Factor theory to identify improvement priorities for express

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Reference no: EM132200217

Using three-factor theory to identify improvement priorities for express and local bus services:

An application of regression with dummy variables in the Twin Cities This study compared the use of importance-performance analysis (IPA or quadrant analysis) in analyzing public transit rider satisfaction to the use of the three factor theory. This study was administered in 2014 by Metro Transit to users of the bus, light rail, and Northstar commuter rail services in the Minneapolis-St. Paul metropolitan area.

IPA is often used to identify service improvement priorities based on customer satisfaction surveys, but because it assumes the relationship between the performance of individual attributes and their influence on overall satisfaction linear and symmetric, it has limitations.

Three-factor theory, classifying transit attributes into basic, performance, and exciting factors, relaxes the linear and symmetric assumptions, thereby creating a hierarchy of improvement priorities.

The three-factor theory was implemented using regression with dummy variables (also called penalty-reward analysis) to convert each attribute into two dummy variables (high performance and low performance) and regress overall satisfaction against these dummy variables. Then, each attribute was classified based on the significance level of both dummy variables. For each of the attributes, the study authors recoded its performance into two mutually exclusive dummy variables indicating “high-performance” and “low-performance” and created a dummy variable to indicate whether “0” is a missing value or is the average performance. This generated 24 dummy variables indicating high performance or low performance of service attributes and 12 dummy variables indicating missing values. The authors regressed the overall satisfaction against the dummy variables for express and local bus users, respectively.

The authors eliminated the attribute, “vehicles are environmentally friendly”, due to multicollinearity. The study used the p-value of 0.05 as the critical significance level. The missing values were almost randomly missing since only two of the 21 missing value dummy variables was significant at the 0.05 level in the models.

IPA identified fewer improvement priorities compared to the three-factor theory. Compared with IPA, the three-factor theory identified more detailed and better-reasoned hierarchy of improvement priorities, indicating that the three-factor theory is superior to the IPA, and offers more implications for transit agencies.

The study identified a hierarchy of improvement priorities that would impact overall satisfaction the most, however it did not consider the cost of improvements. The authors recommended Metro Transit use this analysis as a starting point to identify key focus areas, followed by an analysis of the cost and expected benefits of specific improvement projects.

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Reference no: EM132200217

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