Royalty as a determinant of inbound tourism in the UK

Tourism is a normal good, the demand for which depends primarily on price and income. Other factors that affect the volume of inbound tourism and the revenue derived from it include the ease of obtaining a visa, accessibility (in terms of the availability of frequent flights to the destination) and turmoil (terrorism and political uncertainty). The ability of countries to attract tourists (foreign tourists, to be precise) also depends on a variety of factors, including culture and history, religious landmarks, natural wonders, historic landmarks, entertainment venues, climate, and events such as carnivals, exhibitions and sporting events.

In the United Kingdom, the British monarchy is not merely a historical institution – it is a global brand. This brand is frequently touted as a unique and invaluable asset to the United Kingdom's tourism industry. Prominent voices in the media, monarchist groups and political spheres assert that the royal family draws millions of visitors and generates substantial revenue, with estimates from organizations like Brand Finance valuing its tourism contribution in the billions of pounds. Monarchists suggest that the royal family is a major reason why foreign tourists visit the UK, but it is not clear how the attraction works. Three explanations can be detected from the debate: (1) no explicit reason is given, in which case it is assumed that the attraction lies in the royal persona; (2) foreign visitors are attracted by the desire to see royal landmarks; and (3) visitors are attracted by royal events such as weddings and births. A typical response to the proposition that the royal family attracts tourists is that France is not a monarchy, yet the country attracts almost three times as many tourists.

Evidence for whether visitors are attracted by royal persona or landmarks is not publicly available. This evidence can be obtained through surveying foreign visitors about their motivations to visit the UK. However, evidence on whether foreign visitors are attracted by royal landmarks can be derived from observing where tourists go, what they do and what they spend money on. The proposition that foreign tourists are attracted by royal events can be tested by estimating a model where inbound tourism is explained through traditional factors while accounting for royal events.

A notable gap exists in tourism economics literature regarding empirical validation of the British monarchy's influence on inbound tourism. While reviews like Song and Li (2008) identify income and price as primary determinants of tourism demand, and Palmer and Long (2018) examine European royal families' economic effects, none directly assess the impact of royal events in the UK context. Management literature, including Balmer et al. (2006), has conceptualized the monarchy as a powerful “corporate brand,” supporting claims of its tourism appeal in media and consultancy reports (e.g. Joseph, 2017). However, a disconnect exists between this branding narrative and the absence of formal econometric evidence. This study bridges this gap. We employ a structural time series model with time-varying parameters (UCM-TVP) to test whether royal events have affected UK inbound tourism. This approach quantifies the marginal effect of royal events while controlling for fundamental economic determinants—world GDP and real effective exchange rate (REER). The study extends literature by providing a methodologically rigorous counterpoint to branding narratives.

The objective of this paper is to examine the propositions that foreign visitors to the UK are attracted by royal landmarks and events. The first proposition is examined by observing the revealed preferences of tourists. For the second proposition, formal empirical evidence is presented by estimating and testing a time series model with unobserved components.

This study makes several contributions. Empirically, it delivers a rigorous econometric examination of how distinct royal events influence the UK's inbound tourism. Methodologically, it showcases the analytical strength of the UCM-TVP framework in modelling tourism demand, presenting a dynamic and context-sensitive alternative to conventional static methods. From a policy perspective, the findings challenge a longstanding and widely accepted assumption, providing evidence that can support a more grounded and strategically informed tourism agenda for the UK— anchored in demonstrable national assets rather than assumed symbolic appeal.

Those suggesting that the royal family and royal landmarks represent the biggest attraction for tourists make it sound as if the UK has nothing else to offer. For example, Joseph (2017) suggests that “the royals draw millions of tourists every year”, referring as evidence to the observation that “in 2016, over 2.7 million tourists visited Buckingham Palace, Windsor Castle, the Palace of Holyroodhouse and other key royal attractions”. What he fails to say that the British museum alone, which is not a royal landmark, attracts more than 6 million visitors each year, and the same is true of the National Gallery and Tate Modern. The list of reasons why people want to visit the UK is extensive. Here are examples rather than an exhaustive listing.

The UK offers traditions like cheese rolling in Gloucestershire, fireball whirling in Scotland, Morris Men dancing with sticks, and hobby horses terrorizing villages on May Day. There are magnificent gardens including RHS Wisley Garden, Fountains Abbey and Studley Royal Water Garden, the Gardens of Argyll and Bute, Bodnant Garden Powys Castle, and Blenheim Park and Gardens. People visit the UK to see where the D-Day operation started, where Dunkirk evacuation ended, where Titanic began its only voyage, where Mayflower departed for the new world, and the path of Jarrow marchers to Commons in 1936.

The UK is the country where the industrial revolution started, football was invented (and so were ping pong and rugby), the first grand slam tennis tournament was staged, and the first underground railway system was built. It is the country where the computer, radar, and test-tube baby were invented. It is the country that hosts the largest foreign exchange centre and eurocurrency centre in the world. It is the country of Isaac Newton, Charles Darwin, Stephen Hawking, Michael Faraday, Edmond Halley, Alexander Graham Bell, Alexander Fleming, and Robert Boyle. It is the country of William Shakespeare, Charles Dickens, Graham Greene, Somerset Maugham, Agatha Christie and Arthur Conan Doyle. It is the country of James Bond, Dixon of Dock Green, Sherlock Holmes, Robin Hood, Harry Potter and Winnie the Pooh. It is the land of the Beatles, the Rolling Stones, Queen and Mungo Jerry. It is the land of great economists like J.M. Keynes, Adam Smith, Alfred Marshall, John Hicks, John Stuart Mill, Arthur Pigou, and Thomas Malthus. Last, but certainly not least, it is the land of the great invention: fish and chips wrapped in page three of The Sun. One must not forget other delights such as Bramley apple pie, Cheddar cheeses, Cheshire cheeses, Lancashire hot pot, Yorkshire pudding and the great Scottish invention, haggis.

No evidence is available for the proposition that tourists choose the countries to visit on the basis of whether they are kingdoms or republics. The fastest growing inbound tourist destinations can be found in republics that used to be monarchies. According to the figures supplied by Tourism Alliance (2017), the fastest growing inbound destinations are Serbia (48%), Romania (47%), China (46%), Latvia (45%) and Lithuania (37%). Furthermore, historical places are visited whether or not they are associated with the reigning monarchy. Foreign tourists in the UK do not only visit Buckingham Palace and Windsor Castle, but also Stonehenge, the Neolithic Orkney UNESCO World Heritage Site, the Castles of William the Conqueror, Roman baths and ruins, Big Ben, Canterbury Cathedral, Durham Cathedral, and National Memorial Arboretum.

The facts and figures indicate preference for places of interest other than royal landmarks. According to the figures provided by VisitBritain (2012), visitors are more interested in restaurants, shopping, gardens, museums and pubs than the royal estate. The revenue derived from ticket admissions to the royal estate is nowhere near the alleged hundreds of millions of pounds claimed by monarchists. No royal estate appears on the list of top 20 attractions arranged by the number of visitors ^[1]. The British Museum comes on top with over six million visitors per year. Even Chester Zoo, which not many people have heard of, appears on the top 20 list. Windsor Castle appears as number 23 whereas Buckingham Palace is number 66. The numbers simply do not support the proposition that foreign visitors are attracted mainly by royal landmarks.

A large number of studies have been conducted to identify the determinants of inbound tourism for various countries. These studies include Falk (2014), Brown (2015), Nkosi (2010), Fernando et al. (2013), Seetaram et al. (2016), Jayathilake (2013), Selvanathan (2006), Selvanathan et al. (2009) and Fernando et al. (2013). Determinants of the demand for inbound tourism, as identified by these studies, include the real exchange rate, income level of the tourists' home country, the cost of tourism, travel cost, foreign direct investment, weather, crime against tourists and political instability. None of these studies identify kingdoms versus republics as a determining factor. Table 1 contains a summary of another set of studies that show the most common factors that appear in the literature as determinants of inbound tourism.

Attempts have been made to identify determinants of UK inbound tourism, but no UK studies consider a royal variable. Blake and Cortes-Jiménez (2007) identify main drivers of demand for inbound tourism as foreign income and relative prices, proxied by the real exchange rate. They mention taste and marketing as secondary factors. Taste is sometimes represented by a time trend, implying movement either for or against the destination at a constant rate. This treatment of taste is difficult to justify and interpret, as taste does not necessarily change at a constant rate. Marketing is typically measured by promotional expenditure but rarely appears in tourism demand functions due to lack of reliable data. They also mention one-off events, represented by dummy variables, such as the Olympic Games (positive effect) and terrorism (negative effect). Royal events are not mentioned, though they can be treated like any other one-off event.

Likewise, Durbarry and Sinclair (2001) measure tourism demand sensitivity in the UK to prices, exchange rates and expenditure. They use a gravity model for tourist expenditure from different origins and estimate two versions by including tourism price and exchange rate separately in the first model and effective tourism price (combining both) in the second. One problem with using the gravity model for tourism demand is the implication that the UK should receive more visitors from France, Belgium and the Netherlands than from Italy, the Middle East and North America. This is not necessarily true in this age of cheap air travel. The British Tourist Authority (2001) found that 98% of foreign tourist spending variation was explained by OECD countries' GDP (proxy for source countries' incomes) and the pound's trade weighted exchange rate (proxy for UK visit costs). The study showed foreign tourism demand in the UK is highly cost-sensitive, determined primarily by the effective exchange rate. The exchange rate elasticity of tourism was −1.3—earnings from foreign tourists decline by 13% for a 10% pound appreciation, making UK visits more expensive. An income elasticity of 0.6 means tourism earnings increase by 6% for a 10% rise in foreign tourist income. Thus, the demand for tourism is price elastic but income inelastic.

Other determinants of tourism, which are not easy to measure, are accessibility and visa requirements. The UK excels in accessibility, with numerous flights arriving from worldwide. However, obtaining a British visa hampers tourism growth. VisitBritain (2012) suggests that “the UK visitor visa regime has been singled out as a major factor inhibiting Britain's competitiveness as a destination for international tourism”. British visa application requirements are more elaborate than other European countries. Tourism Alliance (2017) suggests that despite visa offering improvements over five years, since biometric visas were introduced in 2008, the UK's share of outbound tourism from China, Russia and India has declined by 30%. In emerging markets, Sharma et al. (2023) found that GDP per capita, trade openness, exchange rate, and infrastructure significantly influence tourist arrivals in India, confirming that macroeconomic stability attracts foreign tourists. Darıcı et al. (2023) showed that income levels, relative prices, and geopolitical risk indices shape international tourism flows to emerging markets. Din et al. (2025) distinguished between economic and non-economic drivers, revealing that institutional quality and governance effectiveness enhance tourism inflows. Their study showed that institutional quality and good governance strengthen destination image and tourism demand, demonstrating that tourism is influenced by both macroeconomic factors and governance indicators.

The stylized facts and empirical results are based on annual data obtained from the World Bank database and updated from various sources to cover the period 1995–2022 ^[2]. Three indicators represent inbound tourism: the number of tourists arriving in the UK and the revenue generated from foreign tourists (tourism receipts), measured in dollar and pound terms. International tourism receipts are expenditures (in US dollars) by international inbound visitors, including payments to national carriers for international transport and prepayments for goods or services received in the destination country. Dollar receipts are converted into pound receipts at the annual average exchange rate between the two currencies.

Two explicit explanatory variables are used: the pound's REER and world GDP. The REER is a measure of the value of a currency against a weighted average of several foreign currencies divided by a price deflator or index of costs. GDP is measured at constant 2015 prices, converted into US dollars at the official exchange rates.

The original sample period, obtained from the World Bank, covers the period 1995–2019. The sample was subsequently extended up to 2022. The 2022 World GDP figure was estimated by applying the forecast growth rate as suggested by the IMF's World Economic Outlook. Data on the number of arrivals and tourism receipts were obtained for the years 2020–2022 from Osborn (2022). The tourism 2022 figures were obtained by annualizing the latest available quarterly and monthly figures found in Osborn (2022).

Model specification can be inspired by observing the three indicators of inbound tourism as shown in Figure 1 over the period 1995–2022, where the number of tourists is measured in million whereas tourism receipts are measured in billions of dollars and pounds. It is readily observed that tourism indicators move in trends and cycles, closely following economic activity. In the pre-pandemic period, two dips occurred in 2001 and 2009, followed by recoveries. These two dips correspond to the recessions of the early 2000s, which followed the dot.com bubble, and the great recession of 2009, which followed the global financial crisis. The Brexit year, 2016, is interesting as it witnessed a 10% depreciation of the pound in effective terms. As a result, the number of arrivals went up by 6.4%, whereas tourism receipts in dollar terms declined by 6.1%, even though they would have gone up in pound terms. Big dips in the three variables occurred in 2020 and 2021 as a result of the Coronavirus pandemic that forced governments to impose travel restrictions and depressed the tendency of people to travel abroad.

In Figure 2, we can see scatter plots, with correlation ellipses, of tourism indicators as functions of the effective exchange rate and world GDP. Figure 2 provides prima facie evidence for a negative relationship between tourism and the REER, which represents the cost of tourism. On the other hand, tourism indicators are positively correlated with world GDP. Positive correlation with GDP seems to be stronger than negative correlation with the exchange rate, as indicated by the shapes of the correlation ellipses. Three outliers can be observed in the relation between tourism indicators and income, which are the 2020, 2021 and 2022 observations. However, even within those outliers, correlation between inbound tourism and world GDP seems to be positive.

A preliminary idea about how the variables are related can be obtained from the correlation matrix, which is represented by Table 2. Tourism variables correlate positively with world GDP and negatively with exchange rate. Since the effective exchange rate represents the pound's value against other currencies, a high value reduces inbound tourism demand. Tourism receipts show stronger correlation with the explanatory variables than arrivals. Tourism receipts in pounds correlate more strongly with the explanatory variables than dollar receipts.

Given the stylized facts, inbound tourism can be represented by a time series model encompassing trends and cycles. The unobserved components model (UCM) of Harvey (1985, 1989), which is used to decompose an observed time series into unobserved components, is specified as

where $zt$ is an observed time series, $μt$ is the trend component, $φt$ is the cyclical component, and $εt$ is the random component. UCM is used to decompose an observable time series into trend, cyclical and seasonal components, which accounts to any missing variables without the need to express them explicitly, thereby avoiding omitted variables bias, which is bound to be evident in regressions (Moosa, 2018). Moreover, it overcomes many traditional obstacles in time series and econometrics analysis as it can be used to represent a time series with any order of integration.

The UCM provides general specifications for the processes generating the components, thus allowing for any possibility, such as the presence of stochastic (rather than deterministic) trend. The trend and cyclical component are assumed to be uncorrelated, whereas $εt$ is a white noise process. First, The trend, which represents the long-term movement of the underlying series, is represented by the general specification

where $ηt∼N(0,ση2)$ and $ζt∼N(0,σζ2)$⁠. $μt$ is a random walk with a drift factor, $βt$⁠, which follows a first order autoregressive process as represented by equation (3). This process collapses to a simple random walk with drift if $σζ2=0$⁠, and to a deterministic linear trend if $ση2=0$⁠. If, on the other hand, $ση2=0$ while $σζ2≠0$⁠, the process will have a trend that changes relatively smoothly ^[3].

Second, the cyclical component, which is assumed to be a stationary linear process, may be represented by

where $t$ is time, and the amplitude of the cycle is given by $(a2+b2)1/2$⁠. To make the cycle stochastic, the parameters $a$ and $b$ are allowed to evolve over time, while continuity is preserved by writing down a recursion for constructing $φt$ before introducing the stochastic components. By adding disturbances and a damping factor, we obtain:

where $φ*$ appears by construction, such that $ωt$ and $ωt*$ are uncorrelated white noise disturbances with variances $σω2$ and $σω*2$⁠, respectively. The parameters $0≤θ≤π$ and $0≤ρ≤1$ are the frequency of the cycle and the damping factor on the amplitude, respectively, while the period, which is the time taken for the cycle to complete its sequence of values, is given by $2π/θ$⁠.

The univariate time-series model represented by (1) can be extended by adding explanatory variables. Hence, the dependent variable is portrayed as being determined by its components as well as explanatory variables. The full model is

where $zt$ is any of the three indicators of inbound tourism, $yt$ is world GDP, and $et$ is the pound's effective exchange rate. $Dc$ is a dummy variable that represents the Coronavirus outbreak, taking the value of 1 in 2020, 2021 and 2022, and zero otherwise. It is anticipated that $δ1t>0$⁠, $δ2t<0$⁠, and $δ3t<0$⁠. Equation (7) says that the dependent variable is determined by its components (trend and cycle) and two explicit explanatory variables. The underlying idea is that if an explanatory variable is only partially successful in explaining variation in the dependent variable, then there is a role for the components, which may in this case explain the behaviour of omitted variables ^[4]. If, on the other hand, the explanatory variables are completely successful in explaining variation in the dependent variable, then the trend would reduce to a constant term whereas the cycle becomes unnecessary.

In order to find out if a particular royal event has any effect on inbound tourism, a dummy variable is added to equation (7) to represent a particular royal event. Hence

where $Dj$ represents a dummy variable, such that $Dj=1$ on the year of the event and zero otherwise for $j=1,⋯,10$⁠. Table 3 lists the ten dummy variables that correspond to royal events (more than one royal event could occur in one year).

Additionally, the coefficients on the explanatory variables have a time subscript because the model is estimated in a TVP framework. Once the model has been written in state-space form, it is estimated using maximum likelihood, with the Kalman filter updating the state vector, which contains the components and coefficients of the explanatory variables.

The results of estimating equation (7) are presented in Table 4, including the final state vector (with t statistics in parentheses) two goodness of fit measures (⁠$R2$ and $Rd2$⁠) and the diagnostics for serial correlation (DW and Q) and heterocedasticity (H). Harvey (1989) argues that for trended time series data, $Rd2$ is a more appropriate measure of the goodness of fit, as it is based on first differences. Q is the Ljung–Box test statistic with $χ2(1)$ distribution, while the test statistic for heteroscedasticity has an F(7,7) distribution.

The results show that the model has a very high goodness of fit and passes the diagnostics for serial correlation and heteroscedasticity, irrespective of the choice of the dependent variable. World GDP has a significantly positive effect on the number of arrivals and tourism receipts, which is intuitive and consistent with the stylized facts. This finding robustly confirms the results of earlier UK-specific studies, such as Blake and Cortes-Jiménez (2007), who identified foreign income as a primary driver of tourism demand. On the other hand, the effective exchange rate has a negative effect on arrivals and receipts. As the pound appreciates, less people want to visit the UK, but the spending of those who make the trip will be greater (in dollar terms). Whether or not appreciation of the pound boosts receipts in terms of the dollar depends on whether the positive effect on the value in dollar terms is greater or less than the negative effect on the number of arrivals. The outcome depends on the elasticity of demand for UK tourism: with inelastic demand, appreciation of the pound boosts receipts in dollar terms. The significant negative coefficient for the exchange rate is also a well-established result, but our time-varying parameter framework captures its evolving influence more dynamically than the fixed elasticity of −1.3 reported by the British Tourist Authority (2001).

In order to find out if the royal family has a significant role to play in boosting tourism, because visitors are attracted by royal events, equation (8) is estimated with one dummy variable at a time. If the coefficient on the dummy variable is statistically significant and positive, this means that the underlying event attracted tourists over and above what is determined by considerations of income and exchange rate. The results presented in Table 5 show that the coefficients on the dummy variables are insignificant, except for the years 2020, 2021 and 2022. However, they are significantly negative, meaning that the royal events of 2022, 2021 and 2022 had negative effect on inbound tourism, including the Queen's Platinum Jubilee. This null finding stands in direct contrast to the multi-billion-pound valuations often attributed to the monarchy by popular sources (Joseph, 2017). One can also be tempted to interpret this result in terms of the negative royal events: the 2020 decision by Harry and Meghan to step down from royal duties and the 2022 release of the Netflix documentary on Harry and Meghan. However, it is implausible to suggest that a Chinese tourist decides to cancel her trip to the UK, just because members of the royal family quarrel with each other. The finding that the coefficients on the dummy variables D₈, D₉ and D₁₀ are significantly negative can be explained easily because each one of these dummy variables represents one of the Coronavirus years. As robustness test, variable addition test is conducted by estimating equation (8) with and without an event dummy variable. Three test statistics can be calculated from the residual sums of squares of restricted and unrestricted versions: Lagrange multiplier (LM), likelihood ratio (LR) and an F statistic. A significant test statistic implies the royal event is important, contributing to arrivals and tourism receipts. Table 6 shows that adding a dummy variable does not boost the equation's explanatory power. One can conclude that royal events have no effect on inbound tourism.

The findings contribute to understanding tourism's role in socio-economic development by showing that royalty-related factors have measurable implications for public revenues and the socio-economic environment. By stimulating inbound tourism demand, royalty-linked attractions and events can enhance foreign exchange earnings, support tourism-sensitive employment, and provide fiscal space for public investment in infrastructure and services critical for development. Subramaniam et al. (2022) emphasizes tourism's significance for economic inclusion and income distribution, where tourism and institutional quality were statistically linked to reduced income inequality and expanded local economic opportunities. Furthermore, Martín-Cervantes et al. (2025) demonstrate that tourism-induced revenue growth enables governments to allocate resources toward education, healthcare, urban amenities, and tourism infrastructure improvements, which support broader welfare objectives. These outcomes show that tourism development can catalyze public resource reallocation that supports sustainable socio-economic development. Recent empirical studies within this journal confirm that well-designed tourism policy interventions contribute positively to regional employment and economic resilience.

The formal empirical evidence presented in this paper indicates that the influx of foreign tourists to the UK is influenced by two key factors: income and the relative price of tourism, as determined by the effective exchange rate. Regardless of how inbound tourism is measured, the model proves to have very high explanatory power. Adding dummy variables to account for the effects of royal events produces insignificant results, which are confirmed by variable addition tests.

Being counterfactual can be useful for this kind of debate. For example, if the United Kingdom were to become the Republic of Britain, would inbound tourism drop simply because foreigners prefer to visit the United Kingdom rather than the Republic of Britain? Definitely, if the government of President Smith decided to impose more stringent visa restrictions or put restrictions on the consumption of alcohol, it would deprive tourists of the pleasure of enjoying a pint in a traditional British pub. If, however, the government of President Smith relaxed visa restrictions and subsidized pubs, more tourists would visit the Republic of Britain than the United Kingdom.

The attractiveness of the Tower of London will not be affected by a change from a kingdom to a republic, as the current system of governing a country is irrelevant to its heritage. The claim that less people will visit Buckingham Palace in the Republic of Britain is unfounded because tourists still like to visit the Palace of Versailles in the Republic of France. Tourists visit a particular country for a number of reasons, irrespective of whether that country is run by a king, a queen, an emperor, an emir, a sultan, a president, a prime minister, a chancellor or a mulla.

The policy implications suggest a reconsideration of current assumptions. Whereas the idea that the British monarchy generates tourism-driven financial returns is frequently cited, the evidence provided in this study does not substantiate that claim to a meaningful degree, implying that public funding rationales could be reassessed. Government bodies and tourism organizations, including VisitBritain, may therefore benefit from directing attention toward attractions with consistently demonstrated appeal—such as historic sites, museums, landscapes, and cultural experiences—while also improving practical elements like visa accessibility. Likewise, tourism managers could strengthen their strategies by employing data-informed insights into visitor patterns, broadening the focus beyond royal-associated branding and highlighting a wider range of cultural and heritage assets. This approach would support a more sustainable and diversified tourism environment grounded in genuine historical and cultural value.

This study inherently possesses certain limitations that serve as avenues for future research. Firstly, the reliance on annual data—while suitable for capturing long-term national trends—may fail to account for short-term localized impacts of specific royal events. For instance, a royal wedding might induce a temporary increase in tourism centered in London without significantly affecting annual aggregates. Future research could employ higher-frequency data (monthly or quarterly) to investigate these micro-level variations. Secondly, our approach focused on highly publicized royal events. The broader and more nuanced influence of the “royal brand,” embedded in national imagery and destination marketing, is more challenging to quantify and is not fully captured by event-dummy specifications. Qualitative analyses—such as content analysis of tourism campaigns or perception-based visitor surveys—could enhance understanding of this symbolic dimension. Thirdly, the UK's unique status as a mature and iconic tourism destination may limit the generalizability of our findings. A valuable direction for future research would be to apply comparable UCM-TVP modeling to other constitutional monarchies—such as Spain or the Netherlands—to determine whether any royal-related tourism effects manifest in these contexts. Finally, while our model concentrates on inbound international tourism, the monarchy may exert a different influence on domestic tourism behaviour—encouraging UK residents to visit royal sites as part of cultural self-identification. Examining domestic tourist flows to both royal and non-royal heritage locations would provide a more comprehensive understanding of the monarchy's role in national tourism dynamics.

We would like to thank the Editor and three anonymous reviewers for the thoughtful guidance and constructive feedback throughout the review process. We are especially grateful to the late Professor Imad A. Moosa for his pivotal contributions to the conceptualization and intellectual development of this manuscript. His scholarly insight, rigor and unwavering commitment to academic excellence were crucial in shaping this work. Professor Moosa passed away during the publication process, and we respectfully acknowledge his enduring influence on this study and on the broader field to which he dedicated his distinguished career.