This study examines the impact of mobile shopping adoption drivers on the attitude towards mobile shopping apps and mobile shopping loyalty. Furthermore, the mediating role of attitude as well as the moderated mediation role of national culture (i.e., uncertainty avoidance and collectivism) will be examined. Adopting the stimulus–organism–response model in a mobile shopping context, a survey of 250 participants was conducted. AMOS and Hayes’s PROCESS macro were used to test the mediation and the moderated mediation. Findings show that adoption’s drivers have a positive relationship with the attitude towards mobile shopping and loyalty. It also emphasizes the mediating effect of attitude and moderated mediation effects of culture. Originality of this study stems from that it is one of the very few that attempts to examine the attitude towards mobile shopping as a mediator in the relationships between mobile shopping adoption drivers and mobile shopping loyalty. It also highlights the moderated mediation effects of both uncertainty avoidance and collectivism in these relationships, which were also largely ignored in previous research.
AbbasH., & HamdyH. (2015). Determinants of continuance intention factor in Kuwait communication market: Case study of Zain-Kuwait. Computers in Human Behavior, 49, 648–657.
2.
AbunD., MagallanesT., ForondaS., & EncarnacionM. (2020). Students’ attitude toward corruption and their behavioural intention to corrupt or not to corrupt in the future: The Philippines’ context. Journal of the Social Sciences, 77–98.
3.
AjzenI. (1985). From intentions to actions: A theory of planned behavior. Action control (pp. 11–39). Springer.
4.
AlalwanA., AlgharabatR., BaabdullahA., RanaN., QasemZ., & DwivediY. (2020). Examining the impact of mobile interactivity on customer engagement in the context of mobile shopping. Journal of Enterprise Information Management, 33(3), 627–653. https://doi.org/10.1108/JEIM-07-2019-0194
5.
AlhaimerR. (2022). Fluctuating attitudes and behaviors of customers toward online shopping in times of emergency: The case of Kuwait during the COVID-19 pandemic. Journal of Internet Commerce, 21(1), 26–50. https://doi.org/10.1080/15332861.2021.1882758
6.
ArfiW. B., et al. (2020). Understanding acceptance of eHealthcare by IoT natives and IoT immigrants: An integrated model of UTAUT, perceived risk, and financial cost. Technological Forecasting and Social Change, 163(1), 120437.
7.
ArfiW. ben, NasrI. ben, KhvatovaT., & ZaiedY. ben. (2021). Technological forecasting and social change understanding acceptance of eHealthcare by IoT natives and IoT immigrants: An integrated model of UTAUT, perceived risk, and financial cost. Technological Forecasting and Social Change, May, 120437. https://doi.org/10.1016/j.techfore.2020.120437
8.
BaishyaK., & SamaliaH. (2019). Extending unified theory of acceptance and use of technology with perceived monetary value for smartphone adoption at the bottom of the pyramid. International Journal of Information Management, 51, 102036.
9.
BestavrosA. (2000). Banking industry walks ‘tightrope’ in personalization of web services. Bank Systems and Technology, 37(1), 54–56.
10.
BoatengH., KosibaJ., AdamD., OforiK., & OkoeA. (2020). Examining brand loyalty from an attachment theory perspective. Marketing Intelligence and Planning, 38(4), 479–494.
BonninG. (2020). The roles of perceived risk, attractiveness of the online store and familiarity with AR in the influence of AR on patronage intention. Journal of Retailing and Consumer Services, 52, 101938.
13.
BylokF. (2022). Examining the impact of trust on the e-commerce purchase intentions of young consumers in Poland. Journal of Internet Commerce, 21(3), 364-391.
14.
CaoQ., & NiuX. (2019). Integrating context-awareness and UTAUT to explain Alipay user adoption. International Journal of Industrial Ergonomics, 69, 9–13. https://doi.org/10.1016/j.ergon.2018.09.004
15.
ChauhanS., KumarP., & JaiswalM. (2022). A meta-analysis of M-commerce continuance intention: moderating impact of culture and user types. Behaviour & Information Technology, 41(13), 2905-2923.
16.
ChenY., HsuT., & LuY. (2018). Impact of flow on mobile shopping intention. Journal of Retailing and Consumer Services, 41, 281–287.
17.
ChineduA., HaronS., HashimA., & HoJ. (2020). Consumers’ attitudes towards locally manufactured high involvement products: A systematic review. Asian Social Science, 16(12).
18.
ChopdarP. K., KorfiatisN., SivakumarV. J., & LytrasM. D. (2018). Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the unified theory of acceptance and use of technology. Computers in Human Behavior, 86, 109–128. https://doi.org/10.1016/j.chb.2018.04.017
19.
ChopdarP., & SivakumarV. (2019). Understanding continuance usage of mobile shopping applications in India: The role of espoused cultural values and perceived risk. Behaviour and Information Technology, 38(1), 42–64.
20.
CrabbeM., StandingC., StandingS., & KarjaluotoH. (2009). An adoption model for mobile banking in Ghana. International Journal of Mobile Communications, 7(5), 515–543. https://doi.org/10.1504/IJMC.2009.024391
21.
de KervilerG., DemoulinN. T. M., & ZiddaP. (2016). Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers?Journal of Retailing and Consumer Services, 31, 334–344. https://doi.org/10.1016/j.jretconser.2016.04.011
22.
FaqihK., & JaradatM. (2015). Assessing the moderating effect of gender differences and individualism-collectivism at individual-level on the adoption of mobile commerce technology: TAM3 perspective. Journal of Retailing and Consumer Services, 22, 37–52.
23.
FeathermanM. S., & PavlouP. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474.
24.
FishbeinM., & AjzenI. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley.
25.
FornellC., & LarckerD. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
26.
FrankB., EnkawaT., & SchvaneveldtS. J. (2015). The role of individualism vs. collectivism in the formation of repurchase intent: A cross-industry comparison of the effects of cultural and personal values. Journal of Economic Psychology, 51, 261–278.
27.
FranqueF. B., OliveiraT., TamC., & SantiniF. de O. (2021). A meta-analysis of the quantitative studies in continuance intention to use an information system. Internet Research, 31(1), 123–158. https://doi.org/10.1108/INTR-03-2019-0103
28.
GaoT., RohmA., SultanF., & PaganiM. (2013). Consumers un-tethered: A three-market empirical study of consumers’ mobile marketing acceptance. Journal of Business Research, 66(12), 2536–2544.
29.
GarrouchK., & TimoulaliE. (2020). Mobile shopping intentions: Do trustworthiness and culture Matter?The Journal of Distribution Science, 18(11), 69–77.
30.
GoelP., ParayitamS., SharmaA., RanaN. P., & DwivediY. K. (2022). A moderated mediation model for e-impulse buying tendency, customer satisfaction and intention to continue e-shopping. Journal of Business Research, 142, 1–16. https://doi.org/10.1016/j.jbusres.2021.12.041
31.
GrewalD., RoggeveenA., & NordfältJ. (2017). The future of retailing. Journal of Retailing, 93(1), 1–6.
32.
GroßM. (2018). Mobile shopping loyalty: The salient moderating role of normative and functional compatibility beliefs. Technology in Society, 55, 146–159.
33.
GursoyD., ChiO., LuL., & NunkooR. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169.
34.
HasanR., ShamsR., & RahmanM. (2020). Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri. Journal of Business Research, 131, 591–597.
35.
HayesA. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications.
36.
Hernandez-OrtegaB., Aldas-ManzanoJ., Ruiz-MafeC., & Sanz-BlasS. (2017). Perceived value of advanced mobile messaging services. Information Technology and People, 30(2), 324–355. https://doi.org/10.1108/ITP-01-2014-0017
37.
HofstedeG. (1980). Culture’s consequences: International differences in work-related values. Sage Publications.
38.
HomburgC., & GieringA. (2001). Personal characteristics as moderators of the relationship between customer satisfaction and loyalty—An empirical analysis. Psychology and Marketing, 18(1), 43–66.
39.
HubertM., BlutM., BrockC., BackhausC., & EberhardtT. (2017). Acceptance of smartphone-based mobile shopping: Mobile benefits, customer characteristics, perceived risks, and the impact of application context. Psychology and Marketing, 34(2), 175–194.
40.
HwangS., & KimS. (2018). Does MIM experience affect satisfaction with and loyalty toward O2O services?Computers Human Behaviours, 82, 70–80.
41.
ImI., HongS., & KangM. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information and Management, 48(1), 1–8. https://doi.org/10.1016/j.im.2010.09.001
42.
IzogoE., & MpinganjiraM. (2020). Behavioral consequences of customer inspiration: The role of social media inspirational content and cultural orientation. Journal of Research in Interactive Marketing, 14(4), 431–459.
43.
JaM., ChungN., LeeC., & PreisM. (2016). Dual-route of persuasive communications in mobile tourism shopping. Telematics and Informatics, 33(2), 293–308.
44.
JacobyJ., & KaplanL. B. (1972). The components of perceived risk. Proceedings of the 3rd Annual Conference of the Association for Consumer Research.
45.
JadilY., RanaN., & DwivediY. (2021). A meta-analysis of the UTAUT model in the mobile banking literature: The moderating role of sample size and culture. Journal of Business Research, 132, 354–372.
46.
JamieC., RahmanS., RahmanM., WyllieJ., & VoolaR. (2021). Engaging Gen Y customers in online brand communities: A cross-national assessment. International Journal of Information Management, 56, 102252.
47.
JanssensW., WijnenK., De PelsmackerP., & Van KenhoveP. (2008). Marketing research with SPSS. Pearson.
48.
JoséM. C. V. (2018). Usage intensity of mobile medical apps: A tale of two methods. Journal of Business Research, 89, 442–447. https://doi.org/10.1016/j.jbusres.2017.12.026
49.
KambojS., SarmahB., GuptaS., & DwivediY. (2018). Examining branding co-creation in brand communities on social media: Applying the paradigm of Stimulus–Organism–Response. International Journal of Information Management, 39, 169–185. https://doi.org/10.1016/j.ijinfomgt.2017.12.001
50.
KasilingamD. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280.
51.
KhalilzadehJ., BulentA., & BilgihanA. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474. https://doi.org/10.1016/j.chb.2017.01.001
52.
KhanI. U., HameedZ., KhanS. N., KhanS. U., & KhanM. T. (2021). Exploring the effects of culture on acceptance of online banking: A comparative study of Pakistan and Turkey by using the extended UTAUT model. Journal of Internet Commerce, 0(0), 1–34. https://doi.org/10.1080/15332861.2021.1882749
53.
KimM. J., LeeC. K., & JungT. (2020). Exploring consumer behavior in virtual reality tourism using an extended stimulus–organism–response model. Journal of Travel Research, 59(1), 69–89. https://doi.org/10.1177/0047287518818915
54.
KimY., WangQ., & RohT. (2020). Do information and service quality affect perceived privacy protection, satisfaction, and loyalty? Evidence from a Chinese O2O-based mobile shopping application. Telematics and Informatics, 101483.
55.
KumarV., & PansariA. (2016). National culture, economy, and customer lifetime value: Assessing the relative impact of the drivers of customer lifetime value for a global retailer. Journal of International Marketing, 24(1).
56.
KumarS., & SadaranganiP. (2021). An empirical study on shopping motivation among Generation Y Indian. Global Business Review, 22(2), 500–516. https://doi.org/10.1177/0972150918807085
57.
Lancelot MiltgenC., PopovičA., & OliveiraT. (2013). Determinants of end-user acceptance of biometrics: Integrating the ‘big 3’ of technology acceptance with privacy context. Decision Support Systems, 56(1), 103–114. https://doi.org/10.1016/j.dss.2013.05.010
58.
LeeM. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141.
59.
LianJ. W. (2021). Determinants and consequences of service experience toward small retailer platform business model: Stimulus–organism–response perspective. Journal of Retailing and Consumer Services, 62(C). https://doi.org/10.1016/j.jretconser.2021.102631
60.
LuJ., YuC., LiuC., & WeiJ. (2017). Comparison of mobile shopping continuance intention between China and USA from an espoused cultural perspective. Computers in Human Behavior, 75, 130–146.
61.
MacedoI. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior, 75, 935–948. https://doi.org/10.1016/j.chb.2017.06.013
62.
MaddenT., EllenP., & AjzenI. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin, 18(1), 3–9.
63.
MartinsC., OliveiraT., & PopovičA. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13.
64.
MehrabianA., & RussellJ. A. (1974). An approach to environmental psychology. The MIT Press.
65.
McleanG., Osei-FrimpongbK., KhalidAl-Nabhani, & HannahMarriott (2020). Examining consumer attitudes towards retailers’ m-commerce mobile applications – An initial adoption vs. continuous use perspective. Journal of Business Research, 106, 139–157. https://doi.org/10.1016/j.jbusres.2019.08.032
66.
Ministry of Communications and Information Technology. (2020). ICT Indicators Bulletin. https://mcit.gov.eg/Upcont/Documents/Publications_16112020000_ICT_Indicators_Quarterly_Bulletin_Q2_2020.pdf
67.
MohammadiS., & DicksonG. (2021). Online shopping for sporting goods: The role of flow, E-satisfaction, and E-loyalty. Global Business Review. https://doi.org/10.1177/09721509211019516
68.
Molina-CastilloF., Lopez-NicolasC., & de ReuverM. (2020). Mobile payment: The hiding impact of learning costs on user intentions. Journal of Theoretical and Applied Electronic Commerce Research, 15(1).
69.
MortimerG., Fazal e HasanS., AndrewsL., & MartinJ. (2016). Online grocery shopping: The impact of shopping frequency on perceived risk. The International Review of Retail, Distribution and Consumer Research, 26(2), 202–223.
70.
NurcholisL., & FerdiantoR. (2021). How to expand repurchase intention? The intervening impact of attitude towards mobile shopping. Binus Business Review, 12(1), 11–19.
71.
OoiK., & TanG. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33–46.
72.
OzturkA. B., BilgihanA., NusairK., & OkumusF. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2016.04.005
73.
PandeyS., & ChawlaD. (2019). Engaging m-commerce adopters in India: Exploring the two ends of the adoption continuum across four m-commerce categories. Journal of Enterprise Information Management, 32(1), 191–210.
74.
PantanoE., & PriporasC. (2016). The effect of mobile retailing on consumers’ purchasing experiences: A dynamic perspective. Computers in Human Behavior, 61, 548–555.
75.
ParayitamS., KakumaniL., & MuddangalaN. B. (2020). Perceived risk as a moderator in the relationship between perception of celebrity endorsement and buying behavior: Evidence from rural consumers of India. Journal of Marketing Theory and Practice, 28(4), 521–540. https://doi.org/10.1080/10696679.2020.1795687
76.
PaulssenM. (2009). Attachment styles in business-to-business. Psychology and Marketing, 26, 507–533.
77.
PayneE., PeltierJ., & BargerV. (2021). Enhancing the value co-creation process: artificial intelligence and mobile banking service platforms. Journal of Research in Interactive Marketing, 15(1), 68–85.
78.
PedelientoG., AndreiniD., BergamaschiM., & SaloJ. (2015). Brand and product attachment in an industrial context: The effects on brand loyalty. Industrial Marketing Management, 53, 194–206.
79.
PynooB., & Van BraakJ. (2014). Predicting teachers’ generative and receptive use of an educational portal by intention, attitude and self-reported use. Computers in Human Behavior, 34, 315–322. https://doi.org/10.1016/j.chb.2013.12.024
80.
RazaS. A., ShahN., & AliM. (2019). Acceptance of mobile banking in Islamic banks: Evidence from modified UTAUT model. Journal of Islamic Marketing, 10(1), 357–376. https://doi.org/10.1108/JIMA-04-2017-0038
81.
RiquelmeH. E., & RiosR. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328–341.
82.
Rodríguez-TorricoP., ProdanovaJ., San-MartínS., & JimenezN. (2019). The ideal companion: The role of mobile phone attachment in travel purchase intention. Current Issues in Tourism, 23(13), 1–14.
83.
SaleemM., KamarudinS., ShoaibH. M., & NasarA. (2022). Retail consumers’ behavioral intention to use augmented reality mobile apps in Pakistan. Journal of Internet Commerce, 21(4), 497–525.
84.
SaprikisV., AvlogiarisG., & KatarachiaA. (2020). Determinants of the intention to adopt mobile augmented reality apps in shopping malls among university students. Journal of Theoretical and Applied Electronic Commerce Research, 16(3), 491–512.
85.
ScarpiD., PizziG., & VisentinM. (2014). Shopping for fun or shopping to buy: Is it different online and offline?Journal of Retailing and Consumer Services, 21, 258–267. https://doi.org/10.1016/j.jretconser.2014.02.007
86.
ShahidS., IslamJ. U., MalikS., & HasanU. (2022). Examining consumer experience in using m-banking apps: A study of its antecedents and outcomes. Journal of Retailing and Consumer Services, 65, 102870. https://doi.org/10.1016/j.jretconser.2021.102870
87.
ShankarA., & DattaB. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19(3_suppl), S72–S89. https://doi.org/10.1177/0972150918757870
88.
SharmaS., SinghG., GaurL., & AfaqA. (2022). Exploring customer adoption of autonomous shopping systems. Telematics and Informatics, 73, 101861. https://doi.org/10.1016/J.TELE.2022.101861
89.
SreenN., PurbeyS., & SadaranganiP. (2018). Impact of culture, behavior and gender on green purchase intention. Journal of Retailing and Consumer Services, 41, 177–189.
90.
SriteM., & KarahannaE. (2006). The role of espoused national cultural values in technology acceptance. MIS Quarterly, 679–704.
91.
Statista. (2020). E-commerce worldwide—statistics and facts. https://www.statista.com/topics/871/online-shopping/
92.
Statista. (2022). Mobile retail commerce sales as percentage of retail e-commerce sales worldwide from 2016 to 2021. https://www.statista.com/statistics/806336/mobile-retail-commerce-share-worldwide/
StoneR. N., & GrønhaugK. (1993). Perceived risk: further considerations for the marketing discipline. European Journal of Marketing, 27(3), 39–50.
95.
StrömR., VendelM., & BredicanJ. (2014). Mobile marketing: A literature review on its value for consumers and retailers. Journal of Retailing and Consumer Services, (2003), 1–12.
96.
SudhirK., PriesterJ., ShumM., AtkinD., FosterA., IyerG., & QianY. (2015). Research opportunities in emerging markets: An inter-disciplinary perspective from marketing, economics, and psychology. Customer Needs and Solutions, 2(4), 264–276.
97.
Sum ChauV., & NgaiL. W. (2010). The youth market for internet banking services: Perceptions, attitude and behaviour. Journal of Services Marketing, 24(1), 42–60.
98.
SunS., LawR., & SchuckertM. (2020). Mediating effects of attitude, subjective norms and perceived behavioural control for mobile payment-based hotel reservations. International Journal of Hospitality Management, 84, 102331.
99.
TanG. W. H., OoiK. B., ChongS. C., & HewT. S. (2014). NFC mobile credit card: The next frontier of mobile payment?Telematics and Informatics, 31(2), 292–307. https://doi.org/10.1016/j.tele.2013.06.002
100.
TeoT. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers and Education, 52(2), 302–312. https://doi.org/10.1016/j.compedu.2008.08.006
101.
ThakurR. (2018). The role of self-efficacy and customer satisfaction in driving loyalty to the mobile shopping application. International Journal of Retail and Distribution Management, 46(3), 283–303. https://doi.org/10.1108/IJRDM-11-2016-0214
102.
The World Bank. (2021). Populations, total-Egypt, Arab Rep. https://data.worldbank.org/indicator/SP.POP.TOTL?locations=EG
103.
ThomasT., SinghL., & GaffarK. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development Using ICT, 9(3).
104.
ToufailyE., RicardL., & PerrienJ. (2013). Customer loyalty to a commercial website: Descriptive meta-analysis of the empirical literature and proposal of an integrative model. Journal of Business Research, 66(9), 1436–1447.
105.
TranT., FurnerC., & AlbinssonP. (2020). Understanding drivers and outcomes of brand attachment in mobile branded apps. Journal of Consumer Marketing, 38(1), 113–124.
106.
UpadhyayN., UpadhyayS., AbedS. S., & DwivediY. K. (2022). Consumer adoption of mobile payment services during COVID-19: extending meta-UTAUT with perceived severity and self-efficacy. International Journal of Bank Marketing, 40(5), 960–991. https://doi.org/10.1108/IJBM-06-2021-0262
107.
VenkateshV., MorrisM., DavisG., & DavisF. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly(27:3), 425–478.
108.
VenkateshV., ThongJ., & XuX. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
109.
WangC. (2021). New frontiers and future directions in interactive marketing: Inaugural editorial. Journal of Research in Interactive Marketing, 15(1).
110.
YadavR., & MaharaT. (2019). An empirical study of consumers intention to purchase wooden handicraft items online: Using extended technology acceptance model. Global Business Review, 20(2), 479–497. https://doi.org/10.1177/0972150917713899
111.
YangK. (2010). Determinants of US consumer mobile shopping services adoption: Implications for designing mobile shopping services. Journal of Consumer Marketing, 27(3), 262–270.
112.
YangK. (2012). Consumer technology traits in determining mobile shopping adoption: An application of the extended theory of planned behavior. Journal of Retailing and Consumer Services, 19(5), 484–491. https://doi.org/10.1016/j.jretconser.2012.06.003
113.
YangK., & KimH. (2012). Mobile shopping motivation: An application of multiple discriminant analysis. International Journal of Retail and Distribution Management, 40(10), 778–789.
114.
YunZ., & GoodL. (2007). Developing customer loyalty from e-tail store image attributes. Managing Service Quality: An International Journal, 17(1), 4–22.
115.
ZakyA., SaxenaG., & JayawardhenaC. (2021). Conceptualising the role of marketing strategies of tourism providers in inducing risk-taking behaviour. International Journal of Tourism Research, 23(5), 942–957.
116.
ZeithamlV. A., BerryL. L., & ParasuramanA. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46.
117.
ZhangL., ZhuJ., & LiuQ. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902–1911.
118.
ZhouS., LiT., YangS., & ChenY. (2022). What drives consumers’ purchase intention of online paid knowledge? A stimulus–organism–response perspective. Electronic Commerce Research and Applications, 52, 101126. https://doi.org/10.1016/j.elerap.2022.101126
119.
ZhuB., KowatthanakulS., & SatanasavapakP. (2020). Generation Y consumer online repurchase intention in Bangkok: Based on Stimulus–Organism–Response (SOR) model. International Journal of Retail and Distribution Management, 48(1), 53–69. https://doi.org/10.1108/IJRDM-04-2018-0071
120.
ZhuD. H., & ChangY. P. (2014). Investigating consumer attitude and intention toward free trials of technology-based services. Computers in Human Behavior, 30, 328–334.
