Regional Political Risks and Sustainable Tourism Development Tendencies: A Case Study

Regional Political Risks and Sustainable Tourism Development Tendencies: A Case Study

Maia Diakonidze Ercan Özen*

Department of Tourism and Landscape Architecture, Akaki Tsereteli State University, Kutaisi 4600, Georgia

Department of Finance and Banking, Faculty of Applied Science, Usak University, Uşak 64200, Türkiye

Corresponding Author Email: 
ercan.ozen@usak.edu.tr
Page: 
4519-4526
|
DOI: 
https://doi.org/10.18280/ijsdp.191201
Received: 
1 November 2024
|
Revised: 
9 December 2024
|
Accepted: 
14 December 2024
|
Available online: 
30 December 2024
| Citation

© 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

OPEN ACCESS

Abstract: 

The tourism industry is extremely susceptible to political and social unrest. Countries aiming to develop their tourism sector understand this and strive to maintain peace and economic stability. However, for developing nations such as the Georgia, with its strategically attractive location, ensuring socio-economic stability presents a significant challenge. This difficulty is being solved by creating an adaptive environment that fosters both tourism growth and regional economic well-being. The main aim of the article is to examine Georgia as a case study to explore how political issues can influence tourism industry sustainable development. While political instability often hinders tourism, Georgia's case deviates from the norm, potentially offering new approaches for tourism development in such circumstances. This study employs a Vector Autoregression (VAR) model to analyse the impact of various factors, such as - the correlation between the Index of Global Real Economic Activity and tourism revenues, GPRH (Geopolitical Risk Index), and geopolitical events on Georgia's tourism industry from 2006 to 2022. The analysis will focus on the period before and after the war to assess its influence. By analysing the relationships between these variables, the study aims to understand how global economic conditions, geopolitical instability, and the war specifically, have influenced the evolution and economic effects on Georgia's tourism industry. This study's key finding reveals a positive correlation between tourism revenues and geopolitical risks in Georgia, even considering the war. This finding suggests a more nuanced relationship between these factors than previously assumed.

Keywords: 

tourism industry, tourism development, sustainable development, geopolitical risk, global real economic, Georgia

1. Introduction

Tourism is a powerful economic engine, generating jobs, boosting infrastructure development, and driving foreign exchange earnings. Overall, the economic impact of tourism is largely positive, but careful planning and management are crucial to maximize its benefits and minimize its negative consequences [1]. Tourism industry thrives on stability. Political and social unrest can quickly disrupt travel plans, causing significant economic hardship for destinations that rely heavily on tourism. Recognizing this vulnerability, countries with aspirations for a flourishing tourism sector prioritize maintaining peace and economic security. However, developing nations like Georgia face a unique challenge. Despite its strategically advantageous location, fostering both tourism growth and regional economic well-being requires navigating a complex path towards socio-economic stability. To overcome these hurdles, Georgia is pioneering an adaptive approach that aims to cultivate a tourism industry that thrives alongside a strong regional economy. Tourism flourishes as a wealth-generating industry for many nations, particularly when governments establish a stable political environment. This stability fosters the development of leisure destinations – cities and attractions that entice both domestic and international visitors [2]. The very essence of tourism – leisure, and exploration – is fundamentally at odds with an era of volatility and instability. Tourism, a highly vulnerable industry, thrives on political stability, peace, and above all, safety. Tourists and tourism markets are prone to panic, and events like civil unrest or terrorism can cause travelers to scrap travel plans in a heartbeat. The immediate impact and long-term aftermath of such occurrences can be catastrophic, not just for the destination country but for the entire region [3].

We have a wealth of historical examples to evaluate the impact of political instability on tourism development, including the Arab Spring uprisings and the ongoing conflicts in Gaza and the broader Middle East. This paper contributes to analyzing the relationship between political instability caused by the short Russian intervention and its impact on tourism development in Georgia.

Many countries, including Georgia, have come to rely heavily on tourism as a source of economic prosperity and foreign exchange. As a result, tourism plays a vital role on the global stage, and countries strive to attract visitors to reap its benefits [4]. However, the tourism sector is susceptible to external factors, particularly political instability and concerns about safety and security [5]. Recent events in Ukraine and Israel highlight this vulnerability, where ongoing political unrest has led to travel warnings and a significant decline in tourist arrivals. The concept of democratic peace suggests democracies promote stability. However, maintaining peace becomes complex when safety is threatened by regional conflicts or expansionist policies, even for democracies [6]. The concept of democratic peace suggests that democracies tend to promote stability, ultimately leading to a safer environment. Therefore, it's crucial for tourist destinations to take proactive measures to ensure safety and security. By prioritizing safety and security, destinations can create a thriving tourist industry that benefits both visitors and the local economy [7]. Since 2003, Georgia has actively supported tourism development by way of - Investment in Infrastructure. The Georgian Tourism Development Fund focuses on infrastructural projects in regions with high tourism potential. This could involve improvements to roads, transportation, and communication networks, making it easier and safer for tourists to navigate the country. Financial Support for Tourism Services: Programs like the Co-Financing Mechanism by Enterprise Georgia provide financial aid to develop tourism services. This could potentially improve the quality and safety standards of hotels, transportation companies, and other tourist facilities. Strategic Marketing: Explore Georgia, the state's tourism division, and promote Georgia as a tourist destination. By emphasizing safety and security aspects in their marketing efforts, they can attract tourists who prioritize these factors [8, 9].

On August 8, 2008, Russian forces began the invasion of Georgia, marking the start of Europe’s first twenty-first-century war. The conflict itself was over within a matter of days, but the repercussions of the Russo-Georgian War continued to reverberate fifteen years on, shaping the wider geopolitical environment [10].

Georgia is geographically complex. It sits at the crossroads of Eastern Europe and Western Asia, with the Greater Caucasus mountains forming a partial boundary. Georgia is considered a developing country according to the World Bank. Country has a very high Human Development Index, but its economy is still growing [11, 12].

From a statistical viewpoint, Georgia's tourism industry in 2019 defied expectations, experiencing a period of further remarkable growth. The number of non-resident trips surged by an impressive 678,420, shattering the nine million visitor mark for the first time. Domestic and international tourism are important factors in the Georgian economy [13].

Tourism is a significant revenue stream for the Georgian economy, generating a significant portion of its foreign currency earnings. International visitors' spending directly contributes to Georgia's balance of payments, with a staggering 71% of service export revenue stemming from tourism [11, 14].

Georgia's tourism boom translates into significant economic benefits. International tourism revenue soared to a staggering 3.3 billion USD in 2019, showcasing its growing importance. Tourism's significant contribution was undeniable, accounting for a substantial 8.1% of Georgia's total GDP. This impressive figure reflected a 511 million GEL increase in value-added from tourism-related activities. The hospitality sector was well-equipped, with 2,575 registered accommodation units offering 94,438 beds in 2019 [13].

Tourist behavior reveals distinct seasonal patterns. Occupancy rates peak in summer months (August: 77.1%, September: 74.5%) and shoulder seasons (July & October: 66.8%), while dropping in winter (January: 34.6%, February: 36.4%, December: 38.5%) [12, 13].

Popular destinations: Tbilisi reigns supreme as the most popular city for tourists (59.7% occupancy rate), followed closely by Batumi (58.9%). Other regions of Georgia also experience a notable tourist presence (46.7% occupancy rate) [13].

Georgia has been successful in international relations, as well. In 2019 several memoranda and agreements were signed with a number of countries, including Portugal, Argentina, and the Republic of Maldives.

2. Aim and Methodology

This study aims to investigate the specific impact of a military special operation in 2008 on Georgia's booming tourism industry, considering its established growth trajectory before the pandemic. The analysis will explore how the sector functioned during the operation, the resulting consequences, and the pace of recovery [15, 16].

This article delves into Georgia's tourism industry as a case study to explore how political factors, beyond the typical negative influence, can shape tourism development. Georgia's experience, defying the usual correlation with political instability, offers valuable insights and potentially novel approaches for fostering tourism growth even in complex geopolitical settings.

This study utilizes a Vector Autoregression (VAR) model to delve into the multifaceted impact of global and regional factors on Georgia's tourism industry from 2006 to 2022. Specifically, the analysis will explore:

  • The correlation between global economic activity (Index of Global Real Economic Activity) and tourism revenue.
  • The influence of geopolitical risk (Geopolitical Risk Index) on tourism trends.
  • The discrete impact of specific geopolitical events (focusing on the war and the period before and after).

Before diving into the VAR model, it's crucial to clarify the specific aspects of tourism development we need to analyze. Key variables such as Tourism Revenue: Total revenue generated from tourism activities, based on time series it is possible to build VAR model (see Table 1).

Now we introduce the vector auto-regression (VAR) model for time series when it is needed, which is used to capture the linear interdependencies among multiple time series. VAR models generalize the univariate auto-regression (AR) models by allowing for more than one evolving variable. All variables in a VAR underwent pre-processing. All variables were log-transformed to address potential skewness and ensure stationarity; therefore, seasonal components were removed.

Seasonality is one of the key components that make up a time series. Seasonality refers to systematic movements that repeat over a given period with a similar intensity.

Seasonality means the average value in some periods will be different from the average value at other times. This issue causes the series to be non-stationary. This is why it is important to analyze seasonality when building a model.

Seasonality in tourism refers to the fluctuation of tourist arrivals and revenue throughout the year. This phenomenon is influenced by various factors, including climate, cultural events, and school holidays.

Table 1. Revenues from international travel (thousands of USD)

2006 QI

58597.2

2006 QII

82760.9

2006 QIII

101743

2006 QIV

69469.8

2007 QI

63889.6

2007 QII

90174.2

2007 QIII

129086.9

2007 QIV

100595.1

2008 QI

85434.3

2008 QII

132430.9

2008 QIII

125883.1

2008 QIV

102897.6

2009 QI

77676.3

2009 QII

114353.3

2009 QIII

158388.4

2009 QIV

125470.9

2010 QI

100685.8

2010 QII

136565.1

2010 QIII

231913.4

2010 Q IV

190081

2011 QI

163989.8

2011 QII

222249.6

2011 QIII

326568.5

2011 QIV

242100.6

2012 QI

235930.4

2012 QII

330826.5

2012 QIII

487721.4

2012 QIV

356423.4

2013 QI

302817.7

2013 QII

426524.5

2013 QIII

604201.8

2013 QIV

386155.8

2014 QI

315.461

2014 QII

429.69

2014 QIII

649.775

2014 QIV

392.215

2015 QI

306.365

2015 QII

460.056

2015 QIII

741.807

2015 QIV

427.688

2016 QI

342.59

2016 QII

497.441

2016 QIII

813.161

2016 QIV

457.517

2017 QI

425.673

2017 QII

629.854

2017 QIII

1,078,274

2017 QIV

570.538

2018 QI

550.73

2018 QII

809.969

2018 QIII

1,210,561

2018 QIV

650.814

2019 QI

578.442

2019 QII

877.626

2019 QIII

1,126,771

2019 QIV

685.814

2020 QI

427.699

2020 QII

28.842

2020 QIII

42.534

2020 QIV

42.611

2021 QI

53,551

2021 QII

246,123

2021 QIII

565,981

2021 QIV

379,333

2022 QI

393,708

2022 QII

748,822

Source: Georgian Tourism Administration, https://gnta.ge/ [11, 15, 17]

This is a common approach in time series analysis, especially when the primary focus is on understanding the political instability influence as a particular phenomenon, rather than the seasonal fluctuations themselves.

1) All variables are stationary, corrected for seasonal components, and expressed in their logarithm forms (i.e., each variable has been rescaled in order to obtain strict positive values).

2) In a Vector Autoregression (VAR) model, all the variables are considered endogenous. This means that the model explains each variable by its past values (lags) and the past values of all the other variables in the model. Also in a VAR model a process can be affected by other observable variables that are determined outside the system of interest. Such variables are called exogenous (independent) variables. Exogenous variables can be stochastic or non-stochastic. The process can also be affected by the lags of exogenous variables. A model used to describe this process is called a VARX(p,s) model. In our VAR model, the endogenous factors are, Revenues from Tourism Industry in Georgia and Global Real Economic Activity and exogenous factor is, Geopolitical Risk Index. Lag order is the number of previous observations of each variable that are included in the VAR model as explanatory variables. For example, a VAR model uses two lags of each variable to predict the current values of the variables. The lag order of a VAR model reflects how far back the historical data can influence the future outcomes of the variables.

This revised specification captures the interplay between economic and geopolitical factors influencing tourism industry in Georgia.

Endogenous Variables:

L-rev (log of revenue): This remains a core endogenous variable, reflecting the tourism revenue. It's influenced by both global economic conditions and geopolitical risks.

L-gre (log of economic risk): This is now an endogenous variable, suggesting that economic risk may effect on other factors within the model, such as tourism revenue. This could be due to feedback effects, where economic downturns can lead to reduced tourism, which in turn can impact economic sentiment.

Exogenous Variable (Control):

L-gpr (log of geopolitical risk): This is an independent variable that's not influenced by other variables within the model. It's included as a control variable to account for the potential impact of geopolitical risk on revenue, even if it's not the primary focus of the analysis.

By considering L-gre as an endogenous variable, the model acknowledges that economic risk might be influenced by other factors and is not solely determined by external forces. This can lead to more nuanced and accurate analysis of the relationship between economic risk, geopolitical risk, and revenue.

- selected lag is 5 suggested by VAR Lag Order Selection Criteria (see Figure 1)

Figure 1. Eliminated seasonal component for each variable

Source: Vector Autoregressive models performed by author

- endogenous variables:

L_rev=log(rev)

L_gre=log(gre)

- exogenous variable (control):

L_gpr=log(gpr)

3. VAR Lag Order Selection Criteria

Vector Autoregression (VAR) estimates refer to the statistical results obtained by fitting a VAR model to our data (see Table 2). These estimates provide insights into the relationships between the endogenous variables we included in the model (see Table 3).

Here's a breakdown of the key aspects of VAR estimate components:

Coefficients: These are the numerical values associated with each lag of each variable in the model. They show the magnitude and direction of the impact that one variable's past values have on the current value of another variable.

Significance Levels: These values indicate the statistical significance of the estimated coefficients. A statistically significant coefficient suggests that the relationship between the variables is unlikely to be due to chance.

Error Correction Terms: These terms account for any unexplained variations in the model. Diagnostic Tests: These tests assess the overall performance of the VAR model and ensure it meets the underlying assumptions (e.g., stationarity of variables).

This polynomial is a mathematical equation derived from the VAR model's structure. It captures the dynamic properties of the system represented by the model (see Table 4).

Our analysis of the Vector Autoregression (VAR) model's roots confirms that it satisfies the stability condition. This ensures that the estimated relationships between tourism revenue, global economic activity and Geopolitical Risk Index are reliable for further analysis.

Table 2. Vector autoregression estimates

Vector Autoregression Estimates

Date: 10/07/22   Time: 21:35

Sample (adjusted): 2006M06 2022M06

Included observations: 193 after adjustments

Standard errors in ( ) & t-statistics in [ ]

 

L_REV

L_GRE

L_REV(-1)

0.638354

0.017043

 

(0.06892)

(0.02429)

 

[9.26189]

[0.70153]

L_REV(-2)

-0.222242

-0.012617

 

(0.07881)

(0.02778)

 

[-2.82006]

[-0.45421]

L_REV(-3)

0.022581

0.014014

 

(0.07678)

(0.02706)

 

[0.29411]

[0.51785]

L_REV(-4)

-0.023071

0.002117

 

(0.07634)

(0.02691)

 

[-0.30220]

[0.07868]

L_REV(-5)

0.013274

0.019027

 

(0.06678)

(0.02354)

 

[0.19877]

[0.80830]

L_GRE(-1)

0.167641

0.994295

 

(0.21012)

(0.07406)

 

[0.79784]

[13.4250]

L_GRE(-2)

-0.775997

-0.382553

 

(0.29466)

(0.10386)

 

[-2.63356]

[-3.68329]

L_GRE(-3)

1.477228

0.342384

 

(0.29943)

(0.10554)

 

[4.93350]

[3.24401]

L_GRE(-4)

-1.685160

-0.190893

 

(0.30504)

(0.10752)

 

[-5.52433]

[-1.77538]

L_GRE(-5)

1.160526

0.107345

 

(0.21615)

(0.07619)

 

[5.36901]

[1.40891]

C

3.788240

0.675257

 

(2.02932)

(0.71530)

 

[1.86675]

[0.94402]

L_GPR

0.398938

-0.125777

 

(0.35727)

(0.12593)

 

[1.11664]

[-0.99879]

R-squared

0.425021

0.763401

Adj. R-squared

0.390078

0.749022

Sum sq. resids

219.4548

27.26602

S.E. equation

1.101117

0.388125

F-statistic

12.16312

53.09147

Log likelihood

-286.2512

-84.99992

Akaike AIC

3.090686

1.005181

Schwarz SC

3.293547

1.208042

Mean dependent

12.61171

4.680621

S.D. dependent

1.409925

0.774736

Determinant resid covariance (dof adj.)

0.181556

Determinant resid covariance

0.159681

Log likelihood

-370.6736

Akaike information criterion

4.089882

Schwarz criterion

4.495605

Source: Vector Autoregressive models performed by author

Table 3. VAR model

L_REV = C(1,1)*L_REV(-1) + C(1,2)*L_REV(-2) + C(1,3)*L_REV(-3) + C(1,4)*L_REV(-4) + C(1,5)*L_REV(-5) + C(1,6)*L_GRE(-1) + C(1,7)*L_GRE(-2) + C(1,8)*L_GRE(-3) + C(1,9)*L_GRE(-4) + C(1,10)*L_GRE(-5) + C(1,11) + C(1,12)*L_GPR

L_GRE = C(2,1)*L_REV(-1) + C(2,2)*L_REV(-2) + C(2,3)*L_REV(-3) + C(2,4)*L_REV(-4) + C(2,5)*L_REV(-5) + C(2,6)*L_GRE(-1) + C(2,7)*L_GRE(-2) + C(2,8)*L_GRE(-3) + C(2,9)*L_GRE(-4) + C(2,10)*L_GRE(-5) + C(2,11) + C(2,12)*L_GPR

VAR Lag Order Selection Criteria

 

 

 

Endogenous variables: L_REV L_GRE

 

 

 

Exogenous variables: C L_GPR

 

 

 

Date: 10/07/22   Time: 21:35

 

 

 

Sample: 2006M01 2022M06

 

 

 

 

Included observations: 190

 

 

 

Lag

LogL

LR

FPE

AIC

SC

HQ

0

-553.1123

NA

1.207543

5.864340

5.932699

5.892031

1

-397.9532

303.7852

0.245969

4.273192

4.409909*

4.328574

2

-394.7605

6.183799

0.248075

4.281689

4.486765

4.364762

3

-386.1447

16.50597

0.236324

4.233102

4.506536

4.343866

4

-382.8191

6.301117

0.238030

4.240202

4.581994

4.378656

5

-366.6384

30.31758

0.209416*

4.111983*

4.522134

4.278129*

6

-365.1517

2.754363

0.215072

4.138439

4.616948

4.332276

7

-358.8109

11.61371*

0.209893

4.113799

4.660666

4.335326

8

-357.4401

2.481733

0.215857

4.141475

4.756701

4.390694

* indicates lag order selected by the criterion

 

LR: sequential modified LR test statistic (each test at 5% level)

FPE: Final prediction error

AIC: Akaike information criterion

SC: Schwarz information criterion

HQ: Hannan-Quinn information criterion

Source: Vector Autoregressive models performed by author

Table 4. Roots of characteristic polynomial

Roots of Characteristic Polynomial

Endogenous variables: L_REV L_GRE

Exogenous variables: C L_GPR

Lag specification: 1 5

Date: 10/07/22 Time: 21:35

Root

Modulus

0.930632

0.930632

0.388345 - 0.601509i

0.715979

0.388345 + 0.601509i

0.715979

0.633551 - 0.330562i

0.714604

0.633551 + 0.330562i

0.714604

-0.364188 - 0.557640i

0.666029

-0.364188 + 0.557640i

0.666029

-0.051834 - 0.610166i

0.612363

-0.051834 + 0.610166i

0.612363

-0.509732

0.509732

No root lies outside the unit circle.

Source: Vector Autoregressive models performed by author

Table 5. VAR Residual Serial Correlation LM Test

Null Hypothesis: no serial correlation at lag order h

Date: 10/07/22 Time: 21:35

Sample: 2006M01 2022M06

Included observations: 193

Lags

LM-Stat

Prob

1

2.961007

0.5644

2

3.303434

0.5084

3

2.571090

0.6320

4

6.755420

0.1494

5

9.403843

0.0518

6

15.43892

0.0039

Probs from chi-square with 4 df.

Source: Vector Autoregressive models performed by author

In our model, we analyze the relationships between multiple time series variables. A crucial assumption for VAR models is that the errors (residuals) are not serially correlated. This means the errors at any given period are not dependent on the errors at previous periods (see Table 5).

VAR Model - Substituted Coefficients:

L_REV = 0.638354310455*L_REV(-1) - 0.222242402434*L_REV(-2) + 0.0225814561865*L_REV(-3) - 0.0230707596134*L_REV(-4) + 0.0132744660411*L_REV(-5) + 0.167641117497*L_GRE(-1) - 0.775996941178*L_GRE(-2) + 1.47722792278*L_GRE(-3) - 1.68515952577*L_GRE(-4) + 1.1605257563*L_GRE(-5) + 3.78823957643 + 0.398938381063*L_GPR

L_GRE = 0.0170431098973*L_REV(-1) - 0.0126172772917*L_REV(-2) + 0.0140144130223*L_REV(-3) + 0.00211715387649*L_REV(-4) + 0.0190270759934*L_REV(-5) + 0.99429507057*L_GRE(-1) - 0.38255300261*L_GRE(-2) + 0.342384230103*L_GRE(-3) - 0.190892903852*L_GRE(-4) + 0.107345144613*L_GRE(-5) + 0.675257171467 - 0.125777404604*L_GPR

VAR model highlights the strong economic influences on Georgia's tourism revenue. However, external factors like geopolitical tensions can also significantly impact tourism. For instance, Russian intervention in Georgia in 2008 would likely contribute to the decline in tourism flows, revenue, and therefore other important economic indicators. Geopolitical risk can significantly impact the stability of a country in multiple ways, affecting not just the political landscape but also the social and economic well-being. Through the statistical data observed by us using the VAR analysis, we have quite staggering results (see Figure 2).

  • The impulse function of Geopolitical risk, increasing, it is giving volatility for the first 6 months but after a year this incertitude disappears in the Georgian tourism sector.
  • Following the results of our model, in the case of country Georgia, any geopolitical risks are to be absorbed after a year.
  • Geopolitical risk positively correlated with the Georgian tourism revenue (quite strange), which means - Geopolitical Risk increased and revenues have increased as well.

The 2008 Russian intervention in Georgia presents a fascinating case study. While such events often have negative consequences for tourism, Georgia's tourism industry appears to have exhibited a positive trend in the aftermath. Understanding the specific dynamics at play in this instance can provide valuable insights for destinations facing similar challenges. Georgia's tourism industry offers a compelling case study. Despite the 2008 Russian intervention, which is typically associated with a decline in tourism, Georgia's tourism sector appears to have flourished. This unique situation calls for a closer examination of the factors that may have contributed to this positive development (see Table 6) [11, 12, 18].

Figure 2. Response

Source: Vector Autoregressive models performed by author

Table 6. Tourist flows in Georgia (million)

Country

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Sum

559753

763231

1051749

1290108

1500049

203171

2822363

4428221

5392303

5515559

5901094

Source: Georgian National Tourism Administration, Statistical Report, http://www.gnta.ge [11, 12, 18]

Percentage difference

between V1 (2005) = 559753 and V2 (2008) = 1290108

Increase: 78.9632% difference

between V1 (2008) = 1290108 and V2 (2009) = 1500049

Increase: 15.0487% difference

A positive correlation between political instability and tourism flows in Georgia, as suggested by a VAR analysis, may seem counterintuitive. Typically, political instability tends to deter tourists. However, several factors could contribute to this unexpected relationship:

  1. Media Coverage and Sensationalism:
  • Increased Media Attention: Political instability often attracts significant media attention, which can inadvertently put Georgia on the global tourism map.
  • Sensationalized Reporting: News outlets may sensationalize events, creating a sense of excitement and adventure that draws thrill-seeking tourists.
  1. Lower Prices and Discounts:
  • Economic Downturns: Political instability can lead to economic downturns,therefore lower prices for accommodation, transportation, and other tourism services.
  • Promotional Offers: To attract tourists during uncertain times, businesses in Georgia were offering significant discounts and promotions.
  1. Unique Experiences and Adrenaline Rush:
  • Witnessing History in the Making: Some tourists were drawn to Georgia for experiencing political upheaval, seeking to witness firsthand historical events and social change.
  • Adventure Tourism: Political instability can create opportunities for adventure tourism, such as visiting conflict zones or participating in protests. It was boosting tourist to visit conflict zones and further more such as create a hollywood filmmaking -August 8, 2012
  1. Government Initiatives to Boost Tourism:
  • Diversification Strategies: In response to political instability, the government of Georgia prioritized tourism as a source of revenue and employment.
  • Targeted Marketing Campaigns: Government agencies launched marketing campaigns to highlight the country's cultural heritage, natural beauty, and unique experiences, emphasizing safety and security.
  1. Resilience of the Tourism Industry:
  • Adaptability: The tourism industry in Georgia was highly adaptable, quickly adjusting to changing circumstances and implementing safety measures.
  • Strong Domestic Tourism: A robust domestic tourism market that helped offset declines in international tourism during periods of instability.

It's important to note that this positive correlation might be a temporary phenomenon. As political stability returns, it's likely that tourism flows will normalize or even decline if the factors driving the initial increase are no longer present.

To further explore this relationship, it's crucial to consider other factors such as:

  • Specific events and their impact on tourism.
  • Government policies and their effectiveness in mitigating negative impacts.
  • The role of social media and online travel platforms in shaping perceptions of Georgia.

By conducting a more in-depth analysis, it may be possible to identify the primary drivers of this correlation and to predict future trends in tourism flows [6, 9, 19].

4. Conclusive Issues

Potential Reasons Behind Georgia's Unexpected Tourism Growth The positive correlation between political instability and tourism development in Georgia is indeed intriguing. Here are the factors, which might contribute to this phenomenon:

The Russian invasion of Georgia in 2008 had a significant impact on the country's tourism industry. However, the Georgian government has implemented several initiatives to rebuild and revitalize the sector:

  1. Visa Liberalization:
  • Georgia has significantly eased visa requirements for citizens of many countries, making it easier for tourists to visit.
  • This has helped to increase the number of international visitors to Georgia.
  1. Infrastructure Development:
  • The government has invested in improving transportation infrastructure, including airports and roads, to facilitate easier travel within the country.
  • This has made it more convenient for tourists to explore different regions of Georgia.
  1. Marketing and Promotion:
  • Georgia has launched numerous marketing campaigns to promote itself as a tourist destination.
  • These campaigns highlight the country's rich cultural heritage, stunning natural beauty, and unique experiences.
  • The government has also partnered with international travel agencies and tour operators to promote Georgia as a tourist destination.
  1. Investment in Tourism Businesses:
  • The government has provided financial incentives and support to encourage investment in the tourism sector.
  • This has led to the development of new hotels, restaurants, and other tourism-related businesses.
  1. Focus on Sustainable Tourism:
  • The Georgian government has prioritized sustainable tourism practices to protect the country's environment and cultural heritage.
  • This includes initiatives to reduce waste, conserve water, and promote responsible tourism.
  1. Cultural Events and Festivals:
  • The government has supported the organization of various cultural events and festivals throughout the year.
  • These events attract both domestic and international tourists and showcase Georgia's vibrant culture and traditions.
  1. Safety and Security:
  • The government has worked to maintain a safe and secure environment for tourists. Political instability can have a significant impact on a country's economy and social development, leading to either a major setback or a potential catalyst for positive change.
  • This outcome depends heavily on a country's preparedness, resilience, and commitment to a successful rehabilitation process.

The case of Georgia following the 2008 war offers a fascinating example. While war typically deters tourism, Georgia's tourism industry appears to have exhibited a surprising growth trajectory in the aftermath [8, 9, 20].

Our study surprisingly revealed a positive correlation between political instability, specifically the 2008 Russian intervention, and tourism industry development in Georgia [21, 22]. While political instability often deters tourism, it's crucial to consider the specific context. Further research is needed to explore the mechanisms behind this unexpected finding and to see if similar patterns hold true in other cases.

  References

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