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Generative artificial intelligence in business planning and financial budgeting
No full textCorporate planning and financial budgeting are essential for companies to achieve their long-term objectives. While the application of other technologies such as Robotic Process Automation (RPA) in planning and budgeting is already a well-established practice, the potential advantages of incorporating generative artificial intelligence (GenAI), specifically Large Language Models (LLMs) in the domain of planning and budgeting remain rather unclear. Hence, the aim of this research has been to identify potential use cases and review the current state of the discussion. A Multivocal Literature Review (MLR) was conducted to analyze and evaluate both academic and non-academic sources. The literature highlights the transformative potential of GenAI in enhancing business planning and financial budgeting through natural language interfaces, enabling users to access, analyze, and interpret data more effectively. However, the findings also show a gap in the existing academic literature and indicate that some grey literature sources may have to be interpreted with caution. Therefore, further research is required to fully understand and leverage the benefits of GenAI in corporate planning and budgeting
Ambush marketing: creativity vs. controversy
No full textAmbush marketing, a tactic non-sponsors use to associate with major sporting events, raises ethical, legal, and strategic questions. This paper explores the phenomenon from historical, theoretical, and practical perspectives, highlighting its dual nature as a creative marketing tool and a controversial threat to official sponsorships. Drawing on case studies, it categorizes ambush marketing strategies into direct, indirect, and destructive forms, examining their effectiveness and consequences. While ambush marketing enables brands to leverage the visibility of events without official sponsorship costs, it challenges the exclusivity and financial stability of event organizers and sponsors. The discussion considers legal frameworks, consumer perceptions, and the broader impact on sports sponsorship markets. Ultimately, the analysis suggests a balanced approach to regulation, acknowledging ambush marketing’s potential to drive innovation while emphasizing the need for fair competition and protection of sponsorship rights. This paper contributes to a nuanced understanding of ambush marketing’s implications in the evolving landscape of sports marketing
Fachkräftemangel im Vertrieb deutscher IT-Unternehmen
No full textResearch Die vorliegende Studie untersucht Strategien, mit denen IT-Unternehmen erfolgreich Fachkräfte für den Vertrieb rekrutieren und qualifizieren können. Basierend auf 21 qualitativen Interviews, wurden Headhunting, die gezielte Ansprache von Quereinsteigern und Frauen sowie interne Mitarbeiterempfehlungsprogramme als zentrale Maßnahmen identifiziert. Die Ergebnisse zeigen, dass diese Ansätze besonders effektiv sind. Neben technischem Know-how spielen auch Deutschkenntnisse eine wesentliche Rolle, was die Rekrutierung ausländischer Fachkräfte erschwert. Die Studie betont zudem, dass die Kombination von Rekrutierungsstrategien und internen Qualifizierungsprogrammen entscheidend ist, um den Fachkräftemangel im IT-Vertrieb langfristig zu bewältigen
On the relevance of demand pattern categorization
Get PDFThe application of transfer learning to predict sales demand is an emerging topic that has been attracting more and more attention recently. However, the selection of data to be used for the learning process is not trivial. Data resources are usually scarce and often anonymized to a certain extent, so their usability for successful training is not guaranteed. One solution is to use already developed categorization schemes that group time series based on certain calculated parameters, but the derived categories do not necessarily capture the process of time series formation. This research addresses the question of whether categorization schemes are beneficial for transfer learning approaches by conducting an experiment in which Syntetos’, Boylan’s and Croston’s categorization scheme is used in combination with two deep learning architectures for the transfer learning process. The results show that similar patterns are indeed beneficial for prediction, but that models using all available data perform quite similarly
Emotional dynamics in semi-clinical settings: speech emotion recognition in depression-related interviews
Get PDFThe goal of this study was to utilize a state-of-the-art Speech Emotion Recognition (SER) model to explore the dynamics of basic emotions in semi-structured clinical interviews about depression. Segments of N = 217 interviews from the general population were evaluated using the emotion2vec+ large model and compared with the results of a depressive symptom questionnaire. A direct comparison of depressed and non-depressed subgroups revealed significant differences in the frequency of happy and sad emotions, with participants with higher depression scores exhibiting more sad and less happy emotions. A multiple linear regression model including the seven most predicted emotions plus the duration of the interview as predictors explained 23.7 % of variance in depression scores, with happiness, neutrality, and interview duration emerging as significant predictors. Higher depression scores were associated with lesser happiness and neutrality, as well as a longer interview duration. The study demonstrates the potential of SER models in advancing research methodology by providing a novel, objective tool for exploring emotional dynamics in mental health assessment processes. The model’s capacity for depression screening was tested in a realistic sample from the general population, revealing the potential to supplement future screening systems with an objective emotion measurement
The geopolitics, government-business relations, and triangular cooperation of ‘Africa+1’ conferences
Get PDFVarious countries have adopted Africa strategies and established high-level conferences with African nations over the past decades. We contribute to the interdisciplinary literature at the intersection of geopolitics and business by revealing the historical trajectory of government-business relations in the various Africa initiatives. We particularly aim to explore and compare how governments shape international business through their geopolitical initiatives. We conduct a qualitative analysis of 34 policy documents of the Forum on China–Africa Cooperation, the Tokyo International Conference on African Development, and the Türkiye-Africa Partnership conferences held between 1993 and 2022. Based on our longitudinal analysis, we find a complementarity of government-business relations: governments primarily act as facilitators and channelizers of business engagement, while firms are considered crucial enablers for government policies. We further discuss differences in the identified role and ownership of companies in the three Africa initiatives against the backdrop of the respective geopolitical agendas
An unsupervised multi-level fusion domain adaptation method for transfer diagnosis under time-varying working conditions
No full textUnsupervised multi-source domain adaptation can overcome the limitations associated with insufficient information diversity in single-source domain adaptation for intelligent transfer diagnosis. However, the challenges of time-varying working conditions in practical industrial applications, limitation in single-level information fusion along with lack of multi-level information fusion restrict effective applications of unsupervised multi-source domain adaptation in transfer diagnosis. To address these challenges, this research presents a novel unsupervised multi-level fusion domain adaptation methodology for transfer diagnostics under time-varying working conditions, which employs a multi-level fusion domain adaptation network (MLFDAN). Firstly, a multi-sensor data enhancement and fusion module is proposed by combining continuous wavelet transform with an RGB information fusion, which integrates time–frequency and spatial information from multi-sensors. Then, a squeeze and excitation feature fusion module is designed for feature fusion across both time–frequency and spatial domains, which effectively emphasizes domain-invariant features while suppressing less relevant ones. Subsequently, an adaptive collaborative decision module is developed, which employs a weighted fusion strategy to address strong conflicts among multi-subnet predictions and utilizes consensus-based fusion strategy when multi-subnet predictions align, thus ensuring reliable and robust diagnostics decisions. Finally, a promising MLFDAN framework for transfer diagnosis is proposed by incorporating a dual-component domain adaptation approach that integrates a domain discriminator and multi-kernel maximum mean discrepancy. Numerous experiment results show that the presented MLFDAN methodology effectively adapts to transfer diagnosis scenarios from steady to time-varying working conditions, achieving impressive performances and outperforming several prominent unsupervised transfer diagnosis methodologies
Sociophysics model of bubbles with neural-stochastic differential equations : a stochastic inflation model
No full textThis chapter gives an overview of new results concerning a sociophysics’ theory of bubbles. Following a seminal contribution by (Herzog 2015), we generalize the approach to nonlinear stochastic differential equations. We exhibit the mathematical theory of stochastic wave equations and introduce a novel calibration approach by utilizing neural stochastic differential equations. We numerically solve the problem by simulation methods in Julia language. Finally, we apply our method and analyze the presently elevated inflation rates (price bubble)
Digitales Shopfloor-Management : Ein adaptives Informations- und Entscheidungsinstrument im Umfeld von Industrie 4.0 Produktionssystemen
No full textSteigende externe und interne Veränderungen fordern Produktionssysteme in besonderem Maße. Um eine entsprechende Adaptionsfähigkeit sicherzustellen sind klassische Shopfloor-Konzepte im Einsatz, die sich direkt an dem Leistungserstellungs-Prozess orientieren und die Mitarbeiter strukturell einbeziehen. Aufgrund der immanenten analogen Struktur ist der klassische Ansatz aber hinsichtlich der Funktionsweise und der Nutzung neuer technischer Möglichkeiten stark eingeschränkt. Mit der Konzeption des digitalen Shopfloor-Managements werden die Beschränkungen überwunden und neue Möglichkeiten unter Einbeziehung der technologischen, der verhaltensorientierten und der prozessorientieren Dimension ermöglicht. Anhand einer Beispiel-Implementierung wird die praktische Umsetzung der Konzeption gezeigt
Wirksames Projektcontrolling durch die Earned Value Analyse
No full textIn diesem Beitrag wird zunächst das Instrument der Earned Value Analyse (EVA) mit ihren zentralen Kennzahlen beschrieben. Sodann werden die Bearbeitungsschritte der EVA sowie die Beschreibung des empirischen Vorgehens erläutert. Darauf aufbauend werden aus Experteninterviews abgeleitete praxisorientierte Ansätze zur Ermittlung des Projektaufwands, des Fertigstellungsgrads und des Projektfortschritts vorgestellt