Ismail Abdi Changalima, David Amani, Ismail Juma Ismail
{"title":"Social influence and information quality on Generative AI use among business students","authors":"Ismail Abdi Changalima, David Amani, Ismail Juma Ismail","doi":"10.1016/j.ijme.2024.101063","DOIUrl":null,"url":null,"abstract":"<div><div>Despite the increasing utilisation of generative artificial intelligence (AI) in educational settings, its influence on shaping students’ behaviour remains relatively under-researched. This study employs PLS-SEM to explore the relationships between social influence, information quality, and behavioural intentions regarding ChatGPT usage among business students. Drawing from data collected from 477 business students, the study unveils that both social influence and information quality significantly impact behavioural intentions. Moreover, information quality strengthens the influence of social influence on behavioural intentions. The multigroup analysis reveals that the effects of social influence and information quality on behavioural intentions differ between females and males. However, the moderating effect of information quality does not differ significantly between them. Furthermore, the effects observed in all hypothesised relationships do not differ significantly between first-year and second-year students. By empirically validating the proposed model for behavioural intentions regarding ChatGPT usage and identifying statistical differences among males and females, as well as between first-year and second-year students, this study contributes to filling existing knowledge gaps. Furthermore, the study offers potential avenues for future research and serves as a valuable resource for academics, professionals, and policymakers interested in understanding students' engagement with generative AI.</div></div>","PeriodicalId":6,"journal":{"name":"ACS Applied Nano Materials","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Nano Materials","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1472811724001344","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Despite the increasing utilisation of generative artificial intelligence (AI) in educational settings, its influence on shaping students’ behaviour remains relatively under-researched. This study employs PLS-SEM to explore the relationships between social influence, information quality, and behavioural intentions regarding ChatGPT usage among business students. Drawing from data collected from 477 business students, the study unveils that both social influence and information quality significantly impact behavioural intentions. Moreover, information quality strengthens the influence of social influence on behavioural intentions. The multigroup analysis reveals that the effects of social influence and information quality on behavioural intentions differ between females and males. However, the moderating effect of information quality does not differ significantly between them. Furthermore, the effects observed in all hypothesised relationships do not differ significantly between first-year and second-year students. By empirically validating the proposed model for behavioural intentions regarding ChatGPT usage and identifying statistical differences among males and females, as well as between first-year and second-year students, this study contributes to filling existing knowledge gaps. Furthermore, the study offers potential avenues for future research and serves as a valuable resource for academics, professionals, and policymakers interested in understanding students' engagement with generative AI.
期刊介绍:
ACS Applied Nano Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics and biology relevant to applications of nanomaterials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important applications of nanomaterials.