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The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

Previous research reports mixed results regarding the performance impact of servitization in manufacturing firms. To resolve this, the purpose of this paper is to develop a conceptually consistent and comprehensive measurement framework for both dimensions, servitization and its performance effect, and apply in a configurational analysis to reexamine previous evidence, arriving at a configurational theory of the relationship between servitization and firm performance.

Combining systematic literature review (SLR) and inductive reasoning, the existing indicators for servitization and performance are identified and clustered into groups that adequately represent both dimensions. The dataset is reanalyzed against the resulting framework to identify the configurational patterns and to formulate the theoretical propositions.

Financial and nonfinancial indicators of servitization and its performance impact are organized into a comprehensive measurement framework grounded on existing research. The subsequent meta-analysis shows that the positive or negative impacts of servitization on performance depend on how firms implement servitization strategies and which performance aspects are examined.

The results explain when servitization can be successful and confirm the existence of the so-called servitization paradox. The meta-analysis identified patterns that explain the previous mixed results, shaping a configurational theory of servitization. Thus, the measurement framework is conceptually robust and has sufficient detail to capture servitization and its performance outcome as it feasibly distinguished between different organizational configurations.

The framework provides a comprehensive portfolio of indicators for both managers and scholars to measure servitization intensity and performance. This supports managers of servitizing firms in leading this organizational transformation while avoiding its organizational and financial paradoxes.

The purpose of this paper is to provide a method for analysing and improving the operational performance of business processes (BPs).

The method employs two standards, Business Process Modelling Notation (BPMN 2.0) and Business Processes Simulation (BPSim 1.0), to measure key performance indicators (KPIs) of BPs and test for potential improvements. The BP is first modelled in BPMN 2.0. Operational performance can then be measured using BPSim 1.0. The process simulation also enables execution of reliable “what-if” analysis, allowing improvements of the actual processes under study. To confirm the validity of the method the authors provide an application to the healthcare domain, in which the authors conduct several simulation experiments. The case study examines a standardised patient arrival and treatment process in an orthopaedic-emergency room of a public hospital.

The method permits detection of process criticalities, as well as identifying the best corrective actions by means of the “what-if” analysis. The paper discusses both management and research implications of the method.

The study responds to current calls for holistic and sustainable approaches to business process management (BPM). It provides step-by-step process modelling and simulation that serve as a “virtual laboratory” to test potential improvements and verify their impact on operational performance, without the risk of error that would be involved in ex-novo simulation programming.

This study attempts to develop a practical understanding of the positive and negative employee experiences due to artificial intelligence (AI) adoption and the creation of technostress. It unravels the human resource development-related challenges with the onset of Industry 4.0.

Semi-structured interviews were conducted with 32 professionals with average work experience of 7.6 years and working across nine industries, and the transcripts were analyzed using NVivo.

The findings establish prominent adverse impacts of the adoption of AI, namely, information security, data privacy, drastic changes resulting from digital transformations and job risk and insecurity brewing in the employee psyche. This is followed by a hierarchy of factors comprising the positive impacts, namely, work-related flexibility and autonomy, creativity and innovation and overall enhancement in job performance. Further factors contributing to technostress (among employees): work overload, job insecurity and complexity were identified.

The emerging knowledge economy and technological interventions are changing the existing job profiles, hence the need for different skillsets and technological competencies. The organizations thus need to deploy strategic manpower development measures involving up-gradation of skills and knowledge management. Inculcating requisite skills requires well-designed training programs using specialized tools and virtual reality (VR). In addition, employees need to be supported in their evolving socio-technical relationships, for managing both positive and negative outcomes.

This research makes the unique contribution of establishing a qualitative hierarchy of prominent factors constituting unintended consequences, positive impacts and technostress creators (among employees) of AI deployment in organizational processes.