Assessing the impacts of peer-to-peer recommender system on online shopping: PLS-SEM approach
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Author(s)Cuong NguyenLink to ORCID Index: https://orcid.org/0000-0001-7842-0564
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Ninh NguyenLink to ORCID Index: https://orcid.org/0000-0002-7375-8408
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DOIhttp://dx.doi.org/10.21511/im.20(4).2024.01
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Article InfoVolume 20 2024, Issue #4, pp. 1-12
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Creative Commons Attribution 4.0 International License
Peer-to-peer recommender systems play a critical role in online shopping in Vietnam. This paper aims to identify the relationship between Recommendation Quality and Purchase Intention and the moderating effects of Attitude and Trust on this relationship. Partial Least Squares Structural Equation Modeling was used as a research method. The sample consisted of 365 respondents who frequently use recommender system when shopping online. Data were collected using non-probability sampling method. The questionnaire is delivered to online customers who frequently rely on peer-to-peer recommender systems to make a purchase decision. The results show that Recommendation Transparency, Recommendation Accuracy, Recommendation Novelty, and Recommendation Diversity are positively related to Recommendation Quality. Consequently, Recommendation Quality has a positive impact on Attitude, Trust, and Purchase Intention. Besides, Attitude has a positive impact on online Purchase Intention. Trust also has a positive impact on online Purchase Intention. Practical implications are proposed to improve the impacts of peer-to-peer recommender systems on online shopping.
- Keywords
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JEL Classification (Paper profile tab)M31, M37, C20
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References62
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Tables4
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Figures2
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- Figure 1. Research model
- Figure 2. Standardized path coefficient
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- Table 1. Characteristics demographics
- Table 2. Cronbach’s alpha scale reliability analysis results
- Table 3. Discriminant validity for scale measurement of constructs
- Table 4. Path coefficient analysis relationship
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- Amoako, G. K., Caesar, L. D., Dzogbenuku, R. K., & Bonsu, G. A. (2023). Service recovery performance and repurchase intentions: the mediation effect of service quality at KFC. Journal of Hospitality and Tourism Insights, 6(1), 110-130.
- Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.
- Bagozzi, R. P., & Warshaw, P. R. (1992). An examination of the etiology of the attitude-behavior relation for goal-directed behaviors. Multivariate Behavioral Research, 27(4), 601-634.
- Bonicalzi, S., De Caro, M., & Giovanola, B. (2023). Artificial Intelligence and Autonomy: On the Ethical Dimension of Recommender Systems. Topoi, 42, 819-832.
- Bujang, M. A., Omar, E. D., & Baharum, N. A. (2018). A review on sample size determination for Cronbach’s alpha test: a simple guide for researchers. The Malaysian Journal of Medical Sciences: MJMS, 25(6), 85.
- Burke, R. R. (2002). Technology and the Customer Interface: What Consumers Want in the Physical and Virtual Store. Journal of the Academy of Marketing Science, 30(4), 411-432.
- Burke, R., Felfernig, A., & Göker, M. H. (2011). Recommender systems: An overview. Ai Magazine, 32(3), 13-18.
- Köbis, N., Soraperra, I., & Shalvi, S. (2021). The consequences of participating in the sharing economy: A transparency-based sharing framework. Journal of Management, 47(1), 317-343.
- Oliveira, A. S. D., Souki, G. Q., Silva, D. D., Silva, M. A. R., & Medeiros, F. D. A. D. S. (2023). Impacts of service guarantees on consumers’ perceived quality and satisfaction in e-commerce. International Journal of Quality & Reliability Management, 40(10), 2559-2580.
- Chiu, M. C., Huang, J. H., Gupta, S., & Akman, G. (2021). Developing a personalized recommendation system in a smart product service system based on unsupervised learning model.Computers in Industry, 128.
- Chopdar, P. K., Paul, J., Korfiatis, N., & Lytras, M. D. (2022). Examining the role of consumer impulsiveness in multiple app usage behavior among mobile shoppers. Journal of Business Research, 140, 657-669.
- Clark, L. A., & Watson, D. (2016). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309-319.
- Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., & Krishen, A. S. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59.
- Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt brace Jovanovich college publishers.
- Elahi, M., Jannach, D., Skjærven, L., Knudsen, E., Sjøvaag, H., Tolonen, K., Holmstad, Ø., Pipkin, I., Throndsen, E., & Stenbom, A. (2022). Towards responsible media recommendation. AI and Ethics, 2, 103-114.
- Everard, A., & Galletta, D. F. (2005). How presentation flaws affect perceived site quality, Trust, and intention to purchase from an online store. Journal of Management Information Systems, 22(3), 56-95.
- Guttentag, D. (2019). Progress on Airbnb: a literature review. Journal of Hospitality and Tourism Technology, 10(4), 814-844.
- Hair, 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.
- Hanaysha, J. R. (2022). Impact of social media marketing features on consumer’s purchase decision in the fast-food industry: Brand trust as a mediator.International Journal of Information Management Data Insights, 2(2).
- Heinrich, B., Hopf, M., Lohninger, D., Schiller, A., & Szubartowicz, M. (2022). Something’s missing? a procedure for extending item content data sets in the context of recommender systems. Information Systems Frontiers, 24(1), 267-286.
- Himeur, Y., Sohail, S. S., Bensaali, F., Amira, A., & Alazab, M. (2022). Latest trends of security and privacy in recommender systems: a comprehensive review and future perspectives. Computers & Security, 118.
- Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51(1), 57-71.
- Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
- Itani, O. S., Kassar, A.-N., & Loureiro, S. M. C. (2019). Value get, value give: The relationships among perceived value, relationship quality, customer engagement, and value consciousness. International Journal of Hospitality Management, 80, 78-90.
- Jadil, Y., Rana, N. P., & Dwivedi, Y. K. (2022). Understanding the drivers of online Trust and intention to buy on a website: An emerging market perspective. International Journal of Information Management Data Insights, 2(1).
- Jayasankara Prasad, C., & Ramachandra Aryasri, A. (2011). Effect of shopper attributes on retail format choice behaviour for food and grocery retailing in India. International Journal of Retail & Distribution Management, 39(1), 68-86.
- Jiang, H., Ge, J., & Yao, J. (2024). Effects of brand spokes-characters with personal and historical nostalgia on brand attitude: evidence from Generation Z consumers in China. Asia Pacific Journal of Marketing and Logistics, 36(1), 185-205.
- Kumar, S., Kar, A. K., & Ilavarasan, P. V. (2021). Applications of text mining in services management: A systematic literature review.International Journal of Information Management Data Insights, 1(1).
- Kushwaha, A. K., Kar, A. K., & Dwivedi, Y. K. (2021). Applications of big data in emerging management disciplines: A literature review using text mining.International Journal of Information Management Data Insights, 1(2).
- Kemp, E., Briggs, E., & Anaza, N. A. (2020). The emotional side of organizational decision-making: examining the influence of messaging in fostering positive outcomes for the brand. European Journal of Marketing, 54(7), 1609-1640.
- Kline, T. J. B. (2005). Psychological testing: A practical approach to design and evaluation. Sage publications.
- Konuk, F. A. (2018). The role of store image, perceived quality, Trust and perceived value in predicting consumers’ purchase intentions towards organic private label food. Journal of Retailing and Consumer Services, 43, 304-310.
- Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 55.
- Lis, B., & Fischer, M. (2020). Analyzing different types of negative online consumer reviews. Journal of Product & Brand Management, 29(5), 637-653.
- Luan, Y., & Kim, Y. J. (2022). An integrative model of new product evaluation: A systematic investigation of perceived novelty and product evaluation in the movie industry. Plos One, 17(3).
- McDonald, R. P. (1970). The theoretical foundations of principal factor analysis, canonical factor analysis, and alpha factor analysis. British Journal of Mathematical and Statistical Psychology, 23(1), 1-21.
- Möller, J., Trilling, D., Helberger, N., & van Es, B. (2020). Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity. In Digital Media, Political Polarization and Challenges to Democracy (pp. 45-63). Routledge.
- Nguyen, J., Ferraro, C., Sands, S., & Luxton, S. (2022). Alternative protein consumption: A systematic review and future research directions. International Journal of Consumer Studies, 46(5), 1691-1717.
- Osei-Frimpong, K., Donkor, G., & Owusu-Frimpong, N. (2019). The impact of celebrity endorsement on consumer purchase intention: An emerging market perspective. Journal of Marketing Theory and Practice, 27(1), 103-121.
- Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2017). Sense and sensibility in personalized e-commerce: How emotions rebalance the purchase intentions of persuaded customers. Psychology & Marketing, 34(10), 972-986.
- Parasuraman, A., Zeithaml, V. A., & Berry, L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
- Pavlidis, G. (2019). Recommender systems, cultural heritage applications, and the way forward. Journal of Cultural Heritage, 35, 183-196.
- Pecune, F., Callebert, L., & Marsella, S. (2022). Designing persuasive food conversational recommender systems with nudging and socially-aware conversational strategies. Frontiers in Robotics and AI, 8.
- Pu, P., Chen, L., & Hu, R. (2011). A user-centric evaluation framework for recommender systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (pp. 157-164).
- Ramanathan, U., Subramanian, N., & Parrott, G. (2017). Role of social media in retail network operations and marketing to enhance customer satisfaction. International Journal of Operations & Production Management, 37(1), 105-123.
- Raza, S., & Ding, C. (2022). News recommender system: a review of recent progress, challenges, and opportunities. Artificial Intelligence Review, 55, 749-800.
- Sahoo, D., Harichandan, S., Kar, S. K., & Sreejesh, S. (2022). An empirical study on consumer motives and attitude towards adoption of electric vehicles in India: Policy implications for stakeholders. Energy Policy, 165.
- Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146.
- Sivapalan, S., Sadeghian, A., Rahnama, H., & Madni, A. M. (2014). Recommender systems in e-commerce. In Proceedings of 2014 World Automation Congress (WAC) (pp. 179-184).
- Sung, B., La Macchia, S., & Stankovic, M. (2023). Agency appraisal of emotions and brand trust. European Journal of Marketing, 57(9), 2483-2512.
- Supapon, S., & Sukhawatthanakun, K. (2023). The effect of brand equity on Thai cosmetic purchasing decisions. Uncertain Supply Chain Management, 11(4), 1905-1914.
- Teller, P. (2018). Measurement accuracy realism. In The Experimental Side of Modeling (pp. 273-298).
- Tseng, H.-T., Shanmugam, M., Magalingam, P., Shahbazi, S., & Featherman, M. S. (2022). Managing enterprise social media to develop consumer trust. British Food Journal, 124(12), 4626-4643.
- Vaske, J. J., Beaman, J., & Sponarski, C. C. (2017). Rethinking internal consistency in Cronbach’s Alpha. Leisure Sciences, 39(2), 163-173.
- Wang, J. (2023). The relationship between loneliness and consumer shopping channel choice: Evidence from China. Journal of Retailing and Consumer Services, 70.
- Wang, Y., Han, J. H., & Beynon-Davies, P. (2019). Understanding blockchain technology for future supply chains: a systematic literature review and research agenda. Supply Chain Management: An International Journal, 24(1), 62-84.
- Yuwen, H., Guanxing, S., & Qiongwei, Y. (2022). Consumers’ perceived trust evaluation of cross-border e-commerce platforms in the context of socialization. Procedia Computer Science, 199, 548-555.
- Zaizi, F. E., Qassimi, S., & Rakrak, S. (2023). Multi-objective optimization with recommender systems: A systematic review. Information Systems, 117.
- Zhang, H., Wang, Z., Chen, S., & Guo, C. (2019). Product recommendation in online social networking communities: An empirical study of antecedents and a mediator. Information & Management, 56(2), 185-195.
- Zhang, J., Adomavicius, G., Gupta, A., & Ketter, W. (2020). Consumption and performance: Understanding longitudinal dynamics of recommender systems via an agent-based simulation framework. Information Systems Research, 31(1), 76-101.
- Zhao, X., & Keikhosrokiani, P. (2022). Sales Prediction and Product Recommendation Model Through User Behavior Analytics. Computers, Materials & Continua, 70(2), 3855-3874.
- Zhu, L., Li, H., Wang, F.-K., He, W., & Tian, Z. (2020). How online reviews affect purchase intention: a new model based on the stimulus-organism-response (S-O-R) framework. Aslib Journal of Information Management, 72(4), 463-488.
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Green product buying intentions among young consumers: extending the application of theory of planned behavior
Andhy Setyawan 
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Noermijati Noermijati ,
Sunaryo Sunaryo ,
Siti Aisjah
doi: http://dx.doi.org/10.21511/ppm.16(2).2018.13
Problems and Perspectives in Management Volume 16, 2018 Issue #2 pp. 145-154 Views: 3260 Downloads: 1093 TO CITE АНОТАЦІЯThis research reveals the factors explaining the purchase intention toward green products among young consumers. Young consumers are beginner consumers who are going to play an important role to take a responsibility in preserving the environment. Theory of Planned Behavior (TPB) is selected as the main theoretical framework in this research alongside some other variables (environmental concern, environmental knowledge, and willingness to pay), which are added in the research model to expand TPB application. Three hundred and twenty-six respondents were interviewed through a survey and the data are analyzed using Structural Equation Modeling (SEM).
The findings illustrated that not every explanatory variable influenced the purchase intention toward green products among young consumers. Environmental concern and attitude did not influence the purchase intention toward green products among young consumers. -
Adoption of Mobile Banking and Perceived Risk in GCC
Kavita Chavali ,
Ajith Kumar
doi: http://dx.doi.org/10.21511/bbs.13(1).2018.07
Banks and Bank Systems Volume 13, 2018 Issue #1 pp. 72-79 Views: 2456 Downloads: 569 TO CITE АНОТАЦІЯThe study deals with the adoption of mobile banking services by respondents in UAE and the perception of risk factors by them. A model was developed on the Customer Adoption Process of mobile banking. The model is validated based on the data collected using the questionnaire from a sample of 90 respondents in UAE. Factor analysis is used to evaluate and analyze the responses. Belief in technology and the value it creates are the major driving force for respondents to adopt mobile banking. Respondents perceive that mobile banking helps in proper financial planning due to continuous monitoring the transactions and time saving. Lack of privacy in the mobile banking transactions and not all banks offering mobile banking services in UAE are the major challenges perceived by the respondents for non-adoption of mobile banking. Respondents identify time risk, financial risk and performance risk as the most predominant risk factors compared to other risks in the adoption process.
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Short video marketing factors influencing the purchase intention of Generation Z in Vietnam
Thi Thuy An Ngo ,
Phu Quach ,
Thanh Vinh Nguyen ,
Anh Duy Nguyen ,
Thi Minh Nguyet Nguyen
doi: http://dx.doi.org/10.21511/im.19(3).2023.04
Innovative Marketing Volume 19, 2023 Issue #3 pp. 34-50 Views: 2361 Downloads: 1248 TO CITE АНОТАЦІЯIn the digital age and technological advancements, short video platforms have become essential tools for online sales and marketing. In addition, shopping through short video marketing has gained significant attention, especially among Generation Z, as it brings unique and novel shopping experiences. The primary goal of this study is to explore the factors of short video marketing that influence the purchase intentions of Generation Z consumers in Vietnam. To conduct this study, a quantitative approach was employed, utilizing a 5-point Likert scale questionnaire administered online through a non-probability sampling method. The sample comprised 350 respondents aged between 16 and 26 from Vietnam, representing Generation Z, who made purchases through short video marketing. The relationships among various variables were analyzed using Structural Equation Modeling (SEM). The study’s results demonstrated a positive, significant, and direct relationship between all factors of short video marketing, including interesting content, perceived usefulness, scenario-based experience, user interaction, perceived enjoyment, and involvement of celebrities and consumer brand attitude. Among these factors, perceived usefulness is the most influential factor on customer brand attitude. In addition, the study revealed that consumer brand attitude, acting as a mediating variable, had a positive and significant impact on consumers’ purchase intentions. Based on the findings, the study suggested strategies for businesses to enhance the quality and content on short video platforms, thereby improving the effectiveness of their marketing strategies.
Acknowledgment
The authors express a sincere gratitude to all the participants who generously took part in this research study.
