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The purpose of this paper is to evaluate the performance of a modified EWMA control chart (γEWMA control chart), which considers data distribution and incorporate its correlation structure, simulating in-control and out-of-control processes and to select an adequate value for smoothing parameter with these conditions.

This paper is based on a simulation approach using the methodology for evaluating statistical methods proposed by Morris et al. (2019). Data were generated from a simulation considering two factors that associated with data: (1) quality variable distribution skewness as an indicator of quality variable distribution; (2) the autocorrelation structure for type of relationship between the observations and modeled by AR(1). In addition, one factor associated with the process was considered, (1) the shift in the process mean. In the following step, when the chart control is modeled, the fourth factor intervenes. This factor is a smoothing parameter. Finally, three indicators defined from the Run Length are used to evaluate γEWMA control chart performance this factors and their interactions.

Interaction analysis for four factor evidence that the modeling and selection of parameters is different for out-of-control and in-control processes therefore the considerations and parameters selected for each case must be carefully analyzed. For out-of-control processes, it is better to preserve the original features of the distribution (mean and variance) for the calculation of the control limits. It makes sense that highly autocorrelated observations require smaller smoothing parameter since the correlation structure enables the preservation of relevant information in past data.

The γEWMA control chart there has advantages because it gathers, in single chart control: the process and modelling characteristics, and data structure process. Although there are other proposals for modified EWMA, none of them simultaneously analyze the four factors nor their interactions. The proposed γEWMA allows setting the appropriate smoothing parameter when these three factors are considered.

The Sharing Economy (SE) has turned around the concepts of ownership and access, promoting not only an alternative consumption method, but also a more sustainable one. Using digital platforms, this phenomenon expects to achieve a better use of the idle capacity of resources, promote meaningful and trusting communities and contribute to reducing the environmental harm. The huge increase of popularity of this model has lined up with the Sustainable Development Goals proclamation, and the commitment from governments with the search for more sustainable models. This study analyzes the relationship of the SE with sustainability in general, and the Sustainable Development Goals in specific.

Through the analysis of the action plans proposed by governments and the expected contributions of sharing platforms to sustainability, the authors analyze the improvements and assistance that the Sharing Economy could offer to countries.

The main findings suggest that the SE can assist countries to achieve their sustainability goals and to further advance towards a more sustainable consumption and living model in order to fulfill the Sustainable Development Goals.

Although this topic is still to be further developed, the SE seems to be fulfilling the expectations as the path towards sustainability.

Seeking to contribute, from an academic perspective, to the construction of a better tomorrow that leaves no segment of society behind, this final chapter presents arguments for building sustainable futures that are possible through regenerative development. We talk about ‘futures’ in the plural, because there is more than one future that could be sustainable. We explain the importance of prioritising positive values involving the environment, society and markets, ethical considerations of doing no harm and the search for regenerative relationships that lead to collective action. We also explain that regeneration goes beyond restoration. This chapter is divided into four parts. First, we discuss regenerative capitalism. Then, we explain why climate action must be collective and must involve business, governments, academia and civic organisations. The third part presents a concise summary of the findings of the studies presented in this book. Finally, we explain why we need a new social contract to achieve the goal of sustainable futures through regenerative development.

Development pathways for Latin American and the Caribbean countries have been the subject of debates, analyses and controversies. For several decades, countries in this region have struggled with structural barriers to development associated with social inequalities, political turmoil, colonialism, corruption and a dependence on exploiting natural resources, among others. Recently, the COVID-19 pandemic has worsened some of those obstacles, which when added to the global climate crisis and its environmental impact, leaves the region in a highly stressed situation, with many of its countries on the edge of a deep economic depression. This chapter discusses some of the socioeconomic challenges that Latin America and the Caribbean currently face; the roles of COVID-19 and climate crises on these challenges and some opportunities for recovery.

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.

The purpose of this chapter is to solve multi-objective formulation for traveling salesman and transportation problems. Computations are based on real data for the road freight transportation of a Spanish company. The company was selected because of its importance in Spanish economy and market. This company is important in the whole country; however, it has its higher importance in the northern part of Spain. The requirements for these models are the minimization of total distance and the CO₂ emissions. To achieve this, it is required to know and carry out the minimization of the total distance traveled by the trucks during the deliveries. The deliveries are going to be executed between the different locations, nodes, in the region, and Elorrio, where the depot is situated. The data have been used to decide the best route in order to obtain a minimization of cost for the company. As it was mentioned earlier, the problems are focused on the reduction of the amount of CO₂ emissions and minimization of total distance; by studying different parameters, the best solutions of route transportation have been obtained. The software used to solve these models is CPLEX solver with AMPL programming language.

For corporate communications, it is crucial to know how news media outlets report and frame the sociopolitical activities of multinational corporations (MNCs), including their corporate diplomacy, that affect perceptions of their legitimacy. Therefore, this study aims to identify how local news media frame corporate diplomacy in a host country and, in turn, benefit the media legitimacy of MNCs.

To identify media frames in the host country, a quantitative content analysis involving factor and cluster analyses of 385 articles published in newspapers in the United Arab Emirates from 2014 to 2019 addressing the corporate diplomacy of large European MNCs operating in the country was conducted.

This study identified three media frames, two of which establish moral and pragmatic media legitimacy. Results suggest that media legitimacy grows when news media emphasise institutional relationships between MNCs and local, established organisations and corporate diplomacy's benefits for society.

Findings provide insights into how corporate communications can contribute to legitimacy building by emphasising corporations' relationships with institutional actors in host countries and the benefits of corporate activities for local communities.

To the best of the authors’ knowledge, this study was the first in corporate communications to empirically investigate news media's role in corporate diplomacy and how media frames contribute to the media legitimacy of MNCs at the moral, pragmatic, regulative and cognitive levels.

The paper aims to investigate the standpoints and practices of university members from European developing countries regarding the harnessing of the intellectual capital (IC) within online academic social networks.

A questionnaire-based survey with 210 university members was conducted, with the indicators adopting prior measurement scales which were further adapted to a network framework.

The organizational policies and practices relate positively and highly significantly with the valuation of the network-based IC components. Moreover, 63 per cent of the professional and organizational competitiveness of higher education institutions is determined by the exploitation of the IC embedded in online academic networks.

All survey respondents were from the European developing countries, which may limit the general applicability of the findings. Also, the emphasis is laid solely on online academic networks.

This paper brings to the fore both the potential and the state-of-the-art in leveraging the IC of online specialized networks which are indicative of the academic field. When acknowledged as such, the network-based IC is liable to generate substantial competitive advantages at the professional and organizational levels at the same time.

This research adds to the extant literature in two main ways. First, it advances a new construct – network-based IC – in the context of the online academic social networks. Second, it proposes a research model for addressing the network-based IC from a competitive advantage perspective.

Health care systems increasingly demand health professionals who can lead interdisciplinary teams. While physicians recognize the importance of leadership skills, few receive formal instruction in this area. This paper aims to describe how the Student Leadership Committee (SLC) at the Harvard Medical School Center for Primary Care responded to this need by creating a leadership curriculum for health professions students.

The SLC designed an applied longitudinal leadership curriculum and taught it to medical, dentistry, nursing, public health and business students during monthly meetings over two academic years. The perceptions of the curriculum were assessed via a retrospective survey and an assessment of team functioning.

Most teams met their project goals and students felt that their teams were effective. The participants reported increased confidence that they could create change in healthcare and an enhanced desire to hold leadership positions. The sessions that focused on operational skills were especially valued by the students.

This case study presents an effective approach to delivering leadership training to health professions students, which can be replicated by other institutions.

Applied leadership training empowers health professions students to improve the health-care system and prepares them to be more effective leaders of the future health-care teams. The potential benefits of improved health-care leadership are numerous, including better patient care and improved job satisfaction among health-care workers.

Leadership skills are often taught as abstract didactics. In contrast, the approach described here is applied to ongoing projects in an interdisciplinary setting, thereby preparing students for real-world leadership positions.

This paper aims to study the effect of imbalanced data in tourism quality models. It is demonstrated that this imbalance strongly affects the accuracy of tourism prediction models for hotel recommendation.

A questionnaire was used to survey 83,740 clients from hotels between five and two or less stars using a binary logistic model. The data correspond to a sample of 87 hotels from all around the world (120 countries from America, Africa, Asia, Europe and Australia).

The results of the study suggest that the imbalance in the data affects the prediction accuracy of the models used, especially to the prediction provided by unsatisfied clients, tending to consider them as satisfied customers.

In this sense, special attention should be given to unsatisfied clients or, at least, some safeguards to prevent the effect of the imbalance of data should be included in the models.

In the tourism industry, the strong imbalance between satisfied and unsatisfied customers produces misleading prediction results. This fact could have effects on the quality policy of hoteliers.

In this work, focusing on tourism data, it is shown that this imbalance strongly affects the prediction accuracy of the models used, especially to the prediction of the recommendation provided by unsatisfied customers, tending to consider them as satisfied customers; a methodological approach based on the balance of the data set used to build the models is proposed to improve the accuracy of the prediction for unsatisfied customers provided by traditional services quality models.