EconPapers

This article presents a thorough methodology for the planning of new urban metro systems by incorporating multi-regional empirical data alongside city-specific correction factors. This approach facilitates a more precise estimation of the necessary line length and the number of stations required. This research presents a systematic algorithm that produces various design alternatives, taking into account factors such as population, geographical area, and transportation demand, thereby tackling the significant variability noted among current global metro systems. A notable aspect of this work is the incorporation of operational performance forecasting, which includes elements such as crime rates, emergency response times, passenger density, and renewable energy utilization. This illustrates that these indicators are influenced not solely by the physical parameters of the system but also by wider social and infrastructural contexts. The proposed methodology offers urban planners a versatile, data-informed instrument that can be utilized across various urban contexts, thereby improving initial decision-making processes that promote sustainable metro development.

Research background: Governments worldwide prioritize startup ecosystem development, recognizing startups as young, innovative, and fast-growing businesses. However, their growth trajectories and funding dynamics often differ from other high-tech firms, making it important to understand how key innovation ecosystem factors influence their development.Purpose of the article: This study aims to analyze the impact of selected innovation ecosystem factors on the growth of both startups and high-tech firms, focusing on how these effects vary across different growth profiles in Estonia and Lithuania.Methods: The Birch Index turnover metric is used to normalise turnover growth, followed by K-means clustering approach to identify distinct growth profiles. To analyze the relationship be-tween innovation ecosystem variables across different growth profiles, a multivariate regression framework is implemented, combining Ordinary Least Squares and Generalized Additive Models.Findings and value added: The results highlight key differences in how innovation ecosystem factors shape startup and high-tech firm growth. Access to the markets is crucial for startups, while high-tech firms follow a more structured trajectory where access to the markets is insignif-icant. Financial support has no significant effect on either group. Age influences both non-linear-ly — startups grow rapidly before stabilizing, whereas HT firms face a gradual decline. We found that venture capital benefits HT firms but not startups, favoring mature, lower-risk firms, it might suggest that in research countries VC availability may depend on broader market financing con-ditions, with HT firms attracting more investment due to their maturity and lower risk. The study offers valuable insights for policymakers and investors looking to support both startup and high-tech ecosystems effectively.

The aim of this study is to identify the key innovation ecosystem factors influencing the growth of startups at different stages of their development, analysing how these factors differ between startups and other hi-tech companies. The study analyses the growth drivers of startups and other hi-tech companies in Lithuania’s innovation ecosystem.

Research background: In the modern world, one of the most important things is financial stability and the ability to ensure it for a long time. Personal finance management and related processes are receiving more and more attention in the world, because their proper management is the basis for a high-quality and stable life. Every person understands the process of personal finance management differently and is looking for various tools to achieve set financial goals or realistically assess financial situation. Budget planning, achieving financial goals, saving and investing are becoming everyday life processes, but certainly not everyone realizes what tools or technologies could be used to achieve better financial performance. Therefore, the knowledge about the latest financial technologies and the possibilities to use them are becoming especially important and relevant in everyone’s life, also, helps to save time and allows people to successfully achieve set financial goals. Purpose of the article: To analyze the most commonly used financial technologies by households and determine their impact on the personal finance management process. Methods: scientific literature analysis; statistical data analysis; survey; correlation-regression analysis. Findings & value added: The most popular and most used financial technology programs by households were identified and analyzed, also their influence on the personal finance management process was determined. The results obtained showed that using financial programs changes a lot of people financial management habits.

Research background: Digital transformation is a key driver of organizational competitiveness, yet many companies struggle to achieve business objectives after applying digital technologies. This misalignment between strategic objectives and the implementation of digital technologies can lead to inefficiencies, increased operational costs, and missed growth opportunities. The rapid pace of technological advancements further complicates decision making, making it essential for companies to adopt structured frameworks for making investment decisions.Purpose of the article: To address this challenge, this study explores strategic decision making techniques and integrates them into a single framework that helps companies to align the applica-tion of digital technology with strategic business objectives seeking to achieve long-term compet-itive advantage.Methods: The research employs a systematic literature review in the Web of Science Core Collec-tion database to identify research gaps in the management field. Despite extensive studies pro-vided on individual strategic models, their integration into multi-level decision making frameworks for strategic business management is missing in the scientific literature. The paper’s authors syn-thesize and integrate five key techniques: Porter’s competitive strategy model, Importance-Per-formance matrix, Trade-off model, Stacey matrix, and quality-time-cost-flexibility dimensions into a unique framework. The suggested framework allows companies to align the application of digital technology with strategic business objectives, especially in service business cases. This study is supported with qualitative research (semi-structured interviews) and quantitative research (DAG) analysis.Findings and value-added: Findings indicate that the multi-level framework supports strategic decision making. The study presents a practical example demonstrating how this framework can guide companies in selecting technology and its implementation technique. By balancing costs and other dimensions, the firms in the logistics sector can develop their competitive advantages. The research contributes to academic knowledge and practical applications in logistics business management by presenting the integrated framework. It offers valuable insights for decision mak-ers seeking to align investments in digital technology with strategic business goals, ultimately improving competitiveness in an evolving digital landscape.

Research background: Digital transformation is a key driver of organizational competitiveness, yet many companies struggle to achieve business objectives after applying digital technologies. This misalignment between strategic objectives and the implementation of digital technologies can lead to inefficiencies, increased operational costs, and missed growth opportunities. The rapid pace of technological advancements further complicates decision making, making it essential for companies to adopt structured frameworks for making investment decisions. Purpose of the article: To address this challenge, this study explores strategic decision making techniques and integrates them into a single framework that helps companies to align the application of digital technology with strategic business objectives seeking to achieve long-term competitive advantage. Methods: The research employs a systematic literature review in the Web of Science Core Collection database to identify research gaps in the management field. Despite extensive studies provided on individual strategic models, their integration into multi-level decision making frameworks for strategic business management is missing in the scientific literature. The paper's authors synthesize and integrate five key techniques: Porter's competitive strategy model, Importance-Performance matrix, Trade-off model, Stacey matrix, and quality-time-cost-flexibility dimensions into a unique framework. The suggested framework allows companies to align the application of digital technology with strategic business objectives, especially in service business cases. This study is supported with qualitative research (semi-structured interviews) and quantitative research (DAG) analysis. Findings and value-added: Findings indicate that the multi-level framework supports strategic decision making. The study presents a practical example demonstrating how this framework can guide companies in selecting technology and its implementation technique. By balancing costs and other dimensions, the firms in the logistics sector can develop their competitive advantages. The research contributes to academic knowledge and practical applications in logistics business management by presenting the integrated framework. It offers valuable insights for decision makers seeking to align investments in digital technology with strategic business goals, ultimately improving competitiveness in an evolving digital landscape.

As environmental requirements become more stringent and the planet becomes more polluted, the replacement of conventional diesel is attracting more interest. For alternative fuels, such as biodiesel and natural gas, to be used, their effects must be examined not only in terms of the engine’s environmental indicators but also in terms of engine vibrations and sound pressure. This study examined the influence of dual fuel – biodiesel and natural gas – on vibrations and sound pressure of a compression-ignition (CI) engine. Conventional diesel or hydrotreated vegetable oil biodiesel was used as a pilot fuel for gas ignition. The gaseous fuel was natural gas, which was injected into the intake manifold with different energy shares of the gaseous fuel (40%, 60% and 80%). Tests were performed at a constant engine crankshaft speed and a fixed start of pilot fuel injection of 6° BTDC while the fuel composition and engine load were changed. This experiment revealed correlations between gas energy share (GES) in liquid fuel and ecological and energy indicators of a CI engine.

The most-used railway track geometry measurement systems are considered in this study. The advantages and disadvantages of considered systems and the necessity and application of these technologies in European railways were examined. The decisive factors of the operation of measurement equipment were considered. Real-time railway track condition monitoring systems were evaluated by 14 experts according to a scale of 13 criteria. Questionnaire data were processed using Kendall’s rank correlation method, and the mean ranks were normalized using the average rank transformation into weight method. The most relevant evaluation criteria describing the principles of operation and quality of the most promising technologies were defined. The most important criteria for assessing the suitability of technologies were determined by applying the technique for order of preference by similarity to ideal solution. Finally, basic conclusions and recommendations were formulated.

The European Railway Agency has formulated assumptions for a target model of rail transport. ts important premise is digitalization to support the communication and transport services that the railways will make available to the public in the future. Part of the digitalization process is the digital description of the railway infrastructure in a formalized form to allow algorithmic processing. The formal description of infrastructure is not a new issue. However, attempts made so far have not resulted in a permanent definition of a generally accessible formalism allowing for a coherent representation of the physical railway infrastructure in a digital form. This paper presents the results of work carried out within the research project Digital Railway-The Digital Twin of the ETCS Application-Virtual Prototyping and Simulation of Operational Scenarios.