Mallika Roy, Delwar Akbar, Darshana Rajapaksa
et al.
Labour productivity and environmental performance are closely linked in the context of solid waste management. An efficient workforce management system can lead to improved waste handling processes and reduced environmental impacts. However, improving environmental performance, including pro-environmental behaviour among workers, may have a negative impact on labour productivity. Implementing better waste management or pro-environmental practices may initially disrupt operations, requiring time, training, or adjustments that could reduce productivity. This study investigates the dynamic relationships between labour productivity, environmental performance, agricultural output, and capital intensity within the context of sustainable agricultural practices. In Australia, measured environmental performance includes controlled solid waste, including collected food waste, linking it directly to agri-food waste reduction. The results from Spectral Density Analysis of 33 years data show that all agricultural indicators exhibit high spectral power at low frequencies, indicating that their variations are primarily driven by persistent, long-term structural factors rather than short-term cyclical changes. Using an Auto-Regressive Distributed Lag (ARDL) model, we analyse the short- and long-term interactions among key variables, including Labor Productivity, Crop Production Index, Capital Intensity, Agricultural Value Added, and Environmental Performance Index (focusing on controlled solid waste). The study employs two complementary ARDL specifications, one centred on economic drivers and the other integrating environmental economics dimension, to jointly assess the productivity–sustainability nexus in Australian agriculture. Among the two ARDL specifications, the environmental-economics–focused model was further subjected to robustness checks, as it most closely aligned with the study’s objective of analysing the eco-productivity nexus in Australian agriculture. The results reveal a significant long-run relationship between these variables, specifically, past labour productivity has a positive impact on current productivity. Notably, the Environmental Performance Index exhibits a negative impact on productivity, suggesting trade-offs between sustainability and economic performance in the agricultural sector. Capital intensity shows mixed effects, with short-term increases benefiting productivity, but past capital intensity negatively affecting long-term output. The findings also align with global sustainability targets, particularly SDG 2, SDG 8, and SDG 12, by linking productivity improvements with responsible resource use and waste reduction. The findings emphasize the need for balanced agricultural and environmental policies to promote sustainable productivity. However, integrating environmental efforts with economic goals in the agriculture sector requires careful planning. This study provides valuable insights for policymakers by examining the factors influencing labour productivity in agriculture.
The second industrial revolution saw the development of management methods tailored to the challenges of the times: firstly, the need for mass production, and then, the pursuit of improved quality and customer satisfaction, followed by a push to improve operational performances in response to market globalization. If these approaches were initially inspired by rational mechanistic thinking, they have since gradually broadened to integrate other dimensions such as psychology, sociology and systemic analysis. Business enterprises underwent a profound rethink in the 1990s introducing increasingly refined modi operandi, only to find their environment disrupted by the appearance of two new parameters: complexity and uncertainty. Enterprises of the third industrial revolution were able to integrate these parameters at the outset, introducing new styles of management. However, these may well be deficient with regard to activities where an error may be fatal, or a failure intolerable. Caught between the precautionary principle and the principle of experimentation, the third industrial revolution falters to find the right approach, whereas the fourth industrial revolution is almost already upon us, bringing its lot of upheavals. In this regard, faced with increasing complexities and uncertainties, Research and Development is of particular interest since its vocation consists precisely in confronting the complex and the uncertain. This article examines the fundamental principles of the R&D process, and analyses how these may act as a benchmark for contemporary management by providing sources of inspiration.
Zixuan Feng, Katie Kimura, Bianca Trinkenreich
et al.
Open-source software (OSS) community managers face significant challenges in retaining contributors, as they must monitor activity and engagement while navigating complex dynamics of collaboration. Current tools designed for managing contributor retention (e.g., dashboards) fall short by providing retrospective rather than predictive insights to identify potential disengagement early. Without understanding how to anticipate and prevent disengagement, new solutions risk burdening community managers rather than supporting retention management. Following the Design Science Research paradigm, we employed a mixed-methods approach for problem identification and solution design to address contributor retention. To identify the challenges hindering retention management in OSS, we conducted semi-structured interviews, a multi-vocal literature review, and community surveys. Then through an iterative build-evaluate cycle, we developed and refined strategies for diagnosing retention risks and informing engagement efforts. We operationalized these strategies into a web-based prototype, incorporating feedback from 100+ OSS practitioners, and conducted an in situ evaluation across two OSS communities. Our study offers (1) empirical insights into the challenges of contributor retention management in OSS, (2) actionable strategies that support OSS community managers' retention efforts, and (3) a practical framework for future research in developing or validating theories about OSS sustainability.
This paper studies how an online seller designs a menu of return contracts to manage consumer heterogeneity arising from product misfit risk. In the model, informed consumers know the product fits, whereas uninformed consumers face uncertainty about fit. We show that when only misfit risk exists, a single full-refund contract can implement the optimal menu. The return price insures consumers against misfit, and the selling price extracts full surplus. This finding aligns with the widespread adoption of lenient return policies and demonstrates that uniform contracts can emerge endogenously from incentive compatibility rather than as ad hoc assumptions. Introducing quality risk—an additional source of uncertainty in the valuation of fit products—fundamentally changes this outcome. The seller then shifts from full to partial refunds. Under high quality risk, the optimal menu becomes differentiated: a low-price, no-frills option for informed consumers and a high-price, insurance-heavy option for uninformed consumers. To induce self-selection, the seller distorts the return price upward for uninformed consumers to strengthen the insurance effect, trading off allocative efficiency for screening. Interestingly, when quality risk becomes extreme, refunds not only mitigate information rents but also enhance social surplus by preventing consumers from retaining low-quality products. Extensions incorporating misfit valuation, seller-side costs, and return hassle costs show that the uniform contract remains robust when only misfit risk is present, rationalizing diverse return policy practices in online markets.
Basheer Azdi Shahizan, Maisarah Mohd Redwan, Wardina Humaira’ Rostam
et al.
PN17 classification is synonymous with companies that fail to comply with the Bursa Malaysia laws, thus serving as an early warning for potential financial issues. Accurately determining the financial status of PN17 companies often requires an extensive review of the reports and procedures. This study employed a logit model to identify the factors contributing to PN17 classification with a combination of financial distress indicators: stock volatility, leverage, liquidity, profitability, and probability of default (PD). A sample of financial data from 46 companies listed on Bursa Malaysia, comprising both PN17 and non-PN17 companies, from 2017 to 2022 was analysed using logistic regression. The results reveal that stock volatility, liquidity, and leverage are statistically significant to PN17 classification at a 95% confidence interval. The logit function obtained from the logistic regression analysis was able to classify PN17 and non-PN17 companies with 85.1% accuracy. Nonetheless, the accuracy of classifying PN17 (31.9%) is lower compared to non-PN17 (96.4%). This may be due to class imbalance bias (PN17 and non-PN17) and the negative relationship found in leverage, which is contradicted by the financial theory where higher leverage means higher risk. The ROC-AUC analysis supports the struggle in classifying PN17 due to the counterintuitive relationship in leverage. This suggests a complex relationship in leverage that requires further investigation into its linearity, bimodality, and relations with other predictors. Overall, PN17 classification can be improved with stock volatility as the strongest predictor, followed by liquidity and leverage. Meanwhile, profitability and PD were found to be insignificant to PN17 classification. These findings highlight the gaps in the existing literature regarding the predictive power of the logit model when incorporating additional financial indicators (stock volatility and PD) and understanding the relationships of the financial factors in classifying the minority PN17 class. Regulators might consider giving more weight to stock volatility for identifying financially distressed in PN17 companies. Future research may consider data augmentation methods and other model approaches to address the class imbalance bias and counterintuitive relationship in leverage in order to produce a more robust model.
Production management. Operations management, Business
This study examines the impact of employee organizational citizenship behavior (OCB) and engagement on the performance of microfinance institutions, with a focus on the moderating role of managerial decision-making. Data were collected from 100 key microfinance institutions employees in Greater Bandung, with interviews of 10 experienced staff (>5 years) conducted to refine the questionnaire and ensure alignment with microfinance institutions work conditions. Using Structural Equation Modeling using Partial Least Squares (SEM-PLS) for data analysis, this study finds that both organizational citizenship behavior (OCB) and employee engagement significantly enhance microfinance institutions performance. The results further indicate that managerial decision-making serves as a critical mediator between OCB, employee engagement, and organizational performance. These findings underscore the importance of managerial decision-making in amplifying the effects of OCB and employee engagement on microfinance institutions performance. Furthermore, this research introduces alternative metrics for assessing OCB, employee engagement, managerial decision-making, and organizational performance.
Production management. Operations management, Management. Industrial management
Oloiva Sousa, Ludgero Sousa, Fernando Santos
et al.
The main objective of energy balance analysis is to guide farmers in making informed decisions that promote the efficient management of natural resources, optimise the use of agricultural inputs, and improve the overall economic performance of their farms. In addition, it supports the adoption of sustainable agricultural practices, such as crop diversification, the use of renewable energy sources, and the recycling of agricultural by-products and residues into natural energy sources or fertilisers. This paper analyses the variation in energy efficiency between 2019 and 2022 of the main crops in Angola: maize, soybean, and rice, and the forest production of eucalyptus biomass in agroforestry farms. The research was based on the responses to interviews conducted with the managers of the farms regarding the machinery used, fuels and lubricants, labour, seeds, phytopharmaceuticals, and fertilisers. The quantities are gathered by converting data into Megajoules (MJ). The results show variations in efficiency and energy balance. In corn, efficiency fluctuated between 1.32 MJ in 2019 and 1.41 MJ in 2020, falling to 0.94 MJ in 2021 due to the COVID-19 pandemic before rising to 1.31 MJ in 2022. For soybeans, the energy balance went from a deficit of −8223.48 MJ in 2019 to a positive 11,974.62 MJ in 2022, indicating better use of resources. Rice stood out for its high efficiency, reaching 81,541.33 MJ in 2021, while wood production showed negative balances, evidencing the need for more effective strategies. This research concludes that understanding the energy balance of agricultural operations in Angola is essential not only to achieve greater sustainability and profitability but also to strengthen the resilience of agricultural systems against external factors such as climate change, fluctuations in input prices, and economic crises. A comprehensive understanding of the energy balance allows farmers to assess the true cost-effectiveness of their operations, identify energy inefficiencies, and implement more effective strategies to maximise productivity while minimising environmental impacts.
PurposeFocusing on the emerging economies, this study proposes a comprehensive framework for selecting sustainable battery suppliers in the electric vehicle (EV) industry. The study examines how EV manufacturers can prioritise battery suppliers to create a robust and eco-friendly supply chain.Design/methodology/approachThis study integrates the technology-organisation-environment (TOE) framework and the dynamic capability (DC) theory to develop a robust and comprehensive framework for the evaluation of battery suppliers. The fuzzy ordinal priority approach (OPA-F) is used to prioritise the criteria and sub-criteria finalised under the integrated framework. Fuzzy linear programming problems are formulated under this approach with the linguistic opinion of experts as the input parameters. To obtain the global weight of sub-criteria, multiplicative aggregation is performed on the defuzzified local weights of criteria and sub-criteria.FindingsAs per the findings of this study, the battery policy adherence, environment compliance and safety certification emerged as the most important sub-criteria, whereas the liquidity ratio, debt-to-equity ratio and creditworthiness emerged as the least important. These reveal that the environment and technological criteria have great influence on battery supplier selection decisions.Originality/valueBy utilising OPA-F and combining the TOE framework and DC theory, this study offers a theoretical and practical contribution that enables efficient decision-making. The framework provides manufacturers and policymakers with practical insights on improving operational resilience and sustainability in EV battery supply chains, particularly in emerging markets.
Industrial engineering. Management engineering, Production management. Operations management
Against the backdrop of deepening digital and intelligent transformation in human resource management, traditional recruitment models struggle to fully meet enterprises' growing demand for precise talent acquisition due to limited efficiency, high costs, and information asymmetry. As a vital tool for optimizing recruitment processes, reducing labor and time costs, and enhancing core competitiveness, intelligent recruitment management systems have become an indispensable component of modern organizational talent strategies. Compared with the labor intensive tasks of resume screening, candidate position matching, and interview coordination in traditional manual recruitment, intelligent recruitment systems significantly enhance the efficiency and accuracy of the hiring process through automation and data driven approaches. These systems enable rapid parsing of massive resume volumes, intelligent matching of candidates to positions, and automated scheduling of interview processes. This substantially reduces the workload on human resources departments while improving recruitment quality and response speed. This research leverages the Java technology framework to design and implement an intelligent recruitment management system tailored for campus recruitment scenarios. The system establishes a collaborative platform connecting students, enterprises, and administrators through information technology and intelligent solutions, offering comprehensive functionalities including job posting distribution, resume submission, candidate position matching, and process management. Guided by the vision of Smart Campus Recruitment, the project delivers a more convenient job seeking experience for students and provides enterprises with more efficient talent screening and recruitment management services, thereby driving high quality development in university enterprise collaboration.
In practice, new entrants often bypass intermediary retailers and engage in direct competition against incumbents. However, this might place entrants under the burden of integration costs. This paper models the strategic interactions between an incumbent and an entrant, incorporating the integration cost incurred by the entrant and the associated estimation bias exhibited by the incumbent. In the baseline setting with an unbiased incumbent, we find that the entrant’s higher integrative capability (i.e., lower integration cost) always hurts the incumbent, and can even hurt the entrant itself. We further investigate how the incumbent’s estimation bias affects firm performance, and find that increasing the bias can actually benefit the entrant and, intriguingly, the incumbent as well. In particular, a mutually beneficial situation can emerge where a higher level of bias potentially improves profits for both the incumbent and the entrant. Additionally, we extend our analysis to settings with random integration costs, where the bias can be linked to either the mean or the variance of these costs. Our findings offer practical implications for entrants in choosing their integrative capability strategy, and for incumbents in deciding whether to pursue bias reduction.
Ambulance offload delays occur when emergency medical service (EMS) personnel are unable to promptly transfer patients to overwhelmed emergency departments. These delays postpone necessary care for the patient and hinder the EMS system from attending to new emergencies. This study introduces a real-time multi-priority patient transfer policy aimed at reducing these delays. We model the patient transfer problem as a stochastic dynamic program based on post-decision states, and develop approximate dynamic programming approaches to overcome the curse of dimensionality. Additionally, we derive a lower bound for the optimal solution through information relaxation. We evaluate the proposed method using both synthetic and real EMS data from St. Paul, Minnesota. Our solution is close to the lower bound, and the resulting policy results in a triple-win situation. For patients, our policy reduces hospital wait times by 80%; for hospitals, our policy alleviates emergency department congestion, and we find that even partial compatibility leads to notable system-wide improvements; for the EMS system, ambulance utilization is reduced by more than 30%, and ambulances could handle more calls per day, with a reduction in lost calls exceeding 80%.
We study information sharing contracts in which a privately informed retailer offers to share its demand information with the upstream competing manufacturers (suppliers) for a price. We investigate whether such information sharing contracts would ever be accepted by the manufacturers, especially when they can use alternative means, like screening contracts, to obtain the same information. For that we model a three-stage game between the retailer and the manufacturers in which the retailer first determines the optimal payment, per manufacturer, for sharing its demand information. Consequently, the manufacturers simultaneously accept or reject this offer in stage 2 . In stage 3 , both manufacturers simultaneously offer menus of payment-quantity contracts to the retailer, which act as screening contracts if the manufacturer(s) rejected information sharing contract previously. We fully characterize stage- 3 contracts under common agency; the manufacturers’ stage- 2 equilibrium; and the retailer’s stage- 1 decision. We find that common agency can augment the reduction in competition intensity between the manufacturers. As a result, a manufacturer using screening can do better than a manufacturer accepting information-sharing even when information is free, which implies that both manufacturers might never choose information sharing in a pure strategy equilibrium for a positive information sharing price. Despite this, we find that the retailer will optimally set an information sharing price that results in both manufacturers either accepting information sharing as long as demand uncertainty is not too low, or always accepting information sharing, and that the manufacturers can end up in a prisoner’s dilemma. Managerially, our findings provide guidance to both manufacturers as well as retailers on their decisions pertaining to information sharing contracts.
In the dynamic e-commerce environment, social commerce has emerged as a revolutionary force, transforming how consumers interact and transact online. This paper investigates the differences in customers’ search and purchase patterns between a prominent online retailer’s burgeoning social commerce channel, the WeChat mini-program, and its native mobile app. We analyze the customers’ entire journey through a sequential search model that encapsulates decisions from channel selection to product search, search termination, and the final purchase. This study contributes to the search model literature by being the first to estimate both fixed and marginal search costs in a sequential search model in an omnichannel retail environment. We calculate fixed search costs, marginal search costs, and preferences for each channel, revealing differences in customers’ behaviors across channels. Our analysis shows that customers’ fixed search costs are higher, but marginal costs are lower on WeChat channel compared to the App channel. Also, customer characteristics like historical spending levels and search timing influence their search costs. From these insights, we suggest strategies tailored to each channel capitalizing on the differences in customers’ search costs. The first strategy encourages search initiation by lowering fixed search costs through peer-to-peer link sharing in the WeChat channel. The second strategy aims to minimize marginal search costs using search-triggering coupons in the App channel. Implementing these strategies significantly boosts conversion rates and profits for the online retailer. This research is one of the first to explore the differences between traditional retail channels and emerging social commerce channels.
Petrov Viktorija, Drašković Zoran, Ćelić Đorđe
et al.
Background: Research on the topic of determining success of online learning is on the rise. Defining the key success factors, i.e. determinants of online learning success, is extremely important, especially at present as all higher education institutions have been forced to try their hand at teaching with the help of technology. Purpose: Thus a research examining factors of learning outcomes of online learning was conducted. Learning outcomes were modelled as dependent variable, while the set of independent model variables included: course design, student motivation, student self-regulation and dialogue (instructor-student, student-student). Study design/methodology/approach: Five research hypotheses were tested by analysing data collected from the students of the University of Novi Sad. A structured questionnaire was employed to collect data on the attitudes of users (students) to online learning. Respondents expressed their views (perception) about statements and valued them on a 5 point Likert scale. The instrument was applied to a sample of 360 responses using PLS structural equation modelling. Findings/conclusions: All five hypothesis were supported with the analysis, confirming the importance of research from the aspect of contribution to the literature dedicated to identifying the key success factors of online learning. Additional contribution refers to the research conducted in Serbia, i.e. at the University of Novi Sad. Limitations/future research: A more detailed analysis of the model itself and the possibility of finding the interdependence of constructs that affect perceived learning outcomes and user satisfaction remains as an area for further research.
Production management. Operations management, Personnel management. Employment management
The restoration and management of mining ecological environment is an important component of China's ecological civilization construction. The implementation of restoration projects is to avoid the serious impact caused by the continuous deterioration of mining ecological structure, and is a key way to maintain regional ecological security and energy and mineral security. Therefore, efficient and rapid monitoring and evaluation of the effectiveness of mining ecological restoration is an essential and important link. With the rapid development of remote sensing technology, remote sensing monitoring has become an important means to objectively, quickly, and accurately obtaining changes in the mining environment. It can provide timely and long-term monitoring of mining and backfilling conditions in mining areas. This article uses the multi temporal stereo images of the Resource 3 satellite over four years to monitor the mining and backfilling situation of the Zhungeer open-pit coal mine area in Inner Mongolia. By reconstructing the Digital Surface Model (DSM) of the open-pit mining area, the changes and thresholds of the multi temporal DSM data are statistically analyzed to extract the mining and backfilling areas, and the earthwork volume is calculated. The results showed that from 2013 to 2016, the main large-scale mining faces in the study area of Zhungeer open-pit mining area had a total mining operation of about 563.5 million cubic meters, and a total backfilling operation of about 604.29 million cubic meters; During the period from 2016 to 2018, the main large-scale mining faces in the region had a total of approximately 721.71 million cubic meters of mining operations and a total of approximately 805.42 million cubic meters of backfilling operations. The overall operational intensity increased from 2016 to 2018. The research results show that based on satellite image data, it is convenient and efficient to obtain DSM and corresponding changes of multiple time periods in mining areas. Monitoring results can provide important support for regional environmental governance and protection, as well as safety production in mining areas.
Oussama Zoubia, Nagaraj Bahubali Asundi, Adamantios Koumpis
et al.
In the digital age, data has emerged as one of the most valuable assets across various sectors, including academia, industry, and healthcare. Effective data preservation involves the management of data to ensure its long-term accessibility and usability. Given the importance and sensitivity of data, the need for effective management is a crucial necessity. One of the big recent proposed approaches for data management is the FAIR Digital Objects (FDOs) which has emerged to revolutionize the field of data management and preservation. Central to this revolution is the alignment of FDOs with the FAIR principles (Findable, Accessible, Interoperable, Reusable), particularly emphasizing machine-actionability and interoperability across diverse data ecosystems. This paper presents "FDO Manager" a Minimum Viable Implementation of FDOs, tailored specifically for the use case and field of research artefacts such as datasets, publications, and code. The paper discusses the core ideas behind the FDO Manager, its architecture, usage and implementation details, as well as its potential impact, demonstrating a simple and abstract implementation of FDOs in the research realm.
The emerging data-driven methods based on artificial intelligence (AI) have paved the way for intelligent, flexible, and adaptive network management in vehicular applications. To enhance network management towards network automation, this article presents a digital twin (DT) assisted two-tier learning framework, which facilitates the automated life-cycle management of machine learning based intelligent network management functions (INMFs). Specifically, at a high tier, meta learning is employed to capture different levels of general features for the INMFs under nonstationary network conditions. At a low tier, individual learning models are customized for local networks based on fast model adaptation. Hierarchical DTs are deployed at the edge and cloud servers to assist the two-tier learning process, through closed-loop interactions with the physical network domain. Finally, a case study demonstrates the fast and accurate model adaptation ability of meta learning in comparison with benchmark schemes.
Modern cloud infrastructure is powered by cluster management systems such as Kubernetes and Docker Swarm. While these systems seek to minimize users' operational burden, the complex, dynamic, and non-deterministic nature of these systems makes them hard to reason about, potentially leading to failures ranging from performance degradation to outages. We present Kivi, the first system for verifying controllers and their configurations in cluster management systems. Kivi focuses on the popular system Kubernetes, and models its controllers and events into processes whereby their interleavings are exhaustively checked via model checking. Central to handling autoscaling and large-scale deployments is our design that seeks to find violations in a smaller and reduced topology. We also develop several model optimizations in Kivi to scale to large clusters. We show that Kivi is effective and accurate in finding issues in realistic and complex scenarios and showcase two new issues in Kubernetes controller source code.
Sareh Seyedi, Valerie K. Harris, Stefania E. Kapsetaki
et al.
One of the main reasons we have not been able to cure cancers is that drugs select for drug-resistant cancer cells. Pest managers face similar challenges with pesticides selecting for pesticide-resistant organisms. Lessons in pest management have led to four heuristics that can be translated to controlling cancers 1. limit use (of chemical controls or modes of action to the lowest practical levels) 2. diversify use (of modes of action largely through rotations of chemical controls) 3. partition chemistry (modes of action through space and time, which in effect is a refuge management strategy) and 4. include non-chemical methods. These principles are general to all cancers and cancer drugs, and thus should be employed to improve oncology. We review the parallel difficulties in controlling the evolution of drug resistance in pests and cancer cells, and describe the results of single- and multi-drug strategies in agriculture and oncology. We dissect the methods that pest managers use to prevent the evolution of pesticide resistance, showing how integrated pest management inspired the development of adaptive therapy in oncology to stabilize tumor size, and increase progression-free survival and quality of life of patients. Finally, we demonstrate these principles in a proposal for clinical trials in colorectal cancer.
In his 2004 article, Professor Hau Lee argues that the best supply chains are not only fast and cost‐effective but also agile, adaptable, and aligned. The concept of triple‐A supply chains has been extensively studied in academic and trade publications and integrated into numerous operations and supply chain management curricula. It has also influenced the management approach of leaders around the world. Yet since the triple‐A concept was first developed, supply chains have become increasingly global, connected, and interdependent. The increased complexity of global supply chains has reduced much‐needed visibility, further complicating their management, while the growing connectivity and interdependence among different stakeholders have led to many unforeseen environmental and social issues. As a result, Professor Lee’s emphasis on triple‐A supply chains is even more relevant today. In light of these new challenges and demands, we revisit the original triple‐A definitions of agile, adaptable, and aligned, expanding these concepts for a more socially and environmentally conscientious world. We also discuss potential enablers of and barriers to sustainable triple‐A supply chains.