Search results

Public debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.

Using quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.

The results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.

Compared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.

This paper introduces the problematic of the SBP modeling. Our objective is to provide a conceptual analysis related to the concept of SBP. This facilitates, on the one hand, easier understanding by business analysts and end-users, and one the other hand, the integration of the new specific concepts relating to the SBP/BPM-KM domains into the BPMN meta-model (OMG, 2013).

We propose a rigorous characterization of SBP (Sensitive Business Processes) (which distinguishes it from classic, structured and conventional BPs). Secondly, we propose a multidimensional classification of SBP modeling aspects and requirements to develop expressive, comprehensive and rigorous models. Besides, we present an in-depth study of the different modeling approaches and languages, in order to analyze their expressiveness and their abil-ity to perfectly and explicitly represent the new specific requirements of SBP modeling. In this study, we choose the better one positioned nowadays, BPMN 2.0, as the best suited standard for SBP representation. Finally, we propose a semantically rich conceptualization of a SBP organized in core ontology.

We defined a rigorous conceptual specification for this type of BP, organized in a multi-perspective formal ontology, the Core Ontology of Sensitive Business Processes (COSBP). This reference ontology will be used to define a generic BP meta-model (BPM4KI) further specifying SBPs. The objective is to obtain an enriched consensus modeling covering all generic concepts, semantic relationships and properties needed for the exploitation of SBPs, known as core modeling.

This paper introduces the problem of conceptual analysis of SBPs for (crucial) knowledge identification and management. These processes are highly complex and knowledge-intensive. The originality of this contribution lies in the multi-dimensional approach we have adopted for SBP modeling as well as the definition of a Core Ontology of Sensitive Business Processes (COSBP) which is very useful to extend the BPMN notation for knowledge management.

Neurodivergent employees have atypical needs that require distinctive leadership approaches. In this study, the specific nature of a relationship between neurodivergent employees and their neurotypical leaders is explored through the lens of the leader–member exchange (LMX) theory.

This two-phased qualitative study builds on 12 semi-structured interviews with neurodivergent employees and an unstructured focus group with 15 individuals with professional and/or personal interest in neurodiversity. The researcher spent almost 13 h listening to the lived experiences of research participants concerning neurodiversity and leadership.

Leaders who exhibit empathy and understanding were noted to provide greater support. The findings also highlight the complexity of neuro-inclusion in the workplace. Specifically, the delicate balance between accommodation and avoiding stigmatization is emphasized, addressing the concerns raised regarding the legal risks associated with neurodivergent inclusion. Additionally, the findings underscore the necessity for leaders to avoid patronizing behaviors while catering to the diverse needs of neurodivergent employees. This underscores the importance of supporting both neurodivergent employees and leaders navigating such challenges.

The findings help establish inclusive and accommodating employee relations practices that conscientiously address the requirements of neurodivergent employees while providing support for those in leadership roles.

This study constitutes a direct answer to recent calls to develop a more nuanced understanding of workplace neurodiversity, with a specific focus on neuro-inclusive leadership. Acknowledging that we still use inappropriate, old tools in new situations that require novel approaches to leadership helps set the agenda for future research in this area.

This paper aims to examine the role of bullying victimization in predicting psychopathology, encompassing post-traumatic stress disorder (PTSD), risk of developing prodromal psychosis and emotional and behavioural problems, among in-school Nigerian adolescents.

A total of 351 junior secondary students (n = 173 males, 178 females; age range: 9–17 years) were recruited from five randomly selected public secondary schools in Nigeria. Students completed a variety of self-report measures, including a socio-demographic questionnaire, the prodromal questionnaire – brief version, the strengths and difficulties questionnaire (SDQ) and the multidimensional peer victimization scale. They were also interviewed using the PTSD module of the Mini International Neuropsychiatric Interview-Kid Version.

Although bullying victimization was not found to predict the presence of PTSD, it predicted the risk of developing prodromal psychosis. All SDQ subscales also held significant positive associations with bullying victimization. This indicates that higher levels of victimization are associated with increased behavioural and emotional difficulties among adolescents.

The study findings add support to whole system approaches involving relevant stakeholders in health, education, social and criminal justice sectors via protective policies to address the problems of bullying in schools.

The study contributes to evidence demonstrating a need for improved understanding regarding the role of exposure to bullying victimization in predicting various forms of psychopathology. Furthermore, there is specifically a need for research with this focus in developing countries in sub-Saharan Africa and the Nigerian education system.

This article provides an overview of lessons we can learn from human resource (HR) policies and practices related to neurodiversity.

We conducted a practice-based review using information obtained from organizations’ websites, summarized the information and reflected on how scholars can continue to advance this area of research based on what is happening in practice.

The review provided a selective overview of programs and practices per HR cluster: selection and recruitment; onboarding, integration and retention; job design; flexible work options and working remotely; training; employee resource groups (ESGs) and support. The review provides a description of practices and policies implemented within organizations that focus on neurodiversity among employees.

Our review showed that organizations have a multitude of HR practices and policies in place to include neurodivergent individuals in their workforces, though many of these have not been empirically investigated. Sharing this knowledge is important so that research insights and practice can reciprocally influence one another.

This study aims to conceptualize, rethink and systematize methods used for measurement and evaluation (M&E) corporate communication.

The reflection is based on 462 key English-language books and papers devoted to M&E in the fields of corporate communication and public relations from the 1970th to 2023. Keywords in the titles and abstracts found the necessary materials. A critical analysis of the central concepts, models and methods described in the literature was conducted. As a result, a new model that unifies and structures the M&E toolkit is proposed for discussion.

Despite the significant contribution to developing a wide range of M&E models, they are still not perfect and universal. In addition, this system of approaches is continuously self-evolving and changing under the influence of digital innovations, so it requires steady rethinking and updating. On the other hand, most previous studies focused on communication management processes, losing focus on communication aspects. This led to the need for an alternative view based on proven theories to fill this gap. The proposed model combines quantitative and qualitative M&E methods for the five main components of corporate communication (communicator, audience, content, channels and result), covering a wide range of tools, from statistical and sociological research to big data analysis and neuro research.

This work contributes to developing the M&E theory of corporate communication, systematizing existing methods and opening new research perspectives. From a practical point of view, companies can use the presented approach for a more accurate and objective internal evaluation of the main components of corporate communication.

This article examines customer satisfaction in using banking services through Artificial Intelligence (AI) in India. It addresses two questions: first, will customers perceive AI technology as a reliable and efficient alternative to traditional banking practices; second, will AI save customers’ time.

The quantitative research method based on regression analysis models was adopted for hypothesis testing, with data collected from a survey of 189 banking customers from four banks, i.e., State Bank of India, Axis Bank, Punjab National Bank, and HDFC Bank in India.

AI improves banking customers’ experiences by making banking more accessible and enjoyable. Satisfied customers are quick to use cutting-edge AI tools. However, human service is more satisfying than digital service. AI has great potential but works alongside humans rather than replacing them. Even though AI’s novel architecture is helpful, human bank tellers are still needed in enhancing customer satisfaction.

AI’s integration in Indian banking, propelled by customer satisfaction, foresees a transformative landscape. This study uncovers AI’s role in saving time and improving customer satisfaction. While AI revolutionizes financial processes, its harmonious coexistence with human expertise emphasizes personalized and efficient services. This study provides insights for optimal AI utilization in shaping the future of banking.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

This study aims to explore and ranks the factors that might determine attitudes and intentions toward using autonomous vehicles (AVs).

The “technology acceptance model” (TAM) was extended by assessing the moderating influences of personal-related factors. Data were collected from 378 Vietnamese and analysed using a combination of “partial least squares” and the “adaptive neuro-fuzzy inference system” (ANFIS) technique.

The findings demonstrated the power of TAM in explaining the attitude and intention to use AVs. ANFIS enables ranking the importance of determinants and predicting the outcomes. Perceived ease of use and attitude were the most crucial drivers of attitude and intention to use AVs, respectively. Personal innovativeness negatively moderates the influence of perceived ease of use on attitude. Data privacy concerns moderate positively the impact of perceived usefulness on attitude. The moderating effect of price sensitivity was not supported.

These findings provide insights for policymakers and automobile companies' managers, designers and marketers on driving factors in making decisions to adopt AVs.

The study extends the AVs literature by illustrating the importance of personal-related factors, ranking the determinants of attitude and intention, illustrating the inter-relationships among AVs adoption factors and predicting individuals' attitudes and behaviours towards using AVs.