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The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.

This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.

The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.

These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

This article aims to clarify the concepts used to understand, analyze and improve a patient’s progress through a health service system. A patient pathway describes plans and intentions. Within it, we distinguish between the clinical pathway of decisions and interventions and the care pathway of supportive activities. As a patient pathway is implemented, it turns into a patient journey of what is done, what happens to a patient’s medical condition and what is experienced and felt. We introduce “patient journey disruption” (PJD) as a concept describing the events that need to be prevented from happening to accomplish integrated, coordinated and seamless care.

The method used in this paper is concept analysis. First, an expert steering group worked to refine the concept of PJDs; second, an analysis of similar concepts from related fields was done to root the concept into existing theories, and third, semi-structured interviews with professionals and patients were done to test the concept of PJDs in the home care context.

PJDs are agency-based harmful events in the execution of the care pathway that deviate the patient journey from what can be reasonably expected. PJDs are management failures, which is why they should be studied by healthcare operations management (HOM) and service science scholars with the intention to find ways to prevent them from happening.

This study has limitations, including presenting conceptual ideas and preliminary results that are only indicative.

We believe that the introduction of the concept of PJDs into the literature provides a new, systematic way of approaching the different shortcomings in our healthcare production systems. Moreover, by systematically identifying different PJDs, interventions can be designed and targeted more appropriately.

Managerial challenges regarding healthcare processes have been studied but have not been well defined. The concept of PJDs is an original, well-thought-out definition.

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.