Information Systems Frontiers最新文献

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.

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.

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.

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.

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.