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Due to the rapid growth of blockchain technology in recent years, the fusion of blockchain and the Internet of Things (BIoT) has drawn considerable attention from researchers and industrial practitioners and is regarded as a future trend in technological development. Although several authors have conducted literature reviews on the topic, none have examined the development of the knowledge structure of BIoT, resulting in scattered research and development (R&D) efforts.

This study investigates the intellectual core of BIoT through a co-citation proximity analysis–based systematic review (CPASR) of the correlations between 44 highly influential articles out of 473 relevant research studies. Subsequently, we apply a series of statistical analyses, including exploratory factor analysis (EFA), hierarchical cluster analysis (HCA), k-means clustering (KMC) and multidimensional scaling (MDS) to establish the intellectual core.

Our findings indicate that there are nine categories in the intellectual core of BIoT: (1) data privacy and security for BIoT systems, (2) models and applications of BIoT, (3) system security theories for BIoT, (4) frameworks for BIoT deployment, (5) the fusion of BIoT with emerging methods and technologies, (6) applied security strategies for using blockchain with the IoT, (7) the design and development of industrial BIoT, (8) establishing trust through BIoT and (9) the BIoT ecosystem.

We use the CPASR method to examine the intellectual core of BIoT, which is an under-researched and topical area. The paper also provides a structural framework for investigating BIoT research that may be applicable to other knowledge domains.

Although knowledge based on business intelligence (BI) is crucial, few studies have explored the core of BI knowledge; this study explores this topic.

The authors collected 1,306 articles and 54,020 references from the Web of Science (WoS) database and performed co-citation analysis to explore the core knowledge of BI; 52 highly cited articles were identified. The authors also performed factor and cluster analyses to organize this core knowledge and compared the results of these analyses.

The factor analysis based on the co-citation matrix revealed seven key factors of the core knowledge of BI: big data analytics, BI benefits and success, organizational capabilities and performance, information technology (IT) acceptance and measurement, information and business analytics, social media text analytics, and the development of BI. The cluster analysis revealed six categories: IT acceptance and measurement, BI success and measurement, organizational capabilities and performance, big data-enabled business value, social media text analytics, and BI system (BIS) and analytics. These results suggest that numerous research topics related to big data are emerging.

The core knowledge of BI revealed in this study can help researchers understand BI, save time, and explore new problems. The study has three limitations that researchers should consider: the time lag of co-citation analysis, the difference between two analytical methods, and the changing nature of research over time. Researchers should consider these limitations in future studies.

This study systematically explores the extent to which scholars of business have researched and understand BI. To the best of the authors’ knowledge, this is one of the first studies to outline the core knowledge of BI and identify emerging opportunities for research in the field.

Considering both online and offline service scenarios, this study aims to explore the factors affecting doctors' intention to offer consulting services in eHealth and compare the factors between the free- and paid-service doctors. The theory of reasoned action and social exchange theory are integrated to develop the research model that conceptualizes the role of extrinsic motivations, intrinsic motivations, costs, and attitudes in doctors' behavioral intentions.

Partial least square structural equation modeling (PLS-SEM) was leveraged to analyze 326 valid sample data. To provide robust results, three non-parametric multigroup analysis (MGA) methods, including the PLS-MGA, confidence set, and permutation test approaches, were applied to detect the potential heterogeneity between the free- and paid-service doctors.

The results with overall samples reveal that anticipated rewards, anticipated associations, anticipated contribution, and perceived fee are all positively related to attitude, which in turn positively influences behavioral intention, and that perceived fee positively moderates the relationship between attitude and behavioral intention. Attitude's full mediation is also confirmed. However, results vary between the two groups of doctors. The three MGA approaches return relatively convergent results, indicating that the effects of anticipated associations and perceived fee on attitude are significantly larger for the paid-service doctors, while that of anticipated rewards is found to be significantly larger for the free-service doctors.

eHealth, as a potential contactless alternative to face-to-face diagnoses, has recently attracted widespread attention, especially during the continued spread of COVID-19. Most existing studies have neglected the underlying heterogeneity between free- and paid-service doctors regarding their motivations to engage in online healthcare activities. This study advances the understanding of doctors' participation in eHealth by emphasizing their motivations derived from both online and offline service scenarios and comparing the differences between free- and paid-service doctors. Besides, horizontally comparing the results by applying diverse MGA approaches enriches empirical evidence for the selection of MGA approaches in PLS-SEM.

The purpose of this paper is to investigate factors that influence the patients’ intentions to visit doctors face-to-face for consultations from the perspective of online doctor–patient interaction. Justice theory, SERVQUAL and the halo effect are integrated to develop a research model based on the performance-evaluation-outcome framework. The authors hypothesize that perceived justice and service quality are the significant factors in reflecting the performance of online doctor–patient interaction, which influences patient satisfaction evaluation and online and offline behavioral intentions.

The study conducted an online survey to collect data. Patients on a healthcare consulting website were invited to participate in the survey. The research model and hypotheses were tested with 254 collected data from patients and analyzed using the partial least squares method.

The results show that perceived justice and service quality have a positive effect on patient satisfaction, and satisfaction and the intention of online consultation have a positive effect on the intention of face-to-face consultation.

This study offers suggestions on how doctors interact with patients and build their brand image. The findings also offer effective insights into improving doctors’ online services to retain patients and even encourage patients to go to clinics.

Online health consultation is one of the most popular online health services and is growing quickly. After patients consult online doctors, they are able to visit their doctors in person for further diagnosis and treatment if they have the need. This study investigates how patients’ online interactive experience influences their offline behavioral intentions, which are different from most of the past literature on eHealth.

Reading represents a basic way by which humans understand the world and acquire knowledge; it is also central to learning and communicating. However, with the rapid development of mobile reading, an individual's cognition of objective facts may be affected by the reading environment and text genre, resulting in limited memorization and understanding of the reading material. Therefore, this study aimed to investigate the influence of the reading environment and text genre on individuals' cognitive activities from the perspective of motivational activation level using evidence from electroencephalography (EEG) signals.

The study employed a mixed design experiment with two reading environments (quiet and distracting) between subjects, two text genres (entertaining and scientific) within subjects and two reading tasks (memory recall and comprehension) within subjects. There were 50 participants in the experiment, and the data obtained from 44 participants while they read the materials and completed the reading tasks were analyzed.

The results showed that readers are more positively motivated to read in a quiet reading environment than in a distracting reading environment when facing the memory recall tasks of entertaining genre passages and comprehension tasks of scientific genre passages. Entertaining genres are more likely to arouse readers' reading interest but hinder the memory recall of the content details. While scientific genres are not easy to understand, they are helpful for working memory.

This study not only applies a new technology to mobile reading research in the field of library science and addresses the limitations of self-report data, but also provides suggestions for the further improvement of mobile reading service providers. Additionally, the results may provide useful information for learners with different learning demands.

The purpose of this study is to clarify theory and identify factors that could explain the level of fintech continuance intentions with an expectation confirmation model that integrates self-efficacy theory.

With data collected from 753 fintech users, this study applies partial least square structural equation modeling to compare and select the research model with the most predictive power.

The results show that financial self-efficacy, technological self-efficacy and confirmation positively affect perceived usefulness. Among these factors, financial self-efficacy and technological self-efficacy have both direct and indirect effects through confirmation on perceived usefulness. Perceived usefulness and confirmation are positively related to satisfaction. Finally, perceived usefulness and satisfaction positively influence fintech continuance intentions.

To the best of our knowledge, this is one of the earliest studies that investigates the effect of domain-specific self-efficacy on fintech continuance intentions, which enriches the existing research on fintech and deepens our understanding of users' fintech continuance intentions. We distinguish between financial self-efficacy and technological self-efficacy and specify the relationship between self-efficacy and continuance intentions. Moreover, this study highlights the importance of assessing a model's predictive power using the PLSpredict technique and provides a reference for model selection.

Can mobile instant messaging (MIM) make people entering into the state of cognitive absorption (CA)? The purpose of this paper is to investigate whether CA can help explain users’ satisfaction during the process of MIM, while interactivity and interest are operated as determinants of CA as well as directly associated with satisfaction.

This study proposes a satisfaction model that is adapted from the CA theory to investigate MIMs users’ satisfaction with two determinants, interactivity and interest. Specifically, CA is operated as a second-order formative construct with four reflective dimensions, including curiosity, focused immersion, heightened enjoyment and temporal dissociation. Partial least square structural equation modeling was applied to evaluate the causal links of the model with the data collected from 472 LINE users who all had long using experience.

The results showed that CA in MIM, fueled by interactivity and interest, is positively related to satisfaction. Interactivity and interest themselves were also significantly associated with satisfaction. Among them, interactivity has the most influence on satisfaction, followed by interest and CA. Surprised, curiosity and focused immersion did not formative CA in MIM.

The present study focuses on user satisfaction of a specific MIM (LINE) and collects data from users within a specific region (Taiwan). Other researchers must take these constrains into consideration when referencing this study.

To the best of the authors’ knowledge, this is the first study which confirmed that people still enter into the state of CA when using MIM on smartphone, even though the using environment is drastically different from that on desktop. It indicates that prior theories in CA with desktop-based software are still applicable and serve as a basis for more studies in the mobile context to a certain extent, but other factors should also be considered. As interactivity and interest are conducive to CA, leading to user satisfaction, an MIM app can be more popular if the two factors are incorporated.

Emerging social networking sites (SNSs) are less advantageous than leading SNSs in attracting users. They might stand a better chance if they know what users want. The purpose of this paper is to study factors that urge Facebook users to switch to Instagram to reveal how latecomers in the SNSs circuit can win the hearts of users.

This study proposes an SNS switching framework that is adapted from the demographic Push-Pull (PP) migration model to investigate users’ switching intention. Structural equation modeling was applied to analyze the data collected from 260 Instagrammers who all had experience using Facebook before moving on Instagram.

Results show that socializing and system quality of the SNS negatively affect users’ switching intention, while attractiveness of the alternative, peer influence and critical mass do the opposite. Surprisingly, enjoyment is not associated with switching intention.

SNSs switching may not mean a complete abandonment of previous SNSs. In many occasions, users simply become less active in one SNS and more active in other SNSs. The PP migration model provides a useful tool to understand the patterns as well as competing forces that influence the migration of SNS users, pushing them away or pulling them to new alternative sites. Specifically, pulling demonstrates to be a stronger influence than pushing.

This study suggests that SNS operators should satisfy users’ needs for socializing, maintain high system quality, provide peer influence tools and create their own attractive features, in order to retain existing customers or induce new users to switch.

This is one of the earlier empirical studies to investigate users’ switching intention from Facebook to Instagram with a valid sample. In addition, the present study approaches pull and push effects by multiple constructs, providing a clearer picture of what constitutes the pull and push forces.