{"title":"评估人工智能对客户绩效的影响:使用偏最小二乘法的定量研究","authors":"Taqwa Hariguna , Athapol Ruangkanjanases","doi":"10.1016/j.dsm.2024.01.001","DOIUrl":null,"url":null,"abstract":"<div><p>The purpose of this research is to examine the impact of artificial intelligence (AI) on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach, specifically the partial least squares method, to test the hypotheses and explore the relationships between various variables. The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance. Additionally, the results of this study provide valuable insights for both academic and practical communities. This study highlights the importance of specific variables, such as organizational and customer agility, customer experience, customer relationship quality, and customer performance in AI assimilation. By exploring these variables, it contributes significantly to the academic, managerial, and social aspects of AI and its impact on customer performance.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000018/pdfft?md5=ff997f9e6eeea260084310750d46c9aa&pid=1-s2.0-S2666764924000018-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Assessing the impact of artificial intelligence on customer performance: A quantitative study using partial least squares methodology\",\"authors\":\"Taqwa Hariguna , Athapol Ruangkanjanases\",\"doi\":\"10.1016/j.dsm.2024.01.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The purpose of this research is to examine the impact of artificial intelligence (AI) on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach, specifically the partial least squares method, to test the hypotheses and explore the relationships between various variables. The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance. Additionally, the results of this study provide valuable insights for both academic and practical communities. This study highlights the importance of specific variables, such as organizational and customer agility, customer experience, customer relationship quality, and customer performance in AI assimilation. By exploring these variables, it contributes significantly to the academic, managerial, and social aspects of AI and its impact on customer performance.</p></div>\",\"PeriodicalId\":100353,\"journal\":{\"name\":\"Data Science and Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666764924000018/pdfft?md5=ff997f9e6eeea260084310750d46c9aa&pid=1-s2.0-S2666764924000018-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Science and Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666764924000018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science and Management","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666764924000018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assessing the impact of artificial intelligence on customer performance: A quantitative study using partial least squares methodology
The purpose of this research is to examine the impact of artificial intelligence (AI) on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach, specifically the partial least squares method, to test the hypotheses and explore the relationships between various variables. The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance. Additionally, the results of this study provide valuable insights for both academic and practical communities. This study highlights the importance of specific variables, such as organizational and customer agility, customer experience, customer relationship quality, and customer performance in AI assimilation. By exploring these variables, it contributes significantly to the academic, managerial, and social aspects of AI and its impact on customer performance.