<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="other" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.180514.1</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Opinion Article</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Beyond Access: Rethinking Digital Power in Data-Driven Industrial Economies for Sustainable Development</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 1; peer review: awaiting peer review]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Mhlanga</surname>
                        <given-names>David</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-8512-2124</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Financial Governance, University of South Africa, Pretoria, Gauteng, 0002,, South Africa</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:dmhlanga67@gmail.com">dmhlanga67@gmail.com</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>22</day>
                <month>5</month>
                <year>2026</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2026</year>
            </pub-date>
            <volume>15</volume>
            <elocation-id>781</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>29</day>
                    <month>4</month>
                    <year>2026</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Mhlanga D</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/15-781/pdf"/>
            <abstract>
                <p>This paper critically examines the evolving nature of inequality in data-driven industrial economies by moving beyond traditional access-based interpretations of the digital divide toward a more comprehensive concept of digital power. While digital transformation has expanded connectivity and participation, the benefits remain unevenly distributed due to asymmetries in data ownership, algorithmic governance, platform control, and value capture. Drawing on recent literature, the study argues that inequality is increasingly shaped by the capacity to control and leverage digital systems rather than merely access them. It introduces a conceptual perspective that highlights how industrial data systems generate &#x201c;participation without power,&#x201d; particularly affecting workers, SMEs, and developing economies. The paper further explores implications for Sustainable Development Goals, especially SDG 9 and SDG 10, demonstrating how concentrated digital power may hinder inclusive industrialisation and exacerbate global inequalities. Policy recommendations emphasise the need for governance frameworks that prioritise equity, accountability, and inclusive distribution of value.</p>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>Access</kwd>
                <kwd>Digital Power</kwd>
                <kwd>Industrial Economies</kwd>
                <kwd>Financial Inclusion</kwd>
                <kwd>Sustainable Development</kwd>
            </kwd-group>
            <funding-group>
                <funding-statement>The author(s) declared that no grants were involved in supporting this work.</funding-statement>
            </funding-group>
        </article-meta>
    </front>
    <body>
        <sec id="sec1" sec-type="intro">
            <title>1. Introduction</title>
            <p>Digital transformation has emerged as a central driver of economic restructuring, reshaping production systems, governance processes, and social interactions across both advanced and emerging economies as digital technologies become embedded in virtually all sectors of economic activity (
                <xref ref-type="bibr" rid="ref4">David et al., 2025</xref>; 
                <xref ref-type="bibr" rid="ref15">OECD, 2026</xref>). The integration of data-driven technologies, artificial intelligence, and digital infrastructures into industrial systems has accelerated productivity, innovation, and global connectivity, positioning digitalisation as a key enabler of contemporary economic development (
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>; 
                <xref ref-type="bibr" rid="ref6">Hu et al., 2025</xref>). At the same time, the rapid expansion of the digital economy, characterised by exponential growth in data generation, platform ecosystems, and interconnected devices, has fundamentally altered how value is created, distributed, and captured within industrial economies (
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>).</p>
            <p>Historically, scholarly and policy debates on digital inequality have been framed through the lens of the digital divide, which emphasises disparities in access to digital technologies and connectivity among individuals, firms, and regions (
                <xref ref-type="bibr" rid="ref11">Mhlanga 2020</xref>; 
                <xref ref-type="bibr" rid="ref27">Zhang et al., 2025</xref>; 
                <xref ref-type="bibr" rid="ref9">McCarthy, 2022</xref>). This access-oriented perspective has informed a wide range of policy interventions aimed at expanding internet penetration, improving infrastructure, and enhancing digital skills, under the assumption that greater access would lead to more inclusive participation in digital economies (
                <xref ref-type="bibr" rid="ref10">Mhlanga 2021</xref>; 
                <xref ref-type="bibr" rid="ref4">David et al., 2025</xref>). Indeed, empirical evidence suggests that increased digital access is positively associated with economic growth and improvements in human development indicators, particularly in emerging economies (
                <xref ref-type="bibr" rid="ref12">Mhlanga, 2022</xref>; 
                <xref ref-type="bibr" rid="ref4">David et al., 2025</xref>). However, despite these gains, persistent and, in some cases, widening inequalities suggest that access alone is insufficient to ensure equitable outcomes in digital transformation processes (
                <xref ref-type="bibr" rid="ref16">Oxfam, 2026</xref>) .</p>
            <p>Recent evidence indicates that the benefits of digital transformation are unevenly distributed, with wealth, influence, and technological capabilities increasingly concentrated among a small number of dominant actors, including large technology firms and advanced economies (
                <xref ref-type="bibr" rid="ref16">Oxfam, 2026</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). The consolidation of digital platforms and the emergence of data-intensive business models have enabled certain actors to control critical digital infrastructures, data resources, and algorithmic systems that shape economic outcomes (
                <xref ref-type="bibr" rid="ref2">Baley &amp; Veldkamp, 2025</xref>). In this context, inequality is no longer defined solely by whether individuals or organisations can access digital technologies, but increasingly by their capacity to control and leverage data, influence algorithmic decision-making, and capture value within digital ecosystems (
                <xref ref-type="bibr" rid="ref16">Oxfam, 2026</xref>) .</p>
            <p>This shift reflects the emergence of what can be conceptualised as a data divide, where disparities arise between those who generate data and those who possess the capabilities to collect, analyse, and monetise it, thereby creating asymmetries in power and economic advantage (
                <xref ref-type="bibr" rid="ref27">Zhang et al., 2025</xref>; 
                <xref ref-type="bibr" rid="ref9">McCarthy, 2022</xref>). Within industrial contexts, these dynamics are particularly pronounced, as digital technologies such as industrial Internet of Things (IIoT), digital twins, and AI-driven production systems generate vast amounts of operational data that are often controlled by firms and platform providers rather than by workers or smaller enterprises (
                <xref ref-type="bibr" rid="ref1">Al Zami et al., 2025</xref>). Consequently, participation in digital systems does not necessarily translate into meaningful influence or equitable benefit, giving rise to a condition that may be described as participation without power. Against this backdrop, this paper argues that contemporary digital inequality should be reframed as a question of digital power, rather than merely digital access. Digital power refers to the capacity to control data flows, design and govern algorithmic systems, and influence the distribution of economic value within data-driven industrial economies. This perspective shifts the analytical focus from inclusion in terms of connectivity to inclusion in terms of control, agency, and value capture. By advancing this conceptual shift, the paper contributes to ongoing debates on digital transformation by highlighting the structural mechanisms through which power is concentrated, and inequalities are reproduced in the digital age. This paper is particularly relevant to the Sustainable Development Goals (SDGs), especially SDG 9, which emphasises inclusive and sustainable industrialisation, and SDG 10, which focuses on reducing inequalities. While digital transformation is often positioned as a driver of these goals, this study argues that unequal distribution of digital power may instead reinforce structural disparities, limiting the achievement of inclusive and equitable development outcomes.</p>
        </sec>
        <sec id="sec2">
            <title>2. From digital divide to digital power</title>
            <p>The concept of the digital divide has long provided the dominant analytical lens for understanding inequality in the context of technological change, with early scholarship focusing primarily on disparities in access to information and communication technologies (ICTs) across individuals, regions, and nations (
                <xref ref-type="bibr" rid="ref25">van Dijk, 2020</xref>; 
                <xref ref-type="bibr" rid="ref18">Ragnedda, 2020</xref>). This first-level divide emphasised material access, such as internet connectivity, devices, and infrastructure, as the principal barrier to participation in the digital economy, thereby shaping policy responses centred on expanding broadband coverage and reducing affordability constraints (
                <xref ref-type="bibr" rid="ref13">OECD, 2023</xref>; 
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). As digital technologies diffused more widely, however, research began to highlight a second-level divide, which captured inequalities in digital skills, literacy, and the capacity to use technologies effectively, suggesting that access alone does not guarantee meaningful engagement or benefits (
                <xref ref-type="bibr" rid="ref5">Hargittai et al., 2019</xref>; 
                <xref ref-type="bibr" rid="ref24">van Deursen &amp; Helsper, 2018</xref>).</p>
            <p>More recent scholarship has advanced the notion of a third-level digital divide, which shifts attention from access and skills to the outcomes and benefits derived from digital participation, demonstrating that even among connected and digitally literate users, significant disparities persist in terms of economic returns, social capital, and opportunities (
                <xref ref-type="bibr" rid="ref20">Scheerder et al., 2017</xref>; 
                <xref ref-type="bibr" rid="ref18">Ragnedda, 2020</xref>). These evolving perspectives reflect an important progression in the literature, yet they remain limited in their ability to fully capture the structural transformations associated with data-driven and AI-enabled economies, where power is increasingly embedded in the control of digital infrastructures, data ecosystems, and algorithmic systems (
                <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>; 
                <xref ref-type="bibr" rid="ref28">Zuboff, 2023</xref>). In such contexts, inequality is no longer simply a matter of differential participation, but of asymmetrical control over the mechanisms that shape participation itself. This transformation is particularly evident in the rise of the data economy, where data has become a critical factor of production, comparable to capital and labour, and where its collection, aggregation, and analysis underpin competitive advantage and value creation (
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>). Large technology firms and platform-based organisations have developed the capacity to extract, process, and monetise vast quantities of data, thereby consolidating their position within digital ecosystems and creating new forms of market concentration and dependency (
                <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>; 
                <xref ref-type="bibr" rid="ref21">Srnicek, 2017</xref>). These dynamics have given rise to what has been described as data asymmetry, wherein individuals, workers, and smaller firms generate data but lack the capacity to access, interpret, or leverage it effectively, resulting in an imbalance between data producers and data controllers (
                <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>).</p>
            <p>In this context, the limitations of the digital divide framework become increasingly apparent, as it does not sufficiently account for the ways in which power is exercised through data ownership, algorithmic governance, and platform control. Consequently, there is a growing need to reconceptualise digital inequality through the lens of digital power, defined here as the capacity of actors to control data flows, design and govern algorithmic systems, and influence the distribution of value within digital economies. This perspective aligns with emerging critiques that highlight how digital systems are not neutral infrastructures but are shaped by institutional arrangements, corporate strategies, and governance structures that determine who benefits and who is marginalised (
                <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>; 
                <xref ref-type="bibr" rid="ref17">Pasquale, 2015</xref>). Importantly, the shift from digital divide to digital power also reflects a transition from viewing inequality as a problem of exclusion to understanding it as a problem of structural dominance and dependency. In data-driven industrial economies, actors may be formally included within digital systems through participation in platforms, supply chains, or digital production networks yet remain subordinated due to their limited capacity to influence decision-making processes or capture value (
                <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). This condition underscores the emergence of a new form of inequality, where inclusion does not equate to empowerment, and where participation occurs within asymmetrical power relations that reproduce and, in some cases, intensify existing socio-economic disparities (
                <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>; 
                <xref ref-type="bibr" rid="ref19">Ragnedda &amp; Ruiu 2020</xref>). Accordingly, reframing digital inequality as a matter of digital power provides a more comprehensive analytical framework for understanding the contemporary dynamics of technological transformation, particularly in industrial contexts where data-intensive systems and algorithmic decision-making are increasingly central. This perspective not only captures the evolving nature of inequality in the digital age but also foregrounds the importance of governance, regulation, and institutional design in shaping more equitable digital futures.</p>
        </sec>
        <sec id="sec3">
            <title>3. Dimensions of digital power in industrial economies</title>
            <p>The transition toward data-driven industrial systems has fundamentally reconfigured the locus of economic and organisational power, shifting it from traditional factors of production toward the control of data, digital infrastructures, and algorithmic processes (
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>). In this context, digital power is not a singular construct but a multidimensional phenomenon that operates across interconnected domains, including data ownership, algorithmic governance, platform control, and value capture, each of which contributes to shaping asymmetries in influence and economic outcomes within industrial ecosystems (
                <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>; 
                <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>). Understanding these dimensions is critical for explaining how inequalities are reproduced and intensified in contemporary digital economies, particularly as industrial production becomes increasingly reliant on real-time data flows, artificial intelligence, and digitally mediated coordination mechanisms (
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>; 
                <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>).</p>
            <sec id="sec4">
                <title>3.1 Data ownership and control</title>
                <p>Data has emerged as a central strategic asset in industrial economies, underpinning decision-making, optimisation, and innovation across production systems, supply chains, and organisational processes (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). However, the ownership and control of industrial data are highly concentrated, typically residing with firms that possess the technological infrastructure and analytical capabilities to collect, store, and process large-scale datasets (
                    <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>; 
                    <xref ref-type="bibr" rid="ref21">Srnicek, 2017</xref>). While workers, consumers, and smaller enterprises generate substantial volumes of data through their participation in digital systems, they often lack formal ownership rights or meaningful control over how this data is used, shared, or monetised, resulting in asymmetrical power relations between data producers and data controllers (
                    <xref ref-type="bibr" rid="ref28">Zuboff, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). This concentration of data ownership enables dominant actors to reinforce their competitive advantage, as access to proprietary datasets enhances their ability to innovate, predict market trends, and optimise operations, thereby creating barriers to entry for less-resourced participants (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>).</p>
            </sec>
            <sec id="sec5">
                <title>3.2 Algorithmic authority</title>
                <p>The increasing reliance on artificial intelligence and machine learning systems in industrial contexts has introduced a new dimension of algorithmic power, in which decision-making processes are partially or fully delegated to computational systems (
                    <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>; 
                    <xref ref-type="bibr" rid="ref17">Pasquale, 2015</xref>). In sectors such as manufacturing, logistics, and supply chain management, algorithms are used to optimise production schedules, allocate resources, monitor performance, and predict maintenance needs, thereby enhancing efficiency and reducing operational costs (
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>). However, the opacity of many algorithmic systems, often described as &#x201c;black boxes,&#x201d; limits transparency and accountability, making it difficult for affected stakeholders to understand, challenge, or influence the decisions being made (
                    <xref ref-type="bibr" rid="ref17">Pasquale, 2015</xref>; 
                    <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>). These dynamic shifts power toward those who design, own, and control these systems, while marginalising those who are subject to algorithmic decisions, including workers and smaller firms operating within digitally mediated production environments (
                    <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>; 
                    <xref ref-type="bibr" rid="ref28">Zuboff, 2023</xref>).</p>
            </sec>
            <sec id="sec6">
                <title>3.3 Platform dominance</title>
                <p>The rise of digital platforms has further reshaped industrial organisation by creating ecosystems in which economic activity is coordinated through centralised digital infrastructures that facilitate interactions between multiple actors (
                    <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>; 
                    <xref ref-type="bibr" rid="ref21">Srnicek, 2017</xref>). In industrial contexts, platform-based models are increasingly used to manage supply chains, coordinate production networks, and integrate data across organisational boundaries, thereby enhancing efficiency and scalability (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). However, platform operators often exercise significant control over the rules of engagement, including access conditions, pricing structures, data flows, and governance mechanisms, which can create user dependencies and limit their autonomy (
                    <xref ref-type="bibr" rid="ref21">Srnicek, 2017</xref>; 
                    <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>). Network effects and economies of scale further reinforce platform dominance, enabling a small number of firms to consolidate market power and shape the structure of industrial ecosystems, often at the expense of smaller participants who must operate within these controlled environments (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>).</p>
            </sec>
            <sec id="sec7">
                <title>3.4 Value capture and distribution</title>
                <p>A critical dimension of digital power lies in the ability to capture and appropriate economic value generated within digital systems, which is increasingly unevenly distributed across actors in data-driven industrial economies (
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). Firms that control key digital assets such as data, algorithms, and platforms are better positioned to extract value from industrial processes, while those who contribute to data generation or participate in production networks often receive a disproportionately smaller share of the resulting benefits (
                    <xref ref-type="bibr" rid="ref28">Zuboff, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>). This imbalance reflects a broader shift in the distribution of economic rents, where intangible assets and digital capabilities play a dominant role in determining competitive advantage and profitability (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). As a result, digital transformation can simultaneously increase overall productivity while exacerbating inequality, as gains are concentrated among actors with the greatest control over digital infrastructures and resources (
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>). To synthesise the discussion and provide a structured overview of how digital transformation generates and distributes power within industrial systems, 
                    <xref ref-type="fig" rid="f1">
Figure 1</xref> presents a conceptual framework linking digital transformation, industrial data systems, and the multidimensional nature of digital power to inequality outcomes and their implications for sustainable development. The figure illustrates how control over data, algorithms, platforms, and value capture shapes participation and influences progress toward inclusive industrialisation and reduced inequalities.</p>
                <fig fig-type="figure" id="f1" orientation="portrait" position="float">
                    <label>
Figure 1. </label>
                    <caption>
                        <title>Digital power and sustainable development outcomes in data-driven industrial economies.</title>
                        <p>This figure illustrates how digital transformation, mediated through industrial data systems, generates multidimensional forms of digital power (data ownership, algorithmic authority, platform dominance, and value capture), which influence participation, inequality, and progress toward SDG 9 and SDG 10.</p>
                    </caption>
                    <graphic id="gr1" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/199128/55192c68-1dcf-43cb-b701-bdcbc7087b1f_figure1.gif"/>
                </fig>
                <p>This figure illustrates the pathways through which digital transformation, mediated by industrial data systems, produces multidimensional forms of digital power that shape participation, value capture, and inequality, with direct implications for SDG 9 (Industry, Innovation and Infrastructure) and SDG 10 (Reduced Inequalities). As shown in 
                    <xref ref-type="fig" rid="f1">
Figure 1</xref>, digital transformation does not operate as a neutral process but generates structured forms of power that shape economic and social outcomes. While industrial data systems expand participation, the concentration of control over key digital resources results in asymmetrical benefits, reinforcing the condition of &#x201c;participation without power.&#x201d; This framework provides a foundation for understanding how inequalities emerge in digital ecosystems and highlights the importance of governance interventions that redistribute digital power to support more inclusive and sustainable development outcomes.</p>
            </sec>
        </sec>
        <sec id="sec8">
            <title>4. Industrial data systems and unequal participation</title>
            <p>The rapid diffusion of industrial data systems, including the Industrial Internet of Things (IIoT), cyber-physical systems, digital twins, and AI-enabled production platforms, has transformed the organisation of production by embedding data flows and real-time analytics into the core of industrial processes (
                <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>; 
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). These systems enable continuous monitoring, predictive maintenance, and operational optimisation, thereby enhancing efficiency, reducing costs, and improving productivity across manufacturing and supply chain environments (
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). As a result, participation in industrial ecosystems increasingly requires integration into digitally mediated systems, where data generation and exchange are central to value creation (
                <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>; 
                <xref ref-type="bibr" rid="ref21">Srnicek, 2017</xref>). However, while industrial data systems expand opportunities for participation, they simultaneously reproduce and intensify asymmetries in control and influence, leading to uneven outcomes among actors within these ecosystems (
                <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). Workers, suppliers, and small and medium-sized enterprises (SMEs) are often deeply embedded in data-generating processes through their engagement with digital production technologies, yet they typically lack access to the full datasets, analytical tools, and decision-making authority required to shape outcomes or capture value (
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). This creates a structural condition in which participation in digital systems does not equate to empowerment, but rather reinforces existing hierarchies of control (
                <xref ref-type="bibr" rid="ref28">Zuboff, 2023</xref>; 
                <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>).</p>
            <p>Within manufacturing environments, for example, IIoT devices and sensor networks generate vast quantities of operational data related to machine performance, worker productivity, and process efficiency, yet this data is generally owned and managed by firms or platform providers rather than by the individuals or entities that contribute to its creation (
                <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). Similarly, digital supply chains rely on integrated platforms that coordinate logistics, inventory management, and production scheduling across multiple actors, but the governance of these systems is typically centralised, allowing lead firms or platform operators to dictate the terms of participation and control access to critical information (
                <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>; 
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>). These arrangements reinforce dependency relationships, as smaller firms must align with platform standards and protocols to remain competitive while having limited capacity to influence the system&#x2019;s structure or rules (
                <xref ref-type="bibr" rid="ref21">Srnicek, 2017</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). The implications of these dynamics are particularly significant for labour, as workers increasingly operate within digitally monitored and algorithmically managed environments, where performance is tracked, evaluated, and optimised through data-driven systems (
                <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>; 
                <xref ref-type="bibr" rid="ref17">Pasquale, 2015</xref>). While such systems can enhance efficiency and safety, they also raise concerns about autonomy, surveillance, and the redistribution of decision-making authority, as control shifts from workers to managerial and algorithmic systems (
                <xref ref-type="bibr" rid="ref28">Zuboff, 2023</xref>; 
                <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>). In this context, workers become both contributors to and subjects of data systems, generating valuable information while remaining largely excluded from its governance and benefits (
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>).</p>
            <p>At a broader level, these patterns extend to global industrial structures, where firms in the Global South are increasingly integrated into digital value chains but often occupy subordinate positions characterised by limited technological capabilities, restricted access to data, and dependence on foreign platforms and infrastructures (
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). Although digital transformation can facilitate market access and integration into global production networks, it can also reinforce existing inequalities by concentrating high-value activities such as data analytics, platform governance, and innovation within advanced economies and large multinational corporations (
                <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>; 
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>). Consequently, digital participation at the global level often reflects asymmetric integration, in which inclusion in digital systems does not translate into proportional economic gains or strategic control.</p>
            <p>Taken together, these dynamics highlight a critical paradox at the heart of digital transformation in industrial economies: while digital technologies expand participation by connecting actors to data-driven systems, they simultaneously concentrate control over those systems among a relatively small number of powerful actors. This condition can be conceptualised as &#x201c;participation without power,&#x201d; in which individuals and organisations are integrated into digital ecosystems but lack the capacity to influence their operations, governance, or outcomes. Recognising this paradox is essential for advancing a more nuanced understanding of digital inequality, as it underscores the need to move beyond access-based frameworks toward analyses that foreground the distribution of power within industrial data systems.</p>
        </sec>
        <sec id="sec9">
            <title>5. Implications for inequality</title>
            <p>The consolidation of digital power within data-driven industrial economies carries profound implications for the nature, depth, and persistence of inequality, reshaping not only how disparities emerge but also how they are reproduced across economic and social systems (
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). While digital transformation has the potential to enhance productivity, expand market access, and stimulate innovation, its benefits are unevenly distributed, reflecting underlying asymmetries in access to data, technological capabilities, and institutional resources (
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>). As a result, digitalisation operates as a dual force simultaneously enabling inclusion and reinforcing structural inequalities depending on how power is configured within digital ecosystems (
                <xref ref-type="bibr" rid="ref18">Ragnedda, 2020</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>).</p>
            <sec id="sec10">
                <title>5.1 Labour and workplace inequality</title>
                <p>One of the most immediate impacts of digital power is observed in labour markets, where the integration of data-driven technologies and algorithmic management systems is transforming the nature of work and the distribution of control within organisations (
                    <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>; 
                    <xref ref-type="bibr" rid="ref28">Zuboff, 2023</xref>). Workers increasingly operate within environments characterised by continuous data monitoring, performance analytics, and automated decision-making, which can enhance efficiency but also reduce autonomy and bargaining power (
                    <xref ref-type="bibr" rid="ref17">Pasquale, 2015</xref>; 
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>). The shift toward algorithmic governance redistributes authority from workers to those who design and control digital systems, thereby reinforcing hierarchical power structures and limiting workers&#x2019; ability to influence workplace conditions (
                    <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>; 
                    <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>). Furthermore, the growing reliance on automation and AI technologies may exacerbate job polarisation, with high-skill roles benefiting from technological complementarity while low- and medium-skill roles face displacement or deskilling pressures (
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>).</p>
            </sec>
            <sec id="sec11">
                <title>5.2 Firm-Level inequality and market concentration</title>
                <p>At the firm level, digital power contributes to increasing disparities between large, technologically advanced firms and smaller enterprises, particularly SMEs, which often lack the resources and capabilities required to compete effectively in data-driven environments (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>). Large firms benefit from economies of scale in data collection and analysis, as well as from network effects associated with platform-based business models, enabling them to consolidate market power and establish dominant positions within digital ecosystems (
                    <xref ref-type="bibr" rid="ref21">Srnicek, 2017</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). In contrast, smaller firms are frequently dependent on external platforms for access to markets, data, and digital infrastructure, which constrains their strategic autonomy and limits their capacity to capture value (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). This dynamic reinforces a winner-takes-most pattern of accumulation, where economic gains are concentrated among a small number of dominant actors, thereby intensifying firm-level inequality and reducing competitive diversity (
                    <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>).</p>
            </sec>
            <sec id="sec12">
                <title>5.3 Global inequality and the digital divide revisited</title>
                <p>At the global level, the concentration of digital power exacerbates existing inequalities between advanced economies and those in the Global South, as disparities in digital infrastructure, technological capabilities, and institutional capacity shape countries&#x2019; ability to participate in and benefit from the digital economy (
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). While digital technologies can facilitate integration into global value chains, the highest-value activities such as data analytics, platform governance, and innovation are often concentrated in developed economies, leaving developing countries in lower-value segments of digital production networks (
                    <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>). This pattern reflects a form of digital dependency, in which countries rely on foreign technologies and platforms, thereby limiting their ability to exercise sovereignty over data and digital infrastructure (
                    <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). Consequently, digital transformation risks reproducing historical patterns of uneven development, in which technological advancement fails to translate into equitable economic outcomes across regions (
                    <xref ref-type="bibr" rid="ref18">Ragnedda, 2020</xref>; 
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>).</p>
            </sec>
            <sec id="sec13">
                <title>5.4 Inequality in value capture and distribution</title>
                <p>A central implication of digital power is its impact on the distribution of economic value, as control over data and digital infrastructure determines who benefits from the gains of digital transformation (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). Actors who control key digital assets, such as proprietary datasets, advanced analytics capabilities, and platform ecosystems, can capture a disproportionate share of value, while those who contribute to data generation often receive limited returns (
                    <xref ref-type="bibr" rid="ref28">Zuboff, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>). This imbalance reflects a broader shift toward intangible asset-driven economies, where value creation is increasingly decoupled from traditional inputs such as labour and physical capital, thereby altering the distributional dynamics of economic growth (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). As a result, digital transformation can contribute to rising income and wealth inequality, even in contexts where overall economic output is increasing (
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>). These dimensions illustrate that digital transformation does not operate as a neutral or inherently inclusive process, but rather as a structurally embedded system in which power relations determine the distribution of opportunities and outcomes. The concentration of digital power across labour, firms, and global systems highlights the need to move beyond access-based understandings of inequality and toward frameworks that explicitly address issues of control, governance, and value distribution. Without such a shift, digital transformation risks deepening existing inequalities, reinforcing structural asymmetries, and limiting the potential for inclusive and sustainable economic development.</p>
            </sec>
        </sec>
        <sec id="sec14">
            <title>6. Rethinking policy and governance</title>
            <p>The concentration of digital power within data-driven industrial economies necessitates a fundamental reorientation of policy and governance frameworks, as existing approaches, largely centred on expanding access and infrastructure, are insufficient to address the structural dynamics of inequality embedded in contemporary digital systems (
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). While investments in connectivity, digital literacy, and technological adoption remain important, they do not adequately confront the deeper issues of control, ownership, and value distribution that underpin digital power asymmetries (
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>; 
                <xref ref-type="bibr" rid="ref18">Ragnedda, 2020</xref>). Consequently, there is a growing recognition that inclusive digital transformation requires not only broader participation but also more equitable governance of data, algorithms, and digital infrastructures (
                <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>).</p>
            <sec id="sec15">
                <title>6.1 From access-oriented to power-oriented policy frameworks</title>
                <p>Traditional digital policies have prioritised closing the digital divide through infrastructure expansion and affordability measures, reflecting an assumption that increased connectivity will lead to inclusive economic outcomes (
                    <xref ref-type="bibr" rid="ref13">OECD, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). However, as digital economies evolve, it has become evident that access alone does not guarantee equitable participation or benefit, particularly in contexts where power is concentrated among a small number of dominant actors (
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref18">Ragnedda, 2020</xref>). A shift toward power-oriented policy frameworks is therefore required, focusing on how digital systems are governed and who controls critical resources such as data and platforms (
                    <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>; 
                    <xref ref-type="bibr" rid="ref17">Pasquale, 2015</xref>). This shift implies moving beyond inclusion as connectivity toward inclusion as agency and influence within digital ecosystems.</p>
            </sec>
            <sec id="sec16">
                <title>6.2 Data governance and ownership</title>
                <p>Central to this transformation is the development of robust data governance frameworks that address issues of ownership, access, and control over data generated within industrial systems (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). Current arrangements often favour firms that collect and process data, granting them extensive rights over its use and monetisation, while limiting the ability of individuals, workers, and smaller enterprises to access or benefit from the data they generate (
                    <xref ref-type="bibr" rid="ref28">Zuboff, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>). Policy interventions may include establishing data-sharing mechanisms, data trusts, and regulatory frameworks that promote a more equitable distribution of data rights and benefits, thereby reducing asymmetries between data producers and data controllers (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). Such approaches aim to rebalance power within digital ecosystems by recognising data as a collective resource rather than solely a proprietary asset
                    <bold>.</bold>
                </p>
            </sec>
            <sec id="sec17">
                <title>6.3 Algorithmic transparency and accountability</title>
                <p>The growing influence of algorithmic systems in shaping industrial processes and economic outcomes also necessitates greater emphasis on transparency and accountability in algorithmic governance (
                    <xref ref-type="bibr" rid="ref17">Pasquale, 2015</xref>; 
                    <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>). The opacity of many AI systems limits stakeholders&#x2019; ability to understand how decisions are made, raising concerns about bias, fairness, and the concentration of decision-making authority (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). Regulatory frameworks that require explainability, auditability, and oversight of algorithmic systems can help mitigate these risks by ensuring that digital decision-making processes are subject to scrutiny and aligned with broader social and ethical objectives (
                    <xref ref-type="bibr" rid="ref7">Kellogg et al., 2020</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). In industrial contexts, this is particularly important for safeguarding worker rights and ensuring that algorithmic management practices do not undermine autonomy or exacerbate inequality.</p>
            </sec>
            <sec id="sec18">
                <title>6.4 Regulating platform power and market concentration</title>
                <p>Addressing the dominance of digital platforms is another critical dimension of governance, as platform-based business models increasingly shape industrial organisation and market dynamics (
                    <xref ref-type="bibr" rid="ref8">Kenney &amp; Zysman, 2020</xref>; 
                    <xref ref-type="bibr" rid="ref21">Srnicek, 2017</xref>). Competition policy and regulatory interventions must adapt to the realities of digital markets, where network effects, data advantages, and ecosystem control create barriers to entry and reinforce market concentration (
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). Measures such as data portability, interoperability requirements, and antitrust enforcement can help reduce dependency on dominant platforms and promote more competitive and inclusive digital ecosystems (
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>). These interventions are essential for ensuring that smaller firms and new entrants can participate meaningfully in digital markets without being constrained by the power of established actors.</p>
            </sec>
            <sec id="sec19">
                <title>6.5 Redistributing value in digital economies</title>
                <p>Finally, achieving inclusive digital transformation requires addressing the distribution of the economic value generated by digital systems, which is currently skewed toward actors controlling key digital assets (
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>). Policies aimed at redistributing value may include taxing digital activities, supporting innovation among SMEs, and investing in local technological capabilities, particularly in developing economies (
                    <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>). At the global level, there is a need for coordinated efforts to ensure that the benefits of digital transformation are more evenly shared across countries, reducing the risk of digital dependency and reinforcing economic sovereignty (
                    <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref3">Couldry &amp; Mejias, 2019</xref>). Such approaches recognise that equitable outcomes depend not only on participation in digital systems but also on the capacity to capture and retain value within them. These policy and governance considerations underscore the need for a paradigm shift in how digital transformation is approached. Rather than focusing solely on expanding access, policymakers must engage with the deeper structural dimensions of digital power, including who controls data, who governs algorithms, and how value is distributed. By addressing these issues, it becomes possible to move toward a more inclusive and equitable model of digital development, where participation is accompanied by meaningful agency and shared benefits.</p>
            </sec>
        </sec>
        <sec id="sec20">
            <title>6. Digital power and the sustainable development goals</title>
            <p>The concentration of digital power has significant implications for achieving the Sustainable Development Goals (SDGs), particularly in the context of increasingly data-driven industrial systems (
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>). In relation to SDG 9, unequal control over industrial data infrastructures and digital technologies constrains inclusive innovation, limiting the capacity of small and medium-sized enterprises (SMEs) and developing economies to participate meaningfully in digital industrialisation processes (
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>). Evidence suggests that firms and countries with advanced digital capabilities are better positioned to leverage data-driven systems for productivity gains, thereby reinforcing technological and industrial disparities across regions (
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>).</p>
            <p>
Similarly, the realisation of SDG 10 is increasingly challenged by asymmetries in data ownership, algorithmic governance, and value capture, which contribute to widening income and opportunity inequalities both within and between countries (
                <xref ref-type="bibr" rid="ref16">Oxfam, 2026</xref>; 
                <xref ref-type="bibr" rid="ref3">Nick Couldry &amp; Ulises Mejias, 2019</xref>). The dominance of large technology firms and platform-based ecosystems concentrates economic value among actors that control digital infrastructures, while those who generate data, such as workers, consumers, and SMEs, often receive limited returns (
                <xref ref-type="bibr" rid="ref28">Shoshana Zuboff, 2023</xref>; 
                <xref ref-type="bibr" rid="ref8">Martin Kenney &amp; John Zysman, 2020</xref>). These dynamics highlight that achieving the SDGs requires a shift from access-oriented digital policies toward governance frameworks that address the distribution of digital power, including reforms in data ownership, algorithmic accountability, and platform regulation (
                <xref ref-type="bibr" rid="ref14">OECD, 2024</xref>; 
                <xref ref-type="bibr" rid="ref22">UNCTAD, 2024</xref>). Without such interventions, digital transformation risks reinforcing structural inequalities rather than contributing to inclusive and sustainable development outcomes (
                <xref ref-type="bibr" rid="ref26">World Bank, 2025</xref>; 
                <xref ref-type="bibr" rid="ref23">UNIDO, 2023</xref>).</p>
        </sec>
        <sec id="sec21" sec-type="conclusion">
            <title>Conclusion</title>
            <p>This paper has argued that understanding digital transformation through the lens of access alone is no longer sufficient to explain the evolving nature of inequality in data-driven industrial economies. While expanded connectivity has enabled broader participation, it has not ensured equitable outcomes, as power is increasingly concentrated in the control of data, digital infrastructures, and algorithmic systems. The concept of digital power, therefore, provides a more comprehensive framework for analysing how value is created, governed, and distributed within contemporary economic systems. The findings of this study have direct implications for achieving SDG 9 and SDG 10, as unequal control over digital resources constrains inclusive industrialisation and reinforces disparities in economic participation and outcomes. Participation in digital ecosystems often occurs without corresponding influence, resulting in &#x201c;participation without power&#x201d; that perpetuates structural inequalities across firms, labour markets, and global value chains. Achieving more inclusive and sustainable digital futures, therefore, requires a shift toward governance frameworks that prioritise agency, accountability, and equitable distribution of value. Ultimately, progress toward the SDGs will depend not only on expanding digital access but also on ensuring that digital power is more evenly distributed across actors, institutions, and regions.</p>
        </sec>
    </body>
    <back>
        <sec id="sec24" sec-type="data-availability">
            <title>Data availability</title>
            <p>No primary data were generated or analysed in this study. All information used in this paper is derived from publicly available secondary sources, which have been appropriately cited in the reference list.</p>
        </sec>
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