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Available. Open Access. Open Access
Article
Publication date: 15 October 2016

Ann M. Herd, Brittany L. Adams-Pope, Amanda Bowers and Brittany Sims

As the world of healthcare changes rapidly, healthcare leaders and managers must hone their leadership competencies in order to remain effective in their organizations. With…

654

Abstract

As the world of healthcare changes rapidly, healthcare leaders and managers must hone their leadership competencies in order to remain effective in their organizations. With changes such as the Affordable Care Act, increasing medical school costs, decreased graduation rates, and increased needs for care, how are current and future healthcare leaders adapting? In light of the large-scale changes in the healthcare field in recent years, the purpose of this study was to investigate which National Center for Healthcare Leadership (NCHL) competencies were referenced by exemplary healthcare leaders as most important for success in today’s changing healthcare environment. Interviews were conducted with 26 mid- and upper-level healthcare leaders identified by the C-level executives in their organizations as exemplary performers. Change leadership, self-development, talent development, and team leadership were the top four NCHL competencies most frequently referenced, with thematic analysis revealing additional underlying themes in the exemplary leaders’ dialogue.

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Journal of Leadership Education, vol. 15 no. 4
Type: Research Article
ISSN: 1552-9045

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Book part
Publication date: 26 November 2018

Ann M. Herd, Denise M. Cumberland, William A. Lovely and Allan Bird

While international learning programs have received a great deal of attention and have been found to provide valuable learning experiences for participants interested in…

Abstract

While international learning programs have received a great deal of attention and have been found to provide valuable learning experiences for participants interested in developing global leadership competencies (GLCs), they are resource-intensive and variably effective. This chapter examines the relatively unexplored use of assessment center (AC) methodology as a complementary avenue for developing students’ GLCs. Scholarly literature sources pertaining to GLCs and their development, experiential learning theory, and AC methodology are reviewed to develop a conceptual model and propositions related to participants’ learning in an AC designed to develop GLCs. An example is described of one university’s design and facilitation of an AC used to develop students’ GLCs. The role of AC methodology, along with international and other learning experiences for developing students’ GLCs, and recommendations for future research, are discussed.

Available. Content available
Book part
Publication date: 26 November 2018

Abstract

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Advances in Global Leadership
Type: Book
ISBN: 978-1-78754-297-6

Available. Open Access. Open Access
Article
Publication date: 7 June 2023

Enoch Owusu-Sekyere, Helena Hansson, Evgenij Telezhenko, Ann-Kristin Nyman and Haseeb Ahmed

The purpose of this paper was to assess the economic impact of investment in different animal welfare–enhancing flooring solutions in Swedish dairy farming.

1086

Abstract

Purpose

The purpose of this paper was to assess the economic impact of investment in different animal welfare–enhancing flooring solutions in Swedish dairy farming.

Design/methodology/approach

The authors developed a bio-economic model and used stochastic partial budgeting approach to simulate the economic consequences of enhancing solid and slatted concrete floors with soft rubber covering.

Findings

The findings highlight that keeping herds on solid and slatted concrete floor surfaces with soft rubber coverings is a profitable solution, compared with keeping herds on solid and slatted concrete floors without a soft covering. The profit per cow when kept on a solid concrete floor with soft rubber covering increased by 13%–16% depending on the breed.

Practical implications

Promoting farm investments such as improvement in flooring solution, which have both economic and animal welfare incentives, is a potential way of promoting sustainable dairy production. Farmers may make investments in improved floors, resulting in enhanced animal welfare and economic outcomes necessary for sustaining dairy production.

Originality/value

This literature review indicated that the economic impact of investment in specific types of floor improvement solutions, investment costs and financial outcomes have received little attention. This study provides insights needed for a more informed decision-making process when selecting optimal flooring solutions for new and renovated barns that improve both animal welfare and ease the burden on farmers and public financial support.

Details

British Food Journal, vol. 125 no. 12
Type: Research Article
ISSN: 0007-070X

Keywords

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Book part
Publication date: 18 January 2024

Ackmez Mudhoo, Gaurav Sharma, Khim Hoong Chu and Mika Sillanpää

Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However…

Abstract

Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.

Details

Artificial Intelligence, Engineering Systems and Sustainable Development
Type: Book
ISBN: 978-1-83753-540-8

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Article
Publication date: 4 April 2023

Anshita Bihari, Manoranjan Dash, Kamalakanta Muduli, Anil Kumar, Eyob Mulat-Weldemeskel and Sunil Luthra

Current research in the field of behavioural finance has attempted to discover behavioural biases and their characteristics in individual investors’ irrational decision-making…

983

Abstract

Purpose

Current research in the field of behavioural finance has attempted to discover behavioural biases and their characteristics in individual investors’ irrational decision-making. This study aims to find out how biases in information based on knowledge affect decisions about investments.

Design/methodology/approach

In step one, through existing research and consultation with specialists, 13 relevant items covering major aspects of bias were determined. In the second step, multiple linear regression and artificial neural network were used to analyse the data of 337 retail investors.

Findings

The investment choice was heavily impacted by regret aversion, followed by loss aversion, overconfidence and the Barnum effect. It was observed that the Barnum effect has a statistically significant negative link with investing choices. The research also found that investors’ fear of making mistakes and their tendency to be too sure of themselves were the most significant factors in their decisions about where to put their money.

Practical implications

This research contributes to the expansion of the knowledge base in behavioural finance theory by highlighting the significance of cognitive psychological traits in how leading investors end up making irrational decisions. Portfolio managers, financial institutions and investors in developing markets may all significantly benefit from the information offered.

Originality/value

This research is a one-of-a-kind study, as it analyses the emotional biases along with the cognitive biases of investor decision-making. Investor decisions generally consider the shadowy side of knowledge management.

Details

VINE Journal of Information and Knowledge Management Systems, vol. 55 no. 2
Type: Research Article
ISSN: 2059-5891

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Article
Publication date: 17 March 2023

Le Wang, Liping Zou and Ji Wu

This paper aims to use artificial neural network (ANN) methods to predict stock price crashes in the Chinese equity market.

803

Abstract

Purpose

This paper aims to use artificial neural network (ANN) methods to predict stock price crashes in the Chinese equity market.

Design/methodology/approach

Three ANN models are developed and compared with the logistic regression model.

Findings

Results from this study conclude that the ANN approaches outperform the traditional logistic regression model, with fewer hidden layers in the ANN model having superior performance compared to the ANNs with multiple hidden layers. Results from the ANN approach also reveal that foreign institutional ownership, financial leverage, weekly average return and market-to-book ratio are the important variables when predicting stock price crashes, consistent with results from the traditional logistic model.

Originality/value

First, the ANN framework has been used in this study to forecast the stock price crashes and compared to the traditional logistic model in the world’s largest emerging market China. Second, the receiver operating characteristics curves and the area under the ROC curve have been used to evaluate the forecasting performance between the ANNs and the traditional approaches, in addition to some traditional performance evaluation methods.

Details

Pacific Accounting Review, vol. 35 no. 4
Type: Research Article
ISSN: 0114-0582

Keywords

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Article
Publication date: 15 June 2021

Annapoorani Subramanian and Jayaparvathy R.

The solar photovoltaic (PV) system is one of the outstanding, clean and green energy options available for electrical power generation. The varying meteorological operating…

174

Abstract

Purpose

The solar photovoltaic (PV) system is one of the outstanding, clean and green energy options available for electrical power generation. The varying meteorological operating conditions impose various challenges in extracting maximum available power from the solar PV system. The drawbacks of conventional and evolutionary algorithms-based maximum power point tracking (MPPT) approaches are its inability to extract maximum power during partial shading conditions and quickly changing irradiations. Hence, the purpose of this paper is to propose a modified elephant herding optimization (MEHO) based MPPT approach to track global maximum power point (GMPP) proficiently during dynamic and steady state operations within less time.

Design/methodology/approach

A MEHO-based MPPT approach is proposed in this paper by incorporating Gaussian mutation (GM) in the original elephant herding optimization (EHO) to enhance the optimizing capability of determining the optimal value of DC–DC converter’s duty cycle (D) to operate at GMPP.

Findings

The effectiveness of the proposed system is compared with EHO based MPPT, Firefly Algorithm (FA) MPPT and particle swarm optimization (PSO) MPPT during uniform irradiation condition (UIC) and partial shading situation (PSS) using simulation results. An experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

Originality/value

With the proposed MEHO MPPT, it has been noted that the settling period is lowered by 3.1 times in comparison of FA MPPT, 1.86 times when compared to PSO based MPPT and 1.29 times when compared to EHO based MPPT with augmented efficiency of 99.27%.

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Article
Publication date: 24 October 2023

Muhammad Naeem Aslam, Arshad Riaz, Nadeem Shaukat, Muhammad Waheed Aslam and Ghaliah Alhamzi

This study aims to present a unique hybrid metaheuristic approach to solving the nonlinear analysis of hall currents and electric double layer (EDL) effects in multiphase wavy…

170

Abstract

Purpose

This study aims to present a unique hybrid metaheuristic approach to solving the nonlinear analysis of hall currents and electric double layer (EDL) effects in multiphase wavy flow by merging the firefly algorithm (FA) and the water cycle algorithm (WCA).

Design/methodology/approach

Nonlinear Hall currents and EDL effects in multiphase wavy flow are originally described by partial differential equations, which are then translated into an ordinary differential equation model. The hybrid FA-WCA technique is used to take on the optimization challenge and find the best possible design weights for artificial neural networks. The fitness function is efficiently optimized by this hybrid approach, allowing the optimal design weights to be determined.

Findings

The proposed strategy is shown to be effective by taking into account multiple variables to arrive at a single answer. The numerical results obtained from the proposed method exhibit good agreement with the reference solution within finite intervals, showcasing the accuracy of the approach used in this study. Furthermore, a comparison is made between the presented results and the reference numerical solutions of the Hall Currents and electroosmotic effects in multiphase wavy flow problem.

Originality/value

This comparative analysis includes various performance indices, providing a statistical assessment of the precision, efficiency and reliability of the proposed approach. Moreover, to the best of the authors’ knowledge, this is a new work which has not been explored in existing literature and will add new directions to the field of fluid flows to predict most accurate results.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 1
Type: Research Article
ISSN: 0961-5539

Keywords

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Article
Publication date: 31 December 2021

Jyothi N. and Rekha Patil

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

152

Abstract

Purpose

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

Design/methodology/approach

The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.

Findings

A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.

Practical implications

The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.

Originality/value

This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

Details

International Journal of Pervasive Computing and Communications, vol. 18 no. 3
Type: Research Article
ISSN: 1742-7371

Keywords

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