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Interpreting and predicting social commerce intention based on knowledge graph analysis

  • Liu Yuan
  • Zhao HuangEmail author
  • Wei Zhao
  • Pavel Stakhiyevich
Article
  • 23 Downloads

Abstract

There have been significant efforts to understand, describe, and predict the social commerce intention of users in the areas of social commerce and web data management. Based on recent developments in knowledge graph and inductive logic programming in artificial intelligence, in this paper, we propose a knowledge-graph-based social commerce intention analysis method. In particular, a knowledge base is constructed to represent the social commerce environment by integrating information related to social relationships, social commerce factors, and domain background knowledge. In this study, knowledge graphs are used to represent and visualize the entities and relationships related to social commerce, while inductive logic programming techniques are used to discover implicit information that can be used to interpret the information behaviors and intentions of the users. Evaluation tests confirmed the effectiveness of the proposed method. In addition, the feasibility of using knowledge graphs and knowledge-based data mining techniques in the social commerce environment is also confirmed.

Keywords

Social commerce Social commerce intention Knowledge graph Knowledge base Inductive logic programming 

Notes

Acknowledgements

This study was supported by research grants funded by the National Natural Science Foundation of China (Grant No. 61771297), and the “Fundamental Research Funds for the Central Universities” (GK201803062, GK201802013).

Compliance with ethical standards

Conflict of interest

This manuscript has not been published and is not under consideration for publication elsewhere. We have no conflicts of interest to disclose. Both authors have read and approved the final version of the manuscript.

References

  1. 1.
    Zhao, N., & Li, H. (2019). How can social commerce be boosted? the impact of consumer behaviors on the information dissemination mechanism in a social commerce network. Electronic Commerce Research.  https://doi.org/10.1007/s10660-018-09326-3.CrossRefGoogle Scholar
  2. 2.
    Afrasiabi Rad, A., & Benyoucef, M. (2011). A model for understanding social commerce. Journal of Information Systems Applied Research,4(2), 63–73.Google Scholar
  3. 3.
    Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications,12(4), 246–259.CrossRefGoogle Scholar
  4. 4.
    Huang, Z., & Benyoucef, M. (2015). User preferences of social features on social commerce websites: an empirical study. Technological Forecasting and Social Change,95, 57–72.CrossRefGoogle Scholar
  5. 5.
    Attia, A. M., Aziz, N., & Friedman, B. A. (2012). The impact of social networks on behavioral change: A conceptual framework. World Review of Business Research,2(2), 91–108.Google Scholar
  6. 6.
    Baghdadi, Y. (2016). A framework for social commerce design. Information Systems,60(C), 95–113.CrossRefGoogle Scholar
  7. 7.
    Friedrich, T. (2015). Analyzing the factors that influence consumers’ adoption of social commerce—A literature review. In Americas conference on information systems, Puerto Rico, United States.Google Scholar
  8. 8.
    Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management,35(2), 183–191.CrossRefGoogle Scholar
  9. 9.
    Zhang, K. Z., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems,86, 95–108.CrossRefGoogle Scholar
  10. 10.
    Zhang, T., & Chen, P. J. (2018). Customer engagement with social commerce: A motivation analysis. Advances in Global Business and Economıcs,1, 175–183.Google Scholar
  11. 11.
    Herrando, C., Jiménez-Martínez, J., & Martín-De Hoyos, M. J. (2017). Passion at first sight: How to engage users in social commerce contexts. Electronic Commerce Research,17, 701–720.CrossRefGoogle Scholar
  12. 12.
    Li, Q., Liang, N., & Li, E. Y. (2018). Does friendship quality matter in social commerce? An experimental study of its effect on purchase intention. Electronic Commerce Research,18, 693–717.CrossRefGoogle Scholar
  13. 13.
    Hajli, M. N. (2014). The role of social support on relationship quality and social commerce. Technological Forecasting and Social Change,87, 17–27.CrossRefGoogle Scholar
  14. 14.
    Hajli, M. N. (2014). Social commerce for innovation. International Journal of Innovation Management,18(04), 1450024.CrossRefGoogle Scholar
  15. 15.
    Hajli, N. (2015). Handbook of research on integrating social media into strategic marketing. Hershey: IGI Global.CrossRefGoogle Scholar
  16. 16.
    Singhal, A. (2012). Introducing the knowledge graph: Things, not strings. Official google blog, 5.Google Scholar
  17. 17.
    Molinillo, S., Liébana-Cabanillas, F., & Anaya-Sánchez, R. (2018). A social commerce intention model for traditional e-commerce sites. Journal of theoretical and applied electronic commerce research,13(2), 80–93.CrossRefGoogle Scholar
  18. 18.
    Yahia, I. B., Al-Neama, N., & Kerbache, L. (2018). Investigating the drivers for social commerce in social media platforms: Importance of trust, social support and the platform perceived usage. Journal of Retailing and Consumer Services,41, 11–19.CrossRefGoogle Scholar
  19. 19.
    Aladwani, A. M. (2018). A quality-facilitated socialization model of social commerce decisions. International Journal of Information Management,40, 1–7.CrossRefGoogle Scholar
  20. 20.
    Hung, S. Y., Yu, A. P. I., & Chiu, Y. C. (2018). Investigating the factors influencing small online vendors’ intention to continue engaging in social commerce. Journal of Organizational Computing and Electronic Commerce,28(1), 9–30.CrossRefGoogle Scholar
  21. 21.
    Zhou, T. (2019). The effect of social interaction on users’ social commerce intention. International Journal of Mobile Communications,17(4), 391–408.CrossRefGoogle Scholar
  22. 22.
    Dashti, M., Sanayei, A., Dolatabadi, H. R., & Javadi, M. H. M. (2019). Application of the stimuli-organism-response framework to factors influencing social commerce intentions among social network users. International Journal of Business Information Systems,30(2), 177–202.CrossRefGoogle Scholar
  23. 23.
    Yan, S. R., Zheng, X. L., Wang, Y., Song, W. W., & Zhang, W. Y. (2015). A graph-based comprehensive reputation model: Exploiting the social context of opinions to enhance trust in social commerce. Information Sciences,318, 51–72.CrossRefGoogle Scholar
  24. 24.
    Anand, A., & Lee, J. (2016). U.S. Patent No. 9,497,234. Washington, DC: U.S. Patent and Trademark Office.Google Scholar
  25. 25.
    Bai, Y., Yao, Z., & Dou, Y. F. (2015). Effect of social commerce factors on user purchase behavior: An empirical investigation from renren.com. International Journal of Information Management,35(5), 538–550.CrossRefGoogle Scholar
  26. 26.
    Chen, J., & Shen, X. L. (2015). Consumers’ decisions in social commerce context: An empirical investigation. Decision Support Systems,79, 55–64.  https://doi.org/10.1016/j.dss.2015.07.012.CrossRefGoogle Scholar
  27. 27.
    Maryam, S. (2017). Factors affecting social commerce and exploring the mediating role of perceived risk. Iranian Journal of Management Studies,10(1), 63–90.Google Scholar
  28. 28.
    Alhulail, H., Dick, M., & Abareshi, A. (2018). Factors that impact customers’ loyalty to social commerce websites. In International conference on information resources management, Ningbo, China.Google Scholar
  29. 29.
    Lin, X., Featherman, M., & Sarker, S. (2017). Understanding factors affecting users’ social networking site continuance: A gender difference perspective. Information & Management,54(3), 383–395.CrossRefGoogle Scholar
  30. 30.
    Lin, X., Li, Y., & Wang, X. (2017). Social commerce research: Definition, research themes and the trends. International Journal of Information Management,37(3), 190–201.CrossRefGoogle Scholar
  31. 31.
    Wamba, S. F., Bhattacharya, M., Trinchera, L., & Ngai, E. W. (2017). Role of intrinsic and extrinsic factors in user social media acceptance within workspace: Assessing unobserved heterogeneity. International Journal of Information Management,37(2), 1–13.CrossRefGoogle Scholar
  32. 32.
    Akman, I., & Mishra, A. (2017). Factors influencing consumer intention in social commerce adoption. Information Technology & People,30(2), 356–370.CrossRefGoogle Scholar
  33. 33.
    Hajli, N., Sims, J., Zadeh, A. H., & Richard, M. O. (2017). A social commerce investigation of the role of trust in a social networking site on purchase intentions. Journal of Business Research,71, 133–141.CrossRefGoogle Scholar
  34. 34.
    Biucky, S. T., & Harandi, S. R. (2017). The effects of perceived risk on social commerce adoption based on tam model. International Journal of Electronic Commerce Studies,8(2), 173–196.Google Scholar
  35. 35.
    Akram, U., Hui, P., Khan, M. K., Yan, C., & Akram, Z. (2018). Factors affecting online impulse buying: Evidence from Chinese social commerce environment. Sustainability,10(2), 352.CrossRefGoogle Scholar
  36. 36.
    Gruber, T. (2009). Ontology. Encyclopedia of database systems, 1963–1965.Google Scholar
  37. 37.
    Baghdadi, Y. (2016). Towards an ontology for enterprise interactions. In Information and communication technologies in organizations and society (pp. 263–275). Cham: Springer.CrossRefGoogle Scholar
  38. 38.
    Necula, S. C., Păvăloaia, V. D., Strîmbei, C., & Dospinescu, O. (2018). Enhancement of E-commerce websites with semantic web technologies. Sustainability,10(6), 1955.CrossRefGoogle Scholar
  39. 39.
    Brickley, D., & Miller, L. (2016). The friend of a friend (FOAF) vocabulary specification, November 2007. http://xmlns.com/foaf/spec. Accessed February 22, 2019.
  40. 40.
    Westerinen, A., & Tauber, R. (2017). Integrating GoodRelations in a domain-specific ontology. Applied Ontology,12, 323–340.CrossRefGoogle Scholar
  41. 41.
    Hepp, M. (2015). The web of data for e-commerce: Schema.org and GoodRelations for researchers and practitioners. In International conference on Web Engineering (pp. 723–727). Cham: Springer.CrossRefGoogle Scholar
  42. 42.
    Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic web,8(3), 489–508.CrossRefGoogle Scholar
  43. 43.
    Verborgh, R., et al. (2016). Triple pattern fragments: A low-cost knowledge graph interface for the Web. Web Semantics: Science, Services and Agents on the World Wide Web,37, 184–206.CrossRefGoogle Scholar
  44. 44.
    Eder, J. S. (2012). U.S. Patent Application No. 13/404, 109.Google Scholar
  45. 45.
    Muggleton, S., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. The Journal of Logic Programming,19, 629–679.CrossRefGoogle Scholar
  46. 46.
    Gulwani, S., Hernández-Orallo, J., Kitzelmann, E., Muggleton, S. H., Schmid, U., & Zorn, B. (2015). Inductive programming meets the real world. Communications of the ACM,58(11), 90–99.CrossRefGoogle Scholar
  47. 47.
    Hernández-Orallo, J., Muggleton, et al. (2016). Approaches and applications of inductive programming. Dagstuhl Reports, 5(10), 89–111. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.Google Scholar
  48. 48.
    Inoue, K., Ohwada, H., & Yamamoto, A. (2016). Inductive logic programming: Challenges. In AAAI, Phoenix, Arizona USA (pp. 4330–4332).Google Scholar
  49. 49.
    Schmid, U. (2018). Inductive programming as approach to comprehensible machine learning. In Proceedings of the 7th workshop on dynamics of knowledge and belief and the 6th workshop KI & Kognition (Vol. 2194, pp. 4–12).Google Scholar
  50. 50.
    Athanasopoulos, G., Paliouras, G., et al. (2018). Predicting the evolution of communities with online inductive logic programming. In 25th international symposium on temporal representation and reasoning, Warsaw, Poland (Vol. 120, pp. 4:1–4:20).Google Scholar
  51. 51.
    Kitchin, R., & Lauriault, T. P. (2015). Small data in the era of big data. GeoJournal,80(4), 463–475.CrossRefGoogle Scholar
  52. 52.
    Schmid, U., Muggleton, S. H., & Singh, R. (2018). Approaches and applications of inductive programming (Dagstuhl Seminar 17382). In Dagstuhl reports (Vol. 7, No. 9). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.Google Scholar
  53. 53.
    Malec, M., Khot, T., Nagy, J., Blasch, E., & Natarajan, S. (2016). Inductive logic programming meets relational databases: An application to statistical relational learning. In Inductive Logic Programming (ILP).Google Scholar
  54. 54.
    Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Martínez-Plumed, F., Ramírez-Quintana, M. J., & Katayama, S. (2018). General-purpose declarative inductive programming with domain-specific background knowledge for data wrangling automation. arXiv:1809.10054.
  55. 55.
    Ahn, T., Ryu, S., & Han, I. (2007). The impact of web quality and playfulness on user acceptance of online retailing. Information & Management,44(3), 263–275.CrossRefGoogle Scholar
  56. 56.
    Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information & Management,51(8), 1017–1030.CrossRefGoogle Scholar
  57. 57.
    Chang Lee, K., Currás-Pérez, R., Ruiz-Mafé, C., & Sanz-Blas, S. (2013). Social network loyalty: Evaluating the role of attitude, perceived risk and satisfaction. Online Information Review,37(1), 61–82.CrossRefGoogle Scholar
  58. 58.
    Chang, H., & Wen Chen, S. (2008). The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online Information Review,32(6), 818–841.CrossRefGoogle Scholar
  59. 59.
    Liang, T. P., Ho, Y. T., Li, Y. W., & Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. International Journal of Electronic Commerce,16(2), 69–90.CrossRefGoogle Scholar
  60. 60.
    Home, D. C. M. I. (2015). Dublin Core® Metadata Initiative (DCMI).Google Scholar
  61. 61.
    Bechhofer, S., Van Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D. L., Patel-Schneider, P. F., & Stein, L. A. (2004). OWL Web ontology language reference. W3C Recommendation, 10(02).Google Scholar
  62. 62.
    W3C Owl Working Group. (2009). 2 Web ontology language document overview. W3C Recommendation, 27(10).Google Scholar
  63. 63.
    WeChat Economic and Social Impact Report 2017. (2017) China Academy of Information and Communications Technology Industry and Planning Research Institute.Google Scholar
  64. 64.
    Bühmann, L., Lehmann, J., & Westphal, P. (2016). DL-Learner—a framework for inductive learning on the semantic Web. Web Semantics: Science, Services and Agents on the World Wide Web,39, 15–24.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Modern Teaching TechnologyMinistry of EducationXi’anPeople’s Republic of China
  2. 2.School of Computer ScienceShaanxi Normal UniversityXi’anPeople’s Republic of China

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