Artículos de revista INARBE - INARBE aldizkari artikuluak

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Browsing Artículos de revista INARBE - INARBE aldizkari artikuluak by Author "Alzate Barricarte, Miriam"

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    Is review visibility fostering helpful votes? The role of review rank and review characteristics in the adoption of information
    (Elsevier, 2024) Alzate Barricarte, Miriam; Arce Urriza, Marta; Cebollada Calvo, Javier; Gestión de Empresas; Enpresen Kudeaketa; Institute for Advanced Research in Business and Economics - INARBE
    In online environments, where consumers usually face information overload, information regarding the number of helpful votes received by online reviews serves as a trust sign to aid consumers in their purchasing journeys. As consumers can only vote for a review as helpful if they have viewed it, the position of the review in the sequence of reviews is likely to influence the number of helpful votes that the review receives. We propose a model in which review helpfulness depends not only on the characteristics of the review and reviewer, but also on its visibility. Review visibility is defined in this study as the probability of a review being viewed by a consumer, and is measured by the inverse rank order of the review in the sequence of reviews at the online retailer when consumers sort reviews according to different criteria (most helpful and most recent). Using a database of 59,526 online reviews from a popular cosmetics online store in the US, we estimate a zero-inflated negative binomial (ZINB) regression and find evidence that review visibility has a strong impact in explaining the likelihood of a review being read by consumers and subsequently voted as helpful by consumers. This effect is even stronger when sorting is most helpful.
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    PublicationOpen 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.