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Governments in developing countries manage their considerable state assets for public service delivery directly. In Indonesia, the Directorate of State Asset Management responsible for developing the national strategy for state asset optimization requires the determination of key elements and prioritization tools. The purpose of this paper is to show that a simple calculation using the combination of the balanced scorecard (BCS) and analytical hierarchy process (AHP) will help in the prioritization of strategy development.

A questionnaire survey of 131 multistakeholder respondents to identify the most important key elements and the best alternative for asset optimization was done in this study.

The respondents agree on the most important key elements, and that the best alternative for asset optimization is the efficient maintenance of assets. Competitive human resources comprise the recommended second key element, and that improvements in asset performance and value will improve public service as the second-highest alternative. This study also shows the importance of the integration of asset optimization in existing government strategic instruments supported by a comprehensive data set related to public assets and their performance.

This paper provides a new contribution to integrating asset optimization strategies as the core of the organization’s performance and prioritization strategies. Additional BSC perspectives are suggested, with the inclusion of AHP for prioritization. In addition, this study includes the opinions of all the stakeholders, from external users to the central management. The flexibility of the tools to adapt to the existing strategic framework will allow their application by different agencies and in different countries.

The purpose of this paper is to address the complexity of missing persons cases and highlight the linguistic differences that arise in this type of crime. Missing persons cases are typically very complex investigations. Without a body, crime scene forensics is not possible, and police are often left only with witness and suspect statements. Forensic linguistics methods may help investigators to prioritise or remove suspects. There are many competing approaches in forensic linguistic analysis; however, there is limited empirical research available on emerging methods.

This research investigates Statement Analysis, a recent development in linguistic analysis that has practical applications in criminal investigations. Real-world statements of individuals convicted of or found to be not guilty of their involvement in missing persons cases were used in the analyses. In addition, Behaviour Sequence Analysis was used to map the progressions of language in the suspects' statements.

Results indicated differences between the guilty and innocent individuals based on their language choices, for example, guilty suspects in missing [alive] cases were found more likely to use passive language and vague words because of high levels of cognitive load associated with the several types of guilty knowledge suspects in missing persons cases possess. Of particular interest is the use of untruthful words in the innocent suspects’ statements in missing [murdered] cases. While typically seen in deceptive statements, untruthful words in innocent statements may result because of false acquittals.

This research provides some support for Statement Analysis as a suitable approach to analysing linguistic statements in missing persons cases.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

The research questions probe into the heart of participatory learning’s scholarly landscape. They inquire about historical trends, shifts in research interest, and regional contributions, highlighting the global reach of participatory learning discourse. The study also reaches into the most cited papers, emphasizing their pivotal role in shaping the field’s trajectory.

The research design of our study involves a retrospective analysis focused on the topic of participatory learning in education. A retrospective analysis is a methodological approach that entails examining existing literature, data, and publications within a specific field to identify trends, patterns, and insights over a defined period. In the context of participatory learning in education, this research design allows us to gain a comprehensive understanding of the evolution, contributions, and emerging directions within this pedagogical approach.

The culmination of this study lies in formulating 75 thought-provoking future research questions. These questions are designed to guide the future trajectory of participatory learning research, addressing existing gaps and propelling the field toward new horizons. The findings of the study contribute to a panoramic view of participatory learning’s evolution, global impact, and potential for reshaping education. It is a compass for researchers, educators, and policymakers navigating the intricate landscape of participatory learning in education.

Themes and topics are thoroughly explored, unrevealed the multidimensional nature of participatory learning research. The investigation spans time, revealing enduring themes while unveiling emerging areas of inquiry that capture the attention of researchers and practitioners. Current trends and focus areas are scrutinized, offering insights into the ever-evolving scholarly pursuits within participatory learning.

This comprehensive study investigates the evolution and impact of research on participatory learning in education from 1995 to 2023. The research objectives encompass a broad spectrum, including trend analysis, geographical distribution assessment and identification of influential papers, thematic exploration, and the formulation of future research questions. Through a meticulous examination of published literature, this study aims to shed light on the development of participatory learning as a transformative educational approach.

This study’s originality lies in its comprehensive analysis of participatory learning in education from 1995 to 2023, revealing temporal trends, global contributions, influential papers, thematic nuances, and emerging trends. By amalgamating these dimensions, it offers a holistic view of participatory learning’s evolution, impact, and future directions, enriching the understanding of this transformative educational approach and guiding further research and practice.