No abstract

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

Purpose The purpose of this paper is to study the relationship between changes in relative influence between marketing and R&D and new product performance (NPP). The aim is to theorize and test whether relative influence changes are beneficial for NPP. Design/methodology/approach An international survey was sent out to pharmaceutical companies worldwide, resulting in 106 usable questionnaires from knowledgeable senior managers. A model is estimated that relates recent and historic changes in relative influence to NPP. Findings There is a positive relationship between recent relative influence changes and subsequent NPP. Moreover, this paper finds that having a history of adaptation with respect to relative influence can serve organizations to build up capabilities that, in turn, strengthen the positive effects of recent relative influence changes on NPP. Finally, the paper shows that relative influence changes and integration between marketing and R&D positively affect NPP jointly. Originality/value A core finding, that is quite counterintuitive, is that instability with respect to relative influence changes can help organizations to become more competitive in new product development.

Purpose The purpose of this paper is to contribute to the marketing literature and practice by examining the effect of product pricing on consumer behaviours with regard to the assertiveness and the sentiments expressed in their product reviews. In addition, the paper uses new data collection and machine learning tools that can also be extended for other research of online consumer reviewing behaviours. Design/methodology/approach Using web crawling techniques, a large data set was extracted from the Google Play Store. Following this, the authors created machine learning algorithms to identify topics from product reviews and to quantify assertiveness and sentiments from the review texts. Findings The results indicate that product pricing models affect consumer review sentiment, assertiveness and topics. Removing upfront payment obligations positively impacts the overall and pricing specific consumer sentiment and reduces assertiveness. Research limitations/implications The results reveal new effects of pricing models on the nature of consumer reviews of products and form a basis for future research. The study was conducted in the gaming category of the Google Play Store and the generalisability of the findings for other app segments or marketplaces should be further tested. Originality/value The findings can help companies that create digital products in choosing a pricing strategy for their apps. The paper is the first to investigate how pricing modes affect the nature of online reviews written by consumers.

No abstract

Companies use crowdsourcing to solve specific problems or to search for innovation. By using open innovation platforms, where community members propose ideas, companies can better serve customer needs. So far, it remains unclear which factors influence idea implementation in crowd sourcing context. With the research idea that we present here, we aim to get a better understanding of the success and failure of ideas by examining relationships between characteristics of ideators, characteristics of ideas and the likelihood of implementation. In order to test the methodological approach that we propose in this paper in which we investigate for business relevant innovativeness as well as sentiment based on text analytics, data including unstructured text was mined from Dell IdeaStorm using webcrawling and scraping techniques. Some relevant hypotheses that we define in this paper were confirmed on the Dell IdeaStorm dataset but in order to generalize our findings we want to apply to the Lego dataset in our current work in progress. Possible implications of our novel research idea can be used to fill theoretical gaps in marketing literature, help companies to better structure their search for innovation and for ideators to better understand factors contributing to successful idea generation.

No abstract

No abstract