We analyze to what extent climate conditions affect the prevalence of sharecropping as a form of traditional land tenure. We investigate how sharecropping tenure is related to climate risk and how it interacts with fertilizer use and livestock ownership that both influence production risk. We first develop a stylized theoretical model to illustrate the role of climate for land tenure and production. Our empirical analysis is based on more than 9000 households with considerable heterogeneity in climate conditions across several African countries. We find that farmers in areas with low precipitation are more likely to be sharecroppers. We further find evidence for risk management interaction effects as sharecropping farmers are less likely to own livestock and more likely to use fertilizer. In economies where formal kinds of insurance are unavailable, sharecropping thus functions as a form of insurance and reduces the need for potentially costly risk management strategies.

Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established hypothesis is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with considerable heterogeneity among existing studies on the hypothesis and causal evidence still limited, a final verdict on its robustness is still pending. To contribute to this ongoing debate, we conducted a week-long randomized control trial with N = 381 adult Instagram users recruited via Prolific. Specifically, we tested how active SNS use, operationalized as picture postings on Instagram, affects different dimensions of well-being. The results depicted a positive effect on users' positive affect but null findings for other well-being outcomes. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs. <br /> Lay Summary Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established assumption is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with great diversity among conducted studies on the hypothesis and a lack of causal evidence, a final verdict on its viability is still pending. To contribute to this ongoing debate, we conducted a week-long experimental investigation with 381 adult Instagram users. Specifically, we tested how posting pictures on Instagram affects different aspects of well-being. The results of this study depicted a positive effect of posting Instagram pictures on users' experienced positive emotions but no effects on other aspects of well-being. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs on users.

In this study, we contribute to the scholarly conversation on firm-level business model changes following a neoconfigurational approach. By exploring configurations of business model changes over time, we add the direction of business model changes-namely business model convergence or divergence-as a vital avenue to the business model innovation literature. We identify necessary business model convergence and divergence recipes in a sample of N = 217 strategic dyadic alliances. Firstly, technological proximity emerges as a single precondition to both converging and diverging business models. Secondly, business models between competitors either converge through complementarities or tend not to change relative to each other. Thirdly, equity participation enables business model divergence through co-specialization. We conclude with a discussion of business model trajectories and future research directions.

The impact of traits in entrepreneurship has been subject to intense discussion. Apart from favorable traits fostering opportunity recognition, entrepreneurial orientation, venture performance, and other variables, a younger research stream also addresses the role of negative traits. Among them, the dark triad, comprising of narcissism, Machiavellianism, and psychopathy, have gained specific attention. This systematic literature review aims to structure the field, identify current research themes, and provide a better understanding of prior research outcomes. Our results show that dark triad research addresses entrepreneurial activity, opportunity recognition, entrepreneurial orientation, entrepreneurial leadership, the and entrepreneurial motives. Among the dark triad traits, narcissism is stressed most in research so far. It relates to firm performance, risk, and leadership behavior, whereas Machiavellianism and psychopathy relate to opportunity recognition and exploitation. We also identify several research gaps, which can be addressed in future research.

Erfolgreiches Verhandeln stellt einen Schlüsselfaktor für Unternehmenserfolge dar. Es angemessen zu trainieren kann jedoch sowohl zeitaufwendig als auch kostenintensiv werden, erfordert es doch idealerweise wiederholte, persönliche Übungen mit professionellen Verhandlungsführern oder Agenten. Digitale Trainingswerkzeuge können zwar ebenfalls Trainingserfolge erzielen, bieten aber eine mangelnde Authentizität der Übungssituation und erschweren somit den Transfer des Gelernten in den Berufsalltag. Das in diesem Beitrag vorgestellte Verhandlungstraining setzt Virtual Reality (VR) als Technologie für realitätsnahe Simulation ein, um eine räumlich authentische Übungssituation zu schaffen. Weiterhin dient ein sprachlich interagierendes Dialogsystem als automatisierter, virtueller Verhandlungsagent. Dieser wurde mit Interaktionsdaten aus einer Verhandlungsstudie trainiert und bietet Trainingspersonen somit einen wirksamen Übungspartner für das VR-Verhandlungstraining.

Purpose: This study aims to provide probable future developments in the form of holistic scenarios for business negotiations. In recent years, negotiation research did not put a lot of emphasis on external changes. Consequently, current challenges and trends are scarcely integrated, making it difficult to support negotiation practice perspectively. Design/methodology/approach: This paper applies the structured, multi-method approach of scenario analysis. To examine the future space of negotiations, this combines qualitative and quantitative measures to base our analysis on negotiation experts’ assessments, estimations and visions of the negotiation future. Findings: The results comprise an overview of five negotiation scenarios in the year 2030 and of their individual drivers. The five revealed scenarios are: digital intelligence, business as usual, powerful network – the route to collaboration, powerful network – the route to predominance and system crash. Originality/value: The scenario analysis is a suitable approach that enables to relate various factors of the negotiation environment to negotiations themselves and allows an examination of future changes in buyer–seller negotiations and the creation of possible future scenarios. The identified scenarios provide an orientation for business decisions in the field of negotiation.

Purpose Paradoxical leadership (PL) is an emerging perspective to understand how leaders help followers deal with paradoxical demands. Recently, the positive relationship between PL and follower performance was established. This paper builds on and extends this research by interpreting PL as sensegiving and developing theory about mediation in the relationship between PL and adaptive and proactive performance. Design/methodology/approach The paper develops a new measure for PL as sensegiving and provides a test of the mediation model with data from two different sources and two measurement times in a German company. Findings Multilevel mediation analysis (N = 154) supports the mediation model. Originality/value The paper presents sensegiving about paradox as a core element of PL, which informs the choice of change-readiness as mediator. This study also develops and validates a scale to measure PL in future research.

This article merges theoretical literature on non-controlling minority shareholdings (NCMS) in a coherent model to study the effects of NCMS on competition and collusion. The model encompasses both the case of a common owner holding shares of rival firms as well as the case of cross ownership among rivals. We find that by softening competition, NCMS weaken the sustainability of collusion under a greater variety of situations than was indicated by earlier literature. Such effects exist, in particular, in the presence of an effective competition authority.

Creativity – developing something new and useful – is a constant challenge in the working world. Work processes, services, or products must be sensibly adapted to changing times. To be able to analyze and, if necessary, adapt creativity in work processes, a precise understanding of these creative activities is necessary. Process modeling techniques are often used to capture business processes, represent them graphically and analyze them for adaptation possibilities. This has been very limited for creative work. An accurate understanding of creative work is subject to the challenge that, on the one hand, it is usually very complex and iterative. On the other hand, it is at least partially unpredictable as new things emerge. How can the complexity of creative business processes be adequately addressed and simultaneously manageable? This dissertation attempts to answer this question by first developing a precise process understanding of creative work. In an interdisciplinary approach, the literature on the process description of creativity-intensive work is analyzed from the perspective of psychology, organizational studies, and business informatics. In addition, a digital ethnographic study in the context of software development is used to analyze creative work. A model is developed based on which four elementary process components can be analyzed: Intention of the creative activity, Creation to develop the new, Evaluation to assess its meaningfulness, and Planning of the activities arising in the process – in short, the ICEP model. These four process elements are then translated into the Knockledge Modeling Description Language (KMDL), which was developed to capture and represent knowledge-intensive business processes. The modeling extension based on the ICEP model enables creative business processes to be identified and specified without the need for extensive modeling of all process details. The modeling extension proposed here was developed using ethnographic data and then applied to other organizational process contexts. The modeling method was applied to other business contexts and evaluated by external parties as part of two expert studies. The developed ICEP model provides an analytical framework for complex creative work processes. It can be comprehensively integrated into process models by transforming it into a modeling method, thus expanding the understanding of existing creative work in as-is process analyses.

Purpose: Strategic entrepreneurship (SE) depicts the nexus of strategic management and entrepreneurship, suggesting that firms can create superior wealth when simultaneously pursuing advantage-seeking and opportunity-seeking behavior. As the rapid growth in SE research led to a multidisciplinary, scattered and fragmented literature landscape, the authors aim to structure this research field. Design/methodology/approach: The authors employ a bibliographic coupling and literature review of the strategic entrepreneurship research field. Findings: The authors identify and describe five major research streams with 15 sub-themes in recent SE research. Based on our findings, the authors propose an integrated research framework and research gaps for future research. Originality/value: To the authors' knowledge, this is the first review on SE based on a bibliographic coupling.

This paper investigates the views on competition theory and policy of the American institutional economists during the first half of the 20th century. These perspectives contrasted with those of contemporary neoclassical and later mainstream economic approaches. We identify three distinct dimensions to an institutionalist perspective on competition. First, institutionalist approaches focused on describing industry details, so as to bring theory into closer contact with reality. Second, institutionalists emphasized that while competition was sometimes beneficial, it could also be disruptive. Third, institutionalists had a broad view of the objectives of competition policy that extended beyond effects on consumer welfare. Consequently, institutionalists advocated for a wide range of policies to enhance competition, including industrial self-regulation, broad stakeholder representation within corporations, and direct governmental regulations. Their experimental attitude implied that policy would always be evolving, and antitrust enforcement might be only one stage in the development toward a regime of industrial regulation.

Researchers have shown that structuring issues and organizing an agenda before a negotiation lead to improved negotiation performance. By using issue analysis, negotiators become aware of their own and their opponents' preferences on negotiation issues and are able to use this knowledge to optimize their degree of success. Following research on asymmetrical preferences in negotiations, we introduce a new approach for issue analysis that considers the identification of one-sided preferences, specifically a 0-preference for issues from one party. We conducted an experimental study to test if this type of preference for an issue (chance issue) yields strategic potential for a negotiator. We also examined whether the identification of these chance issues could be particularly relevant for a low-power party in negotiations with a power imbalance, to overcome the lower scope of action due to the weaker negotiating position. The results indicate initial verification that no preference at all for one issue could lead to higher individual performance and noneconomic outcomes. Joint performance was positively affected by 0-preference, even in unbalanced power situations.

We collect a network dataset of tenured economics faculty in Austria, Germany and Switzerland. We rank the 100 institutions included with a minimum violation ranking. This ranking is positively and significantly correlated with the Times Higher Education ranking of economics institutions. According to the network ranking, individuals on average go down about 23 ranks from their doctoral institution to their employing institution. While the share of females in our dataset is only 15%, we do not observe a significant gender hiring gap (a difference in rank changes between male and female faculty). We conduct a robustness check with the Handelsblatt and the Times Higher Education ranking. According to these rankings, individuals on average go down only about two ranks. We do not observe a significant gender hiring gap using these two rankings (although the dataset underlying this analysis is small and these estimates are likely to be noisy). Finally, we discuss the limitations of the network ranking in our context.

Accurately solving classification problems nowadays is likely to be the most relevant machine learning task. Binary classification separating two classes only is algorithmically simpler but has fewer potential applications as many real-world problems are multi-class. On the reverse, separating only a subset of classes simplifies the classification task. Even though existing multi-class machine learning algorithms are very flexible regarding the number of classes, they assume that the target set Y is fixed and cannot be restricted once the training is finished. On the other hand, existing state-of-the-art production environments are becoming increasingly interconnected with the advance of Industry 4.0 and related technologies such that additional information can simplify the respective classification problems. In light of this, the main aim of this thesis is to introduce dynamic classification that generalizes multi-class classification such that the target class set can be restricted arbitrarily to a non-empty class subset M of Y at any time between two consecutive predictions. This task is solved by a combination of two algorithmic approaches. First, classifier calibration, which transforms predictions into posterior probability estimates that are intended to be well calibrated. The analysis provided focuses on monotonic calibration and in particular corrects wrong statements that appeared in the literature. It also reveals that bin-based evaluation metrics, which became popular in recent years, are unjustified and should not be used at all. Next, the validity of Platt scaling, which is the most relevant parametric calibration approach, is analyzed in depth. In particular, its optimality for classifier predictions distributed according to four different families of probability distributions as well its equivalence with Beta calibration up to a sigmoidal preprocessing are proven. For non-monotonic calibration, extended variants on kernel density estimation and the ensemble method EKDE are introduced. Finally, the calibration techniques are evaluated using a simulation study with complete information as well as on a selection of 46 real-world data sets. Building on this, classifier calibration is applied as part of decomposition-based classification that aims to reduce multi-class problems to simpler (usually binary) prediction tasks. For the involved fusing step performed at prediction time, a new approach based on evidence theory is presented that uses classifier calibration to model mass functions. This allows the analysis of decomposition-based classification against a strictly formal background and to prove closed-form equations for the overall combinations. Furthermore, the same formalism leads to a consistent integration of dynamic class information, yielding a theoretically justified and computationally tractable dynamic classification model. The insights gained from this modeling are combined with pairwise coupling, which is one of the most relevant reduction-based classification approaches, such that all individual predictions are combined with a weight. This not only generalizes existing works on pairwise coupling but also enables the integration of dynamic class information. Lastly, a thorough empirical study is performed that compares all newly introduced approaches to existing state-of-the-art techniques. For this, evaluation metrics for dynamic classification are introduced that depend on corresponding sampling strategies. Thereafter, these are applied during a three-part evaluation. First, support vector machines and random forests are applied on 26 data sets from the UCI Machine Learning Repository. Second, two state-of-the-art deep neural networks are evaluated on five benchmark data sets from a relatively recent reference work. Here, computationally feasible strategies to apply the presented algorithms in combination with large-scale models are particularly relevant because a naive application is computationally intractable. Finally, reference data from a real-world process allowing the inclusion of dynamic class information are collected and evaluated. The results show that in combination with support vector machines and random forests, pairwise coupling approaches yield the best results, while in combination with deep neural networks, differences between the different approaches are mostly small to negligible. Most importantly, all results empirically confirm that dynamic classification succeeds in improving the respective prediction accuracies. Therefore, it is crucial to pass dynamic class information in respective applications, which requires an appropriate digital infrastructure.

In this paper we examine the relationship between the default risk of banks and sovereigns, i.e. the 'doom-loop'. Specifically, we try to assess the effectiveness of the implementation of the new recovery and resolution framework in the European Union. We use a panel with daily data on European banks and sovereigns ranging from 2012 to 2016 in order to test the effects of the Bank Recovery and Resolution Directive on the two-way feedback process. We find that there was a pronounced feedback loop between banks and sovereigns from 2012 to 2014. However, after the implementation of the European Banking Union, in 2015/2016, the magnitude of the doom-loop decreased and the spillovers became not statistically significant. Furthermore, our results suggest that the implementation of the new resolution framework is a suitable candidate to explain this finding. Overall, the results are robust across several specifications.

Teaching and learning as well as administrative processes are still experiencing intensive changes with the rise of artificial intelligence (AI) technologies and its diverse application opportunities in the context of higher education. Therewith, the scientific interest in the topic in general, but also specific focal points rose as well. However, there is no structured overview on AI in teaching and administration processes in higher education institutions that allows to identify major research topics and trends, and concretizing peculiarities and develops recommendations for further action. To overcome this gap, this study seeks to systematize the current scientific discourse on AI in teaching and administration in higher education institutions. This study identified an (1) imbalance in research on AI in educational and administrative contexts, (2) an imbalance in disciplines and lack of interdisciplinary research, (3) inequalities in cross-national research activities, as well as (4) neglected research topics and paths. In this way, a comparative analysis between AI usage in administration and teaching and learning processes, a systematization of the state of research, an identification of research gaps as well as further research path on AI in higher education institutions are contributed to research.

The term Metaverse is emerging as a result of the late push by multinational technology conglomerates and a recent surge of interest in Web 3.0, Blockchain, NFT, and Cryptocurrencies. From a scientific point of view, there is no definite consensus on what the Metaverse will be like. This paper collects, analyzes, and synthesizes scientific definitions and the accompanying major characteristics of the Metaverse using the methodology of a Systematic Literature Review (SLR). Two revised definitions for the Metaverse are presented, both condensing the key attributes, where the first one is rather simplistic holistic describing “a three-dimensional online environment in which users represented by avatars interact with each other in virtual spaces decoupled from the real physical world”. In contrast, the second definition is specified in a more detailed manner in the paper and further discussed. These comprehensive definitions offer specialized and general scholars an application within and beyond the scientific context of the system science, information system science, computer science, and business informatics, by also introducing open research challenges. Furthermore, an outlook on the social, economic, and technical implications is given, and the preconditions that are necessary for a successful implementation are discussed.

The term Metaverse is emerging as a result of the late push by multinational technology conglomerates and a recent surge of interest in Web 3.0, Blockchain, NFT, and Cryptocurrencies. From a scientific point of view, there is no definite consensus on what the Metaverse will be like. This paper collects, analyzes, and synthesizes scientific definitions and the accompanying major characteristics of the Metaverse using the methodology of a Systematic Literature Review (SLR). Two revised definitions for the Metaverse are presented, both condensing the key attributes, where the first one is rather simplistic holistic describing “a three-dimensional online environment in which users represented by avatars interact with each other in virtual spaces decoupled from the real physical world”. In contrast, the second definition is specified in a more detailed manner in the paper and further discussed. These comprehensive definitions offer specialized and general scholars an application within and beyond the scientific context of the system science, information system science, computer science, and business informatics, by also introducing open research challenges. Furthermore, an outlook on the social, economic, and technical implications is given, and the preconditions that are necessary for a successful implementation are discussed.

Recent research suggests that design thinking practices may foster the development of needed capabilities in new digitalised landscapes. However, existing publications represent individual contributions, and we lack a holistic understanding of the value of design thinking in a digital world. No review, to date, has offered a holistic retrospection of this research. In response, in this bibliometric review, we aim to shed light on the intellectual structure of multidisciplinary design thinking literature related to capabilities relevant to the digital world in higher education and business settings, highlight current trends and suggest further studies to advance theoretical and empirical underpinnings. Our study addresses this aim using bibliometric methods—bibliographic coupling and co-word analysis as they are particularly suitable for identifying current trends and future research priorities at the forefront of the research. Overall, bibliometric analyses of the publications dealing with the related topics published in the last 10 years (extracted from the Web of Science database) expose six trends and two possible future research developments highlighting the expanding scope of the design thinking scientific field related to capabilities required for the (more sustainable and human-centric) digital world. Relatedly, design thinking becomes a relevant approach to be included in higher education curricula and human resources training to prepare students and workers for the changing work demands. This paper is well-suited for education and business practitioners seeking to embed design thinking capabilities in their curricula and for design thinking and other scholars wanting to understand the field and possible directions for future research.

In this paper, we move from the large strand of research that looks at evidence of climate migration to the questions: who are the climate migrants? and where do they go? These questions are crucial to design policies that mitigate welfare losses of migration choices due to climate change. We study the direct and heterogeneous associations between weather extremes and migration in rural India. We combine ERAS reanalysis data with the India Human Development Survey household panel and conduct regression analyses by applying linear probability and multinomial logit models. This enables us to establish a causal relationship between temperature and precipitation anomalies and overall migration as well as migration by destination. We show that adverse weather shocks decrease rural-rural and international migration and push people into cities in different, presumably more prosperous states. A series of positive weather shocks, however, facilitates international migration and migration to cities within the same state. Further, our results indicate that in contrast to other migrants, climate migrants are likely to be from the lower end of the skill distribution and from households strongly dependent on agricultural production. We estimate that approximately 8% of all rural-urban moves between 2005 and 2012 can be attributed to weather. This figure might increase as a consequence of climate change. Thus, a key policy recommendation is to take steps to facilitate integration of less educated migrants into the urban labor market.

Creative thinking is an indispensable cognitive skill that is becoming increasingly important. In the present research, we tested the impact of games on creativity and emotions in a between-subject online experiment with four conditions (N = 658). (1) participants played a simple puzzle game that allowed many solutions (priming divergent thinking); (2) participants played a short game that required one fitting solution (priming convergent thinking); (3) participants performed mental arithmetic; (4) passive control condition. Results show that divergent and convergent creativity were higher after playing games and lower after mental arithmetic. Positive emotions did not function as a mediator, even though they were also heightened after playing the games and lower after mental arithmetic. However, contrary to previous research, we found no direct effect of emotions, creative self-efficacy, and growth- vs. fixed on creative performance. We discuss practical implications for digital learning and application settings.

Dieses Standardwerk zu Geschäftsprozessmanagement in Wirtschaft und Verwaltung gibt gleichzeitig einen Überblick über den aktuellen Stand der Forschung zu diesem Thema und führt Interessierte wie Studierende oder Praktiker in das Thema und seine Facetten ein. Aktuelle Entwicklungen wie Robotic Process Automation und Process Mining werden aufgegriffen. Im Mittelpunkt stehen die drei wesentlichen GPM- Blickwinkel Technik, Organisation und Mensch. Aus Sicht der Forschung werden innovative Methoden zur Modellierung und Analyse von Geschäftsprozessen beschrieben. Aus Sicht der Lehre dient das Buch als Einstiegslektüre und liefert Ansatzpunkte für die vertiefte Befassung mit einzelnen Aspekten des Geschäftsprozessmanagements. Für die Praxis beschreibt dieses Werk die dort bestehenden konzeptionellen und methodischen Hindernisse des Prozessmanagements und zeigt Wege zur Überwindung dieser Hindernisse. Die vorliegende Auflage wurde vollständig überarbeitet und stark erweitert, u. a. mit neuen Kapiteln zu Software für das Geschäftsprozessmanagement und zum Change Management.