With the urgent call for sustainability and the rapid advancement of information technology, especially machine learning and artificial intelligence, the food industry faces unparalleled opportunities and challenges in optimizing resources and enhancing performance. By that call, we foresee a massive change, especially to the way research has been traditionally conducted, it is hypothesized for a surge of research and increasing adoption of innovative tools such as deep learning (DL) and optimization techniques by the research community and the field to address critical challenges in food systems. This study investigates the intersection of commercial, sustainability, and technology domains through advanced bibliometric analysis enhanced by deep document clustering, aims to uncover the multifaceted nature of these interconnected fields, addressing how emerging technologies and sustainability practices shape commercial strategies in the field, providing a panoramic perspective for reader about the dynamic development of the field. Utilizing a multi-tiered literature search strategy and employing deep document clustering techniques, the analysis reveals distinct thematic clusters—ranging from macroeconomic demand challenges to technical advancements in AI-driven agricultural practices and sustainable supply chain management—that underscore the deep connections among commercial, sustainability, and technological domains. The results not only validate the hypothesis of increasing convergence among these fields but also highlight the critical role of advanced methodologies in uncovering latent patterns and facilitating strategic and operational improvements. Moreover, the findings demonstrate that deep document clustering is a powerful tool for bibliometric analysis, offering nuanced insights that can be further refined in future studies. Overall, this research contributes to a holistic understanding of the field, emphasizing the need for continued interdisciplinary collaboration and innovation to drive sustainable development in a global business environment.

This article examines the strategic role of dual-use technologies in advancing defence systems and services, with a focus on artificial intelligence (AI) in the space sector. It explores challenges to broader AI adoption and the sharing of space data across civil and defence domains, highlighting their implications for technological sovereignty. From the European Union (EU) perspective, the study analyses geopolitical constraints on enabling space technologies within the contested environment of near-Earth space, emphasizing their critical role in reinforcing AI-driven capabilities and strategic autonomy. The consolidation of space and defence funding under a single budget line of the European Competitiveness Fund within the EU’s 2028–2034 multiannual financial framework provides a foundation for assessing the impact of AI-enabled, dual-use initiatives on EU technological leadership. Despite inherent risks, the rapid integration of AI in space underscores the importance of robust testing and validation mechanisms, which will shape the global positioning of major powers. The article concludes with policy recommendations to strengthen EU defence space capacities and secure its technological sovereignty and strategic autonomy by using AI as a powerful enabling tool.

The objective of this study is to build a predictive model for the call center of a financial services company using predictive analytics methods. Using this model, the organization would be able to transition from a reactive approach to operational resource allocation to a more proactive one in handling inbound calls. This study uses a quantitative methodology, as it provides a structured, objective, and rigorous framework essential for developing, testing, and validating forecasting models. Quantitative methods’ reliance on numerical data and statistical techniques ensures accuracy, reliability, and the ability to generalize findings. This study contributes valuable insights into the application of time series forecasting models in optimizing resource allocation and enhancing service quality within the call centres, particularly within the financial services industry. By addressing the identified research gap and providing practical recommendations, this research offers an addition to further advancements in call center forecasting methodologies, facilitating more efficient and effective operations within financial organizations.

This study investigates the impact of SERVQUAL dimensions (Assurance, Reliability, Tangibles, Empathy, and Responsiveness) and review scores on customer sentiment. We analyze a large dataset of 920,407 UberEats reviews from the Google Play Store, classifying sentiment based on star ratings and using a Long Short-Term Memory (LSTM) model to predict sentiment from review content. Using text mining and sentiment analysis, the study employs robust feature engineering techniques to extract and quantify SERVQUAL components from customer reviews. The LSTM model demonstrated high accuracy (89.64%) in predicting sentiment, validating the alignment between predicted and assigned sentiments. Our analysis reveals that all SERVQUAL dimensions and review scores have a positive and significant impact on overall sentiment. Specifically, the Ordinary Least Squares (OLS) regression results highlight Empathy as the most influential SERVQUAL component, followed by Responsiveness, Reliability, Tangibles, and Assurance. Furthermore, review score emerged as the strongest predictor of customer sentiment. These findings provide actionable insights for service providers aiming to enhance customer satisfaction by optimizing key SERVQUAL dimensions and addressing review score trends.

The objective of this study is to address existing study gaps by defining what materials are demand-driven material requirements planning (DDMRP) suitable and building a tool that helps to identify such materials. The research problems are approached with three different questions about suitability, features of the identification tool, and financial impact. The research methodology consists of a mixed method case study approach, where semi-structured interviews were conducted with supply chain professionals from the case company, and quantitative data related to the case company’s operations was analyzed. The data consisted of the relevant data of over 10,000 purchased materials. The findings of this study suggest that there are no certain characteristics that materials suitable for DDMRP have, but the potential must be defined individually in the case of every purchased material. The tool initially developed in this study helps to identify materials that meet the requirement of providing potential positive financial impact if brought into DDMRP scope by analyzing historical demand data, lead times, and inventory carrying costs.

This study aims to investigate the impact of digital supply chains and smart technology on the operational performance of the manufacturing industry. Due to the lack of knowledge and guidance in this area, the adoption of smart technology throughout the supply chain is limited, leading to poor operational performance. Therefore, the purpose of this study is to investigate how smart technology and digital supply chain transformation can improve operational performance. To test hypotheses and accomplish study goals, the Resource-Based View (RBV) theory was combined with a quantitative research strategy. The study population of companies was obtained from a manufacturing directory, and a minimum sample size of 107 companies was determined using G*Power. Additionally, 600 online surveys were sent to the manufacturing companies, resulting in a response rate of 17.83%. Data analysis was conducted using Smart-PLS 4.0 software, and eight of the 10 hypotheses were supported. The findings showed that smart technologies completely mediate the link between digital transformation and relationship performance, emphasizing the need for manufacturing organizations to focus on incorporating smart technology into their supply chain to enhance operational performance. The study concludes by presenting theoretical and practical implications, limitations, and recommendations.

In view of the higher demand and customer expectations on the speed and accuracy of e-commerce logistics service as well as the repetitive and time-consuming nature of manual order processing operation. This paper proposes a robotic process automation (RPA) model to liberate human resources over time-consuming, inefficient, non-value-added, and repetitive operational processes occupying workforces. This paper proposed an RPA model that integrates three functional RPA bots in (1) tracking order status, (2) capturing order data, and (3) verifying order data to increase the efficiency and accuracy of logistics operation and the order-handling process in small- and medium-sized enterprise (SME) logistics company. A case study was conducted on an SME logistics service providing customers with inbound and outbound operation services. The result demonstrated that the proposed model significantly improves logistics operations performance against a human approach concerning key indicators after implementation. Logistics companies could free up the workforce for value-creating activities in value-added services.

Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.

Audio classification tasks like speech recognition and acoustic scene analysis require substantial labeled data, which is expensive. This work explores active learning to reduce annotation costs for a sound classification problem with rare target classes where existing datasets are insufficient. A deep convolutional recurrent neural network extracts spectro-temporal features and makes predictions. An uncertainty sampling strategy queries the most uncertain samples for manual labeling by experts and non-experts. A new alternating confidence sampling strategy and two other certainty-based strategies are proposed and evaluated. Experiments show significantly higher accuracy than passive learning baselines with the same labeling budget. Active learning generalizes well in a qualitative analysis of 20,000 unlabeled recordings. Overall, active learning with a novel sampling strategy minimizes the need for expensive labeled data in audio classification, successfully leveraging unlabeled data to improve accuracy with minimal supervision.

The manufacturing industry plays a crucial role in the economy of many countries, but there is a lack of expertise in implementing smart supply chains and smart technologies. This study aims to investigate the relationship between smart supply chains, smart technologies, and operational performance in the manufacturing industry. A survey questionnaire was conducted among registered manufacturing industries, and the results were analyzed using Smart PLS to test 10 hypotheses. Four hypotheses were supported out of 119 responses received through simple random sampling. The study suggests implementing instrumented supply chains using smart technologies can enhance operational performance. The findings provide valuable insights for policymakers, academics, and industry practitioners to improve the competitiveness of the manufacturing industry. This research emphasizes the importance of smart supply chains and smart technologies in achieving operational excellence, and further studies are necessary to address the identified limitations and contribute to a deeper understanding of the role of smart technologies in the supply chain’s digitalization.