Crowdfunding is an important mechanism for supporting innovative projects by connecting creators with distributed contributors. Prior research has identified persistent limitations in both traditional and blockchain-based crowdfunding platforms, including limited transparency, centralized control, passive contributor roles, and inflexible fund management processes. These limitations hinder accountability, equitable participation, and effective decision-making throughout the campaign lifecycle. This paper presents a blockchain-enabled crowdfunding framework designed as a decision-support artifact for adaptive fund allocation and participatory governance. The framework enables contributors to engage in spending-request governance through Quadratic Voting, which balances influence across heterogeneous financial stakes and mitigates dominance by large contributors. To support adaptive campaign management, the framework further integrates Ethereum smart contracts with a Markov Decision Process (MDP), enabling campaign-level decisions to respond to evolving contribution patterns and campaign states. The framework is implemented and evaluated through controlled experiments on the Sepolia Ethereum test network. The evaluation includes both an internal ablation of Quadratic Voting and MDP-based adaptive support and an external comparison against representative blockchain-based baselines. The results show that the combined Quadratic Voting and MDP design achieves lower approval latency and higher throughput than partial or static variants of the framework, and that the full proposed platform outperforms the compared baseline systems under increasing campaign workload. Overall, the study demonstrates how participatory governance, adaptive decision support, and transparent smart-contract execution can be systematically integrated into crowdfunding platforms, providing practical guidance for the design of scalable, efficient, and accountable decentralized crowdfunding systems.

Digital platforms are increasingly being adopted for addressing sustainable supply chain (SC) uncertainties. Digital SC platforms, however, have been conceptualized as strategic or infrastructural assets in previous studies, reporting mixed outcomes depending on their deployment and operational focus. Variations in platform design characteristics may explain these mixed findings, especially when addressing uncertainties specifically regarding sellers’ sustainability. However, no prior study has examined how the characteristics of digital SC platforms, namely, immutability, traceability, transparency level, and platform type, individually or collectively impact SC operational integration to alleviate sustainability-related seller uncertainty. We developed a conceptual model to theorize the effect of IT artifact characteristics on SC operational integration and seller uncertainty. We empirically tested the model through an experiment employing a 2 × 2 factorial survey design accompanied by mock-up user interfaces and collected data from 444 SC managers. The results revealed that immutability and traceability improved SC operational integration, subsequently reducing seller uncertainty. However, their effects were inconsistent, being contingent on the transparency level and platform type. Specifically, a high transparency level lessened the impact of immutability on SC operational integration, whereas blockchain-based platforms enhanced the effect of traceability on SC operational integration. Overall, these findings reveal the dual consequences of digital platform characteristics and connecting digital platform design with SC operational integration in a sustainability-specific context.

In the rapidly evolving business landscape, blockchain technology emerges as a key innovator, enhancing trust, transparency, and security. However, the unique features of blockchain pose challenges in developing blockchain-based software (BSD) systems, demanding improvements in conventional software development processes. This study aims to identify BSD process areas and develop a success probability prediction framework, enhancing BSD process success and progression. We conducted a comprehensive literature survey and a questionnaire-based survey with practitioners to identify BSD process areas and gather training data. The study employs the Grey Wolf Optimizer (GWO) combined with the Naive Bayes Classifier to create a success probability prediction framework for BSD processes. Our research identifies 47 BSD process areas, categorized across five software process improvement (SPI) stages: initial, managed, defined, quantitatively managed, and optimizing. The GWO algorithm facilitates the design of a predictive framework, assessing the success probability of each stage, encompassing various process areas. The framework also prioritizes process areas for each stage, helping practitioners identify critical areas considering implementation cost and success probability. Organizations using BSD can leverage this framework to improve their BSD processes. This study contributes to blockchain technology applications in software development, offering a systematic, predictive approach to augment the effectiveness and success rate of BSD processes.

Context: Software vulnerability detection (SVD) is increasingly challenged by the scale and complexity of modern software systems. Although deep learning–based approaches demonstrate strong detection performance, their adoption in security-critical settings is limited by insufficient interpretability, adversarial resilience, and systematic Responsible AI integration. Objective: This paper aims to design and evaluate a Responsible AI–driven framework for SVD that operationalizes fairness, interpretability, security, reliability, and transparency without compromising detection effectiveness. Method: We propose a model-agnostic vulnerability detection framework that incorporates fairness-aware data preprocessing, multi-model evaluation, and structured verification mechanisms. Interpretability is achieved through multi-view explanations combining global and local SHAP, LIME, and attention-based attribution. Explanation consistency is examined using attention-guided token occlusion, while security is evaluated via multiple white-box adversarial attacks on correctly classified test samples. The framework is validated on three public datasets—CWE-119, CWE-399, and DiverseVul—using deep learning and pre-trained code models. Results: Experimental results demonstrate competitive detection performance across datasets while providing structured explanation and adversarial evaluation evidence. Interpretability analyses reveal dataset-specific vulnerability cues aligned with domain knowledge, and perturbation studies show consistent confidence shifts under controlled token masking. Adversarial experiments further illustrate stable performance trends under attack conditions. Conclusion: The findings indicate that Responsible AI principles can be systematically operationalized within deep learning–based SVD pipelines. By integrating fairness, interpretability, security evaluation, reliability assessment, and transparency mechanisms, the proposed framework supports trustworthy and security-aware deployment of AI-driven vulnerability detection systems.

Federated Learning (FL) operates as a distributed software system in which a central coordinator orchestrates training across heterogeneous and intermittently available clients. In practice, client selection and aggregation policies are configured as static and independent parameters, which become brittle under fluctuating computation capacity, network latency, and client reliability. Consequently, FL systems often suffer from slow convergence, unstable training, and limited adaptability under dynamic execution conditions. This work formulates FL orchestration as a software architecture problem and introduces FedJoint , a modular framework that jointly coordinates client selection and aggregation timing. Unlike prior approaches that optimize these mechanisms independently, FedJoint treats them as coupled runtime control decisions within a unified architecture. The objective is to enable adaptive co-management that improves efficiency, robustness, and operational adaptability under heterogeneous and non-stationary conditions. FedJoint is realized as a modular orchestration architecture composed of three decoupled components: a Selection Manager controlling client participation, an Aggregation Manager governing update integration, and a Deep Reinforcement Learning (DRL) Controller that adapts orchestration decisions based on runtime feedback. The components interact through well-defined interfaces that support configurability, substitution, and integration with existing FL platforms. Within this architecture, a DRL–based policy adapts selection and aggregation parameters to balance accuracy, latency, client dropout, and communication overhead under dynamic execution conditions. Evaluation on CIFAR-10 and MNIST under two heterogeneity levels (Dirichlet α ∈ { 0 . 1 , 0 . 5 } ) and non-stationary conditions shows final accuracy of 63.62%–97.82%, with 5.2–14.0 × speedup over synchronous and semi-asynchronous baselines. Compared to RL-based baselines, FedJoint achieves up to 12.2 × lower wall-clock cost at higher final accuracy. Ablation confirms that gains stem from joint coordination, with single-component variants showing accuracy gaps up to 31.84 percentage points. Treating FL orchestration as a coupled architectural concern enables more robust and manageable distributed learning systems and offers concrete guidance for adaptive FL platform design.

The Software-Defined Networking (SDN) powered Internet of Things (IoT) offers a global perspective of the network and facilitates control and access of IoT devices using a centralized high-level network approach called Software Defined-IoT (SD-IoT). However, this integration and high flow of data generated by IoT devices raises serious security issues in the centralized control intelligence of SD-IoT. Motivated by the aforementioned challenges, we present a new Deep-Learning empowered Threat Hunting Framework named DLTHF to protect SD-IoT data and detect (binary and multi-vector) attack vectors. First, an automated unsupervised feature extraction module is designed that combines data perturbation-driven encoding and normalization-driven scaling with the proposed Long Short-Term Memory Contractive Sparse AutoEncoder (LSTMCSAE) method to filter and transform dataset values into the protected format. Second, using the encoded data, a novel Threat Detection System (TDS) using Multi-head Self-attention-based Bidirectional Recurrent Neural Networks (MhSaBiGRNN) is designed to detect cyber threats and their types. In particular, a unique TDS strategy is developed in which each time instances is analyzed and allocated a self-learned weight based on the degree of relevance. Further, we also design a deployment architecture for DLTHF in the SD-IoT network. The framework is rigorously evaluated on two new SD-IoT data sources to show its effectiveness.

ABSTRACT Many programming languages and technologies have appeared for the purpose of software development. When choosing a programming language, the developers' cognitive attributes, such as the Big5 personality traits (BPT), may play a role. The developers' personality traits can be reflected in their social media content (e.g., tweets, statuses, Q&A, reputation). In this article, we predict the developers' programming language preferences (i.e., the pattern of picking up a language) from their BPT derived from their content produced on social media. We randomly collected data from a total of 820 Twitter (currently X) and Stack Overflow (SO) users. Then, we collected user features (i.e., BPT, word embedding of tweets) from Twitter and programming preferences (i.e., programming tags, reputation, question, answer) from SO. We applied various machine learning (ML) and deep learning (DL) techniques to predict their programming language preferences from their BPT. We also investigated other interesting insights, such as how reputation and question‐asking/replying are associated with the users' BPT. The findings suggest that developers with high openness, conscientiousness, and extraversion are inclined to mobile applications, object‐oriented programming, and web programming, respectively. Furthermore, developers with high openness and conscientiousness traits have a high reputation in the SO community. Our ML and DL techniques classify the developers' programming language preferences using their BPT with an average accuracy of 78%.

Algorithm appreciation, defined as an individual's reliance or tendency to rely on algorithms in decision-making, has emerged as a subject of growing scholarly interest. Inquiries into this subject are crucial to understanding human decision-making processes as in the era of artificial intelligence, algorithms are increasingly being integrated into decision-making. To contribute to this evolving field, this study examines three factors that might play significant roles in enhancing trust in algorithms: familiarity with algorithms, familiarity with tasks, and familiarity with algorithm performance. Drawing upon prior studies, a conceptual model was developed and empirically tested using a scenario study. Data on 327 individuals showed a strong positive association between familiarity with algorithms and trust in algorithms. In contrast, task familiarity appeared to have no significant influence on trust. Trust, in turn, was identified as a key driver of algorithm appreciation. The study also revealed the moderating role of familiarity with algorithm performance in the relationship between familiarity with algorithms and trust in algorithms. Post hoc analysis highlighted that trust fully mediates the relationship between algorithm familiarity and algorithm appreciation. The study underscores the significance of algorithm familiarity and performance transparency in shaping trust in algorithms. The study contributes theoretically by offering important insights about the influences of different forms of familiarity on trust and practically by prescribing practical guidelines to enhance algorithm appreciation.

The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. To enable several secure, intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.

The Industrial Internet of Things (IIoT) is a collection of interconnected smart sensors and actuators with industrial software tools and applications. IIoT aims to enhance manufacturing and industrial processes by capturing and analyzing real-time industrial data. However, the heterogeneous and homogeneous nature of IIoT networks makes them vulnerable to several security threats. As data is transmitted over an insecure communication medium, intruders may intercept communication among different entities and perform malicious activities. Consequently, ensuring the security and privacy of data transmitted in IIoT networks is essential. Motivated by the aforementioned challenges, this article presents a deep-learning-integrated blockchain framework for securing IIoT networks. Specifically, first, we design a private blockchain-based secure communication among the IIoT entities using session-based mutual authentication and key agreement mechanism. In this approach, the Proof-of-Authority (PoA) consensus mechanism is used for verification of the transactions and block creation based on the voting of miners over the cloud server. Second, we design a novel deep-learning-based intrusion detection system that combines contractive sparse autoencoder (CSAE), attention-based bidirectional long short-term memory (ABiLSTM) networks, and softmax classifier for cyberattack detection. The practical implementation of blockchain and deep-learning techniques proves the effectiveness of the proposed framework.

Abstract Development and operations (DevOps) refer to the collaboration and multidisciplinary organizational effort to automate continuous delivery of information systems (IS) development project with an aim to improve the quality of the IS. The flexibility and quality production of software projects motivated the organizations to adopt DevOps paradigm. Organizations face several complexities while management of DevOps process. This study aims to explore and analyze the factors that could positively impact the management of DevOps process. Firstly, literature review was performed and identified 36 success factors that are related to 10 knowledge areas of DevOps project management. Secondly, a questionnaire survey study was conducted to get the insight of industry experts concerning the success factors of DevOps project. Finally, the fuzzy‐AHP was applied for ranking the success factors and examining the relationship between 10 knowledge areas of identified success factors. The results of this study will serve as a body of knowledge for researchers and practitioners to consider the highest priority success factors and develop the effective policies for the successful execution of DevOps project management.

Summary Artificial Intelligence (AI) based cyber threat detection tools are widely used to process and analyze a large amount of data for improved intrusion detection performance. However, these models are often considered as black box by the cybersecurity experts due to their inability to comprehend or interpret the reasoning behind the decisions. Moreover, AI‐based threat hunting is data‐driven and is usually modeled using the data provided by multiple cloud vendors. This is another critical challenge, as a malicious cloud can provide false information (i.e., insider attacks) and can degrade the threat‐hunting capability. In this paper, we present a blockchain‐enabled eXplainable AI (XAI) for enhancing the decision‐making capability of cyber threat detection in the context of Smart Healthcare Systems. Specifically, first, we use blockchain to validate and store data between multiple cloud vendors by implementing a Clique Proof‐of‐Authority (C‐PoA) consensus. Second, a novel deep learning‐based threat‐hunting model is built by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi‐head attention mechanism for improved attack detection. The extensive experiment confirms its potential to be used as an enhanced decision support system by cybersecurity analysts.

The integration of the Internet of Things (IoT) with traditional healthcare systems has improved quality of healthcare services. However, the wearable devices and sensors used in Healthcare System (HS) continuously monitor and transmit data to the nearby devices or servers using an unsecured open channel. This connectivity between IoT devices and servers improves operational efficiency, but it also gives a lot of room for attackers to launch various cyber-attacks that can put patients under critical surveillance in jeopardy. In this article, a Blockchain-orchestrated Deep learning approach for Secure Data Transmission in IoT-enabled healthcare system hereafter referred to as “BDSDT” is designed. Specifically, first a novel scalable blockchain architecture is proposed to ensure data integrity and secure data transmission by leveraging Zero Knowledge Proof (ZKP) mechanism. Then, BDSDT integrates with the off-chain storage InterPlanetary File System (IPFS) to address difficulties with data storage costs and with an Ethereum smart contract to address data security issues. The authenticated data is further used to design a deep learning architecture to detect intrusion in HS network. The latter combines Deep Sparse AutoEncoder (DSAE) with Bidirectional Long Short-Term Memory (BiLSTM) to design an effective intrusion detection system. Experiments on two public data sources (CICIDS-2017 and ToN-IoT) reveal that the proposed BDSDT outperformed state-of-the-arts in both non-blockchain and blockchain settings and have obtained accuracy close to 99% using both datasets.

In this paper, we study popular microcelebrities from the Global South to understand their strategies and practices on Facebook. Unlike traditional celebrities who gain their reputation through different types of physical performance, these microcelebrities attain their status by presenting themselves in a favorable way to their online followers. We conducted interviews with 19 microcelebrities from Bangladesh and analyzed our data using actor-network theory (ANT) and Goffman’s dramaturgical analysis (DA) of human interaction. We discuss the complex socio-technical ecosystem of the microcelebrity and the roles of non-human actors, such as platforms and local internet infrastructure along with human actors’ practices. We explain the microcelebrities’ experience with the process of microcelebritification–the process of being a microcelebrity on social media through impression management, and becoming opinion leaders in local sociopolitical discourses as part of their online identity. Our paper contributes to the emerging literature on microcelebrities by highlighting the process viewed in the context of the Global South.

Location-based games (LBGs) augment urban environments with virtual content turning them into a playground. The importance of understanding how different modes of play impact LBG players’ psychological well-being is emphasized by the enormous and constantly rising popularity of the genre. In this work, we use the two-factor theory of psychological well-being to investigate the associations between five constructs related to game mechanics and personality traits, and psychological well-being and fatigue. We test our proposed structural model with Finnish Pokémon GO players (N = 855). The results show deficient self-regulation and fear of missing out to be positively associated with gaming fatigue. Engagement with cooperative and individual game mechanics had a positive relationship with well-being. Competitive game mechanics were found to have a positive relationship with both well-being and fatigue. Finally, the overall playing intensity had a strong relationship with well-being, but no association with fatigue.

E-commerce platforms have made our life easier and bought plenty of advantages too. However, due to fraud and scams, trust is a concern while buying products online. In this study, we proposed a blockchain-based architecture for the e-commerce sector where data mining technology is used to detect fraudulent users by generating precise and effective rules, and smart contracts are used for enforcement and functionality management within the blockchain network. Our data mining approach yielded a competitive accuracy, precision, recall, and f1-measure of over 99% compared to the state-of-the-art. High performing rules are further tested for ten simulated cycles under completely unseen data to test their rigidity in a real-time scenario. Details analysis of delineation (if any) in rule antecedents has also been analyzed for these cycles. Tested and selected rules are stored in smart contracts deployed within blockchain network for security and immutability. For ensuring strict order criteria maintenance, better return policy, authentic review and scam avoidance, two smart contracts have been written which implement seller reputation mechanism and authentic review maintenance while safeguarding the seller from intentional defaming too. Altogether, the goal of this study is to establish a balance between all the parties within an e-commerce platform so that everyone's right is protected, money is safe and resources are not exploited.

Through Facebook "Group" feature, users often sensitize communionships, join different Facebook groups, and establish imagined communities with known people and strangers. In our interview study with 32 admins and users of Facebook groups, we explored the influential factors of such communionships, the challenges the Facebook group admins face while managing these communities, and how they resolve those. Our findings show that admins set rules for the entry and maintenance of the groups, monitor members' activities, and often limit their actions or mute them during conflicts. Thus, the members and admins of the groups together grow a sensibility of sovereignty within the community on Facebook. While the imagined sovereignty in Facebook groups is empowering, this empowerment may not be perceived and experienced evenly by everyone in such online communities. To explain this, we build on the concept of "Imagined Communities' by Benedict Anderson [16 ] and argue that there is a tension between Facebook admins' perceived sovereignty and other users' empowerment in practice. Our work joins the body of CSCW literature that aims at designing more sustainable and collaborative tools for specific communities on Facebook groups and other similar platforms.

The industrial healthcaresystem has enabled the possibility of realizing advanced real-time monitoring of patients and enriched the quality of medical services through data sharing among intelligent wearable devices and sensors. However, this connectivity brings the intrinsic vulnerabilities related to security and privacy due to the need of continuous communication and monitoring over public network (insecure channel). Motivated from the aforementioned discussions, we integrate permissioned blockchain and smart contract with deep learning (DL) techniques to design a novel secure and efficient data sharing framework named PBDL. Specifically, PBDL first has a blockchain scheme to register, verify (using zero-knowledge proof), and validate the communicating entities using the smart contract-based consensus mechanism. Second, the authenticated data are used to propose a novel DL scheme that combines stacked sparse variational autoencoder (SSVAE) with self-attention-based bidirectional long short term memory (SA-BiLSTM). In this scheme, SSVAE encodes or transforms the healthcare data into new format, and SA-BiLSTM identifies and improves the attack detection process. The security analysis and experimental results using IoT-Botnet and ToN-IoT datasets confirm the superiority of the PBDL framework over existing state-of-the-art techniques.

Women in the global south often seek justice to their online harassment through unveiling the harassers and the screenshots of their sent harassment texts and visual contents before the relevant authorities. Nevertheless, such evidence is often challenged for their authenticity. Our survey (n=91) and interview (n=43) with Bangladeshi online gender harassment victims revealed the depth of the problem, and we set design goals to collect evidence from Facebook Messenger with ensured authenticity. Building on the ‘shame-based model’ of gender justice [12], we designed ‘Unmochon’, a tool that captures authentic evidence and shares with victims’ intended group. Our user-study (n=48) revealed that diminishing authenticity problem may still leave the victim and online gender justice entangled with mob-sentiment, hegemonic legal consciousness, and several privacy aspects. Our findings open up a new discussion on how HCI-design should address online gender justice in such a complex social setting.

Work in Progress: In this paper we outline our initial findings on the potential of state-of-the-art immersive telepresence to support the practical and collaborative group work element in our remotely taught international degree programmes, specifically a Software Engineering Masters degree. Whilst we adopt widely used distance learning approaches generally, the challenge remains in supporting live synchronous practical sessions that depend heavily, on one hand, on the expertise of the educator and their presence in the shared learning environment, and on the other hand, the students' active engagement within teams partially distributed physically. Any technical progress in improving the "sense of presence" can radically renew our conceptions of (distance) teaching and learning, maybe also in terms of the 21st century skills or even those that we are not yet aware of. The question to address is that if sufficiently high-fidelity, and unintrusive, immersive capture technology is used then how could this be game changing in this area? Previous work using similar immersive technology has failed to achieve the level of quality or scope necessary, however, the past 4 years have seen significant progress in the hardware and algorithms required. To support our degree programmes we have designed and developed a custom live 3D capture system for a higher fidelity immersive experience targeting small groups of 2-6 people collaborating both locally and remotely. Here we will present the system and scope out some of the initial plausible affordances for education through the Conceive, Design, Implement, Operate (CDIO)-model. We focus on the affordances offered by the technology to support the learning of the much needed competences of communication, collaboration, critical thinking and creativity which are very challenging to enhance by conventional, content delivery oriented distance learning approaches.

The World Health Organisation has emphasised that misinformation – spreading rapidly through social media – poses a serious threat to the COVID-19 response. Drawing from theories of health perception and cognitive load, we develop and test a research model hypothesising why people share unverified COVID-19 information through social media. Our findings suggest a person’s trust in online information and perceived information overload are strong predictors of unverified information sharing. Furthermore, these factors, along with a person’s perceived COVID-19 severity and vulnerability influence cyberchondria. Females were significantly more likely to suffer from cyberchondria, with males more likely to share news without verifying its reliability. Our findings suggest that to mitigate the spread of COVID-19 misinformation and cyberchondria, measures should be taken to enhance a healthy scepticism of health news while simultaneously guarding against information overload.

Prior research has reported contradictory findings on the relationship between the use of Social Network Sites (SNS) and psychological well-being. We addressed this shortcoming by incorporating a finer measure of SNS user engagement and hypothesizing a U-shaped rather than purely linear relationship between the two. We tested our hypotheses via a Web based questionnaire administered to 289 Facebook users. Ordinary least squares approach confirmed the hypothesized U-shaped relationship. Our results further show that User Engagement, and in turn well-being, is associated with the number of SNS friends. These findings indicate that well-being derived from SNS usage could be optimized by avoiding underuse as well as overuse.

Social Network Sites (SNS) are often characterized as a trade-off where users must give up privacy to gain social benefits. We investigated the alternative viewpoint that users gain the most benefits when SNSs give them the privacy they desire. Applying structural equation modeling to questionnaire data of 303 Facebook users, we examined the complex relationship between privacy and SNS benefits. We found that SNS users whose privacy desires were met reported higher levels of social connectedness (i.e., perceived relational closeness with others) than those who achieved less privacy than they desired. Social connectedness, in turn, played a pivotal role in building social capital (i.e., the benefits derived from relationships with others). These findings suggest that more openness may not always be better; SNSs should aim to achieve 'Privacy Fit' with user needs to enhance user experience and ensure sustained use.

Prior research on learning management system (LMS) continuance focused on either the perspective of educators or that of students. Such studies fall short in advising customised intervention plans based on the user type. This paper investigates perceptions of both educators and students, and tests the moderating role of user type (educators vs. students) in determining the satisfaction and continued use of the LMS. We adopted the information systems (IS) continuance model extended with ease of use and placed user type as a moderator. We test the model by collecting data from 170 educators and 233 students in a Finnish university who use a popular LMS, Moodle. Partial least squares (PLS) technique is employed to test the possible moderation effects. The PLS analysis results revealed that user type moderates most of the relationships in the extended IS continuance model such that the relationships are stronger for students than educators.

Engagement in virtual worlds has become pervasive, particularly among the young. At the same time, the number of virtual environments has increased rapidly. Due to intensifying competition, promoting sustained usage, i.e. continuance, has become a top priority for virtual world operators. Prior research has shown that network externalities play a key role in the adoption of communication technologies. However, a small amount of research has examined the role of network externalities in continued IT usage in general or with respect to the virtual world participation in particular. To fill in this gap, we examine how perceived network externalities affect the continuance of social virtual worlds. To this end, we introduce the concept of perceived aggregate network exposure (PANE). We extend the original information systems (IS) continuance model with perceived enjoyment and position PANE as a moderator. We test the model with data collected from 2134 Finnish Habbo Hotel users and employ structural equation modelling in the analysis. The results demonstrate that PANE moderates the influence of motivational factors on continued use intention and satisfaction.