Table 4.
Detailed future research recommendations—Theme: Customer
| References | Future research directions | Sub-themes | Deficit | Authors recommendations | Themes |
|---|---|---|---|---|---|
| Sharma et al. (2015) | Increase sample size and examine usage trends by banking users | AI and adoption (Customer) | Variables |
• Investigate different variables (e.g., social influence, user trends…) and methods (e.g., longitudinal studies) that impact consumer adoption of AI • Examine practical implications (e.g., experiment AI in different branch locations) of consumer adoption of AI |
Customer |
| Azad (2016) | Investigate mediating and moderator factors of m-banking adoption and conduct cross-country studies | Variables | |||
| Sharma et al. (2017) | Include a) bigger sample size b) data from urban and rural areas c) longitudinal studies | Method | |||
| Payne et al. (2018) | Investigate different effects on different age groups. Examine difference/similarities between m-banking and AI-enabled m-banking | Variables | |||
| Belanche et al. (2019) | perform a longitudinal study, examine other variables, and incorporate different cultures | Variables | |||
| Anouze et al. (2019) | Investigate using more variables and in different countries | Variables | |||
| Königstorfer and Thalmann (2020) | Investigate AI in optimizing branch locations, customers' intention for AI adoption, behavior of decision-makers inside a company, and suitable documentation AI-based services | Implications | |||
| Arif et al. (2020) | Expand and diversify sample examined. Compare moderate and expert users of internet. Test the model in different industries | Variables | |||
| Payne et al. (2021) | Investigate using different age groups and more diverse sample | Variables | |||
| Gallego-Gomez and De-Pablos-Heredero (2020) | Further research in the area of AI impact on consumer banking experience | AI and customer experience | Analyses |
• Utilize different analysis types (e.g., existing heuristic models) to examine the impact of AI on consumer experience • Explore new dimensions of AI (e.g., Chatbots) that influence consumer experience • Investigate different variables (e.g., customer technology readiness, influence of previous experience) impacting consumer perception and the use of AI |
|
| Soltani et al. (2019) | Develop heuristic models and examine different customers' types | Analyses | |||
| Trivedi (2019) | Investigate consumers' experience of using chatbots offered in different industries | New dimension | |||
| Jakšič and Marinč (2019) | Investigate customers' banking relationship needs and the role of AI as a tool to improve banking relationships | New dimension | |||
| Tian et al. (2020) | Increase variables examined and investigate practical uses | Variables | |||
| Xu et al. (2020) | Investigate different variables related to AI and customers' perception | Variables | |||
| Khandani et al. (2010) | develop other models of consumer behavior using ML | AI and marketing | Method |
• Use different research methods (e.g., case studies) and examine different variables (e.g., different countries/cultures) to develop consumer behavior models using ML • Investigate the implications (e.g., experiment marketing models in optimizing bank advertisements) of the use of AI in bank marketing |
|
| Smeureanu et al. (2013) | Research more advanced algorithms that solves the problem of the local minimum aspect problem | Method | |||
| Ekinci et al. (2014) | Test the model in different cultures and longer time horizon | Variables | |||
| Schwartz et al. (2017) | Test the model further and examine practical uses | Implications | |||
| Marinakos and Daskalaki (2017) | Apply method in different industries and sectors | Implications | |||
| Rantanen et al. (2019) | Statistically validate constructs of online corporate reputation | Method | |||
| Frączek (2020) | Present system for more complicated cases | Implications |