IJBMC Journal Issues
Factors affecting consumer’s purchase intention of chatbot commerce in Thailand
Supakchaya Nuanchaona, Supaprawat Siripipatthanakul, Wasutida Nurittamont, Bordin Phayaphrom
ABSTRACT : This study examines the factors influencing consumer’s purchase intention of chatbot commerce in Thailand. The elements of chatbot include the anthropomorphism factor, social presence factor, and perceived enjoyment factor, whereas the outcome variable is purchase intention. This study employed a quantitative approach to gathering data for statistical analysis through stratified random sampling in a total of 388 completed questionnaires were for data analysis. The conceptual framework was examined with the multiple regression analysis (MRA). Findings reveal that the anthropomorphism factor and the perceived enjoyment factor are significant predictors of purchase intention. But social preference factor is not. In future research, comprehensive coverage of chatbot commerce in several online shopping categories is recommended to obtain an extensive result.
Keywords: factors, purchase intention, chatbot commerce, consumer, digital marketing
Bibliography
Nuanchaona, S., Siripipatthanakul, S., Nurittamont, W., & Phayaphrom, B. (2021). Factors affecting consumer’s purchase intention of chatbot commerce in Thailand. International Journal of Business, Marketing and Communication, 1(3), 1–13.
In-text citation
According to new research… (Nuanchaona et al., 2021).
In research from Nuanchaona et al. (2021) …
List of references
’Click
Abou Ali, A., Abbass, A., & Farid, N. (2020). Factors Influencing Customers’ Purchase Intention in Social Commerce. International Review of Management and Marketing, 10(5), 63.
Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 100006. https://doi.org/10.1016/j.mlwa.2020.100006
Amin, S., & Tarun, M. T. (2020). Effect of consumption values on customers’ green purchase intention: A mediating role of green trust. Social Responsibility Journal, ahead-of-print. https://doi.org/10.1108/SRJ-05-2020-0191
Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183-189.
Brandtzaeg, P. B., & Følstad, A. (2017). Why People Use Chatbots. In I. Kompatsiaris, J. Cave, A. Satsiou, G. Carle, A. Passani, E. Kontopoulos, S. Diplaris, & D. McMillan (Eds.), Internet Science (pp. 377–392). Springer International Publishing. https://doi.org/10.1007/978-3-31970284-1_30
Chaves, A. P., Egbert, J., Hocking, T., Doerry, E., & Gerosa, M. A. (2021). Chatbots language design: The influence of language variation on user experience. ArXiv:2101.11089 [Cs]. http://arxiv.org/abs/2101.11089
Chen, J.-S., Le, T.-T.-Y., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/IJRDM-082020-0312
Cheng, Y., & Jiang, H. (2020). How Do AI-driven Chatbots Impact User Experience? Examining Gratifications, Perceived Privacy Risk, Satisfaction, Loyalty, and Continued Use. Journal of Broadcasting & Electronic Media, 64(4), 592–614. https://doi.org/10.1080/08838151.2020.1834296
Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587–595. https://doi.org/10.1016/j.jbusres.2018.10.004
Cui, L., Huang, S., Wei, F., Tan, C., Duan, C., & Zhou, M. (2017, July). Super-Agent: A customer service chatbot for e-commerce websites. In Proceedings of ACL 2017, System Demonstrations (pp. 97-102).
Dahl, D. W., Manchanda, R. V., & Argo, J. J. (2001). Embarrassment in Consumer Purchase: The Roles of Social Presence and Purchase Familiarity. Journal of Consumer Research, 28(3), 473–481. https://doi.org/10.1086/323734
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886. https://doi.org/10.1037/0033-295X.114.4.864
Han, M. C. (2021). The Impact of Anthropomorphism on Consumers’ Purchase Decision in Chatbot Commerce. Journal of Internet Commerce, 20(1), 46–65. https://doi.org/10.1080/15332861.2020.1863022
Hassanein, K., & Head, M. (2005). The Impact of Infusing Social Presence in the Web Interface: An Investigation Across Product Types. International Journal of Electronic Commerce, 10(2), 31–55. https://doi.org/10.2753/JEC1086-4415100202
Hill, J., Randolph Ford, W., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 49, 245–250. https://doi.org/10.1016/j.chb.2015.02.026
Khanna, A., Pandey, B., Vashishta, K., Kalia, K., Pradeepkumar, B., & Das, T. (2015). A Study of Today’s A.I. through Chatbots and Rediscovery of Machine Intelligence. International Journal of U- and e-Service, Science and Technology, 8(7), 277–284. https://doi.org/10.14257/ijunesst.2015.8.7.28
Khwaja, M. G., Jusoh, A., & Nor, K. M. (2019). Does online social presence lead to purchase intentions? International Journal of Economic Policy in Emerging Economies, 12(2), 198–206. https://doi.org/10.1504/IJEPEE.2019.099730
Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280. https://doi.org/10.1016/j.techsoc.2020.101280
Kim, J., Jin Ma, Y., & Park, J. (2009). Are US consumers ready to adopt mobile technology for fashion goods? An integrated theoretical approach. Journal of Fashion Marketing and Management: An International Journal, 13(2), 215–230. https://doi.org/10.1108/13612020910957725
Lee, H., Fiore, A. M., & Kim, J. (2006). The role of the technology acceptance model in explaining effects of image interactivity technology on consumer responses. International Journal of Retail & Distribution Management, 34(8), 621–644. https://doi.org/10.1108/09590550610675949
Liu, C., Bao, Z., & Zheng, C. (2019). Exploring consumers’ purchase intention in social commerce: An empirical study based on trust, argument quality, and social presence. Asia Pacific Journal of Marketing and Logistics, 31(2), 378–397. https://doi.org/10.1108/APJML-05-2018-0170
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases. Marketing Science, 38(6), 937–947. https://doi.org/10.1287/mksc.2019.1192
Nurittamont, W. (2021). Enhancing the factors influence on purchasing decision of endowment insurance: Case of testing mediate and moderate variables. Journal of management Information and Decision Sciences, 24(7), 1-11.
Nurittamont, W. (2021). The Role of E-WOM Communication impact to Consumer’s Purchasing Intention to Healthy Food Products: An Empirical Study to Testing the Mediator and Moderator Variables. International Journal of Innovation, Creativity and Change,15, (4), 639-652
Parsian, N., & Dunning, T. (2009). Developing and validating a questionnaire to measure spirituality: A psychometric process. Global journal of health science, 1(1), 2-11.
Pipitwanichakarn, T., & Wongtada, N. (2019). Leveraging the technology acceptance model for mobile commerce adoption under distinct stages of adoption: A case of micro businesses. Asia Pacific Journal of Marketing and Logistics, 33(6), 1415–1436. https://doi.org/10.1108/APJML-10-2018-0448
Pricilla, C., Lestari, D. P., & Dharma, D. (2018, August). Designing interaction for chatbot-based conversational commerce with user-centered design. In 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA) (pp. 244-249). IEEE.
Puttharak, S. (2020) Factors affecting consumer behaviors in private dental clinic: A case study of Smile Family Dental Clinic (MBA-Thesis, Stamford International University, Thailand).
Puzakova, M., & Aggarwal, P. (2018). Brands as Rivals: Consumer Pursuit of Distinctiveness and the Role of Brand Anthropomorphism. Journal of Consumer Research, 45(4), 869–888. https://doi.org/10.1093/jcr/ucy035
Raman, P. (2020). Examining the importance of gamification, social interaction and perceived enjoyment among young female online buyers in India. Young Consumers, 22(3), 387–412. https://doi.org/10.1108/YC-05-2020-1148
Rauschnabel, P. A., & Ahuvia, A. C. (2014). You’re so lovable: Anthropomorphism and brand love. Journal of Brand Management, 21(5), 372–395. https://doi.org/10.1057/bm.2014.14
Schuetzler, R. M., Grimes, G. M., & Scott Giboney, J. (2020). The impact of chatbot conversational skill on engagement and perceived humanness. Journal of Management Information Systems, 37(3), 875–900. https://doi.org/10.1080/07421222.2020.1790204
Schurink, E. (2019). The role of perceived social presence in online shopping: The effects of chatbot appearance on perceived social presence, satisfaction, and purchase intention (Master’s thesis, University of Twente).
Shawar, B. A., & Atwell, E. (2007). Chatbots: Are they really useful? Ldv Forum, 22(1), 29–49.
Vincze, J. (2017). Virtual reference librarians (Chatbots). Library Hi Tech News, 34(4), 5–8. https://doi.org/10.1108/LHTN-03-2017-0016
Van den Broeck, E., Zarouali, B., & Poels, K. (2019). Chatbot advertising effectiveness: When does the message get through? Computers in Human Behavior, 98, 150–157. https://doi.org/10.1016/j.chb.2019.04.009
Van Pinxteren, M. M. E., Pluymaekers, M., & Lemmink, J. G. A. M. (2020). Human-like communication in conversational agents: A literature review and research agenda. Journal of Service Management, 31(2), 203–225. https://doi.org/10.1108/JOSM-06-2019-0175
Vincze, J. (2017). Virtual reference librarians (Chatbots). Library Hi Tech News, 34(4), 5–8. https://doi.org/10.1108/LHTN-03-2017-0016
Yen, C., & Chiang, M.-C. (2020). Trust me, if you can: A study on the factors that influence consumers’ purchase intention triggered by chatbots based on brain image evidence and self-reported assessments. Behaviour & Information Technology, 0(0), 1–18. https://doi.org/10.1080/0144929X.2020.1743362
Zarouali, B., Van den Broeck, E., Walrave, M., & Poels, K. (2018). Predicting consumer responses to a chatbot on Facebook. Cyberpsychology, Behavior, and Social Networking, 21(8), 491-497.
Zhang, H., Lu, Y., Shi, X., Tang, Z., & Zhao, Z. (2012). Mood and social presence on consumer purchase behaviour in C2C E-commerce in Chinese culture. Electronic Markets, 22(3), 143–154. https://doi.org/10.1007/s12525-012-0097-z
Zhang, M., Li, L., Ye, Y., Qin, K., & Zhong, J. (2020). The effect of brand anthropomorphism, brand distinctiveness, and warmth on brand attitude: A mediated moderation model. Journal of Consumer Behaviour, 19(5), 523–536. https://doi.org/10.1002/cb.1835
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2003). Business research methods 7th ed. Thomson/South-Western.
Malaysian consumers’ purchase intention towards online seafood shopping amid pandemic: The moderating role of the Covid-19 risk perception
Wong Yip Hing, Chok Nyen Vui
Abstracts : Online shopping for groceries has been accelerated to an unprecedented rate and benefitted from various government movement restrictions during the pandemic. However, the challenge remains for online seafood purchasing due to its perishable product nature and the untransferable organoleptic experience through an online platform to the consumer. The purpose of the current study is to determine the factors influencing consumers’ purchase intention toward online seafood shopping with Covid-19 risk perception as the moderating factor. Both The Planned Behavior and Technology Acceptance Model theories have been used to develop the research framework for this study. The five critical factors selected are: perceived usefulness, perceived ease of use, perceived risk, visibility and social influence. Covid-19 risk perception moderating effect is measured from two dimensions: cognitive and affective risk perception. An internet self-administrated questionnaire is used to collect the data from consumers. A total of 182 data were collected from the questionnaire and analysed by using the PLS-SEM method. The findings show that perceived usefulness, perceived ease of use and social influence correlated significantly with purchase intention toward online seafood shopping. The perceived usefulness has the most significant effect on purchase intention. Covid-19 risk perception shows a positive moderation effect on the relationship between perceived usefulness and purchase intention. These findings suggest that online seafood vendors could focus management efforts to improve perceived usefulness. The finding has important implications for developing robust marketing strategies to improve the perceived usefulness and social influence of online seafood shopping.
Keywords: Purchase Intention, Online Seafood Shopping, Perceived Usefulness, Covid-19, Risk Perception
Bibliography
Yip Hing, Wong., & Nyen Vui, Chok. (2021). Malaysian consumers’ purchase intention towards online seafood shopping amid pandemic: The moderating role of the Covid-19 risk perception. International Journal of Business, Marketing and Communication, 1(3), 1–18.
In-text citation
According to new research… (Wong & Chok, 2021).
In research from Wong and Chok (2021) …
List of references
’Click
Ahmed, A. F., Z. Mohamed, and M. M. Ismail. (2011). Determinants of Fresh Fish Purchasing Behavior among Malaysian Consumers. Current Research Journal of Social Sciences 3(2):126–31.
Ajzen, I. (1991). The theory of planned behaviour. Organisational behavior and human decision processes, 50(2), 179-211.
Bae, S. Y., & Chang, P. J. (2021). The effect of coronavirus disease-19 (COVID-19) risk perception on behavioural intention towards ‘untact’tourism in South Korea during the first wave of the pandemic (March 2020). Current Issues in Tourism, 24(7), 1017-1035.
Bauerová, R., & Klepek, M. (2018). Technology acceptance as a determinant of online grocery shopping adoption. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(3), 737-746.
Brug, J., Aro, A. R., Oenema, A., De Zwart, O., Richardus, J. H., & Bishop, G. D. (2004). SARS risk perception, knowledge,precautions, and information sources, the Netherlands. Emerging Infectious Diseases, 10(8), 1486–1489. https://doi.org/10.3201/eid1008.040283
Celik, H. (2011). Influence of social norms, perceived playfulness and online shopping anxiety on customers’ adoption of online retail shopping: An empirical study in the Turkish context. International Journal of Retail & Distribution Management, 39(6), 390-413
Chien, A. W., Kurnia, S., & von Westarp, F. (2003). The acceptance of online grocery shopping. BLED 2003 Proceedings, 52.
Chin, S. L., & Goh, Y. N. (2017). Consumer Purchase Intention Toward Online Grocery Shopping: View from Malaysia. Global Business & Management Research, 9.
Chiu, C.M., Chang, C.C., Cheng, H.L. & Fang, Y.H. (2009). Determinants of customer repurchase intention in online shopping. Online Information Review, 33(4), 761-784.
Choi, Y. M. (2013). A structural equation model of the determinants of repeat purchase behaviour of online grocery shoppers in the UK. Unpublished doctoral dissertation. Newcastle University, Newcastle, England.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Department of Statistic, Malaysia. (2020). Household Expenditure Survey Report 2019. Malaysia.
Driediger, F., & Bhatiasevi, V. (2019). Online grocery shopping in Thailand: Consumer acceptance and usage behavior. Journal of Retailing and Consumer Services, 48, 224-237.
FAOSTAT (Food and Agriculture Organization of the United Nations Statistics Database). (2017). http://www.fao.org/faostat/en
Forsythe, S., Liu, C., Shannon, D., & Gardner, L.C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. J. Interact. Marketing. 20: 55-75
Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing research, 19(4), 440-452.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Geuens, M., Brengman, M., & S’Jergers, R. (2003). Food retailing, now and in the future: a consumer perspective”. Journal of Retailing and Consumer Services. 10(4): 241-251.
Ghazali, E., Mutum, A. D., & Mahbob, N. A. (2006). Attitude towards online purchase of fish in urban Malaysia: An ethnic comparison. Journal of Food Products Marketing, 12(4), 109-128.
Goh, E. V. (2018). The status of fish in Malaysian diets and potential barriers to increasing consumption of farmed species (Ph.D. thesis). The University of Nottingham.
Gong, W., Stump, R. L. & Maddox, L. M. (2013). Factors influencing consumers’ online shopping in China. Journal of Asia Business Studies, 7(3), 214 – 230.
Gutama, W. A., & Intani, A. P. D. (2017). Consumer acceptance towards online grocery shopping in Malang, East Java, Indonesia. Agricultural Socio-Economics Journal, 17(1), 23.
Hair, J., Hult, T. G. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (Second ed.). SAGE Publications, Inc.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.
Hanis, A., Jinap, S., Mad Nasir, S., & Alias, R. (2013). Eliciting Malaysian consumer preferences for marine fish attributes by using conjoint analysis. World Applied Sciences Journal, 28(12), 2054-2060.
Hansen, T. (2005). Consumer adoption of online grocery buying: a discriminant analysis. International Journal of Retail & Distribution Management. 33(2): 101–121.
Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior. International Journal of Information Management, 24(6), 539-550.
Heilig, J., Ernst, S., & Hooker, N. H. (2001). Assessing the e-commerce strategies of grocers. Working paper. Department of Agricultural, Environmental and Development Economics, The Ohio State University.
Henseler, J., Ringle, C. M., Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135.
Hofstede Insights. (2017, August 22). Malaysia. https://www.hofstede-insights.com/country/malaysia/
Hu, L.-t., and Bentler, P. M. (1998). Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecification, Psychological Methods, 3(4): 424-453.
Jöreskog, K.G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409-426.
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS quarterly, 183-213.
Keen, C., Wetzels, M., Ruyter, K. D. & Feinberg, R. (2004). E-tailers versus retailers: Which factors determine consumer preferences. Journal of Business Research, 57(7), 685 – 695.
Kian, T. P., Loong, A. C. W., & Fong, S. W. L. (2018). Customer Purchase Intention on Online Grocery Shopping. International Journal of Academic Research in Business and Social Sciences, 8(12), 1579– 1595.
Kirmani, A. & Rao, A. R. (2000). No Pain, No Gain: A Critical Review of the Literature on Gignaling Unobservable Product Quality. Journal of Marketing, 64 (2), 66 – 80.
Kim, J. & Forsythe, S. (2011). Factors affecting adoption of product virtualisation technology for online consumer electronics shopping. International Journal of Retail and Distribution Management, 38(3), 190-204.
Kongsompong, K., Green, R. T., & Patterson, P. G. (2009). Collectivism and social influence in the buying decision: A four-country study of inter-and intra-national differences. Australasian Marketing Journal (AMJ), 17(3), 142-149.
Konuş, U., Verhoef, P.C., & Neslin, S.A. (2008). Multichannel shopper segments and their covariates. Journal of Retailing. 84(4): 398-413.
Kurnia, S., & Chien, J. A. W. (2003, June). The acceptance of the online grocery shopping. In The 16th Bled Electronic Commerce Conference, Bled, Slovenia. 219-233.
Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127(2), 267. https://doi.org/10.1037/0033-2909.127.2.267
Manecksha, F. (2000) PasarBorong Online Ready to Expand, New Straits Times, 25 September.
MCMC. (2017). INTERNET USERS SURVEY 2017. Malaysian Communications and Multimedia Commission. Https://www.mcmc.gov.my/skmmgovmy/media/general/pdf/mcmc-internet-users-survey-2017.pdf
Odekerken-Schro¨der, G., & Wetzels, M. (2003). Trade-offs in online purchase decisions: two empirical studies in Europe. European Management Journal. 21(6): 731-739.
Ooi, L. P. (2014). Factors influencing purchase intention towards organic food (Doctoral dissertation, Multimedia University).
Peters, E., & Slovic, P. (1996). The role of affect and worldviews as orienting dispositions in the perception and acceptance of nuclear power 1. Journal of Applied Social Psychology, 26(16), 1427–1453. https://doi.org/10.1111/j.1559-1816.1996.tb00079.x
Pett, M. A., Lackey, N. R., & Sullivan, J. (2003). Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research (1st ed.). SAGE Publications, Inc.
Pham, V. K., Do Thi, T. H., & Ha Le, T. H. (2020a). A study on the COVID-19 awareness affecting the consumer perceived benefits of online shopping in Vietnam. Cogent Business & Management, 7(1), 1846882.
Pham, V. K, Tang, M.H. & Nguyen, T. L. (2020b). Risk Perception toward online shopping in Vietnam during the Covid-19 outbreak. Journal of Critical Reviews, 7(18), 1257-1269.
Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of business-to-consumer web sites. Information and Management. 39(6): 457-465.
Rashotte, L. (2007). Social influence. The Blackwell encyclopedia of sociology.
Sarstedt, M., Ringle, C. M., Cheah, J. H., Ting, H., Moisescu, O. I., & Radomir, L. (2020). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531-554.
Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS quarterly, 553-572.
Shmueli, G., Ray, S., Velasquez Estrada, J.M. and Shatla, S.B. (2016). The elephant in the room: evaluating the predictive performance of PLS models. Journal of Business Research, Vol. 69(10), 4552-4564.
Sjöberg, L. (1998). Worry and risk perception. Risk Analysis, 18(1), 85–93. https://doi.org/10.1111/j.1539-6924.1998.tb00918.x
Vodus. (2021, March). Impact on E-commerce in Malaysia since Covid-19 began. https://vodus.com/article/impact-on-e-commerce-since-covid-19-began