Journal of Retailing and Consumer Services, Journal Year: 2024, Volume and Issue: 79, P. 103847 - 103847
Published: April 9, 2024
Language: Английский
Journal of Retailing and Consumer Services, Journal Year: 2024, Volume and Issue: 79, P. 103847 - 103847
Published: April 9, 2024
Language: Английский
Journal of Retailing and Consumer Services, Journal Year: 2022, Volume and Issue: 71, P. 103209 - 103209
Published: Nov. 25, 2022
Language: Английский
Citations
116Journal of Retailing and Consumer Services, Journal Year: 2023, Volume and Issue: 75, P. 103440 - 103440
Published: June 16, 2023
Artificial Intelligence (AI)-powered conversational agents have become ubiquitous tools in the digital transformation of conventional customer-company interactions. Despite widespread implementation agents, there is still a limited understanding how customers use and resist these technologies for shopping. To address this gap, study investigates factors that influence usage resistance AI-based shopping using extended behavioral reasoning theory (BRT) partial least squares-based structural equation modeling (PLS-SEM). test proposed framework, conducted two empirical studies South Korea. Study 1 focused on text-based chatbots with sample 232 participants, while 2 examined voice-based 234 participants. The results both mainly supported hypotheses driven by BRT. Theoretically, contributes offering comprehensive customer motivation, attitudes, intentions toward AI-powered Managerially, provides important insights retail managers developers By drive resistance, managers, can better design deploy innovative to enhance experience improve business outcomes.
Language: Английский
Citations
89Tourism Management, Journal Year: 2023, Volume and Issue: 100, P. 104835 - 104835
Published: Sept. 11, 2023
Language: Английский
Citations
79Journal of Retailing and Consumer Services, Journal Year: 2023, Volume and Issue: 76, P. 103562 - 103562
Published: Sept. 22, 2023
Language: Английский
Citations
74Journal of Retailing and Consumer Services, Journal Year: 2023, Volume and Issue: 75, P. 103494 - 103494
Published: July 13, 2023
This research examines how individuals respond differently to recommendation options generated by ChatGPT, an AI-powered language model, in five studies. In contrast previous on choice overload, Studies 1 and 2 demonstrate that people tend positively a large number of (60 options), revealing diverse consumer perceptions AI-generated recommendations. 3 4 further illustrate the moderating effect agents indicate overload elicits distinct patterns reactions depending whether recommendations are from human or AI agent. Lastly, Study 5 directly measures preferences for agents, general preference particularly when available. These findings have significant implications system design user regarding
Language: Английский
Citations
72Journal of Retailing and Consumer Services, Journal Year: 2024, Volume and Issue: 78, P. 103758 - 103758
Published: Feb. 9, 2024
Language: Английский
Citations
62International Journal of Information Management, Journal Year: 2023, Volume and Issue: 76, P. 102679 - 102679
Published: July 11, 2023
Leveraging the computers are social actors theory, in this study, we explore traits of artificial intelligence-based chatbots that make them perceived as trustworthy, drive consumers to forgive firm for service failure, and reduce their propensity spread negative word-of-mouth against firm. Across two scenario-based studies with UK consumers: one a utilitarian product category (n = 586) another hedonic 508), qualitative our findings suggest safety enhances consumers' ability empathy, anthropomorphism benevolence integrity chatbots, i.e., three affect components trustworthiness differently. Further, these have positive influence on customer forgiveness word-of-mouth.
Language: Английский
Citations
45Journal of Retailing and Consumer Services, Journal Year: 2023, Volume and Issue: 77, P. 103663 - 103663
Published: Dec. 8, 2023
Shifting towards a more data-driven culture is key antecedent of business success in today's digital era. Previous research has paid attention to exploring the influence marketing analytics on performance, and rarely examined how use influences customer agility satisfaction. According dynamic capabilities view using previous studies, we developed conceptual framework explore effect In Study 1, utilised cross-sectional data collected from 468 managers various industries. study 2, employed longitudinal three-wave utilising cross-lagged panel model. 1 indicated that acquisition tool are drivers adopting analytics. Marketing stronger when market turbulence high. They also revealed satisfaction such conditions stronger. 2 at time point T1 significant positive T2, while T2 T3. These findings indicate strong temporal effects between use, agility, The suggest researchers should look beyond direct shift their can be leveraged enable support
Language: Английский
Citations
45Journal of Retailing and Consumer Services, Journal Year: 2023, Volume and Issue: 74, P. 103417 - 103417
Published: May 24, 2023
Language: Английский
Citations
42Journal of Retailing and Consumer Services, Journal Year: 2024, Volume and Issue: 77, P. 103700 - 103700
Published: Jan. 4, 2024
Language: Английский
Citations
37