**Hypothesis 3a (H3a).** *The regulatory environment is expected to significantly affect the implementation of CG by mediating organizational culture.*

Regulation is the management of decisions that are made very complex following a set of rules made by the government and were in force at that time. To their needs, regulations are made according to the context. The regulatory environment requires the compliance of the various parties involved to behave by the established rules of the game so that organizational goals can be achieved effectively. In terms of CG in Indonesia, the government stipulates regulations that must be complied with by companies in Indonesia (Morgan 2006). Gallego-Alvarez and Pucheta-Martinez (2019) states that the regulatory environment will face formal and coercive pressures to comply with social standards within organizations. This coercive power is closely related to regulatory agencies that have the power to sanction companies (e.g., legal mechanisms). For J. L. Campbell (2006), Coercive pressure is closely related to the main regulatory instruments that can sanction companies, such as legal and enforcement mechanisms. Larrinaga (2007) views this type of coercive isomorphism as involving regulations that encourage the disclosure of ecological information, guarantee mandatory compliance, or threaten future regulation. Coercive pressure is usually associated with governments and regulatory agencies. This pressure is closely related to the main regulatory instruments that can sanction companies, such as legal and enforcement mechanisms. J. L. Campbell (2006) supports that companies will behave more responsibly by conducting their activities in an institutional environment with greater coercive pressure and where the legal system is oriented towards protecting stakeholder interests.

**Hypothesis 3b (H3b).** *The regulatory environment is suspected to influence the implementation of CG by mediating the organizational structure.*

The institutional theoretical notion is that the institutional environment can greatly influence the development of formal and informal structures in an organization, often greater than market forces and pressures (Lounsbury 2005). The institutional theory addresses elements of social structure in a deeper and more resilient way: there is a need to consider the processes by which structures, including norms, rules, routines, and schemes, become institutionalized as authoritative parameters or guides for social behavior (Scott 2004). Following the philosophy and logic of this theory, it can be argued that one of the main influences responsible for effective CG compliance within a country is the existence of institutions that can compel organizations to adopt and implement transparent and fair CG practices (Judge et al. 2008). Greenwood et al. (2008) argues that coercive isomorphism occurs because organizations tend to be motivated to avoid sanctions. In research, La Porta et al. (2002) found that best practices in CG can only thrive in the presence of a good legal and regulatory framework. For a CG framework to be effective, legal entities and regulators must be sound so that investors can rely on them when they enter into contractual agreements.

Intense product market competition forces management to improve financial performance and make the best decisions for the future since failure to do so is likely to result in bankruptcy and job loss. Well-managed companies will take over the market from poorly managed companies. The competition will help bring out the best performance from the management team and discipline management. In Allen and Gale's (2000) model, competition is a substitute for external CG mechanisms, particularly the market for firm control.

**Hypothesis 4a (H4a).** *The competitive environment will affect the implementation of CG by mediating organizational culture.*

The competitive environment is considered to influence the discipline of organizations by eliminating inefficient organizations (Udayasankar et al. 2008). Holmstrom (1982) considers that the competitive environment makes monitoring more efficient on the culture of corruption by managers. Udayasankar et al. (2008) and Gallego-Alvarez and Pucheta-Martinez (2019) classify the competitive environment as an isomorphism through a mimetic process. Scott (2001) identifies from mimetic processes due to cognitive institutional influences. He argues that the mimetic institutional perspective through resource dependence as one of the reasons that can explain the effect of competition must be mediated by a productive and useful organizational culture to be able to make CG implementation effective.

**Hypothesis 4b (H4b).** *The competitive environment will influence the implementation of CG by mediating the organizational structure.*

Udayasankar et al. (2008) explained that the competitive environment has a negative effect on CG if the organization has a complex structure. This argument is based on the competitive advantage that arises from CG, which acts as a driving force for organizations to improve CG implementation. However, Udayasankar et al. (2008) demonstrated that, as perceptions of the competitive environment increase with high organizational structure complexity, it weakens CG rather than enhances it. Hatch and Cunliffe (2013) emphasizes that the type of structure is the most important thing in the organization. This opinion is based on the organizational structure that will encourage or restrain an innovation from being implemented. The organizational structure is a boundary that opens up various possibilities. These constraints create the possibility of choice and action. Without any restrictions, possible action will not exist (D. J. Campbell 1985).

**Hypothesis 5a (H5a).** *The national cultural environment will influence the implementation of CG by mediating organizational culture.*

Wibowo (2008) emphasizes that organizational culture is a resource that produces competitive cultural advantage. A company that ultimately allows companies to achieve better results. Hitt et al. (2001) conducted studies that study the relationship between organizational culture and CG, which illustrates that the explanatory power of organizational culture becomes very important when the national cultural environment also supports organizational culture as an invisible resource in generating competitive advantage. The Indonesian context is no exception because organizational culture is a long-lasting resource and provides better company performance. It is because the cultural environment will shape organizational culture, which is valuable, rare, inimitable, and irreplaceable. The unique nature of the cultural environment that shapes organizational culture will differ greatly from country to country (Barney 1986). Related to the importance of organizational culture to CG, culture needs to be studied thoroughly to reveal its role in CG (Schein 1992). From a theoretical point of view, CG is thought to help prevent scandals, fraud, and other potential problems that can damage a company. A company with a good CG image will enhance the company's reputation. Semenov (2000) states that organizational culture significantly impacts a company's ability to realize goals and plans.

**Hypothesis 5b (H5b).** *The national cultural environment will influence the implementation of CG by mediating the organizational structure.*

Khan and Law (2018) states that the cultural environment is composed of values and beliefs and is the programming of the collective mind. The cultural environment system is a set of values, attitudes, and ways structurally and historically developed and shared. The cultural environment will directly or indirectly affect the organization in terms of organizational design, work design, and organizational rewards. In terms of how good CG implementation emphasizes the existence of structural variables where the cultural environment mechanism is translated into a structure within the organization. Feng (2017) states that the complexity, formalization, and centralization of decisions will greatly affect the implementation of CG in a company. According to him, studying the organizational structure is a way to focus on maximizing CG contribution. Gallego-Alvarez and Pucheta-Martinez (2019) state that the cultural environment that influences the organization is the basis of normative isomorphism, where the organizational structure will adapt to the norms, values, and orders that distinguish one society from others throughout the world. Thus, the cultural environment guides the organization in shaping its structure.

#### **3. Methodology**

This research was conducted with a quantitative approach using a questionnaire or survey method, which consists of an explanatory survey with a correlational design and a descriptive survey. The research population is state-owned and private companies in the Bontang Industrial Estate (KIE) Kaltim Industrial Area in Lok Tuan, North Bontang. The BUMN cluster has a population of 289, while the private cluster has a population of 189, bringing the total population in this study to 407 people. A sample survey was conducted from a population of 407 people to test the instruments to be used. Facts were found in the field that line 3 managers have very low awareness. They do not even know about CG, which is the subject of research. With these considerations in mind, line 3 managers who are operators and technical employees were removed from the list of populations to be targeted.

The target population in this study was 199 respondents, with a total of 144 SOE respondents and a total of 55 private respondents. The sampling technique in this research used purposive sampling by considering the size and representativeness of the population. The sample limit measurement used the Slovin formula (Bordens and Abbott 2011), as in Equation (1).

$$m = \frac{N}{1 + Ne^2} \tag{1}$$

where *n* is the sample size, *N* is the population size, and *e* is the error rate.

The proportion of the BUMN sample is 72.3% of the total minimum sample using the Slovin formula, while private companies are 29.3%. Thus, the minimum sample of BUMN is 95 respondents and the private sector is 39 respondents. Sources of research data come from primary data and secondary data. Primary data were obtained by: (1) a questionnaire survey; (2) an interview survey; and (3) non-reactive methods and available statistical data. Collecting primary data using an instrument in the form of a questionnaire consisting of closed questions using intervals and open questions is used to obtain a more comprehensive picture. Open questions use a ratio scale to be coded and analyzed using statistical tools. In contrast, secondary data were obtained by policy documents, statistical documents, or monographs and reporting documents issued by state-owned and private companies. The policy documents used in this study are (1) Law Number 40 of 2007 concerning Limited Liability Companies; (2) BUMN Law Number 19 of 2003; (3) Regulation of the Financial Services Authority Number 21/POJK.04/2015 concerning the Implementation of Public Company Governance Guidelines; and (4) SOE Minister Regulation Number Per 01/MBU/2011 concerning the Implementation of Good Corporate Governance (GCG).

In this research, there are three variable concepts, including the Regulatory Environment, the impact on professionalism will be used the stimulus-organism-response (SOR) model of Mehrabian and Russell (1974). Regulatory Environment (X1), Competition Environment (X2), Cultural Environment (X3), Organizational Culture (M1), Organizational Structure (M2), and Implementation of Corporate Governance (Y1). Scaling indicators variable response indicators using interval scales with scores 1–7, which means that the value one will be worse and the value seven will be better for assessing the variables' attributes (Nachmias and Nachmias 1987). Data will be said as good and quality if using quality measurement instruments. A quality instrument is an instrument that has a reliability of a measure and validity or validity of a measure. The variable reliability test method used the Pearson Product Moment Correlation Reliability method and Cronbach's Alpha. Pearson Product Moment Correlation, to measure the strength of the relationship between variable X and variable Y, and be used to determine the validity of an instrument for several interval data. Validity test of the measure to find out how well the indicators represent the variables following the operational definition of the variable: the better the suitability, and the higher the validity of the measurement (Newman et al. 2013). Validity test of the criteria level carried out in research by testing and calculating the Pearson Correlation coefficient between each indicator with a total score of all indicators.

The research data analysis method using descriptive data analysis and partial least squares analysis (PLS-SEM) makes it possible to simultaneously test the relationship between multiple exogenous and endogenous variables to explore and predict the relationship between latent variables because the theory is undeveloped or weak. Partial Least Square (PLS) is a multivariate statistical analysis that can estimate/test the research model simultaneously both the relationship between variables or between variables and their measurement items with the aim of predictive studies Hair et al. (2006).

Structural analysis in PLS-SEM in this study can be explained in Figure 2. Latent variables are represented in a circle, while the latent variable forming indicators are represented with long ovals. Arrows represent the relationship between latent variables and latent variables with indicators. In PLS-SEM, the relationship is always shown as a one-way arrow. The stages of analysis in PLS-SEM are explained by Sudarmanto (2005). In summary, the PLS-SEM evaluation of the reflective latent variable measurement model and the structural equation model can be seen in Table 1.

**Figure 2.** Research Structure Equation Model.



#### **4. Results**

Analysis of the influence between variables was carried out by analytical methods PLS-SEM (partial least square structural equation modelling) with the aim of predictive or exploratory studies through the development of structural models (Hair et al. 2006). The PLS model consists of measurement and structural models. The measurement model uses second-order factors with variables hierarchically measured by dimensions, and several measurement items further measure these dimensions. The estimation of second-order factors is then carried out using the repeated indicator approach (first-order factor stage), followed by the two-stage approach for evaluating the causality between variables and dimensions (second-order stage) after obtaining a valid and reliable model (Hair et al. 2006). This research focuses more on second-order analysis.

#### *4.1. The Measurement Model at the Variable Level Evaluation*

The measurement model was evaluated at the second-order factor level, which measures the quality of the measurement model of the relationship between the variables and their dimensions. The results of the measurement model at the variable level are presented in Table 2. The quality of the measurement model is seen from the Loading Factor (LF) ≥ 0.70, Composite Reliability (CR) ≥ 0.70, and Average Variance Extracted (AVE) ≥ 0.50, as well as an evaluation of discriminant validity, which is the Fornell-Lacker Criterion, which is the AVE root above the correlation between variables.


**Table 2.** Validity and Reliability of Research Variable Dimensions.

The results show that the regulatory environment variable is measured by two dimensions, namely attitudes and perceptions where there is a very strong relationship between the two dimensions with an LF of 0.952 each. It can be caused by employees/managers having good attitudes and perceptions regarding PJOK, regulations, and laws. LF value greater than 0.7 indicates that the variable indicator has a high level of validity. The variable indicator must be eliminated or removed from the model if the value is smaller. The level of strength or truth is still weak (Ardiansah 2017). The competitive environment variable is measured by five accurate dimensions, where the most dominant dimensions reflecting the competitive environment were competitors (LF = 0.884) and bargaining power of buyers (LF = 0.849).

In contrast, bargaining power of supplier has LF = 0.707, which is good but still needs improvement. The national cultural environment is measured by five valid dimensions with LF, where the most dominant dimensions are certainty with LF = 0.838 and ethics with LF = 0.791. On the other hand, the equality dimension has the lowest LF (0.560), indicating that equality in a national culture still needs improvement.

Organizational culture variables are measured by four valid dimensions, where the very dominant dimensions are coordination (LF = 0.901) and external orientation dimensions (LF = 0.892). In contrast, the autonomy dimension still needs improvement with the lowest loading factor (LF = 0.574). Organizational structure dimensions are measured by three valid dimensions where, overall, there is a strong relationship between the dimensions of complexity, formalization, and decentralization/centralization in measuring organizational structure variables. However, the formalization dimension has the highest LF (0.873), indicating that the organizational structure's most important dimension is formalization. The CG variable has five valid dimensions, and the most important/dominant dimensions are accountability and fairness, with each LF value of 0.883. CG looks stronger in the dimensions of accountability and fairness.

This measurement model has CR values above 0.70 and AVE above 0.50 for each variable. It shows that the dimensions that measure the variables are reliable/reliable or consistent in measuring each variable (Mulyana et al. 2017). The content of dimensional variations in the research variables is more than 50%, indicating that the variables have good convergent validity. These results also indicate that structural testing of the influence between variables can be carried out with the support of a good measurement model.

Discriminant validity was carried out in the PLS analysis of this study to ensure that each dimension/item of focus measurement measures the variables it measures that are related or unrelated (Farrell and Rudd 2009). The method used in evaluating discriminant validity is the Fornell-Lacker criterion, namely, the root of the AVE variable is greater than the correlation between variables. The results of discriminant validity measurements are presented in Table 3. Based on the processing, it can be seen that all the roots of the AVE variable are higher than the correlation with other variables, which indicates that the evaluation of discriminant validity is fulfilled.

**Table 3.** Discriminant validity.


Based on the stages of the model testing process, the VIF value of each variable being tested needs to be calculated to avoid multicollinearity, so that the estimated parameter values and standard errors are not biased. From the data processing presented in Table 4, the variables of regulation, competition, and national culture in influencing organizational culture and organizational structure show a VIF value of <5 or less than the tolerance limit, according to Hair et al. (2006). It can be concluded that there is no high multicollinearity among the variables of regulation, competition, and culture. Likewise, for organizational culture and organizational structure in influencing corporate governance, VIF value < 5.

**Table 4.** Multicollinearity testing.


#### *4.2. Structural Model Evaluation*

The results of testing the model hypothesis as a whole based on each hypothesis statement are presented in Table 5. Hypothesis analysis used the parameter path coefficient value from −1 to 1. The hypothesis is accepted if the T-statistic is less than the *p*-value and the *p*-value < 0.05.


**Table 5.** Results of testing the structural model hypothesis.

The analysis results generally show that all hypotheses have a positive path coefficient direction with different significance for each variable. The hypothesis is not accepted, meaning that the relationship between variables is not significant, which indicates that, every time one variable changes, it does not significantly increase changes in other variables, namely organizational culture and CG implementation; regulatory environment and CG implementation through the mediation of organizational culture and organizational structure; environment competition and CG implementation through the mediation of organizational culture and organizational structure; as well as the cultural environment and CG implementation through the mediation of organizational culture. Conversely, there is a significant influence on the organizational structure variable on CG and the organizational environment and CG implementation mediated by organizational structure.

#### **5. Discussion**

The use of the term corporate governance (CG) has increased when factors involve sustainable investor confidence, shareholder activity, increased social responsibility, and sustainable organizational development. CG is a system of arrangements that directs and controls the company to increase value for all relevant stakeholders (Blau and Schoenher 1971). CG development is an indicator that cannot be separated from the level of investor trust. The increased intention on the influence factor of CG quality becomes important in economic development. In this study, the factors that have a significant or positive influence on the implementation of CG, namely the organizational structure and the national cultural environment through the mediation of the organizational structure, while other variables do not have a significant effect even though they have a positive direction or in the other word, it has a very weak effect on the implementation of CG.

Organizational structure has a significant effect on CG implementation because each structure will direct the behavior of managers in implementing CG in their daily work. Cosset et al. (2016) mentioned that companies with good CG on average have better labor productivity and cost efficiency, and can make acquisitions that can increase company value, meaning that the organizational structure is good. According to Monks and Minnow (2004), CG is a structural mechanism intended to guarantee checks and balances that reflect the long-term sustainability of an organization. In addition, a significant influence on the implementation of CG was also identified in the national cultural and environmental factors through the mediation of the organizational structure, which is like the results of the study DiMaggio and Powell (1983), Scott (2001), and Hofstede (1991). In these studies, it can be interpreted that there is ruler control and scientific selection in the formation of the organizational structure in CG implementation. The dominant political power, or what Hofstede calls the elite (Hofstede 1991), is to apply the norms and standards of the national culture as a model of organizational structure and policies, which then apply years later without being questioned or forming a culture (Bebchuk and Roe 1999).

The success of national cultural variables in influencing CG implementation by mediating organizational structure provides strong support for the argument that isomorphism embedded in national culture will influence CG implementation strategies by establishing a strong organizational structure that aligns with company goals. Contribution to the understanding of the national cultural environment will be driven by the organizational structure, which is considered a residue of cultural norms in that country. This finding provides a theoretical implication that, in an institutional approach, companies try to seek legitimacy in society by conforming to societal norms and culture. Consistent with DiMaggio and Powell (1983), who state that organizational structural conformity is driven by institutional strength that is not related to efficiency in implementing it.

Furthermore, some factors have a weak influence on CG implementation, namely organizational culture, regulatory environment, regulatory environment mediated by organizational culture and organizational structure, competitive environment mediated by organizational culture and organizational structure, and national cultural environment mediated by organizational culture. The CG system initiating from the west will deal with organizational culture, a company resource that has formed the order and is a competitive advantage for companies, making CG a foreign system tasked with controlling and directing management (Hitt et al. 2001). In line with the findings above, Semenov (2000) compared the CG system in western countries, and it turns out that knowledge of these countries is still low. Insufficient knowledge of CG significantly impacts a company's ability to realize CG implementation (Schwartz and Davis 1981). With a lack of knowledge and understanding of CG in the work environment, the work culture in the company where the research is conducted separates the work culture that has been formed from the CG culture.

According to Tabalujan (2002), the regulatory environment in Indonesia requires a fundamental change to the legal culture so that people can become more law-abiding and principle-abiding. Such conditions are needed so that legal instruments and supporting institutions can function optimally following appointed objectives based on the legal culture and culture of the Indonesian people. Traditional cultural values are more dominant than legal-formal institutionalized legal rules (Lukviarman 2004). Johnson et al. (2000) state that the loss and non-functioning of organizational culture does not give life to the existing legal/regulatory system because culture refers to the attitudes, values, and opinions held by members of the organization regarding its implementation. The non-existence of an unsupportive organizational culture in companies makes CG implementation even more complicated. He believes that organizational culture is not a significant determinant of CG implementation in the companies studied. These results are inconsistent with the findings of previous studies such as Wilderom and Van den Berg (2005). However, a weak relationship with the determination of organizational culture was recorded in research by Wibowo (2008). Arogyaswamy (1987) claims that organizational culture in a regulatory environment is not always crucial in determining CG implementation. The mediation of organizational structure in this variable is due to the regulatory environment in its legal products by Bebchuk and Roe (1999), considered to have the dominant power to regulate the structure of the company. This legal force is not always made by officials who side with the public and are not influenced by important groups, but it also has implications for other possible perspectives with the position of the two poles of the shareholding and stakeholder perspectives.

Economic changes influence the competitive environment in the context of company competitiveness, which is expected to lead to the implementation of CG with a management control system. However, Wibowo (2008) indicates that organizational culture is not a determinant of the significance of the company's internal CG. Specifically, Arogyaswamy (1987) claims that culture and competitive environment are not always crucial in determining the success of CG implementation in a company. The application of organizational structure mediation on competitive environment variables also does not significantly affect the implementation of CG. This result is contrary to previous studies such as Pfeffer and Leblebici (1973), Nickell (1996), and Porter (2008). However, the weak influence of the competitive environment on CG mediated by organizational structure was noted in the study by DiMaggio and Powell (1983). The emphasis is on research by Polder et al. (2009) regarding the importance of implementing the best CG in every company in a globally competitive environment as protection against potential risk threats. However, the behavior of CG implementation cannot be predicted even though a company restructuring has been carried out as a limitation in carrying out management. Meanwhile, implementing CG practices is still just to check the compliance box. DiMaggio and Powell (1983) stated that self-awareness and self-interest are very important in improving the development of organizational structures.

The national cultural environment and organizational culture do not strongly influence each other in implementing CG because they explicitly comply with Semenov's (2000) argument that national culture limits variance in organizational culture. However, Hatch also stated that culture also relies on differences besides relying on similarities. It means that not all values are accepted collectively but can be rejected collectively (Hatch and Cunliffe 2013). Specifically, Gerhard (2008) argues that organizational culture does not have to determine national culture in designing and executing management strategies and practices so that national culture acts as a strong boundary. The decision to be unique, as long as the risks and challenges are properly understood and considered, can often offer potential competitive advantages. Therefore, it should not reduce the space for freedom and differentiation. It is appropriate that Hatch and Cunliffe (2013) states that national culture may not be able to answer the challenges faced at any moment. It is the basis for identifying when national culture limits organizational culture and when it is possible to use it.

Different contexts in the form of the legal and regulatory environment, cultural environment, and business patterns (competition) that are predominantly adhered to in a country are the main factors that deserve consideration in identifying CG implementation systems and models. Thus, the effectiveness of governance tools does not depend on the number of existing regulations but depends heavily on the regulatory environment in the form of instruments and law enforcement in a country. It is what Tabalujan (2002) claimed allegedly caused the failure of CG implementation in companies that foreign technical assistance funds mostly assisted. In his research, it was explained that one of the reasons for the non-functioning of law in developing countries, especially in Indonesia, is due to the neglect of the cultural factors of the Indonesian people. The implication is that regulatory issues are not the only dominant factor influencing CG implementation. Other factors interact in Indonesia that influence CG implementation, such as environmental factors on the effectiveness of implementation and its supporting institutions (Lukviarman 2004). From a formal legal standpoint, Tabalujan (2002) believes that Indonesia already has a fairly complete set of laws. What is needed is a fundamental change to the legal culture so that people can become more law-abiding and principle-abiding. Conditions like this are needed so that legal instruments and supporting institutions can function optimally so that they are following the stated goals. Thus, it can be said that traditional cultural values play a more dominant role than formally institutionalized regulations.

#### **6. Conclusions**

This research serves as a basis for identifying factors influencing this research. This research can summarize the modelling of CG implementation in Indonesia based on an institutional approach to three types of isomorphism, which emphasizes the institutionalization of CG implementation. This study confirms the cultural environment as a normative isomorphism from three perspectives of isomorphism in the institutional approach that influences CG implementation. The influencing normative isomorphism is based on national culture mediated by organizational structure. Ruler control and scientific selection occur in the formation of the organizational structure in the implementation of CG. The success of national cultural variables in influencing CG implementation by mediating organizational structure provides strong support for the argument that isomorphism embedded in national culture will influence CG implementation strategies by establishing a strong organizational structure that aligns with company goals. Contribution to understanding the national cultural environment in CG implementation efforts will be driven by the organizational structure, which is considered a residue of cultural norms in that country. The PLS-SEM analysis method with an institutional approach can describe measurement and structural factor models that influence CG on various variables. As a result, this study found that CG practices are strongly influenced by organizational structure and the national cultural environment mediated by organizational structure. In addition, it was also confirmed that sound CG practices that pay attention to the cultural aspects of certain countries would have an optimal impact.

There is no research without limitation. This research used quantitative methods with limited variables. Further research using qualitative methods is needed to deepen the research results regarding the implementation of CG. Future research is expected to be able to carry out limitations or restrictions on focus variables so that research results are sharper and more in-depth about CG in Indonesia, as well as being input into further analysis and application of regulations related to improving the quality of CG in Indonesia. This research can become the foundation for developing a research model with other variables to identify CG implementation.

**Author Contributions:** Conceptualization, S.H.; methodology, S.H.; software, S.H.; validation, S.H.; formal analysis, S.H.; investigation, S.H.; resources, S.H.; data curation, S.H.; writing—original draft preparation, S.H.; writing—review and editing, S.H., M.A.M., K.R. and T.W.A.; visualization, S.H.; supervision, M.A.M., K.R. and T.W.A.; project administration, S.H.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Thank you to the supervisors and all parties involved.

**Conflicts of Interest:** The authors declare no conflict of interest.

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**Frema Apdita \*, Johan Iskandar and Emma Rochima \***

Regional Innovation Graduate School, Universitas Padjadjaran, Bandung 45363, Indonesia; johan.iskandar@unpad.ac.id

**\*** Correspondence: frema21001@mail.unpad.ac.id (F.A.); emma.rochima@unpad.ac.id (E.R.)

**Abstract:** Food security is a requirement for meeting household food demands and is expressed in the availability of enough food that is sufficient both in quantity and quality, safe, equitable, and inexpensive. Academics and practitioners have attempted to revise food security models that may depict disaster-prone places, particularly Pamijahan District; however, these varied models each have their setbacks when compared to the world's various global conditions. This study aims to examine how food security is affected by the availability, accessibility, and consumption of food under the influence of climate change and the COVID-19 outbreak in the period 2017–2022. The methods used in this study were mixed-methods (quantitative and qualitative). In this study, participants underwent SMART PLS 3.0 analysis, followed by quantitative analytic techniques. Study results showed that the total food security condition of Cibunian Village in Pamijahan District in the period 2017–2022 can be categorized as vulnerable. Based on the FSVA analysis, it revealed that Cibunian Village was in the category of being vulnerable to food insecurity in general for the 2017–2022 period, while based on the SKPG analysis from the perspective of food access, there has been a 33.3% increase in food insecurity. The COVID-19 outbreak, climate change, and food consumption are the causes, and they all significantly and positively affect food security. This work advances our knowledge of food security in the COVID-19 outbreak age and the issues posed by global climate change. Everywhere, even in disaster-prone areas, complete food security should be attained.

**Citation:** Apdita, Frema, Johan Iskandar, and Emma Rochima. 2023. The Impact of COVID-19 and Climate Change on Food Security in Pamijahan District, Bogor Regency. *Economies* 11: 271. https://doi.org/ 10.3390/economies11110271

Academic Editors: María del Carmen Valls Martínez, José-María Montero and Pedro Antonio Martín Cervantes

Received: 13 July 2023 Revised: 6 September 2023 Accepted: 8 September 2023 Published: 1 November 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Keywords:** climate change; COVID-19; food security; food availability; food accessibility; food utilization

#### **1. Introduction**

Food is a human right and becomes a government obligation to fulfill (Dohlman et al. 2019). Currently, the world is facing climate change due to global warming. Climate change has increased the frequency of hydrometeorological disasters (Azizah et al. 2022). Since the last decade, climate change has become a major threat to food security.

Indonesia is located on the equator and is influenced by various weather and climate extremes. Variations in weather and climate affect forecasts of planting and harvesting seasons, seed supply distribution systems, and crop yields, causing potential problems in the food security system (Sakya and Mahardh 2010).

The Corona Virus Disease (COVID)-19 outbreak at the end of 2019 exacerbated the challenges of achieving food security. The COVID-19 outbreak created a multidimensional crisis in the health, economic, social, and political sectors, with further implications for food security (Rahmah et al. 2020). The impacts of climate change and the COVID-19 outbreak require further analysis to achieve sustainable food security (Rasul 2021). Achieving sustainable food security to end hunger refers to the SDGs (Sustainable Development Goals) program's 2nd (zero hunger) and 13th (climate action) goals.

Food security is closely related to food availability and food access (Mun'im 2021). Two global crises, the COVID-19 outbreak and climate change, demand appropriate action

from the government so that food security can be realized (Molden et al. 2020). On the other hand, hydrometeorological situations and the COVID-19 outbreak are creating instability in food security.

Food security is mandatory around the world, including in Indonesia. The Economist Group (2022) reported that Indonesia's Food Security Index was ranked 63rd out of 113 countries in 2022. According to the report, the sustainability and adaptation pillars have the lowest indicator performance in the index. They show that productive research related to the development of food security that is adaptive to climate change risks is still seriously needed in Indonesia, including in Bogor Regency.

Alinovi et al. (2010) state that resilience is influenced by stability, social safety nets, access to public services, assets, income, access to food, and adaptive capacity. Alam et al. (2016) stated that the food security model consists of food availability, food accessibility, food utilization, and food security.

Bogor Regency is a second-level administrative region of West Java Province, Indonesia, and is very important as one of the buffer zones for the capital city of Jakarta. According to the Statistical Agency of West Java Province, 6,088,233 people lived in Bogor Regency in 2020, representing 12.19% of the entire population of West Java Province (Yudhanto et al. 2023).

Bogor Regency's Food Security Index is ranked 317 out of 417 districts in Indonesia. Bogor Regency is included in a disaster-prone area. In 2021, the Bogor Regency Regional Disaster Management Agency reported that the frequency of disasters in Bogor Regency from 2017–2020 had continued to increase.

The Food Security Vulnerability Atlas (FSVA) is a thematic map that visualizes the geographical conditions of food insecurity (Food Security Agency 2022). In 2021, the FSVA of Bogor Regency, Pamijahan District, categorized food security, and especially food availability, as vulnerable in terms of access and utilization of food.

The uniqueness of Pamijahan District, surrounded by the Salak Mountain area, is that it has high potential for food resources and also high disaster vulnerability (Regional Disaster Management Agency 2022). This area is located in a dangerous zone. In the period 2020–2022, La Nina winds will come to this area and threaten potential disasters such as floods and landslides. It was recorded that on 22 June and 23 June 2022, flash floods and landslides occurred in Pamijahan District, Bogor Regency, which resulted in 194 heads of families being affected in Cibunian Village and Purwabakti Village (Regional Disaster Management Agency 2022)

Pamijahan District is located at coordinates 106◦38 00 to 106◦42 00 East longitude and 6◦38 00 to 6◦44 00 South latitude. The slope of the surrounding area ranges from 8% to 40%, and the height of the land in Pamijahan District is in the range of 1000–2000 m (Ulfah Rahayu et al. 2019).

The average temperature in Pamijahan District in the period 1991–2002 was 25.60 ◦C, with an increase in temperature from 1991–2022 of 0.60 ◦C and the highest rainfall reaching more than 500 mm per month. It can be categorized as an area with an extreme climate (BPS 2021).

From 1991 until 2022, there were 46 hydrometeorological disasters, which were dominated by floods, landslides, and strong winds. The high incidence in a region will be affected by climate change, increasing the potential for food insecurity in that area. The locations for collecting data for this study were Cibunian Village and Purwabakti Village in Pamijahan District because they are categorized as very disaster-prone villages with high rainfall and steep slopes (Ulfah Rahayu et al. 2019).

Facing various existing obstacles, residents of Pamijahan District must have the ability to maintain their food security for survival in the future. Based on this experience, a model of food security in disaster-prone areas, especially those affected by climate change and those that have also experienced non-natural disasters such as COVID-19, is an interesting thing to develop for the sustainability of the area. This food security model is a planning and prediction technique that has the advantage of dealing with opportunities for similar events in the future.

Food security as a way to deal with hunger is strongly supported by food availability, food access, and food utilization. The support of these three sub-systems must be strong so that they can face various interventions from outside and within the system. Several previous studies have not formulated a model of food security that has been analyzed statistically; instead, they are still conceptual in nature, not referring to empirical studies.

Through this phenomenon, researchers have an interest in conducting empirical studies by identifying the unique characteristics of residents who live in disaster-prone areas and measuring the relationship between model variables of food security that are related to climate change disasters and the COVID-19 outbreak.

Good food security planning can secure the continuity of food availability, access to food, and utilization of food for residents of Pamijahan District. One of the approaches used to deal with external interventions is to develop a model. This research investigates the characteristics of the residents of Pamijahan District and the food security model that was formed to maximize the potential for food availability, food access, and food utilization in the face of climate change and outbreaks of non-natural disasters.

The results of this study will be useful for developing strategies for the government to always maintain suitable food security in disaster-prone areas. In this study, structural equity modeling through partial least squares (PLS-SEM) is also used in the new field of food security.

The appropriate statistical technique for predicting food security management planning in disaster-prone areas is partial least squares structural equation modeling (PLS-SEM). This technique is used because it prioritizes predictive results without requiring normal distribution assumptions, and this technique is very good to use when the sample size is small (Joseph F. Hair et al. 2019).

The PLS-SEM analysis tool is Smart PLS. The use of Smart PLS is highly recommended when you have a limited number of respondents and the model being built is complex. In this study, Smart PLS Series 3.0 is used because this research is predictive and explains latent variables rather than testing a theory with a small number of samples (J. H. Hair et al. 2017).

#### **2. Materials and Methods**

A questionnaire with a sampling method was used to determine the condition of residents in Pamijahan District. Data collection, surveys, direct distribution of questionnaires to respondents, and in-depth interviews with an expert are all parts of the research method.

The research location is Pamijahan District, which supplies food in Bogor Regency (Indonesian Ministry of Agriculture's Food Security Agency 2021). The uniqueness of this district is its susceptibility to food security due to flash floods and landslides in June 2022. Figure 1 provides a map of the area studied in Pamijahan District, Bogor Regency.

**Figure 1.** Pamijahan District map of Bogor Regency.

#### *2.1. Data Collection Method*

In analyzing food security above, researchers combined primary and secondary data. Primary data was collected through surveys using questionnaires and in-depth interviews to determine residents' understanding of the effects of climate change and the COVID-19 outbreak on food security. This study was carried out in Pamijahan District, Bogor Regency, with respondents who were community members who were very affected by the climate change disaster and the COVID-19 outbreak. Secondary data was obtained from the various relevant literature, journals, books, and government statistical data.

The purpose of collecting data through a questionnaire is to access the sub-system of food security (food availability, food access, and food utilization) regarding the presence of climate change and non-natural disasters (COVID-19). The variable measurement of food availability was developed by 2021, while food access was introduced by Béné et al. (2021). Food utilization is measured using the approach from (Baliwati 2019), the climate change variable is measured based on the explanation from (Rasul 2021), and the COVID-19 outbreak is measured using variables from (Hendriks et al. 2022).

The process of collecting information for this study was carried out by interviewing relevant stakeholders. As for the observation process, the authors carried it out directly with respondents. Detailed research stages can be seen in Figure 2.

In Figure 2, this study begins with observing the food security system, which consists of food availability, access, and utilization (Alam et al. 2016). The second step is to determine the food security model with the variables of climate change and the COVID-19 outbreak so that a design model for food security can be produced in disaster-prone locations. The third stage is interviews with respondents and in-depth interviews with key persons using questionnaire media (this research questionnaire can be seen in Appendix A), as well as an interview guide, so that a reflection of the condition of food security is produced, which is affected by climate change and the COVID-19 outbreak. The results of the path analysis using SEM-PLS and the derived food security situation from the results of the FSVA and SKPG analyses, compiled with the results of in-depth interviews with key persons, formulated managerial implications for achieving food security in disaster-prone areas.

**Figure 2.** Research method.

All items were evaluated using a Likert scale of one to five, with five expressing strong agreement. The Likert scale is used to convey how strongly respondents agree or disagree with specific statements about actions, things, people, or events. The suggested scale typically consists of five points. A Likert scale was chosen with five class scores as the measurement. There are a total of five groups, made up of the average value of each informant. The following formula can be utilized to determine class intervals:

Explanation:

*SR* = Range (0.8)

$$SR = \frac{(a-b)}{c}$$

*b* = Minimum scores (1)

*a* = Maximum scores (5)

*c* = Number of class intervals (5)

These calculations enable us to establish that the calculated scale range is 0.8. According to the statement on the research questionnaire, the average range of 1.00–1.80 can be categorized into the Poor category; >0.80–2.60 can be categorized into the Fair category; >2.60–3.40 can be categorized into the Good category; >3.40–4.20 can be categorized into the Very Good category; and >4.20–5.00 can be categorized into the Excellent category. The items used to measure each variable are listed in Table A1 in Appendix A.
