The Business Review, Cambridge

The American Academy of Business Journal

Vol. 25 * Number 2 * March 2020

The Library of Congress, Washington, DC   *   ISSN: 1540 – 7780

Online Computer Library Center, OH  *  OCLC: 805078765

National Library of Australia  *  NLA: 42709473

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The primary goal of the journal will be to provide opportunities for business related academicians and professionals from various business related fields in a global realm to publish their paper in one source. The Journal will bring together academicians and professionals from all areas related business fields and related fields to interact with members inside and outside their own particular disciplines. The journal will provide opportunities for publishing researcher's paper as well as providing opportunities to view other's work. All submissions are subject to a double blind peer review process.  The Journal is a refereed academic journal which  publishes the  scientific research findings in its field with the ISSN 1540-7780 issued by the Library of Congress, Washington, DC.  The journal will meet the quality and integrity requirements of applicable accreditation agencies (AACSB, regional) and journal evaluation organizations to insure our publications provide our authors publication venues that are recognized by their institutions for academic advancement and academically qualified statue.  No Manuscript Will Be Accepted Without the Required Format.  All manuscripts should be professionally proofread / edited before submission. After the manuscript is edited, you must send us the certificate. You can use www.editavenue.com for professional proofreading/editing or other professional editing service etc... The manuscript should be checked through plagiarism detection software (for example, iThenticate/Turnitin / Academic Paradigms, LLC-Check for Plagiarism / Grammarly Plagiarism Checker) and send the certificate with the complete report.

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Classification of Stock Market Price Change by Data Mining

Dr. Nursel Selver Ruzgar, Ryerson University, Toronto, Canada

 

ABSTRACT

In this paper, eight Canadian banks’ daily stock market price changes are examined by three data mining techniques, logistic regression, fuzzy-roughNN and genetic algorithms. Thirty-seven years of data from 1980 to 2017 obtained from NASDAQ for eight Canadian banks with 21 independent variables and one dependent variable, price, were used to classify the daily stock price changes. Daily price changes are divided into three classes, “up”, “down” and “same” according to the previous stock market daily close price. To determine which method makes the better classification, three methods run separately for each bank. Then predicted values for 2018 with each method for each bank, were compared the original 2018 data to see how the predicted values were compatible with the real values. It was seen that, among the three methods, the genetic programming algorithms classified the stock price changes well. This paper demonstrates that the genetic programming method is applicable to a wide range of practical problems pertaining to price changes. Moreover, the results show that the genetic programming is a promising alternative to the conventional methods for financial prediction.  Data mining (DM) and knowledge discovery is a family of computational methods that aim at collecting and analyzing data related to the function of a system of interest to gain a better understanding of the system (Triantaphyllou, 2010). DM attempts to formulate, analyze and implement basic induction processes that help extract meaningful information and knowledge from unstructured data. DM that aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction is the process of using statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and knowledge assembled from large databases (Kusrini, 2009). DM can be used in different disciplines, such as engineering (Carrizosa and Morales, 2013), finance (Cheng, 2010), business, banking (Ferreira, 2018, Manurung, 2015), medicine (Ramamurthy and Chandran, 2011) and science (Singh, 2015).   There are many DM methods to perform the analysis, such as clustering, classification, and association. Classification, which is a work of assessing a data object to include it in a certain class of available classes, is of the widely used DM method to extract information from various high-dimensional data sets. The classification includes the following algorithms; Logistic regression, J48, Discriminant analysis, K-Nearest Neighbor, Naïve Bayes, Decision Tree, Rough sets, fuzzy rough, GA, Associative Classification, Neural Network and Support Vector Machine (Andriansah and Solichin, 2018). Rough sets have been used to classify credit ratings in the global banking industry (Chen and Cheng, 2013). Rough sets were used to classify price movements (Ruzgar, 2014), financial data (Ruzgar, 2015), and incomplete meteorological data (Aprianti and Mukhlash, 2014). Fuzzy rough sets have been used for classification of stock markets (Xiao-feng and Song-song, 2010).  Genetic Programming (GP) has been applied in various fields of knowledge, such as pattern recognition, the utilization of the GA and GP (Grosan and Abraham, 2006), datamining, function regression, decision rule generation, time series forecasting, etc (Cortez, 2002, Alfaro-Cid, 2014,  Kattan, 2015, Kim, 2006). GA has been widely employed on time series prediction problems, such as those illustrated in Cortez (2002), Alfaro-Cid, (2014), Kattan, (2015). GP has been also applied to financial time series prediction (Allen and Karjalainen, 1999, Myszkowski and Rachwalski, 2009, Vasilakis, 2013, Dabhi and Chaudhary, 2015, Pimenta, 2018). Allen and Karjalainen (1999) used GP for generating negotiation rules (Kim, 2006). GP was used for the daily prediction of the exchange rate (Vasilakis, 2013, Pimenta, 2018).  Logistic regression (LR) is another popular linear classifier (Hastie, 2001, Andriansah, 2018). LR measures the relationship between a response variable and independent variables, such as linear regression, LR classifies an observation into one of two classes, and this algorithm analysis can be used when the variables are nominal or binary. LR has been applied in a variety of areas, including the insurance sector (Ruzgar, 2007) and loan performance (Creamer and Freund, 2004)   Classification of the price changes in stock markets has been a major interest of researchers and practitioners for many years. A large number of methods, including LR, cluster analysis, rough sets, fuzzy rough, fuzzy, GP algorithms, and several other techniques, have been used for the classification of price changes. In this paper, three classification algorithms are used to classify the daily stock market closing price change of eight Canadian banks: LR, fuzzyroughNN and GA. In order to perform the analysis, we need software or tools. Weka is a tool, which allows the user to analyze the data from various perspectives and angles, in order to derive meaningful relationships. In this paper, we are studying and comparing three algorithms using Weka 3.7.2 tools.   The remainder of this paper is organized as follows. Section 2 briefly discusses three classification algorithms used in this paper. Section 3 presents the purpose and methodology. Section 4 discusses and findings, and finally Section 5 provides conclusions.  In literature, different classification methods have been proposed. They mainly differ in the statistical assumptions made of the data and type of algorithms needed to construct the classifier (Carrizosa and Morales, 2013). In this section, three classification algorithms are reviewed LR, fuzzyroughNN and GP algorithms. All methods predict the dependent variable by the independent variables.  The LR is a statistical model and a predictive analysis like all regression analyses. LR is applied only when the dependent variable is binary. LR estimates the parameters of a model and it is used to describe data and to explain the relationship between the dependent variable and one, more nominal, ordinal, or interval, independent variables (Andriansah, 2018).   A fuzzy-rough set is a generalization of a rough set, derived from the approximation of a fuzzy set in a crisp approximation space of fuzzy-rough sets (Jensen, 2005). The focus is to define lower and upper approximation of the set after the original data set is partitioned into 10 subsets. One of them is retained as testing data and the remaining 9 subsets are used.

 

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Learning from WhatsApp’s Business Model: The World of Messaging Apps

Dr. Nadeem M. Firoz, Baruch College, CUNY, New York, NY

Atif Noor, Founder & CEO, Adaptly-AI.com, Baruch College, CUNY, New York, NY

 

ABSTRACT

The objective of this proposal is to determine WhatsApp’s positioning within the social media marketplace, analyze its strengths and weaknesses, and propose actionable strategic marketing models which will allow the service to further increase its user base by curtailing weaknesses and exponentiating strengths. This proposal will follow the service from its inception as a startup in Mountain View California to becoming the most popular messaging app in the world after being acquired by Facebook. Its features will be dissected and its value to the marketplace will be closely analyzed. The demographics, geodemographics and user segmentation of the service will be ascertained; along with its business models, marketing positioning, and SWOT analysis. Through diligent and thoughtful analysis of WhatsApp’s SWOT, viable options for growth and further market capitalization will become evident, and recommendations for implementation will be established.  WhatsApp is a cross-platform messaging and Voice over IP (VOIP) service that allows users to send text messages and video calls, along with other rich media such as audio, images, documents, and even video calls. (whatsapp.com) The service was created by WhatsApp Inc. in Mountain View, California by founders Brian Acton and Jan Koum, who previously worked for Yahoo! (Forbes.com) After leaving Yahoo! in 2007, they applied to jobs at Facebook, but failed to get hired. Ironically, after the they were rejected jobs at Facebook, their company was acquired by Facebook in February 2014 for close to $19.3 billion dollars (techcrunch.com). By early 2018, the product had accumulated over 1.5 billion users, making it the most popular messaging app in the market. (Forbes.com) According to the company, “More than 1 billion people in over 180 countries use WhatsApp to stay in touch with friends and family, anytime and anywhere. WhatsApp is free and offers simple, secure, reliable messaging and calling, available on phones all over the world.” (Whatsapp.com) The app was named WhatsApp because it sounded like “What’s up?” and had a viral component to it.  It was officially incorporated on February 24, 2009 (forbes.com) and the founders visited RentACoder.com (now known as freelancer.com) to find a Russian iPhone developer named Igor Solomennikov. In October 2009, Brian Acton was able to secure $250,000 in seed funding from past colleagues at Yahoo! To pay back investors, the app switched from a free model to charging 99 cents per year. In 2011 it became one of the top apps on the App Store and attracted $8 million in funding from Sequoia Capital for 15% stake in the company, and another $50 million by February 2013. This created more than a 5000% return for the VC firm. (BusinessInsider.com) Months after WhatsApp was valued at $1.5 billion on February 19, 2014, Facebook announced it was acquiring WhatsApp for US$19 billion, its largest acquisition to date. Facebook, which was advised by Allen & Co, paid $4 billion in cash, $12 billion in Facebook shares, and (advised by Morgan Stanley) an additional $3 billion in restricted stock units granted to WhatsApp's founders Koum and Acton. (newsroom.fb.com)  WhatsApp’s Business Model is quite simple yet very pervasive; the simplicity of the platform has allowed to grow to stratospheric heights. It solves a very basic market problem: typical mobile SMS services charge too much. By offering free messaging via wi-fi connection, WhatsApp has been able to capture a large portion of the world population who isn’t willing to pay higher prices for messaging purposes. (Lifewire.com)  According to a description from Lifewire, “When WhatsApp launched, people were complaining about the price of SMS texts. SMS was costly and limited. WhatsApp solved this problem. With WhatsApp, you could send messages to other WhatsApp users without counting words, without being deprived of multimedia content, and without being restricted to the number of contacts, all for free. Meanwhile, in some parts of the world, one SMS message could cost as much as a dollar.” (Lifewire.com) WhatsApp has become so popular because they overtook and reinvented an entire industry; the SMS industry. (Businessinsider.com) Similar to how Uber took over Taxis and AirBnb took over hospitality. (Statista)   Perhaps it is the competitive pricing that WhatsApp touts which has allowed it to become a dominant force in India, surpassing all other messaging apps in the region. This will become more evident in the segmentation section of this proposal where the usage by geographic location will be broken down. (Statista) Here are some statistics of the user ecosystem that WhatsApp has developed over the years:  As more and more people purchase mobile devices and gain internet connectivity around the world, the user base of WhatsApp will grow exponentially. The future of WhatsApp is very bright as it rides this technology wave. (businessofapps.com)  The service contains no ads and has very high privacy standards, making it the pinnacle of trust worthiness in users’ minds.  High loyalty user base around the world containing a community of over 1.5 billion people. (nytimes.com)   Owned by FAANG tech giant Facebook, which means it has a powerful support network.  Free and easy to use on all devices and operating systems. (wsj.com)  Great software testing team; low bug count and smooth user experience.  Early mover advantage by being one of the first popular free SMS messaging apps. (inc.com) Large penetration in emerging markets; India, Brazil, UK and parts of Europe. (statista)  Has a small business solution with over 3 million users.  No way to make money at the moment other than to utilize data to improve Facebook’s market share. (businessinsider.com) Users are worried their data may not be safe.  Cyber-bullying and other disturbing digital communications trends (thesun.co.uk)  Hackers finding exploits to crash the app.  Keep a pulse on new internet adopters and have them use WhatsApp before any competitors.  Enforcing the highest level of data privacy, security and encryption.  

 

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Collaborative Strategies in the Context of the Tourism Cluster in the Azores: A Qualitative Analysis

Antonia Canto, University of the Azores, Portugal

Anhelina Bykova, University of the Azores, Portugal

Dr. Joao Couto, University of the Azores, Portugal

 

Abstract

The main objective of this study is to discuss the collaborative strategies within the tourism cluster on the Azores. A qualitative research framework was developed and responses obtained through interviews with 30 regional stakeholders were analyzed using the MaxQDA and NVivo programs. The results highlighted the existence of dynamics and collaboration between the regional tourism partners. The study reveals that the most dynamic partners are car rental companies, restaurants, tours companies, and hotels.  Promoting collaboration is crucial for developing a tourist destination. For collaboration to be successful, it is important to establish collaborative strategies. There are several definitions of the concept, according to Child, Faulkner, Tallman, and Tallman (2005); collaborative strategies are an attempt by organizations to achieve their goals through cooperation with other organizations rather than competing with them.  This work attempts to delineate a framework of collaborative strategies among several tourism cluster actors in the Azores region. A thorough analysis of the interviews collected from the various actors in this sector is undertaken. Particular attention is paid to the type of activity and to the location of such activity, so as to reasonably cover the tourism cluster in the Azores.  First, we present the concepts of collaborative strategies and the tourism cluster, observing their evolution and complexity, and highlight the various contributions of several researchers in this field of study. Second, the method and instruments for gathering information are presented, together with the procedures undertaken in the elaboration of this study. The final section includes the results and discussion, which evaluates the existence and importance of collaborative strategies in the region.  We first analyze the definition of the central concepts; this study follows Canto and Couto (2018), who define the concept of a tourism cluster and collaborative strategies, and observe the importance, dynamics, efficiency, and sustainability of the tourism cluster in the Azores.  According to Beni (2012), a tourism cluster means a group of similar products or activities that developed together to “highlight that this concept” has a strong and significant connotation of junction, union, aggregation, and interaction.  However, according to the same author, it is possible to deepen the concept by considering that the tourism cluster constitutes a permanent set of dynamic actions and reiterates community effort, social mobilization, entrepreneurship in economic investments, efficient inter-organizational communication, engagement of social actors and institutional agents, and interaction of all segments of the supply for the necessary and indispensable synergy in the productive arrangement for the consolidation of its sustainable development (Beni ,2012),  While the definition of a tourism cluster refers to a group/union of activities and similar products that evolve together through the efforts and synergies of its collaborators, its objectives, according to Beni (2012), can be enumerated as follows: reducing operating costs and transactions between companies (1); harnessing and enhancing synergies for the production, marketing, and distribution of products and services (2); sharing technical, productive, and marketing information (3), and disseminating innovation (4).  Addressing the tourism cluster, Cunha and Cunha (2006) highlight that because tourism is a service that can only be consumed in loco, it assumes a prominent role in establishing a local development strategy, highlighting the importance of the strategy as one of the primary elements for the tourism cluster.  The concept of collaborative strategy, according to Child, Faulkner, Tallman, and Tallman (2005), is an attempt by organizations to achieve their goals through cooperation with other organizations rather than competing with them.   The authors further state that different types of collaboration and competition between different actors in the world arena are observable, as presented in the image below.  Another example of high collaboration but low competition in the absorption of one company by another is the case of Fujitsu and ICL. When collaboration and competition are low, there are few results, as seen in the case of Disney and Pixar. Lastly, when collaboration is low, but competition is high, there is a risk of appropriation, as in the case of GM and Daewoo.  According to Figure 1, the authors present four types of strategy: partner, adapter, monoplayer, and contender.  Child, Faulkner, Tallman, and Tallman (2005) state that the development of an alliance is dependent on the nature of cooperation, establishing cooperation, managing cooperation, and past performance and evolution.  Another definition proposed by Wood and Gray (1991) states that collaboration occurs when a group of autonomous stakeholders who dominate a specific subject engage in an interactive process, using the same rules, norms, and structures to act or decide on matters relating to a particular subject.

 

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Efficiency Changes in the Home and Community-based Services of Long-term Care in Taiwan

Chia-Mei Shih, Department of Resources Engineering, National Cheng Kung University, Tainan, Taiwan

Yu-Hua Wang, Institute of Gerontology, National Cheng Kung University, Tainan, Taiwan

Dr. Li-Fan Liu, Professor, Institute of Gerontology, National Cheng Kung University, Tainan, Taiwan

Jung-Hua Wu, Department of Resources Engineering, National Cheng Kung University, Tainan, Taiwan

 

ABSTRACT

Global aging trends have led to dilemmas in resource allocation. Most developed or OECD countries are struggling to ensure the sustainability of their long-term care systems, and Taiwan, a developing country, is not an exception. After years of effort, Taiwan has established home and community-based services in a formal long-term care infrastructure and has developed a nationwide database on long term care. However, the cost-effectiveness of the large amount of resources invested in this system has not yet been analyzed. This study sought to examine the performance of Taiwan’s long-term care system from 2011-2016, using the Data Envelopment Analysis (DEA) based Malmquist Productivity Index (MPI) approach. The results showed a regression in average total factor productivity over 6 years (-5.5%), mostly affected by deteriorating technological change (-6.6%). During that same period of time there was an ascending trend in technical efficiency due to a dramatic increase in financial investment since 2014, which produced an overall growth of 1.1%. Long-term care is a labor-intensive industry. Our study’s findings show that, while change factors within long-term care did help to improve the efficiency of the system to some degree, what really made a difference were factors that impacted the system from exogenous factors, such as improved technology. To sustain the productivity of the long-term care system we must focus on investment in innovations.  The sustainability of long-term care (henceforth LTC) is currently a prominent policy priority in many countries since the aging trend is causing major fiscal issues (Mosca, van der Wees, Mot, Wammes, & Jeurissen, 2017). In recent decades, economic growth has been outpaced by the growth in public funded health expenditures in Organization for Economic Co‐operation and Development (OECD) countries. This trend is shown in the increasing proportion of health expenditure accounted for in calculations of gross domestic product (GDP) (Angelis, Tordrup, & Kanavos, 2017).  A new set of public health and long-term care expenditure projections reaching to 2060 suggest that public spending on health and LTC in OECD countries and in the BRIICS (Brazil, Russia, India, Indonesia, China and South Africa) will rise rapidly over the next 50 years despite cost-containment efforts ( through policy action) or downward cost-pressures (occurring without implicit policy actions). In the prediction, the total health and LTC expenditure across OECD countries will increase by 3.3-7.7 percent of GDP on average till 2060. For the BRIICS, these costs are projected to increase even more steeply by 2.8-7.3 percent of GDP over the same period overall (Maisonneuve & Martins, 2014).  According to OECD Secretariat calculations, the average health care expenditure in OECD countries during 2006-2010 was 5.5 percent of GDP, while in non-OECD countries average health care expenditure was 2.4 percent of GDP. LTC expenditures were 0.8 percent of GDP in OECD countries and 0.1 percent of GDP in non-OECD countries (De la Maisonneuve & Martins, 2013).  The drivers of these expenditure increases differ between health care and long term care. Increases in health care spending were mostly pushed up by the combined effect of technology, relative prices and exogenous factors, while increases in LTC spending were primarily affected by weak productivity gains when compared to the surrounding economy (De la Maisonneuve & Martins, 2013).  The rapid growth in health care and LTC costs are unavoidable, and this is creating competing financial pressure on other social programs. How best to allocate resources is now becoming a first-order policy issue for governments.   In Taiwan, total national health expenditures were 6.63 percent of GDP in 2012, which is much lower than the average number of 9.3 percent of GDP for OECD countries in the same year (Cheng, 2015). Despite its low cost, the Taiwanese single-payer national health insurance (NHI) system, introduced in 1995, has become well known for its good accessibility, short waiting times, and comprehensive population coverage based on egalitarian ethical principles. The government had managed several crises when faced with significant demographic changes through successive policy adjustments and reforms (Wu, Majeed, & Kuo, 2010). Given that health spending as a percentage of GDP is low by international standards, Taiwan appears to have enough economic elbow room to improve the economic and clinical performance of the NHI system (Cheng, 2015).  A similar situation can be seen in Taiwan’s LTC system. The LTC plan 1.0 which the Taiwanese government launched from 2008 to 2016 was aimed at constructing a foundation of home and community-based service (HCBS) resources, and to ensure that the system could provide services to everyone in need of LTC. As response to what was actually experienced, the government revised its policy in 2016, calling the revised policy the LTC plan 2.0. Reforms included expanding recipient coverage, adding service items and greatly increasing the funding for the program.  The LTC plan 2.0 is estimated to cost 472.1 billion TWD (about 15.2 billion USD) in the next ten years, while the cost during plan 1.0 was 81.7 billion TWD (2.63 billion USD) (ExecutiveYuan, 2016).

 

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The Impact of the Tohoku-Oki Earthquake on Tourism Share Prices in Taiwan

Dr. Chun-Huang Liao, Zhao Qing University, Guangdong Province, China

 

ABSTRACT

This study uses the event study method and the recursive Chow test to investigate the impact of the Tohoku-Oki earthquake on tourism share prices in Taiwan. Cumulative abnormal returns and structural changes in return relationships were estimated and tested. The findings show the Tohoku-Oki earthquake had a short-term negative impact on the stock returns of Taiwan’s tourism companies. Significant negative abnormal returns lasted for about 19 trading days, and the structure of the return relationships between the tourism index and market index changed in the short term after the earthquake occurred. By using hierarchical multiple regression analysis, this study found the variables of firm size, debt ratio, ratio of stock market value relative to assets, margin trading of stocks, and percentage of hotel revenue relative to sales can significantly account for the cumulative abnormal returns. To prevent such unexpected event impacts, suitable strategies of risk diversification should be undertaken by hoteliers and tourism operators.  International travel between Taiwan and Japan is very popular, not only because of the close proximity, but also to some extent because of the history of colonization. However, on March 11, 2011, the Tohoku-Oki earthquake (Tajima et al., 2013; Ito et al., 2012) suddenly shut down this busy travel line. International tourism markets between both countries encountered a situation of chaos; many tourists canceled or changed their original itineraries. This unexpected earthquake influenced Taiwan’s tourism revenue and share prices slumped suddenly. Large seismic events are rare in modern history, so they are a natural experiment and a valuable case study that cannot be reproduced (Kollias et al., 2011b; Shan and Gong, 2012). However, literature on this kind of case is still rare. The Tohoku-Oki earthquake occurred at 14:46 on March 11, 2011, in the northeast area of Japan, just around the Miyagi, Fukushima, and Iwate prefectures. The earthquake of 9.0 Richter on the scale triggered tsunami and nuclear disasters, resulting in 86,000 deaths, more than 13,000 missing persons, and more than 550,000 people to flee disaster areas. The disasters caused economic losses amounting to USD 235 billion, much higher than other countries’ cases: the economic losses of USD 9.5 billion from the south Asian tsunami in 2004, USD 81.2 billion loss from U.S. hurricane Katrina in 2005 (Park et al., 2013), USD 1.340 billion losses from the China Wenchuan earthquake in 2008, and USD 8 billion losses from the Haiti earthquake in 2010 (Gao et al., 2012). The Japan Tohoku-Oki earthquake was a composite of natural disasters with earthquake, tsunami, and nuclear crises. The damage was much higher than those from artificial forces or terrorist attacks. It claimed many lives, influencing human economic activities, resulting in psychological panic, and reminding us of the importance of how travel relates to safety. Given the decreasing flows of tourists, the prospect of tourism industry slumps (Mazzocchi & Montini, 2001) and lower tourism-related stock prices was apparent. To the best of my knowledge, this study is the first to explore the impact of the Tohoku-Oki earthquake on the share prices of Taiwan’s tourism industry. There were three purposes, including (1) to measure how long and how deep the negative impact lasted, (2) to measure whether or not there was a structural change in the stock return relationship between Taiwan’s tourism industry and the stock market, and (3) to explain the reasons for abnormal returns from a financial point of view.  The rest of this paper is organized as follows. Section 2 reviews the literature and hypotheses are developed. Section 3 presents the market model with the generalized autoregressive conditional heteroskedasticity (MM-GARCH) model, and the recursive Chow test is designed. Empirical results are reported in Section 4. Section 5 concludes this paper and offers future research directions.  Catastrophic disasters negatively impact the tourism industry and hotel revenue. They trigger uncertainty about tourism demand and increase the risk of hotel management (Chen & Yeh, 2012). For example, a large earthquake causes death and panic, reducing the number of tourists (Mazzocchi & Montini, 2001; Huang & Min, 2002; Tsai & Chen, 2010; Chen, 2011). It pushes hotel industry managers to place more emphasis on risk assessment, risk transfer, and risk diversification issues (Tsai & Chen, 2010; Tsai & Chen, 2011), and it also affects the operating performance of the hotel industry (Chen, 2011). Evidence can be seen in several significant events that occurred in the past, such as the September 21 earthquake in 1999, the U.S. September 11 terrorist attacks in 2001, and the April 12 outbreak of severe acute respiratory syndrome (SARS) in 2003, all of which had negative impacts on Taiwan’s tourism industry (Wang, 2009). Catastrophic disasters affect human psychology and behavior and dominate people’s investment decisions. Silver et al. (2002) pointed out a major national disaster event, such as the September 11 terrorist attacks, influences people’s behavior to some degree, and the psychological effects of a major national trauma are not limited to those who experience it directly. Yamori (2002) studied the negative impact on the insurance industry of Japan’s Hanshin-Awaji earthquake on January 17, 1995, and found investors were affected by this incident; they re-assessed the value of insurance companies and changed their original investment strategies.

 

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Copyright: All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, including photocopying and recording, or by any information storage and retrieval system, without the written permission of the journal.  You are hereby notified that any disclosure, copying, distribution or use of any information (text; pictures; tables. etc..) from this web site or any other linked web pages is strictly prohibited. Request permission / Purchase article (s):  jaabc1@aol.com

 

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Index: The Library of Congress, Washington, DC:    ISSN: 1540 – 7780

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