PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2018 | 11 | nr 4 | 237--253
Tytuł artykułu

A New Model for Customer Purchase Intention in E-Commerce Recommendation Agents

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Recommender systems were introduced to improve the online shopping experience by recommending appropriate products and services to customers according to their preferences. This research develops a new model by identifying the factors that influence customers' purchase intention in recommender systems. The research model of this study was developed by reviewing the previous studies on web-based information systems, e-commerce and recommender systems. Quantitative data was collected from questionnaires conducted among the customers of online shopping websites. The questionnaires was adopted from the previous researches, and validated by the experts in the fields of information systems and recommender systems. Descriptive and hypotheses' analyses were performed on the collected data using statistical analysis software and Partial Least Squares Structural Equation Modeling. The results reveal that Accuracy, Diversity, Ease of Use, Recommendation Quality, Satisfaction, Trust and Usefulness have significant influence on customers' intention to purchase a product recommended by the recommender systems. The developed model and the findings of this research will help e-commerce websites' developers and e-commerce providers to enhance the recommender systems based on the factors that contribute to their quality. (original abstract)
Rocznik
Tom
11
Numer
Strony
237--253
Opis fizyczny
Twórcy
  • Universiti Teknologi Malaysia
  • Universiti Teknologi Malaysia, Johor, Malaysia
  • Universiti Teknologi Malaysia, Johor, Malaysia
  • Lithuanian Institute of Agrarian Economics, Vilnius, Lithuania
  • Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
  • Universiti Teknologi Malaysia
Bibliografia
  • Abdoli, M., Rostamzadeh, R., Feizi, J., & Joksiene, I. (2017). Impact of Perceived Value and Satisfaction on Customer Loyalty in Banking Industry. Transformations in Business & Economics, 16(2A) (41A), 421-441.
  • Abrham, J., Strielkowski, W., Vošta, M., & Šlajs, J. (2015) Factors that influence the competitiveness of Czech rural small and medium enterprises. Agricultural Economics-Zemedelska Ekonomika, 61(10), 450-460. doi:10.17221/63/2015-AGRICECON
  • Adomavicius, G., & Kwon, Y. (2012). Improving aggregate recommendation diversity using ranking-based techniques. Knowledge and Data Engineering, IEEE Transactions on, 24(5), 896-911.
  • Al-Taie, M. Z. (2013). Explanations in recommender systems: overview and research approaches. In Proceedings of the 14th International Arab Conference on Information Technology, Khartoum, Sudan, ACIT (Vol. 13).
  • Bagherifard, K., Rahmani, M., Nilashi, M., & Rafe, V. (2017). Performance improvement for recommender systems using ontology. Telematics and Informatics, 34(8), 1772-1792.
  • Baier, D., & Stüber, E. (2010). Acceptance of recommendations to buy in online retailing. Journal of Retailing and Consumer Services, 17(3), 173-180.
  • Bilan, Y. (2013). Sustainable development of a company: Building of new level relationship with the consumers of XXI century. Amfiteatru Economic Journal, 15(Special No. 7), 687-701.
  • Chen, L., & Pu, P. (2010). Experiments on the preference-based organization interface in recommender systems. ACM Transactions on Computer-Human Interaction (TOCHI), 17(1), 5.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  • Ehrenberger, M., Koudelkova, P., & Strielkowski, W. (2015). Factors influencing innovation in small and medium enterprises in the Czech Republic. Periodica Polytechnica Social and Management Sciences, 23(2), 73-83. https://doi.org/10.3311/PPso.7737
  • Felfernig, A., & Gula, B. (2006). An empirical study on consumer behavior in the interaction with knowledge-based recommender applications (p. 37). IEEE.
  • Furnell, S. M. (2005, December). Considering the security challenges in consumer-oriented eCommerce. In Signal Processing and Information Technology, 2005. Proceedings of the Fifth IEEE International Symposium on (pp. 534-539). IEEE.
  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 27(1), 51-90.
  • Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
  • Hu, R., & Pu, P. (2009, October). Acceptance issues of personality-based recommender systems. In Proceedings of the third ACM conference on Recommender systems (pp. 221-224). ACM.
  • Ilie, M., Moraru, Andreea-Daniela, Ghita-Mitrescu, Silvia. (2017). The Hierarchical Determination of Customer Satisfaction with Banking Services Using an Artificial Neural Network. Transformations in Business & Economics, 16(2A) (41A), 401-421.
  • Jannach, D., Karakaya, Z., & Gedikli, F. (2012, June). Accuracy improvements for multi-criteria recommender systems. In Proceedings of the 13th ACM conference on electronic commerce (pp. 674-689). ACM.
  • Komiak, S. Y., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS quarterly, 941-960.
  • Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12-32.
  • McNee, S. M., Riedl, J., & Konstan, J. A. (2006, April). Making recommendations better: an analytic model for human-recommender interaction. In CHI'06 extended abstracts on Human factors in computing systems (pp. 1103-1108). ACM.
  • Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014). Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 41(8), 3879-3900.
  • Nilashi, M., Jannach, D., bin Ibrahim, O., Esfahani, M. D., & Ahmadi, H. (2016). Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic Commerce Research and Applications, 19, 70-84.
  • Pathak, B., Garfinkel, R., Gopal, R. D., Venkatesan, R., & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27(2), 159-188.
  • Polatidis, N., & Georgiadis, C. K. (2014, June). Factors influencing the quality of the user experience in ubiquitous recommender systems. In International Conference on Distributed, Ambient, and Pervasive Interactions (pp. 369-379). Springer, Cham.
  • Pu, P., Chen, L., & Hu, R. (2011, October). A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 157-164). ACM.
  • Pursel, B., Liang, C., Wang, S., Wu, Z., Williams, K., Brautigam, B., ... & Giles, C. L. (2016, April). BBookX: Design of an Automated Web-based Recommender System for the Creation of Open Learning Content. In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 929-933). International World Wide Web Conferences Steering Committee.
  • Reichheld, F. F., & Schefter, P. (2000). E-loyalty: your secret weapon on the web. Harvard business review, 78(4), 105-113.
  • Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook (pp. 1-35). Springer US.
  • Schafer, J. B., Konstan, J., & Riedl, J. (1999, November). Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce (pp. 158-166). ACM.
  • Sinha, R., & Swearingen, K. (2002, April). The role of transparency in recommender systems. In CHI'02 extended abstracts on Human factors in computing systems (pp. 830-831). ACM.
  • Sinha, R. R., & Swearingen, K. (2001, June). Comparing recommendations made by online systems and friends. In DELOS workshop: personalisation and recommender systems in digital libraries (Vol. 106).
  • Sun, Y., Chong, W. K., Han, Y. S., Rho, S., & Man, K. L. (2015, January). Key factors affecting user experience of mobile recommendation systems. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 2).
  • Sun, Y., Chong, W. K., Man, K. L., Rho, S., & Xie, D. (2016). Exploring critical success factors of mobile recommendation systems: The end user perspective. In Transactions on Engineering Technologies (pp. 45-57). Springer, Singapore.
  • Tintarev, N., & Masthoff, J. (2012). Evaluating the effectiveness of explanations for recommender systems. User Modeling and User-Adapted Interaction, 22(4-5), 399-439.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
  • Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: use, characteristics, and impact. MIS quarterly, 31(1), 137-209.
  • Żelazny, R. (2017). Determinants and measurement of smart growth: evidence from Poland. Journal of International Studies, 10(1), 34-45.
  • Tongxiao (Catherine) Zhang, Agarwal, R., & Lucas Jr, H. C. (2011). The value of IT-enabled retailer learning: personalized product recommendations and customer store loyalty in electronic markets. Mis Quarterly, 859-881.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171542434

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.