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Quality management practices (QMP) have stood as one of the critical strategic differentiators for enhancing firm performance. The production and manufacturing industry is the main driving force of economic growth and social development for any developed or developing country. This study aims to focus on two primary dimensions of QMP: soft quality management practices (SQMP) and hard quality management practices (HQMP) from the socio-technical system perspectives. Based on institutional theory perspectives, the study explores the impact of SQMP and HQMP on quality performance (QP), innovation performance (IVP) and financial performance (FP) in Indian oil processing organizations.

A proposed research model is validated using 289 cross-sectional survey data collected from the senior officials of oil processing firms in India. Covariance-based structural equation modeling is used to verify the proposed theoretical model.

SQMP, directly and indirectly, influenced QP and IVP while only indirectly to FP mediated through QP. HQMP directly impacted only QP while indirectly to IVP and FP mediated through QP.

Impact of organizational legitimacy in proper utilization or application of QMP in achieving the firm sustainable growth. The future study may address the following Research Question (RQ) also: How do QMP enhance the legitimacy of organizations operating in the oil processing industries? Are there specific mechanisms or pathways through which improved performance contributes to enhanced organizational legitimacy? How does legitimacy impact the success and sustainability of organizations, particularly, within the context of the oil processing industries? Are there regulatory requirements or industry certifications that organizations must adhere to in order to maintain legitimacy?

Similarly, manufacturing firms establish QMP of interaction and maintaining relationships with all the stakeholders, total employee empowerment and involvement, workforce commitment and workforce management, helping to control their reputations and maintain legitimacy (Li et al., 2023). Similarly, in the health industry, the health management information system (HMIS), which uses the DHIS2 platform, establishes that isomorphism legitimizes data QMP among health practitioners and, subsequently, data quality. Further, it was concluded that mimetic isomorphism led to moral and pragmatic legitimacy. In contrast, normative isomorphism led to cognitive legitimacy within the HMIS structure and helped to attain the correctness and timeliness of the data and reports, respectively (Msendema et al., 2023). Quality, flexibility and efficiency of Big Data Analytics through better storage, speed and significance can optimize the operational performance of a manufacturing firm (Verma et al., 2023).

The study provides the academician with the different dimensions of QMP. The study demonstrates how a firm develops multiple performance capabilities through proper QMP. Also, it shows how vital behavioral and managerial perspectives are to QMP and statistically solid tools and techniques. The study draws their importance to risk factors involved in the firms. Since the SQMP play a vital role, thus, emphasis on the behavioral dimension of quality requires more investigation and is in line with hard technological advancements in the quality field.

The study of the impact of HQMP and SQMP on performance is still not established. There are inconsistencies in the findings. The study of the impact of HQMP and SQMP in oil processing industries has not dealt with before. The effects of HQMP and SQMP on the firm’s FP have least been dealt. In context to the intended influence of QM implementation, QP has not been examined as a potential mediator between FP. Research carried out in the past is limited to American and European countries. However, a limited study was done in Asia, and no study has been conducted in the Indian context.

This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities.

The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework.

The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field.

The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors.

This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs.

This paper does not discuss social implications

This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.

The related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.

The SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.

The findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.

Existing research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.