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Citation and co-citation analysis to identify core and emerging knowledge in electronic commerce research

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Abstract

The purpose of this paper is to explore the core and emerging knowledge of electronic commerce (e-commerce) research. Data was collected from the top six e-commerce journals from 2006–2010. A total of 1,064 electronic commerce related articles and 33,173 references were identified. There were 48 high value research articles identified using a citation and co-citation analysis. Using statistical analysis including factor analysis, multidimensional scaling, and cluster analysis, we identified five research areas: trust, technology acceptance and technology application, e-commerce task-related application, e-markets, and identity and evaluation. We also identified emerging core knowledge, information systems success. The findings of this study provide core knowledge and directions for researchers and practitioners interested in the electronic commerce field.

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Acknowledgments

The author appreciates Mr. Kang for his help on data collection and analysis. A previous version of this paper had presented in the PACIS 2011.

Author information

Authors and Affiliations

  1. Department of Information Management, Ming Chuan University, No. 5 De Ming Rd., Gueishan Township, Taoyuan County, 333, Taiwan, ROC

    Wen-Lung Shiau

  2. School of Business and Economics, Swansea University, Room #123, Haldane Building, Singleton Park, Swansea, Wales, SA2 8PP, UK

    Yogesh K. Dwivedi

Authors
  1. Wen-Lung Shiau
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  2. Yogesh K. Dwivedi
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Correspondence to Yogesh K. Dwivedi.

Appendices

Appendix 1

The hierarchical framework of electronic commerce (Zwass 1996) and keywords

Level

Function

Keywords

7

Electronic market places and electronic hierarchies

Auction, B2B e-commerce, B2B marketplaces, competitive strategy, consumer behavior, customer relationship management, customer satisfaction, electronic mall, marketing, market intermediaries, market structure, mobile commerce, personalization, pricing, shopping, supply chain management, trust

6

Products and systems

Banking, collaboration, digital content, digital goods, digital music, electronic payment systems, information system, interorganizational information systems, interorganizational systems, MP3, on-line music, on-line video game, open-source software, recommender systems, service

5

Enabling services

Agents, artificial agents, authentication, cash, copyright, decision support, digital libraries, digital rights management, E-mail, enabling services, intelligent agents, mondex, smart-cards, software agents, web site traffic, web traffic

4

Secure messaging

Electronic data interchange, electronic payments, information security, information technology security, internet security, message management, payment method, privacy, security, security breach announcements, security control, security semantics, excess security

3

Hypermedia/multimedia object management

Data mining, information retrieval, Java, multimedia, ontologies, programming languages, semantic web, software development, technology adoption, web interface, web site, web site design, web site domain, web technologies, world wide web, XML

2

Public and private communication utilities

2G, 2.5G, 3G, 3.5G, 4G, bluetooth, cable modem, DSL, ethernet, mobile internet, mobile technology, network structure, Wi-Fi, WiMax, RFID

1

Wide-area telecommunications infrastructure

Cable, coaxial cable, fiber, fiber optic, hybrid fiber coax, infrared waves, information system infrastructures, lightwave, microwave, millimeter waves, radio, satellite, twisted pair, wireless infrastructure

Appendix 2

Publications with high citation

A1. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

A2. Venkatesh, V., Morris, M., Davis, G., Davis, F., Delone, W.H., Mclean, E.R., Jarvis, C.B., MacKenzie, S.B., Podsakoff, P.M., & Chin, W. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478.

A3. Gefen, D., Karahanna, E., & Straub, D.W. (2003a). Trust and tam in online shopping: an integrated model. MIS Quarterly, 27(1), 51–90.

A4. McKnight, D., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: an integrative typology. Information Systems Research, 13(3), 334–359.

A5. Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003.

A6. Podsakoff, P.M., MacKenzie, S.B, Lee, J.-Y., & Podsakoff, N.P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

A7. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

A8. Ba, S., & Pavlou, P.A. (2002). Evidence of the effect of trust building technology in electronic markets: price premiums and buyer behavior. MIS Quarterly, 26(3), 243–268.

A9. Pavlou, P.A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1), 37–59.

A10. Hevner, A., March, S., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.

A11. Delone, W.H., & Mclean, E.R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3(1), 60–95.

A12. Bakos, J.Y. (1997). Reducing buyer search costs: implications for electronic marketplaces. Management Science, 43(12), 1676–1692.

A13. Chin, W., Marcolin, B., & Newsted, P. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217.

A14. Jarvis, C.B., MacKenzie, S.B., & Podsakoff, P.M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Rresearch, 30(2), 199–218.

A15. Malone, T.W., Yates, J., & Benjamin, R.I. (1987). Electronic markets and electronic hierarchies. Communications of the ACM, 30(6), 484–497.

A16. Mayer, R.C., Davis, J.H., & Schoorman, F.D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734.

A17. Brynjolfsson, E., & Smith, M.D. (2000). Frictionless commerce? A comparison of internet and conventional retailers. Management Science, 46(4), 563–585.

A18. Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204.

A19. Podsakoff, P.M., & Organ, D.W. (1986). Self-reports in organizational research: problems and prospects. Journal of Management, 12(4), Winter 86, 531–543.

A20. Moore, G., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.

A21. Delone, W.H., & Mclean, E.R. (2003). The Delone and Mclean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9–30.

A22. Taylor, S., & Todd, P.A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176.

A23. Sambamurthy, V., Bharadwaj, A., & Grover, V. (2003). Shaping agility through digital options: reconceptualizing the role of information technology in contemporary firms. MIS Quarterly, 27(2), 237–263.

A24. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205–223.

A25. Pavlou, P.A. (2003). Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.

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Shiau, WL., Dwivedi, Y.K. Citation and co-citation analysis to identify core and emerging knowledge in electronic commerce research. Scientometrics 94, 1317–1337 (2013). https://doi.org/10.1007/s11192-012-0807-5

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