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IntroductionRecent advances in generative Artificial Intelligence (AI) and Natural Language Processing (NLP) have led to the development of Large Language Models (LLMs) and AI-powered chatbots like ChatGPT, which have numerous practical applications. Notably, these models assist programmers with coding queries, debugging, solution suggestions, and providing guidance on software development tasks. Despite known issues with the accuracy of ChatGPT’s responses, its comprehensive and articulate language continues to attract frequent use. This indicates potential for ChatGPT to support educators and serve as a virtual tutor for students.MethodsTo explore this potential, we conducted a comprehensive analysis comparing the emotional content in responses from ChatGPT and human answers to 2000 questions sourced from Stack Overflow (SO). The emotional aspects of the answers were examined to understand how the emotional tone of AI responses compares to that of human responses.ResultsOur analysis revealed that ChatGPT’s answers are generally more positive compared to human responses. In contrast, human answers often exhibit emotions such as anger and disgust. Significant differences were observed in emotional expressions between ChatGPT and human responses, particularly in the emotions of anger, disgust, and joy. Human responses displayed a broader emotional spectrum compared to ChatGPT, suggesting greater emotional variability among humans.DiscussionThe findings highlight a distinct emotional divergence between ChatGPT and human responses, with ChatGPT exhibiting a more uniformly positive tone and humans displaying a wider range of emotions. This variance underscores the need for further research into the role of emotional content in AI and human interactions, particularly in educational contexts where emotional nuances can impact learning and communication.

Fitness apps are persuasive tools developed to motivate physical activity. Despite their popularity, there is little work on how social-cognitive characteristics such as culture, household size, physical activity level, perceived self-efficacy and social support influence users’ willingness to use them and preference (personal vs. social). Knowing these relationships can help developers tailor fitness apps to different socio-cultural groups. Hence, we conducted two studies to address the research gap. In the first study (n = 194) aimed at recruiting participants for the second study, we asked participants about their app preference (personal vs. social), physical activity level and key demographic variables. In the second study (n = 49), we asked participants about their social-cognitive beliefs about exercise and their willingness to use a fitness app (presented as a screenshot). The results of the first study showed that, in the collectivist group (Nigerians), people in large households were more likely to be active and use the social version of a fitness app than those in small households. However, in the individualist group (Canadians/Americans), neither the preference for the social or personal version of a fitness app nor the physical activity level depended on the household size. Moreover, in the second study, in the individualist model, perceived self-efficacy and perceived self-regulation have a significant total effect on willingness to use a fitness app. However, in the collectivist model, perceived social support and outcome expectation have a significant total effect on the target construct. Finally, we found that females in individualist cultures had higher overall social-cognitive beliefs about exercise than males in individualist cultures and females in collectivist cultures. The implications of the findings are discussed.

Abstract Gamification refers to the attempt to transform different kinds of systems to be able to better invoke positive experiences such as the flow state. However, the ability of such intervention to invoke flow state is commonly believed to depend on several moderating factors including the user’s traits. Currently, there is a dearth of research on the effect of user traits on the results of gamification. Gamer types (personality traits related to gaming styles and preferences) are considered some of the most relevant factors affecting the individual’s susceptibility to gamification. Therefore, in this study we investigate how gamer types from the BrainHex taxonomy (achiever, conqueror, daredevil, mastermind, seeker, socializer and survivor) moderate the effects of personalized/non-personalized gamification on users’ flow experience (challenge-skill balance, merging of action and awareness, clear goals, feedback, concentration, control, loss of self-consciousness and autotelic experience), enjoyment, perception of gamification and motivation. We conducted a mixed factorial within-subject experiment involving 121 elementary school students comparing a personalized version against a non-personalized version of a gamified education system. There were no main effects between personalization and students’ flow experience, perception of gamification and motivation, and enjoyment. Our results also indicate patterns of characteristics that can lead students to the high flow experience (e.g., those who prefer to play multiplayer have a high flow experience in both personalized and non-personalized versions). Based on our results, we provided recommendations to advance the design of gamifed educational systems.

Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modeling, decision support systems, adaptive systems, and evaluation systems. The growing need for dynamic assessment of student characteristics in online educational systems has led to application of machine learning methods in modeling the characteristics. Being able to automatically model student characteristics during learning processes is essential for dynamic and continuous adaptation of teaching and learning to each student's needs. This paper provides a review of 8 years (from 2015 to 2022) of literature on the application of machine learning methods for automatic modeling of various student characteristics. The review found six student characteristics that can be modeled automatically and highlighted the data types, collection methods, and machine learning techniques used to model them. Researchers, educators, and online educational systems designers will benefit from this study as it could be used as a guide for decision-making when creating student models for adaptive educational systems. Such systems can detect students' needs during the learning process and adapt the learning interventions based on the detected needs. Moreover, the study revealed the progress made in the application of machine learning for automatic modeling of student characteristics and suggested new future research directions for the field. Therefore, machine learning researchers could benefit from this study as they can further advance this area by investigating new, unexplored techniques and find new ways to improve the accuracy of the created student models.

Background In the collaborative business environment, blockchain coupled with smart contract removes the reliance on a central system and offers data integrity which is crucial when the transacting parties rely on the shared data. The acceptance of such blockchain-based systems is necessary for the continued use of the services. Despite many extensive studies evaluating the performance of blockchain-based systems, few have focused on users’ acceptance of real-life applications. Objective The main objective of this research is to evaluate the user acceptance of a real-life blockchain-based system (BBS) by observing various latent variables affecting the development of users’ attitudes and intention to use the system. It also aims to uncover the dimensions and role of trust, security and privacy alongside the primary Technology Acceptance Model (TAM)-based predictors and their causal relationship with the users’ behavior to adopt such BBS. Methods We tested the augmented TAM with Trust Model on a BBS that comprises two subsystems: a Shopping Cart System (SCS), a system oriented towards end-users and a Data Sharing System (DSS), a system oriented towards system administrators. We set research questions and hypotheses, and conducted online surveys by requesting each participant to respond to the questionnaire after using the respective system. The main study comprises two separate sub-studies: the first study was performed on SCS and the second on DSS. Furthermore, each study data comprises initial pre-test and post-test data scores. We analyzed the research model with partial least square structural equation modelling. Results The empirical study validates our research model and supports most of the research hypotheses. Based on our findings, we deduce that TAM-based predictors and trust constructs cannot be applied uniformly to BBS. Depending on the specifics of the BBS, the relationships between perceived trust antecedents and attitudes towards the system might change. For SCS, trust is the strongest determinant of attitudes towards system, while DSS has perceived privacy as the strongest determinant of attitudes towards system. Quality of system shows the strongest total effect on intention to use SCS, while perceived usefulness has the strongest total effect on intention to use DSS. Trust has a positive significant effect on users’ attitudes towards both BSS, while security does not have any significant effect on users’ attitudes toward BBS. In SCS, privacy positively affects trust, but security has no significant effect on trust, whereas, in DSS, both privacy and security have significant effects on trust. In both BBS, trust has a moderating effect on privacy that correlates with attitudes towards BBS, whereas security does not have any mediating role between privacy and attitudes towards BBS. Hence, we recommend that while developing BBS, particular attention should be paid to increasing user trust and perceived privacy.

Persuasive strategies are used to influence the behavior or attitude of people without coercion and are commonly used in online systems such as e-commerce systems. However, in order to make persuasive strategies more effective, research suggests that they should be tailored to groups of similar individuals. Research in the traits that are effective in tailoring or personalizing persuasive strategies is an ongoing research area. In the present study, we propose the use of shoppers' online shopping motivation in tailoring six commonly used influence strategies: scarcity, authority, consensus, liking, reciprocity, and commitment. We aim to identify how these influence strategies can be tailored or personalized to e-commerce shoppers based on the online consumers' motivation when shopping. To achieve this, a research model was developed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) and tested by conducting a study of 226 online shoppers. The result of our structural model suggests that persuasive strategies can influence e-commerce shoppers in various ways depending on the shopping motivation of the shopper. Balanced buyers—the shoppers who typically plan their shopping ahead and are influenced by the desire to search for information online—have the strongest influence on commitment strategy and have insignificant effects on the other strategies. Convenience shoppers—those motivated to shop online because of convenience—have the strongest influence on scarcity, while store-oriented shoppers—those who are motivated by the need for social interaction and immediate possession of goods—have the strongest influence on consensus. Variety seekers—consumers who are motivated to shop online because of the opportunity to search through a variety of products and brands, on the other hand, have the strongest influence on authority.

We propose a new platform for user modeling with blockchains that allows users to share data without losing control and ownership of it and applied it to the domain of travel booking. Our new platform provides solution to three important problems: ensuring privacy and user control, and incentives for sharing. It tracks who shared what, with whom, when, by what means and for what purposes in a verifiable fashion. The paper presents a case study of applying the framework for a hotel reservation system as one of the enterprise nodes of Multichain which collects users' profile data and allows users to receive rewards while sharing their data with other travel service providers according to their privacy preferences expressed in smart contracts. The user data from the repository is converted into an open data format and shared via stream in the blockchain so that other nodes can efficiently process and use the data. The smart contract verifies and executes the agreed terms of use of the data and transfers digital tokens as a reward to the user. The smart contract imposes double deposit collateral to ensure that all participants act honestly. The paper also presents a performance evaluation of the new platform by analyzing latency and memory consumption with selected three test-scenarios and measuring the transaction cost for smart contracts deployment. The results show that the node responded quickly in all our cases with a befitting transaction cost.

The number of fitness applications on the market is increasing annually, driven by the increasing awareness of the need to support and motivate physical activity to reduce the incidence of non-communicable diseases worldwide. However, there is limited research on the user-experience (UX) design attributes that drive their adoption and the moderating role culture plays. Consequently, we conducted a study on the Persuasive Technology Acceptance Model (PTAM) for a fitness application aimed at motivating physical activity at home. Using Canada (an individualist culture, n = 189) and Nigeria (a collectivist culture, n = 67) as a case study, we investigated: (1) which of the commonly researched UX design attributes (perceived aesthetics, perceived usability, perceived credibility and perceived usefulness) have the strongest influence on users' intention to use a fitness application; (2) the moderating effect of culture; and (3) how perceived persuasiveness mediates the direct effect of perceived usefulness on the intention to use a fitness application. The results of our path analysis show that, regardless of culture, perceived usefulness and perceived aesthetics are the strongest determinants of users' intention to use a fitness application, with perceived usefulness being stronger in the collectivist culture than in the individualist culture. Secondly, our results show that perceived persuasiveness partially mediates the effect of perceived usefulness on intention to use for the individualist culture, but not for the collectivist culture. Hence, we recommend that designers should invest more in improving functionality (utilitarian benefit) and aesthetics (hedonic benefit) than other UX design attributes such as credibility and usability. However, for the collectivist culture, designers should focus more on usefulness than aesthetics. On the other hand, for the individualist culture, designers should strike a balance between usefulness and aesthetics. Our main contribution is that, our study, to the best of our knowledge, is the first to investigate the moderating effect of culture using subjects from North America and Africa (an understudied population) as a case study.

Fitness applications aimed at behavior change are becoming increasingly popular due to the global prevalence of sedentary lifestyles and physical inactivity, causing countless non-communicable diseases. Competition is one of the most common persuasive strategies employed in such applications to motivate users to engage in physical activity in a social context. However, there is limited research on the persuasive system design predictors of users’ susceptibility to competition as a persuasive strategy for motivating behavior change in a social context. To bridge this gap, we designed storyboards illustrating four of the commonly employed persuasive strategies (reward, social learning, social comparison, and competition) in fitness applications and asked potential users to evaluate their perceived persuasiveness. The result of our path analysis showed that, overall, users’ susceptibilities to social comparison (β T = 0.48, p

Research shows that a well-designed user interface is more likely to be persuasive than a poorly designed one. However, there is a limited understanding of the relationship between user-experience (UX) design attributes and users’ receptiveness to the persuasive features of a persuasive technology aimed at motivating behavior change. To bridge this gap, we carried out an online case study among 228 participants from Canada and the United States to investigate the relationship between perceived UX design attributes and users’ receptiveness to persuasive features. The study serves as exploratory work by focusing on a single prototype (homepage of a fitness app); four commonly researched UX design attributes (perceived aesthetics, perceived usability, perceived credibility, and perceived usefulness); and six commonly employed persuasive features (Goal-Setting/Self-Monitoring, Reward, Cooperation, Competition, Social Comparison, and Social Learning) illustrated on storyboards. The results of the Partial Least Square Path Modeling show that perceived usefulness, followed by perceived aesthetics, has the strongest relationship with users’ receptiveness to the persuasive features of a fitness app. Specifically, perceived usefulness and perceived aesthetics have a significant relationship with users’ receptiveness to all but two of the six persuasive features, respectively, as well as with the overall perceived persuasiveness of the fitness app. These findings are supported by participants’ comments on the perceived UX design attributes of the fitness app and the persuasive features illustrated on the storyboards. However, perceived usability and perceived credibility have weak or non-significant relationships with users’ receptiveness to the six persuasive features. The findings suggest that designers should prioritize utilitarian benefits (perceived usefulness) and hedonic benefits (perceived aesthetics) over perceived usability and perceived credibility when designing fitness apps to support behavior change.

Fitness apps equipped with various persuasive features have become popular worldwide due to the physical inactivity crisis. However, there is a limited understanding of the most important persuasive features that drive their adoption and the moderating effect of age and gender. To bridge this gap, we designed storyboards illustrating six of the commonly employed persuasive strategies in persuasive health applications: Goal-Setting/Self-Monitoring, Reward, Social Learning, Social Comparison, Competition and Cooperation. We conducted an empirical study in which we asked the participants to evaluate their receptiveness to the six persuasive features and their intention to use a fitness app that features them. The result of our Partial Least Square Path Modeling (PLSPM) shows that, overall, Goal-Setting/Self-Monitoring is the strongest predictor of the intention to use a fitness app, followed by Reward and Competition, both of which are in second place. However, Social Learning and Social Comparison turn out to be non-predictors of intention to use. Based on these findings, we recommend that a minimally viable (one-size-fits-all) fitness app, in a personal setting, should support a Goal-Setting/Self-Monitoring feature, coupled with a Reward feature, to increase its appeal to a wide audience. Moreover, in a social setting, it should support a Competition feature to increase its appeal to a wide audience. We discuss these findings and the gender and age differences in the relationships between users’ receptiveness to the six persuasive features and their intention to use a fitness app that support them.

In recent years, the employment of behavior models to motivate behavior change has become a global trend in fitness application design. However, there is hardly any large-scale study of these applications to understand users’ exercise-type preferences, their drivers and barriers, and the potential of employing them for gender-based tailoring. To bridge this gap, we conducted a mixed-method study among 669 participants to investigate users’ exercise-type preferences (their drivers and barriers) and how they and gender can impact users’ social-cognitive beliefs and projected performance of bodyweight exercises. Firstly, we presented to the study participants a behavior model performing push-up or squat bodyweight exercise in a fitness application and asked them to rate their perceived self-efficacy, self-regulation, outcome expectation, and projected (exercise) performance level as observers of the behavior model. Secondly, we presented the study participants with a preselected list of commonly employed exercise types in fitness applications and requested them to identify their most/least preferred, and the reasons behind their choices. Our results showed that there were differences between both genders in their exercise-type preferences, perceived self-efficacy and projected exercise performance level. Males prefer push-up, squat, crunch, plank, and chair dip the most, with effectiveness being the most important driver, followed by ease of performance and improvement of the physique, look, and appearance. On the other hand, females prefer squat, crunch, jumping jack, step up, and plank the most, with ease of performance being the most important driver, followed by improvement of the physique, look, appearance, and effectiveness. Moreover, males prefer running in place the least, while females prefer push-up the least, with perceived difficulty being the greatest barrier for both genders. Moreover, our analysis of variance supported the female’s least preference for a push-up. Females have a lower perceived self-efficacy and projected performance level for push-up than males. We discussed the implications of our findings and provided guidelines for tailoring fitness applications on the market to users’ preferences and gender.

Research has shown that persuasive technologies aimed at behavior change will be more effective if behavioral determinants are targeted. However, research on the determinants of bodyweight exercise performance in the context of behavior modeling in fitness apps is scarce. To bridge this gap, we conducted an empirical study among 659 participants resident in North America using social cognitive theory as a framework to uncover the determinants of the performance of bodyweight exercise behavior. To contextualize our study, we modeled, in a hypothetical context, two popular bodyweight exercise behaviors – push ups and squats – featured in most fitness apps on the market using a virtual coach (aka behavior model). Our social cognitive model shows that users’ perceived self-efficacy (β T = 0.23, p

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In e-commerce, the user interface design of a website is critical to its success. However, there is limited research on how colour and layout design elements influence the perception of e-commerce websites for mobile devices. To bridge this gap, we conducted an empirical study to investigate, how the layout of information and colour temperature of an e-commerce tourism website for mobile device influence essential Technology Acceptance Model (TAM) user experience (UX) design attributes and intention to use the website. The results of our Partial Least Square Path Modelling (PLSPM) showed that both interface design elements significantly influence perceived aesthetics, perceived enjoyment, perceived usefulness and intention to use. Specifically, layout (list = 0 and grid = 1) positively influences perceived aesthetics and perceived enjoyment, while colour temperature negatively influences perceived usefulness and intention to use. The first finding suggests that in tourism website design for mobile devices, a grid layout of products and services provides a better hedonic user experience than a list layout. Moreover, the second finding suggests that cooler-temperature (blue and green) tourism websites are viewed by users as more useful than warmer-temperature (orange and red) tourism websites. We discuss the implications of these findings in the context of website UX design for mobile devices in the tourism domain.

Persuasive technologies have been identified as a potential motivational tool to tackle the rising problem of physical inactivity worldwide, with research showing they are more likely to be successful if tailored to the target audience. However, in the physical activity domain, there is limited research on how culture moderates users’ susceptibility to the various persuasive features employed in mobile health applications aimed to motivate behavior change. To bridge this gap, we conducted an empirical study among 256 participants from collectivist (n = 67) and individualist (n = 189) cultures to determine their culture-specific persuasion profiles with respect to six persuasive features commonly employed in fitness applications on the market. The persuasive features include two personal features (goal-setting/self-monitoring and reward) and four social features (competition, cooperation, social learning and social comparison). We based our study on the rating of storyboards (on which each of the six persuasive features is illustrated) and the ranking of the six persuasive features in terms of perceived persuasiveness. The results of our analysis showed that users from individualist and collectivist cultures significantly differ in their persuasion profiles. Based on our rating measure, collectivist users are more likely to be susceptible to all six persuasive features (personal and social) than individualist users, who are only likely to be susceptible to personal features. However, based on our ranking measure, individualist users are more likely to be susceptible to personal features (goal-setting/self-monitoring and reward) than collectivist users. In contrast, collectivist users are more likely to be susceptible to social features (cooperation and social learning) than individualist users. Based on these findings, we provide culture-specific persuasive technology design guidelines. Our study is the first to uncover the moderating effect of culture on users’ susceptibility to commonly employed persuasive features in fitness applications.