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The outbreak of the COVID-19 pandemic has significantly impacted healthcare systems, presenting unforeseen challenges that necessitated the implementation of change management strategies to adapt to the new contextual conditions. This study aims to analyze organizational changes within the total hip replacement (THR) surgery pathway at multiple levels, including macro, meso and micro. It employs data triangulation from various sources to gauge the complexity of the change process and comprehend how multi-level decision-making influenced an unexpected shift.

A multicentric, single in-depth case study was conducted using a mixed-methods approach. Data sources included patient-reported outcome measures specific to the THR pathway and carefully structured in-depth interviews administered to managers and clinicians in two healthcare organizations serving the same population.

Decisions made at the macro level resulted in an overall reduction in surgical activities. Organizational changes at the meso level led to a complete cessation or partial reorganization of activities. Micro-level actions for change and adaptation revealed diverse and fragmented change management strategies.

Organizations with segmented structures may require a robust and structured department for coordinating change management responses to prevent the entire system from becoming stuck in the absorptive phase of change. However, it is important to recognize that absorptive solutions can serve as a starting point for genuine innovations in change management.

The utilization of data triangulation enables the authors to visualize how specific changes implemented in response to the pandemic have influenced the observed outcomes. From a managerial perspective, it provides insights into how future innovations could be introduced.

The purpose of this article is to investigate the interrelationships between innovation climate and human capital in the development of dynamic capabilities related to innovation. The study presents a set of concepts about variables involved in the innovation process and their interrelationship, addressing the analysis of international scientific production related to the antecedents of the innovation climate, human capital and dynamic capabilities.

A review of the literature from 1998 to 2018 was carried out, using descriptive statistical methods, at first, and qualitative analysis of the results in order to visualize the current configuration of the field of study of innovation background.

The results demonstrate a significant number of studies relating human capital and dynamic capabilities and little quantitative significance in studies relating to a climate of innovation and dynamic capabilities. The research describes how the dynamic capabilities of innovation have been approached in a conceptual model based on the perspective of human capital and innovation climate.

The study did not contemplate the analysis of the interrelationship between the resource configuration construct and the dynamic innovation capacities (part of the gap pointed out by Tuzovic, Wirtz and Heracleous (2018), constituting a perceptible limitation of the analyzes carried out in this article.

The influence of the innovation climate construct and its relationship with dynamic innovation capabilities deserves greater attention in research with an empirical approach, constituting a field to be explored by scientific research in organizations.

The research sought to investigate the gap involving the interrelations between innovation climate and human capital in the development of dynamic capabilities related to innovation, indicating the need for further empirical studies on the subject.

The aim of the paper is to investigate several aspects of bankruptcy prediction within both theoretical and empirical frameworks. In particular, it has focused on the comparison of different techniques used to forecast failure through a balanced sample of companies within a geographical area (the Campania region) located in the south of Italy.

Business failure has been one of the most investigated topics within corporate finance and the empirical approach to bankruptcy prediction has recently gained further attention from financial institutions. The aim of corporate failure prediction is to have a methodological approach which discriminates firms with a high probability of future failure from those which are considered to be healthy. Starting from the seminal paper of Altman (1968), many other significant contributions have been subsequently made to this field (Ravi Kumar and Ravi, 2007). This paper's approach is to compare different statistical techniques based on the analysis of financial data for the prediction and diagnosis of the risk of bankruptcy.

The paper investigates the determinants of bankruptcy in a specific geographical area (Campania region). Empirical evidence on a data‐set of the annual reports of a balanced sample of companies for a given time period has been analyzed. These findings aim to make a contribution to current literature as well as to contribute to the elaboration of efficient prevention and recovery strategies.

Researchers have tended to restrict the scope of their analysis due to the problems related to the collection and storage of financial data. This study focuses on financial information relating to the area of interest in order to provide a comparative analysis of different forecasting techniques.