Conference Agenda

This article analyzes how the anti-bribery management standard ISO 37001 is interpreted and applied in two contrasting governance and cultural environments, Peru and Australia. Both economies are deeply integrated into global markets and face demanding expectations from investors, lenders and international partners, yet they differ sharply in institutional maturity, perceived corruption and compliance traditions. In Australia, ISO 37001 is usually embedded in a long-standing rule of law setting, strong enforcement agencies and consolidated compliance cultures that link anti bribery programs with risk management, corporate governance and environmental social and governance agendas. In Peru, persistent corruption scandals, weaker enforcement capacity and high public distrust coexist with the country’s role as an early regional adopter of ISO 37001, especially in infrastructure, mining and public procurement, often driven by multilateral financing requirements and export market pressures. Using a comparative qualitative design, the article combines secondary literature, governance indicators and case-based evidence to map convergences and differences between both countries. The analysis identifies institutional conditions under which ISO 37001 moves beyond formal certification to become a lever for organizational and cultural change. It shows that leadership commitment, integration with risk management frameworks and sensitivity to local social expectations are decisive for effectiveness and argues that Peru can learn from Australia’s consolidated monitoring and enforcement practices, while Australian organizations can benefit from Peruvian experiences that connect anti bribery systems with community engagement and transparency agendas in high-risk sectors.

Business intelligence is key to identifying opportunities and improving the performance of agricultural export companies. The main objective of this research is to evaluate how the use of business intelligence tools influences the export performance of a company in the city of Piura. The research adopts an applied methodology with a quantitative and correlational approach. A virtual questionnaire with a Likert scale was administered to a sample of 45 people. The results show a strong positive correlation (r = 0.700; p < 0.001), demonstrating that research, analysis, and exploration using business intelligence tools improve the company's export performance..

In highly competitive and dynamic business contexts, strategic decision-making constitutes a critical factor for organizational performance. Although the use of advanced analytical tools, such as predictive analytics, has strengthened the ability to anticipate market behavior, the systematic integration of algorithmic models with managerial intuition—conceptualized as the “Corporate Mind”—remains an underexplored gap in emerging economies. This study analyzes the implementation and effectiveness of predictive analytics in domestic and multinational companies operating in Honduras, with the aim of examining how the combination of algorithmic approaches and human judgment contributes to improving strategic decision-making. A quantitative, descriptive, and correlational research design was employed, using an online structured questionnaire administered to 46 companies. The data were analyzed using Spearman correlations, Cronbach’s alpha, nonparametric tests, and descriptive statistics. The results show that structured training in data analytics significantly increases the perceived impact of predictive models (ρ = 0.459, p = 0.014), while teams’ operational capacity moderates the translation of this perception into tangible organizational outcomes (p = 0.043). The findings confirm that technology alone does not guarantee performance improvements and that its effectiveness is maximized through an integrated approach that combines training, team capacity, and a data-driven organizational culture. This study provides empirical evidence to the discussion on hybrid analytical–intuitive strategies and offers a practical framework applicable to organizations operating in complex and rapidly transforming business environments.

Geomechanical characterization of rock masses is essential for the stability of mining and civil works; however, it is still mostly performed manually, making the process time-consuming and highly dependent on human judgment. This study presents a preliminary evaluation of the performance of a customized GPT model for geomechanical classification using images and input data, comparing its results with field classifications obtained using the Bieniawski RMR system. The research followed a quantitative approach with an applied level, a non-experimental and cross-sectional design, and an exploratory and descriptive scope, analyzing 30 observation points from rock outcrops in the Cajamarca region (Peru). The model evaluated each image in three independent runs and was configured using the technical criteria of the RMR system. The results showed an average accuracy of 83.4%, a simple Kappa of 0.76, and a weighted quadratic Kappa of 0.88, corresponding to substantial to almost perfect agreement, with an overall inter-run reliability of 75.6%. Overall, the customized GPT model demonstrated solid preliminary performance and results consistent with human classification, suggesting potential for future application in the geomechanical characterization of rock masses.

– In the context of accelerating digital transitions and growing sustainability pressures, firm´s ability to simultaneously advance Digital Transformation (DT) and Sustainable Transformation (ST)- referred to as dual transformation- has become a critical determinant of inclusive and resilient development. Yet, empirical evidence on how these processes unfold jointly within emerging and border-region economies remains limited. This study contributes to innovation-for-development research by modeling firm-level dual transformation readiness within the business ecosystem of Tijuana, Mexico.

Using a validated self-diagnosis instrument, data was collected in 2023 from 148 firms operating in Tijuana, encompassing diverse firm sizes, export orientations, certification status, and organizational responsibility structures. A descriptive analysis approach reveals that, on average, sustainability transformation maturity exceeds digital transformation maturity, indicating uneven capability development across two dimensions. Considerable heterogeneity is observed across firms with midsize and non-exporting firms exhibiting higher overall dual transformation maturity, while exporting oriented, large and certified firms tend to cluster at lower maturity levels.

The findings suggest that dual transformation is shaped less by formal compliance mechanisms and international market exposure and more by organizational capabilities, governance arrangements, and strategic autonomy. By providing an empirically grounded maturity model, this research contributes to innovation systems scholarship and offers policy-relevant insights for supporting inclusive upgrading and sustainable development in regions operating at the margins of global value chains. Future research may extend this framework through inferential, longitudinal, and predictive approaches to capture dynamic transformation trajectories over time.

Without a specialized technological solution, web traffic analysis in many companies is performed manually, making it difficult to integrate, consult, and prepare data in a timely manner to support strategic decisions. In scenarios of this type, Business Intelligence tools have established themselves as a fundamental resource for organizations, as they enable large volumes of data to be transformed into useful information through automated analysis and visualization. Therefore, the objective of this study was to optimize web traffic analysis at Grupo Lifcom S.A.C. through the implementation of a Business Intelligence system. The research was applied, with a quantitative approach, a pre-experimental design, and an explanatory level. The population consisted of 80 web traffic records, from which a sample of 66 records was selected through probabilistic sampling. Tools such as Google Analytics and Power BI were used to implement the system, integrating it with a website developed in WordPress. The agile Scrum methodology was used to develop the Business Intelligence system, allowing for iterative and incremental implementation. The results achieved after implementing the Business Intelligence system show a 50.68% increase in the total number of visits, a 75.86% increase in session duration, and a 71.28% reduction in report generation time, demonstrating that the implemented solution significantly optimizes web traffic analysis and strengthens decision-making within the company.