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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.

The rapid development of digital technologies drives digital entrepreneurs to pivot, a behavior that allows entrepreneurs to adjust original opportunities and explore new opportunities. This study aims to investigate the effect of the structural characteristics of digital entrepreneurial teams, the functional heterogeneity, on pivoting from the perspective of digital agility. Moreover, this study also examines the moderating effect of knowledge sharing.

Two-phase survey data were sourced from Chinese digital entrepreneurial teams through the entrepreneurial networks of MBA programs of a Chinese business school and entrepreneurial support organizations in China. The sample of 272 teams with 708 entrepreneurs was collected to test the hypotheses.

The functional heterogeneity of digital entrepreneurial teams, including industry background heterogeneity and occupational experience heterogeneity, positively impacts pivoting by providing heterogeneous knowledge and resources. Moreover, this relationship is mediated by the digital agility of the digital team, and knowledge sharing moderates the relationship between functional heterogeneity and digital agility.

While existing studies have mainly focused on the external factors, this study empirically investigates the team-level internal factors of digital pivoting in digital entrepreneurial teams, enriching the research perspective of pivoting. Moreover, the current study bridges the literature on digital agility with pivoting, broadening the theoretical mechanism of pivoting and expanding the theoretical boundaries of digital agility.

Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems against cyberattacks. This paper aims to report findings from a quantitative analysis of literature within ML security to assess current research trends in ML security.

The study focuses on statistical analysis of literature published between 2000 and 2023, providing quantitative research contributions targeting authors, countries and interdisciplinary studies of organizations. This paper reports existing surveys and a comparison of publications of attacks on ML and its in-demand security. Furthermore, an in-depth study of keywords, citations and collaboration is presented to facilitate deeper analysis of this literature.

Trends identified between 2021 and 2022 highlight an increase in focus on adversarial ML – 40\% more publications compared to 2020–2022 with more than 90\% publications in journals. This paper has also identified trends with respect to citations, keywords analysis, annual publications, co-author citations and geographical collaboration highlighting China and the USA as the countries with highest publications count and Biggio B. as the researcher with collaborative strength of 143 co-authors which highlight significant pollination of ideas and knowledge. Keyword analysis highlighted deep learning and computer vision as the most common domains for adversarial attacks due to the potential to perturb images whilst being challenging to identify issues in deep learning because of complex architecture.

The study presented in this paper identifies research trends, author contributions and open research challenges that can facilitate further research in this domain.