The influence of learning activity on low-skilled workers’ skill improvement in the South Korean manufacturing industry

Figures & data

Figure 1. Research design.

Table 1. Demographic distribution of low-skilled workers.

Section	Frequency	Ratio
Gender
Male	969	67.0
Female	478	33.0
Age (years)
Below 19	21	1.5
20–29	456	31.5
30–39	514	35.5
40–49	362	25.0
50–59	92	6.4
Above 60	2	0.1
Periods of previous schooling
Below middle school	133	9.2
Graduation of high school	415	28.7
Graduation of vocational high school	409	28.3
Graduation of technical high school	490	33.9
Firm size
100–299	503	34.8
300–999	675	46.6
1000–1999	106	7.3
Above 2000	163	11.3

Figure 2. Hierarchical nature of classification system from a skilled worker 1 to 5.

Table 2. Definition of variables.

Variable	Definition
Dependent
SKILL	Degree of workers’ skill improvement (continuous)
Independent
FIRM	How many workers employed in the respondent’s firm?
	(100–299 = 1, 300–999 = 2, 1000–1999 = 3, above 2000 = 4)
Learning
SUPER	Informal learning by supervisor (participation = 1, otherwise = 0)
COWORK	Informal learning by co-workers (participation = 1, otherwise = 0)
SELFLEARN	Self-learning through work (participation = 1, otherwise = 0)
OJT	Formal OJT programme (participation = 1, otherwise = 0)
TFT	Task Force Team (participation = 1, otherwise = 0)
QC	Quality Circle (participation = 1, otherwise = 0)
KNOW	Knowledge Mileage System (participation = 1, otherwise = 0)
SIXSIGMA	Six-Sigma (participation = 1, otherwise = 0)

Table 3. Validity and reliability for variables.

Variable	Total	Component
		1	2
SUPER		.078	.820
COWORK		.099	.840
SELFLEARN		.126	.751
OJT		.648	.255
TFT		.702	.075
QC		.582	.212
KNOW		.631	−.082
SIXSIGMA		.649	.064
Eigenvalue		2.658	1.512
R^2	52.118	33.221	18.897
Cronbach’s alpha	.706	.751	.637

Table 4. Zero-order correlation matrix for variables.

	SKILL	SUPER	COWORK	SELFLEARN	OJT	TFT	QC	KNOW	SIXSIGMA
SKILL	1
SUPER	.056*	1
COWORK	.229**	.579**	1
SELFLEARN	.159**	.431**	.491**	1
OJT	.084**	.229**	.214**	.215**	1
TFT	.089**	.117**	.144**	.156**	.398**	1
QC	.145**	.175**	.185**	.185**	.397**	.243**	1
KNOW	.073**	.042*	.064*	.076**	.197**	.289**	.174**	1
SIXSIGMA	.119**	.129**	.147**	.128**	.246**	.296**	.263**	.304**	1

Note: *p < .05, **p < .01.

Table 5. The comparison among low-skilled workers’ learning activities on their skill improvement.

A	B-1	B-2	B-3
−1	1	0	2
0	63	3	150
1	22	0	98
2	27	2	256
3	31	3	245
4	22	4	216
5	13	4	118
6	2	3	35
Total	181	19	1120

Note: A, the difference between the skill level when a low-skilled worker started work for his or her company and current skill level of the worker; B-1, the number of observations that received no learning at all; B-2, the number of observations that received informal supervisor learning only; B-3, the number of of observations that received the other forms of learning.

Table 6. Influence of learning programme on low-skilled worker’s skill improvement.

	B	t	β	Tolerance	VIF
Constant	1.755	13.159
FIRM	.113*	2.291	.063	.911	1.098
SUPER	−.491**	−4.150	−.139	.625	1.599
COWORK	.880**	7.138	.246	.586	1.705
SELFLEARN	.281*	2.342	.073	.713	1.402
OJT	−.102	−.795	−.025	.705	1.418
TFT	−.093	.459	.014	.764	1.309
QC	.337**	3.082	.091	.797	1.254
KNOW	.112	.438	.013	.844	1.185
SIXSIGMA	.260	1.914	.056	.819	1.221

Note: Dependent variable, SKILL; R² = .087, adjusted R² = .080; Durbin–Watson statistic = 1.568; *p < .05, **p < .01.