The nature of employment relationships has undergone profound transformation in recent decades. Technological advancements, digital infrastructures, and new organizational forms have transformed the coordination and governance of work. Among these developments, digital labor platforms represent one of the most significant changes in contemporary labor markets. Platforms such as ride-hailing, delivery, and online freelancing systems organize work through technological infrastructures rather than traditional managerial hierarchies. Human resource management (HRM) research has long examined employment relationships through frameworks that emphasize reciprocal expectations between employees and organizations. One of the most influential of these frameworks is psychological contract theory, which conceptualizes employment relationships as subjective beliefs regarding mutual obligations between workers and their employers ([27]). Psychological contracts shape employee attitudes and behaviors, including trust, commitment, and perceptions of fairness. However, the structural assumptions underlying psychological contract theory reflect traditional employment contexts characterized by stable organizational boundaries and identifiable managerial actors. Digital platform environments increasingly challenge these assumptions. Platform workers interact with multiple actors simultaneously, including platform firms, customers, algorithmic systems, regulatory institutions, and worker communities. Each of these actors influences expectations regarding compensation, autonomy, fairness, and treatment at work. Recent studies elucidate the way digitally mediated environments allocate governance among technological systems and external stakeholders, consequently transforming employment relationships and workers’ perceptions of reciprocity and obligation ([17]; [33]; [9]).

Despite the growing importance of platform work, existing psychological contract research has only partially addressed these developments. Much of the literature continues to assume a dyadic employment relationship between employee and organization, thereby limiting the theoretical capacity to explain employment relationships characterized by multi-stakeholder governance structures. Critically, existing theory provides no mechanism to explain how workers cognitively integrate potentially conflicting signals from multiple distinct principals or which principal dominates perception under different contextual conditions. To address these gaps, this paper develops the Multi-Stakeholder Psychological Contract (MSPC) framework and advances four formal propositions that specify the mechanisms underlying MSPC formation, breach, and moderation.

This paper makes four primary contributions. First, it extends psychological contract theory by conceptualizing employment relationships as multi-stakeholder governance systems. Second, it theorizes the principal salience mechanism through which workers attribute and weight obligations across multiple principals. Third, it explains how algorithmic opacity generates breach perceptions via informational justice violation. Fourth, it introduces the psychological contract complexity continuum with formal propositions specifying how contextual factors govern movement along this continuum.

Psychological contract theory remains one of the most influential frameworks for understanding the social and relational foundations of employment relationships. Since the foundational work of Denise Rousseau, psychological contracts have been conceptualized as individuals’ beliefs regarding the reciprocal obligations that exist between themselves and their employer ([27]). Over the past three decades, a substantial body of research has examined the formation, maintenance, and consequences of psychological contracts across diverse organizational settings ([22]; [12]; [37]). When workers perceive that organizational obligations are fulfilled, positive relational outcomes emerge, including trust, organizational commitment, and cooperative behavior ([6]; [32]). Conversely, perceived violations of psychological contracts trigger negative reactions such as dissatisfaction, withdrawal behaviors, reduced engagement, and turnover intentions ([37]). Early research distinguished between transactional contracts and relational contracts ([27]).

Despite these advances, a central assumption underlying most psychological contract research is the presence of a clearly identifiable employing organization as the primary contracting party. This assumption becomes increasingly problematic in contemporary labor markets characterized by flexible work arrangements, technological mediation, and decentralized governance structures ([1]; [7]; [3]). Scholars have increasingly called for the extension of psychological contract theory to accommodate these emerging arrangements ([13]; [16]; [25]). An additional theoretical gap warrants attention. Traditionally, psychological contract theory has assumed that intentional agents, capable of making and breaking promises, attribute contractual obligations ([27]). Algorithmic management systems complicate this assumption because they lack intentionality in the conventional sense. Yet research consistently demonstrates that workers form obligation beliefs in response to algorithmic outputs ([21]). We address this tension by drawing on research into anthropomorphism and perceived agency in automated systems ([23]; [30]), arguing that workers attribute quasi-intentional agency to algorithmic systems when these systems consistently govern material outcomes such as earnings, task visibility, and account standing. This attribution process renders algorithmic systems functionally equivalent to contracting agents in the worker’s psychological contract schema.

Digital labor platforms have emerged as one of the most significant transformations in contemporary labor markets, reshaping how work is organized, coordinated, and governed. Unlike traditional organizations that rely on hierarchical managerial structures, digital platforms operate through technological infrastructures that mediate interactions among workers, clients, and organizations ([18]; [29]; [26]).

A defining feature of platform organizations is the reliance on algorithmic management—the use of computational algorithms and data-driven decision systems to allocate tasks, evaluate performance, and control labor processes ([20]; [26]; [17]). Platform work environments replace many interpersonal supervisory interactions with automated decision processes that may appear opaque or difficult to interpret. Research on algorithmic management indicates that this opacity significantly influences worker perceptions of fairness, transparency, and organizational legitimacy ([21]; [17]).

In addition to algorithmic governance, platform work environments incorporate customer-based evaluation systems that further influence worker outcomes. Rating systems in which customers evaluate workers after each service interaction play a critical role in determining worker reputation and continued access to work opportunities ([11]). Platform workers are often classified as independent contractors rather than employees, which means they operate outside many institutional protections associated with standard employment relationships ([8]; [36]). Taken together, these developments suggest that platform work environments are characterized by distributed governance structures in which multiple actors simultaneously shape employment experiences. Workers, therefore, interpret their employment experiences through signals generated by several actors rather than a single organizational authority ([1]).

Digital labor platforms distribute authority across technological infrastructures, customer evaluation systems, and institutional regulations, creating employment environments characterized by multiple interacting actors rather than a single employing organization ([11]; [13]). To address these developments, we propose the Multi-Stakeholder Psychological Contract (MSPC) framework, which treats psychological contracts as constellations of interconnected relational expectations involving multiple stakeholders who collectively shape employment experiences. Figure 1 presents the complete MSPC framework.

Within the MSPC framework, a stakeholder qualifies as a psychological-contract principal when the worker attributes to that stakeholder a capacity to generate, communicate, or stand behind obligations that materially govern the work relationship. A stakeholder is therefore more than an influence when its signals are interpreted as promises, commitments, entitlements, or reciprocal duties directed toward the worker. Customer ratings become contractual signals when repeated evaluations are understood to carry an obligation of fair and respectful assessment and when those evaluations affect access to work or reputation. Regulatory rules

Figure 1. The multi-stakeholder psychological contract (MSPC) framework.

become contractual signals when legal protections or classifications are interpreted as entitlements that institutions are expected to uphold. Algorithmic outputs become contractual signals when workers attribute quasi-intentional agency to systems that repeatedly determine consequential outcomes and therefore expect transparent and consistent allocation, evaluation, or compensation decisions.

The MSPC framework also differs from existing multi-foci psychological contract, justice, and commitment research. Multi-foci approaches can identify separate targets of attachment, fairness judgments, or exchange, but they do not explain how obligation signals originating from different governance sources are cognitively integrated into one employment relationship when those sources jointly determine the same work outcomes. The distinctive theoretical feature of MSPC is therefore the contract portfolio: workers simultaneously hold principal-specific obligation beliefs and weight, reconcile, or experience conflict among those beliefs within a shared psychological contract schema. Treating the platform, customer, and algorithm only as separate exchange foci cannot explain how their signals interact to change which principal is salient, how breach is attributed when governance is distributed, or how conflicting obligations increase the complexity of the overall contract.

Building on the MSPC framework, we introduce the Psychological Contract Complexity Continuum to explain how workers in digital platform environments interpret and evaluate their employment relationships. Traditional psychological contract theory conceptualizes employment relationships primarily as bilateral exchanges between employees and employers ([27]). Platform-mediated work structures alter this assumption by distributing control, governance, and evaluation across multiple actors ([35]; [10]).

Table 1.Stakeholder relationships within the multi-stakeholder psychological contract framework.

Note. The table identifies the five primary stakeholder relationships through which platform workers form psychological contracts. Each relationship involves a distinct governance mechanism, worker expectation set, and illustrative platform example. These five relationships constitute the stakeholder portfolio central to the MSPC framework.

The contract portfolio and the psychological contract complexity continuum describe different but related features of the MSPC framework. The contract portfolio is the set of principal-specific relational expectations that a worker holds at a given time, whereas complexity describes how that portfolio is cognitively organized and evaluated. Complexity is not determined by the number of stakeholders alone. It increases as a greater number of principals become salient, as salience is more evenly distributed rather than concentrated in one dominant principal, and as the obligation signals generated by those principals become more divergent or conflicting. Accordingly, a worker may have a portfolio containing several stakeholder relationships but still experience relatively low complexity when one principal clearly dominates and the remaining signals are weak or consistent with it.

At the lower-complexity end of the continuum, workers primarily evaluate fairness, obligations, and reciprocity through the platform organization itself ([17]). At the opposite end, workers interpret their employment relationships through a broader constellation of actors, customers, regulatory institutions, worker communities, and algorithmic infrastructures, simultaneously ([1]). Between these two poles lies a moderate-complexity midpoint, where workers balance expectations across several actors while still recognizing the platform as a central coordinating entity ([24]). Several contextual factors govern movement along the continuum. Algorithmic transparency determines the degree to which workers understand how decisions about task allocation and performance evaluation are made. Economic dependence on platform income anchors workers toward the low-complexity end, while diversified income sources push workers toward higher complexity. Institutional environments further shape complexity through regulatory interventions that redefine contractual referents, and the presence of worker collectives introduces shared norms that elevate the salience of the collective stakeholder relationship ([33]). Table 2 summarizes the three complexity levels.

Table 2. Psychological contract complexity continuum in platform work.

Note. The table illustrates movement along the complexity continuum from platform-dominant (low complexity) to multi-stakeholder network (high complexity) psychological contract evaluation. Column three describes the corresponding worker perspective at each level.

The MSPC framework generates four formal propositions that specify the mechanisms underlying multi-stakeholder psychological contract formation, breach, moderation, and evolution. This manuscript is a conceptual theory-development paper; it does not report a sample, empirical data source, analytic procedure, or empirical results. The propositions are theoretically derived and empirically testable, consistent with the standards of conceptual contribution in HRM research ([5]; [14]).

The MSPC framework offers several theoretical implications for contemporary HRM research. Platform-mediated work environments contest the bilateral assumption by allocating control among technological systems, customers, regulatory institutions, and worker communities ([24]; [11]). The four propositions advanced in Section 6 translate this conceptual extension into testable predictions that future empirical research can examine using quantitative, experimental, and qualitative designs.

First, the framework extends psychological contract theory beyond the traditional dyadic employee-employer relationship by conceptualizing employment relationships as multi-stakeholder governance systems. This broadens the theoretical scope of psychological contract research by recognizing that contemporary employment relationships increasingly operate within networked governance structures ([1]; [33]).

Second, the MSPC framework highlights the growing importance of algorithmic management as a central mechanism shaping employment relationships in digitally mediated labor markets. Proposition 2 specifically argues that algorithmic opacity functions as a structural breach trigger through the mechanism of informational justice violation, a prediction that can be examined using experimental vignette designs that manipulate algorithmic transparency levels and measure breach perceptions and trust outcomes.

Third, the framework emphasizes the role of institutional environments in shaping employment expectations and perceptions of fairness. Proposition 3 treats regulatory actors as active contractual principals whose interventions generate predictable shifts in worker expectations and breach sensitivity, inviting comparative cross-national research across jurisdictions with varying platform labor regulatory regimes.

The MSPC framework suggests promising research directions. First, researchers could examine the principal salience mechanism in Proposition 1 more closely. Qualitative and experience-sampling methods could track fluctuations in principal salience. These approaches also show how material dependency, governance transparency, and interaction frequency influence which principal shapes contract decisions. Second, Proposition 2 calls for experiments and surveys investigating how algorithmic ambiguity affects perceptions of psychological contract breaches. For instance, using scenarios with varying levels of algorithmic transparency, researchers can evaluate how explicit explanations for task assignments or deactivations shape workers’ breach perceptions, trust in the platform, and intended actions. Third, comparative analyses of regulatory systems enable rigorous tests of Proposition 3. Diverse gig work regulations support quasi-experimental designs. By tracking workers’ expectations before and after regulatory changes, researchers can map regulatory contract renegotiation. Fourth, Proposition 4 highlights collective embeddedness as an outcome factor. Assessing the role of worker communities, unions, or informal online groups can reveal how such membership mitigates negative contract effects on well-being and reshapes contract portfolios.

Longitudinal studies can show how psychological contracts in platform work evolve over time. They can demonstrate how repeated breaches and renegotiations with different principals affect worker well-being and platform sustainability over time.

Digital labor platforms have changed the way work is organized. In this paper, we present the Multi-Stakeholder Psychological Contract (MSPC) framework and outline four main ideas. This framework explains employment relationships in platform work, where digital apps link workers to jobs. It shows how workers understand their obligations and expectations through relationships with groups like platforms, customers, and contractors. The framework also points out four important factors: principal salience (how much workers notice or pay attention to each group they interact with, like the platform or customers), algorithmic opacity (how unclear or difficult it is to understand the rules and decisions made by computer algorithms that guide platform work), regulatory changes (changes in laws or rules that affect how platform work is done), and collective embeddedness (how connected or involved workers feel with groups or communities of workers). These factors help explain how different parties shape the creation, breach, and adjustment of psychological contracts, meaning the unwritten expectations between workers and stakeholders.

These mechanisms show that technology now has a major role in managing today’s labor markets. Algorithms-computer programs that follow set instructions to assign tasks, monitor activity, or evaluate performance-now do many jobs that supervisors used to handle. By learning how people assign responsibility to these systems, we can better understand how employment works in environments organized around platforms.

The MSPC framework provides a foundation for developing more robust Human Resource Management (HRM) theories that explain work in digital and decentralized settings. (HRM is the practice of managing people and workplace policies in organizations.) As platform economies expand, researchers and policymakers need to understand how workers manage complex networks of relationships and obligations with different groups. This knowledge can help create fair and effective work systems.