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It has been argued in the literature that structural changes in the financial markets, such as integration, have the potential to cause herding behavior or correlated behavioral patterns in traders. The purpose of this study is to investigate whether there is any financial herding behavior in the Latin American Integrated Market (MILA), a transnational stock market composed of Chile, Peru, Colombia and Mexico stock exchanges and whether there is any ARCH or GARCH effect in the herding behavior models.

This study uses the modified return dispersion approach on daily index return data. The sample period is from January 03, 2002 to May 07, 2019. The data are obtained from the MILA database. To count time-varying volatilities in herding models, the authors run ARCH family regression with GARCH (1,1) settings. Hwang and Salmon (2004) model is used as a robustness test.

The authors found strong herding behavior under the general market conditions and moderate and partial herding behavior under some specified markets circumstances, such as bull and bear markets and high-low volatility states. Moreover, the pre-MILA period exhibits more herding behavior than the post-MILA period. The empirical results show that most of the ARCH and GARCH effects are statistically significant, implying that the past information of stock returns and market volatility significantly affect the volatility of following periods, which can also explain the formation of herding tendency among investors. Finally, the results of the robustness tests (Hwang and Salmon, 2004) confirm herding in all periods, except full sample period for Mexico and post-MILA period for Mexico and Colombia.

This study investigates the herding behavior in the MILA market in terms of market return, volatility and timing. A limitation of the paper is that the authors have not included other factors on the formation of herding behavior, such as macroeconomic factors, effects of regional or international markets and policy influences. The authors will explore the issue in the extension of the paper.

As MILA is the first virtual integration of stock exchanges without merging, the study provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are useful for academics, investors and policymakers in their investment and decision makings.

The paper provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are not only useful in practical implications, but also in social implications.

This study contributes to the herding literature by examining four different hypotheses in respect of the unique case of transnational stock exchange without fusions or corporate mergers, where each market maintains its independence and regulatory autonomy. The authors also contribute to the literature by including both ARCH and GARCH effects in the herding behavioral models along the Hwang and Salmon (2004) approach.

Despite the growing interest regarding companies' sustainability, its social dimension has mostly been neglected by academics and practitioners. Consequently, this study aims to address this issue by investigating if the adoption of human resource (HR) analytics can positively influence the impact of social sustainable operations practices (SSOP) on employees' motivation and engagement and the effect of these lasts on organizational retention.

Data were collected through online questionnaires addressed to 281 HR managers of heterogeneous companies from Europe and analyzed through a structural equation modeling (SEM) technique.

The findings confirmed the positive effect of SSOP on employees’ motivation and engagement, and of these last on employees’ retention. Furthermore, they confirmed that the usage of HR analytics positively moderates the relationship between SSOP and employees’ motivation and engagement.

This study contributes to both sustainable operations management and HR management literature streams. First, it adopts a multidisciplinary perspective which also considers evidence from HR management literature, allowing the authors to concentrate on the social dimension of sustainability. Second, it provided further insight regarding the adoption of a data-driven approach in relation to social sustainable operations management. Finally, it contributes to HR analytics-related literature by demonstrating its impact also on organizational aspects that are not directly controlled by the HR department.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

Gossip spreads like wildfire, damaging relationships, decaying trust and creating a negative work environment. This study aims to investigate the relationship between negative workplace gossip (NWG) and quiet quitting (QQ), while considering the mediating effects of workplace stress and emotional exhaustion (EE).

Drawing upon the conservation of resource theory, the study aimed to comprehend this association in the context of 267 employees from diverse sectors in India, including health care, IT, banking and education. Through a three-wave time lagged survey design, using partial least squares structural equation modeling, significant findings were uncovered.

The results revealed a positive link between NWG and QQ. There was also a positive correlation between NWG and workplace stress. In addition, workplace stress and EE were found to mediate the relationship between NWG and QQ.

The findings have implications for both theory and practice. Organizations should consider implementing strategies to mitigate the prevalence of negative gossip and foster a healthier work environment, promoting employee well-being and retention.

The study reveals the “black box” between NWG and QQ, adding to the body of knowledge on the novel concept of QQ. Second, the study expands the literature on NWG, by examining impact path of how it leads to stress and EE, leading to QQ.

Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.

The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.

(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.

The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.

This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.

A boundaryless career perspective suggests that career competencies are essential for employees who wish to advance their careers in high uncertainty. This study aims to propose an integrated conceptual model for career competencies to provide insights for employees and organizations by identifying what and how one can prepare and provide support for career development in an uncertain and complex work environment.

The integrated literature reviewed was adapted to provide a conceptual model for career competencies. All 77 studies were reviewed, guided by the intelligent career theory (ICT) and social cognitive career theory (SCCT).

The mechanisms of career competency development were examined through the interrelationship between three types of knowing; knowing-why, knowing-whom and knowing-how. Career competencies can be considered a developmental process, therefore, they could develop through various interventions and accumulate over time. Especially the results indicate that learning is an essential component of career competencies, as it increases self-efficacy and promotes a desire to achieve positive career outcomes.

This study provided a conceptual model, explored the mechanisms of career competency development and considered how career competencies influence career outcomes. Furthermore, it identified the context of the construct of career competencies by integrating the SCCT and ICT. Finally, it showed the inadequacy of existing research on negative factors of career competency outcomes and recommended further research to broaden the general context of career competency studies.

This study aims to examine the potential of Information Ethics (IE) to serve as a coherent ethical foundation for the library and information science profession (LIS profession).

This study consists of two parts: the first part present IE’s central theses and the main critiques it has received; the second part offers the authors' own evaluation of the theory from the LIS perspective in two steps: (1) assessing its internal consistency by testing its major theses against each other; (2) assessing its utility for resolving frequently debated LIS ethical dilemmas by comparing its solutions with solutions from other ethical theories.

This study finds that IE, consisting of an informational ontology, a fundamental ethical assertion and a series of moral laws, forms a coherent ethical framework and holds promising potential to serve as a theoretical foundation for LIS ethical issues; its inclusion of nonhuman objects as moral patients and its levels of abstraction mechanism proved to be particularly relevant for the LIS profession. This study also shows that, to become more solid an ethical theory, IE needs to resolve some of its internal contradictions and ambiguities, particularly its conceptual conflations between internal correctness, rightness and goodness; between destruction, entropy and evil; and the discrepancy between its deontological ethical assertion and its utilitarian moral laws.

This study alerts LIS professionals to the possibility of having a coherent ethical foundation and the potential of IE in this regard.

This study provides a systemic explication, evaluation and field test of IE from the LIS perspective.

The purpose of this study is to explore the impact of work-to-family conflict (WFC) on unethical pro-family behavior (UPFB) and work engagement (WE) among Chinese female leaders. In addition, this study investigates the mediating role of work-to-family guilt (WFG) and the moderating role of family centrality (FC) in these relationships.

A quantitative approach was adopted, involving the collection of data through online questionnaires administered at three time points. These data were analyzed using hierarchical regression and the bootstrapping method to test the proposed hypotheses.

WFC exhibited a significant positive correlation with UPFB and a negative correlation with WE; WFG played a mediating role in the relationships between WFC and both UPFB and WE; and FC had a significant moderating effect on the relationship between WFC and WE.

This study sheds light on a model of WFC and its related effects, reveals how WFC affects UPFB and WE and uncovers the mediating role of WFG and the moderating role of FC; pays attention to a unique organizational behavior, UPFB, which enriches research on the antecedents influencing such behaviors; and examines Chinese female leaders in organizations, their current experience of WFC and the resulting psychological and behavioral outcomes.

The purpose of this paper is to explore and understand the perceptions of residents of Southeastern Nigeria about glocalizing tourism in the region to help improve their support for the sustainability of the industry. Emphasis is laid on their expectations and strategies to maximize the positive impacts while minimizing the negative aspects in a bid to address their specific local needs.

This paper adopts an ethnographic approach to explore the perspectives of key stakeholders in Southeastern Nigeria's tourism industry. These include traditional rulers, men, women and youth representatives, chief priests and local security agents. Decision-making theory is adopted to frame the study.

The findings identified residents' expectations from glocalizing tourism. They see tourism as an avenue for initiating community projects, creating jobs, patronizing farm produces, reviving cultural practices and addressing religious crises.

This research focused only on selected communities within Southeastern Nigeria. The implication is that the findings do not represent what obtains in other communities within the region. Future research should extend to these areas to have a deeper understanding of how residents perceive the glocalization of tourism.

As the government and developers continue to invest in the tourism industry in the study area, glocalization could be a good way to address specific local needs and gain residents' support in the future.

This paper represents a new research approach for understanding the perceptions of residents about the Nigerian tourism industry.