International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 10 (October 2022), Pages: 126-134

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 Original Research Paper

 The effect of interactivity and trust on donation and eWOM on Facebook and Instagram

 Author(s): Dutho Suh Utomo, Naraphorn Paoprasert *, Ramidayu Yousuk

 Affiliation(s):

 Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-6578-3849

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.10.016

 Abstract:

Indonesia is a country that has experienced several earthquakes with adverse impacts. This incident triggered fundraising from various parties to help with the handling. The rise of social media affords the chance to facilitate these fundraising activities. The majority of existing research on donations focused on the role of social media in relation to intentions to donate and eWOM intentions lacked investigating the effect of donating intentions on intentions to eWOM and lacked comparing different social media platforms. Therefore, this study compared the effect of interactivity and trust in influencing Donation Intention and eWOM intention for Indonesian earthquake donations on Facebook and Instagram. The technique used was the Multi-Group Analysis (MGA) on PLS-SEM. This study found that for both Facebook and Instagram, trust and interactivity both influence Donation and eWOM Intention. In addition, donation intention influences eWOM intention. In terms of social media platform comparison, there is no difference between Facebook and Instagram regarding the relationships between variables (intention to donate, interactivity, and trust) in influencing eWOM intention. However, Instagram interactivity has a greater influence in influencing people's intentions to donate, while for Facebook, trust has a greater influence. This may be because the average age of Facebook users is higher than that of Instagram users; hence, Facebook users tend to deal more with trust issues while Instagram users seem to be more focused on interactivity. This research contributes to the understanding of online donations involving social media and charitable donations for earthquake relief in Indonesia.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Donation intention, Interactivity, Trust, eWOM intention, MGA

 Article History: Received 21 March 2022, Received in revised form 8 July 2022, Accepted 9 July 2022

 Acknowledgment 

This study was funded by the Faculty of Engineering, Kasetsart University, Bangkok, Thailand. However, any opinions, findings, conclusions or recommendations in this document are those of the authors and not necessarily the views of the sponsors.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Utomo DS, Paoprasert N, and Yousuk R (2022). The effect of interactivity and trust on donation and eWOM on Facebook and Instagram. International Journal of Advanced and Applied Sciences, 9(10): 126-134

 Permanent Link to this page

 Figures

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 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 Table 12 Table 13 Table 14 

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 References (46)

  1. Abdullah D, Kamal SBM, Azmi A, Lahap J, Bahari KA, Din N, and Pinang CP (2019). Perceived website interactivity, perceived usefulness and online hotel booking intention: A structural model. Malaysian Journal of Consumer and Family Economics, 21: 45-57.   [Google Scholar]
  2. Belanche D, Cenjor I, and Pérez-Rueda A (2019). Instagram stories versus Facebook wall: An advertising effectiveness analysis. Spanish Journal of Marketing-ESIC, 23(1): 69-94. https://doi.org/10.1108/SJME-09-2018-0042   [Google Scholar]
  3. Bhati A and McDonnell D (2020). Success in an online giving day: The role of social media in fundraising. Nonprofit and Voluntary Sector Quarterly, 49(1): 74-92. https://doi.org/10.1177/0899764019868849   [Google Scholar]
  4. Bilgin Y and Kethüda Ö (2022). Charity social media marketing and its influence on charity brand image, brand trust, and donation intention. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations. https://doi.org/10.1007/s11266-021-00426-7   [Google Scholar]
  5. Chawla D and Joshi H (2018). The moderating effect of demographic variables on mobile banking adoption: An empirical investigation. Global Business Review, 19(3): S90-S113. https://doi.org/10.1177/0972150918757883   [Google Scholar]
  6. Chen Y, Dai R, Yao J, and Li Y (2019). Donate time or money? The determinants of donation intention in online crowdfunding. Sustainability, 11(16): 4269. https://doi.org/10.3390/su11164269   [Google Scholar]
  7. Chin WW (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2): 295-336.   [Google Scholar]
  8. Chin WW (2010). How to write up and report PLS analyses. In: Esposito Vinzi V, Chin W, Henseler J, and Wang H (Eds.), Handbook of partial least squares: 655-690. Springer, Berlin, Germany. https://doi.org/10.1007/978-3-540-32827-8_29   [Google Scholar]
  9. Chin WW and Dibbern J (2006). A permutation based procedure for multi-group PLS analysis: Results of tests of differences on simulated data and cross-cultural analysis of the sourcing of information system services between Germany and the USA. In: Mangin JPL and Mallou JV (Eds.), Modelización con estructuras de covarianzas en ciencias sociales: Temas esenciales, avanzados y aportaciones especiales: 501-517. Netbiblo, Madrid, Spain. https://doi.org/10.4272/84-9745-136-8.ch19   [Google Scholar] PMid:17056480
  10. Falk RF and Miller NB (1992). A primer for soft modeling. University of Akron Press, Akron, USA.   [Google Scholar]
  11. Farwell MM, Shier ML, and Handy F (2019). Explaining trust in Canadian charities: The influence of public perceptions of accountability, transparency, familiarity and institutional trust. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 30(4): 768-782. https://doi.org/10.1007/s11266-018-00046-8   [Google Scholar]
  12. Feng Y, Du L, and Ling Q (2017). How social media strategies of nonprofit organizations affect consumer donation intention and word-of-mouth. Social Behavior and Personality: An International Journal, 45(11): 1775-1786. https://doi.org/10.2224/sbp.4412   [Google Scholar]
  13. Filieri R, Alguezaui S, and McLeay F (2015). Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tourism Management, 51: 174-185. https://doi.org/10.1016/j.tourman.2015.05.007   [Google Scholar]
  14. Fornell C and Larcker DF (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   [Google Scholar]
  15. Furneaux C and Wymer W (2015). Public trust in Australian charities: Accounting for cause and effect. Third Sector Review, 21(2): 99-127.   [Google Scholar]
  16. Gao Q, Rau PLP, and Salvendy G (2010). Measuring perceived interactivity of mobile advertisements. Behaviour and Information Technology, 29(1): 35-44. https://doi.org/10.1080/01449290802666770   [Google Scholar]
  17. Hair JF, Hult GTM, Ringle CM, and Sarstedt M (2017). A primer on partial least squares structural equation modeling (PLS-SEM). 2nd Edition, Sage Publications Inc., Thousand Oaks, USA.   [Google Scholar]
  18. Hair JF, Hult GTM, Ringle CM, Sarstedt M, Danks NP, and Ray S (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature, Berlin, Germany. https://doi.org/10.1007/978-3-030-80519-7   [Google Scholar]
  19. Henseler J, Dijkstra TK, Sarstedt M, Ringle CM, Diamantopoulos A, Straub DW, and Calantone RJ (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   [Google Scholar]
  20. Henseler J, Ringle CM, and Sarstedt M (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3): 405-431. https://doi.org/10.1108/IMR-09-2014-0304   [Google Scholar]
  21. Henseler J, Ringle CM, and Sinkovics RR (2009). The use of partial least squares path modeling in international marketing. In: Sinkovics RR and Ghauri PN (Eds.), New challenges to international marketing (advances in international marketing): 277-319. Volume 20, Emerald Group Publishing Limited, Bingley, UK. https://doi.org/10.1108/S1474-7979(2009)0000020014   [Google Scholar]
  22. Hou T, Hou K, Wang X, and Luo XR (2021). Why I give money to unknown people? An investigation of online donation and forwarding intention. Electronic Commerce Research and Applications, 47: 101055. https://doi.org/10.1016/j.elerap.2021.101055   [Google Scholar]
  23. Hu LT and Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1): 1-55. https://doi.org/10.1080/10705519909540118   [Google Scholar]
  24. Hwang G and Chung KS (2020). The dynamics of cause-related marketing platform and interactivity on college sport fans' donations. Sport, Business and Management: An International Journal, 10(2): 227-241. https://doi.org/10.1108/SBM-08-2019-0070   [Google Scholar]
  25. Jalilvand MR, Salimipour S, Elyasi M, and Mohammadi M (2017). Factors influencing word of mouth behaviour in the restaurant industry. Marketing Intelligence and Planning, 35(1): 81-110. https://doi.org/10.1108/MIP-02-2016-0024   [Google Scholar]
  26. Jattamart A, Kwangsawad A, and Boonkasem K (2019). Factors influencing the intentions of customer with regard to the use of E-WOM behavior to promote the use of E-commerce websites. In the 4th Technology Innovation Management and Engineering Science International Conference, IEEE, Bangkok, Thailand: 1-5. https://doi.org/10.1109/TIMES-iCON47539.2019.9024662   [Google Scholar]
  27. Kim S and Park H (2013). Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance. International Journal of Information Management, 33(2): 318-332. https://doi.org/10.1016/j.ijinfomgt.2012.11.006   [Google Scholar]
  28. Li W, Mao Y, and Liu C (2022). Understanding the intention to donate online in the Chinese context: The influence of norms and trust. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 16(1): 7. https://doi.org/10.5817/CP2022-1-7   [Google Scholar]
  29. Liao SH, Chung YC, and Chang WJ (2019). Interactivity, engagement, trust, purchase intention and word-of-mouth: A moderated mediation study. International Journal of Services Technology and Management, 25(2): 116-137. https://doi.org/10.1504/IJSTM.2019.098203   [Google Scholar]
  30. Liebermann Y and Stashevsky S (2002). Perceived risks as barriers to Internet and e‐commerce usage. Qualitative Market Research: An International Journal, 5(4): 291-300. https://doi.org/10.1108/13522750210443245   [Google Scholar]
  31. Matook S, Brown SA, and Rolf J (2015). Forming an intention to act on recommendations given via online social networks. European Journal of Information Systems, 24(1): 76-92. https://doi.org/10.1057/ejis.2013.28   [Google Scholar]
  32. Napoleoncat (2021a). Facebook users in Indonesia-July 2021. Available online at: https://napoleoncat.com/stats/facebook-users-in-indonesia/2021/07/
  33. Napoleoncat (2021b). Instagram users in Indonesia-July 2021. Available online at: https://napoleoncat.com/stats/instagram-users-in-indonesia/2021/07/ 
  34. Naranjo-Zolotov M, Oliveira T, and Casteleyn S (2018). Citizens’ intention to use and recommend e-participation: Drawing upon UTAUT and citizen empowerment. Information Technology and People, 32(2): 364-386. https://doi.org/10.1108/ITP-08-2017-0257   [Google Scholar]
  35. Oliveira T, Thomas M, Baptista G, and Campos F (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61: 404-414. https://doi.org/10.1016/j.chb.2016.03.030   [Google Scholar]
  36. ReliefWeb (2021). Indonesia: Earthquake-Jan 2021. ReliefWeb. Available online at: https://reliefweb.int/disaster/eq-2021-000003-idn
  37. Saprikis V, Avlogiaris G, and Katarachia A (2022). A comparative study of users versus non-users’ behavioral intention towards m-banking apps’ adoption. Information, 13(1): 30. https://doi.org/10.3390/info13010030   [Google Scholar]
  38. Sarstedt M, Henseler J, and Ringle CM (2011). Multigroup analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. In: Sarstedt M, Schwaiger M, and Taylor CR (Eds.), Measurement and research methods in international marketing (advances in international marketing): 195-218. Volume 22, Emerald Group Publishing Limited, Bingley, UK. https://doi.org/10.1108/S1474-7979(2011)0000022012   [Google Scholar]
  39. Schultz C, Einwiller S, Seiffert-Brockmann J, and Weitzl W (2019). When reputation influences trust in nonprofit organizations. The role of value attachment as moderator. Corporate Reputation Review, 22(4): 159-170. https://doi.org/10.1057/s41299-019-00067-z   [Google Scholar]
  40. Tan GWH, Lee VH, Hew JJ, Ooi KB, and Wong LW (2018). The interactive mobile social media advertising: An imminent approach to advertise tourism products and services? Telematics and Informatics, 35(8): 2270-2288. https://doi.org/10.1016/j.tele.2018.09.005   [Google Scholar]
  41. Ventre I, Mollá-Descals A, and Frasquet M (2021). Drivers of social commerce usage: A multi-group analysis comparing Facebook and Instagram. Economic Research-Ekonomska Istraživanja, 34(1): 570-589. https://doi.org/10.1080/1331677X.2020.1799233   [Google Scholar]
  42. Wetzels M, Odekerken-Schröder G, and Van Oppen C (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33: 177-195. https://doi.org/10.2307/20650284   [Google Scholar]
  43. Wong KKK (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1): 1-32.   [Google Scholar]
  44. Wymer W, Becker A, and Boenigk S (2021). The antecedents of charity trust and its influence on charity supportive behavior. Journal of Philanthropy and Marketing, 26(2): e1690. https://doi.org/10.1002/nvsm.1690   [Google Scholar]
  45. Yasin M and Shamim A (2013). Brand love: Mediating role in purchase intentions and word-of-mouth. Journal of Business and Management, 7(2): 101-109. https://doi.org/10.9790/487X-072101109   [Google Scholar]
  46. Zeng CF and Seock YK (2019). Chinese consumers’ perceptions toward social media platform for shopping and eWOM intention: A study of WeChat. International Journal of Fashion Design, Technology and Education, 12(2): 199-207. https://doi.org/10.1080/17543266.2019.1572230   [Google Scholar]