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Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA

The rising population of millennials, coupled with Digital Assistants (DA) and online purchasing trends among consumers have gained increasing attention by global marketers. The study evaluates the influence of DA attributes on the purchasing intention (PUI) of millennials. A combined approach of PL...

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Detalles Bibliográficos
Autores principales: Sharma, Manu, Joshi, Sudhanshu, Luthra, Sunil, Kumar, Anil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510515/
https://www.ncbi.nlm.nih.gov/pubmed/36185777
http://dx.doi.org/10.1007/s10796-022-10339-5
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author Sharma, Manu
Joshi, Sudhanshu
Luthra, Sunil
Kumar, Anil
author_facet Sharma, Manu
Joshi, Sudhanshu
Luthra, Sunil
Kumar, Anil
author_sort Sharma, Manu
collection PubMed
description The rising population of millennials, coupled with Digital Assistants (DA) and online purchasing trends among consumers have gained increasing attention by global marketers. The study evaluates the influence of DA attributes on the purchasing intention (PUI) of millennials. A combined approach of PLS-SEM, Artificial Neural Network (ANN) and Fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to predict the PUI of 345 millennials. Also, multi-group analysis is employed to uncover the influence of gender on the relationship between PUI and DA attributes. The findings suggest that DA attributes may amplify purchasing intention among millennials, especially through perceived interactivity and anthropomorphism. Further, the moderating role of gender was found significant on the inter-relationship of perceived interactivity and PUI. This research is a pioneer study in the area of artificial intelligence, conversational commerce, DA and AI-powered chatbots. This study will help marketers and practitioners to predict millennial purchasing intentions. An evaluation of this paper may help them to foster immersive and effective engagement through DA.
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spelling pubmed-95105152022-09-26 Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA Sharma, Manu Joshi, Sudhanshu Luthra, Sunil Kumar, Anil Inf Syst Front Article The rising population of millennials, coupled with Digital Assistants (DA) and online purchasing trends among consumers have gained increasing attention by global marketers. The study evaluates the influence of DA attributes on the purchasing intention (PUI) of millennials. A combined approach of PLS-SEM, Artificial Neural Network (ANN) and Fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to predict the PUI of 345 millennials. Also, multi-group analysis is employed to uncover the influence of gender on the relationship between PUI and DA attributes. The findings suggest that DA attributes may amplify purchasing intention among millennials, especially through perceived interactivity and anthropomorphism. Further, the moderating role of gender was found significant on the inter-relationship of perceived interactivity and PUI. This research is a pioneer study in the area of artificial intelligence, conversational commerce, DA and AI-powered chatbots. This study will help marketers and practitioners to predict millennial purchasing intentions. An evaluation of this paper may help them to foster immersive and effective engagement through DA. Springer US 2022-09-23 /pmc/articles/PMC9510515/ /pubmed/36185777 http://dx.doi.org/10.1007/s10796-022-10339-5 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Sharma, Manu
Joshi, Sudhanshu
Luthra, Sunil
Kumar, Anil
Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA
title Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA
title_full Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA
title_fullStr Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA
title_full_unstemmed Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA
title_short Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA
title_sort impact of digital assistant attributes on millennials’ purchasing intentions: a multi-group analysis using pls-sem, artificial neural network and fsqca
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510515/
https://www.ncbi.nlm.nih.gov/pubmed/36185777
http://dx.doi.org/10.1007/s10796-022-10339-5
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