Create sustainable competitive advantage and improve sustainable performance by implementing lean and green manufacturing practices: an empirical study on German manufacturing SMEs
Bibliographic record
Abstract
Purpose The aim of this study is to examine the direct and indirect effect between lean manufacturing practices (LMPs), green manufacturing practices (GMPs), firms’ sustainable competitive advantage (SCA) and sustainable performance (SP). Design/methodology/approach A survey questionnaire was used to collect data from 197 manufacturing small and medium-sized enterprises (SMEs) in Germany, and PLS–SEM was used to analyse the collected data and test the research hypotheses. Findings The results of the study show that LMP and GMP positively affect both SCA and SP of manufacturing SMEs in Germany. In addition, SCA positively affects SP and partially mediates the relationship between LMP, GMP and firms’ SP. Practical implications This study provides useful insights to managers of German manufacturing SMEs by emphasizing the importance of implementing both LMP and GMP in order to enhance firms’ SCA and SP. Furthermore, it demonstrates SCA as a critical factor in assessing the sustainable performance of companies, particularly in the context of adopting lean and green manufacturing practices. Originality/value Building on the limited existing literature on LMP, GMP, SCA and SP, this study develops a new framework that connects these concepts. It explores both the direct and indirect effects (via SCA) of lean and green manufacturing practices on the sustainable performance of manufacturing firms in a developed country context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".