1. Survey Data Analysis to Understand the Effect of People's Perceptions on Overall Satisfaction Rating of SFO Airport
Pratyush Gupta1and Anuashka Sharma2
1Management Information System, Oklahoma State University, Stillwater, Oklahoma 74075
2Management Information System, Oklahoma State University, Stillwater, Oklahoma 74075
Introduction
Surveys to collect data from customers to explain and diagnose customer’s satisfaction level with a company are very common in the industry. In this study we used the survey data of San Francisco International Airport of ,872 unique individuals responded to the survey on 68 questions which include customer’s ratings of SFO on different perception variables such as safety at the airport, cleanliness of different areas of airport etc. and overall satisfaction with the SFO airport. In addition, the survey has some demographic variables such as age, household income, etc. Using exploratory factor analysis factor analysis was done to form constructs which represented a set of variables. These factors captured about 87% variance in the twenty questions. We used scales formed by averaging high loading items on these four factors along with the control variables (such as demographics) as input variables to predict and explain overall satisfaction with the SFO airport. Our regression model explained about 36% variance in the overall satisfaction. The most important factors in predicting overall satisfaction were airport facilities, cleanliness and transportation.
Cleanliness – This factor got combined with six variables: Boarding Areas, Domestic Hourly parking garage, Air train, Airport rental car center, Airport restaurants and Restrooms
Transportation – This consists of 5 factors: Signs and Direction on SFO Airport roadways, Airport Parking facilities, Air train, Long term parking lot shuttle and Airport rental car center
Predictive Modeling
A scale was constructed for the four factors formed by taking an average of the variables loading into the factors respectively
These factors were then used as independent variables along with the other present in the data to predict the target variable which is the overall rating of SFO airport
Basic multiple regression model was built to determine the effect of these factors on the overall rating of the airport.
Two multiple regression models were built- one with control variables and other without
The variables used as control variables in predictive modeling are Variables like Age group, Household income category, late code of flight etc.
Methods Exploratory Factor Analysis:
To identify the hidden constructs representing a set of similar variables factor analysis was performed results of which are shown in Fig. 1. We used varimax rotation to form factors
As seen from the results, 4 factors were formed
With no cross loading, the variables loaded really well on the factors formed
Total variance explained by four factors is 87% which is very good for a survey data (Fig. 2)
Reliability of formed constructs can be measured with the Cronbach’s alpha test. In our case the value was greater than 0.85 for each factor.
Results:
Without control variables:
The regression equation that came out to be was
SFO ratings = Transportation * Airport Facilities * Cleanliness * – Encounter Problems * Safety *
Variance Explained by the model: 31.02%
Most significant variables influencing SFO ratings: Transportation and Airport Facilities. Both of these variables are constructs formed using factor analysis
b. With control variables:.
Regression equation was
SFO rating= RestaurantPurchase_1 * – EncounterProblems_1 * Resident_1 * Transportation * Airport Facilties * Cleanliness *
Variance explained by the model: 36.27%
Airport facilities was most significant variable which affected the SFO rating
Discussion
As can be observed by factor analysis, absence of cross loading on variables in factors formed indicated formation of string and mutually independent factors
As the Cronbach alpha value for each factor was greater than 0.85, it could very well be concluded that the formed factors were very reliable to be used as scales to measure respondents’ perceptions.
The results of multiple regression suggested that the factors formed indeed affected the airport rating to a much greater extent
This analysis could be extended to explore different variations in forming factors like promax rotation, no rotation etc. to compare the results with factors formed by different methods
Future scope of this study also includes trying to build other predictive models like decision tree and neural networks which will help to carry out a comparative study of all models and their results to evaluate their performance.
Results of this study can be used to improve consumers’ perception of amenities and facilities of SFO airport and thereby improve the overall satisfaction rating.
Fig. 1 Variable loading on different factors
Fig. 2 Variance explained by the factors
Reference
Morgan and Sonquist, 2012, Problems in the Analysis of Survey Data, and a Proposal
Cook, 1978, A Meta-Analysis of Response Rates in Web- or Internet-Based Surveys
The four factors formed are:
Ease of Info – This consists of two variables Information booth at lower level and Information booth at upper level.
Airport Facilities – It consists of six variables which include Artwork and Exhibitions, Restaurants, Retails shops and concessions, Signs and Direction inside SFO, Escalators/Elevators in moving walkway and Information on Screen
Acknowledgement
Dr Goutam Chakraborthy, Professor, Department of Marketing, Oklahoma State University
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