Modeling Academic Research Collaborator Selection Using an Integrated Model

The present study investigates how online social capital developed through social media (SM) usage influences employees’ innovative job performance in the Malaysian public sector, with work engagement as a mediator. It addresses a limited understanding of how online connections specifically network ties, shared vision, and trust, function digitally to enhance engagement and innovation. Employing a quantitative, cross-sectional survey, data from 313 Malaysian public sector employees were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Established scales measured online social capital, work engagement, and innovative job performance. Results show that network ties and trust significantly enhance work engagement, subsequently improving innovative job performance. Work engagement mediates the relationships between network ties and innovative job performance, as well as trust and innovative job performance. In contrast, a shared vision neither influences work engagement nor indirectly affects innovative performance. These findings highlight that the quality of digital social relationships, rather than shared workplace norms, drives employee involvement and innovation. This study contributes to organizational behavior literature by integrating online social capital and work engagement research in a public sector setting. It underscores the strategic importance of SM platforms in building meaningful digital connections, providing evidence that online trust and network ties serve as catalysts for enhancing employee engagement and innovative performance. The insights gained inform policymakers, industrial leaders, and researchers on optimizing workforce potential within technology-driven environments, ultimately supporting more effective public service innovation.

Situation-oriented directories help to determine the stakeholder responsible for a given situation. Defining all responsibility assignments for a directory can be challenging if situations need to be distinguished by many attributes and some of the attributes have many potential values. In this paper we present the stakeholder matrix, a compact representation of a situation-oriented directory that makes it easier for users to ensure the completeness and consistency of the assignments in the directory. Our evaluation showed that in a real world case the number of responsibility assignments could be compacted from 33700 to 128. We also review related research and show that compact representations similar to the stakeholder matrix will be useful to support validation of definitions in expert finding systems, content-based access systems and content-based routing systems.

Knowledge sharing activity and quality of network ties play a crucial role for mompreneurs as it provide essential support, resources, and opportunities for performance growth. This study aims to examine, from the perspective of women entrepreneurs (mompreneurs), the impact of the quality of mompreneur network ties and knowledge sharing across networks on product innovation, and business performance. This study investigates mompreneurs in the tourism industry, particularly several tourist destinations in Yogyakarta, Indonesia. This study's samples consist of 167 mompreneurs in the tourism industry. We analyzed the data using structural equation modeling partial least squares (SEM-PLS) and WarpPLS version 8.0 was used to process the data. We utilize SEM-PLS due to the relatively small sample size of 167 tourism industry owners or managers. The findings of this study indicate that the quality of mompreneur network ties has a significant impact on knowledge sharing across networks, product innovation, and business performance. Other findings indicate that knowledge sharing across networks positively and substantially affects product innovation. This research offers insights into enhancing the performance of SME enterprises operated by mompreneurs.

With the advancement of immersive technologies, metaverse museum exhibitions have become an increasingly important medium through which audiences access cultural content and experience artistic works. This study aims to identify the key factors influencing visitors’ processual experiences in metaverse museum exhibitions and to explore how these factors collectively contribute to the formation of satisfaction with the visiting experience. Adopting an interdisciplinary theoretical perspective, the study integrates the Experience Economy theory with the Stimulus–Organism–Response (SOR) model to construct a systematic theoretical framework. This framework reveals how exhibition-related stimuli affect visitors’ behavioral intentions through psychological response pathways. Specifically, perceived educational appeal, interactive entertainment, escapist experience, and perceived visual aesthetics are defined as stimulus variables, while psychological immersion, emotional trigger, and cognitive engagement are introduced as organismic variables to explain their effects on satisfaction with the visiting experience and social sharing intention as response variables. Based on 507 valid responses, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for empirical analysis. The results indicate that interactive entertainment and escapist experience have significant positive effects on psychological responses, serving as key drivers of deep visitor engagement. Emotional Trigger acts as a significant mediator between exhibition stimuli and satisfaction with the visiting experience, which in turn significantly predicts social sharing intention. In contrast, perceived educational appeal and perceived visual aesthetics exhibit weaker impacts at the cognitive and behavioral levels. This study not only identifies these weakened pathways but also proposes optimization strategies grounded in experiential construction and cognitive synergy, offering guidance for enhancing the educational function and deep experiential design of metaverse exhibitions. The findings validate the applicability of the Experience Economy theory and the SOR model in metaverse cultural contexts and deepen our understanding of the psychological mechanisms underlying immersive cultural experiences. This study further provides a pathway for shifting exhibition design from a “content-oriented” to an “experience-driven” approach, offering theoretical and practical insights into enhancing audience engagement and cultural communication effectiveness in metaverse museums.

Financial inclusion is a key driver of sustainable development, contributing to poverty reduction (SDG 1), gender equality (SDG 5), and reduced inequalities (SDG 10). Despite extensive financial‐inclusion policies in India, residents of urban slums remain largely excluded from formal banking systems. This study examines the determinants of formal bank account use in Seelampur, one of Delhi's largest informal settlements, interpreting active account ownership as a core indicator of effective financial inclusion. Using original survey data from 221 adults collected in 2019, the analysis combines logistic regression, Partial Least Squares–Structural Equation Modeling (PLS‐SEM), and hierarchical cluster analysis. The results show that education, income, and institutional engagement—proxied by tax compliance and prior borrowing—significantly increase the likelihood of using a formal bank account. PLS‐SEM reveals that institutional engagement mediates the effect of socio‐economic status on financial inclusion, highlighting the central role of formalization pathways. At the same time, a persistent gender gap emerges: women are significantly less likely than men to be financially included, even after controlling for socio‐economic characteristics. Cluster analysis uncovers substantial heterogeneity within the slum population, identifying distinct behavioral profiles and showing that financial exclusion is concentrated among low‐income, female‐dominated groups with weak links to formal institutions. Overall, the findings suggest that sustainable financial inclusion requires policies that go beyond physical access to banking infrastructure and address gender‐specific barriers, institutional participation, and localized outreach in informal urban contexts.

This study aims to investigate the effect of both the affordance of virtual reality (VR) technology and users’ digital media literacy on users’ emotions, which leads to recommendations to use VR experiences in cultural heritage tourism. A survey was conducted at a realistic VR studio in Gyeongju World Culture Expo Park, South Korea using high-quality and sophisticated VR content that accurately reflects users’ physical movements in real-time rendering. A total of 157 visitors participated in the study, and the measurement model and structural model were analysed using partial least square-structural equation modelling (PLS-SEM). The findings show that the cognitive, physical, sensory, and functional affordance of VR devices, as well as user digital media literacy, had a significant effect on the visitors’ positive emotional experiences. In addition, visitors’ positive emotions had a significant effect on the recommendation intention. This empirical result shows that digital media literacy is a new major variable that affects the virtual experience of cultural heritage sites.

Scientists’ collaboration strategies: implications for scientific and technical human capital

Fuzzy learning models in expert systems

This paper uses two modeling tools to analyze the reliability of real-time expert systems: (1) a stochastic Petri net (SPN) for computing the conditional response time distribution given that a fixed number of expert system match-select-act cycles are executed, and (2) a simulation search tree for computing the distribution of expert system match-select-act cycles for formulating a control strategy in response to external events. By modeling the intrinsic match-select-act cycle of expert systems and associating rewards rates with markings of the SPN, the response time distribution for the expert system to reach a decision can be computed as a function of design parameters, thereby facilitating the assessment of reliability of expert systems in the presence of real-time constraints. The utility of the reliability model is illustrated with an expert system characterized by a set of design conditions under a real-time constraint. This reliability model allows the system designers to: (1) experiment with a range of selected parameter values; and (2) observe their effects on system reliability.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

11 Developing a basis for detecting and predicting long-term ecosystem changes

This article focuses on the diagnosis of the bearings of the traction motors of electric railway and subway trains. One of the main sources of mechanical failures in a traction motor is its bearings. The failure of traction motor bearings, the factors that cause these failures, and the diagnostic methods for detecting them are investigated. At this time, faults in traction motor bearing monitoring systems are determined only by temperature. In this work, it is proposed to use a system with temperature, vibration, and noise to determine the technical condition of bearings. Such a multi-parameter system, unlike traditional ones, will help determine specific defects at an early stage. The expert system’s model, based on fuzzy logic and diagnostic parameters, can accurately predict the likelihood of bearing faults in real-time under changing operating conditions. A fuzzy expert system represents knowledge in the form of fuzzy productions and linguistic variables. The expert system model was developed using the Mamdani fuzzy inference algorithm of the Fuzzy Logic Toolbox package in the MATLAB computing environment. The application of fuzzy logic in generating a knowledge base and inference processes enables the formalization of a process for evaluating technical conditions based on incomplete, faulty, and potentially erroneous information and for making decisions about fault identification.

This study explores how bounded rationality through the mechanisms of satisficing and heuristics can be systematically embedded into AI system design to enhance human-likeness, cognitive realism, and decision-making efficiency under uncertainty, time pressure, and information overload. While prior work has largely focused on optimizing accuracy or speed, this research addresses the gap in aligning AI behavior with realistic human cognitive constraints. Using a cross-sectional quantitative design, data were collected via structured questionnaires from domain-relevant respondents. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test hypothesized mediation relationships between environmental factors (time pressure, uncertainty, information overload) and AI design outcomes (human-likeness, cognitive AI design, realism) via bounded rationality mechanisms. Results show that satisficing significantly mediates the relationship between time pressure and cognitive AI design (β = 0.312, p &lt; 0.001) and between information overload and human-likeness (β = 0.287, p &lt; 0.001). Heuristics also positively influence realism (β = 0.295, p &lt; 0.01). Trust, transparency, and engagement demonstrated weaker direct effects (β &lt; 0.10, p &gt; 0.05). With 48 percent of the variation in the most important outcomes explained through mediation effects, the model proved to have a decent fit (SRMR = 0.062; NFI = 0.88). This study presents a new, experimental-verified bounded rationality-based AI design model, which can offer practitioners with actionable changes to make their algorithms appear more human and adapt to new experiences. The potential of further research in this framework is to apply it to AI applications related to a particular domain.

&lt;div class="htmlview paragraph"&gt;An expert system called &lt;u&gt;GMX: The Gasket Material Selection Expert&lt;/u&gt; was presented at the 1989 SAE Conference(&lt;span class="xref"&gt;1&lt;/span&gt;).&lt;span class="xref"&gt;*&lt;/span&gt; The capabilities of this program have since been increased tremendously by the development of a Mathematical Model which predicts physical flange distortion and gasket compression values for any bolted, gasketed flange configuration. The expert system contains the logic (Rules) and facts (Databases) needed to compare gasket material properties to application-specific requirements and to specify gasket materials that will perform best in the application. The Flange Distortion Model is used by the expert system to calculate certain quantities that are needed to compare materials for the application. This paper describes how material properties and gasket engineering are used to make a material recommendation, and how an expert system and mathematical model have been utilized to make this expertise available to the gasket specifiers.&lt;/div&gt;

Explanatory models in expert systems

Individualizing generic decision models using assessments as evidence