Fuzzy-set Qualitative Comparative Analysis Research Articles

Industrial diversity, formal, and informal institutions: a configurational moderation of the innovation input-output relationship

ABSTRACT This study seeks to uncover configurations of industrial diversity and institutions that act as boundary conditions in the conversion of innovation inputs into outputs. Specifically, we analyse how industrial diversity, rule of law, open markets, power distance, and individualism combine to influence countries’ transformation of innovation inputs into innovation outputs. A novel, two-stage fuzzy-set qualitative comparative analysis is used to identify configurational moderation on panel data from 66 countries. Results revealed three distinct equifinal combinations of conditions that moderate the innovation input-output relationship: (1) the presence of open markets and individualism, coupled with the absence of power distance; (2) the presence of industrial diversity, open markets, and individualism; and (3) the presence of rule of law and power distance. These configurations support the presence of high innovation outputs, even when innovation inputs lose importance. This study reconciles the literature on industrial structure with institutional theory and embeds them in the NSI approach. Furthermore, it helps to clarify the current discussions of whether innovation benefits more from industrial diversity or specialisation, or through one cultural profile or another. Thus, the study sheds light on previous inconsistencies in the literature by harmonising different approaches to innovation.

Determinants of Consumers’ Intention to Use Autonomous Delivery Vehicles (ADVs): A Fuzzy-Set Qualitative Comparative Analysis Approach

While numerous studies have investigated the factors associated with autonomous delivery vehicles (ADVs), there remains a paucity of research concerning consumers’ intentions to utilize these technologies. Prior research has predominantly concentrated on the effects of individual variables on outcomes, often neglecting the synergistic influence of various factors on consumer intention. This study seeks to examine the collective impact of pro-environmental motives (including awareness of consequences and ascription of responsibility), normative motives (such as subjective norms and personal norms), risk factors (COVID-19 risk and delivery risk), and individual characteristics (including trust in technology and innovation) on consumers’ intentions to adopt ADVs. Employing a fuzzy-set qualitative comparative analysis (fsQCA), this research analyzed data from 561 Chinese consumers collected via an online platform. The results yielded six distinct solutions, indicating that multiple combinations of antecedent factors could lead to a higher intention to adopt compared to any singular factor. These findings offer significant theoretical and practical implications for the effective implementation of ADVs in the last-mile delivery sector.

Exploring the antecedents of knowledge transfer among stakeholders in green building projects: From the configuration perspective

With the rapid development of green buildings around the world, knowledge transfer among stakeholders is of great significance in promoting the innovation and application of green building technologies. However, due to the complexity and specificity of green building projects, current studies have not fully established a comprehensive understanding of how elements from various dimensions (i.e., subject, relationship, context, and activity) collectively impact knowledge transfer among stakeholders in green building projects. Therefore, this study aims to explore the key factors that affect knowledge transfer among stakeholders in green building projects and how these factors work together to enhance the effectiveness of knowledge transfer (EKT). Firstly, the key factors influencing knowledge transfer were pinpointed: cognitive differences, connection strength, organizational climate, information technology capability, transmit willingness, receive willingness, perceived policy support, and richness of transfer channels. Then, an examination of the collected valid samples was conducted through configuration analysis utilizing the fuzzy set qualitative comparative analysis (fsQCA) method. The findings reveal five configurations, categorized as strong connection-driven, policy support-driven, and both-driven. Lastly, some combination strategies were outlined for different categories to assist green building companies in enhancing EKT. This study not only enhances the existing green building literature by developing a framework of factors that influence knowledge transfer among stakeholders, but also provides a reference for corporate executives to choose the best strategies to enhance EKT in green building projects.

Exploring the dynamic synergistic effect of combined factors on urban energy efficiency: A set-theoretic configurational approach

Addressing global resource scarcity and environmental degradation requires a focus on enhancing energy efficiency. While previous studies have examined the net impact of single factors on energy efficiency, the dynamic synergistic effects of interactive factors remain underexplored. This paper addresses this gap by integrating the Technology, Organization, and Environment (TOE) framework with the Co-evolution Theory. Utilizing the dynamic Fuzzy Set Qualitative Comparative Analysis technique, this study examines 108 Chinese cities to identify configurational pathways for improving energy efficiency while considering social factors such as technological innovation, governmental oversight, demographic scale, foreign capital, economic development, industrial composition, and urban sustainability. Our empirical findings indicate that no single factor is sufficient for achieving energy efficiency independently; rather, it depends on the dynamic interplay and collaboration among these factors. Notably, technological innovation emerges as a critical component in enhancing energy efficiency. This research offers deeper insights into the configurational aspects of energy efficiency and underscores the importance of synergistic dynamics among influencing factors.

The spatial-temporal evolution characteristics and influencing factors of the coupling between the industrial chain and innovation chain in China

Exploring the theoretical logic, development characteristics and improvement paths of the deep coupling of innovation chains and industrial chains (two-chain) is of great importance for building a modern industrial system and developing productive forces. However, empirical evidence on the spatial-temporal characteristics and influencing factors of two-chain coupling is still lacking. In this study, the degree of coupling coordination model is applied to assess the degree of two-chain coupling in China using provincial panel data from 2013 to 2022. In addition, spatial analysis is applied to investigate the spatial-temporal evolution characteristics of the variables in question. On this basis, the fuzzy set qualitative comparative analysis (fsQCA) method is applied to identify influential factors. The results indicate that: (1) Temporally, the coupling level of the two-chain shows a consistent upward trend and gradually develops a distribution pattern characterized by high values in the east and weak values in the west. (2) Spatially, there is a significant positive spatial correlation between the two-chain coupling. (3) In terms of drivers, the increase in two-chain coupling is attributed to multiple factors. Three models can be identified: human-driven, capital-driven and information-driven, which are influenced by the cooperation between government and market. The novelty of this article lies in its exploration of the theoretical logic and developmental characteristics of two-chain coupling, while empirically demonstrating its driving factors. This study provides a new perspective for governments and relevant departments to improve two-chain linkages and promote economic development.

A Study on the Antecedent Grouping Pattern of ESG Decoupling in New Energy Vehicle Manufacturing Companies

This study explores the decoupling phenomenon of listed companies in the new energy vehicle manufacturing category in terms of ESG (environmental, social, and corporate governance) by using fuzzy set qualitative comparative analysis (fsQCA) and necessity condition analysis (NCA) methods, and identifies four different groupings. These groupings reveal the complexity and diversity of firms' ESG performance and disclosure, including high gearing and low environmental commitment, high management compensation and low social responsibility, high media attention and selective disclosure, and high market competition and inadequate governance. The results of the study show that financial pressure, management incentives, media attention and market competition all significantly affect firms' ESG behavior. Based on this, policy and management recommendations are proposed, such as strengthening the regulation of ESG disclosure, incorporating ESG performance assessment in executive incentives, and balancing short-term financial goals with long-term sustainable development. Although this study reveals the complexity of ESG decoupling in new energy automobile manufacturing firms, the limitations of data source, sample scope and methodology still exist. Future research should expand the sample scope, conduct dynamic analysis, and synthesize multiple methods to deepen the understanding of corporate ESG decoupling behavior and provide scientific guidance for management and policy making.

Pathways to Resilient and Sustainable Online Education in the New Normal: Interpreting University Teachers’ Willingness to Use Online Teaching Platforms Consistently

In the new normal, online education has become a norm and is considered equally important as traditional face-to-face education. Although online teaching has improved accessibility, reduced management costs, and expanded educational opportunities, there are also some issues, such as whether technology and internet access are widespread and whether the quality of online education is better or worse compared to previous modes. With the rapid development of online education, it is crucial to investigate the key factors influencing university teachers’ continuous usage of online teaching platforms and their working mechanisms, which is of great significance for improving the level of educational informatization and promoting educational modernization. This study mainly explores the continuous usage intention of online teaching platforms by Chinese university teachers under the new normal and its influencing factors, aiming to provide theoretical support and practical insights for improving educational informatization, promoting educational modernization, and designing and optimizing online teaching platforms. This study builds a new theoretical framework based on the technology acceptance model (TAM), UTAUT model, and the theory of planned behavior (TPB). From April to November 2024, 343 questionnaires were collected from Chinese university teachers. Combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the key factors influencing teachers’ continuous usage of online platforms and their configuration relationships are systematically discussed. Not only are the key factors verified, but the configuration relationships are also revealed, providing deeper insights for universities to design, promote, and optimize online teaching platforms and offering theoretical support and practical guidance for promoting sustainable educational development.

Fuzzy Set Qualitative Comparative Analysis of Loyalty Programmes Powered with Blockchain via an UTAUT2 Framework

(1) Background: Loyalty programmes are business strategies aimed at increasing customer fidelity to brands. One of the most promising technologies of the 21st century is blockchain, whose application to the management of loyalty programmes can increase transparency and reliability, enable interoperability, and facilitate centralised management. (2) Methods: This study evaluates the antecedents of the acceptance of blockchain-based loyalty programmes (BBLPs) using a model grounded in the technology acceptance model UTAUT2. The four basic constructs of UTAUT, which we call the UTAUT-baseline, are considered explanatory factors: performance expectancy (PER), effort expectancy (EFF), social influence (SOC), and facilitating conditions (FAC). Additionally, we consider the constructs introduced in UTAUT2: hedonic motivation (HED) and perceived price value (PRI), to which we add innovativeness (INN) and trust (TRU), and we refer to these as UTAUT-extended, along with the moderating variables of UTAUT and UTAUT2: gender, age, and experience. The analytical approach used is complexity theory, which aims to capture the configurations that lead to both acceptance and rejection positions regarding BBLPs. This analysis is performed via fuzzy set qualitative comparative analysis (fsQCA). (3) Results: Eight explanatory configurations of the acceptance of BBLPs have been obtained, in which the presence of all UTAUT-baseline and UTAUT-extended constructs is a condition in at least three prime implicates. The constructs with the greatest presence as core conditions are PER, EFF, and TRU. On the other hand, nine configurations leading to the rejection of BBLPs have been identified. The absence of all UTAUT-baseline and UTAUT-extended variables is a condition in at least three configurations. The core conditions with the greatest presence in the rejection of BBLPs are the lack of FAC, HED, and PRI. (4) Practical implications: The results obtained are of great interest to business owners, as they allow for the characterisation of different profiles of people potentially engaged with BBLPs, as well as the profiles of consumers reluctant to adopt them.

Exploring the Influencing Factors of Digital Transformation Effectiveness of Specialized and Sophisticated SMEs

Digital transformation is a significant strategy for enterprises to implement long-term business growth and high-quality development. This paper identifies the factors affecting the effectiveness of digital transformation of specialized and sophisticated SMEs; explores the configuration path of the six antecedent factors that jointly drive the enterprise to achieve high-effective digital transformation by using fuzzy-set qualitative comparative analysis. The findings illustrate no single factor can be a necessary and sufficient condition for the formation of the outcome variable; There are three main configuration paths that lead to a high-impact corporate digital transformation, which can be categorized as total factor driven, organization-environment oriented, and organization-environment dominated. As the most energetic and high-quality group of SMEs, Specialized and sophisticated SMEs plays an exemplary leading role in the direction of digital transformation. Therefore, the research sample in this paper is representative and provides some reference indicators for the government to formulate support policies, and also enlighten the thinking of other small and medium-sized enterprises on digital transformation. In addition, this paper explore concludes the key factors and configuration paths that influence the transformation effectiveness of Specialized and Sophisticated SMEs, which could provide practical guidance for managers to formulate digital transformation strategies and paths.

How ecosystems coordinate architectures and AI in humanitarian operations? A configurational view

Recent research in crisis management indicates that business ecosystems are increasingly incorporating artificial intelligence (AI) into humanitarian operations. This integration is designed to coordinate a diverse range of actors and ensure the quick and efficient delivery of aid. One of the key operational issues identified with this undertaking is communal coordination in that the effectiveness of these humanitarian efforts largely hinges on the ability of the actors to foster a sense of community within the architecture of the AI-enabled ecosystem, i.e., a community characterized by trust and cooperation. This architecture is deemed critical for effective humanitarian operations. Yet some studies indicate that AI might impede coordination, potentially obstructing how communal relations develop between actors. Unfortunately, the literature does not adequately explain how ecosystems and architectures should be designed to promote communal coordination among the actors involved. Hence, with this research, we conducted a configurational analysis of humanitarian operations, referencing data from 73 ecosystems observed during the catastrophic floods in China in 2023. Using v4.1 of the fuzzy-set qualitative comparative analysis (fsQCA) tool, we identified correlations between the architectures of various AI-enabled ecosystem configurations and the performance of the humanitarian operations they supported. From our research, we identified two exemplary architectures that led to superior humanitarian performance: one featuring loosely coupled coordination within an AI-enabled architecture, and the other emphasizing aligned coordination within more regulated homogeneity. The key distinction between the two architectural models lies in how the AI deployed influences the dynamics of coordination between the community and the governance bodies. This study reveals that, despite AI's significant potential to enhance humanitarian performance, effective communal coordination depends on aligning the ecosystem architecture with AI-driven coordination. This research contributes to the humanitarian literature by clarifying the complicated interactions between AI and ecosystem architectures in humanitarian scenarios.

Navigating the green wave: Understanding behavioral antecedents of sustainable cryptocurrency investment

This study fills a void in the literature on the intention to invest in sustainable cryptocurrency. It examines the behavioral antecedents of individuals' decision making in this expanding financial industry. A mixed-methods approach that combines partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) is used to gain an understanding of the factors that lead to acceptance of sustainable cryptocurrencies and the obstacles to investing in them. This study is valuable for investors, policymakers, and managers in the digital currency business, providing practical insights and strategic implications. The study's findings underscore the relevance of regulatory frameworks and government assistance in fostering the adoption of sustainable cryptocurrencies. The study emphasizes the importance of customer trust and sustainability in influencing the adoption of sustainable cryptocurrencies. It advances the theory of sustainable finance, technology adoption, and behavioral economics. The study's limitations and recommendations for future research offer a path to further the understanding of this developing topic.

Research on the group path of improving the efficiency of China’s public sports services based on DEA and fsQCA analysis

Public sports services crucially determine the transformation of China into a robust sports country. The enhancement of public sports service efficiency fundamentally manifests its high-quality development. We utilize the data of 30 provinces in China in 2020 as the research object, and we apply data envelope analysis (DEA) to measure and evaluate the efficiency level of China’s public sports services as well as Fuzzy Set Qualitative Comparative Analysis (fsQCA) to identify the critical influencing factors; moreover, we explore the grouping pathways that affect the efficiency improvement of public sports services. The results of the study indicate the following: (1) Although the efficiency of public sports services in China is high, regional differences are apparent. (2) GDP per capita, government support, and years of education are the key factors for efficiency improvement. (3) Population-government-led, economic-resource input, and economic-educational dual-drive are the core pathways for enhancing efficiency. Therefore, the following recommendations are made: boosting the efficiency of China’s public sports services by focusing on the level of regional economic development and rationally allocating public sports resources; increasing the investment of public sports funds to enhance the quality and level of services; strengthening the government’s purchasing efforts to promote the market-based operation of public sports services; and increasing the average years of education to strengthen the public’s awareness and participation in sports. This study integrates Data Envelopment Analysis (DEA) for efficiency assessment and fuzzy-set Qualitative Comparative Analysis (fsQCA) for causal analysis in public sports services, effectively addressing the limitations of single-method approaches. Thus, the scope of research is broadened, and a novel perspective is offered. DEA highlights efficiency disparities within Chinese public sports services, whereas the input–output system enhances the accuracy of evaluations. FsQCA identifies key drivers, inhibitors, and contextual combinations that assist policymakers. Overall, this study enriches theoretical understanding and provides practical guidance, facilitating high-quality development and addressing diverse sports demands.

Optimizing policy mixes for promoting prefabricated construction in developing countries: an fsQCA approach

PurposePrefabricated construction (PC) is increasingly recognized for its sustainability and is being vigorously promoted worldwide. However, its adoption in developing countries remains suboptimal, with existing studies predominantly focusing on policy frameworks or the impact of a single policy instrument. This study addresses this research gap by optimizing the path of PC promotion from the perspective of policy mixes.Design/methodology/approachThe study employs fuzzy-set qualitative comparative analysis (fsQCA) in conjunction with necessary condition analysis, to explore effective policy configurations for PC promotion. A comprehensive collection of 171 PC-related policies issued by the Chinese government was analyzed using text mining to identify antecedent conditions of configuration. Data were further obtained through a questionnaire survey involving 263 valid responses, with fsQCA used to derive the optimal policy configurations.FindingsThe analysis identified six distinct combination paths for effective policy configurations. Land supply and governmental procurement were found to be core conditions, while fiscal and taxation financial measures emerged as marginal conditions prevalent across all paths. These findings suggest that land supply policies are particularly suitable for regions with limited land resources, such as Shanghai, while government procurement is more effective in regions like Xinjiang, where PC awareness is still developing.Practical implicationsIn practice, the conclusions enable policymakers to clearly understand policy instruments, thereby finding differentiated pathways for promoting PC with comparable effects. The proposed recommendations help advance PC development effectively while reducing financial burden and minimizing resource waste. This provides important guidance for PC development across different regions or stages, helps address regional imbalances in PC development, and ultimately contributes to the broader goal of sustainable urban development.Originality/valueThis study significantly enriches the research on PC policy combinations by utilizing more comprehensive and robust data, thereby enhancing the universal applicability of the findings. The results provide valuable references for policymakers in different regions, helping to address regional imbalances in PC development and facilitating the construction industry’s transition towards greater intelligence and sustainability.

Why recommend? Unlocking the factors and configurations influencing word-of-mouth intentions in continuing care retirement communities

In the context of continuing care retirement communities (CCRCs), the synergistic effect of perceived value on older adults’ word-of-mouth intentions remains unclear. This article presents two studies designed to identify and assess the key factors and combinatorial configurations driving older adults’ CCRC recommendations from a perceived value perspective. Study 1 uncovered multiple dimensions (e.g., economic value, contextual value, functional value, emotional value, and social value) of older adults’ perceived value of CCRCs using a grounded theory approach. Structural equation modeling and fuzzy-set qualitative comparative analysis were integrated in Study 2 to validate the impacts of perceived value subdimensions on word-of-mouth intention. Four common combinations, namely “economic–emotional–social,” “contextual–emotional,” “economic–functional–emotional,” and “economic–functional–social,” were found to promote this intention. This study provides a nuanced understanding of enhancing CCRC-related word-of-mouth communication by analyzing the synergistic effects of perceived value, thereby extending the application of perceived value theory to the CCRC field.

Digital Transformation in Family Businesses: An Analysis of Drivers with fsQCA

The digital transformation (DT) encompasses profound technological changes. Businesses face the challenge of adapting their activities to the pace of new technologies to increase the efficiency of business processes, improve the customer experience, and create innovative value propositions while recognizing the importance of sustainability. This research focuses on how family businesses, which are a relevant part of global economic activity and job creation, address the challenges associated with the DT. Based on the case of the automotive retail sector in a small European country characterized by the high acceptance of technologies, we use fuzzy-set qualitative comparative analysis (fsQCA) to understand how critical success factors (causal conditions) combine towards adopting technologies representing different perspectives of the DT. The results confirm the existence of multiple configurations based on the importance of organizational culture, change management, knowledge, human capital, IT systems, and DT strategy towards the adoption of customer relationship management, digital marketing, and social networks technologies, thus helping family businesses to understand the adoption of these technologies leading to organizational success in the context of the DT.

The driving factors, configuration paths and effects of employment polarization under the development of digital economy.

Taking the development of China's digital economy as a background, the article provides an in-depth analysis and summarises the influencing factors of labour force employment polarisation. The study employs provincial panel data from 2010 to 2020, applies fuzzy set qualitative comparative analysis methods, and constructs a fixed-effects model in order to explore the drivers, paths and their effects of employment polarisation. The results of the study show that economic, social and educational environments together have a significant impact on employment polarisation through interactions and synergistic effects; it also identifies four main paths of labour force employment polarisation, which are numerical-social-environment-driven, numerical-educational-environment-driven, numerical-economic-environment-driven, and types driven by other factors; this study also finds that compared to the summed impact of single elements, these grouping pathways have a more significant impact on employment polarisation. These findings not only provide a key perspective for understanding how the digital economy shapes employment polarisation, but also provide an empirical basis and insights for policies based on the findings.

Financial anxiety of university students in Poland and Czechia: fsQCA analysis

PurposeThis study aims to formulate propositions based on combinations of causal conditions that lead to high or low financial anxiety among European students, particularly in Poland and Czechia.Design/methodology/approachThe data for this study were collected in September 2023 from 265 undergraduate and graduate students with their income at one university in Poland and three in Czechia. Students’ views on financial anxiety were explored using a seven-item Likert scale. This study uses fuzzy set qualitative comparative analysis (fsQCA), an emerging marketing research technique.FindingsThere are specific factors that may cause increased financial anxiety among young adults in Poland and Czechia, leading to the following key findings: (1) A mix of factors such as perceived lack of financial knowledge, being female, living with parents, having a low monthly income, single status and working a few hours or not at all is linked to higher financial anxiety. (2) Experiencing financial anxiety is also likely when there is low financial knowledge, female gender, living away from parents, single status and a high number of work hours. (3) The combination of low financial literacy, female gender, living with parents, being single and working more hours can elevate financial anxiety.Originality/valueThis study expands the scope of personal financial research by examining how cultural, socioeconomic and psychological factors affect students’ financial anxiety in two European countries that were infrequently studied in this context. It contributes to identifying the drivers of increased and diminished financial anxiety among young adults in Poland and Czechia.

What Configurational Paths Enhance the High-Quality Construction of Cold Region Rural Landscapes? A Fuzzy-Set Qualitative Comparative Analysis of 66 Villages in Heilongjiang Province

This study addresses the construction of high-quality rural landscapes, crucial for China’s rural revitalization strategy, encompassing economic, social, cultural, and ecological dimensions. Focusing on 66 cold-region villages in Heilongjiang Province, it develops a dual-dimensional quality evaluation system that integrates both objective data and subjective perception indicators. It employs the entropy weight TOPSIS model to evaluate and grade the quality of rural landscapes and uses fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the complex causal relationships influencing high-quality rural landscapes. The results show that (1) The TOPSIS model identifies four grades of rural landscape quality: “Excellent-Good-Average-Poor”, with “Excellent and Good” grades defined as high-quality rural landscape. (2) The fsQCA reveals eight configuration paths that influence high-quality rural landscapes, which are categorized into four models: natural ecology, efficient industry, cultural heritage, and comprehensive development. The main contribution of this study lies in its systematic analysis of the complex causal relationships affecting rural landscape quality, providing a theoretical and technological foundation for guiding the sustainable development of cold-region rural landscapes within the framework of rural revitalization strategy in China.

Exploring the Role of Artificial Intelligence in Achieving a Net Zero Carbon Economy in Emerging Economies: A Combination of PLS-SEM and fsQCA Approaches to Digital Inclusion and Climate Resilience

In this paper, we examine the role of artificial intelligence (AI) in sovereignty and carbon neutrality, emphasizing digital inclusion and climate-resilient AI strategies for emerging markets. Considering the previous studies on AI for carbon neutrality and digital inclusion for climate research along with technology policy frameworks as a guide, this paper undertakes Partial Least Squares Structural Equation Modelling (PLS-SEM) with AI strategies and carbon neutrality outcomes. At the same time, fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to reveal different configurations leading to achieving climate resilience. The model covers various aspects of AI-enabled policy, including technology adoption, policy frameworks, digital literacy, and public engagement. Survey data were collected from key stakeholders in climate policy, technology sectors, and local communities using a structured survey to understand their attitudes towards negative emissions technologies from prominent experts in emerging countries like Vietnam, Italy, Malaysia, and Greece. PLS-SEM results reveal the importance of AI in developing carbon neutrality, a critical AI strategic dimension (Data analytics capability and policy support). Some aspects of the fsQCA findings present heterogeneous outcomes, highlighting complex combinations of digital inclusion, AI adoption, and climate resilience which are industry-specific. This study would further enrich the literature concerning climate strategies by exploring AI, digital inclusion, and carbon neutrality interactions. Theoretically, practical and enriching suggestions for future research are derived to help AI intelligence infuse sustainable climate actions.

Pathways and enhanced evaluation system for green low-carbon development across diverse Chinese regions

Pathways and enhanced evaluation system for green low-carbon development across diverse Chinese regions