Person: Arce Urriza, Marta
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Arce Urriza
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Marta
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Gestión de Empresas
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INARBE. Institute for Advanced Research in Business and Economics
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0000-0002-5095-3788
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7517
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Publication Embargo 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 - INARBEIn 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.Publication Open Access Mining the text of online consumer reviews to analyze brand image and brand positioning(Elsevier, 2022) Alzate Barricarte, Miriam; Arce Urriza, Marta; Cebollada Calvo, Javier; Gestión de Empresas; Enpresen KudeaketaThe growth of the Internet has led to massive availability of online consumer reviews. So far, papers studying online reviews have mainly analysed how non-textual features, such as ratings and volume, influence different types of consumer behavior, such as information adoption decisions or product choices. However, little attention has been paid to examining the textual aspects of online reviews in order to study brand image and brand positioning. The text analysis of online reviews inevitably raises the concept of 'text mining'; that is, the process of extracting useful and meaningful information from unstructured text. This research proposes an unified, structured and easy-to-implement procedure for the text analysis of online reviews with the ultimate goal of studying brand image and brand positioning. The text mining analysis is based on a lexicon-based approach, the Linguistic Inquiry and Word Count (Pennebaker et al., 2007), which provides the researcher with insights into emotional and psychological brand associations.Publication Open Access Online reviews and product sales: the role of review visibility(MDPI, 2021) Alzate Barricarte, Miriam; Arce Urriza, Marta; Cebollada Calvo, Javier; Gestión de Empresas; Enpresen KudeaketaWhen studying the impact of online reviews on product sales, previous scholars have usually assumed that every review for a product has the same probability of being viewed by consumers. However, decision-making and information processing theories underline that the accessibility of information plays a role in consumer decision-making. We incorporate the notion of review visibility to study the relationship between online reviews and product sales, which is proxied by sales rank information, studying three different cases: (1) when every online review isassumed to have the same probability of being viewed; (2) when we assume that consumers sort online reviews by the most helpful mechanism; and (3) when we assume that consumers sort online reviews by the most recent mechanism. Review non-textual and textual variables are analyzed. The empirical analysis is conducted using a panel of 119 cosmetic products over a period of nine weeks. Using the system generalized method of moments (system GMM) method for dynamic models of panel data, our findings reveal that review variables influence product sales, but the magnitude, and even the direction of the effect, vary amongst visibility cases. Overall, the characteristics of the most helpful reviews have a higher impact on sales.Publication Open Access Voice-activated personal assistants and privacy concerns: a Twitter analysis(2023) Alzate Barricarte, Miriam; Arce Urriza, Marta; Cortiñas Ugalde, Mónica; Institute for Advanced Research in Business and Economics - INARBEThis 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.Publication Open Access BRScS: a hybrid recommendation model fusing multi-source heterogeneous data(SpringerOpen, 2020) Ji, Zhenyan; Yang, Chun; Wang, Huihui; Armendáriz Íñigo, José Enrique; Arce Urriza, Marta; Estadística, Informática y Matemáticas; Gestión de Empresas; Estatistika, Informatika eta Matematika; Enpresen KudeaketaRecommendation systems are often used to solve the problem of information overload on the Internet. Many types of data can be used for recommendations, and fusing different types of data can make recommendations more accurate. Most existing fusion recommendation models simply combine the recommendation results from different data instead of fully fusing multi-source heterogeneous data to make recommendations. Furthermore, users’ choices are usually affected by their direct and even indirect friends’ preferences. This paper proposes a hybrid recommendation model BRScS (an acronym for BPR-Review-Score-Social). It fully fuses social data, score, and review together; uses improved BPR model to optimize the ranking; and trains them in a joint representation learning framework to get the top-N recommendations. User trust model is used to introduce social relationships into the rating and review data, PV-DBOW model is used to process the review data, and fully connected neural network is used to process the rating data. Experiments on Yelp public dataset show that the BRScS algorithm proposed outperforms other recommendation algorithms such as BRSc, UserCF, and HRSc. The BRScS model is also scalable and can fuse new types of data easily.Publication Open Access The role of active and passive resistance in new technology adoption by final consumers: the case of 3D printing(Elsevier, 2024) Villanueva Orbaiz, María Luisa; Arce Urriza, Marta; Gestión de Empresas; Enpresen Kudeaketa; Institute for Advanced Research in Business and Economics - INARBE; Universida Pública de Navarra / Nafarroako Unibertsitate PublikoaFrom a model or digital design, 3D printing is a set of “additive” manufacturing technologies capable of creating a 3-dimensional object. The maxim “If you can draw it, you can print it” defines the possibilities this technology offers. Society was surprised that new digital technologies allowed the transformation of tangible products into intangible products. Currently, 3D printing provides the opposite possibility, allowing for the creation of new and customized products at the time and place the user needs. This implies a change of mind from a subtractive to an additive process in the industrial field and a true innovation from buying to home production in the domestic sphere.Publication Open Access An empirical analysis of shopping behavior across online and offline channels for grocery products: the moderating effects of household and product characteristics(Elsevier, 2010) Chu, Junhong; Arce Urriza, Marta; Cebollada Calvo, Javier; Chintagunta, Pradeep; Gestión de Empresas; Enpresen Kudeaketa; Gobierno de Navarra / Nafarroako GobernuaWe study the moderating effects of household (e.g., shopping frequency) and product (e.g., sensory nature) characteristics on household brand loyalty, size loyalty and price sensitivity across online and offline channels for grocery products. We analyze the shopping behavior of the same households that shop interchangeably in the online and offline stores of the same grocery chain in 93 categories of food, nonfood, sensory and nonsensory products. We find that households are more brand loyal, more size loyal but less price sensitive in the online channel than in the offline channel. Brand loyalty, size loyalty and price sensitivity are closely related to household and product characteristics. Light online shoppers exhibit the highest brand and size loyalties, but the lowest price sensitivity in the online channel. Heavy online shoppers display the lowest brand and size loyalties, but the highest price sensitivity in the online channel. Moderate online shoppers exhibit the highest price sensitivity in the offline channel. The online-offline differences in brand loyalty and price sensitivity are largest for light online shoppers and smallest for heavy online shoppers. The online-offline differences in brand loyalty, size loyalty and price sensitivity are larger for food products and for sensory products.