Purpose This study aims to provide a comprehensive framework for sustainable supply chain management (SSCM) by integrating environmental, social, economic and technological dimensions to bridge theoretical and practical gaps. Design/methodology/approach Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, a systematic review of 133 articles from the Scopus database was conducted, with qualitative analysis examining criteria such as SSCM, emerging technologies and sustainability. Findings The results reveal that SSCM drivers include government policies, stakeholder pressure, environmental concerns and managerial commitment, while challenges encompass high costs, technological complexity and data scarcity. Technologies like blockchain and the Internet of Things (IoT) enhanced transparency and efficiency. Practical implications The study offers industry-specific solutions and support for small and medium-sized enterprises in developing economies, though future research on resilience and social sustainability metrics is needed. Originality/value The study enriches the SSCM literature by emphasizing the triple bottom line approach, circular economy principles and models like 6 Rs and GreenSCOR.

Purpose To develop an integrated, biomass-specific framework for identifying, weighting and modeling sustainable supplier selection criteria (SSSC) in biomass energy supply chains. The study aims to address gaps in existing supplier-selection research by capturing economic, social and environmental dimensions and the unique technical and seasonal challenges of biomass feedstocks. Design/methodology/approach A three-step approach was used: (1) a meta-synthesis of 56 studies to extract candidate criteria; (2) a two-round fuzzy Delphi method (12 experts from industry, academia, consultancy and policy) to validate and reduce the list to 18 final criteria and (3) application of the SISMW method to simultaneously weight criteria and model their structural interrelationships. ISM and MICMAC analyses were employed to derive hierarchical levels and driving/dependence classifications. Feedstock-specific considerations (e.g. agricultural residues, forestry residues, energy crops, municipal and industrial organic wastes) were incorporated into the framework. Findings The study produced 18 validated SSSC across economic, social and environmental dimensions. Top-ranked and most influential criteria are: organization and management (EC5), corporate social responsibility (SO3), environmental management system (EN4), organizational culture (SO4) and technology/equipment (EC4). SISMW yielded criterion weights and an ISM hierarchy; MICMAC classified EC4, EC5, SO3, SO4, SO6, EN1 and EN4 as primary driving (independent) criteria, while EC2, EC3, EC6, EC7, EN2 and EN5 were primarily dependent. Practical recommendations include biomass-specific certification, supplier technology development programs, carbon accounting and traceability systems and multi-tier supplier engagement. Originality/value This paper is, to our knowledge, the first application of the SISMW technique to biomass supplier selection, offering an integrated simultaneous-weighting-and-modeling approach tailored to biomass feedstock heterogeneity. The study advances theory by revealing causal links among sustainability criteria in the biomass context and provides actionable, feedstock-aware guidance for managers and policymakers.

Purpose The impacts of localised disruptions within supply chains (SCs) may propagate throughout the entire SC due to the interconnectedness of SC participants. However, there are limited reviews investigating agrifood supply chain resilience (SCRe) from a whole SC perspective. The purpose of this paper is to investigate how SC design characteristics, SC participants, institutions and their interrelationships contribute to improving agrifood SCRe. Design/methodology/approach A systematic literature review (SLR) of 259 articles was conducted to analyse how SC design characteristics, and practices of SC participants and institutions could improve agrifood SCRe. Findings A conceptual framework for improving agrifood SCRe and five related propositions are proposed. Practices that institutions and SC participants can implement individually and collaboratively to improve SC-level resilience are suggested. Recommendations for future research are proposed based on the identified research gaps. Originality/value There are limited industry-specific literature reviews focusing on improving SC-level resilience, especially in agrifood SCs. There is an absence of studies on how agrifood SCRe could be improved holistically. The findings contribute to research advancement of SCRe, proposing a holistic conceptual framework for improving agrifood SCRe.

Purpose This study examines how chain restaurants can improve food quality consistency by employing central kitchens, while addressing operational challenges related to pricing and market coverage. Design/methodology/approach An analytical model is developed to analyze optimal pricing, quality decisions, and market coverage strategies under the central kitchen system. The model is further extended to incorporate customer congestion, variable costs, and kitchen location decisions. Findings Results indicate that full market coverage is optimal when customer sensitivity to quality inconsistency is either low or high, while moderate sensitivity leads to partial coverage. Conditions under which investing in a central kitchen is beneficial are also identified. Originality/value This research introduces a formal analytical framework revealing the strategic implications of central kitchen investment and quality control for chain restaurants. The findings provide actionable guidance for restaurateurs on designing pricing strategies and managing quality consistency.

Purpose This study examines the factors leading to low service quality in the hospitality industry in northwestern Nigeria, emphasising the lack of digitalisation and supply chain integration. The sector faces challenges, such as security issues and reduced investor and tourist numbers, which affect productivity. Our study explores how digital tools and integrated supply chains can enhance service quality by highlighting employee satisfaction and customer service. Design/methodology/approach Using a mixed-methods approach, we conducted a web-based survey with 100 hotel employees, semi-structured interviews with six hotel managers and an analysis of relevant organisational documents. Quantitative data were analysed using linear regression and descriptive statistics. Findings We identified the interconnections between digital transformation, supply chain integration, employee satisfaction and service quality, culminating in the development of a Digital Integration Satisfaction Model (DISM). Research limitations/implications This study provides useful insights into digital transformation in hospitality but has several limitations. It focuses solely on Northwest Nigeria, limiting generalisability. The small interview sample, while achieving thematic saturation, may not reflect broader managerial views. Reliance on self-reported data raises the risk of response bias. Data were collected over six months, so recent changes may be missed. Additionally, the study emphasises service quality and efficiency, omitting financial metrics like ROI or cost savings, which could provide a more comprehensive assessment. Originality/value This study aligns with the technology-organisation-environment (TOE) framework, supply chain management model, Herzberg’s two-factor theory and SERVQUAL model, using the terms digitalisation and technology adoption interchangeably. This study advances the understanding of the critical role of digitalisation and supply chain integration in enhancing hospitality sector performance in a challenging regional context, contributing to both theoretical models and practical insights.

Purpose In today’s volatile business environment, firms are required to balance cost efficiency, sustainability and operational resilience to sustain competitiveness. Supplier selection plays a pivotal role in this context; however, traditional models often fail to capture disruption risks caused by geopolitical tensions, health crises or natural disasters. This study aims to address this gap by developing a framework that explicitly incorporates disruption probabilities into sustainable supplier selection. Design/methodology/approach A chance-constrained goal programming (GP) model, enriched with Value-at-Risk (VaR) metrics, is proposed. The framework integrates disruption probabilities, demand variability, procurement costs and sustainability performance indicators to jointly optimize supplier choice. A real-world case from the automotive sector is used to demonstrate the applicability and robustness of the model under varying risk tolerance levels. Findings The empirical results reveal that the model consistently selects resilient supplier portfolios even under high uncertainty. It ensures cost control, safeguards sustainability thresholds and prevents excessive dependence on a small number of suppliers. The analysis highlights that incorporating probabilistic disruption modeling significantly improves procurement stability compared to conventional approaches. Originality/value Unlike prior studies that consider sustainability and disruption risk separately, this research offers an integrated framework that simultaneously accounts for financial, environmental and disruption-related concerns. By embedding disruption probabilities and diversification constraints into supplier selection, the study addresses a critical gap in procurement planning and contributes a novel methodological advancement for sustainable sourcing under uncertainty.

Purpose With the development and application of new-generation information technology, a wave of intelligent transformations in enterprise services has emerged. As a combination of various information technologies, industrial Internet platforms are becoming the new engine for driving the intelligent transformation of enterprise services. This paper examines how enterprises address pricing and coordination issues in product–service supply chains under industrial Internet platforms. Design/methodology/approach By building a game-theoretic model that includes a manufacturer and seller, with the manufacturer building its own industrial Internet platform, this paper analyzes the changes in service operation modes, service demand and service costs arising from the manufacturer’s service-oriented intelligent transformation. Findings First, with higher cost-saving coefficients and greater sensitivity of service intelligence, both wholesale and retail product prices decline, while service prices first rise and then fall as the cost-saving coefficient increases. Second, the effects of both the cost savings coefficient and service intelligence level sensitivity on service prices are related to the cost coefficient of service intelligence transformation. Specifically, in the centralized decision-making scenario, the service intelligence level is the highest, the product price is the lowest and the supply chain node firms and systems are the most profitable. In addition, manufacturers can incentivize sellers to participate in service intelligence transformation through the “cost-sharing-fixed-transfer-payment” agreement. Originality/value We incorporate industrial Internet platforms into the product–service supply chain framework by modeling the transformation toward service intelligence and propose coordination mechanisms to improve system performance.

PurposeIn the present study, a group of capillary tools are presented with the purpose to reinforce the decision-making (DM) capabilities of supply chain management (SCM) and to transform SCM into smart supply chain management (SSCM). Here, a bunch of DM tools are presented that can assist in providing thinking capabilities to multiple segments of the activities of supply chains (SCs).Design/methodology/approachIn this study, the literature review is considered as a research methodology, where the main thrust of the methodology is to determine the capillary tools for SSCM design based foundation for reinforcing SCM activities. The study mapped the existing literature knowledge related with DM tools to present a surface picture of capillary tools that can provide self-thinking capabilities and DM power to SCM. In this study, a thorough investigation is presented to interpret the overall pictorial representation of SCM assets that can be managed by DM tools.FindingsThe findings of the study have admired the utility of various support tools, i.e. agent-based system (ABS), the genetic algorithm (GA), an expert system (ES), artificial neural network (ANN) system, big data (BD) techniques, deep learning and natural language processing (NLP) prominent to be implicated for handling and maintaining the diverse SCM activities. The study has described support tools that can be implemented under the domain of SCM to import self-thinking capabilities, induce decision-making power for SSCM, create alerts, inform companies about state of mismanagement, forecast true need of concerns and to lead in evading SC troubles.Originality/valueThe study originally presented SCM-related activities and mapped the appropriateness of capillary tool to be used to create an understand-ability about the importance of capillary tools for managerial solutions. The study originally provided a widespread impression of diverse DM capillary tools that can be used to handle widespread stream of SCM activities to make it more sustainable. In this study, discussions pertaining to various support tools are presented for estimating their potential and implementation in handling the accurate SCM activity, and the arguments are presented for building SSCM designs based on the need of capillary tool to handle SCM activity.

PurposeThe study aims to determine in a dual-channel low-carbon supply chain (DCSC with LC), when the adoption of Blockchain technology (BT) can maximize the profits of supply chain (SC) businesses?Design/methodology/approachTo tackle the information opacity issue of low-carbon (LC) products in the dual-channel supply chain (DCSC), this paper constructs a Stackelberg model of different DCSCs with LC and explores the impact of BT on pricing decisions of DCSCs with LC.FindingsThe research finds: (1) Without BT, when the unit direct sales cost (UDC) is within a large threshold, the low-carbon products manufacturer’s (LCPM’s) profits under the distribution model are higher than those under the direct sales model. With BT, in both models, the profits of both LCPMs and retailers grow with the surge in the value coefficient of the shared information. (2) In the direct sales model, regardless of whether BT is adopted, the profits of LCPMs reduce with the increase in UDC; when the emission reduction cost coefficient (ECC) is within a large threshold, profits of traditional retailers (TRs) multiply with the UDC and vice versa; the profits of LCPMs after adopting BT are larger than that those without BT implementation. (3) In the distribution sales model, LCPMs and retailers that incorporate BT are more profitable than those that do not.Originality/valueIn the literature exploring the impact of information-sharing benefits resulting from BT on the pricing decisions of DCSCs with LC, numerous studies are conducted from the perspective that BT application can advance the consumption demand of consumers; however, only a few studies have been conducted from the perspective that the BT application can upgrade the efficiency of manufacturers and thus achieve additional information-sharing merits. Therefore, this paper investigates the pricing decisions of DCSC with LC based on BT, which further enriches the research in DCSC management.

PurposeThis study aims to explore the transformative impact of emerging technologies such as Building Information Modeling (BIM), Artificial Intelligence (AI) and the Internet of Things (IoT), on construction logistics management systems.Design/methodology/approachThis study employs a systematic literature review (SLR) in three phases: focused database searches to identify research on modern construction logistics technologies, qualitative content analysis to examine key emerging technologies and Scientometric analysis using VOS viewer to highlight trends and advancements in smart construction logistics systems.FindingsThe integration of BIM, AI and IoT enhances efficiency and productivity in construction logistics by facilitating real-time data utilization for supply chain optimization, reducing delays and minimizing errors. This transition towards cost-effectiveness and timely project completion signifies a move to streamlined operations and data-driven decision-making. However, challenges such as entrenched conventional practices, resistance to change and concerns regarding privacy and data security present significant challenges.Research limitations/implicationsDespite its significance, key limitations include reliance on existing literature, which may introduce bias due to database access restrictions, potentially overlooking relevant or recent developments in construction logistics technologies and the evolving technological landscape.Originality/valueThis study provides valuable insights for decision-makers and stakeholders within the construction industry, offering guidance on navigating challenges and harnessing the full potential of BIM, AI and IoT. Recommendations include addressing privacy concerns while leveraging innovative solutions for improved construction logistics management. The research sets the groundwork for further studies aimed at understanding and optimising the dynamic influence of emerging technologies on construction practices.