{"title":"Future applications of generative large language models: A data-driven case study on ChatGPT","authors":"Chiarello Filippo , Giordano Vito , Spada Irene , Barandoni Simone , Fantoni Gualtiero","doi":"10.1016/j.technovation.2024.103002","DOIUrl":null,"url":null,"abstract":"<div><p>This study delves into the evolving role of generative Large Language Models (LLMs). We develop a data-driven approach to collect and analyse tasks that users are asking to generative LLMs. Thanks to the focus on tasks this paper contributes to give a quantitative and granular understanding of the potential influence of LLMs in different business areas. Utilizing a dataset comprising over 3.8 million tweets, we identify and cluster 31,747 unique tasks, with a specific case study on ChatGPT. To reach this goal, the proposed method combines two Natural Language Processing (NLP) Techniques, Named Entity Recognition (NER) and BERTopic. The combination makes it possible to collect granular tasks of LLMs (NER) and clusters them in business areas (BERTopic). Our findings reveal a wide spectrum of applications, from programming assistance to creative content generation, highlighting LLM's versatility. The analysis highlighted six emerging areas of application for ChatGPT: human resources, programming, social media, office automation, search engines, education. The study also examines the implications of these findings for innovation management, proposing a research agenda to explore the intersection of the identified areas, with four stages of the innovation process: idea generation, screening/idea selection, development, and diffusion/sales/marketing.</p></div>","PeriodicalId":49444,"journal":{"name":"Technovation","volume":"133 ","pages":"Article 103002"},"PeriodicalIF":11.1000,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016649722400052X/pdfft?md5=91584ae7f5b45865af0050ad7aa3739f&pid=1-s2.0-S016649722400052X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technovation","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016649722400052X","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
This study delves into the evolving role of generative Large Language Models (LLMs). We develop a data-driven approach to collect and analyse tasks that users are asking to generative LLMs. Thanks to the focus on tasks this paper contributes to give a quantitative and granular understanding of the potential influence of LLMs in different business areas. Utilizing a dataset comprising over 3.8 million tweets, we identify and cluster 31,747 unique tasks, with a specific case study on ChatGPT. To reach this goal, the proposed method combines two Natural Language Processing (NLP) Techniques, Named Entity Recognition (NER) and BERTopic. The combination makes it possible to collect granular tasks of LLMs (NER) and clusters them in business areas (BERTopic). Our findings reveal a wide spectrum of applications, from programming assistance to creative content generation, highlighting LLM's versatility. The analysis highlighted six emerging areas of application for ChatGPT: human resources, programming, social media, office automation, search engines, education. The study also examines the implications of these findings for innovation management, proposing a research agenda to explore the intersection of the identified areas, with four stages of the innovation process: idea generation, screening/idea selection, development, and diffusion/sales/marketing.
期刊介绍:
The interdisciplinary journal Technovation covers various aspects of technological innovation, exploring processes, products, and social impacts. It examines innovation in both process and product realms, including social innovations like regulatory frameworks and non-economic benefits. Topics range from emerging trends and capital for development to managing technology-intensive ventures and innovation in organizations of different sizes. It also discusses organizational structures, investment strategies for science and technology enterprises, and the roles of technological innovators. Additionally, it addresses technology transfer between developing countries and innovation across enterprise, political, and economic systems.