Developing Process of Questionnaire Statistics Dataset from
GuangdongTaiwan Agricultural Cooperative Experimental Area in Shaoguan of
China
Li, H. F.^{1,2*} Wei, S. Q.^{2} Wei, S. B.^{2}
1. College of Tourism & Geography, Shaoguan University,
Shaoguan 512005, China;
2. Institute of Geographical Research, Fujian Normal
University, Fuzhou 350007, China
Abstract: The diffusion of innovative
technology is the biggest bottleneck facing the development of agricultural
science and technology in China. There is an urgent need to realize the
strategy of “storing grain in the ground and storing grain in the technology”.
It is important to explore the factors influencing the spread of China’s Taiwan
orchid planting technology in China’s Mainland from the perspective of farmers,
using the GuangdongTaiwan Agricultural Cooperation Pilot Zone in Shaoguan city
as an example, as well as establishing an index system from three dimensions,
namely “farmer dimension”, “China’s Taiwan agriculture dimension”, and
environment dimension”. A total of 217 valid questionnaires were collected in
this questionnaire survey. Each questionnaire contained 25 index data in addition
to basic information. In this paper, data collection method, representative
analysis, data processing process, and data validity were analyzed.
Keywords: farmer’s perspective; China’s Taiwan agricultural technology; influencing
factors; structural equation
Dataset Availability Statement:
The dataset supporting this paper was published and is accessible
through the Digital
Journal of Global Change Data Repository at: https://doi.org/10.3974/geodb.2021.01.01.V1.
1 Introduction
The issue on agriculture, including agriculture, rural
areas, and farmers, is one of keys for China’s economic development, it is always
the very first arrangement each year during the last 18 years in yearly plan of
China. “Implementing fully the strategy of storing grain on the ground and on
technology” was put forward in Central Document No. 1, 2017. Meanwhile,
“Promoting the implementation of food storage on the ground and technology” was
put forward in Central Document No. 1, 2019. The diffusion of innovative
technology is urgently needed to realize the strategy of “storing grain in the
ground and storing grain in the technology”, and it is the biggest bottleneck facing the
development of agricultural science and technology in China today. China has recorded achievements in
agricultural science and technology every year, but its conversion rate is
significantly low^{[1]}. There are many practical cases on S&T
application in Taiwan province’s agriculture, which could help for the S&T
applications in Guangdong province of China.
The
GuangdongTaiwan Agricultural Cooperation Pilot Zone (Wengyuan Core Zone,
hereinafter referred to as the Pilot Zone) in Shaoguan city, Guangdong province,
was established in November 2009, which is the fifth GuangdongTaiwan
Agricultural Cooperation Zone in Guangdong province. The development of the
orchid industry in the pilot area began in 1998 with the drive of Taiwan province
businessmen. Under this drive and the promotion by the local government, the
orchid industry in Wengyuan has developed rapidly. In 2018, Wengyuan Orchid
Industrial Park became the first provincial modern agricultural industrial park
in Guangdong province. At present, the planting area of flowers is 1,787 ha in
the Orchid Industrial Park (orchid planting area of 1,200 ha). The park has orchid
varieties of more than 1,000, forms about a 10 km long orchid corridor, and has
an annual output value of more than 1.8 billion RMB. It has become the largest
national orchid production base, and was regarded as “China’s orchid town”, and
“The first county of China’s orchid”. There are currently 412 enterprises in the park, including one statelevel leading
poverty alleviation enterprise, four provincial leading agricultural
enterprises, 15 municipal leading agricultural enterprises, 46 orchid farmers
professional cooperatives, 24 family farms, and 16 hightech tissue culture,
breeding, and seedling enterprises. Currently, 7,785 households participate in
the production and management of orchids and other characteristics of modern
tourism agriculture, with an average annual income of 15,000 RMB, representing
an annual increase in labor income of 45,000 RMB.
2 Metadata of the Dataset
Table 1 lists the metadata summary, including dataset
title, authors, geographical region , dataset composition, data publishing and
sharing service platform, data policy, etc.^{[2]}.
3 Data Collection Process and Methodology
The collection process of the dataset includes the
following steps:
(1)
The
preparatory stage before the investigation.
First
of all, the main job for the dataset development was to design a questionnaire,
based on the structural equation model principle, our preliminary investigation,
and the existing research literature^{[4–17]}, and by consulting with
relevant experts (Figure 1). The complete questionnaire includes three parts:
the behavior, attitude, and perception of farmers, the inputoutput data of
farmers’ orchid planting, and their social network. The data in this paper only
involves the first part, namely the behavior, attitude, and perception of
farmers, and contains the research hypothesis (Table 2).
(2)
Questionnaire
survey
Second,
the questionnaire survey was conducted from March 2018 to July 2018. A total of
237 questionnaires were collected through centralized questionnaire
distribution and dispersed household survey.
Table 1 Metadata summary
of the Questionnaire statistics dataset from GuangdongTaiwan agricultural
cooperative experimental area in
Shaoguan of China
Items

Description

Dataset full name

Questionnaire statistics
dataset from GuangdongTaiwan agricultural cooperative experimental area in
Shaoguan of China

Dataset short name

QuestionnaireStatisticsShaoguan

Authors

Li, H. F., School of Tourism and
Geography, Shaoguan University,
lihangfei1980@126.com


Wei, S. Q., Institute of Geography,
Fujian Normal University, suqiongwei@126.com


Wei, S. B., Institute of Geography,
Fujian Normal University, 565059315@qq.com

Geographical region

Wengyuan county, Shaoguan city,
Guangdong province:
24°07¢30²N–24°37¢15²N, 113°18¢5²E–114°18¢5²E

Year

2018

Date format

.xls

Date size

69 KB

Date files

Excel data of 217 questionnaires
and 25 indicators

Foundations

National
Natural Science Foundation of China (41771136); Guangdong Province
(GD17CGL13); Fujian Province (2018R11010062); Shaoguan City (2019Sn081)

Data computing environment

The software AMOS 17.0 was used for
structural equation analysis

Data publisher

Global Change Research Data
Publishing & Repository, http://www.geodoi.ac.cn

Address

No. 11A, Datun Road, Chaoyang
District, Beijing 100101, China

Data sharing policy

Data from the Global Change
Research Data Publishing & Repository includes metadata, datasets (in the Digital Journal of Global Change Data Repository), and
publications (in the Journal of Global Change Data & Discovery). Data
sharing policy includes: (1) Data are openly available
and can be free downloaded via the Internet; (2) End users are encouraged to
use Data subject to citation; (3) Users, who are by definition
also valueadded service providers, are welcome to redistribute Data subject
to written permission from the GCdataPR Editorial Office and the issuance of
a Data redistribution license; and (4) If Data are
used to compile new datasets, the ‘ten per cent principal’ should be followed
such that Data records utilized should not surpass 10% of the
new dataset contents, while sources should be clearly noted in suitable
places in the new dataset^{[3]}

Communication
and searchable system

DOI, DCI, CSCD, WDS/ISC, GEOSS, China
GEOSS, Crossref

(3)
Indoor
review stage
The last step is statistics in combination with the
opinions of local village cadres and farmers, we screened the collected
questionnaires and identified 217 valid questionnaires. We used a fivepoint
Likert scale (Strongly disagree =1; Disagree =2; Basically agree =3; Agree = 4;
Strongly agree =5) to quantify the data, and formed a 217×25 excel data sheet.
4 Data
Results and Validation
4.1
Routine Test of Data
4.1.1 Normal Distribution
Test
We determined (Table 3) that the absolute value of skewness
coefficient of all items (indicators) was less than 1; the absolute value of
kurtosis coefficient of all items (indicators) was also less than 1, showing
that the sample data met normal distribution^{[18]}.
Figure 1 The questionnaire
Table 2 Research
hypothesis
Dimension

Latent variable

Research hypothesis

Farmers dimension (A)

Peasant household social network and subjective norm (A1)

HA1: Farmers’ social
networks and subjective norms had positive effect on orchid diffusion

Characteristics of farmers’ entrepreneurship(A2)

HA2: The characteristics of household entrepreneurship had
positive effect on orchid diffusion

Farmers’ cognition to China’s Taiwan agriculture (A3)

HA3: Farmers’ cognition to China’s Taiwan’s agriculture
had positive effect on orchid diffusion

Agricultural dimensions in China’s Taiwan (B)

Agricultural Characteristics of China’s Taiwan (B1)

HB1: Agricultural characteristics in China’s Taiwan had
positive effect on orchid diffusion

The characteristics of agricultural technical service in
China’s Taiwan (B2)

HB2: The characteristics of agricultural technical
services had positive effect on orchid diffusion

Environmental dimensions (C)

Basic conditions (C1)

HC1: Basic conditions had positive effect on orchid diffusion

Policy environment (C2)

HC2: Policy environment had positive effect on orchid
diffusion

Diffusion effect (D)

Table
3 Skewness and kurtosis values of sample
data
Item

Skewness

Kurtosis

Item

Skewness

Kurtosis

Item

Skewness

Kurtosis

A11

‒0.795

0.571

B11

‒0.116

‒0.448

C21

‒0.187

‒0.314

A12

‒0.600

0.042

B12

‒0.098

‒0.780

C22

‒0.091

‒0.778

A13

‒0.337

0.156

B13

0.089

‒0.554

C23

‒0.061

‒0.375

A21

‒0.473

0.415

B21

‒0.233

‒0.104

D1

‒0.439

‒0.369

A22

‒0.601

0.246

B22

‒0.405

‒0.243

D2

‒0.508

0.038

A23

‒0.169

‒0.058

B23

‒0.122

‒0.499

D3

‒0.779

0.357

A31

‒0.068

‒0.178

C11

0.259

‒0.650

D4

‒0.689

0.109

A32

0.074

‒0.255

C12

‒0.254

‒0.471




A33

0.198

‒0.616

C13

‒0.431

‒0.043




4.1.2
Reliability and Validity Test
(1) Reliability Test
Internal consistency reliability analysis is a common
method for reliability analysis, and “Cronbach’s α” coefficient is the most
widely used. Generally, if the coefficient is greater than 0.9, the reliability
is excellent; above 0.8 is considered to be very good, and above 0.7 is
moderate, while 0.5 is the critical value^{[18]}. SPSS19.0 software was
used to conduct the reliability analysis on various factors and overall data.
The result indicated that the reliability test met the requirements (Table 4).
Table 4 Reliability test results
Inspection factors

Cronbach’s α

Inspection factors

Cronbach’s α

Peasant household social network and subjective norm

0.676

Infrastructure

0.708

Characteristics
of farmers’ entrepreneurship

0.706

Policy
environment

0.770

Farmers’
cognition to China’s Taiwan agriculture

0.788

Diffusion
effect

0.865

Agricultural
Characteristics of China’s Taiwan

0.802

The
overall data

0.936

The characteristics of agricultural technical service of China’s
Taiwan

0.856



(2) Validity Test
SPSS19.0 software was used to test the validity of the
sample data in the dataset development, the KMO value was 0.907, greater than
0.9. The significance level of Bartlett’s sphericity test was 0.000, and the
validity of the sample data was good (Table 5).
Table 5 Validity test
Test methods

Results

KaiserMeyerOlkin

0.907

Bartlett’s test for sphericity

The approximate chisquare

3,355.302

df

300

Sig.

0.000

4.1.3 Multicollinearity Test
The correlation coefficient matrix R diagnosis method was
used to conduct multivariate collinearity test for the sample data. The results
showed that the value of the correlation between variables was between 0.064
and 0.650, and that the correlation coefficients of most variables were less
than 0.4, indicating that there was basically no multivariate collinearity
problem among the variables in the questionnaire^{[18]}.
4.2
Structural Equation Model (SEM) Data Verification
4.2.1 Test of Latent Model
Data
Structural equation analysis software Amos17.0 was used to
test the data of the latent variable model A1 (the test method of the other
latent variable such as A2, A3, B1, B2, C1, C2, and D is similar), and the
results are shown in Figure 2.
Figure 2 Standardization path
coefficient diagram of peasant household social network and subjective
normative latent variable (A1) model
As
shown in Figure 2, the path coefficient of A13, at 0.46, was the smallest but
was greater than 0.4, indicating that the model quality was good^{[19]}.
The model fitting results (the relevant fitting standards are shown in Table 6)
showed that the root mean square residual (RMR)=0.00<0.05, GFI=1.00>0.90,
normal fit index (NFI)=1.00>0.90, incremental fit index (IFI)=1.00>0.90,
and comparative fit index (CFI)=1.00>0.90, indicating that the model was
reasonable. In addition, the regression analysis results showed that, except
for the reference index A13, which was set as 1 and not estimated, the other
regression coefficient values were all
significant (significance level was 0.001), indicating that the inherent
quality of the model was good^{[19]}. The data test of the other seven
latent models is similar, and the intrinsic qualities of the eight latent
models were all good. All of them passed the significance test.
Table 6 Commonly used fitting indexes
Fitting index

RMR

GFI

NFI

IFI

CFI

Adaptability criteria or thresholds

＜0.05

＞0.90

＞0.90

＞0.90

＞0.90

4.2.2 Confirmatory Factor Test
Constructive factor analysis was conducted on the sample
data to test whether the relationship
between the latent variables conforms to the theoretical requirements of the
structural equation model, to judge whether it is acceptable or not. Due to
limited space, only the peasant household dimension was taken as an example
here, as the other two dimensions are similar.
First, we performed the model quality evaluation; the test
method we used is consistent with the “latent model data test” method. The peasant
household dimension includes three potential variables: peasant household
social network and subjective norms, peasant household entrepreneurial
characteristics, and peasant household’s cognition to China’s Taiwan
agriculture. All three potential variables passed the test. Amos17.0 software
was used to conduct the confirmatory test on the correlation of the three potential
variables under the farmer dimension. The model fitting results showed that
RMR=0.053>0.05, GFI=0.953> 0.90, NFI=0.915>0.90, IFI=0.954>0.90,
CFI=0.953>0.90, indicating that the model fitting was ideal. The correlation
coefficients were 0.58, 0.41, and 0.44 respectively (Figure 3), all of which
passed the test at the significance level of 0.001. The model thus had good
internal quality and was acceptable^{[19]}.
Second, we conducted the reliability test for the latent
variables. The higher the reliability, the stronger is the internal correlation
between the observation indicators, and the higher is the convergence degree of
the latent variables. It is generally believed that its value should be above
0.6^{[18]}. The calculation formula of reliability is shown in Equation
(1).
ρ_{c} = (∑λ)^{2}/[ (∑λ)^{2}+∑θ] (1)
In Equation (1), ρ_{c} is the reliability; λ is the normalized parameter of the observed variable on the
potential variable, namely the path coefficient. θ is the amount of error variation of the observed index variable.
According to the path coefficient values of the observed variables in Figure 3
(A11 was 0.75) and equation (1), the reliability ρ_{c} values of the potential variables in the farmer household dimension were 0.691,4, 0.728,9, and
0.798,0, respectively, which are all greater than 0.6. The potential variables
thus had a good convergence degree.
Figure 3 Standardized path coefficient diagram of
confirmatory factor analysis model of peasant household dimension

Third,
we tested for the discriminant validity between the latent variables. In the
structural equation model, the relationship between latent variables should be lowgrade relevant or show significant
differences. In general, the method of average amount of variance extraction is
used to test (compare) whether the average variation extraction (ρ_{v}) of two potential
variables is greater than the square of the
correlation coefficient. If the average is larger, it shows there is an obvious
difference between variables. The calculation method of ρ_{v} is shown in Equation (2).
ρ_{v}=∑λ^{2}/(∑λ^{2}+∑θ)
(2)
In Equation (2), ρ_{v}
is the extraction amount of the mean variance of the potential variable. λ is the normalized parameter of the
observed variable on the potential variable, namely the path coefficient. θ is the amount of error variation of
the observed index variable. According to Equation (2), the average variation
extraction amount ρ_{v} of each
factor was calculated and the mean value was calculated. Combining with the
correlation value of the three potential variables in Figure 3, the
discriminant validity among the three potential variables was tested. The
results are shown in Table 7.
Table
7 Differential validity test of the three
latent variables under the farmer household dimension
Dimension

Factor (potential variable)

Peasant household social network and subjective norm

Characteristics of
farmers’ entrepreneurship

Characteristics
of
farmers’
entrepreneurship

r(r^{2})

0.58(0.35)


(ρ_{v})

(0.48, 0.43)


_{}

0.46


Farmers’
cognition to
China’s Taiwan agriculture

r(r^{2})

0.41(0.17)

0.44(0.19)

(ρ_{v})

(0.57, 0.43)

(0.57, 0.48)

_{}

0.50

0.53

Note:
r is the correlation coefficient between factors, r^{2} is the square of the correlation coefficient, ρ_{v} is the extraction amount
of mean variance, _{}is the mean of the extraction amount of
mean variance.
From the above analysis, the three latent variables under
peasant household dimension were verified by confirmatory analysis. Similarly,
the latent variables under Taiwan provinceagricultural dimension and the
environmental dimension were validated.
4.2.3 Influencing
Factors of Diffusion Effect Test
In order to verify the theoretical hypothesis in Table 1
with confirmatory factor analysis, the relationship between factors under the
farmer dimension, Taiwan province agriculture dimension and environment
dimension and diffusion effect should be verified by a full model. Due to space
limitation, this paper only considered the peasant household dimension to
conduct a full model analysis. The correlation fitting index of the model was
as follows: RMR=0.058> 0.05, GFI=0.926>0.90, NFI=0.90, IFI=0.946>0.90,
CFI=0.945﹥0.90, indicating that the fitting result was relatively
ideal.
Table
8 shows the significance test results of the whole model (estimation results of
each parameter). Figure 4 is the full model diagram of the standardized path
coefficient. In Figure 4, 0.68 on the right of “diffusion effect” is the square
of the multiple correlation coefficient of the structural equation,
representing the variation percentage, indicating that the internal potential
variable “diffusion effect” can be explained by the external potential.
Table
8 Estimation results of parameters
Estimated item

Nonstandardized estimates

Value of P

Diffusion effect < Peasant
household social network and subjective norm

1.042

***

Diffusion effect < Characteristics
of farmers’ entrepreneurship

‒0.155

0.293

Diffusion effect < Farmers’
cognition to China’s Taiwan agriculture

0.290

***

A13< Peasant household social network
and subjective norm

1.000


A12< Peasant household social network
and subjective norm

1.382

***

A11< Peasant household social network
and subjective norm

1.404

***

A23< Characteristics of farmers’
entrepreneurship

1.000


A22< Characteristics of farmers’
entrepreneurship

1.463

***

A21< Characteristics of farmers’
entrepreneurship

1.287

***

A33< Farmers’ cognition to China’s
Taiwan agriculture

1.000


A32< Farmers’ cognition to China’s
Taiwan agriculture

0.828

***

A31< Farmers’ cognition to China’s
Taiwan agriculture

0.675

***

D4< Diffusion effect

1.000


D3< Diffusion effect

1.107

***

D2< Diffusion effect

1.062

***

D1< Diffusion effect

1.143

***

Peasant
household social network and subjective norm <> Characteristics of
farmers’ entrepreneurship

0.137

***

Characteristics
of farmers’ entrepreneurship <> Farmers’ cognition to China’s Taiwan
agriculture

0.178

***

Peasant household social network and subjective norm <>
Farmers’ cognition to China’s Taiwan agriculture

0.163

***

Note: ***
indicates that P value is less than
0.001; 2. Standardization estimates were shown in Figure 4.
Figure
4 and Table 8 show that farmers’ social network and subjective norms, as well
as farmers’ cognition of Taiwan province agriculture, had a positive and
significant impact on the technological diffusion of Taiwan province agriculture
(regarding orchids). The hypothesis presented in Table 1 thus passed the test.
However, the characteristic factors of peasant household entrepreneurship did
not pass the significance test at the level of 0.05. The relationship between
the remaining two dimensions and the diffusion effect can be obtained through
similar methods. The test results are shown in Table 9.
5 Discussion
and Summary
Based on the principle of structural equation modeling,
this study constructed index systems influencing the effect of Taiwan province agriculture
(orchid) technology diffusion from three dimensions: farmers dimension
(farmers’ social networks and subjective norms, characteristics of farmers’
entrepreneurship, farmers’ cognition of Taiwan province agriculture), Taiwan
province agriculture dimension (characteristics of Taiwan province agriculture,
characteristics of Taiwan province agricultural technology service), and
environment dimension (basic conditions, policy environment). A total of 217
valid questionnaires were collected through a questionnaire survey. Each
questionnaire contained the qualitative data of 25 observation variables. The
subjective cognition of each observation variable was quantified by a
fivepoint Likert scale. The dataset formed by the questionnaire survey could
better explain the factors influencing the Taiwan province orchid technology
diffusion effect. This had a reference and guiding significance for the
construction of the factors influencing agricultural technology diffusion index
and can be used to research related agricultural technology diffusion. As there
were many factors affecting the technological diffusion of Taiwan province agriculture
(orchids) in the experimental area, this paper only selected a few factors
(latent variables) according to the existing research results and the actual
situation; the design of the questionnaire and its indicators still need to be
further improved.
Figure 4 Standardized path
coefficient diagram of the relationship between peasant household dimension and
diffusion effect
Table 9 The result of test

Hypothesis

Standardized relation coefficient

Value of P

HA1

0.69

***

HA2

‒0.11

0.293

HA3

0.34

***

HB1

0.49

***

HB2

0.25

0.018

HC1

0.61

***

HC2

0.18

0.313

The results of data analysis and test showed that^{[20]}
two external latent variables, the characteristics of peasant household
entrepreneurship and policy environment, had no significant influence on the
technological diffusion effect of China’s Taiwan orchid and failed the significance test. Five potential variables, namely,
peasant households’ social network and subjective norms, peasant households’
cognition of Taiwan province agriculture,
agricultural characteristics, agricultural technical service characteristics,
and basic conditions, had a positive and significant influence on the
technological diffusion effect of Taiwan province orchids in the test area. The results of the data analysis can be used
as a reference for the development of the orchid industry in the pilot area.
The management committee of the pilot area can formulate corresponding
countermeasures according to the specific influencing factors to promote the
quick development of the orchid industry.
Author Contributions
Li, H. F.
and Wei, S. Q. designed the dataset. Li, H. F. and Wei, S. B. contributed to
the data processing and participated in the field survey. Li, H. F. wrote the
data paper.
Conflicts of Interest
The authors declare no conflicts of interest.
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