Wenxin Wang

In group decision-making (GDM), traditional consensus models have primarily focused on cardinal consensus. In reality, irrespective of whether the objective of GDM is to select the optimal alternative or to rank alternatives, it is imperative to establish a ranking that garners the utmost assent from all decision-makers (DMs). When preferences are articulated through fuzzy preference relations (FPRs), cardinal information emerges in numerical form, quantifying the degree of preference for alternatives, while ordinal relations are implicitly embedded within pairwise comparisons. To delve into both cardinal and ordinal consensus among DMs, this study introduces two consensus optimization models that strive to minimize adjustments to FPRs while fostering consensus in terms of preference intensity and ranking. To this end, we first propose two ordinal consensus measurement methods: one precisely discerns whether DMs have achieved consensus on the selection of the best alternative, while the other assesses the consistency of different preference rankings, taking into account the importance of positions. Based on these methods, two systems of inequalities are designed to explicitly govern both types of ordinal consensus. Subsequently, two consensus control rules are formulated, tailored to distinct objectives. These rules necessitate not only cardinal consensus among all DMs but also their alignment in terms of either the selection of the best alternative or the preference ranking. Ultimately, these rules are integrated as constraints into two mixed-integer programming models aimed at minimizing preference adjustments. The proposed models have been applied in a case study, confirming their practicality, with thorough comparative analyses demonstrating their effectiveness.

The internet and emerging technologies have increased the utilisation of digital platforms. This study aims to draw on performance expectancy, social influence, and satisfaction to investigate a proposed model which is geared toward social media healthcare information (SOHI) adoption. The proposed model uses a structured online questionnaire, and 300 responses were evaluated using partial least squares and structural equation modelling [PLS-SEM]. From the findings, performance expectancy of social media (PESM) and satisfaction with social media (SATSM) were revealed to be significant predictors of behavioural intention towards social media (BISM). Satisfaction with social media (SATSM) had the greatest impact on BISM, accounting for 64.7 per cent of the variance in users' intentions to utilise SOHI. Similarly, PESM and social influence on social media (SISM) had the most predictive influence on SATSM, accounting for 50.5 per cent of the variance in users' social media satisfaction, which led to SOHI adoption. Unlike others, the social influence on social media (SISM) did not have a significant effect on BISM. BISM and satisfaction with social media (SATSM) were significant predictors of SOHI adoption, accounting for 54.5 per cent of the variance in SOHI adoption. The recommendations in this study would help healthcare professionals change their approach to digital healthcare engagement.