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This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

This paper aims to propose a novel control scheme and offer a control parameter optimizer to achieve better automatic carrier landing. Carrier landing is a challenging work because of the severe sea conditions, high demand for accuracy and non-linearity and maneuvering coupling of the aircraft. Consequently, the automatic carrier landing system raises the need for a control scheme that combines high robustness, rapidity and accuracy. In addition, to exploit the capability of the proposed control scheme and alleviate the difficulty of manual parameter tuning, a control parameter optimizer is constructed.

A novel reference model is constructed by considering the desired state and the actual state as constrained generalized relative motion, which works as a virtual terminal spring-damper system. An improved particle swarm optimization algorithm with dynamic boundary adjustment and Pareto set analysis is introduced to optimize the control parameters.

The control parameter optimizer makes it efficient and effective to obtain well-tuned control parameters. Furthermore, the proposed control scheme with the optimized parameters can achieve safe carrier landings under various severe sea conditions.

The proposed control scheme shows stronger robustness, accuracy and rapidity than sliding-mode control and Proportion-integration-differentiation (PID). Also, the small number and efficiency of control parameters make this paper realize the first simultaneous optimization of all control parameters in the field of flight control.

This study aims to determine the effects of lodging infrastructure development (LID) on Cordillera Administrative Region’s (CAR) environmental quality and natural resource management and its implication to globally responsible leadership. Specifically, this study sought to determine the contribution of LID to environmental deterioration and natural resource degradation in the CAR. As a result, a mathematical model is developed, which supports sustainability practices to maintain the environmental quality and natural resource management in CAR, Philippines.

This study used a descriptive research design using a mixed-methods approach. Self-structured interview and survey were used to gather the data. The population of this study involved three groups. There were 6.28% (34) experts in the field for the qualitative data, 70.24% (380) respondents for the quantitative data and 23.47% (127) from the lodging establishments. 120 respondents from the Department of Tourism – CAR (DOT-CAR) accredited hotels. Nonparametric and nonlinear regression analysis was used to process the data.

The effects of LID on the environmental quality and natural resource management in CAR as measured through carbon emission from liquefied petroleum gas (LPG), electricity and water consumption in the occupied guest rooms revealed a direct correlation between the LID. Findings conclude that the increase in tourist arrival is a trigger factor in the increase in LID in the CAR. The increase in LID implies a rise in carbon emission in the lodging infrastructure. Any increase in tourist arrivals increases lodging room occupancy; the increased lodging room occupancy contributes to carbon emissions. Thus, tourism trends contribute to the deterioration of the environmental quality and degradation of the natural resources in the CAR. A log-log model shows the percentage change in the average growth of tourist arrival and the percentage increase in carbon emissions. Establishments should observe standard room capacity to maintain the carbon emission of occupied lodging rooms at a minimum. Responsible leadership is a factor in the implementation of policy on standard room capacity.

The result of the study has some implications for the lodging businesses, the local government unit (LGU), the Department of Tourism (DOT) and the Department of Environment and Natural Resources (DENR) in the CAR. The study highlights the contribution of the lodging establishments to CO₂ emission, which can degrade the quality of the environment, and the implication of responsible leadership in managing natural resources in the CAR. The direct inverse relationship between energy use and CO₂ emission in hotels indicates that increased energy consumption leads to environmental degradation (Ahmad et al., 2018). Therefore, responsible leadership among policymakers in the lodging and government sectors – LGU, DOT and DENR – should abound in the CAR. Benchmarking on the model embarked from this study can help in designing and/or enhancing the policy on room capacity standardization, considering the total area with its maximum capacity to keep the carbon emission at a lower rate. Furthermore, as a responsible leader in the community, one should create programs that regulate the number of tourists visiting the place to decrease the number of overnight stays. Besides, having the political will to implement reduced room occupancy throughout the lodging establishments in CAR can help reduce the carbon emissions from the lodging businesses. After all, one of the aims of the International Environment Protection Organization is to reduce CO₂ emissions in the tourism industry. Hence, responsible leadership in environmental quality preservation and sustainable natural resource management must help prevent and avoid greenhouse gas (GHG) emissions.

Most studies about carbon emission in the environment tackle about carbon dioxide emitted by transportation and factories. This study adds to the insights on the existing information about the carbon emission in the environment from the lodging establishments through the use of LPG, electricity and water consumption in the occupied guest rooms. The findings of the study open an avenue for globally responsible leadership in sustaining environmental quality and preservation of natural resources by revisiting and amending the policies on the number of room occupancy, guidelines and standardization, considering the total lodging area with its maximum capacity to keep the carbon emission at a minimum, thus contributing to the lowering of GHG emissions from the lodging industry.

This paper aims to evaluate whether promoter holding influences a bank’s liquidity in India’s leading emerging market. Furthermore, it also evaluates the moderating role of risk-weighted assets (RWA) on the relationship between promoter holding and liquidity.

The data consists of 24 banks for the period of 12 years from 2010 to 2021. Static panel data is used to analyze the relationship between the liquidity coverage ratio (LCR) as the dependent variable, the promoter used as an explanatory variable and RWA used as a moderating variable in this study.

This study concludes that an increase in promoter holding helps to improve the liquidity of Indian banks. Moreover, it also shows that using RWA as a moderating term enhances the relationship between promoter holdings and Indian banks’ liquidity.

This study evaluated the impact of promoter ownership solely on the LCR, a statistic used to measure the short-term liquidity of banks in the Indian setting. Additional corporate governance factors, such as the makeup of the board of directors, relevant ownership concentration factors and external factors with the potential to affect the liquidity position of banks, could potentially be the subject of future investigations.

This paper has both managerial and policy-level implications. It shows that it is advantageous for banks’ ownership composition to include more enormous promoter holdings to enhance banks’ liquidity. Policymakers can, thus, formulate policies to encourage banks to have more extensive promoter holdings.

The impact of promoter ownership on bank liquidity has not been evaluated in earlier research projects. Furthermore, the use of RWA as a moderating variable to determine this link has not been fully investigated, particularly in the context of a developing country like India.

This study aims to theoretically investigate the effect of activating corporate governance (CG) mechanisms on the association between adopting corporate social responsibility (CSR) and tax avoidance (TA). Based on the analyzing of the previous studies, the authors support the results of studies that found a positive effect for activating CG on the adoption of CSR. Also, they found that there is a negative impact of activating CG mechanisms on TA, as CG includes controls and procedures that contribute to limiting opportunistic behaviors of management and ensures making decisions that maximize value for shareholders. To the best of the authors' knowledge, it is the only chapter that examines the effect of activating CG mechanisms on the association between CSR and TA.

Increasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.

To achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).

Results of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R² of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.

Therefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.

By adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.

Economic literature highlights both positive and negative impact of FDI on economic growth. The purpose of this study is to confirm the relationship between various economic factors and FDI equity inflows and find out deviations, if any. This is investigated using standard time-series econometric models. The long and short run relationship is inquired with respect to market size, inflation rate, level of infrastructure, domestic investment and openness to trade. The choice of variables for Indian economy is purely based on empirical observations obtained from scientific literature review.

The study involves application of autoregressive distributive lag (ARDL) model to investigate the relationship. The long run co-integration between FDI and economic growth is tested by Pesaran ARDL model. The stationarity of data is tested by augmented Dickey Fuller test and Phillip–Perron unit root test. Error correction model is applied to study the short run relationship using Johansen’s vector error correction model method besides other tests.

The results show that the domestic investment, inflation rate, level of infrastructure and trade openness influence inward FDI flows. These factors have both long and short-term relationship with FDI inflows. However, market size is insignificant in influencing the foreign investments inflows. There lies an inverse relation between FDI and inflation rate.

To the best of the authors’ knowledge, the study is original. The methodology and interpretation of results are distinct and different from other similar studies.

Adequate means for easily viewing, browsing and searching knowledge graphs (KGs) are a crucial, still limiting factor. Therefore, this paper aims to present virtual properties as valuable user interface (UI) concept for ontologies and KGs able to improve these issues. Virtual properties provide shortcuts on a KG that can enrich the scope of a class with other information beyond its direct neighborhood.

Virtual properties can be defined as enhancements of shapes constraint language (SHACL) property shapes. Their values are computed on demand via protocol and RDF query language (SPARQL) queries. An approach is demonstrated that can help to identify suitable virtual property candidates. Virtual properties can be realized as integral functionality of generic, frame-based UIs, which can automatically provide views and masks for viewing and searching a KG.

The virtual property approach has been implemented at Bosch and is usable by more than 100,000 Bosch employees in a productive deployment, which proves the maturity and relevance of the approach for Bosch. It has successfully been demonstrated that virtual properties can significantly improve KG UIs by enriching the scope of a class with information beyond its direct neighborhood.

SHACL-defined virtual properties and their automatic identification are a novel concept. To the best of the author’s knowledge, no such approach has been established nor standardized so far.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

The main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.

The design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.

The modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.

Business limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.

The modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.

The adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.