Algorithmic Management and Multi-Stakeholder Well-Being: Integrating HR and Marketing Perspectives on Work, Innovation, and Consumer Experience

Dear Colleagues,

Algorithmic systems are increasingly reshaping how organizations manage employees, interact with consumers, allocate resources, evaluate performance, and make operational decisions. In the workplace, algorithmic management—the use of data-driven and algorithmic systems to organize, assign, monitor, supervise, and evaluate work—has expanded beyond digital labor platforms and is increasingly embedded in conventional workplaces and human resource management (HRM) practices (Duggan et al., 2020; Kellogg et al., 2020; Meijerink & Bondarouk, 2023). In parallel, artificial intelligence and algorithmic technologies are transforming marketing by supporting customer analytics, segmentation and targeting, personalization, recommendation, automated service, and customer relationship management (Davenport et al., 2020; Huang & Rust, 2021).

Despite these parallel developments, research on the employee and consumer consequences of algorithmic systems has largely developed in separate disciplinary streams. HRM and organizational research has primarily examined how algorithms affect employees through monitoring, performance management, task allocation, autonomy, control, fairness, and employment relationships (Duggan et al., 2020; Kellogg et al., 2020; Meijerink & Bondarouk, 2023). Marketing and consumer research, by contrast, has examined how AI and algorithms influence customer experience, personalized targeting, dynamic pricing, consumer trust, and responses to automated decision-making (Castelo et al., 2019; Davenport et al., 2020; Huang & Rust, 2021; Puntoni et al., 2021).

This disciplinary separation obscures an important organizational reality: employees and consumers are interconnected stakeholders whose experiences of algorithmic systems can influence one another. The same algorithmic infrastructure that structures employees’ work may simultaneously shape customer interactions, service delivery, personalization, and consumer evaluations. Employee and consumer outcomes should therefore not be understood as independent consequences of organizational algorithms, but as potentially interdependent components of a broader algorithmically mediated organizational system.

Focus and Scope of the Special Issue

This Special Issue examines the dual and potentially paradoxical role of algorithmic systems in shaping work, innovation, employee well-being, and consumer experience. Algorithmic systems can improve coordination, decision-making, personalization, and service delivery. In marketing, AI enables firms to better understand customers, personalize offerings, and automate interactions (Davenport et al., 2020; Huang & Rust, 2021); in the workplace, algorithms can provide employees with information, decision support, and resources that facilitate performance and innovation.

At the same time, algorithmic systems can intensify monitoring and control, constrain discretion and autonomy, and raise concerns about transparency, fairness, privacy, trust, and human agency among both employees and consumers (Kellogg et al., 2020; Meijerink & Bondarouk, 2023; Puntoni et al., 2021). Their consequences therefore depend on how these systems are designed, implemented, and integrated with human judgment. This tension matters for innovation: systems designed for efficiency and standardization may restrict the discretion needed for creativity and responsive service, whereas systems that augment human capabilities while preserving autonomy may foster both innovative work and positive consumer experiences.

A distinctive focus of this Special Issue is to extend algorithmic management beyond the employee–organization dyad to a multi-stakeholder perspective that connects employees and consumers. Employees increasingly work under algorithmic management while serving consumers whose experiences are themselves shaped by algorithms. Marketing algorithms structure targeting, recommendations, and service encounters, while consumer ratings, complaints, and purchasing behaviors may become inputs into algorithmic employee evaluation and performance management. These interdependencies create employee–consumer feedback loops: controlling systems may undermine employee autonomy and service innovation, generating negative consumer responses that further increase employee pressure; supportive systems may enable employee discretion and innovative service, producing positive consumer responses that reinforce employee engagement and performance.

By connecting research on algorithmic management with AI-enabled marketing and consumer experience (Davenport et al., 2020; Huang & Rust, 2021; Puntoni et al., 2021), this Special Issue examines how algorithms jointly shape the employee and consumer sides of value creation and their interconnected consequences for innovation and multi-stakeholder well-being.

We particularly welcome research addressing:

• Algorithmic management, employee autonomy, engagement, well-being, creativity, and innovative behavior;
• Algorithmic monitoring, control, performance evaluation, transparency, fairness, voice, and trust;
• Human–algorithm collaboration, augmentation, decision-making, job design, and meaningful work;
• HR practices and organizational policies that shape the consequences of algorithmic management;
• AI-enabled marketing, personalization, recommendations, automated service, and consumer experience;
• Consumer trust, fairness, privacy, autonomy, vulnerability, and well-being in algorithm-mediated environments;
• Employee–consumer spillovers and feedback loops, including customer ratings, reviews, complaints, and their consequences for employees;
• Employee well-being and innovative behavior as drivers of customer experience and service outcomes;
• Responsible, value-sensitive, and multi-stakeholder approaches to algorithmic and AI governance;
• Cross-cultural, cross-national, occupational, and sectoral variation in algorithmic management and marketing.

These topics are indicative, not exhaustive. We welcome empirical research using diverse methodologies, as well as conceptual and theoretical contributions that advance an interdisciplinary understanding of algorithmic management, innovation, consumer experience, and multi-stakeholder well-being.

Contribution to the Existing Literature

This Special Issue makes three primary contributions.

First, it extends algorithmic-management research from control and efficiency toward work design, innovation, and well-being. Foundational research has emphasized algorithmic control, surveillance, task allocation, and performance management, demonstrating how algorithms reshape managerial authority and employee autonomy (Duggan et al., 2020; Kellogg et al., 2020). Importantly, Parent-Rocheleau and Parker (2022) conceptualize algorithms as work designers, showing how algorithmic management functions—including monitoring, goal setting, performance management, scheduling, compensation, and termination—can reshape job resources and demands central to employee motivation, well-being, and performance. Their work also highlights transparency, fairness, and human influence as conditions that can shape these effects. Together with research emphasizing algorithms' dual capacity to constrain or enable autonomy and value creation (Meijerink & Bondarouk, 2023), this perspective provides a foundation for examining how the design and implementation of algorithmic systems influence employee well-being, discretion, creativity, and innovative behavior.

Second, it bridges algorithmic management with research on AI-enabled marketing and consumer behavior. Marketing research demonstrates that AI is transforming customer understanding, targeting, personalization, service, and consumer experience (Davenport et al., 2020; Huang & Rust, 2021; Puntoni et al., 2021), while consumer acceptance of algorithms varies across decision contexts (Castelo et al., 2019). Yet, these processes are rarely connected to the algorithmic systems governing employees who deliver customer experiences. By integrating these literatures, the Special Issue conceptualizes algorithmic management and algorithmic marketing as interconnected organizational processes, enabling research on employee–consumer spillovers, feedback loops, and joint value creation.

Third, it advances a human-centered, multi-stakeholder perspective on algorithmic governance. Issues of fairness, transparency, autonomy, privacy, trust, and human agency affect both employees and consumers. Rather than evaluating algorithms primarily through efficiency or predictive accuracy, this perspective asks who algorithmic systems create value for, under what conditions, through what mechanisms, and at what cost. It thereby directs attention to complementarities and trade-offs among organizational performance, employee well-being and innovation, consumer experience and well-being, and broader stakeholder interests.

The Special Issue encourages interdisciplinary theorizing drawing on perspectives such as work design theory, self-determination theory, job demands–resources theory, organizational justice, stakeholder theory, employee–customer linkage perspectives, customer experience theory, sociotechnical systems theory, and human–AI interaction.

Ultimately, the Special Issue addresses a central question:

How can organizations design and govern algorithmic systems that generate innovation and organizational value while supporting the well-being, autonomy, fairness, trust, and agency of employees and consumers?

Addressing this question can move scholarship beyond whether algorithms are beneficial or harmful toward identifying the conditions under which they enable sustainable value creation across multiple stakeholders.

Submission Process

We request that, prior to submitting a manuscript, interested authors initially submit a proposed title and an abstract of 300-500 words summarizing their intended contribution. Please send it to the Guest Editors (Email: zheni.wang@southernct.edu) or to the Administrative Sciences editorial office (admsci@mdpi.com). The Guest Editors will review abstracts to ensure they fit the Special Issue's scope. Full manuscripts will undergo double-blind peer review.

Abstract Submission Deadline: 30 November 2026.

References

Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42. https://doi.org/10.1007/s11747-019-00696-0.

Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132. https://doi.org/10.1111/1748-8583.12258.

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9.

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174.

Meijerink, J., & Bondarouk, T. (2023). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human Resource Management Review, 33(1), 100876. https://doi.org/10.1016/j.hrmr.2021.100876.

Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 100838. https://doi.org/10.1016/j.hrmr.2021.100838.

Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151. https://doi.org/10.1177/0022242920953847.

Prof. Dr. Zheni Wang
Guest Editor

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• algorithmic management
• algorithmic marketing
• artificial intelligence (AI)
• human–AI interaction
• human resource management (HRM)
• employee well-being
• consumer well-being
• customer experience
• innovative work behavior
• employee autonomy
• algorithmic control
• algorithmic fairness
• algorithmic transparency
• employee–consumer interface
• multi-stakeholder well-being
• algorithmic governance
• responsible AI
• workplace innovation
• personalization
• human agency