Using structural equation modeling to identify the psychological factors influencing dairy farmers’ intention to diversify agricultural production

Author’s Accepted Manuscript Using structural equation modeling to identify the psychological factors influencing dairy farmers’ intention to diversify agricultural production Igor Senger, João Augusto Rossi Borges, João Armando Dessimon Machado www.elsevier.com/locate/livsci

PII: DOI: Reference:

S1871-1413(17)30217-2 http://dx.doi.org/10.1016/j.livsci.2017.07.009 LIVSCI3260

To appear in: Livestock Science Received date: 24 September 2016 Revised date: 4 July 2017 Accepted date: 24 July 2017 Cite this article as: Igor Senger, João Augusto Rossi Borges and João Armando Dessimon Machado, Using structural equation modeling to identify the psychological factors influencing dairy farmers’ intention to diversify agricultural production, Livestock Science, http://dx.doi.org/10.1016/j.livsci.2017.07.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Using structural equation modeling to identify the psychological factors influencing dairy farmers’ intention to diversify agricultural production Igor Sengera1, João Augusto Rossi Borgesb2, João Armando Dessimon Machadoc a

Federal University of Santa Maria, CESNORS, Linha 7 de Setembro, BR-386 Km40, Frederico Westephalen,

Brazil b

c

Federal University of Grande Dourados, Rodovia Dourados – Itahum, Km 12, Dourados, Brazil

Federal University of Rio Grande do Sul, Avenida João Pessoa, 31, Porto Alegre, Brazil

[email protected] [email protected] [email protected]

Abstract Actions and public policies have been developed to encourage Brazilian farmers to diversify their agricultural production. However, such actions have been unable to encourage production and economic diversification. This article uses the theory of reasoned action (TRA) and the theory of planned behavior (TPB) to understand the intention of dairy farmers to diversify agricultural production. Results showed that the TRA had better explanatory power of farmers’ intention to diversify agricultural production than the TPB model. Corroborating the theory, results revealed that attitude and subjective norm positively influence the intention of farmers to diversify agricultural production. Implications for public policies are discussed.

Keywords: Diversification; Dairy farmers; Attitude; Social pressure; Theory of Reasoned Action; Theory of Planned Behavior.

1 2

Telephone: +55 (55) 37448964. Telephone: +55 (51) 97499180.

1 Introduction In Brazil, actions and public policies have been developed to encourage Brazilian farmers to diversify their agricultural production. The National Program for Strengthening Family Agriculture (Pronaf), the National School Meal Program (PNAE), the Food Acquisition Program (PAA), the National Plan for Sustainable Rural Development and Solidarity and the Shares for the Diversification of Production and Income in Tobacco Cultivated Areas, are some examples designed to encourage farmers to produce food and therefore diversify production on their farms. Although such policies have encouraged farmers to produce food, these actions have been unable to encourage the productive and economic diversification (Gazolla, 2004). In this context, it is relevant to understand the intentions of farmers regarding the diversification and the factors that influence intention. Previous literature on the main drivers of farm diversification found that there is a set of factors influencing farmers’ decisions to diversify their agricultural production and to start new business. For instance, previous studies showed that farm characteristics (i.e., size of the farm, farm type, location of the farm), and farmers characteristics (i.e., educational level, age, gender) influence farmers’ decisions to diversify (Bateman and Ray, 1994; Mcnally, 2001; Mishra et al., 2004; Pfeifer et al., 2009). Others studies focused on the motives underlying farmers’ decisions to diversify. This strand of literature found that farmers’ financial and nonfinancial goals, such as generating additional income, the continuance of farming and ranching, and the enhancement of quality of life influence farmers’ diversification decisions (Barbieri and Mahoney, 2009). In addition, Jongeneel et al. (2008) found that farmers’ trust in the government is an important factor influencing farmers’ decisions to diversify. Moreover, Hansson et al. (2013) found that the decision to start new ventures depends on the situation of the family farm (i.e., if the spouse support the creation of new venture). In summary, previous

literature found that the drivers of farmers’ decisions to diversify and to start new business are complex and includes several factors (Hansson et al., 2013). In this study, the theory of reasoned action (TRA) and the theory of planned behavior (TPB) are used to understand the intention of farmers to diversify3 agricultural production. According to the TRA and the TPB the intention is originated from two latent constructs: attitude and subjective norm, and an additional TPB construct, perceived behavioral control. Both theories have been used to understand different farmers’ decisions. In a first study, which is part of the same research project, Senger et al. (2017) found, by using correlation coefficient, that attitude, subjective norm and perceived behavioral control are significant and positively correlated with intention. Martínez-García et al. (2013) used the TRA to study the decisions of livestock producers and found that attitude and subjective norm are correlated with the intention of farmers to use improved pastures. Borges et al. (2014) correlated the intention of farmers to use pasture improvement with the three constructs of TPB (attitude, subjective norm and perceived behavioral control). However, these three studies used only correlations, evaluating the relation between the constructs of TRA/TPB one at a time. Additionally, this methodology does not allow assessing the relative importance of the TRA/TPB constructs. To overcome the limitations of the use of correlations, Structural Equation Modeling (SEM) has been suggested (Bleakley and Hennessy, 2012). SEM allows the simultaneous estimation of all relationships in the TRA/TPB models and also identifies the relative importance of each construct. The combination of TRA/TPB and SEM has been used in different agricultural contexts to understand: adoption of precision agriculture technologies (Adrian et al, 2005),

3

In the context of this paper, diversification concerns the development of activities inside the farm, focusing on agriculture. By this definition, diversification entails the processing and improvement of products (e.g. making and selling cheese rather than milk), adding value to the products (e.g. creating a cheese brand), and selling products on the market (Barbieri and Mahoney, 2009; Ilbery, 1991; Mahoney et al., 2004; Ploeg and Roep, 2003; Turner et al., 2003).

intention to purchase genetically modified agricultural products (Chen, 2008), intention to adopt precision farming technologies (Kurosh and Saeid, 2010), the use of agricultural information (Sharifzadeh et al., 2012), adoption of pro-environmental agricultural practices (Price and Leviston, 2014). However, to the best of our knowledge, this is the first study using TRA/TPB and SEM to analyze the intention of farmers to diversify agricultural production. In the light of the foregoing, the objective of this study was to use TRA and TPB to determine the effect of attitude, subjective norm and perceived behavioral control on the intention of farmers to diversify agricultural production. 2 Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) The theory of reasoned action (TRA) and the theory of planned behavior (TPB) assume that the intention to act is the immediate determinant of behavior (Ajzen, 2005). According to these theories, the stronger the intention to engage in a behavior, the more likely to be their performance (Ajzen, 1991). Both theories assume that there are independent latent constructs that influence the intention. These constructs are: attitude and subjective norm. Attitude refers to the degree to which a person has a favorable or unfavorable evaluation in relation to behavior (Ajzen and Madden, 1986; Ajzen, 1991; Ajzen, 2005). Individuals form their attitudes based on their perception of what can be true about a particular subject and this perception may or may not be based on information, knowledge or even be an emotional reaction to the object, sometimes supported by beliefs and values (Willock et al., 1999). Subjective norm, which is a social factor, corresponds to the perceived social pressure of performing or not such behavior (Ajzen and Madden, 1986). The theory of planned behavior (TPB) is an extension of TRA. The TPB presents an additional construct called perceived behavioral control. This construct is also assumed to influence intention. Perceived behavioral control is equivalent to the ease or difficulty

perceived by the individual to perform the behavior. In TPB, the more favorable these three constructs are, the stronger is the intention of an individual to express the analyzed behavior (Ajzen, 1991). In this study, the intention of dairy farmers to diversify agricultural production in their farms in the next five years was measured. Therefore, farmers will have high intention to diversify their production when they: understand the production diversification as being more favorable (attitude); when they realize a high social pressure to diversify (subjective norm); and when they perceive their own ability to implement this strategy in their properties as positive (perceived behavioral control). Figure 1 shows the conceptual models to be tested in this study, which originated three hypotheses: H1: Attitude has a positive influence on farmers’ intention. H2: Subjective norm has a positive influence in farmers’ intention. H3: Perceived behavioral control has a positive influence in farmers’ intention.

TRA

TPB ATT

+

ATT

INT

SN

+

SN

PBC

+ +

INT

+

Figure 1: TRA and TPB models. 3 Methodology 3.1 Measures The research instrument used in this study had two sections: the first containing demographic questions and characterization of the farms, and the second with the items to

directly measure the construct intention, and also the items to measure directly and indirectly the constructs attitude, subjective norm and perceived behavioral control. For the purpose of this study, only direct measurements of each construct were used. According to Fishbein and Ajzen (2010), direct measures are considered sufficient to predict intention. Seventeen items were used to represent the TRA/TPB constructs. The statements used to measure each item are shown in Table 1. The items were measured using a five-point scale, with one being the value assigned to negative answers and five to positive answers. Five-point scales have been used in agricultural studies (Bergevoet et al., 2004; Barbieri and Mahoney, 2009; Ferguson and Hansson, 2015; Hansson et al, 2013; Zubair and Garforth, 2006; Heong and Escalada., 1999; Sok et al, 2015), since they are considered short enough for respondents to distinguish between the response options (Hansson et al, 2012). Table 1: Statements used to measure each item of intention (INT), attitude (ATT), subjective norm (SN) and perceived behavioral control (PBC) and the scales used to measure each of them. Item INT1

Statements Do you intend to diversify agricultural production on your farm in the next five years?

Scale (1-5) Definitely Not Definitely Yes

INT2

Your intention to diversify agricultural production on your farm in the next five years is?

Extremely Weak Extremely Strong

INT3

Will you diversify agricultural production on your farm in the next five years?

Certainly Not Certainly Yes

INT4

I am NOT planning to diversify agricultural production on my farm over the next five years.

Fully Agree Fully Disagree

ATT1

The diversification of agricultural production on my farm Extremely Bad in the next five years is: Extremely Good

ATT2

The diversification of agricultural production on my farm Extremely Unnecessary in the next five years is: Extremely Necessary

ATT3

The diversification of agricultural production on my farm Extremely Disadvantageous in the next five years is: Extremely Advantageous

ATT4

The diversification of agricultural production on my farm Extremely Impossible in the next five years is: Extremely Possible

ATT5

The diversification of agricultural production on my farm Extremely Not Important in the next five years is: Extremely important

SN1

Most people who are important to me think I should Strongly Disagree diversify agricultural production on my farm in the next Strongly Agree five years.

SN2

Most people that I hear opinions approve that I diversify Strongly Disagree agricultural production on my farm in the next five years. Strongly Agree

SN3

Do you think that most farmers like you, will diversify their agricultural production on their farms in the next five years?

PBC1

If you want to diversify agricultural production on your Definitely Not farm in the next five years, you have enough knowledge. Definitely Yes

PBC2

If you want to diversify agricultural production on your farm in the next five years, you have enough resources (machinery, financial resources, land, etc.)

Definitely Not Definitely Yes

PBC3

How confident are you to diversify agricultural production on your farm in the next five years?

Extremely Not Confident Extremely Confident

PBC4

The diversification of agricultural production on your farm the next five years depends on you only.

Strongly Disagree Strongly Agree

PBC5

For you, the diversification of agricultural production on Strongly Disagree your farm the next five years is under your control. Strongly Agree

Certainly Not Certainly Yes

3.2 Sampling and data collection Since the objective of this study is to determine the effect of attitude, subjective norm and perceived behavioral control in the intention of farmers to diversify production, initially farmers specialized in milk production were identified. The participation of rural activities in the gross income of the property was used as a criterion to distinguish between specialized farms from diversified ones (Hansson et al., 2010). Thus, if 50% or more of the income came from a single activity, the farmer was considered to be specialized, and the greater this value, the greater their specialization (Hoffmann et al., 1987). According to a report provided by Frederico Westphalen's local government, there were 460 farmers that sold milk during 2013. Agricultural extension technicians that work in the region indicated 120 farmers specialized in milk production to be part of the sample of

farmers. If a farmer was not found or unwilling to participate in the survey, then another farmer was selected. In some cases, a farmer indicated another farmer, often someone in his own community. The final sample consisted of 101 farmers which were personally surveyed by an interviewer, representing 22% of milk farms in the region, or 84% of farm that have 50% or more of income from milk production. The survey was conducted during November and December 2014. 3.3 Data analysis The method used in this study was Structural Equation Modeling (SEM) with latent constructs. To test the proposed model, the two-step approach proposed by Anderson and Gerbing (1988) was used. First, Confirmatory Factor Analysis (CFA) was used to obtain a satisfactory Measurement Model (MM). Second, a structural model was developed and tested. 3.4 Measurement Models (MM) This research tests and compares two Measurement Models (MM), the MM/TRA and the MM/TPB, which are shown in Figure 2. The MM/TRA model had three latent constructs: intention (INT), attitude (ATT), and subjective norm (SN). The MM/TPB model adds up the construct perceived behavioral control (PBC). In both measurement models all latent constructs were allowed to freely inter-relate to each other. However, all the items were allowed to load on only one latent construct. For instance, the item INT1 only loaded on the latent construct INT (intention). Moreover, the errors terms were not allowed to relate to any other item. To assess the construct validity of the measurement models (MM), we examined the convergent and discriminant validity. Convergent validity was verified by checking the magnitude, direction and significance of standardized factor loads on each latent construct. In addition, average variance extracted (AVE) and construct reliability (CR) were used to examine convergent validity. A measurement model presents an acceptable convergent

validity when AVE exceeds the minimum level of 50 percent, and when CR is above the minimum value of 0.7 (Hair et al., 2010). Discriminant validity was assessed by comparing the average variance extracted (AVE) for each latent construct with the squared interconstruct correlations associated with that latent construct. To ensure discriminant validity the AVE for each construct should be greater than the squared inter-construct correlations associated with that latent construct (Borges and Lansink, 2016; Hair et al., 2010). To check the validity of the measurement models a set of indicators was used. Initially it was analyzed the validity of the measurement models by statistical quisquare (x2), together with the degrees of freedom (df), and the probability value (p value). Byrne (2001) recommends that the rate x 2 / df should not be greater than five. It was also checked the root mean square error of approximation (RMSEA), the 90 percent confidence interval for RMSEA, the comparative fit index (CFI), the Tucker-Lewis index (TLI) and the standardized root mean squared residual (SRMR). It was also checked the diagnostics model, as Hair et al. (2010) consider that this may indicate potential improvements to the model or specify issues that were not previously identified. The diagnostic measures used were the standardized residuals and modification indices. In this study, all guidelines and threshold values used to evaluate the validity of the construct, the measurement model validity and diagnostic measures were based on Hair et al. (2010).

MM TRA

MM TPB

Figure 1: Measurement models used for TRA and TPB. 3.5 Structural Model (SM) After obtaining a satisfactory measurement model, we run the structural model (SM). Hair et al. (2010) point out that in the structural model a set of multiple regressions is estimated and the emphasis is on the nature and magnitude of the relation between the latent constructs. According to Borges and Lansink (2016) structural modeling is considered an appropriate method to understand the causal relation between the constructs of TRA/TPB and to test the underlying assumptions. 3.6 Socioeconomic characteristics of the sample Among the 101 farmers in the final sample, 75.2% were male and 24.8% female (woman also work on household chores). Most farmers (69.3%) work full time on the farm. 95% of the farmers have no other income source than agriculture. 37.6% of the farmers have completed elementary school, 32.7% incomplete high school, 17.8% completed high school and 5% higher education (complete and incomplete). No farmer mentioned having postgraduate studies. 84.2 % of farmers rely on technical assistance. Among these farmers, 44.7% have private technical assistance, 20% are mainly using government technical assistance, and 35.3% have both. Other socioeconomic characteristics of the sample are presented in Table 2. Table 2: Socioeconomic characteristics of the sample, minimum values (Min), maximum (Max), mean ( ) and coefficient of variation (CV). Variable

Min.

Max.

Age (years)

20

81

48.8

33.8

Experience (years)

2

62

36.5

40.8

Estimated monthly gross income (R$) a

794

39.708

9.134

68.3

100

75

24.1

Percentage of income from milk production 50

CV (%)

Monthly milk production (liters)

800

28.000

6701

67.7

Number of dairy cows

3

55

16.3

51

Number of people on the farm

1

10

3.5

36.6

Number of children on the farm

0

8

1.1

102.7

Number of agricultural activities on the farm 1

4

1.9

36.8

Size of farms (hectares)

0

96

20.5

66.3

Arable land not used for agriculture

0

21

1.8

181.6

a

calculated based on monthly milk production data, milk participation and other activities in the gross income of the farm. Also, it used the average of the nominal value of the milk price paid to farmers in Rio Grande do Sul state in the year 2014 (CEPEA, 2015).

4. Results 4.1 Constructs of TRA/TPB In general, farmers’ intention to diversify agricultural production in at least some of the activities on their farms in the next five years was low. All items used to measure intention presented mean below 3. See Table A1 in the appendix. Intra-construct correlations for intention were high, ranging from 0.58 to 0.75. In Table A1 in the appendix, the mean, standard deviation and correlations among all the items used to measure each construct are presented. Farmers showed predominantly a positive attitude towards diversification. All items used to measure attitude presented mean above 3. The attitude intra-construct correlations ranged from 0.10 to 0.59. The correlations between the items of intention with the items of the construct attitude ranged from 0.23 to 0.51. The social pressure perceived by farmers towards diversification was moderately high. The three items used to measure subjective norm presented mean of at least 3. The subjective norm intra-construct correlations ranged from 0.11 to 0.55. The correlations between the items of the constructs intention and subjective norm were 0.22 to 0.53.

Farmers did not perceive that they have capability to diversify their production since all items used to measure perceived behavioral control presented a low mean. The highest mean of the five items used to measure this construct was 3.2, and the lowest mean was 2.2. The perceived behavioral control intra-construct correlations were also low. The items PBC3 and PBC4 showed negative correlation (0.22). The other correlations between the items in this construct ranged from 0.05 to 0.44. In general, it was observed that the correlations between the constructs intention and perceived behavioral control are lower when compared to the correlation between intention and attitude, and intention and subjective norm. The exception was the PBC3 item, which correlated with the four items of intention, and these correlations ranged from 0.33 to 0.49. 4.2 Measurement Model of the theory of reasoned action (MM/TRA) According to the guidelines for GOF statistics (Hair et al., 2010), the results for the validity of the measurement model (MM/TRA) showed that the model did not provide a good fit to the data (x2 =76.12; df=54; p=0.0128; and x 2/df =1.4; RMSEA=0.07; 90 percent confidence interval for RMSEA=0,033-0,101; CFI=0.95; TLI=0.94; SRMR=0.07). Results of the factors loadings, average variance extracted (AVE) and construct reliability (CR) are shown in Table A2 in the appendix. The correlation matrix is presented in Table A3 in the Appendix. These results suggested that the MM/TRA should be re-specified, particularly focusing on the subjective norm construct, since the item SN3 had a low factor loading. The results of the re-specified MM (rMM/TRA) without the item SN3 showed a satisfactory fit to the data (x2=56.82; df =44; p=0.0512; and x2/df =1.3; RMSEA=0.062; 90 percent

confidence

interval

for

RMSEA=0.000-0.098;

CFI=0.97;

TLI=0.96;

SRMR=0.06). The insignificant x2 demonstrated that the model is well adjusted. The AVE was recalculated for all the items, which improved mainly the convergent validity of the

subjective norm construct. In addition, the subjective norm construct reliability (CR) also presented better fit (Table 3). The results of the standardized factor loadings of rMM/TRA are shown in Table 3. All items showed the expected sign and were significant to the critical level of 5%. The factor loadings for all items of the constructs intention, attitude and subjective norm were above the minimum value of 0.4. The AVE of the construct attitude was 46.3%, being just below the 50% recommended. For the constructs intention and subjective norm, the AVE was above 50%. The reliability of the construct (CR) for all the analyzed constructs was above the threshold value of 0.7. Therefore, the results of factors loadings, AVE and CR, analyzed together, indicate convergent validity of rMM/TRA. Moreover, all the correlations between the TRA constructs were significant at the 5% critical level and greater than 0.5. Furthermore, the AVE for all constructs was greater than the square of the inter-construct correlations associated with that construct (Table A3). Table 3: Standardized factor loadings for each item of the respective constructs of TRA, with standard errors between brackets, and the average variance extracted (AVE) and construct reliability (CR) for each construct of the rMM / TRA. INT (Intention) INT1 INT2 INT3 INT4 AVE (%) CR

68.4 0.90

ATT (Attitude) 0.89 (0.03) 0.77 (0.04) 0.86 (0.03) 0.78 (0.04)

ATT1 ATT2 ATT3 ATT4 ATT5 46.3 0.81

0.79 (0.05) 0.58 (0.07) 0.66 (0.06) 0.48 (0.09) 0.83 (0.04)

SN (Subjective norm) SN1 SN2

0.95 (0.08) 0,61 (0.08)

66.0 0.79

These results indicate the discriminant validity of the model. The analysis of standardized residuals among the items of the constructs did not identify major

problems. Therefore, it is considered the rMM/TRA satisfactory and that it could then be used to explain the determinants of the intention of farmers to diversify agricultural production. 4.3 Measurement Model of the theory of planned behavior (MM/TPB) The results of the GOF statistics for MM/TPB showed a poor fit of the model to the data (x 2 =191.31; df =117; p=0.0000; and x2/df =1.6; RMSEA=0.08; 90 percent confidence interval for RMSEA=0062-0103; CFI=0.877; TLI=0.852; SRMR=0.086). Moreover, the significance given by x 2 demonstrates that the model needs to be adjusted. Results of the factors loadings, average variance extracted (AVE) and construct reliability (CR) are shown in Table A4 in the appendix. The correlation matrix is presented in Table A5 in the Appendix. These results suggested that the MM/TPB should be re-specified, particularly focusing on the subjective norm and perceived behavioral control constructs, since items SN3, PBC1, PBC4 and PBC5 had low factor loadings. Therefore, it was decided to re-specify and re-estimate the MM/TPB eliminating the items SN3, PBC1, PBC4 and PBC5. The results of the rMM/TPB still showed limitations (x2=78.02, df=63, p=0.0493 e x2/df=1.2, RMSEA = 0.056, 90 percent confidence interval for RMSEA=0.003 – 0.088, CFI = 0.97, TLI = 0.96, e SRMR = 0.065). Results of the factors loadings, average variance extracted (AVE) and construct reliability (CR) are shown in Table A6 in the appendix. The correlation matrix is presented in Table A7 in the Appendix. Particularly, the limitations of the perceived behavioral control construct persisted. Therefore, both the MM/TBP and the rMM/TPB were insufficient models to explain the intention of farmers to diversify agricultural production. 4.4 Comparing TRA and TPB measurement models Before examining the structural model, the measurement models of the TRA and TPB are compared. Using SEM, it was possible to independently test and compare the

models. The results presented in Table 4 showed that rMM/TRA showed better fit to the data compared to the other models. Therefore, the rMM/TRA was used to run a structural model. Table 4: Fit indices of theory of reasoned action (TRA) and theory of planned behavior (TPB) measurement models (MM) and its explanatory power. Fit Indices x2

Recommended Value to N<250 e 12
Df

rMM/TPB

MM/TRA

rMM/TRA

191.31 (p=0.0000) 117

78.02 (p=0.0493) 63

76.12 (p=0.0128) 54

56.82 (p=0.0512) 44

x2/df

≤5

1.6

1.2

1,4

1,3

CFI

Between 0 and 1 0,95 or better

0.88

0.97

0.95

0.97

TLI

Below 0 or above 1 0,95 or better

0.85

0.96

0.94

0.96

< 0,08 with CFI of 0.95 or higher

0.08

0.05

0.07

0.06

0.08

0.06

0.07

0.06

RMSEA a

MM/TPB

SRMR 0.08 or less (with CFI of 0.95 or higher) based on Hair et al. (2010).

4.5 Structural Model (SM) After a satisfactory measurement model (MM) was obtained, a structural model (SM) was estimated to test the assumptions underlying the TRA. The SM has the same GOF statistics of those of the rMM/TRA, as the SM has the same number of correlations between structural relations as the constructs in MM. Results of the structural model are shown in Table 5. The regression coefficient of attitude on intention was significant and positive, indicating that hypothesis H1 (Attitude has a positive influence on the intention of farmers) was not rejected. Furthermore, the regression coefficient of subjective norm on intention was also positive and significant, suggesting that H2 (Subjective norm has a positive influence on the intention of farmers) was not rejected. The hypothesis H3 (perceived behavioral control has a positive influence on the intention of farmers) was rejected, since the TRA model, considered satisfactory in this study, ignores this construct.

Together, the constructs attitude and subjective norm explained 49.3% of the variance in intention. The regression coefficient indicated that attitude was the main determinant of intention. However, subjective norm, despite having lower coefficient of regression, also influences the decision of diversification in rural areas. Table 5: Results of the impact of attitude (ATT) and subjective norm (SN) on intention (INT) Structural relations ATT → INT

Standardized parameter 0.44

p (value) 0.000

SN → INT

0.35

0.002

ATT correlated SN

0.57

0.000

5 Discussion and Conclusions The comparative analysis of the four measurement models showed that the respecified measurement model based on the theory of reasoned action (rMM/TRA) was more parsimonious to our data compared to the measurement models based on the theory of planned behavior. Particularly, in the rMM/TRA the items reliably represent the latent constructs intention, attitude, and subjective norm. Indeed, to obtain a valid measurement model based on the theory of reasoned action only one item was excluded (SN3) from the analysis because it had a low factor loading. On the other hand, the measurement models based on the theory of planned behavior presented some limitations, particularly in the items used to measure the perceived behavioral control construct. Indeed, the perceived behavioral control construct presented low internal reliability in our study, which also occurred in previous studies (Saba and Vassallo, 2002). A possible explanation for the low internal reliability of the perceived behavioral control is that individuals who answered the questionnaire based on the theory of planned behavior interpret the terms ‘control’ and ‘difficulty’ in different ways. In other words, the items used in the questionnaire to measure the perceived behavioral control construct aim to capture whether people think they are under control of the behavior and whether they think it is difficult to perform the behavior. Clearly,

if people think that they have control of the behavior, but still think that they would have difficulty to perform the behavior, the items used to measure these dimensions will not be strong correlated, and therefore, will not reliably represent the perceived behavioral control construct. For instance, in the context of this paper, farmers may have found that diversification of agricultural production is under their control, but at the same time consider difficult to diversify their agricultural production. Based on the results of the rMM/TRA, we run a structural model (SM). In SM, the two TRA constructs attitude and subjective norm explained 49.3% of the variance in farmers’ intention to diversify, which is a common fit in similar studies in agricultural contexts (Adrian et al., 2005; Borges and Lansink, 2016; Price and Leviston, 2014) and in behavioral sciences (Armitage and Conner, 2001). In the structural model, results of the impact of attitude and subjective norm on intention are in line with Hansson et al. (2012) and Senger et al. (2017), who analyze the influence of TRA and TPB constructs on farmers’ intention to diversify. However, Senger et al. (2017) found that the construct perceived behavioral control positively influence farmers’ intention to diversify. As previously explained, we could not check the impact of perceived behavioral control on intention, because the items used to measure did not reliably represent this construct. These contradictory results might be explained because Senger et al. (2017) did not use a confirmatory factor analysis to check whether the items used to measure the perceived behavioral control construct reliably represent this construct, and based their analysis on the correlation between perceived behavioral control and intention. The regression coefficients of the structural model indicated that the effects of attitude, and subjective norm on farmers’ intention to diversify were asymmetric. In particular, the findings showed that attitude had a larger influence than subjective norm of farmers’ intention to diversify. This result is in line with Hansson et al. (2012), who also

found that attitude had a higher impact than subjective norm on Swedish farmers’ intention to diversify. Studies that used the theory of reasoned action and the theory of planned behavior in agricultural contexts presented mixed results of the relatively impact of attitude, subjective norm, and perceived behavioral control on intention and behavior (Borges and Oude Lansink, 2016; Borges et al., 2016; Lalani et al., 2016; Sok et al., 2016; van Dijk et al., 2016). The mixed result of the relative impact of attitude, subjective norm, and perceived behavioral control is expected, because the prediction of intention varies, for instance, across behaviors, situations and cultures (Ajzen, 1991). The high impact of attitude showed that the positive evaluation of diversifying agricultural production was the main determinant of farmers’ intention to diversify. Therefore, a policy intervention target to emphasize the benefits of diversification to farmers could increase farmers’ intention to diversify. For instance, a possible intervention could be the demonstration of cases where others farmers successfully diversified their agricultural production. This strategy has been suggested by other studies in agricultural context to increase farmers’ intention to adopt innovations and to even diversify their agricultural production (Borges and Oude Lansink, 2016; Martínez-Garcia et al., 2013; Senger et al., 2017). Although attitude was the main determinant, subjective norm also influenced farmers’ intention to diversify their agricultural production. This result is in line with previous studies conducted with Brazilian farmers, and emphasizes the important role of social pressure and the opinion of others in influencing farmers’ decision in Brazil (Borges et al., 2016; Senger et al., 2017). According to Burton (2004), there are important referent groups to which people often refer their behavior, because individuals do not act independently from cultural and social influences. Specifically in Brazil, previous research found that family, friends, rural extension agents, and government could be used to increase social pressure and

influence farmers’ behavior (Borges et al., 2016; Senger et al., 2017). We believe that an intervention target to involve family members in the decision to diversify would increase social pressure upon farmers, and this could result in a higher intention to diversify. We believe that because family members seem to play an important role on farmers’ decisions. Because our research focused on a specific Brazilian region and on milk farmers, the implications for policy makers do not necessarily generalize to other regions and to farmers working with different products. However, our results showed that the combination of the theory of reasoned action, the theory of planned behavior with structural equation modeling is appropriate for understanding farmers’ intentions to diversify agricultural production, suggesting that future research could use this approach to study diversification in other contexts.

Appendix Table A1: Mean (x), Standard Deviation (SD) and correlation among all items of intention (INT), attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC) INT1 INT2 INT3 INT4 ATI1 ATI2 ATI3 ATI4 ATI5 SN1 SN2 SN3 1.00 0 .708* 1.00 INT2 * 0 .752* .668* 1.00 INT3 * * 0 * * .704 .589 .674* 1.00 INT4 * * * 0 .395* .464* .404* .375* 1,00 ATI1 * * * * 0 * * * .311 .417 .428 .485* 1.00 ATI2 * .230* * * * 0 .349* .293* .359* .280* .592* .447* 1.00 ATI3 * * * * * * 0 * * * * * * .394 .425 .426 .338 .323 .293 1.00 ATI4 * .107 * * * * * 0 .401* .349* .515* .428* .598* .466* .496* .373* 1.00 ATI5 * * * * * * * * 0 INT1

PBC PBC PBC PBC PBC 1 2 3 4 5

.537* .421* .528* .446* .382* .320* .272* .426* .469* 1.00 * * * * * * * * * 0 * * * * .269 .388 .283 .402 .558* 1.00 SN2 * .227* * .238* .226* .249* .105 * * * 0 * .317 .288* 1.00 SN3 .228* * .235* .234* .142 .235* .150 .161 .172 .117 * 0 * PBC .261 .166 .237* * .154 .051 .114 -.102 -.011 .178 .011 .090 .203* 1 PBC .357* .291* .255* .197* * -.056 .032 -.011 .210* .113 .215* .175 .043 * 2 PBC .469* .357* .496* .338* .331* .317* .271* .428* .359* .411* .282* .055 * * * * * * * * * * * 3 PBC .045 .053 .071 .030 -.002 .027 -.089 -.132 .088 -.047 .020 .176 4 PBC .061 .122 .183 .050 .034 .092 -.038 .094 .087 .149 .137 -.044 5 SN1

2.8 SD

2.7

2.7

3.7

3.2

1.00 0 .280* 1.00 * 0 .287* 1.00 .143 * 0 * .360 1.00 .057 -.022 * 0 .442* 1.00 .210* .144 .174 * 0

2.8

3.7

3.4

3.9

3.2

3.4

3.0

3.2

2.2

3.0

2.5

3.0

1.47 1.28 1.38 1.53

1.0

1.26 1.11 1.23 1.01

1.3

1.3

1.3 1.19 1.13 1.23 1.32 1.31

Table A2: Standardized factors loadings for each item of the theory of reasoned action (TRA) constructs intention (INT), attitude (ATT) and subjective norm (SN), with standard errors between brackets, and the average variance extracted (AVE) and construct reliabily (CR) for each construct of the MM/TRA. TRA

INT INT1 INT2 INT3 INT4

AVE (%) CR

0.88 (0.03) 0.77 (0.04) 0.85 (0.03) 0.78 (0.04)

68.4 0.90

ATT ATT1 ATT2 ATT3 ATT4 ATT5 46.3 0.81

0.78 (0.04) 0.58 (0.07) 0.66 (0.06) 0.47 (0.08) 0.83 (0.04)

SN SN1 SN2 SN3

0.90 (0.07) 0.63 (0.07) 0.21 (0.11)

43.0 0.64

Table A3: Correlation matrix of theory of reasoned action (TRA) latent constructs intention (INT), attitude (ATT), and subjective norm (SN) INT ATT SN INT 1 0.41 0.42 ATT 0.64 1 0.39 SN 0.64 0.62 1 Diagonal elements are the constructs variances; values below the diagonal are the correlations between the latent constructs; values above the diagonal are the square of the correlation among constructs. Table A4: Standardized factor loadings for each item of the theory of planned behavior (TPB) constructs intention (INT), attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC), with standard errors between brackets, and the average variance extracted (AVE) and construct reliability (CR) for each construct of the MM/TPB. TPB

INT INT1

0.88 (0.03)

ATT ATT1

0.78 (0.05)

SN SN1

0.88 (0.06)

PBC PBC1

0.33 (0.12)

INT2

0.77 (0.04)

ATT2

0.58 (0.07)

SN2

0.65 (0.07)

PBC2

0.42 (0.10)

INT3

0.86 (0.03)

ATT3

0.65 (0.06)

SN3

0.22 (0.11)

PBC3

0.69 (0.09)

INT4

0.77 (0.04)

ATT4

0.47 (0.08)

PBC4

0.09 (0.13)

ATT5 AVE (%) CR

68.2 0.89

0.83 (0.04)

46.3 0.81

PBC5 42.2 0.64

0.28 (0.11)

17.5 0.45

Table A5: Correlation matrix of theory of planned behavior (TPB) latent constructs intention (INT), attitude (ATT), and subjective norm (SN), and perceived behavioral control (PBC) INT ATT SN PBC INT 1 0.41 0.42 0.50 ATT 0.64 1 0.39 0.27 SN 0.65 0.63 1 0.35 PBC 0.71 0.52 0.59 1 Diagonal elements are the constructs variances; values below the diagonal are the correlations between the latent constructs; values above the diagonal are the square of the correlation among constructs.

Table A6: Standardized factor loadings for each item of the theory of planned behavior (TPB) constructs intention (INT), attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC), with standard errors between brackets, and the average variance extracted (AVE) and construct reliability (CR) for each construct of the rMM/TPB. rMM/TPB

AVE (%) CR

INT

ATT

SN

PBC

INT1

0.88 (0.03)

ATT1

0.79 (0.04)

SN1

0.95 (0.07)

PBC2

0.38 (0.10)

INT2

0.77 (0.04)

ATT2

0.58 (0.07)

SN2

0.61 (0.07)

PBC3

0.72 (0.13)

INT3

0.86 (0.03)

ATT3

0.66 (0.06)

INT4

0.78 (0.04)

ATT4

0.48 (0.08)

ATT5

0.83 (0.04)

68.3 0.89

46.3 0.81

64.3 0.77

33.0 0.47

Table A7: Correlation matrix of theory of planned behavior (TPB) latent constructs intention (INT), attitude (ATT), and subjective norm (SN), and perceived behavioral control (PBC). INT

INT 1

ATT 0.41

SN 0.38

PBC 0.53

ATT

0.64

1

0,35

0,32

SN

0.62

0.59

1

0,38

PBC

0.73

0.56

0.61

1

Diagonal elements are the constructs variances; values below the diagonal are the correlations between the latent constructs; values above the diagonal are the square of the correlation among constructs.

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Highlights:   

Farmers’ intention to diversify is determined by social pressure Farmers’ intention to diversify is influenced by their attitudes towards diversification TRA performs better than TPB to explain farmers' intention to diversify

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