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.

Research exploring quality management, knowledge management, and innovations in organizations has received significant attention from academics worldwide, providing different insights. Innovation has been widely seen as an essential organizational performance driver. This study aims to accentuate the importance of quality management and knowledge management and their direct, mediating, and total effect on an organization’s process innovations. The double-reflective second-order construct model was analyzed following the most recent methodology guidelines. Eventually, partial least squares structural equation modeling (PLS-SEM) was used to test the research hypotheses and investigate the relations between the latent factors. The results from 264 Serbian companies that implemented ISO 9001 standard point to quality management’s direct effect on process innovations and knowledge management’s mediating effect on process innovation.

Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the reliability, validity, and usefulness of the derived process insights. The literature offers a taxonomy of preprocessing techniques and papers reporting on solutions for data quality issues in particular scenarios without exploring the relationship between the data quality issues and solutions. This research aims to discover how process mining researchers and practitioners solve certain data quality issues in practice and investigates the nature of the relationship between data quality issues and preprocessing techniques. Therefore, a study was undertaken among prominent process mining researchers and practitioners, gathering information regarding the perceived importance and frequency of data quality issues and solutions and the participants’ recommendations on preprocessing technique selection. The results reveal the most important and frequent data quality issues and preprocessing techniques and the gap between their perceived frequency and importance. Consequently, an overview of how researchers and practitioners solve data quality issues is presented, allowing the development of recommendations.

The effective occupational health and safety management system is aimed to reduce risks of injuries and occupational ill health in the organisation. The relation between occupational health and safety management and organisational performance has already been researched and established by some researchers. By creating safe and healthy environment, top management invests in optimal business conditions with agile work power that can fully contribute to achieving company goals, but the expected results and achievement of set goals may vary depending on stress-related working climate. The influence of stress factor as mediator between occupational health and safety management and organisational performance was a subject of research that is shown in this paper. A survey concerning constructs related to the topic was carried out in Serbia, and the results of the survey were analysed using structural equation modeling (SEM). The results of the research proved that occupational health and safety management system has a direct impact on employees' stress, and their stress has a significant, mediating, influence on organisational OHS performance.