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Leadership is a social phenomenon. Therefore, it has to be examined according to its social context. The point to be underlined by the social context is the social network in which the leader emerges. Considering the studies, the social network side of leadership is relatively ignored comparing with sociometric studies. In that sense, the aim of this study is to reveal whether there is a relationship between the positions of the military personnel, who are defined as one of the gray-collar working groups in the literature, within the social network mechanisms of which they are members, and their self-leadership perceptions. To answer the question, a self-leadership scale was applied to 69 gray-collar employees working in a military unit and network analyses were performed. According to results, there is a strong, positive and significant relationship between the network mechanism centrality criteria indegree, reach centrality and closeness and the self-leadership perceptions of individuals. In addition, there was no significant relationship between eigenvector centrality and honest brokerage, which are among the network mechanism criteria, and the actors’ self-leadership perceptions. The study has aimed at accenting and adding different perspectives to the leadership studies and gray-collar literature.

Individual Placement and Support (IPS) is an evidence-based employment model, effective in supporting individuals with severe mental health difficulties to gain competitive employment. Irish mental health policy recognises its value and IPS is being rolled out in a national programme. Employment is recognised an important contributor to mental health recovery and social inclusion. However, research on IPS has tended to focus on competitive job outcomes. The purpose of this study was to explore the non-vocational outcomes of IPS in an Irish context.

A qualitative research approach was used to interview participants taking part in IPS within community mental health teams. Twelve interviews were included in the data analysis process which was informed by a thematic analysis approach.

Participants experienced increased confidence and positivity, both within a work context and whilst job seeking. More purposeful time use, participation in activities and engagement with society were also experienced by those employed and those at the job search stage of IPS.

This study contributes to the literature about the non-vocational benefits of IPS within an Irish context, highlights the mental health recovery benefits of taking part in IPS and supports the need for ongoing development of IPS throughout mental health services in Ireland.

Multiple sclerosis (MS) is a chronic condition with variable physical, cognitive, and quality of life impacts. Little research has investigated how MS outcomes vary by social identity (race, gender, disability, age, sexual orientation, and nationality) and social location (place within systems of power and privilege). However, emerging evidence points to racial and ethnic group disparities in MS outcomes. This chapter integrates core concepts from the life course perspective and an intersectional feminist disability framework to interrogate the role of diagnosis pathways in determining differential MS outcomes. MS diagnosis pathways (the time from symptom onset to the point of diagnosis) are a logical place to begin this work given the varying nature of symptom onset and the importance of a quick diagnosis for optimal MS outcomes. Whereas the life course perspective provides a framework for understanding disability transitions and pathways across the life span, an intersectional feminist disability framework centers disability within an axis of overlapping social identities and locations. The combination of both frameworks provides an approach capable of examining how MS disparities and inequities emerge in different contexts over time. The chapter begins with an overview of MS and current knowledge on disparities (mainly racial) in MS prevalence, diagnosis, and outcomes. The chapter proceeds to describe the utility of key concepts of both the life course perspective and intersectional frameworks when researching health disparities. Finally, the chapter ends with a theoretical application of an intersectional feminist disability life course perspective to investigate disparities in MS diagnosis pathways.

The purpose of this study is to determine whether the fine wine market is efficient between homogeneous lots and heterogeneous lots.

Auction price data for homogeneous (or solid) lots of fine wines was analyzed to create price prediction models. Those models were used to predict the expected auction price for the bottles within heterogeneous lots. Lastly, models were created to explain and predict the differences between expected and realized prices for heterogenous wine lots.

The results show that large inefficiencies exist. The more complex and expensive the heterogeneous lot, the greater the discount relative to what would have been realized if the bottles had been sold individually. This discount can exceed 50% of the expected auction price.

Heterogeneous lots may arise as a practical requirement from the auction house. Restaurant buyers probably have little interest in such lots because of the inclusion of wines the restaurant will be unable to sell. Collectors may be uniquely positioned to benefit from this price discount.

These results are unique in the literature, because the price dynamics of heterogeneous (or mixed) lots of fine wines have not previously been studied.

The principles of a fair, equitable, and quality education are embodied in both federal legislation in the United States and the United Nations 2030 Agenda for Sustainable Development, Goal 4 (SDG 4). However, inclusive education has remained fairly static since passage of SDG 4 in 2015. In this chapter, we posit that the primary levers influencing the inclusion of students with disabilities in general education content and classrooms is a result of the policies governing special education, in addition to the stigmatization of disability. Furthermore, how intersectional identities serve to segregate students with disabilities from their peers is explored.

As learning at the workplace is predominantly self-motivated, this study is aimed to identify and categorize the motivational drivers for working professionals to pursue self-directed learning (SDL) at the workplace.

A total of 23 variables were identified as drivers for working professionals to initiate, pursue and sustain self-directed learning at the workplace through literature review, interviews and focused group discussions with senior leaders from the industry. The participants were a mix of senior professionals responsible for leading large teams in their organizations and those appointed as people development leaders in their respective organizations. A questionnaire was developed based on the identified drivers and administered online. Around 315 working professionals were surveyed. Structural equation modelling and confirmatory factor analysis (CFA) methods were used to verify the scale and assess its validity and reliability.

The results suggested that SDL motivational drivers for working professionals can be categorized into two broad categories, namely extrinsic (14 drivers) and intrinsic (9 drivers). Further, extrinsic drivers were subdivided into three categories: job expectations (6 drivers), negative consequence (2 drivers) and positive reinforcement (6 drivers) based on the initial exploratory and subsequent confirmatory analysis. The results suggest that job expectations and positive reinforcement positively influence the intrinsic motivation of an individual to pursue SDL and negative consequences negatively impact the intrinsic motivation.

The findings from the study will be useful for human resource practitioners, managers and e-Learning companies to draw strategies for building an SDL culture. It also supports the advancement of research in adult education and learning in the workplace.

A new scale to measure motivation for SDL by employees at the workplace is proposed. There are no scales available to measure employee motivation for pursuing SDL at the workplace to the best of our knowledge.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

The British East India Company (EIC) made connections between the Malay Archipelago and Bengal and established a penal settlement at Bencoolen, followed by the Straits Settlements for the Indian convicts. The convicts from different parts of South Asia today were generally described as “Indian”, such generalisation often hides the identity of specific convicts from South Asia. Among the Indian convicts, the Bengalis were transported to Bencoolen and the Straits Settlements. However, the generic term has made it difficult to reconstruct the history of Bengali convicts’ experiences and pathos. Therefore, this paper attempts to “rediscover” the afterlife of transportation of Bengali felons in the Malay Peninsula.

By examining a range of archival records and current scholarships, this article shows the inclusivity, diversity and accessibility of convict labourers with mainstream society. This study will open up a new avenue of convict histories and subaltern studies on Asia.

The Bengali convicts in the Straits Settlements, one of the oldest migrant sections, have largely been ignored in historical literature. Though the Bengalis, among other South Asian convicts, constituted a significant portion, they were categorised under the generic term “Indian” (Rai, 2014). Their manual labour was invaluable for the colonial economy and the development of the Straits Settlements.

Researcher faced difficulties to get the descendant of Bengali convicts.

This article is a research paper based on mostly archival records; therefore, it is an original contribution to the existing knowledge on the convict history.

The valuation of artworks is challenging since their value encompasses economic, social and cultural values. This study examines two specific questions about the economics of Indian art market: first, the determinants of the price of paintings by Indian artists and second, the risk and return characteristics of investment in Indian paintings. The authors also analyze the role of local context for both questions.

This study uses 8,865 paintings that are auctioned between January, 2000 and June, 2018. A generalized additive model (GAM) is employed to identify the determinants of auction prices and estimate art market price index.

The results indicate that the price of paintings in the Indian market is impacted by both global and local factors. Consistent with the previous research, this study finds that provenance, literature, living status of an artist, artist reputation, auction house, location and gender determine prices. However, the unique behavior of artwork medium and art movement affiliation in the Indian art market signifies the importance of local context in the valuation of artworks. An analysis of the second aspect of the study, i.e. risk and return characteristics of art investment, suggests that though overall art market returns are not lucrative, there are sub-sections in the market that outperform stocks and other assets. Further, the Indian art market shows a weak or negative correlation with other assets, thus making it a good candidate for a diversified portfolio. One of the important findings of this study is that artworks created by artists associated with the Bombay Progressive Artists' Group (PAG) command a significant price premium over all other artworks. Moreover, the average return on investment in paintings by artists affiliated to the Bombay PAG is not only significantly better than other art movements but also higher than all other art assets.

This study contributes to the growing literature on the economics of art market by providing a comprehensive analysis of the economics of Indian paintings. This research highlights the importance of local factors in price determination and on the risk and return characteristics of art investment. To the best of the authors’ knowledge, it is the most comprehensive study of the economics of Indian painting market and the first study to identify the relationship between Indian art movements and prices of paintings and returns on investment in paintings.

This paper investigates the factors and how they lead to meat choice decisions based on preferred slaughter practices. The literature has established the role of psychological factors and morality perception in meat choice decisions. However, it explores how consumers' behavioural intention is impacted towards alternative meat when consumer guilt is activated in different cultural settings.

This study included in-depth interviews with consumers from India's emerging market due to its multicultural dimension and diverse religious beliefs about meat consumption. The authors conducted 17 interviews to explore antecedents towards non-halal meat choices.

Utilizing the Theory of planned behaviour (TPB), this paper explores research gaps related to meat consumption preferences based on preferred slaughter practices in an emerging market context. The findings uncover and add to understanding meat preferences in varied cultural contexts that affect consumer choices. The authors advance the current understanding of TPB from the perspective of behavioural intention toward non-halal meat.

The study's findings have significant implications for all the organizations/outlets dealing with non-vegetarian food products, whether packaged or fresh and for meat sellers.

The study is unique in identifying the meat choice preferences based on slaughter practice through the extended prism of TPB. The market chosen for this study is one of the biggest consumer markets and its growing continuously.