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Amid the acceleration of digital transformation and the increasing demand for rapid application development, No-Code and Low-Code (NCLC) development platforms have emerged as an important development approach that enables broader user participation. This study aims to identify the key factors influencing the adoption and intention to use NCLC development platforms. To achieve this objective, this study proposes and empirically tests an integrated research model that combines the Technology-Organization-Environment (TOE) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were collected through an online survey of developers, designers, and general users, and analyzed using partial least squares (PLS) structural equation modeling. The results indicate that relative advantage, cost-effectiveness, competitive environment, compatibility, top management support, and organizational capability significantly influence either performance expectancy or effort expectancy. Furthermore, performance expectancy, effort expectancy, and social influence were found to positively affect usage intention. This study contributes to the literature by integrating organizational and individual perspectives on technology adoption and provides practical implications for organizations considering the adoption of NCLC platforms in digital transformation.

This study draws on microdata from the 2023 NIA Digital Information Gap Survey (N = 17,300) to examine what really drives the gap in AI service adoption. Hierarchical binary logistic regression was used alongside bootstrap- based serial mediation analysis. Among the three dimensions of the digital divide, digital skill turned out to be by far the strongest predictor of AI usage (OR = 1.887 in Block 3), dwarfing the effects of access (OR = 1.143). When usage diversity was added in Block 4, skill remained dominant (OR = 1.708) while usage also showed a significant positive effect (OR = 1.418). The serial mediation path running from access through skill to AI usage was statistically significant (β = 0.02988, 95% CI [0.02522, 0.03466]), which points to skill as the primary bottleneck in the pathway from access to AI adoption. Splitting the sample by population group revealed pronounced inter-group variation : the AI adoption gap was widest among agricultural workers and older adults, while marriage immigrants recorded AI usage rates that, contrary to initial expectations, exceeded those of the general population. On balance, these results suggest that investing in digital competency education is likely to yield greater returns for closing the nascent AI divide than continuing to channel resources primarily into device-distribution programmes. However, the cross-sectional design precludes strict causal inference, the skill measure does not capture AI-specific competencies, and the OLS-based mediation estimates may involve approximation error given the binary outcome. Future research should address these limitations through longitudinal data and AI-tailored literacy instruments.

This study proposes an interpretable framework for corporate bankruptcy prediction by integrating machine learning and explainable artificial intelligence (XAI) techniques. While prior bankruptcy prediction studies have primarily focused on improving predictive accuracy using static financial indicators, limited attention has been given to incorporating dynamic financial trends and translating model interpretation results into practical decision- making frameworks. To address this limitation, this study applies a flatten strategy that transforms recent three-year financial data into a structured vector representation while preserving temporal information. Based on this approach, an XGBoost model was developed using financial statement variables, financial ratios, growth indicators, and delta variables reflecting year-over-year changes. To address the severe class imbalance problem inherent in bankruptcy prediction, repeated undersampling experiments were conducted, and model performance was evaluated using Recall and G-Mean, which are particularly suited for imbalanced classification tasks. The empirical results show that the dataset combining static and dynamic financial variables achieved the best predictive performance, particularly in Recall and G-Mean. SHAP analysis further revealed that market value, liquidity, profitability, and capital structure variables play critical roles in bankruptcy prediction. In particular, delta variables related to profitability and growth trends demonstrated high explanatory power, suggesting that corporate bankruptcy is more closely associated with the deterioration trajectory of financial conditions rather than a snapshot of financial status at a single point in time. Based on the SHAP analysis results, this study systematizes bankruptcy-related financial variables into five interpretive categories: firm size, liquidity, profitability, capital structure, and financial trend. This categorization provides a practical interpretation framework that goes beyond binary bankruptcy prediction, explaining through which financial dimensions risk emerges and supporting financial risk diagnosis and decision-making. This study contributes to the literature by integrating temporal financial trends with interpretable machine learning and by proposing a practical SHAP-based financial risk interpretation framework for corporate bankruptcy analysis.

This study aims to examine the effect of employees’ negative affectivity (NA) on organizational citizenship behavior directed towrd individuals (OCBI) and to investigate the moderating role of leader-member exchange (LMX) in this relationship. While prior research has largely emphasized the detrimental effects of negative affectivity, this study posits a functional perspective on negative affectivity, suggesting it may also lead to positive behavioral outcomes depending on contextual factors. The results of the empirical analysis indicate that negative affectivity has a positive effect on OCBI when this relationship is significantly moderated by LMX. Specifically, when LMX is high, the positive relationship between negative affectivity and OCBI becomes stronger, whereas when LMX is low, the relationship is relatively weaker. These findings suggest that LMX amplifies the effect of negative affectivity on OCBI. Drawing on social exchange theory and the affective events theory, the results can be interpreted as follows. In high-quality LMX relationships, employees are more likely to transform the problem awareness arising from negative affectivity into constructive behaviors such as OCBI due to perceived reciprocity and mutual trust. In contrast, when LMX is low, negative affectivity is more likely to result in defensive responses or withdrawal behaviors. This study contributes to the literature by extending the conventional view of negative affectivity as solely detrimental, demonstrating that its effects can vary depending on relational context. Furthermore, it highlights the importance of fostering high-quality leader-member relationships in organizations, as such relationships play a critical role in channeling employees’ negative affectivity into productive behaviors.

This study analyzes the correlation between the generative architecture of the slop phenomenon and the resulting cognitive aversion. This phenomenon has emerged through the proliferation of generative artificial intelligence and the revenue models of digital platforms. Slop refers to low-quality synthetic media mass-produced by diffusion models for the purpose of algorithmic reward. These materials generally lack meticulous planning or coherent aesthetic intent. This paper investigates the visual artifacts and perceptual inconsistencies arising from the probabilistic computation and black box nature of AI models. It interprets the roots of the cognitive discomfort and disgust responses elicited by such content through the frameworks of cognitive and evolutionary psychology, including the Uncanny Valley, the disease avoidance mechanism, and the effort heuristic. Functioning as residual noise within the digital information ecosystem, slop raises concerns regarding content quality and epistemological reliability. This study suggests the necessity of technical filtering, institutional governance, and the cultivation of critical literacy. This research interprets the slop phenomenon as a byproduct of a technical transition period and a data-driven experimental stage in the evolution of intelligent media. It provides a multifaceted perspective that defines slop as systemic noise that may threaten the long-term sustainability of the information ecosystem.

This study examines the effect of user experience (UX) design on brand trust and repurchase intention in a digital environment. UX is conceptualized as a multidimensional construct including information accessibility, interface intuitiveness, visual trust cues, emotional experience value, and personalization fit. Data from 312 Vegemil consumers were analyzed using PLS-SEM. The results show that UX significantly influences brand trust, which in turn affects repurchase intention and mediates the relationship between UX and repurchase intention. These findings highlight the importance of UX in building trust and enhancing repurchase behavior in high-involvement food contexts.

The purpose of this study is to identify cognitive and emotional factors necessary for Generative AI services to continue to establish themselves in the lives of users beyond simple technical achievements. To this end, based on the value-based acceptance model (VAM), the effects of five service characteristics: personalization, interactivity, contextual cognition, information quality, and learning ease on perceived value and satisfaction through perceived utility and perceived sacrifice were empirically analyzed through a structural equation model. As a result of the analysis, personalization, information quality, and learning ease had a positive effect on perceived utility, but interaction performance and context were not significant. In addition, personalization was found to be a factor that increased perceived sacrifice, and both utility and sacrifice had a positive (+) effect on perceived value, confirming that sacrifice can be perceived as a 'worth investment' rather than a simple cost. Furthermore, perceived value was verified as a key variable that strongly mediates satisfaction. This study expands the technology acceptance model to present a balanced consideration of utility and sacrifice, and provides practical implications for designing customized functions and establishing rate policies for service diffusion.

Live streaming has been increasingly linked to high-profile incidents of deviant content production, yet academic understanding of the conditions that produce streamers’ deviant intention remains fragmented and largely variable-centered. Drawing on integrated deterrence theory, self-control theory, and Wikström’s situational action framework, this study examines the deviant streaming intention of 195 active streamers on a major Korean live-streaming platform using fuzzy-set qualitative comparative analysis (fsQCA). Seven conditions—formal platform sanction salience, anticipated shame, social criticism, low self-control, moral belief, viewer-driven normative pressure, and perceived deviant reward—are organized into three theoretical blocks and examined configurationally. Three findings reframe the phenomenon. First, viewer pressure and perceived reward function as necessary conditions for high deviant intention (consistencies of 0.95 and 0.91, respectively), positioning the platform’s attention economy as a structural prerequisite rather than a secondary consideration in the rational-choice calculus. Second, no single configuration is sufficient for high intention even at relaxed consistency thresholds—a pattern of disorganized causation above the necessary- condition substrate. Third, five distinct configurations are sufficient for low intention (solution consistency = 0.86, coverage = 0.77), none of which is the logical negation of pathways to high intention, confirming asymmetric causation. A subgroup analysisfurther reveals that moral belief becomes a necessary condition for low intention among high-tenure streamers, indicating that causal structure shifts with career stage. The findings extend deterrence theory into the platform economy and offer configuration-specific intervention strategies that move beyond uniform sanction-intensification approaches.