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The Effects of Product’s Visual Preview and Customer Review on Sale Performance in Mobile Commerce

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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2021)

Abstract

Due to the development of mobile communication and the spread of mobile devices, the use of mobile commerce (m-commerce), which enables commercial activities anytime, anywhere, through the Internet, is becoming more active. Compared with e-commerce, m-commerce has excellent convenience, but consumer behavior such as product searching, preference and purchase are performed on a relatively smaller screen. Therefore, this paper extracted images attributes through vision API and Deep-CNNs, and sentiment analyzed customer reviews by separating them into Material, Size, Price, and Delivery. In order to study the effect of such information on consumer behavior, regression analysis and mediating effect analysis based on the Stimulus-Organism-Response (S-O-R) model. The model states that external stimuli affect the individual's psychological state and ultimately the actual behavior. This paper has classified the characteristics of information on customer behavior in m-commerce, and it had been confirmed that images and reviews had a significant effect on the performance of m-commerce sales products through S-O-R model.

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References

  1. Zheng, X., Men, J., Yang, F., Gong, X.: Understanding impulse buying in mobile commerce: an investigation into hedonic and utilitarian browsing. Int. J. Inf. Manag. 48, 151–160 (2019)

    Article  Google Scholar 

  2. Omonedo, P., Bocij, P.: E-commerce versus m-commerce: where is the dividing line. World Acad. Sci. Eng. Technol. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 8(11), 3488–3493 (2014)

    Google Scholar 

  3. Huang, L., Lu, X., Ba, S.: An empirical study of the cross-channel effects between web and mobile shopping channels. Inf. Manag. 53(2), 265–278 (2016)

    Article  Google Scholar 

  4. Rana, N.P., Barnard, D.J., Baabdullah, A.M.A., Rees, D., Roderick, S.: Exploring barriers of m-commerce adoption in SMEs in the UK: developing a framework using ISM. Int. J. Inf. Manag. 44, 141–153 (2019)

    Article  Google Scholar 

  5. Xia, H., Pan, X., Zhou, Y., Zhang, Z.J.: Creating the best first impression: designing online product photos to increase sales. Decis. Support Syst. 131, 113235 (2020)

    Article  Google Scholar 

  6. Wang, S.W., Ngamsiriudom, W., Hsieh, C.: Trust disposition, trust antecedents, trust, and behavioral intention. Serv. Ind. J. 35(10), 555–572 (2015)

    Article  Google Scholar 

  7. Koksal, M.H.: The intentions of Lebanese consumers to adopt mobile banking. Int. J. Bank Mark. 34(3), 327–346 (2016)

    Article  Google Scholar 

  8. Chin, A.G., Harris, M.A., Brookshire, R.: A bidirectional perspective of trust and risk in determining factors that influence mobile app installation. Int. J. Inf. Manag. 39, 49–59 (2018)

    Article  Google Scholar 

  9. Malu, G., Bapi, R.S., Indurkhya, B.: Learning photography aesthetics with deep CNNs (2017). arXiv:1707.03981

  10. Mehrabian, A., & Russell, J.A.: An approach to environmental psychology. MIT Press (1974)

    Google Scholar 

  11. Kim, M., Lennon, S.: The effects of visual and verbal information on attitudes and purchase intentions in internet shopping. Psychol. Mark. 25(2), 146–178 (2008)

    Article  Google Scholar 

  12. Chen, Y.C., Shang, R.A., Kao, C.Y.: The effects of information over-load on consumers’ subjective state towards buying decision in the internet shop-ping environment. Electron. Commer. Res. Appl. 8(1), 48–58 (2009)

    Article  Google Scholar 

  13. Jiang, Z., Benbasat, I.: The effects of presentation formats and task complexity on online consumers’ product understanding. MIS Q. 475–500 (2007)

    Google Scholar 

  14. Koufaris, M.: Applying the technology acceptance model and flow theory to online consumer behavior. Inf. Syst. Res. 13(2), 205–223 (2002)

    Article  Google Scholar 

  15. Li, X., Wang, M., Chen, Y.: The impact of product photo on online consumer purchase intention: an image-processing enabled empirical study. In: Proceedings of PACIS, June 2014, p. 325

    Google Scholar 

  16. Li, X., Wu, C., Mai, F.: The effect of online reviews on product sales: a joint sentiment-topic analysis. Inf. Manag. 56(2), 172–184 (2019)

    Article  Google Scholar 

  17. Wang, W., Wang, H., Song, Y.: Ranking product aspects through sentiment analysis of online reviews. J. Exp. Theor. Artif. Intell. 29(2), 227–246 (2017)

    Article  Google Scholar 

  18. Archak, N., Ghose, A., Ipeirotis, P.G.: Deriving the pricing power of product features by mining consumer reviews. Manag. Sci. 57(8), 1485–1509 (2011)

    Article  MATH  Google Scholar 

  19. Gutt, D., Neumann, J., Zimmermann, S., Kundisch, D., Chen, J.: Design of review systems—a strategic instrument to shape online reviewing behavior and economic outcomes. J. Strateg. Inf. Syst. 28(2), 104–117 (2019)

    Article  Google Scholar 

  20. Hu, N., Koh, N.S., Reddy, S.K.: Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decis. Support Syst. 57, 42–53 (2014)

    Article  Google Scholar 

  21. Ketelaar, P.E., Willemsen, L.M., Sleven, L., Kerkhof, P.: The good, the bad, and the expert: how consumer expertise affects review valence effects on purchase intentions in online product reviews. J. Comput. Mediat. Commun. 20(6), 649–666 (2015)

    Article  Google Scholar 

  22. Hu, N., Pavlou, P.A., Zhang, J.J.: On self-selection biases in online product reviews. MIS Q. 41(2), 449–471 (2017)

    Article  Google Scholar 

  23. Choi, A.A., Cho, D., Yim, D., Moon, J.Y., Oh, W.: When seeing helps believing: the interactive effects of previews and reviews on E-book purchases. Inf. Syst. Res. 30(4), 1164–1183 (2019)

    Article  Google Scholar 

  24. Wang, J.C., Chang, C.H.: How online social ties and product-related risks influence purchase intentions: a Facebook experiment. Electron. Commer. Res. Appl. 12(5), 337–346 (2013)

    Article  Google Scholar 

  25. Zhu, L., Li, H., Wang, F.K., He, W., Tian, Z.: How online reviews affect purchase intention: a new model based on the stimulus-organism-response (SOR) framework. Aslib J. Inf. Manag. (2020)

    Google Scholar 

  26. Peng, C., Kim, Y.G.: Application of the stimuli-organism-response (SOR) framework to online shopping behavior. J. Internet Commer. 13(3–4), 159–176 (2014)

    Article  Google Scholar 

  27. Mo, Z., Li, Y.F., Fan, P.: Effect of online reviews on consumer purchase behavior. J. Serv. Sci. Manag. 8(03), 419 (2015)

    Google Scholar 

  28. Bigne, E., Chatzipanagiotou, K., Ruiz, C.: Pictorial content, sequence of conflicting online reviews and consumer decision-making: the stimulus-organism-response model revisited. J. Bus. Res. (2020)

    Google Scholar 

  29. Kim, M.: Digital product presentation, information processing, need for cognition and behavioral intent in digital commerce. J. Retail. Consum. Serv. 50, 362–370 (2019)

    Article  Google Scholar 

  30. Moshagen, M., Thielsch, M.T.: Facets of visual aesthetics. Int. J. Hum Comput Stud. 68(10), 689–709 (2010)

    Article  Google Scholar 

  31. Bauerly, M., Liu, Y.: Computational modeling and experimental investigation of effects of compositional elements on interface and design aesthetics. Int. J. Hum. Comput. Stud. 64(8), 670–682 (2006)

    Article  Google Scholar 

  32. Cyr, D., Head, M., Larios, H., Pan, B.: Exploring human images in website design: a multi-method approach. MIS Q. 539–566 (2009)

    Google Scholar 

  33. Wang, M., Li, X., Chau, P.Y.: The impact of photo aesthetics on online consumer shopping behavior: an image processing-enabled empirical study. In: 37th International Conference on Information Systems, ICIS 2016, Dec 2016. Association for Information Systems

    Google Scholar 

  34. Blei, D.M., Lafferty, J.D.: A correlated topic model of science. Ann. Appl. Stat. 1(1), 17–35 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  35. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    Google Scholar 

  36. Wang, Y., Lu, X., Tan, Y.: Impact of product attributes on customer satisfaction: an analysis of online reviews for washing machines. Electron. Commer. Res. Appl. 29, 1–11 (2018)

    Article  Google Scholar 

  37. Kong, S., Shen, X., Lin, Z., Mech, R., Fowlkes, C.: Photo aesthetics ranking network with attributes and content adaptation. In: European Conference on Computer Vision, Oct 2016, pp. 662–679. Springer, Cham

    Google Scholar 

  38. kevinlu1211: Deep photo aesthetics. GitHub repository (2018). https://github.com/kevinlu1211/deep-photo-aesthetics

  39. Wang, T., Cai, Y., Leung, H.F., Lau, R.Y., Li, Q., Min, H.: Product aspect extraction supervised with online domain knowledge. Knowl. Based Syst. 71, 86–100 (2014)

    Article  Google Scholar 

  40. Kim, Y., Moon, H.S., Kim, J.K., Lim, S.H., Sung, J., Kim, D., Noh, G.Y., et al.: Analyzing the effect of electronic word of mouth on low involvement products. Asia Pac. J. Inf. Syst. 27, 139–155 (2017)

    Google Scholar 

  41. Gaudenzi, F.: Bias in purchase decisions: correlation between expectations and procrastination in high and low involvement products (2020)

    Google Scholar 

  42. Baron, R.M., Kenny, D.A.: The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 1173–1182 (1986)

    Article  Google Scholar 

  43. Sobel, M.E.: Asymptotic confidence intervals for indirect effects in structural equation models. Sociol. Methodol. 13, 290–312 (1982)

    Article  Google Scholar 

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Correspondence to Eun Tack Im .

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Im, E.T., Phuong, H.T., Oh, M.S., Lee, J.Y., Gim, S. (2021). The Effects of Product’s Visual Preview and Customer Review on Sale Performance in Mobile Commerce. In: Lee, R., Kim, J.B. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SNPD 2021. Studies in Computational Intelligence, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-67008-5_17

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