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Investigating Iranian farmers’ satisfaction with agricultural extension

programs using the American Customer Satisfaction Index

Abstract

The aim of this paper is to investigate and measure farmers’ satisfaction regarding extension

services to better understand why farmers do or do not participate in such programs in Iran. Our

study sample consisted of 240 farmers selected through simple quota random sampling (160 male

and 80 female) from a list of names from the extension organization. Data were gathered through

a face-to-face survey of farmers. Structural equation modeling analysis revealed that perceived

quality and perceived value significantly predicted farmers’ overall satisfaction. Furthermore,

farmer satisfaction can predict farmers’ loyalty to extension program activities. From a practical

point of view, the present study provides a justification for using perceived quality and perceived

value in policy and decision making that seeks to encourage farmers to use agricultural extension

programs (AEPs).

Keywords: farmers’ satisfaction, extension programs, extension class, perceived value,

Perceived quality, Iran

Introduction

Evaluation of customer satisfaction (CS) is a very important issue for all types of business

organizations (Baker & Crompton, 2000; Grigoroudis & Siskos, 2002; Gomez, McLaughlin, &

Wittink, 2004; Lee, Yoon, & Lee, 2007; Siskos & Grigoroudis, 2002; Verhoef, Franses, &

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Hoekstra, 2001). CS is an important determinant of customer retention which, in turn, has a very

strong effect on profitability (Hsu, 2008; Johnson & Fornell, 1991). CS can also help businesses

acquire new customers and maximize their lifetime value (Onut, Erdem, & Hosver, 2008). In other

words, CS is a heuristic tool by which managers, policymakers and other stakeholders can learn

the customer’s opinion regarding the services received and also obtain a quality indicator of a

firm’s functioning. CS also provides the opportunity for firms to evaluate their current and future

performance (Anderson, Fornell, & Lehmann. 2004; Grigoroudis & Siskos, 2004; Gomez et al.,

2004; Nilsson, Johnson, & Gustafson, 2001; Yazdanpanah, Zamani, Hochrainer-Stigler,

Monfared, & Yaghoubi, 2013). Moreover, CS can promote employees’ motivation by providing a

sense of achievement and accomplishment (Mihelis, Grigoroudis, Siskos, Politis, & Malandrakis,

2001). As such, many companies and service providers choose CS as their main performance

indicator; it is on the mission statements of many large corporations and has become a sought-after

source of competitive advantage (Mihelis et al., 2001; Spiteri & Dion, 2004). For these and other

reasons, CS must be translated into a measurable parameter.

While researchers recognize that CS plays a key role in a successful company strategy,

surprisingly little (if any) published research has investigated farmers' satisfaction regarding the

agricultural extension program (AEP). AEPs in Iran and other developing countries provide a

range of educational services for rural people. AEPs attempt to reach a very wide and

heterogeneous group of farmers, providing demonstrations and training courses free of charge.

They help farmers make efficient, productive and sustainable use of their land and other

agricultural resources, through the provision of information, advice, education and training

(Bernet, Ortiz, Estrada, Quiroz, & Swinton, 2001; Budak, Darcan, & Kantar, 2005; Qtaishat &

AL-Sharafat, 2012; Valentine, Hurley, Reid, & Allen, 2007). As such, asking questions about what

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makes farmers satisfied or dissatisfied is important when the role of AEPs worldwide has been

highlighted but demand for it has steadily decreased in the face of old and new challenges facing

the agricultural sector: climate change, globalization, and environmental degradation, etc.

(Leeuwis, 2013; Ozor & Nnaji, 2011; Rivera, 2011).

Vanclay and Lawrence (1994) argued that the severity of environmental problems in

agriculture means that there is a great need for effective extension in agriculture. Regarding the

worldwide global warming challenge, Davis (2009) argued that agricultural extension and

advisory services, both public and private, have a major role to play in providing farmers with

information and technology to cope with climate change and contribute to greenhouse gas

mitigation. Christoplos (2012) also points out that agricultural extension, as an important meso-

level actor, has a pivotal role in the processes of famers’ adaptation to climate change. Because of

environmental challenges, the FAO (2011) argued that we need well-trained, experienced, and

innovative extension service providers more than ever, to teach farmers ways of improving farm

productivity sustainably, using superior, yield-enhancing technologies (See also Zobeidi,

Yazdanpanah, Forouzani & Khosravipour, 2016 and Bakhtiyari, Yazdanpanah, Forouzani, &

Kazemi, 2017 ). Furthermore, AEPs all over the world rely on feedback from farmers as their

clients. CS measurement may be the most reliable feedback system, considering that it provides

the clients’ preferences and expectations in an effective, direct, meaningful and objective way

(Grigoroudis & Siskos, 2002).

Moreover, measuring farmers’ satisfaction with AEPs is justifiable from a crisis

perspective. Agricultural extension faces a crisis of legitimacy because farmers do not believe that

extension is relevant to them (Vanclay & Lawrence, 1994). Many blame the extension services

themselves. They claim that extension services deliver their message to farmers without

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considering whether the technology is appropriate and assume that farmers have nothing to

contribute to the discussion about what is needed (Vanclay & Lawrence, 1994). In response to this

criticism, researchers (Bernet et al., 2001; Friday, McArthur, & Watson, 2006; Leeuwis, 2013;

Worth, 2006) have emphasized the importance of farmers’ participation in extension programs and

declared that they must target the needs and expectations of farmers within specific environmental

and socio-economic settings. They point out that extension services must create space to allow

farmers to actively engage in the process. Farmers are more likely to be motivated to engage with

extension programs once they realize that their own needs and expectations are incorporated into

the design of such programs (Boateng, 2006). Furthermore, AEPs are costly (Cain, Anwar, &

Rowlinson, 2007; Marsh, Pannell, & Lindner, 2004). To ensure that they can face the challenges

regarding their effectiveness (Bernet et al., 2001), policymakers and those working in agricultural

extension need to select efficient decision-support tools to efficiently target the needs of farmers.

CS can provide such tools.

AEPs clearly play an important role in accelerating technological, social, and economic

development. Many rural and agricultural development policies transfer through these

demonstrations and training courses, reaching groups nationwide. Success of any AEP, therefore,

depends largely on optimum selection of extension programs by farmers and the farmers’

preference for extension demonstrations and training courses. Therefore, the active participation

of farmers (and other groups) is very important and there needs to be a well-defined strategy for

assisting them in their participation. Not surprisingly, farmers will participate in these programs

when they meet their preferences, needs and expectations. In other words, their participation in

AEPs is probably equal with their satisfaction regarding them. As such, assessment of farmers’

satisfaction with educational programs has been strongly recommended as a tool to improve AEPs.

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Farmers’ satisfaction should therefore be translated into a number of measurable parameters, and

extension managers should undertake farmer satisfaction surveys to ascertain farmers’ specific

needs and expectations about extension activities. This will draw farmers into the problem

identification process and help create better understanding and evaluation of AEPs (see

Yazdanpanah et al., 2013; Yazdanpanah, Zamani, & Moghadam Rezaei, 2009).

In summary, evaluation of AEP client satisfaction is essential for both internal and external

reasons (Anderson, 1994). Internally, client satisfaction will help monitor performance, allocate

resources, and compensate employees. Externally, it will provide information to farmers,

investors, policy makers, and even taxpayers. Despite this, surprisingly little published research

has investigated farmers' satisfaction and, to our knowledge, no study of this kind has been

undertaken in Iran. In light of these facts, the aim of this paper was to measure farmers’ satisfaction

with AEPs in Iran and to better understand why farmers do or do not participate in extension

programs. This research should fill knowledge gaps on the theoretical as well as practical side of

extension program satisfaction in developing countries. On the theoretical side, we propose and

empirically test a CS index, the American Customer Satisfaction Index (ACSI) model. On the

practical side, the model can serve as a diagnostic tool to determine why farmers are satisfied or

dissatisfied, whether the complaint handling procedure is effective, how to improve CS, and how

effective the efforts at improving farmers’ satisfaction are.

American Customer Satisfaction Index

The ACSI was developed in 1994 and is rooted in a Swedish customer satisfaction index. The

model consists of six constructs (Figure 1). In the model, CS has three parts: perceived quality,

customer expectations, and perceived value. These variables directly determine a person’s

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satisfaction. The model has two distinct outputs: customer loyalty and complaint behavior. In the

model, perceived quality and CS jointly determine perceived value. In turn, perceived quality,

customer expectations, and perceived value determine CS. Finally, CS determines customer

loyalty and complaint behavior (for more details please see Fornell, Johnson, Anderson, Cha, &

Bryant, 1996; Grigoroudis & Siskos, 2004 ; Hsu, 2008; Johnson, Gustafson, Andreassen, Lervik,

& Cha , 2001; Yazdanpanah et al., 2013; Yazdanpanah et al., 2009).

Perceived quality, the first determinant of satisfaction, refers to a judgment regarding a

supplier's current offering (Anderson et al., 1994) or the consumer’s judgment about a product's

overall standard (Zeithaml, 1988). Perceived quality is conceptualized as a measure of a provider's

output. It is as a gestalt attitude toward a service which is acquired over time after multiple

experiences with it (Baker & Crompton, 2000). The model assumes that it has direct and positive

effect on both perceived value and CS (Cronin, Brady, & Hult, 2000).

Customer expectation is the second determinant of satisfaction. This variable has a direct

and positive effect on perceived quality, perceived value, and CS. It refers to how well the

customer expected the product or service to perform and captures a customer’s prior experience

with good or bad services (Johnson et al., 2001). Perceived value, the third determinant of CS, is

related to the price paid and has a direct and positive effect on CS. Perceived value is

conceptualized as the consumer's evaluation of the utility of perceived benefits and perceived

sacrifices (Yazdanpanah et al., 2013). In other words, there is a positive association between

perceived value and CS (Fornell et al., 1996). CS refers to an overall assessment of usefulness of

a product or service and is a comparison of what the customer has expected and what he/she has

received (Parasuraman, Zeithaml, & Berry, 1988). Hsu (2008) has argued that it is a tradeoff

between benefit and cost, which can be either monetary or non-monetary. The consequences of

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increased CS, according to Hirschman's exit-voice theory, are decreased customer complaints and

increased customer loyalty (Fornell et al., 1996; Johnson et al., 2001). Oliver (1999, p. 34) defined

loyalty as a “deeply held commitment to rebuy or repatronize a preferred product/service

consistently in the future”. There is considerable evidence that an increase in CS causes greater

intention to use more of a firm’s products or services, that is, loyalty. Loyalty is important in

marketing because there is a strong positive link between loyalty and long-term profitability

(Yazdanpanah et al., 2013). For AEPs, farmers’ loyalty to the extension services and their intention

to re-participate in future programs is an index of success. In other words, intention to participate

in future programs means that past and current programs fit well with the farmers’ needs and

expectations and reveals that the extension organization is working well, with the right targets.

The last factor is customer complaints. In the original ACSI, this referred to customer

dissatisfaction and a decision to no longer use the firm. Complaints can be defined as a conflict

between the customer and the organization (Hsu, 2008). However, researchers agree that complaint

resolution has become more important than complaints per se and thus “customer complaints” has

become “complaint management” (Johnson et al., 2001). In our framework, we used “complaint

handling” instead of customer complaints.

METHOD

The study was designed as a cross-sectional survey. The population of interest consisted of farmers

in the Aleshtar district, Lorestan Province of western Iran who have participated at least once in

an extension program presented by an extension organization. Our study sample consisted of 240

farmers who were selected through a quota random sampling method (160 male and 80 female)

based on a list of names from the extension organization. Our data were gathered through a face-

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to-face questionnaire survey of farmers in fall 2013. All farmers were given the right to refuse to

participate and to refuse to answer any question they deemed too sensitive or they felt

uncomfortable about. No payment was made to the farmers. Most of the data were collected from

the farmers either during working hours on their farm, at home in their villages, or at other places

in their villages. The questions were scored on a five-point scale (very low; low; moderate; high;

very high) to reduce the statistical problem of extreme skewness (Fornell, 1992). Our scales,

containing multiple items, were developed to measure each of the following psycho-social

variables: perceived expectation, quality, value, satisfaction, loyalty, and complaint handling.

Note that for assessed ACSI variables we used items that closely follow the methods used in past

studies in other areas. Table 1 shows some examples of the survey items contained in the

questionnaire. The validity of the questionnaire was approved by a panel of experts. Additionally,

Cronbach alpha reliability coefficients of the pilot study assessment were used to refine the

questions for the final questionnaire. All scales indicated excellent reliability (Table 1).

Table 1. Questionnaire items

varia

bles

item

s

relia

bilit

y statements

Perc

eive

d

expe

ctat

io

4 0.78 To what extent do you think your expectation regarding customization has been realized by extension programs?

To what extent do you think your expectation regarding quality has been realized by extension programs?

To what extent do you think your expectation regarding reliability has been realized by extension programs?

Overall, To what extent do you think your expectations have been realized by extension programs?

9

Perc

eive

d qu

ality

29 0.95 How good do you think extension services treats its farmers?

How good are extension services at creating an environment of trust for you? How good do you think extension services are at allocating time to help their farmers? How good do you think extension service agents are at understanding farmer’s needs? How good do you think extension service agents are at providing quick service? How good are extension services’ equipment and facilities?

How good is the information communication from extension services?

How good is the timing of extension services?

How willing and able are the agents to provide good quality service?

How competent are the agents (i.e., knowledgeable and skillful)?

How courteous, polite, and respectful are the agents?

How good the agents are at listening to me and speaking in a language that I can understand

How good the agents are at making an effort to understand my needs

How good the agents are at empathizing with you

How good is the agent’s attention to you personally?

How trustable are the agents?

How good are the agents in assigning time to you?

How fast are the agents in helping you when you need it?

Perc

eive

d

valu

e

4 0.78 The choice to use extension services was the right decision. I obtained good results from extension services. The value of extension services was more than what I expected Overall, use of extension services is valuable and worth it

satis

fact

ion

12 0.89 To what degree does the extension services exceed your expectations overallTo what degree are you satisfied with extension

services when considering invested time and effort?

To what degree was your choice to use extension service a wise one? To what degree do you think that you did the right thing when using extension services? To what degree do you think that information and education from extension services are exactly what you needed?

To what degree do you think that information and education from extension services are applicable to your agriculture?

To what degree do you think that information and education from extension services are useful for your agriculture?

To what degree do you think that information and education from extension services are easy to use?

To what degree do you think that information and education from extension services are improving your performance?

To what degree do you think that information and education from extension services are improving your productivity?

To what degree do you think that information and education from extension services are improving your effectiveness?

How satisfied are you overall with the extension services?

Loya

lty

4 0.85 How likely is it that you would use extension services the next time you are in need of information and education services?” How likely is it that you will speak favorably of the extension services to others? How likely is it that you would recommend the extension services to a friend?

com

plai

nt

hand

ling

4 0.85 How well do you think extension services provide compensation?

How well agents treated you politely when you complained

How well agents treated you with respect when you complained.

How well agents treated you promptly when you complained

RESULTS

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Descriptive analysis revealed that the age of farmers participating in the survey ranged from 16 to

86 years, with an average age of 37.63 years (S.D. = 13.44). The participants in the survey

comprised 80 female farmers (33%) and 160 male farmers (67%). Farmers’ agricultural experience

was distributed across a 1–75 year range, with an experience of 19.12 years (S.D. = 16.15). While

two farmers did not specify their educational level, of the majority of the participants, 96 (40%)

had a college degree; the remainder included 73 (30.4%), 26(10.8%), 23 (9.6%), and 20 (8.3%)

farmers who had, respectively, graduated from high school, middle school, finished primary

education, or had no education. In Iran, primary school lasts 5 years, middle school 3 years, and

high school 4 years: in total 12 years. More than 12 years of education represents a college degree.

Respondents grew different crops, including wheat, barley, sugar beet, canola, and vegetables

(beans, cucumbers, tomatoes) in different seasons. The main horticultural products were walnuts,

apples, and peaches. However, it is important to note that this study has focused on wheat, as this

constitutes the main crop in the area.

ACSI: relationship between variables

A Pearson correlation test was used to investigate the relationship between all variables (see Table

2). As expected, the Pearson correlation demonstrated that there was a significant positive and

strong relationship between CS and loyalty, as well as with all other variables.

Table 2. Relationship between the variables variables Expectations Quality Value Complaints Satisfaction Loyalty

Expectations 1

Quality 0.568** (0.0001) 1

Value 0.646** (0.0001)

0.619** (0.0001) 1

Complaints 0.437** (0.0001)

0.659** (0.0001)

0.434** (0.0001) 1

Satisfaction 0.643** (0.0001)

0.759** (0.0001)

0.742** (0.0001)

0.526** (0.0001) 1

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Loyalty 0.514** (0.0001)

0.636** (0.0001)

0.581** (0.0001)

0.441** (0.0001)

0.712** (0.000) 1

** P<01

Figure 2: Structural equation modelling and path coefficients between variables (ACSI)

0.857

0.612

0.483

0.499

0.298

0.62

0.618 Satisfactio

n (R2=0.89)

Expectations

Value (R2=0.70

Loyalty (R2=0.69

)

Complaints

(R2=0 37)

0.75 0.72 0.60 0.75

0.72

0.65

0.65

0.68

0.86 0.65 0.79 0.79

0.57

0.55

0.62

0.59

0.67

0.68

0.56

0.72

0.69

0.67

0.71

0.69

0.75 0.74 0.76 0.82

0.65

0.67

0.71

0.71

0.72

0.70

0.68

0.70

0.71

0.64

0.62

0.58

0.70

0.61

0.71

0.55

0.71

0.64

0.62

0.73

Quality (R2=0.38

)

0.62

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Scale validation or measurement model evaluation

The test of the structural equation model (SEM) includes an estimation of the model fit and the

path coefficients. Using the maximum likelihood method, the model gives reasonably good fit

estimates:

i) The measurement model should demonstrate robustness for the empirical data and meet

the requirements of certain indexes; for example, chi-square normalized by degrees of

freedom (λ/df) should be less than five (Bentler, 1989). In our study, it was 2952/1168=

2.53.

ii) The comparative fit index (CFI) should be exceed 0.9. Here, it was 0.96.

iii) The root mean square error (RMSEA) should be less than 0.10 (Henry & Stone, 1994).

In our study, it was 0.080. This suggests an adequate model fit for the empirical data.

In summary, the indices show that the model, which is a representation of the ACSI, can be

accepted from an empirical point of view as being robust (for more details, see Hu & Bentler,

1999; Kaiser & Scheuthle, 2003). In the next step, ACSI was tested consecutively. The results of

the SEM revealed that the (standardized) path coefficients indicated the strength of the

relationships between the variables. The separation of direct and indirect causal effects of the

components can be found in Figure 2, as well as in Table 3. Regarding the direct and indirect

effects, the following observations can be made. A farmer's expectation has significant positive

effects (0.62, p<0.0001) on the perceived quality, as well as a significant direct effect (0.62,

p<0.0001) on the perceived value, while the effect of a farmer's expectation on his or her

satisfaction level is not significant. Perceived expectation predicted 38% of the variance in

perceived quality (Figure 2). Regarding perceived quality, analysis revealed that it has a direct

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positive effect both on value (0.62, p<0.0001) and satisfaction (0.30, p<0.0001). Farmers’

expectations have indirect effects on perceived value (β= 0.18) through perceived quality.

Perceived expectation (directly and indirectly) and quality jointly predicted 70% of the variance

in perceived value. Perceived value has a direct positive effect on satisfaction (0.50, p<0.0001).

Farmers’ expectations, through the effects on perceived quality and perceived value, also have an

indirect effect on satisfaction (β= 0.70).

Perceived value, together with perceived quality and perceived expectation, (indirectly)

accounted for 89% of variance in farmers’ satisfaction. Finally, farmers’ satisfaction has a direct

positive effect on both complaint handling (0.61, p<0.0001) and farmers’ loyalty (0.86, p<0.0001).

Perceived expectation and perceived quality indirectly affect complaint handling (β= 0.46) and

Loyalty (β= 0.39), respectively. Moreover, perceived expectation has an indirect effect on loyalty

(β= 0.63). These variables directly and indirectly account for 69% and 37% of the variance in

farmers’ loyalty and complaint handling, respectively. Complaint handling has a significant effect

on loyalty. The results of the study hypotheses are presented in Table 4.

Table 3. Variable effects of American Customer Satisfaction Index Standardized Direct Effects

Variables Expectations Quality Value Satisfaction Quality 0.618 - - - Value 0.620 0.298 - - Satisfaction - 0.483 0.499 - Complaints - - - 0.612 Loyalty - - - 0.857

Standardized Indirect Effects Quality - - - - Value 0.184 - - - Satisfaction 0.70 0.149 - - Complaints 0.641 0.386 0.305 - Loyalty 0.625 0.524 0.414 -

Standardized Total Effects Quality 0.618 - - - Value 0.805 0.298 - -

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Satisfaction 0.754 0.632 0.499 - Complaints 0.461 0.386 0.305 0.612 Loyalty 0.625 0.524 0.414 0.829

DISCUSSION

The aim of this paper was to investigate Iranian farmers’ satisfaction with agricultural extension

programs through the ACSI. Our study is the first in the literature to use ACSI specifically to

assess farmers’ satisfaction with AEPs. The model allows extension program staff to understand

the specific factors that significantly influence farmers’ satisfaction using the causal relationship

in the model and the strategic management map.

Table 4. research hypotheses Research hypotheses Results Hypothesis 1 Perceived expectation will positively affect perceived

quality

Hypothesis 2 Perceived quality will positively affect perceived value Hypothesis 3 Perceived expectation will positively affect perceived

value

Hypothesis 4 Perceived value will positively affect overall satisfaction Hypothesis 5 Perceived quality will positively affect overall

satisfaction

Hypothesis 6 Perceived expectation will positively affect overall satisfaction

Hypothesis 7 overall satisfaction will positively affect loyalty Hypothesis 8 overall satisfaction will positively affect complaint

handling

Hypothesis 9 complaint handling will positively affect loyalty Indicates that the hypothesis shown in Table 4 was proved

Indicates that the hypothesis shown in Table 4was not proved

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SEM analyses revealed that, as predicted, perceived quality and perceived value are drivers

of farmers’ satisfaction. Hence, the value of contracts is not the only factor to determine the success

or failure of AEPs. The results support the conclusion that both value and quality of the services

that farmers receive have the greatest effect in raising farmer satisfaction. In other words, focusing

on only a single aspect may reduce farmers’ satisfaction. This means that, although the value of

the extension program is very important to farmers and will help them make decisions, other

aspects of quality (Johnson et al., 2001; Parasuraman et al., 1988), such as tangibility, reliability,

responsiveness, assurance, and empathy, are also very important for farmers’ satisfaction. We

conclude that service quality is very important, particularly in rural environments and for

traditional farmers in Iran. In this case, all aspects are very important; however, assurance and

empathy are the most important. This is because, in traditional Iranian culture, still common in

rural areas, respect, politeness, personal attention, attention to special needs, and prompt service

are very important. As such, any attempt to provide these qualities by extension services will result

in more satisfaction and increased loyalty, which will ultimately lead to success in extension goals

and targets. Our finding is supported by the work of Sayers, Kilmer, Lee and Flambert (1996) who

found client dealings and quality of service had a direct impact on farmers' satisfaction with milk

handlers. Furthermore, extension agencies should improve their services by delivering services at

the right time, as well as providing a range of information because, in contrast to Western farms,

there is a huge heterogeneity in production. Our results also revealed that farmers’ satisfaction is

the leading factor in determining farmers' loyalty (Hsu, 2008; Yazdanpanah et al., 2013). In rural

areas, particularly in developing countries, local relationships and interactions between people are

still very influential and, as a result, farmers’ satisfaction and loyalty are very important for AEPs.

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One farmers’ positive experience with an AEP, for instance, can be disseminated among many

potential extension clients and other farmers, which may not be the case in other environments.

Thus, understanding the factors that influence farmers’ satisfaction is of great importance to

extension organizations.

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