 Title: the strategic use of QM practices for organizational learning and
business performance
 Author: Seok-Young Oh
 Author’s current affiliation: Korean Research Institute for Vocational
Education and Training(KRIVET)
 Address: Korea Research Institute for Vocational Education and
Training(KRIVET), Room# 407B, 15-1, Chungdam-Dong, Kangnam-Gu,
Seoul, Korea, 135-949
 Author’s current email address: ohsey22@gmail.com
 Stream: KM, the Learning Organization/Organizational Learning and
HRD in 2020
 Submission type: Refereed paper
1
Abstract
This study investigates the relationships of two distinct sets of quality management
practices (QM) called core QM practices and infrastructure QM practices, with two
organizational learning (OL) elements called learning stocks and learning flows
conceptually proposed by Crossan and Hulland (1997). This study also examines the
mediation effect of OL elements on business performance in Korean industrial
manufacturing settings. This study theoretically develops a conceptual model with 7
hypotheses regarding how these two different types of QM approaches influence
learning accumulations and learning transfer processes and how the organizational
learning activities meditate between the QM practices and business performance.
To examine the hypotheses, all manufacturing firms listed in the Korea Composite Stock
Price Index (KOSPI) of the Korea Stock Exchange (KSE) are utilized and of the 453
manufacturing firms listed, 206 firms participated.
In the hypothesized model, this study found that infrastructure QM practices have strong
positive relationships with learning stocks and core QM practices have positive
relationship with learning flows. Moreover, only learning flows influenced by core QM
practices and learning stocks are significantly associated with business performance.
Based on the results, this study concludes that the effects of both core QM practices and
infrastructure QM practices on OL activities are complementary when knowledge
accumulated by members is freely transferred to other members and groups or
throughout organizations.
Keywords: quality management practices, organizational learning, learning stocks,
learning flows, business performance
2
1. Introduction
Popular interest in organizational learning (OL) has been demonstrated in the past
several decades. The concept of organizational learning has been considered to be one
of the fundamental strategic processes that extends organizational competitiveness
(DeGeus, 1988), leads to organizational innovation (Stata, 1989), and responds to
dynamic business circumstances (Fiol & Lyles, 1985).
Although growing in popularity, the concept of organizational learning has been
understood in a variety of ways due to its teleological, ontological, and methodological
ambiguity (Easterby-Smith, Snell, & Gherardi, 1998) and its varied disciplinary
background (Argyris & Schon, 1978; Easterby-Smith, 1997). In an effort to decrease the
conceptual diversity of why organizational learning is necessary, who learns, and how to
implement OL, some scholars explain this concept with a strategic management
perspective.
A strategic management perspective focuses on how firms achieve and sustain
competitive advantage (Teece, Pisano, & Shuen, 1997) and views firms’ resources as
strengths that can be used to implement strategy (Barney, 1991). From this perspective,
organizational learning is considered to be a dynamic process based on heterogeneous
resources, which implies retaining, transferring, and integrating varied ideas and actions
of an organization’s members for its competitiveness (Lei, Hitt, & Bettis, 1996).
Crossan and her colleagues have a parallel insight for defining the concept of
organizational learning. They propose that organizational learning is “a principal means
of achieving the strategic renewal of an organization” (Crossan, Lane, & White, 1999, p.
522) and emphasize two essential dimensions underlying its conceptualization: learning
stocks and learning flows (Crossan & Hulland, 1997; Crossan et al., 1999; Bontis,
Crossan, & Hulland, 2002). Learning stocks refer to learning outcomes generated from
changes in cognitions and behaviors, which are respectively stored at individual, group,
and organizational levels. In comparison, learning flows refer to the process of learning
outcomes moving from either the individual to the group and then to the organizational
levels or vice versa. Thus, learning stocks include knowledge and actions within a level,
while learning flows facilitate interactions between knowledge and actions. As a result
new knowledge and actions are created or applied across the three levels.
Both learning stocks and flows are regarded as a key resource for strategic renewal
(Crossan & Berdrow, 2003) as well as a competitive advantage of an organization
(Prieto & Revilla, 2006). Strategic renewal is achieved when firms appropriately maintain
a balance between creating new knowledge and exploiting existing knowledge (Crossan
& Berdrow, 2003; March, 1991). In order to more effectively gain strategic renewal, it is
necessary to strategically align learning resources with organizational goals.
Misalignment between learning stocks and learning flows leads to decreased employees
motivation, thereby decreasing a firm’ capability to learn how to deal with external
changes (Bontis et al., 2002).
In regards to the implementation and facilitation of organizational learning, some
efforts have been made to link other relevant resources or competencies to
organizational learning. Crrossan and Vera (2004) argue that learning capability is
improved more in the strategic context of the organization, where the constructs of
learning stocks and learning flows are combined with other resources of an organization
3
according to their strategic direction.
Recently, quality management, which is popularly implemented in manufacturing and
service firms, has been contended as particularly relevant for the promotion of learning
in business settings (Choo, Linderman, & Schroeder, 2007a, 2007b; Linderman,
Schroeder, Zaheer, Liedtke, & Choo, 2004; Ruiz-Moreno, Morales, & Llorens-Montes,
2005).
Quality management (QM) is defined as a management approach which aims to
“achieve and sustain high quality output, focusing on the maintenance and continuous
improvement of processes and defect prevention at all levels and in all functions of the
organization in order to meet or exceed customer expectations” (Flynn, Schroeder, and
Sakakibara, 1994, p. 342). Due to continuous improvement and change-oriented
aspects inherent in the QM definition, quality management is considered as
management practices that facilitate other management practices such as organizational
learning (Ruiz-Monreno et al., 2005). Moreover, commonly accepted specific behaviors,
so-called QM practices (e.g. process management, information and analysis, customer
focus, people management, and leadership) (Powell, 1995; Samson & Terziovski, 1999)
provide learning opportunities for people to develop their competencies in various ways
(Chilie & Choi, 2000).
In fact, many QM researchers propose the strategic use of QM practices within
organizations (Flynn, Schroeder, & Sakakibara, 1994; Sousa & Voss, 2002). They
classify QM practices into two groups: core QM practices and infrastructure QM
practices. The former are relevant to structured methods and systematic techniques
specifically related to quality (e.g. process management and information analysis) while
the latter are related to socio-environmental practices which support effective
implementation of core practices and increase an employee’s commitment to promote
their overall quality-related activities (e.g. leadership, customer focus, and people
management). Quality management requires not only analytical tools and standardized
procedures to increase work efficiency, but also requires contextual elements which lead
to physiological safety and work commitment, thereby effectively improving quality
(Choo et al., 2007b). Choo and his colleagues (2007a, 2007b) argue that independent
implementation of each group of QM practices leads to different intended outcomes
within organizations. Due to particular characteristics of core QM practices and
infrastructure QM practices, their strategic uses enable a firm to operate its other
relevant resources in accordance with environmental conditions.
Indeed, the relationship between quality management and organizational learning
has received much attention both in business and academic fields. In the relevant
literature, two different perspectives concerning the relationship have been proposed.
The first perspective explains why organizational learning is important for firms’
implementation of QM. Some QM scholars argue that the concept of learning is
embedded in quality practices. They believe that learning is a means to develop
organizational capabilities by helping to identify customers’ needs by adding a unique
value to products which is difficult to imitate (Chiles & Choi, 2000; Hackman & Wageman,
1995). In other words, organizational learning in quality practices is regarded as a
means which enables firms to explore new markets, hence contributing to their
competitive advantage (Crossan, et al., 1999; Ruiz-Moreno et al., 2005; Sitkin, Sutcliffe,
& Schroeder, 1994).
4
The second perspective explains why some learning activities are more effective
across quality management processes. QM practices provide a good learning
environment both by mitigating anxiety aroused from market changes and by supplying
a rich and diverse set of learning tools. Infrastructure QM practices encourage people to
think creatively, to develop common knowledge about customer needs, and to align that
knowledge with their business strategy, while core QM practices provide learners with
stable and necessary learning tools to help them clearly understand what they learn as
well as to share it without distortion (Hackman & Wageman, 1995).
Thus, this study explores how organizational learning elements play mediating roles
in the relationship between quality management practices and business performance.
Since organizational learning activities are closely embedded in QM practices and, in
turn, are the principal means by which QM practices affect business performance,
organizational learning meditates the relationship between QM practices and
performance.
Given these theoretical assumptions about this relationship, it is meaningful to
identify how the two distinctly different concepts of QM practices influence learning
stocks and learning flows and how these two organizational learning activities influence
business performance. This study will identify these relationships in a Korean
manufacturing industry setting. In cross-cultural studies, some scholars argue that the
different economic structures and different cultural backgrounds of countries could lead
to different ways of implementing quality management (Tan & Khoo, 2002) or different
styles of organizational learning (Tsang, 1997). Thus, some aspects of business
behavior emphasized more in Korea (e.g. social relations and harmony (Hofstede, 2005))
compared with Western firms could be reflected more in the relationships. In fact, Tan
and Khoo (2002) describe the relevance of Confucian philosophy in the effective
application of QM excellence in Asia. Since most of the studies on the relationship
between QM and OL have been based only on Western firms, this study brings a fresh
angle to the relevant research literature which lacks a non-Western organizational
perspective.
1.1 Purpose of the Study
This study addresses two main purposes. The first purpose of this study is to identify
the extent to which the core QM practices (e.g. quality process management and
information analysis tools) and the infrastructure QM practices (e.g. leadership,
customer focus, and people management) relate to learning stocks at the individual,
group and organizational levels as well as to the learning flows across levels in Korean
manufacturing firms. The second purpose of this study is to explore the extent to which
core QM practices, infrastructure QM practices, learning stocks, and learning flows
jointly relate to business performance in Korean manufacturing firms.
In order to achieve the purposes of this study, the following research question is
addressed:
What kinds of structural relationships among core QM practices, infrastructure QM
practices, learning stocks, learning flows, and business performance exist?
Seven hypotheses relevant to the question are explicated based on a review of
5
existing literature.
H1: Core QM practices (process management and information and analysis) relate
positively to learning stocks at the individual, group, and organizational levels.
H2: Core QM practices (process management and information and analysis) relate
positively to learning flows (feedback and feed-forward).
H3: Infrastructure QM practices (leadership, customer focus, and people
management) relate positively to learning stocks at the individual, group, and
organizational levels.
H4: Infrastructure QM practices (leadership, customer focus, and people
management) relate positively to learning flows (feedback and feed-forward).
H5: Learning stocks relate positively to learning flows
H6: Learning stocks relate positively to business performance (market performance
and financial performance).
H7: Learning flows relate positively to business performance (market performance
and financial performance).
Process
management
Information &
Analysis
Individual
learning
H1
Core QM
Practices
H2
Group learning
Organizational
learning
Learning
Stocks
H6
Bus
Perfor
H5
H3
Infrastructure
QM Practices
Leadership
Customer focus
H4
People
management
H7
Learning
Flows
Feed-forward
learning
Feedback
learning
2. Research Methods
The research design used in this study is a non-experimental survey study using self6
administered questionnaires. For the purpose of this study, structural equation modeling
(SEM) was conducted in order to verify the degree to which both core QM practices and
infrastructure QM practices are jointly associated with learning stocks and learning flows
as well as how these four constructs influence business performance in Korean
manufacturing firms. For this cross-cultural research, forward and backward translations
were carried out to ensure equivalence between the Korean and English languages. The
target samples were all of the manufacturing firms listed on the Korea Stock Exchange
(KSE) and the instrument was distributed to quality managers who are familiar with both
QM practices and employee learning in organizations. Paper-based surveys were used.
SPSS 15.0 and AMOS 7.0 were used as statistical tools.
The target sample in this study is all Korean manufacturing companies listed on the
Korea Composite Stock Price Index (KOSPI) of the Korea Stock Exchange (KSE) as of
May 2008. The reason for selecting these firms is threefold. First, quality management is
prominent in the manufacturing industry (Flynn et al., 1994). Second, the QM instrument
used in this study was developed for measuring the QM practices of manufacturing firms
(Samson & Terzivoski, 1999; Prajogo & Sohal, 2003). Third, the firms listed in the KOSPI
of the KSE are representative firms which lead the Korean economy and its
competitiveness. The KOSPI is the representative stock market index of Korea and the
firms listed on the index are mostly domestic firms. As of May 2008, 453 manufacturing
firms and 323 services firms were listed on the index. The 200 largest firms out of the
756 listed firms have over 70% of the market value of the KOSPI. The range of
manufacturing industries covers food and beverage, textile and apparel, rubber,
chemical compound and Chemical product, iron and metals, machinery and equipment,
electronic parts and communication equipment, motor and transport equipment, and
furniture and other products.
Out of 756 listed firms, all 453 manufacturing firms were selected from the KSE
database, based on following two considerations: (a) a minimum sample size that
provides a converged and proper solution for the structural equation modeling (SEM)
analysis is at least 150 cases (Bentler & Chou, 1987) and (b) the expected response
rate is 30 percent.
The 453 manufacturing companies that are listed on the Korea Stock Exchange
(KSE) were contacted through telephone or email. A total of 206 responses were
received (a 45.5% response rate), among which 182 were email responses (88.3%) and
25 were mail or fax responses (12.1%). Among the respondents, 85.9% are working in
the QM department of a company and 14.1% are working in other departments that are
in charge of QM or QM training, such as the Business Strategy, Marketing department,
or Human Resource Development. In terms of job level, 70.8% of the respondents are
above or at the level of middle management and 6.3% are at the non-management level.
Furthermore, 64.9% of them have more than 5 years work experience in the QM field.
In terms of industry type and firm size, 28.2% of the 206 companies are in the
chemical-related industry sector, 16.0% are in the electronic and communication
equipment sector, and 15.0% are in the motor and transport equipment sector. In
addition, 34.5% of them have more than 1000 employees, 27.2 % have an employee
body of between 500 and 999, and 19.9% have between 100 and 299 employees.
3. Results
7
In order to test the hypotheses, the two-step procedures of SEM were conducted.
The first step was to assess the measurement model and the second step was to
evaluate the structural model. According to Anderson and Gerbing (1988), assessing the
measurement model before the structural model is required because it helps not only to
verify the unidimensionality (e.g. the validity and reliability) of the observed and latent
variables but also leads to a better structural model based on their respecified model fits.
The measurement model aims to investigate the degree of association between
observed variables and their latent variables. This study has five latent constructs, that is,
learning stocks, learning flows, core QM practices, infrastructure QM practices, and
business performance. Five measurement models are tested and each model fit indexes
and Cronbach’s Alpha are presented in table 1.
Table 1
Summary of Fit Indexes for Five Measurement Models and Their Reliabilities
Cronbach’s
χ2 /df
P
CFI GFI
TLI RMSEA
Alpha
earning stocks
159.277/132 .053 .984 .922 .982
.032
.93
Learning flows
72.051/53 .042 .981 .944 .976
.042
.90
Infrastructure QM
182.271/74 .000 .942 .884 .928
.085
.93
practices
Core
QM
75.534/34 .000 .959 .933 .946
.078
.91
practices
Business
53.900/8
.000 .951 .924 .909
.168
.93
performance
Based on the five measurement models which obtained acceptable construct validity
and reliability, the relationships among the five major constructs, namely, learning stocks,
learning flows, infrastructure QM practices, core QM practices, and business
performance were examined. In order to develop the structural model, the item scores of
each sub-construct were added up, and the sum of the scores were used as a measured
variable of each main construct. This item parceling technique leads to increased
simplicity of the structural model as well as decreased estimates of latent errors
(Bandalos & Finney, 2001).
A hypothesized structure model (figure 2, table 2) was developed from the structural
analysis. The overall model fit indexes show moderately acceptable fit except chi-square
value. Moreover, the results of path analysis indicated that core QM practices were
positively and significantly associated with learning flows (t-value = 2.29), while
infrastructural QM practices had a significant relationship only with learning stocks (tvalue = 7.37). In addition, learning stocks significantly influenced learning flows (t-value
= 3.42) which were significantly associated with business performance (t-value = 2.50).
8
Core QM
Practices
-.04
.19*
Learning
Stocks
.09
Business
Performance
.55***
.95***
Infrastructure
QM Practices
.30
.57*
Learning
Flows
Figure 1. Hypothesized structural model with five major constructs
Table 2
Summary of Fit Indexes for a Hypothesized Model
χ2 /df
P
CFI
Hypothesized
102.010/46
.000
.969
model
GFI
.925
TLI
.955
RMSEA
.077
4. Implications
This study examined the relationships of QM practices and organizational learning
elements and business performance. In the hypothesized model, which is designed to
identify the separate and independent role of learning stocks and learning flows within
the structural relationship, infrastructure QM practices had strong positive relationships
with learning stocks and core QM practices were positively related to learning flows.
Learning stocks had positive effect on learning flows and only learning flows influenced
by the core QM practices and learning stocks were significantly associated with
business performance. Thus, out of the 7 hypotheses, hypotheses 2, 3, 5, and 7 were
supported by the hypothesized structural model.
Given the result of the hypothesized model, core QM practices directly influence
learning flows. Linderman and colleagues (2004) stress that core QM practices provide
structured and goal-oriented learning opportunities for organizational members so that
they can conceptualize the common goal of their work and constantly maintain their
actions in conformance with the goal. From a parallel viewpoint, Choo and his
colleagues (2007a, 2007b) mention that these methodological practices regularly
provide tools and techniques to distribute information throughout organizations and
efficiently help organizational members to share information. As a result of the
hypothesized model, core QM practices increase organizational learning capability
which effectively delivers knowledge accumulated at the individual, the group, and the
organizational levels into organizational systems, products or procedures and also
efficiently distribute knowledge embedded in the organizational system and procedures
throughout organizations.
With respect to its relationship with learning stocks, core QM practices show no
9
significant influence on knowledge accumulation at each level. Some scholars mention
that core QM practices could restrict learning activities by minimizing the differences in a
person’s actions by simply following readymade solutions linked with the routines of an
organization (Argyris & Schon, 1978). Since core QM practices emphasize the
refinement of existing techniques and current customers’ needs rather than new
technology and future customers’ needs, it could limit the learning activities of
organizational members (Barrow, 1993). Thus, even if core QM practices can efficiently
move already stored knowledge to other levels, the QM practices are not helpful for the
learning subjects to create and store new knowledge.
In the other hand, the infrastructure QM practices only directly impact learning stocks.
Leadership, customer focus, and people management, which represent the
infrastructure QM practices, play a role in increasing an employee’s commitment to
overall learning activities by providing psychological safety and various learning
opportunities resulting from new challenges (Choo et al., 2007b; Samson & Terziovski,
1999; Prajogo & Sohal, 2003). According to Choo and colleagues (2007a), the
infrastructure QM practices tend to promote tacit knowledge creation rather than explicit
knowledge creation due to their non-structured and flexible characteristics. In addition,
the infrastructure QM practices promote the exploratory learning process (i.e. a
variance-seeking learning process) rather than the exploitive learning process (i.e. a
mean-seeking learning process) due to their change-oriented behaviors.
Taking into consideration the previous theoretical framework, the findings of this
study proposed that learning stocks take place when organizational members are
exposed to a work environment in which they can freely explore new knowledge with
mutual trust and emotional consensus rather than merely sharing explicit information by
using tools and techniques.
Furthermore, although the findings showed a somewhat weak relationship, the
infrastructure QM practices increase learning flows. The infrastructure QM practices
perform better in storing learning outcomes in the models, but these QM practices also
tend to promote transfer of the stored knowledge to other levels. Based on the findings
of the alternative model, it seems that infrastructure QM practices could indirectly help
learning flows. For example, when new customers’ needs are detected, members start
to collect relevant knowledge through spontaneous learning activities. When the
knowledge accumulation is completed, members try to look for the places or systems in
which their knowledge can be used and transfer their knowledge to where it is needed.
While core QM practices could directly promote learning flows by providing practical and
exogenous systems and tools which effectively or enforcedly move along the
accumulated learning, infrastructure QM practices help to transfer the learning outcomes
through alignment processes between learning stocks and learning flows.
For the relationships between learning stocks, learning flows and business
performance, learning flows positively influence business performance, while learning
stocks did not impact business performance. However, when the influence of learning
stocks on learning flows was taken into consideration, the learning stocks showed
indirect influence through learning flows on business performance (indirect effect = .62,
p < .05).
In previous empirical studies, two studies examined the relationship of learning
stocks and learning flows with business performance (Bontis et al., 2002; Prieto &
10
Revilla, 2006). Bontis and colleagues (2002) suggested that three learning stocks (i.e.
individual, group, and organization) positively relate to business performance and the
misalignment of learning stocks and learning flows is negatively associated with
business performance. On the other hand, Prieto and Revilla (2006) found that learning
capability presented by learning stocks and learning flows positively influences both nonfinancial performance and financial performance. Unlike Bontis et al.’s (2002) findings,
this study found that learning stocks did not independently influence business
performance. Instead, learning flows were positively and directly associated with
business performance while learning stocks influenced business performance through
learning flows when a link from learning stocks to learning flows was connected.
It seems that the concept of learning flows, which is the capability of organizations to
absorb learning outcomes into organizations’ systems as well as disseminate the
collective knowledge throughout organizations, is a more critical factor than learning
stocks to improve business performance. Bontis and his colleagues (2002) mention that
learning surpluses which are not being absorbed by organizations causes misalignment
between learning stocks and flows and hence decrease performance. Thus, although
knowledge resources accumulated in organizations are fundamental to competitiveness,
this study found that their synthesizing processes are more crucial to improve business
performance.
The findings of this study also supported Choo and colleagues’ (2007b) empirical
research in that two different sets of QM practices (i.e. structure method and
psychological safety) independently influence learning constructs (i.e. learning behaviors
and knowledge created) and indirectly impact performance through the learning
constructs. However, this study points out that the existence of the relationships could
be extended from the team level to the organizational level and from a single firm setting
to industrial settings. Moreover, this study found that the flows of learning within the
relationships are an important factor in linking quality management to business
performance.
4.1 Implication for future of HRD Practitioners
The present study empirically emphasizes that the concept of organizational learning
in manufacturing firms implementing quality management can be a crucial factor in the
improvement of business performance. Although many QM and HRD managers
recognize learning as an important resource, they could overlook how to facilitate
learning at a workplace and how to deliver outcomes of learning throughout the
organization.
As appears by conclusions of the research, this study provide two important
contributions for future of HRD practitioners
First, it contributes to the understanding of how organizational learning is facilitated in
manufacturing firms implementing quality management. Building on the integrated model
which links two developed fields, this study proposes that the strategic use of two
characteristic sets of QM practices leads to the effective alignment of fundamental
learning elements with an organization’s goals. According to Chiles and Choi (2000),
quality management is linked with the theoretical foundation of organizational learning
through its continuous quality improvement efforts, cooperative knowledge creation, and
adaptation to dynamic changes in customer needs. However, ineffective implementation
of QM practices can cause learning failures and misalignment between firm strategy and
11
market needs (Hackman & Wageman, 1995). Structured and formal practices of quality
management continuously provide stable and accurate learning resources to individuals,
groups, and organizations and bundle the multi-level learning units to a single system
pursuing the same strategy. In contrast, flexible and informal QM practices lead to an
open and learning-driven atmosphere which encourages all learning levels to
cooperatively create new common ideas, values, and routines. Moreover, these two
propensities of QM practices strategically bring about accumulation of learning within the
three learning units as well as effectively create a desirable tension between feedback
flow of learning (exploitation) and feed-forward flow of learning (exploration). Thus, this
study contributes to the understanding of how QM based-culture drives organizational
learning efficiently and effectively.
Second, this study contributes to understanding the importance of organizational
learning in future manufacturing firms. In previous QM literature, Sousa and Voss (2002)
argue that most empirical studies have shown some evidence that QM practices
contribute to the improvement of short-term performance, such as operational/product
performance, rather than long-term performance, such as market and financial
performance. From a strategic management perspective, this study considers learning
activities as learning stocks and learning flows and identifies each role for improving
long-term performance. Thing future HRD professionals have to know in order to
achieve long-term performance through learning is that activities for sharing knowledge
more directly influence on business performance rather than activities for accumulating
knowledge. Learning occurred at the workplace should be team-based rather than
individual-based and informal rather than formal in order for people to find some outlet to
which their knowledge can be delivered and shared.
In short, as seen in the findings of this study, core QM practices help a firm to
efficiently develop learning flows. Core QM practices help to operate the flows of
learning outcomes by providing systems and tools. Process management and
information and analysis practices enable individuals to easily share information, enable
groups to think of themselves as a part of the whole system, and enable a firm to
efficiently deliver or disseminate knowledge gained in one unit to other upper or lower
units with connected tasks. In contrast, infrastructure QM practices play roles in
establishing unrestricted work environments in which organizational members freely
learn with inquiry and openness which thereby continuously accumulates their personal
knowledge in their memories. Moreover, they facilitate the flows of learning by providing
new directions which people can follow and to which they can align their knowledge.
Thus, the influence of infrastructure QM practices on learning flows could be
spontaneous and voluntary while core QM practices facilitate the learning flows with
enforced systems and tools.
Linderman and colleagues (2004) believe that organizations can create more
knowledge by deploying quality management practices with the knowledge creation
processes. Effective deployment of QM practices does not mean merely implementing
quality practices for the organizations’ own purposes, but arranging them with the
learning activities considering the degree of their involvement. Choo and colleagues
(2007a) also stress that the managers should know about the strategic effects of QM
practices on learning activities in order to manage learning associated with work
performance.
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Thus, future HRD professionals need to understand the intertwined but strategically
associated nature of relationships between quality management practices and
organizational learning elements. Under the basic assumption from the contingent view
of quality management practices, at certain times organizational learning is fostered by
core QM practices, and at other times it is facilitated by infrastructure QM practices.
HRD professionals need to understand how to effectively manage the different uses of
both sets of quality practices in facilitating learning and to cooperate with QM managers
in order to align the learning processes with organizational goals.
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