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Digital transformation (DT) is a priority for the healthcare sector. In many countries, it is still considered in the early stages with an underestimation of its benefits and potentiality. Especially in Italy, little is known about the impact of digitalization – particularly of the Internet of Things (IoT) – on the healthcare sector, for example, in terms of clinician's jobs and patient's experience. Drawing from such premises, the paper aims to focus on an overlooked healthcare area related to the chronic heart diseases field and its relationship with DT. The authors aim at exploring and framing the main variables of remote Monitoring (RM) adoption as a specific archetype of healthcare digitalization, both on patients and medical staff level, by shedding some lights on its overall implementation.

The authors empirically inquiry the RM adoption within the context of the Cardiology Department of the Casilino General Hospital of Rome. To answer our research question, the authors reconstruct the salient information by using induction-type reasoning, direct observation and interviewees with 12 key informants, as well as secondary sources analysis related to the hospital (internal documentation, presentations and technical reports).

According to a socio-technical framework, the authors build a model composed of five main variables related to medical staff and patients. The authors classify such variables into an input-process-output (I-P-O) model. RM adoption driver represents the input; cultural digital divide, structure flexibility and reaction to change serve the process and finally, RM outcome stands for the output. All these factors, interacting together, contribute to understanding the RM adoption process for chronic disease management.

The authors' research presents two main limitations. The first one is related to using a qualitative method, which is less reliable in terms of replication and the interpretive role of researchers. The second limitation, connected to the first one, is related to the study's scale level, which focuses on a mono-centric consistent level of analysis.

The paper offers a clear understanding of the RM attributes and a comprehensive view for improving the overall quality management of chronic diseases by suggesting that clinicians carefully evaluate both hard and soft variables when undertaking RM adoption decisions.

RM technologies could impact on society both in ordinary situations, by preventing patient mobility issues and transport costs, and in extraordinary times (such as a pandemic), where telemedicine contributes to supporting hospitals in swapping in-person visits with remote controls, in order to minimize the risk of coronavirus disease (COVID-19) contagion or the spread of the virus.

The study enriches the knowledge and understanding of RM adoption within the healthcare sector. From a theoretical perspective, the authors contribute to the healthcare DT adoption debate by focusing on the main variables contributing to the DT process by considering both medical staff and patient's role. From a managerial perspective, the authors highlight the main issues for RM of chronic disease management to enable the transition toward its adoption. Such issues range from the need for awareness of the medical staff about RM advantages to the need for adapting the organizational structure and the training and education process of the patients.

The purpose of this paper is to investigate the relationship between intellectual capital (IC), categorized in terms of four sub-constructs – namely, human capital (HC), relational capital (RC), innovation capital (InnC) and process capital (PrC) – and business performance in the agribusiness industry.

Based on a sample of international agribusiness companies observed over a five-year period, this paper uses correlation and multiple regression analysis to test for the existence of a positive relationship between each IC component and conventional business performance metrics.

The empirical results support the hypotheses that RC and PrC have a positive impact on corporate performance. Counter to the expectations, InnC by itself is negatively associated with performance. Results also failed to confirm the hypothesis that HC directly and positively affects performance. However HC positively moderates the relation between InnC and performance, which suggests that firms that heavily invest in HC are better placed to gain returns from their research and development (R & D) investments.

This study expands the existing research on the link between IC and performance by adding fresh evidence from a highly knowledge-intensive sector which has been under-researched thus far. It may also contribute to the specific literature on R & D and performance as it uncovers that the value-generating effect associated with R & D investments is contingent on the levels of HC.