Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations

This research examines how individuals respond differently to recommendation options generated by ChatGPT, an AI-powered language model, in five studies. In contrast to previous research on choice overload, Studies 1 and 2 demonstrate that people tend to respond positively to a large number of recommendation options (60 options), revealing diverse consumer perceptions of AI-generated recommendations. Studies 3 and 4 further illustrate the moderating effect of recommendation agents and indicate that choice overload elicits distinct patterns of consumer reactions depending on whether the recommendations are from a human or AI agent. Lastly, Study 5 directly measures consumer preferences for recommendation agents, revealing a general preference for ChatGPT, particularly when a large number of options are available. These findings have significant implications for recommendation system design and user preferences regarding AI-powered recommendations.     

On ChatGPT and beyond: How generative artificial intelligence may affect research,teaching, and practice

How does ChatGPT, and other forms of Generative Artificial Intelligence (GenAI) affect the way we have been conducting—and evaluating—academic research, teaching, and business practice? What are the implications for the theory and practice of marketing? What are the opportunities and threats, and what are some interesting avenues for future research? This editorial aims to kick off an initial discussion and stimulate research that will help us better understand how the marketing field can fully exploit the potential of GenAI and effectively cope with its challenges.     

类ChatGPT模型介入国家科技计划项目评审的应用探讨

国家科技计划是打造科技强国和人才强国的重要抓手,而项目评审是对其进行风险控制和目标实现的关键环节。生成式人工智能模型如ChatGPT的突破性发展将大大推动其与项目管理等多领域的交融发展。目前,项目评审面临评审专家失信、国际化评审滞后、评审负荷过重以及专家库管理不足等瓶颈问题。为解决这些问题,可充分利用类ChatGPT在项目评审工作中广阔的应用前景,包括教育培训、翻译、评阅和专家库管理助手,为推动项目管理数字化转型升级提供参考。     

ChatGPT and consumers: Benefits,Pitfalls and Future Research Agenda

The need of the hour is to encourage research on topics with newness and novelty. In this context, this article discusses multidimensional benefits and potential pitfalls of using artificial intelligence-based Chat Generative Pre-trained Transformer (ChatGPT), and provides numerous ideas for future research in consumer studies and marketing in the context of ChatGPT. ChatGPT provides algorithm-generated conversational responses to text-based prompts. Since its launch in the late 2022, ChatGPT has generated significant debate surrounding its hallmarks, benefits and potential pitfalls. On the one hand, ChatGPT can offer enhanced consumer engagement, improved customer service, personalization and shopping, social interaction and communication practice, cost-effectiveness, insights into consumer behaviour and improved marketing campaigns. On the other hand, potential pitfalls include concerns about consumer well-being, bias, misinformation, lack of context, privacy concerns, ethical considerations and security. The article concludes by outlining a potential future research agenda in the area of ChatGPT and consumer studies. Overall, this article provides valuable insights into the benefits and challenges associated with ChatGPT, shedding light on its potential applications and the need for further research.     

An analysis of three chatbots: BlenderBot,ChatGPT and LaMDA

Google, Facebook, OpenAI, and others have released access to versions of language chatbots that they have developed. These chatbots have been trained on massive amounts of text using neural networks for language processing. Using an approach similar to security penetration testing, this paper investigates and compares three different chatbots, assessing potential strengths and limitations of these systems. The paper presents several findings, including a comparison of those systems across answers to common questions, an analysis of the use of names and activities to guide discussion in two systems, an analysis of the extent of differences in responses arising from “regeneration” of a question, the determination of a weakness in a system of knowing “who” invented something, development of a potential new subfield, sensitive topic classifiers, and an analysis of some of the implications of these findings. As part of this analysis, I find emerging topics in chatbots, such as “topic stalemate” and the use of sensitive topic classifiers.     

Enterprise large language models: Knowledge characteristics,risks, and organizational activities

Since the release of OpenAI's ChatGPT, there has been substantial interest in and concern about generative AI systems. This paper investigates some of the characteristics, risks, and limitations with the enterprise use of enterprise large language models. In so doing, we study the organizational impact, continuing a long line of research on that topic. This paper examines the impact on expertise, the organizational implications of multiple correlated but different responses to the same query, the potential concerns associated with sensitive information and intellectual property, and some applications that likely would not be appropriate for large language models. We also investigate the possibility of agents potentially manipulating the content in these large language models for their own benefit. Finally, we investigate the emerging phenomenon of “ChatBot Enterprise” versions, including some of the implications and concerns of such enterprise large language models.