AI Meets the Shopper: Psychosocial Factors in Ease of Use and Their Effect on E-Commerce Purchase Intention

Overview

Journal Behav Sci (Basel)

Date 2024 Jul 27

PMID 39062439

Authors

Joao M Lopes

L Filipe Silva

Ilda Massano-Cardoso

Affiliations

Soon will be listed here.

Abstract

The evolution of e-retail and the contribution of artificial intelligence in improving algorithms for greater customer engagement highlight the potential of these technologies to develop e-commerce further, making it more accessible and personalized to meet individual needs. This study aims to explore the psychosocial factors (subjective norms; faith; consciousness; perceived control) that affect AI-enabled ease of use and their impact on purchase intention in online retail. We will also assess the mediating effect of AI-enabled ease of use between psychosocial factors and consumer purchase intention. A quantitative methodology was used, and 1438 responses were collected from Portuguese consumers on e-retail. Structural equation modeling was used for the statistical treatment. The findings indicate that subjective norms do not positively impact AI-enabled ease of use, whereas factors such as faith, consciousness, and perceived control do enhance it. Furthermore, AI-enabled ease of use itself boosts purchase intention. Additionally, the effects of subjective norms, faith, consciousness, and perceived control on purchase intention are significantly enhanced when mediated by AI-enabled ease of use, highlighting the crucial role of usability in shaping consumer purchase behavior. The contribution of this study has been made through the formulation model that provides a systematized perspective about the influencers of purchase intentions and extends the knowledge about the impact of artificial intelligence in e-retail. Furthermore, this study offers insights into the impact of artificial intelligence in e-commerce-artificial intelligence directly affects purchase intentions and plays an important mediator role in the interaction mechanisms between psychosocial factors and purchase intentions.

References

Almogren A, Al-Rahmi W, Dahri N . Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon. 2024; 10(11):e31887. PMC: 11154614. DOI: 10.1016/j.heliyon.2024.e31887. View

Uzir M, Bukari Z, Al Halbusi H, Lim R, Wahab S, Rasul T . Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context. Heliyon. 2023; 9(8):e18666. PMC: 10407215. DOI: 10.1016/j.heliyon.2023.e18666. View

Rahmani A, Azhir E, Ali S, Mohammadi M, Ahmed O, Yassin Ghafour M . Artificial intelligence approaches and mechanisms for big data analytics: a systematic study. PeerJ Comput Sci. 2021; 7:e488. PMC: 8053021. DOI: 10.7717/peerj-cs.488. View

Wang C, Ahmad S, Bani Ahmad Ayassrah A, Awwad E, Irshad M, Ali Y . An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon. 2023; 9(8):e18349. PMC: 10382301. DOI: 10.1016/j.heliyon.2023.e18349. View

Esmaeilzadeh P . Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives. BMC Med Inform Decis Mak. 2020; 20(1):170. PMC: 7376886. DOI: 10.1186/s12911-020-01191-1. View

Bashir T, Zhongfu T, Sadiq B, Niaz U, Anjum F, Mahmood H . An assessment of influential factors developing the intention to use social media sites: A technology acceptance model based approach. Front Psychol. 2022; 13:983930. PMC: 9596138. DOI: 10.3389/fpsyg.2022.983930. View

Uzir M, Al Halbusi H, Lim R, Jerin I, Hamid A, Ramayah T . Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19. Technol Soc. 2021; 67:101780. PMC: 8528563. DOI: 10.1016/j.techsoc.2021.101780. View

Chen Y, Prentice C, Weaven S, Hisao A . The influence of customer trust and artificial intelligence on customer engagement and loyalty - The case of the home-sharing industry. Front Psychol. 2022; 13:912339. PMC: 9386451. DOI: 10.3389/fpsyg.2022.912339. View

Bawack R, Wamba S, Carillo K, Akter S . Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electron Mark. 2022; 32(1):297-338. PMC: 8932684. DOI: 10.1007/s12525-022-00537-z. View

10.

Guingrich R, Graziano M . Ascribing consciousness to artificial intelligence: human-AI interaction and its carry-over effects on human-human interaction. Front Psychol. 2024; 15:1322781. PMC: 11008604. DOI: 10.3389/fpsyg.2024.1322781. View

11.

Jain R, Garg N, Khera S . Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model. Front Psychol. 2022; 13:893691. PMC: 9251489. DOI: 10.3389/fpsyg.2022.893691. View

12.

Ameen N, Tarhini A, Reppel A, Anand A . Customer experiences in the age of artificial intelligence. Comput Human Behav. 2020; 114:106548. PMC: 7463275. DOI: 10.1016/j.chb.2020.106548. View

13.

Canhoto A, Keegan B, Ryzhikh M . Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment. Inf Syst Front. 2023; :1-20. PMC: 9840426. DOI: 10.1007/s10796-023-10369-7. View

14.

Ye T, Xue J, He M, Gu J, Lin H, Xu B . Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study. J Med Internet Res. 2019; 21(10):e14316. PMC: 6913088. DOI: 10.2196/14316. View

15.

Mogi K . Artificial intelligence, human cognition, and conscious supremacy. Front Psychol. 2024; 15:1364714. PMC: 11130558. DOI: 10.3389/fpsyg.2024.1364714. View

16.

Zhou C, Liu X, Yu C, Tao Y, Shao Y . Trust in AI-augmented design: Applying structural equation modeling to AI-augmented design acceptance. Heliyon. 2024; 10(1):e23305. PMC: 10771990. DOI: 10.1016/j.heliyon.2023.e23305. View

17.

Erkut S . Developing Multiple Language Versions of Instruments for Intercultural Research. Child Dev Perspect. 2011; 4(1):19-24. PMC: 3060794. DOI: 10.1111/j.1750-8606.2009.00111.x. View

18.

Cohen J . A power primer. Psychol Bull. 2009; 112(1):155-9. DOI: 10.1037//0033-2909.112.1.155. View