Christo Bisschoff

A model to measure the employee engagement was developed by researching historical employee engagement models. These models, consisting of employee engagement constructs and their measuring criteria, have been empirically validated and factorized into seven employee engagement factors. The seven employee engagement factors (of which factor one consists of two sub-factors) were subjected to confirmatory factor analysis to ensure the inclusion of the factors in the validated model to measure employee engagement. The model was also tested for goodness of fit, and the model shows good fit indices with the Comparative Fit Index (0.799), while the good model fit of the secondary fit indices RMSEA (0.078 within a narrow margin of 0.004) and Hoelter (113 at p <= 0.1; 111 at p <= 0.05) also show satisfactory model fit. Management can use the model as diagnostic tool to measure employee engagement and to apply it in managerial decision-making. On the other hand, academics could apply the model to extend their research in employee engagement

This study investigated the drivers which influence consumers’ beef buying behavior. A validated questionnaire was used to collect the data and to evaluate consumer beef purchasing behavior. Exploratory factor analysis was employed to analyze the data while Cronbach alpha coefficient was used to calculate the reliability of the drivers. Satisfactory levels of reliability were recorded. The analysis identified eight drivers that influence the buying behavior of consumers when they purchase beef products. These drivers were: quality of the meat, buying preference, farming practices, intention to buy, health, convenience, packaging & presentation and future purchase. In addition, correlational analysis indicates that additional important attributes to buying behavior are supplier characteristics and packaging & presentation. The study culminates in a frame of reference for beef (and possibly other meat products) buying behavior analysis whilst it also provides a frame of reference for marketers to better understand their customers’ behavior when they are selling beef. As a result it is recommended that retailers focus their actions on the more important beef purchasing drivers and that the study be repeated on a larger scale so that the results of the present study can either be confirmed or further refined.

Keywords: beef cattle, buying behavior, consumer preferences of beef, packaging, farming practices, factor analysis.
JEL Classification: M30

Fertilizer not only plays a major role in the profitability of the farmer’s business, but also represents an expensive farm input. The emerging farmer market segment is expanding and holds a great deal of potential for fertilizer companies to supply the growing need for fertilizer in this market. Almost all fertilizer marketing strategies of South African companies have been designed to cater for the commercial farming sector; however, if fertilizer suppliers want to focus on the emerging farmer market segment, they need to understand buying behavior of emerging farmers, as well as their needs when developing strategies to utilize opportunities in this developing market. This study aims to identify factors playing an important role in the buying behavior of emerging farmers in the Free State when purchasing fertilizer. The results show that service, brand, product, and learning or psychological factors highly influence emerging farmers’ fertilizer purchase decision. The study also finally draws recommendations and conclusions for managerial perusal.

This article compares an existing model to measure competence of managers educated within the business school environment to a similar model at another business school setting. The existing management model initially developed by Thekiso’s is evaluated to determine if the model can be operationalised, hence can be regarded as valid, to apply to another business school’s educated manager sample to measure their managerial competence. Resultantly, Thekiso’s original model was applied to another similar sample at another business school educated manager population in KwaZulu-Natal. These respondents also studied towards a Master of Business Administration degree but their specific cultural, language and business school variables differed. The objective was then to validate the Thekiso model in different application settings. The results showed that the data were suitable to use to determine if the existing model can be used as it is to measure managerial competence. Also, the results indicated that Thekiso’s existing model is not generic and cannot be operationalised as it is school-specific. The anbalysis showed that the measuring criteria developed by Thekiso are relevant but the specific factors identified differ in nature and number, hence altering the core of the measuring model itself. As a result, the article formulates an alternative model for use in KwaZulu-Natal to measure skills for managerial competence in the province. This research is of value to management, academia and researchers because it renders an alternative model to measure managerial competence whilst also warning against operationalization of any of the two models without further validation.

Microinsurance is an insurance product offered to low-income earners charactrized by low profitability resulting from low premiums and high transaction costs. Insurance companies are socially challenged to also include this market segment in their portfolio of insurance products to contribute to economic development and servicing the low-income market. Business success in the microinsurance segment is, therefore, more than calculating profits. This article offers guidance to measure business success in this market. Two models were constructed to measure business success: one generalized and the other an industry specific model. These models are compared to determine which one would be the more suitable to employ as a tool to measure business success in the microinsurance industry. The analysis indicated that the generalized model is better model to use. However, the industry specific model also proves to be valuable and is more suitable for specific company applications than general industry analysis.

South African universities experience increased pressure to comply with and implement environmentally friendly practices. Specifically, state-funded universities need to enhance environmental management efficiency and environmental awareness. However, measuring the implementation of green initiatives in higher education takes time and effort. South African models for state-funded universities are absent, and international models are inapplicable. Therefore, this study aims to develop and empirically test the model by investigating existing theories and models and identifying potential factors for higher education. The paper determined ten initial factors from 31 environmental studies, limiting their number to five. The finally selected factors are cost of green products, awareness, training and education, top management attitude and commitment, committee for sustainable accountability, and digital transformation. This qualitative study uses a five-point Likert-scale questionnaire sampling 149 university managers. Structural equation modeling retained three of the original five factors in the model: cost of green products, top management attitude and commitment, and digital transformation. However, knowledge of the environment (SRW = 0.76) is also crucial. Ten theoretical measuring criteria are retained as valid measures of implementing green initiatives. The model has good fit indices (CMin/Df = 4.07, CFI = 0.944, GFI = 0.909), despite RMSEA exceeding 0.10. The developed conceptual model can be used to measure the implementation of green initiatives by South African state-funded universities.

This study investigates the role of brand loyalty in South African banking. More specifically, the study identifies brand loyalty factors in South Africa’s banking industry. Brand loyalty can significantly impact a bank’s competitiveness. Loyal customers do not switch banks and use more banking products; this earns a better income for a bank in the long run (such as home loans). The study’s primary aim is to identify factors of brand loyalty in South African banks. Data were gathered from South African banking customers using a Google Forms digitized format questionnaire with a 5-point Likert scale. More than 1,000 questionnaires were distributed, and 150 completed questionnaires were returned (representing a 15% response rate). The results show that the data are suitable for multivariate analysis. It has an adequate sample (Kaiser, Meyer and Olkin measure > 0.70), acceptable sphericity (p ≤ 0.05), and satisfactory reliability (α ≥ 0.70). The exploratory factor analysis identified four factors explaining a cumulative variance of 55%. These factors are: 1) customer service and satisfaction (34%); 2) negative publicity (7.5%); 3) regulatory compliance and trust (7.1%); and 4) trust and reputation (6.5%). South African bank managers can use the results to focus their brand loyalty strategies on their quest to be more competitive to face the strong competition in the banking industry.

The study investigates the relationship between customers’ loyalty, trust and satisfaction concerning an organization’s reputation in the South African banking sector. High service levels exist in this highly competitive and price-sensitive market. Access to banking has also digitized significantly, and banks adapted their service strategies to comply with COVID-19 restrictions such as hard lockdowns and limited movements. Customers were not able to attend banks. Hence the whole personal (contact) service and loyalty scenario required aggressive reengineering. A bank’s competitiveness can be impacted significantly by service quality, price competitiveness, and product diversity. As a result, the study with the primary objective is to determine the new relationships between customer loyalty and antecedents such as service quality, customer satisfaction, customer trust, brand image, reputation, customer loyalty, and word of mouth. Data were gathered from South African customers using a 5-point Likert scale distributed via an electronic platform (Google Forms). More than 1,000 questionnaires were distributed, and 150 were completed and returned (representing a 15% response rate). The reliability is satisfactory (Cronbach alpha coefficient on all antecedents exceeded 0.775). The literature model was confirmed using confirmatory factor analysis. The analysis showed that the model possesses convergent- (r2 < Sqrt AVE) and discriminant (AVE > 0.5) validity and possesses satisfactory fit indices (CFI = .951, TL = .941, NFI = .922, RMSEA = .089, CMIN/df = 129.072/592.188 = 2.188). This indicates that the model can be operationalized in South Africa to measure post-COVID-19 bank loyalty.

This study investigates the brand loyalty of consumers in the online booking platform industry. The Mabalingwe Nature Reserve served as a case study, while the Moolla and Bisschoff brand loyalty model was used to measure brand loyalty across twelve antecedents. This study aimed to, firstly, validate the model for use in online booking platforms, secondly, to measure the reliability of the data, and finally, to measure brand loyalty across twelve antecedents in online booking platforms. Online questionnaires were distributed via an online link by the booking managers of the game reserve, and 131 responses were captured; this represented a statistically adequate sample as per the KMO measure (.741). The descriptive statistics, using a 5-point Likert scale, showed that Brand trust (4.03) and Customer satisfaction (3.96) are the most important brand loyalty antecedents, while Culture (2.34) is the least important brand loyalty antecedent in an online booking platform. Exploratory factor analysis validated the questionnaire for online booking platforms, while Cronbach’s alpha coefficient (.701) indicated that the reliability of the data is acceptable. Regarding latent variable identification, Brand quality and Brand relationship are two most important factors, respectively, explaining variance of 13.1% and 8.7%. The study culminated in a model to measure and manage brand loyalty of online booking platforms. This model can be operationalized for use by managers, researchers, and academia.

The purpose of the study is to measure behavioral, attitudinal and other brand loyalty antecedents, and to develop an operating model for measuring and managing brand loyalty of commercial banks clients. A random sample of 500 members of the South African Commercial Institute, who are also commercial banks’ clients, received a 5-point Likert scale questionnaire to be completed online via Twitter and Facebook. About 196 people completed the questionnaire. The data possess construct validity and reliability (α ≥ 0.70). The results show that seven of the 12 original antecedents are banking related, namely five Attitudinal antecedents (r2 = 0.557) and two Other antecedents (r2 = 0.442). Behavioral antecedents were not important to bank clients. All the antecedents have factor loadings above 0.60, and there is a significant positive correlation between Attitude and the Other antecedents (r = 0.75; p ≤ 0.01). This means that the model is useful for managers in managing brand loyalty at their banks. It is also of value to researchers and academia looking to conduct further research on how to measure and manage brand loyalty. However, a caution is that the data originated from South African banks’ clients. Country-specific influences can cause different brand loyalty preferences among international banks’ clients.

Acknowledgment
I wish to acknowledge Mr. Sarel Salim for his contribution to administering the data collection for the original research on brand loyalty in banking (see also Salim and Bisschoff, 2014 in the reference list).