Impact Of Capital Structure On The Firm Performance

Impact Of Capital Structure On The Firm Performance In The Manufacturing Sector Of The United Kingdom

[Student ID]

[Name of Institution]

[Date of Submission]

Abstract

The aim of this study was to analyse the impact of capital structure on firm performance with specific sample of manufacturing companies in the UK. This study adopted quantitative methodology using panel data analysis with a sample of 10 large manufacturing companies in the UK for the period 2010-2020. Based on panel data this study concludes that manufacturing companies in the UK have higher leverage because they rely more on debt as source of finance for their operations as compared to equity and assets. Furthermore, there is also evidence that manufacturing companies are more relying on short term debt, yet they are able to maintain adequate level of liquidity which counters the risk of default due to high leverage. Furthermore, it can also be inferred that due to good liquidity the manufacturing companies are able to manage risks due to economic downturn and similar event and maintain their profitability and performance. For further analysis of performance following discussion is focused on analysing the profitability ratios of manufacturing sector. Overall, using debt-to-equity, long term debt, and short term debt as capital structure indicators whereas RAO and ROE as performance indicators, this study found that there negative impact of debt on ROA but positive impact with ROE. However, it is important for manufacturing companies in the UK to make effective debt financing decisions as there are several risks associated with this choice of capital structure.

Chapter 1 Introduction

1.1 Background of the Study 

When a business wants to run and grow, it uses a mix of debt and stock. This is called its capital structure. It is one of the most important choices a business can make about its money. A company's capital structure has a big effect on how much it makes, how much risk it takes, and how much it is worth altogether. The link between capital structure and company success has been studied a lot over the years because it is so important. Many important things happen in the UK economy because of the manufacturing sector (Vuong et al., 2017). About 10% of the UK's GDP comes from manufacturing, which employs more than 2.6 million people. Even though globalization and automation have caused some problems, UK industry is still strong, and there is a lot of room for growth in new technology areas like electric cars and green energy (Kautto Penttinen, 202). Because this industry is so important, improving capital structures can have a big effect on how well manufacturing businesses can spend, come up with new ideas, and make long-term value for their shareholders.

Prior research on the capital structure-performance relationship has largely focused on developed markets like the US. However, there are some key differences in capital market conditions across countries that suggest findings may not be directly generalized. For instance, the UK has proportionately greater public ownership of large companies when compared to the US. Venture capital and private equity funding are also relatively more prominent sources of corporate finance in the UK (Basit and Irwan, 2017). These country-specific factors indicate a need to examine capital structure issues within the unique context of the UK market. Understanding firm performance implications is also important within the manufacturing industry specifically, as it has its own dynamics and growth priorities. Manufacturing companies often have substantial capital expenditure needs to support research/development, product innovation and adoption of advanced technologies like robotics/AI. Capital structure choices could differentially impact such firms versus others with purely financial/service-based business models (Nenu et al., 2018). Past capital structure studies have not adequately focused on manufacturing as an independent sector of interest.

The inconclusive and sometimes conflicting results from prior capital structure literature also suggest the relationship may be more complex than usually depicted. Moderating factors like firm size, asset tangibility, profitability, growth opportunities could shape how debt and equity financing influence performance measures in certain scenarios. More granular analysis is needed to recognize potential contingent relationships and contextual dependencies. Also, most of the study on capital structure that was available used accounting-based measures like return on assets (ROA) and return on equity (ROE) to represent how well a company was doing (Muhammad et al., 2021). Even though these measures are helpful, they might not fully show a company's promise or long-term wealth creation. Using different result factors based on economic theory, such as Tobin's Q (ratio of market value to substitute prices of assets), might help paint a fuller picture. Because of these gaps, the goal of this research study is to look at the link between capital structure and success for manufacturing companies in the UK market. It will use multivariate analysis to look at how different debt-to-equity ratios affect both financial and economic measures of how well a company is doing.

The main dependent variable that will show long-term worth will be Tobin's Q (Ozcan, 2019). It will also be looked at how firm traits like growth, revenue, etc. affect the results. Researchers hope that the results of this study will help people make good decisions about capital structure, especially in the UK industry sector. It will not only add to the existing literature but also guide policymakers and firms on strategies to optimize balance sheets in a way that boosts long-term competitiveness and value creation for shareholders (Yousef, 2019). In summary, gaining a more nuanced understanding of how capital structure influences performance of UK manufacturers is highly relevant both theoretically and practically.

1.2 Problem Statement 

Capital structure is one of the most important aspects of corporate financial decision making as it can significantly impact a firm’s value, risk level, and overall performance. While there is substantial research examining the relationship between capital structure and performance, several gaps remain. More specifically, there are enough solid evidence to determine how capital structure affects the success of manufacturing companies in the UK (Nenu et al., 2018). Previous research on this subject has mostly been limited to big, developed countries with unique capital markets, such as the United States. People may not be able to directly apply those results to other countries because ownership structures, regulations, and macroeconomic conditions vary from one country to the next (Demirgüc-Kunt et al., 2020; Kyere and Ausloos, 2021; Javeed and Lefen, 2019). Things that are unique to the UK, like the fact that the government owns more big businesses and private equity plays a big role, need to be taken into account. Capital structure problems need to be looked at from the point of view of the British market.Also, previous studies have mostly looked at capital structure across whole industries, not just certain areas that might work in different ways. There are some things that make the industrial business different that could change the link between capital structure and success. 

When manufacturers want to come up with new products and processes, use advanced manufacturing technologies like robots, or buy new plants and equipment, they often have to make big, long-term investments in capital (Jiang et al., 2021). Unlike service sector firms, they have a lot of fixed assets and take a long time to make investments. For manufacturing companies with these unique growing needs and ways of doing business, the effects of debt and stock capital on creating value could be different. Most earlier research has also used standard accounting performance measures such as return on assets (ROA) and return on equity (ROE), which show how profitable a business has been in the past but might not accurately show its long-term potential or economic value (Makhija and Trivedi, 2021; Hamdan, 2018). New metrics with deeper roots in financial theory, such as Tobin's Q, which compares the market value of a company to the cost of replacing its assets, might give a better picture of how choices about capital structure will affect the company in the long run. Also, the data have on dependent relationships has been all over the place, and it's possible that firm-specific factors may change how capital structure affects performance results. 

There are important factors that affect the capital structure-performance relationship in important ways that are not well known in the UK industrial context (Pathak and Chandani, 2023) such as the size of the company, how tangible its assets are, its level of profitability, and its growth prospects. Because of the gaps in the knowledge, it's still not clear how the make-up of a company's capital affects its long-term economic performance and its ability to create value in the UK's capital markets and economy (Poutziouris et al., 2022). UK makers want to be as competitive as possible and maximize company value over time in this fast-paced global industry. They need to know how to make good capital structure policies and financing choices. 

1.3 Research Aim

The aim of this study is to analyse the impact of capital structure on the long-term performance and value of manufacturing firms in the United Kingdom.

1.4 Research Objectives

• To investigate the relationship between capital structure, as measured by debt-to-equity ratio, and accounting performance, as measured by return on assets (ROA), of UK manufacturing firms.
• To examine the impact of capital structure on the economic performance and value, as measured by Tobin's Q ratio, of UK manufacturing firms.
• To determine whether firm characteristics such as size, asset tangibility, profitability, and growth opportunities moderate the relationship between capital structure and firm performance.

1.5 Research Questions 

1. Does the capital structure, as represented by debt-to-equity ratio, influence the accounting performance, as measured by ROA, of manufacturing firms operating in the UK?
2. Does the capital structure impact the economic performance and value, as measured by Tobin's Q ratio, of UK-based manufacturing companies?
3. Do firm characteristics such as size, asset tangibility, profitability, and growth opportunities condition the relationship between capital structure and firm performance for UK manufacturers?

1.6 Significance of the Study 

This research study on the impact of capital structure on the performance of UK manufacturing firms holds both theoretical and practical significance. From a theoretical perspective, the findings will contribute to advancing academic knowledge on the long-standing debate around the relationship between capital structure and corporate performance. Most prior studies have examined this issue at an aggregate level without isolating specific industries or accounting for country-level differences (Mwangi, 2021). This study aims to address some of the gaps in the literature by focusing on a single sector—manufacturing—within the context of the UK economy and capital markets.

The use of alternative measures like Tobin's Q ratio to examine economic performance adds richness beyond traditional accounting-based metrics used in earlier studies. The evaluation of potential moderating effects of firm characteristics will also provide more nuanced insights on contingent relationships. Overall, the research enhances theoretical understanding of how debt-equity decisions impact long-term value creation for companies with different profiles. From a practical standpoint, the results carry important implications for capital structure policymaking and financing decisions within the UK manufacturing industry (Alfawareh et al., 2022). Understanding which debt-equity mixes are optimal for maximizing performance at both operational and strategic levels arms managers with valuable insights. It informs people how to use their resources to make the country more competitive in the long run, encourages investment in new ideas, and raises the chances of long-term growth. 

Manufacturing companies often need to spend a lot of money on capital, so having well-optimized balance sheets is important for staying ahead of their global competitors. The results would help these companies come up with financial models that work with the way their business works and their long-term goals as a company. It also helps buyers who are looking for safe ways to put their money to work in the field. The study tells capital sources like banks and private equity firms how to match different types of loans to UK makers based on their risk-return profiles in the most efficient way (Alitani, 2020). The right way to structure debt can free up cash for growth while keeping potential risks in check. This makes it easier for lenders to back and give money to the business as a whole.

The manufacturing industry is a big part of the UK's economy as a whole because it creates jobs, sells goods, boosts productivity, and helps regions grow. Maximizing performance of these firms through capital structure strategies that augment investment capability, adoption of advanced technologies, new product development and global expansion would significantly boost economic output, job creation and prosperity over the long run. The insights are also relevant for policymakers aiming to foster sustainable growth of the manufacturing industry through facilitating productive capital allocation. It provides empirical basis for designing prudent regulations and incentives supporting optimal balance sheet management within this vital sector. In summary, deeper understanding of capital structure impact holds both theoretical and practical value for varied stakeholders (Do, 2020). The significance of this research lies in advancing knowledge while guiding strategic finance decisions central to performance, competitiveness and long-term prosperity of UK manufacturing enterprises.

1.7 Structure of the Study 

Table 1 Structure of the Study 

Source: Author

1.8 Key Definitions

Table 2 Key Definitions

 Source: Author

Chapter 2: Literature Review

2.1 Introduction 

The chapter consists of a review of literature relevant to the research topic listed below. The concentration is on the findings of research showing the connection between capital and firm performance and a significant attention to those originating from the UK and those which concentrate on manufacturing. The goals are to comprehend the main theoretical bases of the choices of capital structure, investigate the direct and indirect correlations between debt ratios and the performance measures, and explore the underlying factors of the UK market and the manufacturing industry and to find the gaps in the existing body of knowledge. It is therefore necessary to bridge the gaps and add to the previous research in order to establish new conclusions on the interference of capital structure with the long-term prosperity of UK manufacturers.

2.2 Capital Structure Theories

Capital structure is the composition of a company's long-term debt and common shares that it uses to support operating needs. The capital structure that a firm decides to take on has a real effect on its overall value, risk and how well it performs. The manner in which managers of businesses usually determine a company’s optimal debt to equity ratio has over the years, been revealed by several theoretical models proposed by corporate finance researchers (Herciu and Ogrean, 2017). This paragraph reviews the three wealth structure concepts advocated in the literature, spanning trade-off, pecking order, and agency cost theories.

2.2.1 Trade-off Theory

Among the theories advanced in an attempt of explaining funding structure, trade off theory is one of the most prominent and influential ones. The tradeoff theory of capital structure aims to show a financier's evaluation of marginal costs and benefits connected with the common stock as well as with the debt appearing as a measure criterion of a firm's optimal capital structure (Abel, 2018). On the one hand, it is possible to receive tax shield as a result of interest payment on which tax is payable (Ai et al., 2020). The firm's overall tax burden thus is reduced. It serves to control that managers use resources efficiently as their obligations to debt need to be met. Debt too creates an amplifying impact, doubling gains to shareholders as long as earnings surpass their general interest component.

On the one hand, debt provides capital for business growth and investment, while on the other, it puts the company at the risk of bankruptcy during financial challenges when debt may cease to be repaid due to missed interest or principal payments. Thus, it can result in a lot of financial stress load which may have to be borne by the individual. Additional issues with high leverage include restrictive debt covenants that limit managerial flexibility, agency costs of debt, and potential underinvestment problems as risk-averse managers pass up worthwhile projects that increase risk of default. According to the trade-off theory, firms strive for an optimal debt ratio which equates the marginal tax benefits and marginal financial distress costs of additional debt (Nicodano and Regis, 2019). This optimal level trades off the costs and benefits associated with further borrowing. The theory implies that firms in stable industries with steady cash flows can take on higher debt safely compared to riskier firms. Firm-specific factors like profitability, asset tangibility, firm size and growth options also determine how much debt a company can support.

2.2.2 Pecking Order Theory

The viewpoint set forth by Jensen and Meckling in 1976 is opposed by the pecking order theory, which was presented by Myers and Majluf in 1984. Unlike the trade-off theory which on grounds that managers are aiming to maximize shareholder value, pecking order theory is founded on asymmetric information among the insiders and outsiders of the company. First of all, this hypothesis suggests that managers are more informed about the future of a firm, meaning its opportunities and risks, than investors. This problem creates a 'lemon' gap where the "good" and the "bad" firms cannot be acutely distinguished (Martinez et al., 2019). To prevent the adverse selection problem, firms are proposed to adhere to a pecking order of capital when deciding where to source the funds based on the lowest preferred options for capital, which begins with internal funds from retained earnings before going on to borrowing from outside sources where managers may be forced to disclose the firm’s information. If a further package of funds is required, a company would turn to safer debt as opposed to the possibility of riskier equity. Another reason relates to the fact that capital raising from debt does not diminish share ownership and voting control for existing shareholders, while equity financing would do (Frank et al., 2020). At the end of the belt, companies are likened to stock sales that signal overvaluation and also cause existing shareholders, who would get redistributed to new investors, to suffer a wealth transfer.

In contrast to the trade-off theory, the pecking order theory implies that firms do not actively aim for a target debt ratio. Instead, their funding needs determine the capital structure endogenously. Firms with more profitable investment opportunities and higher retained earnings will tend to have lower leverage (Wiagustini et al., 2017). The theory also argues that market timing considerations do not affect capital structure decisions. Empirical evidence has found support for many predictions of the pecking order theory.

2.2.3 Agency Cost Theory

Proposed by Jensen and Meckling in 1976, the agency cost theory examines capital structure choice from a corporate governance perspective. It considers conflicts of interest that arise due to separation of ownership and control in corporations. The central issue arises from divergence between the risk preferences of managers as agents of shareholders, and the risk preference of shareholders as principals who bear ownership. Managers have incentive to shirk and engage in behaviors that benefit themselves rather than maximizing shareholder value (Jensen and Meckling, 2019). Debt financing mitigates this agency problem by disciplining managers through fixed debt obligations, covenant restrictions and threat of bankruptcy in case of default. Equity owners also want to incur more debt up to a point where marginal costs of financial distress offset benefits of reduced agency costs (Panda and Leepsa, 2017). In essence, debt works as a governance mechanism that constrains managerial discretion and reduces perquisite consumption, causing managers to work harder to service debt and avoid bankruptcy.

2.3 Literature on Capital Structure-Performance Relationship

The capital structure-performance relationship remains an area of debate. Trade-off theory proposes an optimal debt level that balances tax benefits and distress costs, with some studies finding an inverted U-shape. However, many find a negative linear relationship, questioning the target capital structure assumption. Higher leverage may lower value by restricting flexibility, increasing refinancing risk and bankruptcy costs. Pecking order theory also suggests leverage signals private information leading to underinvestment (Al-Thuneibat, 2018). The relationship depends on the performance measure used, as leverage lowers accounting returns but its impact on Tobin's Q is inconclusive. Some positive relationships emerge too, as leverage can align management incentives and induce efficiency. 

Moderating firm factors importantly condition the debt effect, such as asset tangibility mitigating underinvestment problems. Larger firms with stable earnings benefit more from leverage. However, high growth firms may find debt restrictive. Country differences also produce heterogeneity (Opoku-Asante et al., 2022). Weaker shareholder protections erode capital structure impacts in some emerging markets. Overall, while positive and negative relationships have been found, moderating factors demand consideration to avoid oversimplifying this complex relationship between capital structure decisions and performance outcomes.

2.4 Moderating Effects of Firm Characteristics

The relationship between a firm's capital structure and its performance is often more complex than a simple direct link, with empirical evidence suggesting this association is contingent on characteristics of the individual firm. This section provides a more in-depth review of four key firm-level moderators that have been extensively analysed in prior studies - firm size, asset tangibility, profitability, and growth opportunities.

2.4.1 Firm Size

Larger firms tend to exhibit less variability in cash flows and revenues compared to smaller enterprises due to economies of scale, product diversification, and established customer networks. This stable earnings profile reduces the costs of financial distress associated with higher leverage. Informational asymmetries are also weaker for larger firms that are typically well-known and widely followed, facilitating easier access to both debt and equity capital. Empirically, size has shown to positively influence the capital structure-performance relationship. Using a sample of Chinese listed companies, Ibhagui and Olokoyo, (2018) reported leverage enhances firm performance more strongly for large firms versus small firms. Similarly, a study found size amplifies the inverted U-shape between debt ratios and profitability proposed by trade-off theory (, A.S. and Gao, 2023). This moderating effect indicates higher debt levels are more suitable and yield greater benefits for larger versus smaller businesses.

2.4.2 Asset Tangibility

Tangible assets can be pledged as collateral, lowering borrowing costs and mitigating issues of adverse selection and moral hazard associated with leverage. Debt agreements often include covenants linked to tangible asset ratios which provide lenders additional protection against downside risks. Highly tangible firms also face less pressure to fulfill restrictive debt obligations that could curb capital reinvestment (Feldman et al., 2021). Notably, empirical tests show asset tangibility positively conditions whether leverage enhances or impairs performance outcomes. Capital structure decisions have a stronger positive alignment with firm value when businesses hold more tangible versus intangible assets. Supporting this, Indian companies with higher tangible asset compositions experienced more favorable effects from leverage on profits and market valuations.

2.4.3 Profitability

Sustainable earnings streams indicate higher quality firms with stable cash flows to meet debt obligations. Leverage impacts are thus expected to differ based on profit generating abilities, with more profitable enterprises facing lesser information asymmetry costs. Unprofitable businesses conversely impose greater risks on lenders. Indeed, profitability proves a key moderator in leveraged firms. Highly profitable Chinese firms saw debt ratios positively impact value, whereas unprofitable peers exhibited stronger inverse debt-performance sensitivity. Similarly, Phuong et al., (2020) found leverage affects profits less negatively for UK companies with history of strong earnings. Profitability conditions the magnitude and direction of capital structure influence.

2.4.4 Growth Opportunities

Innovative projects entail higher intangible assets and complexity, exacerbating asymmetries versus mature low-growth businesses. Leverage may also curb managerial discretion needed to realize growth potential through risky ventures. However, ul Islam, (2023) argued debt monitoring encourages efficient investment of funds into the most productive opportunities. Prior empirical work reports mixed evidence on growth's moderating role. Higher uncertainty associated with growth weakened positive debt impacts for Pakistani firms. Yet a meta-analysis found growth amplified leverage benefits on performance metrics through better governance effects (White et al., 2022). Clearly, the relationship is contingent on firm context and measured outcomes.

2.5 Capital Structure in UK Context

While capital structure theories and much prior empirical work are based on evidence from the US, it is important to examine issues within the unique context of the UK as well. Capital markets exhibit cross-country variations in regulations, shareholder protection laws, ownership structures and financing trends impacting corporate financial decisions (Zogning, 2017). This section outlines some distinguishing aspects of the UK environment and their implications.

2.5.1 Ownership Structure

In comparison to dispersed ownership in the US, UK equity markets have a higher concentration of institutional investors like pension funds and insurance companies holding large ownership stakes. Around two-thirds of UK market capitalization is held by such institutional investors (Franks and Mayer, 2017). This concentrated ownership enables greater involvement in firm governance and monitoring, with potential to positively influence capital structure choices towards optimizing shareholder value. Government ownership also remains prominent historically through enterprises like British Petroleum, British Telecom, British Steel Corporation prior to privatization initiatives since 1980s. While most state-owned giants have now been divested, this legacy imparts a distinctive public sector influence on the UK business landscape and capital allocation norms.

2.5.2 Access to External Finance

Private equity activity forms a substantial source of long-term investment capital, accounting for around 25% of total enterprise value transactions in the UK (Armstrong et al., 2019). Similarly, venture capital investments also exert visible impacts on the financial component for new and small/medium enterprises. Such disposition of the private funds’ owners as immediate liquidity that they tend to make UK firms contemplate equity versus debt financing in terms of future growth. The fact remains however that UK businesses, thanks to the conservative lending policies from the banks, have been required to access the capital from the public market as well as retained earnings instead of relying on relationship banking in cases of other countries (Allen et al., 2018). Regarding bank lending, it should be noted that crisis-related circumstances have brought unforeseen monetary policies, however the refinancing dynamics in bank-firm transaction have been quite conservative.

2.5.3 Regulatory Environment

In contrast to corporate governance frameworks based on stronger shareholder protection measures as in the US and UK, whereby boards and managers have very limited degree of authority, there is much greater latitude endowed to board directors and managers in developing their corporate governance framework. The anti-unsolicited-takeover measures tend to be the deterrents to the power of the shareholders. Disclosure in the UK tends to be easier to ‘finesse’ compared to strict accounting standards imposed in some countries, making it one of the flexible conditions for favorable capital structure view (Coffee et al., 2021). The presence of these characteristics in being UK debt market implies that their managers may find the performance of their debt obligations to be less tight than their US competitors, and that this affects their capital structure decisions. In spite of the fact that institutional investors possess a very great influence, unfortunately, it helps curb the potential problems associated with agency.

2.6 Capital Structure in Manufacturing Sector

Industrial production is a category of expenditure that has a particular way of accumulating capital and carrying its operations that will entail distinct aspects when it comes to the capital structure as opposed to sectors just like services or trade. One must mention the expanatory note on manufacturing due to its pervasive role as a boosting factor of productivity, innovation and employment (Jaisinghani and Kanjilal, 2017). This part fabricates the notion that different industries have specific characteristics that should be considered when capital structure decision making is involved.

2.6.1 Capital Intensity

Firstly, manufacturing mainly uses a lot of factories/machines and production equipment as a form of capital, ranging from the engineering plants to R&D laboratories and distribution networks. The extensive investment as long-term investors are usually made, even before there is a reasonable time for yields to be seen, for establishing the firm’s competitive strengths. Renewal of technologies is another area that need regular flow of capital to keep them up to date. It is here that capital intensity influences strategic capital structure choices. The presence of extremely costly machinery raises asset specificity thus the idea of getting debt finance through collateral (Matsa, 2018). This also means sound financial management formula namely dedication in capital investment, which together reflect financial flexibility and debt capacity will have greater importance, thus will prevent problem of borrowing extensively and severe distress due to insolvency. Retained earnings or an issuance can be more appropriate way to finance if a continuous flow of allocation for this purpose is needed.

2.6.2 Research & Development

Product/process innovation constitutes a lifeline for sustaining competitiveness through technological upgrades. Manufacturing R&D typically yields benefits over long gestation periods with highly uncertain payoffs. Measuring up to US$ billions annually for leading pharmaceutical/technology firms, R&D requires stable long-term finance (Kimoro, 2019). Debt covenants demanding short-term cash flows may curb managers' discretion over multi-year R&D budgets, impairing growth potential. Conversely, equity financing preserves financial flexibility and aligns investors with innovation-led value creation (Sun and Xiaolan, 2019). These knowledge-capital factors influence whether leverage facilitates or hampers innovation/performance.

2.6.3 Business Cyclicality

Demand and profitability patterns of manufacturing sectors are more susceptible to macroeconomic and industry cycles compared to other industries. For instance, automobile sales plunge during recessions while booms drive capacity expansions in semi-conductors. Such cyclicality implies higher risk of default or refinancing difficulties at the trough of downturns for leveraged manufacturers. Excessive debt exacerbates pro-cyclical swings damping stability. Equity cushions better alleviate short-term liquidity pressures supporting continued operations over a horizon of fluctuating cash flows. Empirically, studies specifically examining manufacturing lend support to its differentiated capital needs. Hiroki et al. (2022) observed debt positively impacted Taiwanese electronics firms' returns, albeit nonlinearly, reflecting their asset intensity. Using Korean SME data, Xie et al. (2022) found capital structures exhibited heterogeneity by industry. Firms invest more in R&D and capital witnessed weaker debt-value relationships.

2.7 The Impact of Capital Structure on the Economic Performance

Capital structure refers to the mix of debt and equity that firms use to finance their operations. It is one of the most important financial decisions firms make as it impacts various dimensions of performance. While prior research has examined accounting-based measures like profitability and returns, a firm's economic performance in the long-run reflects its true value creation potential better (Masavi et al., 2017). This section analyzes some key economic performance indicators and how capital structure influences them.

2.7.1 Tobin's Q

Tobin's Q ratio compares a firm's market value to the replacement cost of its assets. It is a forward-looking measure that captures long-term growth opportunities not fully reflected in accounting books. Capital structure affects Q by its impact on cash flows, risk and agency costs. Theoretically, leverage can improve Q up to an optimal point by reducing free cash flows and aligning management interests. But excessive debt raises financial risk weighing on growth options (Vuong et al., 2017). Empirically, studies report leverage generally lowers Q after controlling for other determinants, supporting the notion that high leverage constrains future investments (Ayange et al., 2021). However, some find leverage positively impacts Q when firms have strong tangible assets and stable cash flows. This suggests capital structure influences Q in a context-specific, often nonlinear manner depending on firms' risk profiles.

2.7.2 R&D Investments

Research intensity reflects innovative capabilities impacting long-run competitive edge and profitability. Leverage may hinder R&D expenditures needed to create and commercialize new technologies if rigid debt covenants curb managerial flexibility. However, debt monitoring can also motivate efficient R&D resource allocation. Empirically, debt negatively impacts European firms' R&D intensities but this effect weakens for highly innovative industries (He et al., 2022). Effective capital structures balance control benefits versus flexibility costs of leverage over technological innovations.

2.7.3 Productivity Growth

Capital investments in physical and knowledge assets sustain productivity enhancement over time. While debt supports capital spending related to tangible assets, overleveraging carries refinancing and underinvestment risks constraining reinvestment potential. Using U.S. manufacturing data, Jiao et al. (2019) found higher debt ratios associate with lower total factor productivity growth and attribute the effect to underinvestment problems. This highlights how leverage influences the very foundation of long-run economic advancement via compromising efficient capital allocation.

2.7.4 Patent Counts

Patenting activity captures firms' technological outcomes translating R&D efforts to new commercial applications. Leverage impacts innovation incentives as debt becomes riskier for output with delayed cash flows. Huang et al. (2021) reported E.U. firms' patent counts decline significantly with leverage but this negative relationship is moderated by creditor controls in common law countries. Capital structure thus shapes real economic consequences through bottom-line innovations outcomes. Overall, the evidence indicates that while appropriate use of debt financing can incentivize value-adding investments and operations, excessive leverage often hinders economic performance in key non-accounting dimensions increasingly tied to sustainable competitive advantage and progress. This underscores the need for optimized, context-aware capital structures.

2.8 Gaps in Literature

There are several gaps and inconsistencies in the previous literature on capital structure and firm performance, particularly regarding the context of UK manufacturing firms. Many existing studies focus their analyses on countries like the US rather than specifically examining capital structure implications within the UK market environment. While some work looks at manufacturing sectors internationally, the unique regulatory, economic, and industry-related factors present in the UK warrant a more context-specific investigation (Nenu et al., 2018; Allen et al., 2018). In addition, much of the prior research considers only direct linear relationships between capital structure and performance metrics. However, capital structure theories suggest these associations are likely contingent on moderating characteristics of the firm. Conditional effects have not been fully explored, representing an opportunity for richer analyses (La Rocca et al., 2019). Further, studies tend to rely on either accounting or market-based measures of performance, but utilizing both short-term and long-term indicators could provide more robustness to the findings.

Another inconsistency is that nonlinear relationships proposed by trade-off theory are not always supported by empirical evidence. More flexible functional forms in statistical models may better capture how firms optimize capital structures over varying levels of leverage. Additionally, endogeneity poses challenges which prior work has not always fully addressed. Strong identification strategies are needed to isolate the true impacts of capital structure from reverse causality (Jouida, 2018). This study aims to fill several of these gaps. It will focus specifically on the under-researched context of UK manufacturing firms in recent decades. Additionally, multigroup analyses can evaluate contingent effects, while system GMM helps address endogeneity concerns (Mishra and Kapil, 2017). Nonlinear specifications may better represent optimization patterns. In doing so, this research intends to generate novel contextually relevant insights for UK businesses while also improving on methodological rigor. The findings will provide more robust evidence regarding capital structure implications within an important sector and economy.

2.9 Conceptual Framework

This framework examines the relationship between capital structure and financial performance of UK manufacturing firms. The capital structure is represented by the independent variables - debt to assets ratio, debt to equity ratio, long-term debt ratio and short-term debt ratio. These ratios measure the extent of debt financing through total debt obligations as well as the composition of long-term and short-term debt sources. The dependent variables measuring financial performance are return on assets (ROA) and return on equity (ROE). ROA indicates the efficiency with which a company generates net income relative to total assets. ROE reflects the profitability level in relation to shareholders' equity. These accounting-based metrics are commonly used to gauge firm performance.

According to trade-off theory, firms seek an optimal capital structure that balances costs and benefits of debt financing. With a low to moderate amount of poundages, tax shields and a leverage take place and would result to decided returns (Martinez et al., 2019; Abbana and Marimuthu, 2023). Nevertheless, from a particular point, the profitability of higher leverage is reduced, and it has an adverse effect on the assets and increases the potential bankruptcy costs.

Based on this theoretical perspective, the framework proposes:

The companies that practice optimal standard throughout their debt levels in industry are proposed to realize higher ROA and ROE than those that do not have very low or very high debt ratio level. In this context, the hypothesis predicts that debt ratios follow an inverted U-shaped relationship with the performance measures, that is, the returns initially increase with debt but decline after that as proportions of debt rather than the most efficient use is reached. This hypothesized nonlinear association will be empirically tested using data from UK manufacturing enterprises while controlling for firm size to account for scale effects. The results can provide useful insights for capital structure optimization.

2.10 Chapter Summary

This chapter reviewed the major capital structure theories that seek to explain how firms determine their optimal debt-equity mix, including the trade-off theory, pecking order theory and agency cost theory. It examined past empirical studies investigating the relationship between capital structure and firm performance. The moderating roles of firm characteristics such as size, asset tangibility, and profitability and growth opportunities were also discussed. Further, it explored the contextual factors specific to capital markets and financing trends in the UK. Finally, the chapter described characteristics of the manufacturing sector relevant to capital structure considerations and identified gaps in the existing literature, particularly regarding UK manufacturers.

Chapter 3: Research Methodology

3.1 Introduction

This chapter outlines the methodology. The research aims to analyze capital structure's impact on UK manufacturers' long-term performance and value. A quantitative approach is used to test hypotheses derived from literature. The population is manufacturing firms listed on the LSE from 2010-2020. Financial data is from Thomson Reuters Eikon. Variables are defined using established measures. Regression analysis has examined the relationship between capital structure, measured by debt ratio, and accounting and economic performance. It has also assess how firm characteristics moderate this relationship. The statistical tools aim to provide insights into capital structure impacts to address the research objectives.

3.2 Research Philosophy

This study employs a positivist philosophy as the most appropriate approach to meet its objectives. Positivism applies methods of natural sciences to quantify variables and test hypotheses deductively using empirical data. This allows hypotheses relating capital structure and firm performance to be objectively tested through quantitative analysis, suiting the aims. An interpretivist view seeks subjective meanings which does not align with analysing secondary financial data (Marsonet, 2019). Relativism denies objective knowledge conflicting with intended testing, while critical realism explains structures through experiences rather than empirical testing proposed. Given the clear aim to analyse UK manufacturer performance and capital structure based on theories, a positivist quantitative approach provides the strongest philosophical underpinning to achieve valid, generalizable conclusions through statistical techniques minimizing bias in assessing predefined hypotheses on a large secondary dataset (Park et al., 2020). Standardized measurements also facilitate comparisons to prior literature. Therefore, a positivist philosophy best fulfils the objectives of empirically investigating capital structure theory scientifically in line with related research.

3.3 Research Approach

This study employs a deductive approach which involves testing predefined hypotheses developed from established theories, as opposed to an inductive approach of deriving theory from data. The deductive method was chosen because clear conceptual frameworks in the form of theories such as trade-off and pecking order theory already exist regarding the capital structure-performance relationship (Casula et al., 2021). These well-established theories provide the foundation for formulating specific, testable hypotheses. The deductive process enables examining established theory through hypothesizing expected relationships, thereby addressing the research questions. It allows for quantitatively testing hypotheses against secondary data on UK manufacturing firms in line with the research aim of applying capital structure concepts. Conversely, an inductive approach starting with raw exploratory data collection would not directly assess existing theories or predefined relationships (Grinchenko and Shchapova, 2020). With clear hypotheses stemming from strong theoretical roots, a deductive approach provides the most rigorous means of theory evaluation required to meet the objectives.

3.4 Research Design

This study adopts a quantitative research design. A quantitative design statistically examines relationships between variables by collecting and analyzing numerical data. This approach suitably facilitates hypothesis testing through statistical analysis of secondary data. In contrast, a qualitative design understands opinions through interviews, observations or open-ended questions rather than statistically analyzing predefined relationships as required. A mixed methods approach combining quantitative and qualitative elements was also rejected as the objective is empirical theory testing using numerical data rather than nuanced perspectives (Tashakkori et al., 2020). Key factors contributing to the quantitative selection include obtaining secondary financial data to objectively measure capital structure and performance metrics over time according to standard industry definitions. This enables statistical examination of effects and comparative analysis against prior quantitative empirical studies. A large sample also facilitates hypothesis testing, statistical modelling and generalizable results aligned with the deductive theoretical testing approach (Strijker et al., 2020). Therefore, a quantitative methodology offers the most robust means to examine effects and address the stated research objectives.

3.5 Methods of Data Collection

Secondary data is information previously collected and re-analyzed to answer new research questions. Financial and market data on UK-listed manufacturers from 2010-2020 has been obtained from Thomson Reuters Eikon. There are several benefits to secondary data. It provides a large panel dataset of annual firm-level information, enabling robust panel analysis. Using financial databases also enhances reliability as it utilizes official records previously validated. It is more time and cost-effective than primary data collection, facilitating a large sample size (Mahy et al., 2022). Thomson Reuters compiles consistent variables according to standard definitions, promoting comparability to prior literature. While secondary data has limitations, it adequately meets this study's needs by offering a validated, streamlined dataset for hypothesis testing. Primary sources were deemed unnecessary given availability of suitable secondary information from Thomson Reuters.

3.6 Sampling Techniques

This study uses sampling to select cases from the target population. Non-probability purposive sampling is employed given secondary data use. Purposive sampling involves deliberately selecting units based on the researcher's knowledge to ensure representativeness. The target population is UK manufacturing companies listed 2010-2020. From this, purposive criteria are used to sample relevant firms classified as manufacturers with continuously available financial data for 2010-2020 to conduct panel analysis. Excluding outliers provides a high quality coherent sample increasing validity. This purposive approach allows a large, representative sample selection based on predefined criteria, ensuring inclusion of complete, quality data while omitting anomalous records (Campbell et al., 2020). Given reliance on secondary data where random selection may not yield usable cases, purposive sampling pragmatically acquires a suitable sample for hypothesis testing.

3.7 Data Analysis

This study has employed quantitative data analysis methods to test the hypotheses developed from capital structure theories. Specifically, regression analysis techniques has been used. Regression analysis is a statistical process for estimating relationships between variables (Flatt and Jacobs, 2019). It seeks to determine the association between an independent or predictor variable and a dependent or criterion variable. Panel data regression analysis has been conducted due to the longitudinal panel nature of the dataset spanning 2010-2020. Panel data involves observations of multiple phenomena over multiple time periods, allowing analysis of changes and trends. Fixed effects regression has control for extraneous firm-specific factors that do not change over time through the inclusion of individual dummy variables. Random effects models the extent variance within firms is correlated across time periods (Kaloudis and Tsolis, 2018). Diagnostic tests has check assumptions and select the appropriate model between fixed, random and pooled OLS regression. Regressions has measured the effect of capital structure variables like leverage, asset structure etc. on performance indicators like ROA, ROE and Tobin's Q. This quantitative analytical approach is appropriate given the deductive theory testing purpose and presence of numerical panel data meeting parametric assumptions. Regression analysis has established relationships and therefore enable hypotheses to be empirically evaluated.

3.8 Validity and Reliability

Validity and reliability ensure quality, trustworthy findings. Internal validity is established through a deductive research approach basing hypotheses on established, empirically supported theories. External validity is improved by a large UK manufacturer sample enhancing generalizability. Reliability is increased by documenting research processes allowing replication. Construct validity is strengthened using standardized Thomson Reuters metrics which align with operationalizing capital structure conceptually (Coleman, 2022). Statistical conclusion validity is bolstered via robust regression techniques and diagnostic testing. External reliability is improved through using annual reports from reputable secondary sources. Implementing validity and reliability protocols helps ensure findings accurately and consistently represent the phenomenon studied.

3.9 Ethical Consideration

This study has uphold high ethical standards throughout the research process. As secondary data is used from public company reports, no direct human participants are involved. However, ethical research practices are still required. Permission to access and use the Thomson Reuters Eikon database has been obtained to ensure authorized data collection. The database contents are owned by Thomson Reuters and require their consent for analysis. Company’s selected using purposive sampling has remain anonymous in reporting and publication. Only sector and size characteristics may be disclosed, with no sensitive commercial information divulged. Analysis and findings has been presented in an objective, unbiased manner without attempting to influence conclusions in any way (Arifin, 2018). Results and limitations has been communicated transparently without misrepresentation. Data has securely stored according to data protection regulations. Individual records or company-identifiable information has not be shared externally without permission. Where external studies or theories are referenced, accurate citations and acknowledgement of ideas has provided to avoid plagiarism (Fleming et al., 2018). Overall the research has conducted with honesty, integrity and respect for intellectual property. Approval are sought from the relevant university research ethics committee to ensure all procedures comply with ethical guidelines prior to commencing the study.

3.10 Chapter Summary

This chapter outlined the methodology used for the study. A quantitative, deductive approach was selected to test predefined hypotheses from capital structure theories. Secondary financial and market data from Thomson Reuters on UK manufacturers from 2010-2020 has been obtained and purposively sampled. Panel data regression analysis techniques has assessed relationships between leverage, performance and other variables. Validity and reliability protocols as well as ethical standards for anonymous, approved secondary data use were established. The quantitative methods aim to provide an objective evaluation of capital structure theories for UK manufacturers.

Chapter 4: Results and Discussion

4.1 Introduction 

In this chapter main results and analysis are presented which show the relationship between capital structure and organisational performance for manufacturing companies in the UK. The results begin with descriptive statistics and then continue to correlation analysis. The regression analysis would be conducted to determine impact of capital structure variables on the firm performance. Finally the chapter ends with a discussion of results.

4.2 Results 

4.2.1 Descriptive Statistics

Descriptive statistics can be defined as tools that summarize a dataset using a number of measures such as mean, standard deviation, minimum and maximum values among others. These measures offer a quick overview of key characteristics of dataset (Peck, Short, and Olsen, 2020). Table 1 below shows descriptive statistics of dataset used in this study. 

Table 3: Descriptive Statistics

Total number of observations in the dataset is 110 which comprise of five ratios for 10 companies for a period of 11 years. The mean of debt to equity ratio is 2.02 which shows that financial leverage of the manufacturing companies for 11 years period. In other words, debt to equity ratio indicates the degree to which the operations of manufacturing companies are financed by debt as compared to equity (Muhammad, et al., 2021). An average ratio of 2.02 shows that manufacturing are heavily financing their operations using debt because debt is almost double the equity of manufacturing companies. This is a potential risk as debt requires interest payments, debt repayments, and put strain on cash flow (Nukala and Prasada Rao, 2021). In other words, debt to equity ratio of manufacturing companies in the UK as indicator of capital structure shows that there is pressure on profitability due to current capital structure arrangements. However, it is also clear that manufacturing companies continue to show positive returns. This implies that companies are fully utilising debt as source of finance (Sihombing, Astuty, and Irfan, 2021). 

The average of long term debt to capital ratio for manufacturing companies in the UK is .3905. This ratio is more comprehensive in its ability to show reliance of a company on long term debt because it includes not only debt, but also considers preferred stock which are somewhere between equity and debt. Their characteristics and impact are similar to debt but there are no interest payments (Pham, 2020). Higher long term debt ratio shows greater reliance of company on the long-term debt as a source of finance for its operations. Similar to debt to equity ratio it also means higher risk of default. Furthermore, higher ratio also implies that there economic downturns or similar events would have severe impact on operations and profitability of company (Arhinful, Mensah, and Owusu-Sarfo, 2023). Current level is 0.39 which is much lower than debt to equity ratio and implies less risk. Therefore there is less impact on profitability of the company. However, it implies that the companies are more reliant on short term debts which can be assessed using short term debt ratio.  

The average value of short term debt ratio is 1.289. This result clearly justifies the argument presented in previous paragraph and support the assertion that manufacturing companies in the UK are relying more on short term debt as a source of finance for their operations as compared to long term debts and equity. The proxy used for short term debt to equity in this study is current ratio which compares short term assets with short term liabilities and reflects the degree of short term liquidity of a company. Ideally, the ratio should be 2:1 but manufacturing companies higher than 1 ratio is considered good liquidity (Dini, Vanessa, and Juvina, 2022). Currently manufacturing companies have 1.289 which shows that there is adequate level of liquidity in the manufacturing sector of the UK. 

Overall it can be determined that manufacturing companies in the UK have higher leverage because they rely more on debt as source of finance for their operations as compared to equity and assets. Furthermore, there is also evidence that manufacturing companies are more relying on short term debt, yet they are able to maintain adequate level of liquidity which counters the risk of default due to high leverage. Furthermore, it can also be inferred that due to good liquidity the manufacturing companies are able to manage risks due to economic downturn and similar event and maintain their profitability and performance. For further analysis of performance following discussion is focused on analysing the profitability ratios of manufacturing sector. 

The average of return on assets of manufacturing companies in the sample is 0.098. the return of asset is a measure of the efficiently of a business in terms of generating profits from its assets. The ROA shows the portion of profit generated from every dollar invested in assets (Marito and Sjarif, 2020). An average of .098 implies that the assets of manufacturing companies in the UK generate only .09 dollars of profit. From this result the main inference appears is that manufacturing companies need to utilize their assets more effectively into managing their operations. 

On the other hand, the return on equity ROE of manufacturing companies in the sample is on average .42897. The ROE shows the ability of a company to generate profit from its equity. In other words, the ROE shows how much profit is being generated from every dollar invested in equity (Choiriyah, et al., 2020). Current average shows that manufacturing sector is efficiently using its equity to generate profits. However, this results also justifies previous assertions that majority of the operations of the companies are being financed by external debt (with heavy reliance of short term debt). 

Overall it can be inferred that the manufacturing companies in the UK have shown profitability during 2010-2020. Since, external debt is major source of finance in the industry therefore there is significant level of strain on profitability which is reflected in lower profitability ratios. There is therefore clear indication for the companies to undertake strategic changes and focus more on increasing efficiency of their assets to generate more profits as compared to external debt.

4.2.2 Correlation Analysis 

The correlation technique is a commonly used statistical measure of relationship between two variables. The correlation provides a single measure i.e. correlation coefficient which reflects the strength of nature of change in two variables.  A strong positive correlation is indicated by a positive coefficient greater than 0.5 which shows that if there is an increase in one variable there will also be an increase in other variable. However, a negative coefficient indicates an inverse movement which means that if there is an increase in one variable there will be a decrease in other (Coolidge, 2020). Following table 2 presents correlation of all ratios in the dataset:

Table 4: Correlation

First, this study analyses how capital structure ratios correlate with ROA. The correlation coefficient between ROA and debt to equity ratio is (r = -0.2206, p = .02). This result shows that ROA has weak negative correlation with debt to equity ratio. In other words, if there is an increase in debt to equity ratio there will be a decrease in ROA. The correlation is statistically significant also. The theoretical underpinning as discussed in Chandra and Juliawati, (2020) also indicate that higher debt to equity ratio puts pressure on return on assets. Therefore, it can be concluded that the results above are consistent with theoretical literature as well as empirical evidence. Similarly, the correlation between long term debt and ROA is also negative (r = -0.0293, p = .809). This implies that an increase in long term debt will put pressure on return on assets. However, the correlation is statistically insignificant. Comparing this result with previous studies show consistency as Li, (2020) also posited that an increase in long term external debt causes a negative impact on efficiency of assets. Finally, the correlation between ROA and shot term debt ratio is (r = -0.1579, p = .099). This result indicates that an increase in current ratio has significant negative relation with ROA but the correlation is very weak despite being significant. The relationship between ROA and current ratio has also been discussed in previous studies and similar results and explanations have been drawn because an increase in debt puts pressure on assets ability to generate profit because much of the operations are being financed by external debt as compared to internal assets (Legesse and Guo, 2020).      

However, the correlations between ROA and capital structure ratio are not consistent with correlations between capital structure and ROE. Consider for example, the correlation between ROE and debt to equity ratio (r = 0.1693, p = .077). This result although shows positive correlation but statistically insignificant. Theoretically, an increase in debt to equity ratio implies a positive impact on return on equity because increasing operations are being financed through external debt (Sukma, Nurtina, and Nainggolan, 2022). Furthermore, in case of correlation between ROE and long term debt the results show (r = 0.3452, p = .0002). Hence, if there is an increase in long term debt there is likely to be an increase in return on equity. This result also is consistent with theoretical and empirical evidence as shown in (Sukma, Nurtina, and Nainggolan, 2022). However, results in case of manufacturing sector of the UK show that there is significant negative correlation between ROE and short term debt (r = -0.2590, p = 0.0063). This results contradicts with previous results because increase in debt is theoretically positively related with ROE.  

Overall the correlation table shows that the manufacturing sector of the UK which is heavily relying on external debt as source of finance shows positive performance in terms of profitability and is using external debt efficiently to generate positive return on equity. This results is also consistent with general empirical literature on relationship between capital structure and firm profitability.

4.2.3 Hausman Test 

This test is a simple statistical tool which enables analyst to choose most appropriate regression model for panel data analysis. The null hypothesis is that fixed effect model is the most appropriate choice which implies that predictors and error terms do not have significant correlation (Abiahu et al., 2018). Alternatively random effect model is selected if null hypothesis is rejected provided that p>0.05 (Hahs-Vaughn and Lomax, 2020). Following table shows that null hypothesis is accepted (p = 0.2127>0.05) and thus random effects model is more appropriate. 

Table 5: Hausman Test

4.2.4 Generalized Least Square (GLS)

GLS regression is statistical tool which is applied when the dataset shows violation of assumptions of Ordinary least square regression model. GLS enhances the accuracy and efficiency of regression analysis because it addresses the issue of autocorrelation and heteroscedasticity in the data. GLS is a valuable technique that provides more robust analysis (Giraud, 2021). Following table shows GLS model results with return on assets as dependent variable and debt to equity, long term debt, and short term debt as independent variables.  

Table 6: Random Effect GLS

The results above clearly indicate that debt to equity ratio has negative impact on return on asset (C: -.0028, p = .063 > 0.05). Furthermore, long term debt also shows negative impact on ROA (C: -.00188, p =.972 > 0.05). Finally, there is also negative impact of short term debt on ROA (C: -.0126, p =.562 > 0.05). This result is consistent with correlation results which indicated that there is negative correlation between ROA as profitability indicator and debt to equity as capital structure indicator. Previous studies have also reported similar results. For example, Nguyen and Nguyen, (2020) conducted a panel data study to analyse the relationship between capital structure and profitability of non-financial companies in Vietnam and reported negative correlation between ROA and all forms of debt (short-term debt, long-term debt and total debt).

4.3 Discussion 

Based on results above it can be inferred that in case of manufacturing companies in the UK, majority of the firms show inclination towards debt as major element of capital structure. The analysis above shows that despite the fact that manufacturing companies in the UK are showing profitable trends and using debt as major source of finance, there is negative impact of debt inclined capital structure on profitability. This section compares the findings above with previous studies to check consistency and find discrepancies. 

A study conducted by Habibniya, et al., (2022) analysed the impact of financial leverage through the indebtedness of companies and its relationship with profitability of assets and equity. The study was based on the hypothesis that a higher level of debt could lead to better results in financial profitability. The results show that higher leverage earns higher returns, on average, than those with less leverage, but the returns of the higher leveraged firm are more volatile. This study clearly contradicts with results presented in previous section because in case of manufacturing companies the relationship between debt and ROA is negative while other studies posit that leverage can increase profitability.  

Ngo, Tram, and Vu, (2020) analysed the impact of financial leverage on company performance in terms of profitability and collected data from industrial sector for a period of 9 years. The results of panel data indicated that there is a significant influence of financial leverage on profitability of industrial sector companies. The leverage explained 21.85% variability in profitability and 23.18% variability in the share price. Furthermore, there was no impact of operating margin and expenses on the profitability. In addition, the analysis also showed that there was a significant combined impact of financial leverage and operating margin on profitability. These explained 23.56% of variability in the ROE. The study concluded that although financial leverage influences profitability yet there are various other variables that explain variability in the profitability of a company. These may include share price, operating margin and operating expenses, among others.

Yoon and Jang, Soo Cheong (2005) also studied the relationship between financial leverage and profitability with ROE as proxy. This study collected data from the restaurant industry and used OLS regressions for panel data ranging from 1998 to 2003. The results in the study indicated that, for the period under analysis, leverage had significant impact on ROE of restaurants however, company size was more influential as compared to debt in explaining variability in the ROE. The study also opined that larger restaurants had larger debt. The study concluded that the risk of default was higher in case of smaller restaurants although larger restaurants were showing higher leverage and smaller restaurants showed less leverage. Hence, the study confirmed that leverage had less impact on profitability of larger companies as compared to other variables such as size.

Karimi (2020) conducted analysis of impact of financial leverage on the volatility of stock prices in companies in Iran for a period of eight years from 2011 to 2018. The study employed systematic elimination method and showed that there was a significant effect of financial leverage on volatility of stock price. In other words, if there was a change in financial leverage the stock price also fluctuated confirming the signaling theory. Another similar study was Karimi and Kheiri (2017) which evaluated relationship between capital structure and profitability using financial leverage as proxy for a period five years from 2010 to 2014. This study confirmed that there was no impact of capital structure on profitability and company value in case of Iranian companies. 

Ramnoher and Seetah (2020) studied the relationship between leverage and profitability in case of Mauritius, which is a developing economy. In this study the sample included 34 companies that were listed on the Mauritius Stock Exchange. The panel data included a period of 11 years from 2007 to 2017. The results indicated that in Mauritius the financial leverage had significant positive impact on profitability of listed companies. Based on what was previously stated, this research contributes to the aforementioned literature, analyzing the impact of variables linked to the company's capital structure, as well as other relevant factors, on share profitability. Thus, the work is of utmost importance because it analyzes a poorly studied emerging market, using financial ratios of a sample of 27 companies during a period that includes stages of relative calm and the immediate effects of the so-called Covid-19 crisis.

Ahmed and Bhuyan, (2020) also argued that it is evident that larger companies maintain higher levels of debt, followed by a higher cost of debt, in line with the positive relationship with capital intensity, which suggests that these companies have greater guarantees and, therefore, greater access to financing. The positive relationship with tax protection shows a more intensive use of tax shields in order to reduce the pressure of taxes on the most active firms. Finally, a higher level of activity represents a higher level of liquidity to cover short-term obligations. Furthermore, those companies that generate a higher return based on the investment made in assets, have higher levels of liquidity and greater tax protection through depreciation expenses. This study seems to explain the tendency of manufacturing firms in the UK to use debt as major source of finance.  

The negative relationship between debt and the ROA suggests that companies with a higher level of profitability prefer to use fewer third-party resources, which is consistent with what was suggested by Al-Homaidi et al. (2021). There is also evidence to negative relationship between the level of debt and the capacity of firms to cover their short-term obligations, largely explained by the concentration of current obligations maintained by companies in the sector. The negative relationship between the cost of debt and indebtedness affirms that, as the financial burden derived from debt increases, firms decrease the use of external resources, which is consistent with the research carried out by Rahman, Saima, and Jahan, (2020).

Taking into account the different theories of capital structure determinants, some considerations can be made about the results. Among the factors that determine the financial structure of the companies, the volume of fixed assets stands out, which according to the results achieved in the estimation show a negative relationship which may be a consequence of the low participation of fixed assets in the total assets of the companies. It is also possible that investment in inventory (current assets) reflects better consideration as collateral for debt, given that they have a greater relationship with the operating part of the company (Sunardi, Husain, and Kadim, 2020). From a performance perspective, it is determined that the most profitable companies are able to generate sufficient profits with their own resources; Therefore, they resort to fewer financial obligations; and, as financial expenses cause a greater impact on entities, the lower their debt level will be. Finally, furniture companies with high liquidity ratios or that have a greater investment in short-term assets will use these to operate rather than leverage themselves (Horobet, et al., 2021).

Additionally, Neves, et al., (2020) argued that a larger size positively influences the level of debt, non-current assets and liquidity of companies; as well as the negative effect that the cost of debt, payment capacity, capital intensity and profitability has on the level of leverage of organizations. This undoubtedly marks a very important guideline regarding the preference of companies in the use of internal resources to finance their operations, and that there are many factors that influence and negatively affect the choice of external debt in an entity.

The results found show the preference that companies have to finance their operations with short-term external resources. However, it shows an indication of the substitution of financing with own resources. The study of the determinants of the capital structure reveals the relationship of variables such as liquidity, profitability, capital intensity and cost of debt, with the debt presented by companies; valuable information that provides additional input to management for making business decisions, focused on maximizing the value of a firm.

Grozdić, et al., (2020) tested the Pecking Order Theory (POT) and the Static Trade off Theory (STT) and designed their panel data analysis based study with a sample of non-financial business organisations. This study confirmed that companies with lower indebtedness outperformed their counterparts in terms of profitability. They explained that higher profitability could be credited to using own finance for new projects instead of debt and/or equity. The authors argued that the main driver of change in debt was the deficit of funds for companies which they calculated as the difference between capital investments and working capital. According to STT business managers plan a certain level of capital structure and achieve their targets over a time period. This study assessed the impact of debt (leverage) on the market/book value ratio, the tangibility of assets, the sales and profitability as proxies for business performance. They concluded that companies having more tangible assets had higher leverage as tangible assets are often committed as collateral. Since, the sample of manufacturing companies in the UK included largest companies therefore it is possible that the results show an inclination towards external debt as source of finance. In other words it is quite possible that there is a different trend and inclination in case of small and medium sized companies 

Finally, at this point it is important to reiterate that there are several other factors that affect choice of capital structure and profitability of a firm. The study done by He et al. (2021) found that firms adjusted their capital structure faster in the face of strong economic crises such as COVID-19. The change was even faster in countries where the pandemic had the greatest impact. As Myers mentions, there is no universal theory regarding capital structure, so some hypotheses are true for certain data sets and others are not. As you can see, the capital structure depends on factors that are not necessarily homogeneous between countries, industries, investment in research and development, technological development. In this sense, the size, age, proportion of financing through social or venture capital also influences. Each of these variables is influenced by macroeconomic and regional conditions.

Chapter 5: Conclusions & Recommendations

5.1 Conclusions

Companies need resources to carry out their operation and there are various sources from which they can be obtained, which are subject to dissimilar conditions, therefore, administrators must carry out a thorough analysis of what is the best way to obtain these resources. Thus, financial literature has developed theories and analyses to provide elements that facilitate and improve efficiency in decision-making, such that the value of the company and the returns of the shareholder partners are maximized. Thus, financing decisions within the company impact its financial structure. It refers to the combination of own and external resources used to finance its investments. Determining the financial structure is a complex task that depends on various factors, so there is no single model or theory that explains said financial decision; However, various approaches have been developed that are of great relevance in financial literature. 

The capital structure of a company means what combination of funds, in the form of equity securities (shares) or debt, the company has to finance and develop its activities. These funds receive compensation for use, which determines the flow of interest to creditors (third-party capital) and dividends to shareholders (own capital). The remuneration comes from the cash flows generated by the company's assets. The issuance of debt and equity securities involves the division of cash flows into two streams, a comparatively safer one destined for holders of debt securities, and a relatively riskier one destined for shareholders. The combination of the different sources of financing configures the capital structure of the company. In the literature on capital structure, the notion of whether an optimal capital structure of the company exists is highlighted. The analysis of the existence of an optimal capital structure begins to be systematized from the pioneering works of Williams and Durand, who advocated the existence of an optimal capital structure: as long as the limits are observed accepted by the market, every company should increase its debt, since this would increase its market value. 

Clearly this conception implied an important financial policy recommendation. Subsequently, Modigliani and Miller made a fundamental contribution due to its theoretical and analytical relevance, which implied a financial policy recommendation with fundamental differences with those existing up to that time. They proposed that decisions on financial structure were irrelevant, and therefore there was no optimal capital structure. The idea underlying proposition of MM is that one cannot expect an increase (or fall) in the value of the firm simply by changing the capital structure. 

The aim of this study was to analyse the impact of capital structure on firm performance with specific sample of manufacturing companies in the UK. This study adopted quantitative methodology using panel data analysis with a sample of 10 large manufacturing companies in the UK for the period 2010-2020. Based on panel data this study concludes that manufacturing companies in the UK have higher leverage because they rely more on debt as source of finance for their operations as compared to equity and assets. Furthermore, there is also evidence that manufacturing companies are more relying on short term debt, yet they are able to maintain adequate level of liquidity which counters the risk of default due to high leverage. Furthermore, it can also be inferred that due to good liquidity the manufacturing companies are able to manage risks due to economic downturn and similar event and maintain their profitability and performance. For further analysis of performance following discussion is focused on analysing the profitability ratios of manufacturing sector. 

Furthermore, it can be concluded that the manufacturing companies in the UK have shown profitability during 2010-2020. Since, external debt is major source of finance in the industry therefore there is significant level of strain on profitability which is reflected in lower profitability ratios. There is therefore clear indication for the companies to undertake strategic changes and focus more on increasing efficiency of their assets to generate more profits as compared to external debt.

In addition, the correlation results shows that the manufacturing sector of the UK which is heavily relying on external debt as source of finance shows positive performance in terms of profitability and is using external debt efficiently to generate positive return on equity. This result is also consistent with general empirical literature on relationship between capital structure and firm profitability.

5.2 Recommendations

Based on the results and discussion in previous chapter this section presents recommendations for manufacturing companies in the UK to achieve an optimal capital structure.

It is recommended that the companies attempt to adopt optimal capital structure but consider their individual and industrial characteristics to identify a target such as 70% debt and 30% equity. Particularly those manufacturing companies who require high resources in working capital and those for whom the return of capital is slower than the outflow of money to financial entities and suppliers. This structure would allow such companies to finance operations with debt, obtaining a lower cost, investing in fixed infrastructure assets that would be the initial support for general growth. 

• Furthermore, it is recommended that the companies must choose a balanced approach to finance its debt from financial entities, through its suppliers and by improving payment agreements with its customers. It is recommended that supplier rotation be increased to more days according to purchase negotiations. 

• It is recommended to establish new negotiation parameters with the clients to obtain shorter deadlines for collection, through advances at the start and progress of sales, thus reducing the days of portfolio turnover that generate cash. 

• Furthermore, it is recommended that companies should make investment in infrastructure which is necessary to improve production capacity and venture into new lines of business that cover different markets and generate dynamism to the operation. For example, car manufacturers may diversify into equipment assembly that reduces imports, positively affecting the company's cash flow and decreasing manufacturing costs. This could enhance profitability or enable them to decrease sales prices to be more competitive. 

• Due to the configuration of payment agreements for works in progress, it is recommended to generate cash flow through financial instruments offered by the market such as factoring and development credits, backed by contracts between clients and the company in a manner that the entry of money is anticipated and thus be able to have greater working capital. 

• It is recommended to alternate the diversification of production lines focused on supply such as self-service for manufacturers of retail sector. These would allow for more effective operations with respect to customer payment for the immediacy of product delivery as well as access to new markets. 

• It is recommended to establish new market management strategies that lead the company to move from sustained growth to more accelerated growth that allows its evolution; strategies focused on new markets, new products with development and innovation in engineering and own assemblies, investment in new technologies that support the manufacturing process, an improvement in delivery times and training of personnel in new refrigeration trends.

5.3 Limitations

The primary limitation in this study is that the sample contains only ten large manufacturing companies in the UK for a period of 11 years from 2010 to 2020. A change in this sample size and period could lead to differing conclusions. Furthermore, the results are pplicable on large manufacturing companies only which implies that there could be a different picture in case a sample of small and medium sized manufacturing companies are analysed using same data analysis. Future researchers encouraged to collect data from a larger and more diversified sample with a longer time period to obtain more comprehensive trends and analysis.    

References

• Abbana, S. and Marimuthu, F., 2023. Financing of state-owned entities: can the trade-off theory explain the debt structure?. International Journal of Entrepreneurship and Sustainability Studies; Vol. 3, Issue 1.
• Abel, A.B., 2018. Optimal debt and profitability in the trade‐off theory. The Journal of Finance, 73(1), pp.95-143.
• Ahmed, R. and Bhuyan, R., 2020. Capital structure and firm performance in Australian service sector firms: A panel data analysis. Journal of Risk and Financial Management, 13(9), p.214.
• Ai, H., Frank, M.Z. and Sanati, A., 2020. The trade-off theory of corporate capital structure.
• Alarussi, A.S. and Gao, X., 2023. Determinants of profitability in Chinese companies. International Journal of Emerging Markets, 18(10), pp.4232-4251.
• Alfawareh, F.S., Almashaqbeh, A., Al-Kofahi, M., Alshirah, M., Ananzeh, H. and Al-Kofahi, B., 2022. The relationship between capital structure and performance of non-financial firms listed on the Amman stock exchange. Res. J. Financ. Account, 13(2), pp.96-102.
• Al-Homaidi, E.A., Farhan, N.H., Alahdal, W.M., Khaled, A.S. and Qaid, M.M., 2021. Factors affecting the profitability of Indian listed firms: a panel data approach. International Journal of Business Excellence, 23(1), pp.1-17.
• Alitani, S., 2020. Determinants of Capital Structure in GCC and G7 Countries (Doctoral dissertation, University of Brighton).
• Alkhudary, R. and Gardiner, P., 2021. Stages in project managers’ careers: Learning and growth opportunities. International Journal of Project Management, 39(5), pp.536-545.
• Allen, F., Bartiloro, L., Gu, X. and Kowalewski, O., 2018. Does economic structure determine financial structure?. Journal of International Economics, 114, pp.389-409.
• Allen, F., Gu, X. and Kowalewski, O., 2018. Financial structure, economic growth and development. In Handbook of finance and development (pp. 31-62). Edward Elgar Publishing.
• Al-Thuneibat, A., 2018. The relationship between the ownership structure, capital structure and performance. Journal of Accounting, Business and Management (JABM), 25(1), pp.1-20.
• Ambrosi, D., Ben Amar, M., Cyron, C.J., DeSimone, A., Goriely, A., Humphrey, J.D. and Kuhl, E., 2019. Growth and remodelling of living tissues: perspectives, challenges and opportunities. Journal of the Royal Society Interface, 16(157), p.20190233.
• Arhinful, R., Mensah, L. and Owusu-Sarfo, J.S., 2023. The Impact of capital structure on the financial performance of financial institutions in Ghana. International Journal of Finance and Banking Research, 9(2), pp.19-29.
• Arifin, S.R.M., 2018. Ethical considerations in qualitative study. International journal of care scholars, 1(2), pp.30-33.
• Ayange, A., Emmanuel, N.C., Rosemary, I.H., Ndudi, U.C. and Samuel, U.E., 2021. Effect of capital structure on firms performance in Nigeria. Universal Journal of Accounting and Finance, 9(1), pp.15-23.
• Basit, A. and Irwan, N.F., 2017. The impact of capital structure on firms performance: evidence from Malaysian industrial sector-A case based approach. International Journal of Accounting & Business Management, 5(2), pp.131-148.
• Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D. and Walker, K., 2020. Purposive sampling: complex or simple? Research case examples. Journal of research in Nursing, 25(8), pp.652-661.
• Casula, M., Rangarajan, N. and Shields, P., 2021. The potential of working hypotheses for deductive exploratory research. Quality & Quantity, 55(5), pp.1703-1725.
• Chandra, S. and Juliawati, C., 2020. Effects of long term debt to total assets, short term debt to total assets, total asset turnover, and inventory turnover on profitability of manufacturing companies in consumer goods subsector listed on IDX. Journal of Applied Business and Technology, 1(3), pp.212-222.
• Choiriyah, C., Fatimah, F., Agustina, S. and Ulfa, U., 2020. The effect of return on assets, return on equity, net profit margin, earning per share, and operating profit margin on stock prices of banking companies in Indonesia Stock Exchange. International Journal of Finance Research, 1(2), pp.103-123.
• Coffee, J.C., Gilson, R.J. and Quinn, B.J., 2021. Cases and materials on corporations. Aspen Publishing.
• Coleman, P., 2022. Validity and reliability within qualitative research for the caring sciences. International Journal of Caring Sciences, 14(3), pp.2041-2045.
• Coolidge, F.L., 2020. Statistics: A gentle introduction. Sage Publications.
• Dang, C., Li, Z.F. and Yang, C., 2018. Measuring firm size in empirical corporate finance. Journal of banking & finance, 86, pp.159-176.
• Demirgüç-Kunt, A., Peria, M.S.M. and Tressel, T., 2020. The global financial crisis and the capital structure of firms: Was the impact more severe among SMEs and non-listed firms?. Journal of Corporate Finance, 60, p.101514.
• Dini, S., Vanessa, V. and Juvina, J., 2022. Effect of Current Ratio, Return on Equity, Managerial Ownership, Sales Growth, and Asset Structure on Capital Structure in Retail Trade Companies Listed on the Indonesia Stock Exchange for the 2016-2020 Period. Budapest International Research and Critics Institute-Journal (BIRCI-Journal), 5(2).
• Do, H.T., 2020. Relationship between capital structure and firm performance: a comparative study (Doctoral dissertation, Dissertation, Flensburg, Europa-Universität Flensburg, 2020).
• Feldman, N., Kawano, L., Patel, E., Rao, N., Stevens, M. and Edgerton, J., 2021. Investment differences between public and private firms: Evidence from US tax returns. Journal of Public Economics, 196, p.104370.
• Flatt, C. and Jacobs, R.L., 2019. Principle assumptions of regression analysis: Testing, techniques, and statistical reporting of imperfect data sets. Advances in Developing Human Resources, 21(4), pp.484-502.
• Fleming, J. and Zegwaard, K.E., 2018. Methodologies, methods and ethical considerations for conducting research in work-integrated learning. International Journal of Work-Integrated Learning, 19(3), pp.205-213.
• Frank, M.Z., Goyal, V.K. and Shen, T., 2020. The pecking order theory of capital structure: Where do we stand?.
• Franks, J. and Mayer, C., 2017. Evolution of ownership and control around the world: the changing face of capitalism. In The handbook of the economics of corporate governance (Vol. 1, pp. 685-735). North-Holland.
• Giraud, C., 2021. Introduction to high-dimensional statistics. Chapman and Hall/CRC.
• Grinchenko, S. and Shchapova, Y.L., 2020. The deductive approach to Big History’s Singularity. The 21st Century Singularity and Global Futures: A Big History Perspective, pp.201-210.
• Grozdić, V., Marić, B., Radišić, M., Šebestová, J. and Lis, M., 2020. Capital investments and manufacturing firms’ performance: Panel-data analysis. Sustainability, 12(4), p.1689.
• Habibniya, H., Dsouza, S., Rabbani, M.R., Nawaz, N. and Demiraj, R., 2022. Impact of capital structure on profitability: panel data evidence of the telecom industry in the United States. Risks, 10(8), p.157.
• Hahs-Vaughn, D.L. and Lomax, R.G., 2020. An introduction to statistical concepts. Routledge.
• Hamdan, A., 2018. Intellectual capital and firm performance: Differentiating between accounting-based and market-based performance. International Journal of Islamic and Middle Eastern Finance and Management, 11(1), pp.139-151.
• He, G., Li, A.Z. and Shen, D., 2022. The role of earnings management in equity valuation. In Encyclopedia of Finance (pp. 2061-2094). Cham: Springer International Publishing.
• He, W., Hu, M., Mi, L., y Yu, J. (2021). How stable are corporate capital structures? International evidence. Journal of Banking and Finance, 126, 106103.
• Herciu, M. and Ogrean, C., 2017. Does capital structure influence company profitability?. Studies in Business and Economics, 12(3), pp.50-62.
• Hertina, D., 2021. The influence of current ratio, debt to equity ratio and company size on return on assets. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(8), pp.1702-1709.
• Hiroki, T., Iwatsubo, K. and Watkins, C., 2022. Does firm-level productivity predict stock returns?. Pacific-Basin Finance Journal, 72, p.101710.
• Horobet, A., Radulescu, M., Belascu, L. and Dita, S.M., 2021. Determinants of bank profitability in CEE countries: Evidence from GMM panel data estimates. Journal of Risk and Financial Management, 14(7), p.307.
• Huang, H.J., Habib, A., Sun, S.L., Liu, Y. and Guo, H., 2021. Financial reporting and corporate innovation: a review of the international literature. Accounting & Finance, 61(4), pp.5439-5499.
• Ibhagui, O.W. and Olokoyo, F.O., 2018. Leverage and firm performance: New evidence on the role of firm size. The North American Journal of Economics and Finance, 45, pp.57-82.
• Jaisinghani, D. and Kanjilal, K., 2017. Non-linear dynamics of size, capital structure and profitability: Empirical evidence from Indian manufacturing sector. Asia Pacific Management Review, 22(3), pp.159-165.
• Javeed, S.A. and Lefen, L., 2019. An analysis of corporate social responsibility and firm performance with moderating effects of CEO power and ownership structure: A case study of the manufacturing sector of Pakistan. Sustainability, 11(1), p.248.
• Jensen, M.C. and Meckling, W.H., 2019. Theory of the firm: Managerial behavior, agency costs and ownership structure. In Corporate governance (pp. 77-132). Gower.
• Jiang, X., Shen, J.H., Lee, C.C. and Chen, C., 2021. Supply-side structural reform and dynamic capital structure adjustment: Evidence from Chinese-listed firms. Pacific-Basin Finance Journal, 65, p.101482.
• Jiao, F., Nishihara, M. and Zhang, C., 2019. Operating leverage and underinvestment. Journal of Financial Research, 42(3), pp.553-587.
• Jouida, S., 2018. Diversification, capital structure and profitability: A panel VAR approach. Research in International Business and Finance, 45, pp.243-256.
• Kaloudis, A. and Tsolis, D., 2018. Capital structure in US, a quantile regression approach with macroeconomic impacts. arXiv preprint arXiv:1801.06651.
• Karimi, G. (2020). Effect of Financial Leverage on the Trend of Stock Pricing Fluctuations in Companies Listed in Tehran Stock Exchange. Propósitos y Representaciones, 8 (SPE2), e654.
• Kautto Penttinen, A., 2023. An Analysis of the Impact of Capital Structure on Corporate Performance in the United States and United Kingdom.
• Kimoro, J.N., 2019. Factors influencing capital structure choice of commercial banks in Kenya (Doctoral dissertation, JKUAT-COHRED).
• Kyere, M. and Ausloos, M., 2021. Corporate governance and firms financial performance in the United Kingdom. International Journal of Finance & Economics, 26(2), pp.1871-1885.
• La Rocca, M., Staglianò, R., La Rocca, T., Cariola, A. and Skatova, E., 2019. Cash holdings and SME performance in Europe: the role of firm-specific and macroeconomic moderators. Small Business Economics, 53, pp.1051-1078.
• Legesse, T.S. and Guo, H., 2020. Does firm efficiency matter for debt financing decisions? Evidence from the biggest manufacturing countries. Journal of Applied Economics, 23(1), pp.106-128.
• Li, Y., 2020. The effects of external debt financing and internal financing on firm performance: empirical evidence from automobile listed firms (Master's thesis, University of Twente).
• Lim, S.C., Macias, A.J. and Moeller, T., 2020. Intangible assets and capital structure. Journal of Banking & Finance, 118, p.105873.
• Maeenuddina, R.B., Hussain, A., Hafeez, M., Khan, M. and Wahi, N., 2020. Economic value added momentum & traditional profitability measures (ROA, ROE & ROCE): A comparative study. Test Engineering and Management, 83(April), pp.13762-13774.
• Mahy, B., Rycx, F., Vermeylen, G. and Volral, M., 2022. Productivity and wage effects of firm‐level upstreamness: Evidence from Belgian linked panel data. The World Economy, 45(7), pp.2222-2250.
• Makhija, H. and Trivedi, P., 2021. An empirical investigation of the relationship between TSR, value-based and accounting-based performance measures. International Journal of Productivity and Performance Management, 70(5), pp.1118-1136.
• Marito, B.C. and Sjarif, A.D., 2020. The impact of current ratio, debt to equity ratio, return on assets, dividend yield, and market capitalization on stock return (Evidence from listed manufacturing companies in Indonesia Stock Exchange). Economics, 7(1), pp.10-16.
• Marsonet, M., 2019. Philosophy and logical positivism. Academicus International Scientific Journal, 10(19), pp.32-36.
• Martinez, L.B., Scherger, V. and Guercio, M.B., 2019. SMEs capital structure: trade-off or pecking order theory: a systematic review. Journal of Small Business and Enterprise Development, 26(1), pp.105-132.
• Martinez, L.B., Scherger, V. and Guercio, M.B., 2019. SMEs capital structure: trade-off or pecking order theory: a systematic review. Journal of Small Business and Enterprise Development, 26(1), pp.105-132.
• Masavi, J.M., Kiweu, M. and Kinyili, J., 2017. Capital structure and financial performance of agricultural companies listed in Nairobi securities exchange, Kenya. International Journal of Economics, Commerce and Management, 5(11), pp.653-665.
• Matsa, D.A., 2018. Capital structure and a firm's workforce. Annual Review of Financial Economics, 10, pp.387-412.
• Mishra, R. and Kapil, S., 2017. Effect of ownership structure and board structure on firm value: evidence from India. Corporate Governance: The international journal of business in society, 17(4), pp.700-726.
• Muhammad, H., Migliori, S. and Mohsni, S., 2021. Capital structure and firm performance: The role of corporate governance. International Journal of Business Governance and Ethics, 15(4), pp.436-458.
• Muhammad, M., Ei Yet, C., Tahir, M. and Nasir, A.M., 2021. Capital structure of family firms: the effect of debt and equity market timing. Journal of Family Business Management, 11(1), pp.1-18.
• Mwangi, E., 2021. Effect of Capital Structure on the Financial Performance of Small and Medium Enterprises in the ICT Sector, Kenya A Case of Selected SMEs in Nairobi County, Kenya (Doctoral dissertation, Daystar University, School of Business and Economics).
• Nenu, E.A., Vintilă, G. and Gherghina, Ş.C., 2018. The impact of capital structure on risk and firm performance: Empirical evidence for the Bucharest Stock Exchange listed companies. International Journal of Financial Studies, 6(2), p.41.
• Nenu, E.A., Vintilă, G. and Gherghina, Ş.C., 2018. The impact of capital structure on risk and firm performance: Empirical evidence for the Bucharest Stock Exchange listed companies. International Journal of Financial Studies, 6(2), p.41.
• Nenu, E.A., Vintilă, G. and Gherghina, Ş.C., 2018. The impact of capital structure on risk and firm performance: Empirical evidence for the Bucharest Stock Exchange listed companies. International Journal of Financial Studies, 6(2), p.41.
• Neves, M.E., Serrasqueiro, Z., Dias, A. and Hermano, C., 2020. Capital structure decisions in a period of economic intervention: Empirical evidence of Portuguese companies with panel data. International Journal of Accounting & Information Management, 28(3), pp.465-495.
• NGO, V.T., TRAM, T.X.H. and VU, B.T., 2020. The impact of debt on corporate profitability: evidence from Vietnam. The Journal of Asian Finance, Economics and Business, 7(11), pp.835-842.
• Nguyen, H.T. and Nguyen, A.H., 2020. The impact of capital structure on firm performance: Evidence from Vietnam. Journal of Asian Finance, Economics and Business, 7(4), pp.97-105.
• Nicodano, G. and Regis, L., 2019. A trade-off theory of ownership and capital structure. Journal of Financial Economics, 131(3), pp.715-735.
• Nukala, V.B. and Prasada Rao, S.S., 2021. Role of debt-to-equity ratio in project investment valuation, assessing risk and return in capital markets. Future Business Journal, 7(1), p.13.
• Opoku-Asante, K., Winful, E.C., Sharifzadeh, M. and Neubert, M., 2022. The relationship between capital structure and financial performance of firms in Ghana and Nigeria. European Journal of Business and Management Research, 7(1), pp.236-244.
• Özcan, İ.Ç., 2019. Capital structure and firm performance: Evidence from the airport industry. European Journal of Transport and Infrastructure Research, 19(3).
• Panda, B. and Leepsa, N.M., 2017. Agency theory: Review of theory and evidence on problems and perspectives. Indian journal of corporate governance, 10(1), pp.74-95.
• Park, Y.S., Konge, L. and Artino Jr, A.R., 2020. The positivism paradigm of research. Academic medicine, 95(5), pp.690-694.
• Pathak, M. and Chandani, A., 2023. The nexus between capital structure and firm-specific factors: evidence from Indian companies. Journal of Economic and Administrative Sciences, 39(2), pp.470-487.
• Peck, R., Short, T. and Olsen, C., 2020. Introduction to statistics and data analysis. Cengage Learning.
• Petruzzelli, A.M., Ardito, L. and Savino, T., 2018. Maturity of knowledge inputs and innovation value: The moderating effect of firm age and size. Journal of Business Research, 86, pp.190-201.
• Pham, C.D., 2020. The effect of capital structure on financial performance of Vietnamese listing pharmaceutical enterprises. The Journal of Asian Finance, Economics and Business, 7(9), pp.329-340.
• Phuong, N.T.T., Hung, D.N., Van, V.T.T. and Xuan, N.T., 2020. Effect of debt structure on earnings quality of energy businesses in vietnam. International Journal of Energy Economics and Policy, 10(3), pp.396-401.
• Poutziouris, P., Markou, D., Glyptis, L. and Hadjielias, Ε., 2022. Capital structure of UK SMEs: an integrated understanding. International Journal of Entrepreneurship and Small Business, 46(1), pp.64-95.
• Rahman, M.M., Saima, F.N. and Jahan, K., 2020. The impact of financial leverage on firm's profitability: an empirical evidence from listed textile firms of Bangladesh. Asian Journal of Business Environment, 10(2), pp.23-31.
• Ramnoher, L. & Seetah, K. (2020) Profitability and Financial Leverage, Evidence from a Small Island. Asian Journal of Finance & Accounting. Vol 12 (1), pp. 58-71.
• Sihombing, L., Astuty, W. and Irfan, I., 2021. Effect of capital structure, firm size and leverage on firm value with profitability as an intervening variable in manufacturing companies listed on the Indonesia Stock Exchange. Budapest International Research and Critics Institute-Journal (BIRCI-Journal), 4(3), pp.6585-6591.
• Singh, S., Tabassum, N., Darwish, T.K. and Batsakis, G., 2018. Corporate governance and Tobin's Q as a measure of organizational performance. British journal of management, 29(1), pp.171-190.
• Strijker, D., Bosworth, G. and Bouter, G., 2020. Research methods in rural studies: Qualitative, quantitative and mixed methods. Journal of Rural Studies, 78, pp.262-270.
• Sukma, R.P., Nurtina, A.R. and Nainggolan, B.M., 2022. Effect of Debt Ratio, Long-Term Debt To Equity, and Firm Size on Profitability. Journal of Management and Leadership, 5(1), pp.27-37.
• Sun, Q. and Xiaolan, M.Z., 2019. Financing intangible capital. Journal of Financial Economics, 133(3), pp.564-588.
• Sunardi, N., Husain, T. and Kadim, A., 2020. Determinants of debt policy and company’s performance. International Journal of Economics and Business Administration, 8(4), pp.204-213.
• Tashakkori, A., Johnson, R.B. and Teddlie, C., 2020. Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences. Sage publications.
• ul Islam, Z., 2023. Capital Structure and Firm Performance: Exploring the Moderating Role of Size (Doctoral dissertation, CAPITAL UNIVERSITY).
• Vuong, N.B., Vu, T.T.Q. and Mitra, P., 2017. Impact of capital structure on firm’s financial performance: Evidence from United Kingdom. Journal of Finance & Economics Research, 2(1), pp.16-29.
• Vuong, N.B., Vu, T.T.Q. and Mitra, P., 2017. Impact of capital structure on firm’s financial performance: Evidence from United Kingdom. Journal of Finance & Economics Research, 2(1), pp.16-29.
• White, J.V., Markin, E., Marshall, D. and Gupta, V.K., 2022. Exploring the boundaries of business model innovation and firm performance: A meta-analysis. Long Range Planning, 55(5), p.102242.
• Wiagustini, N.L.P., Ramantha, I.W., Sedana, I.B.P. and Rahyuda, H., 2017. Indonesia’s capital structure: Pecking order theory or trade-off theory. International Journal of Applied Business and Economic Research, 15(16), pp.119-131.
• Xie, G., Wu, S. and Song, Z., 2022. Focus on Innovation or Focus on Sales? The Influences of the Government of China’s Demand-Side Reform during COVID-19 and Their Sustainability Consequences in the Consumer Products Industry. Sustainability, 14(20), p.13348.
• Yoon, Eunju and Jang, SooCheong (2005) The Effect of Financial Leverage on Profitability and Risk of Restaurant Firms Journal of Hospitality Financial Management: Vol. 13. (1), 35-47.
• Yousef, I., 2019. The determinants of capital structure: evidence from GCC and UK real estate sectors. Real Estate Management and Valuation, 27(2), pp.108-125.
• Zogning, F., 2017. Comparing financial systems around the world: Capital markets, legal systems, and governance regimes. Economics, Management, and Financial Markets, 12(4), pp.43-58.