With the rapid development of technology, an increasingly competitive environment, and evolving consumer behaviour, the use of modern marketing tools has become a key challenge for companies of various types (manufacturing, providing services, sports organizations, universities, etc.). Although sustainable digital communication methods are gaining prominence, existing research often focuses merely on describing communication trends without providing decision-making frameworks for strategy optimisation. This paper addresses this gap by mapping the current state of marketing communication strategies among large telecommunication companies in Slovakia and assessing their impact on customer behaviour and market position. Data were analysed through a combination of qualitative and quantitative research methods, including document analysis, annual reports, surveys, and personal observations. One enterprise was selected for detailed data analysis. The results confirm a significant relationship between the use of communication channels and the company’s market position, brand popularity, and the strong influence of employee recommendations. Unlike previous studies, which predominantly describe marketing communication trends and tools, this research integrates the evaluation of communication strategy effectiveness with a systematic management decision-making model based on the PDCA (Plan-Do-Check-Act) continuous improvement cycle. This approach enables continuous optimisation of sustainable communication strategies and provides actionable managerial guidance for improving resource allocation, market position, and organisational adaptability in dynamic market environments.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

Sustainability has become increasingly essential for companies to remain competitive. The automotive industry is one of the sectors most affected by sustainable regulatory standards. Companies face pressures, changes in consumer behaviour, and evolving expectations alongside growing demands for environmentally sustainable practices. However, sustainability implementation practices and main initiatives remain inconsistent across the sector. This study aims to identify key indicators for successful sustainability implementation by analysing best practices among the four largest companies within the European automotive industry through quantitative and qualitative content analysis. The findings define main approaches and challenges companies faced, categorising key activities within the environmental, social, and governance (ESG) dimensions, and underscoring the importance of embedding sustainability into corporate strategy. The analysis also highlights inconsistencies between stated objectives and actual approaches of analysed companies, which may jeopardise the effectiveness of sustainability implementation, companies’ competitive positions, and performance. Based on the analysis of best practices, the study provides practical recommendations and guidelines for smaller manufacturers, suppliers, and other industries and organizations to adapt and successfully implement sustainability initiatives.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

The analysis examines how historical project data can be used to identify factors associ-ated with project schedule delays and to support the development of an explainable Early Warning Framework. The analysis is based on data from 83 completed projects in a me-dium-sized IT service organization. After data preprocessing and descriptive analysis, Classification and Regression Trees (CART) were used to identify combinations of pro-ject-management conditions related to schedule performance. The resulting classification structure highlighted several factors associated with delays, including missed interim deadlines, communication and coordination problems, delayed responses to issues, diffi-culties in estimating work scope, and planning-related factors. The results are not intend-ed to represent a validated real-time prediction model. Instead, the identified decision rules provide an interpretable starting point for developing a prospective Early Warning Framework that combines historical organizational knowledge, explainable analytics, managerial review, and organizational learning. Further prospective testing is needed to assess whether these rules can provide sufficient predictive accuracy and lead time to support practical project governance.