Skip to main content
SpringerLink
Log in

Prediction of Consumer Repurchase Intention with Food Delivery Apps: The Mediating Role of Prior Online Experience Using PLS-SEM-ANN Approach

  • Chapter
  • First Online:
Current and Future Trends on Intelligent Technology Adoption

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1128))

  • 267 Accesses

Abstract

The snowballing approval of food delivery apps (FDAs) in developing countries, including Palestine, calls for a deep insight into this trait. However, limited research has been conducted in Palestine regarding the acceptance and usage of FDAs. Addressing this research gap, this study examines the mediating role of prior online experience (POE) in the relationship between UTAUT2 constructs “performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivations” and consumer repurchase intention (RI) from FDAs. With collecting a sample of 392 customers, a two-step hybrid approach is employed, utilizing partial least squares structural equation modelling (PLS-SEM) for testing hypotheses and artificial neural networks (ANN) for assessing the significance of the construct. The results confirm all hypotheses, indicating that UTAUT2 constructs positively influence POE. Furthermore, POE significantly affects RI, acting as a mediator between UTAUT2 constructs and RI. The analysis of the ANN demonstrates that effort expectancy and facilitating conditions are the most influential factors on POE, while social influence shows relatively lower importance. This research contributes to raise awareness of FDA adoption and usage patterns in developing countries, providing valuable insights for policymakers, businesses, and consumers. The findings emphasize the crucial role of POE in shaping consumers’ RI from FDAs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Amofah, D.O., Chai, J.: Sustaining consumer E-commerce adoption in Sub-Saharan Africa: do trust and payment method matter? Sustainability 14, 1–20 (2022). https://doi.org/10.3390/su14148466

    Article  Google Scholar 

  2. Kenney, M., Zysman, J.: COVID-19 and the increasing centrality and power of platforms in China, the USA, and beyond. SSRN Electron. J. (2020). https://doi.org/10.2139/ssrn.3636509

    Article  Google Scholar 

  3. Pitchay, A., Ganesan, Y., Zulkifli, N.S., Khaliq, A.: Determinants of customers’ intention to use online food delivery application through smartphone in Malaysia. Br. Food J. 124, 732–753 (2022). https://doi.org/10.1108/BFJ-01-2021-0075

    Article  Google Scholar 

  4. Wang, Y., Wang, H., Xu, H.: Understanding the experience and meaning of app-based food delivery from a mobility perspective. Int. J. Hosp. Manag. 99, 103070 (2021). https://doi.org/10.1016/j.ijhm.2021.103070

  5. Data.ai: State of Mobile 2023 (2022)

    Google Scholar 

  6. Yeo, S.F., Tan, C.L., Teo, S.L., Tan, K.H.: The role of food apps servitization on repurchase intention: a study of FoodPanda. Int. J. Prod. Econ. 234, 108063 (2021). https://doi.org/10.1016/j.ijpe.2021.108063

    Article  Google Scholar 

  7. Lee, L.-H., Braud, T., Zhou, P., Wang, L., Xu, D., Lin, Z., Kumar, A., Bermejo, C., Hui, P.: All one needs to know about metaverse: a complete survey on technological singularity, virtual ecosystem, and research agenda. (2021). https://doi.org/10.13140/RG.2.2.11200.05124/8

  8. Pigatto, G., Machado, J.G.C.F., Negreti, A.S., Machado, L.M.: Have you chosen your request? Analysis of online food delivery companies in Brazil. Br. Food J. 119, 639–657 (2017). https://doi.org/10.1108/BFJ-05-2016-0207

  9. Kaur, P., Dhir, A., Talwar, S., Ghuman, K.: The value proposition of food delivery apps from the perspective of theory of consumption value. Int. J. Contemp. Hosp. Manag. 33, 1129–1159 (2020). https://doi.org/10.1108/IJCHM-05-2020-0477

    Article  Google Scholar 

  10. Ahn, J., Kwon, J.: Examining the relative influence of multidimensional customer service relationships in the food delivery application context. Int. J. Contemp. Hosp. Manag. 33, 912–928 (2021). https://doi.org/10.1108/IJCHM-09-2020-0973

    Article  Google Scholar 

  11. Statista: Online Food Delivery—Worldwide (2022)

    Google Scholar 

  12. Lau, T., Ng, D.: Online food delivery services: making food delivery the new normal. J. Mark. Adv. Pract. 1, 17 (2019)

    Google Scholar 

  13. Meena, P., Kumar, G.: Online food delivery companies’ performance and consumers expectations during Covid-19: an investigation using machine learning approach. J. Retail. Consum. Serv. 68, 103052 (2022). https://doi.org/10.1016/j.jretconser.2022.103052

    Article  Google Scholar 

  14. Alalwan, A.A.: Mobile food ordering apps: an empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. Int. J. Inf. Manage. 50, 28–44 (2020). https://doi.org/10.1016/j.ijinfomgt.2019.04.008

  15. Ramesh, R., Venkatesa Prabhu, S., Sasikumar, B., Kiruthika Devi, B.S., Prasath, P., Praveena Rachel Kamala, S.: An empirical study of online food delivery services from applications perspective. Mater. Today Proc. (2022). https://doi.org/10.1016/j.matpr.2021.05.500

  16. Marcus, N.B., Shariff, S.H., Bujang, I.: Behavioural intention on e-Government adoption among the Malaysian baby boomers: the moderating effect of technology readiness. Bus. Manag. Rev. 10, 29–36 (2019)

    Google Scholar 

  17. Venkatesh, V., Thong, J.Y.L., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. Manag. Inf. Syst. 36, 157–178 (2012). https://doi.org/10.2307/41410412

    Article  Google Scholar 

  18. Fakfare, P.: Influence of service attributes of food delivery application on customers’ satisfaction and their behavioural responses: the IPMA approach. Int. J. Gastron. Food Sci. 25, 100392 (2021). https://doi.org/10.1016/j.ijgfs.2021.100392

    Article  Google Scholar 

  19. Lee, S.W., Sung, H.J., Jeon, H.M.: Determinants of continuous intention on food delivery apps: extending UTAUT2 with information quality. Sustainability 11 (2019). https://doi.org/10.3390/su11113141

  20. Ray, A., Dhir, A., Bala, P.K., Kaur, P.: Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. J. Retail. Consum. Serv. 51, 221–230 (2019). https://doi.org/10.1016/j.jretconser.2019.05.025

    Article  Google Scholar 

  21. Zhao, Y., Bacao, F.: What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? Int. J. Hosp. Manag. 91, 102683 (2020). https://doi.org/10.1016/j.ijhm.2020.102683

    Article  Google Scholar 

  22. Morosan, C., DeFranco, A.: It’s about time: revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. Int. J. Hosp. Manag. 53, 17–29 (2016). https://doi.org/10.1016/j.ijhm.2015.11.003

    Article  Google Scholar 

  23. Florenthal, B.: Young consumers’ motivational drivers of brand engagement behavior on social media sites: a synthesized U&G and TAM framework. J. Res. Interact. Mark. 13, 351–391 (2019). https://doi.org/10.1108/JRIM-05-2018-0064

    Article  Google Scholar 

  24. Dash, G., Kiefer, K., Paul, J.: Marketing-to-millennials: marketing 4.0, customer satisfaction and purchase intention. J. Bus. Res. 122, 608–620 (2021). https://doi.org/10.1016/j.jbusres.2020.10.016

  25. Sebastianelli, R., Tamimi, N.: E-tailer website attributes and trust: understanding the role of online reviews. Online Inf. Rev. 42, 506–519 (2018). https://doi.org/10.1108/OIR-02-2016-0039

    Article  Google Scholar 

  26. Su, K., Hsu, M., Wang, J.: Exploring the online shopping—the role of prior experience. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, vol. I. no. I, pp. 18–21 (2009)

    Google Scholar 

  27. Rose, S., Hair, N., Clark, M.: Online customer experience: a review of the business-to-consumer online purchase context. Int. J. Manag. Rev. 13, 24–39 (2011). https://doi.org/10.1111/j.1468-2370.2010.00280.x

    Article  Google Scholar 

  28. Nordhoff, S., Louw, T., Innamaa, S., Lehtonen, E., Beuster, A., Torrao, G., Bjorvatn, A., Kessel, T., Malin, F., Happee, R., Merat, N.: Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: a questionnaire study among 9118 car drivers from eight European countries. Transp. Res. Part F Traffic Psychol. Behav. 74, 280–297 (2020). https://doi.org/10.1016/j.trf.2020.07.015

  29. Park, M.: Factors affecting consumers’ intention to use online music service and customer satisfaction in South Korea. Degree Proj. Inf. Commun. Technol. 1, 14 (2020)

    Google Scholar 

  30. Shaw, N., Sergueeva, K.: The non-monetary benefits of mobile commerce: extending UTAUT2 with perceived value. Int. J. Inf. Manage. 45, 44–55 (2019). https://doi.org/10.1016/j.ijinfomgt.2018.10.024

    Article  Google Scholar 

  31. Agarwal, V., Sahu, R.: Predicting repeat usage intention towards O2O food delivery: extending UTAUT2 with user gratifications and bandwagoning. J. Foodserv. Bus. Res. 25, 434–474 (2022). https://doi.org/10.1080/15378020.2021.1951074

    Article  Google Scholar 

  32. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. 13, 319–340 (1989). https://doi.org/10.2307/249008

  33. Troise, C., O’Driscoll, A., Tani, M., Prisco, A.: Online food delivery services and behavioural intention—a test of an integrated TAM and TPB framework. Br. Food J. 123, 664–683 (2021). https://doi.org/10.1108/BFJ-05-2020-0418

    Article  Google Scholar 

  34. Marangunic, N., Granic, A., Marangunić, N., Granić, A.: Technology acceptance model: a literature review from 1986 to 2013. Univ. Access Inf. Soc. 14, 81–95 (2015). https://doi.org/10.1007/s10209-014-0348-1

    Article  Google Scholar 

  35. Kang, J.-W., Namkung, Y.: The role of personalization on continuance intention in food service mobile apps. Int. J. Contemp. Hosp. Manag. 31, 734–752 (2019). https://doi.org/10.1108/IJCHM-12-2017-0783

    Article  Google Scholar 

  36. Agarwal, R., Karahanna, E.: Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Q. 24, 665 (2000). https://doi.org/10.2307/3250951

    Article  Google Scholar 

  37. Venkatesh, V., Morris, M., Davis, G., Davis, F.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478 (2003). https://doi.org/10.2307/30036540

    Article  Google Scholar 

  38. Al-Sharafi, M.A., Al-Emran, M., Arpaci, I., Iahad, N.A., AlQudah, A.A., Iranmanesh, M., Al-Qaysi, N.: Generation Z use of artificial intelligence products and its impact on environmental sustainability: a cross-cultural comparison. Comput. Hum. Behav. 143, 107708 (2023). https://doi.org/10.1016/j.chb.2023.107708

    Article  Google Scholar 

  39. Surya, A.P., Sukresna, I.M., Mardiyono, A.: Factors affecting intention to use food order-delivery feature of ride-hailing applications: the UTAUT approach. Int. J. Bus. Soc. 22, 1363–1383 (2021). https://doi.org/10.33736/ijbs.4306.2021

  40. Abu-Shanab, E., Al-Sharafi, M.A., Al-Emran, M.: The influence of network externality and fear of missing out on the continuous use of social networks: a cross-country comparison. Int. J. Hum.–Comput. Interact. 1–13 (2023). https://doi.org/10.1080/10447318.2023.2208990

  41. Gangwar, H., Date, H., Ramaswamy, R.: Developing a cloud-computing adoption framework. Glob. Bus. Rev. 16, 632–651 (2015). https://doi.org/10.1177/0972150915581108

    Article  Google Scholar 

  42. Thabet, Z., Albashtawi, S., Ansari, H., Al-Emran, M., Al-Sharafi, M.A., AlQudah, A.A.: Exploring the factors affecting telemedicine adoption by integrating UTAUT2 and IS success model: a hybrid SEM-ANN approach. IEEE Trans. Eng. Manag. (2023). https://doi.org/10.1109/TEM.2023.3296132

    Article  Google Scholar 

  43. Mohd Rahim, N.I., Iahad, N.A., Yusof, A.F., A. Al-Sharafi, M.: AI-based chatbots adoption model for higher-education institutions: a hybrid PLS-SEM-neural network modelling approach. Sustainability 14, 12726 (2022). https://doi.org/10.3390/su141912726

  44. Al-Amri, R., Al-Shami, S., Esmail Abualrejal, H.M., Al-Sharafi, M.A., Yahya Alormuza, T.K.: Role of Shariah compliance on cryptocurrency acceptance among Malaysians: an empirical study. Int. Conf. Intell. Technol. Syst. Serv. Internet Everything, ITSS-IoE 2021 (2021). https://doi.org/10.1109/ITSS-IoE53029.2021.9615291

  45. Albanna, H., Alalwan, A.A., Al-Emran, M.: An integrated model for using social media applications in non-profit organizations. Int. J. Inf. Manage. 63, 102452 (2022). https://doi.org/10.1016/j.ijinfomgt.2021.102452

    Article  Google Scholar 

  46. AlQudah, A.A., Al-Emran, M., Daim, T.U., Shaalan, K.: Toward an integrated model for examining the factors affecting the acceptance of queue management solutions in healthcare. IEEE Trans. Eng. Manag. 1–17 (2022). https://doi.org/10.1109/TEM.2022.3223520

  47. Singh, M., Matsui, Y.: How long tail and trust affect online shopping behavior: an extension to UTAUT2 framework. Pacific Asia J. Assoc. Inf. Syst. 9, 1–24 (2017). https://doi.org/10.17705/1pais.09401

  48. Macedo, I.M.: Predicting the acceptance and use of information and communication technology by older adults: an empirical examination of the revised UTAUT2. Comput. Hum. Behav. 75, 935–948 (2017). https://doi.org/10.1016/j.chb.2017.06.013

    Article  Google Scholar 

  49. Shaikh, A.A., Glavee-Geo, R., Karjaluoto, H.: How relevant are risk perceptions, effort, and performance expectancy in mobile banking adoption? Int. J. E-Bus. Res. 14, 39–60 (2018). https://doi.org/10.4018/IJEBR.2018040103

    Article  Google Scholar 

  50. Li, C.Y., Fang, Y.H., Sukoco, B.M.: Value proposition as a catalyst for innovative service experience: the case of smart-tourism destinations. Serv. Bus. 15, 281–308 (2021). https://doi.org/10.1007/s11628-021-00443-y

    Article  Google Scholar 

  51. Bilgihan, A., Kandampully, J., Zhang, T.: (Christina): Towards a unified customer experience in online shopping environments. Int. J. Qual. Serv. Sci. 8, 102–119 (2016). https://doi.org/10.1108/IJQSS-07-2015-0054

    Article  Google Scholar 

  52. No, E., Kim, J.K.: Determinants of the adoption for travel information on smartphone. Int. J. Tour. Res. 16, 534–545 (2014). https://doi.org/10.1002/jtr.1945

    Article  Google Scholar 

  53. Wang, K.: Determinants of mobile value-added service continuance: the mediating role of service experience. Inf. Manag. 52, 261–274 (2015). https://doi.org/10.1016/j.im.2014.11.005

    Article  Google Scholar 

  54. Pham, T.S.H., Ahammad, M.F.: Antecedents and consequences of online customer satisfaction: a holistic process perspective. Technol. Forecast. Soc. Change. 124, 332–342 (2017). https://doi.org/10.1016/j.techfore.2017.04.003

    Article  Google Scholar 

  55. Alalwan, A.A., Dwivedi, Y.K., Rana, N.P.: Factors influencing adoption of mobile banking by Jordanian bank customers: extending UTAUT2 with trust. Int. J. Inf. Manage. 37, 99–110 (2017). https://doi.org/10.1016/j.ijinfomgt.2017.01.002

    Article  Google Scholar 

  56. Ramos, K.: Factors influencing customers’ continuance usage intention of food delivery apps during COVID-19 quarantine in Mexico. Br. Food J. 124, 833–852 (2022). https://doi.org/10.1108/BFJ-01-2021-0020

    Article  Google Scholar 

  57. Wakefield, R., Wakefield, K.: Social media network behavior: a study of user passion and affect. J. Strateg. Inf. Syst. 25, 140–156 (2016). https://doi.org/10.1016/j.jsis.2016.04.001

    Article  Google Scholar 

  58. Okumus, B., Ali, F., Bilgihan, A., Ozturk, A.B.: Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants. Int. J. Hosp. Manag. 72, 67–77 (2018). https://doi.org/10.1016/j.ijhm.2018.01.001

  59. Cachero-Martínez, S., Vázquez-Casielles, R.: Developing the marketing experience to increase shopping time: the moderating effect of visit frequency. Adm. Sci. 8 (2018). https://doi.org/10.3390/admsci8040077

  60. Srivastava, M., Kaul, D.: Social interaction, convenience and customer satisfaction: the mediating effect of customer experience. J. Retail. Consum. Serv. 21, 1028–1037 (2014). https://doi.org/10.1016/j.jretconser.2014.04.007

    Article  Google Scholar 

  61. Cambra-Fierro, J.J., Fuentes-Blasco, M., Huerta-Álvarez, R., Olavarría, A.: Customer-based brand equity and customer engagement in experiential services: insights from an emerging economy. Serv. Bus. 15, 467–491 (2021). https://doi.org/10.1007/s11628-021-00448-7

    Article  Google Scholar 

  62. Riaz, H., Davidaviciene, V., Ahmed, H., Meidute-Kavaliauskiene, I.: Optimizing customer repurchase intention through cognitive and affective experience: an insight of food delivery applications. Sustainability 14 (2022). https://doi.org/10.3390/su141912936

  63. Rehman, A.U., Bashir, S., Mahmood, A., Karim, H., Nawaz, Z.: Does e-shopping service quality enhance customers’ e-shopping adoption? An extended perspective of unified theory of acceptance and use of technology. PLoS ONE 17, 1–22 (2022). https://doi.org/10.1371/journal.pone.0263652

    Article  Google Scholar 

  64. Yeo, V.C.S., Goh, S.K., Rezaei, S.: Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. J. Retail. Consum. Serv. 35, 150–162 (2017). https://doi.org/10.1016/j.jretconser.2016.12.013

    Article  Google Scholar 

  65. Syifa, I., Indriyani, R., Vincēviča-Gaile, Z.: Measuring repurchase intention on fashion online shopping. SHS Web Conf. 76, 01015 (2020). https://doi.org/10.1051/shsconf/20207601015

    Article  Google Scholar 

  66. Herjanto, H., Amin, M.: Repurchase intention: the effect of similarity and client knowledge. Int. J. Bank Mark. 38, 1351–1371 (2020). https://doi.org/10.1108/IJBM-03-2020-0108

    Article  Google Scholar 

  67. Liao, C., Lin, H.N., Luo, M.M., Chea, S.: Factors influencing online shoppers’ repurchase intentions: the roles of satisfaction and regret. Inf. Manag. 54, 651–668 (2017). https://doi.org/10.1016/j.im.2016.12.005

    Article  Google Scholar 

  68. Aref, M.M., Okasha, A.A.O.: The driving factors of online repurchase intention among Egyptian internet users: an extended UTAUT perspective. Sci. J. Financ. Commer. Stud. Res. 4, 511–545 (2023). https://doi.org/10.21608/cfdj.2023.258054

  69. Naszariah, R., Naseri, N., Mohd Esa, M., Abas, N., Zamratul, N., Ahmad, A., Azis, R.A., Norazmi Bin Nordin, M.: An overview of online purchase intention of halal cosmetic product: a perspective from Malaysia. Turkish J. Comput. Math. Educ. 12, 7674–7681 (2021)

    Google Scholar 

  70. Lee, J., Lee, H., Chung, N.: The impact of customers’ prior online experience on future hotel usage behavior. Int. J. Hosp. Manag. 91, 102669 (2020). https://doi.org/10.1016/j.ijhm.2020.102669

    Article  Google Scholar 

  71. Shi, S., Chow, W.S.: Trust development and transfer in social commerce: prior experience as moderator. Ind. Manag. Data Syst. 115, 1182–1203 (2015). https://doi.org/10.1108/IMDS-01-2015-0019

    Article  Google Scholar 

  72. Miao, M., Jalees, T., Zaman, S.I., Khan, S., Hanif, N.A., Javed, M.K.: The influence of e-customer satisfaction, e-trust and perceived value on consumer’s repurchase intention in B2C e-commerce segment. Asia Pac. J. Mark. Logist. 34, 2184–2206 (2022). https://doi.org/10.1108/APJML-03-2021-0221

  73. Khalid, S., Jalees, T., Malik, K.: Extending the TAM Model for understanding antecedents to online purchase intentions. Mark. Forces. XIII, 90–112 (2018)

    Google Scholar 

  74. Suhaily, L., Soelasih, Y.: What effects repurchase intention of online shopping. Int. Bus. Res. 10, 113 (2017). https://doi.org/10.5539/ibr.v10n12p113

    Article  Google Scholar 

  75. Ahmad, F., Mustafa, K., Hamid, S.A.R., Khawaja, K.F., Zada, S., Jamil, S., Qaisar, M.N., Vega-Muñoz, A., Contreras-Barraza, N., Anwer, N.: Online customer experience leads to loyalty via customer engagement: moderating role of value co-creation. Front. Psychol. 13, 1–15 (2022). https://doi.org/10.3389/fpsyg.2022.897851

    Article  Google Scholar 

  76. Amoako, G.K., Doe, J.K., Neequaye, E.K.: Online innovation and repurchase intentions in hotels: the mediating effect of customer experience. Int. Hosp. Rev. 37, 28–47 (2021). https://doi.org/10.1108/ihr-02-2021-0008

    Article  Google Scholar 

  77. Anshu, K., Gaur, L., Singh, G.: Impact of customer experience on attitude and repurchase intention in online grocery retailing: a moderation mechanism of value co-creation. J. Retail. Consum. Serv. 64, 102798 (2022). https://doi.org/10.1016/j.jretconser.2021.102798

    Article  Google Scholar 

  78. Jaiswal, S., Singh, A.: Influence of the determinants of online customer experience on online customer satisfaction. Paradigm 24, 41–55 (2020). https://doi.org/10.1177/0971890720914121

    Article  Google Scholar 

  79. Lakhal, S., Khechine, H., Mukamurera, J.: Explaining persistence in online courses in higher education: a difference-in-differences analysis. (2021).https://doi.org/10.1186/s41239-021-00251-4

  80. Dharmawirya, M.: Analysis of consumer repurchase intention towards online shopping in Indonesia’s online retail business market. Int. J. e-Education, e-Business, e-Management e-Learning 2, 3–6 (2012). https://doi.org/10.7763/ijeeee.2012.v2.109

  81. Aravindan, K.L., Ramayah, T., Thavanethen, M., Raman, M., Ilhavenil, N., Annamalah, S., Choong, Y.V.: Modeling positive electronic word of mouth and purchase intention using theory of consumption value. Sustainability 15, 1–19 (2023). https://doi.org/10.3390/su15043009

    Article  Google Scholar 

  82. Hsu, Y.-C., Lin, C.-Y.: Industrial management and data systems, total quality management and business excellence. Int. J. Technol. Manag. Int. J. Manuf. Technol. Manag. 15, 104–117 (2017)

    Google Scholar 

  83. San Martín, H., Herrero, Á.: Influence of the user’s psychological factors on the online purchase intention in rural tourism: integrating innovativeness to the UTAUT framework. Tour. Manag. 33, 341–350 (2012). https://doi.org/10.1016/j.tourman.2011.04.003

  84. Kim, C., Galliers, R.D., Shin, N., Ryoo, J.H., Kim, J.: Factors influencing Internet shopping value and customer repurchase intention (2012).https://doi.org/10.1016/j.elerap.2012.04.002

  85. Suhartanto, D., Dean, D., Leo, G., Triyuni, N.: Millennial experience with online food home delivery: a lesson from Indonesia. Interdiscip. J. Inf. Knowl. Manag. 14, 277–294 (2019). https://doi.org/10.28945/4386

  86. Wang, E.S.T., Chu, Y.H.: Influence of consumer’s long-term orientation and safety consciousness on intention to repurchase certified functional foods. J. Food Prod. Mark. 26, 247–261 (2020). https://doi.org/10.1080/10454446.2020.1757554

    Article  Google Scholar 

  87. Hair, J.F., Jr., Matthews, L.M., Matthews, R.L., Sarstedt, M.: PLS-SEM or CB-SEM: updated guidelines on which method to use. Int. J. Multivar. Data Anal. 1, 107 (2017). https://doi.org/10.1504/ijmda.2017.10008574

    Article  Google Scholar 

  88. Hair, J.F., Sarstedt, M., Ringle, C.M., Mena, J.A.: An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 40, 414–433 (2011). https://doi.org/10.1007/s11747-011-0261-6

    Article  Google Scholar 

  89. Alkawsi, G.A., Ali, N., Mustafa, A.S., Baashar, Y., Alhussian, H., Alkahtani, A., Tiong, S.K., Ekanayake, J.: A hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: challenges perspective. Alexandria Eng. J. 60, 227–240 (2021). https://doi.org/10.1016/j.aej.2020.07.002

    Article  Google Scholar 

  90. Kalinić, Z., Marinković, V., Kalinić, L., Liébana-Cabanillas, F.: Neural network modeling of consumer satisfaction in mobile commerce: an empirical analysis. Expert Syst. Appl. 175, 114803 (2021). https://doi.org/10.1016/j.eswa.2021.114803

    Article  Google Scholar 

  91. Lee, V.-H.H., Hew, J.-J.J., Leong, L.-Y.Y., Tan, G.W.-H.H., Ooi, K.-B.B.: Wearable payment: a deep learning-based dual-stage SEM-ANN analysis. Expert Syst. Appl. 157, 113477 (2020). https://doi.org/10.1016/j.eswa.2020.113477

  92. Ooi, K.B., Lee, V.H., Tan, G.W.H., Hew, T.S., Hew, J.J.: Cloud computing in manufacturing: the next industrial revolution in Malaysia? Expert Syst. Appl. 93, 376–394 (2018). https://doi.org/10.1016/j.eswa.2017.10.009

    Article  Google Scholar 

  93. Qasem, Y.A.M., Asadi, S., Abdullah, R., Yah, Y., Atan, R., Al-Sharafi, M.A., Yassin, A.A.: A multi-analytical approach to predict the determinants of cloud computing adoption in higher education institutions. Appl. Sci. 10, 4905 (2020). https://doi.org/10.3390/app10144905

    Article  Google Scholar 

  94. Al-Emran, M., Mezhuyev, V., Kamaludin, A.: PLS-SEM in information systems research: a comprehensive methodological reference. Springer Nature (2018).https://doi.org/10.1007/978-3-319-99010-1_59

  95. Khayer, A., Talukder, M.S., Bao, Y., Hossain, M.N.: Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: a dual-stage analytical approach. Technol. Soc. 60, 101225 (2020). https://doi.org/10.1016/j.techsoc.2019.101225

    Article  Google Scholar 

  96. Morris, S.A., Greer, T.H., Hughes, C., Clark, W.J.: Prediction of CASE adoption: a neural network approach. Ind. Manag. Data Syst. 104, 129–135 (2004). https://doi.org/10.1108/02635570410522099

    Article  Google Scholar 

  97. Wang, Y.M., Elhag, T.M.S.: A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks. Expert Syst. Appl. 32, 336–348 (2007). https://doi.org/10.1016/j.eswa.2005.11.029

    Article  Google Scholar 

  98. Ali, Z., Gongbing, B.B., Mehreen, A.: Understanding and predicting academic performance through cloud computing adoption: a perspective of technology acceptance model. J. Comput. Educ. 5, 297–327 (2018). https://doi.org/10.1007/s40692-018-0114-0

    Article  Google Scholar 

  99. Leong, L.-Y., Hew, T.-S., Ooi, K.-B., Chong, A.Y.-L.: Predicting the antecedents of trust in social commerce—a hybrid structural equation modeling with neural network approach. J. Bus. Res. 110, 24–40 (2020). https://doi.org/10.1016/j.jbusres.2019.11.056

    Article  Google Scholar 

  100. Armstrong, J.S., Overton, T.S.: Estimating nonresponse bias in mail surveys. J. Mark. Res. 14, 396–402 (1977). https://doi.org/10.1177/002224377701400320

    Article  Google Scholar 

  101. Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P.: Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903 (2003). https://doi.org/10.1037/0021-9010.88.5.879

    Article  Google Scholar 

  102. Kock, N.: Common method bias in PLS-SEM. Int. J. e-Collab. 11, 1–10 (2015). https://doi.org/10.4018/ijec.2015100101

    Article  Google Scholar 

  103. Hair Jr, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage publications (2021)

    Google Scholar 

  104. Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43, 115–135 (2015). https://doi.org/10.1007/s11747-014-0403-8

    Article  Google Scholar 

  105. Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, Hillsdale, NJ (1998)

    Google Scholar 

  106. Chin, W.W.: The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 295, 295–336 (1998)

    Google Scholar 

  107. Al-Sharafi, M.A., Al-Emran, M., Arpaci, I., Marques, G., Namoun, A., Iahad, N.A.: Examining the impact of psychological, social, and quality factors on the continuous intention to use virtual meeting platforms during and beyond COVID-19 pandemic: a hybrid SEM-ANN approach. Int. J. Hum. Comput. Interact. (2022). https://doi.org/10.1080/10447318.2022.2084036

    Article  Google Scholar 

  108. Al-Sharafi, M.A., Al-Qaysi, N., Iahad, N.A., Al-Emran, M.: Evaluating the sustainable use of mobile payment contactless technologies within and beyond the COVID-19 pandemic using a hybrid SEM-ANN approach. Int. J. Bank Mark. 40, 1071–1095 (2022). https://doi.org/10.1108/IJBM-07-2021-0291

    Article  Google Scholar 

  109. Al-Sharafi, M.A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N.A., Arpaci, I.: Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interact. Learn. Environ. 1–20 (2022). https://doi.org/10.1080/10494820.2022.2075014

  110. Leong, L.Y., Hew, J.J., Lee, V.H., Tan, G.W.H., Ooi, K.B., Rana, N.P.: An SEM-ANN analysis of the impacts of blockchain on competitive advantage. Ind. Manag. Data Syst. 123, 967–1004 (2023). https://doi.org/10.1108/IMDS-11-2021-0671/FULL/PDF

    Article  Google Scholar 

  111. Lo, P.S., Dwivedi, Y.K., Wei-Han Tan, G., Ooi, K.B., 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. 147, 325–337 (2022). https://doi.org/10.1016/J.JBUSRES.2022.04.013

    Article  Google Scholar 

  112. Hsu, L.C., Wang, K.Y., Chih, W.H., Lin, K.Y.: Investigating the ripple effect in virtual communities: an example of Facebook fan pages. Comput. Hum. Behav. 51, 483–494 (2015). https://doi.org/10.1016/j.chb.2015.04.069

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Palestine Technical University—Kadoorie, Tulkarm, Palestine

    Fadi Herzallah & Amer J. Abosamaha

  2. Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, 43000, Putrajaya Campus, Kajang, Malaysia

    Mohammed A. Al-Sharafi

Authors
  1. Fadi Herzallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amer J. Abosamaha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed A. Al-Sharafi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fadi Herzallah .

Editor information

Editors and Affiliations

  1. Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia

    Mohammed A. Al-Sharafi

  2. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  3. UCSI University, Kuala Lumpur, Malaysia

    Garry Wei-Han Tan

  4. UCSI University, Kuala Lumpur, Malaysia

    Keng-Boon Ooi

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Herzallah, F., Abosamaha, A.J., Al-Sharafi, M.A. (2023). Prediction of Consumer Repurchase Intention with Food Delivery Apps: The Mediating Role of Prior Online Experience Using PLS-SEM-ANN Approach. In: Al-Sharafi, M.A., Al-Emran, M., Tan, G.WH., Ooi, KB. (eds) Current and Future Trends on Intelligent Technology Adoption. Studies in Computational Intelligence, vol 1128. Springer, Cham. https://doi.org/10.1007/978-3-031-48397-4_14

Download citation

Publish with us

Policies and ethics

Search

Navigation