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This study aims to examine the price and income elasticities of short- and long-run industrial electricity demand in Brazil between 2003 and 2020. The research also examines the impacts of COVID-19 in Brazil’s industrial electricity sector, including an analysis in states more and less industrialized.

Dynamic adjustments models in panel data are used to present robust estimates and analyze the impact of different methodologies on reported elasticities.

The short-run price elasticity is estimated at −0.448, while the long-run values are around −1.60. Regarding income elasticity, the value is 0.069 in the short-run and is concentrated in 0.25 in the long-run. The inelastic results of income show that the industrial demand for electric energy follows the trend of loss of competitiveness of the Brazilian industry in the past years. In addition, the price of natural gas, the level of employment, and, in specific cases, the level of imports also influence industrial electricity demand.

The research is a pioneer in the investigation of the industrial behavior of electricity of the Brazilian industrial branch, using as control variables, the average temperature, and the level of rainfall, this one, so important for a country whose main source is hydroelectric. In addition, to the best of the authors’ knowledge, it is the first study, which is prepared to analyze the effects of COVID-19 on electric consumption in the industrial sector, investigating these impacts, including in the states considered more and less industrialized. The estimates generated may help in the design of the Brazilian energy policy.

This research work aims to arise from developing new communication channels for customer service in micro, small and medium enterprises (MSMEs), such as chatbots. In particular, the results of the usability testing of three chatbots implemented in MSMEs are presented.

The methodology employed includes participants, chatbot development platform, research methodology, software development methodology and usability test to contextualize the study's results.

Based on the results obtained from the System Usability Scale (SUS) and considering the accuracy of the chatbot's responses, it is concluded that the level of satisfaction in using chatbots is high; therefore, if the chatbot is well integrated with the communication systems/channels of the MSMEs, the client receives an excellent, fast and efficient service.

The paper analyzes chatbots for customer service and presents the usability testing results of three chatbots implemented in MSMEs.

Technological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.

To capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.

Our analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).

The paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.

This paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.

The present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.

While previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.

This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports.

The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency.

Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction.

There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil.

The process of knowledge creation is recognized as an essential process for organizational learning and innovation. Creating knowledge to solve the problems and complexities of today's world is like opening a black box. Hence, the higher education system and universities are exploring ways to overcome the complexities and cope with global changes. In this regard, interdisciplinary collaborations and activities are crucial in creating knowledge and innovation to counter these changes. This study aimed to know the experiences of Shiraz university interdisciplinary researchers in the field of humanities and also design and explain the conceptual model of knowledge creation in interdisciplinary research teams in the field of humanities.

In this qualitative research, grounded theory was implemented based on Strauss and Corbin's systematic approach. The sampling method was purposeful, and the participants included sixteen faculty members of shiraz university who had at least one experience of performing an interdisciplinary activity in one of the humanities fields. The first participant was selected as a pilot, and the rest were selected by snowball sampling. Semi-structured interviews were also used to collect data and continued until theoretical saturation was attained. After collecting the available information and interviewing the people, the data were organized and analyzed in three stages, open coding, axial coding, and selective coding, using the proposed framework of Strauss and Corbin. Finally, the researcher reached a final and meaningful categorization.

In this research, the results were presented as a paradigm model of knowledge creation in the interdisciplinary research teams in the field of humanities. The paradigm model of the study consists of causal factors (internal and external factors), main categories (specialized competencies, scientific discourse, understanding of knowledge domains), strategies (structuring and synchronizing), context (individual and organizational), interfering factors (leadership, industry, and society), and consequences (individual and group achievement).

The present study aimed to explore the experiences of researchers in the interdisciplinary humanities research teams on knowledge creation in qualitative research. The study used Strauss and Corbin's systematic approach to recognize the causal factors of knowledge creation and the contexts. Discovering the main category of knowledge creation in interdisciplinary research teams, the authors analyze the strategies and consequences of knowledge creation.

Using the lenses of social exchange and reactance theories, this study examines the relationships between abusive supervision and both turnover intentions and job satisfaction. The moderating role of employee depression in the relationship between abusive supervision and these specific work outcomes is also investigated, by incorporating the conservation of resources theory.

Quantitative data were collected from a sample of 221 frontline retail employees, across shopping malls in the Caribbean nation of Trinidad. A 3-step multiple hierarchical regression analysis was performed to test the relationships.

The findings provided support for the propositions that abusive supervision predicts job satisfaction and turnover intentions, respectively. Employee depression moderated the relationship between abusive supervision and job satisfaction but did not moderate the relationship between abusive supervision and turnover intentions.

While existing research has explored the relationships between abusive supervision, job satisfaction and turnover intentions, limited studies have investigated the moderating role of employee depression. This study contributes to understanding this pervasive workplace issue by investigating a relatively unexplored moderating effect.