Journal Screenshot

International Journal of Academic Research in Economics and Management Sciences

Open Access Journal

ISSN: 2226-3624

Factors Influencing Bingobox Technology Adoption on Consumers' Behavioral Intentions in Malaysia Post-COVID-19

Nurulizwa Rashid, Norshahira Shaharin, Samer Ali Al- Shami

http://dx.doi.org/10.6007/IJAREMS/v13-i3/22369

Open access

China introduced its pioneering concept of human-free and cashless convenience shops, Bingobox, into the Malaysian market. This innovative retail model allows customers to shop independently, without the assistance of store employees, requiring only the scan of a QR code for access and automatic registration of purchases. Given that this technological system is relatively new to users in Malaysia, the motivation behind this study is to understand the challenges and opportunities presented by this novel retail concept, which has the potential to significantly transform consumer behavior and retail practices in the country. To achieve this, the study aimed to investigate the factors influencing the adoption of Bingobox technology in relation to consumers' behavioral intentions in Malaysia, utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The research questions were designed to identify the factors that statistically significantly affect consumer behavioral intentions. The modified UTAUT2 model comprises seven independent variables: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Perceived Risk, Hedonic Motivation, Price Value, and Habit. Data was collected through a questionnaire distributed to 150 targeted respondents via URLs or links to Google Forms, disseminated through platforms such as WhatsApp, Telegram, Facebook, and other network-based applications. Analysis was conducted using SPSS for coding and SmartPLS 3.0 for performing Partial Least Squares Structural Equation Modeling (PLS-SEM) path coefficient analysis. The findings revealed that all seven independent variables have significant relationships with the dependent variable, providing valuable insights for practitioners and policymakers in leveraging technology for sustainable business practices. This study contributes to a deeper understanding of the factors driving the adoption of innovative retail technologies in Malaysia, offering guidance for businesses and policymakers in effectively implementing and promoting new technological solutions.

Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261.
Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of experimental social psychology, 22(5), 453-474.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta?analytic review. British journal of social psychology, 40(4), 471-499.
Babbie, E. (2010). Research design. The practice of social research, 12, 90-123.
Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the association for information systems, 8(4), 3.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
Benbasat, I., & Barki, H. (2007). Quo vadis TAM?. Journal of the association for information systems, 8(4), 7.
Brewer, P., & Sebby, A. G. (2021). The effect of online restaurant menus on consumers’ purchase intentions during the COVID-19 pandemic. International Journal of Hospitality Management, 94, 102777.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Diaz, M. C., & Loraas, T. (2010). Learning new uses of technology while on an audit engagement: Contextualizing general models to advance pragmatic understanding. International Journal of Accounting Information Systems, 11(1), 61-77.
Gulati, R., & Smith, R. (2009). Maintenance and reliability best practices. Industrial Press Inc..
Helena Chiu, Y. T., Fang, S. C., & Tseng, C. C. (2010). Early versus potential adopters: Exploring the antecedents of use intention in the context of retail service innovations. International Journal of Retail & Distribution Management, 38(6), 443-459.
Hong, H. T. (2018, November 2). A visit to BingoBox unmanned convenience store in KL [Review of A visit to BingoBox unmanned convenience store in KL]. Minime Insights. https://www.minimeinsights.com/2018/11/02/a-visit-to-bingobox-unmanned-convenience-store-in-the-heart-of-kuala-lumpur/
Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. International journal of medical informatics, 78(6), 404-416.
Koivumaki, T., Ristola, A., & Kesti, M. (2008). The perceptions towards mobile services: an empirical analysis of the role of use facilitators. Personal and Ubiquitous Computing, 12, 67-75.
Keong, M., Ramayah, T., Kurnia, S., & May Chiun, L. (2012). Explaining intention to use an enterprise resource planning (ERP) system: an extension of the UTAUT model. Business Strategy Series, 13(4), 173-180.
Malhotra, N.M. (2012), “Conference note”, International Journal of Marketing Research, Vol. 54, pp. 432-433.

Misirlis, N., & Vlachopoulou, M. (2018). Social media metrics and analytics in marketing–S3M: A mapping literature review. International Journal of Information Management, 38(1), 270-276.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
Ogunsola, K., & Olojo, T. P. (2021). Towards Connected Governance: Citizens' Use of Web 2.0 in Nigeria. In Web 2.0 and cloud Technologies for implementing connected government (pp. 68-94). IGI Global.
Park, H. J., & Zhang, Y. (2022). Technology readiness and technology paradox of unmanned convenience store users. Journal of Retailing and Consumer Services, 65, 102523.
Polacco, A., & Backes, K. (2018). The amazon go concept: implications, applications, and sustainability. Journal of Business and Management, 24(1), 79-92.
Retail Insight Network. (2019). BingoBox leads the space race in cashierless convenience retail. [online] Available at: https://www.retail-insight network.com/comment/bingobox-cashierless-stores/ [Accessed 14 Jun. 2022].
SafetyCulture. (2022). RetailTheft: How to Prevent Theft in Retail Stores. [online] Available at: https://safetyculture.com/topics/retail-theft/ [Accessed 14 Jun. 2022].
San Martin, H., & Herrero, Á. (2012). Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism management, 33(2), 341-350.
Sarmah, R., Dhiman, N., & Kanojia, H. (2021). Understanding intentions and actual use of mobile wallets by millennial: an extended TAM model perspective. Journal of Indian Business Research, 13(3), 361-381.
Schaupp, L. C., Carter, L., & McBride, M. E. (2010). E-file adoption: A study of US taxpayers’ intentions. Computers in Human Behavior, 26(4), 636-644.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & management, 44(1), 90-103.
Shin, D. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354.
Shishah, W., & Alhelaly, S. (2021). User experience of utilising contactless payment technology in Saudi Arabia during the COVID-19 pandemic. Journal of Decision Systems, 30(2-3), 282-299.
Soodan, V., & Rana, A. (2020). Modeling customers' intention to use e-wallet in a developing nation: Extending UTAUT2 with security, privacy and savings. Journal of Electronic Commerce in Organizations (JECO), 18(1), 89-114.
Surin Murugiah / theedgemarkets com (2018). Scientific Retail eyes 500 retailers using humanless technology by end-2019. [online] The Edge Markets. Available at: https://www.theedgemarkets.com/article/scientific-retail-eyes-500-retailers-using humanless-technology-end2019 [Accessed 6 Jun. 2022].
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Visionify. (2021). Facial Recognition Use Cases in Retail. [online] Available at: https://www.google.com/amp/s/visionify.ai/facial-recognition-use-cases-in-retail/ [Accessed 14 Jun. 2022].
Wyer Jr, R. S., Jiang, Y., & Hung, I. W. (2008). Visual and verbal information processing in a consumer context: Further considerations. Journal of Consumer Psychology, 18(4), 276-280.

(Rashid et al., 2024)
Rashid, N., Shaharin, N., & Shami, S. A. A.-. (2024). Factors Influencing Bingobox Technology Adoption on Consumers’ Behavioral Intentions in Malaysia Post-COVID-19. International Journal of Academic Research in Economics and Management Sciences, 13(3), 276–293.