User Intention Research Articles

Continuous intention usage of artificial intelligence enabled digital banks: a review of expectation confirmation model

PurposeThis study builds on a conceptual model by integrating AI features – Perceived intelligence (PIN) and anthropomorphism (PAN) – while extending expectation confirmation theory (ECT) factors – interaction quality (IQU), confirmation (CON), and customer experience (CSE) – to evaluate the continued intention to use (CIU) of AI-enabled digital banking services.Design/methodology/approachData were collected through an online questionnaire administered to 390 digital banking customers in India. The data were further analysed, and the presented hypotheses were evaluated using partial least squares structural equation modelling (PLS-SEM).FindingsThe research indicates that perceived intelligence and anthropomorphism predict interaction quality. Interaction quality significantly impacts expectation confirmation, consumer experience, and the continuous intention to use digital banking services powered by AI technology. AI design will become a fundamental factor; thus, all interactions should be user-friendly, efficient, and reliable, and the successful implementation of AI in digital banking will largely depend on AI features.Originality/valueThis study is the first to demonstrate the effectiveness of an AI-ECT model for AI-enabled Indian digital banks. The user continuance intention to use digital banking in the context of AI has not yet been studied. These findings further enrich the literature on AI, digital banking, and information systems by focusing on the AI's Intelligence and Anthropomorphism variables in digital banks.

Predictive analysis of Metaverse usage intention in the Spanish University

Predictive analysis of Metaverse usage intention in the Spanish University

The path to cashless transaction: A study of user intention and attitudes towards quick response mobile payments

This study explores the factors influencing users' behavioral intentions, attitudes and actual adoption of quick response (QR) mobile payment in the least developed country (LDC) of Bangladesh, by extending the original unified theory of acceptance and use of technology (UTAUT) model. The study conducts a mixed-methods investigation by combining the partial least squares (PLS) and focus group discussion (FGD) methods to empirically evaluate the research model and cross-validate the findings. Using purposive sampling, data were gathered from 412 respondents, followed by 10 respondents who took part in the FGD, who all met the sample criteria. The study findings indicate that performance expectancy, effort expectancy, and social influence significantly positively influence users' behavioral intention, while self-concept, perceived self-efficacy, and habit substantially influence their attitudes towards using QR mobile payments. The findings also confirm a positive effect of users’ attitudes toward using QR mobile payment on both behavioral intention and actual use, and a positive effect of behavioral intention on the actual use of QR mobile payments. These findings offer several important theoretical and managerial implications.

Intent mining framework for understanding online conversations on vaping to inform social media-based intervention design

ABSTRACT The recent surge in the usage of e-cigarettes amongst youth has highlighted a long-standing societal crisis. To assist public health agencies in policymaking, past research often employed traditional survey-based methods to understand youth behavior, which suffer from response biases and scalability, are time-consuming, and their findings often lag the fast-changing public behavior. Our study fills this gap by using social media as a complementary data source to understand user intentions for vape usage at a large scale, thus, providing an alternative to traditional survey-based methods. In this paper, we propose a novel user intent mining framework under the guidance of social cognitive theory for health behavioral interventions that helps study user intentions across different social media platforms. We then employ this framework to investigate the feasibility of automated intent mining on social media by formulating a multi-class classification task, employing machine learning algorithms to classify a social media message across relevant intent classes: Accusational, Anecdotal, Informational, Justificational and Promotional. The analyses indicate that Accusational tweets and Anecdotal messages were most prevalent on X/Twitter and Reddit respectively. We further provide novel insights on the conversational context using topic modeling analysis and psychometric analysis consequently, informing intervention designs and assisting health analysts.

The effects of tourists’ digital attachment on behavioural intention in live-streaming tourism

ABSTRACT Live-streaming tourism emerged as an innovative form of socio-technical tourism. This research explores the nexus between the digital technology experiences of tourists engaged in live-streaming tourism and behavioural intentions. Study 1 relied on social media dependence theory to analyze the impact of digital attachment on usage intention and explored tourists’ willingness to use digital technology. The results indicate that digital attachment comprises digital addiction, self-connection with digital technology, digital involvement, and social connection through digital technology. Study 2 blended trust and risk theory and technology acceptance models to strengthen these insights through a quantitative study involving tourists in live-streaming tourism. The results show that tourists’ perceived value (usefulness and ease of use) mediates the relationship between tourists’ digital attachment and behavioural intention. Despite the risks encountered in live-streaming tourism, results suggest that these risks do not significantly sway tourists’ behavioural intentions. The research highlights that the engagement and connection achieved via participation in live-streaming tourism can lessen perceived risks and bolster subjective trust, thus influencing their behavioural intentions.

Consumer Usage of Mobile Visual Search in China

As an emerging information retrieval technology, mobile visual search (MVS) has considerable development potential and broad market application prospects. Although many studies have explored user behaviors with various information technology artifacts, few have specifically investigated consumers' MVS intentions and behaviors. For filling the gap, this study is to integrate perceived contextual offer (PCO) and implementation intention into the theoretical model of UTAUT2, aiming to explore potential predictors of Chinese consumers' MVS usage intention and behavior. The results showed that consumers' perceptions of contextual offer, performance expectancy, hedonic motivation, and their using habit had significant effects on consumers' MVS usage intention. Habit, usage intention, and facilitating conditions showed significant predictive power for consumers' MVS implementation intention and their further usage behavior. Additionally, habit and usage intention indirectly influenced consumers' MVS usage through the complementary mediation of implementation intention.

An integrative research framework to investigate factors influencing citizen’s intention to adopt e-health applications: post-COVID-19 perspective

Purpose The use of e-health applications has remained popular during pandemic for medical treatments. Nevertheless, adoption of e-health applications among individuals is considerably low in post-COVID-19 world. To address this issue, the current research integrates three renowned theories, namely unified theory of acceptance and use of technology model, diffusion of innovation and DeLone and McLean model and investigates individual intention to adopt e-health application. Design/methodology/approach Research design is grounded in quantitative and cross-sectional methods and uses a survey questionnaire. Survey questionnaire is administered toward smartphone users. Overall, 238 valid responses were analyzed with structural equation modeling approach. Findings Results indicate that altogether performance expectancy, effort expectancy, information quality, system quality, service quality, facilitating condition and social influence explained substantial variance (R2 = 76.5%) in user intention to adopt e-health applications. Similarly, effect size analysis has revealed substantial impact of social influence on user intention to adopt e-health applications. Geiser and Stone’s Q2 analysis discloses that research model has substantial power to predict user intention to adopt e-health applications. Practical implications This study integrates three known information system models to investigate individual behavior toward adoption of e-health applications. In practice, it suggests that managers should pay attention in improving performance expectancy, social influence, facilitating condition, system quality, service quality and information quality which develop positive behavior and encourage smartphone users to adopt e-health applications. Originality/value This study is original as it integrates three models and investigates individual intention to adopt e-health applications. To the best of the author’s knowledge, this is the first study to test the moderating impact of patient health value between user intention to adopt and actual use of e-health applications. It has established that compatibility and innovativeness are essential antecedents of performance and effort expectancy and hence make this research more unique and valuable.

Antecedents and consequences of social media group users' fatigue: A mixed-methods study under educational context

Social media platforms have radically changed the education section in recent years. Along with benefits, the negative aspects of these tools have also begun to emerge and cannot be ignored. Based on the stress-strain–outcome model, this study examines the notion of fatigue in the context of teacher-parent social media groups through a mixed-methods approach. Among the three stressors (information overload, information irrelevance, and group member value unfit) that we identified in the qualitative content analysis, information overload, information irrelevance and group member value unfit were found significantly influence fatigue, which further leads to the user's intention to leave and mute the group in the quantitative study. By taking a negative approach, our study contributes to a better understanding of social media usage in the educational context. In addition to the theoretical contributions, the findings of the current study offer valuable practical insights for combating the social media fatigue phenomenon.

Enhancing smart home interaction through multimodal command disambiguation

AbstractSmart speakers are entering our homes and enriching the connected ecosystem already present in them. Home inhabitants can use those to execute relatively simple commands, e.g., turning a lamp on. Their capabilities to interpret more complex and ambiguous commands (e.g., make this room warmer) are limited, if not absent. Large language models (LLMs) can offer creative and viable solutions to enable a practical and user-acceptable interpretation of such ambiguous commands. This paper introduces an interactive disambiguation approach that integrates visual and textual cues with natural language commands. After contextualizing the approach with a use case, we test it in an experiment where users are prompted to select the appropriate cue (an image or a textual description) to clarify ambiguous commands, thereby refining the accuracy of the system’s interpretations. Outcomes from the study indicate that the disambiguation system produces responses well-aligned with user intentions, and that participants found the textual descriptions slightly more effective. Finally, interviews reveal heightened satisfaction with the smart-home system when engaging with the proposed disambiguation approach.

Exploring continued usage of an AI teaching assistant among university students: A temporal distance perspective

Although technological developments have made AI chatbot teaching assistants a lived reality, limited insights exist indicating how students perceive and use these new technologies. Recognizing that technology usage research adopts a static view that overlooks the role of temporal distance, we draw on temporal construal theory to examine user intentions and actual usage behavior over time by re-approaching the same set of participants after developing and deploying an AI chatbot in the educational setting. Our results highlight the significance of temporal distance in shaping user perceptions of the technology, with the need for interaction level to play a moderating role.

Factors of Self-Checkout Counter towards Customer Usage Intention in Malaysia Drugstore

Factors of Self-Checkout Counter towards Customer Usage Intention in Malaysia Drugstore

The Effect of Product Involvement, Perceived Value, and Attitude as Mediation on the Intention to Use “POSPAY” Apps by PT Pos Indonesia (Persero) to Create the Right Marketing Strategy through a Marketing 4.0 Perspectives

Pospay is an app from PT Pos Indonesia (Persero). The research’s background highlights the competitive pressures in the financial services industry and the necessity for transformational companies to innovate through digital offerings such as the Fintech app. The implications of this research complete a new understanding for the marketing department at PT Pos Indonesia on how to increase customer intention to use. This research can provide evaluation materials and references for implementing the right marketing strategy from the perspective of Marketing 4.0, especially to increase intention to use it with variable mediation of consumer attitudes. The results of this research can provide an overview of the fact that perceived value is the most powerful variable that contributes to efforts to increase intention to use the Pospay App. The key research problems are the need to identify the determinants of user intention in adopting FinTech apps, the role of consumer attitude in mediating these determinants, product involvement, and the practical implications for marketing strategy development. The research employs a mixed method with a descriptive approach and uses a type-3 problem-solving scenario. Quantitative data is collected through surveys, and qualitative insights are gathered via interviews or focus group discussions. Structural Equation Modelling (SEM) – Partial Least Square (PLS) with WarpPLS software is used to analyze data testing the hypothesis. The research results are as follows: Product involvement does not have a significant effect on the intention to use but has a significant effect on consumer attitudes; Perceived value has a significant effect on the intention to use and consumer attitudes; Consumer attitude has a significant effect on the intention to use and mediates the influence of perceived value on intention to use but does not moderate the influence of product involvement on intention to use. The research will provide actionable recommendations for PT Pos Indonesia, enabling them to tailor their marketing strategies better through a Marketing 4.0 perspective to meet customer needs and preferences.

Applying the extended UTAUT-2 to assess the factors contributing to consumers’ usage intention towards over-the-top video streaming platforms

The current study aims to examine consumers' over-the-top (OTT) video streaming platform usage intention and apply the extended unified theory of acceptance and use of technology version 2 (UTAUT-2) to develop a holistic evaluation model. Specifically, a decision-making trial and evaluation laboratory (DEMATEL) approach was utilised to discover the determinants of OTT video streaming service selection. This study comprehensively analyses data collected from 498 users of an OTT video streaming platform. The research rigorously examines and validates the proposed research questions by utilising multiple attribute causality modelling. The findings demonstrated that the primary factors for platform improvements were performance and effort expectancies, with facilitating conditions, personal innovativeness, hedonic motivations, and social influences significantly impacting consumers’ preferences. The findings provided insights into OTT platform providers to acquire more subscribers while improving respective competitiveness. The study discusses both theoretical and practical implications in depth.

Disentangling User Cognitive Intent with Causal Reasoning for Knowledge-Enhanced Recommendation

The primary objective of an effective recommender system is to provide accurate, varied, and personalized recommendations that align with the user’s cognitive intents. Given their ability to represent structural and semantic information effectively, knowledge graphs (KGs) are increasingly being utilized to capture auxiliary information for recommendation systems. This trend is supported by the recent advancements in graph neural network (GNN)-based models for KG-aware recommendations. However, these models often struggle with issues such as insufficient user-item interactions and the misalignment of user intent weights during information propagation. Additionally, they face a popularity bias, which is exacerbated by the disproportionate influence of a small number of highly active users and the limited auxiliary information about items. This bias significantly curtails the effectiveness of the recommendations. To address this issue, we propose a Knowledge-Enhanced User Cognitive Intent Network (KeCAIN), which incorporates item category information to capture user intents with information aggregation and eliminate popularity bias based on causal reasoning in recommendation systems. Experiments on three real-world datasets show that KeCAIN outperforms state-of-the-art baselines.

An Approach to Potentially Increasing Adoption of an Artificial Intelligence-Enabled Electronic Medical Record Encounter in Canadian Primary Care: Protocol for a User-Centered Design.

Primary care physicians are at the forefront of the clinical process that can lead to diagnosis, referral, and treatment. With electronic medical records (EMRs) being introduced and, over time, gaining acceptance by primary care users, they have now become a standard part of care. EMRs have the potential to be further optimized with the introduction of artificial intelligence (AI). There has yet to be a widespread exploration of the use of AI in primary health care and how clinicians envision AI use to encourage further uptake. The primary objective of this research is to understand if the user-centered design approach, rooted in contextual design, can lead to an increased likelihood of adoption of an AI-enabled encounter module embedded in a primary care EMR. In this study, we use human factor models and the technology acceptance model to understand the results. To accomplish this, a partnership has been established with an industry partner, TELUS Health, to use their EMR, the collaborative health record. The overall intention is to understand how to improve the user experience by using user-centered design to inform how AI should be embedded in an EMR encounter. Given this intention, a user-centered approach will be used to accomplish it. The approach of user-centered design requires qualitative interviewing to gain a clear understanding of users' approaches, intentions, and other key insights to inform the design process. A total of 5 phases have been designed for this study. As of March 2024, a total of 14 primary care clinician participants have been recruited and interviewed. First-cycle coding of all qualitative data results is being conducted to inform redesign considerations. Some limitations need to be acknowledged related to the approach of this study. There is a lack of market maturity of AI-enabled EMR encounters in primary care, requiring research to take place through scenario-based interviews. However, this participant group will still help inform design considerations for this tool. This study is targeted for completion in the late fall of 2024. DERR1-10.2196/54365.

Task-Motion Planning System for Socially Viable Service Robots Based on Object Manipulation.

This paper presents a software architecture to implement a task-motion planning system that can improve human-robot interactions by including social behavior when social robots provide services related to object manipulation to users. The proposed system incorporates four main modules: knowledge reasoning, perception, task planning, and motion planning for autonomous service. This system adds constraints to the robot motions based on the recognition of the object affordance from the perception module and environment states from the knowledge reasoning module. Thus, the system performs task planning by adjusting the goal of the task to be performed, and motion planning based on the functional aspects of the object, enabling the robot to execute actions consistent with social behavior to respond to the user's intent and the task environment. The system is verified through simulated experiments consisting of several object manipulation services such as handover and delivery. The results show that, by using the proposed system, the robot can provide different services depending on the situation, even if it performs the same tasks. In addition, the system demonstrates a modular structure that enables the expansion of the available services by defining additional actions and diverse planning modules.

User segmentation in human–robot interactions: insights from sports betting patrons using diffusion of innovation theory

ABSTRACT Previous research has assumed uniformity in user response to service robots regardless of user profile. This study sought to understand the nature of human–robot interactions in a competitive setting by applying the diffusion of innovation theory. Through a survey of 447 sports betting patrons, we used unsupervised learning to cluster the patrons based on human/robot interaction, relational and psychological variables, usage intention, and socioeconomic information. Four clusters were identified: Tech laggards, Optimists, Skeptics, and Enthusiasts. Trust was a key determining factor of cluster membership, especially among frequent betters. Further, the study examined each cluster through the lens of psychological theories and ethical perspectives, identified associated risks, and offered mitigation strategies. Practical implications are discussed.

Decoding tourist perceptions and behavioral intentions to use electric vehicles for sustainable tourism with the lens of technology continuance theory

Purpose This study aims to explore tourist perceptions and behaviors toward electric vehicles (EVs) in the Delhi National Capital Region using the technology continuance theory. Design/methodology/approach An online survey involving 226 respondents uses structural equation modeling to analyze correlations among factors, including perceived enjoyment, facilitating conditions, ease of use, satisfaction, cost, image and performance. Findings This study reveals that enhancing perceived enjoyment and facilitating conditions can improve the user-friendliness of EVs. Additionally, reducing perceived cost, enhancing image and improving perceived performance can increase the perceived usefulness of EVs. Perceived ease of use strongly influences user satisfaction, while perceived usefulness and satisfaction positively impact users’ attitudes and intentions to use EVs. Although factors such as experience, environmental consciousness, age and gender influence perceptions, focusing on enjoyment, facilitating conditions, cost, image and performance can significantly enhance user satisfaction and intention to use EVs. Research limitations/implications The findings underscore several actionable recommendations for businesses and policymakers to boost EV adoption at tourist destinations. The potential benefits of EV adoption, such as improved environmental sustainability, enhanced technological image and increased tourist satisfaction, can serve as a source of inspiration and motivation. Enhancing user experience by prioritizing comfort and convenience in EV design is crucial. Addressing cost concerns through incentives and cost-effective pricing strategies can make EVs more appealing. Marketing campaigns highlighting environmental benefits and technological advancements can improve EV image and performance perception. Prioritizing tourists’ satisfaction and support services is essential, along with educational campaigns to increase awareness. Infrastructure development, including expanding charging networks, and supportive policies like tax incentives, can further encourage EVs adoption, accelerating the transition to sustainable transportation. Originality/value This research contributes to understanding tourist perspectives on EV adoption within the context of sustainable tourism and technology adoption.

Mitigating the influence of message features on health misinformation sharing intention in social media: Experimental evidence for accuracy-nudge intervention

RationaleThe escalating dissemination of health misinformation on social media platforms poses a significant threat to users' well-being. It is imperative to identify the types of health misinformation that are more susceptible to widespread dissemination and to explore strategies to curb its spread. MethodThis study designed a 2 (emotional appeal type: positive vs. negative) × 2 (fabricated source type: pseudo-common vs. pseudo-authoritative) × 2 (accuracy-nudge label: No vs. Yes) online between-subjects experiment controlling for factors such as e-health literacy, prior sharing experience, and personal involvement. A snowball sampling approach was used to recruit 1952 participants through social media, resulting in a final sample of 1393 valid responses. ResultsCompared to positive emotional appeal and pseudo-common sources, negative emotional appeal and pseudo-authoritative sources resulted in higher levels of sharing intention. Under the condition of negative emotional appeal, the promotion effect of pseudo-authoritative sources on sharing intention was intensified. The accuracy-nudge intervention could significantly mitigate this tendency. The underlying mechanisms revealed more details: both negative emotional appeals and pseudo-authoritative sources increased the perceived credibility of health misinformation, thereby increasing users' sharing intention. However, in contrast to pseudo-authoritative sources, excessive negative emotional appeal induced vigilant verification behavior among users, which reduced sharing to some extent. Adding an accuracy-nudge label to health misinformation reduced users' misguided trust in health misinformation features and stimulated information verification, ultimately reducing health misinformation sharing intention. ConclusionsNegative emotional appeal and pseudo-authoritative sources can enhance the perceived credibility of health misinformation, thereby strengthening the sharing intention of social media users. Therefore, health misinformation with negative emotional appeal and pseudo-authoritative sources is more likely to be widely shared. The accuracy nudge intervention can trigger users' information verification behavior, suppress the persuasive effects of the misinformation features mentioned above, and help prevent the spread of health misinformation on social media.

Pengaruh Moderasi Gender terhadap Niat Penggunaan Aplikasi Seabank di Kalangan Mahasiswa Yogyakarta dengan Analisis Model UTAUT2

This study aims to analyze the moderating effect of gender on the intention to use the Seabank application among Yogyakarta university students using the Unified Theory Of Acceptance And Use Of Technology 2 (UTAUT2) model. The research method used is a quantitative survey by distributing questionnaires to 110 university students. The data obtained were analyzed using Structural Equation Modeling (SEM) techniques, while the analysis tool used was SmartPLS. The results showed that gender moderation was not significant in influencing the relationship between facilitating conditions, hedonic motivation, price value and habit on behavioral intention to use the Seabank application. This finding suggests that these factors influence usage intention independently of gender. The implication of this study is that digital financial application marketing strategies can be focused on increasing performance expectations and effort without the need to consider gender differences.