Skip to main content

Advertisement

Springer Nature Link
Log in

Exploring the key factors affecting the usage intention for cross-border e-commerce platforms based on DEMATEL and EDAS method

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

Cross-border e-commerce (CBEC) is developing into a new business and an important part of the international trade channel. While understanding the usage intention that facilitates platform adoption is a critical issue for any CBEC firm, academia has paid scant attention to this topic. This study thus explores the critical determinants affecting consumers’ usage intention of CBEC platforms. First, this study combines the technology acceptance model (TAM) and the DeLone and McLean (D&M) information system success model (ISSM) as a framework to identify the relevant evaluation factors. Second, the proposed model integrates Decision Making Trial and Evaluation Laboratory (DEMATEL) with the Evaluation based on Distance from Average Solution (EDAS) approach. Moreover, we employ DEMATEL to measure the importance of the factors and alternatives to CBEC platforms as ranked by the EDAS method. According to the results of ranking we are able to identify the best preference among the alternatives to CBEC platforms. Finally, our findings offer some suggestions for developing a model on consumers’ usage intention of CBEC platforms for related businesses.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. van Heel, B., Lukic, V., & Leeuwis, E. (2014). Cross-border e-commerce makes the world flatter. The Boston Consulting Group.

    Google Scholar 

  2. Wang, Y., & Lee, S. H. (2017). The effect of cross-border e-commerce on China’s international trade: An empirical study based on transaction cost analysis. Sustainability, 9(11), 2028.

    Google Scholar 

  3. Yin, Z. H., & Choi, C. H. (2021). The effects of China’s cross-border e-commerce on its exports: a comparative analysis of goods and services trade. Electronic Commerce Research, 1–32.

  4. Mi, C., Wang, Y., & Xiao, L. (2021). Prediction on transaction amounts of China’s CBEC with improved GM (1, 1) models based on the principle of new information priority. Electronic Commerce Research, 21(1), 125–146.

    Google Scholar 

  5. Cheng, X., Su, L., & Zarifis, A. (2019). Designing a talents training model for cross-border e-commerce: A mixed approach of problem-based learning with social media. Electronic Commerce Research, 19(4), 801–822.

    Google Scholar 

  6. Cui, Y., Mou, J., Cohen, J., & Liu, Y. (2019). Understanding information system success model and valence framework in sellers’ acceptance of cross-border e-commerce: A sequential multi-method approach. Electronic Commerce Research, 19(4), 885–914.

    Google Scholar 

  7. Han, J. H., & Kim, H. M. (2019). The role of information technology use for increasing consumer informedness in cross-border electronic commerce: an empirical study. Electronic Commerce Research and Applications, 34, 100826.

    Google Scholar 

  8. Cui, Y., Mou, J., Cohen, J., Liu, Y., & Kurcz, K. (2020). Understanding consumer intentions toward cross-border m-commerce usage: A psychological distance and commitment-trust perspective. Electronic Commerce Research and Applications, 39, 100920.

    Google Scholar 

  9. Ai, W., Yang, J., & Wang, L. (2016). Revelation of cross-border logistics performance for the manufacturing industry development. International Journal of Mobile Communications, 14(6), 593–609.

    Google Scholar 

  10. Valarezo, Á., Pérez-Amaral, T., Garín-Muñoz, T., García, I. H., & López, R. (2018). Drivers and barriers to cross-border e-commerce: Evidence from Spanish individual behavior. Telecommunications Policy, 42(6), 464–473.

    Google Scholar 

  11. Gomez-Herrera, E., Martens, B., & Turlea, G. (2014). The drivers and impediments for cross-border e-commerce in the EU. Information Economics and Policy, 28, 83–96.

    Google Scholar 

  12. Lu, C. W., Lin, G. H., Wu, T. J., Hu, I., & Chang, Y. C. (2021). Influencing factors of cross-border e-commerce consumer purchase intention based on wireless network and machine learning. Security and Communication Networks, 2021.

  13. Shao, B., Cheng, Z., Wan, L., & Yue, J. (2021). The impact of cross border E-tailer’s return policy on consumer’s purchase intention. Journal of Retailing and Consumer Services, 59, 102367.

    Google Scholar 

  14. Zhu, W., Mou, J., & Benyoucef, M. (2019). Exploring purchase intention in cross-border E-commerce: A three stage model. Journal of Retailing and Consumer Services, 51, 320–330.

    Google Scholar 

  15. Mou, J., Zhu, W., & Benyoucef, M. (2019). Impact of product description and involvement on purchase intention in cross-border e-commerce. Industrial Management & Data Systems, 120(3), 567–586.

    Google Scholar 

  16. Sun, Y., & Li, Y. (2021). The impact of risk-aware consumer trust on CB e-commerce platforms and purchase intention. Journal of Global Information Management, 30(3), 1–13.

    Google Scholar 

  17. Mou, J., Ren, G., Qin, C., & Kurcz, K. (2019). Understanding the topics of export cross-border e-commerce consumers feedback: An LDA approach. Electronic Commerce Research, 19(4), 749–777.

    Google Scholar 

  18. Hazarika, B. B., & Mousavi, R. (2021). Review of cross-border e-commerce and directions for future research. Journal of Global Information Management (JGIM), 30(2), 1–18.

    Google Scholar 

  19. Liu, Y. (2021). Use “cross-border e-commerce” multi-dimensional teaching to enhance the core competence of international economics and trade students. The International Journal of Electrical Engineering & Education. https://doi.org/10.1177/00207209211013472

    Article  Google Scholar 

  20. Hsu, C. W., & Yeh, C. C. (2017). Understanding the factors affecting the adoption of the Internet of Things. Technology Analysis & Strategic Management, 29(9), 1089–1102.

    Google Scholar 

  21. Hsu, C. W., & Yeh, C. C. (2018). Understanding the critical factors for successful M-commerce adoption. International Journal of Mobile Communications, 16(1), 50–62.

    Google Scholar 

  22. Si, S. L., You, X. Y., Liu, H. C., & Zhang, P. (2018). DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/3696457

    Article  Google Scholar 

  23. Bai, C., & Sarkis, J. (2013). A grey-based DEMATEL model for evaluating business process management critical success factors. International Journal of Production Economics, 146(1), 281–292.

    Google Scholar 

  24. Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435–451.

    Google Scholar 

  25. Kutlu Gündoğdu, F., Kahraman, C., & Civan, H. N. (2018). A novel hesitant fuzzy EDAS method and its application to hospital selection. Journal of Intelligent & Fuzzy Systems, 35(6), 6353–6365.

    Google Scholar 

  26. Cho, H., & Lee, J. (2017). Searching for logistics and regulatory determinants affecting overseas direct purchase: An empirical cross-national study. Asian Journal of Shipping & Logistics, 33(1), 11–18. https://doi.org/10.1016/j.ajsl.2017.03.002

    Article  Google Scholar 

  27. Sinkovics, R. R., Mo, Y., & Hossinger, M. (2007). Cultural adaptation in cross border e-commerce: A study of German companies. Journal of Electronic Commerce Research, 8(4), 221–235.

    Google Scholar 

  28. Huang, S. L., & Chang, Y. C. (2017). Factors that impact consumers' intention to shop on foreign online stores. In Proceedings of the 50th Hawaii international conference on system sciences.

  29. Zhang, D., & Yoon, S. (2018). Social media, information presentation, consumer involvement, and cross-border adoption of pop culture products. Electronic Commerce Research and Applications, 27, 129–138.

    Google Scholar 

  30. Mou, J., Cohen, J., Dou, Y., & Zhang, B. (2019). International buyers’ repurchase intentions in a Chinese cross-border e-commerce platform: A valence framework perspective. Internet Research, 30(2), 403–437.

    Google Scholar 

  31. Lee, J. Y., Choo, H. J., & Lee, H. (2015). Fashion consumers’ purchase intention on cross-border online shopping. Fashion & Textile Research Journal, 17(5), 741–753.

    Google Scholar 

  32. Hong, J. C., Hwang, M. Y., Hsu, H. F., Wong, W. T., & Chen, M. Y. (2011). Applying the technology acceptance model in a study of the factors affecting usage of the Taiwan digital archives system. Computers & Education, 57(3), 2086–2094.

    Google Scholar 

  33. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).

  34. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13, 319–340.

    Google Scholar 

  35. Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.

    Google Scholar 

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

    Google Scholar 

  37. Chung, B. G. (2018). Comparative analysis of technology acceptance model: Focusing on UTAUT1 and UTAUT2. Journal of Venture Innovation, 1(2), 109–121.

    Google Scholar 

  38. Vahdat, A., Alizadeh, A., Quach, S., & Hamelin, N. (2021). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal, 29(2), 187–197.

    Google Scholar 

  39. Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593.

    Google Scholar 

  40. Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 25(6), 4961–5002.

    Google Scholar 

  41. Subaeki, B., Rahman, A. A., Putra, S. J., & Alam, C. N. (2019). Success model for measuring information system implementation: Literature review. In Journal of Physics: Conference Series (Vol. 1402, No. 7, p. 077015). IOP Publishing.

  42. Jeyaraj, A. (2020). DeLone & McLean models of information system success: Critical meta-review and research directions. International Journal of Information Management, 54, 102139.

    Google Scholar 

  43. Rizal, H., Yussof, S., Amin, H., & Chen-Jung, K. (2018). EWOM towards homestays lodging: Extending the information system success model. Journal of Hospitality and Tourism Technology, 9(1), 94–108.

    Google Scholar 

  44. Antonopoulou, M., & Kotsilieris, T. (2019). A literature review of user satisfaction models towards information system success. International Journal of E-Services and Mobile Applications, 11(2), 71–87.

    Google Scholar 

  45. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information systems research, 3(1), 60–95.

    Google Scholar 

  46. 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.

    Google Scholar 

  47. Urbach, N., & Müller, B. (2012). The updated DeLone and McLean model of information systems success. In Information systems theory (pp. 1–18). Springer

  48. Delibasic, B., Vukicevic, M., & Jovanovic, M. I. L. O. (2013). White-box decision tree algorithms: A pilot study on perceived usefulness, perceived ease of use, and perceived understanding. International Journal of Engineering Education, 29(3), 674–687.

    Google Scholar 

  49. Raza, S. A., Umer, A., & Shah, N. (2017). New determinants of ease of use and perceived usefulness for mobile banking adoption. International Journal of Electronic Customer Relationship Management, 11(1), 44–65.

    Google Scholar 

  50. Yang, L., Bian, Y., Zhao, X., Liu, X., & Yao, X. (2021). Drivers’ acceptance of mobile navigation applications: An extended technology acceptance model considering drivers’ sense of direction, navigation application affinity and distraction perception. International Journal of Human-Computer Studies, 145, 102507.

    Google Scholar 

  51. Rana, N. P., Dwivedi, Y. K., & Williams, M. D. (2015). A meta-analysis of existing research on citizen adoption of e-government. Information Systems Frontiers, 17(3), 547–563.

    Google Scholar 

  52. Xu, F., & Du, J. T. (2018). Factors influencing users’ satisfaction and loyalty to digital libraries in Chinese universities. Computers in Human Behavior, 83, 64–72.

    Google Scholar 

  53. Xie, H. (2003). Supporting ease-of-use and user control: Desired features and structure of Web-based online IR systems. Information processing and management, 39(6), 899–922.

    Google Scholar 

  54. Mariani, M. M., & Borghi, M. (2020). Online review helpfulness and firms’ financial performance: An empirical study in a service industry. International Journal of Electronic Commerce, 24(4), 421–449.

    Google Scholar 

  55. Wang, X., Tang, L. R., & Kim, E. (2019). More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness? International Journal of Hospitality Management, 77, 438–447.

    Google Scholar 

  56. Kripesh, A. S., Prabhu, H. M., & Sriram, K. V. (2020). An empirical study on the effect of product information and perceived usefulness on purchase intention during online shopping in India. International Journal of Business Innovation and Research, 21(4), 509–522.

    Google Scholar 

  57. Grover, P., Kar, A. K., Janssen, M., & Ilavarasan, P. V. (2019). Perceived usefulness, ease of use and user acceptance of blockchain technology for digital transactions–insights from user-generated content on Twitter. Enterprise Information Systems, 13(6), 771–800.

    Google Scholar 

  58. Koo, C., Chung, N., & Nam, K. (2015). Assessing the impact of intrinsic and extrinsic motivators on smart green IT device use: Reference group perspectives. International Journal of Information Management, 35(1), 64–79.

    Google Scholar 

  59. Mangin, J. P. M. L., Guerrero, M. M., Bourgault, N., & Egea, J. M. O. (2013). Exploring the influence of price and convenience on perceived usefulness of on-line banking within the TAM framework: A cross national (Canada and Spain) decision model. Journal of Business Theory and Practice, 1(2), 342–362.

    Google Scholar 

  60. Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233–244.

    Google Scholar 

  61. Al-Mamary, Y. H., Shamsuddin, A., & Aziati, N. (2014). The relationship between system quality, information quality, and organizational performance. International Journal of Knowledge and Research in Management & E-Commerce, 4(3), 7–10.

    Google Scholar 

  62. Bossen, C., Jensen, L. G., & Udsen, F. W. (2013). Evaluation of a comprehensive EHR based on the DeLone and McLean model for IS success: Approach, results, and success factors. International journal of medical informatics, 82(10), 940–953.

    Google Scholar 

  63. Bauk, S., Šćepanović, S., & Kopp, M. (2014). Estimating students’ satisfaction with web based learning system in blended learning environment. Education Research International. https://doi.org/10.1155/2014/731720

    Article  Google Scholar 

  64. Lin, H. F. (2010). An investigation into the effects of IS quality and top management support on ERP system usage. Total Quality Management, 21(3), 335–349.

    Google Scholar 

  65. Lin, H. F. (2008). Determinants of successful virtual communities: Contributions from system characteristics and social factors. Information & Management, 45(8), 522–527.

    Google Scholar 

  66. Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002). AIMQ: A methodology for information quality assessment. Information & management, 40(2), 133–146.

    Google Scholar 

  67. Arbanas, K., & Žajdela Hrustek, N. (2019). Key success factors of information systems security. Journal of Information and Organizational Sciences, 43(2), 131–144.

    Google Scholar 

  68. Harold, L., & Thenmozhi, M. (2014). Information quality and banking success: A theoretical model with empirical validation. International Journal of Information Quality, 3(3), 251–272.

    Google Scholar 

  69. Chen, J. V., Rungruengsamrit, D., Rajkumar, T. M., & Yen, D. C. (2013). Success of electronic commerce Web sites: A comparative study in two countries. Information & management, 50(6), 344–355.

    Google Scholar 

  70. Shen, X. L., Wang, N., Sun, Y., & Xiang, L. (2013). Unleash the power of mobile word-of-mouth: An empirical study of system and information characteristics in ubiquitous decision making. Online Information Review, 37(1), 42–60.

    Google Scholar 

  71. Kaisara, G., & Pather, S. (2011). The e-Government evaluation challenge: A South African Batho Pele-aligned service quality approach. Government information quarterly, 28(2), 211–221.

    Google Scholar 

  72. Yen, C. H., & Lu, H. P. (2008). Effects of e-service quality on loyalty intention: An empirical study in online auction. Managing Service Quality: An International Journal, 18(2), 127–146.

    Google Scholar 

  73. Zhou, T., Lu, Y., & Wang, B. (2009). The relative importance of website design quality and service quality in determining consumers’ online repurchase behavior. Information Systems Management, 26(4), 327–337.

    Google Scholar 

  74. Wang, Y. S. (2008). Assessing e-commerce systems success: A respecification and validation of the DeLone and McLean model of IS success. Information systems journal, 18(5), 529–557.

    Google Scholar 

  75. Ojo, A. I. (2017). Validation of the DeLone and McLean information systems success model. Healthcare informatics research, 23(1), 60–66.

    Google Scholar 

  76. Dowsett, R. M., & Harty, C. F. (2019). Assessing the implementation of BIM–an information systems approach. Construction management and economics, 37(10), 551–566.

    Google Scholar 

  77. Mostafa, M. M. (2006). A comparison of SERVQUAL and IP analysis: Measuring and improving service quality in Egyptian private universities. Journal of Marketing for Higher Education, 16(2), 83–104.

    Google Scholar 

  78. Agrawal, V., Seth, N., & Dixit, J. K. (2020). A combined AHP–TOPSIS–DEMATEL approach for evaluating success factors of e-service quality: an experience from Indian banking industry. Electronic Commerce Research, 1–33.

  79. Zhang, X., Gong, B., Cao, Y., Ding, Y., & Su, J. (2020). Investigating participants’ attributes for participant estimation in knowledge-intensive crowdsourcing: a fuzzy DEMATEL based approach. Electronic Commerce Research, 1–32.

  80. Peng, X., & Liu, C. (2017). Algorithms for neutrosophic soft decision making based on EDAS, new similarity measure and level soft set. Journal of Intelligent & Fuzzy Systems, 32(1), 955–968.

    Google Scholar 

  81. Stanujkić, D., Jevtić, M., & Ivanov, B. (2018). An approach for laptop computers evaluation using multiple-criteria decision analysis. In Proc. of International Scientific Conference UNITECH (pp. 263–267).

  82. Karabasevic, D., Maksimovic, M., Stanujkic, D., Brzakovic, P., & Brzakovic, M. (2018). The evaluation of websites in the textile industry by applying ISO/IEC 9126–4 standard and the EDAS method. Industria Textila, 69(6), 489.

    Google Scholar 

  83. Vesković, S., Stević, Ž, Karabašević, D., Rajilić, S., Milinković, S., & Stojić, G. (2020). A new integrated fuzzy approach to selecting the best solution for business balance of passenger rail operator: Fuzzy PIPRECIA-fuzzy EDAS model. Symmetry, 12(5), 743.

    Google Scholar 

  84. Ghorabaee, M. K., Zavadskas, E. K., Amiri, M., & Turskis, Z. (2016). Extended EDAS method for fuzzy multi-criteria decision-making: An application to supplier selection. International Journal of Computers Communications & Control, 11(3), 358–371.

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research (MOST 110-2221-E-141-008). Special thanks are extended to the anonymous reviewers for their time and valuable comments.

Author information

Authors and Affiliations

  1. Department of Business Administration, National Taipei University of Business, No.321, Sec. 1, Jinan Rd., Zhongzheng District, Taipei City, 100, Taiwan

    Yi-Hsiang Lu, Ching-Chiang Yeh & Tz-Wei Liau

Authors
  1. Yi-Hsiang Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ching-Chiang Yeh
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Tz-Wei Liau
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ching-Chiang Yeh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, YH., Yeh, CC. & Liau, TW. Exploring the key factors affecting the usage intention for cross-border e-commerce platforms based on DEMATEL and EDAS method. Electron Commer Res 23, 2517–2539 (2023). https://doi.org/10.1007/s10660-022-09548-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-022-09548-6

Keywords