Quality Control versus Quality Learning:
Measurement, Antecedents,
and Performance Implication
Dongli Zhang
PhD Candidate
Operations and Management Science Department
Carlson School of Management
University of Minnesota
August 12, 2006
OM Division PhD Consortium
Annual meeting of AoM, Atlanta
3/16/2016
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Research overview
Committee Members:
Dr. Kevin Linderman (Advisor, OMS)
Dr. Roger Schroeder (Advisor, OMS)
Dr. Susan Meyer Goldstein (OMS)
Dr. Geoffrey Maruyama (Educational Psychology)
Stage:
Proposal development
Primary research methodology:
Cross-sectional survey
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Agenda
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Motivation
Research questions
Part I: Description of major concepts
Part II: Antecedents of implementation of QC versus QL
Part III: Performance implication of QC versus QL
Methods
Conclusions
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Motivation
Practical
 Same QM practices, different results
 Some observations from my working experience: one set
fits all?
 Implement or focus on different QM practices according to
some contingency factors. But how?
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Motivation
Research
 Results of QM practices impact on performance is
inconsistent.
 Contingency approach rather than an assumption of
universal applicability is needed (Nair, 2005; Kaynak,
2003; Dale, et al., 2001)
 One limitation of existing studies: all QM practices are
treated as one set when examining their implementation
and influence on performance (Sitkin et al., 1994)
No testing of this theory
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Research Questions
A central premise of this study is that there exist two
different aspects of QM practices that have different
objectives: quality control (QC) and quality learning (QL)
(Sitkin et al., 1994; Sutcliffe et al., 2000).
 Q1: How do we discriminate and measure QC and QL?
 Q2: What are the antecedents that influence the
implementation of QC and QL?
 Q3: What is the relationship between QC, QL, and plant
performance? What factors may moderate the
relationship (organizational structure, environmental
uncertainty)?
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Part I: Description of QC and QL
Common QM precepts
Two widely used frameworks:
Dean and Bowen, Customer Focus
1994
Continuous Improvement
Team Work
Sitkin, Sutcliffe , Customer Satisfaction
and Schroeder,
Continuous Improvement
1994
Systems View of Organization
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Part I: Description of QC and QL-continued
 QC: a set of QM practices that aim to manage the known
problems and processes. The objective of QC is to
ensure the reliability of outcomes.
 QL: a set of QM practices that aim to explore the
unknown and to identify and pursue novel solutions. QL
keeps organizations open and flexible to new ideas.
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Part I: Description of QC and QL-continued
QC
QL
Customer
Focus
Identify and fulfill current
customers’ needs
Anticipate customers’
needs and respond
Continuous
Improvement
Monitor current processes
to make sure they are
under control
Improve process
incrementally or radically
Systems
View of
Organization
Working within each
function
Focus on integration
between functions
Task-related training
Multi-functional training
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Part II: Antecedents of
implementation of QC versus QL
Institutional
view
Institutional mechanisms
(Westphal et al., 1997; Ketokivi
and Schroeder, 2004)
QC
QL
Proposition 1a. QC practices are implemented
through institutional mechanisms.
Proposition 1b. QL practices are implemented
through institutional mechanisms.
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Institutional
view
QC
QL
Rational
view
Rational view
(Scott, 2003; Linderman et al., 2005; Evans and Lindsay, 2005 )
Proposition 2a. The implementation of QC practices
is driven by the organization’s goals and objectives of
low cost and on-time delivery.
Proposition 2b. The implementation of QL practices
is driven by the organization’s goals and objectives of
flexibility and innovation.
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Part III: Performance implication
of QC versus QL
QC
Performance
outcome
QL
• Org structure
• Environmental
uncertainty
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Define the dependent variable
Plant level performance (Klassen and Whybark, 1999; Roth
and Miller, 1990)
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Cost
Quality
Delivery
Flexibility
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Organizational structural as a
moderator
Two types of organizational structure: mechanistic and
organic (Burns and Stalker, 1961; Douglas and Judge,
2001)
 Mechanistic structure: structured hierarchically and
centrally controlled by an authority
 Organic structure: more flexible and open-type internal
arrangements
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Organizational structure as a
moderator
Proposition 3a. Organizations with mechanistic structure
that focus on QC result in higher plant level performance
than those that focus on QL.
Proposition 3b. Organizations with organic structure that
focus on QL result in higher plant level performance than
those that focus on QC.
Focus on QC
Focus on QL
Mechanistic
High performance
Low performance
Organic
Low performance
High performance
Org structure
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Environmental uncertainty as a
moderator
Environmental uncertainty: is proposed as having an
influence on the relationship between QM practices and
performance in several studies (Benson et al. 1991; Sitkin
et al. 1994; Nair, 2005)
Environmental uncertainty:
 degree of competition, change of customer needs, and
rate of product/process change (Benson et al., 1991).
 task uncertainty, product/process uncertainty, and
organizational uncertainty (Sitkin et al., 1994).
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Environmental uncertainty as a
moderator
Proposition 4a. When environmental uncertainty is low,
organizations that focus on QC result in higher plant level
performance than those that focus on QL.
Proposition 4b. When environmental uncertainty is high,
organizations that focus on QL result in higher plant level
performance than those that focus on QC.
Focus on QC
Focus on QL
Low
High performance
Low performance
High
Low performance
High performance
Uncertainty
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Methods
 Unit of analysis: plant
 Data: a cross-sectional survey, from a research project that
lasted for 15 years and collected data for three rounds
Round 3: High Performance Manufacturing (HPM) project
HPM data base:
N=189
Three industries:
Automotive, electronics, and machinery
Six countries:
Japan, Sweden, Finland, Korea, Germany, USA
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Methods-continued
 Measurement Instrument development
Based on a comprehensive literature review, draw items
from the HPM dataset
 Reliability and validity analysis
 Structural Equation Modeling (SEM)
 Hierarchical moderated regression analysis
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Conclusions
Potential Contributions
 Among the first attempts that address the theoretical
underpinnings of QM by distinguishing its two goals:
control and learning
 The first empirical test for discriminating them
 Incorporating insights from organization theory and
management theory into the research on QM
 Providing insights for practitioners on implementing QM
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Thank You
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