Predictors of Local Non-Governmental Organisations’ (NGOs’) Adoption of Self-Regulation in Ghana
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Emmanuel Kumi
,
Dennis Boahene Osei
und
Antoinette Tsiboe-Darko
Abstract
Self-regulation has emerged as one of the strategies employed by non-governmental organisations (NGOs) to address their legitimacy crisis resulting from the lack of accountability and transparency in countries with weak government regulatory frameworks. However, our understanding of the predictors of local NGOs’ adoption of self-regulation in contexts of weak government regulations remains limited. Drawing on the concept of isomorphism and resource dependency and using data from local NGOs in Ghana, we find that an increase in the number of donors of local NGOs reduces their likelihood of adopting self-regulation practices. We show that donor diversification serves as a disincentive for adopting self-regulation among local NGOs in Ghana. We also find that older and experienced local NGOs are more likely to adopt self-regulation practices than their younger counterparts. Implications of the findings for NGO management are discussed.
1 Introduction
Non-governmental organisations (NGOs) perform multiple accountabilities to diverse stakeholders (e.g., government, donors, the public and peer organisations) with different demands and expectations. NGOs are therefore required to remain accountable by adhering to regulations which enable them to demonstrate their effectiveness and efficiency. Adhering to such regulations has become important in recent years when NGOs have been hit by highly publicized scandals including sexual harassment (Scurlock et al. 2020). Against this backdrop, regulations have the potential to enhance eroded public trust, legitimacy, and credibility of NGOs (Prakash and Gugerty 2010). Regulations ensure that NGOs adhere to accountability and transparency requirements as part of measures to promote institutional legitimacy (Breen et al. 2016).
Informed by these developments, there has been steady growth in calls for regulating the NGO sector through statutory regulations by governments (Breen et al. 2016). Statutory regulations which are government-driven are in some instances aimed at promoting accountability and transparency, improving governance structures, and strengthening the supervisory and investigatory power of government agencies, among many others (Breen et al. 2019). For instance, according to Gugerty (2010), government regulations among NGOs in Africa are in response to the growth of the sector and the quest for the government to exert its control in governing their activities especially when they are perceived as potential threats. The enforcement of heavy-handed regulations limits NGOs’ autonomy and room for manoeuvre. At the same time, the absence of effective regulations undermines the credibility of the sector as issues of accountability and transparency become a challenge.
Informed by the need for autonomy and credibility, many NGOs have engaged in self-regulation practices (Gugerty 2010; Breen et al. 2016). The concept of self-regulation has been defined from diverse perspectives (Gugerty 2008; Bies 2010; Breen et al. 2016). That notwithstanding, the consensus is that self-regulation involves regulatory governance that relies less on command-and-control mechanisms by the government than on networks and collaborative arrangements (Gugerty 2008). Thus, self-regulation refers to a set of institutions where standards and conducts are usually set by industry-level organisations.
However, in the Ghanaian context, given the high demands placed on NGOs, especially by donors, self-regulation is in direct response to donor requirements and the sector’s quest for legitimacy. For this reason, self-regulation in Ghana reflects what Sidel (2010:1042) calls intranet regulation which is understood as ‘‘private governance of a bounded range of NGOs brought together largely because of their relationship with a dominant funder, a form of collective action required, mandated, and led by powerful domestic [or external] funders’’. In countries like Ghana with weak regulatory frameworks, NGOs are increasingly being criticised for their perceived lack of accountability and transparency which negatively affects their legitimacy and resource mobilisation potential (Gugerty 2010; Bawole and Langnel 2016). For example, some NGOs have been accused of defrauding people and embezzling funds meant for development projects (Adobea-Owusu 2022; Daily Graphic 2018). A corollary of this is the distrust of the NGO sector by the public in Ghana.
To this end, self-regulation becomes crucial for NGOs as it enhances their legitimacy by solidifying their accountability and transparency (Burger 2012). Self-regulation also helps the NGO sector to build public trust and legitimacy given that it limits the entry of ‘bad apples’ and distinguishes them from the ‘good apples’ (Prakash and Gugerty 2010). It also becomes a mechanism for sending a ‘positive signal’ to stakeholders about the trustworthiness and legitimacy of the NGO sector, hence helping in addressing concerns about the declining trust arising in part from episodic scandals and incidences of corruption (Scurlock et al. 2020). While discussions of self-regulation have received much attention (Bies 2010; Gugerty 2010; Breen et al. 2016), there is relatively little empirical research on the predictors of self-regulation among local NGOs in sub-Saharan Africa (SSA) with weak regulatory frameworks. The application of self-regulation mechanisms is perceived to be a ‘‘cost-effective means of fostering discipline and benchmarking quality assurance by sector stakeholders’’ (Government of Ghana 2021:13). However, little is known about what predicts the adoption of self-regulation practices by local NGOs in Ghana. This article seeks to address this knowledge gap by answering the research question: what are the predictors of Ghanaian local NGOs’ adoption of self-regulation in their institutional environment?
Understanding the predictors of self-regulation practices among local NGOs, especially in contexts of weak government regulation is important for NGO management because resource dependence and the need for sector-wide legitimacy leads to conformance of institutional norms and values and the adoption of self-regulation. Moreover, in an uncertain environment, organisations conform to institutionalised norms and values which shape their behaviour and access to critical resources for their survival. For example, the regulative aspects of institutions focus on coercion through rules and laws while the normative aspects are about the acceptance of values and norms where compliance with norms and values is perceived as a social responsibility. Conformance to institutional pressures (e.g., donor demands and requirements) results in the homogenization of the institutional field.
Drawing on the concept of isomorphism and resource dependency theory, this article examines what factors predict the adoption of self-regulatory practices among local NGOs in their uncertain environment characterised by weak regulatory framework, aid withdrawal and changing donor priorities (Kumi 2017). Given the uncertainty, NGOs respond to uncertainty by conforming to donor demands, while others mimic the behaviour of their successful counterparts. Conforming to institutional rules enhances their legitimacy and increases their resource mobilisation potential and long-term organisational survival. For this reason, understanding the predictors of local NGOs’ adoption of self-regulation practices has implications for NGOs’ management. This article therefore draws on the case of local NGOs operating in the health, education and agriculture sub-sectors in five regions of Ghana. It uses data from 118 local NGOs to examine the predictors of the adoption of self-regulation practices. This article positions itself in the literature on self-regulation and makes two important contributions. First, it provides empirical evidence on the predictors of self-regulation practices among NGOs from a sub-Saharan African perspective. Although the Ghanaian context is used, the issue of self-regulation is similar to other countries where NGOs are increasingly facing accountability and transparency scrutiny in the absence of strong government regulation. In doing so, this article contributes to the literature by expanding the scope of self-regulation to different institutional settings by providing perspectives from sub-Saharan Africa, a region where self-regulation by NGOs has been relatively less explored in the literature. Second, this article demonstrates the limits of resource dependency theory and the concept of isomorphism in explaining the predictors for NGOs’ adoption of self-regulation. Our findings suggest that an increase in the number of donors reduces the likelihood of adopting self-regulation practices. This indicates that donor diversification is a disincentive for adopting self-regulation among local NGOs in Ghana. Our finding is therefore contrary to what conventional theory on self-regulation and resource dependency suggests that the diversity of funding sources is positively associated with the adoption of self-regulation (AbouAssi 2015). This article expands our understanding of self-regulation by showing how the interaction of coercive isomorphism resulting from resource dependency and organisational characteristics such as age influence local NGOs’ adoption of self-regulation. The findings show that older local NGOs with diversified funding are less likely to adopt self-regulation compared to their newer or younger counterparts with limited funding diversification. This highlights the importance of resource and age effects in explaining the adoption of self-regulation mechanisms among NGOs in sub-Saharan Africa.
The remainder of the article is structured as follows: Section 2 reviews the literature on isomorphism, resource dependency and self-regulation in developing the research hypotheses. Section 3 discusses the research context focusing on self-regulation among NGOs, state-NGO relations and the legal and regulatory frameworks in Ghana. Section 4 presents the research methodology while the results are presented in Section 5. Section 6 discusses the findings while the last section concludes.
2 Isomorphism, Resource Dependency and Self-Regulation
Neo-institutional theory has become one of the fundamental theories for explaining organisational behaviour. Given that organisations operate within an institutional environment which is largely controlled by the government, donors and the general public, they are susceptible to external pressures which make them conform to rules and norms in seeking legitimacy (Suchman 1995).
The environment tends to prescribe socially acceptable practices and failure to adhere to such rules results in punitive measures. However, conformance also leads to homogeneity in organisational structures and culture. In the case of NGOs, prescribed rules, and norms such as accountability requirements become templates for organising their behaviour (DiMaggio and Powell 1983; Yanacopulos 2005). They are perceived as best practices to which members must adhere in promoting sector-wide legitimacy in times of uncertainty (Gugerty 2010). For NGOs, homogenization occurs through the process of isomorphism – a situation where an organisation adopts or models the characteristics of dominant organisational forms. DiMaggio and Powell (1983:149) argue that diversity in organisational forms reflects environmental variation. Informed by this, when organisations face similar environmental constraints or uncertainty, they tend to copy one another and over time, they become similar to each other and their operating environment.
DiMaggio and Powell (1983) identified three drivers of institutional isomorphism that exist in organisational structures, processes, and decision-making. These are i) coercive isomorphism; ii) normative isomorphism; and iii) mimetic isomorphism.
2.1 Coercive Isomorphism and NGOs’ Resource Dependence
Coercive isomorphism mostly comes from political influence and the need for legitimacy and occurs when superiors impose changes on subordinates. Coercive isomorphism is concerned with the direct imposition of force, persuasion, and invitation from a superior to a subordinate organisation to join in collusion. According to DiMaggio and Powell (1983), coercive isomorphism stems from legal and technical regulations from governments. Another coercive mechanism employed by state agencies is in defining the structures of organisations through enforcement mechanisms such as codes of conduct. The exertion of coercive isomorphic pressures increases when dependence on a critical resource provider is high (Pfeffer and Salancik 2003). From a resource dependence perspective, resource providers exert pressure on organisations to conform to certain patterns of behaviours because of their dependence. For this reason, resource providers can demand conditions such as adherence to regulatory requirements. According to Pfeffer and Salancik (2003), organisations acquiesce when there is high-level coercion. On the other hand, when dependence on resources or means for enforcing regulations is limited, NGOs can flout such regulations by employing the tactics of manipulation and deviance.
The adoption of self-regulation by NGOs is therefore influenced by resource dependence and the need for legitimacy. NGOs that are highly dependent on resources from resource providers are prone to comply with their demands. The degree of dependence increases with the concentration and criticality of the resource (Pfeffer and Salancik 2003). For this reason, NGOs that depend on more donors are likely to face constraining effects from the different donors, hence there is a likelihood for them to adopt self-regulation arising from coercive demands to ensure their broad-based legitimacy. For this reason, there is a likelihood that NGOs will adopt self-regulation because of their resource dependence on different donors who come with their own rules. Thus, the diversity of resources affects the adoption of self-regulation as NGOs with diverse sources of funding are more likely to adopt self-regulation. Similar assumptions about how resource dependency affects the adoption of self-regulation by NGOs have been highlighted in the literature (see for example AbouAssi and Bies 2018).
H1:
In an environment where local NGOs have a higher number of donors, they are more likely to adopt self-regulation practices through coercive isomorphism.
2.2 Normative Isomorphism
According to DiMaggio and Powell (1983), normative isomorphism is concerned with the professionalisation of organisations within institutional fields. This occurs when professional associations impose homogenous norms and values on organisations to ensure that their operations meet certain standards. These institutionalised norms and values about appropriate organisational behaviour are taken for granted and serve as prescriptions for organising actions. Normative isomorphism is driven by professionalisation through formal education which leads to the spread of insights and normative rules (DiMaggio and Powell 1983). The professionalisation of the NGO sector has become a means for securing legitimacy and resources from donors where skilled staff who can speak and understand ‘donor language’ are employed. More importantly, professionalisation indicates donors’ normative outlook of what an NGO should be like. NGOs’ legitimacy is therefore equated to their perceived level of professionalism (Appe 2016; Kamstra and Schulpen 2015). Professionalisation also drives and promotes normative isomorphism through formal education and training which leads to the spread of insights, models and normative rules (DiMaggio and Powell 1983; Zorn et al. 2011). Formal education helps in the transfer of norms and beliefs into organisations which creates normative pressures (Hersberger-Langloh et al. 2021). Therefore, staff qualification such as having a university degree becomes an important mechanism through which normative isomorphism is transmitted among professionalised NGOs which helps them in seeking professional legitimacy (Suchman 1995). Similarly, Kamstra and Schulpen (2015) highlight how professionalisation leads to homogenization among NGOs in Ghana. It is therefore hypothesized that staff qualification is more likely to influence the adoption of self-regulation practices es through normative isomorphism for professionalised NGOs in Ghana.
H2:
Professionalised local NGOs with more university graduates are likely to adopt self-regulation mechanisms through normative isomorphism.
2.3 Mimetic Isomorphism
Mimetic isomorphism occurs in periods of uncertainty where organisations scan their environment and model themselves around their perceived successful and legitimate counterparts. By learning from these organisations, they can address their inefficiencies. In the case of NGOs, networks and coalitions are important mechanisms for the spread of mimetic isomorphism through the adoption of best practices that are diffused through the sector. Self-regulation is also influenced by peer pressures as organisations seek social acceptance from their networks. Many networks and coalitions establish and adopt self-regulatory practices as part of their internal governance processes. Membership in networks and coalitions is motivated by information sharing and access to resources including finances and capacity building (Yanacopulos 2005). Therefore, NGOs can learn and copy the behaviour of their perceived successful counterparts in their networks and coalitions. The process of mimetic isomorphism in networks and coalitions is different from that of normative isomorphism which occurs mainly through professionalisation such as the adoption of managerialist practices. However, for mimetic isomorphism, organisations imitate others when there is uncertainty in their environment (DiMaggio and Powell 1983). Organisations respond to uncertainty using diverse mechanisms such as imitation of best practices, replication of innovations, adoption of new organisational forms and using multiple institutional logics (AbouAssi and Bies 2018). This enhances their potential for social acceptability by network members. It is therefore expected that belonging to NGO networks and coalitions increases the likelihood of adopting self-regulation practices through mimetic isomorphism which occurs at the sectoral level.
H3:
Self-regulation practices through mimetic isomorphism are more likely to be adopted by local NGOs that are engaged in networks and coalitions.
3 The Research Context: Self-Regulation Among NGOs in Ghana
Ghana presents an interesting case for understanding the drivers of self-regulation practices among NGOs as several attempts by the government (e.g., through the National Policy for Strategic Partnerships in 2000 and the Trust Bill in 2006) to regulate the sector have failed due to strong opposition from CSOs for fear of state encroachment of the sector and have therefore led to the emergence of self-regulation by NGOs (Olugbuo 2014; WACSI 2020). Despite this, the Government of Ghana has renewed its efforts to regulate the activities of CSOs through the drafting of the NGO regulation bill (i.e. the Trust Bill in 2021) and the passage of the Nonprofit Policy (NPO policy) (Government of Ghana 2021). This has also intensified calls by NGOs to adopt self-regulation practices to promote quality assurance for the sector (WACSI 2020).
Faced with allegations of corruption and the lack of accountability to beneficiaries (Bawole and Langnel 2016; Adobea-Owusu 2022), some NGO coalitions and networks in Ghana have taken the responsibility of regulating their activities through the adoption of voluntary self-regulatory mechanisms (WACSI 2007). Thus, for many NGOs, self-regulation has therefore become an accountability mechanism where they develop norms and standards about how they operate. Specifically, the self-regulatory mechanisms practised include written constitutions, governance policies, performance evaluation accountability and disciplinary mechanisms as well as auditing and reporting (Gugerty 2010; WACSI 2007; Olugbuo 2014). The norms of conduct adopted especially by network members represent a form of ‘intranet’ self-regulation (Sidel 2010). However, given that there is no sector-wide code of conduct for NGOs, adherence to self-regulation is voluntary (Olugbuo 2014). For example, networks and coalitions such as the Coalition of NGOs in Health, Coalition of NGOs in Water and Sanitation, Northern Network for Educational Development and Ghana National Education Campaign Coalition, among many others, have instituted written constitutions, standards and rules of conduct for their members (Kumi 2017). Table 1 lists the forms of self-regulation mechanisms practised by the sampled NGOs in this study.
Self-regulatory mechanisms therefore promote peer learning where NGOs mimic best practices among peers as part of efforts to enhance their reputation and credibility. According to Lloyd and de las Casas (2006), NGOs that fail to adopt self-regulatory mechanisms accepted by their peers could face questions regarding their credibility. Moreover, adopting self-regulation mechanisms helps NGOs to ‘stand out’ and some donors use membership in a code or certification scheme as criteria for receiving grants. Self-regulation therefore provides an avenue for meeting the interests of stakeholders such as donors, government and the public as NGOs become accountable and transparent. This in turn enhances their legitimacy and autonomy. The NPO policy in Ghana therefore acknowledges the significance of self-regulation in the sector. The Government of Ghana (2021) also highlights the need for peer review among CSOs because it promotes discipline in the sector. Additionally, the adoption of self-regulatory helps in promoting high and generally acceptable ethical standards and operational norms for the sector.
Moreover, the self-regulation of the sector is also fuelled in part by donor demands for accountability by NGOs given their high dependency on external donor resources which account for about 80–90 % of all NGOs’ annual budgets (Kumi 2017). This makes external donors demand strict accountability and transparency requirements which leads to homogenization of the institutional field (Kamstra and Schulpen 2015). Thus, self-regulation by NGOs in Ghana is also a means for accessing donor funding to ensure their survival (Sidel 2010). The emergence of self-regulation by NGOs is therefore in response to criticisms about their accountability and sector-wide legitimacy. Given the weak government regulation, self-regulation is employed by NGOs as a collective action to improve public perceptions of the sector not being accountable and transparent due to the mismanagement of funds, corruption and fraud which causes reputational damage to the NGO sector (Adobea-Owusu 2022). Self-regulation therefore helps NGOs to marginalise or exclude the ‘bad apples’ that cause reputational damage to the sector which in turn enhances the legitimacy of the sector. This creates opportunities for strengthening their resource mobilisation potential because it becomes a means for signalling donors that the NGO sector is credible and legitimate. Thus, it highlights how donors influence the adoption of self-regulation among NGOs (Sidel 2010).
Despite the significance of self-regulation, concerns have been raised about the weak enforcement nature of self-regulatory mechanisms in the country due in part to institutional capacity challenges among network and coalition secretariats (WACSI 2007).
4 State-NGO Relations in Ghana
Understanding the predictors of self-regulation by NGOs in Ghana requires situating the discussions within the context of state-NGO relationships. NGOs in Ghana operate within a favourable environment with no significant restrictions or interference from the government. However, there have been instances of closing civic space (Kumi 2022). State-NGO relationship in Ghana has a long history dating back to the colonial and the present-day period (Gary 1996; Yarrow 2011). During the military regimes and civil regimes of post-independent Ghana, the relationship between government and civil society was characterised by a mixture of alliance formation and opposition (Anderson et al. 2024). For instance, civil society including student unions supported military revolutions including the National Liberation Council and the Provisional National Defense Council (PNDC) because they were perceived to promote the ideals of revolutionary development (Anderson et al. 2024). However, factors such as corruption, lack of transparency and abuse of human rights by the military regimes led to suspicion and mistrust of the government by civil society (Yarrow 2011). During the PNDC era, government-civil society relations were repressive where civil society actors were perceived as enemies of the state and there were many instances of media censorship (Gyimah-Boadi 2007).
While acknowledging that the formation of civil society in Ghana dates to the 17th Century, the proliferation of NGOs happened especially during the Structural Adjustment era of the 1980s and 1990s (Gary 1996). The implementation of the Economic Recovery Programme (ERP) affected all sectors of the Ghanaian economy and had detrimental effects on the citizens (Gyimah-Boadi 2007). As part of the government’s efforts to address the negative effects of the ERP, the Programme of Action to Mitigate the Social Costs of Adjustment (PAMSCAD) was adopted in 1987 by implementing 23 poverty reduction programmes (Gary 1996). This created opportunities for NGOs to serve as service delivery partners of the state. Thus, the neoliberal agenda of the SAP provided opportunities for NGOs to cooperate with the government. The provision of external donor funding to NGOs during PAMSCAD changed state-civil society relations from repression to cooperation or collaboration.
The adoption of multi-party democracy in Ghana in 1993 changed the political landscape and opened spaces for civil society including NGOs to operate freely as part of efforts to promote democratisation (Yarrow 2011). For this reason, state-NGO relations have improved significantly following the adoption of democracy especially after 2000 when NGOs were recognised as partners for national development. According to Kamstra and Schulpen (2015), state-NGO relations improved because the New Patriotic Party while in opposition had collaborated with NGOs which resulted in improving their mutual understanding and trust. This reduced the extent of NGOs using confrontational approaches.
Notwithstanding, Bawole and Hossain (2015) highlight that the relationship has been characterised by superficial cordiality and mutual suspicion. Moreover, while the government’s regulation of the civil society space in Ghana has a long history, there has been renewed interest by the government to regulate the activities of CSOs through the drafting of the NGO regulation bill (i.e. the Trust Bill in 2021) and the passage of the Nonprofit Policy (NPO policy) (Government of Ghana 2021). Among the factors cited by the government for regulating CSOs include their perceived lack of accountability, transparency and sometimes involvement and susceptibility to corrupt practices such as terrorist financing and money laundering (Government of Ghana 2021). Next, we discuss the regulatory environment of CSOs in Ghana.
5 The Regulatory and Legal Frameworks for CSOs in Ghana
In terms of regulatory and legal frameworks, CSOs are required by law to formally register with the Office of the Registrar of Companies as Companies Limited by Guarantee under the revised Companies Act, 2019 (Act 992) (i.e. previously the Companies Act 1963) and the NPO Policy approved in 2020. Additionally, they are required to formally register with the Nonprofit Organisations Secretariat as not-for-profit organisations which gives them legal recognition (USAID 2023). As part of the regulations, NGOs are required to undergo an annual renewal of their operating license to be recognised as functional organisations or in ‘good standing’ with the government which gives them legitimacy.[1] However, as USAID (2023) highlighted, out of the 11,161 registered CSOs in 2022, only 687 renewed their annual license. The lack of renewal by many CSOs has been attributed to factors including the lack of resources and the bureaucratic processes involved (WACSI 2020). The non-renewal of licenses highlights the administrative lapses in the regulatory framework for CSOs in Ghana. Aside from the annual license renewal, CSOs are required to report to government agencies including the Ghana Revenue Authority and the Registrar of Companies annually. Registration makes NGOs qualify for tax exemptions when they apply for it through the NPO Secretariat.
NGOs in Ghana are free to engage in fundraising activities to support their activities although there are regulations regarding, for example, terrorist financing and money laundering. The Government of Ghana exercises coercive pressures on NGOs in terms of how they mobilise resources such as funding through the Anti-Money Laundering Act 2008 (Act 749). The Act mandates the government to freeze the account of CSOs that receive funds from individuals or donors including organisations both domestically or internationally with terrorist links. In addition, NGOs are required to report financial transactions above GH¢50,000 (US$10,368) (Financial Intelligence Centre 2016). As part of the new NPO policy and efforts to tackle terrorist financing, the NPO Secretariat is required to conduct a risk assessment of the sector. The Department of Social Development also issues certificates for newly established and registered CSOs before they are allowed to open a bank account (Government of Ghana 2021). As Kumi (forthcoming) highlights, the regulatory framework for CSOs in Ghana is one of command-and-control rather than incentive-based, hence many CSOs are unwilling to comply with such regulations. The command-and-control nature of the regulations demonstrates how the government uses administrative coercion measures to regulate the activities of CSOs which often results in lapses.
Aside from the government, donors (i.e. bilateral, multilateral and philanthropic organisations) impose conditionalities and demand adherence to practices such as submission of registration certificates, monthly and quarterly review reports, use of auditors and standardization of project-based reporting formats (Kamstra and Schulpen 2015). The imposition of donor requirements is due to NGOs’ high dependency on external donor resources (Kumi 2017). Donors, therefore, use their funding to regulate NGOs by setting standards and guidelines within which they should operate. As Kamstra and Schulpen (2015) show, donors tend to apply blueprint approaches which promote homogenization among NGOs. While the control of NGOs might not be the end goal of external donor funding, NGOs’ conformance to such funding requirements shapes their activities.
6 Data Collection and Methods
This study relies on data from 118 local NGOs in the health, education and agriculture sectors in Ghana. The rationale for focusing on local NGOs is that given their high dependency on donor resources for their survival, they have led calls for the adoption of self-regulation practices in Ghana (WACSI 2020). Therefore, their selection was empirically motivated to understand what predicts their adoption of self-regulation. The health, education and agriculture sectors were selected for the following reasons. First, they are the largest recipients of external donor funding as they serve as important priority areas for development partners. For instance, between 2013 and 2018, US$ 125.1million, US$ 130.5 million and US$216.4 million have been channelled through NGOs in education, health and agriculture sectors respectively in Ghana compared to US$ 10.6 million for democratic participation and US$ 0.8 million for conflict, peace and security (OECD 2023). This has made many local NGOs focus their operations on these sectors (Kwao and Amoak 2022). Second, because of the increasing presence of NGO networks and coalitions in these sectors. Examples include the Ghana Coalition of NGOs in Health, Ghana National Education Campaign Coalition, Ghana Coalition of NGOs in Malaria and Coalition of NGOs in water and sanitation, among others, who have developed self-regulatory mechanisms to regulate the activities of NGOs who are their members.
The five regions were also selected to reflect different geographic areas and the spatial distribution of local NGOs in Ghana. These regions have a high presence of local NGOs in Ghana (WACSI 2023). For example, Northern Ghana (i.e. Northern, Upper West, Upper East, Savannah and North East regions) has the highest concentration of NGO activities in Ghana due partly to the high incidence of poverty (Kwao and Amoak 2022). According to the Ghana Statistical Service (2020), the Northern Region (80.0 %) and the Upper East Region (68.0 %) have the highest incidence of multidimensional poverty in Ghana. The Greater Accra Region also serves as the national headquarters for many local NGOs in Ghana.
Using stratified random sampling, data were obtained from NGOs in the Northern, Upper West, Upper East, Ashanti and Greater Accra regions. Data collection took place in two phases: i) phase 1 between July 2015 and July 2016; and ii) phase 2 between January and August 2018. Phase 1 involved collecting data from local NGOs in the Northern, Upper West and Greater Accra regions as part of a broader doctoral research on changing aid landscape for NGOs in Ghana. For phase 1, the survey questionnaire was sent to 127 local NGOs of which 59 completed the survey.
The scope of the study was expanded to the Ashanti and Upper West regions in phase 2 of data collection. These regions were selected because of the high presence of local NGOs. In total, the survey was sent to 123 local NGOs of which 65 returned completed the survey. Out of the 65 completed surveys, 6 were dropped during the data cleaning process because they were incomplete. For this reason, a total of 59 local NGOs participated in phase 2. In collecting the data, the same survey questionnaire was used for both phases. A comparison of the local NGOs that participated in phases 1 and 2 revealed that there were no significant differences among them regarding their size, age and thematic areas. For phases 1 and 2, a total of 118 out of the 250 sampled NGOs participated in the study. This led to a response rate of 47.2 %.
The survey questionnaire captured self-reported organisational information (e.g., year of establishment, operating regions and scope of operations etc.), financial information (e.g., number of external donor funding etc.), human resources, structure and governance (e.g., number of staff (permanent and part-time staff), volunteers etc.) and relationships with stakeholders (e.g., membership of networks and coalitions etc.). The design of the survey questionnaire was informed by insights from existing literature and in-depth interviews with key informants in the Ghanaian civil society sector. The questionnaire consisted of closed-ended and open-ended questions.
As mentioned earlier, the focus of this article is on the predictors for the adoption of self-regulation practices by local NGOs in the health, education and agriculture sectors in five regions in Ghana. Against this background, some of the local NGOs were members of networks and coalitions while others were not. Thus, not all the sampled NGOs belonged to networks and coalitions. In terms of self-regulation mechanisms, NGO representatives were asked to identify the self-regulation forms they adopted. The design of the list of self-regulation mechanisms drew insights from existing literature (Gugerty 2010; WACSI 2007) and interviews with key informants in the civil society sector. The list of self-regulation mechanisms includes adherence to networks’ constitution and governance and administrative policies, disciplinary mechanisms and auditing and reporting frameworks (see Table 1). The self-regulatory mechanisms by NGOs in Ghana were centred on organisational governance, financial accountability and transparency, resource mobilisation and legal and public reporting requirements (WACSI 2007). These regulatory mechanisms employed by NGOs in Ghana are similar to what has been reported in the literature (Prakash and Gugerty 2010; Gugerty 2010).
7 Measurement of Variables
7.1 Dependent Variable
In this study, the dependent variable is the adoption of self-regulation practices by local NGOs in Ghana. In following AbouAssi and Bies (2018), a binary variable was created and coded as 1 to indicate the adoption or adherence to any form of self-regulation practices (see Table 1) and 0 if the response was that the NGO did not adopt or adhere to any self-regulation practices. Due to the binary nature of the dependent variable, a probit regression model including the computation of marginal effect was considered the most appropriate analysis method. It is assumed NGOs adopt self-regulation practices to promote collective legitimacy at the sectoral level (Appe 2016). In addition, self-regulation is used as a mechanism for responding to demands from stakeholders including donors, government and the public for transparency and accountability (Bawole and Langnel 2016).
It is noteworthy that this article is concerned about the predictors of local NGOs’ adoption of self-regulatory practices. For this reason, the dependent variable is not about testing for specific self-regulation practices separately nor it is about the creation of an index or score for measuring the extent of the specific self-regulation practices.
7.2 Independent Variables
Given that this study is interested in understanding the predictors for adopting self-regulatory practices, mimetic, normative and coercive isomorphism were chosen as the independent variables. This is consistent with the approach used in the existing literature (AbouAssi and Bies 2018; Hersberger-Langloh, Stühlinger, and von Schnurbein 2021; Kamstra and Schulpen 2015).
For mimetic isomorphism, membership of networks and coalitions was chosen as an independent variable. The rationale is that in times of uncertainty, members of networks and coalitions respond by adopting best practices, innovations and multiple institutional logics to ensure their survival (Yanacopulos 2005; Kumi and Saharan 2022). In the existing literature, membership in networks and coalitions or umbrella organisations has been employed in representing mimetic isomorphism (Verbruggen et al. 2011; AbouAssi and Bies 2018).
While a previous study by AbouAssi and Bies (2018) measured mimetic isomorphism as a continuous variable based on NGOs joining various networks or umbrella organisations, mimetic isomorphism in this study is measured as a binary variable which takes the value of 1 if an NGO is a member of at least one network or coalition and 0 if NGO is not a member of any network. The rationale for measuring mimetic isomorphism using a binary variable rather than a continuous variable is that the sample local NGOs in this study include those who are not members of any network or coalition. For this reason, it is impossible to use the number of networks and coalitions in measuring mimetic isomorphism. However, in uncertain times, NGOs are likely to adopt similar services and also learn innovative ways of working from their peers which has the potential of promoting mimetic isomorphism (Hersberger-Langloh et al. 2021).
Following Zorn et al. (2011) and Hersberger-Langloh et al. (2021), normative isomorphism is measured as a continuous variable by the definite number of staff with a university degree in an NGO (e.g., bachelor’s, master’s, PhD). This is consistent with the existing literature (AbouAssi and Bies 2018). Previous studies have used educational qualification as a measure of normative isomorphism (Zorn et al. 2011; Hersberger-Langloh et al. 2021). Staff education is considered an independent rather than a control variable because formal education is one of the mechanisms through which normative values are diffused in organisations.
While acknowledging that coercive isomorphism stems from legal requirements and resource dependence (Pfeffer and Salancik 2003; Verbruggen, Christiaens, and Milis 2011), in this study, it was based on resource dependence, specifically using the number of donors supporting an NGO. In doing so, we do not measure the dominance of any one donor in the financial base of the NGOs or the proportion of revenue from the different donors, rather we show the diversity of donors available to the NGOs. While acknowledging that resource dependency has been measured using, for example, the percentage of revenues from external donors to internal revenues (see AbouAssi and Bies 2018), this measure was not possible because NGOs’ financial data (e.g., percentage of reported external and internal revenues) are not publicly available in many African countries including Ghana. Many organisations are also reluctant to share their financial data which raises accountability and transparency concerns (Barr et al. 2005). Although some previous studies (e.g., Hung and Hager 2019) have measured resource dependency (i.e. revenue concentration) using the Herfindahl–Hirschman Index, its application in this study was limited mainly because of the lack of information on the proportion of revenue generated from the different funding streams by the sampled NGOs in Ghana. Thus, the local NGOs only provided self-reported information on their number of donors without specifying the proportion of each donor’s contribution to their annual budget.
For this reason, coercive isomorphism is operationalised using the number of donors and measured as a continuous variable based on the number of external donors of an NGO (Verbruggen et al. 2011). The rationale is that reliance on external donors comes with the need to comply with both financial and annual reporting as well as project accountability requirements which in turn puts coercive pressures on NGOs. This is because given that NGOs depend on external donors for their survival, donors tend to put formal and informal pressures on NGOs which compel them to respond to such demands through coercive isomorphism because of the need to ensure their organisational survival (Kamstra and Schulpen 2015). The desire to practice self-regulation is higher when resource dependence is higher because of the increasing pressures to comply with external demands such as the submission of narrative and financial reports by NGOs to donors.
7.3 Control Variables
The study further adds the following control variables: age, size, scope and geographic regions. Age represents the year an NGO was established. It is considered a continuous variable and measured based on the number of years of operation. Age is chosen as a control variable because existing studies have shown that age influences NGOs’ adoption of self-regulation (Tremblay-Boire et al. 2016). Organisational size is also considered a continuous variable and measured based on the number of paid staff working for an NGO (Zorn et al. 2011). Scope refers to the sector(s) of operation. It is measured as a binary variable which takes the value of 1 if the NGO is a specialist and 0 if it is a generalist. Generalist was chosen as a reference for the probit analysis. Generalists operate in multiple domains which enhances their access to different stakeholders and makes them have more collaborative capacity (Uvin et al. 2000). Another control variable is geographic region. This is measured as a continuous variable based on the number of operational regions within which an NGO undertakes activities. Regions of operation influence the extent to which NGOs engage with stakeholders and peer organisations which also has the potential of putting normative and mimetic pressures on NGOs. More importantly, in the case of Ghana, given that some regional networks restrict membership to organisations operating in specific regions, it is expected that it will influence the adoption of self-regulation practices (Tremblay-Boire et al. 2016).
8 Results
8.1 Descriptive Statistics
Table 2 presents the summary statistics in terms of means and standard deviation for the variables in the sample.
Table 3 presents results on the correlation coefficient of the variables. We observe that, apart from scope, staff education and membership of networks, self-regulation is negatively related to all the variables. We also find that except for region and age, scope is positively correlated with all the variables. Membership of networks and coalitions appears to have a positive link with all the variables except the number of operational regions. Similarly, for the most part, the number of donors has a positive association except for self-regulation and the number of operational regions. We present the empirical results in the next section.
8.2 Probit Regression Results
Table 4 presents the result of the marginal effect of the full probit regression models. The models include the number of donors, staff education (i.e. number of university degree holders) and membership of networks and coalitions as independent variables in addition to the standard controls.
For H1, the results show a negative relationship between the number of donors and self-regulation. The marginal effect of the model indicates that the coefficient is negative but significant at 5 % which suggests that an increase in the number of donors of an NGO reduces the probability or likelihood of adopting self-regulation by 0.03 (see Model 1 of Table 4). The results, therefore, suggest that as local NGOs in Ghana increase the number of their donors by becoming resource-dependent, they are less likely to adopt self-regulation practices. This finding contradicts our hypothesis that in an environment where NGOs have a higher number of donors, they are more likely to adopt self-regulation practices through coercive isomorphism.
The results further show that among the control variables, the coefficient for the age of an NGO is negative but significantly associated with self-regulation. This suggests that an increase in the age of an NGO reduces the probability of it adopting self-regulation by 0.21. To capture the possible non-linear relationship between age and self-regulation, we included the quadratic term of age. The result of the marginal effect suggests that age squared is significant and positively related to self-regulation. This means that, as an NGO grows over time and becomes experienced, there is a greater likelihood of adoption of self-regulation practices. Our finding shows that NGOs’ self-regulation decreases with age up to a point above which further increases in age improve the likelihood of adopting self-regulation. To find the turning point of this non-linear relationship, we partially differentiate self-regulation with respect to age using the estimated coefficients of the probit regression model and set the result to zero. We find a threshold value of 15.9 years suggesting that NGOs below this age threshold are less likely to adopt self-regulation mechanisms while those above this age limit are more inclined to be self-regulated.
Moreover, the number of operating regions has a negative effect on the probability or likelihood of NGOs adopting self-regulation although the effect is marginally significant. The findings, therefore, suggest that as NGOs increase the number of their operating regions, it reduces their likelihood of adopting self-regulation by 0.03 (see Model 1 of Table 4).
For H2, results from the probit model on the relationship between staff education and the adoption of self-regulation through normative isomorphism are presented in Table 4 (see Model 2). The results show a negative and insignificant association between staff education (i.e. the number of university degree holders in an NGO) and the probability of adopting self-regulation practices. Furthermore, the result of the non-linear relationship between age and self-regulation remains the same when number of university degree holders was used as the main independent variable. NGOs in their early stages of operation are less likely to adopt self-regulation practices. However, when NGOs’ organisational age increases beyond 15.9 years, the likelihood of adopting self-regulation becomes positive. This implies a greater probability of older NGOs adopting self-regulatory practices.
In relation to H3, Table 4 shows the results of the probit regression analysis (see Model 3). As evident from the results of the marginal effect model, the coefficient of membership of networks and coalitions is negative and insignificant, contradicting H3. The results show that belonging to a network does not predict the adoption of self-regulation through mimetic isomorphism. Regarding the control variables, it is observed that the age of an NGO has a negative but significant effect on the probability or likelihood of adopting self-regulation practices. When we account for possible non-linearities, the earlier result of a U-shaped relationship is observed. This means that inexperienced NGOs are less likely to adopt self-regulatory practices until their organisational age exceeds 15.9 years, where the likelihood of adoption of self-regulatory practices changes from negative to positive. We also found that NGOs’ number of operating regions, number of staff and scope in this model are insignificantly related to self-regulation.
9 Discussion
This study examines the predictors for local NGOs in the health, education and agriculture sectors in Ghana to adopt self-regulatory practices. Using the concept of isomorphism and resource dependency, this study finds that in terms of coercive isomorphism, resource dependency measured by the number of donors is a significant predictor of self-regulation by local NGOs in Ghana. However, the result on the negative relationship between the number of donors and self-regulation, therefore, does not support our hypothesis that, in an environment where NGOs have a higher number of external donors, they are more likely to adopt self-regulation through coercive isomorphism because of the need to ensure their broad-based legitimacy.
A plausible explanation for this finding is that while local NGOs that are dependent on many external donors are vulnerable to their accountability, transparency and reporting demands through coercive pressures to maintain their legitimacy, having a diversified donor base also gives the local NGOs some leeway in manoeuvring the conditions of donors that are considered burdensome. For this reason, local NGOs with diverse funding base are less worried about the exit of a single donor. For this reason, they are more likely to flout or defy the demands imposed on them by ‘stringent donors’, hence their unwillingness to adopt ‘intranet’ self-regulation mechanisms (Elbers and Arts 2011; Kumi 2017). Diversification, therefore, gives NGOs autonomy from the imposition of stringent funding conditions (Hung and Hager 2019). In this regard, given their diversified donor base, they are able to resist institutional pressures from donors whose requirements are considered burdensome (Elbers and Arts 2011).
For this reason, our finding on the negative relationship between the number of donors and self-regulation contradicts earlier studies suggesting that NGOs with diversified funding are more likely to adopt self-regulation (AbouAssi 2015). One plausible explanation is that in the case of Ghanaian NGOs, while donor requirements have the potential to cause local NGOs to adopt self-regulatory practices, maintaining a diversified donor funding base limits the extent of coercive pressures and demands. This suggests that resource diversification gives local NGOs some level of flexibility and autonomy in their engagements with their donors (Hung and Hager 2019; Kumi 2022).
Our findings further demonstrate the significance of organisational characteristics like age in explaining the adoption of self-regulation practices. NGOs that have operated for a longer number of years have experience and this may influence their adoption of self-regulation. As highlighted in the findings, older NGOs are able to attract different funding from external donors because of their ability to overcome the liability of newness associated with newer NGOs (Hager et al. 2004). Our findings from the marginal effect model show that an increase in the age of an NGO reduces its likelihood of adopting self-regulatory practices by 0.21 (Table 4). Thus, NGOs in their initial stages of operation are less likely to adopt self-regulation practices. As NGOs grow over time and become experienced, they are more likely to adopt self-regulatory practices. This is because older NGOs have built their reputation over time and are able to design structures to guide their operations which serves as an important mechanism for sending signals to their stakeholders about their accountability and transparency (Tremblay-Boire et al. 2016). Thus, the adoption of self-regulation practices enhances their credibility and legitimacy in the eyes of stakeholders, including donors, government and the public. Additionally, we find that operating regions of NGOs have a significant negative effect on the adoption of self-regulation practices. This suggests that, as NGOs increase their operating regions, it reduces the likelihood of adopting self-regulation. The findings, therefore, highlight the limitations of isomorphism in explaining the adoption of self-regulation among NGOs in Ghana because of its neglect of other organisational factors, including how the age and the operating regions of a local NGO influence its likelihood of adopting self-regulation.
For mimetic isomorphism, we expected that membership in networks would facilitate organisational learning of best practices among NGOs. However, the findings do not support the initial hypothesis that members of networks are more likely to adopt self-regulation through mimetic isomorphism. A possible explanation is the lack of effective collaboration among local NGOs in Ghana due partly to competition for scarce resources. This is consistent with the observation by Kamstra and Schulpen (2015) that competition for donor funding results in the lack of collaboration and distrust among democracy-promoting NGOs in Ghana which results in duplication of programmes. Although local NGOs claim to be members of networks and coalitions, this is mostly a result of donor conditions that require them to engage in networks or consortia to access funding opportunities (Kumi 2017). For this reason, in the absence of donor funding, collaboration is sporadic or takes place infrequently (e.g., during workshops) rather than daily engagements. This suggests that donor funding, especially through earmarking, affects the extent of collaborations among local NGOs in Ghana. Many local NGOs, therefore, collaborate among themselves in fulfilling donor demands. Similar findings have been highlighted in the literature (Kamstra and Schulpen 2015).
Another reason for the limited collaboration relates to the lack of information sharing among network members (Yanacopulos 2005). The effectiveness of networks is also hampered by the lack of trust, transparency, accountability and power imbalance, especially between network secretariats and members. In the case of Ghana, network secretariats compete with their members for funding and recognition from donors which affects their effectiveness (Kumi 2017). The fear of losing members also serves as a disincentive for enforcing strict self-regulatory mechanisms by NGO networks (Burger 2012).
Regarding normative isomorphism, we find a negative and insignificant association between staff education and the adoption of self-regulation practices among local NGOs in Ghana. This is not consistent with the initial hypothesis that NGOs with more university graduates are likely to adopt self-regulation through normative isomorphism. A plausible explanation for this finding is that having an educational qualification does not automatically lead to an NGO staff becoming ‘professionals’ who adhere to normative demands. Rather, years of experience and training on the job are key determinants of the extent of their professionalisation, which in turn influences their adoption of self-regulation practices. However, many local NGOs in Ghana are faced with the challenge of turnover, which in turn affects their ability to acquire the needed experience on the job (Gyambrah, Hanson, and Nottinson 2017).
10 Conclusions
This article examines the predictors for local NGOs in Ghana to adopt self-regulation. Using the concept of isomorphism and resource dependency, the findings show a negative and significant effect of coercive isomorphism through the number of donors (i.e. resource dependency) in explaining the adoption of self-regulation practices by local NGOs. The findings suggest that the adoption of self-regulation practices is rather influenced by organisational and environmental characteristics, particularly the age and operating region(s) of an NGO, because they significantly influence the willingness of NGOs to adopt self-regulation. We also find that older and experienced local NGOs are more likely to adopt self-regulation practices than their younger counterparts. At the same time, as NGOs increase their operating regions, they are less likely to adopt self-regulation practices. The findings, therefore, have some significance for donors in their quest to promote accountability and transparency within the NGO sector. The findings show that using coercive demands, including accountability requirements, in promoting adherence to self-regulation practices might not be effective, especially when NGOs have a broad donor base. Therefore, understanding and awareness by donors and NGO managers of the drivers of self-regulations outside of resource dependency and institutional explanations is important.
Directly related to the above is the statistically insignificant relationship between membership of networks and coalitions and the adoption of self-regulation. The findings, therefore, point to the importance of informal accountability mechanisms in contexts where the collaboration among NGOs is largely informal. Promoting self-regulation through informal accountability incentives including socially constructed norms and trust might provide a better alternative for NGOs to adopt self-regulation. In addition, ensuring effective adherence to self-regulation requires paying particular attention to strengthening NGOs in forming genuine collaborations rather than ‘artificial marriages’ including donor-promoted consortia and networks in response to institutional pressures. As the findings suggest, for self-regulation practices to be effectively adopted, it requires that the design and implementation of practices evolve from NGOs themselves. In this regard, decision-making in networks is expected to be mutual because power asymmetries have the potential to affect ownership of self-regulation mechanisms.
Moreover, it is worth highlighting that the findings of this study have implications for the government’s attempt to regulate the civil society sector in Ghana through the NPO policy. As the findings show, the use of coercive pressures through, for example, command and control mechanisms reduces the likelihood of local NGOs adopting self-regulation practices. For this reason, there is a need for the government to collaborate and also engage in more consultations with CSOs in mainstreaming peer review mechanisms which when effectively implemented will help ensure the accountability and transparency of the sector. Additionally, given that many local NGOs are inclined to counter strong government control over the sector, the introduction of the NPO policy is likely to result in collective action by CSOs through the adoption of self-regulation practices.
While the findings in this study provide some useful insights into understanding self-regulation among local NGOs in Ghana, we also recognise the limitations of the study. First, this article draws on primary data collected between 2015 and 2018. For this reason, the data might be considered as old data. However, as Ketchen et al. (2023:2544) argue, using old data becomes necessary ‘‘when data was painstakingly collected’’ that allows the researcher(s) to develop unique insights into a phenomenon that is not readily available to other scholars. Thus, the data generated from this study although could be considered as old, it is a strategic resource for understanding the predictors of self-regulation among NGOs in Ghana and other countries with weak regulatory frameworks because recent data are not available. Moreover, the underlying phenomenon of self-regulation among NGOs in Ghana has not changed significantly from the time the data was collected. For this reason, the data provides some useful insights and makes significant contributions to the literature on NGO self-regulation. Notwithstanding, we acknowledge that supplementing the ‘old data’ with newer data would offer fresh perspectives and insights into understanding the predictors of self-regulation among NGOs.
A second limitation is that the absence of financial information on the NGOs meant that resource dependency using the Herfindahl–Hirschman Index could not be measured because there was no information on the proportion of revenue generated from different funding streams. Instead, the number of donors was used. While acknowledging the need for a robust measure of revenue concentration such as the Herfindahl–Hirschman Index, in the context of Ghana and other developing countries, this would mean the need to improve the financial data of NGOs. This also requires the need for further studies on the transparency and accountability of NGOs in sharing their financial information with the public.
Third, we recognise that the small number of NGOs that participated in this study is a limitation of the study. Therefore, the findings are limited to NGOs in the selected sector and regions which affects the generalisability of the research findings across all sectors and regions in Ghana. There is, therefore, the need to expand the focus of this study to other sectors and regions in Ghana.
Self-regulation mechanism employed by sampled NGOs (n = 118).
| Form of self-regulation | Percentage of NGOs sampled practicing self-regulation |
|---|---|
| Governance and administrative policies | 30.5 % |
| Disciplinary mechanisms | 6.8 % |
| Code of ethics and conduct | 69.5 % |
| Accountability and transparency frameworks | 25.4 % |
| Auditing and reporting frameworks | 5.1 % |
-
Source, Author.
Variable definition and descriptive statistics.
| Variables | Description | Obs | Mean | SD | Min | Max |
|---|---|---|---|---|---|---|
| Self–regulation |
Dummy: 1: If NGO adopts self–regulation mechanisms; 0 otherwise |
118 |
0.915 |
0.279 |
0 |
1 |
| Scope |
Dummy: 1 = if the NGO is a specialist; 0 = otherwise (generalist) |
118 |
0.423 |
0.496 |
0 |
1 |
| Region | Continuous: Number of operational regions | 118 | 3.288 | 1.227 | 1 | 5 |
| Age (in years) | Continuous: Number of years of operation | 118 | 13.118 | 6.599 | 5 | 32 |
| Size | Continuous: Number of staff | 118 | 11.915 | 9.666 | 4 | 51 |
| Staff education | Continuous: Definite number of staff with university degree | 118 | 9.534 | 9.781 | 2 | 51 |
| Resource dependence | Continuous: Number of donors | 118 | 5.254 | 1.7205 | 2 | 7 |
| Membership of network or coalition |
Dummy: 1 = if the NGO is a member of at least one network or coalition; 0 = otherwise |
118 | 0.678 | 0.469 | 0 | 1 |
-
Obs, SD, Min and Max respectively denote the number of observations, standard deviation, minimum and maximum.
Correlation coefficient.
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) |
|---|---|---|---|---|---|---|---|---|
| (1) Self-regulation | 1.000 | |||||||
| (2) Scope | 0.015 | 1.000 | ||||||
| (0.875) | ||||||||
| (3) Number of operational regions | −0.078 | −0.034 | 1.000 | |||||
| (0.403) | (0.717) | |||||||
| (4) Age | −0.078 | −0.094 | −0.030 | 1.000 | ||||
| (0.402) | (0.313) | (0.750) | ||||||
| (5) Number of staff | −0.022 | 0.136 | −0.265 | 0.172 | 1.000 | |||
| (0.816) | (0.142) | (0.004) | (0.062) | |||||
| (6) Number of university degree holders | 0.001 | 0.089 | −0.148 | 0.066 | 0.478 | 1.000 | ||
| (0.991) | (0.340) | (0.109) | (0.479) | (0.000) | ||||
| (7) Number of donors | −0.132 | 0.013 | −0.051 | 0.198 | 0.009 | 0.087 | 1.000 | |
| (0.153) | (0.890) | (0.582) | (0.032) | (0.927) | (0.350) | |||
| (8) Membership of a network or coalition | 0.051 | 0.151 | −0.223 | 0.029 | 0.099 | 0.004 | 0.123 | 1.000 |
| (0.585) | (0.104) | (0.015) | (0.755) | (0.284) | (0.963) | (0.183) |
-
Values in parentheses are the p-values.
Marginal effect results of the full probit regression models.
| (1) | (2) | (3) | |
|---|---|---|---|
| Number of donors | −0.032** | – | – |
| (0.015) | |||
| Number of university degree holders | – | −0.002 | – |
| (0.003) | |||
| Membership of a network or coalition | – | – | −0.015 (0.061) |
| Age | −0.215*** | −0.2581** | −0.257** |
| (0.070) | (0.100) | (0.106) | |
| Age squared | 0.007*** | 0.008** | 0.008** |
| (0.002) | (0.003) | (0.004) | |
| Number of staff | −0.002 | 0.000 | −0.001 |
| (0.002) | (0.003) | (0.003) | |
| Scope (ref = generalist) Specialist |
0.010 (0.050) | 0.015 (0.054) | 0.017 (0.056) |
| Number of operating regions | −0.032* | −0.327 | −0.035 |
| (0.019) | (0.023) | (0.024) | |
| Number of observations | 118 | 118 | 118 |
-
*, ** and *** respectively denote significance at 10 %, 5 % and 1 %. Values in parentheses are the standard errors.
Results of probit regression model for coercive isomorphism and self-regulation.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Constant | 2.282 *** (0.670) | 2.447*** (0.721) | 17.80** (5.862) | 18.12*** (5.859) | 18.33*** (5.907) | 21.74*** (6.906) |
| Number of donors | −0.163 (0.112) | −0.152 (0.114) | −0.286 (0.156) | −0.315* (0.173) | −0.319* (0.179) | −0.342* (0.179) |
| Age | −0.016 (0.026) | −2.021*** (0.753) | −2.014*** (0.739) | −2.029*** (0.736) | −2.293*** (0.848) | |
| Age squared | 0.064*** (0.025) | 0.064*** (0.024) | 0.064*** (0.024) | 0.072*** (0.028) | ||
| Number of staff | −0.012 (0.022) | −0.019 (0.026) | −0.022 (0.026) | |||
| Scope (ref = generalist) Specialist |
0.287 (0.519) | 0.105 (0.539) | ||||
| Number of operational regions | −0.341 (0.216) | |||||
|
|
||||||
| Diagnostics | ||||||
|
|
||||||
| Number of observations | 118 | 118 | 118 | 118 | 118 | 118 |
| LR χ 2 | 2.33 | 2.73 | 24.07 | 24.35 | 24.66 | 27.43 |
| Prob > χ 2 | 0.1270 | 0.2558 | 0.0000 | 0.0001 | 0.0002 | 0.0001 |
| Pseudo R 2 | 0.0340 | 0.0398 | 0.3515 | 0.3555 | 0.3600 | 0.4005 |
| Log-likelihood | −33.080 | −32.881 | −22.208 | −22.071 | −21.915 | −20.531 |
-
*, ** and *** respectively denote significance at 10 %, 5 % and 1 %. Values in parentheses are the standard errors.
Results of probit regression model for normative isomorphism and self-regulation.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Constant | 1.372*** (0.235) | 1.680 *** (0.423) | 16.030 *** (5.806) | 16.250*** (6.009) | 16.430*** (6.037) | 20.490*** (7.880) |
| Number of university degree holders | 0.000 (0.017) | 0.001 (0.018) | −0.006 (0.022) | −0.009 (0.027) | −0.010 (0.027) | −0.016 (0.029) |
| Age | −0.023 (0.025) | −1.983** (0.778) | −2.018** (0.809) | −2.032** (0.807) | −2.412** (1.018) | |
| Age squared | 0.063** (0.025) | 0.064** (0.027) | 0.064** (0.026) | 0.076** (0.033) | ||
| Number of staff | 0.005 (0.026) | 0.000 (0.026) | 0.001 (0.029) | |||
| Scope (ref = generalist) Specialist |
0.265 (0.495) | 0.143 (0.505) | ||||
| Number of operating regions | −0.327 (0.220) | |||||
|
|
||||||
| Diagnostics: | ||||||
|
|
||||||
| Number of observations | 118 | 118 | 118 | 118 | 118 | 118 |
| LR χ 2 | 0.00 | 0.79 | 20.25 | 20.30 | 20.59 | 23.06 |
| Prob > χ 2 | 0.9905 | 0.6733 | 0.0002 | 0.0004 | 0.0010 | 0.0008 |
| Pseudo R 2 | 0.0000 | 0.0115 | 0.2957 | 0.2963 | 0.3006 | 0.3367 |
| Log-likelihood | −34.244 | 33.849 | −24.118 | −24.096 | −23.949 | −22.714 |
-
** and *** respectively denote significance at 5 % and 1 %. Values in parentheses are the standard errors.
Results of probit regression model for mimetic isomorphism and self-regulation.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Constant | 1.252*** (0.273) | 1.570*** (0.443) | 15.70*** (5.828) | 15.650*** (5.805) | 15.850*** (5.831) | 20.170*** (8.067) |
| Membership of a network or coalition | 0.187 (0.343) | 0.209 (0.346) | 0.161 (0.458) | 0.180 (0.491) | 0.124 (0.508) | −0.137 (0.559) |
| Age | −0.024 (0.026) | −1.951** (0.782) | −1.939** (0.783) | −1.959** (0.782) | −2.380** (1.045) | |
| Age squared | 0.062** (0.026) | 0.061** (0.026) | 0.062** (0.026) | 0.076** (0.035) | ||
| Number of staff | −0.002 (0.023) | −0.006 (0.025) | −0.005 (0.026) | |||
| Scope (ref = generalist) Specialist |
0.220 (0.508) | 0.157 (0.520) | ||||
| Number of operating regions | −0.328 (0.227) | |||||
|
|
||||||
| Diagnostics: | ||||||
|
|
||||||
| Number of observations | 118 | 118 | 118 | 118 | 118 | 118 |
| LR χ 2 | 0.29 | 1.15 | 20.31 | 20.32 | 20.51 | 22.83 |
| Prob > χ 2 | 0.5875 | 0.5637 | 0.0001 | 0.0004 | 0.0010 | 0.0009 |
| Pseudo R 2 | 0.0043 | 0.0167 | 0.2965 | 0.2967 | 0.2995 | 0.3334 |
| Log-likelihood | −34.098 | −33.672 | −24.089 | −24.084 | −23.989 | −22.829 |
-
** and *** respectively denote significance at 5 % and 1 %. Values in parentheses are the standard errors.
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