Research note

# Cross-border mergers and acquisitions activity in the tourism industry: A spatial econometrics analysis

Muhammad Farooq Ahmad<sup>a</sup>, Christos Alexakis<sup>b</sup>, Saqib Aziz<sup>b</sup>, Konstantinos Eleftheriou<sup>c, *, D</sup>

ARTICLE INFO

Keywords:

Tourism industry

Mergers and acquisitions

Cross-border deals

Spatial econometrics

A B S T R A C T

The tourism industry is one of the largest sectors of the international economy with a 10.3 percent share in global GDP while cross-border mergers and acquisitions (M&A) activity contributes 42–45 percent to aggregate foreign direct investment (FDI) flows. However, little is known about the extent to which tourism activity may affect cross-border M&A activity. We employ a spatial econometrics framework over a sample of 3849 M&A transactions from 34 countries to examine the role of tourism activity in cross-border M&A in the tourism sector. Our results indicate that tourism activity reduces M&As investment uncertainty. In addition, cultural distance negatively affects tourism’s role as a channel for M&A activity.

1. Introduction

The tourism industry is vital globally, as tourism positively affects economic growth across the globe (Paramati et al., 2017) with multiplier effects for socio-economic development indicators such as income, employment, infrastructural development, international trade, capital flows and foreign direct investments (Nunkoo et al., 2020, Ehigiamusoe et al., 2023). The scholarly contributions examining the relationship between economic growth and tourism activity have mainly focused on the tourism-led growth theory (Balaguer and Cantavella-Jorda, 2002) and acculturation theory (Segall, 1979) with these studies employing standard econometric models as well as nonlinear and nonparametric approaches (Brida et al., 2020). Tourism may also help countries improve their balance of payments, providing a crucial source of foreign currency. For many countries, especially those with limited exports in other sectors, tourism is essential in offsetting trade deficits. Furthermore, the tourism sector attracts significant levels of FDI, contributing to infrastructure development, such as hotels, resorts, and transportation networks. Hence, spending in the tourism sector positively affects the broader economy of many countries. Although providing a single tourism “general multiplier” for the international economy is not that easy, due to the diversity of economic contexts across different countries

and regions, the United Nations Environment Programme (UNEP) (https://www.unep.org/) and the UN World Tourism Organization (UNWTO) (https://www.unwto.org/) have reported multipliers in the range of 2–3 for different regions. Some studies have indicated the role of tourism activity in regional development and growth as well (Calero and Turner, 2020).

The benefits of tourism go beyond its direct impact on economic variables like GDP and employment, with indirect benefits found in the tourism ecosystem. Tourism, through a bidirectional “causality” relationship, enables socio-economic development (Pulido-Fernandez and Cárdenas-García, 2021) with more jobs, and poverty reduction, especially for women, young workers and minorities. The negative effects of COVID-19 emphasize the importance and positive contribution of the global tourism industry. According to the World Travel and Tourism Council (2022), during the first year of the pandemic (2020), 62 million jobs were lost in the travel and tourism sector. In monetary terms, the travel and tourism sector experienced losses of almost USD 4.9 trillion, a 50.4% year to year decline of its global contribution to GDP, when compared to a much smaller decline in the global economy estimated at 3.3%.

Nevertheless, according to recent reports,<fnref n="1" /> around 975 million international tourists were recorded between January and September

<footnote><sup>a</sup> SKEMA Business School – Université Côte d’Azur, France</footnote>

<footnote><sup>b</sup> Rennes School of Business, France</footnote>

<footnote><sup>c</sup> Department of Economics, University of Piraeus, Greece</footnote>

<footnote>* Correspondence to: Department of Economics, University of Piraeus, 80 Karaoli & Dimitriou Street, Piraeus 18534, Greece.
E-mail addresses: farooq.ahmad@skema.edu (M.F. Ahmad), christos.alexakis@rennes-sb.com (C. Alexakis), saqib.aziz@rennes-sb.com (S. Aziz), keleft@unipi.gr (K. Eleftheriou).</footnote>

<footnote n="1">¹ See https://ec.europa.eu/eurostat/web/products-eurostat-news/w/DDN-20230118-1 and https://data.oecd.org/industry/tourism-receipts-and-spending.htm</footnote>
2023 (+38% compared to 2022, but still -13% than 2019, before the pandemic), with international tourism revenues projected at around USD 1.4 trillion for 2023 (UNWTO, 2023). For the first three quarters of 2023, a robust post pandemic recovery was observed in air passenger numbers and hotel occupancy rates (IATA, 2023). Despite economic challenges like high inflation and the recent geopolitical tensions, international tourism is on the course of a full recovery to pre-pandemic levels in 2024 (IMF, 2023). The industry is expected to grow further in the future but will face significant challenges due to evolving environmental conditions, technological innovations, and demographic shifts (American Express Travel, 2023).

Given the greater economic significance of tourism activity (Paramati et al., 2017; Ehigiamusoe et al., 2023), the motivation of our research is to investigate, with the use of spatial quantitative analysis, the mechanisms through which tourism affects another important economic activity like mergers and acquisitions (M&A). On one hand, existing studies on tourism have mainly explored the effect of tourism activity in spurring aggregate FDI flows (Malik and Latif, 2021), inward FDI (Bannò and Redondi, 2014; Tomohara, 2016), and trade flows (Keum, 2011). These studies have substantiated tourism-led growth theory (Balaguer and Cantavella-Jorda, 2002) and acculturation theory (Segall, 1979) positing that tourist visit to a host country nurtures cultural adaptation through contact and communication with culturally dissimilar groups (Gibson, 2001). On the other hand, it is worthwhile to note that cross-border M&A activity contributes a sizable share of 42-45 percent in aggregate FDI flows across the world (UNCTAD, 2022). However, despite such a substantial economic contribution, evidence is rather scant on whether and how international tourism affects cross-border M&A activity (Zhang et al., 2020). The extant literature on determinants of cross-border M&A has focused on a wide range of factors. These include differences in economic growth and development (Alimov and Officer, 2017), institutional quality and corporate governance (Erel et al., 2012), collective bargaining and favorable tax laws (Ahmad and Lambert, 2019), trade flows (Ahmad et al., 2021), corporate social responsibility and environmental sustainability (Ahmad et al., 2024; Arouri et al., 2019), cultural distance between countries of acquirer and target firms (Ahern et al., 2015), cultural orientation of target firms (Ahmad et al., 2022) and official state visits (Aleksanyan et al., 2021) in spurring cross-border M&A activity.

In this backdrop, we observe a clear research gap that exists in investigating how tourism activity may affect cross-border M&A activity. More specifically, triggered by the limited existing evidence, and the encouragement for future research for cross-border M&A in the hotel industry (Zhang et al., 2020), we attempt to investigate the potential role of tourism activity in spurring cross-border M&A activity in the tourism sector employing sophisticated spatial econometrics tools. The novelty of spatial econometrics models lies in their ability to analyze and model spatial dependencies, which are manifested through the cross-border M&A activity in our case. In technical terms, spatial econometrics allows overcoming the issue of biased estimation of traditional techniques, in the presence of spatial dependencies.

We document a direct positive effect of tourism activity on the aggregate cross-border M&A activity in the tourism sector in both the short- and long-run. Our analysis also highlights a negative indirect effect of tourism activity on cross-border M&A in both short- and long-run suggesting that amid cultural distance between two countries, a rise in tourism activity in one country will lead to a decline in the cross-border M&A activity in the other country, potentially due to the substitution effect. Finally, the total effect (defined as the summation of the direct and the indirect effects) of tourism activity on cross-border M&A activity remains negative. Our results remain robust after addressing concerns

relating to the econometric specification such as criticism over identification of the Spatial Durbin Model (SDM) and using a different spatial matrix to perform the analysis.

This study makes several contributions to the existing literature. First, we expand the limited evidence of cross-border M&A activity in the tourism industry by adding significant explanatory variables and including a large number of international M&A transactions making our study a cross-country/territory study. To the best of our knowledge, this is the first study that investigates the role of tourism in spurring M&A activity using a spatial framework. Unlike the work of Aleksanyan et al. (2021) that focuses on the effect of official state visits over cross-border M&A, we investigate the effect of general tourism activity over cross-border M&A activity in the tourism sector. Second, we connect the dots between two widely explored theories such as the tourism-led growth theory (Balaguer and Cantavella-Jorda, 2002) and the acculturation theory (Segall, 1979; Gibson, 2001) in the cross-border M&A setting by showing the way cultural distance matters in the nexus between tourism and cross-border M&A activity – a vital external growth strategy for corporations. Moreover, we also contribute to the literature on determinants of cross-border investment flows by moving beyond the widely investigated relation of tourism on aggregate FDI flows (Tomohara, 2016; Malik and Latif, 2021) and provide a deeper insight by particularly investigating the relation between tourism activity and cross-border M&A through the lens of cultural proximity among nations. Finally, the spatial models we use can capture the impact of cultural proximity giving a clearer picture of the relationship between tourism and financial activity such as cross-border M&A.

## 2. Theoretical background

The tourism industry is extremely important globally, bringing economic prosperity and feelings of happiness to millions of people every year. The tourism output amounted to USD 3.5 trillion in 2019, or 4% of world GDP, measured in tourism direct gross domestic product (TDGDP). Before the COVID-19 pandemic, travel and tourism (including its direct and indirect, impacts) was one of the world's largest industries, accounting for 1 in 4 of all new jobs created globally, 10.3% of all jobs, with women making up to 54% of the tourism workforce, and 10.3% of global GDP (World Travel and Tourism Council, 2022). In 2019, USD 1.8 trillion in export revenues from international tourism was produced, equivalent to 28% of the world's trade in services and 7% of overall exports of goods and services. It is important to note that, for some weaker economic countries, e.g. small island developing states, tourism represented over 50% of total exports (UNWTO, 2023). In macroeconomic terms the tourism industry has positive effects on the balance of payments, exports and trade deficits, job creation, output, FDI and, infrastructure development. The COVID-19 pandemic had an extremely negative effect on tourism, while a strong recovery of the tourism sector is observed after the pandemic, bringing optimism to the industry. Global tourism is expected to experience further growth in the future, triggered by globalization, demographic changes and technology, which is expected to simplify travel planning, booking and other tourism logistics (American Express Travel, 2023).

The economic significance of the tourism industry motivated several research papers to relate tourism activity to significant economic variables like political stability, exports, GDP or trade and investigate the impact of tourism on foreign direct investments (Malik and Latif, 2021). In addition to economic variables, financial variables like hotels' assets, revenues and operational costs were also used, in an effort to relate hotel customers' satisfaction to the hotels' financial performance (Alexakis et al., 2021). Tomohara (2016), analyzed international tourism inward
FDIs relationships with Japanese data for the period 1996–2011. The results of that study indicate that inbound foreign tourism affected the inward FDI beyond tourism-related sectors. Based on the results obtained, coordination of the Japanese government authorities is proposed for FDI promotion and economic development. In another FDI study, statistical evidence indicates that new airline destinations increased inward FDI, because of lower transportation costs and improved knowledge of the host country (Bannò and Redondi, 2014), with a more pronounced effect for non-stop flights (Fageda, 2017). Similar results are reported for trade flows by Keum (2011) suggesting that tourism Granger “causes”<fnref n="2" /> trade in Korea.

These results can also be viewed through the lens of acculturation theory (Segall, 1979) corroborating widely investigated tourism-led growth theory (Balaguer and Cantavella-Jorda, 2002). These results also suggest that tourist visit to a host country nurtures cultural adaptation through contact and communication with culturally dissimilar groups (Gibson, 2001). This, in turn, may soften possible perceived ideas regarding the host country’s entrepreneurial ecosystem, reducing possible economic uncertainties related to investments in the host country.

Cross-border M&A activity is considered as a significant contributor (42%-45%) to aggregate FDI flows (UNCTAD, 2022). A plethora of research exists on a wider set of firms, industry, and countries' social and economic determinants of cross-border M&A activity (Cumming et al., 2023). For instance, the extant literature suggests that differences in acquirer and target countries' economic development and growth (Alimov and Officer, 2017), exchange rate differences (Erel et al., 2012), trade flows (Ahmad et al., 2021) and stock market valuations (Harford, 2005) may propagate M&A activity. Some other studies explain international M&A activity in the context of regulatory standards, institutional and corporate governance related issues (Xie et al., 2017). For instance, collective bargaining processes (Ahmad and Lambert, 2019) and favorable tax laws (Erel et al., 2012) may positively influence M&A activity.

A rather nascent stream of literature examines the role of culture and cultural distance in driving cross-border M&A (Ahern et al., 2015; Ahmad et al., 2022; Dikova et al., 2010; Popli et al., 2016). These studies find that cultural origins and cultural distance (Ahern et al., 2015) between home and host countries along with cultural orientation (Ahmad et al., 2022) of target firms are important to understand M&As outcomes. While cultural distance is a widely used proxy of distance to study the effect of informal institutions in the international business setting (Popli et al., 2016), its effect on firms' decisions and economic outcomes is not homogenous (Ahern et al., 2015; Dikova et al., 2010). More specifically, the effect of cultural distance is contingent upon firms' cultural experience reserve and industry affiliation (Dikova et al., 2010; Popli et al., 2016). Therefore, paying attention to the role of contextual factors such as international tourism activity in understanding the effect of cultural distance or proximity over M&A activity is of vital significance. However, we observe a void in the literature that delves into the role of international tourism in spurring cross-border M&A activity through the lens of cultural distance. Closer to the context of tourism, Aleksanyan et al. (2021) study the effect of state visits over M&As between two countries. The authors find a higher likelihood of visiting country firms acquiring firms in the host country primarily due to increased business networking and clearer investment environment. Moreover, Zhang et al. (2020) in a comprehensive study discovered limited empirical and conceptual papers related to M&A in the hotel industry, and proposed directions for future research in the field. In this setting, this study attempts to fill the gap in the literature by investigating the extent to which tourism activity affects cross-border M&A in the tourism sector. Our research focuses on the effect of

tourism activity on M&A, based on the fact that when two countries are distant in terms of culture, the administrative investigation costs of a merger can be high and thus discouraging for the involved parties. In such a case, exchange of cultural goods may help to reduce information costs and “breed investment” (Huberman, 2001). Empirical evidence suggests the positive effect of cultural imports on M&A performance (Li and Yang, 2020).

In the empirical part which follows, motivated by the significance of the tourism sector in an economic system and people's lives, as well as the surge towards future research on cross-border M&A in the hotel industry (Zhang et al., 2020), we attempt with the use of spatial econometrics modeling, to investigate the potential role of tourism activity in spurring cross-border M&A activity in the tourism sector. In this respect, we expand the existing literature on FDI by focusing on M&A and we extend the tools used with the use of spatial econometrics. Spatial econometric models are useful in analyzing economic data that has a geographical or spatial dimension especially in the field of empirical economics and finance, where a 'virtual explosion' of studies has been noticed ranging from asset correlation in different geographical markets and risk contagion to credit risk analysis (Abdallah et al., 2022). To the best of our knowledge, this is the first study to make this combination of M&A in the tourism industry with spatial modeling.

### 3. Data and methodology

We use data for M&A deals in the tourism sector announced during the period 2009–2019. Following Ahmad et al. (2021), we drop M&A transactions in which the bidder and target firms are government, joint ventures, or mutual funds, and in the cases when the acquisition type is a buyback, exchange offer, recapitalization, or acquisition of partial interest. Moreover, countries without M&A data were excluded from our sample. The year used as reference for the M&A transaction is the year of the M&A announcement. The application of the above filters yielded a sample of 3849 M&As transactions, for 34 countries over the sample period. Finally, all missing values were extrapolated/interpolated using the nearest neighbor method.<fnref n="3" />

Table 1 provides the variables used in the model (together with references from studies using similar metrics for the variables), definition and data sources. The variables are selected to reflect the M&A activity, the economic environment (GDP growth rate), and the size of the tourism market (number of tourists and number of rooms). The number of tourists is obviously related to the tourism market from the side of demand, while the number of rooms from the side of supply. In addition, the number of rooms as a fixed asset, is related to customer satisfaction as this is important for profitability (Alexakis et al., 2021). From an M&A perspective, variables such as the number of rooms can help acquirers assess potential avenues of synergies, efficiency improvement and identify growth opportunities within the target country's tourism sector. In tourism quantitative studies, several variables can be used to measure tourism activity like tourism revenue per capita, average expenditure per tourist or hotel occupancy rates. Our variables (number of tourists and number of rooms) are characterized as simple and direct measures of a tourism market and are not affected by factors like inflation, exchange rates, or spending patterns, making them more representative for our research question which is related to cross-border business activity. In addition, hotel occupancy ratios can be noisy indicators since they can be influenced by factors like seasonality or can be distorted by business travel or temporary events.

A geographical picture of cross-border M&A activity and tourism

<footnote n="2">² The term “causality” in the Granger “causality” tests is used to describe in econometric terms linear time precedence (Granger, 1969).</footnote>

<footnote n="3">³ The selection of the nearest neighbor method for handling missing values in our dataset was based on its advantage in preserving the structure of the original dataset (Yan et al., 2021; Beretta and Santaniello, 2016). Moreover, this method may exhibit better performance than other more complex methods (Li et al., 2022).</footnote>
<table><caption>**Table 1**
Variable definitions and data sources (numbers and dollar values are in millions).</caption><thead><tr><th>Variables</th><th>Definition</th><th>Source</th><th>Reference</th></tr></thead><tbody><tr><td>A. Dependent variable:</td><td></td><td></td><td></td></tr><tr><td>CBM&amp;A</td><td>Total dollar value of cross-border M&amp;A deals (inbound and outbound) / corresponding number of M&amp;A deals</td><td>Refinitiv Eikon<br>database</td><td>Authors calculation following Ahmad et al. (2021)</td></tr><tr><td>B. Independent variable and control variables</td><td></td><td></td><td></td></tr><tr><td>TE</td><td>Total dollar value of tourism (inbound and outbound) travel expenditures</td><td>UNWTO</td><td>Ehigiamusoe et al. (2023), Malik and Latif (2021)</td></tr><tr><td>ITE</td><td>Number of inbound tourists from Europe</td><td>UNWTO</td><td>Ehigiamusoe et al. (2023), Tomohara (2016)</td></tr><tr><td>ITAP</td><td>Number of inbound tourists from East Asia and Pacific</td><td>UNWTO</td><td>Ehigiamusoe et al. (2023), Tomohara (2016)</td></tr><tr><td>NR</td><td>Number of rooms</td><td>UNWTO</td><td>Abrate and Viglia (2016)</td></tr><tr><td>GDP</td><td>GDP growth rate (%)</td><td>World Bank</td><td>Aleksanyan et al. (2021)</td></tr></tbody></table>

<figure><img src="image_1.png" /><figcaption>**Fig. 1.** Average CBM&A (in millions USD). Source: Map (GIS Boundary Files) was retrieved from https://international.ipums.org/international/gis.shtml.</figcaption></figure>

<figure><img src="image_2.png" /><figcaption>**Fig. 2.** Average TE (in millions USD). Source: Map (GIS Boundary Files) was retrieved from https://international.ipums.org/international/gis.shtml.</figcaption></figure>

expenditure is presented in Figs. 1 and 2.

The Pesaran (2004) test, for cross-sectional dependence, indicates that CBM&A exhibits cross-sectional dependence (p-value=0.000) confirming the need for a spatial specification. This result is also consistent with the possible hypothesis that M&A activity in the tourism sector may be affected by the counties' cultural distance. Within this context, the advantage of using spatial econometrics modeling is twofold: (i) we

control for the bias due to the existence of spatial dependence (Elhorst, 2014) – which in our case is manifested through cultural distance and (ii) given (i), we quantify the size of the spillover effects – which is important from a policy-making point of view.

We estimate the following Dynamic Spatial Durbin Model (SDM)<fnref n="4" />:

<footnote n="4">⁴ The Spatial Durbin Model (SDM) nests both the Spatial Autoregressive Model (SAR) and the Spatial Error Model (SEM) as special cases. In this respect, it can be considered as a generalization of the above models (SEM, SAR). Furthermore, our model selection is based on the reasonable assumption that both dependent and independent variables may exhibit spatial dependencies.</footnote>
$$ CBM\&A_{it} = a + \tau CBM\&A_{it-1} + \rho \sum_{j=1}^{N} \hat{w}_{ij} CBM\&A_{jt} + \beta_1 TE_{it} + \theta_1 \sum_{j=1}^{N} \hat{w}_{ij} TE_{jt} \\ + \beta_2 ITE_{it} + \theta_2 \sum_{j=1}^{N} \hat{w}_{ij} ITE_{jt} + \beta_3 ITAP_{it} + \theta_3 \sum_{j=1}^{N} \hat{w}_{ij} ITAP_{jt} \\ + \beta_4 NR_{it} + \theta_4 \sum_{j=1}^{N} \hat{w}_{ij} NR_{jt} + \beta_5 GDP_{it} + \theta_5 \sum_{j=1}^{N} \hat{w}_{ij} GDP_{jt} + \mu_i \\ + \gamma_t + \varepsilon_{it} \quad (1), $$

where i denotes the country and t denotes the year, μᵢ and γᵢ denote the country and time fixed effects, and εᵢt denotes the disturbance term. Furthermore, ̂wᵢⱼ is the ith element of a row standardized weighting matrix W, with ̂wᵢⱼ = Σⱼ wᵢⱼ, Σⱼ wᵢⱼ = 1, and wᵢⱼ = { 1/dᵢⱼ if i ≠ j & dᵢⱼ ≠ 0 where dᵢⱼ is the measure of the cultural distance between countries i and j.<fnref n="5" /> For the calculation of dᵢⱼ, we rely on the six dimensions model of national culture (Hofstede et al., 2010).<fnref n="6" /> This data was added to create a single value for each country and the absolute difference between pairs of countries was taken. We use the inverse of the absolute difference so that lower values correspond to more cultural dissimilarity. The lagged dependent variable CBM&Aᵢᵢ₋₁ and the country effects in (1) allow us to address any endogeneity issues due to omitted variables (Baltagi, 2005). Wooldridge (2010) test of strict exogeneity is used to test for the endogeneity of tourism expenditure (TE) variable due to potential simultaneity, and suggests that the above variable is exogenous (p-value = 0.5681). Finally, we use a Quasi-Maximum Likelihood Estimator for the estimation of (1) since it is the preferred option when the time-lagged dependent and the spatially lagged variables are the only endogenous variables (Lee and Yu, 2010).

## 4. Results and discussion

The obtained results for our analysis are presented in Table 2. Our results indicate that the total amount of tourism travel expenditure (variable TE), as an indicator of tourism activity, has a direct positive effect on the aggregate tourism sector M&A activity (CBM&Aᵢᵢ) in both the short- and long-run. In other words, an increase in the tourism activity in a specific country will positively affect its tourism sector M&A activity. This result is outside the standard framework of the M&A activity which focuses mainly on financial factors and mainstream economic theory, e.g. M&A due to vertical or horizontal operational expansion or economies of scale for cost reduction. Supporting the acculturation hypothesis (Segall, 1979), our findings suggest that a tourism-induced acculturation and information exchange creates an environment of trust and reduces investment uncertainty. These results broadly resonate with the findings for state visits (Aleksanyan et al., 2021) mentioned above. Moreover, these findings also corroborate with the tourism-led growth theory (Balaguer and Cantavella-Jorda, 2002) given the vital contribution cross-border M&As activity makes to a significant economic indicator such as aggregate FDI flows (Ehigiamusoe et al., 2023).

Concerning the corresponding indirect (spillover) effect, we observe a negative and statistically significant coefficient in both the short and

long-run. This implies that, in the presence of cultural distance, an increase in the tourism expenditure in a country, say j, will lead to a decrease in the cross-border M&A activity in another country, say i, potentially due to a substitution effect. Specifically, greater tourism activity in competitor countries (as defined by the cultural distance) may drive away M&A activity from the country under consideration. The accumulation of this effect across countries can substantially increase the size of spillover effects above that of direct effects (LeSage and Dominguez, 2012). These results resonate with the findings documented in Ahmad et al. (2022) on the role of culture in M&A activity. We believe that the above findings add to the growing evidence that factors other than pure economic variables like profits or costs may influence investment decisions (Kräussl et al., 2024). Finally, in terms of results, the total effect (the sum of the direct and the indirect effects) is negative implying that the indirect effect dominates the direct effect in driving the cross-border M&As activity in the tourism industry.

Our findings indicate that the benefits of tourism in the economy of a country could be optimized through a synchronized effort of different national authorities and a harmonization of national policies regarding enhancement of cultural proximity. Nowadays, diplomacy is evaluated based on the achievement of good economic deals. A close collaboration of the national Ministries of Foreign Affairs with business and tourism authorities like chambers of commerce and national tourism organizations could create benefits for cross-border economic agreements like M&A that could bring economic prosperity to the involved partners.

### 4.1. Robustness checks

To further test the validity of our results, we perform two robustness tests. First, based on the critique by Gibbons and Overman (2012) on the identification of the SDM, and the fact that from the explanatory variables, only TE and GDP appear to have significant spatially lagged coefficients, we re-estimate our specification by spatially lagging the above two independent variables. Second, taking as premise the specification of the first robustness check, we perform the estimation using a different spatial matrix. Specifically, the ith element of the row standardized weighting matrix ̂W is now defined as ̂wᵢⱼ = Σⱼ wᵢⱼ, Σⱼ wᵢⱼ = 1, and ̂wᵢⱼ = { 1 if i ≠ j & 1/dᵢⱼ ≥ m where m is the average of the upper triangular (or lower triangular) elements of the symmetric (non-standardized) version of the spatial matrix W (as defined earlier in our baseline specification) where 1/dᵢⱼ = max(1/dᵢⱼ) if dᵢⱼ = 0 for i ≠ j. The obtained results, which are available upon request, corroborate the findings of our baseline model. A visual inspection of the changing colorations in Figs. 1 and 2 above can serve as an initial indication of the obtained statistical results.

## 5. Conclusion

Based on our results, an increase in the tourism activity in a specific country will positively affect its tourism sector M&A activity. More analytically, our analysis reveals a direct (indirect) positive (negative) effect of tourism activity on the aggregate cross-border M&A activity in the tourism sector in both the short- and long run. On one side, results on direct effect support acculturation hypothesis as tourism-induced acculturation and information exchange creates an environment of trust and reduces investment uncertainty to increase cross-border M&A activity in one country amid rise in tourism activity in the other country.

We believe that the obtained results are important for market practitioners in the tourism and financial sectors, as well as for policymakers involved in designing national tourism attraction strategies. Policy makers should consider the fact that tourism activity may accelerate

<footnote n="5">⁵ Given that the travel constraints imposed by physical distance in the past are now not met, due to the development of transportation means, we consider cultural distance as a more appropriate metric for capturing tourism demand in our analysis (an assumption consistent with the relevant literature – see Ng et al., 2007). Furthermore, there is a strong correlation between cultural and physical distance (Joo et al., 2017).</footnote>

<footnote n="6">⁶ https://geerthofstede.com/research-and-vsm/dimension-data-matrix/ Six cultural dimensions include power distance, individualism, uncertainty avoidance, masculinity, long-term orientation, and indulgence.</footnote>
<table><caption>**Table 2**
Estimation results – baseline model.</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2">Coefficients (1)</th><th colspan="2">Panel A: Short-run effect</th><th rowspan="2">Total effects (4)</th><th colspan="3">Panel B: Long-run effects</th></tr><tr><th>Direct effects (2)</th><th>Indirect effects (3)</th><th>Direct effects (5)</th><th>Indirect effects (6)</th><th>Total effects (7)</th></tr></thead><tbody><tr><td>CBM&amp;A<sub>t-1</sub></td><td>-0.0113<br>(0.0532)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TE</td><td>0.00112 *<br>(0.000612)</td><td>0.00144 ***<br>(0.000553)</td><td>-0.0119 **<br>(0.00476)</td><td>-0.0104 **<br>(0.00493)</td><td>0.00142 **<br>(0.000547)</td><td>-0.0118 **<br>(0.00471)</td><td>-0.0104 **<br>(0.00489)</td></tr><tr><td>ITE</td><td>6.521<br>(4.279)</td><td>7.123 *<br>(4.154)</td><td>-7.358<br>(11.07)</td><td>-0.235<br>(11.30)</td><td>7.041 *<br>(4.107)</td><td>-7.274<br>(10.96)</td><td>-0.233<br>(11.20)</td></tr><tr><td>ITAP</td><td>-7.542 ***<br>(2.469)</td><td>-7.910 ***<br>(2.395)</td><td>12.75<br>(11.54)</td><td>4.842<br>(11.89)</td><td>-7.818 ***<br>(2.368)</td><td>12.62<br>(11.43)</td><td>4.797<br>(11.78)</td></tr><tr><td>NR</td><td>-41.98<br>(42.24)</td><td>-40.33<br>(40.85)</td><td>-14.26<br>(116.0)</td><td>-54.59<br>(124.1)</td><td>-39.89<br>(40.39)</td><td>-14.24<br>(114.9)</td><td>-54.13<br>(123.0)</td></tr><tr><td>GDP</td><td>2.201<br>(5.734)</td><td>3.400<br>(5.478)</td><td>-42.25 ***<br>(13.84)</td><td>-38.85 ***<br>(13.08)</td><td>3.349<br>(5.417)</td><td>-41.86 ***<br>(13.70)</td><td>-38.51 ***<br>(12.96)</td></tr><tr><td>W*CBM&amp;A</td><td>0.277 ***<br>(0.0868)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>W*TE</td><td>-0.0142 **<br>(0.00568)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>W*ITE</td><td>-6.668<br>(14.89)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>W*ITAP</td><td>12.27<br>(14.58)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>W*NR</td><td>-34.97<br>(145.6)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>W*GDP</td><td>-51.65 ***<br>(16.59)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>340</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of countries</td><td>34</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Log-Likelihood</td><td>-2276</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td colspan="8">Specification tests</td></tr><tr><td>Spatial Durbin Model (SDM) vs. Spatial Error Model (SEM) likelihood ratio test (χ<sup>2</sup>(5))</td><td></td><td></td><td></td><td></td><td>17.89 ***</td><td></td><td></td></tr><tr><td>Spatial Durbin Model (SDM) vs. Spatial Autoregressive Model (SAR) (χ<sup>2</sup>(5))</td><td></td><td></td><td></td><td></td><td>18.54 ***</td><td></td><td></td></tr></tbody></table>

Notes: Robust standard errors are reported in parentheses. * **, * * and * indicate significance at the 1 %, 5 % and 10 % level, respectively. The SDM vs. SEM likelihood ratio tests for the common factor constraint (Florax et al., 2003). The SDM vs. SAR test reports the Wald test of the joint significance of all the spatially lagged explanatory variables. The results of both specification tests point to a Spatial Durbin Model (SDM). The regression results were produced by the Stata command -xsmle- (Belotti et al., 2017).

general economic activity with positive effects for the involved economies. Based on that evidence, more policies should be designed targeting to build cultural bridges as tourism. For example, campaigns promoting tourism may lead to more tourism activity, which in turn could boost economic activity and bring economic prosperity in the societies of the bridged countries. Given the significant contribution of cross-border M&A activity to FDI, policymakers might focus on developing effective tourism strategies to help overcome possible cultural and informational barriers between acquiring and target ethnic groups. Such strategies could promote greater cross-border M&A activity overall and within the tourism sector specifically. For the case of market practitioners, cultural exchange projects, like possibly financing research collaboration of research centers or universities, may contribute to cultural proximity, reduce information costs and “breed investment” like M&A (Huberman, 2001).

In nutshell, this study underlines the important role that tourism activity can play in stimulating cross-border M&A activity. This, in turn, provides firms with opportunities for rapid growth across borders while fostering increased employment opportunities and economic development in target countries.

### CRediT authorship contribution statement

**Alexakis Christos:** Writing – original draft, Supervision, Conceptualization. **Ahmad Muhammad Farooq:** Writing – review & editing, Supervision, Conceptualization. **Eleftheriou Konstantinos:** Writing – original draft, Methodology, Formal analysis, Conceptualization. **Aziz Saqib:** Writing – review & editing, Data curation, Conceptualization.

### Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

We thank three anonymous referees for constructive comments and suggestions and Konstantinos Christopoulos for providing us with excellent research assistance. The usual disclaimer applies.

### Data availability

The authors do not have permission to share data.

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