{"title":"人工智能助手感知真实性的分析建模:PLS-预测算法和重要性-性能图分析的应用","authors":"Palima Pandey, Alok Kumar Rai","doi":"10.1108/sajbs-04-2023-0102","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<p>The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.</p><!--/ Abstract__block -->","PeriodicalId":55618,"journal":{"name":"South Asian Journal of Business Studies","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analytical modeling of perceived authenticity in AI assistants: application of PLS-predict algorithm and importance-performance map analysis\",\"authors\":\"Palima Pandey, Alok Kumar Rai\",\"doi\":\"10.1108/sajbs-04-2023-0102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.</p><!--/ Abstract__block -->\\n<h3>Practical implications</h3>\\n<p>The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.</p><!--/ Abstract__block -->\",\"PeriodicalId\":55618,\"journal\":{\"name\":\"South Asian Journal of Business Studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"South Asian Journal of Business Studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/sajbs-04-2023-0102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"South Asian Journal of Business Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/sajbs-04-2023-0102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
Analytical modeling of perceived authenticity in AI assistants: application of PLS-predict algorithm and importance-performance map analysis
Purpose
The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.
Design/methodology/approach
Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.
Findings
A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.
Practical implications
The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.
Originality/value
The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.