Results and Hypotheses Testing

The final step in the data analysis process is to examine the different relationships in the model to determine whether the constructs are significantly and directionally related as predicted by theoretical hypotheses. Following Cheung and Lau’s (2008) suggestion, instead of total effects, regression weights were used to interpret the effect of independent variables on the dependent variable.

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As discussed in the previous section, Model III, as an abbreviated version of Model II, had fewer paths when using composite variables. In this section, the relationships are analysed using the results from both the revised model (Model II) and its equivalent, Model III.

Figure 7- 2 Result of Model III

Note: 1. For the sake of simplicity, all the observed variables except four variables of behavioural intentions, all errors, and all paths with insignificant path coefficients were concealed. The paths between latent variables of values and attitudes are simplified and their path coefficients are omitted.

2. The numbers beside the paths are the standardized path coefficients. Paths in thin purple are statistically significant at 0.1 probability level with positive coefficients;

paths in bold purple are statistically significant at 0.05 probability level with positive coefficients; paths in thin blue are statistically significant at 0.1 probability level with negative coefficients; and paths in bold blue are statistically significant at 0.05 probability level with negative coefficients.

3. The numbers in red over four variables of behavioural intentions are the squared multiple correlations, which are the R²’s in regression.

4. Composite variables of personal, household, and farm business characteristics are presented in hexagon.

Figure 7- 1 Result of Model III

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7.3.1 Results for the Hypotheses Related to Behavioural Intentions

As illustrated in Figure 7-1, the influential factors have quite different patterns of effects on the four behavioural intentions. For respondents’ response intentions to volumetric pricing (BI_1), the effects of farm business characteristics (FBC), and the Universalism value related to nature (UN_N), and attitude to farm business (AFB) are statistically significant. For respondents’ willingness to transfer historically unused water (BI_2), the effects of FBC, and Universalism values both for nature and human (UN_N and UN_H), and attitude to land attachment (ALA) are statistically significant. For respondents’

intentions to improve irrigation equipment (BI_3), the effect of attitude to farm business (AFB) is statistically significant, and for respondents’ intentions to not reduce irrigation water if the price of water increases (BI_4), the only statistically significant effect is from past behaviour (PB). Table 7-6 shows coefficient estimates for all paths directly to four behavioural intentions from Model III. Those paths correspond to the hypotheses of HA1, H_B1, H_C1, H_D1, and H_E.The overall conclusions for the hypotheses related to behavioural intentions are summarised in Table 7-7.

As shown in Table 7-7, individual and household characteristics (IC and HC) had no statistically significant effects on all four behavioural intentions, which indicates that H_A1 and HB1 are not supported. HC1 is partly supported because the composite variable of farm business characteristics (FBC) has statistically significant effects on both BI_1 and BI_2, but not on B1_3 and BI_4.

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Table 7- 6 Estimates for the Paths to Behavioural Intentions from Model III

Paths Unstandardized

Regression Weights S.E. C.R. P Standardized

Regression Weights

Note: 1.Boldface type indicates significance at p<0.05; boldface italic type at p<0.1. 2. BI_1: Water pricing should be based on actual and recorded volume of water used; BI_2: You would be willing to transfer water that,

historically, you have not used; BI_3: You intend to make changes to your irrigation equipment in the next five year; BI_4: Increasing the price of water will not reduce the use of water for irrigation.

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Table 7- 7 Testing Results for the Hypotheses Related to Behavioural Intentions Hypotheses Associated

Variable ^{BI_1 BI_2 BI_3 BI_4}

Testing Result HA1: Response intentions to

possible water policy changes

H_B1: Response intentions to possible water policy changes

H_C1: Response intentions to possible water policy changes

H_E: Response intentions to possible water policy changes

Note: 1.The results are based on Model III;

2. √ indicates the association is statistically significant; × indicates the association is not statistically significant.

3. BI_1: Water pricing should be based on actual and recorded volume of water used;

BI_2: You would be willing to transfer water that, historically, you have not used;

BI_3: You intend to make changes to your irrigation equipment in the next five years;

BI_4: Increasing the price of water will not reduce the use of water for irrigation.

Based on Table 7-6, some values and attitudes have statistically significant effects on behavioural intentions (BI_1 to BI_3) except BI_4. HD1 is partly supported (shown in Table 7-7). Theoretically, value is always a core concept in the field of behaviour and decision study. The literature shows that farmers’ values often have been used to identify and distinguish farmers’ behaviour.

The intention to agree that water pricing should be based on actual and recorded volume of water used (BI_1) was significantly influenced by values of Universalism for nature

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(UN_N) and Attitude to Farming Business (AFB). Farmers who place more importance on “Protecting the environment” (VI_1), “United with nature” (VI_3), “Curious” (VI_16), and “A world of beauty” (VI_17), were more likely to agree that water pricing should be based on actual and recorded volume of water used. Farmers who had a more positive Attitude to Farming Business (AFB), which means they are more likely to agree with the statement “A maximum annual net financial return from your farm is an important goal for your family” (AFB_1), “Increasing the asset value or net worth of your farming operation is very important to your family” (AFB_2), and “You view your farming operation as first and foremost a business investment” (AFB_3), also were more likely to agree that water pricing should be based on actual and recorded volume of water used.

The intention to transfer historically unused water (BI_2) was influenced by values of Universalism for human (UN_H) and Attitude to Land Attachment (ALA). Famers who more highly value “Inner harmony” (VI_7) and “Equality” (VI_8), and who have a more positive Attitude to Land Attachment (ALA), that is they were more likely to agree with the statements of “Having land to pass down to future generations is more important than selling it for the highest price (ALA_1)”, and “You feel a responsibility to keep your land in the family” (ALA_2), were less willing to transfer their historically unused water. The intention to improve irrigation equipment in the next five years (BI_3) was influenced by Attitude to Farming Business (AFB). That is,farmers who had a more positive Attitude to Farming Business (AFB), which means they were more likely to agree with the statement “A maximum annual net financial return from your farm is an important goal for your family” (AFB_1), “Increasing the asset value or net worth of your farming operation is very important to your family” (AFB_2), and “You view your farming