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In this study, the authors explore students' and industry’s perceptions about the challenges and opportunities of participating in a large-scale, non-compulsory, individual, in-person and unpaid business placement programme at an Australian university. The placement programme aims to support students' workplace transition by emphasising the development of key employability skills through reflective learning and linking theory to practice.

Utilising a case study methodology and integrating survey questionnaires, the authors collected both quantitative and qualitative data with large sample sizes.

The results highlight curriculum areas for improvement, emphasising tailored feedback to manage placement expectations and addressing employability skill strengths and weaknesses.

Recommendations include co-partnering with students to develop short, tailored and hot tip videos along with online learning modules, including the presentation of evidence-based statistics to inform students about post-programme employment prospects.

The study contributes to benchmarking good practices in non-compulsory, individual, in-person and unpaid placement pedagogy within the business education context.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.