Transformational leadership and human capital within the disruptive business environment of academia

Abstract

Purpose – The purpose of this paper is to examine the impact of transformational leadership on the cultivation of human capital in an academic setting from the perspective of university employees Design/methodology/approach – Quantitative data was collected from a range of staff working within three universities located in Jordan using an online questionnaire survey. The focus was on identifying which factors of transformational leadership were perceived as having the greatest potential contribution towards developing human capital and if there was a consensus amongst staff about these factors Results – 131 staff members submitted a completed questionnaire giving a response rate of 93%. Respondents generally had a good understanding of the concepts of human capital and transformational leadership, but the way in which transformational leadership was perceived to impact on human capital differed. Conclusion – Overall, the findings from the survey, consistent with other similar studies, confirmed that there was a correlation between transformational leadership and human capital in the context of academia from the perspective of staff. Research limitations/implications – The study confirms the need for leaders in academia to consider the leadership approach that is adopted to ensure that human capital, which is one of its main assets, is supported and developed appropriately Practical implications – The paper describes implications for leaders in higher education, as well as more broadly for educators who are attempting to develop human capital in their organisations Originality/value – This paper expands upon a developing field in leadership in higher education
 
 Keywords: Transformational Leadership; Human Capital; Academia; Disruptive Business Environment

RETRACTED ARTICLE: Feature selection for diagnose coronavirus (COVID-19) disease by neural network and Caledonian crow learning algorithm

“…COVID-19 disease causes high unemployment and increases poverty in the world. And more people are falling below the poverty line (Tang et al 2020 ; Fernandes 2020 ). COVID-19 disease has negatively affected all aspects of human life.…”

“…In image processing methods, CT scans of lungs are considered as training input, and then a machine learning method is taught to identify the affected area (Ozkaya et al 2020 ). Artificial neural networks (Mollalo et al 2020 ), support vector machine (Sethy et al 2020 ), deep learning (Gozes et al 2020 ), and random forest (Yeşilkanat 2020 ) are some of the practical methods for analyzing lung images in the diagnosis of COVID-19 disease. The use of CT scan images of the lungs to diagnose COVID-19 disease has its advantages, for example, the possibility of diagnosing the disease in the early stages and creating a three-dimensional image of the lungs and identifying the affected areas are some of the advantages of this method.…”

“…Machine learning algorithms have been applied for decades for medical applications such as diagnosing disease, proposing treatment, and prescribing medication (Alhayani and Abdallah 2020 ; Alhayani and llhan 2021 ; Alhayani et al 2021 ). Today, neural networks such as multilayer neural networks are used to classify images or analyze patient records (Al-Hayani and Ilhan 2020 ; Kwekha-Rashid et al 2021 ).…”

“…Despite the development of a large number of studies in the field of lung imaging to diagnose COVID-19 disease (Hasan and Alhayani 2021 ; Yahya et al 2021 ; Abu-Rumman 2021 ), a small number has been developed based on patient information and records for the diagnosis of COVID-19 disease. Various methods have been developed to diagnose COVID-19 disease, and one of these methods is the analysis of information collected by medical centers with machine learning methods (Abu-Rumman et al 2021 ; Mohamed et al 2017 ). So far, a large number of data sets related to COVID-19 disease have been uploading to valid databases.…”

“…The next SARS epidemic was between 2002 and 2004, infecting 8,000 people and killing 800. The next swine flu epidemic broke out between 200 and 2012, infecting 1.6 million–1.4 billion people and causing a death toll of about 18,000 (Debone et al 2020 ).…”