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Drawing from the conservation of resources theory and organisational justice theory, this study examined the direct and indirect (via career resilience) effects of organisational career growth on work engagement. We further examined the boundary condition of procedure justice in the relationship between organisational career growth, career resilience and work engagement.

We employed time-lagged data collected in three waves from 431 employees working in the public health sector. Hierarchical regression analysis and Hayes Process Macro were used to test the study hypotheses.

The findings revealed that organisational career growth has a significant positive direct and indirect influence on work engagement in the presence of career resilience. Also, the boundary condition of procedural justice was significant for employees who perceived procedural justice to be high as opposed to low.

Organisational career growth has a positive influence on employee work engagement. Therefore, public health organisations need to prioritise employee career growth by creating an enabling environment that will help employees' career prospects and mitigate employees’ perception of low procedural justice.

The originality of this study is in empirically establishing career resilience as an underlying mechanism in the relationship between organisational career growth and work engagement while considering the interactive effect of procedural justice. Additionally, the originality of this paper is demonstrated by empirically establishing that a perceived high level of procedural justice helps healthcare employees improve their work engagement, thus deepening our understanding of work engagement amongst health professionals.

The purpose of this paper is to implement a genetic algorithmic geared toward building an optimized investment portfolio exploring data set from stocks of firms listed on the Nigerian exchange market. To provide a research-driven guide toward portfolio business assessment and implementation for optimal risk-return.

The approach was to formulate the portfolio selection problem as a mathematical programming problem to optimize returns of portfolio; calculated by a Sharpe ratio. A genetic algorithm (GA) is then applied to solve the formulated model. The GA lead to an optimized portfolio, suggesting an effective asset allocation to achieve the optimized returns.

The approach enables an investor to take a calculated risk in selecting and investing in an investment portfolio best minimizes the risks and maximizes returns. The investor can make a sound investment decision based on expected returns suggested from the optimal portfolio.

The data used for the GA model building and implementation GA was limited to stock market prices. Thus, portfolio investment that which to combines another capital market instrument was used.

Investment managers can implement this GA method to solve the usual bottleneck in selecting or determining which stock to advise potential investors to invest in, and also advise on which capital sharing ratio to reduce risk and attain optimal portfolio-mix targeted at achieving an optimal return on investment.

The value proposition of this paper is due to its exhaustiveness in considering the very important measures in the selection of an optimal portfolio such as risk, liquidity ratio, returns, diversification and asset allocation.