The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

The objective of the study was to analyze green marketing in the promotion of environmentally responsible and sustainable practices in the development of resilient infrastructure in Peru. The methodology used was qualitative and interpretative, the documentary design based on the systematic review of scientific literature. The PRISMA model was applied for the selection of units of analysis, resulting in 36 articles out of an initial total of 950. Content analysis was used to examine the documents, following a detailed procedure that included the use of Grounded Theory to categorize and analyze the data. The results highlighted the importance of integrating green marketing and sustainable practices into resilient infrastructure planning and development. Key strategies were identified that include promoting environmental responsibility, adopting sustainable technologies in construction, and implementing policies that foster urban resilience and sustainability. The findings highlight the adoption of a comprehensive approach that combines green marketing with resilient infrastructure planning and development to address environmental challenges and promote sustainable development in Peru.

The study aims to investigate the impact of digital leadership on sustainable competitive advantage, digital talent, and knowledge workers. Additionally, it explores the mediating role of digital talent (DT) and knowledge workers (KW) in the relationship between digital leadership (DL) and sustainable competitive advantage (SC), using the Technology Acceptance Model (TAM) as its theoretical foundation. The researchers employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine survey data from 784 employees working in Egyptian travel agencies and tour operators. The results demonstrate that DL significantly enhances SC, DT, and KW. Moreover, DT and KW were shown to positively contribute to SC and serve as partial mediators in the relationship between DL and SC. The findings highlight the crucial role of developing DT and creating an environment that embraces technological acceptance and innovation. This approach amplifies the strategic effectiveness of DL, ultimately contributing to long-term organizational success.

Universities play a crucial role in supporting sustainable development. In recent decades, indicator-based assessment tools have emerged to quantify universities’ efforts towards sustainability. The most widely known is the UI GreenMetric World University Rankings (UI-GWUR): In our paper, we examine the sustainability performance of the three greenest Hungarian universities. The University of Pécs, the University of Szeged and the University of Sopron were among the top 200 higher education institutions (HEIs) in the UI-GWUR in 2023, which proves that they have successfully integrated sustainable development into the components of their system. The aim of the paper is to identify the sustainability measures implemented by the three-top Hungarian HEIs. Their experiences shed light on how it is possible to move forward in the UI GWUR for a Hungarian higher education institution. In order to evaluate the sustainability efforts of the universities, the UI GWUR database was first examined. The websites and sustainability reports of the three universities were also analyzed to gain insight into their activities. Identifying the sustainability actions of the three institutions will help other universities to successfully plan and implement their sustainability initiatives. In the last part of our paper, we evaluate how the three Hungarian universities communicate sustainability through their websites. The results show that advancement in the UI Green Metric World University Rankings primarily requires conscious planning, which means a deeper understanding of the ranking methodology on the one hand, and a clear strategy creation and implementation on the other hand.

The United Nations General Assembly declared 2023 the “International Year of Millets” in order to promote millet cultivation, consumption, and conservation. Millets play an important role in food security, livelihoods, and biodiversity. Despite its numerous benefits, millet cultivation and consumption in Uttarakhand have declined due to a variety of constraints. This paper examines the effects of regiocentrism and materialism on intention towards Uttarakhand’s regional food products (millets). It employs PLS-SEM to investigate relationships between latent variables and generate results on a sample of 460 participants. This study elucidates the intricate interplay between materialism, regiocentrism, and intention towards regional food products in the Himalayan region, enriching the theory of planned behavior (TPB) with a nuanced understanding of personal values and regional identity. It reveals materialism’s positive association with attitudes towards regional food products, suggesting materialistic individuals may view these products as status symbols, thus affecting behavioral intentions. Additionally, the research highlights regiocentrism’s dual influence—enhancing attitudes yet deterring purchase intentions—underscoring the complexity of regional pride in consumer decision-making. These findings advance TPB by integrating broader value systems and cultural context, offering significant theoretical and practical insights for promoting sustainable consumption patterns.