Good Governance in Rural Local Administration

Abstract

The governance principle is an important aspect of good governance, and its implementation is believed to have a good impact on governance outcomes, such as corruption reduction and performance improvement. The governance principle has been developed for several types of organisations. However, previous studies did not focus on the rural local administration’s governance. This study aims to develop a good governance principle for the rural local administration. There are four objectives of this study: first, to identify governance principles and their indicators in the literature; second, to create a governance principle using exploratory factor analysis; third, to model the governance principle using the structural equation model (SEM); and finally, to analyse any different perceptions about the governance principles for the rural local administration using univariate analysis. The result showed that 33 indicators of governance principles were identified through the literature. Using 238 usable questionnaires and exploratory factor analysis, we found 6 governance principles: fairness and capability, inclusivity, legitimacy and direction, participation, performance and information, and transparency and accountability. Using the second-order SEM in SmartPLS, we developed a governance principle model for the rural local administration. A few indicators of governance principles found were deleted through measurement model validation. In addition, the univariate analysis concluded that perceptions did not differ by the sex, education level, and occupation type of respondents. In other words, they agreed with the governance principle for the rural local administration. This study has practical and theoretical implications, which are discussed in detail in this article.

1. Introduction

Since the late 1980s, good governance has been a crucial point for programme implementation and decision making for many nations worldwide (Pomeranz and Stedman 2020), including the central, local, and rural local administration. Governance is one of the buzzwords in the development field (Yong and Wenhao 2012) and in modern social sciences (Almqvist et al. 2013). Other authors believe that good governance is important for sustainable development in several dimensions, such as the accountability of decisions, the transparency of transactions, and the rule of law (Da Cruz and Marques 2017). Any good nation is confronted by a key challenge in developing a unified nation, while eluding the pitfall of depending on a centralised government and the bureaucracy that fails to address various government issues (Tang 2021). Governance is the institutional capacity of public organisations to prepare the public and other goods demanded by a country’s citizens in an effective, impartial, transparent, accountable manner and subject to resource constraints (Katsamunska 2016). In addition, Bekele and Ago (2020) mention that one ingredient of poverty reduction and economic growth is good governance.

In the 1980s and 1990s, ‘New Public Governance’ (NPG) became a theoretical paradigm that was more adjusted for contemporary government public administration (Runya et al. 2015). It originates in the radical transformation proposed by public policy. It can be partly regarded as a response to the NPM-oriented developments in the public sector, especially concerning ‘marketisation’ and ‘accountingisation’ (Osborne 2006). NPG is a line on ‘New Public Management’ in two areas: (1) NPG is the main point in the public sector area, and (2) NPG begins from the perspective of networks of organisations (Almqvist et al. 2013). Thus, ‘New Public Governance’, as a new model of public administration science, paid attention to organisational governance, emphasised pluralism, and attached high intention to the association between external and internal organisations (Runya et al. 2015).

The good-governance idea is widely accepted around the world, and private and public sectors give importance to this idea and adopt it in managing the organisation to increase sustainable development of the organisation (Channuwong 2018). Good governance consists of mechanisms and processes in which citizens and groups articulate their interests and exercise their legal rights (UNDP 1997). In addition, its point elements consist of participation, responsiveness, the rule of law, consensus orientation, equity, effectiveness, and efficiency (UNDP 1997). Since the governance principles were introduced, several studies have examined these principles using various public sectors, such as central government, local government, and other non-profit-oriented organisations (Bekele and Ago 2020; Berkel et al. 2022; Channuwong 2018; Da Cruz and Marques 2017; Pomeranz and Stedman 2020). For example, Bekele and Ago (2020) identified the indicator framework for the principles of good governance(transparency, accountability, participation, corruption, the rule of law, and public service delivery) and explored the practice of these principles using the local government in Ethiopia. Hence, another study developed some indicators for evaluating programme achievement: fairness, inclusivity, transparency, capability, accountability, direction, legitimacy, and performance (Pomeranz and Stedman 2020).

Further, Da Cruz and Marques (2017) produced the multi-criteria of good governance principles for the local government based on the operation research literature and decision analysis. The governance principles are (i) political stability, voice, and accountability; (ii) market access and regulation; (iii) government effectiveness; and (iv) the rule of law and corruption prevention (Da Cruz and Marques 2017). A study using a south-east Asian country (Channuwong 2018) validated governance principles: transparency, the rule of law, virtue, accountability, and participation. Berkel et al. (2022) applied the governance principles (legitimacy and transparency) as an indicator of local governance quality. In Indonesia, several studies have also been conducted using the rural local administration context (Aziiz and Prastiti 2019; Rahajeng 2020; Sofyani et al. 2020). Aziiz and Prastiti (2019) identified the governance principles in rural or rural funds and concluded that the principles had been moderately applied, especially accountability, transparency, and participation. Thus, Rahajeng (2020) examined the factor affecting the rural fund accountability in the rural local administration and found that staff competency, the use of information technology, and the internal control system are determinants of accountability. Finally, Sofyani et al. (2020) examined the implementation of governance principles (responsiveness, transparency, professionalism, vision strategies, and the rule of law) in rural-owned enterprises and their effect on enterprise performance. No previous studies have validated the governance principle for the rural local administration in Indonesia from a societal perspective. Furthermore, Szumowski (2019) argues that there is no previous empirical evidence that public administration units function according to the principle of good governance based on citizens’ perceptions. Therefore, it motivated the authors to validate the governance principles identified in the literature. Thus, this paper tries to validate governance principles using exploratory factor analysis. Specifically, there are four objectives of this study: first, to identify governance principles and their indicators in the literature; second, to create a governance principle using exploratory factor analysis; third, to model the governance principle using the structural equation model (SEM); and finally, to analyse any different perceptions about the governance principles for the rural local administration using univariate analysis. The study will benefit the next investigator to use these validated principles. This paper comprises five sections: research background, literature review, research method, results and discussion, and conclusions and recommendations.

2. Literature Review

The UNDP asserts governance as a policy, value, and institutional system by which a society manages its political, economic, and social affairs, with the association among civil society, the private sector, and the state (UNDP 1997). In addition, the World Bank indicates that governance refers to the institutions and traditions by which a authority is exercised in a country for the common good, including (1) selection, monitoring, and replacement of the process by authority and (2) the ability of the government to manage its implementation and resources effectively. Previous researchers have documented the importance of the governance principle. The UNDP (1997) proposed several governance principles: the rule of law, participation, consensus orientation, responsiveness, equity, effectiveness, and efficiency. In addition, other authors have identified governance principles, such as accountability and transparency, participation, the rule of law, corruption, and public service delivery (Bekele and Ago 2020). Furthermore, Pomeranz and Stedman (2020) recommended governance principles, such as transparency, inclusivity, legitimacy, fairness, accountability, direction, capability, and performance. Szumowski (2019) operationalised the good governance concept by formulating an action model of local government administration according to the principles of good governance and compiled good governance principles from previous research: transparency, participation, efficiency and effectiveness, accountability, and cohesion. In addition, Szumowski (2019) identified several good governance dimensions: transparency, stakeholders’ needs, participation, cohesion, efficiency and effectiveness, and accountability. Finally, Da Cruz and Marques (2017) produced the governance principles for the local government, such as voice, accountability, government effectiveness, political stability, market access, the rule of law, regulation, and corruption prevention. Based on the governance principles identified in the literature, we found 33 indicators (observed variables), and they are summarised in

Table 1. Indicator of governance principle.

Governance Principle Indicator	Code	References
The rural communities are allowed to express their opinions about the rural local administration.	vggp1	(Bekele and Ago 2020; Berkel et al. 2022; Channuwong 2018; Da Cruz and Marques 2017; Graham et al. 2003; Lockwood 2010; Pomeranz and Stedman 2020; Sofyani et al. 2020, 2021)
All important aspects have been discussed at a meeting with the rural local administration.	vggp2
Many rural communities are influential in decision making.	vggp3
All rural communities could provide input/opinions.	vggp4
The elected rural head tries to allow the community to give its opinion.	vggp5
The decision-making process by the rural local administration apparatus follows applicable regulations.	vggp6
Rural government officials are sincere in carrying out every rural development activity.	vggp7
The rural local administration is the right authority to make decisions about future rural development.	vggp8
The rural communities trust the rural local administration to manage the rural communities well.	vggp9
This rural community is managed according to the proper process.	vggp10
The rural community tends to like the existing rural-development-planning system.	vggp11
The rural development programme will benefit future generations.	vggp12
There is a long-term positive impact of the rural development programme.	vggp13
The rural administration should be able to make decisions about rural development in a shorter time.	vggp14
The rural development decision-making process is effective.	vggp15
The cost of services/services charged by the rural local administration is according to the ability of the rural community to pay.	vggp16
The performance of the rural local administration is according to what has been planned.	vggp17
The rural community feels the benefits of the rural government programme.	vggp18
The rural local administration answers questions from rural communities about rural development according to its ability.	vggp19
The rural local administration updates information regarding the performance of the rural government to rural communities.	vggp20
The rural local administration updates information regarding changes related to rural development.	vggp21
The rural communities know where to ask if they want to know about the management of the rural local administration.	vggp22
The rural communities know where to get information about rural development programmes.	vggp23
The rural development programme has been communicated before being carried out by the rural local administration apparatus.	vggp24
The rural local administration communicates how it makes decisions about rural development.	vggp25
The rural communities are aware of their opportunities to participate in decision making.	vggp26
The rural communities are satisfied with the information provided by the rural local administration.	vggp27
The decision-making process regarding rural development programmes prioritises common interests rather than individual or group interests.	vggp28
The rural local administration respects the opinion of the rural community in the decision-making process.	vggp29
The opinion of the rural community influences future rural development planning.	vggp30
The rural local administration considers the needs of rural communities who will bear the inconvenience of implementing rural development.	vggp31
In the decision-making process, the rural local administration considers that rural communities will benefit from rural development.	vggp32
The rural development programmes benefit the wider community.	vggp33

.

The rural local administration has good governance in terms of the rural stakeholders’ chance to be involved in and affect decision making (Lockwood 2010). Rural participation may involve the rural communities, who can express their opinions, discuss all aspects, influence the decision making, provide ideas or input, and give their opinion. The second good governance principle is the rural local administration and its decision-making process hold heterogeneity stakeholder views in high esteem without bias (Lockwood 2010). Therefore, the rural local administration must follow the applicable regulation, be sincere in carrying out rural development, make the appropriate decision, gain the community’s trust to administrate the rural community, manage according to the proper process, and have a suitable development planning system that benefits the next generation. The third principle of good governance is that the rural local administration meets its strategic objective, while making the use of economic resources (Graham et al. 2003). Therefore, the rural development programme should have a long-term positive impact, make decisions in a shorter time, have an effective decision-making process, charge a reasonable cost of service, show a well-planned performance of the rural local administration, benefit the rural communities, respond to the rural communities, and update the information about the rural local administration. The next principle of good governance is that information be freely available and accessible (Sheng 2009). These principles compose several aspects, such as updated information regarding rural development, information availability, information communication, and rural communities’ awareness and satisfaction with information availability. Another good governance principle is that the rural local administration uses authority with integrity (Lockwood 2010). The aspect consists of the common interest, appreciation of the stakeholders’ opinion in the decision-making process, considering the inconvenience of the rural community with regard to its needs, and rural development benefits to rural and wider communities.

3. Materials and Methods

This research used rural society as the object. Societies from eight tourist destination villages in Pariaman City participated in this study. The village names (population) are Apar (1053), Tungkal Selatan (1548), Kampung Gadang (1719), Kampung Kandang (1548), Pasir Sunur (1265), Marunggi (3280), Taluk 3497), and Pauh Barat (1956). Hence, the total population was 15,869, and the sample size using the formula n = N/(1 + N × e²) was 390 (e = 5%). Proportional random sampling was applied to get the sample per village. For example, the Pasir Sunur village had 31 (1268/15,869 × 390) respondents. In addition, 390 questionnaires were distributed to the respondents. The primary data applied in this study were collected through a survey. The surveyors distributed the questionnaires by visiting each rural residence. Governance principles have 33 indicators (see Appendix A), which were developed and used by previous researchers (Bekele and Ago 2020; Berkel et al. 2022; Channuwong 2018; Da Cruz and Marques 2017; Graham et al. 2003; Lockwood 2010; Pomeranz and Stedman 2020; Sofyani et al. 2020). Based on the study’s objective, the first objective was achieved through a literature review. The second objective was revealed by using exploratory factor analysis (EFA) using SPSS (Hair et al. 2014). EFA was used because this study had many observed variables. In addition, Yong and Pearce (2013) argued that large datasets that consist of several variables can be minimised by observing ‘groups’ of variables (i.e., factors)—that is, factor analysis assembles common variables into descriptive categories. Factor analysis is useful for studies that involve a few or hundreds of variables, items from questionnaires, or a battery of tests that can be reduced to a smaller set to get at an underlying concept and to facilitate interpretations (Rummel 1970). The second-order SEM was used to achieve the third objective of this study using SmartPLS (Chin 2010; Hair et al. 2017). The model was validated using convergent validity (outer loading, Cronbach alpha, composite reliability, and average variance extracted; Bagozzi and Yi 1988; Henseler et al. 2015; Hulland 1999) and discriminant validity (Fornell–Lacker criterion and heterotrait heteromethod (HTMT); Fornell and Larcker 1981; Henseler 2010). The cut-off for outer loading, Cronbach alpha, and composite reliability was greater than 0.70 (Bagozzi and Yi 1988; Hulland 1999), and the average variance extracted was above 0.5 (Henseler et al. 2015). In addition, the HTMT was below 0.85 (Henseler 2010). Finally, univariate analysis was used to achieve the fourth objective of this study using SPSS (Hair et al. 2010). In addition, the normality test was performed before selecting the parametric or non-parametric statistic.

4. Results and Discussion

This section explains the results and discussion. Of the 390 distributed questionnaires, 238 were filled out by the respondents and returned to the surveyors, with a return rate of 61.02%. Demographic data are demonstrated in

Table 2. Demographic data.

Demographic Data	Category	Frequency	%
Age	16–30 years old	88	36.97
	31–40 years old	44	18.49
	41–50 years old	51	21.43
	>50 years old	55	23.11
Sex	Male	87	36.55
	Female	151	63.45
Education	Senior high school	126	52.94
	Diploma	34	14.29
	Bachelor’s degree	19	7.98
	Postgraduate	1	0.42
	Other	58	24.37
Working as	Public servant	11	4.62
	Entrepreneur	101	42.44
	Student	36	15.13
	Other	90	37.82
Monthly income	<Rp. 3 million	200	84.03
	Rp. 3–6 million	38	15.97

. There were five segments of demographic information: age, sex, education, occupation, and income. Based on age, the respondents were dominated by those aged 16–30 years (36.97%), and the rest were above 50 years old (23.11%), 41–50 years old (21.43%), and 31–40 years old (18.49%). Regarding sex, 63.45% of the respondents were female and the rest were male (36.55%). The education level of the respondents consisted of senior high school (52.94%), and the rest were junior high school graduates and below (24.37%), diploma holders (14.29%), bachelor degree holders (7.98%), and postgraduates (0.42%). Regarding the respondents’ occupations, most of them were entrepreneurs (42.44%) and the rest were from other occupations (37.82%), students (15.13%), and public servants (4.62%). In addition, the monthly income of the respondents was below Rp. 3 million (84.03%) and the rest earned Rp. 3–6 million (15.97%).

4.1. Exploratory Factor Analysis

The first goal of this research was to investigate the largely observed variables in several factors. Exploratory factor analysis (EFA) was used to achieve this first objective. EFA is a powerful tool to decrease a set of observed variables to a small number of factors (Thompson 2007). It enables the researcher to emphasise the principal components to gain knowledge about the dynamics of their relationship. In this paper, EFA was used first to measure the factor structure of the governance principle. To conduct exploratory factor analysis, it is required to ensure that the data matric has sufficient correlation (Lin 2012). The Kaiser–Meyer–Olkin (Kaiser 1970) measure of sampling accuracy and Bartlett’s (Bartlett 1950) test of sphericity were run to assess the appropriateness of using the EFA method. In addition, the anti-image correlation was produced to support the sample adequacy. Thus, data extraction was used for principle of component analysis (Hair et al. 2010). Observed variables were placed together according to their mutual correlations and then incorporated into a specific number of components (Choudhry et al. 2009). The Eugenie value was assessed and compared to the parallel analysis result to obtain the number of factors extracted. Thus, parallel analysis is more precise for determining the number of factors to be presented (Pallant 2007). The loading factor picked an item to load on a latent factor using the cut-off of 0.50 (Lingard and Sublet 2002).

Table 3. Sampling adequacy test.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.784
Bartlett’s test of sphericity	Approx. chi-square	2419.775
	df	528
	Sig.	0.000

demonstrates the results of the sampling adequacy test. Based on that table, the result indicated that the Kaiser–Meyer–Olkin (KMO) measure of sampling accuracy was 0.784 and Bartlett’s test of sphericity was significant (p < 0.001), showing that the data were appropriate for factor analysis (Kaiser 1974).

Table 4. Anti-image correlation.

GP	vggp1	vggp2	vggp3	vggp4	vggp5	vggp6	vggp7	vggp8	vggp9	vggp10	vggp11	vggp12	vggp13	vggp14	vggp15	vggp16	vggp17	vggp18	vggp19	vggp20	vggp21	vggp22	vggp23	vggp24	vggp25	vggp26	vggp27	vggp28	vggp29	vggp30	vggp31	vggp32	vggp33
vggp1	0.74	−0.52	−0.09	−0.09	−0.03	−0.12	0.02	−0.12	0.06	0.06	−0.04	0.00	0.06	−0.02	0.05	−0.13	0.15	−0.07	0.19	0.07	−0.02	0.07	0.05	−0.01	−0.18	−0.04	−0.08	0.07	−0.09	0.06	−0.03	0.09	−0.11
vggp2		0.77	−0.35	0.10	0.08	0.14	−0.15	0.02	0.02	−0.11	−0.07	−0.12	−0.13	0.00	0.10	−0.03	−0.06	−0.04	−0.12	0.07	−0.07	0.02	−0.11	0.04	0.14	−0.01	0.08	0.01	−0.02	0.03	−0.05	−0.05	0.01
vggp3			0.81	−0.29	0.02	0.00	0.03	0.06	−0.07	0.10	0.10	0.03	−0.06	−0.02	−0.17	0.01	0.01	0.04	0.03	−0.21	0.12	−0.11	−0.03	0.02	−0.06	0.10	−0.05	−0.06	0.06	−0.09	−0.04	0.04	0.10
vggp4				0.73	−0.34	−0.08	0.01	0.11	0.03	−0.19	0.03	−0.10	−0.01	−0.08	−0.21	0.18	−0.23	0.14	−0.20	0.00	−0.04	−0.15	−0.09	0.03	0.15	−0.07	0.20	0.05	0.00	−0.02	0.01	−0.13	0.06
vggp5					0.67	0.07	−0.04	−0.05	−0.02	0.04	−0.23	0.09	−0.06	−0.03	0.05	−0.18	0.02	−0.16	0.13	−0.01	−0.03	0.06	0.24	−0.06	0.12	0.07	−0.10	0.00	−0.04	−0.04	−0.18	0.12	−0.04
vggp6						0.69	−0.27	0.04	0.17	−0.01	−0.11	0.07	0.05	−0.17	0.09	0.07	−0.13	0.03	−0.07	−0.04	−0.11	−0.13	−0.10	0.12	0.03	−0.09	−0.08	0.07	0.00	−0.06	0.03	−0.06	−0.13
vggp7							0.80	−0.20	0.05	0.14	0.04	−0.01	0.05	0.01	−0.04	0.01	−0.07	0.07	−0.16	0.07	−0.02	0.00	0.09	0.03	−0.13	−0.08	−0.06	−0.09	−0.14	0.00	−0.08	0.02	0.07
vggp8								0.83	−0.17	−0.05	−0.07	−0.06	0.11	−0.18	−0.11	0.14	−0.05	−0.05	−0.02	−0.16	0.11	−0.09	−0.15	0.01	0.07	0.11	0.07	−0.05	0.03	−0.15	0.02	−0.19	−0.02
vggp9									0.82	−0.30	0.04	0.03	0.00	0.01	0.01	−0.09	−0.13	−0.02	−0.08	0.01	−0.11	−0.07	−0.24	0.14	−0.10	−0.02	0.06	0.11	−0.08	−0.02	−0.10	0.00	−0.16
vggp10										0.78	−0.02	−0.08	0.00	−0.01	−0.11	−0.01	0.16	−0.04	0.08	0.00	−0.11	0.12	0.18	−0.14	−0.10	−0.02	−0.26	0.02	0.10	−0.01	0.12	−0.11	−0.10
vggp11											0.84	−0.39	−0.02	0.06	−0.06	−0.18	−0.09	0.08	−0.07	−0.06	0.12	0.10	−0.03	0.10	−0.10	−0.04	−0.01	−0.11	0.09	−0.01	0.03	0.01	−0.02
vggp12												0.87	−0.18	0.00	0.11	0.02	−0.08	−0.07	0.05	0.05	−0.07	−0.04	0.00	−0.08	−0.09	0.07	−0.14	0.10	−0.12	−0.15	−0.09	−0.02	0.06
vggp13													0.82	−0.23	−0.02	0.18	−0.11	−0.04	0.02	0.09	−0.06	−0.02	−0.01	0.01	−0.05	−0.11	−0.17	−0.01	0.05	−0.09	0.09	−0.01	−0.16
vggp14														0.88	−0.13	−0.04	0.05	0.04	−0.01	−0.01	−0.04	0.02	0.05	0.02	−0.03	−0.04	−0.06	−0.19	0.02	−0.03	−0.03	0.07	0.11
vggp15															0.84	−0.29	0.09	0.02	−0.01	0.08	−0.14	0.02	0.11	−0.13	0.02	−0.03	−0.11	0.02	−0.18	0.01	0.02	−0.03	0.02
vggp16																0.78	−0.41	0.08	−0.10	−0.03	−0.04	−0.13	−0.14	0.03	−0.07	−0.01	0.00	0.08	−0.06	0.01	0.05	−0.06	−0.03
vggp17																	0.75	−0.10	0.12	−0.08	0.12	0.13	0.06	−0.09	−0.07	0.06	0.00	−0.05	0.08	0.05	0.05	−0.07	−0.01
vggp18																		0.73	−0.43	−0.10	0.10	−0.03	−0.12	0.14	0.06	−0.04	−0.02	−0.09	0.03	−0.05	−0.03	−0.16	0.00
vggp19																			0.70	−0.22	0.10	0.05	0.16	−0.27	−0.09	0.12	−0.07	0.06	−0.08	0.01	−0.03	0.16	0.02
vggp20																				0.81	−0.48	0.04	0.00	0.08	−0.05	−0.29	−0.07	0.04	−0.12	0.06	−0.07	0.04	0.00
vggp21																					0.78	−0.05	−0.09	0.09	−0.03	0.06	−0.01	−0.12	0.14	−0.12	−0.07	−0.02	0.02
vggp22																						0.73	−0.08	0.02	0.06	0.01	−0.12	−0.11	0.09	−0.12	0.02	0.06	−0.08
vggp23																							0.68	−0.48	0.098	0.14	−0.14	0.04	−0.14	0.06	0.02	0.00	0.02
vggp24																								0.75	−0.20	−0.28	−0.01	−0.20	0.10	−0.07	−0.14	−0.03	−0.06
vggp25																									0.86	−0.13	0.02	−0.05	0.05	−0.05	−0.01	−0.08	0.02
vggp26																										0.75	−0.04	0.03	−0.09	−0.09	0.17	−0.08	0.06
vggp27																											0.82	−0.26	0.12	0.18	0.05	−0.04	−0.03
vggp28																												0.76	−0.35	0.06	0.01	0.04	0.02
vggp29																													0.67	−0.19	0.06	0.15	−0.22
vggp30																														0.86	−0.20	−0.04	0.06
vggp31																															0.82	−0.25	−0.09
vggp32																																0.82	−0.09
vggp33																																	0.76

shows the anti-image correlation, and the result indicated that all observed variables were correlate by 0.500 (see bold numbers). Therefore, it can be concluded that the consequence supports the sampling adequacy, and all observed variables can be used for further analysis.

The first run of principal component analysis (PCA) produced 11 factors (components) due to their initial Eugene value above 1 and are shown in the scree plot in Figure 1 (Churchill and Iacobucci 2004). However, this number of factors is too large, and a parallel analysis was conducted. As seen in

Table 5. Parallel analysis.

Root	Mean (Parallel Analysis)	Initial Eugene Value	Decision
1	1.78	7.20	Accepted
2	1.67	1.95	Accepted
3	1.59	1.84	Accepted
4	1.52	1.64	Accepted
5	1.47	1.60	Accepted
6	1.41	1.51	Accepted
7	1.35	1.30	Rejected
8	1.31	1.20	Rejected
9	1.26	1.17	Rejected
10	1.22	1.12	Rejected
11	1.18	1.09	Rejected
12	1.14	0.92	Rejected
13	1.10	0.88	Rejected
14	1.06	0.83	Rejected
15	1.03	0.74	Rejected

, the mean of parallel analysis above the initial Eugene value was six components. Therefore, the number of factors is suggested to be six. In addition, principal component analysis was run again to produce the factor extraction using six factors.

Table 5 indicates the results of the second run for factor extraction. The extraction of the sums of square loading showed that six factors were created, with a total value ranging from 7.20 to 1.51 and a percentage of variance from 9.42% to 6.40%. Loading factors resulted from rotation using Variamax since there was cross-loading among indicators. The loading factor per indicator is also demonstrated in Table 5. Loading factors varied from 0.50 to 0.79. In brief, we produced six governance principles for the rural local administration.

Table 6. Exploratory factor analysis of governance principles.

Governance Principle	Code	New Code	Loading Factor	Eugene Value	Variance
Legitimacy and direction	vggp8	gp_ld1	0.50	7.20	9.42
	vggp9	gp_ld2	0.70
	vggp23	gp_ld3	0.60
	vggp31	gp_ld4	0.50
	vggp32	gp_ld5	0.54
Transparency and accountability	vggp10	gp_ta1	0.51	1.95	8.94
	vggp24	gp_ta2	0.51
	vggp25	gp_ta3	0.51
	vggp26	gp_ta4	0.56
	vggp27	gp_ta5	0.69
	vggp28	gp_ta6	0.51
Participation	vggp4	gp_pa1	0.71	1.84	8.44
	vggp21	gp_pa2	0.56
Performance and information	vggp7	gp_pi1	0.65	1.64	7.11
	vggp19	gp_pi2	0.57
	vggp29	gp_pi3	0.60
Fairness and capability	vggp11	gp_fc1	0.68	1.60	6.97
	vggp16	gp_fc2	0.55
	vggp17	gp_gc3	0.64
Inclusivity	vggp1	gp_in1	0.77	1.51	6.80
	vggp2	gp_in2	0.79
	vggp3	gp_in3	0.56

also shows the rural local administration’s new code of governance principles.

4.2. Modelling the Rural Local Administration Governance Principles

To model the governance principle for the rural local administration, we used second-order analysis using SmartPLS. This research used measurement model validity assessment to develop the rural government governance principle. In this case, convergent and discriminant validity was used (Hair et al. 2017). Convergent validity has three statistical properties: average variance extracted, composite reliability, and Cronbach alpha (Vinzi et al. 2010).

Table 7. Model validity: convergent validity.

Governance Principle	Indicator	Outer Loading	Cronbach Alpha	Composite Reliability	Average Variance Extracted (AVE)
Fairness and capability	gp_fc1	0.769	0.693	0.830	0.619
	gp_fc2	0.823
	gp_fc3	0.768
Inclusivity	gp_in1	0.809	0.746	0.856	0.666
	gp_in2	0.886
	gp_in3	0.746
Legitimacy and direction	gp_ld1	0.755	0.793	0.786	0.551
	gp_ld2	0.766
	gp_ld3	0.705
Participation	gp_pa1	0.799	0.716	0.774	0.631
	gp_pa2	0.790
Performance and information	gp_pi1	0.751	0.730	0.774	0.632
	gp_pi2	0.837
Transparency and accountability	gp_ta2	0.845	0.781	0.827	0.705
	gp_ta3	0.833

demonstrates the results of convergent validity. Based on Table 7, fairness and capability had three indicators, and all indicators had an outer loading above 0.70 (Hulland 1999). In addition, the Cronbach alpha, composite reliability, and average variance extracted satisfied the requirements suggested by experts (Bagozzi and Yi 1988; Henseler 2010). Therefore, the governance principle of fairness and capability supports convergent validity. The second principle, inclusivity, also maintained the number of indicators from the exploratory factor analysis (three indicators). The outer loading of all indicators of this principle was above 0.700; it can be concluded that it satisfies the requirement (Hulland 1999). Composite reliability and Cronbach alpha as a measurement of governance principle reliability met the standard (Bagozzi and Yi 1988). In addition, the average variance extracted was above 0.500, and it can be concluded that it supports the convergent validity (Henseler 2010).

The legitimacy and direction governance principle previously had five indicators and when run on SmartPLS also resulted in three valid indicators with an outer loading above 0.700 (Hulland 1999). The percentage variance extracted was also above the requirement recommended by experts (Henseler 2010). In addition, this principle’s composite reliability and Cronbach alpha were above 0.700 (Bagozzi and Yi 1988). The participation principle also supports the convergent validity due to its outer loading, Cronbach alpha, composite reliability, and average variance extracted to meet the requirement (Bagozzi and Yi 1988; Henseler 2010; Hulland 1999). The performance and information principle had two valid indicators with an outer loading above 0.700 (Hulland 1999). In addition, its Cronbach alpha, composite reliability, and average variance extracted also met the requirement (Bagozzi and Yi 1988; Henseler 2010). Finally, the transparency and accountability principle previously had six indicators and produced two valid indicators after the validity test (gp_ta2 and gp_ta3). Its composite reliability, Cronbach alpha, and average variance extracted also met the requirement (Bagozzi and Yi 1988; Henseler 2010). Based on these findings, the model’s convergent validity is achieved. The subsequent analysis was for discriminant validity.

This discriminant validity is of two types: the Fornell–Lacker criterion and the heterotrait heteromethod (HTMT).

Table 8. Model discriminant validity: Fornell–Lacker criterion.

Governance Principle	GP_FC	GP_IN	GP_LD	GP_PA	GP_PI	GP_TA
Fairness and capability	0.787
Inclusivity	0.332	0.816
Legitimacy and direction	0.368	0.314	0.742
Participation	0.277	0.344	0.317	0.795
Performance and information	0.287	0.259	0.270	0.226	0.795
Transparency and accountability	0.363	0.245	0.393	0.237	0.339	0.839

shows the results of discriminant validity using the Fornell–Lacker criterion (Fornell and Larcker 1981). As seen in Table 8, the value of the square root of a governance principle’s AVE (bold number) was higher than the correlation of this governance principle with another. For example, the governance principle of fairness and capability had a square root of its AVE as 0.787, and this value was greater than its coefficient correlation with the governance principle of inclusivity (0.322). In addition, another result also indicated the same conclusion. Therefore, this result supports the discriminant validity requirement using the Fornell–Lacker criterion.

The second discriminant validity used the heterotrait heteromethod. Average heterotrait heteromethod correlations are relative to the average monotrait heteromethod correlation (Hair et al. 2017; Henseler et al. 2015). Thus, the monotrait heteromethod correlation is the correlation of indicators measuring the same construct. In addition, the heterotrait heteromethod correlation is the correlation of indicators across constructs measuring different phenomena. The HTMT value was close to 1, indicating a lack of discriminant validity. HTMT values greater than 0.85 indicate a lack of discriminant validity (Kline 2011). The results of the HTMT are shown in

Table 9. Model discriminant validity: heterotrait heteromethod (HTMT).

Governance Principle	GP_FC	GP_IN	GP_LD	GP_PA	GP_PI	GP_TA
Fairness and capability
Inclusivity	0.456
Legitimacy and direction	0.569	0.471
Participation	0.515	0.621	0.634
Performance and information	0.531	0.464	0.527	0.526
Transparency and accountability	0.572	0.374	0.672	0.483	0.669

. The value of the HTMT for all governance principles was below the cut-off (0.85); therefore, it can be concluded that it satisfies the discriminant validity requirement.

Figure 2 shows the validated model of the rural local administration governance principle—the weight of each governance principle was at least 0.500. In addition, the predictive power was at least 0.300. The R-square for the governance principle of legitimacy and direction was 0.469, which shows moderate predictive power (Hair et al. 2014). Thus, the R-square for transparency and accountability was 0.451 and was classified as having substantial predictive power (Cohen 1992). Further, the governance principle of participation had an R-square value of 0.330 and was grouped into moderate predictive power (Cohen 1998). Moreover, the inclusivity governance principle had an R-square of 0.422 and was classified as having substantial predictive power (Cohen 1992). Finally, the fairness and capability and the performance and information governance principles had R-squares of 0.474 and 0.300, respectively; the predictive power was moderate (Cohen 1998; Hair et al. 2014).

4.3. Univariate Analysis

The third goal of the paper was to determine any difference in the rural local administration’s governance principle based on the respondents’ sex, education level, and occupation. The test of normality for all indicators was conducted, and the results showed that all indicators for the six governance principles were not normal due to their Asymp. Sig. using the Kolmogorov–Smirnov test. Therefore, the non-parametric statistic was used for a univariate test (Hair et al. 2010). In addition, the Mann–Whitney U test (two independent t-tests) was performed for sex differences and the Kruskal–Wallis H test (k-independent t-test) for education level and occupation. Table 9 shows the results of the Mann–Whitney U test for sex differences. Based on sex, all sex categories (male vs. female) agreed that all indicators for the rural local administration governance principle are due to no differences between males and females, which is shown by an Asymp. Sig. value greater than 0.05 (see

Table 10. Test of difference: sex.

Governance Principle		Sex	N	Mean	Mann–Whitney U Test (Asymp. Sig.)
Legitimacy and direction	gp_ld1	Male	87	3.64	0.27
		Female	151	3.47
	gp_ld2	Male	87	3.62	0.68
		Female	151	3.52
	gp_ld3	Male	87	3.54	0.56
		Female	151	3.46
Transparency and accountability	gp_ta2	Male	87	3.34	0.60
		Female	151	3.40
	gp_ta3	Male	87	3.44	0.76
		Female	151	3.39
Participation	gp_pa1	Male	87	3.45	0.76
		Female	151	3.49
	gp_pa2	Male	87	3.51	0.73
		Female	151	3.51
Performance and information	gp_pi1	Male	87	3.47	0.97
		Female	151	3.49
	gp_pi2	Male	87	3.39	0.76
		Female	151	3.37
Fairness and capability	gp_fc1	Male	87	3.41	0.62
		Female	151	3.48
	gp_fc2	Male	87	3.82	0.71
		Female	151	3.83
	gp_fc3	Male	87	3.54	0.55
		Female	151	3.62
Inclusivity	gp_in1	Male	87	3.56	0.25
		Female	151	3.75
	gp_in2	Male	87	3.45	0.24
		Female	151	3.62
	gp_in3	Male	87	3.48	0.56
		Female	151	3.45

).

The second test of difference was for the education level. There were four categories of this education level: senior high school, diploma, bachelor’s degree, and other education level. Since the education level consisted of four levels (categories), we used the Kruskal–Wallis H test (k-independent t-test). The Asymp. Sig. value of the Kruskal Wallis H test indicated that all indicators had an Asymp. Sig. value above 0.05 (see

Table 11. Test of difference: education level.

Governance Principles		Education Level	N	Mean	Kruskal–Wallis H Test (Asymp. Sig.)
Legitimacy and direction	gp_ld1	Senior high school	126	3.52	0.80
		Diploma	34	3.74
		Bachelor’s degree	19	3.32
		Postgraduate	1	4.00
		Other	58	3.52
	gp_ld2	Senior high school	126	3.53	0.55
		Diploma	34	3.68
		Bachelor’s degree	19	3.58
		Postgraduate	1	5.00
		Other	58	3.52
	gp_ld3	Senior high school	126	3.56	0.24
		Diploma	34	3.62
		Bachelor’s degree	19	3.00
		Postgraduate	1	4.00
		Other	58	3.41
Transparency and accountability	gp_ta2	Senior high school	126	3.39	0.63
		Diploma	34	3.41
		Bachelor’s degree	19	3.05
		Postgraduate	1	4.00
		Other	58	3.43
	gp_ta3	Senior high school	126	3.40	0.50
		Diploma	34	3.38
		Bachelor’s degree	19	3.53
		Postgraduate	1	5.00
		Other	58	3.38
Participation	gp_pa1	Senior high school	126	3.48	0.17
		Diploma	34	3.82
		Bachelor’s degree	19	3.21
		Postgraduate	1	1.00
		Other	58	3.40
	gp_pa2	Senior high school	126	3.63	0.26
		Diploma	34	3.62
		Bachelor’s degree	19	3.21
		Postgraduate	1	2.00
		Other	58	3.31
Performance and information	gp_pi1	Senior high school	126	3.38	0.50
		Diploma	34	3.62
		Bachelor’s degree	19	3.47
		Postgraduate	1	3.00
		Other	58	3.64
	gp_pi2	Senior high school	126	3.32	0.81
		Diploma	34	3.62
		Bachelor’s degree	19	3.32
		Postgraduate	1	3.00
		Other	58	3.40
Fairness and capability	gp_fc1	Senior high school	126	3.50	0.88
		Diploma	34	3.44
		Bachelor’s degree	19	3.58
		Postgraduate	1	4.00
		Other	58	3.33
	gp_fc2	Senior high school	126	3.76	0.63
		Diploma	34	3.76
		Bachelor’s degree	19	4.05
		Postgraduate	1	4.00
		Other	58	3.93
	gp_fc3	Senior high school	126	3.52	0.44
		Diploma	34	3.74
		Bachelor’s degree	19	3.68
		Postgraduate	1	5.00
		Other	58	3.62
Inclusivity	gp_in1	Senior high school	126	3.64	0.84
		Diploma	34	4.00
		Bachelor’s degree	19	3.68
		Postgraduate	1	4.00
		Other	58	3.57
	gp_in2	Senior high school	126	3.52	0.43
		Diploma	34	3.91
		Bachelor’s degree	19	3.47
		Postgraduate	1	3.00
		Other	58	3.47
	gp_in3	Senior high school	126	3.40	0.08
		Diploma	34	3.85
		Bachelor’s degree	19	3.42
		Postgraduate	1	5.00
		Other	58	3.34

). Therefore, there was no difference in the perceptions towards rural government governance principles among respondents with different education levels. In other words, all respondents with various education levels agreed with this rural local administration governance principle.

The third of the k-independent t-tests was for occupation. The respondents had four occupations: public servant, entrepreneur, student, and others. The Kruskal–Wallis H test was conducted to investigate any differences in the rural local administration governance principle indicators among respondents with different occupations. The results of the test can be seen in

Table 12. Test of difference: occupation.

Governance Principle		Working as	N	Mean	Kruskal–Wallis H Test (Asymp. Sig.)
Legitimacy and direction	gp_ld1	Public servant	11	3.45	0.49
		Entrepreneur	101	3.50
		Student	36	3.75
		Other	90	3.50
	gp_ld2	Public servant	11	3.91	0.48
		Entrepreneur	101	3.52
		Student	36	3.72
		Other	90	3.49
	gp_ld3	Public servant	11	3.18	0.26
		Entrepreneur	101	3.45
		Student	36	3.81
		Other	90	3.46
Transparency and accountability	gp_ta2	Public servant	11	3.27	0.93
		Entrepreneur	101	3.37
		Student	36	3.47
		Other	90	3.37
	gp_ta3	Public servant	11	3.73	0.60
		Entrepreneur	101	3.47
		Student	36	3.42
		Other	90	3.30
Participation	gp_pa1	Public servant	11	2.91	0.05
		Entrepreneur	101	3.45
		Student	36	3.92
		Other	90	3.40
	gp_pa2	Public servant	11	3.45	0.90
		Entrepreneur	101	3.54
		Student	36	3.64
		Other	90	3.42
Performance and information	gp_pi1	Public servant	11	3.45	0.35
		Entrepreneur	101	3.54
		Student	36	3.22
		Other	90	3.52
	gp_pi2	Public servant	11	3.18	0.19
		Entrepreneur	101	3.57
		Student	36	3.22
		Other	90	3.24
Fairness and capability	gp_fc1	Public servant	11	3.91	0.31
		Entrepreneur	101	3.50
		Student	36	3.58
		Other	90	3.31
	gp_fc2	Public servant	11	4.00	0.75
		Entrepreneur	101	3.85
		Student	36	3.86
		Other	90	3.77
	gp_fc3	Public servant	11	4.09	0.06
		Entrepreneur	101	3.61
		Student	36	3.86
		Other	90	3.40
Inclusivity	gp_in1	Public servant	11	3.82	0.57
		Entrepreneur	101	3.86
		Student	36	3.50
		Other	90	3.53
	gp_in2	Public servant	11	3.27	0.25
		Entrepreneur	101	3.73
		Student	36	3.39
		Other	90	3.47
	gp_in3	Public servant	11	3.18	0.06
		Entrepreneur	101	3.62
		Student	36	3.64
		Other	90	3.24

. Based on the results, all indicators of the rural local administration governance principle did not differ among respondents’ occupations due to the value of Asymp. Sig. being above 0.05. In addition, the respondents with various working backgrounds aligned with each other.

5. Discussion

Based on the exploratory factor analysis, governance principle modelling, and univariate analysis, we conclude that the rural local administration has six governance principles: fairness and capability, inclusivity, legitimacy and direction, participation, performance and information, and transparency and accountability. From a community perspective, the rural local administration’s first governance principle is fairness and capability. This governance principle is also identified in other types of organisations, such as environmental governance (Pomeranz and Stedman 2020), city government (Channuwong 2018), and sports governance (Parent and Hoye 2018). In addition, the UNDP (1997) and other experts (Graham et al. 2003) suggest this governance principle. The second governance principle for rural governance is inclusivity. Inclusivity refers to society having a voice in decision making, directly or through legitimate intermediate institutions representing their intention (Graham et al. 2003; Lockwood 2010). Environmental governance principles have been identified (Pomeranz and Stedman 2020). In addition, legitimacy and direction is the third governance principle for rural local administration governance. Legitimacy refers to the governing body given authority to make decisions by the rule of law or by stakeholders; authority is used with integrity (Graham et al. 2003; Lockwood 2010). Thus, the direction is related to strategic vision. The rural local administration leaders and the community have a broad and long-term perspective on human development and good governance and a sense of what is needed for such development. Thus, they must understand the historical, cultural, and social complexities in which that perspective is grounded (Graham et al. 2003). The fourth governance principle of rural local administration governance is participation. This governance principle regarding a voice in decision making and good governance mediates differing interests to reach a broad consensus on what is in the group’s best interest and, where possible, policies and procedures (Graham et al. 2003). This principle has also been used in city government governance (Channuwong 2018). The fifth governance principle of rural local administration governance is performance and information. Performance is characterised by responsiveness, effectiveness, and efficiency (Graham et al. 2003). Information is the availability of information that is needed by the public. This performance principle is used in environmental governance (Pomeranz and Stedman 2020). Finally, transparency and accountability is the last governance principle for the rural local administration. Transparency refers to the rationale for decision making being clearly communicated and information being freely available and accessible (Graham et al. 2003; Lockwood 2010). Accountability refers to the governing body taking responsibility and being answerable for its decisions (Graham et al. 2003; Lockwood 2010). This governance principle is also used by many organisations and structures (Channuwong 2018; Pomeranz and Stedman 2020).

6. Conclusions and Recommendations

Previous studies have documented the governance principles for various types of organisations. However, they failed to give attention to the rural local administration, the kind of government in Indonesia. The implementation of a governance principle drives the governance outcome in an organisation, such as corruption reduction, performance improvement, and organisational development. This study developed a governance principle for the rural local administration. There were four objectives of this study. The first was to identify the indicators of the governance principle (observed variables) through a literature review. The second was to produce factors (governance principle) based on the first aim’s governance principle. In addition, exploratory factor analysis (EFA) was used to create the governance principle. The third objective was to model the governance principle for the rural local administration using the second-order structural equation model (SEM) applying SmartPLS. The fourth objective was to investigate different perceptions towards the governance principle developed. In this case, the univariate analysis is conducted using statistics non-parametric for two independent and k-independent samples. The two and k-independent sample tests apply Mann Whitney U and Kruskal Wallis H, respectively.

The result shows that thirty-two indicators of governance principles were identified through literature. Using two hundred thirty-eight usable questionnaires and the exploratory factor analysis, we found six governance principles: fairness and capability, inclusivity, legitimacy and direction, participation, performance and information, transparency and accountability. Using the second-order SEM in SmartPLS, we developed a governance principle model for the rural local administration. A few indicators of the governance principle found in the second objective were deleted through measurement model validation. In addition, the univariate analysis concluded that there is the perception differs by the sex, education level, and occupation type of respondents. In other words, they agreed with the governance principle of the rural local administration. The result has a practical implication in which the rural local administration can implement these governance principles. Theoretically, this study contributes to the agency, stakeholder, and legitimacy theories. This study has several limitations, and future investigators can consider it a venue for the next study. First, this study developed a governance principle from the community perspective. Future research can extend the governance principle from another stakeholder perspective. Second, this study used a rural community that is a tourist destination in Pariaman City. The next study can investigate using diverse rural communities in other towns or regencies. Third, the next study can also use different types of software programmes when developing structural equation models. Finally, this research also has a limitation in research procedures, and future investigators can overcome the research procedure limitation by conducting confirmatory research to confirm this finding.