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This study aims to explore the impact of board size, board gender diversity and federation age on the likelihood of having a female chair in National Sports Federations (NSF).

A quantitative methodology compares 300 sports boards in five countries (Italy, Portugal, Spain, Turkey and the UK), using data collected from NSF’s websites.

The board size and federation age have no significant impact on having a female board chair when the countries and the percentage of female directors are included in the model. When the number of women is measured in absolute value rather than in relative terms, the only variable that predicts a woman chair is the country. When the model does not include country differences, the percentage of female directors is key in predicting a chairwoman, and when the number of women is used as a variable instead of the percentage, a board’s smaller size increases the odds of having a chairwoman.

There are some limitations to this study which we believe provide useful directions for future research. Firstly, the authors have not considered the role of gender typing in sports activities which explains the extent that women participate in specific sports (Sobal and Milgrim, 2019) and the related perception of such sports in society. The social representation of sports activities classified as masculine, feminine or gender-neutral can hypothetically influence women’s access to that specific federations’s leadership. The authors included the country factor only partially, as a control variable, as the social representation of sports usually goes beyond national boundaries.

This study has implications for sport policymakers and stakeholders, and for institutions such as the IOC or the European Union that implement equality policies. If the aim is to increase female presence in the highest position of a sports board and to achieve gender equality more generally, other policies need to be implemented alongside gender quotas for the sports boards, namely, those specifically related to the recruitment and selection of the sports board chairs (Mikkonen et al., 2021). For example, given the implications of critical mass and its ability to increase more female’s engagement then the role of existing chairs acting as mentors and taking initiative in this objective may be warranted. Furthermore, attention should be paid to the existing gender portfolio of each board and its subsequent influence on recruiting a female chair, regardless of the organization’s age. Knoppers et al. (2021) concluded that resistance to gender balance by board members is often related to discriminatory discourses against women. The normalization of the discourses of meritocracy, neoliberalism, silence/passivity about the responsibility of structures and an artificial defence of diversity emphasise that equality should not only be determined by women (Knoppers et al., 2021).

When countries are included in the model, the results suggest that the social representation of a female board member is different from that of a female board chair.

The originality of the study is that it shows the factors that constrain women taking up a chair position on NSFs. Theoretically, it contributes to existing literature by demonstrating how a critical mass of females on boards may also extend to the higher and most powerful position of chair.

To understand how organizations, public or private, must increase their productivity perception (PP), independently of the sector. This article aims to analyze PP in the digital transformation (DT) process to determine how it is affected by technostress (TS) and work engagement (WE), two concepts that seem to be forces opposing PP.

The authors use data from a questionnaire addressed to personnel in two organizations (public and private). The analysis applies partial least squares technique to the 505 valid responses obtained from these organizations. This analysis is based not on representativeness but on uniqueness.

The results suggest a positive, significant relationship between DT and PP. This article integrates DT and its effects on aspects of people's health, PP and WE. The model thus includes interactions of technology with human elements. In both business and administrative environments, PP is key to optimizing resources and survival of organizations.

DT processes are different and complex because every organization is different. The authors recommend expanding this study to other sectors in both spheres, public and private. Aligning the objectives of the institutions for aid with DT is also quite complicated.

This study contributes to improving participating organizations. It also provides government institutions with a clear foundation from which to encourage actions that promote the health and WE of their workforce without reducing productivity. In addition, this study adds novelty to the research line.

The authors have deepened this line of research by developing fuller knowledge of the relationships among novel and necessary variables in organizations. The authors provide complementary, different and inspiring value in addressing this line of research.

A separate study of the different behavioral biases does not allow for a full understanding of the complexity and stability of the heterogeneity of beliefs. Therefore, through a more global view of these anomalies, the authors wish to show that they can converge on a single concept, which is the heterogeneity of beliefs.

It is therefore essential to stress that the importance of this study is mainly reflected in the methodological approach used in the construction and analysis of the map and not only in the results achieved. This contribution states that structural analysis, as a means of building the cognitive map, can facilitate the task of investors and other decision-makers, in the identification and analysis of the heterogeneity of beliefs that can therefore guide investors' strategy in decision-making.

The authors have studied the behavior of the investor and its way of interpreting the information and the authors have emphasized the value of studying the concept of heterogeneity of beliefs in its complexity. So that part of the work seems to be relevant and crucial to filling, if you will, that void. In this sense, the authors have shown that behavioral abnormalities are multidimensional concepts: “self-deception”, “cognitive bias”, “emotional bias” and “social bias”.

In particular, this article will aim to achieve the objective of proposing a model for measuring the heterogeneity of beliefs. Thus, the authors want to show that the heterogeneity of beliefs can be measured directly through the different behavioral anomalies.

This paper aims to analyse the effects of events image from host communities’ perspective on the city’s overall image and the intention to recommend the events and the city as a tourism destination.

The research used a bivariate data analysis based on Spearman’s correlation and regression analysis to determine useful variables to predict the intention to recommend the city as a tourism destination. Data collection was face-to-face and online with a non-probabilistic sample of Viseu city residents, the second largest city in the central region of Portugal.

The findings had implications for researchers, governments and stakeholders. From the resident’s point of view, there is a high correlation between the overall city image and the intention to recommend it as a tourism destination. Event image and the intention to recommend the event participation affect the overall city image. Results point out the resident as natural promoters of events and their city if the local events have an appeal that generates their participation. Conclusions indicated that cities need to re-thinking tourism from the citizen’s perspective as staycation is a grown option.

Event image by host-city residents’ perceptions is an underdevelopment theme in the literature, although residents’ participation is essential to the success of most events. Local events can promote tourist citizenship and reinforce the positioning of tourism destinations, associating them with an image of desirable places to visit and live.

In this study, the authors want to identify current possible causes for citing and referencing errors in scholarly literature to compare if something changed from the snapshot provided by Sweetland in his 1989 paper.

The authors analysed reference elements, i.e. bibliographic references, mentions, quotations and respective in-text reference pointers, from 729 articles published in 147 journals across the 27 subject areas.

The outcomes of the analysis pointed out that bibliographic errors have been perpetuated for decades and that their possible causes have increased, despite the encouraged use of technological facilities, i.e. the reference managers.

As far as the authors know, the study is the best recent available analysis of errors in referencing and citing practices in the literature since Sweetland (1989).

This research mainly intends to ascertain the stimulus of investor investment tendencies on the amount of capital investment in the share market.

Utilizing a sample of 477 individual investors who actively trade on the Bangladesh capital market, this empirical study was conducted. The objective of this examination is to ascertain the investment trading behavior of retail investors in the Bangladesh capital market using multiple regression, hypothesis testing and correlation analysis.

The coefficients of market categories, preferred share price ranges and investment source reveal negative predictor correlations; all predictors are statistically significant, with the exception of investment source. Positive predictive correlations exist between investor category, financial literacy degree, investment duration, emotional tolerance level, risk consideration, investment monitoring activities, internal sentiment and correct investment selection. Except for risk consideration and investment monitoring activities, all components have statistically significant predictions. The quantity of capital invested in the stock market is heavily influenced by the investment duration, preferred share price ranges, investor type, emotional toleration level and decision-making accuracy level.

This investigation was conducted exclusively with Bangladeshi individual stockholders. Therefore, the existing study can be extended to institutional investors and conceivably to other divisions. It is possible to conduct this similar study internationally. And the query can enlarge with more sample size and use a more sophisticated econometric model. Despite that the outcomes of this study help the regulatory authorities to arrange more informative seminars and consciousness programs.

The conclusions have practical implications since they empower investors to modify their portfolios based on elements including share price ranges, investment horizons and emotional stability. To improve chances of success and reach financial objectives, they stress the significance of bettering financial understanding, active monitoring and risk analysis. Results can also be enhanced by distributing ownership over a number of market sectors and price points. The results highlight the value of patience and giving potential returns enough time.

This study on the trading behavior of investors in Bangladesh is unique and based on field study, and the findings of this study will deliver information to the stakeholders of the capital market regarding the investors’ trading behavior belonging to different categories, financial literacy level, investment duration, emotional tolerance level and internal feeling.

The authors provide a novel interpretation of the relationship between skill demand and labour market concentration based on the training rationale.

The authors use a novel data set on Italian online job vacancies during 2013–2018 to analyse the relationship between labour market concentration and employers' skill demand. The authors construct measures of market concentration and skill intensity in the local labour market. The authors regress the measures of skill demand on market concentration, controlling for sector, occupations and other features of the labour market. The authors also use the Hausman–Nevo instrument for market concentration.

The authors show that employers in a highly concentrated labour market demand competencies associated with the ability of workers to learn faster (e.g. social skills) rather than actual knowledge. They also require less experience but higher education. These results are consistent with the hypothesis that employers in more concentrated labour markets are more prone to train their employees. Instead of looking for workers who already have job-specific skills, they look for workers who can acquire them faster and efficiently. The authors provide a theoretical framework within which to analyse these aspects as well as providing a test for the relevant hypotheses.

In addition to cross-countries differences in labour market regulations, the authors' findings suggest that policy authorities should consider the local labour market structure when studying workforce development programmes aimed at bridging the skill gap of displaced workers. Moreover, the authors show that market concentration can have relevant implications for human resource (HR) managers by affecting their recruitment behaviour through the demand for skills. In fact, concentrated markets tend to favour firms' collusion and anti-competitive behaviour that could strongly affect HR management practices.

The authors' paper innovates on the literature in a number of ways. First, the authors provide evidence of local labour market concentration in Italy. Second, the authors provide evidence of skill demand at the local level using a detailed skill taxonomy that goes beyond the classical distinction between high and low skills. Third, and most importantly, the authors provide evidence of the relationship between skill demand and labour market concentration. By analysing detailed skills and competencies, the authors take one step beyond understanding the features of labour demand in monopsonistic markets.

This paper intends to verify the extent to which Clean Development Mechanism (CDM) projects intend to contribute to sustainable development (SD) in Brazil, one of the top three leading countries in terms of the number of CDM projects. The authors assess the impact of CDMs not only in environmental aspects, but also social and economic ones.

The authors define a set of qualitative sustainability indicators and scrutinize documents regarding a sample of almost half of all the projects registered in Brazil between 2004 and 2020 (219 projects).

The findings of this study contradict many previous studies finding very limited evidence of SD in CDMs in many different countries: most projects in Brazil intend to contribute to some extent with SD, with 91% and 75% claiming to improve social and economic aspects, respectively.

The authors derive lessons from Brazil that can be used in other researches.

The authors derive lessons from Brazil and propose paths for public policy toward encouraging sustainable development.

The empirical data set relies on data collected directly from each of the projects in Brazil (roughly half of all of them) between 2004 and 2020. This is not only up to date, but pushes further the analytical scope of previous works.

This paper aims to analyse residents’ perceptions of tourism growth in Porto prior to the COVID-19 pandemic, aiming to determine the most appropriate strategies to mitigate negative tourism impacts. Studies on resident perceptions of tourism impacts are still scarce, particularly the ones addressing the topic in the context of Portuguese urban tourism areas.

Data was collected through an online survey, focusing on three categories of impacts: (i) economic, (ii) sociocultural (iii) and spatial-environmental, and the respective mitigation strategies, analysed from the perspective of Porto’s residents. Descriptive and bivariate statistics – T-test and Eta correlation – were used to analyse the collected data.

Respondents who live in the city centre experience specific tourism impacts more negatively, when compared to those living outside the inner-city area. Furthermore, no strong correlation is found between the said impacts and the respective mitigation strategies. However, creating awareness among tourists about acceptable behaviour in shared spaces is the strategy that stands out, as it has a medium correlation with all three impact categories. Most impact-strategy associations are weak, meaning that the defined strategies are not the most case-appropriate, which is something that policymakers should address.

To the best of the author’s/authors’ knowledge, this is the first study to adopt this approach in tackling the negative impacts of rapid tourism growth in Porto.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。