Dynamic Effect of Flow on Impulsive Consumption: Evidence from Southeast Asian Live Streaming Platforms
Abstract
:1. Introduction
2. Literature Review
2.1. Flow
2.2. Temperament
2.3. Audio-Visual Experience
2.4. Impulsive Buying
2.5. Moderating Effect of Temperament on Impulse Buying
2.6. Mediating Effect of Flow on Impulse Buying
3. Hypotheses
3.1. Audio-Visual Experience
3.2. Methodology
3.3. Quantitative
3.4. Qualitative
3.5. Workshop
3.6. Interviewing Techniques for Analysis
3.7. Questionnaire
3.8. Reliability
3.9. Validity
3.10. Workshop
3.11. Employing a Double Diamond Design Method
3.12. Recruitment
3.13. Profile of Overall Respondents
3.14. Data Analysis
4. Quantitative Findings
4.1. Data Collection
4.2. Research Instrument
4.3. Statistical Analysis
4.4. Common Method Bias
4.5. Testing Mediation
4.6. Intermediary Model Test
4.7. Model Building
- X: The audio-visual experience, which corresponds to the four observable variables (Q1, Q2, Q3, and Q4).
- M: Flow experience, which corresponds to the four observed variables (Q5, Q6, Q7, and Q8).
- W: Consumer temperament type, which after categorisation includes 4 observable factors and corresponds to Q12–Q15.
- Y: Impulse buy, corresponding to Q21, Q22, and Q23; there were three variables detected in all.
4.8. Model Fit Index
4.9. Model Fitting Results Comparison
4.10. Hypothesis Validation
4.11. Investigation of Mediation Effect
4.12. Survey Findings
4.13. Population
4.14. Audio-Visual Experience
4.15. Flow Experience
4.16. Temperament of the Consumer
4.17. Temperament Types
5. Live Streaming Insights
Impulsive Purchase
6. Qualitative Findings
7. Hypothesis Validation
8. Discussion
8.1. Theoretical Contributions
8.2. Practical Implications
8.3. Circumscription, Contemporary Research and Future Development Directions
9. Conclusions and Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ma, Y. To shop or not: Understanding Chinese consumers’ live-stream shopping intentions from the perspectives of uses and gratifications, perceived network size, perceptions of digital celebrities, and shopping orientations. Telemat. Inform. 2021, 59, 101562. [Google Scholar] [CrossRef]
- Frater, P.; Frater, P. Premium Video Grows in Southeast Asia as Chinese Content Strikes a Chord in Thailand, Says Study. [online] Variety. 2021. Available online: https://variety.com/2021/global/asia/premium-video-grows-in-southeast-asia-1235117197/ (accessed on 6 February 2022).
- Southeast Asia Internet Economy to Hit $1 Trillion by 2030, Report Says. (n.d.). The Economic Times. [online]. Available online: https://economictimes.indiatimes.com/tech/technology/southeast-asia-internet-economy-to-hit-1-trillion-by-2030-report-says/articleshow/87620147.cms?from=mdr (accessed on 6 October 2022).
- Irfan, M.; Zhao, Z.Y.; Li, H.; Rehman, A. The influence of consumers’ intention factors on willingness to pay for renewable energy: A structural equation modeling approach. Environ. Sci. Pollut. Res. 2020, 27, 21747–21761. [Google Scholar] [CrossRef]
- Jeffery, S.A.; Hodge, R. Factors influencing impulse buying during an online purchase. Electron. Commer. Res. 2007, 7, 367–379. [Google Scholar] [CrossRef]
- Lu, Z.; Xia, H.; Heo, S.; Wigdor, D. You Watch, You Give, and You Engage. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada, 21–26 April 2018; pp. 1–13. [Google Scholar]
- Rook, D.W. The Buying Impulse. J. Consum. Res. 1987, 14, 189–199. [Google Scholar] [CrossRef]
- Webb, A.M.; Wang, C.; Kerne, A.; Cesar, P. Distributed liveness: Understanding how new technologies transform performance experiences. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative WORK & social Computing, San Francisco, CA, USA, 27 February–2 March 2016; ACM: New York, NY, USA, 2016; pp. 432–437. [Google Scholar]
- Wells, J.D.; Parboteeah, V.; Valacich, J.S. Online impulse buying: Understanding the interplay between consumer impulsiveness and website quality. J. Assoc. Inf. Syst. 2011, 12, 32–56. [Google Scholar] [CrossRef] [Green Version]
- Andén-Papadopolous, K. Media Witnessing and the ‘Crowd-sourced Video Revolution’. Vis. Commun. 2013, 12, 341–357. [Google Scholar] [CrossRef]
- Abdelsalam, S.; Salim, N.; Alias, R.A.; Husain, O. Understanding Online Impulse Buying Behavior in Social Commerce: A Systematic Literature Review. IEEE Access 2020, 8, 89041–89058. [Google Scholar] [CrossRef]
- Xie, C.; Yu, J.; Huang, S.; Zhang, J. Tourism E-Commerce live streaming: Identifying and testing a value-based marketing framework from the live streamer perspective. Tour. Manag. 2022, 91, 104513. [Google Scholar] [CrossRef]
- Kim, M. How can I Be as attractive as a Fitness YouTuber in the era of COVID-19? The impact of digital attributes on flow experience, satisfaction, and behavioral intention. J. Retail. Consum. Serv. 2021, 64, 102778. [Google Scholar] [CrossRef]
- Parboteeah, D.V.; Valacich, J.S.; Wells, J.D. The influence of website characteristics on a consumer’s urge to buy impulsively. Inf. Syst. Res. 2009, 20, 60–78. [Google Scholar] [CrossRef]
- Hallanan, L. Live Streaming Drives $6 Billion USD in Sales During the 11.11 Global Shopping Festival; Forbes: Washington, DC, USA, 2020. [Google Scholar]
- Koufaris, M. Applying the technology acceptance model and flow theory to online consumer behavior. Inf. Syst. Res. 2002, 13, 205–223. [Google Scholar] [CrossRef] [Green Version]
- Lăzăroiu, G.; Neguriţă, O.; Grecu, I.; Grecu, G.; Mitran, P.C. Consumers’ decision-making process on social commerce platforms: Online trust, perceived risk, and purchase intentions. Front. Psychol. 2020, 11, 890. [Google Scholar] [CrossRef] [PubMed]
- Lin, X.; Wang, X.; Hajli, N. Building E-Commerce Satisfaction and Boosting Sales: The Role of Social Commerce Trust and Its Antecedents. Int. J. Electron. Commer. 2019, 23, 328–363. [Google Scholar] [CrossRef]
- Lin, J.; Luo, Z.; Cheng, X.; Li, L. Understanding the interplay of social commerce affordances and swift guanxi: An empirical study. Inf. Manag. 2019, 56, 213–224. [Google Scholar] [CrossRef]
- Lin, K.; Fong, L.H.; Law, R. Live streaming in tourism and hospitality: A literature review. Asia Pac. J. Tour. Res. 2022, 27, 290–304. [Google Scholar] [CrossRef]
- Merritt, K.; Zhao, S. The power of live stream commerce: A case study of how live stream commerce can be utilised in the traditional British retailing sector. J. Open Innov. Technol. Mark. Complex. 2022, 8, 71. [Google Scholar] [CrossRef]
- Oppotus. Livestream Shopping: The Future of E-commerce? 2021. Available online: https://www.oppotus.com/livestream-shopping-the-future-of-e-commerce/ (accessed on 24 May 2021).
- Kim, J.; Kim, M. Spectator e-sport and well-being through live streaming services. Technol. Soc. 2020, 63, 101401. [Google Scholar] [CrossRef]
- Zaman, M.; Ananda Rajan, M.; Dai, Q. Experiencing flow with instant messaging and its facilitating role on creative behaviors. Comput. Hum. Behav. 2010, 26, 1009–1018. [Google Scholar] [CrossRef]
- Nguyen, M.; Khoa, B. Customer Electronic Loyalty towards Online Business: The role of Online Trust, Perceived Mental Benefits and Hedonic Value. J. Distrib. Sci. 2019, 17, 81–93. [Google Scholar] [CrossRef]
- Ooi, K.B.; Tan GW, H. Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Syst. Appl. 2016, 59, 33–46. [Google Scholar] [CrossRef]
- Ooi, K.B.; Foo, F.E.; Tan GW, H.; Hew, J.J.; Leong, L.Y. Taxi within a grab? A gender-invariant model of mobile taxi adoption. Ind. Manag. Data Syst. 2021, 121, 312–332. [Google Scholar] [CrossRef]
- Ozkara, B.Y.; Ozmen, M.; Kim, J.W. Examining the effect of flow experience on online purchase: A novel approach to the flow theory based on hedonic and utilitarian value. J. Retail. Consum. Serv. 2017, 37, 119–131. [Google Scholar] [CrossRef]
- Esmaeili, L.; Mardani, S.; Golpayegani, S.A.H.; Madar, Z.Z. A novel tourism recommender system in the context of social commerce. Expert Syst. Appl. 2020, 149, 113301. [Google Scholar] [CrossRef]
- Adelaar, T.; Chang, S.; Lancendorfer, K.M.; Lee, B.; Morimoto, M. Effects of media formats in emotions and impulse buying intent. J. Inf. Technol. 2003, 18, 247–266. [Google Scholar] [CrossRef]
- Agarwal, R.; Sambamurthy, V.; Stair, R. Cognitive absorption and the adoption of new information technologies. Acad. Manag. 1997, 1997, 293–297. [Google Scholar] [CrossRef]
- Agarwal, R.; Karahanna, E. Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Q. 2000, 24, 665–694. [Google Scholar] [CrossRef]
- Akram, U.; Junaid, M.; Zafar, A.U.; Zhiwen, L.; Mingyue, F. Online purchase intention in Chinese social commerce platforms: Being emotional or rational? J. Retail. Consum. Serv. 2021, 63, 102669. [Google Scholar] [CrossRef]
- Castleberry, A.; Amanda, N. Thematic analysis of qualitative research data: Is it as easy as it sounds? Curr. Pharm. Teach. Learn. 2018, 10, 807–815. [Google Scholar] [CrossRef]
- Celsi, R.L.; Olson, J.C. The role of involvement in attention and comprehension processes. J. Consum. Res. 1988, 15, 210–224. [Google Scholar] [CrossRef]
- Chen, Y.; Lu, Y.; Wang, B.; Pan, Z. How do product recommendations affect impulse buying? An empirical study on WeChat social commerce. Inf. Manag. 2018, 56, 236–248. [Google Scholar] [CrossRef]
- Chen, C.C.; Lin, Y.C. What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telemat. Inform. 2018, 35, 293–303. [Google Scholar] [CrossRef]
- Changsong, W.; Kerry, L.; Marta, R.F. Film distribution by video streaming platforms across Southeast Asia during COVID-19. Media Cult. Soc. 2021, 43, 1542–1552. [Google Scholar] [CrossRef]
- Privette, G. Peak experience, peak performance, and flow: A comparative analysis of positive human experiences. J. Personal. Soc. Psychol. 1983, 45, 1361–1368. [Google Scholar] [CrossRef]
- Finneran, C.M.; Zhang, P. A Person-Artefact-Task (PAT) Model of Flow Antecedents in Computer-Mediated Environments. Int. J. Hum. Comput. Stud. 2003, 59, 475–496. [Google Scholar] [CrossRef]
- Suppatvech, C.; Godsell, J.; Day, S. The Roles of Internet of Things Technology in Enabling Servitized Business Models: A Systematic Literature Review. Ind. Mark. Manag. 2019, 82, 70–86. [Google Scholar] [CrossRef]
- Cloninger, C.R.; Svrakic, D.M.; Przybeck, T.R. A psychobiological model of temperament and character. Arch. Gen. Psychiatry 1993, 50, 975–990. [Google Scholar] [CrossRef]
- Clover, V.T. Relative importance of impulse-buying in retail stores. J. Mark. 1950, 15, 66–70. [Google Scholar] [CrossRef]
- Creswell, J. Research Design: Qualitative, Quantitative and Mixed Methods Approaches, 3rd ed.; Sage: Thousand Oaks, CA, USA, 2009. [Google Scholar]
- Csíkszentmihályi, M. Finding Flow: The Psychology of Engagement with Everyday Life; Basic Books: New York, NY, USA, 1997. [Google Scholar]
- Csíkszentmihályi, M.; LeFevre, J. Optimal experience in work and leisure. J. Personal. Soc. Psychol. 1989, 56, 815–822. [Google Scholar] [CrossRef]
- Csikszentmihalyi, M. Foward. Occup. Ther. Health Care 1990, 6, xv–xvii. [Google Scholar] [CrossRef]
- de Matos, C.A.; Krielow, A. The effects of environmental factors on B2B e-services purchase: Perceived risk and convenience as mediators. J. Bus. Ind. Mark. 2019, 34, 4. [Google Scholar] [CrossRef]
- Design Council. The ‘Double Diamond’ Design Process Model; Design Council: London, UK, 2005. [Google Scholar]
- Kemp, S. Digital 2022. Available online: https://datareportal.com/reports/digital-2022-april-global-statshot (accessed on 2 December 2021).
- Donetto, S.; Pierri, P.; Tsianakas, V.; Robert, G. Experience-based Co-design and Healthcare Improvement: Realizing Participatory Design in the Public Sector. Des. J. 2015, 18, 227–248. [Google Scholar] [CrossRef] [Green Version]
- Edwards, J.R.; Lambert, L.S. Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychol. Methods 2007, 12, 1–22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Flick, U. An Introduction to Qualitative Research, 6th ed.; SAGE: Los Angeles, CA, USA, 2019; Volume 85. [Google Scholar]
- Ford, K.L.; West, A.B.; Bucher, A.; Osborn, C.Y. Personalized Digital Health Communications to Increase COVID-19 Vaccination in Underserved Populations: A Double Diamond Approach to Behavioral Design. Front. Digit. Health 2022, 4, 831093. [Google Scholar] [CrossRef] [PubMed]
- Foroudi, P.; Gupta, S.; Sivarajah, U.; Broderick, A. Investigating the effects of smart technology on customer dynamics and customer experience. Comput. Hum. Behav. 2017, 80, 271–282. [Google Scholar] [CrossRef]
- Furukawa, I.; Jin, C.; Nuttapol, A.; Hahn, D.; Kao, M.H.; Shi, Z. Why we buy what we do not want to buy? Effect of filed pressure on willingness to buy in face to face service encounter. J. Mark. Thought 2014, 1, 1–12. [Google Scholar] [CrossRef]
- Gibreel, O.; AlOtaibi, D.A.; Altmann, J. Social commerce development in emerging markets. Electron. Commer. Res. Appl. 2018, 27, 152–162. [Google Scholar] [CrossRef]
- Gil-Gomez, H.; Guerola-Navarro, V.; Oltra-Badenes, R.; Lozano-Quilis, J. Customer Relationship Management: Digital Transformation and Sustainable Business Model Innovation. Econ. Res.-Ekon. Istraz. 2020, 33, 2733–2750. [Google Scholar] [CrossRef] [Green Version]
- Grabowska, S.; Saniuk, S. Assessment of the Competitiveness and Effectiveness of an Open Business Model in the Industry 4.0 Environment. J. Open Innov. Technol. Mark. Complex. 2022, 8, 57. [Google Scholar] [CrossRef]
- Goldstein, H.I.; McDonald, R.P. A General Model for the Analysis of Multilevel Data. Psychometrika 1988, 53, 455–467. [Google Scholar] [CrossRef] [Green Version]
- Grizzle, J.W.; Zablah, A.R.; Brown, T.J.; Mowen, J.C.; Lee, J.M. Employee customer orientation in context: How the environment moderates the influence of customer orientation on performance outcomes. J. Appl. Psychol. 2009, 94, 1227–1242. [Google Scholar] [CrossRef]
- Golan, O.; Martini, M. Religious live streaming: Constructing the authentic in real time. Inf. Commun. Soc. 2019, 22, 437–454. [Google Scholar] [CrossRef]
- Guo, J.; Li, Y.; Xu, Y.; Zeng, K. How Live Streaming Features Impact Consumers’ Purchase Intention in The Context of Cross-Border E-Commerce? A Research Based on SOR Theory. Front. Psych. 2021, 12, 767876. [Google Scholar] [CrossRef]
- Halskov, K.; Hansen, N. The diversity of participatory design research practice at PDC 2002–2012. Int. J. Hum.-Comput. Stud. 2015, 74, 81–92. [Google Scholar] [CrossRef] [Green Version]
- Hair, J.F. Multivariate data analysis: An overview. Int. Encycl. Stat. Sci. 2011, 29, 904–907. [Google Scholar]
- Hilligoss, B.; Rieh, S.Y. Developing a Unifying Framework of Credibility Assessment: Construct, Heuristics, and Interaction in Context. Inf. Processing Manag. 2008, 44, 1467–1484. [Google Scholar] [CrossRef]
- Hirschman, E.C.; Bellenger, D.N.; Robertson, D.H. An application of simulation in retail management. Simulation 1977, 28, 185–188. [Google Scholar] [CrossRef]
- Huang, L. Flow and social capital theory in online impulse buying. J. Bus. Res. 2016, 69, 2277–2283. [Google Scholar] [CrossRef]
- Hostler, R.E.; Yoon, V.Y.; Gui, Z.; Guimaraes, T.; Forgionne, G. Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior. Inf. Manag. 2011, 48, 336–343. [Google Scholar] [CrossRef]
- Reuters. 2021. Available online: https://www.reuters.com/world/asia-pacific/southeast-asia-internet-economy-hit-1-trln-by-2030-report-says-2021-11-10/ (accessed on 6 June 2021).
- Irfan, M.; Elavarasan, R.M.; Hao, Y.; Feng, M.; Sailan, D. An assessment of consumers’ willingness to utilize solar energy in china: End-users’ perspective. J. Clean. Prod. 2021, 292, 126008. [Google Scholar] [CrossRef]
- Irfan, M.; Hao, Y.; Ikram, M.; Wu, H.; Akram, R.; Rauf, A. Assessment of the public acceptance and utilization of renewable energy in Pakistan. Sustain Prod. Consum. 2021, 27, 312–324. [Google Scholar] [CrossRef]
- Irfan, M.; Zhao, Z.Y.; Rehman, A.; Ozturk, I.; Li, H. Consumers’ intention-based influence factors of renewable energy adoption in Pakistan: A structural equation modeling approach. Environ. Sci. Pollut. Res. 2020, 28, 432–445. [Google Scholar] [CrossRef] [PubMed]
- Irfan, M.; Ahmad, M. Modeling consumers’ information acquisition and 5G technology utilization: Is personality relevant? Pers. Individ. Differ. 2021, 188, 111450. [Google Scholar] [CrossRef]
- Irwin, H. Communicating with Asia: Understanding People and Customs; Allen & Unwin: Crows Nest, NSW, Australia, 1996. [Google Scholar]
- Jackson, S.A.; Marsh, H.W. Development and Validation of a Scale to Measure Optimal Experience: The Flow State Scale. J. Sport Exerc. Psychol. 1996, 18, 17–35. [Google Scholar] [CrossRef]
- Jervis, M.; Drake, M. The Use of Qualitative Research Methods in Quantitative Science: A Review. J. Sens. Stud. 2014, 29, 234–247. [Google Scholar] [CrossRef]
- Johnson, R.; Onwuegbuzie, A.; Turner, L. Toward a Definition of Mixed Methods Research. J. Mix. Methods Res. 2007, 1, 112–133. [Google Scholar] [CrossRef]
- Mulia, K. Bigo Fuels the Global Creator Economy with Livestreaming E-Commerce, Gaming, and Entertainment Content (KRASIA). 25 April 2021. Available online: https://kr-asia.com/bigo-fuels-the-global-creator-economy-with-livestreaming-e-commerce-gaming-and-entertainment-content (accessed on 20 June 2021).
- Music Press Asia Staff. BIGO LIVE Is Reporting Massive Growth in Streaming 2022. (Music Press Asia). Available online: https://www.musicpressasia.com/2022/01/26/bigo-live-is-reporting-massive-growth-in-streaming-2022/ (accessed on 15 March 2022).
- Kang, H.J.; Shin, J.-H.; Ponto, K. How 3D Virtual Reality Stores Can Shape Consumer Purchase Decisions: The Roles of Informativeness and Playfulness. J. Interact. Mark. 2020, 49, 70–85. [Google Scholar] [CrossRef]
- Kang, K.; Lu, J.; Guo, L.; Li, W. The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms. Int. J. Inf. Manag. 2021, 56, 102251. [Google Scholar] [CrossRef]
- Kim, S.; Baek, H.; Kim, D.H. OTT and live streaming services: Past, present, and future. Telecommun. Policy 2021, 45, 102244. [Google Scholar] [CrossRef]
- Kim, D.; Ko, Y.J. The impact of virtual reality (VR) technology on sport spectators’ flow experience and satisfaction. Comput. Hum. Behav. 2019, 93, 346–356. [Google Scholar] [CrossRef]
- Kim, M.S.; Stepchenkova, S. Examining the impact of experiential value on emotions, self-connective attachment, and brand loyalty in Korean family restaurants. J. Qual. Assur. Hospit. Tourism. 2018, 19, 298–321. [Google Scholar] [CrossRef]
- Kimiagari, S.; Asadi Malafe, N.S. The role of cognitive and affective responses in the relationship between internal and external stimuli on online impulse buying behaviour. J. Retail. Consum. Serv. 2021, 61, 102567. [Google Scholar] [CrossRef]
- Tomitsch, M.; Borthwick, M.; Ahmadpour, N.; Cooper, C.; Frawley, J.; Hepburn, L.A.; Kocaballi, A.B.; Loke, L.; Núñez-Pacheco, C.; Straker, K.; et al. Design. Think. Make. Break. Repeat. A Handbook of Methods; BIS Publishers: Amsterdam, The Netherlands, 2018. [Google Scholar]
- Lim, T. Managing Digital Platforms in the age of Chinese streaming services in Southeast Asia. In Proceedings of the 10th International Research Symposium in Service Management @ Dubai (IRSSM-10), Dubai, United Arab Emirates, 6–9 October 2019. [Google Scholar]
- Lee, S.Y. Multilevel Analysis of Structural Equation Models. Biometrika 1990, 77, 763–772. [Google Scholar] [CrossRef]
- Longford, N.T. A Fast-Scoring Algorithm for Maximum Likelihood Estimation in Unbalanced Mixed Models with Nested Effects. Biometrika 1987, 74, 817–827. [Google Scholar] [CrossRef]
- Hoffman, D.L.; Novak, T.P. Marketing in hypermedia computer-mediated environments: Conceptual foundations. J. Mark. 1996, 60, 50–68. [Google Scholar] [CrossRef]
- Lenzner, B. The emergence of occupy Wall Street and digital video practices: Tim pool, live streaming and experimentations in citizen journalism. Stud. Doc. Film. 2014, 8, 251–266. [Google Scholar] [CrossRef]
- Lin JH, T.; Bowman, N.; Lin, S.F.; Chen, Y.S. Setting the digital stage: Defining game streaming as an entertainment experience. Entertain. Comput. 2019, 31, 100309. [Google Scholar]
- Leih, S.; Linden, G.; Teece, D. Business Model Innovation and Organizational Design. Bus. Model Innov. 2015, 15, 24–42. [Google Scholar]
- Liao, S.H.; Widowati, R.; Hsieh, Y.C. Investigating online social media users’ behaviors for social commerce recommendations. Technol. Soc. 2021, 66, 101655. [Google Scholar] [CrossRef]
- Lo, P.; Dwivedi, Y.K.; Wei-Han Tan, G.; Ooi, K.; Cheng-Xi Aw, E.; Metri, B. Why do consumers buy impulsively during live streaming? A deep learning-based dual-stage SEM-ANN analysis. J. Bus. Res. 2022, 147, 325–337. [Google Scholar] [CrossRef]
- MacInnis, D.J.; Price, L.L. The Role of Imagery in Information Processing: Review and Extensions. J. Consum. Res. 1987, 13, 473–491. [Google Scholar] [CrossRef]
- McKinsey & Company. It’s Showtime! How Live Commerce Is Transforming the Shopping Experience. 2021. Available online: https://www.mckinsey.com/business-functions/mckinsey-digital (accessed on 5 September 2021).
- Mechinda, P.; Patterson, P.G. The impact of service climate and service provider personality on employees’ customer-oriented behavior in a high-contact setting. J. Serv. Mark. 2011, 25, 101–113. [Google Scholar] [CrossRef]
- Meng, L.; Duan, S.; Zhao, Y.; Lü, K.; Chen, S. The impact of online celebrity in livestreaming e-Commerce on purchase intention from the perspective of emotional contagion. J. Retail. Consum. Serv. 2021, 63, 102733. [Google Scholar] [CrossRef]
- Ming, J.; Jianqiu, Z.; Bilal, M.; Akram, U.; Fan, M. How social presence influences impulse buying behavior in live streaming commerce? The role of SOR theory. Int. J. Web Inf. Syst. 2021, 17, 300–320. [Google Scholar] [CrossRef]
- Molinillo, S.; Aguilar-Illescas, R.; Anaya-Sánchez, R.; Liébana-Cabanillas, F. Social commerce website design, perceived value and loyalty behavior intentions: The moderating roles of gender, age and frequency of use. J. Retail. Consum. Serv. 2021, 63, 102404. [Google Scholar] [CrossRef]
- Money Compass. 57% of Southeast Asian Viewers Are Streaming More OTT Video Content Due to COVID-19. 9 December 2020. Available online: https://moneycompass.com.my/2020/12/09/57-of-southeast-asian-viewers-are-streaming-more-ott-video-content-due-to-covid-19/ (accessed on 6 March 2022).
- Muthén, B. Moments of the censored and truncated bivariate normal distribution. Br. J. Math. Stat. Psychol. 1990, 43, 131–143. [Google Scholar] [CrossRef]
- Kim, M.J.; Bonn, M.; Lee, C.; Kim, J.S. Effects of employees’ personality and attachment on job flow experience relevant to organizational commitment and consumer-oriented behavior. J. Hosp. Tour. Manag. 2019, 41, 156–170. [Google Scholar] [CrossRef]
- Mostafa, A.E.; Inkpen, K.; Tang, J.C.; Venolia, G.; Hamilton, W.A. Social Stream Viewer: Guiding the viewer experience of multiple streams of an event. In Proceedings of the 19th International Conference on Supporting Group Work, Sanibel Island, FL, USA, 13–16 November 2016; ACM: New York, NY, USA; pp. 287–291. [Google Scholar]
- Nakayama, M.; Wan, Y. The cultural impact on social commerce: A sentiment analysis on yelp ethnic restaurant reviews. Inf. Manag. 2019, 56, 271–279. [Google Scholar] [CrossRef]
- Chin, W.W.; Thatcher, J.B.; Wright, R.T. Assessing common method bias: Problems with the ULMC technique. MIS Q. 2012, 36, 1003. [Google Scholar] [CrossRef] [Green Version]
- Platania, S.; Castellano, S.; Santisi, G.; Di Nuovo, S. Correlati di personalità della tendenza allo shopping compulsivo. G. Ital. Psicol. 2017, 64, 137–158. [Google Scholar]
- Paraman, P.; Ali, L. Mesoscopic laddering in consumer behaviour: Analysing the modalities of consumption on a micro-individual scale in the Meeting, Incentive, Conference, Exhibitions (MICE) sector. Spat. Stat. 2021, 44, 100527. [Google Scholar] [CrossRef]
- Mueser, D.; Vlachos, P. Almost like being there? A conceptualisation of live-streaming theatre. Int. J. Event Festiv. Manag. 2018, 9, 183–203. [Google Scholar] [CrossRef]
Variables | Question No. | Min | Max | AVG | SD | Kurtosis |
---|---|---|---|---|---|---|
Audio-visual experience | Q1 | 1 | 5 | 2.96 | 1.45 | −1.35 |
Q2 | 1 | 5 | 2.99 | 1.4 | −1.29 | |
Q3 | 1 | 5 | 3.04 | 1.39 | −1.28 | |
Q4 | 1 | 5 | 3 | 1.44 | −1.34 | |
Flow Experience | Q5 | 1 | 5 | 3 | 1.43 | −1.33 |
Q6 | 1 | 5 | 3 | 1.38 | −1.24 | |
Q7 | 1 | 5 | 3.03 | 1.38 | −1.22 | |
Q8 | 1 | 5 | 3.06 | 1.36 | −1.16 | |
Temperament type | Q9 | 1 | 5 | 3.02 | 1.434 | −1.346 |
Q10 | 1 | 5 | 2.96 | 1.42 | −1.311 | |
Q11 | 1 | 5 | 3.03 | 1.418 | −1.294 | |
Q12 | 1 | 5 | 2.97 | 1.427 | −1.322 | |
Q13 | 1 | 5 | 3.03 | 1.447 | −1.343 | |
Q14 | 1 | 5 | 2.95 | 1.433 | −1.332 | |
Q15 | 1 | 5 | 2.92 | 1.42 | −1.307 | |
Q16 | 1 | 5 | 3.01 | 1.417 | −1.319 | |
Q17 | 1 | 5 | 2.93 | 1.415 | −1.31 | |
Q18 | 1 | 5 | 2.98 | 1.418 | −1.324 | |
Q19 | 1 | 5 | 2.94 | 1.405 | −1.274 | |
Q20 | 1 | 5 | 2.97 | 1.376 | −1.222 | |
Impulse Buying | Q21 | 1 | 5 | 3.04 | 1.416 | −1.303 |
Q22 | 1 | 5 | 3.03 | 1.418 | −1.289 | |
Q23 | 1 | 5 | 3.01 | 1.385 | −1.257 |
Item | Regression Coefficient | Standard Error | T | P | LLCI | ULCI |
---|---|---|---|---|---|---|
X | −0.4003 | 0.1411 | −2.8262 | 0.0211 | −0.6127 | −0.058 |
W | −3.6183 | 0.6422 | −5.1119 | 0 | −5.0375 | −3.64 |
X*W | 0.3009 | 0.0333 | 5.8128 | 0 | 0.1546 | 0.4325 |
Item | Regression Coefficient | Standard Error | T | P | LLCI | ULCI |
---|---|---|---|---|---|---|
W | 2.4194 | 0.6129 | 3.7432 | 0.0009 | 0.9771 | 3.6551 |
M | 0.6334 | 0.2252 | 4.9803 | 0 | 0.451 | 0.8457 |
M*W | −0.1566 | 0.0375 | −4.9408 | 0 | −0.2245 | −0.0996 |
X | 0.3899 | 0.0985 | 4.285 | 0 | 0.3112 | 0.5782 |
X*W | −0.0089 | 0.0244 | −0.2946 | 0.7685 | −0.0544 | 0.0413 |
Index | Threshold | Values |
---|---|---|
χ²/DF | <3 | 2.714 |
CFI | >0.88 | 0.890 |
NNFI | >0.91 | 0.959 |
RMR | <0.106 | 0.061 |
RMSEA | <0.106 | 0.109 |
Model Name | χ²/DF | CFI | TLI | RMR | RMSEA |
---|---|---|---|---|---|
Original Model | 2.716 | 0.905 | 0.938 | 0.046 | 0.048 |
Correction Model | 2.706 | 0.918 | 0.926 | 0.055 | 0.042 |
No. | Description | Result |
---|---|---|
H1 | An impulse purchase is positively impacted by audio-visual experience of LST. | Supported |
H2 | The high-quality audio-visual experience of LST has a positive impact on the flow experience | Supported |
H3 | Flow experience has a positive effect on impulse buying. | Supported |
H4 | Consumer temperament type moderates the effect of the audio-visual experience of LST on impulse purchase | Not supported |
H5 | Consumer temperament type has a moderating effect on audio-visual experience over the flow experience. | Supported |
H6 | Consumer temperament type moderates the effect of flow experience on impulse buying. | Supported |
H7 | Flow experience mediates the relationship between audio-visual experience and impulse buying. | Supported |
Variable | Direct Effect | Indirect Effect | Total Effect | Mediation Type | Hypothesis |
---|---|---|---|---|---|
Flow experience | --- | 0.708 * | 0.708 * | Full mediation | Supported |
Serial Multiple Mediator Analysis | ||||||
---|---|---|---|---|---|---|
Estimate (β) | p Value | Hypotheses | ||||
Flow experience | Audio-visual experience | Impulse buying | 0.441 | 0.02 | Supported | H7 |
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Paraman, P.; Annamalah, S.; Vlachos, P.; Ahmed, S.; Balasubramaniam, A.; Kadir, B.; Raman, M.; Hoo, W.C. Dynamic Effect of Flow on Impulsive Consumption: Evidence from Southeast Asian Live Streaming Platforms. J. Open Innov. Technol. Mark. Complex. 2022, 8, 212. https://doi.org/10.3390/joitmc8040212
Paraman P, Annamalah S, Vlachos P, Ahmed S, Balasubramaniam A, Kadir B, Raman M, Hoo WC. Dynamic Effect of Flow on Impulsive Consumption: Evidence from Southeast Asian Live Streaming Platforms. Journal of Open Innovation: Technology, Market, and Complexity. 2022; 8(4):212. https://doi.org/10.3390/joitmc8040212
Chicago/Turabian StyleParaman, Pradeep, Sanmugam Annamalah, Peter Vlachos, Selim Ahmed, Arunnan Balasubramaniam, Baharudin Kadir, Murali Raman, and Wong Chee Hoo. 2022. "Dynamic Effect of Flow on Impulsive Consumption: Evidence from Southeast Asian Live Streaming Platforms" Journal of Open Innovation: Technology, Market, and Complexity 8, no. 4: 212. https://doi.org/10.3390/joitmc8040212
APA StyleParaman, P., Annamalah, S., Vlachos, P., Ahmed, S., Balasubramaniam, A., Kadir, B., Raman, M., & Hoo, W. C. (2022). Dynamic Effect of Flow on Impulsive Consumption: Evidence from Southeast Asian Live Streaming Platforms. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 212. https://doi.org/10.3390/joitmc8040212