Companies have realized the importance of “big data” in creating a sustainable competitive advantage, and user-generated content (UGC) represents one of big data’s most important sources. From blogs to social media and online reviews, consumers generate a huge amount of brand-related information that has a decisive potential business value for marketing purposes. Particularly, we focus on online reviews that could have an influence on brand image and positioning. Within this context, and using the usual quantitative star score ratings, a recent stream of research has employed sentiment analysis (SA) tools to examine the textual content of reviews and categorize buyer opinions. Although many SA tools split comments into negative or positive, a review can contain phrases with different polarities because the user can have different sentiments about each feature of the product. Finding the polarity of each feature can be interesting for product managers and brand management. In this paper, we present a general framework that uses natural language processing (NLP) techniques, including sentiment analysis, text data mining, and clustering techniques, to obtain new scores based on consumer sentiments for different product features. The main contribution of our proposal is the combination of price and the aforementioned scores to define a new global score for the product, which allows us to obtain a ranking according to product features. Furthermore, the products can be classified according to their positive, neutral, or negative features (visualized on dashboards), helping consumers with their sustainable purchasing behavior. We proved the validity of our approach in a case study using big data extracted from Amazon online reviews (specifically cell phones), obtaining satisfactory and promising results. After the experimentation, we could conclude that our work is able to improve recommender systems by using positive, neutral, and negative customer opinions and by classifying customers based on their comments.

Sustainability is an important challenge for wineries. Although sustainability represents a way to differentiate wines and to meet some market segment demands, it also constitutes a necessary strategy to guarantee the future development of the wine sector. However, some wineries state that production costs are higher for sustainable wines than for conventional wines. Thus, the goal of this paper is to analyze the premium price that consumers are willing to pay for a sustainable wine with respect to the price of a conventional wine with similar characteristics. An experiment based on contingent valuation was designed to test the differences in the willingness to pay for sustainable wines among market segments. Further, a Heckit model was estimated, which avoids the problems of the methodologies applied to date. Results reveal that most consumers are willing to pay a higher price for sustainable wines, and that there are differences among the main market segments. In addition, several variables regarding socio-economic characteristics of the consumer (age, gender and income) affect the willingness to pay. Results show that as consumers’ knowledge increases, willingness to pay a premium decreases and that consumers that are more concerned about the environment are willing to pay a higher premium

Purpose: To estimate the economic efficiency of supermarket chains in the Spanish retailing industry.Design/methodology/approach: The methodology applied is based on the non‐parametric technique of data envelopment analysis. The empirical application is carried out on a sample of 100 supermarket chains between 1995 and 2001.Findings: The results reveal high levels of economic inefficiency in the Spanish retailing sector.Research limitations/implications: The generalisation of the conclusions of the study to the whole sector should be made with caution, given the fact that only one of the players in the channel of distribution has been analysed.Practical implications: Efficiency analysis facilitates the management of the intermediaries themselves at a horizontal level, allowing the realisation of “benchmarking” analysis. Producers can also identify the efficient retailers, what could be useful for vertical relationships in the channel.Originality/value: For the first time, this study analyses the economic efficiency of the intermediaries in the Spanish retailing sector.