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

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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

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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

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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).

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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 StudyThis 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

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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 flowsH6: 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).

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2. Research Methods

The research design used in this study is a non-experimental survey study using self-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

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H6

H5

H7H4

H3

H2

H1

Feedback learningFeed-forward learning

People management

Organizational learningGroup learning

Customer focusLeadership

Individual learning

Information & Analysis

Process management

Business Performance

Core QM Practices

Learning Stocks

Infrastructure QM Practices

Learning Flows

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

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

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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 1Summary of Fit Indexes for Five Measurement Models and Their Reliabilities

χ2 /df P CFI GFI TLI RMSEACronbach’s

Alphaearning stocks 159.277/132 .053 .984 .922 .982 .032 .93Learning flows 72.051/53 .042 .981 .944 .976 .042 .90Infrastructure QM practices 182.271/74 .000 .942 .884 .928 .085 .93

Core QM practices 75.534/34 .000 .959 .933 .946 .078 .91

Business performance 53.900/8 .000 .951 .924 .909 .168 .93

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 (t-value = 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).

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Figure 1. Hypothesized structural model with five major constructs

Table 2Summary of Fit Indexes for a Hypothesized Model

χ2 /df P CFI GFI TLI RMSEAHypothesized model

102.010/46

.000 .969 .925 .955 .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 10

.09

.55***

.57*.30

.95***

.19*

-.04

Business Performanc

e

Core QM Practices

Learning Stocks

Infrastructure QM Practices

Learning Flows

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 &

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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 non-financial 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 PractitionersThe 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

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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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