Volume 22, Number 2 (2022): 103-109 E-ISSN: 2252-6757
CITATION: Arissaryadin, Yuliati, Y., Hidayat, K., Cahyono, E. D., (2022). A QUESTIONNAIRE TO DETERMINE THE WORK MOTIVATION OF AGRICULTURAL EXTENSION WORKERS IN INDONESIA, Agricultural Socio-
A QUESTIONNAIRE TO DETERMINE THE WORK MOTIVATION OF AGRICULTURAL EXTENSION
WORKERS IN INDONESIA
Arissaryadin
1*, Yayuk Yuliati
2, Kliwon Hidayat
2, Edi Dwi Cahyono
21Department of Civil Engineering, College of Engineering of Bima, Indonesia
2Faculty of Agriculture, Brawijaya University, Indonesia
*corresponding author: [email protected]
Abstract: A review of the literature reveals that most previous studies only used achievement motivation theory to assess the work motivation of agricultural extension workers in Indonesia. So far, no research questionnaire has been designed using expectancy theory. The purpose of this study is to develop a work motivation questionnaire based on expectancy theory and to test the convergent validity, discriminant validity, construct reliability and composite reliability of this questionnaire. This study drew on data from 107 people who work as agricultural extension workers in Indonesia. The questionnaire distributed via google forms was completed by all respondents.
All statistical analyses were performed using JASP, version 0.16, and a confirmatory factor analysis technique was used. This study resulted in the development of a work motivation questionnaire in Indonesian. There are nine- item questions, each with three questions for construct expectancy, three questions for constructs instrumentality, and three questions for constructs valence. Confirmatory factor analysis confirmed that all of the question items from the expectancy theory three constructs met the convergent validity, discriminant validity, construct reliability, and composite reliability criteria. This questionnaire can be used in future research on agricultural extension workers' work motivation in Indonesia.
Keywords: Expectancy theory, questionnaire, motivation, agricultural extension workers
http://dx.doi.org/10.21776/ub.agrise.2022.022.2.4 Received 23 November 2021 Accepted 26 April 2022 Available online 30 April 2022
INTRODUCTION
The agricultural sector has played an important role in providing jobs for the Indonesian population. Out of the total workforce of 129.3 million people, 38.1 million people are working in the agricultural sector (BPS, 2020). Agricultural extension is one of the main agricultural policies of the Indonesian government to enhance agricultural human resources (Mardikanto, 2009; Syahyuti, 2014).
Agricultural extension workers carry out agricultural extension programs and also serve as facilitators, motivators, innovators, and educators for farmers in agricultural development (Soraya et al., 2021). Therefore, the high performance of
agricultural extension workers can help achieve Indonesia's agricultural development goals.
Many studies have found that motivation positively affects the performance of workers in agriculture (Bahua, 2018; Erwina, 2018; Hanafiah, Rasyid & Purwoko, 2013; Indrawati, 2019;
Lesmana, 2016; Pello, 2019; Putri, 2019) and that greater the motivation, the greater the number of agricultural products of farmers (Arifianto, Satmoko
& Setiyawan, 2018; Pello, 2019; Rosnita, 2016).
Motivation is important in almost all aspects of human behaviour. This is because motivation is a psychological force that arises from the mind that leads to personal goals (Kanfer, 2009). Motivation is the process by which energy is placed to maximize
the satisfaction of needs (Pritchard & Ashwood, 2008), it is the process by which an individual moves into action (Chulef, Read & Walsh, 2001; Deckers, 2010). Work motivation is a set of energetic forces that come from within individuals as well as their environment to initiate work-related behaviours in the workplace (Latham & Pinder, 2005).
Many motivation theories have been developed to understand employee motivation, including a hierarchy of needs (Maslow, 1943), human motivation theory (McClelland, 1965), Herzberg's motivation-hygiene theory (Herzberg, Mausner, &
Snyderman, 1959), expectancy theory (Vroom, 1964), cognitive evaluation theory (Deci, 1971), equity and justice theory (Adams, 1963).
So far, much research in Indonesia (Bahua, 2018; Erwina, 2018; Hubeis, 2007; Lesmana, 2016;
Rosnita, 2016), has only measured the motivation of agricultural extension workers using based on the questionnaire Achievement-Motivation theory was developed by Atkinson, McClelland and Veroff and focuses on aspects of personality characteristics and proposes three forms of motivation in work situations (McGee, 2006). The three forms of motivation in the Achievement-Motivation Theory are achievement, power, and affiliation (McClelland, 1965). In his works, McClelland highlighted that human actions are influenced and controlled by subconscious motives (Al-Akeel &
Jahangir, 2020).
There is not yet been developed a questionnaire on the work motivation of agricultural extension workers based on Vroom theory. According to Vroom, the expectancy theory postulates that motivation is a product of expectancy, instrumentality, and valence (Vroom, 1964). This theory identifies process cognitive variables that reflect individual differences in work motivation (Vroom, Porter & Lawler, 2005). This theory belongs to the group of process theories, so it has a different form from the Hierarchy of needs theory (Maslow, 1943), Achievement-Motivation theory (McClelland, 1965), Herzberg's motivation-hygiene theory (Herzberg, Mausner, & Snyderman, 1959), are in the category of content theory, and these theories fall under the category of content theory, which focuses on the types of needs that motivate a person.
The objective of this research is to develop a questionnaire based on Vroom's expectancy theory and to test construct validity including convergent validity and discriminant validity, while the reliability includes constructing reliability and composite reliability
RESEARCH METHODS
This work employed a descriptive research design.
This design is frequently chosen and used as a first step toward a more quantitative research design by providing some valuable clues about a variable that should be tested quantitatively (Anastas, 1999).
This study took place in Indonesia. The time for the research is three weeks, starting on February 7, 2021 and ending on February 27, 2021. This study included 107 people who are still actively working as agricultural extension workers, 70 of whom were women and 60 of whom were men. There are 47 male extension workers. The number of respondents corresponds to the number of sample questionnaire trials recommended (Meyers & Gamst, 2006;
Nunnally & Berstein, 1994; Sapnas & Zeller, 2002).
Data collection was carried out by distributing questionnaires via a google form, after which respondents were asked to fill in and provide answers. The questionnaire was created by four researchers and the elements of the question were adapted to Vroom's theory of expectation. This theory is based on three main constructs: 1) Expectation (Exp), which is a type of belief that a person will achieve performance goals if adequate efforts are made; 2) the construct of instrumentality (Ins) refers to a person's expectation that rewards will follow when performance standards are met; 3) Valence construct (Val), which is the value a person believes he will receive from the award (Vroom, 1964).
The questionnaire was designed to include a Likert-type with a 5-point scale: strongly disagree (score 1), disagree (score 2), neutral (score 3), agree (score 4), and strongly agree (score 5). (score 5).
Statistical tests are using a software package JASP (Jeffrey's Amazing Statistics Program). JASP also provides factor analysis methods which consist of Principal Component Analysis (PCA), Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA) (Goss- Sampson, 2019).
CFA procedure begins by assessing how well the overall model is performing, absolute fit measure and increment fit measure (Hair, et al., 2019).
Absolute Fit Measure, is a method of measuring a model as a whole using several criteria such as Chi- square value, Goodness-of-fit index (GFI), and Root Mean Square Error of Approximation (RMSEA).
Increment fit measure, a method of determining a fit model by comparing the proposed model to the baseline model: Normal Fit Index (NFI), Comparative Fit Index (CFI), and Relative Fit Index
(RFI) is the measurement criteria (RFI). The fit index criteria are shown in table 1.
Table 1. Fit Indexs
The after testing fit model and factor loading analysis, then the reliability test is carried out. To compute the value Average Variance Extracted (AVE) and Composite Reliability (CR), using the formula (Ghozali & Fuad, 2005).
AVE
= ∑Standardized Loading2
∑Standardized Loading2+∑Measurement Error
CR= (∑Standardized Loading)2
(∑Standardized Loading)2+(∑Measurement Error)
According to Hair, et al. (2019), reliability testing with the CFA method includes constructing reliability (CR) and variance extracted (AVE). CR value 0.7 is in the category of good reliability and the CR value between 0.6 and 0.7 is including acceptable reliability.
RESULTSANDDISCUSSION
Work motivation questionnaire that was produced, using Indonesian. In total there are 9 item questions consisting of three-item construct expectations, three questions item construct instrumentality and three construct item questions valence. The questions of the questionnaire are presented as follows:
Expectancy:
1. I want to be an outstanding agricultural extension worker
2. I want to increase the yields of the assisted farmers
3. I can improve the class of the assisted farmer groups from the beginner class to the main class farmer group
Expectancy is the belief that if individual agricultural extension worker increases their efforts, then the rewards will increase as well. Expectancy is also a source of encouragement for extension workers to collect the right tools to complete the work, which can include: Raw materials and
resources, Skills to do the work, Support and information from the leadership. Factors that include self-efficacy, difficulty carrying out work, and ability to control work are related to the level of expectations.
Instrumentality
1. If I am a professional at work, then I will be selected as an outstanding agricultural extension worker
2. If I provide extension materials according to farmers' needs, their yields will increase 3. If I succeed in increasing the class of the
farmer group, then it will be easy for me to get a promotion in the rank of the farmer group Instrumentality is the belief that the reward that an extensionist receives will depend on their performance at work. An agricultural extension worker must trust that the leadership and organization they work for will reward them appropriately for their efforts. Some of the most common awards include A raise, A promotion, Recognition, and a sense of accomplishment.
Valence
1. For me, being an outstanding agricultural instructor will make my family and institution proud
2. I am happy, if farmers' yields continue to increase, this will improve the lives of their families
3. I am responsible if I am not able to improve the class of the assisted farmer groups
Valence is the importance of an agricultural extensionist placing the expected results of his performance. This often depends on what the individual's needs, goals, values, and sources of motivation are.
The goodness of fit evaluation
Furthermore, the testing fit model seeks to demonstrate the theoretical model's compatibility with the empirical model. In reality, there is no single rule for determining modelling fit. Anderson
& Gerbing (1992), define three criteria: p-value, Relative Fit Index (RFI), and Normed Fit Index (NFI). Meanwhile, Hu & Bentler (1999) proposed two criteria for assessing model fit, including the Comparative Fit Index (CFI) and Root Mean Square Error of Approximation (RMSEA). The fit indices shown in this study include CFI, Tucker-Lewis Index (TLI), RMSEA, GFI, NFI, RFI and p-value.
Table 2.Model fit parameters
Index Recommended Value Decision
CFI ≥0.90 0.999 Good fit
TLI >0.90 0.998 Good fit
RMSEA <0.08 0.014 Acceptable
GFI >0.90 0.954 Good fit
Indexs Recommended
Minimum Fit Function Chi-Square p >0.05 Root Mean Square Error of
Approximation (RMSEA)
< 0.08 Goodness of Fit Index (GFI) ≥0.90 Normed Fit Index (NFI) ≥0.90 Comparative Fit Index (CFI) ≥0.90 Relative Fit Index (RFI) ≥0.90
TLI ≥0.90
Index Recommended Value Decision
NFI >0.80 0.949 Good fit
RFI ≥0.90 0.923 Good fit
p-value P>0.05 0.432 Acceptable As shown in table 2, the test results goodness of fit: value CFI = 0.999, TLI = 0.998, RMSEA = 0.014, GFI = 0.954, NFI = 0.949, RFI = 0.923, and p-value = 0.432. The p-value > 0.05, indicates that the model is fit, meaning that there is no significant difference between the ideal model and the model based on observational data. The values of CFI, TLI, RMSEA, GFI, NFI, RFI, were also in accordance with the model fit criteria.
Factor Loading
The correlation of each variable and factor is referred to as factor loading (McCoach, Gable, &
Madura, 2013). Factor loading is a method of interpreting each variable's role in defining its factor (Field, 2009). The factor loading of the three factors and their items is shown in table 3.
Table 3. Factor loading
Factor Indicator Std.
Estimate Std.
Error Expectancy
Exp-1 0.91 0.17 Exp-2 0.89 0.21 Exp-3 0.80 0.35 Instrumentality
Ins-1 0.75 0.45 Ins-2 0.86 0.26 Ins-3 0.73 0.47 Valence
Val-1 0.67 0.51 Val-2 0.91 0.16 Val-3 0.72 0.49 Table 3 shows in column standardized estimate the value factor loadin of the construct expectancy with three question items in the indicators Exp-1 = 0.91, Exp-2 = 0.89, and Exp-3 = 0.80. For Instrumentality constructs with indicators Ins-1 = 0.75, Ins-2 = 0.86, and Ins-3 = 0.73. Construct valence of the indicators Val-1 = 0.67, Val-2 = 0.91, and Val-3 = 0.72. Hair et al. (2019) assert that a loading factor of 0.50 is practically significant. As a result, the three constructs, expectancy, instrumentality, and valence and their items have loading factor > 0.5, indicating that the questionnaire items can be used to determine agricultural extension workers' work motivation Construct Validity
Construct validity is a method for ensuring that a set of variables accurately represents the theoretical latent construct being measured (Hair, et al, 2019)
The extent to which the measurement score reflects the latent construct to be measured is referred to as construct validity (Furr & Bacharach, 2013).
Convergent validity and discriminant validity tests are the two types of construct validity tests (Fornell & Larcker, 1998; Agarwal, 2013).
Convergent Validity
Convergent validity, as defined by Hill & Hughes, (2007), refers to the extent to which similar constructs are measured by different variables. The magnitude of the factor loading is a critical factor in determining convergent validity (Wang, French, &
Clay, 2015). According to Igbaria, Zinatelli, Cragg
& Cavaye (1997), a variable is said to be good if the latent variable has a factor loading of 0.50.
Table 4 Average variance extracted
Factor The average variance extracted (AVE)
Expectancy 0,75
Instrumentality 0,61
Valence 0,60
As shown in Table 4, the AVE values for the three constructs are 0.75, 0.61, and 0.60. This AVE value exceeds the AVE threshold value > 0.50, so the three constructs meet the criteria of convergent validity. Hair, et al. (2019) recommends AVE of 0.5 or higher values can be accepted as convergent validity.
Discriminant validity
The goal of discriminant validity is to demonstrate that one construct is vastly different from another (Voorhees, Brady, Calantone, & Ramirez, 2015).
Discriminant validity, also known as divergent validity (DeVelis, 2017), requires the two concepts to have conceptually significant differences.
Discriminant validity reveals how distinct a construct is from other constructs in a model (Hair, et al. 2019; Wang, French, & Clay, 2015).
Discriminant validity is achieved when two latent constructs are not theoretically and empirically proven to be correlated by scores indicating one construct is greater than the other (Wang, French, & Clay, 2015). According to Hair, et al. (2019), if the correlation value between the two constructs is less than 0.85, discriminant validity.
Table 5 Average Variance Extracted (AVE) and Shared Variance Estimates
Construct Item Exp Ins Val
Expectancy 3 0,75
Instrumentality 3 0,05 0,61
Valence 3 0,32 0,01 0,60
The number below the diagonal is the correlation coefficient (R2), and the diagonal number is AVE. The results, shown in table 5, show that each of the three constructs has a square root of AVE of 0.75, 0.61, and 0.60. Because the AVE value of the three constructs is greater than the correlation value between constructs, it meets the discriminant validity criteria
Reliability
According to Brown, (2015) refers to reliability in the consistency of measurement results. Roberts, Priest, & Traynor, (2006) stated that a reliable instrument can maintain the consistency of measurement results within a certain range. This study reports two types of reliability, namely, construct and composite reliability.
Construct reliability
Construct reliability is used to measure the extent to which the variables underlying the construction are represented in structural equation modelling (Zinbarg, Revelle, Yovel, & Li, 2005). Construct reliability can be estimated after construct validity is proven by using confirmatory factor analysis (Geldhof, Preacher & Zyphur, 2014). According to Gefen, Straub, & Boudreau, (2000) show that the construct reliability coefficient higher than 0.70 is acceptable.
Tabel 5. Construct reliability
Factor Construct reliability
Expectancy 0,90
Instrumentality 0,82
Valence 0,82
The results showed that the three constructs: expectancy, instrumentality, and valence had CR coefficients of 0.90, 0.82, and 0.82, so the three constructs were declared acceptable.
Composite Reliability
Composite reliability, known as internal consistency,(Fornell & Larcker, 1998), is the combined reliability of the latent constructs that underlie the scale (Voorhees, Brady, Calantone, , &
Ramirez, 2015). Viladrich, Angulo-Brunet, &
Doval, (2017), claim that the threshold coefficient of composite reliability higher than 0.70 is reliable.
Table 6. Composite reliability
Factor Composite reliability
Expectancy 0,90
Instrumentality 0,82
Valence 0,81
Table 6 shows the composite reliability of the three constructs expectancy, instrumentality, and
valenced which is higher than 0.70. Thus, the work motivation questionnaire is reliable.
CONCLUSION
Based on the results of the CFA , it was concluded that the expectancy theory model consists of three dimensions, namely expectancy, instrumentality, and valence, which has a good fit model. The questionnaire that has been designed with this theory can be used to identify the work motivation of agricultural extension workers in Indonesia.
We also assert that expectancy theory can be used to increase the motivation of agricultural instructors. This can be done by following these steps: an agricultural extension agency leader assigns tasks that match the individual agricultural instructor's expertise and makes the task challenging but achievable. In addition, a leader establishes a clear relationship between performance and rewards given.
The limitations of the data collection process of this study are because using the google form, it is possible that respondents hide their real identities and ask others to answer the questionnaire. For this reason, future research should consider face-to-face questionnaires. In addition, further researchers need to examine the relationship of work motivation, with the variables of performance, the personality of the instructor and support from the organization where they work.
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