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A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

This study employs bibliometric analysis to map the research landscape of social media trending topics during the COVID-19 pandemic. The authors aim to offer a comprehensive review of the predominant research organisations and countries, key themes and favoured research methodologies pertinent to this subject.

The authors extracted data on social media trending topics from the Web of Science Core Collection database, spanning from 2009 to 2022. A total of 1,504 publications were subjected to bibliometric analysis, utilising the VOSviewer tool. The study analytical process encompassed co-occurrence, co-authorship, citation analysis, field mapping, bibliographic coupling and co-citation analysis.

Interest in social media research, particularly on trending topics during the COVID-19 pandemic, remains high despite signs of the pandemic stabilising globally. The study predominantly addresses misinformation and public health communication, with notable focus on interactions between governments and the public. Recent studies have concentrated on analysing Twitter user data through text mining, sentiment analysis and topic modelling. The authors also identify key leading organisations, countries and journals that are central to this research area.

Diverging from the narrow focus of previous literature reviews on social media, which are often confined to particular fields or sectors, this study offers a broad view of social media's role, emphasising trending topics. The authors demonstrate a significant link between social media trends and public events, such as the COVID-19 pandemic. The paper discusses research priorities that emerged during the pandemic and outlines potential methodologies for future studies, advocating for a greater emphasis on qualitative approaches.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2023-0194.

In the context of innovation-driven development strategy, open innovation has become an important way for enterprises to gain competitive advantages on the path of innovation and development. However, with the increasing competition, enterprises' open innovation is restricted by some constraints. How to promote open innovation in the restricted situations has become an existing research gap. Based on the perspective of digital transformation, this paper discusses how to promote the open innovation of enterprises under the restricted situations and find its breakthrough path, and analyzes the moderating effect of innovation persistence and political relevance.

Due to the complexity and confidentiality of military–civilian integration enterprises, they have become typical innovation-restricted enterprises. In this study, it selects a-share listed companies in the field of military–civilian integration in China in 2016–2020 as the research sample, and uses the two-way fixed-effect model to analyze the proposed variables. Finally, the robustness of the results in this paper is verified by a series of robustness tests and endogeneity tests.

The results show that digital transformation facilitates open innovation in military–civil integration enterprises, and that innovation persistence and political relevance positively moderate the relationship between the two. Further, digital transformation can promote open innovation in military–civil integration enterprises by easing the financing constraints and reducing information asymmetry. Innovation persistence has a more pronounced positive moderating effect among civilian-to-military and SMEs, and digital transformation of firms in the South has a negative effect on open innovation, but innovation persistence and political relevance dampen this negative effect.

Previous studies on the restrictions of open innovation or its dark side are mostly case studies and qualitative research. In contrast, the superiority and novelty of this study is in the form of a typical innovation-restricted enterprises “civil-military integration enterprise” as the research sample, based on the perspective of digital transformation, through empirical analysis method to explore how to better implementation of open innovation in the restricted situations. The findings of the study can not only enrich the application of digital transformation and open innovation theory, but also provide practical guidance for military–civil integration innovation in restricted situations.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

This study aims to investigate the responses of young Chinese consumers with different cultural characteristics to negative brand information about electric vehicles.

The current study is quantitative research with an experimental method. It shows two different levels of severity for negative publicity and asks participants to self-report through questionnaires.

Chinese young consumers, being collectivist and of high uncertainty avoidance, tend to search for and spread information; consumers with low power distance search and share information more under low information severity. In addition, information search positively affects brand attitude under lower severity; negative word-of-mouth intention negatively affects brand attitudes at both severity levels.

The current study examines the influence of personal cultural values on information searching and negative information dissemination among young consumers, providing insights to complement previous studies. Furthermore, it explores how such exposure influences young consumers’ brand attitude and intention to purchase. Limitations include simple sample scopes and single-product stimuli.

This research highlights the importance of cultural dimensions in shaping young consumers’ responses to negative publicity. Marketers worldwide should consider cultural influence and develop specific strategies to address negative information about different products. Understanding customers’ unique characteristics and preferences can help marketers effectively tailor their approaches to counter negative publicity.

This study originally provides a supplement to prior studies on cultural dimensions and consumer behavior and provides suggestions to marketers on young Chinese consumers.

The purpose of this paper is to synthesize a polyacrylate-based dispersant with a determined target molecular weight for oily systems and to determine the optimal dispersant level and monomer ratio of the dispersant.

The dispersant was synthesized by conventional radical polymerization using methacrylic acid, butyl acrylate and dimethylamino ethyl methacrylate as the monomer. It was characterized by Fourier transform infrared spectroscopy, nuclear magnetic hydrogen spectroscopy, gel permeation chromatography and thermogravimetric analysis. The dispersant was used to disperse TiO₂, and the performance of the dispersant was evaluated by measuring the viscosity, particle size and dispersive force of the slurry.

The dispersant exhibited high thermal stability and was successfully anchored to the surface of the TiO₂ pigment. When used to disperse a TiO₂ slurry, it effectively made the TiO₂ slurry more fluid, indicating its strong viscosity-reducing properties. The viscosity, particle sizes and dispersion capabilities of the TiO₂ slurry were found to vary depending on the contents and monomer ratios of the dispersant.

P(MAA-BA-DM) dispersant increases the wettability of TiO₂ only in oily solvents but not in aqueous solvents.

P(MAA-BA-DM) dispersant makes it easier to disperse TiO₂ pigments in oily solvents, increasing the amount of pigment in the solvent and making the preparation of highly pigmented pastes easier.

A dispersant containing suitable carboxyl and tertiary amine groups was initially synthesized to disperse TiO₂ in an oily system. The findings are anticipated to be used in the formulation of pigment concentrates, industrial coatings and other solvent-based coatings.

This study explores the influence of heritage brand extension on consumer purchase intention and analyses the effects of pop culture involvement. The extension of heritage brands is becoming increasingly difficult because such an extension needs to be consistent with the unique characteristics of brands and resonate with consumers. However, few scholars discuss the influence of consumers' level of pop culture involvement on brand extension and purchasing behaviour.

Taking time-honoured brands as an example, this study established a conceptual model based on a comprehensive review of the literature, and then tested the model using a sample of 255 respondents who were familiar with one of the selected Chinese time-honoured brands. Structural equation modelling was used to analyse the relationships amongst brand extension fit, pop culture involvement, perceived value and purchase intention.

Time-honoured brand extension fit has a positive impact on consumer purchase intention, and this path is significantly influenced by the mediation mechanisms of perceived value. Situational pop culture involvement can significantly strengthen the relationship between time-honoured brand extension fit and perceived value, whereas enduring pop culture involvement does not.

The results clarify and expand on the different roles of cultural involvement in time-honoured brands and broaden research on the influence of cultural involvement in this regard. This study has significant theoretical value for the inheritance and revival of heritage brands and provides a reference for the practice of time-honoured brands.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.