{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T23:04:18Z","timestamp":1781046258985,"version":"3.54.1"},"reference-count":139,"publisher":"Emerald","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,4]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Generative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users\u2019 guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI\u2019s cognitive and emotional conversational skills affect users\u2019 guidance through different types of user engagement, and how these effects are moderated by users\u2019 relationship norm orientation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>Based on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>The results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users\u2019 guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>First, drawing on social exchange theory, this study empirically examined the drivers of users\u2019 guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users\u2019 guidance. The findings will deepen our understanding of users\u2019 guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/k-04-2024-0894","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T11:25:34Z","timestamp":1723634734000},"page":"7511-7547","source":"Crossref","is-referenced-by-count":17,"title":["From general AI to custom AI: the\u00a0effects of generative conversational AI\u2019s cognitive and\u00a0emotional conversational skills on user's guidance"],"prefix":"10.1108","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0864-089X","authenticated-orcid":true,"given":"Kun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Management, Huazhong University of Science and Technology , ,","place":["Wuhan, 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