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Based on a lexical analysis of publications on 529 Facebook pages, published between 2013 and 2017, this research explores how Brazilian left-wing activist groups participate on Facebook to coordinate their opposition and engage in social struggles. This chapter shows how activist groups set up two main digital network repertoires of action when mobilizing on Facebook. First, in direct connection with major political events, the platform is used as a media arena to challenge governments’ political actions and second, it is employed as a tool to coordinate mobilization, whether these mobilizations are demonstrations on the street or at cultural events, such as at a music concert. These repertoires of action exemplify ways in which contemporary Brazilian activism is carried out at the intersection of online and offline engagements. While participants engage through these two repertoires, this network of activists is held together over time through a more mundane type of event, pertaining to the repertoire of action allowing the organization of mobilization. Stepping aside from opposition and struggles brought to the streets, the organization of cultural activities, such as concerts and exhibitions, punctuates the everyday exchanges in activists’ communications. Talk about cultural events and their related social agendas structures activist networks on a medium-term basis and creates the conditions for the coordination of (future) social movements, in that they offer the opportunities to stay in contact, in addition to taking part in occasional gatherings, between more highly visible social protests.

Smart tourism (ST) needs the development of smart business. The purpose of this study is to evaluate the future of the smart component of tourism companies, what their perspectives are and what factors can help to accelerate it.

A survey of 133 managers of tourist companies is the basis of the empirical information. The study was a personal survey carried out during FITUR 2019 International Tourism Fair of Madrid. The main element of the study is the future development of Information and Communication Technologies (ICTs) and smart tourism (ST) in business.

The results indicate that there is little development at present of the smart business eco-system and that development will continue to be slow in the future. Moreover, this is not a critical issue in the agendas of companies. It was found that tourists pressure tourism through the extensive use of their smartphones, but only at the level of tourism resources. Furthermore, it will be the consolidation of the smart tourism destination that marks the medium and long-term design of smart business.

The limitations concern the problems of a sampling procedure. Firstly, it operates with a database of managers’ opinions; secondly, there are specificities of each company in particular.

The design of the smart tourism destination must incorporate the integration of tourism companies, both with a useful vision of ICTs towards the creation of experiences.

Research on smart business tourism is very scarce compared to smart destination and smart tourists. Also, the data are supported by managers of important tourism companies, as their companies are present at FITUR.

The purpose of this paper is to create an automatic interpretation of the results of the method of multiple correspondence analysis (MCA) for categorical variables, so that the nonexpert user can immediately and safely interpret the results, which concern, as the authors know, the categories of variables that strongly interact and determine the trends of the subject under investigation.

This study is a novel theoretical approach to interpreting the results of the MCA method. The classical interpretation of MCA results is based on three indicators: the projection (F) of the category points of the variables in factorial axes, the point contribution to axis creation (CTR) and the correlation (COR) of a point with an axis. The synthetic use of the aforementioned indicators is arduous, particularly for nonexpert users, and frequently results in misinterpretations. The current study has achieved a synthesis of the aforementioned indicators, so that the interpretation of the results is based on a new indicator, as correspondingly on an index, the well-known method principal component analysis (PCA) for continuous variables is based.

Two (2) concepts were proposed in the new theoretical approach. The interpretative axis corresponding to the classical factorial axis and the interpretative plane corresponding to the factorial plane that as it will be seen offer clear and safe interpretative results in MCA.

It is obvious that in the development of the proposed automatic interpretation of the MCA results, the authors do not have in the interpretative axes the actual projections of the points as is the case in the original factorial axes, but this is not of interest to the simple user who is only interested in being able to distinguish the categories of variables that determine the interpretation of the most pronounced trends of the phenomenon being examined.

The results of this research can have positive implications for the dissemination of MCA as a method and its use as an integrated exploratory data analysis approach.

Interpreting the MCA results presents difficulties for the nonexpert user and sometimes lead to misinterpretations. The interpretative difficulty persists in the MCA's other interpretative proposals. The proposed method of interpreting the MCA results clearly and accurately allows for the interpretation of its results and thus contributes to the dissemination of the MCA as an integrated method of categorical data analysis and exploration.

The purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.

Within the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.

Software utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.

The proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.

The practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.

The proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

The purpose of the current study is to investigate occupational stereotypes among a professional sample of recruiters and other employees on the two fundamental dimensions of warmth and competence.

The authors conducted a survey to collect professionals´ (mostly recruiters´) ratings of preselected occupations. Participants were asked to rate warmth and competence attributes. Factor and cluster analysis were employed to investigate the two-dimensional structure of the warmth/competence space and how and whether occupations cluster as predicted by the stereotype content model (SCM).

Almost all occupations showed a clear two-factorial structure, corresponding to the warmth/competence dimensions. A five-cluster solution was deemed appropriate as depicting how occupations disperse on these dimensions. Implications for stereotyping research, the design of hiring discrimination experiments, and HRM are discussed.

In contrast to previous related research, in which participants select the included occupations themselves, the authors included prespecified common occupations, which should be important for representativeness. In addition, previous research has been conducted in the United States, while the authors conduct this study in a European context (Sweden). Finally, instead of studying students or participants with unspecified work experience, the authors focus on professionals (mostly recruiters).

The purpose of this study is to develop the self-initiated work adjustment for learning (SIWAL) scale that measures the adjustments that employees make in their work to enhance learning, based on theories and research on workplace learning, work adjustment and work design.

The SIWAL scale was validated in two independent studies. Study 1 (n = 208) focused on the internal consistency and factor structure of the SIWAL scale. Study 2 (n = 178) re-examined the factorial structure using confirmatory factor analysis and investigated scale validity.

In both studies, the SIWAL scale showed good psychometric characteristics, i.e. a clear two-factorial structure and internal reliable sub-scales. The findings also indicated convergent, divergent and concurrent validity.

Using the SIWAL scale, future research could focus on the individual, social and organizational predictors and outcomes of SIWAL, collect supervisor and peer ratings to further validate this self-report scale and investigate lower-educated workers.

Organizations might try to enhance their employees' SIWAL through organizational policies, such as supportive leadership, and a learning climate.

This study provides a first step toward a better understanding of what workers do to enhance their workplace learning. The study findings indicate that employees address two adaptive behaviors: adjusting job responsibilities and adjusting social interactions.

Sociological researches about tourism of lesbian, gay, bisexual and transgender (LGBT) people are growing in number. These studies are carried out mainly in Anglo-Saxon countries, although nowadays tour operators, travel agencies, cruise and airline companies have started to reserve more and more services and promotions to this group of travellers all around the world. To fill this gap, the paper presents the results of a research that involved 650 Italian LGBT Millennial travellers.

Using the exploratory technique of multiple correspondence analysis, the research focussed on the study of decision-making processes, finding out the factors that drive young LGBT people to prefer one destination over the others, distinguishing motivations between pull and push factors.

For Italian LGBT Millennials, tourism means more than just recreation. Tourism could also represent a strategy that gives them temporary escape from social prejudice and inequality, since tourist experiences provide an opportunity to re-build LGBT people' sexual identity and enjoy social freedom that LGBT people are deprived of whilst being at home.

The analysis allowed to underline some differences. Even if LGBT people share the burden of being as a member of a sexual and gender minority, LGBT people attribute a slightly different meaning to tourism, considering distinct push and pull factors.

The need to improve the efficiency and quality of public services has increased the interest in innovation. This study seeks to understand the relationship between the Public Service Motivation (PSM) index and the profile of strategic-level public servants who are considered innovative.

Questionnaires were applied to the group of government managers who registered their projects in an innovation competition. Data were analyzed using t-test, multiple correspondence analysis (MCA) and hierarchical cluster analysis (HCA).

This paper concluded that the PSM level of these innovative public servants is significantly greater than public servants overall. In terms of the profile of these innovative government managers, it was found that the majority were female, with a high level of education and a background in Information Technology.

The discovery that innovative government managers have a significantly higher PSM (p < 0.05) than public servants overall may be relevant, because it confirms a statistical tendency that it would be advantageous for policy-makers to invest in actions that increase public servant PSM since these servants with high PSM are more innovative.

This article has sought to fill a gap in studies that associate the level of PSM with the innovation practices in the public sector, as well as verify the profile of public servants with high levels of motivation in public service (PSM).

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The purpose of this paper is to identify under what circumstances advisor gender and advice justification influence advice taking by managers.

The authors designed a quasirational managerial decision experiment with both analytic and intuitive cues. The design was a 2 × 2 between-subjects factorial, in which gender (male/female) and advice justification (intuitive/analytic) were crossed. The experiment involved two independent samples, taken from Amazon Mechanical Turk workers and Brazilian professionals.

Results suggest that, in general, analytic justification is more valued than intuitive justification. The findings also infer that depending on the advisees’ sample and providing that advice justification is analytic, quasirational scenarios seem to favor male advisors (MTurk sample) or both male and female advisors with “male values” (professional sample), as analysis is traditionally considered a “male value.”

Analytic justification will likely lead to more advice utilization in quasirational managerial situations, as it may act as a safeguard for the accuracy of the offered advice.

The results might signal an ongoing, but slow, process leading to the mitigation of gender stereotypes, considering that the male gender stereotype was active in the MTurk sample, but not in the professional one.

This study contributes to the advice-taking research field by showing the interplay between advisor gender and advice justification in a quasirational managerial decision setting with both analytic and intuitive cues. In advice-taking literature, observations are usually collected from students. However, as this study focused on managerial decisions, the authors collected independent samples from MTurk workers and Brazilian professionals.