high performance work practices and firm training
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※ This is a revised version of the paper presented at the KLI/ ILO Tripartite Workshop on Skill
Development, High Performance Work Organization and Social Dialogue, held in Seoul, Korea, on
March 6, 2003.
High Performance Work Practices and Firm Training
Dongbae KimResearch Fellow
Korea Labor Institutee-mail: dongbae@kli.re.kr
April 2003
1. Introduction
Advances in IT technology gave rise to new industries while bringing changes
to existing businesses, intensifying global competition, shifting the source of
comparative advantage, and ultimately resulting in globalization. Growing importance
is being placed on human resources as a determinant of corporate competitiveness.
Competition among corporations in the 21st century is shifting from that based on
capital or tangible resources to one dominated by human resources (Pfeffer, 1994).
In order for a company to gain a competitive edge, it must continuously invest
in workforce training to increase their knowledge and skills. At the same time, it needs
to inspire the employees to become committed to the organization. Several requisites
that are called for in order to achieve the organizational capacity to attain competitive
advantage include: accumulation of human capital through sustained training, sharing of
individual knowledge within the organization, accumulation and constant revision of
organizational routine to become a learning organization.
The work system that maximizes worker’s competence and organizational
commitment is referred to as a “high-performance/involvement work system.” The
high-performance work system is a non-Tayloristic or high-involvement work
organization, characterized by high degree of autonomy of work groups, high level of
task integration and employee participation in innovative activities at the workplace.
The high-performance work system is also supported by human resources
management(HRM) that reinforces workers’ role structure.
As indicated in its description as a system based on the autonomy of skilled
workers, the high-performance work system is closely related to firm training.
(MacDuffie & Kochan, 1995). Under this system, quality and maintenance tasks
previously performed by special departments are delegated to workers, and work groups
independently take charge of the task-related decision-making formerly undertaken by
supervisors or managers. The system is also marked by workplace participation in
which workers suggest ideas for problem-solving and continuous improvement.
Since high-performance work systems require workers to take on broader roles,
work competence as well as self-governing and problem-solving capabilities must
accompany the workers in order to effectively carry out their roles. Thus, high-
performance work systems also call for continuing upgrading of workforce skills. Based
on the premise that high-performance work systems require enhanced workforce skills,
this study will analyze the effect of high-performance work system on firm training and
make some policy suggestions.
2. Review of Literature and Hypotheses
2.1 High-performance work system
High-performance work systems have been an important topic for studies on
HRM and industrial relations ever since it was first mentioned in the report “America’s
Choice” of the National Commission on Skills of the Workplace in 1989 (Cappelli &
Newmark, 1999). This work system emerged in the 1980s when the U.S. was trying to
emulate the success of Japan. Key features of this system take after the work
organization and HRM practices of large Japanese corporations (Doeringer et al., 1998;
Cappelli & Newmark, 1999).
Critics of the term “high-performance” instead use such adjectives as
“innovative,” “transformational,” “alternative,” “flexible,” and “involvement.” The use
of the term “high-performance” may create confusion as to whether the term is merely
an expression of desire or actually a proven means to enhance performance. Moreover,
the term invites misunderstanding as any system, regardless of its characteristics, can be
deemed high-performance as long as it generates good performance records.
According to some researchers, the key to high-performance work systems lies
in worker participation in the decision making at the workplace (Cotton, 1993; Parks,
1995; Delaney, 1996). A high-performance work system entails a high-involvement
work organization that requires increased workers participation and roles in the
workplace, not a Taylorized organization that minimizes the role of workers. Instead of
employing HRM policies and practices designed to control workers, the high-
performance system employ high-commitment HRM practices that promote workers’
voluntary involvement and dedication as well as skill formation. Some researchers
separate non-Taylorized or high-involvement work organization as the core of high-
performance work systems and commitment-oriented HRM as a complement to work
organization (MacDuffie, 1995; Pil & MacDuffie, 1996).
High-involvement work organization can be further categorized into three
dimensions: task integration, autonomy of work groups, and workplace participation,
although the combination among these dimensions may differ by county or by company.
Task integration negates excessive specialization and reintegrates job-related tasks into
a single job. Indicators of task integration include workers’ responsibility for
maintenance or quality work, job rotation and task composition. Autonomy of work
groups relaxes vertical specialization of management hierarchy, allowing work groups
to independently decide on the planning and control functions usually reserved for
managers. Autonomy is assessed by whether autonomous work teams are in place or by
how much autonomy is retained by each work group. Workplace participation negates
the monopoly of problem-solving and improvement processes by engineers or managers
and its indicators are off-line activities like QC or worker suggestions.
Although there is no consensus on what constitutes commitment-oriented HRM
practices, most agree that workers should be recognized as important stakeholders just
as shareholders and workers’ interests should be faithfully represented. In addition,
HRM should be based on trust, humanistic philosophy and long-term perspective.
Recent studies also classify commitment-oriented or supporting HRM into the
dimensions of motivation, skill formation and empowerment or information sharing
(Appelbaum et al., 2000; Gardner et al., 2001; Wright & Boswell, 2002). The dimension
of motivation is comprised of such practices as life long employment, internal
promotion and profit sharing, and skill formation consists of worker training. Lastly,
empowerment or information sharing is built on communication, workplace
participation or information sharing practices. Although some researchers of HRM do
not set aside work organization as a separate subject but include it in empowerment, it
would be better to set up a separate dimension for work organization as some study call
for an area clearly distinct from HRM. High-performance work systems can be
illustrated as in Figure 1.
<Figure 1> High-performance Work System
Motivation
Skill formation
Informationsharing
High-involvement work organization
Supporting HRM
Task integration
Workplaceparticipation Autonomy
2.2 High-performance work systems and firm training
Studies on the relationship between high-performance work systems and firm
training follow two broad trends. One examines firm training as an element of the high-
performance work systems and the other studies the effect of high-performance work
systems, excluding the element of firm training, on firm training. Most studies on high-
performance work systems consider skills formation as a crucial element, and thus
examine what kind of effect the work system as a whole has on firm performance or
workers. As seen in Figure 1, high-performance work systems consist of non-Taylorized
or high-involvement work organization and supporting HRM, under which skills
formation takes up a dimension.
It was in the mid-1990s when high-performance work systems and firm training
began to be thought of as separate concepts and the effect of the former on the latter
came under scrutiny (MacDuffie & Kochan, 1995; Osterman, 1995; Wagar, 1997;
Lynch & Black, 1998; Frazis et al., 2000; Whitfield, 2000).
Figure 1 classified training as an element of high-performance work systems.
However, system elements may be loosely connected so that the internal consistency
among elements is not automatically guaranteed. For instance, the adoption of high
involvement work organization may not lead to the full-fledged introduction of
supporting HRM, including skills formation, and vice versa. Exploring the relationship
between high-performance work systems and skill formation as a separate dimension is
necessary in order to verify the internal consistency of the system.
Increased interests in examining separately the high-performance work systems
and skill formation stemmed from the need to increase investment in firm training in
order to enhance competitiveness. However, since corporate investment in training is
usually determined by the characteristics of work system, companies eventually face a
practical matter of introducing high-performance work systems to increase investment
in training (Osterman, 1995; MacDuffie & Kochan, 1995). In short, as high-
performance work systems create the demand for high-skilled workers, skill-related
government policies should also change in order to encourage the introduction and
dissemination of high-performance work systems that stimulate such demand.
<Table 1> High-performance work system and firm trainingData Measurement of
trainingMeasurement of
work system Result
MacDuffie &Kochan(1995)
AutomobileAssembly plant(N=57)
Hours of training(Off-JT & OJT)
Team, off-line team,suggestion, jobrotation,responsibility forquality, selectioncriteria, profitsharing etc.
Positive
Osterman(1995)
U.S.A.(N=878)
Proportion of coreworkers trained
Team, TQM, QC,SPC, job rotation,problem-solvinggroup
Positive
Wagar(1997) Canada(N=569)
Num. ofoccupational groupsreceived training ,proportion ofworked trained
Team, QC, QWL,problem-solvinggroup
Positive
Lynch &Black(1998)
U.S.A.(Educational Qualityof WorkplaceNational EmployerSurvey)
Proportion ofworkers trained,types of training
Team, TQM, jobrotation,Benchmarking,number ofHierarchy, span ofcontrol
Benchmarking,TQM, team(positive)
Fraziset al.(2000)
U.S.A.(N=. 1,062)
Training incidence,hours of training,trainingexpenditures
Team, QC, TQM,job rotation, skill-based pay,workplaceparticipation, jobredesign, re-engineering, JIT,peer-rating
Positive
Whitfield(2000)
U. K.(N=657)
Proportion ofworkerstrained*days spentin training
Team, QC, teambriefing, flexibleassignment
QC, team briefing,flexibleassignment(positive)
From a theoretical point of view, the studies on the effect of high-performance
work system on firm training have opened a new horizon in the debates on
skills(Osterman, 1995). While the debates on skills gave rise to a series of trends,
including case studies since Braverman, studies using the DOT, and researches using
corporate information, the debate on “de-skilling” versus “up-skilling” is not over. Thus,
it is worthwhile to identify which work system increase investment in training since it
looks at training in terms of corporate demands. Table 1 shows the summarized review
of literature on high-performance work systems and firm training.
As seen in Table 1, although previous studies on the relationship between high-
performance work systems and firm training focused on the U.S., the U.K., Canada and
other countries, and employed different measurement of high-performance work system
and training indicators, they all resulted in similar outcomes. In sum, high-performance
work system and its elements are factors that demand an increased firm training.
2.3 Hypotheses
As the saying “you leave your head in the dressing room and take only your
hands and feet to the workplace” implies, Taylorized work organizations minimize the
role of workers at the workplace. On the contrary, in high-involvement work
organizations, quality and maintenance tasks previously supervised by specialized
departments are now under the responsibility of workers. In addition, work groups are
in charge of work-related decision-making, which used to be under the exclusive control
of supervisors and managers. Workers also propose ideas and take part in a range of
activities concerning problem-solving and improvements(H. Kim & D. Kim, 2001).
Since high-involvement work organization requires much more input from
workers, workers need to develop their capabilities in order to carry out their roles
effectively. To take charge of maintenance and quality work, for instance, workers need
training in relevant fields as well as multifunctional training to enable flexible work
allocation like job rotation. Furthermore, members of work groups should be provided
training on decision-making process, communication and conflict management, and
managerial work.
If high-involvement work organization is a role structure of workers,
supporting HRM plays a complementary role that provides competence, motivation and
resources (such as information) needed to put such work organization in place. Because
of the complementarity between work organization and HRM, the more deeply
established the involvement-oriented work organization, the more likely for the
supporting HRM comprised of motivation, information sharing and skill formation to be
adopted.
As seen in the HR philosophy of human investment model, the principle of
supporting HRM is grounded on investment in workers. For example, motivation
consists of employment guarantee, relatively high wage and internal promotion, and
information sharing has to do with sharing business and task-related information with
workers. Skill formation, which we categorized as a separate dimension, signifies direct
investment in workers. There is also complementarity among the dimensions of
supporting HRM, with higher motivation or information sharing leading to more
investment in skill formation. Such reasoning supports the hypotheses concerning
corporate investment return in the theories of human capital or internal labor market,
which predicted HRM encouraging long-term employment would increase investment
in firm training (Frazis et al., 2000).
Studies on high-performance work systems focus on the effect of a “system”
of interconnected practices rather than individual practices, given the complementarity
of elements. Complementarity among work practices is observed, for instance, when
performance improvement gained from adopting two practices together is greater than
the sum of improvements gained from adopting the two practices individually (Pil &
MacDuffie, 1996). When complementarity among elements exists, the elements
combined as a system create a greater synergy effect than the sum of individual effects.
Therefore the following hypotheses can be derived from the above discussion.
H1: High-performance work system would increase firm training.
H 1-1: High-involvement work organization would increase firm training.
H 1-2: Supporting HRM would increase firm training.
The variables that could possibly impact corporate investment in training other
than high-performance work systems, such as the firm age, industry, listings on the
stock market, labor unions, competitive pressure, competitive strategy and introduction
of Six-Sigma, have been controlled.
The younger the company, the more likely it is for it to invest in training to
catch up with the skill level of existing companies. In a study by Whitfield (2000), for
example, the older the company, the less it invested in training. However, social
structure and practices of the time tend to be imprinted on the organization at the time of
its founding. The relationship between the age of an organization and training may
differ, depending on the social and institutional environment at the time of the
establishment, discouraging such a definite prediction of the impact of years in
operation.
It is generally predicted that the larger the company, the greater the investment
in training (Jang-soo Ryu, 1997), because larger companies tend to enjoy more slack to
invest in training and the economy of scale related to training. Moreover, growth of
organization is accompanied by increased specialization, which brings about a greater
need for monitoring in order to coordinate the work efficiently. The cost of monitoring
increases concurrently with size, which makes training more appealing than costly
monitoring. In short, the management would find it more tempting to augment
investment in training if internalized monitoring through training turns out to be more
effective than direct monitoring (Scott & Meyer, 1994). However, a word to the wise in
this case is that the relationship between firm size and training investment may be either
linear or non-linear (Knoke & Kalleberg, 1994). Previous studies by Wagar (1997),
Frazis et al. (2000), and Whitfield (2000) demonstrated a positive relationship, but those
by Knoke & Kalleberg (1994), Osterman (1995), Felstead & Green (1996) and J. Ryu
(1997) found no significant relationship.
High capital intensity is a characteristic of heavy industries. When capital
intensity is high, discretionary actions of workers substantially affect performance and
make it harder to monitor worker activities. Therefore, it may be more efficient to rely
on norms through training and autonomy of skilled workers. Heavy industries, therefore,
would invest in training more heavily than light industries. Multivariate analyses
indicated that result controlling 3-digit manufacturing industry showed similar outcome
compared with heavy industry dummy variable without controlling for 3-digit industry
dummy variables.
Listed companies or KOSDAQ-registered public corporations may also show
different levels of investment in training as compared to unlisted companies. Public
corporations are considerably exposed to governmental or public monitoring, thus very
likely to implement mandatory training. If the overall social atmosphere appears to
favor firm training, public companies would probably feel the normative pressure. On
the other hand, although the stock market may apply different degrees of pressure on
companies, public corporations may invest little in training, because the stock market is
generally skeptical about substantially uncertain investment in training.
Given our keen interest in the skill formation projects led by the labor-
management partnership, the relationship between labor unions and training investment
is a crucial research subject. However, both theoretical and experimental studies on the
effects of labor unions on corporate investment in training demonstrate a variety of
contradicting arguments and research findings. Labor unions may enhance investment
in training, for they reduce the turnover rate of workers by providing a collective voice
rather than exit. On the other hand, however, very likely promotion of unqualified
candidates allowed under the seniority-based scheme of labor unions will cut down on
firms’ incentive to invest in training (Freeman & Medoff, 1984; Knoke & Kallberg,
1994). In the meantime, Smith & Dowling (2001) hypothesized that the degree of
union-employer partnership and corporate investment in training are in an inverse U-
shaped relationship. This hypothesis is based on the reasoning that when the degree of
partnership between the labor and management is either very low or very high, attention
is placed more on the overall relationship rather than on the training of individual
workers.
Osterman (1995) found a significant positive correlation between labor unions
and firm training. However, Ryu (1997) in Korea and Whitfield (2000) in the U.K.
detected no significant relationship among the variables. It appears that labor union’s
policy on training is more important than the presence of unions itself, and the union’s
training policy is closely linked to the overall labor-management history of a given
country and the current environment.
The variable of product market is also a vital factor in training investment.
Fiercer competition in the product market could lead to increased investment in training
to strengthen organizational capability. As Keenoy (1995) and Osterman (1994) pointed
out, however, the reverse could be observed when control-oriented management style
takes hold in response to intense competition. Therefore, competitive strategies, rather
than the mere intensification of competition, has more impact on training investment.
Researchers maintain that the introduction of high-performance work systems in the
U.S. is greatly influenced by quality strategies (Cappelli et al., 1997; Lawler et al.,
1998). Furthermore, according to strategic HRM, investment in training is more likely
to jump when the management steadily pursues high road strategies based on quality,
diversity and speed, rather than low-cost strategy.
Six-Sigma is a recently adopted quality management tool. When Six-Sigma is
introduced as a quality management tool, training concerning quality management will
expand. A study by Felstead & Green (1996) could provide explanation for the effect of
Six-Sigma on training. They found that the acquisition of BS 5750 or ISO certificates,
outside control and changes in training features explained the unexpected phenomenon
of increased training in the companies suffering from a recession-induced fall in
revenue (N=27). Acquisition or maintenance of industry standards requires investment
in training and some companies or clients request standard certificates as a condition of
transaction. Therefore, companies that obtained such standard certificates can inject a
great sum of money in training. Six Sigma is expected to generate similar effect the one
observed in Felstead & Green’s (1996) study on standard acquisition.
3. Data and Measurement
3.1 Data
This study is based on the data of the Workplace Panel Survey (WPS)
conducted in 2002 by the Korea Labor Institute and utilizes training-related data from
the employment insurance database and corporate financial data from NICE Credit
Bureau.
The WPS data was analyzed by combining questionnaires for HR managers
(N=1,395) and labor affairs. The questionnaires for labor affairs were filled out by labor
affairs managers at workplaces together with worker representatives. This study first
matched HR managers response with the labor affairs questionnaires (N=1,245), and
only the representatives’ responses were included (N=73) from the workplaces where
labor affairs managers were not available for the survey. The remaining 77 workplaces
submitted responses only from HR managers.
Subjects of this study were limited to manufacturers, because, except for a few
customized survey questions, the tools to measure the work systems of non-
manufacturing industries were not fully developed, and the same limitation existed in
the data used in this study. Actually Cappelli & Newmark (2001) revealed that high-
performance work practices had completely different meanings for the manufacturing
and service industries. So, this study is confined to manufacturers because the study
employed the conventional work system measurement items developed by the
manufacturing industry. Originally there were 691 respondent manufacturers, but only
598 manufacturers were included in the final analysis, as 35 did not answer the question
that asked for the number of production workers and 38 companies answered “0” to the
same question. Out of the 598 workplaces, 529 included labor affairs management
questionnaires from labor affairs managers and 31 from worker representatives.
Therefore, the valid sample size was 560 workplaces, and the sample used in the final
analysis, excluding missing observations, totaled 554.
In order to add information on training investment, data on the 2001 Vocational
Capability Development Training were extracted from the employment insurance
database. The extracted data included the amount of insurance refund generated by the
implementation of the 2001 Vocational Capability Development Training(including paid
training leave) and the annual count of beneficiaries. Since the sample population of the
WPS coincided with that of the employment insurance database, matching of two sets
of data did not create any problems other than few minor adjustments. In addition,
corporate financial data from NICE Credit Bureau were utilized to obtain another data
on training expenditure.
3.2 Measurement
3.2.1 Dependent variables
Measurement of training investment varies by researchers. Osterman (1995)
measured the percentage of core workers receiving official training, and MacDuffie &
Kochan (1995) combined the OJT and Off-JT hours of newly recruited employees and
workers with more than one year of job experience. Ryu (1997) calculated the
proportion of official training investment by adding the training investment costs from
the balance sheets and manufacturing cost invoices. The problem with this measurement,
however, is that training cost is not a mandatory item on the financial statement.
Whitfield (2000) estimated the training figures by multiplying the percentage of
workers who completed official training courses over the last year by the hours. Frazis
et al. (2000) used three measurements – provision of official training, training hours and
training costs.
It is a well-known fact that accurate data on training investment is quite lacking.
Given the absence of reliable data, this study quantified the degree of training
investment into training expenditure, training coverage and annual training hours by
using different data sources. The training expenditure and the coverage data were
derived from the employment insurance database. Insurance premium refunded through
the 2001 Vocational Capability Development Training, including the paid training leave,
and the number of annual beneficiaries were divided by the total number of workplace
employees at the beginning of 2002.
Training investment data from the insurance database could also be limited, so
the training investment amounts recorded in the corporate financial statements obtained
from the NICE Credit Bureau were divided by the total number of employees at firm in
early 2002 to arrive at the per capita training expenditure. This variable is named
training expenditures1(See Table 2). However, this data is also problematic in that
financial data can be disclosed only to outside auditors and even they very seldom
record the amount of training investment in both the balance sheet and the
manufacturing cost invoice, substantially shrinking the number of usable cases.
Moreover, training investment is not a mandatory item on the financial statement and
could be classified under a different item to suit the circumstance of each company, so
the data does not accurately reflect the status of training investment. Therefore the
training expenditure1 obtained from the financial statements was used for the sole
purpose of comparing the result with that gained from the employment insurance
database.
Lastly, hours of training per person was created by using the number of the Off-
JT and OJT beneficiaries in 2001 and the yearly per capita training duration. This data
is drived from WPS. Hours of training index was calculated by dividing the number of
workers that received the Off-JT in 2001 by the number of employees in early 2002, and
then multiplying it by the day of training. The same calculation was applied to obtain
the duration of the OJT and finally hours of training calculated by summing up both
Off-JT and OJT.
The distribution of investment variables was skewed so the logarithmic series
was used in analysis. There were many instances where training investment amount and
beneficiary rate were “0” so the value of 1 was added to each figure and then the
logarithm was taken. In this case, if the value of training investment originally “0,” it
would still retain the value of “0” even after the transformation.
3.2.2 Independent variables
Supporting HRM was measured with motivation and information sharing. In
measuring motivation, 5-point scales were used to identify the strictness of selection
and the wage level, and the value of 1 was given when HR merit rating system and at
least one out of profit sharing or other group performance pay or employee stare
ownership were adopted. One point was also allotted when employment was guaranteed
by refraining from employee downsizing, such as layoffs or early retirement, after the
economic crisis. Motivation was quantified by combining these standardized variables.
Information sharing was measured by adding up the indices for the presence of business
briefings, newsletters with management information, hotline between the workers and
the management, and team briefing. The addition of standardized values of motivation
and information sharing items made up the supporting HRM index. The internal
consistency of the HRM index was α=.6124.
High-involvement work organization was measured by averaging the indices of
work group autonomy, task integration and workplace participation. Autonomy of work
group is a composite index of dummy variables, which were given one point when
answers were either “somewhat autonomous” or “completely autonomous” in regards to
work fare, work method, and work speed(each on a 5-point scale). Task integration was
obtained by adding one dummy variable, which was given the value of 1 when
production workers are responsible for quality, and another dummy variable that earned
one point when production workers were rotated. Workplace participation consisted of
variables that each received one point when the percentage of its production workers
participating in small group activities stood at 50% or more and when workers were
actively making suggestions. The standardized values of autonomy, task integration and
workplace participation were added up to obtain the work system index. The internal
consistency of the index registered α=.5963.
The age of workplace was calculated by subtracting the year of establishment
from 2002. The size of workplace was estimated from the number of employees and the
logarithm of it was used. Heavy industry dummy followed the categorization of Jeong
(1999), giving the value of 1 to manufacturing industry codes 23-24, 27, 28-35, and 371.
The dummy variable of labor union received one point when labor unions existed.
Competition intensification was identified by the factor score (α=.6122) of “the number
of competitors,” “changes in existing products or services,” “new product
development ,” “product demand,” and “the importance of quality” (5-point scale) in
the core product or services market during the last three years. The characteristics of
core products were categorized into low cost, quality, variety, speed and technological
edge (each on a 5-point scale) for the competitive strategy variable. These
characteristics were put through a factor analysis to yield two factors. Four items except
for low cost were classified as the first factor, which can be interpreted as high-road
strategy(α=.7352). Adoption of Six-Sigma was a dummy variable with the value of 1.
Descriptive statistical figures of these variables are presented in Table 2.
<Talbe 2> Means and Standard DeviationN Mean S.D.
Training Expenditures(1,000 Won) 552 36.06 237.54Percentage of Workers Trained 552 0.27 1.35Hours of Training(day) 498 4.43 10.45Training Expenditures1(1,000 Won) 189 237.70 343.06Work System Index 384 0.03 0.48
HRM Index(α =.6124) 468 0.01 0.49Motivation 477 0.01 0.49
Selective hiring 541 2.42 1.07Personnel rating 512 0.50 0.50Wage level 534 3.05 0.78Profit sharing 539 0.33 0.47Employment security 519 0.63 0.48
Information sharing 544 2.13 1.17Business briefing 545 0.66 0.47News letter 551 0.20 0.40Hot line 552 0.50 0.50Team briefing 552 0.78 0.42
Work Organization Index(α =.5963) 433 0.04 0.66Work Group Autonomy 517 1.05 1.19
Work fare 520 0.33 0.47Work method 519 0.36 0.48Work speed 520 0.37 0.48
Workplace Participation 516 0.77 0.77QC 521 0.22 0.42Suggestion 527 0.56 0.50
Task Integration 460 0.78 0.70Responsibility for quality 466 0.52 0.50Job rotation 532 0.23 0.42
Establishment Age 543 20.92 14.70Num. of Employee 554 565.18 2797.80Heavy Industry 554 0.59 0.49Listed Company 554 0.21 0.41Trade Union 554 0.37 0.48Competitive Pressure(α =.6122) 515 0.00 1.00High-road Strategy(α =.7352) 518 0.00 1.00Six-Sigma 533 0.17 0.38
4. Results
4.1 The status of investment in training
The descriptive statistics in Table 2 and correlations in Table 3 reveal the status
of how much investment is being made in training.
Under the assumption that no refund of insurance premium implies non-
implementation of paid training leave and other vocational capability development
training, 369 (66.85%) out of 552 companies were found to have invested in training in
2001 and the rest did not conduct any training. Annual per capita training investment
estimated from the refund amounted to an average of 36,000 won.
The investment amount may be considerably underestimated than the actual
training investment made by the companies, because, even when the companies poured
money into training, they were omitted from the analysis if employees did not get their
employment insurance refund for various reasons or if grants or other types of
investment were not included in the training investment category. For instance, Table 2
shows the annual per capita training expenditures1 in the corporate financial statements
standing at 237,000 won. Therefore, if we base our analysis on the financial statement
figures, the amount of employment insurance refund accounts for only 15.17% of the
actual corporate investment in training.
The annual benefit receipt rate of paid training leave and other vocational
capability development training posted 27%. The training coveragee in the employment
insurance database is derived from the annual number of beneficiaries, which could lead
to one person being counted twice, so the data should be interpreted with caution. The
annual hours of training per person measured by questionnaires was 4.43 days,
including Off-JT and OJT, which somewhat exceeded the findings of other studies in
Korea (Kim, 2000; Kim & Roh, 2002).
Three sources used to measure the level of investment in training each have
limits. Correlation among the training investment indices from three data sources (see
Appendix 1) all revealed positive relationships that were significant at 99% level,
except for the correlation between the hours of training obtained from questionnaires
and the training expenditures1 from financial statements. Therefore, each measurement
was found to have the minimum level of reliability, but it is difficult to identify which
data source most accurately reflects the degree of investment in training.
<Table 3> Correlation (N=336)1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
2 0.78
3 0.39 0.35
4 0.37 0.31 0.34
5 0.36 0.29 0.38 0.74
6 0.28 0.22 0.33 0.68 0.94
7 0.33 0.32 0.29 0.47 0.58 0.28
8 0.27 0.22 0.21 0.87 0.32 0.28 0.24
9 0.07 0.03 0.08 0.50 0.12 0.10 0.08 0.62
10 0.35 0.30 0.30 0.68 0.36 0.30 0.29 0.70 0.16
11 0.11 0.10 0.03 0.54 0.16 0.15 0.09 0.64 0.05 0.20
12 0.24 0.22 0.16 0.04 0.00 -0.04 0.08 0.06 0.04 0.11 -0.03
13 0.46 0.34 0.23 0.30 0.33 0.24 0.35 0.19 0.00 0.26 0.10 0.40
14 0.20 0.07 0.19 0.13 0.05 0.05 0.03 0.15 0.12 0.07 0.09 -0.11 0.04
15 0.22 0.17 0.15 -0.01 0.02 -0.01 0.07 -0.02 -0.02 0.00 -0.02 0.32 0.29 0.01
16 0.32 0.32 0.13 0.15 0.15 0.12 0.16 0.10 0.04 0.18 -0.03 0.42 0.51 -0.09 0.21
17 0.07 0.08 0.18 0.32 0.26 0.23 0.18 0.27 0.13 0.19 0.20 -0.01 0.12 0.06 0.08 -0.02
18 0.05 0.02 0.06 0.25 0.25 0.24 0.14 0.17 0.06 0.14 0.13 -0.04 0.12 -0.09 0.04 -0.04 0.37
19 0.25 0.19 0.15 0.25 0.20 0.14 0.24 0.20 0.07 0.24 0.09 -0.04 0.24 0.08 0.02 0.16 0.12 0.18
Notes: 1). Correlation greater than |.14| are significant at .01; r’s greater than |.11| are significant at .05. 2) 1=Training expenditures, 2=Percent of workers trained, 3=Days of training, 4=High-
performance work system, 5=Supporing HRM, 6=Motivation, 7=Infoamtion sharing, 8=High-involvement work organization, 9=Work group autonomy, 10=Workplace participation,11=Task integration, 12=Establishment age, 13=Establishment size, 14=Heavy industry,15=Listed company, 16=Trade Union,17=Competitive pressure, 18=High-road strategy, 19=Six-sigma
As shown in Table 3, the three indices of investment training are significantly
and positively correlated with: the high-performance work system indicator and its sub-
dimensional indicators of supporting HRM and high-involvement work organization;
motivation and information sharing, the sub-dimensions of high-involvement HR
management; and workplace participation from the sub-dimensions of high-
involvement work organization. The correlation coefficient of supporting HRM and
high-involvement work organization stood at 0.32 (p<.01), thus the effect of high-
involvement work organization without supporting HRM was also examined in the
multivariate analysis.
Contrary to the prediction that new companies would make a considerable
investment in training to catch up, the age of organization showed a positive
relationship with the amount of training investment. As projected, the correlation
between organization size and investment in training was positive one as was the
correlation between the dummy variables of heavy industry and Six-Sigma and training
investment.
Meanwhile, the unpredictable direction of the relationship between training
investment and two variables of public corporations and labor unions turned out to be a
positive one. The variables of competition intensification and competitive strategy show
positive yet very low correlation coefficient, while the correlation between these
variables and those of high-performance work system were quite high. Therefore, the
variables of competition intensification and competitive strategy appear to have fostered
corporate investment in training through high-performance work systems, which merit
future studies.
4.2 Factors influencing the degree of investment in training
Tobit analysis was used to estimate a coefficient since the training investment
indicator oftentimes recorded “0” when the amount of investment in training on the
financial statements was taken out. Hence, linear regression analysis (OLS) was used to
analyze training expenditures1, which is not affected by the value of “0.” (see appendix
2). Although not reported in this paper, OLS yielded results similar to those by Tobit
analysis.
Model 1 is the result high-performance work system index together with control
variables and Model 2 shows the result of injecting supporting HRM index and high-
involvement work organization index, the two sub-dimensions of high-performance
work systems. Model 3 presents the outcome of putting in only the work organization
index, taking into consideration the correlation between the supporting HRM and high-
involvement work organization. Model 4 had the sub-dimensions of supporting HRM
and high-involvement work organization instead of those indices themselves, and
Model 5 had only the sub-dimensions of high-involvement work organization, not those
of supporting HRM.
<Table 4> High-performance work system and Training Expenditure (Tobit)
M1 M2 M3 M4 M5
-2.510*** -2.383*** -2.496*** -3.058*** -2.781***Constants(0.534) (0.535) (0.508) (0.534) (0.506)0.012 0.013 0.006 0.012 0.005Age
(0.009) (0.009) (0.008) (0.009) (0.008)0.568*** 0.532*** 0.561*** 0.492*** 0.531***Size(0.110) (0.111) (0.106) (0.112) (0.107)
0.713*** 0.743*** 0.846*** 0.757*** 0.861***Heavy industry(0.231) (0.231) (0.223) (0.229) (0.222)0.501* 0.488* 0.546** 0.511* 0.589**Listed company(0.272) (0.271) (0.271) (0.268) (0.269)0.305 0.318 0.471* 0.310 0.466*Trade Union
(0.267) (0.266) (0.263) (0.264) (0.261)-0.126 -0.120 -0.062 -0.119 -0.059Competitive
Pressure (0.119) (0.118) (0.116) (0.117) (0.116)-0.093 -0.110 -0.036 -0.094 -0.028High-road strategy(0.125) (0.125) (0.122) (0.124) (0.121)
0.850*** 0.852*** 0.961*** 0.740** 0.878***Six-Sigma(0.293) (0.291) (0.288) (0.293) (0.288)
1.184***HPWS(0.263)
0.981***HRM(0.259)
0.608**Motivation(0.243)0.251**Information
sharing (0.106)0.374** 0.455***WO(0.181) (0.170)
0.024 0.053Work groupautonomy (0.093) (0.091)
0.440*** 0.496***Workplaceparticipation (0.154) (0.147)
0.013 0.021Taskintegration (0.156) (0.153)
Log L -589.43*** -587.91*** -672.47*** -584.78*** -669.65***N 358 358 401 358 401
Note: 1) Standard Errors in Parentheses 2) *p<.1, **p<.05, ***p<.01 (two-tailed)
<Table 5> High-performance work system and Training Coverage (Tobit)
M1 M2 M3 M4 M5
-0.523*** -0.508*** -0.514*** -0.643*** -0.569***Constants(0.101) (0.102) (0.094) (0.102) (0.094)0.001 0.001 0.000 0.001 0.000Age
(0.002) (0.002) (0.002) (0.002) (0.002)0.092*** 0.087*** 0.089*** 0.080*** 0.084***Size(0.021) (0.021) (0.020) (0.021) (0.020)0.039 0.043 0.062 0.045 0.064Heavy industry
(0.044) (0.044) (0.041) (0.044) (0.041)0.067 0.066 0.065 0.069 0.072Listed company
(0.051) (0.051) (0.050) (0.051) (0.050)0.093* 0.095* 0.110** 0.095* 0.110**Trade Union(0.050) (0.050) (0.048) (0.050) (0.048)-0.011 -0.010 -0.004 -0.011 -0.003Competitive
Pressure (0.023) (0.023) (0.022) (0.022) (0.022)-0.020 -0.022 -0.009 -0.019 -0.008High-road strategy(0.024) (0.024) (0.023) (0.024) (0.023)0.130** 0.130** 0.149*** 0.110** 0.137***Six-Sigma(0.055) (0.055) (0.053) (0.055) (0.053)
0.204***HPWS(0.050)
0.151***HRM(0.049)
0.084*Motivation(0.046)0.049**Information
sharing (0.020)0.075** 0.079**WO(0.034) (0.031)
0.010 0.010Work groupautonomy (0.018) (0.017)
0.069** 0.079***Workplaceparticipation (0.029) (0.027)
0.013 0.011Taskintegration (0.030) (0.028)
Log L -164.02*** -163.37*** -187.12*** -161.06*** -185.31***N 358 358 401 358 401
Note: 1) Standard Errors in Parentheses 2) *p<.1, **p<.05, ***p<.01 (two-tailed)
<Table 6> High-performance work system and hours of training (Tobit)
M1 M2 M3 M4 M5
-0.921** -0.718* -1.500*** -1.302*** -1.673***Constants(0.444) (0.434) (0.434) (0.440) (0.430)0.015** 0.018** 0.014** 0.017** 0.012*Age(0.007) (0.007) (0.007) (0.007) (0.007)0.111 0.049 0.240*** 0.022 0.208**Size
(0.094) (0.093) (0.092) (0.094) (0.091)0.643*** 0.692*** 0.627*** 0.715*** 0.652***Heavy industry(0.184) (0.180) (0.179) (0.178) (0.176)0.431** 0.422** 0.319 0.432** 0.360*Listed company(0.217) (0.211) (0.217) (0.207) (0.212)0.029 0.004 0.090 -0.033 0.052Trade Union
(0.216) (0.210) (0.213) (0.207) (0.209)0.165* 0.160* 0.238*** 0.168* 0.238***Competitive
Pressure (0.094) (0.092) (0.092) (0.090) (0.090)-0.072 -0.101 0.012 -0.083 0.024High-road strategy(0.101) (0.099) (0.099) (0.098) (0.096)0.244 0.270 0.219 0.170 0.110Six-Sigma
(0.237) (0.230) (0.237) (0.229) (0.232)1.133***HPWS(0.214)
1.202***HRM(0.204)
0.789***Motivation(0.190)
0.264***Informationsharing (0.082)
0.215 0.391***WO(0.139) (0.137)
0.024 0.030Work groupautonomy (0.071) (0.071)
0.384*** 0.558***Workplaceparticipation (0.117) (0.115)
-0.160 -0.119Taskintegration (0.122) (0.122)
Log L -451.23*** -444.48*** -515.96*** -438.518** -500.75***N 336 336 374 336 374
Note: 1) Standard Errors in Parentheses 2) *p<.1, **p<.05, ***p<.01 (two-tailed)
The results indicate that high-performance work systems had a significant
positive correlation with the three indicators of training investment – training
expenditures, training coverage and hours of training – as they did with the degree of
training investment in financial statements (see Appendix 2), supporting Hypothesis 1.
Hypothesis 1-2 is also substantiated by the fact that the supporting HRM index,
a sub-dimension of high-performance work system, as well as its elements of
motivation and information sharing were positively and significantly correlated with all
three indices of training investment.
High-involvement work organization index, the core of high-performance work
systems, is in a significant positive relationship, except with the hours of training per
worker and training expenditures1 in financial statements. When supporting HRM is
omitted from the analysis, it shows a significant positive correlation with the per capita
hours of training and training expenditures1 in financial statements as well, generally
supporting Hypothesis 1-1. As seen in the correlation table, workplace participation, an
indicator of workers participation in developmental activities at workplace, displayed
the strongest relationship with training investment out of all other elements of high-
involvement work organization.
The coefficients of other factors were mostly similar in results to those of
correlation analysis. However, the regression coefficient of competition intensification
and competitive strategy often showed negative, though statistically not significant. As
mentioned in the correlation analysis, since the variable of competition intensification
and competitive strategy could impact training investment through the work system, a
method such as two-stage regression analysis could be employed to deduce the effect.
5. Summary
Based on the hypothesis that high-performance work system is a demand factor
for workforce skills in companies, this study analyzed the effects of high-performance
work practices on firm training. The results showed that high-performance work system
and its elements of supporting HRM and high-involvement work organization increased
corporate investment in training. This finding is similar to other studies that looked into
the relationship between high-performance work systems and firm training.
Corporate investment in training is one dimension of supporting HRM, which
is the sub-dimension of high-performance work system. Therefore it would be more
reasonable to interpret the link between high-performance work system and training
investment as a co-variance, rather than a causal one.
In should also be noted that high-performance work system requires not only
task-related skills but also social skills such as problem solving or self-governance,
suggesting that workers need to be trained in these skills as well.
Although this study is about the demand aspect of firm training, it also makes
suggestions on the supply of skills in general. The overall supply of high-quality skills
required by high-performance work systems would expedite the introduction of those
systems. Given the nature of skills as quasi-public goods, high-performance work
systems do not have enough enticements for individual companies because they require
investments in workforce skills. Therefore, individual companies would find more
incentives to adopt high-performance work systems when high-quality skills are
provided by the society.
The policy suggestions derived from the analysis could be summarized into the
following three points.
First, government policies promoting firm training should be driven in
conjunction with the demand factors of work system. The introduction of high-
performance work systems could prove to be more effective in promoting firm training
than any incentive or regulation. The contents of employee training should also cover
not only task-related skills, but also social skills or self-governing skills. In step with
changes in the nature of work brought about by the replacement of machinery with
electronic equipment, basic education on electronics should be strengthened as well.
Second, tripartite partnership among workers, employers and the government is
needed to supply skills to the society. The supply of high-quality skills would indeed
expedite the introduction of high-performance work systems, which in turn would
increase corporate investment in training. This would ultimately lead to a virtuous cycle
of skill formation in the society. Besides enhancing corporate competitiveness, high-
performance work systems would also raise the quality of working life, resulting in a
win-win game for both the labor and the management. Thus, tripartite dialogues at the
national, regional or by-industry level on high-performance work systems and
workforce skills should be further encouraged.
Third, data on training need to be further developed. As pointed out in this
study, data obtained from the employment insurance database, corporate financial
statements and survey questionnaires all have limitations as measurements of corporate
investment on training.
Although this study attempted to identify the demand factors of firm training, it
came short of analyzing them completely. The demand factor of high-performance work
systems, which in turn is a demand factor of firm training, may be a corporate
competitive strategy in the product market. Researches on the sequential relationship
among corporate competition strategies, high-performance work systems and firm
training are needed in the future.
Regarding the assumption that social supply of skills would accelerate the
introduction of high-performance work systems, researches into the relationship
between training and high-performance work systems should be conducted on the
corporate level. For instance, companies that make heavy investment in employee
training may show higher high-performance work systems adoption rate. Such likely
hypothesis should be verified by using longitudinal data at corporate level.
※ The author welcomes any use of this material provided the source is acknowledged. Nothing
written here is to be construed as necessarily reflecting the views of the Korea Labor Institute.
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<Appendix 2> High-performance work system and Training Expenditures(OLS)
M1 M2 M3 M4 M5
3.645*** 3.719*** 3.421*** 3.044*** 3.006***Constants
(0.523) (0.511) (0.531) (0.529) (0.524)0.002 0.001 0.001 0.001 0.000
Age(0.009) (0.009) (0.008) (0.009) (0.008)0.090 0.040 0.155 0.033 0.152
Size(0.098) (0.098) (0.099) (0.100) (0.098)0.008 0.102 0.044 0.098 0.021
Heavy industry(0.228) (0.225) (0.231) (0.226) (0.228)0.110 0.174 -0.045 0.201 0.034
Listed company(0.224) (0.220) (0.226) (0.220) (0.223)0.377 0.503** 0.352 0.447* 0.299
Trade Union(0.246) (0.244) (0.251) (0.247) (0.249)0.123 0.105 0.200 0.107 0.218*Competitive
Pressure (0.122) (0.119) (0.123) (0.121) (0.122)-0.059 -0.055 -0.073 -0.044 -0.055
High-road strategy(0.130) (0.127) (0.130) (0.127) (0.128)0.026 0.117 0.126 0.042 0.006
Six-Sigma(0.267) (0.263) (0.278) (0.267) (0.276)
1.144***HPWS
(0.234)1.136***HRM(0.239)
0.803***Motivation(0.236)0.224**Information
sharing (0.110)0.223 0.424**WO
(0.173) (0.165)0.111 0.122Work group
Autonomy (0.098) (0.099)0.243 0.483***Workplace
Participation (0.153) (0.143)-0.083 -0.095Task
integration (0.149) (0.152)F 5.27*** 5.71*** 3.08*** 4.62*** 3.26***
Adj. R2 .224 .262 .116 .262 .148N 134 134 144 134 144
Note: 1) Standard Errors in Parentheses 2) *p<.1, **p<.05, ***p<.01 (two-tailed)
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