This research aims to develop and to examine a measurement of customers’ experiences in the last-mile delivery process, which is a critical step towards their satisfaction and future intention to order products from the same retailer again. The CMX-LMD scale is proposed as a self-report tool whose psychometric properties where then examined. The total sample of N = 907 participants (63.4% females) was randomly split into two subsamples, then EFA and CFA were conducted, followed by item and correlation analysis. Six factors were revealed (Delivery efficiency, Parcel tracking, Smooth delivery, Visual appeal, Joyful anticipation, and Convenience), explaining about 60% of the total variance. The structure was supported by CFA (χ2(237) = 584.71, p < 0.001, CFI = 0.941, TLI = 0.932, RMSEA = 0.057, SRMR = 0.058). The relations of the derived scales and other variables, such as trust in courier services, were also examined. The CDX-LMD offers acceptable properties and provides a useful measurement for researchers and practitioners in the cross-section between e-commerce and logistics.

The primary goal of this research is to identify which factors most significantly influence customer satisfaction in the last-mile delivery (LMD) process. The sample comprised 907 participants (63.4% female) with a mean age of 34.90. All participants completed three questionnaires regarding LMD, customer satisfaction, and trust in courier service. Furthermore, participants answered questions related to significant aspects of the delivery process: speed, price, and courier call before delivery. To determine which factors most significantly influence customer satisfaction in LMD, structural equation modeling (SEM) was applied. The tested SEM model showed a good fit. The results indicated that within the LMD dimension, visual appeal was a significant predictor in a negative direction, and all other LMD dimensions (except parcel tracking) were positive and significant predictors of customer satisfaction. Trust in courier service, delivery price, speed, and courier call before delivery were statistically significant predictors of customer satisfaction in last-mile delivery, all in a positive direction.

The evolution from traditional industrial maintenance to sustainable maintenance (SM) is pivotal within an existing industrial ecosystem. This study, utilising an umbrella review (UR), critically examines this transition, highlighting its increased importance in maintenance decision-making (MDM). Using a sample (n = 20) of reviews, we synthesised meta-, methodological-, and content-based evidence and performed bibliometric, thematic and statistical analyses. For the bibliometric and thematic/conceptual analyses, we used the R bibliometrix package. The results show that the early research focuses mainly on theoretical aspects, while recent studies examine the practical implications. Also, comprehensive studies evaluating the benefits of implementing environmental and social aspects within MDM are still lacking. For that reason, we switched the attention to content-based data, from which we identified 43 distinct criteria discussed. For the analysis of criteria, the Bayesian Network Analysis with Gaussian Copula Graphical Model (BNA-GCGM) method was used. Although the evidence shows that environmental pollution, energy consumption and health and safety of workers are the most discussed criteria, the BNA-GCGM suggests that labour costs, resource consumption, employee satisfaction and energy consumption, among others, are the most influential criteria in the network analysis. Interestingly, after distinguishing studies into pre- and post-2021 research, the results show that pre-2021 research is primarily focused on economic and technical factors, reflecting a profit-oriented approach. The post-2021 analysis suggests a discernible shift towards more balanced considerations by incorporating social and environmental factors, suggesting a more socially responsible approach. Finally, while SM is gaining momentum, further empirical and practical research are required to demonstrate the advantages that SM offers in the light of the upcoming Industry 5.0.

As the digital procurement transformation continues to evolve, it is crucial to adopt a risk-aware approach to ensure successful outcomes. This paper delves into the necessity of a risk-aware approach to digital procurement transformation, specifically focusing on the role of professional procurement management and the significance of supplier partnerships in the digital environment. The research aimed to examine the critical role of risk management in the digital transformation of procurement. A total of 318 respondents from Serbia participated in the study. The role of professional management in procurement must encompass risk management to guarantee success in digital procurement transformation. Furthermore, the study identified that adequate preparation, information, and training for individuals involved are crucial for a seamless transition to digital procurement. The digital transformation of procurement contributes to sustainability by enhancing efficiency, transparency, and collaboration across the supply chain, ultimately fostering environmentally conscious practices and responsible resource management. In summary, the research underscores the need for a comprehensive and risk-aware approach to digital procurement transformation, considering the pivotal roles of procurement professionals, supplier partnerships, and the integration of new technologies.

The rapid growth of e-commerce has transformed customer behaviors, demanding deeper insights into how demographic factors shape online user preferences. This study performed a threefold analysis to understand the impact of these changes. Firstly, this study investigated how demographic factors (e.g., age, gender, education) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, conditional dependencies between demographics and user preferences were tested. From a hypothetical framework of 24 tested hypotheses, this study successfully rejected 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency and reasons for quitting the purchase. However, although the reported test statistics suggested an association, understanding how interactions between categories shape e-customer profiles was still required. Therefore, the second part of this study considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis revealed three main clusters: (1) young, female, unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; and (3) employed, highly educated, male, middle-aged adults who prioritize fast and accurate delivery over price. In the third stage, the clusters are used as labels for Machine Learning (ML) classification tasks. Particularly, Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF), and Support Vector Machine (SVM) were used. The results suggested that GBM, RF, and SVM had high classification performance in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggested that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies.

With the growing trend of urbanisation and the growing number of people migrating to cities, the demand for the development and construction of new buildings and infrastructure has risen, meaning that the construction industry must adapt to these trends. Growing demands with shorter deadlines for an industry already known for its high costs and late delivery means that productivity must be increased without increasing costs. The solution for this might lie in the application of the Lean philosophy to the construction industry. This paper analyses the application of the Lean philosophy in order to increase the productivity of construction work for an airport project. This paper highlights the potential for enhancing productivity in construction workplaces by concurrently fostering continuous improvement and sustainability through the implementation of the A3 methodology and Lean principles, resulting in waste reduction and increased value.