Open Access
Electronic word of mouth (eWOM) and marketing implications
(2021) Alzate Barricarte, Miriam; Cebollada Calvo, Javier; Arce Urriza, Marta; Gestión de Empresas; Enpresen Kudeaketa
Esta tesis doctoral estudia varios aspectos relacionados con un tipo concreto de boca a boca electrónico (en inglés electronic Word-of-Mouth o eWOM), que son las reseñas de consumidores. Por un lado, se estudia cómo ciertas características no textuales y textuales de esas reseñas, junto a su visibilidad, influye en varios tipos de comportamiento de consumidor, en concreto en la adopción de información y en el comportamiento de compra. Por otro lado, se estudia el contenido textual de las reseñas y cómo este se puede utilizar tanto para estudiar el posicionamiento y segmentación de marcas, como para estudiar el posicionamiento y asociaciones con productos. Previamente al análisis empírico, se revisa principalmente la literatura sobre procesamiento de información, toma de decisiones e imagen de marca y minería de textos.
Open Access
What are consumers saying online about your products?: mining the text of online reviews to uncover hidden features
(Henry Stewart Publications, 2021) Alzate Barricarte, Miriam; Arce Urriza, Marta; Cebollada Calvo, Javier; Gestión de Empresas; Enpresen Kudeaketa
Thanks to the growth of the internet and the increasing use of social networks, companies can now access huge volumes of online texts in order to understand consumers¿ preferences and needs. This article illustrates some methods to extrapolate information from such texts. The text-mining analysis covers such issues as word frequency, sentiment analysis, paired words, similarities in textual content and the main topics discussed in online reviews. From a practical point of view, brand managers can use the proposed methods to gain consumer insights into products and brands, to be able to improve and adapt their marketing strategies.
Open Access
Consumers' awareness and attitudes in circular fashion
(Elsevier, 2023) Jiménez Fernández, Anne; Aramendia Muneta, María Elena; Alzate Barricarte, Miriam; Gestión de Empresas; Enpresen Kudeaketa
Circular fashion is a new concept shift towards a more sustainable fashion industry. Consumers' awareness of the fashion industry's environmental and social damage is increasing, along with the transformation of their purchasing habits. Circular economy appears to be a solution to these issues. Nonetheless, users' awareness of the principles of circular economy might benefit their application in real life. This research studies the relationships between awareness and attitudes towards the principles of circular economy as well as the mediators, which are namely, benefits and enablers. The main purpose of this study is to test a hypothesised model through structural equation modeling with Partial Least Squares. The results show that the principles have a positive relationship to the attitudes, benefits, and enablers of circular economy. Additionally, the effect of gender on consumers' attitudes towards sustainable fashion is worth analysing since it seems to play a significant role.
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 Kudeaketa
When 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.
Embargo
Is review visibility fostering helpful votes? The role of review rank and review characteristics in the adoption of information
(Elsevier, 2024-04-01) 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.
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 Kudeaketa
The 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.
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.