An integrated picture fuzzy standard deviation and pivot pairwise assessment method for assessing the drivers of digital transformation in higher education institutions

Information and communication technologies play a significant role in all aspects of modern society. It has become a subject of high significance in all perspectives, mainly in the workstation. Consequently, the main objective for universities and schools is to develop future specialists for handling the problems and finding the effective outcomes by their digital competency as a significant ability. In higher education institutions, assessing the key drivers of digital transformation is a complicated decision-making problem as the multiple factors are involved in it. To this aim, a novel picture fuzzy information-based decision-making framework is proposed to assess the key drivers of digital transformation in higher education institutions. In this regard, an improved score function is proposed to rank the picture fuzzy numbers. In addition, it is used to determine the weights of decision experts. Next, some generalized Dombi aggregation operators are proposed to aggregate the picture fuzzy information. Further, an integrated criteria weighting model is presented based on the combination of objective weights through standard deviation-based model and subjective weights through pivot pairwise relative criteria importance assessment (PIPRECIA) model in the context of picture fuzzy sets. To rank the alternatives, a picture fuzzy extension of alternative ranking order model accounting for two step normalization (AROMAN) approach is proposed using the score function, Dombi operators and integrated weighting model. The usefulness of the proposed approach is illustrated using a case study of key drivers' assessment of digital transformation in higher education institutions. Sensitivity and comparative analyses are discussed to reveal the consistency, robustness and efficiency of introduced model. This study offers a new decision-making framework, which makes a significant contribution to the key drivers' assessment process from uncertainty perspective.