Tourism Management and Sustainable Development (Contributions to Economics) 3030746313, 9783030746315

Table of contents :
Preface
Contents
Part I: Tourism Management Performance
Tourism Development in North Greece
1 Introduction
2 Literature Review
2.1 Theoretical Framework
2.1.1 Tourism´s Contribution to Greek Economy
2.1.2 EPAnEK: Comparative Analysis Between Greece and Region of Central Macedonia
3 Data and Research Methodology
3.1 Methodology and Data
3.2 Descriptive Statistics and Correlations
4 Main Results and Discussion
4.1 Findings
4.2 Discussion
4.3 Policy Imprecations: Proposals for Further Research
5 Conclusion
References
Tourism Destination Product Characteristics Based on Twitter Sentiment Analysis: A Case Study of Penang, Malaysia
1 Introduction
1.1 Problem Statement
1.2 Research Objectives
2 Theoretical Framework
3 Data and Research Methodology
4 Findings
5 Conclusion
References
Tourism Demand Modelling and Forecasting: Evidence from EU Countries
1 Introduction
2 Literature Review
3 Model Specifications and Variable Definition
4 Methodology
5 Empirical Results and Discussion
6 Conclusions and Recommendations
References
Millennials and Digital Marketing in Tourism: The Greek Case
1 Introduction
2 Methodology
2.1 The Conceptual Model
2.2 Methodology
3 Results-Discussion
3.1 Factors Affecting Attitudes towards those Digital Applications
3.2 Profiling each Group of Millennials According to their Demographic Characteristics
3.3 Profiling of each Group of Tourists According to their Preferences Regarding their Holidays/Travel
4 Conclusions
References
Determinants of Operating Revenues: Travel Agencies vs Tour Operators in European Union
1 Introduction
2 Literature Review
3 Data and Methodology
4 Results and Discussion
5 Conclusion
Bibliography
A Unified Business Model Canvas for Digital Intermediaries in Tourism Industry
1 Introduction
2 Background Theory
3 Approach
4 Results
4.1 Value Proposition
4.2 Target Customers
4.3 Channels
4.4 Customer Relationship
4.5 Key Activities
4.6 Key Partners
4.7 Key Resources
4.8 Revenue Streams
4.9 Cost Structure
5 Discussion and Conclusions
References
Part II: Tourism and Sustainable Development
The Role and Importance of Transport within the Tourism Supply Chain
1 Introduction
2 The Theoretical Aspect of the Tourism Supply Chain
3 Transportation as a Component of the Tourism Supply Chain
4 Empirical Research on the Role and Importance of Transport in the Western Serbia Tourism Sector
4.1 Research Methodology
4.2 Research Results
5 Discussion and Conclusions
6 Theoretical/Practical Implications, Limitations, and Further Lines of Research
References
The Economics of Fisheries in the Mediterranean Basin: A Scoping Review by Means of Citation Patterns Analysis
1 Introduction
2 Methods
3 Results
3.1 The Orange Core Network on Bio-Economic Models
3.2 The Yellow Core Network on Small-Scale Fisheries, Sustainability, and Eco-Tourism
3.3 The Violet Cluster: Med Fisheries in the Climate Change
4 Concluding Remarks
References
The Efficiency of Croatian Seaport Authorities
1 Introduction
2 The Efficiency of Seaport Authorities
2.1 The Role of Port Authority in the Seaport System
2.2 The Literature Review
3 Maritime Transport and Seaport Authorities in Croatia
4 Data, Model And Results
4.1 Data and the Model
4.2 Results and Discussion
5 Conclusion
References
The Impact of Maritime Passenger Traffic on the Development of Seaports and Their Surroundings
1 Introduction
2 Research Methodology
3 Structural Analysis of Tourists Arrived at the Destinations by Passengers Ships with Regard to the Generation of Economic Im...
4 Economic Analysis of the Impact of Maritime Passengers Traffic on Destination Development
4.1 The Financial Impact of Maritime Passenger Traffic on the Port of Zadar System
4.2 Defining the Future Directions for Development of Maritime Passenger Traffic
5 Conclusion
References
Environmental Investments in Hotel Budgets: A Case Study on Croatian Hotels
1 Introduction
2 Background
3 Research
4 Methodology
5 Research
6 Conclusion
References

Citation preview

Contributions to Economics

Goran Karanovic Persefoni Polychronidou Anastasios Karasavvoglou Helga Maskarin Ribaric   Editors

Tourism Management and Sustainable Development

Contributions to Economics

The series Contributions to Economics provides an outlet for innovative research in all areas of economics. Books published in the series are primarily monographs and multiple author works that present new research results on a clearly defined topic, but contributed volumes and conference proceedings are also considered. All books are published in print and ebook and disseminated and promoted globally. The series and the volumes published in it are indexed by Scopus and ISI (selected volumes).

More information about this series at http://www.springer.com/series/1262

Goran Karanovic • Persefoni Polychronidou • Anastasios Karasavvoglou • Helga Maskarin Ribaric Editors

Tourism Management and Sustainable Development

Editors Goran Karanovic Faculty of Tourism and Hospitality Management University of Rijeka Opatija, Croatia

Persefoni Polychronidou Department of Economics International Hellenic University Serres, Greece

Anastasios Karasavvoglou Department of Accounting and Finance International Hellenic University Kavala, Greece

Helga Maskarin Ribaric Faculty of Tourism and Hospitality Management University of Rijeka Opatija, Croatia

ISSN 1431-1933 ISSN 2197-7178 (electronic) Contributions to Economics ISBN 978-3-030-74631-5 ISBN 978-3-030-74632-2 (eBook) https://doi.org/10.1007/978-3-030-74632-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The digital transformation of the economy, combined with manifest climate changes, demographic imparity and mobility (south/north, west/east, poor/wealthy), as well as a wide range of changes in consumer behavior is significantly shaping and developing tourism. Evident changes in nature—melting glaciers, global warming, enormous deforestation, pollution of every kind, extension of warm nature cycles, and contraction of cold ones—are precipitously influencing the economy, especially with regard to sustainable tourism development. Lifestyles and the perception on what is conventional are changing on a daily basis. Tourism management performance requires comprehensive analysis that compels the inclusion of numerous exogenous and endogenous variables. Most importantly, the uniqueness of tourism as an industry arises from the fact that it incorporates various industries. The undergoing paradigm shift towards green and blue economy as well as sustainable economy unequivocally affects tourism and tourism performance. Tourism management is under enormous pressure as well as in discrepancy with the attempt of upholding all contemporary sustainability principles while assuring efficiency and company value maximization. This volume investigates the various relationships between tourism development and sustainability, uncovering diverse powers of change which shape the current trends in tourism management performance in central and southeastern European counties. Consequently, this volume is a collection of studies which explore how the tourism industry responds to the challenges it faces while managing risks with the aim of increasing tourism management performance. Furthermore, it offers an insight into the interconnection of other industries with tourism at the juncture point. Academics and professional researchers whose works are included in this volume offer an innovative quantitative and qualitative scientific approach along with conclusions and recommendations for further discussions. The international conference “Economies of the Balkan and Eastern European Countries (EBEEC),” through its twelve previous editions, has become a recognized forum where the knowledge and experiences gained by academics specializing in Economics and Business in the region of Central and Southeastern Europe are v

vi

Preface

exchanged, debated, and validated. In addition, the results of the EBEEC conference include numerous publications disseminated to the scientific public and practitioners all over the world. In this effort, substantial assistance and contribution come from renowned publishers such as Springer. The 12th International Conference “Economies of the Balkan and Eastern European Countries” was organized jointly in Opatija, Croatia, by the International Hellenic University, Department of Accounting and Finance and University of Rijeka, Faculty of Tourism and Hospitality Management, in May 29–31, 2020. The aim of the conference was to gather scientists and practitioners who would present academic papers and exchange theoretical and empirical results on contemporary issues in economy with a specific focus on sustainable tourism in Central and Southeastern European countries. Due to the epidemic situation of SARS-CoV-2 and numerous restrictions regarding traveling, the conference was held for the first time online. The conference brought together more than 101 manuscripts by more than 150 authors from 20 countries from Europe and all over the world. A broad range of issues—Finance and Banking; Sustainable Tourism Development; International Political Economy; Macroeconomic and Economic Policy; Management and Marketing; Knowledge Economics; Business Information Systems; Entrepreneurship; Labor Markets; Corporate Governance and Corporate Performance; Regional Integration with special reference to the EU—were discussed and debated at the conference and in the resulting published manuscripts. As one of the publications resulting from the 12th International Conference “Economies of the Balkan and Eastern European Countries” (EBEEC), Opatija, Croatia, May 2020, this volume has the goal to present to the worldwide audience new and original research and conclusions in a specific field of tourism performance management and sustainability tourism in Europe. The volume includes 11 manuscripts selected on the basis of their quality and originality within the field of tourism performance management and sustainability tourism, arranged and presented at 12th EBEEC Conference in Opatija, Croatia. The entire manuscript selection process was managed by the Board of Editors in compliance with the highest standards and best practice guidelines on publishing ethics, paying special attention to issues regarding plagiarism, peer-review, objectivity, funding, privacy, and conflict of interest. All selected manuscripts underwent a rigorous peer-review process, being carefully edited in order to render a significant contribution to the broader field of economics. Research ideas and applied quantitative methods indicate that scientists emerging from Central and Southeastern Europe are developing new attention-worthy knowledge in this field. This is a result of the changing nature of the global economy being reflected in Central and Southeastern European countries, thus meriting attention and an increase in the scientific quality of the works presented. The manuscripts included in this volume cover a wide range of tourism performance management and sustainability tourism. The articles selected for

Preface

vii

publication are independent and do not constitute joint research; their appearance in the volume aims at presenting analogous topics in the field of economics and tourism that would attract the attention of the scientific public as well as practitioners in the field. The manuscripts in this volume are presented in two parts. Opatija, Croatia Serres, Greece Kavala, Greece Opatija, Croatia

Goran Karanovic Persefoni Polychronidou Anastasios Karasavvoglou Helga Maskarin Ribaric

Contents

Part I

Tourism Management Performance

Tourism Development in North Greece . . . . . . . . . . . . . . . . . . . . . . . . . Balomenou Chrysanthi, Lagos Dimitrios, Maliari Marianthi, Semasis Simeon, and Mamalis Spyridon Tourism Destination Product Characteristics Based on Twitter Sentiment Analysis: A Case Study of Penang, Malaysia . . . . . . . . . . . . . Nor Hasliza Md Saad and Zulnaidi Yaacob Tourism Demand Modelling and Forecasting: Evidence from EU Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Athanasia Mavrommati, Konstantina Pendaraki, and Achilleas Kontogeorgos Millennials and Digital Marketing in Tourism: The Greek Case . . . . . . Polina Karagianni, Lambros Tsourgiannis, Vasilios Zoumpoulidis, and Giannoula Florou Determinants of Operating Revenues: Travel Agencies vs Tour Operators in European Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vinko Zaninović and Alen Host A Unified Business Model Canvas for Digital Intermediaries in Tourism Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vasilios Zoumpoulidis, Stavros Valsamidis, Stefanos Nikolaidis, and Lambros Tsourgiannis Part II

5

27

39

51

63

71

Tourism and Sustainable Development

The Role and Importance of Transport within the Tourism Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Slobodan Aćimović, Veljko M. Mijušković, Ivan Todorović, and Ana Todorović Spasenić

93

ix

x

Contents

The Economics of Fisheries in the Mediterranean Basin: A Scoping Review by Means of Citation Patterns Analysis . . . . . . . . . . . . . . . . . . . 107 Matteo Belletti, Adele Finco, Deborah Bentivoglio, and Giorgia Bucci The Efficiency of Croatian Seaport Authorities . . . . . . . . . . . . . . . . . . . 129 Blanka Šimundić and Lana Kordić The Impact of Maritime Passenger Traffic on the Development of Seaports and Their Surroundings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Alen Jugović Environmental Investments in Hotel Budgets: A Case Study on Croatian Hotels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Vanja Vejzagić and Peter Schmidt

Part I

Tourism Management Performance

The book opens with the paper written by Balomenou Chrysanthi (EPOKA University, Greece), Lagos Dimitrios (University of the Aegean, Greece), Maliari Marianthi (School of Agriculture, Aristotle University of Thessaloniki, Greece), Semasis Simeon (School of Agriculture, Aristotle University of Thessaloniki, Greece), and Mamalis Spyridon (International Hellenic University, Greece) who examine how Greece should decrease the intraregional disparities by utilizing European Operational programs and grants for tourism development. The main tasks and objectives of the European programs are ensuring equal and balanced development between all member states of the European Union, while providing opportunities for less developed regions to catch up with the levels of developed regions in each member country. Taking the above into consideration, the main goal of this article is to investigate how the Operational Program Competitiveness, Entrepreneurship, and Innovation 2014–2020 (EPAnEK) intended for touristic enterprises impacted the balanced regional development in the region of Central Macedonia. The paper’s findings indicate that majority investments were realized in already developed areas. In stark opposition to the idea and the purpose of the Operational programs, less developed areas of the investigated region remained neglected and toward the rear in terms of development. The conclusion and suggestions of the authors are very interesting and deserving of the attention of both policy makers and academia. The second paper of the volume authored by Nor Hasliza Md Saad and Zulnaidi Yaacob (both from Universiti Sains, Malaysia) explores how sentiment analysis can be used in tourism. The main objective of this paper is to investigate tourist sentiment regarding tourism destination product of the region of Penang in Malaysia. In the right hands, social networks can be a powerful tool for tourism destination management. In today’s world—and especially in the economy—the most valuable item is information. Social networks facilitate free information of persons’ habits, biases, feelings, and opinions that can be used in tourism destination management. Furthermore, through the use of social networks the tourism industry may offer information to tourists and influence their decision-making process.

2

Part I

Tourism Management Performance

The authors utilized a qualitative data analysis and investigated Twitter data related to the tourism destination and tourism product of Penang region. The findings reveal that positive tourist perception is more likely to spread positive recommendations to associates. Likewise, positive perception and satisfaction arising from tourism experience and developing a positive image as a destination obviously contribute to an important competitive strength of a tourism product. The next paper prepared by Mavrommati Athanasia, Pendaraki Konstantina, and Kontogeorgos Achilleas (all from University of Patras) empirically investigates the determinants of tourism demand for a statistically significant sample of eleven European countries for the years 1996–2015. The growing trend of tourism and its share in the world economy in the last decade is significant, consequently the correct tourism demand is highly valuable information for the tourism management. It should be highlighted that the tourism industry significantly impacts the European economy as is distinctly evident in some countries (Italy, Spain, Greece, Malta, Croatia, France, and other ones). Time-series models have been frequently utilized for the forecast demand. The authors used a panel data model to investigate a set of variables in order to predict the tourist demand expressed by the variable total number of tourists in tourist destination. The countries that authors have included in the study are Austria, Cyprus, Italy, France, Spain, Greece, Germany, Netherlands, Portugal, Finland, and Ireland. The results of the panel data model suggest that per capita income, expenditure price, population, and advertising expenses of promoting the tourism product are the most significant explanatory factors of tourism demand. The authors recommend several highly essential and functional policy proposals. Millennials and Digital Marketing in Tourism is the topic of the manuscript prepared by Polina Karagianni (International Hellenic University, Greece), Lambros Tsourgiannis (Directorate of Public Health and Social Care of Regional District of Xanthi), Vasilios Zoumpoulidis (International Hellenic University, Greece), and Giannoula Florou (International Hellenic University, Greece). The authors investigate the attitudes of millennials (Generation Y) towards digital marketing applications related to peer-to-peer short-term rental services within the sharing economy in the tourism sector. Shifts in individuals’ behaviors and rise of awareness towards the environment and climate change are radically changing the well-known economic paradigms. The sharing economy emerged as the result of a consciousness regarding resource scarcity. Most importantly, the sharing economy can be defined as a peer-to-peer (P2P) activity of sharing community-based goods and services through on-line platforms. This peer-to-peer short-term rental services is altering the tourism sector from the roots, which is especially evident in the accommodation industry. A multivariate analysis technique was applied by the authors of this study. The paper’s results contribute significantly to the understanding of the Greek millennials’ behavior towards the adoption of digital marketing applications related to peer-to-peer rental services within the sharing economy in the tourism sector. The following paper arranged by Vinko Zaninović and Alen Host (University of Rijeka, Faculty of Economics and Business) explores variables determining

Part I

Tourism Management Performance

3

operating revenues (OR) of more than 15,000 Travel Agency and Tour Operators Firms across European Union countries. Company performance is permanently in the focus of business professionals and still today one of the furthermost investigated concepts in business economics. Due to the idiosyncratic nature of tourism towards numerous exogenous and endogenous variables, performance measurement in tourism remains very challenging. The authors examined the impact of firm-level (total assets, number of employees) and country-level variables (number of arrivals) on operating revenues by analyzing data obtained from BvD Amadeus for the period 2010–2016. The findings and conclusion indicate significant differences between North and South countries, and also differences within variables that affect operations of Tourist Agencies and Tour Operators. The first part of the volume closes with the paper written by Vasilios Zoumpoulidis, Stavros Valsamidis, Stefanos Nikolaidis, and Lambros Tsourgiannis (all from the Department of Accounting and Finance, International Hellenic University) who analyzed the business models of four digital intermediaries in tourism: Airbnb, TripAdvisor, Expedia, and Booking.com. The tourism industry is constantly confronting global disruptions and tourist companies are facing a new economy that is comprised of new technologies and global outsourcing. The Internet has become the imperative communication channel featuring the main tools of information for both tourists and tourism companies. The Internet of Things and communication through apps have influenced the tourism industry in a variety of ways, resulting in fundamental changes in industry structures and behavior. The authors used the Business Model Canvas tool and proposed a new unified model consisting of the best features of the aforementioned four models.

Tourism Development in North Greece Balomenou Chrysanthi, Lagos Dimitrios, Maliari Marianthi, Semasis Simeon, and Mamalis Spyridon

Abstract Tourism now considered being as one of the world’s largest industries and maybe one of the fastest growing economic sectors. As for many other countries and for Greece, tourism supposed as a main instrument for regional development. Furthermore depression in Greece has become a subject of global interesting in scientific community. In such an adverse macroeconomic environment, tourism’s contribution to Greek economy is a matter of great importance. In this context, Greek state participating in the Operational Program for Competitiveness, Entrepreneurship and Innovation 2014–2020 (EPAnEK), that focuses on balanced regional development and taking into account the particular needs of each of its thirteen Greek regions, announced on the 11th of February 2016, the application of the program “Strengthening SME Tourism” aiming in the modernization and improving of touristic services.

B. Chrysanthi EPOKA University, Tirana, Albania European International University (EIU), Paris, France Hellenic Open University, Patras, Greece e-mail: [email protected]; [email protected] L. Dimitrios Department of Business Administration, University of the Aegean, Chios, Greece e-mail: [email protected] M. Marianthi (*) · S. Simeon Department of Agricultural Economics, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki, Greece Master of Science in Banking, School of Social Science of Hellenic Open University, Patras, Greece e-mail: [email protected] M. Spyridon Hellenic Open University, Patras, Greece Department Of Management Science and Technology, International Hellenic University, Thessaloniki, Greece e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. Karanovic et al. (eds.), Tourism Management and Sustainable Development, Contributions to Economics, https://doi.org/10.1007/978-3-030-74632-2_1

5

6

B. Chrysanthi et al.

The main scope of the current paper is to examine the potential of EPAnEK programs on Region of Central Macedonia, taking under consideration the significant intraregional disparities on tourist development in the region. The first theoretical part of this paper consists of an approach of analyzing all the aforementioned above. The empirical research was conducted in October 2018. All the touristic enterprises in Central Macedonia Region (184), which were selected to receive subsidies from EPAnEK, participated in the research. The statistical methodology that has been used is the calculation of the main descriptive statistical measures, such as the average, standard deviation, coefficient of variation, and the correlation coefficient. According to the main results of the research, new enterprises are not willing to invest in the less developed areas and consequently regional disparities will not be reduced under the influence of application of the EPAnEK. Finally, it should be pointed out that the results of the empirical part of the research in general are relevant to the known literature.

1 Introduction Greece’s economy during last decade has suffered from a severe economic crisis also known as Greek depression. According to Krugman, in Greece “we live in the shadow of economic catastrophe.” Within this period of downturn which was characterized among others by drastic loan providing shortcuts, Greek entrepreneurs have been very reluctant to invest. According to Stinglitz “. . .in Greece, more than a third (of SMEs) continue to report ‘access to finance’ as the single largest obstacle to doing business.” Greek state struggling for providing funds to boost especially tourist sector introduced EPAnEK Programs. The main objective of this article is to examine the influence of this program upon Greek’s region of Central Macedonia tourism development. Tourism services’ sector in Greece is an economic activity of an utmost importance and in terms of that sector’s net receipts amount to the 73% of the total services’ net receipts from abroad and in addition cover the 67.6% of the trade balance’s deficit. However apart from the generally positive statistic depiction of Greek tourism the fact is that arrivals from abroad started to increase from 2014 and onwards. That was not only due to the services’ improvement but also for the political turmoil widely took place in the coastal and touristically attractive northern African and middle Asian countries thrusting tourists to other destinations. According to per capita expenditure rate on tourism, Greek tourism has been declined from 2000 to 2016. The reduction in tourism’s receipts was mainly due to the declination of the average expenditure per destination, and attraction of relatively lower number of highincome level clients (Gaki et al. 2018). Two years earlier (Chen et al. 2016) mentioned that “Because of the economic crisis in recent years, the demand for yachts has declined in Greece, although there is a significant increase of yachting

Tourism Development in North Greece

7

tourism worldwide (Hall 2001). Obviously, this form of tourism is directly related to the disposable income of consumers, the existing infrastructure, the number of marinas, and the dock costs. Nevertheless, yachting tourism plays a significant role in employment creation, and its sustainable development could help the local economy to recover from the economic depression.” According to the current indications, Greek tourism’s quality level has not been improved, in recent years. Long-term deficiencies and structural problems led Greek tourist industry to a less competitive model. Greece is nowadays been considering as a low budget tourist destination and because the offering services are more or less of moderate level, price is the most critical element for the consumers’ choice. Therefore, tourist services demand, tend to shift to less developed destinations and markets priced lower than Greece. In addition, the sea, sand, and sun model of tourism which comprised the comparative advantage of Greece is considered to be outdated and no longer attracts significant number of tourists. Moreover the internal tourism had been reduced due to the reduction of the Greek disposable income, the tourism period had been shortened. Also, the tourism sector has been characterized by seasonality (56% of arrivals from abroad are at July, August, and September), and gradual degradation of the average tourist level. According to the figures of the Gini-Hirschman rate for the period between 2000 and 2015, peripheral inequalities of Greek tourism have been slightly reduced (Gaki et al. 2018). This fact reveals a trend towards a more balanced development. The existing inequalities as far as Greek tourism concerned focused mainly on inland regions. The current Greek model exploits very few competitive advantages focusing mainly on islands and coastal areas of the country. In fact, the Greek model is focusing only in the country’s favorable climate conditions and its special connection with the sea. Moreover, it has to be pointed out a concentration of the tourist activity phenomenon in specific destinations that enhances tourist urbanization and creates polar development conditions resulting to some extend in intraregional and interregional asymmetry. In particular, during 2016, the 45.83% of the total tourist activity and the 64.53% of the total night stops was concentrated on the islands (regions of Ionian Islands, Northern Aegean, Southern Aegean, and Crete) (WTTC 2018; Gaki et al. 2018) In order to improve Greek tourism’s quality level, Greek state announced on February 2016 the program “Strengthening SME Tourism.” The present research tries to evaluate the effectiveness of the program in reducing inequalities among regions. Moreover, the results can be used from policy makers for planning and implementation of similar progams in the future in order to include alternative forms of tourism. The main research question addressed is whether these programs contribute to tourism development on peripheral level. Data for this research have been extracted from SETE, Hellenic Statistical Authority and Ministry of Economy, Development and Tourism—EPAnEK. Moreover, in the second part, a quantitative research has been conducted and the results of the research have been presented in order to suggest improvements. According to the findings, there is a remarkable increase on

8

B. Chrysanthi et al.

tourism investments in urban areas and specifically in this case, the core region of Thessaloniki. On the contrary winter—alternative tourism is facing a slump. As inequalities on tourism development are increased after the implementation of the program, it is to be appointed that programs which aim to reduce disparities, not always achieve their goal. In addition, while intraregional disparities yet characterize Greek Regions development, a potential solution may be the change of the criterion for providing subsidies and other motives from income at regions-NUTS 2 level, to income at regional unities-NUTS 3 level. Finally, there presented our research’s main conclusions, policy implications, and proposals for further research.

2 Literature Review 2.1

Theoretical Framework

Tourism is an activity with social, cultural, and economic dimensions. It involves people moving between places, beyond their permanent place of residence. Tourism is a dynamic sector, which has beneficial effects on enterprises and regions as well. It is well known that tourism is one of the most important elements for a country’s growth and development (Brida and Risso 2009; Tang and Tan 2013). There have been many studies that highlighted the crucial role of tourism on national and regional development, by identifying the benefits to the population and the economy as well (Andereck and Vogt 2000; Andraz et al. 2015; Hall and Page 2009; McGehee and Andereck 2004; Katircioglu 2009; Yang and Wong 2012). Moreover, many researchers have tried to identify whether tourism reduces regional inequalities leading to a more balanced regional development. According to Andraz et al. (2015) although all regions benefit from tourism, those benefits are not equally distributed, among them. Due to the important role of tourism in their development, many countries have tried to create a tourism image that could be for their benefit, meaning attracting visitors (Botti et al. 2009; Dwyer et al. 2011; Gomezelj and Mihalič 2008; Ritchie and Crouch 2005). On the other hand, researches such as Webster and Ivanov (2014) have pointed that there is no direct positive relationship between a region’s competitiveness and tourism’s contribution to economic growth. Regarding the effects of tourism on development, those can be economic, e.g., increased economic activity, economic development of the regions, social, e.g., increase in employment, improvement of the quality of life, and environmental, e.g., conservation of natural environment (e.g., Besculides et al. 2002). In addition, tourism’s affects are not limited only to the travel and tourism industry. On the contrary, tourism influences other economic sectors and services, such as local agriculture production, retail, transport, and constructions. WTTC in its report for 2018 claims that tourism’s total contribution (directly and indirectly) to the Greek GDP amounted at 2017 to 35 bn Euros that means to the

Tourism Development in North Greece

9

19.7% of GDP. And as it has also been calculated in the same report, tourism’s activity for 2018 will contribute to a 5.3% increase of the GDP and in addition there will be a constant 3.7% increase each year for the next ten years. In 2017 the direct contribution of tourism’s activity and traveling amounted to 8%. According to also with WTTC’s estimations the direct contribution of tourism will increase by 5.6% within 2018 and each year by 3.5% for the next ten years and consequently 2028 it will reach the sum of 21.3 bn Euros that means the 9.1% of the Greek GDP. According to EU statistics, tourism is the largest service industry in the European Union. It accounts for more than 4% of the Community’s GDP and employees about 4% of the total labor force (this is only for hotels and travel agencies). It is a laborintensive and a fast-growing sector, which is not affected by the financial crisis, at least not as much as other sectors and economic activities. Of course, the contribution of tourism expanded to other activities as well. A study of International Labor Organization and World Tourism Organization reports that one job in tourism generates 1.5 jobs elsewhere (UNWTO and ILO 2014). The EU was, according to the United Nations World Tourism Organization (UNWTO), a major tourist destination and five of its Member States where among the world’s top 10 destinations in 2014. In 2015, the countries of EU where the most frequently visited ones, receiving more the half (51.4%) of all international tourist arrivals, which accounts for 609 million persons. Greece on the other hand has been a major tourist destination and attraction in Europe and has attracted 26.5 million visitors in 2015 making Greece one of the most visited countries in Europe and the world. The financial and economic crisis of the recent years has affected every aspect of the economy and the society. According to EU date, the result of the crisis was among others the increase in unemployment, the reduction of income, public, and private cut offs and the general feeling of uncertainty. Employment is an indicator of a country or region’s development. Especially for unemployment “And the response. . . has pushed unemployment all around Europe’s periphery to Great Depression levels” (Krugman 2012). It is of great importance and has always been on the core of research. Unfortunately, total employment has fallen during these last years and specific categories of employees such as young employees, low-skilled ones, and self-employed have been the ones that have been influenced the most by the crisis, especially South European countries (Barbieri and Scherer 2009; Dunford 2012). Therefore, it is acceptable to say that employment has been affected by the ongoing financial crisis largely. However, beyond these alarming data on employment, tourism sector seems to be a bright spot. Recent data show that tourism industry has not been affected by the economic crisis, at least not to the extent that this has happened to other industries. For example, accommodation has an average annual growth rate of 0.9% since 2008. This illustrates the dynamic character of tourism sector and its potential as a growth sector.

10

B. Chrysanthi et al.

The purposes of this paper includes analyzing new trends of touristic investments, examination of correlations between incomes and tourism development, highlighting intraregional disparities in the under examination Region of Central Macedonia. The methodological framework used in this analysis includes a question-based empirical research on all the eligible under the umbrella of National Strategic Reference Program enterprises of Region of Central Macedonia.

2.1.1

Tourism’s Contribution to Greek Economy

Greece has been a major tourist destination and attraction in Europe and has attracted 33 million visitors in 2018 making Greece one of the most visited countries in Europe and the world. Tourism’s increasing contribution to Greek economy depicted in Table 1 (Fig. 1). Arrivals from abroad exceed the number of 33 million tourists, which corresponded in 210 mn overnights and in 16.6 bn Euros in tourist revenue. In last 13 years arrivals over doubled (from 14 million passenger to 33 million passengers) Table 1 Greek tourism 2017—facts and figures Contribution to GDP International tourism arrivals Average per capita tourism expenditure Contribution to employment Seasonality Top international tourism arrivals by country of origin (in thousands visitors)

27.3%/48.5 billion Euros 27,194 millions 522.3 Euros Direct 12.2% Indirect 24.8% 57.3% of international tourist arrivals take place in the period July–August–September 2017 Germany 3.706 United Kingdom 3.002 Bulgaria 2.546 North Macedonia 1.571 Italy 1.441 France 1.420

Source: SETE 2018

Fig. 1 International tourism arrivals (in millions top line) and tourism expenditure (in million Euros bottom line). (Source: SETE 2018)

Tourism Development in North Greece

11

Table 2 Tourism in Greece’s regions 2017 Greek regions East Mac. & Thrace Central Macedonia West Macedonia Epirus Sterea Greece Thessaly Ionian Islands Peloponnese West Greece North Aegean South Aegean Crete Attica

Contribution to regional development (%) 2

Direct contribution of tourism to the 2017 GDP region (%) 5

13

10

0.3

1

2 1 2 12 2 1 9 26 23 15

7 3 4 73 5 3 9 77 47 3

Source: SETE 2018

and their expenditure increased from 10.7 billion euros to 16 billion Euros. These data reflect the importance of tourism sector in Greece’s economic development and justify the relevant research that has been contacted on the topic these last years. In Table 1 there are depicted main Greek Tourism’s Facts and Figures. According to the above table, Greek tourism sector employing the 24.8% of total employment and that means that 934,500 employees in Greece, directly or indirectly worked in tourism activities (SETE 2018). In comparison to EU, according to EU statistics, tourism is the largest service industry in the European Union. It accounts for more than 4% of the Community’s GDP and employees about 4% of the total labor force (this is only for hotels and travel agencies). Furthermore, Greece during 2017 welcomed 27.2 million arrivals from abroad and that is a share of 2% of the global and the 4% of the European market of tourism. These data reflect the importance of tourism sector in Greece’s economic development and justifies the relevant research that has been contacted on the topic these last years. Although Germany and the United Kingdom consist the main origin of visitors, in the last decade, new countries emerged in the international tourism market, mainly the neighboring Balkan countries as Bulgaria and North Macedonia, from which Greece attracts a large number of visitors. On the other hand, visitors form countries such as Australia, Canada, China, or Japan are at a low percentage, which indicates that there are important markets from which Greece could gain an even greater share.

12

B. Chrysanthi et al.

Table 2 presents data on regional level. According to these data, the contribution of tourism to the GDP of several regions is quite impressive. In 2017, this contribution reached 47% in Crete, 77% in South Aegean, and 73% in the Ionian Islands. The above table reveals the uneven development of tourism and its contribution among the Greek regions. It is worth noticing that there are regions, such as West Macedonia and Sterea Greece, who can be characterized as non-tourism regions as have a low percentage of tourists and overnight stays. Tourism has a significant impact in Greece’s economic development as it can be considered a facilitator for investments and employment. It is therefore reasonable to say that tourism could be the key for the country’s recovery from the ongoing financial crisis.

2.1.2

EPAnEK: Comparative Analysis Between Greece and Region of Central Macedonia

Under the circumstances where “The firms were less willing to undertake investment or even increase employment at any interest rate” (Stinglitz 2016), the Ministry of Economy, Development and Tourism under the framework of Operational Program Competitiveness, Entrepreneurship and Innovation 2014–2020 (EPAnEK) taking into account on the one hand the specific needs of the thirteen Greek Regions and on the other hand—as above described tourism’s contribution to Greek economy, announced on February 11th 2016 the program “Strengthening SME Tourism for modernizing and improving the quality of their services (11/2/2016).” According to the announcement: “The program aims to strengthen the investment plans of existing micro, small and medium-sized tourism enterprises in order to modernize their infrastructure and operation, improve their quality and enrich, upgrading and certifying the products and services offered, to improve their position on the domestic and international tourist market”(www.antagonistikotita.gr). As the amount of state support per enterprise is up to 110.000€, thousands of enterprises are going to benefit from this program. The final catalogue of the eligible touristic enterprises was announced on July the 8th 2018 (Announcement 5010/1662/Α3/8-7-2018). The focus of this research is the region of Central Macedonia. In the Region of Central Macedonia the direct contribution of tourism to GDP is 10% (Table 2). In this program, 184 enterprises were reluctant to invest in this sector. Generally, improvement of touristic services will end to GDP’s increase in regional level. On the following figure it is depicted the Map of Greece, where Region of Central Macedonia is marked (Fig. 2). The presentation of this program starts with a comparative analysis between Greece and the under examination Greek region of Central Macedonia. On the table followings Tables 3, 4, and 5 there are presented the number, the total amount of touristic investments, and the average amount of eligible touristic investments. The GDP of Central Macedonia Region as it depicted on Table 3 is 13.3% of total Greece GDP. 184 local enterprises (10.8%) of the eligible enterprises are in Central

Tourism Development in North Greece

13

Fig. 2 Map of Greece-region of Central Macedonia Table 3 Number of touristic investments (2018)

Area I. N.

Greece 1699

Region of Central Macedonia 184

Source: EPANEK (http://epan2.antagonistikotita.gr/uploads/ 20180807-apofasi_oristikos_katalogos_tour_MME.pdf) Table 4 Total amount of touristic investments (2018)

Area Amount

Greece 95.661.181€

Region of Central Macedonia 21.341.609€

Source: EPANEK (http://epan2.antagonistikotita.gr/uploads/ 20180807-apofasi_oristikos_katalogos_tour_MME.pdf) Table 5 Amount of investments per investor (2018)

Area Average

Greece 56.604€

Region of Central Macedonia 115.987€

Source: EPANEK (http://epan2.antagonistikotita.gr/uploads/ 20180807-apofasi_oristikos_katalogos_tour_MME.pdf)

Macedonia area while the amount of 21.341.609€—22.3% of the total amount is going to be distributed in this area. Thus, it is worth to examine the fact that the average amount of investments in Central Macedonia is approximately the double in comparison to the average in Greece.

14

B. Chrysanthi et al.

3 Data and Research Methodology 3.1

Methodology and Data

The quantitative research has been conducted in the Region of Central MacedoniaGreece during October 2019. The sample was equal to the population as all the benefited enterprises (180) in the area included in the sample. The research questionnaire contained 180 participants. Questions regarding investment priorities, type and place of investment and future plans has been included in the questionnaire. The initial part of the questionnaire included questions that first classified the respondent as belonging to a specific group and then to a sub-group. Respondents also asked to express themselves on specific attitudes and opinions about the potential investment and the effectiveness of the program. 180 respondents took part in the research. The research data collected by researchers employing personal interviews. After initial verification of the questionnaire, the answers from the 164 respondents (86.95%) selected for further analysis.

3.2

Descriptive Statistics and Correlations

The results of the research were analyzed using descriptive statistics and correlations. The Data were categorized in two categories, the first one was startups and the second one Existing firms. Then, we calculate the main statistical measures.

4 Main Results and Discussion 4.1

Findings

While Greek economy is still facing an economic slump, entrepreneurs usually hesitate to invest. On the other hand, tourism is the only sector in Greek economy that developed during this last decade. In Fig. 3, it is presented in which Regional Unity of Region of Central Macedonia entrepreneurs are going to invest on tourism. As 24 of the participants did not clarified the place that the investment will take place they are named as unknown Regional Unity. At R.U. of Serres only two enterprises are going to invest. From research four years ago concerning the previous (2013) NSFR program which is referred to R.U. of Serres “There is only 1 new touristic enterprise. . . . new enterprises are not established on poor areas” (Balomenou and Maliari 2015). R.U. of Serres is the poorest Greek R.U. In Fig. 4 there are presented Gross Domestic Product (GDP) and

Tourism Development in North Greece

15

Fig. 3 Distribution of touristic investments per Regional Unity in Region of Central Macedonia. (Source: Authors’ research)

Fig. 4 GDP per Capita in R.U. of Region of Central Macedonia/NUTS 3 (2014). (Source: Hellenic Statistical Authority)

the Number of eligible under the umbrella of these specific EPAnEK program enterprises per Regional Unity. Thus, as all subsidies provided based on income criteria on NUTS 2 level in combination to the fact that there are intraregional disparities in Region on Central Macedonia our proposal is to change income criteria from NUTS2 level to NUTS3 level. According to the Data results intraregional disparities were emerged (also reffered in Christofakis et a. 2019). A first finding that emerges from the above analysis is that the crisis has not left the regional inequalities unaffected. More specifically, it has emerged that during the total period 2000–2016, the regional disparities increased. However, if we divide this period into two sub-periods 2000–2008 and 2008–2016, we see that the regional disparities increased during the first period while in the next period of crisis they declined, but without reaching the levels they were at in 2000. This fact means that the regional distribution of employment during the development period was more imbalanced than the corresponding distribution during the crisis. In other words, we can say that the crisis hit the regions in a more balanced way.

16

B. Chrysanthi et al.

Fig. 5 Distribution of touristic investments per category (3S, Urban, Winter). (Source: Authors’ research)

Fig. 6 Distribution of touristic enterprises per amount of investment. (Source: Authors’ research)

While at R.U. as Serres, where only two enterprises that are going to invest, in order to facilitate our research, we examine the differentiated result concerning the investments on three categories of tourism in region of Central Macedonia as follows: • Regional Unities of Chalkidiki and Pieria which are characterized by sea-sun and sand (3S) • The city of Thessaloniki (Municipalities of Thessaloniki and Kalamaria) (urban) • Enterprises of Regional Unities of Pella, Imathia, Kilkis, Serres and rural or semi urban areas on R.U. of Thessaloniki which are addressed to different target groups as those who visit thermal springs or ski centers (winter) In Fig. 5 we present distribution of touristic investments per as above described category. As it is expected, the majority of entrepreneurs invest on areas as Chalkidiki or Pieria where there is the typical Greek touristic product. The development of urban tourism in Thessaloniki is an under further examination issue. Ski centers or thermal springs (winter tourism) after ten years since the beginning of great depression are no longer attractive. The amount of the investments is another parameter we examine. In Fig. 6 we present distribution of touristic investments per amount of investment. Up to 36% have chosen to invest the maximum amount 150.000€. Taking under consideration the fact that the average in Region of Central Macedonia is doubled than in Greece, the great invests in this area are an under examination issue. On the following figure we analyze where greater investments will take place.

Tourism Development in North Greece

17

Fig. 7 Touristic enterprises per enterprise’s place of investment and per amount of investment. (Source: Authors’ research)

Fig. 8 Percentage of investments per amount of investment. (Source: Authors’ research)

In order to facilitate our research we separate the sample in two categories as it depicted on the following Figs. 7 and 8. In 3s areas investments are greater than those in urban area of Thessaloniki. Smaller ones are taking place in areas focusing on winter tourism. In Fig. 9, we present distribution of touristic investments per enterprise’s age. Older firms have greater possibilities to invest and consequently to develop their business than startups ones. Problems new enterprises face are analyzed by Nobel nominated Porter and Stinglitz. “As a result of newness, the high level of uncertainty, customer confusion, and erratic quality, the emerging industry’s image and credibility with the financial community may be poor” (Porter 1998). Especially in Greece and the GIPSI countries (Greece, Ireland, Portugal, Spain, Italy)

18

B. Chrysanthi et al.

Fig. 9 Distribution of touristic investments per enterprise’s age. (Source: Authors’ research)

Fig. 10 Touristic enterprises per enterprise’s investment and per enterprise’s age. (Source: Authors’ research)

entrepreneurs face more obstacles. “The rich and well performing could invest in better schools and infrastructure. Their banks could lend more, making it easier for entrepreneurs to start a new business” (Stinglitz 2016). On the following Fig. 10 we analyze where startups are going to invest and on Fig. 11 the percentage of startups. It is worth mentioning the fact that more startups prefer to begin in urban Thessaloniki than in other places. It is worth remembering that “in periods of recession, the crisis policy responses focus on more resources in core regions” (Myrdal 1969 cited in Konsolas 2003; Balomenou 2003). “More specifically, during these periods, it is preferable to enforce, via ‘ backwash effects’, the metropolitan centers (like Athens and Thessaloniki), which, in any case, had the appropriate socioeconomic background, in order in periods of economic growth, to support the peripheral, developing and under developing regions, via “spread effects” (Balomenou and Maliari 2016). In order to examine the kind of investment the sample separated in two categories, one for Hotels and Pensions and the other for travel agencies and services. In Fig. 12 we present distribution of touristic investments per kind of investment.

Tourism Development in North Greece

19

Fig. 11 Percentage of startups. (Source: Authors’ research)

Fig. 12 Distribution of touristic investments per kind of investment. (Source: Authors’ research)

80 70 60 50 ACCOMODATION

40

SERVICES

30 20 10 0 WINTER

URBAN

3S

Fig. 13 Touristic enterprises per enterprise’ kind of investment per place. (Source: Authors’ research)

20

B. Chrysanthi et al.

Fig. 14 Percentage of accommodation/ services per place. (Source: Authors’ research)

Fig. 15 Touristic enterprises per enterprise’ kind (accommodation/services) and size. (Source: Authors’ research)

Investments on accommodation cover a great part of the budget. On the following Fig. 13 we analyze the place (3S, urban, winter tourism focusing areas) investments on accommodation covers greater part of the budget than those in services and on Fig. 14 their percentage. Examining the above figures, we conclude that in urban areas investments target on touristic agencies and generally services, versus 3s areas investments which target on hotels and pensions. In the next step we examine correlations between size of investments and kind of investments. Thus, on the following Fig. 15 we analyze whether small100.000€ investments on accommodation cover greater part of the budget than those in services and on Fig. 16 their percentage. As services demand less funding than hotels, investments on accommodation expected to be greater than those on services do. The paradox of our results is an under further examination issue. In Fig. 17, the allocation of investments presented between startups and existing firms. Accommodation covers a greater part of the budget than those in services (Fig. 18). As it mentioned above, services demand less funding than hotels, thus startups are less likely to invest on accommodation. Thus, on the following Fig. 19, the size of investment presented. Again, accommodation covers greater part of the investment than those in services (Fig. 20). All new enterprises (100%) invest less than 100.000€ while the mature ones invest more than 100.000€. As we referred in Region of Central Macedonia, the

22

B. Chrysanthi et al.

Fig. 18 Percentage of investment per enterprise’s kind and age. (Source: Authors’ research)

140 120 100 100000

60 40 20 0 START UP

EXISTING FIRMS

Fig. 19 Touristic enterprises per enterprise’ kind of investment per size. (Source: Authors’ proposal)

average amount of investments is the double in comparison to the average amount in Greece (Table 5). Thus, existing firms in this region invest bigger amounts.

4.2

Discussion

The research results are in line with previous researches. The main findings suggested that startups are not willing to invest in less developed peripheral unities. On the contrary, they invest in core urban regions like Thessaloniki. Myrdal, in 1969 and Balomenou (2003) suggested that enterprises prefer to invest in already

Tourism Development in North Greece

23

100 90 80 70 60 50 40 30 20 10 0 START UPS

EXISTING FIRMS

Fig. 20 Percentage of investment per kind and per size. (Source: Authors’ proposal)

developed countries. The same concluded Balomenou and Maliari (2015) in their research paper. In periods of Economic crisis, enterprises invest, in metropolitan centers such as Athens and Thessaloniki, where there is the infrastructure to support economic development. In periods of economic growth, policies via “spread effect” support peripheral development (Balomenou and Maliari 2016). The results suggested that investments developed a new product in tourism offer. The investments focused on the development of city breaks in urban areas and particularly in Thessaloniki area. Urban tourism and city breaks are a global trend. Nevertheless, this trend is underdeveloped in Greece Tourism offer. EPAnEK program focused mainly on the development of this type of tourism. Especially startup enterprises focused mainly in this type of tourism. City breaks can lengthen the period and reduce the tourism seasonality. At the same time city, breaks have been developed in urban places and in more developed unities. In this way the inequalities have extended. According to the results it was revealed that the existing firms have greater opportunities to invest and develop their business than startup enterprises. Porter (1998) underlined and analyzed the financial problems that new enterprises face when they try to invest. In a more analytical way, there it was indicated that “As a result of newness, the high level of uncertainty, customer confusion, and erratic quality, the emerging industry’s image and credibility with the financial community may be poor.” Another finding of the current research is referring to the fact that while the European programs’ main aim is to enhance a balanced development, it was found out that the current EPAnek Program did not really achieve its objectives, as this program focused mainly on providing funds to more developed Regional Unities as Chalkidiki and Pieria. This outcome is also in compliance, to the existing literature. Stiglitz refers that the GIPSI countries (i.e., Greece, Ireland, Portugal, Spain, Italy) entrepreneurs face more obstacles. “The rich and well performing could invest in

24

B. Chrysanthi et al.

better schools and infrastructure. Their banks could lend more, making it easier for entrepreneurs to start a new business,” whereas in our case we are coming to the same conclusion but at an interregional level.

4.3

Policy Imprecations: Proposals for Further Research

The research inevitably has many limitations. Firstly, the questionnaire used in the present research has not validated in previous research. Secondly, some of the respondents have not identified the place of investment. Finally, the research has not examined previous investments. It would be interesting to expand our research by elaborating an interregional analysis in both peripheral and core regions at a country level. Furthermore, it should be more thoroughly investigated, in a future paper, why the urban tourism in Thessaloniki has remarkably developed. In addition, a comparative analysis with other core regions of Greece should be conducted in the future.

5 Conclusion Economic crisis in Greece, widely known as Greek depression, which has emerged before even the era of the current decade has had seriously damaged each and every sector of the Greek economy decreasing among others the Greek GDP by over than 25%. In such an extremely adverse environment, Greek tourism succeeded to not only be stabilized but also even to slightly incline. Greece’s tourism great importance is inevitable on the one hand, because its total contribution to GDP in 2018 is calculated to 19.7%, the contribution to total employment is calculated to 24.8% boosting synergies and interrelations with the other sectors and on the other hand, the potential prospect of tourism is to increase by 3.7% each year for the next ten years. In addition, tourism’s contribution to GDP at some regions such as Grete, South Aegean, or Ionian islands overcomes the 55%. Taking under consideration the importance of tourism to Greece’s economy recovery EPAnEK Program is examined on the grounds that it is supposed to provide the necessary funds to touristic enterprises. European programs are supposed to support balanced development. However, the findings of this research are revealing that EPAnek Program did not really fulfill its purpose, because as a matter of fact, this program focuses on providing funds to touristic Regional Unities as Chalkidiki and Pieria. The fact that urban tourism in Thessaloniki has been remarkably developed is a matter under further examination. Although the region of Central Macedonia is not characterized as a touristic developing region, it is worth examining EPAnEK touristic program’s effects on the second in size—according to GDP and population—Greek region but unfortunately the less developed areas which are not characterized by sea-sun and sand

Tourism Development in North Greece

25

concept, received a low amount of investments. Thus, in order to achieve balanced development, programs should focus on providing funds to poor non-touristic regional unities by induce criteria such as per capital GDP. Taking for granted that new enterprises do not invest more than 100.000€, it is concluded that especially startup entrepreneurship is developing slowly. For further research, our scope is to focus on ex-post evaluation of the implementation and the efficacy of this program. The question whether EPANEK managed to reduce inequalities and enhance convergence among regions has not been supported by the results. So, a future research should be conducted in order to investigate the program effects in long term. Finally, this paper contributes to the current understanding of attitudes towards tourism within the SET framework, especially relating to covering those more emotional elements of social interactions. Moreover, there seems to be a gap in current research relating to small urban destinations that aimed to be covered in this research.

References Andereck KL, Vogt CA (2000) The relationship between residents’ attitudes toward tourism and tourism development options. J Travel Res 39(1):27–36 Andraz M, Norte M, Gonçalves S (2015) Effects of tourism on regional asymmetries: empirical evidence for Portugal. Tour Manag 50(3):257–267 Balomenou C (2003) The Greek banking system and the procedures of evaluating investment plants. Papazisi, Athens Balomenou C, Maliari M (2015) Support of local entrepreneurship during periods of crisis: a case study for Serres-Greece on NSRF programmes. Procedia Economics and Finance, Elsevier, pp 535–551 Balomenou C, Maliari M (2016) Entrepreneurship in Greece: an intraregional sectoral analysis. Rom J Reg Sci 10(2):35–49 Barbieri P, Scherer S (2009) Labour market flexibilization and its consequences in Italy. Eur Sociol Rev 25(6):677–692 Besculides A, Lee ME, McCormick PJ (2002) Residents’ perceptions of the cultural benefits of tourism. Ann Tour Res 29(2):303–319. https://doi.org/10.1016/S0160-7383(01)00066-4 Botti L, Peypoch N, Robinot E, Solonadrasana B (2009) Tourism destination competitiveness: the French regions case. Eur J Tourism Res 2(1):5–24 Brida JG, Risso WA (2009) Tourism as a factor of long-run economic growth: an empirical analysis for Chile. Eur J Tourism Res 2(2):178e185 Chen J, Balomenou C, Nijkamp P, Poulaki P, Lagos D (2016) The sustainability of yachting tourism: a case study on Greece. IJRTH 2(2):42–49 Christofakis M, Gaki E, Lagos D (2019) Τhe impact of economic crisis on the regional disparities and the allocation of economic branches in Greek regions. Bull Geogr Soc Econ Ser 44:7–21. https://apcz.umk.pl/czasopisma/index.php/BGSS/issue/view/1576/showToc Dunford M (2012) Regional inequality in the EU: how to finance greater cohesion. Eur Plann Stud 20(6):895–922 Dwyer L, Forsyth P, Papatheodorou A (2011) Economics of tourism. Goodfellow Publishers, Limited, United Kingdom

26

B. Chrysanthi et al.

Gaki E, Christofakis M, Lagos D, Balomenou Chr (2018) The spatial structure of financial crisis on Greek tourism. 54rd ERSA 2018 Congress “Places for People: Inclusive and Liveable Regions”, Cork, Ireland, 28–31 August 2018 Gomezelj DO, Mihalič T (2008) Destination competitiveness – applying different models, the case of Slovenia. Tour Manag 29(2):294–307 Hall CM (2001) Trends in ocean and coastal tourism: the end of the last frontier? Ocean and Coast Manag 44(9–10):601–618. https://doi.org/10.1016/S0964-5691(01)00071-0 Hall CM, Page SJ (2009) Progress in tourism management: from the geography of tourism to geographies of tourism: a review. Tour Manag 30(1):3–16 Hellenic Statistical Authority. http://www.statistics.gr & http://dlib.statistics.gr/portal/page/portal/ ESYE/. Accessed 23 Oct 2018 Katircioglu ST (2009) Revisiting the tourism-led-growth hypothesis for Turkey using the bounds test and Johansen approach for cointegration. Tour Manag 30(1):17e20 Konsolas N (2003) Modern regional economic policy. Papazisi, Athens Krugman P (2012) End this depression now. W. W. Norton & Company, New York and London McGehee NG, Andereck KL (2004) Factors predicting rural residents’ support of tourism. J Travel Res 43(2):131–140 Porter M (1998) Competitive strategy-techniques for analyzing industries and competitors. The Free Press, New York Ritchie JRB, Crouch GI (2005) The competitive destination: a sustainable tourism perspective. Oxford University Press, Oxford SETE, Greek Tourism Confederation (2018) The contribution of tourism in the Greek economy at 2017. Study of SETE, Intelligence, Athens. https://sete.gr›media›sete-report2017. Accessed 7 Jan 2020 Stinglitz J (2016) The euro-how a common currency threatens the future of Europe. W.W. Norton & Company Inc, New York Tang CF, Tan EC (2013) How stable is the tourism-led growth hypothesis in Malaysia? Evidence from disaggregated tourism markets. Tour Manag 37:52e57 Webster C, Ivanov SH (2014) Transforming competitiveness into economic benefits: does tourism stimulate economic growth in more competitive destinations? Tour Manag 40:137–140 World Tourism Organization and International Labour Organization (2014) Measuring employment in the tourism industries – guide with best practices, UNWTO, Madrid. https://doi.org/10. 18111/9789284416158 WTTC (2018) https://www.wttc.org/ & https://sete.gr›media›sete-report2017. Accessed 7 Jan 2020 Yang Y, Wong KKF (2012) A spatial econometric approach to model spillover effects in tourism flows. J Travel Res 51(6):768e778

Tourism Destination Product Characteristics Based on Twitter Sentiment Analysis: A Case Study of Penang, Malaysia Nor Hasliza Md Saad and Zulnaidi Yaacob

Abstract Sentiment analysis using Twitter offers businesses an effective way to provide and monitor people’s opinion towards their products and services. Sentiment analysis has been valuable in the tourism industry, which creates essential implications for understanding public opinion and experience about tourism destination. This study presents a sentiment analysis of tourist opinion in Penang, Malaysia. The main objectives are to identify destination product messages that utilise the hashtag #penang; to categorise the dominant messages that include #penang; and to explore the sentiment expressed by #penang appearances on Twitter as positive or negative towards the destination product. This study adopts the destination product framework (Murphy et al. 2000, The destination product and its impact on traveller perceptions. Tour Manag 21:43–52) that consists of two essential components: service infrastructure (shopping services, recreation and attraction services, food services, travel services, transportation services and accommodation services) and destination environments (natural environment factors, political factors, technological factors, cultural factors, economic factors and social factors). The findings provide a range of opinion on Penang, Malaysia, a popular tourist destination appropriate for understanding tourist experience and views on the subject of this tourist destination product. Besides, the findings can be used for assessment purposes to enhance efficiency and to create competitive advantage in the tourism industry in Penang, Malaysia.

N. H. Md Saad (*) School of Management, Universiti Sains Malaysia, Minden, Penang, Malaysia e-mail: [email protected] Z. Yaacob School of Distance Education, Universiti Sains Malaysia, Minden, Penang, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. Karanovic et al. (eds.), Tourism Management and Sustainable Development, Contributions to Economics, https://doi.org/10.1007/978-3-030-74632-2_2

27

28

N. H. Md Saad and Z. Yaacob

1 Introduction Tourism has now become one of Malaysia’s leading industries, the second major contributor to Malaysia’s Gross Domestic Product (GDP) after manufacturing (Government of Malaysia 2010). According to Department of Statistics Malaysia (2018), in 2018, the Gross Value Added of Tourism Industries (GVATI) has recorded tourism industry contributed 15.2% to Malaysia’s GDP, as compared to 14.6% in 2017. The Ministry of Tourism, Arts and Culture Malaysia launched Malaysia Integrated Plan 2018–2020 (Tourism Malaysia 2018) to further promote Malaysia’s extensive tourism offering and boost tourist arrivals and receipts. Tourism Malaysia reported that in 2019, tourism contributed RM86.1 billion to the national economy, 2.3% more than the previous year’s RM84.1 billion (Tourism Malaysia 2019). Furthermore, tourist arrivals in Malaysia are expected to increase by 4% annually to 36 million by 2020, with tourism receipts rising by 13.6% to RM168 billion. Subsequently, predictions for 2020 anticipate that the tourism industry will provide 2.34 million jobs. Accordingly, to stimulate the industry’s contribution to the economy, the Eleventh Malaysia Plan prioritises high-yield income (Government of Malaysia 2015). Domestic tourism provides another mechanism by which to further increase the vibrancy of the industry, highlighting Malaysia’s uniqueness and strengths through targeted promotional activities. As such, Malaysia’s tourism industry is shifting towards knowledge-intensive niches areas, creating high-income jobs and transforming the industry from high volume to high yield. Moving forward, these strategies will be pursued across five areas, namely enhancing tourism products, upgrading service quality, enhancing marketing promotion, improving governance and intensifying domestic tourism. Accordingly, to stimulate the industry’s contribution to the economy, the Eleventh Malaysia Plan prioritises high-yield tourism (Government of Malaysia 2015). Domestic tourism provides another mechanism by which to further increase the vibrancy of the industry, highlighting Malaysia’s uniqueness and strengths through targeted promotional activities. As such, Malaysia’s tourism industry is shifting towards knowledge-intensive niche areas, creating high-income jobs and transforming the industry from high volume to high yield. Moving forward, these strategies will be pursued across five key areas, namely enhancing tourism products, upgrading service quality, enhancing marketing promotion, improving governance and intensifying domestic tourism. Penang, known as the ‘Pearl of Orient’, has emerged as one of the leading tourism destinations in Malaysia. Located on the northwest coast of Peninsular Malaysia, the island has its own identity, a unique fusion of eastern and western influences. It is a melting pot of different cultures, from vibrant multi-ethnic races to its variety of outstanding local cuisine, rich history, lovely scenic attractions and an exceptional mix of modern and heritage architecture. These characteristics enable Penang to offer a comprehensive and diverse range of tourism product attractions. Known not only locally but across the world, Penang is a premier tourist destination.

Tourism Destination Product Characteristics Based on Twitter Sentiment. . .

29

On seventh July 2008, UNESCO declared the historic capital city of George Town a World Cultural Heritage Site. This capital city encompasses a collection of historic buildings with diverse architecture, especially its assemblages of pre-war colonial British and Chinese shophouses along the streets. Complementing these is an extensive collection of different types of impressive architectural styles and designs of mosques, churches and Chinese and Indian temples, including Chinese, Indian-Muslim and Peranakan clan houses within the inner city. In fact, at the international level, Penang has received great recognition as an outstanding tourist destination. Time magazine honoured Penang for having the ‘Best Street Food 2004’ in Asia. The New York Times chose Penang as the second-best destination among ‘44 Places to Go’. The leading travel guide in the world, Lonely Planet, listed Penang as the top food destination in 2014 (The Star 2014) and included it on the list of top 10 cities for Best in Travel 2016 (The Star 2015). Recently, renowned American food writer James Oseland recognised it as the world’s best food destination in 2017; CNN Travel named Penang as second of the 17 top tourist destinations to visit in 2017 (The Star 2017) and again in 2019 as one of Asia’s 17 best destinations to consider for ‘the next adventure’ (The Star 2019).

1.1

Problem Statement

Social network sites transform the ways in which tourists get, share and deliver information about their experience with tourism activities. The growth in usage of social network sites has attracted the attention of tourism researchers interested in generating a potential source of information for understanding tourist opinion and behaviour, to use in developing a tourism marketing strategy. Recently, research related to social network sites has drawn unprecedented attention from customers and businesses in many disciplines. Tourism is one of the research areas that has realised vast benefits from utilising social network sites’ data content as practical alternative data resources; most previous studies in tourism research have extensively relied on survey methods (Drus and Khalid 2019). Despite the increasing importance of using social network site content (e.g. interest expressed in comments) to predict current trends in tourist perceptions, few empirical types of research analyses tweet from Tweeter in the context of Penang, Malaysia tourism. Analysing Twitter for tourism research represents an emerging topic (Ćurlin et al. 2019). Similarity, this study contributes to utilising tourist sentiment analysis relating to Penang as a Malaysia tourist destination product. This research examines the social network site content from Twitter to find sentiment about Penang as one of the famous tourism destinations in Malaysia. Since it launched in 2006, Twitter has become one of the most popular sites in the world for microblogging as an online communication medium. The growth of Twitter has attracted the attention of tourism research for acquiring tourist sentiment about the tourist destinations that users have visited or will visit. Twitter allows the tourist to generated content and share views and experiences, including accessing

30

N. H. Md Saad and Z. Yaacob

other users’ information. The prevalence of Twitter generates significant implications for the tourism industry, in terms of reputation, marketing and performance (Philander and Zhong 2016). Research using sentiment analysis in tourism is still in its infancy, even though many studies identify and prove the excellent strategy of social network sites to market tourism destination products. Sentiment analysis is the perception of an individual experience of opinion as positive or negative. Such sentiment can apply to identifying the tourist’s perception of experience with a tourism destination through the content of a posted tweet, including its words, image, tone and emotion. Within the tourism industry, Twitter can be a source of tourism information that influences tourist’s decision-making process. Since its founding in 2006, Twitter has become a popular microblogging service among social network sites. Twitter allows users to write a 140-character instant message, called a ‘tweet’. As Twitter evolves, add-ons function to include links to other content, such as photos or videos, and websites that make it more powerful for sharing information among its members. Communication and interaction are a primary reason for the use of such social networking sites as Twitter. Currently, the Twitter static review shows that in average Twitter has 330 million monthly active users and 500 million tweets a day in 2020 (Aslam 2020). Twitter users can also use the hashtag (starting words, acronyms or phrases with the ‘#’ symbol) to associate a tweet with a particular context, topic, issue or event. The use of the hashtag enables Twitter users to search for exciting information or flow or contribute to a discussion on the particular information the hashtag designates. Hashtags are essential to organising information on specific topics shared by a group of Twitter users. Within the tourism sector, Twitter provides a source of tourism information that influences tourists in their decision-making process (Alaei et al. 2017). Tourists can find and gather up-to-date information from the opinions expressed there, such as reviews and feedback about location, food, people, accommodations, transportation and other services. Going one step further, Twitter can also play an essential role in digital marketing for the tourism sector, especially for promotion and communicating with consumers. The content of Twitter enables this study to provide a range of opinion expressed about the tourism product destination of Penang, Malaysia. Analysing how people talk about or market Penang, Malaysia holds interest for researchers.

1.2

Research Objectives

This research has three objectives: first, to identify the message about the destination product reflected by #penang; second, to categorise the dominant messages categorised by #penang and, finally, to explore the sentiment shared on Twitter around #penang as positive or negative towards this destination product.

Tourism Destination Product Characteristics Based on Twitter Sentiment. . .

31

2 Theoretical Framework Tourism destinations refer to the nature of geographic locations or places that tourists visit (Zemla 2016). They are the essential units of analysis influencing tourist attractions and experiences. This study adopted Murphy et al.’s (2000) destination product model, namely the tourism destination model. They define a destination product as an ‘amalgam of individual products and experience opportunities that combine to form a total experience of the area visited’. This framework consists of two essential components: service infrastructure (shopping services, recreation and attraction services, food services, travel services, transportation services and accommodation services) and destination environments (natural environment factors, political factors, technological factors, cultural factors, economic factors and social factors). According to Smith (1994), service infrastructure appears to be included within the larger macro-environment or the physical plant of the destination. The elements of service infrastructure are as follows. • Shopping services include all sorts of retail shopping facilities, such as duty-free shopping in airports, luxury-goods shopping and shopping mall or outlet visits. Shopping activities have always been an integral part of tourism activities where tourists tend to purchase goods. • Recreation and attraction services include sites for physical and social activities, such as picnic areas, some rides, local fitness centres, entertainment, small water parks and clubs. The activities include visiting attractive places for relaxation purposes. • Food services include any food-related activities, from food searching to food consumption. • Travel services typically refer to travel agency and tourist guides that offer service for accommodation and transportation packages with specific discounts, special events and festival listings. • Transportation services, discussed widely in the tourism literature, include various transportation service modes, such as air, land and water transportation. • Accommodation services include temporary lodging services for tourists, from low-budget lodges to world-class luxury hotels. Destination environments consist of the physical, social and political nature of the surrounding destination. The elements of the destination environment are: • The natural environment factor made up of various tourism experiences involving flora, fauna and climate of the encompassing destination. • The political factor comprising political instability, such as wars, corruption, conflicts, crime rate, crisis and terrorism. • The technological factor, integrating information and communication technologies in enhancing tourism-related activities, including improving tourism service productivity in delivering, communicating and entertaining.

32

N. H. Md Saad and Z. Yaacob

• The culture factor, referring to activities related to destination history, institutions, customs, architectural features, language, cuisine, traditions, artwork, music and handicrafts that attract the tourist. • The economic factor, considering the nature of world economic trends that determine the currency exchange rate, behaviours of stock and foreign exchange markets and economic recession. • The social factor, considering collective lifestyles, moral conduct, traditional ceremonies and community organisation. In an effort to consider the importance of competitive tourism products, one should primarily recognise the tourist’s destination experience as the core of a tourism product, which influences the combined management of both service infrastructure and destination environment (Murphy et al. 2000). On a practical level, managing and coordinating a tourism destination are a complex task, due to the intangibility and heterogeneity of the service delivered. The concept of destination competitiveness requires the specific ability of the host destination to offer better services and environment conductive to interacting with tourists and, consequently, to shape the tourist experience during the trip. Furthermore, Murphy et al. (2000) elaborate that the tourism experience regarding both destination quality and perceived trip value can influence the tourist’s intention to return. In the context of this research, destination image gains a substance advantage by encouraging word-of-mouth recommendations to family and friends, using social network sites such as Twitter (Luo and Zhong 2015). In the last decade, the destination product model has been used in a number of tourism studies in addressing the important to consider the tourist perceived value of the tourism experience (Frías-Jamilena et al. 2019; Saikia et al. 2019; Shuhao et al. 2020). Tourist of the destination product often varies in their perceptions, expectation and satisfaction (Yoon and Uysal 2005; Hui et al. 2007; Karayilan and Cetin 2016; Saikia et al. 2019). The multiplicity of components that make up the destination product plays an important role in influencing travel behaviour and motivation.

3 Data and Research Methodology For data collection and analysis, Tweets with the hashtag #penang were collected using Ncapture from Nvivo 11 Pro, a qualitative data-analysis tool. Initially, the dataset consisted of 799 tweets, captured from 1st April until 7th May 2017. Then, the data screening selected data regarding tourist-related opinion, excluding all spam and advertising messages. The remaining 248 tweets were manually analysed and coded, using an iterative process of coding tweets. To extract consumers’ opinions and sentiments from the Twitter Application Programming Interface (API) using computer programming language is not a novel task for social-science researchers. The manual coding mechanism has the advantage of analysing data thoroughly and comprehensively. These Twitter posts

Tourism Destination Product Characteristics Based on Twitter Sentiment. . .

33

were analysed individually, to determine the nature of the content and match the post with the Tourism Product framework. The Twitter post was the unit of analysis. Data were coded manually by two independent coders who were categorised based on two essential components, namely service infrastructure (shopping services, recreation and attraction services, food services, travel services, transportation services and accommodation services) and destination environments (natural environment factors, political factors, technological factors, cultural factors, economic factors and social factors). Tweets with an unclear message were discussed, in an attempt to resolve disagreement and find consensus. Interpreting the characteristic of tweet content for each category is challenging, especially for the combination of images, hyperlink, symbol and text. Manual coding presents a challenge with a large volume of data analysis, since it consumes a great amount of time and is workintensive (Kirilenko et al. 2018).

4 Findings In general, the service infrastructure characterised more Twitter posts in the Penang tourism destination than destination environments, as Tables 1 and 2 show. A total of 60.89% were retrieved from service infrastructure, while 39.11% of Twitter posts describe the destination environment. For the service infrastructure as Table 1 depicts it, interestingly, the findings of sentiment analysis for service infrastructure tweets indicate that 100% of the Twitter postings convey a positive sentiment. The recreation and attraction services topic dominates with 31.45% of messages, followed by 25.4% of messages regarding food services, 3.23% concerning transportation services and 0.81% relating to accommodation services. Meanwhile, none of the Twitter posts talks about shopping services and travel services. For the destination environment as Table 2 depicts it, the findings indicate that almost 99% of the Twitter postings reflect positive rather than negative sentiment. The natural environment factors dominate with 25%, while the cultural factors are 6.05%, followed by 3.32% for the historical factors, 2.02% for the social factors, 1.21% for the technological factors, 0.81% for the political/legal factors and 0.4% for the economic factors. The negative sentiment appeared in 0.4% of the natural environment factor tweets.

5 Conclusion The findings from sentiment analysis using Twitter posts create a new opportunity to reach the content of comments or discussion on Twitter among general users or tourists, regarding the hashtag #penang. By examining and understanding Twitter posts from around the world, this study provides some indication of central

Recreation & attraction services F % 78 31.45 0 0

F ¼ frequency, % ¼ percentage, n ¼ 248

Service infrastructure Shopping services Sentiment F % Positive 0 0 Negative 0 0

Table 1 Summary of sentiment analysis in service infrastructure Food Services F % 63 25.4 0 0

Travel services F % 0 0 0 0

Transportation services F % 8 3.23 0 0

Accommodation services F % 2 0.81 0 0

34 N. H. Md Saad and Z. Yaacob

F ¼ frequency, % ¼ percentage, n ¼ 248

Destination environment Natural Environment Factors Sentiment F % Positive 62 25 Negative 1 0.4

Political/Legal Factors F % 2 0.81 0 0

Technological Factors F % 3 1.21 0 0

Table 2 Summary of sentiment analysis in destination environment Economic Factors F % 1 0.4 0 0

Cultural Factors F % 15 6.05 0 0

Social Factors F % 5 2.02 0 0

Historical Factors F % 8 3.23 0 0

Tourism Destination Product Characteristics Based on Twitter Sentiment. . . 35

36

N. H. Md Saad and Z. Yaacob

perceptions of Penang, Malaysia, as a tourism destination product with its more detailed components, namely, services infrastructure (shopping services, recreation and attraction services, food services, travel services, transportation services and accommodation services) and destination environments (natural environment factors, political factors, technological factors, cultural factors, economic factors and social factors). First, this study found that most tourists have positive perceptions of Penang as a tourism destination product. Almost 99% of Twitter posts reflected positive sentiment, concrete proof that Penang has received local and international acclaim as a popular tourism destination. Prior research shows that a favourable experience or perception of a tourism destination leads to influencing the tourist’s intention to return (Alegre and Cladera 2009; Sthapit et al. 2019). Furthermore, the positive tourist perception is more likely to spread positive recommendations to friends and relatives (Litvin, et al. 2018; Pourfakhimi et al. 2020). Second, a large number of positive Twitter posts are linked to the tourism product of service infrastructure, in terms of recreation and attraction services and food services. Penang has many tourist places to visit, with very nice sightseeing, recreation and sports attractions. This finding supports the Penang Tourism Survey 2017 (Omar and Mohamed 2018), which reports that 41.1% of tourists listed ‘Experiencing local food’ as the number-one must-do activity in Penang and ‘sightseeing in the city’ as the number-two must-do activity there. In addition to this report, the majority of tourists were satisfied with accessibility to tourist attractions around Penang. Third, this study reveals that the natural environment factor is the dominant category among positive Twitter posts for the destination-environment factor within tourism products. Malaysia’s ‘Pearl of the Orient’ carries the natural beauty of pre-served forests and beautiful beaches that make the tourism experience both attractive and demanding. Furthermore, these findings demonstrate the strategic importance of the wide range of tourism product for assisting decision-making in their analysis of tourism competitiveness. Positive perceptions and satisfaction from the tourism experience and developing a positive image as a destination clearly contribute to an important competitive strategy for a tourism product (Sthapit et al. 2019). Thus, understanding tourism perceptions of both service infrastructure and the tourism destination with excellent service will provide a positive tourism experience, in terms of both destination quality and perceived trip value. This study has several limitations. First, the data collection period was relatively short, and the quantity of data was quite small. To further understand the broader perception of Penang as a tourism destination product, future studies could collect a larger amount of data during a more extended period, to see the detailed pattern of the output. Second, the manual process of data analysis requires a longer time than computer programming mechanisms. Third, this study provides a brief descriptive analysis of Twitter posts; future studies could apply other advanced statistical analysis to producing complex findings with more valuable information.

Tourism Destination Product Characteristics Based on Twitter Sentiment. . .

37

References Alaei AR, Becken S, Stantic B (2017) Sentiment analysis in tourism: capitalizing on big data. J Travel Res 58(2):175–191 Alegre J, Cladera M (2009) Analysing the effect of satisfaction and previous visits on tourist intentions to return. Eur J Mark 43(5/6):670–685 Aslam S (2020) Twitter by the numbers: stats, demographics & fun facts. Omnicore. https://www. omnicoreagency.com/twitter-statistics/ Ćurlin T, Miloloža I, Jaković B (2019) Twitter usage in tourism: literature review. Bus Syst Res 10 (1):102–119 Department of Statistic Malaysia (2018) Tourism satellite account. https://www.dosm.gov.my/v1/ index.php?r¼column/cthemeByCat&cat¼111&bul_id¼Wk1KWlpxZTRDWnVhVW NMV21ZVVY3Zz09&menu_id¼TE5CRUZCblh4ZTZMODZIbmk2aWRRQT09 Drus Z, Khalid H (2019) Sentiment analysis in social media and its application: systematic literature review. Procedia Comput Sci 161:707–714 Frías-Jamilena DM, Castañeda-García JA, Del Barrio-García S (2019) Self-congruity and motivations as antecedents of destination perceived value: the moderating effect of previous experience. Int J Tour Res 21(1):23–36 Government of Malaysia (2010) 10th Malaysian Plan (2011–2015) Government of Malaysia (2015) 11th Malaysian Plan (2016–2020) Hui TK, Wan D, Ho A (2007) Tourists’ satisfaction, recommendation and revisiting Singapore. Tour Manag 28(4):965–975 Karayilan E, Cetin G (2016) Tourism destination: design of experiences. In: Sotiriadis M, Gursoy D (eds) The handbook of managing and marketing tourism experiences. Emerald Group Publishing Limited, Bingley, pp 65–83 Kirilenko AP, Stepchenkova SO, Kim H, Li X (2018) Automated sentiment analysis in tourism: comparison of approaches. J Travel Res 57(8):1012–1025 Litvin SW, Goldsmith RE, Pan B (2018) A retrospective view of electronic word-of-mouth in hospitality and tourism management. Int J Contemp Hosp Manag 30(1):313–325 Luo Q, Zhong D (2015) Using social network analysis to explain communication characteristics of travel related electronic word-of-mouth on social networking sites. Tour Manag 46:274–282 Murphy P, Pritchard M, Smith B (2000) The destination product and its impact on traveler perceptions. Tour Manag 21:43–52 Omar SI, Mohamed B (2018) Penang tourist survey 2017. Penang Global Tourism Sdn Bhd Philander K, Zhong Y (2016) Twitter sentiment analysis: capturing sentiment from integrated resort tweets. Int J Hosp Manag 55:16–24 Pourfakhimi S, Duncan T, Coetzee WJL (2020) Electronic word of mouth in tourism and hospitality consumer behaviour: state of the art. Tour Rev 75(4):637–661 Saikia J, Buragohain PP, Choudhury HK (2019) Attribute perception and tourist’s choice for wildlife tourism destination. Int J Cult Tour Hosp Res 13(3):346–358 Shuhao L, Min W, Hailin Q, Shangzhi Q (2020) How does self-image congruity affect tourists’ environmentally responsible behavior? J Sustain Tour 28(12):2156–2174 Smith SLJ (1994) The tourism product. Ann Tour Res 21:582–595 Sthapit E, Del Chiappa G, Coudounaris DN, Björk P (2019) Tourism experiences, memorability and behavioural intentions: a study of tourists in Sardinia, Italy. Tour Rev 75(3):533–558 The Star (2014) Lonely Planet picks Penang as top spot for foodies in 2014. https://www.thestar. com.my/news/nation/2014/02/04/lonely-planet-penang-food-top-spot/ The Star (2015) Lonely Planet lists George Town among world’s 10 best cities. https://www.thestar. com.my/news/nation/2015/10/28/george-town-listed-lonely-plane The Star (2017) Penang among must visit destinations this year in CNN list. https://www.thestar. com.my/news/nation/2017/01/09/cnn-recognises-penang-as-among-17-must-visit-destinationsthis-year/

38

N. H. Md Saad and Z. Yaacob

The Star (2019) CNN Travel: Penang among best destinations for ultimate Asia experience. https:// www.thestar.com.my/news/nation/2019/06/04/cnn-travel-penang-among-best-destinations-forultimate-asia-experience/ Tourism Malaysia (2018) Tourism Malaysia integrated promotion plan 2018–2020. Tourism Malaysia and Ministry of Tourism and Culture Malaysia, Kuala Lumpur Tourism Malaysia (2019) Malaysia tourism: key performance indicators 2019. https://www. tourism.gov.my/files/uploads/KPI2019-web-nov2020.pdf Yoon Y, Uysal M (2005) An examination of the effects of motivation and satisfaction on destination loyalty: a structural model. Tour Manag 26(1):45–56 Zemla M (2016) Tourism destination: the networking approach. J Morav Geographical Rep 24(4):2–14

Tourism Demand Modelling and Forecasting: Evidence from EU Countries Athanasia Mavrommati, Konstantina Pendaraki, and Achilleas Kontogeorgos

Abstract The purpose of this study is to investigate tourism demand and its determinants with panel data models. This paper empirically investigates the determinants of tourism demand for a statistically significant sample of eleven European countries for the years 1996–2015. Various potential determinants are investigated, including gross domestic product, consumer price index, the average per capita tourism expenditure, and the marketing expenses to promote tourism industry. The empirical results indicate that the explanatory variables affect the tourism demand of the EU countries and play an important role in strategies that affect total cost, demand, and structure of the market. As the marketing and advertising expenses revealed a dynamically interacts with tourist demand, their implications in decision making policies were discussed.

1 Introduction Tourism has a substantial indirect contribution to the economy through its multiplier effects. The annual analysis of the World Travel & Tourism Council’s in 185 countries and 25 regions showed that the Travel and Tourism sector is translated to 10.4% of global GDP and 319 million jobs or 10% of total employment in 2018. These numbers show that travel and tourism industries are rapidly growing in contrast to other sectors of the economy and become one of the major factors of socio-economic progress throughout the generation of jobs, the strengthening of export income, and the enforcement of infrastructure development. This growing trend for the tourism industry and its importance in the economic development of many countries makes it necessary for related government agencies and private sector to know which main factors are affecting tourism demand. Tourism demand is usually measured by the number of tourist visits from an origin

A. Mavrommati (*) · K. Pendaraki · A. Kontogeorgos University of Patras, Agrinio, Greece e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. Karanovic et al. (eds.), Tourism Management and Sustainable Development, Contributions to Economics, https://doi.org/10.1007/978-3-030-74632-2_3

39

40

A. Mavrommati et al.

country to a destination, in terms of tourist nights spent in the destination country or in terms of tourist expenditures by visitors. The present study empirically investigates the determinants of tourism demand for a statistically significant sample of eleven European countries for the years 1996–2015. To our knowledge, this is the first research work that uses the “advertising expenses” made to promote the tourism product of European countries as an explanatory variable. Following is given the literature review, while the rest of the paper is organized as follows: Sect. 3 presents the model specification and data set. The methodological framework is given in Sect. 4. Empirical results are discussed in Sect. 5, and conclusions are given in the Sect. 6.

2 Literature Review The growth of the world’s tourism industry led researchers to develop several modelling approaches to forecast tourism demand. Numerous studies have shown that forecasting tourism demand remains important to predict the future of tourism (Brand 1973; Chan 1979; Vanhove 1980; Sheldon and Var 1985; Crouch 1994; Witt and Witt 1995; Lim 1997a, b, 1999; Li et al. 2006; Song and Li 2008; Karlaftis 2010; Goh and Law 2011; Moro et al. 2017; Khaidi et al. 2019; Ghalehkhondabi et al. 2019). These review studies categorize demand models and methods into three main approaches: time series, econometric, and artificial intelligence models. Time series models have been broadly applied because they provide simplicity in data collection, cost effectiveness in the application and interpretation of forecasting demand, and allow comparisons for benchmarking purposes (Andrew et al. 1990; Goh and Law 2002; Cho 2003; Chan et al. 2005; Coshall 2006; Adhikari and Agrawal 2012; Baldigara and Mamula 2015; Tang et al. 2015). Econometric models, on the other hand, enrich the study of forecasting tourism demand by linking the causal relationship between tourism demand and its influencing factors (Clements and Hendry 1998; Kulendran and Wilson 2000; Song and Witt 2000; Lim and McAleer 2001; Turner and Witt 2001, Dritsakis 2004; Song and Wong 2003; Algieri 2006; Han et al. 2006). Moreover, artificial intelligence is recently introduced by the emerging of programming systems in analysing and predicting tourism demand (Kon and Turner 2005; Li et al. 2006; Palmer et al. 2006; Claveria and Torra 2014; Cankurt and Subasi 2016; Karakitsiou and Mavrommati 2017). Nevertheless, econometric models overtake both time series and artificial models in predicting tourism demand given their advantage in linking the dependent variable with its explanatory ones (Khaidi et al. 2019). In terms of the variables used in the models major studies are focusing on tourist arrivals (Coshall 2005; Rosselló 2001; Tang et al. 2015; Cankurt and Subasi 2016; Rafidah et al. 2017), while there also a few studies which measure the number of overnight stays such us these of Claveria and Torra (2014) and Constantino et al. (2016). The most common explanatory variables used are the real gross domestic products for approaching the tourist incomes, the exchange rate, the consumer

Tourism Demand Modelling and Forecasting: Evidence from EU Countries

41

price index, the tourism price of destination relative to country of origins, the living cost as well as the price of competing destination (Song et al. 2003, 2011; Constantino et al. 2016; Cankurt and Subasi 2015; Gunter and Onder 2015; Assaf et al. 2019). Country of origin (Claveria and Torra 2014) and allowance for visitors (Liang 2014) are also chosen as explanatory variable in certain studies. Also, some researchers use tourism related keywords from the search engines (Liang 2014; Yang et al. 2015; Önder 2017; Kirilenko AP and Stepchenkova, S. 2018).

3 Model Specifications and Variable Definition In this study eleven European countries were considered and the data used were obtained from the following sources: World Bank Reports; World Travel and Tourism Council; European Central Bank, Statistical Data; Media Services S.A.; Research Institute of Tourism; Bank of Greece. The data were analysed from 1996 to 2015. The dependent variable is the number of visitors (internal and external) in tourism destinations (VIS) for each country, since it is the most widely used variable in studies on tourism demand (Tang et al. 2015; Cankurt and Subasi 2016; Rafidah et al. 2017). Five explanatory variables have been used to measure the influence on tourism demand (VIS) in the current model. The Gross Domestic Product per capita (GDP) in each country, as a measure variable for analysing income, has a positive impact on tourism arrivals (Surugiu et al. 2011; Deng and Athanasopoulos 2011). The Consumer Price Index in each country (CPI), as a representative of prices, has been shown as a negative indicator of tourism arrivals (Lathiras and Siriopoulos 1998; Kulendran and Wilson 2000; Hanafiah and Harun 2010). The average per capita tourism expenditure in each country (EXP), as a representative of the component cost of travel to the destination, negatively influences the tourist arrivals (Au and Law 2002; Brida and Risso 2009). Population in each country (POP) that positively affects tourism demand (Oigenblick and Kirschenbaum 2002) was also included in the model. The current study is further enhanced by the inclusion of an important variable in the model which is related to the tourism advertising expenses in each country (ADV) as a representative of tourism marketing. Tourism advertising expenses has been suggested by Chinnakum and Boonyasana (2017) as an explanatory variable, which has not been yet broadly studied in the tourism demand forecasting models. All variables are measured in US dollars. Hence, assuming the existence of panel data, each country’s tourism demand (VIS) is determined according to the following equation: VISit ¼ ci þ b1 GDP þ b2 ADV þ b3 POP þ b4 EXP þ b5 CPI þ uit , where i refers to cross-sections and t refers to time periods.

42

A. Mavrommati et al.

The variables in the econometric model include the population and income of the destination country, the visitors’ purchasing power for the demand of international tourism services, the costs of living in the destination country and marketing expenses to promote each country’s tourism industry. This paper mainly adopts the total number of tourists in tourist destination as the dependent variable, so the selection of explanatory variables does not consider the variables of individual countries of origin but only that of the destination. The average per capita tourism expenditure is associated with travel expenses at the destination. Consumer price index in the destination country is a representative of prices and the component cost of living at the destination. All variables were selected for eleven European countries that use euro with yearly collected historical data, including: Austria, Cyprus, Italy, France, Spain, Greece, Germany, Netherlands, Portugal, Finland, and Ireland.

4 Methodology As far as tourism demand is concerned, econometric analysis has its empirical usefulness in interpreting the change of tourism demand and evaluating the effectiveness of the existing tourism policies. Panel data models consider the crosssectional and time series properties of the data, for example, tourism revenue observed by origin and over time. In the present study, the term “panel data” refers to the pooling of observations in a cross-section of the eleven selected countries over a period of twenty years (1996–2015). The combination of cross-section and time series data should be conducted in an appropriate statistical way, otherwise the coefficients will not be efficient. A variety of estimation techniques for panel data models have been developed in the literature that enable relaxation of many of the restrictive assumptions of the single crosssectional stochastic model and give rise to alternative measures of efficiency. These include the fixed effects model and the least squares dummy variable (LSDV) estimation, the random effects model and the generalized least squares (GLS) estimation and finally, maximum likelihood estimation (MLE). The fixed effect model explores the relationship between predictor and outcome variables within an entity (country, company, etc.). Each entity has its own individual characteristics that may or may not influence the predictor variables. The model requires relatively weak assumptions and allows αi (i.e. the unknown intercept for each entity) to differ across the cross-section units, and the estimates for the constants are different for each cross-section. It provides a convenient means of allowing for differences in coefficients, which may occur for different samples or for different sample distributions (Carter et al. 1988). That is,

Tourism Demand Modelling and Forecasting: Evidence from EU Countries

Y it ¼

Z X

43

βz X it þ αi þ uit ,

z¼1

with i ¼ 1. . .Z and t ¼ 1. . .T,where Yit represents the value of the dependent variable for entity i at time t, Xit is the value of any Z explanatory variable for entity i at time t, and uit is the error term with the standard assumption, βz is the coefficient for the explanatory variables, and αi is the unknown intercept for each entity. The fixed effects model is a classical regression model and controls all time-invariant differences between the entities, so the estimated coefficients of the fixed effects models cannot be biased because of omitted time-invariant characteristics. The rationale behind the random effects model is that, unlike the fixed effects model, the variation across entities is assumed to be random and uncorrelated with the predictor or independent variables included in the model. More specifically, the random effects model assumes that the term αit is the sum of a common constant α and a time-invariant cross-section specific random variable ui that is uncorrelated with the disturbance term εit. This means that, Y it ¼

Z X

βz X it þ αit þ uit þ εit ,

z¼1

where E[u(i)] ¼ 0, Var[u(i)] ¼ σ 2(u), Cov[ε(i, t), u(i)] ¼ 0. The random effects model is a generalized regression model. All disturbances have a variance of Var[ε(i, t) + u(i)] ¼ σ 2 ¼ σ 2(ε) + σ 2(u). For a given i, the disturbances in different periods are correlated because of their common component, u(i), Corr[ε(i, t) + u(i), ε(i, t) + u(i)] ¼ ρ ¼ σ 2(u)/σ 2. Random effects assume that the entity’s error term is not correlated with the predictors, which allows for time-invariant variables to play a role as explanatory variables and the efficient estimator is the generalized least square. Each one of the above methods of estimation makes different assumptions about the distribution of technical efficiency and its potential correlation with the regressors. If observations on statistical noise, as well as on firm effects, are assumed independent over time and across entities, following a specific distribution, then the stochastic frontier specification is not different from the maximum likelihood estimates of the panel model [Madalla (1987, 1991)]. In order to decide between fixed or random effects we run a Hausman test where the null hypothesis is that the preferred model is random effects vs. the alternative fixed effects.

44

A. Mavrommati et al.

5 Empirical Results and Discussion The model constructed in this study is based on the classical economic theory which assumes that income, advertising expenses, cost of living and price factors play an important role in determining the demand for tourism. We estimate a model to explain the tourism demand for eleven European countries by using data on number of tourists—visitors. These eleven countries are: Austria, Cyprus, Italy, France, Spain, Greece, Germany, Netherlands, Portugal, Finland, and Ireland. The data set pointed out the annual arrivals during the period between 1996 and 2015. The conjunction of time series and cross-sectional data allows for higher degrees of freedom in the estimation process, has the advantage to include specific country effects, gives more data information, reduces the multicollinearity effects, and allows for dynamic specification. All estimates were obtained from Stata. The regression results of pooled OLS are shown in Table 1, which gives the estimated coefficients of the independent variable with corresponding standard errors and the t-statistic test. VISit ¼ ci þ b1 GDP þ b2 ADV þ b3 POP þ b4 EXP þ b5 CPI þ uit , The OLS regression results are shown in Table 1, which gives the estimated coefficients of the independent variable with corresponding standard errors and the tstatistic test. The model has an R2 and adjusted-R2 of 0.69 and 0.68, respectively, and an F-value of 96.17. The variable GDP is statistically significant, with an expected positive sign. The higher the income per capita, the higher the tourism arrivals in European countries. The variable ADV is positive, meaning that advertising and promotion expenses for tourism, has positive impact on tourism demand. An increase in promotion expenses would increase tourism arrivals. Nevertheless, tstatistics shows that this variable is insignificant. The coefficient of variable POP confirms a positive sign according to the literature and is strongly significant. The higher the population in European countries the higher the tourism demand. The Table 1 Pooled OLS regression of the tourism demand model (1996–2015) C (constant) GDP ADV POP EXP CPI Total panel obs. R2 R2 Adjusted F-Statistic

Coefficient 619347.3 2053.687 0.671428 0.185497 10142.94 761.0683

Source: Survey Results

Std. Error 272243.5 410.7131 0.084306 0.000905 19506.79 2535.562 220 0.6920 0.6848 96.17

t-Statistic 2.27 5.00 0.80 20.49 0.52 0.30

Prob.- Value 0.024 0.000 0.427 0.000 0.604 0.764

Tourism Demand Modelling and Forecasting: Evidence from EU Countries

45

Table 2 Fixed Effect model estimation of the tourism demand (1996–2015) C (constant) GDP ADV POP EXP CPI Total panel obs. R-sq (overall) F (5, 204) Prob > F Rho

Coefficient 1,283,517 855.7973 0.0581578 0.0535183 14569.81 60.57613

Std. Error 240046.4 382.5305 0.029494 0.009472 6944.478 13303.67 220 0.6597 14.13 0.000 0.9859

t-Statistic 5.35 2.24 1.97 5.65 2.10 0.05

Prob.- Value 0.000 0.026 0.050 0.000 0.037 0.963

Source: Survey Results

variable EXP is negative meaning that expenditure per journey in each country has negative impact on tourism demand. An increase in journey expenses in the host country would reduce tourism arrivals. The coefficient of CPI is insignificant and has a negative sign. The application of the Hausman test for fixed effects or random effects in our study shows that the fixed effect model is the advisable estimation method for the model. The results of the fixed effects estimator are described in Table 2. The general performance of the model is very satisfactory. Results of the tourism demand model show that the coefficients are all statistically significant, with the exception of CPI. The coefficient of income per capita (GDP) is positive and significant, which means that the higher the income per capita, the higher the tourism arrivals in European countries. The coefficient of advertising expenses (ADV), as expected, has a significant positive effect. An increase in promotion expenses would increase tourism arrivals. The results also show that the higher the population (POP) in European countries, the higher the tourism demand. The coefficient of the journey expenses (EXP) is statistically significant and negative and confirms the economic hypothesis which states that an increase in journey expenses in the host country would reduce tourism arrivals. Since the CPI variable was not significant in the original model it was eliminated and the new regression results for the OLS regression and Fixed Effect model are shown in Table 3. The estimated model continues to display an excellent adjustment to the data (it explains about 78% of tourism demand). The independent variables are significant towards the dependent variable showing significant relationships. In other words, the original relationships between tourism demand and income per capita, advertising expenses, population, and journey expenses are confirmed even when the CPI variable is eliminated. Though it was expected the CPI to have a significant negative effect on tourism arrivals (Lathiras and Siriopoulos 1998; Kulendran and Wilson 2000; Hanafiah and Harun 2010) the current analysis has shown that CPI does not affect the number of

46

A. Mavrommati et al.

Table 3 Pooled OLS regression/Fixed Effect model estimation of the tourism demand (1996– 2015)

C (const.) GDP ADV POP EXP Total panel obs.

OLS Regression Coefficient 15223.3 354.721 0.96123 1.02002 942.35 R2 R2 Adjusted F-Statistic

Prob.- Value 0.000 0.012 0.006 0.000 0.008 220 0.78 0.76 97.15

Fixed Effect Model Coefficient 26253.8 156.110 1.12012 1.56058 827.581

Prob.- Value 0.000 0.002 0.000 0.000 0.006

R-sq (overall) F (5, 204) Prob > F rho

0.76 15.18 0.000 0.9868

Source: Survey Results

arrivals. However, the EXP variable has a significant negative impact in the model as expected (Au and Law 2002; Brida and Risso 2009). Thus, it can be concluded that prices do affect arrivals in a negative way. According to the findings of previous research (Surugiu et al. 2011; Deng and Athanasopoulos 2011) the GDP variable had a positive impact on tourism arrivals, as well as the population in each country (Oigenblick and Kirschenbaum 2002). Advertising expenses have also shown a significant relationship in increasing tourism demand (Song and Jiang 2019).

6 Conclusions and Recommendations The Tourism sector is an important sector in Europe in terms of contribution to growth and profitability. This paper examines the effects of structural and performance variables on tourism demand, taking into consideration the component cost of travel to the destination, the income per capita, the population, and the travel expenses among others. The most important contribution of the current research is the study of the tourism advertising expenses as an impact factor to the tourism demand model. In order to measure tourism demand we used a constructed panel database for eleven European countries that use Euro for the period 1996–2015. This study investigated the impact of various specific factors on tourist demand in European countries. Panel data using fixed effects model results suggested that 69% of the variation in eleven European countries tourist inflows could be explained by real income per capita, advertising expenses, population, and prices. All of the independent variables were significant in the panel data analysis, fixed effects model, except for the consumer price index. More specifically, the panel data estimation approach, by using a static specification, provides evidence that per capita

Tourism Demand Modelling and Forecasting: Evidence from EU Countries

47

income, expenditure price, population and advertising expenses of promoting the tourism product are the most significant explanatory factors. Based on estimation results, we can make several policy suggestions to the decision-makers and tourism suppliers. Correct prediction of tourism demand is necessary for making planning efficient. In order to sustain the tourist arrivals in Europe it is necessary to increase the income per capita and the results suggest that the tourist flows are sustained by promotion and advertising expenses, low travel prices, and cost of living in host destinations. It seems that although tourists are sensitive to prices and travel expenses, they are attracted by advertising and promotion when deciding to travel and choose a destination. Even though there is not a specific-cost-of-tourism-variable that best serves the tourist product policies, countries should find ways to remain competitive and attract tourists in relevance to prices and tourist expenditures. On the other hand, tourism advertising is an information source that dynamically affects tourists’ price decisions and spending (Song and Jiang 2019). However, the impact of advertising expenditure on generating tourists arrivals is difficult to be estimated and, therefore, countries seek to eliminate these costs. In addition to this, the different utility function faced by different consumers (given their incomes and reference prices) displays different behaviours in choosing and purchasing tourist products. Effective advertising might be, therefore, the answer to this tricky equation. The recent evolution of technology and social media in the last decades could be exploited by countries to generate arrivals with low-cost promotion campaigns. Marketing campaigns in the form of social media content (e.g. YouTube, podcasts, photos, information) affects the behaviour of tourists, as well as their decision making towards a destination (Tsiakali 2018). In this vein, the importance of advertising the tourist product can be supported by promoting tourism in less costly ways, such as social media campaigns. More importantly, given that smartphones and gadgets are part of the everyday life of the potential visitors this can be broadly interfere in tourists decision making. All in all, the cost-quality relationship of a country’s tourist products can be efficiently promoted and advertised via social media in ways that achieve the objective of attracting higher number of tourists with lower price-sensitive behaviours. As with any research, limitations exist. Research has focused only on a small number of countries based on Europe. Moreover, the countries involved in the model are members of the European union with the same currency which might affect the robustness of the results. Further research should include a higher number of European countries, as well as non-European with important GDP contribution of tourism industry. Future research is also recommended related to the explanatory factor of advertising and promotion expenses that the current research revealed.

48

A. Mavrommati et al.

References Adhikari R, Agrawal RK (2012) Forecasting strong seasonal time series with artificial neural networks. J Sci Ind Res 71:657–666 Algieri B (2006) An econometric estimation of the demand for tourism: the case of Russia. Tour Econ 12:5–20 Andrew WP, Crange DA, Lee CK (1990) Forecasting hotel occupancy rates with time series models: a empirical analysis. Hosp Res J 14(2):173–181 Assaf AG, Li G, Song H, Tsionas MG (2019) Modelling and forecasting regional tourism demand using the Bayesian global vector autoregressive (BGVAR) model. J Travel Res 58(3):383–397 Au N, Law R (2002) Categorical classification of tourism dining. Ann Tour Res 29:819–833 Baldigara T, Mamula M (2015) Modelling international tourism demand using seasonal ARIMA models. Tour Hosp Manag 21(1):19–31 Brand D (1973) Travel demand forecasting: some foundations and a review. Highway Research Board Special Report, No. 143, Williamsburg, VA Brida J, Risso W (2009) A dynamic panel data study of the German demand for tourism in south Tryrol. Tour Hosp Res 9(4):305–314 Cankurt S, Subasi A (2015) Developing tourism demand forecasting models using machine learning techniques with trend, seasonal and cyclic components. Balk J Electr Compt Eng 3 (1):42–49 Cankurt S, Subasi A (2016) Tourism demand modelling and forecasting using data mining techniques in multivariate time series: a case study in Turkey. Turk J Electr Eng Comput Sci 24(5):3388–3404 Carter HR, Griffiths WE, Liitkepohl H, Tsounh CL (1988) Introduction to the theory and practice of econometrics, 2nd edn. John Wiley, New York Chan Y (1979) Review and compilation of demand forecasting experiences: an aggregation of estimation procedures, Department of Transportation, No. DOT-P-30-80-25, Washington, DC Chan F, Lim C, McAleer M (2005) Modelling multivariate international tourism demand and volatility. Tour Manag 26:459–471 Chinnakum W, Boonyasana P (2017) Modelling Thailand tourism demand: a dual generalized maximum entropy estimator for panel data regression models. Thai J Math. Special Issue on Entropy in Econometrics, 67–78 Cho V (2003) A comparison of three different approaches to tourist arrival forecasting. Tour Manag 24:323–330 Claveria O, Torra S (2014) Forecasting tourism demand to Catalonia: neural networks vs. time series models. Econ Model 36(C):220–228 Clements MP, Hendry DF (1998) Forecasting economic time series. Cambridge University Press, Cambridge Constantino HA, Fernandes PO, Teixeira JP (2016) Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study. Rev Appl Manag Stud 14 (2):113–124 Coshall JT (2005) A selection strategy for modelling UK tourism flows by air to European destinations. Tour Econ 11:141–158 Coshall J (2006) Time series analyses of UK outbound travel by air. J Travel Res 44:335–347 Crouch GI (1994) The study of international tourism demand: a review of practice. J Travel Res 33:41–54 Deng M, Athanasopoulos G (2011) Modelling Australian domestic and international inbound travel: a spatial-temporal approach. Tour Manag 32(5):1075–1084 Dritsakis N (2004) Cointegration analysis of German and British tourism demand for Greece. Tour Manag 25:111–119 Ghalehkhondabi I, Ardjmand E, Young WA, Weckman GR (2019) A review of demand forecasting models and methodological developments within tourism and passenger transportation industry. J Tour Futures 5(1):75–93

Tourism Demand Modelling and Forecasting: Evidence from EU Countries

49

Goh C, Law R (2002) Modelling and forecasting tourism demand for arrivals with stochastic no stationarity seasonality and intervention. Tour Manag 23:499–510 Goh C, Law R (2011) The methodological progress of tourism demand forecasting: a review of related literature. J Travel Tour Mark 28(3):296–317 Gunter U, Onder I (2015) Forecasting international city tourism demand for Paris: accuracy of uniand multivariate models employing monthly data. Tour Manag 46:123–135 Han Z, Dubarry R, Sinclair MT (2006) Modelling US tourism demand for European destinations. Tour Manag 27:1–10 Hanafiah MHM, Harun MFM (2010) Tourism demand in Malaysia: a cross-sectional pool timeseries analysis. Int J Trade Econ Financ 1(1):80–83 Karakitsiou A, Mavrommati A (2017) Machine learning methods in tourism demand forecasting: some evidence from Greece. MIBES Transactions 11(1):92–105 Karlaftis MG (2010) Critical review and analysis of air-travel demand: forecasting models, computational models, software engineering, and advanced technologies in air transportation: Next Generation Applications, IGI Global, 71–87 Khaidi SM, Abul N, Sarah NM (2019) Tourism demand forecasting—a review on the variables and models. J Phys Conf Ser 1366:012111. https://doi.org/10.1088/1742-6596/1366/1/012111 Kirilenko AP, Stepchenkova S (2018) Tourism research from its inception to present day: subject area, geography, and gender distributions. PLoS One 13(11):e0206820. https://doi.org/10.1371/ journal.pone.0206820 Kon SC, Turner WL (2005) Neural network forecasting of tourism demand. Tour Econ 11:301–328 Kulendran N, Wilson K (2000) Modelling business travel. Tour Econ 6:47–59 Lathiras P, Siriopoulos C (1998) The demand for tourism to Greece: a Cointegration approach. Tour Econ 4(2):171–185 Li G, Wong KF, Song H, Witt SF (2006) Tourism demand forecasting: a time varying parameter error correction model. J Travel Res 45:175–185 Liang YH (2014) Forecasting models for Taiwanese tourism demand after allowance for mainland China tourists visiting Taiwan. Comput Ind Eng 74:111–119 Lim C (1997a) Review of international tourism demand models. Ann Tour Res 24:835–849 Lim C (1997b) An econometric classification and review of international tourism demand models. Tour Econ 3:69–81 Lim C (1999) A meta-analysis review of international tourism demand. J Travel Res 37:273–284 Lim C, McAleer M (2001) Cointegration analysis of quarterly tourism demand by Hong Kong and Singapore for Australia. Appl Econ 33:1599–1619 Maddala GS (1987) Recent developments in the econometrics of panel data analysis. Transp Res 21:303–326 Maddala GS (1991) To Pool or not to Pool: that is the question. J Quant Econ 7:255–263 Moro S, Rita P, Cortez P (2017) A text mining approach to analysing annals literature. Ann Tour Res 66:208–210 Oigenblick L, Kirschenbaum A (2002) Tourism and immigration. Ann Tour Res 29(4):1086–1100. https://doi.org/10.1016/s0160-7383(02)00023-3 Önder I (2017) Forecasting tourism demand with Google trends: accuracy comparison of countries versus cities. Int J Tour Res 19:648–660. https://doi.org/10.1002/jtr.2137 Palmer A, Montaño JJ, Sesé A (2006) Designing an artificial neural network for forecasting tourism time-series. Tour Manag 27:781–790 Rafidah A, Shabri A, Nurulhuda A, Suhaila Y (2017) A wavelet support vector machine combination model for Singapore tourist arrival to Malaysia. IOP Conf Ser Mater Sci Eng 226 (1):012077 Rosselló J (2001) Forecasting turning points in international visitor arrivals in the Balearic Islands. Tour Econ 7:365–380 Sheldon PJ, Var T (1985) Tourism forecasting: a review of empirical research. J Forecast 4 (2):183–195

50

A. Mavrommati et al.

Song H, Jiang Y (2019) Dynamic pricing decisions by potential tourists under uncertainty: the effects of tourism advertising. Tour Econ 25(2):213–234 Song H, Li G (2008) Tourism demand modelling and forecasting. Tour Res 29(2):203–220 Song H, Witt SF (2000) Tourism demand modelling and forecasting: modern econometric approaches. Pergamon, Cambridge Song H, Wong KF (2003) Tourism demand modelling: a time-varying parameter approach. J Travel Res 42:57–64 Song H, Witt SF, Li G (2003) Modelling and forecasting the demand for Thai tourism. Tour Econ 9:363–387 Song H, Li G, Witt SF, Athanasopoulos G (2011) Forecasting tourist arrivals using time-varying parameter structural time series models. Int J Forecast 27(3):855–869 Surugiu C, Leitão NC, Surugiu MR (2011) A panel data modelling of international tourism demand: evidences for Romania. Ekonomska istraživanja/Econ Res 24(1):134–145 Tang J, Sriboonchitta S, Yuan X (2015) Forecasting inbound tourism demand to China using time series models and belief functions. In: Econometrics of risk. Springer, Cham, pp 329–341 Tsiakali K (2018) User-generated-content versus marketing-generated-content: personality and contentinfluence on traveler’s behavior. J Hosp Market Manag 27(8):946–972 Turner LW, Witt SF (2001) Forecasting tourism using univariate and multivariate structural time series models. Tour Econ 7:135–147 Vanhove N (1980) Forecasting in tourism. Tour Rev 35(3):2–7 Witt SF, Witt CA (1995) Forecasting tourism demand: a review of empirical research. Int J Forecast 11:447–475 Yang X, Pan P, Evans JA, Lv B (2015) Forecasting Chinese tourist volume with search engine data. Tour Manag 46:386–397

Millennials and Digital Marketing in Tourism: The Greek Case Polina Karagianni, Lambros Tsourgiannis, Vasilios Zoumpoulidis, and Giannoula Florou

Abstract This paper aims to explore the attitudes of millennials (Generation Y) towards digital marketing applications related to peer-to-peer short-term rental services within the sharing economy in tourism sector. In particular it aims to identify the factors that influence Greek millennials to use those applications and to classify them into groups according to their attitudes towards those websites. A primary survey conducted in November–December of 2019 to a random selected sample of Greek tourists. Multivariate statistical techniques including principal component analysis (PCA) conducted to identify the main factors that affect tourists in using digital marketing application related to peer-to-peer short-term rental services within the sharing economy in tourism sector. Cluster analysis performed to classify tourists into groups according to their attitudes towards the use of those websites while discriminant analysis conducted to check cluster predictability. Non-parametric tests including chi-square test performed to profile each strategic group according to their demographic characteristics and their preferences regarding their holidays/travel.

1 Introduction To many countries, tourism plays an important role in generating revenue for the nation. Tourism is a crucial export industry for many countries and cities and as an important and necessary industry, it is considered a sector of the economy that can benefit from the various technological resources available (Liberato et al. 2018). Tourism is an important sector worldwide. In fact, in 2015, the direct contribution of travel and tourism to the world GDP was USD 2229.8 billion (3.0% of total GDP), P. Karagianni · V. Zoumpoulidis · G. Florou Accounting and Finance Department, International Hellenic University, Kavala, Greece L. Tsourgiannis (*) Accounting and Finance Department, International Hellenic University, Kavala, Greece Directorate of Public Health and Social Care of Regional District of Xanthi, Region of Eastern Macedonia and Thrace, Xanthi, Greece © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. Karanovic et al. (eds.), Tourism Management and Sustainable Development, Contributions to Economics, https://doi.org/10.1007/978-3-030-74632-2_4

51

52

P. Karagianni et al.

while its total contribution rose to 9.8% (USD 7.2 trillion), providing 1 in 11 jobs on the planet (WTTC 2016). Generation Y or millennials are people born roughly between 1980 and the mid-2000s. According to Kasriel-Alexander (2015), millennials are savvy shoppers. Generation Y is a hero generation, with a focus on brands, friends, fun, and digital culture; confident, relaxed, conservative, and the most educated generation ever (Benckendorff et al. 2010). The same researchers argued that for millennials, safety is very important, they value teams and collaboration, are multitaskers, strongly influenced by friends and peers. Millennials are traveling more often, book more over the internet and usually spend more on travel, are avid of information and experiences as well as willing to explore more destination (Benckendorff et al. 2010). Furthermore, according to Futurecast (2016) 55% of millennials agree that travel is all about discovery and adventure, 70% of them want to explore and learn from the communities they visit, 45% agree that traveling is about getting connected to other cultures and 90% like to experience new things while on vacation. The three quarters of millennials have travel apps on their phones, 90% have researched travel on a laptop/desktop computer, 74% have used their smartphone/tablet, and 62% have used only a smartphone. One-half of millennials say they are “travel hackers,” meaning they know all of the best sites and methods to get the best travel deals. A vast majority (62%) of millennials extended their business trips into personal vacations to squeeze in more value while weekend trips make up almost half of all millennial vacation (Futurecast 2016). Furthermore, millennials check on average 10 sources before travel purchases (Futurecast 2016; Lee 2013). However, Kressmann Jeremy and the Skift Team (2016) argued that millennials prioritize travel and experiences over other purchases, spending more on travel than on other items. The buying behavior and decision making process of digital consumers is influenced by quite different factors in comparison with those of conventional consumers (Constantinides 2004). Some of them are the functionality of the websity, the psychological elements aiming in reducing the uncertainty of the client, the trust and reliability of the on line seller and the website. According to (Monga and Kaplash 2016) consumers use the web for their purchases in order to gain from the easiness of the booking, the comparative pricing while the on line organizations provide flexibility, access and convenience to the interest parties to seek and buy traveling products and services in a short period of time. A variety of emerging platforms including Airbnb, and Couchsurfing disrupt the traditional corporate business models associated with rental housing and hospitality and develop new peer-to-peer marketplaces that challenge established industries. These new platforms facilitate connections between hosts who rent spaces in their homes or secondary properties and guests from around the world contrary to a single company managing buildings, terms, and leases. The sharing economy is often related to Internet and mobile technologies, and it involves consumers maintaining access to goods and services such as bike-sharing without owning them, and ordinary individuals renting out or otherwise offering access to their underused assets (e.g., ride-hailing services like UberX) (Belk 2014; Guttentag et al. 2017).

Millennials and Digital Marketing in Tourism: The Greek Case

53

The rise peer-to-peer short-term rental services within the sharing economy represent a transformative innovation within the tourism accommodation industry (Guttentag et al. 2017). On the other hand there are some other studies that identified a variety of key attributes influencing hotel decisions, including cleanliness, location, reputation, price, staff friendliness and helpfulness, room comfort, and security (Chu and Choi 2000; Dolnicar and Otter 2003). The literature about non-hotel forms of accommodation (e.g., bed-and-breakfasts, homestays) has focused on the choice to use these alternative forms of accommodation more generally. Hence, McIntosh and Siggs (2005) argued that alternative accommodation guests enjoyed the unique character and homely feel of the accommodations, the personalized service and personal interaction with the hosts, and the opportunity to receive useful local knowledge from the hosts. Moreover, Stringer (1981) examined guests of British bed-and-breakfasts and found they were drawn by both the experience and the economical price. Many studies indicated many factors that are influencing tourists in choosing digital platforms including economic benefits (Nowak et al. 2015; Tussyadiah 2015) household amenities and space (Quinby and Gasdia 2014) authenticity (Lamb 2011; Nowak et al. (2015), interaction with locals (Tussyadiah 2015; Tussyadiah and Pesonen 2016; Guttentag et al. 2017) and location (Nowak et al. 2015). Furthermore, Gong and Zheng (2018) identified that “price value” and “enjoyment” have a significant influence both on American and Canadian consumers and Chinese consumers. Both the two groups are not influenced by “authenticity,” “social interaction,” or “perceived risk” significantly. Potential motivations to use websites like Airbnb were proposed as relating to six different dimensions—price, functional attributes, unique and local authenticity, novelty, bragging rights, and sharing economy ethos (Guttentag 2016). Brand personality, communication channels, trip characteristics and travel decisions, and satisfaction and loyalty are also related to the selection of web applications like Airbnb according to (Guttentag 2016). Tsourgiannis and Valsamidis (2019) identified that some factors included unique and local authenticity, functional rights, novelty, sharing economic ethos, low cost, and convenience, influence Greek tourists to use digital applications like Airbnb website. They also classified tourists into three groups according to their attitudes towards the use of such applications: (a) pioneers, (b) convenience seekers, and (c) conscious. Moreover, tourists’ age, sex, and educational level and occupation as well as the aim and duration of their trip, their annual expenses for holidays, type of preferred accommodation and the type of other co-travelers, have a significant impact on the adoption of marketing application related to peer-to-peer rental services within sharing economy in tourism sector by Greek tourists (Tsourgiannis and Valsamidis 2019). This paper aims to explore the attitudes of millenials towards digital marketing applications related to peer-to-peer short-term rental services within the sharing economy in tourism sector. In particular it aims to identify the factors that make Greek millennials to use those applications and to classify them into groups according to their attitudes towards those websites.

54

P. Karagianni et al.

2 Methodology 2.1

The Conceptual Model

This paper adopted the conceptual model that was developed by Tsourgiannis and Valsamidis (2019) to place key concepts into an identifiable framework (Fig. 1), in order to investigate the relationships between factors affecting millenials’ behavior in Greece towards digital marketing applications. The null research hypotheses this study aimed to reject were: Ho1 Millennials cannot be classified into groups according to their attitudes towards digital marketing applications in tourism sector. Ho2 The demographic and personal characteristics of millenials are not significantly related to their attitudes towards digital marketing applications in tourism sector. Ho3 Millenials’ preferences regarding their holidays/travel are not associated with a particular attitude towards digital marketing applications in tourist sector.

Ho2

Factors affecting millenials’ attitudes towards digital marketing applications in tourist sector

Ho1

Classification of milllenials into groups according attitudes towards digital marketing applications in tourist sector

Demographic millenials’ characteristics (age, education, occupation, etc)

Ho3

Millenials’ preferences regarding their holidays/travel

Fig. 1 A Conceptual Model for classifying millenials according to their attitudes towards digital marketing applications in tourism sector (adopted by Tsourgiannis and Valsamidis 2019)

Millennials and Digital Marketing in Tourism: The Greek Case

2.2

55

Methodology

A survey based on phone interviews was conducted throughout Greece to gather the necessary information. Cluster sampling method was used to form the sample. In particular, the general population was stratified into two levels: regions and prefectures. Based on the methodology presented by Oppenheim (2000), in order to have representative sample for the geographical area of Greece, one prefecture from each of the thirteen regions existed in Greece was randomly selected. The sampling took place at the capital of each prefecture. The total number of people that were questioned at each sampled prefecture was 30 respondents, randomly selected. A total productive sample of 370 people came up from the survey methodology while the 149 of them are millennials. Prior to the main sampling, a pilot survey took place to evaluate if the research objectives were met by the designed questioner. The pilot survey was performed for a total of 30 respondents in the town of Xanthi. Based on the analyzed results, the survey sample was considered adequate to conduct the main survey with no further modification. Multivariate analysis techniques were applied in three stages to the responses for the total of 149 respondents of Generation Y to reveal the key information these contained. Principal Component Analysis (PCA) was used to identify the variables that accounted for the maximum amount of variance within the data in terms of the smallest number of uncorrelated variables (components). The anti-image correlation matrix, as well as, the Bartlett’s test of sphericity and the Measure of Sampling Adequacy (MSA) were used in order to check the fitness of the data for subsequent factor analysis. The variables with a high proportion of large absolute values of antiimage correlations and MSA less than 0.5 were removed before analysis. An orthogonal rotation (varimax method) was conducted and the standard criteria of eigenvalue ¼ 1, scree test and percentage of variance were used in order to determine the factors in the first rotation (Hair et al. 1998). Different trial rotations followed, where factor interpretability was used to compare the reduced through PCA variables. These PCA scores were then subjected to cluster analysis to classify millennials with similar patterns of scores into similar clusters of attitudes towards those digital applications. Both hierarchical and non-hierarchical methods were used (Hair et al. 1998) in order to develop a typology of the millenials’ attitudes towards those applications. Quadratic Discriminant Analysis (QDA) was performed to assess how accurately the key identified factors could predict and discriminate cluster membership through factor analysis. Furthermore, the chi-square analysis was performed to develop the profile of each group of respondents regarding their demographic characteristics as well as to explore the association between millenials’ preferences regarding their holidays/travel and their attitudes towards digital marketing applications in tourism sector.

56

P. Karagianni et al.

3 Results-Discussion 3.1

Factors Affecting Attitudes towards those Digital Applications

Principal components and factor analyses (through a varimax rotation) were conducted to identify the key attitude variables, and the latent root criterion (eigenvalue ¼1), and the percentage of variance were used to determine the number of factors (Table 1). Several different trial rotations were conducted to compare factor interpretability as suggested by Hair et al. (1998). PCA identified two key factors that affect millennials to use digital marketing applications related to peer-to-peer short-term rental services within the sharing economy in tourism sector (Table 2). In the next stage, hierarchical and non-hierarchical clustering methods were used to develop a typology of the attitudes of millennials towards the use of digital marketing application related to peer-to-peer short-term rental services within the sharing economy in tourism sector (Hair et al. 1998). Cluster analysis was conducted on the 149 observations, as there were no outliers. It identified three groups that were named according to the attitudes of millenials towards the adoption of those applications (Table 3). These are: (a) the opportunists, (b) conscious, (c) convenience seekers. In particular opportunists comprise 15% of the sample. They use digital marketing applications like Airbnb platform probably due to curiosity as they are not influenced by any identified factor. On the other hand convenience seekers comprise 57% of the sample. They pay attention to: (a) convenient location of the accommodation, (b) low cost accommodation, (c) access to household amenities (e.g., fridge, stove, washing machine), (d) large amount of space—big rooms, (e) ease of resolving unexpected problems (e.g., no hot water), (f) easy and fast booking, (g) cleanliness. Table 1 Results of Principal Component Analysis regarding tourists’ attitudes towards those digital applications Component 1 2 3 4 5 6 7 8 9 10

Eigenvalue 4.630 2.353 0.993 0.692 0.462 0.331 0.224 0.164 0.092 0.059

%Variance 46.300 23.529 9.927 6.917 4.625 3.313 2.236 1.640 0.921 0.592

KMO MSA ¼ 0.730 Bartlett test of Sphericity ¼ 1205.389 P < 0.001

%Cumulative variance 46.300 68.829 79.756 86.673 91.928 94.610 96.846 98.487 99.408 100.00

Millennials and Digital Marketing in Tourism: The Greek Case

57

Table 2 The main factors that affect millennials to use digital marketing applications in tourism sector derived from principal component analysis Main factors affecting millennials in using digital marketing applications like Airbnb Convenience Convenient location of the accommodation Low cost of accommodation Access to household amenities (e.g., fridge, stove, washing machine) Large amount of space—Big rooms Ease of resolving unexpected problems (e.g., no hot water) Easy booking Cleanliness Getting experience Authentic local experience Opportunity to stay in a non-touristy, residential neighborhood Living a unique experience

Factor loadings 0.952 0.886 0.875 0.833 0.734 0.634 0.568 0.882 0.841 0.581

Table 3 Classification of millennials regarding their attitudes towards the use of digital platforms related to peer-to-peer short-term renting within sharing economy in tourism sector Main factors affecting tourists in using digital marketing applications related to peer-to-peer rental services within sharing economy in tourism sector Convenience Getting experience Number of persons (n ¼ 149)

Opportunists 2.20012 0.12166 22

Convenience seekers 0.33010 0.61781 85

Conscious 0.48438 1.18660 42

P 0.001 0.001

Besides, the conscious comprise 28% of the sample. They are motivated in using digital platforms related to peer-to-peer short-term renting within sharing economy including Airbnb by the opportunity to live an authentic local and unique experience as well as to stay in a non-touristic, residential neighborhood. They are also interested in factors including a convenient location of the accommodation, low cost accommodation, access to household amenities (e.g., fridge, stove, washing machine, large amount of space—big rooms) ease of resolving unexpected problems (e.g., no hot water) as well as easy booking and cleanliness. Moreover discriminant analysis was conducted to evaluate the prediction of group membership by the predictors derived from the factor analysis. The summary of the cross validation classification derived by the quadratic discriminant analysis is shown in Table 4. Thus, the two key factors that affect Greek millennials in using digital marketing applications related to peer-to-peer rental services within sharing economy in tourism sector could accurately predict and discriminate consumers’ group membership. Therefore, the hypothesis Ho1: Millennials cannot be classified into groups according to their attitudes towards digital marketing applications in tourism sector maybe rejected.

58

P. Karagianni et al.

Table 4 Summary of classification with cross-validation Actual Classification Opportunists Convenience seekers Conscious Total N N correct Proportion Number of hotels (n ¼ 179)

Predicted Classification Opportunists Convenience seekers 22 0 0 85 0 0 22 85 22 85 100% 100% N correct ¼ 179 Proportion Correct ¼ 100%

Conscious 0 0 42 42 42 100%

Table 5 Profiling each group of tourists according to their demographic characteristics Demographic characteristics Gender x2 ¼ 111.563 P < 0.001 Occupation x2 ¼ 122.673 P < 0.001

Education x2 ¼ 46.251 P < 0.001

3.2

Opportunists Male Female Private employee Civil servant Free licensed Retiree Student Unemployment Primary School Secondary School High School University Degree Postgraduate Degree

Convenience seekers (%) 45.2 54.8 24.1 15.3 40.2 5.1 14.8 0.5 18.2

Conscious (%) 49.4 73.8 50.6 17.3 19.7 40.2 43.6 34.3 28.0 21.9 7.8 2.4 9.3 0.6 1.6 0.0 0.0 0.0

35.4 9.1 37.4

50.6 36.7 11.8

50.0 50.0 0.0

Profiling each Group of Millennials According to their Demographic Characteristics

A chi-square analysis was also performed for each group of tourists in order to develop their profile regarding their demographic characteristics. As Table 5 indicates, Opportunists are mainly female, have a post graduate degree and are free licensed. Convenience seekers are mainly women, attended the high school and are civil servants. Finally conscious are male, private employees who finished the high school. Therefore the hypothesis Ho2: Ho2: The demographic and personal characteristics of millennials are not significantly related to their attitudes towards digital marketing applications in tourism sector, may be rejected.

Millennials and Digital Marketing in Tourism: The Greek Case

3.3

59

Profiling of each Group of Tourists According to their Preferences Regarding their Holidays/Travel

A chi-square analysis was also conducted for each consumer group in order to explore the association between tourists’ attitudes towards the adoption of digital marketing application related to peer-to-peer rental services within sharing economy in tourism sector and their preferences regarding their holidays/travel. As Table 6 indicates most of opportunists use digital marketing application related to peer-topeer rental services when they travel for business purposes. The duration of their accommodation is for 2–3 nights and they tend to spend less than 500 euros and prefer to rent an entire house or apartment, through those applications. The majority of the convenience seekers spend 4–7 nights for their holidays, prefer to stay in a private bedroom, their holiday budget is between 7 nights