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Purpose – The marketing literature does not provide a satisfactory explanation for the role of consumer's attention in the process of how Country-of-Manufacture (COM) information influences consumer product evaluations. The research contributes to an improved understanding of this process by integrating the construct of “attention to Country-of-Manufacture” into the model and examining its relationship with the influence of COM.

Design/methodology/approach – Survey data are collected from American consumers aged 18 years and above. To test the research hypotheses, MANOVA and canonical correlation analysis are performed in analyzing the data.

Findings – COM has more influence on the attentive group (consumers consciously paying attention to the COM information on a product label), on their evaluations of abstract product attributes such as durability and reliability than it does on the inattentive group (consumers not paying conscious attention to such information). In contrast, COM's influences on evaluating concrete product attributes such as style, model, availability, and quality are all significantly related to involvement with COM, but not to attention.

Research limitations/implications – The product assessments sought from respondents are generally on “foreign” products. Future research needs to obtain product-specific evaluations within each product category in testing the model and see how the results may differ or not differ across product categories.

Practical implications – Marketers selling products with high performance in abstract attributes such as durability and reliability should increase consumers’ attention to the COM through effective product labeling.

Originality/value – This research identifies and empirically investigates the difference of COM effects on consumers’ product judgment between consumers who are attentive and the ones who are inattentive to COM information.

This paper focuses on customer value analysis and measurement, framing customer value management as one of the main antecedents of the company value-creation process. The paper builds on three main pillars. First, the paper highlights the critical role of customer value in business-to-business markets, focusing on the links between the company's ability to manage customer value-creation processes and the positive financial and economic outcomes generated by loyalty effects. Secondly, the paper develops key analytical stages for an understanding of customer value. The focus is on the customer value-chain concept, including consideration of the customer information and acquisition process and its decision rules. Third, the paper illustrates the measurement process, offering an organizational framework for selecting the most suitable method for measuring perceived customer value. The methodological alternatives range from desk measures (e.g., technical computation of the total cost of ownership (TCO)) to field analysis, like those considered under both compositional and the decomposition approaches (e.g., conjoint analysis). The paper concludes with remarks on the managerial implications of these measures, as well as offering suggestions for further research on value for the customer.

Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.

Discrete choice models are widely used for estimating the effects of changes in attributes on a given product's likely market share. These models can be applied directly to situations in which the choice set is constant across the market of interest or in which the choice set varies systematically across the market. In both of these applications, the models are used to determine the effects of different attribute levels on market shares among the available alternatives, given predetermined choice sets, or of varying the choice set in a straightforward way.

Discrete choice models can also be used to identify the “optimal” configuration of a product or service in a given market. This can be computationally challenging when preferences vary with respect to the ordering of levels within an attribute as well the strengths of preferences across attributes. However, this type of optimization can be a relatively straightforward extension of the typical discrete choice model application.

In this paper, we describe two applications that use discrete choice methods to provide a more robust metric for use in Total Unduplicated Reach and Frequency (TURF) applications: apparel and food products. Both applications involve products for which there is a high degree of heterogeneity in preferences among consumers.

We further discuss a significant challenge in using TURF — that with multi-attributed products the method can become computationally intractable — and describe a heuristic approach to support food and apparel applications. We conclude with a summary of the challenges in these applications, which are yet to be addressed.

This chapter addresses how firms buying information technology (IT) products select their suppliers. We argue that social embeddedness, in the sense of own experiences with suppliers and information about experiences of third parties, influences these types of selection decisions. More specifically, we claim that social embeddedness is more important if: (1) the potential damage for the buyer from receiving an inferior product is larger and (2) if it is more difficult for the buyer to monitor the quality of the product. We use large-scale surveys of IT transactions in the Netherlands and Germany to test these hypotheses. In general, our hypotheses about the effects of social embeddedness on partner selection are supported. We find that buyers tend to assign greater weight to product quality if the potential damage for the buyer is larger. Negative third-party information is particularly important if the buyer has large problems to monitor the quality of a product.

A special event requiring little capital for development is regarded as a unique type of attractions. Art festival is a popular form among all special events, famous example including Edinburgh Festival in England. However, the literature on the measurement of successful factors of art festivals is limited. This study uses Chiayi International Band Festival for a case study to evaluate performance factors of art festivals. The performance attributes are developed based on Kotler's Three Levels of Product framework. Importance–Performance Analysis (IPA) is used to evaluate the performance attributes perceived by the visitors. Further, a SWOT analysis is deployed to provide managerial strategies and implications.

In this chapter, we describe how random utility maximization (RUM) discrete choice models are used to estimate the demand for commodity attributes in quality-differentiated goods. After presenting a conceptual overview, we focus specifically on the conditional logit model. We examine technical issues related to specification, interpretation, estimation, and policy use. We also discuss identification strategies for estimating the role of price and non-price attributes in preferences when product attributes are incompletely observed. We illustrate these concepts via a stylized application to new car purchases, in which our objective is to measure preferences for fuel economy.

The study applies a multimodal approach to position aesthetic innovation, i.e., the strategic use of aesthetic design attributes, such as color and shape, as an institutionalized aspect of competition, rather than as a firm-specific differentiation strategy, in settings that favor the symbolic meanings of products. Empirically, the study offers a detailed case study of the personal computer (PC) industry to examine the institutionalization of aesthetic innovation as a dimension of competition across industrial firms. The study examines the color and shape of PCs over the 1992–2003 period and situates changes to these attributes in the competitive conditions that characterized the industry, paying particular attention to the introduction of the Apple iMac in 1998. Furthermore, it examines the discursive manifestations of aesthetic innovation by content analysis of reviews of PCs and interviews with industry executives. Findings demonstrate that, in a period coinciding with a decline in demand for PCs and an overall mature market as well as with the introduction of the iMac, the majority of firms engaged in aesthetic innovation and used a greater number of aesthetic words in describing their PCs.