Lucía Isabel García-Cebrián, Fabíola Zambom-Ferraresi and Fernando Lera-López
The purpose of this paper is to analyze efficiency and its evolution in teams that played in the UEFA Champions League during nine seasons. The aim is to present a research…
Abstract
Purpose
The purpose of this paper is to analyze efficiency and its evolution in teams that played in the UEFA Champions League during nine seasons. The aim is to present a research procedure for determining the most accurate data envelopment analysis to estimate and compare the efficiency.
Design/methodology/approach
First, the authors analyzed the existence of a temporal trend using the S-statistic. The authors calculated the Kruskal–Wallis statistic to verify if there is stability in relative ranks. The results of the aforementioned tests have indicated that window analysis is an accurate methodology to apply to the sample. The authors analyzed 94 clubs with a sample of 288 observations, obtaining 768 efficiency ratios. They have been calculated using super-efficiency which enables to discriminate efficient units.
Findings
Results indicate that there is a low efficiency level in the nine seasons observed. There is a strong correlation between sports results and the efficiency of semifinalists. The authors conclude that improvement in a club’s efficiency could enhance its sports results. Finally, as practical implications, the authors highlight benchmark teams and alternative sports tactics to help clubs become more efficient and achieve better sports results.
Originality/value
This paper contributes to sports efficiency literature by presenting a research procedure to identify the most accurate methodology to be applied to panel data. To the best of the authors’ knowledge, this paper is the first empirical study on international football competitions applying WindowDEA to incomplete panel data.
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Damian Tago, Henrik Andersson and Nicolas Treich
This study contributes to the understanding of the health effects of pesticides exposure and of how pesticides have been and should be regulated.
Abstract
Purpose
This study contributes to the understanding of the health effects of pesticides exposure and of how pesticides have been and should be regulated.
Design/methodology/approach
This study presents literature reviews for the period 2000–2013 on (i) the health effects of pesticides and on (ii) preference valuation of health risks related to pesticides, as well as a discussion of the role of benefit-cost analysis applied to pesticide regulatory measures.
Findings
This study indicates that the health literature has focused on individuals with direct exposure to pesticides, i.e. farmers, while the literature on preference valuation has focused on those with indirect exposure, i.e. consumers. The discussion highlights the need to clarify the rationale for regulating pesticides, the role of risk perceptions in benefit-cost analysis, and the importance of inter-disciplinary research in this area.
Originality/value
This study relates findings of different disciplines (health, economics, public policy) regarding pesticides, and identifies gaps for future research.
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The progress of semiconductor fabrication technology, particularly the heteroepitaxial technology (MOCVD, MBE, etc.) has permitted the fabrication of structures and devices whose…
Abstract
The progress of semiconductor fabrication technology, particularly the heteroepitaxial technology (MOCVD, MBE, etc.) has permitted the fabrication of structures and devices whose behaviour is dominated by ballistic and/or quantum‐interference effects through heterojunctions.
In the UK, Masters level discipline-specific courses in sustainability integrate modules on the social, economic, and environmental issues of sustainable development. The…
Abstract
In the UK, Masters level discipline-specific courses in sustainability integrate modules on the social, economic, and environmental issues of sustainable development. The postgraduate faculty teaching on these courses and the student cohorts enrolling in such courses bring varying attitudes, experiences, and beliefs to the ecological and anthropological discourses and practices about sustainable development. Existing studies of education for sustainable development (ESD) have identified strengths and weaknesses in the knowledge and attitudes of students and faculty although few studies have focused on postgraduate cohorts and fewer still have attempted to compare and contrast students and lecturers. This mixed method case study analyses findings from data collected (2016–2017) from student surveys (n = 121) and semi-structured interviews with faculty (n = 21) recruited from multiple university departments, centers, and programs (n = 12) to identify prevailing anthropocentric and eco-centric ideas and rationales about sustainable development and ESD. Findings suggest a strong orientation to mainstream sustainable development in both groups but analysis identifies reasons for resisting a focus on extremes of “deep green” or “green wash” approaches. In addition, prevailing belief in academic neutrality, institutional and disciplinary factors, student pragmatism, and other drivers are highlighted. The study concludes by identifying potential paths from prevailing (experiential) education in sustainable development to more transformational approaches.
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Stephanos Anastasiadis, Stephanie Perkiss, Bonnie A. Dean, Leopold Bayerlein, Maria Alejandra Gonzalez-Perez, Alec Wersun, Pilar Acosta, Hannah Jun and Belinda Gibbons
Sustainability is one of the leading challenges of our age, and higher education plays a vital role in supporting the implementation of sustainability initiatives. There has been…
Abstract
Purpose
Sustainability is one of the leading challenges of our age, and higher education plays a vital role in supporting the implementation of sustainability initiatives. There has been substantial progress in business schools introducing sustainability into courses with extant literature detailing case studies of sustainability education and student perceptions of their learning. The purpose of this paper is to address the gap in literature from educators' perspectives on their experiences of introducing sustainability teaching using specific teaching tools for sustainability.
Design/methodology/approach
This paper presents a case study on a sustainability teaching tool, WikiRate, that was embedded into business and management courses at seven higher education institutions from across the globe. Interviews were conducted after course delivery to gain insights into the practical challenges of designing and implementing a sustainability education activity.
Findings
The findings show that educators perceive sustainability as a complex issue, presenting a challenge to teaching in university systems whose normative curricula are rooted in instrumental problem-solving. Furthermore, educators described challenges to their own learning in order to implement sustainability into curricula including the need for compromises and adaptions.
Originality/value
This empirical study reports on educators' experiences embedding sustainability into their courses through an innovative teaching tool, WikiRate. This paper has implications for reframing how we can approach sustainability education and presents discussion ways to teach complexity without reduction or simplification.
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Social democratic unionism has arguably been one of the most successful worker organisations in modern history. Through collective bargaining and political influence, this type of…
Abstract
Social democratic unionism has arguably been one of the most successful worker organisations in modern history. Through collective bargaining and political influence, this type of unionism has been effective in redistributing the gains from capitalist markets. This paper reviews the challenges, pathways and dilemmas social democratic unions face in the knowledge economy. Similar to industrialisation, the knowledge economy has the potential to fundamentally change the social fabric that trade unions derive their power resources from. There are three major and interrelated challenges: (1) technological change and the knowledge economy, (2) new socio-political coalitions and (3) keeping employers in. Focussing on Denmark and Sweden, it is argued that these three challenges strike the core of social democratic unionism, as they can undermine the ability to encompass the whole labour market because of polarisation or upgrading of jobs. The paper goes on to outline three possible pathways: ‘going radical’, ‘going academic’ and ‘going old-school’. ‘Going radical’ entails a sharper focus on fighting precarious work with other regulatory means other than collective bargaining. ‘Going academic’ entails a focus on education and lifting all occupational groups. ‘Going old-school’ entails adapting the principle of collective bargaining to new types of companies and occupations while sticking to the regulatory means as before. It is argued that none of the strategies is a silver bullet to the challenges, but that a key to the success of any of the strategies is that minimum wage levels are defended, as this will fuel investment in education for lower-paid work.
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Giovanni De Luca and Monica Rosciano
The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty…
Abstract
Purpose
The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.
Design/methodology/approach
The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.
Findings
The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.
Originality/value
The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.
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Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the…
Abstract
Purpose
Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the sensational hyperbole of AI in teaching and learning. Instead, this paper aims to explore the role of AI in educational leadership.
Design/methodology/approach
To explore the role of AI in educational leadership, I synthesized the literature that intersects AI, decision-making, and educational leadership from multiple disciplines such as computer science, educational leadership, administrative science, judgment and decision-making and neuroscience. Grounded in the intellectual interrelationships between AI and educational leadership since the 1950s, this paper starts with conceptualizing decision-making, including both individual decision-making and organizational decision-making, as the foundation of educational leadership. Next, I elaborated on the symbiotic role of human-AI decision-making.
Findings
With its efficiency in collecting, processing, analyzing data and providing real-time or near real-time results, AI can bring in analytical efficiency to assist educational leaders in making data-driven, evidence-informed decisions. However, AI-assisted data-driven decision-making may run against value-based moral decision-making. Taken together, both leaders' individual decision-making and organizational decision-making are best handled by using a blend of data-driven, evidence-informed decision-making and value-based moral decision-making. AI can function as an extended brain in making data-driven, evidence-informed decisions. The shortcomings of AI-assisted data-driven decision-making can be overcome by human judgment guided by moral values.
Practical implications
The paper concludes with two recommendations for educational leadership practitioners' decision-making and future scholarly inquiry: keeping a watchful eye on biases and minding ethically-compromised decisions.
Originality/value
This paper brings together two fields of educational leadership and AI that have been growing up together since the 1950s and mostly growing apart till the late 2010s. To explore the role of AI in educational leadership, this paper starts with the foundation of leadership—decision-making, both leaders' individual decisions and collective organizational decisions. The paper then synthesizes the literature that intersects AI, decision-making and educational leadership from multiple disciplines to delineate the role of AI in educational leadership.
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Claire M. Mason, Haohui Chen, David Evans and Gavin Walker
This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of…
Abstract
Purpose
This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications.
Design/methodology/approach
Using the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations.
Findings
This study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers.
Originality/value
This study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.