Kuntara Pukthuanthong, Thomas J. Walker, Dolruedee Nuttanontra Thiengtham and Heng Du
– The purpose of this paper is to examine whether and how family ownership enhances or damages firm value.
Abstract
Purpose
The purpose of this paper is to examine whether and how family ownership enhances or damages firm value.
Design/methodology/approach
The paper studies a sample of Canadian companies listed on the Toronto Stock Exchange (TSX) between 1999 and 2007 and apply multivariate regression with firm value as a dependent variable. The paper measures firm value as Tobin ' s Q and ROA based either on net income or EBITDA. The independent variables include family firm dummy and ownership percentage.
Findings
It is found that control-enhancing mechanisms which are often employed by family companies add value to companies. Furthermore, it is found that agency conflicts between ownership and management are less costly than those between majority and minority shareholders, suggesting that family ownership helps resolve the agency conflicts between ownership and management and in turn enhances firm value. Finally, it is found that family companies with founders as CEOs outperform those with descendants as CEOs.
Research limitations/implications
The paper studies Canadian family firms; as such, the sample size is not relatively large. Nonetheless, the results should be generalized as Canada is one of the largest markets in the world and have high integration with the rest of the world.
Practical implications
The results suggest investors should invest in family ownership firms.
Originality/value
The paper shows whether firm ownership increases firm value and the determinant of family firm value.
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What insights might attending to the cyclical history of colonially imposed environmental change experienced by Indigenous peoples offer to critical intellectual projects…
Abstract
What insights might attending to the cyclical history of colonially imposed environmental change experienced by Indigenous peoples offer to critical intellectual projects concerned with race? How might our understanding of race shift if we took Indigenous peoples' concerns with the usurpation and transformation of land seriously? Motivated by these broader questions, in this chapter, I deploy an approach to the critical inquiry of race that I have tentatively been calling anticolonial environmental sociology. As a single iteration of the anticolonial environmental sociology of race, this chapter focuses on Native (American) perspectives on land and experiences with colonialism. I argue that thinking with Native conceptualizations of land forces us to confront the ecomateriality of race that so often escapes sight in conventional analyses. The chapter proceeds by first theorizing the ecomateriality of race by thinking with recent critical theorizing on colonial racialization, alongside Native conceptualizations of land. To further explicate this theoretical argument, I then turn to an historical excavation of the relations between settlers, Natives, and the land in Rhode Island that is organized according to spatiotemporal distinctions that punctuate Native land relations in this particular global region: the Reservation, the Plantation, and the Narragansett.
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Ismail Abiodun Sulaimon, Hafiz Alaka, Razak Olu-Ajayi, Mubashir Ahmad, Saheed Ajayi and Abdul Hye
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully…
Abstract
Purpose
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.
Design/methodology/approach
To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM2.5, PM10, NO2 and O3 in the atmosphere at various times of the day.
Findings
The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.
Research limitations/implications
This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.
Practical implications
This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.
Originality/value
The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.
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Xiaoping Zhang, Yanhui Li, Meixiu Li, Heng Zheng, Qiuju Du, Hong Li, Yuqi Wang, Dechang Wang, Cuiping Wang, Kunyan Sui, Hongliang Li and Yanzhi Xia
The purpose of this paper is to purify the wastewater in the garment industry.
Abstract
Purpose
The purpose of this paper is to purify the wastewater in the garment industry.
Design/methodology/approach
The preparation of the calcium alginate (CA)/activated carbon (AC) composite membrane was achieved by vacuum freeze-drying and the cross-linking reaction between sodium alginate and CaCl2. Effective parameters in the methylene blue (MB) adsorption such as temperature, dose, contact time and pH were discussed. The adsorption properties of the composite membrane were investigated by isotherm, kinetics and thermodynamic analysis. The adsorption equilibrium data were described by the adsorption isotherm Langmuir model and the Freundlich model. The pseudo-first-order, pseudo-second-order and intra-particle diffusion equations were selected to evaluate the kinetics. The thermodynamic study described that the adsorption reaction was spontaneous and exothermic.
Findings
The AC/CA membrane is an efficient and powerful adsorbent to remove MB in printing and dyeing wastewater, and provides a new idea for the selection of adsorption materials for industrial printing and dyeing wastewater.
Practical implications
The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials.
Social implications
The paper contributes to its wider and convenientapplication in wastewater treatment.
Originality/value
Studies on the combination of CA and AC into adsorption membranes and for the removal of dyes from printing and dyeing wastewater have not been reported. A novel composite material is provided for treatment dyeing wastewater in garment production. The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.
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Wei Lu, Vivian W.Y. Tam, Heng Chen and Lei Du
Addressing global warming challenge, carbon emissions reduction potential of the construction industry has received additional attentions. The decoupling of construction industry…
Abstract
Purpose
Addressing global warming challenge, carbon emissions reduction potential of the construction industry has received additional attentions. The decoupling of construction industry and carbon emissions through policies, technologies and model innovations is an effective way for reducing environmental pollution and achieve eco-urban target. The paper aims to discuss these issues.
Design/methodology/approach
Within the scope of green building carbon emissions (GB-CO2) research, a large number of scientific literature has been published in construction discipline over the past few decades. However, it seems that a systematic summary of strategies, techniques, models and scientific discussion of future direction of GB-CO2 is lacking. Therefore, this paper carries out data mining on authoritative journals, identified the key research topics, active research areas and further research trends through visualization studies.
Findings
This study contributes to the body of knowledge in GB-CO2 by critically reviewing and summarizing: professional high-quality journals have a greater influence in the scope of research, developed countries and developing countries are all very concerned about sustainable buildings, and the current hot topics of research focus on the application of the life cycle models, energy efficiency, environmental performance of concrete material, etc. Moreover, further research areas that could expand the knowledge of cross-national long-term carbon mechanisms, develop comprehensive life cycle carbon emissions assessment models, build technical standards and tests for the sustainable building material and systems, and exploit multi-objective decision models considering decarbonizing design and renewable energy.
Originality/value
This study is of value in systematic insight the state-of-the-art of GB-CO2 research in the more recent decade. A more vividly and effectively method is documented in extending the traditional bibliometric review to a deeper discussion. This study can also benefit construction practitioners by providing them a focused perspective of strategy and technologies innovations for emerging practices in green building projects.
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Heng-Yang Lu, Yi Zhang and Yuntao Du
Topic model has been widely applied to discover important information from a vast amount of unstructured data. Traditional long-text topic models such as Latent Dirichlet…
Abstract
Purpose
Topic model has been widely applied to discover important information from a vast amount of unstructured data. Traditional long-text topic models such as Latent Dirichlet Allocation may suffer from the sparsity problem when dealing with short texts, which mostly come from the Web. These models also exist the readability problem when displaying the discovered topics. The purpose of this paper is to propose a novel model called the Sense Unit based Phrase Topic Model (SenU-PTM) for both the sparsity and readability problems.
Design/methodology/approach
SenU-PTM is a novel phrase-based short-text topic model under a two-phase framework. The first phase introduces a phrase-generation algorithm by exploiting word embeddings, which aims to generate phrases with the original corpus. The second phase introduces a new concept of sense unit, which consists of a set of semantically similar tokens for modeling topics with token vectors generated in the first phase. Finally, SenU-PTM infers topics based on the above two phases.
Findings
Experimental results on two real-world and publicly available datasets show the effectiveness of SenU-PTM from the perspectives of topical quality and document characterization. It reveals that modeling topics on sense units can solve the sparsity of short texts and improve the readability of topics at the same time.
Originality/value
The originality of SenU-PTM lies in the new procedure of modeling topics on the proposed sense units with word embeddings for short-text topic discovery.
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This paper aims to investigate a firm’s incentive to innovate its basic product to be socially responsible and its decision on the product line. By constructing a competition…
Abstract
Purpose
This paper aims to investigate a firm’s incentive to innovate its basic product to be socially responsible and its decision on the product line. By constructing a competition model, the paper examines the factors that affect the firm’s choice on its product line with the socially responsible innovation in the presence of altruistic consumers. Such factors include the proportion of the altruistic consumers, the firm’s coordination cost with the basic and innovative products, as well as the consumer’s transportation cost.
Design/methodology/approach
In a model of differentiated products with the competition, the author assumes that a portion of consumers has a strong preference for the socially responsible product (e.g. altruistic consumers). A firm is able to attract altruistic consumers with a socially responsible innovation but it may incur a coordination cost when both the basic and the innovated products are manufactured and sold. In a framework of a sequential game, the firms make a decision on the prices, innovation inputs, as well as the choice on its product line to achieve the expected profit maximization.
Findings
The firm has the incentive to engage in socially responsible innovation to better compete with its rivals. More importantly, the results of the paper explain why some firms wish to manufacture and sell the basic product even though the innovation is successful. The main factors that affect such a firm’s decision include the proportion of the altruistic consumers, the aggregate benefit to all the consumers who purchase the innovative product, the firm’s potential coordination cost and the consumer’s transportation cost.
Originality/value
The paper sheds light on a firm’s corporate social responsibility innovation and its product line determination. The results of this paper can be widely applied in the firm’s strategy of engaging in corporate social responsibility with eco-friendly elements that can attract altruistic consumers in the market. In addition, the findings of the paper can also contribute to policy formulation in terms of innovation. Such a result enables the policymakers to understand the factors that affect the firm’s motivation on innovation and helps them to better guide the firms efficiently participate in the research and development activities.
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Saeed Loghman and Azita Zahiriharsini
Research focusing on psychological capital (PsyCap) has been mainly conducted at the individual level. However, recent research has expanded investigations to the collective level…
Abstract
Research focusing on psychological capital (PsyCap) has been mainly conducted at the individual level. However, recent research has expanded investigations to the collective level with a greater focus on team-level PsyCap. Although, as demonstrated by recent systematic reviews and meta-analyses, the relationships between individual-level PsyCap and the desirable/undesirable outcomes are fairly established in the literature, less is known about such relationships for team-level PsyCap. One of these important, yet least investigated, research areas is the research stream that focuses on the relationship between team-level PsyCap and the outcomes of health, Well-Being, and safety. This chapter aims to highlight the role of individual-level PsyCap as an important predictor of employees’ health, Well-Being, and safety outcomes, but also to go beyond that to provide insights into the potential role of team-level PsyCap in predicting such outcomes at both individual and team levels. To do so, the chapter first draws upon relevant theories to discuss the empirical research findings focusing on the relationship between individual-level PsyCap and the outcomes of health, Well-Being, and safety. It then focuses on team-level PsyCap from theoretical, conceptualization, and operationalization perspectives and provides insights into how team-level PsyCap might be related to health, Well-Being, and safety outcomes at both individual and team levels. Thus, this chapter proposes new research directions in an area of PsyCap that has been left unexplored.