With the advancement of technology, many habits of people have changed. One of these is the habit of ordering food. While people used to place food orders through traditional means such as phone or email, nowadays, they can do so more quickly and easily through mobile food ordering applications (MFOAs). The increasing use of MFOAs has necessitated the exploration of factors influencing individuals’ intention to reuse these applications. The aim of this study is to investigate the factors influencing individuals’ intention to reuse MFOAs. The study is based on marketing theory and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. In addition to the seven variables proposed by UTAUT2, variables related to the characteristic features of MFOAs, such as online reviews, online ratings, and online order tracking, have been added to the same model. The research data were obtained from surveys conducted with 441 MFOA users between December 2021 and March 2022. The collected data were analyzed using structural equation modeling in the R Software. As a result, it was observed that online ratings, effort expectation, online order tracking, price value, habit, and hedonic motivation variables have a significant and positive impact on the intention to reuse MFOAs. Social influence, facilitating conditions, performance expectation, and online reviews were found to have no effect on customers’ reuse intentions toward MFOAs. The findings of the study provide insights into understanding consumer preferences and purchase intentions, offering a prediction for MFOA service providers competing for a larger market share.
Предпросмотр статьи
Идентификаторы и классификаторы
- SCI
- Социология
- УДК
- 316. Социология
The modern lifestyle, with its fast pace and heavy reliance on technology, is driving consumers to shop via mobile applications [Çelik, Özköse, 2023]. Individuals are always looking for new ways to simplify their daily duties and fit them into their schedules. Innovative solutions enable a variety of possibilities, from home deliveries to logistics service providers depositing e-commerce goods at authorized collection places, particularly in the last-mile logistics services. Urban logistics services include home delivery services, which are a convenient choice for online customers. Additionally, home delivery services are becoming much more important since customers want their purchases to arrive at the appropriate time, location, amount, and condition owing to mobile phone applications [Belanche, Flavian, ve Pérez-Rueda, 2020].
Список литературы
1. AbuShanab E., Pearson J.M. (2007). Internet banking in Jordan: The unified theory of acceptance and use of technology (UTAUT) perspective. Journal of Systems and Information Technology, vol. 9, no. 1, pp. 78-97. DOI: 10.1108/13287260710817700
2. Afacan Adanır G., Çınar M. (2021). The acceptance and use of an online exam system by online learners: Implementation of the UTAUT model. Sakarya University Journal of Education, vol. 11, no. 3, pp. 412-430. DOI: 10.19126/suje.830529 EDN: TESEUF
3. Agarwal V., Sahu R. (2022). Predicting repeat usage intention towards O2O food delivery: Extending UTAUT2 with user gratifications and bandwagoning. Journal of Foodservice Business Research, vol. 25, no. 4, pp. 434-474. DOI: 10.1080/15378020.2021.1951074 EDN: LFBUBT
4. Ajzen I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, vol. 50, issue 2, pp. 179-211. DOI: 10.1016/0749-5978(91)90020-T
5. Ajzen I., Fishbein M. (1980). Understanding attitudes and predicting social behavior. London: Pearson.
6. Alalwan A.A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued ıntention to reuse. International Journal of Information Management, vol. 50, pp. 28-44. DOI: 10.1016/j.ijinfomgt.2019.04.008 EDN: FEVOAH
7. Alalwan A.A., Dwivedi Y.K., Rana N.P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, vol. 37, issue 3, pp. 99-110. DOI: 10.1016/j.ijinfomgt.2017.01.002
8. Alavi S.A., Rezaei S., Valaei N., Wan Ismail W.K. (2016). Examining shopping mall consumer decision-making styles, satisfaction and purchase ıntention. The International Review of Retail, Distribution and Consumer Research, vol. 26, no. 3, pp. 272-303. DOI: 10.1080/09593969.2015.1096808
9. Ali F., Nair P.K, Hussain K. (2016). An assessment of students’ acceptance and usage of computer supported collaborative classrooms in hospitality and tourism schools. Journal of Hospitality, Leisure, Sport & Tourism Education, vol. 18, pp. 51-60. DOI: 10.1016/j.jhlste.2016.03.002
10. Al-Okaily M., Lutfi A., Alsaad A., Taamneh A., Alsyouf A. (2020). The determinants of digital payment systems’ acceptance under cultural orientation differences: The case of uncertainty avoidance. Technology in Society, vol. 63. DOI: 10.1016/j.techsoc.2020.101367 EDN: WFBVXI
11. Amoroso D., Lim R. (2017). The mediating effects of habit on continuance ıntention. International Journal of Information Management, vol. 37, issue 6, pp. 693-702. DOI: 10.1016/j.ijinfomgt.2017.05.003
12. Andaleeb S., Conway C. (2006). Customer satisfaction in the restaurant industry: An examination of the transaction-specific model. Journal of Services Marketing, vol. 20, no. 1, pp. 3-11. DOI: 10.1108/08876040610646536
13. Ayaz A., Yanartaş M. (2020). An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS). Computers in Human Behavior Reports, vol. 2, 100032. DOI: 10.1016/j.chbr.2020.100032 EDN: HWFDDY
14. Baabdullah A.M., Alalwan A.A., Rana N.P., Kizgin H., Patil P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management, vol. 44, pp. 38-52. DOI: 10.1016/j.ijinfomgt.2018.09.002
15. Belanche D., Flavian M., ve Pérez-Rueda A. (2020). Mobile apps use and WOM in the food delivery sector: The role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability, vol. 12, no. 10, 4275. DOI: 10.3390/su12104275 EDN: PTFDOY
16. Bert F., Giacometti M., Gualano M.R., Siliquini R. (2014). Smartphones and health promotion: A review of the evidence. Journal of Medical Systems, vol. 38, no. 1, pp. 1-11. DOI: 10.1007/s10916-013-9995-7 EDN: TLJTTG
17. Chai L.T., Yat D.N.C. (2019). Online food delivery services: Making food delivery the new normal. Journal of Marketing Advances and Practices, vol. 1, no. 1, pp. 62-77.
18. Chandrasekhar N., Gupta S., Nanda N. (2019). Food delivery services and customer preference: A comparative analysis. Journal of Foodservice Business Research, vol. 22, no. 4, pp. 375-386. DOI: 10.1080/15378020.2019.1626208
19. Cheng C.C., Chang Y.Y., Chen C.T. (2021). Construction of a service quality scale for the online food delivery industry. International Journal of Hospitality Management, vol. 95, pp. 29-38. DOI: 10.1016/j.ijhm.2021.102938 EDN: PSZIJV
20. Cheng Y., Sharma S., Sharma P., Kulathunga K.M.M.C.B. (2020). Role of personalization in continuous use intention of Mobile news apps in India: Extending the UTAUT2 model. Information, vol. 11, no. 1, 33. DOI: 10.3390/info11010033 EDN: YINHMA
21. Chiu C.M., Wang E.T. (2008). Understanding web-based learning continuance ıntention: The role of subjective task value. Information & Management, vol. 45, no. 3, pp. 194-201. DOI: 10.1016/j.im.2008.02.003
22. Cho M., Bonn M.A., Li J. (2019). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journalof Hospitality Management, vol. 77, pp. 108-116. DOI: 10.1016/j.ijhm.2018.06.019
23. Chotigo J., Kadono Y. (2021). Comparative analysis of key factors encouraging food delivery app adoption before and during the COVID-19 pandemic in Thailand. Sustainability, vol. 13, no. 8, 4088. DOI: 10.3390/su13084088 EDN: KUCSVQ
24. Correa J.C., Garz on W., Brooker P., Sakarkar G., Carranzaa S.A., Yunado L., Rincon A. (2019). Evaluation of collaborative consumption of food delivery services through web mining techniques. Journal of Retailing and Consumer Services, vol. 46, pp. 45-50. DOI: 10.1016/j.jretconser.2018.05.002
25. Çelik K., Ayaz A. (2022). Validation of the Delone and McLean information systems success model: A study on student information system. Education and Information Technologies, vol. 27, pp. 4709-4727. DOI: 10.1007/s10639-021-10798-4 EDN: MFMBQQ
26. Çelik K. (2021). The effect of e-service quality and after-sales e-service quality on e-satisfaction. BMİJ, vol. 9, no. 3, pp. 1137- 1155. DOI: 10.15295/bmij.v9i3.1898 EDN: DJMJWS
27. Çelik K., Özköse H. (2023). Investigation of factors affecting mobile banking ıntention to use: Extended Technology Acceptance Model. Journal of Business Research-Turk, vol. 15, no. 2, pp. 1517-1530. DOI: 10.20491/isarder.2023.1663 EDN: UUFQVV
28. Çelik K., Sökmen A. (2018). Understanding distance education continuance ıntention: Extended technology acceptance model. Journal of Management, Economic and Marketing Research, vol. 2, no. 3, pp. 1-23.
29. Das J. (2018). Consumer perception towards online food ordering and delivery services: An empirical study. Journal of Management, vol. 5, no. 5, pp. 155-163.
30. Daud D., Yoong H.M. (2019). The relationship between consumers’ price-saving orientation and time-saving orientation towards food delivery intermediaries (fdi) services: An exploratory study. Global Scientific Journals, vol. 7, no. 2, pp. 175-190.
31. Davis F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, vol. 13, no. 3, pp. 319-340. DOI: 10.2307/249008
32. Eckhardt A., Laumer S., Weitzel T. (2009). Who ınfluences whom? Analyzing workplace referents’ social ınfluence on it adoption and non-adoption. Journal Informaiton Technology, vol. 24, no. 1, pp. 11-24. DOI: 10.1057/jit.2008.31
33. Elwalda A., Lü K., Ali M. (2016). Perceived derived attributes of online customer reviews. Computers in Human Behavior, vol. 56, pp. 306-319. DOI: 10.1016/j.chb.2015.11.051
34. Escobar-Rodríguez T., Carvajal-Trujillo E. (2013). Online drivers of consumer purchase of website airline tickets. Journal of Air Transport Management, vol. 32, pp. 58-64. DOI: 10.1016/j.jairtraman.2013.06.018
35. Filieri R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, vol. 68, no. 6, pp. 1261-1270. DOI: 10.1016/j.jbusres.2014.11.006
36. Fishbein M., Ajzen I. (1975). Belief, attitude, ıntention and behaviour: An ıntroduction to theory and research. Reading, MA, USA: Addison-Wesley.
37. Fornell C., Larcker D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, vol. 18, no. 1, pp. 39-50. DOI: 10.2307/3151312
38. Gefen D. (2002). Customer loyalty in e-commerce. Journal of the Association for Information Systems, vol. 3, pp. 27-51. DOI: 10.17705/1jais.00022
39. Gunden N., Morosan C., DeFranco A. (2020). Consumers’ ıntentions to use online food delivery systems ın the USA. International Journal of Contemporary Hospitality Management, vol. 32, no. 3, pp. 1325-1345. DOI: 10.1108/ijchm-06-2019-0595 EDN: OGRZTH
40. Guo Y., Zhu Y., Barnes S.J., Bao Y., Li X., Le-Nguyen K. (2018). Understanding cross-product purchase ıntention in an IT brand extension context. Psychology and Marketing, vol. 35, no. 6, pp. 392-411. DOI: 10.1002/mar.21094
41. Gupta A., Dogra N., George B. (2018). What determines tourist adoption of smartphone apps? An analysis based on the UTAUT-2 framework. Journal of Hospitality and Tourism Technology, vol. 9, no. 1, pp. 50-64. DOI: 10.1108/jhtt-02-2017-0013
42. Gupta V. (2019). The influencing role of social media in the consumer’s hotel decision-making process. Worldwide Hospitality and Tourism Themes, vol. 11, no. 4, pp. 378-391. DOI: 10.1108/WHATT-04-2019-0019
43. Gutierrez A., O’Leary S., Rana N.P., Dwivedi Y.K., Calle T. (2019). Using privacy calculus theory to explore entrepreneurial directions in mobile location-based advertising: Identifying intrusiveness as the critical risk factor. Computers in Human Behavior, vol. 95, pp. 295-306. DOI: 10.1016/j.chb.2018.09.015
44. Hair J.F., Black W.C., Babin B.J., Anderson R.E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Education.
45. Hobbs J.E. (2020). Food supply chains during the COVID-19 pandemic. Canadian Journal of Agricultural Economics, vol. 68, no. 2, pp. 171-176. DOI: 10.1111/cjag.12237 EDN: QIDRNJ
46. Holbrook M.B., Hirschman E.C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, vol. 9, no. 2, pp. 132-140. DOI: 10.1086/208906
47. Huang J., Baptista J., Newell S. (2015). Communicational ambidexterity as a new capability to manage social media communication within organizations. The Journal of Strategic Information Systems, vol. 24, no. 2, pp. 49-64. DOI: 10.1016/j.jsis.2015.03.002
48. Hwang J., Kim H., Kim W. (2019). Investigating motivated consumer innovativeness in the context of drone food delivery services. Journal of Hospitality and Tourism Management, vol. 38, pp. 102-110. DOI: 10.1016/j.jhtm.2019.01.004
49. Hwang J., Kim J.J., Lee K.W. (2021). Investigating consumer innovativeness in the context of drone food delivery services: Its impact on attitude and behavioral intentions. Technological Forecasting and Social Change, vol. 163, 120433. DOI: 10.1016/j.techfore.2020.120433 EDN: PNTJCJ
50. Iacobucci D., Duhachek A. (2003). Advancing alpha: Measuring reliability with confidence. Journal of Consumer Psychology, vol. 13, no. 4, pp. 478-487. DOI: 10.1207/s15327663jcp1304_14
51. Jiang Z., Benbasat I. (2004). Virtual product experience: Effects of visual and functional control of products on perceived diagnosticity and flow in electronic shopping. Journal of Management Information Systems, vol. 21, no. 3, pp. 111-147. DOI: 10.1080/07421222.2004.11045817
52. Jin B., Gu Suh Y. (2005). Integrating effect of consumer perception factors in predicting private brand purchase in a Korean discount store context. Journal of Consumer Marketing, vol. 22, no. 2, pp. 62-71. DOI: 10.1108/07363760510589226 EDN: HLBVLN
53. Kabra G., Ramesh A., Akhtar P., Dash M.K. (2017). Understanding behavioural intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics, vol. 34, no. 7, pp. 1250-1261. DOI: 10.1016/j.tele.2017.05.010
54. Kang J.-W., Namkung Y. (2019). The ınformation quality and source credibility matter in customers’ evaluation toward food O2O commerce. International Journal of Hospitality Management, vol. 78, pp. 189-198. DOI: 10.1016/j.ijhm.2018.10.011
55. Kapoor A.P., Vij M. (2018). Technology at the dinner table: Ordering food online through mobile apps. Journal of Retailing and Consumer Services, vol. 43, pp. 342-351. DOI: 10.1016/j.jretconser.2018.04.001
56. Karlı H. (2019). Investigation of the factors that affect the purchase intention of omnichannel customers with UTAUT2. M. Sc. Thesis, Bandırma Onyedi Eylül University.
57. Kaur P., Dhir A., Talwar S., Ghuman K. (2021). The value proposition of food delivery apps from the perspective of theory of consumption value. International Journal of Contemporary Hospitality Management, vol. 33, no. 4, pp. 1129-1159. DOI: 10.1108/IJCHM-05-2020-0477 EDN: WJUPXM
58. Khalifa M., Liu V. (2007). Online consumer retention: Contingent effects of online shopping habit and online shopping experience. European Journal of Information Systems, vol. 16, no. 6, pp. 780-792. DOI: 10.1057/palgrave.ejis.3000711
59. Khalilzadeh J., Ozturk A.B., Bilgihan A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, vol. 70, pp. 460-474. DOI: 10.1016/j.chb.2017.01.001
60. Kijsanayotin B., Pannarunothai S., Speedie S.M. (2009). Factors ınfluencing health ınformation technology adoption in Thailand’s community health centers: Applying the UTAUT model. International Journal of Medical Informatics, vol. 78, no. 6, pp. 404-416. DOI: 10.1016/j.ijmedinf.2008.12.005
61. Kim S.S., Malhotra N.K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, vol. 51, no. 5, pp. 741-755. DOI: 10.1287/mnsc.1040.0326
62. Kim S., Lee K.H., Hwang H., Yoo S. (2016). Analysis of the factors influencing healthcare professionals’ adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Medical Informatics and Decision Making, vol. 16, 12. DOI: 10.1186/s12911-016-0249-8
63. King R.A., Racherla P., Bush V.D. (2014). What we know and don’t know about online word-of-mouth: A review and synthesis of the literature. Journal of Interactive Marketing, vol. 28, no. 3, pp. 167-183. DOI: 10.1016/j.intmar.2014.02.001
64. Koay K.Y., Cheah C.W., Chang Y.X. (2022). A model of online food delivery service quality, customer satisfaction and customer loyalty: A combination of PLS-SEM and NCA approaches. British Food Journal, vol. 124, issue 12, pp. 1-17. DOI: 10.1108/BFJ-10-2021-1169 EDN: XJXOZG
65. Lee J.-H. (2019). Effects of service and mobile app quality on customer satisfaction and repurchase intention in the context of O2O food delivery services in Korea and China. Test Engineering and Management, vol. 81, no. 11, pp. 335-345.
66. Lee S.W., Sung H.J., Jeon H.M. (2019). Determinants of continuous ıntention on food delivery apps: Extending UTAUT2 with ınformation quality. Sustainability, vol. 11, no. 11. DOI: 10.3390/su11113141
67. Li C., Mirosa M., Bremer P. (2020). Review of online food delivery platforms and their impacts on sustainability. Sustainability, vol. 12, no. 14. https://doi.org/.3390/su12145528. EDN: KHMZOR
68. Liao C., Chen J.L., Yen D.C. (2007). Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computer Human Behaviour, vol. 23, pp. 2804-2822. DOI: 10.1016/j.chb.2006.05.006
69. Limayem M., Cheung C.M.K. (2008). Understanding information systems continuance: The case of internet-based learning technologies. Information and Management, vol. 45, no. 4, pp. 227-232. DOI: 10.1016/j.im.2008.02.005
70. Lu J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, vol. 24, no. 2, pp. 134-159. DOI: 10.1108/intr-05-2012-0100
71. Miranda M.J. (2009). Engaging the purchase motivations to charm shoppers. Marketing Intelligence & Planning, vol. 27, no. 1, pp. 127-145. DOI: 10.1108/02634500910928335
72. Morganti E., Seidel S., Blanquart C., Dablanc L., Lenz B. (2014). The impact of e-commerce on final deliveries: alternative parcel delivery services in France and Germany. Transportation Research Procedia, vol. 4, pp. 178-190. DOI: 10.1016/j.trpro.2014.11.014
73. Morosan C., DeFranco A. (2016). It’s about time: Revisiting UTAUT2 to examine consumers’intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, vol. 53, pp. 17-29. DOI: 10.1016/j.ijhm.2015.11.003
74. Mort G.S., Rose T. (2004). The effect of product type on value linkages in the means-end chain ımplications for theory and method. Journal of Consumer Behaviour, vol. 3, no. 3, pp. 221-234. DOI: 10.1002/cb.136
75. Mudambi S.M., Schuff D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS Quarterly, vol. 34, no. 1, pp. 185-200.
76. Nunnally J.C. (1978). Psychometric theory (2nd ed.). New York: McGraw Hill.
77. Okumus B., Ali F., Bilgihan A., Ozturk A.B. (2018). Psychological factors ınfluencing customers’ acceptance of smartphone diet apps when ordering food at restaurants. International Journal of Hospitality Management, vol. 72, pp. 67-77. DOI: 10.1016/j.ijhm.2018.01.001
78. Parasuraman A., Zeithaml V.A., Berry L. (1994). Alternative scales for measuring service quality: A comparative assessment based on psychometric and diagnostic criteria. Journal of Retailing, vol. 70, no. 3, pp. 201-230. DOI: 10.1016/0022-4359(94)90033-7
79. Park D.H., Lee J., Han I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, vol. 11, no. 4, pp. 125-148.
80. Philips P., Barnes S., Zigan K., Schegg R. (2017). Understanding the ımpact of online reviews on hotel performance: An empirical analysis. Journal of Travel Research, vol. 56, no. 2, pp. 235-249. DOI: 10.1177/0047287516636481
81. Podsakoff P.M., MacKenzie S.B., Lee J.Y., Podsakoff N.P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, vol. 88, no. 5, pp. 879-903. DOI: 10.1037/0021-9010.88.5.879 EDN: GWUHYP
82. Ramos K. (2022). Factors influencing customers’ continuance usage intention of food delivery apps during COVID-19 quarantine in Mexico. British Food Journal, vol. 124, no. 3, pp. 833-852. DOI: 10.1108/BFJ-01-2021-0020 EDN: XIOSLO
83. Ray A., Dhir A., Bala P.K., ve Kaur P. (2019). Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. Journal of Retailing and Consumer Services, vol. 51, pp. 221-230. DOI: 10.1016/j.jretconser.2019.05.025
84. Raza A., Asif M., Akram M. (2022). Give your hunger a new option: Understanding consumers’ continuous intention to use online food delivery apps using trust transfer theory. International Journal of Consumer Studies, vol. 47, no. 2, pp. 474-495. DOI: 10.1111/ijcs.12845 EDN: XPEADN
85. Reibstein D.J. (2002). What attracts customers to online stores, and what keeps them coming back? Journal of the Academy of Marketing Science, vol. 30, no. 4, pp. 465-473. DOI: 10.1177/009207002236918 EDN: DTOWYP
86. Roh M., Park K. (2019). Adoption of O2O food delivery services in South Korea: The moderating role of moral obligation in meal preparation. International Journal of Information Management, vol. 47, pp. 262-273. DOI: 10.1016/j.ijinfomgt.2018.09.017
87. Roy Dholakia R., Zhao M. (2010). Effects of online store attributes on customer satisfaction and repurchase intentions, International Journal of Retail and Distribution Management, vol. 38, no. 7, pp. 482-496.
88. Saad A.T. (2021). Factors affecting online food delivery service in Bangladesh: An empirical study. British Food Journal, vol. 123, no. 2, pp. 535-550. DOI: 10.1108/BFJ-05-2020-0449 EDN: ZIXGRN
89. Salloum S.A., Al-Emran M., Shaalan K., Tarhini A. (2018). Factors affecting the E-learning acceptance: A case study from UAE. Education and Information Technologies, vol. 24, pp. 509-530. DOI: 10.1007/s10639-018-9786-3
90. San Martín H., Herrero A. (2012). Influence of the user’s psychological factors on the online purchase ıntention in rural tourism: Integrating ınnovativeness to the UTAUT framework. Tourism Management, vol. 33, no. 2, pp. 341-350. DOI: 10.1016/j.tourman.2011.04.003
91. Saumya S., Singh J.P., Dwivedi Y.K. (2019). Predicting the helpfulness score of online reviews using convolutional neural network. Soft Computing, pp. 1-17.
92. Schepers J., Wetzels M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, vol. 44, issue 1, pp. 90-103. DOI: 10.1016/j.im.2006.10.007
93. Shareef M.A., Dwivedi Y.K., Stamati T., Williams M.D. (2014). SQ mGov: A comprehensive service-quality paradigm for mobile government. Information Systems Management, vol. 31, no. 2, pp. 126-142. DOI: 10.1080/10580530.2014.890432
94. Shaw N., Sergueeva K. (2019). The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value. International Journal of Information Management, vol. 45, pp. 44-55. DOI: 10.1016/j.ijinfomgt.2018.10.024
95. Shugan S.M. (2004). The impact of advancing technology on marketing and academic research. Marketing Science, vol. 23, no. 4, pp. 469-631. DOI: 10.1287/mksc.1040.0096
96. Simonson I., Rosen E. (2014). What marketers misunderstand about online reviews. Harvard Business Review, vol. 92, no. 1, pp. 23-25.
97. Singh S., Sharma M., Dhir S. (2021). Modeling the effects of digital transformation in Indian manufacturing industry. Technology in Society, vol. 67, 101763. DOI: 10.1016/j.techsoc.2021.101763
98. Singh S., Singh G., Dhir S. (2022). Impact of digital marketing on the competitiveness of the restaurant industry. Journal of Food- service Business Research, vol. 27, issue 2, pp. 109-137. DOI: 10.1080/15378020.2022.2077088 EDN: OSOKTD
99. Singh S., Srivastava R.K. (2018). Predicting the intention to use mobile banking in India. International Journal of Bank Marketing, vol. 36, no. 2, pp. 357-378. DOI: 10.1108/IJBM-12-2016-0186
100. Slade E.L., Dwivedi Y.K., Piercy N.C., Williams M.D. (2015). Modeling consumers’ adoption ıntentions of remote mobile payments in The United Kingdom: Extending UTAUT with ınnovativeness, risk, and trust. Psychology and Marketing, vol. 32, no. 8, pp. 860-873. DOI: 10.1002/mar.20823
101. Song H., Ruan W.J., Jeon Y.J.J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, vol. 95. DOI: 10.1016/j.ijhm.2021.102943 EDN: IEUSEN
102. Su D.N., Nguyen N.A.N., Nguyen L.N.T., Luu T.T., Nguyen-Phuoc D.Q. (2022). Modeling consumers’ trust in mobile food delivery apps: Perspectives of technology acceptance model, mobile service quality and personalization-privacy theory. Journal of Hospitality Marketing & Management, vol. 31, no. 5, pp. 535-569. DOI: 10.1080/19368623.2022.2020199 EDN: RLBSUW
103. Sun J., Chi T. (2018). Key factors influencing the adoption of apparel mobile commerce: An empirical study of Chinese consumers. The Journal of the Textile Institute, vol. 109, no. 6, pp. 785-797. DOI: 10.1080/00405000.2017.1371828
104. Tamilmani K., Rana N.P., Prakasam N., Dwivedi Y.K. (2019). The battle of brain vs. heart: A literature review and meta-analysis of “hedonic motivation” use in UTAUT2. International Journal of Information Management, vol. 46, pp. 222-235. DOI: 10.1016/j.ijinfomgt.2019.01.008
105. Tosuntas S.B., Karadag E., Orhan S. (2015). The factors affecting acceptance and use of ınteractive whiteboard within the scope of FATIH project: A structural equation model based on the unified theory of acceptance and use of technology. Computers Education, vol. 81, pp. 169-178. DOI: 10.1016/j.compedu.2014.10.009
106. Tsang P.M., Tse S. (2005). A hedonic model for effective web marketing: An empirical examination. Industrial Management & Data Systems, vol. 105, no. 8, pp. 1039-1052. DOI: 10.1108/02635570510624437
107. Van Raaij E.M., Schepers J.J.L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, vol. 50, no. 3, pp. 838-852. DOI: 10.1016/j.compedu.2006.09.001
108. Venkatesh V., Morris M.G., Davis G.B., Davis F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, vol. 27, no. 3, pp. 425-478. EDN: EABIEZ
109. Venkatesh V., Thong J.Y.L., Xu X. (2012). Consumer acceptance and use of ınformation technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, vol. 36, no. 1, pp. 157-178. DOI: 10.2307/41410412
110. Verhoef P.C., Broekhuizen T., Bart Y., Bhattacharya A., Dong J.Q., Fabian N., Haenlein M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, vol. 122, pp. 889-901. DOI: 10.1016/j.jbusres.2019.09.022 EDN: BUXXSM
111. Verkijika S.F. (2018). Factors influencing the adoption of mobile commerce applications in Cameroon. Telematics and Informatics, vol. 35, no. 6, pp. 1665-1674. DOI: 10.1016/j.tele.2018.04.012
112. Vinaik A., Goel R., Sahai S., Garg V. (2019). The study of interest of consumers in mobile food ordering apps. International Journal of Recent Technology and Engineering, vol. 8, no. 1, pp. 3424-3429.
113. Vinerean S., Budac C., Baltador L.A., Dabija D.-C. (2022). Assessing the effects of the COVID-19 pandemic on m-commerce adoption: An adapted UTAUT2 approach. Electronics, vol. 11, no. 8, 1269. DOI: 10.3390/electronics11081269
114. Wang Y.S., Tseng T.H., Wang W.T., Shih Y.W., Chan P.Y. (2019). Developing and validating a mobile catering app success model. International Journal of Hospitality Management, vol. 7, pp. 19-30.
115. Wang Y.-S., Wu M.-C., Wang H.-Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, vol. 40, no. 1, pp. 92-118. DOI: 10.1111/j.1467-8535.2007.00809.x
116. Wei P.S., Lu H.P. (2013). An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior. Computers in Human Behavior, vol. 29, no. 1, pp. 193-201. DOI: 10.1016/j.chb.2012.08.005
117. Yeo V.C.S., Goh S.K., Rezaei S. (2017). Consumer experiences, attitude and behavioral ıntention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services, vol. 35, pp. 150-162. DOI: 10.1016/j.jretconser.2016.12.013
118. Yıldız Durak H. (2018). Examining the acceptance and use of online social networks by preservice teachers within the context of unified theory of acceptance and use of technology model. Journal of Computing in Higher Education, vol. 31, no. 1, pp. 173-209. DOI: 10.1007/s12528-018-9200-6
119. Yılmaz M.B., Kavanoz S. (2017). The validity and reliability of Turkish version of unified theory of acceptance and use of Technology-2. Electronic Turkish Studies, vol. 12, no. 32, pp. 127-146. DOI: 10.7827/TurkishStudies.12064
120. Zanetta L.D., Hakim M.P., Gastaldi G.B., Seabra L.M.J., Rolim P.M., Nascimento L.G.P., Medeiros C.O., Cunha D.T. (2021). The use of food delivery apps during the COVID-19 pandemic in Brazil: The role of solidarity, perceived risk, and regional aspects. Food Research International, vol. 149, 110671. DOI: 10.1016/j.foodres.2021.110671 EDN: FGMPZN
121. Zhou T., Lu Y., Wang B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, vol. 26, no. 4, pp. 760-767. DOI: 10.1016/j.chb.2010.01.013
122. Zhou T., Lu Y. (2011). Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Computers in Human Behavior, vol. 27, no. 2, pp. 883-889. DOI: 10.1016/j.chb.2010.11.013
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Проблематика «зеленого» маркетинга и его влияния на потребительское поведение, а также вопросы эволюции собственных торговых марок (СТМ) розничных сетей достаточно хорошо освещены, но до сих пор нет комплексных исследований, посвященных взаимосвязи этих трех аспектов. Статья направлена на изучение факторов, влияющих на решение потребителей о приобретении «зеленых» СТМ, и определение характеристик товаров, которые российские потребители воспринимают как «зеленые», то есть безопасные для самого потребителя и окружающей среды на всех стадиях производственного, реализационного и утилизационного циклов. Методология работы опирается на теории маркетинга отношений, потребительского выбора, восприятия ценности (value perception theory) и концепцию устойчивого развития. Информационной базой для эмпирического исследования послужили глубинные экспертные интервью с представителями розничных сетей и результаты анкетирования 373 респондентов. Полученные данные обработаны с помощью методов корреляционного и регрессионного анализа, контент-анализа. Результаты исследования показали, что цена СТМ не является единственным фактором потребительского выбора: на него влияют как социально-демографические характеристики респондентов, так и различные дополнительные свойства СТМ, в числе которых – набор «зеленых» характеристик, информативность упаковки, социальная ориентация СТМ и др. Полученные выводы могут быть востребованы в планировании маркетинговых кампаний по продвижению собственных торговых марок сетей
На российском рынке маргариновой продукции наблюдаются изменения моделей потребительского поведения, отрасль столкнулась с падением продаж ряда брендов. Исследование направлено на выявление новых трендов на рынке масложировой продукции и проверку гипотез о сокращении потребления маргарина конечными потребителями по причине популярности здорового образа жизни, быстро растущего спроса на готовую еду и выпечку, высокой чувствительности покупателей к цене. Методологическую основу исследования составили теории бренд-менеджмента и маркетинга. Использованы методы кабинетного и полевого маркетинговых исследований, опрос, статистические методы анализа. Расчеты проведены с помощью статистического программного обеспечения SPSS и JASP. Для оценки брендменеджмента компаний применялись индекс лояльности бренду, показатель знания брендов в Яндекс Wordstat. Информационную базу работы составили панельные данные Nielsen по ритейл-аудиту на рынках товаров повседневного спроса за 2021–2023 гг., аналитической платформы QlickView, отчет ООО «Профи Исследования» по итогам полевого исследования брендов маргарина в ноябре 2023 г., материалы опроса, проведенного авторами в ноябре – декабре 2023 г. Получен вывод о сокращении доли маргарина в общем объеме производства и продаж твердых жиров. Подтвердились гипотезы об уменьшении покупок маргаринов из-за стремления к здоровому образу жизни и по рекомендации врача. Выявлено, что цена является ключевым фактором при выборе маргариновой продукции.
С учетом адаптивности покупателей к устоявшимся маркетинговым стимулам возникает необходимость совершенствования способов продвижения текстильной продукции. Применение нейротехнологий позволяет определить потенциальные «якоря» в виде этнических орнаментов для сувенирной продукции. Исследование направлено на выявление нейромаркетинговых метрик потребительского восприятия этнических орнаментов для сувенирной одежды на основе искусственного интеллекта. Методологической базой работы послужили положения сенсорного маркетинга. Использовались общие маркетинговые и специальные нейромаркетинговые методы исследования. Информационную базу составил массив данных, полученных в рамках электроэнцефалографических экспериментов и фокус-группы, в которых участвовало 12 чел., и выборочного предварительного опроса, охватывающего 90 чел. Исследования были проведены в период с января по февраль 2024 г. Разработаны подходы к нейромаркетинговой потребительской оценке этнических орнаментов для сувенирной одежды, основанные на биометрических данных. Выявлены реакции потребителей на разные виды этнических орнаментов одежды. На базе электроэнцефалографического обследования и вербального опроса экспериментальной группы были выбраны наиболее сильные эмоциональные реакции на отдельные виды орнаментов. Полученные результаты были обработаны с помощью искусственного интеллекта для создания современного дизайна сувенирной продукции, в котором использованы этнические национальные узоры. Результаты исследования вносят вклад в понимание влияния ароматической нейростимуляции на восприятие элементов одежды, а также возможностей искусственного интеллекта в формировании обоснованных маркетинговых решений
The introduction of new technologies provides consumers with additional shopping opportunities and allow them to test new products. The study was conducted to determine the effects of brand experience on customer inspiration and behavioral intention and to establish the mediating role of openness to experience. The theoretical framework of the study resides in the theory of marketing and cognitive appraisal theory. Convenience sampling method and structural equation modelling were used within the study. The population of the study consists of customers who order from the online takeaway portal yemeksepeti. com. The data were gathered using questionnaires collected online in June, 2023. A total of 416 valid responses were received. Smart PLS 4 statistical program was used to test the hypotheses. As a result of the tests, it was found that brand experience has a positive effect on customer inspiration and openness to experience. It was also determined that openness to experience has a positive effect on customer inspiration by partially mediating the positive effect between brand experience and inspiration. Furthermore, the hypothesis that customer inspiration positively affects behavioral intention was also confirmed. The research will contribute to expanding the literature on consumer inspiration and openness to experience. Among possible directions for further research is to test the proposed model in relating industries or using statistical data from other countries
Тенденция старения населения обусловливает необходимость анализа поведения так называемых серебряных потребителей. Одной из ключевых особенностей этого поведения является низкая готовность к совершению онлайн-покупок. Статья посвящена оценке роли индивидуальных характеристик, воспринимаемых выгод и рисков в онлайн-покупках лиц старшего возраста. Методологическую базу исследования составила концепция воспринимаемых выгод и рисков. В качестве методов использовались качественные интервью, количественный опрос, факторный анализ и моделирование структурными уравнениями. Информационной базой исследования послужили материалы 10 интервью и 244 анкет потребителей в возрасте 50 лет и старше. По результатам анализа впервые выявлены значимые предикторы интернет-покупок российских потребителей старшей возрастной группы. Так, риск оценки, производственный и финансовый риски снижают вероятность покупок онлайн, а разнообразие ассортимента повышает намерение совершать такие покупки в будущем. При этом наличие опыта покупок в интернете снижает восприятие рисков и повышает восприятие выгод. Значимую роль играют и навыки пользования интернетом: чем они лучше, тем выше вероятность онлайн-покупок. Также выявлено положительное влияние разницы биологического и когнитивного возраста: чем моложе ощущает себя индивид, тем выше вероятность совершения покупок в интернете. Полученные результаты позволяют онлайн-ритейлерам скорректировать практики работы с потребителями старшего поколения.
Переход рынка торговых центров в стадию зрелости трансформирует поведение покупателей и требует пересмотра подходов к их сегментации. Статья посвящена объяснению покупательского поведения россиян при совершении покупок в торговых центрах, их отношения к ценности шопинга и атрибутам торгового центра с учетом поколенческой и гендерной разницы для обоснования подхода к сегментации покупателей на стадии зрелости рынка. Методология исследования строится на теориях поколений и потребительской ценности. В качестве методов обработки данных использовались частотный, факторный, регрессионный, дисперсионный и кластерный анализ. Информационная база включает данные онлайн-опроса 531 респондента, проведенного в декабре 2023 г. – январе 2024 г. Результаты исследования показывают, что принадлежность покупателей к поколенческой когорте оказывает статистически значимое влияние на их поведение и отношение к атрибутам торгового центра. Гендерная разница лишь частично объясняет поведение потребителей при покупках. Поколенческие и гендерные различия выявлены в отношении гедонистической ценности шопинга: статистически значима реакция молодых покупателей и покупателей-женщин. Уровень дохода не определяет разницу в поведении потребителей. Кластерный анализ доказал, что основой сегментации покупателей являются психографические переменные – отношение к ценности шопинга и важность атрибутов торгового центра. Демографические факторы сегментации демонстрируют общую вторичность в условиях перехода рынка торговых центров в стадию зрелости. На первый план выходят психографические факторы: утилитарная и гедонистическая ценности шопинга, важность гигиенических и опытных атрибутов торговых центров
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