Purpose: The development of mobile technology has changed the traditional financial industry and banking sector. While traditional banks have adopted artificial intelligence (AI) techniques to deepen the development of mobile banking applications (apps), the current literature lacks research on the use of AI-based constructs to explore users' mobile banking app adoption intentions. To fill this gap, based on stimulus-organism-response (SOR) theory, two AI feature constructs as stimuli are considered, namely, perceived intelligence and anthropomorphism. This study then develops a research model to investigate how intelligence and anthropomorphism affect task-technology fit (TTF), perceived cost, perceived risk and trust (organism), which in turn influence users' AI mobile banking app adoption (response). Design/methodology/approach: This study used a convenience nonprobability sampling approach; a total of 451 responses were collected to examine the model. The partial least squares technique was utilized for data analysis. Findings: The results show that intelligence and anthropomorphism increase users' willingness to adopt mobile banking apps through TTF and trust. However, higher levels of anthropomorphism enhance users' perceived cost. In addition, both intelligence and anthropomorphism have insignificant effects on perceived risk. The results provide theoretical contributions for AI-based mobile banking app adoption and offer practical guidance for bank planning to use AI to retain users. Originality/value: Based on SOR theory, this study reveals that as features, AI-enabled intelligence and anthropomorphism help us further understand users' perceptions regarding cost, risk, TTF and trust in the context of AI-enabled app adoption intentions.

Artificial intelligence (AI) has been proven to be a disruptive financial technology in the context of mobile banking that can provide more practical value to users and banks. AI is a critical way of facilitating user acceptance and adoption of mobile banking applications (apps). Nevertheless, the ways in which AI features influence users’ continuance intention towards AI-enabled mobile banking apps have not been investigated from the perspective of an expectation confirmation model (ECM). To address this research gap, this paper develops a research model by combining two constructs pertaining to AI characteristics, namely, perceived intelligence and perceived anthropomorphism, and by using the ECM to explore users’ continuance intentions in this context. We employed a survey research method using a random sampling approach to collect 365 valid responses. A partial least squares approach was used to examine the model. The results show that both intelligence and anthropomorphism can increase user satisfaction via confirmation and perceived usefulness, which in turn fosters users’ willingness to continue to engage in mobile banking. This paper offers theoretical advancements, discusses future directions for mobile banking research and provides practical guidance to app developers with respect to designing and developing proper mobile banking apps using AI technology.

Purpose: Artificial intelligence (AI) has been widely used as a financial technology (fintech) in the mobile banking (M-banking) domain. However, in the literature, how AI affects users' perceptions of social support and the users' satisfaction and continuance intention (CI) remains unknown. To fill this gap, the two core characteristics of AI, perceived intelligence (PI) and perceived anthropomorphism (PA), are combined with social support theory (SST) (including informational support (IS) and emotional support (ES)) to develop a research model to investigate how PI and PA affect IS and ES, which in turn affect users’ M-banking satisfaction and CI.Design/methodology/approach: This study adopted a random probability sampling method to collect a total of 360 valid responses to verify the proposed model. Partial least squares (PLS) was employed for data analysis.Findings: The results showed that PI and PA both have a significant positive impact on consumers' perception of social support (IS and ES). IS was a direct driver of satisfaction and CI. Surprisingly, although ES was positively associated with satisfaction, the study found that higher levels of ES will decrease CI. This study exposed how AI affects consumers’ satisfaction and CI through SST, and the role of AI in M-banking applications has been further confirmed.Originality/value: This study expanded the SST to creatively integrate with AI features to reveal the impact of PI and PA on IS and ES, which in turn influence users' M-banking usage.

Purpose: Due to the popularity of mobile devices and the development of artificial intelligence (AI), AI-powered mobile fitness applications (MFAs) have entered people's daily lives. However, the extant literature lacks empirical investigations that explore users' continuance usage intentions regarding AI-powered MFAs. To fill this research gap, this paper employs goal-setting theory to establish a research model for exploring how AI-enabled features (i.e. intelligence and anthropomorphism) affect users' perceptions of goal difficulties and goal specificities, which in turn affect their MFA continuance usage intentions.Design/methodology/approach: This paper uses a survey method to analyze the research model, and a total of 223 responses are collected. The partial least squares (PLS) technique is utilized for data analysis.Findings: The results show that intelligence and anthropomorphism affect the continuance usage intention of MFA users through their goal difficulty and specificity. Both intelligence and anthropomorphism positively influence goal specificity, whereas they negatively affect goal difficulty. In addition, goal specificity increases users' MFA continuance usage intention, whereas goal difficulty decreases users' continuance usage intention. The findings of this study provide theoretical contributions for AI technology adoption research and offer practical strategies for firms to retain MFA users.Originality/value: Based on goal-setting theory, this study reveals that as two primary AI features of contemporary mobile fitness apps, intelligence and anthropomorphism, can increase comprehension of users' perceptions regarding goal difficulty and specificity in the context of users' continuance usage intentions toward AI-powered MFAs.

Purpose: To improve the frequency of adoption of mobile health services (MHSs) by users (consumers), it is critical to understand users' MHS adoption behaviors. However, the literature primarily focuses on MHS adoption-related factors and lacks consideration of the joint impacts of reasons for (RF) and reasons against (RA) on users' attitudes and adoption behaviors regarding MHSs. To fill this gap, this study integrates behavioral reasoning theory (BRT) and protective motivation theory (PMT) to develop a research model by uncovering the reasoning process of personal values, RF and RA, adoption attitudes and behavior toward MHSs. In particular, health consciousness (HC) is selected as the value. Comparative advantage, compatibility and perceived threat severity are considered the RF subconstructs; value barriers, risk barriers and tradition and norm barriers are deemed the RA subconstructs.Design/methodology/approach: A total of 281 responses were collected to examine the model with the partial least squares structural equation modeling (PLS-SEM) method.Findings: The results show that HC positively affects attitude through RA and RF. Additionally, RF partially mediates the relationship between HC and adoption behavior. This study contributes to a deeper understanding of user adoption behavior in MHS and provides practical guidance for the health services industry.Originality/value: This study contributes to the existing MHS literature by understanding the joint influences of personal values, RF and RA on user attitude, which eventually determines users' adoption decisions regarding MHSs.

Purpose: Financial technology (Fintech) brings about innovative financial services, such as the possibility of introducing mobile wealth management applications (apps) into consumers' lives. Despite the rapid development of such apps, few studies have focused on users' switching intentions from traditional wealth management services to mobile settings (apps). Design/methodology/approach: Through a survey research method, a total of 378 responses were collected to examine the model. The partial least squares (PLS) technique was employed for data analysis. Findings: To fill this research gap, this paper adopts a push-pull-mooring (PPM) theoretical framework to develop a model for exploring users' switching intentions. According to the empirical results, several push (i.e. perceived inconvenience), pull (i.e. transaction efficiency, perceived personalization and mobile wealth management scenarios) or mooring (i.e. product market expertise and affective commitment) factors are identified that significantly affect switching intention. This study provides theoretical contributions and practical implications for the existing wealth management literature and also offers future research directions. Originality/value: This study innovatively extends the PPM framework to the traditional and mobile wealth management domains to understand users' switching intentions from offline wealth management services to mobile wealth management apps. The authors uncover several push, pull and mooring factors that are critical for determining users’ switching decisions.