Abstract
For 2014-2016, “Developing Marketing Analytics for a Data-Rich Environment” is determined as a “Tier 1 Priority” by the Marketing Science Institute (MSI). Understanding customers by analyzing their experiences is very important to build long-term and profitable relationships with them. In the hospitality sector, travelers create huge amounts of data called “big data” by sharing their experiences in travel websites. If these experiences are analyzed and transformed into information, they can provide a competitive advantage; thus, data mining tools are used to find hidden patterns and relationships in large data sets. So, the purpose of this paper is to explore the cultural differences in the hospitality sector with the help of big data. For this purpose, customers’ online reviews are analyzed by using text mining, as a data mining tool, according to the factors based on positive and negative reviews collected from “booking.com” (1000 customer reviews, 500 for each city) for the hotels in London and Tokyo. Then, the findings are discussed according to Hertzberg’s Two-Factor Theory of Motivation and the cultural differences. In addition, some managerial implications were given to hotel managers to enlighten them about gaining competitive advantage in their industry.
References
- Akerkar, R., (2012) “Big Data & Tourism. To promote innovation and increase efficiency in the tourism sector.”(TMRF Report 11-2012), Big Data & Tourism. To Promote Innovation and Increase Efficiency In The Tourism Sector. Chan J. & Baum T., (2007) “Determination of Satisfiers and Dissatisfiers Using Herzberg’s Motivator and Hygiene Factor Theory: An Exploratory Study” Tourism, Culture & Communication, 7 (2), 117-131. Davenport, T.H. (2013) “At the Big Data Crossroads: Turning Towards a Smarter Travel Experience”. Amadeus IT Group Report, Available at: http://amadeusblog.com/wp-content/uploads/Amadeus-Big-Data-Report.pdf, Accessed 01.04.2016. Dirsehan, T. (2015). “An Application of Text Mining to Capture and Analyze eWOM: A Pilot Study on Tourism Sector” In: S. Rathore, & A. Panwar (Eds.), Capturing, Analyzing, and Managing Word-of-Mouth in the Digital Marketplace, Hershey: IGI Global, 168-186. Dirsehan, T. (2016) “Text Mining in the Hospitality Sector to Extend the Motivation Theory”. International Marketing Trends Conference, Venice, Available at: http://www.marketing-trends-congress.com/archives/2016/pages/PDF/DIRSEHAN.pdf, Accessed 01.04.2016. Feldman, R., & Sanger, J. (2006). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge: Cambridge University Press. Filiz, Z. & Kolukısaoğlu, S. (2013). “Servqual Yöntemi ve Bir Hizmet İşletmesinde Uygulaması”, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, EYİ 2013 Özel Sayısı, 253-266. Hair, J., Robert, B. R., & Ortinau, D. (2009). Marketing Research in a Digital Information Environment (4th ed). New York: McGraw Hill/Irwin. Hasegava, J. (2009). Motivation Level of Japanese Hotel Managers and the Effects of Variables on Their Levels of Managerial Motivation, 184-195. Hedrick-Wong Y. & Choong D. (2015), “MasterCard 2015 Global Destination Cities Index”, Available at: https://newsroom.mastercard.com/wp-content/uploads/2015/06/MasterCard-GDCI-2015-Final-Report1.pdf, Accessed 01.04.2016. Hofmann M. & Klinkenberg R. (ed) (2014), RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series), CRC Press, Taylor & Francis Group. Hofstede, G. (2011). “Dimensionalizing Cultures: The Hofstede Model in Context”, Online Readings in Psychology and Culture, 2(1). International Business Machines Corporation (IBM) (2012), IBM Annual Report, Available at: https://www.ibm.com/annualreport/2012/bin/assets/2012_ibm_annual.pdf, Accessed 01.04.2016. Jouan, C. (2014) “Big Data Insight for Travel and Tourism”, 14th WTTC (World Travel & Tourism Council) Global Summit, Hainan, China. Available at: https://www.wttc.org/research/other-research/big-data-the-impact-on-travel-tourism/, Accessed 01.04.2016. Kaplan, M. (2013) “Analyzing Big Data: The Path to Competitive Advantage”. Ziff Davis B2B White Paper. Laudon, K., & Laudon, J. (2014). Management Information Systems: Managing the Digital Form (13th Global Ed), USA: Pearson Education Limited. Min, H., Min, H., & Emam, A. (2002). “A Data Mining Approach to Developing The Profiles of Hotel Customers”, International Journal of Contemporary Hospitality Management, 14(6), 274-285. Peneva, M.R. (2013). The Importance of Employee Satisfaction to Hotel’s Operations, Thesis Submitted to the Department of Service Hospitality and Tourism Management, University College Nordjylland. The Hofstede Center, Cultural Dimensions (n.d.), Available at: https://geert-hofstede.com/countries.html, Accessed 01.04.2016.
Abstract
2014-2016 yılları için Pazarlama Bilim Enstitüsü (MSI), “Veri Bakımından Zengin Ortamlarda Pazarlama Analitiğini Geliştirme” konusunu “Birinci Derecede Öncelikli” konu olarak belirlemiştir. Müşterilerin deneyimlerini inceleyerek onları anlamak, uzun dönemde kârlı müşteri ilişkileri oluşturabilmek için önemlidir. Konaklama sektöründe ziyaretçiler, deneyimlerini internetteki seyahat sitelerinde paylaşarak “büyük veri” olarak adlandırılan büyük miktarda veri yaratmaktadır. Eğer bu deneyimler analiz edilip bilgiye dönüştürülebilirse rekabet avantajı sağlanabilir, bu amaçla veri madenciliği araçları büyük veri setlerindeki gizli örüntü ve ilişkileri bulmak için kullanılır. Dolayısıyla bu çalışmanın amacı, büyük veri yardımıyla konaklama sektöründe kültürel farklılıkları keşfetmektir. Bu amaçla, müşterilerin Londra ve Tokyo’ya ilişkin “booking.com” üzerinden toplanan çevrimiçi görüşleri (her bir şehirden 500 olmak üzere toplam 1000 müşteri görüşü) olumlu ve olumsuz faktörler üzerinden bir veri madenciliği aracı olan metin madenciliği kullanımıyla analiz edilmiştir. Ardından, bulgular Hertzberg’in İki Faktörlü Motivasyon Teorisi’ne ve kültürel farklılıklara göre incelenmiştir. Ek olarak, otel yöneticilerine sektörlerinde rekabet avantajı kazanabilmeleri için yönetimsel çıkarımlar sunulmuştur.
Keywords
Büyük Veri Metin Madenciliği Hertzberg’in İki Faktörlü Motivasyon Teorisi Turizm Pazarlaması
References
- Akerkar, R., (2012) “Big Data & Tourism. To promote innovation and increase efficiency in the tourism sector.”(TMRF Report 11-2012), Big Data & Tourism. To Promote Innovation and Increase Efficiency In The Tourism Sector. Chan J. & Baum T., (2007) “Determination of Satisfiers and Dissatisfiers Using Herzberg’s Motivator and Hygiene Factor Theory: An Exploratory Study” Tourism, Culture & Communication, 7 (2), 117-131. Davenport, T.H. (2013) “At the Big Data Crossroads: Turning Towards a Smarter Travel Experience”. Amadeus IT Group Report, Available at: http://amadeusblog.com/wp-content/uploads/Amadeus-Big-Data-Report.pdf, Accessed 01.04.2016. Dirsehan, T. (2015). “An Application of Text Mining to Capture and Analyze eWOM: A Pilot Study on Tourism Sector” In: S. Rathore, & A. Panwar (Eds.), Capturing, Analyzing, and Managing Word-of-Mouth in the Digital Marketplace, Hershey: IGI Global, 168-186. Dirsehan, T. (2016) “Text Mining in the Hospitality Sector to Extend the Motivation Theory”. International Marketing Trends Conference, Venice, Available at: http://www.marketing-trends-congress.com/archives/2016/pages/PDF/DIRSEHAN.pdf, Accessed 01.04.2016. Feldman, R., & Sanger, J. (2006). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge: Cambridge University Press. Filiz, Z. & Kolukısaoğlu, S. (2013). “Servqual Yöntemi ve Bir Hizmet İşletmesinde Uygulaması”, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, EYİ 2013 Özel Sayısı, 253-266. Hair, J., Robert, B. R., & Ortinau, D. (2009). Marketing Research in a Digital Information Environment (4th ed). New York: McGraw Hill/Irwin. Hasegava, J. (2009). Motivation Level of Japanese Hotel Managers and the Effects of Variables on Their Levels of Managerial Motivation, 184-195. Hedrick-Wong Y. & Choong D. (2015), “MasterCard 2015 Global Destination Cities Index”, Available at: https://newsroom.mastercard.com/wp-content/uploads/2015/06/MasterCard-GDCI-2015-Final-Report1.pdf, Accessed 01.04.2016. Hofmann M. & Klinkenberg R. (ed) (2014), RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series), CRC Press, Taylor & Francis Group. Hofstede, G. (2011). “Dimensionalizing Cultures: The Hofstede Model in Context”, Online Readings in Psychology and Culture, 2(1). International Business Machines Corporation (IBM) (2012), IBM Annual Report, Available at: https://www.ibm.com/annualreport/2012/bin/assets/2012_ibm_annual.pdf, Accessed 01.04.2016. Jouan, C. (2014) “Big Data Insight for Travel and Tourism”, 14th WTTC (World Travel & Tourism Council) Global Summit, Hainan, China. Available at: https://www.wttc.org/research/other-research/big-data-the-impact-on-travel-tourism/, Accessed 01.04.2016. Kaplan, M. (2013) “Analyzing Big Data: The Path to Competitive Advantage”. Ziff Davis B2B White Paper. Laudon, K., & Laudon, J. (2014). Management Information Systems: Managing the Digital Form (13th Global Ed), USA: Pearson Education Limited. Min, H., Min, H., & Emam, A. (2002). “A Data Mining Approach to Developing The Profiles of Hotel Customers”, International Journal of Contemporary Hospitality Management, 14(6), 274-285. Peneva, M.R. (2013). The Importance of Employee Satisfaction to Hotel’s Operations, Thesis Submitted to the Department of Service Hospitality and Tourism Management, University College Nordjylland. The Hofstede Center, Cultural Dimensions (n.d.), Available at: https://geert-hofstede.com/countries.html, Accessed 01.04.2016.
Details
| Subjects | Business Administration |
|---|---|
| Journal Section | Makaleler |
| Authors | |
| Publication Date | June 30, 2017 |
| Published in Issue | Year 2017 Volume: 2 Issue: 1 |