Search Results (273)

This research introduces a new spectral pruning approach using the Adjacency Edge Index (AEI), which is a centrality measure from the theory of spectral graphs and first-order matrix perturbation theory. The AEI score reflects the contribution of each neuron to the network’s dynamic synchronizability via the Fiedler vector. The AEI approach thus offers a mathematically motivated saliency score in the context of data-driven neuron co-activation graphs. The proposed approach has been tested on MNIST, Fashion MNIST, KMNIST, and a real-world social network dataset. The approach has been extended to convolutional filter pruning on the CIFAR-10 dataset using spatial global average pooling. The AEI approach has been compared to magnitude-based pruning methods like L1 and L2 norms and gradient-based pruning methods like SNIP and GraSP. The robustness of the proposed approach has been established by comparing the results over five random seeds. The AEI approach is proposed as a principled, interpretable, structure-aware pruning criterion rather than an accuracy-maximising method. Spectral analysis demonstrates that AEI is the only evaluated method that systematically targets structurally peripheral neurons, whereas magnitude-based methods prune broadly across the structural spectrum and GraSP actively removes structurally central neurons, an effect most pronounced on more complex datasets and deeper architectures (CIFAR-100, ResNet-20), where it causes substantial accuracy degradation at high sparsity. The AEI approach has been extended to the Hybrid approach by combining the AEI and L2 norms. The Hybrid approach has been seen to improve the accuracy gap at 40% sparsity from 5.46 to 2.26 percentage points over the state of the art. The robustness of the proposed approach has been established by conducting ablation studies on the robustness of the approach to the selection of the graph. The approach has been seen to be moderately robust to the selection of the graph with Spearman’s $\rho$ ≈ 0.70. The accuracy gap has been seen to be less than 0.2%. Full article

Eco-compensation is not only an important supplementary source of funding for ecological conservation in national parks, but also an important mechanism through which ecological beneficiaries share the costs of national park conservation. In the context of the public welfare of national parks, free admission does not imply the absence of responsibility. Therefore, it is essential to strengthen tourists’ awareness of ecological conservation for national park management. Taking Wuyishan National Park as the study area, this study employed a questionnaire survey, distributed 630 questionnaires in total, and obtained 582 valid responses. The contingent valuation method (CVM) and a multiple-bounded dichotomous choice (MBDC) design were used to examine tourists’ willingness to pay (WTP) for eco-compensation and its influencing factors: (1) The tourists’ estimated average WTP for eco-compensation in Wuyishan National Park is approximately ¥451.72, which is higher than the total cost of admission and basic experience activities in the park. It implies that tourists have a relatively high willingness to pay for eco-compensation in the national park. (2) Based on Value–Belief–Norm (VBN) theory, the hierarchical OLS regression results showed that personal norms (β = 0.636, p < 0.01), awareness of consequences (β = 0.259, p < 0.01), and ascription of responsibility (β = 0.277, p < 0.01) were positively associated with lnWTP. The strength of these associations varied across VBN dimensions, with personal norms showing the strongest positive association with tourists’ WTP for eco-compensation. (3) Income differences have a clear effect on WTP. Higher-income tourists reported greater WTP, which was more strongly associated with awareness of consequences and personal norms, whereas lower-income tourists were more responsive to environmental values and ascription of responsibility. This study identifies an association between tourists’ environmental responsibility and willingness to pay for eco-compensation in the context of national park tourism. The findings offer empirical support for policy measures to regulate tourist behavior, enhance national park revenue, and reinforce the public welfare function of national parks. Full article

As cities worldwide pursue sustainable and inclusive mobility, understanding the travel behavior of vulnerable populations in peri-urban resettlement areas is essential for designing equitable built environments. In Shanghai, as in many rapidly urbanizing megacities, government-led redevelopment in central Shanghai displaces residents to the urban periphery, creating accessibility deficits and raising transport inclusivity concerns. Using travel diary data from five peri-urban affordable housing communities collected in 2012 (N = 8390)—a critical baseline before major infrastructure improvements—this study documents limited mobility (residents make significantly fewer trips than the city average) and examines its correlates via logistic regression. A core finding is a type-based threshold effect: awareness of a nearby large shopping mall is associated with higher shopping trip frequency (coefficient −0.332, p = 0.024), whereas awareness of small-to-medium supermarkets shows no significant association. This pattern is consistent with elastic demand theory and challenges the assumption that any retail facility improves accessibility equally. Our models explain very little variance (Nagelkerke R² = 0.078 for overall trip frequency; 0.048 for shopping frequency). This low explanatory power is itself a diagnostic signal—and a core motivation for our hypothesis-generating approach—indicating that unmeasured psychological (attitudes, perceived behavioral control), social (networks, norms), and technological (e-commerce) factors likely dominate travel decisions—directing future research priorities. Methodologically, the 2012 data provide an uncommon early-stage baseline for policy-induced resettlement, enabling future longitudinal comparisons and mechanism identification (e.g., before/after metro or mall openings). For contemporary policy, the findings suggest a testable hypothesis that anchor-type facilities (e.g., mid-sized community hubs with mixed use, rather than merely small supermarkets) may activate suppressed demand, requiring re-evaluation with recent data and causal designs. Readers should not extrapolate numeric trip rates to today’s Shanghai. The primary contributions are theoretical (type-based threshold effects, the anchor-vs.-routine facility distinction, the role of basic spatial awareness) and methodological (historical baseline). The 2012 data predate widespread e-commerce; digital substitution may substantially attenuate these relationships in contemporary contexts. Full article

Residents’ pro-environmental behavior is important for sustainable tourism destination governance, particularly in ecologically fragile and culturally distinctive regions. Drawing on Value–Belief–Norm theory and the Norm Activation Model, this study examines how the New Environmental Paradigm, social norms, and nature bonding influence residents’ pro-environmental behavior through perceived obligation, and whether Religious–Cultural Embeddedness moderates the perceived obligation–behavior relationship. A structured questionnaire survey was conducted among 608 residents in Xizang, China, and the data were analyzed using confirmatory factor analysis and structural equation modeling. The results show that the New Environmental Paradigm, social norms, and nature bonding all have significant positive effects on residents’ pro-environmental behavior, with nature bonding showing the strongest direct effect. Perceived obligation mediates the effects of social norms and nature bonding, but not the effect of the New Environmental Paradigm. Religious–Cultural Embeddedness negatively moderates the relationship between perceived obligation and pro-environmental behavior, indicating that the behavioral effect of individual obligation becomes weaker in highly embedded religious–cultural contexts. These findings suggest that residents’ environmental behavior is shaped by cognitive, social, affective, moral-responsibility, and cultural mechanisms, offering implications for culturally sensitive sustainable destination governance. Full article

This article examines violence as a tool used within criminal enterprises of trafficking of women for sexual exploitation. Its use is not seen as an isolated or incidental phenomenon but is placed in the broader context of women’s vulnerability to violence linked to structural inequalities, socio-economic marginalization, and persistent culture norms. In this context, criminal enterprises involved in human trafficking exploit both individual vulnerability and broader socio-economic conditions, institutional dysfunctions, and specific cultural norms. In this article, data obtained from court decisions are combined with findings from a separate field study that employs qualitative and quantitative data collection methods. Viewed through the prism of enterprise theory, it is shown that violence against women within this criminal activity has a functional role and serves an economic purpose. The findings reveal that various techniques of coercion and submission are used as control mechanisms to maintain victims’ compliance, reduce the risk of business interruption, and ensure the profitability of criminal enterprises. Additionally, parallels between the coercive techniques employed by traffickers and the methods used in general violence against women were identified. In conclusion, the article highlights the need for multidisciplinary responses that integrate criminal justice measures, victim protection, social support, and policies aimed at reducing structural inequalities. It underscores the importance of future research on the relationship between violence against women and vulnerability to coercive control and exploitation. Full article

In this paper, consideration is given to the class of birth-and-death processes with possibly state-dependent and time-varying transition intensities and a finite state space. Several techniques are available in the literature for the computation of the long-run (limiting) time-dependent performance characteristics of such processes. Whenever a solution technique is combined with a limiting regime detection method, its efficiency may be improved. It is intuitively reasonable to expect that, if additional information about the process is available, a limiting regime detection method may allow one to save more computation effort. In this paper, we demonstrate that the logarithmic norm method, which is one of the methods with which to provide ergodicity bounds for continuous-time Markov chains with discrete state space, can be utilized in such a way. When the exact values of the transition intensities of the (ergodic) birth-and-death process are known and are such that it is clear that one group of states is visited less often than the other, the method allows one to detect the limiting regime rapidly. We illustrate numerically the results obtained within the queueing theory context by considering the activity of the total number of customers in a multi-server finite-capacity queue with periodic arrival and service intensities. Full article

This study examines how Indonesian university students negotiate the meanings of extremism and tolerance across digital and campus environments. Using phenomenology, the research examines the lived experiences of 35 students from Yogyakarta, Jakarta, Semarang, Surabaya, and Bandung to understand how digital platforms, everyday interactions, and symbolic practices mediate their religious identity work. The analysis applies Castells’ concept of communication power and Bourdieu’s notions of habitus and symbolic power to explore how digital platforms shape ideological narratives and emotional framing. Key Findings reveal that extremism is experienced not merely as ideological deviation but as a communicative rupture—manifested through coercive discourse, exclusionary framing, and digitally amplified moral authority—while tolerance emerges as a socially cultivated practice embedded in relational norms, affective positioning, and strategic self-presentation in mediated environments. Social media functions simultaneously as a site of exposure, contestation, and subtle resistance, enabling students to reframe, soften, or oppose dominant narratives through micro-activism and selective curation. This study offers a novel contribution by integrating data-driven phenomenological insights with a theory-driven account of mediated power, demonstrating how symbolic power circulates through digital infrastructures and shapes the cultural politics of religious pluralism among youth in contemporary Indonesia. Full article

Environmental education is crucial for promoting sustainable behavior, yet maintaining engagement across educational transitions remains challenging. Drawing on the Theory of Planned Behavior (TPB), this study examined how high school experiences influence pro-environmental behavior (PEB) during the transition to university. We integrated sense of school belonging (SB) and pro-environmental innovative behavior (IB) as school-based antecedents to the TPB framework. A retrospective survey was conducted with 1879 first-year university students, and structural equation modeling (SEM) was used to test the hypothesized relationships. Results provided robust support for the integrated model. Consistent with TPB, attitudes, subjective norms, and perceived behavioral control significantly predicted pro-environmental intention, which subsequently predicted actual PEB. Both IB and SB emerged as significant predictors of these cognitive factors, with SB also promoting engagement in innovative activities. The model explained 72.4% of the variance in intention and 37.5% in actual behavior. Multigroup analysis revealed that while the structural model was stable across gender, certain pathway strengths varied. These findings offer empirical evidence for the retrospective associations of pro-environmental behavior, demonstrating that the recalled social–emotional and experiential contexts of high school are consistently related to university students’ current environmental actions. This study extends the TPB by highlighting the developmental role of school environments in fostering long-term environmental responsibility. Full article

This qualitative study explores perceptions of gender equity and leadership in three higher education institutions in Eswatini. The research involved nine senior management members—deans, registrars, and bursars—and eighteen lecturers (nine women and nine men). Employing narrative inquiry, data were gathered through semi-structured interviews and focus groups, capturing rich individual stories and social dynamics. The study explores how gender influences access to leadership roles, the barriers faced, and potential strategies for fostering inclusive environments. Guided by social role theory, the analysis was deductive, examining how cultural norms and stereotypes shape perceptions of leadership and reinforce gender disparities. Women occupy only 22% of senior management roles, while among lecturers, women constitute 50% of the workforce but only 30% of leadership positions, illustrating persistent under-representation. Findings reveal that despite existing policies, cultural norms rooted in hegemonic masculinity continue to impede gender equity, with organisational biases and societal stereotypes maintaining male dominance in leadership. Participants emphasised that policies must be actively enforced, and cultural change initiatives are essential to challenge stereotypes and reshape societal narratives about gender roles. The study underscores the importance of institutional support, mentorship programmes, and visibility initiatives to empower women and promote gender-inclusive leadership. Engaging men as allies is also critical in transforming organisational culture. These findings contribute to advancing understanding of gender dynamics in Eswatini’s higher education sector and highlight the need for comprehensive, context-specific interventions. Addressing these challenges requires coordinated efforts involving policy enforcement, cultural transformation, capacity building, and ongoing evaluation to ensure sustainable progress toward gender equality in academic leadership. Full article

With the rapid expansion of the pet economy, owner–pet matching outfits have grown increasingly popular among pet owners. Grounded in the extended theory of planned behavior, this study investigates the key determinants of pet owners’ purchase intentions and actual purchase behaviors toward owner–pet matching outfits, and explores the moderating effect of aesthetic risk on the intention–behavior transition. Questionnaire survey data from 222 pet owners were collected for empirical analysis, and regression analysis was adopted to verify the proposed research hypotheses. The empirical results reveal that subjective norms exert a direct promotional effect on consumer purchase behavior and indirectly boost such behavior through the partial mediating role of purchase intention. By contrast, behavioral attitude is positively associated with purchase intention and further stimulates purchase behavior via a full mediating pathway of purchase intention. Perceived behavioral control displays a significant positive direct impact on purchase behavior yet yields no significant effect on purchase intention. Furthermore, purchase intention serves as a robust positive predictor of purchase behavior, whereas aesthetic risk significantly weakens the association between purchase intention and purchase behavior. Brands are suggested to foster consumers’ favorable behavioral attitudes by optimizing product design, enriching practical functions, and minimizing potential risks to pets in owner–pet matching outfits. Meanwhile, enterprises should actively shape supportive subjective norms to popularize the owner–pet matching outfit wearing lifestyle. Additionally, brands need to enhance consumption accessibility through diversified sales channels, reasonable pricing strategies and abundant product style options. This study pioneers the application of the extended theory of planned behavior to the emerging field of owner–pet matching outfits, empirically verifying the positive effects of behavioral attitude, subjective norms, and perceived behavioral control on consumers’ purchase intention and purchase behavior. Full article

Racial diversity is normatively desirable in public organizations, but the social and psychological processes it activates may lead to employees’ negative work attitudes. Combining social categorization theory, perceived organizational support theory, and psychological contract theory, this study investigates whether racial diversity is negatively related to employee work engagement in U.S. federal agencies and whether the perceived effectiveness of agency ethics programs moderates this relationship. Using multilevel mixed-effects regression analyses with data from 10,088 employees nested within 24 federal agencies drawn from the 2016 Merit Principles Survey, we find that racial diversity was negatively associated with work engagement. However, this negative relationship was reduced when employees perceived their agency’s ethics program as more effective. At high levels of perceived effectiveness, the negative association was no longer statistically significant. These findings suggest that the perceived effectiveness of ethics programs is a meaningful organizational condition under which the negative association between racial diversity and work engagement may be attenuated. This pattern has implications for diversity management and human resource practice in ethical, high-performing, and sustainable public organizations. Full article

Artificial intelligence (AI) is increasingly embedded in marketing work, yet employees who use AI tools may not always disclose AI’s role in producing their outputs. This study examines AI disclosure silence, defined as employees’ intentional withholding of information about the use, role, or contribution of AI tools in work-related outputs after AI has already been used. Unlike AI avoidance or resistance, this construct concerns post-adoption concealment; unlike general employee silence, it focuses on the hidden technological contribution behind visible work. Drawing on Conservation of Resources Theory and Psychological Safety Theory, the study investigates how threat-based conditions, safety and governance conditions, and AI-related capability are associated with AI disclosure silence. Data were collected through a two-wave survey of 635 marketing employees who actively used AI tools at work. The analysis combined measurement validation, Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and explainable machine learning. The findings show that no single condition operated as a strong necessary bottleneck. Instead, AI disclosure silence appeared through multiple pathways involving AI anxiety, fear of negative evaluation, perceived creativity threat, perceived job insecurity, low trust in management, weak psychological safety, and unclear AI policy. SHapley Additive exPlanations (SHAP)-based interpretation further indicated that fear of negative evaluation, AI anxiety, perceived creativity threat, and trust in management had the strongest model-based predictive relevance. The study contributes to workplace AI and employee silence research by positioning AI disclosure silence as an emerging post-adoption disclosure construct. It also highlights the need for clear AI disclosure norms, non-punitive managerial responses, AI-assisted authorship guidelines, and psychologically safe AI-governance practices. The findings should be interpreted as configurational and predictive evidence rather than causal effects, and further scale validation across sectors and cultures is encouraged. Full article

This study investigated the factors that drive the behavioral intention towards cryptocurrency usage among individuals in Jordan. The Theory of Reasoned Action (TRA) was used as base. The study respondents were active Facebook users. The questionnaires were delivered online to 500 respondents, yielding 320 usable responses which were analyzed using Structural Equation Modeling (SEM). Results demonstrated positive effects of subjective norms, perceived risk, perceived usefulness, perceived enjoyment, and trust on the intention to use cryptocurrency. Results also showed the effect of attitude towards cryptocurrency on the intention to use cryptocurrency. Results further showed the impact of perceived risk, perceived usefulness and perceived enjoyment on attitude towards cryptocurrency, while facilitating conditions and perceived ease of use showed no impact on attitude towards cryptocurrency. In addition, perceived ease of use did not affect the intention to use cryptocurrency. Moreover, results showed the positive effect of intention to use cryptocurrency towards cryptocurrency adoption. Results also showed the mediating effect of attitude towards cryptocurrency in the link between subjective norms, perceived risk, perceived usefulness, perceived enjoyment, and intention to use cryptocurrency. Full article

With the large-scale integration of wind farms, the spatial distribution characteristics of system dynamic frequency response become increasingly prominent. Existing wind farm planning methods considering frequency constraints mostly rely on predefined disturbance scenarios or system-level average frequency indicators, failing to account for the spatial randomness of disturbance locations. This leads to potential safety risks of local bus frequency limit violations when the systems planned accordingly are subjected to uncertain disturbances in actual operation. To address this issue, this paper proposes an optimal wind farm planning method based on frequency response upper bound constraints. The proposed method utilizes the algebraic connectivity index to characterize the theoretical upper bound of system frequency response under uncertain disturbances, which is then incorporated into the planning model as a constraint, thereby avoiding the risk of frequency limit violations in weak areas of the network. First, based on the closed-loop transfer function model and infinity norm theory, the theoretical upper bound of frequency response under uncertain disturbances is derived, and an upper bound evaluation index explicitly correlated with algebraic connectivity is constructed to identify the worst-case frequency response under uncertain disturbances. Second, the upper bound evaluation index is transformed into constraints for the wind farm siting and sizing model and an optimal planning method based on the frequency upper bound constraint is proposed. Simulation results on the IEEE 39-bus system show that, when a 1.2 p.u. active-power disturbance is applied at each bus individually, the proposed planning method reduces the average maximum frequency deviation by 36.78% relative to the unoptimized siting-and-sizing scheme (arithmetic mean across 39 bus-wise scenarios); for eight preset topology change scenarios, it yields a further 7.03% average reduction in maximum frequency deviation relative to the unoptimized scheme under each scenario topology. Full article

Artificial intelligence (AI) is increasingly embedded in managerial decision-making, yet innovation research has not fully explained how AI-enabled decision environments condition the influence of CEO traits on innovation strategy and outcomes. This conceptual paper examines CEO self-monitoring—leaders’ tendency to adapt behavior to social cues, manage impressions, and respond to external evaluation—as a trait that shapes innovation in AI-enabled decision environments. The problem addressed is that existing research often treats CEO traits, innovation, and AI-enabled decision-making separately, leaving underdeveloped how AI amplifies the leadership conditions under which innovation strategies and outcomes vary. Drawing on upper-echelons theory, self-monitoring research, the ability–motivation–opportunity framework, and the AI-enabled decision-making literature, we develop propositions explaining how AI-enabled decision environments condition the relationship between CEO self-monitoring and innovation-strategy volatility, innovation-strategy alignment, innovation-outcome quality, and innovation-outcome variability. The framework suggests that high self-monitoring CEOs may recalibrate innovation priorities more frequently while keeping innovation activity closer to recognizable industry norms. It further proposes that self-monitoring may improve innovation-outcome quality by mobilizing employees toward visible, high-potential initiatives, but it may also widen innovation-outcome variability through high-visibility, high-uncertainty innovation bets. AI-enabled decision environments are theorized to amplify these relationships by increasing algorithmic visibility, feedback velocity, and signal density. This paper concludes that AI should be understood not as an autonomous engine of innovation performance but as a contextual amplifier of leadership-driven innovation variance. Full article