Home equity lines of credit and the unemployment rate: Have unemployed consumers borrowed themselves into the next financial crisis?

Some economists argue the recent recovery has been so meager because many consumers have lost their main source of income and maxed-out their home-equity borrowings. Further, banks that were able to make consumer loans did so with less security because home prices fell so dramatically. This paper argues that at least part of that recovery story is purely anecdotal and, in fact, incorrect. In spite of the precipitous decline in home prices, the original price increases were so large that many homeowners still have/had adequate equity in their homes to borrow. The paper presents evidence that the average quarterly increase in aggregate home equity line of credit (HELOC) lending after housing prices began their decline is, statistically, no different than the average quarterly increase in HELOC lending before housing prices began their downward trend. The evidence also suggests that increased HELOC lending during the recession is not correlated with higher unemployment.     

Payday loans and consumer financial health

The annualized interest rate for a payday loan often exceeds 10 times that of a typical credit card, yet this market grew immensely in the 1990s and 2000s, elevating concerns about the risk payday loans pose to consumers and whether payday lenders target minority neighborhoods. This paper employs individual credit record data, and Census data on payday lender store locations, to assess these concerns. Taking advantage of several state law changes since 2006 and, following previous work, within-state-year differences in access arising from proximity to states that allow payday loans, I find little to no effect of payday loans on credit scores, new delinquencies, or the likelihood of overdrawing credit lines. The analysis also indicates that neighborhood racial composition has little influence on payday lender store locations conditional on income, wealth and demographic characteristics.     

Demand for ecolabeled seafood in the Japanese market: A conjoint analysis of the impact of information and interaction with other labels

The use of seafood ecolabels is expanding in the world marketplace, but so are labels indicating other product attributes, such as country of origin and wild vs. farmed. The interactive effects of these labels and attributes in evaluating consumers’ preferences for ecolabeled seafood are relatively unexplored. In this paper we investigate (1) the direct and interactive effects of seafood ecolabels with other common fish labels, and (2) how consumers’ perceptions about the state of marine stocks and the valuation of ecolabels may be affected by different information. We find moderate interactive effects between ecolabels and country of origin labels, whereas the valuation for seafood ecolabels is fairly high. In terms of information, we find that consumers’ perceptions about fish stock levels changed (negatively) after receiving information on declining stock levels, and more sensationalized information led to increased change. However, valuation for a seafood ecolabel increases only when the information was perceived positively (credible/interesting); whereas exaggerated information (which was also perceived less credible) had insignificant effects on WTP.     

How virtual brand community traces may increase fan engagement in brand pages

Brand pages in social media are a great way to foster consumer gathering around a brand, but it can be challenging to keep fans engaged and coming back to see updated content. Brands with millions of fans on Facebook have seen organic reach fall below 2% of their base. In this article, we describe how the creation of virtual brand communities can help brand managers increase fan engagement. We suggest the steps, conditions, advantages, and limitations involved in nurturing a brand page as an online social gathering that assumes some of the characteristics of a virtual brand community. The results of our study show that a brand page can have some of the characteristics of a virtual brand community—topical information exchange, identity communication, and establishment and internalization of cultural norms. We also show the importance of having celebrities among fans in order to foster social interactions and legitimate social practices on brand pages.     

Consumer cooperatives as an alternative form of governance: The case of the broadband industry

With a growing number of consumer cooperatives in non-agrarian industries such as energy and broadband, there is a need to better understand their emergence as a viable form of governance. In this context, the paper uses Mikami’s (2010) model on consumer cooperatives to explain their emergence as a result of their ability to generate additional equity financing through the membership market. It focuses on the motivations of consumers to financially contribute to a cooperative by examining the risks and benefits associated with the investment. Based on a survey of 759 consumers, it links their motivations for joining a cooperative to different forms of risks and benefits associated with the investment. It shows that the risks related to cost sharing and switching costs are important determinants for consumers. Other factors that affected the likelihood of joining a cooperative were expected benefits with respect to user network externalities and infrastructure gains.     

A neural network based price sensitive recommender model to predict customer choices based on price effect

The impact of price and price changes should not be ignored while designing algorithms for predicting customer choice. Consumer preferences should be modeled with consideration of price effects. Businesses need to consider for efficient prediction of an individual's purchase behaviour. Personalized recommendation systems have been studied with machine learning algorithms. However, the price-aware personalized recommendation has received little attention. In this paper, we attempt to capture insightful economic results considered in the marketing and economics disciplines by employing modern machine learning architecture for predicting customer choice in a large-scale supermarket context. We extract personalized price sensitivities and examine their importance in consumer behaviour. The employed data collected from a supermarket chain in Germany consists of implicit feedback based on customer-product interactions and the price of every interaction. We propose a two-pathway matrix factorization (2way-MF) model that is price-aware and tries to memorize customer-product interaction's implicit feedback. The proposed models achieve better model performance than standard Matrix Factorization models widely used in the industry. The approach was re-validated with data from supermarket chain in Taiwan. Other industries can adopt the proposed framework of modeling customer's preferences based on price sensitivity. We suggest that further research and analyses could help understand the cross-price elasticities.     

Refining e-shoppers’ perceived risks: Development and validation of new measurement scale

Due to the rapid expansion of perceived e-shopping risks, and highly isolated and inconsistent presentation of literature about this concept, understanding e-shopper's behavior has become more difficult. In this regard, this study brings together different views, evidences and facts about perceived e-shopping risks from both scholars and practitioners of South-East Asian and Western countries. It then interprets the information in the form of a new scale which offers more adequacy, assemblage and uniformity than the existing models of perceived risks of e-shoppers. The new scale of construct has 11 dimensions comprising a pool of 38 items, which has been empirically validated through the data collected from 537 Malaysian e-shoppers. The dimensions are: high price risk, deception risk, transaction failure risk, dissimilar product risk, incapable service risk, illegitimate product risk, isolation risk, unease risk, displeasure risk, prior-purchase time delays risk, and post-purchase time delays risk. The theoretical and managerial implications and research limitations have also been discussed.     

Exploring the elements of consumer nostalgia in retailing: Evidence from a content analysis of retailer collages

This article focuses on consumer nostalgia in the retailing environment to identify the types of nostalgic memories evoked by remembering a retailer and the elements associated with such nostalgic memories. A projective technique involving collage construction enabled informants to reflect their feelings about, memories of, experiences with, or relationships with a retailer that they have selected. The findings uncovered three distinct types of retailer-related consumer nostalgic memories and six elements that comprise retailer-related consumer nostalgic memories. The findings of the study offer a comprehensive understanding of retailer consumer nostalgia. Theoretical and managerial implications are discussed.     

Random regret minimization for consumer choice modeling: Assessment of empirical evidence

This paper introduces to the field of marketing a regret-based discrete choice model for the analysis of multi-attribute consumer choices from multinomial choice sets. This random regret minimization (RRM) model, which has recently been introduced in the field of transport, forms a regret-based counterpart of the canonical random utility maximization (RUM) paradigm. This paper assesses empirical results based on 43 comparisons reported in peer-reviewed journal articles and book chapters, with the aim of finding out to what extent, when, and how RRM can form a viable addition to the consumer choice modeler's toolkit. The paper shows that RRM and hybrid RRM–RUM models outperform RUM counterparts in a majority of cases, in terms of model fit and predictive ability. Although differences in performance are quite small, the two paradigms often result in markedly different managerial implications due to considerable differences in, for example, market share forecasts.