Preliminary human safety assessment (PHSA) for the improvement of the behavioral aspects of safety climate in the construction industry

Occupational safety in the construction industry still represents a relevant problem at a global level. In fact, the complexity of working activities in this sector requires a comprehensive approach that goes beyond normative compliance to guarantee safer working conditions. In particular, empirical research on the factors influencing the unsafe behavior of workers needs to be augmented. Thus, the relationship between human factors and safety management issues following a bottom-up approach was investigated. In particular, an easy-to-use procedure that can be used to better address workers' safety needs augmenting the company's safety climate and supporting safety management issues was developed. Such an approach, based on the assessment of human reliability factors, was verified in a real case study concerning the users of concrete mixer trucks. The results showed that the majority of human failures were action and retrieval errors, underlining the importance of theoretical and practical training programs as a means to improve safety behavior. In such a context, information and communication activities also resulted beneficially to augment the company's safety climate. The proposed approach, despite its qualitative nature, allows a clearer understanding of workers' perceptions of hazards and their risk-taking behavior, providing practical cues to monitor and improve the behavioral aspects of safety climate. Hence, these first results can contribute to augmenting safety knowledge in the construction industry, providing a basis for further investigations on the causalities related to human performances, which are considered a key element in the prevention of accidents

Residents' support for tourism development: The role of residents' place image and perceived tourism impacts

Drawing on the triple bottom line approach for tourism impacts (economic, socio-cultural and environmental) and adopting a non-forced approach for measuring residents' perception of these impacts, this study explores the role of residents' place image in shaping their support for tourism development. The tested model proposes that residents' place image affects their perceptions of tourism impacts and in turn their support for tourism development. The results stress the need for a more flexible and resident oriented measurement of tourism impacts, revealing that more favorable perceptions of the economic, socio-cultural and environmental impacts lead to greater support. Moreover, while residents' place image has been largely neglected by tourism development studies, the findings of this study reveal its significance in shaping residents' perception of tourism impacts as well as their level of support. The practical implications of the findings for tourism planning and development are also discussed

Municipal transitions: The social, energy, and spatial dynamics of sociotechnical change in South Tyrol, Italy

With the aim of proposing recommendations on how to use social and territorial specificities as levers for wider achievement of climate and energy targets at local level, this research analyses territories as sociotechnical systems. Defining the territory as a sociotechnical system allows us to underline the interrelations between space, energy and society. Groups of municipalities in a region can be identified with respect to their potential production of renewable energy by means of well-known data-mining approaches. Similar municipalities linking together can share ideas and promote collaborations, supporting clever social planning in the transition towards a new energy system. The methodology is applied to the South Tyrol case study (Italy). Results show eight different spatially-based sociotechnical systems within the coherent cultural and institutional context of South Tyrol. In particular, this paper observes eight different systems in terms of (1) different renewable energy source preferences in semi-urban and rural contexts; (2) different links with other local planning, management, and policy needs; (3) different socio-demographic specificities of individuals and families; (4) presence of different kinds of stakeholders or of (5) different socio-spatial organizations based on land cover. Each energy system has its own specificities and potentialities, including social and spatial dimensions, that can address a more balanced, inclusive, equal, and accelerated energy transition at the local and translocal scale

Return migration to home country: a systematic literature review with text mining and topic modelling

A crucial (and less developed) part of migration studies is the exploration of migrant’s further mobility and the intention of return to home country at some point in life. Knowing who, why and when returns matters for both the host and the home country. Very few studies have focused on return in a wider perspective, adopting a comparative approach. The present study aims at providing a systematic review of peer-reviewed literature indexed in Scopus database, to understand how return has been dealt with by researchers. The main objectives are: collecting and synthetizing previous studies; comparing approaches, conceptualizations of return, methods and variable of interest. A bibliometric analysis on metadata and content analysis based on text mining and topic modelling techniques has been conducted on a sample of approximately 3,000 publications. With our contribution, we expect to implement a baseline for theoretical development and empirical research, presenting an overview on the evolution of trend topics, with regional and temporal patterns of research focus and identifying knowledge gaps in literature

Creating an environment for free education and technology-enhanced learning

The purpose of this paper is to present a project aimed at making knowledge publically available through opene ducational resources (OER). The focus is on open online courses which will be created by educational institutions and best practice examples offered by leading companies, with the purpose to support life-long education and enhancement of academic education with practical knowledge. The goal is to create diverse high quality educational materials in electronic format, which will be publically available. The educational material will follow basic pedagogical-didactic principles, in order to best meet the needs of the potential learners. In accordance with that a review of didactic principles that can contribute to producing OER content of excellence is given. The choice of a convenient platform, as well as the application of appropriate information technologies enable content representation in a suitable, innovative and meaningful way

Managing labour: UK and Australian employers in comparative perspective, 1900-50

The exceptionalism of Australian industrial relations has long been asserted. In particular, the Australian system of industrial arbitration has been argued to contrast markedly with other countries, such as Britain, which developed a more 'voluntarist' model of industrial regulation. However this distinction relies upon limited historical research of workplace-level developments. In this paper, we focus on a comparative analysis of employer practice in British and Australian workplaces during the first half of the twentieth century. While we find some differences in the nature and extent of management control between the British and Australian experience, what is more striking are the strong similarities in employer practice in work organisation, employment and industrial relations. While economic and institutional factors explain differences in employer practice, fundamental similarities appear to relate to the close economic and social linkages between British and Australian business

Multimodal Document Analytics for Banking Process Automation

In response to growing FinTech competition and the need for improved operational efficiency, this research focuses on understanding the potential of advanced document analytics, particularly using multimodal models, in banking processes. We perform a comprehensive analysis of the diverse banking document landscape, highlighting the opportunities for efficiency gains through automation and advanced analytics techniques in the customer business. Building on the rapidly evolving field of natural language processing (NLP), we illustrate the potential of models such as LayoutXLM, a cross-lingual, multimodal, pre-trained model, for analyzing diverse documents in the banking sector. This model performs a text token classification on German company register extracts with an overall F1 score performance of around 80\%. Our empirical evidence confirms the critical role of layout information in improving model performance and further underscores the benefits of integrating image information. Interestingly, our study shows that over 75% F1 score can be achieved with only 30% of the training data, demonstrating the efficiency of LayoutXLM. Through addressing state-of-the-art document analysis frameworks, our study aims to enhance process efficiency and demonstrate the real-world applicability and benefits of multimodal models within banking.Comment: A Preprin

Transforming Sentiment Analysis in the Financial Domain with ChatGPT

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an additional evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35\% enhanced performance in sentiment classification and a 36\% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.Comment: 10 pages, 8 figures, Preprint submitted to Machine Learning with Application

A proposed model for Process Mining Adoption: Using a Mixed-Methods Approach

Driven by digital transformation, Process Mining represents one of the biggest analytical trends in the Software-as-a-Service technology market, providing companies with transparency of their processes in place. As such, there has been little research about what are the factors that influence the decision of companies to adopt Process Mining in their organization. Hence, this study aims on developing a comprehensive research model that sheds light on the most decisive Process Mining adoption drivers among European firms. A Mixed-Method design was applied to ensure a tailored IT adoption model for Process Mining. Based on a qualitative pre-study with expert interviews as well as a thorough literature review about the IT adoption theories of TOE, DOI as well as OIPT we derived the most essential antecedents of Process Mining adoption and proposed to our knowledge the first Process Mining adoption research model on firm-level