Scientific projects at the PUEB

General description:

There are several different ways to obtain cryptocurrencies. Currently, the most popular method is their purchase, which can be made via specially designed websites, commonly referred to as cryptocurrency exchanges. They provide their services in different parts of the world and offer the possibility of exchanging cryptocurrencies for traditional currencies, as well as exchanging cryptocurrencies for other cryptocurrencies. In addition to the largest cryptocurrency exchanges that attract capital from many countries, there are also smaller entities whose competitive advantage stems from the fact that they offer the opportunity to trade in local currency, and not only in major world currencies. The main goal of the project is to determine the characteristics of various types of cryptocurrency trading platforms and to assess their diversity in this respect. The importance of the topic results from the growing popularity of cryptocurrencies on the financial market. Currently, they attract not only technology enthusiasts and individual speculators, but also institutional investors. At the same time, the regulations of the cryptocurrency market is developed. The high dynamics of changes within and around the cryptocurrency market causes that the importance of intermediaries on this market may change.

General description

The housing issues addressed in the project focus on social housing and housing policy aimed at meeting the needs of low-income and middle-income households.

The aim of the planned study is to implement a single research task consisting of identifying the criteria for the allocation of premises from the municipal resource. The planned research activity (pilot study) will include conducting interviews according to a prepared questionnaire. It is assumed that the respondents will be people managing the municipal housing stock, e.g. directors of housing management departments. The research will be carried out in municipalities associated with the Social Housing Initiative “KZN – Zachodni” Sp. z o. o. (14 municipalities as of July 2023). The selection of communes results from the fact that they have just started implementing social housing projects and are demographically, economically and socially diverse. A kind of benchmark for these municipalities will be three central centers from this region – the cities of Poznań, Gorzów Wielkopolski and Zielona Góra.

General description:

The project delves into the phenomenon of the continuous development of digital technologies and factors motivating their acceptance (intention to use) by the consumer. Interactive technologies are transforming retailing by e-commerce development and increasing the competitiveness of local and international companies.

Augmented reality (AR) is one of the digital technologies that can be considered impactful in redefining the concept of retail stores. Despite the wide possibilities of AR application and its advantages (e.g. better product selection, fewer online shopping returns) AR is still not widespread in e-commerce. One of the reasons is connected with the costs of AR`s implementation, the other is associated with consumers` acceptance of new technologies and the risks connected with them.

The main objective of the project is the identification of factors (motives and risks) affecting the acceptance of AR technologies by young consumers in e-commerce in three countries (South Korea, Poland, and the United States).

To execute the main objective, detailed objectives have been formulated:
– theoretical conceptualization and empirical verification of the consumer acceptance model for using AR in e-commerce;
– determining the impact of gender, consumers` experience, and country of origin towards their acceptance of AR usage in e commerce.

The study will create a cyoung onsumer acceptance model for using AR in e-commerce considering the gender, experience, and country of origin. The model will have a great cognitive significance as an input to consumer behavior, motivation theories, and theory of perceived value concerning innovations such as AR.

Showing the acceptance of AR`s use in selected countries the project may contribute to the development of guidelines for enterprises to be more successful in e-commerce in the international marketplace. Authors plan to publish papers mainly in English in reputable journals accessible worldwide, therefore, the knowledge gathered while working on the project will be internationally disseminated.

General description:

This application aims to carry out a 14-week research stay at the Institute of Geography and Sustainability at the University of Lausanne (UNIL), which is to be conducted within the research group M3 (Materialities | Multiplicities | Metropolis) headed by Professor Martin Müller, one of the most distinguished and cited researchers specialised in analysing the effects of global mega-events in the vein of sustainability transition.

This research stay is substantially in line with their recently conducted research, which concerns the socioeconomic and sustainability aspects of the largest mega-events – the Olympic Games, the Men’s Football World Cup and Expo. These works are empirically underpinned by their custom-built databases, which are the first to represent systematic data to evaluate the outcomes of global mega-events across time and space and ameliorate the evidence base for treating such events as types of public policy. The main aim of this research stay is to embark on a scientific collaboration with Prof. Müller and the research group M3 to conduct joint research on evaluating sports mega-events as public policy. In the literature, there is a lack of studies on their impacts on host cities that would be based on systematically collected data. Such an approach would give appropriate evidence and arguments in public debate on whether it is worth hosting the biggest global sports mega-event as the Olympic Games and therefore improve sound decision-making in this respect.

The goal of the project: The research aims to verify to what extent the Russo-Ukrainian conflict affects the prices and volatility of commodities (with an emphasis on crops) and, in turn, impacts the creditworthiness of the European Union economies. We aim to assess which European Union economies suffer the most (indirectly) from the conflict.

Expected results: The outcomes of the project will fill the gap in the base research field. By discovering how the impulses from the fossil fuels market spill over to crop one, we will gain more knowledge on the theoretical nature of this relationship. At the same time, such information can be utilized by policymakers to properly adjust to the changing prices of energy and fuels during crisis times and diversify agricultural production.
The part of the research devoted to the opportunity of hedging crop market uncertainty is of interest for investors in commodity markets and allows them to protect against possible market fluctuations.

Eventually, the analysis of the volatility of spot prices may be useful for agricultural producers.

The goal of the project:

The project examines how rising oil prices affected travel demand and the shift to sustainable transport. In particular, we investigate the effect of the demand (mobility bans during the COVID- 19 pandemic) and supply shocks (cutting-off Russia from the main part of global supply chains) on the dynamics of oil and alternative fuels’ prices, and hence – on the change of travel preferences. The last aspect is studied in-depth in the Vysehrad 4 group (further V4), based on survey data. The V4 economies rely heavily on Russian oil supplies. Among the V4 countries, Slovakia is the most dependent on Russian oil and petroleum products. In 2020, they accounted for over 78 % of the country’s total imports of these commodities. The analogous measures for the rest of the countries are 67.5% for Poland, 44.6% for Hungary, and 29.1% for Czechia. We expect that the countries with lower GDP per capita would be more affected by the rising oil prices. Therefore, we also study the vulnerability of oil and alternative fuels’ returns to two main risk factors: geopolitical situation and pressures from the financial market (which tend to intensify during turmoils). If we show that the prices of alternative fuels are more immune than oil ones to the international crises, that would add one more argument for a gradual switch to green energy sources.

Expected results: We expect that the result of our project would allow us to better understand the behavioral aspects governing the decision-making process and enhance the theoretical models of transport choice. Such information can also be used by the central authorities to accelerate the process of de-carbonization and adjust the infrastructure to the changing transport mode. The outcomes of our study would contribute to a better understanding of the factors that shape fuel prices. By discovering how the impulses from the financial market spill over to the crude one, we will gain more knowledge of the theoretical nature of this relationship. At the same time, such information can be utilized by policymakers to properly adjust to the changing prices of energy and fuels during crisis times. Exploring different variability of different fuels (traditional versus green ones) to the impulses from the markets may provide some insight into how the usage of the two kinds of resources may be effectively varied during turmoils.

The project is run in cooperation with the Czech Travel Research Centre (CDV).

General description:

Can Machines Help us Understand the Market?
“Predicting the future isn’t magic, it’s artificial intelligence.”
~ Dave Waters

The stock market history had come full circle: from when we knew too little to make wise investment decisions to when we know too much. It has been nearly seven decades since William Sharpe, John Lintner, and Jan Mossin offered the capital asset pricing model (CAPM). A simple and elegant model seemed to explain what drives the returns in the market very well. According to the CAPM, only one variable predicted future returns: the risk. The higher the risk, the higher the returns. That’s it—one variable to rule them all.

But then, the theory started to crack. Suddenly, it came to light that other predictors seemed to be working well. In the 1970s, we discovered valuation ratios. Then, 1980 brought with it both firm size and long-run returns. In the 1990s, we uncovered momentum. After that, the research exploded. As of now, the researchers have literally discovered hundreds of different return predictors. This “factor zoo;” as it was christened by John Cochrane, the president of the American Finance Association; has not only made CAPM out-of-date, it also raised a preponderance of entirely new questions. Which of these predictors really work? How many of them are real? Which of them captures the same phenomena? Do they interact with each other? Do they survive the test of time? And, finally, how should we find our way in this mess?

Alas, the traditional research tools used in finance have not been well-posed to answer these burning questions. They do not cope well with such vast amounts of data. The finance profession needed new tools to face its new reality. And so, here came the machines.

The recent merger between traditional asset pricing research and developments in machine learning brought with it a breakthrough. Indeed, machine learning models have several unique features that have attracted the academic community’s attention. First, they can simultaneously crunch vast amounts of big data and numerous predictors. Second, they can tell apart the variables that matter
from those that are irrelevant. Third, they can identify how different variables interact with each other. Fourth, they can capture various nonlinear dependencies in returns. In short, machine learning is poised to revolutionize asset pricing research over the coming years.

The initial evidence is very promising. The studies from both the U.S. and China demonstrate that machine learning models, such as regression trees or neural networks, can effectively explain and predict stock returns. They can work with accuracy that has been hardly seen before. Most importantly, they can simultaneously handle, select, or even discover hundreds of predictors. In short,
they can crunch decades of asset pricing research in mere minutes.

Nevertheless, machine learning algorithms working in individual markets still do not unleash their ultimate power: to digest vast amounts of data. Therefore, this study will combine machine learning research with broad international datasets in order to gain novel insights into asset pricing. Machine learning will go fully global. We want to see what new lessons we can learn from applying machine
learning to broad international data.

This study will explore various questions that could not be answered without the recent development in machine learning combined with big international data. What drives the return predictability around the world? Does information from one asset class help predict returns in other asset classes? Can we predict comovement between countries and asset classes? These are just a few questions we will attempt to answer.

Let us see whether machines can help us understand the markets better.

General description:

Research on artificial intelligence has been conducted for more than 60 years, but in recent years there has been a significant acceleration in the development of this technology. We have more and more contact with it in our private lives (for example, using household appliances, car navigation, and cell phones) and at work (using various systems to streamline tasks). We see it changing how we perform multiple activities, facilitating them, and creating new challenges. While many people are fascinated by artificial intelligence because of its computing and learning capabilities, others may be concerned that it will take jobs away from people or control them. Many companies are implementing such solutions to improve work but encounter reluctance among employees to use them to make various decisions.

Our project focuses on how people interact with artificial intelligence in companies, especially in companies’ interactions with customers. This is where more and more data are coming in that managers can no longer analyze. So, they are using systems that apply machine learning and deep learning to develop analyses, predictions, and recommendations. These sophisticated systems are good at analyzing massive data sets on customers but struggle to recognize their non-standard behavior. We assume that the best results can be achieved not by humans and artificial intelligence competing but by performing tasks together. We refer to this as a partnership between humans and machine learning. This is a new, poorly recognized issue on which a discussion in global science is beginning. Its results will have significant implications for the development of artificial intelligence research and the work of humans in many future professions.

In our research, we aim to understand what emotions are associated with the fear of such collaboration and how they combine with the awe of artificial intelligence. We also want to determine under what conditions people will be willing to share their knowledge and experience with artificial intelligence so that it learns from them to make better decisions and to what extent they take artificial intelligence’s prompts into account when making decisions in companies. Finally, we are also looking for ways to reduce reluctance to cooperate with artificial intelligence, assuming that if it is more understandable to humans, it will be easier for them to follow
its prompts.

These issues occur in developed economies, so our project is international. In it, we are undertaking a study of managers responsible for companies’ interactions with customers since they are the ones who already make decisions using artificial intelligence and will do so in large part in the future. First, we plan to distinguish types of managers according to their emotions and behaviors related to working with artificial intelligence. Then we will study in which situations they are willing to share their knowledge with such systems and take their suggestions into account when making marketing decisions. Finally, expecting that a limitation of cooperation is the difficulty of understanding how artificial intelligence works (due to the so-called “black box problem”), we will look for such ways of designing artificial intelligence to reduce its aversion. The research is international; we plan to survey 1,700 managers throughout a project lasting three years.

The project will result in the development of knowledge about how humans manage and cooperate with artificial intelligence, a better understanding of the role played by the analysis, predictions, and recommendations it generates in customer relationship management, a better understanding of the impact of emotions associated with artificial intelligence on the willingness to cooperate with it, and an indication of how to design this technology so that it is acceptable to users as reliable.

General description

The main goal of a single scientific activity undertaken in the Miniature is to conduct preliminary research on a selected supply chain within the industrial automation sector for the purposes of verification of initial knowledge about the tested environment, operationalization of the resilience and flexibility of supply chains including the preparation of the concept of survey research and thus development of a measurement instrument to study the relationship between the resilience of modern supply chains and their flexibility and to study the impact of the location of the decoupling point resulting from the MST (Make To Stock), ATO (Assembly To Order), MTO (Make To Order) or ETO (Engineering to Order) strategy implemented by the analyzed supply chains on the level of resilience of the supply chain. In-depth literature studies, the desire to specify the main research problem and the project’s contribution to science allowed to define the following research questions: Is there a statistically significant relationship between the resilience of modern supply chains and their flexibility? Is the level of resilience of adaptive supply chains related to the decoupling point and strategies implemented by supply chains: MTS, ATO, MTO, ETO? Does moving the decoupling point from MTO towards ETO affect the level of resilience of supply chains? Obtaining answers to the above research questions will allow to achieve the goal of the main scientific activity.