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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.

This study aims to obtain governance strategies for managing the complexity of megaprojects by analyzing the impact of individual factors and their configurations using the fuzzy-set qualitative comparative analysis (fsQCA) method and to provide references for project managers.

With the continuous development of the economy, society and construction industry, the number and scale of megaprojects are increasing, and the complexity is becoming serious. Based on the relevant literature, the factors affecting the complexity of megaprojects are determined through case analysis, and the paths of factors affecting the complexity are constructed for megaprojects. Then, the fsQCA method is used to analyze the factors affecting the complexity of megaprojects through 245 valid questionnaires from project engineers in this study.

The results support the correlation between the complexity factors of megaprojects, with six histological paths leading to high complexity and seven histological paths leading to low complexity.

It breaks the limitations of the traditional project complexity field through a “configuration perspective” and concludes that megaproject complexity is a synergistic effect of multiple factors. The study is important for enriching the theory of megaproject complexity and providing complexity governance strategies for managers in megaproject decision-making.

Three-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of decision-making. They are proposed to assist decision-makers in better managing incomplete or imprecise information under conditions of uncertainty or fuzziness. However, it is easy to cause decision losses and the personal thresholds of decision-makers cannot be taken into account. To solve this problem, this paper combines picture fuzzy (PF) multi-granularity (MG) with 3WD and establishes the notion of PF MG 3WD.

An effective incomplete model based on PF MG 3WD is designed in this paper. First, the form of PF MG incomplete information systems (IISs) is established to reasonably record the uncertain information. On this basis, the PF conditional probability is established by using PF similarity relations, and the concept of adjustable PF MG PRSs is proposed by using the PF conditional probability to fuse data. Then, a comprehensive PF multi-attribute group decision-making (MAGDM) scheme is formed by the adjustable PF MG PRSs and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, an actual breast cancer data set is used to reveal the validity of the constructed method.

The experimental results confirm the effectiveness of PF MG 3WD in predicting breast cancer. Compared with existing models, PF MG 3WD has better robustness and generalization performance. This is mainly due to the incomplete PF MG 3WD proposed in this paper, which effectively reduces the influence of unreasonable outliers and threshold settings.

The model employs the VIKOR method for optimal granularity selections, which takes into account both group utility maximization and individual regret minimization, while incorporating decision-makers' subjective preferences as well. This ensures that the experiment maintains higher exclusion stability and reliability, enhancing the robustness of the decision results.

The purpose of this paper is to analyze the effect and mechanism of platform incentives on users’ knowledge collaboration performance (KCP) and the configuration leading to high KCP in online knowledge communities (OKCs) in the post-COVID-19 pandemic era from a cross-culture perspective.

A survey method and a standard questionnaire were applied. The data was analyzed using multiple regression and fuzzy set qualitative comparative analysis.

The results indicate that, for both kinds of users, self-enhancement and communication positively affect the KCP. User engagement significantly mediates the relationship between communication and KCP and knowledge absorptive capacity moderates the relationship between user engagement and KCP. In contrast, material incentive positively affects the KCP of Chinese users, while hurting the cross-cultural sample. And the promotion of KCP for cross-cultural samples does not require a higher engagement and knowledge absorptive capacity, while paying more attention to short-term interests, such as communication and self-enhancement.

The study only divides users into Chinese and cross-cultural foreign users, without a distinction between foreign users in different countries. In addition, the research is based on cross-sectional data and failed to try to explore the long-term effects of these incentives from the time dimension.

This study explores the incentive mechanism and configuration of OKC platforms to achieve high KCP for different users from a cross-cultural perspective. It provides new ideas and solutions for precise incentives for users of OKC platforms.

This study aims to investigate the relationship between personality traits and innovative behaviour, using a mixed-methods approach to provide deeper insights into these dynamics.

The authors used a mixed-methods approach, integrating fuzzy set qualitative comparative analysis (fsQCA) with traditional multiple linear regression analysis. This study was conducted among 76 university graduates, using the Big Five personality model and the Innovator DNA model to assess innovative behaviour.

The findings reveal significant positive correlations between conscientiousness, extraversion and innovative behaviour. The inclusion of fsQCA allowed for a more nuanced understanding of the complex interactions between personality traits and innovative behaviour, highlighting configurations of traits that traditional methods may overlook.

This study's sample size and focus on university graduates may limit the generalisability of the findings. Future research should explore these relationships in more diverse populations and settings to enhance generalisability.

The insights gained from this study can inform the development of more effective talent management strategies, helping organisations to better align personality traits with roles that demand high innovation. This approach can optimise team composition and improve innovative output.

Understanding the configurations of personality traits that lead to innovative behaviour can help educational institutions and organisations foster environments that support diverse and innovative thinking, ultimately contributing to societal progress.

This research contributes to the literature by demonstrating the efficacy of fsQCA in capturing the complexities of human behaviour, particularly in the context of personality traits influencing innovation. By combining qualitative and quantitative analyses, this study provides a comprehensive perspective that enhances both methodological rigour and the depth of understanding in psychological and innovation studies.

Waste classification plays an important role in reducing pollution, promoting waste recycling and resource utilization. This paper aims to explore the multiple reasons that affect the performance of waste classification governance.

Content analysis of the existing waste classification policies is conducted using the Latent Dirichlet Allocation (LDA) model. Based on this analysis, influencing factors are identified through the technology-organization-environment (TOE) research framework. The condition configurations and action paths that cause differences in governance performance are derived using the fuzzy-set qualitative comparative analysis method (fsQCA).

The results show that there are spatial and temporal disparities in waste classification policies among different provinces/cities. In most situations, the implementation effect of policy combinations is better than that of a single type of policy, with mandatory policies playing a key role. Additionally, a single influencing factor cannot constitute the bottleneck of high governance performance. Policy topics coordinate with environmental and technical factors to influence governance performance. Finally, in light of China's actual governance situation, several targeted implications are proposed for the practical optimization of local government waste classification governance.

This paper presents a novel approach by integrating multiple heterogeneous data sources from both online and offline channels, adopting a public-government perspective and applying the fsQCA method to investigate the combined effects of technical, organizational and environmental factors on waste classification governance performance across 31 provinces and cities in China.

This study aims to investigate the causal complexity of ECF investors’ peer effect through two different paths of structural social influence.

Using the fuzzy-set qualitative comparative analysis (fsQCA) approach, we employ 157 samples from a Chinese ECF source to explore how peer-effect are caused by both informational and normative mechanisms.

The findings suggests that there are multiple configurations could lead to ECF investors’ high level peer-effect through both informational and normative mechanisms, and the informational mechanism' role depends on the normative mechanism, while the normative mechanism could lead to peer-effect independently.

The findings enrich the literature on ECF investors’ behaviors by revealing the diverse configurations resulting in investors’ peer-effect and shedding new light on investigating the decision-making driven by information asymmetry and relationship settings for individuals at a disadvantage.

This is the first study that investigates the multiple-driven of ECF investors’ decision-making and the importance of mutual norms in individuals' decision-making by complex network analysis approach and qualitative comparative analysis from the perspective of complexity. The results reveal the complexity of investors’ decision-making in ECF.

Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance.

Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized.

The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down.

This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development.

Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events.

This study aims to investigate how a firm’s sustainable competitive advantage is influenced by the combination of entrepreneurial ecological orientation, digital transformation and dynamic capabilities.

Based on the qualitative comparative analysis method, this study systematically explores the significant key conditions and configuration effects that affect the growth of sustainable competitive advantage. This study uncovers the causal relationship and complex mechanisms underlying the sustainable and unsustainable competitive advantages for new ventures, by examining the grouping effects of the above three factors – entrepreneurial ecological orientation, digital transformation and dynamic capabilities on those advantages.

A single factor fails to constitute a necessary condition for sustainable competitive advantage. Three types of configurations are beneficial to sustainable competitive advantage, namely, flexible and responsive type, dynamically adjusted type and type of opportunity resource integration, whereas four types of configurations lead to the unsustainable competitive advantage of new ventures, namely, type of organizational rigidity, informal entrepreneurial type, information-blocking type and technology-deficient type.

According to this study, adopting an entrepreneurial ecological orientation is a novel strategic move. This study offers an extensive review of three aspects of entrepreneurial ecological orientation, dynamic capacities and digital transformation and their mutually synergistic cascading effects on the sustainable competitive advantage of new ventures. This study investigates how three dimensions interact to achieve sustainable competitive advantage for firms, ultimately contributing to the study of sustainable competitive advantage strategies from an entrepreneurial ecosystem perspective.

The objective of this investigation is to examine the correlation between green transformational leadership (GTL) and organizational green innovation (GI) by employing a mediation model. Drawing upon the social cognitive theory and natural resource-based view, the present study explores the role of green creativity (GC) as a mediating variable in the relationship between GTL and GI in the hotel industry.

The research was conducted in Italian luxury hotels to assess the efficacy of our conceptual framework among workers in the hospitality industry. The study utilized a three-wave 2-week time-lagged design (N = 303). In addition, the study also intends to apply the Partial Least Squares – Structural Equation Modelling (PLS-SEM) and the fuzzy qualitative comparative analysis (fsQCA) to have distinctive discernment into model rapport.

The results of the study indicate the linkage between GTL and GI. Furthermore, the study also found the partial mediation of GC of employees. The results show numerous combinations using fsQCA that can be utilized to increase green performance (GP).

The study helps the hotel industry maximize its performance by unravelling irregular relationships. Therefore, it contributes by explaining previously unexplored factors and elucidating causal recipes to build a higher GP base through GTL, GC and GI. The research findings hold significant implications for comprehending the effects of GTL on organizational GI as well as its influence on GP. The limitations of the study are discussed for avenues of future research.