Extension of grid soil sampling technology; application of extended Technology Acceptance Model (TAM)

Extension of grid soil sampling technology:

application of extended Technology Acceptance Model (TAM)

Keywords: Grid soil sampling technology, Technology acceptance model, intention, attitude, Iran.

ABSTRACT:

Grid soil sampling technology is one of the most important information technologies in agriculture. Application of these technologies is a way to understand the extent of needed nutrient elements of soil. The purpose of this research is to investigate the attitude and intention to the extension of grid soil sampling technologies among agricultural specialists in Iran. A survey was used to collect data from 249 specialists. The results using Structural Equation Modeling (SEM) showed that attitude to use is the most important determinant of intention to extension. Attitude of confidence, observability and triability positively affect intention to extension of these technologies. Perceived ease of use indirectly influences the intention to extension through attitude to use.

078-087 | JRA | 2012 | Vol 2 | No 1

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www.jagri.info

Journal of Research in

Agriculture An International Scientific

Research Journal

Authors: Kurosh. Rezaei-Moghaddam1,

Saeid. Salehi2,

Abdol-azim. Ajili3.

Institution:

1. Assistant Professor, Dept.

of Agricultural Education

and Extension, College of

Agriculture, Shiraz

University, Fars Province,

Iran.

2. M.S in Agricultural

Education and Extension,

3. Assistant Professor,

Dept. of Agricultural

Education and Extension,

Ramin University of natural

resource and agriculture,

Ramin, Khuzestan Province,

Iran.

Corresponding author: Kurosh. Rezaei-Moghaddam.

Email:

[email protected]

[email protected]

Web Address:

http://www.jagri.info

documents/AG0013.pdf.

Dates: Received: 20 Dec 2011 Accepted: 16 Jan 2012 Published: 30 May 2012

Article Citation: Kurosh. Rezaei-Moghaddam, Saeid. Salehi, Abdol-azim. Ajili.

Extension of grid soil sampling technology: application of extended Technology Acceptance Model (TAM). Journal of Research in Agriculture (2012) 1: 078-087

Original Research

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INTRODUCTION

Application of new technologies based on

"high-input and high-output" conventional strategy has

caused fundamental changes in the process of

production. Technological advances have contributed to

increased productivity of crop production in Iran. For

example, yields of irrigated wheat and barley increased

from 1700 kg/ha and 1670 kg/ha in 1980

(Abdulhosainzadeh, 1986) to 3054 kg/ha and 2594 kg/ha

in 2000 (Iran Statistical Center, 2002). Despite these

successes, the agricultural production system has been

criticized for technical and allocative inefficiencies

(Torkamani & Hardaker, 1996). Environmental

technology is usually considered to comprise products

and services developed for purposes of environmental

improvement. Use of these technologies can decrease

demand on natural systems and increase ability to control

the environmental consequences of production

(Rezaei-Moghaddam et al., 2005). This is the goal of

precision farming that implies the maturity of wisdom-

oriented technologies and aims at "optimized input-

output solution" (Shibusawa, 2002).

The concept of precision agriculture, based on

information technology, is becoming an attractive idea

for managing natural resources and realizing modern

sustainable agricultural development (Maohua, 2001).

The main activities of precision agriculture are data

collection, processing and targeted application of inputs

(Fountas et al., 2005). The central ideas of precision

agriculture are understanding spatial variability of soil

properties, crop status and yield within a field;

identifying the reasons for yield variability; making

farming prescription and crop production management

decisions based on variability and knowledge

implementing site-specific field management operations;

evaluating the efficiency of treatment; and accumulating

spatial resource information for further management

decision making (Maohua, 2001).

Precision farming technologies have designed to

provide extensive information and data to assist farmers

when making site-specific management decisions.

By making more informed and better management

decisions, farmers can become more efficient, paying

lower production costs, and, in turn, become more

profitable (Arnholt et al., 2001).

Grid soil sampling is based on GPS technology.

This is a method of breaking a field into square grids that

generally range from 1 to 2.5 acres, and sampling soils

within those grids to determine appropriate application

rates (Grisso et al., 2002). Grid soil sampling involves

partitioning a field into grids of a specified size and

pulling soil samples from the grids. This technology

allows measurement of within-field variability of soil

fertility. Another type of soil sampling involves taking

samples from several management zones which are

identified by characteristics such as soil type or

topography. Information gathered from soil sampling, as

well as other informat ion such as soil

electro-conductivity, may then be used to generate

variable rate lime or fertilizer recommendations for

d i f f e r e n t g r id s o r ma na g e me n t z o ne s

(English et al., 2000).

Conceptual model and research hypotheses

The "Technology Acceptance Model (TAM)" of

Davis and his colleagues (1989) is perhaps most widely

applied to explain or predict application of information

technologies (Yi et al., 2006). Davis (1989) based the

TAM on the Theory of Reasoned Action (TRA)

(Fishbin & Ajzen, 1975) by defining perceived

usefulness and perceived ease of use as constructs that

predict behavior intention and usage of technologies.

This structural equation model demonstrated the

simultaneous effects of potential information system

users' perceptions of usefulness and ease of the use of

technology on both the intention to adopt technology and

the actual use of technology (Adrian et al., 2005).

079 Journal of Research in Agriculture (2012) 1: 078-087

Rezaei-Moghaddam et al.,2012

The innovation adoption literature showed

technology characteristics that can affect adoption.

The factors compatibility of new technologies with

current practices, triability and observability of their

results affect in the decision process of adoption

(Rogers, 1983, 1995). Also, Adrian et al., (2005) have

shown that attitude to confidence is used to measure the

confidence of a producer to learn and use precision

agriculture technologies. We extended the TAM with

new variables (Fig. 1). The purpose of this research is to

investigate the attitude and intention to extension of grid

soil sampling technologies among Iranian agricultural

specialists.

Based on Fig.1, the following hypotheses are

proposed:

H1. Attitude of confidence will affect perceived ease of

use (H1a), attitude to use (H1b), perceived usefulness

(H1c) and intention to extension (H1d) of grid soil

sampling technologies.

H2. Perceived ease of use will affect attitude to use

(H2a), perceived usefulness (H2b) and intention to

extension (H2c) of grid soil sampling technologies.

H3. Perceived usefulness will affect attitude to use (H3a)

and intention to extension (H3b) of grid soil sampling

technologies.

H4. Attitude to use will affect intention to extension of

grid soil sampling technologies.

H5. Observability will affect perceived ease of use

(H5a), perceived usefulness (H5b), attitude to use (H5c)

and intention to extension (H5d) of grid soil sampling

technologies.

H6. Triability will affect perceived ease of use (H6a),

perceived usefulness (H6b), attitude to use (H6c) and

intention to extension (H6d) of grid soil sampling

technologies.

H7. Compatibility will affect perceived ease of use

(H7a), perceived usefulness (H7b), attitude to use (H7c)

and intention to extension (H7d) of grid soil sampling

technologies.

Research method

A cross-sectional survey was used to collect

information using questionnaire. Data to test the model

was gathered among agricultural specialists in Khuzestan

and Fars, two southern provinces in Iran. A stratified

random sampling was used to gather data. The sample

consists of 249 agricultural specialists from the

population of 705. The study was conducted in two

phases. First, the questionnaire was pilot-tested with 30

randomly selected agricultural specialists from out of

sample. Based on the feedback from the pilot test, the

questionnaire was refined and a revised final

questionnaire was developed. The Cronbach’s alpha for

all variables were well above the cited minimums of 0.70

(Nunnally, 1978, Nunnally & Bernstein, 1994) and,

ranged from 0.71 to 0.91. Second, questionnaires were

distributed to agricultural specialists in Khuzestan and

Fars provinces. Data were analyzed using the LISREL

software version 8.54 and SPSS software version 11.5.

Variables Definitions

Perceived Ease of Use (PEOU)

This is defined as the belief that using a

particular technology (grid soil sampling technology in

this study) will be free of physical and mental

effort (Davis, 1989). The scale consisted of

four items (alpha = 0.72).

Journal of Research in Agriculture (2012) 1: 078-087 080


Fig.1 Research model

Perceived Usefulness (PU)

This variable measuring the extent to which a

person believed that the grid soil sampling technology

was capable of being used advantageously and provided

expected outcomes. The scale consisted of four items

(alpha = 0.71).

Attitude to Use (ATU)

Taylor and Todd (1995) defined attitude scale

which measured whether individuals like or dislike using

the technology and how they felt using the technology.

We operationally defined attitude to use as the

prospective specialist's positive or negative feeling about

the adopting grid soil sampling technologies. The scale

consisted of three items (alpha = 0.74).

Attitude of Confidence (AOC)

This variable measures the confidence of a

producer to learn and use grid soil sampling

technologies. Adrian et al., (2005) argued that the

attitude of having the ability to learn and use precision

agriculture technologies, influence the perception of ease

of use. The scale consisted of three items (alpha = 0.79).

Intention to Extension (INE)

Behavioral intention is defined as the strength of

the prospective adopter's intention to make or to support

the adoption decision (Phillips et al., 1994). We

measured the intention to extension as specialist's

intention to extension of grid soil sampling technologies

among farmers. This variable consisted of four items

(alpha = 0.71).



Table 1: Confirmatory factor analysis (CFA) for research model of grid soil sampling technology

Variable Item Mean Standard

Deviation Factor

Loading t-value α-Cronbach (>0.7) ρc (>0.6)

AVE

(>0.5)

Intention to

Extension 0.71 0.860 0.672

INE1 3.95 0.77 0.77 16.69

INE2 3.97 0.73 0.87 45.77

INE4 3.90 0.80 0.81 19.37

Attitude to

Use 0.74 0.875 0.701

ATU1 4.31 0.69 0.80 25.28

ATU2 4.08 0.75 0.84 26.33

ATU3 3.97 0.76 0.87 50.38

Perceived

Usefulness 0.71 0.845 0.645

PU2 3.90 0.80 0.81 23.19

PU3 3.90 0.80 -0.78 15.83

PU4 3.90 0.80 0.81 23.73

Perceived Ease of Use 0.72 0.803 0.674

PEOU2 3.18 0.98 0.70 8.02

PEOU3 3.72 0.85 0.92 36.12

Compatibility 0.91 0.840 0.725

COM2 3.24 1.01 0.83 8.79

COM3 3.01 1.01 0.87 10.63

Triability 0.74 0.796 0.661

TRI1 4.01 0.78 0.78 9.90

TRI2 4.10 0.76 0.85 9.52

Observability 0.77 0.834 0.716

OBS1 4.01 0.72 0.82 16.00

OBS2 4.03 0.77 0.87 26.93

Attitude of Confidence 0.79 0.775 0.634

AOC1 2.16 0.89 -0.86 22.05

AOC2 4.15 0.71 0.72 6.42

Observability (OBS)

This means the extent to observe the results of an

innovation (grid soil sampling technology in this study)

for others. The scale consisted of two items

(alpha = 0.77).

Triability (TRI)

This variable is defined as probability to test an

innovation (grid soil sampling technology in this study)

in a small area (of farm). The scale consisted of three

items (alpha = 0.74).

Compatibility (COM)

It is defined as individual's interpretation of

economic advantages of grid soil sampling technology

with existing values, past experiences and future needs.

The scale consisted of three items (alpha = 0.91).

RESULTS

Measurement model

We evaluated the proposed model using

Structural Equation Modeling (SEM). The items used for

the variables are included in table 1. We tested the data

for reliability and validity using Confirmatory Factor

Analysis (CFA). We see in Table-1 that all factor items

for PEOU, PU, ATU, INE, AOC, OBS, TRI and COM

fit were all above 0.7. Factor loadings indicate the

correlation between the item and the latent variable.

When the coefficients exceed the 0.7, then the empirical

data fit the proposed model (Fornell & Larcker, 1981).

The composite reliability was estimated to

evaluate the internal consistency of the measurement

model. Table 1 shows the composite reliabilities (ρc) of

the variables in the model ranged from 0.775 to 0.875.

Then, all variables have suitable reliabilities

(Fornell & Larcker, 1981). These showed that all

measures had strong and adequate reliability and

discriminate validity. As shown in Table 1, the Average

Variance Extracted (AVE) for all measures also

exceeded 0.5. The completely standardized factor

loadings and individual item reliability for the observed

variables were presented in Table 1.

Table 2 shows the results of the goodness of fit

measures. Goodness of fit measures includes

Ch i- S quar e /Degr ee o f Fr eedo m (χ 2 /df) ,

Goodness-of-Fit (GFI), Normed Fit Index (NFI),

Comparative Fit Index (CFI), Root Mean square

Residual (RMR), Root Mean Square Error of

Approximation (RMSEA) and Adjusted Goodness of Fit

Index (AGFI). As we see the measurement model test

presented a good fit between the data and the proposed

measurement model. The χ2/df value was 1.32, less than

J ö r e s k o g a n d S ö r b o m ( 1 9 8 3 & 1 9 9 3 ) ,



goodness of

fit measure

Measure

recommended *

Results in

this survey

χ2/df ≤3 1.32

p-value ≥005 0.56

NFI ≥0.90 0.98

NNFI ≥0.90 0.98

CFI ≥0.90 0.99

GFI ≥0.90 0.99

AGFI ≥0.90 0.95

RMR ≤0.05 0.026

RMSEA ≤0.10 0.039

Table 2: Model evaluation overall fit measurements

Source: Jöreskog, & Sörbom, 1983 & 1993;

Gefen et al., 2000; Markland, 2006 Fig. 2: SEM analysis for grid soil sampling technology

Gefen et al., (2000) and Markland (2006) suggestion

fewer than three. The GFI is 0.99. RMSEA was less than

the recommended range of acceptability (≤0.10)

suggested by Jöreskog and Sörbom (1983 & 1993),

Gefen et al., (2000) and Markland (2006). Then the

goodness of fit indices such as χ2/df, NFI, NNFI, CFI,

GFI, AGFI, RMR and RMSEA are acceptable (Table 2).

Structural Model

Hypotheses testing

The results of inter-correlations have been shown

in Table 3. We see in this table that the variables are inter

-correlated. Also, the Fig-2 presents the standardized

coefficients for each of the paths. Attitude of confidence

has significant direct effect on perceived ease of use

(γ= 0.17, p<0.05), perceived usefulness (γ=0.15, p<0.05)

and intention to extension (γ=0.15, p<0.05) of grid soil

sampling technologies. These are consistent with

H1a, H1c and H1d. The attitude of confidence has no

significant direct effect on attitude to use. This is not

consistent with H1b. But, the attitude of confidence has a

significant indirect effect on attitude to use through

perceived ease of use.

Based on agricultural specialists' worldviews,

perceived ease of use directly affect attitude to use

(ß=0.33,p<0.01) and perceived usefulness

(γ=0.22, p<0.01), consistent with H2a and H2b. The

perceived ease of use has no significant direct effect on

the intention to extension of grid soil sampling

technologies. This is not consistent with H2c. But, fig.2

showed that perceived ease of use has a significant

indirect effect on intention to extension through attitude

to use.

The results showed that perceived usefulness has

no direct effect on attitude to use and intention to

extension (Fig.2). Consistent with H4, attitude to use has

the highest direct effect on the intention to extension

(ß=0.43,p<0.01) of grid soil sampling technologies.

Similarly, observability has direct effect on

perceived ease of use (γ=0.19, p<0.05), perceived

usefulness (γ=0.14, p<0.05), attitude to use (γ=0.28,

p<0.01) and intention to extension (γ=0.21, p<0.01) of

grid soil sampling technologies (Fig.2). These results are

consistent with H5a, H5b, H5c and H5d.

For hypothesis 6, we see in fig.2 that triability

has significant and positive effects on perceived ease of

use (γ=0.16, p<0.05) and intention to extension

(γ=0.21, p<0.01) of grid soil sampling technologies.

These results are consistent with H6a and H6d. Also,



Table 3: Scale properties and correlations for grid soil sampling technology

*: significant in p<0.05 **: significant in p<0.01

- Parentheses are variation range of Likert scale

Mean SD INE ATU PU PEOU COM TRI OBS AOC

INE

(4-20) 15.53 2.36

ATU

(3-15) 12.36 1.84 0.54**

PU

(4-20) 13.16 1.83 0.26** 0.20**

PEOU

(4-20) 13.54 2.02 0.41** 0.37** 0.34**

COM

(4-20) 12.99 3.05 -0.09 -0.04 0.13 0.04

TRI

(3-15) 11.27 2.02 0.49** 0.25** 0.54** 0.29** 0.15**

OBS

(2-10) 8.05 1.27 0.49** 0.32** 0.16* 0.25** -0.06 0.53**

AOC

(3-15) 9.77 1.56 0.23** -0.11 0.21* 0.29** 0.03 0.28** 0.09

triability has indirect effect on intention to extension

through perceived ease of use and attitude to use. Fig.2

showed that triability has not direct effect on attitude to

use and perceived usefulness.

Compatibility has a direct effect on perceived

usefulness (ß=0.22, p<0.01), consistent with H7b.

But the effects of compatibility on perceived ease of use,

attitude to use and intention to extension are not

significant.

Fig.2 showed that the explained variance (SMC)

in perceived ease of use, perceived usefulness, attitude to

use and intention to extension are 0.15, 0.17, 0.34 and

0.51, respectively.

DISCUSSION

The results showed that attitude to use is the

most important factor to intention to extension of grid

soil sampling technologies. The role of attitude in

changing intention and behavior is emphasized (Fishbin

& Ajzen, 1975; Rezaei-Moghaddam et al., 2005). Both

perceived ease of use and perceived usefulness have no

direct effect on intention to extension of grid soil

sampling technologies. This is in accord with the results

of Adrian et al. (2005). Koufaris (2002) showed that

perceived ease of use is not a significant determinate for

intension to use. Also, the results of

Venkatesh and Davis (1996) and Venkatesh (2000)

implies that perceived ease of use has a direct and

significant effect on behavioral intention to use in the pre

-implementation test, but little influence on intentions

over a period. However, perceived ease of use has

significant positive effect on attitude to use and

indirectly influences the intention to extension of grid

soil sampling technologies. This is consistent

with the results of Hung et al., (2006) and

Schepers and Wetzels (2007). Prior studies indicated that

perceived ease of use has direct effect on perceived

usefulness (Fu et al., 2006; Davis, 1989; Schepers &

Wetzels, 2007). Our study presents a causal

relationship between these two factors.

Attitude of confidence has a significant direct

effect on intention to extension. Also, this factor

indirectly affect on intention to extension of grid soil

sampling technologies through perceived ease of use and

attitude to use. The findings are consistent with the

results of Adrian et al., (2005). In fact, an attitude of

confidence can lead to a better understanding of the

technology's usefulness, and then leading to a propensity

to adopt the technology. Producers who indicated

confidence about using and learning technologies

showed greater propensity to adopt precision agriculture

technologies (Adrian et al., 2005).

One of the exciting aspects of our study is the

influence of innovation characteristics on intention to

extension. Many researchers emphasized on the

characteristics of innovation to adoption (Rogers, 1983,

1995). The results showed that observability has

significant direct effect on all dependent variables i.e.

perceived ease of use, perceived usefulness, attitude to

use and intention to extension of grid soil sampling

technologies. Also, observability indirectly affect on

attitude to use through perceived ease of use.

This variable has an indirect effect on intention to

extension through perceived ease of use and attitude to

use. The importance of observability has been

emphasized in previous studies (Karahanna et al., 1999).

Compatibility only has significant direct effect

on perceived usefulness. The results of Wu and Wang

(2005) showed that compatibility affects positively and

has direct influences on perceived usefulness.

Chau and Hu (2002a) showed that compatibility is a

significant determinant of perceived usefulness but not

perceived ease of use.

Another characteristic is triability. Our study

showed that triability has significant direct effect on

perceived ease of use. Also, the results imply that

triability has indirect effects on both attitude to use

through perceived ease of use and intention to extension



through perceived ease of use and attitude to use. Many

studies confirm that test of a technology in a small area

of farm leads to better decisions related to adoption by

farmers (Rogers, 1983).

CONCLUSION

In this research, we tried to test the extended

technology acceptance model. We integrated the attitude

of confidence, and characteristics of innovation by

Rogers (1983, 1995) i.e. observability, compatibility and

triability to it. The findings indicated that the explained

variance by this model was higher than the previous

studies related to information technologies in agriculture.

Then, careful attention should be paid to characteristics

of grid soil sampling technologies.

The results showed that the most important

determinant for intention to extension of grid soil

sampling technologies is attitude to use. The relationship

between attitude and behavioral intention has been

emphasized. This is important to develop positive

attitudes towards technology for successful adoption.

This finding has policy implications for agricultural

development policy makers so that can help extension

agents, agricultural educators and agricultural

administrators to present suitable training and services to

change attitude of clients.

Our study provides a starting point for

agricultural development decision makers in Iran to

extension and application of information system

technologies in agriculture. However, additional research

is needed to apply the extended technology acceptance

model proposed by this study to other contexts. A survey

would be useful to predict attitude and intension of

agricultural specialists in other provinces towards

information technologies. Also, further development of

the model with additional constructs such as

environmental impacts of these technologies are

proposed.

As with all empirical researches, this study has a

few limitations. The most important is that the model

was tested in only one context i.e. grid soil sampling

technologies. The research should be extend to another

precision agriculture technologies such as

VRT-technologies (irrigation, spraying, tillage …) and

Yield monitoring.

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