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Abstract Current legislation and market dynamics require and encourage systems to become more open and interoperable. Given this shift, the challenge arises as to how service providers can navigate such environments and whether and how known platform economics are affected by that change. In this context, our work investigates behavior when users interact with services in highly interoperable environments, examining the influence of service attributes and platform economics on service selection and switching decisions, with a focus on the role of transaction cost and time, onboarding time, privacy, ownership, and community. For this purpose, we designed and conducted an extensive survey study with more than 500 respondents that combined a conjoint study with an experimental part on switching scenarios. Our findings suggest that transaction features such as cost, time, and privacy are the main factors in service selection with part-worth utilities being 2.1 to 14.7 times higher than non-transactional features. Additionally, building a strong community and offering ownership opportunities to users are effective strategies for customer retention. Further, we observe that rational choice theory does not explain switching decisions in many cases. Our study has important implications for both industry practitioners and policymakers. Practitioners can use our results to carefully manage effective customer retention strategies, while policymakers can use them to better regulate digital markets.

Devices such as cell phones, vehicles, and smart home systems often generate time-series data that are crucial for developing effective AI applications. However, directly analyzing and using these local data could violate user privacy. Although synthetic data offer a promising alternative to real data, current methods for synthesizing time-series data either require servers to first collect large amounts of real user data or fail to offer adequate privacy protection for training generative models. In order to address the limitations in existing solutions, we introduce FedSTDG, an innovative framework for the distributed synthesis of time-series data under strict local differential privacy (LDP) guarantees. Our approach combines federated learning (FL) with a recurrent Wasserstein autoencoder (RWAE) to train generative models without the need to collect real local data directly. Additionally, we propose an enhanced LDP-FL mechanism that incorporates a private dimension selection algorithm and an adaptive learning rate adjustment algorithm. Our approach substantially reduces privacy risks during FL training and improves the model utility compared to existing LDP-FL methods. Extensive experiments on open-source datasets show that synthetic data generated by our framework can reduce downstream prediction errors by 20% to 85% compared to baseline models under equivalent privacy constraints. Furthermore, the framework offers stronger privacy protection and robustness compared to traditional nonprivate FL mechanisms.

Abstract In recent years, credit reporting agencies are under pressure to reform themselves to reach higher levels of transparency. In this context, the critically considered digitally-implemented credit reporting system in China—the Chinese Social Credit System (SCS)—is of particular interest. Compared to traditional credit reporting systems, the SCS has a wider range of goals, including enhancing social control, establishing a state-defined notion of trustworthiness and increasing market efficiency. It covers not only Chinese citizens, companies, and organizations but also foreign ones which have activities in or with China. This paper studies the transparency of the SCS from the perspective of foreign organizations. We conducted interviews with employees from German organizations that are engaged in commercial activities in or with China to understand how transparent the SCS is to them and to gather their perceptions about the role of the system in increasing the transparency of the Chinese business landscape. Our analysis of the interviews showcased an interesting contrast between reservations with respect to the system’s transparency and the belief that the SCS would enhance the transparency of the Chinese business landscape. Drawing on our analysis of the interviews, we discuss factors affecting SCS transparency and the role of the SCS in increasing corporate transparency. We further highlight that there are inherent limits when credit rating systems (such as the SCS) aim to provide transparency about market participants, while pursuing other objectives at the same time.

The emergence of the COVID-19 pandemic in early 2020 has transformed how individuals work and learn and how they can apply cyber-security requirements in their, mostly remote, environments. This transformation also affected the university student population; some needed to adjust to new remote work settings, and all needed to adjust to the new remote study environment. In this online research study, we surveyed a large number of university students (n = 798) to understand their expectations in terms of support and help for this new remote work and study environment. We also asked students to report on their practices regarding remote location and Wi-Fi security settings, smart home device usage, BYOD (bring your own device) and personal device usage and social engineering threats, which can all lead to compromised security. A key aspect of our work is a comparison between the practices of students having work experience with the practices of students having no such additional experience. We identified that both the expectations and the level of cyber-security awareness differ significantly between the two student populations and that cyber-security awareness is increased by work experience. Work experience students are more aware of the cyber-security risks associated with a remote environment, and a higher portion of them know the dedicated employee whom they can contact in the event of incidents. We present the organizational security practices through the lens of employees with initial work experience, contributing to a topic that has so far received only limited attention from researchers. We provide recommendations for remote study settings and also for remote work environments, especially where the existing research literature survey results differ from the findings of our survey.

In recent years, several high-profile incidents have spurred research into games of timing. A framework emanating from the FlipIt model features two covert agents competing to control a single contested resource. In its basic form, the resource exists forever while generating value at a constant rate. As this research area evolves, attempts to introduce more economically realistic models have led to the application of various forms of economic discounting to the contested resource. This paper investigates the application of a two-parameter economic discounting method, called generalized hyperbolic discounting, and characterizes the game’s Nash equilibrium conditions. We prove that for agents discounting such that accumulated value generated by the resource diverges, equilibrium conditions are identical to those of non-discounting agents. The methodology presented in this paper generalizes the findings of several other studies and may be of independent interest when applying economic discounting to other models.

The Chinese social credit system (SCS) is a digital sociotechnical credit system that rewards and sanctions the economic and social behaviors of individuals and companies. This article uses classic social-control theories—the shaming theory and the labeling theory—to analyze the SCS, thereby contributing to a better understanding of the Chinese social-control approach to the digital transformation of society. Our research relies not only on government documents and media reports, but also on first-hand data collected from in-depth interviews conducted in China. We found that the perceived effectiveness of the shaming and labeling mechanisms is enhanced by the design of the SCS framework and the assistance of digital technology but weakened by a lack of transparency and questionable justification criteria, as well as privacy and fairness concerns.

Users are often educated to follow advices from security experts. For example, using a password manager is considered an effective way to maintain a unique and strong password for every website. However, user surveys reveal that most users are not willing to adopt this tool. They feel uncomfortable when they grant password managers the privilege to automate access to their digital accounts. We propose VaultIME to nudge more users towards the adoption of password managers by offering them a tangible benefit, while only slightly interfering with their current usage practices. Instead of “auto-filling” password fields, we propose to “auto-correct” passwords in case of minor typos. VaultIME integrates the functionality of a password manager into the input method editor. Running as an app on mobile phones, VaultIME remembers user passwords on a per-app basis, and corrects mistyped passwords within a typo-tolerant set. We show that VaultIME achieves high levels of usability and security.

Cybersecurity has become a key factor that determines the success or failure of companies that rely on information systems. Therefore, investment in cybersecurity is an important financial and operational decision. Typical information technology investments aim to create value, whereas cybersecurity investments aim to minimize loss incurred by cyber attacks. Admittedly, cybersecurity investment has become an increasingly complex one, since information systems are typically subject to frequent attacks, whose arrival and impact fluctuate stochastically. Furthermore, cybersecurity measures and improvements, such as patches, become available at random points in time making investment decisions even more challenging. We propose and develop an analytical real options framework that incorporates major components relevant to cybersecurity practice, and analyze how optimal cybersecurity investment decisions perform for a private firm. The novelty of this paper is that it provides analytical solutions that lend themselves to intuitive interpretations regarding the effect of timing and cybersecurity risk on investment behavior using real options theory. Such aspects are frequently not implemented within economic models that support policy initiatives. However, if these are not properly understood, security controls will not be properly set resulting in a dynamic inefficiency reflected in cycles of over or under investment, and, in turn, increased cybersecurity risk following corrective policy actions. Results indicate that greater uncertainty over the cost of cybersecurity attacks raises the value of an embedded option to invest in cybersecurity. This increases the incentive to suspend operations temporarily in order to install a cybersecurity patch that will make the firm more resilient to cybersecurity breaches. Similarly, greater likelihood associated with the availability of a cybersecurity patch increases the value of the option to invest in cybersecurity. However, the absence of an embedded investment option increases the incentive to delay the permanent abandonment of the company's operation due to the irreversible nature of the decision.