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Despite differing strategies towards environmental sustainability in developed and developing nations, the manufacturing sector in these regional domains faces substantial environmental issues. The purpose of this study is to examine the green-lean-six sigma (GLSS) enablers and outcomes for enhancing environmental sustainability of manufacturing firms in both, a developed and developing country context by using an environment-centric natural resource-based view (NRBV).

First, a framework of GLSS enablers and outcomes aligned with the NRBV strategic capabilities is proposed through a systematic literature review. Second, this framework is used to empirically investigate the GLSS enablers and outcomes of manufacturing firms through in-depth interviews with lean six sigma and environmental consultants from New Zealand (NZ) and Pakistan (PK) (developed and developing nations).

Analysis from both regional domains highlights the use of GLSS enablers and outcomes under different NRBV capabilities of pollution prevention, product stewardship and sustainable development. A comparison reveals that NZ firms practice GLSS to comply with environmental regulatory requirements, avoid penalties and maintain their clean-green image. Conversely, Pakistani firms execute GLSS to reduce energy use, satisfy international customers and create a green image.

This paper provides new insights on GLSS for environmental sustainability which can assist industrial experts and academia for future strategies and research.

This is one of the early comparative studies that has used the NRBV to investigate GLSS enablers and outcomes in manufacturing firms for enhancing environmental performance comparing developed and developing nations

Manufacturing companies continue to encounter a diverse set of obstacles while embracing sustainable development goals. Accordingly, the purpose of this study is to explore critical sustainable development-related barriers to flexible packaging manufacturing companies in the New Zealand context.

Drawing on a qualitative multiple case studies approach, the authors collected data from the New Zealand flexible packaging industry. Semistructured interviews were conducted with the senior corporate managers in two large flexible packaging companies. Following the thematic analysis approach, the authors analyzed the information collected from the participants and synthesized our findings under the key dimensions of internal and external barriers to sustainable development.

The findings revealed that internal barriers to sustainable flexible packaging are linked to economic, operational and technical issues. Conversely, external barriers include global crises and disruption, customer behavior and preferences and institutional and infrastructural-related aspects. Based on the analysis of empirical findings, the authors further identified the underlying reasons for sustainable flexible packaging barriers and recommended guidelines that could assist corporate managers and policymakers in addressing obstacles inhibiting the flexible packaging industry from adopting sustainable business practices.

The authors argue that this study is one of the early studies to consider inhibiting factors to incorporate sustainable development into the New Zealand flexible packaging industry context. Building on a range of theoretical perspectives, the authors extend the current body of knowledge seeking to advance the sustainable development agenda in the New Zealand flexible packaging industry and offer recommended pathways fostering sustainable development in a distinctive manufacturing context.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.