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This research aims to investigate the relationship between knowledge capabilities, engagement capabilities, operational capabilities and sustainable supply chain flexibility in the steel production industry. In addition, it seeks to understand how these elements contribute toward achieving a net-zero supply chain, under the moderation of industry dynamism.

In total, 371 people have responded to the self-administered survey that the authors developed. The nonresponse bias analysis was carried out before diving into fundamental assumptions, such as homoscedasticity and normality. The data's reliability and construct validity were assessed by using confirmatory factor analysis. The hypothesized conclusions were supported by subsequent regression outputs, strengthening the body of existing academic research.

The research's empirical results highlight the positive relationship among knowledge capabilities, operational capabilities, sustainable supply chain flexibility and net-zero supply chain, particularly under the influence of industry dynamism. Information obtained from the steel production industry corroborates these findings. Moderation role of industry dynamism in the relationship between operational capabilities, engagement capability and sustainable supply chain flexibility was found significant.

This conceptual framework clarifies how knowledge capability, dynamic capabilities and the flexibility of a sustainable supply chain interact. It highlights how these factors collectively work together to attain a sustainable supply chain with net-zero environmental impact.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

The purpose of this study is to explore how big data analytics (BDA) as a potential information technology (IT) innovation can facilitate the retail logistics supply chain (SC) from the perspective of outbound logistics operations in the United Kingdom. The authors' goal was to better understand how BDA can be integrated to streamline SCs and logistical networks by using the technology, organisational and environmental model.

The authors applied existing theoretical foundations for theory building based on semi-structured interviews with 15 SC and logistics managers.

The perceived benefits of using BDA in outbound retail logistics comprised the strongest predictor amongst technological, organisational and environmental issues, followed by top management support (TMS). A framework was proposed for the adoption of BDA in retail logistics. Contextual concepts from previous literature have helped us understand how environmental changes impact BDA decision-making, as such: (i) SC maturity levels and connectivity affect BDA utilisation, (ii) connected SCs improve data accessibility and information exchange, (iii) the benefits of BDAs also affect adoption and (iv) outsourcing complex tasks to experts allows companies to focus on core businesses instead of investing in IT infrastructure.

Outside the key findings listed, this study shows that there is no one-size-fits-it-all approach for use within all organisational settings. The proposed framework reveals that the perceived benefit of BDA is non-transferrable and requires top-level management support for successful implementation.

The existing literature focusses on the approaches to applying BDA in SC and logistics but fails to present a deep dive into retail outbound logistics activity. This study addresses the “how” and proposes a social-inclusive framework for a technology-enabled topic.

Using a dynamic capability view, this study examined the relationships between big data analytics capability (BDAC), entrepreneurial orientation (EO) and sustainable supply chain performance (SSCP) by exploring the moderating role of trust among supply chain partners.

Questionnaires were collected from 300 manufacturing organizations using snow sampling. The moderating connections and direct relationships were examined using Hays' process macro and structural equation modeling.

BDAC was positively related to EO and SSCP. When supply chain partners experienced low levels of trust, an increase in BDAC did not enhance SSCP. As trust increased, the relationship between BDAC and SSCP became more positive, underpinning the moderating effects of trust. Moreover, trust did not moderate the relationship between BDAC and EO. The moderating effect of trust on the relationship between EO and SSCP showed a positive relationship between EO and SSCP when trust was low; however, the relationship became negative when trust was high.

Developing technology alone may not be sufficient, as supply chain managers need to establish a strong business relationship based on mutual trust. However, they also need to be aware of the dangers of high levels of trust because these may negatively affect performance. Therefore, supply chain managers need to achieve an optimal level of trust that is neither excessive nor insufficient.

Advances in technology and entrepreneurial drive for supply chain sustainability make it pertinent to examine trust levels among supply chain partners and the varying impact on BDAC, EO and SSCP. The current study shows the negative aspects of too much trust among supply chain partners.

This paper aims to examine the interconnectedness between stakeholder expectations (SE), inter-organizational coordination (IOC) and procurement practices within humanitarian organizations (HOs) based in Uganda.

Employing a quantitative cross-sectional design, data were gathered from 43 HOs and analyzed using SmartPLS 4.0.8.3. Variance-based structural equation models (VB-SEMs) were employed to examine both direct and indirect effects.

The findings show a significantly positive relationship between SE, IOC and procurement practices. Additionally, the mediating role of IOC in the relationship between SE and procurement practices is evident.

While this study offers insights into procurement practices in HOs, the use of a quantitative approach might limit capturing dynamic changes over time. Future research could benefit from a nuanced approach involving interviews and longitudinal studies to uncover incremental changes.

During relief management, HOs need to understand their SE through information sharing and capacity building. This understanding can aid in selecting procurement practices that align with SE and ensure the delivery of relief.

Leveraging stakeholder theory, this research contributes to the understanding of how SE and IOC influence the adoption of procurement practices in HOs during relief delivery.