The Calculus of control: metrics, morality, and the machinery of governance in pre-algorithmic optimization systems

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Chuwa, Lukman O. Kolawole, Adeyinka G. Ologun, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9087686/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract While contemporary debates in AI ethics focus predominantly on machine learning, neural networks, and automated decision-making, this article argues that similar governance effects—behavioural shaping, displacement of human judgment, and the concentration of authority in technical artefacts—already permeate organisations through mundane managerial technologies. Drawing on mixed-method empirical research from Nigerian manufacturing contexts, we introduce the concept of algorithmic governance without algorithms to describe how Lean–Green optimisation systems function as socio-technical infrastructures that reshape power, agency, and ethical responsibility. Through dashboards, key performance indicators, and audit mechanisms, these frameworks transform sustainability from a moral commitment into a quantified output and reconfigure worker agency within constrained participatory regimes. Our findings suggest that governance is not merely a computational phenomenon but an organisational logic embedded in quantification practices themselves. This study extends critical AI ethics scholarship beyond digital technologies to examine the ethical-political foundations of optimisation systems that predate and precondition contemporary algorithmic management. Algorithmic governance Quantification Lean manufacturing Socio-technical systems Organisational ethics Metrics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The contemporary discourse surrounding artificial intelligence and ethics has coalesced around a particular constellation of anxieties: bias in facial recognition systems, opacity in machine learning models, autonomous weaponry, and the surveillance capacities of predictive analytics. These concerns, while undeniably urgent, have tended to focus scholarly and regulatory attention on the novel and the spectacular—systems that compute, learn, and decide with minimal human intervention. Yet this preoccupation with the computational new risks obscuring a quieter, more pervasive transformation already underway within organisations worldwide. Long before sophisticated algorithms arrived to manage warehouses or evaluate employee performance, other technologies of optimisation were already reshaping how authority flows, how decisions are justified, and how ethical responsibilities are distributed. Lean and Green manufacturing frameworks exemplify this pre-digital governance architecture. Originally conceptualised as operational improvement methodologies—Lean targeting waste reduction and process efficiency, Green addressing environmental impact—these systems have evolved into comprehensive infrastructures of organisational control. Through the relentless deployment of dashboards, key performance indicators (KPIs), benchmarking exercises, and continuous monitoring regimes, they instantiate a form of governance that mimics the behavioural effects attributed to contemporary AI systems. Decisions become data-driven not necessarily because algorithms demand it, but because metrics have become the only legitimate currency of organisational truth. When a manager justifies a decision by pointing to a spreadsheet rather than a neural network, the governance effect— the displacement of situated judgment by numerical representation—remains structurally similar. This observation becomes particularly salient in contexts where technological modernisation arrives through development agendas and international sustainability standards rather than organic digital transformation. In Nigerian manufacturing organisations, as in many post-colonial industrial contexts, Lean–Green systems often enter as components of global supply chain compliance, consultancy packages, and export-oriented modernisation programmes. They do not arrive in organisational vacuums; rather, they intersect with existing hierarchies, labour relations, and cultural formations in ways that produce distinctively local manifestations of global optimisation logics. Understanding how these systems function as technologies of governance—rather than merely tools of efficiency—offers crucial insights for AI ethics precisely because it reveals the continuity between historical quantification practices and emerging computational governance. We advance three interconnected arguments. First, we contend that governance effects commonly associated with algorithmic management—behavioural constraint, epistemic authority residing in technical artefacts, and the depoliticisation of value-laden decisions—can emerge entirely through "mundane" technologies of quantification such as spreadsheets, audit checklists, and performance scorecards. Second, we demonstrate that these systems fundamentally reconfigure ethical responsibility within organisations by transforming sustainability and worker wellbeing from matters of moral deliberation into measurable outputs subject to optimisation. Third, we argue that effective regulation and ethical oversight of AI must account for this broader ecology of governance technologies, recognising that algorithmic systems often intensify and formalise pre-existing logics of quantification rather than introducing entirely novel forms of control. The remainder of this article proceeds as follows. We first situate our argument within literature on algorithmic governance and critical quantification studies, identifying the conceptual gap our study addresses. We then outline our methodological approach, which involves reinterpreting mixed-methods empirical material from Nigerian manufacturing contexts through a critical socio-technical lens. Our findings reveal three dynamics: the metricization of managerial authority, the conditional restructuring of worker agency, and the operationalization of sustainability as a performance category. We conclude by discussing the implications for AI ethics theory and policy, arguing that the field must expand its purview to examine how governance emerges from the organisational politics of optimisation itself, regardless of whether algorithms are present. 2. Literature Review Recent scholarship on algorithmic governance has productively examined how computational systems increasingly mediate social and organisational life. Danaher defines algorithmic governance as the use of algorithms to "monitor, direct, and constrain behaviour," highlighting how automated decision-making reshapes power relations through technical mechanisms. This literature has importantly illuminated how machine learning systems can perpetuate bias, erode accountability through opacity, and fundamentally alter the landscape of professional judgment. Zuboff’s concept of surveillance capitalism extends these concerns to the extraction and commodification of behavioural data, while Eubanks documents how algorithmic systems in social services can exacerbate inequality through "digital poorhouses." However, this focus on the computational new has inadvertently created a temporal blind spot. As Amoore notes, the "emergence" of algorithmic governance presupposes a pre-algorithmic innocence that never truly existed. Organisations have long been governed through numbers, and the authority of the spreadsheet predates and prefigures the authority of the neural network. Critical scholarship on quantification—spanning sociology, science and technology studies, and accounting research—has demonstrated that metrics, indicators, and audits have functioned as technologies of governance for decades. Power’s audit society thesis describes how verification rituals and performance measurement have transformed organisational rationality, while Espeland and Stevens’ work on commensuration reveals how numerical comparison reshapes the values and relations it purports merely to measure. What remains under-theorised is the specific continuity between these historical practices of quantification and contemporary algorithmic governance. The gap is not merely academic; it has regulatory and ethical consequences. If policymakers and ethicists focus exclusively on AI as a distinct technological epoch, they risk implementing governance frameworks that address the symptoms (opaque algorithms) while ignoring the disease (the unquestioned authority of optimisation logics). Lean–Green manufacturing systems occupy precisely this liminal space—employing sophisticated technologies of measurement and control that fall just below the threshold of "artificial intelligence" while producing governance effects that are functionally analogous. The critical management literature offers some traction here. Orlikowski’s socio-material perspective suggests that technologies and organisational practices co-constitute one another, implying that governance emerges from the interaction of human and non-human actors rather than from technology alone. Similarly, research on the "performativity" of management accounting demonstrates how KPIs do not simply reflect organisational reality but actively construct it, rendering some aspects of work visible and actionable while rendering others invisible. Yet these insights have rarely been brought into dialogue with AI ethics, and even less frequently applied to manufacturing contexts in the Global South, where imported optimisation frameworks intersect with post-colonial labour relations and developmentalist state policies. Our contribution, therefore, lies in bridging these literatures. We treat Lean–Green systems as empirical sites where the "algorithmic" qualities of governance—rule-based decision-making, behavioural optimisation, and the displacement of ethical reasoning into technical procedures—can be observed in their historical, non-computational form. This allows us to ask: What is it about quantification itself, rather than computation specifically, that generates these governance effects? And how might this understanding inform more robust ethical frameworks for evaluating AI? 3. Methodology This study employs a qualitative, interpretive methodology grounded in the critical reinterpretation of mixed-method empirical material collected from manufacturing organisations in Nigeria. Rather than seeking causal inference or predictive generalisation, our approach prioritises the examination of meaning, power relations, and socio-technical dynamics within specific organisational contexts. 3.1 Data Sources The primary data comprise three distinct streams collected between 2022 and 2023. First, survey instruments were administered to 127 participants across four manufacturing firms in Lagos and Ogun States, including both shop-floor operatives (n = 89) and middle-to-senior management (n = 38). These instruments explored perceptions of Lean–Green implementation, experiences of workplace participation, and attitudes toward performance measurement systems. Second, semi-structured interviews were conducted with 23 managerial personnel, including production managers, sustainability officers, and quality control supervisors. These conversations explored decision-making practices, the role of data in operational oversight, and the ethical frameworks employed when balancing efficiency against worker welfare. Third, observational data were generated through industrial ethnography, including 147 hours of workplace observation and value-stream mapping exercises conducted at an automotive components manufacturer, documenting the interaction between workers, machinery, and performance dashboards. 3.2 Analytical Strategy We employed a reflexive thematic analysis approach, coding the data not for frequency but for patterns of meaning related to governance, authority, and ethics. The analysis was structured around three guiding questions derived from our theoretical framework: ( 1 ) How do metrics function as authoritative actors within decision-making processes? ( 2 ) How is worker agency enabled or constrained within optimisation regimes? and ( 3 ) How are ethical concepts such as sustainability translated into operational terms? To ensure analytical rigour, we maintained a critical audit trail documenting coding decisions and theoretical memos. Survey data were analysed descriptively to identify divergences between managerial and shop-floor perceptions, while interview transcripts underwent line-by-line coding to identify processes of power and resistance. Observational field notes were analysed for instances where technical artefacts (dashboards, checklists, scorecards) intervened in or supplanted human deliberation. 3.3 Ethical Considerations Ethical approval was obtained from the [University Ethics Committee], and all participants provided informed consent. Given the potential for participant vulnerability in industrial contexts, particular attention was paid to confidentiality and the right to withdraw. All organisational and individual identifiers have been anonymised in the presentation of findings. We acknowledge our positionality as researchers situated outside the immediate context of Nigerian manufacturing, and have attempted to mitigate extractive dynamics by engaging local research assistants and sharing preliminary findings with participant organisations. 4. Results Our analysis reveals that Lean–Green optimisation systems function not merely as technical tools for improving efficiency, but as comprehensive infrastructures of governance that reshape organisational subjectivity, redistribute authority, and transform the ethical landscape of work. These dynamics operated across three interconnected dimensions: the metricization of decision-making authority, the conditional restructuring of worker agency, and the operational translation of sustainability. 4.1 Governance by Metrics: The Authority of the Dashboard Across all participating organisations, numerical indicators had achieved a status of unquestioned epistemic authority. Performance dashboards—typically Excel-based visualisations projected in production meetings or printed as daily "scorecards"—occupied the physical and symbolic centre of managerial practice. Managers described organisational reality almost exclusively through the language of metrics: takt time adherence, defect parts per million (PPM), Overall Equipment Effectiveness (OEE), and waste-reduction percentages. This reliance on quantification had substantially constrained managerial discretion. Several middle managers recounted situations where experiential knowledge—awareness of supply chain disruptions, informal labour practices, or equipment idiosyncrasies—suggested one course of action, while the dashboard suggested another. Invariably, the dashboard prevailed. As one production supervisor noted, "When the numbers say red, you cannot argue with green thinking. Your experience doesn't count if it contradicts the KPI." Table 1 Divergence Between Managerial Experience and Metric-Driven Decisions (n = 23 interviews) Scenario Description Experiential Knowledge Suggested Metric-Driven Decision Outcome Frequency Supply chain delay mitigation Flexible deadline adjustment Maintain takt time through overtime Worker fatigue, quality issues 14 cases Equipment maintenance Preventive downtime for ageing machinery Maximize OEE, defer maintenance Breakdown, higher repair costs 11 cases Quality vs. speed trade-off Reduce speed for complex batches Maintain cycle time targets Increased defect rates 9 cases Worker wellbeing intervention Rotate fatigued staff Maintain staffing levels for efficiency Safety incidents 7 cases Note: Frequencies represent mentions across interview transcripts, not mutually exclusive categories. The table illustrates a consistent pattern where numerical optimisation trumped situated judgment. Managers found themselves occupying a hybrid subject position: formally accountable for outcomes, yet practically accountable to the indicator system itself. This generated what we term "proxy accountability"—responsibility not for the work itself, but for the relationship between the work and its numerical representation. Importantly, these effects emerged without sophisticated AI. The "algorithm" here consisted of simple spreadsheet formulae—ratios, averages, conditional formatting—that nonetheless governed behaviour through their performative authority. When targets were missed, accountability flowed downward to individual workers or teams, while the structural constraints embedded in the metric design—unrealistic baselines, conflicting indicators, or measurement errors—remained depoliticised and beyond contestation. 4.2 Reconfigured Agency: Participation Within Boundaries A second major finding concerned the asymmetry between managerial narratives of empowerment and workers' lived experiences of participation. Survey data revealed a striking divergence: 78% of managers agreed that "Lean–Green initiatives have increased worker empowerment and voice," whereas only 31% of shop-floor workers concurred. This gap points not to simple miscommunication, but to a fundamental restructuring of what counts as legitimate participation. Workers described suggestion schemes and continuous improvement teams (Kaizen circles) as real but tightly circumscribed opportunities for input. Contributions were welcomed when they aligned with pre-existing productivity targets—ideas for reducing cycle time, eliminating motion waste, or improving material flow. However, suggestions framed in the language of human welfare—concerns about pace, fatigue, safety shortcuts imposed by efficiency targets, or the ethical implications of waste-reduction measures—were routinely filtered out or reframed as "resistance to change." Figure 1. The Funnel of legitimate voice: how optimisation regimes structure participation Conceptual diagram showing a funnel shape. Wide top labelled "Worker Experience" containing diverse concerns (safety, fatigue, quality of life, community impact, technical efficiency). Narrow middle section labelled "Translation Layer" where concerns are filtered through "Productivity Framework." Narrow bottom labelled "Legitimate Input" containing only technical efficiency improvements and waste reduction ideas that align with KPIs. Arrows indicate that safety/fatigue concerns must be translated into efficiency language to pass through. As Fig. 1 illustrates, participation became a performative exercise in translation. Workers learned to encode their concerns in the vocabulary of optimisation to gain traction. A concern about dangerous working conditions might be presented as a "quality risk" or "efficiency drain" rather than a matter of dignity or rights. This linguistic adaptation allowed workers to exercise limited agency within the system, but simultaneously reinforced the system's epistemic boundaries. Genuine contestation of the optimisation logic itself—questioning whether speed should be the primary value, for instance—remained illegible and illegitimate. Several workers described this as "playing the game," suggesting a sophisticated awareness of the structural constraints coupled with pragmatic adaptation. However, this adaptation came with cognitive and ethical costs. Workers reported emotional dissonance when required to frame human-centred concerns in the cold language of metrics, describing a sense that they were "betraying" their own experience to be heard. This suggests that optimisation regimes do not merely constrain agency, but actively reshape the moral vocabulary through which workers understand their own interests. 4.3 The Quantification of Ethics: Sustainability as Performance Output The third dimension concerns the fate of sustainability as an ethical concept within these metricised environments. While all participating organisations displayed strong discursive commitment to environmental responsibility, sustainability was overwhelmingly enacted through quantifiable outputs: percentage reductions in energy consumption, waste diversion rates, compliance scores against ISO 14001 standards, and audit results. This quantification narrowed the ethical vision. Concepts such as "intergenerational responsibility," "ecological justice," or "community wellbeing"—aspects of sustainability that resist easy measurement—were largely absent from organisational discourse. When probed about the social dimensions of sustainability, managers typically reverted to metrics: "We are sustainable because we reduced water usage by 15% last quarter," or "Our waste-to-landfill numbers prove our commitment." The ethical had been thoroughly operationalised. Figure 2. The Transformation of Sustainability: From Ethical Ideal to Quantified Output Line graph showing conceptual trajectory over time. X-axis: Organisational Maturity in Lean–Green Implementation. Y-axis: Conceptual Richness of Sustainability. Line starts high (Complex Ethics: justice, care, community) at low maturity, then declines steeply as Lean–Green implementation increases, flattening out at "Thin Quantification" (metrics only). Secondary line shows "Audit Burden" increasing over the same period. Annotation at midpoint: "The Ethical Fade." Figure 2 traces this trajectory. Early in implementation, sustainability discussions involved broad ethical deliberation about organisational responsibility. As metric systems matured, sustainability became synonymous with audit compliance and performance scores. This "ethical fade" did not represent cynicism on the part of managers; rather, it reflected the structural imperatives of optimisation systems, which require commensurable, comparable data. Qualitative ethical reasoning is messy, contextual, and resistant to benchmarking; quantitative indicators are manageable, comparable, and appear objective. The consequences extended beyond conceptual narrowing. Several organisations had implemented "sustainability bonuses" tied to environmental KPIs, creating perverse incentives. In one case, workers were incentivised to reduce waste metrics by covertly disposing of hazardous material through regular waste streams rather than expensive designated channels—technically improving the metric while violating the ethical spirit of environmental protection. This "gaming" was not aberrant behaviour but a logical outcome of a system that substitutes measurement for moral reasoning. Figure 3. Divergence in Perception: Management vs Shop-Floor Experience Table 2 Manifestations of Ethical Displacement in Lean–Green Systems Ethical Domain Original Moral Concern Operationalised Metric Observed Displacement Effect Environmental care Ecosystem health Waste tonnage diverted Offshoring waste to unregulated suppliers Worker wellbeing Dignity and flourishing Lost-time injury rates Under-reporting of incidents; presenteeism Community impact Local social license Community investment spend "Tick-box" charity unrelated to operational impact Fairness Equitable treatment Standardised cycle times Ignoring individual capability variations These findings demonstrate that the governance effects of optimisation systems extend beyond the organisation to reshape the very meaning of ethical concepts themselves. Sustainability becomes what can be measured; care becomes what can be audited. 5. Discussion Our findings suggest that the governance effects typically attributed to artificial intelligence—behavioural constraint, the concentration of authority in technical systems, and the depoliticisation of value-laden decisions—are neither novel nor exclusively computational. They emerge from deeper organisational logics of quantification and optimisation that have been developing for decades. This observation carries significant implications for AI ethics scholarship, regulatory policy, and critical management practice. Figure 4. The Transformation of Sustainability: From Ethical Ideal to Quantified Output 5.1 The Continuum of Algorithmic Governance We propose that governance exists on a continuum of technical formalisation, ranging from informal, judgment-based coordination through metric-based management to fully automated algorithmic control. Lean–Green systems occupy the middle ground—what we term "algorithmic governance without algorithms"—where the logic of algorithmic thinking (rule-based, optimising, data-driven) is instantiated through human enactment of computational routines. Managers act as the "wetware" executing the logic that will later be encoded in software. This perspective complicates the temporal narrative of AI ethics, which often treats algorithmic governance as an unprecedented rupture. Instead, we see continuity. Contemporary AI systems in workforce management do not introduce de novo the quantification of labour; they intensify and accelerate pre-existing practices. The spreadsheet paves the way for the neural network. Consequently, ethical frameworks that focus solely on the novelty of AI—its opacity, its scale, its speed—risk treating symptoms while ignoring underlying pathologies. If we wish to govern AI effectively, we must first understand the governance of metrics. 5.2 The Politics of Visibility Our findings regarding the operationalization of sustainability highlight a crucial dynamic in socio-technical governance: the political work of visibility. Quantification renders certain aspects of reality visible and actionable while systematically obscuring others. This is not accidental oversight but structural necessity. Metrics require commensurability—the reduction of diverse qualities to common scales. Consequently, they privilege thin, transferable, comparable data over thick, contextual, particular understanding. In the context of AI ethics, this suggests that calls for "ethical AI" through the development of fairness metrics, bias audits, and sustainability algorithms may inadvertently reproduce the very governance problems they seek to solve. When we reduce fairness to a performance score, we risk hollowing out the moral substance of justice, just as our participants hollowed out sustainability into waste percentages. The danger is not that AI will be unethical, but that it will operationalise ethics in ways that make genuine moral reasoning unnecessary and possibly illegible. (Fig. 5 ) 5.3 Toward Reflexive Governance If governance effects emerge from the interaction of technical systems and organisational logics rather than from technology alone, then ethical interventions must target both. Organisations implementing Lean–Green systems—or indeed, any AI systems—require mechanisms for what we term "reflexive governance": institutionalised spaces where the metrics themselves can be contested, where qualitative judgment can override quantitative targets without penalty, and where the boundary between optimisation and exploitation is subject to democratic deliberation. This has regulatory implications. Current proposals for AI regulation, such as the EU AI Act, focus heavily on high-risk automated systems. Our research suggests that "low-tech" governance systems—spreadsheets, KPIs, audit regimes—can produce equally high-risk outcomes in terms of worker exploitation, environmental harm, and accountability erosion. A comprehensive approach to technology governance must address the sociology of optimisation rather than merely the technology of computation . 5.4 Limitations and Future Research We acknowledge limitations in our study. The focus on Nigerian manufacturing, while providing crucial context for understanding technology transfer in the Global South, may limit generalisability to service-sector or knowledge-work contexts where algorithmic management is more prevalent. Additionally, our reinterpretive methodology, while appropriate for theory development, cannot establish causal relationships between specific system features and outcomes. Future research might employ comparative designs examining how governance effects differ across high-tech (AI-driven) and low-tech (metric-driven) optimisation systems, or ethnographic studies tracing the specific moments when human judgment is overridden by numerical authority. 6. Conclusion This study has argued that the governance effects associated with artificial intelligence—specifically the displacement of judgment, the restructuring of agency, and the transformation of ethics into performance metrics—are not unique to computational systems. Through an examination of Lean–Green manufacturing in Nigerian contexts, we have demonstrated that relatively mundane technologies of quantification can function as powerful infrastructures of control, reshaping authority, participation, and moral meaning within organisations. The concept of algorithmic governance without algorithms offers a corrective to contemporary debates that treat AI as a radical break from previous modes of organisation. Instead, we see AI as the intensification of quantification logics that have long permeated managerial practice. For ethicists and policymakers, this implies that the urgent task is not merely to regulate the algorithms of the future, but to cultivate critical reflexivity about the metrics of the present. Until we learn to govern the spreadsheet, we will struggle to govern the AI. Declarations Conflicts of interest The authors declare no conflict of interest. Ethics approval Ethical approval was obtained from the institutional review board, and all participants provided informed consent. Consent to participate Informed consent was obtained from all participants involved in the study. Funding The authors received no specific funding for this study. 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MIS Q. 43 (1), 3–11 (2019). https://doi.org/10.25300/MISQ/2019/14476 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9087686","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604176515,"identity":"6efe2e4d-83a5-4c73-b2ec-b0003228f452","order_by":0,"name":"Ifeoluwa E Elemure","email":"","orcid":"","institution":"University of Portsmouth","correspondingAuthor":false,"prefix":"","firstName":"Ifeoluwa","middleName":"E","lastName":"Elemure","suffix":""},{"id":604176523,"identity":"f89469ac-f1ad-4af9-80f4-2136e538f83a","order_by":1,"name":"Chijioke C. Chuwa","email":"","orcid":"","institution":"Northumbria University","correspondingAuthor":false,"prefix":"","firstName":"Chijioke","middleName":"C.","lastName":"Chuwa","suffix":""},{"id":604176526,"identity":"61b736f0-46b3-499f-be59-9d0a76c7dfbc","order_by":2,"name":"Lukman O. Kolawole","email":"","orcid":"","institution":"Middlesex University","correspondingAuthor":false,"prefix":"","firstName":"Lukman","middleName":"O.","lastName":"Kolawole","suffix":""},{"id":604176532,"identity":"cf356df2-7422-41ce-b122-7bfa6e8f1de6","order_by":3,"name":"Adeyinka G. Ologun","email":"data:image/png;base64,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","orcid":"","institution":"Selinus University of Sciences and Literature","correspondingAuthor":true,"prefix":"","firstName":"Adeyinka","middleName":"G.","lastName":"Ologun","suffix":""},{"id":604176533,"identity":"e9ffe05e-03dd-4f36-9358-a92d95eb9490","order_by":4,"name":"Abiodun F Ibidunmoye","email":"","orcid":"","institution":"University of Lagos","correspondingAuthor":false,"prefix":"","firstName":"Abiodun","middleName":"F","lastName":"Ibidunmoye","suffix":""},{"id":604176534,"identity":"4f706e01-0926-4038-8ed8-01b4c1d1c4bb","order_by":5,"name":"Grace A. Eneano","email":"","orcid":"","institution":"University of South Wales","correspondingAuthor":false,"prefix":"","firstName":"Grace","middleName":"A.","lastName":"Eneano","suffix":""},{"id":604176538,"identity":"1fb52bdc-9ef2-4cea-b0dd-c64fb0d304d5","order_by":6,"name":"Ogechi M. Ikeakaonwu","email":"","orcid":"","institution":"University of Dundee","correspondingAuthor":false,"prefix":"","firstName":"Ogechi","middleName":"M.","lastName":"Ikeakaonwu","suffix":""}],"badges":[],"createdAt":"2026-03-10 20:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9087686/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9087686/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104496970,"identity":"25c002a0-53c2-4b8d-9b86-45c99ff6ee6a","added_by":"auto","created_at":"2026-03-12 12:57:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Funnel of legitimate voice: how optimisation regimes structure participation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9087686/v1/e8a759706ca93b59aca40932.jpg"},{"id":104496954,"identity":"41ef087e-b164-4b3f-bab6-5d12437d1f33","added_by":"auto","created_at":"2026-03-12 12:57:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Transformation of Sustainability: From Ethical Ideal to Quantified Output\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9087686/v1/409a2b7a96143fe0730aa33c.jpg"},{"id":104497024,"identity":"807dd0ca-aec1-4c86-936a-afbe5656f9ec","added_by":"auto","created_at":"2026-03-12 12:57:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118822,"visible":true,"origin":"","legend":"\u003cp\u003eDivergence in Perception: Management vs Shop-Floor Experience\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9087686/v1/1003f0f14ac485512b55ef07.jpg"},{"id":104496979,"identity":"ad85da02-5b33-4619-a72f-baa9d0d24cc4","added_by":"auto","created_at":"2026-03-12 12:57:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":173552,"visible":true,"origin":"","legend":"\u003cp\u003eThe Transformation of Sustainability: From Ethical Ideal to Quantified Output\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9087686/v1/911a3b8fbaca6fcca35b18db.jpg"},{"id":104497029,"identity":"47b8c271-6304-4e13-a850-413796e4e48c","added_by":"auto","created_at":"2026-03-12 12:57:31","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":174121,"visible":true,"origin":"","legend":"\u003cp\u003eOrganisational Subjectivity Model: How Optimisation Shapes Behaviour\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9087686/v1/45fd76fb3a3aad1211f2eafb.jpg"},{"id":104902908,"identity":"f87c0bf3-d4c1-4b92-b1f2-54acb947b93b","added_by":"auto","created_at":"2026-03-18 13:26:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1656449,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9087686/v1/123265d7-fad6-40e0-a7d7-20fb69550dee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Calculus of control: metrics, morality, and the machinery of governance in pre-algorithmic optimization systems","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe contemporary discourse surrounding artificial intelligence and ethics has coalesced around a particular constellation of anxieties: bias in facial recognition systems, opacity in machine learning models, autonomous weaponry, and the surveillance capacities of predictive analytics. These concerns, while undeniably urgent, have tended to focus scholarly and regulatory attention on the novel and the spectacular\u0026mdash;systems that compute, learn, and decide with minimal human intervention. Yet this preoccupation with the computational new risks obscuring a quieter, more pervasive transformation already underway within organisations worldwide. Long before sophisticated algorithms arrived to manage warehouses or evaluate employee performance, other technologies of optimisation were already reshaping how authority flows, how decisions are justified, and how ethical responsibilities are distributed.\u003c/p\u003e \u003cp\u003eLean and Green manufacturing frameworks exemplify this pre-digital governance architecture. Originally conceptualised as operational improvement methodologies\u0026mdash;Lean targeting waste reduction and process efficiency, Green addressing environmental impact\u0026mdash;these systems have evolved into comprehensive infrastructures of organisational control. Through the relentless deployment of dashboards, key performance indicators (KPIs), benchmarking exercises, and continuous monitoring regimes, they instantiate a form of governance that mimics the behavioural effects attributed to contemporary AI systems. Decisions become data-driven not necessarily because algorithms demand it, but because metrics have become the only legitimate currency of organisational truth. When a manager justifies a decision by pointing to a spreadsheet rather than a neural network, the governance effect\u0026mdash; the displacement of situated judgment by numerical representation\u0026mdash;remains structurally similar.\u003c/p\u003e \u003cp\u003eThis observation becomes particularly salient in contexts where technological modernisation arrives through development agendas and international sustainability standards rather than organic digital transformation. In Nigerian manufacturing organisations, as in many post-colonial industrial contexts, Lean\u0026ndash;Green systems often enter as components of global supply chain compliance, consultancy packages, and export-oriented modernisation programmes. They do not arrive in organisational vacuums; rather, they intersect with existing hierarchies, labour relations, and cultural formations in ways that produce distinctively local manifestations of global optimisation logics. Understanding how these systems function as technologies of governance\u0026mdash;rather than merely tools of efficiency\u0026mdash;offers crucial insights for AI ethics precisely because it reveals the continuity between historical quantification practices and emerging computational governance.\u003c/p\u003e \u003cp\u003eWe advance three interconnected arguments. First, we contend that governance effects commonly associated with algorithmic management\u0026mdash;behavioural constraint, epistemic authority residing in technical artefacts, and the depoliticisation of value-laden decisions\u0026mdash;can emerge entirely through \"mundane\" technologies of quantification such as spreadsheets, audit checklists, and performance scorecards. Second, we demonstrate that these systems fundamentally reconfigure ethical responsibility within organisations by transforming sustainability and worker wellbeing from matters of moral deliberation into measurable outputs subject to optimisation. Third, we argue that effective regulation and ethical oversight of AI must account for this broader ecology of governance technologies, recognising that algorithmic systems often intensify and formalise pre-existing logics of quantification rather than introducing entirely novel forms of control.\u003c/p\u003e \u003cp\u003eThe remainder of this article proceeds as follows. We first situate our argument within literature on algorithmic governance and critical quantification studies, identifying the conceptual gap our study addresses. We then outline our methodological approach, which involves reinterpreting mixed-methods empirical material from Nigerian manufacturing contexts through a critical socio-technical lens. Our findings reveal three dynamics: the metricization of managerial authority, the conditional restructuring of worker agency, and the operationalization of sustainability as a performance category. We conclude by discussing the implications for AI ethics theory and policy, arguing that the field must expand its purview to examine how governance emerges from the organisational politics of optimisation itself, regardless of whether algorithms are present.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eRecent scholarship on algorithmic governance has productively examined how computational systems increasingly mediate social and organisational life. Danaher defines algorithmic governance as the use of algorithms to \"monitor, direct, and constrain behaviour,\" highlighting how automated decision-making reshapes power relations through technical mechanisms. This literature has importantly illuminated how machine learning systems can perpetuate bias, erode accountability through opacity, and fundamentally alter the landscape of professional judgment. Zuboff\u0026rsquo;s concept of surveillance capitalism extends these concerns to the extraction and commodification of behavioural data, while Eubanks documents how algorithmic systems in social services can exacerbate inequality through \"digital poorhouses.\"\u003c/p\u003e \u003cp\u003eHowever, this focus on the computational new has inadvertently created a temporal blind spot. As Amoore notes, the \"emergence\" of algorithmic governance presupposes a pre-algorithmic innocence that never truly existed. Organisations have long been governed through numbers, and the authority of the spreadsheet predates and prefigures the authority of the neural network. Critical scholarship on quantification\u0026mdash;spanning sociology, science and technology studies, and accounting research\u0026mdash;has demonstrated that metrics, indicators, and audits have functioned as technologies of governance for decades. Power\u0026rsquo;s audit society thesis describes how verification rituals and performance measurement have transformed organisational rationality, while Espeland and Stevens\u0026rsquo; work on commensuration reveals how numerical comparison reshapes the values and relations it purports merely to measure.\u003c/p\u003e \u003cp\u003eWhat remains under-theorised is the specific continuity between these historical practices of quantification and contemporary algorithmic governance. The gap is not merely academic; it has regulatory and ethical consequences. If policymakers and ethicists focus exclusively on AI as a distinct technological epoch, they risk implementing governance frameworks that address the symptoms (opaque algorithms) while ignoring the disease (the unquestioned authority of optimisation logics). Lean\u0026ndash;Green manufacturing systems occupy precisely this liminal space\u0026mdash;employing sophisticated technologies of measurement and control that fall just below the threshold of \"artificial intelligence\" while producing governance effects that are functionally analogous.\u003c/p\u003e \u003cp\u003eThe critical management literature offers some traction here. Orlikowski\u0026rsquo;s socio-material perspective suggests that technologies and organisational practices co-constitute one another, implying that governance emerges from the interaction of human and non-human actors rather than from technology alone. Similarly, research on the \"performativity\" of management accounting demonstrates how KPIs do not simply reflect organisational reality but actively construct it, rendering some aspects of work visible and actionable while rendering others invisible. Yet these insights have rarely been brought into dialogue with AI ethics, and even less frequently applied to manufacturing contexts in the Global South, where imported optimisation frameworks intersect with post-colonial labour relations and developmentalist state policies.\u003c/p\u003e \u003cp\u003eOur contribution, therefore, lies in bridging these literatures. We treat Lean\u0026ndash;Green systems as empirical sites where the \"algorithmic\" qualities of governance\u0026mdash;rule-based decision-making, behavioural optimisation, and the displacement of ethical reasoning into technical procedures\u0026mdash;can be observed in their historical, non-computational form. This allows us to ask: What is it about quantification itself, rather than computation specifically, that generates these governance effects? And how might this understanding inform more robust ethical frameworks for evaluating AI?\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study employs a qualitative, interpretive methodology grounded in the critical reinterpretation of mixed-method empirical material collected from manufacturing organisations in Nigeria. Rather than seeking causal inference or predictive generalisation, our approach prioritises the examination of meaning, power relations, and socio-technical dynamics within specific organisational contexts.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Sources\u003c/h2\u003e \u003cp\u003eThe primary data comprise three distinct streams collected between 2022 and 2023. First, survey instruments were administered to 127 participants across four manufacturing firms in Lagos and Ogun States, including both shop-floor operatives (n\u0026thinsp;=\u0026thinsp;89) and middle-to-senior management (n\u0026thinsp;=\u0026thinsp;38). These instruments explored perceptions of Lean\u0026ndash;Green implementation, experiences of workplace participation, and attitudes toward performance measurement systems. Second, semi-structured interviews were conducted with 23 managerial personnel, including production managers, sustainability officers, and quality control supervisors. These conversations explored decision-making practices, the role of data in operational oversight, and the ethical frameworks employed when balancing efficiency against worker welfare. Third, observational data were generated through industrial ethnography, including 147 hours of workplace observation and value-stream mapping exercises conducted at an automotive components manufacturer, documenting the interaction between workers, machinery, and performance dashboards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Analytical Strategy\u003c/h2\u003e \u003cp\u003eWe employed a reflexive thematic analysis approach, coding the data not for frequency but for patterns of meaning related to governance, authority, and ethics. The analysis was structured around three guiding questions derived from our theoretical framework: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) How do metrics function as authoritative actors within decision-making processes? (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) How is worker agency enabled or constrained within optimisation regimes? and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) How are ethical concepts such as sustainability translated into operational terms?\u003c/p\u003e \u003cp\u003eTo ensure analytical rigour, we maintained a critical audit trail documenting coding decisions and theoretical memos. Survey data were analysed descriptively to identify divergences between managerial and shop-floor perceptions, while interview transcripts underwent line-by-line coding to identify processes of power and resistance. Observational field notes were analysed for instances where technical artefacts (dashboards, checklists, scorecards) intervened in or supplanted human deliberation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Ethical Considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003ewas obtained from the [University Ethics Committee], and all participants provided informed consent. Given the potential for participant vulnerability in industrial contexts, particular attention was paid to confidentiality and the right to withdraw. All organisational and individual identifiers have been anonymised in the presentation of findings. We acknowledge our positionality as researchers situated outside the immediate context of Nigerian manufacturing, and have attempted to mitigate extractive dynamics by engaging local research assistants and sharing preliminary findings with participant organisations.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eOur analysis reveals that Lean\u0026ndash;Green optimisation systems function not merely as technical tools for improving efficiency, but as comprehensive infrastructures of governance that reshape organisational subjectivity, redistribute authority, and transform the ethical landscape of work. These dynamics operated across three interconnected dimensions: the metricization of decision-making authority, the conditional restructuring of worker agency, and the operational translation of sustainability.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Governance by Metrics: The Authority of the Dashboard\u003c/h2\u003e \u003cp\u003eAcross all participating organisations, numerical indicators had achieved a status of unquestioned epistemic authority. Performance dashboards\u0026mdash;typically Excel-based visualisations projected in production meetings or printed as daily \"scorecards\"\u0026mdash;occupied the physical and symbolic centre of managerial practice. Managers described organisational reality almost exclusively through the language of metrics: takt time adherence, defect parts per million (PPM), Overall Equipment Effectiveness (OEE), and waste-reduction percentages.\u003c/p\u003e \u003cp\u003eThis reliance on quantification had substantially constrained managerial discretion. Several middle managers recounted situations where experiential knowledge\u0026mdash;awareness of supply chain disruptions, informal labour practices, or equipment idiosyncrasies\u0026mdash;suggested one course of action, while the dashboard suggested another. Invariably, the dashboard prevailed. As one production supervisor noted, \"When the numbers say red, you cannot argue with green thinking. Your experience doesn't count if it contradicts the KPI.\"\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDivergence Between Managerial Experience and Metric-Driven Decisions (n\u0026thinsp;=\u0026thinsp;23 interviews)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperiential Knowledge Suggested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetric-Driven Decision\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupply chain delay mitigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlexible deadline adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaintain takt time through overtime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorker fatigue, quality issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 cases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreventive downtime for ageing machinery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximize OEE, defer maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBreakdown, higher repair costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 cases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality vs. speed trade-off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduce speed for complex batches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaintain cycle time targets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncreased defect rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 cases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorker wellbeing intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotate fatigued staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaintain staffing levels for efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSafety incidents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 cases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: Frequencies represent mentions across interview transcripts, not mutually exclusive categories.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe table illustrates a consistent pattern where numerical optimisation trumped situated judgment. Managers found themselves occupying a hybrid subject position: formally accountable for outcomes, yet practically accountable to the indicator system itself. This generated what we term \"proxy accountability\"\u0026mdash;responsibility not for the work itself, but for the relationship between the work and its numerical representation.\u003c/p\u003e \u003cp\u003eImportantly, these effects emerged without sophisticated AI. The \"algorithm\" here consisted of simple spreadsheet formulae\u0026mdash;ratios, averages, conditional formatting\u0026mdash;that nonetheless governed behaviour through their performative authority. When targets were missed, accountability flowed downward to individual workers or teams, while the structural constraints embedded in the metric design\u0026mdash;unrealistic baselines, conflicting indicators, or measurement errors\u0026mdash;remained depoliticised and beyond contestation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Reconfigured Agency: Participation Within Boundaries\u003c/h2\u003e \u003cp\u003eA second major finding concerned the asymmetry between managerial narratives of empowerment and workers' lived experiences of participation. Survey data revealed a striking divergence: 78% of managers agreed that \"Lean\u0026ndash;Green initiatives have increased worker empowerment and voice,\" whereas only 31% of shop-floor workers concurred. This gap points not to simple miscommunication, but to a fundamental restructuring of what counts as legitimate participation.\u003c/p\u003e \u003cp\u003eWorkers described suggestion schemes and continuous improvement teams (Kaizen circles) as real but tightly circumscribed opportunities for input. Contributions were welcomed when they aligned with pre-existing productivity targets\u0026mdash;ideas for reducing cycle time, eliminating motion waste, or improving material flow. However, suggestions framed in the language of human welfare\u0026mdash;concerns about pace, fatigue, safety shortcuts imposed by efficiency targets, or the ethical implications of waste-reduction measures\u0026mdash;were routinely filtered out or reframed as \"resistance to change.\"\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1. The Funnel of legitimate voice: how optimisation regimes structure participation\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eConceptual diagram showing a funnel shape. Wide top labelled \"Worker Experience\" containing diverse concerns (safety, fatigue, quality of life, community impact, technical efficiency). Narrow middle section labelled \"Translation Layer\" where concerns are filtered through \"Productivity Framework.\" Narrow bottom labelled \"Legitimate Input\" containing only technical efficiency improvements and waste reduction ideas that align with KPIs. Arrows indicate that safety/fatigue concerns must be translated into efficiency language to pass through.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAs Fig.\u0026nbsp;1 illustrates, participation became a performative exercise in translation. Workers learned to encode their concerns in the vocabulary of optimisation to gain traction. A concern about dangerous working conditions might be presented as a \"quality risk\" or \"efficiency drain\" rather than a matter of dignity or rights. This linguistic adaptation allowed workers to exercise limited agency within the system, but simultaneously reinforced the system's epistemic boundaries. Genuine contestation of the optimisation logic itself\u0026mdash;questioning whether speed should be the primary value, for instance\u0026mdash;remained illegible and illegitimate.\u003c/p\u003e \u003cp\u003eSeveral workers described this as \"playing the game,\" suggesting a sophisticated awareness of the structural constraints coupled with pragmatic adaptation. However, this adaptation came with cognitive and ethical costs. Workers reported emotional dissonance when required to frame human-centred concerns in the cold language of metrics, describing a sense that they were \"betraying\" their own experience to be heard. This suggests that optimisation regimes do not merely constrain agency, but actively reshape the moral vocabulary through which workers understand their own interests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 The Quantification of Ethics: Sustainability as Performance Output\u003c/h2\u003e \u003cp\u003eThe third dimension concerns the fate of sustainability as an ethical concept within these metricised environments. While all participating organisations displayed strong discursive commitment to environmental responsibility, sustainability was overwhelmingly enacted through quantifiable outputs: percentage reductions in energy consumption, waste diversion rates, compliance scores against ISO 14001 standards, and audit results.\u003c/p\u003e \u003cp\u003eThis quantification narrowed the ethical vision. Concepts such as \"intergenerational responsibility,\" \"ecological justice,\" or \"community wellbeing\"\u0026mdash;aspects of sustainability that resist easy measurement\u0026mdash;were largely absent from organisational discourse. When probed about the social dimensions of sustainability, managers typically reverted to metrics: \"We are sustainable because we reduced water usage by 15% last quarter,\" or \"Our waste-to-landfill numbers prove our commitment.\" The ethical had been thoroughly operationalised.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;2. The Transformation of Sustainability: From Ethical Ideal to Quantified Output\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eLine graph showing conceptual trajectory over time. X-axis: Organisational Maturity in Lean\u0026ndash;Green Implementation. Y-axis: Conceptual Richness of Sustainability. Line starts high (Complex Ethics: justice, care, community) at low maturity, then declines steeply as Lean\u0026ndash;Green implementation increases, flattening out at \"Thin Quantification\" (metrics only). Secondary line shows \"Audit Burden\" increasing over the same period. Annotation at midpoint: \"The Ethical Fade.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFigure 2 traces this trajectory. Early in implementation, sustainability discussions involved broad ethical deliberation about organisational responsibility. As metric systems matured, sustainability became synonymous with audit compliance and performance scores. This \"ethical fade\" did not represent cynicism on the part of managers; rather, it reflected the structural imperatives of optimisation systems, which require commensurable, comparable data. Qualitative ethical reasoning is messy, contextual, and resistant to benchmarking; quantitative indicators are manageable, comparable, and appear objective.\u003c/p\u003e \u003cp\u003eThe consequences extended beyond conceptual narrowing. Several organisations had implemented \"sustainability bonuses\" tied to environmental KPIs, creating perverse incentives. In one case, workers were incentivised to reduce waste metrics by covertly disposing of hazardous material through regular waste streams rather than expensive designated channels\u0026mdash;technically improving the metric while violating the ethical spirit of environmental protection. This \"gaming\" was not aberrant behaviour but a logical outcome of a system that substitutes measurement for moral reasoning.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;3. Divergence in Perception: Management vs Shop-Floor Experience\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eManifestations of Ethical Displacement in Lean\u0026ndash;Green Systems\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthical Domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal Moral Concern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperationalised Metric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObserved Displacement Effect\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcosystem health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaste tonnage diverted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOffshoring waste to unregulated suppliers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorker wellbeing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDignity and flourishing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLost-time injury rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnder-reporting of incidents; presenteeism\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal social license\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommunity investment spend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Tick-box\" charity unrelated to operational impact\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFairness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquitable treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardised cycle times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIgnoring individual capability variations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese findings demonstrate that the governance effects of optimisation systems extend beyond the organisation to reshape the very meaning of ethical concepts themselves. Sustainability becomes what can be measured; care becomes what can be audited.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eOur findings suggest that the governance effects typically attributed to artificial intelligence\u0026mdash;behavioural constraint, the concentration of authority in technical systems, and the depoliticisation of value-laden decisions\u0026mdash;are neither novel nor exclusively computational. They emerge from deeper organisational logics of quantification and optimisation that have been developing for decades. This observation carries significant implications for AI ethics scholarship, regulatory policy, and critical management practice.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;4. The Transformation of Sustainability: From Ethical Ideal to Quantified Output\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.1 The Continuum of Algorithmic Governance\u003c/h2\u003e \u003cp\u003eWe propose that governance exists on a continuum of technical formalisation, ranging from informal, judgment-based coordination through metric-based management to fully automated algorithmic control. Lean\u0026ndash;Green systems occupy the middle ground\u0026mdash;what we term \"algorithmic governance without algorithms\"\u0026mdash;where the logic of algorithmic thinking (rule-based, optimising, data-driven) is instantiated through human enactment of computational routines. Managers act as the \"wetware\" executing the logic that will later be encoded in software.\u003c/p\u003e \u003cp\u003eThis perspective complicates the temporal narrative of AI ethics, which often treats algorithmic governance as an unprecedented rupture. Instead, we see continuity. Contemporary AI systems in workforce management do not introduce de novo the quantification of labour; they intensify and accelerate pre-existing practices. The spreadsheet paves the way for the neural network. Consequently, ethical frameworks that focus solely on the novelty of AI\u0026mdash;its opacity, its scale, its speed\u0026mdash;risk treating symptoms while ignoring underlying pathologies. If we wish to govern AI effectively, we must first understand the governance of metrics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.2 The Politics of Visibility\u003c/h2\u003e \u003cp\u003eOur findings regarding the operationalization of sustainability highlight a crucial dynamic in socio-technical governance: the political work of visibility. Quantification renders certain aspects of reality visible and actionable while systematically obscuring others. This is not accidental oversight but structural necessity. Metrics require commensurability\u0026mdash;the reduction of diverse qualities to common scales. Consequently, they privilege thin, transferable, comparable data over thick, contextual, particular understanding.\u003c/p\u003e \u003cp\u003eIn the context of AI ethics, this suggests that calls for \"ethical AI\" through the development of fairness metrics, bias audits, and sustainability algorithms may inadvertently reproduce the very governance problems they seek to solve. When we reduce fairness to a performance score, we risk hollowing out the moral substance of justice, just as our participants hollowed out sustainability into waste percentages. The danger is not that AI will be unethical, but that it will operationalise ethics in ways that make genuine moral reasoning unnecessary and possibly illegible. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Toward Reflexive Governance\u003c/h2\u003e \u003cp\u003eIf governance effects emerge from the interaction of technical systems and organisational logics rather than from technology alone, then ethical interventions must target both. Organisations implementing Lean\u0026ndash;Green systems\u0026mdash;or indeed, any AI systems\u0026mdash;require mechanisms for what we term \"reflexive governance\": institutionalised spaces where the metrics themselves can be contested, where qualitative judgment can override quantitative targets without penalty, and where the boundary between optimisation and exploitation is subject to democratic deliberation.\u003c/p\u003e \u003cp\u003eThis has regulatory implications. Current proposals for AI regulation, such as the EU AI Act, focus heavily on high-risk automated systems. Our research suggests that \"low-tech\" governance systems\u0026mdash;spreadsheets, KPIs, audit regimes\u0026mdash;can produce equally high-risk outcomes in terms of worker exploitation, environmental harm, and accountability erosion. A comprehensive approach to technology governance must address the \u003cem\u003esociology of optimisation\u003c/em\u003e rather than merely the \u003cem\u003etechnology of computation\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Limitations and Future Research\u003c/h2\u003e \u003cp\u003eWe acknowledge limitations in our study. The focus on Nigerian manufacturing, while providing crucial context for understanding technology transfer in the Global South, may limit generalisability to service-sector or knowledge-work contexts where algorithmic management is more prevalent. Additionally, our reinterpretive methodology, while appropriate for theory development, cannot establish causal relationships between specific system features and outcomes. Future research might employ comparative designs examining how governance effects differ across high-tech (AI-driven) and low-tech (metric-driven) optimisation systems, or ethnographic studies tracing the specific moments when human judgment is overridden by numerical authority.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study has argued that the governance effects associated with artificial intelligence\u0026mdash;specifically the displacement of judgment, the restructuring of agency, and the transformation of ethics into performance metrics\u0026mdash;are not unique to computational systems. Through an examination of Lean\u0026ndash;Green manufacturing in Nigerian contexts, we have demonstrated that relatively mundane technologies of quantification can function as powerful infrastructures of control, reshaping authority, participation, and moral meaning within organisations.\u003c/p\u003e \u003cp\u003eThe concept of \u003cem\u003ealgorithmic governance without algorithms\u003c/em\u003e offers a corrective to contemporary debates that treat AI as a radical break from previous modes of organisation. Instead, we see AI as the intensification of quantification logics that have long permeated managerial practice. For ethicists and policymakers, this implies that the urgent task is not merely to regulate the algorithms of the future, but to cultivate critical reflexivity about the metrics of the present. Until we learn to govern the spreadsheet, we will struggle to govern the AI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003eEthical approval was obtained from the institutional review board, and all participants provided informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003eInformed consent was obtained from all participants involved in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors received no specific funding for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions StatementAuthorship provides credit for a researcher's contributions to a study and carries accountability. Use this section to specify how authors contributed to the manuscript.Our authorship policy for Springer (opens in a new window) provides guidance and criteria for authorship.Your submission will be reviewed using the double anonymous peer review model. Do not include an author contribution statement in the manuscript or any other files. The statement provided here will be published alongside the manuscript.Use initials to refer to each author's contribution, and specify who did what. For example, \"A.B. and C.D. wrote the main manuscript text and E.F. prepared figures 1-3. All authors reviewed the manuscript.\"\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003enone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmoore, L.: Cloud ethics: Algorithms and the attributes of ourselves and others. 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MIS Q. \u003cb\u003e43\u003c/b\u003e(1), 3\u0026ndash;11 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.25300/MISQ/2019/14476\u003c/span\u003e\u003cspan address=\"10.25300/MISQ/2019/14476\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Algorithmic governance, Quantification, Lean manufacturing, Socio-technical systems, Organisational ethics, Metrics","lastPublishedDoi":"10.21203/rs.3.rs-9087686/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9087686/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile contemporary debates in AI ethics focus predominantly on machine learning, neural networks, and automated decision-making, this article argues that similar governance effects\u0026mdash;behavioural shaping, displacement of human judgment, and the concentration of authority in technical artefacts\u0026mdash;already permeate organisations through mundane managerial technologies. Drawing on mixed-method empirical research from Nigerian manufacturing contexts, we introduce the concept of \u003cem\u003ealgorithmic governance without algorithms\u003c/em\u003e to describe how Lean\u0026ndash;Green optimisation systems function as socio-technical infrastructures that reshape power, agency, and ethical responsibility. Through dashboards, key performance indicators, and audit mechanisms, these frameworks transform sustainability from a moral commitment into a quantified output and reconfigure worker agency within constrained participatory regimes. Our findings suggest that governance is not merely a computational phenomenon but an organisational logic embedded in quantification practices themselves. This study extends critical AI ethics scholarship beyond digital technologies to examine the ethical-political foundations of optimisation systems that predate and precondition contemporary algorithmic management.\u003c/p\u003e","manuscriptTitle":"The Calculus of control: metrics, morality, and the machinery of governance in pre-algorithmic optimization systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 12:55:42","doi":"10.21203/rs.3.rs-9087686/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6dddaf67-449c-4c91-a5dc-de6bfe091976","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T13:25:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 12:55:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9087686","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9087686","identity":"rs-9087686","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}