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This paper investigates the relationship between the sustainable finance disclosure regulation (SFDR) and the performance of unlisted real estate funds.

While existing literature has primarily focused on the impact of voluntary sustainability disclosure, such as certifications or reporting standards, this study addresses a significant research gap by constructing and analyzing the financial J-Curve of 40 funds under the SFDR. The authors employ a panel regression analysis to examine the effects of different SFDR categories on fund performance.

The findings reveal that funds categorized under Article 8 of the SFDR do not exhibit significantly poorer performance compared to funds categorized under Article 6 during the initial phase after launch. On average, Article 8 funds even demonstrate positive returns earlier than their peers. However, the panel regression analysis suggests that Article 8 funds slightly underperform when compared to Article 6 funds over time.

While investors may not anticipate lower initial returns when opting for higher SFDR categories, they should nevertheless be aware of the limitations inherent in the existing SFDR labeling system within the unlisted real estate sector.

To the best of our knowledge, this study represents the first quantitative examination of unlisted real estate fund performance under the SFDR. By providing unique insights into the J-Curves of funds, our research contributes to the existing body of knowledge on the impact of sustainability regulations in the financial sector.

This study examines whether green buildings enjoy more favorable financing terms compared to their non-green counterparts, exploring the presence of a green discount in commercial real estate lending. Despite the extensive research on green premiums on the equity side, lending has received limited attention in the existing literature, even as regulations have increased and ambitious net-zero targets have been set in the banking sector.

In this study, the authors leverage a unique dataset comprising European commercial loan data spanning from 2018 to 2023, with a total loan value exceeding €30 billion. Hedonic regression analysis is used to isolate a potential green discount. Specifically, the authors rely on property assessments conducted by lenders to investigate whether green properties exhibit lower interest rate spreads and higher loan-to-value (LTV) ratios.

The findings reveal the existence of a green discount in European commercial real estate lending, with green buildings enjoying a 5.35% lower contracted loan spread and a 3.92% lower target spread compared to their non-green counterparts. However, this analysis does not indicate any distinct advantage in terms of LTV ratios for green buildings.

This research contributes to a deeper understanding of the interaction between green properties and commercial real estate lending, offering valuable insights for both lenders and investors.

This study, to the best of the authors’ knowledge, represents the first of its kind in a European context and provides empirical evidence for the presence of a green discount.

An ideal learning analytics tool for programming exercises performs the role of a lecturer who monitors the code development, provides customized support and identifies students at risk to drop out. But a reliable prediction and prevention of drop-out is difficult, due to the huge problem space in programming tasks and variety of solutions and programming strategies. The purpose of this paper is to tackle this problem by, first, identifying activity patterns that indicate students at risk; and, second, finding reasons behind specific activity pattern, for identification of instructional interventions that prevent drop-out.

The authors combine two investigation strategies: first, learning analytic techniques (decision trees) are applied on features gathered from students, while completing programming exercises, in order to classify predictors for drop-outs. Second, the authors determine cognitive, motivational and demographic learner characteristics based on a questionnaire. Finally, both parts are related with a correlation analysis.

It was possible to identify generic variables that could predict early and later drop-outs. For students who drop out early, the most relevant variable is the delay time between availability of the assignment and the first login. The correlation analysis indicates a relation with prior programming experience in years and job occupation per week. For students who drop out later in the course, the number of errors within the first assignment is the most relevant predictor, which correlates with prior programming skills.

The findings indicate a relation between activity patterns and learner characteristics. Based on the results, the authors deduce instructional interventions to support students and to prevent drop-outs.

In the ever-evolving digital landscape, this study aims to explore which specific personality traits contribute to the innovativeness of startups, with a particular emphasis on understanding how technology adoption mediates this relationship. By doing so, the authors strive to unveil the nuanced dynamics of personality, technology adoption and startup innovativeness in the digital era.

The authors conduct a quantitative empirical analysis using a sample of 1,314 German startups. This study utilizes a mediation analysis to examine the effects of personality traits on the innovativeness of startups, taking technology adoption as a mediator into account.

The empirical results show certain personality traits have direct effects on innovativeness. Also, the results show that technology adoption is a driver of startup innovativeness. In addition, these traits are (partially) mediated by technology adoption.

The results shed new light on the interplay of entrepreneurs' personality and technology adoption in relation to startup innovativeness and therefore underline the importance of technology in this triangular relationship. The authors employ secondary data from startups in Germany, which complicates generalization of the results to other geographical and cultural contexts.

This study contributes to the scientific debate on the role of personality traits in entrepreneurship by providing empirical evidence on the mediating effect of technology adoption in the relationship between personality traits and startup innovativeness. The findings offer valuable insights for researchers, entrepreneurs and policymakers interested in understanding and promoting innovativeness in the context of startups.

The purpose of this study was to investigate the online shopping intention of customers by watching artificial intelligence (AI)–based deepfake video advertisements using media richness (MR) theory and Information Manipulation Theory 2 (IMT2).

A conceptual model was developed to understand customers' online shopping intention by watching deepfake videos. A quantitative survey was conducted among the 1,180 customers using a structured questionnaire to test the conceptual model, and data were analyzed with partial least squares structural equation modeling.

The outcome of this research provides the antecedents of the online shopping intention of customers after watching AI-based deepfake videos. These antecedents are MR, information manipulation tactics, personalization and perceived trust. Perceived deception negatively influences customers' online shopping intention, and cognitive load has no effect. It also elucidates the manipulation tactics used by the managers to develop AI-based deepfake videos.

The distinctive model that emerged is insightful for senior executives and managers in the e-commerce and retailing industry to understand the influence of AI-based deepfake videos. This provides the antecedents of online shopping intention due to deepfakes, which are helpful for designers, marketing managers and developers.

The authors amalgamate the MR and IMT2 theory to understand the online shopping intention of the customers after watching AI-based deepfake videos. This work is a pioneer in examining the effect of AI-based deepfakes on the online shopping intention of customers by providing a framework that is empirically validated.

In Germany up until now, there has been very little research on staff development in schools. The purpose of this paper is to comprehensively assess school-based staff development and to describe the interplay between different instruments of staff development (e.g. classroom observations, development discussions) at the school level.

Considering that different constellations of organizational management tools may be differentially effective in different contexts (see Mintzberg, 1983/1992), an approach that takes a combination of different staff development instruments into account was chosen. Data were gathered from principals of primary and secondary schools in two federal states of Germany. Using regression, cluster analysis, and analysis of variance, the authors examined different instruments and patterns of staff development used in everyday school practice and determined how these affected the professional development of teachers.

Five staff development patterns could be identified. With regard to the extent of professional development activities of teachers, these patterns have been proven to have a different impact. Furthermore, the use of the different staff development patterns seems to be heavily dependent on the type of school.

Further research would be needed that examines if the three most relevant staff development patterns identified in this study can also be proven to be effective with regard to somewhat “harder” criteria than the extent of professional development activities of teachers. Such criteria could be teachers’ teaching skills or even student achievement.

The current study is the first to examine staff development in German schools systematically. The results provide some good leads for further studies in this area.