*Article* **Predicting Behavioral Intention of Rural Inhabitants toward Economic Incentive for Deforestation in Gilgit-Baltistan, Pakistan**

**Saif Ullah <sup>1</sup> , Ali Abid <sup>1</sup> , Waqas Aslam <sup>1</sup> , Rana Shahzad Noor <sup>2</sup> , Muhammad Mohsin Waqas <sup>3</sup> and Tian Gang 1,\***


**Abstract:** The conservation of forest in the northern areas of Pakistan is the major priority of the national environmental policy to fight against global warming. Despite the policy for the protection of forest, rural residents' behavior toward economic incentives for deforestation may undermine their conservation goals. Therefore, the purpose of this study was to understand the factors that affect the illegal behaviors related to deforestation in the northern areas of Pakistan. The present study applied the socio-psychological theory of planned behavior to predict the behavioral intention of rural residents toward economic incentives for deforestation. Correlations were explored between background factors toward motivations for deforestation based on positive and negative views through open-ended questions. Attitude and descriptive norm were found good predictors to perceive the behaviors. The findings of the study suggest that rural communities' support for compliance with policies is vital for the long-term efficacy and protection of the forest in the region. Further, change in the behaviors of inhabitants toward the ecosystem through training can be improved to manage the forest.

**Keywords:** forest conservation; forest management; rural residents; economic incentives; Pakistan

## **1. Introduction**

Globally, forests have been receiving ever-growing attention to fight against global warming. Forest not only conserves biodiversity but also provides necessary ecosystem services to society. Nevertheless, recently several studies have demonstrated that restrictions on the use of natural resources have negative behavior among the rural residents who rely on the forest for their livelihood, which creates a lot of hurdles for the management of forests [1–5]. The mountainous rural area resident's major source of income is from the forest resources; therefore, the economic incentive from forests directs rural residents toward deforestation [6–12]. Therefore, comprehensive attention is needed to understand the relationship between rural residents, economic incentives, and forest conservation.

Scientific literature has explored the relationship between rural residents and deforestation [13–16]. Such literature has helped to identify the preferences and beliefs of residents on conservation issues [17]. Therefore, the evaluation of plausible relationships with intentions is important to predict the actions that influence and change the behavior of rural communities [18–21]. In the past, many scholars have suggested incorporating the beliefs of rural residents toward the conservation of natural resources in global forest policy [22–25].

**Citation:** Ullah, S.; Abid, A.; Aslam, W.; Noor, R.S.; Waqas, M.M.; Gang, T. Predicting Behavioral Intention of Rural Inhabitants toward Economic Incentive for Deforestation in Gilgit-Baltistan, Pakistan. *Sustainability* **2021**, *13*, 617. https:// doi.org/10.3390/su13020617

Received: 1 December 2020 Accepted: 6 January 2021 Published: 11 January 2021

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**Copyright:** © 2021 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/).

According to the National Forest Policy of Pakistan 2015, the lawmakers have taken steps to manage forests and improve public awareness of the ecological and cultural values of forests [26–29]. Nevertheless, forests have been weakly preserved in recent years and it is a challenging task in northern areas of Pakistan to apply strict rules of preservation due to the high reliance of peoples on the natural resources of forests [30–32]. In the scarcely facilitated area of Gilgit-Baltistan, resident depends on natural resources for their livelihoods [32,33]. According to Reference [32], porters cut trees for cooking, walking sticks, and fuel. This is because people have no source of income in the region, and they used natural resources to fulfill their basic needs; therefore, policies regarding the conservation of forests adversely affect the behavior of rural inhabitants. In the northern areas of Pakistan, rural resident's relationships with the forest authorities and continuous illegal activities to use fuel-wood are of particular concern for deforestation. This is due to the existence of strong (informal) links between the authorities, influential groups, and timber mafia [34,35]. The increasing level of illegal activities also negatively affects the rest of the areas. Deforestation in the developed countries is considered due to expansion in agricultural land [36–42]. While in the case of developing countries like Africa, deforestation is continued for subsistence agriculture farming and wood production for local markets [43,44]. Agriculture and charcoal production are the main causes of deforestation in Tanzania [45] and fuel-wood in Senegal [46]. In Turkey, the main causes of deforestation are rapid urbanization and industrialization [47].

Previous studies related to deforestation in Pakistan are mainly focused on timber production, conversion to agriculture, roadbuilding, and human-caused fire [48]. According to the studies of Ullah et al. [14,49] and Ali et al. [14,49], deforestation in northern areas is due to the construction of roads and high population growth in the last few years. However, to the best knowledge of authors, no research has so far been conducted to examine the behavior of rural residents toward deforestation, especially the study related to the economic motivation of rural residents. Therefore, the purpose of this study is to investigate the intention of people toward economic incentives to control deforestation and help the government institutes to make effective policies for the livelihoods of rural residents as for as law enforcement agencies.

To perceive human behavior, Ajzen's theory of planned behavior, an extension of the theory of reasoned action, has been used [50]. Nonetheless, few studies on forest biodiversity have utilized such frameworks to analyze multiple predictors of behavior. [18]. According to the theory, the behavioral intentions arise from an individual's attitude (ATT), norms, and Planned Behavior Constructs (PBC), which can further be predicted by actual behavior in question [50]. Furthermore, a number of background factors including socioeconomic, knowledge, education, and past experience of the individual can influence the ATT, Normative (N), and PBC. [51]. Contextual aspects such as rules and legislation of government may often interfere and evaluate a behavior [52]. Attitude is defined as the extent to which an individual has a favorable or unfavorable view of a particular behavior. Descriptive normative (DN) is defined as the opinions of the people rather than what approve or disapprove by others [18,53]. PBC is the perception of how an individual feels ease or difficulty to perform a specific behavior [50,54,55]. Understanding behavioral intent toward, and factors that affect, illegal behaviors can help managers emphasize their actions to enhance people's compliance with laws and protect forest within those areas.

Therefore, our study investigated the ATT, DN, PBC, and Behavior Intention (BI) of the rural inhabitants toward their economic incentive for deforestation. By applying the theory of planned behavior, the purpose of the present study was to (a) identify background factors that may influence the intention of rural residents; (b) identify inhabitants' attitudes, descriptive norms, perceived behavioral control toward economic incentives for deforestation; and (c) identify rural inhabitant's illegal behavior toward the economic incentives for deforestation.

### **2. Theoretical Model 2. Theoretical Model**

### *2.1. Theory of Planned Behavior 2.1. Theory of Planned Behavior*

economic incentives for deforestation.

Many contemporary studies have used intentions as a key component to understand the behavior in question [50,56–60]. In psychology, the theory of planned behavior is an attempt to shape an individual's behavioral intentions with a combination of three factors: attitudes toward the behavior, norms, and perceived behavioral control [51,61,62] as given in Figure 1. Many contemporary studies have used intentions as a key component to understand the behavior in question [50,56–60]. In psychology, the theory of planned behavior is an attempt to shape an individual's behavioral intentions with a combination of three factors: attitudes toward the behavior, norms, and perceived behavioral control [51,61,62] as given in Figure 1.

incentives for deforestation; and (c) identify rural inhabitant's illegal behavior toward the

*Sustainability* **2021**, *13*, x FOR PEER REVIEW 3 of 18

**Figure 1.** Factors affecting the Behavior Intention: Theory of Planned Behavior Model [50]. **Figure 1.** Factors affecting the Behavior Intention: Theory of Planned Behavior Model [50].

### *2.2. Expectancy-Value Model 2.2. Expectancy-Value Model*

The theory of planned behavior follows an expectancy-value model to predict the behavior of individual under question [50,51,63,64]. The beliefs-based measures are probably regarded to get a more accurate prediction of cognitive intention than its direct predictors alone. The theory of planned behavior follows an expectancy-value model to predict the behavior of individual under question [50,51,63,64]. The beliefs-based measures are probably regarded to get a more accurate prediction of cognitive intention than its direct predictors alone.

The attitude is comprised of (silent beliefs -bsi) and evaluation of the outcomes (oei) and it can be obtained according to the following formula: The attitude is comprised of (silent beliefs -bs<sup>i</sup> ) and evaluation of the outcomes (oe<sup>i</sup> ) and it can be obtained according to the following formula:

$$\text{ATT} \propto \sum \text{bs}\_{\text{i}} \,\text{oe}\_{\text{i}} \tag{1}$$

Strength of behavioral belief (bsi) is described as a possibility that can produce a particular outcome by performing a behavior (i) and the outcome evaluation (oei) can be termed as the utility obtained if the result (i) occurs [50,51,63,64]. The descriptive norm (DN) describes the whereas descriptive norms refer to Strength of behavioral belief (bs<sup>i</sup> ) is described as a possibility that can produce a particular outcome by performing a behavior (i) and the outcome evaluation (oe<sup>i</sup> ) can be termed as the utility obtained if the result (i) occurs [50,51,63,64].

perceptions that others are or are not performing the behavior. Normative beliefs can be explored by assessing a person's identification with the referent (iwri), multiplying the measures of descriptive normative beliefs (dnbi) regarding given referents by the corresponding identity measures, and then summing the normative belief by identity The descriptive norm (DN) describes the whereas descriptive norms refer to perceptions that others are or are not performing the behavior. Normative beliefs can be explored by assessing a person's identification with the referent (iwr<sup>i</sup> ), multiplying the measures of descriptive normative beliefs (dnb<sup>i</sup> ) regarding given referents by the corresponding identity measures, and then summing the normative belief by identity products [65,66]. A belief-based measure of the descriptive norm (DN) can be obtained according to the following formula:

$$\text{DN} \propto \sum \text{dnb}\_{\text{i}} \text{iwr}\_{\text{i}} \tag{2}$$

Strength of descriptive norms is formed by considering multiple descriptive normative beliefs (dnb<sup>i</sup> ), or beliefs that behavior is normative for peers and individuals we look up to in social groups [65,66].

"Perceived behavioral control—This refers to a person's perception of the ease or difficulty to perform the behavior of interest. It consisted of personal control beliefs (cb<sup>i</sup> ) and the perceived strength of these specific control factors to facilitate or impede actions (power to affect—pc<sup>i</sup> ) [50,67,68].

$$\text{PBC } \alpha \sum \mathbf{c} \mathbf{b}\_{\mathbf{i}} \text{ pc}\_{\mathbf{i}} \tag{3}$$

The strength of each control belief (cb<sup>i</sup> ) is weighted by the perceived power (pc<sup>i</sup> ) of the control factor to perform a specific behavior [50,51].

Behavioral intention (BI) refers to "a person's subjective probability that he will perform some behavior". BI is the function of three antecedents, namely, attitude, norms, and PBC. By incorporating the belief-based measures, BI can be calculated according to the following formula:

$$\text{BII} = \beta\_1 \text{ATT} + \beta\_2 \text{DN} + \beta\_3 \text{PBC} + \varepsilon \tag{4}$$

β1, β2, and β<sup>3</sup> are the coefficients to evaluate each component, and (ε) is an error term.

### **3. Materials and Methods**

### *3.1. Study Area*

The present study was conducted in the three districts (Skardu, Gilgit, and Astore) of Gilgit-Baltistan, Pakistan (Figure 2), where rural residents not only use forest resources to meet their livelihood but also generate substantial cash income through trade in forest products. *Sustainability* **2021**, *13*, x FOR PEER REVIEW 5 of 18

**Figure 2. Figure 2.** Study map Study map. .

### *3.2. Data Collection 3.2. Data Collection*

For the present study, survey data were collected from January 2019 to April 2019 (Figure 3). Face-to-face interviews were conducted with the residents. All the meetings were scheduled with the consent of the participants and their privacy was ensured. For the present study, survey data were collected from January 2019 to April 2019 (Figure 3). Face-to-face interviews were conducted with the residents. All the meetings were scheduled with the consent of the participants and their privacy was ensured.

**Figure 3.** Continuous decline in forest cover area in northern Pakistan from 2001 to 2019. (Taken from https://www.globalforestwatch.org/dashboards/country/PAK). **Figure 3.** Continuous decline in forest cover area in northern Pakistan from 2001 to 2019. (Taken from https://www. globalforestwatch.org/dashboards/country/PAK).

From 2001 to 2019, Northern Areas lost 112 ha of tree cover, equivalent to a 0.23% decrease in tree cover since 2000. The survey also consisted of some close-ended questions of (a) socioeconomic factors: age, education, time duration to stay in the region, family size, and support from the government (''royalty'' income offered to local residents for not utilizing the forest), (b) attitudes toward economic incentives for deforestation; descriptive norms defined in the study as the opinions of other people's behavior toward deforestation; perceived behavioral control as the respondent's views about the presence of law enforcement in the region; and behaviors toward economic incentives for deforestation. Moreover, an open-ended questionnaire has been used to explore the perceptions of the respondents on natural resources including the one where they lived. Categorization of these opinions was done according to the values of nature described by Kellert [69–71]. The opinions of the respondents were categorized into only two groups of natural values "(moralistic values, which represent a respect to the nature; and utilitarian values, which represents the material benefits that a person obtain from nature; Kellert, From 2001 to 2019, Northern Areas lost 112 ha of tree cover, equivalent to a 0.23% decrease in tree cover since 2000. The survey also consisted of some close-ended questions of (a) socioeconomic factors: age, education, time duration to stay in the region, family size, and support from the government ("royalty" income offered to local residents for not utilizing the forest), (b) attitudes toward economic incentives for deforestation; descriptive norms defined in the study as the opinions of other people's behavior toward deforestation; perceived behavioral control as the respondent's views about the presence of law enforcement in the region; and behaviors toward economic incentives for deforestation. Moreover, an open-ended questionnaire has been used to explore the perceptions of the respondents on natural resources including the one where they lived. Categorization of these opinions was done according to the values of nature described by Kellert [69–71]. The opinions of the respondents were categorized into only two groups of natural values "(moralistic values, which represent a respect to the nature; and utilitarian values, which represents the material benefits that a person obtain from nature; Kellert, 2005)".

### 2005)". *3.3. Measurement of Variables*

*3.3. Measurement of Variables*  Behavioral intention was defined for this research as the intention of rural residents toward the economic incentive of deforestation by replacing small-scale agriculture. The question was designed as follows: Do you have the intention to replace secondary forest with small-scale agriculture to get economic incentives? To evaluate the construct, a five-Behavioral intention was defined for this research as the intention of rural residents toward the economic incentive of deforestation by replacing small-scale agriculture. The question was designed as follows: Do you have the intention to replace secondary forest with small-scale agriculture to get economic incentives? To evaluate the construct, a five-point bipolar Likert scale was used ranging from 4—very likely to 0—very unlikely.

point bipolar Likert scale was used ranging from 4—very likely to 0—very unlikely. Attitude was measured directly by utilizing five points "bipolar Likert scale ranging from very unlikely (0) to very likely (4)". For assessment of attitude, question was formed as follows: Do you think forest should be replaced with small-scale agriculture to get economic incentives? For the indirect assessment of belief-based items, (b) were measured on five points "Likert scale ranging from strongly disagree (0) to strongly agree (4)", while Attitude was measured directly by utilizing five points "bipolar Likert scale ranging from very unlikely (0) to very likely (4)". For assessment of attitude, question was formed as follows: Do you think forest should be replaced with small-scale agriculture to get economic incentives? For the indirect assessment of belief-based items, (b) were measured on five points "Likert scale ranging from strongly disagree (0) to strongly agree (4)", while for (e) very important (4) to not very important (0). For the belief-based measures,

three behavioral beliefs were presented to the respondents concerning that rural residents think by replacing forest with small-scale agriculture will provide food for the family, increase livelihood, and increase tourism (see Table 1).

**Table 1.** Items used for evaluation of Theory of Planned Behavior constructs.


Talking about the actions of others (descriptive norms) was found more comfortable for the respondent than perceived social pressure from others for an individual to behave in a certain manner (subjective norms). Descriptive norm was measured directly by utilizing a five-point unipolar Likert scale ranging from not at all (0) to a large extent (4). The item was designed as follows: Do you think that rural residents in the area replacing the forest with small-scale agriculture for economic incentives? The indirect assessment of belief-based items (dnb<sup>i</sup> ) was performed by using unipolar Likert scale, from 0—strongly disagree to 4 strongly agree, while for (iwr<sup>i</sup> ) "unipolar Likert scale was used ranging from 0—strongly disagree to 4—strongly agree".

For the measurement of perceived behavioral control, a five-point "unipolar Likert scale from (0) strongly disagree to (4) strongly agree" was used. The statement was formed as follows: In my view, law enforcement is sufficient in the regions to control rural resident's activities. For the indirect assessment of perceived behavioral control, control beliefs (cb<sup>i</sup> ) were assessed on a Likert scale ranging from (0) strongly disagree to (4) strongly agree, while p was measured on a scale ranging from (0) strongly disagree to (4) strongly agree.

### *3.4. Analysis Methods*

Descriptive statistics were used to present the results, and "Pearson's correlation coefficients and regression analysis were chosen for interpretation". The Pearson correlation coefficients are used in statistics to measure how strong a relationship is between two variables. A value of 0 demonstrates that there is no correlation between the two variables. A value higher than 0 describes a positive relationship and a value less than 0 indicates a negative relationship. Regression analyses investigate the relationship between dependent and independent variables.

To determine the relationship between dependent variables (attitude, descriptive norms, perceived behavioral control, behavioral intention) and independent variables (∑ bsioe<sup>i</sup> , ∑ dnb<sup>i</sup> iwr<sup>i</sup> , and ∑cb<sup>i</sup> pc<sup>i</sup> ), a linear relationship was presumed (as mentioned in the mathematical formulation of TPB). Therefore, a multiple linear regression technique was used to analyze the data. P-values were used to interpret the significance level of regression analysis and coefficients. β weights and t-values were used to interpret the results. R<sup>2</sup> was applied for the evaluation of the explanatory power of the regression analysis. F-test was used for the assessment of the overall significance level of the models. Data analysis was performed using "Microsoft Excel for Windows version 19 and SPSS ver. 25 software".

### **4. Results**

## *4.1. Background Factors*

The survey was conducted with 207 interviewers, 92 percent of whom were males and 8 percent were females. the detail of background factors is presented in Table 2.


**Table 2.** Demographic survey of rural residents in protected areas of northern Pakistan.

Based on the feedback of the open-ended question about the participants' opinions on deforestation, 86% of participants reported negative beliefs, including 42% people cannot work, 20% people claimed that government does not pay a royalty for the properties, 11% think an increase in unemployment, and 10% thinks that usage of firewood is prohibited. Only 14% mentioned just negative values. Respondents' beliefs categorized into negative values including restrictions and positive values including forest protection, relationship between education and profession, education and government assistance, Duration of Residence and government assistance, Age and government assistance, and found that the regression relationship is significant and multicollinearity is less than 5. So, these re-

sults indicate that there is an influence of these variables regarding deforestation if it is from the government sector or local communities [72]. variables regarding deforestation if it is from the government sector or local communities [72].

multicollinearity is less than 5. So, these results indicate that there is an influence of these

### *4.2. Components of Theory of Planned Behavior 4.2. Components of Theory of Planned Behavior*

*Sustainability* **2021**, *13*, x FOR PEER REVIEW 9 of 18

### 4.2.1. Components of Behavioral Intention 4.2.1. Components of Behavioral Intention

The majority of the participants (46%) had thought to carry out the behavior in question, while 23% of them declared that it is an illegal activity to deforest. Attitude toward performing behavior was 59%. At the same time, descriptive norms for the respondent's views toward other people were 42% that were engaging in illegal activities. As far as PBC is concerned, 47% of respondents were not satisfied with the performance of law enforcement. The majority of the participants (46%) had thought to carry out the behavior in question, while 23% of them declared that it is an illegal activity to deforest. Attitude toward performing behavior was 59%. At the same time, descriptive norms for the respondent's views toward other people were 42% that were engaging in illegal activities. As far as PBC is concerned, 47% of respondents were not satisfied with the performance of law enforcement.

### 4.2.2. Components of Attitude 4.2.2. Components of Attitude

Most of the participants (61%) believed that the replacement of forests with smallscale agriculture will provide food for the family (agree and strongly agree on the "*bipolar Likert scale*"). With regards to other important beliefs that categorized the respondents are livelihood (82%), however, respondents' beliefs toward replacing forest with tourism were adverse (Figure 4a). As far as outcome evaluation is concerned, 94% of the participants perceived that provide food to the family is important or very important "*3 and 4 on the bipolar Likert scale*", while only 28% of the respondents stated that, for them, increase livelihood is of some importance. Most of the participants (61%) believed that the replacement of forests with smallscale agriculture will provide food for the family (agree and strongly agree on the "*bipolar Likert scale*"). With regards to other important beliefs that categorized the respondents are livelihood (82%), however, respondents' beliefs toward replacing forest with tourism were adverse (Figure 4a). As far as outcome evaluation is concerned, 94% of the participants perceived that provide food to the family is important or very important "*3 and 4 on the bipolar Likert scale*", while only 28% of the respondents stated that, for them, increase livelihood is of some importance.

**Figure 4.** (**a**,**b**) Evaluating the percentage of components of attitude (behavioral belief and outcome evaluation). **Figure 4.** (**a**,**b**) Evaluating the percentage of components of attitude (behavioral belief and outcome evaluation).

exogenous variables was significant.

The results obtained from Pearson correlation coefficients described that beliefassessment (biei) had a relatively strong association with the direct measurement of attitude (the values of Pearson coefficient lie between 0.2 and 0.4), with the notable exception of increasing tourism (see Table 3). The regression analysis verified the relationship between endogenous variable attitude and the exogenous variables biei: the β-tourism coefficient was not found significant, whilst the β-coefficient for all other The results obtained from Pearson correlation coefficients described that belief-assessment (bie<sup>i</sup> ) had a relatively strong association with the direct measurement of attitude (the values of Pearson coefficient lie between 0.2 and 0.4), with the notable exception of increasing tourism (see Table 3). The regression analysis verified the relationship between endogenous variable attitude and the exogenous variables bie<sup>i</sup> : the β-tourism coefficient was not found significant, whilst the β-coefficient for all other exogenous variables was significant.


Increase tourism × tourism is 0.223 \* 0.069 0.984

Increase livelihood x increase revenue is 0.327 \*\* 0.138 \*\* 1.767 Increase tourism x tourism is 0.223 \* 0.069 0.984

**Table 3.** Regression analysis to predict Attitude (dependent variable) from beliefs—evaluation (independent variables) Pearson correlation coefficients between bie<sup>i</sup> and Attitude (F = 14.671, R<sup>2</sup> = 0.178 and significance level = 0.000). **Table 3.** Regression analysis to predict Attitude (dependent variable) from beliefs—evaluation (independent variables) Pearson correlation coefficients between biei and Attitude (F = 14.671, R2 = 0.178 and significance level = 0.000).

> \*\* significant for *p* ≤ 0.01. \* significant for *p* ≤ 0.05. \*\* significant for *p* ≤ 0.01. \* significant for *p* ≤ 0.05.

### 4.2.3. Components of Descriptive Norm 4.2.3. Components of Descriptive Norm

With regards to the perception of other people, it was perceived (78%, say Yes) rural inhabitants replace the secondary forest with small-scale agriculture. Respondents generally confirmed their most people (85%) have illegal activities in the forest (Figure 5). With regards to the perception of other people, it was perceived (78%, say Yes) rural inhabitants replace the secondary forest with small-scale agriculture. Respondents generally confirmed their most people (85%) have illegal activities in the forest (Figure 5).

**Figure 5.** Percentage distribution of descriptive beliefs on the protection of forest. **Figure 5.** Percentage distribution of descriptive beliefs on the protection of forest.

By assessing correlation coefficients of descriptive normative beliefs (dnbi) and identification with the referent (iwri), the descriptive norm was found correlated with these two products. The regression results demonstrated 20 percent of the variation in descriptive norm that confirmed illegal activities of the people in the region (Table 4). By assessing correlation coefficients of descriptive normative beliefs (dnb<sup>i</sup> ) and identification with the referent (iwr<sup>i</sup> ), the descriptive norm was found correlated with these two products. The regression results demonstrated 20 percent of the variation in descriptive norm that confirmed illegal activities of the people in the region (Table 4).


\* *p* < 0.000.

\* *p* < 0.000.

4.2.4. Components of PBC

4.2.4. Components of PBC The statement on unsuitable legislation was supported by a majority of respondents, while the power of this belief to impede law enforcement was also perceived as high. Majority of respondents agreed without suitable training illegal behavior of residents The statement on unsuitable legislation was supported by a majority of respondents, while the power of this belief to impede law enforcement was also perceived as high. Majority of respondents agreed without suitable training illegal behavior of residents would not stop (Figure 6a,b).

would not stop (Figure 6a,b). With regards to the evaluation of control belief x perceived power to influence products (cbip<sup>i</sup> ), it was found that both products were correlated with the PBC (see Table 5). However, insufficient legislation demonstrated a major factor affecting the perceived power of control. Both the Pearson correlation coefficient and the regression analysis results demonstrated that insufficient legislation as well as unsuited training significantly predicted PBC.

level = 0.000).

**Figure 6.** (**a**,**b**): Percentage distribution of control beliefs (**a**) and compliance with the beliefs (**b**) in law enforcement to protect the forest. **Figure 6.** (**a**,**b**): Percentage distribution of control beliefs (**a**) and compliance with the beliefs (**b**) in law enforcement to protect the forest.

With regards to the evaluation of control belief x perceived power to influence products (cbipi), it was found that both products were correlated with the PBC (see Table 5). However, insufficient legislation demonstrated a major factor affecting the perceived **Table 5.** Regression analysis to predict perceived behavior control (dependent variable) from beliefs—evaluation (independent variables) Pearson correlation coefficients between biei and attitude (F = 37.679, R<sup>2</sup> = 0.270 and significance level = 0.000).


\*\* significant for *p* ≤ 0.01.

(independent variables) Pearson correlation coefficients between biei and attitude (F = 37.679, R2 = 0.270 and significance
