Retailing evolution research: Introduction to the special section on retailing research

This JBR Special Issue on Retailing includes several high quality research studies presented at the 10th and 11th Society for Marketing Advances Retail Strategy and Patronage Behavior Symposiums. The articles cover a wide range of interesting and important retailing topics including topics such as atmospherics, self-esteem and advertising. This introduction offers a few comments on the 2007 and 2008 Symposiums' content including a brief overview of each article.     

Do interest groups affect US immigration policy?

While anecdotal evidence suggests that interest groups play a key role in shaping immigration policy, there is no systematic empirical analysis of this issue. In this paper, we construct an industry-level dataset for the United States, by combining information on the number of temporary work visas with data on lobbying activity associated with immigration. We find robust evidence that both pro- and anti-immigration interest groups play a statistically significant and economically relevant role in shaping migration policy across sectors. Barriers to migration are lower in sectors in which business interest groups incur larger lobbying expenditures and higher in sectors where labor unions are more important.     

A state-dependent model of intermediate goods pricing

Recent analyses of transaction-level data sets have generated new stylized facts on price setting and greatly influenced the empirical open- and closed-economy macroeconomics literatures. This work has uncovered marked heterogeneity in price stickiness, demonstrated that even non-zero price changes do not fully “pass through” exchange rate shocks, and offered evidence of synchronization in the timing of price changes. Further, intrafirm prices have been shown to differ from arm's length prices in each of these characteristics. This paper develops a state-dependent model of price setting by strategic intermediate goods producers that anticipate and respond to their competitors' actions. The model, which allows for both arm's length and intrafirm transactions, is able to generate all of these empirical pricing patterns.     

Managing customer experiences in online product communities

Many companies have established online product communities or forums as a vehicle to extend product support services to their customers. Customers' interactions in such online forums with peer customers and vendor representatives resolve their product-related queries as well as inform on their product purchase decisions. Despite the significance of such interactions, there has been limited theoretical attention so far on how companies can manage customer experiences in online product communities. Drawing on theories and concepts from diverse areas including computer-mediated communication, consumer psychology, and online communities, this study proposes a four dimensional construct - Online Community Experience (OCE) - to capture customer experiences in such online product communities, and examines its impact on customer attitudes regarding the product, the company, and the quality of service. Data collected from customers in online product forums offered by four companies is used to test the study hypotheses. Implications for research on online consumer behavior and marketing are discussed.     

Customers acting badly: Evidence from the hospitality industry

The activities of misbehaving customers represent a significant problem for organizations across diverse sectors and industries. Customer misbehavior signifies behavior within the exchange setting that deliberately violates the generally accepted norms of conduct in such situations. The core aim of this study is to advance understanding of the antecedents to past customer misbehavior and examine the relationship between past misbehavior and future misbehavior intentions. Although prior studies offer insights into individual events or types of customer misbehavior, to date, empirical evidence of the antecedents to past misbehaviors and future behavioral intentions is lacking. Synthesizing literature from varied theoretical backgrounds, this study puts forth five personality-based variables (consumer alienation, Machiavellianism, sensation seeking, aggressiveness, and self-esteem) and four demographic variables (gender, income, age, and education) as antecedents to past customer misbehavior. In turn, this study reveals an association between past customer misbehavior and future misbehavior intent. Structural equation modeling procedures using a sample of 380 respondents reveals support for eight of the nine specified hypotheses. The article concludes with a discussion of the study's implications for theory and practice.     

Beyond the technology adoption: Technology readiness effects on post-adoption behavior

Technology readiness (TR) refers to people's propensity to embrace and use new technologies. Nowadays, the proliferation of technology-based products and services brings consumers not only benefits but also frustration over ineffective use of products and services. A key factor, therefore, in the diffusion and success of these products and services is how well-prepared consumers are for new technologies. Although some studies examine the relationships between TR and technology adoption, the long-term survival and substantial success of firms rely on the continued use of such technology rather than first use. This study focuses on post-adoption behavior and investigates how TR affects the continued use intention of new technology. Specifically, the study classifies usage behavior into the usage rate of basic functions, the usage rate of innovative functions, and the variety of use of innovative functions. The article then, examines how each dimension (optimism, innovativeness, discomfort and insecurity) of TR influences consumer usage patterns, and how usage patterns affect repurchase intention through consumer satisfaction. The empirical results from IPTV users in Korea show that each dimension of TR has a significantly different influence on usage patterns. The findings show that usage patterns, particularly the use of innovative functions, have a significantly positive impact on consumer satisfaction and repurchase intention. Finally, the article suggests several managerial implications and directions for further studies.     

Valuing customers for social network services

In the current Internet environment, many online service companies based on community factors (networks) produce and sell new types of digital products. For example, SNS (social network service) companies now sell customers digital decorative products for the adornment of online avatars. Not only do individual customers consume these digital products, but they also exchange them as gifts in their local neighborhoods. While previous studies on customer valuation (e.g. CLTV, RFM, etc.) focus on some important issues for identifying valuable customers, the literature does not resolve issues related to the effects on customer value of products purchased as gifts for another person. This study attempts to verify empirically the effects of two representative social network properties (tie strength and the number of ties) on customer monetary value.This study selects 2615 customers from a South Korean SNS website who enrolled on the site on the same day. Data include diverse purchase-related information, social network properties in terms of gift-giving for six months, and demographic information. The primary results of this study are as follows. First, the proposed model, which includes social network properties, has a great deal more explanatory power than the baseline model — the RFM-based customer value model. Second, tie strength and the number of ties in a gift-giving network increase customer value. Third, tie strength has a more profound impact on customer value than does the number of ties. The results of this study theoretically expand the domain of customer valuation studies due to the inclusion of social network properties, and suggest important practical implications for some online SNS companies in their efforts to find valuable customers and improve customer value.     

Extending the BG/NBD: A simple model of purchases and complaints

Extant customer-base models like the beta geometric/negative binomial distribution (BG/NBD) predict future purchasing based on customers' observed purchase history. We extend the BG/NBD by adding an important non-transactional element that also drives future purchases: complaint history. Our model retains several desirable properties of the BG/NBD: it can be implemented in readily available software, and estimation requires only customer-specific statistics, rather than detailed transaction-sequence data. The likelihood function is closed-form, and managerially relevant metrics are obtained by drawing from beta and gamma densities and transforming these draws to a sample average. Based on more than two years of individual-level data from a major U.S. internet and catalog retailer, our model with complaints outperforms both the original BG/NBD and a modified version. Even though complaints are rare and non-transactional events, they lead to different substantive insights about customer purchasing and drop-out: customers purchase faster but also drop out much faster. Furthermore, there is more heterogeneity in drop-out rates following a purchase than a complaint.     

Investigating the immediate and long-term effects of job stressors on frontline service employees

Two studies investigate the immediate and long-term effects of job stressors on frontline service employees (FSEs). Using cognitive appraisal theory, we develop and test a conceptual model of two job stressors (crowding and emotional labor) that affect coping strategies and job outcomes. Study 1, which is a field experiment, investigates the immediate effects of crowding in a single firm. Study 2 extends the findings of Study 1 and investigates the long-term effects of emotional labor and crowding on FSEs across multiple firms. The results show that crowding has a negative impact on coping strategies and job-related outcomes. In addition, emotional labor can lead to long-term negative outcomes, such as emotional exhaustion and decreased job retention, for some FSEs. Consequently, service organizations should consider strategies or tactics that prevent high levels of customer crowding and help FSEs deal with emotional labor.     

一种基于支持向量机的带钢表面缺陷识别方法

鉴于支持向量机（SVM）在小样本、高维模式分类中具有的优良分类性能,可以基于多分类支持向量机来检测带铜表面的缺陷。本文构造了一类有向无环图支持向量机（DAGSVM）,利用交叉验证进行了参数和模型的选取,对冷轧带钢中几种现场易出现的缺陷进行分类,并与BP神经网络进行比较分析。实验结果表明,这类基于SVM的算法识别效率较高,较好地解决了小样本学习问题,避免了BP神经网络出现的过学习、收敛速度慢、泛化能力弱等缺点,可有效地应用于带铜表面缺陷检测。