Organizational justice is a key component in ensuring an equitable, functional, and sustainable work environment. The objective of the research was to analyze the adaptation and validation of an organizational justice scale, in order to guarantee its applicability in the Peruvian labor context. The methodology used was a quantitative, cross-sectional and instrumental approach. It was analyzed through the AFE, AFC and PLS-SEM; the sample was 250 workers in the transport sector. The results demonstrated three dimensions: procedural justice, distributive justice and interpersonal justice. The indicators were adequate Chi-square norm (X^2/gl) = 2.564, RMSEA = 0.079, SRMR = 0.021, TLI = 0.972 and CFI = 0.981. It is concluded that the instrument presents adequate levels of validity and reliability, to be applied in other sectors of the Peruvian context.
Construction companies require strategic tools to integrate sustainability into their management; therefore, this study sought to analyze how environmental accounting serves as a strategic tool for corporate sustainability in small businesses in the construction sector, using a quantitative, basic-research study that involved experimentation with variables and a cross-sectional design. The population consists of business entities in the construction sector in the city of Cajamarca, duly registered with the National Superintendency of Tax Administration. The sample consists of 51 companies in the same sector, identified through convenience sampling. The research method employed was a survey, and the instrument was a well-structured questionnaire with Likert-scale responses, which was validated with expert opinions for greater reliability; Cronbach’s alpha coefficient was calculated. The collected data were processed using IBM SPSS Statistics 27; a scatterplot with boxplots was also generated. The findings confirm that environmental accounting provides useful information for business management and is significantly associated with corporate sustainability. It is concluded that environmental accounting enables the generation of valid reports that provide useful information for strengthening sustainability in companies in the construction sector; it also allows companies to align themselves with the principles of social responsibility and environmental protection.
The purpose of this research was to explore how user-generated content (UGC) and brand-generated content (BGC) shape and influence consumer impressions. A mixed-methods approach was used, incorporating data from an online survey (n = 171) and semi-structured interviews (n = 5). This research was grounded in the authenticity framework. The results revealed that UGC was perceived as more authentic, leading to higher levels of trust and stronger consumer–brand associations. Relatable peer experiences were also found to be more persuasive than polished brand messaging. In contrast, BGC was more effective in strengthening brand identity and maintaining consistent brand narratives, especially when aligned with user feedback. The interview results further emphasized the importance of balancing brand-controlled content with authentic user voices to maximize positive consumer impressions. The results emphasize the need for brands to nurture and strategically develop UGC by leveraging its perceived authenticity. At the same time, BGC should establish transparency and genuinely serve consumer interests. This research delivers practical insights for marketing and brand practitioners aiming to integrate UGC and BGC to strengthen consumer impressions.
This study investigates interpretable photometric and geometric features for deepfake detection under realistic conditions. The dedicated DeepFake RealWorld (DFRW) dataset, comprising 46 371 clips generated by diffusion, reenactment, and face-swap models, was used to evaluate lighting and shape consistency. Key descriptors, including light direction mismatch (Δθ), luminance deviation (ΔL), shading ratio (r_shade), shadow coherence (χ_shadow), and head-torso alignment, achieved Δp≈0.20–0.23 and PR up to 4.25. The results confirm that physically grounded descriptors of illumination and geometry enable reliable, explainable deepfake detection in forensic contexts.
This study introduces an interpretable framework for detecting synthetic audiovisual content using deep neural representations, applied to the DeepFake RealWorld (DFRW) dataset (46 371 clips; 77% with audio). Visual, acoustic, and cross-modal embeddings from ResNet, Vision Transformer, SlowFast, Wav2Vec2, and ECAPA-TDNN were evaluated with frequency-based metrics (Δp ≥ 0.15, PR ≥ 1.5). The strongest indicators were facial embedding variance (Δp = 0.29, PR = 3.4), Mahalanobis distance (Δp = 0.25), and audiovisual coherence (Δp = 0.23), all stable within 15% under compression and re-capture. In teledentistry and telemedicine, such explainable AI markers enhance authenticity verification of digital evidence and strengthen medico-legal reliability.
