and assessing research quality in a transdisciplinary context. Research Evaluation, 25(1), 1–17.

    Google Scholar 

  • Bertot, J. C., Jaeger, P. T., & Grimes, J. M. (2010). Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies. Government Information Quarterly, 27(3), 264–271.

    Google Scholar 

  • Bolívar, R. M. P., Muñoz, A. L., & Hernández, L. A. M. (2010). Trends of e-government research: Contextualization and research opportunities. The International Journal of Digital Accounting Research, 10, 87–111.

    Google Scholar 

  • Bollen, J., Rodriquez, M. A., & Van de Sompel, H. (2006). Journal status. Scientometrics, 69(3), 669–687.

    Google Scholar 

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1–7), 107–117.

    Google Scholar 

  • Carley, S., & Porter, A. L. (2011). A forward diversity index. Scientometrics, 90(2), 407–427.

    Google Scholar 

  • Carr, G., Loucks, D. P., & Blöschl, G. (2018). Gaining insight into interdisciplinary research and education programmes: A framework for evaluation. Research Policy, 47(1), 35–48.

    Google Scholar 

  • Carter, L., & Bélanger, F. (2005). The utilization of e-government services: Citizen trust, innovation and acceptance factors. Information Systems Journal, 15(1), 5–25.

    Google Scholar 

  • Chi, X., Streicher-Porte, M., Wang, M. Y., & Reuter, M. A. (2011). Informal electronic waste recycling: A sector review with special focus on China. Waste Management, 31(4), 731–742.

    Google Scholar 

  • Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487.

    Google Scholar 

  • Ding, Y., Yan, E., Frazho, A., & Caverlee, J. (2009). PageRank for ranking authors in co-citation networks. Journal of the Association for Information Science and Technology, 60(11), 2229–2243.

    Google Scholar 

  • Dwivedi, Y. K., & Weerakkody, V. (2010). A profile of scholarly community contributing to the International Journal of Electronic Government Research. International Journal of Electronic Government Research, 6(4), 1–11.

    Google Scholar 

  • Falagas, M. E., Kouranos, V. D., Arencibia-Jorge, R., & Karageorgopoulos, D. E. (2008). Comparison of SCImago journal rank indicator with journal impact factor. The FASEB Journal, 22(8), 2623–2628.

    Google Scholar 

  • Frodeman, R., Klein, J. T., & Pacheco, R. C. D. S. (2017). The Oxford handbook of interdisciplinarity. Oxford: Oxford University Press.

    Google Scholar 

  • Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471–479.

    Google Scholar 

  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51–90.

    Google Scholar 

  • Haveliwala, T., Kamvar, S., & Jeh, G. (2003). An analytical comparison of approaches to personalizing pagerank. Technical Report, Stanford University, California.

  • Heeks, R., & Bailur, S. (2007). Analyzing e-government research: Perspectives, philosophies, theories, methods, and practice. Government Information Quarterly, 24(2), 243–265.

    Google Scholar 

  • Hoffman, D. L., Novak, T. P., & Peralta, M. (1999). Building consumer trust online. Communications of the ACM, 42(4), 80–85.

    Google Scholar 

  • Horst, M., Kuttschreuter, M., & Gutteling, J. M. (2007). Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in the Netherlands. Computers in Human Behavior, 23(4), 1838–1852.

    Google Scholar 

  • Jiang, X., Sun, X., Yang, Z., Zhuge, H., & Yao, J. (2016). Exploiting heterogeneous scientific literature networks to combat ranking bias: Evidence from the computational linguistics area. Journal of the Association for Information Science and Technology, 67(7), 1679–1702.

    Google Scholar 

  • Joseph, R. C. (2013). A structured analysis of e-government studies: Trends and opportunities. Government Information Quarterly, 30(4), 435–440.

    Google Scholar 

  • Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23, 183–213.

    Google Scholar 

  • Klein, J. T. (2008). Evaluation of interdisciplinary and transdisciplinary research. American Journal of Preventive Medicine, 35(2), S116–S123.

    Google Scholar 

  • Larivière, V., & Gingras, Y. (2010). On the relationship between interdisciplinarity and scientific impact. Journal of the Association for Information Science and Technology, 61(1), 126–131.

    Google Scholar 

  • Layne, K., & Lee, J. (2001). Developing fully functional E-government: A four stage model. Government Information Quarterly, 18(2), 122–136.

    Google Scholar 

  • Levitt, J. M., & Thelwall, M. (2008). Is multidisciplinary research more highly cited? A macrolevel study. Journal of the Association for Information Science and Technology, 59(12), 1973–1984.

    Google Scholar 

  • Levitt, J. M., Thelwall, M., & Oppenheim, C. (2011). Variations between subjects in the extent to which the social sciences have become more interdisciplinary. Journal of the Association for Information Science and Technology, 62(6), 1118–1129.

    Google Scholar 

  • Leydesdorff, L. (2007a). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the Association for Information Science and Technology, 58(9), 1303–1319.

    Google Scholar 

  • Leydesdorff, L. (2007b). Mapping interdisciplinarity at the interfaces between the Science Citation Index and the Social Science Citation Index. Scientometrics, 71(3), 391–405.

    Google Scholar 

  • Leydesdorff, L., & Goldstone, R. L. (2014). Interdisciplinarity at the journal and specialty level: The changing knowledge bases of the journal Cognitive Science. Journal of the Association for Information Science and Technology, 65(1), 164–177.

    Google Scholar 

  • Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the Association for Information Science and Technology, 60(2), 348–362.

    Google Scholar 

  • Leydesdorff, L., & Rafols, I. (2011). Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations. Journal of Informetrics, 5(1), 87–100.

    Google Scholar 

  • Leydesdorff, L., Rafols, I., & Chen, C. (2013). Interactive overlays of journals and the measurement of interdisciplinarity on the basis of aggregated journal–journal citations. Journal of the Association for Information Science and Technology, 64(12), 2573–2586.

    Google Scholar 

  • Linders, D. (2012). From e-government to we-government: Defining a typology for citizen coproduction in the age of social media. Government Information Quarterly, 29(4), 446–454.

    Google Scholar 

  • Linton, J. D., Tierney, R., & Walsh, S. T. (2012). What are research expectations? A comparative study of different academic disciplines. Serials Review, 38(4), 228–234.

    Google Scholar 

  • Liu, X., Tanaka, M., & Matsui, Y. (2006). Generation amount prediction and material flow analysis of electronic waste: A case study in Beijing. China. Waste Management & Research, 24(5), 434–445.

    Google Scholar 

  • Mansilla, V. B., Feller, I., & Gardner, H. (2006). Quality assessment in interdisciplinary research and education. Research Evaluation, 15(1), 69–74.

    Google Scholar 

  • Morillo, F., Bordons, M., & Gómez, I. (2001). An approach to interdisciplinarity through bibliometric indicators. Scientometrics, 51(1), 203–222.

    Google Scholar 

  • Morillo, F., Bordons, M., & Gómez, I. (2003). Interdisciplinarity in science: A tentative typology of disciplines and research areas. Journal of the Association for Information Science and Technology, 54(13), 1237–1249.

    Google Scholar 

  • Moya-Anegón, F., Vargas-Quesada, B., Herrero-Solana, V., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., & Munoz-Fernández, F. (2004). A new technique for building maps of large scientific domains based on the cocitation of classes and categories. Scientometrics, 61(1), 129–145.

    Google Scholar 

  • Muñoz, A. L., & Bolívar, R. M. P. (2015). Understanding e-government research: a perspective from the information and library science field of knowledge. Internet Research, 25(4), 633–673.

    Google Scholar 

  • Muñoz, A. L., Bolívar, R. M. P., Cobo, M. J., & Viedma, H. E. (2017a). Analysing the scientific evolution of e-government using a science mapping approach. Government Information Quarterly, 34(3), 545–555.

    Google Scholar 

  • Muñoz, A. L., Bolívar, R. M. P., & Hernández, L. A. M. (2017b). Transparency in governments: A meta-analytic review of incentives for digital versus hard-copy public financial disclosures. The American Review of Public Administration, 47(5), 550–573.

    Google Scholar 

  • Nykl, M., Ježek, K., Fiala, D., & Dostal, M. (2014). PageRank variants in the evaluation of citation networks. Journal of Informetrics, 8(3), 683–692.

    Google Scholar 

  • Porter, A. L., & Chubin, D. (1985). An indicator of cross-disciplinary research. Scientometrics, 8(3–4), 161–176.

    Google Scholar 

  • Porter, A. L., Cohen, A., David Roessner, J., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72(1), 117–147.

    Google Scholar 

  • Porter, A. L., Roessner, J. D., Cohen, A. S., & Perreault, M. (2006). Interdisciplinary research: Meaning, metrics and nurture. Research Evaluation, 15(3), 187–195.

    Google Scholar 

  • Qin, J., Lancaster, F. W., & Allen, B. (1997). Types and levels of collaboration in interdisciplinary research in the sciences. Journal of the American Society for Information Science and Technology, 48(10), 893–916.

    Google Scholar 

  • Raasch, C., Lee, V., Spaeth, S., & Herstatt, C. (2013). The rise and fall of interdisciplinary research: The case of open source innovation. Research Policy, 42(5), 1138–1151.

    Google Scholar 

  • Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82(2), 263–287.

    Google Scholar 

  • Rao, C. R. (1982). Diversity: its measurement, decomposition, apportionment and analysis. Sankhyā: The Indian Journal of Statistics, Series A, 44, 1–22.

    MathSciNet  MATH  Google Scholar 

  • Rinia, E., Van Leeuwen, T., & Van Raan, A. (2002). Impact measures of interdisciplinary research in physics. Scientometrics, 53(2), 241–248.

    Google Scholar 

  • Saha, S., Saint, S., & Christakis, D. A. (2003). Impact factor: A valid measure of journal quality? Journal of the Medical Library Association, 91(1), 42.

    Google Scholar 

  • Salton, G., & Bergmark, D. (1979). A citation study of computer science literature. IEEE Transactions on Professional Communication, 22(3), 146–158.

    Google Scholar 

  • Scholl, H. J. J., & Dwivedi, Y. K. (2014). Forums for electronic government scholars: Insights from a 2012/2013 study. Government Information Quarterly, 31(2), 229–242.

    Google Scholar 

  • Small, H., & Garfield, E. (1985). The geography of science: Disciplinary and national mappings. Information Scientist, 11(4), 147–159.

    Google Scholar 

  • Snead, J. T., & Wright, E. (2014). E-government research in the United States. Government Information Quarterly, 31(1), 129–136.

    Google Scholar 

  • Soós, S., & Kampis, G. (2011). Towards a typology of research performance diversity: The case of top Hungarian players. Scientometrics, 87(2), 357–371.

    Google Scholar 

  • Steele, T. W., & Stier, J. C. (2000). The impact of interdisciplinary research in the environmental sciences: A forestry case study. Journal of the Association for Information Science and Technology, 51(5), 476–484.

    Google Scholar 

  • Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society, Interface, 4(15), 707–719.

    Google Scholar 

  • Tat-Kei Ho, A. (2002). Reinventing local governments and the e-government initiative. Public Administration Review, 62(4), 434–444.

    Google Scholar 

  • Teo, T. S., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: An empirical study. Journal of Management Information Systems, 25(3), 99–132.

    Google Scholar 

  • Tolbert, C. J., & Mossberger, K. (2006). The effects of e-government on trust and confidence in government. Public Administration Review, 66(3), 354–369.

    Google Scholar 

  • Van Leeuwen, T., & Tijssen, R. (2000). Interdisciplinary dynamics of modern science: Analysis of cross-disciplinary citation flows. Research Evaluation, 9(3), 183–187.

    Google Scholar 

  • Van Rijnsoever, F. J., & Hessels, L. K. (2011). Factors associated with disciplinary and interdisciplinary research collaboration. Research Policy, 40(3), 463–472.

    Google Scholar 

  • Vanclay, J. K. (2012). Impact factor: Outdated artefact or stepping-stone to journal certification? Scientometrics, 92(2), 211–238.

    Google Scholar 

  • Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly, 23, 239–260.

    Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27, 425–478.

    Google Scholar 

  • Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5(1), 14–26.

    Google Scholar 

  • Wang, X., Wang, Z., Huang, Y., Chen, Y., Zhang, Y., Ren, H., et al. (2017). Measuring interdisciplinarity of a research system: Detecting distinction between publication categories and citation categories. Scientometrics, 111(3), 2023–2039.

    Google Scholar 

  • West, D. M. (2004). E-government and the transformation of service delivery and citizen attitudes. Public Administration Review, 64(1), 15–27.

    MathSciNet  Google Scholar 

  • Yan, E., & Ding, Y. (2010). Weighted citation: An indicator of an article’s prestige. Journal of the Association for Information Science and Technology, 61(8), 1635–1643.

    Google Scholar 

  • Yan, E., Ding, Y., & Sugimoto, C. R. (2011). P-Rank: An indicator measuring prestige in heterogeneous scholarly networks. Journal of the Association for Information Science and Technology, 62(3), 467–477.

    Google Scholar 

  • Yildiz, M. (2007). E-government research: Reviewing the literature, limitations, and ways forward. Government Information Quarterly, 24(3), 646–665.

    Google Scholar 

  • Yu, D., & Shi, S. (2015). Researching the development of Atanassov intuitionistic fuzzy set: Using a citation network analysis. Applied Soft Computing, 32, 189–198.

    Google Scholar 

  • Yu, D. J., Wang, W. R., Zhang, S., Zhang, W. Y., & Liu, R. Y. (2017a). A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals. Scientometrics, 111(1), 521–542.

    Google Scholar 

  • Yu, D., Xu, Z., Pedrycz, W., & Wang, W. (2017b). Information Sciences 1968–2016: A retrospective analysis with text mining and bibliometric. Information Sciences, 418, 619–634.

    Google Scholar 

  • Zhou, D., Orshanskiy, S. A., Zha, H., & Giles, C. L. (2007). Co-ranking authors and documents in a heterogeneous network. In: Proceedings of the seventh IEEE international conference on data mining, October 28–31, Omaha, USA (pp. 739–744).

  • Zitt, M. (2005). Facing diversity of science: A challenge for bibliometric indicators. Measurement: Interdisciplinary Research and Perspectives, 3(1), 38–49.

    Google Scholar 

  • Zitt, M., Ramanana-Rahary, S., & Bassecoulard, E. (2005). Relativity of citation performance and excellence measures: From cross-field to cross-scale effects of field-normalisation. Scientometrics, 63(2), 373–401.

    Google Scholar 

Download references

Acknowledgements

The work has been supported by National Natural Science Foundation of China (Nos. 51475410, 51875503, 51775496), Zhejiang Natural Science Foundation of China (No. LY17E050010).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wenyu Zhang.

Appendix

Appendix

See Table 5.

Table 5 The abbreviation and full name of categories in Figs. 5 and 7

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, S., Zhang, W., Zhang, S. et al. Does prestige dimension influence the interdisciplinary performance of scientific entities in knowledge flow? Evidence from the e-government field. Scientometrics 117, 1237–1264 (2018). https://doi.org/10.1007/s11192-018-2914-4

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-018-2914-4

Keywords

Navigation