The power of generative marketing: Can generative AI reach human-level visual marketing content?

Hartmann, Jochen; Exner, Yannick; Domdey, Samuel

doi:10.2139/ssrn.4597899

Cited by 6 publications

(2 citation statements)

References 75 publications

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“…The emergence of Generative AI is revolutionizing the marketing landscape through its dual role: it serves to both generate [9,22,55] and analyze content [36,38,41,54,66]. This dual capacity makes it a flexible and dynamic tool that can handle a wide range of marketing tasks.…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysis in the Age of Generative AI

Krugmann,

Hartmann

2024

Cust. Need. and Solut.

Self Cite

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In the rapidly advancing age of Generative AI, Large Language Models (LLMs) such as ChatGPT stand at the forefront of disrupting marketing practice and research. This paper presents a comprehensive exploration of LLMs’ proficiency in sentiment analysis, a core task in marketing research for understanding consumer emotions, opinions, and perceptions. We benchmark the performance of three state-of-the-art LLMs, i.e., GPT-3.5, GPT-4, and Llama 2, against established, high-performing transfer learning models. Despite their zero-shot nature, our research reveals that LLMs can not only compete with but in some cases also surpass traditional transfer learning methods in terms of sentiment classification accuracy. We investigate the influence of textual data characteristics and analytical procedures on classification accuracy, shedding light on how data origin, text complexity, and prompting techniques impact LLM performance. We find that linguistic features such as the presence of lengthy, content-laden words improve classification performance, while other features such as single-sentence reviews and less structured social media text documents reduce performance. Further, we explore the explainability of sentiment classifications generated by LLMs. The findings indicate that LLMs, especially Llama 2, offer remarkable classification explanations, highlighting their advanced human-like reasoning capabilities. Collectively, this paper enriches the current understanding of sentiment analysis, providing valuable insights and guidance for the selection of suitable methods by marketing researchers and practitioners in the age of Generative AI.

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Section: Discussionmentioning

confidence: 99%

Sentiment Analysis in the Age of Generative AI

Krugmann,

Hartmann

2024

Cust. Need. and Solut.

Self Cite

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“…Level 5 might be used in tasks which require the use of GenAI tools as part of addressing learning outcomes or when the skills and knowledge being assessed can be tested irrespective of AI usage. This level is also designed to allow for the exploration of GenAI as a collaborative and creative tool and reflects ways in which these technologies are being used outside of education, in fields such as journalism and marketing, where AI-generated content is increasingly used but still requires human editorial oversight (Hartmann et al, 2023;Kshetri et al, 2023). Example tasks include:…”

Section: Level 5: Full Aimentioning

confidence: 99%

The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

Perkins,

Furze,

Roe

et al. 2024

JUTLP

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Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this backdrop, we outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment: the AI Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate level of GenAI usage in assessments based on the learning outcomes they seek to address. The AIAS offers greater clarity and transparency for students and educators, provides a fair and equitable policy tool for institutions to work with, and offers a nuanced approach which embraces the opportunities of GenAI while recognising that there are instances where such tools may not be pedagogically appropriate or necessary. By adopting a practical, flexible approach that can be implemented quickly, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education. As a secondary objective, we engage with the current literature and advocate for a refocused discourse on GenAI tools in education, one which foregrounds how technologies can help support and enhance teaching and learning, which contrasts with the current focus on GenAI as a facilitator of academic misconduct.

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Understanding model power in social AI

Brandtzaeg,

Skjuve,

Følstad

2024

AI & Soc

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Given the widespread integration of Social AI like ChatGPT, Gemini, Copilot, and MyAI, in personal and professional contexts, it is crucial to understand their effects on information and knowledge processing, and individual autonomy. This paper builds on Bråten’s concept of model power, applying it to Social AI to offer a new perspective on the interaction dynamics between humans and AI. By reviewing recent user studies, we examine whether and how models of the world reflected in Social AI may disproportionately impact human-AI interactions, potentially leading to model monopolies where Social AI impacts human beliefs, behaviour and homogenize the worldviews of its users. The concept of model power provides a framework for critically evaluating the impact and influence that Social AI has on communication and meaning-making, thereby informing the development of future systems to support more balanced and meaningful human-AI interactions.

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The power of generative marketing: Can generative AI reach human-level visual marketing content?

Cited by 6 publications

References 75 publications

Sentiment Analysis in the Age of Generative AI

Sentiment Analysis in the Age of Generative AI

The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

Understanding model power in social AI

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