The Influence of Personal Motivation and Innovative Climate on Innovative Behavior: Evidence from University Students in China

Figures & data

Figure 1 Conceptual Framework.

Table 1 Variable Measurements

Variable Type	Variable Name	Variable Symbol	Description
Dependent variable	Project outcomes	PO	Publish articles/apply for patents/ technology transfer=1, otherwise=0
Independent variable	Mentor guidance	MG	Do nothing =0, otherwise =1
	Mentor’s role	MR	Facilitator =0, leader=1
	Insufficient funding	IF	Yes=1, no=0
	Poor laboratory conditions	PLC	Yes=1, no=0
	Methodological and technological barriers	MTB	Yes=1, no=0
	Weak team members	WTM	Yes=1, no=0
	Insufficient time	IT	Yes=1, no=0
	Knowledge of innovative activities	KIA	Do not know—know well 0–4
	Literature review	LR	Yes=1, no=0
	Participation in academic reports	PAR	Continuous variable
	Knowledge of academic articles	KAA	Do not know—know well 0–4
	Participation in trainings	PT	Yes=1, no=0
	Participation of classmates	PC	Yes=1, no=0
	Interest in scientific activity	ISA	Yes=1, no=0
	Participation in mentor’s project	PMP	Yes =1, otherwise=0
	Plan to attend graduate school	PAGS	Yes=1, no=0
	Experience of research or experiments	ERE	No=0, otherwise =1
	Time to attend innovative activities	TIME	No participation=0, Freshman=4, sophomore=3, junior=2, senior =1
	Self-designed project	SD	Yes =1, otherwise=0
Control variable	Gender	GENDER	Male=1, female=0
	Nationality	NATION	Han nationality=1, minority=0
	Grade	GRADE	Freshman=1, sophomore=2, junior=3, senior=4
	Served as student cadre	SC	Yes=1, no=0
	Served in university association for science and technology	UAST	Yes=1, no=0
	One-child families	OCF	Yes=1, no=0
	Location of student’s home	LSH	Set up dummy variables with reference to East
			Urban=1, rural=0

Table 2 Results of Stratified Regression Analysis

Independent variable	M1	M2	M3	M4	M5
MG	1.08 (1.04)		0.96 (0.71)	1.72 (1.23)	6.16* (1.83)
MR	0.09 (0.27)	0.76 (0.91)	−0.49 (−1.00)	0.50 (1.20)	−2.83* (−1.82)
IF	0.04 (0.11)	1.59* (1.70)	−0.31 (−0.61)	−0.04 (−0.09)	1.50 (1.23)
PLC	−0.31 (−0.90)	−1.58** (−2.08)	0.09 (0.16)	−0.28 (−0.64)	−0.60 (−0.52)
MTB	−0.57* (−1.63)	−0.30 (−0.33)	−1.27** (−2.41)	−0.53 (−1.20)	−0.52 (−0.53)
WTM	−0.08 (−0.25)	−0.34 (−0.37)	−0.69 (−1.36)	−0.38 (−0.90)	1.61 (1.12)
IT	−0.25 (−0.74)	−0.83 (−0.89)	0.15 (0.30)	−0.13 (−0.33)	−0.57 (−0.46)
KIA	−0.06 (−0.33)	0.11 (0.23)	−0.25 (−1.00)	−0.04 (−0.20)	0.45 (0.81)
LR	−0.23 (−0.35)		−0.60 (−0.69)	0.74 (0.88)	−2.02 (−1.00)
PAR	0.16 (1.46)	0.02 (0.07)	0.28* (1.69)	0.06 (0.45)	0.38 (1.00)
KAA	0.06 (0.26)	0.99** (1.93)	−0.05 (−0.17)	−0.09 (−0.29)	−0.50 (−0.95)
PT	−0.23* (−0.65)	−0.54 (−0.64)	−0.41 (−0.73)	0.28 (0.62)	−2.23* (−1.88)
PC	1.06*** (3.26)	0.79 (0.88)	1.70*** (3.23)	0.78** (1.94)	4.21*** (2.77)
ISA	−0.38 (−0.87)	−2.25* (−1.72)	−0.11 (−0.18)	−0.96* (−1.78)	−0.04 (−0.03)
PMP	1.71*** (3.12)	1.00 (0.84)	2.00*** (2.49)	1.75*** (2.63)	4.86*** (2.65)
PAGS	0.32 (0.89)	0.57 (0.53)	0.39 (0.77)	−0.07 (−0.16)	2.92** (2.01)
ERE	1.02 (1.41)	2.00* (1.45)	1.58 (1.22)	1.10 (1.39)	1.26* (1.66)
TIME	−0.16 (−0.61)	−1.16* (−1.54)	0.17 (0.44)	−0.37 (−1.14)	0.78 (0.76)
SD	−0.57* (−1.77)	−0.05 (−0.05)	−0.70* (−1.51)	−1.20*** (−2.84)	3.33** (2.03)
Control variable	YES	YES	YES	YES	YES
_cons	−3.15* (−1.63)	−2.72 (−0.72)	−3.73 (−1.37)	−2.57 (−1.12)	−16.09** (−2.15)
LRchi2	47.02	28.46	44.89	44.88	36.17
Prob>chi2	0.01	0.29	0.02	0.01	0.07
PseudoR2	0.15	0.30	0.24	0.19	0.43

Note: z-scores in parentheses, *p<0.10, **p<0.05, ***p<0.01.