Venkatram Ramaswamy

Effects of Brand Local and Nonlocal Origin on Consumer Attitudes in Developing Countries

This study tested whether, among consumers in developing countries, brands perceived as having a nonlocal country of origin, especially from the West, are attitudinally preferred to brands seen as local, for reasons not only of perceived quality but also of social status. We found that this perceived brand nonlocalness effect was greater for consumers who have a greater admiration for lifestyles in economically developed countries, which is consistent with findings from the cultural anthropology literature. The effect was also found to be stronger for consumers who were high in susceptibility to normative influence and for product categories high in social signaling value. This effect was also moderated by product category familiarity, but not by consumer ethnocentrism. The results, thus, suggest that in developing countries, a brands country of origin not only serves as a “quality halo” or summary of product quality (cf. Han, 1989), but also possesses a dimension of nonlocalness that, among some consumers and for some product categories, contributes to attitudinal liking for status-enhancing reasons.

Market Segmentation with Choice-Based Conjoint Analysis

Choice-based conjoint analysis has increased in popularity in recent years among marketing practitioners. The typical practice is to estimate choice-based conjoint models at the aggregate level, given insufficient data for individual-level estimation of part-worths. We discuss a method for market segmentation with choice-based conjoint models. This method determines the number of market segments, the size of each market segment, and the values of segment-level conjoint part-worths using commonly collected conjoint choice data. A major advantage of the proposed method is that current (incomplete) data collection approaches for choice-based conjoint analysis can still be used for market segmentation without having to collect additional data. We illustrate the proposed method using commercial conjoint choice data gathered in a new concept test for a major consumer packaged goods company. We also compare the proposed method with ana priori segmentation approach based on individual choice frequencies.

Latent Class Metric Conjoint Analysis

A latent class methodology for conjoint analysis is proposed, which simultaneously estimates market segment membership and part-worth utilities for each derived market segment using mixtures of multivariate conditional normal distributions. An E-M algorithm to estimate the parameters of these mixtures is briefly discussed. Finally, an application of the methodology to a commercial study (pretest) examining the design of a remote automobile entry device is presented.

Data Envelopment Analysis and its Use in Banking

Data envelopment analysis (DEA) is a linear-programming-based method for assessing the performance of homogeneous organizational units and is increasingly being used in banking. The unit of assessment is normally the bank branch. Studies are mostly centered on deriving a summary measure of the efficiency of each unit, on estimating targets of performance for the unit, and on identifying role-model units of good operating practice. Additional uses for DEA in banking include the measurement of efficiency in light of resource and output prices, the estimation of operating budgets that are conducive to efficiency, the assessment of financial risk at bank-branch level, and the measurement of the impact of managerial change initiatives on productivity.

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.

Analyzing channel structures of business markets via the structure-output paradigm

Abstract While numerous conceptual ideas regarding distribution channel structures for industrial markets have been advanced in the marketing literature, there has been little integration of relevant theory and hardly any empirical testing of these ideas to confirm or define best managerial practice. We seek to integrate logistical and informational issues affecting channel structure into a single framework labeled as the Structure-Output Paradigm. This model regards the channel as the means by which services are provided to end-users and their needs as constituting the chief inputs into the channel design process. We test hypotheses derived from our framework using the PIMS database entailing businesses of major corporations serving both the European and North American markets. Our results provide strong empirical support for the influence of logistical and informational service outputs in industrial channel structures, and underscores the pivotal role of the end-user in designing industrial channels.

A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.

Coupon characteristics and redemption intentions: A segment-level analysis

The authors investigate how different segments of consumers react to different coupon characteristics, such as face value and method of distribution. They utilize a latent segmentation approach to identify the underlying segments. The empirical analysis suggests that different segments of consumers place varying emphasis with regard to economic benefits, psychic benefits, effort costs, and substitution costs. A further examination of the derived segments with respect to consumer correlates such as psychological, attitudinal, behavioral, and demographic characteristics reveals that coupon-related consumer characteristics, rather than demographics, exhibit significant and meaningful differences across these segments. Implications of the segment-level analysis for evaluating coupon drops and managing promotional expenditures are also discussed. ©1998 John Wiley & Sons, Inc. With 269 billion coupons distributed in 1996 in the United States, and approximately 5.3 billion coupons redeemed, for a total savings of

Latent Class Models for Conjoint Analysis

3.7 billion (Brown, 1997), coupons continue to be among the most important promotional vehicles being used today. To improve the profitability of promotions, an in-depth understanding of the impact of promotions

On Estimating Finite Mixtures of Multivariate Regression and Simultaneous Equation Models

Conjoint analysis was introduced to market researchers in the early 1970s as a means to understand the importance of product and service attributes and price as predictors of consumer preference (e.g., Green and Rao 1971; Green and Wind 1973). Since then it has received considerable attention in academic research (see Green and Srinivasan 1978, 1990 for exhaustive reviews; and Louviere 1994 for a review of the behavioral foundations of conjoint analysis). By systematically manipulating the product or service descriptions shown to a respondent with an experimental design, conjoint analysis allows decision-makers to understand consumer preferences in an enormous range of potential market situations (see Cattin and Wittink 1982; Wittink and Cattin 1989; and Wittink, Vriens, and Burhenne 1994 for surveys of industry usage of conjoint analysis).