Information Systems Frontiers

Cybersecurity incidents damage not only the organizations attacked, but also society in general, harming customers and stakeholders. Through the text mining of the incident database, we observed that the impact of cybersecurity incident trends became more outward-oriented causing increased risks associated with social responsibility. Thus, this study aims to validate the potential effect of cybersecurity incidents on social responsibility risks and stock price drops. To derive meaningful factors from the description of incidents, we mined the texts to extract the features of the severity of incidents and their direction of impact whether inward or outward. The severity score is derived from sentiment analysis and the impact direction by topic modeling and machine learning models including SVM, LSTM, and BERT. The effects of these incident features are studied through regression models with social responsibility risk and stock price drops as dependent variables. To conduct this study, we collected incident texts from the Privacy Rights Clearinghouse database, and social responsibility risk indices from the Privacy and Data Security index and Cyber Risk Rating scores. The subsequent short-term stock price drops are measured by Cumulative Abnormal Returns and their variations. Our analysis revealed a profound impact of cybersecurity incidents on social responsibility risk indices and stock price drops with the moderating effect of outward impact in both models. However, we recognize the incompatibility between an annual index of social responsibility risk and short-term stock price drops. Therefore, we propose a short-term social responsibility risk index for cybersecurity which can be derived from the disclosed incidents. All these scenarios support the premise that cybersecurity incidents significantly impact the social responsibility risk and may lead to potential stock price drops.

The proposed native intelligent network by 6G networks has provided a boost to network security capabilities. Unlike intelligent networks built by intelligent network elements, plug-in AI applications require transmission bandwidth for traffic analysis and consume computation and storage resources of security devices. This cannot meet the real-time requirements for detecting and processing DDoS attacks. This paper proposes the intelligent network element that combines programmable switch technology and AI algorithms. The intelligent network element is used to build a distributed intelligent network defense system that analyzes the packet header information of the traffic to classify the packets, thus realizing network intelligence at the network layer. We analyzes a total of 14 types of DDoS attack traffic categorized into application layer DDoS, low-rate DDoS, and DRDoS. The machine learning model is used to sink to the network layer.In conclusion, the performance of the k-means, random forest, and decision tree algorithms is evaluated by comparing the performance of single-point and multi-point deployment scenarios on intelligent network elements in multiple dimensions. The results demonstrate that the multi-point intelligent network element system can reduce the packet loss rate by approximately 10% when the client transmits packets at a rate of 1000 pkts/s, while exhibiting a slight increase in resource consumption. This enables the intelligent network element detection accuracy to reach 98.03%.

With the increasing deployment of robots to support humans in various activities, a crucial factor that has surfaced as a precondition for successful human-robot interaction (HRI) is the human’s level of trust in the robotic companion. A phenomenon that has recently shifted into the foreground for its potential to influence cognitive and affective dimensions in humans is gamification. However, there is a dearth of knowledge whether and how gamification can be employed to effectively cultivate trust in HRI. The present study investigates and compares the effects of three design interventions (i.e., non-gamified vs. gameful design vs. playful design) on cognitive and affective trust between humans and an autonomous mobile collaborative robot (cobot) in a virtual reality (VR) training experiment. The results reveal that affective trust and specific trust antecedents (i.e., a robot’s likability and perceived intelligence) are most significantly developed via playful design, revealing the importance of incorporating playful elements into a robot’s appearance, demeanor, and interaction to establish an emotional connection and trust in HRI.

The integration of technological innovations and data analytics into sustainable development presents an opportunity to address pressing global challenges such as climate change, resource scarcity, and social inequities. This editorial introduces the Sustainable Development Impact Through Technological Innovations and Data Analytics (SDITIDA) framework, offering a conceptual foundation for aligning technology with the United Nations Sustainable Development Goals (SDGs). Through a rigorous review process, nine articles were selected for this special issue, showcasing interdisciplinary approaches and diverse applications of technology in sustainability. These contributions examine areas such as smart home technologies, AI maturity frameworks, blockchain-enabled agricultural practices, and big data analytics for organizational performance. Collectively, the issue highlights actionable strategies for researchers, practitioners, and policymakers, advancing the discourse on the socio-technical dimensions of sustainability and promoting equitable, sustainable outcomes.

In the year 2020, two real-world vigilantism incidents invited nationwide discourses on social media: the fatal shooting of two men by Kyle Rittenhouse (an aggressor) and the murder of Ahmaud Arbery (a victim). The public engaged vigorously in social media discussions of approval or disapproval of the aggressor or victim in such vigilantism incidents. While diversity of opinions is a healthy driver of advancement, extreme polarization can be a powerful barrier to achieving societal progress and human flourishing. In this paper, we first examine public opinion regarding these vigilantism incidents. We identify various issues expressed in social media conversations and find that compared to victim-oriented discourse, aggressor-oriented discourse on vigilantism displays more opinion polarization. The discourses show that aggressor-oriented vigilantism discussions largely support vigilantism, self-defense, and the right to bear arms. On the other hand, victim-oriented discourses largely disapprove of vigilantism incidents. We also find that positive emotions in discourses are more polarized compared to negative emotions. Our work has practical implications concerning polarization on social media after devastating events.

With the expansion of business activities around the world and the importance of sustainability in various fields, corporate sustainability has become a strategic imperative for management plans and investment decision. Therefore, this study focuses on examining the contribution of sustainability variables, i.e., economic, social, and environmental (ESG), to corporates profitability at 5936 companies distributed globally in an industry sectors using the data mining methods. The data extracted from Thomson Reuters database (ASSET4 ESG) for the period of 2002–2017 was used for modelling. Different algorithms, such as decision tree, support vector machine, and Naïve Bayes, were used for modelling. Since the current study uses a multi-class classification, the Kappa criterion was used to assess the quality of the classification algorithm. The results of the study confirmed that none of the sustainability dimensions had a negative impact on corporate profitability.

Although many countries have already forayed into 5G deployment, 4G still comprises the largest share of mobile subscribers, especially in emerging economies such as India. In this paper, we quantitatively assess the diffusion characteristics of ‘pure’ 4G mobile communication in India using popular nonlinear diffusion models, and scrutinize the association of two external factors, namely Human Development and Urbanization, with formal and informal communication channels of diffusion, in the context of 4G. Our findings highlight the crucial role external factors play in the speed and pattern of 4G diffusion across India. Notably, the 4G diffusion is predominantly driven by informal communication channels, such as word of mouth and interpersonal signalling. Typically, in regions with lower levels of Human Development and Urbanization, the informal communication channels have a greater influence on the diffusion. However, as the levels of Human Development and Urbanization go up, the formal communication channels start gathering momentum. Thus, our preliminary study sheds light on how Human Development and Urbanization interact with formal and informal communication channels to shape the diffusion of 4G in emerging economies. Our findings could furnish valuable perspectives for policymakers and stakeholders toward refining their strategies concerning infrastructure deployment, socio-economic development, and regulatory interventions.

Smart home technology (SHT) offers numerous economic, social, and environmental benefits, positioning it as a sustainable option for individuals and families seeking eco-friendly living solutions. Despite these advantages, adoption rates for SHT remain paradoxically low. Recognizing the ecological potential of SHT, this study investigates the psychological processes that influence the perceived sustainable value of SHT offerings within website content and how these perceptions affect adoption behavior. By integrating innovation diffusion theory with perceived value theory, this research provides a comprehensive framework for understanding the adoption of complex innovations like SHT. Empirical findings reveal that imagery processing during the online purchasing experience significantly enhances the perception of sustainable benefits and reduces the perceived sacrifices associated with adopting SHT, highlighting the importance of visual content in promoting sustainable technology adoption.

Contactless methods are widely used to measure vital signs from recorded or live videos using remote photoplethysmography (rPPG), which takes advantage of the slight skin color variation that occurs periodically on specific body regions with each blood pulse. However, existing rPPG-based solutions are typically expensive and not suitable for daily use at home for personal healthcare. To address this issue, we have recently developed a low-cost device that allows for the real-time estimation of vital signs using rPPG and can be easily integrated into any common home environment. The device consists of a smart mirror equipped with a camera that captures facial videos and extracts rPPG signals by processing video frames. One major limitation of this solution was its high sensitivity to abrupt head movements during video acquisition. This paper presents some advancements in the development of our smart device aimed at obtaining a more robust measurement of vital signs. Experimental results on live videos show that the new version of our system overcomes the limitations of the previous version, offering a more stable performance. Moreover, the new methodology shows improved performance compared to other state-of-the-art rPPG algorithms when tested on pre-recorded in-house videos from the UBFC-RPPG database.

Online health communities (OHCs) offer emotional and informational support to their users. However, past research has primarily treated these supports as separate, but they coexist in messages, making it essential to consider the emotional valence of text to understand the support being provided. This study examines how aligning questions and responses in OHCs reduces information gaps, and enhances support quality and perceived helpfulness. We use a labeled data set of question-response pairs to develop multimodal machine learning models to predict support interactions. Using explainable AI, we reveal the emotions within support exchanges, underscoring how emotional valence in the text determines informational support in OHCs and provide insight into the interaction between emotional and informational support. This study refines social support theory and establishes a foundation for decision aids and emotion-sensitive AI systems to deliver personalized social support tailored to users’ informational and emotional needs.

Building on the perspectives of the uses & gratification (U&G) theory and stimulus-organism-response (S–O-R) model, this article develops and tests an integrative framework to examine the underlying factors influencing customers’ experiences with chatbots as a form of virtual conversational agent (VCA) in the UK and Vietnam. In addition to utilitarian and hedonic factors, anthropomorphism and social presence are also investigated, which are considered important experiential dimensions in a customer-machine relationship. We also explore how stimuli such as functionality, communication style similarity, and aesthetics indirectly affect outcomes like customer satisfaction and reuse intention, mediated by four types of customer experiences. Data collected from a sample of 417 and 359 participants in the UK and Vietnam respectively revealed that, in general, perceived informativeness, credibility, enjoyment, functionality, and communication style similarity are crucial for customer satisfaction in both countries. Interesting differences in the effects of customer experience between developed and developing countries were observed. For instance, the effects of anthropomorphism and social presence on satisfaction are only effective for customers from developed country, while those from developing country only need information provided by chatbots be transparent. Our findings offer a novel way to understand customer experience with chatbots and provide important theoretical and managerial implications.

Credit risk assessment has drawn great interests from both researcher studies and financial institutions. In fact, classifying an applicant as defaulter or non-defaulter customer helps banks to make a reasonable decision. The classification of applicants is based on a set of historical information of past loans. Data sets for analysis may include different features, many of which may be irrelevant to the decision making process. Keeping irrelevant features or leaving out relevant ones may be harmful, causing generation of poor quality patterns that may lead to confusion decision. Determining an appropriate set of predictors is an important challenge in credit risk prediction research which guarantees better decision-making. It is the task of searching the smallest subset of features that provide the highest accuracy and comprehensibility. Thus, this study proposes feature selection-based classification model on credit risk assessment. To this end, five algorithms are applied, Speed-constrained Multi-objective PSO (SMPSO), Non-dominated Sorting Algorithm (NSGA-II), Sequential Forward Selection (SFS), Sequential Forward Floating Selection (SFFS), and Random Subset Feature Selection (RSFS). The selected subset is evaluated based on three classifiers K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN). Our proposed model is validated using three real-world credit datasets. The obtained results confirm the efficiency of SMPSO-KNN model to select the most significant features and provide the highest classification accuracy compared to existing models.

As per agenda-setting theory, political agenda is concerned with the government’s agenda, including politicians and political parties. Political actors utilize various channels to set their political agenda, including social media platforms such as Twitter (now X). Political agenda-setting can be influenced by anonymous user-generated content following the Bright Internet. This is why speech acts, experts, users with affiliations and parties through annotated Tweets were analyzed in this study. In doing so, the agenda formation during the 2019 European Parliament Election in Germany based on the agenda-setting theory as our theoretical framework, was analyzed. A prediction model was trained to predict users’ voting tendencies based on three feature categories: social, network, and text. By combining features from all categories logistical regression leads to the best predictions matching the election results. The contribution to theory is an approach to identify agenda formation based on our novel variables. For practice, a novel approach is presented to forecast the winner of events.

Network slicing is a 5G concept that virtualizes the physical network infrastructure to accommodate multiple service requirements on the same network, where each slice manages diverse needs and ensures their coexistence. In this work, we leverage blockchain technology to strengthen the security of handover authentication (HA) processes in network slicing systems.The proposed system addresses the challenge of reducing latency during handovers by incorporating a hybrid on-chain/off-chain model, optimizing the balance between security and speed. It employs the Raft consensus mechanism, which offers lower latency compared to more traditional consensus protocols such as PBFT. It establishes a decentralized registry for recording transfer events, streamlining user equipment (UE) identification verification, and improving HA efficiency. Moreover, we also introduce a three-component model: network slicing, user environments, and a Hyperledger Fabric (HLF) blockchain for authentication and authorization, which enhances the user experience by minimizing delays, ensuring data privacy, and providing scalability. By leveraging edge computing in conjunction with network slicing, the system further reduces latency, making it more efficient for real-time applications in dynamic mobile environments. Performance experiments indicate satisfactory scalability and maintained service quality under increasing throughput, affirming the suitability of the HLF-based system for managing network scenarios. Furthermore, the system’s modular design ensures compatibility with existing authentication protocols, such as AKA and EAP, enabling seamless integration with legacy systems. Consequently, this work enhances network security and service quality, especially in network slicing, HA, and employing HLF for privacy and security solutions. As 5G networks continue to evolve toward 6G, this system’s scalability and flexibility offer a promising approach to addressing future challenges in secure and efficient handover authentication.

With the rapid development of information technology, the gig labor marketplace is fast growing, with digital platform-based instant messaging (IM) playing an important role in raising freelancers’ orders, serving the intention for the crowdsourcing platforms to increase capacity to balance supply and demand. Using a large-scale field experiment on a crowdsourcing freight platform, this study investigates the impact of IM on freelancers’ response rate of orders. Our findings suggest the effects of IM depend on its content and information richness level. Task-relevant information in IM increases the freelancers’ response rate, especially for the priority commitment information, compared with order price information. In addition, although adding task-irrelevant information in IM decreases the freelancers’ response rate, it does not mean the less task-irrelevant information results in a weaker negative IM effect. Rather than that, including task-irrelevant information with a medium information richness level in IM harms the freelancers’ response to the most significant extent. Moreover, our findings reveal crowdsourcing platforms’ actions of IM to increase freelancers’ response rate are consistent with the actions to improve the order acceptance rate, thus demonstrating the critical role of increasing freelancers’ response rate in raising their interest in the final acceptance of the order serving. Our findings guide crowdsourcing platforms to design effective digital platform-based IMs to communicate with freelancers to arouse their response and interest in serving the orders. The capacity of crowdsourcing platforms thus can be dynamically adjusted and expanded to benefit their profitability.

This paper provides a comprehensive examination of the ongoing debate surrounding Artificial Intelligence (AI) and its societal implications, with a particular focus on job displacement. The release of generative AI tools for public use, particularly ChatGPT, has created numerous concerns on how these tools will be used and adverse impacts on society. Augmented Intelligence has been introduced as a concept utilizing AI to enhance human capabilities but its distinction as an assistive role is ill-defined. This research provides insights into the reconceptualization of AI as Augmented Intelligence examining their differences in terms of knowledge development, decision-making, and outcomes. Through three case studies, we demonstrate the assistive role of Augmented Intelligence and how it can serve as a catalyst for job creation and cognitive enhancement. We also explore the impact of AI and IA tools as a sociotechnical system and their effect on human cognitive abilities through the theoretical lens of the Dunning Kruger Effect. We conclude with a research agenda to stimulate future directions of research.

Online reviews are effective information-sharing tools due to their word-of-mouth characteristics. The extant literature has considered reviews as independent variables that influence business performance, while the environmental factors shaping these reviews remain under-explored. We examine the impact of COVID-19-related environmental uncertainties on changes in review prosumption (production and consumption) behavior. Based on the stimulus-response theory, with COVID-19 as the stimulus and prosumption as the response, we examined the changes in the characteristics of online reviews. Using the difference-in-differences methodology, we analyze online reviews of restaurants in two US cities that experienced different levels of COVID-19 impact. On the production side, we find an increased use of contextual terms and negative sentiments. On the consumption side, we find an increase in review usefulness and a decline in funniness. The results are robust, supported by coarsened exact matching and falsification tests. We conclude with a discussion of the study’s implications and contributions.

A recent trend in data management research investigates whether machine learning techniques could improve or replace core components of traditional database architectures, such as the query optimizer or selectivity and cardinality cost estimators. The preliminary approaches leverage cost-based optimizers and cost models to avoid a cold-start as they train and build learning models. In this work, we investigate whether learning could also be beneficial in rule-based optimizers, which instead of driving query execution decisions via a cost model they rely on a set of fixed rules and pre-defined heuristics. Our experimental testbed employs MonetDB, an open-source, column-store analytics data engine, and explore whether a learning model using Graph Neural Networks (GNNs) that is trained on a cost-based engine, such as PostgreSQL, could improve MonetDB optimizer’s decisions. Our initial findings reveal that our approach could improve significantly MonetDB’s query execution plans, especially as the query complexity increases whet it involves many join operators.

The well-organized structure of Policy Texts (PTs) is fundamental to intelligent governance, yet most PTs lack fine-grained category labels. PTs from different domains follow different classification systems, and traditional encoder-only models cannot directly handle scenarios where the label spaces of the source and target domains differ significantly, as their output layer typically is a fixed-dimensional classification head. Therefore, we propose a Cross-Domain Policy Text Classification (CDPTC) task. We introduce a method for the task called InstructCDPTC. This method, within an instruction tuning framework, transforms the classification task into a generation task, using the decoder-only model BigBird to predict masked tokens. We wrap the original PT within an instruction template containing a task description, a label description, and a mask sequence, which serve as input to BigBird. During training, we use the names of gold categories as the prediction targets for masked positions. During inference, we determine the final predicted category by computing the semantic distance between the averaged representations of the mask predictions and each candidate label. We constructed a dataset of 20,189 labeled policy texts from five different policy domains to evaluate InstructCDPTC. Experimental results demonstrate that InstructCDPTC achieves an F1 score of 0.824 under conditions where the sample distribution and label space of the target domain are entirely unseen, surpassing other baselines.

Shared services using digital platforms have increasingly gained prominence in recent times. Existing studies have studied several facets of ride-sharing services, but mobile app technology’s impact on user’s experience has not been explored meticulously. We attempt to study the technological artifacts which can signal about the capability of the service and thereby, reducing the informational asymmetry, stemming from lack of information and in-person communication. To address that, we adopt the Signaling Theory and Value Framework to understand the apps’ features, reflecting the shared mobility service quality to the users. We mine 212,000 and 150,000 user reviews on India’s two most extensively used shared mobility services- OLA and UBER, respectively and identify the factors affecting user experiences. We provide a novel framework by mapping these factors to theoretical lexicons. Multiple regression models show that time resources, monetary resources, perceived information protection, app usage controllability, perceived safety in e-payment mechanism, informational trust-related advantage, and participation in decision making influence the user experience of both the services significantly.

Since the Industrial Revolution, significant technological advancements have revolutionized various manual processes and workflows entrenched for decades. Artificial Intelligence (AI) offers similar transformative potential across diverse industrial and social domains. The rapid pace of change in the AI-driven digital age presents unprecedented opportunities and challenges for sustained progress. Given the potentially profound impact of AI, this study seeks to explore its disruptive effects and challenges within organizational contexts. Drawing on the Social Exchange Theory, this research examines the relationship between psychological contract (PC) fulfillment and organizational commitment, with trust acting as a mediator and AI acceptance as a moderator. Data were collected from the service industry using a time-lagged design. The findings indicate that PC fulfillment positively influences workers’ trust and organizational commitment. Furthermore, AI acceptance attenuates the direct and indirect positive effects of PC fulfillment on job-related outcomes. This study offers valuable insights into building and maintaining trust and fostering a committed workforce amidst the digitalization era. It underscores the importance of fulfilling promissory expectations in fostering trust and commitment. Additionally, it sheds light on the disruptive effects of AI technology on critical job outcomes, emphasizing the societal and industrial implications, the future of work, and avenues for further advancements in AI technology.

The role of information systems (IS) were widely discoursed during the spread of the COVID-19 outbreak. We have focused on developing a decision support systems (DSS) based on a combined prediction model, that can essentially be used at the start of any pandemic. Convalescent plasma therapy is generally applied during the spread of a pandemic as a therapy method that transfuses blood plasma from the people, who have recovered from an illness to treat critical cases. We observe, analyse, and predict the risks associated with the treatment effects of convalescent plasma therapy on COVID-19 patients. Based on the secondary data, we build a prediction model to evaluate and predict the trends in the clinical characteristics and laboratory findings for critically ill patients infected with COVID-19 and treated with convalescent plasma. Here, we use a combined prediction model utilizing three models; the grey prediction model (GM (1, 1)), the residual prediction model (residual GM (1, 1)), and a back propagation artificial neural network (BP-ANN) based residual sign prediction model. Also, a validation of the results of the study has been presented at two levels. On analysis of the results from the prediction model, it is observed that the convalescent plasma therapy can show progressive signs on COVID-19 infected patients. Health practitioners can understand, analyze, and predict the potential risks of convalescent plasma therapy based on the proposed model.

In contemporary society, where chronic stress is increasingly prevalent, this study aims to propose a multi-parametric wearable platform suitable for real-life monitoring and to validate its ability to acquire four physiological signals relevant for the stress response (electrocardiogram, respiration, galvanic skin response, photoplethysmogram). Secondly, it seeks to conduct a statistical analysis on the derived features both to identify the physiological signals necessary for a comprehensive analysis of the stress response and to understand the distinct contribution of each one. The results obtained revealed at least two statistically significant features from each of the physiological signals considered, confirming the importance of a multi-parametric approach for an accurate stress response analysis. Additionally, the proposed statistical hypotheses allowed to determine how each physiological signal contributes differently to characterize various aspects of the stress response. For these reasons, this study could represent a benchmark for future investigations aiming to classify the stress response.

The domain of decision support systems (DSS), focusing on developing various systems to aid decision-making, is receiving increasing attention. The proliferation of the domain necessitates a more comprehensive categorization of DSS research towards a unified representation that can contribute to a shared understanding. We thereby resort to re-examining the DSS research to assimilate, unveil, and re-structure the DSS scholarship. We perform an automated content analysis of the abstracts to investigate the structural commonalities of the DSS articles featuring in the Scopus database and published in the last five decades. Furthermore, we supplement our findings by exploring and classifying the emergent sub-structures. For this, we resort to the scenario classification framework, which draws from information systems, human-computer interaction, and requirements engineering domains, and adapt it in the context of DSS. Our overall results led to a framework for classifying DSS research with four levels: decision environment, DS (decision support) artifact, DS application, and context. We show the framework’s applicability by systematically classifying a sample of publications shortlisted to demonstrate its usefulness.

While the research on online consumer reviews is immense, it has largely focused on products which can also be purchased online. However, the high level of digital engagement of individuals today along with a reported fall in physical store visits indicate that digital content can also affect products available only for purchase offline. This research examines the effects of trust, uncertainty, and topics extracted from online consumer reviews on two outcomes in the India car market, namely the search for online information and sales. The study finds that while uncertainty does not affect sales, and has a negative effect on online information search, trust is positively associated with both. The topics extracted using Latent Dirichlet Allocation from the review corpus fall under the category of experiential or functional. The different topics have direct and mediating impacts on search and sales.