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Article
Publication date: 1 March 2024

Daniel Walzer

In the following theoretical article, the author generates a theory of Leadership Pedagogy and its connection to Creative Arts Education.

Abstract

Purpose

In the following theoretical article, the author generates a theory of Leadership Pedagogy and its connection to Creative Arts Education.

Design/methodology/approach

The article analyzes Leadership Theory across three pillars: Socio-relational, Cognitive and Creative, and how these areas underscore thoughtful and caring pedagogy and inclusive teaching in undergraduate education.

Findings

Drawing on the Scholarship of Teaching and Learning (SoTL), the article advocates for a flexible, multifaceted approach to curricular design rooted in theoretical pluralism, prioritizing interdisciplinary methods to bridge theory and practice in Creative Arts Education.

Originality/value

The article concludes with implications for future research and collaboration connecting Leadership Studies and the Arts.

Details

Journal of Leadership Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1552-9045

Keywords

Article
Publication date: 6 September 2023

Lenka Papíková and Mário Papík

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors…

Abstract

Purpose

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

Design/methodology/approach

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

Findings

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

Originality/value

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

Details

Gender in Management: An International Journal , vol. 39 no. 3
Type: Research Article
ISSN: 1754-2413

Keywords

Article
Publication date: 8 February 2024

Juho Park, Junghwan Cho, Alex C. Gang, Hyun-Woo Lee and Paul M. Pedersen

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major…

Abstract

Purpose

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

Design/methodology/approach

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

Findings

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

Originality/value

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

Details

International Journal of Sports Marketing and Sponsorship, vol. 25 no. 2
Type: Research Article
ISSN: 1464-6668

Keywords

Article
Publication date: 28 March 2024

Elisa Gonzalez Santacruz, David Romero, Julieta Noguez and Thorsten Wuest

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework…

Abstract

Purpose

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

Design/methodology/approach

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

Findings

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

Originality/value

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 7 July 2023

Xiaojie Xu and Yun Zhang

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important…

Abstract

Purpose

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

Design/methodology/approach

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

Findings

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

Originality/value

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Details

Journal of Financial Management of Property and Construction , vol. 29 no. 1
Type: Research Article
ISSN: 1366-4387

Keywords

Article
Publication date: 26 March 2024

Ángela Ximena Campos García, Victoria Eugenia Cabrera-García, María del Carmen Docal-Millán, Lina María Acuña Arango and Fernando Riveros Munevar

Remote work has been intensifying in organizations, and the recent pandemic forced an immediate increase in it, ignoring its effect on the family. The purpose of this study was to…

Abstract

Purpose

Remote work has been intensifying in organizations, and the recent pandemic forced an immediate increase in it, ignoring its effect on the family. The purpose of this study was to analyze the work and personal-family life balance of Colombian workers during the lockdown and the effects on post-pandemic times.

Design/methodology/approach

Quantitative correlational study with a non-probabilistic sample of 1,069 participants: 349 (32.64%) men and 720 (67.35%) women.

Findings

A total of 44.8% of the participants reported that their work interfered with their personal life; 61.6% reported that their work exceeded their habitual time; 72.2% felt comfortable with the remote work; and women perceived more affectation, as did participants with children. No interaction was present between these variables. There are more interruptions for workers with children younger than 12 years.

Practical implications

There is satisfaction with remote work. However, there are difficulties regarding work-personal life balance that must be addressed to improve quality of life, with an emphasis on women and workers with children, especially younger children.

Social implications

This study provides empirical evidence for the foundation of public and organizational policies aimed at managing remote work and the work-personal life balance to reduce the risk of loss of female labor force and effects on the quality of life of workers.

Originality/value

Studies on the work-personal life balance with Latin American samples are scarce. This research contributes to the literature about satisfaction with working from home modality and the work-personal life balance during COVID-19 confinement, with a look at the differences by gender and the evaluation of the family conditions of Colombian workers, contributing to a regional perspective.

Details

Gender in Management: An International Journal , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2413

Keywords

Article
Publication date: 2 April 2024

Yixue Shen, Naomi Brookes, Luis Lattuf Flores and Julia Brettschneider

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging…

Abstract

Purpose

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

Design/methodology/approach

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

Findings

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Originality/value

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

Details

International Journal of Managing Projects in Business, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8378

Keywords

Article
Publication date: 21 March 2024

Thamaraiselvan Natarajan, P. Pragha, Krantiraditya Dhalmahapatra and Deepak Ramanan Veera Raghavan

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and…

Abstract

Purpose

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

Design/methodology/approach

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

Findings

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Research limitations/implications

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

Social implications

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

Originality/value

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 7 June 2023

Hafiz Muhammad Muien, Sabariah Nordin and Bazeet Olayemi Badru

As the benefit of gender diversity continues to receive significant attention, a holistic investigation of its effect on corporate financial distress (CFD) is lacking. Therefore…

Abstract

Purpose

As the benefit of gender diversity continues to receive significant attention, a holistic investigation of its effect on corporate financial distress (CFD) is lacking. Therefore, this study examines the effects of board gender diversity, measured in different forms, such as the presence and proportion of female directors, family-affiliated female directors and the chief executive officer (CEO) gender, on CFD in Pakistan. The study also investigates the interacting effects of family-controlled (20 and 50% family-owned) companies on the association between board gender diversity and CFD.

Design/methodology/approach

The study applied the pooled cross-sectional logistic regression model to examine the effect of board gender diversity (presence and proportion of female directors, family-affiliated female directors and CEO gender) on CFD through a sample of 285 non-financial companies in Pakistan over the period of 2006–2017.

Findings

The results reveal that gender diversity on boards is significantly and negatively associated with CFD in Pakistan. In addition, when family ownership is 50% or more, the interacting effect of family control is found to be significant, while gender effects remain negative. The results suggest that female directors contribute to the long-term viability of companies, especially family-owned companies. Female directors are also found to be more prevalent in family-owned companies compared to their non-family counterparts.

Research limitations/implications

The findings imply that female directors may efficiently manage and control all functions necessary to guarantee the company's long-term prosperity. Similarly, gender effects can outweigh the detrimental impact of family control when female directors are in reasonable numbers and of high quality in the boardroom.

Practical implications

The practical relevance of the findings is that female directors play a significant role on the corporate board. Thus, it is a wakeup call for Pakistani companies to recognize the critical role and uniqueness of women on the corporate ladder. Family companies can also galvanize on the uniqueness of women to improve their governance structure.

Originality/value

This study adds to the literature on the benefits of gender diversity in family and non-family-owned companies. Specifically, this study applied multiple measures of gender diversity and family control in a single study. In addition, the study was conducted in a country that is ranked as the second worst country in the Global Gender Gap Index 2022, implying that investigating this type of research would go a long way towards changing the minds of corporate executives and regulators about the critical role that women can play in the economy.

Details

Journal of Family Business Management, vol. 14 no. 1
Type: Research Article
ISSN: 2043-6238

Keywords

Article
Publication date: 24 January 2022

Muhammad Ayat, Sheheryar Mohsin Qureshi and Changwook Kang

The purpose of this study is to propose an improved framework for managing Private Participation in Infrastructure ICT (PPI-ICT) projects in the context of developing countries as…

Abstract

Purpose

The purpose of this study is to propose an improved framework for managing Private Participation in Infrastructure ICT (PPI-ICT) projects in the context of developing countries as the requirements to manage them are different in several aspects.

Design/methodology/approach

The framework has been proposed based on an exhaustive literature review and statistical analysis of the PPI-ICT projects’ data set using logistic regression, F-test and student’s t-test. The proposed framework was also applied to the PPI-ICT projects.

Findings

The framework is an extension to NTCP (novelty, technology, complexity and pace) approach by including extrinsic factors such as income of the country, climate risk, religious diversity, political stability, regularity quality and control of corruption. The proposed framework was used to analyze project characteristics and their external conditions in the context of developing countries. Based on the analyses, the authors have presented a detailed set of recommendations for project managers, practitioners and governments to improve the success rate of these projects.

Originality/value

The major contribution of this study is the framework, which encompasses the NTCP model as well as extrinsic characteristics of PPI-ICT projects. The proposed framework is meant to assist the project managers to comprehend the project characteristics and its external environment to identify an adequate approach for managing projects successfully.

Details

Journal of Engineering, Design and Technology , vol. 22 no. 2
Type: Research Article
ISSN: 1726-0531

Keywords

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