IDC-CDR: Cross-domain Recommendation based on Intent Disentanglement and Contrast Learning DOI
Feng Xu, Mingxin Gan, H. Y. Zhang

и другие.

Information Processing & Management, Год журнала: 2024, Номер 61(6), С. 103871 - 103871

Опубликована: Авг. 29, 2024

Язык: Английский

Generative artificial intelligence (GenAI) revolution: A deep dive into GenAI adoption DOI Creative Commons
Aman Kumar, Amit Shankar, Linda D. Hollebeek

и другие.

Journal of Business Research, Год журнала: 2025, Номер 189, С. 115160 - 115160

Опубликована: Янв. 2, 2025

Процитировано

1

AI for marketing: Enabler? Engager? Ersatz? DOI
Sreedhar Madhavaram,

Radha Appan

AMS Review, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Язык: Английский

Процитировано

1

Anthropomorphic generative AI chatbots for enhancing customer engagement, experience and recommendation DOI
Aman Kumar, Amit Shankar, Abhishek Behl

и другие.

Journal of Consumer Marketing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 31, 2025

Purpose This research focuses on developing and testing a conceptual model that explores customer behavioural responses (engagement, experience recommendation) towards generative artificial intelligence (AI)-enabled chatbots. It highlights the significant influence of anthropomorphic characteristics in enhancing perceptions competence warmth, further perceived authenticity. In addition, this study aims to investigate how need for social interactions moderates these relationships. Design/methodology/approach used self-administered questionnaire distributed Prolific Academic gather data from 282 eligible participants worldwide. uses structural equation modelling approach answer questions. Findings The findings reveal AI-enabled chatbots are positively associated with competence. Moreover, show warmth significantly Furthermore, results highlight authenticity is engagement, recommendation. Finally, illustrate impact moderated by interaction. Originality/value enriches AI literature guides organizations understanding consumer leveraging contributes response theory as investigates user intentions influenced their level characteristics.

Язык: Английский

Процитировано

1

Impact of generative artificial intelligence models on the performance of citizen data scientists in retail firms DOI
Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Keng‐Boon Ooi

и другие.

Computers in Industry, Год журнала: 2024, Номер 161, С. 104128 - 104128

Опубликована: Июль 21, 2024

Язык: Английский

Процитировано

5

Digital Transformation of Grocery In-Store Shopping-Scanners, Artificial Intelligence, Augmented Reality and Beyond: A Review DOI Creative Commons
Radosław Wolniak, Kinga Stecuła,

Barış Aydın

и другие.

Foods, Год журнала: 2024, Номер 13(18), С. 2948 - 2948

Опубликована: Сен. 18, 2024

This paper reviews the digital transformation of grocery shopping, focusing on technological innovations that have redefined consumer experiences over past decades. By analyzing both academic literature and up-to-date information from websites, study provides a review evolution shopping traditional methods to modern, technology-driven approaches. The categorizes developments into two primary areas: in-store online shopping. In-store has progressed interactions implementation self-service checkouts, handheld scanners, mobile apps, AI-based solutions, including augmented reality (AR) facial recognition. first area which are solutions. aims highlight revolution in perspective, present most significant achievements, outline future possibilities for further advancements this field.

Язык: Английский

Процитировано

5

Human-AI agency in the age of generative AI DOI Creative Commons
Sebastian Krakowski

Information and Organization, Год журнала: 2025, Номер 35(1), С. 100560 - 100560

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Leveraging AI to Ignite Innovation in Small and Medium Enterprises: Challenges and Opportunities DOI Creative Commons
Peiqian Wu, Yan Zhu, Wenli Chen

и другие.

Опубликована: Март 5, 2025

Small and medium enterprises (SMEs) form the backbone of many economies, yet they often struggle to remain competitive innovative under resource constraints. Rapid advances in artificial intelligence (AI) offer fresh possibilities for SMEs transform their operations, discover untapped market segments, foster resilient business models. AI tools can enhance decision-making reduce operational inefficiencies, from automating repetitive processes generating predictive insights. At same time, ethical considerations data privacy concerns underscore importance implementing responsibly. By embracing cross-sector collaboration, developing robust training programs, advocating supportive policy frameworks, harness AI’s immense potential without compromising social values or organizational integrity. This paper highlights both opportunities challenges poses, proposing actionable strategies that enable drive sustainable, inclusive growth.

Язык: Английский

Процитировано

0

Human-AI coordination for large-scale group decision making with heterogeneous feedback strategies DOI
Jing Zhang, Ning Wang, Ming Tang

и другие.

Journal of the Operational Research Society, Год журнала: 2025, Номер unknown, С. 1 - 21

Опубликована: Март 7, 2025

Язык: Английский

Процитировано

0

Empowering GenAI stakeholders DOI
Erik Hermann, Stefano Puntoni

Journal of the Academy of Marketing Science, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

Язык: Английский

Процитировано

0

Using LLMs in sensory service research: initial insights and perspectives DOI Creative Commons
Monika Imschloß, Marko Sarstedt, Susanne Adler

и другие.

Service Industries Journal, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Март 28, 2025

Язык: Английский

Процитировано

0