tourism in tanzania case study

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Tanzania Economic Update: How to Transform Tourism into a More Sustainable, Resilient and Inclusive Sector

Stone Town, Zanzibar

Photo credit: Christian Morgan/World Bank.

STORY HIGHLIGHTS

• The latest Tanzania Economic Update highlights the huge untapped potential of the tourism sector to drive the country’s development agenda
• The new analysis discusses long-standing issues facing tourism in Tanzania as well as new challenges brought on by the COVID-19 pandemic
• The report says that the pandemic offers an opportunity for policy actions for the sector to recover in the near term and become a sustainable engine of private-sector-driven growth, social and economic inclusion, and climate adaptation and mitigation over the long term

DAR ES SALAAM, July 29, 2021— Tourism offers Tanzania the long-term potential to create good jobs, generate foreign exchange earnings, provide revenue to support the preservation and maintenance of natural and cultural heritage, and expand the tax base to finance development expenditures and poverty-reduction efforts.

The latest World Bank Tanzania Economic Update, Transforming Tourism: Toward a Sustainable, Resilient, and Inclusive Sector highlights tourism as central to the country’s economy, livelihoods and poverty reduction, particularly for women, who make up 72% of all workers in the tourism sector.

“Without tourism, the situation would be bad,” said Rehema Gabriel, a hotel attendant in Dar es Salaam. “I have been working in the tourism industry for eight years now, so I do not know what it would be like without it.”

The economic system around tourism had grown in value over the years and in 2019 was the largest foreign exchange earner, the second largest contributor to the gross domestic product (GDP) and the third largest contributor to employment, the report says. On the semiautonomous Zanzibar archipelago, the sector has also experienced rapid growth, accounting for almost 30% of the island’s GDP and for an estimated 15,000 direct and 50,000 indirect jobs. However, the report notes, only a small fraction of Tanzania’s natural and cultural endowments has been put to economic use through tourism development.

“Tourism offers countries like Tanzania, with abundant natural and cultural endowments, access to many foreign markets,” said Shaun Mann, World Bank Senior Private Sector Development Specialist and co-author of the Tanzania Economic Update. “But the absence of tourism revenues, as we have seen during this pandemic, compromises the integrity and viability of not only endowments, but also the economic, environmental and social ecosystems built up around those endowments.”

Amid the ongoing COVID-19 (coronavirus) pandemic, the World Bank estimates that Tanzania’s GDP growth decelerated to 2.0% in 2020. Business slowed across a wide range of sectors and firms, especially export-oriented sectors such as tourism and manufacturing. The report highlights the impact of the crisis on tourism specifically, which has had consequences beyond just the industry, given the many other sectors that support, and are supported by, tourism. The 72% drop in the sector’s revenues in 2020 (from 2019 levels) closed businesses and caused layoffs.

Zanzibar’s economy was even more severely impacted with GDP growth slowing to an estimated 1.3%, driven by a collapse of the tourism industry. As the hospitality industry shut down between March and September 2020, occupancy rates dropped to close to zero. While the Zanzibar tourism sector started slowly rebounding in the last quarter of 2020, with tourist inflows in December 2020 reaching almost 80% of those in 2019, receipts from tourism fell by 38% for the year.

As the tourism sector transitions gradually into recovery mode with the rest of the world, the report urges authorities to look toward its future resilience by addressing long running challenges that could help position Tanzania on a higher and more inclusive growth trajectory. Areas of focus include destination planning and management, product and market diversification, more inclusive local value chains, an improved business and investment climate and new business models for investment that are built on partnership and shared value creation.

Tanzania is a globally recognized destination for nature-based tourism, a competitive market segment in eastern and southern Africa. Beyond attracting tourists, the country’s landscapes and seascapes produce a wide range of ecosystem services, including carbon sequestration and biodiversity co-benefits that are not efficiently priced and often generate little or no financial return. The global climate crisis has created significant demand for investment in these forms of natural capital, and Tanzania is well positioned to take advantage of nature-positive investment opportunities. The additional revenue derived from global climate programs could be an opportunity to ease the government’s fiscal constraints while also supporting the livelihoods of local communities.

“While restoring the trade and financial flows associated with tourism is an urgent priority, the disruption of the sector has created an opportunity to realign tourism development with economic, social, and environmental resilience,” said Marina Bakanova, World Bank Senior Economist, and co-author of the report. “The pandemic has created an opportunity to implement long-discussed structural reforms in the sector and use tourism as a leading example of improvement of the overall business climate for private investment.”

The authors suggest five priorities for a sustainable and inclusive recovery that lay the foundation for the long-term transformation of the tourism sector:

• Creating an efficient, reliable, and transparent business environment to reduce red tape and multiple distortions and inefficiencies, hindering decisions on private investments, domestic and foreign
• Establishing an information-management system that consolidates data from tourists and firms, enabling policymakers to improve sectoral planning and identify viable investment opportunities
• Ensuring that firms across the sector, as well as those in downstream value chains, have access to affordable transitional finance
• Consistently promoting, monitoring, and reporting on adherence to health and safety protocols.
• Developing co-investment and partnership arrangements to support nature-based landscape and seascape management
• Press Release: Tanzania has an Opportunity to Ignite Inclusive Economic Growth by Transforming its Tourism Sector
• Report: 16th Tanzania Economic Update: ‘Transforming Tourism: Toward a Sustainable, Resilient, and Inclusive Sector’
• Video: Launch event: 16th Tanzania Economic Update
• The World Bank in Tanzania
• The World Bank in Eastern and Southern Africa
• The World Bank in Africa

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Economic impacts of COVID-19 on the tourism sector in Tanzania

Martin henseler.

a EDEHN – Equipe d'Economie Le Havre Normandie, Le Havre Normandy University, Le Havre, France

b Partnership for Economic Policy (PEP), Nairobi, Kenya

Helene Maisonnave

Asiya maskaeva.

c University of Dodoma, Dodoma, Tanzania

Associated Data

The worldwide COVID-19 pandemic has affected the tourism sector by closing borders, reducing both the transportation of tourists and tourist demand. Developing countries, such as Tanzania, where the tourism sector contributes a high share to gross domestic product, are facing considerable economic consequences. Tourism interlinks domestic sectors such as transport, accommodation, beverages and food, and retail trade and thus plays an important role in household income. Our study assesses the macroeconomic impacts of COVID-19 on the tourism sector and the Tanzanian economy as a case study of an impacted developing economy. We use a computable general equilibrium model framework to simulate the economic impacts resulting from the COVID-19 pandemic and quantitatively analysed the economic impacts.

Graphical abstract

1. Introduction

The COVID-19 pandemic has severely affected the tourism sector worldwide by closing borders, reducing the transportation of tourists, and decreasing tourist demand. Tourism is the hardest-hit sector. Indeed, in 2020, it was predicted that international tourism would fall by 80% ( OECD, 2020 ). Countries whose tourism sectors contribute a high share to gross domestic product (GDP) are facing considerable economic impact as the tourism sector is an important driver of economic development ( Faber & Cecile, 2019 ; Sinclair, 1998 ), particularly in transitioning and developing countries ( Chou, 2013 ; Khan, Bibi, Lorenzo, Lyu, & Babar, 2020 ; Liu & Wall, 2006 ; Pelizzo & Kinyondo, 2015 ). For example, in Africa, the tourism sector contributes around 9% to real GDP and supports approximately 7% of all jobs. Thus, during the last few decades, the tourism sector has received attention from both tourism researchers and development economists alike ( Brown & Hall, 2008 ; De Kadt, 1979 ; Ghimire, 2001 ; Mings, 1981 ; Rogerson, 2008 ).

In developing countries, where the tourism sector is of high importance to the economy, the COVID-19 pandemic has had a significant negative impact. First, the pandemic has directly affected the whole economy and society through health consequences and measures against it (e.g., increased hospitalisation and many lethal cases, economic lockdown, closure of schools). Second, the pandemic has impacted the tourism sector in particular, which is very important for economic growth and employment. Third, since tourism is linked to many other economic sectors ( Faber & Cecile, 2019 ; Sinclair, 1998 ), the negative impacts of COVID-19 on the tourism sector are channelled to linked sectors. These impacts are therefore of high interest to researchers and politicians. The differentiated information on these impacts is relevant for the design of measures and policy decisions in counteracting the negative economic impacts of COVID-19. Particularly in developing countries, which are vulnerable to any economic shock, such information could help support economic growth and reduce the increase in poverty.

Tanzania is a developing country where tourism is a key sector for economic growth ( Antonakakis, Dragouni, Eeckels, & Filis, 2016 ; Curry, 1990 ; Wade, Mwasaga, & Eagles, 2001 ). In 2019, the tourism sector was the second-largest component of GDP, with a contribution of 17%. In terms of employment, the sector is the third-largest source of employment, with 850,000 workers ( World Bank, 2021a ). Moreover, the sector has strong linkages with other domestic sectors such as transport, accommodation, beverage and food, and the retail trade ( Mayer & Vogt, 2016 ). Tourism creates direct and indirect jobs for low and unskilled workers, making it an important driver of economic growth and the fight against poverty ( Pelizzo & Kinyondo, 2015 ). Tourism stimulates domestic and foreign investments in new infrastructure and management of hotels, aviation, training, and travel services, tour operators' businesses, marketing, and promotion of tourism activities ( Mwakalobo, Kaswamila, Kira, Chawala, & Tea, 2016 ). Furthermore, foreign currency earnings from tourism allow for the importation of capital goods that support domestic production ( Brida, Gomez, & Segarra, 2020 ).

Since March 2020, the Tanzanian government has adopted key measures to curb the COVID-19 outbreak ( BOT, 2020 ). These measures have had an impact on all sectors, including the Tanzanian tourism sector, as one of the most important industries for economic growth and employment. The real GDP growth rate declined from 6.9% in 2019 to 4.8% in 2020 owing to regional trade disruptions and contraction in tourism and related sectors as a result of the COVID-19 pandemic ( NBS, 2019b ). Our study assesses the macroeconomic impact of COVID-19 on the tourism sector and the Tanzanian economy. We use a dynamic computable general equilibrium model to assess the macroeconomic impacts of the COVID-19 pandemic on the tourism sector and the Tanzanian economy. Our analysis provides results that will be of interest to researchers and policymakers. (i) We analyse the short and long-term impacts of COVID-19 in Tanzania on the tourism sector, the interlinked sectors, and the macro-economic indicators (e.g., GDP), (ii) we analyse the impacts on labour demand and household income, and (iii) we complement the existing academic literature with a dynamic CGE modelling study on COVID-19 impacts in Tanzania.

2. Literature review

2.1. tourism and computable general equilibrium model studies.

The tourism sector is linked multiple times in the economy to many sectors and economic agents ( Dwyer, 2015 ; Dwyer, Forsyth, & Spurr, 2004 ). It is, therefore, important to capture the links between the tourism sector and the rest of the economy, especially in countries where tourism is important for economic development, as in Tanzania ( Curry, 1990 ; Wade et al., 2001 ). Computable general equilibrium (CGE) models are tools used to assess the impact of external shocks on specific economic sectors, such as tourism, as they consider retroactive effects. The CGE model is applied to analyse the impacts of fiscal policy reforms on tourism in both developed and developing countries (e.g., Ihalanayake, 2012 ; Mabugu, 2002 ; Meng, 2012 ; Ponjan & Thirawat, 2016 ), to evaluate the impacts of investment projects (e.g., Banerjee, Cicowiez, & Cotta, 2016 ), and to assess changes in international commodity markets (e.g. Becken & Lennox, 2012 ; Yeoman et al., 2007 ), or in tourism demand (e.g., Blake et al., 2006 ). Dwyer (2015) and van Truong and Shimizu (2017) present an overview of CGE applications on tourism-related research questions. They conclude that despite its relative suitability, “CGE modelling remains relatively under-used in tourism policy analysis” ( Dwyer, 2015 , p. 124) and that it is even rare in the analysis of specific tourism-related topics like transportation ( van Truong & Shimizu, 2017 ).

In developing countries, the tourism sector has been identified as a potential channel for increasing economic growth and alleviating poverty ( Alam & Paramati, 2016 ; Honey & Gilpin, 2009 ; Khan et al., 2020 ; World Tourism Organization and International Labour Organization, 2013 ). Thus, tourism studies in developing countries often address research questions on economic growth, employment, and income. Several phenomena described in theoretical and empirical studies have also been described in studies using input-output tables or CGE models as analytical frameworks for African case studies.

2.2. Inter-sectoral linkages

Tourism has significant backward linkages to sectors that supply tourists' consumption demand, such as accommodation, restaurants, beverages and food, retail trade, and transport ( Eric, Semeyutin, & Hubbard, 2020 ; Mayer & Vogt, 2016 ; Njoya & Nikitas, 2020 ; Suau-Sancheza, Voltes-Dortac, & Cugueró-Escofeta, 2020 ). Transport and accommodation tourism is indirectly linked to the construction sector, which builds infrastructure for both ( Adam, Bevan, & Gollin, 2018 ; Kweka, 2004 ). In an input-output analysis for Tanzania, Kweka, Morrisey, and Blake (2003) find that tourism can contribute to increasing tax revenue and exchange earnings resulting from the linked sectors. In addition, linkages to natural resource sectors can be highly relevant to the tourism value chain ( Damania & Scandizzo, 2017 ). In agriculture, the tourism sector has relatively weak backward linkages as a traditional sector for exports and subsistence production. Thus, tourism expansion does not necessarily result in income generation for rural farming households. Expansion of tourism can even create a contraction in sectors with weak linkages, caused by sectoral competition for production factors or by the Dutch disease effect ( Kweka et al., 2003 ; Njoya & Seetaram, 2018 ).

2.3. Competition for production factors and Dutch Disease

An expanding tourism sector can compete with other sectors for production factors (e.g., land or labour), resulting in non-tourism sectors being deprived of production factors (e.g., land for agriculture). The sectoral competition for production factors depends on the regional economic situation and the type of tourism. Less labour-intensive tourism often uses intensive natural resources and land (e.g., large-scale resorts, national parks, and safaris) ( Damania & Scandizzo, 2017 ; Karim & Njoya, 2013 ; Njoya & Seetaram, 2018 ). Expanding tourism (as inbound tourism) increases the export of tourism as a service to foreign tourists and thus can change the current account balance and appreciation of the local currency. If the changes in currency appreciation increase the value of the local currency, then the prices of locally produced non-tourism goods and services increase. Becoming more expensive, the traditional exporting sectors (such as agriculture) can lose their competitiveness because relatively cheaper imported products are in high demand. By contracting the production of domestic non-tourism commodities, a growing tourism sector can have negative impacts on the growth of the non-tourism exporting sector(s). Several authors describe these phenomena in Kenya and Tanzania in CGE studies (see Damania & Scandizzo, 2017 ; Jensen, Rutherford, & Tarr, 2010 ; Karim & Njoya, 2013 ; Kweka, 2004 ; Njoya & Seetaram, 2018 ).

2.4. Economic policies

Identified as a pro-growth and pro-poor sector, the support of tourism by economic policies (e.g., taxation, trade reforms or investments) is an interesting research topic in developing countries. However, the impact of economic policies on growth, employment, and poverty can vary between countries, regions, and socioeconomic groups. For example, in their study Gooroochurn and Sinclair (2005) point out that in Mauritius, taxation of tourism sectors or tourists can be more efficient and equitable than levying other sectors and can create a high income for government and households. However, enterprises can suffer income losses if tourist consumption decreases, caused by increased prices from tourists' consumption ( Kweka, 2004 ). The liberalisation of barriers against domestic and multinational service providers in the tourism sector can reduce the production cost of tourism. Thus, trade reform policies could support the expansion of tourism ( Jensen et al., 2010 ).

Investment in transport infrastructure can be a measure to reduce production costs and increase efficiency in the tourism sector, and it has been found that the impacts on poverty and income can be unevenly distributed in the economy. Kweka (2004) describes how investments in transport infrastructure have more positive effects for rural than for urban households. However, Njoya and Nikitas (2020) find in a CGE study that air transport expansion in South Africa creates employment effects with more benefits for wealthy households and highly skilled workers than for poor households and unskilled workers. Thus, investments that should target the alleviation of poverty require caution and good knowledge of the impacts ( Adam et al., 2018 ). To avoid unwanted effects such as widening income inequality, accompanying measures might be required. Such measures could improve education and training for low-skilled workers if highly skilled workers benefit from the positive outcomes of the investment ( Njoya & Nikitas, 2020 ).

2.5. Economic growth

Tourism stimulates domestic production, employment and creates tax income ( Blake, 2008 ; Sharma, 2006 ; Wamboyea, Nyarongab, & Sergic, 2020 ). Indeed, several studies have reported the quantifiable positive effect of tourism on economic development in different African countries: Kenya (e.g., Honey & Gilpin, 2009 ), Mauritius (e.g., Durbarry, 2002 ), Botswana, Namibia, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe (e.g., Manrai, Lascu, & Manrai, 2020 ). Tourism expansion can be initiated by economic policies, but expansion and contraction can also result from the development of economic settings, either from a trend over time or from event-based economic shocks (e.g., catastrophes, terrorist activity, outbreaks of pandemics). Several CGE studies, find that growing inbound tourism created a net benefit to the national economy in Kenya and Tanzania (see Karim & Njoya, 2013 ; Kweka, 2004 ; Njoya & Seetaram, 2018 ). Karim and Njoya (2013) find in Kenya that tourism expansion is pro-growth, particularly for hotels, restaurants, construction, and the agricultural sector. However, there was decreased growth in the manufacturing sector. This finding differs from those of Kweka (2004) and Njoya and Seetaram (2018) , who explain how the expansion of tourism in Kenya and Tanzania caused Dutch disease and a contraction of the agricultural sector as a weakly linked sector for traditional exports.

2.6. Tourism and labour markets

Tourism has many benefits for middle- and upper-income households or tour operators–often foreign owners–while households not linked to tourism attractions (e.g., in rural areas) benefit less ( Eric et al., 2020 ; Kweka, 2004 ). Foreign inbound tourism contributes relatively more to less-skilled wage earners than to high-income workers, making it regarded as pro-poor ( Incera & Fernandez, 2015 ). Thus, in rural regions with tourism activities, tourism can have a positive effect on economic empowerment. Informal jobs allow even women in rural regions to simultaneously engage in childcare and earn money; hence tourism can assist in elevating the social status of women, helping them to afford education, and contribute to household income ( Buzinde, Kalavar, & Melubo, 2014 ). Njoya and Seetaram (2018) show that for Kenya, industries with linkages to the tourism sector increase labour demand in contrast to non-tourism sectors, which has a reverse effect. The demand for unskilled labour increases faster than the demand for skilled and semi-skilled labour ( Njoya & Seetaram, 2018 ).

2.7. Household income

The distribution of tourism income varies across rural and urban households. Urban households gain higher income from tourism-related industries than rural households ( Eric et al., 2020 ; Kweka, 2004 ; Njoya and Seetaram, 2018 ). Poor (and rural) households receive less income in tourism-related industries and more from other activities such as the primary sector (e.g. agriculture) ( Blake, 2008 ). Thus, some authors consider the redistribution of the tourism sector's benefits to be a measure to counteract poverty and inequality in developing countries ( Alam & Paramati, 2016 ; Gascon, 2015 ; Hall, 2007 ; Scheyvens, 2009 ). Karim and Njoya (2013) find in a CGE study on Kenya that tourism expansion benefits mostly rural households. Kweka (2004) and Njoya and Seetaram (2018) describe rural and farming households as benefitting less than urban households. Both studies find that tourism expansion causes unevenly distributed increases in income among middle- and upper-income households in rural and urban regions. Poverty falls faster in urban than rural areas due to decreased labour demand and earnings in rural households from the agricultural sector.

2.8. Negative impacts on tourism

While most of the reviewed studies have analysed the impacts of policies or scenarios with a positive impact on tourism, until the COVID-19 crisis, only a few studies have analysed scenarios with negative impacts on tourism Damania and Scandizzo (2017) simulated the impacts of reducing the wildlife population, which is the natural capital for safari tourism. They find a negative impact on sectoral growth and an impact from the exchange rate, which spills over to the whole economy. These impacts are especially large among (poor) rural households, resulting from the reduction of foreign exchange flows. Even measures to increase agricultural productivity cannot compensate for losses in tourism and bushmeat hunting.

2.9. Computable general equilibrium studies on tourism and COVID-19

Although the economy-wide impacts of COVID-19 have already been analysed using CGE models, to date, only a few studies have quantitatively evaluated the impacts of COVID-19 on the tourism sector ( Zenker & Kock, 2020 ), and few used the CGE or dynamic stochastic general equilibrium (DSGE) framework. Gopalakrishnan, Peters, Vanzetti, and Hamilton (2020) used a CGE model to analyse the short- and medium-term impacts of COVID-19 on the tourism sector in countries with major tourist destinations and those highly dependent on tourism. The authors identified strong linkages and spillover effects between tourism and other sectors. The decline in tourism demand impacts employment and income in many economic sectors. Pham, Dwyer, Su, and Ngo (2021) find comparable significant short-run impacts on job losses in Australia's tourism sector and industries linked to it, ranging from 152,000 jobs (for the tourism sector only) to more than 400,000 (for tourism and tourism linked industries).

Leroy de Morel, Wittwer, Gämperle, and Leung (2020) find for New Zealand that the economic impacts on the tourism sector spill over to the sectors directly linked to tourism industries (e.g. accommodation, food, and transportation services). Travel bans and mobility reductions severely decreased demand for these sectors. At the same time, the imposition of restrictive and isolation measures decreased the output of sectors not directly linked to tourism because of reduced availability of labour and capital (e.g. manufacturing, construction, and other services). Aydin and Ari (2020) show that for Turkey, COVID-19 reduced the output and exports of the tourism and transport sectors, and falling world crude oil prices compensated partially for this fall by reducing energy costs. In addition, in industries not directly linked to the tourism sector, the decreased oil price reduced production costs and increased output. Yang, Zhang, and Chen (2020) evaluated the impact of COVID-19 on the tourism sector with health status and health disaster indicators and quantified the impacts of different levels of infection risks on the tourism sector's output and labour productivity. For the longer and greater infection risk of COVID-19, the authors expect significant losses for the tourism sector and the whole economy.

Simulating the impact of COVID-19 on the whole economy, including all sectors, agents and labour market impacts, results in greater effects on GDP and employment than a partial analysis focused only on the tourism sector. A relatively high number of studies have applied CGE models to analyse tourism-related questions for Tanzania (e.g., Adam et al., 2018 ; Damania & Scandizzo, 2017 ; Jensen et al., 2010 ; Kweka, 2004 ; Kweka et al., 2003 ). Njoya (2022) uses a social accounting matrix (SAM) multiplier model to measure the impacts of the COVID-19 tourism crisis in Tanzania, considering the intersectoral and inter-institutional linkages. The SAM multiplier allows for the economy-wide analysis of COVID-19 impacts for the short term. Our study complements the existing academic literature by using a dynamic CGE modelling framework, which provides a quantitative analysis of the COVID-19 impact in Tanzania for the short and the long term.

3. Tourism in Tanzania and COVID-19

The tourism sector in Tanzania has experienced rapid development in steering the Tanzanian economy ( Curry, 1990 ; Wade et al., 2001 ). After independence in 1961, tourism development faced many challenges, namely, poor transportation, accommodation, and information facilities, weak internal tourism education, and poorly funded tourism institutional frameworks ( Wade et al., 2001 ). During the mid-1970s, Tanzania tourism shifted from regional to international tourism involving an expansion of investment, mainly through governmental programmes (e.g., new beaches and holiday projects). During the period 1964–1976, the Tanzanian government contributed 88% of the total investment in the tourism sector. However, government investments were met by accumulating losses and a decrease in revenue caused by declining terms of trade ( Curry, 1990 ).

Recently, the Tanzania tourism sector has grown significantly and has contributed considerably to economic growth in Tanzania ( Kyara, Rahman, & Khanam, 2021 ). Between 2016 and 2019, international tourism arrivals increased by 18.9%, while foreign exchange receipts from international tourism grew by about 25% during the same period. Thus, Tanzania is ranked tenth among 50 African countries in tourism growth. 1 ( WEF, 2019 ). Until April 2020, tourism earnings accounted for more than 24% of the total share of exports, making tourism the second largest foreign exchange earner after agriculture ( NBS, 2019a ). The major source markets for Tanzania's international tourism are the United States of America (13.2%) and the United Kingdom (9.5%).

Tanzania is endowed with a wide variety of landscapes, culture, and wildlife attractions and ranks eighth out of 136 countries globally in natural resource endowments ( WEF, 2017 ). Tanzania's tourist destinations comprise several cultural sites and many natural sites, including six World Heritage sites. Worldwide, Tanzania is the only country that has set aside more than 25% of the total reserve land for wildlife and other resources. 2 Thus, natural amenities and wildlife resources represent a large growth potential for nature-based tourism ( Kweka et al., 2003 ; Sekar, Weiss, & Dobson, 2014 ).

In Tanzania, most tour operators are owned by foreign entrepreneurs. 3 There is evidence that up to 60% of the total profits from the tourism industry are repatriated abroad. Thus, foreign ownership prevents Tanzania from engaging a full array of economic benefits to the booming tourism industry ( Ankomah & Crompton, 1990 ; Kinyondo & Pelizzo, 2015 ). However, Tanzanian tourism contributes significantly through its direct and indirect links to the domestic production of other sectors and economic development. The fact that around 80% of Tanzanian tourism firms are small enterprises makes them vulnerable to financial stress. Since 2000 different global disruptive events (‘black swan’ events) have negatively impacted worldwide international tourism (e.g., the September 11, 2001 attacks, the 2003 SARS epidemic and the 2008–2009 global economic crisis). However, compared to historical black swan events, the COVID-19 pandemic has caused the most significant decline in global tourism ( Mwamwaja & Mlozi, 2020 ).

In March 2020, the Tanzanian government adopted key measures to curb the COVID-19 outbreak, such as travel restrictions on international travel or a mandatory 14-day quarantine for international travellers and social distancing. All of these measures affected the tourism sector. These measures were reduced when the government stopped reporting on COVID-19 test results and cases in May 2020. However, Tanzania continued to suffer from a drop in tourist arrivals. Indeed, in 2020, the number of visitors dropped by 60%, while the revenues of public sector tourism institutions decreased by 72% (from 489.4 billion Tanzanian shilling in 2019 to 136.2 billion Tanzanian shilling in 2020) ( World Bank, 2021a ). Unlike many other countries, Tanzania did not implement specific lockdown measures. However, the reduction in tourism travel activities impacted interlinked sectors, particularly air transport, the hotel business, and retail trade. The impact of COVID-19 on tourism in Tanzania is accompanied by various other COVID-19 related impacts, which are not linked to tourism, namely a decrease in oil prices in 2020. Others include a decline in private investment and remittances, a disruption of international trade with China, India, and some European countries (e.g., for agricultural commodities), and a temporary decline in domestic consumption.

4. Methodology

A dynamic CGE model was used to evaluate the short- and long-term impacts of COVID-19 on the Tanzanian economy. Computable general equilibrium models represent the entire economy, linking different sectors such as tourism to other sectors and institutions such as households. This type of macroeconomic model has been utilised to assess the impacts of pandemics ( Beutels et al., 2009 ; Fofana, Odjo, & Collins, 2015 ; Keogh-Brown, Wren-Lewis, Edmundsa, Beutels, & Smith, 2010 ), and to evaluate the impacts of COVID-19 on the world economy ( Laborde, Martin, & Vos, 2020 ; Maliszewska, Mattoo, & Van Der Mensbrugghe, 2020 ), on a single country ( Chitiga-Mabugu, Henseler, Mabugu, & Maisonnave, 2021a ; Erero & Makananisa, 2020 ; Kinda, Zidouemba, & Ouedraogo, 2020 ), and on households' economic behaviour with gender implications ( Chitiga-Mabugu, Henseler, Mabugu, & Maisonnave, 2021b ; Escalante & Maisonnave, 2021 ; Maisonnave & Cabral, 2021 ).

Fig. 1 presents the flow of the values in a CGE model. In the economy, tourism is linked to other domestic sectors and produces export commodity tourism services for the commodity market. The domestic commodity market provides a supply for the export market, where tourism services are sold as inbound tourism to foreign tourists. The demand for tourism services by foreign tourists determines the price of the service, thus driving the domestic production in the tourism sector. Reduced demand reduces the production of the tourism sector with a corresponding interaction with other sectors via intermediate demand. Reduced production also means a reduced payment for production factor labour to households (i.e., decreased household income). The decrease in exported tourism services and spillover effects on other sectors, income, and consumption results in a decrease in taxes paid as income to the government. Decreased governmental income forces the government to reduce investments in production, which is required to retain economic growth in the long term. Consequently, reduced investment negatively impacts economic growth.

Schematic presentation of the flows in a CGE model. Source: authors' presentation.

We used the dynamic PEP 1-t model developed by Decaluwé, Lemelin, Robichaud, and Maisonnave (2013) and modified it to reflect the Tanzanian economy. The model database is a social accounting matrix (SAM) which represents a snapshot of the Tanzanian economy for 2015. Indeed, this matrix is a consistent framework that retrieves all the flows recorded in the economy for a given year and provides the structure of the economy ( Round, 2003 ). The SAM used in this study was developed by Randriamamonjy and Thurlow (2017) . In line with the matrix, our model has 55 activities and 56 commodities. Of these, 25 are agricultural, 19 belong to the industrial sector, and 11 are in tourism-related sectors such as accommodation and restaurants, retail, and transportation.

We assumed that the production process is nested. At the top level, production is a Leontief-type function between intermediate consumption and value addition. In other words, there is no possibility of substituting value addition with intermediate consumption. At the next level, value addition is a constant elasticity of substitution function between aggregate labour and capital. At the last level, it was assumed that aggregate labour is a constant elasticity of substitution function between the different types of labour, while aggregate capital is a constant elasticity of substitution function between the different types of capital (e.g., land, livestock, machines). For instance, mining capital is used only by mining industries, while crop capital is used only in crop-based agricultural sectors.

Among the different types of labour, workers are disaggregated according to their level of education (no school education, primary education from grades 0 to 4, medium education from grades 5 to 11, and grade 12 or above). If we look closely at the hotel and restaurant sector in Tanzania, we find that 63.5% of its production relies on intermediate consumption, and among the workers hired, more than 90% have primary to secondary education levels (up to grade 11). The information on the factor demand is important for interpreting the results and explaining the links between the tourism sector and the rest of the economy, such as the link to the labour market and other sectors.

Along with the SAM, the model distinguishes three different types of institutions: households, the government, and the rest of the world. Households are disaggregated per quintile of income and whether they are in urban or rural areas. Among households living in rural areas, there is a distinction between farming and non-farming households. All households receive income from labour and capital but in different proportions. For instance, farming households belonging to the lowest quintile of income mainly receive income from unskilled and low-skilled labour, while urban households belonging to the richest quintile of income mainly receive income from mining capital and non-agricultural capital and, to a lesser extent, skilled labour and remittances. Households spend their income on consumption, pay transfers to other institutions and direct taxes, and save the remainder.

The government's income is composed of direct taxes paid by households and firms, indirect taxes, transfers from the rest of the world, and a share of capital income. Indirect taxes account for 46% of the government's income, while direct taxes account for 25%, capital income (mainly non-agricultural capital) accounts for 10%, and transfers from the other institutions for 19%. Government savings are equal to government income less its consumption and transfers paid to other institutions (e.g., social grants, pensions, etc.). Tanzania is linked to the rest of the world via its exports and imports of commodities and through receipts and payment of transfers. Almost 40% of total Tanzanian exports are derived from the service sector, such as the tourism and transport sectors. Agricultural exports account for almost 30% of total exports (e.g., tobacco, coffee, tea, cotton, cashews, and sisal). Given the origin of the commodities, we modelled the links between the rest of the world and Tanzania according to the traditional approach based on the assumption of imperfect substitutability. If Tanzanian producers want to increase their market share in the international market, they need to be more competitive, which is technically translated to set up a finite elasticity for export demand.

Compared to the standard ‘PEP 1-t model’, we changed the assumption of full employment following the modelling of Blanchflower and Oswald (1995) . We assumed a negative slope between wage rates and unemployment rates. In other words, an increase in the unemployment rate leads to a decrease in wage rates. We assumed that labour is mobile across sectors while capital is sector-specific. According to the dynamic assumptions, labour supply increases with an increase in the population rate, while the stock of capital for each sector increases as per the investments made in the sector during the year. The equation that determines the allocation of new investments follows Jung and Thorbecke (2003) . In terms of other closure rules, we took the nominal exchange rate as the numeraire of our model. Following the supposition of small countries, world prices are exogenous; thus we have also presumed that the rest of the world's savings are exogenous, as well as government spending.

5. Scenario design

The COVID-19 pandemic is affecting the Tanzanian economy in many ways through international channels of transmission that are used to inform the design of the scenarios, which are presented in Table 1 . In contrast to other countries, Tanzania did not apply a strict lockdown in 2020. Therefore, COVID-19 shocks are modelled exclusively through international channels. Among them, we identify three channels: remittances, world prices of commodities, and exports. Tanzanian households receive transfers from friends and relatives living and working abroad. Given the economic recession worldwide, this source of income has dwindled. From the BOT (2020) , we can see that it reduced by 29.5% compared to the previous year in 2020. For 2021, we assume that the drop will be 10% and 5% in 2022 for the severe scenario.

Scenarios implemented (in per cent to the business as usual scenario).

Source: Authors' assumptions. Notes: a) The shocks in 2020 are based on observed data and applied to both the mild and severe scenario.

The COVID-19 outbreak has had an impact on world prices on different commodities in 2020, and we expect some impacts in the following years. There was indeed a drop in oil prices due to the drop in international demand, while for gold, wheat, and sugar, we observed a positive impact on world prices. This will impact the Tanzanian economy since Tanzania is a net oil importer and exporter of many minerals. The exports of coal, gold, and manganese account for 30% of total exports. Finally, given the economic situation in trading partner countries, the country faces a decrease in demand for exports. Indeed, Tanzania's main trade partners (India, China, and the United Arab Emirates) face a decrease in their economy, reducing their demand for imports from Tanzania. For instance, India, which accounts for almost 30% of Tanzanian's exports, is expected to face a decrease of 10.3% in 2020 ( IMF, 2020 ), while the second trade partner, the United Arab Emirates, is expected to fall by 6.6% of its GDP ( IMF, 2020 ). The biggest drops in 2020 were observed in the tourism, transportation, and communication sectors.

Indeed, it is already clear that COVID-19 will impact the tourism sector not only in 2020 and 2021 but also in 2022. Thus, long-term effects on tourism, linked sectors, and economies are predicted. Fotiadis, Polyzos, and Huan (2021) find that tourist arrivals could drop to 76.3% and last until at least June 2021. In a study on a group of 20 countries, Kourentzes et al. (2021) expect that under severe scenario assumptions, countries will recover on average to only 34% of their total tourist arrivals in the last quarter of 2021 compared to the same period in 2019. Under a mild scenario assumption, the average recovery was 80%. Polyzos, Samitas, & Spyridou (2021) find that the recovery of Chinese tourists arriving at the pre-crisis level in the USA and Australia could take between 6 and 12 months.

More than 40% of international tourism experts expect the global tourism sector to recover to its 2019 level in 2024 or later, with only 15% of experts anticipating a recovery by 2022 (UNWTO Panel of Tourism Experts, January 2021). Moreover, it is likely that COVID-19 will modify the behaviours of tourists who may prefer to travel to familiar and trusted places. For instance, for China, Huang, Shao, Zeng, Liu, and Li (2021) found that tourists avoided travelling to places with more confirmed cases of COVID-19 compared to their places of origin. This argument is relevant in Tanzania's case. Indeed, by refusing in 2020 to acknowledge the existence of the coronavirus in Tanzania, the previous government only fell behind in providing care and access to vaccination. It is possible that tourists will be afraid to return to Tanzania if only a small proportion of the population is vaccinated.

It is quite challenging to estimate a “back to normal” situation in the tourist arrivals, as most countries are experiencing a third or fourth wave of COVID-19. Therefore, we designed two scenarios: a mild scenario and a severe scenario. These two scenarios differ in the magnitude and duration of the shocks from 2021 onwards. However, for both scenarios, in 2020, the same magnitude is applied. Since based on observed data and we label the year 2020 as a “historical” simulation year. In the mild scenario, we estimate a return to normal in 2022, whereas in the severe scenario, the return to normal will be in 2023. The model runs from 2015 (year of the SAM), a so-called ‘business as usual’ scenario until 2030, without any shocks assuming a regular path of economic growth. The economic shocks caused by COVID-19 are applied in 2020 and 2021 for the mild scenario and in 2020, 2021, and 2022 for the severe scenario. When analysing the results, we compared the values obtained with the mild and severe scenarios to those of the “business as usual” scenario. We present the values obtained for 2020, 2021, and 2030.

For 2020, we computed the magnitudes of the shocks using the BOT (2020) . For the scenario year 2020, information is available that remittances (inflows excluding those going to the government) dropped by almost 30% compared to the business as usual scenario. It should be noted that for the scenario years 2021 and 2022, the simulated decreases in remittances are not based on official sources but on the authors' assumptions. Given the ongoing pandemic for 2021 and the coming years, official estimates of changes could not be researched. We assume in the mild scenario that the Tanzanian economy would continue to be affected but not as severely as 2020 and return to its business as usual situation in 2022. For the severe scenario, in 2021, the economy will still be heavily affected, and for 2022, we envisage that only a couple of sectors (mainly tourism and transport) would remain stricken. We assume that discovering new COVID-19 variants (such as Omicron) leads to preventive reactions from different countries that directly affect the tourism and transport sectors. The economy would return to its business as usual level in 2023.

6.1. The macroeconomic impacts

The COVID-19 related shocks are quite harsh in the Tanzanian economy. Indeed, as mentioned above, the effects on Tanzania are via a number of channels (e.g. drop in tourists, decrease in exports, differing world prices, and decreased remittances). In 2020, Tanzania experienced a decrease in GDP of 1.88%. In 2021, in the mild scenario, the decline in GDP is slightly lower than in 2020, while under the severe scenario, Tanzania suffers a higher loss. In both scenarios, in the long run, GDP would still be lower than what it would have been without the pandemic, with a drop in GDP by 0.38% in the mild scenario compared to 0.54% in the severe scenario (see Table 2 ). The negative impact on economic growth caused by a contraction of the tourism sector, as well as trade shocks, is in line with studies describing the positive impact of tourism expansion (e.g., Kweka, 2004 ; Njoya & Seetaram, 2018 ). Since Tanzania's tourism sector was expanding until 2019 ( Kyara et al., 2021 ; WEF, 2019 ), the negative shock caused by COVID-19 has created an inverted (negative) impact on growth. The macroeconomic impacts simulated for Tanzania are comparable to the results of other studies on COVID-19 impact on tourism (e.g. Leroy de Morel et al., 2020 ; Pham et al., 2021 ).

Macroeconomic impacts, selected indicators (in per cent change to the business as usual scenario).

Source: Authors' simulation results. Notes: a) The shocks in 2020 are based on observed data and applied to both the mild and severe scenario.

The drop in tourism demand, combined with the linked drop in transportation and communication services, forces sectors to reduce production and lay off workers. These sectors reduce their intermediate demand, which in turn negatively impacts other sectors. These results are consistent with the strong backward linkages described by Adam et al. (2018) and Kweka (2004) . In contrast to these studies, Tanzanian tourism suffers a contraction, thus creating a negative impact on the growth of the strongly linked sectors. Consequently, we observe a drop in total labour demand from tourism and linked sectors by more than 3.3% in 2021. This drop in labour demand impacts households by reducing their income and then their consumption, ceteris paribus. Households' real consumption decreases by 5.1% in 2020, and in the long run, for both scenarios, it is still below the level of the business as usual scenario.

6.2. Impacts on the tourism sector and other sectors

In 2020, given the massive reduction in travellers, the production of the tourism sector is declining by more than 13% (see Table 3 ). The sector is laying off workers and is no longer attractive for private investment. The drop in production continues throughout the period under mild and severe scenarios. In the long run, production is still slightly below what it would have been without the pandemic. We can point out that the sector hires again and attracts more investment at the end of the period in 2030. However, the investments in the current period will be effective as capital in the next period (i.e., after 2030). To reach the level of services, as would have been the case without COVID-19, policy measures (e.g., more investments) will be needed to stimulate the expansion of the tourism sector (e.g. more tourism activities to Tanzania) and more investments. Pro-tourism measures would indirectly support the recovery of sectors linked to tourism. The strong linkages between tourism and other sectors ( Eric et al., 2020 ; Mayer & Vogt, 2016 ; Njoya & Nikitas, 2020 ; Suau-Sanchez et al., 2020) cause a decrease in the production of the linked sectors (see Table 4 ).

Impacts on labour demand, investments, and production in the tourism sector (in per cent change).

Source: Authors' simulations results. Notes: a) The shocks in 2020 are based on observed data and applied to both the mild and severe scenario.

Impacts on labour demand and production of the tourism sector and linked sectors (in per cent change).

For instance, the transport sector faces a drop of 11.7% of its production in 2020, and the decline continues even after the economy goes to its business as usual level. Note that under the severe scenario, the sector's production is almost as affected as in 2020, resulting in layoffs in the sector. These reduced labour demands from the transportation sector are in line with the inverted observations of Njoya and Seetaram's (2018) study, which describes the significant positive impact on tourism caused by expanding the transportation sector. For the construction sector, the drop in production is also linked to the drop in total investment ( Table 2 ), thereby affecting investment goods such as construction commodities.

6.3. Impacts on Tanzanian households

The analysis of household income shows that in the short-term (in 2020 and 2021), rich and poor households suffered a significant decrease in income (see Table 5 ). Among the poor households, the loss is almost uniform given the place of residence. For farming households, the income losses are smaller than for non-farming rural households. This finding is comparable to Njoya's (2022) findings. Njoya (2022) explains that rural farm households have more diversified sources of income than rural non-farm households, which mainly earn income from labour ( Njoya, 2022 ). In the long run, farming households, also perform better than the others. Eric et al. (2020) , Kweka (2004) and Njoya and Seetaram (2018) note that under the positive development of tourism, the benefits for rural households are smaller than for rural households. This observation confirms the weaker dependency of rural households also on the negative development of the tourism sector.

Impact on households' total income (in % change to the BAU).

Wealthy urban households lose relative less income than their rural counterparts. This is because wealthy urban households are the only ones who receive capital income from mining. The increase in the world price for gold in 2020, therefore, benefits the richest urban households, which can compensate parts of for their losses. The findings are in line with the World Tourism Organization and International Labour Organization (2013) , who revealed that during the economic slowdown, poor households tend to suffer more than households with high and middle income ( World Tourism Organization and International Labour Organization, 2013 ). However, our findings of smaller income losses for rural than for urban households differ from Njoya's (2022) findings. Njoya (2022) reports smaller income losses for urban than for rural households. This difference may result from different scenario design in both studies. We simulate with a dynamic CGE model the decrease of tourism demand and changes of international trade and prices (e.g., increased gold prices). Njoya's (2022) SAM multiplier model simulates the reduction in international tourism receipts and excludes the changes in trade and prices and their impacts on income ( Njoya, 2022 ).

For the long-term, we find that poor households recover better in rural than in urban regions. Rural households' income is derived from work in the primary sectors (e.g., agriculture, fishery, forestry), and thus they are able to recover from pandemic crises independently from the tourism sector ( Blake, 2008 ). This means that COVID-19 measures have hit Tanzanian society at all income levels, even at a comparable relative range. For poor households, the loss of income might have a bigger effect on their purchasing power; thus the impacts are much harsher for these households. The observation of the harder hit is in line with the findings of Damania and Scandizzo (2017) , who find that a contracting tourism sector has the heaviest impact on extremely poor rural households caused by the increase in local currency value and the corresponding increase in local prices.

6.4. Comparison with other black swan events

The impacts of the COVID-19 pandemic on worldwide tourism are considered as the most devastating in the history of tourism ( Aldao, Blasco, Poch Espallargas, & Palou Rubio, 2021 ; Gössling, Scott, & Hall, 2021 ). Figs. 2a and ​ andb b present the impacts of COVID-19 and other historical black swan events on Tanzanian tourism and the aggregate economy by selected economic indicators. The indicators “tourism arrivals” and “value-added of services” inform on the impact of disruptive events on the operation of the tourism sector. The indicator “labour force”, “consumption”, and “Real GDP” inform on the impact on the aggregate economy. The solid lines represent historical data from 2000 to 2020, and the other lines present simulated data based on our CGE simulations for 2020 to 2023. The dotted lines represent the hypothetical economic development without any disruptive events, i.e., the business as usual. We computed these data as the linear interpolation between the historical level in 2019 and 2030, which we simulated with the CGE modal according to the business as usual assumptions. The dashed lines represent the development in the mild and the severe COVID-19 scenarios. We compute these data by applying the relative change simulated by the CGE model to the historical data of the year 2019 as the last year before COVID-19.

a. Historical development of indicators between 1999 and 2015. Per cent change with 2015 = 100%. Source: World Bank (2021b) .

Historical development of indicators between 2015 and 2020 and simulated development for the mild and severe scenario. Per cent change with 2015 = 100%. Source: World Bank (2021b) and authors' computations.

The impacts cause by the COVID-19 pandemic exacerbate the historical black swan events. The 9/11 Attacks in 2001 and the 2003-SARS epidemic flatten the curve of tourist arrivals compared to the increasing trend from 2000 to 2002 ( Aldao et al., 2021 , Gössling et al., 2021 ). The 2008–2009 global economic crisis causes a drop in tourism arrivals and a slight decrease in the value-added of the service sector. However, the global crisis decreased consumption via other channels than tourism. The global economic crisis also causes changes in international markets and the exporting sectors (e.g., agricultural sectors and minerals) and prices ( Ngowi, 2010 ), spilling over to consumption. COVID-19 pandemic in 2019 reduces the tourism arrivals, which had been steadily increasing since 2000, by 80 percentage points. Compared to the business as usual, all macro-economic indicators drop in 2019 and approach the business as usual only after 2023. The COVID-19 pandemic has not only reduced the number of tourists arrivals and tourist operations, as it seems to be the case in the 9/11 Attacks and 2003-SARS crisis. In addition to tourism services, it significantly decreases GDP, consumption, and labour demand.

6.5. Ex-post comparison

For 2020 we obtain data for the historical development and the development simulated with the CGE model based on observed data. In Fig. 2 , the differences between the historical and the simulated data appear as a gap between the end of the solid line and the beginning of the dashed line. Table 6 compares the data numerically and presents a slight deviation between historical and simulated data of 1 to 3 percentage points. The underestimation of labour demand, private consumption and value-added of the service sector explain the overestimation of GDP. The CGE model underestimates the negative impact of COVID19 on sectors, consumption and labour market and consequently simulate a less negative impact for the economic development, i.e., the GDP. This ex-post comparison presents a relatively small deviation between simulated and historical results and thus a relatively good model-based reality replication. Thus, we assume the model results could deviate 1 to 3 percentage points for the other years. The application of CGE models to the COVID-19 crisis and the availability of historical data for recent years allow for ex-post analysis for many countries and models. To the best of our knowledge, we examined the first ex-post comparison on COVID-19 for a CGE model on COVID19 impacts in Tanzania.

Ex-post comparison between historical and simulated development of the year 2000.

7. Conclusions

The results presented in this study illustrate the significant impact of COVID-19 measures on the tourism sector and allied sectors in Tanzania. Without any policy measures in place, in the long run, GDP, production, and household income would still be below the baseline than would have been the case without the COVID-19 crisis. The comparison between the historical development of tourism and macro-economic indicators shows that the COVID-19 crisis exacerbates the impact of historical black swan events on tourism and economic development. The shocks via international and domestic channels create negative impacts in all Tanzanian sectors. The impact on the aggregate economy is much more substantial than observed in former black swan events.

The results suggest that policy measures focusing on supporting the tourism sector could be an important means to stimulate the Tanzanian economy after the COVID-19 pandemic. Such measures could, for example, be the development of hygienic concepts, improved infrastructure, and advertising to make tourism in Tanzania attractive for tourists after the pandemic ( Kyara et al., 2021 ). The potential to expand nature-based tourism ( Kweka et al., 2003 ; Sekar et al., 2014 ) could represent a competitive advantage compared to cultural tourism. In nature-based tourism activities, contact between people is less than that of cultural tourism, and the potential risk of infection is thus lower than that in mass tourism (e.g., holiday beach resorts).

Investing in road infrastructure could be a very interesting option: it would reduce transport costs for all the different sectors in the economy and encourage the growth of the tourism sector (see Adam et al., 2018 ; Eric et al., 2020 ; Kweka, 2004 ). It would also respond to a recommendation from tourists, 42% of whom believe that roads and other infrastructure should be improved ( NBS, 2018 ). Government and foreign investments could be channelled into building tourism infrastructure that could serve two objectives simultaneously: an improved infrastructure that could reduce transport costs and improve logistics. Investment in the construction sector in building infrastructure could help create new jobs and thus increase household income and domestic consumption to stimulate the economy. The dominant role of tour operators' foreign owners needs to be considered in counteracting measures. With governmental investments in tourism, foreign company owners could be engaged in boosting Tanzanian nature-based tourism, making it more competitive than in other countries after the COVID-19 pandemic ( Ankomah and Crompton, 1990 ; Kinyondo & Pelizzo, 2015 , Kyara et al., 2021 ).

Since the relative loss of income is high for both wealthy and poor households, political measures could find broad acceptance in both populations, notwithstanding that poor households are hit harder by income losses than wealthy households, and thus, the former might need different additional support. Further simulations of the impact on households by linking a micro-simulation model to the dynamic macroeconomic model could provide a more differentiated analysis of the short- and long-term impact of COVID-19 among households ( Njoya, 2022 ) and different socioeconomic groups (e.g., women).

Tanzanian history has shown that government investments in tourism have been essential in the past, which could now be crucial in helping the recovery of the Tanzanian tourism sector, the economy, and households/residents. The findings from the Tanzanian case may also apply to other developing countries where tourism is an important economic driver. In addition, governments might need to take measures to help tourism and the economic growth of their countries.

The following are the supplementary data related to this article.

Supplementary video

Acknowledgement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We thank three anonymous reviewers and the editor whose comments and suggestions helped improve this article.

Biographies

Martin Henseler is researcher at EDEHN research center (Le Havre Normandy University). He works with quantitative economic models mainly on the areas of agriculture, environment and development.

Helene Maisonnave is Professor at Le Havre Normandy University, she works with computable general equilibrium models on development, gender and poverty issues.

Asiya Maskaeva is Senior Lecturer at the University of Dodoma. She applies economy-wide models to analyse the economic development in African countries

Editor: Li Gang

1 Tanzania's tourism industry accounts for 2.6 billion US dollars.

2 Cultural sites: Dar es Salaam and the historic island of Zanzibar, Ruins of Kilwa Kisiwani and Ruins of Songo Mnara. Natural sites: Serengeti National Park, Selous Game Reserve, Kilimanjaro National Park, and Stone Town of Zanzibar, Ngorongoro Conservation Area. The conservation area includes 16 national parks, 28 game reserves, 44 controlled conservation areas, and two marine parks.

3 There are 543 tour operator companies located in Arusha, Dar es Salaam, and Zanzibar. Arusha has the largest (401) number of tour operator enterprises, followed by Dar es Salaam (95) and Zanzibar (47).

Appendix A. Description of extrapolating the historical data by using CGE model results

We scaled the development of World Band Development indicators relative to 2015 to display the historical indicators ( INDHt ) from 2000 to 2020 (Eq. A.1). In a second step, we simulate the post-COVID-19 years from 2020 to 2030 as simulated indicators ( INDSt ) (Eq. A.2). To project the post-COVID-19 development, we use the last pre-COVID-19 year (i.e., 2019) as the base and apply a projection factor ( PROJt ). This projection factor is computed based on the CGE model results as a relative change compared to the last pre-COVID-19 year (Eq. A.3). For 2020 we obtain two values: one historical value for the indicator ( INDH 2020 ) and one simulated value for ( INDS 2020 ). The values from the CGE model are percentage change related to the base year of the CGE model (i.e., the year 2015) (Eq. A.4). Finally, we simulate the business as usual scenario ( BAUSINDI t ) as a linear interpolation between the CGE model result for the year 2030 and the last pre-COVID19 year (Eq. A.5 and A.6).

t : all thirty years of the presented period pre-COVID-19 and post-COVID-19 year = {2000, …., 2030},

tPreCOVID : twenty years with historical data before COVID-19 including the first year under COVID-19 as historical data = {2000, …,2019, 2020},

tPostCOVID : ten years with simulated data after COVID-19 including the first year under COVID-19 as simulated data = {2020, 2021, …,2030},

tBAU : ten ears simulated as projected business as usual scenario = {2020, …,2030}.

INDH tPreCOVID : macroeconomic indicators historical values for the years pre-COVID-19,

INDSt tPostCOVID : macroeconomic indicators simulated values for the years post-COVID-19,

WOBAINDI tPreCOVID : macroeconomic indicators for the years pre-COVID-19 (incl. 2020) provided by the World Bank Development indicators ( World Bank, 2021b ),

PROJ tPostCOVID : factor for projecting the post COVID-19 years relative to the last pre-COVID-19 year (i.e., 2019),

CGEMPERC tPostCOVID : macroeconomic indicator for the post COVID-19 years from the CGE model simulations relative to the CGE model results for the year 2015 (i.e., the base year 2015),

CGEMABSO tPostCOVID : macroeconomic indicator for the post COVID-19 years from the CGE simulations as a ratio to the CGE base year 2015,

BAUSINDI tBAU : macroeconomic indicator values projected for the years for the business as usual for the ten years from 2020 to 2030,

INDH 2019  = macroeconomic indicators historical values for the last year pre-COVID-19 (i.e., 2019) relative to 2015,

DILI : Difference for linear interpolation between the last year of the BAU (i.e., 2030) and the last historical year (i.e., 2019),

TSBAU tBAU : number of years in simulated business as usual (i.e., 2020 to 2030), i.e., TSBAU 2020  = 1, TSBAU 2021  = 2, … TSBAU 2030  = 10,

• Adam C., Bevan D., Gollin D. Rural-urban linkages, public investment and transport costs: The case of Tanzania. World Development. 2018; 109 :479–510. [ Google Scholar ]
• Alam M.S., Paramati S.R. The impact of tourism on income inequality in developing economies: Does Kuznets curve hypothesis exist? Annals of Tourism Research. 2016; 61 :111–126. doi: 10.1016/j.annals.2016.09.008. [ CrossRef ] [ Google Scholar ]
• Aldao C., Blasco D., Poch Espallargas M., Palou Rubio S. Modelling the crisis management and impacts of 21st century disruptive events in tourism: The case of the COVID-19 pandemic. Tourism Review. 2021; 76 (4):929–941. [ Google Scholar ]
• Ankomah K., Crompton J. Unrealised tourism potential: The case of sub-Saharan Africa. Tourism Management. 1990; 11 (1):11–28. [ Google Scholar ]
• Antonakakis N., Dragouni M., Eeckels B., Filis G. Tourism and economic growth: Does democrat matter? Annals of Tourism Research. 2016; 61 :231–267. [ Google Scholar ]
• Aydin L., Ari I. The impact of Covid-19 on Turkey’s non-recoverable economic sectors compensating with falling crude oil prices: A computable general equilibrium analysis. Energy Exploration & Exploitation. 2020; 0 (0):1–21. doi: 10.1177/0144598720934007. [ CrossRef ] [ Google Scholar ]
• Banerjee O., Cicowiez M., Cotta J. Economics of tourism investment in data scarce countries. Annals of Tourism Research. 2016; 60 :115–138. doi: 10.1016/j.annals.2016.06.001. [ CrossRef ] [ Google Scholar ]
• Becken S., Lennox J. Implications of a long-term increase in oil prices for tourism. Tourism Management. 2012; 33 (1):133–142. doi: 10.1016/j.tourman.2011.02.012. [ CrossRef ] [ Google Scholar ]
• Beutels P., Jia N., Zhou Q.Y., Smith R., Cao W.C., de Vlas S.J. The economic impact of SARS in Beijing, China. Tropical Medicine and International Health. 2009; 14 (1):85–91. doi: 10.1111/j.1365-3156.2008.02210.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blake A. Tourism and income distribution in East Africa. International Journal of Tourism Research . 2008; 10 :511–524. [ Google Scholar ]
• Blake A., Durbarry R., Eugenio-Martin J., Gooroochurn N., Hay H., Lennon J.…Yeoman I. Integrating forecasting and CGE models: The case of tourism in Scotland. Tourism Management. 2006; 27 :292–305. [ Google Scholar ]
• Blanchflower D., Oswald A. An introduction to the wage curve. Journal of Economic Perspectives. 1995; 9 (3):153–167. [ Google Scholar ]
• BOT . Bank of Tanzania (BOT); 2020. Monthly Economic Review. https://mof.go.tz/docs/THE%20UNITED%20REPUBLIC%20OF%20TANZANIA-copy%206.11.2020.pdf [ Google Scholar ]
• Brida J., Gomez D., Segarra V. On the empirical relationship between tourism and economic growth. Tourism Management. 2020; 81 :104–131. [ Google Scholar ]
• Brown F., Hall D. Tourism and development in the global south: The issues. Third World Quarterly. 2008; 29 (5):839–849. doi: 10.1080/01436590802105967. [ CrossRef ] [ Google Scholar ]
• Buzinde C., Kalavar J., Melubo K. Tourism and community well-being: The case of the Maasai in Tanzania. Annals of Tourism Research. 2014; 44 :20–35. [ Google Scholar ]
• Chitiga-Mabugu M., Henseler M., Mabugu R., Maisonnave H. Economic and distributional impact of COVID-19: Evidence from macro-Micro modelling of the south African economy. South Afr. J. Econ. 2021; 89 :82–94. doi: 10.1111/saje.12275. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chitiga-Mabugu M., Henseler M., Mabugu R.E., Maisonnave H. How COVID-19 pandemic worsens the economic situation of women in South Africa. European Journal of Development Research. 2021 doi: 10.1057/s41287-021-00441-w. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chou M.C. Does tourism development promote economic growth in transition countries? A panel data analysis. Economic Modelling. 2013; 33 :226–232. doi: 10.1016/j.econmod.2013.04.024. [ CrossRef ] [ Google Scholar ]
• Curry S. Tourism development in Tanzania. Annals of Tourism Research. 1990; 17 (1):133–149. [ Google Scholar ]
• Damania R., Scandizzo P.L. The Serengeti ecosystem—Burden or bounty? Journal of Policy Modeling. 2017; 39 :185–205. [ Google Scholar ]
• De Kadt E., editor. Tourism – Passport to development? Oxford University Press; 1979. [ Google Scholar ]
• Decaluwé B., Lemelin A., Robichaud V., Maisonnave H. PEP; Static Dynamic Version: 2013. PEP-1-1 t single-country. https://www.pep-net.org/pep-1-1t-single-country-staticdynamic-version [ Google Scholar ]
• Durbarry R. The economic contribution of tourism in Mauritius. Annals of Tourism Research. 2002; 29 (3):862–865. doi: 10.1016/s0160-7383(02)00008-7. [ CrossRef ] [ Google Scholar ]
• Dwyer L. Computable general equilibrium modelling: An important tool for tourism policy analysis (December 4, 2015) Tourism & Hospitality Management. 2015; 21 (2) https://ssrn.com/abstract=2708544 [ Google Scholar ]
• Dwyer L., Forsyth P., Spurr R. Evaluating Tourism’s economic effects: New and old approaches. Tourism Economics. 2004; 25 :307–317. doi: 10.1016/S0261-5177(03)00131-6. [ CrossRef ] [ Google Scholar ]
• Erero J.L., Makananisa M.P. Impact of Covid-19 on the South African economy: A CGE, Holt-Winter and SARIMA model’s analysis. Turkish Economic Review. 2020; 7 (4):193–213. [ Google Scholar ]
• Eric T., Semeyutin A., Hubbard N. Effects of enhanced air connectivity on the Kenyan tourism industry and their likely welfare implications. Tourism Management. 2020; 78 :1–16. [ Google Scholar ]
• Escalante L., Maisonnave H. Gender and Covid-19: Are women bearing the brunt? A case study for Bolivia. Journal of International Development. 2021 doi: 10.1002/jid.3603. [ CrossRef ] [ Google Scholar ]
• Faber B., Cecile G. Tourism and economic development: Evidence from Mexico’s coastline. American Economic Review. 2019; 109 (6):2245–2293. [ Google Scholar ]
• Fofana I., Odjo S., Collins J. An assessment of Ebola-related food security threat in Guinea. Center for Global Trade Analysis. 2015 https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=4695 [ Google Scholar ]
• Fotiadis A., Polyzos S., Huan T.C. The good, the bad and the ugly on COVID-19 tourism recovery. Annals of Tourism Research. 2021; 87 doi: 10.1016/j.annals.2020.103117. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gascon J. Pro-poor tourism as a strategy to fight rural poverty: A critique. Journal of Agrarian Change. 2015; 15 :499–518. [ Google Scholar ]
• Ghimire K.B. Regional tourism and south-south economic cooperation. The Geographical Journal. 2001; 167 (2):99–110. doi: 10.1111/1475-4959.00010. [ CrossRef ] [ Google Scholar ]
• Gooroochurn N., Sinclair M.T. Economics of tourism taxation: Evidence from Mauritius. Annals of Tourism Research. 2005; 32 :478–498. [ Google Scholar ]
• Gopalakrishnan B., Peters R., Vanzetti D., Hamilton P. United Nations Conference on Trade and Development. 2020. COVID-19 and tourism: Assessing the economic consequences. https://unctad.org/system/files/official-document/ditcinf2020d3_en.pdf [ Google Scholar ]
• Gössling S., Scott S., Hall C.M. Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism. 2021; 29 (1):1–20. doi: 10.1080/09669582.2020.1758708. [ CrossRef ] [ Google Scholar ]
• Hall C. Pro-poor tourism: Do tourism exchanges benefit primarily the countries of the south? Current Issues in Tourism. 2007; 10 (2/3):111–118. [ Google Scholar ]
• Honey M., Gilpin R. United States Institute of Peace; 2009. Tourism in the developing world: Promoting peace and reducing poverty. https://www.jstor.org/stable/pdf/resrep12508.pdf?refreqid=excelsior%3Ae77ba3c811d536612bb767735d47ba93 [ Google Scholar ]
• Huang S.S., Shao Y., Zeng Y., Liu X., Li Z. Impacts of COVID-19 on Chinese nationals’ tourism preferences. Tourism Management Perspectives. 2021; 40 doi: 10.1016/j.tmp.2021.100895. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ihalanayake R. Economic effects of tourism tax changes in Australia: Empirical evidence from a computable general equilibrium model. Tourism Economics. 2012; 18 (1):181–202. [ Google Scholar ]
• IMF . nternational Monetary Fund (IMF); 2020. World Economic Outlook: A Long and Difficult Ascent. https://www.imf.org/en/Publications/WEO/Issues/2020/09/30/world-economic-outlook-october-2020 [ Google Scholar ]
• Incera A., Fernandez M. Tourism and income distribution: Evidence from a developed regional economy. Tourism Management. 2015; 48 :11–20. [ Google Scholar ]
• Jensen J., Rutherford T.F., Tarr T.G. Modeling services liberalisation: The case of Tanzania. Journal of Economic Integration. 2010; 25 (4):644–675. [ Google Scholar ]
• Jung H., Thorbecke E. The impact of public education expenditure on human capital, growth, and poverty in Tanzania and Zambia: A general equilibrium approach. Journal of Policy Modeling. 2003; 25 (8):701–725. [ Google Scholar ]
• Karim M., Njoya E.T. Impact of inbound tourism in Kenyan economy: An analysis using a computable general equilibrium model. Asian Journal of Empirical Research. 2013; 3 (7):911–932. [ Google Scholar ]
• Keogh-Brown M., Wren-Lewis S., Edmundsa W., Beutels P., Smith R. The possible macroeconomic impact on the UK of an influenza pandemic. Health Economics. 2010; 19 :1345–1360. doi: 10.1002/hec.1554. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Khan A., Bibi S., Lorenzo A., Lyu J., Babar Z. Tourism and development in developing economies: A policy implication perspective. Sustainability. 2020; 12 (4):1–19. doi: 10.3390/su12041618. [ CrossRef ] [ Google Scholar ]
• Kinda R., Zidouemba P., Ouedraogo I. How could the covid-19 pandemic impact the economy of Burkina Faso? Economics Bulletin. 2020; 40 (3):2034–2046. [ Google Scholar ]
• Kinyondo A., Pelizzo R. Tourism, Development, and Inequality: The Case of Tanzania. Poverty and Public Policy. 2015; 7 (1):64–79. [ Google Scholar ]
• Kourentzes N., Saayman A., Jean-Pierre P., Provenzano D., Sahli M., Seetaram N., Volo S. Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team. Annals of Tourism Research. 2021; 88 doi: 10.1016/j.annals.2021.103197. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kweka J., Morrisey O., Blake A. The economic potential of tourism in Tanzania. Journal of International Development. 2003; 15 :335–351. [ Google Scholar ]
• Kweka J.P. Tourism and the economy of Tanzania: A CGE analysis. 2004. https://www.semanticscholar.org/paper/TOURISM-AND-THE-ECONOMY-OF-TANZANIA%3A-A-CGE-Kweka/c7d3c1f406c9d8e163aba5acd450885c261140e9
• Kyara V.C., Rahman M.M., Khanam R. Tourism expansion and economic growth in Tanzania: A causality analysis. Heliyon. 2021; 7 doi: 10.1016/j.heliyon.2021.e06966. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Laborde D., Martin W., Vos R. In: COVID-19 and global food security. Part one: Food security, poverty, and inequality. Swinnen J., McDermott J., editors. International Food Policy Research Institute (IFPRI); 2020. Poverty and food insecurity could grow dramatically as COVID-19 spreads; pp. 16–19. [ CrossRef ] [ Google Scholar ]
• Leroy de Morel L., Wittwer G., Gämperle D., Leung C. New Zealand Institute of Economic Research (NZIER) 2020. The potential local and regional impacts of COVID-19 in New Zealand with a focus on tourism. [ Google Scholar ]
• Liu A., Wall G. Planning tourism employment: A developing country perspective. Tourism Management. 2006; 27 :159–170. [ Google Scholar ]
• Mabugu R. Short-term effects of policy reform on tourism and the macroeconomy in Zimbabwe: Applied CGE analysis. Development Southern Africa. 2002; 19 (3):419–430. doi: 10.1080/03768350220150206. [ CrossRef ] [ Google Scholar ]
• Maisonnave H., Cabral F. The impact of Covid-19 on the Senegalese economy: From a gender perspective. 2021. https://portal.pep-net.org/document/download/37094
• Maliszewska M., Mattoo A., Van Der Mensbrugghe D. The potential impact of COVID-19 on GDP and trade: A preliminary assessment. The World Bank. 2020 http://documents.worldbank.org/curated/en/295991586526445673/pdf/The-Potential-Impact-of-COVID-19-on-GDP-and-Trade-A-Preliminary-Assessment.pdf [ Google Scholar ]
• Manrai L., Lascu D., Manrai A. A study of safari tourism in sub-Saharan Africa: An empirical test of tourism A-B-C (T-ABC) model. Journal of Business Research. 2020; 119 :639–651. [ Google Scholar ]
• Mayer M., Vogt L. Economic effects of tourism and its influencing factors. Zeitschrift für Tourismuswissenschaft. 2016; 8 (2):169–198. [ Google Scholar ]
• Meng X. Is a tourism subsidy the best response to the global financial crisis? A short-run CGE simulation for Singapore. Asia Pacific Journal of Tourism Research. 2012; 19 (3):325–341. [ Google Scholar ]
• Mings R. Tourism development in Latin America and related research needs. Proceedings of the Conference of Latin Americanist Geographers. 1981; 8 :336–346. [ Google Scholar ]
• Mwakalobo A., Kaswamila A., Kira A., Chawala O., Tea T. Tourism regional multiplier effects in Tanzania: Analysis of Singita Grumeti reserves tourism in the Mara region. Journal of Sustainable Development. 2016; 9 (4):44–60. [ Google Scholar ]
• Mwamwaja E.E., Mlozi S. An assessment of the impact of COVID-19 pandemic in the tourism sector in Tanzania. International Tourism and Hospitality Journal. 2020; 3 (4):1–15. [ Google Scholar ]
• NBS . National Bureau of statistics (NBS); 2018. Tanzania tourism sector survey: The 2017 international Visitors’ exit survey report. [ Google Scholar ]
• NBS . National Bureau of Statistics (NBS); 2019. Highlights on the fourth quarter (October-December) , 2019: the base year 2015 . https://www.nbs.go.tz/nbs/takwimu/na/Highlights_on_the_Q4_GDP,_2019.pdf [ Google Scholar ]
• NBS . National Bureau of Statistics (NBS); 2019. National Account Statistics: popular version 2018. https://www.nbs.go.tz/nbs/takwimu/na/National_Accounts_Statistics_Popular_Version_2018.pdf [ Google Scholar ]
• Ngowi H.P. The current global economic crisis and its impacts in Tanzania. African Journal of Business Management. 2010; 4 (8):1468–1474. [ Google Scholar ]
• Njoya E.T. Assessing the poverty impact of the COVID-19-induced tourism crisis in Tanzania: A social accounting matrix microsimulation analysis. Journal of Sustainable Tourism. 2022 doi: 10.1080/09669582.2021.2024552. [ CrossRef ] [ Google Scholar ]
• Njoya E.T., Nikitas A. The role of air transport in employment creation and inclusive growth in the global south: The case of South Africa. Journal of Transport Geography. 2020; 85 :1–15. [ Google Scholar ]
• Njoya E.T., Seetaram N. Tourism contribution to poverty alleviation in Kenya: A dynamic computable general equilibrium analysis. Journal of Travel Research. 2018; 57 (4):513–524. doi: 10.1177/0047287517700317. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• OECD . Mitigating the impact of COVID-19 on tourism and supporting recovery. OECD Publishing; 2020. [ CrossRef ] [ Google Scholar ]
• Pelizzo R., Kinyondo A. Tourism, development, and inequality: The case of Tanzania. Poverty and Public Policy. 2015; 7 (1):55–67. [ Google Scholar ]
• Pham T., Dwyer L., Su J., Ngo T. COVID-19 impacts of inbound tourism on Australian economy. Annals of Tourism Research. 2021; 88 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Polyzos S., Samitas A., Spyridou A. Ef. Tourism demand and the COVID-19 pandemic: An LSTM approach. Tourism Recreation Research. 2021; 46 (2):175–187. doi: 10.1080/02508281.2020.1777053. [ CrossRef ] [ Google Scholar ]
• Ponjan P., Thirawat N. Impacts of Thailand’s tourism tax cut: A CGE analysis. Annals of Tourism Research. 2016; 61 :45–62. [ Google Scholar ]
• Randriamamonjy J., Thurlow J. International Food Policy Research Institute (IFPRI) 2017. 2015 social accounting matrix for Tanzania: A Nexus project SAM. http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/131509/filename/131719.pdf [ Google Scholar ]
• Rogerson C. Botswana Notes and Records. Vol. 39. 2008. Developing small tourism businesses in Southern Africa; pp. 23–34. [ Google Scholar ]
• Round J. Constructing SAMs for development policy analysis: Lessons learned and challenges ahead. Economic Systems Research. 2003; 15 (2):162–183. [ Google Scholar ]
• Scheyvens R. Pro-poor tourism: Is there value beyond the rhetoric? Tourism Recreation Research. 2009; 34 (2):191–196. [ Google Scholar ]
• Sekar N., Weiss J., Dobson A. Willingness-to-pay and the perfect safari: Valuation and cultural evaluation of safari package attributes in the Serengeti and Tanzanian circuit. Ecological Economics. 2014; 97 :34–41. [ Google Scholar ]
• Sharma A. Economic impact and institutional dynamics of small hotels in Tanzania. Journal of Hospitality and Tourism Research. 2006; 30 :76–94. [ Google Scholar ]
• Sinclair T. Tourism and economic development: A survey. The Journal of Development Studies. 1998; 34 (5):1–51. [ Google Scholar ]
• Suau-Sancheza P., Voltes-Dortac A., Cugueró-Escofeta N. An early assessment of the impact of COVID-19 on air transport: Just another crisis or the end of aviation as we know it? Journal of Transport Geography. 2020; 86 :1–15. doi: 10.1016/j.jtrangeo.2020.102749. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Van Truong N., Shimizu T. The effect of transportation on tourism promotion: Literature review on application of the computable general equilibrium (CGE) model. Transportation Research Procedia. 2017; 25 :3096–3115. doi: 10.1016/j.trpro.2017.05.336. [ CrossRef ] [ Google Scholar ]
• Wade D., Mwasaga B., Eagles P. A history and market analysis of tourism in Tanzania. Tourism Management. 2001; 22 :93–101. [ Google Scholar ]
• Wamboyea E., Nyarongab P., Sergic B. What are the determinant of international tourism in Tanzania? World Development Perspectives. 2020; 17 :2–15. [ Google Scholar ]
• WEF . World Economic Forum (WEF); 2017. The travel & tourism competitiveness report 2017: Paving the way for a more sustainable and inclusive future. http://www3.weforum.org/docs/WEF_TTCR_2017_web_0401.pdf [ Google Scholar ]
• WEF . World Economic Forum (WEF); 2019. Travel and tourism competitive report 2019: Travel and tourism at a tipping point. http://www3.weforum.org/docs/WEF_TTCR_2019.pdf [ Google Scholar ]
• World Bank . The World Bank Group; 2021. Transforming tourism: Toward a sustainable, resilient, and inclusive sector. http://documents.worldbank.org/curated/en/794611627497650414/Transforming-Tourism-Toward-a-Sustainable-Resilient-and-Inclusive-Sector [ Google Scholar ]
• World Bank . The World Bank; 2021. World development indicators. https://databank.worldbank.org/source/world-development-indicators/ [ Google Scholar ]
• World Tourism Organization and International Labour Organization . World Tourism Organization (UNWTO) and International Labour Organization (ILO); 2013. Economic Crisis , International Tourism Decline and its Impact on the Poor. [ Google Scholar ]
• Yang Y., Zhang H., Chen X. Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak. Annals of Tourism Research. 2020; 83 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Yeoman I., Lennon J., Blake A., Galt M., Greenwood C., McMahon-Beattie U. Oil depletion: What does this mean for Scottish tourism? Tourism Management. 2007; 28 (5):1354–1365. [ Google Scholar ]
• Zenker S., Kock F. The coronavirus pandemic – A critical discussion of a tourism research agenda. Tourism Management. 2020; 81 doi: 10.1016/j.tourman.2020.104164. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Demographic factors and travel motivation among leisure tourists in Tanzania

International Hospitality Review

ISSN : 2516-8142

Article publication date: 3 April 2020

Issue publication date: 14 July 2020

To examine demographic factors and travel motivations among leisure tourists in Tanzania. Specifically by examining the influence of demographic factors on travel motivation among local and international leisure tourists in Tanzania.

Design/methodology/approach

Approach is quantitative and applied descriptive statistics, independent t -test and ANOVA.

The findings showed that age, gender and family size as demographic factors significantly influenced travel motivation among local and international leisure tourists.

Research limitations/implications

Future studies to consider different approaches including collection of data during the peak season, use qualitative method and conduct studies in other parts of the country to explore demographic factors and travel motivations of tourists.

Practical implications

To assist tourism stakeholders in their design of promotional tools to market tourism products/services to different tourists as opposed to homogeneous marketing campaigns.

Originality/value

Examined the influence of demographic factors and travel motivation among local and international leisure tourists in the context of Tanzania.

Demographic factors

Travel motivation.

• Leisure tourists

Kara, N.S. and Mkwizu, K.H. (2020), "Demographic factors and travel motivation among leisure tourists in Tanzania", International Hospitality Review , Vol. 34 No. 1, pp. 81-103. https://doi.org/10.1108/IHR-01-2020-0002

Emerald Publishing Limited

Copyright © 2020, Nasra Shoka Kara and Kezia Herman Mkwizu

Published in International Hospitality Review . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Travel motivation is commonly acknowledged as a crucial concept to most tourism professionals and theorists ( Lam and Hsu, 2006 ). Travel motivation has been known as a driving force behind understanding behavior ( Venkatesh, 2006 ). The concept of travel motivation is not new ( Pearce and Caltabiano, 1983 ). Researchers around the globe have applied travel motivation to determine individual's satisfaction level ( Snepenger et al. , 2006 ; Lemmetynen et al. , 2016 ; Celik and Dedeoglu, 2019 ; Preko et al. , 2019 ), predict leisure participation levels ( Yan and Halpenny, 2019 ), identify travel patterns ( Pearce, 1987 ; Cavagnaro and Staffieri, 2015 ), understand tourists’ travel decisions and consumption behavior ( Chang et al. , 2015 ) as well as to develop more effective strategies and policies to increase demand for tourism ( Heung et al. , 2001 ; Papatheodorou, 2006 ). The complex nature of this concept has pushed many researchers to come up with different travel motives. However, the central themes behind it revolved around push and pull factors/motives. Push and pull factors have been extensively employed to assess tourists' travel motivations ( Kanagaraj and Bindu, 2013 ; Michael et al. , 2017 ; Wijaya et al. , 2018 ).

In Tanzania, tourism plays a significant role in the country's economy and one among the crucial sectors in generating foreign exchange ( Tanzania Tourism Sector Survey, 2018 ). The sector indirectly offered 1,452,000 jobs in 2017 from 1,389,000 jobs offered in 2016 ( WTTC, 2017 ). Tourism in Tanzania generates about 17.5% of the total country's GDP and 25% of total foreign currency earnings ( Tanzania Tourism Sector Report, 2017 ). Tanzania is famously known for tourist attractions and home to the famous Roof of Africa, the Mount Kilimanjaro. Following these attractions, Tanzania has pulled thousands of international visitors from different parts of the world, thereby making the country be known as one of the competitive tourist destinations in sub-Saharan Africa ( Mkumbo, 2010 ). The WTTC (2017) projects a rising trend by 6.8% in 2027 of 2,267,000 international tourists to Tanzania.

On the other hand, the arrivals of domestic tourists to various tourist attractions in the country are not in the same pace as international travel market. Factors such as limited promotion, awareness, low income, inadequate information, media usage, marketing and service quality challenges such as infrastructure and trained staff have been reported to be among the factors affecting the performance of domestic tourism in Tanzania ( Wade et al. , 2001 ; Mariki et al. , 2011 ; Mkwizu, 2018a ; 2019 ; Mkwizu et al. , 2018 ). Some of the initiatives were done by the government to boost the travel market including setting preferential rates, establishment of the tourism training college for training purposes and introduction of intensive marketing campaigns to create awareness of tourism attractions. Despite all these efforts, there are more international tourists than locals visiting national parks. In 2018–2019, there were 731,351 international tourists compared to 464,933 locals that visited national parks ( Tanzania National Parks, 2019 ). The differences in tourist numbers can be attributed to the fact that Tanzania is the only country in the world that has allocated 25% of her land for wildlife and game-controlled reserves ( Tanzania Tourism Sector Survey, 2018 ). On the other hand, domestic tourists have been seen traveling mainly to visit their friends or relatives and sometimes they travel for leisure ( Mariki et al. , 2011 ). Therefore, there is need for more studies on whether the importance of travel motivations differs among the two groups.

Literature on consumer behavior acknowledges that travel motivation and needs are related ( Goodall, 1988 ), and this means that tourists may decide to take a vacation to satisfy their physiological needs such as food, health and learning. However, the decision of choosing a given destination to visit has been closely linked with sociodemographic characteristics. Woodside and Lysonski (1989) , Um and Crompton (1990) and Moscardo et al. (1996) are among the earliest studies that examined the role of demographic factors on tourists' destination choice with findings showing a link between demographic factors and visitors’ participation in tourism activities. For instance, increasing free time and disposable income have provided people with an opportunity to take part in outdoor activities ( Ibrahim and Cordes, 1993 ). Factors such as age and family structure have an impact on the decision of an individual to participate in leisure activities ( Foot, 2004 ) .

Demand for leisure is also affected by individuals' age and gender ( Mieczkowski, 1990 ; Collin and Tisdell, 2002 ). Collin and Tisdell (2002) found that demographic factors have a role to play in influencing visitors' participation in tourism activities as well as the selection of vacation destination. What is not known is the role that demographic factors such as age, gender and family size play in influencing tourists’ travel motivation in Tanzania. Studies that examined the influence of demographic factors on travel motivation in Tanzania are limited. Existing literature in Tanzania has mainly examined demographic factors in relation to nature-based tourism and media such as Mariki et al. (2011) and Mkwizu (2018a) . Therefore, this study intends to uncover the missing gap by examining demographic factors and travel motivation among local and international leisure tourists in Tanzania.

Furthermore, this study is important in providing insight information on various demographic factors such as age, gender and marital status in influencing tourists’ travel motivation particularly for Tanzania. The information from this study can help tourism stakeholders to segment tourists based on their demographic traits.

Literature review

Travel motivation is viewed as an internal force that arouses and pushes an individual from choosing a particular destination with the intention of getting the desired benefits and satisfaction ( Pyo et al. , 1989 ; Yoon and Uysal, 2005 ). Motivation is viewed as a sociopsychological factor that pushes an individual to a new destination and take part in leisure activities ( Iso-Ahola, 1982 ; Beard and Ragheb, 1983 ). This study defines travel motivation as an internal motive that drives a particular tourist to take a leisure trip in Tanzania.

The complex nature of travel motivation has caused many researchers to come up with different travel motives. However, a good number of them focused on push and pull factors. These dimensions have been used extensively in most of motivation studies ( Kim and Lehto, 2013 ). Due to the importance of these two factors, researchers such as Dann (1977) , Crompton (1979) , Iso-Ahola (1982) and Epperson (1983) developed different motivation dimensions based on the idea of push and pull travel motives.

Mazilu and Mitroi (2010) defined demographic factors as descriptive segmentation technique, whereby sociodemographic factors are directly involved. Examples of sociodemographic factors commonly used by tourism experts ( Ma et al. , 2018 ; Mkwizu, 2018a , 2018b ) include age, gender, family life cycle, education, income and nationality. These variables are believed to be accurate in describing tourism market and predicting travel behavior patterns ( Weaver and Oppermann, 2000 ).

Age is considered to be a crucial demographic factor by tourism stakeholders because leisure demand can effectively be predicted through visitors' age ( Mieczkowski, 1990 ). Age is reported to have positive influence on individual's desire for relaxation and nature exploration ( Ma et al. , 2018 ). According to Spence (2002) , the probability of an individual to participate in wildlife activities varies with age, meaning that the probability of activity participation increases when an individual is young and decreases as that individual grows old.

Gender is one of the major factors influencing travel demand ( Collin and Tisdell, 2002 ). The travel patterns between men and women vary based on their travel motivation. According to Collin and Tisdell (2002) , men travel more than women. Men travel for business-related activities while women do travel mainly for visiting friends and relatives and prefer taking shorter-distance trips compared to men ( Moriarty and Honnery, 2005 ). Females are reported to be highly involved in shopping and are more affected by intrapersonal or structural constraints than men ( Josiam et al. , 2005 ; Andronikidis et al. , 2008 ). Cost, time and family commitments are among limitations for women to be active in travel activities ( Scott, 2005 ; Alexandris and Carrol, 1997 ). As a result, women have been seen participating more in shopping, dining and cultural activities than outdoor activities such as skiing while men are more likely to participate in adventure activities ( Xie et al. , 2008 ).

Marital status is one of the factors that affect vacation decisions ( Kattiyapornpong and Miller, 2008 ). It is important for marketers to have such information because they can use such details to predict one's travel patterns. For instance, Lee and Bhargava (2004) found that married couples spend less time enjoying leisure than singles. This is due to the fact that married couples have social and family obligations that limit their time to undertake holiday vacation or participate in sports activities ( Henderson, 1990 ; Downward and Rasciute, 2010 ). Singles prefer shorter but frequent trips ( Biearnat and Lubowiecki-Vikuk, 2012 ). Singles are assumed to have more free time to engage in various activities compared to those with a family, for example, more time playing musical instruments, singing, dancing, watching TV and traveling for social activities ( Lee and Bhargava, 2004 ). The literature further highlights that Passias et al. (2017) found that never-married mothers have more time to spend on leisure than married mothers. In contrast, Vernon (2010) suggests that married women have more time to engage in leisure than single mothers. For the purpose of this study, age, gender and marital status were included in the analysis. The reason for these factors is due to the fact that there is limited information regarding the roles they play in influencing travel motivation of tourists in the context of Tanzania.

The Beard and Ragheb travel motivation theory

Beard and Ragheb (1983) developed the leisure motivation variables based on the idea from the work of Maslow (1970) . The leisure motivation theory contains four major travel motives, which determine satisfaction that a visitor may gain from taking part in leisure activities. The travel factors identified were: “Intellectual” – these include items such as learning and exploring; “social” – covers the desire for developing friendship and esteem of others; “competence-mastery” – involves issues such as health and fitness and lastly “stimulus avoidance” – which simply describes the desire to relax and escape the routine life. This study employs the Beard and Ragheb theory for the purpose of assessing tourist travel motivation. Beard and Ragheb's theory was chosen because since its establishment in 1983, many researchers ( Mohsin et al. , 2017 ; Albayrak and Caber, 2018 ; Jia et al. , 2018 ) have employed and validated it.

In 1983, Beard and Ragheb also noted that using leisure motivation scale (LMS) to study travel motivation is reliable due to the 32 items measuring Cronbach's alpha ranging from 0.89 to 0.91. Past scholars such as Yusof and Shah (2008) and Chen et al. (2018) have used LMS by Beard and Raghed (1983) to study motivation in tourism. For example, Chen et al. (2018) explored travel motivation for Chinese residents using LMS of 32 items to measure motivation due to its reliability and validity. Chen et al. (2018) found that there were significant differences of gender, marital status and education in leisure behaviors. This study not only used the Beard and Ragheb theory but also applied LMS by Beard and Ragheb (1983) due to its reliability and validity.

Demographic factors and travel motivation

Several researchers have examined travel motivation in relation to demographic factors. Some of these works include a work by Saayman and Saayman (2009) . Researchers examined the relationship between sociodemographic, behavioral and motivational factors for tourists that visited Addo Elephant National Park. The findings of this study revealed that tourists were motivated to travel to the national park because of the need for nature, activities, escape, attractions, photography, family and socialization. It was further pointed out that both sociodemographic and motivational factors influence visitors’ spending decisions.

Differences in travel motivation are noted in past studies such as You et al. (2000) , Kozak (2002) , Jönsson and Deonish (2008) , Kim and Prideaux (2005) , Fan et al. (2015) , Gu et al. (2015) , Albayrak and Caber (2018) and Marques et al. (2018) . The findings of these studies concluded that travel motives differ among travelers from different countries ( You et al. , 2000 ; Kim and Prideaux, 2005 ), among students from different countries ( Marques et al. , 2018 ), across various destinations and nationalities ( Kozak, 2002 ), among tourists participated in white water rafting activity ( Albayrak and Caber, 2018 ), across different forms of tourism ( Gu et al. , 2015 ) as well as those from different countries visiting the same destination ( Jönsson and Deonish, 2008 ).

Yung-Kun et al. (2015) explored factors related to tourists' motivation to visit Taiwan as well as the demographic segmentation of these foreign tourists. The results indicated that push motivation factors such as enlightenment, freedom, shopping, diverse attractions, culture connections, sport facilities and wildlife play a crucial role in the motivation of foreign tourists. These tourists were later clustered into five main motivation groups to include scenery/knowledge seekers, accessibility/expenditure seekers, relaxation/relation seekers, novelty/experience seekers, sport/service seekers based on five demographic traits (gender, age, marital status, nationality and income).

Additionally, Fan et al. (2015) compared motivation and intention of cruise passengers from different demographic profiles in China. They found that travelers from different demographic caliber differ in terms of their travel motivation. For example, singles had higher mean values for travel motivations such as discovering and exploring nature than those who were married. Researchers believed that singles have ample time and freedom to try new and exciting things compared to married travelers. Furthermore, Ma et al. (2018) examined the relationship among tourists' sociodemographic characteristics, motivation and satisfaction as a way of predicting their visitation patterns and travel behaviors to forest nature reserves in Guangdong. The findings from multiple regression analysis revealed that some of the sociodemographic factors had a role to play in influencing travel motivation. For example, age was positively correlated with travel motivation called sense of relaxation and nature exploration. However, education level negatively influenced social travel motivation.

Older people or senior travelers are motivated by the desire for novelty ( Jönsson and Deonish, 2008 ). However, a study by Luo and Deng (2008) found age negatively influenced travel motivation and that younger tourists prefer seeking for novelty compared to older travelers. A study by Mohsin (2008) was done to examine the impact of sociodemographic variables on Mainland Chinese holidaymakers who traveled to New Zealand. The overall findings of one-way ANOVA revealed that there is a significant relationship between travel motivation and demographic factors such age and educational level. The findings are supported by previous studies of Park and Mok (1998) that travel motivation varies with age. Irimias et al. (2016) conducted a study aimed at exploring demographic characteristics in influencing religious tourism behavior among 345 Hungarians who traveled for pilgrimage. It was found that their travel motives differ with age; senior travelers see educational purposes and feelings of national identity related to sacred sites as crucial travel motives while young tourists did not picture that to be of any value to their travel motives. Njagi et al. (2017) conducted a study to provide an in-depth understanding of the factors affecting travel motivation of youth travelers in Kenya. The study revealed that push factors are more crucial in influencing youth travelers in Kenya than the pull travel motives.

The overall findings from the previous studies confirmed that sociodemographic factors have a role to play in influencing tourists’ travel motivation. However, these studies focused more on push and pull factors among youth travelers in Kenya ( Njagi et al. , 2017 ) and among travelers who traveled to Taiwan ( Yung-Kun et al. , 2015 ). Furthermore, the existing studies also looked at the relationships between sociodemographic factors and travel motivation among cruise passengers who traveled to China ( Fan et al. , 2015 ), those who traveled to national parks ( Saayman and Saayman 2009 ) and those who traveled to sacred places for religious purposes ( Irimias et al. , 2016 ). From the reviewed literature, it is evident that sociodemographic factors are crucial in predicting travel patterns of tourists.

However, there are still inconclusive remarks regarding the influence of sociodemographic factors on travel motivation. For example, age was reported to be among the key factors affecting travel motivation ( Irimias et al. , 2016 ; Ma et al. , 2018 ). On the other hand, age was reported to have a negative effect on travel motivation ( Luo and Deng, 2008 ). Other demographic factors such as education were also reported to have a negative effect on travel motivation ( Ma et al. , 2018 ) while marital status was seen to be a significant factor in influencing travel motivation among cruise passengers ( Fan et al. , 2015 ). Furthermore, the existing studies such as Baniya and Paudel (2016) have examined the effects of demographic factors on travel motivation using push and pull items. Other studies in Tanzania ( Wade et al. , 2001 ; Mariki et al. , 2011 ; Mkwizu, 2018a ; 2018b ; 2019 ; Mkwizu et al. , 2018 ) have focused on nature-based tourism, history, market analysis and media. Therefore, this study specifically intended to examine the extent to which demographic factors such as age, gender and marital status influenced travel motivation among local and international leisure tourists guided by the motivation theory and scale items developed by Beard and Ragheb (1983) .

Methodology

Research instrument.

The research questionnaire was divided into two major parts. The first part covered general information about the respondents. Demographic information such as age, gender, marital status and family size. This section composed of six questions. The second part comprised information related to tourists' travel motivation. Respondents were asked to rank the list of travel motivation statements according to their level of importance, indicating whether those statements describe their travel motivation on a Likert scale of 1 ( Not important at all ) to 7 ( Extremely important ). Examples of travel motivation items were to learn things around me, to challenge my abilities and to relax mentally. This study employed Likert scale developed by Kozak (2002) , who highlighted that Likert scale is appropriate to be used in tourist-based studies. This study adopted the shortest version of LMS, which consists of 32 items to measure different travel motives because of its Cronbach's alpha reliability ranging from 0.89 to 0.91 as pointed out by Beard and Ragheb (1983) . The shortest version is appropriate to be used in a research constrained by time and can be applied within less time compared to 48 items from the original scale ( Beard and Ragheb, 1983 ).

Sampling design

A convenience sampling technique was adopted to get the appropriate sample for the study. Ferber (1977) noted that convenience sampling as one form of nonprobability sampling can reduce the impact of nonrandom convenience sampling by making sure that the generated findings are a true representative of the population. Additionally, convenience sampling is one among the appropriate sampling technique s to be used when collecting data from the actual tourist settings ( Madrigal and Kahle, 1994 ).

Data collection

This study used a quantitative approach and survey strategy as the research design. Before collection of data, the survey instrument was pretested by distributing the questionnaires to 50 international tourists found on the beaches of Zanzibar and Pemba islands. Respondents were randomly and conveniently selected to take part in the study. The pretesting exercise was done to assess the survey suitability, readability, eliminate any vague items and determine the response rate. Data was collected from January 2017 to May 2017. A self-administered open-ended questionnaire was employed to 300 local and international tourists who traveled to and within Tanzania for leisure. Tourists at the Julius Nyerere International Airport lounges and those on the beaches of the islands of Zanzibar and Pemba were conveniently approached and asked to take part in the study. The decision to take part in the study was left entirely to tourists. Those who agreed to participate in the study were given a survey questionnaire to fill in.

Data analysis

The collected data was analyzed using the aid of a Social Science Statistical Package (SPSS) version 20. This study selected SPSS, which has descriptive statistics such as frequencies and percentages in order to avail demographic characteristics of the respondents. In addition, the independent sample t -test was used to test the differences in travel motivation among local and international tourists. ANOVA assisted this study to test the effect of the independent variable (demographic factors) on the dependent variable (travel motivation). Data was cleaned first to check whether there was missing data, outliers and determine the data distribution pattern before analysis. Cronbach's alpha coefficients were employed for purposes of examining internal data consistency. Content, construct, convergent and discriminant validities were tested using CFA.

Respondents' demographic characteristics

Out of 300 surveys from each group, only 250 from each group were recognized as a useable survey, representing a token useable return rate of 83%. The overall descriptive statistics from Table 1 shows that most tourists from each group were between the ages of 18 and 30 (45.6% for internationals and 49.2% for locals), and less than 10% were covered by the senior tourists (4.4% for internationals and 6% for locals). The gender distribution showed that majority of international tourists were males (61.2%) and also for local tourists most were males (61.8%). Over 50% of all tourists had a university education and employed in different fields. On marital status, 53.2% of all the international tourists were married while 49% of all locals were married.

The findings in Table 1 further indicate that 47% of internationals and 51.2% of all locals were singles. On family size distribution, the majority of international tourists have three children and above while 40% of all locals proved to have less than three children. This suggests that the sampled respondents were mostly young educated male tourists who are employed. In addition, the differences between the international and local tourists are noted in marital status.

Furthermore, Table 2 indicates that the largest group of international tourists was from South Africa (10%) followed by Australia (8.8%) and Kenya (8%). There were very few international tourists from countries such as Bangladesh, Brazil, Cameroon and Zurich. These results suggest that the young educated male international tourists were mostly from South Africa.

Reliability results

The alpha coefficient for the total scale was 0.933 and the alpha values for each of the subscales ranged from 0.880 to 0.907, which are above the acceptable threshold (0.70) as suggested by Hair et al. (1998) . The summary of the results is presented in Table 3 .

Validity results

All 32 travel motivation items were subjected to CFA for validity testing as it is presented in Table 4 . Content validity for the observed items was tested for consistency, easy of understanding and appropriateness by members of the academic staff together with tourist experts. Construct validity was examined using composite reliability (CR) and average variance explained (AVE). The overall findings indicate that CR and AVE surpassed the threshold values of 0.70 and 0.50, respectively ( Yap and Khong, 2006 ). Therefore, it can be concluded that the indicators for all constructs met the reliability thresholds and thus qualified for further analyses. Convergent validity indicated that the standardized factor loadings for all the items were above the acceptable range of 0.5 as indicated by Tabachnick and Fidell (2007) . In this study, all the CR and AVE were above the recommended value of 0.7 and 0.5 respectively. Discriminant validity was assessed using Fornell and Larcker’s approach of 1981. In order to achieve discriminant validity, AVE of each construct was compared with the shared variance between two constructs. For all the items, the AVE was higher than the shared variance (MSV). The results indicated that all the constructs had acceptable discriminant validity as presented in Table 4 .

Assumptions guiding independent t -test

Data normality.

Before testing for the differences in travel motivation among the tourists, data normality was performed using descriptive statistics. Skewness and kurtosis values were used to determine data normality. Meyers et al. (2006) highlighted that if the values of skewness and kurtosis range within ± 1.00, these are evidence of data normality. Pallant (2011) advised that when one is dealing with large enough sample sizes (e.g. 30+), the violation of normality assumption may not cause any significant problems. For this study, the skewness and kurtosis values were within the cutoff points as was highlighted by Meyers et al. (2006) and Pallant (2011) .

Differences in the importance of travel motivation among international and local leisure tourists

An independent sample t -test was conducted to test whether the importance of travel motivation differs among international and local tourists. This meant comparing travel motivation mean scores for international and local tourists. First of all Levene's test was performed to see whether there was equal variance in the data set. The overall results show that this assumption was met in eight travel motivation items ( p  ≥ 0.005) while for the rest of the travel motivation items, the assumption was violated as it is presented in Table 5 , Table 6 , Table 7 and Table 8 . The results in Table 5 , Table 6 , Table 7 and Table 8 indicate that there was significant difference in scores for travel motivation among international and local leisure tourists. In Table 5 , the findings show that local tourists had higher mean values than international tourists for travel motivation (intellectual motivation) such as to learn about myself ( M  = 5.67, SD = 1.288), to explore new ideas ( M  = 5.73, SD = 1.294), to expand my knowledge ( M  = 6.05, SD = 1.136), to be creative ( M  = 5.68, SD = 1.494), to use my imagination ( M  = 5.22, SD = 1.757) and to satisfy my curiosity ( M  = 5.81, SD = 1.265).

In Table 6 , the findings show that local tourists had higher mean values compared to international tourists for travel motivation (social motivation) such as to build friendship with others ( M  = 5.70, SD = 1.353), to interact with others ( M  = 5.66, SD = 1.428), to develop close friendships ( M  = 5.47, SD = 1.573), to reveal my thoughts ( M  = 5.11, SD = 1.657), to be socially competent and skillful ( M  = 5.66, SD = 1.425), to gain a feeling of belonging ( M  = 5.62, SD = 1.387) and to gain others' respect ( M  = 5.24, SD = 1.827).

Table 7 indicates that local tourists had higher mean values than international tourists for travel motivation (mastery competency motivation) such as to be active ( M  = 5.76, SD = 1.296), to develop physical skills and abilities ( M  = 5.59, SD = 1.375), to keep in shape physically ( M  = 5.39, SD = 1.702), to use my physical abilities ( M  = 5.28) and to develop physical fitness ( M  = 5.21, SD = 1.685). The remaining mastery competency motives had no significant differences.

Table 8 reveals that local tourists had higher mean values for travel motivation (stimulus avoidance motivation) such as to calm down ( M  = 4.89, SD = 1.674), to be alone ( M  = 3.32, SD = 2.064), to relax physically ( M  = 5.39, SD = 1.499), to relax mentally ( M  = 5.63, SD = 1.426), to rest ( M  = 5.53, SD = 1.508), to relieve stress and tension ( M  = 5.48, SD = 1.506) as well as to unstructure my time ( M  = 5.48, SD = 1.506) compared to international tourists. The remaining stimulus avoidance motives had no significant differences.

Differences in travel motivation among tourists by age, gender and family size

Univariate ANOVA tests the interaction between each dependent variable with an independent variable; in short, ANOVA explains changes in the dependent variable, which are caused by the interaction between the independent variables. First, multivariate tests were performed to assess whether there is a significant effect between independent and dependent variables. Second, univariate ANOVA was applied to examine the effect of independent variables on specific dependent variable. Previous scholars have also used ANOVA in examining demographic factors with motivation such as Urosevic et al. (2016) . Using Pillai's trace results in Table 9 indicated that there was significant effect between travel motivation across age F (96.000) = 1.396, p  = 0.008, across gender F (32.000) = 2.005, p  = 0.001, across family size F (32.000) = 2.610, p  = 0.000, across the interaction between age and family F (96.000) = 1.154, p  = 0.023 as well as the interaction between age, gender and family size F (96.000) = 1.514, p  = 0.001.

A separate ANOVA shown in Table 10 was performed to each travel motivation at alpha level of 0.005, and it was found that there were significant difference s among age groups on the need to develop physical skills and abilities F (312.594) = 4.972, p  = 0.002 while for males and females results show the desire to explore new ideas among age groups F (18.906) = 4.451, p  = 0.035 and the desire to discover new things F (16.081) = 3.899, p  = 0.049.

Furthermore, the results indicated that desire to develop physical skills and abilities was significantly different among tourists who have small and large family size F (156.811) = 22.428, p  = 0.000. Other differences were reflected on travel motivation such as the desire to develop physical fitness F (167.625) = 18.772, p  = 0.000 as well as to unstructure my time F (150.424) = 14.955, p  = 0.000.

This study also examined the contribution of the interaction effects of the independent variables on the dependent variable. Table 10 shows that the interaction between age and family size was significant to travel motivation such as to relieve stress and tension F (319.051) = 6.112, p  = 0.000, to develop physical fitness F (320.517) = 5.695, p  = 0.001, ŋ 2  = 0.034, to unstructure my time F (318.159) = 5.386, p  = 0.001, as well as to use my physical abilities F (311.260) = 3.322, p  = 0.020. Additionally, the interaction effect between age, gender and family size was significant to travel motivation such as to satisfy my curiosity F (35.223) = 2.693, p  = 0.046, as well as to develop close friendships with others F (38.729) = 2.634, p  = 0.049.

Discussions of findings

This study reveals that leisure tourists from Australia, Kenya, South Africa, Germany, France, the United Kingdom and United States were motivated to travel to the country with the intention of discovering and learning new things. Furthermore, similar groups of tourists were extremely motivated to visit Tanzania for the sake of relaxing mentally, revealing stress and tensions of their daily routine activities. The results imply that leisure tourists may have more than a single travel motive when visiting a particular destination. These findings support the idea developed by Crompton (1979) that tourists' motivations are multiple and because of that they may have different reasons of taking either domestic or international trips ( Mayo and Jarvis, 1981 ). Researchers also add that some people take trips not only to fulfill their physiological desires (food, climate and health) but also to satisfy their psychological needs.

Furthermore, the study also found that tourists from the United Kingdom and United States had strong views that they were motivated to visit the country for social reasons such as building friendship with others. This can be explained by differences in tourists' culture. It has been identified that there are motivational differences between nationalities ( Kozak, 2002 ). Culture associated with nationality has been extensively acknowledged to be one among the crucial factors differentiating individuals’ attitudes, beliefs and behaviors ( Chen, 2000 ). National culture can be employed to reveal variations in the social behavior of different nationalities, especially in international settings such as tourism experiences ( Kim et al. , 2002 ). The findings of this study confirmed the results reported by Özdemir and Yolal (2016) that Americans and British people prefer to interact and socialize with other tourists when they travel. Additionally, Kozak (2002) pointed out that British tourists enjoy mixing themselves and having fun with other tourists when they travel. It seems that Tanzania is attracting tourists who have psychocentric personality. Individuals of this nature prefer visiting familiar places, having fun and relaxing when visiting new destinations ( Plog, 1974 ).

Surprisingly, this study found that tourists, mainly from Kenya and South Africa, were motivated strongly to travel to the country for the intention of competing and being good at participating in leisure activities. This can be explained by the differences in the level of novelty seeking among tourists. Novelty seeking is one among the key reasons why tourists travel to new destinations ( Dayour and Adongo, 2015 ). The findings of this study show that there is a possibility that tourists from Kenya and South Africa are sensation seekers. Individuals of this nature are risk takers, and this is why they prefer to travel to unfamiliar destinations ( Pizam et al. , 2004 ). Generally, tourists are attracted differently to different tourist attractions, and this is because they have different levels of tolerance for tourism experiences. Some people choose destinations where they can unwind their daily routine life while others look for destinations that can offer adventure life. The choice of a destination can sometimes be linked to tourists' personality traits. The findings of this study imply that Kenyans and South Africans may be allocentrics. Individuals of this caliber are usually seeking for arousal from unexpected and surprising stimuli ( Ryan, 1997 ), they are outgoing, confident, relatively anxiety free, like to feel in control, prefer to visit new destinations, desire to explore the world around them and are moderately risk takers ( Plog, 1973 , 1974 ).

This study found that there was no significant differences in travel motivation among leisure tourists who are single and those who are married. However, a minor difference was revealed on intellectual travel motives to single leisure tourists. It was revealed that single leisure tourists were highly motivated to travel to Tanzania for intellectual purpose. This finding is consistent with a study by Fan et al. (2015) that single people place higher value when it comes to discovering and learning new things compared to married ones. The finding of this study is not surprising since Tanzania is blessed with multiple tourist attractions ranging from game reserves, controlled conservation areas and national parks ( URT, 2014 ). Other attractions include Mount Kilimanjaro, museums, historical sites and buildings. Following these attractions, it is not surprising to see single leisure tourists travel to the country for intellectual reasons.

The findings further indicated that married leisure tourists were more motivated to travel to the country, mainly by their desire to unwind their daily life's hustle. This could be due to the fact that married couples spend less time enjoying leisure than singles. In addition, married couples have social and family obligations that limit their time to undertake holidays ( Henderson, 1990 ) or participate in learning activities as singles. For them, escaping travel motive makes sense since they have been experiencing routine hectic daily life; therefore, it is understandable to see them ranking this motive important. This finding somehow corroborates the views of Leonard and Onyx (2009) that relaxation and escape motivations are two key psychological motives that drive people to take overseas trips. The desire to take a vacation is closely associated with the desire to escape ( Jarvis and Peel, 2010 ). Therefore, tourists often choose to take a vacation to a new destination with the intention of breaking from the daily routine life of home and work ( Kim and Ritchie, 2012 ). The break gives people an opportunity to refresh their minds by taking active role in nonroutine leisure activities ( Ritchie et al. , 2010 ) as well as offering a platform for them to liberate themselves from tension and anxiety.

Furthermore, the study revealed that married leisure tourists traveled to the country for social reasons. This finding is somehow consistent with the study by Passias et al. (2017) that married mothers prefer to spend quality time with their children by engaging themselves in both active and social leisure compared to single mothers. Generally, tourism offers opportunity to bring people of different cultural backgrounds together ( Brown and Lehto, 2005 ), but also offers avenue for them to meet and communicate with others ( Dayour, 2013 ). This study also found that married leisure tourists had higher mean scores for mastery competency travel motives compared to singles. This finding implies that may be Tanzania attracts married leisure tourists who are sensational seekers because tourists differ in the way they consume and obtain novel experience ( Lee and Crompton, 1992 ). Tourists who are high sensational seekers prefer to engage in adventure activities such as scuba diving ( Heyman and Rose, 1980 ) as well as mountain climbing ( Robinson, 1985 ). This group also prefers to travel to new places or meeting new people ( Zuckerman, 1979 ). This finding can be supported by the fact that Tanzania is endowed with more than eight known mountains that attract international tourists from all over the world. Moreover, the country is surrounded with both sandy and clean beaches that offer opportunity for tourists to take part in scuba diving and other water sports activities.

Therefore, the discussion of results for this study has theoretical, practical and policy implications, which are further highlighted in the implications section of this paper.

Conclusions

Based on the findings and discussions, this study can conclude that in examining demographic factors and travel motivation among leisure tourists, there are influential factors. The demographic factors that influence travel motivation (intellectual, social, mastery competency and stimulus avoidance) among local and international leisure tourists in the context of Tanzania are age, gender and family size.

Implications

Theoretical implication.

The overall findings from this study imply that theoretically, the Beard and Ragheb leisure motivation theory and scale can be used to determine tourists’ travel motives in Tanzania. Age, gender and family size significantly influenced intellectual, social, mastery competency and stimulus avoidance motives among local and international leisure tourists.

Practical implication

From a practical implication, the differences in travel motivation among tourists are not homogeneous; therefore, they are not supposed to be treated equally. What is important to tourists from South Africa may not be important to tourists from other countries. Therefore, the government of Tanzania through the Ministry of Tourism and Natural Resources (MNRT) and Tanzania Tourists Board (TTB) should make sure that they promote Tanzania as a destination for people to discover new things, hence attract tourists from South Africa, Kenya, Australia, Germany as well as tourists from France. Furthermore, Tanzania can also be segmented as a friendly and social destination as this will attract tourists from the United States and the United Kingdom. Additionally, destination managers need to make use of the existing attractions such as mountains, beaches, national parks and game reserves to position the country as an adventurous destination. This can help to attract more tourists from Kenya and South Africa.

Policy implication

From a policy perspective, the government, destination marketers, policymakers and tourism stakeholders should make use of the tourists' marital status data because such data can develop better promotion campaigns that match their travel motives. For example, single tourists had higher mean value for intellectual travel motives. This implies that tourist attractions such as museums, historical sites, rock paintings, old town and old buildings can be used to segment this target group. Since singles travel more and spend more time enjoying leisure than married couples, then it would be better for destination managers as well as policymakers to use this opportunity to position the country as a destination that helps tourists to discover new things. On the other hand, married tourists were reported to have higher mean values for most of mastery competency and social and stimulus avoidance travel motives. This implies that the destination managers should advertise tourist activities such as boat cruising, shopping, swimming, as well as beach sports activities for this group. These activities will help them to meet other people, to relax near the sandy beaches as well as to take part in various adventurous games.

Limitations and suggestions for further studies

This study examined travel motivation differences among leisure tourists who were married and those who were single. It did not cover widowers and those who were divorced. Focus was on international tourists who traveled to Northern tourist circuit and islands of Zanzibar and Pemba for leisure. Therefore, the results from the study may not be generalized beyond the selected population. This geographically limited survey may produce different results and conclusions in terms of the magnitude and the strength of relationships among variables. Tourists who visited other circuits (Southern tourist circuit) may have different opinion preferences regarding the importance of travel motives. Replication of similar studies in other tourist circuits should be done to see whether similar findings could be generated.

Additionally, this study employed nonprobability sampling. Therefore, this may affect the external validity. Other studies should try to adopt probability sampling design so as to avoid this problem. Furthermore, the data collection was done between January and May, which is the low season. Thus, the findings of this study are limited to this particular period. Therefore, the tourists who travel in different seasons, for instance, high peak season, might have different opinions regarding the importance of their travel motives. In tourism, seasonality limits the generalization of the study findings and should always be taken into consideration in the interpretation stage. Future research should conduct similar studies in different seasons to overcome this limitation. The obtained results can then be compared to identify similarities and differences between them. Also, the generated findings can be used to validate the findings of this study.

Tourists’ demographic characteristics (age, gender, education, occupation, marital status and family size )

Independent t -test results for intellectual motivation (IL) among tourists

Tests between subjects effects for age, gender and family size on travel motivation

Albayrak , T. and Caber , M. ( 2018 ), “ A motivation-based segmentation of holiday tourists participating in white-water rafting ”, Journal of Destination Marketing and Management , Vol. 9 , pp. 64 - 71 .

Alexandris , K. and Carrol , B. ( 1997 ), “ Demographic differences in the perception of constraints on recreational sports participation: result from a study in Greece ”, Leisure Studies , Vol. 16 , pp. 107 - 125 .

Andronikidis , A. , Vassiliadis , C. and Masmanidis , T. ( 2008 ), “ Evaluation of the compatibility degree of constraint variables for the demand of ski center services ”, Journal of Vacation Marketing , Vol. 14 No. 3 , pp. 211 - 220 .

Baniya , R. and Paudel , K. ( 2016 ), “ An analysis of push and pull travel motivations of domestic tourists in Nepal ”, Journal of Management and Development Studies , Vol. 27 , pp. 16 - 30 .

Beard , J. and Ragheb , M. ( 1983 ), “ Measuring leisure motivation ”, Journal of Leisure Research , Vol. 15 , pp. 219 - 228 .

Biernat , E. and Lubowiecki-Vikuk , A. ( 2012 ), “ Tourist activity among urban singles in view of socio-demographic factors. Leisure and recreation ”, Studies in Physical Culture and Tourism , Vol. 19 No. 2 , pp. 86 - 93 .

Brown , S. and Lehto , X. ( 2005 ), “ Travelling with a purpose: understanding the motives and benefits of volunteer vacationers ”, Current Issues in Tourism , Vol. 8 No. 6 , pp. 479 - 496 .

Cavagnaro , E. and Staffieri , S. ( 2015 ), “ A study of students' travelers values and needs in order to establish future patterns and insights ”, Journal of Tourism Futures , Vol. 1 No. 2 , pp. 94 - 107 .

Celik , S. and Dedeoglu , B.B. ( 2019 ), “ Psychological factors affecting the behaviour intention of the tourist visiting Southeastern Anatolia ”, Journal of Hospitality and Tourism Insights , Vol. 2 No. 4 , pp. 425 - 450 .

Chang , L. , Yeh , N. and Tung , T. ( 2015 ), “ How do motivation, pre-visit information search, and destination image affect post visit behaviour intentions?, the case of an Island destination ”, European Journal of Tourism Research , Vol. 9 , pp. 8 - 23 .

Chen , J. ( 2000 ), “ Cross-cultural differences in travel information acquisition among tourists from three pacific-rim countries ”, Journal of Hospitality and Tourism Research , Vol. 24 No. 2 , pp. 239 - 251 .

Chen , M. , Xue , S. and Shi , Y. ( 2018 ), “ Leisure activities and leisure motivations of Chinese residents ”, PloS One , Vol. 13 No. 11 , pp. 1 - 12 .

Collins , D. and Tisdell , C. ( 2002 ), “ Gender and differences in travel life cycles ”, Journal of Travel Research , Vol. 41 , pp. 133 - 143 .

Crompton , J. ( 1979 ), “ Motivations for pleasure vacation ”, Annals of Tourism Research , Vol. 6 No. 4 , pp. 408 - 424 .

Dann , M. ( 1977 ), “ Anomie ego- enhancement and tourism ”, Annals of Tourism Research , Vol. 4 No. 4 , pp. 184 - 194 .

Dayour , F. ( 2013 ), “ Are backpackers a homogeneous group? A study of Backpackers' motivations in the cape coast-Elmina conurbation, Ghana ”, European Journal of Tourism, Hospitality and Recreation , Vol. 4 No. 3 , pp. 69 - 94 .

Dayour , F. and Adongo , C. ( 2015 ), “ Why they go there: international tourists motivations and revisit intention to Northern Ghana ”, American Journal of Tourism Management , Vol. 4 No. 1 , pp. 7 - 17 .

Downward , P. and Rasciute , S. ( 2010 ), “ The relative demands sports and leisure in England ”, European Sports Management Quarterly , Vol. 10 No. 2 , pp. 189 - 214 .

Epperson , A. ( 1983 ), “ Why people travel? ”, Journal of Physical Education, Recreation and Dance , Vol. 4 , pp. 53 - 54 .

Fan , X. , Qiu , H. , Hsu , C. and Liu , Z. ( 2015 ), “ Comparing motivations and intentions of potential cruise passengers from different demographic groups: the case of China ”, Journal of China Tourism Research , Vol. 11 No. 4 , pp. 461 - 480 .

Ferber , R. ( 1977 ), “ Research by convenience: editorial ”, Journal of Consumer Research , Vol. 4 , pp. 57 - 58 .

Foot , D. ( 2004 ), “ Leisure futures: a change in demography? ”, in Weiermair , K. and Mathies , C. (Eds), The Tourism and Leisure Industry: Shaping the Future , The Haworth Hospitality Press , pp. 21 - 33 .

Goodall , B. ( 1988 ), “ How tourists choose their holidays: an analytical framework ”, in Goodall , B. and Ashworth , G. (Eds), Marketing in the Tourism Industry: The Promotion of Destination Regions , International Travel, International Trade , London , pp. 1 - 10 .

Gu , X. , Lewis , B. , Li , Y. , Wang , Z. , Sheng-nan , W. and Li-Min , D. ( 2015 ), “ Travel motivations of domestic tourists to Changbai Mountain biosphere reserve in Northerneastern China: a comparative study ”, Journal of Mountain Science , Vol. 12 No. 6 , pp. 1582 - 1597 .

Hair , J. , Rolph , E. , Anderson , E. , Tatham , R. and Black , W. ( 1998 ), Multivariate Data Analysis , 5th ed. , Prentice Hall , Upper Saddle River, NJ .

Henderson , K. ( 1990 ), “ Anatomy is not destiny: a feminist analysis of the scholarship on women's leisure ”, Leisure Sciences , Vol. 12 , pp. 229 - 236 .

Heung , V.C.S. , Qu , H. and Chu , R. ( 2001 ), “ The relationship between vacation factors and socio-demographic and travelling characteristics: the case of Japanese leisure travellers ”, Tourism Management , Vol. 22 No. 3 , pp. 259 - 269 .

Heyman , S. and Rose , K. ( 1980 ), “ Psychological variables affecting scuba performance ”, in Nadeau , C. , Halliwell , W. , Newell , K. and Roberts , G. (Eds), Psychology of Motor Behaviour and Sport , Human Kinetics Press , Champaign, IL .

Ibrahim , H. and Cordes , K. ( 1993 ), Outdoor Recreation , Brown and Benchmark Publishers , Madison, WI .

Irimias , A. , Mitev , A. and Michalko , G. ( 2016 ), “ Demographic characteristics influencing religious tourism behaviour: evidence form a Central-Eastern-European country ”, International Journal of Religious Tourism and Pilgrimage , Vol. 4 No. 4 , p. 3 .

Iso-Ahola , S. ( 1982 ), “ Toward a social psychology theory of tourism motivation ”, Annals of Tourism Research , Vol. 12 , pp. 256 - 262 .

Jarvis , J. and Peel , V. ( 2010 ), “ Flash packing in Fiji: reframing the global nomad in a developing destination ”, in Hannam , K. and Diekmann , A. (Eds), Beyond Backpacker Tourism: Mobilities and Experiences , Channel View Publications , Bristol .

Jia , K.Y. , Rahman , S. , Kassim , N.M. and Shabbir , M.S. ( 2018 ), “ The influence of sensation seeking, travel motivation and social media networks towards risk perception among Malaysians to travel internationally ”, International Journal of Engineering and Technology , Vol. 7 Nos 4/25 , pp. 348 - 356 .

Jönsson , C. and Deonish , D. ( 2008 ), “ Does nationality, gender, and age affect travel motivation? A case of visitors to the Caribbean Island of Barbados ”, Journal of Travel and Tourism Marketing , Vol. 25 Nos 3-4 , pp. 398 - 408 .

Josiam , B. , Kinley , T. and Kim , Y. ( 2005 ), “ Involvement and the tourist shopper: using the involvement construct segment the American tourist shopper at the mall ”, Journal of Vacation Marketing , Vol. 11 No. 2 , pp. 135 - 154 .

Kanagaraj , C. and Bindu , T. ( 2013 ), “ An Analysis of push and pull travel motivations of domestic tourists to Kerala ”, International Journal of Management and Business Studies , Vol. 3 No. 2 , pp. 112 - 118 .

Kattiyapornpong , U. and Miller , K.E. ( 2008 ), “ A practitioner’s report on the interactive effects of socio-demographic barriers to travel ”, Journal of Vacation Marketing , Vol. 14 No. 4 , pp. 357 - 371 .

Kim , S. and Lehto , X. ( 2013 ), “ Projected and perceived destination brand personalities: the case of South Korea ”, Journal of Travel Research , Vol. 52 , pp. 117 - 130 .

Kim , S. and Prideaux , B. ( 2005 ), “ Marketing Implications arising from a comparative study of international pleasure tourist motivations and other travel related characteristics of visitors to Korea ”, Tourism Management , Vol. 26 No. 3 , pp. 347 - 357 .

Kim , S. , Prideaux , B. and Kim , S. ( 2002 ), “ A cross-sectional study on casino guests as perceived by casino employees ”, Tourism Management , Vol. 23 No. 5 , pp. 511 - 520 .

Kim , J. and Ritchie , B. ( 2012 ), “ Motivation-based typology: an empirical study of golf tourists ”, Journal of Hospitality and Tourism Research , Vol. 36 No. 2 , pp. 251 - 280 .

Kozak , M. ( 2002 ), “ Comparative analysis of tourist motivations by nationality and destinations ”, Tourism Management , Vol. 23 No. 3 , pp. 221 - 232 .

Lam , T. and Hsu , C.H.C. ( 2006 ), “ Predicting behavioral intention of choosing a travel destination ”, Tourism Management , Vol. 27 No. 4 , pp. 589 - 599 .

Lee , T. and Crompton , J. ( 1992 ), “ Measuring novelty seeking in tourism ”, Annals of Tourism Research , Vol. 19 No. 4 , pp. 732 - 751 .

Lee , Y. and Bhargava , V. ( 2004 ), “ Leisure time. Do married and single individuals spend it differently? ”, Family and Consumer Sciences Research Journal , Vol. 32 , pp. 254 - 274 .

Lemmetyinen , A. , Dimitrovski , D. , Nieminen , L. and Pohjola , T. ( 2016 ), “ Cruise destination brand awareness as a moderator in motivation-satisfaction relation ”, Tourism Review , Vol. 71 No. 4 , pp. 245 - 258 .

Leonard , R. and Onyx , J. ( 2009 ), “ Volunteer tourism: the interests and motivations of grey nomads ”, Annals of Leisure Research , Vol. 12 Nos 3/4 , pp. 315 - 332 .

Luo , Y. and Deng , J. ( 2008 ), “ The new environmental paradigm and nature-based tourism motivation ”, Journal of Travel Research , Vol. 46 , pp. 392 - 402 .

Ma , A. , Chow , A. , Cheung , L. , Lee , K. and Liu , S. ( 2018 ), “ Impacts of tourists' socio-demographic characteristics on the travel motivation and satisfaction: the case of protected areas in South China ”, Sustainability , Vol. 10 No. 10 , p. 3388 .

Madrigal , R. and Kahle , L. ( 1994 ), “ Predicting vacation activity preferences on the basis of value system segmentation ”, Journal of Travel Research , Vol. 32 No. 3 , pp. 22 - 28 .

Mariki , S.B. , Hassan , S.N. , Maganga , S.L.S. , Modest , R.B. and Salehe , F.S. ( 2011 ), “ Wildlife-based domestic tourism in Tanzania: experiences from the northern tourist circuit ”, Ethiopian Journal of Environment Studies and Management , Vol. 4 No. 4 , pp. 62 - 73 .

Marques , C. , Mohsin , A. and Lengler , J. ( 2018 ), “ A multinational comparative study highlighting students' travel motivations and touristic trends ”, Journal of Destination Marketing and Management , Vol. 10 , pp. 87 - 100 .

Maslow , A. ( 1970 ), Personality and Motivation , Harper and Row , New York .

Mayo , E. and Jarvis , L. ( 1981 ), The Psychology of Leisure Travel: Effective Marketing and Selling of Travel Services , CBI Publishing Company , Boston .

Mazilu , M. and Mitroi , S. ( 2010 ), “ Demographic, social, economic and geographic features shaping factors of the tourist market ”, Romanian Economic and Business Review , Vol. 5 No. 1 , pp. 159 - 166 .

Meyers , S. , Gamst , G. and Guarino , A. ( 2006 ), Applied Multivariate Research: Design and Interpretation , SAGE Publications , Thousand Oaks, London .

Michael , N. , Wien , C. and Reisinger , Y. ( 2017 ), “ Push and pull escape travel motivations of Emirati nationals to Australia ”, International Journal of Culture, Tourism and Hospitality Research , Vol. 11 No. 3 , pp. 274 - 296 .

Mieczkowski , Z. ( 1990 ), The World Trends in Tourism and Recreation , Peter Lang , New York .

Mkumbo , P. ( 2010 ), “ Tourism distribution channels in the northern tourist circuit of Tanzania ”, Masters' thesis , Victoria University of Wellington , pp. 1 - 182 , available at: https://core.ac.uk/download/pdf/41336564.pdf ( accessed 2 January 2020 ).

Mkwizu , K.H. ( 2018a ), “ Analysis of sources of information and income of domestic tourists to naitonal parks in Tanzania ”, ATLAS Tourism and Leisure Review , Vol. 2018 No. 2 , pp. 28 - 43 .

Mkwizu , K.H. ( 2018b ), “ The influence of television advertising on domestic tourism: a case of southern tourist attractions in Tanzania ”, available at: http://repository.out.ac.tz/1878/ ( accessed 4 January 2020 ) .

Mkwizu , K.H. , Matama , R. and Atuzarirwe , C. ( 2018 ), “ Media and education: domestic tourists' perspective ”, in Ahmed , Z.U. , Yajid , M.S.A. and Raj , S. (Eds), Defining the Frontiers of Global Business Research across Emerging Countries , McGraw Hill Education , Chennai , pp. 257 - 275 .

Mkwizu , K.H. ( 2019 ), “ Digital marketing and tourism: opportunities for Africa ”, International Hospitality Review , doi 10.1108/IHR-09-2019-0015 .

Mohsin , A. ( 2008 ), “ Analysis of Chinese travellers' attitudes toward holidaying in New Zealand: the impact of socio-demographic variables ”, Journal of Hospitality and Leisure Marketing , Vol. 16 Nos 1/2 , pp. 21 - 40 .

Mohsin , A. , Lengler , J. and Chaiya , P. ( 2017 ), “ Does travel interest mediate between motives and intention to travel? A case of young Asian travelers ”, Journal of Hospitality and Tourism Management , Vol. 31 , pp. 36 - 44 .

Moriarty , P. and Honnery , D. ( 2005 ), Determinants of Urban Travel in Australia , 28th Australasian Transport Research Forum (ATRF) , Melbourne .

Moscardo , G. , Morrison , A. , Pearce , P. , Lang , C. and O’Leary , J. ( 1996 ), “ Understanding vacation destination choice through travel motivation and activities ”, Journal of Vacation Marketing , Vol. 2 No. 2 , pp. 109 - 122 .

Njagi , C. , Ndivo , R. and Manyara , G. ( 2017 ), “ Understanding the travel motivation among youth travelers in Kenya: the push and pull paradigm ”, African Journal of Hospitality, Tourism and Leisure , Vol. 6 No. 1 , pp. 1 - 16 .

Özdemir , C. and Yolal , C. ( 2016 ), “ Cross-cultural tourist behaviour: an Examination of tourists' behaviour in guided tours ”, Tourism and Hospitality Research , Vol. 17 No. 3 , pp. 1 - 11 .

Pallant , J. ( 2011 ), SPSS Survival Manual: A Step by Step Guide to Data Analysis Using SPSS , 4th ed. , Allen and Unwin , Australia .

Papatheodorou , A. ( 2006 ), Managing Tourism Destinations , Edward Elgar Publishers , Northampton .

Park , J. and Mok , C. ( 1998 ), “ Travel motivational factors and their relationship to demographics: the Korean market ”, Pacific Tourism Review , Vol. 2 Nos 1/2 , pp. 109 - 120 .

Passias , E.J. , Sayer , L. and Pepin , J.R. ( 2017 ), “ Who experiences leisure deficits? Mothers marital status and leisure time ”, Journal of Marriage and Family , Vol. 79 No. 4 , pp. 1001 - 1022 .

Pearce , P.L. ( 1987 ), “ Psychological studies of tourists behaviour and experience ”, Australian Journal of Psychology , Vol. 39 No. 2 , pp. 173 - 182 .

Pearce , P.L. and Caltabiano , M.L. ( 1983 ), “ Inferring travel motivation from travelers' experiences ”, International Journal of Travel Research , Vol. 22 No. 2 , pp. 16 - 20 .

Pizam , A. , Jeong , G. , Reichel , A. , Boemmel , H. , Lusson , J. , Steynberg , L. , State-Costache , O. , Volo , S. , Kroesbacher , C. , Kucerova , J. and Mountmany , N. ( 2004 ), “ The relationship between risk taking, sensation-seeking and the tourist behaviour of young adults: a cross-cultural study ”, Journal of Travel Research , Vol. 42 No. 3 , pp. 251 - 260 .

Plog , S. ( 1973 ), “ Why destination areas rise and fall in popularity ”, Cornell Hotel and Restaurant Administration Quarterly , Vol. 14 No. 3 , pp. 13 - 16 .

Plog , S. ( 1974 ), “ Why destination areas rise and fall in popularity ”, Cornell Hotel and Restaurant Administration Quarterly , Vol. 14 No. 4 , pp. 55 - 58 .

Preko , A. , Doe , F. and Dadzie , S.A. ( 2019 ), “ The future of youth tourism in Ghana: motives, satisfaction and behaviour intentions ”, Journal of Tourism Futures , Vol. 5 No. 1 , pp. 5 - 21 .

Pyo , S. , Mihalik , B. and Uysal , M. ( 1989 ), “ Attraction attributes and motivations. A canonical correlation analysis ”, Annals of Tourism Research , Vol. 16 No. 2 , pp. 277 - 282 .

Ritchie , B. , Tkaczynski , A. and Faulks , P. ( 2010 ), “ Understanding the motivation and travel behaviour of cycle tourists using involvement profiles ”, Journal of Travel and Tourism Marketing , Vol. 27 No. 4 , pp. 409 - 425 .

Robinson , D. ( 1985 ), “ Stress seeking: selected behavioural characteristics of elite rock climbers ”, Journal of Sports Psychology , Vol. 7 , pp. 400 - 404 .

Ryan , C. (Ed.) ( 1997 ), The Tourist Experiences, A New Introduction , Cassell , London .

Saayman , M. and Saayman , A. ( 2009 ), “ Why travel motivational and socio-demographics matter in managing a national park ”, Koedoe , Vol. 51 No. 1 , pp. 1 - 9 .

Scott , D. ( 2005 ), “ The relevance of constraints researches to leisure service delivery ”, in Jackson , E.L. (Ed.), Constraints to Leisure , Venture Publishing , State College, PA , pp. 279 - 293 .

Spence , M. ( 2002 ), “ The effect of age on the probability of participation in wildlife-related activities: a birth year cohort study ”, American Journal of Agricultural Economics , Vol. 84 No. 5 , pp. 1384 - 1389 .

Snepenger , D. , King , J. , Marshall , M. and Uysal , M. ( 2006 ), “ Modeling iso-ahola's motivation theory in the tourism context ”, Journal of Travel Research ,Vol. 45 No. 2 , pp. 140 - 149 .

Tabachnick , G. and Fidell , L. ( 2007 ), Using Multivariate Statistics , 5th ed. , Person , New York .

Tanzania National Parks ( 2019 ), “ Arrival trends ”, available at: https://www.tanzaniaparks.go.tz/uploads/publications/en-1568717749-TEN%20YEARS%20ARRIVAL%20TRENDS.pdf ( accessed 29 December 2019 ).

Tanzania Tourism Sector Survey ( 2017 ), “ The 2016 international visitors' exit survey report ”, available at: http://nbs.go.tz/nbs/takwimu/trade/The2016_International%20_Visitors'_Exit_Survey_Report.pdf ( accessed 2 January 2020 ).

Tanzania Tourism Sector Survey ( 2018 ), “ The 2017 international visitors' exit survey report ”, available at: https://www.nbs.go.tz/nbs/takwimu/trade/International_Visitors_Exit_Survey_2017.pdf ( accessed 2 January 2020 ).

Um , S. and Crompton , J. ( 1990 ), “ Attitude determinants in tourism destination choice ”, Annals of Tourism Research , Vol. 17 , pp. 432 - 448 .

United Republic of Tanzania URT ( 2014 ), “ Tanzania country special report: diplomat, April 2014 ”, available at: http://www.tic.co.tz/media/TANZANIA%20COUNTRYSPECIAL%20REPORT.pdf .

Urosevic , S. , Karabasevic , D. , Maksimovic , M. and Stanujkic , D. ( 2016 ), “ Impact of demographic factors on motivation and satisfaction of employees in the leather and footwear industry ”, Tekstilna Industija , Vol. 3 , pp. 45 - 55 .

Venkatesh , S.A. ( 2006 ), “ Sociology and katrina ”, City and Community , Vol. 5 No. 2 , pp. 115 - 118 .

Vernon , V. ( 2010 ), “ Marriage: for love, for, money…and for time? ”, Review of the Economics of the Household , Vol. 8 No. 4 , pp. 433 - 457 .

Wade , D.J. , Mwasaga , B.C. and Eagles , P.F.J. ( 2001 ), “ A history and market analysis of tourism in Tanzania ”, Tourism Management , Vol. 22 No. 1 , pp. 93 - 101 .

Weaver , D. and Oppermann , M. ( 2000 ), Tourism Management , John Wiley , Milton .

Wijaya , S. , Wahyudi , W. , Kusuma , C. and Sugiano , E. ( 2018 ), “ Travel motivation of Indonesian seniors in choosing destination overseas ”, International Journal of Culture, Tourism and Hospitality Review , Vol. 12 No. 2 , pp. 185 - 197 .

Woodside , A. and Lysonski , S. ( 1989 ), “ A general model of traveller destination choice ”, Journal of Travel Research , Vol. 27 No. 4 , pp. 8 - 14 .

WTTC ( 2017 ), “ Travel and tourism economic impact in Tanzania ”, available at: https://www.wttc.org/-/media/files/reports/economic-impact-research/countries-2017/tanzania2017.pdf .

Xie , H. , Costa , C. and Morais , D. ( 2008 ), “ Gender differences in rural tourists motivation and activity participation ”, Journal of Hospitality and Leisure Marketing , Vol. 16 No. 4 , pp. 1 - 27 .

Yan , N. and Halpenny , E. ( 2019 ), “ The role of cultural differebce and travel motivation in event participation: a cross-cultural perspective ”, International Journal of Events and Festival Management , Vol. 10 No. 2 , pp. 155 - 173 .

Yap , B. and Khong , K. ( 2006 ), “ Examining the effects of Customer Service Management (CSM) on perceived business performance via structural equation modelling ”, Applied Stochastic Models in Business and Industry , Vol. 22 No. 5 , pp. 587 - 606 .

Yoon , Y. and Uysal , M. ( 2005 ), “ An examination of the effects of motivation and satisfaction on destination loyalty. A structural model ”, Tourism Management , Vol. 26 No. 1 , pp. 45 - 56 .

You , X. , O'leary , J. , Morrison , A. and Hong , G. ( 2000 ), “ A cross-cultural comparison of travel push and pull factors: United Kingdom vs Japan ”, International Journal of Hospitality and Tourism Administration , Vol. 1 No. 2 , pp. 1 - 26 .

Yung-Kun , S. , Kuo-Chien , C. and Yuan-Feng , S. ( 2015 ), “ Market segmentation of international tourists based on motivation to travel: a case study of Taiwan ”, Asia Pacific Journal of Tourism Research , Vol. 21 No. 8 , pp. 1 - 21 .

Yusof , A. and Shah , P.M. ( 2008 ), “ Application of leisure motivation scale to sport tourism ”, The International Journal of The Humanities , Vol. 6 , pp. 8 - 16 .

Zuckerman , M. ( 1979 ), Sensation Seeking: Beyond the Optimal Level of Arousal , Lawrence Erlbaum , Hillsdale, NJ .

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Community Involvement and Participation in Tourism Development in Tanzania: A Case Study of Local Communities in Barabarani Village, Mto wa Mbu, Arusha-Tanzania

This thesis discusses the involvement and participation of local communities in tourism development in Tanzania using a case study of local communities in Barabarani village, Mto wa Mbu, Arusha. To explore this research topic, the thesis examines three key concepts: community participation in the tourism development decision-making process; community participation in the sharing of tourism benefits; and the contribution of tourism development to poverty alleviation. To achieve these systematically, the research is guided by five inter-related research questions: (1) what are the views of local people towards community involvement in tourism development; (2) what are appropriate roles of local people in tourism development; (3) to what extent do local people participate in the tourism development decision-making process; (4) to what extent have tourism businesses developed benefit-sharing schemes; and (5) what are the views of the local people on the contribution of tourism development towards poverty reduction. To gain a rich understanding of the context of the research, the thesis employs a case study approach, which enables: investigation at the community level to bring together perspectives from the grass-root level, where little research on this topic has been done; involvement of multiple stakeholders that explores perspectives from a range of stakeholders (ordinary members of the community, decision-makers within the community, tourism professionals, tourism businesses and NGOs); and the use of multiple methods (household survey, interviews, field observations, document analysis, and informal discussions). Such an approach improves the validity of the findings and successfully addresses the central research questions. Both quantitative and qualitative data generated from these techniques are analysed, integrated and compared, and are used to complement each other. Based on the findings obtained from multiple methods, this research concludes that local people wish to play a role in the tourism development decision-making process. In general, local people want to see decisions about tourism development in their area made jointly by government officials and local leaders in consultation with the local community. They also want to be involved in the sharing of tourism benefits. Tourism businesses have developed benefit-sharing schemes that favour local people to access tourism benefits. These schemes include local employment, local capacity building, and sharing tourism profits with the wider community. Tourism development is contributing positively towards poverty alleviation, and has made improvement on accessibility, prices of goods and services, employment, entrepreneurial training, income-generating projects, household incomes and general quality of life though the extent of contribution vary from one aspect to another.

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ANALYSIS OF CHALLENGES FACING TANZANIA TOURISM PROMOTION: A SURVEY OF SELECTED TOURISM DESTINATION

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Domestic tourism status in Tanzania: a case study of Tanzania national parks

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Facing pressure from rights groups, World Bank suspends funding for Tanzania tourism project

A U.S.-based rights group says the World Bank has suspended funding for a tourism project in Tanzania that allegedly caused the suffering of tens of thousands of villagers

KAMPALA, Uganda -- The World Bank has suspended funding for a tourism project in Tanzania that caused the suffering of tens of thousands of villagers, according to a U.S.-based rights group that has long urged the global lender to take such action.

The World Bank's decision to suspend the $150 million project, which aims to improve the management of natural resources and tourism assets in a remote part of southern Tanzanian, was “long overdue,” the Oakland Institute said in a statement Tuesday, charging that the bank's "failure to take immediate action resulted in serious harms for the local communities.”

At least $100 million has already been disbursed for the project, which started in 2017. The suspension of World Bank financing took effect April 18.

The Oakland Institute, a California-based rights watchdog whose work focuses on marginalized communities, for years led calls for the World Bank to stop funding the project known by the acronym REGROW, documenting serious rights abuses suffered by Indigenous communities in the area.

The group in a report released in November accused the World Bank of failing to hold Tanzanian authorities accountable for extrajudicial killings and sexual assault s relating to the expansion of Ruaha National Park.

The report said the Tanzanian government’s tactics to force communities away and increase tourism in Ruaha National Park, a goal of the REGROW project, were “inextricably tied to its financing by the World Bank.”

The World Bank said at the time that it “has zero tolerance for violence in the projects it finances," adding that a panel of inspectors was reviewing a complaint related to REGROW “to determine whether a compliance audit into the concerns raised is warranted.”

In recent correspondence between the World Bank and the Oakland Institute seen by The Associated Press, the lender confirmed the suspension of further disbursements to REGROW “until we are confident that the project is upholding our environmental and social standards.”

Anuradha Mittal, executive director of the Oakland Institute, said the World Bank's decision to suspend funding for “a dangerous project” is a victory for marginalized communities in the East African country.

“It sends a resounding message to the Tanzanian government that there are consequences for its rampant rights abuses taking place across the country to boost tourism,” Mittal said. “The days of impunity are finally coming to an end.”

It was not immediately possible to obtain a comment from Tanzanian authorities.

The Oakland Institute documented at least 12 disappearances or extrajudicial killings allegedly carried out by rangers, in addition to multiple sexual assaults of women. Government agencies allegedly seized and auctioned large numbers of cattle, imposing a heavy financial strain aimed at pressuring herders to leave.

“During the first months of 2024, rangers illegally seized and auctioned off thousands of cattle from herders while preventing farmers from cultivating their land -– devastating countless livelihoods as a result,” it said in its statement Tuesday.

Tanzania relies heavily on tourism to finance its budget, and the country has long been trying to develop its extensive national parks to attract more visitors.

Tens of thousands of communities in other parts of Tanzania have been caught up in the efforts, putting local authorities under the spotlight over civilian abuses. These efforts, cited by Amnesty International and others, include the violent eviction of 70,000 Maasai from grazing lands in the Loliondo area to clear vast tracts of land for trophy hunting.

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Tourism Marketing Report for Tanzania

The methodology adopted for data collection was to check on internet, books and where possible looking for academic articles to give an objective assessment of the Tanzania tourism strategy. 3. FINDINGS The first analysis in the APPENDIX, gave me the impression that Tanzania is trying to shape the first rudiments of a marketing strategy. It is important to note the country’s awareness first at the customers coming to visit Tanzania and second to the products offered.

Also, it is important to note the country’s poor quality services. 4. DISCUSSION First at all, it is important to explain what marketing is.Respectable associations from all around the world have coined so many definitions of marketing. By my opinion, the one which suits the situation of the Tanzania country, is the one given by Gronroos (1997) as follows: ? Marketing is to establish, maintain and enhance relationship with customers and other partners, at a profit, so that the objectives of the parties involved are met.

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This is achieved by mutual exchange and fulfilment of promises. The Gronroos definition contains one more aspect that others definitions does not. The Chartered Marketing Institute defines marketing as: Marketing is the management process responsible for identifying, anticipating and satisfying customer requirements profitably (CIM 2010). Also the American Marketing Association definition for marketing is: ? Marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large (AMA 2007). Definitions from both Institutions, the CIM and the AMA, are more focused on the organization, without including elements from the external organisation environment.Tanzania has a lack of quality services due to internal and external factors (road networks and limited flights) which can badly influence the tourist’s perspective of a nice holiday.

The marketing concept focuses its activities driving the organization to the customer needs. Secondarily (but not for importance! ), the marketing concept takes in consideration two elements: ? Internal Environment; ? External Environment; Internal environment is more focused on the internal elements of an organization; elements of an organization which influence its activities and choices, capable of giving a quality service.The external environment instead, is composed by all the external elements of an organisation, capable of influencing directly the organisation. These elements are as follows: ? Customers and Consumers; ? Competitors; ? Intermediaries; ? Suppliers. The limited flights to Tanzania, are part of the external environment which can damage from the tourists perspective a nice holiday, making them willing to change holiday’s destination.

Substantially, the marketing concept could be summarized in three steps: ? Focus on customer needs, before product’s development; ?Aligning all company functions to focus on customer needs; ? Realizing a profit satisfying customer needs over the long period. Another point which can influence the tourists is their behaviour. Mr. Bwento is right in thinking that if the tourists have the perception that there is a problem in the country, they will change their holiday’s destination. Marketing gives instruments to explain how this process goes through potential buyer’s behaviour, also taking in consideration the competitors influence.

The decision-making process goes through a few steps: st – Problem recognition:A product or a service has to solve buyer problems to be sold. If a buyer does not recognize having a problem, how can decide to purchase something? 2nd – Information search:Before deciding to purchase (especially if the product is expensive or can be rarely purchased as a holiday for example! ) the potential buyer will be starting to find information about the product which suits its needs; 3rd – Information evaluation:After collecting all the information needed about the product, the potential buyer will evaluate all the possibilities (being influence by many factors, and competitors too! .Marketers have to work hard at this stage, for example in our case, for holiday decision it will be useful to prepare point-of sales material with the Tanzania’s attractions to giveto the travel agencies of the targeted market(s); 4th – Decision:This is a crucial point. If something goes wrong with one factor (i. e. , the quality of the service, lack of assistance, bureaucratic procedures) the buyer can still change his mind and go elsewhere or purchase another product; th – Post-purchase evaluation:Monitoring this stage for marketing is important because is possible indentify where the products falls short of expectations (Brassington F.

& Pettitt S. , 2007). Tanzania’s main products to promote are all the attractions offered to any potential consumers willing to enjoy its beaches and mountains. To be sure to develop a competitive service, a “branding” approach by promotional activity could be adopted. Promotional activity is an integral part to achieve marketing objectives.For example, the marketing strategy could be to develop a promotional activity in the medium term to gain financial resources necessary in the long term to build a strong brand name.

“Brand Tanzania” consists of those elements of a product or service which can be easily linked to the business (on hearing at the word Tanzania people will think immediately of Mount Kilimanjaro! ); the benefits carried by using this approach are for all the parties involved in the exchanging process (potential tourists; travel agencies; airline companies; etc… . One more point is that branding is a real challenge for competitors to compete, creating stronger competitive advantages for the country. The competitor’s identification should be done looking firstly at potential competitors, then focus at the extent to which market needs are satisfied and new needs are emerging (Brassington F.

& Pettitt S. , 2007). Before planning a marketing campaign placing advertising the CNN, it is important to give some data about costs and benefits to this marketing approach.Benefits using this method are a high impact offers by combination of colours, sounds, motion and entertainment, capable to grab a high attention (Brassington F. & Pettitt S. , 2007).

Costs otherwise could be highly derived by advertising development campaign through its steps: targeting an audience, giving campaign objectives, advertising development and testing. Other important costs could be using an international media (like CNN! ). Being an expensive method, it is also important to measure the effectiveness of its usage by two stages: ? Interim evaluation Exit evaluation The first step is useful to revise and adjust the campaign before completion; the second step is used to measure if the targeted audience has received and understood the message. One more consideration, using a pull strategy with the above mentioned advertising method, Tanzania will be able to solve its limited flights to the country because, targeting the potential tourists, more will want to come to visit Tanzania and its attractions, compelling the airline companies to strengthen the flights into the country due to higher market demand.The distribution process plays an important role for any activity. Being in into a technological era, it should be developed a different kind of distribution method by using Internet.

Internet has few benefits, by recent analysis in 2010, 30. 1 million adults in the UK (Office for national statistic. 2010) accessed the Internet every day or almost every day. Considering the fact which British population is around 62 million people (Office for national statistic. 010) means that 50% of one Tanzania main market could be targeted using internet, collecting feed-back for analysis purposes, giving services like information, flight tickets, spending much less rather than using other marketing techniques.

According to the forecast incomes analysis furnished at the APPENDIX, Tanzania seems to be more sales oriented than market oriented, probably because of its favourable money tax exchange (1 British Pound – GBP = 2,414. 35 Tanzanian Shilling – TZS).Adopting a market oriented strategy; an organisation will be driven by the customer needs, aiming at the best customer satisfaction, assuming that customers are looking more for the things that best suit their needs instead that price driven. Market Tanzania and its attractions focusing at tourist needs, will increase customer loyalty if the main elements which compose this strategy are managed properly. The main elements are as follows: ? Price; ? Information; ? Availability; Peripheral benefits (to add value to the products); Determining the price of a product / service is a delicate balance between Tanzania’s tourism objectives and market condition. The price must be linked to both country and marketing strategic objectives.

Tanzania needs to gain cash quickly to invest in its infrastructures (as improving the road networks and the limited accommodations), to do so in the short term could be useful to dropping prices in favour of a higher demand for its inflow cash benefits.Tanzania should follow the demand based pricing (most used to service sector like tourism) linked with competition-based pricing, where the prices are set considering the demand of the market and its fluctuation on time, and the structure of market (less competitors are in the market and more autonomy the organisation has to set the price) and the perceived value of the product by the tourists. Tanzania’s tourism employs almost 200,000 Tanzanians (Appendix), this meaning which many local people have direct responsibility of the quality of the product / service provided.Elements of the extended marketing mix, takes in consideration three elements in addition to the traditional 4P(s) model (Brassington F. & Pettitt S.

, 2007): Place, Price, Product and Promotion. These additional 3P(s) are often used in the service sector, to better explain the marketing activity: ? People: In service sector people have a lot of responsibility having direct interaction with tourist for the final quality of the service provided (many people are working in the Tanzanian tourism sector this means that 200,000 people have direct responsibility for the quality of the service provided.This topic should be taken in carefully consideration); ? Process:Any mechanism which can prevent a long waiting from customers to leave before purchasing (this can involve issues from the flights problems to Tanzania, to the poor road networks); ? Physical evidence:These elements is the last one which can add a physical value to the service (the scarcity of accommodations around the Serengeti national park could be solved by building more high quality accommodations, giving added value to the attraction and the wider Tanzanian tourism to the tourists coming to visit it). 5. CONCLUSIONSBuild a marketing / customer oriented organisation is a long way which will definitely bring more benefits than costs, because most of the costs sustained will change in benefits at guarantee of the efforts made to purse this goals. Maintain a competitive advantage is a dynamic process, and Tanzania should work in this direction setting firstly its objectives and secondly plan how to achieve them with the marketing techniques explained in this report.

Listen at the market mean listen at the consumer, and having the necessary flexibility to satisfy, and where possible “spoil”, them is the key of success. . RECOMMENDATIONS After deep consideration, I have figured some recommendations for the Country, to achieve better results by its marketing strategy. TIP1: Improve quality of services; TIP2:Understand the buyers behaviour to prevent ; TIP3:Brand Tanzania to strengthen the consumer loyalty and gain more competitive advantage; TIP4:Plan advertising campaign, considering the budget and those marketing techniques used to verify its efficiency to reach the targeted segment; TIP5:Improve technology to lighten derived costs by marketing costs and to reach a higher segment of the targeted market;TIP6:Determine the price of the product or service offered, in function of marketing and Tanzania objectives. By your request, I’ve been analyzing a product to segment more markets for the Tanzanian tourism benefits.

Mount Kilimanjaro has snow almost 365 days per year. It could be segmented the skiers market, to attract them offering a new product “made in Tanzania” with dedicated “skiers packages” for tourists.7. BYBLIOGRAPHY • American Marketing Association. (2010).

AMA definition of marketing: New Definition of Marketing [online]. AMA: American Marketing Association. Available from: [Accessed 20 November 2010]. • Brassington, F. and Pettitt S. (2007).

Essential of Marketing. 2nd ed. Prentice Hall: Financial Times. • Chartered Institute of Marketing. (2010). CIM definition of marketing: Definition of Marketing [online].

Chartered Institute of Marketing. Available from: [Accessed 20 November 2010]. • CoinMill. com (2010). The currency converter [online].

CoinMill. com. Available from: [Accessed 22 November 2010]. Office for National Statistic. (2010). Internet Access: 60% of adults access internet everyday in 2010 [online].

National Statistic: Office for National Statistic. Available from: [Accessed 18 November 2010]. • Office for National Statistic. (2010). Population Estimates: UK population grows to 61.

8 million [online]. National Statistic: Office for National Statistic. Available from: ;lt; http://www. statistics. gov.

uk/cci/nugget. asp? id=6;gt; [Accessed 29 November 2010]. • •

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World Bank suspends funding for Tanzania tourism project

The World Bank has suspended funding for a tourism project in Tanzania that caused the suffering of tens of thousands of villagers, according to a U.S.-based rights group that has long urged the global lender to take such action.

The World Bank's decision to suspend the $150 million project, which aims to improve the management of natural resources and tourism assets in a remote part of southern Tanzanian, was "long overdue," the Oakland Institute said in a statement Tuesday, charging that the bank's "failure to take immediate action resulted in serious harms for the local communities."

At least $100 million has already been disbursed for the project, which started in 2017. The suspension of World Bank financing took effect April 18.

The Oakland Institute, a California-based rights watchdog whose work focuses on marginalized communities, for years led calls for the World Bank to stop funding the project known by the acronym REGROW, documenting serious rights abuses suffered by Indigenous communities in the area.

The group in a report released in November accused the World Bank of failing to hold Tanzanian authorities accountable for extrajudicial killings and sexual assaults relating to the expansion of Ruaha National Park.

The report said the Tanzanian government's tactics to force communities away and increase tourism in Ruaha National Park, a goal of the REGROW project, were "inextricably tied to its financing by the World Bank."

The World Bank said at the time that it "has zero tolerance for violence in the projects it finances," adding that a panel of inspectors was reviewing a complaint related to REGROW "to determine whether a compliance audit into the concerns raised is warranted."

In recent correspondence between the World Bank and the Oakland Institute seen by The Associated Press, the lender confirmed the suspension of further disbursements to REGROW "until we are confident that the project is upholding our environmental and social standards."

Anuradha Mittal, executive director of the Oakland Institute, said the World Bank's decision to suspend funding for "a dangerous project" is a victory for marginalized communities in the East African country.

"It sends a resounding message to the Tanzanian government that there are consequences for its rampant rights abuses taking place across the country to boost tourism," Mittal said. "The days of impunity are finally coming to an end."

It was not immediately possible to obtain a comment from Tanzanian authorities.

The Oakland Institute documented at least 12 disappearances or extrajudicial killings allegedly carried out by rangers, in addition to multiple sexual assaults of women. Government agencies allegedly seized and auctioned large numbers of cattle, imposing a heavy financial strain aimed at pressuring herders to leave.

"During the first months of 2024, rangers illegally seized and auctioned off thousands of cattle from herders while preventing farmers from cultivating their land -– devastating countless livelihoods as a result," it said in its statement Tuesday.

Tanzania relies heavily on tourism to finance its budget, and the country has long been trying to develop its extensive national parks to attract more visitors.

Tens of thousands of communities in other parts of Tanzania have been caught up in the efforts, putting local authorities under the spotlight over civilian abuses. These efforts, cited by Amnesty International and others, include the violent eviction of 70,000 Maasai from grazing lands in the Loliondo area to clear vast tracts of land for trophy hunting.

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