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This paper aims to explore the impact of fourth-party logistics (4PL) adoption on the agility, adaptability and alignment (AAA) capabilities within humanitarian supply chains (HSCs).

Semi-structured interviews with individuals from a large non-government organisation were combined with secondary data to assess the influence of 4PL adoption on AAA capabilities in HSCs.

The results indicate that HSCs exhibit some of the AAA antecedents but not all are fully realised. While 4PL positively affects the AAA capabilities of HSCs, its adoption faces challenges such as the funding environment, data security/confidentiality and alignment with humanitarian principles. The study suggests an AAA antecedent realignment, positioning alignment as a precursor to agility and adaptability. It also identifies three core antecedents in HSCs: flexibility, speed and environmental uncertainty.

The study shows the positive impact 4PL adoption can have on the AAA capabilities of HSCs. The findings have practical relevance for those wishing to optimise HSC performance through 4PL adoption, by identifying the inhibiting factors to its adoption as well as strategies to address them.

This research empirically explores 4PL’s impact on AAA capabilities in HSCs, highlighting the facilitating and hindering factors of 4PL adoption in this environment as well as endorsing a realignment of AAA antecedents. It also contributes to the growing research on SC operations in volatile settings.

This study conducts a thorough literature review and meta-analysis to explore the adoption of blockchain technology (BCT) in supply chain management (SCM). It aims to identify the potential benefits, challenges, and critical factors influencing the implementation of this technology in supply chains.

A systematic analysis of 157 highly cited publications is performed, offering insights into research trends, citations, industries, research methods, and contextual aspects. Thematic analysis is employed to uncover key findings regarding enablers, barriers, drivers, challenges, benefits, and drawbacks associated with BCT adoption in supply chains.

The analysis highlights the complexities and opportunities involved in adopting BCT in SCM. A proposed model aligns with five dimensions, including inter-organizational, intra-organizational, technological, legal, and to assist businesses in harnessing the potential of BCT, overcoming obstacles, and managing challenges. This model provides practical recommendations for navigating the intricacies of BCT implementation while balancing associated challenges and risks.

Organizations operating in supply chains can leverage the insights gained from this investigation to position themselves at the forefront of BCT adoption. By implementing the proposed model, they can unlock benefits such as increased transparency, efficiency, trust, and cost reduction.

The novelty of this paper lies in its extensive review of publications on Blockchain Technology adoption in supply chains. It offers insights into various aspects such as enablers, barriers, drivers, challenges, benefits, and drawbacks. Additionally, the paper presents a comprehensive model specifically designed for successful adoption of Blockchain Technology in supply chains. This model addresses multiple dimensions including inter-organizational, intra-organizational, technological, legal, and financial.

This study aims to examine the impact of the decision makers’ risk preference on logistics routing problem, contributing to logistics behavior analysis and route integration optimization under uncertain environment. Due to the unexpected events and complex environment in modern logistics operations, the logistics process is full of uncertainty. Based on the chance function of satisfying the transportation time and cost requirements, this paper focuses on the fourth party logistics routing integrated optimization problem considering the chance preference of decision makers from the perspective of satisfaction.

This study used the quantitative method to investigate the relationship between route decision making and human behavior. The cumulative prospect theory is used to describe the loss, gain and utility function based on confidence levels. A mathematical model and an improved ant colony algorithm are employed to solve the problems. Numerical examples show the effectiveness of the proposed model and algorithm.

The study’s findings reveal that the dual-population improvement strategy enhances the algorithm’s global search capability and the improved algorithm can solve the risk model quickly, verifying the effectiveness of the improvement method. Moreover, the decision-maker is more sensitive to losses, and the utility obtained when considering decision-makers' risk attitudes is greater than that obtained when the decision-maker exhibits risk neutrality.

In an uncertain environment, the logistics decision maker’s risk preference directly affects decision making. Different parameter combinations in the proposed model could be set for decision-makers with different risk attitudes to fit their needs more accurately. This could help managers design effective transportation plans and improve service levels. In addition, the improved algorithm can solve the proposed problem quickly, stably and effectively, so as to help the decision maker to make the logistics path decision quickly according to the required confidence level.

Considering the uncertainty in logistics and the risk behavior of decision makers, this paper studies integrated routing problem from the perspective of opportunity preference. Based on the chance function of satisfying the transportation time and cost requirements, a fourth party logistics routing integrated optimization problem model considering the chance preference of decision makers is established. According to the characteristics of the problem, an improved dual-population ant colony algorithm is designed to solve the proposed model. Numerical examples show the effectiveness the proposed methods.

This paper aims to explore the use of digital technologies in enabling circular ecosystems. We apply supply network (SN) configuration theory and a novel resource pooling lens, more typically used in financial systems, to identify inventory pools, information repositories and financial exchange models among network actors.

Five in-depth circular SN case studies are examined where digital technologies are extensively deployed to support circularity, each case representing alternative SN configurations. Data collection involved semi-structured interviews to map SN and resource pooling configurations across each circular ecosystem, with cross-case analysis used to identify distinct pooling and digital strategies.

Results suggest three digitally enabled circular ecosystem archetypes and their related governance modalities: consortia-based information pooling for resource recovery, intermediary-enabled material and financial pooling for remanufacturing and platform-driven information, material and financial pooling for resource optimisation.

Drawing on SN configuration and resource pooling literature, we recognise distinct configurational, stakeholder and resource pooling dimensions characterising circular ecosystems. While this research is exploratory and the identified archetypes not exhaustive, the combination of resource pooling and configuration lenses offers new insights on circular ecosystem configurations and the critical role of resource pools and enabling digital technologies.

We demonstrate the utility of the resource pooling and configuration approach in the design of digitally enabled circular ecosystems. These archetypes provide practitioners and policymakers with alternative design frameworks when considering circular SN transformations.

This paper introduces a resource netting and pooling configuration lens to circular ecosystems, analogous to financial systems, where cyclical flows and stock are critical and enabled through digital technologies.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

This study aims to understand client food preferences and how pantry offerings can be optimized by those preferences.

This study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry.

This study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry.

This research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains.

This study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences.

An emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions.

This study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

This study aims to use the knowledge management perspective to examine the mechanism through which entrepreneurship drives firms’ technological innovation in the digital age. The objective is to develop a multi-stage integrated theoretical model to explain how entrepreneurship exerts its influence on firms’ technological innovation with a particular focus on the knowledge management perspective. The findings can be used for the cultivation of entrepreneurship and for the promotion of continuous technological innovation activities.

This study uses a case-based qualitative approach to examine the relationship between entrepreneurship and technological innovation. The authors first analyze the case of SANY and then explore the mechanism of how entrepreneurship can promote a firm’s technological innovation from the perspective of knowledge management based on the technology-organization-environment framework. An integrated theoretical model is then developed in this study.

Based on a case study, the authors propose that there are three main processes of knowledge management in firms’ technological innovation: knowledge acquisition, knowledge integration and knowledge creation. In the process of knowledge acquisition, the joint effects of innovation spirit, learning spirit, cooperation spirit and global vision drive the construction and its healthy development of firms’ innovation ecosystem. In the process of knowledge integration, the joint effects of innovation spirit, cooperation spirit and learning spirit help complete the integration of knowledge and further the accumulation of firms’ core knowledge resources. In the process of knowledge creation, the joint effects of mission spirit, learning spirit and innovation spirit encourage the top management team to establish long-term goals and innovation philosophy. This philosophy can promote the establishment of a people-oriented incentive mechanism that helps achieve the transformation from the accumulation of core knowledge resources to the research and innovation of core technologies. After these three stages, firms are passively engaged in the “reverse transfer of knowledge” step, which contributes to other firms’ knowledge management cycle. With active knowledge acquisition, integration, creation and passive reverse knowledge transfer, firms can achieve continuous technological innovation.

This study has important theoretical implications in entrepreneurship research. This study helps advance the understanding of entrepreneurship and literature on the relationship between entrepreneurship and technological innovation in the digital age, which can broaden the application of knowledge management theories. It can also help better understand how to develop healthy firm-led innovation ecosystems to achieve continuous optimization of knowledge and technological innovation in the digital age.

This study proposes an integrated theoretical model to address the issues of entrepreneurship and firms’ technological innovation in the digital age, and it is also one of few studies that focuses on entrepreneurship and innovation from a knowledge management perspective.

The number of passengers in the aviation sector following COVID-19 has recovered in 2023 and is 5% higher than it was in 2019. The average annual growth of air travel is predicted to be 3.2% between 2019 and 2039. This means the need for aircraft maintenance, repair and overhaul (MRO) services will also increase. Moreover, the stakeholders require lower costs, higher effectiveness/market share and sustainability. These expectations can be realized only with the identification, development and implementation of new innovations while improving and optimizing the already used processes and procedures. Since only highly qualified graduates can reach these requirements, the need for profession-specific MSc and PhD level engineers has elevated significantly. The purpose of this paper is to introduce the development and implementation of a new MRO higher educational framework program in strong cooperation with enterprises and universities.

The emphasis is placed on the program’s scouting, investigation, development, realization and evaluation by defining key performance indexes and aiming for the optimal solution for all participants.

The result of this study is the establishment of a new educational framework, the reinvention of the MSc curriculum and the integration of PhD-level researchers in the industry in a way that fulfills the needs and requirements of the MRO sector. In return, it will give various benefits to all parties involved.

The novelty of this work comes from creating a new educational MSc and PhD level framework that can push the MRO industry forward and fill the gap of missing engineers in this field. Plus, the newly developed program is highly flexible and can be used by other players in the economy after making some adaptions and modifications.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.