Search Results (46)

Serious games (SGs) have been widely employed in the digital preservation and transmission of architectural heritage. However, the key determinants and underlying mechanisms driving users’ continuance intentions toward ancient-architecture cultural heritage serious games (CH-SGs) have not been thoroughly investigated. Accordingly, a conceptual model grounded in the stimulus–organism–response (S–O–R) framework was developed to elucidate the affective and behavioral effects experienced by CH-SG users. Partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANNs) were employed to capture both the linear and nonlinear relationships among model constructs. By integrating sufficiency logic (PLS-SEM) and necessity logic (necessary condition analysis, NCA), “must-have” and “should-have” factors were identified. Empirical results indicate that cultural authenticity, knowledge acquisition, perceived enjoyment, and design aesthetics each exert a positive influence—of varying magnitude—on perceived value, cultural identification, and perceived pleasure, thereby shaping users’ continuance intentions. Moreover, cultural authenticity and perceived enjoyment were found to be necessary and sufficient conditions, respectively, for enhancing perceived pleasure and perceived value, which in turn indirectly bolster CH-SG users’ sustained use intentions. By creating an immersive, narratively rich, and engaging cognitive experience, CH-SGs set against ancient architectural backdrops not only stimulate users’ willingness to visit and protect heritage sites but also provide designers and developers with critical insights for optimizing future CH-SG design, development, and dissemination. Full article

Understanding consumer behavior in digital environments is increasingly becoming crucial. This study investigates the influence of key metaverse characteristics—sociability, immersiveness, and environmental fidelity—on continuance intention to use the metaverse by examining the mediating role of consumer experience. In study 1, a Delphi method involving 26 experts was employed to establish a consensus on the significance of metaverse characteristics in shaping consumer experience and behavior. In study 2, data from 391 metaverse users were collected via an online survey, and a two-step PLS-SEM approach was used to evaluate the hypothesized relationships. The Delphi study confirmed the theoretical relevance of the identified metaverse characteristics, which were subsequently validated through user data. The results show that consumer experience fully mediates the influence of immersiveness and environmental fidelity on continuance intention and partially mediates between sociability and continuance intention. Necessary Condition Analysis (NCA) further established consumer experience as a prerequisite for continued metaverse use, while importance performance analysis (IMPA) identified sociability as the most influential driver of continuance intention. This study advances our theoretical understanding of consumer behavior in virtual settings and offers practical insights for enhancing consumer engagement and continued metaverse use. Full article

The rapid development of autonomous driving technology has been a key driver for the emergence of driverless delivery vehicles. To promote wider adoption, it is essential to address consumers’ concerns about safety and reliability, leverage psychological factors, and implement supportive policies that encourage technology adoption while ensuring public safety and privacy. Therefore, it is necessary to explain and predict consumers’ behavior and intention to adopt driverless delivery vehicles. To this end, this study extends the Technology Acceptance Model (TAM) to include technological complexity and perceived trust. This study evaluates the model by applying necessary condition analysis (NCA) and partial least squares structural equation modeling (PLS-SEM) to analyze data from 579 respondents from Jiangsu Province, China. This study explores the sustainability implications of autonomous delivery vehicles, highlighting their potential to reduce environmental impact and promote a more sustainable transportation system. The outcomes indicate that perceived ease of use (PEU), attitude, perceived trust, technological complexity (TECOM), and perceived usefulness (PU) are significant determinants and necessary conditions of consumers’ intention to adopt driverless delivery vehicles. Perceived trust and TECOM had a significant and indirect influence on consumers’ intention to adopt driverless delivery vehicles via PU and PEU. Perceived trust and technological complexity had a substantial impact on consumers’ adoption intention of driverless delivery vehicles. The study recommends that managers work closely with regulators to ensure their technologies meet all local standards and regulations. It also recommends its potential to reduce carbon emissions, improve energy efficiency, and contribute to a more sustainable transportation system. Full article

This study investigates the determinants of eco-buying behavior by drawing from the Theory of Planned Behavior (TPB) and considering green consumption values and prosocial attitudes. Using a cross-sectional survey of 436 adults in the UK and employing a two-step analysis with Partial Least Squares Structural Equation Modelling (PLS-SEM) and Necessary Condition Analysis (NCA), this research examines both sufficient and necessary conditions for sustainable purchasing behavior. The findings reveal that green consumption values and social influence are positively associated with green buying behavior, with green consumption values exerting the strongest influence. Conversely, prosocial attitudes and perceived behavioral control show no significant direct effects, yet age moderates the relationship between prosocial attitudes and green buying behavior. Moreover, NCA identifies green consumption values and perceived behavioral control as necessary conditions at specific thresholds, underscoring their importance in fostering high levels of green buying behavior. Additionally, a logarithmic relationship is observed between green consumption values and green buying behavior, suggesting diminishing returns at higher levels of green consumption values. Notably, the influence of green consumption values on green buying behavior is stronger among older individuals (35+), highlighting age-based differences in sustainable consumption. By integrating both sufficient and necessary conditions, this research addresses the attitude-behavior gap in sustainable consumption, offering novel insights into the roles of intrinsic values and social influences. These findings challenge established constructs like perceived behavioral control and highlight the relevance of advanced analytical methods in sustainable consumer research. The study contributes theoretical insights and practical implications for sustainable marketing strategies targeting value-driven consumers. Full article

Digital transformation is a crucial strategic decision for achieving sustainable development. It emphasizes disruptive upgrades in production, operations, and management thinking, thereby infusing vitality into long-term corporate sustainability. While extensive research has explored digital transformation drivers, the role of employees is often overlooked. To address how to activate employees’ behavioral support for digital transformation, this study first identified antecedent conditions of employees’ behavioral support for digital transformation through content analysis within the AMO framework. Subsequently, by combining NCA and fsQCA methods, the study explored the impact of coupling antecedent conditions on behavioral support for digital transformation. The findings are as follows. First, a single factor does not constitute a necessary condition for high behavioral support for digital transformation. Second, there are four configurations that cause high behavioral support for digital transformation: “motivation-driven and leadership-supported”, “experience-led and motivation-driven”, “efficacy-dominated and opportunity-empowered”, and “individually-driven and opportunity-enabled”. Third, learning goal orientation and perceived usefulness are important for activating high behavioral support for digital transformation. This study can provide insights to inspire employees’ support for digital transformation, facilitating corporate digital transformation and further achieving sustainability. Full article

This research explores the interaction between inter-organizational trust, marketing channels, and market and financial performance (FP) in establishing sustainable global marketing channels using Industry 4.0 technologies. It is conducted within the relational exchange theory (RET) framework and transaction cost economics (TCE). The sample (N = 131) was collected through the marketing research firm Centiment. PLS-SEM and Necessary Condition Analysis (NCA) were utilized as statistical methods. All hypotheses except the relationship between marketing channel operational performance and FP were accepted. This research highlights the vital role of inter-organizational trust in enhancing operational efficiency, profitability, and sustainability. It finds that trust fosters collaboration in global distribution channels, improving performance across multiple dimensions. Specifically, trust positively impacts marketing channel operations, boosting market performance. Nevertheless, all exogenous constructs were essential—“must-have” conditions for the endogenous FP construct. Applying the novel NCA is distinctive, primarily as it demonstrates that the relationship between marketing channel operational performance and FP is a necessary “must-have” condition, despite the insignificant path coefficient between the constructs. This is a crucial finding, as further investment in marketing channel operational performance and other antecedents of FP may be futile if the necessary conditions have not been met. Full article

Improving the green total factor productivity (GTFP) of publicly listed companies in the livestock sector is essential for achieving sustainable and high-quality development in China’s agricultural industry. This study proposes an integrated analysis framework for the advancement of GTFP, focusing on internal resource allocation and external business environment configurations. Using panel data from 32 publicly listed companies in China’s livestock sector covering the period 2016 to 2021, we apply the dynamic qualitative comparative analysis (QCA) and necessary condition analysis (NCA) methodologies to explore the configuration pathways for multiple factors that influence GTFP, aiming to identify the mechanisms that drive these pathways in publicly listed livestock companies. The findings reveal that individual antecedent conditions are not essential for achieving high green total factor productivity (GTFP) in firms. Rather, internal and external factors jointly facilitate GTFP enhancement, resulting in three distinct configurational pathways that share the equivalence of “diverse configuration pathways leading to the same objective”. Over time, the consistency level of each configuration pathway fluctuates above 0.94, demonstrating their stability over the study period. In terms of individual companies, the explanatory power of each configuration remains uniform across enterprises, exhibiting no significant differences. This study expands the scope of GTFP-related research and advances the application of the dynamic QCA method. It also provides enlightenment for policymakers to refine sectoral regulations and for companies seeking strategies to improve GTFP. Full article

As a key factor driving economic growth, the connotation and extension of new quality productivity have been expanding. This has a profound impact on the path of total factor productivity improvement. Therefore, this paper combines the qualitative comparative analysis (QCA), necessary conditions analysis (NCA), and data envelopment analysis (DEA) methods and adopts a group study to analyze the driving mechanism of total factor productivity (TFP) enhancement in 30 provinces in China from the perspective of new quality productivity. The study finds that (1) a single new quality productivity component does not constitute a necessary condition for TFP; (2) three new types of ecological factors can drive regions to realize high total factor productivity, including innovation-driven, green-led, and service-embedded factors, reflecting the complex mechanism of high-quality development in China’s provinces; and (3) the high factor productivities of the eastern, central, and western regions are clearly differentiated, with different grouping paths, which are manifested in the ”leading” development of the east, the “following” development of the center, and the “accompanying” development of the west, revealing the regional “leading” development and the “accompanying” development and showing the diversified path of regional high-quality development.This study not only deepens our understanding of the relationship between new quality productivity and total factor productivity in theory, but also provides scientific basis and decision support for different regions to formulate differentiated high-quality development strategies empirically. Full article

Supply chain resilience (SRES) is essential for firms aiming to alleviate the impact of interruptions and maintain operational continuity and sustainability in performance. In the context of the FMCG industries of Pakistan and Saudi Arabia, characterized by intricate and vulnerable supply chains, there exists an urgent necessity for solutions that bolster resilience. This study examines the influence of critical factors resilient suppliers (RS), transportation capacity (TC), flexibility (FLEX), network complexity (NC), and supply chain dynamism (SPD) on SRES. A quantitative methodology was utilized, examining survey data from 611 participants with Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). The results indicate that RS and TC are essential for directly improving SRES, whereas FLEX and SPD facilitate increased adaptability and reactivity. The NCA emphasizes the need to control NC to avert bottlenecks that may impede resilience. This study indicates that SRES emerges from the dynamic interplay of several elements, rather than from separate enhancements. Although NC exerts a negligible direct influence, adeptly managing complexity is crucial for reducing disruptions and inefficiencies. The results underscore that fortifying RS, TC, and FLEX in unison improves resilience and adaptation to market volatility and disturbances. This study provides various theoretical and managerial insights. A systems theory approach highlights the interdependence of supply chain components, whereas the Theory of Constraints (TOC) posits that excessive NC can hinder resilience. Management should concentrate on maximizing RS and TC until declining returns are evident, thereafter redirecting efforts towards improving FLEX and minimizing NC. Furthermore, optimizing processes and facilitating swift decision-making are essential for maintaining resilience. Full article

This study examines how individual, organisational, and societal factors influence blockchain technology (BCT) adoption in supply chain management (SCM). Using Partial Least Squares Artificial Neural Networks (PLS-ANNs) and Necessary Condition Analysis (NCA), it identifies key determinants of sustainable BCT adoption among small- and medium-sized enterprises (SMEs). The results show that compatibility, top management support, and relative advantage are critical for adoption. This study focuses on SMEs, and further research is needed to assess whether these findings apply to larger organisations. Insights from this research provide a foundation for improving BCT adoption in high-impact sectors and inform strategic adoption practices. By analysing multi-level factors, the study enhances understanding and guides policy development for equitable and sustainable supply chain innovations. Additionally, the findings refine existing BCT adoption models by introducing and validating new determinants, contributing to both theory and practice in SCM. This comprehensive approach bridges research gaps and offers actionable insights for improving BCT adoption, supporting broader economic and social benefits. Full article

The purpose of this study is to examine the interaction between Industry 4.0 technologies, inter-organizational trust, sustainable distributor channel development, and marketing channel operational performance. The research employed a quantitative approach, collecting data from 131 respondents in Canadian and U.S. global firms with over 400 employees. The analysis utilized partial least squares structural equation modelling (PLS-SEM) and Necessary Condition Analysis (NCA). The study revealed that inter-organizational trust is both a significant determinant and a necessary condition for marketing channel operational performance. While Industry 4.0 technologies emerged as a significant determinant, they were not identified as a “must-have” necessary condition. Notably, distributor sustainability development proved to be an insignificant determinant, but still a “must-have” necessary condition for marketing channel operational performance. This study uniquely contributes to understanding Industry 4.0 and marketing channel dynamics by integrating inter-organizational trust analysis with NCA methodology. By identifying trust as a significant determinant and a “must-have necessary condition”, the research provides practical guidance for managers navigating technological adoption in global marketing channels. The findings challenge conventional assumptions about sustainable development while emphasizing trust’s crucial role in the digital age, offering valuable insights for achieving high marketing channel operational performance during the transformation to Industry 4.0. Full article

The high-quality development of specialized, refined, distinctive, and innovative enterprises (SRDIEs) is essential for advancing an innovation-driven strategy. This paper investigates the impact of financial technology (Fintech) on sustainable innovation within SRDIEs that face financing challenges, analyzing it from supply-side, demand-side, and environmental perspectives. We utilize fuzzy-set Qualitative Comparative Analysis (fSQCA) and Necessary Condition Analysis (NCA) to explore the configurational paths and complex causal effects of Fintech in facilitating the innovation of SRDIEs amid financing challenges. By employing a combination of NCA and fsQCA, this study identifies several effective pathways through which Fintech enhances the innovation efficiency of SRDIEs. We develop an integrative model to enhance innovation inputs, outputs, and sustainability. The key findings include the following: (1) Fintech significantly enhances innovation output, supported by business efficiency and digital intelligence; (2) two distinct pathways for achieving high-innovation inputs are identified, driven by Fintech intensity and effective credit allocation, with specialization and financial mismatches serving as auxiliary factors; (3) the core conditions of Fintech intensity and the financing environment, along with competitive banking, promote innovation motivation and sustainability in highly specialized enterprises. The conclusions of this study provide both theoretical and practical insights for SRDIEs to tackle innovation challenges characterized by an “inability to innovate”, a “lack of willingness to innovate”, and “ineffectiveness in innovation”, enabling their transition from merely being “able to innovate” and “daring to innovate” to becoming “proficient in sustainable innovation”. These findings offer differentiated sustainable innovation solutions for enterprises through three avenues: capacity building on the demand side, channel optimization on the supply side, and ecological cultivation on the environmental side. Full article

Background/Objectives: This study investigates consumers’ purchase intention (PI) toward health and wellness foods (HWF) in China by examining key factors, such as health-consciousness (HC), desire, and attitude toward organic food. Methods: Data were collected via online surveys completed by Chinese respondents aged 50–65 years. Of the 270 distributed surveys, 230 valid responses (85.2% effectiveness) were analyzed. A multi-analytic approach was employed, integrating Partial Least Squares–Structural Equation Modeling (PLS–SEM), Necessary Condition Analysis (NCA), and Combined Importance-Performance Map Analysis (cIPMA), to explore the hypothesized relationships. Results: The results reveal consumers’ HC has a strong influence on their PI toward HWF, with attitude and desire sequentially mediating this relationship. Furthermore, the results of NCA confirm that HC and desire are necessary conditions for purchasing HWF, whereas attitude is not. Conclusions: Based on these findings, the study provides suggestions for future research and practical recommendations for HWF businesses. Full article

In the context of global carbon emissions and climate change, identifying context-specific low-carbon pathways for urban areas is critical for achieving socio-environmental sustainability. This study applies the technology–organization–environment (TOE) framework to examine the driving mechanisms and the diversity in carbon reduction pathways across 81 cities in China. Utilizing partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA), this research assesses the roles of technological, organizational, and environmental drivers in urban carbon reduction. Fuzzy-set qualitative comparative analysis (fsQCA) is employed to uncover distinct carbon reduction pathways and causal asymmetries between cities. The findings reveal that technological, organizational, and environmental factors significantly drive carbon reduction, with technological and organizational factors playing the central roles. Environmental factors exert primarily indirect effects, interacting with technological and organizational drivers. This study categorizes cities into three distinct carbon reduction models: cities with high carbon-neutral potential primarily leverage technological innovation and energy efficiency optimization; cities with moderate potential integrate technology and policy, emphasizing green landscape planning to achieve balanced development; and cities with lower carbon reduction potential are mainly policy-driven, constrained by technological and resource limitations. This study underscores the role of computational modeling in providing valuable insights for the development of context-tailored carbon reduction strategies. It highlights the synergetic interactions among technological, organizational, and environmental factors, offering essential guidance for advancing sustainable development planning and facilitating the low-carbon transition of cities and communities. Full article

In recent years, policymakers have increasingly focused on environmental quality and economic growth. While various factors influence green growth, two important factors that have been overlooked in research are the global innovation index and the fragile states index. This study employs novel methods, such as necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA), to analyze green growth across 90 countries in 2019, surpassing traditional regression techniques. The NCA model identifies essential variables for green growth, revealing that global innovation, institutional quality, human development, and globalization are crucial conditions. Conversely, the fsQCA model offers intricate solutions by combining key variables for green growth. It presents five solutions for achieving high green growth, each tailored to specific groups of countries. For instance, Solution 1, with a consistency of 0.96%, suggests that increased consumption of renewable energy, greater trade openness, and reduced fragility in states lead to higher green growth in countries like Denmark and Austria. Thus, policymakers can foster both economic growth and environmental improvement by promoting renewable energy adoption, enhancing global trade management, and strengthening institutional quality and political stability. Full article