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Risk management for small and medium sized incoming tour operators

Shown at the example of Skylimit Travel S.A., Isla Margarita, Venezuela

Bachelor Thesis 2009 140 Pages

Tourism

Excerpt

Table of Contents

1 Introduction

2 Statistics used throughout the paper
2.1 Detailed Company Statistic Arrivals / Products / Departures April 2005 - March 2008 (CS-APD)
2.2 Company Statistic Passenger Totals by Source Market, Jan 2001 - Dec 2007 (CS-PSM)
2.3 Government Statistic Monthly Arrivals to Nueva Esparta by Transportation Type (Air/Sea) Jan 2001 - Dec 2006 (GS-ANE)
2.4 Common Measures

3 The Company
3.1 History
3.2 Current Situation and Products
3.3 The Main Destination - Venezuela
3.3.1 Overview
3.3.2 Main geographical Areas of Activity for Skylimit Travel S.A
3.4 The Home Port Nueva Esparta
3.4.1 Overview
3.4.2 The Touristic Value Chain and the Companies Position in it
3.4.3 Characteristics of seasonality

4 Risk Management
4.1 COSO’s Enterprise Risk Management - Integrated Framework
4.2 Risk Management for SMEs
4.2.1 Internal Environment
4.2.2 Objective Setting
4.2.3 Event Identification
4.2.4 Risk Assessment
4.2.5 Risk Response
4.2.6 Control Activities
4.2.7 Information and Communication
4.2.8 Monitoring
4.2.9 Summary

5 Conclusion

6 Lists of Tables, Figures and Abbreviations
6.1 Figures:
6.2 Tables
6.3 Abbreviations

7 Sources

8 Appendix
8.1 Software Algorithms
8.1.1 Data Import and Sorting
8.1.2 Data Analysis
8.1.3 User defined Functions (UDF)
8.2 Data Tables
8.2.1 Detailed Company Statistic Arrivals / Products / Departures April 2005 - March 2008 (CS-APD)
8.2.2 Company Statistic Passenger Totals by Source Market, Jan 2001 - Dec 2007 (CS-PSM)
8.2.3 Government Statistic Monthly Arrivals to Nueva Esparta by Transportation Type (Air/Sea) Jan 2001 - Jun 2007 (GS-ANE)

9 Eidesstattliche Erklärung

1 Introduction

“Take calculated risks. That is quite different from being rash.”

George S. Patton, US-General (1885-1945)1

Every decision we make - be they our own, private, decision to buy a pair of shoes or our workplace decision to approve that multi-million dollar contract - carries consequences. These consequences can be positive or negative for us, but every decision will influence our life. Trying to foresee the consequences of our decisions is often difficult and frequently impossible, yet we must try to foresee them. The possibility that a decision has positive consequences we regard as an opportunity, while we regard the possibility of negative consequences as a risk. Only by balancing the possible consequences we can decide between alternatives. Thinking about the consequences of our actions is as old as humanity itself.

Similarly people conducting any kind of business had to think about the consequences of their decisions for their organisation since the dawn of times. The minimisation of the risk that a decision carries negative consequences has long been the goal of businesses and their leaders. In the early 14th century Italian merchants developed a system of bets that shared the risk that a ship would not return from a journey and the opportunity of high profit if the ship returned from its journey between a number of punters. From these roots grew the modern insurance industry (Bernstein, 1996). In modern times the view of risks faced by enterprises has expanded from looking solely at insurable risks via technical and financial risks to a holistic view of all risks faced by an enterprise in what has come to be called the “Risk Management” approach. The first known publication on Risk Management is the book “Die Unternehmensrisiken”2 by Leitner, published 1915 in Berlin, Germany (Brühwiler, 2003, p. 19). The recent attention to Risk Management in both theory and practical application can be traced to two recent trends - the trend to recognise stakeholder value3 and therefore Corporate Governance to be important and the trend to realise that risks do not only exist within the company but that developments that can not, or only minimally, be affected by the company can put the very existence of a company in jeopardy, leading to the holistic view of risks Risk Management entails nowadays.

In the wake of the ENRON and Worldcom scandals at the beginning of the 21st century Corporate Governance became a hot topic. The idea behind Corporate Governance that enterprises and their management should act in the interests of their stakeholders is old. As early as 1932 Berle and Means identified in their book “The Modern Corporation and Private Property” the potential for conflict between the interests of the owners of a company, who are interested in the sustainability and long-term profitability of the company, and the interests of the paid managers of the company, who might have different, short-term goals, which they called the Principal-

Agent-Conflict (Berle & Means, 1932). Since then, good Corporate Governance came to be mostly understood to mean that the managers of a company should act in the interest of the owners, mostly understood to be shareholders, of the company. This definition, however, has been recently extended vastly to include the interests of all stakeholders. The Organisation for Economic Co-Operation and Development (OECD) defines Corporate Governance as follows:

“Corporate governance is one key element in improving economic efficiency and growth as well as enhancing investor confidence. Corporate governance involves a set of relationships between a company’s management, its board, its shareholders and other stakeholders. Corporate governance also provides the structure through which the objectives of the company are set, and the means of attaining those objectives and monitoring performance are determined. Good corporate governance should provide proper incentives for the board and management to pursue objectives that are in the interests of the company and its shareholders and should facilitate effective monitoring. […] Corporate governance is only part of the larger economic context in which firms operate that includes, for example, macroeconomic policies and the degree of competition in product and factor markets. The corporate governance framework also depends on the legal, regulatory, and institutional environment. In addition, factors such as business ethics and corporate awareness of the environmental and societal interests of the communities in which a company operates can also have an impact on its reputation and its long-term success.” (OECD, 2004, pp. 13 - 14)

The principles of Corporate Governance were first published by the OECD in 1999 and updated in 2004. The key principles of a Corporate Governance framework are, according to the OECD:

„The corporate governance framework:

- should promote transparent and efficient markets, be consistent with the rule of law and clearly articulate the division of responsibilities among different supervisory, regulatory and enforcement authorities.
- should protect and facilitate the exercise of shareholders’ rights.
- should ensure the equitable treatment of all shareholders, including minority and foreign shareholders. All shareholders should have the opportunity to obtain effective redress for violation of their rights.
- should recognise the rights of stakeholders established by law or through mutual agreements and encourage active co-operation between corporations and stakeholders in creating wealth, jobs, and the sustainability of financially sound enterprises.
- should ensure that timely and accurate disclosure is made on all material matters regarding the corporation, including the financial situation, performance, ownership, and governance of the company.
- should ensure the strategic guidance of the company, the effective monitoring of management by the board, and the board’s accountability to the company and the shareholders.” (OECD, 2004, pp. 19-26)

As part of the principle of timely and adequate disclosure, the OECD advocates the disclosure of foreseeable risk factors:

“Users of financial information and market participants need information on reasonably foreseeable material risks that may include: risks that are specific to the industry or the geographical areas in which the company operates; dependence on commodities; financial market risks including interest rate or currency risk; risk related to derivatives and off-balance sheet transactions; and risks related to environmental liabilities.” (OECD, 2004, p. 55) The principles of Corporate Governance have been put into law and practice in numerous countries, for instance in Germany with the 1998 law “Gesetz zur Kontrolle und Transparenz im Unternehmensbereich (KonTraG)” and the German Corporate Governance Codex of 2006, in Switzerland with the “Swiss Code of Best Practice” of 2002 or in the United States of America with the Sarbanes-Oxley Act of 2002.

The US-American Sarbanes-Oxley Act of 2002 demands in section 404 reporting on the responsibility of the management ”for establishing and maintaining an adequate internal control structure and procedures for financial reporting; and […] an assessment […] of the effectiveness of the internal control structure“ of a company (107th US Congress, 2002). It is widely understood that following the rules set out in “Internal Control - Integrated Framework” by the Committee of Sponsoring Organizations (COSO)4 of 1992 satisfies the demands of the Sarbanes- Oxley Act of 2002.

Applying COSO’s integrated framework for internal control “can help an entity achieve its performance and profitability targets, and prevent loss of resources. It can help ensure reliable financial reporting. And it can help ensure that the enterprise complies with laws and regulations, avoiding damage to its reputation and other consequences“ (COSO, 1992). According to the executive summary the five major components of the “Internal Control - Integrated Framework” are (COSO, 1992):

- Control Environment

Describes the way a company operates, the integrity, ethical values and competence of the entity's people and the management's philosophy and operating style.

- Risk Assessment

Is the identification and analysis of relevant risks to achievement of the objectives, forming a basis for determining how the risks should be managed.

- Control Activities

Are the policies and procedures that help ensure management directives are carried out.

- Information and Communication

Pertinent information must be identified, captured and communicated in a form and timeframe that enable people to carry out their responsibilities.

- Monitoring

Is a process that assesses the quality of the system's performance over time.

It is interesting to note that as early as 1992 the assessment, monitoring, reporting and management of risks was seen as an integral management task by a major professional body in the United States.

The framework was expanded by COSO considerably in 2004 to form the “Enterprise Risk Management - Integrated framework”, which provides a more robust and extensive focus on enterprise-wide risk management.

The debates on both Corporate Governance and its component Enterprise Risk Management have so far been focussed on publicly traded enterprises in developed economies (COSO, 1992; OECD, 2004, p. 13; OECD, 2006, p. 4). Brühwiler (2003, pp. 148-152) argues that effective Risk Management can lower the equity requirements of a company and raise the companies value and credit-worthiness as do COSO (1992; 2004, p. 7) and Chong & López-de-Silanes (2007, S. 3). The question therefore is how the lessons of good Corporate Governance and effective Risk Management can be applied to non-publicly traded companies and in particular to SMEs.

The tourism industry worldwide consists of a few large players that are publicly listed, such as large tour operators, cruise lines and air lines that are therefore subject to the relevant Corporate Governance laws and codices. Beside these main players a majority of small- to medium-sized enterprises provide the actual tourism services in the destinations such as ground-transport or accommodation to the end-user, which are typically privately-owned enterprises and as such not usually subject to Corporate Governance laws and codices. Hystad and Keller (2008, pp. 152, 161) as well as Pechlaner and Glaeßer (2005, p. 5) argue that there is only minimal proactive planning for and the management of risks in the tourism industry. This paper will use the example of the small incoming tour operator Skylimit Travel S.A. based in Venezuela to demonstrate how SMEs in the tourism industry can implement an effective Risk Management process in order to lower their equity requirements, raise the company value and increase their performance.

The paper is split into two distinctive parts. In the first two chapters following this introduction the stage is set by an explanation of the main statistics used throughout the paper, their data sources, setup and utility and by an introduction to Skylimit Travel S.A., its home country Venezuela and its home destination Nueva Esparta, Venezuela. In the second part of the paper, chapters 4 and 5, the theory and methodology of Risk Management is explained and its application to the specific circumstances faced by Skylimit Travel S.A. is demonstrated. Selected risks faced by Skylimit Travel S.A. and possible coping strategies for risks identified are discussed and conclusions are drawn.

The goal of the paper is to encourage readers working in small- to medium sized enterprises in the tourism industry to be more conscious of risks faced by their organisation and to give those readers an example of how, with a comparatively low level of effort, Risk Management procedures might be implemented for their enterprise. To that end the attempt is made to develop on the basis of COSO’s “ERM - Integrated Framework” (2004) a risk management system for SMEs.

2 Statistics used throughout the paper

This paper utilizes three main sets of statistical data to describe the operating environment of the company. As these datasets are referred to throughout the paper, their data sources, setup and utility shall be described briefly here. In addition to these three main sets, some other statistical data, in particular demographical data of the main destination Venezuela is referred to in this paper, but not described in this chapter. Common to the three statistics is that they allow the analysis of patterns of demand over time, or, in other words, the patterns and strength of seasonality5 experienced by both Skylimit Travel S.A. and their home destination Nueva Esparta, Venezuela.

The following may serve as a short overview of these three statistics:

- Detailed Company Statistic Arrivals / Products / Departures April 2005 - March 2008

(CS-APD):

A highly detailed statistic for the period of three years, allowing an in-depth analysis with regards to arrival and departure point and time, purchased products and length of stay for reservations executed and passengers handled by Skylimit Travel S.A. for the period 01 April 2005 to 31 March 2008.

- Company Statistic Passenger Totals by Source Market, Jan 2001 - Dec 2007 (CS-PSM)

A statistic of passenger numbers for a period of seven years, cumulated by passenger country of residence (source market), demonstrating the long-term development of passenger numbers handled by Skylimit Travel S.A. in respect to total numbers handled and to each individual source market.

- Government Statistic Monthly Arrivals to Nueva Esparta by Transportation Type

(Air/Sea)

Jan 2001 - Dec 2006 (GS-ANE)

A statistic of passenger numbers for the period of six years; allowing a long-term view of the development of arrivals to the destination Nueva Esparta, home port of Skylimit Travel S.A., by arrival mode (sea / air) as well as analysis of the characteristics of seasonality of the destination as a whole

The chapter closes with an explanation of the common measure for the strength of seasonality used for all three statistics, the GINI Coefficient.

2.1 Detailed Company Statistic Arrivals / Products / Departures April 2005 - March 2008 (CS-APD)

This set of statistical data was derived in April 2008 from reservation reports of the proprietary reservations system of Skylimit Travel S.A. (SKY). The reservation system is a dbase -based database with a purpose-built user interface. Due to the limitations of the database, no reliable data older than three years is contained within the reservations system. The precise structure of the database is unknown and it was therefore necessary to export all data in the form of system generated reports. These reports have a standard text (.txt) format and show the following information:

- Product
- Operator
- Booking number
- Number of Passengers
- Passenger Name(s)
- Date of arrival
- Date of departure
- Room type.

The information is sorted by operator. An example of the report format for the hotel “Hesperia Playa el Aqua”, Isla Margarita, Venezuela follows:

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Figure 1: Reservations Report; Source: own, SKY Reservations System

The reports only show reservations that were active at the time of arrival. Reservations that had been entered into the system and have subsequently been cancelled are not part of the report. The data derived from the reports therefore reflects the true situation of reservations executed and passengers handled by SKY.

One report was generated for each product in the database and imported into Microsoft Excel 2003 using a self-developed software algorithm6 programmed with the Microsoft Excel macro language Microsoft VBA. Each report was placed in a separate worksheet within Microsoft Excel. Subsequently, using within the importing software algorithm, the data set as cleared up to only show the following relevant information:

- Operator7
- Reservation number
- Number of Passengers
- Arrival Date
- Departure Date
- Product Name

Based on the above report the Microsoft Excel Data set would now have the following format:

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Figure 2: Dataset after import and clear-up; Source: own

In a next step, all data was combined in one Microsoft Excel worksheet through another software algorithm8 and sorted by booking number, reconstituting the original bookings. At this point the following location codes, the weekday of arrival and departure as well as the following product type codes were added:

Location Codes:
- PMV = Nueva Esparta, Venezuela
- CCS = Caracas, Venezuela
- RTR = Roundtrip / Excursion

Product Type Codes:
- HOTEL = Accommodation
- RTR = Roundtrip / Excursion9

The combined data now had the layout shown in Figure 3:

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Figure 3: Layout combined Data10 ; Source: own

Looking through the dataset it became apparent, that, as was expected, some bookings were non-continuous. Consider for example the following booking number 73418:

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Figure 4: Booking Nr. 73418 - raw data11 ; Source: own

The passengers arrived on 12th of January 2005 in the hotel ”Avila” in Caracas, stayed there for 2 nights and had a later booking for the 22nd of January 2005 for the hotel “Las Palmeras” on Isla Margarita. Although the activity of the passenger for missing periods can not be determined, experience shows that in all likelihood the passengers stayed in country but had organised their stay in the intervening week through another company or privately. With regards to risk management and in particular the risk of changing purchasing behaviour, it is important to be able to determine if the number of passengers that choose not to purchase all services they are consuming during their stay from SKY is changing over time and if so, which trend this may signify. Therefore, using another software algorithm12, missing dates of stay were added with to the dataset and denoted with the operator identifier “OTHER”, the location identifier “???” and the product type identifier “OTHER”. Figure 4 shows booking number 73418 including the missing link:

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Figure 5: Booking Nr. 73418 - completed with missing link13 ; Source: own

Through yet another self-developed software algorithm14 the data set was then analyzed by booking number for the following criteria:

- Weekdays of arrival and departure (Arrival Day / Departure Day)
- Arrival and Departure Location
- Total number of products booked
- Count of individual products booked (Hotel / Roundtrip / Other)
- Length of Stay in Days.

Figure 6 demonstrates the Layout of the data table.

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Figure 6: Data table layout - Detailed Company Statistic Arrivals / Products / Departures April 2005 - March 200815 ; Source: own

As the data taken from the reservations system in early April 2008 is only reliable for the last three years and all future bookings, all following analysis was performed for the period 01 April 2005 to 31 Mach 2008 and, where appropriate, sorted into the following seasons:

- 2005 / 2006: 01 April 2005 to 31 March 2006
- 2006 / 2007: 01 April 2006 to 31 March 2007
- 2007 / 2008: 01 April 2007 to 31 March 2008

As can be seen in Chapter 3.4.3 “Characteristics of seasonality”16, these seasons also fit well with the seasonality of business for Skylimit Travel S.A.

The dataset was analyzed for the following criteria:

- Distribution by Weekday - Arrivals & Departures for Destination - Bookings, giving the count of bookings for arrival / departure on each weekday for the destinations: All destinations (ALL), Isla Margarita (PMV), Caracas (CCS) and bookings that start / end with a roundtrip / excursion, independent of the departure / arrival point of the roundtrip / excursion (RTR). See appendix 8.2.1.1 for the corresponding tables.

- Distribution by Weekday - Arrivals & Departures for Destination - Passengers, giving the count of Passengers arriving / departing on each weekday for the destinations: All destinations (ALL), Isla Margarita (PMV), Caracas (CCS) and bookings that start / end with a roundtrip / excursion, independent of the departure / arrival point of the roundtrip / excursion (RTR). See appendix 8.2.1.2 for the corresponding tables.

- Distribution by Month - Arrivals & Departures for Destination - Bookings, giving the count of bookings of arrivals / departures for each month for the destinations: All destinations (ALL), Isla Margarita (PMV), Caracas (CCS) and bookings that start / end with a roundtrip / excursion, independent of the departure / arrival point of the roundtrip / excursion (RTR). See appendix 0 for the corresponding tables.

- Distribution Average Number of Arrivals & Departures for Destination / day per month - Bookings, giving the average number of bookings of arrivals / departures per day per month for each month for the destinations: All destinations (ALL), Isla Margarita (PMV), Caracas (CCS) and bookings that start / end with a roundtrip / excursion , independent of the departure / arrival point of the roundtrip / excursion (RTR). See appendix 0 for the corresponding tables.

- Distribution by Month - Arrivals & Departures for Destination - Passengers, giving the count of Passengers arriving / departing for each Month for the destinations: All destinations (ALL), Isla Margarita (PMV), Caracas (CCS) and bookings that start / end with a roundtrip / excursion, independent of the departure / arrival point of the roundtrip / excursion (RTR). See appendix 8.2.1.5 for the corresponding tables.

- Distribution Average Number of Arrivals & Departures for Destination / day per month - Passengers, giving the average number of Passengers arriving / departing per day per month for each Month for the destinations: All destinations (ALL), Isla Margarita (PMV), Caracas (CCS) and bookings that start / end with a roundtrip / excursion , independent of the departure / arrival point of the roundtrip / excursion (RTR). See appendix 8.2.1.6 for the corresponding tables.

- Distribution Average Length of Stay in Month - Bookings, giving the average length of stay for each booking in each month. See appendix 8.2.1.7 for the corresponding table.

- Distribution by Month - Total Number of Products in Month - Bookings, giving the count of bookings for each month for the products: All products (ALL), Hotels (HOTEL), Roundtrips / Excursions (RTR) and Other as defined above (OTHER). See appendix 8.2.1.8 for the corresponding table.

- Distribution by Month - Total Number of Products in Month - Passengers, giving the count of passengers for each month for the products: All products (ALL), Hotels (HOTEL), Roundtrips / Excursions (RTR) and Other as defined above (OTHER). See appendix 8.2.1.9 for the corresponding table.

As has been shown above, the data in this statistical data set allows an in-depth analysis with regards to arrival and departure point and time, purchased products and length of stay for reservations executed and passengers handled by Skylimit Travel S.A. for the period 01 April 2005 to 31 March 2008. Of particular interest are the distribution of arrivals by weekday to judge the risk of dependency on a particular carrier, the distribution of arrivals by month and the distribution of the total number of products per month to evaluate the specific patterns of seasonality experienced by SKY and the risks associated with these patterns.17

2.2 Company Statistic Passenger Totals by Source Market, Jan 2001 - Dec 2007 (CS-PSM)

Similar to the report shown in Figure 1 the Skylimit Travel S.A. reservations system allows the user to generate a reservations report for reservations by operator within a user-defined timeframe. The report shows essentially the same information as the report shown in Figure 1 for all reservations whose first arrival date falls within the defined timeframe for all products in all arrival locations that have been booked through the operator. The following Figure 7 demonstrates the layout of the system-generated report.

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Figure 7: First and Last Page of the Passenger per Operator Report; Source: own (Adapted Screenshot of SKY Reservations System)

Since February 2001 this report is generated at the beginning of each month for the previous month for all operators Skylimit Travel S.A. is working with. Based on theses reports, the number of passengers per operator that have arrived in the previous month (“TOTAL PASJEROS”) is entered in a Microsoft Excel Workbook. As the base data is updated monthly from SKY’s reservation system, and the entered data is cross-checked by a senior member of staff, the data can be considered to be sufficiently reliable for the complete period January 2001 - December 2007.

Based on the arrivals dataset, each operator was linked to a source market - defined as the operator’s home market and therefore most likely the individual passenger’s country of residence - by a senior member of staff of the reservations department. The source markets are identified by their corresponding international vehicle registration codes of which the most important follow:

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Table 1: International vehicle registration codes (wikipedia.org, 2008)

All independent travellers are booked, regardless of their source market, under operator Code “SKY” by the company. The arrivals booked under operator code “SKY” have therefore been added to the source market bin “OTHER”. The total number of arrivals with operator code “SKY” for the years 2001 to 2007 is 2,505 out of a total of 68,233 arrivals in the same period or 3.67%. The error introduced into the analysis by this is therefore negligible.

Figure 8 shows the layout of the dataset after each operator was linked with its source market identifier:

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Figure 8: Layout Company Statistic Monthly Arrivals by Operator with Source Market Codes; Source: own

In a next step the data was cumulated by source market and analyzed for the GINI-Coefficient18 for each source Market per year. Figure 9 shows an example of analyzed data for the year 2003. The complete dataset can be found in appendix 8.2.2.1.

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Figure 9: Cumulated Arrivals by Source Market 2003; Source: own

This information was then summarized for the years 2001 to 2007 and analyzed for the GINICoefficient for each source market and each month. The complete table can be found in Appendix 8.2.2.2. The timeframe has been chosen in order to be able to depict the development of business for whole calendar years.

Further analysis was performed for the following criteria:

- Distribution GINI-Coefficients by Source Market per Year19
- Total Passengers per Year by Source Market20
- Average Number of Passengers per Month all Years by Source Market21
- Average Number of Passengers per Day per Month all Years by Source Market22
- Average Number of Passengers per Month all Years - Percent of Total by Source Market23

The dataset allows demonstrating the long-term development of passenger numbers handled by Skylimit Travel S.A. in respect to total numbers handled and to each individual source market. It further allows the analysis of changes to the GINI-Coefficients for the observed period, allowing a judgement of the volatility of bookings from the analysed source markets, which in turn is necessary to judge the risks posed by the exposition of SKY to various source markets.

2.3 Government Statistic Monthly Arrivals to Nueva Esparta by Transportation Type (Air/Sea) Jan 2001 - Dec 2006 (GS-ANE)

The Venezuelan federal State of Nueva Esparta geographically consists of the islands Margarita, Coche and Cubagua in the Caribbean Sea and is only reachable by air or ship24. The government agency “Corporación de Turismo del Estado Nueva Esparta“, CORPOTUR, is an “autonomous institute charged with the management of tourism on the islands Margarita and Coche, the design of the development policies for the tourism sector, including the formulation and execution of programs for sustainable tourism development” (CORPOTUR, 2005). Its department of statistic and research is tasked with “generating trustworthy statistics through the realization of studies of the tourism system, which provide information on the state of tourism in the federal state and support the decision making process and policy formulation for the tourism sector” (CORPOTUR, Unidad de Investigaciones Turísticas, 2005). Its areas of research are “Touristic Demand, Touristic Supply, Quality Evaluation of Tourism Services, Income generated through tourism and Hotel Occupation” (CORPOTUR, Unidad de Investigaciones Turísticas, 2005).

CORPOTUR and in particular the research department compiles a range of monthly publicly available statistics, amongst others the statistic “Entrada mensual de viajeros al estado Nueva Esparta por medio de transporte” - Monthly Arrivals to Nueva Esparta by Transportation Type. The complete list of publicly available statistics can be found on CORPOTUR’s research department website.25 The download facility for publicly available statistics was not working at the time of writing. Up-to-date statistics can be requested by email from “estadisticas@corpoturmargarita.gov.ve” (CORPOTUR, Unidad de Investigaciones Turísticas, 2005). The statistic “Entrada mensual de viajeros al estado Nueva Esparta por medio de transporte” used in this paper was received by the author by email in August 2007 with data for the period January 2001 to June 2007. Figure 9 shows the layout of the statistical data compiled by the research department of CORPOTUR. The statistic is compiled for the two possible arrival modes by air (“Via Aérea”) and by sea (“Via Maritima”).

The arrival mode by air is subdivided into the two categories:

- Travellers in national flights (“Viajeros en Vuelos Nacionales”)
- Travellers in international flights (“Viajeros en Vuelos Internacionales”).

The arrival mode by sea is subdivided into the following categories:

- Travellers in Boats (“Viajeros en Lanchas”)
- Travellers in Ferries / Barges (“Viajeros en Ferrys/Chalanas”)
- Tourists in Yachts and Sailing boats (“Turistas en Yates - Veleros”)
- Cruise Ship Shore Excursionists (“Excursionistas Cruceros”)
- Travellers in Ferries (“Viajeros en Ferrys”)

Data is listed for the months January (“Enero”) through to December (“Diciembre”) as well as the totals for the year and the market share of each type of arrival mode as a percentage.

In the analysis of the data the two categories arrival by sea - Travellers in Ferries and Barges and - Travellers in Ferries have been combined into the category - by Ferry as there is no discernible difference between the two original categories. The complete data set for the full years 2001 to 2006 can be found in Appendix 8.2.3.1.

The data was analyzed for the following criteria:

- Distribution GINI-Coefficients per Year by

Arrival Mode26

- Average Number of Arrivals per Month all

Years by Arrival Mode27

- Average Number of Arrivals per Day per

Month all Years by Arrival Mode28

- Total Number of Arrivals per Year and

Percentage of Total per Year by Arrival Mode29

The data in this statistic allows a long-term view of the development of arrivals to the destination Nueva Esparta including Margarita Island and Coche Island as well as research on the seasonality of the destination as a whole. In chapter 3.4.3 the seasonality patterns experienced by Nueva Esparta as a whole and in particular the seasonality patterns for arrivals by international flights are compared to the seasonality patterns experienced by SKY, supporting the evaluation of risks posed by the phenomenon of seasonality to SKY in chapter 4.3.

Figure 10: Layout Government Statistic Monthly Arrivals by Transportation Type; Source: CORPOTUR

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2.4 Common Measures

All three main statistics used in this paper describe primarily the distribution of arrivals and/ or departures throughout a set time period. In particular the three datasets can commonly be used to describe the characteristics of seasonality30 of the main destination for travellers handled by Skylimit Travel S.A., Nueva Esparta, based on the monthly arrival figures.

The best method for measuring seasonality in tourism it has not yet been agreed on. As Koenig & Bischoff (2004) outline, a wide variety of approaches to measure the seasonality of tourism demand are used. A few of these studies make the attempt at comparing these measures. They suggest a combination of different approaches as the best way to analyse seasonal demand variations at a destination level. A lack of standards in quantification methods makes comparisons particularly difficult. Seasonality of demand can be measured in terms of arrivals, overnights stays, departures or length of stay in a given time period. Lundtorp (2001) would prefer to measure the economic impact, but this data is usually not available. Yacoumis (1980) introduced as one of the first measures the standard deviation and the seasonality ratio (the largest value of a data population divided by the average for the population). Wanhill (1980) added the Gini coefficient as a useful measurement for the seasonality. Lundtorp’s (2001) comprehensive overview of the different seasonality measures defined the seasonality indicator (the average value of a data population divided by the largest value of this population) as a reciprocal of the seasonality ratio. Koenig & Bischoff (2003) used the latter three measures for their study of seasonality in Wales simultaneously. Lundtorp (2001) demonstrated that the GINI Coefficient is sensitive to variations outside the peak season and therefore especially suited to as a measure of seasonality. As an in-depth analysis of seasonality would exceed the scope of this paper, only the GINI Coefficient has been chosen as the common measure for all three datasets.

The GINI Coefficient measures inequalities, in other words, the deviation from a uniform distribution of demand and is a function of the LORENZ Curve. According to Hartung (2005, pp. 50-55) is the LORENZ Curve the graphic representation of the concentration of property values of a dataset. All further explanations will refer to the following example (Hartung, Elpelt, & Klösener, 2005, p. 51) of the distribution of farm sizes within an area.

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Table 2: Example Data set for LORENZ Curve (Hartung, Elpelt, & Klösener, 2005)

On the abscissa of a coordinate system the values for:

are marked. In other words, the feature characteristics are sorted in ascending order and their relative frequencies are cumulated and marked on the abscissa. In the example: first the percentage of farms with the smallest area is obtained, than the percentage of farms with the second-smallest area is added to that and so on. On the ordinate the values for:

illustration not visible in this excerpt

are marked; the characteristics are cumulated by size and referred to the sum of all characteristics.

In the example: the shares of the total area are cumulated in ascending order by size.

A point (ki ; li) of the LORENZ Curve therefore expresses that x % of the analysis units express x% of the characteristics analyzed. For example, in the point ki = 100 % li = 100% as well; in the example 100% of the farms analyzed own 100 % of the farmed area. By connecting the points (ki ; li) with (ki + 1 ; li +1) for i = 0, ..., k -1 the LORENZ Curve is obtained. By definition k0 = l0 = 0 and kq = lq= 1. Figure 11 shows the LORENZ Curve (blue, middle) for the example.

illustration not visible in this excerpt

Figure 11: LORENZ Curve (example), Source: own, (Hartung, Elpelt, & Klösener, 2005)

The LORENZ Curve can exhibit two extreme cases:

- There is no concentration to be measured; all analysis units have the same share of the

total. In that case the LORENZ curve would be the diagonal, in Figure 11 exemplified by the red diagonal line on the far left.

- There is a (near) total concentration; the total is (almost completely) concentrated in one analysis unit. The LORENZ curve gets closer and closer to the abscissa with rising concentration and rises almost vertically from the right corner point (1;0) to the corner point (1;1). The green line on the far right in Figure 11 demonstrates this.

With the help of the LORENZ Curve it is possible to calculate a measure of concentration, whose value is 0 if there is no concentration and 1 if there is total concentration. The larger the concentration is; the larger is the area between the diagonal, shown as a red line in Figure 11, and the LORENZ Curve.

The LORENZ CONZENTRATION MEASURE (LCM), also known as the GINI-Coefficient (GC), is defined as double the area F between the diagonal and the LORENZ Curve

LCM = GC = 2F

The area F can be calculated as the sum of the areas of the trapezes Fi minus the area of the triangle above the diagonal as Figure 12 demonstrates.

illustration not visible in this excerpt

Figure 12: Calculation of the GINI Coefficient; Source: own , (Hartung, Elpelt, & Klösener, 2005)

illustration not visible in this excerpt

Therefore the GINI Coefficients is calculated as follows:

illustration not visible in this excerpt

By definition 0 and[Abbildung in dieser Leseprobe nicht enthalten] are the lower and upper bound for the GINI Coefficient;[illustration not visible in this excerpt]

If all observed values of the analysis units are equal GC = 0. If one analysis unit has the total of observed values [illustration not visible in this excerpt]

For time-period related datasets such as the data in the statistics used in this paper, the GINI Coefficient can be calculated as follows:

The number of time periods observed is regarded as the population of analysis units. With the monthly values yi ordered by increasing size, the GINI Coefficient calculates as:

illustration not visible in this excerpt

(Dixon, Weiner, Mitchell-Olds, & Woodley, 1988; Damgaard & Weiner, 2000)

A User Defined Function (UDF) for Microsoft Excel was developed in order to automate the task of sorting the data in ascending order and calculating the GINI Coefficient. The User Defined Function has the following syntax:

=Gini (Range holding values to calculate, Include Values = 0 yes (TRUE) / no (FALSE))

An example of the usage is: =Gini(B40:B51;TRUE).

The code for the UDF can be found in Appendix 8.1.3.1. The reader is advised to note that the UDF will return the definition value of 0 if the sum of values in the range is 0, rather than an error as would be expected due to the division by 0.

Although the GINI Coefficient can be the same for two separate sets of data, this does not necessarily signify an equal structure of concentration within the datasets (Hartung, Elpelt, & Klösener, 2005, P. 53, 54). The GINI Coefficient therefore does not give any information about the structure of the observed inequalities, but is a measure of absolute inequality and is therefore especially suited for comparisons of inequalities between datasets with different structures of inequality. For instance:

The GINI Coefficient for the two following sets of data would be equally 0.24074, while the distribution of arrivals per arrival month is different:

illustration not visible in this excerpt

Table 3: Example for Equal GINI Coefficients for differently structured Data; Source: own

The example shows that even if the structure of arrivals per month is different between the two examples, the measure of their inequality, the GINI Coefficient, is the same and therefore the seasonality of both examples is the same. Furthermore, as noted above, demonstrated Lundtorp (2001) that the GINI Coefficient is sensitive to variations outside the peak season and therefore especially suited as a measure of seasonality.

3 The Company

As this paper is concerned with risks to Skylimit Travel S.A. (SKY), the reader needs to have an understanding of the conditions SKY is operating under. In this chapter the reader will first be familiarised with SKY’s history and current scope of activities.

In the second part of this chapter the reader is given background information on Venezuela, including geography, climate, land use, environmental issues, the history and current political developments. In particular both internal politics and foreign policies can have a tremendous impact on SKY’s business activities as shall be shown on the following two examples. During the general strike and associated disturbances from December 2002 to May 2003, a marked decline in international tourism arrival to Venezuela took place which hit SKY particularly hard. This can be seen from the data in GS-ANE and CS-PSM31. In March 2008 the prospect of a border-war between Venezuela and Colombia seemed to be quite real, with potentially very heavy consequences for SKY.

Through the second part of this chapter the reader should gain a broad understanding of the general conditions SKY is operating under and is influenced by. The main geographical areas of activity for SKY are briefly introduced to the readers in order to give an impression of the variety of conditions and associated risks faced in the various geographical regions of Venezuela SKY is operating in.

The third part of this chapter is intended to familiarise the reader further with the immediate business environment of SKY in its home port and main destination Nueva Esparta. To do this, a more detailed overview of Nueva Esparta; including geographical, demographical, hydrographical and economic data is given and the reader is introduced the touristic value chain32 of Nueva Esparta, the company’s positioning within said value chain is shown and the characteristics of seasonality experienced by Nueva Esparta and SKY are demonstrated.

3.1 History

The company Skylimit Travel S.A. was founded as SKYLIMIT Venezuela 1987 in Caracas as an Incoming Tour Operator and became an IATA Partner in 1989. One year later, the German tour operators Touristik Union International (TUI) and ITS elected SKYLIMIT Venezuela as their exclusive agent in Venezuela. Following the award of these contracts, a branch office was opened on Isla Margarita in 1991, which allowed SKYLIMIT Venezuela to specialize on providing guest service through dedicated guides in the hotels and the organization and sale of excursions and roundtrips.

An incentive travel department was added to the organization in 1992. The range of services offered and the number of clients grew consistently in the following years. In 1997 the car rental agency FUN CAR RENTAL for the rental of 4-wheel drive vehicles and the jeep-safari company FUN TOURS were founded as part of the SKYLIMIT Venezuela family. SKYLIMIT Venezuela was renamed to Skylimit Travel S.A. and acted from now on as the mother company to its subsidiaries. For the newly founded subsidiary SKYTRANS a fleet of busses with a total capacity of 180 seats, predominantly to provide airport - hotel - airport transfer services to incoming tourists was purchased in the same year. Another subsidiary, SKYTRAVEL, was founded in 1997 to take over the tour operations and passenger reservations handling from SKYLIMIT Venezuela. In 1998 the subsidiary SKYCRUISE was added to sell and handle shore-excursions for cruise lines calling at Isla Margarita, Venezuela; Caracas, Venezuela and Cartagena, Colombia.

In 2002 a new subsidiary, Turismo Skylimit, with its main office in Santiago de Chile was opened to act as the central for all South-America excursions and roundtrips outside of Venezuela offered by Skylimit. (Skylimit Travel S.A., 2008)

Figure 13 demonstrates the current structure and business areas of SKY.

illustration not visible in this excerpt

Figure 13: Skylimit Travel S.A. - Subsidiaries and their Business Areas, Source: own

3.2 Current Situation and Products

SKY acts predominantly as a destination agency for foreign tour operators. In this capacity SKY serves approximately between 4,500 (2003) and 14,000 (2007)33 passengers every year with the following:

- Meet & Greet at the airport and assistance at the airport
- Transfers with multilingual guides
- Guest service through representatives in hotels
- Emergency assistance
- Organisation and sale of more than 500 day trips per week - on shore, the sea and by air
- Hotel reservations service for all of Venezuela
- F.I.T, S.I.B. and modular tours with 40 - 200 passengers / week
- Organising individual and group tours tailored to the clients interests and budget
- Connections to all of South-America
- Incentive - creative programs / Logistic
- Special interest tours, such as trekking-, bird watching-, and eco-tourism tours

SKY organises shore excursions, day trips and other activities for more than 12,000 cruise passengers per year in Venezuela and Colombia as well as providing independent transportation to clients of its subsidiary FUN CAR RENTAL.

In the offices of the various subsidiaries of SKY on Isla Margarita and Caracas in Venezuela and Santiago de Chile, Chile, work a total of 28 employees with a wealth of experience in tourism, administration and IT. Six experienced representatives provide guest services in the hotels on the Islands of Coche and Margarita. Skylimit can also draw from a pool of 20 experienced freelance tour guides for tours and excursions in 5 languages. (Skylimit Travel S.A., 2008). According to the definitions by the European Commission and the Inter-American Development Bank SKY with its 21-50 employees falls into the category of small- to medium- sized enterprises (SMEs) (European Commission, 2008; Inter-American Development Bank, 2007).

The main source markets and clients of SKY are (Skylimit Travel S.A., 2008):

Germany: REWE Group (ITS, Meier’s Weltreisen, Jahn Reisen, …), DET Reisen, VTours

Holland: Arke Reizen, Holland International, Kras, Fly Brasil

Switzerland: Salina Tours, Hotelplan, Palm Tours, Direkt Reisen, FTI, Voegele

Austria: Poncho Tours, Siesta Reisen, Ruefa, GEO Reisen, Gulliver Reisen

Japan: Eurasia,Thompson Tours

Italy: Blue Reef

Denmark: Strobel-Travel

Cruise Lines: Princess Cruises, Celebrity Cruises Inc., P&O Cruises, Aida Vita Cruises,

Royal Caribbean International, Phoenix Reisen

3.3 The Main Destination - Venezuela

“Venezuela is a country of unique features and one of the most irresistible and overlooked destinations in South America. Its astounding geography, the amazing variety of its cultural heritage, the warmth of its people and the modernity of a country with unlimited potential are just some of its many attractions. From the snow-capped Andean peaks to the sun-soaked Caribbean coral atoll Los Roques, from the dense jungles to the wide, open savannah Los Llanos, from the enormous rivers to the deserts of Peninsula Paraguana, from the uninhabited tops of the biggest mesas in the world to the bustling cities - Venezuela offers the traveller almost all experiences of South-America in one country. Whether travellers visit Angel Fall, the highest waterfall in the world, observe the diverse wildlife of the Llanos and the Orinoco Delta, climb Mount Roraima, experience the traditional way of living of the indigenous people, go white- water rafting or trek through unspoiled nature in the Venezuelan Andes, experience the bustle of Caracas, enjoy the well preserved colonial city centres of Coro or Ciudad Bolivar or just relax on one of the beautiful beaches on Venezuela's over 3000 km of Caribbean coastline outside the Hurricane Belt, a trip through Venezuela will leave them with unforgettable experiences” (Skylimit Travel S.A., 2008).

This chapter will introduce the reader to Venezuela, home and main business area of SKY, its geography, demography, history and current political situation as well as to the characteristics of the geographical areas of Venezuela SKY is active in to further understanding of the local conditions faced by the company. Understanding the conditions a company is subject to is vital in order to evaluate risks arising from those conditions as well as for formulating appropriate response strategies.

3.3.1 Overview

This subchapter gives the reader a general overview of the main information concerning Venezuela, in particular geographical, demographical, political and historical information.

3.3.1.1 Geography, Climate, Land Use, Environmental Issues and General Information

The following information was taken from (CONAPRI, 2005; OECD, 2006; Library of Congress- Federal Research Division, 2008; U.S. Departement of State, 2008) and is quoted were appropriate.

“Venezuela is a Federal Republic, consisting of 23 federal states, one federal district (Caracas) and one federal dependency (72 Islands).” (U.S. Departement of State, 2008) Located in northern South America, Venezuela is bordered to the north by the Caribbean Sea and the North Atlantic Ocean, to the east by Guyana, to the south by Brazil, and to the west by Colombia. Venezuela has a total area of 912,050 square kilometres (land: 882,050 square kilometres; water: 30,000 square kilometres).

Figure 14 shows a map of Venezuela.

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Figure 14: Map of Venezuela (World Travel Guide, 2008)

Located entirely within the tropics, Venezuela’s climate is tropical, hot, and humid. The country has two distinct seasons: the rainy season called “invierno” (winter) from June to October and the dry season called “verano” (summer) from November to May. The months from August to October are the wettest months, with an average rainfall of 145 millimetres; whereas January to April are the driest months with only 8 millimetres rainfall on average. Concerning the temperature, the hottest months are May- September, averaging 18º C to 32º C; the coldest month is January, averaging 2º C to 13º C. However, the climate varies in the four distinct regions of Venezuela due to their different elevations: The dry, windless, and hot Maracaibo Lowlands in the far northwest and the vast central Orinoco plains (llanos), mostly less than 50 metres in elevation, are hot year-round. In the north-western Andean mountains and highlands with an elevation of up to 5007 metres and the tropical Guiana Highlands with an elevation of up to 3500 metres in the southeast a more moderate climate is found. The most important river of the country is, at between 2,140 and 2,500 kilometres, the Orinoco. It is the third longest river in South America and is of high economical importance. The Orinoco plain covers one third of the country. More than half of the area of the country is comprised by the Guiana Highlands that host the world’s highest waterfall, the Angel Fall with a free fall height of almost 1000 metres.

Venezuela’s natural resources, hydropower and minerals, including bauxite, diamonds, gold, iron, ore, natural gas and petroleum are vast. Its huge oil reserves - billions of barrels of extra- heavy crude oil and bitumen deposits are located in Central Venezuela - are the largest in South America and the sixth largest in the world. They ensure that the country will remain a major oil producer for at least the next 100 years. With a total of 148 trillion cubic feet, the country’s largely untapped natural gas reserves are the second largest in the Western Hemisphere and the eighth largest in the world. Venezuela also has vast forest reserves, although they are dwindling rapidly as a result of the constant expansion of cattle-grazing land. Arable land constitutes 2.95 percent of the country’s area; permanent crops, 0.92 percent; and other, 96.13 percent. During the last years approximately 10 million hectares of forest have been allocated for timber production.

Nevertheless, Venezuela has the third-highest deforestation rate in South America at 1.1 percent. This is only one indication for the environmental degradation Venezuela has suffered from. The country is subject to earthquakes, floods, rockslides, mudslides, and periodic droughts. It ranks among the top 10 of the world’s most ecologically diverse countries. Environmental issues include sewage pollution into Lago de Valencia, located not far to the west of Caracas; oil and urban pollution of Lago de Maracaibo, located in north-western Zulia State; deforestation; soil degradation; and urban and industrial pollution, especially along the Caribbean coast. The rain-forest ecosystem and indigenous peoples are currently endangered by irresponsible mining operations. In order to protect their nature successive governments have attempted to develop environmental regulations. However, only 35 percent to 40 percent of Venezuela's land is regulated thus far, 29 percent as part of about 100 national parks. Along the sea coast a strong fishing industry can be found.

„From 1984 to 2004 extraction of oil and gas and refining of these accounted for 24% of the country’s gross domestic product (GDP). Most activities in the non-oil economy are in the services sector, which accounts for 46% of the non-oil GDP, with trade and real estate standing out above the rest. In the case of production of goods, manufacturing holds first place, contributing 15%, followed by construction with 6% and agriculture with 5%.“ (CONAPRI, 2005) „The SME sector constitutes 95% of the Venezuelan market and 90% of the country’s industry, and is one of the main generators of employment in the country. “ (OECD, 2006)

Since December 2007 Venezuela Standard Time is four and a half hours behind Greenwich Mean Time (GMT-4.5). Venezuela’s population is very religious with 96% of all Venezuelans adhering to the Roman Catholic religion, 2% being Protestants and the remaining 2% adhering to other religions. (U.S. Departement of State, 2008)

[...]


1 in a letter to George S. Patton IV, 06 Jun 1944 (Morgan, 1996)

2 Translatable as „Corporate Risks“

3 Stakeholder value is a concept based on the recognition that a company and its management are not only responsible for returning a profit to its owners but can also be held responsible for the welfare of all its stakeholders, be they the owners, the employees, its suppliers, its customers, or anyone else affected by the activities of the company.

4 COSO is a voluntary private-sector organization comprising the professional associations American Accounting Association (AAA), American Institute of Certified Public Accountants (AICPA), Financial Executives International (FEI), Institute of Management Accountants (IMA) and the Institute of Internal Auditors (IIA).

5 See Chapter 3.4.3 for a definition of seasonality

6 See Appendix 8.1.1.1

7 Operators are (predominantly foreign) tour operators whose clients are handled by Skylimit Travel S.A. and are the main clientele of the company.

8 See Appendix 8.1.1.2

9 Pax = Passengers

10 See Appendix 0 for explanation and translation of Arrival / Departure Day codes

11 See Appendix 0 for explanation and translation of Arrival / Departure Day codes

12 See Appendix 8.1.2.1

13 See Appendix 0 for explanation and translation of Arrival / Departure Day codes

14 See Appendix 8.1.2.2

15 See Appendix 0 for explanation and translation of Arrival / Departure Day codes

16 See there also for definitions and measures of seasonality

17 Compare chapters 3.4.3.2, 4.2.3 and 4.2.4

18 See Chapter 2.4 “Common Measures“ for details on the GINI-Coefficient

19 See Appendix 8.2.2.3

20 See Appendix 8.2.2.4

21 See Appendix 8.2.2.5

22 See Appendix 8.2.2.6

23 See Appendix 8.2.2.7

24 See Chapter 3.4 for detailed information on Nueva Esparta

25 www.corpoturmargarita.gov.ve/v2/planificacion/estadisticas.asp.

26 See Appendix 8.2.1.2

27 See Appendix 0

28 See Appendix 0

29 See Appendix 8.2.1.5

30 See Chapter 3.4.3 for a definition of seasonality

31 See also Chapter 3.4.3 “Characteristics of seasonality“ and Fehler! Verweisquelle konnte nicht gefunden werden. “Strategic Risks“

32 See Chapter 3.4.2 for definition

33 Source: CS-PSM

Details

Pages
140
Year
2009
ISBN (eBook)
9783640561940
ISBN (Book)
9783640561896
File size
3.1 MB
Language
English
Catalog Number
v145466
Institution / College
Stralsund University of Applied Sciences
Grade
1,7
Tags
risk management SME KMU Venezuela Tourismus Tourism Risiko Management

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Title: Risk management for small and medium sized incoming tour operators