Introduction

In the summer of 2021, a significant increase in people quitting their jobs was observed in many different fields, particularly in the United States and numerous other countries. This phenomenon, called “Great Resignation” or “The Big Quit,” represents a significant shift in people’s importance of work (Kuzior et al., 2022). This phenomenon has been and is still being discussed in the international media and academic and corporate spheres. Well-known newspapers reported data from the United States Department of Labor, according to which in September 2021, 4.4 million Americans left their jobs, an increase of more than 1.1 million from September 2020, and a record number of open positions are being noted (Gallup, 2021; The Washington Post, 2021).

Moreover, 36% of U.S. workers plan to change their employment due to a lack of flexibility in remote work arrangements (Workhuman, 2022). Low income, a lack of promotion opportunities, and feeling disrespected at work were the primary reasons Americans left their jobs in 2021 (Pew Research Center, 2022a).

One potential triggering event for the Great Resignation is the pandemic-induced shift toward remote work (Gittleman, 2022; Tessema et al., 2022). Over 50% of U.S. workers who were able to work from home during the pandemic want to do so in the future (Pew Research Center, 2022b). As it has been underlined, home working increased the work-to-life and life-to-work conflicts, triggered greater work-related fatigue, and worsened the perceived work–life balance. These negative effects are mediated by positive work engagement (Palumbo, 2020). In this light, the COVID-19 pandemic has driven some workers to reassess their work priorities and values. In particular, it has forced them to confront the reality that work is not just a way to make a livelihood but also a significant part of their identity as persons, social connections, and personal fulfillment. Recent studies have shown that those who feel disconnected from their work or lack a sense of purpose may experience burnout, stress, and reduced motivation (Adamopoulos and Syrou, 2022; Anjum et al., 2023). Conversely, people who find meaningful work tend to have better mental and physical health, higher levels of job satisfaction, and greater engagement and productivity at work (Ahmadi et al., 2022; Niebuhr et al., 2022).

Another potential factor contributing to the Great Resignation is the changing perspective on work among the younger generation of workers (Montaudon-Tomas et al., 2022). Several studies have shown that millennials and Gen Z workers tend to prioritize work–life balance, meaningful employment, and a sense of purpose in their careers (Fraley et al., 2022; Kuzior et al., 2022). They are less likely to be prompted by extrinsic incentives such as salary and job security and more likely to seek out employers that align with their intrinsic values and provide opportunities for personal growth. These values are driven not only by material interests but also by a sense of morality that shapes how people think about work and its role in their lives.

We will argue that the so-called “Moral Economy” is especially pertinent when analyzing the shifting views toward work and employment manifested for some workers in the Great Resignation phenomena. Moral Economy refers to a set of moral and ethical principles that both guide and justify economic behavior and decision-making in economic life (Polanyi, 2018). It suggests that economic transactions are not built only on self-interest but also on a sense of social responsibility, justice, and morality. The concept of Moral Economy was originally applied by E.P. Thompson (1971) in the illustration of the English food riots in the 18th century. In his seminal work, Thompson made the key claim about what distinguishes morally legitimate from morally illegitimate practices in economic life. This difference is—as Thompson maintained—capable of inspiring individual and collective action based on some sense of what individuals owe to the collectivities in which they live. In this vein, the riots were not simply a reaction to unfulfilled basic needs, for they reflected a strive for a better world (Carrier, 2018).

Respect, recognition, reciprocity, solidarity, justice, fairness, regard, appreciation, thoughtfulness, consideration, esteem: the Moral Economy conception points to the fact that people want and hope to be considered by others as “moral persons” whose needs are considered worth in themselves. It is key to clarify the way the Moral Economy conceives values and morality as key mechanisms. The Moral Economy approach treats morality as a social phenomena. As Fourcade and Healy (2007, p. 301) put it: “[This] approach is broadly Durkheimian. Morality does not refer here to some universal ethical standard; rather, it means what a society, or a group, defines as good or bad, legitimate or inappropriate” (Fourcade and Healy, 2017). As we will argue in the following, Great Resignations phenomena refers to the “violation” of the Moral Economy of people. It is key to spell out two analytical implications.

First and foremost, it should be made clear that the concept of the Moral Economy lost its analytical power in a process of “trivialization” that reduced it to a “moralized economy” (Hann, 2018). It became a symbol to be invoked rather than a concept to be applied in social theory and scientific research (Carrier, 2018). Descriptive and prescriptive elements overlapped in a confusing manner (Arnold, 2001). This overlapping obscured the key added value of the concept, namely the social value of mutual obligations that arise when people transact with each other over the course of time. Secondly, it should be stressed that these obligations are both psychological motives explaining the choices (Bowles, 2016). We thus maintain that it is key to draw a line between the “psychological motivations” and the “vocabularies of motives” of social actors, as argued by C. Wright Mills (1940) and—more recently—by the so-called economy of conventions or conventions theory (Thévenot, 2017). The perspective of the vocabularies of motives goes beyond simply listing the supposed reasons given by an individual and interpreting them as psychological forces behind one’s action. It rather examines how people express their motives, how motives are clustered, and how these vocabularies relate to the larger social structure and society-level changes. This conception, thus, is especially effective for exploring the moral and ethical implications of a large-scale social change that the vocabularies of motives express and reflect. “Motives” here need to be framed not as “inner psychological forces”, namely as intentions that causally explain individual behaviors, but as public justifications that support/justify one’s decision in the eyes of others. They are public because their rationale is one that members of the public can accept. They are a sort of “moral recognition rules” that have larger socio-cultural meanings and consequences, as well as public conventions that situate social action within widely accepted normative models (Borghi and Vitale, 2007). In this vein, assessing empirically the relevance of the Moral Economy is a way to understand the changing symbolic order of capitalism (Chiapello and Boltanski, 1999).

On this premise, the key research questions of this paper are: RQ1 What are the predominant reasons that individuals on Reddit cite for participating in the ‘Great Resignation’?

RQ2 How can these prevalent reasons be analyzed and understood through the application of the Moral Economy lens?

RQ3 What is the ‘vocabulary of motives’ and/or public justifications expressed by individuals, as derived from the Moral Economy analysis?

From the empirical viewpoint, this study seeks first to identify the common reasons associated with the ‘Great Resignation,’ then apply a Moral Economy lens to these reasons, and finally distill the ‘vocabulary of motives’ from the analysis. Interpreting this vocabulary of motives as potential indicators of a societal shift in evaluating the moral aspects of work.

Data from the r/anti-work sub-Reddit (Reddit, 2023) from February 2020 to February 2022 on Reddit’s social media platform was analyzed to understand the reasons behind the Great Resignation phenomenon. The Big Quit has been widely associated with Reddit r/antiwork (Jiskrova, 2022), a forum dedicated to discussing worker exploitation, labor rights, and associated left-wing political ideologies. The Great Resignation, as it came to be known in the mainstream media, coincided with the time when r/antiwork experienced its highest growth on Reddit in late 2021, gaining 1.7 million by the end of 2021 and reaching 2.3 million by October 2022. The r/antiwork subreddit’s popularity and subsequent expansion were credited to the same media coverage (Medlar et al., 2022). The antiwork subreddit group collects many testimonies and reports of voluntary resignation and, more generally, opinions about the situation in the contemporary world of work, providing valuable insights into the different vocabularies of motive employed by individuals during this phenomenon.

The BERTopic method (Grootendorst, 2022) was used to analyze text and extract topics from the antiwork sub-reddit group, which were subsequently semantically interpreted and grouped using the moral economy framework (Bolton and Laaser, 2013; Sayer, 2007) to identify the different vocabularies of motives behind the Great Resignation and prospective changes in how future generations consider the work.

Literature review and conceptual framework

The Great Resignation,” also called as “The Big Quit,” is a trend that began in the early part of 2021, characterized by a considerable number of individuals voluntarily choosing to leave their jobs. Over the past years, several studies have proposed explanations for the Great Resignation phenomenon.

According to Mitchell and Dill (2021), many workers have left their full-time positions to work freelance jobs to dedicate more time to their families. In particular, the COVID-19 epidemic had a great influence on people’s short and long-term job prospects, experiences, and trajectories (Akkermans et al., 2020).

Serenko (2022) proposed other explanations for the Big Quit phenomenon. First, so many people were comfortable working from home without long and exhausting commutes that they refused to return to mandatory office presence after reopening. Moreover, during the lockdown, people had the opportunity to reexamine their relationship with work and redefine life priorities and long-term career goals. As a result, they decided to move closer to family and friends, enjoy peace and quiet outside a busy city, seek more work-life balance, and accelerate retirement plans.

Moon et al. (2023) used the data from full-time employees to examine the role of extraversion in the Great Resignation phenomena. The findings show that extroverted individuals experience reduced burnout at work. Schmiedehaus et al. (2023) analyze the resignations during the COVID-19 epidemic to investigate the main causes and predictors of quitting academia. According to their findings, poor perceived organizational support, excessive tiredness, and low satisfaction were the main characteristics linked to the intent to leave. Similarly, academic staff who planned to quit academia had higher rates of stress and anxiety.

Lambert (2023) examines data on US employment patterns from 2003 to 2021 to show that resignations have been moving up in the overall economy, with quit rates mostly increasing among numerous industries. The study hypothesizes that this phenomenon may be explained by high unemployment and underemployment rates, pay stagnation, management oversight, and minority group composition within industries.

Kundu et al. (2022) explored and pinpointed the leading causes that created the context or built the groundwork for the Great Resignation trend. After determining the variables influencing the great Resignation, they used the fuzzy analytical hierarchy process to quantify each variable’s importance. The variables “Toxic Workplace” and “Uninspiring Work” were shown to be the most significant in predicting Great Resignation.

Shukla et al. (2022) performed a sentiment analysis of tweets referring to the great Resignation phenomena. Their findings indicate that employees are deciding to leave their current jobs and embark on a new journey toward their business ventures or freelance work as a result of poor work-life balance, workplace uncertainty, a lack of mutual trust between the employee and the employer, low pay, and a lack of career advancement are accountable for turnover intention.

Using text analysis, Del Rio-Chanona et al. (2022) looked at how Reddit posts on work and quitting have changed between 2018 and 2021. They discovered that the dynamics of the U.S. quit, and layoff rates are similar to the evolution of Reddit discussions. Additionally, discussions about working from home, changing jobs, work-related stress, and mental health surged when the COVID-19 epidemic broke out. Their key result is that from the start of the epidemic, quit-related posts have disproportionately increased in content about mental health and workplace distress, which is likely what caused the Great Resignation.

Moreover, the phenomenon has been affecting some work fields in specific ways; according to Avitzur (2021), the COVID-19 pandemic has caused significant stress and exhaustion among various groups of healthcare professionals during the past months. According to a Morning Consult survey of 1000 American healthcare professionals, about a fifth of workers had resigned from their employment due to the epidemic, and a further fifth was thinking about doing so (Morning Consult, 2022). The hospitality industry was also one of the earliest and most severely affected sectors when Covid forced the nation to a stop. The data suggest that burnout contributes to greater attrition rates in specific industries. According to Sull et al. (2022), the Great Resignation equally impacted both white-collar and blue-collar workers. Of all the industries examined, fast food, specialty retail, and apparel retail are the most badly affected and employ the most blue-collar people.

What is strongly challenged by the phenomenon of the Big Quit is the conception of “work ethics”, namely the conviction that hard work and perseverance have moral benefits and an innate capacity, virtue, or value to develop one’s abilities and character. Weeks (2011) defines it as the result of a process of reification whereby the fact that it is necessary to work to “earn a living” is seen as a part of the natural order rather than a social convention. The naturalization of wage labor as the only viable way for individuals to make a living is accompanied by glorifying the time and resources spent on work tasks. Given the relevance of moral judgment to the choices that may have led to the resignation decision of many individuals, one can refer to the concept of moral economy as a theoretical key to interpreting the phenomenon.

Despite the wealth of studies that have advanced our understanding of the Great Resignation, there remain critical gaps in our knowledge. Most current research has focused on individual-level factors such as personal satisfaction, burnout, or personality traits like extraversion. There is also discussion on job-related factors like perceived organizational support, working conditions, and pay.

Yet, these studies do not fully capture the collective and societal aspects of the Great Resignation, which could be instrumental in understanding this phenomenon. There is an evident lack of exploration into the interplay between individual, organizational, and societal factors. For example, the contribution of societal norms and attitudes towards work, such as the conception of work ethics, to this trend is a relatively uncharted area.

In addition, most current studies utilize traditional data collection methods, such as surveys or interviews. These methods might not entirely capture the array of reasons behind the Great Resignation, especially considering the rapidly evolving dynamics of work and employment in the digital age. The need for research leveraging new forms of data, like social media posts, to probe into the Great Resignation is palpable.

Our study aims to bridge research gaps by investigating the Great Resignation through the lens of moral economy and utilizing data from Reddit posts as a novel data source. By adopting the moral economy framework, we want to reveal the Great Resignation as a complex social transition taking place across personal, organizational, and collective domains, rather than as a series of individual actions or a trend. This method is designed to provide a complete explanation of the socio-psychological dynamics underlying the Great Resignation, providing a deep, multi-dimensional perspective on this contemporary event. It enables us to see and investigate the interdependence of individual motivations, structural circumstances, and wider social trends, resulting in a more comprehensive perspective about the Great Resignation.

Moral economy framework

The concept of “moral economy” refers to a conceptual framework for understanding the moral norms, values, and ethical concerns that form and influence economic practices (Sayer, 2007). It recognizes that economic acts are influenced not just by self-interest or market pressures but also by larger social, political, and moral circumstances. Accordingly, moral economy emphasizes the interaction between economic activities and the moral attitudes and larger ethical principles that govern them.

The development of the moral economy approach may be attributed back to the writings of authors such as Karl Polanyi (1957) and Edward Thompson (1971). Polanyi, in his seminal work “The Great Transformation,” criticized the concept of a self-regulating market that ignores the social and human repercussions of economic activity (Polanyi, 1957). For instance, he stressed that land, labor, and money are not just commodities but are interconnected in social interactions, and their commercialization can have negative consequences. The idea of the moral economy was further explored by Edward Thompson in his work “The Moral Economy of the English Crowd in the Eighteenth Century”, which looked at particular class conflicts and collective activities motivated by moral and ethical concerns (Thompson, 1971). Generalizing from the particulars of food riots, Thompson conceives of the moral economy as a popular consensus on what distinguishes legitimate from illegitimate practices, a consensus rooted in the past and capable of inspiring action. According to the moral economy approach, collective action is a response to violations of norms and moral standards to which the marginalized class has become accustomed and which it expects ruling élites to maintain. Thompson thus emphasized the importance of understanding—along with material interests—the moral and ethical dimensions of economic behavior and decision-making (Granovetter, 2017).

The key finding offered by Polanyi and Thompson is that the market is constituted by institutions, people’s moral principles, and communitarian dimensions, and that this is what allows market forces and society to persist over time together. People recognize the material realities of living and working in a market-driven society and do what they must do to survive. Besides, markets need moral legitimacy to function.

Finally, Sayer’s idea of “lay normativity” (Sayer, 2011, 2005) provides the last component of this moral economy framework, which bridges the gap between institutional/community norms and people’s everyday reflexive moral capabilities. It brings to attention questions related to “what is of value, how to live, what is worth striving for and what is not” (Sayer, 2005), demonstrating the richness and complexity of social and moral existence. Humans are evaluative entities capable of accepting or rejecting community standards and providing reasons for engaging or not participating in economic behaviors. Sayer’s notion is based on the reciprocal nature of social relationships and an understanding of people as needy and fragile individuals who may flourish or suffer under specific conditions. It contends that in order to be effective, efforts to promote more sustainable aims must take into consideration these lay normativities (Wheeler, 2014). Social action can be explained not just by intentions/reasons nor by its behavioral outcomes but by the evaluation of others and their social judgment. Judgment depends on the way an action is received in a “circle of recognition” that judges it as being socially valid. To make sense of social action, theories of judgment look at the exogenous contextual changes and macro-level dynamics. When social structure changes, agents face a sort of ‘symbolic tsunami’: the moral meaning of social action changes accordingly, often abruptly (Pizzorno, 2007).

As we argued, the literature on the moral economy contains both descriptive and prescriptive elements (Barbera, 2023). The descriptive element concerns the investigation of the various moral norms and obligations that mediate the central economic relations of a given context. The prescriptive element refers to the moral economy as a tool for giving an ethical judgment on economic practices (Sayer, 2007). Moral economists provide effective means of thinking about work, employment, and society by providing a comprehensive analytical lens (Bolton and Laaser, 2013). Bolton and Laaser (2013) proposed a moral economy framework based on Karl Polanyi (1957), E.P. Thompson (1971), and Andrew Sayer’s (2005, 2007, 2011) work. As noted above, Polanyi’s thesis reflects the contradiction between a stable, moral, and human society and the market economy’s self-regulating practices. Thompson’s approach to community-based moral economy offers a rich historical perspective on the interplay between economic practices and social ideals. Sayer’s comprehension of lay morality and political norms reveals the underlying foundations that drive market economies. The relevance of the Moral Economy is key in the provisioning of all commodities where human well-being as such is deemed to be an intrinsically significant goal (Morgan, 2020).

By examining the Great Resignation phenomenon through the lens of the moral economy framework, we gain a deeper understanding of the dynamics between institutions, communities, and individuals. Institutions, including organizations, establish the rules and conventions that regulate behavior in the workplace. Communities serve as central points for collective action and social change, bringing people together to challenge established conventions and fight for their own interests. Individuals, in the meantime, negotiate their own wants, values, and motives within these larger institutional and social structures.

On this background framework, our research focuses on exploring the vocabulary of motives or public reasons that individuals refer to when justifying their resignation, as presented in the r/antiwork channel. Although various researchers have investigated and proposed reasons for the Great Resignation, our approach offers a novel perspective. This comprehensive approach combines the Moral Economy framework and the BERTopic model to identify the variety of vocabularies of the motives associated with the Great Resignation phenomena. Combining the Moral Economy framework and the BERTopic model, this approach sheds light on the large-scale moral implications of the Great Resignation phenomenon, providing a more nuanced understanding of the complex interplay between individual motivations, cultural values, and societal factors.

Methodology

The present study employs a topic modeling method to shed light on the vocabulary of motives associated with the Great Resignation phenomena. Figure 1 shows the methodological framework employed, comprised of distinct phases:

Fig. 1
figure 1

Framework methodology.

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Data collection

The study started by collecting data from Reddit using the Python Reddit API Wrapper (PRAW) tool (PRAW, 2023). This tool allows for extracting posts and related information from the Reddit platform. A substantial part of posts’ content is composed of images, primarily screenshots of chats or posts shared on other social platforms or, to a lesser extent, photographs and memes. An extension for Google Chrome called optical character recognition (OCR) was utilized to collect this information manually (Google, 2023). OCR enables extracting and copying text from images, facilitating the process of gathering textual content from several types of images.

Filtering data

The collected data were filtered based on the “Top” classification. This means that only posts with a high level of engagement, such as upvotes and comments, were considered for further analysis. By focusing on the “Top” posts, the study aimed to capture the most relevant and influential discussions within the r/antiwork subreddit. This resulted in a dataset containing 955 posts shared from February 2020 to February 2022. The output dataset was obtained considering the post’s information (ID post, title, score, post, number of comments, time of sharing, and URL).

Data preprocessing

In this phase, several steps were taken to clean and prepare the data for text analysis. Stopwords frequently used words that do not have significant meaning, were removed from the text to improve the quality of the extracted topics. Missing values were also addressed. Additionally, posts containing fewer than 15 words were filtered out, ensuring that only posts with sufficient content were included in the analysis. The output dataset after this phase consisted of 783 posts.

Application of BERTopic model

BERTopic, a topic modeling technique based on the BERT language model, was applied to the preprocessed data (Grootendorst, 2022). This model utilizes a transformer-based neural network architecture to identify coherent topics within a collection of documents. By applying BERTopic, the analysis aimed to uncover distinct themes or topics present in the r/antiwork subreddit and explore the potential reason behind Big Quit.

Topic extraction

Once the BERTopic model was applied, the study focused on extracting the topics from the analyzed data. This involved identifying clusters of posts that shared similar content or themes. The resulting topics provided an overview of the main discussions taking place within the subreddit. In order to assess the BERTopic model, we applied ‘c_v’ method to calculate the coherence score (Abdelrazek et al., 2022; Chen et al., 2023) to evaluate the quality of the topics generated by our BERTopic model, providing a quantitative measure to supplement our analysis. C_V metric is a method used to evaluate the consistency of topic models. C_V can measure both the internal coherence of a topic and the coherence between topics. Specifically, C_V utilizes the co-occurrence information of words to calculate the relevance within a topic and the separation between topics, thus assessing the quality of topics (Cheng et al., 2023).

Semantic interpretation of topics

The final phase involved the semantic interpretation of the extracted topics. This step aimed to understand the underlying meaning and implications of each topic within the context of the research question. Interpreting the topics within the moral economy framework (Bolton and Laaser, 2013) seeks to gain insights into the motivations and perspectives behind the Great Resignation phenomenon.

The research methodology involved data collection, filtering, preprocessing, topic modeling using BERTopic, topic extraction, and semantic interpretation of the extracted topics. These steps allow us to analyze the motivations and perspectives of individuals participating in the r/antiwork subreddit during the Great Resignation phenomenon.

Topic modeling with BERTopic

In the framework of natural language processing (NLP) techniques, topic modeling is often performed as an unsupervised learning problem in which algorithms use statistics to determine which words are related and then organize them into groups (De Leo et al., 2023; Jónsson and Stolee, 2015; Kherwa and Bansal, 2018).

Topic modeling technique has become a prevalent way to analyze unstructured data; latent Dirichlet allocation (LDA) is frequently applied in prior studies (Mo et al., 2015; Zhu et al., 2022). LDA is a stochastic generative model for discrete datasets such as text corpora and is typically viewed as the standard approach (Newman et al., 2009; Gallagher et al., 2017). However, LDA’s efficacy in analyzing social media data has been highly criticized as it is effective at identifying topics, mainly when the corpus of data includes longer and larger documents (Baird et al., 2022; Egger and Yu, 2022). Consequently, researchers have reinforced the value of newly developed algorithms as alternatives since they often exceed LDA, especially when analyzing short text data on social media (Vayansky and Kumar, 2020; Egger and Yu, 2022).

Therefore, the BERTopic (Bidirectional Encoder Representations from Transformers) model can provide more contextually specific topics while understanding some of the nuances of language that LDA might miss (Baird et al., 2022). Specifically, the BERTopic is a topic model technique that generates document integrations with pre-trained transform-based language models and groups these integrations. Finally, it generates topic representations with the class-based TF-IDF procedure (Grootendorst, 2022).

This method generates topic representations through three steps (Saidi et al., 2022; Baird et al., 2022):

  • Extract word embeddings: The first stage is to turn the textual data into numerical representations called word embeddings. In order to perform this transformation, BERTopic utilizes the sentence-transformers library (Devika et al., 2021), which contains a variety of pre-trained language models designed for natural language processing (NLP) applications. The resulting transformation enhances machine learning models’ understanding of textual data and is a foundation for later processing.

  • Cluster-reduced embeddings: When text is converted into word embeddings, each document is represented as a high-dimensional vector, which is effectively a lengthy list of numbers. Because clustering approaches aren’t always effective at dealing with high-dimensional data, BERTopic implements dimensionality reduction by default using the uniform manifold approximation and projection (UMAP) algorithm (McInnes et al., 2018). UMAP is employed because it preserves part of the data’s structure, which is necessary for grouping text based on similarity. After reducing the dimensionality, BERTopic clusters the data using the hierarchical density-based spatial clustering of applications with noise (HDB-SCAN) (McInnes et al., 2017) since it can generate clusters of various forms and find outliers. The resulting topic representations are less noisy since it does not push text into clusters. When BERTopic uses HDB-SCAN to cluster the data, the resultant clusters might have variable degrees of density and form. BERTopic employs a bag-of-words technique to create topics without making assumptions about the anticipated structure of the clusters by counting how frequently each word appears in each cluster.

  • Topic representation: Finally, the BERTopic model creates topics and generates keywords for each topic by extracting class-specific words by using the TF–IDF (Class-based-Term Frequency–Inverse Document Frequency) called c-TF-IDF. TF-IDF is a popular technique for identifying the most relevant “documents” given a term or set of terms. c-TF-IDF turns this on its head by finding the most relevant terms given all the “documents” within a cluster. c-TF-IDF is well explained by the following Eq. (1):

$$w_{t,c} = tf_{t,c} \cdot \log \left( {1 + \frac{A}{{tf_t}}} \right)$$
(1)

where the “term frequency” in this context refers to how frequently a particular word, represented as t appears in a group or ’class,’ represented as c. A class in this context is a collection of documents that have been merged. Then, the term “inverse document frequency” is thus replaced with “inverse class frequency.” This clarifies how much information a particular word may provide about a class. This is calculated by dividing the log of the average terms for class A divided by number of words t across every class.

These approaches enable BERTopic to produce topic representations that are relevant while maintaining the contextual richness of the source text, making it a viable tool for text analysis and information extraction tasks. Moreover, BERTopic offers a significant advantage over traditional methods such as latent Dirichlet allocation (LDA) due to its ability to infer the optimal number of topics from the data. Unlike LDA, which requires the number of topics to be predefined, BERTopic can autonomously identify the appropriate number of topics based on the content of the documents. This eliminates potential bias or inaccurate representations of the data’s thematic structure, yielding more accurate and naturally coherent topics (Wang et al., 2023).

Results

In Fig. 2, we illustrate the distribution of word counts across all posts within the r/antiwork subreddit. The histogram was created by dividing posts into bins according to their word length, with each bin representing a specific range of word counts. The y-axis denotes the number of posts that correspond to each bin, thus representing the frequency of posts with a given word length. To give a clearer representation of the underlying data trend, a Kernel density estimation (KDE) was overlaid on the histogram, providing a smoothed approximation of the frequency distribution. The average word count, represented by a vertical dashed red line, stands at 143.72 words. This figure serves as a central tendency measure of word lengths within the dataset, providing a representative value for overall post length. Interestingly, the distribution appears to be positively skewed. This suggests that while the majority of posts are relatively short, a small number of lengthier posts skew the average to a higher word count.

Fig. 2
figure 2

Histogram of post word count in the r/antiwork subreddit.

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Upon conducting an initial exploration of our data, we then applied the BERTopic model to extract dominant topics from the posts. The results of this analysis are displayed in Fig. 3, which presents the topics along with their respective probability scores, sorted in descending order, starting from Topic 0, which comprises 139 posts, down to Topic 11, which contains 15 posts (Table 1). Additionally, the model identified a category, Topic-1, with 244 posts, which represents a collection of documents that did not align closely with any specific topic. These could be outlier texts or ones with diverse themes that didn’t fit neatly into the other identified topics. In particular, Table 1 provides an interpretation and labels assigned to each topic based on the most representative words within each topic. This semantic interpretation was generated by thoroughly analyzing the posts associated with each identified topic. The labels aim to encapsulate the core theme or discussion in each topic, ranging from personal work experiences (Topic 0) to issues of sick leave (Topic 11).

Fig. 3
figure 3

Topics extracted using BERTopic model.

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Table 1 Interpretation and labels Assigned to each topic based on representative words.
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Topic 0—Workplace dynamics and management

with words such as “work,” “job,” “manager,” and “company,” this topic is related to employment and work settings, including workplace rules, career advancement, and office politics. The topic collects stories that generally describe bad job experiences that frequently lead to resignation, usually involve supervisor abuse, and include labor exploitation that is not compensated monetarily or through promotion.

Topic 1—Work–life balance and personal satisfaction

The topic concerning with work–life balance and job happiness. Keywords like “life,” “time,” and “rich” imply that they may cover topics like locating meaningful employment, scheduling one’s time effectively, and reaching financial stability. The idea that working long hours and investing all one’s efforts has value in and of itself or allows one to obtain a successful or well-paid position is questioned. Here is reported an example of posts shared by a Reddit user, regarding one of the topics that are part of this group:

“I love how Millennials and GenZ are collectively rejecting Hustle Culture. No we will not work on our rest days. No we will not sleep for 2 h. No there is no glory in blind devotion to the grind No it is not admirable to—very literally—work oneself to the grave.”

Topic 2—Wages and compensation

With words like “wage”, “paid”, “minimum wage”, and “work”, this topic is about salaries and benefits. It could touch on topics including fair compensation, economic inequality, and the effects of minimum wage regulations. In this case, the hourly wage for low-skilled jobs (such as fast food) and the minimum wage are mentioned. A significant portion of the posts claim that the minimum salary in the U.S. is insufficient to support a decent lifestyle. An example of a post posted by a Reddit user on one of the topics in this group is shown below:

“There is no state in the U.S. where a 40-h minimum wage work week is enough to afford a two-bedroom apartment.”.

Topic 3—Wealth and Income Inequality

This topic contains terms like “wealth”, “money”, “taxes”, and “capitalism”. It might include a discussion on income inequality, tax policy, and capitalism’s structure of power. The discussion is about celebrated billionaires, unequal wealth distribution, and taxation. Many posts emphasize the importance of taxing these multi-billionaire figures in proportion to the required contribution of US citizens.

Topic 4—Work schedules and communication

This topic is related to work schedules and communication, with keywords such as “tomorrow”, “time”, and “see”. The discussions regarding about managing one’s work schedule and communicating with colleagues and managers. In particular, the discussions consist primarily of screenshots of messages exchanged between employees and employers. The dialogs in these messages frequently include demands for changes in work hours, while vacation, or rest. Many posts emphasize the urgent work culture in the workplace.

Topic 5—Job interviews and employment experiences

This topic is about job interviews and work experiences, and it contains terms like “interview”, “pay,” “hour”, and “experience”. It comprises conversations about job searching and application, negotiating wages, and getting work experience. Many discussions concerning the interview process for obtaining a job. Many users agree that recruiters should disclose the salary range for the position that is open. Additionally, other discussions about the imbalance between the job obtained and the proposed salary.

Topic 6—Labor strikes and worker rights

This topic is about labor strikes and worker rights, and it contains terms like “strike”, “workers”, “unions”, and “employees”. Discussions regarding collective bargaining, worker organizing, and labor laws are all part of it. The posts in the topic group are related to events planned by subreddit users in opposition to Kelloggs’ decision to employ new workers to replace those on strike. Users discuss the usefulness and efficacy of strikes in these posts.

Topic 7—Education and employment

This topic is about education and employment, and it contains terms like “student”, “jobs”, “loan”, and “debt”. It includes discussions concerning the labor market for new graduates, college costs, and student loan debt. Specifically, the topic is related to the American economic system and the more severe challenges that citizens face due to rising living costs. Several posts discuss wages that are insufficient to support a non-public health care system, having a home and family, the significant expenses required to enroll in college, and unexpected expenses.

Topic 8—Anti-work and alternative employment

With keywords like “antiwork”, “workers”, and “reddit”, this topic is related to the anti-work movement. Discussions regarding the gig economy, the reasons why individuals are abandoning regular employment, and the possibility of life after work are all be part of this topic. Furthermore, the topic collects posts in which people discuss the antiwork subreddit, with the most common discussions focusing on the channel’s popularity outside of Reddit. Other posts discuss internal subreddit issues such as organization and rules.

Topic 9—Workplace safety and natural disaster

With words like “amazon”, “tornado”, “factory”, and “safe”, this topic concerning workplace safety. The topic includes discussions regarding the risks of working in certain sectors, the responsibilities of employers to protect employee safety, and the effects of natural disasters on workplaces. The posts on this topic mostly discuss the tornado that demolished an Amazon-owned warehouse in Edwardsville, Illinois, on December 10, 2021, which resulted in the deaths of six workers. Users debate the shortcomings of Amazon’s performance-based rules in the r/anti- work forum. Following is an example of a post shared by a Reddit user about one of the topics covered by this group:

“Amazon employees work harder as Jeff Bezos becomes wealthier. It’s almost as if the workers are the ones who create the money.”

Topic 10—Holiday traditions and celebrations

With words like “holiday”, “Christmas”, “family”, and “favorite”, this topic relates to holiday customs and festivities. The influence of consumerism on Christmas festivities, family relationships over the holidays, and cultural traditions are all discussed. In particular, this topic discusses the importance of spending free time and vacation days with family.

Topic 11—Sick leave policies and worker benefits

This topic is about sick leave policies and employee benefits, and it contains terms like “sick”, “paid”, “workers”, and “shifts”. It comprises talks about employee rights, company obligations, and the impact of sick leave policies on employee health and productivity. Furthermore, the topic contains posts about sick leave days from work. In particular, the posts discuss the working person’s rights and the sick leave days denied during the pandemic period.

In evaluating our topic model results, we employed the coherence score as a quantitative measure of the semantic interpretability of the topics. The model achieved a coherence score of 0.6081, suggesting that the topics generated by the model were, on average, fairly semantically coherent.

Applying the Moral Economy framework to the Great Resignation phenomenon

Examining all the extracted topics, we identify patterns and themes concerning work and employment (organizational dimension), social justice and activism (community-based dimension), and health, well-being, and lifestyle (individual dimension) illustrated in Table 2. Within the context of the moral economy, these groupings represent the diverse nature of human experiences and interactions. By drawing ideas from the writings of Karl Polanyi, E.P. Thompson, and Andrew Sayer, we may comprehend the fundamental ideas that influence these groups more deeply.

Table 2 Topics grouped based on Moral Economy framework.
Full size table

Work and Employment

The moral economy approach developed by Polanyi emphasizes the necessity of integrating labor and employment practices with social and moral structures. This view highlights the significance of ethical hiring procedures, fair pay scales, and respectful working conditions. It urges businesses to go beyond simply market-driven strategies and take into account their workers’ wellbeing, promoting a work environment that values social ties, meaningful employment, and human dignity. Organizations may build more sustainable and inclusive workplaces that support individuals to flourish within a wider ethical and social structure by embracing the moral economy’s guiding principles.

Social Justice and Activism

Thompson’s community-based moral economy approach focuses on how communities may fight for social justice and challenge inequalities. This interpretation emphasizes the value of teamwork, solidarity, and dedication to the pursuit of greater justice and equity in economic interaction. It highlights the necessity of communities banding together to fight for workers’ rights, combat income disparity, and advance social justice. Individuals may strive toward a moral economy that is in line with communal values, where economic activities are dictated by principles of fairness, justice, and human dignity, through organizing communities, taking part in action, and confronting systematic injustices.

Health, Well-being, and Lifestyle

Sayer’s comprehension of political norms and lay morality draws attention to the political and moral implications of market economies. This view highlights the value of acknowledging and appreciating people’s personal choices, their well-being, and the larger social and cultural settings in which economic transactions take place. Individual well-being must be prioritized, work–life balance must be supported, and policies and procedures must be in line with personal beliefs. A moral economy that respects the many needs and ambitions of people within society may be formed by considering the consequences of labor on people’s physical and mental health, valuing their personal choices, and recognizing the social and cultural components of well-being.

The moral economy framework helps us comprehend the dynamic interactions that exist between organizations, communities, and individuals within the context of work and employment, social justice and activism, and health, well-being, and lifestyle. In the context of ‘Work and Employment,’ it is essential for the US government to draw attention to workers’ rights and safety as a primary institutional provider. This priority can help establish policies that promote fair and ethical employment practices. In the field of ‘Social Justice and Activism,’ supporting mechanisms such as anti-discrimination legislation, equal opportunity regulations, and collective bargaining rights may empower people and communities. It improves their ability to fight for fair treatment, protect workers’ rights, and promote overall social equality.

Finally, in the context of “Health, Well-being, and Lifestyle” policymakers should prioritize overall wellness, such as universal healthcare, paid leave policies, and mental health assistance, to contribute to a better and more balanced lifestyle.

Discussion and conclusion

In our study, we adopt a moral economy framework and a research design that combines multiple methodologies to gain comprehensive insights concerning the reasons behind Big Quit. We utilize the BERTopic methodology to analyze data from the social media platform Reddit, apply the Moral Economy perspective to examine the broader societal and ethical dimensions and identify distinct vocabularies of motives expressed by the users of r/antiwork. By examining these potential vocabularies of motives within the context of the groups “Work and Employment,” “Social Justice and Activism”, and “Health, Well-being, and Lifestyle”, we gain a deeper understanding of the public justifications of the choices. A key motive is the pursuit of meaningful work, with younger workers increasingly seeking employment that is fulfilling, aligned with personal beliefs, and allows them to have a positive effect on the world while pursuing personal interests (Kuzior et al., 2022). Another prominent vocabulary of motive is flexibility according to individual schedules of time, as individuals prioritize work-life balance over long-term commitment and stability. They value opportunities to work remotely or on flexible schedules, and they are less likely to remain with a single company throughout their career (Fraley et al., 2022; Niebuhr et al., 2022).

Furthermore, social responsibility plays a significant role, as younger generations become more aware of the structural injustice inherent in the existing economic system. They are socially conscious and less willing to accept the status quo, often engaging in activism and group action. Additionally, the focus on self-care is a driving factor, as the younger generation prioritizes their physical and emotional well-being (Montaudon-Tomas et al., 2022). They understand the value of maintaining a healthy work-life balance and prioritize self-care to succeed in their jobs.

These results indicate a shift towards a more personalized, human-centered, and socially responsible approach to work and employment. This is in line with the argument that younger generations seek careers that allow them to balance personal and professional obligations, make a positive impact on the world, and prioritize their own well-being (Fraley et al., 2022; Kuzior et al., 2022). The Great Resignations are a signal of a larger social change at the macro-level: people judge and evaluate jobs and work environments in a “holistic” way, refusing to segment their jobs from a broader evaluation of the other roles they occupy. In this light, the Great Resignations phenomena points to the stronger intersection of people’s role-set as worker, citizen, activist, family-member, and the like (Merton, 1957). We can tentatively speculate that the people who left their jobs judged more and more their work environment through quality conventions and public justifications that point to their entire role-set as community activists (social justice), family members (work–life balance), and the like. This is entirely speculative because we do have a date on the changing role-set of “quitters” vis-à-vis the one on “stayers”. The conjecture is nonetheless coherent with the well-established finding that people typically pursue multiple purposes simultaneously in intersecting social formations (Granovetter, 2000). Understanding these vocabularies thus provides valuable insights into the changing landscape of work and employment in society as a whole and the underlying factors contributing to the Great Resignation phenomenon.

The findings have important implications for comprehending the phenomena of the Great Resignation. It offers insights that can help companies, policymakers, and stakeholders build strategies for adapting to changing work dynamics and improving work environments. The identification of a shift in the perception of work importance highlights the need for a more human-centered and socially responsible approach to work.

There are various limitations to the study to be carefully considered. First of all, data from the social media network Reddit does not reflect the full population. R/antiwork subreddit was created in 2013 as a forum for discussion of anti-work thought within post-left anarchism and it is clearly a self-selected sub-population with peculiar traits. Looking at the whole population, workers' attitudes and moral dimensions seem to be less pertinent. As it has been argued: “The record percentage of workers who are quitting their jobs, known as the “Great Resignation,” is not a shift in worker attitudes in the wake of the pandemic. Evidence on which workers are quitting suggests that it reflects the strong rebound of the demand for younger and less-educated workers” (Hobijn, 2022). Moreover, the BERTopic technique has inherent limitations in capturing the intricate details of human language and context. Finally, the use of the Moral Economy Framework is susceptible to many different analytical interpretations.

In conclusion, our study on the Great Resignations has provided empirically grounded insights into the vocabulary of motives that a sub-population of individuals who quitted their jobs report publicly as meaningful justifications for their choices. The identified motivations of flexibility, meaningful work, social responsibility, and self-care reflect a paradigm shift in the perception of work importance vis-à-vis other dimensions and social roles. We tentatively attributed our finding to the intersections of role-set triggered by the pandemic and home working; the dynamics of such intersections are an important topic well beyond the scope of this research (Granovetter, 2017).

From the human-resources management viewpoint, our study highlights the need for organizations and policymakers to adapt to these changes by prioritizing flexibility, meaningful work, social impact, and personal well-being. However, limitations exist in the reliance on data from social media and the employed methodologies. Further study might look at other data sources and frameworks to have a better understanding of the micro-level mechanisms at work. In summary, our research adds to our understanding of the Great Resignation as a “moral” phenomenon for a specific sub-population in connection to broader social change, enabling future research to identify different mechanisms for different kinds of sub-populations, economic sectors, and country-specific dimensions.