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Logics and Agency in Public Management Research

Tony kinder.

1 Administrative Science, Tampere University, Tampere, Finland

Jari Stenvall

Antti talonen.

2 Faculty of Law, University of Helsinki, Helsinki, Finland

The article analyses the negative effects of the use of logics in public organisation research on active human agency. We build up a new conceptual model with which to approach logics in current research on organising public services; suggesting ways in which current models using logics in public organisation research can be strengthened. Our contribution is two-fold: we argue that Elder-Vass’ approach benefits from close synthesis with social learning theory (including recent thinking on trust, emotions, and distributed learning) and secondly, that grounding all usage of logics in logic-of-practice helps avoid a reification of logics and thirdly that situated learning better suits public organisation problem solving that the application of ‘new’ universal solutions.

Introduction

A set of POR papers analysing COVID-19 responses, take as part of their explanatory tool kit how logics influences people and events. Christensen ( 2021 ) contrasts the logics flowing from March and Olsen’s ( 1983 ) homogeneous leadership, with those based on Selznick’s ( 1957 ) notion of homogeneous groups resulting in a situated logic of negotiation and compromise. Mattei and Vigevano’s ( 2021 ) analysis of the Italian COVID response interrelating national and local bodies, identifies a logic of underlaying policy integration cooperation that enhanced effectiveness. Mozumder ( 2021 ) suggests that the logic of learning from practice was made difficult in the UK because of diminished trust in agents and institutions.

Our paper picks up this issue of logics in how public organisations react to events. This is important as public managers increasingly focus on organising processes rather than organisational imperative: an essential change when most major policy responses now call for partnership, networks and/or ecosystem cooperation and integration. Situated and customised responses to events, taking advantage of available strengths and opportunities are perhaps particularly important, where user/client/customer feedback points to ways, as Normann ( 2002 ) notes, in which services can be improved. Our focus is on the potential conflict between the logics inherent in organisational form or organising in relation to the preferences of users and managers based on situated and social learning. Bourdieu (1984) and Bernstein ( 2000 ) emphasised how conduct of conduct rules (governance) are influenced by ‘soft’ socio-cultural practice, which they in turn reproduce and reshape. Faced with volatile and rapidly altering service environments, street-level decisions (Lipsky, 1980 ) can become patterned into ‘pop-up” governance-as-legitimacy (Laclau 1990).

Research about public organisations increasingly references logics: isomorphic logic (DiMaggio & Powell, 1983 ); public service logic (Ngoye et al., 2019 ; Osborne, 2017 ); management decision logics (Thornton et al., 2015 ) [ framing of public institutions and rules of the game logics (Scott, 2008 ); network management logics (Kooiman, 2003 ); and service-dominant logic (Lusch & Vargo,  2014 , cf. Lopes & Alves, 2020 ). Bourdieu’s (1984) logic-of-practice is intended to ground logics in practice.

Logics is now a prominent idea in research on organisation from post-structural and socio-cultural process perspectives. March and Olsen ( 1989 ) for example, argued that logic of appropriateness has limited scope giving way to logic of consequences , apportioned by a hierarchy of logics. For social scientists seeking to generalise research results positing logics is an opportunity, provided as Kinder (2000) argues, they are recontextualised. Where logics are not re-grounded there is danger of assuming the future is dictated by yesterday, discounting human intervention. Determinism of this sort is a major issue in social research. Few social theorists now aim to ‘discover’ the ‘iron’ laws of society beloved by nineteenth century theorists. Logics properly applied are mediated by cognitive, emotional, (possibly) trusting people and especially so in services for people. As Jacobsen ( 2021 ) shows, in partnerships between the public and private sectors admixtures of potentially competing logics can be hybridised or combined to achieve public value.

Important thinkers criticise using logics from the perspective of diminishing agency include Wittgenstein ( 2001 ), Arendt ( 1951 ) and Chomsky ( 1969 ). Others highlight problems in agents choosing between multiple logics (Berman, 2012 ), or conflicting logics (Lounsbury, 2007 ), or over-reliance on logics to predict solutions (Dewey ( 1938 ), leading Archer ( 2000 ) and Toulmin ( 2003a , 2003b ) for example to call for more research on the use of logics and contingency in social research.

Elder-Vass ( 2010 ) suggests a synthesis between Bourdieu’s ( 1990 ) idea of habitus as structuring thinking with Archer’s ( 2000 ) idea of inner conversations reflecting on choices in context. We find this synthesis inadequate for PM research since it unsatisfactorily addresses the complexity people face in public services and the nature of the learning processes they undertake, implying a clear view of how situated learning occurs and is used. Our research question is: are there negative effects of the use of logics in public organisation research on active human agency?

Our contribution is two-fold: we argue that Elder-Vass’ approach benefits from close synthesis with social learning theory (including recent thinking on trust, emotions, and distributed learning) and secondly, that grounding all usage of logics in logic-of-practice helps avoid a reification of logics. In pursuing these arguments, we build up a new conceptual model with which to approach logics in current research on organising in the public sector; suggesting ways in which frameworks using logics in can be strengthened.

The paper proceeds by exploring and defining the meaning of logics. Illustrating how the use of logics has become important in public organisation theory, one example being Bright’s ( 2021 ) recent use of Klijn et al. ,  ( 2016 ) bureaucratic logic to analyse how organisational identity interrelates with motivation. We argue that logics are only valid when grounded in situated experience from logic-of-practice in a particular service setting. Since contexts and cultures differ, it cannot be assumed that logics applicable in one setting are appropriate to another. We give a short exposition of how active agents in a particular setting learn logics and apply them using Vygotsky’s social learning theory. We then consider how logics are currently deployed in public organisation research taking the example of Vargo and Lusch’s ( 2008 ) service-dominant logic and Klijn et al., ( 2016 ) and Kooiman’s ( 2003 ) network management. We show that in both cases logics are regarded as universals; there is an absence of active human agency in both cases. From this we argue for a review of how logics and active agency and currently used in public organisation research citing logics.

Logics: Genealogy and Use

Thornton and Ocasio ( 1999 :804) define institutional logics as the socially constructed, historical patterns of material practices, assumptions, values, beliefs, and rules by which individuals produce and reproduce their material subsistence, organize time and space, and provide meaning to their social reality . Linking individual cognition to social structures they Thornton and Ocasio ( 2017 ) trace the idea to Selznick ( 1948 ) and later Zucker ( 1983 ), noting that Olson’s ( 1970 ) collective action emphasised individual consciousness and Fleck ( 1979 ) the notion of thought style as micro-social conditioning.

Douglas ( 1987 :63) argued that organisations produce and reproduce sameness by embedding knowledge and when organisations interact, (DiMaggio & Powell, 1983 ) isomorphic logic diffused sameness, even according to Friedland and Alford ( 1991 ) across governances and between Governments. Wary of imitating symbolisms, Jackall ( 1988 ) argues imitating practice is more important; importantly suggesting the embedded agency is more important than imitative structures. Jackall’s framing of enablers and constraints continues to be cited though less is paid to the idea that only logics based on practice evidence have value.

Evidencing logics remains contentious for Toulmin ( 2003a :213) who says, Warm hearts allied with cool heads seek a middle way between the extremes of abstract theory and personal impulse , a wariness of deduced logics shared by Arendt ( 1958 ) who worries about thoughtlessness ( 1958 :62) displacing empirical inquiry. Her emphasis on active agency (discussed below), including socially generated trust, is echoed by Popper ( 2007 ) who distrusts any logic not empirically founded.

Processes creating logics are subject to close scrutiny. For example, Van Benthem and Pacuit’s ( 2010 ) idea of temporal logics captures the point that logic in one time-frame may be non-logical in another. Epstein ( 1995 ) points to people internalising multiple logics and researchers need to justify their choices arguing that splitting and splicing of logics by agents are often unconscious. Kahneman and Tversky ( 1982 ) would agree, noting that slower (rational) selection of logics is post-facto justification of emotional preferences. Janik and Toulmin ( 1996 ) fear that transferring logics between locations is problematic.

Learning and Logics

The idea of logics is widespread in public organisation research in justifying interpretations and actions as the recent POR articiles demonstrate. Dunn and Jones ( 2010 ) argue that following the introduction of new public management (NPM), Doctors adopt a new array of logics, including management heuristics and processes. Suggesting Finnish Doctors are more accepting than their Norwegian colleagues of NPM, Berg et al. ( 2017 ) suggest this due to identity change. Also investigating Doctors and NPM, Berg et al. ( 2017 ) argue that identity change is more profound amongst Norwegian than Finnish Doctors, since the former have less acceptance of NPM logics. It is now common-place for researchers to follow Scott ( 2008 ) and speak of logics in and between organisations or follow Freidson ( 2001 ) and comment on changing logics within professions without citing grounded empirical evidence. Bjerregaard ( 2011 :195) argues that organisational logics are derived from institutional logics, giving a hierarchical authority of logics: these hierarchies of logics he says are somehow learned and accepted as justifying actions.

This short review supports our argument that new research into the use of logics in public organisation research is needed. Bourdieu ( 1990 ) and Zacka ( 2017 ) would support this conclusion; they draw attention to logic-of-practice – active learning by agents of patterned behaviour in contrast to the Habermasian deduction of logics from theory and their generalised usage. Bourdieu uses logic-of-practice to explain stability and change: practice-based habituations and frameworks and metaphors for thinking. Bourdieu’s logic-of-practice grounds logics in situated practice, not to be confused with Gidden’s ( 1984 ) use of the term for whom logic-of-practice results in new social structures.

We conclude that conceptual development in public organisation theory often features the idea of logics: isomorphic logic, public service logic, logics in management decision taking, logics in framing the rules of the game , logic in the management of networks, service-dominant logic. How conceptually robust, grounded and evidentially-situated are these logics in public organisation research? How do different logics relate to one another? We turn now to look further inside logics from the perspective of human agency.

Agent-Centred Social Theory

Problematising logics in public organisation research and highlighting Bourdieu’s point that logics are learned, draws attention to active human agency as learners. Agency too is a contested idea: are cognition and intent essential characteristics, (b) what constitutes collective agency and (c) is agency contingent on context? Our perspective is that agency necessarily involves cognitive intent, and this precludes non-human ‘actants’ from agency, though in the special sense of distributed learning, collectives of people may be said metaphorically to possess agency.

Following Elder-Vass’s ( 2010 ) we agree that Archer’s ( 2003 ) internal conversation shaping agency and Bourdieu’s (1984) idea of habitus influencing agency are reconciled by the idea of emergence from complexity theory. This aligns with Morin’s ( 1959 , 1982 , 1986 ) contribution to active agency in French social theory, which can be underestimated; he spoke of recursive causality , in similar terms to Whitehead’s ( 1929 ) being and becoming : social order both creates and is created by active human agency.

Agentic acting with intent suggests intention-to-act (future) and intention-in-action (current activity). Intention therefore introduces psychological deliberation into agency, often as Elster ( 1979 ) notes from pre-commitment to particular goals citing ends-means coherence. Intention seeks control over future outcomes resulting from present behaviour; it is volition to act as Bratman (1987; 1991) says, based on cognitive reflection and/or beliefs, what Dewey calls reflective intelligence .

Since individuals are continually interpreting and responding to events and the activities of other agents, this catalyses new flows of conduct (Giddens, 1979 :55) that continually emerge making human cognitive agency the micro-foundation of social research. Individual cognition and learning is then central to active agency, this the opposite of structural theory (Parsons, Althusser) which accords agency to non-cognitive structures such as bureaucracy and organisation. Similarly, we reject actor-network approaches (Latour, 1992 ), that attribute agency to non-human actants.

We employ Vygotsky’s ( 1986 ) social learning approach in which learning is social; featuring cognitions, relationality and emotions (especially trust); these mediate learning through the individual’s context and culture. Language, frameworks, concepts, social morès and norms influence learning. As Wertsch et al. ( 1995 :25) says, we can never speak from nowhere . Learning cannot be reduced to bio-deterministic synaptic processing. Bernstein ( 2000 ) blends agent-centred and social learning approaches to explaining social change (see Hasan, 2001 , 2005 . He argues that restricted and elaborated codes of interpretation are the result of interaction between active agents’ identity and inherited cultural meanings. New social constructions, emergences that may constitute logics, can be weak or strong for Elder-Vass, yet always—as Arthur’s (2015) complexity theory suggests—arise from non-linear thinking and active processes with unforeseen results, the result of emotional attachments. Social learning aligns closely with Bourdieu’s logic-of-practice: concrete experiences and their interpretation by cognitive human agents, balance stability (morphostasis) and change (morphogenesis). Sense-making (which includes possible logics) are always provisional and transitory, since social life is always dynamically responding to the actions and ideas of other people and external events. Agentic intent then is ontologically founded on social learning, which is always relational and in arenas of complexity, continuous; context mediates without deterministically shaping learning and intent.

Context and Active Agency

Cognitive human agents have both the capability to act and the capacity for cognition. Capability allows intent to result in social effects; those intended or not (Hvinden et al. 2018 ). Distinctive human agency (Ci 2011) is power, since intentions always subjectively mobilises bias (Schattschneider, 1975a , 1975b ). All agents operate in domains in which this subjective power is recognised as plausible (or not); contexts therefore give content (intention) and modality to agency. As Kiser ( 1999 ) argues this makes agents and their context inseparable. “Logic” becomes an accumulation of behaviour at an individual level from which the individual learns; it is not a superstructural imposition.

Agency is relational, enlivened only in social processes (Burkitt,  2016 ). It is in Whitehead’s ( 1929 ) terms becoming , not being : an emergence that is coproduced (Weber, 2006 ). The interactivity of agents creates intent not something inherent in objects (Dépelteau 2010 ). This perspective closely aligns with relational sociology, which focuses on processual relationships (Emirbayer and Mishe 1998; Daniels, 2001 ). Unlike Vandenberghe (2010), who suggests human agents can operate without prior intent, we follow Gergen (2009 and Archer (2012; 2013) who insist that agency presumes reflexivity, which by iterational morphogenesis explains how agents both produce and reproduce social structures. Social structures are important Elder-Vass ( 2010 ) argues not because they dictate logics, but rather because they mediate learning, which may become logics: social structures cannot act independently of human intent.

Elder-Vass ( 2010 , 2007a , 2007b ) points out that although at first sight (a) Archer’s ( 2003 ) emphasis on reflexivity ( internal conversation ) shaping agency (and creating personal and social identity), and (b) Bourdieu’s (1984) accent on habitus as socially conditioning (generative capacity to produce, reproduce, change), appear irreconcilable, the idea of emergence (from complexity theory) reconciles the two approaches. Agency itself is always emergent, always becoming . Welcoming these ideas, our view is that further steps are needed to explore how the social learning processes occur the give rise to and interpret emergences, leading us towards Vygotskian social learning.

Collective Agency?

What then of collective agency, such as Marx’s ( 1852 ) class for itself or collective unlearning in public organisations (Stenvall et al., 2018 ), or corporate responsibility? Law often ascribes moral responsibility to corporate bodies. However, the agency of collective bodies (state, working-class, companies) is quite different from individual agency, which presumes cognitive ability and as Arendt ( 1969 ) argued, collective actions cannot abrogate the inalienable culpability for their actions. Nor can agency be confused or conflated with finance principal-agency theory, which often ignores context and presumes rational choice (Becker, Williamson). For Perrow ( 1990 :121) this approach is not only wrong but also dangerous . Since agency presumes learners and intent, collective agency such as learning organisations are illusory – organisations cannot learn, since only individual cognisant individuals can think.

Grounded logics are the human/social construction working on nature and in social relationships, the dialectical logics Marx developed in Capital ( 1993 ), applied historically ( 1852 ) and justified philosophically ( 1976 ), grounded in his theory of capitalism ( 1973 ) and which Engels (1859) related to dialectics of nature. Whereas some social theorists (Schatzki 2019 being an example) sayings and doings shape social change, Marx ( 1973 ) that it is not ideals (ideas ungrounded in material practice) that drive social change. Instead, humans being the only animal capable of advanced cognition, including intentionality (design) and purposive labour enhancing the productivity of nature, we use self-consciousness to create social consciousness. Shared with others and becoming collective intentionality and consciousness, social change results. Humans are capable of thought-through collective agency. It is from patterns of agency that new logics are created and in turn, individual cognitions and collective intent make use of previously formulated logics. Logics then are actively constructed, quite unlike abstracted ideas such as Kant’s imperative or Smith invisible hand.

Whereas individual consciousness is the result of cognition and affect (Vygotsky,  1986 ), collective consciousness is embedded from external sources. Blackmore’s ( 2003 ) memes and Durkheim’s ( 1893 ) analysis of religious show this occurring in wider society. In organisations and organising however, Wittgenstein’s follow the rules is replaced by follow the leader , since as Schattschneider ( 1975a , 1975b :71) says, organisations are the mobilisation of bias they necessarily privilege certain outcomes. Collectivities of people in organisations instead of following an ideology, and ideal in Ilyenkov’s ( 2008 ) terms, require a dynamic narrative connecting problem and solution, creating organic solidarity: collective consciousness in organisations is necessarily highly situated and, where leadership is effective, is guided towards understanding the germ-cell or essence of the problem, recognising contradictions in the current state-of-affairs, and takes collective active to achieve what the leader has explained as a preferred solution (Prilleltensky ( 1997 :525). Individuals still must make sense of the leader’s narrative; often this is helped by framing, frameworks, metaphors and language supplied by the leader. Part of the leader’s role in ecosystems is legitimising the other agents in the ecosystem, with whom the organisation’s members must work in order to deliver the preferred solution. For Blunden ( 2015 ) effective project work in organisations relies on leadership building this collective consciousness. Such leaders will combine vision with the leadership, management and administrative competences to which Hartley and Allison ( 2000 ) refer. Creating collective consciousness in a public or other organisation, requires a leader able to make sense of problem–solution in context and culture and to marshal the collective into activity.

One of the more contentious results of what Bernstein ( 2000 ) terms the cultural turn in social theory, is the attribution of agency to culture. As Harvey ( 1982 ) and others make clear, scaling between levels of analysis only makes sense if not employed as a deterministic hierarchy of simplified causalities from the general to the particular.

Socio-Cultural Theory

Learning and deploying new knowledge is then an essential aspect of active agency: learning patterns of activity from logic-of-practice creates new bottom-up governances and learning from user feedback helps personalise the design and delivery of local public services. How then does this occur? Figure  1 is a simplified exposition of how social learning occurs using Vygotsky’s ( 1986 ; 1997) perspective and drawing on the work of Engeström et al., ( 1999 ), Nardi ( 1996 ) and Illeris ( 2004 ). Individual thinking is shaped by logics and in distribution new logics are formulated, in turn influenced by context and culture. Arrows 1, 2 and 3 indicate these interactions constituting the activity centre in the middle of Fig.  1 illustrating how logics influences learning and in turn by learning and patterned practice, new logics are created. A key point is the sense-making by cognitive individuals references their emotional attachments both to old ways-of-working and to a vision of how new services and governances might operate drawing on heuristics (thinking frameworks) evolved from formal education and in practice. As Vygotsky ( 1986 :282) says, [Thought] is not born of other thoughts. Thought has its origins in the motivating sphere of consciousness, a sphere that includes our inclinations and needs, our interests and impulses, and our affect and emotions Figs.  2   3 .

Simplified Vygotskian social learning framework (derived from Illeris, 2004 ; Engeström ( 1996 , 1996 ) and Nardi ( 1996 )

Individual learning (top-left) is distributed during organising service delivery (top-right). All learning occurs in a specific context (bottom-left) meaning ‘hard’ rules and norms such as budgets, regulations, ethical standards and (bottom-right) ‘soft’ cultural norms such as general social culture, occupational culture. While taking account of ‘objective’ facts, the individual learning is non-rational.

Trust is an especially important emotion in individual learning and its distribution for PM since the services target vulnerable people reliant on trust and trust accompanies representatives between sets of professionals. As Weibel and Six ( 2013 ) argue, this willing acceptance of vulnerability between agents in turn presupposes relatedness between agents, competence and autonomy. Trust and control are in once sense opposites and in another complementary. More trust in a service system means less need for formal management structures and oversight accountability. When trouble occurs Six (2005) notes, trust is either dissipated or strengthened.

Social learning then is relational and offers a way of improving Elder-Vass’s position by explaining how agent learning reframes and reformulates logics in situated theorisations of public service dynamics.

Logics in Public Service Organisation Research

Taking two oft-cited uses of logics in public organisation research as examples, here we consider how far they are grounded in learning by active agents.

We have chosen (a) Vargo and Lusch’s ( 2004 , 2007 , 2008 and 2017 ) service-dominant logic and (b) Kooiman ( 2003 ); and Klijn and Koppenjan’s ( 2014 ) management of networks. We choose Vargo and Lusch and Kooiman and Klijn because they will be familiar to readers, and each apply logics at the level of organisations delivering services. The choices are for illustrative purposes; we are not in any way suggesting that these pieces of research are other than valuable. In each case, we present a table of how logics are referenced in these two bodies of research relative to social learning by active agent in logic-of-practice.

Service-Dominant Logic and Logic Evaluation

Vargo and Lusch ( 2004 , 2007 , 2008 , 2017 ) offer an integrated marketing perspective on services, arguing that value (in use) is co-created by service providers and users; focusing on private services little mention is made of public services. They argue that a goods-dominant logic (GDL, featuring tangible goods, a supply-side mindset and objective success criteria) is being superseded by a SDL, which concentrates on intangible services, a customer-focused mindset and subjective success criteria: goods are a distribution mechanism for services. Our focus here is on the use Vargo and Lusch make of logics and the extent to which the logics are grounded in practice and agent learning.

Using Lusch and Vargo ( 2014 ), a 224-page exposition of SDL, Table ​ Table1 1 summarises the stance taken in relations to factors constituting grounded logics. In this welcome exhortation to adopt customer-focused activity, non-exchange (public and some 3S) people and public organisations are seldom mentioned. New service dominant logics arise in abstract exchange relationships. Few individual service providers or people are mentioned; the logics are derived from market exchange. SDL is presented as a new paradigm evolving from firms’ exchange activity, without any reference to the people constituting the firms. For example, new knowledge arises from exchange, without mention of learning, cognition, affectations. The SDL world is populated by firms exchanging services with other firms and customers, none of which reference in any detailed way the context and culture in which the exchanges occur. SDL is a switch from GDL the processes of which do not feature human agentic involvement.

Logics in service-dominant logic (page references from Lusch and Vargo 2014 )

Our point is this: while Lusch and Vargo refer often to being actor-centric, there are few people in their exposition of logics and almost no public sector. The logics arise from market exchange between economic units (firms) within ecosystems and institutions envisaged from a market exchange perspective. There is no human thinking, feeling, or (human) relations agency. Agency is ascribed to inanimate entities: abstracted customers and firms. Although the services ecosystems self-adjust, they appear to do so without human decisions, responses, creativities. The only practice referenced are those of resource integration and market exchange; not real people, using real services. No competitive or conflicting logics exist, apart from GDL. This new SDL paradigm exists without reference to particular and situated contexts and cultures. SDL is a metaphysic, a belief system not grounded in practice or reality, not subject to verification or disproof. The logic is without reference to human intervention and can only be categorised as deterministic.

Network of Management Governances and Logic Evaluation

Kooiman’s 249-page exposition ( 2003 ) and later work including Klijn ( 2008 ) and Klijn and Koppenjan ( 2014 ) presents a logic for governance analysis based on management of networks, used in a 427-page study of fisheries governance ( 2005 ). Kooiman and Bavink, ( 2013 ) sets out to explain how interdependency and relationships between agencies can best operate as society becomes more complex, dynamic and diverse. Table ​ Table2 2 summarises the stance taken by network management theorists towards grounding logics in practice and learning.

Logics network management of governances

taken from Kooiman ( 2003 ); Klijn ( 2008 ) and Klijn and Koppenjan ( 2014 )

For Kooiman networks are the preferred structure to solve problems and a Central-Controller led governance manages the network. Central Controllers, usually Government, bring resources to problem-solving; networks are characterised by rational-cognitive agency and reference formal knowledge in decision taking. NPM efficiency is a desirable goal of the networks. Networks form governance hierarchies with first-order governance (day-to-day), institutional arrangements (second order) and (third order) meta-governance (similar to Habermas’ communicative rationality. Network governance is appropriate to policy networking, inter-organisational delivery of service and policy implementation: it is not confined to second-order policy making. The logic in network management then is compliance with the preferences of the Central Controller; often the targets and processes associated with new public management.

To assess the implications of Table ​ Table2 2 in steps. Kooiman and his colleagues argue that governances are best analysed and constructed as networks, that networks are best centrally directed, network participants ought to act and think rationally, the possessing resources (power) gives legitimacy to network central directors and that these logics apply whatever the context and culture.

Network management as a logic is not grounded in active agency; it is not derived from lessons learned by cognitive-emotional humans. The approach appears to be a quirky ideal type justifying the adopting of new public management and legitimating the power of central Government in public service design and delivery.

Discussion and Conclusions

Tables ​ Tables1 1 and ​ and2 2 illustrate little evidence of learning by active agents and little evidence of grounding their prescribed logics in practice. Why is this absence of social learning in SDL and the Rotterdam group’s network management approach important? The answer is that social research bereft of cognisant people is questionable: the social constructions resulting from such research are from inside the mind of the researchers not logic-of-practice.

It is of course possible to argue that logics are an implicit or sub-conscious aspect of agent behaviour. None of the theorists mentioned take this position. Assuming an unverifiable set of beliefs would open new methodological cans of worms. Does this invalidate the approaches suggested by SDL and network management? Not necessarily, however, it makes it important to know the epistemic stance of the researchers.

Both Lusch and Vargo and Kooiman and his colleagues are suggesting that there are alternative mindsets to (for example) market transactionality (price) or hierarchy (power). Both sets of researchers are proposing new principles to guide thinking and action. Our central point is that each offer a play without actors, a world without people; principles decided deductively not grounded, ways of operating where the key units of analysis are not cognitive-emotional persons, but instead firms/exchanges (SDL) or organisations in networks.

Both SDL and network management are logics oft cited in public organisation research. Llewelyn ( 2003 ) argued that there are five types of theorising available to qualitative researchers: (a) metaphors; (b) differentiation; (c) conceptualisation; (d) context-bound theorising of settings and (e) context-free ‘grand’ theorizing. Both SDL and network management are context-free theorising: meta-narratives, in each case produced from deep conceptual reflection on enduring social relationships and causalities of how structures and agency interact. The fact that such theorisations are not derived from empirically substantiated agent learning that grounds logics in practice, does not in itself deprive them of usefulness.

Nonetheless, deduced theorisation should be acknowledged, and the assumptions laid bare; so that when applied to particular types of human agency or situated contexts and cultures, it is clear what evidence from structures and agency are appropriate for researchers using these approaches to seek. If there are contexts and cultures to which SDL and network management do not apply, then this too should be acknowledged to avoid using the approaches to inappropriately frame research problems and/or embed assumptions in empirical work unknowingly. This is especially important for public organisation research which is international in nature, transgressive of contexts and cultures and needs to know if conceptual tools are proposed as universally applicable or if of limited generalisability, the nature of the limitations. Our own view is that the logics in SDL is a metaphysic and network management epistemologically flawed, given its assumptions of rationality and privileging of power. Neither approach is universally applicable—contexts and cultures vary considerably.

General theories are stronger on explanation (attributed causality, i.e. why) and weaker on understanding (i.e. what—identifying the existence of outcomes predicted by the theory); since social research phenomena are constructions. This can lead to confusing the map with the terrain i.e. finding what one sets out looking for, making theory falsification impossible. This of course is the great advantage of grounding social research by investigating agent interpretations and actions: instead of beginning with a theory and seeking evidence of its usefulness. As Glaser and Strauss ( 1967 :2) propose grounded theory discovers theory from systematic data. Groundedness itself being a metaphor for linking to a feedback loop, such as ethnographic studies or qualitative interviews producing case studies. For Archer ( 2000 ) grasping both agency and structures is essential to explaining social activity: neither SDL nor network management do this.

We conclude that some researchers continue to find Lusch and Vargo’s SDL and Kooiman’s network management logics, or theories, useful. Their use however should be constrained by a clear understanding of how the key concepts relate to the particular context and culture being studied. Further, researchers should note that in focus on logics comes at the cost of not focusing on human agency – a controversial choice in public management research. As Fig.  1 illustrates use of logics is not passive, from using logics new logics emerge addressing contradictions and conflicts in the previous patterns of logics, as they apply to current problems.

For public organisation managers the clear implication of this research is to avoid off-the-shelf, transformative, paradigm-switching new tools. There is no alternative but deeply investigating problems in situation, digging to identify the germ-cell essence of any problem and to propose solutions accordingly. Public manager should always to wary of universal tools or solutions and instead be unafraid to conclude that their organisation, their problem requires tools of analysis and solutions uniquely suiting their organisation and its capabilities. Our contribution revolves around viewing logics not as passive ‘ideas’ isolated from practice, but instead as ‘actively’ helping to socially construct what current practice is and thereby create new logics. In short, logics occupy a dialectical place in learning and problem solving and are not static nor fixed.

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Contributor Information

Tony Kinder, Email: [email protected] .

Jari Stenvall, Email: [email protected] .

Antti Talonen, Email: [email protected] .

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This paper is in the following e-collection/theme issue:

Published on 16.4.2024 in Vol 8 (2024)

Experiences, Lessons, and Challenges With Adapting REDCap for COVID-19 Laboratory Data Management in a Resource-Limited Country: Descriptive Study

Authors of this article:

Original Paper

• Kagiso Ndlovu 1 , BSc, MSc, PhD   ; 
• Kabelo Leonard Mauco 2 , BSc, MSc, PhD   ; 
• Onalenna Makhura 1 , BSc, MSc, PhD   ; 
• Robin Hu 3 , BA   ; 
• Nkwebi Peace Motlogelwa 1 , BSc, MSc   ; 
• Audrey Masizana 1 , BSc, MSc, PhD   ; 
• Emily Lo 3 , BA   ; 
• Thongbotho Mphoyakgosi 4 , BSc   ; 
• Sikhulile Moyo 5 , BSc, MSc, MPH, PhD  

1 Department of Computer Science, University of Botswana, Gaborone, Botswana

2 Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden

3 College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States

4 National Health Laboratory, Ministry of Health, Gaborone, Botswana

5 Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana

Corresponding Author:

Kagiso Ndlovu, BSc, MSc, PhD

Department of Computer Science

University of Botswana

Private Bag UB 0022

Gaborone, 00267

Phone: 267 71786953

Email: [email protected]

Background: The COVID-19 pandemic brought challenges requiring timely health data sharing to inform accurate decision-making at national levels. In Botswana, we adapted and integrated the Research Electronic Data Capture (REDCap) and the District Health Information System version 2 (DHIS2) platforms to support timely collection and reporting of COVID-19 cases. We focused on establishing an effective COVID-19 data flow at the national public health laboratory, being guided by the needs of health care professionals at the National Health Laboratory (NHL). This integration contributed to automated centralized reporting of COVID-19 results at the Ministry of Health (MOH).

Objective: This paper reports the experiences, challenges, and lessons learned while designing, adapting, and implementing the REDCap and DHIS2 platforms to support COVID-19 data management at the NHL in Botswana.

Methods: A participatory design approach was adopted to guide the design, customization, and implementation of the REDCap platform in support of COVID-19 data management at the NHL. Study participants included 29 NHL and 4 MOH personnel, and the study was conducted from March 2, 2020, to June 30, 2020. Participants’ requirements for an ideal COVID-19 data management system were established. NVivo 11 software supported thematic analysis of the challenges and resolutions identified during this study. These were categorized according to the 4 themes of infrastructure, capacity development, platform constraints, and interoperability.

Results: Overall, REDCap supported the majority of perceived technical and nontechnical requirements for an ideal COVID-19 data management system at the NHL. Although some implementation challenges were identified, each had mitigation strategies such as procurement of mobile Internet routers, engagement of senior management to resolve conflicting policies, continuous REDCap training, and the development of a third-party web application to enhance REDCap’s capabilities. Lessons learned informed next steps and further refinement of the REDCap platform.

Conclusions: Implementation of REDCap at the NHL to streamline COVID-19 data collection and integration with the DHIS2 platform was feasible despite the urgency of implementation during the pandemic. By implementing the REDCap platform at the NHL, we demonstrated the possibility of achieving a centralized reporting system of COVID-19 cases, hence enabling timely and informed decision-making at a national level. Challenges faced presented lessons learned to inform sustainable implementation of digital health innovations in Botswana and similar resource-limited countries.

Introduction

The onset of the COVID-19 pandemic resulted in a global public health crisis [ 1 ]. The pandemic stretched almost all health care systems to their limits and exposed their weaknesses [ 2 ]. Previously documented COVID-19 challenges for the health sector include a lack of the health care services needed for the pandemic, inadequate resources, limited testing ability and capacity for a COVID-19 response, as well as overall poor data management within existing health care systems [ 1 ]. It was previously projected that countries in sub-Saharan Africa could see a sharp rise in COVID-19 infection rates and deaths, as such challenges are prominent in resource-limited countries [ 3 ]. This projected disproportionate impact of COVID-19 in resource-limited countries comes as no surprise. Currently, the health sector in high-income countries is deemed as underfunded, while in most resource-limited countries, it is reportedly heavily underfunded [ 4 ]. It is not a surprise that, in 2001, African leaders through the African Union’s Abuja Declaration agreed to “allocate 15% of the state’s annual budget to the improvement of the health sector” [ 5 ]. However, in 2013, only 5 African countries had achieved this target, while in 2018, only 2 countries achieved the target [ 6 ].

Access to accurate and current information has been recognized globally as a critical requirement for timely COVID-19 pandemic responses [ 7 ]. As such, the pandemic presented the need for robust data management systems in response to the evolving nature of COVID-19 [ 8 ]. Ideally, eHealth—“cost-effective and secure use of information and communications technologies (ICT) in support of health and health-related fields, including health-care services, health surveillance, health literature, and health education, knowledge and research” [ 9 ]—could ensure reliable information reporting and timely decision-making by governments and relevant stakeholders. However, requirements for such eHealth solutions present a greater challenge for resource-limited countries with documented weak ICT infrastructure, limited maintenance budgets, a lack of health human resource capacity to utilize eHealth systems, and nonuniform unique patient identifiers [ 10 ]. According to Archer et al [ 11 ], other factors affecting successful implementation of eHealth solutions in resource-limited countries include the absence of eHealth agendas, ethical and legal considerations, common system interoperability standards, and reliable power supplies.

Similar to other nations responding to the pandemic, the government of Botswana, through the Ministry of Health (MOH), identified eHealth as a means to improve COVID-19 data management and address complex data capture and transfer processes at various port of entries and the National Health Laboratory (NHL). Botswana’s eHealth infrastructure was previously reported as generally adequate, with functioning computers and some communication systems such as telephones, email, and Internet services [ 12 ]. According to Seitio-Kgokgwe et al [ 12 ], almost all public health facilities in Botswana are now connected to the government data network (GDN) with an average Internet connectivity of about 2 Mbps. The authors further highlighted that Botswana's eHealth initiatives have always operated within a very weak policy and regulatory framework characterized by inadequate health information legislation, national policy, and strategic plan. Fragmentation and inefficient eHealth initiatives were noted as other contributing factors to poor utilization of information in Botswana's health sector, as well as the lack of appreciation of the important role played by health information in managing health services. Currently, over 52 health laboratories operate in Botswana, 8 of which are accredited with national certifications like the South African National Accreditation System (SANAS), Clinical Laboratory Improvement Amendments (CLIA), International Organization for Standardization (ISO) 15189, or the Southern African Development Community Accreditation Services (SADCAS) [ 13 , 14 ].

The Botswana NHL is one of the accredited laboratories tasked with the management of national COVID-19 testing. The NHL has decentralized its COVID-19 testing services from the capital city Gaborone, by operating satellite testing centers in selected districts with sizable populations and key ports of entry into the country. The NHL data flow is such that specimen data go through each of the 4 laboratory stages of (1) Reception Lab (all incoming lab specimens are captured using a barcode scanner), (2) Extraction Lab (specimen processing for nucleic acid isolation and purification), (3) Detection Lab (sample amplification and detection), and (4) Resulting and Verification Lab (lab results are captured and verified for release to clients). The need to scan laboratory specimen samples at each of the laboratory phases is an important quality assurance step. Accession numbers in laboratory processes are critical to link the specimen with a participant. Despite having an already existing eHealth system at the NHL, electronic data transfers within and across the 4 laboratories were considered tedious, time consuming, and a risk to both data quality and timely COVID-19 results reporting. These limitations required immediate attention.

The authors volunteered their technical support toward implementation of a customizable COVID-19 data management system—Research Electronic Data Capture (REDCap)—at the NHL. REDCap was suggested by the authors following its documented benefits including its utility within a resource-limited country context [ 15 , 16 ], as well as its availability locally through the University of Botswana (UB). REDCap is a secure, web-based platform designed to support electronic data capture. It was developed at Vanderbilt University in the United States in 2004 and can be set up to support a variety of health care environments and scenarios. REDCap is compliant with international standards such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR).

In order to support timely reporting of COVID-19 cases, the REDCap platform was linked to the District Health Information System version 2 (DHIS2) at the MOH. DHIS2 is another open-source platform for collecting, processing, and analyzing health data. It was developed and first implemented in 1998 by the Health Information System Programme (HISP) in South Africa and offers a secure web-based electronic data capture, analysis, and reporting tool [ 17 ]. Similar to REDCap, the DHIS2 platform has a mobile application (DHIS Tracker), enabling its use in case of weak and fluctuating Internet connectivity. The DHIS2 Tracker allows for auto-syncing of data with the central server once stable Internet connectivity returns. The data elements shared between DHIS2 and REDCap include COVID-19 test results and demographic information of those who were tested including their names, gender, ages, locations, and related underlying medical conditions.

Through a participatory design approach [ 18 , 19 ], the REDCap platform was customized to support the NHL data process needs, with the goal of increasing operational efficiency and reducing overreliance on paper-based manual approaches. The authors’ preference for a participatory design approach aligns with that of Berntsen [ 20 ] who acknowledged that initial involvement of end users is central to the development of a health information system. This study reports on the experiences, challenges, and lessons learned while designing, adapting, and implementing REDCap and the DHIS2 platform to support COVID-19 data management processes at the NHL in Botswana.

A participatory design approach was adopted to guide the design, customization, and implementation of REDCap for COVID-19 data management at the NHL.

Study Population, Setting, and Design

Participants.

All NHL and MOH personnel responsible for processing COVID-19 specimen samples and data were invited to participate. All potential participants were sent an introductory email describing the background and objectives of the study. A consent form was subsequently shared with all those who showed interest. All invited participants agreed to participate in the study, of which 29 were NHL personnel and 4 were based at the MOH. Of the 29 NHL personnel, 12 were based at the Reception Lab, and 17 were based at the Extraction, Detection, and Resulting and Verification Labs. The study spanned from March 2, 2020, to June 30, 2020.

Data collection was conducted in 3 phases.

Phase 1: Participant Engagement

The authors facilitated a 1-day consultative physical meeting or workshop with the study participants to solicit specific requirements for the COVID-19 data management system. Study participants were requested to define requirements for an ideal COVID-19 data management system. The authors recorded all participants' responses during the session which lasted for 1 hour. Based on the insights gathered from this exercise, iterative design and testing approaches were adopted for each key deliverable from design, customization, and implementation of the REDCap platform to support COVID-19 data management. A minimum of 2 iterations and a maximum of 4 iterations were incurred per deliverable, and this was influenced by the complexity or noncomplexity of the tasks.

Phase 2: Implementation and Assessment of the Platform to Meet User Requirements

Design and customization of the REDCap platform were followed by implementation of the solution as well as evaluating its feasibility to address the previously noted requirements and specifications. This involved participants testing the REDCap platform and providing feedback on any issues or challenges they encountered.

Phase 3: Human Resource Capacity Development

At each phase of the design, customization, and implementation of the REDCap platform, participants were trained on the various system components and had the opportunity to share their feedback to inform next steps.

Ethical Considerations

This study was approved by the Ethics Committee of the University of Botswana (Reference: UBR/RES/IRB/BIO/GRAD/244). All data experiments were performed in accordance with relevant guidelines and regulations such as the Declaration of Helsinki. All participants were informed of the objective of the exercise as well as their voluntary participation, and all gave informed consent for this study. Those who consented to participate were immediately sensitized and granted access to the REDCap instance at the UB. No compensation was provided for participating in the study.

Data Analysis

NVivo 11 software was used for thematic analysis [ 21 ] of data to determine participants’ requirements for an ideal COVID-19 data management system and their postimplementation experiences.

Study participants consisted of all personnel involved with handling COVID-19 specimens and managing all relevant laboratory data at both the NHL and MOH ( Table 1 ).

Consultative meetings with study participants led to the identification of user requirements for an ideal COVID-19 data management system at the NHL. These were categorized under the following 2 themes: functional and nonfunctional requirements ( Table 2 ).

a REDCap: Research Electronic Data Capture.

b DHIS2: District Health Information System version 2.

Although this paper reports on challenges and resolutions while implementing REDCap at the NHL ( Table 3 ), Figures 1 - 7 are intended to illustrate the design, customization, and implementation of the REDCap platform at the NHL.

The authors used participants’ feedback to guide the design of an ideal COVID-19 data flow architecture for this study ( Figure 1 ).

The REDCap data security architecture at UB separates the web server from the database server ( Figure 2 ).

In order to facilitate timely decision-making and address a key technical requirement (“Automated COVID-19 results sync with the DHIS2 system” in Table 2 ), the REDCap system was linked with the DHIS2 system at the MOH for aggregate data reporting.

Interfacing between DHIS2 and REDCap was controlled by the DHIS2 link application programming interface (API; Figure 3 ). The DHIS2 API link supported 3 functionalities: (1) data format conversions between DHIS2 and REDCap, (2) avoiding duplicate synchronizing of data between REDCap and DHIS2, and (3) enabling data access for the DHIS2 front end, which relied on the API to make consistent data available for the 2 systems.

In order to access the DHIS2 API, a RestTemplate object short code was implemented with basic authentication secured by username and password ( Figure 4 ).

Study participants accessed REDCap using their personal account details (unique usernames and passwords) before proceeding to collect, manage, and report on COVID-19 at the MOH ( Figure 5 ).

Of important note, the REDCap platform triggered automated email alerts whenever a COVID-19 positive result was recorded. The email alerts were sent to study coordinators at the MOH.

A third-party web application simplified the process of pulling records from the DHIS2 system into REDCap ( Figure 6 ) and enabled the viewing of specimen barcodes through a web browser ( Figure 7 ).

Challenges and resolutions while implementing the REDCap platform to support COVID-19 data management at the NHL are summarized in Table 3 .

This paper describes the experiences, challenges, and lessons learned while designing, customizing, and implementing the REDCap platform for COVID-19 data management at the NHL in Botswana. REDCap implementation challenges and lessons identified during the study were categorized under the following 4 themes: (1) infrastructure, (2) capacity development, (3) platform constraints, and (4) interoperability.

Infrastructure

The literature emphasizes the critical need for robust ICT infrastructure to serve as the backbone for successful and sustainable digital health implementation [ 22 - 25 ]. The national eHealth strategy tool kit by the World Health Organization (WHO) and International Telecommunication Union (ITU) identifies essential eHealth infrastructure components as high-speed data connectivity, computing infrastructure, identification and authentication services, directory services, health care provider systems, electronic health record repositories, and health information data sets, all of which underpin a national eHealth environment [ 26 ]. The tool kit further urges countries to secure long-term funding for investment in national eHealth infrastructure and services. It is against this backdrop that the WHO recognizes a pressing need for countries to invest in infrastructure to support digital transformation; Internet connectivity; and issues related to legacy infrastructure, technology ownership, privacy, and security while adapting and implementing globally recognized standards and technologies [ 27 ].

In this study, infrastructural challenges experienced include weak Internet speed and misalignment of computer network firewalls between the GDN and the UB network. These issues resonate with the identified ICT infrastructural challenges within the Botswana National eHealth strategy [ 28 ]. During implementation of the REDCap platform at the NHL, initial Internet download and upload speeds were 12.59 Mbps and 16.84 Mbps, respectively ( Table 3 ). The Federal Communications Commission (FCC) considers minimum Internet speeds of 5 Mbps to 25 Mbps as ideal to support online tasks such as file download and telecommuting [ 29 ]. However, despite meeting the FCC requirement, study participants reported the need for the Internet speed to be upgraded to adequately support frequent data transactions and data sharing between REDCap and the DHIS2 platform. This could be due to multiple factors. The following are some example quotes from study participants pertaining to their dissatisfaction with the Internet bandwidth:

Internet connectivity is still slow. [participant, NHL Reception Lab]

Internet bandwidth needs to be upgraded. [participant, NHL Resulting and Verification Lab]

As a mitigation for the slow Internet connectivity, relevant government departments were engaged, and measures were put in place to increase the Internet speed. This includes procurement of mobile Internet routers from private internet service providers (ISPs) for use during the study. The engagement of private sector stakeholders to support implementation of eHealth initiatives is also highlighted as essential within the “Strategy and Investment” pillar of the Botswana National eHealth Strategy, which emphasizes “eHealth planning, with involvement of major stakeholders and sectors” [ 28 ].

Another challenge was the restriction introduced by computer network firewalls resulting in data flow constraints between the GDN and UB networks. A similar issue was encountered in another study, in which implementation of the REDCap platform incurred network firewall challenges hindering participants from using computers outside the Veterans Health Administration network to complete a survey on REDCap [ 30 ]. According to Nagpure et al [ 31 ], firewall policy conflicts can be complex to eliminate but could be addressed through practical resolution methods such as the “first-match resolution” involving “identifying which firewall policy rule involved in a conflict situation should take precedence when multiple conflicting rules (with different actions) filter a particular network packet simultaneously.” At the NHL, the authors resolved the network firewall challenges by engaging relevant ICT authorities at both governmental and UB IT departments to facilitate reconfiguration of the firewall settings to allow data flow between the 2 networks. This necessitated effective measures for data security, privacy, and confidentiality throughout the study. To achieve this, the authors ensured that the REDcap and DHIS2 servers communicate through an encrypted Internet connection using a 128-bit Secure Sockets Layer (SSL) certificate. The use of SSL has been previously considered a standard technology for securing electronic commerce and electronic banking transactions over the Internet [ 32 ]. However, recent advances in cybersecurity attacks call for using technologies to detect compromised SSL network traffic [ 33 ]. This consideration was brought to the attention of both governmental and UB IT departments. Compliance with the Botswana-specific Data Protection Act [ 34 ] could improve the necessary safeguards for the right to privacy of individuals and the collection and transfer of their personal data.

Capacity Development

Some study participants lacked the requisite skills to effectively use the REDCap platform, which was another challenge encountered during the study. The lack of competent and experienced REDCap users in some low and middle-income countries (LMICs) has been linked to poor “REDCap penetration” in those countries [ 35 ]. For example, in Botswana, only 3 institutions have REDCap instances [ 36 ], and unless affiliated with any of these institutions, most health care workers are unfamiliar with the platform. Low ICT literacy is common among health care workers in most LMICs [ 25 ] and could affect the ability of some participants to competently use the REDCap platform. Recognizing this challenge, the Botswana National eHealth strategy included “Workforce Development” as one of its pillars dedicated to eHealth capacity building among health care workers [ 28 ].

A reported benefit of having a health workforce trained in ICT prior to engaging with a health information system is minimizing avoidable errors, which, in this case, may be influenced by a lack of familiarity with the REDCap platform or poor ICT competency. To ensure the effective use of REDCap at the NHL, the authors continuously trained participants at all stages in the study. The ever-evolving nature of the REDCap platform [ 16 , 36 ] also necessitates that those working with the system must continuously familiarize themselves with its core functionalities and data workflows through support from the REDCap Consortium. Almost all training needs for this study were identified during weekly virtual progress report meetings with all study participants. Training content was also informed by the identified system requirements for the REDCap platform ( Table 2 ). According to Nsaghurwe et al [ 37 ], “functional requirements describe what the system should do—such as its ability to exchange client-level data in a single repository, search for records with data quality issues; while non-functional requirements describe how the system should perform,” including system performance, reliability, and user-friendliness.

In essence, a central aspect to the successful implementation of any health information management system is the need to train users on how to appropriately utilize the system to capture and manage data [ 25 ]. Recommended approaches from the existing literature include ensuring continuous engagement and availing training materials for trainees' access and reference at a later stage. In this study, the authors made all training material and manuals available via the REDCap “File Repository” feature for the participants to access at their convenience posttraining.

Informed by the level of participants’ familiarity with the REDCap platform, other measures were put in place such as denying participants “data deletion” privileges on the REDCap system. Instead, participants could add, edit, and view data they required to complete their respective tasks. This minimized accidental data deletion and resonated with the famous “principle of least privilege” by Saltzer and Schroeder [ 38 ], which states that “every program and every user of the system should operate using the least set of privileges necessary to complete the job.”

Platform Constraints

An important lesson learned was that, although REDCap is often regarded as a complete solution for data management [ 16 ], in some instances, support from other software applications could augment its limitations. In this study, the REDCap platform was not able to generate barcodes for the NHL personnel to scan to retrieve previously captured information. This limitation was resolved by developing a web-based application supported by the Angular framework [ 39 ] for quick production of scannable barcodes required during the Results and Verification Lab stage at the NHL. Some documented benefits of the Angular framework include support for lightweight web applications; faster software development capabilities; and easily readable, testable, and interoperable software solutions [ 39 ]. For this study, the authors leveraged the ability to easily link the Angular web application with the REDCap platform. Moreover, only one username and password combination was used to access the data capture forms between the Angular-based web application and REDCap.

Another constraint encountered was the inability of the REDCap platform to automatically store COVID-19 specimen barcodes on the data collection form. Further, the REDCap system requires that the barcode scanner accept a “tab key” to move to the next field, but the scanner configuration used at the NHL accepted a “carriage return key” instead to move to the next field. This meant that each time a barcode was scanned by the study participants, REDCap would not capture it into the appropriate field unless the user pressed the “tab key” on the keyboard to trigger a move to the next field. This became tedious to participants considering the high number of barcodes requiring scanning at the NHL during the COVID-19 pandemic. To resolve this, the authors reconfigured the barcode scanners to align with REDCap requirements, that is, use of the “tab key” to capture barcodes versus the initial configuration of the “carriage return key.” Moreover, the MOH procured dedicated barcode scanners for use with the REDCap platform as the previous ones accompanied a different system at the NHL. Overall, REDCap ensured auto-saving of the barcodes as they are being scanned, minimizing data loss in case of network or power failure.

Interoperability

The Healthcare Information and Management Systems Society (HIMSS) defines interoperability as “the ability of different information systems, devices, and applications (‘systems’) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally” [ 40 ]. Some documented benefits of interoperable health care information systems include improved patient management, quality of care, and decision-making, as well as reduced health care costs [ 41 ]. However, several challenges have been noted that hinder the interoperability of health care systems, affecting their successful and sustainable implementation, especially in resource-limited countries [ 10 ].

Despite the documented interoperability challenges, REDCap has been successfully linked to other systems previously. For example, a study in the United Kingdom automated the process by which COVID-19 clinical trial registration records were exported from the WHO International Clinical Trials Registry Platform into external software [ 42 ]. A linking script subsequently pulled relevant data, aligned it with the data dictionary in REDCap, and directly imported it, removing any duplicates in the process. Other researchers utilized REDCap as a data harmonizer, managing and combining cancer research data from multiple registries and allowing for the reconciliation of disparate data sets as well as their conversion [ 43 , 44 ]. In another previous study, REDCap served as the platform housing all global data collected with modified survey questions to capture nuances and allowed for individualization to study public health interventions for COVID-19 [ 45 ]. Consequently, these applications reinforced REDCap’s utility for cross-continental collaborations, extending beyond any singular institution or setting.

In this study, although linking the REDCap platform with the DHIS2 platform was achieved, the failure of the REDCap system to automatically pull data from the DHIS2 platform was noteworthy. This limitation was a result of constraints within the Dynamic Data Pull (DDP) feature in REDCap. The REDCap DDP is a special feature for importing data into REDCap from an external source system [ 46 ]. The DDP feature provides an adjudication process whereby REDCap users can approve all incoming data from the source system before the data are officially saved in their REDCap project. Because the REDCap DDP assumes that all incoming data from the source system may not be trusted as valid or that only a subset of the data coming from the source system needs to be imported, it utilizes an adjudication web page inside REDCap’s interface to allow manual review of the data obtained from the source system before confirming that it be imported into the REDCap repository [ 46 ]. It is precisely the adjudication process that hindered the automatic data import from the DHIS2 system into REDCap. To address this challenge, a custom API was developed by the authors to support accessing health records from the DHIS2 platform, staging them for quality assurance as a batch and automatically loading them into the REDCap system. Further, this approach helped minimize network traffic whenever data synchronization between REDCap and the DHIS2 systems occurred.

Limitations

The REDCap system was implemented at the NHL in response to the emergency COVID-19 pandemic. Consequently, a systematic approach to its implementation could have been compromised, and essential considerations could have been overlooked. Most notably, multiple training and feedback sessions had to be conducted virtually using the Zoom platform due to restrictions on in-person gatherings. Hence, the authors were unable to frequently visit the NHL in person to provide the necessary technical support and training. Participation in the study was limited and often disrupted, as some NHL personnel were reassigned to efforts such as vaccine distribution or other scenarios not applicable for REDCap use. Last, increased workloads for participants due to COVID-19 surges in Botswana likely contributed to the lack of thorough quality checks when interacting with the system.

Implementation of the REDCap platform to support COVID-19 data management at the NHL in Botswana was successful, albeit with challenges. It is worth noting that, like any software, REDCap as a system possesses limitations that were addressed with other applications to meet requirements for this study. Most challenges encountered were exacerbated by the lack of pandemic response preparedness, as was the case in Botswana and around the world. As such, most of these challenges will not be unique to this specific case of REDCap implementation but will continue to affect the sustainable implementation of eHealth innovations until conscious efforts using key digital health strategies are made that create an enabling environment to support implementation of digital health innovations. Another lesson learned is the essential need for collaborations with key stakeholders to minimize technological barriers while implementing eHealth solutions. Despite the challenges encountered, the REDCap and DHIS2 platforms served as readily available and customizable platforms to address COVID-19 data management at the NHL. To this end, effective planning is essential, including the engagement and training of key personnel to optimize the use of eHealth systems beyond the COVID-19 pandemic.

Acknowledgments

The authors express their great appreciation to the National Health Laboratory (NHL) and Ministry of Health (MOH) management for supporting the implementation of Research Electronic Data Capture (REDCap) at their facilities as well as the supportive study participants for their valued contributions. No generative artificial intelligence (AI) such as ChatGPT was used in any portion of this manuscript.

Data Availability

The datasets used and/or analyzed during the study are available from the corresponding author on reasonable request.

Authors' Contributions

All authors jointly conceived the study, and jointly contributed to its design. KN, OM, AM, and NPM contributed to the customization of the Research Electronic Data Capture (REDCap) platform for use at the National Health Laboratory (NHL) and provided REDCap training sessions to the study participants. KN, KLM, and OM completed the initial data analysis and wrote the first draft of the manuscript. SM, TM, RH, and EL provided substantial editorial and intellectual input and contributed to subsequent revisions. All authors approved the final manuscript.

Conflicts of Interest

TM is an employee of the National Health Laboratory (NHL). KN, AM, OM, and NPM form part of the REDCap technical team in Botswana. The remaining authors have no competing interests.

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Abbreviations

Edited by A Mavragani; submitted 16.07.23; peer-reviewed by L Walter, K Gierend; comments to author 28.08.23; revised version received 10.10.23; accepted 02.11.23; published 16.04.24.

©Kagiso Ndlovu, Kabelo Leonard Mauco, Onalenna Makhura, Robin Hu, Nkwebi Peace Motlogelwa, Audrey Masizana, Emily Lo, Thongbotho Mphoyakgosi, Sikhulile Moyo. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

Maxwell School Ranks #1 for Public Affairs in 2024-25

April 9, 2024

Colleen Heflin

David M. Van Slyke

Departments

Public Administration & International Affairs Department

Awards & Honors

Syracuse University’s Maxwell School of Citizenship and Public Affairs ranked #1 overall in the latest U.S. News & World Report Best Public Affairs Schools rankings. Maxwell has received this top honor in all but one of the years the rankings have been determined, which are based on a survey of the school’s peer institutions. In addition, the school remains highly ranked in ten subspecialties.

“As the Maxwell School celebrates its 100-year anniversary, we remain as focused as ever on preparing students to be public servants, leaders and scholars who will have a positive impact on their communities and the world around them,” says David M. Van Slyke, dean of the Maxwell School. “We are honored that our peers again recognized this work. We are also thankful to be counted among so many schools, colleges and universities preparing students to make a positive impact on democratic institutions and governance. And we share this honor with our students, faculty, staff and the vast network of Maxwell alumni who seek evidence-based solutions, encourage civil discourse, and ever strive to leave the world better than they found it.”

The Best Public Affairs Schools rankings are based solely on surveys of deans, directors and department chairs representing 271 master’s programs in public affairs and administration. Each school is numerically ranked by peer school leadership on a 5-point scale, with the average score determining the school’s overall rank. Additionally, survey respondents can nominate up to 15 schools for excellence in 12 subspecialties, with the number of nominations determining each school’s position in the ranking.

U.S. News began ranking graduate programs in public affairs in 1995. Since then, the Maxwell School has been ranked #1 in every survey but one.

This year, the Maxwell School is highly ranked in ten subspecialty categories:

• Environmental Policy and Management
• Health Policy and Management
• International Global Policy and Administration
• Information and Technology Management
• Local Government Management
• Non-Profit Management
• Public Finance and Budgeting
• Public Management and Leadership
• Public Policy Analysis
• Social Policy

“We are thankful for this recognition from our peers and for the students who, throughout the years, put their trust in us,” says Colleen Heflin, associate dean, chair and professor of public administration and international affairs. “I think our greatest attribute is our ability to foster creativity, critical thinking, collaboration and leadership for the common good. Students come here to learn tools for change, and leave as leaders ready to tackle complex problems, with the courage to challenge the status quo and the preparation to succeed in leading at all levels of government and in the private and non-profit sectors across the United States and around the world.”

The Maxwell School was founded in 1924, thanks to the support of George Holmes Maxwell and his vision to establish a "School of American Citizenship." As the world has changed and faced new challenges, the Maxwell School too has evolved to meet those challenges in its storied history. Today, Maxwell is home to 15 research centers and institutes, where students and scholars grapple with a range of issues, including environmental sustainability, autonomous systems policy, population health and aging, law and security, conflict resolution, democracy and journalism, global affairs, and regional studies. And the school continues to evolve to meet the challenges of tomorrow.

Just this year, the Maxwell School launched the new Master of Science in Sustainable Organizations and Policy , a joint degree program in coordination with the Martin J. Whitman School of Management. It is co-directed by Maxwell’s Jay S. Golden, Pontarelli Professor of Environmental Sustainability and Finance and founding director of the Dynamic Sustainability Lab, and leverages the strengths of both schools to prepare students to be versatile, multidisciplinary, forward-looking leaders ready to take on the important challenges across the globe related to sustainability.

“Our strength, over the last 100 years and today, lies in Maxwell’s continuing ability to evolve as we leverage an interdisciplinary and collaborative approach to address the greatest challenges faced by humanity,” says Van Slyke. “Today that list of issues and challenges includes environmental sustainability, artificial intelligence, international security, conflict resolution, poverty and health equity, to name a few. Within the walls of Maxwell and across the globe, our students, faculty, staff and alumni do that work on a daily basis. That is the greatest reward we can receive.”

About the Maxwell School

The Maxwell School of Citizenship and Public Affairs at Syracuse University offers highly regarded professional programs in public administration and international affairs as well as undergraduate and graduate degrees across the social sciences, and signature interdisciplinary undergraduate programs in policy, civic engagement, environmental sustainability and international relations. It is home to 15 interdisciplinary research centers focused on topical areas within public affairs. With campuses in Syracuse, New York, and Washington, D.C., and academic partnerships around the globe, the Maxwell network provides access to a world of opportunity.

About Syracuse University

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7 facts about americans and taxes.

Spring reliably brings a whirlwind of number-crunching and form-filing as Americans finish their tax returns. Altogether, the IRS expects to process more than 160 million individual and business tax returns this season.

Ahead of Tax Day on April 15, here are seven facts about Americans and federal taxes, drawn from Pew Research Center surveys and analyses of federal data.

Ahead of Tax Day 2024, Pew Research Center sought to understand Americans’ views of the federal tax system and outline some of its features.

The public opinion data in this analysis comes from Pew Research Center surveys. Links to these surveys, including details about their methodologies, are available in the text.

The external data comes from the U.S. Office of Management and Budget and the IRS Data Book . Data is reported by fiscal year, which for the federal government begins Oct. 1 and ends Sept. 30. For example, fiscal 2024 began Oct. 1, 2023, and ends Sept. 30, 2024.

A majority of Americans feel that corporations and wealthy people don’t pay their fair share in taxes, according to a Center survey from spring 2023 . About six-in-ten U.S. adults say they’re bothered a lot by the feeling that some corporations (61%) and some wealthy people (60%) don’t pay their fair share.

Democrats are far more likely than Republicans to feel this way. Among Democrats and Democratic-leaning independents, about three-quarters say they’re bothered a lot by the feeling that some corporations (77%) and some wealthy people (77%) don’t pay their fair share. Much smaller shares of Republicans and GOP leaners share these views (46% say this about corporations and 43% about the wealthy).

Meanwhile, about two-thirds of Americans (65%) support raising tax rates on large businesses and corporations, and a similar share (61%) support raising tax rates on households with annual incomes over $400,000. Democrats are much more likely than Republicans to say these tax rates should increase.

Just over half of U.S. adults feel they personally pay more than what is fair, considering what they get in return from the federal government, according to the same survey.

This sentiment has grown more widespread in recent years: 56% of Americans now say they pay more than their fair share in taxes, up from 49% in 2021. Roughly a third (34%) say they pay about the right amount, and 8% say they pay less than their fair share.

Republicans are more likely than Democrats to say they pay more than their fair share (63% vs. 50%), though the share of Democrats who feel this way has risen since 2021. (The share among Republicans is statistically unchanged from 2021.)

Many Americans are frustrated by the complexity of the federal tax system, according to the same survey. About half (53%) say its complexity bothers them a lot. Of the aspects of the federal tax system that we asked about, this was the top frustration among Republicans – 59% say it bothers them a lot, compared with 49% of Democrats.

Undeniably, the federal tax code is a massive document, and it has only gotten longer over time. The printed 2022 edition of the Internal Revenue Code clocks in at 4,192 pages, excluding front matter. Income tax law alone accounts for over half of those pages (2,544).

The public is divided in its views of the IRS. In a separate spring 2023 Center survey , 51% of Americans said they have an unfavorable opinion of the government tax agency, while 42% had a favorable view of the IRS. Still, of the 16 federal agencies and departments we asked about, the IRS was among the least popular on the list.

Views of the IRS differ greatly by party:

• Among Republicans, 29% have a favorable view and 64% have an unfavorable view.
• Among Democrats, it’s 53% favorable and 40% unfavorable.

On balance, Democrats offer much more positive opinions than Republicans when it comes to most of the federal agencies we asked about. Even so, the IRS ranks near the bottom of their list.

Individual income taxes are by far the government’s largest single source of revenue, according to estimates from the Office of Management and Budget (OMB).

The federal government expects to collect about $2.5 trillion in individual income taxes in fiscal year 2024. That accounts for nearly half (49%) of its total estimated receipts for the year. The next largest chunk comes from Social Security taxes (including those for disability and retirement programs), which are projected to pull in $1.2 trillion this fiscal year (24%).

By comparison, corporate income taxes are estimated to bring in $612.8 billion, or 12% of this fiscal year’s federal receipts. And excise taxes – which include things like transportation trust fund revenue and taxes on alcohol, tobacco and crude oil – are expected to come to $99.7 billion, or 2% of receipts.

American tax dollars mostly go to social services. Human services – including education, health, Social Security, Medicare, income security and veterans benefits – together will account for 66% ($4.6 trillion) of federal government spending in fiscal 2024, according to OMB estimates.

An estimated 13% ($907.7 billion) will go toward defense spending. Another 13% ($888.6 billion) will repay net interest on government debt, and 10% ($726.9 billion) will fund all other functions, including energy, transportation, agriculture and more.

Related: 6 facts about Americans’ views of government spending and the deficit

The vast majority of Americans e-file their taxes, according to IRS data . In fiscal 2022, 150.6 million individual federal income tax returns were filed electronically, accounting for 94% of all individual filings that year.

Unsurprisingly, e-filing has become more popular since the turn of the century. Fiscal 2000, the earliest year for which comparable data is available, saw 35.4 million individual income tax returns filed electronically (including those filed over the phone). These accounted for just 28% of individual filings that year.

By fiscal 2005, more than half of individual income tax returns (52%) were filed electronically.

Note: This is an update combining information from two posts originally published in 2014 and 2015.

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Top tax frustrations for Americans: The feeling that some corporations, wealthy people don’t pay fair share

Growing partisan divide over fairness of the nation’s tax system, public has mixed expectations for new tax law, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

World Bank RAMP Trust Fund to Improve Public Asset Management Announces Second Beneficiary, Cabo Verde

From left to right: Óscar Santos, Governor, Central Bank of Cabo Verde and Jorge Familiar, Vice President & Treasurer, World Bank at signing ceremony in Washington, DC

WASHINGTON April 16, 2024 – The World Bank announced the Central Bank of Cabo Verde (CBCV) as the second beneficiary of a multi-donor trust fund for advancing public asset management worldwide. Representatives from CBCV and the World Bank signed the technical assistance agreement, which delivers technical advisory services under the World Bank’s Reserve Advisory & Management Partnership (RAMP) program.

Announced in October, the trust fund brings the World Bank’s public asset management expertise to lower-income, fragile, or conflict-affected countries that could not otherwise afford it. CBCV becomes the 75 th member of the Partnership’s global network of public asset managers and second beneficiary of the trust fund.

“A warm welcome to the Central Bank of Cabo Verde as the newest RAMP member supported by the trust fund,” said Jorge Familiar, World Bank Vice President & Treasurer . “We are grateful for the continuous support and generosity of the Norwegian Agency for Development Cooperation (Norad) to share our asset management expertise, expanding RAMP’s global impact.”

World Bank RAMP

The World Bank Reserve Advisory & Management Partnership program delivers advisory services, executive training, and asset management services in a global network of public asset managers, contributing to the Sustainable Development Goals of quality education, decent work and economic growth, climate action, strong institutions, and partnerships. Established in 2001, RAMP promotes countries’ stability, resiliency, and prosperity through public asset management advisory. With over 70 members, RAMP membership includes central banks, international financial institutions, public pension funds, and sovereign wealth funds. In the context of RAMP, the World Bank has advised over 100 public institutions and trained over 10,000 public asset management staff on sound public asset management practices. Learn more at ramp.worldbank.org .

World Bank Asset Management

The World Bank is the trusted partner for public asset managers and the largest external asset manager in the development community, with nearly $200 billion of assets under management. As a global fixed-income leader, the World Bank has advised member countries on asset management since the early 1980s. Central banks and public asset managers manage over $40 trillion in reserves and assets worldwide, underscoring their importance in the global financial system and the need for high-quality asset management & advisory services. Learn more at worldbank.org/assetmanagement .

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Biden Administration Raises Costs to Drill and Mine on Public Lands

For the first time since 1920, the government has raised the rates that companies pay. The fossil fuel industry says it will hurt the economy.

By Coral Davenport

The Biden administration on Friday made it more expensive for fossil fuel companies to pull oil, gas and coal from public lands, raising royalty rates for the first time in 100 years in a bid to end bargain basement fees enjoyed by one of the country’s most profitable industries.

The government also increased more than tenfold the amount of the bonds that companies must secure before they start drilling.

The new rules are among a series of environmental regulations that are being pushed out as President Biden, in the last year of his term in the White House, seeks to cement policies designed to protect public lands, lower fossil fuel emissions and expand renewable energy.

While the oil and gas industry is strongly opposed to higher rates, the increase is not expected to significantly discourage drilling. The federal rate had been much lower than what many states and private landowners charge for drilling leases on state or private property.

“These are the most significant reforms to the federal oil and gas leasing program in decades, and they will cut wasteful speculation, increase returns for the public, and protect taxpayers from being saddled with the costs of environmental cleanups,” Interior Secretary Deb Haaland said.

The government estimates that the new rules, which would also raise various other rates and fees for drilling on public lands, would increase costs for fossil fuel companies by about $1.5 billion between now and 2032. After that, the minimum royalty rate could increase again.

About half of that money would go to states, approximately a third would be used to fund water projects in the West, and the rest would be split between the Treasury Department and Interior.

“This rule will finally curtail some of these wasteful handouts to the fossil fuel industry,” s aid Josh Axelrod, senior policy advocate with the Natural Resources Defense Council. “Communities, conservationists, and taxpayer advocates have been demanding many of these changes for decades.”

The rate increase was mandated by Congress under the 2022 Inflation Reduction Act, which directed the Interior Department to raise the royalty fee from 12.5 percent, set in 1920, to 16.67 percent. Congress also stipulated that the minimum bid at auctions for drilling leases should be raised from $2 per acre to $10 per acre.

But the sharp jump in bond payments — the first increase since 1960 — was decided by the Biden administration, not Congress. It came in response to arguments from environmental advocates, watchdog groups and the U.S. Government Accountability Office that the bonds do not cover the cost of cleaning up abandoned, uncapped wells, leaving taxpayers with that burden.

“Taxpayers have been losing billions of dollars on a broken leasing system with these ridiculously low royalty rates, rents, and minimum bids for far too long,” said Autumn Hanna, vice president of Taxpayers for Common Sense, a fiscal watchdog group. “Adding insult to injury, taxpayers were left holding the bag for damages from wells oil and gas companies left behind, long after they had already profited from them. We own these resources and it’s about time we are fairly compensated.”

The new rules increase the minimum bond for an individual drilling lease from $10,000 to $150,000. The amount of a bond for a drilling lease on multiple public lands in one state would rise from $25,000 to $500,000. The changes would replace an existing requirement that companies secure a single $150,000 bond as insurance against multiple damaged, abandoned wells anywhere in the country.

Oil and gas companies said the changes, which could take effect in as few as 60 days, would hurt fossil fuel production and damage the economy.

“The true losers with this misguided policy are states and localities that rely on revenues from federal land extractive industries to meet their budget obligations year after year,” said Dan Naatz, chief operating officer for the Independent Petroleum Association of America. “Rather than taking their mandate to be good stewards of federal land for the betterment of the American people seriously, the Biden Administration continues to ignore the people in local towns and communities across the West in order to placate a small group of environmentalists and to further reduce American oil and natural gas production.”

Last year , the United States produced more oil than any country, ever.

The oil and gas industry will continue to receive nearly a dozen federal tax breaks, including incentives for domestic production and write-offs tied to foreign production. Total estimates vary widely but the Fossil Fuel Subsidy Tracker, run by the Organization for Economic Cooperation and Development, calculated the total to be about $14 billion in 2022.

But more expensive bonds could put drilling out of reach for smaller oil and gas producers, said Kathleen Sgamma president of Western Energy Alliance, an association of independent oil and gas companies. “They are ludicrously high, ludicrously out of whack with the problem,” she said. “They could actually put companies out of business and create new orphan wells.”

The Interior Department estimates that there are 3.5 million abandoned oil and gas wells in the United States. When oil and gas wells are discarded without being properly sealed, which can happen when companies go bankrupt, the wells can leak methane, a powerful planet-warming pollutant that is a major contributor to global warming.

The Biden administration has had to navigate challenging terrain when it comes to extraction of fossil fuels on public lands and in federal waters, which is responsible for almost a quarter of the nation’s greenhouse gas emissions.

As a candidate, Mr. Biden promised “no more drilling on federal lands, period. Period, period, period.” He also campaigned to end billions of dollars in annual tax breaks to oil and gas companies within his first year in office.

But since Mr. Biden took office, his administration has continued to sell leases to drill, compelled by court decisions. The Biden administration approved more permits for oil and gas drilling in its first two years (over 6,900 permits) than the Trump administration did in the same period (6,172 permits). Congress has done nothing to end tax breaks for oil and gas companies.

Environmentalists excoriated Mr. Biden for his administration’s final approval earlier last year of an enormous $8 billion oil drilling project in Alaska known as Willow.

At the other end of the political spectrum, Republicans have accused the administration of waging a “war” on fossil fuels that threatens the nation’s economy and national security.

At rally in January, former President Donald J. Trump blamed economic inflation on Mr. Biden’s policies. “His inflation that he caused and would’ve been so easy not to. All it was — is energy. Remember this, gasoline, fuel, oil, natural gas went up to a level that it was impossible,” said Mr. Trump, who is running to unseat Mr. Biden. “That’s what caused inflation, and we’re going to bring it down because we’re going to go drill, baby, drill. We drill, baby, drill. We’re bringing it way down.”

Last month, the Republican-majority House passed a bill, sponsored by Representative Lauren Boebert of Colorado, that would force the administration to withdraw the new royalty regulation , although the measure has little chance of passage in the Democratic-majority Senate.

Coral Davenport covers energy and environment policy, with a focus on climate change, for The Times. More about Coral Davenport

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3 Questions: Enhancing last-mile logistics with machine learning

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Across the country, hundreds of thousands of drivers deliver packages and parcels to customers and companies each day, with many click-to-door times averaging only a few days. Coordinating a supply chain feat of this magnitude in a predictable and timely way is a longstanding problem of operations research, where researchers have been working to optimize the last leg of delivery routes. This is because the last phase of the process is often the costliest due to inefficiencies like long distances between stops due to increased ecommerce demand, weather delays, traffic, lack of parking availability, customer delivery preferences, or partially full trucks — inefficiencies that became more exaggerated and evident during the pandemic.

With newer technology and more individualized and nuanced data, researchers are able to develop models with better routing options but at the same time need to balance the computational cost of running them. Matthias Winkenbach, MIT principal research scientist, director of research for the MIT Center for Transportation and Logistics (CTL) and a researcher with the MIT-IBM Watson AI Lab, discusses how artificial intelligence could provide better and more computationally efficient solutions to a combinatorial optimization problem like this one.

Q: What is the vehicle routing problem, and how do traditional operations research (OR) methods address it?

A: The vehicle routing problem is faced by pretty much every logistics and delivery company like USPS, Amazon, UPS, FedEx, DHL every single day. Simply speaking, it's finding an efficient route that connects a set of customers that need to be either delivered to, or something needs to be picked up from them. It’s deciding which customers each of those vehicles — that you see out there on the road — should visit on a given day and in which sequence. Usually, the objective there is to find routes that lead to the shortest, or the fastest, or the cheapest route. But very often they are also driven by constraints that are specific to a customer. For instance, if you have a customer who has a delivery time window specified, or a customer on the 15th floor in the high-rise building versus the ground floor. This makes these customers more difficult to integrate into an efficient delivery route.

To solve the vehicle routing problem, we obviously we can't do our modeling without proper demand information and, ideally, customer-related characteristics. For instance, we need to know the size or weight of the packages ordered by a given customer, or how many units of a certain product need to be shipped to a certain location. All of this determines the time that you would need to service that particular stop. For realistic problems, you also want to know where the driver can park the vehicle safely. Traditionally, a route planner had to come up with good estimates for these parameters, so very often you find models and planning tools that are making blanket assumptions because there weren’t stop-specific data available.

Machine learning can be very interesting for this because nowadays most of the drivers have smartphones or GPS trackers, so there is a ton of information as to how long it takes to deliver a package. You can now, at scale, in a somewhat automated way, extract that information and calibrate every single stop to be modeled in a realistic way.

Using a traditional OR approach means you write up an optimization model, where you start by defining the objective function. In most cases that's some sort of cost function. Then there are a bunch of other equations that define the inner workings of a routing problem. For instance, you must tell the model that, if the vehicle visits a customer, it also needs to leave the customer again. In academic terms, that's usually called flow conservation. Similarly, you need to make sure that every customer is visited exactly once on a given route. These and many other real-world constraints together define what constitutes a viable route. It may seem obvious to us, but this needs to be encoded explicitly.

Once an optimization problem is formulated, there are algorithms out there that help us find the best possible solution; we refer to them as solvers. Over time they find solutions that comply with all the constraints. Then, it tries to find routes that are better and better, so cheaper and cheaper ones until you either say, "OK, this is good enough for me," or until it can mathematically prove that it found the optimal solution. The average delivery vehicle in a U.S. city makes about 120 stops. It can take a while to solve that explicitly, so that's usually not what companies do, because it's just too computationally expensive. Therefore, they use so-called heuristics, which are algorithms that are very efficient in finding reasonably good solutions but typically cannot quantify how far away these solutions are from the theoretical optimum.

Q: You’re currently applying machine learning to the vehicle routing problem. How are you employing it to leverage and possibly outperform traditional OR methods?

A: That's what we're currently working on with folks from the MIT-IBM Watson AI Lab. Here, the general idea is that you train a model on a large set of existing routing solutions that you either observed in a company’s real-world operations or that you generated using one of these efficient heuristics. In most machine-learning models, you no longer have an explicit objective function. Instead, you need to make the model understand what kind of problem it's actually looking at and what a good solution to the problem looks like. For instance, similar to training a large language model on words in a given language, you need to train a route learning model on the concept of the various delivery stops and their demand characteristics. Like understanding the inherent grammar of natural language, your model needs to understand how to connect these delivery stops in a way that results in a good solution — in our case, a cheap or fast solution. If you then throw a completely new set of customer demands at it, it will still be able to connect the dots quite literally in a way that you would also do if you were trying to find a good route to connect these customers.

For this, we're using model architectures that most people know from the language processing space. It seems a little bit counterintuitive because what does language processing have to do with routing? But actually, the properties of these models, especially transformer models, are good at finding structure in language — connecting words in a way that they form sentences. For instance, in a language, you have a certain vocabulary, and that's fixed. It's a discrete set of possible words that you can use, and the challenge is to combine them in a meaningful way. In routing, it's similar. In Cambridge there are like 40,000 addresses that you can visit. Usually, it's a subset of these addresses that need to be visited, and the challenge is: How do we combine this subset — these "words" — in a sequence that makes sense?

That's kind of the novelty of our approach — leveraging that structure that has proven to be extremely effective in the language space and bringing it into combinatorial optimization. Routing is just a great test bed for us because it's the most fundamental problem in the logistics industry. 

Of course, there are already very good routing algorithms out there that emerged from decades of operations research. What we are trying to do in this project is show that with a completely different, purely machine learning-based methodological approach, we are able to predict routes that are pretty much as good as, or better than, the routes that you would get from running a state-of-the-art route optimization heuristic.

Q: What advantages does a method like yours have over other state-of-the-art OR techniques?

A: Right now, the best methods are still very hungry in terms of computational resources that are required to train these models, but you can front-load some of this effort. Then, the trained model is relatively efficient in producing a new solution as it becomes required. 

Another aspect to consider is that the operational environment of a route, especially in cities, is constantly changing. The available road infrastructure, or traffic rules and speed limits might be altered, the ideal parking lot may be occupied by something else, or a construction site might block a road. With a pure OR-based approach, you might actually be in trouble because you would have to basically resolve the entire problem instantly once new information about the problem becomes available. Since the operational environment is dynamically changing, you would have to do this over and over again. While if you have a well-trained model that has seen similar issues before, it could potentially suggest the next-best route to take, almost instantaneously. It's more of a tool that would help companies to adjust to increasingly unpredictable changes in the environment.

Moreover, optimization algorithms are often manually crafted to solve the specific problem of a given company. The quality of the solutions obtained from such explicit algorithms is bounded by the level of detail and sophistication that went into the design of the algorithm. A learning-based model, on the other hand, continuously learns a routing policy from data. Once you have defined the model structure, a well-designed route learning model will distill potential improvements to your routing policy from the vast amount of routes it is being trained on. Simply put, a learning-based routing tool will continue to find improvements to your routes without you having to invest into explicitly designing these improvements into the algorithm.

Lastly, optimization-based methods are typically limited to optimizing for a very clearly defined objective function, which often seeks to minimize cost or maximize profits. In reality, the objectives that companies and drivers face are much more complex than that, and often they are also somewhat contradictory. For instance, a company wants to find efficient routes, but it also wants to have a low emissions footprint. The driver also wants to be safe and have a convenient way of serving these customers. On top of all of that, companies also care about consistency. A well-designed route learning model can eventually capture these high-dimensional objectives by itself, and that is something that you would never be able to achieve in the same way with a traditional optimization approach.

So, this is the kind of machine learning application that can actually have a tangible real-world impact in industry, on society, and on the environment. The logistics industry has problems that are much more complex than this. For instance, if you want to optimize an entire supply chain — let's say, the flow of a product from the manufacturer in China through the network of different ports around the world, through the distribution network of a big retailer in North America to your store where you actually buy it — there are so many decisions involved in that, which obviously makes it a much harder task than optimizing a single vehicle route. Our hope is that with this initial work, we can lay the foundation for research and also private sector development efforts to build tools that will eventually enable better end-to-end supply chain optimization.

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White-sounding names get called back for jobs more than Black ones, a new study finds

Joe Hernandez

A sign seeking job applicants is seen in the window of a restaurant in Miami, Florida, on May 5, 2023. Joe Raedle/Getty Images hide caption

A sign seeking job applicants is seen in the window of a restaurant in Miami, Florida, on May 5, 2023.

Twenty years ago, two economists responded to a slew of help-wanted ads in Boston and Chicago newspapers using a set of fictitious names to test for racial bias in the job market.

The watershed study found that applicants with names suggesting they were white got 50% more callbacks from employers than those whose names indicated they were Black.

Researchers at the University of California, Berkeley and the University of Chicago recently took that premise and expanded on it, filing 83,000 fake job applications for 11,000 entry-level positions at a variety of Fortune 500 companies.

Their working paper , published this month and titled "A Discrimination Report Card," found that the typical employer called back the presumably white applicants around 9% more than Black ones. That number rose to roughly 24% for the worst offenders.

The research team initially conducted its experiment in 2021, but their new paper names the 97 companies they included in the study and assigns them grades representing their level of bias, thanks to a new methodology the researchers developed.

"Putting the names out there in the public domain is to move away from a lot of the performative allyship that you see with these companies, saying, 'Oh, we value inclusivity and diversity,'" said Pat Kline, a University of California, Berkeley economics professor who worked on the study. "We're trying to create kind of an objective ground truth here."

America Reckons With Racial Injustice

From jobs to homeownership, protests put spotlight on racial economic divide.

The names that researchers tested include some used in the 2004 study as well as others culled from a database of speeding tickets in North Carolina. A name was classified as "racially distinctive" if more than 90% of people with that name shared the same race.

Applicants with names such as Brad and Greg were up against Darnell and Lamar. Amanda and Kristen competed for jobs with Ebony and Latoya.

What the researchers found was that some firms called back Black applicants considerably less, while race played little to no factor in the hiring processes at other firms.

Dorianne St Fleur, a career coach and workplace consultant, said she wasn't surprised by the findings showing fewer callbacks for presumed Black applicants at some companies.

"I know the study focused on entry-level positions. Unfortunately it doesn't stop there. I've seen it throughout the organization all the way up into the C-suite," she said.

St Fleur, who primarily coaches women of color, said many of her clients have the right credentials and experience for certain jobs but aren't being hired.

"They are sending out dozens, hundreds of resumes and receiving nothing back," she said.

What the researchers found

Much of a company's bias in hiring could be explained by its industry, the study found. Auto dealers and retailers of car parts were the least likely to call back Black applicants, with Genuine Auto Parts (which distributes NAPA products) and the used car retailer AutoNation scoring the worst on the study's "discrimination report card."

"We are always evaluating our practices to ensure inclusivity and break down barriers, and we will continue to do so," Heather Ross, vice president of strategic communications at Genuine Parts Company, said in an email.

AutoNation did not reply to a request for comment.

The companies that performed best in the analysis included Charter/Spectrum, Dr. Pepper, Kroger and Avis-Budget.

Workplace Diversity Goes Far Past Hiring. How Leaders Can Support Employees Of Color

Several patterns emerged when the researchers looked at the companies that had the lowest "contact gap" between white and Black applicants

Federal contractors and more profitable companies called back applicants from the two racial groups at more similar rates. Firms with more centralized human resources departments and policies also exhibited less racial bias, which Kline says may indicate that a standardized hiring workflow involving multiple employees could help reduce discrimination.

When it came to the sex of applicants, most companies didn't discriminate when calling back job-seekers.

Still, some firms preferred one sex over another in screening applicants. Manufacturing companies called back people with male names at higher rates, and clothing stores showing a bias toward female applicants.

What can workplaces — and workers — do

Kline said the research team hoped the public would focus as much on companies doing a bad job as those doing a good one, since they have potentially found ways to remove or limit racial bias from the hiring process.

"Even if it's true, from these insights in psychology and behavioral economics, that individuals are inevitably going to carry biases along with them, it's not automatic that those individual biases will translate into organizational biases, on average," he said.

St Fleur said there are several strategies companies can use to cut down on bias in the hiring process, including training staff and involving multiple recruiters in callback decisions.

Companies should also collect data about which candidates make it through the hiring process and consider standardizing or anonymizing that process, she added.

St Fleur also said she often tells her job-seeking clients that it's not their fault that they aren't getting called back for open positions they believe they're qualified for.

"The fact that you're not getting callbacks does not mean you suck, you're not a good worker, you don't deserve this thing," she said. "It's just the nature of the systemic forces at play, and this is what we have to deal with."

Still, she said job candidates facing bias in the hiring process can lean on their network for new opportunities, prioritize inclusive companies when applying for work and even consider switching industries or locations.

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