Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You and Your Team.

Learn More →

Periodicity analysis and a model structure for consumer behavior on hotel online search interest in the US

Periodicity analysis and a model structure for consumer behavior on hotel online search interest... PurposeThis paper aims to analyze and model consumer behavior on hotel online search interest in the USA.Design/methodology/approachDiscrete Fourier transform was used to analyze the periodicity of hotel search behavior in the USA by using Google Trends data. Based on the obtained frequency components, a model structure was proposed to describe the search interest. A separable nonlinear least squares algorithm was developed to fit the data.FindingsIt was found that the major dynamics of the search interest was composed of nine frequency components. The developed separable nonlinear least squares algorithm significantly reduced the number of model parameters that needed to be estimated. The fitting results indicated that the model structure could fit the data well (average error 0.575 per cent).Practical implicationsKnowledge of consumer behavior on online search is critical to marketing decision because search engine has become an important tool for customers to find hotels. This work is thus very useful to marketing strategy.Originality/valueThis research is the first work on analyzing and modeling consumer behavior on hotel online search interest. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Contemporary Hospitality Management Emerald Publishing

Periodicity analysis and a model structure for consumer behavior on hotel online search interest in the US

Loading next page...
 
/lp/emerald-publishing/periodicity-analysis-and-a-model-structure-for-consumer-behavior-on-wM8VTE1W3T
Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0959-6119
DOI
10.1108/IJCHM-06-2015-0280
Publisher site
See Article on Publisher Site

Abstract

PurposeThis paper aims to analyze and model consumer behavior on hotel online search interest in the USA.Design/methodology/approachDiscrete Fourier transform was used to analyze the periodicity of hotel search behavior in the USA by using Google Trends data. Based on the obtained frequency components, a model structure was proposed to describe the search interest. A separable nonlinear least squares algorithm was developed to fit the data.FindingsIt was found that the major dynamics of the search interest was composed of nine frequency components. The developed separable nonlinear least squares algorithm significantly reduced the number of model parameters that needed to be estimated. The fitting results indicated that the model structure could fit the data well (average error 0.575 per cent).Practical implicationsKnowledge of consumer behavior on online search is critical to marketing decision because search engine has become an important tool for customers to find hotels. This work is thus very useful to marketing strategy.Originality/valueThis research is the first work on analyzing and modeling consumer behavior on hotel online search interest.

Journal

International Journal of Contemporary Hospitality ManagementEmerald Publishing

Published: May 8, 2017

References