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Student-faculty interactions that promote learning are essential contributors to student retention, academic success and satisfaction. But the factors that causally initiate and frame these interactions are not well understood. Only if students evaluate these interactions as positive will they seek them. We conducted a survey experiment with students (n = 375) from a tuition-fee-free German business school, using conditional process analysis to assess which factors frame effective interactions. We focus on out-of-classroom standard and non-standard requests that students make to faculty, then investigate how faculty and student gender and students’ academic entitlement influence the interaction. Our study examines how students evaluate the interaction with faculty: when they seek interaction, their expectations of getting their requests approved, and their disappointment when their requests are declined. We find a significant influence of the request type along with moderating effects of faculty gender, student gender and student entitlement, particularly for non-standard work requests. We conclude with policy implications for university management: developing target-group-specific measures that facilitate the desired and positively evaluated student-faculty interactions might benefit all university stakeholders.
Purpose
The authors study the valuation effect of corporate diversification in the initial phase of the COVID-19 pandemic in 2020 in Europe.
Design/methodology/approach
Applying a cross-sectional regression model to a sample of public companies headquartered in the European Union, the authors investigate the existence of and the change in a diversification discount between 2018 and 2020. By applying the Excess Q methodology, the authors make an industry adjustment of diversified companies to measure the value effect of corporate diversification.
Findings
The authors find an economically and statistically significant diversification discount that increases from an average Excess Q of −0.05 in 2019 to −0.10 in 2020. The diversified companies' inferior fundamental financial performance in 2020 accompanies the discount. The results deviate from those of previous research, which mostly show a decrease in the diversification discount in economic crises, and thereby, shed doubt on whether diversification provides insurance against pandemic-induced adverse value effects.
Originality/valueThe study distinguishes the role of corporate diversification during recessionary periods by establishing that the valuation effect of diversification depends on the nature of the crisis. The analysis incorporates criticism of previous studies concerning a biased methodology and uniform data source by applying the Excess Q methodology and using FactSet industry segment data.
Unternehmertum spielt sowohl für die Entwicklung afrikanischer Länder eine Rolle, als auch für ausländische Unternehmen mit Markteintrittsplänen. Die infrastrukturellen und institutionellen Rahmenbedingungen für Unternehmertum sind nach wie vor schwierig, wobei aber die fortschreitende Digitalisierung zu einer zunehmend aktiven Start-Up Szene in vielen afrikanischen Ländern führt. Nach wie vor existiert ein Mismatch zwischen den Bereichen in denen Start-Ups entstehen und den Bereichen, wo ausländische Unternehmen Partner für den Markteintritt suchen. Somit bleibt es trotz positiver Entwicklung beim Unternehmertum in absehbarer Zeit schwierig adäquate Partner zu finden.
Artificial intelligence is considered to be a significant technology for driving the future evolution of smart manufacturing environments. At the same time, automated guided vehicles (AGVs) play an essential role in manufacturing systems due to their potential to improve internal logistics by increasing production flexibility. Thereby, the productivity of the entire system relies on the quality of the schedule, which can achieve production cost savings by minimizing delays and the total makespan. However, traditional scheduling algorithms often have difficulties in adapting to changing environment conditions, and the performance of a selected algorithm depends on the individual scheduling problem. Therefore, this paper aimed to analyze the scheduling problem classes of AGVs by applying design science research to develop an algorithm selection approach. The designed artifact addressed a catalogue of characteristics that used several machine learning algorithms to find the optimal solution strategy for the intended scheduling problem. The contribution of this paper is the creation of an algorithm selection method that automatically selects a scheduling algorithm, depending on the problem class and the algorithm space. In this way, production efficiency can be increased by dynamically adapting the AGV schedules. A computational study with benchmark literature instances unveiled the successful implementation of constraint programming solvers for solving JSSP and FJSSP scheduling problems and machine learning algorithms for predicting the most promising solver. The performance of the solvers strongly depended on the given problem class and the problem instance. Consequently, the overall production performance increased by selecting the algorithms per instance. A field experiment in the learning factory at Reutlingen University enabled the validation of the approach within a running production scenario.
Supply chains have evolved into dynamic, interconnected supply networks, which increases the complexity of achieving end-to-end traceability of object flows and their experienced events. With its capability of ensuring a secure, transparent, and immutable environment without relying on a trusted third party, the emerging blockchain technology shows strong potential to enable end-to-end traceability in such complex multitiered supply networks. This paper aims to overcome the limitations of existing blockchain-based traceability architectures regarding their object-related event mapping ability, which involves mapping the creation and deletion of objects, their aggregation and disaggregation, transformation, and transaction, in one holistic architecture. Therefore, this paper proposes a novel ‘blueprint-based’ token concept, which allows clients to group tokens into different types, where tokens of the same type are non-fungible. Furthermore, blueprints can include minting conditions, which, for example, are necessary when mapping assembly processes. In addition, the token concept contains logic for reflecting all conducted object-related events in an integrated token history. Finally, for validation purposes, this article implements the architecture’s components in code and proves its applicability based on the Ethereum blockchain. As a result, the proposed blockchain-based traceability architecture covers all object-related supply chain events and proves its general-purpose end-to-end traceability capabilities of object flows.
This paper presents a description model for smart, connected devices used in a manufacturing context. Similar to the wide spread adoption of smart products for personal and private usage, recent developments lead to a plethora of devices offering a variety of features and capabilities. Manufacturing companies undergoing digital transformation demand guidance with respect to the systematic introduction of smart, connected devices. The introduction of smart connected devices constitutes a strategic decision cost due to the high future committed cost after introduction and maintaining a smart device fleet by a vendor. This paper aims to support the introduction efforts by classifying the devices and thus helping companies identify their specific requirements for smart, connected devices before initiating widespread procurement. By mapping the features of these devices based on various attributes, allows the clustering of smart, connected devices including a requirement list for their implementation on the shopfloor. Four individual commercially available smart connected devices were analyzed using the description model.
Parallel grippers offer multiple applications thanks to their flexibility. Their application field ranges from aerospace and automotive to medicine and communication technologies. However, the application of grippers has the problem of exhibition wear and errors during the execution of their operation. This affects the performance of the gripper. In this context, the remaining useful life (RUL) defines the remaining lifespan until failure for an asset at a particular time of operation occurs. The exact lifespan of an asset is uncertain, thus the RUL model and estimation must be derived from available sources of information. This paper presents a method for the estimation of the RUL for a two-jaw parallel gripper. After the introduction to the topic, an overview of existing literature and RUL methods are presented. Subsequently, the method for estimating the RUL of grippers is explained. Finally, the results are summarized and discussed before the outlook and further challenges are presented.
In diesem Beitrag wird der Einfluss von Zuschauern nach den pandemiebedingten Geisterspielen auf den Heimvorteil in der ersten Fußball-Bundesliga analysiert. Für die empirische Untersuchung werden die fünf Spielzeiten 2017/18 bis 2021/22 betrachtet. Während in der Geisterspielphase in der Saison 2019/20 der Heimvorteil vollständig verschwindet, steigt der Heimvorteil in der zweiten Geisterspielphase in der Saison 2020/21 mit der Zeit wieder auf vorpandemisches Niveau an. In der Saison 2021/22 ist nach einer kurzen Phase mit eingeschränkten Zuschauerkapazitäten ein signifikant größerer Heimvorteil als in der Zeit vor der Pandemie zu beobachten. Der überdurchschnittlich positive Effekt der Zuschauer in dieser letzten Phase verschwindet mit der Zeit trotz steigender Zuschauerzahlen. Der Einfluss von Fans auf den Heimvorteil ist insgesamt insbesondere auf psychologische Effekte auf die Spieler der Heimmannschaften zurückzuführen.
Artificial Intelligence (AI) in der Markenführung: Künstliche Neuronale Netze zur Markenimagemessung
(2023)
Da Künstliche Neuronale Netze die Modellierung nichtlinearer und vielschichtiger Beziehungen ermöglichen, befasst sich dieser Beitrag mit deren Einsatzmöglichkeiten für die methodisch anspruchsvolle Analyse und Messung des Markenimages. Zur Veranschaulichung des konzeptionellen Ansatzes wird am empirischen Beispiel des Sportartikelherstellers adidas ein mehrschichtiges Künstliches Neuronales Netz zwischen den Bewertungen spezifischer Markenattribute und der Gesamtbewertung der Marke erzeugt. Auf der Grundlage einer Analyse der Verbindungsgewichte des Künstliches Neuronales Netzes wird die Bedeutung verschiedener Markenattribute für die Markenbewertung gemessen, wodurch sich konkrete Implikationen für die Praxis der Markenführung ableiten lassen.