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The United Nations' Principles of Responsible Management Education initiative aspires to transform the relationship between business and society by ensuring that the next generation of business leaders are shaped by management education that conceptualises businesses as generators of sustainable value. Simplistic economic models overemphasize the role of narrow profit maximization on the part of firms in generating broader economic wellbeing. More nuanced views of the relationships between firms and the societies in which they operate, such as those that allow for market power in product and labour markets, for the presence of externalities in the production of goods and services, for a role of the state in the provision of public goods, and for the existence of market failures more generally, offer profoundly different advice to aspiring practitioners of responsible management. This article proposes an introductory economics curriculum for management students that gives due emphasis to these more nuanced perspectives and thus equips aspiring business leaders with the skills they will need to build profitable enterprises that also fulfil the objective of generating sustainable value as envisioned by the Principles of Responsible Management Education.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.