Conjoint Analysis Like Qualitative Researchers, Quantitative Researchers

Conjoint Analysis

Like qualitative researchers, quantitative researchers also have a number of approaches available to them today. The selection of the research approach will depend on what type of information is being sought, what type of information is available, and the goals of the researcher. One research methodology that is gaining increasing popularity is conjoint analysis, a quantitative methodology that is discussed further below, followed by a summary of the research and important findings in the conclusion.

Description of Conjoint Analysis and Examples of Business Applications

Conjoint analysis is a quantitative methodology that measures the perceived values of various possible product designs (Calantone & Di Benedetto, 1990). Respondents participating in conjoint analyses view several variations in product concepts and then assign ranks with respect to their individual preferences (Calantone & Di Benedetto, 1990). The analysis of these responses can be used to identify the respective utility that is associated with each of the attributes ranked by the respondents, and these findings can then be used to identify the combination of attributes that consumers would likely most prefer (Calantone & Di Benedetto, 1990). According to Orme (2009), "Conjoint analysis has become one of the most widely-used quantitative methods in marketing research. When used properly, it provides reliable and useful results" (p. 1). Although there are a number of different conjoint analytical methods that employ different pairings of example products, the general goal is to "measure the perceived values of specific product features, to learn how demand for a particular product or service is related to price, and to forecast what the likely acceptance of a product would be if brought to market" (What is conjoint analysis?, 2011, para. 1).

Types of Management Problems Addressed by Conjoint Analysis

A number of researchers have used conjoint analysis to examine different aspects of management problems, including Wind, Grashof, and Goldhar (1978) who applied conjoint analysis to scientific and technical information services; Pekelman and Sen (1979) who used the method to improve the predictability of product acceptance and concept choice. In addition, conjoint analysis also appears to have the potential to play an increasingly useful role in the identification of consumer preferences with respect to environmental issues (Bateman & Willis, 1999).

Relationship between the Management Problem and the Research Purpose/Objectives addressed by Conjoint Analysis

As the nation's largest healthcare provider, the Department of Veterans Affairs (VA) Healthcare Management System has a mandate to provide the best possible care for its veteran patients, making the identification of reliable and efficient data analysis methods an important and timely enterprise. For instance, although conjoint analysis has not been found particularly useful as a peer-review tool by healthcare providers, it has been shown to be increasingly popular as a "basis for examination of patients' preferences and valuation of options in decision making" (Bowling & Ebrahim, 2005, p. 346).

Representative Examples of the Research Objectives Addressed by Conjoint Analysis

Detailed Description of the Methodology

According to Bateman and Willis (1999), "Conjoint analysis is based on judgments on a carefully constructed set of hypothetical cases that allow for capture of the 'partworths' of each cue-utility at each of its levels. So linearity of cue use is not assumed, but simplification of cues into different levels is necessary" (p. 346). A sample protocol for developing a conjoint analysis is provided in Table 1 below:

Table 1

Conjoint Analysis Protocol

Step

Description

Identify key attributes (or factors) and levels

This is probably the most important step in the design of conjoint analysis studies since the selection of the proper attributes will have an impact on our ability to reflect how buyers make purchase decisions. Qualitative research or other research sources should be used to provide realistic attributes. The presentation of attribute levels is not restricted to text. We can use images when appropriate; here, Mora cites Orme's attributes or factors that should be used to develop this step in the conjoint analysis as follows:

1. Cover the full range of possibilities for existing products;

2. Be independent from each other with no overlapping meaning;

3. Be mutually exclusive;

4. Have a balanced number of levels across attributes (when possible).

Create an experimental design

This step should provide:

1. Frequency balance (each attribute appears the same number of times),;

2. Orthogonality (each item is paired with other items the same number of times); and,

3. Position balance (each items appears the same number of times in each position).

The experimental design is then used to generate a certain number of choice tasks for respondents. Using this approach for the case of online market research courses would not only provide a more realistic scenario for respondents, but also prevents them from focusing solely on price, decreasing the natural tendency to lowball when price becomes the center of attention, as it happens in price research approaches using direct questions about willingness to pay and purchase intent.

Estimate utility coefficients.

These are measures of desirability for each of the attribute levels and can be estimated with different methods including aggregated multinomial logit, latent class analysis, or Hierarchical Bayes estimation (this approach is more common today).

Develop a market simulator

This uses the utility coefficients to predict which product configurations are more likely to be chosen among many product configurations, including those that were not presented to respondents. This is the most valuable tool for managers as they can conduct "what-if" analysis to predict shares of preferences for different product configurations.

Source: Adapted from Mora, 2011

Critique of an Actual Application of Conjoint Analysis in Business/Management