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Market evaluation of products is the basis for product innovation, yet traditional expert-based evaluation methods are highly dependent on the specialization of experts. There exist a lot of weak expert-generated texts on the Internet of their own subjective evaluations of products. Analyzing these texts can indirectly extract the opinions of weak experts and transform them into decision-support information that assists product designers in understanding the market.

In social networks, a subset of users, termed “weak experts”, possess specialized knowledge and frequently share their product experiences online. This study introduces a comparative opinion mining framework that leverages the insights of “weak experts” to analyze user opinions.

An automotive product case study demonstrates that evaluations based on weak expert insights offer managerial insights with a 99.4% improvement in timeliness over traditional expert analyses. Furthermore, in the few-shot sentiment analysis module, with only 10% of the sample, the precision loss is just 1.59%. In addition, the quantitative module of specialization weighting balances low-specialization expert opinions and boosts the weight of high-specialization weak expert views. This new framework offers a valuable tool for companies in product innovation and market strategy development.

This study introduces a novel approach to opinion mining by focusing on the underutilized insights of weak experts. It combines few-shot sentiment analysis with specialization weighting and AHP, offering a comprehensive and efficient tool for product evaluation and market analysis.

Recent studies suggested that owner dynamic capabilities (ODCs) enabling public owners of megaprojects to activate, orchestrate and reorganize resources to uncertainties were beneficial to improving the project resilience of megaprojects. However, most of them pay insufficient attention to the specific context of long cycles and deep uncertainty in megaprojects, neglecting the causal complexity that different dimensions of ODCs and learning mechanisms interact with each other in terms of enhancing project resilience. Therefore, this study aims to systematically unveil the complex causality among ODCs, learning mechanisms and project resilience of megaprojects.

This study introduces a configurational perspective to explore how multi-dimensional ODCs combine to improve project resilience in megaprojects along with different organizational learning mechanisms and learning orientations. Based on 330 ODC events in 19 construction megaprojects, a multi-temporal crisp-set qualitative comparative analysis method is adopted to extract configurations of ODCs for project resilience improvement and unveil their evolution features over the whole megaproject lifecycle.

Six configurations are identified for improving project resilience in megaprojects, including cognition-dominant, cognition-deficient, transformation-dominant, innovation-driven, value-co-creation and exploitative-transformation configurations. The results also indicate that distinct megaproject stages appeal to corresponding ODC configurations for project resilience improvement under unique uncertain contexts.

This study not only makes theoretical contributions to the literature on dynamic capability and project resilience in the megaproject management field but also provides useful practical guidance for public owners of megaprojects to better utilize ODCs for project resilience improvement.

Addressing users’ migration is a prerequisite for the sustainable development of the sharing economy. Ethical concerns that may lead to users’ migration are frequent in sharing economy platforms. Therefore, this study explores whether the long-term governance of sharing economy platforms can effectively mitigate users’ migration caused by ethical concerns.

Using a questionnaire survey of 549 participants, this study investigated the mechanism of users’ migration and governance strategies in the platform ecosystem based on trust theory.

The results indicate that users’ ethical concerns regarding the platform ecosystem significantly and positively influence their migration. Furthermore, users’ continued trust played a significant mediating role in the relationship between ethical concerns and users’ migration. The results also showed that future orientation and resilience significantly moderated the impact of users’ ethical concerns on their continued trust, thereby weakening this effect.

The author clarified the relationship between ethical concerns and users’ migration, identified the underlying mechanisms and provided guidance on how to mitigate migration behavior. However, users’ migration is influenced by various factors beyond ethical concerns. In addition to some factors that lead to migration, other factors make users stay on the platform. Future research should integrate multiple factors.

This study reveals the mechanism of action between users’ migration and ethical concerns in the platform ecosystem and sheds light on the output of long-term orientation practices of the platform.

Assessing project criticality in a project portfolio (PP) is of great practical significance to improve robustness from damage. While project criticality assessment has increased diversity in approaches, the understanding of vulnerable project impacts is still limited. To promote a better understanding of assessing project criticality, a vulnerability measurement model is constructed.

First, integrating the tasks, projects and corresponding relationships among them, a project portfolio network (PPN) is constructed. Second, the project's vulnerability is measured by combining the topological structure and functional attributes. Third, project criticality is assessed by the vulnerability measurement results. Lastly, the proposed model is applied in a numerical example to illustrate its suitability and effectiveness.

For academia, this study provides a novel perspective on project vulnerability measurement and expands project criticality assessment tools. For practitioners, the straightforward model provides an effective tool for assessing project criticality and contributes to enhancing project portfolio management (PPM).

The impact of the task on the project is considered in this study. Topological structure and functional attributes are also integrated for measuring project vulnerability due to the impact of random attacks in an uncertain environment, providing a new perspective on the requirements of project criticality assessment and the measurement of project vulnerability.

This paper aims to investigate the relationship between in situ monitoring characteristics and surface defects in laser-based directed energydeposited Ti-6Al-4V.

In situ monitoring was conducted to extract and quantify the monitoring characteristics of each frame. A two-dimensional contour map was generated using the quantified characteristics to determine the defect formation locations. Computational thermal-fluid dynamics software was used to determine which surface tension terms or shielding gas had a significant effect on the depression of the molten pool.

This study has made a significant contribution by revealing the direct correlation between the molten pool size and brightness with defect formation in laser-based DED of Ti-6Al-4V. It was found that in regions of reduced height, the molten pool exhibited increased size and brightness, leading to surface depressions due to vapor recoil pressure flattening the molten pool. Moreover, the results highlighted that the enhanced Marangoni forces, caused by a high-temperature gradient, hindered the proper accumulation of molten metal, exacerbating height reductions. This insight provides a deeper understanding of how molten pool dynamics directly influence surface quality, which is a critical factor in DED processes.

This study contributes to understanding of the relationship between in situ monitoring characteristics and surface defects in laser-based directed energy-deposited Ti-6Al-4V. Additionally, by using in situ monitoring and computational analysis, significant insights were gained into the factors influencing molten pool behavior and subsequent surface defects.

Relationship conflict between the owners and contractors is inevitable, which could induce negative consequences. Yet, the existing literature mostly focused on its direct effects on project performance and ignored the process by which relationship conflict gradually deteriorates cooperation as well as corresponding managerial approaches. Given the fact that relationship conflict originates from interdependent tasks, the proposed theoretical model is intended to measure relational behavior as an instant outcome of relationship conflict, and explore the buffering role of contract enforcement approach.

This paper develops the conceptual model based on the literature review. Then the questionnaire survey was conducted. The dyadic data obtained from 168 Chinese construction project professionals were analyzed by the Partial Least squares Structural Equation Modeling (PLS-SEM) technique.

The results show that relational behavior partially mediates the link between relationship conflict and project performance. Besides, three types of contract enforcement approaches are found to differentially change the negative link between relationship conflict and relational behavior. Rigid contract enforcement can worsen the adverse effects of relationship conflict on relational behavior, whereas flexible contract enforcement can alleviate these negative effects. The level of mitigation hinges on whether compromising behaviors or obliging behaviors are chosen.

The study extends the knowledge of conflict theory and contract theory in the construction field. Based on the proposed conceptual model and PLS-SEM results, this study contributes to the understanding of relationship conflict’s consequences between the owners and contractors and enriches conflict management approaches in the construction field.

This study explores the effects of virtual streamer characteristics, virtual scene characteristics and streamer image-scene fit on users’ watching intention from the perspective of flow experience.

The survey data for this study were collected from the QQ fan group of virtual streamers between November 26th 2022 and December 5th 2022. The authors survey 274 viewers who have experience of watching virtual streaming and employ the partial least squares structural equation model to test the research hypotheses.

Among the characteristics of virtual streamers, interactivity significantly influences users’ perceived enjoyment and concentration, while vividness only affects perceived enjoyment. In addition, the novelty of the virtual scene has a notable impact on users’ perceived enjoyment and concentration, whereas aesthetic appeal serves as an important indicator solely for concentration. Furthermore, the virtual streamer image-scene fit also affects users’ perceived enjoyment and concentration. Finally, perceived enjoyment and concentration equally contribute to users' watching intention.

This study explores the impact of virtual streamer characteristics, virtual scene characteristics and streamer image-scene fit on users’ watching intention, which enriches the research on user behavioral intention in virtual streaming. Additionally, this study attempts to combine the stimulus-organism-response (S-O-R) model and flow theory in the field of virtual streaming, expanding the research areas. Finally, this study also provides valuable insights for virtual streamers and virtual streaming platforms. By enhancing their virtual personas and optimizing their streaming strategies, virtual streamers can more effectively retain users and maintain audience engagement. Meanwhile, virtual streaming platforms can gain a deeper understanding of user preferences, enabling them to launch high-quality events that sustain user popularity. These efforts collectively contribute to the advancement of the virtual streaming industry.