obsr

Evaluation of Online Booking System Reviews: Greek Tourists’ perspective in Booking Behavior Evaluation Explored via Data Mining Techniques during Economic Recession

Ioanna Giannoukou1, Constantinos Halkiopoulos1, Hera Antonopoulou1, Evgenia Gkintoni2, Panagiotis Togias1, Gerasimos Panas3

 

[Abstract]

The purpose of this paper is to illuminate the perspective that tourists and particularly Greeks demonstrate as far as their booking behavior concerned travel reviews during the period of economic recession. Especially online travel reviews written by consumers are ever more available and used to inform travel-related decisions.

The present research concentrated on the web platforms of intermediary firms and agencies that constitute the e-tourism sector. These are organizations that facilitate communication and transactions between the primary providers of travel and accommodation services (e.g. airlines, hotels, car hire firms), and potential consumers of those services. Blogs, online reviews (ORs), and social networking platforms are enabling travelers to share information, opinions, and knowledge about all kinds of goods and services in e-Tourism. Specifically, online reviews (ORs) can be considered as electronic versions of traditional WOM (e-WOM) and consist of comments published by travelers.
For the data collection, three self- report questionnaires were administered: a) E-WOM and Accommodation Scale (E-WOM), b) Emotion-Based Decision-Making Scale (EBDMS,) c) Emotional Intelligence Questionnaire (TEIQue) clarifying four factors of emotional state; well-being, self-control, emotionality and sociability.

There were being created their electronic versions* through Google Forms service and posted through the website “https://www.cicos.gr/iccmi2017/obsr”. Then the collected data were selected for analysis, with relevant transformations in order to have a suitable form for the implementation of the respective machine learning algorithms included in the software package R. The methodology, that was adopted, consists of two concrete phases. During the first phase, questionnaires were created and submitted. During the second phase, the data set were collected, preprocessed and analyzed based on Data Mining techniques evaluating the results. More specifically, classification algorithms were utilized so as to manage to describe hidden patterns. Also, the parameters of the algorithms were set, depending on the application cases, and the results were correlated with the demographic characteristics of the respondents, in order to evaluate and assess the significance of exported rules / conclusions.

Keywords: Booking Behavior, Data Mining, Economic Recession, e-Tourism, R

 

References:

  1. Cooper, A., Petrides, K.V. (2010). A Psychometric Analysis of the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) Using Item Response Theory. Journal of Personality Assessment, 92 (5), 449-457.
  2. Alloway, T., Copello, E., Loesch, M., Soares, C., Watkins J., Miller, D., Campell. G., Tarter, A., Law, N., Soares, C., Ray, S. (2016). Investigating the reliability and validity of the Multidimensional Emotional Empathy Scale. Measurement, 90: 438-442.
  3. Gkintoni, E., Halkiopoulos, C., Antzoulatos, G., Giannopoulou, G., “Emotional Intelligence Evaluation in Greek Adolescents: A Data Mining Approach”, International Conference on Adolescent Medicine and Child Psychology, September 28-30, 2015, Houston, USA, [Online] Available from: https://www.cicos.gr/eiega/, [Accessed: 30th June 2015].
  4. Gkintoni, E., Halkiopoulos, C., Antonopoulou, H., Togias, P., Mitropoulos, A., “Emotional Intelligence in Social Network Consumers”, International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  5. James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning with Applications in R. Springer; 2013.
  6. Cohen H., Lefebvre C. editors. Handbook of Categorization in Cognitive Science. Elsevier; 2005.
  7. Baron-Cohen, S. and Wheelwright, S. (2004) The Empathy Quotient: An Investigation of Adults with Asperger Syndrome or High Functioning Autism, and Normal Sex Differences. Journal of Autism and Developmental Disorders, 34, 163-175. http://dx.doi.org/10.1023/B:JADD.0000022607.19833.00
  8. Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction to Data Mining”, Addison-Wesley, 2006.

1Dept. of Psychology, University of Crete, Greece

2Dept. of Business Administration, Technological Educational Institute of Western Greece, Greece

3Dept. of Digital Media and Communication, Technological Educational Institute of Ionian Islands, Greece

[Conference Program: ICCMI 2017]

© The Author(s) 2017. Published by 5th International Conference Proceedings on behalf of C.I.CO.S Research Team and the Entrepreneurship & Digital Innovation LAB of the Business Administration Department (Technological and Educational Institute of Western Greek) and Department of Psychology (University of Crete).

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

*Research Questionnaire: Evaluation of Online Booking System Reviews: Greek Tourists’ perspective in Booking Behavior

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