Exploring quality challenges and the validity of excellence models

Author:

Eriksson Henrik,Gremyr Ida,Bergquist Bjarne,Garvare Rickard,Fundin Anders,Wiklund Håkan,Wester Michael,Sörqvist Lars

Abstract

Purpose The purpose of this paper is to identify and explore important quality-related challenges facing organizations, and investigate how current excellence models incorporate these challenges. Design/methodology/approach The paper is based on a Delphi study of Swedish organizations. Forty-nine challenges were generated and ranked according to importance and the ten top-ranked challenges were compared to the principles of four excellence models. Findings The excellence models still seem to be relevant since their content matches many of the identified challenges. The Malcolm Baldrige National Quality Award and the Swedish Institute for Quality models were found to have the most comprehensive coverage, while the International Organization for Standardization model had limited coverage. Research limitations/implications Three areas for further research were identified: first, how quality management (QM) can evolve in different contexts that have varying needs in terms of adaptive and explorative capabilities; second, the interfaces of QM and sustainability, and ways to understand how customers and stakeholders can be active contributors to improvements; and third, the roles of the owners and board of directors regarding QM, and how to organize and distribute responsibilities of the QM work. Practical implications There are three important challenges that future revisions of excellence models could address: first, making QM a strategic issue for company owners; second, involving customers in the improvement activities; and third, developing processes that are robust yet still easily adaptable. Originality/value The Delphi study identified upcoming challenges in the QM area based on input from 188 quality professionals.

Publisher

Emerald

Subject

Management of Technology and Innovation,Strategy and Management,General Decision Sciences

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