Customer Satisfaction

Customer Satisfaction & how can we measure it

By: Omid Nasrollah Mazandarani

BACKGROUND OF THE WRITER

As a student of Masters of Business Administration specialization in general management with the background of Bachelor of Industrial engineering the writer has four years experiences in the automobile industry. These experiences and background help me to understand the role of customer satisfaction in terms of organization profitability.

Abstract:

This proposal examines customer satisfaction models for assessing the relationship of overall satisfaction with a product or service and satisfaction with specific aspects of the product or service for organizations having multiple units or subunits. These units could be stores, markets, dealers, divisions, and so on. The authors suggest a method for studying whether the drivers of overall satisfaction vary across such units. For cases where the drivers do vary across subunits, they show how additional variables can be included in a model to account for the variation. The authors illustrate this approach by studying customer satisfaction in the newspaper and health care industries. They use generalizability theory to evaluate the reliability of scales from multistage cluster sample designs. It is argued that the approach has important implications for both theory and practice.

Key words:

Customer satisfaction

Introduction:

Many studies have related overall satisfaction with some product or service to satisfaction with specific aspects of the product or service (Anderson and Sullivan 1993; DeWulf, Odekerken-Schrцder, and Iacobucci 2001; Garbarino and Johnson 1999; Oliver 1980, 1993; Parsuraman, Berry, and Zeithaml 1988, 1991). Customers may explain their satisfaction with a product or service in terms of specific aspects such as the product attributes, price, customer service, or a combination of these various features. The objective of such studies is to understand how specific types of customer satisfaction affect overall satisfaction, usually by examining the slopes from a regression analysis. This article extends this approach by allowing the slopes to vary over predefined "subunits" of customers. We hypothesize that different subunits within an organization or industry may show different relationships between specific aspects of satisfaction and overall satisfaction, that is, there may be different utilities for the specific aspects of satisfaction. The problem of whether the relationship between specific aspects of satisfaction and overall satisfaction varies by subunits has both practical and theoretical importance. As a practical matter, such variation could be important for marketing decisions. For example, an automotive manufacturer may have multiple dealers (the subunit). A marketing manager would want to know if all dealers should focus on the same aspects of satisfaction or whether the customers of one dealer may have different priorities than another. If there is variation in the utilities across subunits, can the variation be explained by, for example, the geographic location of the dealership? A second example is a national retailer with multiple stores (the subunit). It would not be surprising for consumers in densely populated urban areas to place a high utility on dimensions such as location and convenience, whereas these same dimensions might be less important in sparsely populated rural areas. A third example is a media organization with multiple properties (subunits). Newspaper owners often own several newspapers (subunits) in different markets. Should all of the owner's newspapers focus on the same customer satisfaction dimensions? Banks have multiple branches. Perhaps the drivers of satisfaction for large branches are different than for those of small branches? Variation in the specific-general satisfaction relationship across organizational subunits also has important theoretical implications for satisfaction research. The goal of theoretical research is to test universal hypotheses that apply across observational units (Calder, Phillips, and Tybout 1981; Calder and Tybout 1999). Research attempts to expose these hypotheses to rejection by the empirical test. A study of the specific-general satisfaction relationship in a single organization provides such a test. However, testing the relationship across several organizational units provides an even stronger test in that the theoretical relationship is exposed to additional opportunities for empirical rejection. And, beyond this, if the hypothesized relationship is not found for some units, this offers the possibility of developing richer theoretical hypotheses that take into account the effects of other variables. Much of academic services marketing research is of the single organization sort. It often posits certain effects and evaluates the extent to which the effects hold using a random sample of customers from a single company (Bolton 1998; Garbarino and Johnson 1999; Hallowell 1996; Loveman 1998; Schlesinger and Zornitsky 1991). Occasionally, the effect will be evaluated on a small convenience sample of companies (Parasuraman, Berry, and Zeithaml 1991; Zeithaml, Berry, and Parasuraman 1996). Although such studies are certainly important, they are not strong tests in the above sense. The ideal study would be one with a random sample of organizational units and a random sample of consumers from each selected unit. Thus, for both practical and theoretical reasons, this article focuses on the extent to which specific-general satisfaction effects vary across units. If the effects are the same across units, a manager may be able to use one strategy for all units. To the extent that effects vary across units, the company would want to consider different strategies for different units. And, at the theoretical level, the multiple units provide a stronger test of a hypothesized general effect. We also stress the importance of explaining the variation in effects across units or subunits. One way to approach this question is to partition the units or subunits into strata. For example, the locations of retail stores could be classified into rural, small city, suburban, and urban types. We want to quantify how much variation in effects there is both within and across strata. If the within-stratum variation is small and the between-stratum variation is great (e.g., rural stores all have the same needs, but rural stores have different needs than urban ones), the manager might develop separate strategies for each