Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Hussaini, Tajwar; Hussaini, Nilofer Title A name given to the resource Transforming Customer Relations: Emotional AI and Behavioural Insights as Strategic Enablers in the Automotive Industry Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Integrating 4IR and 5IR Technologies for Digital Business Innovation;pp.185-196 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1108/978-1-83608-578-220251019" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/978-1-83608-578-220251019</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029196965?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029196965?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Hussaini T., Amity University Dubai, United Arab Emirates; Hussaini N., Christ University, Central Campus, India Description An account of the resource This research examines how emotional AI deepens customer relations within the American automotive industry and how behavioural insights (BLI) mediate this process. Underlying framework: In the automotive industry, prioritising customer needs is crucial. Using emotional AI that analyses emotions and behavioural patterns can contribute to customer loyalty and satisfaction. The research employs a quantitative survey research approach. The sample pool comprised 237 customer experience managers representing various automotive companies in Texas. Emotional AI, business intelligence (BI), and consumer research are assessed using survey questionnaires, and the responses are recorded electronically. Descriptive regression and correlation analysis methods are employed to understand the connections between emotional AI, BLI, and customer relations. Evidence: The survey results indicated a positive relationship between the developed emotional AI and BLI, highlighting aspects of customer relations, including satisfaction and trust levels. The researchs findings suggest that emotional AI and BI could be a strategic intervention for enhancing customer loyalty. 2026 Haitham M. Alzoubi and Shanmugan Joghee and 2026 The authors. Subject The topic of the resource Automotive industry; Behavioural insights; Customer experience; Customer relations; Customer satisfaction; Emotional AI Publisher An entity responsible for making the resource available Emerald Group Publishing Ltd. Relation A related resource ISBN: 978-183608578-2; 978-183608579-9; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online