Open Access
From familiarity to acceptance: the impact of Generative Artificial Intelligence on consumer adoption of retail chatbots
(Elsevier, 2025-01-17) Arce Urriza, Marta; Chocarro Eguaras, Raquel; Cortiñas Ugalde, Mónica; Marcos Matas, Gustavo; Gestión de Empresas; Enpresen Kudeaketa; Institute for Advanced Research in Business and Economics - INARBE
This study investigates the influence of Generative Artificial Intelligence (GenAI) on consumer adoption of retail chatbots, focusing on how GenAI impacts key adoption determinants, the role of familiarity and assessing its effects across different stages of the customer journey. We conducted two waves of surveys, one pre- and one post-GenAI integration, to compare consumer perceptions across three customer service tasks. Using the Service Robot Acceptance Model (SRAM) as a framework, we found that GenAI enhances consumer perceptions of chatbot usefulness, human-likeness, and familiarity, thereby increasing adoption intentions. However, trust remains largely unchanged, and privacy concerns have risen post-GenAI. Additionally, the relationships remain stable across customer journey stages, with familiarity playing a key role. Our findings extend SRAM to the retail context with GenAI, offering new insights into the temporal stability of chatbot adoption factors. It underscores familiarity's dual role (direct and indirect) in fostering adoption, while highlighting that GenAI impacts specific aspects of consumer interaction. These findings provide insights for retailers to leverage GenAI-powered chatbots to enhance customer engagement and satisfaction.
Open Access
Voice-activated personal assistants and privacy concerns: a Twitter analysis
(Emerald, 2023) Alzate Barricarte, Miriam; Arce Urriza, Marta; Cortiñas Ugalde, Mónica; Institute for Advanced Research in Business and Economics - INARBE
This study aims to understand the extent of privacy concerns regarding voice-activated personal assistants (VAPAs) on Twitter. It investigates three key areas: (1) the effect of privacy-related press coverage on public sentiment and discussion volume; (2) the comparative negativity of privacy-focused conversations versus general conversations; and (3) the specific privacy-related topics that arise most frequently and their impact on sentiment and discussion volume. Design/methodology/approach – A dataset of 441,427 tweets mentioning Amazon Alexa, Google Assistant, and Apple Siri from July 1, 2019 to June 30, 2021 were collected. Privacy-related press coverage has also been monitored. Sentiment analysis was conducted using the dictionary-based software LIWC and VADER, whereas text mining packages in R were used to identify privacy-related issues. Findings – Negative privacy-related news significantly increases both negativity and volume in Twitter conversations, whereas positive news only boosts volume. Privacy-related tweets were notably more negative than general tweets. Specific keywords were found to either increase or decrease the sentiment and discussion volume. Additionally, a temporal evolution in sentiment, with general attitudes toward VAPAs becoming more positive, but privacy-specific discussions becoming more negative was observed. Originality/value – This research augments the existing online privacy literature by employing text mining methodologies to gauge consumer sentiments regarding privacy concerns linked to VAPAs, a topic currently underexplored. Furthermore, this research uniquely integrates established theories from privacy calculus and social contract theory to deepen our analysis.

Arce Urriza, Marta

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