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ISSN : 2233-4165(Print)
ISSN : 2233-5382(Online)
Journal of Industrial Distribution & Business Vol.11 No.6 pp.41-53
DOI : http://dx.doi.org/10.13106/jidb.2020.vol11.no6.41

The effect of image search, social influence characteristics and anthropomorphism on purchase intention in mobile shopping

Won-Gu KIM1,Hyeonsuk PARK2
* Acknowledgements (if any): If there is any acknowledgement that authors would like to mention, please mention here.
1 First Author, ABD, Dept. of Convergence Industry, Seoul Venture University, Korea. Email: kwg1004@gmail.com

© Copyright: The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
2 Corresponding Author, Professor, Dept. of Convergence Industry, Seoul Venture University, Korea. Tel: +82-2-3470-5244, Email: hspark@svu.ac.kr
April 21, 2020. May 30, 2020. June 05, 2020

Abstract

Purpose: The purpose of this study is to review the previous studies on the characteristics of the image search service provided by using artificial intelligence, the social impact characteristics, and the moderating effect of perceived anthropomorphism, and conduct empirical analysis to identify the constituent factors affecting purchase intention. To clarify. Through this, I tried to present theoretical and practical implications. Research design, data, and methodology: Research design was that characteristics of image search service (ubiquity and information quality) and social impact characteristics (subjective norms, electronic word of mouth marketing) are affected by mediation of satisfaction and flow, therefore, control of perceived anthropomorphism have an effect on purchase intention to increase. For analysis, research conducted literature review, and developed questionnaires, so that EM firm which is a specialized research institute has collected data. This was conducted on 410 people between the 20s and 50s who have mobile shopping experiences. SPSS Statistics 23 and AMOS 23 had been used to perform necessary analysis such as exploratory factor analysis, reliability analysis, feasibility analysis, and structural equation modeling based on this data. Results: first, ubiquity, information quality and subjective norms were found to have a positive effect on purchase intention through satisfaction and flow parameters. Second, satisfaction and flow were found to have a mediating effect between ubiquity, information quality, and subjective norms and purchase intentions. However, there was no mediating effect between eWOM information and purchase intention. Third, perceived anthropomorphism was found to have a moderating effect between information quality and satisfaction, and it was found that there was no moderating effect on the relationship between information quality and flow. Conclusions: The information quality of image search services using artificial intelligence has a positive effect on satisfaction, and it has been found that there is a positive moderate effect of perceived anthropomorphism in this relationship, which may be an academic contribution to the distribution science utilizing artificial intelligence. Therefore, it is possible to propose a distribution strategy that improves purchase intention by utilizing image search service and anthropomorphism in practical business and providing a more enjoyable immersive experience to customers.

JEL Classification Code: M15, M30, M31

초록


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    Reference

    1. Aggarwal, P., & Mcgill, A. L. (2007). Is that car smiling at me? Schema congruity as a basis for evaluating anthropomorphized products. Journal of Consumer Research, 34(4), 468-479.
    2. Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22(C), 16-23.
    3. Ahn, T., Ryu, S., & Han, I. (2004). The impact of the online and offline features on the user acceptance of Internet shopping malls. Electronic commerce research and applications, 3(4), 405-420.
    4. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
    5. Ángela, P. L., & Ángel, V. R. (2017). Hedonic and utilitarian effects of the adoption and use of social commerce. Cooperative and Networking Strategies in Small Business, 1, 155–173.
    6. Astrid, M., Kramer, N. C., Gratch, J., & Kang, S. H. (2010). It doesn't matter what you are! Explaining social effects of agents and avatars. Journal Computers in Human Behavior, 26(6), 1641-1650.
    7. Au, Y. A., & Kauffman, R. J. (2008). The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application. Electronic Commerce Research and Applications, 7(2), 141-164.
    8. Baker, D. A., & Crompton, J. L. (2000). Quality, satisfaction and behavioral intentions. Annals of Tourism Research, 27(3), 785-804.
    9. Bao, Z., & Huang, T. (2018). Exploring stickiness intention of B2C online shopping malls (A perspective from information quality). International Journal of Web Information Systems, 14(2), 177-192.
    10. Bilgihan, A., Nusair, K., Okumus, F., & Cobanoglu, C. (2015). Applying flow theory to booking experiences: An integrated model in an online service context. International Journal of Social Economics, 45(1), 57-81.
    11. Carolina, L. N., Francisco, J. M. C., & Harry, B. (2008). An assessment of advanced mobile services acceptance: contributions from TAM and diffusion theory models. Information & Management, 45(6), 359-364.
    12. Cha, S. S., & Lee, S. H. (2020). The effect of convenience store dessert on consumers value and satisfaction. Journal of Asian Finance, Economics and Business, 7(3), 191-199.
    13. Cha, S. S., & Seo, B. K. (2019). The effect of brand trust of home meal replacement on repurchasing in online shopping. Journal of Business, Economics and Environmental Studies, 9(3), 21-26.
    14. Cha, S. S., & Lyu, M. S. (2019). Influence of SNS characteristics on the brand Image of infant food products. International Journal of Industrial Distribution & Business, 10(8), 7-15.
    15. Chandler, J., & Schwarz, N. (2010). Use does not wear ragged the fabric of friendship: Thinking of objects as alive makes people less willing to replace them. Journal of Consumer Psychology, 20(2), 138-45.
    16. Chang, E. C., & Tseng, Y. F. (2013). E-store image, perceived value and perceived risk. Journal of Business Research, 66(7), 864−870.
    17. Chatterjee, S., Kar, A. K., & Gupta, M. P. (2018). Success of IoT in smart cities of India: An empirical analysis. Government Information Quarterly, 35(3), 349-361.
    18. Chen, L. Y. (2013). Antecedents of customer satisfaction and purchase intention with mobile shopping system use. International Journal of Services and Operations Management, 15(3), 259-274.
    19. Cho, B. D., Potluri, R. M., & Youn, M. K. (2020). A Study on the effect of product recommendation system on customer satisfaction: focused on the online shopping mall. Journal of Industrial Distribution & Business, 11(2), 17-23.
    20. Cho, S. H. (2019). The effect of mobile tourism App characteristics on perceived value, satisfaction and behavioral intention. International Journal of Industrial Distribution & Business, 10(9), 45-52.
    21. Cho, Y. C. (2020). Exploring determinants of performance indicator and customer satisfaction of accommodation sharing. Journal of Asian Finance, Economics and Business, 7(3), 201-210.
    22. Clarke, I. (2008). Emerging value propositions for M-commerce. Journal of Business Strategies, 18(2), 133-148.
    23. Craig, S. D., & Schroeder, N. L. (2017). Reconsidering the voice effect when learning from a virtual human. Computers & Education, 114, 193-205.
    24. Csikszentmihalyi, M. (1997). Creativity: Flow and the psychology of discovery and invention. New York, NY: Harper Perennial.
    25. 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.
    26. DeLone, W. H., & McLean, E. R. (2016). Information Systems Success Measurement. Foundations and Trends in Information Systems, 2(1), 1-116.
    27. Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2016). A generalized adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, 33(1), 174-187.
    28. Engel, J., Blackwell, L., Roger, D., & Miniard, P. W. (1995). Consumer behavior, Chicago. Journal of Retailing, 58(1), 34-57.
    29. Epley, N., Waytz, A., Akalis, S., & Cacioppo, J. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864-886.
    30. Epley, N., Waytz, A., Akalis, S., & Cacioppo, J. (2008). When we need a human: Motivational determinants of anthropomorphism. Social Cognition, 26(2), 143-155.
    31. Erkan, I., & Evans, C. (2016). The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61, 47-55.
    32. Estela, F. S., & Sergio, R. (2016). The multichannel customer’s service experience: Building satisfaction and trust. Service Business, 10(2), 423-445.
    33. Fetscherin, M., & Lattemann, C. (2008). User acceptance of virtual worlds. Journal of Electronic Commerce Research, 9(3), 231-242.
    34. Fishbein, M. (1967). Readings in attitude theory and measurement (Ed.). New York, NY: John Wiley & Sons.
    35. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. The Journal of Marketing Research, 18(1), 39-50.
    36. Frolick, M. N., & Chen, L. D. (2004). Assessing m-commerce opportunities. Information Systems Management, 21(2), 53-61.
    37. Fu, X., Bin, Z., Xie, Q., Liuli, X., & Yu, C. (2011). Impact of quantity and timeliness of eWOM information on consumer’s online purchase intention under C2C environment. Asian Journal of Business Research, 1(2), 37–52.
    38. Ghani, J. A., & Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human-computer interaction. The Journal of psychology, 128(4), 381-391.
    39. Hausman, A., & Siekpe, J. (2009). The effect of web interface features on consumer online purchase intentions. Journal of Business Research, 62(1), 5-13.
    40. Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Concep-tual foundations. Journal of Marketing, 60(3), 50-68.
    41. Horst, M., Kuttschreuter, M., & Gutteling, J. M. (2007). Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in the Netherlands. Computers in Human Behavior, 23(4), 1838-1852.
    42. Hsiao, C. H., Chang, J. J., & Tang, K. Y. (2016). Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value persp- ectives. Telematics and Informatics, 33(2), 342-355.
    43. Huang, L., Lu, X., & Ba, S. (2016). An empirical study of cross-channel effects between web and mobile shopping channels. Information & Management, 53(2), 265-278.
    44. Iriobe, O. C., & Elizabeth, A. O. (2019). Moderating effect of the use of eWOM on subjective norms, behavioural control and religious tourist revisit intention. International Journal of Religious Tourism and Pilgrimage, 7(3), 38-47.
    45. Jalilvand, M. R., & Samiei, N. (2012). The impact of electronic word of mouth on a tourism destination choice testing the theory of planned behavior(TPB). Internet Research, 22(5), 591-612.
    46. Jamshidi, D., Keshavarz, Y., Kazemi, F., & Mohammadian, M. (2018). Mobile banking behavior and flow experience: An integration of utilitarian features, hedonic features and trust. International Journal of Social Economics, 45(1), 57–81.
    47. Jun, Z., & Lee, H. Y. (2015). Factors influencing Chinese customers’ selection of health care service countries: Focusing on word-of-mouth moderating effects. Journal of Distribution Science, 13(12), 41-52.
    48. Jun, Z., & Lee, H. Y. (2016). The word-of-mouth effects on the Chinese customers’ choice intention of medical tourism destination. Journal of Distribution Science, 14(7), 21-31.
    49. Kannan, P. K., Chang, A. M., & Whinston, A. B. (2001). Electronic communities in e-business: Their role and issues. Information System Frontier, l(4), 415-426.
    50. Kim, M. S., Kim, M. O., Hur, S. E., Seo, M. S., & Seo, W. J. (2020). Promoting word-of-mouth communication: The moderating role of leisure sport social media. Journal of Distribution Science, 18(4), 61-72.
    51. Kleijnen, M., Ruyter, K., & Wetzels, M. (2007). An assessment of value creation in mobile service delivery and the moderating role of time consciousness. Journal of Retailing, 83(1), 33-46.
    52. Koo, G. Y., Shoffner, S., & Ryu, J. (2017). Use of animated pedagogical agent in sport management education: effect on students’ situational interest. Sport Management Education Journal, 11(1), 34-44.
    53. Kwak, J. G., Kim, N. E., & Kim, M. S. (2019). The relationship among chatbot’s characteristics, service value, and customer satisfaction. International Journal of Industrial Distribution & Business, 10(3), 45-58.
    54. Lee, W. S., & Kim, B. Y. (2019). The effects of career orientations on entrepreneurial satisfaction and business sustainability. Journal of Asian Finance, Economics and Business, 6(4). 235-248.
    55. Lim, H. Y., Jo, S. H., Kho, H. S., & Han, K. S. (2019). A study on the effects of image search service on customers’ purchase intention in mobile shopping mall. Journal of Digital Contents Society, 20(2), 363-375.
    56. Limayem, M., & Cheung, C. M. (2008). Understanding information systems continuance: The case of internet-based learning technologies. Information & Management, 45(4), 227-232.
    57. Lin, Y. H., Hsu, C. L., Chen, M. F., & Fang, C. H. (2017). New gratifications for social word-of-mouth spread via mobile SNSs: Uses and gratifications approach with a perspective of media technology. Telematics and informatics, 34(4), 382-397.
    58. Mariné, A. S., Forsythe, S., Kwon, W. S., & Chattaraman, V. (2012). The role of product brand image and online store image on perceived risks and online purchase intentions for apparel. Journal of Retailing and Consumer Services, 19(3), 325-331.
    59. McDougall, G. H. G., & Levesque, T. (2000), Customer satisfaction with services: putting perceived value into the equation. Journal of Services Marketing, 14(5), 392-410.
    60. Mohammad, I., & Razli, C. R. (2011). The determinant factors influencing young consumers’ acceptance of mobile marketing in Malaysia. African Journal of Business Management, 5(32), 12531-12542.
    61. Moreno, R., Mayer, R. E., Spires, H. A., & Lester, J. C. (2001). The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, 19(2), 177-213.
    62. Negash, S., Ryan, T., & Igbaria, M. (2003). Quality and effectiveness in web-based customer support systems. Information & Management, 40(8), 757-768. 
    63. Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73.
    64. Novak, T. P., & Hoffman, D. L. (1996). Marketing in hyperme dia computer- mediated environments: Conceptual foun dations. Journal of Marketing, 60(3), 50-68.
    65. Novak, T. P., Hoffman, D. L., & Yung, Y. F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42.
    66. Okazaki, S., & Mendez, F. (2013). Perceived ubiquity in mobile services. Journal of Interactive Marketing, 27(2), 98-111
    67. Osatuyi, B., & Turel, O. (2019). Social motivation for the use of social technologies: An empirical examination of social commerce site users. Internet Research, 29(1), 24-45.
    68. Park, S. Y., & Park, E. J. (2013). The effects of flow on consumer satisfaction through e-impulse buying for fashion products. Fashion & Textile Research Journal, 15(4), 533-542.
    69. Pedersen, P. E. (2005). Adoption of mobile internet services: An exploratory study of mobile commerce early adopters. Journal of Organizational Computing, 15(2), 203-222.
    70. Petter, S., & McLean, E. R. (2009). A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level. Information & Management, 46(3), 159-166.
    71. Pitt, L. F., Watson, R. T., & Kavan, C. B. (1995). Service quality: A measure of information systems effectiveness. MIS Quarterly 19(2), 173-187.
    72. Sarkara, S., Chauhanb, S., & Khare, A. (2020). A meta-analysis of antecedents and consequence influence of expectation confirmation, network externalities, and flow on use of s of trust in mobile commerce. International Journal of Information Management, 50, 286–301.
    73. Sarkar, S., & Khare, A. (2019). Influence of expectation confirmation, network externalities, and flow on use of mobile shopping Apps. International Journal of Human–Computer Interaction, 35(16), 1449-1460.
    74. Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information systems research, 8(3), 240-253.
    75. Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44, 65–75.
    76. Shaw, N., & Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value. International Journal of Information Management, 45, 44-45.
    77. Siau, K., Lim, E. P., & Shen, Z. (2001). Mobile commerce: Promises, challenges, and research agenda. Journal of Database Management, 12(3), 4-13.
    78. Sonia, S. M., Jana, P., & Blanca, L. C. (2016). What makes services customers say “Buy it with a mobile phone?”. Journal of Services Marketing, 30(6), 601–614.
    79. Swan, M. (1985). A critical look at the communicative approach (1). English Language Teaching Journal, 39(1), 2-12.
    80. Tojib, D., & Tsarenko, Y. (2012). Post-adoption modeling of advanced mobile service use. Journal of Business Research, 65(7), 922-928.
    81. Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication electronic mail and voice mail evaluation and impacts. Communication research, 19(5), 539-573.
    82. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.
    83. Venkatesh, V., & Davis, F. D. (2000). A theoretic al extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
    84. Venkatesh, V., Morris, M. G., & Davis. F. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425-478.
    85. Wan, E. W., Chen, R. P., & Jin, L. (2017). Judging a book by its cover? The effect of anthropomorphism on product attribute processing and consumer preference. Journal of Consumer Research, 43(6), 1008-1030.
    86. Wang, K., & Huang, S. T. (2014). How flow experience affects intention to use musicstreaming service: Model development. Proceeding of the 12th International Conference on Advances in Mobile Computing and Multimedia, 451–457.
    87. Wang, N., Shen, X. L., & Sun, Y. (2013). Transition of electronic word-of-mouth services from web to mobile context: A trust transfer perspective. Decision Support Systems, 54(3), 1394-1403.
    88. Waytz, A., Morewedge, C. K., Epley, N., Monteleone, G., Gao, J. H., & Cacioppo, J. T. (2010). Making sense by making sentient: Effectance motivation increases anthropomor phism. Journal of Personality and Social Psychology, 99(3), 410-435.
    89. Webster, J., Trevino, L. K., & Ryan, L. (1993). The dimension ality and correlates of flow in human-computer interac tions. Computers in Humans Behavior, 9(4), 411-426.
    90. Wells, J. D., Parboteeah, V., & Valacich, J. S. (2011). Online impulse buying: Understanding the interplay between consumer impulsiveness and website quality. Journal of the Association for Information Systems, 12(1), 32-56.
    91. Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.
    92. Wolf, L., Bick, M., & Kummer, T. F. (2017). The influence of situation dependent factors on mobile shopping usage. Proceedings of the 50th Hawaii International Conference on System Sciences, 4169-4178. Honolulu, HI: HICSS. Retrieved from http://hdl.handle.net/10125/41664.
    93. Wu, J. J., & Chang, Y. S. (2005). Towards understanding members’ interactivity, trust, and flow in online travel community. Industrial Management & Data Systems, 105(7), 937-954.
    94. Xiang, L., Zheng, X., Lee, M. K. O., & Zhao, D. (2016). Exploring consumers’ impulse buying behavior on social commerce platform: The role of parasocial interaction. International Journal of Information Management, 36(3), 333-347.
    95. Yadav, R., Sharma, S.K., & Tarhini, A. (2015). A multi analytical approach to understand and predict the mobile commerce adoption. Journal of Enterprise Information and Management, 29(2), 222-237.
    96. Yoo, C. K., & Kim, G. P. (2019). An empirical analysis on the compromised delivery model of traditional market using delivery application. International Journal of Industrial Distribution & Business, 10(10), 45-51.
    97. Zhou, T., & Lu, Y. (2011). The effect of interactivity on the flow experience of mobile commerce user. International journal of mobile communication, 9(3), 225-242.
    98. Zhu, F. X., Wymer, W., & Chen, I. (2002). IT-based services and service quality in consumer banking. International Journal of Service Industry Management, 13(1), 69-90.