Scientific projects at the PUEB

The subject of this project aligns with the research trend concerning the legal services market and the analysis of individual clients’ expectations in the context of service delivery. Legal services, due to their specific nature—a high degree of intangibility, clients’ emotional involvement, information asymmetry, and the requirement of trust (Grönroos, 2007)—constitute a unique area of research. In recent years, this market has been observed to be developing dynamically in Poland, accompanied by a rise in consumer awareness and demands. Furthermore, as a result of market evolution and ongoing digital transformation, the expectations of individual clients towards lawyers are changing (Bosman and Hakanson, 2020). Despite this, there is still a lack of research in Polish academic literature concerning the needs and expectations of individual clients in relation to law firms. While there are studies on market regulation (Sowiński, 2019), there is a shortage of deep, qualitative analysis focused on the perception of client expectations by lawyers themselves (advocates and legal advisors). During research on the Polish legal services market, literature reviews, and cooperation with the Polish Bar Council (NRA) and the National Council of Legal Advisors (KRRP), as well as collaboration with Western European academic centers, the author has observed a research gap in the subject matter of this proposal. This gap is particularly noticeable and significant in the context of the development of global customer-centricity trends in the service sector (Zeithaml et al., 2024). The objective of the planned study is to carry out a single research task consisting of exploring lawyers’ perceptions of individual clients’ expectations in the context of the legal services provided. The focus on the individual client’s perspective is justified by their key role as a recipient of legal services. Individual clients, unlike business entities, often encounter legal problems incidentally, in life situations of great significance (e.g., family, inheritance, consumer, or criminal matters), which generates specific emotional, communicational, and procedural needs and expectations. Researching these expectations from the perspective of expert lawyers is essential, as they are the ones who directly provide the services and have unique insight into the realities of client interactions. Additionally, they not only respond to expectations but also co-create them through their communication style and the scope of services offered. The planned research activity will involve conducting expert interviews with lawyers who provide services to individual clients. During these interviews, the experts’ perceptions of the key categories of individual clients’ expectations, identified, among others, in the author’s previous research among clients themselves, will be examined in detail. These criteria will cover expectations regarding: • Communication: e.g., the lawyer’s availability, the frequency and form of information provided about the case’s progress, the comprehensibility of legal language, and proactivity in contact. • Professionalism and competence: e.g., substantive knowledge, reliability, professional ethics, the effectiveness of actions taken, and trust-building skills. • Transparency and the service delivery process: e.g., clarity in presenting costs and fees, the expected duration of the case, procedural matters, and the risks and opportunities associated with a legal solution. • Interpersonal aspects of the relationship: e.g., empathy, understanding the client’s situation, an individual approach, and providing psychological support in difficult circumstances. • The outcome/resolution of the case: e.g., a realistic assessment of prospects, striving for an optimal solution, and presenting alternative courses of action. Expert interviews are the optimal research method, allowing for the collection of in-depth qualitative data that is not available through surveys or statistical data analysis. They will be conducted taking into account the diversity of lawyers’ specializations, their work experience, and their geographical location. Thanks to the author’s ongoing cooperation with the legal professional bodies (NRA, KRRP), it will be possible to conduct some of the interviews independently, while commissioning certain tasks to a professional external entity. The final outcome of the project will be the development of a research report containing a qualitative analysis of the interview content, the identification of key expectations of individual clients, and recommendations for lawyers on improving the quality of their services. In addition to the data obtained from the interviews, it is planned to include information from other sources, such as the applicable codes of professional ethics and existing academic studies and industry reports on the lawyer-client relationship. Such an approach will strengthen the credibility and comprehensiveness of the obtained results.

The integration of Artificial Intelligence (AI) into professional environments creates both opportunities and challenges. While AI systems can enhance decision-making, their growing autonomy and human-like features raise critical ethical questions: Who is responsible when decisions made with AI lead to harm? This project focuses on the “Responsibility Gap”—a phenomenon where responsibility for actions taken in human-AI collaborations becomes unclear. It aims to explore how responsibility is attributed between managers and AI systems, how this affects behavior, and what can be done to mitigate unethical decisions. Our research tackles the urgent need to understand the behavioral and ethical consequences of working with AI. Managers often face situations where they must balance organizational goals, such as profit maximization, with ethical considerations, like consumer privacy and safety. The involvement of AI in these decisions can lead to responsibility shifting, where managers feel less accountable, potentially increasing harmful or unethical actions. By investigating these dynamics, we aim to uncover the factors that influence responsibility attribution and propose interventions to reduce negative behaviors. The project will use innovative research methods, including experimental studies with real interactions between professionals and AI agents powered by advanced language models. These experiments will provide unprecedented insights into how managers collaborate with AI and how responsibility is shared in such partnerships. Our research will also test practical solutions to ensure that AI is used ethically in professional settings, safeguarding public trust and societal well-being. The findings of this project will contribute to the responsible use of AI in business. By identifying the mechanisms that drive harmful behaviors and designing interventions to mitigate them, we aim to support managers in making ethical decisions while leveraging AI’s potential. This work will also guide policymakers and organizations in developing frameworks for accountability and transparency, ensuring that AI technologies benefit society as a whole.

Recent technological developments and improved data-gathering techniques have changed real estate evaluation from applied and theoretical perspectives. Large volumes of data are being collected, transformed and analysed to predict market trends. Moreover, access to public resources became easier. In many countries, real property data has significantly improved regarding geographic coverage, depth and granularity, availability of new information, the currency of information, and the integrity and accuracy of available data sources. In the case of Poland, there was a massive change in the possibility of obtaining information on the property transaction. Nowadays, these data are to be accessed in electronic form from the Land Registry, while ten years ago, one had to rewrite it directly from notarial deeds. This information has limitations in describing properties; however, it is always a huge step forward. Considering the ease of gathering data from different online sources (e.g. information on properties or sales, GIS data – coordinates, layers shared by municipalities or data from OpenStreetMap project) gives the possibility to merge data, estimate models with new variables, and elaborate new ideas and solutions. The value of the project lies primarily in presenting the idea of AVMs (automated valuation models), which have not yet been explored, not only in Polish conditions but also in others (e.g. offer indices as a way of dealing with transaction delays in AVMs and accuracy in different phases of the housing cycle). Furthermore, we will compare the valuations of dwellings for mortgages prepared by valuers with values determined by AVMs. The models will be estimated based on different assumptions. The data will be split into sub-periods to test their effectiveness in various phases of housing cycles. The research will be conducted on own appraisal emulations, hedonics, and machine learning approaches in the emerging market. Moreover, the study will be investigated on large, original datasets with a detailed description of properties in long-horizon time.

Measuring the size and characteristics of the demand for workers is not an easy task. Increasing non- response and under-reporting in business sample surveys may introduce bias in the job vacancy estimates. The limited scope of job vacancy surveys by official statistics does not allow to provide estimates of the demand for skills and competencies or to cover all types of work contracts (e.g. contracts for specific work, contracts of mandate, business-to-business contracts). Administrative data and the Internet (the so-called “found data”) cover thousands of job ads at any moment that are full of valuable information. However this information take an unstructured form (e.g. text) and requires the application of machine learning and natural language processing (ML/NLP) algorithms. Direct use of these data as sources of statistics is limited due to the fact that Internet data, although detailed, may be selective and do not conform to official classifications. Plenty of applications of job ads for describing the labour demand and employer – job seeker search and matching use simple error correction methods (e.g. post-stratification) while additional errors resulting from imperfect ML/NLP methods are ignored. Such a naive approach may lead to biased estimates, and thus incorrect policy recommendations. The main objective of the project is to develop new methods to measure the size and characteristics of the job vacancies (including flexible work contracts) based on survey, online and administrative data, and apply it to analyse fast changes observed in the vacancy market in the latest years, including the COVID-19 recession, refugees’ inflow and technological advancements (e.g. artificial intelligence), as well as the job seeker – employer search and matching. The novelty of the project results from: 1) proposing a new approach to estimate the total number of vacancies based on solely on non-statistical data sources; 2) correcting non-representativeness of admin and online data using data integration techniques under mis-classification error; 3) inferring about the demand for explicitly defined skills and other characteristics of workers and companies, such as types of contract or work mode – remote, hybrid, mobile, traditional; 4) accounting for uncertainty due to non-random character of data to answer questions regarding the structural changes observed in the vacancy market (e.g. the ones resulting from the COVID-19 pandemic, refugees’ inflow, artificial intelligence); 5) the analysis of search and matching between job seekers and employers using unit-level data instead of common aggregate data approach. The study will bring new statistical and economic solutions to labour market research. Innovative nature of the proposed statistical approach is connected to the use of big data (from administrative and online sources) supported with methods designed for inference based on non-probability samples with full representativeness treatment to support official statistics. The developed new methods with big data will enable recognition of labour market transformation, such as new job search and matching possibilities (online platforms), flexible work contracts (e.g. B2B), human capital measurement – skills and qualifications. Innovative nature of this approach results from the fact that this transformation cannot be fully tracked by traditional methods. The project will be based on a unique collection of micro data, in particular: Administrative job ads (Central Job Offers Database; CBOP), Online job advertisements, the Demand for Labour survey; the Labour Force Survey and the Register of Unemployed. We aim to enhance national scientific cooperation. The project will be led by a consortium of two Polish Universities: University of Information Technology and Management in Rzeszów (Leader; UITM) and Pozna´n University of Economics (PUEB). Tasks associated with these goals will be assigned to the research team of the Principal Investigator (dr hab. Robert Pater), co-investigator from PUEB (dr Maciej Ber˛esewicz), 2 co-investigators and 2 PhD students.

This research aims to examine the behaviour of prices of critical metals like copper, lithium, cobalt, and nickel, which play a pivotal role in enabling the transition to a net zero emissions economy in the face of climate change. Several key factors that may influence metal prices will be analyzed, including geopolitical events, economic uncertainties, the impact of traditional energy asset prices, the influence of other metal prices, and climate change awareness. The study highlights the increasing importance of understanding metal market dynamics due to the rising demand for these metals driven by the global push towards a low-carbon economy. While traditional energy markets like oil and gas have been extensively studied in financial markets, metal markets have received less attention despite their growing relevance in the transition to clean energy technologies. The research aims to address seven main questions: 1) assessing the level of financialization and investment opportunities for critical metals, 2) examining dependencies between metal prices, 3) exploring interdependencies between traditional energy sources and metals used in the low-carbon transition, 4) analyzing the influence of geopolitical risk on metal prices, 5) investigating whether economic uncertainty is priced into metal market returns and their potential hedging properties, 6) examining the impact of inflation on metal-related assets, and 7) exploring any causal relationship between metal, prices and climate change awareness. By delving into these research questions, the study seeks to provide insights into the complex interdependencies between metal markets and various factors relevant to financial markets, ultimately contributing to a better understanding of the pricing dynamics of these critical metals in the context of the global transition towards a net-zero emissions economy. The transition towards a net-zero emissions economy has made understanding the dynamics of critical metals like lithium, cobalt, copper, and nickel crucial. However, the risks associated with investing in these “green metals” remain understudied compared to traditional energy sources. This research aims to comprehensively examine the behaviour of critical metal prices by investigating several key factors. Firstly, the study will assess the financialization of metal markets and construct a database that includes prices from various sources like commodity exchanges and mining company stocks. It will explore interdependencies between metal prices themselves, as well as their linkages with traditional energy sources like oil and gas. Another focus is analyzing how geopolitical risks and economic uncertainties influence metal prices and returns. The impact of inflation and potential hedging properties of metal-based assets against other asset classes will also be evaluated. Finally, the research will investigate whether a causal relationship exists between critical metal prices and climate change awareness. The proposed project will fill a gap in the research carried out on international markets. The project’s innovative contribution lies in its multifaceted approach, simultaneously considering diverse factors affecting metal markets within the context of the ongoing energy transition. The findings are expected to have significant implications for investors, portfolio managers, regulatory bodies, and market facilitators. By shedding light on the complex dynamics surrounding critical metals, this study aims to inform risk mitigation strategies, asset allocation approaches, and policy frameworks essential for enabling a smooth shift from fossil fuels to renewable sources while maintaining financial stability.

The transformation of rural areas in terms of socio-economic, structural changes, have led to their economic diversification, and spatial polarization comparing to urban and suburban regions. Agritourism has emerged as a significant factor in rural development, and a diversification of agricultural activities of individual farmers. The availability of public goods, such as quality of infrastructure, and public services, with an access to resource endowment, play a crucial role the attractiveness of rural tourism destinations. Effective regional policies are vital for supporting agritourism in local and regional level. Policies that promote sustainable development of rural tourism destinations, provide financial incentives, and invest in infrastructure improvements significantly boost agritourism. Agritourism can generate positive spillover effects for rural areas in terms of socio-economic development. The study will highlight the importance of spatial dependence in agritourism activities taking into account integrated approaches of regional policies, spatial planning, and engagement of local communities to foster sustainable rural development through agritourism. The research problem concerns spatial dependence of agritourism development answering particularly the question how this dependence affects the behaviour of touristic service providers. These research questions were formulated in the project: 1) Which factors influence the development of agritourism? 2) How to measure the impact of agritourism on rural development? 3) How to fit regional and local planning into spatial development patterns? 4) How spatial dependence and development plans affect farmers’ intentions to provide tourist services? To answering the above questions, it will allow to identify socio-economic factors significantly affected agritourism development. The analysis will also assess the significant role of public goods, including natural resources and environmental quality, in enhancing the attractiveness of rural tourism destinations. It will measure spatial effects on rural development and agritourism activities respectively, and the alignment of regional planning with spatial development patterns, and the impact of spatial dependence on the farmers’ intention to engage in agritourism activity. This study will be based on the extended theory of planned behavior shaping spatial effects on agritourism activities and a farmer’s intention to proceed or engage in agritourism activities. The project will use the most up-to-date research methods to reliably establish causal relationships between spatial dependences and individual farmer’s intention potentially engaged in agritourism activities. In the course of the project, data obtained from statistical databases GUS BDL, relevant institutions in rural economy, agriculture, environmental protection, tourism sphere. To answering spatial dependence and their effect on farmers’ intentions to provide agritourism activities, it will be conducted a survey with a sample of approximately 200 individual farmers potentially engaged as touristic service producers. The results of research will be presented at international scientific conferences and will be published in scientific journals in a field of rural studies, tourism economics, behavioral economics.

The main goal of the research is to define the roles played by multinational enterprises (MNEs) in adopting responsible Environmental, Social, and Governance (ESG) practices. Given the complexity of MNEs, the main contribution will be to define the roles played by headquarters (HQ) and foreign subsidiaries (FSs) in ESG adoption in their activities, especially in Central and Eastern European Countries (CEECs), where this topic currently evolves. The analysis aims to precisely identify the factors influencing ESG adoption, the roles of HQs and FSs in this process, and the ESG strategies employed by these entities. The results of the study will enable the creation of a model that shows the relationship between the determinants, roles and strategies of MNEs in in the implementation of ESG concept.

The research project employs both qualitative and quantitative research methods. The study begins with a thorough literature review on MNEs and sustainability to build a solid theoretical foundation.Empirical research includes both secondary data analysis and primary data collection. Secondary data will be analyzed through available statistics, while primary data will be obtained through qualitative and quantitative research. The qualitative research will include semi-structured interviews with MNEs managers to create case studies, and the use of Total Interpretative Structural Modeling (TISM) to identify preliminary relationships between MNEs’ determinants, roles and strategies for adopting ESG practices. Quantitative research will be conducted using the Computer-Assisted Web Interview (CAWI) method on a sample of 220 HQs of MNEs based in Poland. Statistical analysis using Structural Equation Modeling (SEM) will present the relationship between determinants and roles of MNEs adopting ESG practices, which will allow to develop a comprehensive model depicting the actual relationship between determinants, roles and strategies of MNEs in adopting ESG practices.

Recent European Union regulations on sustainability reporting have imposed obligations on MNEs to report on their non-financial practices. To properly adopt these frameworks, it is essential to investigate the relationships between the HQ and their FSs within MNEs. This is crucial due to the dynamically changing environments of their operations, their geographical dispersion, and their embeddedness in institutionally and culturally distinct contexts. The relationships between the HQ and FSs can significantly impact the effectiveness of ESG actions and optimizing initiatives in this area. ESG strategies contribute to attracting investors and improving financial performance in the long-term period, which is crucial for the sustainable development of the enterprise. There is a visible gap in the context of the geographical area of a given topic. ESG concept is primarily prevalent in Western markets, which raises concerns about the suitability and correctness of transplanting these ESG frameworks to CEECs markets. Examining the relationship between HQ and FSs and the adoption of ESG practices by MNEs in this region is key to understanding how this framework can be effectively implemented to specific market conditions.

The findings regarding ESG strategies adopted by HQ and FSs could offer actionable insights for MNEs in structuring their ESG initiatives and guide the development of cohesive ESG strategies that align with global standards and local requirements. The results will be relevant for HQs, FSs, managers, and policymakers, thus contributing to international business and sustainable development economics. This integration provides a nuanced understanding of ESG adoption in MNEs, offering a comprehensive framework for analyzing the complex dynamics between HQs and FSs in shaping ESG practices. Focusing on Polish companies investing abroad, the thesis adds empirical evidence from a relatively underexplored context, contributing to a more global understanding of ESG practices within MNEs. The study will provide insights into how companies from CEECs navigate global ESG demands and adopt concepts initially introduced by Western European countries.

Project Description

Our project is dedicated to exploring how artificial intelligence (AI) can guide people toward making more sustainable food choices. We all know that our food system has a big impact on the environment.

Issues like climate change, deforestation, and loss of biodiversity are heavily influenced by the way we produce and consume food. By focusing on how AI can help us choose foods that are better for the planet, this project aims to contribute to a healthier environment and a more sustainable future for everyone.

Project Goal

The main goal of this project is to understand how personalized AI recommendations can encourage people to make food choices that are kinder to the environment. We want to see how AI can help make it easier for people to identify and choose sustainable food products, guiding them towards options that have a lower environmental impact.

Description of Research

We will be using a variety of advanced research methods to see how people react to AI recommendations. This includes:

• Focus Groups
  We will talk to different groups of people to learn about the challenges they face when trying to make sustainable food choices. This helps us understand what information they need to make better decisions.
• Eye-Tracking Studies
  We will look at how people read food labels and what catches their eye. This will help us understand which sustainability labels are most effective in grabbing attention.
• EEG Studies
  By monitoring brain activity, we can see how people’s brains react to AI-generated recommendations. This will tell us how AI influences their decision-making processes.
• Surveys
  We’ll ask a lot of people about their attitudes toward sustainability and how much they know about sustainable practices. This will help us see how these factors affect their food choices.
• Shopping Behavior Analysis
  In a real store setting, we’ll see how people use AI tools on their phones to get real-time advice about the sustainability of different products. We’ll observe how this influences their shopping decisions.

Reasons for Attempting This Research

Our research is motivated by the need to make sustainable food choices easier and more accessible for everyone. Many people want to make more environmentally friendly choices but find it hard to understand what makes a product sustainable.

Labels can be confusing, and the information is often overwhelming. AI has the potential to simplify this process by providing clear, personalized recommendations. By making it easier for people to understand and choose sustainable options, we hope to reduce the environmental impact of our food system and contribute to a more sustainable future.

Substantial Results Expected

We expect to achieve several important outcomes from this project. First, we aim to develop AI tools that provide personalized and easy-to-understand recommendations, making it simpler for people to choose sustainable food products.

We also hope to identify which types of sustainability information are most effective in influencing people’s choices. This could lead to better labeling practices and more informed consumers.

Ultimately, our goal is to help people make choices that support a healthier planet and encourage more sustainable consumption patterns. By demonstrating the effectiveness of AI in promoting sustainable choices, we hope to inspire wider use of this technology in other areas of our lives.

With the growth of the market economy in Poland, some markets which played a marginal role in the pre-transition economic system have grown in importance. The housing market is undoubtedly one of them. The significant role this segment of the real estate market plays in the economy may be attributed to the fact that real estate is perceived as capital, which makes it possible to create added value for the owner as well as for the local and national economy. Moreover, the global financial crisis caused by a collapse in the real estate market in the United States, led to the growing interest in the relationship between the housing market and the economic situation of individual countries. The fact that economists are paying increasing attention to the issue of changes in the real estate market is the result of fears concerning the consequences of the drop in housing prices. The housing market is affected by changes in the economy. On the other hand, through different channels, it can also influence the economic development of regions or countries. Changes in this market affect business cycles, have a significant impact on the transmission of monetary policy impulses, and, under some circumstances, on the stability of the financial system. Studies conducted in various countries show that there is a close connection between fluctuations in this market and changes in business activity in particular countries. Generally speaking, changes in housing prices precede changes in business cycles. Moreover, prices of flats may influence household spending – higher prices make property owners richer, which may, in turn, lead to increased consumption. What is a direct effect of the fluctuations of housing prices in the economic sphere are changes in the volume of real estate investments. An increase in housing prices makes such investments more profitable. Taking into account above-mentioned issues the main objective of the project is to identify and classify the determinants of variation in housing prices in 18 cities (provincial cities) in Poland in years 2000-2017, and on this basis to create price prediction models.

Project Description:

The project “The Quality of Decisions in the Innovation Process in Biotechnological Enterprises” aims to develop a model and comprehensive approach that specifies and contextualizes the innovation process from a decision-making perspective. The primary objective of this approach is to enhance the efficiency of innovation processes in the biotechnology industry, facilitating the regular creation of new products. The lack of a comprehensive theoretical framework hinders the effective development and implementation of sustainable innovation processes, making research in this area both theoretically and practically significant.

This project innovatively integrates research within decision theory. It builds on current discussions around innovation processes and addresses existing research gaps in areas such as:

1. Shared interpretative frameworks among innovation process participants,
2. Rationality of decision-makers,
3. Participation in decision-making processes, and
4. Transactional memory.

Biotechnology is widely regarded as a cornerstone of the knowledge-based economy.

The primary role of managers is to make decisions, and the quality of decisions concerning innovation is critical to the effectiveness of the innovation process. In the conceptual model of the innovation process, successive stages are separated by decision points, where managers decide whether to proceed to the next phase. The absence of comprehensive theoretical foundations in this area complicates the effective development of innovation processes. Meanwhile, numerous studies of enterprises confirm that nearly all businesses strive to increase innovation and create organizational solutions leading to the establishment of a continuous innovation process. This process is complex, with each stage involving different approaches to decision-making. Decision-making within the innovation process represents the most significant challenge for companies in the biotechnology sector.

The research problem of the project is expressed as the following question: How does improving the quality of decisions affect the efficiency of the innovation process in biotechnological enterprises? The main objective of the project is to determine how improving the quality of decisions impacts the efficiency of the innovation process in biotechnological companies. This objective is supplemented by ten specific goals. The main hypothesis states: The quality of innovation-related decisions impacts the efficiency of the innovation process in such a way that higher decision quality contributes to increased process efficiency.

The research plan proposed in the project is divided into seven parts, ultimately creating a comprehensive view of the innovation process from the perspective of decision quality in biotechnological enterprises.