Research on Improvement of Entrepreneurial Ability of Science and
Technological Small-micro Enterprise Based on Entrepreneurial Learning Perspective
Dayong XU
1 Department of Business Administration, University of Science and Technology Liaoning,Anshan,liaoning,China
Abstract: This article first combs literature of entrepreneurial learning style, learning style and entrepreneurship ability; Second, according
to the related research, puts forward the assumption that four types of entrepreneurial learning style affect the five kinds of entrepreneurial
learning methods, five kinds of entrepreneurial learning style affect the entrepreneurial ability, and according to the hypothesis, structure
model about science and technological small-micro enterprise entrepreneurship ability; Then, collect data to research 590 science and
technological small-micro enterprises in our country. Finally, combined with the data, this article discussed impact of entrepreneurial
learning to science and technological small-micro enterprises entrepreneurial ability based on the structural equation model,for
fundamentally breakthrough entrepreneurship promotion bottleneck and achieve rapid ascension a new path for technology-based small
micro enterprise build entrepreneurial ability and implementation.
Keywords: structural equation model; entrepreneurial learning; entrepreneurial style; science and technological small-micro enterprises;
entrepreneurial ability
1. Introduction
Small micro enterprise is common term of the small and micro enterprises. In small micro enterprise, science and technological
small-micro enterprises is the small or micro enterprise as the key point of science and technology, it is the most active and the most
potential technology innovation group, mainly in high and new technology industries and the industrial chain link of high technical content
in traditional industries, natural chasers from emerging industries and the new economy form. with society's increasing dependence on
technology, its number develops rapidly.In the report to eighteen clearly put forward to promote the development and stimulate vitality of
science and technological small-micro enterprises, it is a very important strategic significance to support the development of society,
economy and deepening innovation system in China.
2 Literature review and hypothesis
Entrepreneurial activity is key to promote the development of science and technological small-micro enterprises. However, exist many
problems about science and technological small-micro enterprises in our country, mainly in terms of lack of entrepreneurial ability. Wood &
Bandura (1989) argued that entrepreneurial ability. is ability of the entrepreneur self-assessment accomplish specific entrepreneurial
behavior, the ability of self assessment is the core of individual self-efficacy belief. Framework includes: the framework of above functions
and technical skills[11] and is built on startup entrepreneurs need skills framework[12]. For specific dimension of entrepreneurial
ability[1-4], Man (2002) divided it into six dimensions: the opportunity ability, relationship skills, conceptual ability, organization
ability, strategy ability and commitment ability. Tang Jing etc. (2008) pointed out entrepreneurial ability is 6 dimensions of two order
for opportunity recognition and development ability and operation management ability on the basis of the Man. In conclusion, the existing
literature mainly discussed entrepreneurs to self assessment, self-efficacy, skills and how to promote ability of (Science and technology) small
micro enterprises, laid the foundation for this study, however, It is very few to combine the entrepreneurial capacity with entrepreneurial
learning[5-8].
From the perspective of experiential learning, entrepreneurial learning is study of background of entrepreneurship about the application
of some valuable experience, gain experience from the trial-and-error continuously and improve the learning motivation, it’s a learning
process that deal effectively with information to review of the experience, analysis summary, learning and understanding of reflection. ;
From the cognitive perspective, entrepreneurial learning is a process that acquisition and storage of entrepreneurial knowledge, and
regard it as a kind of expert knowledge, actively make use [9-13]. From the perspective of dynamic evolution, entrepreneurial learning is
a kind of process of processing information, trial and error, update the decision model, improve performance , it includes two types of
learning of exploratory and developing, performance strongly phase features. Politis (2005)Pointed out, entrepreneurial learning is
process of entrepreneurial opportunities information acquisition and conversion on the entrepreneur, namely the entrepreneurial learning
(get) and entrepreneurial learning style (conversion), the differences of entrepreneurial learning is fundamental to differences of
entrepreneurial ability between enterprises. Chandler&Lyon (2009), entrepreneurial learning style can be divided into" initial learning,
experience learning, imitation learning, search and insight learning and grafting learning". Kolb (1984),divided learning style into"
adaptive learning style, divergent learning styles, absorption type style and convergent learning style"[14-17].
Due to changed entrepreneurial environment, the original knowledge structure of entrepreneurs can't adapt to the demand to solve new
problem,make the expected result cannot achieve, at the same time, the chance of mistakes for entrepreneurs will also increase, the gap
between the actual result and target especially error, will make the strong learning needs for entrepreneurs, this kind of learning, under the
promotion of different learning style, quick update the original knowledge structure, and on this basis to trial and error, to adapt to the new
problem finally, so the cycle, entrepreneurial ability of science and technological small-micro enterprises also will be significantly increased.
Therefore, facing the height change of the complicated competition environment, science and technological small-micro enterprises must
establish effective learning mechanism to improve enterprise knowledge acquisition, sharing, communication, interpretation, paraphrase and
the quality of the decisions, improve the adaptability of enterprises, so as to improve enterprise's ability of entrepreneurship[18-19]. And
improvement of entrepreneurial ability requires that enterprises must keep on learning, development and utilization of knowledge and ability
of the individual, entrepreneurial learning here , refers to the learning associated with entrepreneurial activity, especially start-ups in the
establishment and growth stage of knowledge creation and accumulation of experience.Especially creation and accumulation of knowledge
for new starting enterprise in the establishment and growth stage The entrepreneurial learning is a important factor of constructing
entrepreneurial ability and the foundation of a competitive advantage, promote growth performance of science and technological
small-micro enterprises.The positive role of entrepreneurial learning to start-ups grow has been confirmed by the above-mentioned research
scholars[20-24].
This paper argues that:
H1: Entrepreneurial learning style is positively related to the proper entrepreneurial learning way;
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©2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/8/9
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H2: Entrepreneurial learning style is positively related to opportunity recognition and the development ability of science and
technological small-micro enterprises;
H3: Entrepreneurial learning style is positively related to operation management ability of science and technological small-micro
enterprises;
H4: Entrepreneurial learning way is positively related to opportunity recognition and the development ability of science and
technological small-micro enterprises;
H5: Entrepreneurial learning way is positively related to operation management ability of science and technological small-micro
enterprises;
Therefore, this article constructed the following basic structure model and hypothesis as shown in figure 1 based on the above
assumptions.
Figure 1 Basic structure model and hypothesis
Notice: LS is learning style;LW is learning way;OA is opportunity ability;MA is management ability
3 Sample and variable metric
3.1 sample
Research object of this research mainly to small micro enterprises, research activities from the beginning of January, 2014 to the end of
April 2014, the whole research work for 4 months. Specific questionnaire distribution and recovery situation as shown in table 1. The
specific situation of the samples are shown in table 2.The scope of the issuance of the questionnaire chosed 10 cities from Beijing, Shanghai,
zhengzhou, shenyang, tianjin, ningbo, shenzhen, guangzhou, wenzhou and anshan, selected samples in this article is typical and
representative in science and technological small-micro enterprises.
Table 1 questionnaire distribution and recovery situation
No.
City
Questionnaire (total)
Recycling questionnaire (copy)
Recycling effective questionnaire (copy)
1
Beijing
65
58
52
2
Shanghai
72
62
58
3
Zhengzhou
56
45
43
4
Shenyang
71
62
59
5
Shenzhen
60
47
45
6
Guangzhou
65
43
42
7
Wenzhou
68
52
48
8
Ningbo
55
41
37
9
Tianjin
43
35
32
10
Anshan
35
30
29
Total
590
475
445
Note: the recovery rate of questionnaire was 80.51%，The total effective rate of questionnaire was 75.42%.
3.2 Variable Metric
Based on the related literature research, the paper measured learning styles to use the four indicators:①adaptive,② divergent ③
convergent ④absorption;Measured entrepreneurial learning to use the five indicators:①initial study; ② experiential learning; ③
imitation; ④ Search and insight learning; ⑤ grafting learning;Measured opportunity ability to use the six indicators:①Relational skills;
②learning ability; ③knowledge sharing capabilities; ④innovation; ⑤the ability to identify opportunities; ⑥capacity development
opportunities;Measured operational management ability to use the five indicators:①organizational capacity; ②coordination; ③risk
management; ④strategic capabilities; ⑤conceptual ability.
4 Research Design
1 Reliability test
Journal of Residuals Science & Technology, Vol. 13, No. 8, 2016
©2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/8/9
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The reliability is mainly refers to accurate of the questionnaire(precision). Reliability analysis involves the consistency and stability of
the questionnaire test results, the purpose is how to control and reduce the random error.The following formula can be used to test reliability
of questionnaire:
rXX =
S2
ST2
S X2
rXX = 1 −
or
S E2
S X2
S2
S2
X represents the variance of actually geting scores;
E represents the variance of the
In the formula, T is true score variance;
error.
Six kinds of reliability coefficient commonly are used① retest reliability; ② replica reliability; ③ split-half reliability; ④Kuder
─ Richardson reliability; ⑤ Cronbach reliability coefficient; ⑥ rater reliability.
Reliability inspection in the article mainly adopts the general indicators for Cronbach 'a consistency coefficient, DeVellis believes that
its minimum value in 0.65 to 0.70 for the acceptable values; If value within 0.70 0.80, that the reliability of the questionnaire is good; If
value within 0.80 0.90, that the reliability of the questionnaire is very good. Therefore, reliability coefficient of questionnaire above 0.80 is
good,the SPSS17.0 used to test recycling effective questionnaire, get reliability statistics test (table 2) and the correlation matrix of 20 topics
(table 3), from view of the reliability of measurement model of the variable Cronbach 'a ,Cronbach' a value of overall questionnaire reached
0. 890, very good reliability showed that the emerging technology enterprises entrepreneurial ability ascension path concept dimension.
Table 2 The reliability statistics
Cronbach's
Based on the standardized Item
Alpha
Cronbachs Alpha
.890
b1
b2
b3
b4
b5
b6
b7
b8
b9
b10
b11
b12
b13
b14
b15
b16
b17
b18
b19
b20
b1
1.000
.427
.035
.387
.274
.442
.352
.301
.157
.215
.193
.340
.368
.458
.360
.287
.350
.245
.215
.287
b2
.427
1.000
-.231
.422
.164
.361
.282
.267
.136
.232
.101
.323
.198
.348
.258
.228
.266
.221
.243
.223
b3
.035
-.231
1.000
.002
.213
-.031
.044
.113
.145
.060
.087
-.075
.117
.048
.090
.114
.033
.059
-.008
.016
b4
.387
.422
.002
1.000
.278
.405
.328
.245
.267
.236
.155
.337
.325
.378
.323
.235
.368
.214
.192
.234
b5
.274
.164
.213
.278
1.000
.373
.357
.380
.214
.266
.291
.288
.376
.296
.293
.353
.231
.247
.240
.321
b6
.442
.361
-.031
.405
.373
1.000
.524
.362
.293
.408
.269
.489
.469
.554
.442
.414
.496
.318
.316
.404
b7
.352
.282
.044
.328
.357
.524
1.000
.359
.213
.361
.183
.414
.477
.443
.377
.358
.337
.174
.381
.352
Item
.894
Table 3 The topics correlation matrix
b8
b9 b10 b11 b12 b13
.301 .157 .215 .193 .340 .368
.267 .136 .232 .101 .323 .198
.113 .145 .060 .087 -.075 .117
.245 .267 .236 .155 .337 .325
.380 .214 .266 .291 .288 .376
.362 .293 .408 .269 .489 .469
.359 .213 .361 .183 .414 .477
1.000 .238 .330 .209 .291 .354
.238 1.000 .249 .200 .278 .341
.330 .249 1.000 .226 .384 .347
.209 .200 .226 1.000 .344 .372
.291 .278 .384 .344 1.000 .595
.354 .341 .347 .372 .595 1.000
.389 .325 .397 .270 .518 .520
.284 .278 .328 .244 .398 .439
.322 .250 .385 .166 .337 .390
.254 .311 .333 .215 .501 .419
.292 .180 .270 .134 .278 .286
.333 .196 .135 .315 .377 .302
.389 .305 .280 .162 .360 .381
20
b14
.458
.348
.048
.378
.296
.554
.443
.389
.325
.397
.270
.518
.520
1.000
.593
.409
.585
.320
.430
.483
b15
.360
.258
.090
.323
.293
.442
.377
.284
.278
.328
.244
.398
.439
.593
1.000
.393
.549
.363
.349
.431
b16
.287
.228
.114
.235
.353
.414
.358
.322
.250
.385
.166
.337
.390
.409
.393
1.000
.395
.423
.153
.433
b17
.350
.266
.033
.368
.231
.496
.337
.254
.311
.333
.215
.501
.419
.585
.549
.395
1.000
.313
.395
.465
b18
.245
.221
.059
.214
.247
.318
.174
.292
.180
.270
.134
.278
.286
.320
.363
.423
.313
1.000
.112
.305
b19
.215
.243
-.008
.192
.240
.316
.381
.333
.196
.135
.315
.377
.302
.430
.349
.153
.395
.112
1.000
.271
b20
.287
.223
.016
.234
.321
.404
.352
.389
.305
.280
.162
.360
.381
.483
.431
.433
.465
.305
.271
1.000
2 Validity test
In measurement theory, the validity is defined as the ratio of measuring the true variance (i.e.,the effective variable) and the total
variance that related to the purpose in a series of measurement:
rxy2 =
rxy2
indicates measurement of validity coefficient,
S v2
S x2
S v2 represents valid number of variation, S x2 represents the total number of
variation.
Commonly used validity indicators, there are three: ① content validity; ② criterion validity; ③ construct validity.
KMO and Bartlett ball inspection shown in table 4 in the factor analysis, chi-square value is 3059.575 (df=190), there was no
significant difference for observations and expectations. KMO statistics(.918) also suggests that sample is very suitable for factor analysis.
So the sample can better support scale, namely validity is good.
Table 4 KMO and Bartlett’s test
Sampling enough degrees of Kaiser - Meyer - Olkin measurements
Journal of Residuals Science & Technology, Vol. 13, No. 8, 2016
©2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/8/9
.918
9.3
Bartlett sphericity test
The approximate chi-square
3059.575
df
190
Sig.
.000
5 Model test
The fitting test of the structural equation model, test Whether the consistency for hypothesis model and real data sample. There are
many measurement standard about the overall fit of the model, the most commonly used fitting index is goodness-of-fit card square test.In
fact, chi-square fitting substandard measurement, that is, a small card square value shows that the fitting is good, but the chi-square values
associated with a sample size, it is not good for determining model fitting, in order to reduce the influence of sample size for fitting test,
there is a rough regular that associated directly with chi-square, Chi-square value to degrees of freedom is less than 3, then we can think that
model has good fitting. Besides, there are a lot of fitting test indicators of model, but different performance characteristics have been shown
about different indicators from different sample size, model complexity, it must consider according to the specific circumstances. In this
paper, according to the results of the correlation matrix in table 3 item,maximum likelihood method is used to estimate the model in
AMOS17.0, preliminary results are shown in figure 2.
Figure 2 Roadmap of entrepreneurial ability of science and technological small-micro enterprise based on
entrepreneurial learning perspective
We only focused on the preset model (the Default model) according to the output results of the AMOS17.0 and the view of the
practical research. For Saturated model, it is minimum limit model that AMOS can fitting with, because it does not provide the
corresponding value in many cases, cause unable to judge the pros and cons of the model, so don't care; And independent model fits with the
most restrictive model in the AMOS, namely it is a calculated result that is no correlation relationship between the scalar of introduction, so
we usually only focus on the results of the prediction model.
Table 5 Concept fitting model index of roadmap of entrepreneurial ability of science and technological small-micro enterprise based on
entrepreneurial learning perspective
Macro
Default
Saturated model
Independence model
Evaluation standard
model
CMIN
RMR,GFI
Baseline Comparisons
NPAR
45
210
20
CMIN
519.971
.000
3112.145
DF
160
0
190
P
.000
.000
>0.05
CMIN/DF
3.151
16.380
<3
RMR
.066
.000
.362
the smaller,the better
GFI
.891
1.000
.336
>0.9
AGFI
.862
.266
>0.9
PGFI
.700
.304
>0.5
NFIDelta1
.833
.000
>0.9
1.000
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RFIrho1
.808
.000
>0.9
IFIDelta2
.880
.000
>0.9
TLIrho2
.860
.000
>0.9
CFI
.879
1.000
.000
>0.9
Parsimony-Adjusted
PRATIO
.868
.000
1.000
Measures
PNFI
.723
.000
.000
>0.5
PCFI
.763
.000
.000
>0.5
NCP
354.971
.000
2922.145
the smaller,the better
LO90
289.933
.000
2745.049
HI90
427.625
.000
3106.574
FMIN
1.171
.000
7.009
F0
.799
.000
6.581
LO90
.653
.000
6.183
HI90
.963
.000
6.997
RMSEA
.070
.186
LO90
.063
.180
HI90
.076
.192
PCLOSE
.000
.000
the smaller,the better
AIC
609.971
420.000
3152.145
the smaller,the better
BCC
614.439
440.851
3154.131
the smaller,the better
BIC
794.385
1280.596
3234.106
the smaller,the better
CAIC
839.385
1490.596
3254.106
the smaller,the better
ECVI
1.374
.946
7.099
the smaller,the better
LO90
1.227
.946
6.701
HI90
1.537
.946
7.515
MECVI
1.384
.993
7.104
1.000
NCP
FMIN
the smaller,the better
the smaller,the better
RMSEA
AIC
ECVI
HOELTER
the smaller,the better
HOELTER
>200
168
32
180
34
.05
HOELTER
>200
.01
Table 5 data is sorted out according to the AMOS output, the part of the index is not model fitting effect evaluation indexes, so there
isn’t the corresponding evaluation standard in the last column. chi-square value is not reached acceptable significant level for the fitting
effect from the model in absolute indicators fitting effect, because it is easily influenced by such factors as the sample size, negligible P
values here, the only part indexes reach acceptable level in the table 4, in some indicators such as absolute index, GFI = 0.891, AGFI =
0.862, in the index of relative fitting effect, NFI = 0.833, IFI = 0.880, TLI = 0.860, the RFI = 0. 808,in alternative indicators, CFI = 0.879,
close to 0.9. Therefore, synthesizes above all kinds of evaluation index, we think that the fitting effect of the model is general, We need to
modify it.
6 Correction Model
The results of coefficient evaluation are shown in Table 6 .
Table 6 Regression Weights: (Group number 1 - Default model)
Estimate
S.E.
C.R.
P
Label
Learning way
<---
Learning style
.893
.123
7.250
***
par_21
Opportunity ability
<---
Learning style
-.197
.101
-1.949
.051
par_17
Management ability
<---
Learning way
1.128
.181
6.220
***
par_18
Opportunity ability
<---
Learning way
.883
.143
6.151
***
par_19
Journal of Residuals Science & Technology, Vol. 13, No. 8, 2016
©2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/8/9
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Management ability
<---
Learning style
Estimate
S.E.
C.R.
P
Label
-.354
.142
-2.496
.013
par_20
From table 6, it is not significant that the path coefficient below the 0.05 level, other parameters are fair, this path from the learning
style to the opportunity ability first should be considered to delete, After test, structure coefficient is still not clear (coefficient is negative, P
= 0.076) from learning style to management ability, and should be considered to delete, the modified model is shown in figure 3:
Figure 3 The entrepreneurial ability revised roadmap of science and technology small-micro enterprises based on entrepreneurial
learning perspective
According to figure 3, maximum likelihood estimation is used in Amos, part of the fitting index results such as table 7:
Table 7 Results of common fitting index calculation
Fitting index
Chi-square value/degrees of freedom
GFI
AGFI
PGFI
NFI
RFI
IFI
TLI
CFI
RMSEA
Results
527.839(160)
0.891
0.863
0.708
0.830
0.807
0.877
0.860
0.877
0.070
According to the fitting results shown in Table 7, it requires further corrections to the figure 3 combining with the path coefficient, after
four correction to the model, the results are shown in Table 8:
Table 8 Results of common fitting index calculation
Fitting index
Chi-square value/degrees of freedom
GFI
AGFI
PGFI
NFI
RFI
IFI
TLI
CFI
RMSEA
Results
361.212(146)
0.923
0.900
0.709
0.912
0.901
0.925
0.911
0.924
0.058
As can be seen from Table 8, the model is well fitted, ultimately,concept model of Roadmap of entrepreneurial ability of science and
technological small-micro enterprise based on entrepreneurial learning perspective is shown in Figure 4:
Figure 4 Roadmap of entrepreneurial ability of science and technological small-micro enterprise based on
entrepreneurial learning perspective
Journal of Residuals Science & Technology, Vol. 13, No. 8, 2016
©2016 DEStech Publications, Inc.
doi:10.12783/issn.1544-8053/13/8/9
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7 Conclusion
The article mainly discussed to promote entrepreneurial ability of science and technological small-micro enterprise development ability
from the perspective of entrepreneurial learning and implements the organic integration of the entrepreneurial theory and enterprise
management theory. From the result of the operation, there is no direct positive correlation in two dimensions between entrepreneurial
learning style and entrepreneurial ability, To improve the entrepreneurial ability in the entrepreneurial process, the enterprise must be
unceasingly continuous learning through the different learning way. In fact, the entrepreneurial process is a learning process, The ascension
of entrepreneurial ability is the steady accumulation process of entrepreneurial learning.Entrepreneurial learning way and entrepreneurial
learning style constitute the entrepreneurial learning,the article clarify an important impact on the different entrepreneurial learning styles to
different entrepreneurial learning ways, it is a positive relevant function that different entrepreneurial learning ways to entrepreneurial
ability,which is able to help fundamentally breakthrough promotion bottleneck of entrepreneurial ability, so as to provides a new path to
build and how to rapid promote entrepreneurial capacity of science and technological small-micro enterprise.
Acknowledgements: Philosophy and social sciences prosperity project of University of Science and Technology Liaoning: Research
on Improvement of Entrepreneurial Ability of Science and Technological Small-micro Enterprise Based on Entrepreneurial Learning
Perspective (2015FR01); Liaoning Province Education Science "Twelfth Five Year Plan" project "Internet + University Entrepreneurship
Education" platform design (JG15DB176); Teaching reform project of University of Science and Technology Liaoning: Research on
Business Management Professional Innovative Entrepreneurial Talent Cultivation Model(cxcy-2015-36);The project supported by Anshan
city social science research project:Research on path of Entrepreneurial Ability of Anshan Science and Technological Small-micro
Enterprise Based on Entrepreneurial Learning Perspective(as20162016).
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Journal of Residuals Science & Technology, Vol. 13, No. 8, 2016
©2016 DEStech Publications, Inc.
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