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Electric bicycles and public transport tickets: Ownership and car use patterns
No full textRiding electric bicycles and using public transport are popular alternatives to private car use. Utilizing data from a 2022 survey of 6,285 participants in Germany, we examine who typically owns e-bikes or public transport tickets. We firstly employ regression analyses to identify correlations between individual characteristics and ebike as well as ticket ownership, respectively. We find that e-bike owners tend to be older, earn higher incomes, often reside in more rural areas, and are more likely to be male. For public transport ticket ownership, these associations are largely reversed. Through latent class analyses, we identify distinct groups of e-bike and ticket owners. In a second step, we investigate associations between e-bike or ticket ownership and car use through propensity score matching and regression analyses. We find that, compared to non-owners, owners of either alternative exhibit lower car use, with the difference being larger for public transport ticket owners.Sowohl die Nutzung von E-Bikes (Pedelecs) und als auch von öffentlichen Verkehrsmitteln sind bereits beliebte Alternativen zur Autonutzung. Auf Basis von Daten aus einer 2022 Befragung mit 6,285 Teilnehmenden aus Deutschland untersuchen wir, wer die typischen Besitzerinnen und Besitzer von E-Bikes oder Zeitkarten für den ÖPNV sind. Mithilfe von Regressionsanalysen identifizieren wir Korrelationen zwischen verschiedenen Merkmalen und dem Besitz von E-Bikes bzw. Zeitkarten. Wer ein E-Bike besitzt ist tendenziell älter, hat ein höheres Einkommen und lebt eher in ländlichen Gebieten. Bei Menschen die Zeitkarten für den ÖPNV besitzen kehren sich diese Korrelationen größtenteils um. Durch Latent Class Analysen klassifizieren wir typische Nutzergruppen von beiden Verkehrsmitteloptionen. In einem zweiten Schritt untersuchen wir mithilfe von Propensity-Score-Matching und Regressionsanalysen Zusammenhänge zwischen der Intensität der Autonutzung und dem Besitz von E-Bikes oder Zeitkarten und finden heraus, dass in beiden Fällen der Besitz mit einer geringeren Autonutzung zusammenhängt. Allerdings ist diese Korrelation bei ÖPNV-Karten-Besitz wesentlich größer
Consumer Response to Anthropomorphism of Text‐Based AI Chatbots: A Systematic Literature Review and Future Research Directions
No full textThe rapid advancement of artificial intelligence (AI) has not only integrated chatbots like ChatGPT into everyday life but also transformed customer relations in various industries. Despite their growing adoption, research on consumer responses and preferences toward AI chatbots remains limited. Anthropomorphism, the attribution of human characteristics to nonhuman entities, plays a crucial role in shaping these interactions. This systematic literature review (SLR) analyzes 84 research articles within the theories‐context‐characteristics‐methods (TCCM) framework to provide a comprehensive overview of consumer responses to chatbot anthropomorphism. The findings reveal predominantly positive effects, with humanlike communication styles and emotional characteristics eliciting trust, empathy, and a sense of social presence. However, negative outcomes in the form of privacy concerns and AI anxiety from overly humanlike chatbots highlight the complex nature of humanized chatbots. New generative AI models amplify these risks, widening gaps in current research. Future research should address these gaps by exploring the opportunities and challenges of generative AI, emotional adaptability, personalized communication strategies, and the role of consumer technology affinity. By focusing exclusively on text‐based chatbots, this review offers a unique perspective that augments existing literature on consumer responses to AI chatbots, contributing to a better overall understanding of the underlying effects of anthropomorphism within the context of human–computer interactions
Differential Development of Professional Knowledge and Problem-Solving Skills During VET: The Role of Cognitive Resources, School-Leaving Certificates, and Sociodemographic Background
No full textIn this study, we investigated the development of apprentices’ professional knowledge and problem-solving skills during the second half of dual vocational education and training (VET). We analyzed (1) the average level of development, (2) interindividual differences in this development, and (3) the effects of cognitive resources, school-leaving certificates, and sociodemographic background as covariates of the development. Professional knowledge (n = 473) and problem-solving skills (n = 322) were assessed towards the end of the second (T1) and third year (T2) of VET. Using latent change score analyses, we found (1) an average increase in professional knowledge, (2) substantial variance in change, and (3) associations between the apprentices’ change and their cognitive resources (specifically, fluid intelligence and the prior level of professional knowledge). Beyond that, there were no effects of school-leaving certificates, socioeconomic status and migration background on the change between T1 and T2 but we found these covariates to be partly associated with the apprentices’ prior level of professional knowledge at T1. As we detected large estimation uncertainties in the results for problem-solving skills, no reliable interpretation was possible for this outcome. In sum, our study reveals inconclusive findings on the effectiveness of VET programs in promoting learning and, at the same time, emphasizes the large heterogeneity in learning trajectories that needs to be accounted for in VET practice to improve success for all apprentices
Tokenomics and digital economy in China: Analyzing the influence of blockchain technology integration on traditional business models
No full textThis study investigates the impact of tokenomics and the integration of blockchain technology on China’s digital economy, focusing on how blockchain adoption influences traditional business models. As China becomes a global leader in digital transformation, understanding the role of the blockchain in economic modernization is critical. The aim of this research is to quantify the effects of blockchain adoption on key economic indicators such as GDP growth, investment levels, and business innovation. Using panel data analysis and regression models, this study provides empirical evidence on the positive correlation between blockchain integration and improved economic performance. Key results reveal that a 1% increase in blockchain adoption is associated with a 0.3% rise in GDP growth, while tokenization contributes significantly to investment levels and business innovation. These findings emphasize the transformative potential of the blockchain in enhancing economic stability, increasing liquidity, and fostering new business opportunities. In conclusion, this research highlights the critical role of the blockchain and tokenomics in driving economic modernization in China, offering valuable insights for policymakers, business leaders, and investors aiming to leverage digital technologies for sustainable growth. Future research should explore the broader global implications of blockchain adoption and tokenomics in emerging markets
Mixed orthogonality graphs for continuous‐time state space models and orthogonal projections
No full textIn this article, we derive (local) orthogonality graphs for the popular continuous‐time state space models, including in particular multivariate continuous‐time ARMA (MCARMA) processes. In these (local) orthogonality graphs, vertices represent the components of the process, directed edges between the vertices indicate causal influences and undirected edges indicate contemporaneous correlations between the component processes. We present sufficient criteria for state space models to satisfy the assumptions of Fasen‐Hartmann and Schenk (2024a) so that the (local) orthogonality graphs are well‐defined and various Markov properties hold. Both directed and undirected edges in these graphs are characterised by orthogonal projections on well‐defined linear spaces. To compute these orthogonal projections, we use the unique controller canonical form of a state space model, which exists under mild assumptions, to recover the input process from the output process. We are then able to derive some alternative representations of the output process and its highest derivative. Finally, we apply these representations to calculate the necessary orthogonal projections, which culminate in the characterisations of the edges in the (local) orthogonality graph. These characterisations are given by the parameters of the controller canonical form and the covariance matrix of the driving Lévy process
Towards autonomous learning and optimisation in textile production: data-driven simulation approach for optimiser validation
No full textThe textile industry is a traditional industry branch that remains highly relevant in Europe. The industry is under pressure to remain profitable in this high-wage region. As one promising approach, data-driven methods can be used for process optimisation in order to reduce waste, increase profitability and relieve mental burden on staff members. However, approaches from research rarely get adopted into practice. We identify the high dimensionality of textile production processes leading to high model uncertainty as well as an incomplete problem formulation as the two main problems. We argue that some form of an autonomous learning agent can address this challenge, when it safely explores advantageous, unknown new settings by interacting with the process. Our main goal is to facilitate the adoption of promising research into practical applications. The main contributions of this paper include the derivation and formulation of a probabilistic optimisation problem for high-dimensional, stationary production processes. We also create a highly adaptable simulation of the textile carded nonwovens production process in Python that implements the optimisation problem. Economic and technical behavior of the process is approximated using both Gaussian Process Regression (GPR) models trained with industrial data as well as physics-motivated explicit models. This ’simulation first’-approach makes the development of autonomous learning agents for practical applications feasible because it allows for cheap testing and validation before physical trials. Future work will include the comparison of the performance of different agent approaches
Forecasting e-commerce consumer returns: a systematic literature review
No full textThe substantial growth of e-commerce during the last years has led to a surge in consumer returns. Recently, research interest in consumer returns has grown steadily. The availability of vast customer data and advancements in machine learning opened up new avenues for returns forecasting. However, existing reviews predominantly took a broader perspective, focussing on reverse logistics and closed-loop supply chain management aspects. This paper addresses this gap by reviewing the state of research on returns forecasting in the realms of e-commerce. Methodologically, a systematic literature review was conducted, analyzing 25 relevant publications regarding methodology, required or employed data, significant predictors, and forecasting techniques, classifying them into several publication streams according to the papers' main scope. Besides extending a taxonomy for machine learning in e-commerce, this review outlines avenues for future research. This comprehensive literature review contributes to several disciplines, from information systems to operations management and marketing research, and is the first to explore returns forecasting issues specifically from the e-commerce perspective
Gaps between de jure entitlements and de facto benefits: Institutional drift and non‐take‐up in China's maternity benefit system
No full textChinese female workers have a de jure right to maternity benefits, enshrined in law and policy since the 1950s. Using the concept of institutional drift, this article examines why entitlements are not awarded as legally stipulated. It finds that the transition from a command to a market economy undermined the effectiveness of maternity benefit entitlements. Although maternity insurance was introduced in 1994 to alleviate drift, employer non‐compliance and lax enforcement resulted in non‐take‐up of benefits. The non‐contributory design of the insurance makes employers both contributors to and distributors of the maternity benefits to which formally employed workers are entitled. Combining historical research, interviews and quantitative data, this article documents the historical evolution of maternity benefits in China, identifies drift as the mechanism underlying uneven insurance coverage and declining benefit levels, and argues that a comprehensive understanding of non‐take‐up must go beyond the individual worker level to include the role of employers and local governments
The Impact of Paid Paternity Leave Reforms on Divorce Rates in Europe
No full textUsing a panel dataset covering 27 European countries over a 53-year period, this study examines the relationship between paid paternity leave reforms and divorce rates. Controlling for policyrelated factors and other legislative changes affecting divorce, the dynamic analysis reveals that while the introduction of any paid paternity leave is initially associated with higher divorce rates, the effect becomes negative when focusing on policies offering 2 weeks or more of leave. The decrease in divorce rates becomes more significant as the length of leave increases and grows over time. Specifically, providing fathers with at least 2 weeks of paid leave after childbirth reduces divorce rates by 0.36 percentage points 15 years after implementation. Additional analyses of the underlying mechanisms suggest that, in the absence of extended paternity leave, the results are likely driven by improved labor market opportunities for women-a factor that may unintentionally increase the likelihood of divorce
Temperature and quarterly economic activity: Panel data evidence from Mexico
No full textIn this paper, we estimate the effect of temperature on the economic activity of Mexico using 42 years of quarterly panel data on economic growth at the state level. Our findings reveal a concave relationship between quarterly economic growth and quarterly average temperature that is maximized at around 20 degrees Celsius. Average temperatures above this level are associated with lower economic growth rates with sharper declines for agricultural and low-income states. Temperature affects aggregate economic growth mainly through the effect it has on the growth of the primary and secondary sectors. The findings of this paper suggest that by 2100, in the absence of adaptation and under an intermediate scenario of climate change, global warming might cause a statistically significant reduction of quarterly economic growth