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The study has two aims: first, it aimed to investigate the impact of contextual factors (such as information quality, service quality, system quality, trust in applications (app) and COVID-19 health anxiety) on the intention to use the Mobile Payment (MP) app, and subsequently, the actual use of the app. Second, the aim of this study is whether the COVID-19 threat has a moderating influence on the relationship between customers' intent to use MP app and the actual use of MP app.

The data are collected through an online survey from 341 Mobile Banking (MB) app users from Pakistan to empirically analyze the relationship between service quality, system quality, information quality, trust in the app, COVID-19 health anxiety and COVID-19 threat, intentions to use MB-app and actual use of MB-app.

The empirical analysis of the data collected from MB-app users from Pakistan shows that service quality, system quality, information quality, trust in the app and COVID-19 health anxiety positively related to intentions to use MB-app, consequently affect the actual use of MB-app. Furthermore, the results demonstrate that the COVID-19 threat positively moderates the relationship between intentions to use MB-app and actual use of MB-app.

Although, prior research established a positive impact of mobile apps on customer service and consumer satisfaction. Yet, it is not clear which factors influence customers to adopt MB-app. This study contributes to the research on MB-apps based on adaptive structuration theory and examines the technological factors and contextual factors that collectively explain when and how individuals decide to adopt MB-app.

This study aims to investigate the consumer behavior toward telemedicine services in India during the COVID-19 pandemic onset. With lockdown restrictions and safety concerns in visiting brick-and-mortar clinics or hospitals during the pandemic, Telemedicine had emerged as a potent alternative for seeking redressal to health issues. Based on theory and focus interviews with the telemedicine users, the researchers proposed a model to understand the intent and actual usage of telemedicine in India.

The cross-sectional study undertaken used a questionnaire designed on a seven-point Likert scale and administered to respondents with the objective of identifying the determinants of intent and actual usage of telemedicine services. Simple random sampling was used to collect primary data. The data was cleaned and finally a sample of 405 responses complete in all respects was considered for analysis. The questionnaire comprised of 34 items and following the recommendation of Hair et al. (2016), which says the minimum sample size in structural equation modeling should be ten times the number of indicator variables, a sample size of 405 was deemed adequate.

The research paper finds that performance expectancy, attitude, credibility and self-efficacy positively impact the intention of consumers to use telemedicine services. As the effort expectancy or risk perception toward telemedicine increases the intent and actual usage of telemedicine decreases. The intention to use telemedicine emerged as a strong predictor of the actual usage of telemedicine. Intent to use telemedicine was explained 81.4% by its predictors of performance expectancy, effort expectancy, attitude, risk, credibility and self-efficacy, and actual usage was explained 79.9% by its predictors. This study also reports that telemedicine was found to be popular among chronic as well as episodic patients though the preference was skewed in favor of the episodic patients. One of the advantages of telemedicine is its availability round the clock, and the study found that 8 a. m. to 12 noon time slot as the most preferred slot for seeking telemedicine services.

Chang (2004) opined that telemedicine can fulfill the needs of all stakeholders: citizens, health-care consumers, medical doctors and health-care professionals, policymakers, and so on. Considering the promise telemedicine holds, this realm must be studied and leveraged to the full potential. The study found that patients were using telemedicine even for their day-to-day aliments. This indicates a growing popularity of telemedicine and as such an opportunity for telemedicine companies to leverage it. In India, pharmaceutical companies cannot give commercial advertisements for medicines, and the same can only be sold through a registered medical practitioner’s prescription. As such there is total dependency on the medical practitioner for the sale of medicines. Telemedicine companies offer services of home delivering medicines clubbed with medical consultation thus giving them forward integration in their business models. Using telemedicine the patients had control over the timings of the services offered, and as such the waiting time to get a consultation and subsequent treatment was reduced considerably. Best medical advice from across the globe is available to the patient at less cost. Medical practitioners also stand to benefit as they can treat a variety of cases, collaborate among the medical fraternity and give consultation safely in case of fatal contagious diseases.

This study points to a definite growing popularity of telemedicine services not only in episodic patients but also chronic patients. Telemedicine with its unique advantages holds the promise to grow exponentially in the future and is a compelling health-care segment to focus on for delivering health-care solution to the geographically distant consumers.

With the rise of cryptocurrency and its influence on the financial industry, this paper aims to explore cryptocurrency affordances that lead to approach–avoidance behavioral intentions for non-users (potential) and the intention to continue use for users (actual), drawing upon affordance theory and chasm theory.

The authors collected data from 480 potential and actual users in Germany and used maximum likelihood structural equation modeling (ML-SEM) to analyze it. In particular, the data consisted of 301 cryptocurrency users in Germany\ the authors used ML-SEM to test the post-adoption model. Additionally, logistic regression was utilized to determine the dominant actual usage method (store of value or medium of exchange) for various cryptocurrency coins.

According to the study's results, the perceived value benefits have a positive impact on the behavioral intention of potential users to adopt cryptocurrency, and they influence the intention of actual users to continue using it. However, both perceived volatility and financial risk tolerance are the most crucial factors hindering cryptocurrency adoption, whether in the pre-adoption or the post-adoption stage.

This is the first study to reveal cryptocurrency affordances and examine their effect on behavioral intentions toward cryptocurrency adoption based on the differences between non-users (potential) and users (actual). Furthermore, the authors explore how cryptocurrency holders perceive and invest in different coins (e.g. NFTs), which sheds light on factors such as financial risk tolerance that affect their decision making.

The purpose of this study was to identify existing users' acceptance of the multidimensional health and fitness features of wrist-worn wearable devices (WWDs) required for each stage of physical activity (i.e. before, during and after) and examine the relationship between its acceptance (i.e. knowledge acquisition, perceived usefulness and perceived ease of use) and the actual use of its health and fitness attributes.

Both qualitative and quantitative approaches were taken to analyze the relationships. A focus group interview was conducted (N = 9) to design the research model, including the operationalized definition of the study constructs. A questionnaire survey was conducted with respondents in South Korea (N = 480). Partial least squares structural equation modeling via Smart PLS 3.0 was employed to test the hypotheses.

When users learned to use fitness functions and perceived them as useful for physical activity without causing any difficulty, they tended to use those functions more, which provided enhanced health benefits in the digitalized interactive environment of WWDs.

This research is one of the first to examine the relationship between the perceived user value of WWDs and their actual usage within a digitalized and interactive environment. The results are expected to offer theoretical insights into how well users accept the health and fitness components of WWDs. Practically, it will build awareness of what makes users adopt and use WWDs, helping practitioners design better health promotions and campaigns associated with WWDs.

The purpose of this study is to investigate the factors affecting behaviour intention (BI) to use and actual usages of investment-related FinTech services among the zoomers (Gen Z) and millennials (Gen M) retail investors of India.

The study explores the predictive relevance of actual adoption behaviour among the two different age categories of Indian retail investors. It uses the Unified Theory of Acceptance and Use of Technology-2 and the prospect theory framework as guiding frameworks. Data has been collected from 294 retail investors, actively engaged in the investment-related FinTech services. The multi-group analysis using variance-based partial least square structured equation modelling has been used to compare the two groups. The invariance between the two groups was achieved through measurement invariance assessment.

The study reveals distinct factors significantly affecting BI to use investment-related FinTech services among Gen Z and Gen M retail investors are performance expectancy (PE) to BI, perceived risk (PR) to BI, price value (PV) to BI and PR to service trust (ST).

This study provides insights for financial providers and policymakers, emphasizing different factors influencing BI to use investment-related FinTech services in both age groups. Notably, habit emerges as a common factor influencing the actual usage of investment-related FinTech services across Gen M and Gen Z retail investors in India.

This study explores the heterogeneous behaviour of the heterogenous population in the domain of technological adoption of investment-related FinTech services in India.

The objective of this study was to analyse the effects of major determinants on VCT adoption intention among higher educational leaders. Also, this study aims to investigate how perceived risk and perceived benefits influence the educational leaders, VCT actual use intention.

The authors used the online survey method to collect the 440 responses through purposive sampling procedure. Structural equation modelling (SEM) technique and Multi Group Analysis procedure were used to test the proposed model and moderating effects.

The findings revealed that all the four determinants (PE, EE, SI and FC) based on UTAUT model have positive and significant effects on intention to use VCT. Besides this there is a significant and positive effect of intention to use VCT on actual usage of VCT by the educational leaders. The moderating effect of perceived risk and perceived benefits on actual usage of virtual communication also found significant.

This paper makes its contribution to the literature related to virtual communication technology adoption by including two moderator variables (perceived risk and benefits) that are expected to affect educational leaders' actual usage of VCT. The results can also help researchers and practitioners better understanding the factors that influence higher educational leaders to adopt VCT.

This study proposed a model incorporating the perceived risk and perceived benefits in the UTAUT model to predict the actual use of VCT. The study endeavours to investigate the moderating effects of perceived risk and perceived benefits between ITUVCT and AUVCT in Indian educational context.

The emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified theory of acceptance and use of technology to explore how effort expectancy (EEC) and performance expectancy (PEE) individually, jointly, congruently and incongruently affect higher education students’ intentions and actual uses of ChatGPT for their learning.

An advanced methodology – polynomial regression with response surface analysis – and a sample of 1,461 higher education students recruited in Vietnam through three-phase stratified random sampling approach were adopted to test developed hypotheses.

Both EEC and PEE were found to have a direct positive impact on the likelihood of higher education students’ intention to use ChatGPT, which in turn promotes them actually use this tool for learning purposes. Conversely, a large incongruence between EEC and PEE will lower the level of intentions and actual uses of ChatGPT for learning. However, when there is a growing incongruence between EEC and PEE, either in a positive or negative direction, the likelihood of students’ intentions to use ChatGPT for learning decreases.

Some practical implications are subsequently recommended to obtain advantages and address potential threats arising from the implementation of this novel technology in the education context.

This study shed the new light on the educational setting by testing how higher education students’ intentions to use ChatGPT and subsequent actual uses of ChatGPT are synthesized from the balance between high EEC and PEE.

This study aims to examine how the theory of planned behaviour and technology acceptance theory can be used to understand the adoption of non-pharmaceutical interventions (NPIs). The relationships between attitudes, behavioural intentions towards using NPIs, actual use of NPIs and word-of-mouth (WOM) were examined and compared between early and late adopters.

A survey was conducted to test the hypotheses with partial least squares structural equation modelling (n = 278).

The results indicate that relationships between attitudes, intentions and behavioural intentions were positive and significant in the whole data set – and that there were differences between the early and late adopters. WOM had no substantial relationship with actual usage and early adopters’ behavioural intentions.

This research gives a better sense of how WOM impacts attitudes, behavioural intentions and actual usage among early and late adopters of NPIs and highlights the effectiveness of WOM, especially among late adopters of NPIs. Furthermore, using the TAM allows us to make specific recommendations regarding encouraging the use of NPIs. A new three-stage communications model is introduced that uses early adopters as influencers to reduce the NPI adoption time by late adopters.

This study aims to investigate how artificial intelligence (AI)-powered smart home devices affect young consumers' requirements for convenience, support, security and monitoring, as well as their ability to advance environmental sustainability. This study also examines the variables that impact users' motivation to use AI-powered smart home devices, such as perceived value, ease of use, social presence, identity, technology security and the moderating impact of trust.

The responses from residents of Sarawak, Malaysia, were collected through online questionnaires. This study aimed to examine the perceptions of millennials and zillennials towards their trust and adoption of AI-powered devices. This study used a quantitative approach, and the relationships among the study constructs were analysed using partial least squares - structural equation modelling.

The present study found that perceived usefulness, ease of use and social presence were the main motivators among actual and potential users of smart home devices, especially in determining their intentions to use and actual usage. Additionally, there was a moderating effect of trust on the relationship between perceived ease of use, social presence, social identity and intention to use AI-powered devices in smart homes.

To the best of the authors’ knowledge, this is one of the first studies to examine the factors influencing smart technology adoption. This study provided meaningful insights on the development of strategies for the key stakeholders to enhance the adoption and usage of AI-powered smart home devices in Sarawak, one of the promising Borneo states. Additionally, this study contributed to the growing body of knowledge on the associations between technology acceptance model dimensions, intention and actual usage of smart technology, with the moderating impact of trust.

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.