1. Introduction

2. Materials and Methods

2.1. Study Areas

In this paper, Tanzania, Africa’s nature reserves were selected as the study area. The Mikumi-Selous region of Tanzania is an area with a concentration of nature reserves, where Mikumi National Park, Nyerere National Park and Selous Sanctuary are located, covering an area of more than 5 million hectares and is known for its diverse landscape and great variety of wildlife (Figure 1). The area has a variety of ecosystems such as woodlands, grasslands, riverine forests and swamps, all becoming important habitats for wildlife, such as elephants (Loxodonta africana), black rhinoceros (Diceros bicornis), cheetahs (Gattus pardus), giraffes (Giraffa camelopardalis), hippopotamuses (Hippopotamus amphibius) and crocodile (Crocodylus siamensis). The wild animals cause damage to crops, livestock and houses of the neighboring communities, which often leads to human-animal conflicts. At the same time, there are many communities around the reserve, and in recent years, due to the growing population and the gradual increase in the use of natural resources, the fringe area adjacent to the reserve has become more fertile, which has attracted a lot of people interested in occupying the land for cultivation and animal husbandry. The conflict between conservation and development in this area is obvious, so to better protect biodiversity, it is of great significance to explore and study the willingness and action of community residents to participate in conservation as a way to resolve the harm caused by the conflict.

Figure 1. Map of the study area.

Figure 1. Map of the study area.

2.2. Data Collection

In 2019, our research team travelled to the Mikumi-Selous region of Tanzania to conduct a pre-survey, with the main methodology being semi-structured interviews. The objectives of the pre-survey included: to understand the current situation of conflict between wildlife and neighboring communities in the reserve and collect relevant secondary data; to listen to the opinions of various stakeholders, and to amend and improve the questionnaire and the survey method in terms of problems and applicability; and to select suitable areas for the subsequent survey of community residents. Our survey targets were mainly local government departments, scientific research institutions and communities in conflict. We travelled to TAWA, Morogoro District Council and Mvomero District Council, three different types of nature reserves (former Selous Sanctuary, Mikumi National Park and Jukumu WMA) and four surrounding communities (Mkata, Bonye, Kisaki and Matambwe), and conducted semi-structured interviews with many experienced staff members to obtain relevant information. Through the pre-survey, we selected 4 villages in Mikumi-Selous area as the official research area and used the questionnaire method to obtain accurate data at the level of the community residents. Based on the pre-survey and semi-structured interviews, we listened to all the parties and adjusted the questionnaire by deleting and modifying the options that did not fit the local conditions to make the questionnaire more comprehensible.

Due to COVID-19, our formal research was postponed until November 2022. We travelled to 4 villages located around Mikumi National Park and Nyerere National Park in Tanzania to conduct a questionnaire survey with 200 local residents. The entire survey was conducted in commonly spoken languages in Tanzania (Kiswahili and English) by a total of 4 surveyors, with a local university student volunteering to assist us with translation to facilitate our survey smoothly. Each questionnaire took about 30 minutes to complete. The questionnaire was divided into 3 parts: the first part dealt with basic demographic and household information, including gender, age, education level, occupation and income from agriculture, forestry and animal husbandry; the second part investigated the conflict between the national park and the community residents, including the type of conflict, conflict losses, etc.; and the third part investigated the residents’ protection cognition, attitudes toward and protection actions taken to protect the national park. The basic information of the questionnaire respondents is shown in Table 1.

We also conducted interviews with experienced employees of Mikumi National Park and Selous Sanctuary (now Nyerere National Park) and Tanzania National Parks (TANAPA) staff, and administrators of the administrative board and tourists were invited to discuss some key issues separately, totaling 14 people. Most of the interviews were recorded with the permission of the participants. By comparing answers from different viewpoints, we could make unbiased judgments on the various issues.

Table 1. Basic profile of respondents (n = 200)

Table 1. Basic profile of respondents (n = 200)

diagnostic property	form	frequency	Frequency (%)
gender	male	139	69.5
	female	61	30.5
age	30 years and under	56	28
	30-50 years	105	52.5
	50 and over	39	19.5
education level	below secondary	109	54.5
	secondary school - university	72	36
	university and above	19	9.5
occupation	peasants	163	81.5
	the rest	37	18.5
length of residence	1-10 years	37	18.5
	11-20 years	52	26
	21-30 years	45	22.5
	31-40 years	39	19.5
	More than 40 years	27	13.5
area of residence	Mloka	47	23.5
	Bonye	35	17.5
	Matambwe	61	30.5
	Kisaki	57	28.5

2.4. Model Setting

Based on the above analysis, there is a complex relationship between residents’ perception cognition, willingness and action. The traditional measurement method has some limitations, because although it allows the dependent variable to have measurement error, it needs to assume that the independent variable does not have error, so the traditional measurement cannot deal with the situation where the independent variable cannot be measured, and structural equations can deal with the latent variable and its indicators at the same time, so it is chosen to use Structural Equation Model (SEM) to analyze the link between them. Currently, structural equation model is divided into two mainstream research approaches: one is covariance-based structural equation model(CB-SEM) and partial least squares structural equation model (PLS-SEM). CB-SEM estimates the relationship between variables by minimizing the difference between the theoretical and estimated covariance matrices, and PLS-SEM determines the relationship between the variables by maximizing the explanatory variance of endogenous latent variables. In this paper, the PLS-SEM model is selected for further analysis based on SmartPLS 4.0 software. The PLS-SEM model allows for the estimation of complex causal relationships using empirical data, and is an approach based on latent variable path modelling. Its advantage is that it can minimize the residuals of endogenous variables and construct the minimization of residuals [38] and can effectively overcome the problem of covariance between observed variables. Thus, the model can well meet the needs of this study to conduct research and analysis in the state of small sample size.

PLS-SEM consists of two sets of theoretical models, one is the structural model, which is used to define the linear relationship between the latent independent variables and the latent dependent variables, and the other is the measurement model, which is used to define the relationship between the latent variables and the measurement variables. Since the reflective indicators selected in this paper can be directly reflected to the latent variables and have one-way correlation, the reflective model in the measurement model is selected for model construction. The equations of each model are shown below:

Structural equations:

$$\mathsf{\eta }=\mathsf{\beta }\mathsf{\eta }+\mathsf{\gamma }\mathsf{\xi }+\mathsf{\xi },$$

(1)

Measurement equations of internal variables (dependent variables):

$$\mathrm{Y}=\mathsf{\mu }\mathsf{\eta }+\mathsf{\epsilon }$$

(2)

Measurement equations of external variables (independent variables):

$$\mathrm{X}=\mathsf{\lambda }\mathsf{\xi }+\mathsf{\delta }$$

(3)

Where $\mathsf{\eta }$ is the endogenous latent variable; $\mathsf{\beta }$ represents the relationship between endogenous latent variables; $\mathsf{\xi }$ is the exogenous latent variable; $\mathsf{\gamma }$ represents the linear relationship between endogenous and exogenous latent variables. In the measurement equations of internal and external variables: $\mathsf{\mu }$ represents the factor load matrix of endogenous observed variable to endogenous latent variable $\mathsf{\eta }$; $\mathsf{\lambda }$ represents the factor load matrix of exogenous observed variable to exogenous latent variable $\mathsf{\xi }$; $\mathsf{\delta }$ and $\mathsf{\epsilon }$ are the parts that cannot be explained by the measurement model and are the measurement error terms.

Figure 2. Theoretical framework based on structural equation modelling.

Figure 2. Theoretical framework based on structural equation modelling.

3. Results

3.1. Status of Conflicts between Nature Reserves and Neighboring Communities

Conflicts between Tanzanian conservancies and community residents are mainly characterized by biodiversity conservation policies that severely restrict the use and exploitation of natural resources by community residents, and by wildlife-human conflicts. The latter is a two-pronged conflict that includes both the problems of habitat destruction, overhunting, and overexploitation of wildlife by residents, as well as the destruction of agricultural products by wildlife (stealing and eating of crops, trampling of farmland and pasture, stealing of honey, etc.), harm to livestock and poultry (predation on livestock, raiding of livestock), damage to household property (destruction of houses, breaking and entering and stealing of food, and destruction of means of production), and damage to personal safety (trampling, toppling, cannibalism). The survey found that 63% (n = 200) of the respondents indicated that they have limited the use of natural resources, and 36.5%, 32.5% and 12.5% of the respondents were affected by wildlife destroying crops, attacking livestock and destroying houses respectively (n = 200). It can be observed that many residents are paying a huge price for wildlife conservation and their livelihoods are severely affected.

Figure 3. Status of conflicts between nature reserves and neighboring communities

Figure 3. Status of conflicts between nature reserves and neighboring communities

3.3. Model Reliability and Validity Tests

The measurement model needs to be validated before the model test, which contains the reliability test and validity test. Cronbach’s α coefficient and composite reliability (CR) were used as the test of internal consistency. Usually, a Cronbach’s α of <0.35 indicates low reliability; 0.35 ≤ Cronbach’s α < 0.70 indicates moderate reliability; Cronbach’s α > 0.70 indicates high reliability. The higher the CR value, the higher the reliability. As a rule of thumb, a CR value between 0.60 and 0.70 is considered "acceptable in exploratory studies", and a CR value between 0.7 and 0.9 indicates "satisfactory to good" reliability. As can be seen in Table 3, the CR values and Cronbach’s α for all latent variables satisfy the criteria, providing evidence of good internal consistency in the measurement model. In addition, in general, the standardized factor loading of the observed variables is greater than 0.5 indicating that each observed variable has good explanatory power for the corresponding latent variable. All the standardized factor loadings in this study are greater than the test criteria, which again indicates that the measurement model has high reliability. In addition, a VIF value of less than 5 indicates that there is no problem of multicollinearity.

Next, the validity of the measurement model needs to be assessed by testing convergent validity and discriminant validity. The metric used to assess convergent validity is the average variance of all items extracted on each construct, i.e., the average variance extracted (AVE).An acceptable value for the AVE is 0.50 or higher, which indicates that the construct explains at least 50% of the variance in its items. According to Table 3, it can be seen that the data in this part of the study meet the requirements. And discriminant validity, which represents the degree of effective differentiation between latent variables. It can be tested by the criterion that the square root of the AVE value is greater than the correlation coefficient of the other latent variables. From Table 4, it can be seen that the square root of AVE of the latent variables in this part is greater than the correlation coefficient between the latent variables, which indicates that the measurement model has good discriminant validity.

In addition, recent research suggests that this criterion may not be sufficiently robust in discriminant validity assessments, especially when structured with only slightly different indicator loadings (e.g., all factor loadings between 0.65 and 0.85) [39].As an alternative, Henseler proposed heterotrait-monotrait ratio (HTMT) as a new metric for discriminant validity assessment [39]. When the HTMT value is high, it indicates low discriminant validity, and Henseler et al. [39] proposed a threshold of 0.9 for the structural model. As can be seen in Table 5, the HTMT values for all latent variables in this part of the study were less than 0.9, indicating high discriminant validity of the measurement model in this part of the study.

Table 3. Results of model reliability test

Table 3. Results of model reliability test

Constructs	Items	Factor loadings	VIF	Cronbach’s alpha	CR	AVE
action	action1	0.955	2.233	0.853	0.929	0.868
	action2	0.908	2.233
cognition	cognition1	0.800	1.351	0.715	0.82	0.535
	Cognition2	0.682	1.421
	Cognition3	0.783	1.524
	Cognition4	0.648	1.257
cost	cost1	0.785	1.44	0.762	0.85	0.589
	cost2	0.848	2.258
	cost3	0.803	2.15
	Cost4	0.613	1.164
family	family1	0.888	2.252	0.736	0.831	0.561
	family2	0.809	1.46
	family3	0.519	1.266
	family4	0.728	1.618
revenue	revenue1	0.628	1.626	0.766	0.814	0.697
	revenue2	1.000	1.626
willingness	Willingness1	0.920	2.206	0.705	0.837	0.636
	Willingness2	0.631	1.328
	Willingness3	0.816	1.778

Table 4. Square root of AVE and correlation coefficient between latent variables.

Table 4. Square root of AVE and correlation coefficient between latent variables.

	action	cognition	cost	family	revenue	willingness
action	0.932
cognition	0.536	0.731
cost	-0.105	-0.338	0.768
family	-0.002	-0.234	0.386	0.749
revenue	0.202	0.264	-0.02	0.068	0.835
willingness	0.545	0.548	-0.49	-0.212	0.045	0.798

Table 5. Heterotrait-monotrait ratio (HTMT) results.

Table 5. Heterotrait-monotrait ratio (HTMT) results.

	action	cognition	cost	family	revenue	willingness
action	-
cognition	0.603	-
cost	0.142	0.453	-
family	0.053	0.315	0.51	-
revenue	0.181	0.249	0.141	0.12	-
willingness	0.681	0.739	0.652	0.27	0.119	-

4. Discussion

5. Conclusion

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