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Quality Control versus Quality Learning: Measurement, Antecedents, and Performance Implication

Dongli ZhangPhD Candidate

Operations and Management Science DepartmentCarlson School of Management

University of Minnesota

August 12, 2006OM Division PhD Consortium

Annual meeting of AoM, Atlanta

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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

Research overview

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Agenda

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

Some observations from my working experience: one set fits all?

Practical

Same QM practices, different results

Implement or focus on different QM practices according to some contingency factors. But how?

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Motivation

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

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)

Research

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Research Questions

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)?

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).

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Part I: Description of QC and QL

Common QM precepts

Two widely used frameworks:

Dean and Bowen, 1994

Customer FocusContinuous ImprovementTeam Work

Sitkin, Sutcliffe , and Schroeder, 1994

Customer SatisfactionContinuous ImprovementSystems 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

Task-related training

Focus on integration between functions

Multi-functional training

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Institutional view

QC

QL

Part II: Antecedents of implementation of QC versus QL

Institutional mechanisms (Westphal et al., 1997; Ketokivi and Schroeder, 2004)

Proposition 1a. QC practices are implemented through institutional mechanisms.Proposition 1b. QL practices are implemented through institutional mechanisms.

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Institutional view

Rational view

QC

QL

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

QL

Performance outcome

• Org structure• Environmental uncertainty

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Define the dependent variable

Plant level performance (Klassen and Whybark, 1999; Roth and Miller, 1990)

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)

Organic structure: more flexible and open-type internal arrangements

Mechanistic structure: structured hierarchically and centrally controlled by an authority

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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.

Org structure Focus on QC Focus on QL

Mechanistic High performance Low performance

Organic Low performance High performance

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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.

Uncertainty Focus on QC Focus on QL

Low High performance Low performance

High Low performance High performance

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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=189Three industries:Automotive, electronics, and machinery Six countries:Japan, Sweden, Finland, Korea, Germany, USA

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Measurement Instrument development Based on a comprehensive literature review, draw items

from the HPM dataset

Methods-continued

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