Measuring the Level of Responsible Consumption Influenced by the Digital Environment: A Case Study of University of Barcelona and Bielefeld University Students

Abstract

The problem of consumerism is very relevant in the global context, as it is directly linked to the deteriorating ecological situation. Since the physical and digital environments are closely linked, the authors try to explore their interdependence in the context of responsible consumption. This research aimed to construct a tool for a comparative assessment of responsible consumption within physical and digital environments. Based on deep theoretical analysis, the authors developed the methodology of measuring the level of responsible consumption represented by the following categories: food, waste, transport, clothing, energy and water consumption, and active participation. According to this analytical system, a survey was developed and conducted among students at the University of Barcelona and the University of Bielefeld, and the results were transformed into a fuzzy-multiple model. In the digital environment analysis, the authors used an algorithm for assessing the relative presence of certain tonal and content-thematic components in the digital background of the subject under study through using the Python 3 programming language. The algorithm was tested for the assessment of the level of responsible consumption among members of two social network student communities, represented by the official accounts of the University of Barcelona and the University of Bielefeld on Instagram. The correlation between the indicators of the physical and digital environment was proved as a result of comparative analysis. The scientific novelty of the study lies in the proposed methodology, which aims to conduct a comparative analysis of responsible consumption in the physical and digital environments, while using valid but different indicators to assess consumer behavior in each of the environments. The practical implementation of the study is that the proposed methodology allows universities to investigate how their students adhere to responsible consumption and to what extent the topic of responsible consumption is expressed in informational accounts of universities and, in accordance with this, to develop measures to influence both of these parameters.

1. Introduction

Climate change is the most serious environmental problem of our time. The ice melting, rising sea levels, changing soil moisture, and other processes associated with global warming could have catastrophic consequences if the situation does not change drastically in the next 11 years. This means that the need to address this issue is a primary challenge facing humanity in the context of environmental sustainability.

One of the key factors affecting the environment is consumerism. In the modern world, consumerism is a part of our reality and philosophy. The marketing area is engulfed with appeals to consume more and more, while the availability of the various good is astonishing for the customers [1].

Currently, there is a trend to purchase the amount of material and non-material goods which significantly outweighs people’s need [2]. Adam Smith considered consumption as an aim of production, which leads to the connection between welfare and consumption. According to this, the amount of consumed goods equals welfare, while welfare can be indicated by the level of production [3]. Thus, people are trying to reach a high level of welfare linking it with happiness and satisfaction with the help of consuming goods.

Another significant factor influencing this behavioral pattern in the framework of consumerism is the highly developed digital sphere, which became an integral part of our reality. Society is adapting to new challenges, such as the COVID-19 pandemic, which accelerated the process of digitalization around the world [4]. The pandemic made society use virtual space as much as it was possible in various spheres such as business, education, social interaction, entertainment, etc. It allowed the advertising sector to use the digital environment for promoting goods and benefit from it, while the influence of the advertisement practices on consumer behaviour is essential [1].

Thus, humanity is currently consuming more resources than the term sustainability requires. The concept of “sustainability” was introduced when the scientific community realized that modern society is dynamic and uses more finite resources than it can replenish, depriving future generations of the existing resources. The European Union has established the 17 Sustainable Goals to preserve resources for future generations. These goals address different parts of the complex concept of sustainability, and Goal number 12 addresses consumption and production processes.

The harmful effects of consumerism include two types—those directly related to the phenomenon in question, such as waste disposal, and those related to production, which can be demonstrated in the example of farming and meat production, as this part of agriculture contributes to CO2 emissions the most which consequently significantly affects global warming [5].

Formerly, the opinion was that changes in consumerism should be initiated by the state, as individual choice has no impact. However, according to scientific research, it is the change in personal consumption patterns towards responsible consumption, which includes an environmentally friendly approach, that can reduce climate change by one-third.

Thus, individual-conscious consumer choice, focused on sustainability, can significantly reduce the production and consumption currently represented by the consumerism culture that thrives in modern society. That is why measuring the level of already existing responsible consumption among the social groups where this trend is most prevalent is an urgent and necessary step for further research and public action to accelerate the shift towards consciousness-raising in the context of consumption.

However, “responsible consumption” is an ambiguous and difficult to measure term. It is necessary to understand what responsible consumption consists of and how to measure it to take steps towards improving the environment. Once these actions are implemented, this research can tackle the problem of a lack of responsible consumption that is harmful to the environment.

This research project aims to construct a tool for a comparative assessment of responsible consumption among students of Bielefeld University and the University of Barcelona. To measure the level of responsible consumption in the context of a physical environment, a survey among the students of the University of Barcelona and the University of Bielefeld is used, from which a fuzzy-multiple model of the level of responsible consumption is constructed.

The hypothesis is that the level of responsible consumption in the physical environment will be higher at the University, where the indicator of responsible consumption in the digital environment is also higher. The authors understand the physical environment as an expression of the material world that surrounds a person, with which they come into contact empirically through their senses. In the digital environment, contact with reality occurs through its informational representation.

The empirical objects of this research are students of Bielefeld University and the University of Barcelona, and the official Instagram accounts of Bielefeld University and the University of Barcelona in social media. The theoretical object of this research is represented by responsible consumption in the students’ daily life, limited by the physical and digital space of the campus. The subject is the correlation between the level of responsible consumption in digital and physical environments.

The theoretical framework is based on the concept of sustainability in terms of European policies within the theories of Green growth and Degrowth, mainly referring to Ian Gough and his discussion regarding the transition to sustainable Europe and responses towards climate change. The theoretical basis of consumption is discussed within Baudrillard’s Consumer Society, and Bourdieu and Fromm’s conceptual framework.

This research project is highly relevant because it is crucial to act to reduce the impact on the environment right now when there is still no irreversible damage to the consequences of humanity’s consumer attitude to nature. The problem of resource depletion and hunger can also be tackled by increasing the level of responsible consumption. Currently, many governments, and the EU in particular, are implementing a sustainable development policy that includes responsible consumption. That is why this problem is relevant and requires close attention at the very initial stage—determining the level of responsible consumption to implement further actions that will help improve the environmental situation and act following the principles of sustainable development.

This hypothesis allows us to approbate constructed methodological tools for sociological research on the example of students of Bielefeld University and the University of Barcelona. The research has a clear structure with a step-by-step description of the process of construction of a methodological tool for assessing the level of responsible consumption in the physical and digital environment. The first part includes an analysis of the theoretical basis for the distinction of “responsible consumption” on the subcategories and specific indicators. The second part consists of the preparatory phase, which includes the development and conduction of the survey, and the development of the evaluation of each specific indicator. The realization phase of physical environment analysis contains the construction of a fuzzy-multiple model and the calculation of the results of the survey. The realization phase of the digital environment analysis contains a description of the tokenization algorithm, which was introduced in Python 3 and used for the assessment of the level of responsible consumption. The concluding part consists of a comparative analysis, a transcription of results, and recommendations for increasing the level of responsible consumption regarding the digital environment component.

The construction of the model and data collection for approbation in the physical environment was developed as an online questionnaire in three languages: English, Spanish, and German. This survey allowed us to receive data for the calculation of the level of responsible consumption on the territory of campus, which included their habits within six defined categories, the details of their actions, and their reasoning. The tokenization algorithm helped to determine the level of responsible consumption in the digital environment for comparative analysis. As a result, the comparative assessment tool was constructed, and the hypothesis was proved.

2. Literature Review

2.1. Consumption and Consumerism

The problem of consumer society is one of the most relevant in social philosophy today. The period of the formation of the “consumer society” was a transition from an industrial to a post-industrial society. From an economic point of view, the stage of social development that can be characterized as industrial represents a moment of drastic large-scale growth in various spheres, the predominance of the industrial sector over the agrarian one, mass production, and the division of labor. Due to these changes, competition increased, and there was a need to give products a greater significance to emphasize the consumer’s belonging to a certain social layer of the population.

During the transition to the information society, a reorientation emerged from the production of physical goods to information and the provision of services [6]. The information society characterizes the techno-progressive side of social relations, while the consumer society characterizes the system of values and the transformation of reality in the information age.

One of the key roles influencing the total formation of the “consumer society” has been played by the development of virtual reality and the transition of a significant part of modern individual lives to the digital environment.

The genesis and functioning of consumer society are major contemporary global issues that attract the attention of researchers from different fields of scientific knowledge. The modern consumer society is based on the following pillars: economic (mass consumption), social (urbanization), and ideological (the introduction of the attitude “to seem rather than to be” into the mass consciousness).

Baudrillard, in his work The Consumer Society, writes that in contemporary realities people are not surrounded by other individuals, but by objects of consumption, which constitute complex systems of functioning in everyday life. This concept includes the receipt of goods continuously, from everyday objects to the consumption of an advertising information component containing the message of consumption [7].

Initially, the consumer society was viewed solely from an economic perspective—as being determined by the relationship between unlimited needs and limited resources. The economic approach defines consumer behaviour as a system of consumer actions: preferences, demand for goods and services, consumption patterns, and ways of disposing of income [8].

Later, consumption was interpreted simultaneously as a function of income and as a consciously chosen alternative to saving: consumers have random and temporary changes in the level of income at different stages of the life cycle, but their consumption does not change proportionally [9].

Thus, the economic approach generally treats consumption as one of the elements of the individual’s economic activity. Consumer behaviour is rational, limited by the available resources, and it demonstrates a stability and independence of choice from other consumer groups, i.e., the economic approach does not address the fact that consumers can be motivated by social rather than economic reasons.

The socio-psychological approach focuses on the irrational nature of consumption and the personal characteristics of consumers. It contains the psychological characteristics of personality and the emotional reactions of individuals in the market for goods and services as it considers these, as consumer behaviour in general and any social actions, to be reactions to external influences.

According to Baudrillard, in the post-industrial era, consumer capacity has become a criterion of social differentiation: the only limitation to the acquisition of goods and services is a lack of money as a result of their mass replication that made everything more available. However, consumption has also acquired a symbolic character: People consume images, and through its prism they define the present and their place in it, appealing to familiar cultural codes. The idea of consuming symbols rather than objective characteristics is the basis of the concept of the post-mass consumption society.

In modern society, consumer behaviour turns out to be the main form of human social behaviour and fully reflects the social status of the group and the individual. Moreover, it acquires a symbolic nature: through the purchase of expensive goods and services people can demonstrate their high position, their ability to follow trends, etc. For example, consumption performs the function of communication, the transfer of information about a particular consumer to the surrounding people, thus affecting all areas of human life, not only purely economic but also social and political. In modern society, consumption becomes a tool that helps to construct social identity and adapt from a socially cultural perspective. Production is flexible and mass-produced so that through various goods and services a person can create and demonstrate individuality.

Modern society has shifted from consumption from a position of scarcity to a surplus. The current reality is characterized by the overconsumption of products and information, and the difficulty is related to filtering consumption by moving away from marketing-imposed desires to what a person really needs for happy and healthy life. In this case, a striking example is the minimalism movement, which is the opposite of the artificially created picture of “success” and “happiness” [10].

Sometimes the basis of the problem is deeply psychological because it is hard for people in an over-saturated physical and virtual space to separate the social construction integrated into the structure of society from the core of their interests and motivations. In other words, overconsumption is a solution for the lack of happiness and emotional satisfaction for many people. It is clear that there is a short-term effect of immediate gratitude when a purchase has been made, but with the increasing intensity of the consuming process, this effect diminishes, and a parallel with the law of unit utility in economics becomes noticeable.

Overconsumption becomes a tool imposed by the culture and drivers of capitalism, which people use to fill internal gaps. However, this is not a solution to the root of the problem but often exacerbates it, leading to the growth of mental health problems. On the contrary, minimalism as a lifestyle and the rejection of consumerism has emerged as one of the philosophical components of the degrowth theory discussed below.

In addition to the minimalist approach, there are types of consumption that can influence the economic and political systems. For example, there is the phenomenon of political consumerism, which the authors discuss below in detail.

2.2. Political Consumerism as an Action to Tackle Global Problems

Political consumerism is a specific form of consumer behaviour which consists of the choice of goods based on political and ethical convictions. This consumption may take the form of a boycott or buycott of a particular product. A boycott is a refusal to buy a product as an expression of a political position. A buycott involves labelling environmentally and ethically “fair” products that are recommended for purchase, such as those that do not infringe on animal rights or use only environmentally friendly raw materials.

The heterogeneity of expressions of political consumerism makes it impossible to identify an established social group. Some consumers boycott the products of firms that test on animals, while others choose products that do not contain environmentally harmful ingredients or buy exclusively from local producers, ignoring the big retailers.

Global examples of the manifestation of political consumption include a boycott of Nike due to a controversial case with Colin Kaepernick, the boycott of Israeli products due to conflict with Palestine, and others.

The consumer is driven by personal gain, which theoretically contrasts with the citizen, for whom the common good is more important than their own. However, with the development of mass production, politics has gradually moved into the public sphere, functioning according to market laws. While, in the past, the pursuit of individual interests was rather encouraged, it is now generating a wave of social criticism.

The notion of political participation is also changing. Initially, its forms were considered actions that influence the behaviour of the state—the exercise of voting rights, direct appeals to the authorities, or membership in parties. However, perceptions of civic duty are evolving, and the collective action of people who intend to change the world through their efforts, without the involvement of administrative resources, is already recognized as a more effective means of influencing society.

Thus, there is a reorientation towards a more flexible format—creative participation. It is considered one of the criteria of politicization, which does not contradict the traditional understanding of civic engagement, as the buyer, guided by political and ethical norms, takes responsibility for public welfare. For example, a boycott of goods produced using child labor is an endorsement of the dominant belief that child exploitation is unacceptable and a boycott by African-Americans under the slogan “Don’t Buy Where You Can’t Work” is a way of combating discrimination in the labor market and advocating social justice per se [11].

Shopping is a private sphere, but consumption patterns are not just shaped by individual choice. They vary according to institutional and cultural contexts, interfacing with the development of the state. That is, the state, and hence politics, determine patterns of consumer behaviour. This varies from country to country.

Consumption is consequently a process which has become part and parcel of the culture of capitalism, and of the concept of sustainable development which is being embedded in it. Consumption is now a structural component, which is also involved in global environmental problems such as climate change. The duality of this situation is underlined in consumption as one of the major causes of the ecological crisis and in the attempt to transform consumerism to solve global problems of environmental damage and social inequality [12].

In terms of the social constructionist approach, the situation becomes a social problem only when it is perceived in this way by society. According to a Eurobarometer survey on climate change in 2021, 78% of Europeans perceive it as a “very serious problem”. We can see a tendency towards changing the perspective on the issue of global warming in comparison with the years 2017 and 2021. In 2017, poverty, hunger, and a lack of drinking water were the most important global problem for 28% of Europeans, international terrorism was the most for 24%, and only 12% believed climate change was the most important global problem. In 2021, the results were completely different because 18% of Europeans thought that climate change was the most serious problem whilst poverty, hunger, and a lack of drinking water were chosen only by 17%, the same 17% chose the spread of infectious diseases as the most important global problem [13].

It is necessary to refer to the Sustainable Development Goals to form the basis for the indicator characteristics of responsible consumption [14].

Within the framework of this goal, the following targets were indicated:

• Implement the 10-year sustainable consumption and production framework [15];
• Sustainable management and use of natural resources;
• Halve global per capita food waste;
• Responsible management of chemicals and waste;
• Substantially reduce waste generation;
• Encourage companies to adopt sustainable practices and sustainability reporting;
• Promote sustainable public procurement practices;
• Promote universal understanding of sustainable lifestyles;
• Support developing countries’ scientific and technological capacity for sustainable consumption and production;
• Develop and implement tools to monitor sustainable tourism [16];
• Remove market distortions that encourage wasteful consumption.

Thus, after detailed comprehension of the processes behind every target, we can indicate four categories: waste (including waste management and recycling), food (with an emphasis on food waste as a global problem), natural resources (represented by energy and water in this research), and promotion or active participation (indicated as awareness and taking action to achieve SDG). With deepening initiatives aimed to promote a sustainable lifestyle, we can determine two more categories: transport (how to reduce CO2 emissions and carbon footprint) and clothes (opposition to fast-fashion brands and re-use practices).

Responsible consumption is one of the aspects of sustainable development that is addressed to the greatest extent now [17]. Sustainability includes political aspects aimed at improving the social environment and achieving equal opportunities for all citizens, as well as environmental and economic measures aimed at the efficient use of resources and reducing environmental damage. It is related to the development of consumer behaviour culture and increased consumption, as these interrelated phenomena lead to increased environmental damage.

Responsible consumption can be considered on different scales, and equitable resource allocation can be carried out not only among states but also between states and regions [18].

The following concepts were considered in the context of the principles of sustainable consumption:

• Sufficiency;
• Eco-friendly consumption;
• Reducing the use of natural resources;
• Reducing waste.

Sufficiency refers to the consumption of material goods that achieves a better quality of life and meets current needs. The essence of eco-friendly consumption is a preference for those goods and services whose production and provision of services by companies cause the least possible damage to the environment and meet all environmental standards [19].

Reducing the use of natural resources also addresses the definition of sustainability, related to the need to provide the next generations with enough resources to meet their needs. Thus, this aspect aims to conserve exhaustible natural resources and develop alternatives to these resources for their further safe consumption.

Reducing waste and reducing it to zero, i.e., carrying out recycling, is the basis of the circular economy [20]. Which, in turn, seeks to maximize the efficient use of all resources and not to pollute the environment with hazardous waste from production and consumption [21]. Thus, each of the above aspects demonstrates the manifestation of the objective of all sustainable initiatives, the reduction of the negative impact on the environment. Examples of that influence from unsustainable consumer behaviour are climate change, eutrophication, and stratospheric ozone layer reduction.

2.3. Sociological Perspective of “Sustainability”

The sociological approach to sustainability differs from the most common approach of sustainability studies, where sustainability is perceived more as a principle, in the context of societal conditions and requirements for implementation [22]. On the other hand, sociology takes a problem-oriented stance in the context of sustainability, which opens up a discussion about the paradoxes and dilemmas of the phenomenon.

Thus, sustainability can become another field of study of society and its changes due to the integration of sustainability criteria and principles into institutional structures. This approach opens up a space for considering the formation of inequalities, social and economic changes, and the forms of social order arising from the implementation of sustainable policies.

Indeed, a sociological perspective on sustainability includes the study of political structures as major actors reshaping the social order to act following sustainable principles. It also allows us to identify groups of actors with various levels of “power to name” and opportunities to reap the benefits [23].

For example, a sociological perspective on sustainability can look at increasing social inequalities, using the example of the social-environmental initiative of Ecological Basic Income [24]. In terms of social justice, this initiative can be controversial. Because of the redistributive effect, all groups of the population can choose more environmentally friendly products, and the main source of this income will be the high-income groups of the population. Thus, in addition to a regulatory price policy, EBI represents freedom and the opportunity to choose more sustainable and expensive products. However, there are also criticisms that some of the most environmentally damaging products or services, such as air travel, will only be available to the high-income group of the population, which will worsen social inequalities and segregation [25].

Therefore, sustainability is a socially unstable category that can be approached by sociology as a value model open to public discourse and redefinition. The approach requires a reflective perspective that cannot perceive sustainability in isolation from the structures of global capitalism. These conditions represent a cultural approach, shaping ways of thinking in addition to economic and social implications [22].

The main current debate in terms of sustainability and capitalism revolves around the nature of the interaction of these concepts and the study of their interaction. Can sustainability be turned into profit, or is there a need for a transition of the economy?

The problem of such research is global because the Anthropocene ecological crises (climate change) have a worldwide scale. These ecological crises also exacerbate the problem of global inequality between the developed North and the South, which is much more dependent on local livelihoods. Thus, sustainable development becomes one of the reasons for the modernization of capitalism, especially in the context of ecology [26].

The problems of social reproduction related to sustainable development are the necessity to ensure that future development is possible even with the current threats of resource problems. Furthermore, all renewable resources currently contributing to the energy sector need to be preserved for future exploitation.

From a sociological perspective, the integration of sustainability into existing systems of capitalism represents a broad research perspective, i.e., exploration of the possibilities for societal transformation, communal eco-efficient economies, and lifestyles. The research on these topics with a liberal values approach represents an interesting scientific area.

2.4. Human Behaviour and Sustainability

The problem with sustainability in terms of human behaviour is the disconnection between the individual’s view of the world and personal history. The individual feels no impact on the future, which leads to a lack in the visibility of causality. In other words, people do not feel an impact in their lives with more sustainable patterns of behaviour, which makes them less likely to be implemented. The problem with sustainability is the lack of a powerful and personally affecting story to break the habitual chain of consumer behaviour built up to automatism and it is deeply embedded habits that drive our behaviour, that cannot be ignored or easily changed, therefore, habits have predominance above reasoning. As a result, the shift towards sustainability can be carried out either through behavioral practice reprogramming or changes in values and beliefs [27].

In addition, one of the motivations for this kind of shift is the focus on a catastrophic future scenario where climate change is inevitable. However, the dystopian perspective is a less effective strategy for inducement due to the nature of the emotional basis of fear and hopelessness rooted in this approach. Moreover, the correlation between global crises and daily life matters is difficult to be reconciled with an awareness of impending danger. This mechanism does not work because fear is only a powerful tool when the threat is immediate. The abstract fear of losing something unidentifiable after death cannot be compared with the power of immediate gratification that an individual experiences when buying something.

Thus, the term “intention behaviour gap” occurs, which describes the phenomenon when people do not behave sustainably even if they have intentions to do so [28]. The initial problem could be the lack of information about climate change crises, but the absolute majority of the European population thinks it is the most important global problem. Thus, the level of awareness of climate change is indeed high so why do people not live sustainably? The fact is that people are very good at rationalizing their unsustainable decisions by blaming corporations, industries, and governments. By absolving ourselves of responsibility, we justify unsustainable behaviour and continue to harm the planet, and this is the problem with our way of life. We are part of the problem.

To solve this, we need to create stories that are directly connected to our lives, happiness, and well-being. We need to create connections between personal well-being and planetary sustainability, so we can experience these interactions in our daily choices. Behavioral change organizations work according to this principle by addressing both the demand and the beneficial components. The Planetary Health Diet, for example, does not call for the abandonment of local traditions in the context of meat consumption but adjusts the diet to be both healthy and sustainable [29]. An individual has a direct interest in increasing their life expectancy and improving their physical condition, and this is an example of a personal story with sustainability principles. Another example is the slow-food movement, created as a protest to a fast pace of life and a similar way of thinking.

According to a Eurobarometer opinion survey, the importance of environmental protection in the European Union is described by 53% as “very important” and 41% as “important”. Climate change as the single most serious problem facing the world as a whole was chosen by 18% of the population of the European Union in 2021, for the first time becoming a leading problem with an absolute majority of votes. Moreover, 78 % of people in the EU consider climate change as “a very serious problem” [13].

According to the environmental survey, the public opinion is that the most effective way of tackling environmental problems is by changing the way we consume [30]. Given the possibility of multiple choice, 33% of the EU population (an absolute majority) believe that this is the solution to the problem.

Surprisingly, if you look at the statistics of answers concerning who within the EU is responsible for tackling climate change, the ranking is as follows (with a multiple choice option):

• National governments—63%;
• Business and industry—58%;
• The European Union—57%;
• Regional and local authorities—43%;
• You personally—30%.

These statistics illustrate the mechanism for rationalizing unsustainable behaviour and the shifting of responsibility mentioned earlier. While most people are convinced that it is possible to solve environmental problems by changing consumer behaviour patterns, when it comes to taking responsibility for this, personal choice is only in fifth place.

The Internet is the second largest source of environmental information, but after the pandemic, this figure may increase due to the greater integration of virtual reality into our lives. The focus of this study is to explore the relationship and difference between responsible consumption in the physical and digital environments, which led to the formation of the following research methodology.

3. Methodology

3.1. Explanation of Limitation of the Research Project by University Space and Comparability

As a validation of the use of the proposed comparative toolkit, the authors propose a consideration of the physical and digital environment of the University of Bielefeld and the University of Barcelona. This choice is motivated by the reasons described below.

Firstly, students represent the best option of a social group to demonstrate the most pronounced results of the study. The age range of the individuals surveyed mostly corresponds to the younger generations, who, according to research, are most concerned about current environmental issues. In a 2018 survey conducted by global consultancy Deloitte, 77% of Generation Z respondents said it was important for them to work for organizations whose values align with theirs. Social values matter to this group, and especially the issue of climate change—in the US, members of Generation Z (people in their teens to mid-20s) are much more concerned about climate change than older generations. Similarly, in the UK, health insurer Bupa found in 2021 that 64% of 18- to 22-year-olds surveyed believe it is important for employers to take action on environmental issues, and 59% are willing to work longer with responsible employers. In Australia, young workers are leaving companies that do not do enough to respond to climate change.

In addition to environmental concerns, Generation Z is the most adapted to, and therefore more influenced by, the digital environment. Because of this, the correlation between the physical and the digital environment can be demonstrated more clearly.

Finally, university students are united by a desire for knowledge and education, and since sustainability issues have gained considerable traction and support from states and organizations in Europe in recent decades, students represent the most knowledgeable group of people in the context of climate change and other global issues.

Due to the limitation of the analyzed physical and information media to the university space, the data obtained in the study are comparable, which is a prerequisite for comparative analysis.

Although there are significant differences between the University of Bielefeld and the University of Barcelona, the sociological tool constructed allows us to measure the level of responsible consumption of each of them in a comparative way. The universities differ in terms of location and country (Central Europe, Northern Germany in case of Bielefeld University; Southern Europe, Catalonia in case of University of Barcelona), year of foundation, number of students, and campus distribution. In the case of Bielefeld, all faculties are located in the same location, whereas the University of Barcelona is represented by different buildings within the city. However, what they have in common is the focus on sustainable development, in the case of Bielefeld this is due to the developed Faculty of Social Sciences’ environmentally oriented attitude around the city and region, in the case of the University of Barcelona this is due to the strategic development plan 2030, a separate online resource dedicated to the goals of sustainable development within the framework of the university.

In addition, the consideration of the percentage of students‘ involvement in social networks (official Instagram accounts of universities), the diversity of the response sample, including representatives of different levels of education, undergraduate, graduate, postgraduate, and research internships, and the overall assessment of reasons and approaches, allows us to talk about the comparability of the study objects and the representativeness of the information obtained.

Thus, the authors compare the level of responsible consumption in the physical environment of the university campus by conducting a survey. Moreover, when considering the virtual environment, the digital space of universities is used, which allows us to further calculate the correlation between the obtained ratios of responsible consumption levels.

3.2. Identification of Components of Level of Responsible Consumption in the Physical Environment

Based on a review of the literature, the authors propose to divide the concept of “responsible consumption” into the following six categories, which were discussed earlier: clothing, food, waste, energy and water, transport, and active participation.

Each of these categories will be represented by specific indicators measured by means of a survey with the help of developed system of evaluation criteria. Each of the individual indicators represents a consumer choice based on personal desire, an opportunity provided at the organizational level (the university in this case), or an opportunity provided by the government.

This is due to the need to consider the most important reasons for the exercise of certain elections, which may be represented by more than just a personal expression of will. The set of reasons formulated to assess each of the 6 categories of responsible consumption was identified in the observation conducted by the author of this study in both universities under the Erasmus+ student mobility programme.

Therefore, for each particular indicator, the most relevant decision influencing factor will be identified. For example, a student’s participation at a university conference on waste-management issues will correspond to the organizational level, while the choice of public transport because of the price policy for students will correspond to the government level. This ranking is necessary to assign the most accurate specific weights to each individual indicator. Since the aim of this study is to identify the impact of the level of responsible consumption in the digital environment on the level of responsible consumption in the physical environment, the private indicators will be ranked in the following order according to decision-making factors:

Personal level, identified as “1”;

Organizational level, identified as “2”;

Governmental level, identified as “3”.

The distribution of the specific weights of the individual indicators was carried out in accordance with combination of Fishburne’s law and relativeness to levels’ structure [31]. Below is the formula for calculating the significance of each indicator in the ranked series:

$${\mathrm{r}}_{\mathrm{i}}=\frac{2\times \left(\mathrm{N} - \mathrm{i} +1\right)}{\left(\mathrm{N} +1\right)\times \mathrm{N}}$$

(1)

where N is the number of indicators in the ranking series;

i is the order number of the indicator in the ranking series.

Thus, the lowest weight will be given to private indicators describing the government level, followed by the organizational level, and the highest weight will be given to private indicators describing the personal level. This is due to the specific nature of this study, in which the digital environment in the context of the university has the greatest impact on students’ personal private choices.

However, those indicators that reflect the organizational and governmental levels also reflect the level of responsible consumption and should be taken into account, but for the most reliable result, these categories of indicators reflect a lower specific weight, respectively.

Based on the results of the survey, the frequent indicators will be ranked according to the degree of relevance of each category, depending on the students’ goalsetting. Every indicator out of 6 categories is evaluated in the range from 0% to 100%. The final indicator value of each category is calculated individually for every respondent, depending on the track of the answers. The minimum and maximum values are used for further fuzzy multiple model construction. The average values are used for comparative analysis and approbation of the model illustrated on the Figure 1.

3.3. Survey Development for the Assessment the Level of Responsible Consumption in the Physical Environment

The service “Examinare” was chosen for the survey, for several reasons: ability to use skip logic in the questions to shorten the time to complete the survey, security policy of data, etc. Furthermore, as the survey was carried out among university students from two different countries, it was necessary to present the survey in three languages: English, German, and Spanish.

All of the specific indicators with reasoning options were compiled based on the observation made by one of the authors during individual study experience at the University of Bielefeld and at the University of Barcelona as an exchange student. In this study, the indicators and reasons most relevant to students are examined to assess the level of responsible consumption.

A 5-category scale with a minimum value of 1 and a maximum value of 100 is used to quantify each of the indicators. As the nature of the distribution of weights is determined by Fishburne’s law, the minimum value cannot be 0, otherwise the final value of specific indicators such as “food”, “waste” etc. may be distorted. In addition, a number of questions are characterized by a positive indicator orientation, where the highest degree corresponds to the maximum indicator of responsible consumption, but there are also questions with a reverse logic, where the highest degree of compliance is characterized by the minimum value of the indicator. For this category of questions, it is also necessary to use a maximum value equal to one for informative calculations. Below is

Table 1. Correspondence between semantic and numerical values of indicators.

Semantic Value	Direct Numerical Value	Reverse Numerical Value
To a very small degree	1	100
To a small degree	25	75
Partially	50	50
To a high degree	75	25
To a very high degree	100	1

showing the correspondence between semantic and numerical values.

3.3.1. Food Category

This category, along with the category “waste”, represents the longest branch of components and indicators of several sub-levels, which are:

• Self-prepared food;
• Cafeteria.

Each of these sub-categories is made up of several elements: reasoning, food leftovers, and meat consumption. Within self-prepared food, buying habits are also taken into account, which influence the level of responsible consumption within a meal. The “buying habits” indicator consists of the place where food is purchased (local markets, supermarkets), the removal of eco-labelling, as well as the importance for the student of the producer company’s compliance with the principles of sustainability. Thus, the following formula is used to calculate the relative level of buying habits 𝐿_𝐵𝐻:

$${\mathrm{L}}_{\mathrm{BH}}=\frac{\left({\mathrm{l}}_1+{ \mathrm{l}}_2+{ \mathrm{l}}_3+{ \mathrm{l}}_4\right)}{4\times 100}$$

(2)

where 𝑙₁ is “Buy food products in supermarkets”;

𝑙₂ is “Buy food products at local markets, from local farmers”;

𝑙₃ is “Pay attention to eco-friendly labelling”;

𝑙₄ is “Pay attention to the company producer and check if it is sustainable”.

In addition, for each sub-category, the specific weights of each specific indicator are calculated according to Fishburne’s Law on the basis of belonging to one of the three levels of importance: individual, organizational, and governmental. For example, the table below shows the calculations and specific weights for the indicator “reasoning” in the sub-category “self-prepared food”.

Thus, for the calculation of the “reasoning for self-prepared food” indicator authors use the following formula:

$${\mathrm{R}}_{\mathrm{Sp}}={ \mathrm{L}}_{\mathrm{BH}}\ast {\sum }_{\mathrm{i}=1}^4{\mathrm{I}}_{{\mathrm{sp}}_{\mathrm{i}}}{\ast \mathrm{r}}_{{\mathrm{sp}}_{\mathrm{i}}}$$

(3)

where 𝐿_𝐵𝐻 is “Relative level of buying habits”;

${\mathrm{I}}_{{\mathrm{sp}}_{\mathrm{i}}}$ is indicator described in

Table 2. Reasoning indicators for self-prepared food.

Fishburne Weights Calculation	Redistributed Weights (𝑟𝑠𝑝)	Indicator (𝐼𝑠𝑝)	Level of Indicator
0.4	0.35	I can control the producer from which I buy the product	1
0.3	0.35	It is healthier (the quality is better than in the cafeteria)	1
0.2	0.2	There is no appropriate option for me in the cafeteria (no vegan option, for example)	2
0.1	0.1	Cheaper	3

below;

${\mathrm{r}}_{{\mathrm{sp}}_{\mathrm{i}}}$ is redistributed weight of each indicator from Table 2.

To calculate the next indicator, which is “Reasoning for canteen”, the calculation is made according to the indicators presented in

Table 3. Reasoning indicators for canteen option.

Fishburne Weights Calculation	Redistributed Weights (𝑟c)	Indicator (𝐼c)	Level of Indicator
0.4	0.4	I can control the producer from which I buy the product	1
0.3	0.2	It is healthier (the quality is better than in the cafeteria)	2
0.2	0.2	There is no appropriate option for me in the cafeteria (no vegan option, for example)	2
0.1	0.2	Cheaper	2

below. This table also shows the level and unit weights.

Thus, for the calculation of the “reasoning for canteen” indicator authors use the following formula:

$${\mathrm{R}}_{\mathrm{c}}={ \mathrm{L}}_{\mathrm{BH}}\ast {\sum }_{\mathrm{i}=1}^4{\mathrm{I}}_{{\mathrm{c}}_{\mathrm{i}}}{\ast \mathrm{r}}_{{\mathrm{c}}_{\mathrm{i}}}$$

(4)

where ${\mathrm{I}}_{{\mathrm{c}}_{\mathrm{i}}}$ is indicator of reasoning for canteen food described Table 3 above;

${\mathrm{r}}_{{\mathrm{c}}_{\mathrm{i}}}$ is redistributed weight of each indicator from Table 3.

The next subcategory in terms of food is “food leftovers”. There are two branches of responses in this subcategory, which are indexed according to the value. If the respondent, when asked what they do with leftovers, chooses “Mostly take away and eat later”, the index is 1, if they choose “mostly throw away”, the index is 0.5. The logic of indexation is also due to Fishburne’s Law and demonstrated in

Table 4. Index system for “food leftovers” indicator.

Fishburne Weights Calculation	Index (𝐼𝐿)	Option
0.67	1	Mostly take away and eat later
0.33	0.5	Mostly throw away

below.

Thus, the first branch of the “reasoning” indicator is related to the reasons why the respondent takes it away and is presented in

Table 5. Reasoning indicators for taking away food leftovers.

Fishburne Weights Calculation	Redistributed Weights (rLt/a)	Indicator (ILt/a)	Level of Indicator
0.5	0.42	I can eat it later and spend less money	1
0.333	0.42	I do not want to contribute to the waste of food	1
0.167	0.17	The portion is huge for me, but the quality and/or taste are good	2

below with the distribution of unit weights and belonging to a certain level.

In the following

Table 6. Reasoning indicators for throwing away food leftovers.

Fishburne Weights Calculation	Redistributed Weights (rLt)	Indicator (ILt)	Level of Indicator
0.5	0.5	It is not convenient to carry leftover food with me	1
0.333	0.25	I throw it away when the quality and/or taste are bad	2
0.167	0.25	There is no option to take it away (when I eat at the canteen)	2

below, there are indicators for reasoning for throwing away food leftovers with the distribution of unit weights and belonging to a certain level.

Thus, for the calculation of the “reasoning for food leftovers” indicator authors use the following formula in case of taking away:

$${\mathrm{R}}_{\mathrm{L}}={ \mathrm{I}}_{\mathrm{L}}\ast {\sum }_{\mathrm{i}=1}^3{\mathrm{I}}_{{\mathrm{L}}_{\mathrm{t}/{\mathrm{a}}_{\mathrm{i}}}}{\ast \mathrm{r}}_{{\mathrm{L}}_{\mathrm{t}/{\mathrm{a}}_{\mathrm{i}}}}$$

(5)

where ${\mathrm{I}}_{\mathrm{L}}$ is index of taking away option with food leftovers;

${\mathrm{I}}_{{\mathrm{L}}_{\mathrm{t}/\mathrm{a}}}$ is indicator of reasoning for taking away food leftovers described in Table 5 above;

${\mathrm{r}}_{{\mathrm{L}}_{\mathrm{t}/\mathrm{a}}}$ is redistributed weight of each indicator from Table 5.

And in case of throwing away food leftovers, the formula below is used:

$${\mathrm{R}}_{\mathrm{L}}={ \mathrm{I}}_{\mathrm{L}}\ast {\sum }_{\mathrm{i}=1}^3{\mathrm{I}}_{{\mathrm{L}}_{{\mathrm{t}}_{\mathrm{i}}}}{\ast \mathrm{r}}_{{\mathrm{L}}_{{\mathrm{t}}_{\mathrm{i}}}}$$

(6)

where 𝐼_𝐿 is index of throwing away option with food leftovers;

𝐼_𝐿𝑡 is indicator of reasoning for throwing away food leftovers described in Table 6 above;

𝑟_𝐿𝑡 is redistributed weight of each indicator from Table 6.

The next sub-category describes meat consumption, and is divided into two survey lines: (1) people who do not consume meat, specifically vegans and vegetarians, (2) people who do consume meat, namely flexitarians and meat-consumers. Each response category is indexed according to Fishburne’s Law and these indexes are presented in

Table 7. Index system for type of meat consumption.

Fishburne Weights Calculation	Index (𝐼𝑚𝑐)	Option
0.4	1	Vegan
0.3	0.75	Vegetarian
0.2	0.5	Flexitarian
0.1	0.25	Meat consumer

below.

The first branch of the “reasoning” indicator is related to the respondents who do not consume meat at all is presented in

Table 8. Reasoning indicators for not consuming meat.

Fishburne Weights Calculation	Redistributed Weights (rmcv)	Indicator (Imcv)	Level of Indicator
0.5	0.4167	It is healthier not to eat meat	1
0.333	0.4167	It is better for the environment not to eat meat	1
0.167	0.167	I cannot eat meat for medical reasons	2

below with the distribution of unit weights and belonging to a certain level.

In the following

Table 9. Reasoning indicators for consuming meat.

Fishburne Weights Calculation	Redistributed Weights (rmcn/v)	Indicator (Imcn/v)	Level of Indicator
0.5	0.4167	I do not have any reasons to stop eating meat	1
0.333	0.4167	It affects my physical condition and energy and I cannot reject eating meat	1
0.167	0.167	It is part of my (my family/region/country) food culture to eat meat	2

below, there are indicators for reasoning behind eating meat with the distribution of unit weights and belonging to a certain level.

Thus, for the calculation of the “reasoning for meat consumption” indicator authors use the following formula in case of non-consuming meat options:

$${\mathrm{R}}_{\mathrm{mc}}={ \mathrm{I}}_{\mathrm{mc}}\ast {\sum }_{\mathrm{i}=1}^3{\mathrm{I}}_{{\mathrm{mc}}_{{\mathrm{v}}_{\mathrm{i}}}}{\ast \mathrm{r}}_{{\mathrm{mc}}_{{\mathrm{v}}_{\mathrm{i}}}}$$

(7)

where 𝐼_𝑚𝑐 is Index according to categories “vegan” or “vegetarian”;

${\mathrm{I}}_{{\mathrm{mc}}_{\mathrm{v}}}$ is indicator of reasoning for not consuming meat described in Table 8 above;

${\mathrm{r}}_{{\mathrm{mc}}_{\mathrm{v}}}$ is redistributed weight of each indicator from Table 8.

And in case of consuming meat options, the formula below is used:

$${\mathrm{R}}_{\mathrm{mc}}={ \mathrm{I}}_{\mathrm{mc}}\ast {\sum }_{\mathrm{i}=1}^3{\mathrm{I}}_{{\mathrm{mc}}_{\mathrm{n}/{\mathrm{v}}_{\mathrm{i}}}}{\ast \mathrm{r}}_{{\mathrm{mc}}_{\mathrm{n}/{\mathrm{v}}_{\mathrm{i}}}}$$

(8)

where ${\mathrm{I}}_{\mathrm{mc}}$ is index according to categories “flexitarian” or “meat consumer”;

${\mathrm{I}}_{{\mathrm{mc}}_{\mathrm{n}/\mathrm{v}}}$ is indicator of reasoning for consuming meat described in Table 9 above;

${\mathrm{r}}_{{\mathrm{mc}}_{\mathrm{n}/\mathrm{v}}}$ is redistributed weight of each indicator from Table 9.

Thus, the final formula for Specific Indicator of “Food” category with the line of “Self- prepared food” is presented below:

$${\mathrm{SI}}_{\mathrm{F}}=\frac{\left({\mathrm{R}}_{\mathrm{sp}}+{ \mathrm{R}}_{\mathrm{L}}+{ \mathrm{R}}_{\mathrm{mc}}\right)}{3}$$

(9)

and the final formula for Specific Indicator of “Food” category with the line of “Canteen” is presented below:

$${\mathrm{SI}}_{\mathrm{F}}=\frac{\left({\mathrm{R}}_{\mathrm{C}}+{ \mathrm{R}}_{\mathrm{L}}+{ \mathrm{R}}_{\mathrm{mc}}\right)}{3}$$

(10)

and the skip-logic algorithm, used for questionnaire is schematically demonstrated in Figure 2 below:

3.3.2. Waste Category

This category, along with the category “Food”, represents the longest branch of components and indicators of several sub-levels, which are:

• Drinking options;
• Separation of waste;
• Dishes.

In the context of the drinking options indicator, two possible lines of response are considered, the first is “multiple-use bottle and/or cup”, and the second is “vending machines”. These options are indexed in terms of sustainability in the context of the category “Waste”. As the contribution to plastic waste differs in these sub-categories, this needs to be taken into account and the index system is shown in the

Table 10. Index system for type of meat consumption.

Fishburne Weights Calculation	Index (𝐼w)	Option
0.67	1	Bring drinks with me in multiple-use bottle and/or cup
0.33	0.5	Buy drinks in vending machines

below.

The following

Table 11. Reasoning indicators for consuming meat.

Fishburne Weights Calculation	Redistributed Weights (rwm/b)	Indicator (Iwm/b)	Level of Indicator
0.4	0.4	It is better for the environment, I am trying to avoid buying single-use plastic	1
0.3	0.2	I can fill the bottle on the territory of the university (special fountain, tap)	2
0.2	0.2	It is cheaper, I am not spending money on drinks	2
0.1	0.2	There is a special promotion if use your cup (discounts, benefits)	2

below shows the different reasons and the weightings of their indicators reflecting the value of reasoning for multiple-use bottles/cups in the “Waste” category.

Thus, for the calculation of the reasoning for multiple-use bottles/cups indicator authors use the following formula:

$${\mathrm{R}}_{\mathrm{dr}}={ \mathrm{I}}_{\mathrm{w}}\ast {\sum }_{\mathrm{i}=1}^4{\mathrm{I}}_{{\mathrm{w}}_{\mathrm{m}/{\mathrm{b}}_{\mathrm{i}}}}{\ast \mathrm{r}}_{{\mathrm{w}}_{\mathrm{m}/{\mathrm{b}}_{\mathrm{i}},}}$$

(11)

where 𝐼_𝑤 is an index of sustainability of this type of drinking option;

${\mathrm{I}}_{{\mathrm{w}}_{\mathrm{m}/\mathrm{b}}}$ is indicator described in Table 11 above;

${\mathrm{r}}_{{\mathrm{w}}_{\mathrm{m}/\mathrm{b}}}$ is redistributed weight of each indicator from Table 11.

For the drinking option “vending machines”, the authors also provide indexing in

Table 12. Index system for vending machine option.

Fishburne Weights Calculation	Index (𝐼vm)	Option
0.67	1	Hot beverages
0.33	0.5	Drinks in bottles

below related to the different level of significance in the context of responsible consumption, as well as materials when buying hot drinks and bottled beverages.

The following

Table 13. Reasoning indicators for vending machine option.

Fishburne Weights Calculation	Redistributed weights (rwv)	Indicator (Iwv)	Level of Indicator
0.66	0.5	It is not convenient to bring my bottle/cup with me	1
0.33	0.5	I drink rarely and buy drinks only if I need	1

below shows the different reasons and the weightings of their indicators reflecting the value of reasoning for vending machines in the “Waste” category.

Thus, for the calculation of the reasoning for vending machines indicator authors uses the following formula:

$${\mathrm{R}}_{\mathrm{dr}}={ \mathrm{I}}_{\mathrm{vm}}{\ast \mathrm{I}}_{\mathrm{w}}\ast {\sum }_{\mathrm{i}=1}^2{\mathrm{I}}_{{\mathrm{w}}_{{\mathrm{v}}_{\mathrm{i}}}}{\ast \mathrm{r}}_{{\mathrm{w}}_{{\mathrm{v}}_{\mathrm{i}}}},$$

(12)

where 𝐼_𝑤 is an index of sustainability of this type of drinking option;

𝐼_𝑣𝑚 is an index of sustainability of this type of drinks in vending machine;

${\mathrm{I}}_{{\mathrm{w}}_{\mathrm{v}}}$ is indicator described in Table 13 above;

${\mathrm{r}}_{{\mathrm{w}}_{\mathrm{v}}}$ is redistributed weight of each indicator from Table 13.

Since the degree of impact on the environment of these options regarding waste separation varies,

Table 14. Index system for waste separation.

Fishburne Weights Calculation	Index (𝐼vm)	Option
0.67	1	Mostly separate waste
0.33	0.5	Mostly do not separate waste

below presents a system of indexes that takes these differences into account.

The following

Table 15. Reasoning indicators for waste separation.

Fishburne Weights Calculation	Redistributed Weights (rs/w)	Indicator (Is/w)	Level of Indicator
0.4	0.4	It is better for the environment	1
0.3	0.2	There is a convenient functioning system of waste separation on the territory of the University	2
0.2	0.2	There are only separate waste bins on the territory of campus, no other options	2
0.1	0.2	I can receive a fine/reprimand for not doing it	2

below shows the different reasons and the weightings of their indicators reflecting the value of reasoning for waste separation in the “Waste” category.

Thus, for the calculation of the reasoning for waste separation indicator author uses the following formula:

$${\mathrm{R}}_{\mathrm{sw}}={ \mathrm{I}}_{\mathrm{sw}}\ast {\sum }_{\mathrm{i}=1}^4{\mathrm{I}}_{\mathrm{s}/{\mathrm{w}}_{\mathrm{i}}}{\ast \mathrm{r}}_{\mathrm{s}/{\mathrm{w}}_{\mathrm{i}}}$$

(13)

where ${\mathrm{I}}_{\mathrm{sw}}$ is an index of sustainability of waste separation option;

${\mathrm{I}}_{\mathrm{s}/\mathrm{w}}$ is indicator described in Table 15 above;

${\mathrm{r}}_{\mathrm{s}/\mathrm{w}}$ is redistributed weight of each indicator from Table 15.

The following

Table 16. Reasoning indicators for not separating waste.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} (r_{N_{s/w}})$	$\mathbf{Indicator} (I_{N_{s/w}})$	Level of Indicator
0.5	0.42	It is not convenient for me personally	1
0.33	0.42	I do not think about it	1
0.17	0.17	There are no options to separate waste on	2

below shows the different reasons and the weightings of their indicators reflecting the value of reasoning for not separating waste in the “Waste” category.

Thus, for the calculation of the reasoning for not separating waste indicator author uses the following formula:

$${\mathrm{R}}_{\mathrm{sw}}={ \mathrm{I}}_{\mathrm{sw}}\ast {\sum }_{\mathrm{i}=1}^3{\mathrm{I}}_{{\mathrm{N}}_{{\frac{\mathrm{s}}{\mathrm{w}}}_{\mathrm{i}}}}{\ast \mathrm{r}}_{{\mathrm{N}}_{\mathrm{s}/\mathrm{w}}}{}_{\mathrm{i}}$$

(14)

where ${\mathrm{I}}_{\mathrm{sw}}$ is an index of sustainability of waste separation option;

${\mathrm{I}}_{{\mathrm{N}}_{\mathrm{s}/\mathrm{w}}}$ is indicator described in Table 16 above;

${\mathrm{r}}_{{\mathrm{N}}_{\mathrm{s}/\mathrm{w}}}$ is redistributed weight of each indicator from Table 16.

The different categories of dishes (multiple-use or single-use) used by students in the canteen should also be indexed, as they have different impacts on the environment as part of sustainability. This indexation is shown in

Table 17. Index system for dishes category.

Fishburne Weights Calculation	Index (𝐼D)	Option
0.67	1	Mostly multiple-use
0.33	0.5	Mostly single-use

below.

The following

Table 18. Reasoning indicators for multiple-use dishes.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left(r_{D_m}\right)$	$\mathbf{Indicator} \left(I_{D_m}\right)$	Level of Indicator
0.67	0.67	Prefer multiple-use because it is better for the environment	1
0.33	0.33	There is only a multiple-use dishes option	2

below shows the different reasons and the weightings of their indicators reflecting the value of reasoning for using multiple-use dishes in the “Waste” category.

Thus, for the calculation of the multiple-use dishes indicator author uses the following formula:

$${\mathrm{R}}_{\mathrm{D}}={ \mathrm{I}}_{\mathrm{D}}\ast {\sum }_{\mathrm{i}=1}^2{\mathrm{I}}_{{\mathrm{D}}_{{\mathrm{m}}_{\mathrm{i}}}}{\ast \mathrm{r}}_{{\mathrm{D}}_{\mathrm{m}}}{}_{\mathrm{i}}$$

(15)

where ${\mathrm{I}}_{\mathrm{D}}$ is an index of sustainability of dishes option;

${\mathrm{I}}_{{\mathrm{D}}_{\mathrm{m}}}$ is indicator described in Table 18 above;

${\mathrm{r}}_{{\mathrm{D}}_{\mathrm{m}}}$ is redistributed weight of each indicator from Table 18.

The following

Table 19. Reasoning indicators for single-use dishes.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left(r_{D_s}\right)$	$\mathbf{Indicator} \left(I_{D_s}\right)$	Level of Indicator
0.67	0.5	It is not convenient to bring back multiple-use dishes after I eat	1
0.33	0.5	I want to take away my food	1

below shows the different reasons and the weightings of their indicators reflecting the value of reasoning for using single-use dishes in the “Waste” category.

Thus, for the calculation of the single-use dishes indicator author uses the following formula:

$${\mathrm{R}}_{\mathrm{D}}={ \mathrm{I}}_{\mathrm{D}}\ast {\sum }_{\mathrm{i}=1}^2{\mathrm{I}}_{{\mathrm{D}}_{{\mathrm{s}}_{\mathrm{i}}}}{\ast \mathrm{r}}_{{\mathrm{D}}_{\mathrm{s}}}{}_{\mathrm{i}}$$

(16)

where ${\mathrm{I}}_{\mathrm{D}}$ is an index of sustainability of dishes option;

${\mathrm{I}}_{{\mathrm{D}}_{\mathrm{s}}}$ is indicator described in Table 19 above;

${\mathrm{r}}_{\mathrm{D}}{}_{\mathrm{s}}$ is redistributed weight of each indicator from Table 19.

And the final formula for Specific Indicator of “Waste” category is presented below:

$${\mathrm{SI}}_{\mathrm{W}}=\frac{\left({\mathrm{R}}_{\mathrm{Dr}}+{ \mathrm{R}}_{\mathrm{sw}}+{ \mathrm{R}}_{\mathrm{D}}\right)}{3}$$

(17)

The skip-logic algorithm, used for the questionnaire, is schematically demonstrated in Figure 3 below:

3.3.3. Transport Category

Category of active participation consists of 5 possible lines of answers of respondents. Depending on which activity they do the most, following options are:

• Walking by foot;
• Private bicycle/electric scooter;
• Municipal bicycle/electric scooter;
• Public transport;
• Private car/motorcycle.

Since the degree of impact on the environment of these types of transport varies considerably,

Table 20. Index system for types of transport.

Fishburne Weights<break>Calculation	Index (𝐼T)	Option
0.5	1	Walking by foot
0.5	1	Bicycle/electric scooter private (parents’)
0.5	1	Bicycle/electric scooter municipal
0.333	0.66	Public transport
0.167	0.33	Car/motorcycle

below presents a system of indexes that takes these differences into account.

The following

Table 21. Reasoning indicators for walking by foot.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left({\mathit{r}}_{wf}\right)$	$\mathbf{Indicator} \left({\mathit{I}}_{wf}\right)$	Level of Indicator
0.4	0.35	It is healthier	1
0.3	0.35	It is better for the environment	1
0.2	0.2	It is free and I do not have to pay for transport	2
0.1	0.1	I live close to the university in a student residence	3

below shows the different reasons and the weightings of their indicators reflecting the value of “walking by foot” in the “Transport” category.

Thus, the final formula for Specific Indicator of “Transport” category with the line of “Walking by foot” is presented below:

$${\mathrm{SI}}_{\mathrm{T}}={ \mathrm{I}}_{\mathrm{T}}\ast {\sum }_{\mathrm{i}=1}^4{\mathrm{I}}_{{\mathrm{wf}}_{\mathrm{i}}}{\ast \mathrm{r}}_{\mathrm{wf}}{}_{\mathrm{i}}$$

(18)

where ${\mathrm{I}}_{\mathrm{wf}}$ is indicator of reasoning for walking by foot described in Table 21 above;

${\mathrm{r}}_{\mathrm{wf}}$ is redistributed weight of each indicator from Table 21;

${\mathrm{I}}_{\mathrm{T}}$ is an index of sustainability of this type of “Transport” category.

The following

Table 22. Reasoning indicators for private bicycle/electric scooter.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left(r_{Pb}\right)$	$\mathbf{Indicator} \left(I_{Pb}\right)$	Level of Indicator
0.333	0.3	It is healthier	1
0.267	0.3	It is better for the environment	1
0.2	0.167	I live close to the university in a student Residence	2
0.133	0.1	It is cheaper in comparison with other types of Transport	3
0.067	0.1	Many bike/scooter lanes in my city, which is Convenient	3

below shows the different reasons and the weightings of their indicators reflecting the value of “Private bicycle/electric scooter” in the “Transport” category.

Thus, the final formula for Specific Indicator of “Transport” category with the line of “Private bicycle/electric scooter” is presented below:

$${\mathrm{SI}}_{\mathrm{T}}={ \mathrm{I}}_{\mathrm{T}}\ast {\sum }_{\mathrm{i}=1}^5{\mathrm{I}}_{{\mathrm{Pb}}_{\mathrm{i}}}{\ast \mathrm{r}}_{\mathrm{Pb}}{}_{\mathrm{i}}$$

(19)

where ${\mathrm{I}}_{\mathrm{Pb}}$ is reasoning indicator for private bicycle/electric scooter described in Table 22 above;

${\mathrm{r}}_{\mathrm{Pb}}$ is redistributed weight of each indicator from Table 22;

${\mathrm{I}}_{\mathrm{T}}$ is an index of sustainability of this type of “Transport” category.

The following

Table 23. Reasoning indicators for municipal bicycle/electric scooter.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left(r_{Mb}\right)$	$\mathbf{Indicator} \left(I_{Mb}\right)$	Level of Indicator
0.29	0.26	It is healthier	1
0.24	0.26	It is better for the environment	1
0.19	0.19	I live close to the university in a student Residence	2
0.14	0.10	It is cheaper in comparison with other types of Transport	3
0.10	0.10	Many bike/scooter lanes in my city, which is Convenient	3
0.05	0.10	Availability of shared municipal bicycles	3

below shows the different reasons and the weightings of their indicators reflecting the value of “Municipal bicycle/electric scooter” in the “Transport” category.

Thus, the final formula for Specific Indicator of “Transport” category with the line of “Municipal bicycle/electric scooter” is presented below:

$${\mathrm{SI}}_{\mathrm{T}}={\mathrm{I}}_{\mathrm{T}}\ast {\sum }_{\mathrm{i}=1}^6{\mathrm{I}}_{{\mathrm{Mb}}_{\mathrm{i}}}{\ast \mathrm{r}}_{\mathrm{Mb}}{}_{\mathrm{i}}$$

(20)

where ${\mathrm{I}}_{\mathrm{Mb}}$ is indicator of reasoning for municipal bicycle/electric scooter described in Table 23 above;

${\mathrm{r}}_{\mathrm{Mb}}$ is redistributed weight of each indicator from the Table 23;

${\mathrm{I}}_{\mathrm{T}}$ is an index of sustainability of this type of “Transport” category.

The following

Table 24. Reasoning indicators for public transport.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} (r_{Pt})$	$\mathbf{Indicator} \left(I_{Pt}\right)$	Level of Indicator
0.33	0.33	It is the most environmental-friendly option of transport available for me	1
0.27	0.23	I live far from the university in a student residence	1
0.20	0.23	I live far because I did not receive a place in a student residence	2
0.13	0.10	Good connection of public transport in my city	3
0.07	0.10	Price policy for youngsters/students	3

below shows the different reasons and the weightings of their indicators reflecting the value of “Public transport” in the “Transport” category.

Thus, the final formula for Specific Indicator of “Transport” category with the line of “Public transport” is presented below:

$${\mathrm{SI}}_{\mathrm{T}}={\mathrm{I}}_{\mathrm{T}}\ast {\sum }_{\mathrm{i}=1}^5{\mathrm{I}}_{{\mathrm{Pt}}_{\mathrm{i}}}{\ast \mathrm{r}}_{\mathrm{Pt}}{}_{\mathrm{i}}$$

(21)

where ${\mathrm{I}}_{\mathrm{Pt}}$ is indicator of reasoning for public transport described in Table 24 above;

${\mathrm{r}}_{\mathrm{Pt}}$ is redistributed weight of each indicator from Table 24;

${\mathrm{I}}_{\mathrm{T}}$ is an index of sustainability of this type of “Transport” category.

And the last category, “Private car/motorcycle”, is also indexed according to the type of vehicle. If the respondent does not know what type of vehicle he/she is using, the calculation is based on the lowest default index value. For this study, three vehicle options were considered, categorized according to their energy/fuel consumption type: Electric, Hybrid, Fossil fuel. Since the degree of impact on the environment of these types of transport varies considerably,

Table 25. Index system for the type of vehicle.

Fishburne Weights Calculation	Index (𝐼v)	Option
0.5	1	Electric
0.333	0.66	Hybrid
0.167	0.33	Fossil-fuel

below presents a system of indexes that takes these differences into account.

The following

Table 26. Reasoning indicators for private car/motorcycle.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left(r_{c/m}\right)$	$\mathbf{Indicator} \left(I_{c/m}\right)$	Level of Indicator
0.4	0.4	I need a private vehicle because of my special needs (I cannot use public transport)	1
0.3	0.25	I live far from the university in student residence	2
0.2	0.25	I live far because I did not receive a place in student residence	2
0.1	0.1	Poor public transport connection between university and place where I live	3

below shows the different reasons and the weightings of their indicators reflecting the value of “Private car/motorcycle” in the “Transport” category.

Thus, the final formula for Specific Indicator of “Transport” category with the line of “Private car/motorcycle” is presented below:

$${\mathrm{SI}}_{\mathrm{T}}={\mathrm{I}}_{\mathrm{T}}{\ast \mathrm{I}}_{\mathrm{v}}\ast {\sum }_{\mathrm{i}=1}^4{\mathrm{I}}_{\mathrm{c}/{\mathrm{m}}_{\mathrm{i}}}{\ast \mathrm{r}}_{\mathrm{c}/\mathrm{m}}{}_{\mathrm{i}}$$

(22)

where ${\mathrm{I}}_{\mathrm{c}/\mathrm{m}}$ is indicator of reasoning for private car/motorcycle described in Table 26 above;

${\mathrm{r}}_{\mathrm{c}/\mathrm{m}}$ is redistributed weight of each indicator from Table 26;

${\mathrm{I}}_{\mathrm{v}}$ is an index of sustainability of this type of vehicle category;

${\mathrm{I}}_{\mathrm{T}}$ is an index of sustainability of this type of “Transport” category.

The skip-logic algorithm, used for the questionnaire is schematically demonstrated in Figure 4 below:

3.3.4. Active Participation Category

Category of active participation consists of 3 possible lines of answers of respondents.

Depending on which activity they do the most, following options are:

• Discussion;
• Participation;
• Organization.

Since the degree of effectiveness of these variations in activity varies considerably,

Table 27. Index system for type of active participation.

Fishburne Weights Calculation	Index (Ap)	Option
0.5	1	Organization
0.333	0.66	Participation
0.167	0.33	Discussion

below presents a system of indexes that takes these differences into account.

The following

Table 28. Reasoning indicators for discussion.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} (r_D)$	$\mathbf{Indicator} \left(I_D\right)$	Level of Indicator
0.333	0.233	It was my interest	1
0.267	0.233	I want to demonstrate my involvement in this topic to other people	1
0.2	0.233	I want people to be aware of the situation	1
0.133	0.233	I want to act for sustainability and this is my way to do that	1
0.067	0.067	It was part of the curriculum activity	2

below shows the different reasons and the weightings of their indicators reflecting the level of active participation in the discussions.

Thus, the final formula for Specific Indicator of “Active participation” category with the line of “Discussion” is presented below:

$${\mathrm{SI}}_{\mathrm{Ap}}={\mathrm{I}}_{\mathrm{Ap}}\ast {\sum }_{\mathrm{i}=1}^5{\mathrm{I}}_{{\mathrm{D}}_{\mathrm{i}}}{\ast \mathrm{r}}_{\mathrm{D}}{}_{\mathrm{i}}$$

(23)

where ${\mathrm{I}}_{\mathrm{D}}$ is indicator of every reason of discussion as a part of active participation described in Table 28 above;

${\mathrm{r}}_{\mathrm{D}}$ is redistributed weight of each indicator from Table 28;

${\mathrm{I}}_{\mathrm{Ap}}$ is an index of effectiveness of this type of “Active participation” category.

The following

Table 29. Reasoning indicators for participation.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} (r_P)$	$\mathbf{Indicator} \left(I_P\right)$	Level of Indicator
0.333	0.233	It was my interest	1
0.267	0.233	I want to demonstrate my involvement in this topic to other people	1
0.2	0.233	I want people to be aware of the situation	1
0.133	0.233	I want to act for sustainability and this is my way to do that	1
0.067	0.067	It was part of the curriculum activity	2

below shows the different reasons and the weightings of their indicators reflecting the level of active participation in the “Participation” line of answers.

Thus, the final formula for Specific Indicator of “Active participation” category with the line of “Participation” is presented below:

$${\mathrm{SI}}_{\mathrm{Ap}}={\mathrm{I}}_{\mathrm{Ap}}\ast {\sum }_{\mathrm{i}=1}^5{\mathrm{I}}_{{\mathrm{P}}_{\mathrm{i}}}{\ast \mathrm{r}}_{\mathrm{P}}{}_{\mathrm{i}}$$

(24)

where ${\mathrm{I}}_{{\mathrm{P}}_{\mathrm{i}}}$ is indicator of every reason of participation in events as a part of “Active participation” category described in Table 29 above;

${\mathrm{r}}_{\mathrm{P}}$ is redistributed weight of each indicator from Table 29;

${\mathrm{I}}_{\mathrm{Ap}}$ is an index of effectiveness of this type of “Active participation” category.

The following

Table 30. Reasoning indicators for organization.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left(r_O\right)$	$\mathbf{Indicator} \left(I_O\right)$	Level of Indicator
0.333	0.233	It was my interest	1
0.267	0.233	I want to demonstrate my involvement in this topic to other people	1
0.2	0.233	I want people to be aware of the situation	1
0.133	0.233	I want to act for sustainability and this is my way to do that	1
0.067	0.067	It was part of the curriculum activity	2

below shows the different reasons and the weightings of their indicators reflecting the level of active participation in the “Organization” line of answers.

Thus, the final formula for Specific Indicator of “Active participation” category with the line of “Organization” is presented below:

$${\mathrm{SI}}_{\mathrm{Ap}}={\mathrm{I}}_{\mathrm{Ap}}\ast {\sum }_{\mathrm{i}=1}^5{\mathrm{I}}_{{\mathrm{O}}_{\mathrm{i}}}{\ast \mathrm{r}}_{\mathrm{O}}{}_{\mathrm{i}}$$

(25)

where ${\mathrm{I}}_{{\mathrm{O}}_{\mathrm{i}}}$ is indicator of every reason of organization as a part of “Active participation” category described in Table 30 above;

${\mathrm{r}}_{\mathrm{O}}$ is redistributed weight of each indicator from Table 30;

${\mathrm{I}}_{\mathrm{Ap}}$ is an index of effectiveness of this type of “Active participation” category.

The skip-logic algorithm, used for questionnaire is schematically demonstrated in Figure 5 below.

3.3.5. Clothing Category

Clothing is an important component of responsible consumption, however the choices related to this category are made outside of the University campus. Consequently, the value of Clothing indicator is calculated according to this statement.

Therefore, for this category, the authors exclude the reasoning layer of responsible consumption and there is no need of usage of logic of skipping tool in the survey. Category of clothing consists of 2 factors:

• Wearing habits;
• Buying habits.

Therefore, in

Table 31. Reasoning indicators for wearing habits.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left(r_{C{l}_w}\right)$	$\mathbf{Indicator} (I_{C{l}_w)}$	Level of Indicator
0.5	0.33	I wear clothes which I repaired by myself	1
0.333	0.33	I have clothes which I wear for more than 1 year	1
0.167	0.33	We practice sharing items of wardrobe with my friends from the University	1

below there are factors which indicate wearing habits with redistributed weights and level of choice which they represent.

Thus, wearing habits indicator W can be determined according to the following formula:

$$\mathrm{W}={\sum }_{\mathrm{i}=1}^3{\mathrm{I}}_{{\mathrm{Cl}}_{\mathrm{w}}{}_{\mathrm{i}}}{\ast \mathrm{r}}_{{\mathrm{Cl}}_{\mathrm{w}}}{}_{\mathrm{i}}$$

(26)

where ${\mathrm{I}}_{{\mathrm{Cl}}_{\mathrm{w}}}$ is indicator of every wearing habit described in Table 31 above;

${\mathrm{r}}_{{\mathrm{Cl}}_{\mathrm{w}}}$ is redistributed weight of each indicator from Table 31.

For the buying habits indicator calculation, the authors determine factors which indicate buying habits with redistributed weights and level of choice which they represent in

Table 32. Reasoning indicators for buying habits.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left(r_{C{l}_b}\right)$	$\mathbf{Indicator} \left(I_{C{l}_b}\right)$	Level of Indicator
0.5	0.5	I wear clothes bought in second-hand shops	1
0.333	0.333	I wear expensive high-quality production	2
0.167	0.167	I wear fast-fashion brands (Zara, Uniqlo, etc.)	3

below.

Thus, buying habits indicator B can be determined according to the following formula:

$$\mathrm{B}={\sum }_{\mathrm{i}=1}^3{\mathrm{I}}_{{\mathrm{Cl}}_{\mathrm{b}}{}_{\mathrm{i}}}{\ast \mathrm{r}}_{{\mathrm{Cl}}_{\mathrm{b}}}{}_{\mathrm{i}}$$

(27)

where ${\mathrm{I}}_{{\mathrm{Cl}}_{\mathrm{b}}{}_{\mathrm{i}}}$ is indicator of every buying habit described in Table 32 above;

${\mathrm{r}}_{{\mathrm{Cl}}_{\mathrm{b}}}{}_{\mathrm{i}}$ is redistributed weight of each indicator from Table 32.

Thus, the final formula for Specific Indicator of “Clothing” 𝑆𝐼_𝐶𝑙 category is presented below:

$${\mathrm{SI}}_{\mathrm{Cl}}=\frac{\left(\mathrm{W} + \mathrm{B}\right)}{2}$$

(28)

3.3.6. Energy and Water category

Energy and water are also important components of responsible consumption, however, this category cannot be fully represented on the territory of campus of the University due to limitation of choices available to students. Consequently, the value of energy and water as indicators is calculated according to this statement.

Therefore, for this category, the authors exclude the reasoning layer of responsible consumption and there is no need of usage of logic of skipping tool in the survey.

Category of clothing consists of 1 factor which is “Usage of University facilities” and it is described in

Table 33. Reasoning indicators for usage of university facilities.

Fishburne Weights Calculation	$\mathbf{Redistributed} \mathbf{Weights} \left(r_E\right)$	$\mathbf{Indicator} \left(I_E\right)$	Level of Indicator
0.4	0.25	Turning off the water as fast as I finish washing my hands	1
0.3	0.25	Turning off the light when I leave a room	1
0.2	0.25	Avoiding the usage of hands-dryer	1
0.1	0.25	Choosing a place at the library with natural light instead of using the electric one	1

below.

Thus, the final formula for Specific Indicator of “energy and water” 𝑆𝐼_𝐸 category is presented below:

$${\mathrm{SI}}_{\mathrm{E}}={\sum }_{\mathrm{i}=1}^4{\mathrm{I}}_{{\mathrm{E}}_{\mathrm{i}}}{\ast \mathrm{r}}_{{\mathrm{E}}_{\mathrm{i}}}$$

(29)

Based on developed system of evaluation of every factor, the questions for survey were created. The next step of the research contains collection of data and transformation of received answers into proposed system of evaluation for further construction of fuzzy-multiple model.

3.4. Fuzzy-Multiple Model Construction

Expected result of the constructed model is the calculation of levels of responsible consumption on the territory of the universities in percentage, as it is illustrated in Figure 6 below.

Authors suggest using the methodology of fuzzy-multiple modeling for calculation of the integral indicator. The choice of the methodology is related to uncertain nature of human behavioral choices, which leads to difficulties of precise segregation of categories of indicators. Thus, we can see the whole picture by making the numeric calculation representing specific indicators that belong to categories which are difficult to identify [32].

The indicators of responsible consumption in the fuzzy-multiple model are presented in

Table 34. Indicators of the level of responsible consumption.

Key Indicator	Specific Indicators
The generic level of responsible consumption RC	Food (𝑆𝐼𝐹)
	Waste (𝑆𝐼𝑊)
	Transport (𝑆𝐼𝑇)
	Active participation (𝑆𝐼𝐴𝑝)
	Clothing (𝑆𝐼𝐶)
	Energy and water (𝑆𝐼𝐸)

below. The model generated has two linguistic variables: the level of responsible consumption and the level of each individual indicator under consideration (six indicators in total). The term set of each linguistic variable consists of five subsets.

The following

Table 35. Term-sets of linguistic variables.

Linguistic Variable RCi—Responsible Consumption	Term Set
γ—the generic level of responsible consumption
RC1	Extremely low level of the integral indicator
RC2	Low level of the integral indicator
RC3	Average level of the integral indicator
RC4	High level of the integral indicator
RC5	Extremely high level of the integral indicator
Linguistic Variable SIi Is the Level of the Value of Indicator Xi	Term Set
γ—the level of specific indicator
SI1	Extremely low level of the specific indicator
SI2	Low level of the specific indicator
SI3	Average level of the specific indicator
SI4	High level of the specific indicator
SI5	Extremely high level of the specific indicator

presents the term sets for the linguistic variables. The first term set refers to the linguistic variable describing the level of responsible consumption, while the second term set refers to the linguistic variable describing private indicators.

Each of the indicators is assigned a different level of significance r_i. There are 2 levels of importance. It is indicated by reasoning and possibility of impact of digital environment. Because authors consider responsible consumption in the context of University campus, one of the criteria of differentiation is the availability to choose between various options—sustainable or not. As was identified earlier, Clothing and Energy and Water are important components of responsible consumption, however the choices related to these categories are made outside of the University campus. Consequently, the value of these indicators is calculated according to this statement.

The distribution of the specific weights of the individual indicators was carried out according to Fishburne’s law [31]. The illustration of calculated individual indicator weights is presented in Figure 7 below.

In

Table 36. Indicators of the level of responsible consumption.

T-Numbers {y} for the Values of the Linguistic Variable
Indicator designation	Extremely low level of the specific Indicator	Low level of the specific indicator	Average level of the specific indicator	High level of the specific indicator	Extremely high level of the specific indicator
Indicator designation RC	(0; 0; 10; 21,43)	(10; 21,43; 32,86; 44,29)	(32,86; 44,29; 55,71; 67,14)	(55,71; 67,14; 78,57; 90)	(78,57; 90; 100; 100)

below the fuzzy-multiple classifier of an integral indicator, necessary for following calculations, is presented.

3.5. Analysis of Digital Environment

According to a survey conducted among students of Bielefeld University and the University of Barcelona, about 70% of those subscribed to the university’s official social media account preferred Instagram as a social network. Therefore, the analysis of the digital environment was conducted using the data from the University of Barcelona @unibarcelona and Bielefeld University @bielefelduniversity Instagram accounts.

The characteristics of responsible consumption can be represented in two parallel environments: physical and digital. At the same time, the most frequent variations in the assessment of sustainable components are associated with the physical environment, although the digital environment is much more accessible for analysis. This fact is a consequence of the development of the information society and the digitalization of basic economic and social processes. The development of technology and the consequences of the pandemic have triggered the digitalization of social communication, resulting in the ubiquitous penetration of Internet media and social networks. The digital environment is non-hierarchical and non-centralized, indicating the potential validity of the information flows generated and broadcast within it. The influence in our lives of a highly integrated digital environment suggests that transformations of the digital environment are inevitably reflected in the physical environment and this research hypothesizes that responsible consumption in the digital environment and its transformations are reflected in the objects of the physical environment. This research was conducted by quantifying the level of responsible consumption in both environment, using a survey and fuzzy-multiple modelling within the physical environment and the analysis of the digital environment within the virtual space.

The objects of the digital environment are elementary information units, encoded in textual or audiovisual form and forming information background, centered on the elements of the physical world and comprehensively describing the process of their transformation and interaction. These elementary information units are generated by subjects of the digital environment within the framework of specialized sources of information concentration (media, social networks, thematic, and professional communities, etc.). The level of development of the digital environment can be characterized by the complexity of this information background. At the moment, within a certain part of information concentration sources, not only primary information is significant, but also meta information, differentiated on many levels, and containing such components as the business reputation of the generation source, the business reputation of the vast majority of consumers, correlation with general information context and much more.

Both elementary information units and their aggregates can be differentiated into 2 basic components:

• Content-thematic component, reflecting multidimensional lexical specificity of an elementary information unit or their totality;
• The tonal component, reflecting the properties of emotional specificity of the elementary information unit or their totality.

Thus, the identification, structuring, quantification, and comparative analysis of the content-thematic and tonal components of an elementary information unit or their totality will allow the characterization of the current properties and the process of their transformation, in relation to the complex elements of the digital environment. The tools of computational linguistics, in particular tokenization algorithms, can be used for the purpose of research [33]. Based on the results of the tokenization process an array of lexemes is formed, which can act as a reflection of a certain content-thematic and tonal component of the digital environment [34].

By parsing the data using Python, the following information can be obtained within the posts: the frequency of use of the highlighted responsible consumption tokens in the post descriptions, audience engagement reflected in the number and length of comments, and emotional characteristics of comments. In addition, the frequency of token usage in the text of the comments themselves can be determined, for which the length of the comment and its emotional coloring can also be identified.

This information is enough to identify the level of responsible consumption in the digital environment of each of the considered Instagram accounts for further comparative analysis and summarizing the results of the research.

Thus, using the tokenization algorithm (Figure 8), the data in the account of the University of Barcelona @unibarcelona and Bielefeld University @bielefelduniversity were analyzed.

4. Results

4.1. Survey Participants Overview

The survey was conducted among students of two universities: University of Barcelona and Bielefeld University. The authors received 100 responses, 50 from each University. According to official data, 25,000 students are receiving their degree at Bielefeld University, while 73,926 students are receiving their degree at the University of Barcelona. With an accuracy of 90% and an error of 11.7% the statistical sample is representative in this research. The survey was conducted among students of various levels of education and areas of study. In the Figure 9 below, the redistribution of respondents in terms of levels of education is presented. The majority of respondents are bachelor students; the second most popular level or education is Master Degree.

The ratio of regular and exchange students is presented in the Figure 10 below. At the University of Barcelona, 1/3 of the participants of the research were exchange students, while in the case of Bielefeld University, the majority of respondents were regular students.

In terms of area of study, Bielefeld University is represented with a more diverse sample with the majority of respondents studying natural sciences. At the University of Barcelona, 68% of respondents were from the Social Sciences and Management faculty, which can be explained by the far location of campuses around the city of Barcelona and the ability of the author of this research project to mostly operate on site on the territory of the Social Sciences and Management faculty (see Figure 11).

The age range of respondents varies from 18 to 26 for University of Barcelona and 18 to 30 for Bielefeld University. The most frequent age is 21 for both Universities.

One of the main indicators for further research is the subscription on the official account of the university on social media. At the University of Barcelona, students are mostly not subscribed, while at Bielefeld University 62% are subscribed (see Figure 12).

In order to be able to choose which social media to use for the analysis of the digital environment, the respondents were asked to indicate to which social media they are subscribed and, in both cases, around 70% of respondents answered “Instagram” (see Figure 13). Therefore, the official account of the University of Barcelona @unibarcelona and the official account of Bielefeld University @bielefelduniversity will be analyzed as a part of the digital analysis.

The respondents were asked how important protecting the environment is for them personally. At the University of Barcelona, according to the received data, the importance is slightly higher than at Bielefeld University. “To a very high degree” was chosen by 30% of students at Barcelona and 28% at Bielefeld, while “To a high degree” was chosen by 56% of respondents in Barcelona and 52% in Bielefeld.

Another question indicated agreement with the following statement: “My personal consumption habits (in food, clothes, transport, energy, plastic) have a strong impact on the environment and social problems of nowadays reality”. In both Universities 18% of students participating in the survey agreed with this statement “To a very high degree”. However, the situation differed for the option “To a high degree”, because at the University of Barcelona, 38% of respondents chose it, while at Bielefeld University only 28% chose it.

Thus, according to the results of the survey, respondents of the University of Barcelona consider the protection of the environment for them personally more important than respondents at Bielefeld University, and are also more willing to believe that personal consumption habits have a strong impact.

The authors focused on the universities due to their crucial role as special places that are pivotal in the process of social and political action aimed at changing societal development towards sustainability [35]. There are also opinions that universities should be the first to embrace the process of transformation [36].

As can be seen from the survey results, the majority of respondents in both universities are undergraduate students, but the ratio of undergraduate and graduate students corresponds to the national figures in Germany and Spain, respectively. The number of students enrolled in various levels of tertiary education in Germany and Spain in 2020 are presented in

Table 37. Number of students enrolled in different levels of tertiary education in 2020, EUROSTAT.

	2020
GEO	Bachelor’s or equivalent level	Master’s or equivalent level	Tertiary education (levels 5–8)
Germany	2,002,583	1,084,652	3,280,033
Spain	1,224,186	365,556	2,145,333

below.

Further,

Table 38. Ratio of students in Bachelor’s and Master’s courses in the questionnaire and on national levels.

		Questionnaire Level	National Level
Germany	Bachelor	60%	61.05%
	Master	26%	33.07%
Spain	Bachelor	76%	57.06%
	Master	14%	17.04%

presents the percentages of undergraduate and graduate students in relation to students at all levels of tertiary education at the national level and at the questionnaire level.

Thus, authors calculated the percentage of two of the most represented categories of respondents: bachelor’s and master’s students. The correlation between questionnaire level and national level is 0.94 which speaks to the direct connection between indicators. The predominance of undergraduate students in the survey reflects the general trend of higher education in each country, proportionally.

In this research, there appeared to be technical limitations related to the availability of respondents during the data collection phase. However, in the context of this methodological study aimed at developing an analytical tool, these limitations may be applicable.

4.2. Digital Environment Data Obtained

The Bielefeld University account @bielefelduniversity has 18,700 followers, which quantitatively represents 74.8% of the student population. The account was registered on 19 September 2015. The language of the account is German and the number of publications at the time of data analysis was 1417. The account of the University of Barcelona @unibarcelona is represented by 50,500 subscribers, which quantitatively represents 68.3% of the number of students. The account was registered on 28 March 2014. The language of the account is Catalan and the number of publications at the time of the data analysis was 1904.

As part of the data environment analysis, 1170 publications in each of the accounts were reviewed. Thus, 82.57% of all publications were analyzed within the Bielefeld University account and 61.45% of all publications were analyzed within the Barcelona University account. In both cases, this quantitative ratio represents a majority, indicating that the data obtained is representative.

In

Table 39. Result of collection the data from Instagram accounts.

Specific Indicators	Bielefeld	Barcelona
Number of tokens mentioned in account	20	97
Number of posts with comments with token	15	73
Average lengths of comments with token	12,379	14,786
Average tonal negative characteristic of comments with token	−0.22	−0.64
Average tonal positive characteristic of comments with token	0.77	0.36
Number of posts with token	5	11
Average lengths of comments under posts with tokens	16.16	7.95
Average tonal negative characteristic of comments under posts with token	−0.86	−0.76
Average tonal positive characteristic of comments under posts with token	0.14	0.24

below, there is collected data regarding digital analysis.

Thus, the presence of a content component can be calculated as the ratio of the number of tokens identifying the level of responsible consumption to the total number of posts analyzed, which is 1170. The ratios of average negative and average positive components of comments with tokens can be calculated. To calculate this indicator, we take the number of posts where tokens were encountered in the comments, multiply it by the average tone indicator (positive or negative) and by the average comment length. Thus, we obtain a quantitative reflection of the subscribers’ engagement, taking into account the tonality characteristic. Next, the ratio between these figures for one account and the other can be calculated. The average ratio is R_Av = 0.505.

4.3. General Outcomes and Recommendations

The following results were obtained from the study for the level of responsible consumption in the physical environment:

𝑅𝐶_𝐴𝑣1 = 28.88%;

𝑅𝐶_𝐴𝑣2 = 34.55%.

The following quantitative results were obtained within the digital environment:

𝑅𝐶_𝐵𝑖𝑒 = 1.8 ∗ 0.505 = 0.91 %;

𝑅𝐶_𝐵𝑎𝑟 = 8.3 ∗ 1 = 8.3 %.

Thus, the hypothesis that the level of responsible consumption in the physical environment is higher in the university where the level of responsible consumption in the digital environment is higher was confirmed. In addition, the Pearson correlation coefficient between the indicators obtained is 1, indicating a direct and strong correlation between the indicators.

Based on the results of this study, a comparative tool was constructed and tested to empirically measure patterns of consumer behavior and the various components of sustainability as a social phenomenon. This is reflected in the deliberate approach of action within the highlighted six categories assessed by the survey.

This research project is a methodological development of sociological research tools, which can be adapted and extended for more in-depth study within the considered research topic or adjusted for other topics. The research is based on a comparative approach and assessment of individuals’ actions in terms of “awareness” under the influence of the digital environment.

Two different quantitative data analyses were carried out as part of this study, which became part of the toolkit and can be used in further research as proposed by the author, or as separate methodologies for measuring physical and digital environment indicators in the context of behavioral practices.

The strength of the study is the interdisciplinary approach and the proposed methodology for assessing the level of responsible consumption using the theory of fuzzy sets. In addition, research on the digital environment and its interaction with physical reality has a high potential, as every year the penetration of the Internet in everyday life is increasing.

Eventually, the fuzzy-multiple model can be complemented with indicators measuring the responsible consumption with different units, such as CO2 emissions, or the number of plastic bottles bought in the last month.

The limitation of the project is the consideration of a specific space and social group of respondents. This can be solved by developing this research project into a larger scale study to test the constructed instrument with a differently defined space and a larger sample of respondents.

Thus, the result of the research project is a constructed analytical tool for assessing the level of responsible consumption in two environments, the work of which is confirmed by the approbation of the results in the example of Bielefeld University and Barcelona University.

The quantitative data shows the interconnection of the two environments—physical and digital—and this information can be used to increase the level of responsible consumption within the university space.

This research confirmed the hypothesis that, among the subscribed respondents, the level was higher in the university where the digital environment was also characterized by a higher level of responsible consumption. Therefore, the author proposes a number of recommendations to increase the level of responsible consumption:

Firstly, it is recommended to increase student engagement and to carry out a series of measures to increase the number of students subscribed to the university’s official social media accounts, preferably focusing on Instagram, as research shows that 70% of respondents from both universities prefer this particular network.

Secondly, it is proposed to increase the presence of the content component, namely to increase the number of publications dedicated to specific actions and practices on the university campus that are in line with the goals of sustainable development, in particular responsible consumption.

Thirdly, it is recommended to focus on audience engagement and to implement account management strategies to maximize engagement with the audience, e.g., through reposts and comments.

Thus, the author suggests implementing the above actions within the digital environment and thereby also influencing the physical environment, which is limited to the university space.

5. Discussion and Conclusions

This research explored the concept of sustainability, responsible consumption, and its indicators within the framework of the 12th sustainable development goal to develop an analytical tool to evaluate these concepts within physical and digital environments [13].

Responsible consumption has no worldwide defined framework of assessment. There are various approaches to how it can be evaluated and which elements it contains. This is due both to the different definitions of this phenomenon and the complexity of the development of a unified qualitative and quantitative measurement methodology. Surveys regarding the predisposition of consumers to purchase more environmentally friendly products, particularly their willingness to pay extra for sustainable production or to boycott companies that do not comply with the principles of sustainable development, are one of the main trends in the methodological assessment of responsible consumption.

The other concepts and tools, such as Environmental Carrying Capacity or Consumer Footprint measured using the approach of a basket of products (BoP), mainly focus on the evaluation of absolute figures assessing the numerical impact on the environment [35,37]. While authors in this research propose an analytical tool for comparative analysis, which allows the performance of calculations that avoid absolute figures if necessary. In addition, the methodology of fuzzy-multiple modelling provides the opportunity to include indicators with different units of measurement and to assess human behaviour more efficiently due to uncertainty intervals.

Furthermore, the digitalization process became an inseparable part of our reality after its speeded-up integration during the pandemic [37]. As a result, the authors suggest developing a methodology which includes this aspect of digitalization [34]. Thus, we proposed a new way of evaluating responsible consumption based on the conducted theoretical analysis which explains what are considered to be factors reflecting responsible consumption in the physical and digital environments in the closed social structure represented by the universities in this particular research.

The authors focused on the universities due to their crucial role as special places that are pivotal in the process of social and political action aimed at changing societal development towards sustainability [35]. There are also opinions that universities should be the first to embrace the process of transformation [36].

Thus, the project aim was to develop a comparative analytical tool for sociological research in the context of responsible consumption within two environments. To achieve it, the authors developed an indicative system for assessing the level of responsible consumption, built a fuzzy-multiple model, tested it, analyzed the digital environment, and compared the levels of responsible consumption of the two universities in both environments. Hence, a research project of a methodological nature has been presented, which can be considered as a pilot application for further research in this area, and the goal was achieved.

The introduction considered the relevance of the research topic in the context of the global climate change problem, which highlights the necessity of improvement of the assessment methodology, enhancing its efficiency and complexity.

The “Consumer society” of Baudrillard is one of the major elements of the theoretical basis of this research. The authors compare the statements of Jean Baudrillard with the official data of public opinion in Europe, Eurobarometer, and construct their research on the theoretical results, received out of previous analytics. Two issues were considered regarding the “Consumer society”. Firstly, today reality can be characterized by an information and commodities overabundance. Secondly, consumerism today does not have only an economic nature but also a socio-psychological one, which was confirmed by analytics of public opinion surveys. The authors take into consideration the complexity of the nature of consumption while proposing this analytical tool, thanks to which the strategies of the shift towards sustainability can be developed and carried out. Furthermore, the interrelation of the physical and digital environments is theoretically explored in this section, which becomes the basis of the developed methodology.

Considering the phenomenon of political consumerism as a continuation of the discussion of the social element of consumption, the authors explore the nature of interconnection between capitalism and consumerism, which is supported by the work [12]. The subsequent development of the study is dictated by the feature of consumerism, which is the ability to express its social position and be dictated not only by economic motives. This was identified as a result of theoretical analysis and considered by the authors of the previously mentioned works and it allows us to assume that change in the pattern of overconsumption towards responsible consumption is possible.

The authors then proceeded to a theoretical concept of “sustainability”, referring to the terms and definitions formulated by the European Commission and the United Nations, revealing “sustainability development goals”. Based on a detailed study of the categories of sustainable development goals, in particular the 12th goal, obtained from the official documentation of the European Commission, the authors formulated indicators of responsible consumption and an assessment system of indicators of the level of responsible consumption in the physical environment, limited by the university space.

As part of a theoretical analysis, the authors also explored the regulatory policies framework regarding sustainable development and examined sustainability from a sociological perspective.

This theoretical analysis is necessary for a deeper understanding of sustainability and the further development of a methodological toolkit for sociological research. Furthermore, the relevance and necessity of sustainability actions were formulated. The correlation between the digital environment and the digitalization process was also considered.

The methodological tool developed by the authors can find wide application as a basis for the development of similar analytical instruments in other areas of sociological research. This case demonstrates the structural approach of information analysis in sociological research, which can be extended in the context of space—the tool can be used to measure the level of responsible consumption within corporate systems of companies, various associations, schools, and on a larger scale such as neighborhoods or cities with appropriate detailing.

The tool can be used to quantitatively measure the reflection of certain factors of the digital environment in everyday reality within closed social structures to further influence the physical environment.

The ultimate goal of the analytical process is the data needed to develop the most effective process for influencing behavioral patterns to shift them towards sustainability, in particular responsible consumption. However, to develop strategies similar to the work of behavioral change organizations, the initial relationship between the physical environment and the digital environment needs to be defined to use the result as a tool for developing these strategies.

To carry out this analysis, the authors identify certain factors that reflect the same indicator, responsible consumption, by measuring its characteristics present in different environments, and then quantitatively calculate their relationships in a comparative analysis.

Thus, we consider a universal relative analytical tool that can be used to determine the relationship between the characteristics of a single indicator presented in different environments—physical and digital—to measure social phenomena in closed social structures that can be characterized by a single information background. The unique combination developed in this project assessment system of responsible consumption, a theory of fuzzy sets, and the content-thematic and tonal analysis of the digital environment forms a new tool for the comparative assessment of responsible consumption which can be considered as a pilot version. The approach developed by the authors can be modified at a more detailed functional level and reoriented to assess other social phenomena in the context of sustainable development in the business environment. Thus, the results of this study can form the basis for the development of further research on measuring the sustainability of working conditions within companies to compare the functioning of different sections and identify problem areas for further improvement.