Corporate Social Responsibility in Higher Education: A PLS-SEM Neural Network Approach

Abstract

Corporate social responsibility (CSR) has become vital to the competitiveness of organizations. However, there is very limited research on CSR in the context of higher education. Therefore, this study investigates the impact of CSR factors on the university competitiveness with other universities. The contribution of this study includes applying a multi-analytical methodology by applying Partial Least Squares-Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). First, PLS-SEM analysis was applied to measure which CSR factor has the most significant influence on the increased competitiveness of Saudi Arabian universities. Then, an ANN model analysis was applied to rank the relative influence of the significant CSR factors attained from the PLS-SEM analysis. The findings show that the CSR factors such as market-oriented CSR activities, society-oriented CSR activities and workforce-oriented CSR activities have a significant positive impact on increased competitiveness of Saudi Arabian universities. However, based on the ANN findings, society-oriented CSR is the most significant predictor of a university's increased competitiveness, followed by workforce-oriented CSR and finally by market-oriented CSR. The practical implications of this study provide Saudi universities to focus on society-oriented CSR to maintain and sustain their competitive advantage over other universities.INDEX TERMS ANN, corporate social responsibility, education, PLS SEM, Saudi Arabia.

Hybrid artificial neural network and structural equation modelling techniques: a survey

“…Social responsibility is essential for the competitiveness of organisations. Therefore, the effects of the social responsibility factors of organisations on a university’s competitiveness towards other universities were investigated using SEM and ANN [ 16 ]. From another point of view, university students were selected as subjects to determine the effects of emotional eating, body shape concerns and measures of body appreciation on preventive behaviour [ 84 ].…”

“…As mentioned in the previous section, one benefit of SEM is its capability to explain linear and compensatory causal relationships amongst constraints. However, SEM cannot explore nonlinear relationships amongst constructs [ 16 ]. Hence, ANN analysis has become an essential method that complements PLS-SEM in recent research [ 88 ].…”

“…PLS-SEM exhibits high potential in solving industrial and academic issues [ 11 ]; hence, a new research direction is the prediction of important factors using a dual-stage model of SEM and an artificial neural network (ANN) because SEM is unable to explore nonlinear relationships amongst constructs [ 12 – 15 ]. The purpose of merging SEM with ANN is to explain nonlinear and non-compensatory relationship amongst constraints [ 16 ]. In particular, ANN depends on the black box process of the algorithm, which is the basis for prediction [ 17 ].…”

7S Model for Technology Protection of Organizations

“…According to the hypotheses testing results, H1, H3, and H5 were significant at p < 0.001, p < 0.05, and p < 0.01 respectively. Among the 7S factors, it was confirmed that shared value (SV), strategy (SG), and staff (SF) has a significant influence on technology protection (TP) [44]. Figure 2 shows the overall structural model testing results.…”

“…The two-stage approach integrating PLS-SEM and ANN enables meaningful analysis by complementing the weaknesses of each methodology. This study used both methods to increase the accuracy of the research results [44]. Here, factor and element are considered the same concepts.…”