Contributions in Merlin

Temperament is claimed to be the basis for personality; therefore, discovering the temperamental underpinnings of borderline personality disorder and its facets is crucial for understanding this personality disorder. In this article, we explore these underpinnings by using a new model of temperament, based on the Regulative Theory of Temperament, the Big Two of temperament, and the Circumplex of Personality Metatraits. Two studies were conducted on adults—the first was in a general population sample (N = 315) and the second was in a clinical sample (N = 113) in people with a diagnosis of borderline personality disorder. The following measurements were used: The Screening Instrument for Borderline Personality Disorder (SI‐Bord), the Five‐Factor Borderline Inventory‐Short Form (FFBI‐SF), and the Temperament Metadimensions Questionnaire (TMQ). General borderline was explained by Reactivity (high Sensitivity) and Activity (high Dynamism). At the facet level, the Borderline Internalizing Facet was mainly explained by Reactivity (high Sensitivity), while the Borderline Externalizing Facet was explained by Activity (high Dynamism) in addition to Reactivity (high Sensitivity). The results of our study revealed specific temperamental underpinnings of borderline and its facets. Reactivity underlies all borderline facets, while Activity differentiates between the Borderline Externalizing Facet and Borderline Internalizing Facet.

Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group members’ preferences. However, few of them have empirically evaluated how the specific distribution of preferences in a group determines which strategy is the most effective. Furthermore, only a few studies evaluated the impact of providing explanations for the recommendations generated with social choice aggregation strategies, by evaluating explanations and aggregation strategies in a coupled way. To fill these gaps, we present two user studies (N=399 and N=288) examining the effectiveness of social choice aggregation strategies in terms of users’ fairness perception, consensus perception, and satisfaction. We study the impact of the level of (dis-)agreement within the group on the performance of these strategies. Furthermore, we investigate the added value of textual explanations of the underlying social choice aggregation strategy used to generate the recommendation. The results of both user studies show no benefits in using social choice-based explanations for group recommendations. However, we find significant differences in the effectiveness of the social choice-based aggregation strategies in both studies. Furthermore, the specific group configuration (i.e., various scenarios of internal diversity) seems to determine the most effective aggregation strategy. These results provide useful insights on how to select the appropriate aggregation strategy for a specific group based on the level of (dis-)agreement within the group members’ preferences.

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting spectrum sensing data falsification (SSDF) attacks. However, the amount of data needed to train models and the scenario privacy concerns limit the applicability of centralized ML/DL. Federated learning (FL) addresses these drawbacks but is vulnerable to adversarial participants and attacks. The literature has proposed countermeasures, but more effort is required to evaluate the performance of FL detecting SSDF attacks and their robustness against adversaries. Thus, the first contribution of this work is to create an FL-oriented dataset modeling the behavior of resource-constrained spectrum sensors affected by SSDF attacks. The second contribution is a pool of experiments analyzing the robustness of FL models according to i) three families of sensors, ii) eight SSDF attacks, iii) four FL scenarios dealing with anomaly detection and binary classification, iv) up to 33% of participants implementing data and model poisoning attacks, and v) four aggregation functions acting as anti-adversarial mechanisms. In conclusion, FL achieves promising performance when detecting SSDF attacks. Without anti-adversarial mechanisms, FL models are particularly vulnerable with > 16% of adversaries. Coordinate-wise-median is the best mitigation for anomaly detection, but binary classifiers are still affected with > 33% of adversaries.

Digitalization allows retailers to target customers with personalized promotions when they enter the store. Although traditional promotional retailer objectives, such as store visit, become obsolete once the shopper is already in the store, retailers still tend to target customers based on indicators that drive store visit, such as recency, frequency, and monetary value (RFM). In order to improve promotional efficiency, the authors propose targeting shoppers based on information derived from regularity patterns in individual interpurchase times at the point of sale. When compared to RFM-based targeting, the proposed live targeting approach translates into higher redemption rates (+ 10.5 percentage points), revenues (+ 42.3 percentage points), and purchase frequencies (+ 44.2 percentage points). The findings emphasize the importance of promotional timing and of considering customers’ outside potential for dynamic in-store targeting.

Decentralized Finance (‘DeFi’) has gained tremendous momentum over the past three years by using novel approaches to disintermediating financial institutions in the provision of financial services. However, empirical research in this field is still rare, and a more comprehensive understanding of the domain is a missing component in academic research. This paper develops a taxonomy based on a comprehensive literature analysis to structure this emerging field systematically. The taxonomy includes three perspectives (strategy, organization, technology) and seven dimensions (blockchain, value proposition, token type, business process, price mechanism, protocol type, integration type) as well as thirty-six characteristics. The application of the taxonomy to 278 DeFi start-ups reveals that most of the DeFi start-ups focus on Ethereum (36.3%) and have a focus on analytics and automation (52%), while, surprisingly only a few incorporate decentralized governance approaches (3.3%), provide decentralized exchanges (14%) or integrate off-chain data.

Artificial intelligence (AI)-enabled voice assistants (VAs) are transforming firm-customer interactions but often come across as lacking empathy. This challenge may cause business managers to question the overall effectiveness of VAs in shopping contexts. Recognizing empathy as a core design element in the next generation of VAs and the limits of scenario-based studies in voice commerce, this article investigates how empathy exhibited by an existing AI agent (Alexa) may alter consumer shopping responses. AI empathy moderates the original structural model bridging functional, relational, and social-emotional dimensions. Findings of an individual-session online experiment show higher intentions to delegate tasks, seek decision assistance, and trust recommendations from AI agents perceived as empathic. In contrast to individual shoppers, families respond better to functional VA attributes such as ease of use when AI empathy is present. The results contribute to the literature on AI empathy and conversational commerce while informing managerial AI design decisions.

Understanding the heterogeneous role of individuals in large-scale information spreading is essential to manage online behavior as well as its potential offline consequences. To this end, most existing studies from diverse research domains focus on the disproportionate role played by highly-connected “hub” individuals. However, we demonstrate here that information superspreaders in online social media are best understood and predicted by simultaneously considering two individual-level behavioral traits: influence and susceptibility. Specifically, we derive a nonlinear network-based algorithm to quantify individuals’ influence and susceptibility from multiple spreading event data. By applying the algorithm to large-scale data from Twitter and Weibo, we demonstrate that individuals’ estimated influence and susceptibility scores enable predictions of future superspreaders above and beyond network centrality, and reveal new insights on the network position of the superspreaders.

Habits pose a fundamental puzzle for those aiming to understand human behavior. They pervade our everyday lives and dominate some forms of psychopathology but are extremely hard to elicit in the lab. In this Registered Report, we developed novel experimental paradigms grounded in computational models, which suggest that habit strength should be proportional to the frequency of behavior and, in contrast to previous research, independent of value. Specifically, we manipulated how often participants performed responses in two tasks varying action repetition without, or separately from, variations in value. Moreover, we asked how this frequency-based habitization related to value-based operationalizations of habit and self-reported propensities for habitual behavior in real life. We find that choice frequency during training increases habit strength at test and that this form of habit shows little relation to value-based operationalizations of habit. Our findings empirically ground a novel perspective on the constituents of habits and suggest that habits may arise in the absence of external reinforcement. We further find no evidence for an overlap between different experimental approaches to measuring habits and no associations with self-reported real-life habits. Thus, our findings call for a rigorous reassessment of our understanding and measurement of human habitual behavior in the lab.

We study the long-term effects of a randomized intervention targeting children's socio-emotional skills. The classroom-based intervention for primary school children has positive impacts that persist for over a decade. Treated children become more likely to complete academic high school and enrol in university. Two mechanisms drive these results. Treated children show fewer attention deficit/hyperactivity disorder symptoms: they are less impulsive and less disruptive. They also attain higher grades, but they do not score higher on standardized tests. The long-term effects on educational attainment thus appear to be driven by changes in socio-emotional skills rather than cognitive skills.

The computing device deployment explosion experienced in recent years, motivated by the advances of technologies such as Internet-of-Things (IoT) and 5G, has led to a global scenario with increasing cybersecurity risks and threats. Among them, device spoofing and impersonation cyberattacks stand out due to their impact and, usually, low complexity required to be launched. To solve this issue, several solutions have emerged to identify device models and types based on the combination of behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques. However, these solutions are not appropriate for scenarios where data privacy and protection are a must, as they require data centralization for processing. In this context, newer approaches such as Federated Learning (FL) have not been fully explored yet, especially when malicious clients are present in the scenario setup. The present work analyzes and compares the device model identification performance of a centralized DL model with an FL one while using execution time-based events. For experimental purposes, a dataset containing execution-time features of 55 Raspberry Pis belonging to four different models has been collected and published. Using this dataset, the proposed solution achieved 0.9999 accuracy in both setups, centralized and federated, showing no performance decrease while preserving data privacy. Later, the impact of a label-flipping attack during the federated model training is evaluated using several aggregation mechanisms as countermeasures. Zeno and coordinate-wise median aggregation show the best performance, although their performance greatly degrades when the percentage of fully malicious clients (all training samples poisoned) grows over 50%.