{"title":"Can decision intelligence help organizations retain employees? Serial multiple mediation of job characteristics and meaningful work","authors":"Miriam O’Callaghan","doi":"10.1080/23311908.2023.2258475","DOIUrl":null,"url":null,"abstract":"New artificial intelligence (AI) powered technologies such as OpenAI’s ChatGPT model, intelligent decision support systems, and autonomous robots are transforming decision making leading to the increased prevalence of decision intelligence in organizations. This paper explores the relationship between decision intelligence, job characteristics, meaningful work, and employees’ intentions to leave the organization or turnover intentions. The research model is based on robust theoretical foundations and was tested with data collected from a survey on Prolific. The study utilizes PLS SEM (partial least squares structural equation modeling) method to test the hypotheses. Three categories of model fit indices are used to assess the final model. The results interpreted from direct effects revealed a positive relationship between decision intelligence and intention to leave. Nevertheless, the mediation analysis within the path model demonstrated that this relationship transformed into a negative one when mediated by job characteristics and meaningful work. In its conclusion, the paper discusses research findings, addresses limitations, and underscores contributions, thus paving the path for integrating decision intelligence into academic literature and industry practices.","PeriodicalId":46323,"journal":{"name":"Cogent Psychology","volume":"11 1","pages":"0"},"PeriodicalIF":1.6000,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cogent Psychology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23311908.2023.2258475","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
New artificial intelligence (AI) powered technologies such as OpenAI’s ChatGPT model, intelligent decision support systems, and autonomous robots are transforming decision making leading to the increased prevalence of decision intelligence in organizations. This paper explores the relationship between decision intelligence, job characteristics, meaningful work, and employees’ intentions to leave the organization or turnover intentions. The research model is based on robust theoretical foundations and was tested with data collected from a survey on Prolific. The study utilizes PLS SEM (partial least squares structural equation modeling) method to test the hypotheses. Three categories of model fit indices are used to assess the final model. The results interpreted from direct effects revealed a positive relationship between decision intelligence and intention to leave. Nevertheless, the mediation analysis within the path model demonstrated that this relationship transformed into a negative one when mediated by job characteristics and meaningful work. In its conclusion, the paper discusses research findings, addresses limitations, and underscores contributions, thus paving the path for integrating decision intelligence into academic literature and industry practices.
期刊介绍:
One of the largest multidisciplinary open access journals serving the psychology community, Cogent Psychology provides a home for scientifically sound peer-reviewed research. Part of Taylor & Francis / Routledge, the journal provides authors with fast peer review and publication and, through open access publishing, endeavours to help authors share their knowledge with the world. Cogent Psychology particularly encourages interdisciplinary studies and also accepts replication studies and negative results. Cogent Psychology covers a broad range of topics and welcomes submissions in all areas of psychology, ranging from social psychology to neuroscience, and everything in between. Led by Editor-in-Chief Professor Peter Walla of Webster Private University, Austria, and supported by an expert editorial team from institutions across the globe, Cogent Psychology provides our authors with comprehensive and quality peer review. Rather than accepting manuscripts based on their level of importance or impact, editors assess manuscripts objectively, accepting valid, scientific research with sound rigorous methodology. Article-level metrics let the research speak for itself.