Modelling the signifcance of strategic orientation for competitive advantage and economic sustainability : the use of hybrid SEM–neural network analysis - Marvello, Yang; Norizan, Jaafar; Abdullah, Al Mamun; Anas, A. Salameh; Noorshella, Che Nawi (Springer Nature, 2022)
Economic sustainability involves the development of an organisation that meets its future needs through an integrated policy, planning, and social learning process. The purpose of this study was to investigate the mediating role of competitive advantage in the relationship between strategic orientation and economic sustainability under unpredictable circumstances. This study collected quantitative data from a total of 284 halal small and medium enterprises (SMEs) from Indonesia through structured interviews. Data were analysed using partial least squares structural equation modelling (PLS-SEM). Moreover, this study adopted artifcial neural network (ANN) analysis for a model-free estimation using non-linear, multilayer, and parallel regression. The results revealed statistically signifcant and positive efect of strategic orientation on economic sustainability. Additionally, this study found that competitive advantage expanded the efect of strategic orientation on economic sustainability. Findings of ANN analysis confrm high prediction accuracy of the model. Findings of the sensitivity analysis highlighted the importance of innovation, network and technological orientation, and the positive efect of competitive advantage on halal SMEs economic sustainability. In order to achieve long-term economic sustainability, halal SMEs should therefore focus on innovation capacity, vertical and horizontal networking and adoption of the latest technologies. The uniqueness of this study focused on the strategic orientation and value of competitive advantage of halal SMEs towards economic sustainability. Additionally, this study was the frst to develop hybrid SEM–neural network analysis to apply sensitivity analysis for the evaluation of the contribution of each exogenous predictor towards the endogenous construct.