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Poor maintenance management leads to non-negligible economic, environmental and social impacts and obstacles to the sustainable manufacturing paradigm. Studies evaluating maintenance impacts on sustainability underline growing interest in the topic, but reports on the industrial field are lacking. Therefore, this paper investigates the industrial environment and the indicators that manufacturing companies use for measuring their maintenance impacts.

In this pilot survey study, several stakeholders of production enterprises in the south of Italy were interviewed to unveil the spread of the measurement of maintenance impacts on sustainability and the indicators used by those companies.

The interview results showed a low level of awareness among stakeholders about maintenance impacts on sustainability. Maintenance stakeholders are mainly focused on technical and economic factors, whereas environmental, quality and safety stakeholders are becoming more aware of maintenance impacts on environmental and social factors. However, both groups need guidelines to define sustainability indicators to assess such impacts.

This exploratory study allowed us to investigate the current situation in industrial organisations and achieve the first variegated and diversified vision of the awareness of company stakeholders on maintenance impacts on the sustainability of several business functions. This paper provides a valuable contribution to “maintenance and sustainability” research area in production contexts and sheds light on non-negligible maintenance impacts on sustainability, providing preliminary insights on the topic and an effective basis for defining future research opportunities. Moreover, this study enables increased awareness among internal and external manufacturing company stakeholders on the role of maintenance in sustainable production.

The increasing number of natural disasters has increased the attention on emergency plans aimed at providing fast support to affected communities. In this context, inventory pre-positioning management, which involves positioning the materials required to meet the affected community's needs early, has been increasingly acknowledged, but many challenges persist. The purpose of the paper is to provide a decision support system for the optimal quantification and location of humanitarian aid, trying to enhance and extend the existing literature on this topic.

The paper develops a numerical model for inventory pre-positioning of humanitarian aid to reduce both emergency response times and costs connected to goods procurement for seismic events. By examining the characteristics of the territory and the affected population, the model defines the optimal stock levels for four basic need items (hygienic sanitary kits, beds, blankets and camp tents) to be pre-allocated in the territory.

The model was validated using data obtained from the two severe earthquakes that occurred in Italy. The case study showed how the simulated outputs differ from the real case data and the economic benefits of adopting inventory pre-positioning considering the cost reductions (purchase, storage, transport and fulfilment of requirements).

The proposed decision support system allows the pre-positioning of emergency supplies in local areas in order to reduce response times and increase the speed of intervention in the event of seismic events, exploiting the advantages of a simulation model. Numerical and graphical results can easily support improvements in humanitarian logistics, providing those affected with rapid, cost-effective and better-adapted responses.

This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image.

This work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study.

Results show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination.

The originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.

The wide development of digital platforms permitted the birth of new financing modalities, namely, crowdfunding, where the crowd of individuals and investors can supply the necessary financial resources for venture creation and growth. While the extant literature has focused on analyzing the dynamics and features of crowdfunding campaigns, few studies have focused on understanding how crowd investors decide which ventures to invest in and which factors influence their decision-making process. Due to this gap, the purpose of this paper is to analyze the factors influencing the choice to invest in an equity crowdfunding campaign, by defining a set of indicators useful to evaluate the risk of the campaign.

An empirical research study of Italian equity crowdfunding campaigns has been conducted to identify quantitative indicators useful for evaluating the risk in a crowdfunding campaign.

Findings demonstrate that the risk indicators proposed to represent important gauges that investors can usefully consider ex ante to assess the degree of riskiness of the investment in the equity crowdfunding campaign.

The limitations of the study regarding the size of the sample that is small due to the necessity to extract enough information in pre and post-equity campaigns. Also, the lack of historical data is another limitation.

The originality of the studies relies on the proposal of quantitative indicators for the evaluation of the risk in equity crowdfunding campaigns for “crowd” investors to reduce information asymmetries.

Recognizing novel entrepreneurial opportunities arising from a crisis is of paramount importance for firms. Hence, understanding the pivotal factors that facilitate firms in this endeavor holds significant value. This study delves into such factors within a representative empirical context impacted by a crisis, drawing insights from existing literature on opportunity recognition during such tumultuous periods.

The authors conducted a qualitative inspection of 14 Italian firms during the COVID-19 pandemic crisis. The authors collected a rich body of multi-source qualitative data, including 34 interviews (with senior managers and entrepreneurs) and secondary data (press releases, videos, web interviews, newspapers, reports and academic articles) in two phases (March–August 2020 and September–December 2020).

The results suggest the existence of a process model of opportunity recognition during crises based on five entrepreneurial influencing factors (entrepreneurial knowledge, entrepreneurial alertness, entrepreneurial proclivity, entrepreneurial personality and entrepreneurial purpose).

Various scholars have highlighted that, in times of crises, it is not easy and indeed very challenging for entrepreneurs to identify novel entrepreneurial opportunities. However, recent research has shown that crises can also positively impact entrepreneurs and their capacity to identify new entrepreneurial opportunities. Given these findings, not much research has analyzed the process by which entrepreneurs identify novel entrepreneurial opportunities during crises. This study shows that some entrepreneurial influencing factors are very important to identify new entrepreneurial opportunities during crises.

The purpose of this paper is to discuss the dynamics of the evolution of damage to the residential buildings within the city walls of Norcia during the six-month seismic swarm that hit Central Italy in the period 24th August 2016 to end of January 2019. This is accomplished by comparing the damage recorded by the Italian Civil Protection usability form (AEDES form) during this period after each event.

First, these outputs are compared with a qualitative assessment conducted by means of omnidirectional camera (ODC) imagery collected on site by the authors, to explore the ability of this technology to support post-earthquake damage assessment. The damage level attributed with these two techniques is then further compared with the output of the analytical vulnerability assessment method FaMIVE, which allows to correlate damage to vulnerability. Specifically, the objective is to investigate the efficacy and performance of historic and recent strengthening interventions.

Results show that there is a good correspondence between AeDES and ODC assessments for low to medium damage grades (DG). Discrepancies in higher DGs are discussed in light of the different levels of information that can be recorded by using the two tools. The efficacy of strengthening is also well captured by the FaMIVE method. The procedure estimates a decrease of almost 40, 25 and 20 per cent of the total number of buildings failing out-of-plane, respectively, for the three seismic events considered, when restraining elements are in action.

The analysis conducted in this work make use of deterministic values of Norcia’s masonry fabric characteristics that have been found in literature, thus implying that neither the probabilistic aspects nor the related uncertainties have been properly investigated and addressed. However, this limitation is to be considered within the more general context of the legislation for the preservation of historic buildings which limits substantially any type of semi-destructive tests, hence limiting the reliability of the values available in literature. This in turn affects the decisions informing the design and implementation of strengthening interventions which can be confidently considered reliable and effective.

The paper addresses for the first time a systematic investigation of damage progression in historic masonry structures, part of urban aggregates in heritage cities. The current urban fabric is discussed in view of historic building codes as the basis for determining the present seismic vulnerability of the historic city centre of Norcia. The study provides new data sets for the city of Norcia and develops a statistical correlation between cumulative damage and analytical vulnerability functions for heritage buildings exposed to a swarm of earthquakes. The analytical assessment of the effect of historic strengthening is totally novel.