Article Sidebar

Submitted
February 1, 2024
Accepted
March 11, 2024
Published
March 18, 2024
Download
Full text PDF
Statistic
Read Counter : 336 Download : 341

Downloads

Download data is not yet available.

Main Article Content

Abstract

In recent years, sales in online platforms have received huge attention from generation Z in Indonesia, along with live streaming business model which has undergone rapid development in recent years. Although the way live streaming with consumers has been known as the key to influence consumer behavior, only a few research has studied the style of streaming communication in affecting purchase intentions. Based on the social influence theory, this study attempts to examine the influence of interaction orientation, parasocial relation, and digital influencer credibility on purchase intention. In addition, the present study also investigates the role of product involvement advertised by streamers as moderator. The quantitative approach with the survey is used through samples of 200 respondents selected by purposive sampling. The data is collected online through disseminating questionnaires using Google Form, which is then analyzed using SEM-PLS technique. The findings indicate that interaction orientation positively influences purchase intention and parasocial relation, while parasocial relation positively influences digital influencer credibility, and digital influencer credibility positively influences purchase intention. In addition, product involvement has a moderating role that can strengthen the influence of interaction orientation and purchase intention. This study provides implications for how an interaction can be delivered online and how the involvement of a credible influencer can affect purchase intention.

Keywords

Interaction orientation Parasocial relation Digital influencer credibility Product involvement Purchase intention

Article Details

License

Copyright (c) 2023 Adjeng Mariana Febrianti, Nurul Hermina, Maman Suratman

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
How to Cite
Febrianti, A. M., Hermina, N., & Suratman, M. (2024). Will you purchase what I recommend? The role of interaction orientation, parasocial relation, and product involvement. Asian Management and Business Review, 4(1), 137–152. https://doi.org/10.20885/AMBR.vol4.iss1.art9
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1080/10410236.2018.1493416.
  2. Ambroise, L., Bérard, C., & Prim-Allaz, I. (2020). Performance implications of exploration and exploitation in SMEs: The mediating role of interaction orientation. Journal of Business and Industrial Marketing, 35(12), 1971-1981. https://doi.org/10.1108/JBIM-06-2019-0279.
  3. Aparicio, M., Costa, C. J., & Moises, R. (2021). Gamification and reputation: Key determinants of e-commerce usage and repurchase intention. Heliyon, 7(3), e06383. https://doi.org/10.1016/j.heliyon.2021.e06383.
  4. Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79-95. https://doi.org/10.1007/s11747-019-00695-1.
  5. Arsenyan, J., & Mirowska, A. (2021). Almost human? A comparative case study on the social media presence of virtual influencers. International Journal of Human Computer Studies, 155, 102694. https://doi.org/10.1016/j.ijhcs.2021.102694.
  6. Ayuni, R. F. (2019). The online shopping habits and e-loyalty of gen Z as natives in the digital era. Journal of Indonesian Economy and Business, 34(2), 168-184. https://doi.org/10.22146/jieb.39848.
  7. Balaban, D. C., Szambolics, J., & Chirică, M. (2022). Parasocial relations and social media influencers’ persuasive power: Exploring the moderating role of product involvement. Acta Psychologica, 230(April), 103731. https://doi.org/10.1016/j.actpsy.2022.103731.
  8. Bharadwaj, N., & Shipley, G. M. (2020). Salesperson communication effectiveness in a digital sales interaction. Industrial Marketing Management, 90, 106-112. https://doi.org/10.1016/j.indmarman.2020.07.002.
  9. Book, L. A., & Tanford, S. (2020). Measuring social influence from online traveler reviews. Journal of Hospitality and Tourism Insights, 3(1), 54-72. https://doi.org/10.1108/JHTI-06-2019-0080.
  10. Chen, Y., & Xiong, F. (2019). The business model of live streaming entertainment services in China and associated challenges for key stakeholders. IEEE Access, 7, 116321-116327. https://doi.org/10.1109/ACCESS.2019.2935005.
  11. Cooper, D. R., & Schindler, P. S. (2014). Business Research Methods, 12th edition. US: McGraw-Hill Education.
  12. Creswell, J. W., & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. California, US: Sage Publications.
  13. Crisafulli, B., Quamina, L. T., & Singh, J. (2022). Competence is power: How digital influencers impact buying decisions in B2B markets. Industrial Marketing Management, 104, 384-399. https://doi.org/10.1016/j.indmarman.2022.05.006.
  14. Dash, G., Kiefer, K., & Paul, J. (2021). Marketing-to-millennials: Marketing 4.0, customer satisfaction and purchase intention. Journal of Business Research, 122, 608-620. https://doi.org/10.1016/j.jbusres.2020.10.016.
  15. Efrat, K., & Øyna, S. (2021). An interaction orientation approach to SME-intermediaries relationships. European Management Journal, 39(4), 508-520. https://doi.org/10.1016/j.emj.2020.10.004.
  16. Ferchaud, A., Grzeslo, J., Orme, S., & LaGroue, J. (2018). Parasocial attributes and YouTube personalities: Exploring content trends across the most subscribed YouTube channels. Computers in Human Behavior, 80, 88-96. https://doi.org/10.1016/j.chb.2017.10.041.
  17. Ferreira, A. G., & Coelho, F. J. (2015). Product involvement, price perceptions, and brand loyalty. Journal of Product & Brand Management, 24(4), 349-364. https://doi.org/10.1108/JPBM-06-2014-0623.
  18. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104.
  19. Gong, X., & Jiang, X. (2023). Understanding consumer impulse buying in livestreaming commerce: The product involvement perspective. Frontiers in Psychology, 14, 1104349. https://doi.org/10.3389/fpsyg.2023.1104349.
  20. Guo, M., Tan, C. L., Wu, L., Peng, J., Ren, R., & Chiu, C. H. (2021). Determinants of intention to purchase bottled water based on business online strategy in China: The role of perceived risk in the theory of planned behavior. International Journal of Environmental Research and Public Health, 18(20), 10729. https://doi.org/10.3390/ijerph182010729.
  21. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12.
  22. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414-433. https://doi.org/10.1007/s11747-011-0261-6.
  23. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., ... & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182-209. https://doi.org/10.1177/1094428114526928.
  24. Herzallah, D., Muñoz-Leiva, F., & Liebana-Cabanillas, F. (2022). Drivers of purchase intention in Instagram Commerce. Spanish Journal of Marketing - ESIC, 26(2), 168-188. https://doi.org/10.1108/SJME-03-2022-0043.
  25. Itani, O. S., Krush, M. T., Agnihotri, R., & Trainor, K. J. (2020). Social media and customer relationship management technologies: Influencing buyer-seller information exchanges. Industrial Marketing Management, 90(October 2019), 264–275. https://doi.org/10.1016/j.indmarman.2020.07.015.
  26. Kautish, P., Paço, A., & Thaichon, P. (2022). Sustainable consumption and plastic packaging: Relationships among product involvement, perceived marketplace influence and choice behavior. Journal of Retailing and Consumer Services, 67(May), 103032. https://doi.org/10.1016/j.jretconser.2022.103032.
  27. Kwon, W., Lee, M., Back, K. J., & Lee, K. Y. (2021). Assessing restaurant review helpfulness through big data: Dual-process and social influence theory. Journal of Hospitality and Tourism Technology, 12(2), 177-195. https://doi.org/10.1108/JHTT-04-2020-0077.
  28. Lacap, J. P. G., Cruz, M. R. M., Bayson, A. J., Molano, R., & Garcia, J. G. (2023). Parasocial relationships and social media interactions: Building brand credibility and loyalty. Spanish Journal of Marketing - ESIC, 28(1), 77-97 https://doi.org/10.1108/SJME-09-2022-0190
  29. Li, M., Wang, Q., & Cao, Y. (2022). Understanding consumer online impulse buying in live streaming e-commerce: A stimulus-organism-response framework. International Journal of Environmental Research and Public Health, 19(7), 4378. https://doi.org/10.3390/ijerph19074378.
  30. Liao, J., Chen, K., Qi, J., Li, J., & Yu, I. Y. (2023). Creating immersive and parasocial live shopping experience for viewers: The role of streamers’ interactional communication style. Journal of Research in Interactive Marketing, 17(1), 140–155. https://doi.org/10.1108/JRIM-04-2021-0114.
  31. Lou, C. (2022). Social media influencers and followers: Theorization of a trans-parasocial relation and explication of its implications for influencer advertising. Journal of Advertising, 51(1), 4-21. https://doi.org/10.1080/00913367.2021.1880345
  32. Ma, L., Gao, S., & Zhang, X. (2022). How to use live streaming to improve consumer purchase intentions: Evidence from China. Sustainability (Switzerland), 14(2), 1045. https://doi.org/10.3390/su14021045.
  33. Melnychuk, H. A., Arasli, H., & Nevzat, R. (2024). How to engage and attract virtual influencers’ followers: A new non-human approach in the age of influencer marketing. Marketing Intelligence & Planning, vol. ahead-of-print, no. ahead-of-print. https://doi.org/10.1108/MIP-06-2023-0251.
  34. Mittal, B. (1995). A comparative analysis of four scales of consumer involvement. Psychology & Marketing, 12(7), 663–682. https://doi.org/10.1002/mar.4220120708.
  35. Munnukka, J., Maity, D., Reinikainen, H., & Luoma-aho, V. (2019). “Thanks for watching”. The effectiveness of YouTube vlogendorsements. Computers in Human Behavior, 93, 226-234. https://doi.org/10.1016/j.chb.2018.12.014.
  36. Palmié, M., Miehé, L., Oghazi, P., Parida, V., & Wincent, J. (2022). The evolution of the digital service ecosystem and digital business model innovation in retail: The emergence of meta-ecosystems and the value of physical interactions. Technological Forecasting and Social Change, 177, 121496. https://doi.org/10.1016/j.techfore.2022.121496.
  37. Penttinen, V., Ciuchita, R., & Čaić, M. (2022). YouTube it before you buy it: The role of parasocial interaction in consumer-to-consumer video reviews. Journal of Interactive Marketing, 57(4), 561-582. https://doi.org/10.1177/10949968221102825.
  38. Pöyry, E., Reinikainen, H., & Luoma-Aho, V. (2022). The role of social media influencers in public health communication: Case COVID-19 pandemic. International Journal of Strategic Communication, 16(3), 469-484. https://doi.org/10.1080/1553118X.2022.2042694.
  39. Pradhan, D., Kuanr, A., Anupurba Pahi, S., & Akram, M. S. (2023). Influencer marketing: When and why gen Z consumers avoid influencers and endorsed brands. Psychology and Marketing, 40(1), 27-47. https://doi.org/10.1002/mar.21749.
  40. Rahim, H. L., Sarifah Radiah Shariff, S., Ngah, R., & Jalil, S. A. (2022). The resilience of SMEs in fighting COVID-19 through online business acceleration. International Journal of Applied Economics, Finance and Accounting, 13(1), 10-19. https://doi.org/10.33094/ijaefa.v13i1.570.
  41. Reinikainen, H., Munnukka, J., Maity, D., & Luoma-aho, V. (2020). ‘You really are a great big sister’–Parasocial relationships, credibility, and the moderating role of audience comments in influencer marketing. Journal of Marketing Management, 36(3–4), 279–298. https://doi.org/10.1080/0267257X.2019.1708781.
  42. Richins, M. L., & Bloch, P. H. (1986). After the new wears off: The temporal context of product involvement. Journal of Consumer Research, 13(2), 280-285. https://doi.org/10.1086/209067
  43. Rokonuzzaman, M., Harun, A., Al-Emran, M., & Prybutok, V. R. (2020). An investigation into the link between consumer’s product involvement and store loyalty: The roles of shopping value goals and information search as the mediating factors. Journal of Retailing and Consumer Services, 52(June 2019), 101933. https://doi.org/10.1016/j.jretconser.2019.101933.
  44. Salmiah, Sahir, S. H., & Fahlevi, M. (2024). The effect of social media and electronic word of mouth on trust and loyalty: Evidence from generation Z in coffee industry. International Journal of Data and Network Science, 8(1), 641–654. https://doi.org/10.5267/j.ijdns.2023.8.021.
  45. Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197-211. https://doi.org/10.1016/j.ausmj.2019.05.003.
  46. Sesar, V., Martinčević, I., & Boguszewicz-Kreft, M. (2022). Relationship between advertising disclosure, influencer credibility and purchase intention. Journal of Risk and Financial Management, 15(7), 276. https://doi.org/10.3390/jrfm15070276.
  47. Sokolova, K., & Kefi, H. (2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. Journal of Retailing and Consumer Services, 53, 101742. https://doi.org/10.1016/j.jretconser.2019.01.011.
  48. Song, W., Ma, X., & Yu, H. (2019). Entrepreneurial orientation, interaction orientation, and innovation performance: A model of moderated mediation. SAGE Open, 9(4). https://doi.org/10.1177/2158244019885143.
  49. Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 37, 100886. https://doi.org/10.1016/j.elerap.2019.100886.
  50. Todd, P. R., & Melancon, J. (2018). Gender and live-streaming: Source credibility and motivation. Journal of Research in Interactive Marketing, 12(1), 79-93. https://doi.org/10.1108/JRIM-05-2017-0035.
  51. Torres, P., Augusto, M., & Matos, M. (2019). Antecedents and outcomes of digital influencer endorsement: An exploratory study. Psychology and Marketing, 36(12), 1267-1276. https://doi.org/10.1002/mar.21274.
  52. Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 2. https://aisel.aisnet.org/jitta/vol11/iss2/2.
  53. Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two‐stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527-555. https://doi.org/10.1111/j.1365-2575.2011.00373.x.
  54. Wang, J., Shahzad, F., Ahmad, Z., Abdullah, M., & Hassan, N. M. (2022a). Trust and consumers’ purchase intention in a social commerce platform: A meta-analytic approach. SAGE Open, 12(2). https://doi.org/10.1177/21582440221091262.
  55. Wang, Y., Lu, Z., Cao, P., Chu, J., Wang, H., & Wattenhofer, R. (2022b). How live streaming changes shopping decisions in e-commerce: A study of live streaming commerce. Computer Supported Cooperative Work (CSCW): An International Journal, 31(4), 701-729. https://doi.org/10.1007/s10606-022-09439-2.
  56. Weismueller, J., Harrigan, P., Wang, S., & Soutar, G. N. (2020). Influencer endorsements: How advertising disclosure and source credibility affect consumer purchase intention on social media. Australasian Marketing Journal, 28(4), 160-170. https://doi.org/10.1016/j.ausmj.2020. 03.002.
  57. Xu, Y., Vanden Abeele, M., Hou, M., & Antheunis, M. (2023). Do parasocial relationships with micro- and mainstream celebrities differ? An empirical study testing four attributes of the parasocial relationship. Celebrity Studies, 14(3), 366–386. https://doi.org/10.1080/19392397.2021.2006730.
  58. Yuan, C. L., Kim, J., & Kim, S. J. (2016). Parasocial relationship effects on customer equity in the social media context. Journal of Business Research, 69(9), 3795-3803. http://dx.doi.org/10.1016/ j.jbusres.2015.12.071.
  59. Yuan, S., & Lou, C. (2020). How social media influencers foster relationships with followers: The roles of source credibility and fairness in parasocial relationship and product interest. Journal of Interactive Advertising, 20(2), 133–147. https://doi.org/10.1080/15252019.2020.1769514.
  60. Zhu, X., Li, Y., & Shang, H. (2022). The impact of online sales on recovery from COVID-19. Engineering Economics, 33(4), 444-457. https://doi.org/10.5755/j01.ee.33.4.30472.
  61. Zhou, T. (2022). Examining online health community users’ sharing behaviour: A social influence perspective. Information Development, 38(4), 599-608. https://doi.org/10.1177/02666669211 007188.