A Theoretical Model to Measure Managerial and Leadership Competence of Business School Managers

Abstract

A theoretical model to measure managerial competence was developed by evaluating two previous models with similar aims. In addition to the original eight management competencies, another eight competencies were identified and applied to determine the management competencies needed in the Fourth Industrial Revolution with its fast-changing business environment. The article narrows down eleven management competencies and develops measuring criteria for each; in total 42 criteria. The eleven business competencies are leading change, cultural intelligence, team building, conflict management, communication skills, a global leader mindset, emotional intelligence, career awareness, personal value system, and external and ethical influences. The model is a theoretical model and presents management competencies relevant to the modern business environment. The value of this study resides in a strong theoretical basis for postmodern business environmental skills that managers need to maintain completeness of their organizations as well as providing a sound point of departure for other researchers of managerial competence.

Factors to Measure the Performance of Private Business Schools in South Africa

“…Reduction of the Measuring Criteria: Exploratory factor analysis offers the advantage to simplify the dataset; this results in an easier understanding of the results. Simplification, according to Gaskin (2014), not only improves the validity of the analysis, but also enables better application and operationalisation of factor models in practice (Shaikh et al, 2017). In this case, varimax rotation is particularly suitable because it attempts to load criteria strongly onto specific factors, thereby reducing criteria that load onto more than one factor.…”

“…Furthermore, based on the success of numerous exploratory studies to simplify and purify measuring instruments from dual-loading or low-loading criteria by successfully using exploratory factor analysis (Naidoo, 2011;Fields & Bisschoff, 2013a;2013b;Bisschoff & Moolla, 2014;Hamid, 2015;Shaikh et al, 2017), the statistical decision criteria that are used are to accept factors if the KMO is equal to or higher than 0.70, Bartlett's sphericity is smaller or equal to 0.05, and the cumulative variance explained exceeds 60%. Factors' reliability should equal or exceed Cronbach's alpha coefficient of 0.70 (Field, 2013:675).…”

“…Sphericity (Bartlett tests) remained unchanged below the required 0.05 level. However, Shaikh et al (2017) point out that the real value of purification resides with the reduction of the number of factors (from 15 to 10), thereby creating a much more measurable and manageable model to apply in practice. In this case, Hill and Hughes (2007:380) state that a marginal decline in total variance explained is "but a small price to pay for the additional validity gained by the reduction in the number of factors and measuring criteria".…”

“…Factor analysis can also be used to determine the validity of factors and its measuring criteria; in practice, this means that the criteria measuring a specific antecedent or construct can be confirmed as true measures of the specific construct (Patel, 2015). Many studies have successfully applied factor analysis to do so and to weed out unworthy measuring criteria from measuring instruments (for example Bisschoff & Moolla, 2014;Imandin, Bisschoff & Botha, 2016;Fields & Bisschoff, 2013a;Shaikh, Bisschoff & Botha, 2017). The two main factor analysis techniques are confirmatory factor analysis (CFA) and exploratory factor analysis (EFA); these techniques are discussed below.…”

“…This study selected the orthogonal varimax rotation, mainly because of its ability to successfully extract factors in exploratory research settings (Shaikh et al, 2017;Hamid, 2015;Naidoo, 2011;Fields & Bisschoff, 2013b). The cut-off factor loadings to develop a conceptual model in this study retained only those criteria with factor loadings of 0.40 and higher; this decision criterion was implemented based on the success of similar research in various application settings (Shaikh et al, 2017;Hamid, 2015;Bisschoff & Moolla, 2014;Fields & Bisschoff, 2014;Naidoo, 2011;). The study aimed for a 60% cumulative variance explained as this signifies a good fit to the data (Field, 2013:672).…”