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:
Salehi.saeid85@gmail.com
rezaei@shirazu.ac.ir
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
Journal of Research in Agriculture
JournalofResearchinAgriculture An International Scientific Research Journal

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 information such as soil
electro-conductivity, may then be used to generate
variable rate lime or fertilizer recommendations for
different grids o r ma nage ment zo nes
(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
Chi-Square/Degree of Freedom (χ2
/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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