2.1 Characteristics of Meaningful Work

Research based on social psychology and management sciences has identified relevant elements or factors related to meaningful work (Bailey et al. Reference Bailey, Yeoman, Madden, Thompson and Kerridge2019; Hackman and Oldham Reference Hackman and Oldham1975; Lips-Wiersma and Morris Reference Lips-Wiersma and Morris2009; Rosso, Dekas, and Wrzesniewski Reference Rosso, Dekas and Wrzesniewski2010). On the one hand, factors related to the content of work contribute to its meaningfulness (Martela Reference Martela2023; Michaelson Reference Michaelson2021; Veltman Reference Veltman2016). These factors entail:

• • Skill variety: the number and diversity of different skills used by workers.

• • Task integrity: the extent to which workers perform a job from start to finish.

• • Task significance: the extent of the achievement or the contribution to a worthy goal; its point and purpose.

On the other hand, factors related to the subjective experience of work influence whether workers perceive it as meaningful or not (Breen Reference Breen, Yeoman, Bailey, Madden and Thompson2019; Lips-Wiersma and Morris Reference Lips-Wiersma and Morris2009; Weeks and Schaffert Reference Weeks and Schaffert2019; Yeoman Reference Yeoman2014). These factors include:

• • Autonomy: the degree to which the job provides substantial freedom, independence, and discretion to the individual in determining the procedures to carry it out; this trait includes participation in decision-making regarding work and job crafting and design.

• • Self-development: this trait describes how work facilitates skill cultivation and use, and its impact on the realization of a worker’s professional and personal flourishing.

• • Self-esteem: the extent to which work allows integration with other components of one’s life and leads to a meaningful life.

• • Social relationships and social recognition: meaningful work provides a sense of belongingness and relatedness, of being accepted by colleagues, being mentored, and supported; and a shared sense of agency and common purpose, a sense of connection and “unity with others.”

The subjective experience and the objective content of work are connected. Work is often experienced as meaningful when its content has a worthy purpose, in the achievement of which the worker participates from start to finish using a varied set of skills. However, this connection does not always hold (Michaelson Reference Michaelson2021). Sometimes, dull and fragmented work, which objectively does not involve an actual achievement or require a set of professional skills, is experienced as meaningful. Conversely, sometimes work that does involve achievement, allows participation in a complete process, and helps to develop one’s skills, is subjectively experienced as meaningless. This possible dissociation between subjective experience and objective content shows that the worker’s motives for engaging in work depend not only on its content but also on other external motives (such as salary and status). In this regard, some authors have pointed out ethical problems concerning workers’ motivation and its potential manipulation by managers, that is, how managers might promote their own interests against those of the workers by implementing certain types of work (Breen Reference Breen, Yeoman, Bailey, Madden and Thompson2019) and why workers should desire a job with certain objective characteristics over one that reflects other kinds of preferences (Ciulla Reference Ciulla2000; Mortimer Reference Mortimer2023).

Understanding the connection between work’s subjective and objective aspects and avoiding possible manipulation requires a normative approach (Michaelson Reference Michaelson2021; Sison Reference Sison2024). Objectively, work is meaningful to the extent that it contributes to a meaningful life (Ciulla Reference Ciulla2000; Mejia Reference Mejia2023). Subjectively, work is meaningful to the extent that workers have convincing reasons to engage in it (Mortimer Reference Mortimer2023).

Alasdair MacIntyre’s moral sociology (Reference MacIntyre2007; 2016) offers an approach that considers both dimensions from a normative perspective. To show that something has “meaning” or is “meaningful” is to clarify its place in a broader context that makes it intelligible (MacIntyre Reference MacIntyre1999b). Work, like other human activities, has a teleological structure. Subjective experiences are meaningful not only because they are experienced as such but because “there are good non-circular reasons for that experience” (Michaelson Reference Michaelson2021, 421). Meaningful life is not the kind of life one considers good and worth living, whatever that may be, but the kind of life one has reason to consider good and worth living (MacIntyre Reference MacIntyre2016, 17ff). Accordingly, work is meaningful when the one who works “has good reasons to experience it as meaningful, that others have good reasons to perceive as socially worthwhile, and that is independently (intersubjectively or objectively) meaningful” (Michaelson Reference Michaelson2021, 420). MacIntyre’s Neo-Aristotelian ethics offers such reasons in three stages.

First, MacIntyre (Reference MacIntyre2007) presents “practices” (socially established complex activities realizing internal goods and whose excellence is defined mainly by expert practitioners) in contrast to “institutions” (activities or organizations pursuing external goods, such as wealth, status, and power, whose effectiveness can be judged by anyone). Second, he examines how individual practitioners render compatible various roles and practices by ordering different types of goods in their quest for a good life. Third, he argues how excellence in practices contributes not only to practitioners’ good lives but also to the advancement of their communities. It is this normative perspective that justifies and substantiates the legitimacy of the subjective experience of meaningful work (Beadle and Knight Reference Beadle and Knight2012). In this article, we adopt this normative perspective.

In the following sections, we present in more detail the MacIntyrean approach to: 1) meaningful work, 2) the tension between meaningful work and professional management, and 3) the conditions that management must meet to sustain and promote meaningful work. We then contrast this approach with the characteristics of algorithmic management and, finally, elucidate whether and how organizations using algorithmic management leave room for meaningful work.

2.2 A MacIntyrean Approach to Meaningful Work

According to MacIntyre (Reference MacIntyre2007), there are goods internal to certain activities that he calls “practices,” in which individuals can work and be educated in a way that informs their sense of meaning (Beadle and Knight Reference Beadle and Knight2012, 438). To clarify this claim, it is necessary to explain, first, what kind of activities practices are; second, what kind of goods are internal to practices; and third, why people would prefer those internal goods over other external goods such as money, prestige, or power.

First, a practice, according to an oft-quoted definition by MacIntyre, is “any coherent and complex form of socially established cooperative human activity through which the goods internal to that form of activity are realized in the course of attempting to achieve those standards of excellence which are appropriate to, and partially definitive of, that form of activity, with the result that human powers to achieve excellence, and human conceptions of the ends and goods involved, are systematically extended” (MacIntyre Reference MacIntyre2007, 187). Examples of practices are found in sciences, arts, and sports. Many professions can also qualify as practices (Higgins, Reference Higgins2010; MacIntyre, Reference MacIntyre2007, 187–189; Moore, Reference Moore2017), including interpersonal services like nursing (Sellman Reference Sellman2000), as well as productive activities like farming (Sinnicks Reference Sinnicks2019).

Second, MacIntyre follows Aristotle in identifying two kinds of goods internal to practices (Reference MacIntyre2007, 189–90). On the one hand, there is the excellence of the product: this encompasses both the excellence of practitioners’ performance (e.g., the skill of the artist who makes portraits or the teacher who lectures) and the excellence of the result (the portrait or the lecture). A productive practice such as painting has standards of excellence, the actualization of which by individual painters produces excellent portraits. The same can be said for teachers (Dunne Reference Dunne2003), nurses who care for people (Sellman Reference Sellman2000), and farmers who cultivate fields (Sinnicks Reference Sinnicks2019). On the other hand, MacIntyre argues that the actualization of these standards also actualizes something else: “the good of a certain kind of life…. it is the painter’s living out of a greater or lesser part of his or her life as a painter that is the second kind of good internal to painting” (Reference MacIntyre2007, 190). Something similar can be affirmed of the lifestyle of the teacher, the nurse, and the farmer as kinds of life that realize different modes of human excellence, only accessible from within each profession.

Third, practitioners can prefer internal goods over other goods, such as a better salary or better working conditions, because internal goods are valued according to the standards of excellence proper to the practice. Goods such as money, power, or prestige can be attained in many different ways, while internal goods can be obtained only in each specific practice: having more money does not make one a better painter, a better teacher, or a better nurse. Certainly, external goods are necessary to sustain practices and practitioners: good painters, teachers, and nurses need adequate resources and conditions to carry out their work in an excellent way, as do art schools, universities and hospitals. However, for practitioners committed to their profession, it is the internal goods that are essential in the evaluation of their work. The meaning of their work lies in achieving excellence in their profession, both in the results produced and the skills developed, and in the kind of life, specific to their profession, that they aspire to live.

To identify and obtain goods of excellence, workers need some skills and virtues that can only be acquired by becoming expert practitioners. As they become better in the practice, workers also acquire higher levels of skills and insights (better reasons) that were not previously available, that beginners do not know, do not understand well or do not value enough. “Not only skills, maxims and rules but also practical judgment and moral character are learned through work that actualizes the goods of a certain kind of life” (Beadle and Knight Reference Beadle and Knight2012, 438). Work is perceived as meaningful when workers take the achievement of its internal goods to be constitutive of their own good.

Meaningful work is thus preferred over other possible goods for moral reasons: it is valued as part of the kind of life one has good reasons to consider good and worth living. “There is a close connection between being a good human being and doing good work” (MacIntyre Reference MacIntyre, Blackledge and Knight2011, 323). This criterion is not purely subjective, nor does it depend on individual preferences. Rather, it depends on work endowing practice-like features (Beadle and Knight Reference Beadle and Knight2012). Thus, a work may be meaningful insofar as it is: a) complex and coherent enough to require skill variety and task integrity; b) socially established and cooperative enough to promote social relationships within which a sense of belongingness, unity, and common purpose can be developed; c) able to actualize internal goods, providing purpose, as well as a sense of achievement and self-development; d) able to hold standards of excellence according to which products are judged, and practitioners obtain self-esteem and social recognition; e) with the result that extension of human powers and human conceptions of the ends and goods involved can be pursued with autonomy and discretion in such a way that that work becomes morally educative; and, f) its contribution to society is realized in the advancement of practitioners’ communities.

Table 1 summarizes what has just been described, associating the different characteristics of a “practice” according to MacIntyre (Reference MacIntyre2007) with the characteristics of meaningful work highlighted above.

Table 1:

Characteristics of MacIntyrean Practices and Meaningful Work

These categories help us argue why some kinds of work are meaningful and others are not. Some types of work have such limited content that they do not allow the development of professional competencies in quantity and quality. Other types of work are organized in such a way that they do not allow for the development of social and moral competencies conducive to a fulfilling life. Other types of work do not contribute to the good of society. MacIntyre’s moral sociology connects objective and subjective dimensions of meaningful work as it captures the elements that the literature associates with meaningful work and explains the reasons that lead workers to engage in it. Besides, this approach also identifies some important threats to the very existence of meaningful work, which are discussed in the next sections.

2.3 Institutions and Meaningful Work

For practices to thrive they need to be institutionalized (MacIntyre Reference MacIntyre2007, 193). Practitioners must organize themselves to establish standards, obtain resources, and advance other common objectives. For this purpose, academies of arts and sciences, schools, hospitals, sport federations, professional bodies, and business enterprises emerge. These organizations are a combination of practice and institution (Moore Reference Moore2012). While practices are concerned with internal goods (also called goods of excellence), institutions focus on the acquisition of external goods (or goods of effectiveness), such as money, power, and prestige necessary for the sustaining and flourishing of practices (MacIntyre Reference MacIntyre1988). Institutions are supposed to protect practices and promote their healthy development.

The simultaneous pursuit of excellence and effectiveness typically causes tensions in organizations. The acquisition of goods of effectiveness is a necessary and valuable function of an organization, as long as it is subordinate to the sustenance and development of the practices it hosts (Moore and Beadle Reference Moore and Beadle2006). However, there is a constant risk that the order of priorities will be reversed and effectiveness considerations will prevail: “The ideals and the creativity of the practice are always vulnerable to the acquisitiveness of the institution, in which the cooperative care for common goods of the practice is always vulnerable to the competitiveness of the institution” (MacIntyre Reference MacIntyre2007, 194). For MacIntyre, the custodians of the delicate relationship between practices and institutions are the virtues. The integrity of a practice causally requires the exercise of the virtues by at least some of the members of the organization, both managers and employees; conversely, the corruption of institutions is always in part at least an effect of their vices (MacIntyre Reference MacIntyre2007, 195).

In After Virtue, MacIntyre (Reference MacIntyre2007) warns about the manipulative nature of professional management that arises from rationalization processes in bureaucratic organizations, as described by Max Weber. Professional managers leading all kinds of private and public bureaucratic organizations claim the authority to manage workers on the pretense that they have superior knowledge and experience on how to be effective. Such expertise and authority are assumed to be universally applicable, and transferable from one organization to another regardless of the particular type of activity carried out there (Reference MacIntyre2007, 26–27). However, according to MacIntyre (Reference MacIntyre2007, 90–108), social and organizational studies do not provide a basis for such knowledge and, therefore, do not truly justify the belief in managerial expertise that has become institutionalized in business.

In business organizations, the harshness of competition and the immoderate drive for profit trigger managers to prioritize the achievement of effectiveness objectives over the pursuit of goods of excellence (MacIntyre Reference MacIntyre, Bielskis and Knight2015). Professional management tends to manipulate human resources by treating them as mere means to the fulfillment of the manager’s ends. Managers characteristically insist on dividing the work in such a way that they reserve to themselves the power to decide and expect their subordinates to execute their decisions. As a result, “practices are often distorted by their modes of institutionalization, when irrelevant considerations relating to money, power and status are allowed to invade the practice” (MacIntyre Reference MacIntyre, Horton and Mendus1994, 289).

Nevertheless, MacIntyre admits that institutions are necessary to sustain practices, formalize and enforce rules, and obtain and distribute resources. To avoid manipulation, management must aim to defend the goods internal to the practice, the standards of excellence and the common goods of the organization’s members (Beadle and Moore Reference Beadle and Moore2006). These goods and standards give meaning and purpose to the acquisition of external goods. A management that “defends” internal goods is only possible if managers are also practitioners who have learned to value internal goods through participation in the practice (West Reference West2024). In organizations with this kind of management, managers and practitioners engage in rational critical dialogue regarding how to adequately order the goods of excellence and the goods of effectiveness (MacIntyre Reference MacIntyre2016). This is the institutional context that promotes meaningful work, based on trust and cooperation relationships between managers and employees (Moore Reference Moore2012). However, meaningful work is compromised when power structures deny workers control over their activity and, consequently, prevent them from cultivating goods of personal excellence (Beadle and Knight Reference Beadle and Knight2012).

As explained in Section 1, the current use of algorithmic management systems introduces new forms of control and power relations in organizations (Noponen et al. Reference Noponen, Feshchenko, Auvinen, Luoma-aho and Abrahamsson2024). Its increasing diffusion is generating significant consequences for the relationship between practices and institutions: on the one hand, it generates new ways of sustaining practices but, on the other hand, it introduces new threats to the proper order between goods of excellence and of effectiveness. As we have seen above, this order is closely linked to the meaning of work. Therefore, in the following section, we will analyze the threats that algorithmic management specifically poses to meaningful work.

2.4 How Algorithmic Management Threatens Meaningful Work

In the reality of algorithmic management, the three components identified in Section 1 (matching users and service providers; helping companies monitor workers; providing mechanisms for feedback on workers’ performance) are dynamically interrelated. In addition to the many advantages that algorithmic management brings to organizations in terms of improved efficiency, there are specific aspects which threaten exactly those practice-like characteristics identified in Table 1.

The use of algorithmic management tends to heavily affect the aspects related to skill variety and task significance: indeed, algorithmic management divides tasks and assigns them automatically to different workers (Baiocco et al. Reference Baiocco, Fernandez-Macías, Rani and Pesole2022; Wood Reference Wood2021), causing workers to lose the context of their work, being assigned a specific task without necessarily knowing how it contributes to the final product or service. This directly affects variety, integrity, and significance, as described above, and it causes deskilling and fragmentation.

Algorithmic-managed platforms use reviews to measure workers’ performance and give or deny them access to the critical resource of visibility. In doing so, the use of algorithmic management indirectly induces workers to care more about ratings and rankings than the achievement of the standards of excellence (Bucher, Schou, and Waldkirch Reference Bucher, Schou and Waldkirch2021; Roberts and Zietsma Reference Roberts and Zietsma2018). Workers maintain, therefore, a double dependency: they depend on users, who evaluate them (or not), and the platform; this gives them visibility (or not), according to the calculation that the algorithm makes based on the evaluations received, following frequently opaque rules. Thus, work no longer contributes to the moral education and excellence of workers, since it disregards critical thinking and personal growth.

Platform algorithms sustain workers’ as practitioners, as they provide them access to reputation, prestige, and income, but they do so by establishing a power structure that exerts control over workers’ activity, and thus undermines the achievement of internal goods, making the workers prioritize their reputation and standing (Möhlmann et al. Reference Möhlmann, Zalmanson, Henfridsson and Gregory2021; Vallas and Schor Reference Vallas and Schor2020). Moreover, algorithmic management is usually implemented through software or apps, which move the conversation between manager and worker from a personal social relationship to a system of automated communication (Cameron et al. Reference Cameron, Lamers, Leicht-Deobald, Lutz, Meijerink and Möhlmann2023; Cutolo, Hargadon, and Kenney Reference Cutolo, Hargadon and Kenney2021).

Platform control is also deployed in other forms of power, in particular, the discourse of the worker as an independent contractor (Galière Reference Galière2020). Like the repressive power of algorithmic control, the discourse of worker autonomy and entrepreneurship aims to achieve the same compliance, only by other means. The platform presents itself as a tool, an aid for workers to better organize their work. In this discourse, the role of the algorithm is always legitimized by its effectiveness. However, this discourse is not free from contradictions. A paradigmatic example is that of Uber (Roberts and Zietsma Reference Roberts and Zietsma2018; Rosenblat and Stark Reference Rosenblat and Stark2016). On the one hand, the company’s official discourse refers to drivers as partners who join Uber to set up a transportation business together: “Be your own boss.” On the other hand, the company itself implies that drivers are at its disposal as cogs in an automated service: “Uber drops people off everywhere in the city at the press of a button” (Roberts and Zietsma Reference Roberts and Zietsma2018, 208). The high availability of drivers has allowed Uber to relate to drivers as mere executors of orders under the threat of being replaced by other drivers eager to take their position in the ranking (Möhlmann et al. Reference Möhlmann, Zalmanson, Henfridsson and Gregory2021). “The conflicts and contradictions between … a driver-bot role … and driver-partner role … closed off … the positive aspects of the driver-partner identity by eliminating the autonomy and freedom that constructed the work as entrepreneurial, and thus meaningful” (Roberts and Zietsma Reference Roberts and Zietsma2018, 218). The discourse of the independent contractor thus becomes, in this case and many others, a form of manipulative relationship between management and workers that happens through the implementation of algorithmic management. This clearly contrasts the practice-like characteristic related to the extension of human powers and, in terms of meaningful work, the expression of autonomy through work, substituting this trait with surveillance and control.

Indeed, algorithmic management manifests the characteristics that MacIntyre attributes to professional management (Reference MacIntyre2007, 26–32).Footnote ¹ It establishes a division of labor in which the algorithm holds the power of decision and assigns the execution to the workers. This authority is exercised under the claim of superior knowledge on how to achieve the greatest effectiveness (algorithmic calculation), which is assumed to be universally applicable, regardless of the type of activity being managed. This approach results in the treatment of workers as mere means in the service of pre-established objectives, clearly moving work from the domain of an intentional positive contribution to society, to a new form of alienation, where ends cannot be put into question.

Table 2 summarizes the threats to meaningful work posed by algorithmic management, following the scheme emerged from the analysis synthetized in Table 1. The first two columns report the findings of Table 1, and the third column pairs them with the threats to meaningful work described in this section.

Table 2:

How Algorithmic Management Threatens Meaningful Work

As we have just explored, algorithmic management undoubtedly poses a threat to meaningful work, affecting exactly those practice-like characteristics which constitute the nucleus of meaningful work. Consequently, and given its pervasive diffusion, it is compelling to ask what the conditions for work to be meaningful under algorithmic management are, and who has the responsibility to enable meaningful work under algorithmic management. The next section addresses exactly those questions.

3.1 The Practice-Institution Scheme Revisited in the Age of Algorithmic Management

As we noted above, for MacIntyre, managers can promote meaningful work by enabling workers to engage in rational critical dialogues about how to appropriately advance the goods of the practice and the organization (MacIntyre Reference MacIntyre2016). These shared deliberations should be based on relationships of trust and cooperation among members and managers. To do this, managers need virtues such as courage, justice, constancy, and, above all, practical wisdom (Moore Reference Moore2017; Moore and Beadle Reference Moore and Beadle2006; Sison, Beabout, and Ferrero Reference Sison, Beabout and Ferrero2017).

However, in organizations managed by algorithms, these conditions do not seem to be met: the relationship between management and workers is based on automated communication, monitoring and control, rather than trust and cooperation (see Table 2). Thus, decision processes are not based on shared deliberations among members but on opaque algorithmic calculations. Moreover, algorithmic management cannot find a virtuous relation between goods of excellence and goods of effectiveness since it does not have virtues in itself.Footnote ²

What algorithms do is to act as an interface between workers and the organization. On the one hand, algorithms improve managerial activities’ effectiveness. On the other hand, they constrain workers’ activities and limit their autonomy by enforcing predetermined patterns of action. However, the outcomes of the deployment of this kind of technologies in organizations are often unintended. As Sandberg and Tsoukas affirm, referencing Orlikowski (Reference Orlikowski2000):

In Dependent Rational Animals, MacIntyre (Reference MacIntyre1999a) argues that people’s reasons for acting are biologically and socially molded, but they can still regain some autonomy by using their practical reasoning capacity to become aware of and evaluate those reasons. Similarly, we may argue that human work, in addition to being biologically and socially shaped, is technologically mediated (Waelbers Reference Waelbers2011).Footnote ³ This technological mediation also allows for the exercise of practical reasoning by workers. In one of his few references to the use of technology at work, MacIntyre states that, even when work seems to be purely mechanical, human workers (he offers the example of ticket sellers) always face choices that machines never confront:

MacIntyre refers this comment to the social structures in which people find themselves as moral agents, but we can also apply it to algorithmic management, that is, the technological structure in which workers find themselves in organizations managed by algorithms. This technological structure sets specific constraints and affordances for human work, and workers usually conform more or less willingly to its requirements, even deflecting the responsibility for morally questionable courses of action to technology (Köbis, Bonnefon, and Rahwan Reference Köbis, Bonnefon and Rahwan2021; Krügel, Ostermaier, and Uhl Reference Krügel, Ostermaier and Uhl2023). Algorithmic management guides the way work is performed and, as a result, how workers perceive their work as meaningful at different levels or according to different aspects (as detailed above).

This relationship between workers and algorithmic management happens within organizations. For this reason, two aspects must be investigated: 1) what conditions must algorithmic management meet for an organization to enable and even promote meaningful work; 2) how workers can act beyond and even sometimes against the requirements of algorithmic management when an organization is not capable or willing to meeting those conditions. The following sections are dedicated to the development of these normative conditions (Section 3.2) and to the exploration of the scenarios where organizations are not willing or capable to enable them (Section 3.3).

3.2 The Normative Conditions for Meaningful Work Under Algorithmic Management

MacIntyre’s scheme has helped us to reframe what meaningful work is and why people engage in it (Section 2.2). Work becomes meaningful when workers take the achievement of specific goods to be part of the kind of life they consider meaningful and worth living. This criterion is not purely subjective, nor does it depend on individual preferences. Rather, it depends on work endowing practice-like features (see Table 1), that is, work that allows for professional and moral excellence and contributes to the advancement of workers’ communities. In addition, we have explored how algorithmic management threatens meaningful work exactly along those characteristics that make work meaningful (Table 2).

The implication of this analysis is, according to our argument, that work can be meaningful under algorithmic management when organizations meet some normative conditions, which are now presented and explained. Thus, to sustain and promote meaningful work, organizations must design and manage jobs in such a way that their employees can actually meet these same conditions. The normative conditions are described in this section, and they are summarized in Table 3, which gives evidence of how each condition enables a specific trait of meaningful work, corresponding to a practice-like characteristic.

Table 3:

Normative Conditions for Meaningful Work Under Algorithmic Management

Job crafting: Organizations using algorithmic management should allow workers enough autonomy to craft their activities with some degree of skill variety, task integrity, and task significance. For example, telemedicine increasingly uses algorithms to monitor patients’ health, replacing some of the tasks typically performed by nurses. Nurses must now acquire new skills, such as expertise in reading screens with information about patients and integrating these tasks into a new style of caring (Nicolini Reference Nicolini2012). Similarly, teachers must know how to use specialized software for their students’ education (Sandberg and Tsoukas Reference Sandberg and Tsoukas2011).

Alternative use of technology: Organizations should use their improved capacity for monitoring and control to help workers extend their powers and put their talents to their highest and best uses. For example, automated patient monitoring introduces the risk that nurses find themselves “treating the numbers instead of the patient” (Jones Reference Jones2014, 907), that is, paying more attention to the data on the screen than to the actual condition of the person, but this automated monitoring allows them to devote their time to more careful attention to each patient. Similarly, teachers can easily access many sophisticated technological devices to conduct their lessons, but they must learn how to use them wisely for the good of their students (Higgins Reference Higgins2010).

Improving results: Organizations should reward workers who take advantage of the system’s affordances to improve results in terms of excellence and not only in terms of effectiveness, reputation, and standing. For example, organizations should use rating and ranking systems not only to help workers access resources and share good practices, but also to further standards of excellence together with colleagues.

Help assess achievements: Organizations should help workers obtain information about their achievements and benchmark their performance. For example, teachers whose performance is evaluated quantitatively may seek to improve their rating to secure rewards, but they may also find out the most valued teaching qualities and try to acquire them, thus improving their teaching. In this way, they can build professional prestige and strengthen their self-esteem.

Connecting people: Organizations should sustain human work by facilitating forms of communication that promote rather than preclude the development of significant social relationships in the workplace. For instance, in large companies, algorithms can be used to identify and connect employees with similar profiles, interests, or abilities, and sustain possible collective initiatives.

Sharing goods: Organizations using algorithmic management should strengthen cooperation and workers’ deliberation about common goods. For example, professional groups of nurses must deliberate on what it means to be a good nurse in a technological context where traditional tasks and standards of excellence have changed. The same applies to teachers and other professions. Participation and shared deliberation about how to reframe one’s work can occur not only in service professions but also in jobs such as drivers or other blue-collar occupations (Acemoglu and Johnson Reference Acemoglu and Johnson2023; Wrzesniewski, Dutton, and Debebe Reference Wrzesniewski, Dutton and Debebe2003; Schwartz Reference Schwartz2015). Those workers’ communities may develop guidance and direction not only for colleagues but also for human managers and other stakeholders, such as designers, marketers, and regulators.

Empowerment: Organizations should ensure a working environment that favors professional and moral development. Due to the adoption of algorithmic management systems, human managers also undergo task automation and risk of redundancy. However, those human managers who keep their jobs have a new task of undoubted moral relevance: to ensure that algorithmic systems not only increase the effectiveness in achieving the organization’s objectives but also help obtain its members’ excellence, that is, empower employees to further their professional and moral development. Managing the relationship between algorithms and workers in this way is the ultimate necessary condition for sustaining and providing meaningful work in their organizations.

Table 3 summarizes the essence of what has been explained so far, connecting the normative conditions that organizations using algorithmic management systems need to guarantee to sustain and promote meaningful work with the traits of meaningful work and the corresponding practice-like characteristics. An organization that wants to enable meaningful work for its employees under algorithmic management should enforce the conditions presented in the left column. In this way, the organization ensures that traits typical of meaningful work are realized (second column) in the course of an activity which has practice-like characteristics and is so ordered towards a meaningful life (third column).

The formulation of the normative conditions for work to be meaningful under algorithmic management takes the perspective of the organization: in this sense, the realization of the normative conditions represents the ideal situation, when organizations are willing to make meaningful work possible under algorithmic management and assume the responsibility to ensure the realization of these conditions. However, the responsibility to make work meaningful relies not only on organizations but also on workers. Organizations design and manage workers’ activities in ways that help or hinder their orientation toward goods of excellence and their integration in the workers’ quest for a good life. Organizations influence, but do not determine, the experience that workers have of their own work. Some organizations provide good working conditions, but some individuals do not take advantage of them for their professional and personal development. Other organizations threaten workers’ development by promoting deskilling, dependency, and even alienation, but workers may react in various ways to that influence. Some workers conform to the distressing situation and blame the organization for it; other workers resist the power of algorithmic management and put in place strategies to make their work meaningful. These “strategies of resistance” are described in the next section.

3.3 Strategies of Resistance: How Workers Can Make Meaningful Work a Reality

The normative conditions presented in this article provide a perspective on how to sustain meaningful work under algorithmic management from the point of view of the organization. When organizations are not responsive or do not engage with these conditions, workers do not receive adequate support to make their work meaningful. However, workers are still responsible for their own reaction to the lack of organizational effort to systematically enable the conditions for meaningful work under algorithmic management. If, in the case of an empowering organization, it is anyway the worker who needs to decide whether to engage in their work in a meaningful way or not, the worker’s determination to engage in meaningful work plays an even more determinant role when the organization does not actively pursue or even prevents the realization of the normative conditions. In this latter case, workers are in the position of having to resist such organizational threat and try to create the conditions for meaningful work themselves. This last section explores some strategies of resistance that workers can put in place in non-collaborative organizations.

In a traditional workplace, workers can defend their rights and improve their conditions through various strategies, such as collective action, individual resistance, or collaboration with management (Crozier and Friedberg Reference Crozier, Friedberg and Goldhammer1980). In organizations managed by algorithms, in an attempt to balance power relations, in addition to the demands for legislative regulation, some collective action initiatives have arisen. These include the Online Merchants Guild, which brings together entrepreneurs who sell their products on Amazon, and the YouTubers Union, which unites some content creators (Cutolo, Hargadon, and Kenney Reference Cutolo, Hargadon and Kenney2021).Footnote ⁴ Cooperatives of platform workers have also emerged (Scholz Reference Scholz2016). However, these attempts are proving insufficient (Schor Reference Schor2021).

Some platform workers, based on the practical knowledge they are gaining about how the system works, are developing individual strategies to reduce their dependence on the algorithm and achieve some autonomy (Bucher, Schou, and Waldkirch Reference Bucher, Schou and Waldkirch2021; Möhlmann et al. Reference Möhlmann, Zalmanson, Henfridsson and Gregory2021). For example, on professional services platforms such as Freelancer, Upwork, and Fiverr, some workers, who know how the platform works better than their occasional clients, know how to circumvent the system’s supervision, establish direct communication with the beneficiaries of their services, and secure assignments and positive evaluations (Cameron and Rahman Reference Cameron and Rahman2022).

An illustrative ethnographic study of platform drivers (Cameron Reference Cameron2022) explores how these workers develop individual strategies that help them find meaning in their work and engage with it. Working in a technologically structured and often socially isolated environment, instead of experiencing alienation, these drivers set their own specific goals in their work through two different games, the “relational game” and the “efficiency game.” In the “relational game,” drivers look for meaning by “crafting their job as a positive encounter service” (Reference Cameron2022, 231). To do this, drivers personalize their interactions with clients, trying to generate a connection and, where appropriate, offer physical or emotional support. For example, some drivers offer water bottles or tissues (without being obliged to do so by the system); others listen empathetically to customers’ outbursts; others carefully choose the music they play on each ride. These initiatives allow them to establish rich social exchanges. The purpose of all this work is “to connect with customers and provide good customer service” (Reference Cameron2022, 237). The app’s rating system allows them to closely monitor their achievement, based on the customers’ assessment of their efforts. A good rating is “a source of professional pride” (Reference Cameron2022, 238). What drivers consider “succeeding” in this game is also what the company wants. Therefore, the company supports this type of behavior with recognition and awards. However, the “relational game” goes beyond incentives and rewards, and incorporates a certain sense of excellence. For drivers, there is no concrete benefit in improving their rating from 4.8 to 4.9, nor in decorating their car with care, and yet they still do it. These drivers see the app as a facilitator of their work, a tool that gives them valuable information to do their job better (Reference Cameron2022, 247).

The “efficiency game,” on the other hand, aims to complete rides as quickly as possible, ideally at the highest fare (Cameron Reference Cameron2022, 244). For drivers who play this game, the goal of their work is to optimize the time spent on a trip and the revenue earned. These drivers choose to depersonalize their encounters with customers by emphasizing physical and social boundaries. For example, they do not comfort crying passengers so that their help is not misinterpreted, nor do they offer extra services to protect themselves from potential liability (Reference Cameron2022, 242). Some have themselves installed cameras in the car, not as instruments of surveillance and control, but as an effective means of defense against unfounded accusations by clients. This game is about limiting interactions with customers, which can be time-consuming, emotionally draining and financially unprofitable. The purpose of work performed in this way is simply to get passengers to their destination quickly (Reference Cameron2022, 242). The measure of success is different in this game than in the relational game. The app does not provide information on the yield of each ride. Drivers distrust the algorithm. They think that it does not always assign them profitable trips and, therefore, they keep their own financial records to monitor their activity and try to secure their income. In this game, drivers see themselves repeatedly trying to thrive in a system in which they are at a disadvantage, and often “feel trapped in a game they cannot win” (Reference Cameron2022, 245). Thus, they view the algorithm as an adversary that they must “cheat” to obtain more lucrative results.

In both games, workers relate to the system in such a way that they gain some autonomy to set their own goals and craft their jobs. Algorithmic management indicates to both types of drivers which rides to take and which route to follow, but it does not tell them the purpose of their work, beyond the task of driving. Some drivers set for themselves the goal of providing good service. These drivers find in the algorithm an ally to monitor their achievements, which helps them excel in their job providing valuable information. On the contrary, drivers who mainly aim to optimize revenue experience the algorithm as an obstacle to their work. The system imposes work patterns on them, withholds crucial information from them, and thus stands in their way of achieving their goals. Their only chance of getting away with it is the unlikely strategy of cheating the system.

These examples give us insights into meaningful work under algorithmic management in a non-collaborative organizational environment: they are illustrative of the behaviors and strategies that workers can put in place in such a setting. These strategies reaffirm the central role of workers, as free human beings, in characterizing the ultimate purpose and content of their work, thus highlighting once more the dynamic interrelation between the objective and subjective dimensions of meaningful work. In addition, these examples demonstrate that, ultimately, the threats posed by algorithmic management to meaningful work constitute an additional and novel stimulus for workers who, even in an adverse organizational environment, can creatively use the affordances of the technological structure to find and nurture meaning in their work.