Eurasian Economic Perspectives: Proceedings of the 24th Eurasia Business and Economics Society Conference [1st ed.] 978-3-030-18564-0;978-3-030-18565-7

Table of contents :
Front Matter ....Pages i-xiv
Front Matter ....Pages 1-1
The Financial Benefits of Training on the Labor Market: Evidence from Romania (Madalina Ecaterina Popescu, Ramona-Mihaela Paun)....Pages 3-12
The Nexus Between Economic Development and Environmental Pollution: The Case of Lithuania (Lina Sineviciene, Aura Draksaite, Violeta Naraskeviciute, Oleksandr Kubatko)....Pages 13-23
The Impact of Changes in Time to Maturity on the Risk of Geometric Options (Ewa Dziawgo)....Pages 25-35
Front Matter ....Pages 37-37
A Conceptual Paper of the Smart City and Smart Community (Normalini M. D. Kassim, Jasmine Ai Leen Yeap, Saravanan Nathan, Nor Hazlina Hashim, T. Ramayah)....Pages 39-47
Tourist Destination Assessment by Revised Importance-Performance Analysis (Olimpia I. Ban, Victoria Bogdan, Delia Tușe)....Pages 49-68
Front Matter ....Pages 69-69
Corrupt Practices in Public Procurement: Evidence from Poland (Arkadiusz Borowiec)....Pages 71-82
Evaluation System for the Public Institutions Employees (Stefania Cristina Mirica, Liliana Mihaela Moga, Bucur Iulian Dediu)....Pages 83-91
Front Matter ....Pages 93-93
An Overview of the Trends in the Evaluation System for the Public Management Performance (Bucur Iulian Dediu, Liliana Mihaela Moga, Stefania Cristina Mirica)....Pages 95-103
Assessment of the Importance of Agri-Food Products Trade Between the European Union and China (Aneta Jarosz-Angowska)....Pages 105-123
Economic and Environmental Performance of Post-Communist Transition Economies (Lina Sineviciene, Oleksandra V. Kubatko, Iryna M. Sotnyk, Ausrine Lakstutiene)....Pages 125-141

Citation preview

Eurasian Studies in Business and Economics 11/1 Series Editors: Mehmet Huseyin Bilgin · Hakan Danis

Mehmet Huseyin Bilgin Hakan Danis Ender Demir Ugur Can Editors

Eurasian Economic Perspectives Proceedings of the 24th Eurasia Business and Economics Society Conference

Eurasian Studies in Business and Economics 11/1

Series editors Mehmet Huseyin Bilgin, Istanbul, Turkey Hakan Danis, San Francisco, CA, USA Representing Eurasia Business and Economics Society

More information about this series at http://www.springer.com/series/13544

Mehmet Huseyin Bilgin • Hakan Danis • Ender Demir • Ugur Can Editors

Eurasian Economic Perspectives Proceedings of the 24th Eurasia Business and Economics Society Conference

Editors Mehmet Huseyin Bilgin Faculty of Political Sciences Istanbul Medeniyet University Istanbul, Turkey Ender Demir Faculty of Tourism Istanbul Medeniyet University Istanbul, Turkey

Hakan Danis MUFG Union Bank San Francisco, CA, USA Ugur Can Eurasia Business & Economic Society Fatih, Istanbul, Turkey

The authors of individual papers are responsible for technical, content, and linguistic correctness. ISSN 2364-5067 ISSN 2364-5075 (electronic) Eurasian Studies in Business and Economics ISBN 978-3-030-18564-0 ISBN 978-3-030-18565-7 (eBook) https://doi.org/10.1007/978-3-030-18565-7 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved 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, express 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

This is Volume 1—Eurasian Economic Perspectives of the 11th issue of the Springer’s series Eurasian Studies in Business and Economics, which is the official book series of the Eurasia Business and Economics Society (EBES, www. ebesweb.org). This issue includes selected papers presented at the 24th EBES Conference—Bangkok, which was held on January 10–12, 2018, in Bangkok, Thailand, jointly organized by the Faculty of Business Administration Kasetsart University with the support of the Istanbul Economic Research Association. Distinguished colleagues Jonathan Batten from University Utara Malaysia, Malaysia; Euston Quah from the Nanyang Technological University, Singapore; Naoyuki Yoshino from the Asian Development Bank Institute, Japan; and Partha Sen from Delhi School of Economics, India, joined the conference as keynote speakers. During the conference, participants had many productive discussions and exchanges that contributed to the success of the conference where 178 papers by 334 colleagues from 47 countries were presented. In addition to publication opportunities in EBES journals (Eurasian Business Review and Eurasian Economic Review, which are also published by Springer), conference participants were given the opportunity to submit their full papers for this issue. Theoretical and empirical papers in the series cover diverse areas of business, economics, and finance from many different countries, providing a valuable opportunity to researchers, professionals, and students to catch up with the most recent studies in a diverse set of fields across many countries and regions. The aim of the EBES conferences is to bring together scientists from business, finance, and economics fields, attract original research papers, and provide them publication opportunities. Each issue of the Eurasian Studies in Business and Economics covers a wide variety of topics from business and economics and provides empirical results from many different countries and regions that are less investigated in the existing literature. All accepted papers for the issue went through peer-review process and benefited from the comments made during the conference

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as well. The current issue covers fields such as economic development, finance, tourism, public economic, and regional studies. Although the papers in this issue may provide empirical results for a specific county or regions, we believe that the readers would have an opportunity to catch up with the most recent studies in a diverse set of fields across many countries and regions and empirical support for the existing literature. In addition, the findings from these papers could be valid for similar economies or regions. On behalf of the series editors, volume editors, and EBES officers, I would like to thank all presenters, participants, board members, and the keynote speakers, and we are looking forward to seeing you at the upcoming EBES conferences. Istanbul, Turkey

Ender Demir

Eurasia Business and Economics Society (EBES)

EBES is a scholarly association for scholars involved in the practice and study of economics, finance, and business worldwide. EBES was founded in 2008 with the purpose of not only promoting academic research in the field of business and economics but also encouraging the intellectual development of scholars. In spite of the term “Eurasia,” the scope should be understood in its broadest term as having a global emphasis. EBES aims to bring worldwide researchers and professionals together through organizing conferences and publishing academic journals and increase economics, finance, and business knowledge through academic discussions. Any scholar or professional interested in economics, finance, and business is welcome to attend EBES conferences. Since our first conference in 2009, around 11,157 colleagues from 98 countries have joined our conferences and 6379 academic papers have been presented. Also, in a short period of time, EBES has reached 2050 members from 84 countries. Since 2011, EBES has been publishing two academic journals: Eurasian Business Review (EABR) and Eurasian Economic Review (EAER). While both journals are indexed in Scopus, EABR and EAER are indexed in Social Science Citation Index and Emerging Sources Citation Index, respectively. Furthermore, EABR is in the fields of industrial organization, innovation, and management science, and EAER is in the fields of applied macroeconomics and finance. Both journals are published quarterly, and they have been published by Springer since 2014. Moreover, since 2014 Springer has started to publish a new conference proceedings series (Eurasian Studies in Business and Economics) which includes selected papers from the EBES conferences. The 10th, 11th, 12th, 13th, 14th, 15th, 16th, 17th, 19th, and 20th (Vol. 2) EBES Conference Proceedings have already been accepted for inclusion in the Thomson Reuters’ Conference Proceedings Citation Index. The 18th and subsequent conference proceedings are in progress.

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Eurasia Business and Economics Society (EBES)

On behalf of the EBES officers, I sincerely thank you for your participation and look forward to seeing you at our future conferences. In order to improve our future conferences, we welcome your comments and suggestions. Our success is only possible with your valuable feedback and support. I hope you enjoy the conference and Bangkok. With my very best wishes, Klaus F. Zimmermann, PhD President EBES Executive Board Klaus F. Zimmermann, UNU-MERIT, Maastricht University, The Netherlands Jonathan Batten, University Utara Malaysia, Malaysia Iftekhar Hasan, Fordham University, U.S.A. Euston Quah, Nanyang Technological University, Singapore Peter Rangazas, Indiana University-Purdue University Indianapolis, U.S.A. John Rust, Georgetown University, U.S.A. Marco Vivarelli, Università Cattolica del Sacro Cuore, Italy

EBES Advisory Board Hassan Aly, Department of Economics, Ohio State University, U.S.A. Ahmet Faruk Aysan, Istanbul Sehir University, Turkey Michael R. Baye, Kelley School of Business, Indiana University, U.S.A. Mohamed Hegazy, School of Management, Economics and Communication, The American University in Cairo, Egypt Cheng Hsiao, Department of Economics, University of Southern California, U.S.A. Philip Y. Huang, China Europe International Business School, China Noor Azina Ismail, University of Malaya, Malaysia Irina Ivashkovskaya, State University – Higher School of Economics, Russia Hieyeon Keum, University of Seoul, South Korea Christos Kollias, Department of Economics, University of Thessaly, Greece William D. Lastrapes, Terry College of Business, University of Georgia, U.S.A. Brian Lucey, The University of Dublin, Ireland Rita Martenson, School of Business, Economics and Law, Goteborg University, Sweden Steven Ongena, University of Zurich, Switzerland Panu Poutvaara, Faculty of Economics, University of Munich, Germany Peter Szilagyi, Central European University, Hungary Amine Tarazi, University of Limoges, France Russ Vince, University of Bath, United Kingdom Wing-Keung Wong, Department of Finance, Asia University, Taiwan Naoyuki Yoshino, Faculty of Economics, Keio University, Japan

Eurasia Business and Economics Society (EBES)

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Organizing Committee Klaus F. Zimmermann, PhD, Maastricht University, The Netherlands Mehmet Huseyin Bilgin, PhD, Istanbul Medeniyet University, Turkey Hakan Danis, PhD, Union Bank, U.S.A. Alina Klonowska, PhD, Cracow University of Economics, Poland Jonathan Tan, PhD, Nanyang Technological University, Singapore Sofia Vale, PhD, ISCTE Business School, Portugal Ender Demir, PhD, Istanbul Medeniyet University, Turkey Orhun Guldiken, PhD, University of Arkansas, U.S.A. Ugur Can, EBES, Turkey

Reviewers Sagi Akron, PhD, University of Haifa, Israel Ahmet Faruk Aysan, PhD, Central Bank of the Republic of Turkey, Turkey Mehmet Huseyin Bilgin, PhD, Istanbul Medeniyet University, Turkey Hakan Danis, PhD, Union Bank, U.S.A. Ender Demir, PhD, Istanbul Medeniyet University, Turkey Giray Gozgor, PhD, Istanbul Medeniyet University, Turkey Orhun Guldiken, University of Arkansas, U.S.A. Peter Harris, PhD, New York Institute of Technology, U.S.A. Mohamed Hegazy, The American University in Cairo, Egypt Gokhan Karabulut, PhD, Istanbul University, Turkey Christos Kollias, University of Thessaly, Greece Davor Labaš, PhD, University of Zagreb, Croatia Chi Keung Marco Lau, PhD, University of Northumbria, United Kingdom Gregory Lee, PhD, University of the Witwatersrand, South Africa Nidžara Osmanagić-Bedenik, PhD, University of Zagreb, Croatia Euston Quah, PhD, Nanyang Technological University, Singapore Peter Rangazas, PhD, Indiana University-Purdue University Indianapolis, U.S.A. Doojin Ryu, PhD, Chung-Ang University, South Korea Sofia Vale, PhD, ISCTE Business School, Portugal Manuela Tvaronavičienė, PhD, Vilnius Gediminas Technical University, Lithuania

Contents

Part I

Growth, Development and Finance

The Financial Benefits of Training on the Labor Market: Evidence from Romania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Madalina Ecaterina Popescu and Ramona-Mihaela Paun The Nexus Between Economic Development and Environmental Pollution: The Case of Lithuania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lina Sineviciene, Aura Draksaite, Violeta Naraskeviciute, and Oleksandr Kubatko The Impact of Changes in Time to Maturity on the Risk of Geometric Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ewa Dziawgo Part II

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Tourism

A Conceptual Paper of the Smart City and Smart Community . . . . . . . Normalini M. D. Kassim, Jasmine Ai Leen Yeap, Saravanan Nathan, Nor Hazlina Hashim, and T. Ramayah Tourist Destination Assessment by Revised Importance-Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Olimpia I. Ban, Victoria Bogdan, and Delia Tușe Part III

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Public Economics

Corrupt Practices in Public Procurement: Evidence from Poland . . . . . Arkadiusz Borowiec

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Evaluation System for the Public Institutions Employees . . . . . . . . . . . . Stefania Cristina Mirica, Liliana Mihaela Moga, and Bucur Iulian Dediu

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Part IV

Contents

Regional Studies

An Overview of the Trends in the Evaluation System for the Public Management Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bucur Iulian Dediu, Liliana Mihaela Moga, and Stefania Cristina Mirica

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Assessment of the Importance of Agri-Food Products Trade Between the European Union and China . . . . . . . . . . . . . . . . . . . . . . . . 105 Aneta Jarosz-Angowska Economic and Environmental Performance of Post-Communist Transition Economies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Lina Sineviciene, Oleksandra V. Kubatko, Iryna M. Sotnyk, and Ausrine Lakstutiene

List of Contributors

Olimpia I. Ban Department of Economics and Business, University of Oradea, Oradea, Romania Victoria Bogdan Department of Finance and Accounting, University of Oradea, Oradea, Romania Arkadiusz Borowiec Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland Bucur Iulian Dediu The School for Doctoral Studies in the Socio-Humanities, Dunarea de Jos University of Galati, Galati, Romania Aura Draksaite School of Economics and Business, Kaunas University of Technology, Kaunas, Lithuania Ewa Dziawgo Faculty of Administration and Social Sciences, Kazimierz Wielki University, Bydgoszcz, Poland Nor Hazlina Hashim School of Communication, Universiti Sains Malaysia, Penang, Malaysia Aneta Jarosz-Angowska Department of Economics and Agribusiness, University of Life Sciences in Lublin, Lublin, Poland Normalini M. D. Kassim School of Management, Universiti Sains Malaysia, Penang, Malaysia Oleksandra V. Kubatko Department of Economics and Business Administration, Sumy State University, Sumy, Ukraine Ausrine Lakstutiene School of Economics and Business, Kaunas University of Technology, Kaunas, Lithuania Stefania Cristina Mirica Department of Juridical Sciences, The School for Doctoral Studies in the Socio-Humanities, Dunarea de Jos University of Galati, Galati, Romania xiii

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List of Contributors

Liliana Mihaela Moga Department of Economics, Dunarea de Jos University of Galati, Galati, Romania Violeta Naraskeviciute School of Economics and Business, Kaunas University of Technology, Kaunas, Lithuania Saravanan Nathan Telekom Malaysia Berhad, Penang, Malaysia Ramona-Mihaela Paun George Herbert Walker School of Business and Technology, Webster University Thailand, Bangkok, Thailand Madalina Ecaterina Popescu The National Scientific Research Institute for Labor and Social Protection, The Bucharest University of Economic Studies, Bucharest, Romania T. Ramayah School of Management, Universiti Sains Malaysia, Penang, Malaysia Lina Sineviciene School of Economics and Business, Kaunas University of Technology, Kaunas, Lithuania Iryna M. Sotnyk Department of Economics and Business Administration, Sumy State University, Sumy, Ukraine Delia Tușe Department of Mathematics and Informatics, University of Oradea, Oradea, Romania Jasmine Ai Leen Yeap School of Management, Universiti Sains Malaysia, Penang, Malaysia

Part I

Growth, Development and Finance

The Financial Benefits of Training on the Labor Market: Evidence from Romania Madalina Ecaterina Popescu and Ramona-Mihaela Paun

Abstract In this paper we study the direct effects of vocational training upon earnings, and bring empirical evidence for the case of Romania. In order to determine whether vocational training generates an increase in earnings, a quantitative analysis is conducted and econometric tools are applied on both a treatment and a control group. Survey micro-datasets are used together with a probit model, a Kernel matching algorithm and an ordered logit. The main findings are consistent with the international literature in the field, arguing in favor of a positive return on training, as higher earnings are to be expected after attending training programs. Keywords Vocational training · Earnings · Impact evaluation · Ordered logit

1 Introduction As vocational training has become one of the popular active labor market policies, more and more studies have focused on evaluating its impact on the labor market. Although the vast majority of empirical studies focused on its effects on employability, significant concerns are also raised on the financial benefits of training. This paper is, therefore, dedicated to the direct effects of vocational training on earnings. The case of Romania is discussed, and empirical evidences are brought in favor of the human capital theory. The research question underpinned in this study refers to the impact of vocational training upon earnings. Since official databases could not provide specific wage information at individual level, survey micro-datasets were used in the analysis. The methodological approach M. E. Popescu The National Scientific Research Institute for Labor and Social Protection, The Bucharest University of Economic Studies, Bucharest, Romania R.-M. Paun (*) George Herbert Walker School of Business and Technology, Webster University Thailand, Bangkok, Thailand e-mail: [email protected] © Springer Nature Switzerland AG 2019 M. Huseyin Bilgin et al. (eds.), Eurasian Economic Perspectives, Eurasian Studies in Business and Economics 11/1, https://doi.org/10.1007/978-3-030-18565-7_1

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relies on a quasi-experiment which assumes building a counterfactual scenario based on two distinct groups of individuals that were registered as unemployed in 2013 by the Romanian National Agency of Employment (NAE). The two groups were made up of individuals that shared rather similar observable characteristics, but with the difference that the individuals assigned to the treatment group attended a training program, while the control group consisted of individuals who did not attend such a program. The structure of the paper is the following: Sect. 2 provides a brief state of the art of previous empirical studies in the field, followed by Sect. 3 which is dedicated to data description and methodology. The main findings of the study are presented in Sect. 4, while the conclusions are summarized in the last section.

2 Literature Review Economists have tried for quite some time to provide an explanation for wage differentials. According to the early work of Becker (1964) and Mincer (1958), if other things are being equal, then personal income varies depending on the amount of investment in human capital. Such investments refer to education and training undertaken by individuals or groups of people. There are several empirical studies that looked into the financial benefits of training although the vast majority focused on the impact of trainings on employability. For instance, Bartel (1995) used a large US company data covering the period between 1986 and 1990 and found that wages of workers that have attended on-the-job training were 10.6% higher than those who did not undertake training, and that one additional day of training raised their wages by 1.6%. When considering the advantages of long versus short training programs based on the Swedish Level of Living Survey for 1968, 1981 and 1991, Regner (2002) found that employees with jobs that require long on-the-job training earn 15.7% more than workers with jobs that have short training requirements. In addition, the wage effects of on the job training vary significantly between men and women, and the private and public sectors. The wage premium of long training is 20.8% for men versus 13.5% for women, and 18.1% for employees working in the private sector versus 11% for those working in the public sector. The study also highlighted that publicsector employees benefit more from specific training, whereas those working for the private sector gain more from general training, while the wage effect of on-the-job training is larger for recently hired employees than for senior employees. In another study, Greenberg et al. (2003) checked the effects of 15 different training programs sponsored by the US government between 1964 and 1998 for disadvantaged, low-income individuals. They considered three groups: adult women, adult men and youth, and concluded that the effects of training are the highest for women, rather small for men and negligible for youths. For adults, the effects persisted for several years. Another interesting finding of this study shows that training programs that are more expensive are not necessarily the ones generating the highest returns.

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Budria and Pereira (2004) used pooled data for Portugal covering the period from 1998 to 2000 and found that the returns to training are much higher for women than men but the effects differ depending on education and experience. No significant differences in returns to training were found between the private sector and the public sector even though some particularities were noticed indicating that experience matters in the private sector while education leads to higher earnings in the public sector. Arulampalam et al. (2004) examined the returns to training for ten European countries to see if they are similar for employees that are part of different wage groups. Their findings show that, except for Belgium, training yields similar percentage returns across the conditional wage distribution. In Belgium, employees that are part of the lower wage group seem to enjoy a larger return on training than those in higher wage group. In another study, Muehler et al. (2007) applied non-parametric matching estimation in order to compare the effects of general continuous training versus firmspecific training programs on wages. Based on the German Socio-Economic Panel (GSOEP) datasets, they found that general training yields up to 5–6% wage increase, while firm-specific trainings have rather insignificant effects. Their findings are consistent with the human capital theory, as general training is normally associated with higher financial benefits than firm-specific training. Heng et al. (2007) used data from a labor force survey on training conducted in 2004 in Singapore that included 2400 respondents and concluded that the welleducated and higher earning individuals are more likely to attend training programs but the ones that benefit the most are the relatively low paid workers. Workers with relatively low earnings and less than 10 years of experience are more likely to consider the training successful and get a pay rise or promotion. For workers under 37 years old, age has a small positive impact on training participation which becomes negative for older workers. A possible explanation might be the smaller incentive for employers to send their senior workers for training, either due to the higher opportunity cost or the narrower time horizon of getting the benefits out of the training program. For the case of Romania, there are less impact evaluation studies on the impact of vocational training on the labor market (see Popescu and Roman 2018; Pirciog et al. 2015; Roman and Popescu 2015; Rodriguez-Planas and Benus 2006). Among them, however, only Roman and Popescu (2015) actually focused on evaluating the effects of training on the Romanian migrants’ income through a propensity score matching approach. The empirical study was based on datasets collected through an online survey conducted in 2010 upon Romanian migrants worldwide that were divided between a control and a treatment group. The conclusions once again supported the theory that higher incomes are expected after attending training programs. More recently, Popescu and Roman (2018) studied the effects of training upon employability in Romania and found a positive, but rather modest impact on employability for persons below 55 years old. More precisely, the participation to training increased the chances of employment by 15%, being more successful for women living in urban areas. The authors also made some recommendations on better targeting and profiling the vocational training participants in Romania in order

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to facilitate a smoother insertion on the labor market and a better match between people’s skills and the requirements of the labor market.

3 Data Description and Methodology Since the official NAE database lacks wage information available at individual level, survey micro-datasets were used in the analysis. The survey was conducted in October 2014 by the National Scientific Research Institute for Labor and Social Protection on approximately 400 respondents one and a half years after the training measure was implemented. In order to quantify the impact of vocational training on earnings, we built a counterfactual scenario that allows estimating what would have happened to the treated persons in the absence of the intervention. A quasi-experimental approach was thus applied, and individuals registered as unemployed in 2013 by the NAE were randomly selected from the NAE database and placed in a treatment or a control group. The treatment group was made up of individuals that attended a training program by the end of 2013, while the control group consisted of individuals that shared similar observable characteristics except that they did not attend such training programs. The standard procedure for estimating the effectiveness of a particular intervention when several observable characteristics are considered includes: the estimation of the propensity score, the selection of a matching algorithm that will use the estimated propensity scores to match the individuals in the control group to those in the treatment group, and the estimation of the impact of the intervention with the matched sample. The propensity score shows the probability that one individual in the full sample receives the treatment, given a set of observed variables, thus reducing the matching problem to a single dimension. Therefore, rather than matching all the values of the variables, individuals can be compared based on the propensity scores only. A major challenge in most empirical program evaluation studies is related to the selection of the set of covariates to be included in the analysis that describe background information that is relevant for the performance of each individual in the labor market. In this paper, both socio-demographic and economic variables were considered according to the economic theory and prior empirical studies in the field (Popescu and Roman 2018). The selected covariates are summarized in Table 1. The initial data samples consisted of 116 respondents in the treatment group and 276 in the control group. Studying the structural differences based on the main sociodemographic characteristics of both the treated and the control groups revealed several particularities. In the search of a better match between the treated individuals and those belonging to the control group, we turned to a matching technique, namely the Kernel matching algorithm implemented in STATA. The Kernel matching technique is a non-parametric estimator that estimates the counterfactual outcome using a weighted average of all the comparison units. The

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Table 1 The main covariates considered in the analysis Types of information Socio-demographic characteristics

Economic variables

Individual characteristics Age Gender Residence area Level of education Activity sector

Codifications based on sub-categories Age 18–24/age 25–44/age over 45 Male/female Urban/rural Low level of education/medium level of education/ high level of education Services sector/industry sector/construction/other sectors

main advantage of this method consists in the use of more information in order to reduce the variance. Even though the algorithm could still generate some poor matches, we accepted such biases due to data limitations. For that, a probit model was first estimated (see Table 2) to predict the propensity scores of each individual representing each individual’s chance of attending a training program. For this purpose, a binary treated variable was built by taking value 1 in case the individual attended the training program and 0 otherwise. Next, the Kernel matching algorithm was applied based on the previously estimated propensity score of each individual. The common support restriction generated a drop of seven individuals belonging to the treated group and eight individuals from the control group, summing up to a total of 377 respondents remaining in the analysis. Since the background information regarding individuals’ earnings at the moment the survey was conducted could only be collected as a categorical variable, it requires logistic model estimation. More precisely, the net earnings variable is described through the following categorical variable: 8 < 1,if net earnings are below 600 lei Net earnings ¼ 2,if net earnings are between 600 and 1500 lei : 3,if net earnings are over 1500 lei The classes used for collecting net earnings data, although not equal in width, do reflect to a certain extent how the respondents’ income compare to the net minimum earnings1 and to the net average earnings2 for 2014. The first category includes net earnings that are below the estimated net minimum wage, the second included earnings that are roughly between the minimum wage but less than the net average earnings, while the last include observations that are close or above the net average earnings.

1 Gross minimum earnings in Romania were 850 lei in January–June 2014, and increased to 900 lei in July 2014, corresponding to a net minimum earnings of around 678 lei. 2 Net average earnings in September 2014 were 1698 lei.

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Table 2 The probit model output

Covariates Age 18–24 Medium level of education Low education level Urban Male Industry sector Services sector Constant Pseudo R2 Observations

Coefficients 0.34 (0.21) 0.72 (0.22) 0.57 (0.31) 0.78 (0.20) 0.92 (0.15) 1.1 (0.45) 0.27 (0.19) 1.16 (0.27) 0.17 392

Note: Standard errors are in parentheses. p < 0.01, p < 0.05, p < 0.10

The ordinal structure of this outcome variable does not allow for an average estimation of the impact of training upon net earnings. Instead, a quite suitable alternative consists in estimating an ordered logit model for the individuals’ chance to earn more. An ordered logit model allows modelling the relationship between a categorical variable and a set of independent variables by estimating a score based on a linear function of independent variables, along with a set of thresholds. The general form of the model is the following:     P outcomej ¼ i ¼ P ki1 < β1 x1 j þ β2 x2 j þ . . . þ βk xkj þ u j  ki

ð1Þ

where β1, β2, . . . , βk are the estimated coefficients together with k1, k2, . . . , kk  1, where k represents the number of possible outcomes. The outputs of the logistic estimation as well as the main findings of the empirical study are presented in the following section.

4 Findings The propensity scores of each individual that represent their chance of attending a training program are predicted through a probit model that is provided in Table 2. The binary treated variable takes a value of 1 in case the individual attended the training program and 0 otherwise. According to Table 2, the estimated probit model is valid, being statistically significant with respect to the LR test. Although the pseudo R2 level is modest (only 17%) we have to accept such limitation because no additional explanatory variable was available to be included in the model. The estimated coefficients have the expected sign and are statistically significant. The dummy variable corresponding to the Construction sector had to be eliminated so that the balancing property be satisfied. In general, we can conclude that men

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working in Industry or Services sectors have less chance to attend training programs compared to women and individuals working in other economic sectors. Moreover, chances tend to increase with the number of years of education (as higher educated persons tend to attend more training programs than low educated individuals). Similar conclusions can be drawn for urban area individuals as compared to those living in rural areas. One last finding suggests that young people have higher chances to attend training programs than those that are 25 and above. A Kernel matching algorithm was then applied based on the previously estimated propensity score of each individual. The common support restriction generated a drop of seven individuals belonging to the treated group and eight individuals from the control group, summing up to a total of 377 respondents remaining in the analysis. An ordered logit model was then estimated to quantify the impact of vocational training on earnings. Using the backward approach, we built an ordered logit model based on the following explanatory variables: the respondent’s age, gender, education level, residence area and economic activity sector. The dummy variable (treated) indicating whether the respondents benefited from a training program or not was also inserted in the estimation in order to check how training influences the chances of individuals to earn more (meaning to get a boost on the earnings scale, rising from a lower level to an upper sequential one). Even though new explanatory variables should be added to assure a better specification, due to data limitation we were unable to generate any new variable from the initial database. The general model is therefore presented in Table 3. The estimated model is statistically significant, as confirmed by Likelihood Ratio test (LR test) and the level of pseudo R2 of 38.2%. Although it is desirable to get a value closer 1, the rather low level of the pseudo R2 is not surprising, since binary models are expected, to certain extent, to have modest values for the pseudo R2. In order to interpret the results obtained from the ordered logit model, we must bear in mind that since this is not a linear regression model, the interpretation of the

Table 3 The output of net earnings ordered logit model

Covariates Treated Age 18–24 Medium level of education Low education level Urban Male Industry sector Services sector Construction sector Pseudo R2 Observations

Coefficients 2.88 (0.89) 0.42 (0.18) 0.25 (0.13) 0.31 (0.11) 2.50 (0.85) 1.93 (0.56) 48.80 (19.34) 102.08 (58.94) 102.23 (48.19) 0.3821 377.0

Note: Standard errors are in parentheses. p < 0.01, p < 0.05, p < 0.10

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M. E. Popescu and R.-M. Paun

coefficients requires a distinct approach, based on the odds ratio determined out of the exponential model. The general findings yielded from the binary model confirm the fact that less educated people have lower chances to benefit from an increase in earnings as compared to highly educated ones. Moreover, age plays an important role in stimulating an increase in net earnings. More precisely, the likelihood of an increase in net earnings for young people aged between 18 and 24 years old is 58% smaller as compared to persons that are over 25 years old. Regarding the economic activity sector, the econometric results suggest that those working in Constructions, Services or Industry have higher chances to get an increase in earnings as compared to other activity sectors. As expected, out of the three activity sectors considered in the analysis, people working in Constructions and in the Services sector (very close to it, based on the odd ratios) have the highest chances of getting an increase in income as compared to other economic activity sectors. Less surprising is also the fact that people living in urban areas have 2.5 more chances to benefit from an increase in income as compared to those living in rural areas and that males are expected to earn 1.93 times more than females, keeping all other variables constant. Regarding the impact of vocational training upon earnings, the results suggest that the likelihood of an increase in earnings is 2.9 times higher for those who attended a training program than for those who did not. The findings of the present empirical study encourage us to argue in favor of vocational training and to confirm the research hypothesis that vocational training does generate a post program increase in training beneficiaries’ net earnings in Romania. The main findings of the counterfactual scenario approach are consistent with the international literature in the field, arguing in favor of a positive return on training, as higher earnings are to be expected after attending training programs. To conclude, if we were to build up a general profile of the respondents who are expected to earn more based on the current binary model, then it will be mostly described as high educated men, that are over 25 years old, living in urban areas and working in Constructions or in the Services sector. Moreover, additional chances to benefit from an increase in earnings rise in favor of those who attended a training program, as compared to those who did not participated in such programs.

5 Conclusions Vocational training has become one of the most popular active labor market policies, and more and more studies have focused on evaluating its impact on the labor market, mainly its effect on employability. Recently, significant concerns are also raised on the financial benefits of training. This paper is therefore focused on the direct effects of vocational training upon earnings, and brings empirical evidence for the case of Romania in favor of the human capital theory. In order to determine whether vocational training generates an increase in earnings, a quantitative analysis was conducted and econometric tools were applied on

The Financial Benefits of Training on the Labor Market: Evidence from Romania

11

both a treatment and a control group. Since official databases could not provide specific wage information at individual level, survey micro-datasets were used in the analysis. The main findings suggest a positive return on training, as higher earnings are to be expected after attending training programs. In general, the results are consistent with the international literature in the field, arguing that vocational training should indeed lead to a positive return on training investment, as higher earnings are to be expected after attending training programs. Based on the main econometric results of the paper regarding the impact of trainings on earnings, we can conclude that chances of benefitting from an increase in earnings rise in favor of those who attend a training program, that are high educated men, over 25 years old, living in urban areas and working in Constructions or in the Services sector. While the present study has supplied much useful information on the impact of vocational trainings on earnings, it has several limitations that must be acknowledged. One refers to the small number of variables used in the analysis, and another to the fact that the net earnings were collected as a categorical variable with rather few available categories which restricted the impact evaluation analysis. Therefore, our model has a limited power to estimate the chances of an individual to increase the net earnings. While it will estimate the chances of switching from a lower level on the earnings scale to an upper one, it will not be able to estimate the actual difference in net income resulted from attending a training program. Further investigation is therefore required to bring new and more consolidated proofs of the financial benefits of training on the labor market in transition countries, such as Romania. Acknowledgements The authors would like to thank the Romanian National Scientific Research Institute for Labor and Social Protection (INCSMPS) for providing access to the micro-survey database. The authors would also like to thank Webster University Thailand for financially supporting this research.

References Arulampalam, W., Booth, A. L., & Bryan, M. L. (2004). Are there asymmetries in the effects of training on the conditional male wage distribution? (IZA Discussion Paper, No. 984). Bartel, A. P. (1995). Training, wage growth, and job performance: Evidence from a company database. Journal of Labor Economics, 13, 401–425. Becker, G. (1964). Human capital (2nd ed.). New York: Columbia University Press. Budria, S., & Pereira, P. T. (2004). On the returns to training in Portugal (IZA Discussion Paper No. 1429). Greenberg, D. H., Michalopoulos, C., & Robins, P. K. (2003). A meta-analysis of governmentsponsored training programs. Industrial and Labor Relations Review, 57, 31–53. Heng, A. B., Cheolsung, P., Haoming, L., Thangavelu, S. M., & Wong, J. (2007). The impact of structured training on workers’ employability and productivity (SCAPE Working Paper Series, Paper No. 2007/02).

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Mincer, J. (1958). Investment in human capital and personal income distribution. Journal of Political Economy, LXVI(4), 281–302. Muehler, G., Beckmann, M., & Schauenberg, B. (2007). The returns to continuous training in Germany: New evidence from propensity score matching estimators. Review of Managerial Science, 1(3), 209–235. Pirciog, S., Ciuca, V., & Popescu, M. E. (2015). The net impact of training measures from active labor market programs in Romania – Subjective and objective evaluation. Procedia Economics and Finance, 26, 339–344. Popescu, M. E., & Roman, M. (2018). Vocational training and employability: Evaluation evidence from Romania. Evaluation and Program Planning, 67, 38–46. Regner, H. (2002). The effects of on-the-job training on wages in Sweden. International Journal of Manpower, 23, 326–344. Rodriguez-Planas, N., & Benus, J. (2006). Evaluating active labor market programs in Romania (IZA Discussion Paper Series, No. 2464). Roman, M., & Popescu, M. E. (2015). The effects of training on Romanian migrants’ income: A propensity score matching approach. Economic Computation and Economic Cybernetics Studies and Research, 43(1), 85–108.

The Nexus Between Economic Development and Environmental Pollution: The Case of Lithuania Lina Sineviciene, Aura Draksaite, Violeta Naraskeviciute, and Oleksandr Kubatko

Abstract The aim of this research is to assess a link between economic development and environmental pollution in the case of Lithuania. The research methods used in this research are as follows: systemic, logical and comparative analysis of literature, and statistical methods: descriptive statistics analysis, correlation analysis. Empirical analysis of this study focuses on the data of Lithuanian counties. The study covers 2000–2015 years using annual data. The cross-sectional data of Lithuanian counties showed that the pollution is higher in the higher GDP per capita counties. However, annual data analysis showed that there is a positive tendency in the sustainable development of the counties as there is the evidence that a strong negative correlation between the pollution and economic development exists in the majority of Lithuanian counties. It means that the decrease of pollution is related to the growth of economic development or vice versa. Keywords Economic development · Sustainability · Environmental pollution · Lithuania

1 Introduction Energy intensive economies face growing environmental pollution problems if the energy efficiency does not increase with growing economic development as societies consume more energy resources what increases environmental pollution. Economic development of countries highlights new issues for scientist and policy-makers as it is important to ensure economic growth avoiding the increment of environmental pollution. The relationship between economic development and environmental pollution has been a focus of research by economists for many years. According to L. Sineviciene (*) · A. Draksaite · V. Naraskeviciute School of Economics and Business, Kaunas University of Technology, Kaunas, Lithuania e-mail: [email protected]; [email protected]; [email protected] O. Kubatko Department of Economics and Business Administration, Sumy State University, Sumy, Ukraine © Springer Nature Switzerland AG 2019 M. Huseyin Bilgin et al. (eds.), Eurasian Economic Perspectives, Eurasian Studies in Business and Economics 11/1, https://doi.org/10.1007/978-3-030-18565-7_2

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L. Sineviciene et al.

Yevdokimov et al. (2011), there are two basic competing views explaining this relationship: the first one states that economic growth is harmful to the environment due to ineffective use of resources while the second one states that technological progress and economic growth improve environmental quality due to technological innovations. Chandran and Tang (2013) argue that energy consumption and economic development goals, which countries pursue at the expense of the environment quality, are the most important variables that are associated with environmental degradation. In the majority of the studies, the energy consumption and economic development are main indicators used in the analysis of the relationship between economic growth and pollution. Understanding the main factors of environmental quality is crucial for the development of green growth policies. There is an evidence in the scientific literature that there is a link between economic development and environmental pollution. This evidence is proved by Cialani (2007) who has found a positive linear relationship between CO2 emissions and GDP per capita in the case of Italy over the period 1861–2002. Akbostanci et al. (2008) argue that there is a monotonically increasing relationship between CO2 emissions and per capita income in the case of Turkey over the period of 1968–2003. De Groot et al. (2002) in the case of China over the period 1982–1997 has found a linear relationship between income and pollution: an increase in per capita income is associated with a decline in pollution. Lacheheb et al. (2015) also argue that there is a relationship between income and population. Luo et al. (2014) presents the results on the relationship between air pollutants and economic development of the provincial capital cities in China. The analysis of the relationship between air pollutants and the three major industries (the primary, secondary and tertiary industries) shows the declining trend of the pollutant concentrations with the improvement of energy efficiency and implementation of environmental protection policies. Many studies show the interconnections between processes and factors of economic growth and environmental pollution. These research results are based on studies conducted in the case of many countries and regions and in different periods (Soytas and Sari 2003; Lee 2006; Lee and Chien 2010; Haggar 2012; Shahbaz et al. 2013; Hwang and Yoo 2014; Zeng et al. 2014; Erol and Yu 1998; Narayan and Smyth 2008; Bowden and Payne 2009; Ozcan 2013; Wesseh and Zoumara 2012). However, the contradiction of research results makes it difficult to formulate precise guidelines for the implementation of an effective sustainable energy policy in certain areas and countries. There are many studies concerning the relationship between economic development and environmental pollution. The results differ depending on the study sample, i.e. the countries used in the research development level and the period chosen. Despite single studies of regional problems, there is a lack of studies analyzing regional problems, or local level problems. The understanding of the relationship between economic development and environmental pollution in regions or at a local level becomes of particular importance in order to apply environmental pollution decrement tools effectively. The aim of the study is to assess a link between

The Nexus Between Economic Development and Environmental Pollution: The. . .

15

economic development and environmental pollution in the case of Lithuania. The research object: a link between economic development and environmental pollution.

2 Research Methodology The link between economic development and environmental pollution in the case of Lithuania is investigated in this chapter. The research methods used in this research: systemic, logical and comparative analysis of literature, and statistical methods: descriptive statistics analysis, correlation analysis (Spearman’s rho correlation). Lithuanian counties annual data ranging from 2000 to 2015 are used in the empirical analysis. The cross-sectional data technique using arithmetic averages is applied. The source of the data is the database of the Lithuanian department of statistics. The indicator of air emissions from stationary sources is chosen to represent the pollution. In order to make comparisons better, air emissions from stationary sources indicator is expressed in ton, per capita and square kilometer. Economic development is represented by real GDP per person indicator.

3 Findings According to the European Commission (2010), in the Europe 2020 Strategy, it is argued that the transition towards a green, low carbon and resource efficient economy in achieving smart, sustainable and inclusive growth is very important. The inefficient use of resources, the unsustainable pressure on the environment, and climate change, as well as social exclusion and inequalities, pose challenges to long-term sustainable economic growth (European Commission 2014). Therefore, the transition to a green economy depends on the natural and human resources of each state and on the level of economic development. Strategic priorities and principles of sustainable development of Lithuania are set out in the national legislation taking into account the national interests and peculiarities of Lithuania, the provisions of the EU Sustainable Development Strategy and other program documents. The analysis of Lithuanian changes in pollution and real GDP per capita has shown that there is a tendency that the pollution has decreased while the real GDP increased during the period of 2000–2015 (see Fig. 1). However, the decrease in pollution per capita is not sustainable. The sharp decline in pollution was observed during the years of the economic crisis from 2007 until 2009. However, the economic recovery caused the increase of pollution, and the sharp increment of the pollution is observed in the last year of the analysis. In order to better understand the situation of Lithuania, all data of Lithuania and Lithuanian counties were investigated. The descriptive statistics of Lithuanian all sample data and Lithuanian counties data (see Tables 1 and 2) shows that large

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L. Sineviciene et al.

30 25 20 15 10 5 0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Pollution per capita, kg

RGDP per person, th EUR

Fig. 1 The tendencies of pollution and real GDP per capita in Lithuania. Source: compiled by the authors, Data source: the Lithuanian Department of Statistics (2017) Table 1 Descriptive statistics of Lithuania all sample data Indicators Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR

N 16 16

Range 41,767.80 9.00

MIN 56,513.70 19.00

MAX 98,281.50 28.00

Mean 76,472.44 23.69

Std. Dev. 13,865.6 2.8

16

639.00

866.00

1505.00

1171.06

212.3

16

4.93065

5.81040

10.74105

8.111

1.9

Source: authors’ calculations, Data source: the Lithuanian Department of Statistics (2017)

differences exist between the Lithuanian counties economic development and the pollution indicators. The results of descriptive statistics, i.e. standard deviation, using all sample data shows that there is a high variability in the data. High differences between pollution indicators and real GDP per capita comparing Lithuanian counties’ data also exist. The results of descriptive statistics of Lithuanian counties show that the highest economic development is in the largest counties of Lithuania with the largest cities, however, the pollution (per capita) is not the highest in all these counties. The exception is the capital of Lithuania (Vilnius) where the high value-added service sector is well developed. In the case of Vilnius county, the indicator of pollution per capita is only in the seventh position if compared with other counties (according to the indicator of pollution per km2 is in the fourth place due to the high density of population). The lower pollution (per capita and per square km) is in the lower economic development counties as the population is smaller and industry sector is weaker developed but with exception of Telsiai county, which indicators of pollution

The Nexus Between Economic Development and Environmental Pollution: The. . .

17

Table 2 Descriptive statistics of Lithuanian counties data Indicators Alytus Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Kaunas Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Klaipeda Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Marijampole Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Panevezys Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Siauliai Pollution, ton Pollution per capita, kg

N

Range

MIN

MAX

Std. Dev.

Mean

16 16

1299.30 7.00

1211.40 7.00

2510.70 14.00

1657.29 9.69

416.8 2.0

16

240.00

223.00

463.00

305.62

76.9

5.47

1.0

16

2.68716

4.22374

6.91091

16 16

8470.40 10.00

10,104.90 16.00

18,575.30 26.00

13,267.39 20.25

2689.5 3.0

16

1025.00

1249.00

2274.00

1639.69

330.3

7.76

2.0

16

5.52865

5.04587

10.57452

16 16

11,087.70 27.00

3484.70 11.00

14,572.40 38.00

8926.72 24.56

3912.7 9.4

16

2131.00

667.00

2798.00

1700.88

745.0

8.59

2.1

16

5.14062

5.96107

11.10169

16 16

605.20 5.00

1633.30 9.00

2238.50 14.00

1878.22 11.06

151.2 1.4

16

136.00

366.00

502.00

420.88

33.9

5.08

1.2

16

3.19559

3.49933

6.69492

16 16

2554.10 7.00

2625.60 10.00

5179.70 17.00

3501.06 13.12

699.0 2.1

16

324.00

333.00

657.00

444.19

88.7

6.21

1.2

6386.66 19.75

987.3 4.0

16

16 16

3.33208

3411.10 13.00

4.74453

4837.80 15.00

8.07660

8248.90 28.00

(continued)

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L. Sineviciene et al.

Table 2 (continued) Indicators Pollution per km2, kg RGDP per person, thousands EUR Taurage Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Telsiai Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Utena Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Vilnius Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR

N 16 16

Range 400.00 3.82106

MIN 566.00 4.17227

MAX 966.00 7.99334

Mean 746.88

Std. Dev. 116.1

6.01

1.4

16 16

1251.40 9.00

567.90 6.00

1819.30 15.00

1175.06 9.69

473.2 3.2

16

283.00

129.00

412.00

269.00

108.9

4.26

1.3

16

3.16862

2.82638

5.99500

16 16

15,451.90 59.00

21,416.10 148.00

36,868.00 207.00

29,233.51 178.88

4501.1 17.3

16

3551.00

4924.00

8475.00

6742.56

1043.2

6.58

1.4

16

3.72584

4.57604

8.30189

16 16

983.80 7.00

1359.10 7.00

2342.90 14.00

1767.27 10.75

315.6 2.1

16

136.00

189.00

325.00

245.38

43.8

6.13

0.9

16

2.47185

4.84522

7.31707

16 16

7700.90 9.00

5196.70 6.00

12,897.60 15.00

8679.26 10.38

2514.2 2.9

16

803.00

534.00

1337.00

892.19

258.9

11.97

2.5

16

7.00767

8.56269

15.57036

Source: authors’ calculations, Data source: the Lithuanian Department of Statistics (2017)

are the highest compared with other counties. The most problematic county is Marijampole where real GDP per person is the lowest but pollution indicators are rated on average. Results of Spearman’s correlations (see Table 3) using cross-sectional data show that a strong negative relationship between economic development and pollution

The Nexus Between Economic Development and Environmental Pollution: The. . .

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Table 3 Results of Spearman’s correlations (all sample data) Indicators Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR

Pollution, ton 1.000 0.964

0.999

0.885



Pollution per capita, kg 0.964

1.000 0.960

0.794



Pollution per km2, kg 0.999

0.960

1.000 0.892



RGDP per person, thousands EUR 0.885

0.794

0.892

1.000

Source: authors’ calculations Notes:

. Correlation is significant at the 0.01 level (2-tailed);
Correlation is significant at the 0.05 level (2-tailed)

exists in the case of Lithuania. This means that pollution is lower in the counties in which real GDP per capita is higher on the average. The Spearman’s correlations results (see Table 4) using the separate data of Lithuanian counties show that only in six cases from ten a statistically significant negative correlation between the economic development and pollution is assessed. The results show that there is a tendency that pollution decrease is associated with the increment of economic development and vice versa but, in the majority of cases, only in higher economic development counties. The negative correlation between economic development and pollution indicators indicate that the counties are sustainably developing. The correlations results (see Table 4) show that, in the two cases, i.e. Marijampole and Siauliai counties, a statistically significant positive relationship between pollution and real GDP per capita is found. This means that the development of these counties is not sustainable. The attention should be paid to the data of these counties (see Table 2). If to range analyzed data, real GDP per person is in the lowest position compared with other counties but pollution indicators are rated on average in the case of Marijampole. Siauliai is relatively clean county but the problem arise due to low economic development of this county and the increment of the development in the expense of the environment. High levels of emigration, high unemployment, a decline in the working-age population, which do not lead the new investment, hinders the economic development of these regions. The obtained results indicate that Lithuania can face challenges of sustainable development in the future due to unequal development of the lower development counties as the uneven development was observed in these counties during the past decade. The high emigration from these counties deepens the problems of sustainable development and may be a great challenge for policy-makers to stabilize the situation in the future.

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L. Sineviciene et al.

Table 4 Results of Spearman’s correlations (Lithuanian counties data) Indicators Alytus Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Kaunas Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Klaipeda Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Marijampole Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Panevezys Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Siauliai Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Taurage Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR

Pollution, ton

Pollution per capita, kg

Pollution per km2, kg

RGDP per person, thousands EUR

1.000 0.861

0.999

0.497

0.861

1.000 0.862

0.071

0.999

0.862

1.000 0.496

0.497 0.071 0.496 1.000

1.000 0.928

1.000

0.818



0.928

1.000 0.928

0.704



1.000

0.928

1.000 0.818



0.818

0.704

0.818

1.000

1.000 0.993

1.000

0.953



0.993

1.000 0.993

0.949



1.000

0.993

1.000 0.953



0.953

0.949

0.953

1.000

1.000 0.804

0.999

0.162

0.804

1.000 0.809

0.668



0.999

0.809

1.000 0.171

0.162 0.668

0.171 1.000

1.000 0.882

1.000

0.488

0.882

1.000 0.882

0.091

1.000

0.882

1.000 0.488

1.000 0.827

0.999

0.135

0.827

1.000 0.827

0.567


0.999

0.827

1.000 0.138

1.000 0.975

0.990

0.788



0.975

1.000 0.964

0.739



0.990

0.964

1.000 0.792



0.488 0.091 0.488 1.000

0.135 0.567
0.138 1.000

0.788

0.739

0.792

1.000 (continued)

The Nexus Between Economic Development and Environmental Pollution: The. . .

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Table 4 (continued) Indicators Telsiai Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Utena Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR Vilnius Pollution, ton Pollution per capita, kg Pollution per km2, kg RGDP per person, thousands EUR

Pollution, ton

Pollution per capita, kg

Pollution per km2, kg

RGDP per person, thousands EUR

1.000 0.856

0.991

0.724



0.856

1.000 0.815

0.365

0.991

0.815

1.000 0.735



0.724

0.365 0.735

1.000

1.000 0.810

0.999

0.482

0.810

1.000 0.824

0.024

0.999

0.824

1.000 0.462

0.482 0.024 0.462 1.000

1.000 0.983

1.000

0.824



0.983

1.000 0.983

0.829



1.000

0.983

1.000 0.824



0.824

0.829

0.824

1.000

Source: authors’ calculations Notes:

Correlation is significant at the 0.01 level (2-tailed);
Correlation is significant at the 0.05 level (2-tailed)

4 Conclusion Strategic priorities and principles of sustainable development of Lithuania are set out in the national legislation taking into account the national interests and peculiarities of Lithuania, the provisions of the European Union Sustainable Development Strategy and other program documents. The analysis of Lithuanian changes in pollution and real GDP per capita growth has shown that there is a tendency that the pollution has decreased while the real GDP increased during the period of 2000–2015. However, the decrease in pollution per capita is not even and sustainable and depends on the economic conditions. The research results have proved that there is a link between the pollution and economic development. The cross-sectional data of Lithuanian counties showed that the pollution is higher in the higher GDP per capita counties but not in all cases. However, annual data showed that there is a positive tendency in the sustainable development of the counties as there is the evidence that a strong negative correlation between the pollution and economic development exists in the majority of Lithuanian counties. It means that the decrease of pollution is related to the growth of economic development or vice versa. However, the results show that not all counties develop equally and sustainably.

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L. Sineviciene et al.

Acknowledgement This research was funded by a grant (No. TAP LU-4-2016) from the Research Council of Lithuania.

References Akbostanci, E., Turut-Asik, S., & Tunc, G. I. (2008). The relationship between income and environment in Turkey: Is there an environmental Kuznets curve? Energy Policy, 37(3), 861–867. Bowden, N., & Payne, J. E. (2009). The causal relationship between US energy consumption and real output: A disaggregated analysis. Policy Model, 31, 180–188. Chandran, V. G. R., & Tang, C. F. (2013). The impacts of transport energy consumption, foreign direct investment and income on CO2 emissions in ASEAN-5 economies. Renewable and Sustainable Energy Reviews, 24, 445–453. Cialani, C. (2007). Economic growth and environmental quality: An econometric and a decomposition analysis. Management of Environmental Quality: An International Journal, 18(5), 568–577. De Groot, H., Withagen, C., & Minliang, Z., (2002). Dynamics of China’s regional development and pollution (Tinbergen Institute Discussion Paper, TI 2001–036–3). Erol, U., & Yu, E. S. H. (1998). On the causal relationship between energy and income for industrialized countries. Energy Development, 13, 113–122. European Commission. (2014). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions Green Employment Initiative: Tapping into the job creation potential of the green economy/
COM/2014/0446 final
/, [online]. Accessed January 10, 2018, from https://eur-lex.europa.eu/ legal-content/EN/TXT/?uri¼CELEX:52014DC0446 European Commission. (2010). EUROPE 2020: A strategy for smart, sustainable and inclusive growth/
COM/2010/2020 final
/ [online]. Accessed January 10, 2018, from http://eur-lex. europa.eu/legal-content/LT/TXT/?uri¼CELEX%3A52014DC0446 Haggar, M. H. (2012). Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis from Canadian industrial sector perspective. Energy Economics, 34, 358–364. Hwang, Y. H., & Yoo, S. H. (2014). Energy consumption, CO2 emissions, and economic growth: Evidence from Indonesia. Quality & Quantity, 48, 63–73. Lacheheb, M., Rahim, A. S. A., & Sirag, A. (2015). Economic growth and carbon dioxide emissions: Investigating the environmental Kuznets curve hypothesis in Algeria. International Journal of Energy Economics and Policy, 5(4), 1125–1132. Lee, C. (2006). The causality relationship between energy consumption and GDP in G-11 countries revisited. Energy Policy, 34, 1086–1093. Lee, C., & Chien, M. (2010). Dynamic modelling of energy consumption, capital stock, and real income in G-7 countries. Energy Economics, 32, 564–581. Luo, Y., Chen, H., Zhu, Q., Peng, C., Yang, G., Yang, Y., & Zhang, Y. (2014). Relationship between air pollutants and economic development of the provincial capital cities in China during the past decade. PLoS One, 9(8), e104013. https://doi.org/10.1371/journal.pone.0104013. Narayan, P. K., & Smyth, R. (2008). Energy consumption and real GDP in G7 countries, new evidence from panel cointegration with structural breaks. Energy Economics, 30, 2331–2341. Ozcan, B. (2013). The nexus between carbon emissions, energy consumption and economic growth in Middle East countries: A panel data analysis. Energy Policy, 62, 1138–1147. Shahbaz, M., Khan, S., & Tahir, M. I. (2013). The dynamic links between energy consumption, economic growth, financial development and trade in China: Fresh evidence from multivariate framework analysis. Energy Economics, 40, 8–21.

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Soytas, U., & Sari, R. (2003). Energy consumption and GDP: Causality relationship in G-7 countries and emerging markets. Energy Economics, 25, 33–37. The Lithuanian Department of Statistics. (2017). [online]. Accessed October 15, 2017, from https:// osp.stat.gov.lt/statistiniu-rodikliu-analize#/ Wesseh, P. K., & Zoumara, B. (2012). Causal independence between energy consumption and economic growth in Liberia: Evidence from a non-parametric bootstrapped causality test. Energy Policy, 50, 518–527. Yevdokimov, Y. V., Melnyk, L. G., & Kubatko, O. V. (2011). The environmental Kuznets curve and regional convergence in Ukraine. International Journal of Ecological Economics & Statistics, 22, 72–86. Zeng, L., Xu, M., Liang, S., Zeng, S., & Zhang, T. (2014). Revisiting drivers of energy intensity in China during 1997–2007: A structural decomposition analysis. Energy Policy, 67, 640–647.

The Impact of Changes in Time to Maturity on the Risk of Geometric Options Ewa Dziawgo

Abstract Asian geometric options are path-dependent exotic options whose pay-off function is based on the geometric average price of the underlying instrument in the past (in a fixed period, leading up to the expiration date). This paper presents the issues connected with the geometric option: the instrument’s structure, types options, pay-off function, the pricing model, the effect of time to maturity and the price of underlying instrument on the option price and the value coefficients: delta, gamma, vega, theta, rho. These coefficients are the sensitivity measures. The sensitivity measures are important in managing the options risk. They indicate the influence the change in the option price for a change in the value of a risk factor. The objective of this paper is to present the effect of time to maturity on the price and the values of the risk measures (coefficients: delta, gamma, vega, theta, rho). The empirical illustrations included in the paper are presented based on a simulation of valuations of currency geometric call options (on the EUR/USD). Keywords Financial instruments · Risk management · Measures of risk

1 Introduction The increase of the risk of running a business influences the growth of demand for new financial instruments and methods that would allow more effective management of risk. The option offers the buyer the right, but not obligation, to buy (call option) or sell (put option) an underlying instrument at the agreed price in the option exercise time. Options, due of the asymmetry of the rights and obligations of the transaction parties, are a very attractive instrument of risk management. The exotic options are characterized by the income structure different from structure of standard options (Ong 1996; Nelken 2000; Zhang 2001; Dziawgo 2013; Musiela and Rutkowski E. Dziawgo (*) Faculty of Administration and Social Sciences, Kazimierz Wielki University, Bydgoszcz, Poland e-mail: [email protected] © Springer Nature Switzerland AG 2019 M. Huseyin Bilgin et al. (eds.), Eurasian Economic Perspectives, Eurasian Studies in Business and Economics 11/1, https://doi.org/10.1007/978-3-030-18565-7_3

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E. Dziawgo

1997; Garman and Kohlhagen 1983). Asian geometric options are path-dependent exotic options. These options give the holder a pay-off that depends on the geometric average price of the underlying instrument over some prescribed period. The objective of the article is analysis the impact of the time to the option expiry date on the formation of the price geometric call option and value of the risk measures. In the case of options, it is very important to analyze sensitivity measures. They are the measures and they define the influence of changes in the risk factor on the price of an option. The measures of options risk include the following coefficients: delta, gamma, theta, vega and rho (Hull 2005; Dziawgo 2010).

2 Price of the Geometric Call Options If on the expiry date the average price of the underlying instrument is higher than the strike price than the call geometric option is realized. In this case the option is in-the-money. However, if on the expiry date the average price of the underlying instrument is lower than the strike price than the call option is out-of-the-money. In this situation the value of the pay function for this option amounts to zero. At the expiration date the pay-off function for the geometric call option is the following form (Zhang 2001; Dziawgo 2013):
 W c ¼ max S^T  K; 0

ð1Þ

Wc—the pay-off function of the geometric call option, K—the option’s strike price, S^T —the geometric average price of the underlying instrument over period [0; T], T—time to maturity. The pricing model of the geometric call option is in the form (Vorst 1992): C ¼ St e0,5



2

rτþσ6



e0,5qτ N ðd1 Þ  Kerτ N ðd2 Þ

ð2Þ

C—the price of the geometric call option, St—the price of the underlying instrument in the time t, t 2 [0; T], N(d )—cumulative probability function for a standard normal variable distribution, rffiffiffi   ln SKt þ 0,5τðr  q  0; 5σ 2 Þ 1 pffiffi d1 ¼ d2 þ σ , d2 ¼ , 3 σ 3τ r—the domestic interest rate, σ—the price volatility of the underlying instrument, q—the foreign interest rate, and other designations are the same as in the formula (1).

The Impact of Changes in Time to Maturity on the Risk of Geometric Options

27

0.04 0.035 0.03 0.025 0.02 0.015 0.01 0.005

geometric option

19.03

14.03

06.03

09.03

01.03

26.02

21.02

13.02

16.02

08.02

05.02

26.01

31.01

23.01

18.01

15.01

10.01

05.01

02.01

0

standard option

Fig. 1 Changes in the price of geometric and standard call options. Source: elaborated by the author based on own calculations Table 1 The impact of the price of the underlying instrument and the time to maturity on the shaping of the price of geometric call options

The price of the underlying instrument [USD] 1.19 1.21 1.22 1.23 1.24 1.25 1.26

The price option [USD] Time to maturity T ¼ 3 [month] 0.0004 0.0031 0.0045 0.0055 0.0161 0.0205 0.0312

The price option [USD] Time to maturity T¼6 [month] 0.0005 0.0048 0.0063 0.0109 0.0179 0.0273 0.0396

The price option [USD] Time to maturity T ¼ 9 [month] 0.0042 0.0079 0.0102 0.0165 0.0267 0.0329 0.0494

Source: elaborated by the author based on own calculations

The considerations are focused on the impact of the price of the underlying instrument and the time to maturity on the price of geometric and standard call options. These options are on EUR/USD. The strike price is 1.23 USD. The maturity time is 4 months. The simulation was carried out for the period: 02 January, 2018–19 March, 2018. Figure 1 shows the shaping of the price of geometric and standard options. Table 1 presents the impact of the price of the underlying instrument and the time to maturity on the shaping of the price analyzed options. During the analyzed period the call options were out-of-the-money on the following dates: 02 January 2018–23 January 2018, 08 February 2018–12 February 2018, 22 February 2018, 28 February 2018–02 March 2018, 19 March 2018. In the

28

E. Dziawgo 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

geometric option

14.03

19.03

09.03

06.03

01.03

26.02

21.02

16.02

13.02

08.02

05.02

31.01

26.01

23.01

18.01

15.01

10.01

05.01

02.01

0

standard option

Fig. 2 Changes in the delta coefficient of geometric and standard call options. Source: elaborated by the author based on own calculations

other periods the options were in-the-money. The following properties of the geometric call option result from the analysis of the shaping of the price: • the standard options are much more expensive than the geometric options, • the increase/decrease in the price of the underlying instrument influences the growth/decline in the price of the option, • the option with a longer expiration date is more expensive.

3 Delta Coefficient of the Geometric Call Options The delta coefficient is one of the most important risk measures of the option’s price. This coefficient shows how much the option’s price will change when the price of the underlying instrument changes by a unit (Dziawgo 2003; Hull 2005; Wilmott 1998). Figure 2 illustrates the changes in the delta coefficient of the geometric and standard call options. Table 2 demonstrates the impact of the price of the underlying instrument and time to maturity on the shaping of the price discussed options. In the case of the analyzed options, the values of the delta coefficient fluctuate significantly in time. The delta coefficient of call options takes positive values, which mean that the increase/decrease in the price of the underlying instrument influences the rise/fall in the price of option. The values of the delta coefficient for the call geometric options and standard options belong to the interval [0; 1]. If the option has a greater value of the delta coefficient, then the growth sensitivity of the price of the option to changes in the price of the underlying instrument can be

The Impact of Changes in Time to Maturity on the Risk of Geometric Options

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Table 2 The impact of the price of the underlying instrument and the time to maturity on the shaping of the delta coefficient of geometric call options

The price of the underlying instrument [USD] 1.19 1.21 1.22 1.23 1.24 1.25 1.26

Delta of option Time to maturity T ¼ 3 [month] 0.0331 0.1897 0.3401 0.5170 0.6927 0.8303 0.9592

Delta of option Time to maturity T ¼ 6 [month] 0.1028 0.2769 0.3984 0.5220 0.6456 0.7534 0.8954

Delta of option Time to maturity T ¼ 9 [month] 0.1983 0.3517 0.4404 0.5235 0.6114 0.6886 0.8117

Source: elaborated by the author based on own calculations

observed. If the case of the out-of-the-money and at-the-money (when the price option amounts to the strike price) call options: • the values of the standard options coefficient are higher than the delta coefficient of the geometric options, • the option with the longer expiration date is characterized by a greater value of the delta coefficient. If the call options are in-the-money, and then: • the values of the standard options coefficient are lower than the delta coefficient of the geometric options, • the option with the shorter expiration date have the greater value of the delta coefficient. The increase/decrease in the price of the underlying instrument contributes to the growth/decline in the value of the delta coefficient of the call options. When the options are deep-in-the-money, the values of the delta coefficient are close to 1. If the options are deep-out-of-the-money, the value of the delta coefficient decreases to 0.

4 Gamma Coefficient of the Geometric Call Options Another measure of risk of the options price is the gamma coefficient. It shows by how much the delta coefficient will change, when the value of the of the price of the underlying instrument changes by one unit. Figure 3 shows the shaping of the gamma coefficient of discussed geometric and standard call options. Table 3 presents the impact of the price of the underlying instrument and the time to maturity on the forming of the gamma coefficient geometric options.

30

E. Dziawgo 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02

geometric option

19.03

14.03

09.03

06.03

01.03

26.02

21.02

16.02

13.02

08.02

05.02

31.01

26.01

23.01

18.01

15.01

05.01

10.01

02.01

0

standard option

Fig. 3 Changes in the gamma coefficient of geometric and standard call options. Source: elaborated by the author based on own calculations Table 3 The impact of the price of the underlying instrument and the time to maturity on the shaping of the gamma coefficient of the geometric call options

The price of the underlying instrument [USD] 1.19 1.21 1.22 1.23 1.24 1.25 1.26

Gamma of option Time to maturity T ¼ 3 [month] 0.0351 0.1278 0.1719 0.1844 0.1587 0.1102 0.0284

Gamma of option Time to maturity T ¼ 6 [month] 0.0608 0.1113 0.1265 0.1284 0.1167 0.0953 0.0465

Gamma of option Time to maturity T ¼ 9 [month] 0.0658 0.0854 0.0891 0.0880 0.0822 0.0728 0.0489

Source: elaborated by the author based on own calculations

The values of the gamma coefficient of the call options are positive. Then the increase/decrease in the price of the underlying instrument affects the growth/decline in the value of the delta coefficient. The highest value of the gamma coefficient is observed in the option is the at-the-money. Namely, for this type of the option, the slightest change in the price of the underlying instrument influences in the significant change of the delta coefficient. In the case of the increase/decrease in the price of the underlying instrument in relation to the strike price, the gamma coefficient decreases. If the option is deep-in-the-money and deep-out-of-the-money the value of gamma coefficient of standard option is greater than the value of gamma of geometric option. In other cases, the gamma coefficient of the geometric option is greater than gamma coefficient of the standard option. If the options are deep-in-the-money

The Impact of Changes in Time to Maturity on the Risk of Geometric Options

31

0.003 0.0025 0.002 0.0015 0.001 0.0005

02.01 04.01 08.01 10.01 12.01 16.01 18.01 22.01 24.01 26.01 30.01 01.02 05.02 07.02 09.02 13.02 15.02 19.02 21.02 23.02 27.02 01.03 05.03 07.03 09.03 13.03 15.03 19.03

0

geometric option

standard option

Fig. 4 Changes in the vega coefficient of geometric and standard call options. Source: elaborated by the author based on own calculations

and deep-out-of-the-money, the gamma coefficient of the option with the longer expiration date has a higher value. In the other cases the increase/decrease in the time to maturity influences the fall/rise in the value of gamma coefficient of the option.

5 Vega Coefficient of the Geometric Call Options The vega coefficient indicates by how much the option price will change when the volatility of the price of the underlying instrument changes by one unit. Figure 4 illustrates the development of the vega coefficient of the geometric and standard call options. Table 4 presents the impact of the price of the underlying instrument and the time to maturity on the shaping of the vega coefficient analyzed geometric options. The values of the vega coefficient are positive. The increase/decrease in the price volatility of the underlying instrument impacts the growth/decline of the options price. The high value of the vega coefficient demonstrates a significant impact of fluctuations in the price volatility of the underlying instrument on price of the option. The highest vega value is reached for the at-the-money option. The value of the vega coefficient is close to zero if the option is deep-in-the-money and deep-out-of-themoney. The value of vega coefficient of the standard options are greater than the value of vega coefficient of the geometric option. The option with the longer time to maturity has the greater value of the vega coefficient.

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E. Dziawgo

Table 4 The impact of the price of the underlying instrument and the time to maturity on the shaping of the vega coefficient of geometric call options Vega of option Time to maturity T ¼ 3 [month] 0.0002 0.0090 0.0012 0.0014 0.0011 0.0008 0.0001

The price of the underlying instrument [USD] 1.19 1.21 1.22 1.23 1.24 1.25 1.26

Vega of option Time to maturity T ¼ 6 [month] 0.0008 0.0016 0.0018 0.0019 0.0017 0.0014 0.0006

Vega of option Time to maturity T ¼ 9 [month] 0.0018 0.0024 0.0026 0.0027 0.0024 0.0021 0.0015

19.03

14.03

09.03

06.03

01.03

26.02

21.02

13.02

16.02

08.02

05.02

31.01

26.01

23.01

18.01

15.01

10.01

02.01

05.01

Source: elaborated by the author based on own calculations

0 -0.005 -0.01 -0.015 -0.02 -0.025 -0.03 -0.035

geometric option

standard option

Fig. 5 Changes in the theta coefficient of geometric and standard call options. Source: elaborated by the author based on own calculations

6 Theta Coefficient of the Geometric Call Options The theta coefficient is another measure of risk of the option price. This coefficient is determining the influence of the decrease in time to expiration on the option price. Figure 5 illustrates the development of the value the theta coefficient of the geometric and standard call options. However, Table 5 presents the impact of the price of the underlying instrument and the time to maturity on the shaping of the theta coefficient for discussed geometric call options. The values of the theta coefficient are negative. Therefore, the option with the longer expiration date is more expensive than the option with the shorter expiration date. If the option has a greater absolute value of the theta coefficient, then the

The Impact of Changes in Time to Maturity on the Risk of Geometric Options

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Table 5 The impact of the price of the underlying instrument and the time to maturity on the shaping of the theta coefficient of geometric call options

The price of the underlying instrument [USD] 1.19 1.21 1.22 1.23 1.24 1.25 1.26

Theta of option Time to maturity T ¼ 3 [month] 0.0031 0.0120 0.0170 0.0191 0.0173 0.0133 0.0055

Theta of option Time to maturity T ¼ 6 [month] 0.0056 0.0110 0.0129 0.0137 0.0131 0.0115 0.0062

Theta of option Time to maturity T ¼ 9 [month] 0.0062 0.0088 0.0093 0.0099 0.0092 0.0085 0.0071

Source: elaborated by the author based on own calculations

increase sensitivity of the price of the option to changes in the time to maturity can be observed. The highest absolute value of the theta coefficient occurs when the option is at-the-money. If the price of underlying instrument increases/decreases in relation to the strike price, then the absolute value of the theta coefficient decreases. The absolute value of theta coefficient of the standard options is greater than the value of theta coefficient of the geometric option. If the option is deep-in-the-money and deep-out-of-the-money, the absolute value of theta coefficient of the option with the longer time to maturity is greater than the absolute value of theta coefficient of option with the shorter time to maturity. In the other cases the increase/decrease in the time to maturity influences the fall/rise in the absolute value of the theta coefficient.

7 Rho Coefficient of the Geometric Call Options The rho coefficient indicates by how much an option’s price will change, if the interest rate of risk–free assets changes by one unit. Figure 6 illustrates the changes in the rho coefficient of the geometric and standard call options. Table 6 demonstrates the impact of the price of the underlying instrument and time to maturity on the shaping of the rho coefficient discussed geometric options. The values of the rho coefficient are positive. This means that the increase/ decrease in the interest rate of risk-free assets influences the increase/decrease in the option price. The high value of the rho coefficient indicates a significant impact of changes in the interest rate of risk-free assets on price of the option. The values of the rho coefficient of standard option are higher than the rho coefficient of the geometric options. The increase/decrease in the price of the underlying instrument impacts the growth/decline in the value of the rho coefficient. The value of the rho coefficient of the option with the longer expiration date is greater than the value of the rho coefficient of the option with the shorter expiration date. Then the option

34

E. Dziawgo 0.0035 0.003 0.0025 0.002 0.0015 0.001 0.0005 02.01 04.01 08.01 10.01 12.01 16.01 18.01 22.01 24.01 26.01 30.01 01.02 05.02 07.02 09.02 13.02 15.02 19.02 21.02 23.02 27.02 01.03 05.03 07.03 09.03 13.03 15.03 19.03

0

geometric option

standard option

Fig. 6 Changes in the rho coefficient of geometric and standard call options. Source: elaborated by the author based on own calculations Table 6 The impact of the price of the underlying instrument and the time to maturity on the shaping of the rho coefficient of geometric call options

The price of the underlying instrument [USD] 1.19 1.21 1.22 1.23 1.24 1.25 1.26

Rho of option Time to maturity T ¼ 3 [month] 0.0001 0.0002 0.0005 0.0007 0.0010 0.0012 0.0014

Rho of option Time to maturity T ¼ 6 [month] 0.0003 0.0008 0.0011 0.0015 0.0019 0.0022 0.0026

Rho of option Time to maturity T ¼ 9 [month] 0.0011 0.0020 0.0025 0.0030 0.0035 0.0039 0.0046

Source: elaborated by the author based on own calculations

with the longer expiration date has the greater sensitivity to fluctuations in the interest rate.

8 Conclusion Geometric options are cheaper than the standard options. Due to the cost, geometric options are attractive instrument used in risk management. Strategies formulated on the basis of options should be resistant to changes in market conditions. Hence, risk

The Impact of Changes in Time to Maturity on the Risk of Geometric Options

35

analyses need to be conducted. The values of the risk measures of the geometric option fluctuate significantly in time. The time to maturity is more important factor impact on the price geometric option and on the value of the risk measures. The properties of the geometric option cause that the options used mostly in hedging transactions.

References Dziawgo, E. (2003). Modele kontraktów opcyjnych [Models of the options]. Torun: UMK. Dziawgo, E. (2010). Wprowadzenie do strategii opcyjnych [Introduction to options strategy]. Torun: UMK. Dziawgo, E. (2013). Miary wrażliwości opcji jednoczynnikowych [Sensitivity measures of one-factor exotic options]. Warsaw: CeDeWu. Garman, M. B., & Kohlhagen, S. W. (1983). Foreign currency options value. Journal of International Money and Finance, 2(3), 231–237. Hull, J. C. (2005). Options, futures and other derivatives. New Jersey: Pearson Prentice Hall. Musiela, M., & Rutkowski, M. (1997). Martingale methods in financial modelling. Berlin: Springer. Nelken, I. (2000). Pricing, hedging and trading exotic options. New York: McGraw-Hill. Ong, M. (1996). Exotic options: The market and their taxonomy. In I. Nelken (Ed.), The handbook of exotic options (pp. 3–44). Chicago, IL: IRWIN Professional Publishing. Vorst, T. (1992). Prices and hedge ratios of average exchange rate options. International Review of Financial Analysis, 1(3), 179–193. Wilmott, P. (1998). Derivatives. Chichester: Wiley. Zhang, P. G. (2001). Exotic options: A guide to second generation options. Singapore: World Scientific.

Part II

Tourism

A Conceptual Paper of the Smart City and Smart Community Normalini M. D. Kassim, Jasmine Ai Leen Yeap, Saravanan Nathan, Nor Hazlina Hashim, and T. Ramayah

Abstract This chapter aims to provide some insights on the different definitions of smart city and smart community concepts which is done through a comprehensive literature review and gap recognition that exists between these terms. Besides this, the forthcoming issues and threats as a result of infrastructure dependence during smart city development are addressed, through integration of smart community elements to provide a comprehensive and balanced view to improve the quality of life. The implementation of smart city or community is presented through examples from China, Japan and Malaysia. Conclusion is drawn at the end of the paper that there is no absolute answer that which concept is preferred than the other while it is built upon the desired outcomes of the governments and developers within their own context that suit well for the country’s needs, social-culture, economic, technology advancement and values. Keywords Smart community · Smart city · Smart citizen · Smart government

1 Introduction The use of Information and Communication Technology (ICT) in cities in various forms for different activities has led to the increased effectiveness of city operations. These cities have been labelled using many terms such as “smart community” and “smart city”. However, Albino et al. (2015) clarified that the smart city concept is no longer limited to the diffusion of ICT, but it looks at people and community needs. N. M. D. Kassim (*) · J. A. L. Yeap · T. Ramayah School of Management, Universiti Sains Malaysia, Penang, Malaysia e-mail: [email protected]; [email protected] S. Nathan Telekom Malaysia Berhad, Penang, Malaysia N. H. Hashim School of Communication, Universiti Sains Malaysia, Penang, Malaysia e-mail: [email protected] © Springer Nature Switzerland AG 2019 M. Huseyin Bilgin et al. (eds.), Eurasian Economic Perspectives, Eurasian Studies in Business and Economics 11/1, https://doi.org/10.1007/978-3-030-18565-7_4

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Lazaroiu and Roscia (2012) defined a smart community as a community of average technology size, interconnected and sustainable, comfortable, attractive and secure. Smart community can be characterized as a next generation community in which the management and optimized control of various infrastructures such as electricity, water, transportation, logistics, medicine, and information are integrated. Hence, it presented that the definition of smart city and smart community is not consistent and apparently a universal definition that shares between these terms does not exist. Therefore, this chapter aims to present some insights on definitions of smart city and smart community concepts, whether there are similarities or differences among these two concepts. This chapter also discuss on the issues and threats as a result of infrastructure dependence during smart city development are addressed, through integration of smart community elements to provide a comprehensive and balanced view to improve the quality of life. Examples of smart city or community implementation in China, Japan and Malaysia have also been addressed. It is hoped that this chapter would be able to capture and determine the concept of smart city or smart community. This would help scholars to further enhance the substantial variables and framework of smart city/community in the near future.

2 Definition of Smart City vs. Smart Community The ‘Smart Community’ will provide comprehensive solutions encompassing energy, water, and medical systems in order to realize a synergetic balance between environmental considerations and comfortable living. International Telecommunication Union—Telecommunication Standardization Sector of ITU (ITU-T) (2014) Focus Group on Smart Sustainable Cities established a concrete definition for smart sustainable cities, which can be used worldwide. They reviewed 116 definitions of smart sustainable city from different sources such as academic, government, and corporate. In their report, smart community and smart city are considered as a same term. Similarly, Albino et al. (2015) did not differentiate the terms of smart city and smart community and they treat these two terms with the same meaning. They clarified the meaning of the word “smart” in the context of cities through an approach based on an in-depth literature review of relevant studies as well as official documents of international institutions. However, a smart city initiative needs to create a community where all citizens can engage more easily and effectively (Paskaleva 2009). Ishida and Isbister (2000) debated some similar terms such digital community, smart community, digital city, information city and e-city. Tanabe et al. (2002) considered all of those alternative terms are used to refer to a connected community that combines broadband communications infrastructure. The geographical dimension (space) of smart communities is varied; it can be extended from a city district up to a multimillion metropolis (van den Besselaar and Koizumi 2005). And Harrison et al. (2010) agreed that Instrumented, Interconnected and Intelligent were the three fundamental factors for a smarter city.

A Conceptual Paper of the Smart City and Smart Community

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Nam and Pardo (2011) in their study attempted to conceptualise smart city with multi-dimensions stated that there are various definition of smart city and majority of them are focusing on hardware development by providing infrastructures and services such as education, health care, transportation and etc. which congruent with other existing literature. Whereas Eger (2005) pointed out that the key factors in succeeding a smart community is all about collaborations and connectivity where internet, knowledge creation and innovation and the core elements in the community. Apparently, these presented the distinctiveness among smart city and smart community through definition and gap exists between these concepts that require further clarifications which agreed by researchers as well (Stratigea 2012). And it is notable to take note as how it is defined will determine the development path.

3 Smart City Threats and Issues Gurstein (2014) argued that the current trend of developing more and more smart cities has deviated from its original objectives that attempt to improve the quality of live. As for now, many smart cities development planning are emphasising on installing edge cutting technology rather than shifting its attention on educating innovative citizen and empowering in community development projects. Eger (2005) agreed that it should not just about technology and ICTs, it should be concerning jobs, dollars and quality of life. And emphasising not just deploying technology as it is but to understand how the local citizens perceive the infrastructure and facilities to their daily life and works which it acts as one of the objectives of this study in measuring the impacts ICTs implementation initiatives. Besides, Gurstein (2014) criticized on current imbalance situation where politicians and major technology corporation utilise “smart city” as a Public Relations (PR) tool and mainly focus in urban and desirable area and tend to ignore those rural and less attractive suburb particularly in less developed countries, where the living standard of the citizen were barely reach the basic requirement, and government initiative to develop towards “smart cities” is equal to transferring the resources from the poor to the rich as much resources is putting on installing high technology and expensive devices, whereas there are still people who has limited access to basic living facilities such as health care, environmental management and security. Incontestably, providing critical and basic infrastructure is essential while it could be said that smart city development plan should separate to several stages while the first priority is the provision of basic facilities including healthcare, education, transportations and security which particularly important for developing countries as in order to develop a smart city it should first fulfil the requirement to be a city and thereafter high technology infrastructure such as IoT, broadband and other ICT initiative could only take place on top of the fundamental and act as catalyst to drive smarter development (Nam and Pardo 2011). Hence, there are opinions stating that the development direction should focus on producing smart community rather than smart city and the ultimate goals of a smart community should not just rely on

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high technologies and hardware while nurturing the smarter citizen that could ensure these infrastructure and facilities able to optimise their daily life and work and at the same time the community itself able to sustain by its own as they have acquired the knowledge and are well prepared to be part of the smart community (Eger 2005).

4 Characteristic of Smart Community 4.1

Smart Citizen

Evidently, it shows the needs to shift to develop a smart community rather than smart city where social inclusion, enabling citizen, supporting community should be included in the city development agenda (Gurstein 2014). Wongbumru and Dewancker (2014)’s study focusing on how next-generation technology could be utilised and integrated in community particularly through improving citizen innovation and participation in reaching the goal of improving quality of life demonstrating example from Japan that not only focusing on smart energy management, but going further to smart community by involving all the stakeholders and aims towards behavioural change through lifestyle innovation. Moreover, author pointed the challenges where there is situation although infrastructure on smart energy system is provided, citizen shows lack of participation and concluded that to bring up “smart citizen” should put as priority before succeeding a smart community (Wongbumru and Dewancker 2014). A smarter city should be treated as an organic system - as a network, as a linked system and smart citizen is part of it (Nam and Pardo 2011). In order to enable this part of “smart citizen” to be fully integrated to the entire system, first the citizen should be educated in a way to “infuse” themselves in the smart system. As without the knowledge, it could not maintain the sustainable cycle of the system and it will become a huge hinder for long term sustainable development. If only with a smart and innovative citizen that act as motivator for the entire system to ensure the smooth network and linkage among the smart initiatives; they themselves could only be the beneficiary from the smart system.

4.2

Training

Stratigea (2012)’s work focusing on relating smart cities concept to community development adapting the concepts from Intelligent Community Forum (ICF) that demonstrated the critical success factors for cities “going smart” and presented in a pyramid structure. As Broadband infrastructure act as the first fundamental base, knowledge-based workforce is the second level from bottom proven that apart from infrastructure and facilities, it is important to educate skilled workforce to support the further development. As it is agreed by Eger (2005) as well that internet and

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broadband access is just the first step and an integrated intelligent community is a catalyst to economic growth (Hughes and Spray 2002). Indeed, the installation of internet and broadband could offer citizen the accessibility to be linked and connected with each other as well as with the government and businesses that stimulated the local growth in terms of both economy and socially (Nam and Pardo 2011). Moreover, introducing innovation energy system, information and communication technology could encourage and support private companies, government and SMEs in providing various services to the community (Wongbumru and Dewancker 2014).

4.3

Smart Governance

Apart from installing ICTs to the city, the citizens should introduce with a new decision-making mechanism which incorporate the community into the city planning (Eger 2005). Hollands (2008) emphasise that the way citizen interacts and becoming a member to the society is the goal of succeeding a smart community and information technology is playing a role not just as a physical infrastructure that drive towards smart cities but offering opportunity to empower and educate “smart citizen”. Frost and Sullivan (2014), a consulting and research firm identifies eight key aspects that define a smart city and smart citizen to be part of it which acts as the main role in building the smart community (Singh 2014). Besides, argument exists as according to Gurstein (2014) that current situation was centralised and top down approach and the voices and opinion of the citizen were put as lowest priority when comes to city development. While in the process of development towards smarter community, all stakeholders from every segment in the society should be included and each party should assume the responsibility and attention as none of the segment should be neglected. Public private partnership to involve locals and realising a connected society is an example (Hughes and Spray 2002). Smart community that should focus on cooperation, emphasise on shared governance and participation of the citizen is essential to include the voice and opinions of the community to local development. The role of government and policy in providing governance to the city is a strong support for smart city initiative (Nam and Pardo 2011). Besides, in their study of conceptualising smart city with dimensions pointed the element in institutional factors which includes integrated and transparent governance, strategic and promotional activities, networking and partnership. Other than that, with the launches of e-government in many smart cities as one of the initiatives, it encourages the participation of citizen to more community development projects. In such way, government should be more transparent and accountable to share information to citizen, so they could voice their opinion and be part of decision making process which affects their daily lives. Smart governance it set to be the cornerstone for smart city, therefore, partnership and collaboration could prove as an effective approach to connect public, private and individual towards

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structuring an effective bottom-up mechanism where it is the key steps in transforming citizen towards a smarter way.

5 Implementation of Smart Cities in Asia 5.1

China

Smart city development is favourable by governments and developers as to its benefits that been proven with the help of ICT it could improve the quality of life for the people. For instance, many smart cities projects have been carried out around the world while different developing models have been implemented and various initiatives were introduced to achieve respective targets. In China, smart community planning took place in Yishanwan, Jiangxia District, Wuhan. The community was developed alongside with a comprehensive framework to achieve the targets of construction of infrastructure, establish a sharing platform, develop and research of an application system and develop service portal (Anrong et al. 2016). In the development plan, the synchronous development of informatization, new industrialisation, agricultural modernisation and new urbanisation act as the core of developing the smart community. The authority especially emphasis on agricultural modernisation as it plays as an important role in rural area and act as the fundamental for the suburban area to develop through urbanisation. Anrong et al. (2016) agreed that it is insufficient to improve only on infrastructure but also stress on the innovation ability of the society by integrating the concept of sustainable to city development.

5.2

Japan

In Japan, Smart Community often define as taking full advantage of IT technology to effectively control power flow and provide new services for power supplies and demand side users (Gao et al. 2016). And it is different from the definition in other countries and the smart community model can be described in four parts which are new information network, new energy system, new transportation system and new urban development. Emphasis is putting on smart energy grid implementation as a way to change citizen towards smarted lifestyle in term of daily life, work or office and transportation. There are four large scale demonstration cities in Japan which implementing smart community concepts which are Kyoto Keihanna District, Yokohama City, Kitakyushu City and Toyota City. All the demonstration cities having the similar target which is reducing the emission of Greenhouse Gas (GHG) and with the assistant of IT technology smart-grid electricity management was introduced and most of the researches about smart community are about using of renewable resource. For instance, eco-town project was introduced for the aim to build a system

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by using all the waste products of an industry as raw materials for another industry and realising a zero-waste recycling-oriented society.

5.3

Malaysia

Medini Smart City in Iskandar Puteri, Malaysia was chosen to be the pilot for smart city projects which focuses on three areas, economy, environment and social and promotes six dimensions: smart economy, smart governance, smart environment, smart mobility, smart people and smart living (Ghazali et al. 2016). This is a project collaborating the university and regional development agency. The objective of this project was focus on infrastructure provision and change of quality of life and environment as long term target by developing smart living through engaging local citizen for sustainable development. However, Gil and Navarro (2013) argued that in terms of governance and policy context which were one of the factors in analysing smart city initiatives, the participation of local seems lacking from the technology partnership. While there was a noticeable effort from the aspect of education where there are few internationally well-known universities such as University of Newcastle plan to open up campus in Medini.

6 Conclusion It could be seen that the countries that mentioned at the above have different approaches and initiatives in achieving their desired goals and finally realizing a smart city or community in their own context. Therefore, it is not a total deny that smart city is distinctive from smart community while it should be integrated as one of the multi-dimensional aspects of smart city that receive similar attention and develop simultaneously without neglecting any aspect. Citizens should be provided the chances and opportunities to prepare themselves through educations, participation in community development, taking the ownership of their own community and equipped themselves with soft skills that are needed to face the global challenges (Eger 2005). “To be connected” has found to be the fundamental element in realizing both smart city and smart community and this is what city is all about where people come and live together to form a concentrated settlement (Kumar and Dahiya 2017). Since ancient time, through the utilization of horses that form the connected path between people and cities where information, knowledge, culture and economies are connected. And for now, the ICT is playing the similar role. And integrating smart city with community development through provision of affordable broadband and internet facilities and training for ICT skills should be the priority particularly for developing countries which are lacking of technology innovation and implementation of ICT facilities is high investment and returns on investment is major concern.

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Hence, the availability of the resources should be utilized and shared among the community in order to generate the maximum benefit for the citizen (Wongbumru and Dewancker 2014). Moreover, there is no a universal template or model of smart city or community that is suitable for all the countries as the definition and target to be achieve for a specified city may be varied from another while the development model of smart city for Europe countries might not suit well for countries in Southeast Asia as they are different from the aspect of social-culture, political background, economic, technological situation and values. Acknowledgement The authors acknowledge all reviewers for their comments on this manuscript and are grateful to the committee members of the 24th Eurasia Business and Economic Society (EBES) Conference - Bangkok for organizing a beneficial conference that provided a good platform for research. This article is supported by the USM- MCMC Grant (No: 304/PMGT/650865/M147).

References Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart cities: Definitions, dimensions, performance, and initiatives. Journal of Urban Technology, 22(1), 3–21. Anrong, D., Li, G., Li, J., & Kong, X. (2016). Research on smart community planning of Yishanwan, China towards new urbanization. International Review for Spatial Planning and Sustainable Development, 4(1), 78–90. Eger, J. M. (2005). Smart communities, universities, and globalization: Educating the workforce for tomorrow’s economy. Metropolitan Universities, 16(4), 28–38. Frost and Sullivan. (2014). Smart Cities – Frost & Sullivan Value Proposition. Accessed May 12, 2018, from https://ww2.frost.com/wp-content/uploads/2019/01/SmartCities.pdf Gao, W., Fan, L., Ushifusa, Y., Gu, Q., & Ren, J. (2016). Possibility and challenge of smart community in Japan. Procedia-Social and Behavioral Sciences, 216, 109–118. Ghazali, M., Okamura, T., Abdullah, T., Sunar, M.S., Mohamed, F., & Ismail, N., (2016, May). In the quest of defining smart digital city in Medini Iskandar Malaysia, Iskandar Puteri, Malaysia. In Proceedings of the SEACHI 2016 on smart cities for better living with HCI and UX (pp. 19–23). ACM. Gil, O., Navarro, C., Gil, O., & Navarro, C. (2013, July). Innovations of governance in cities and urban regions: Smart cities in China, Iskandar (Malaysia), Japan, New York and Tarragona (Spain). In EURA conference: Cities as Sheedbeds for innovation (pp. 4–6). Gurstein, M. (2014). Smart cities vs. smart communities: Empowering citizens not market economics. The Journal of Community Informatics, 10(3). Harrison, C., Eckman, B., Hamilton, R., Hartswick, P., Kalagnanam, J., Paraszczak, J., & Williams, P. (2010). Foundations for smarter cities. IBM Journal of Research and Development, 54(4), 1–16. Hollands, R. G. (2008). Will the real smart city please stand up? Intelligent, progressive or entrepreneurial? City, 12(3), 303–320. Hughes, C., & Spray, R. (2002). Smart communities and smart growth-maximising benefits for the corporation. Journal of Corporate Real Estate, 4(3), 207–214. International Telecommunication Union – Telecommunication Standardization Sector of ITU. (2014). Smart sustainable cities: An analysis of definitions. Geneva: International Telecommunication Union.

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Ishida, T., & Isbister, K. (Eds.). (2000). Digital cities: Technologies, experiences, and future perspectives (Vol. 1765). Berlin, Germany: Springer. Kumar, T. V., & Dahiya, B. (2017). Smart economy in smart cities (pp. 3–76). Singapore: Springer. Lazaroiu, G. C., & Roscia, M. (2012). Definition methodology for the smart cities model. Energy, 47(1), 326–332. Nam, T. & Pardo, T.A., (2011, June). Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th annual international digital government research conference: Digital government innovation in challenging times (pp. 282–291). ACM. Paskaleva, K. A. (2009). Enabling the smart city: The progress of city e-governance in Europe. International Journal of Innovation and Regional Development, 1(4), 405–422. Singh, S. 2014. Smart cities – A $1.5 trillion market opportunity. Forbes. Accessed February 14, 2018, from http://www.forbes.com/sites/sarwantsingh/2014/06/19/smart-cities-a-1-5-tril lion-market-opportunity/#3b9495237ef9 Stratigea, A. (2012). The concept of ‘smart cities’. Towards community development? Netcom. Réseaux, Communication et Territoires, 26(3/4), 375–388. Tanabe, M., van den Besselaar, P., & Ishida, T. (2002). Digital cities II: Computational and sociological approaches. Berlin: Springer. van den Besselaar, P., & Koizumi, S. (Eds.). (2005). Digital cities III. Information technologies for social capital: Cross-cultural perspectives. Berlin: Springer. Wongbumru, T., & Dewancker, B. (2014). Smart communities for future development: Lessons from Japan. International Journal of Building, Urban, Interior and Landscape Technology, 3, 70–75.

Tourist Destination Assessment by Revised Importance-Performance Analysis Olimpia I. Ban, Victoria Bogdan, and Delia Tușe

Abstract The goal of this research is to improve the Importance-Performance Matrix by more accurately fitting the attributes of quality into matrix quadrants. The working methodology implied the use of the mathematical apparatus, respectively of five validated mathematical indexes (Dunn validity index, Silhouette validity index, validity index Calinski-Harabasz, Pakhira-Bandyopadhyay-Maulik validity index and Davies-Bouldin validity index). It will consider the position of an attribute in a quadrant validated if it resulted in all five index-generated situations. It was tested the method for the data collected from the customers accommodated in the International Hotel in Băile Felix Spa, Romania. The result shows that the number of attributes that retain their quadrant position in all situations is greatly reduced, but the certainty about the position of the attributes is greatly increased. This method increases the rigor of IPA and helps managers in their decision-making about quality of services. Keywords Importance-Performance Analysis · Critics · Validation index · Method · Touristic destinations · Romania

1 Introduction A marketing tool with strategic valences that also has the capacity to be widely used by theoreticians and practitioners is the Importance-Performance Analysis (IPA), created by Martilla and James (1977). Through this tool, the quality attributes of O. I. Ban (*) Department of Economics and Business, University of Oradea, Oradea, Romania V. Bogdan Department of Finance and Accounting, University of Oradea, Oradea, Romania e-mail: [email protected] D. Tușe Department of Mathematics and Informatics, University of Oradea, Oradea, Romania e-mail: [email protected] © Springer Nature Switzerland AG 2019 M. Huseyin Bilgin et al. (eds.), Eurasian Economic Perspectives, Eurasian Studies in Business and Economics 11/1, https://doi.org/10.1007/978-3-030-18565-7_5

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Table 1 Some IPA application domains Application domain Supply of medical services Education Service quality On-campus catering services Service quality and strategic development of banks E-business strategies and resource allocation Cultural services Service quality on national highways Computer industry Tourism (tourist accommodation services, tourist guide, strategy development, the image and the performance of the destination etc.)

Evaluation of the performance of suppliers Recreational services

Authors who applied the ImportancePerformance Analysis Dolinsky and Caputo (1991), Hawes and Rao (1985), Yavas and Shemwell (2001) Alberty and Mihalik (1989), Ford et al. (1999), Nale et al. (2000), Ortinau et al.(1989) Ennew et al. (1993) Aigbedo and Parameswaran (2004) Matzler et al. (2003) Lavenburg and Magal (2005) Baker and Draper (2013) Huang et al. (2006) Lee et al. (2009) Bush and Ortinau (1986), Lin et al. (2005), Deng (2007), Chu and Choi (2000), Pike (2002), Lee and Lee (2009), Oh (2001), O’Leary and Deegan (2005), Smith and Costello (2009), Wade and Eagles (2003), Caber et al. (2012), Hudson and Shephard (1998) Lee et al. (2009) Ko and Pastore (2007)

Source: created by authors

products or services are analyzed in terms of perceived importance and performance to enable marketing managers to take the decisions necessary to raise the quality of supply and increase in consumer satisfaction. The obviously importance of consumer satisfaction determines firms to conduct periodically surveys for established what satisfies the consumers (Wu 2014). IPA has been applied in different domains and in various tourism sectors (see Table 1). The Importance-Performance Analysis method proposed by Martilla and James (1977), is building a matrix that is a instrument for developing marketing strategies based on the importance and performance of each attribute of service quality. The key objective of this method is to diagnose, identify the attributes with a determined importance that have a performance below or above expectations. Thus, the vertical axis will represent the “importance of attributes” while the horizontal axis will represent the “performance of attributes”. The two axes will divide the array into four quadrants through a point that is given by the mean of values obtained in importance and performance. Each quadrant has some strategic recommendations from “keep up the good work”, “concentrate here”, “low priority”, and “possible overkill”. Satisfaction is an important component of behavior of tourist (Moll-de-Alba et al. 2016). For tourist destinations, the comparison of the perceived importance and performance represented in the IPA matrix allows managers and tourism authorities to identify the characteristics that contribute to the success of destinations (Zhang

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and Chow 2004, p. 82 cited in Caber et al. 2012). Many studies carried out at the tourist destination level have been limited to classifying attributes by the performance at tourist destinations. The comparison of perceived importance and performance, represented in the IPA matrix, allows managers and tourism authorities to identify the characteristics that contribute to the success of destinations (Zhang and Chow 2004 cited in Caber et al. 2012) and to the success of companies.

2 Weaknesses of Importance-Performance-Analysis: Theoretical Approaches In tourism literature, the Importance-Performance Analysis (IPA) was used to measure the quality of services, image and performance of the destination by numerous researchers (Table 1). Although it is a very used tool, the IPA has often been criticized and improved, also questionable. Criticisms of the original IPA variant of Martilla and James (1977): • the use of direct data collection on the importance of attributes (Likert scale or metric scale), has the disadvantages that it makes data collection difficult, the scores have a small inter-item variation, the values of the answers are raised uniformly, therefore unnecessary (Bacon 2003); • using direct data collection on the importance of attributes that has many leaks, with few respondents knowing the real answer concerning them (Van Ryzin and Immerwahr 2007); • inaccuracies related to registering the importance of attributes. “When consumers evaluate attributes, it is not clear whether the attribute is important for its presence or its absence” (Slevitch and Oh 2010, p. 561); • the assumption that the relationship between the perceived performance of each attribute and global satisfaction is symmetrical, when in fact it is actually asymmetric (Kano et al. 1984; Deng and Pei 2009; Matzler and Sauerwein 2002; Matzler et al. 2003); • not taking into account the possibility that the relationship between the perceived attribute performance and global satisfaction is non-linear and asymmetric for the basic attributes and the qualitative stimulus attributes, while for the performance attributes, the relationship is linear and symmetrical (Deng 2007); • not taking into account the fact that the relationship between the performance of attributes and the importance of attributes is causal (Matzler et al. 2003; Oh 2001; Ryan and Huyton 2002; Sampson and Showalter 1999); • evaluation of the performance without taking into account the competition (Burns 1986; Keyt et al. 1994); • IPA building for all audiences and not for each segment, as it would be right (Abalo et al. 2006, 2007; Caber et al. 2012) (this observation concerns the application of the IPA and not its construction);

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• traditional IPA erroneously implies that quality attributes are independent, in reality some influence others, therefore the matrix interpretation generates decisions with unpredictable impact (Hu et al. 2009); • the grid partition into areas with distinct significance for decision-makers. The majority of the attributes can fell into the category, like “Keep up the good work” with not clear implications (Milman and Xu 2012); • in connection with the above observation, we can add the very common IPA situations where many attributes are positioned in the matrix at the boundary between two dials. This position at the border raises issues of correct interpretation (Ban et al. 2016); • the arbitrary tracing of the axes, which leads to arbitrary managerial decisions (Sever 2015; Ortinau et al. 1989; Mount 2000; Martilla and James 1977; Crompton and Duray 1985); • another criticism is related to the number of IPA quadrants that in the original form are four, but it is considered that there should be another number to cover the possible situations (starting with Burns 1986). The serious criticism is that in the Martilla and James version the quadrants are subjectively constructed (Mount 2000). Burns (1986) proposes transforming IPA into SIPA by including and reporting to competition, proposing a method of prioritizing the attributes by comparing the consumer scores to importance and performance. The attributes are given degrees of “low, medium or high” and performance grades (“satisfaction” is used here) differentiated by “A, B and C”. In the original IPA matrix there is a gray area (also called an area of indifference) that includes the attributes to which the respondents show indifference, both in terms of importance and performance. In the model proposed by Albrecht and Bradford (1990) nine quadrants are obtained. Keyt et al. (1994) used a single-score z-test on average scores to determine the remarkable (salient) attributes. Those attributes that had values significantly higher than the mean scores for importance were considered remarkable. This procedure has led to the placement of the attributes in one of the sixteen cells in Keyt’s matrix. Ortigueira-Sánchez et al. (2015) propose the Importance-Performance Matrix with a priority line, the so-called “diagonal pattern.” The method of drawing axes in building the Importance-Performance Matrix by the point that represents the average of the values obtained is the most widely used. Oh (2001) suggests drawing the axes by the point representing the mean of the measurement scales (4 points, 5, 7 etc.) and not by the mean of the recorded values. The proposed method has the disadvantage of placing (almost totally) the attributes in the upper areas of importance and performance, which makes it impossible to distinguish between them. In addition, the division would be the same for all the studies regardless of the data recorded and for the same study different interpretations can be obtained depending on the chosen scale (Araña and León 2013). Azzopardi and Nash (2013) and Ziegler et al. (2012) use an iso-valuation line dividing the plan into two. Above the line there are the attributes that have a higher performance than importance and those below the line require improvement.

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Abalo et al. (2006) modify the original IPA in the sense that it forms a partition that combines the dials diagonally, widening the top left dial. The newly formed area extends over the entire surface above the diagonal that divides the ImportancePerformance Matrix into two. Thus, any attribute with greater importance than the performance is a candidate for improvement. Under the diagonal, the three quadrants are reconstructed, but the mathematical rule used to construct them is not specified.

3 Research Methodology To build the performance matrix we need to evaluate the importance of the attributes/resources and their performance associated with the Băile Felix destination. We have chosen to perform the Classical Importance-Performance Analysis proposed by Martilla and James (1977) as compared to Abalo’s Revised ImportancePerformance Analysis (Abalo et al. 2006). The tourist destination chosen for the research of the quality attributes is a balneoclimatic resort, Băile Felix. This resort is the largest from Romania in terms of accommodation capacity. Băile Felix resort is positioned in the northwestern part of Romania, in Bihor county, near Oradea and 22 km from the border crossing point to Hungary. It has a ideal climate for balneary tourism. The resort is open all year with a great influx of tourists in the especially in summer months. Băile Felix resort supplies conditions for relaxation, vacation, break and recuperation. The resort’s very valuable natural resources are the thermal waters. The temperature of the thermal waters is between 20–49
C. The thermal waters in the resort are used to treat diseases such as: inflammatory rheumatic diseases; degenerative rheumatic diseases; abarticular rheumatic disorders; post-traumatic conditions; peripheral neurological and gynecological disorders; associated diseases. The resort has numerous accommodation structures: 16 hotels (of which 1 are 5 stars), boarding houses, villas (4), etc. with a total of over 7000 accommodation places. The International Hotel is a four-star hotel and has a modern spa and a modern conference center, recently renovated. The International Hotel is renowned for its therapy programs and is the only hotel unit in Romania that has been awarded the EUROPESPA-med logo, which attests the fulfillment of the standards of the European Association of Spa Resorts (ESPA) on the general infrastructure of therapies, hygiene and safety of tourists (Turism Felix 2013). The research method used was that of the survey based on the questionnaire. The research was exploratory on the basis of a sample based on availability. Between November and December 2016 a questionnaire was applied to 120 tourists accommodated at the International Hotel in Băile Felix. The questionnaire was applied at the reception of the International Hotel in Băile Felix, observing the condition that the respondent stayed in the resort for at least one night. The questionnaire used in this research was that one applied by Mihalič (2013) and adapted at this survey conditions. It was tested before application on 20 people. Following the test, the questionnaire was simplified by removing the questions

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considered secondary in terms of importance for the research objectives. The final form of the questionnaire includes 13 questions divided as follows: 5 questions about how visitors have chosen and arrived at Băile Felix; 2 questions about the global evaluation of the destination; 2 questions about the importance of destination attributes/resources and the performance of Băile Felix destination on these attributes/resources and 4 questions related to the respondent profile. We have decided to directly determine the importance of attributes, although there is also the possibility of indirect determination with very good results (Ban 2012; Ban and Bogdan 2013). The questionnaire was applied in Romanian for the Romanian tourists and in English for foreign tourists. The destination was evaluated from the perspective of 19 attributes/resources as proposed by Mihalič (2013) and their importance and performance was recorded with a 5-step Likert scale. Despite the simplification of the questionnaire, its completion rate was 80% for the 120 questionnaires prepared at the hotel reception. The assessment of the importance and satisfaction of Băile Felix destination was achieved with a 5-step Likert scale.

4 Results and Discussions The socio-demographic characteristics of the respondents were: nationality, socioprofessional status, age and gender. The structure of the respondents by nationality is that of Table 2 with 76% of Romanians and 24% of foreign visitors. We have found among those surveyed a high share of employees and entrepreneurs (70%), only 16% are retired and the rest are students /pupils or do not have a job. Employability is also explained by the age of visitors between 40 and 60 years old for 45%, between 20 and 40 years old 36%, only 19% over 60 years old. The male/female ratio is balanced (52% men and 48% women). The overall satisfaction assessment regarding Băile Felix destination received an average score of 4.35 out of a maximum of 5.00, with 44 maximum ratings. The evaluation of Băile Felix destination on attributes/resources in terms of importance and performance can be seen in Table 3. We calculated the intersection of the Importance-Performance Matrix axes as a point of the mean of the values for the value and the mean of the performance values. On the vertical axis we represented the importance of the researched resources/ attributes and on the horizontal axis we represented the performance of the destination from the point of view of the researched resources/attributes. The Importance-

Table 2 Structure of respondents by country of residence

Country of residence of tourists Romania Israel Germany Source: created by authors

Weight in total (%) 76 15 9

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Table 3 The importance of resources and the performance of Băile Felix destination from the perspective of these resources

Resources/attributes 1. “Personal safety and security” 2. “The destination can be easily reached” 3. “Overall cleanliness of the destination” 4. “Unspoiled nature” 5. “Climate conditions” 6. “Diversity of cultural/historical attractions” (architecture, tradition and customs. . .) 7. “The quality of the accommodation (hotel, motel, apartment. . .)” 8. “Friendliness of the local people” 9. “Organization of the local transportation services” 10. “The supply of local cuisine” 11. “Possibilities for shopping” 12. “Night life and entertainment” 13. “Opportunity for rest” 14. “Availability of sport facilities and recreational activities” 15. “Supply of cultural and other events” 16. “Thermal spa supply” 17. “Wellness supply” 18. “Casino and gambling supply” 19. “Conference supply” Mean of values

Importance associated to resources 4.15 4.14 4.23 3.36 3.02 3.40

Performance of destination from the resurge perspective 3.25 4.17 4.28 3.51 3.69 3.21

4.08

4.15

3.63 3.63

3.65 3.48

3.64 3.13 3.17 4.31 3.32

3.62 3.17 3.07 4.30 3.26

3.21 4.67 4.49 2.62 3.12 3.64

3.31 4.58 4.63 2.67 3.25 3.64

Source: created by authors

Performance Analysis performed for Băile Felix destination is shown in Fig. 1 (Importance-Y axis, Performance-X axis). The results of the Importance-Performance Analysis after Martilla and James (1977) show the following distribution of quality attributes/resources (Fig. 1): • quadrant I “Keep up the good work”—six attributes (16. “Thermal spa supply”, 17. “Wellness supply”, 2. “The destination can be easily reached”, 3. “Overall cleanliness of the destination”, 7. “The quality of the accommodation (hotel, motel, apartment. . .)”, 13. “Opportunity for rest”); • quadrant II “Concentrate here”—one attribute (1. “Personal safety and security”); • quadrant III “Low priority”—nine attributes (18. “Casino and gambling supply”, 19. “Conference supply”, 11. “Possibilities for shopping”, 12. “Night life and entertainment”, 14. “Availability of sport facilities and recreational activities”, 6. “Diversity of cultural/historical attractions (architecture, tradition and customs. . .)”, 15. “Supply of cultural and other events”, 4. “Unspoiled nature”,

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O. I. Ban et al.

5 4.634.58

4.5

4.3 4.28 4.17 4.15

4

3.69

3.65 3.64 3.62 3.51 3.48 3.31 3.26 3.25 3.21 3.17 3.07

3.5 3

3.25

2.67

2.5 2 1.5 1 0.5 0

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Fig. 1 Martilla and James Importance-Performance Matrix (1977) representing the attributes/ resources of Băile Felix destination with four quadrants (the top right quadrant I, the top left quadrant II, the bottom left quadrant III, the bottom right quadrant IV). Source: created by authors. Note: the graph shows the values of Performance

9. “Organization of the local transportation services”, 10. “The supply of local cuisine”); • quadrant IV “Possible overkill”—două attributes (5. “Climate conditions”, 8. “Friendliness of the local people”). Three attributes are very close to the quadrant demarcation axes. These attributes are: 8.”Friendliness of the local people”, 10. “The supply of local cuisine”, 9. “Organization of the local transportation services”. Attribute 10. “The supply of local cuisine” is right on the vertical axis, being either in the quadrant III or in quadrant IV. The results show the strengths of Băile Felix destination through the most appreciated attributes: thermal spa supply, wellness supply, personal safety and security, overall cleanliness of the destination, the quality of the accommodation (hotel, motel, apartment), opportunity for rest. We note that these resources are in direct correlation with the main motivations of the chosen destination. When asked about the main motivation for the destination of Băile Felix, 59% mentioned “rest and relaxation”, 33% said “health”, only 8% having other reasons. Quadrant II is the main strategic base for increasing consumer satisfaction by improving the quality of resources that are positioned here. Positioning in this quadrant the “Personal safety and security” attribute draws the attention to a very sensitive issue, which also enters the attractiveness of the accommodation. The issue of personal safety and security is demotivated tourism. Romania, generally as a tourist destination, has a problem promoted in the international media-that of personal insecurity and travelers’ goods. This issue must also be solved as a problem in itself and as a way of communication. The eight attributes located in quadrant III

Tourist Destination Assessment by Revised Importance-Performance Analysis

57

were distributed there circumstantially, in a close relationship with the interests of the surveyed tourists. It is about the low interest in: conferences, shopping, casino, sports, sightseeing and even unspoiled nature. An additional reason for the tourists’ disinterest could be the period of the year when they used the tourist product, namely November and December. This is also the reason why “climatic conditions” were not too important for the respondents, being located in quadrant IV. We note that three attributes are located very close to the quadrant demarcation axes. These attributes are: “Friendliness of the local people”, “Local cuisine supply”, “Organization of local transport services”. This sensitive positioning raises many issues because an error of completeness can lead to dramatically different strategic solutions. For example, “Friendliness of the local people” is an attribute that always enjoys the appreciation of the public as an asset (Ban 2012) and here it has an uncertain positioning. The situation may be the result of an error in completing the questionnaire. In Fig. 2(a) we applied the solution proposed by Oh (2001) to divide the quadrant matrix into the point representing the mean of the scale chosen. As the Likert scale applied to the research was a 5-stage, the mean is 2.5. The definite disadvantage of the method is that it determines the positioning of all the attributes in the top right quadrant “keep up the good work” (Fig. 2). According to this positioning, all the attributes can be maintained at the current quality level. This situation is not isolated, it occurs frequently due to the positioning of responses, in many cases, in the upper part of the scale (Bacon 2003). Consequently, the method does not prove its usefulness in this study. Abalo et al. (2006) modifies the original IPA by drawing a diagonal through the points where the importance is equal to the performance. He believes that everything above the diagonal is a candidate for improvement. The attributes located under the diagonal are divided into three quadrants, yet the division rule is not specified. In the case of the study undertaken, most of the attributes are near the diagonal (Fig. 2(b)) which means that decision are discussable. We note that the strategic decision is absolutely different from that obtained by Oh’s method (2001). The major differences noticed call into question the validity of the methods, at least for this particular case. There are other methods of attribute distribution, as mentioned in the first part of the paper It is necessary to find a verifiable axis tracking method that works for each specific case.

5 Determining the Axes in the Importance-Performance Matrix Using the Validity Indices In Ban et al. (2016) it is proposed an approach based on classification theory methods. Thus, it eliminates the possibility of positioning the attribute on axes or near axes in the Importance-Performance Matrix.

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a 5.00

4.50

4.31 4.23 4.14 4.08

4.15

4.00

4.67 4.49

3.64 3.63 3.63 3.40 3.36 3.32 3.21 3.17 3.13 3.12 3.02

3.50 3.00 2.62

2.50

2.00 1.50

1.00 0.50

0.00 0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

b 5.00 4.50

4.31 4.23 4.14 4.08

4.15

4.00

4.67 4.49

3.64 3.63 3.63 3.40 3.36 3.32 3.21 3.17 3.13 3.12 3.02

3.50

3.00 2.62

2.50

2.00 1.50

1.00 0.50

0.00 0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

Fig. 2 (a) Importance-Performance Matrix representing the attributes/resources of Băile Felix destination after the quadrants division proposed by Oh (2001). (b) Importance-Performance Matrix representing the attributes/resources of Băile Felix destination after the quadrants division proposed Abalo et al. (2006). Source: created by authors. Note: the graph shows the values of Performance

Tourist Destination Assessment by Revised Importance-Performance Analysis

59

The central problem of the classification theory is that starting from a set of objects X ¼ {x1, . . . , xn} in general elements of ℝs, that is each characterized by s parameters, we determine a position of that, i.e. a set P ¼ {A1, . . . , Ac} so that: i. Ai 6¼ ∅, 8i 2 f1; . . . ; cg; ii. Ai \ Ai ¼ ∅, 8ij 2 f1; . . . ; cg, i 6¼ j; iii. c U i¼1 Ai ¼ X:

Validity indices have been introduced to measure the quality of a partition generally obtained from a classification algorithm. Taking into account their simplicity and the comparative results obtained in Arbelaitz et al. (2013) and Milligan and Coofer (1985), we use the following indices in this paper: Dunn validity index (V_D), Silhouette validity index (V_S), Calinski-Harabasz validity index (V_CH), Pakhira-Bandyopadhyay-Maulik validity index (V_PBM) and Davies-Bouldin validity index (V_DB). Dunn validity index (V_D): An old validity index having many subsequent variants and often used in applications in the Dunn index (denoted V_D) introduced as follows: min

V DðPÞ ¼

min

iЄf1;...;cg kЄf1;...;cg\ fig


 δ A j ; Ak

max ΔðAi Þ

ð1Þ

iЄf1;...;cg

where

 δ A j ; Ak ¼ min min d x j ; xi

ð2Þ


 ΔðAi Þ ¼ max d x j ; xi x j , xi ЄAi

ð3Þ

x j ЄAi xi ЄAk

and

Considering “d” the Euclidean distance between two elements of Rs, both in (2), (3) and further on. We mention that an upper value of V-D means a better partition.

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Silhouette validity index (V_S): According with some criteria the Silhouette index it is the best validity index. It is defined as follows:

 c X b x j ; Ai  a x j ; Ai 1X 

 V SðPÞ ¼ n i¼1 x ЄA max a x j ; Ai ; b x j ; Ai j i

ð4Þ

where

 1 X a x j ; Ai ¼ d x j ; xk x ЄA k i jAi j X

 1 d x j ; xk b x j ; Ai ¼ min xk ЄAk Ak ЄP\ fAi g jAk j

ð5Þ ð6Þ

and |M| denotes the cardinality of M. An upper value of V_S means a better partition. Calinski-Harabasz validity index (V_CH): it is obtained the best results in some comparative studies and it is defined as follows: V CH ¼

Pc n  c i¼1 jAi jdðvi, vÞ
 Pc Pc c  i¼1 x j ЄAi d x j, vi,

ð7Þ

Where v denotes the center of the entire set x and vi denotes the center of the atom Ai and i 2 {1, . . . , c}. An upper value of V_CH means a better partition. Pakhira-Bandyopadhyay-Maulik validity index (V_PBM): By comparison with other well-known measures (Davies-Bouldin, Dunn and Xie-Beni validity indices) this cluster validity index gives better results. The validity index, denoted by V_PBM, is defined as follows:  V PBM ðPÞ ¼

1 E  D c J

2 ð8Þ

Where, E¼ D¼ J¼

Xn


 d x j, v
 max d vi, v j j¼1

i, jЄf1;...;cg

Xn

j¼1

Xc


 u d x ; v ij j i i¼1

ð9Þ ð10Þ ð11Þ

= Ai, i 2 {1, . . . , c}, , j 2 {1, . . . , n}, v and vi, and uij ¼ 1 if xi 2 Ai, uij ¼ 0 if xi 2 i 2 {1, . . .c} being introduced above. An upper value of V-PBM means a better partition.

Tourist Destination Assessment by Revised Importance-Performance Analysis

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Davies-Bouldin validity index (V_DB): it is one of the most used indices in comparative studies. Its value (denoted by V_DB) seems to give very good results. It is defined as follows: 0 1 max ðSðAi Þ þ SðAk ÞÞ 1 X c @kЄf1;...;cgfig A V DBðPÞ ¼ i¼1 c min d ð vi ; vk Þ

ð12Þ

kЄf1;...;cg\ fig

where SðAi Þ ¼

1 X d ð xk ; vi Þ jAi j xkЄA

ð13Þ

i

for every i2 {1, . . . , c} with above notations. A lower value of V_DB means a better partition. Let {a1, . . . , an} be a set of attributes. By denoting xj, j 2 {1, . . . , n} the pairs (performance, importance) corresponding to the attribute aj, that is xj ¼ ( pj, wj), the problem is to find the optimum choice of axes p and w such that for the partition P ¼ {A1, A2, A3, A4} of the set X ¼ {x1, . . . , xn}, obtained as:
   A1 ¼ xi ¼ p j ; w j : p j > p; w j > w
   A2 ¼ xi ¼ p j ; w j : p j < p; w j > w
   A3 ¼ xi ¼ p j ; w j : p p; w