预测供应链金融创新的采用意向

IF 3.6 3区管理学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Organizational and End User Computing Pub Date : 2023-11-16 DOI:10.4018/joeuc.333689

Ronnié Figueiredo, Maria Emilia Camargo, João J. Ferreira, J. Zhang, Yulong Liu

{"title":"预测供应链金融创新的采用意向","authors":"Ronnié Figueiredo, Maria Emilia Camargo, João J. Ferreira, J. Zhang, Yulong Liu","doi":"10.4018/joeuc.333689","DOIUrl":null,"url":null,"abstract":"Based on the mixed model unified technology acceptance and utilization theory (UTAUT) and spinner innovation model (SPINNER), a theoretical model is suggested to explain the determinant of behavioral intention to predict innovation in the context of a financial sector firm. A questionnaire was developed to collect primary data, which was subsequently processed through the artificial intelligence technique (deep learning). The constructs (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, public knowledge, private knowledge, and innovation) supported the model, including mediating hypotheses. It was observed that the mixed methodological approach (SEM and ANN) can help to find the linear and non-linear relationships better, being that the error of the predicted model is 0.104, that is, 10.4% relatively low, which evidences that ANN can be used to predict the dependent variable innovation safely.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"26 2","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting the Intention to Adopt Innovation in Supply Chain Finance\",\"authors\":\"Ronnié Figueiredo, Maria Emilia Camargo, João J. Ferreira, J. Zhang, Yulong Liu\",\"doi\":\"10.4018/joeuc.333689\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the mixed model unified technology acceptance and utilization theory (UTAUT) and spinner innovation model (SPINNER), a theoretical model is suggested to explain the determinant of behavioral intention to predict innovation in the context of a financial sector firm. A questionnaire was developed to collect primary data, which was subsequently processed through the artificial intelligence technique (deep learning). The constructs (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, public knowledge, private knowledge, and innovation) supported the model, including mediating hypotheses. It was observed that the mixed methodological approach (SEM and ANN) can help to find the linear and non-linear relationships better, being that the error of the predicted model is 0.104, that is, 10.4% relatively low, which evidences that ANN can be used to predict the dependent variable innovation safely.\",\"PeriodicalId\":49029,\"journal\":{\"name\":\"Journal of Organizational and End User Computing\",\"volume\":\"26 2\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Organizational and End User Computing\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.4018/joeuc.333689\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Organizational and End User Computing","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.4018/joeuc.333689","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

摘要

基于统一技术接受与利用理论（UTAUT）和斯宾纳创新模型（SPINNER）的混合模型，提出了一个理论模型来解释预测金融行业企业创新的行为意向的决定因素。我们编制了一份问卷来收集原始数据，随后通过人工智能技术（深度学习）对数据进行处理。研究结果表明，该模型的建构（绩效预期、努力预期、社会影响、促进条件、行为意向、公共知识、私人知识和创新）支持包括中介假设在内的模型。据观察，混合方法（SEM 和 ANN）有助于更好地发现线性和非线性关系，因为预测模型的误差为 0.104，即相对较低的 10.4%，这证明 ANN 可用于安全地预测因变量创新。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Predicting the Intention to Adopt Innovation in Supply Chain Finance

Based on the mixed model unified technology acceptance and utilization theory (UTAUT) and spinner innovation model (SPINNER), a theoretical model is suggested to explain the determinant of behavioral intention to predict innovation in the context of a financial sector firm. A questionnaire was developed to collect primary data, which was subsequently processed through the artificial intelligence technique (deep learning). The constructs (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, public knowledge, private knowledge, and innovation) supported the model, including mediating hypotheses. It was observed that the mixed methodological approach (SEM and ANN) can help to find the linear and non-linear relationships better, being that the error of the predicted model is 0.104, that is, 10.4% relatively low, which evidences that ANN can be used to predict the dependent variable innovation safely.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Organizational and End User Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

6.00

自引率

9.20%

发文量

期刊介绍： The Journal of Organizational and End User Computing (JOEUC) provides a forum to information technology educators, researchers, and practitioners to advance the practice and understanding of organizational and end user computing. The journal features a major emphasis on how to increase organizational and end user productivity and performance, and how to achieve organizational strategic and competitive advantage. JOEUC publishes full-length research manuscripts, insightful research and practice notes, and case studies from all areas of organizational and end user computing that are selected after a rigorous blind review by experts in the field.