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The main objective of this paper is to assess how the degree of regulation that a company has to comply with affects its profitability. There is great variation within the internet value chain in the profitability of different players. The paper aims to analyse a large sample of companies that are leaders in different internet‐based businesses (network operators, search engines and other ASP, software, electronic retailing, content delivery networks, device manufacturers …). The paper's hypothesis is that regulation plays an important role in the profitability of a company and therefore also in how the market values them.

The methodology used to check the authors' hypothesis includes the following steps: identify leaders in the internet space; identify their core asset and group them according to it; calculate their profitability across a series of dimensions, with focus on return on fixed assets (ROFA); assess the degree of regulation of each group; and assess the statistical relationship between regulation and profitability and look for significant results.

The paper analyses the degree of regulation of the core asset of these companies and finds that is statistically related to their profitability. Companies with core assets free from regulation yield much higher profits on their investments than those with core assets curtailed by regulation.

This finding can cast light on some policy proposals under debate (net neutrality, access regulation, privacy …), especially how they can increase or decrease the current imbalance in the relative profitability of companies and the internet balance of payments and power between the European Union and the USA.

This chapter investigates the trends in international and European legal and policy regulation of the process related to carbon capture and storage (CCS). The global endeavor that seeks to limit carbon dioxide emissions has come to recognize CCS as an indispensable ally. This chapter offers an up-to-date and comprehensive commentary to the relatively new and developing area of international regulation of the process of CCS, a dimension that might yield significant effects on the environment and, overall, sustainable development. It reveals a constantly growing trend of an enhanced awareness about the indispensable role and effects of the CCS on wider climate aspirations and, to that effect, also a need for a stable and effective international regulatory framework. The key barriers that are preventing the wider implementation of CCS projects, however, relate primarily to two extra-regulatory processes, which is the policy uncertainty at national levels and financial shortcomings. This background presents a window of opportunity for entrepreneurship and policy invention.

This paper aims to determine how important cultural events organized by hotels are when guests select the establishment, determine the most influential source to attract customers to the hotels’ cultural events and measure their ability to meet guests’ expectations.

A qualitative exploratory methodology was adopted. Data collection was performed through voluntary and non-probabilistic face-to-face interviews to 2,229 guests and six directors from eight hotels that organize cultural events.

The data revealed that, although cultural events organized by hotels were not a selection factor, these were attended by the majority of guests, who felt valued for being asked for feedback by the hotels and were more influenced by marketing communications within the establishments than by word-of-mouth.

Contrary to cultural events organized by local authorities, cultural events organized by hotels have received little to no academic attention. This research allowed the authors to better understand the factors that may influence hotel selection and guests’ satisfaction.

This study analyzed the impact on the persistence of Chilean university students who had received a government-guaranteed loan (CAE).

Using academic and administrative data from 2016 to 2019, provided by 11 Chilean universities, a discrete-time survival model was constructed. The model was based on data of 5,276 students in the 2016 cohort and included sociodemographic variables, academic background prior to entering university and academic performance once in university. As a robustness check of our results to observable confounding, the analysis was repeated using a control group constructed using propensity score matching (PSM).

The results reveal that students who receive a bank loan (CAE) were more likely to remain in undergraduate studies for at least the first two years of university, as opposed to their peers who did not receive financial aid. In addition, they show the importance of academic performance in retention.

The article advances in the identification of the impact of bank loans on permanence. Although previous research has evaluated the impact of the CAE, it has been conducted on small samples of students. These studies also lacked student records associated with their academic performance at the university. The present research overcomes both weaknesses, allowing us to estimate the impact of the CAE on a larger population of students that is representative of the system.

The relationship between the role played by corporate governance (CG) mechanisms and shareholder wealth is an important and mature topic in some countries and regions. However, despite the considerable number of studies, the results are still inconclusive. The purpose of this paper is to contribute to the debate around the theme in Latin America through a meta-analysis.

The study used meta-analytic procedures to review 42 articles produced by researchers from Latin American countries, whose samples were composed of Latin American firms.

The results suggest that CG best practices are associated with better Latin American firm performance. The evidence also suggests that results are moderated by the characteristics of boards of directors, the ownership, and control structure and various simultaneous CG mechanisms, through broad indices and special CG trading segments.

The relationship between GC and firm performance possesses certain peculiarities in the case of Latin American countries and the literature on the region is certainly not as abundant and mature. As most of the articles reviewed were written in Portuguese and Spanish and published in local journals, the consolidation produced should also be useful for researchers throughout the world by enabling them to access their ideas.

A relação entre o papel desempenhado pelos mecanismos de governança corporativa (GC) e a riqueza dos acionistas é um tópico importante e maduro em alguns países e regiões. No entanto, os resultados ainda se apresentam ambíguos, apesar do grande número de estudos. Este ensaio pretende contribuir para o debate do tema na região através de uma meta análise.

O trabalho fez uso de procedimentos meta-analíticos para revisar 42 artigos produzidos por pesquisadores da região e de outras localidades, cujas amostras são compostas por empresas de países da América Latina.

Os resultados sugerem que de forma geral melhores práticas de GC são associadas a um melhor desempenho das empresas latino-americanas. Este trabalho sugere também que os resultados encontrados são moderados pelas características dos conselhos de administração, da estrutura de propriedade e controle e de diversos mecanismos simultâneos de GC, como índices amplos e segmentos especiais de GC.

Esta relação possui peculiaridades quando se tratam de países latino-americanos e a literatura sobre a região certamente não se apresenta tão abundante e madura. Acredita-se que a consolidação produzida será útil para pesquisadores ao redor do mundo, uma vez que a maioria dos artigos revisados foi escrita em português ou espanhol e publicada em periódicos locais, o que não a torna acessível a todos.

This study aims to compare the rating dynamics of the same hotels in two online review platforms (Booking.com and Trip Advisor), which mainly differ in requiring or not requiring proof of prior reservation before posting a review (respectively, a verified vs a non-verified platform).

A verified system, by definition, cannot host fake reviews. Should also the non-verified system be free from “ambiguous” reviews, the structure of ratings (valence, variability, dynamics) for the same items should also be similar. Any detected structural difference, on the contrary, might be linked to a possible review bias.

Travelers’ scores in the non-verified platform are higher and much more volatile than ratings in the verified platform. Additionally, the verified review system presents a faster convergence of ratings towards the long-term scores of individual hotels, whereas the non-verified system shows much more discordance in the early phases of the review window.

The paper offers insights into how to detect suspicious reviews. Non-verified platforms should add indices of scores’ dispersion to existing information available in websites and mobile apps. Moreover, they can use time windows to delete older (and more likely biased) reviews. Findings also ring a warning bell to tourists about the reliability of ratings, particularly when only a few reviews are posted online.

The across-platform comparison of single items (in terms of ratings’ dynamics and speed of convergence) is a novel contribution that calls for extending the analysis to different destinations and types of platform.

Social development is the ultimate goal of every nation, and climate change is a major stumbling block. Climate Risk Index has documented several climate change events with their devastations in terms of lives lost and economic cost. This study aims to link the climate change and renewable energy with the social progress of extreme climate affected countries.

This research used the top 50 most climate-affected countries of the decade and estimated the impact of climate risk on social progress with moderation effects of renewable energy and technology. Several competing panel data models such as quantile regression, bootstrap quantile regression and feasible generalized least square are used to generate robust estimates.

The results confirm that climate hazards obstruct socioeconomic progress, but renewable energy and technology can help to mitigate the repercussion. Moreover, improved institutions enhance the social progress of nations.

Government should improve the institutional quality that enhances their performance in terms of Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law and Control of Corruption to increase social progress. In addition, society should use renewable energy instead of fossil fuels to avoid environmental degradation and health hazards. Innovation and technology also play an important role in social progress and living standards, so there should be free hand to private business research and development, encouraging research institutes and universities to come forward for innovation and research.

The ultimate goal of all human struggle is to have progress that facilitates human beings to uplift their living standard. One of the best measures that can tell us about a nation’s progress is Social Progress Index (SPI), and one of many factors that can abruptly change it is the climate; so this study is an attempt to link the relationship among these variables and also discuss the situation where the impact of climate can be reduced.

Although social progress is an important concept of today’s economics discussion, relatively few studies are using the SPI to measure social well-being. Similarly, there is consensus about the impact of climate on people, government and crops but relatively less study about its overall impact on social progress, so this study attempts to fill the gap about the relationship between social progress and climate change.

The main contribution of this study is the solution for the impact of climate risk. Climate risk is not in human control, and we cannot eliminate it, but we can reduce the negative impacts of climate change. Moderator impact of renewable energy decreases the negative impact of climate change, so there is a need to use more renewable energy to mitigate the bad consequences of climate on social progress. Another moderator is technology; using technology will also mitigate the negative consequences of the climate, so there is a need to facilitate technological advancement.

Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.

This paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.

The R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.

The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.