Factors Influencing Consumer's Intention to Use Recommendation Agents

Seyedeh OMSalameh Pourhashemi, Ehram Safari, Amirmohammad Alidoust Moazezi Lahijan

Abstract


The internet allows us to quickly access any information we want, but the increased volume of information causes an overload. In e-commerce, Recommender Systems (RS) assist users of e-service providers as a tool to access appropriate information proportionate to their needs with the least amount of effort and complete confidence in a short period of time. The research under consideration seeks to influence consumers' attitudes toward recommendation agents in recommender systems on e-commerce websites. We conducted our research on the Digikala website, which is one of Iranian's most popular e-commerce sites. The data came from 384 customers, and structural equation modeling was used to test statistical hypotheses. Our analysis of the collected data revealed that all of the hypotheses in the model were accepted. According to the findings, all factors such as perceived accuracy, perceived diversity, perceived novelty, recommendation quality, recommendation transparency, explanation, perceived risk, ease of use, usefulness, satisfaction, trust, and loyalty have a significant impact on the intention to purchase a product recommended by recommender systems. The proposed model assists online store managers in improving their website RSs and increasing product sales through improved customer satisfaction. Furthermore, it can help them gain loyalty and thus increase trust.


Keywords


Recommender System, Satisfaction, Trust, Intention to Use, E-Commerce

Full Text:

Abstract

References


Abdullah, F., Ward, R., 2016. Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior 56, 238-256.

Abumalloh, R.A., Asadi, S., Nilashi, M., Minaei-Bidgoli, B., Nayer, F.K., Samad, S., Mohd, S., Ibrahim, O., 2021. The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education. Technology in Society 67.

Ahani, A., Nilashi, M., Yadegaridehkordi, E., Sanzogni, L., Tarik, A.R., Knox, K., Samad, S., Ibrahim, O., 2019. Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services 51, 331-343.

Ahani, A., Nilashi, M., Zogaan, W.A., Samad, S., Aljehane, N.O., Alhargan, A., Mohd, S., Ahmadi, H., Sanzogni, L., 2021. Evaluating medical travelers’ satisfaction through online review analysis. Journal of Hospitality and Tourism Management 48, 519-537.

Ahrholdt, D.C., Gudergan, S.P., Ringle, C.M., 2019. Enhancing loyalty: When improving consumer satisfaction and delight matters. Journal of business research 94, 18-27.

Al-Hadi, I.A.A.Q., Sharef, N.M., Sulaiman, M.N., Mustapha, N., Nilashi, M., 2020. Latent Based Temporal Optimization Approach for Improving the Performance of Collaborative Filtering. PeerJ Comput. Sci. 6.

Alalwan, A.A., Dwivedi, Y.K., Rana, N.P., Algharabat, R., 2018. Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services 40, 125-138.

Alba, J., Lynch, J., Weitz, B., Janiszewski, C., Lutz, R., Sawyer, A., Wood, S., 1997. Interactive home shopping: consumer, retailer, and manufacturer incentives to participate in electronic marketplaces. Journal of marketing 61(3), 38-53.

Ali Abumalloh, R., Ibrahim, O., Nilashi, M., 2020. Loyalty of young female Arabic customers towards recommendation agents: A new model for B2C E-commerce. Technology in Society 61.

Bagherifard, K., Rahmani, M., Nilashi, M., Rafe, V., 2017. Performance improvement for recommender systems using ontology. Telematics and Informatics 34(8), 1772-1792.

Bagherifard, K., Rahmani, M., Rafe, V., Nilashi, M., 2018. A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset. J. Inf. Knowl. Manage. 17(1).

Bamfield, J., 2013. Retail futures 2018: Shop numbers, online and the high street: A guide to retailing in 2018. Centre for Retail Research Limited 16(2), 5-14.

Benazić, D., Tanković, A.Č., Music, M., 2015. Impact of perceived risk and perceived cost on trust in the online shopping websites and customer repurchase intention, Proceedings of the 24th CROMAR congress: Marketing Theory and Practice-Building Bridges and Fostering Collaboration. pp. 104-122.

Benlian, A., Titah, R., Hess, T., 2012. Differential effects of provider recommendations and consumer reviews in e-commerce transactions: An experimental study. Journal of Management Information Systems 29(1), 237-272.

Bilge, A., Kaleli, C., 2014. A multi-criteria item-based collaborative filtering framework, 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, pp. 18-22.

Bilgic, M., Mooney, R.J., 2005. Explaining recommendations: Satisfaction vs. promotion, Beyond Personalization Workshop, IUI. p. 153.

Bodapati, A.V., 2008. Recommendation systems with purchase data. Journal of marketing research 45(1), 77-93.

Brunk, J., Mattern, J., Riehle, D.M., 2019. Effect of transparency and trust on acceptance of automatic online comment moderation systems, 2019 IEEE 21st Conference on Business Informatics (CBI). IEEE, pp. 429-435.

Carmichael, D., Kay, J., Kummerfeld, B., Niu, W., 2006. Why did you show/tell/hide that? The need for scrutability in ubiquitous personalisation, ECHISE Workshop Exploiting Context Histories in Smart Environments at UbiComp, Irvine, CA, USA.

Chen, J., Ren, Y., Riedl, J., 2010. The effects of diversity on group productivity and member withdrawal in online volunteer groups, Proceedings of the SIGCHI conference on human factors in computing systems. pp. 821-830.

Chen, M., Hao, Y., Hwang, K., Wang, L., Wang, L., 2017. Disease prediction by machine learning over big data from healthcare communities. Ieee Access 5, 8869-8879.

Chen, Y.-H., Barnes, S., 2007. Initial trust and online buyer behaviour. Industrial Management & Data Systems 107(1), 21-36.

Chin, W.W., 1998. Commentary: Issues and opinion on structural equation modeling. JSTOR.

Chintagunta, P.K., Chu, J., Cebollada, J., 2012. Quantifying transaction costs in online/off-line grocery channel choice. Marketing Science 31(1), 96-114.

Cho, Y.H., Kim, J.K., Kim, S.H., 2002. A personalized recommender system based on web usage mining and decision tree induction. Expert systems with Applications 23(3), 329-342.

Cohen, J., 1988. Statistical power analysis for the behavioral sciences.

Cyr, D., 2008. Modeling web site design across cultures: relationships to trust, satisfaction, and e-loyalty. Journal of management information systems 24(4), 47-72.

Dabholkar, P.A., 2006. Factors influencing consumer choice of a" rating Web site": An experimental investigation of an online interactive decision aid. Journal of Marketing Theory and Practice 14(4), 259-273.

Dabholkar, P.A., Sheng, X., 2012. Consumer participation in using online recommendation agents: effects on satisfaction, trust, and purchase intentions. The Service Industries Journal 32(9), 1433-1449.

Dai, B., Forsythe, S., Kwon, W.-S., 2014. The Impact of Online Shopping Experience on Risk Perceptions and Online Purchase Intentions: Does Product Category Matter? Journal of Electronic Commerce Research 15(1), 13.

Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.

Esmaeili, L., Hashemi G, S.A., 2019. A systematic review on social commerce. Journal of Strategic Marketing 27(4), 317-355.

Fernández-Tobías, I., Braunhofer, M., Elahi, M., Ricci, F., Cantador, I., 2016. Alleviating the new user problem in collaborative filtering by exploiting personality information. User Modeling and User-Adapted Interaction 26(2-3), 221-255.

Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research 18(1), 39-50.

Foroughi, B., Nhan, P.V., Iranmanesh, M., Ghobakhloo, M., Nilashi, M., Yadegaridehkordi, E., 2023. Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS. Journal of Retailing and Consumer Services 70.

Forrester, 2015. Online Retail Sales to Top $480 Billion by 2019.

Gefen, D., Pavlou, P.A., 2006. An inverted-U theory of trust: The moderating role of perceived regulatory effectiveness of online marketplaces, Twenty Seventh International Conference on Information System.

Gefen, D., Straub, D., 2003. Managing user trust in B2C e-services. e-Service 2(2), 7-24.

Gregor, S., Benbasat, I., 1999. Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS quarterly, 497-530.

Grenci, R.T., Todd, P.A., 2002. Solutions-driven marketing. Communications of the ACM 45(3), 64-71.

Guo, X., Ling, K.C., Liu, M., 2012. Evaluating factors influencing consumer satisfaction towards online shopping in China. Asian Social Science 8(13), 40.

Hafez, L., Elakkad, E., Gamil, M., 2021. A Study on the Impact of Logistics Service Quality on the Satisfaction and Loyalty of E-Shoppers in Egypt. Open Journal of Business and Management 9(5), 2464-2478.

Hair, J.F., Black, W.C., Anderson, R.E., Tatham, R.L., 1998. Multivariate data analysis.

Hair, J.F., Ringle, C.M., Sarstedt, M., 2011. PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice 19(2), 139-152.

Hair Jr, J.F., Howard, M.C., Nitzl, C., 2020. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research 109, 101-110.

Hair Jr, J.F., Hult, G.T.M., Ringle, C., Sarstedt, M., 2013. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE.

Han, H., Xu, H., Chen, H., 2018. Social commerce: A systematic review and data synthesis. Electronic Commerce Research and Applications 30, 38-50.

Häubl, G., Murray, K.B., 2006. Double agents: assessing the role of electronic product recommendation systems. Sloan Management Review 47(3), 8-12.

Hausman, A., 2000. A multi-method investigation of consumer motivations in impulse buying behavior. Journal of consumer marketing 17(5), 403-426.

Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A., Straub, D.W., Ketchen Jr, D.J., Hair, J.F., Hult, G.T.M., Calantone, R.J., 2014. Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational research methods 17(2), 182-209.

Herlocker, J.L., Konstan, J.A., Riedl, J., 2000. Explaining collaborative filtering recommendations, Proceedings of the 2000 ACM conference on Computer supported cooperative work. ACM, pp. 241-250.

Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T., 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22(1), 5-53.

Höök, K., 2000. Steps to take before intelligent user interfaces become real. Interacting with computers 12(4), 409-426.

Hook, K., Karlgren, J., Waern, A., Dahlback, N., Jansson, C.G., Karlgren, K., Lemaire, B., 1996. A Glass Box Approach to Adaptive Hypermedia. User Modeling and User-Adapted Interaction 6(2-3), 157-184.

Hosanagar, K., Fleder, D., Lee, D., Buja, A., 2014. Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation. Management Science 60(4), 805-823.

Huang, L., Tan, C.-H., Ke, W., Wei, K.-K., 2013. Comprehension and assessment of product reviews: A review-product congruity proposition. Journal of Management Information Systems 30(3), 311-343.

Jameson, A., 2003. Adaptive Interfaces and Agents. The HCI Handbook: Fundamentals, Evolving Technologies and Emerging Applications. Erlbaum, Mahwah.

Jannach, D., Resnick, P., Tuzhilin, A., Zanker, M., 2016. Recommender systems—beyond matrix completion. Communications of the ACM 59(11), 94-102.

Jiang, P., Zhu, Y., Zhang, Y., Yuan, Q., 2015. Life-stage prediction for product recommendation in e-commerce, Proceedings of the 21th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 1879-1888.

Jin, R., Chen, K., 2021. Impact of value cocreation on customer satisfaction and loyalty of online car-hailing services. Journal of theoretical and applied electronic commerce research 16(3), 432-444.

Kay, J., 2006. Scrutable adaptation: Because we can and must, International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems. Springer, pp. 11-19.

Khristianto, W., Suyadi, I., 2012. THE INFLUENCE OF INFORMATION, SYSTEM, AND SERVICE ON CUSTOMER SATISFACTION AND LOYALTY IN ONLINE SHOPPING OF FORUM JUAL BELI KASKUS. US, MALANG REGION. International Journal of Academic Research 4(2).

Kramer, R.M., 1999. Trust and distrust in organizations: Emerging perspectives, enduring questions. Annual review of psychology 50(1), 569-598.

Kumar, N., Benbasat, I., 2006. Research note: the influence of recommendations and consumer reviews on evaluations of websites. Information Systems Research 17(4), 425-439.

Lee, M.K., Turban, E., 2001. A trust model for consumer internet shopping. International Journal of electronic commerce 6(1), 75-91.

Lewicki, R.J., Bunker, B.B., 1995. Trust in relationships: A model of development and decline. Jossey-Bass.

Li, Y., Chen, M., 2015. Software-defined network function virtualization: A survey. IEEE Access 3, 2542-2553.

Liang, T.-P., Yang, Y.-F., Chen, D.-N., Ku, Y.-C., 2008. A semantic-expansion approach to personalized knowledge recommendation. Decision Support Systems 45(3), 401-412.

Liu, C.H., Zhang, Z., Chen, M., 2017. Personalized Multimedia Recommendations for Cloud-Integrated Cyber-Physical Systems. IEEE Systems Journal 11, 106-117.

Luqman, A., Razak, R.C., Ismail, M., Alwi, M.A.M., 2016. Predicting continuance intention in mobile commerce usage activities: The Effects of Innovation Attributes, 8th International Conference on Humanities and Social Sciences held on. pp. 27-29.

Mayer, R., 1995. An Integrative Model of Organizational Trust. Academy of Management Review 20(3), 709-734.

McNee, S.M., Lam, S.K., Konstan, J.A., Riedl, J., 2003. Interfaces for eliciting new user preferences in recommender systems, International Conference on User Modeling. Springer, pp. 178-187.

McNee, S.M., Riedl, J., Konstan, J.A., 2006. Being accurate is not enough: how accuracy metrics have hurt recommender systems, CHI'06 extended abstracts on Human factors in computing systems. pp. 1097-1101.

Meimand, S.E., Mardani, A., Khalifah, Z., Nilashi, M., Ismail, H.N., Skare, M., 2019. Religious concerns and residents’ attitude toward tourism development: A comparative study. Transform. Bus. Econ. 18(3), 21-42.

Mukherjee, A., Nath, P., 2007. Role of electronic trust in online retailing: A re-examination of the commitment-trust theory. European Journal of Marketing 41(9-10), 1173-1202.

Nilashi, M., Abumalloh, R.A., Alghamdi, A., Minaei-Bidgoli, B., Alsulami, A.A., Thanoon, M., Asadi, S., Samad, S., 2021a. What is the impact of service quality on customers’ satisfaction during COVID-19 outbreak? New findings from online reviews analysis. Telematics and Informatics 64.

Nilashi, M., Abumalloh, R.A., Almulihi, A., Alrizq, M., Alghamdi, A., Ismail, M.Y., Bashar, A., Zogaan, W.A., Asadi, S., 2021b. Big social data analysis for impact of food quality on travelers’ satisfaction in eco-friendly hotels. ICT Express.

Nilashi, M., Abumalloh, R.A., Minaei-Bidgoli, B., Abdu Zogaan, W., Alhargan, A., Mohd, S., Syed Azhar, S.N.F., Asadi, S., Samad, S., 2022a. Revealing travellers’ satisfaction during COVID-19 outbreak: Moderating role of service quality. Journal of Retailing and Consumer Services 64.

Nilashi, M., Abumalloh, R.A., Zibarzani, M., Samad, S., Zogaan, W.A., Ismail, M.Y., Mohd, S., Akib, N.A.M., 2022b. What Factors Influence Students Satisfaction in Massive Open Online Courses? Findings from User-Generated Content Using Educational Data Mining. Education and Information Technologies 27(7), 9401-9435.

Nilashi, M., Ahmadi, H., Arji, G., Alsalem, K.O., Samad, S., Ghabban, F., Alzahrani, A.O., Ahani, A., Alarood, A.A., 2021c. Big social data and customer decision making in vegetarian restaurants: A combined machine learning method. Journal of Retailing and Consumer Services 62.

Nilashi, M., Ali Abumalloh, R., Mohd, S., Nurlaili Farhana Syed Azhar, S., Samad, S., Hang Thi, H., Alghamdi, O.A., Alghamdi, A., 2023a. COVID-19 and sustainable development goals: A bibliometric analysis and SWOT analysis in Malaysian context. Telematics and Informatics 76.

Nilashi, M., Ali Abumalloh, R., Samad, S., Minaei-Bidgoli, B., Hang Thi, H., Alghamdi, O.A., Yousoof Ismail, M., Ahmadi, H., 2023b. The impact of multi-criteria ratings in social networking sites on the performance of online recommendation agents. Telematics and Informatics 76.

Nilashi, M., Asadi, S., Minaei-Bidgoli‬, B., Ali Abumalloh, R., Samad, S., Ghabban, F., Ahani, A., 2021d. Recommendation agents and information sharing through social media for coronavirus outbreak. Telematics and Informatics 61.

Nilashi, M., Bagherifard, K., Ibrahim, O., Alizadeh, H., Nojeem, L.A., Roozegar, N., 2013. Collaborative filtering recommender systems. Res. J. Appl. Sci. Eng. Technol. 5(16), 4168-4182.

Nilashi, M., Bagherifard, K., Ibrahim, O., Janahmadi, N., Ebrahimi, L., 2012. Ranking parameters on quality of online shopping websites using multi-criteria method. Res. J. Appl. Sci. Eng. Technol. 4(21), 4380-4396.

Nilashi, M., Bagherifard, K., Rahmani, M., Rafe, V., 2017. A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Comput Ind Eng 109, 357-368.

Nilashi, M., Bin Ibrahim, O., Ithnin, N., Sarmin, N.H., 2015a. A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS. Electronic Commerce Research and Applications 14(6), 542-562.

Nilashi, M., Fallahpour, A., Wong, K.Y., Ghabban, F., 2022c. Customer satisfaction analysis and preference prediction in historic sites through electronic word of mouth. Neural Computing and Applications 34(16), 13867-13881.

Nilashi, M., Fathian, M., Gholamian, M.R., Ibrahim, O.B., Talebi, A., Ithnin, N., 2011. A comparative study of adaptive neuro fuzzy inferences system (ANFIS) and fuzzy inference system (FIS) approach for trust in B2C electronic commerce websites. J. Convergence Inf. Technol. 6(9), 25-43.

Nilashi, M., Ibrahim, O., Bagherifard, K., 2018a. A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications 92, 507-520.

Nilashi, M., Ibrahim, O., Reza Mirabi, V., Ebrahimi, L., Zare, M., 2015b. The role of Security, Design and Content factors on customer trust in mobile commerce. Journal of Retailing and Consumer Services 26, 57-69.

Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E., Alizadeh, A., 2018b. Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of Computational Science 28, 168-179.

Nilashi, M., Ibrahim, O.B., 2012. A model for detecting customer level intentions to purchase in B2C websites using TOPSIS and fuzzy logic rule-based system. Arabian Journal for Science and Engineering 39(3), 1907-1922.

Nilashi, M., Ibrahim, O.B., Ithnin, N., 2014a. Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications 41(8), 3879-3900.

Nilashi, M., Ibrahim, O.B., Ithnin, N., 2014b. Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems 60, 82-101.

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.

Nilashi, M., Jannach, D., Ibrahim, O.B., Ithnin, N., 2015c. Clustering- and regression-based multi-criteria collaborative filtering with incremental updates. Inf Sci 293, 235-250.

Nilashi, M., Mardani, A., Liao, H., Ahmadi, H., Manaf, A.A., Almukadi, W., 2019a. A hybrid method with TOPSIS and machine learning techniques for sustainable development of green hotels considering online reviews. Sustainability (Switzerland) 11(21).

Nilashi, M., Minaei-Bidgoli, B., Alghamdi, A., Alrizq, M., Alghamdi, O., Khan Nayer, F., Aljehane, N.O., Khosravi, A., Mohd, S., 2022d. Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches. Expert Systems with Applications 199.

Nilashi, M., Samad, S., Alghamdi, A., Ismail, M.Y., Alghamdi, O.A., Mehmood, S.S., Mohd, S., Zogaan, W.A., Alhargan, A., 2022e. A New Method for Analysis of Customers' Online Review in Medical Tourism Using Fuzzy Logic and Text Mining Approaches. Int. J. Inf. Technol. Decis. Mak. 21(6), 1797-1820.

Nilashi, M., Samad, S., Yusuf, S.Y.M., Akbari, E., 2020a. Can complementary and alternative medicines be beneficial in the treatment of COVID-19 through improving immune system function? Journal of Infection and Public Health 13(6), 893-896.

Nilashi, M., Yadegaridehkordi, E., Ibrahim, O., Samad, S., Ahani, A., Sanzogni, L., 2019b. Analysis of Travellers’ Online Reviews in Social Networking Sites Using Fuzzy Logic Approach. International Journal of Fuzzy Systems 21(5), 1367-1378.

Nilashi, M., Yadegaridehkordi, E., Samad, S., Mardani, A., Ahani, A., Aljojo, N., Razali, N.S., Tajuddin, T., 2020b. Decision to adopt neuromarketing techniques for sustainable product marketing: A fuzzy decision-making approach. Symmetry 12(2).

Nisar, T.M., Prabhakar, G., 2017. What factors determine e-satisfaction and consumer spending in e-commerce retailing? Journal of Retailing and Consumer Services 39, 135-144.

Oh, J., Park, S., Yu, H., Song, M., Park, S.-T., 2011. Novel recommendation based on personal popularity tendency, 2011 IEEE 11th International Conference on Data Mining. IEEE, pp. 507-516.

Olever, R., 1997. Satisfaction: a behavioral perspective on the customer. New York: Irwin McGraw Hill.

Parboteeah, D.V., Valacich, J.S., Wells, J.D., 2009. The influence of website characteristics on a consumer's urge to buy impulsively. Information systems research 20(1), 60-78.

Park, C., Jun, J.-K., 2003. A cross-cultural comparison of Internet buying behavior: Effects of Internet usage, perceived risks, and innovativeness. International Marketing Review 20(5), 534-553.

Pu, P., Chen, L., 2010. A User-Centric Evaluation Framework of Recommender Systems.

Raisian, K., Minouei, A., Khosravi, A., Hashemi, A., Nilashi, M., Ibrahim, O., Zakaria, R., Nazari, M., 2014. Multi-criteria approach for customer trust model in internet banking: A case of UTM CIBM bank. Life Sci. J. 11(6), 81-93.

Ren, K., Qin, J., Fang, Y., Zhang, W., Zheng, L., Bian, W., Zhou, G., Xu, J., Yu, Y., Zhu, X., 2019. Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction, Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 565-574.

Ringle, C.M., Sarstedt, M., Straub, D., 2012. A Critical Look at the Use of PLS-SEM in MIS Quarterly. MIS Quarterly (MISQ) 36(1).

Roudposhti, V.M., Nilashi, M., Mardani, A., Streimikiene, D., Samad, S., Ibrahim, O., 2018. A new model for customer purchase intention in e-commerce recommendation agents. Journal of International Studies 11(4), 237-253.

Rupani, P.F., Nilashi, M., Abumalloh, R.A., Asadi, S., Samad, S., Wang, S., 2020. Coronavirus pandemic (COVID-19) and its natural environmental impacts. International Journal of Environmental Science and Technology 17(11), 4655-4666.

Saeidi, P., Saeidi, S.P., Sofian, S., Saeidi, S.P., Nilashi, M., Mardani, A., 2019. The impact of enterprise risk management on competitive advantage by moderating role of information technology. Comput Stand Interfaces 63, 67-82.

Shani, G., Gunawardana, A., 2011. Evaluating recommendation systems, Recommender systems handbook. Springer, pp. 257-297.

Shanmugam, M., Jusoh, Y.Y., 2014. Social commerce from the Information Systems perspective: A systematic literature review, 2014 International Conference on Computer and Information Sciences (ICCOINS). IEEE, pp. 1-6.

Sharma, A., Cosley, D., 2013. Do social explanations work? Studying and modeling the effects of social explanations in recommender systems, Proceedings of the 22nd international conference on World Wide Web. pp. 1133-1144.

Sinha, R., Swearingen, K., 2002a. The role of transparency in recommender systems, CHI '02 Extended Abstracts on Human Factors in Computing Systems. ACM, Minneapolis, Minnesota, USA, pp. 830-831.

Sinha, R., Swearingen, K., 2002b. The role of transparency in recommender systems, CHI'02 extended abstracts on Human factors in computing systems. pp. 830-831.

Sinha, R.R., Swearingen, K., 2001. Comparing recommendations made by online systems and friends. DELOS 106.

Su, H.J., Comer, L.B., Lee, S., 2008. The effect of expertise on consumers' satisfaction with the use of interactive recommendation agents. Psychology & Marketing 25(9), 859-880.

Subramanian, N., Gunasekaran, A., Yu, J., Cheng, J., Ning, K., 2014. Customer satisfaction and competitiveness in the Chinese E-retailing: Structural equation modeling (SEM) approach to identify the role of quality factors. Expert Systems with Applications 41(1), 69-80.

Swearingen, K., Sinha, R., 2001. Beyond algorithms: An HCI perspective on recommender systems, ACM SIGIR 2001 workshop on recommender systems. Citeseer, pp. 1-11.

Thirumalai, S., Sinha, K.K., 2009. Customization strategies in electronic retailing: Implications of customer purchase behavior. Decision Sciences 40(1), 5-36.

Thong, J.Y., Hong, S.-J., Tam, K.Y., 2006. The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-computer studies 64(9), 799-810.

Tian, D., Zhou, J., Sheng, Z., 2017. An adaptive fusion strategy for distributed information estimation over cooperative multi-agent networks. IEEE Transactions on Information Theory 63(5), 3076-3091.

Tian, D., Zhou, J., Wang, Y., Lu, Y., Xia, H., Yi, Z., 2015. A dynamic and self-adaptive network selection method for multimode communications in heterogeneous vehicular telematics. IEEE transactions on intelligent transportation systems 16(6), 3033-3049.

Tintarev, N., Masthoff, J., 2007. A survey of explanations in recommender systems, 2007 IEEE 23rd international conference on data engineering workshop. IEEE, pp. 801-810.

Tintarev, N., Masthoff, J., 2011. Designing and evaluating explanations for recommender systems, Recommender systems handbook. Springer, pp. 479-510.

Tintarev, N., Masthoff, J., 2012. Evaluating the effectiveness of explanations for recommender systems. User Modeling and User-Adapted Interaction 22(4-5), 399-439.

Tsai, C.-H., Brusilovsky, P., 2017. Providing control and transparency in a social recommender system for academic conferences, Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. pp. 313-317.

Vahid, M., Farokhi, M., Ibrahim, O., Nilashi, M., 2016. A user satisfaction model for e-commerce recommender systems. Journal of soft computing and decision support systems 3(3), 42-54.

Waari, D., 2018. The effect of customer satisfaction on customer loyalty: The moderation roles of experiential encounter and customer patronage. IOSR Journal of Business and Management 20(4).

Wang, F.-H., Shao, H.-M., 2004. Effective personalized recommendation based on time-framed navigation clustering and association mining. Expert systems with applications 27(3), 365-377.

Wang, L., Prompanyo, M., 2020. Modeling the relationship between perceived values, e-satisfaction, and e-loyalty. Management Science Letters 10(11), 2609-2616.

Warrington, T.B., Abgrab, N.J., Caldwell, H.M., 2000. Building trust to develop competitive advantage in e-business relationships. Competitiveness Review 10(2), 160-168.

Wasilewski, J., Hurley, N., 2016. Intent-aware diversification using a constrained PLSA, Proceedings of the 10th ACM Conference on Recommender Systems. pp. 39-42.

Weisberg, J., Te’eni, D., Arman, L., 2011. Past purchase and intention to purchase in e-commerce: The mediation of social presence and trust Article information. 21 (1), 82–96.

West, P.M., Ariely, D., Bellman, S., Bradlow, E., Huber, J., Johnson, E., Kahn, B., Little, J., Schkade, D., 1999. Agents to the Rescue? Marketing letters 10(3), 285-300.

Wu, W., Chen, L., He, L., 2013. Using personality to adjust diversity in recommender systems, Proceedings of the 24th ACM conference on hypertext and social media. pp. 225-229.

Xiao, B., Benbasat, I., 2007. E-commerce product recommendation agents: use, characteristics, and impact. MIS quarterly 31(1), 137-209.

Yadegaridehkordi, E., Nilashi, M., Nasir, M.H.N.B.M., Ibrahim, O., 2018. Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method. Tourism Management 66, 364-386.

Yang, X., 2021. Determinants of consumers’ continuance intention to use social recommender systems: A self-regulation perspective. Technology in Society 64, 101464.

Zenebe, A., Norcio, A.F., 2009. Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy sets and systems 160(1), 76-94.

Zhang, Y., Chen, M., Huang, D., Wu, D., Li, Y., 2017. iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Generation Computer Systems 66, 30-35.

Zhao, Y., Deng, S., Zhou, R., 2015. Understanding mobile library apps continuance usage in China: a theoretical framework and empirical study. Libri 65(3), 161-173.

Zibarzani, M., Abumalloh, R.A., Nilashi, M., Samad, S., Alghamdi, O.A., Nayer, F.K., Ismail, M.Y., Mohd, S., Mohammed Akib, N.A., 2022. Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology. Technology in Society 70.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.