René Algesheimer’s research while affiliated with University of Zurich and other places

Voice-based assistants (VAs) are transforming digital corporate communication by fundamentally reshaping how organizations interact with stakeholders. Recent innovations in generative artificial intelligence, powered by large language models (LLMs), have led to the widespread adoption of VAs capable of perceiving their environment, making decisions, and automating processes in real-time. Their ability to simulate human-like empathy, combined with increased agency, enhances the naturalness and relatability of their interactions. Consequently, these assistants function as multimodal, customizable tools that can adapt to the varied ways organizations choose to integrate them into their workflows. In this article, we illustrate the current applications of VAs through real-world examples.

Recent advances in artificial intelligence have led to the proliferation of artificial agents in social contexts, ranging from education to online social media and financial markets, among many others. The increasing rate at which artificial and human agents interact makes it urgent to understand the consequences of human-machine interactions for the propagation of new ideas, products, and behaviors in society. Across two distinct empirical contexts, we find here that artificial agents lead to significantly faster and wider social contagion. To this end, we replicate a choice experiment previously conducted with human subjects by using artificial agents powered by large language models (LLMs). We use the experiment's results to measure the adoption thresholds of artificial agents and their impact on the spread of social contagion. We find that artificial agents tend to exhibit lower adoption thresholds than humans, which leads to wider network-based social contagions. Our findings suggest that the increased presence of artificial agents in real-world networks may accelerate behavioral shifts, potentially in unforeseen ways.

We test the theory that personality incoherence may instigate personality change in the context of personal values. Values’ near-universal organization makes value incoherence assessment straightforward. The study included 13 longitudinal samples from seven cultures (Australia, Israel Palestinian citizens, Israel Jewish majority, Italy, Poland, Portugal, and Switzerland), total N = 7,126, and T1 M age ranging between 6 and 18. Each participant reported values between two- and six-times. Using unfolding analysis, we calculated the fit of the internal value structure of each participant at the first time point to the value structure in their sample (normative structure) and to the theoretical structure of values. We estimated value change using Growth Curve Modeling (when at least three measurement times were available) and the difference between T1 and T2 in each sample. We correlated value incoherence with value change and estimated the effect across samples using a meta-analysis. Incoherence with the structure of values predicted greater value change. The associations were stronger when participant’s value structures were compared to the normative value structure at T1 than when they were compared to the theoretical structure. A meta-regression analysis indicated that effects were not moderated by age. We discuss possible underlying processes and implications for personality development.

Addressing global challenges – from public health to climate change – often involves stimulating the large-scale adoption of new products or behaviors. Research traditions that focus on individual decision making suggest that achieving this objective requires better identifying the drivers of individual adoption choices. On the other hand, computational approaches rooted in complexity science focus on maximizing the propagation of a given product or behavior throughout social networks of interconnected adopters. The integration of these two perspectives – although advocated by several research communities – has remained elusive so far. Here we show how achieving this integration could inform seeding policies to facilitate the large-scale adoption of a given behavior or product. Drawing on complex contagion and discrete choice theories, we propose a method to estimate individual-level thresholds to adoption, and validate its predictive power in two choice experiments. By integrating the estimated thresholds into computational simulations, we show that state-of- the-art seeding methods for social influence maximization might be suboptimal if they neglect individual-level behavioral drivers, which can be corrected through the proposed experimental method.

This study proposes a novel conceptualization of work values designed to quantify the degree of incongruity between personal values and workplace demands. We define work values as the priorities individuals wish to be recognized for in their workplace, while personalvalues are those the individual personally identifies with. By contrasting personal and work values, we provide evidence for value incongruence among employees and showed that this measurement of value incongruence effectively predicts key job-related metrics. Value incongruence directly reduces job satisfaction, but its primary impact is indirect. Our multiple mediation analysis reveals that it mainly affects job satisfaction through perceived job meaningfulness, relationships with supervisors, and opportunities for career advancement. We discuss the implications of our findings for various stakeholders and suggest potential improvements for individual and societal well-being linked to the future of work.

The aim of our study was twofold: (1) to explore the role of value preferences on peer relations in school classes (selection effect) and (2) to explore the role of peers’ values on adolescents’ values (influence or socialization effect) in three types of networks (friendship, advice, and trust). To answer these questions, we used a longitudinal social network approach in a study of N = 903 adolescents (57% girls) from 34 secondary school classes in Poland. Pupils began participating in the study when they joined their secondary school and were followed over two and a half years. Panel data were collected at six measurement time points during this period. Values were conceptualized according to the values theory proposed by Schwartz and measured by the Portrait Value Questionnaire. The collection of network data followed a roster design. Pupils were asked to evaluate the strength of their friendships, as well as the frequency with which they approached peers to ask for advice about school or homework or to talk about things that are important to them in the last 2 weeks. We found empirical support for both selection and socialization effects, especially for protection values (Conservation and Self-enhancement). The selection effect was most evident in advice and trust networks and the socialization effect was particularly prevalent in friendship and trust networks.

Addressing global challenges -- from public health to climate change -- often involves stimulating the large-scale adoption of new products or behaviors. Research traditions that focus on individual decision making suggest that achieving this objective requires better identifying the drivers of individual adoption choices. On the other hand, computational approaches rooted in complexity science focus on maximizing the propagation of a given product or behavior throughout social networks of interconnected adopters. The integration of these two perspectives -- although advocated by several research communities -- has remained elusive so far. Here we show how achieving this integration could inform seeding policies to facilitate the large-scale adoption of a given behavior or product. Drawing on complex contagion and discrete choice theories, we propose a method to estimate individual-level thresholds to adoption, and validate its predictive power in two choice experiments. By integrating the estimated thresholds into computational simulations, we show that state-of-the-art seeding methods for social influence maximization might be suboptimal if they neglect individual-level behavioral drivers, which can be corrected through the proposed experimental method.

Corporate decision-makers (DMs) are increasingly being challenged to adopt emerging technologies with undefined market potential while being susceptible to biases. Failure to achieve the expected benefits may affect collective and individual-level performance. Fear of missing out (FOMO) influences the ability to make rational decisions. Although FOMO can lead DMs to prioritize popular but immature technologies, there remains a limited understanding of the notion in organizational settings. Drawing on semi-structured interviews and archival data corroborated by insights from key stakeholders, our research investigates the role of FOMO when adopting emerging technology. Findings reveal that FOMO (i) is experienced by DMs experience in one of three performance levels (firm, team, employee), each differentiated by specific targets and responses, and (ii) influences the decision process both directly and via inflated expected outcomes. The mere presence of FOMO does not constitute a bias in the decision. Further, we suggest how to regulate FOMO in organizations.

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.