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Abstract Paper aims Assess the sustainability of sugarcane production in Sofala Province, Mozambique, using the SustenAgro Index approach and the Entropy Weight Method. Originality This paper provides several original contributions. First it enhances our understanding about the sustainability of sugarcane production in Mozambique, a topic that has been few studied. Second, it proposes a robust and comprehensive sustainability assessment framework based on the SustenAgro Index tailored to Mozambique. Finally, an innovative solution using an entropy approach is employed to determine the weights of criteria. Research method An intentional sample of 30 sugarcane producers from the districts of Nhamatanda and Búzi was selected. The sustainability indicators and dimensions were weighted using the entropy method, and the sustainability index was determined using the SustenAgro Index approach. Main findings Sugarcane production systems present positive sustainability scores. The social dimension has highest contribution to the sustainability index, followed by the economic and environmental dimension. Inefficient water management and the considerable distance between production fields and the sugar factory, significantly impacts the sustainability of sugarcane production. Implications for theory and practice This article presents a reliable framework for assessing sustainability in sugarcane production, leading policymakers and stakeholders to prioritize critical factors in designing policies and interventions.

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Abstract: Paper aims The research aims to investigating the impact of implementing Internet of Things (IoT) using Bayesian networks in the supply chain of manufacturing of Indian auto components enterprises to achieve enhanced productivity and reduced failure rates. Originality The research's originality lies in exploring IoT's impact with Bayesian Networks in Indian auto component manufacturing, showcasing Industry 4.0 applications. Research method The research utilizes Bayesian Network analysis to investigate IoT's impact in Indian auto component manufacturing supply chains, validating findings through Industry 4.0-based IoT implementation and a pilot study. Main findings Implementing IoT in Indian auto component manufacturing enhanced industry performance, productivity, and reduced failure rates with Industry 4.0 technologies. Implications for theory and practice The research offers theoretical insights into IoT and Industry 4.0's impact on the automotive industries and practical solutions for practitioners

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Abstract Paper aims This article identifies and discusses the barriers older adults face when using internal bus elements, which affect providing a safe, accessible, and desirable service, aiming to improve their commuting experience and quality of life. Originality By integrating qualitative approaches with aging simulation kits, this study provides new insights into the barriers users face in an aging developing city, emphasizing the influence of cultural attitudes, vehicle design, and service features. Research method An exploratory qualitative approach was used, involving simulation with an aging kit, semi-structured interviews, questionnaires, real-time observations, and postural risk assessments. Data were collected from 75 autonomous bus users aged 60 and over. Main findings The study reveals that the primary barriers for older adults using bus services are linked to vehicle design, service characteristics, and societal attitudes towards aging. Significant postural issues were identified during vehicle entry and exit, the use of priority seats, the height of horizontal handles, and interaction with turnstiles. Implications for theory and practice The findings highlight the need for bus design and service provision improvements to accommodate older adults better. These insights can inform future transportation policies, design standards, and training programs, enhancing safety, accessibility, and overall quality of life for elderly commuters.

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Abstract Paper aims This study aims to explore the relationships between barriers to agile methodologies, focusing on identifying reinforcement mechanisms between these barriers and effective mitigation strategies. Originality This study contributes original insights by examining the interconnectedness of barriers to agile adoption and introducing reinforcement mechanisms as a novel concept. The findings add both theoretical depth and practical value to the existing literature on agile methodology implementation. Research method The research is based on qualitative evidence gathered from five projects. A comprehensive literature review was conducted, followed by in-depth content analysis of interviews using the N-VIVO® software. A coding schema was developed to systematically analyze the data and uncover key insights. Main findings The study identified four distinct reinforcement mechanisms that exacerbate the challenges of transitioning from traditional to agile methodologies. Additionally, the research highlights specific mitigation strategies that facilitate pattern recognition and suggest appropriate interventions for different stages of agile implementation. Implications for theory and practice The identification of reinforcement mechanisms and corresponding mitigation strategies provides practical guidance for organizations aiming to implement agile methodologies. This framework can help managers recognize patterns of resistance and apply targeted solutions during different phases of the agile transition.

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Abstract Paper aims This study investigates inventory performance in Brazil’s commerce sector (retail, wholesale, and automotive) from 1996 to 2021. It examines how internal and macroeconomic factors influence inventory turnover, a key operational efficiency metric. Originality Using Annual Commerce Survey data by the Brazilian Institute of Geography and Statistics, this research offers a comprehensive analysis. A quasi-experimental approach explores a 2009 tax change, revealing how factors influence inventory performance in developing economies. Research method The study applies panel data econometrics with fixed and random effects models. Robustness checks include substituting inventory turnover with inventory days. A Difference-in-Differences approach is used to assess the tax reform’s impact on inventory turnover in the retail sector. Main findings During the period under analysis, the data reveals a general decline in inventory performance. Retail firms are primarily influenced by factors such as profit margins, GDP growth, and both consumer and industrial confidence, whereas wholesale firms exhibit greater sensitivity to interest rates and wage levels. Capital intensity plays a affected inventory performance across all sectors. Surprise had no significant effect on inventory performance, challenging previous research. Implications for theory and practice Findings emphasize sector-specific inventory management strategies. Macroeconomic factors impact sectors differently, suggesting that tailored strategies are essential for improving operational efficiency and policy development in emerging markets.

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Abstract Paper aims To explore the pioneering application of the QFD methodology in cartographic production in Brazil, focusing on aligning customer expectations with technical specifications in the development of orthoimage products. Originality This study represents a novel approach to employing the QFD methodology in the Brazilian cartographic sector, highlighting its potential to transform customer requirements (VOC) into prioritized technical attributes, an approach not widely used in this context. Research method The research employs a QFD-based approach, integrating qualitative and quantitative analyses to translate customer expectations into technical specifications. The methodology was applied in the design of orthoimage maps and extracts, with a focus on customer needs and operational requirements. Main findings Nine customer expectations (WHATs) related to image information were identified and addressed through fifteen technical descriptors (HOWs) using the QFD methodology. The study highlights the effectiveness of QFD while emphasizing the need for clearer definitions of cartographic products, scale, and operational requirements to improve the translation of VOC into VOE. Implications for theory and practice This study demonstrates the feasibility and challenges of applying the QFD methodology in cartographic production. It provides insights into bridging the gap between customer expectations and technical production, emphasizing the need for structured approaches in geoinformation projects. The findings can guide future research and practice in improving cartographic product development, particularly in aligning user needs with technical capabilities.

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Abstract Paper aims This paper presents a novel computational tool for managing risks related to human fatigue, applied at the First Integrated Center for Air Defense and Air Traffic Control (CINDACTA I). Distinct from existing systems, the proposed tool integrates a data‐oriented approach with a rigorous implementation of the Fatigue Risk Management System (FRMS) methodology, explicitly addressing the transition from acute to chronic fatigue. Originality The Fatigue Risk Management System (FRMS) tool was developed using an innovative approach that differentiates it from previous solutions. In contrast to validated international tools, our system emphasizes real-time data monitoring and adaptive risk mitigation strategies based on operational and scientific evidence (Gander et al., 2017; Maslach & Jackson, 1981). Research method we carried out exploratory, descriptive, and applied research with a qualitative approach. Main findings (i) The tool comprehensively covers all essential stages of the fatigue risk management process for Air Traffic Controllers (ATCOs); (ii) It provides actionable guidelines for reducing fatigue risks among ATCOs through an easily replicable computational prototype. Implications for theory and practice The tool contributes both to the theoretical understanding of human fatigue progression and to practical improvements in operational safety by prioritizing human resource management.

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Abstract Paper aims This research explores proposed integrated policies and supports for university spin-offs by considering the growth level of the spin-offs. Originality According to the literature, different types of support are needed to make spin-offs become established companies. However, the literature lacks clarity in addressing the specific types of support required at each stage of spin-off growth. Research method This research employs a qualitative research method in which the data collection is based on nine interviews with the founders of the spin-offs, the inventor, the director of the technology transfer office, the head of the university incubator, the manager of the university technopark, and the director of university’s company. Main findings Each level of spin-offs’ growth has to be supported by specific policies and supports. There are different types of support expected at the pre-incubation stage, which are Intellectual Property Rights (IPRs) protection, patent incentives, royalties, matching funds between university and industry, and the Technology Transfer Office as a matchmaker of inventor and startup founders. Implications for theory and practice This study provides a theoretical contribution to the policy framework for university spin-offs and offers practical guidance for university management and incubator managers.

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Abstract Paper aims Elucidating the barriers for an active participation of field workers in Operational Experience Feedback (OEF) processes and identifying potential ways forward. Originality Although the literature on OEF already addresses its challenges and strategies, we identified an opportunity for delving deeper into the (inter)subjective issues involved. Research method We conduct a review of the technical-scientific literature on OEF and elaborate a discussion in light of the theory of Psychodynamics of Work (PDW). Main findings Silence and disengagement in OEF can be the result of field workers and managers resorting to defensive strategies against the risks of questionings and critiques, which are nevertheless necessary for discussing and deliberating issues reported via OEF. The deliberation gap (i.e., the exclusion of field workers from deliberation of issues they report) can be an important element in the distrust and distance between field and management. Implications for theory and practice In order to strengthen OEF processes, we propose the development of collective resources that shall enable stakeholders dealing with questionings more constructively. For this purpose, we suggest strategies that consider the expectations on OEF results, performance evaluation criteria, and the conditions for field workers to participate in the deliberation of issues reported.

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Abstract Paper aims This study aims to explore the relationship between Total Quality Management (TQM), Technology Management (TM), and sustainable performance in manufacturing. Originality The originality of this study lies in its integrated approach, combining TQM and TM to assess their collective impact on Corporate Sustainability Performance (CSP). While prior research has examined these concepts separately, this study provides a comprehensive framework that highlights their synergies in driving sustainability. Research method This study employs a mixed-methods approach, combining surveys and expert interviews. The quantitative phase assesses TQM and TM practices' impact on sustainability in manufacturing firms, while qualitative interviews provide deeper insights into key success factors, challenges, and mechanisms driving the adoption of these strategies. Main findings The findings indicate that TQM and TM collectively enhance CSP by improving operational efficiency, reducing waste and emissions, fostering sustainable innovation, and promoting a culture of continuous improvement and employee involvement. These findings highlight the need to integrate quality management and technology for sustainability goals. Implications for theory and practice Theoretically, this study enriches the understanding of how TQM and TM interact to drive sustainable performance. Practically, it provides organizations with actionable strategies to align quality management and technology for long-term sustainability.

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Abstract Paper aims Introduce a new statistical test to verify whether two small samples of variable classified into multiple categories are drawn from the same population. This problem can be represented by a contingency table of order (m x 2). Originality We do not have adequate asymptotic texts to treat this issue in all instantiation of the problem, and exact methods require substantial computational effort and specialized algorithms. The proposed test covers this gap. Research method It can be classified within design science research. The result, as well as the research process, meets the guidelines of that research method. Main findings Computational experiments show that the proposed test has similar effectiveness to the exact test, even when dealing with sparse data contingency tables and small values of m. Furthermore, examples show that it can work well in cases where the chi-square test, its numerous variations, and even in situation where the more recently developed methods fail. Implications for theory and practice This type of decision problem has received significant attention in the literature because it represents many real-life situations. The test proposed is as useful for small samples as Chi-square is for larger samples.

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Abstract Paper aims This study aims to assess the network efficiency of the bar and profile rolling process in a steel manufacturing company using Network DEA (NDEA). Originality This research presents the first application of NDEA in combination with internal benchmarking, illustrating its feasibility in supporting significant performance improvements. Research method A case study was conducted in a steel plant to analyze the overall network efficiency. Main findings The average efficiency was 41.99%, with minimum and maximum values of 15.12% and 99.23%, respectively. Internal benchmarking revealed that the third stage of the rolling process negatively affected overall efficiency. Additionally, critical incidents influencing performance were identified, with 66.67% occurring in the fourth quarter each year. Implications for theory and practice Combining NDEA with internal benchmarking enables a continuous improvement framework, allowing the company to monitor and adjust operations for enhanced efficiency.

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Abstract Paper aims This study investigates the influence of digital transformation, technology use, and business management on the digital maturity of real estate agencies in Peru. We strive to provide an empirical model to assess digitalization's role in enhancing operational efficiency within the real estate sector. Originality This is the first study to systematically quantify digital maturity levels in Peruvian real estate agencies through structural equation modeling. Our research bridges a critical gap by linking digitalization practices with tangible business outcomes in emerging markets. Research method The study employed a quantitative methodology, gathering data from 116 real estate agents and analyzing it using partial least squares structural equation modeling (PLS-SEM). It also utilizes robust statistical techniques, including confirmatory factor analysis and model fit indices, to validate constructs and ensure reliability. Main findings Digital transformation significantly enhances business management practices and the digital maturity level, while digital networking showed no direct impact. Technology use emerges as a pivotal driver of digital transformation, emphasizing its foundational role in real estate innovation. Implications for theory and practice The findings highlight a novel framework for real estate professionals to benchmark digital maturity and optimize digital investments. By outlining key enablers of digital transformation, this study provides actionable insights for policymakers aiming to foster innovation in the real estate sector.

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Abstract Paper aims This article introduces a novel hybrid methodology in order to optimise dual-axis photovoltaic tracking systems in three Argentinian provinces by combining artificial intelligence, swarm intelligence and the productive chain. It identifies the most suitable strategy by balancing fixed-panel worst-case scenarios with continuous-tracking best-case scenarios and incorporating the decision makers’ preferences. Originality Firstly, the novel research methods listed below combine mathematical modelling and graphical analysis, and highlighting their complementarity and distinct contributions. Secondly, theoretical, methodological and practical gaps are identified and addressed in Argentina and other under-explored regions. This offers decision-makers a viable interim solution. Research method Firstly, it involves the novel mathematical modelling, simulation, optimisation, comparison of dual-axis solar tracking in fixed and mobile cases using multi-criteria techniques, while also validating across provinces and extreme scenarios. Secondly, it consists of a novel hybrid multi-criteria optimisation model combining particle swarm optimisation with constriction factor and a fuzzy-guided feedback metaheuristic system. It is for dynamic boundary-reflected constraints, the Analytic Hierarchy Process, and radial basis function neural networks. Thirdly, this survey is based on data obtained through the present line of research, including government and meteorological station data, manufacturer data and independent research. Main findings This methodology improves energy efficiency by 10–27% and economic performance by 40–110% compared to fixed panels, depending on regional and technical conditions. Implications for theory and practice This novel, scalable hybrid methodology combines the aforementioned research methods (theory) with support for decision-making in the planning of renewable energy projects in constrained economies (practice).

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Abstract Paper aims This study aims to identify the main risks affecting food supply chains (FSCs) in Brazil, analyze their interrelationships, and propose mitigation strategies. Originality This research presents a structured approach to modeling the interdependencies among FSC risks, offering original insights with practical applications for risk managers in the Brazilian food sector. Research method The methodology consists of three phases: (1) an Exploratory Literature Review (ELR) to identify and categorize FSC risks; (2) a survey with industry experts to collect qualitative data on the interrelationships among the identified risks; and (3) the application of Interpretive Structural Modeling (ISM) and the Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis to explore the relationships among risks and propose mitigation strategies. Main findings Ten key risks were identified and ranked. Natural risks and macro-level risks emerged as the most influential, while demand, supply, and operational risks were found to be more dependent on these factors. The ISM model illustrated the interconnections among the risks, and the MICMAC analysis classified them according to their driving and dependence power. Implications for theory and practice The findings support the development of targeted mitigation strategies, strengthening risk resilience and decision-making within FSCs. Additionally, the study contributes to academic discourse by integrating structural analysis into food supply chain risk management.

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Abstract Paper aims This study seeks to investigate the accuracy of machine learning algorithms for estimation of the effort required for software development in the manufacturing sector to identify the most effective algorithms according to the nature and complexity of the data and the number of available attributes. Originality This work distinguishes itself from other studies in the field of effort prediction by utilizing a data repository that consists exclusively of projects from the manufacturing sector. This approach ensures that the specific characteristics of manufacturing projects are reflected in the predictions, addressing a gap in the existing literature. Another notable contribution of this study is the comparative analysis of various machine learning algorithms assessed under different dimensionality scenarios (three and five variables). Although this factor is crucial for enhancing effort estimation accuracy, it has received limited attention in the literature. Research method The investigated techniques in this work were (i) Support Vector Regression, (ii) Gradient Boosting Machines (GBM), (iii) eXtreme Gradient Boosting (XGBoost), (iv) Random Forest (RF), (v) Extreme Learning Machine (ELM); and (vi) Linear Regression (LR). Performance measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) were used to compare the results achieved by each model, considering a dataset of 230 records originating from various countries. Main findings The comparison among machine learning models revealed significant performance variations depending on the number of variables and the evaluation metrics adopted. GBM stood out for its robustness in complex scenarios, while SVR achieved the lowest mean absolute error. ELM, in turn, proved effective with fewer variables but showed sensitivity to outliers and less stability in more complex contexts. Among all the techniques evaluated, XGB yielded the worst performance across all parameters. Implications for theory and practice This study contributes by applying these models to the manufacturing sector and comparing scenarios with three and five variables. The results support a more informed selection of models based on project complexity and data dimensionality. The more research conducted in this area, the stronger the theoretical and practical conclusions can be drawn.

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Abstract Paper aims This study aimed to identify the main risks present in Food Supply Chains (FSCs) and the strategies employed to mitigate them through a Systematic Literature Review (SLR). Originality The originality of this research lies in the systematization of risks and mitigation strategies from an extensive international body of literature, providing an integrated and updated perspective on the challenges facing FSCs. Research method The review used PRISMA and Joanna Briggs Institute (JBI) protocols. Sources included the Scopus, Web of Science, and Google Scholar databases, along with grey literature. Main findings The study identified fourteen key risks, including disruptions, forecasting failures, and operational, environmental, logistical, and intellectual property risks. Mitigation strategies were grouped into proactive, reactive, and concurrent approaches, and involved technologies such as IoT, blockchain, and big data, as well as practices like supplier diversification, traceability, and sustainability. Implications for theory and practice Findings support managerial and policy decision-making and contribute to building more resilient, efficient, and secure FSCs. They also highlight the need for further research tailored to the Brazilian context and reinforce the importance of digital and sustainable strategies in supply chain risk management.

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Abstract Paper aims This paper explores the integration of Industry 4.0 digital technologies and the Theory of Constraints (TOC) in manufacturing systems, focusing on their reciprocal enhancement to drive operational improvements. Originality It pioneers an examination of how TOC’s principles can effectively guide the adoption of I4.0 innovations while also showing how emerging technologies can extend the practical applications of TOC in operations management. Research method A systematic literature review was conducted following the PRISMA protocol, ensuring a comprehensive synthesis of existing studies at the intersection of TOC and I4.0. Main findings The review identifies three key elements: (i) the critical role of systems analysis in understanding manufacturing constraints, (ii) the effective implementation of I4.0 strategies guided by TOC principles, and (iii) the support provided by advanced technologies—such as artificial intelligence, digital twins, and RFID—in enhancing TOC applications. Implications for theory and practice The study offers a robust theoretical framework that bridges traditional operations management with modern digital strategies. Practically, it provides actionable insights for managers seeking to optimize technology adoption and operational efficiency in manufacturing, ultimately paving the way for future research on integrated digital and constraint-based management systems.

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Abstract Paper aims This study investigates integrating digital transformation, human factors, business process management, and emerging technologies to improve organisational efficiency and employee well-being. The research aims to develop a conceptual model that optimises digital processes while reducing the cognitive load on employees. Originality The research fills a gap in the literature by emphasising the intersection of human factors and digital transformation. It introduces a human-centric approach that balances operational efficiency with employee well-being, which has been underexplored in previous studies. Research method A systematic literature review was conducted using Scopus and Web of Science databases to identify relevant studies. Content analysis was used to extract criteria for each domain, and Structural Equation Modelling (SEM) was applied to analyse complex relationships between digital transformation and human factors. Main findings The results indicate that integrating digital tools into organisational processes optimises workflows and decision-making while mitigating cognitive overload. The proposed model prioritises employee engagement, usability, and well-being alongside technological advancement. Implications for theory and practice This study contributes to the theoretical understanding of digital transformation by integrating human factors. The findings provide a structured pathway for organisations to enhance operational efficiency while safeguarding employee well-being, offering a balanced approach to digitalisation that can be applied in real-world scenarios.

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Abstract Paper aims Production Engineering, as a knowledge field, was born with Frederick Taylor and his seminal analysis of work in industry. Over time, work design gradually changed, due to a complex set of factors. Nowadays, although work is at the center stage of societal concerns, work design modeling is understudied and undervalued, as if everything depended on technological advances only. Originality To assess the evolution of work design models, I created a framework based on five elements: Technology, Model of Person, Management System, Work Blueprint, and targeted Outcomes. It allows for a comparative analysis of the models and reveals their path dependence. Research method In this article, I perform a historical analysis. I use seminal works that established the foundations of Production Engineering and Operations Management, combined with those that polemicize and establish the state-of-the-art in work design. Main findings Using that framework, I highlight the key features of each work design model, disclose their path dependence to cope with increasing complexity, and discuss the options currently available to understand better the challenges associated with work design in the future. Implications for theory and practice Evolutionary analysis is valuable for research and teaching, whereas the analytical framework may help practice, organization and work design.

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Abstract Paper aims This study characterizes Purchasing and Supply Management (PSM) projects in the context of Industry 4.0 (I4.0) at a Brazilian steel company, using the reference architecture model industry 4.0 (RAMI4.0) as a basis. Originality The article adapts RAMI4.0 to the PSM context and empirically evaluates the model, promoting digitalization practices in PSM. Research method An in-depth case study was conducted using participant observation, document analysis, individual interviews, and focus groups with PSM professionals. Main findings By adapting the life cycle (dimension) to the stages of PSM deployment, this study establishes a framework for monitoring and optimizing activities throughout the PSM process, offering valuable insights. Implications for theory and practice The customization and evaluation of the RAMI4.0 model for PSM can serve as a reference framework for companies seeking to drive the digital transformation of the PSM function. This model offers a strategic perspective on how to integrate Industry 4.0 technologies across various organizational levels, with the goal of promoting digital PSM.

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Abstract Paper aims The main objective is to determine which Industry 4.0 (I4.0) technologies significantly impact the scale efficiency of 3PLs’ (Third Party Logistics). Originality This paper provides a significant academic contribution given that it is the first quantitative research endeavor to evaluate the influence of I4.0 applications on productivity within the Brazilian 3PL industry. Research method A two-stage Data Envelopment Analysis (DEA) model was adopted. The first stage of the DEA enabled the measurement of 3PL efficiency, and the second stage (Bootstrap Truncated Regression) allowed us to explore the relationship between efficiency and the I4.0 technologies. Secondary data from Revista Tecnologística provided the inputs, outputs, and contextual variables for this analysis. Main findings In the first stage of the analysis, a high average technical inefficiency was identified, suggesting managerial failures to efficiently use available resources. However, 3PLs demonstrated low-scale inefficiency, operating close to the optimal production scale. In the second stage, the contextual variables Drones, Big Data, and Business Intelligence were positively significant, while Internet of Things technology was negatively significant. Implications for theory and practice Our study enhances 3PL efficiency literature by applying DEA, considering contextual aspects, and exploring the adoption challenges of I4.0 technologies in emerging economies.

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Abstract Paper aims This study explores sustainability adoption in Indonesian construction firms by (a) describing current levels of the three sustainability pillars, (b) analyzing associations between key variables influencing sustainability performance, and (c) providing managerial insights and recommendations for improving sustainable construction practices in Indonesia. Originality It extends the literature by proposing and testing a theoretical model that explains the interaction between sustainability attitudes, practices, and performance, tailored to the Indonesian construction context. Research method A cross-sectional, self-administered survey targeted Indonesian construction firms, achieving a 22.8% response rate with 104 usable responses. Moderation analysis evaluated the association of ‘sustainability attitudes’ and ‘sustainability performance’ with ‘management practices’ as the moderating variable. Main findings Management practices partially moderate the association between sustainability attitudes and performance. Firms prioritize compliance-driven environmental sustainability, internal stakeholder well-being, and short-term economic benefits but lack strategic vision and sustainability teams. Implications for theory and practice The study contributes to the theoretical understanding of sustainability performance in construction by extending the Attitude–Behavior (A–B) framework to a firm-level context. It also addresses practical gaps in sustainability practices among firms in emerging economies. Findings highlight Indonesian construction firms’ priorities and challenges, guiding intervention strategies such as policy reforms, market incentives, and capacity-building programs.

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Abstract Paper aims Businesses are facing a challenge to transform their supply chain operations and contribute to sustainable development through circular economy practices. Whilst the transformation takes time and requires financial resources, many businesses like small and medium sized enterprises (SMEs) in developing world are struggling to survive and need to set priorities. This work aims to understand whether and how business survivability (SRV) and supply chain finance (SCF) can drive circular economy practices (CEP) amongst SMEs in an emerging country. Originality We contribute by offering a novel perspective of need hierarchy and showing that SRV is a critical driving factor of CE adoption amongst SMEs in the developing world. Research method We survey 515 SMEs from a diverse range of industries in Indonesia, one of the largest emerging economies in the world, and analyse the data using Partial Least Squares Structural Equation Modeling (PLS-SEM). Main findings This research shows that SRV is a critical driving factor of CE adoption amongst SMEs in Indonesia. Whilst higher SRV could increase chances to access SCF, non-financial support from SC actors is needed to make the financing effective and to motivate SMEs to adopt CEP. Without SCF, SMEs could adopt CEP when their business performs well. Otherwise, their focus is on surviving their business operations. Implication for theory and practice Our research challenges current research in developed countries showing that CE practices could affect SME performance and business survival. We provide evidence on the opposite argument and extend the applicability of Maslow’s hierarchy of needs theory in the business context.

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Abstract Paper aims This paper investigates how variability in certification requirements, organizational capabilities, and traceability technologies influences halal traceability system readiness in the Indonesian cosmetics industry. Guided by Institutional Theory, which explains how external regulatory pressures shape organizational behavior, and the Resource-Based View (RBV), which highlights the role of internal capabilities, this study aims to identify the determinants crucial for ensuring halal traceability system readiness amid regulatory diversity and supply chain complexity. Originality This study fills a significant research gap by focusing on halal traceability in the cosmetics sector, an area predominantly studied in the food industry. It offers an integrated conceptual framework that links institutional Theory and RBV to enhance traceability readiness. Research method Qualitative analysis was conducted using semi-structured interviews with key stakeholders, including regulatory bodies, industry representatives, and material suppliers. Data was analyzed systematically with ATLAS.ti 23, ensuring reliability through inter-coder agreement. Main findings Institutional pressures influence traceability readiness, with organizational capabilities and traceability technologies playing critical roles in addressing compliance challenges. Key factors include adaptive compliance, organizational responsiveness, and the traceability integration of material tracking, regulatory systems, and compliance Implications for theory and practice As a qualitative and exploratory study, the findings are context-specific and not intended for statistical generalization. They extend Institutional Theory and RBV by demonstrating how regulatory pressures and internal capabilities shape halal traceability readiness. The study provides actionable strategies for policymakers to harmonize certification systems and for industry stakeholders to enhance training, adopt adaptive compliance, and integrate traceability technologies to support halal system integrity.