Results of the RIGE project

This study focuses on the conceptualization of sustainable entrepreneurship, aiming to develop a comprehensive profile of a sustainable entrepreneur.

Research Methodology

A qualitative study was conducted through interviews with 20 entrepreneurs operating vegan and vegetarian restaurants in Poznań. The research examines:

• The specifics of running a plant-based food business
• Motivations and competencies of entrepreneurs
• Collaboration with other entities
• Sustainability-related aspects of business operations

The interviews were transcribed and analyzed using Maxqda software, which facilitates the identification of research patterns and themes.

Context and Importance

The consumption and promotion of plant-based food are crucial, as meat production has a significantly greater environmental impact. It contributes to:

• Higher greenhouse gas emissions
• Excessive water consumption
• Soil pollution

Vegan and vegetarian restaurants serve as examples of sustainable entrepreneurship, integrating environmental responsibility with business operations.

Key Challenges Faced by Vegan and Vegetarian Entrepreneurs

1. Higher costs – Plant-based ingredients are more expensive than meat products, increasing operating expenses.
2. Consumer perception – The term “vegan” carries negative connotations for some consumers, potentially reducing demand.
3. Higher consumer expectations – Vegan customers demand higher quality and ethical standards compared to meat-eaters.
4. Industry-wide challenges – Entrepreneurs face inflationary pressures and gastronomic sector difficulties, regardless of cuisine type.

Collaboration and Business Relationships

A crucial aspect of success in sustainable entrepreneurship is building relationships with industry stakeholders. However, the study reveals a lack of long-term partnerships among vegan and vegetarian business owners.

• Many entrepreneurs do not prioritize long-term relationships with suppliers or competitors.
• A limited business network restricts access to resources, knowledge, and know-how.
• Collaboration with competitors could foster innovation, while strong supplier relationships ensure stability in crisis situations.
• However, resource scarcity forces entrepreneurs to work with suppliers who may not share sustainability or animal welfare values.

One major factor limiting collaboration is entrepreneurs’ personality traits, such as:

• A strong need for independence
• Fear of cooperation
• Reluctance to share information

Conclusion

The study highlights both the potential and challenges of sustainable entrepreneurship in the plant-based food sector. While vegan and vegetarian businesses contribute to environmental sustainability, their success is often hindered by high costs, consumer perception, and limited collaboration within the industry.

Social phenomena are often interpreted through the lens of historical institutionalism, within which critical moments or turning points shape the basic contours of social and economic life. An analysis of international research on labor markets clearly indicates that the problem of precarious employment is global. Researchers distinguish exogenous shocks that play a key role in the occurrence of precarious employment. In social terms, it is commonly defined as a job that accumulates several unfavorable characteristics of job quality, such as job insecurity, inadequate income for qualifications, and limited worker rights and weak social protection. The social groups most vulnerable to precarious employment are mainly the poorly educated, migrants, young people and women. Workers suffering from precarious employment often find themselves in situations where their governments and employers do not provide access to sufficient social and health protection, which can be associated with a variety of negative health consequences, such as chronic stress or mental and cardiovascular diseases. This article aims to show how critical moments in recent world history – the Covid-19 pandemic, the Fourth Industrial Revolution and migration crises – have affected precarious employment in social enterprises and, more broadly, in the third sector. The results of analyses of international reports by global agencies such as the International Labor Organization, the World Economic Forum and the Organization for Economic Cooperation and Development suggest that labor market insecurity will be one of the most important challenges of today. Although social enterprises are making efforts to mitigate the effects of precarious employment by helping those affected, they are themselves vulnerable to external shocks. Facing numerous barriers to operations, they are limited in their ability to hire full-time employees.

Research Objective

The main goal of this study is to examine the impact of technological innovations on the sustainable functioning of packaging industry enterprises in Poland. The insights gained will help understand how selected packaging manufacturers use digital technologies to optimize production while improving societal well-being. Additionally, the study evaluates the role of digitalization in promoting sustainable production management.

Specific Objectives

1. Assess the extent to which sustainable development strategies are implemented by packaging companies in Poland.
2. Identify industry expectations regarding digital technologies and explore opportunities for improving production and distribution processes.
3. Evaluate the impact of Industry 5.0 on the development of innovative packaging solutions, including smart packaging and personalized packaging.
4. Identify legal, technological, and social barriers limiting the industry’s transformation.
5. Analyze collaboration between companies, research institutions, and technology startups in the sector’s digital transformation.

Research Methodology

The study involved a survey conducted among organizations producing plastic, paper, and cardboard packaging in Poland. A total of 82 companies from across the country participated.

To achieve the research objectives, advanced statistical methods will be employed, including:

• Factor analysis
• Correspondence analysis
• Multidimensional scaling
• Selected statistical tests

The data analysis will be performed using R and Python, leveraging their specialized packages for data processing and statistical analysis.

The study is still in progress.

The survey consisted of two main sections. In the first section, respondents were asked about their pro-environmental attitudes, including their involvement in environmental protection and their sense of agency in this area. The second section explored respondents’ approach to sustainable consumption, including their willingness to adopt products that incorporate eco-friendly technologies.

Key Findings:

1. Human-environment interdependence:
   • A majority (74.2%) of respondents believe that humans and the environment are interconnected.
   • Nearly half (48.8%) reported feeling good when they engage in activities that benefit the environment.
2. Collective responsibility:
   • Respondents predominantly agreed that solving environmental protection issues requires collective societal effort.
   • 25.8% strongly agreed with the statement that only by working together can society address environmental challenges.
3. Sense of personal agency:
   • Most respondents reported a moderate sense of agency regarding environmental protection.
   • The largest share (35.4%) moderately agreed that they are capable of effectively dealing with environmental problems.
4. Willingness to reduce consumption:
   • Respondents demonstrated moderate willingness to reduce consumption for environmental reasons.
   • 53.0% moderately agreed with the statement that they intend to lower their consumption for environmental protection purposes.
5. Openness to green technologies:
   • Respondents exhibited moderate openness toward adopting eco-friendly technologies.
   • When asked about their intentions to use green technologies, 47.6% expressed moderate willingness.

Conclusion:

The study indicates that while respondents are aware of the interdependence between humans and the environment, this awareness only moderately translates into pro-environmental consumption intentions.

The findings will contribute to a forthcoming academic publication, which is currently under review.

In the marketing of tourist destinations (receptive areas that host tourists), there is growing emphasis on highlighting which sustainable development and sustainable tourism goals are being implemented in these areas. This messaging can enhance the image of the destination, potentially attracting tourists, especially those with pro-ecological attitudes. A key research question arises regarding the influence of this type of communication on the decision-making process of potential consumers.

Due to the lack of empirical data to evaluate the impact of specific marketing messages on tourist decisions, the study employed an advanced economic modeling technique—Agent-Based Modeling (ABM)—to answer the research question: How do the features of sustainable tourism, as reflected in a destination’s image, influence tourists’ decisions to visit that destination?

The simulations were based on a custom-designed system architecture developed for the study, which included decision-making algorithms representing agent behaviors. These agents—virtual representations of tourists—interacted with external environmental agents, simulating real-world decision-making and behavior over time.

Agent-Based Models simulate the behavior of multiple virtual entities, each possessing its own goals, personality traits, and varying susceptibility to external factors, all of which shape their decision-making processes. By analyzing these simulations, researchers can assess how agents’ behavior changes in response to various factors, enabling the verification of behavioral scenarios. This approach provides new insights into the importance of sustainability features in the marketing image of tourist destinations and their impact on tourists’ destination choices.

Co-authors:
Justyna Majewska, Szymon Truskolaski

The purchase of the MAXQDA Analytics Pro + AI Assist software enabled an in-depth qualitative analysis of data collected during extended focus group interviews. These interviews were conducted across six groups (two interviews each in South Korea, Poland, and the United States). The study explored young consumers’ acceptance of augmented reality (AR) during online shopping, using eyewear as the product under examination. The focus group study identified both similarities and differences among cultural groups regarding the motivations and risks associated with using AR for online shopping. The most significant differences were noted in hedonistic motivation, while similarities emerged concerning perceived risks and ease of use of the technology.

MAXQDA facilitated the coding of results based on interview transcripts and the generation of visual representations of the findings, with breakdowns by country and participant gender. Code clouds were used to illustrate the results, playing an integral role in the analysis and subsequent academic publications.

Study outcome:

Publication:
Bartosik-Purgat, M., & Rakowska, W. (2024). Gender in acceptance of augmented reality in e-commerce: An international perspective, International Entrepreneurship Review, 10(4), 113-127. DOI:10.15678/IER.2024.1004.08.

This quantitative study was conducted in collaboration with Brno University of Technology.

The objective of the research was to analyze the factors influencing the success of construction projects in terms of cost, completion time, and quality. The study was conducted among three respondent groups involved in construction projects: investors, designers, and contractors. A total of 171 responses were collected through an online questionnaire, using a 7-point Likert scale.

The study found that both advanced communication and change management, as well as effective and precise project supervision, positively impact project success concerning the three key factors—time, cost, and quality—aligned with the “iron triangle” concept.

The findings indicate that companies in the construction sector should focus on digital communication practices to effectively manage changes.

Additionally, the research revealed that developing flawless technical documentation for construction projects requires time and proper resource coordination. However, this cost is justified, as high-quality documentation is crucial for project success, and its preparation costs are negligible compared to overall construction expenses.

Two publications containing the results of the study are currently underway.

The survey study was conducted in two stages.

The first stage, involving a sample of 120 respondents, aimed to validate research tools and determine the role of social exclusion in the context of biases against artificial intelligence. A survey method was used, and the questionnaire was distributed to participants via the online platform Prolific. The survey contained closed-ended questions, primarily based on previously tested attitude measurement scales found in the literature.

The second stage, conducted with a sample of 330 respondents, aimed to assess the extent to which the frequency and quality of both positive and negative interactions with conversational AI influence biases against AI. Data were collected on Prolific, utilizing the attitude measurement scales validated in the first stage.

The conceptual framework for this study is based on Gordon Allport’s contact hypothesis concerning intergroup prejudice, along with later research summarized in several meta-analyses (e.g., Pettigrew & Tropp, 2006, 2013). These studies identify three main mediating factors in the relationship between contact and prejudice: anxiety, knowledge, and empathy. In the context of AI-related research, this framework was expanded to include anthropomorphism (as a variable preceding empathy). Bias against AI was operationalized as beliefs about AI’s ability to deceive.

Key Findings:

• Socially excluded individuals perceive AI as more likely to engage in deception compared to those less at risk of exclusion.
• Aversion to AI strengthens prejudices against AI.
• Individuals with more frequent positive interactions with conversational AI exhibit lower levels of bias against AI (measured as beliefs about AI engaging in deceptive practices), in line with Allport’s contact hypothesis.
• Empathy mediates the relationship between positive interactions and reduced prejudice, consistent with Pettigrew and Tropp’s contact model.
• Knowledge and anxiety do not mediate the relationship between positive interactions with AI and prejudice toward AI.

The study will result in two publications, which are currently under review.

Artificial Intelligence and Sales Forecasting: Applications of Machine Learning

The modern business world faces the challenge of predicting the future based on uncertain and complex data. In response to these needs, advanced machine learning methods are increasingly being employed, revolutionizing sales forecasting. One of the latest advancements in this field is the combination of fuzzy logic and neural networks.

The research conducted focused on analyzing and evaluating the effectiveness of these techniques in real-world business scenarios. Fuzzy logic, as a method that enables a more flexible approach to uncertainty, was applied in the forecasting model to support neural networks in unexpected and dynamic situations. This system was tested using real sales data, allowing for the verification of its practical utility.

The results proved to be highly promising. We demonstrated that integrating fuzzy logic with machine learning not only enhances forecasting accuracy but also improves the system’s ability to handle the unpredictability of business processes. The hybrid model was found to be more resilient to changing market conditions compared to traditional approaches.

Contemporary applications of artificial intelligence in economics and business represent a milestone toward more efficient management. Our research confirms that fuzzy logic combined with machine learning is the future of sales forecasting—offering greater precision, adaptability, and effectiveness in the face of uncertainty.

Co-authors: Marcin Bartkowiak, Piotr Cyplik, Tomasz Górecki, Adam Karolewski

The art market is gaining increasing significance as an alternative form of investment, which creates the need for reliable price indices. The main challenge in this area is the heterogeneity of artworks, which complicates the comparison of price changes over time. The solution to these challenges lies in art price indices, which allow for the tracking of price trends and the assessment of artwork values over time. Practical applications of such indices include providing investors and collectors with data on price trends and potential returns. They also assist policymakers and researchers in understanding the dynamics of the art market, supporting the development of cultural policies and the creation of investment strategies. The methods traditionally used in this field include hedonic regression and repeated sales regression (RSR), although each has its limitations.

The research presented at the conference titled “A Comparative Analysis of Hedonic and Repeated Sales Regression Indices for the Polish Art Market: Towards a Mixed Approach” aimed to analyze and compare these methods and propose a mixed approach that combines the strengths of both techniques. The analyses conducted revealed that the mixed approach can provide a more comprehensive and accurate picture of price changes in the art market. The combination of hedonic indices and repeated sales, as in the proposed mixed model for the Polish art market, allows for a more precise capture of the complexity and specific characteristics of this market by integrating qualitative analysis with transactional data.

The aggregated transaction data presented in the form of indices indicate that the Polish art market shows low correlation with traditional financial assets, making it an attractive tool for portfolio diversification. The proposed mixed approach, combining hedonic regression and RSR, enables more precise tracking of price dynamics by accounting for both the distinctive features of artworks and price changes of the same objects over time.

Modern environmental protection efforts place significant emphasis on changing dietary habits. Consumers’ daily choices impact the health of our planet—from the amount of waste produced and greenhouse gas emissions to the exploitation of natural resources. This article discusses how different forms of interventions (e.g., educational or economic) can help consumers make more sustainable dietary decisions, as well as the level of acceptance of these interventions among the studied group.

A survey conducted on a sample of 600 people identified three groups of consumers: those open to interventions promoting sustainable consumption, those resistant to such interventions, and those with mixed feelings on the issue. The findings show that educational interventions, such as initiatives in schools, and those offering tangible benefits to consumers, like discounts on healthy products, garnered the highest acceptance. On the other hand, proposals such as higher taxes on unhealthy food faced resistance, particularly among opponents of intervention.

Many people do not adopt more sustainable food consumption practices due to a lack of awareness of their importance, insufficient knowledge on how to implement such changes, and the perception that change requires too much effort. The article highlights that the key to the effectiveness of interventions aimed at sustainable consumption is tailoring them to consumers’ needs and readiness for change. Therefore, it is essential to develop intervention strategies that account for the diverse motivations of consumers—ranging from health and convenience to financial considerations.

The use of new technologies in interventions, such as mobile apps or Internet of Things (IoT)-based solutions, which help consumers, for example, select products with a low carbon footprint, can support sustainable dietary choices. These solutions are particularly appealing to consumers who are open to innovation. By using these tools, changing habits becomes easier, and consumers receive greater support in making daily, more sustainable dietary decisions. Importantly, this support is tailored to their actual needs.

Stock liquidity refers to how easily these stocks can be bought and sold on the market. But how do we measure this ease? It’s not a straightforward task, which is why the literature offers many different metrics for liquidity. Even the term “ease” itself is vague, as each of us may interpret it differently—does it mean transactions are quick (?), cheap (?), or allow for large volumes (?). To define such imprecise concepts, like “today is warm” or “these stocks are highly liquid,” the concept of directed fuzzy numbers can be applied. Directed fuzzy numbers are useful for processing imprecise data. In our article, we use directed fuzzy numbers to assess how liquid stocks are. But this raises a question: can such an approach provide any real benefits, meaning can it more accurately reflect stock liquidity levels?

To answer this question, we decided to investigate whether investors could benefit from it by developing a profitable investment strategy. Since 1986, research has suggested that less liquid stocks should yield higher returns than more liquid ones. Therefore, if a liquidity measure based on the concept of directed fuzzy numbers reflects stock liquidity better than other metrics, it should also enable the development of an investment strategy that delivers more significant returns. The goal of our article is to analyze whether a liquidity metric based on directed fuzzy numbers can be used to build an effective investment strategy.

Our research findings indicate that by using a strategy based on directed fuzzy numbers, investors can achieve higher returns than if liquidity were measured using other metrics. The strategy presented in the article yields an average weekly return of 0.184% to 0.250%, translating into an annual return of 9.568% to 13.000%. Importantly, this strategy remains profitable even when transaction costs are taken into account. The maximum allowable transaction cost (for the strategy to remain profitable) ranges from 0.462% to 0.794%. Even if transaction costs reach 0.295% to 0.414%, the strategy based on liquidity measurement using directed fuzzy numbers would still outperform the average market investment.