How can Eastern Europe help American manufacturers?

The end of the Soviet Union and the creation of the Commonwealth of Independent States will definitely increase Western involvement in the former East Bloc countries. Recent emphasis has been placed on either developing Eastern markets or using inexpensive human capital to manufacture goods to be sold in the West. A third option does exist: the use of Eastern knowledge and technology in the West. Application of this knowledge and technology, currently not feasible in the former East Bloc because of a deficient infrastructure and lack of incentives, could be practical in the fertile environment of the West. Western firms are advised to catch up with their Japanese competitors in taking advantage of the low-risk opportunities that the break up of the Soviet Union has made available. © 1993 John Wiley & Sons, Inc.     

Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media

The increasing volume of firm-related conversations on social media has made it considerably more difficult for marketers to track and analyse electronic word-of-mouth (eWOM) about brands, products or services. Firms often use sentiment analysis to identify relevant eWOM that requires a response to consequently engage in webcare. In this paper, we show that sentiment analysis of any kind might not be ideal for this purpose, because it relies on the questionable assumption that only negative eWOM is response-worthy and it is not able to infer meaning from text. We propose and test an approach based on supervised machine learning that first decides whether eWOM is relevant for the brand to respond, and then—based on a categorization of seven different types of eWOM (e.g., question, complaint)—classifies three customer satisfaction dimensions. Using a dataset of approximately 60,000 Facebook comments and 11,000 tweets about 16 different brands in eight different industries, we test and compare the efficacy of various sentiment analysis, dictionary-based and machine learning techniques to detect relevant eWOM. In doing so, this study identifies response-worthy eWOM based on the content instead of its expressed sentiment. The results indicate that these machine learning techniques achieve considerably higher accuracy in detecting relevant eWOM on social media compared to any kind of sentiment analysis. Moreover, it is shown that industry-specific classifiers can further improve this process and that algorithms are applicable across different social networks.     

Risky business: How social psychology can help improve corporate risk management

Drawing on research in management theory, risk analysis, and the social sciences, this installment of Business Law & Ethics Corner promotes diversity of thought in corporate governance as not only beneficial to business growth and creativity, but also imperative to managing risk successfully. The article begins with a review of four major worldviews and the risk preferences of each. Next, it examines psychological processes that guide human decision making and greatly influence risk perception. The article then applies these worldviews and psychological phenomena to the case of risk management. It offers a critique of current risk management practices, drawing on evidence from the 2007–2008 financial crisis. The article concludes by promoting increased diversity of worldviews in corporate governance as a way to prevent the same risk blindness that led to the Great Recession.     

Vulnerable consumer engagement: How corporate social media can facilitate the replenishment of depleted resources

Technology may facilitate health and wellbeing consumer engagement. When there is scant public health provision and socio-cultural norms marginalize consumers stigmatized from cancer, we reveal how a brand’s corporate social media campaign can support vulnerable consumers with resource constraints. Drawing from a transformative consumer research lens, we investigate five years of computer-mediated communications facilitated by the Indian brand Dabur Vatika. Through a grounded theory and an abductive reasoning approach, we unveil how vulnerable consumers directly or indirectly affected by cancer leverage brand's social media to replenish resources. First, we identify how vulnerable consumers engage to replenish depleted emotional and social support resources. We further expand consumer engagement scholarship by offering a preliminary definition of “vulnerable consumer engagement”. Second, we provide a nascent classification of vulnerable consumers in a consumer-producer role, Principal Vulnerable Consumers and Associate Vulnerable Consumers, distinguished by their proximity to the vulnerable context. Lastly, we reveal how brands may perform a transformative role, to replenish social, emotional and operant resources at the micro level through the engagement of vulnerable consumers with corporate social media. This insight is informative for policymakers, advertising practitioners and transformative consumer research academics.     

How category characteristics affect the number of store brands offered by the retailer: a model and empirical analysis

Our research examines why retailers offer, not one, but multiple store brands in some product categories. More specifically, we are interested in how certain product category characteristics affect the number of store brands. We model a product category consisting of two incumbent national brands that may differ in strength. The retailer may introduce one or two store brands depending on which maximizes category profits. Our analysis suggests that the retailer is likely to carry two store brands in categories where (i) the national brands are similar in strength; and (ii) the price sensitivity between the national brands is low. Interestingly, the conditions that support the introduction of more than one store brand are quite different than the conditions that would facilitate the introduction of additional national brands. We provide empirical evidence that support our model-based predictions.