Digitalisation of the Tourism Industry and Self-Employment: Challenges of the Gig-Economy

Abstract

The study focuses on the challenges that the gig-economy brings to the labour market. Digitalisation is transforming the nature of labour relations, and the ratio of the employed to the self-employed is changing. By expanding the scope of digital technology and the use of digital platforms, changes are affecting all areas, including the tourism industry. The article assesses the relationship between tourism development, digitalisation, and self-employment indicators. The study covers 112 countries for 2021. The paper uses the Travel & Tourism Development Index, Network Readiness Index and the World Bank’s self-employment indicator. The analysis showed that the features of the relationship between tourism development, digitalisation and self-employment vary across the three groups of countries identified as a result of cluster analysis. The study found that there is a positive relationship between the level of tourism sector development and the level of the country’s digitalisation. The relationship between the level of self-employment and tourism development is variable across groups. The relationship between self-employment and digitalisation was confirmed only for the sample as a whole.

1. Introduction

It is difficult to imagine modern business activities without the use the new digital tools. Digital technologies are spreading into more and more areas. The digitalisation of society is changing not only the external environment of business entities, but also the essence of labour relations in all spheres of the economy. Digitalisation has caused significant changes in the workplace and laid the foundation for the formation of the gig-economy, which is understood to be a system of freelance and flexible employment. Major transformations are also taking place in the tourism industry. Considering the trends in the spread of digital technologies and the flexible employment opportunities in tourism, the gig-economy will cover the tourism industry in the near future.

Digitalisation is fundamentally changing the system of relationships in society, at industries and between individuals. The nature and essence of labour in the digital age is transforming. Information technologies are gradually evolving from a tool of labour in its classic sense into a key resource for modern business. Changes in the value chain and in the way of doing business are causing corresponding transformations in approaches to labour organisation. New forms and types of employment, organisation of the workplace, approaches to managing employment relationships, etc., are emerging in the information society. Part-time, temporary, virtual, self-employment and remote work are forms of employment that are already commonplace responses of the labour market to the challenges of the digital economy. A new conception of economic relations, called the gig-economy, is shaping up on their basis.

Starting with mobile apps for food delivery and taxi services, the gig-economy is gradually spreading around the world and covers more and more areas of the economy and society. For instance, according to Mastercard, the global gig-economy generates a gross volume of $204 billion, and it is projected that the size of gig-economy transactions will grow by 17% per year and the gross volume will be around $455 billion by 2023 (Mastercard & Kaiser Associates, 2019). Every year, the gig-economy involves more and more participants, both on the supply side and on the demand side. As Gahene (2020) points out, the gig-economy connects 40.7 million freelancers on digital platforms worldwide, generating $193 billion in gross volume and $127 billion in payments to freelancers. In the US alone, the gig-economy had 55 million participants as of 2017. In addition, 33% of U.S. companies make extensive use of gig-workers and 36% of all U.S. workers are employed in the gig-economy to some degree (Duszyński, 2023).

In terms of platforms providing work, their number increased from 142 in 2010 to 777 in 2020. Most of them are in the delivery sector, followed by online platforms providing taxi services and a small number of hybrid platforms offering various services, including e-commerce (ILO, 2022). The expansion of the number of digital platforms leads to the transformation of labour relations in the direction of their dependence on digital technologies, as well as to a corresponding increase in the number of people employed both directly in the digital space and employed through digital tools. Thus, the OECD estimates the number of workers on platforms in the range from 1 to 3% of all workers (Schwellnus et al., 2019). According to ILO studies (2022), the number of platform workers worldwide ranges from 0.3 to 22 percent of the adult population. And the number of platform workers in the world will increase from 43 million in 2018 to 78 million in 2023 (Mastercard, 2020). In line with to some estimates, 30% of online platform workers use them as their main source of income, while the remaining 70% use platform work as an additional source of income. These proportions differ in developing countries, where 44% of online platform workers derive their main income from this work (ILO, 2022).

Despite the widespread implementation of digital technologies in all areas, most digital platforms are used in the services sector. However, there is a lack of sectoral research on the gig-economy and its impact on employment, including in the field of tourism. Most of the gig-employed work without formal employment or on a part-time basis. This fact complicates the calculations of the real scale of the gig-economy and imposes corresponding restrictions on economic research.

The digitalisation of the economy expands employment opportunities through the spread of flexible employment, self-employment, part-time work, etc. As digital labour platforms proliferate, the number of gig-workers will increase, and the tourism industry is no exception. The use of booking, reservation, route-building, augmented and virtual reality tools and other modern technological applications by travel companies is changing the nature of their activity. It is also very relevant for travel agencies to employ gig-workers during peak periods.

The tourism sector has long been characterised by the labour market challenges that the gig-economy brings with it, such as part-time and short-term contracts, seasonal employment, underemployment, etc. Therefore, the expansion of digital platforms actualises the need to address the regulation of these and other types of platform employment.

Digital technologies make it possible to work remotely. This empowers travel companies and expands the range of services they provide. At the same time, the requirements for the qualifications of staff in travel firms are increasing primarily in terms of their communication and digital skills, soft-skills and the ability to work in a team, including virtual teamwork. The wide dissemination of digital tools is significantly changing the labour relations in tourism. Flexible employment and self-employment are increasingly spreading in the tourism sector.

Labour law systems cannot respond quickly to the regulation of labour relations that arise with the emergence of new forms of employment, as technology spreads faster than legal norms change. Also, due to the high rate of technological change, there are some problems in the literature regarding the determination of the impact of digitalisation on the types and forms of employment in many sectors of the economy. In this context, the aim of the article is to explore the nature of the relationship among the self-employment rate, the level of tourism industry development, and the degree of digitalisation in the context of the gig-economy.

To achieve this goal, the study puts forward the following hypotheses:

H1. 

There is a relationship between tourism development and the degree of digitalisation.

H2. 

There is a relationship between the level of self-employment and tourism development.

H3. 

There is a relationship between the level of self-employment and the degree of digitalisation.

2. Literature Overview

In general, the gig-economy is an updated format of economic relations based on the use of digital platforms and modern technologies. In particular, Montgomery and Baglioni (2021) note that in most cases the gig-economy is understood as an economy of non-standard employment. At the same time, the gig-economy can include significant technological changes that exacerbate problems of employment. In recent years, scientists have been studying the expansion of the gig-economy, as well as the challenges in extending it to the labour sphere.

The gig-economy is understood as informal temporary employment through digital platforms where payment is made for completed tasks. Thus, Ali et al. (2023) identify the gig-economy as the labour markets for temporary and task-based contract work. The gig-economy is a special segment of the labour market defined by short-term freelance contracts (Yaroshenko et al., 2024). In the gig-economy, a new intermediary—a digital platform—appears between labour supply and demand. Modern digital technologies and digital labour platforms are creating a fundamentally new employment environment, promoting decentralised employment, flexible working arrangements, and expanded opportunities for independent team building. Panackal et al. (2024) point out the characteristics of the gig-economy, such as flexibility, autonomy, choice, and multiple sources of income. In the context of the gig-economy, flexible employment is often identified with temporary, yet independent, employment rather than working for an employer. At the same time, Pangrazio et al. (2023) define gig-work as freelance, short-term or contractual employment.

The concept of the gig-economy covers not only short-term contracts and part-time- and self-employment, but also flexible working style, independence of workers, use of modern technologies, etc. The gig-economy studies focus on various areas, among them labour law issues in the use of digital platforms, regulation of labour relations, social and pension problems, taxation of various forms of non-standard employment, geographical and sectoral expansion of flexible employment, self-employment and many others.

Researchers define the essence and features of the gig-economy as a modern concept of social and labour relations development. For example, Huđek and Širec (2023) consider the gig-economy as a new business model that connects employees and employers via digital platforms and uses non-traditional labour mechanisms. Despite the widespread expansion of online platforms, many problems of ensuring labour rights and social support remain unresolved, both at the level of national economies and internationally. The increasing digital component in labour relations is changing the nature of the interaction between employees and employers. Accordingly, Kuhn et al. (2021) argue that the gig-economy is expanding, which is changing traditional human resource management.

In addition, the gig-economy is spreading unevenly across space (Johnes, 2019). The reason for the uneven coverage of countries by the gig-economy is the different level of technological development and the quality of the Internet. Thus, as per to Olorundare et al. (2022), one of the obstacles to the spread of gig-employment in Nigeria is the poor quality of electricity and the Internet.

Digital technologies are the basis and a decisive factor in the development and functioning of the gig-economy. However, the roll-out of digital platforms into the labour relations system is changing the principles of their implementation. As McDonnell et al. (2021) point out, gig-work differs from other forms of employment relationships because of the crucial role technology plays in its organisation. Since the working conditions in the gig-economy are highly individualised and digitalised, HRM must be adapted to the new requirements.

On the one hand, the labour market is more and more dependent on digital technologies; on the other hand, digital technologies are increasing employment opportunities and thus broadening the labour market through the expanded use of digital platforms. Gig platforms are expanding their scope and function in the US labour market (Baber, 2023). Digital platforms connect self-employed workers with employers through apps, thereby benefiting from the relationship between labour and capital (P. Williams et al., 2021). Digital platforms connect consumers and workers to perform tasks on demand, changing the nature of economic and labour relations (Lata et al., 2023).

Scientists also point to the benefits that the gig-economy provides. Arcidiacono et al. (2019) highlight that digital platforms have a significant impact on the labour market by expanding employment opportunities, balancing labour supply and demand, transforming risk and control, etc. Despite the fact that gig-workers are divided, some forms of organising their collective actions have been observed recently. As Barrios et al. (2022) argue, the gig-economy stimulates new business creation by helping to reduce risk for entrepreneurs through providing potential additional income and insurance against volatility.

Although the gig-economy decreases the transaction costs of labour contracts (Oranburg & Palagashvili, 2021), it also brings certain problems to the labour market. As Barratt et al. (2020) note, platforms are transforming the relationship between capital and labour by creating new forms of workplace behaviour in the gig-economy. Instability and risks for gig-workers are growing and workers are becoming more vulnerable to a labour market that is increasingly regulated by platforms.

At the same time, the development of the gig-economy is causing some contradictions in the labour market. Uchiyama et al. (2022) stress the ambiguous nature of gig-work. On the one side, the platforms contribute to reducing unemployment; on the other, they completely transfer the risk and responsibility of the work to the gig-workers. This expansion of the labour market comes amid a lack of social protection mechanisms for gig-workers. Such work is highly susceptible to fluctuations in demand. Au-Yeung and Qiu (2022) also focus on this fact, noting that the gig-economy is causing new labour contradictions between digital platforms and the workforce in terms of changing forms of control over employees, transformation of professional norms, and increasing competition in the labour market, etc. (Hong Kong case).

It should be noted that non-standard employment, while contributing to reducing unemployment, also creates many problems. For example, with a high share of the informal economy in Africa, gig-workers face precarious working conditions and have less opportunities to exercise their free will (Anwar & Graham, 2020). In addition, gig-employment is temporary and greatly prejudices the rights of the employed in most cases. Y. Li et al. (2023) highlight the unreliable nature of gig-work in the sharing economy compared to traditional employment. This leads to the concept of digital exploitation. Altenried (2021) remarks that platforms focusing on changing labour dynamics and on non-standard and ultra-flexible employment are gradually becoming a tool for the exploitation of migrant workers. Also, the gig-economy is changing the nature of work, causing a number of ethical issues such as discrimination against gig-workers, the precarious nature of employment, and the unfair treatment of gig-workers, which requires the development of appropriate policy measures to address them (Tan et al., 2021).

To summarise the above, it should be noted the advantages and threats that the gig-economy brings to employees and employers (

Table 1. Advantages and threats for employees and employers in the gig-economy.

	For Employers	For Employees
Advantages	ease of recruitment; low social responsibility; reduction in the amount of taxable payments	balance of working and personal time; expanding employment opportunities; tax avoidance
Threats	uneven distribution of labour resources; dependence on the quality of the Internet; lack of interest in the company’s development	lack (or low level) of insurance; unfavourable contract terms; low legal protection of interests; income instability

Source: Compiled by the authors.

).

One of the responses of the labour market to the digitalisation of economic relations is the increasing share of the self-employed. A lot of scholars confirm this trend. Thus, L. Li et al. (2022) point to the fact that labour market transformation is taking place in the modern platform economy. More and more often self-employment is replacing the traditional full-time job. D’Elia and Gabriele (2022) note that the number of self-employed is increasing in different countries. Mccrystal and Hardy (2021) highlight that as the gig-economy grows, the number of self-employed increases, therefore the importance of labour regulation affairs in the gig-economy rises. Self-employed workers represent a significant share of the labour market in OECD countries (Bradley, 2016); in particular, the number of self-employed people has been increasing in the UK over the past forty years (Barnard & Georgiou, 2023). Against the background of the growing number of self-employed in EU countries, many issues of social protection, labour justice, and working conditions of the self-employed require addressing (Queralt, 2023). For example, Tyc (2023) emphasises the importance of the social rights of the self-employed in France and Italy, and Barwaśny (2023) points to the need to protect the rights of the self-employed as EU legal norms in this area are fragmented. In general, it is noted that there is no unified definition of self-employment in the context of international law, and there are no unified legal norms regulating concerns of self-employment. However, as Porras-Arena and Martín-Román (2019) highlight, European authorities have policies to support self-employment in the gig-economy. Taking into account the above, it is necessary to determine whether there is a relationship between the level of self-employment and tourism development (hypothesis H2).

However, digitalisation contributes to the creation of new jobs, and it also initiates the spread of non-standard forms of employment, including self-employment (Niederfranke & Drewes, 2017). According to MacDonald and Giazitzoglu (2019), the gig-economy is primarily characterised by the de-standardisation of employment, the development of its new forms, such as the low-pay, no-pay cycle, self-employment, and zero-hours contracts. At the same time, new forms of self-employment are developing in the gig-economy (Malzani, 2020). Ravizki and Purnami (2023), also suggesting the fact that there is a strong relationship between the gig-economy and self-employment.

Digitalisation affects labour markets, working conditions and employment in different ways (Horváth et al., 2021). In addition, digitalisation affects self-employment to varying degrees. For example, Eichhorst et al. (2017) observe that digitalisation is driving changes in the labour market, including the transformation of professions, and forms of employment in Germany and other developed countries. The role of platforms and self-employment is increasing. Henley (2021) highlights the significant growth in self-employment in the UK in recent years. Thus, changing the working environment in the gig-economy necessitate a closer examination of the relationship between self-employment and digitalisation in countries and sectoral contexts (confirmation (refutation) of hypothesis H3).

Digital platforms are changing labour relations everywhere, including in tourism. The relationships of gig-workers with trade unions are changing, as an alternative, various associations and representations of gig-workers are being formed (Dazzi, 2019). Sánchez-Bayón (2023) notes the transformation of the tourism employment system towards the replacement of unskilled labour with technologies in the context of digitalisation. Often gig-earnings have a stressful, short-term and unstable nature, and gig-work is characterised by a lack of social security and pension provision. At the same time, the gig-economy provides a job search opportunity for persons who, in the absence of digital platforms, are much less likely to be in full-time employment—older employees, students, caretakers of children, etc., i.e., all people who are looking for flexible working hours. As the pandemic has shown, gig-employment in the hospitality industry contributes to minimising risks for both businesses and workers (El Hajal & Rowson, 2021). The COVID-19 pandemic has also found that, despite negative characteristics such as irregular working hours and low income, jobs in tourism generate a wide range of skills that may be in demand in the IT sector, retail, business process outsourcing, etc. (Yancheva & Ilieva, 2022). Along with this, Binder and Miller (2020) draw attention to the significant challenges of the tourism industry’s workforce in the context of the evolving digital environment. For example, in the tourism and hospitality industry in Britain, migrants make up a large part of the workforce. However, their abilities often remain underestimated, including due to part-time employment. The work of migrants in tourism and hospitality are often below their level of education and status in their country of origin (Hack-Polay et al., 2022). From the perspective of changing labour relations in tourism under the influence of digital technologies, it is advisable to determine whether there is a relationship between tourism development and the degree of digitalisation (hypothesis H1).

Technologies are significantly changing activities in the tourism sector, exacerbating inequalities and insecurities for workers, limiting their freedom of action and affecting their well-being. Rydzik and Kissoon (2022) suggest that low-skilled workers will be the most affected by the coming expansion of automation, which actualises issues of government regulation to minimise the negative social impacts in the tourism sector. Mamatzakis et al. (2022), examining the relationship between efficiency and labour market regulation, conclude that strengthening the mechanism of state regulation of the labour market in the tourism industry can negatively affect the labour costs, thereby causing its transition to the shadow sphere.

The spread of non-standard forms of employment, including self-employment, provides both advantages associated with greater personalisation of tourism enterprises and creates certain difficulties, for example in the area of legal relations and the distribution of responsibilities between the employer and the gig-worker. Gig-workers are often not interested in career growth and long-term cooperation with a travel company and are not oriented towards ensuring its long-term success, as short-term contracts are used in such employment relationships. However, travel companies are increasingly hiring such workers in order to reduce staff costs. Employees are interested in flexible working hours and pay-per-performance, despite the lack of social guarantees from the employer.

3. Materials and Methods

The study uses the Network Readiness Index (NRI) as a measure of a country’s level of digitalisation. The NRI is an indicator of the level of the network economy development and information and communication technology by countries. The NRI indicates the countries’ level of development in the field of digital technologies. The Index assesses 131 countries (130 in 2021) on the level of network readiness using 58 indicators (60 in 2021) combined into four parameters: Technology; People; Governance; and Impact.

One of the main tourism development indicators is the Travel & Tourism Development Index (TTDI), which shows the level of tourism development by countries and allows for cross-country comparisons. The TTDI (formerly Travel & Tourism Competitiveness Index (TTCI)) provides information on the strengths and weaknesses of tourism development in a country, and highlights the role of tourism in economic and social development. The TTDI assesses a country’s capacity for sustainable tourism and travel. The index consists of 11 indicators, 17 main components that form 5 sub-indices. The index is calculated every two years and covers 117 countries in 2021.

To assess employment, this study uses World Bank indicators such as Employment-Employment to population ratio, 15+, total (%) (modelled ILO estimate) and Self-employment-Self-employed to population ratio, 15+, total (%) (modelled ILO estimate).

The research methodology includes analysis of data for 2021. The study uses such data analysis methods such as the following: correlation analysis, cluster analysis, descriptive statistics methods, and graphical methods. Figure 1 shows the log-frame of the study.

4. Results

Digital technologies are increasingly spreading to the tourism industry. The tourism sector is undergoing a significant digital transformation (Moreno-Izquierdo et al., 2022). Christensen (2023) stresses the role of digital platforms in promoting the tourism industry. Digitalisation of the tourism industry has greatly intensified during the COVID-19 pandemic (Ilhan, 2021). Travel companies and independent travellers have long been using reservation and booking technologies, applications for route and excursion planning, hitchhiking services, websites with reviews and recommendations, etc. In addition, the travel industry has been making increasing use of digital platforms in recent years. Figure 2 presents the main tourism sectors where platforms are used.

The increasing number of digital platforms in tourism and, consequently, the expansion of self-employment in this sector, necessitates a more thorough investigation of the relationship between tourism development, digital technologies and self-employment. The sample for the analysis covers 112 countries with comparable data for 2021. The year of the study is due to the fact that this is the last year for which TTDI data are available, since the index is calculated and published every two years.

The initial stage of the study involves the calculation of Pearson’s correlation coefficients to determine the relationship between the analysed indicators (Figure 3). The TTDI and NRI indices and their sub-indices, as well as employment and self-employment indicators of the World Bank were used as indicators for the analysis.

Figure 3 demonstrates that there is a sufficiently strong correlation between all the analysed indicators, except for the relationship of TTDI and NRI and their sub-indices with employment, so the employment indicator will not be used in the following calculations. It should also be noted that TTDI and NRI and their sub-indices are negatively related to self-employment.

The next stage of the study determines whether the relationship is homogeneous or varies from country to country. To do this, the article determines whether there are stable relationships for groups of countries. The study uses TTDI and NRI and the self-employment rate to identify country groups. As the analysed data are presented in different types of scales, the data have been standardised. This stage of the analysis verifies whether the analysed indicators form “natural” clusters. The study uses the Complete Linkage Method as the rule for combining variables and the Euclidean Distance Square as a measure of proximity. Figure 4 presents the results of the cluster analysis.

As can be seen in Figure 4, the variables form three natural clusters. To verify this assumption, it is necessary to divide the data by the K-means method into three clusters and test the significance of the differences between the resulting groups. The paper uses a procedure of analysis of variance to determine the significance of differences between the obtained clusters.

Table 2. Analysis of Variance.

Variable	Between SS	df	Within SS	df	F	Signif. p
TTDI	80.92	2	30.08	109	146.64	0.00
NRI	88.15	2	22.85	109	210.30	0.00
Self-employment	80.24	2	30.76	109	142.16	0.00

Source: Author’s computation with data from TTDI, NRI and self-employment using Statistica.

presents the results of the analysis of variance.

The p < 0.05 value shows that the differences between the clusters are significant. Based on the distribution of countries into clusters, basic descriptive statistics for each cluster can be calculated.

Table 3. Descriptive Statistics.

Variable	Mean	Min	Max	Standard Deviation
1 Cluster (43 cases)—Australia, Austria, Belgium, Bulgaria, Canada, China, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong SAR, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Rep., Latvia, Lithuania, Luxembourg, Malaysia, Malta, Netherlands, New Zealand, Poland, Portugal, Qatar, Saudi Arabia, Singapore, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Arab Emirates, United Kingdom, United States
TTDI	4.63	4.09	5.25	0.32
EE	5.45	4.90	6.13	0.29
TTPEC	4.58	3.56	5.46	0.33
Infrastructure	4.62	3.48	5.57	0.54
TTDD	3.37	1.76	6.23	1.27
TTS	4.58	3.87	5.24	0.30
NRI	69.75	56.17	82.06	7.63
Technology	65.52	45.40	87.81	10.48
People	64.94	46.94	80.63	8.36
Governance	77.89	63.61	90.23	7.50
Impact	70.63	53.69	84.77	7.48
Self-employment	8.28	0.33	28.53	4.31
2 Cluster (45 cases)—Albania, Argentina, Armenia, Bahrain, Bosnia and Herzegovina, Botswana, Brazil, Cabo Verde, Chile, Colombia, Costa Rica, Dominican Republic, Egypt, El Salvador, Georgia, Guatemala, India, Indonesia, Jordan, Kazakhstan, Kuwait, Kyrgyz Republic, Lebanon, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Morocco, Namibia, North Macedonia, Pakistan, Panama, Philippines, Romania, Serbia, South Africa, Sri Lanka, Tajikistan, Thailand, Trinidad and Tobago, Tunisia, Turkey, Uruguay, Vietnam
TTDI	3.85	3.34	4.39	0.31
EE	4.50	3.74	5.29	0.41
TTPEC	4.36	3.28	5.06	0.35
Infrastructure	3.34	2.35	4.40	0.49
TTDD	2.60	1.36	5.60	1.10
TTS	3.98	3.50	4.54	0.27
NRI	48.18	34.55	56.89	5.96
Technology	41.95	23.43	51.99	7.06
People	45.86	26.48	60.22	7.25
Governance	52.00	35.05	65.78	8.03
Impact	52.89	36.43	64.10	6.36
Self-employment	17.02	1.16	38.18	8.90
3 Cluster (24 cases)—Angola, Azerbaijan, Bangladesh, Bolivia, Cambodia, Cameroon, Chad, Côte d’Ivoire, Ecuador, Ghana, Honduras, Kenya, Lao PDR, Lesotho, Malawi, Mali, Nepal, Nigeria, Paraguay, Peru, Rwanda, Senegal, Tanzania, Zambia
TTDI	3.28	2.49	3.96	0.36
EE	3.69	2.64	5.09	0.50
TTPEC	3.92	3.03	5.03	0.60
Infrastructure	2.56	1.68	3.41	0.41
TTDD	2.15	1.27	3.51	0.49
TTS	3.78	3.23	4.31	0.29
NRI	36.71	21.85	47.96	6.63
Technology	29.57	14.61	45.93	7.15
People	34.58	16.61	51.13	8.41
Governance	41.37	25.06	57.25	8.19
Impact	41.32	23.48	54.48	7.73
Self-employment	41.12	27.27	67.58	9.77

Source: Author’s computation with data from TTDI (with sub-indices) and NRI (with sub-indices) and self-employment using Statistica.

shows the descriptive statistics for each cluster.

The next stage of the analysis involves plotting the mean and confidence intervals for the variables in each cluster (Figure 5).

The graphs in Figure 5 illustrate the structure of the clusters in the context of the analysed indicators. As can be concluded from Table 2 and Figure 4, the first cluster includes countries with high TTDI and NRI values and low self-employment rate. Most of these countries are high-income countries. In other words, we can say that developed countries have both a high level of tourism development and a high level of digital development with a low level of self-employment. Indicators with average values represent the second cluster. Finally, the third cluster shows low TTDI and NRI values and high level of self-employment. As it turns out, a high level of self-employment is not an indicator of a country’s success in developing tourism and digital platforms.

Notably, the highest rates of self-employment of the population are observed in countries with low incomes and a low level of economic development. However, it should be noted that the self-employment indicators used in the study include all self-employed persons in the economy. Also, the high level of self-employment in developing countries may indirectly reflect the low level of industrialisation of the economy, when most of the population is employed in small family businesses and does not work in large industrial companies.

For a more detailed analysis, the article determines the correlations between the indicators by clusters. Since the number of variables for each cluster does not exceed 100, this study uses Spearman’s rank correlation coefficient to determine relationships. Figure 6 presents the results of the correlation analysis.

As Figure 6 shows, there are both similarities and differences between the clusters. Thus, cluster 1 demonstrates a negative correlation of TTPEC with NRI and its sub-indices. This indicates that tourism and travel policy is not consistent with the digital technology development strategy in this group of countries. While this relationship is positive, but also weak, in other clusters. Other TTDI sub-indices show a significant positive correlation with NRI and its sub-indices in all clusters. TTS has a significant relationship with NRI and its sub-indices in only cluster 1.

In the case of self-employment, no statistically significant relationship was found with any of the cluster indicators. At the same time, in all groups there is a relationship both between TTDI and NRI, as well as between their sub-indices, in most cases with the exception of TTPEC. The TTPEC shows a non-significant negative correlation in the first cluster, a positive weak correlation in the second and a moderate correlation in the third.

It is noteworthy that cluster 3 shows strong correlations between the TTDI infrastructure indicator and NRI components, with no such correlation in other clusters. This may confirm the need to improve the infrastructure for the development of the digital environment in this group of countries.

Thus, the study revealed certain trends in tourism and travel in the context of digitalisation. Overall, the fact of the relationship between the level of tourism sector development of and the level of country digitalisation was confirmed. Nevertheless, the study found that the self-employment indicator does not correlate with the TTDI and NRI indices in any of the groups of countries identified as a result of the analysis.

5. Discussion

The emergence of the gig-economy has changed the nature of work around the world. Thus, India’s labour market has faced job cuts and rising unemployment in recent years. Left without work, people seek freelancing opportunities, while depriving themselves of many social guarantees such as pensions and paid holidays, as well as a system of financial security. Due to these features, many gig-workers consider this type of employment as temporary (Mehta, 2020). It follows that the challenges posed by the digitalisation of labour relations and the further expansion of the gig-economy to all sectors of society actualise the affairs of state regulation of the labour market. However, in some countries, such as China, government intervention in the gig-employment market has had mixed effect. On the one hand, the welfare of gig-workers has improved, while on the other hand, operating costs have increased (Lin et al., 2023). Since the gig-economy has a significant impact on the economy and society, Cho and Cho (2020) propose appropriate business models to raise its social focus. Williams & Horodnic note that the growing fictitious self-employment in the gig-economy is forcing governments to address the declining quality of labour conditions (C. C. Williams & Horodnic, 2017).

Digital platforms create economic opportunities in part-time employment. Hunt and Samman (2020) emphasise the need to protect labour rights and protect gig-workers, change social norms, equalise the rights of gig-workers and traditionally contracted workers, ensure the right to fair pay, social protection, safety and health, etc. Nevertheless, there is a growing number of independent workers offering their services via platforms, which necessitates a review of their employment status and legal protection (Poon, 2019). It should also be noted that the gig-economy predominantly involves young people, bringing a number of negative features to youth employment, such as restricted rights and opportunities, low wages, lack of income guarantees, precarious employment, exploitation, etc. (MacDonald & Giazitzoglu, 2019). Labour relations are changing significantly with the development of the gig-economy, requiring corresponding changes in labour law (Todolí-Signes, 2017b).

Despite the benefits that the gig-economy offers to gig-workers, it raises a number of risks and costs. It is important to note that the digital labour market has developed unevenly geographically. In this context, digital work can be presented as a tool for economic development (Graham et al., 2017). It is also observed that there are more entrepreneurs and small firms in countries with higher levels of development (Congregado et al., 2019). Moreover, one of the features of the gig-economy is the uneven coverage of not only countries, but also sectors of the economy.

The results of this study provide grounds for the conclusion that digitalisation is a mediating tool between tourism and self-employment. It is also necessary to highlight that there are country-specific features in the manifestation of relationships between the analysed indicators, so further study of these issues should pay attention to the analysis of data by countries (groups of countries). The findings are somewhat consistent with the results of other studies. For example, Banik and Padalkar (2021) note that the gig-economy is spreading unevenly and has different impacts on industries, professions and skill levels. Meanwhile, in tourism, through digital platforms, the boundaries between host and guest are gradually blurring (Bakas & Salman, 2024). The coverage of the tourism sector by platforms is increasing, as in the case of Airbnb, where the platform is expanding its services from home rentals to tourism-related services (Capineri & Romano, 2021).

Obviously, standard personnel management schemes in the gig-economy will not be effective, as they focus on a completely different model of labour relations. For example, in the gig-economy, many companies do business through self-employed workers (Todolí-Signes, 2017a). It is also important that the majority of online platform workers are classified as self-employed, but consider themselves dependent workers (ILO, 2022). Furthermore, the variety of flexible employment forms in the digital economy makes it difficult to measure its productivity. Often, gig-workers are not officially employed, which makes it hard to keep statistics on sectors of the economy where such forms of employment are widespread. However, despite the challenges of calculating productivity and informal employment, there is a clear trend towards the growth of non-traditional forms of employment in the economy. Approaches to the analysis of employment, labour productivity, job allocation, etc., must change, respectively. In this context, the development of theoretical approaches to analysing the labour force in a gig-economy is of particular relevance.

6. Conclusions

Universal digitalisation of all areas of the economy and society is now underway. These processes affect all countries, albeit to a different extent, regardless of their level of economic development. Digital technologies are changing the external environment for business entities. One of the manifestations of digital relations is the gig-economy and its increasing spread to all sectors. Tourism is also under the influence of digital changes, but at the same time it is gaining new opportunities through the use of digital platforms. Although digitalisation has an impact on the labour market (Stryzhak, 2023), this study found a statistically significant negative relationship between self-employment and the TTDI and NRI indices.

Hypothesis H1, that there is a relationship between tourism development and the level of digitalisation, was confirmed for the sample as a whole. Also, Hypothesis H1 was confirmed for cluster 1, except for the TTPEC indicator. In the second cluster, a weak positive relationship was found between NRI and tourism development indicators other than TTPEC. In the third cluster, the relationship is positive for all indicators.

As a result of testing hypothesis H2, which assumes a relationship between the level of self-employment and tourism development, the study showed that this relationship is negative for the entire sample. For clusters 1 and 2, the relationship is weakly positive (except for the EE indicator), the relationship is positive for cluster 3 (except for TTDD).

Hypothesis H3, suggesting a relationship between the level of self-employment and the degree of digitalisation, was confirmed for the sample as a whole, but was not confirmed for individual groups, where the relationship turned out to be weak.

The research therefore shows that the relationship between tourism development, digitalisation and self-employment is uneven, which necessitates further analysis of the causes of such differences. However, the empirical findings from this study should be considered somewhat limited, as they are based on data from only one base year, which included the COVID-19 pandemic.

The results of the study point to the necessity of changing the system of gig-employed registration and the legal framework for the regulation gig-employment. The findings of the study actualise the need to include platform workers in general employment statistics as well as to adapt labour law to the challenges of the gig-economy. Prospects for further research, in our opinion, are in more detailed study of self-employment issues by countries and regions and industries in the context of digitalisation of economic activity. For example, Margolis (2014) argues that a large proportion of workers in developing countries are low-productivity self-employed.

In this aspect, the importance of further studies on accounting and protecting the gig-workers’ rights must be observed. Thus, Minter (2017) notes that the realities of the expansion of the gig-economy through the widespread use of digital platforms require addressing gaps in legislation by revising labour standards and regulating employment conditions, including the level of the minimum wage. Kerikmäe and Kajander (2022) highlight the fact that the role of the gig-economy has changed in recent years: for many people in the EU, it has turned from a source of additional income to the main income earning activity, which necessitates a review of labour legislation. Schmidt et al. (2023) point out that the expansion of the gig-economy is driving the need to rethink employment relations. Pulignano (2019) reached similar conclusions, arguing that the social implications of the expansion of the gig-economy lead to the need to develop an appropriate regulatory framework and policy measures to ensure workers’ rights. Based on an empirical analysis of self-employment between generations in nine European countries Giménez-Nadal et al. (2022) note that the level of self-employment depends on the legal norms of the state and the entrepreneurial culture established in the society.

Thus, this article makes a theoretical contribution to understanding the features of the relationship between self-employment, tourism development and digitalisation in the context of the expansion of the gig-economy, but the study has some limitations. These limitations are the lack of a clear definition of self-employment in international law and unified universal legal norms for regulating self-employment; therefore, the study uses the assessment of the self-employment level based on the World Bank indicators. In addition, there is bogus self-employment, which distorts the overall statistics, because in order to avoid taxation, employers, by agreement, register employees as self-employed. Barnard and Georgiou (2023) focus on this fact, noting that in the total number of self-employed is a significant part of bogus self-employment, including through imperfections in the legislation in the field of labour law. Also, in addition to ordinary self-employed entrepreneurs for whom self-employment is the main activity, there are hybrid forms of self-employment that mainly operate as a side business (Bögenhold et al., 2017).

It is obvious that in the current conditions of digital technology development, interactions in the tourism sector are increasingly dependent on digital tools (mobile offers, digital platforms, reservation and booking systems, etc.), therefore, state policy should focus primarily on the development and support of digital tourism resources. Such assistance, including through tax preferences and grant support, will expand opportunities for self-employment, as well as contribute to the creation of new jobs and the transition of those employed in tourism from informal to legal employment. At the regional level, support for the development of self-employment in tourism can be achieved through the creation of working platforms and business incubators to support start-ups in the tourism sector and partner search platforms for tourism business entities.

It should be noted that in a digitalised environment, not only gig-workers have to adapt to operations in the digital sphere, but also travel companies have to rethink their activities in response to the challenges of the gig-economy, including in the field of HRM. In this regard, the practical application of the study can be realised in identifying key changes in the relationship between gig-workers and employers in the context of the digitalisation of the tourism industry, as well as developing guidelines for harmonising their interests.