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This study addresses current research gaps by integrating resilience literature with crisis management theories, focussing on SMEs. Specifically, the authors examine how the entrepreneurial decision-making process, via the interplay of causation and effectuation logic, impacts a firm's ability to respond to unpredictable events. This paper aims to present an investigation that seeks to unearth the potentially complex interplay between causation and effectuation logic in fostering organisational resilience, particularly in the face of unprecedented disruptions such as the COVID-19 pandemic.

This study includes the responses of 80 Italian entrepreneurs operating in the hospitality sector. The paper deployed a joint analysis through a partial least squares structural equation modelling technique (PLS-SEM) and a necessary condition analysis (NCA) to assess how the decision-making logics impact the entrepreneurs' decision when reacting to the pandemic.

The findings show that how entrepreneurs make decisions influence how they react to the crisis. Causation was found as a direct cause of resilience and preparedness, and effectuation was a direct cause of resilience and agility. Moreover, causation indirectly caused resilience through preparedness, and effectuation indirectly caused resilience through agility. Finally, both preparedness and agility are direct causes of resilience.

This research generated insights into why and how some SMEs respond more effectively to uncertainty than others. It provides actionable strategies that business owners and managers can employ to enhance their ability to withstand and recover from crises.

This study's originality and novelty lie in its empirical investigation of the roles of causation and effectuation logic in entrepreneurial decision-making and, consequently, their influence on SME resilience. Focused on the Italian hospitality sector, it provides unique insights into resilience strategies under severe, real-world conditions, contributing to theoretical development and practical applications in crisis management.

This paper aims to investigate the impact of knowledge spillover effects (KSE) on employment levels using a sample of 245 Italian Innovative startup companies created as a result of the legislative changes of Law Decree 179/12 introduced in Italy in 2012.

This study uses a parsimonious model with the employment level as the dependent variable. The paper tests for the impact that the measures of industry competition, specialization and diversity have on the level of employment in the Innovative Startup sector in Italy. The data uses a sample of 245 firms, across 20 geographic regions in Italy for three economic sectors at the 2-Dig NAICS classification.

The empirical results provide evidence in favor of regional specialization as the main force to create and transfer knowledge resulting in increased employment; while higher levels of competition and a more diverse regional production bases result in lower firm employment levels. Employment levels for these firms are also time-dependent, and thus mainly determined at the time of the firm’s creation. This study also found a lack of technological convergence across regions, that are inherent regional differences are not bridged by knowledge spillover effects.

This paper is based on a sample of Italian Innovative Startups and consequently, further research with a potentially larger sample and, perhaps, a sample across countries could also shed some light on the issues relating to KSE and their effects on employment generation and firm formation.

From a practical point of view, the results indicate that regional disparity and limited transmission of KSE across regions remain an impediment to the flow of knowledge. This in turn may limit the development of entrepreneurial activities and further development of new firms. Practical implications regarding knowledge management indicate that firms face time and spatial challenges when developing, transferring and acquiring knowledge. In sum, the evidence points out in favor of existent and persistent regional heterogeneity in terms of economic and technological specialization as sources of employment.

This research adds to the empirical evidence focusing on the effects of knowledge spillover effects in the Innovative Startup segment of the economy. This research highlights the applicability of knowledge spillover effects accounting for levels of industry competition, specialization and diversity. We also provide a measure of cluster formation and concentration at the sectoral and regional levels. Thus, the research provides a better understanding under which conditions knowledge is more likely to have positive or negative effects on employment generation.

The objective of this study is to examine current business and management research on “Industry 4.0 base technologies” and “business models” to shed light on this vast literature and to point out future research agenda.

The authors conducted a bibliometric analysis of scientific publications based on 482 documents collected from the Scopus database and a co-citation analysis to provide an overview of business model studies related to Industry 4.0 base technologies. After that a qualitative analysis of the articles was also conducted to identify research trends and trajectories.

The results reveal the existence of five research themes: smart products (cluster 1); business model innovation (cluster 2); technological platforms (cluster 3); value creation and appropriation (cluster 4); and digital business models (cluster 5). A qualitative analysis of the articles was also conducted to identify research trends and trajectories.

First, the dataset was collected through Scopus. The authors are aware that other databases, such as Web of Science, can be used to deepen the focus of quantitative bibliometric analysis. Second, the authors based this analysis on the Industry 4.0 base technologies identified by Frank et al. (2019). The authors recognize that Industry 4.0 comprises other technologies beyond IoT, cloud computing, big data and analytics.

Drawing on these analyses, the authors submit a useful baseline for developing Industry 4.0 base technologies and considering their implications for business models.

In this paper, the authors focus their attention on the relationship between technologies underlying the fourth industrial revolution, identified by Frank et al. (2019), and the business model, with a particular focus on the developments that have occurred over the last decade and the authors performed a bibliometric analysis to consider all the burgeoning literature on the topic.

This article aims to compare the LiDAR handheld mobile laser scanner (HMLS) scans with traditional survey methods, as the tree gauge and the hypsometer, to study the efficiency of the new technology in relation to the accuracy of structural forest attributes estimation useful to support a sustainable forest management.

A case study was carried out in a high forest located in Tuscany (Italy), by considering 5 forest types, in 20 different survey plots. A comparative analysis between two survey methods will be shown in order to verify the potential limits and the viability of the LiDAR HMLS in the forest field.

This research demonstrates that LiDAR HMLS technology allows to obtain a large amount of valuable data on forest structural parameters in a short span of time with a high level of accuracy and with obvious impact in terms of organisational efficiency.

Findings could be useful for forest owners highlighting the importance of investing in science and technology to improve the overall efficiency of forest resources management.

This article adds to the current knowledge on the precision forestry topic by providing insight on the feasibility and effectiveness of using precision technologies for monitoring forest ecosystems and dynamics. In particular, this study fills the gap in the literature linked to the need to have practical examples of the use of innovative technologies in forestry.

This study examines the adaptation to future climate conditions of energy retrofitting measures, the performance of which is considered to be effective today. It is aimed to analyse the energy performance of retrofit interventions that respond positively to today's climate conditions in future climate conditions.

In the methodology of the study, the diversity of energy retrofit interventions in the literature is constructed within the framework of three different conceptualised intervention packages. Each conceptual intervention package is evaluated under three different climatic conditions for the years 2023, 2050 and 2080 and 9 different energy improvement scenario models are created. The base model in the study represents a cultural heritage building and the study presents a case study.

According to the findings, the most effective energy retrofit package in the climatic conditions of all years analysed is the intervention package created with renewable energy sources. The second package in energy optimisation is the package with an innovative concept using aerogel materials. Although the base model improves the energy performance, the poorest performance in the comparison of the three packages belongs to the package that includes interventions that are frequently used in the current literature.

This study presents original research to the literature in terms of addressing the role of current and future climate conditions in energy retrofit. The study findings provide quantitative data on the revision requirements for new studies of energy retrofit interventions that are frequently used today.