An empirical assessment of Lean Six Sigma Awareness in manufacturing industries: construct development and validation

V. Raja Sreedharan*, R. Raju, R. Rajkanth, M. Nagaraj

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)

Abstract

Lean Six Sigma (LSS) is one of the managerial practices for organisations to achieve operational excellence. But, prior research suggests that many companies have failed in the LSS Implementation (IMP) due to lack of Lean Six Sigma Awareness (LSSA). In this study, we assess LSSA in the manufacturing industries in India. Through extant literature review, we have identified and developed four new constructs for LSSA comprising the Impact of LSS (IM), Acceptance towards LSS (ACC), top management commitment (TMC) and IMP using the three-stage technique proposed by Churchill [(1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73]. Using these constructs, an online survey was conducted in manufacturing industries yielding a response rate of 65.4%. The survey results evaluated reliability, convergent validity and discriminant validity of the constructs and were found to be satisfactory. To test the model fit and hypotheses between the LSSA constructs, we used structural equation modelling (SEM). The SEM result shows that there is strong evidence to support the hypothesised model where IM has positive influences on TMC. Whereas ACC has a positive influence on both TMC and IMP, and TMC has positive influences on IMP. Based on the SEM results, it is clear that LSSA is essential for successful IMP across different manufacturing industries in India.

Original languageEnglish
Pages (from-to)686-703
Number of pages18
JournalTotal Quality Management and Business Excellence
Volume29
Issue number5-6
DOIs
Publication statusPublished - 14 Sept 2016
Externally publishedYes

Keywords

  • Lean Six Sigma Awareness (LSSA)
  • constructs
  • manufacturing industries
  • structural equation modelling

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