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.

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.

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.