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The advancement in Internet technology has played a significant role in revolutionizing the Internet banking services. Therefore, little is discussed about factors that motivate technology user to continue the use of Internet banking services. The current study investigates Internet banking user continuance behavior toward the use of Internet banking services with the integration of two-well known information system (IS) theories namely task technology fit (TTF) and technology continuance theory (TCT).

The research design of this study is based on positivist paradigm and followed quantitative research approach. Data were collected from 360 Internet banking users of commercial banks across Pakistan. The research model was tested with structural equation modeling (SEM).

The research model had explained 53.9% variance in Internet banking user continuance intention. Next to this, the predictive relevance of the research model was tested with Stone-Geisser's Q² values using blindfolding procedure. Results revealed that the newly developed integrated technology continuance research model has substantial power to predict Internet banking user continuance intention. Moreover, the effect size analysis revealed that factors like satisfaction and user expectation were the most important factors in determining Internet banking user continuance intention.

For practical implications importance performance matrix analysis (IPMA) has used to see the importance and performance of the underpinned factors. Findings indicate that managers and policy makers should focus on user satisfaction, perceived usefulness and expectation confirmation in order to enhance the Internet banking user continuance intention toward the use of Internet banking services. Some of the ways banks can do this is to develop esthetic Internet banking website with charm of novelty, relevant information and smooth flows with less complex redirects.

Unlike prior studies that focus on Internet banking user pre-adoption issues, the current study examines post-adoption issue of Internet banking users and investigates Internet banking user continuance intention. This study is significant as it integrates two-well known theories namely TCT and TTF in Internet banking user continuance intention and augments the IS literature by developing an integrated technology continuance model (TCM).

Mobile health (mHealth) services are considered an important means of relieving the problems of the aging population. The efficiency of mHealth services can be enhanced by engaging more elderly users and guaranteeing their continued use. However, limited attention has been directed toward investigating elderly users' continuance intention regarding mHealth services. The purpose of this paper is to explain elderly users' continuance intention by investigating the contingent role of technology anxiety and health anxiety on affective trust and cognitive trust.

Survey data were collected from 232 elderly users to verify the research model and hypotheses based on structural equation modeling (SEM).

This study revealed that both affective and cognitive trust enhance elderly users' continuance intention regarding their use of mHealth services. Health anxiety strengthens the effect of cognitive trust but weakens the effect of affective trust with regard to continuance intention. Furthermore, technology anxiety strengthens the effect of affective trust but not the effect of cognitive trust with regard to continuance intention.

This study is one of the first to examine elderly users' continuance intention regarding mHealth services use from the perspective of affective and cognitive trust, thus enriching the extant literature on the use of mHealth services. Additionally, this study sheds light on the contingent effects of technology anxiety and health anxiety on affective and cognitive trust, which have been neglected by previous research.

Based on the post-acceptance model of information system continuance (PAMISC), this study investigates the influence of the early-stage users' personal traits (specifically personal innovativeness and technology anxiety) and ex-post instrumentality perceptions (specifically price value, hedonic motivation, compatibility and perceived security) on social diffusion of smart technologies measured by the intention to recommend artificial intelligence-based voice assistant systems (AIVAS) to others.

Survey data from 400 US AIVAS users were collected and analyzed with Statistical Product and Service Solutions (SPSS) 18.0 and the partial least square technique using advanced analysis of composites (ADANCO) 2.1.

AIVAS technology is presently at the early stage of market penetration (about 25% of market penetration in the USA). A survey of AIVAS technology users reveals that personal innovativeness is directly and indirectly (through confirmation and continuance) associated with a stronger intention to recommend the use of the device to others. Confirmation is associated with all four ex-post instrumentality perceptions (hedonic motivation, compatibility, price value and perceived security). Among the four, however, only hedonic motivation and compatibility are significant predictors of satisfaction, which lead to use continuance and, eventually, intention to recommend. Finally, technology anxiety is found to be indirectly (but not directly) associated with a lower intention to recommend.

This is the first study conducted on the early-stage AIVAS users that evaluates the influence of both personal traits and ex-post instrumentality perceptions on users' intention for continuance and recommendation to others.

The long-term success of internet banking (IB) services is connected with continuous use. Therefore, examining factors which influence IB user continuance intention is important. Grounded in technology continuance theory, bank transparency and task technology fit (TTF) model, the purpose of this study is to develop a unified technology continuance model to investigate IB user continuance intention.

The research model was empirically tested with 348 responses. Respondents included in this study had prior experience with IB services. For hypotheses testing, the structural equation modelling approach was used.

Findings indicate that the research model has substantial power in explaining IB user continuance intention. Importance performance matrix analysis revealed that bank transparency had the highest importance in determining IB user continuance intention. Therefore, factors like user satisfaction and perceived usefulness have shown a medium level of importance in determining IB user continuance intention. Satisfaction is influenced by perceived usefulness and confirmation and established a significant impact on IB user continuance intention.

The findings of this research are useful for bank managers and policymakers to develop and design IB services according to user’s expectations. Results demonstrate that expectation confirmation and perceived usefulness are antecedents of IB user satisfaction and IB user continuance intention. In addition to that bank, transparency has shown a significant effect on user expectation and IB user continuance intention. These findings established that accuracy in sharing bank information and clarity in transaction charges encourage the user to continue the use of IB services.

The current study develops a unified technology continuance model based on the TTF model and technology continuance theory and investigates IB user continuance intention. Moreover, bank transparency is added in the technology continuance model and established a significant impact towards user expectation confirmation and continuance intention. These findings contribute to the limited body of research in the context of IB user continuance intention and enrich information system literature.

Although many studies have sought to address the topic of continuance intention among Fintech customers, the reported findings are fragmented. Therefore, the present study proposes a research model that integrates the main constructs involved in Fintech continuance intention.

The current study uses a meta-analytic-based correlation analysis of effect sizes, meta-regression analysis and meta-analytic structural equation modeling, with 247 effect sizes in 69 studies involving 26,140 respondents.

The results reveal continuance intention is driven by satisfaction and trust, with ease of use and usefulness being antecedents of satisfaction and trust. The authors also found evidence to show satisfaction partially mediates the relationship between ease of use and continuance intention and that trust fully mediates the relationship between ease of use and continuance intention and partially mediates the relationship between usefulness and continuance intention. In addition, the authors found that in Western countries, with higher Human Development Index levels and greater of use of electronic payment, satisfaction has more impact on continuance intention.

From a theoretical standpoint, this meta-analytic study has implications for the literature on Fintech by offering an empirical generalization on the strength of the antecedents of Fintech continuance intentions and by testing possible moderators in a wide range of countries and studies. In other words, this study’s goal is to broaden the scope of the research. Regarding managerial implications, it is important to listen to user opinions regarding the positive and negative points of their experience with these technologies and take them into consideration when planning improvements. Additionally, the analysis shows the importance of using data from user interaction with technology, obtained, for example, through big data analytics, whereby companies can see how users behave, how much time they spend accessing certain functions and which technological features they use most, and thus seek to improve whatever is needed.

This meta-analytic study advances the understanding of Fintech continuance intentions. Using the proposed approach, it is possible to generate accurate estimates of the effect size of each analyzed antecedent as the meta-analytic method jointly evaluates the results produced by a wide variety of studies performed in different contexts, allowing more accurate conclusions to be drawn.

The purpose of this study is to propose an integrated model based on expectation-confirmation model (ECM) and technology acceptance model (TAM) to examine whether environmental, human, organizational and technology factors as the antecedents to physicians’ beliefs can affect their satisfaction and continuance intention of the cloud-based hospital information system (HIS).

Sample data for this study were collected from physicians at six hospitals in Taiwan. A total of 600 questionnaires were distributed, and 471 (78.5 per cent) usable questionnaires were analyzed using structural equation modeling in this study.

This study proposes a solid research model that is based on ECM and TAM, four types of factors, environmental factor, human factor, organizational factors and technology factors, as antecedents to physicians’ continuance intention of the cloud-based HIS have been examined. This study’s results strongly support the research model with all hypothesized links being significant, and control variables have no confounding effects on physicians’ continuance intention of the cloud-based HIS.

This study contributes to physicians’ continuance intention toward the cloud-based HIS based on ECM, TAM, technology-organization-environment framework and human-organization-technology fit framework, and reveals deep insights into the evaluation of determinants in the field of physicians’ continuance intention of the cloud-based HIS. Accordingly, it is particularly worth mentioning that IS and non-IS determinants are simultaneously evaluated and taken into consideration in this study’s theoretical development of physicians’ continuance intention of the cloud-based HIS to acquire a more comprehensive and robust analysis.

This study aims to examine the relationship between emotional attachment (EA) and intelligent personal assistant (IPA) continuance intention. While existing theories emphasize purely rational and goal-oriented factors in terms of information technology (IT) continuance intention, this research examines how users' EA toward technology impacts their continuance intention in the absence of cognitive and habitual factors.

This study contextualizes attachment theory from the social psychology/consumer psychology literature to an IT application and formulates and tests a new model that is proposed in the context of IPA continuance. Five research hypotheses developed from contextualization and application of the theory were posited in a structural model and empirically validated using survey results from IPA users.

The results show that users' EA to IPA use significantly influences their IPA continuance intention, along with emotional trust and interaction quality with the IPA.

This study contextualizes attachment theory developed in the social psychology/consumer psychology literature to formulate and test a new model in the context of IPA continuance. This work contributes to the theoretical understanding by investigating IPA continuance intention in the absence of cognitive or habitual factors and fills a critical research gap in IT post-adoption literature. IPA is just one example of technologies to which individuals can form attachments and this research provides an important foundation for future research by positing and testing the value of EA in IT post-adoption behavior. This research also contributes to practical knowledge by inferring that IPA manufacturers, managers and vendors could extend their revenue streams by integrating product features that capture emotion.

The purpose of this study is to propose an integrated model based on expectation–confirmation model (ECM), flow theory and human–organization–technology fit framework to examine whether human, organizational and technology factors as antecedents to medical professionals' beliefs can affect their continuance intention of the cloud-based e-learning system.

Sample data for this study were collected from medical professionals at five hospitals in Taiwan. A total of 500 questionnaires were distributed, and 368 (73.6%) useable questionnaires were analyzed using structural equation modeling in this study.

Synthetically speaking, human, organizational and technology factors, as antecedents to medical professionals' continuance intention of the cloud-based e-learning system have been examined, and the results strongly support the research model with all hypothesized links being significant.

Particularly, it is worth mentioning that the application of capturing both ECM and flow theory for completely explaining three types of factors (i.e. human, organizational and technology factors) as external variables to medical professionals' cloud-based e-learning continuance intention is well documented, that is, information systems (IS) and nonIS determinants are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into consideration in this study's theoretical development of medical professionals' cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.

The purpose of this paper is to examine the roles of task-technology fit (TTF), learning-technology fit (LTF) and cognitive absorption (CA) in determining medical professionals’ cloud-based electronic learning (e-learning) system continuance intention and performance outcomes and evaluate whether medical professionals’ perceived impact on learning can affect their perceived impact on tasks within medical institutions.

Sample data for this study were collected from medical professionals at six hospitals in Taiwan. A total of 600 questionnaires were distributed, and 373 (62.2%) usable questionnaires were analyzed using structural equation modeling in this study.

In this study, medical professionals’ perceived TTF and LTF as antecedents to their cloud-based e-learning continuance intention and performance outcomes were validated, and medical professionals’ perceived impact on learning had a positive effect on their perceived impact on tasks. Synthetically speaking, this study’s results strongly support the research model with all hypothesized links being significant.

It is particularly worth mentioning that this study introduces a new construct, “LTF,” to conceptualize, define and measure it, and further contributes to the application of capturing both expectation–confirmation model and CA (i.e. an intrinsic motivator) for completely explaining medical professionals’ perceived TTF and LTF as external variables to their cloud-based e-learning continuance intention and performance outcomes.

This study looks to understand the opposing forces that would influence continuance intention. This is significant as users will take into account the positive and negative use experiences in determining their continuance intention. Therefore, this study looks to highlight the opposing forces of users’ continuance intention by proposing the Expectation-Confirmation-Resistance Model (ECRM).

Through an online survey, 411 responses were obtained from mobile payment users. Subsequently, a hybrid approach comprised of the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was utilized to analyze the data.

The results revealed that all hypotheses proposed in the ECRM are supported. More precisely, the facilitating and inhibiting variables were found to significantly affect continuance intention. In addition, the ECRM was revealed to possess superior explanatory power over the original model in predicting continuance intention.

This study successfully developed and validated the ECRM which captures both facilitators and inhibitors of continuance intention. Besides, the relevance and significance of users’ innovative resistance to continuance intention have been highlighted. Following this, effective business and research strategies can be developed by taking into account the opposing forces that affect users’ continuance intention.