Measuring organisational governance capacity in healthcare organisations: a scale development and validation study

Abstract

Background

Research on governance has shown that many important outcomes have important relationships and essentially provide a framework that enables an organisation to achieve its goals, operate efficiently and behave ethically. Although this area of research is interesting, no scale has been developed in organisational settings or, made applicable to organisations. Therefore, this study aimed to develop and validate a scale of organisational governance capacity in healthcare organisations.

Methods

This is a scale development and validation study. A revised DeVellis and Hinkin scale development process was applied. While 12 experts in the field of governance, health and crisis assessed the content validity, 1013 employees evaluated the scale. The scale development process consisted of exploratory factor analysis, confirmatory factor analysis, test–retest analysis, and discrimination analysis. The internal consistency of the scale was analysed using Cronbach's α value.

Results

The final scale is a 5-point Likert-type scale consisting of 37 items based on four sub-factors. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were applied to ensure construct validity. The total variance explained by the scale was calculated as 82.22%, and CFA results showed that the four-factor structure consisting of 37 items exhibited good fit values. The internal consistency of the scale was evaluated with Cronbach's alpha value and this value was found to be .991. In addition, in the test–retest analysis, it was found that the correlation values between the responses received before and after were positive and very high (r = .998). These results reveal that the scale has a strong structure in terms of validity and reliability.

Conclusion

The “Organisational Governance Capacity Scale”, which assesses the governance capacity of organisations, is the first scale developed in the Turkish context. The development study has demonstrated excellent psychometric properties.

Introduction and literature review

Crisis situations, whether triggered by global events, economic downturns, or internal challenges, demand agile and robust responses. In such times, governance emerges as a critical framework for regulating organisational structures, policies, and processes, playing a pivotal role in determining how an organisation navigates uncertainty and disruption. Effective governance practices are especially crucial during crises, as they ensure resilience, maintain organisational effectiveness, and support long-term success. By providing clear direction and a structured approach, governance enhances an organisation's capacity to adapt and thrive, underscoring its importance as a core capability for sustained success and stability.

Organisational governance

Governance was used in the early 1990s to describe changes in government and transitions to a market economy [1]. Kooiman (1993) [2] considered governance as patterns emerging from the management activities of social, political and administrative actors, while Rhodes (1996:660) [3] defined governance as ‘networks of self-organising inter-organisations’ and Jessop (1998) [4] defined it as any coordination of interdependent activities or self-organisation. Stoker (1998) [5] focuses on characteristics such as uncertainty, power dependence and autonomous dynamics in governance, while Hirst (2000) [6] contributed to the literature by proposing five governance styles: good governance, global governance, corporate governance, networks and new public management. The work of Pierre & Peters (2000) is particularly important in terms of the distinction between ‘governance as a structure’ and ‘governance as a dynamic process’. Further elaborating this distinction, Levi-Faur (2012) [7] broadened the understanding of governance by attributing four meanings to the concept (structure, process, mechanism and strategy).

Organisational governance is a concept without a universally agreed definition, but there is general agreement that it involves the direction, control and accountability of a business [8, 9]. It essentially involves balancing the interests of an organisation’s many stakeholders such as shareholders, senior managers, customers, suppliers, financiers, government, employees and society [10]. It requires the formulation, monitoring and implementation of organisational structures [11]. It promotes the efficient use of resources and improves overall organisational performance [10].

The focus of this study is on the organisational level and how organisations are governed in the field of business. The term organisational governance is often used to refer to governance at this level. Accordingly, it is important to distinguish organisational governance from public or political governance. It is also argued that governance capacity should include formal, structural and procedural features of the government administrative apparatus, as well as informal elements, i.e. how these features work in practice, and that each organisation should include these features. In this study, governance is adapted to healthcare organisations, such as hospitals, based on the definitions and frameworks used in public administration and a new framework is introduced.

Organisational governance capacity

Governance and governance capacity have been extensively analysed in the public administration, development, capacity building and environmental governance literatures. Although theoretical and empirical work on these concepts has increased, there is a lack of knowledge on what constitutes governance capacity, fragmented and non-integrative approaches, which hinders a full understanding of governance capacity. Governance capacity is a complex and multidimensional construct. Although various studies have defined different dimensions or components, most of them do so from a theoretical point of view, and hardly any of them have designed a measurement tool based on the defined dimensions. However, many studies focus on specific aspects of governance capacity, which does not provide a holistic view of the concept.

Some emphasise "collaborative capacity", "collaborative governance" [12,13,14,15,16,17,18], while others focus on "adaptive capacity" [19,20,21]. Others focus on concepts such as "network governance" [22], "policy capacity" [23, 24], "reflexive governance" [25]. These concepts provide various strategies to account for multiple frames. Stoker (2019) [5] presented a framework for analysing the combination of formal and interactional capacities, while Koop et al. (2017) [26] presented a framework for knowing, wanting and enabling capacities.

Governance capacity is a complex concept characterised by a combination of many factors. These include the quality of relationships between actors, the utilisation of resources, the availability of knowledge and skills, continuity of participation, institutional structures and the effectiveness of governance processes. In addition to these factors, governance capacity can be influenced by external factors such as the characteristics of the context, cultural factors, social norms and political structures. Therefore, to analyse governance capacity, all these factors need to be examined together.

Van Popering-Verkerk et al. (2022) [27] take an “integrated approach” to governance capacity, focusing on five elements: collective action, coordination, resilience, learning and resources. These elements emphasise the relationship and space for action between actors in collective action. Coordination capacity involves information sharing and co-operation based on mutual trust, while resilience refers to the ability to cope with opportunities and threats. Learning takes place in the cognitive, social and institutional dimensions, while resources include not only the ability to possess but also the ability to manage. Yang & Meng (2023) [28] showed that governance capabilities are ‘governance response speed’, ‘mutual support and assistance’, emergency response capability using ‘expertise’, collective and cooperation capability, professional leadership and capability to learn from the past.

Dang, Visseren-Hamakers & Arts (2016) [29] developed a framework for assessing governance capacity based on a “policy arrangement approach”. While this framework emphasises three key elements—enabling rules of the game, converging discourses and facilitating resources, and their inter-linkage, Termeer, Dewulf, Breeman & Stiller (2015) [30] identify four key governance capabilities: reflexivity, resilience, responsiveness and revitalisation. These capabilities cover the observation, action and enabling dimensions of governance. These concepts emphasise how policymakers and management systems can be effective while identifying key elements of governance capacity.

Lodge & Wegrich (2014) [31] and Christensen, Lægreid & Rykkja (2016) [32] distinguish four types of governance capacities: coordination capacity about bringing together disparate organisations to engage in joint action; analytical capacity is about analysing information, providing advice as well as risk and vulnerability assessments; regulation capacity is about control, oversight, adulting and surveillance; and delivery capacity is about about handling the crisis, exercising power, and providing public services in practice.

Various concepts are commonly referenced in the literature on governance capacity. At the most basic level, organisations need capacity (coordination, the ability to synthesise information, to link observations to actions and to operationalise those actions effectively, cooperation, learning…) to respond successfully to shocks. Governance capacity building is therefore an important goal for governments as well as for organisations and involves building the skills, processes and systems necessary to support effective governance. A strong organisational governance capacity is essential to ensure the long-term sustainability, resilience and success of an organisation. In summary, governance capacity is a strategic priority for organisations as part of conversations that address resilience, success and sustainability by using factors such as cooperation, management, communication, resource management, problem solving, learning to achieve organisations' goals.

In the context of this study, governance capacity is understood as follows: “the multifaceted and dynamic capability of an organisation to effectively design, adapt and implement governance structures and processes.” It reflects the capability of individuals or organisations to achieve and protect a common goal, coordinate actions, mobilise resources, learn from challenges, manage risks, ensure stakeholder engagement and align strategic decision-making with resilience, and manage governance as a living system in a constantly changing environment, while maintaining transparency, accountability and ethical standards.

The healthcare sector operates in a dynamic ecosystem characterised by uncertainty, diversity and interactive complexities. In this complex landscape of diverse elements and stakeholders, healthcare organisations need to deal with uncertainty from a perspective that embraces diversity, adapts to interactive complexities, and focuses on both short-term crisis management and long-term sustainability. Crises such as pandemics, natural disasters or other emergencies can profoundly affect health systems, and the specific structures and operational dynamics of health organisations such as hospitals require preparedness for these challenges. By focusing on crisis management and governance processes in various types of hospitals in Turkey, this study aims to understand how health systems respond to challenges and the effectiveness of these responses.

Answers were sought to the following questions in the study:

• What are the main components and sub-dimensions of governance capacity?

• How can a scale that meets the criteria of validity and reliability be designed to measure organisational governance capacity?

• How can a scale that meets the criteria of validity and reliability be designed to measure organisational governance capacity?

• Is the "Organisational Governance Capacity Scale" a valid and reliable measurement tool for measuring the governance capacity of organisations?

Methodolgy

In this scale development and validation study, the accepted scale development approach proposed by DeVellis (2022) [33] and Hinkin (1995) [34] was followed and summarised in three phases: Phase I: Item Generation and Development; Phase II: Item Refinement; and Phase III: Instrument Testing and Validation. Figure 1 represents the scale development approach used in this study to assess the structure and validity of the OGCS.

Fig. 1

Qualitative and quantitative phases of scale development and validation (Flow chart)

Phases of scale development

Phase I: item generation

The goal of the item generation phase is to develop the items that will be included in the measurement tool. In the item generation phase, which is the most important part of the scale development process, a combination of both deductive and inductive approaches were used. The deductive method involves creating items based on a comprehensive literature review and pre-existing scales [34], while the inductive method is based on qualitative information obtained from the opinions collected from the target population [35]. At this phase of the study, interviews were conducted with the target population in addition to a comprehensive literature review, which is the first and necessary basis for creating a new scale. Evaluations on issues such as clarity and comprehensibility of these statements, easy reading, answering and understanding were also taken into consideration, and superficial validity was ensured. This phase includes two steps: (1) Literature Review; (2) Individual Interviews.

Step I- literature review

Literature review was limited to online sources like Scopus, Web of Science, PubMed, Ebsco Host, Elsevier-Science Direct, JStor, Taylor & Francis and TUBITAK Ulakbim (National Academic Network Information Center). These sources were searched without any time period, language and discipline selection; and it was limited to refereed journal articles, books and book chapters. Firstly, all entries with ‘governance’, ‘governance capacity’, ‘governance capability’, ‘governance in times of crisis’ and ‘crisis governance’ in the title, abstract and keywords were selected, and then studies defining governance capacity as a distinctive concept were prioritised. Furthermore, for item generation according to the definition followed in this research, concepts referring to crisis management or organisation were taken into account, although care was taken to consider examples that generally reflect the application of governance at the organisational level. As part of the item generation process, existing governance and crisis management measurement tools were also considered.

Step II- individual interviews

Interviews were conducted with different professional groups from different hospital groups in the health sector. Care was taken to ensure that these samples were diverse in terms of gender, age, years of employment, education and that they represented both Ministry of Health, University and private hospitals in Istanbul. The reason for conducting individual interviews was to determine the applicability of the identified organisational governance capacity structures and what employees experience, to reveal what factors affect this phenomenon, to find common meanings, and to find new governance dimensions not captured in the literature. In addition, interpretations were made in terms of the responses and meanings attributed by employees in their own settings and conditions, considering national and organisational culture. In this step, an interval of 5 to 15 interviews is considered ideal, covering two to three rounds or until saturation is reached where relatively few new insights emerge [36].

Phase II: item improvement

The next phase after item generation is to assess clarity of expression, redundancy of expression, choice of response format, as well as face and content validity. Achieving good face and content validity depends not only on how accurately a construct is defined, but also on the extent to which experts agree on the dimensions and measurement items [37]. This phase includes two steps: (1) Expert Panel; (2) Target Population Opinions /Pilot Testing (pre-test).

Step I- expert panel

To select the most valuable items, Lawshe's (1975) [38] content validity ratio (CVR) formula was used. 12 experts were consulted to assess the content validity of the 62 items of the draft Organisational Governance Capacity Questionnaire. Since governance capacity is a multidimensional and multidisciplinary concept, experts from different fields, academic researchers (governance, public administration, business administration, crisis management and political science, linguistics) and various professionals (disaster and emergency, health, quality, occupational health and safety, measurement and statistical analysis) were consulted. The expert panel also included various stakeholders who would use the scale in a practical setting to ensure that it reflects real-world needs and challenges. In this study, the critical cut-off value for 12 experts was 0.56. Further deletions and modifications were made following additional comments from the panel regarding clarity, length, and duplication.

Step II- Target Population Opinions /Pilot Testing (pre-test)

The steps so far have been based on theoretical foundations, previous empirical evidence and expert opinions. This step and next steps involve the application of these constructs on appropriate samples. These are considered as the summary of the scale development process after item development [33]. In this step, the draft questionnaire form, which was finalised by making corrections to the items after receiving expert opinions, was applied to the sample group. The draft scale items were scored on a 5-point Likert-type scale and were graded as 1 "Strongly Disagree", 2 "Disagree", 3 "Neutral, 4 "Agree", or 5 "Strongly Agree".

Phase III: Construct validity

This phase consists of selecting the most appropriate item to be included in the final scale and measuring the performance of individual items to examine the dimensionality of the scale after the initial item pool has been developed and pilot tested (pre-tested) in a representative sample [33]. This is the stage where no expectations can be made about the nature and number of factors. The dimensionality of a scale was examined by Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) [39, 40]. The estimated time required for employees with different positions and years of experience to complete the questionnaire without interruption is 15–20 min.

Population and sample of the research

In scale development studies, it is considered common and ideal that the sample size should be between 5 and 10 times the number of items [41,42,43,44]. In this study, for the stability of factor analysis, the sample size is calculated approximately 15 times the 62 items in the initial scale, and approximately 20 times the 48 items that survived the first stages of scale development. As a result, data were collected from a sample of 1092 people working in University, Ministry of Health and Private hospitals by questionnaire method. In the selection of the employees, questionnaires were distributed by simple random sampling method. The questionnaires that had too many missing values or showed obvious reaction tendencies were eliminated, as a result 1013 questionnaires were taken into consideration.

Data collection

Data were collected by face-to-face questionnaire method. A questionnaire form consisting of 48 items to measure governance capacity, and 6 items related to the demographic and organisational characteristics of the participants, totalling 54 items were used.

Data analysis

The data were analysed using IBM SPSS 23 (StatisticalPackage for the Social Sciences) and AMOS 23. Descriptive information, item analyses, content and construct validity, reliability analyses and confirmatory factor analyses were performed. The kurtosis and skewness values of the normal distribution of the scale items were analysed. Kolmogorov–Smirnov test was used to test the suitability of the data for normal distribution, sperman rho correlation analysis was used to determine item total score correlations, regression analysis was used for item residual correlation, Independent Samples t-test was used for item discrimination analysis.

The Kaiser–Meyer–Olkin (KMO) sample fit scale and Bartlett’s test of sphericity was performed with a KMO value ≥ 0.50 and a p-value for Bartlett’s test < 0.05 to examine the appropriateness of the factor analysis. KMO coefficient and Bartlett's sphericity test were used for Exploratory Factor Analysis, Cronbach's Alpha, AVE, CR coefficients were used to test internal consistency, Wilcoxon test was used to test invariance against time, and Confirmatory Factor Analysis was used to test construct validity. The reliability of the whole scale and each factor was deemed to be acceptable if the Cronbach’s α > 0.65 [33].

Results

Scale development: development and content validitiy of initial items

Based on a comprehensive literature review and individual interviews, an item pool consisting of a total of 62 items was initially created. While 56 of these items were derived from the literature, the remaining 6 items were added after individual interviews. Since these interviews did not reveal new insights and the responses were repetitive, data saturation was considered to have been reached and the data collection process was terminated after 20 participants. In the interviews, participants were asked to provide concrete examples of crises they had experienced in relation to organisational governance and governance capacity. Seventy percent of the participants were employees (40% with a postgraduate degree and 60% with a bachelor's degree) and 30% were managers. In terms of tenure, 40% had 6–10 years of experience, 30% had 11–15 years of experience and 30% had 16–20 years of experience. Some common concepts frequently mentioned in the literature review and interviews were analysed and categorised (Appendix 1).

After the interviews, the items were refined and the item pool was evaluated by a panel of 12 experts. The 15 items below the critical value of 0.56 were removed from the scale, 3 items were revised and 1 new item was added. At the end of this process, a draft scale consisting of 48 items was prepared. The draft was tested on a sample of 100 people with similar characteristics to the target group. A section was also added to the questionnaire where the participants could share their feedback and suggestions about the statements. 51.7% of the participants work in university hospitals, 26.8% in private hospitals and 21.4% in hospitals affiliated to the Ministry of Health. As a result of the positive feedback received regarding the comprehensibility of the statements, the scale was started to be applied.

Normality test and item discrimination analyses

The kurtosis and skewness values of the normal distribution of the scale items were analysed. In this study, since the skewness values of the scale items are out of range and negative, they are left skewed away from the normal distribution; and since the kurtosis coefficients are negative, the normal distribution curve is flattened compared to normal. According to the skewness and kurtosis values, no item was removed (Table 1).

Table 1 Distribution statistics and normal distribution results of organisational governance capacity questionnaire

Sperman rank correlation test was performed to determine the item total score correlation and the correlation values between the items were found in the range of 0.376–0.895 (Appendix 2). In the regression analysis performed to determine the item residual score correlation, the lowest correlation was found in the first item with 0.514 and the correlation values for the other items were in the range of 0.77–0.909 and all items were found significant. Since there is a positive and linear relationship between all items in the scale and total item scores, all items are discriminative (Table 2).

Table 2 Item total and residual score correlation results of scale scores

In the Comparison of the Item Score Averages of the Lower–Upper 27% groups, the mean scores of the lowest and highest 27% extreme groups were calculated in the ranking formed by ranking the scores of each item from lower to higher, and the difference between the mean scores was determined as a result of the test between these groups and a statistically significant difference was found (p < 0.05). The respondents in the upper group have the characteristic that the scale wants to measure in a positive way, while the respondents in the lower group have the characteristic in a negative way. The respondents in the upper group have the characteristic that the scale wants to measure in a positive way, while the respondents in the lower group have the characteristic in a negative way (Table 3).

Table 3 Comparison of 27% lower and upper group score means

Exploratory Factor Analysis (EFA) of the OGCS

According to Table 4, 41.6% of the participants were between 21–30 years of age, and 44.9% had a bachelor's degree. It was determined that 50% of the employees had 1–5 years of experience, 54.5% were nurses and 51.7% worked in a university hospital. The Kaiser‒Meyer‒Olkin (KMO) value and the Bartlett’s sphericity value are shown in Table 5.

Table 4 Participants’ personal and professional characteristics

Table 5 Kaiser‒Meyer‒Olkin and bartlett’s test

According to Table 5, the KMO value was 0.988, Bartlett's sphericity value was significant (p < 0.05).

As a result of the factor analysis performed with the principal components method by including all items, items 1, 18, 19, 20, 28, 29, 31, 32, 33, 41 and 45 were removed and the factor analysis was completed with 37 items in the final state. After these items were removed from the scale, EFA was conducted once again. The results of the factor analysis are shown in Table 6. Accordingly, the highest contribution was provided by factor 1 with 75.88% and 4 factors explained 82.22% of the total variance.

Table 6 Explanatory factor analysis

At the end of the factor rotation process using the Promax Rotation Method, items 1, 18, 19, 20, 28, 29, 31, 32, 33, 41, 45, which had less than 0.1 difference between factor loadings or factor loadings below 0.4, were excluded from the scope. As a result of EFA, 37 items were included under 4 factors. There are 11 items in factor 1, 7 items in factor 2, 12 items in factor 3 and 7 items in factor 4. The correlation values between the factors ranged between 0.746–0.821. In this case, it is concluded that there is a positive, linear and high degree of relationship between them (Table 7).

Table 7 Rotated factor loadings

The factors were named by considering the common characteristics of the items in the factor dimensions: Factor 1 “Responsibility Capacity (RC)”, Factor 2 “Coordination and Cooperation Capacity (CCC)”, Factor 3 “Analytical Capacity (AC)”, Factor 4 “Self-Organising Capacity (SOC)”.

Confirmatory Factor Analysis (CFA) of the OGCS

CFA was performed to test whether the factors determined by the EFA were appropriate for the factor structures. The relationship between observable variables consisting of Likert-type questions and unobservable variables called factors or latent variables was measured. Thus, it was aimed to reveal how much the observable variables explain the latent variables. Figure 2 shows the first-level factor analysis results of the scale.

Fig. 2

First-Level Confirmatory Factor Analysis Path (PATH) Analysis of the OGCS

According to Fig. 2, the correlation between the factors is between 0.88 and 0.95. The correlations between the items of the sub-dimensions are generally high and there is a strong relationship between them.

After the CFA path diagram was determined, the fit indices were examined to see the compatibility of the data with the model. Table 8 shows the reference ranges and the values calculated in this study.

Table 8 Evaluation of fit indices related to confirmatory factor analysis for the OGCS

According to Table 8, when CFA results and fit indices are evaluated as a whole, it shows that a correct model was established in CFA and it is compatible with the results of EFA and CFA results show that the construct validity of the scale is appropriate.

Reliability analysis

Reliability is the degree to which a test or scale consistently and consistently measures the phenomenon it is intended to measure. If the phenomenon to be measured consists of question items, it is the consistency between the answers given by individuals to the test items. In order to measure the internal consistency of a scale, a value between 0 and 1 must be reached. Internal consistency shows the extent to which all items measure the same concept or construct and thus establish the relationship between the items [45]. Cronbach's alpha coefficient was used to determine internal consistency.

Convergent validity is tried to be understood with various criteria. The first of these is "individual item reliability". In order to fulfil this constraint, Cronbach's Alpha value should be 0.50 and above. The second is the “Composite Reliability (CR)” value, which is similar to Cronbach's Alpha value, and the standard is above 0.70. The third is the “Average Variance Extracted (AVE)” value, which should be above 0.50. According to Psailla and Wagner (2007) [46], AVE values above 0.40 and CR values greater than 0.7 are acceptable values.

Although AVE is a value that should be given under the confirmatory factor, composite reliability and its coefficients are shown together with Cronbach's Alpha reliability coefficients in order to give the reader a better idea and comparison.

Convergent validity and reliability coefficient values are shown in Table 9.

Table 9 Reliability analysis results regarding the organisation governance capacity scale

According to Table 9, the overall scale and subscales are highly reliable. Since the AVE value showing the similarity in the scale is in the range of 0.626–0.761 in the sub-dimensions, convergent validity is ensured. The CR criterion is 0.7 and the sub-dimension CR values are in the range of 0.822–0.902, which provides additional evidence for item reliability. Therefore, the scale's reliability is ensured.

Test–retest, which is one of the reliability analyses, is performed to evaluate the invariance of the scale over time. The test–retest method analysis results are shown in Table 10.

Table 10 Comparison of test–retest results of the items included in the organisation governance capacity scale

The 37 items included in the final version of the scale used within the scope of this research were applied to the group of 38 people twice with two weeks intervals. According to the results in the table, there were no significant differences between the responses of the participants to the subscales and the total score for both applications (p > 0.05). The fact that no significant difference was found indicates that the answers given by the participants at different times were consistent and therefore the questions were understood in a similar way at different times. The correlation values between both applications are in the same direction, positive and very high (r: 0.998). Therefore, the measurement ranges of the scale questions are consistent.

Discussion

This study developed the first scale designed to measure the OGC of hospitals. The OGCS showed good reliability and met the criteria for content and construct validity. The validity of the scale was determined by identifying the constructs of the concepts and developing items that effectively measure each construct [33]. This measurement tool can be used in the health sector and different sectors in studies on this subject.

The Organizational Management Capacity Scale provided four factors explaining 82.22% of the total variance. The factor loading values of the scale items ranged from 0.493 to 0.905. The subscales were named responsibility capacity, coordination and cooperation capacity, analytical capacity and self-organization capacity. CFA was performed to test the accuracy of the structure formed as a result of EFA. The fit indices calculated for the model were Chi-square (p) = 0.000, Chi-square (sd) = 4.92; RMSEA = 0.062; CFI = 0.95; GFI = 0.93; AGFI = 0.86; RMR = 0.026 for CFA. These values indicate that the fit indices are within the acceptable range. After the construct validity of the scale was established, Cronbach's alpha coefficient was calculated to determine internal consistency and reliability was ensured using the test–retest method. Cronbach's alpha coefficient was calculated as 0.991. As a result of the test–retest, the correlation coefficient was calculated as 0.998 and thus it was concluded that there was no significant difference between the participants' responses at the two time points (p > 0.05). The findings of this study demonstrate the validity and reliability of the Organizational Governance Capacity Scale as evidenced by its strong factor structure and high internal consistency. These results support the relevance of the scale for assessing the governance capacity of organizations and provide valuable insights for future research and practical applications.

Consisting of 37 items and 4 subscales, the "Organisational Governance Capacity Scale" was designed to be easy to understand and transparent and was developed with end-users (including decision makers, managers and employees) in mind. This is crucial to facilitate constructive discussions, joint knowledge production and co-operation. The OGCS is the first questionnaire developed in the Turkish context and has demonstrated excellent psychometric properties. The English language version is presented in Appendix 3.

Responsibility capacity is a multifaceted concept that includes transparency, accountability, innovation, adaptability and stakeholder engagement. Transparency builds trust in decisions and processes, while effective control and audit mechanisms ensure accountability. Innovation underpins progress by providing creative solutions to problems. A responsible organisation takes these elements beyond corporate social responsibility and adopts a strategic approach for long-term success and sustainability.

Coordination and co-operation capacity is critical for an organisation to achieve its objectives. Coordination involves the effective organisation of resources, information and activities, ensuring the harmonious alignment of units, roles and efforts [47]. Co-operation strengthens the capacity for joint action by bringing together leadership, information sharing and resource management towards common goals [18]. Effective coordination and co-operation allows blind spots to be avoided, innovation to be encouraged and quick solutions to problems to be found. Especially in complex organisations, this capability improves the quality of care and services by providing agility and flexibility [48].

Analytical capacity is the collective ability of an organisation to collect, analyse, interpret and use data to inform strategic decisions, solve complex problems and achieve goals [32]. It goes beyond individual skills and encompasses the organisation's culture, processes and the technology infrastructure that supports data-driven applications. This non-static capacity is shaped by the need for continuous improvement and adaptation to changing needs.

Self-Organising Capacity is based on the principles of complexity theory and systems thinking, recognising that organisations are complex systems composed of interconnected components such as people, processes, and technologies. This capacity refers to the ability of components to reorganise their operating patterns, adapting to changing needs, capacities and environmental demands [49]. This adaptability makes organisations resilient and flexible in the face of changing environments [50]. Furthermore, visioning, building trusting relationships, effective coordination and the design of an organisational structure that facilitates cooperation are essential elements of self-organising capacity.

Healthcare organisations are faced with an environment that requires continuous adaptation and rapid response due to the fact that they operate in a complex and dynamic environment. This dynamism keeps organisations in the health sector in a constant effort to cope with various factors and provide the best service. In this context, it is of utmost importance that governance systems are resilient to external disturbances, uncertainties and surprises; at the same time, they can rapidly adapt to environmental variables in line with ecosystem dynamics. Crisis situations, whether caused by global events, economic downturns or internal challenges, require agile and robust responses. This is where governance, as a regulator of organisational structures, policies and processes, becomes a key element in determining how organisations move forward in turbulent times. In particular, the successful management of these processes depends on the strength of organisational governance capacity. Organisational governance capacity stands out as a critical capability that increases an organisation's resilience to crises, sustainable success and effectiveness. Therefore, making organisational governance capacity measurable is important both for organisations to assess their current situation and to make strategic improvements.

The development of such a scale is a necessary tool to measure and strengthen the effectiveness of organisations in crisis management, adaptation and decision-making processes. In this framework, systematic measurement of organisational governance capacity in hospitals, which are the cornerstone of the health sector, provides opportunities for improvement in crisis management by revealing the strengths and weaknesses of hospitals in their current governance structures. Given the dynamic nature of the health sector, developing such a scale is a strategic necessity for hospitals to ensure sustainable success and effectiveness both in routine periods and in crisis situations. Moreover, since the health sector involves different disciplines, this study offers a holistic perspective by strengthening the link between medicine, social sciences and public policy. In this context, the Organisational Governance Capacity Scale (OGCS) developed in this study stands out as a valid and reliable tool developed to measure the governance capacities of healthcare organisations, especially hospitals, and to improve their crisis management processes.

Limitations

This research was conducted at a time when the effects of a global pandemic that caused serious effects and changes around the world, and the earthquake that occurred in Turkey in 2023. Although research conducted during periods of extraordinary events such as pandemics or earthquakes can help us understand the effects of events and better prepare to deal with similar situations in the future, the stress, anxiety and difficulties experienced by the participants may affect their answers to the questions. In addition, the qualitative research part is limited to the answers given by the participants to the research questions, and the quantitative research part is limited to the answers given to the scales used in the research.

Conclusions

The results showed that the OGCS is a valid and reliable scale for measuring the governance capacity of hospitals. Measuring the organisational governance capacity of hospitals can be useful to assess the ability to respond quickly and effectively to crises, use resources efficiently, increase coordination among stakeholders and achieve long-term strategic goals. In addition, by identifying strengths and weaknesses in governance processes, this measurement can enable improvements to be made to improve service quality and support sustainability goals. The scale was developed in Turkish and then translated into English, but the validity of the translated scale has not been verified.