Conjoint Analysis Experimental Design for Services Involving Multiple Options

Presented by: Neda Javanmardi

Airline flights usually include multiple additional services to differentiate their offer from the competition. In particular, the number of stimuli (combination of attributes) becomes huge and difficult to evaluate through conjoint analysis. In this paper, a new hybrid method is proposed in order to produce relevant card systems of reasonable sizes. First, a pre-selection of stimuli is conducted based on logical survey and expert testing procedures. Second, an algorithm using the criterion of D-optimality is programmed to select the final set of stimuli. The method has been employed for the design of flights between Tehran and Dubai.

E. Fragniere, N. Javanmardi, and F. Moresino, “Conjoint Analysis Experimental Design for Services Involving Multiple Options”, IEEE International Conference on Service Operations and Logistics, and Informatics, China, 2010.

Context

Context Research question Literature review Findings 2/6Methodology

The research is customized for Airline industry.

An integrated model is applied to find the best flight design, which is combination of Share-of-Choice model and Mathematical Model.

Researchers try to solve the problem of the designs which are not realistic.

Research question

Context Research question Literature review Findings 3/6Methodology

Research question:

What is the best service design for our partner airline company for the flight from Tehran to Dubai?

Research objective:

Finding the most desirable flight design (ticket design) which brings the highest profit to our partner company.

Literature review

Context Research question Literature review Findings 4/6Methodology

Paul Green

“Full-profile methods cannot be conducted with the whole possible stimuli set. The number of stimuli presented in a survey shouldn't be too high. In order to reduce the number of stimuli, Green proposed the use of orthogonal design. The main drawback of the orthogonal designs lies in the fact that it can contain stimuli that are impossible or irrelevant.”

P. E.Green, "On the Design of Choice Experiments Involving Multifactor Alternatives", Journal of Customr Research, Vol. 1, PP. 61-68, 1974.

“By breaking down attributes, to derive the part-worth associated with each level of a product based on the overall preferences of alternatives, by a group of respondents, conjoint analysis helps the marketing manager to determine which of the product's or service's qualities are most important to the Customer.”

T. Reutterer, H. W. Kotzab, ''The use of Conjoint Analysis for Measuring Preferences in Supply Chain Design", Industrial Marketing Management, Vol. 29, PP. 27- 35, 2000.

Thomas Reutterer

Methodology

Context Research question Literature review Findings 5/6Methodology

Pre-survey:a. Self-explicated conjoint analysis

Interview Questionnaire

Survey part I:b. Conjoint analysis

Vignette study Part-worth estimation (Quantitative method)

Survey part II:c. Mathematical model

Optimization (Quantitative method)

Findings

Context Research question Literature review Findings 6/6Methodology

A new model is proposed based on a self-explicated card-based method, which reduces all combinations of attributes only to those which can be potentially possible.

The method is applied to an airline industry case. By comparing the algorithm results with some traditional orthogonal designs, it is found that our cards are more realistic than the orthogonal cards.

Limitations:Dependency on expert judgment.Final selected set is not completely orthogonal.

Future works: Since some attributes are impractical to evaluate, information

may be difficult to obtain and complex to analyze, so further study can be based on the Fuzzy Set Theory (FST) which could effectively helps to better model uncertainties and imprecision.