Adaptive treatment strategies (ATS) are sequential decision-making processes that enable personalized care by dynamically adjusting treatment decisions in response to evolving patient symptoms. While reinforcement learning (RL) offers a promising approach for optimizing ATS, its conventional online trial-and-error learning mechanism is not permissible in clinical settings due to risks of harm to patients. Offline RL tackles this limitation by learning policies exclusively from historical treatment data, but its performance is often constrained by data scarcity—a pervasive challenge in clinical domains. To overcome this, we propose Treatment Stitching (TreatStitch), a novel data augmentation framework that generates clinically valid treatment trajectories by intelligently stitching segments from existing treatment data. Specifically, TreatStitch identifies similar intermediate patient states across different trajectories and stitches their respective segments. Even when intermediate states are too dissimilar to stitch directly, TreatStitch leverages the Schrödinger bridge method to generate smooth and shortest possible bridging trajectories that connect dissimilar states. By augmenting these synthetic trajectories into the original dataset, offline RL can learn from a more diverse dataset, thereby improving its ability to optimize ATS. Extensive experiments across multiple treatment datasets demonstrate the effectiveness of TreatStitch in enhancing offline RL performance. Furthermore, we provide a theoretical justification showing that TreatStitch maintains clinical validity by avoiding out-of-distribution transitions.

The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images. Previous approaches for radiology report generation have shown impressive performance. However, there remains significant potential to improve accuracy by ensuring that retrieved reports contain disease-relevant findings similar to those in the X-ray images and by refining generated reports. In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation (DART) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive learning. This approach ensures the retrieval of reports with similar disease-relevant findings that closely align with the input X-ray images. In the second stage, we further enhance the initial reports by introducing a self-correction module that re-aligns them with the X-ray images. Our proposed framework achieves state-of-the-art results on two widely used benchmarks, surpassing previous approaches in both report generation and clinical efficacy metrics, thereby enhancing the trustworthiness of radiology reports.

Molecular discovery has attracted significant attention in scientific fields for its ability to generate novel molecules with desirable properties. Although numerous methods have been developed to tackle this problem, most rely on an online setting that requires repeated online evaluation of candidate molecules using the oracle. However, in real-world molecular discovery, the oracle is often represented by wet-lab experiments, making this online setting impractical due to the significant time and resource demands. To fill this gap, we propose the Molecular Stitching (MolStitch) framework, which utilizes a fixed offline dataset to explore and optimize molecules without the need for repeated oracle evaluations. Specifically, MolStitch leverages existing molecules from the offline dataset to generate novel `stitched molecules' that combine their desirable properties. These stitched molecules are then used as training samples to fine-tune the generative model using preference optimization techniques. Experimental results on various offline multi-objective molecular optimization problems validate the effectiveness of MolStitch. The source code is available online.

Two-photon calcium imaging is a powerful technique for recording neuronal activities over extended periods. However, reliably capturing neuronal signals from the this data poses a significant challenge due to non-uniform neuropil distribution and densely packed neuronal populations. In this study, we leverage deep learning (DL) techniques to directly capture true neuronal signals from calcium imaging data, addressing these challenges effectively. Utilizing publicly available datasets, we demonstrate that our DL-based approach, which directly extracts neuronal signals from images, outperforms existing non-DL methods in accurately capturing neuronal signals. This highlights the significant potential of DL methods for unveiling neuronal activity hidden in calcium imaging data. Furthermore, we investigate the impact of calcium imaging data quality on the performance of DL models. While our approach demonstrates significant promise, ongoing improvements in the quality of calcium imaging data will further enhance DL techniques, leading to a deeper understanding of brain mechanisms.

Transfer learning for motor imagery-based brain-computer interfaces (MI-BCIs) struggles with inter-subject variability, hindering its generalization to new users. This paper proposes an advanced implicit transfer learning framework, META-EEG, designed to overcome the challenge arising from inter-subject variability. By incorporating gradient-based meta-learning with an intermittent freezing strategy, META-EEG ensures efficient feature representation learning, providing a robust zero-calibration solution. A comparative analysis reveals that META-EEG significantly outperforms all the baseline methods and competing methods on three different public datasets. Moreover, we demonstrate the efficiency of the proposed model through a neurophysiological and feature-representational analysis. With its robustness and superior performance on challenging datasets, META-EEG provides an effective solution for calibration-free MI-EEG classification, facilitating broader usability.

Resting-state functional magnetic resonance imaging (rs-fMRI) has gained attention as a reliable technique for investigating the intrinsic function patterns of the brain. It facilitates the extraction of functional connectivity networks (FCNs) that capture synchronized activity patterns among regions of interest (ROIs). Analyzing FCNs enables the identification of distinctive connectivity patterns associated with mild cognitive impairment (MCI). For MCI diagnosis, various sparse representation techniques have been introduced, including statistical- and deep learning-based methods. However, these methods face limitations due to their reliance on supervised learning schemes, which restrict the exploration necessary for probing novel solutions. To overcome such limitation, prior work has incorporated reinforcement learning (RL) to dynamically select ROIs, but effective exploration remains challenging due to the vast search space during training. To tackle this issue, in this study, we propose an advanced RL-based framework that utilizes a divide-and-conquer approach to decompose the FCN construction task into smaller sub-problems in a subject-specific manner, enabling efficient exploration under each sub-problem condition. Additionally, we leverage the learned value function to determine the sparsity level of FCNs, considering individual characteristics of FCNs. We validate the effectiveness of our proposed framework by demonstrating its superior performance in MCI diagnosis on publicly available cohort datasets.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

Major Depressive Disorder (MDD) is a pervasive disorder affecting millions of individuals, presenting a significant global health concern. Functional connectivity (FC) derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) serves as a crucial tool in revealing functional connectivity patterns associated with MDD, playing an essential role in precise diagnosis. However, the limited data availability of FC poses challenges for robust MDD diagnosis. To tackle this, some studies have employed Deep Neural Networks (DNN) architectures to construct Generative Adversarial Networks (GAN) for synthetic FC generation, but this tends to overlook the inherent topology characteristics of FC. To overcome this challenge, we propose a novel Graph Convolutional Networks (GCN)-based Conditional GAN with Class-Aware Discriminator (GC-GAN). GC-GAN utilizes GCN in both the generator and discriminator to capture intricate FC patterns among brain regions, and the class-aware discriminator ensures the diversity and quality of the generated synthetic FC. Additionally, we introduce a topology refinement technique to enhance MDD diagnosis performance by optimizing the topology using the augmented FC dataset. Our framework was evaluated on publicly available rs-fMRI datasets, and the results demonstrate that GC-GAN outperforms existing methods. This indicates the superior potential of GCN in capturing intricate topology characteristics and generating high-fidelity synthetic FC, thus contributing to a more robust MDD diagnosis.

Molecular property prediction has gained substantial attention due to its potential for various bio-chemical applications. Numerous attempts have been made to enhance the performance by combining multiple molecular representations (1D, 2D, and 3D). However, most prior works only merged a limited number of representations or tried to embed multiple representations through a single network without using representation-specific networks. Furthermore, the heterogeneous characteristics of each representation made the fusion more challenging. Addressing these challenges, we introduce the Fusion Transformer for Multiple Molecular Representations (FTMMR) framework. Our strategy employs three distinct representation-specific networks and integrates information from each network using a fusion transformer architecture to generate fused representations. Additionally, we use self-supervised learning methods to align heterogeneous representations and to effectively utilize the limited chemical data available. In particular, we adopt a combinatorial loss function to leverage the contrastive loss for all three representations. We evaluate the performance of FTMMR using seven benchmark datasets, demonstrating that our framework outperforms existing fusion and self-supervised methods.

In recent years, deep learning methods have shown promising capabilities for extracting informative and discriminative features from electroencephalography (EEG) data. However, several studies have reported that the feature selection process followed by feature extraction can be beneficial to achieve further performance improvement. Even though a recent work achieved promising results by using the single-agent reinforcement learning (RL)-based framework to select task-relevant features in the temporal domain, it still failed to consider other significant features in the spatial–spectral domain. To overcome such limitations, we propose a cooperative multiagent RL-based framework (MARS) that performs feature selection in both the spatial–spectral and temporal domains simultaneously for a motor imagery (MI)-EEG classification task. In this framework, we enable our RL agents to collaborate with each other as a team to solve a complex multiobjective feature selection problem. Furthermore, we adopt a counterfactual advantage function to overcome the free-rider problem, which is associated with the credit assignment issue in multiagent cases. To assess the MARS framework, we conduct extensive experiments with two public MI datasets under subject-dependent and subject-independent scenarios and we apply the MARS to different backbone networks. The experimental results demonstrate that our MARS outperforms other competing methods in terms of mean accuracy and achieves statistically significant improvements.

Through the lens of trust–commitment and co-creation theories, this study explores the effect of trust on commitment and co-creation intentions within the free-to-play (F2P) gaming industry, to extend our understanding of co-creation within a service context. Responses from an online survey were used as inputs into a structural equation model (SEM). The findings reveal that trust in a F2P game influences commitment to continue playing the F2P game and elicit behaviour in the form of co-creation intentions. The findings also found that involvement and achievement motivations partially mediate the relationship between trust and commitment and escapism significantly moderates the relationship between trust and involvement.

This study conceptualises a framework of technological affordances for augmented reality games (ARG) relative to the affordances of internalised and embodied experiences by users. This study examines players’ affordances and investigate how they influence user experience in ARG. It explores how affordances are perceived and enacted by users in an augmented environment to maximise user experience of ARG. A multimethod research approach was utilised that integrated ethnographic and statistical methods. Qualitative study confirmed the general structure of affordance framework, while also revealing relational structures with other variables to explore. Based on the affordance factors identified from the ethnographic methods, a survey questionnaire was created to map and investigate the effects of affordance on the user’s cognitive processes and the influence of affordance on the gameplay process. The results show that technological properties of the ARG system affect opportunities for action available in the environment (affordances). The heuristic role of immersion and presence affordances through underlying cues appear to trigger a player’s sensory representations of affective affordances.

Although algorithms have been widely used to deliver useful services, how users actually experience algorithm-driven news remains unclear. This study examines user attitude and perception of algorithmic journalism and identifies the similarities and differences in experience and satisfaction formation. A comparative study between the United States (U.S.) and South Korea was conducted to examine how the two countries' users experience the quality of algorithm-driven news services and how individuals perceive the topics of fairness, accountability, and transparency. The notable similarities and differences are found by performing a comparison of cognitive processes. The major attitudes toward algorithm news are similar between the two countries, although the weights placed on the qualities differ. South Korean users put more weight on performance qualities, and U.S. users place relatively greater emphasis on procedural features. Different patterns of algorithm news experience imply the contextual nature of algorithm: how users perceive and feel about topics in algorithm news and how they use and engage with algorithm news depend on the context where the experience is taking place. The analysis suggests the importance of user-perceived issues and the contextual nature of such issues.

Technology acceptance has been studied extensively within the IS discipline. Few, if any, have studied end users’ acceptance of newly implemented technologies within organisational contexts before end users start using the technology. Thus, by integrating variables from multiple relevant literature, this research attempts to answer this research question: will the introduction of a richer model for technology acceptance in a mandatory adoption environment, specifically in the pre-implementation phase, allow us to capture and account for the complexities of organisational technology implementations? The research model was tested in an organisational setting where a new content management system was being implemented. A total of 148 employees participated in this survey and partial least squares method was used to analyse the data to test the model. Implementation climate, valence, attitude, and perceived ease of use positively influence goal commitment to technology acceptance in a mandatory environment, and the model has displayed relatively large explanatory and predictive power. Theoretical and practical implications are discussed in the paper.

E-services remain characterised by uncertainty despite their proliferation. Consumer trust beliefs are therefore considered an important determinant of e-service adoption. However, the research has not yet considered the potentially dynamic nature of these trust beliefs or how early-stage trust might influence later-stage adoption and use. To address this gap, this study draws on the theory of reasoned action and expectation–confirmation theory in a longitudinal study of trust in e-services. We examine how trust interacts with other consumer beliefs such as perceived usefulness (PU) and how these beliefs together influence consumer intentions and behaviours concerning e-services at both the initial and latter stages of use. The empirical context is online health information services. Data collection on a student population occurred during two time periods approximately five weeks apart. The results show that PU and trust are important at both the initial and latter stages in the consumer acceptance of online health services. Consumers’ actual usage experiences modify perceptions of usefulness and influence the confirmation of their initial expectations. These results have implications for our understanding of the dynamic nature of trust and PU as well as their roles in the long-term sustainability of e-services.

The popularity of online health information has increased because of its easy accessibility. The present study employs longitudinal data to investigate perceived ease of use, perceived usefulness, and trust in the online health information context. More specifically, the authors classified trust as both trust in the provider and in the website for health information. In light of this discussion, the purpose of this study is twofold: (1) To better understand the multidimensional and dynamic nature of trust in the online health information context and (2) To carry out a longitudinal investigation to study the technology acceptance model (TAM) in the online health information context. This study focuses on college students’ behaviors related to online health information services; this is because engagement with online health information is a dynamic area for research among young populations. By analyzing the longitudinal data, the authors found that both trust in website and trust in provider can integrate TAM variables well to influence college students’ behavior for seeking out online health information. Moreover, when college students gain more experience with online health information servers, perceived ease of use becomes less important to form positive behavior intentions.

This paper investigates the influence of goal awareness and IT self-efficacy on job satisfaction based on the motivation sequence model, goal-setting theory, and social cognitive theory. Using a large-scale field survey of healthcare enterprise resource planning (ERP) system users (n = 352), this study investigates these relationships and provides important insight to healthcare ERP system researchers and managers. Both goal awareness and IT self-efficacy influence positive job satisfaction of healthcare ERP system users, as expected. Furthermore, the influence of goal awareness is stronger when the role of ERP systems is highly perceived for decision-making of the job. There was no interaction effect between goal awareness and IT self-efficacy in the post hoc analysis. The model is significantly supported by the empirical test with the large number of field data from healthcare ERP system users in the healthcare company. Practical and academic implications are discussed in the paper.

App install advertising not only serves as a revenue source for mobile app developers and mobile ad platforms, but also is considered an effective channel for app exposure to mobile app users. Owing to their benefits to the main players, such ads are the driving force of mobile advertising growth. For the purpose of improving the effectiveness of app install ads, this study investigated how to select potential active users for advertised apps based on users’ previous app usage behaviors. By analyzing large-scale field data on game app usage behaviors, users’ engagement level, specifically daily number of purchase activity in the medium app was found as the significant factor for targeting potential active users for the advertised app. However, users with a longer app session time in medium app and users who have more game apps in their mobile devices have a lower probability of becoming active users of the advertised app due to their limited time. It was also found that users who have played game apps in the same category as the advertised app are more likely to become active users of the advertised app. Both the theoretical and practical implications of the findings are discussed for improving the effectiveness of app install ads.

Cross-platform services provide unified entertainment experiences across multiple devices between which users can toggle when watching content using televisions, tablets, personal computers, and smartphones. The software automatically adapts the programming to fit the diverse formats. This study analyzed user experiences (UX) of cross-platform services with a mixed methods (quantitative and qualitative) approach. It used a multi-state analytical approach, in which the user model was tested in a statistical model and accompanying experiment. A variety of methods were used to best understand the complexities of UX. Heuristic results revealed the ways that UX of cross-platform services are formed, moderated, and improved, and the ways that users’ intentions are determined through the relationships among factors. The results revealed that the key elements of cross-platform UX include access, mobility, and coherence, which imply the importance of seamless UX of cross-platform services. Based on those key factors, the study proposed the idea of inter-usability for designing user-centered systems.

To understand market dynamics relating to net neutrality better, in particular from the end-user perspective, this study examines consumer perception of neutrality and the public value under debate within the neutrality discussions. Focusing on the user perspective, it analyzes the policy effectiveness of current net neutrality by analyzing user perception and opinion. A value model is proposed to empirically test the policy effectiveness by incorporating factors representing net neutrality. The factors are drawn from people's perceived concepts of net neutrality. The findings show that while competition and regulation are the two main factors constituting net neutrality, each of them influences the formation of attitude toward policy effectiveness differently. This study contributes to policymakers by increasing an understanding of market dynamics relating to net neutrality, in particular from the end-user perspective.

A cloud learning environment enables an enriched learning experience compared to conventional methods of learning. Employing a value-sensitive approach, we undertook theoretical and empirical analyses to explore the values that influence potential users’ adoption of cloud courseware, by integrating cognitive motivations and user values as primary determining factors. We found that users’ intentions and behaviours are largely influenced by their perceptions of what is valuable about the cloud courseware in terms of sociability, learnability, and usability. These evaluations were found to be significant antecedents of cloud-computing intentions. This study makes a contribution to theory development as our model extends existing technology acceptance models and can be used to design user interfaces and promote the acceptance of cloud computing. For practical applications, the study findings can be used by industries promoting cloud services to increase user acceptance by addressing user values and incorporating them into cloud-computing design.

Although autonomous vehicles are increasingly becoming a reality, eliminating human intervention from driving may imply significant safety and trust-related concerns. To address this issue from a psychological perspective, this study applies layers of anthropomorphic cues to an artificial driving agent and explicates the process in which these cues promote positive evaluations and perceptions of an unmanned driving system. In a between-subjects factorial experiment (N = 89) consisting of three unmanned driving scenarios, participants interacted with an artificial driving agent with different levels of anthropomorphic cues induced by the variations in appearance (human-like vs. gadget-like) and autonomy (high vs. low) of the agent. The results indicated that human-like appearance and high autonomy were more effective in eliciting positive perceptions of the agent. In addition, a mediation analysis revealed that the greater level of anthropomorphism induced by human-like appearance and high autonomy in the agent evoked the feelings of social presence, which in turn positively affected the perceived intelligence and safety of and trust in the agent, suggesting that the extent to which users perceive the driving agent as intelligent, safe, and trustworthy is largely determined by the feelings of social presence experienced during their interaction.

The purpose of this study was to investigate the relationships between the softness of two mobile touchscreens and three direct input devices on user's task performance and subjective evaluation for tapping tasks. In the within-subjects design, 44 participants were asked to perform tasks as quickly and accurately as possible across the six combinations. After completing tasks in each condition, they filled out a questionnaire to evaluate the perceived pleasantness and degree of elasticity. The result showed a main effect of different touchscreen types on the task performance and degree of elasticity while different input device types showed a main effect on all three dependent variables. An interaction effect of two independent variables on the degree of elasticity was found. Along with the result, the practical implications of these findings are also discussed.

Social commerce (s-commerce), a new form of commerce that involves using social media, has been rapidly developing. While the adoption of social technology is well studied, new theoretical development is needed to explain the specific characteristics of s-commerce and their interactions with the user. This study analyses consumer behaviours in s-commerce, focusing on the role of social influence in s-commerce. A model is created to validate the relationship between the subjective norm and trust, social support, attitude, and intention. The results of the model show that the subjective norm is a key behavioural antecedent to use s-commerce. In the extended model, the moderating and mediating effects of the subjective norm on relationships among variables were found to be significant. The new set of variables adapted from previous research can be s-commerce-specific, acting as factors that enhance attitudes and behavioural intentions in s-commerce. The implications of the findings are discussed in terms of building a theory of social interaction and providing practical insights into developing user-centered s-commerce as a platform.

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This study examines the users' experiences with three-dimensional (3D) virtual environments to investigate the areas of development as a learning application. For the investigation, the modified technology acceptance model (TAM) is used with constructs from expectation-confirmation theory (ECT). Users' responses to questions about cognitive perceptions and continuous use were collected and analysed with factors that were modified from TAM and ECT. Whilst the findings confirm the significant roles by users' cognitive perceptions, the findings also shed light on the possibility of 3D application serving as an enabler of learning tools. In the extended model, the moderating effects of confirmation/satisfaction and demographics of the relationships amongst the variables were found to be significant.

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Tables in this paper, originally published in Behaviour & Information Technology, Volume 32, Number 2 (2013) pp. 156–172 are incorrect.Tables should read:AgeNumberPercentage (%)Under 204814.621–301...

Smart TV (STV), a new digital television service, has been rapidly developing, particularly in Korea. With the conceptual model of interactivity, this study empirically investigates the effects of perceived interactivity on the motivations and attitudes towards STV in Korea. The model is created to validate the relationship of perceived interactivity to performance, attitude and intention. Further, the model examines the mediating roles of perceived interactivity in the effect of performance on attitude towards STV. Empirical evidence supports the mediating role of perceived interactivity. Implications of the findings are discussed in terms of building a theory of interactivity and providing practical insights into developing a user-centred STV interface.

Smartphone users in the U.S. and Korea were cross-surveyed to determine country-specific differences in product value perceptions. Usability factors and aesthetic values were combined using the theory of reasoned action (TRA). The strengths of the model’s relationships are discussed. The models were analyzed cross-nationally to explore differences in the compositions of technology adoption motives in the two countries. Although the results illustrate the importance of both usability and aesthetic values, the two countries show different value preferences as well as intention and adoption patterns. The results of this study suggest practical implications for employing cross-cultural strategies in the global marketing of smartphones as well as theoretical implications for cross-country studies, which are recommended accordingly.

Social network services (SNS) focus on building online communities of people who share interests and/or activities, or who are interested in exploring the interests and activities of others. This study examines security, trust, and privacy concerns with regard to social networking Websites among consumers using both reliable scales and measures. It proposes an SNS acceptance model by integrating cognitive as well as affective attitudes as primary influencing factors, which are driven by underlying beliefs, perceived security, perceived privacy, trust, attitude, and intention. Results from a survey of SNS users validate that the proposed theoretical model explains and predicts user acceptance of SNS substantially well. The model shows excellent measurement properties and establishes perceived privacy and perceived security of SNS as distinct constructs. The finding also reveals that perceived security moderates the effect of perceived privacy on trust. Based on the results of this study, practical implications for marketing strategies in SNS markets and theoretical implications are recommended accordingly.

This study explores the factors influencing the adoption of IPTV, and tests the applicability of the technology acceptance model (TAM) in a new convergent technology. The behavioural constructs from TAM were tested for predicting user acceptance of IPTV. Structural equation modelling was used to analyse data and to design a theoretical model predicting the individual's intention to adopt IPTV. A modified TAM for IPTV proposes that new constructs determine user-perceived usefulness and enjoyment of using IPTV. Although this study confirms the impact of information quality and system quality on consumers' technology experience, it specifically shows that the perceived quality of content and system were found to have a significant effect on users' perceived usefulness and perceived enjoyment. In addition, social influences had a positive effect on the intention to use IPTV. These findings suggest an extension of the TAM model for convergence technologies. This research advances theory and contributes to the foundation for future research aimed at improving the understanding of users' adoption behaviour of convergence technologies. Implications of these findings for practice and research are examined.

As mobile TV is becoming increasingly popular, this study examines the factors affecting consumers' intentions to use and adopt DMB. By integrating a motivational perspective into the technology acceptance model (TAM), this study examines the socioeconomic determinants of DMB adopters and nonadopters in South Korea. Perceived availability and perceived quality are proposed as new constructs that reflect DMB-specific features. The empirical results overall support a modified TAM in explaining consumers' behavioral intentions to use/adopt DMB. In particular, the results of structural equation modeling suggest that perceived availability is positively associated with perceived benefit and the attitude toward DMB. In addition, the results suggest that adopters and nonadopters of DMB perceive its value differently, which implies how to promote the diffusion of DMB to nonadopters more effectively. Implications of this study are important for both researchers and practitioners.

This study surveyed mobile users in the United States and Korea to determine the key differences between the two countries. Survey questions, developed in two languages, were presented in each country to explore the influences of informativeness, entertainment, interactivity, and availability on mobile user dimensions. The study design methods were based on the revision of a uses and gratifications approach, and a relational model of antecedents and consequences was tested with a structural equation modeling approach. Mobile Internet service uses and gratifications were analyzed cross-nationally in a comparative fashion focusing on the differences in the composition of motives in the two countries. Based on the results of this study, practical implications for marketing strategies in mobile service markets and theoretical implications for cross-country studies are recommended accordingly.

This study analyzes consumer purchasing behavior in Web 2.0, expanding the technology acceptance model (TAM), focusing on which variables influence the intention to transact with virtual currency. Individuals’ responses to questions about attitude and intention to transact in Web 2.0 were collected and analyzed with various factors modified from the TAM. The results of the proposed model show that subjective norm is a key behavioral antecedent to using virtual currency. In the extended model, the moderating effects of subjective norm on the relations among the variables were found to be significant. The new set of variables is virtual environment-specific, acting as factors enhancing attitudes and behavioral intentions in Web 2.0 transactions.

Abstract This study compares the United States of America and Korea's cases of national information infrastructure (NII) development, focusing on the role of the governments in the development of their NIIs and on the realization of the next generation of information infrastructure vision. The important similarities and differences can be seen by comparison on sociotechnical dimensions: government function, histories, visions, policy design, implementation plans, and realities and prospects. Findings show different patterns of NII development, providing insights for the next generation of NIIs. This study provides a prospect towards future information infrastructure needs in the context of dynamic sociotechnical changes.

Using the Technology Acceptance Model as a conceptual framework and a method of structural equation modeling, this study analyzes the consumer attitude toward Wi-Bro drawing data from 515 consumers. Individuals’ responses to questions about whether they use/accept Wi-Bro were collected and combined with various factors modified from the Technology Acceptance Model. The result of this study show that users’ perceptions are significantly associated with their motivation to use Wi-Bro. Specifically, perceived quality and perceived availability are found to have significant effect on users’ extrinsic and intrinsic motivation. These new factors are found to be Wi-Bro-specific factors, playing as enhancing factors to attitudes and intention.