Institut für Marketing & Management

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/29

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Celebrating 30 years of Innovation System research : what you need to know about Innovation Systems
(2016) Klein, Malte; Sauer, Andreas
On the occasion of the 30th anniversary of Innovation System research, this paper presents an extensive literature review on this large field of innovation research. Building on an analytical basis of the commonalities “system” and “innovation”, the authors analyze the four main Innovation System approaches: National Innovation Systems (NIS), Regional Innovation Systems (RIS), Sectoral Innovation Systems (SIS) and Technological Innovation Systems (TIS). The analysis is structured systematically along ten comprehensive criteria. Starting with the founder(s) of each theory and the research program within each Innovation System approach was developed (1), the basic thoughts of each Innovation System approach are explained (2). For five case studies most cited (3), spatial boundaries are examined (4) and units of analyses are derived (5). By comparing the underlying theoretical concept and empirical results, the authors show patterns in the evolution of Innovation System research overall. By studying the basic components (6) and a functional analysis (7), each Innovation System approach is broken down into structural pieces and functional processes. If available, the authors present one or several taxonomies (8) for each Innovation System approach and summarize similar approaches (9), in order to classify and integrate the approaches into the ongoing innovation research. The identification of further research (10) shows which steps will need to be taken in the next years in order to evolve Innovation System research further and deeper. After the conclusion, the extensive table of comparison is presented which can serve as a guideline for academics and practitioners from basic and applied science, industry or policy that need to understand which Innovation System approach may be best for their specific analytical purposes.
The international sales accelerator : a project management tool for improving sales performance in foreign target markets
(2018) Gerybadze, Alexander; Wiesenauer, Simone
There is a current research gap in the marketing and management literature regarding the setup of sales and distribution structures as well as the rollout in foreign target markets in order to establish countrywide presences. Due to this gap, we developed the International Sales Accelerator Model. The data collection and verification of the model took place during a thirdparty funds project with Baden-Württemberg’s business development agency, and environmental agency. The results reveal that the model represents a summary of best practices from different internationalization processes of very large companies. It is a seven-stage project management tool with the objective to improve the sales performance of companies entering foreign target markets.
Unlocking the power of generative AI models and systems such asGPT-4 and ChatGPT for higher education
(2023) Vandrik, Steffen; Urbach, Nils; Gimpel, Henner; Hall, Kristina; Decker, Stefan; Eymann, Torsten; Lämmermann, Luis; Mädche, Alexander; Röglinger, Maximilian; Ruiner, Caroline; Schoch, Manfred; Schoop, Mareike
Generative AI technologies, such as large language models, have the potential to revolutionize much of our higher education teaching and learning. ChatGPT is an impressive, easy-to-use, publicly accessible system demonstrating the power of large language models such as GPT-4. Other compa- rable generative models are available for text processing, images, audio, video, and other outputs – and we expect a massive further performance increase, integration in larger software systems, and diffusion in the coming years. This technological development triggers substantial uncertainty and change in university-level teaching and learning. Students ask questions like: How can ChatGPT or other artificial intelligence tools support me? Am I allowed to use ChatGPT for a seminar or final paper, or is that cheating? How exactly do I use ChatGPT best? Are there other ways to access models such as GPT-4? Given that such tools are here to stay, what skills should I acquire, and what is obsolete? Lecturers ask similar questions from a different perspective: What skills should I teach? How can I test students’ competencies rather than their ability to prompt generative AI models? How can I use ChatGPT and other systems based on generative AI to increase my efficiency or even improve my students’ learning experience and outcomes? Even if the current discussion revolves around ChatGPT and GPT-4, these are only the forerunners of what we can expect from future generative AI-based models and tools. So even if you think ChatGPT is not yet technically mature, it is worth looking into its impact on higher education. This is where this whitepaper comes in. It looks at ChatGPT as a contemporary example of a conversational user interface that leverages large language models. The whitepaper looks at ChatGPT from the perspective of students and lecturers. It focuses on everyday areas of higher education: teaching courses, learning for an exam, crafting seminar papers and theses, and assessing students’ learning outcomes and performance. For this purpose, we consider the chances and concrete application possibilities, the limits and risks of ChatGPT, and the underlying large language models. This serves two purposes: • First, we aim to provide concrete examples and guidance for individual students and lecturers to find their way of dealing with ChatGPT and similar tools. • Second, this whitepaper shall inform the more extensive organizational sensemaking processes on embracing and enclosing large language models or related tools in higher education. We wrote this whitepaper based on our experience in information systems, computer science, management, and sociology. We have hands-on experience in using generative AI tools. As professors, postdocs, doctoral candidates, and students, we constantly innovate our teaching and learning. Fully embracing the chances and challenges of generative AI requires adding further perspectives from scholars in various other disciplines (focusing on didactics of higher education and legal aspects), university administrations, and broader student groups. Overall, we have a positive picture of generative AI models and tools such as GPT-4 and ChatGPT. As always, there is light and dark, and change is difficult. However, if we issue clear guidelines on the part of the universities, faculties, and individual lecturers, and if lecturers and students use such systems efficiently and responsibly, our higher education system may improve. We see a greatchance for that if we embrace and manage the change appropriately.

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