Capturing Relative Importance of Customer Satisfaction Drivers Using Bayesian Dominance Hierarchy

[We’re pleased to welcome authors Philippe Duverger and Xiaoyin Wang of Towson University. Duverger and Wang recently published an article in Cornell Hospitality Quarterly entitled “Capturing Relative Importance of Customer Satisfaction Drivers using Bayesian Dominance Hierarchy,” which is currently free to read for a limited time. Below, they reflect on the inspiration for conducting this research:]

cqxb_58_2.cover

What motivated you to pursue this research?

We observed that most research on the drivers of customer satisfaction (CS) used large samples, often aggregated from several month and/or several properties. Although this is a fine method to look at CS trends it is not practical at the property level for immediate action. The current methods require large samples in order to achieve sufficient power and find significant estimates in models. Unfortunately, most hotel property monthly survey yield samples of less than 100 that will make driver analyses problematic and more likely most drivers will have non-significant estimates.

We asked ourselves if there would not be a method that could circumvent the problem of property managers that want and need to address CS drivers on a monthly basis, if not on a daily basis.

What has been the most challenging aspect of conducting your research? Were there any surprising findings?

We used a Bayesian statistical framework, borrowing from several literature areas to construct a model. Bayesian statistical analysis is still a fairly new method in practice, often not well understood, and can be computationally heavy. Therefore we first needed to explain the advantages of the method in a way that was pragmatic enough because our goal in this paper was to appeal to the hospitality manager.

Bayesian statistics work from the belief that the unknown parameter is a random variable and is associated with a probability distribution (prior distribution). The information in the sample data is used to adjust the prior perception of the unknown parameter and results in the final estimation of the parameter (posterior distribution). Therefore, even if the sample is small, significance can be determined.

Maybe more pragmatically, Bayesian analysis is “often a more direct way to tackle questions we usually want to know, such as: is the hypothesis likely to be true?” Bayesian analysis does not use double negatives, such as we often encounter, e.g., “we failed to reject the null hypothesis that there is no difference.” Bayesian analysis reports are straight forward: “given these data, it is likely that the difference is X% probable” (Chapman and McDonnell Fei 2015, p. 150).

There are many other advantages that we discuss in the paper.

In what ways is your research innovative, and how do you think it will impact the field?

We believe that our Bayesian model, for which we share the code at http://tinyurl.com/kdqjf4u, could be used by hospitality properties or hospitality corporate departments, to enhance monthly reporting along with other marketing metrics, and shared via dashboards.

 

Stay up-to-date with the latest research from Cornell Hospitality Quarterly and sign up for email alerts today through the homepage!

This entry was posted in Customer Satisfaction, Hospitality Management, Uncategorized and tagged , , , , , , , , by Cynthia Nalevanko, Senior Editor, SAGE Publishing. Bookmark the permalink.

About Cynthia Nalevanko, Senior Editor, SAGE Publishing

Founded in 1965, SAGE is the world’s leading independent academic and professional publisher. Known for our commitment to quality and innovation, SAGE has helped inform and educate a global community of scholars, practitioners, researchers, and students across a broad range of subject areas. With over 1500 employees globally from principal offices in Los Angeles, London, New Delhi, Singapore, Washington DC, and Melburne, our publishing program includes more than 1000 journals and over 900 books, reference works and databases a year in business, humanities, social sciences, science, technology and medicine. Believing passionately that engaged scholarship lies at the heart of any healthy society and that education is intrinsically valuable, SAGE aims to be the world’s leading independent academic and professional publisher. This means playing a creative role in society by disseminating teaching and research on a global scale, the cornerstones of which are good, long-term relationships, a focus on our markets, and an ability to combine quality and innovation. Leading authors, editors and societies should feel that SAGE is their natural home: we believe in meeting the range of their needs, and in publishing the best of their work. We are a growing company, and our financial success comes from thinking creatively about our markets and actively responding to the needs of our customers.

One thought on “Capturing Relative Importance of Customer Satisfaction Drivers Using Bayesian Dominance Hierarchy

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

w

Connecting to %s