Tangible and Intangible Determinants of FDI and FPI Inflows: Evidence from BRICS Countries

Abstract

While extensive research has explored the determinants of foreign direct investment (FDI) and foreign portfolio investment (FPI) in BRICS nations, there remains a notable gap in understanding the influence of intangible factors, particularly soft power and nation branding. Historically, academic discourse has underemphasized the role of nation branding as a crucial emotional and perceptual component in investment decision-making processes. Consequently, governments in BRICS countries must enhance their national branding efforts to attract both capital and portfolio investment flows. The principal aim of this study was to jointly analyse the tangible and intangible determinants influencing FDI and FPI in BRICS from 1994 to 2024. Employing advanced econometric techniques, specifically the Autoregressive Distributed Lag (ARDL) bounds testing approach for cointegration and Vector Error Correction Models (VECM) for estimation. This study makes a unique contribution to existing literature by examining the nexus between nation branding, FDI and FPI, thereby introducing a novel perspective on the factors driving investment in the BRICS context with an emphasis on non-tangible determinants. The findings indicate that nation branding, along with exchange rate stability, property rights, and financial market development, are significant positive determinants of FPI in these countries. Conversely, capital openness demonstrated a negative relationship with FPI. Moreover, the positive impact of nation branding on FDI within BRICS nations was reaffirmed. This study substantiates the critical role of nation branding as a pivotal driver for both FDI and FPI, emphasising its strategic importance in the economic landscape of BRICS countries.

1. Introduction

The economic liberalisation and the integration of international capital markets during the 1990s marked a pivotal shift in global capital dynamics, catalysing substantial capital flows between economies (F. Ahmed et al., 2005). Prior to this, from the 1980s, net capital inflows to developing countries remained modest, predominantly manifesting as foreign direct investment (FDI). However, as outlined by Agrawal (2015), the 1990s witnessed an accelerated increase in global capital flows, particularly FDI, which outpaced overall global economic growth. A notable surge occurred between 1998 and 2000, with FDI inflows spiking nearly 50% annually, culminating in a peak of USD 1.5 trillion in global FDI by 2000, significantly driven by large-scale cross-border mergers and acquisitions (Agrawal, 2015).

The simultaneous evolution of local financial markets in developing nations and their enhanced openness to foreign investments from the early 1990s brought about a transformation in the composition of capital inflows. There was a discernible rise in foreign portfolio investment (FPI) and other investment avenues, such as loans, trade finance, and deposits with unaffiliated parties (Humanicki et al., 2013).

Traditionally, research on foreign investment has emphasized tangible economic fundamentals. While institutional factors—non-monetary determinants influencing foreign capital flows—have garnered significant attention, the realm of intangible factors that can impact investment behavior remains underexplored (Kalamova & Konrad, 2010). Tangible determinants typically include country-specific characteristics like locational advantages, ownership structures, labor costs, economic policies, and market proximity. Conversely, intangible determinants encompass a nation’s soft power elements, which significantly influence investor perceptions of potential investment locations, including nation branding, social stability, and investment promotion policies (Papadopoulos et al., 2018).

Despite the extensive analysis of tangible factors such as infrastructure, macroeconomic performance, and natural resource availability (as cited in works by Seyoum et al., 2015; Asongu & Nwachukwu, 2015; Kotenkova et al., 2015; Labes, 2015; Agbloyor et al., 2016; Makoni, 2016; M. Ahmed, 2017; Rafat & Farahani, 2019; Alshamlan et al., 2021; Tsaurai, 2022b; Islam & Beloucif, 2024; Alalade et al., 2024; Ditta et al., 2025), the significance of soft power factors in attracting foreign direct and portfolio investment remains inadequately addressed. Ignoring these intangible elements undermines the psychological dimensions that govern investor behavior (Fan, 2010).

Institutional aspects, such as political stability, property rights, and levels of corruption—considered as part of the intangible framework—have been identified as significant determinants in both developing and developed contexts (Bénassy-Quéré et al., 2007; Walsh & Yu, 2010; Dutta & Osei-Yeboah, 2013; Esew & Yaroson, 2014; Gammoudi & Cherif, 2015; Nxumalo & Makoni, 2021). Moreover, factors like nation branding and social stability, though largely overlooked in scholarly discourse, hold influential sway over the decision-making processes of prospective foreign investors. A nation may possess abundant wealth, yet a negative international reputation can thwart its attractiveness to foreign capital.

Recognizing the potential consequences of disregarding intangible factors in the context of international capital flows is paramount, as it can lead to diminished global competitiveness, reduced investor confidence, strained diplomatic relations, and compromised soft power, particularly among BRICS nations. Thus, despite its overlooked status, the concept of nation branding merits further investigation as it plays a critical role in shaping foreign investment decisions made by multinational corporations (MNCs).

In recent years, nations have actively sought to implement competitive strategies and policy frameworks aimed at attracting essential foreign capital inflows. Consequently, the domains of foreign direct investment and foreign portfolio investment have garnered significant attention in both academic literature and investment practice, traditionally conceptualized as distinct avenues of foreign capital influx.

In terms of contribution, this study delineated the power of intangible factors as determinants of foreign capital inflows, which has been undervalued and neglected by the literature. In addition, this study made a significant contribution by constructing a more comprehensive nation branding index within the context of the BRICS countries. Through the construction of the nation branding index, a more comprehensive and accurate representation of the nation branding variables in BRICS countries was achieved. Rather than solely relying on the government effectiveness indicator, as commonly employed in the Anholt (2005) NBI measure, this study incorporated all six world governance indicators (WGIs). Policy-wise, the impact of nation branding on FDI and FPI in BRICS calls for governments to leverage their cultural, social, and economic strengths to enhance their soft power and create a positive image that appeals to foreign investors. BRICS countries have diverse cultures and economic strengths; hence, both country-level and collaborative regional efforts are eminent to manage BRICS’ nation brands and reputation strategically for FDI and FPI purposes.

2. Theoretical Literature Review

2.1. Foreign Direct Investment Theories

The literature on foreign direct investment (FDI) is extensive, with numerous theories and models dating back to the 1770s. Early international trade theories, proposed by Adam Smith in 1776 and David Ricardo in 1817, laid the groundwork for FDI theories. Following these foundational concepts, a variety of other theories and models emerged, including the MacDougall (1958) model, the international product life cycle (Vernon, 1966), the capital market theory (Aliber, 1970), the macroeconomic approach (Kojima, 1973, 1978, 1982), the firm-specific advantage theory (Hymer, 1976), the internationalization theory (Hymer, 1976), the incremental internationalization theory (Johanson & Vahlne, 1977), and Dunning’s (1977) eclectic paradigm theory.

MacDougall (1958) and Kemp (1964) introduced the MacDougall–Kemp hypothesis, which articulates the dynamics of foreign direct investment (FDI) in competitive markets characterized by unfettered capital mobility across borders. This hypothesis operates under the premise of uniform capital prices, optimal capital allocation, and enhanced welfare outcomes. Capital inflows were understood to encompass both debt instruments and FDI, contributing positively to the host country’s GDP and promoting sectoral improvements, ceteris paribus, as evidenced by Agyapong and Bedjabeng (2020). Nevertheless, existing literature presents a patchwork of findings regarding the implications of inward FDI on host economies, as discussed by Ramzan et al. (2019), Chaudhury et al. (2020), and Demena and Afesorgbor (2020).

Subsequent scholarly contributions from Frankel (1965), Pearce and Rowan (1966), and Caves (1971) provided frameworks for understanding FDI within the context of perfect markets, yet these theories have come under scrutiny for their idealistic assumptions. Stigler (1957) questioned the very existence of perfect markets, while Vernon (1966) and Kindleberger (1969) posited that firms pursue foreign production primarily due to monopolistic advantages. This led to the formulation of theories emphasizing market imperfections, notably advanced by Vernon (1966) and Hymer (1976).

Vernon (1966) articulated the Product Life Cycle (PLC) theory, situating FDI within an international trade context. The PLC theory delineates a product’s journey through stages of innovation, growth, maturity, and decline. During the innovation phase, firms leverage superior technology and export surplus production to overseas markets. As products reach maturity, competitive pressures in domestic markets catalyze FDI as companies seek to stave off local market share erosion. The imperative for cost competitiveness and market expansion drives firms to establish foreign subsidiaries during this phase.

However, the PLC theory is criticised for inadequately addressing the rationale behind firms’ preference for FDI over sustained exporting. Boddewyn (1983) pointed out its empirical shortcomings, challenging its applicability in real-world scenarios. Additionally, Vernon (1979) indirectly critiqued the PLC framework, highlighting its inability to fully elucidate the behavioural nuances of firms in choosing FDI over export strategies. The international business milieu is also undergoing significant transformations due to the proliferation of multinational corporations and rapid technological advancements.

Hymer (1976) emerged as a pivotal figure in the systematic examination of FDI, proposing a theory rooted in imperfect market structures. His framework highlights the challenges posed by domestic competition, foreign exchange risks, and the pursuit of market power to mitigate these risks. Firm-specific advantages—such as brand equity, technological prowess, and managerial competencies—are vital to maximising outcomes from foreign investments. Hymer posited that in perfectly competitive markets, FDI would be untenable; instead, structural imperfections necessitate that foreign entrants possess technological and managerial capabilities to surmount the liabilities associated with foreignness (Zaheer, 1995).

Hymer’s theory represented a significant shift from traditional neoclassical frameworks, garnering support from various scholars, including Dunning (1974) and Buckley and Casson (1976). Distinct from prior models, Hymer framed foreign direct investment (FDI) not solely as the transfer of capital, but also as a mechanism involving retained earnings and borrowing. Although Hymer’s insights greatly enhanced the understanding of FDI within imperfect markets, they encountered criticism for failing to elucidate firms’ preferences for foreign investment over alternatives such as exporting and licensing (Robock & Simmonds, 1983). Furthermore, it did not adequately address FDI destination choices, locational determinants, or the timing of investment (Morgan & Katsikeas, 1997; Nayak & Choudhury, 2014).

Dunning (1977) built upon the foundations laid by imperfect market theories by incorporating locational factors into the foreign investment conversation. His framework, known as the ‘eclectic paradigm’ or OLI paradigm, asserts that for successful FDI, a firm must harness Ownership (O) advantages, Location (L) advantages, and Internalisation (I) advantages, rather than merely transferring these attributes. Ownership advantages encompass intangible assets such as patents, trademarks, and proprietary technology, all of which confer a competitive advantage. Location advantages pertain to country-specific characteristics, including GDP growth rates, political stability, and labor costs. Internalisation refers to a firm’s decision to produce in a foreign market while retaining control over its advantages. For FDI to materialize, alignment of all three OLI components is essential.

Critiques of the OLI paradigm highlight its home country-centric bias in terms of ownership factors and its challenges related to generalizability, given the specificity of contextual variables. Nevertheless, Dunning’s paradigm remains the most recognised and extensively utilised framework for analysing FDI. While numerous theories seek to account for FDI dynamics, the eclectic paradigm notably emphasizes the significance of intangible factors in shaping investment location decisions, rendering it particularly relevant in the context of this investigation.

2.2. Foreign Portfolio Investment Theories

According to Dunning and Dilyard (1999), the concept of foreign investment initially framed the discourse on international capital flows as it pertained to portfolio movements until the 1960s. During this period, capital traversed borders predominantly through the intermediation of international capital markets, driven by the pursuit of superior interest rates. The primary instruments facilitating these cross-border capital movements included bonds, equities, money market instruments, and financial derivatives. Notably, foreign portfolio investment (FPI) is posited to have historically preceded foreign direct investment (FDI); for instance, in the early nineteenth century, European investments in the U.S. were typically conducted via loans or minority stakes rather than direct ownership (Dunning & Dilyard, 1999).

With the acceleration of international financial asset movements—outpacing the trade of goods and services in recent decades—scholars have sought to elucidate the phenomenon of FPI (Bartram & Dufey, 2001). Most theoretical frameworks surrounding FPI have concentrated on the allocation and pricing of financial vehicles, often neglecting a comprehensive definition of FPI itself. Attempts by Iversen (1936) and Dunning and Dilyard (1999) to contextualize FPI from an FDI perspective warrant further exploration.

Iversen (1936) advanced a portfolio theory paradigm, reframing FDI as essentially a portfolio of equity rather than purely tangible investments. This theory, also referred to as the differential rates of return theory, proposed that, ceteris paribus (in the absence of barriers and risk), capital would migrate from low-interest to high-interest countries. In contrast, Tobin (1958) posited that investors engage in cross-border portfolio allocations not solely to seek higher returns but also for purposes of risk diversification. This perspective underscored a limitation of the portfolio theory, as it did not account for capital movement from high to low-interest environments.

Supporting Tobin’s argument, H. M. Markowitz (1959) further elucidated that portfolio investment could be redirected towards lower-yield countries as part of a strategy to achieve diversification benefits and mitigate overall portfolio risk. However, the theoretical underpinnings of the Portfolio Theory attracted substantial critique due to its pessimistic assumptions concerning capital mobility and its assumption that capital flows exclusively in one direction. Factors such as risk-return dynamics, market barriers, and other exogenous variables undermined the robustness of the portfolio theory (Denisia, 2010).

Dunning and Dilyard (1999) introduced the Ownership-Location-Externalization (OLE) framework, building on Dunning’s eclectic paradigm. They posited that FPI reflects wealth transfer through financial markets in a manner that aligns with the eclectic model. In this framework, Ownership (O) encompasses capital accessibility, reinforced by additional advantages such as information asymmetries, while Location (L) evaluates the profitability-determining factors that influence dividend and interest rate expectations. As FPI demonstrates greater responsiveness to ‘L’ factors than FDI, it involves investments that are less tied to fixed assets. Importantly, the internalization aspects of the eclectic model were substituted with Externalization (E) factors, which rationalize the preference for foreign over local investment through considerations of transaction costs, market correlation, and information availability.

Contrary to Dunning and Dilyard’s perspective, H. Markowitz (1952), through his modern portfolio theory, argued that investors pursue FPI primarily for diversification benefits. The ‘L’ factors in the OLE framework corroborate Markowitz’s assertion regarding rational portfolio investment behaviors in response to interest rate variations. However, Markowitz extended his analysis to incorporate risk within a quantitative approach to portfolio construction and selection. The OLE model, as articulated by Dunning and Dilyard (1999), partially integrates the risk considerations posited by Markowitz, who proposed that international portfolio diversification serves to minimize risk while maximizing returns.

2.3. Nation Branding Theories

Investor perceptions and national stereotypes significantly influence the decision-making process regarding investment location (Kalamova & Konrad, 2010). Intangible factors, such as nation branding, play a crucial role in shaping the judgments of ostensibly rational investors by affecting their perceptions and emotions. Nation branding is defined as “the construction of a country’s unique competitive and differentiating reputation,” which is built on dimensions such as tourist appeal, foreign investment potential, and political and cultural attributes (Anholt, 2011). Despite the acknowledged impact of these intangible factors on attracting foreign capital flows, their significance is often undervalued in both theoretical and practical contexts.

The concept of nation branding has been received with mixed sentiments, leading to a certain level of confusion regarding its construct. However, increasing academic attention is apparent due to the phenomenon’s growing prominence (see Kotler & Gertner, 2002; Papadopoulos & Heslop, 2002; Lee, 2009; Anholt, 2011). As Van Ham (2001) notes, a nation’s reputation and image are critical components of its strategic equity, making it extremely difficult for unbranded nations to garner the essential economic and political attention they require.

Nation branding transcends mere verbal promotion; it involves a country’s strategic maneuvering across political, social, economic, and cultural dimensions, which resonates with public perceptions and emotions, thereby establishing a competitive advantage and a favorable global image (Anholt, 2007; Yousaf, 2017). For nation branding to be effective, it must be grounded in the realities of the nation itself, aligning the brand with what the country can authentically deliver, thus ensuring relevance for both local and international stakeholders (Gilmore, 2002).

Anholt (2011) contends that the term “nation branding” is frequently misconstrued by some scholars, who erroneously confine it to product and firm references. Unlike product brands, a nation brand is primarily emotional and lacks tangible offerings (Fan, 2006). It encompasses a multitude of intangible factors, including a nation’s culture, history, language, demographic attributes, natural resources, social institutions, and political and economic frameworks. Simultaneously, nation branding involves the application of various brand management strategies aimed at enhancing a country’s influence, reputation, trade, tourism, and foreign investment outcomes (Dinnie, 2015).

Historically, nations have engaged in branding themselves through various symbols such as names, currencies, national anthems, and flags, and by showcasing their natural beauty to attract tourists (Olins, 2002). Despite the longstanding practical applications of nation branding, theoretical exploration of the concept has only gained traction recently. Theoretically, the foundations of nation branding can be traced back to the 1940s, originating from trade theories, notably Ricardo’s (1817) theory of comparative advantage. This theory posits that a nation’s competitiveness relative to others arises from its factor endowments (Cho, 1998). However, as Fan (2006) argues, mere resource endowment does not guarantee brand competitiveness, as a nation might possess abundant natural resources yet still maintain an unfavorable global image that deters investment and tourism.

The exploration of nation branding’s origins is quite expansive, extending beyond the scope of this study (Dinnie, 2008, 2015; Lee, 2009). According to Dinnie (2008), nation branding theoretically derives from marketing principles and evolves through four foundational pillars: public diplomacy, country of origin (COO), national identity, and place development. Conversely, Lee (2009) identifies three primary pillars for nation branding’s origins: COO, place development, and public diplomacy.

The evaluation and quantification of nation brands have largely stemmed from perceptual frameworks originating in private sectors rather than academic discourse. A prominent tool for assessing nation branding was introduced by Anholt in 2005, encapsulating essential non-monetary elements that can influence a nation’s global reputation. Anholt identified critical metrics for a nation’s brand, including exports, tourism, foreign investment, immigration patterns, governance standards, cultural heritage, and the demographic characteristics of its populace. As of 2021, Brand Finance publishes the Nation Brand Index (NBI) annually, reflecting how nations are perceived globally. However, since the NBI relies on subjective perceptions, the incorporation of quantitative methodologies could provide a more comprehensive analysis. As noted by Dinnie et al. (2010), alternative models utilising objective secondary data can enrich the construction of nation brand indices, offering a clearer exposition of the factors underlying nation brand strength.

Nation branding, akin to other economic phenomena, faces inherent challenges. First, the perception of a nation brand is context-dependent, complicating the communication of a unified image across diverse cultural audiences. Each target demographic may interpret branding messages through culturally influenced lenses, creating complexities in international marketing strategies. Secondly, the internal diversity of ethnicities and cultures complicates the definition of a cohesive national identity (Fan, 2006). While effective nation branding seeks clarity, distinctiveness, and visibility, articulating a coherent national image is often fraught with ambiguities. The challenge persists in catering to the divergent needs of investors, tourists, and foreign stakeholders, which necessitates a brand image that strives for universal appeal (Jordan, 2014). Furthermore, nation branding transcends basic marketing campaigns; it demands a sustained and holistic evolution of the foundational elements of a nation’s brand. Lastly, temporal factors can significantly shape a nation’s image. The case of Zimbabwe illustrates this, as the country transitioned from being recognised as Africa’s breadbasket to facing severe economic challenges, reflected in a child poverty rate exceeding 76%, with a majority of affected children residing in rural areas (UNICEF, 2020).

Despite its complexities and paradoxes, nation branding has been identified as a crucial strategy for developing countries aiming to enhance their positioning on the global stage (Olins, 2002; Fan, 2006; Kahn, 2006; Dinnie, 2007). Fan (2006) articulates that nation branding can serve as a formidable instrument for driving developmental initiatives. Literature underscores that a nation’s brand or image can significantly influence foreign capital inflows, prompting this study to investigate the ramifications of nation branding on economic dynamics.

3. Empirical Literature Review

The determinants of foreign capital movements have been analysed at different levels which include inter-country, inter-regional, inter-blocs, inter-industry and even inter-sector.

Table 1. Empirical evidence on FDI, FPI and nation branding.

Author	Unit of Analysis	Findings
Asteriou et al. (2005)	European transition economies 1990–2003	The study’s results revealed that planned foreign investments have a positive and significant impact on the economic growth of the studied economies. However, portfolio investments were found to have a negative and insignificant effect on economic growth.
Nunes et al. (2006)	Latin America 1991–1998	The results showed a significant impact of factors such as market size, infrastructure availability, and trade openness on attracting FDI to Latin America. However, wage rates and inflation rates were found to have a negative influence on attracting foreign investment.
Kalamova and Konrad (2010)	34 developed and developing countries 2005–2006	A nation’s brand or image can have a significant impact on foreign capital inflows for both developed and developing countries.
Khachoo and Khan (2012)	32 developing countries 1982–2008	Countries with large market size and a higher GDP attract large amounts of FDI
Jadhav (2012)	BRICS 2000–2009	Market size had the most significant impact on attracting BRICS FDI
Alam and Zulfiqar Ali Shah (2013)	Pakistan 2012	A link between nation branding and FDI was observed; a poor brand for Pakistan resulted in FDI plummeting by 67% in the first quarter of 2012.
Akpan et al. (2014)	BRICS and MINT 2001 to 2011	Availability of infrastructure, trade openness and market availability were found to be significant common drivers of FDI in BRICS and MINT countries. However, the availability of natural resources and institutional quality were concluded to be insignificant in attracting FDI in BRICS and MINT.
Polat (2015)	Turkey and Central and Eastern European Countries (CEECs) 2001–2012	The EU country risk (CR), as represented by the EU CR indices, GDP growth rates, and global financial crises, has influential power in explaining FDI inflows of Turkey and CEECs.
Haider et al. (2016)	China 1997–2014	Exchange rate, GDP, inflation rate, population growth and external debt had a significant effect on attracting FPI.
M. Ahmed (2017)	Oil-exporting countries (OECs) 1984–2012	Trade openness and countries’ composite risk were observed as the critical determinants of FDI inflow in OECs.
P. L. R. Makoni (2016)	9 selected African countries 1980–2014	It was detected that FDI to African host countries is driven by low inflation, infrastructural development, past FDI inflows and real GDP growth rate. FPI inflows, on the other hand, were found to be affected by previous FPI inflows, availability of developed infrastructure, the real exchange rate and the inflation rate. Developed financial markets were also observed to exert a positive influence on FPI inflows.
Al-Smadi (2018)	Jordan 2000–2016	A stable macro-economic environment, good governance, low corruption levels and opportunities for risk diversification were significant determinants of FPI in Jordan.
Rafat and Farahani (2019)	Iran 1985–2016	Political risk was concluded to be a significant determinant of FDI
Maryam and Mittal (2020)	BRICS 1994–2018	Trade openness, gross capital formation, GDP growth, exchange rate and availability of infrastructure were some of the determinant factors that were found to significantly attract FDI in the long run
Lahrech et al. (2020)	Top ten list of countries on the Nation Branding Index 2008–2014	A significant role of nation branding in attracting FDI in all the countries studied was observed
Alshamlan et al. (2021)	UAE 2015–2019	Macro-economic variables such as political stability, stable currency, strong and well-developed financial sector, good governance and currency stability were the main drivers of FDI. In addition, the signing of the Abraham Accords in 2020 stimulated foreign capital inflows
Tsaurai (2022b)	BRICS 1994–2020.	While trade openness, currency rates and growth were observed to exert a positive effect on FDI, inflation, financial sector development and human capital were observed to be negatively related to FDI.
Tsaurai (2022a)	BRICS 1998–2020	Economic growth, savings and stock market development were concluded to be significant FPI determinants. However, human capital development, economic growth and foreign direct investments were observed to have a negative effect on FPI in BRICS.
Islam and Beloucif (2024)	Systematic Literature Review 2000–2018	Size of the host market, trade openness, infrastructure quality, labour cost, macroeconomic stability, human capital, and the growth prospect of the host country were observed to be the most robust determinants of FDI from 112 empirical studies examined.
Zaharum et al. (2024)	Malaysia 1992–2021	The impact of macro-economic variables on FDI was investigated. While GDP, inflation and exchange rate impact was positive and significant, real interest rate, trade openness and unemployment rate were observed to be negatively related to FDI.
Alalade et al. (2024)	Nigeria 1993–2023	Interest rate and exchange rate evidenced a significant positive impact on FPI.
Ditta et al. (2025)	GCC countries 2005–2023	The impact of fiscal and monetary policy variables on FDI was investigated. Only government expenditure was observed to exert a significant impact on FDI. On the contrary, GDP growth, inflation, interest rate differentials, exchange rates, and tax revenue were observed to have no significant effect on FDI.

summarises the empirical evidence of FDI and FPI in developed, developing and BRICS countries.

The literature review indicates that the determinants of Foreign Direct Investment (FDI) and Foreign Portfolio Investment (FPI) are influenced by macroeconomic fundamentals and institutional frameworks. In developing economies, factors such as market size, inflation, interest rates, trade openness, and infrastructure quality are frequently explored. Conversely, in developed nations, the focus often shifts to macroeconomic stability and the quality of institutions. Core determinants like market size, trade openness, and GDP growth consistently emerge as pivotal in influencing both FDI and FPI across varying economic contexts.

While the majority of studies, such as Jadhav (2012), Haider et al. (2016), Al-Smadi (2018), Lahrech et al. (2020), Tsaurai (2022b), Alalade et al. (2024), and Ditta et al. (2025), have predominantly analyzed FDI and FPI in isolation, a few, notably Makoni (2016), have examined their determinants concurrently. This study follows suit by mutually investigating FDI and FPI, leveraging insights from the UNCTAD (2018) report alongside findings by Dunning and Dilyard (1999), which highlight an increasing interplay between these forms of capital. The UNCTAD (2018) report points out that the lines separating FDI and FPI are increasingly indistinct, as global FDI—when measured through balance of payments—includes flows characterized by FPI behavior. Additionally, FPI inflows can be converted into FDI, reinforcing the rationale for concurrent analysis.

Specifically, this study zeroes in on the role of nation branding as a potential driver of FDI and FPI, particularly within BRICS nations. Several countries have successfully employed nation branding strategies to achieve favorable economic outcomes. A pertinent example is Spain, which, following the dictatorship of Francisco Franco from 1936 to 1975, battled a tarnished reputation marked by civil conflict and poverty. Today, as a stable democracy and EU member, Spain has transformed its image, largely attributed to strategic rebranding efforts (Blanco, 2021).

In this context, nation branding is posited to exert a beneficial impact on inward FDI and FPI within BRICS economies. The methodology includes constructing a standardized quantitative measure of nation branding for BRICS, utilizing the Country Brand Strength Index as proposed by Fetscherin (2010). Given this framework, the present study will test the following hypotheses:

• Long-Run Relationship

H₀. 

There is nolong-run relationship between nation branding (intangible determinants) and FDI and FPI in BRICS.

H₁. 

There is a long-run relationship between nation branding (intangible determinants) and FDI and FPI in BRICS.

• Short-Run Relationship

H₀. 

There is no short-run relationship between nation branding (intangible determinants) and FDI and FPI in BRICS.

H₁. 

There is a short-run relationship between nation branding (intangible determinants) and FDI and FPI in BRICS.

4. Research Methodology

The analysis of the determinants of Foreign Direct Investment (FDI) and Foreign Portfolio Investment (FPI) from 1994 to 2024 across BRICS nations was conducted utilizing panel data methods. The selection of BRICS is justified by their substantial contribution to global economic cooperation and the distinctive characteristics of their economies, which exhibit high growth potential, positioning them as exemplars among developing nations (Roberts et al., 2017). Notably, the Brand Finance Nation Brands (2024) study recognized four BRICS countries among the top 30 fastest improving nation brands globally, with China ranked second, underscoring the bloc’s relevance for analysis.

The data for this study were primarily sourced from World Development Indicators and the Heritage Foundation. Key independent variables analyzed included economic growth, infrastructure availability, trade and capital openness, natural resource endowment, nation branding, financial market development, and investment freedom. To synthesize these dimensions, Principal Component Analysis (PCA) was employed, resulting in three composite indices: nation branding, financial market development, and infrastructure availability.

In line with Anholt’s (2005) foundational framework for measuring nation brands—which incorporates variables such as exports, tourism, investment, emigration, governance, and cultural factors—this study focused on four pillars of nation branding: tourism, exports, net migration, and governance effectiveness. The governance dimension was operationalized using the six World Governance Indicators (WGI): rule of law, voice and accountability, government effectiveness, regulatory quality, control of corruption, and political stability.

Furthermore, the financial market development index was constructed with key metrics including Stock Market Value Traded (SMVT), Stock Market Capitalization (SMC), the ratio of domestic credit to the private sector as a percentage of GDP (PCred), and the turnover ratio of domestic stocks traded (STT). Lastly, the infrastructure availability composite index (InA) integrated three variables: communication expenditure as a percentage of GDP, electricity production per 1000 population, and transport expenditure relative to GDP.

In some cases where data gaps were relatively small, Kalman (1960) filtering and smoothing interpolation methods were used to fill in the gaps. The Kalman (1960) filtering technique was used as a recursive algorithm to predict what the system state should be on the next step based on noisy measurements. In addition, the Kalman (1960) smoothing approach was also used to estimate the state of the system at any given time, that is past, present and future estimates. The Kalman (1960) technique was deemed appropriate since it is useful for situations where data is missing or incomplete, as it can use measurements both before and after the missing data to produce a more accurate estimate. The Kalman (1960) filtering approach supports estimations of past, present, and even future states even when the precise nature of the modeled system is not known, which results in a smoother and more accurate estimate compared to other interpolation methods (Welch, 2020).

The composite indices for nation branding, financial market development and infrastructure availability were generated using the PCA formula as stated below:

$$n_j= {wj}_1{x}_1+ {wj}_2{x}_2+{wj}_3{x}_3+\dots + {wj}_p{x}_p$$

(1)

where

n_j = estimate of the nth factor, wj = weight on the factor score coefficient, xj = variable under consideration and p = number of variables.

The values of FDI and FPI were scaled as ratios of GDP. Among the independent variables investigated were the natural resource endowments (NRE) which were signified by total natural resources rents (% of GDP), which is the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. Financial market development was represented by an FMD index constructed by means of PCA; using financial market development measures, which are: stock market value (SMVT), stock market capitalisation (SMC), deposit banks’ domestic credit to the private sector as a percentage of GDP (PCred), as well as stocks traded, turnover ratio of domestic shares percentage (STT). Nation branding attractiveness index (NB) was represented by a composite index created using PCA. The NB measures included tourism, exports, immigration, and governance quality (represented by the six WGI variables). While the data for tourism and exports came from the World Bank WDI database, the data for immigration came from the United Nations international migrant stock data.

Economic growth (EG) was scaled by real gross domestic product growth rate (RGDP) while infrastructure availability (InA) was represented by a composite index constructed by use of PCA, using infrastructural development measures such as communication expenditure as a percentage of GDP, electricity production per 1000 people of the population and transport expenditure scaled by GDP. For the exchange rate variable, real effective exchange rate (REER) was utilised. While trade openness (TO) was represented by the sum of exports and imports of goods and services measured as a share of gross domestic product, capital openness (CO) was scaled using the capital account openness index (KAOPEN), developed by Chinn and Ito (2002, 2006, 2008). For the property rights (PR) and investment freedom (IFr) variables, the data was taken from the Heritage Foundation through the Economic Freedom Index.

Table 2. Pairwise correlation matrix for the variables used for the FDI and FPI determinants.

Variable	FDI	FPI	NRE	EG	ER	TO	CO	PR	IFR	NB	FMD	INA
FDI	1.000
FPI	0.000	1.000
NRE	−0.138	0.109	1.000
EG	0.325 ***	−0.052	0.002	1.000
ER	0.477 ***	−0.074	−0.265 ***	0.299 ***	1.000
TO	−0.055	0.065	0.549 ***	0.087	−0.153	1.000
CO	−0.002	0.115	0.461 ***	−0.294 ***	−0.079	0.018	1.000
PR	−0.323 ***	−0.120	−0.388 ***	−0.329 ***	−0.171 **	−0.253 **	−0.132	1.000
IFR	−0.479 ***	−0.046	−0.244 **	−0.274 ***	−0.368 ***	−0.185 **	−0.216 *	0.486 ***	1.000
NB	0.209 *	0.130	0.608 ***	0.137	−0.136	0.213 *	0.285 **	−0.552 **	−0.583 ***	1.000
FMD	0.759 ***	0.132	0.148	0.320 ***	0.503 ***	−0.048	−0.099	−0.286 **	−0.533 **	0.000	1.000
INA	0.419 ***	0.437 ***	0.114	0.591 ***	−0.095	0.092	0.327 **	0.487 ***	−0.512 **	−0.376 **	0.359 ***	1.000

Note: *** p < 0.01, ** p < 0.05, * p < 0.1. NRE, EG, ER, TO, CO, PR, IFR, NB, FMD and INA stand for natural resource endowments, economic growth, trade openness, capital openness, property rights, investment freedom, nation branding, financial market development and infrastructure availability, respectively.

presents the results of the pairwise correlation matrix on the variables investigated:

Having examined the pairwise correlations among the key variables under investigation, the data was evaluated for the presence of multicollinearity. To address this concern, the variance inflation factor (VIF) was utilised as a diagnostic tool to assess the extent of multicollinearity present in the models. The degree of multicollinearity observed was not concerning; hence all variables were retained for subsequent econometric estimation.

Table 3. Summary statistics.

Variable	N	Mean	Std. Dev.	Min	Max	Skew	Kurt
FDI	150	4.870	6.860	−3.980	3.440	2.232	7.732
FPI	150	−5.970	2.820	−1.070	1.620	1.345	12.988
NRE	150	5.552	5.000	−0.864	21.996	1.652	4.652
EG	150	4.390	3.987	−7.878	14.231	−0.531	3.499
ER	150	89.718	17.089	47.952	129.977	−0.830	2.778
TO	150	42.665	12.662	15.636	69.339	−0.179	2.198
CO	150	−0.788	1.021	−2.440	1.379	0.880	2.969
PR	150	−0.439	0.121	−0.110	0.630	−0.864	2.507
IFR	150	−0.419	1.217	−3.150	2.870	0.217	2.443
NB	150	−0.042	1.987	−4.181	6.180	0.150	3.084
FMD	150	0.814	0.687	−1.510	1.780	2.052	7.137
InA	150	0.035	1.319	−2.835	2.371	0.075	1.941

provides a detailed account of the summary statistics for the examined variables within the BRICS countries, including measures such as the mean, standard deviation, minimum, and maximum values.

As can be adduced in Table 3, the data for most of the variables was positively skewed (FDI, FPI, natural resource endowments, capital openness, investment freedom, nation branding, financial market development and infrastructure availability). On the other hand, the data for economic growth, exchange rate, trade openness, and property rights were negatively skewed. The kurtosis values for FDI, FPI, economic growth, NRE and financial market development indicated that the data for the five variables was abnormally distributed. The data for all the variables was therefore converted into natural logarithms to manage the abnormal distribution and outlier values, as well as to cater for the multilinearity problem of the data.

After running the cross-cutting pre-estimation tests and conducting a battery of tests to inspect the variables for cross sectional dependence and stationarity properties, it was concluded that the examined series were integrated on either level zero I(0) or I(1) and none of the variables were integrated on level I(2). To confirm the cross-sectional independence of the data, this study utilised the Breusch and Pagan (1980), Pesaran (2004) and Frees (1995, 2004) tests. The first-generation panel unit root tests, such as the Im et al. (2003) test, as well as the Augmented Dickey–Fuller (ADF) were utilised to check for panel unit root. The Panel unit root tests confirmed that most of the series were integrated of order one [I(1)], thus validating the applicability of the ARDL approach.

The first objective of this study was to identify the tangible and intangible determinants that enhance the attraction of FDI and FPI inflows to BRICS countries. Having assessed the presence of unit roots, the next stage was to proceed with the model specification. If some variables are stationary at level while others are of first difference, the application of the panel ARDL model is deemed most appropriate. Furthermore, the panel data utilised have a smaller sample size (N) and a large time span (T), which disqualifies some methodological approaches, such as the GMM. Hence, this study settled for the panel ARDL approach. In order to ascertain the FDI and FPI determinants in the context of BRICS countries, the following ARDL equations based on a Pooled Mean Group (PMG) approach were estimated, as adopted from Yimer (2017) and Maryam and Mittal (2020).

As enunciated by Pesaran et al. (2001), the ARDL modelling technique, based on the PMG approach, can be applied in three steps, taking the simple form of ARDL with advanced panel settings, which include an intercept, short-run coefficients, and a co-integration term. While the first step is to determine the cointegration relationship between the variables under investigation, the second stage involves establishing the conditional ARDL long-run model underlying the estimate. Finally, the third stage is attaining the short-run parameters by approximating the ECM associated with the long-run parameters. The 3-step PMG ARDL modelling technique is ideal for this study, as it suits the moderate panel sample size under investigation, thereby producing robust econometric results. In addition, the 3-step PMG ARDL model allows for the investigation of cointegration relationships even when variables have mixed integration orders (i.e., I(0) and I(1)), which is the case in this study. The three steps of the PMG ARDL model can be specified as follows:

Step 1:

$$\begin{array}{l}{∆LFDI}_{it}={\propto }_0+{\beta }_1{LFDI}_{it-j}+ {\beta }_2{NRE}_{it-j}+{\beta }_3{LNB}_{it-j}+{\beta }_4{LEG}_{it-j}+{\beta }_5{LInA}_{it-j}+{\beta }_6{LER}_{it-j}+{\beta }_7{LTO}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\beta }_8{LCO}_{it-j}+{\beta }_9{LPR}_{it-j}+{\beta }_{10}{LIFr}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_1∆{LFDI}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_2∆{LNRE}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\displaystyle \sum _{j=1}^n}{\delta }_3∆{LNB}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_4∆{LEG}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_5∆{LInA}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_6∆{LERi}_{t-j}+{\displaystyle \sum _{j=1}^n}{\delta }_7∆{LTO}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\displaystyle \sum _{j=1}^n}{\delta }_8∆{LCO}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_9∆{LPR}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_{10}∆{LIFr}_{it-j}+{{\mathsf{Ɣ}}_{1}DUMMY}_{it}+ {\epsilon }_{it}\end{array}$$

(2)

$$\begin{array}{l}{∆LFPI}_{it}={\propto }_0+{\beta }_1{LFPI}_{it-j}+ {\beta }_2{LNB}_{it-j}+{\beta }_3{LEG}_{it-j}+{\beta }_4{LFmD}_{it-j}+{\beta }_5{LER}_{it-j}+{\beta }_6{LCO}_{it-j}+{\beta }_7{LPR}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\displaystyle \sum _{j=1}^n}{\delta }_1∆{LFPI}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_2∆{LNB}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_3∆{LEG}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_4∆{LFmD}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_5∆{LER}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\displaystyle \sum _{j=1}^n}{\delta }_6∆{LCO}_{it-j}+{\displaystyle \sum _{j=1}^n}{\delta }_7∆{LPR}_{it-j}+{{\mathsf{Ɣ}}_{1}DUMMY}_{it}+ {\epsilon }_t\end{array}$$

(3)

where LFDI is the log of FDI and LFPI is the log of FPI, $∆$ = first difference operator, LNRE is the log of natural resource availability, LNB is the log of nation branding, LEG is the log of the domestic economic growth rate, LInA is the log of infrastructure availability, LFmD is the log of financial market development, LER is the log of exchange rate, LTO is log of trade openness, LCO is log of capital openness, LPR is log of property rights and LIFr is the log of investment freedom. The ${{\mathsf{Ɣ}}_{1}DUMMY}_t$ represents the global crisis and the pandemic periods.

Step 2:

$$\begin{array}{l}{LFDI}_t= {\propto }_0 +{\displaystyle \sum _{j=1}^n}{\beta }_1{LFDI}_{it-j}+{\displaystyle \sum _{j=1}^o}{\beta }_2{LNRE}_{it-j}+{\displaystyle \sum _{j=1}^q}{\beta }_3{LNB}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^r}{\beta }_4{LEG}_{it-j}+{\displaystyle \sum _{j=1}^s}{\beta }_5{LInA}_{it-j}+{\displaystyle \sum _{j=1}^u}{\beta }_6{LER}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^v}{\beta }_7{LTO}_{it-j}+{\displaystyle \sum _{j=1}^v}{\beta }_8{LCO}_{it-j}+{\displaystyle \sum _{j=1}^x}{\beta }_9{LPR}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^y}{\beta }_{10}{LIFr}_{it-j}+{{\mathsf{Ɣ}}_{1}DUMMY}_{it}+ {\epsilon }_t\end{array}$$

(4)

$$\begin{array}{l}{LFPI}_t= {\propto }_0 +{\displaystyle \sum _{j=1}^n}{\beta }_1{LFPI}_{it-j}+{\displaystyle \sum _{j=1}^q}{\beta }_2{LNB}_{it-j}+{\displaystyle \sum _{j=1}^r}{\beta }_3{LEG}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^s}{\beta }_4{LFmD}_{it-j}+{\displaystyle \sum _{j=1}^u}{\beta }_5{LER}_{it-j}+{\displaystyle \sum _{j=1}^w}{\beta }_6{LCO}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^x}{\beta }_7{LPR}_{it-j}+{{\mathsf{Ɣ}}_{1}DUMMY}_{it}+ {\epsilon }_t\end{array}$$

(5)

where the variables remain as explained in Equations (2) and (3) above. The ${{\mathsf{Ɣ}}_{1}DUMMY}_t$ represents the global crisis and the pandemic periods.

Step 3

$$\begin{array}{l}{∆LFDI}_t= {\propto }_0 +{\displaystyle \sum _{j=1}^n}{\delta }_1{∆LFDI}_{it-j}+{\displaystyle \sum _{j=1}^o}{\delta }_2{∆NRE}_{it-j}+{\displaystyle \sum _{j=1}^q}{\delta }_3{∆LNB}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^r}{\delta }_4{∆LEG}_{it-j}+{\displaystyle \sum _{j=1}^s}{\delta }_5{∆LInA}_{it-j}+{\displaystyle \sum _{j=1}^u}{\delta }_6{∆LER}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^v}{\delta }_7{∆LTO}_{it-j}+{\displaystyle \sum _{j=1}^v}{\delta }_8{∆LCO}_{it-j}+{\displaystyle \sum _{j=1}^x}{\delta }_9{∆LPR}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^y}{\delta }_{10}{∆LIFr}_{it-j}+{{\mathsf{Ɣ}}_{1}DUMMY}_{it}+{\theta ECM}_{it-1}+ {\epsilon }_t\end{array}$$

(6)

$$\begin{array}{l}{∆LFPI}_t= {\propto }_0 +{\displaystyle \sum _{j=1}^n}{\delta }_1{∆LFPI}_{it-j}+{\displaystyle \sum _{j=1}^q}{\delta }_2{∆LNB}_{it-j}+{\displaystyle \sum _{j=1}^r}{\delta }_3{∆LEG}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^s}{\delta }_4{∆LFmD}_{it-j}+{\displaystyle \sum _{j=1}^u}{\delta }_5{∆LER}_{it-j}+{\displaystyle \sum _{j=1}^w}{\delta }_6{∆LCO}_{it-j}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{j=1}^x}{\delta }_7{∆LPR}_{it-j}+{{\mathsf{Ɣ}}_{1}DUMMY}_{it}+{\theta ECM}_{it-j}+ {\epsilon }_t\end{array}$$

(7)

where ${\delta }_1$ … ${\delta }_{12}$ are the short-run dynamic coefficients of the model’s convergence to equilibrium, $\theta$ is the speed of adjustment, and ECM represents the error correction model. The other variables remain as described earlier.

5. Empirical Results and Discussion of Findings

To examine the presence of cointegration between the variables under investigation, a Kao cointegration test was conducted. The results of the Kao test are presented in

Table 4. Kao panel cointegration test.

Model	Dependent Variable	Independent Variables	Modified DF Statistic	DF Statistic
1	FDI		−3.959 ***	−2.114 **
		NRE, NBA, EG, InA, ER, TO, CO, PR, IFr, DUMMY	(0.001)	(0.0173)
2	FPI		−8.418 ***	−5.617 ***
		NBA, EG, FmD, ER, CO, PR, DUMMY	(<0.001)	(<0.001)

Note: *** p < 0.01, ** p < 0.05.

.

The cointegration analysis presented in Table 4 highlights a significant long-run relationship among the variables, thereby justifying the application of panel ARDL methodologies. This study employed two discrete models to evaluate the cointegration between foreign direct investment (FDI) and foreign portfolio investment (FPI) alongside their respective determinants. Model 1 was utilised to explore the cointegration of FDI with determinants such as natural resource endowments, nation branding, economic growth, infrastructure, exchange rates, trade openness, capital openness, and property rights. Conversely, Model 2 focused on the cointegration of FPI with its determinants, which include nation branding, economic growth, financial market development, exchange rates, capital openness, and property rights.

The Kao panel cointegration test aimed to assess the null hypothesis asserting the absence of a long-run relationship among the variables within the panel dataset. Specifically, the null hypothesis posits that there is no cointegration, suggesting that the variables do not exhibit a stable equilibrium relationship. The alternative hypothesis posits that a long-run relationship exists, indicating cointegration. The findings from the Kao cointegration test reaffirmed the existence of a long-run relationship among the variables, aligning with previous studies by Agrawal (2015), Maryam and Mittal (2020), and Tsaurai (2022b).

Consequently, the null hypothesis of no cointegration was rejected, as evidenced by the detection of a long-run relationship among the variables. Typically, a p-value threshold of less than or equal to 0.05 is deemed statistically significant, providing sufficient evidence for a long-run relationship. In this analysis, both the Modified Dickey–Fuller (DF) Statistic and the standard DF Statistic yielded results with 1% statistical significance, reinforcing the conclusion of a long-run relationship among the variables.

The results of the panel PMG ARDL estimation regarding the determinants of FDI and FPI within the BRICS region are detailed in

Table 5. Panel ARDL estimation results.

Variable	Model 1: ∆FDI	Model 2: ∆FPI
Panel A: Long-Run Results
NRE	0.256 ***
	(0.061)
NB	0.204 ***	0.417 ***
	(0.032)	(0.108)
EG	0.156 **	0.017
	(0.073)	(0.048)
InA	0.514 ***
	(0.172)
ER	0.515 ***	0.815 ***
	(0.013)	(0.012)
TO	0.016
	(0.02)
CO	−0.112	−0.328 ***
	(0.499)	(0.032)
PR	4.069 **	16.01 ***
	(1.273)	(3.128)
IFR	3.926 ***
	(1.034)
FMD		0.127 **
		(0.016)
CRISIS	−14.258 ***	−31.792 ***
	(2.084)	(3.836)
Variable	Model 1: ∆FDI	Model 2: ∆FPI
Panel B: Short-Run Results
ECT [−1]	−0.688 ***	−0.759 ***
	(0.179)	(0.142)
∆NRE	0.084
	(0.075)
∆NB	0.26 **	0.857
	(0.122)	(0.752)
∆EG	0.045	−0.036
	(0.082)	(0.044)
∆InA	0.745
	(1.668)
∆ER	−0.027	−0.059 *
	(0.02)	(0.032)
∆TO	−0.088 **
	(0.04)
∆CO	−0.091	0.478
	(0.056)	(0.294)
∆PR	−3.103	−18.448
	(7.147)	(11.996)
∆IFR	−0.122
	(1.435)
∆FMD		−1.86
		(1.421)
∆CRISIS	−9.81 ***	23.014 ***
	(2.424)	(4.788)
Constant	−3.077 **	−5.264 ***
	(1.24)	(1.975)
Observations	150	150
Diagnostic Checks

Note: *** p < 0.01, ** p < 0.05, * p < 0.1.

.

The study’s results, as reported in Table 5, indicate that over the long term, factors such as natural resource endowment (NRE), nation branding (NB), economic growth (EG), exchange rates (ER), property rights (PR), investment freedom (IFr), and infrastructure availability (InA) significantly and positively influence foreign direct investment (FDI) in the BRICS nations. Conversely, nation branding, exchange rates, property rights, and financial market development (FmD) were found to have significant positive relationships with foreign portfolio investment (FPI). However, the crisis dummy showed a negative and significant relationship with both FDI and FPI. Additionally, the relationship between FPI and capital openness displayed a long-term negative correlation.

In panel B of Table 5, the study found that nation branding had a positive and significant effect on FDI in the short run, while trade openness had a significant negative relationship with FDI. Regarding FPI, a significant negative short-term relationship with exchange rates was observed. The short-run findings presented in Panel B indicated that the estimated speed of adjustment was at least 68% for FDI and 76% for FPI (as illustrated by the Error Correction Term, or ECT), which is close to unity, suggesting a rapid adjustment rate. These results align with expectations, as the negative signs indicate a correct direction of adjustment. This suggests that short-term disequilibrium in FPI and FDI within the BRICS region is quickly corrected towards equilibrium levels.

By examining the selected factors influencing FDI in BRICS countries from 1994 to 2024, the study found that natural resource endowments, economic growth, exchange rates, property rights, investment freedom, nation branding, and infrastructure availability have a positive and significant impact on FDI in the BRICS nations. These findings are consistent with the works of Matiza (2017); Asongu et al. (2018); Cung and Nhung (2020); Corbet et al. (2020); Azam and Haseeb (2021); Tsaurai (2022b); Azharuddin and Mehra (2022); and Gupta et al. (2023), among others. Furthermore, the study confirmed that nation branding, exchange rates, property rights, and financial market development positively and significantly determine FPI in BRICS countries; however, capital openness was found to have a negative impact on FPI. This finding regarding FPI determinants aligns with the research of Mercado and Park (2011); Nielsen and Bjørnskov (2012); Haider et al. (2016); Giritli and Ibrahim (2020); and Makoni (2020).

Nation branding, an intangible variable, was assessed using a Nation Brand Attractiveness Index, which indicated a statistically significant positive impact on FDI. Previous literature has noted the limited academic attention given to the concept of nation branding on a global scale. In this context, the current study represents one of the few investigations exploring nation branding as a determinant of FDI in the BRICS countries. The empirical results of this research align with the conclusions drawn by Kalamova and Konrad (2010) and Matiza (2017) regarding the critical role of nation branding as a determinant of FDI.

Kalamova and Konrad (2010), as well as Lahrech et al. (2020), utilised the Anholt Nation Brands Index to measure nation branding while focusing on both developed and developing countries. Their findings revealed a considerable and robust positive effect of nation branding on FDI flows. In contrast, Matiza (2017) expanded upon the commonly used components of Anholt’s Brands Index by adding three additional factors—infrastructure, factor endowments, and the legal and regulatory framework—to measure nation branding. The author then developed a qualitative measure of nation branding through primary data surveys. Similar to the findings of Kalamova and Konrad (2010) and Lahrech et al. (2020), Matiza (2017) confirmed the positive effect of nation branding on FDI. This current study followed the quantitative approach of Fetscherin (2010) to create an alternative measure of nation brand attractiveness, which was used to assess the nation branding variable. The findings of this investigation reveal a new perspective on the determinants of foreign capital flows in BRICS, suggesting that intangible factors, such as nation branding, have a positive and significant impact on FDI.

For the robustness check, the Seemingly Unrelated Regression Equations (SURE) approach, which closely mirrored the results obtained from the ARDL approach, was utilised, thereby affirming the robustness of the results. The consistency observed from the two distinct analytical methods employed enhanced the credibility and validity of the empirical findings. Moreover, the application of both the panel ARDL and SURE methodologies reinforced the stability of the identified patterns and associations within the examined dataset. This alignment not only underscores the reliability of the employed analytical techniques but also strengthens confidence in the substantive implications derived from the study.

6. Contribution of the Study

6.1. Contribution to Body of Knowledge

This study makes a significant contribution by analysing the role of nation branding as a catalyst for foreign direct investment (FDI) and foreign portfolio investment (FPI) in BRICS countries. Historically, scholarship in this area has overlooked nation branding’s emotional, perceptual, and intangible dimensions in the investment decision-making process. This research posits that traditional determinants of FDI and FPI, along with intangible factors, are insufficient on their own for attracting substantial foreign capital. By integrating nation branding into the analysis, this study offers a nuanced understanding of investment flows.

Furthermore, while BRICS nations were chosen to illustrate the characteristics of developing economies, the findings may be extrapolated to other emerging markets despite the variations in national branding. Another key aspect of this research is the simultaneous examination of the drivers of FDI and FPI, recognising them as interrelated phenomena rather than isolated entities. This study also enriches the existing literature by exploring the interplay between FDI, FPI, and nation branding—an area that has been notably underexplored in the context of BRICS economies.

6.2. Methodological Contribution

This study offers a significant methodological advancement by creating a more comprehensive nation branding index specifically for the BRICS countries. The nation branding attractiveness index was developed by taking into account various factors, including exports, net migration, tourism, and six world governance indicators (WGIs). By constructing this index, the study provides a more accurate and holistic representation of the nation branding variables in BRICS nations.

Unlike the Anholt (2005) Nation Branding Index (NBI), which primarily relies on the government effectiveness indicator, this study uniquely incorporates all six WGIs. This shift is motivated by inconsistencies in the existing literature regarding the construct validity of these indicators and the debate over whether they represent distinct concepts or are causally related. Gerged et al. (2023) noted that the six WGI variables are not easily distinguishable, as they tend to reflect a broader underlying concept. Given this context, along with the complex nature of the data related to the six WGI variables, this study makes a valuable contribution by integrating all six indicators into the nation branding attractiveness index.

6.3. Policy Implications and Recommendations

The findings derived from this study outline several key policy implications and actionable recommendations regarding foreign capital inflows into BRICS nations. Notably, alongside traditional macroeconomic and microeconomic determinants of Foreign Direct Investment (FDI) and Foreign Portfolio Investment (FPI), nation branding has emerged as a significant influencer of foreign capital attraction within the BRICS framework.

It is essential to recognise that while intangible elements play a pivotal role, they are insufficient in isolation to draw foreign investments. Consequently, BRICS governments must prioritise the enhancement of both macroeconomic environments and investor-centric policies to bolster confidence among foreign investors. In today’s geopolitical landscape, soft power has become a vital strategic asset, particularly for countries aiming for sustained long-term growth; thus, enhancing soft power components to attract foreign capital effectively is imperative.

The influence of nation branding on FDI and FPI necessitates that BRICS governments capitalise on their distinct cultural, social, and economic attributes to elevate their soft power and cultivate a favourable investment climate. Given the diverse cultural and economic contexts across BRICS nations, there is a need for strategic initiatives that manage each country’s reputation and branding for investor appeal. Significantly, collaborative regional branding initiatives could be explored, considerably amplifying the joint attractiveness of BRICS as an investment destination.

The economic implications of the BRICS on the global stage are profound, underscoring the potential effectiveness of cohesive branding efforts that position the BRICS bloc as a formidable regional investment hub. Establishing a dedicated BRICS branding council could facilitate collaborative branding strategies, fostering a platform for the exchange of best practices and ensuring coordinated approaches to enhance the collective brand image.

Additionally, BRICS nations must identify and leverage shared values and themes, such as economic growth, cultural diversity, and sustainable development, that resonate with both potential investors and broader global audiences. By articulating a unified narrative, BRICS can augment its credibility and strengthen its influence in international policy discussions, thereby enhancing the region’s perception as a desirable and stable investment environment.

Beyond branding and diplomatic efforts, BRICS governments must address the underlying perceptual factors associated with soft power that impact investment decisions. Significant attention should be given to creating conducive investment climates characterised by political stability, legal integrity, anti-corruption measures, favourable incentives, and the reduction in bureaucratic impediments. Moreover, effective nation branding is further complemented by robust investor aftercare initiatives; governments must actively support investors, address their concerns, and foster relationships to mitigate risks of capital flight.

As they strive to establish resilient brands, BRICS governments must acknowledge that nation branding is a long-term endeavour that demands continuous commitment and resources. The evolution of nation brands necessitates ongoing attention with a long-range vision to build, maintain, and reinforce the desired global image. Accordingly, relevant authorities in BRICS countries need to implement monitoring and evaluation mechanisms to assess the impact of branding initiatives on FDI and FPI. Regular perception surveys can be instrumental in gauging the efficacy of these branding strategies, facilitating necessary adjustments to enhance their appeal.

Furthermore, proactive engagement with potential foreign investors through targeted outreach initiatives, such as investment forums and business matchmaking events, can provide BRICS governments with valuable insights into the effectiveness of their branding efforts and their overall influence on investment attraction. By fostering direct dialogue with potential investors, BRICS nations can solicit feedback that informs their branding strategies and drives improvements in investment competitiveness.

7. Conclusions

This study explored the tangible and intangible determinants influencing foreign capital flows within the BRICS nations, utilising panel data spanning from 1994 to 2024. It highlighted a notable knowledge gap in existing literature, particularly emphasising the disproportionate focus on Foreign Direct Investment (FDI) compared to Foreign Portfolio Investment (FPI) determinants. To assess the long-term relationships between FDI, FPI, and the selected determinant variables, the PMG Panel ARDL methodology was employed, providing insights into both short- and long-term drivers for these forms of investment.

The findings revealed that several factors significantly and positively impacted FDI in the BRICS context, including natural resource endowments, nation branding, economic growth, exchange rates, property rights, infrastructure availability, and investment freedom. Conversely, the determinants affecting FPI included nation branding, exchange rates, property rights, and the maturity of financial markets, all exhibiting significant positive associations.

Interestingly, capital openness emerged as a negatively correlated factor with FPI, highlighting complex dynamics in capital flow behaviour, while the introduction of a crisis dummy variable indicated a significant negative impact on both FDI and FPI. The study advocates for BRICS governments to strategically leverage their cultural, social, and economic attributes to enhance their soft power, thereby cultivating a favourable image that would attract foreign investors.