Keywords: B Corp certification, Small and Medium Enterprises, Funding rounds, Empirical

In the pursuit of enhancing their credibility with investors and facilitating access to new financial resources, sustainable small and medium enterprises (SMEs) often undertake the process of obtaining certifications from reputable third-party entities. One such organization of significance is B Lab, renowned for its provision of the B Corp certification, which is awarded to enterprises that successfully meet the organization's rigorous environmental and social standards. This master's dissertation studies the relationship between B Corp certification and the capacity of certified SMEs to attract new investments. To investigate the economic benefits of B Corp certification for SMEs and the role of social and environmental sustainability in driving economic interest, the study employs a complete theoretical framework, a literature assessment, and an intensive empirical analysis using the R program. The empirical models confirm that B Corp certification has a positive impact on the ability of certified SMEs to attract new investments, with higher funding levels and increased funding success, although the influence may vary across industry sectors, and additional factors beyond BIA ratings play a role in funding outcomes.

ACKNOWLEDGMENT

Me gustaría aprovechar esta oportunidad para expresar mi más profundo agradecimiento a todos los que me apoyaron y guiaron durante la realización de mi trabajo final de master.

En primer lugar, me gustaría expresar mi más sincero agradecimiento a mis padres y hermanas. Su amor incondicional, aliento y confianza en mis habilidades ha sido la fuerza impulsora detrás de mi éxito académico. Su constante apoyo y sacrificios han sido fundamentales para mi viaje educativo. Estaré eternamente agradecido por su guía, paciencia y comprensión durante este difícil proceso.

También me gustaría expresar mi más sincero agradecimiento a mi supervisor Geoffroy Enjolras por su excelente orientación, experiencia y apoyo constante. Sus valiosos comentarios, retroalimentación constructiva y fuerte compromiso con la excelencia han sido invaluables para guiar y mejorar la calidad de esta investigación.

Quisiera expresar mi más profunda gratitud a la facultad y al personal académico de Grenoble IAE. Estoy agradecido por las oportunidades que me brindaron para mi desarrollo intelectual y por crear un ambiente propicio para el aprendizaje. También me gustaría agradecer a mis amigos por su constante apoyo y aliento. Su creencia en mis habilidades y su presencia en momentos difíciles han sido una fuente constante de motivación y fortaleza para mí.

Gracias a todos por su valioso apoyo, orientación y aliento.

Miguel Ángel Escribano Poza

INTRODUCTION

At a time when stakeholders are increasingly becoming environmentally conscious, from a small business perspective it makes sense to capitalise on that by demonstrating your commitment to solid social responsibilities that will result in measurable social and environmental impact. Growing social and environmental awareness has opened doors for creative, mixed entrepreneurial strategies that aim to significantly improve society while continuing to operate profitably [START_REF] Doherty | Social Enterprises as Hybrid Organizations[END_REF]. To achieve sustainability, state and non-state market actors must be involved in governance, whether at the local, national, or international levels. In fact, scholars, policymakers, and business leaders have acknowledged that private governance and public-private governance are essential examples of "governance beyond government" for solving some of the most urgent social and ecological problems, including addressing climate change, eradicating poverty, and halting deforestation. Corporate social responsibility (CSR) activities, including as conduct codes, sustainability reporting, and the adoption of voluntary norms in the social and ecological sphere, can be considered in this context as an example of private governance. Voluntary CSR and sustainability standards involving governments, nongovernmental organizations, and businesses have arisen as a potential form of public-private governance in efforts to cooperatively realize public values and corporate goals. The use of these public-private governance models has grown significantly in recent years, and public authorities and policymakers are increasingly acknowledging their value. Public-private governance as a result has been integrated into the range of policy settings [START_REF] Marx | Global governance and the certification revolution: Types, trends and challenges[END_REF].

Companies want to get financial and economic benefit from their CSR operations in addition to advancing social objectives. Firms must make sure their stakeholders can recognize and evaluate their "CSR quality," which is defined as a firm's CSR promises, actions, and performance, to maximize the return on their efforts. Firms encounter at least two problems here. First, because internal processes predominate, the relative observability of an organization's CSR quality is typically low [START_REF] Johnston | Signalling Social Responsibility: On the Law and Economics of Market Incentives for Corporate Environmental Performance[END_REF]. Second, because the CSR idea is fundamentally controversial, businesses must create unique interpretations of the notion that consider their unique circumstances [START_REF] Matten | Implicit' and 'explicit' CSR: A conceptual framework for a comparative understanding of corporate social responsibility[END_REF]. The ability of stakeholders to evaluate various interpretations may be compromised by the coexistence of numerous firm-specific meanings attributed to CSR. Information asymmetries in firm-stakeholder relationships are a result of these issues. Firms seek sensegiving and sensemaking communication strategies using a variety of platforms and messaging to expose their genuine CSR quality to eliminate information asymmetries. However, research shows that the majority of stakeholders appear to believe that businesses do not speak about CSR honestly, and instances of corporate wrongdoing and symbolically implemented CSR have created questions about the credibility of businesses [START_REF] Basu | Corporate social responsibility: A process model of sensemaking[END_REF]. Thus, a major difficulty for businesses is effective CSR signalling. Voluntary sustainability standards serve as an example of private governance in the context of public objectives, and they provide a means for businesses to prove that their CSR efforts are credible. CSR standards in this context not only have a function in disciplining firm behavior, but also function as a signaling device. To reach the highest standards of environmental and social performance, companies often collaborate with third parties and apply for environmental certifications. Several approaches have emerged to help companies tackle this new shift and put social and environmental impact right next to financial return on the priority list. Amongst these initiatives, we can identify the Sustainable Development Goals of the United Nations, the ISO26000, the Global Reporting Initiatives, Conscious Capitalism and the B Corp certification. This latter one is a set of standards that has been developed by a non-profit, the B Lab, with the aim of helping companies with a certified measurement of social and environmental progress that could be openly marketed using the name [START_REF] David | How is the B Corp Movement influencing the future of Capitalism? An analysis of the motivations and the factors impacting the B Corp certification and decertification[END_REF]. The group assesses an organization's social and environmental impact and awards the B Corp accreditation to those who successfully meet its requirements. To get the B Corp certification, companies must pass the B Impact Assessment (BIA), in which five areas are assessed: Governance, Workers, Community, Environment and Client. The rigorous audit procedure by B Lab adds credibility to the certification. To become accredited as B Corps, organizations voluntarily modify their fundamental operating procedure.

In 2019, [START_REF] Villela | B Corp Certification and Its Impact on Organizations Over Time[END_REF] defined the B Corp certification as "maybe the best available tool" to measure extra-financial performance. This suggestion is supported by the significant increase in popularity that the B Corp Movement has undergone in the past few years. Nonetheless, it appears that, within 3 years, one third of the certified companies decided to quit this Movement. This raises questions on the significance of the B Corp certification and on its future relevance. Indeed, there is a gap in the literature that fails to determine the reasons why the B Corp Movement is losing members while becoming increasingly important in the eyes of its stakeholders. The B Corp Movement has drawn a lot of interest in academia for this and many other reasons. The phenomena, its effects on a company's environmental performance, and its role in creating a more circular economy have been the subject of an increasing amount of academic research (Diez-Busto, Sanchez-Ruiz and Fernandez-Laviada, 2021).

A certification like B Corp could be a helpful tool from a financial standpoint for a company to stand out, recruit brilliant employees, gain exposure to investors, and attract new, socially conscious customers and clients. However, there is limited research investigating whether B Corp certification provides investors with greater confidence in environmentally and socially responsible companies.

Therefore, this master's dissertation aims to explore the emergence of B Corp certification over the past decade. The primary focus of this research is to examine the potential impact of obtaining B Corp certification on companies' ability to attract new investments. Furthermore, this study aims to investigate whether this relationship is contingent upon the extent of social and environmental impact improvements achieved by the certified companies over time. Additionally, the dissertation aims to study the possible benefits of firms aligning with recognized standards, such as B Corp, in enhancing their likelihood of securing funding rounds. Lastly, we investigate the potential positive effect of B Corp certification on Small and Medium Enterprises.

LITERATURE REVIEW

I. CORPORATE SOCIAL RESPONSABILITY

The concept of corporate social responsibility (CSR), which refers to firms having a duty to society and a wider range of stakeholders than just their shareholders, became popular in the 1960s. Since then, CSR has received more attention from academic and professional circles worldwide (Wang et al,2016). [START_REF] Bowen | Social responsibilities of the businessman[END_REF] defines the social responsibilities of business at that time as "the obligations of businessmen to pursue those policies, to make those decisions or follow those lines of action which are desirable in terms of the objectives and values of society." He did not simply talk about decisions made within the company; he focused on the decisions made by these managers and their responsibilities to society. In contrast, modern CSR places more emphasis on institutional and corporate policies than the managerial decision-making of specific individuals. Economic, legal, ethical, and charitable responsibilities are the four categories [START_REF] Carroll | A three-dimensional conceptual model of corporate performance[END_REF] defines for business's social obligations. Even though this categorization has faced criticism in recent years, many people still use it today [START_REF] Enderle | Clarifying the terms of business ethics and CSR[END_REF].

Carroll explains the justification for corporate social responsibility in his book, saying that "after considering the pros and cons of CSR, most businesses today embrace the concept, but insist on linking discretionary aspects of corporate citizenship with business objectives and strategy" (Carroll, 2009, p. 21). Therefore, executives keep insisting that there needs to be a "business case" for CSR.

Firms with better CSR performance face lower capital constraints, which are market frictions that may prevent a firm from funding all desired investments [START_REF] Cheng | Corporate social responsibility and access to finance[END_REF]. There are various causes for this. First, higher CSR performance is associated with increased stakeholder engagement, which lowers the probability of short-term opportunistic behavior [START_REF] Eccles | The impact of corporate sustainability on organizational processes and performance[END_REF][START_REF] Benabou | Individual and corporate social responsibility[END_REF], hence lowering overall contracting costs [START_REF] Jones | Instrumental stakeholder theory: a synthesis of ethics and economics[END_REF]. Second, firms with better CSR performance are more likely to disclose their CSR activities to the market [START_REF] Dhaliwal | Voluntary non-financial disclosure and the cost of equity capital: the case of corporate social responsibility reporting[END_REF] to signal their long-term focus and differentiate themselves [START_REF] Spence | Job market signalling[END_REF][START_REF] Benabou | Individual and corporate social responsibility[END_REF].

Numerous theoretical and empirical studies have examined the relationship between CSR and financial performance. Neoclassical economics-based studies stated that CSR unnecessarily increases a corporation's costs, placing the firm at a competitive disadvantage with its rivals. [START_REF] Friedman | The social responsibility of business is to increase its profits[END_REF][START_REF] Aupperle | An empirical examination of the relationship between corporate social responsibility and profitability[END_REF][START_REF] Mcwilliams | The role of money managers in assessing corporate social responsibility research[END_REF][START_REF] Jensen | Value maximization, stakeholder theory, and the corporate objective function[END_REF]. Predominantly based on agency theory, some studies have argued that employing valuable firm resources to engage in CSR results in significant managerial benefits rather than financial benefits to the firm's shareholders [START_REF] Brammer | Does it pay to be different? An analysis of the relationship between corporate social and financial performance[END_REF]. However, other academics contend that CSR can benefit society by improving access to important resources (Waddock and Graves, 1997), attracting and retaining higher quality employees [START_REF] Turban | Corporate social performance and organizational attractiveness to prospective employees[END_REF], enabling improved marketing of goods and services [START_REF] Moskowitz | Choosing socially responsible stocks[END_REF], creating unforeseen opportunities [START_REF] Fombrun | Opportunity platforms and safety nets: corporate citizenship and reputational risk[END_REF], and helping to establish social legitimacy [START_REF] Hawn | How operational legitimacy conditions the impact of changes in social legitimacy on firm's economic value: the Dow Jones sustainability index addition and deletion[END_REF]. Additionally, CSR may work similarly to advertising, boosting demand for goods and services while lowering price sensitivity among consumers [START_REF] Dorfman | Optimal advertising and optimal quality[END_REF][START_REF] Sen | Does doing good always lead to doing better? Consumer reactions to corporate social responsibility[END_REF], and even enabling firms to develop intangible assets (Gardberg and Fombrun, 2006;Waddock and Graves, 1997). From a stakeholder theory point of view [START_REF] Freeman | Strategic Management: A Stakeholder Perspective[END_REF][START_REF] Freeman | Managing for Stakeholders: Survival, Reputation, and Success[END_REF][START_REF] Freeman | Stakeholder Theory: The State of the Art[END_REF], which suggests that CSR includes managing multiple stakeholder ties concurrently, scholars have argued that CSR can mitigate the likelihood of negative regulatory, legislative, or fiscal action [START_REF] Freeman | Strategic Management: A Stakeholder Perspective[END_REF][START_REF] Berman | Does stakeholder orientation matter? The relationship between stakeholder management models and firm financial performance[END_REF][START_REF] Hillman | Shareholder value, stakeholder management, and social issues: what's the bottom line?[END_REF], attract socially conscious consumers [START_REF] Hillman | Shareholder value, stakeholder management, and social issues: what's the bottom line?[END_REF], or attract financial resources from socially responsible investors [START_REF] Kapstein | The corporate ethics crusade[END_REF]. By selecting a sample of worldwide non-financial listed firms, Martnez-Ferrero and Frias-Aceituno (2015) investigated the association between CSR and firm performance as well as the direction of causality. Their findings supported the presence of a beneficial and reciprocal relationship between CSR and financial performance.

Advocates of Freeman's school contend that as business exist in society, they are social institutions and should give back to the community (Bello et al., 2016). For an enterprise to be sustainable, it must meet the needs of its major stakeholders (Supriti Mishra & Suar, 2010). According to Baric (2017), the success of the business in its goal of differentiating itself from competitors and building sustained competitive advantage depends heavily on the quality of the relationship between the firm and its stakeholders.

The relationship between CSR and the success of commercial organizations has been examined in several studies. The relationship between these two, however, has emerged as the most contentious topic in the literature on CSR due to conflicting prior findings [START_REF] Fassin | CSR and related terms in SME owner-managers' mental models in six European countries: National context matters[END_REF]. Concerning the connection between CSR and business performance, no agreement has been established. According to research, there is either no association at all [START_REF] Hao | A study on the impact of corporate social performance on corporate financial performance: From a view of social capital[END_REF][START_REF] Liping | An empirical study on the relationship between corporate social responsibility and financial performance-based on the analysis and interpretation based on the perspective of corporate reputation[END_REF], positive correlation [START_REF] Oeyono | An examination of corporate social responsibility and financial performance[END_REF], negative correlation (W. [START_REF] Yang | An empirical study on the relationship between corporate social responsibility and financial performance under the Chinese context-based on the contrastive analysis of large, small and medium-size listed companies[END_REF], or no correlation at all [START_REF] Oeyono | An examination of corporate social responsibility and financial performance[END_REF]. Among others, [START_REF] Galant | Corporate social responsibility and financial performance relationship: A review of measurement approaches[END_REF] acknowledged that measurement difficulties with regard to both of the categories of interest are to blame for the results' inconsistent nature. Research examining the relationship between CSR and firm success will be required as long as the conclusion on the link remains debatable in theory and practice.

Recent research focuses on how capital markets function as an intermediary mechanism for CSR to generate long-term benefit. [START_REF] Lee | Corporate sustainability performance and idiosyncratic risk: a global perspective[END_REF], for instance, demonstrate that companies with high CSR ratings have lower idiosyncratic risk, whereas [START_REF] Goss | Corporate social responsibility and financial distress[END_REF] demonstrates that companies with low CSR scores are more likely to encounter financial distress. Furthermore, [START_REF] Goss | The impact of corporate social responsibility on the cost of bank loans[END_REF] discover that companies with the lowest CSR ratings pay seven to 18 basis points more on their bank loans than companies with higher scores, but Ioannou and Serafeim (2013) demonstrate a beneficial impact of CSR on sell-side analysts' recommendations. Similarly, [START_REF] Dhaliwal | Voluntary non-financial disclosure and the cost of equity capital: the case of corporate social responsibility reporting[END_REF] discover that the firm's cost of capital decreases because of the voluntary disclosure of CSR operations, which also draws focused institutional investors and analyst coverage. El Ghoul et al. (2011) examines a sample of American businesses and discover that those with higher CSR ratings have lower equity capital costs.

To improve performance and gain a competitive advantage, businesses make beneficial investments. However, the unique capital restrictions that each organization must deal with directly affect the capacity to finance such strategic expenditures. The investment function, which is derived from the firm's profit-maximizing optimization, postulates that investment is dependent on capital's marginal productivity, interest rates, and tax laws (Mankiw, 2009). As Stein (2003: 125) notes, according to this paradigm 'nothing else should matter: not the firm's mix of debt and equity financing, nor its reserves of cash and securities, nor financial market 'conditions,' however defined.' However, later research on the equity and debt markets demonstrates that cash flow is also crucial in determining the firm's degree of investment. The ability of a firm to grow over time is significantly and adversely affected by financial constrained firms' tendency to reduce investments in a variety of strategic activities, such as investments in inventory and R&D activities, pricing for market share, and labor hoarding during recessions. Understanding that capital-constrained organizations are compelled to forgo investments they would otherwise make is crucial for understanding firm survival and success.

In other words, they are profitable investment opportunities that are not being pursued because of finance issues. Therefore, it follows that, all things being equal, the easing of capital restrictions for such enterprises would allow them to make more profitable investments and perform better. For instance, Faulkender and Petersen (2012) found that the American Jobs Creation Act (AJCA) of 2004 did, in fact, cause significant increases in investment, but only among the subgroup of enterprises that were capital restricted. They used the AJCA as a temporary shock to the cost of internal financing.

The impact of capital limitations on a firm's decisions to enter and leave markets or industries has been examined in a second group of studies. Holtz-Eakin et al., (1994a) found that the quantity of an individual's inheritance-considered an exogenous shock to one's wealth-had a significant beneficial effect on the likelihood of becoming an entrepreneur. They did this using personal tax return data on entrepreneurs. A follow-up study demonstrates that businesses created by entrepreneurs with greater inheritances (therefore, less capital limitations) are likewise more likely to endure (Holtz-Eakin et al., 1994b). By comparing the post-entry growth trajectory of newly established firms and firm-level data from 16 economies, Aghion, Fally, and Scarpetta (2007) describe a comparable mechanism.

For a thorough overview of this third stream of research, see [START_REF] Levine | Finance and growth: theory and evidence[END_REF]. It contends that capital limitations have a disproportionately greater negative impact on smaller, more recent, and riskier businesses, directing resources to those with the highest marginal returns. As a result, industrial expansion is more rapid in nations with better finance systems that can relax these restrictions. Given the idiosyncratic levels of constraints faced by companies of various sizes, scholars turned to capital constraints as an explanation for why small companies pay lower dividends, become more highly leveraged, and grow more slowly. For instance, [START_REF] Carpenter | Is the growth of small firms constrained by internal finance[END_REF] demonstrate that small U.S. enterprises' internal capital limits their ability to develop their assets, and that firms that can raise additional external money experience better growth rates. Using a sample of Indian businesses, [START_REF] Becchetti | The determinants of growth of small-and medium-sized firms: the role of the availability of external finance[END_REF] discovered qualitatively comparable findings, while Desai, Foley, and Forbes (2008) confirmed the relationship in the context of a currency crisis. Finally, [START_REF] Beck | Financial and legal constraints to growth: does firm size matter[END_REF] demonstrate that firm performance is susceptible to different financial restraints and that small businesses are disproportionately impacted owing to stricter restrictions by analyzing survey data from international corporations. In conclusion, research to date has shown that finding ways to loosen capital restrictions is essential for business survival and growth, industry expansion, and even national development. [START_REF] Hennessy | How costly is external financing? Evidence from a structural estimation[END_REF]Whited (2007: 1705) assert that "a CFO can neither create nor destroy value through his financing decisions in a world without frictions" based on neoclassical economic assumptions that postulate a flat supply curve for funds in the capital market at the level of the risk-adjusted real interest rate. At levels of capital that exceed the firm's net worth, however, the supply curve for funds is upward sloping rather than horizontal due to market imperfections like informational asymmetries [START_REF] Greenwald | Information imperfections and macroeconomic fluctuations[END_REF][START_REF] Myers | Corporate financing and investment decisions when firms have information that investors do not have[END_REF] and agency costs (Bernanke andGertler, 1989, 1990). In other words, capital providers are reimbursed for their information (and/or monitoring) costs by charging a higher interest rate when the likelihood of agency costs is high and the amount of capital the company needs for investments exceeds its net worth (and is, therefore, uncollateralized). Therefore, the supply curve for funds is successfully made less steep by adopting and putting into practice company methods that lessen informational asymmetries or the likelihood of agency costs.

By enabling the firm to make significant investments that would not have been profitable otherwise and/or by influencing the capital structure decisions of the firm, improved access to funds reduces the idiosyncratic capital constraints the firm is facing, favorably affecting its strategic objectives [START_REF] Hennessy | How costly is external financing? Evidence from a structural estimation[END_REF].

It is argued that the adoption and implementation of CSR strategies that lead to superior CSR performance result in lower idiosyncratic capital constraints for the firm because of two complementary mechanisms [START_REF] Cheng | Corporate social responsibility and access to finance[END_REF]. First, a company's commitment to and involvement with stakeholders based on trust and cooperation are captured by exceptional CSR performance [START_REF] Andriof | Unfolding stakeholder engagement[END_REF][START_REF] Jones | Instrumental stakeholder theory: a synthesis of ethics and economics[END_REF]. Therefore, according to Jones (1995:420), "firms that contract with their stakeholders on the basis of mutual trust and cooperation will experience reduced agency costs, transaction costs, and costs associated with team production because ethical solutions to commitment problems are more efficient than mechanisms designed to curb opportunism."

According to Jones (1995: 422), these agency and transaction expenses would include "monitoring costs, bonding costs, search costs, warranty costs, and residual losses." Additionally, better engagement with stakeholders can increase a company's revenue or profit generation, which helps to maintain superior profitability, through higher quality relationships with clients, business partners, and coworkers. This, in turn, enhances customer interaction and new product development. In other words, superior stakeholder engagement may directly limit the likelihood of short-term opportunistic behavior [START_REF] Benabou | Individual and corporate social responsibility[END_REF] and it also represents a more efficient form of contracting with key stakeholders [START_REF] Jones | Instrumental stakeholder theory: a synthesis of ethics and economics[END_REF] that could lead to enhanced revenue or profit generation which, in turn, is rewarded by the markets.

Second, previous research has demonstrated that companies with superior CSR performance are more likely to publicly disclose their CSR strategies by issuing sustainability reports [START_REF] Dhaliwal | Voluntary non-financial disclosure and the cost of equity capital: the case of corporate social responsibility reporting[END_REF] and are more likely to provide third-party assurance of such reports, increasing the credibility of such reports [START_REF] Simnett | Assurance on sustainability reports: an international comparison[END_REF]. As a result, CSR reporting: (1) promotes openness with regard to organizations' social and environmental impacts and their governance structure; and (2) may trigger changes in internal control systems that further enhance regulatory compliance and reporting accuracy. As a result, the knowledge asymmetry is further reduced and there are fewer capital constraints because more reliable information about the company's CSR plans is now available in addition to its financial disclosures [START_REF] Hubbard | Capital-market imperfections and investment[END_REF]. Moreover, the resulting changes in internal managerial practices [START_REF] Ioannou | The consequences of mandatory corporate sustainability reporting[END_REF]) may also reduce the likelihood of agency costs in the form of short-termism.

In brief, it is said that companies that excel in corporate social responsibility (CSR) are less likely to encounter individual capital restrictions. This is due to two reasons: firstly, they benefit from reduced agency expenses and an increase in revenue and profits because of effective engagement with stakeholders. Secondly, they experience a reduction in information asymmetry due to the implementation of more extensive and credible CSR disclosure practices, leading to greater transparency. [START_REF] Cheng | Corporate social responsibility and access to finance[END_REF] provided evidence that firms with better CSR performance face lower capital constraints. They argued that this negative relation between CSR performance and capital constraints materializes via two distinct mechanisms. First, better CSR performance is associated with superior stakeholder engagement that, in turn, significantly reduces the likelihood of opportunistic behavior and introduces a more efficient form of contracting with key constituents. In other words, by encouraging managers to adopt a long-term rather than a short-term focus, stakeholder engagement based on mutual trust and cooperation decreases possible agency costs. Additionally, greater stakeholder engagement improves the company's ability to generate revenue or profits through fostering better relationships with clients, partners, and staff. Second, companies that perform better in CSR are more likely to publicly disclose their CSR efforts, which leads to increased accountability and transparency [START_REF] Dhaliwal | Voluntary non-financial disclosure and the cost of equity capital: the case of corporate social responsibility reporting[END_REF]. Greater transparency lessens informational imbalances between the company and investors, hence reducing perceived risk. Cheng, [START_REF] Cheng | Corporate social responsibility and access to finance[END_REF] demonstrate that firms with better CSR performance face a capital supply curve that is actually less steep, contrary to the literature's contention that market frictions like informational asymmetries and agency costs are the primary causes of firms' upward sloping supply curves in the capital markets.

These findings have ramifications for the ongoing discussion about whether CSR programs result in value creation and, more importantly, how they do so. They showed that companies with stronger CSR records are better positioned to access capital markets financing. The ability of businesses to make successful strategic investments that they otherwise would not is positively impacted by the relaxation of capital restraints, and stock market performance is likewise positively impacted. This capital allocation process is significantly impacted by CSR since market players are more inclined to allocate limited capital resources to companies that perform well in this area. Additionally, they demonstrated that both the social and the environmental aspects of CSR activities minimize capital restrictions at a more fine-grained level by breaking down the CSR performance into its constituent parts.

II. CSR LABELS AND CERTIFICATIONS

Differentiation by CSR attributes might increase consumer welfare, but it also creates an asymmetric-information problem. Labeling can be used to signal CSR to consumers and to address this market failure. Most CSR labels are provided to businesses following a certification process completed by an impartial organization (a private or public third party), assuring that the production process fulfills a specific standard of quality. However, many businesses decide to make CSR claims in marketing campaigns and on product packaging even though they are not accredited. This practice is often seen as one element of a more general brand-building strategy aimed at generating consumer awareness and loyalty [START_REF] Hoeffler | Building Brand Equity through Corporate Societal Marketing[END_REF][START_REF] Blumenthal | Brand Councils that Care: Towards the Con-vergence of Branding and Corporate Social Responsibility[END_REF] These CSR claims could be entirely or partially unsupported, and they could correspond to superficial or nonexistent CSR engagements; they are therefore unreliable quality indicators. Their widespread use has allegedly increased consumer skepticism of CSR programs, particularly when those projects are thought to lack values that prioritize helping others [START_REF] Bronn | Corporate Social Responsibility and Cause-Related Marketing: An Overview[END_REF][START_REF] Ellen | Building Corporate Associations: Consumer Attri-butions for Corporate Socially Responsible Programs[END_REF]. However, according to economic theory, the interaction of business owners' social preferences, media, non-governmental organization (NGO) or state oversight, and reputation may be enough to encourage organizations to self-regulate (i.e., not deceive customers about their CSR commitments; McCluskey, 2000; [START_REF] Baron | Private Politics, Corporate Social Responsibility, and Integrated Strategy[END_REF][START_REF] Baron | Corporate Social Responsibility and Social Entrepreneurship[END_REF][START_REF] Baksi | Credence Goods, Efficient Labeling Policies, and Regulatory Enforcement[END_REF]. Brands play the same signalling role as certified labels.

The popularity of third-party certifications has grown as the field of management system standards (MSS) has matured (Heras-Saizarbitoria and [START_REF] Heras-Saizarbitoria | ISO 9001 and ISO 14001: towards a research agenda on management system standards[END_REF]. The definition of management system standards in the literature, also known as meta-standards, is "voluntary codes, guidelines, or processes used by organizations to formalize, systematize, and legitimize a very diverse set of managerial activities or tasks" (Boiral and Heras-Saizarbitoria, 2015, p. 390). According to these authors, "standardization constitutes a mechanism of coordination and an instrument of regulation comparable with other instruments, such as public regulations, markets, hierarchies, or formal organizations". They claimed that "standards are important for the promotion of economic efficiency, as they provide a basis for reducing information-related transaction costs."

The management system standards issued by the International Organization for Standardization (ISO) are the most famous. With members from 164 nations and a portfolio of over 22,000 standards, guidelines, and certificates on topics like health and safety, services, transportation, climate change, and sustainable development, ISO is an international standard-setting organisation (ISO, 2023).

The MSS itself, in general, "do not denote compliance with an objective or result," but rather provide "guidelines to systematize and formalize a series of company processes into procedures and to document this implementation" (Boiral & Heras-Saizarbitoria, 2015, p. 390). In accordance with MSS, adherence to ISO, Fairtrade, Forestry Standards Council (FSC), and B Corp standards is all evaluated and confirmed by third-party accreditors to testify to such standards' conformance.

Several ideas have been put out in respect to many of the various ISO standards, even though the literature does not provide consensus regarding the reasons for the general application of MSS and related certifications. According to [START_REF] Bansal | Why companies go green: A model of ecological responsiveness[END_REF], who concentrate on the ISO 14001 environmental management standard, three main factors-ethical, competitive, and relational-drive businesses to adopt this standard. Environmental responsibility is a component of ethical motivation, pursuing competitive advantage is the driving force behind competitive motivation, and improving stakeholder relations is the driving force behind relational motivation.

Furthering his investigation of ISO 9000, Boiral (2012) uses the degree-purchasing syndrome in education to frame the preparation for and acquisition of the ISO certification. "ISO management standards and certifications audits are ultimately what organizations want them to be," he claims (2012, p. 652). "They can be tools for the improvement of practices or just organizational degrees useful for marketing purposes, or both." Standard-setting has developed into a rapidly expanding sector of its own because of increased interest in the veracity of policies and activities (EcoLabel Index,

2022).

As was previously mentioned, there are many reasons why socially conscious companies would want to operationalize and truly integrate social responsibility into every aspect of their enterprises.

Due to this, there are now many standards and a complex landscape of guidelines for CSR implementation that can be found in codes of conduct, principles, auditable standards, or reporting frameworks (EcoLabel Index, 2022). Companies are resorting to certificates as an unbiased sign of the validity of their CSR since they are being pressed more and more to show that their actions and policies adhere to various social and ethical standards set forth by third parties (Heras-Saizarbitoria & Boiral, 2013). So much so that according to research conducted in 2013 by the Organisation for Economic Cooperation and Development (OECD), the quantity of environmental labeling and information schemes more than quintupled between 1988 and 2009 [START_REF] Gruère | A Characterisation of Environmental Labelling and Information Schemes[END_REF]. Although that trend peaked in 2021, there are still over 455 eco-labels in 199 countries, according to a global database of eco-labels (EcoLabel Index, 2022).

The proliferation of ethical branding, self-regulation, and third-party certification rests on the assumption that consumers believe they are an effective means of product differentiation. The importance of certification is championed, and it is expected that customers can rely on it to give them fair information about how products are sourced, produced, and/or distributed to final consumers.

The company can assert legitimacy, authenticity, and "truth" in adhering to recognised best practices to a greater extent the more widely accepted and recognized the label or standard is (Heras-

Saizarbitoria & Boiral, 2013).
The ability of businesses to "measure, track, and optimize their sustainability impact" is restricted by internal processes, which makes it challenging to assess the validity of CSR programs (Wang et al., 2016, p. 535). These assertions, which can be correct, misleading, or erroneous, are frequently not supported by evidence. As a result, it is not surprising that researchers have discovered that sometimes consumers find it difficult to distinguish between a company's objectives and its behavior.

Over time, 'greenwashing' has become the critical term for the false representation of corporate actions. Greenwashing originally referred to misrepresentations of environmental effects, but many researchers have extended a broader meaning to greenwashing to also include misrepresentations of social and economic concern [START_REF] Lyon | Greenwash: Corporate environmental disclosure under threat of audit[END_REF]Seele & Gatti, 2015). According to Seele and Gatti (2015, p. 3), "a green message must combine with a falsity (information-related element) and an accusation of being misleading (external-distortion element)" to be deemed "greenwashing". Most academics concur that organizations are under pressure to spread messages of social and environmental responsibility to project credibility and authenticity, which heightens the temptation to make exaggerated claims in order to compete in the market [START_REF] Hrasky | Visual disclosure strategies adopted by more and less sustainability-driven companies[END_REF][START_REF] Testa | Internalization of environmental practices and institutional complexity: Can stakeholders pressures encourage greenwashing[END_REF].

Companies have sought differentiation through third-party CSR labeling and certification to counteract the distrust that greenwashing has generated (Chipperfield, 2013; [START_REF] Testa | Internalization of environmental practices and institutional complexity: Can stakeholders pressures encourage greenwashing[END_REF]. In the past, certifications at the product level have dominated the market. When it comes to third-party certification, businesses nowadays have a wide range of options. In terms of their scope, concentration, reputation, and price, certifications differ. As a result, there don't seem to be any literature reviews or synthesis of study findings that concentrate on the depth and breadth of CSR certifications. This has led to a widely fragmented body of research on certifications.

A. B Corp Certification : Motivations and Adoption

The number of certifications has dramatically increased for product attribute certifications over the past thirty years [START_REF] Delmas | Sustainable practices and product quality: Is there value in eco-label certification? The case of wine[END_REF]. A certified B Corp represents adopting a collective identity like that of Fairtrade certification for coffee and LEED certification for green buildings for the entire organization, in contrast to and consistent with the more recent trend, where B Corp certification is applicable to the entire organization rather than at the product or process level. Many certified businesses will then include the B Corporation symbol on their products, brands, corporate reports, and websites as a public testament to their commitment to broad stakeholder values, like other well-known certifications like those just mentioned and the Forest Stewardship Council.

As certified B Corps continue to gain global adoption, scholarly research interest is increasing [START_REF] Gehman | Why we care about certified B Corporations: From valuing growth to verifying values practices[END_REF][START_REF] Diez-Busto | The B Corp Movement: A Systematic Literature Review[END_REF]. Role modeling this possibility is crucial because early adopters saw certification as a potent tool for societal change [START_REF] Stubbs | Blending Institutional Logics: The Emergence of B Corps in Australia[END_REF]. [START_REF] Hickman | Explaining the location of mission-driven businesses: An examination of B-Corps[END_REF] investigated what influences businesses' mission alignment and orientation toward sustainability through B Corp certification. They conclude that pursuing B Corp certification is a sign of sustainable company operations and CSR dedication.

Several academics looked more closely at the B Corp certification process to investigate the impact of outside factors. According to [START_REF] Kim | Why Companies Are Becoming B Corporations[END_REF], companies have been "forced" to pursue B Corp certification as a reaction to more traditional companies stepping up their efforts to be perceived as "green" and to "help consumers sort through the marketing hype to find businesses and products that are truly socially and environmentally responsible", as a signal of authenticity (p. 32). Additionally, they contend that businesses selected B Corp accreditation as a capitalistic critique to "join the movement of creating a new economy with a new set of rules" (p. 49), rather than solely because of the leader's inclination. In line with this discovery, [START_REF] Kim | Why Companies Are Becoming B Corporations[END_REF] discovered that the number of new B Corp entry into a given industry was positively correlated with the presence of larger CSR activities in that industry. Four other pieces that specifically addressed certified B Corps were published in this special issue.

Using the B Corp certification as an example, Grimes et al. (2018) expanded on their earlier research to examine the influence of identity on early acceptance of certifications. Women-owned firms were discovered to have a twofold increase in certification eligibility and a threefold increase in certification retention rates (p. 130). They contend that early adoption of certifications, especially those lacking in legitimacy or gaining it, is "not driven by efforts to gain social approval, but efforts to authenticate values that the owners perceive as contextually distinctive" (p. 148). They contend that these distinctive features do not simply relate to identity or gender but go beyond to role identities and social identities; a sense of affirming one's authenticity. to further investigate the degree to which obtaining B Corp certification has a detrimental impact on the financial performance of the smallest and youngest B Corps. They looked at the connection between prosocial motivation, or the desire to act in ways that benefit others, and entrepreneurial orientation (EO), which was defined as a behavioral characteristic of a venture typically composed of three or more coexisting strategic concepts of innovation, risk-taking, and proactivity. In contrast to Parker et al.'s (2019) proposed growth penalty, they found that pursuing B Corp certification did not lead EO to decrease; rather, they found that those businesses pursuing B Corp certification had lower EO at the beginning and that this lasted through time. While EO remained constant, they found that prosocial motivation was positively related to EO and that companies with higher levels of prosocial motivation saw increased EO over time. Independently by deciding to apply for the certification or not, the BIA is a free tool that can be used by any company for improving their social and environmental performance. Understanding what they were doing well and what they could do better has proven to be incredibly illuminating for many companies [START_REF] Honeyman | The B Corp Handboo[END_REF]. In this regard, there has been a significant rise in the number of businesses that express interest and dedication in becoming B Corps throughout time. This pattern demonstrates how society is becoming more interested in hybrid business models, where firms combine social, environmental, and economic objectives. [START_REF] Villela | B Corp Certification and Its Impact on Organizations Over Time[END_REF] investigated the various factors that impact a company's decision to become part of the B Corp community. They discovered that a strong dedication to social and ethical business practices is the main motivation behind this choice. This is particularly evident among companies that were initially established as social enterprises, as they perceive a significant alignment between their values and principles and those of the B Corp movement. Additionally, there are those who are inspired by the prospect of helping to establish a B Corp movement in their home nations and promoting superior business practices.

According to certain research, entrepreneurs and founders who want to achieve social objectives view the B Corp Certification as an important tool for establishing their credibility and reputation [START_REF] Gamble | Measuring the Integration of Social and Environmental Missions in Hybrid Organizations[END_REF]. The certification is the beginning of a challenging path where entrepreneurs can evaluate themselves, compete with other community members on their effect, and learn how to further integrate their business's social, environmental, and financial activities. In addition, according to their website, B Lab audits ten percent of all B Corps every year to make sure the firms are following their promise. Organizations may better explain who they are, what they believe in, and how they and their product stand out from the competition thanks to this arduous path. This context contributes to reinforcing trust with partners and stakeholders, and it simplifies access to investors who share the same concern about social and environmental impact.

According to Honeyman & Jana (2019), many businesses recognize the value of establishing connections with a community of leaders who share their desire to use business to improve the world.

Entrepreneurs who become a part of the B Corp movement are enthusiastic about the connections they make with their peers and the new relationships they form within this community. Companies may benefit from using this certification to attract new customers and clients. Many people still find it difficult to accept the companies' goals for and level of commitment to their social responsibility initiatives [START_REF] Ferreti | B CORP CERTIFICATION AND ITS IMPACT ON FUNDING ROUNDS OVER TIME[END_REF]. B Corps helps firms to attract talent. Studies have indicated that young people are not only interested in achieving a work-life balance, which refers to having enough time and energy to enjoy their personal lives outside of work. They also aim for work-life integration, which means pursuing something they feel passionate about and fulfilling both their financial and higher purpose needs. If a company become a B Corporation, it can attract, retain, and motivate employees through the company's higher purpose and the B Corp community's collective aim to redefine business success worldwide.

In fact, obtaining B Corp certification can help recruit the most qualified MBA students. Due to student demand, some prestigious business schools, such as Columbia, New York University, and Yale, now forgive the student loans of their MBA graduates who work for Certified B Corporations. According to the Wall Street Journal, "more companies are using the B Corp logo, an independent seal of environmental and social credentials, to lure young job seekers who desire an employer committed to both a social mission and profitability."

Using the B Impact Assessment framework can serve as a valuable tool for engaging your team and establishing a strong, purpose-driven company culture. New generations of employees have a stronger desire than ever to align personal and corporate values. People are not only searching for financial reward, but also for positions that fulfil their passions and their need to contribute for a higher purpose. A successful example of this approach was demonstrated at Etsy. After earning the required 80 points to obtain B Corp certification, Chad Dickerson, Etsy's CEO, felt a sense of pride in the accomplishment. However, he also recognized that it was just the beginning of the company's ongoing efforts to build a better business [START_REF] Godelnik | How B Corp Certification Helps Attract and Engage Employees[END_REF] To further improve Etsy's social and environmental performance, Dickerson and his executive team organized a B Corp Hack Day, where the entire workforce could set aside their regular duties to brainstorm ideas for enhancing their B Corp score. The initiative generated 22 innovative proposals, including a carbon footprint tracking program, promoting women's leadership, strengthening partnerships with local community organizations, and increasing access to art studios for employees.

These efforts also gained recognition in a Harvard Business Review article.

III. INFORMATION ASYMMETRIES

A. Signalling

The signalling theory, which grew out of the work of Akerlof (1970), [START_REF] Spence | Job market signalling[END_REF], and [START_REF] Stiglitz | Information and economic analysis: A perspective[END_REF], is a significant body of academic research in economic contract theory that focuses on information asymmetries between various entities, such as people or organizations. Signalling theory is particularly interested in how one entity-the agent or insider-may take steps to convey its underlying quality to lessen information asymmetries. This fundamental feature is frequently difficult or impossible for another entity-the principal or an outsider-to observe. As a result, "problems of social selection under conditions of imperfect information" are at the center of signalling theory [START_REF] Connelly | Signalling theory: A review and assessment[END_REF].

The definition of a signal is a snapshot pointing to an unobservable signaler quality at a specific moment in time. They represent transmitted images or messages from one entity to another. The term "quality" refers to the signaler's characteristics or capacities to meet the requirements of a third party who is observing the signal [START_REF] Connelly | Signalling theory: A review and assessment[END_REF].

The fundamental premise is that an entity that lacks certain information will typically be ready to pay a material premium to an entity that can expose its qualities through signals. In essence, signalling theory develops hypotheses regarding action plans in the context of costs and benefits under various degrees of opacity or transparency, from the perspectives of both the signaller and the signal receiver.

According to signalling theory, businesses disseminate information that individuals or constituency groups may use to generate opinions about the company, its values, and its general future course [START_REF] Jones | Signalling positive corporate social performance: An event study of family-friendly firms[END_REF]. It primarily addresses "the deliberate communication of positive information in an effort to convey positive organizational attributes" that represent imperceptible underlying qualities and can be a powerful explanation for the conduct of firms and their constituents and their patterns of interaction.

Efficacy of signalling by high-quality and low-quality firms.

Even while it's crucial, a signal's ability to be observed does not guarantee its effectiveness. According to Connelly et al. [START_REF] Carpenter | Is the growth of small firms constrained by internal finance[END_REF], signals must also have the quality of being expensive. Signals that require signallers to incur expenditures reveal that certain signallers may be more able than others to do so. Even if they result in financial losses, some businesses may pursue social projects (Windsor,D, 2001). The signal may therefore convey the idea that the signaller is more trustworthy or sincere in its claim to possess a particular trait.

Signal honesty and signal fit. Signal honesty is defined as "the extent to which the signaller actually has the underlying quality associated with the signal" [START_REF] Connelly | Signalling theory: A review and assessment[END_REF], p. 45), bearing some resemblance to the distinction between signal intent and signal quality previously discussed. It has to do with the coupling of formal plans and subsequent actions. The level of correlation between a signal and the unobservable characteristic of the signaller is known as signal fit. This notion implies that situations may occur in which a signaller sends out signals that do not correlate well with the signaller's unobservable quality [START_REF] Busenitz | Signaling in venture capitalist-new venture team funding decisions: Does it indicate long-term venture outcomes?[END_REF].

Such misalignment is typically viewed as greenwashing in the context of CSR [START_REF] Laufer | Social accountability and corporate greenwashing[END_REF]. Poor signaling, which may be brought on by either the integrity of the signaller or the quality of the signal, is the reason of the difference between the signal and the signaller's quality, whether actual or perceived. Signal reliability, which is closely related to the idea of credibility [START_REF] Connelly | Signalling theory: A review and assessment[END_REF], is made up of signal fit and signal honesty.

B. Certifications and the Theory of Institutions

The concept of new institutionalism has also been used to examine certifications. According to [START_REF] Dimaggio | The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields[END_REF], external influences cause organizations to become more homogeneous. These forces are described as "coercive," "mimetic," and "normative" in their seminal work (DiMaggio and Powell, 1983, p. 147). Pressure from outside stakeholders leads to "coercive isomorphism" (DiMaggio and Powell, 1983, p. 150). For instance, [START_REF] Bansal | Why companies go green: A model of ecological responsiveness[END_REF] identify consumer pressures and competition as reasons for ecolabelling adoption. According to DiMaggio and Powell (1983, p. 150), "mimetic isomorphism" is the tendency of an organization to replicate the acts or behaviors of another organization when there are perceived benefits to doing so. According to various studies cited by Heras-Saizarbitoria and Boiral (2013), both ISO 9001 and ISO 14001 have seen widespread acceptance and have therefore evolved into competitive expectations, notably across national boundaries. Additionally, they note that firms were driven to comply to prove their validity and that, in order to prevent unfavorable outcomes, organizations were more motivated to adhere to standards than to exceed them. According to DiMaggio and Powell (1983, p. 152) "Normative isomorphism" is the process by which expectations and standards set by professions are met. This pressure is most closely related to that of certification. Heras-Saizarbitoria and Boiral (2013) reference several authors in their analysis who argue that when a standard-setter has authority by issuing a seal or label that certifies compliance, this turns into a normative pressure. Within the context of their work, critical scholars have highlighted that such standards can be implemented superficially by the "quest for social legitimacy rather than the search for improvement of internal practices".

The underlying premise of the theory of institutions is that organizations require social legitimacy and credibility to survive and flourish. Organizations are expected to behave in a way that is accepted as appropriate in the larger context, which includes abiding by the prevalent laws, rules, values, and conventions. As a result, legitimacy is the widespread belief that a particular action is being taken by a given entity is "socially desirable" and within the bounds of the institutional frameworks that have been socially built. This concept was further examined and recreated by [START_REF] Deephouse | Organizational legitimacy: Six key questions. The SAGE handbook of organizational institutionalism[END_REF] as the "perceived appropriateness of an organization to a social system in terms of rules, values, norms, and definitions" as the word "desirable" started to raise concerns due to its nod to reputation and status.

There are two distinct approaches to legitimacy: strategic and institutional. In this perspective, legitimacy is seen as a tactical resource in and of itself. Institutional legitimacy highlights the influence of external, cultural, and contextual elements in the creation of organizations and the metrics used to assess them [START_REF] Suchman | Managing legitimacy: Strategic and institutional approaches[END_REF]. The difference, according to [START_REF] Suchman | Managing legitimacy: Strategic and institutional approaches[END_REF], is "a matter of perspective, with institutional theorists taking the viewpoint of society looking "in," whereas strategic theorists adopt the viewpoint of organizational managers looking "out."

Building on this, Suchman (1995) offers three types of organizational legitimacy: pragmatic, moral, and cognitive legitimacy.

• Pragmatic legitimacy emerges from a self-interested exchange with the organization's most immediate stakeholders. • Moral legitimacy specifically refers to "the right thing to do," also known as normative legitimacy, from the universal values and morality of society and an evaluation of what society deems socially appropriate (Suchman, 1995, p. 579). • Cognitive legitimacy is a "taken-for-grantedness" assumption, and can be broadly defined as how well organizations execute their activities from their stakeholder's point of view (Suchman, 1995, p. 575) More recently, Bowen (2019), using the perspective of industry self-regulation (ISR), widened the body of knowledge on pragmatic and moral validity. In addition to extending recent studies beyond pragmatic considerations of ISR benefits, Bowen addressed issues between pragmatic and moral legitimacy. He also brought attention to the dual experience for ISRs, since enterprises are frequently drafting the regulations and determining how to participate. Most recently, in a 10-year longitudinal study conducted in France of 14 firms, Hudson and Descubes (2021) found that organizations are increasingly turning to CSR 53 certifications. They suggest that certain certifications are growing in their legitimacy and propose that organizations view the voluntary nature of CSR as congruent with moral legitimacy, citing that legislation would, in fact, "detract" from such legitimacy (p. 43). They also found that some organizations pursued certification for instrumental reasons based on perceived value by their internal and external stakeholders; thus, having positive business implications.

IV. SMALL AND MEDIUM ENTERPRISES

Small and medium enterprises (SMEs) account for over 90% of firms worldwide. In 2010, joint research by the IFC and McKinsey estimated that there were 420-510 million formal and informal micro, small, and medium-sized businesses operating worldwide, with most of those businesses-365-445 million-found in emerging nations. SME's are essential to the global economy. They account for most occupations in developing nations and are one of the biggest contributors to employment.

Additionally, SMEs make a significant contribution to the total value added in these economies. Despite this, SMEs suffer more financing challenges than larger companies since they have limited access to outside funding and must pay higher transaction costs and risk premiums. A further 15% of SMEs are underfinanced, and nearly 70% do not use external borrowing from financial institutions. 14% of the GDP of all emerging economies (Ramalho and Croci, 2018). These barriers for acquiring new resources from external parties are mainly attributed to their inability of narrowing the gap of information asymmetry [START_REF] Islam | Signalling by early stage startups: US government research grants and venture capital funding[END_REF]. Debt financing is usually not an option for such enterprises, since they

are not yet profitable and lack tangible assets [START_REF] Denis | Entrepreneurial Finance: An Overview of the Issues and Evidence[END_REF]. Consequently, entrepreneurial finance is mostly focused on alternative sources of capital, such as crowdfunding, venture capitalists (VC) and business angels (BA), as well as friends and family lenders [START_REF] Bergset | Green start-up financewhere do particular challenges lie?[END_REF].

Despite claims that CSR affects businesses of all sizes, most of the academic and professional research to date has consistently focused on large multinational corporations (Bikefe et al., 2020;

Bondy & Starkey, 2014; [START_REF] Grimstad | SMEs motivations for CSR: An exploratory study[END_REF]. The concern that the current standards and guidance to implement sustainable business practices may not be appropriate for SMEs as they were developed for larger business frameworks and infrastructure is growing as the contribution of SMEs to the overall economy is recognized more and more [START_REF] Grimstad | SMEs motivations for CSR: An exploratory study[END_REF][START_REF] Maldonado-Erazo | Corporate social responsibility and performance in SMEs: scientific coverage[END_REF].

The concern that the current standards and guidance to implement sustainable business practices may not be appropriate for SMEs as they were developed for larger business frameworks and infrastructure has grown with the increased recognition of SMEs' contribution to the overall economy [START_REF] Grimstad | SMEs motivations for CSR: An exploratory study[END_REF][START_REF] Maldonado-Erazo | Corporate social responsibility and performance in SMEs: scientific coverage[END_REF].

SMEs support CSR and are linked to the social, ecological, and economic advancement of society.

As a result, they are associated with the CSR movement. Developing countries experience societal and ecological problems, such as labor and human rights violations and environmental degradation, but by engaging in CSR activities, firms can help to mitigate these problems. CSR is a strategic tool that may be utilized to increase customer loyalty and happiness, employee motivation, and public fund availability for SMEs. These benefits come from an improved company reputation and increased sales.

CSR is a crucial element in maintaining a positive business reputation and is regarded as a crucial strategic asset that contributes to a firm's competitive advantage, according to

H1b: The presence of B Corp certification, along with factors such as certification cycles, firm sizes, industry categories, and funding round scheduling, will significantly influence the amount raised by certified SMEs through different types of investments, including equity crowdfunding, seed funding, Serie A funding, and debt financing.

The hypothesis posits that the presence of B Corp certification serves as a credible signal of a company's commitment to social and environmental responsibility. This certification is expected to enhance the reputation and attractiveness of SMEs to potential investors, consequently increasing their likelihood of securing funding. Additionally, larger firm sizes are often associated with greater resources, financial stability, and a higher level of operational capacity. These factors may provide advantages to SMEs in terms of their ability to demonstrate growth potential and manage potential risks, making them more appealing to investors.

By examining the relationship between B Corp certification, firm size, and funding success, this study aims to contribute to the understanding of the funding dynamics in the sustainable and socially responsible business sector.

Furthermore, we hypothesize that the strength of this effect is dependent on the SMEs' level of social and environmental impact improvement over time. In other words, we anticipate that SMEs with higher BIA ratings will benefit more from B Corp accreditation in terms of attracting new investments.

H2: The beneficial effect of B Corp certification on investment attractiveness is greatest for SMEs with higher BIA ratings that demonstrate greater social and environmental impact improvement.

Moreover, our aim is to examine whether the influence of B Corp certification on the appeal for investment differs among different industry sectors. To answer this question, we propose the following hypothesis:

H3: The impact of B Corp certification on investment attractiveness varies by industry sector, with some gaining a higher positive impact than others.

This thesis attempts to add to the understanding of the possible financial benefits that B Corp certification can bring to SMEs by investigating these assumptions, as well as to shed light on the role of social and environmental impact improvement in affecting investment attractiveness outcomes. To examine the data and test the provided hypotheses, relevant statistical procedures, such as regression analysis, will be used throughout the study. This study's findings have the potential to add useful insights and empirical research to the existing literature.

METHODOLOGY I. B CORP CERTIFICATION

The B Lab is a social movement organization focused on prosocial organizing [START_REF] Sharma | A configural framework of practice change for B corporations[END_REF].

To establish an organizational architecture that would drive an economic change toward a more (Moroz, 2018). According to these 3 criteria, the BIA will create a customized 'track' for the respondents of the survey. Each of the questions of this adapted track is weighted by the B Lab and its weight is indicated on the questionnaire. Following the BIA, the B Lab thoroughly audits the assessment's results, and if the applied firm satisfies all the criteria, it will be certified. The company joins the B Corp community and the accreditation is good for three years. Companies may compare their outcomes to those of thousands of enterprises thanks to B Lab, which compiles anonymized data from BIA users. This setting provides access to tools and best practice manuals that businesses may use to gradually enhance their social and environmental performance. Compared to what was formerly required for satisfying the standards, this certification necessitates investing more resources.

Additionally, businesses that pass the audit process are required to sign an agreement with the B Lab and pay an annual fee that currently ranges from 1,000€ to 500,00€ depending on the region and the company's annual revenues. Companies must retake the evaluation and earn the required minimum score once more after the three-year mark to keep their accreditation.

II. DATA COLECTION AND SAMPLE

Crunchbase and the data available from the B Lab platform are the two sources of data used in this dissertation. Crunchbase is a platform that provides business-related information for organizations.

Specifically, data regarding the firm's year of establishment, the number of funding rounds, amount of raised capital, type of investment, size, industry and country of operation were used. This collection includes data on about 2636448 firms. The B Lab's official website includes information about the certified companies, including the year of certification, the sector and industry in which they operate, and the B assessment score. By April 2023, 7884 distinct companies had information available on the B Lab website. As we can see in the next figure, there has been an important increase in the number of first certification in the recent last years. To build a comprehensive dataset for certified B Corp SMEs, the two datasets were merged based on the names of the companies and the websites of such companies. The R programming language was employed to merge the datasets. As there was no shared foreign key between the databases, a series of data transformations were carried out to clean and merge the datasets. To ensure data quality, the data was filtered to exclude records in Crunchbase with missing values in the "domain"

and "name" columns, as well as B Corp data with missing values in the "website" column.

Subsequently, the website columns in both datasets were modified by removing certain characters, such as "https," to facilitate more accurate matching, using the gsub() function in R. After cleaning the data and merging the two datasets we obtained a comprehensive dataset made of 4872 unique firms.

As the current and former number of B Corps available in the B Corp dataset is 7884 firms, the selection process resulted in 61.79% of this quantity.

After filtering, cleaning, and merging both databases, we proceeded to filter the data to exclusively obtain information on small and medium-sized enterprises (SMEs). To accomplish this task, the data underwent filtration, adhering to the European Commission's definition, which stipulates that a small and medium-sized enterprise (SME) must have a maximum of 250 employees (European Commission, 2003). While firms certified as B Corps are primarily SMEs, it is worth noting that, according to the B Corp website (B Corp, 2023), an increasing number of large enterprises are also joining their movement. By excluding these larger enterprises, we were left with a total of 3200 unique SMEs in our dataset. In addition to the certified B Corporations, our study incorporates a sample of 2993 noncertified firms. This inclusion allows us to provide valuable insights and comparisons between the certified and non-certified firms.

34

In this dissertation, it is pertinent to underscore certain fields that deserve particular emphasis. A crucial aspect to address is the field of B_Corporation, which plays a significant role in this study.

B_Corporation is represented as a binary variable, taking a value of 0 when a company is not certified and 1 when it becomes a B Corp. This variable remains 0 during the initial periods for all firms and transitions to 1 in subsequent years. The field overall score represents the grade obtained based on the B Impact Assessment score. To maintain certification, companies are required to undergo recertification and complete a fresh B Impact Assessment every three years, resulting in periodic changes to their scores.

35

In the case of the "category_list" and "category_groups_list" fields, the individual items within these lists were transformed into distinct dummy variables. Each item, separated by commas (e.g., "Brand Marketing, Consulting, Graphic Design"), was assigned a value of 1 if it was mentioned and 0 otherwise, effectively creating new variables for each item. This transformation will prove to be useful when conducting descriptive statistics. Another field that is fundamental for this research "total_funding_usd" and all the fields related to the funding rounds. Those fields describe the amount of US dollars raised by a given firm that reveals its funding amount. Within our sample, there are a total of 2996 individual funding rounds. Among these, there are 1024 firms that have participated in at least one funding round.

Table 4 Summary of the field Total_Funding_USD

For the applications of the models, it has been created a panel data with the information of the funding rounds. Previously, a pivot of the data has been done to create new columns to be more precise about the type of the investment. The type of investment that can be found in our sample are (Investopedia, 2023):

1. Series A: The first significant round of funding after the initial seed capital. It is usually aimed at helping the company grow and develop its product or service. 2. Series B: The second round of financing that occurs after a company has achieved certain milestones or demonstrated growth potential. It is often used to scale operations and expand market reach. 3. Series C: The third round of funding, which typically occurs when a company has established a solid market presence and aims to further accelerate growth, expand into new markets, or acquire other businesses. 4. Series D: Subsequent funding rounds that follow Series C. They represent further rounds of financing as a company continues to scale its operations and pursue growth opportunities. 5. Angel: Early-stage funding provided by individual investors (angels) who offer capital, mentorship, and expertise to startups in exchange for equity. 6. Pre-Seed: The initial stage of funding before the seed round, usually involving small amounts of capital to support concept validation or the development of a minimum viable product (MVP). 7. Seed: The first official round of funding for a startup. It helps the company launch its product or service and covers initial operational expenses. 8. Debt Financing: Funding obtained through loans or other forms of debt. This type of financing requires repayment with interest over a specified period. 9. Grant: Non-repayable funds awarded to startups or organizations by government bodies, foundations, or institutions to support specific projects or initiatives.

10. Equity Crowdfunding: A method of raising funds from a large number of people, often through online platforms, in exchange for equity in the company. 11. Product Crowdfunding: Crowdfunding campaigns that offer early access to a company's product or service in exchange for financial support. 12. Convertible Note: A debt instrument that can convert into equity at a later stage, usually during a future funding round. 13. Private Equity: Investment in companies that are not publicly traded, typically involving larger amounts of capital and more mature businesses. Where:

• 𝑌 {𝑖𝑡} represents the dependent variable for firm i in year t.

• 𝑋 {𝑛𝑖𝑡} denotes the independent variables for firm i in year t

• α is the intercept term.

• β measures the effect of the BIA score on the funding amount, while controlling for firm and year fixed effects

• 𝐹𝑖𝑟𝑚 {𝑖} represents the fixed effects at the firm level, capturing the unobserved heterogeneity across firms.

• 𝑌𝑒𝑎𝑟 {𝑡} represents the fixed effects at the year level, capturing the time-specific factors.

• 𝜖 {𝑖𝑡} is the error term.

By including firm fixed effects (𝜆𝐹𝑖𝑟𝑚 {𝑖} ), it can be controlled for time-invariant firm-specific characteristics that may influence the dependent variable but are not explicitly assessed or accounted for in the model. This helps to account for unobserved corporate heterogeneity that may influence funding amount. Likewise, by inserting year fixed effects (𝛿 * 𝑌𝑒𝑎𝑟 {𝑡} ), it can be controlled for timespecific elements that affect all firms in a particular year but are not represented by the independent variables. This helps to account for common time-specific shocks or trends that may impact the funding amount.

The output provides the estimated coefficients for each variable in the regression model, along with their standard errors, t-values, and p-values.

The coefficient estimates represent the expected change in the response variable for a one-unit increase in the corresponding predictor variable while holding other variables constant.

The t-values measure the number of standard deviations the coefficient estimate is away from zero. It is used to assess the statistical significance of the coefficient. Generally, smaller p-values indicate more statistically significant results.

The p-values indicate the probability of observing the coefficient estimate (or an even more extreme value) if the null hypothesis (no relationship between the predictor variable and the response variable) is true. Lower p-values suggest stronger evidence against the null hypothesis.

The fixed effects model estimates the coefficients (β) of the independent variable(s) while controlling for firm and temporal effects. The coefficients show the average influence of the independent variable(s) on the dependent variable, taking firm and year fixed effects into consideration.

The analysis is conducted using panel data comprising our sample of certified SMEs and their corresponding funding amounts over a specific time period. The data is transformed into panel format, and the fixed effects model is estimated using the "plm" package in R. Running the regression with the BIA score as the independent variable and the funding amount as the dependent variable, while adjusting for firm and year fixed effects, is what the estimate entails.

B. Pooled OLS regression model

Another panel data model that can be used it the Pooled OLS regression model. It can be used to investigate the correlation between various variables, namely "overall_score," "certification cycle," "year_fundround," and "size" with the amount raised by SMEs through different types of investments.

In this study, we have focused on four primary types of investments, namely seed funding, series A funding, debt financing, and equity crowdfunding. These types of investments were chosen based on their high frequency of occurrence and relevance.

In general, these models' equation are like:

𝑌 {𝑖} = 𝛽 {0} + 𝛽 {1} * 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 {1} + 𝛽 {2} * 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 {2} … + 𝛽 {𝑛} * 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 {𝑛} + 𝜀
where:

• 𝑌 {𝑖} is the dependent variable for observation i.

• 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 {1} , 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 {2} , … , 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 {𝑛} are the independent variables.

• β₀, β₁, β₂, and βn are the coefficients estimated by the regression model for the intercept.

• ε is the error term, representing the unobserved factors and random variation that affect the dependent variable.

The Pooled OLS model assumes that no individual-specific fixed effects or time-specific random effects exist. It aggregates all the data and estimates a single set of coefficients for all people and time periods for the independent variables.

The purpose is to determine whether or if there is a statistical relationship or correlation between these variables and the amount raised through different types of investment by SMEs. By estimating these regression models, it may be determined the significance and size of the independent variables' impact on the dependent variable. The regressions aid in identifying the variables that may influence the type of investment. It allows you to see if variables like overall_score (which could represent the overall quality or attractiveness of the investment opportunity), company size, certification_cycle, and year_fundround (which represents the year the funding round occurred) have a significant impact on the amount raised.

C. Fixed effect logit model

The fixed effect logit model will be used to estimate the effect of B Corp certification on the likelihood of funding success for SMEs while controlling for individual firm specific characteristics that remain constant over time.

The formula used in the fixed effect logit model is:

𝑙𝑜𝑔𝑖𝑡(𝑃(𝑌 {𝑖} = 1)) = 𝛽 {0} + 𝛽 {1} * 𝑋 {𝑖} + 𝛼 {𝑖}
Where:

• 𝑌 {𝑖} represents the binary outcome variable, such as funding success, for the i-th SME.

• 𝑋 {𝑖} represents the independent variable(s) of interest, such as B Corp certification, for the ith SME.

• 𝛼 {𝑖} represents the individual-specific fixed effect, capturing unobserved heterogeneity across firms that remain constant over time. • 𝛽 {0} , 𝛽 {1} are the coefficients to be estimated.

The model implies that the success probability (P(Yi = 1)) is related to the independent variable(s) via the logit link function, which converts the linear combination of the independent variables and fixed effects to a probability scale between 0 and 1. The fixed effects 𝛼 {𝑖} account for time-invariant individual-specific characteristics that may affect the outcome variable but are not directly observable.

Including these fixed effects helps control for unobserved heterogeneity across firms.

The calculated coefficients, such as 𝛽 {1} , represent the effect of the independent variable(s) on the outcome variable's log-odds while adjusting for individual-specific fixed effects. After controlling for fixed effects, a positive coefficient for B Corp certification would imply that acquiring the certification relates to a greater log-odds (or probability) of funding success for SMEs.

RESULTS

I. DESCRIPTIVE STATISTICS

In total, the sample consists of 3200 unique firms of certified firms. Based on the information provided in Appendix 1, a significant portion of the observed firms originate from North America and various European countries. Consequently, one can posit that most of these firms are part of developed nations.

There are a total of 2815 distinct financing rounds in our sample. There are 1024 firms that have participated in at least one funding round among these. Only firms that successfully obtained certification and participated in at least one investment round were included in the pie chart displayed in Appendix 2. According to the graph, only 27.5% of these companies were certified prior to their first founding round. This observation illustrates the fact that most of the organizations obtained certification at a later stage, and typically after becoming certified, they get their first round of funding.

As shown in Appendix 3, the number of enterprises with at least one funding round has increased over time.

Table 5 details the investments made in BCorp-certified SMEs by various sorts of investors. We can learn a lot about the impact of BCorp certification on SMEs by evaluating these investment trends.

Understanding investment dynamics is critical for assessing the impact of BCorp certification on promoting sustainable and socially responsible behaviors among SMEs.

Table 5 Amount raised and number of investments by investor types

The table displays the quantity and number of investments made by various investor types. It demonstrates the presence of venture capital firms, with a total investment value of $70651011790 spread across 1277 investments. Private equity firms are also heavily involved, investing $1840000000 in 46 enterprises. Accelerators, angel clubs, incubators, and micro-VCs have also made investments.

II. THE IMPACT OF B CORP OVERALL SCORE ON FUNDING ROUNDS IN SMES

We can assess the association between BCorp certification, as indicated by the BIA score, and investment quantities in SMEs by analyzing the estimated coefficients from the fixed effects model. A positive and statistically significant BIA score coefficient would imply that a higher BCorp certification score relates to increasing investment quantities, demonstrating that investors appreciate certified SMEs' social and environmental performance. A non-significant or negative coefficient, on the other hand, would show that BCorp certification has no substantial impact on funding levels. Furthermore, including company fixed effects accounts for time-invariant firm-specific characteristics that may influence funding quantities but are not explicitly quantified or included in the model. Firm fixed effects represent firm heterogeneity, allowing us to isolate the influence of BCorp certification in particular. Similarly, incorporating year fixed effects accounts for time-specific elements that affect all firms in a particular year but are not represented by the independent variable. This takes into account typical shocks or trends that may have an impact on financing quantities, such as macroeconomic conditions or industry-specific factors.

We get useful insights into the influence of BCorp certification on fostering sustainable and socially responsible behaviors among SMEs by using this panel data analysis and investigating the link between BCorp certification and financing levels while accounting for company and year fixed effects.

This section illustrates the analysis of the B Corp grades effect on the SME's ability to attract new investments. The following table summarizes the outcomes of the fixed effects model. The coefficient for the field "overall_score" is -4771.8. This coefficient shows the estimated effect of BCorp certification's overall score on the total amount raised by SMEs. A negative coefficient in this situation indicates that an increase in the overall score relates to a decrease in the total amount raised, but the coefficient is not statistically significant (p-value > 0.05).

Coefficients:

The R-squared value is 3.9633e-05, showing that the overall BCorp certification score explains a very little part of the variation in the total amount raised by SMEs. The adjusted R-squared value is -0.22104, indicating that include the overall score as an independent variable has no effect on model fit.

The F-statistic evaluates the model's overall significance. The F-statistic is 0.576525 in this situation, with a p-value of 0.44769, indicating that the whole model is not statistically significant.

In conclusion, there is no sufficient evidence to support the premise that the overall score of BCorp certification has a major impact on the total amount generated by SMEs based on the findings. The low R-squared and modified R-squared values imply that other factors not included in the model may have a greater impact on SMEs' funding levels.

III. RELATIONSHIP BETWEEN INVESTMENT TYPE AND DEPENDENT VARIABLES IN FUNDING ROUNDS

This section illustrates the analysis of the relationship between the type of investment, independent variable, and the dependent variables (such as overall_score, size, certification_cycle, year_fundround). The following table summarizes the outcomes of the study of the relationship between Equity Crowfunding and the dependent variables.

Coefficients: The intercept term (β₀) reflects the predicted value of the dependent variable (equity_crowdfunding_raised_amount_usd) when all independent variables (overall_score, certification_cycle, and year_fundround) are zero. The intercept, in this case, is predicted to be -3.5096e+07. However, the p-value (0.217930) suggests that the intercept is not statistically significant.

Overall Score (β₁): The overall_score coefficient is calculated to be -30,583. It is, however, not statistically significant (p-value: 0.99), demonstrating that changes in the total score have no effect on the amount raised via equity crowdfunding.

Certification Cycle (β₂):

The certification_cycle coefficient is calculated to be 14946. It is not, however, statistically significant (p-value: 0.7665), demonstrating that the certification cycle has no effect on the amount raised through equity crowdfunding.

Year Fundround (β₃): The year_rounds coefficient is expected to be 30302. It is statistically significant (p-value: 0.022), demonstrating that variations in the year of the fundround affect the amount raised through equity crowdfunding.

The coefficients for different size categories represent the expected differences in the equity crowdfunding raised amount compared to the reference category. For example, the coefficient for size 10-49 is 218180, indicating that, on average, firms with 10-49 employees have 218,180 units higher equity crowdfunding raised amount compared to firms with 0-1 employees. However, the p-values for size categories are generally not statistically significant.

The coefficient for the "Food and Beverage" category is 200090. This suggests that, on average, firms categorized under "Food and Beverage" have a higher equity crowdfunding raised amount compared to the reference category. The positive coefficient indicates an expected increase in the raised amount for firms in this category. Additionally, the p-value associated with this coefficient is 0.014144, which is below the conventional significance level of 0.05. This indicates that the association between the "Food and Beverage" category and the equity crowdfunding raised amount is statistically significant. In other words, there is evidence to suggest that being categorized under "Food and

Beverage" is associated with a higher amount raised through equity crowdfunding.

The coefficient for the "Apps" category is 2.6112e+05. This indicates that, on average, firms categorized under "Apps" have a higher equity crowdfunding raised amount compared to the reference category. However, the p-value (0.133360) suggests that this result is not statistically significant at the conventional significance level.

Catgr_Design, catgr_Information.Technology, catgr_Mobile, and other category variables are similar to the "Apps" category, the coefficients for these variables represent the expected differences in the equity crowdfunding raised amount compared to the reference category. Positive coefficients indicate higher amounts, while negative coefficients indicate lower amounts. However, the p-values for most of these category variables are not statistically significant, indicating that the associations may not be reliable.

Overall, the size 10-49 appears to be the sole meaningful predictor of the amount raised through equity crowdfunding, based on the statistics. The "catgr_Food.and.Beverage" and "catgr_others" term has a considerable influence as well, however the total score and certification cycle have no effect on the dependent variable.

The R-squared value (0.077736) suggests that the independent variables in the model can explain about 7.77% of the variation in the equity crowdfunding raised amount. The adjusted R-squared value (0.0074163) accounts for the number of variables and observations, indicating that the model only explains a small percentage of the variation.

F-statistic: The F-statistic evaluates the model's overall significance. The F-statistic value in this situation is 1.10547, and the accompanying p-value is 0.2886. We cannot reject the null hypothesis that all the coefficients are zero because the p-value is bigger than the standard significance level of 0.05, indicating that the entire model is not statistically significant.

The following table summarizes the outcomes of the study of the relationship between Seed and the dependent variables.

Coefficients: Overall Score (β₁): The overall_score coefficient estimate is 1334.2. However, the p-value associated with this coefficient is 0.74741, indicating that this relationship is not statistically significant.

Certification Cycle (β₂):

The certification_cycle coefficient estimate is -16231. his suggests that, on average, a longer certification cycle is associated with a decrease in the seed raised amount. However, the p-value (0.86934) indicates that this relationship is not statistically significant.

Year Fundround (β₃): The year_fundround coefficient estimate is 57942. This coefficient's p-value is 0.036, which is less than the customary threshold of 0.05. This shows that the year_fundround variable have a statistically significant effect on the seed_raised_amount_usd. It implies that the year of the investment round have a impact on the amount obtained through seed funding.

The coefficients for the "size" variables represent the estimated effects of different company sizes on the amount raised. None of these coefficients are statistically significant, as their p-values are greater than 0.05.

The model incorporates an abundant number of category variables that reflect various industries or sectors. Each coefficient for a single category variable shows the projected change in the amount of seed grown in comparison to the reference category (omitted category). For example, the coefficient for "catgr_Mobile" is -697790, showing that enterprises classified as "Mobile" have a lower average seed raised quantity than the reference group. The statistical significance of these coefficients, however, varies, with some being significant (p-value 0.05) and others not.

The R-squared value is 0.093261, indicating that the independent variables in the model explain approximately 9.33% of the variation in the seed raised amount. The adjusted R-squared value, which accounts for the number of variables in the model, is 0.024126.

The following table summarizes the outcomes of the study of the relationship between Series A and the dependent variables.

Coefficients: Certification Cycle (β₂): The coefficient for "certification_cycle" is -90937, and it is not statistically significant (p-value = 0.72510).

Year rounds (β₃): The coefficient for "year_rounds" is 72715, and it is not statistically significant (pvalue = 0.31469).

The "size" coefficients represent the estimated effects of different firm sizes on the amount raised.

For example, the coefficient for "size 1-9" is 209686. This means that, on average, companies in the size range of 1-9 employees have an expected increase in the seed raised amount by 209686 units compared to the reference category (larger companies), holding other variables constant. However, the coefficients for all three size categories are not statistically significant (p-values > 0.05), indicating that the relationship between company size and Serie A raised amount is not statistically supported in this model.

The coefficient for "catgr_Software" is 1051667, indicating that companies in the software industry category are projected to raise 1051667 units more than the reference category (other industry categories), after controlling for other factors. At the 0.05 significance level, this effect is statistically significant (p-value = 0.02446). The coefficients for other "catgr_" variables, including the "size" variable, show the estimated effects of being in certain industrial groups on the amount of Serie A raised. Some industrial categories, such as "catgr_Science.and.Engineering" and "catgr_Transportation," exhibit statistically significant effects (p-values 0.05), whereas others do not.

The R-squared and modified R-squared values are also used to measure overall model fit. The Rsquared value in this case is 0.10751, indicating that the model explains approximately 10.75% of the variation in seed raised quantity. The adjusted R-squared value is 0.039459, which accounts for the 51 number of variables in the model.The F-statistic measures the overall significance of the model and has a value of 1.57986 with a p-value of 0.0070187 in this example. This implies that the model as a whole has a statistically significant link with the amount of seed raised.

The following table summarizes the outcomes of the study of the relationship between Debt financing and the dependent variables.

Coefficients: The intercept of -406715676.582 represents the estimated amount of debt financing raised when all predictor variables are zero.

Overall Score (β₁): The "overall_score" coefficient is -33926.258, and it is not statistically significant (p-value = 0.403563). As a result, the total score does not appear to have a substantial impact on the amount of debt funding raised.

Certification Cycle (β₂):

The "certification_cycle" coefficient is -533890 and is not statistically significant (p-value = 0.580990). This shows that the certification procedure has no impact on the amount raised through debt financing.

Year Fundround (β₃): The coefficient for "year_fundround" is 203891.491, and it is not statistically significant (p-value = 0.450935). This indicates that the timing of the funding round does not have a significant impact on the amount raised through debt financing.

The "size" coefficients represent the estimated effects of different firm sizes on the amount raised.

The "size 50-249" coefficient is statistically significant (p-value = 0.073086), showing that companies with 50-249 employees likely to raise more Debt funding than other size groups. The remaining size categories' coefficients are not statistically significant.

The coefficients and p-values indicate the impact of each category "catgr_" on the debt financing raised amount. However, many of these coefficients are not statistically significant (p-values > 0.05), suggesting that the categories may not have a significant relationship with the debt financing raised amount in this model. The variable "catgr_Energy" stands out with a statistically significant coefficient, indicating that campaigns related to energy have a positive association with the debt financing raised amount.

The R-squared value of 0.075817 suggests that the model explains only a small portion of the variation in the debt financing raised amount. The Adjusted R-squared value is even lower at 0.0053514, indicating that the model's explanatory power is limited.

In summary, the results indicate that the model variables (overall_score, certification_cycle, year_fundround, and size (except size 50-249)) have no meaningful impact on the amount obtained through debt financing. The variable "catgr_Energy" appears to have a significant positive association, but further analysis and consideration of other factors are necessary to draw more conclusive insights.

IV. BETWEEN B CORP SUBSCORES AND OVERALL SCORE

This study's regression approach allows for the examination of many predictor variables related to Based on the data provided:

The intercept term has a coefficient estimate of 2.6630e-01, indicating the expected value of the response variable when all predictor variables are zero.

Several predictor variables have highly significant coefficients (p < 0.001), such as impact_area_community, impact_area_customers, impact_area_environment, impact_area_governance, and impact_area_workers.

Other variables, such as ia_community_it_diversity_inclusion, ia_environment_it_inputs, ia_environment_it_land_office_plant, and ia_governance_it_ethics_transparency, also have statistically significant coefficients (p < 0.05).

Some variables, including ia_community_it_job_creation, ia_environment_it_land_life, ia_environment_it_outputs, and ia_environment_it_water, have coefficients that are not statistically significant (p > 0.05).

The R-squared value is 0.99996, suggesting that the model accounts for a significant percentage of the variance in the response variable. The adjusted R-squared is the same, indicating that adding more predictor variables has no effect on the model's explanatory ability. In summary, the regression findings indicate that there is a very significant association between the predictor variables and the response variable, with most of the predictors having a positive effect on the answer. However, it is critical to evaluate the data context, model assumptions, and the presence of multicollinearity or other factors that may influence the interpretation of the results.

V. EXPLORING FACTORS AFFECTING TOTAL FUNDINGS AND SUBSCORES

This study's regression approach allows for the examination of many predictor variables related to The aim of this study was to examine the factors influencing the total amount raised by subscores.

A panel data model using the "pooling" approach was employed to analyze the data.

The study's findings revealed that the model's overall performance in explaining the total amount raised was rather weak, as evidenced by the low R-squared value of 0.0272. This implies that the model's variables explain just roughly 2.72% of the entire variation in the total amount raised by impact-oriented programs.

Examining the coefficients of the independent variables provides insights into their individual effects on the total amount raised. Notably, the coefficients of the impact area variables, such as impact_area_community, impact_area_customers, impact_area_environment, impact_area_governance, and impact_area_workers, indicate the expected change in the total amount raised when compared to their respective base levels.

Exploring the dimensions within the community impact area, variables such as ia_community_it_civic_engagement_giving and ia_community_it_diversity_equity_inclusion did not exhibit a significant relationship with the total amount raised. This implies that specific dimensions within the community impact area may not be strong determinants of fundraising success.

However, it was found that projects emphasizing customer stewardship, as represented by the variable ia_customers_it_customer_stewardship, tend to attract higher total amounts raised.

Conversely, grades related to water management or conservation, as indicated by ia_environment_it_water, were associated with lower total amounts raised. This suggests that the specific focus of a project, particularly in terms of customer stewardship and water-related initiatives, can significantly influence the fundraising outcomes of SMEs.

Additionally, the presence of worker ownership, captured by the variable ia_workers_it_worker_ownership, was found to have a positive and statistically significant effect on the total amount raised. This implies that projects involving worker ownership tend to attract higher levels of funding.

In conclusion, while several specific dimensions within impact areas and project features revealed significant relationships with total amount raised, the model's overall explanatory power was very modest. This shows that, in addition to the variables considered in this study, there may be other unexplored elements or contextual impacts that contribute to the fundraising outcomes of impactoriented programs.

VI. INDUSTRY SECTORS AND B CORP CERTIFICATION

The total amount raised is the dependent variable in this section. The independent variables are a collection of dummy variables representing various industry sectors and also the dummy variable B_Corporation.

Consider the coefficient for the "catgr_Software" dummy variable, for example. A positive coefficient shows that working in the software industry is related with a higher total amount raised as compared to the reference group (usually the excluded sector). A negative coefficient, on the other hand, indicates that working in the software industry is related with a lower total amount raised.

Here are the results of the model:

Coefficients: In this study, a pooling model was employed to examine the factors influencing the total amount raised by impact-oriented projects. The results of the pooling model reveal several significant findings.

Firstly, the coefficients of the categorical variables representing different industry categories indicate their impact on the total amount raised. Notably, the categories of "Professional Services," "Food and Beverage," "Financial Services," "Sustainability," "Healthcare," "Internet Services," "Energy," "Science and Engineering," "Transportation," "Agriculture and Farming," "Biotechnology," and "Payments" were found to have statistically significant effects on the total amount raised. These categories exhibited positive coefficients, suggesting that projects falling within these industries tend to attract higher levels of funding.

Conversely, several industry categories, such as "Commerce and Shopping," "Other," "Sales and Marketing," "Design," "Media and Entertainment," "Consumer Goods," "Real Estate," "Manufacturing," "Education," "Lending and Investments," "Clothing and Apparel," "Information Technology," "Advertising," "Hardware," "Data and Analytics," "Content and Publishing," "Natural Resources," "Consumer Electronics," "Events," "Video," "Artificial Intelligence," "Government and Military," "Privacy and Security," "Messaging and Telecommunications," "Music and Audio," "Gaming," "Navigation and Mapping," and "Platforms," did not demonstrate statistically significant effects on the total amount raised.

Furthermore, the coefficient for the dummy variable "B_Corporation" was determined to be highly significant. A positive coefficient indicates that being a B Corp certified company has a positive impact on the overall amount raised by firms.

The overall performance of the model in explaining the variation in the total amount raised was relatively weak, as indicated by the low R-squared value of 0.0079. This implies that the variables included in the model collectively explain only around 0.79% of the total variation in the total amount raised by frims.

It should be noted that the coefficient estimates should be viewed with caution because several variables had no statistically significant effects on the overall amount raised. Furthermore, the low Rsquared value shows the inclusion of unknown elements or contextual impacts that contribute to the fundraising outcomes of impact-oriented programs in addition to the variables considered in this study.

These findings are useful for practitioners and policymakers working on impact-oriented programs.

Professional Services, Food and Beverage, Financial Services, Sustainability, Healthcare, Internet Services, Energy, Science and Engineering, Transportation, Agriculture and Farming, Biotechnology, and Payments have a better chance of attracting more funding for their impact-oriented projects.

Obtaining B Corp certification can also considerably increase the overall amount raised.

VII. THE IMPACT OF B CORP CERTIFICATION, FIRM SIZE, AND FIRM AGE ON FUNDING SUCCESS FOR SMES:

The outcomes of the logistic regression analysis investigating the influence of B Corp certification, company size, and firm age on the likelihood of funding success for SMEs are presented in this section.

The dependent variable, funding_success, is a binary variable indicating whether an SME obtained funding [START_REF] Aghion | Credit constraints as a barrier to the entry and post-entry growth of firms[END_REF] or not (0). The independent variables of interest include B Corp certification, firm size, and age of the firm. The results show that B Corp certification has a statistically significant positive effect on the odds of funding success for SMEs (β = 0.6773, p < 0.001). This means that, even after controlling for other variables, SMEs with B Corp accreditation are more likely to get funding than those without certification. This research implies that B Corp certification serves as a signal of social and environmental responsibility, which may attract investors and enhance funding opportunities.

Furthermore, firm size has a substantial association with funding performance. The coefficient estimates for firm sizes 1-9, 10-49, and 50-249 are all positive and statistically significant. This suggests that larger SMEs, in terms of employee numbers, have a better chance of securing funding than smaller SMEs. This finding is consistent with earlier research emphasizing the benefits of scale and resources that larger organizations have in attracting investors and accessing funding options.

Interestingly, the age of the firm exhibits a negative and statistically significant effect on funding success (β = -0.0029, p < 0.01). This implies that as the firm's age increases, the chances of obtaining capital diminish slightly. One possible explanation is that older organizations may suffer hurdles in terms of flexibility, creativity, or reputation, which may limit their capacity to acquire capital when compared to younger and more dynamic firms.

Overall, the findings emphasize the importance of B Corp certification and firm size in impacting the likelihood of SME funding success. B Corp certification is a distinguishing element that makes SMEs more appealing to potential investors. Furthermore, the positive association between firm size and funding success highlights the advantages that larger firms have in gaining access to financial resources. However, the negative effect of business age shows that older enterprises may face specific funding constraints that need more examination.

CONCLUSION

In this master's dissertation, we aimed to explore the relationship between B Corp certification and the ability of certified SMEs to attract new investments. Our findings reveal that the B Corp The second hypothesis (H2) proposed that the strength of the influence of B Corp certification on investment attractiveness is depending on the BIA ratings of the SMEs. In terms of soliciting new investments, we believed that SMEs with better BIA ratings would benefit more from B Corp accreditation. This hypothesis has been rejected because of the results of table 6. The field "overall_score" has a coefficient of -4771.8. In this case, a negative coefficient implies that an increase in overall score is associated with a decrease in total amount raised, but the coefficient is not statistically significant (p-value > 0.05). Based on the findings, there is insufficient evidence to support the notion that the overall score of BCorp certification has a significant impact on the total amount generated by SMEs. The low R-squared and modified R-squared values show that additional factors not accounted for in the model may have a higher impact on the funding levels of SMEs. The findings produced by the model, table 12, that analyzed the relationship between total funding raised and the set of subscores, on the other hand, revealed an existing relationship between certain dimensions within impact areas and project aspects, and total financing raised by SMEs. Firms that promote customer stewardship receive more financing, whereas grades relating to water management or conservation receive less cash. Furthermore, projects featuring worker ownership tend to receive more funding. However, the model's overall explanatory ability is limited.

The third hypothesis (H3) investigated how the impact of B Corp certification differed across industry sectors in terms of investment attractiveness. We hypothesized that certain industries would be more positively impacted than others. This hypothesis acknowledges that contextual factors and industry-specific dynamics may influence the perceived value and desirability of B Corp certification across industries. To demonstrate this hypothesis, a pooling model was employed, as it can be seen in Table 13. The results of the pooling model yield significant findings. Particularly, the coefficients of categorical variables representing different industry categories indicate their impact on the total funding raised. Noteworthy industry categories such as "Professional Services," "Food and Beverage," "Financial Services," "Sustainability," "Healthcare," "Internet Services," "Energy," "Science and Engineering," "Transportation," "Agriculture and Farming," "Biotechnology," and "Payments" exhibit statistically significant effects on the total funding raised. These industry categories demonstrate positive coefficients, implying that projects within these sectors tend to attract higher levels of funding.

It is also relevant to note that this study has limitations, keeping avenues open for further research.

Firstly, even though our study is cross-sectional, and the scope involves multiple countries, the sample used is relatively small. Secondly, the matching used for merging both databases, B Corporation and Crunchbase, can be improved. We found 61.79% matching results despite of the lack of common foreign keys to join both datasets. Finally, our study could be more exhaustive by regressing more types of funding rounds and including more dependent variables in the models. By addressing these limitations and further exploring these aspects, future research can contribute to a more robust understanding of B Corp certification on SME funding outcomes. # Company_id is repeated colnames(prueba3) names(prueba3) [START_REF] Berman | Does stakeholder orientation matter? The relationship between stakeholder management models and firm financial performance[END_REF] <-"city_org" prueba$date_certified <as.Date(prueba$date_certified) colnames(prueba) columns_to_delete <c("state", "b_corp_profile","website","ia_customers_it_marketing_recruiting_and_transp arency", "ia_customers_it_student_experience", "ia_environment_it_environmentally_innovative_agricultural_process", "ia_customers_it_mission_aligned_exit", "ia_customers_it_student_outcomes_special_interest_students_", "ia_customers_it_infrastructure_market_access", "ia_environment_it_designed_to_conserve_wholesale_process", "ia_environment_it_environmentally_innovative_manufacturing_process", "ia_community_it_national_economic_development", "ia_customers_it_educational_models_and_engagement", "ia_customers_it_infrastructure_market_access_building", "ia_community_it_microfranchise_poverty_alleviation", "ia_environment_it_green_investing", "ia_community_it_producer_cooperative", "ia_customers_it_student_outcomes_traditional_students_", "ia_customers_it_student_outcomes", "ia_environment_it_construction_practices", "ia_community_it_microdistribution_poverty_alleviation", "ia_environment_it_safety", "ia_environment_it_designed_to_conserve_agriculture_process", "ia_customers_it_basic_services_for_the_underserved", "ia_customers_it_business_model_and_engagement", "ia_customers_it_educational_outcomes", "ia_customers_it_privacy_and_consumer_protection", "ia_customers_it_quality_and_continuous_improvement", "ia_environment_it_environmentally_innovative_wholesale_process", "ia_environment_it_energy_water_efficiency", "ia_customers_it_serving_underserved_populations_direct_", "ia_community_it_designed_for_charitable_giving", "ia_environment_it_green_lending", "ia_environment_it_material_energy_use", "ia_environment_it_designed_to_conserve_manufacturing_process", "ia_customers_it_health", "ia_environment_it_materials_codes", "ia_workers_it_worker_owned", "ia_customers_it_arts_media_culture", "ia_customers_it_economic_empowerment_for_the_underserved", "num_exits", "ia_customers_it_mission_lock", "ia_customers_it_fund_governance", "ia_environment_it_certification", "ia_customers_it_capacity_building", "ia_customers_it_current_fund", "ia_customers_it_past_performance", "ia_customers_it_positive_impact", "ia_environment_it_training_collaboration", "ia_environment_it_community", "ia_environment_it_renewable_or_cleaner_burning_energy", "ia_workers_it_workforce_development", "ia_workers_it_human_rights_labor_policy", "ia_customers_it_health_wellness_improvement", "ia_customers_it_portfolio_management", "ia_customers_it_leadership_outreach", "ia_environment_it_environmental_education_information", "ia_customers_it_investment_criteria", "ia_customers_it_targeted_for_investment", "ia_environment_it_land_wildlife_conservation", "ia_customers_it_education", "ia_customers_it_portfolio_reporting", "ia_customers_it_improved_impact", "ia_community_it_supply_chain_poverty_alleviation", "ia_customers_it_impact_improvement", "ia_community_it_local_economic_development", "ia_community_it_designed_to_give", "ia_community_it_suppliers_distributors", "ia_environment_it_environment_products_services_introduction", "ia_environment_it_resource_conservation", "ia_workers_it_occupational_health_safety", "ia_environment_it_transportation_distribution_suppliers", "ia_environment_it_toxin_reduction_remediation", "ia_governance_it_governance", "ia_customers_it_support_for_underserved_purpose_driven_enterprises", "ia_workers_it_worker_benefits", "ia_governance_it_ethics", "ia_workers_it_job_flexibility_corporate_culture", "ia_workers_it_benefits", "ia_customers_it_serving_in_need_populations", "test", "type", "permalink", "cb_url", "legal_name", "roles", "domain", "homepage_url", "address", "postal_code", "short_description", "email", "phone", "facebook_url", "linkedin_url", "twitter_url", "logo_url", "alias1", "alias2", "alias3", "total_funding", "total_funding_currency_code", "rank", "country_code", "state_code", "region", "city_org", "status" )

TABLE OF TABLES

# Delete the specified columns from the dataset Dataset10 <prueba[, -which(names(prueba) %in% columns_to_delete)] colnames(Dataset10) Dataset10$description <gsub("\n"," ",Dataset10$description) Dataset10$description <gsub("[^[:alnum:]]", " ",Dataset10$description) Dataset10$products_and_services <gsub("\n"," ",Dataset10$products_and_services) Dataset10$products_and_services <gsub("[^[:alnum:]]", " ",Dataset10$products_and_services) write.csv(Dataset10, "C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis/IJ_BCORP-0-1.csv", row.names = FALSE) colnames(Dataset10) #We did a groupby count further in the code and we realise that there are some company_id that doesn't match properly. Since we have selected random firms, careful with the next line of the code because I might be diferent borrado <sqldf('select * from Dataset10 where company_id in("0013b00001s7i4wAAA","001C000001QzVAgIAN","001C000000zl8noIAA","001C0000 01lM5KKIA0","0013b00001tBGnQAAW","001C000001QzgRJIAZ","0013b00001qEcsnAAC", "0013b00001srTp5AAE","0013b00001vAIxWAAW")') Dataset10 <-Dataset10 %>% filter(!company_id %in% borrado$company_id) #Can you convert the items in "category_list" and "category_groups_list" into separate dummy variables? All items listed with the commas, such as "Brand Marketing,Consulting,Graphic Design" should become new variables with a value of 1 if the item is mentioned and "0" otherwise. In this example, value of 1 for "Brank Marketing" variable, but 0 for "Architecture", etc. # Split the items in the "category_list" column into dummy variables categories <strsplit(as.character(Dataset10$category_list), ",") category_names <unique(unlist(categories)) category_names <as.character(category_names) # Convertir a caracteres # Create an empty dataframe for the dummy variables dummy_vars <data.frame(matrix(0, nrow = nrow(Dataset10), ncol = length(category_names))) colnames(dummy_vars) <paste("cat_", category_names, sep = "") # Set the values of dummy variables to 1 where appropriate for (i in 1:length(categories)) { dummy_vars[i, paste("cat_", categories[[i]], sep = "")] <-1 } # Split the items in the "category_groups_list" column into dummy variables category_groups <strsplit(as.character(Dataset10$category_groups_list), ",") category_groups_names <unique(unlist(category_groups)) category_groups_names <as.character(category_groups_names) # Convertir a caracteres # Create an empty dataframe for the dummy variables dummy_vars_category_groups <data.frame(matrix(0, nrow = nrow(Dataset10), ncol = length(category_groups_names))) colnames(dummy_vars_category_groups) <paste("catgr_", category_groups_names, sep = "") # Set the values of dummy variables to 1 where appropriate for (i in 1:length(category_groups)) { dummy_vars_category_groups[i, paste("catgr_", category_groups[[i]], sep = "")] <-1 } # Combine the original data with the dummy variables data <cbind(Dataset10, dummy_vars, dummy_vars_category_groups) #-Once it's done, drop the commas "," in all variables, as they will corrupt the csv files when opened in Excel or Stata. data$category_groups_list <gsub(",", ";", data$category_groups_list) data$category_list <gsub(",", ";", data$category_list) Dataset10 <data setwd("C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis\\Crunchbase") Fund_rounds <read.csv("funding_rounds.csv") a10 <sqldf('SELECT * FROM Fund_rounds where org_uuid IN (select uuid from Dataset10)' ) colnames(a10) a10$year <year(a10$announced_on) Pivot_fund_rounds_10 <-a10 %>% group_by(org_uuid, org_name, investment_type) %>% mutate(column_index = row_number()) %>% ungroup() %>% pivot_wider( id_cols = c( "org_uuid", "org_name"), names_from = c("investment_type", "column_index"), values_from = c("uuid", "name", "created_at", "updated_at", "announced_on", "raised_amount_usd", "post_money_valuation_usd","year"), names_glue = "{.value}_{investment_type}_{column_index}" ) %>% select(-starts_with("investment_type_"), -starts_with("column_index_")) series_g_raised_amount_usd, secondary_market_raised_amount_usd, post_ipo_secondary_raised_amount_usd, initial_coin_offering_raised_amount_usd, non_equity_assistance_raised_amount_usd, series_h_raised_amount_usd, product_crowdfunding_raised_amount_usd, equity_crowdfunding_raised_amount_usd, series_j_raised_amount_usd, na.rm = TRUE)) #DESCRIPTIVE_STATISTICS paneldatafinal_10 <subset(paneldatafinal_10, !(size %in% c(" 250-999", " 1000+" , " 250+"))) empresaunica <unique(paneldatafinal_10$uuid) print(length(empresaunica)) paneldatafinal_10$B_Corporation <ifelse(is.na(paneldatafinal_10$overall_score), 0, 1) campo <-"B_Corporation" numero_columna <which(colnames(paneldatafinal_10) == campo) library(ggplot2) # for creating plots library(dplyr) # for data manipulation colnames(Dataset10) # Get the columns that start with "cat_" columnas_cat <names(Dataset10)[grepl("^cat_", names(Dataset10))] # Get the category names without the "cat_" prefix categorias <gsub("^cat_", "", columnas_cat) # Calculate the frequencies of occurrence of one in each field, considering only a unique uuid frecuencias <sapply(columnas_cat, function(columnas_cat) { unique_uuids <unique(Dataset10$uuid) sum(data[Dataset10$uuid %in% unique_uuids, columnas_cat] == 1) }) # Create the data frame with the "Category" and "Frequency" columns tabla <data.frame("Category" = categorias, "Frequency" = frecuencias) # Sort the table by the "Frequency" column from highest to lowest tabla <tabla[order(-tabla$Frequency), ] # Limit the table to the 10 most frequent categories tabla <head(tabla, 10) # Change row names tabla <-rownames_to_column(tabla, "Row") # remove the first column tabla <select(tabla, -1) write.csv(tabla, "C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis/Category_list.csv", row.names = FALSE) # Change the names of the column headers colnames(investment_table) <c("Investor Type", "Invested Amount ($)", "Number of Investments") colnames(investment_table) investment_table_kable <kable(investment_table, "html") %>% kable_styling(bootstrap_options = "striped", full_width = FALSE) %>% column_spec(1, bold = TRUE) %>% column_spec(2:3, width = "100px") %>% add_header_above() # Eliminar el título grande reemplazándolo con un vector vacío investment_table_kable paneldatafinal_10 <-paneldatafinal_10[paneldatafinal_10$current_status != "de-certified", ] # Load the 'plm' package library(plm) count_by_year <-paneldatafinal_10 %>% group_by(year, uuid) %>% summarize(uuid_count = n()) %>% filter(uuid_count > 1)

# View the resulting count by year for combinations with count > 1 print(count_by_year) borrar <sqldf('select * from paneldatafinal_10 where uuid in (select uuid from count_by_year) ') paneldatafinal_10 <subset(paneldatafinal_10, year != 2024) paneldatafinal_10$B_Corporation <as.numeric(paneldatafinal_10$B_Corporation) paneldatafinal_10$B_Corporation <as.integer(paneldatafinal_10$B_Corporation) class(paneldatafinal_10$cat_Mobile.Apps) class(paneldatafinal_10$cat_Mobile.Apps) # Get the columns that start with "cat_" columnas_catgr <names(paneldatafinal_10)[grepl("^catgr_", names(paneldatafinal_10))] # Define the grouping variable group_var <-paneldatafinal_10$uuid # Calculate the frequencies of occurrence of one in each column grouped by uuid frecuencias <aggregate(paneldatafinal_10[columnas_catgr], by = list(group_var), FUN = function(x) sum(x == 1)) # Rename the grouping variable column names(frecuencias) [START_REF] Aghion | Credit constraints as a barrier to the entry and post-entry growth of firms[END_REF] <-"uuid" # Get the category names without the "cat_" prefix categorias <gsub("^catgr_", "", columnas_catgr) # Create the data frame with the "Category" and "Frequency" columns tabla <data.frame("Category" = categorias, "Frequency" = colSums(frecuencias # Create a flextable object from the data frame flex_table <flextable(summary_data) # Save the table to a Word file save_as_docx(flex_table, path = "C:/Users/mikel.escribano/Documents/Master/Master Thesis/rlogit.docx")

  Gehman and Grimes (2017) aimed to comprehend why businesses pursued certification while passing up possibilities to market membership. According to Gehman and Grimes' research with 49 companies, (a) alignment with a company's mission, purpose, values, or identity (b) validating and legitimizing a company's commitment to sustainability (c) innovation and practice improvement (d) membership as a community-are the main drivers of B Corp membership. According to their research, pursuing B Corp accreditation was most frequently motivated by congruence with the organization's mission, purpose, values, or identity (p. 2311). They speculate that the reason why their businesses sought B Corp membership was for "identity enactment and validation" (p. 2311). A special edition of the Journal of Business Venturing, edited by Moroz et al. (2018), had six articles about B Corp certification and official Social Purpose Company designations. Incorporating imprinting theory into entrepreneurship According to Moroz et al. (2018), Certified B Corps are a powerful example of social entrepreneurship. According to their argument, the B Corp certification "offers an explicitly temporal perspective, punctuated by distinct and potentially impactful experiential events, that reveal the prosocial motivations, opportunity processes, and actions entailed in entrepreneurship" (p. 119).

A

  few more articles that are specific to B Corps have been published since the release of the special issue. According to Parker et al. (2019), pursuing third-party B Corp certification entails trade-offs because it typically results in a 20-61% short-term income deceleration compared to companies that have not yet obtained certification. Given the tiny size of the majority of certified B Corporations, they claim that the internal resources diverted to comply with certification take limited attention away from topline revenue development. Furthermore, they claim that compared to their older and larger certified B Corp competitors, the smallest and young certified B Corps (those with fewer than 10 employees and under 10 years of age) face this slowdown more severely. According to Gehman et al. (2019), organizations seek for and promote sustainability certifications for reasons other than financial ones in response to Parker et al.'s findings. They further contend that the growth penalty experienced by newly certified firms may be because many stakeholders do not yet value or understand what it means to be a recognized B Corp. In addition, they question whether metrics should be developed to attest to societal well-being and social impact. They conclude by arguing that founders' motivations for pursuing B Corp certification may differ and that conventional measures of organizational performance may not be appropriate for businesses choosing to pursue sustainability certifications. Building on the conclusions of Parker et al. (2019), Pollack et al. (2021) recently conducted research

Finally, in order

  to investigate the effects of B Corp certification on both financial and nonfinancial goals, Patel and Dahlin (2022) undertook quantitative research on a sample of 355 B Corps and 623 non-B Corp enterprises. They discovered that while B Corp accreditation increases sales in the short term, it has little effect on financial stability over the following three to five years. They contend that the B Corp accreditation didn't lead to either economic growth or financial stability. They contend that their findings need investigating a wider variety of reasons for pursuing B Corp certification, and they speculate that stewardship or benevolence may present an interesting area of study for the future.

  Connelly et al. (2011) condensed a few important theoretical ideas in their review of signalling theory. These include: (1) signals of quality and intent; (2) the efficacy of signalling by high-quality and low-quality firms; (3) signal honesty and signal fi; and (4) signal frequency and consistency Signals of quality and intent. In ST, a fundamental difference is drawn between information signaling an organization's quality and intent. To gain credibility with signal receivers, a certain organizational attribute must be communicated through quality signals (such as CSR quality). The phrase "indicate future action, possibly conditional on the receiver's response" is used to describe signals of purpose (Connelly et al. (2011), p. 60). Firms communicate with stakeholders about their goals or resolutions via these signals.

  Signal frequency and consistency. Businesses can improve the efficacy of their signaling by emitting a wider range of observable signs or by emitting more signals often, which is referred to as signal frequency. Connelly et al. (2011) highlighted the potential for signalers to repeatedly signal to keep lowering information asymmetries and improve the efficacy of the signaling process. This is especially true when a company employs various signal kinds to deliver the same message (Balboa, M et al., 2007). Signal consistency, which Connelly et al. defined as the agreement between several signals from one specific source, is a term linked to signal frequency. Signal consistency could reduce the issue of communication being less effective as the receiver becomes confused by mismatched or contradictory messages.

Figure 1 :

 1 Figure 1: Sum of the companies that have firstly become B corps for each year since B Corp began in 2007.

B

  Corp subscores and their link to the B Corp score. The regression model produced interesting results, revealing the links between predictor factors and B Corp score. The computed coefficients for each variable represent the predicted change in CSR performance for a one-unit increase in the related predictor variable while maintaining other factors fixed. Here are the results of the model. Coefficients:

  certification has a positive relationship with firms' funding rounds. We aimed to analyse the economic benefits that B Corp certification may provide to SMEs through a review of the current literature and a theoretical framework, as well as the role of social and environmental sustainability in the impact of the ensuing economic interest and with an exhaustive empirical study with the R program.The first hypothesis (H1) proposed that B Corp certification has a positive effect on the ability of certified SMEs to attract new investments. According to Appendix 2, only 27.5% of the companies were certified prior to their initial funding round. This observation underscores the prevalent trend among firms to achieve B Corp Certification later in their development. Frequently after getting certificated, they receive their first round of funding. Furthermore, as shown in Appendix 3, the number of firms getting at least one investment round has increased significantly over time. The findings presented in

  (country = factor(country, levels = country)) ggplot(country_counts, aes(x = country, y = count)) + geom_bar(stat = "identity", fill = "steelblue") + geom_text(aes(label = count), vjust = -0.5, hjust = 0.5, size = 3.5) + # Add labels on bars xlab("Country") + ylab("Number of Firms") + ggtitle("Number of Firms per Country (Top 10)") + theme(axis.text.x = element_text(size = 9, angle = 45, hjust = 1), # Adjust label size, rotation, and position plot.title = element_text(size = 12, face = "bold"), # Adjust title appearance axis.title = element_text(size = 10, face = "bold"), # Adjust axis label appearance panel.background = element_blank()) # Remove plot background print(plot) #Descriptive_statistics related to founding_rounds. To load the data first run Founding_rounds_20230605 colnames(dataset_fund_rounds_10) dfr1 <subset(dataset_fund_rounds_10, B_Corporation == 1) dfr0 <subset(dataset_fund_rounds_10, B_Corporation == 0)summary_stats_fund <c( "Minimum" = min(dataset_fund_rounds_10$total_funding_usd), "1st Quartile" = quantile(dataset_fund_rounds_10$total_funding_usd, 0.25), "Median" = median(dataset_fund_rounds_10$total_funding_usd), "Mean" = mean(dataset_fund_rounds_10$total_funding_usd), "3rd Quartile" = quantile(dataset_fund_rounds_10$total_funding_usd, 0.75), "Maximum" = max(dataset_fund_rounds_10$total_funding_usd) )# Format values to two decimal places summary_stats_fund <format(round(summary_stats_fund, 2), nsmall = 2) # Create the summary

  coefficients <coef(summary(model_sector))[, "Estimate"] std_errors <coef(summary(model_sector))[, "Std. Error"] t_values <coef(summary(model_sector))[, "t-value"] p_values <coef(summary(model_sector))[, "Pr(>|t|)"] # Extract variable names variable_names <names(coefficients) # Create a data frame summary_data <data.frame(Variable = variable_names, Coefficient = coefficients, Std_Error = std_errors, t_value = t_values, p_value = round(p_values, digits = 6) ) # Create a flextable object from the data frame flex_table <flextable(summary_data) # Save the table to a Word file save_as_docx(flex_table, path = "C:/Users/mikel.escribano/Documents/Master/Master Thesis/rounds_sector.docx") # View model results summary(model_sector) #logit # Load the required packages #install.packages("bife") library(bife) # 'certification' is a binary variable indicating B Corp certification status # 'funding_success' is a binary variable indicating funding success paneldatafinal_10$funding_success <ifelse(paneldatafinal_10$total_raised_amount > 0, 1, 0) colnames(paneldatafinal_10) paneldatafinal_10$founded_on <as.Date(paneldatafinal_10$founded_on) paneldatafinal_10$year_founded <year(paneldatafinal_10$founded_on) paneldatafinal_10$year_founded[is.na(paneldatafinal_10$year_founded)] <-0 class(paneldatafinal_10$year) # Convert Age of the Firm to a continuous variable (if not already done) paneldatafinal_10$Age <as.numeric(paneldatafinal_10$year -paneldatafinal_10$year_founded) # Fit the logistic regression model logit_age_model <glm(funding_success ~ B_Corporation + size + Age, data = paneldatafinal_10, family = "binomial") # View the summary of the model summary(logit_age_model) coefficients <coef(summary(logit_age_model))[, "Estimate"] std_errors <coef(summary(logit_age_model))[, "Std. Error"] t_values <coef(summary(logit_age_model))[, "z value"] p_values <coef(summary(logit_age_model))[, "Pr(>|z|)"] # Extract variable names variable_names <names(coefficients) # Create a data frame summary_data <data.frame(Variable = variable_names, Coefficient = coefficients, Std_Error = std_errors, z_value = format(t_values, scientific = TRUE), p_value = format(p_values, scientific = TRUE) )

Corp certification has a positive effect on the ability of certified SMEs to attract new investments.

  Growing evidence suggests that CSR initiatives implemented by SMEs can have positive strategic effects when they are in line with their primary business objectives[START_REF] Bikefe | Corporate social responsibility (CSR) by small and medium Enterprise (SMEs): A systematic review[END_REF]. Legislation, managerial and owner values, stakeholder participation, and the business case have all been linked to the motivation for CSR engagement[START_REF] Bikefe | Corporate social responsibility (CSR) by small and medium Enterprise (SMEs): A systematic review[END_REF][START_REF] Tang | Stakeholder corporate social responsibility orientation congruence, entrepreneurial orientation and environmental performance of Chinese small and medium-sized enterprises[END_REF].Although SMEs engage in CSR practices and actions, they do so less frequently than their bigger corporate counterparts. According to scholars, this is because people are afraid of criticism, lack the necessary skills and resources, and rely on informal word-of-mouth advertising from colleagues, clients, and third parties (Lee-Wong & More, 2016).SMEs. Specifically, it is important to investigate whether B Corp certification enhances the ability of SMEs to attract new investments, which can play a crucial role in their growth and sustainability.Building upon the existing body of research, this master's thesis aims to explore the relationship between B Corp certification and the ability of certified SMEs to attract new investments. We propose

	the following hypothesis:
	H1a: B
	nature of the interactions small business executives have with their employees and other critical Impact Assessment (BIA) scores. Several types of research have supported this perspective, revealing
	stakeholders. that B Corps enjoy reputational and legitimacy gains, expanded networking opportunities, and even
	External stakeholders are increasingly requesting that SMEs publicly commit to CSR as their impact potential financial advantages.
	and overall size continue to grow (Hudson & Descubes, 2021; Kechiche & Soparnot, 2012). In addition, However, while there is evidence of the reputational benefits associated with B Corp certification,

[START_REF] Park | Corporate social responsibilities, consumer trust and corporate reputation: South Korean consumers' perspectives[END_REF]

. CSR is thought to be an effective method for enhancing financial performance, intangible assets, and competitive power. Its connection to business reputation, however, is only recently discovered and poorly understood (Šontaitė-Petkevičienė, 2015).

According to

[START_REF] Torugsa | Capabilities, proactive CSR and financial performance in SMEs: Empirical evidence from an Australian manufacturing industry sector[END_REF]

, SMEs can achieve high financial yield by proactively making progress in their CSR programmes. Juarez (2017) confirmed that social and economic CSR actions have a definite influence on the business performance of SMEs. According to

[START_REF] Roblek | Impact of knowledge management on sustainable development in the innovative economy[END_REF]

, businesses that adhere to the sustainability concept and invest in new inventive concepts like "ecoinnovation" and "social innovation" are predicted to provide superior value and social development in a competitive, low-carbon, and knowledge-based economy. Businesses may be more effective and efficient in society, which will favorably impact their business models and help them achieve higher levels of sustainability, by enhancing corporate responsibility policies and creating distinctive nonfinancial reporting

[START_REF] Vukic | Leadership for sustainability: connecting corporate responsibility reporting and strategy leadership styles, innovation, and social entrepreneurship in the era of digitalization[END_REF]

.

According to

[START_REF] Spence | Small business social responsibility: Expanding core CSR theory[END_REF]

, the extension of CSR ideas to the context of small businesses can be accomplished through the ethic of caring. According to Spence (2016), prioritizing human relationships and valuing emotions are related to the use of informal systems of governance and informal communication techniques. In contrast to their bigger counterparts, she argues that examining SMEs via a feminist lens of care allows researchers to analyze the extremely personalized and intimate it has been thought that adopting sustainable business practices may give them a competitive edge and be associated with other significant organizational outcomes (Bikefe et al., 2020; Kechiche &

Soparnot, 2012).

There is a need for studies that are specifically focused on SMEs because SMEs are more than just "little big firms" (Pedersen, 2009). The size, structure, resources, and management of large and small businesses differ, which has a big impact on their ability to carry out CSR projects, why they want to do it, and how much they do it. The resources and competencies of SMEs are severely constrained, and they frequently use informal methods and lack strategic approaches to CSR

[START_REF] Bikefe | Corporate social responsibility (CSR) by small and medium Enterprise (SMEs): A systematic review[END_REF]

. A new set of theoretical and conceptual frameworks must be created to address the competitive challenges and institutional constraints that SMEs face, according to research that has started to examine the potential value proposition of implementing sustainable business practices in SMEs.

V. RESEARCH QUESTIONS AND HYPOTHESIS

In recent years, as it was mentioned before, there has been a growing interest in understanding the impact of B Corp certification on the evolution of organizations. B Corp certification has gained significant attention as a means for businesses to demonstrate their commitment to sustainability and responsible business practices. Numerous studies (Gamble et al., 2019; Villela et al., 2019; Parket et al., 2019; amongst others) have explored the motivations behind obtaining B Corp certification and its perceived benefits. It has been observed that organizations often view certification as a tool for reputation building, with less emphasis on making substantial internal changes to improve their B it remains unclear whether such certification translates into tangible financial outcomes for certified 29

  socially and environmentally sustainable future, Jay Coen Gilbert, Bart Houlahan, and Andrew Kassoy made the decision to found the B Lab in June 2006[START_REF] Marquis | B Lab: Building a New Sector of the Economy[END_REF]. Houlahan was chosen as the new president and was in charge of operations and certification while Kassoy oversaw capital efforts and Gilbert sought out new companies to join (Cao et al., 2017). The B Lab was founded on the principle that market-based organizations can be used to further higher social and environmental goals

(Ballesteros-Sola et al., 2018).

In 2007, year of the B Lab launch, the initial number of B Corps was 43. In 2015, 16,000 companies had used the B Impact Assessment (BIA) and there were more than 1,700 B Corps existing

[START_REF] Gehman | Hidden badge of honor: How contextual distinctiveness affects category promotion among certified B Corporations[END_REF]

. In 2018, there were 2,441 certified B Corps spread in 40 different countries and 130 sectors

[START_REF] Winkler | Employees as conduits for effective stakeholder engagement: An example from B Corporations[END_REF]

.

Even though other B Lab organizations have sprouted up all over the world, the US B Lab is the only one that can provide the B Corp accreditation

[START_REF] Villela | B Corp Certification and Its Impact on Organizations Over Time[END_REF]

. The fact that businesses freely self-select into this certification by adhering to an audit of their procedures is a crucial element of it (Cao et al., 2017). Additionally, it was decided that certification would be an ongoing process to guarantee that businesses would continually raise their bar for participation and impact as they develop over time. Indeed, a revised examination of the CBCs is required after the third year of certification. This enables the businesses to improve their performance in accordance with the most recent benchmarks and criteria

[START_REF] Honeyman | The B Corp Handboo[END_REF]

.

The B Corp model does not represent a new kind of corporate legal entity, but it offers a framework that any company in the world can use to build a better business. According to Honeyman and Jana (2019), this framework is relevant for business-to-business (B2B) or a business-to-consumer (B2C) business, a sole local proprietor or a global brand, a start-up or a third-generation family business, a limited liability company or a partnership, an employee-owned company or a cooperative, a C corporation or an S corporation, or even when deciding on the right structure for a new business.

Through the certification process, companies must demonstrate and show their social and environmental performance, legal accountability, and public transparency. The BIA is the certification tool of the B Lab measuring the impact of the companies desiring to certify as B Corps (Bauer & Umlas, 2017). The BIA is a self-administered synthesis of CSR best practices concerning various impact areas

[START_REF] Villela | B Corp Certification and Its Impact on Organizations Over Time[END_REF]

on which a set of questions are incorporated. This set depends on 3 factors: company's size (number of employees and annual sales), sector (type of activities) and geographical location

Table 1 Overview of the field B_Corporation

 1

	B Corporation	Firms	Percentage
	1	3200	51.66%
	0	2993	48.34%
	Total	6193	100%

Table 2 Summary of the field Overall Score

 2

Table 3 The following are the top 10 most frequently occurring categories among our sample firms.

 3

	Category	Frequency
	Professional Services	1300
	Software	1070
	Sales and Marketing	880
	Commerce and Shopping	863
	Information Technology	747
	Design	695
	Media and Entertainment	693
	Financial Services	684
	Health Care	635
	Internet Services	596

  14. Non-Equity Assistance: Funding or support provided to startups or organizations without requiring equity in return, such as mentorship, networking, or business development assistance. 15. Post-IPO Equity: Additional equity financing raised by a company after its initial public offering (IPO) to fund further growth or strategic initiatives. 16. Secondary Market: The market where existing shares of privately held companies are bought and sold between investors, often providing liquidity to early investors and employees. 17. Undisclosed: Funding rounds where the specific details or terms are not publicly disclosed. 18. Corporate Round: Funding provided by established corporations to startups as a strategic investment or partnership opportunity. 19. Post-IPO Debt: Debt financing obtained by a company after going public, which can be used for various purposes, such as refinancing existing debt or funding acquisitions. 𝑌 {𝑖𝑡} = 𝛼 + 𝛽 {1} 𝑋 {1𝑖𝑡} + 𝛽 {2} 𝑋 {2𝑖𝑡} + ⋯ + 𝛽 {𝑛} 𝑋 {𝑛𝑖𝑡} + 𝜆𝐹𝑖𝑟𝑚 {𝑖} + 𝛿 * 𝑌𝑒𝑎𝑟 {𝑡} + 𝜖 {𝑖𝑡}

III. MODELS

A. Fixed effects model

Understanding how BCorp certification affects financing quantities is critical for evaluating the certification's efficacy in fostering sustainable and socially responsible practices among SMEs. A panel data analysis is used to investigate this association, integrating company fixed effects and year fixed effects to account for unobserved heterogeneity and time-specific characteristics. To evaluate the link between BCorp certification and financing levels, the fixed effects model is used. The following is the general equation for the fixed effects model with panel data applied to our study:

Table 6 Model estimations for the effect of B Corp scores on funding rounds

 6

	Variable	Estimate	Std.Error	t-value	Pr(>|t|)
	Overall_score	-4771.8	6284.5	-0.7593	0.4477

Table 7 Model estimations for the analysis of the relationship between Equity Crowfunding and the dependent variables

 7

	Variable	Coefficient	Std_Error	t_value	p_value
	(Intercept) overall_score certification_cycle size 1-9 size 10-49 size 50-249 year_rounds catgr_Professional.Services catgr_Apps catgr_Design catgr_Information.Technology catgr_Mobile catgr_Privacy.and.Security catgr_Software catgr_Agriculture.and.Farming catgr_Community.and.Lifestyle catgr_Commerce.and.Shopping catgr_Manufacturing catgr_Real.Estate catgr_Artificial.Intelligence catgr_Data.and.Analytics catgr_Science.and.Engineering catgr_Other catgr_Consumer.Goods catgr_Transportation catgr_Media.and.Entertainment catgr_Music.and.Audio catgr_Financial.Services catgr_Payments catgr_Food.and.Beverage catgr_Sales.and.Marketing catgr_Education catgr_Events catgr_Internet.Services catgr_Administrative.Services catgr_Hardware catgr_Navigation.and.Mapping catgr_Gaming catgr_Health.Care catgr_Content.and.Publishing catgr_Sports catgr_Consumer.Electronics catgr_Advertising catgr_Clothing.and.Apparel catgr_Government.and.Military catgr_Sustainability catgr_Lending.and.Investments catgr_Travel.and.Tourism catgr_Energy catgr_Natural.Resources catgr_Video catgr_Messaging.and.Telecommunications catgr_Biotechnology	-35,096,459.66698 -30.58338 14,945.50952 97,179.06029 218,182.82469 116,325.52355 17,357.98843 -6,277.80722 261,116.06017 129,064.66591 -98,108.14203 -78,773.70948 199,345.63600 -18,278.33637 9,714.62667 47,994.82439 -81,191.31097 320,961.93999 -18,740.97622 5,029.28042 25,698.43519 -152,235.82144 -152,108.76594 17,651.59305 -148,590.27565 -73,523.75734 97,090.11311 25,035.71936 -77,873.56633 200,085.63904 -214,681.99478 -14,660.09244 47,048.28331 88,239.33271 -14,499.23542 143,232.92671 268,050.74844 -32,981.17739 -27,772.64272 -77,470.39326 -145,797.94596 -124,951.44121 192,625.27639 246,392.64323 -265,821.37649 111,964.07807 98,913.31109 -76,654.09360 -210,399.57175 5,199.31029 -67,361.56048 58,762.53596 -51,892.51464	28,459,904.634 2,112.154 50,308.800 131,896.312 132,396.702 148,367.214 14,065.331 108,442.441 173,755.951 185,460.922 108,574.700 166,194.918 499,832.998 90,771.025 131,224.177 93,807.082 89,330.737 107,779.621 117,894.836 273,818.650 191,770.736 145,066.117 84,980.103 96,450.190 108,840.034 241,820.473 829,871.621 105,656.288 290,228.174 81,338.355 188,646.668 137,494.393 390,851.313 109,207.465 129,416.031 138,482.378 378,869.613 247,983.822 83,616.858 781,340.156 154,805.036 192,396.939 326,031.905 205,212.925 746,130.563 87,725.521 166,240.311 158,207.963 130,901.252 138,801.122 917,009.614 760,087.840 221,153.413	-1.23318964 -0.01447971 0.29707545 0.73678376 1.64794758 0.78403793 1.23409737 -0.05789069 1.50277478 0.69591299 -0.90360039 -0.47398386 0.39882448 -0.20136752 0.07403077 0.51163327 -0.90888437 2.97794645 -0.15896350 0.01836719 0.13400603 -1.04942370 -1.78993389 0.18301253 -1.36521710 -0.30404273 0.11699414 0.23695437 -0.26831842 2.45991744 -1.13801106 -0.10662320 0.12037387 0.80799726 -0.11203585 1.03430436 0.70750131 -0.13299729 -0.33214167 -0.09915066 -0.94181655 -0.64944610 0.59081726 1.20066825 -0.35626657 1.27629996 0.59500196 -0.48451476 -1.60731519 0.03745871 -0.07345786 0.07731019 -0.23464487	0.217929889 0.988451503 0.766499362 0.461507232 0.099824015 0.433290071 0.217591708 0.953852660 0.133360012 0.486720395 0.366526382 0.635663117 0.690147377 0.840471252 0.941007610 0.609073308 0.363732231 0.003004798 0.873744689 0.985351295 0.893437339 0.294354882 0.073908066 0.854842580 0.172634979 0.761188076 0.906899144 0.812763344 0.788535363 0.014143890 0.255515483 0.915119266 0.904222446 0.419373892 0.910827944 0.301360513 0.479496688 0.894234752 0.739884340 0.921047784 0.346620050 0.516268735 0.554838608 0.230296768 0.721751126 0.202283956 0.552039518 0.628176155 0.108448079 0.970130227 0.941463318 0.938399459 0.814554806
	---Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
	Residuals:

Table 8 Model estimations for the analysis of the relationship between Seed and the dependent variables

 8

	Variable	Coefficient	Std_Error	t_value	p_value
	(Intercept) overall_score certification_cycle size 1-9	-116,405,271.162 1,334.244 -16,231.312 193,075.768	55,799,934.924 4,141.197 98,637.989 258,602.610	-2.08611840 0.32218802 -0.16455437 0.74661183	0.03733891 0.74740899 0.86934351 0.45555520

Table 9 Model estimations for the analysis of the relationship between Series A and the dependent variables

 9

	Variable	Coefficient	Std_Error	t_value	p_value
	(Intercept) overall_score certification_cycle size 1-9 size 10-49 size 50-249 year_rounds catgr_Professional.Services catgr_Apps catgr_Design catgr_Information.Technology catgr_Mobile catgr_Privacy.and.Security catgr_Software catgr_Agriculture.and.Farming catgr_Community.and.Lifestyle catgr_Commerce.and.Shopping catgr_Manufacturing catgr_Real.Estate catgr_Artificial.Intelligence catgr_Data.and.Analytics catgr_Science.and.Engineering catgr_Other catgr_Consumer.Goods catgr_Transportation catgr_Media.and.Entertainment catgr_Music.and.Audio catgr_Financial.Services catgr_Payments catgr_Food.and.Beverage catgr_Sales.and.Marketing catgr_Education catgr_Events catgr_Internet.Services catgr_Administrative.Services catgr_Hardware catgr_Navigation.and.Mapping catgr_Gaming catgr_Health.Care catgr_Content.and.Publishing catgr_Sports catgr_Consumer.Electronics catgr_Advertising catgr_Clothing.and.Apparel catgr_Government.and.Military catgr_Sustainability catgr_Lending.and.Investments catgr_Travel.and.Tourism catgr_Energy catgr_Natural.Resources catgr_Video catgr_Messaging.and.Telecommunications catgr_Biotechnology	-144,938,599.63 -16,885.68 -90,937.11 209,686.29 968,105.02 401,498.35 72,715.13 470,183.47 456,508.56 -53,592.27 -971,757.92 114,285.77 -3,449,428.62 1,051,666.68 -638,281.58 404,547.75 -194,155.02 -500,576.02 122,487.86 -92,699.71 294,067.67 1,474,159.20 -762,000.68 191,900.64 1,526,898.38 -1,065,996.33 545,810.40 1,025,632.24 1,104,602.36 222,026.61 -937,599.91 -1,168,971.41 2,188,867.42 69,331.32 -1,037,880.15 -1,240,558.28 -1,648,729.31 583,117.73 -259,679.76 529,693.32 439,324.27 853,894.44 1,378,232.12 -458,191.81 -1,301,867.65 685,305.52 1,266,649.21 442,667.35 -872,368.69 353,134.70 -528,410.01 -726,300.81 712,504.52	146,229,871.50 10,852.46 258,491.71 677,696.60 680,267.66 762,325.77 72,269.10 557,188.24 892,775.67 952,917.00 557,867.80 853,926.32 2,568,192.55 466,390.72 674,243.11 481,990.29 458,990.37 553,782.60 605,755.61 1,406,907.95 985,337.46 745,364.39 436,636.37 495,570.84 559,231.12 1,242,498.07 4,263,964.41 542,872.71 1,491,221.74 417,924.70 969,285.68 706,460.11 2,008,233.62 561,119.01 664,952.67 711,536.48 1,946,670.43 1,274,165.99 429,631.88 4,014,604.83 795,403.95 988,554.95 1,675,184.94 1,054,404.79 3,833,694.38 450,742.61 854,159.55 812,888.53 672,583.89 713,174.22 4,711,688.24 3,905,408.28 1,136,308.63	-0.99116958 -1.55593037 -0.35179893 0.30941027 1.42312369 0.52667555 1.00617187 0.84385031 0.51133625 -0.05624022 -1.74191434 0.13383563 -1.34313474 2.25490482 -0.94666385 0.83932760 -0.42300456 -0.90392153 0.20220673 -0.06588897 0.29844361 1.97776982 -1.74516080 0.38723151 2.73035304 -0.85794606 0.12800538 1.88926837 0.74073649 0.53125985 -0.96731019 -1.65468849 1.08994661 0.12355904 -1.56083312 -1.74349217 -0.84694835 0.45764660 -0.60442386 0.13194158 0.55232850 0.86378045 0.82273430 -0.43455020 -0.33958566 1.52039214 1.48291876 0.54456095 -1.29704072 0.49515909 -0.11214876 -0.18597308 0.62703433	0.32195431 0.12018855 0.72509773 0.75710396 0.15515757 0.59859016 0.31468975 0.39904894 0.60928112 0.95516691 0.08197426 0.89357203 0.17967513 0.02445564 0.34414538 0.40157961 0.67242528 0.36635619 0.83981541 0.94748554 0.76545551 0.04835682 0.08140727 0.69870563 0.00649002 0.39122365 0.89818245 0.05927984 0.45910818 0.59541170 0.33373189 0.09844780 0.27612162 0.90170083 0.11902702 0.08169829 0.39732101 0.64735212 0.54576289 0.89506941 0.58090427 0.38801219 0.41094667 0.66402640 0.73427312 0.12887591 0.13855794 0.58623352 0.19505579 0.62064732 0.91073845 0.85252117 0.53084658
	---Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
	Residuals:

Overall Score (β₁): The overall_score coefficient estimate is -16886. It is not statistically significant (p-value = 0.12019). It implies a negative link between overall score and amount raised.

Table 10 Model estimations for the analysis of the relationship between Debt financing and the dependent variables

 10

	Variable	Coefficient	Std_Error	t_value	p_value

Table 11 Model estimations for the analysis of the relationship between Overall Score and the subscores

 11

	Coefficients:
		Estimate	Std. Error	t-value	Pr(>|t|)
	(Intercept) impact_area_community impact_area_customers impact_area_environment impact_area_governance impact_area_workers impact_area_community_na_score impact_area_customers_na_score impact_area_environment_na_score impact_area_workers_na_score ia_community_it_civic_engagement_giving ia_community_it_diversity_equity_inclusion ia_community_it_diversity_inclusion ia_community_it_economic_impact ia_community_it_job_creation ia_community_it_local_involvement ia_community_it_suppliers_distributors_product ia_community_it_supply_chain_management ia_customers_it_customer_stewardship ia_environment_it_air_climate ia_environment_it_environmental_management ia_environment_it_inputs ia_environment_it_land_life ia_environment_it_land_office_plant ia_environment_it_outputs ia_environment_it_water ia_governance_it_corporate_accountability ia_governance_it_ethics_transparency ia_governance_it_mission_engagement ia_governance_it_mission_locked	2.6630e-01 9.9994e-01 1.0004e+00 1.0003e+00 6.3390e-01 9.9991e-01 1.0051e+00 1.1836e+00 1.0048e+00 1.0066e+00 1.9928e-03 2.1656e-03 3.1577e-03 -2.3808e-03 -3.8825e-05 -1.9295e-03 1.0178e-03 -2.4694e-03 -9.9418e-04 2.4064e-03 -2.6085e-03 4.1391e-03 -1.1954e-03 -5.8233e-03 -1.9685e-03 -3.7041e-03 1.3802e-03 3.7494e-01 3.5533e-01 3.7010e-01	2.4486e-02 3.5092e-04 2.3292e-04 3.4170e-04 5.3033e-02 5.2999e-04 3.2148e-03 6.0377e-03 1.2883e-03 1.4918e-03 1.7387e-03 1.6556e-03 1.5777e-03 1.3462e-03 1.4824e-03 1.0971e-03 1.3551e-03 1.8994e-03 2.6096e-03 1.5880e-03 1.8036e-03 1.8091e-03 1.8335e-03 1.5029e-03 2.2381e-03 3.9603e-03 3.1883e-03 5.3094e-02 5.2968e-02 5.3025e-02	10.8755 2849.4485 4295.0649 2927.2532 11.9529 1886.6714 312.6440 196.0280 779.8817 674.7296 1.1461 1.3081 2.0014 -1.7685 -0.0262 -1.7587 0.7511 -1.3001 -0.3810 1.5154 -1.4463 2.2880 -0.6520 -3.8747 -0.8795 -0.9353 0.4329 7.0618 6.7084 6.9797	<2.2e-16** <2.2e-16** <2.2e-16** <2.2e-16** <2.2e-16** <2.2e-16** <2.2e-16** <2.2e-16** <2.2e-16** <2.2e-16** 0.2518898 0.1910066 0.0454801* 0.0771308 0.9791076 0.0787771 0.4526864 0.1937155 0.7032622 0.1298290 0.1482526 0.0222436* 0.5144681 0.0001102*** 0.3792190 0.3497548 0.6651514 2.264e-12*** 2.559e-11*** 4.018e-12***

B Corp subscores and their link to the B Corp score. The regression model produced interesting results, revealing the links between predictor factors and B Corp score. The computed coefficients for each variable represent the predicted change in CSR performance for a one-unit increase in the related predictor variable while maintaining other factors fixed. Here are the results of the model. • Total Sum of Squares: 592000 • Residual Sum of Squares: 25.785 • R-Squared: 0.99996 • Adj. R-Squared: 0.99996 • F-statistic: 1198660 on 38 and 1984 DF, p-value: < 2.22e-16

Table 12 Model estimations for the analysis of the relationship between Total fundings and the subscores

 12

	Estimate	Std. Error	t-value	Pr(>|t|)

Table 13 Model estimations for the analysis of the relationship between Total funding amount and the different sectors and B_Corporation

 13

	Estimate	Std. Error	t-value	Pr(>|t|)

---Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table 14 Model estimations for the analysis of the relationship between the probability of funding success and dependent variables

 14

	Estimate	Std. Error	t-value	Pr(>|t|)

Table 13

 13 reveal that obtaining a B Corp Certification, because of the positive coefficient has a positive impact on the overall amount raised by firms. However, it is important to note that the model used in table 13 exhibited relatively weak predictive power, as indicated by the low R-squared value of 0.0079. This implies that the variables included in the model collectively explain only around 0.79% of the total variation in the total amount raised by firms. Despite the model's weak explanatory power, the results show that B Corp Certification has a significant beneficial impact on the amount of finance secured by enterprises. While the model may not capture all the factors driving fundraising outcomes, the data suggest that B Corp Certification plays an important role in encouraging higher finance for firms. The findings in Table15demonstrate that B Corp certification has a statistically significant positive effect on SMEs' probability of funding success. This implies that SMEs with B Corp certification are more likely to secure funding to those without certification. Other results of that model are that firm size influences funding performance, with larger SMEs having higher odds of obtaining funding. Finally, firm age shows a negative and statistically significant effect on funding success. As the firm's age increases, the chances of obtaining capital slightly diminish. This suggests that older organization may face specific constrains related to flexibility, creativity, and reputation.

TABLE 1 OVERVIEW

 1 OF THE FIELD B_CORPORATION .........................................................................................................

TABLE 2 SUMMARY

 2 OF THE FIELD OVERALL SCORE ............................................................................................................

TABLE 3 THE

 3 FOLLOWING ARE THE TOP 10 MOST FREQUENTLY OCCURRING CATEGORIES AMONG OUR SAMPLE FIRMS. ...................

TABLE 4 SUMMARY

 4 OF THE FIELD TOTAL_FUNDING_USD .................................................................................................

TABLE 5 AMOUNT

 5 RAISED AND NUMBER OF INVESTMENTS BY INVESTOR TYPES .......................................................................

TABLE 6 MODEL

 6 ESTIMATIONS FOR THE EFFECT OF B CORP SCORES ON FUNDING ROUNDS ........................................................

TABLE 7 MODEL

 7 ESTIMATIONS FOR THE ANALYSIS OF THE RELATIONSHIP BETWEEN EQUITY CROWFUNDING AND THE DEPENDENT VARIABLES ........................................................................................................................................................TABLE 8 MODEL ESTIMATIONS FOR THE ANALYSIS OF THE RELATIONSHIP BETWEEN SEED AND THE DEPENDENT VARIABLES .............. TABLE 9 MODEL ESTIMATIONS FOR THE ANALYSIS OF THE RELATIONSHIP BETWEEN SERIES A AND THE DEPENDENT VARIABLES .........

TABLE 10 MODEL

 10 ESTIMATIONS FOR THE ANALYSIS OF THE RELATIONSHIP BETWEEN DEBT FINANCING AND THE DEPENDENT VARIABLES ......................................................................................................................................................................

TABLE 11 MODEL

 11 ESTIMATIONS FOR THE ANALYSIS OF THE RELATIONSHIP BETWEEN OVERALL SCORE AND THE SUBSCORES .............

TABLE 12 MODEL

 12 ESTIMATIONS FOR THE ANALYSIS OF THE RELATIONSHIP BETWEEN TOTAL FUNDINGS AND THE SUBSCORES ........... TABLE 13 MODEL ESTIMATIONS FOR THE ANALYSIS OF THE RELATIONSHIP BETWEEN TOTAL FUNDING AMOUNT AND THE DIFFERENT SECTORS AND B_CORPORATION ........................................................................................................................... TABLE 14 MODEL ESTIMATIONS FOR THE ANALYSIS OF THE RELATIONSHIP BETWEEN THE PROBABILITY OF FUNDING SUCCESS AND DEPENDENT VARIABLES .......................................................................................................................................

TABLE OF FIGURES

 OF FIGURE 1: SUM OF THE COMPANIES THAT HAVE FIRSTLY BECOME B CORPS FOR EACH YEAR SINCE B CORP BEGAN IN 2007. ............. FIGURE 2 CERTIFIED BCORPS SME BY COUNTRY .............................................................................................................. FIGURE 3 SMES AND FIRST FUNDING ROUND...................................................................................................................FIGURE 4 NUMBER OF B CORP'S THAT HAD AT LEAST ONE FUNDING ROUND PER YEAR .............................................................

	FROM orga_fi
	LEFT JOIN BCORP
	ON BCORP.company_name = orga_fi.name
	OR BCORP.test = orga_fi.domain'
	)

table as

 as

	investment_count = n()
	) %>%
	arrange(desc(total_amount_invested))
	a data frame
	summary_table_fund <-data.frame(
	Statistic = names(summary_stats_fund),
	Value = summary_stats_fund,
	stringsAsFactors = FALSE
	)
	# Remove row names
	row.names(summary_table_fund) <-NULL
	# Add the sign "$" to the values in the Value column
	summary_table_fund$Value <-paste0(summary_table_fund$Value, "$")
	# Create the summary table with kableExtra
	summary_table_fund_kable <-kable(summary_table_fund, "html") %>%
	kable_styling(bootstrap_options = "striped", full_width = FALSE) %>%
	kable_paper(full_width = TRUE) %>%
	add_header_above(c("Total Funding" = 2))
	# Print the table
	summary_table_fund_kable
	#investors
	setwd("C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis")
	dataset_investments <-read.csv("IJ_investments.csv")
	# Calculate the average number of investors by company_id
	media_inversores <-dataset_investments %>%
	group_by(company_id) %>%
	summarize(media_inversores = mean(investment_count, na.rm = TRUE))
	print(media_inversores)
	colnames(dataset_investments)
	# Create the table of the amount invested by type of investor
	investment_table <-dataset_investments %>%
	filter(!is.na(total_funding_usd.1) & investor_types != "") %>%
	group_by(investor_types) %>%
	summarise(
	total_amount_invested = sum(total_funding_usd.1, na.rm = TRUE),

1])/17)

  [, -# Sort the table by the "Frequency" column from highest to lowest tabla <tabla[order(-tabla$Frequency), ] Word file save_as_docx(flex_table, path = "C:/Users/mikel.escribano/Documents/Master/Master Thesis/sum_crowf.docx") coef(summary(model_seed))[, "Estimate"] std_errors <coef(summary(model_seed))[, "Std. Error"] t_values <coef(summary(model_seed))[, "t-value"] p_values <coef(summary(model_seed))[, "Pr(>|t|)"] coef(summary(model_seriea))[, "Estimate"] std_errors <coef(summary(model_seriea))[, "Std. Error"] t_values <coef(summary(model_seriea))[, "t-value"] p_values <coef(summary(model_seriea))[, "Pr(>|t|)"] Save the table to a Word file save_as_docx(flex_table, path = "C:/Users/mikel.escribano/Documents/Master/Master Thesis/sum_debt.docx")

	# Limit the table to the top 10 most frequent categories tabla <-head(tabla, 11) catgr_Consumer.Goods catgr_Transportation catgr_Media.and.Entertainment catgr_Music.and.Audio catgr_Platforms catgr_Financial.Services catgr_Payments catgr_Food.and.Beverage catgr_Sales.and.Marketing # Estimate the regression model model_seed <-plm(formula = seed_raised_amount_usd ~ overall_score + + + + + + + + + + certification_cycle + size + year_rounds+ catgr_Professional.Services + catgr_Apps + catgr_Health.Care + catgr_Content.and.Publishing + catgr_Commerce.and.Shopping + catgr_Manufacturing + flex_table <-flextable(summary_data) # Create a data frame ia_workers_it_compensation_wages + ia_workers_it_engagement_satisfaction + catgr_Health.Care + catgr_Information.Technology + coefficients <-# Extract variable names variable_names <-names(coefficients) # Create a data frame catgr_Sports + catgr_Consumer.Electronics + catgr_Advertising + catgr_Clothing.and.Apparel + catgr_Government.and.Military + catgr_Sustainability + catgr_Lending.and.Investments + catgr_Real.Estate + catgr_Artificial.Intelligence + catgr_Data.and.Analytics + catgr_Science.and.Engineering + catgr_Other + catgr_Consumer.Goods + catgr_Transportation + summary_data <-data.frame(Variable = variable_names, Coefficient = coefficients, Std_Error = std_errors, t_value = t_values, p_value = p_values) ia_workers_it_financial_security + ia_workers_it_health_wellness_safety + ia_workers_it_management_worker_communication + ia_workers_it_training_education + ia_workers_it_worker_ownership, data = paneldatafinal_10, catgr_Advertising + catgr_Consumer.Goods + catgr_Real.Estate + catgr_Manufacturing + catgr_Education + catgr_Lending.and.Investments + # #scores # Create a flextable object from the data frame model = "pooling",na.action = na.omit) catgr_Administrative.Services +
	catgr_Education catgr_Design summary_data <-data.frame(Variable = variable_names, catgr_Travel.and.Tourism catgr_Media.and.Entertainment flex_table <-flextable(summary_data) catgr_Clothing.and.Apparel +	+ +	+	+
	# Print the resulting catgr_Information.Technology catgr_Events Coefficient = coefficients, catgr_Energy catgr_Music.and.Audio catgr_Energy +	+ +	+	+
	# Change row names # Estimate the regression model catgr_Internet.Services catgr_Mobile Std_Error = std_errors, catgr_Natural.Resources catgr_Platforms # Save the table to a Word file coefficients <-coef(summary(model_scores_round))[, "Estimate"] + + + + catgr_Science.and.Engineering +
	tabla <-rownames_to_column(tabla, "Row") catgr_Administrative.Services catgr_Privacy.and.Security t_value = t_values, catgr_Video catgr_Financial.Services model_scores <-plm(formula = overall_score ~ impact_area_community + + + + + save_as_docx(flex_table, path = std_errors <-coef(summary(model_scores_round))[, "Std. Error"] catgr_Hardware +
	catgr_Hardware catgr_Software p_value = p_values) catgr_Messaging.and.Telecommunications+ + catgr_Payments + impact_area_customers + "C:/Users/mikel.escribano/Documents/Master/Master Thesis/scores.docx") + t_values <-coef(summary(model_scores_round))[, "t-value"] catgr_Data.and.Analytics +
	# remove the first column catgr_Navigation.and.Mapping catgr_Agriculture.and.Farming catgr_Biotechnology , data = paneldatafinal_10, + + catgr_Food.and.Beverage + impact_area_environment + p_values <-coef(summary(model_scores_round))[, "Pr(>|t|)"] catgr_Transportation +
	tabla <-select(tabla, -1) catgr_Gaming catgr_Community.and.Lifestyle # Create a flextable object from the data frame model = "pooling") catgr_Sales.and.Marketing impact_area_governance + catgr_Agriculture.and.Farming +	+ +	+
	catgr_Health.Care catgr_Commerce.and.Shopping flex_table <-flextable(summary_data) catgr_Education impact_area_workers + # Extract variable names catgr_Sports	+	+ +	+
	write.csv(tabla, "C:\\Users\\mikel.escribano\\Documents\\Master\\Master catgr_Content.and.Publishing + catgr_Manufacturing + catgr_Events + impact_area_community_na_score + variable_names <-names(coefficients) catgr_Apps +
	Thesis/Category_list.csv", row.names = FALSE) catgr_Sports catgr_Real.Estate # Save the table to a Word file catgr_Internet.Services impact_area_customers_na_score + # View model results catgr_Content.and.Publishing +	+ +	+
	catgr_Consumer.Electronics catgr_Artificial.Intelligence save_as_docx(flex_table, path = # View model results catgr_Administrative.Services impact_area_environment_na_score + summary(model_scores) # Create a data frame catgr_Travel.and.Tourism +	+ +	+
	catgr_Advertising catgr_Data.and.Analytics "C:/Users/mikel.escribano/Documents/Master/Master Thesis/sum_seed.docx") + + summary(model_seriea) catgr_Hardware + impact_area_governance_na_score + summary_data <-data.frame(Variable = variable_names, catgr_Natural.Resources +
	unique(paneldatafinal_10$B_Corporation) catgr_Clothing.and.Apparel catgr_Science.and.Engineering catgr_Navigation.and.Mapping impact_area_workers_na_score + Coefficient = coefficients, catgr_Consumer.Electronics +	+ +	+
	write.csv(paneldatafinal_10, "C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis/PanelData10.csv", row.names = FALSE) # Fit the fixed effects model with firm and year fixed effects catgr_Government.and.Military + catgr_Sustainability + catgr_Lending.and.Investments + catgr_Travel.and.Tourism + catgr_Energy + catgr_Natural.Resources + catgr_Other + catgr_Consumer.Goods + catgr_Transportation + catgr_Media.and.Entertainment + catgr_Music.and.Audio + catgr_Platforms + #series A funding # Estimate the regression model model_seriea <-plm(formula = series_a_raised_amount_usd ~ overall_score + catgr_Gaming + catgr_Health.Care + catgr_Content.and.Publishing + catgr_Sports + catgr_Consumer.Electronics + ia_community_it_civic_engagement_giving + ia_community_it_diversity_equity_inclusion + ia_community_it_diversity_inclusion + ia_community_it_economic_impact + ia_community_it_job_creation + #scores # Estimate the regression model Std_Error = std_errors, t_value = t_values, p_value = p_values) # Create a flextable object from the data frame catgr_Events + catgr_Video + catgr_Biotechnology + catgr_Artificial.Intelligence + catgr_Government.and.Military + coefficients <-# Extract variable names catgr_Advertising + ia_community_it_local_involvement + model_scores_round <-plm(formula = total_raised_amount ~ flex_table <-flextable(summary_data) catgr_Privacy.and.Security +
	model <-plm(total_raised_amount ~ overall_score, data = catgr_Video catgr_Financial.Services certification_cycle + size+year_rounds + + + variable_names <-names(coefficients) catgr_Clothing.and.Apparel + ia_community_it_suppliers_distributors_product + impact_area_community + catgr_Payments +
	paneldatafinal_10, model = "within", effect = "twoways") catgr_Messaging.and.Telecommunications+ catgr_Payments + catgr_Professional.Services + catgr_Government.and.Military + ia_community_it_supply_chain_management + impact_area_customers + # Save the table to a Word file catgr_Messaging.and.Telecommunications +
	catgr_Biotechnology catgr_Food.and.Beverage catgr_Apps # Create a data frame catgr_Sustainability ia_customers_it_customer_stewardship + + + + impact_area_environment + save_as_docx(flex_table, path = catgr_Music.and.Audio	, data = +
	# Print the model summary paneldatafinal_10, catgr_Sales.and.Marketing catgr_Design summary_data <-data.frame(Variable = variable_names, catgr_Lending.and.Investments ia_environment_it_air_climate + impact_area_governance + "C:/Users/mikel.escribano/Documents/Master/Master Thesis/scores-+ + + catgr_Gaming +
	summary(model) rounds.docx")	model = "pooling") catgr_Education catgr_Information.Technology Coefficient = coefficients, catgr_Travel.and.Tourism ia_environment_it_environmental_management + + + + impact_area_workers + catgr_Navigation.and.Mapping +
		catgr_Events catgr_Mobile Std_Error = std_errors, catgr_Energy ia_environment_it_inputs + impact_area_community_na_score + + + + catgr_Platforms + B_Corporation	,
	summary_table <-summary(model) catgr_Internet.Services catgr_Privacy.and.Security t_value = t_values, catgr_Natural.Resources ia_environment_it_land_life + impact_area_customers_na_score + + + + data = paneldatafinal_10,
	catgr_Administrative.Services catgr_Software p_value = p_values) catgr_Video ia_environment_it_land_office_plant + + + impact_area_environment_na_score + + # View model results model = "pooling")
	write.csv(summary_table, # View model results catgr_Hardware catgr_Agriculture.and.Farming catgr_Messaging.and.Telecommunications+ + ia_environment_it_outputs + impact_area_governance_na_score + + summary(model_scores_round)
	"C:\\Users\\mikel.escribano\\Documents\\Master\\Master summary(model_crowd_10) catgr_Navigation.and.Mapping catgr_Community.and.Lifestyle # Create a flextable object from the data frame catgr_Biotechnology, data = paneldatafinal_10, + + ia_environment_it_water + impact_area_workers_na_score +
	Thesis/Psummary_table.csv", row.names = FALSE) class(paneldatafinal_10$year_rounds) catgr_Gaming catgr_Commerce.and.Shopping flex_table <-flextable(summary_data) model = "pooling") ia_governance_it_corporate_accountability + + + ia_community_it_civic_engagement_giving + # Change values of the field based on conditions
	paneldatafinal_10$year_rounds <-as.integer(paneldatafinal_10$year_rounds) catgr_Health.Care + catgr_Manufacturing + ia_governance_it_ethics_transparency + ia_community_it_diversity_equity_inclusion + paneldatafinal_10$B_Corporation[paneldatafinal_10$B_Corporation == 1] <-
	catgr_Content.and.Publishing catgr_Real.Estate # Save the table to a Word file ia_governance_it_mission_engagement + + ia_community_it_diversity_inclusion + + "yes"
	# Estimate the regression model catgr_Sports catgr_Artificial.Intelligence save_as_docx(flex_table, path = ia_governance_it_mission_locked + ia_community_it_economic_impact + + + paneldatafinal_10$B_Corporation[paneldatafinal_10$B_Corporation == 0] <-
	model_crowd_10 <-plm(formula = equity_crowdfunding_raised_amount_usd ~ coefficients <-coef(summary(model_crowd_10))[, "Estimate"] catgr_Consumer.Electronics + catgr_Data.and.Analytics + "C:/Users/mikel.escribano/Documents/Master/Master Thesis/sum_sseriea.docx") # View model results ia_governance_it_transparency + ia_community_it_job_creation + "no"
	overall_score + std_errors <-coef(summary(model_crowd_10))[, "Std. Error"] catgr_Advertising + catgr_Science.and.Engineering summary(model_debt) ia_workers_it_career_development + ia_community_it_local_involvement + + paneldatafinal_10 <-subset(paneldatafinal_10, select = -B_Corporation)
	certification_cycle + size + year_rounds + t_values <-coef(summary(model_crowd_10))[, "t-value"] catgr_Clothing.and.Apparel + catgr_Other + ia_workers_it_compensation_wages + ia_community_it_suppliers_distributors_product paneldatafinal_10$B_Corporation <-
	catgr_Professional.Services p_values <-coef(summary(model_crowd_10))[, "Pr(>|t|)"] + catgr_Government.and.Military catgr_Consumer.Goods coefficients <-coef(summary(model_debt))[, "Estimate"] ia_workers_it_engagement_satisfaction + + + + ifelse(!is.na(paneldatafinal_10$overall_score), 1, 0)
	catgr_Apps catgr_Sustainability catgr_Transportation std_errors <-coef(summary(model_debt))[, "Std. Error"] #, debt financing ia_workers_it_financial_security + ia_community_it_supply_chain_management + + + +
	catgr_Design # Extract variable names catgr_Lending.and.Investments catgr_Media.and.Entertainment t_values <-coef(summary(model_debt))[, "t-value"] ia_workers_it_health_wellness_safety + + + ia_customers_it_customer_stewardship + + # Estimate the regression model
	catgr_Information.Technology variable_names <-names(coefficients) catgr_Travel.and.Tourism catgr_Music.and.Audio # Estimate the regression model p_values <-coef(summary(model_debt))[, "Pr(>|t|)"] ia_workers_it_management_worker_communication + + + + ia_environment_it_air_climate + model_sector <-plm(formula = total_raised_amount ~
	catgr_Mobile catgr_Energy catgr_Platforms model_debt <-plm(formula = debt_financing_raised_amount_usd ~ overall_score + + + ia_workers_it_training_education + ia_environment_it_environmental_management + catgr_Professional.Services +
	catgr_Privacy.and.Security catgr_Natural.Resources # Create a data frame catgr_Financial.Services + # Extract variable names ia_workers_it_worker_ownership, ia_environment_it_inputs + catgr_Commerce.and.Shopping +	+	+	+
	catgr_Software summary_data <-data.frame(Variable = variable_names, catgr_Video catgr_Payments certification_cycle + size + year_rounds + + + + variable_names <-names(coefficients) data = paneldatafinal_10, ia_environment_it_land_life + catgr_Other +
	catgr_Agriculture.and.Farming Coefficient = coefficients, catgr_Messaging.and.Telecommunications+ catgr_Food.and.Beverage catgr_Professional.Services + model = "pooling") ia_environment_it_land_office_plant + + + catgr_Sales.and.Marketing +
	catgr_Community.and.Lifestyle Std_Error = std_errors, catgr_Biotechnology , data = paneldatafinal_10, + catgr_Sales.and.Marketing + catgr_Apps + # Create a data frame ia_environment_it_outputs + catgr_Software +
	catgr_Commerce.and.Shopping t_value = t_values, model = "pooling") catgr_Education catgr_Design summary_data <-data.frame(Variable = variable_names, coefficients <-coef(summary(model_scores))[, "Estimate"] ia_environment_it_water + catgr_Food.and.Beverage +	+	+	+
	catgr_Manufacturing p_value = p_values) catgr_Events catgr_Information.Technology Coefficient = coefficients, std_errors <-coef(summary(model_scores))[, "Std. Error"] ia_governance_it_corporate_accountability + + + + catgr_Financial.Services +
	catgr_Real.Estate catgr_Internet.Services catgr_Mobile Std_Error = std_errors, t_values <-coef(summary(model_scores))[, "t-value"] ia_governance_it_ethics_transparency + + + + catgr_Sustainability +
	catgr_Artificial.Intelligence catgr_Administrative.Services catgr_Privacy.and.Security t_value = t_values, p_values <-coef(summary(model_scores))[, "Pr(>|t|)"] ia_governance_it_mission_engagement + + + + catgr_Design +
	catgr_Data.and.Analytics catgr_Hardware catgr_Software # View model results p_value = p_values) ia_governance_it_mission_locked + + + + catgr_Media.and.Entertainment +
	catgr_Science.and.Engineering catgr_Navigation.and.Mapping catgr_Agriculture.and.Farming # Extract variable names summary(model_seed) ia_governance_it_transparency + + catgr_Community.and.Lifestyle +	+	+
	catgr_Other catgr_Gaming catgr_Community.and.Lifestyle # Create a flextable object from the data frame variable_names <-names(coefficients) ia_workers_it_career_development + + + + catgr_Internet.Services +

# Create a flextable object from the data frame flex_table <flextable(summary_data) # Save the table to a

mutate(total_raised_amount = sum(angel_raised_amount_usd, series_a_raised_amount_usd, series_b_raised_amount_usd, seed_raised_amount_usd, series_c_raised_amount_usd, series_d_raised_amount_usd, series_unknown_raised_amount_usd, debt_financing_raised_amount_usd, series_e_raised_amount_usd, private_equity_raised_amount_usd, series_f_raised_amount_usd, pre_seed_raised_amount_usd, grant_raised_amount_usd, post_ipo_equity_raised_amount_usd, convertible_note_raised_amount_usd, undisclosed_raised_amount_usd, post_ipo_debt_raised_amount_usd, corporate_round_raised_amount_usd, series_i_raised_amount_usd,

# set the working directory .libPaths("C:\\Mesprogrammes\\R\\RLibraries") setwd("C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis") library(dplyr) library(tidyverse) library(sqldf) library(lubridate) # read the csv file setwd("C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis\\Crunchbase") CR_org <read.csv("organizations.csv") CR_org_desc <read.csv("organization_descriptions.csv") colnames(CR_org) setwd("C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis") # read the csv file BCORP <read.csv("B Corp Impact Data.csv") orga_filtered <subset(CR_org, !is.na(name)) orga_filtered <subset(orga_filtered, domain != "") orga_filtered <subset(orga_filtered, status == "operating") orga_filtered <subset(orga_filtered, primary_role == "company") orga_filtered <sqldf('SELECT * FROM orga_filtered WHERE employee_count IN ("1-10", "11-50", "101-250", "51-100")') # Generate the dataframe with all the years for each company_id all_years <expand.grid(year = min(min_year$first_year):max(max_year$last_year), uuid = unique(Dataset10$uuid)) panel_data_10 <-all_years %>% left_join(Dataset10, by = c("year", "uuid")) %>% arrange(uuid, year) %>% group_by(uuid) panel_data_10 <-panel_data_10[order(panel_data_10$uuid, panel_data_10$year), ] colnames(panel_data_10) panel_data_10 <-panel_data_10 %>% group_by(uuid) %>% fill( "company_name" , "date_first_certified" , "name", "created_at", "updated_at", "B_Corporation", "company_id", "current_status" , "description" , "industry" , "industry_category" , "products_and_services" , "country" , "city" , "sector" , "size" , "uuid" , "category_list" , "category_groups_list" , "num_funding_rounds" , "total_funding_usd" , "founded_on" , "last_funding_on" , "closed_on" , "employee_count" , "primary_role" , "cat_Employment" , "cat_IT. Management , .direction = "downup") %>% ungroup() panel_data_10 <-panel_data_10 %>% group_by(uuid) %>% fill("date_certified" , "assessment_year" , "overall_score" , "impact_area_community" , "impact_area_customers" , "impact_area_environment" , "impact_area_governance" , "impact_area_workers" , "impact_area_community_na_score" , "impact_area_customers_na_score" , "impact_area_environment_na_score" , "impact_area_governance_na_score" , "impact_area_workers_na_score" , "ia_community_it_civic_engagement_giving" , "ia_community_it_diversity_equity_inclusion" , "ia_community_it_diversity_inclusion" , "ia_community_it_economic_impact" , "ia_community_it_job_creation" , "ia_community_it_local_involvement" , "ia_community_it_suppliers_distributors_product" , "ia_community_it_supply_chain_management" , "ia_customers_it_customer_stewardship" , "ia_environment_it_air_climate" , "ia_environment_it_environmental_management" , "ia_environment_it_inputs" , "ia_environment_it_land_life" , "ia_environment_it_land_office_plant" , "ia_environment_it_outputs" , "ia_environment_it_water" , "ia_governance_it_corporate_accountability" , "ia_governance_it_ethics_transparency" , "ia_governance_it_mission_engagement" , "ia_governance_it_mission_locked" , "ia_governance_it_transparency" , "ia_workers_it_career_development" , "ia_workers_it_compensation_wages" , "ia_workers_it_engagement_satisfaction" , "ia_workers_it_financial_security" , "ia_workers_it_health_wellness_safety" , "ia_workers_it_management_worker_communication" , "ia_workers_it_training_education" , "ia_workers_it_worker_ownership" , "certification_cycle" , .direction = "down") %>% ungroup() # read the csv file setwd("C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis\\Crunchbase") Fund_rounds_eco_10 <read.csv("funding_rounds.csv") Fund_rounds_eco_10$year <year(Fund_rounds_eco_10$announced_on) # DELETE SOME COLUMNS Fund_rounds_eco_10 <subset(Fund_rounds_eco_10, select =c(announced_on,uuid,name, type, permalink, cb_url, rank, created_at, updated_at, country_code, state_code, region, city, raised_amount, raised_amount_currency_code, post_money_valuation, post_money_valuation_currency_code, investor_count, lead_investor_uuids)) Fund_rounds_eco_10 <-Fund_rounds_eco_10 %>% mutate( raised_amount_usd = as.numeric(raised_amount_usd) ) colnames(Fund_rounds_eco_10) Fund_rounds_eco_10 <-Fund_rounds_eco_10[!(is.na(Fund_rounds_eco_10$raised_amount_usd) & is.na(Fund_rounds_eco_10$post_money_valuation_usd)), ] pivot_data_eco_10 <-Fund_rounds_eco_10 %>% group_by(org_uuid, org_name, year, investment_type) %>% summarise( raised_amount_usd = sum(raised_amount_usd, na.rm = TRUE), post_money_valuation_usd = sum(post_money_valuation_usd, na.rm = TRUE) ) %>% pivot_wider( id_cols = c(org_uuid, org_name, year), names_from = investment_type, values_from = c(raised_amount_usd, post_money_valuation_usd), names_glue = "{investment_type}_{.value}", names_sep = "_", values_fill = 0 ) fund_rounds_panel_data_10 <sqldf('SELECT * FROM panel_data_10 LEFT JOIN pivot_data_eco_10 ON panel_data_10."uuid" = pivot_data_eco_10.org_uuid and panel_data_10."year" = pivot_data_eco_10.year' ) fund_rounds_panel_data_10$company_name <gsub("\n"," ",fund_rounds_panel_data_10$company_name) fund_rounds_panel_data_10$company_name <gsub("[^[:alnum:]]", " ",fund_rounds_panel_data_10$company_name) write.csv(fund_rounds_panel_data_10, "C:\\Users\\mikel.escribano\\Documents\\Master\\Master Thesis/PanelData10.csv", row.names = FALSE) paneldatafinal_10<-fund_rounds_panel_data_10 colnames(paneldatafinal_10) # Get column number for a specific field campo <-"year" numero_columna <which(colnames(paneldatafinal_10) == campo) # Company_id is repeated names(paneldatafinal_10)[749] <-"year_rounds" colnames(paneldatafinal_10) # sum the fields x_raised_money por uuid y year paneldatafinal_10 <-paneldatafinal_10 %>% group_by(uuid, year) %>%