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• > Volume 44 Issue 2
• > Systems approaches to public service delivery: methods...

Article contents

Introduction, review method, defining systems approaches, macro-systems approaches, micro-systems approaches, systems approaches and impact evaluation, supplementary material, data availability statement, competing interests, systems approaches to public service delivery: methods and frameworks.

Published online by Cambridge University Press:  27 December 2023

• Supplementary materials

Researchers and practitioners are increasingly embracing systems approaches to deal with the complexity of public service delivery and policy evaluation. However, there is little agreement on what exactly constitutes a systems approach, conceptually or methodologically. We review and critically synthesize systems literature from the fields of health, education, and infrastructure. We argue that the common theoretical core of systems approaches is the idea that multi-dimensional complementarities between a policy and other aspects of the policy context are the first-order problem of policy design and evaluation. We distinguish between macro-systems approaches, which focus on the collective coherence of a set of policies or institutions, and micro-systems approaches, which focus on how a single policy interacts with the context in which it operates. We develop a typology of micro-systems approaches and discuss their relationship to standard impact evaluation methods as well as to work in external validity, implementation science, and complexity theory.

Across the social sciences, researchers and practitioners working to use evidence to improve public service delivery are increasingly turning to systems approaches to remedy what they see as the limitations of traditional approaches to policy evaluation. This includes increasing calls from disciplines like economics and management to adopt systems approaches to understanding the complexities of government bureaucracies (Pritchett Reference Pritchett 2015 ; Bandiera et al. Reference Bandiera, Callen, Casey, La Ferrara and Landais Camille 2019 ; Besley et al. Reference Besley, Burgess, Khan and Xu 2022 ). While those turning to systems approaches are united in viewing standard impact evaluation methods (at least in their more naïve applications) as overly simplistic, deterministic, and insensitive to context, the alternative methods they have developed are hugely varied. Studies that self-identify as systems approaches include everything from ethnographic approaches to understanding citizen engagement with public health campaigns during the 2014 Ebola outbreak in West Africa (Martineau Reference Martineau 2016 ) to high-level World Health Organization (WHO) frameworks (De Savigny and Adam Reference De Savigny and Adam 2009 ), multi-sectoral computational models of infrastructure systems (e.g. Saidi et al. Reference Saidi, Kattan, Jayasinghe, Hettiaratchi and Taron 2018 ), diagnostic surveys to identify system weaknesses (Halsey and Demas Reference Halsey and Demas 2013 ), and “whole-of-government” governance approaches to address the new cross-sectoral coordination challenges (Organization for Economic Cooperation and Development 2017 ), such as those imposed by COVID-19. This extreme diversity in concepts and methods can make systems approaches seem ill-defined and opaque to researchers and policymakers from outside the systems tradition and has limited engagement with their insights.

What, then, is the common theoretical core of systems approaches to public service delivery? What are the key distinctions among them, and to which kinds of questions or situations are different types of systems approaches best suited? And what is the relationship between systems approaches and standard impact evaluation-based approaches to using evidence to improve public service delivery?

We address these questions by reviewing and synthesizing the growing literature on systems approaches. We focus our review on three policy sectors in which systems approaches have gained increasing currency in high- as well as middle- and low-income countries alike: health, education, and infrastructure. These approaches have developed largely independently in each sector, which not only creates opportunities for learning across sectors but also allows us to distill a common set of conceptual underpinnings from a diverse array of methods, contexts, and applications.

Our article thus has two linked goals. First, we aim to provide shared conceptual foundations for engagement between researchers within the systems tradition and those who work outside the systems community but share an interest in the role of context and complexity in public service delivery and policy evaluation. Second, we aim to cross-pollinate ideas and facilitate discussion within the systems research community, among researchers and practitioners from different sectoral backgrounds or disciplinary communities.

Based on our review, we argue that systems approaches can best be understood not as a single method but as a diverse set of analytical responses to the idea that multi-dimensional complementarities between a policy and other aspects of the policy’s context (e.g. other policies, institutions, social and economic context, cultural norms, etc.) are the first-order problem of policy design and evaluation. Such complementarities are present when the impact of a group of variables on an outcome is greater than the sum of its parts. For example, the impact of a new pay-for-performance scheme on health service delivery might depend not just on multiple characteristics of the scheme’s design but also on the presence of effective data monitoring and auditing systems, on health workers’ intrinsic motivation and career incentives, on the availability of resources to pay bonuses, and on whether political economy considerations permit the payment of bonuses – as well as potentially dozens of other dimensions along which contexts might vary. Whereas standard impact evaluation methods typically seek to address these complexities by finding a way to “hold all else constant” in order to causally identify the impact of a policy intervention on an outcome variable, systems approaches focus on the “all else” in order to better understand the complex ways in which policies’ effectiveness might vary across contexts and time or depend on the presence of complementary policy interventions. The systems character of a piece of research can thus pertain to its question, theoretical approach, and/or empirical methodology.

Within the broad umbrella of systems approaches, we distinguish between “macro-systems” approaches and “micro-systems” approaches. The former is primarily concerned with understanding the collective coherence of a set of policy interventions and various other elements of context, whereas the latter focuses on a single policy intervention (like most standard impact evaluations) but focuses on understanding its interactions with contextual variables and other policy interventions (rather than necessarily obtaining an average treatment effect). We further review and distinguish among different analytical methods within each of these two categories, and link these different methods to different questions and analytical purposes. In particular, we suggest that the choice of which micro-systems approach to adopt depends on the degree to which contextual complementarities affect a policy’s efficacy (i.e. the extent to which a given policy has consistent impacts across contexts) and implementability (i.e the extent to which a given policy can be delivered or implemented correctly). We combine these two dimensions to construct four stylized types of linked question types and research approaches: “what works” – style impact evaluation (consistent efficacy, consistent implementability); external validity (inconsistent efficacy, consistent implementability); implementation science (consistent efficacy, inconsistent implementability); and complex systems (inconsistent efficacy, inconsistent implementability). While not necessarily straightforward to apply in practice, this parsimonious framework helps explain why and when researchers might choose to adopt different systems-based methods to understand different policies and different questions – as well as when adopting a systems perspective may be less necessary.

Of course, these questions are also of interest to impact evaluators outside the systems tradition, and many of the methodological tools that systems researchers use are familiar to them. Whereas systems approaches are sometimes perceived as being from a different epistemological tradition than standard impact evaluation methods (e.g. Marchal et al. Reference Marchal, Van Belle, Van Olmen, Hoerée and Kegels 2012 ), we view the underlying epistemology of systems approaches as consistent with that of impact evaluation. The main difference is the extent to which multi-dimensional complementarities are thought to be relevant, and hence how tractable it is to estimate the impacts of these complementarities using standard evaluation methods (given constraints of limited statistical power and/or counterfactual availability. While issues of heterogeneity, complementarity, and external validity can be addressed using standard impact evaluation methods (e.g. Bandiera et al. Reference Bandiera, Barankay and Rasul 2010 ; Andrabi et al. Reference Andrabi, Das, Khwaja, Ozyurt and Singh 2020 ), systems approaches presume (implicitly or explicitly) that such interactions are often high-dimensional (i.e. across many different variables) and thus intractable with limited sample sizes. Footnote 1 What distinguishes systems approaches, then, is mainly a different prioritization of these questions, and consequently a greater openness to methods other than quantitative impact evaluation in answering them. In this view, systems approaches and impact evaluation are thus better understood as complements, not mutually inconsistent alternatives, for creating and interpreting evidence about policy effectiveness.

The remainder of our article proceeds as follows. “Review method” briefly discusses our review method. “Defining systems approaches” presents a range of definitions of systems approaches from the literature, then synthesizes them into what we characterize as their common theoretical core. “Macro-systems approaches” reviews and typologizes macro-systems approaches across health, education, and infrastructure and offers a conceptual framework for synthesis, and “Micro-systems approaches” does the same for micro-systems approaches. “Systems approaches and impact evaluation” discusses how researchers and practitioners should go about selecting which type of systems approach (if any) is best suited for their purposes, and “Conclusion” concludes by discussing the connections between systems approaches to public service delivery and other well-established theoretical and methodological concerns in economics, political science, and public administration.

Our review of systems approaches in public service delivery focuses primarily on three sectors in which they have increasingly gained popularity: health, education, and infrastructure. However, the purpose of this article is not to provide a comprehensive survey of the systems literature in each of these sectors, as there already exist several excellent sector-level survey papers on systems approaches (e.g. Gilson Reference Gilson 2012 ; Carey et al. Reference Carey, Malbon, Carey, Joyce, Crammond and Carey 2015 ; Hanson Reference Hanson 2015 for health; Pritchett Reference Pritchett 2015 for education; Saidi et al. Reference Saidi, Kattan, Jayasinghe, Hettiaratchi and Taron 2018 for infrastructure). Instead, this article’s main contribution is to synthesize ideas and insights from these divergent sectoral literatures to make them more accessible to each other and to readers from outside the systems tradition.

We conducted selective literature reviews within each sector aimed at synthesizing the breadth of questions, theories, methods, and empirical applications that comprise the range of methods used in the systems literature across these sectors. In doing so, we drew on a combination of foundational systems texts of which we were already aware, the existing sectoral review papers listed above, input from sectoral experts, and keyword searches in databases. We then used the citations and reference lists of these to iteratively identify additional articles of interest, stopping when we reached a point of saturation. Footnote 2 The result is not a systematic review in the formal sense of the term but nevertheless provides a detailed and consistent picture of the state of the literature in each sector. In that sense, our methodology shares overlaps with a “problematizing” (Alvesson and Sandberg Reference Alvesson and Sandberg 2020 ) or a “prospector” review (Breslin and Gatrell Reference Breslin and Gatrell 2023 ) where our focus is more on defining a new set of domains and boundaries that can allow us to critically reimagine the existing literature, challenge pre-existing conceptions, and build new theory, rather than offering a representative description of the field through a narrow lens. In addition to literature specifically about each of these sectors, we also draw on non-sector-specific work on systems approaches to understanding service delivery in complex and unpredictable systems more generally. We include in our review texts that self-describe as systems-based, as well as many that share similar questions, theoretical approaches, and empirical methods but which do not necessarily adopt the language of systems approaches.

For clarity and brevity, and in line with the article’s purpose, we focus the main text on presenting an overall synthesis with illustrative examples rather than on decreasing readability by trying to cover as many studies as possible. We include a more detailed (though still inevitably selective) sector-by-sector summary in an Online Appendix for interested readers.

Our review and synthesis are not necessarily intended as an argument in favor of systems approaches being used more widely, nor as a critique of research outside the systems tradition. Neither should it be read as a critique of systems approaches. While we do believe that both the general thrust of systems approaches and many of the specific ideas presented by them are important and useful, our goal is merely to present a concise survey and a set of clear conceptual distinctions so that readers can determine what might be useful to them from within this diverse array of perspectives and methods and can better converse across disciplinary and sectoral boundaries without the caricaturing and misrepresentation that have often marred these conversations. Doing this inevitably creates a tension between staying faithful to the way in which researchers in these fields view their work and the necessity of communicating about it in ways that will be intelligible to readers from other fields. We hope that we have struck this balance well and that readers will understand the challenges of doing so on such a broad-ranging topic.

Systems approaches are defined in different ways across different sectors but tend to share a common emphasis on the multiplicity of actors, institutions, and processes within systems. For example, the WHO ( 2007 , p. 2) defines a health system as consisting of “all organizations, people and actions whose primary intent is to promote, restore or maintain health.” In education, Moore ( Reference Moore 2015 , p. 1) defines education systems as “institutions, actions and processes that affect the ‘educational status’ of citizens in the short and long run.” In infrastructure, Hall et al. ( Reference Hall, Tran, Hickford and Nicholls 2016 , p. 6) define it as “the collection and interconnection of all physical facilities and human systems that are operated in a coordinated way to provide a particular infrastructure service.”

Despite their differences, these definitions imply a focus of systems on “holism” (Midgley Reference Midgley 2006 ; Hanson Reference Hanson 2015 ), or the idea that individual policies do not operate in isolation. Whereas a great deal of research and evidence-based policymaking focuses on studying the effectiveness of a single policy in isolation – often by means of using impact evaluation to estimate an average treatment effect – in practice, each policy’s effectiveness depends on other policies and various features of the contextual environment (Hanson Reference Hanson 2015 ). As De Savigny and Adam ( Reference De Savigny and Adam 2009 , p. 19) write in their seminal discussion of health systems, “every intervention, from the simplest to the most complex, has an effect on the overall system, and the overall system has an effect on every intervention.” This emphasis on interconnection has made the study of complexity (e.g. Stacey Reference Stacey 2010 ; Burns and Worsley Reference Burns and Worsley 2015 ) a natural source of inspiration for those seeking to apply systems approaches to the study of development and public service delivery.

But despite the growing popularity of systems approaches, there remains significant ambiguity around their meaning, with no universally accepted definition or conceptual framework beyond their shared emphasis on holism, context, and complexity (Midgley 2006). Even those writing within the systems tradition have pointed out that the field has used “diverse” and “divergent” concepts and definitions, leading the field as a whole to be sometimes characterized as “ambiguous” and “amorphous” (Cabrera et al. Reference Cabrera, Colosi and Lobdell 2008 ). This lack of a commonly agreed definition and theoretical basis has made a precise and concise response to the question “what is a systems approach to public service delivery, and how is it different to what already exists?” difficult to obtain.

We argue that instead of viewing a systems approach as a specific method, system approaches are better understood as a diverse set of analytical responses to the idea that the first-order challenge of policy design and evaluation is to understand the multi-dimensional complementarities between a policy and other aspects of the policy’s context (e.g. other policies, institutions, social and economic context, cultural norms, etc.). By complementarities, we refer to the formal definition under which two variables – e.g. a variable capturing the presence of a particular policy and another variable capturing some aspect of the policy’s context – are considered complements when their joint effect on an outcome variable is greater than the sum of their individual effects on that variable. Footnote 3 By multi-dimensional, we refer to the idea that these complementarities might not just be among two or three variables at a time (as impact evaluations often seek to estimate) but among so many variables that estimating them in a standard econometric framework often becomes intractable. While this definition is limited in its precision by the need to adequately encompass the enormous diversity of systems approaches we discuss in subsequent sections, it captures the theoretical core – the emphasis on understanding multi-dimensional complementarities – that ties them all together.

Advocates of systems approaches often contrast this emphasis with the naïve use of impact evaluation to obtain an average treatment effect of a policy, which is then used to guide adoption decisions across a wide range of contexts and populations. Of course, the rapid growth in attention toward and research on issues of external validity and implementation within economics and political science (Deaton Reference Deaton 2010 ; Pritchett and Sandefur Reference Pritchett and Sandefur 2015 ; Bold et al. Reference Bold, Kimenyi, Mwabu, Ng’ang’a and Sandefur 2018 ) makes this something of a “straw-man” characterization in many cases. In practice, both “impact evaluators” and “systems researchers” care about average treatment effects as well as about heterogeneity, mechanisms, and interactions. Indeed, it is noteworthy that the groups of researchers and practitioners with whom systems approaches have gained the most currency in the past two decades are (at least in the health and education sectors) those who most often find themselves working with, arguing against, or attempting to expand the boundaries of impact evaluators. But while easily over-exaggerated, the distinction does capture the different frame of mind with which systems researchers approach evidence-based policy, in which understanding complementarities among policies and their context is the primary focus of analysis, prioritized (in many cases) even overestimating the direct effect of a policy itself. Whereas a standard impact evaluation seeks primarily to understand the impact of a specific policy holding all else constant, a systems approach to the same policy seeks primarily to understand how the “all else” affects the policy’s impacts.

Among studies that self-identify as focusing on systems, one can draw a conceptual distinction between studies that are system-focused in substance (due to their scale or topic) and those that are system-focused in approach (due to their methodological or theoretical emphasis on issues of context, complementarity, and contingency). This article focuses mainly on the latter category. Although in practice these categories overlap significantly and the distinction is a blurry one, it nonetheless helps avoid the excessive conceptual spread that could result from referring to every study on “the health system” (or the education or infrastructure systems) as a “systems approach.”

Before we proceed to draw distinctions among different types of systems approaches, it is worth noting two additional characterizations of systems approaches that are often made by systems researchers. First, systems approaches are sometimes viewed as being more question- or problem-driven than standard research approaches, with a focus on real-world issues and linkages to actual government policy choices (e.g. Gilson Reference Gilson 2012 ; Mills Reference Mills 2012 ; Hanson Reference Hanson 2015 ). While this characterization risks giving short shrift to the policy relevance of a great deal of research outside the systems tradition, there is also a natural linkage between embeddedness in an actual policy decision and a concern for understanding how a wide range of factors interlock, since policymakers must often deal with a breadth of challenges that researchers might choose to abstract away in the pursuit of parsimony. Second, some systems researchers emphasize that service delivery is not only complicated (in the sense of involving many moving parts) but also complex (in the sense of possessing dynamics that are non-linear and/or fundamentally unpredictable) (Sheikh et al. Reference Sheikh, Gilson, Agyepong, Hanson, Ssengooba and Bennett 2011 ; Snyder Reference Snyder 2013 ). We do not include this aspect of complexity in our core definition presented above, since it is far from universally shared among systems approaches, but return to discuss this issue further in “Systems approaches and impact evaluation” below.

One branch of systems approaches responds to the challenge posed by the presence of multi-dimensional complementarities across policies and contextual factors by taking a step back to try to examine questions of policy effectiveness from the standpoint of the entire system. These macro-systems approaches are focused not on the impact of a specific policy in isolation, but on understanding how the entire system functions to deliver desired outcomes. Macro-systems approaches thus focus on understanding coherence and interconnectedness between different policies, structures, and processes. In doing so, they also tend to define boundaries of the system in question, although this is often a challenging task (Carey et al. Reference Carey, Malbon, Carey, Joyce, Crammond and Carey 2015 ).

Our review of macro-systems approaches across the health, education, and infrastructure sectors highlights that these approaches lie on a spectrum of the specificity with which they define causal relationships between different system components. This includes approaches ranging from those that merely outline lists or typologies of various system components to those that tend to specify causal relationships between system components through specific numerical parameters. Along this spectrum, it is possible to distinguish three types of macro-systems approaches:

Inventory approaches, which are primarily descriptive and use typologies or lists to define a comprehensive universe of system components such as the types of stakeholders, functions, institutions, or processes within a system;

Relational approaches, which go a step further to posit broad causal relationships or complementarities between system features, based mainly on theory Footnote 4 ; and

Systems modeling, which conceptualizes the system through precise mathematical causal relationships between different system components.

Inventory approaches list different components and/or typologies within a system with the aim of cataloging the whole range of factors that determine the outcomes or performance of a given system (usually defined sectorally). An example of such an approach is the seminal WHO health systems framework, which characterizes the health system as comprising six key functional building blocks – service delivery, health workforce, information, medical products (including both vaccines and technologies), financing, and leadership and governance – and links them to the broader health system goals (WHO 2007 ). As Fig.  1 shows, the strength of such inventory frameworks is their very wide scope in terms of identifying the full range of potential determinants and outcomes of a system, but this breadth is achieved by limiting the specificity of the causal relationships they posit. Similarly, the World Bank Systems Approach for Better Education Results (SABER) defines the education system in terms of thirteen different functions (e.g. education management information systems, school autonomy and accountability, and student assessment) with a link to improved student learning without specifying the relationship between these functions (Halsey and Demas Reference Halsey and Demas 2013 ).

Figure 1. World Health Organizationhealth system framework.

Source : (Reprinted with permission): De Savigny and Adam ( Reference De Savigny and Adam 2009 ).

Like inventory approaches, relational macro-systems approaches list different system components, but go a step further in specifying the nature or direction of specific relationships or complementarities between them. For example, Gilson ( Reference Gilson 2003 ) conceptualizes the health system as a set of trust relationships between patients, providers, and the wider institutions. This differs from an inventory approach in more narrowly specifying both the content and direction of relationships among actors, which makes it more analytical but also limits its scope. It also demonstrates how such frameworks may also consider the software (i.e. institutional environment, values, culture, and norms) in addition to the hardware (i.e. population, providers, and organizations) of a health system (Sheikh et al. Reference Sheikh, Gilson, Agyepong, Hanson, Ssengooba and Bennett 2011 ). In the education sector, Pritchett ( Reference Pritchett 2015 ) adopts a relational approach to characterizing the education system through accountability links between different actors such as the executive apparatus of the state, organizational providers of schooling (such as ministries and schools), frontline providers (such as head teachers and teachers), and citizens (such as parents and students). Footnote 5 He argues that the system of education works when there is an adequate flow of accountability across the key actors in the system across four design elements: delegation, financing, information, and motivation (see Fig.  2 ). Similarly, in the infrastructure sector, Ottens et al. ( Reference Ottens, Franssen, Kroes and Van De Poel 2006 ) propose a high-level framework to characterize how technical elements in an infrastructure system may interact with human actors and social institutions to determine system performance. But while such relational approaches are more specific than inventory approaches in their definition of elements and causal relationships, they are still broad enough that their use is more as a conceptual framework for arraying factors and nesting hypotheses than as an operationalizable model of the system.

Figure 2. Education system framework.

Source : (Reprinted with permission): Pritchett ( Reference Pritchett 2015 ).

Systems modeling approaches take this next step of precisely specifying variables, causal relationships among these system components, and numerical parameters on these relationships. Such models typically combine theory with statistical methods and draw on a range of quantitative techniques such as systems dynamics, structural equation modeling, and structural econometric modeling (e.g. Homer and Hirsch, Reference Homer and Hirsch 2006 ; Reiss and Wolak Reference Reiss and Wolak 2007 ). Footnote 6 Thacker et al. ( Reference Thacker, Pant and Hall 2017 ), for example, develop a network-based systems-of-systems model for critical national infrastructures, where each type of infrastructure such as water or electricity is a sub-system comprising a group of nodes and edges with their specific flows (see Fig.  3 ). They use this model to perform a multi-scale disruption analysis and draw predictions on how failures in any individual sub-systems can potentially lead to large disruptions. In the health sector, Homer and Hirsch ( Reference Homer and Hirsch 2006 ) develop a causal diagram of how chronic disease prevention works and then use systems dynamic methodology to develop a computer-based model to test alternate policy scenarios that may affect the chronic disease population. In the education sector, Kaffenberger and Pritchett ( Reference Kaffenberger and Pritchett 2021 ) combine a structural model with parameter values from existing empirical literature to predict how learning outcomes would be affected under different policy scenarios such as expanding schooling to universal basic education, slowing the pace of curriculum, and increasing instructional quality.

Figure 3. Infrastructure system representation with six critical national infrastructures.

Source : (Reprinted with permission): Thacker et al. ( Reference Thacker, Pant and Hall 2017 ).

The three macro-systems approaches outlined above can have different types of uses and benefits depending on the question of interest. For example, systems researchers often use frameworks developed through inventory approaches to develop diagnostic tools to understand strengths and weaknesses of systems, such as the World Bank’s use of its SABER framework, which has been implemented in more than 100 countries to identify potential constraints to system effectiveness (World Bank 2014 ). Relational frameworks in turn can be used to array key relationships between system actors, which may be useful for generating important insights for policy design or generating more precise hypotheses for empirical research. Finally, systems modeling approaches are one way of making complex systems analytically tractable by narrowing down on a set of key causal relationships within a system to generate useful predictions and insights about a system (Berlow Reference Berlow 2010 ). Although systems modeling has been used in the health and education sectors to generate useful predictions, such models have been used more extensively in infrastructure systems research, possibly because the variables are more quantitative in nature and relatively easier to model in comparison to more human or intangible contextual features in health or education. While conceptually distinct, in practice, these three types of macro-systems approaches can overlap, and not every framework is easily classifiable within a single category (Table  1 ).

Table 1. Summary of macro-systems approaches with selected examples

Source: Authors’ synthesis.

While macro-systems approaches offer big-picture frameworks to understand coherence between many system components and policies, micro-systems approaches focus on the effectiveness of a specific policy – just like impact evaluations. However, the central presumption of micro-systems approaches is that policies cannot be viewed in isolation, but rather need to be designed, implemented, evaluated, and scaled taking the wider context and complementarities within the system into account (Travis et al. Reference Travis, Bennett, Haines, Pang, Bhutta, Hyder, Pielemeier, Mills and Evans 2004 ; De Savigny and Adam Reference De Savigny and Adam 2009 ; Snyder Reference Snyder 2013 ; Pritchett Reference Pritchett 2015 ), and so questions and methods mainly revolve around these issues rather than average treatment effects.

Across the health, education, and infrastructure sectors, a diverse range of analytical approaches fit our description of systems approaches. Each of these approaches is likely to be familiar to readers in some disciplines and unfamiliar to others. They include approaches that aim to help evaluators better understand the roles of mechanisms and contextual factors in producing policy impact, such as realist evaluation (Pawson and Tilley Reference Pawson and Tilley 1997 ) and theory-driven evaluation (Coryn et al. Reference Coryn, Noakes, Westine and Schröter 2011 ), as well as a range of qualitative or ethnographic (e.g. George Reference George 2009 ; Bano and Oberoi Reference Bano and Oberoi 2020 ) and mixed method approaches (e.g. Mackenzie et al. Reference Mackenzie, Koshy, Leslie, Lean and Hankey 2009 ; Tuominen et al. Reference Tuominen, Tapio, Varho, Järvi and Banister 2014 ) more broadly. They also encompass fields such as implementation science (Rubenstein and Pugh Reference Rubenstein and Pugh 2006 ), some types of meta-analysis and systematic review (e.g. Greenhalgh et al. Reference Greenhalgh, Macfarlane, Steed and Walton 2016 ; Leviton et al. Reference Leviton 2017 ; Masset Reference Masset 2019 ), and adaptive approaches to policy design and evaluation (e.g. Andrews et al. Reference Andrews, Pritchett and Woolcock 2017 ). These micro-systems approaches can focus on a variety of levels of analysis, from individuals to organizations to policy networks, but are united by their analytical focus on a single policy at a time rather than on the entire system (as in macro-systems approaches). We briefly summarize each of these methods or approaches in this section, before the next section develops a framework to link them back to standard impact evaluation and help prospective systems researchers select among them.

Micro-systems approaches’ emphasis on heterogeneity is perhaps best captured by the mantra of the “realist” approach to evaluation, which argues that the purpose of an evaluation should be to identify “what works in which circumstances and for whom?,” rather than merely answering the question of “does it work”? (Pawson and Tilley Reference Pawson and Tilley 1997 ). More specifically, instead of looking at simple cause-and-effect relationships, realist research typically aims to develop middle-range theories through developing “context-mechanism-outcome configurations” in which the role of policy context is integral to developing an understanding of how the policy works (Pawson and Tilley Reference Pawson and Tilley 1997 ; Greenhalgh et al. Reference Greenhalgh, Macfarlane, Steed and Walton 2016 ). For example, Kwamie et al. ( Reference Kwamie, van Dijk and Agyepong 2014 ) use a realist evaluation to evaluate the impact of the Leadership Development Programme delivered to district hospitals in Ghana. Focusing on a district hospital in Accra, they used a range of qualitative data sources to develop causal loop diagrams to explain interactions between contexts, mechanisms, and outcomes. They found that while the training produced some positive short-term outcomes, it was not institutionalized and embedded within the district processes. They argue that this was primarily due to the structure of hierarchical authority in the department, due to which the training was seen as a project coming from the top, and thus reduced initiative on the part of the district managers to institutionalize it.

A related approach is theory-driven evaluation, in which the focus is not just on whether an intervention works but also on its mediating mechanisms – the “why” of impact (Coryn et al. Reference Coryn, Noakes, Westine and Schröter 2011 ). Theory-driven evaluations take as their starting point the underlying theory of how the policy is intended to achieve its desired outcomes (often expressed in the form of a theory of change diagram) and seek to evaluate each step of this causal process. As with realist approaches, the role of context is critical for theory-driven evaluations, as it is these mechanism-context complementarities that drive heterogeneity of impact across contexts and target populations, and hence the external validity and real-world effectiveness of policies or interventions. Theory-based and realist evaluations both tend to rely on qualitative methods, either alone or as a supplement to a quantitative impact evaluation (i.e. mixed methods), as limitations of sample size, counterfactual availability, and measurement often make it infeasible to document multiple potential mechanisms quantitatively at the desired levels of nuance and rigor. Footnote 7

Another form of qualitative method widely used by systems researchers is ethnography and participant observation. These are used mainly for the diagnosis of policy problems, refining research hypotheses, or designing new policy interventions, rather than evaluating policy impact ex post . For example, George ( Reference George 2009 ) conducts an ethnographic analysis to examine how formal rules and hierarchies affect informal norms, processes, and power relations in the Indian health system in Koppal state. The study shows that the two key functions of accountability in Koppal’s health system – supervision and disciplinary action – are rarely implemented uniformly as these are negotiated by frontline staff in various ways depending on their informal relationships. In the education sector, Bano and Oberoi ( Reference Bano and Oberoi 2020 ) use ethnographic methods to understand how innovations are adopted in the context of an Indian non-governmental organization that introduced a Teaching at the Right Level intervention and tease out lessons for how innovations can be scaled and adopted in state systems. In this sense, ethnographic research is a more structured and rigorous version of the informal discussions or anecdotal data that policymakers and evaluators often draw upon in making policy or evaluation decisions, and can be integrated into these processes accordingly (alone or alongside some form of impact evaluation).

Systems research often has a specific focus on the implementation, uptake, and scale-up of policy (Hanson Reference Hanson 2015 ). The discipline of implementation science in the health sector, for example, is specifically targeted toward understanding such issues (Rubenstein and Pugh Reference Rubenstein and Pugh 2006 ). Research in implementation science is usually less concerned with the question of what is effective (where there is strong prior evidence of an intervention’s efficacy in ideal conditions) and is more concerned with how to implement it effectively. Systems researchers who study implementation cater to a set of concerns such as methods for introducing and scaling up new practices, behavior change among practitioners, and the use and effects of patient and implementer participation in improving compliance. Greenhalgh et al. ( Reference Greenhalgh, Wherton, Papoutsi, Lynch, Hughes, A’Court and Shaw 2017 ), for example, combine qualitative interviews, ethnographic research, and systematic review to study the implementation of technological innovations in health. They develop the non-adoption, abandonment, scale-up, spread, and sustainability framework to both theorize and evaluate the implementation of health care technologies. Like realist and theory-based evaluation, implementation science research often relies heavily (though not exclusively) on qualitative methods, although these can also be combined with experimental or observational quantitative data.

While these micro-systems approaches are by definition used to analyze the effectiveness of a single policy, some systems researchers have also adapted evidence aggregation methods like systematic reviews and meta-analysis to the interests of systems researchers. While these methods are typically used to summarize impacts or identify an average treatment effect of an intervention by summarizing studies across several contexts, systems researchers focus on using these methods to identify important intervening mechanisms across contexts. For example, Leviton (2017) argues that systematic reviews and meta-analyses can offer bodies of knowledge that support better understanding of external validity by identifying features of program theory that are consistent across contexts. To identify these systematically, she identifies several techniques to be used in combination with meta-analyses such as a more thorough description of interventions and their contexts, nuanced theories behind the interventions, and consultation with practitioners. While many of these applications rely on integrating qualitative information into the evidence aggregation process, other researchers use these methods in their traditional quantitative formats but focus specifically on systems-relevant questions of mechanisms, contextual interactions, and heterogeneity. For example, Masset ( Reference Masset 2019 ) calculates prediction intervals for various meta-analyses of education interventions and finds that interventions’ outcomes are highly heterogeneous and unpredictable across contexts, even for simple interventions like merit-based scholarships. Used in this way, there is methodological overlap between meta-analysis in the systems tradition and how it is commonly used in mainstream impact evaluation. This illustrates one of many ways in which the boundaries between “systems” and “non-systems” research are porous, which both increases the possibilities for productive interchange among research approaches but also creates terminological and conceptual confusion that inhibits it.

Stakeholder mapping or analysis is another method used by systems researchers, to either understand issues of policy implementation or policy design. For example, Sheikh and Porter ( Reference Sheikh and Porter 2010 ) conducted a stakeholder analysis to identify key gaps in policy implementation. Using data from in-depth interviews with various stakeholders across five states in India, they highlight bottlenecks in human immunodeficiency virus policy implementation (from nine hospitals selected by principles of maximum variation). Like ethnography, stakeholder mapping is an example of a micro-systems approach (because it focuses on the effectiveness of a single policy) but which asks different questions about that policy’s effectiveness than standard impact evaluations do.

A final set of micro-systems approaches is grounded in the reality that many questions of policy design and evaluation are situated in complex settings, where policy-context complementarities are so numerous and specific to the contextual setting that the effectiveness of a policy is impossible to predict, for all intents and purposes. Systems researchers argue that for such complex systems , which have many “unknown unknowns” with few clear cause-and-effect relationships, various negative and positive feedback loops, and emergent behaviors (Bertalanffy Reference Bertalanffy 1971 ; Snowden and Boone Reference Snowden and Boone 2007 ), there is a need for a different set of analytical approaches to policy design and evaluation (e.g. Snyder Reference Snyder 2013 ). This perspective eschews not only the idea of “best practice” policies but also sometimes the idea of basing adoption decisions on policies’ effectiveness in other contexts because policy dynamics are viewed as so highly context-specific.

A core idea in complex systems theory is that the processes of policy design and implementation should involve an ongoing process of iteration with feedback from key stakeholders and decision-makers in the system. For example, Andrews et al. ( Reference Andrews, Pritchett and Woolcock 2013 ) argue that designing and implementing effective policies for governments in complex settings require locally driven problem-solving and experimentation, and propose an approach called problem-driven iterative adaptation that emphasizes local problem definition, design, and experimentation. In a different vein, Tsofa et al.’s ( Reference Tsofa, Molyneux, Gilson and Goodman 2017 ) “learning sites” approach envisions a long-term research collaboration with a district hospital in which researchers and health practitioners work together over time to uncover and address thorny governance challenges. While the learning site serves to host a series of narrower research studies, the most important elements include formal reflective sessions being regularly held among researchers, between researchers and practitioners, and across learning sites to study complex pathways to change. Such approaches are also closely linked to the living lab methodology, which relies on innovation, experimentation, and participation for diagnosing problems and designing solutions for more effective governance (Dekker et al. 2019).

The types of micro-systems approaches discussed above and presented in Table  2 are neither mutually exclusive nor collectively exhaustive of all possible micro-systems approaches but illustrate the breadth and diversity of such approaches. Table  2 also illustrates the variation across sectors in the range of approaches that are commonly used. The health sector has the broadest coverage across different types of methods. The education sector also shows fairly broad coverage across methods, while also demonstrating growing attention toward systems approaches in response to greater concerns of external validity following the surge of education-related impact evaluations (especially in international development) over the last decade. The use of micro-systems approaches in infrastructure is comparatively limited. This is possible because infrastructures have high up-front costs that demand more ex ante cost-benefit analysis and planning (often through macro-systems approaches) rather than ex post evaluations of the impacts of specific infrastructures through micro-systems approaches.

Table 2. Summary of micro-systems approaches

The review of systems approaches in the preceding two sections illustrates the sheer diversity of topics, questions, theories, and methods that can fall within the broad label of systems approaches. It also shows that while systems approaches are sometimes rhetorically positioned in opposition to standard impact evaluation approaches, many of the concerns motivating systems researchers (such as attention to mechanisms, heterogeneity, external validity, implementation and scale-up, and the use of qualitative data) can and increasingly are being addressed within the impact evaluation community. At the same time, it is also generally true that systems approaches differ substantially in their prioritization of questions and hence the types of evidence in which they are most interested, so these differences are not purely semantic.

How, then, should a researcher or policymaker think about whether they need to adopt a systems approach to creating and interpreting evidence? And if so, which type of systems approach might be most relevant? In this section, we offer a brief conceptual synthesis and stylized framework to guide thinking on these questions.

For macro-systems approaches, the relationship to standard impact evaluation methods is fairly clear. Macro-systems approaches array the broad range of policies and outcomes relevant to understanding the performance of a given sector, and impact evaluations examine the effect of specific policies on specific outcomes within this framework. Macro-systems frameworks can thus add value to impact evaluation-led approaches to studying policy effectiveness by providing a framework with which to cumulate knowledge, suggesting important variables for impact evaluations to focus on (and potential complementarities among them), and highlighting gaps in an evidence base. Being more explicit in couching impact evaluations in some kind of broader macro-system framework – whether inventory, relational, or systems modeling – could thus enhance the evidentiary value of systems approaches, as indeed it has begun to do in the systems literature in the health, education, and infrastructure sectors (e.g. Silberstein and Spivack Reference Silberstein and Spivack 2023 ).

For micro-systems approaches, however, the relationship to (and distinction from) standard impact evaluation methods is more blurry. Among other reasons, this is because our definition of systems approaches as being concerned with multi-dimensional complementarities does not give much guidance as to which types of systems questions and methods might be related to different types of potential complementarities.

We, therefore, propose a simple framework that uses a policy’s consistency of implementability and consistency of efficacy to guide choices about the appropriateness of different evidence-creation approaches. Footnote 8 By consistency of implementability, we mean the extent to which a given policy can be delivered or implemented correctly (i.e. the desired service delivery outputs can be produced) across a wide range of contexts. Policies whose effective implementation depends on important and numerous complementarities with other policies or aspects of context will tend to have lower consistency of implementability, since these complementary factors will be present in some contexts but not others, whereas policies for whom these complementarities are relatively fewer or less demanding will be able to be implemented more consistently across a wide range of contexts. By consistency of efficacy, we mean the extent to which delivery of a given set of policy outputs results in the same set of outcomes in society across a wide range of contexts. As with implementability, policies whose mechanisms rely on many important complementarities with other policies or aspects of context will tend to have lower consistency of efficacy across contexts, and vice versa.

Putting these two dimensions together (Fig.  4 ) yields a set of distinctions among four different stylized types of evidence problems, each of which can be addressed most effectively using different methods for creating and interpreting evidence. In interpreting this diagram, several important caveats are in order. First, this framework is intended to help readers organize the extraordinarily diverse range of micro-systems approaches identified in our review and summarized in our preceding sections and to identify when they might want to adopt a systems approach and which type might be most useful. However, it is not comprehensive taxonomy of all micro-systems approaches, nor do all methods reviewed fit neatly into one category. Second, while we present four stylized “types” of evidence problems for simplicity, the underlying dimensions are continuous spectrums. Finally, complementarities exist and context matters for all policies to at least some extent; the distinctions presented here are intended to be relative in nature, not absolute. With these caveats in mind, we discuss each of these types in turn, highlighting their relationship both to different micro-systems methods as well as to standard impact evaluation approaches.

Figure 4. Synthesizing micro-systems approaches.

Source: Author’s synthesis.

The top-left quadrant of Fig.  4 corresponds to types of policies that are consistently efficacious across contexts, but which are challenging to implement effectively. We refer to these problems as “implementation science” problems. Handwashing in hospitals is an example of a type of policy that falls in this quadrant, as it is simple and universally effective in reducing hospital-acquired infections but also extremely difficult to get health workers to do routinely. Increasing rates of childhood immunization is another example, as well-established vaccinations are consistently efficacious but many children fail to receive immunizations every year. If a policymaker were considering adopting a policy of promoting vaccinations of children, she ought to be less interested in reading existing evidence (or creating new evidence through research) on the efficacy of the vaccines themselves than in evidence about how to increase vaccination rates.

As discussed in the previous section, implementation science researchers have used a range of methods – qualitative, quantitative, and mixed – and theoretical perspectives (e.g. realist evaluation) to address implementation-type problems. Outside of the systems tradition, this concern with the nitty-gritty details of how to better deliver policies and the consequences of minor variations in implementation for take-up is perhaps most closely paralleled by Duflo's ( Reference Duflo 2017 ) vision of economists (and presumably evidence-creators in other disciplines) as “plumbers” helping governments to improve delivery by varying and evaluating program details. So while implementation is clearly a core focus of many types of systems approaches, this is not to say that researchers who do not self-identify as systems researchers are uninterested in it. That said, systems researchers perhaps tend to be more willing to focus their attention exclusively on implementation issues, as distinct from the policy’s impact on final outcomes – a choice that is justifiable for the type of evidence problems posed by policies that share the features of consistent efficacy but inconsistent implementability.

This contrasts with the scenario in the bottom-right quadrant, where a policy is simple to implement but has highly variable efficacy across contexts. This is the classic external validity question: will a policy or intervention that works in one context work in a different context? Footnote 9 An example of such a problem is merit-based scholarships for education, which are relatively easy to implement in most contexts but can have high variance in effectiveness across contexts (Masset Reference Masset 2019 ). In terms of methodological responses to such problems, realist and theory-driven evaluations are commonly used by systems researchers to understand these issues of heterogeneous effects and fit with context. Meta-analysis and systematic reviews are also commonly used within the systems tradition to aggregate evidence across studies, but typically with a focus on identifying how context influences policy efficacy more than on estimating an overall average treatment effect, often by supplementing quantitative impact estimates with qualitative data and attention to mechanisms and context (e.g. Greenhalgh et al. Reference Greenhalgh, Macfarlane, Steed and Walton 2016 ; Leviton 2017). Of course, impact evaluation researchers outside the systems tradition are also increasingly recognizing these issues as important, so once again the difference is largely one of prioritization of questions and of methodological pluralism in addressing them.

Policies that are both inconsistently implementable and inconsistently efficacious fall into the category of complex systems . This exhibit features that arise from important and numerous complementarities with other policies and with features of the context, such as emergent behaviors that are not explained by those interactions in isolation; non-linearities; and system self-organization whilst operating across multiple levels and time periods (Sabelli Reference Sabelli 2006 ). Examples of complex system-type problems in public service delivery include many organization- and sector-level reform efforts, which by their nature affect numerous actors (some of whom are organized and strategic), and depend on the existing state of the system and presence of other related policy interventions. Evidence creation and use takes on very different forms for these type of problems since knowing that a particular policy worked in another context is unlikely to be informative about its effect in a new context. Footnote 10 Evidence generation and learning therefore have to take on very local forms, such as the adaptive experimentation methods (e.g. Andrews et al. Reference Andrews, Pritchett and Woolcock 2017 ) and learning sites and living labs (e.g. Sabel and Zeitlin Reference Sabel and Zeitlin 2012 ; Tsofa et al. Reference Tsofa, Molyneux, Gilson and Goodman 2017 ; Dekker et al. 2019) discussed in “Micro-systems approaches” above.

Finally, some policies may fall in the bottom-left quadrant of Fig.  4 (consistent implementability, consistent efficacy). Such policies are actually relatively amenable to straightforward evaluate-and-transport or evaluate-and-scale-up forms of evidence-based policy, so delving deeply into the complexities of context and broader systems may be unnecessary – or at least not a priority for scarce attention and resources. While context matters for the implementability and efficacy of all policies to some degree, policies such as cash transfers have been shown to be consistently effective in achieving poverty reduction outcomes across a wide range of contexts and are relatively simple to implement. As Bates and Glennerster ( Reference Bates and Glennerster 2017 ) note, it is a fallacy to think that all interventions must be re-evaluated in every context in which they are tried, and for policies in this bottom-left quadrant, systems approaches might not be necessary at all. Just as there are complex system-type policy problems for which evidence is not generalizable and nearly all learning must be local, there are also “what works”-type policy problems for which evidence is highly generalizable. The challenge for selecting a method of evidence generation and interpretation, then, is being able to predict ex ante which type of policy problem one is facing.

How might a researcher or policymaker actually go about deciding which quadrant of this framework they are in when deciding what type of evidence they need in order to make decisions about the adoption and design of a new policy? Several approaches are possible, although each faces its own challenges. First, one might approach the question of consistency of implementability and efficacy empirically, by aggregating evidence across multiple contexts and/or target groups through systematic review and meta-analysis. Indeed, multi-intervention meta-analyses such as Vivalt ( Reference Vivalt 2020 ) demonstrate that some interventions exhibit much higher heterogeneity of impact across contexts. Unfortunately, such meta-analyses do not routinely distinguish between implementation and efficacy as causes for this heterogeneity, although, in principle, they could – particularly when quantitative methods are supplemented with qualitative data in trying to aggregate evidence about interventions’ full causal chains (e.g. Kneale et al. Reference Kneale, Thomas, Bangpan, Waddington and Gough 2018 ). Second, one could approach the question theoretically, by developing priors about the complexity of each policy’s theory of change (i.e. intended mechanism) and its scope for complementarities with other policies or aspects of context in terms of implementation and efficacy. Finally, Williams ( Reference Williams 2020 ) proposes a methodology of mechanism mapping that combines theory-based and empirics-based approaches to developing predictions about how a policy’s mechanism is likely to interact with its context, and thus how heterogeneous its implementability and efficacy are likely to be. All of these approaches have obvious limitations – limited evidence availability, and the difficulty of foreseeing all potential complementarities and their consequences – and in practice would likely need to be combined. Fig.  4 is thus likely to be of more use as a conceptual framework or heuristic device than as a device for formally classifying different types of policies. But it may nonetheless help researchers and practitioners structure their thinking about why different types of policies might present different needs in terms of evidence generation.

This article has synthesized a wide range of literature that falls under the broad label of systems approaches to public service delivery, drawing key distinctions within it and linking it to standard impact evaluation-led approaches to evidence-based policymaking. Based on our review of studies in health, education, and infrastructure, we have argued that systems approaches are united in their focus on multi-dimensional complementarities between policies and aspects of context as the key challenge for creating and using evidence. This results in a different prioritization of types of questions and greater methodological pluralism, and also gives rise to a range of different types of systems approaches, each suited to different situations and questions.

Our systems-perspective synthesis in some ways echoes, but goes beyond, discipline-specific attempts to grapple with these issues. It also illustrates ways in which the relevance of systems approaches extends beyond being a set of considerations about how best to undertake policy evaluations. In economics, for instance, issues of complementarity among management structures and processes are perhaps the central focus of the field of organizational economics (Brynjolfsson and Milgrom Reference Brynjolfsson, Milgrom, Robert and Roberts 2013 ) as well as common focuses (at least along one or two dimensions) of impact evaluations (Bandiera et al. Reference Bandiera, Barankay and Rasul 2010 ; Andrabi et al. Reference Andrabi, Das, Khwaja, Ozyurt and Singh 2020 ). Indeed, Besley et al.’s recent ( Reference Besley, Burgess, Khan and Xu 2022 ) review of the literature on bureaucracy and development (which also calls for a systems perspective) highlights the potential for this literature to draw increasingly on organizational economics and industrial organization. Similarly, understanding the impact of policies in general rather than partial equilibrium has long been valued (Acemoglu Reference Acemoglu 2010 ) and issues of external validity, implementation, and policy scale-up are now at the forefront of impact evaluation (e.g. Duflo Reference Duflo 2017 ; Vivalt Reference Vivalt 2020 ). In comparative politics, discussion of scope conditions for theories and mixed methods are frequently used to understand mechanisms and heterogeneity (e.g. Falleti and Lynch Reference Falleti and Lynch 2009 ). And in public administration, questions around how to incorporate complexity of policy implementation and governance networks in research methods (Klijn Reference Klijn 2008 ), and new governance approaches to address policy design in the face of such complexity are being increasingly discussed (OECD 2017 ).

Among practitioners, there is growing recognition that policies are designed and implemented in systems where different layers of administration, personnel, and institutions are intertwined. This has resulted in the production of various guides and frameworks on how policymakers can use tools from systems approaches to design and implement policy. For example, Woodhill and Millican ( Reference Woodhill and Millican 2023 ) offer a framework for how the UK Foreign, Commonwealth, and Development Office and its partners can employ systems thinking in their working practices and business processes. Similarly, OECD ( 2017 ) offers a discussion on how systems approaches can be used by governments to design policy and (among several other examples) describes how the Prime Minister’s Office in Finland developed a new framework for experimental policy using the tools from systems approaches. At both the macro and micro levels, systems approaches are increasingly being adopted by practitioners to navigate many of the same challenges of complexity, context, and uncertainty with which academic researchers are also grappling.

These convergences of interest, theory, and method present opportunities for cross-sectoral and cross-disciplinary learning. And while these overlaps of questions and methods do serve as a warning against strawman characterizations of other disciplines, so too can they serve to conceal real differences in the specifics of choosing and combining analytical methods, in how theoretical frameworks are constructed and tested, and – most of all – in the extent to which questions about context and complementarity are prioritized when thinking about policy effectiveness. It is our hope that this article provides readers from a range of backgrounds with a better understanding of the current state of literature on systems approaches, ideas for new avenues of connection with their work, and a common conceptual foundation on which to base dialog with researchers from different traditions who share the goal of using evidence to improve public service delivery.

To view supplementary material for this article, please visit https://doi.org/10.1017/S0143814X23000405 .

This study does not employ statistical methods and no replication materials are available.

Acknowledgements

We are grateful for funding from the Bill and Melinda Gates Foundation and helpful conversations and comments from Seye Abimbola, Dan Berliner, Lucy Gilson, Guy Grossman, Kara Hanson, Rachel Hinton, Dan Honig, Adnan Khan, Julien Labonne, Lant Pritchett, Imran Rasul, Joachim Wehner, and workshop participants at the Blavatnik School of Government and 2018 Global Symposium on Health Systems Research. The authors are responsible for any remaining faults.

1 Pritchett ( Reference Pritchett 2015 ), and Williams ( Reference Williams 2020 ), among others, for related discussions.

2 The Online Appendix gives further details on our literature review methodology.

3 The prevalence of complementarities in bureaucracies has also been emphasized in organizational research (e.g. Ichniowski and Shaw Reference Ichniowski and Shaw 2003 ; Brynjolfsson and Milgrom Reference Brynjolfsson, Milgrom, Robert and Roberts 2013 ) and used in explaining institutional path dependence (Deeg Reference Deeg 2007 ).

4 The “inventory” and “relational” terms are drawn from Hanson’s ( Reference Hanson 2015 ) excellent review of the health systems literature.

5 Pritchett’s ( Reference Pritchett 2015 ) framework builds upon the World Bank’s ( 2003 ) well-known “accountability triangle,” which is itself a relational framework.

6 Systems dynamics methodology involves computer simulation models to capture processes of accumulation and feedback using numerical values (Homer and Hirsch Reference Homer and Hirsch 2006 ). This is related methodologically to the type of formal theoretical and empirical structural modeling methods often used in the social sciences; the distinction between them lies less in the methods themselves than in the intent to model relationships across an entire system or sub-system.

7 Magrath et al. ( Reference Magrath, Aslam and Johnson 2019 ) cite various examples of mixed methods research studies under the Raising Learning Outcomes in Education Systems (RLO) research program.

8 Other authors have made similar distinctions among policies with respect to questions of implementation, external validity, and scale-up (e.g. Pritchett and Woolcock Reference Pritchett and Woolcock 2004 ; Bates and Glennerster Reference Bates and Glennerster 2017 ; Pritchett Reference Pritchett 2017 ) or with respect to the complexity of problems (Snowden and Boone Reference Snowden and Boone 2007 ). We build on these distinctions and deploy them for a different purpose.

9 We call this quadrant “pure” external validity because in practice, many impact evaluations (and hence discussions of external validity) combine efficacy and implementation when measuring policy impact or effectiveness, whereas we distinguish between external validity as a matter of a policy’s efficacy across contexts (which abstracts from implementation quality) rather than its effectiveness across contexts (which includes implementation quality).

10 The subset of systems studies that view complexity as generating fundamental uncertainty and unpredictability in outcomes (e.g. Sheikh et al. Reference Sheikh, Gilson, Agyepong, Hanson, Ssengooba and Bennett 2011 ; Snyder Reference Snyder 2013 ) could be viewed as an extreme case within this quadrant. The underlying epistemological question of whether the outcomes of such systems are impossible to predict or just very difficult to predict is beyond the scope of this article.

Figure 1. World Health Organizationhealth system framework. Source : (Reprinted with permission): De Savigny and Adam (2009).

Figure 2. Education system framework. Source : (Reprinted with permission): Pritchett (2015).

Figure 3. Infrastructure system representation with six critical national infrastructures. Source : (Reprinted with permission): Thacker et al. (2017).

Figure 4. Synthesizing micro-systems approaches. Source: Author’s synthesis.

Mansoor and Williams supplementary material

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• Volume 44, Issue 2
• Zahra Mansoor (a1) and Martin J. Williams (a2)
• DOI: https://doi.org/10.1017/S0143814X23000405

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Open Access

Peer-reviewed

Research Article

Public service motivation and organizational performance: Catalyzing effects of altruism, perceived social impact and political support

Contributed equally to this work with: Syed Sohaib Zubair, Mukaram Ali Khan, Aamna Tariq Mukaram

Roles Conceptualization, Project administration, Supervision, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Administrative Sciences, University of the Punjab, Jhelum, Pakistan

Roles Formal analysis, Validation

Affiliation Institute of Administrative Sciences, University of the Punjab, Lahore, Pakistan

Roles Data curation, Investigation, Writing – original draft

Affiliation Islamia University Bahawalpur, Bahawalpur, Pakistan

• Syed Sohaib Zubair, 
• Mukaram Ali Khan, 
• Aamna Tariq Mukaram

• Published: December 2, 2021
• https://doi.org/10.1371/journal.pone.0260559
• Reader Comments

With the increasing pressures and demands from the public sector to be more efficient and effective and accountable, the idea of Public Service Motivation (PSM) and Organization Performance (OP) has become more relevant and critical. This quantitative research hypothesizes that PSM leads towards higher level of organizational performance among public sector officials and also explores the intervening effects of Altruism (ALT), Perceived Social Impact (PSI) and Political Support (PS) in this context. Based on self-administered questionnaire, data was collected from 405 public officials using random sampling strategy. Covariance Based Structural Equation Modelling was used to test the hypothesized model. Following the validation of the measurement model, structural model was developed to test the various paths predicted in the hypotheses. Analysis revealed that PSM, PS and ALT have a positive relationship with OP whereas PSM relationship with PS could not be established.

Citation: Zubair SS, Khan MA, Mukaram AT (2021) Public service motivation and organizational performance: Catalyzing effects of altruism, perceived social impact and political support. PLoS ONE 16(12): e0260559. https://doi.org/10.1371/journal.pone.0260559

Editor: Rogis Baker, Universiti Pertahanan Nasional Malaysia, MALAYSIA

Received: September 14, 2021; Accepted: October 26, 2021; Published: December 2, 2021

Copyright: © 2021 Zubair et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data is available within the paper and Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Governance and Government related issues are becoming increasingly complex and it is the need of the hour to focus on various possible solutions in the light of “dynamics of modern societies” [ 1 ]. Among various issues, the idea of motivating public sector employees has always been one of the major challenges. The literature in public administration has long endeavored to distinguish the characteristic of public and private administration. Public administration theorists and scholars have incorporated an enormous amount of time in anticipating what motivates public sector employees. Due to the reason that public sector lacks in providing explicit financial incentives to the employees and the fact that government employees look up to a clear and meaningful service, the available research in this realm has been majorly tilted towards non-financial factors [ 2 ]. Two vastly researched non-financial factors among these are goal clarity i.e. [ 3 ] and public service motivation [ 4 ]. The understanding of motivation for individuals working in public organizations is a prerequisite for the management and structure of public sector and for a prosperous provision of public services. Motivation in its general description withholds a stimulus that strengthens, sustains and directs the behavior of individuals, while for public service employees these motivational stimuli are specifically associated with the desire contributing in the social world and serving its citizens [ 5 – 7 ]. Studies such as [ 8 ] and [ 9 ] clearly presents that Public Service Motivation (PSM) cultivate higher performance in organizations only when managers get the instinct of employees feeling that they can hold a constructive influence on society.

According to pioneer studies including [ 10 ] and [ 11 ], it is assumed that employees in public sectors carry a motivation and zeal of serving public which is not present in private sector employees. Since the very beginning, public sector has been highlighted as a responsibility, a duty and a calling instead of merely being a job because, these employees are supposed to be motivated by the ethics of serving public in contrast to employees working in private sector organizations. While on the other hand, the rational choice theories of public administration view public administrators as self-interest maximizers not paying credits to those moral responsibilities which are not specifically reflecting any explicit goals and the external rewards associated with their achievement [ 12 ]. While many organizational theorists and behavioral scientists have tried to indicate the significance of non-selfish motivational elements such as loyalty, altruism and sense of responsibility in overcoming the most highly reported malpractices in public sector organizations such as self-aggrandizement, free riding and opportunism.

A study has highlighted the failures and challenges of traditional incentives in motivating public sector employees [ 13 ]. Moreover, [ 14 ] demonstrated the negative impacts of pay for performance on public sector and also depicted that these negative effects are more likely to persist in public when compared to the management of private sector. In short, studies such as [ 15 ] suggest that by adopting the practices of private sector may not necessarily lead towards the similar perks and advantages of performance in public sector organizations.

Scholars and practitioners in the field have been active in the process of deepening our understanding of why employees in public sector urge to act more in the favor of common good as compared to private sector employees. The leading theoretical perspective which explains the reason why public employees are more active in serving society is public service motivation [ 16 ]. According to [ 17 ], public service motivation has been defined as the belief, values and attitudes that go beyond self-interest and organizational interest, that concern the interest of a larger political entity and that motivate individuals to act accordingly whenever appropriate.

The growing volume of research in the domain of public service motivation is the spirit of this study. [ 18 ] and [ 19 ] report that, beforehand the research on public service motivation has predominantly been conducted in European and American context and Asia has generally being under-researched. The notion to improve the performance of public sector organizations in Pakistan carries equal importance. Since, public sector reforms in the country have specifically endorsed ‘merit-based systems’ and a performance oriented culture which is altogether different from traditional practices that levies growth demands on public sector employees and public organizations in general. The study hypothesizes that public service motivation leads towards higher level of organizational performance among public sector officials because they value organizational results and fate as their own. It contributes to theory and evidence by providing meaningful insights into how public service motivation increases organizational performance amidst the presence of altruism, perceived social impact and political support.

2. Literature review and theoretical underpinnings

Over a period of more than two decades, studies such as that of [ 20 ] and [ 21 ] in public administration research have compiled the need to understand the context of work motivation in public sector organizations. Undeniably, work motivation is a complex subject, and no single theory of motivation can address all the contextual settings of work motivation [ 22 ]. The advocates of goal theory i.e. [ 23 ] promoted that goal theory is conceivably potentially appropriate in the motivational settings of public sector. This assertion may not be true for the reason that it relies on “personal significance reinforcement” instead of monetarist incentives, rather it is considered convincible due to the vital share it carries into many other motivational techniques.

The motivational explanation presented by goal theory illustrates that variations in the performance of employees are not due to the situation or ability rather due to their diverse performance objectives [ 24 ]. Likewise, according to the social cognitive theory, goals do not provide enough explanations to motivate employees to perform, rather these are the discrepancies that individuals shape to compare their actual performance with their desired performance which motivate their behaviors [ 25 ]. The outcome of these discrepancies is a feeling of self-disapproval or approval which encourages individuals to perform in a way that increases self-approval.

As per [ 22 ], the integration of goal and cognitive theories is practical and significantly important to understand motivation in the domain of public sector. Resultantly, if public sector carries ambiguous goals or some conflicting contextual or procedural constraints, these characteristics put a potential influence on the attitudes of employees which as per social cognitive theories are the keystones of work motivation. The present study contributes to the understanding of public service motivation and its relationship with organizational performance by considering altruism, perceived social impact and political support as possible imminent factors that can significantly influence.

Political environment and its corresponding governmental reforms can be a challenge in the path of stimulating the provision of public service motivation despite of having synchronization between organizational and employees’ values. Since, organizations in public sector are typically engrossed in high bureaucratic systems and political structures where individual service providers work within the confined system of directives, rules and regulations and are accountable to their political heads. This does influence their potential of public service motivation and their abilities to uphold [ 26 ]. Policies and political environment carry power to influence the working conditions of service providers [ 8 ] and when such policy reforms are perceived by service providers as a source of their work support, motivation can be multiplied rather than being curtailed [ 7 , 27 ].

Studies such as those by [ 28 ] show that employees who carry higher level of public service motivation do take part in social and political activities, and these organizations promote several values associated to their motivation. Similarly [ 29 ] reveal that people with an orientation for doing good to authors’ value social impact and are likely to be more helpful in certain public services.

Undertaking an institutional and organizational framework, this study argues that the presence of altruism, perceived social support and political support in public service motivation-Organizational Performance relationship demonstrates exactly how this association unfolds. Finally, the study asserts that the presence of contextual factors such as altruism, perceived social impact and political support as potential mediators can assist the relationship between public service motivation and organizational performance. This discussion leads to the elaboration of key constructs in this study, followed by development of research hypotheses that are to be tested.

2.1 Public service motivation

The term public service motivation was first coined by [ 30 ] which was further elaborated by Perry and Wise formally and in consequence of it the research in the realm of public service motivation was sprouted. The description of [ 10 ] states public service motivation as “ the individual predisposition to respond to motives primarily or uniquely found in public institutions ”(p.368). Moreover, in addition to this description, public service motivation is also portrayed as a general orientation of individuals towards delivering services to people with the intention of doing good for society at large [ 31 ].

Research about public service motivation has uprooted rapidly since two decades. [ 10 ] elaborate that public service motivation is to influence employees’ behavior in three different manners i.e. (a) as the level of public service motivation escalates, individuals are more oriented towards working in public sector organizations (b) public service motivation is significantly correlated with job performance of employees in public organizations and that (c) public organizations are comprised of higher number of employees having higher degrees of public service motivation and are not necessarily in need for extrinsic incentives to fulfill their motivation. While [ 17 ] emphasizes on the altruistic component present in public service motivation and further describes it as the beliefs, values and attitudes that go beyond self-interest and organizational interest, that concern the interest of a larger political entity and that motivate individuals to act accordingly whenever appropriate. Perry et al. [ 32 ] argue that due to the blend of altruism, public service motivation has to be considered as a specific type of motivation. According to [ 33 ], some normative concerns such as political ideologies are also a part of public service motivation. As per [ 34 ] and [ 35 ], public service motivation does not only measure motivation in public sector employees, it is rather equally applicable for studying the motivation of volunteer workers. Furthermore, in the perspective of public service motivation, employees’ motivation is slanted towards realizing the importance of goals and services in the public sector because they are a part of some specific public employees and hence get to justify their performance and behaviors accordingly [ 34 ].

Public service motivation and its role as an independent variable is of special consideration because of the proposed welcoming outcomes research has found it with. Studies have found public service motivation’s association with individual and organizational performance [ 36 ]. As per [ 37 ], this relationship has been relatively under researched. Furthermore, [ 18 ] in their comprehensive literature review have lately reported 34 studies out of around 300 articles over a span of twenty five years. In that review 21 studies demonstrated a positive relation between public service motivation and performance, while the rest represented assorted or neutral finding.

Public administration scholars advocate that the true spirit of public service-motivated employees resides in serving the abstract notion of public interest through contributing and serving the society at large. It is also reasoned that public service motivation which focuses on societal well-being primarily resonates with “ societal altruism ” [ 38 ].

2.2 Altruism

Altruism comprises of behaviors a person, a group or an organization takes part in for the sake of providing benefits or to improve the wellness of the beneficiaries. It can also be describes as exhibiting one’s own personal resources to benefit others. It works as an ethical doctrine in which the moral values of an individual’s action are dependent solely on their influence over others regardless of their consequences and outcomes on the individual itself. It is also similar to the concept of formal utilitarianism which advocates maximizing acts which hold good consequences for whole society. Moreover, according to [ 39 ], altruism is defined as “acting on genuinely selfless motives to enhance another’s welfare” . It suggests that altruism is a special behavior grounded on particular sets of fundamental yet theoretically distinctive motives.

In psychology research the concept of altruistic motivation and altruism are considered to describe the motivational dimension. However, studies such as [ 40 ] consider it as an ambiguous psychological terminology and argue that it is important to noticeably explain altruism as a behavior, otherwise it may hold identical meanings as the description of prosocial motivation. In line with this description, the present study undertakes the explanation of altruism in the perspective of [ 41 ] i.e. “evolutionary biology” which expresses altruism as “conferring a benefit ‘b’ on the recipient at a cost ‘c’ to the donor” , this definition explicitly withhold the conceptual basis of altruism and align with the concept of a behavior and not of a motivation . Through the discussion these narrow differences among motivations and behaviors scholars are more able to reduce the complexities by ultimately steering towards conceptual clarity [ 42 ]. As per [ 10 ] altruism contributes in building normative and affective motives among individuals i.e. the normative aspiration of serving and working for the public interest can be regarded as being altruistic. Scholars such as [ 43 ] studied the potential connection of the affective dimension of altruism and selflessness. Piatak and Holt [ 44 ] comprehensively describe that public service motivation and altruism undoubtedly measure some intersecting fragments of prosocial motives for behavior but on the other hand they are different concepts where public service motivation is founded to be more likely predicting voluntary behaviors as compared to altruism.

2.3 Perceived social impact

The concept of perceived social impact is described in terms of degree to which employees analyze their actions while positively influencing their recipients, for instance, by offering such services and products that create a positive impact in the lives of customers [ 45 , 46 ]. In some of the pioneer research, the connection between perceived social impact and job performance has been demonstrated clearly. Grant in a series of experiments [ 45 , 47 , 48 ] demonstrated that connection with recipients amplified social impact’s perception and consequently instigated higher persistence and improved work performance.

In a study on public sanitation department, [ 49 ] have concluded that perceived social impact significantly curtails emotional collapse and increases administrative performance ratings among employees.

2.4 Political support

Easton [ 50 ] (p.436) describe political support as the “degree to which individuals evaluate political objects positively , that is , the mix of attitudes about political leaders , institutions and the system as a whole” . According to [ 51 – 53 ] there are different faces of political support. Tausendpfund and Schäfer [ 54 ] distinguishes “overt support”, that are “supportive activities”, such as vote casting in favor of some political candidate and “covert support”, that is associated with “supportive behaviors” i.e. party loyalty. Moreover, according to [ 55 ] the concept of political support acts as multidimensional because it includes contentment with policies as well as a general assessment which reports how well a political system, its authorities or institutions are meeting the normative expectations of its residents. As per [ 56 ] and [ 57 ], political support elevates in the presence of direct democratic instruments which are considered while political decision making. Moreover, with reference to the procedural fairness theory, [ 58 ] argues that just procedures curtail the negative consequences of unsuitable decisions, which means that citizens may not receive the desired outcome but since, they held a support for raising their voice in the processes, they endorse the processes and call them just and fair which in consequence amplify their political support.

Furthermore, Bowler and Donovan [ 59 ] (p.376) explains that citizens due to the notion of direct and democratic decision-making hold an “occasional voice in government”, which means that their voices are given a considerable attention and they are able to take decisions on specific issues and are listened to. This notion of feeling themselves as a credible part of decision making signifies their perception of influence and political support. According to [ 60 ] this practice largely illustrates their sense of self-determination along with a significant sense of control on their society and living conditions. Shomer et al. [ 61 ] illustrates that the higher degrees of people’s involvement and participation in electoral procedures for the political parties amplifies political support.

2.5 Organizational performance

Organizational performance is generally theorized in terms of the actual output of an organization which are measured against its desired or intended results, objectives or goals and meet the expectations of different groups of stakeholders [ 62 ]. The level of organizational performance is evaluated through several elements consisting of operational efficiencies, levels of diversification, mergers, acquisitions, composition of top management and organizational structures and manipulation of social or political effects interfering with the market conformity [ 63 ]. Although, the measuring criteria for organizational performance has been remained controversial. Studies such as [ 64 ] endorse adopting a multi-dimensional approach to measure organizational performance which reflect a broader range of interests of stakeholders. However, Rouse and Putterill [ 65 ] demonstrates that there is no single performance criteria that is suffice enough to be applicable for all organizations. Hence, organizational performance being a complex subject should always be studied in the contextual settings of the existing context [ 66 ]. Exceptional results are maintained by organizations when they meet the expectations of stakeholders within society [ 67 ]. Based on all this discussion and the objectives of the study, Fig 1 below depicts the research model developed for the study.

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2.6 Hypothesis development

Distinct studies such as [ 68 , 69 ] illustrate that public service motivation leads towards individual performance. [ 17 ] validates the potential evidence that public service motivation is positively connected with job performance. Moreover, an empirical study conducted on medical staff i.e. nurses in Italy proliferates that public service motivation carries a significant positive association with performance [ 70 ]. In some relative studies comprising of small samples from nurses, school teachers and other government employees represented a positive relationship of public service motivation and job performance i.e. [ 70 , 71 , 36 ].

Public administration scholars and experts are captivated in knowing the way PSM amplifies organizational performance of employees in public sector organizations [ 37 ]. In the available literature PSM has been associated to primarily positive consequences, such as organizational citizenship behavior as depicted by [ 72 ], organizational commitment as studied by [ 73 ] and job satisfaction as portrayed by [ 74 ]. However, there is a lack of empirical research on the relationship between public service motivation and organizational performance and this relationship is still inconclusive in the available literature [ 37 ].

In addition to this discussion, [ 75 ] describes that the conceptuality of performance and what creates performance in public sector is complex because it might comprises of some private sector measures such as efficiency or it may carry orientation towards more of public setting objectives such as transparency, access to public, and alleviation of corruption. A meta-analysis [ 76 ] demonstrated that a performance surge and a higher possibility of better performance can be seen with the help of intrinsic motivational sources in contrast with extrinsic motivators. According to [ 77 ], there is a significant positive connection between public service motivation and organizational performance. In the light of these findings this study leads towards hypothesizing that public service motivation is potentially related with organizational performance.

• H1: Public service motivation is significantly positively related with organizational performance.

As far as the relationship between public service motivation and political support is concerned, there are quite a few studies which have been conducted on said variables. According to a research conducted on undergraduate students public service motivation is identified as one of the major factors in increasing political participation and support [ 78 ]. Another study conducted on 300 civil servants found a positive and significant relationship between political support/loyalty and public service motivation [ 79 ].

As far as relationship between political support and organizational performance is concerned, a study conducted by [ 80 ] highlighted the positive role of organizational performance in unfolding the role of political support and concluded that political support is inevitable in accessing organizational performance. According to [ 81 ], a study conducted on elected officials found a positive relationship between political support and organizational performance. Based on following studies, following hypotheses have been developed;

• H2: Public service motivation holds a significant positive relationship with political support.
• H2a: Political support is significantly and positively associated with organizational performance.
• H2b: Political support performs as a potential mediator between PSM and organizational performance.

As far as relationship between public service motivation and Altruism is concerned, there are very few studies which are conducted on the relationship between these two concepts as number of studies tried to distinguish these two concepts [ 42 , 44 ]. As per [ 82 ], a late study conducted in 1870 on university students resulted into finding that public service motivation and altruism are significantly positively correlated with each other and also found that public service motivation may act as a potential predictor of Altruism.

There are quite few studies steered on trying to develop the connection between Altruism and social impact. According to [ 83 ], it was concluded that there is a crucial role of altruism in the society which can eventually create positive social impact. According to [ 84 ], a study conducted on US public and non-profit employees concludes that performance metrics are more likely to be used by those public servants who consider social impact as an important aspect of their tasks. According to [ 46 ], study concluded the positive relationship between perceived social impact and performance. Based on the aforementioned discussion, subsequent hypotheses are developed;

• H3: Public service motivation is significantly positively associated with altruism.
• H3a: Altruism is expected to have a significant positive relationship with perceived social impact.
• H3b: Perceived social impact is anticipated to be positively associated with organizational performance.
• H3c: Altruism and perceived social impact mediates the relationship of PSM and organizational performance.

3. Methodology

Design and protocols developed or followed for a study are of critical nature [ 85 ]. They add that no matter how advanced statistical tool a researcher uses, the research effort might not have sound weightage if the fundamentals of research design and methodology are not carefully taken care off. This research on various factors associated with public officials and their performance has been evaluated by following a quantitative research methodology and a cross-sectional research design. The sample included officers from public organizations/departments under the federal and provincial governments in Pakistan, where the population is 1343, as per the list available with the central bank i.e. the State Bank of Pakistan. On the basis of [ 86 ], the minimum sample size calculated was to be 308. The questionnaires were sent to 475 civil servants using random sampling strategy and 405 were received as duly filled making the response rate of approximately 85.26%. The reason for sending 475 questionnaires was the potential issue of no response, however, the response rate was good in actual. The said individuals in the sample representing their organizations were from top-tier management. In order to tap the organizations, simple random sampling strategy was used and organizations were selected from the frame available. It is imperative to mention that verbal informed consent was taken from the respondents and all details regarding the purpose of data collection and the research work were shared in a cover letter attached with the instrument.

Furthermore, in order to collect data, a structured questionnaire was adapted after extensive review of the literature and responses were recorded using a 5 point Likert Scale. Table 1 highlights the scale used to measure all variables, the number of items used and a sample item for each construct:

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The model developed earlier, and the collected valuable responses were put to inferential analysis using Co-Variance Based Structural Equation Modelling (SEM) through AMOS. Prior to the testing of the hypothesis through the structural model, several perquisites were established and ensured using the confirmatory factor analysis (CFA). The model fitness was tested and ensured, followed by the confirmation of the convergent and discriminant validities. Table 2 summarizes the demographical characteristics of the officers that were part of the final sample:

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The sample included 84% male officers and 16% female officers playing a lead role in the organizations under study. 33.6% of the total sample was officers with experience of less than 15 years whereas 66.4% were of more than 15 years of service in the public sector. Referring to the education of such officers, 33.3% were bachelors, 64% had a Masters/M.Phil degree whereas 2.7% had a PhD. Public organizations or offices in Pakistan range from federal, provincial and district level. 40.5% respondents were from federal organizations, 49.1% from provincial organizations, whereas 10.4% were currently serving in district level organizations.

This study has used descriptive statistics including the Means and Standard Deviations of the latent constructs whereas measurement and structural model using covariance based SEM. As far as descriptives are concerned, the mean values of all latent constructs are between 2.74 and 3.68 whereas standard deviations are from 0.54 to 0.81 shows the dispersion of mean.

4.1 Measurement model

The purpose of measurement model is to check the reliability and validity of the model. It also identifies the model fitness indices which ultimately decide the fitness of the model. At first stage, it is highlighted that the Standardized Factor Loading (SFL) of each item should be at least 0.60. However, as per the initial model, the only item whose factor loading was found to be less than the threshold value was PSM 1. After removing the said item, the model was run again and found all the items more than the threshold value of 0.60. At first stage, model fitness indices were tested using covariance based Structural Equation Modeling (SEM). As far as relative chi-square value is concerned, its threshold value is up to 3 [ 92 ] which stands true in this case as the value was found to be 2.90. Moving on, Goodness of Fit index (GFI) [ 93 ], Normed Fit Index (NFI) [ 94 ], and Tucker Lewis index (TLI) [ 95 ] have threshold values of minimum of 0.90 and in this case, all values meets the minimum threshold with the value i.e. 0.901, 0.927 and 0.941 respectively. Furthermore, Comparative Fit Index (CFI) minimum threshold is 0.940 [ 96 ] and its obtained value is 0.950. Lastly, RMSEA minimum threshold is up to 0.080 and in this case, it is 0.069 meeting the minimum threshold [ 97 ].

4.1.1 Composite reliability and convergent validity.

Table 3 highlights the composite reliability and convergent validity. Convergent validity which refers to the accuracy of convergence of items towards their respective latent constructs [ 98 ]. For fulfilling the criteria of convergent validity, three criteria must be fulfilled. One the minimum SFLs must be at least 0.60 which is the case in this study. Secondly, Composite reliability (CR) refers to the internal consistency of the items and its values should be at least 0.70 [ 99 ] which in this case stands true as CR of public service motivation, altruism, social impact, political support, and organizational performance is 0.826, 0.838, 0.854, 0.820 and 0.939 respectively. Thirdly, Average Variance Extracted (AVE) should be at least 0.50 [ 97 ] which also stands true in this case as AVE of public service motivation, altruism, social impact, political support, and organizational performance is 0.544, 0.567, 0.593, 0.604 and 0.660 respectively. Looking at the aforementioned discussion, it is concluded that convergent validity exist in the model.

https://doi.org/10.1371/journal.pone.0260559.t003

4.1.2 Discriminant validity.

As far as discriminant validity is concerned, it refers to the level to which participants were able to differentiate between the items of latent constructs [ 97 ]. For meeting the criteria, all the values of the correlations should be less than the square roots of AVEs. As per Table 4 , it can be seen that all the values of the correlations are less than the square roots of AVE which means that discriminant validity exist in the model.

https://doi.org/10.1371/journal.pone.0260559.t004

Lastly, as far as Common method Bias (CMB) is concerned, “Harman Single Factor Test” (HSFT) is used which is referred to see whether “change in single factor affects all the variables in the data and that variance should be less than 0.5 to avoid CMB” and in this study, value of HSFT is found to be 0.09, therefore it is reported that data is not suffering from CMB [ 100 ]. However, there are few limitations associated with technique [ 101 ], hence, “Common Latent Factor” (CLF) test is used through SEM by “comparing standardized regression weights (SRWs) with and without CLF and found that SRWs without CLF were higher than SRWs with CLF with the difference of less than 0.05”, ultimately concludes that data is not having CMB [ 102 ].

4.2 Structural model

Fig 2 is the structural model developed for testing the hypotheses of the study. As per Table 5 , it can be seen that public service motivation is directly and positively related to organizational performance at β = 0.41 which approves first hypothesis. As far as public service motivation relationship with political support is concerned, the relationship was not found to be significant at β = 0.05 and rejected second hypothesis. As far as political support relationship with organizational performance is concerned, it was found to be significantly positive at β = 0.29 and approved H2a. Due to the rejection of H2, mediation path due to political support between public service motivation and organizational performance was also found to be insignificant at β = 0.015. As far as relationship between public service motivation and altruism is concerned, the relationship was found to be significantly positive at β = 0.30 leading to the acceptance of H3. Similar relationship was found between altruism and social impact and social impact and organizational performance at β = 0.38 and β = 0.30 accepting the H3a and H3b. Due to these significant relationships, serial mediation due to altruism and social impact between public service motivation and organizational performance was found to be significant at β = 0.034 approving H3c and similar results were found by taking altruism as mediator between public service motivation and social impact at β = 0.11 leading to the acceptance of H3d and by taking social impact as mediator between altruism and organizational performance at β = 0.41 ultimately the acceptance of H3d.

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5. Discussion and conclusion

This study aims to respond the recent for empirical research into the association between public service motivation and organizational performance. The relationship between public service motivation and organizational performance carries an utmost significance for researchers’ community because scholars are eager in identifying some predictable connection between what motivates employees and stems their organizational performance in the public sector. It is direly important to look into these concepts and strengthened them owing to the high stakes involved in the public sector.

Results found that public service motivation is significantly and positively related with organizational performance as reflected in H 1 . The pioneer of the idea of public service motivation i.e. [ 10 ] argued that employees having greater level of public service motivation carry greater chances of performing better in public sector organizations. The positive insights regarding the relationship between public service motivation and organizational performance supports a recent empirical study [ 70 ] in this domain.

Moreover, two highly cited studies i.e. [ 103 ] and [ 104 ] based on sectoral comparison reported that employees from public sector held a greater enthusiasm towards helping others and working for societal benefits. Moreover, the results from public and private sectors confirmed that public sector employees are more altruistic in their behaviours and are more prosocial as compared to other members of society. Likewise, [ 73 ] examined a significant positive relationship of public service motivation with performance and support for governmental reinvention activities.

Furthermore, [ 105 ] elaborate two premises in this domain. The first premise describes that public service motivation is more like a behavioral trail where altruistic characteristic motivates prosocial behavior among employees. The second premise holds description that people in every walk of life can have altruistic traits and be motivated to perform social service, public service motivation is more likely to be present in public sector employees as compared to private sector and elsewhere.

Moreover, the findings support the serial mediation path in the conceptual model (H3c) i.e. PSM → ALT → PSI →OP which hypothesizes that altruism and perceived social impact mediates the relationship of public service motivation and organizational performance. The results also suggest that public service motivation is strongly and positively related with altruism hence, approving the assumption of H3. On the basis of similarity between public service motivation and altruism some scholars encourage to establish some conceptual boundaries between them [ 18 ]. Scholars such as [ 21 ] have used public service motivation as some general interchangeable concept of altruism. While others have distinguished public service motivation as a prosocial motivational element that is primarily grounded in public sector employees and altruism as a general motivational dimension which aids to serve more specific subgroups among people. Scholars also agree that altruism is one the multiple dimensions of public service motivation [ 32 , 43 , 106 ]. Public service motivation is more likely understood as a general motivation directed towards society or individuals; it is highly expected that public service motivated employees indulge in different types of altruistic behaviors or societal altruism. Moreover, it is argued that public service motivation which potentially directs towards society is associated with societal altruism. The results of this study which show that public service motivation is positively associated with altruism, which are in line with [ 9 , 38 , 42 ].

Moreover, the results indicated that altruism is positively related with perceived social impact and validated the postulation of H3a. In relation with these findings [ 107 ] suggest that public service motivation potentially predicts employees’ perception of social impact of their jobs. Moreover, [ 45 ] showed that employees’ motivation can be amplified when linked with the prosocial impact of their jobs.

In addition to this, the last path of serial mediation approves that perceived social impact is significantly and positively related with organizational performance hence supporting H3b. The results equate with [ 45 ] which found that perceived social impact brings about dedication and is positively related with performance. Furthermore, [ 46 ] describes that perceived social impact plays a positive role in determining employees’ motivation to perform their jobs well. Existing empirical research in this realm such as [ 17 , 74 , 78 ] provide evidence that the real benefits of public service motivation may rely on employees’ perception that their work provides them with enough opportunities to serve others. Moreover, [ 49 ] and [ 108 ] argue that higher degrees of perceived social impact lower emotional exhaustion of employees and stimulate them towards higher performance. [ 84 ] present that when public sector employees are pro-socially motivated and perceive a meaningful influence and purpose of their job on others, they provide organization with high end performance gifts.

The data did not show support for the overall mediation path i.e. H2b which hypothesized that political support performs as a potential mediator between public service motivation and organizational performance.

Noticeably, the results did not validate the assumption of path A of mediation i.e. H2 which hypothesized that public service motivation is positively related with political support. [ 109 ] support the findings by illuminating that public sector employees having higher levels of public service motivation are more vulnerable to perceptions of politics as compared to those having lower levels of public service motivation. In addition, [ 110 ] emphasize that public sector employees carry higher levels of self-efficacy and can be more productive when they perceive their organization to be less political or non-political. Keeping this view it can be assumed that public service motivation is a behavioral trait and public service motivated employees are not necessarily reliant or in wait for political support in their respective organizations.

While, the path B of mediation i.e. H2a which postulated that political support is positively related with organizational performance was supported by the data. It is normally argued that the firms which bear high political support carry easy access ability towards long term governmental loans and other governmental privileges. The findings of this study equate with [ 111 – 114 ] and suggest that being politically supported ultimately upsurges organizational ability to showcase higher performance. In addition to this, [ 112 ] demonstrates the importance of political regimes by approving that the performance of politically supported organizations in Pakistan increased during political regimes when compared with military regimes.

The study generates enough evidence that the presence of public service motivation carries a positive impact on employees’ job behavior and organizational performance in particular. It is therefore inevitable for public sector organizations to seek ways to maximize and encourage public service motivation among their employees. It concludes that altruism and perceived social impact positively mediates the association of public service motivation and organizational performance. While political support does not validate itself as a potential mediator between public service motivation and organizational performance. However, political support individually proves itself to be a potential predictor of organizational performance. To sum it up, Public Service Motivation is a concept that is not just of scholarly interest to academicians but it equally interests and applies to practitioners particularly public administrators and managers that need to deal with multiple complexities and challenges, varying from efficient use of financial and human resources in order to make sure that the public offices and organizations are responsive to the public, and meeting its objectives [ 115 ].

5.1 Managerial implications

The present study provides relevant insights and practical implications for public sector organizations, their employees and managers by adding its valuable evidence which supports the role of public service motivation and its contribution in achieving organizational performance. It provides a meaningful contribution by providing a practical usefulness of undertaken constructs i.e. public service motivation, organizational performance, social support and political support in the field of research in public administration. The observed relationship between public service motivation and organizational performance can be useful in measuring the behavioral traits and channeling the performance and motivation of public sector employees. Moreover, the findings are useful for practitioners because they demonstrate the importance of employees’ perceptions of social impact and emphasize their positive role in relation with organizational performance. It is reiterated that organizational performance in the context of public sector are very crucial, owing to the fact that high stakes involved and increasing demand for efficiency and effectiveness along with the demand for accountability. Therefore, the model developed in this study syncs with the emerging requirements of the global public sector.

5.2 Limitations and future directions

The study acknowledges few limitations. First, the cross sectional nature of the study limits it to assert the possibility of causation among variables. Another possible threat is related to the validity and truthfulness of employees’ belief and the reliance on them because, they cannot be observed or measured directly such as public service motivation and perceived social impact. An earlier research i.e. [ 116 ] found that diverging personality traits may influence research related to such concepts. Hence, an inability and limitation to control some personality traits such as altruism, public service motivation or perceived social impact always prevail in such research. Furthermore, demographic factors have not been controlled in this study making it as one of the limitations. Moreover, the generalizability of these empirical findings is limited since, it comprises the contextual settings of public sector organizations in Pakistan, however some findings may be attributed to the developing countries with a similar political and administrative infrastructure.

Future research may introduce a longitudinal research design to study the influence of time lag between the exogenous i.e. public service motivation; mediators i.e. altruism, perceived social impact and political support; and endogenous variable i.e. organizational performance. Furthermore, a multilevel analysis with data from affectees of certain public sector organizations can enrich the literature and provide further insights.

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Journal of Service Management

ISSN : 1757-5818

Article publication date: 22 September 2017

Issue publication date: 19 October 2017

In providing a fine-grained analysis of public service management, the purpose of this paper is to make an important contribution to furthering research in service management, a body of literature that has tended to regard public services as homogenous or to neglect the context altogether.

Design/methodology/approach

Integrating public management and service management literatures, the past and present of public service management are discussed. Future directions for the field are outlined drawing on a service-dominant approach that has the potential to transform public services. Invited commentaries augment the review.

The review presents the Public Service Network Framework to capture the public value network in its abstraction and conceptualizes how value is created in public services. The study identifies current shortcomings in the field and offers a series of directions for future research where service management theory can contribute greatly.

Research limitations/implications

The review encourages service management research to examine the dynamic, diverse, and complex nature of public services and to recognize the importance of this context. The review calls for an interdisciplinary public service management community to develop, and to assist public managers in leveraging service logic.

Originality/value

The review positions service research in the public sector, makes explicit the role of complex networks in value creation, argues for wider engagement with public service management, and offers future research directions to advance public service management research.

• Co-creation
• Public sector
• Service logic
• Co-production
• Public service-dominant logic
• Goods-logic

Hodgkinson, I.R. , Hannibal, C. , Keating, B.W. , Chester Buxton, R. and Bateman, N. (2017), "Toward a public service management: past, present, and future directions", Journal of Service Management , Vol. 28 No. 5, pp. 998-1023. https://doi.org/10.1108/JOSM-01-2017-0020

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Transforming public service delivery: a comprehensive review of digitization initiatives.

1. Introduction

2. materials and methods, 3. results and discussion, 3.1. benefits of digitalization in public service delivery.

• Improved Decision Making: Big data facilitates more informed and evidence-based decision making by providing access to larger amounts of information and deeper insights into patterns and trends.
• Predictive Analytics: By analyzing past and current data, predictive models can forecast future trends and needs, which allows public administration to allocate resources more efficiently and prepare for upcoming demands.
• Real-time Operations Management: Big data technologies enable the monitoring and managing of operations in real time, which allows for immediate adjustments and optimizations that reduce waste and improve response times.
• Enhanced Resource Allocation: With the proper use of big data, public organizations can better understand the distribution of resources and thus optimize their allocation to match the actual needs of citizens.
• Process Automation: Big data can identify areas where processes can be automated, leading to reduced processing times and lower costs.
• Customized Service Delivery: Big data analysis can help tailor public services to individual needs, which increases effectiveness and user satisfaction.
• Fraud Detection and Compliance: Big data enables more effective detection of fraud and non-compliance in real-time, potentially saving significant public resources.
• Collaboration and Transparency: Big data can foster greater collaboration between government entities and enhance transparency, both of which can improve efficiency and trust in public service delivery.

3.2. Technology and Governance Solutions to Address Challenges in AI Implementation

3.3. citizen engagement in digitalization transformation.

• A city should provide user-friendly and citizen-centered digital services that improve the quality of life and business environment [ 11 , 13 ]. This requires understanding the needs and preferences of citizens through conjoint analysis and other methods [ 13 , 26 ].
• Digital transformation enhances citizen participation in public service delivery and governance through digital channels [ 14 , 24 , 27 ]. This can improve citizens’ evaluation of public services and encourage broader participation.
• Digital transformation leverages big data and digital technologies to meet shifting societal expectations and create value for the public [ 17 ]. However, this requires a robust big data governance framework.
• Other research addresses the challenges of implementing artificial intelligence and other technologies, including accuracy, bias, legality, responsibility, accountability, transparency, and explanation [ 12 , 18 ]. This is to ensure technology serves public interest.
• Governments should implement citizen-focused digital transformation and user experience [ 14 , 16 ]. This involves institutional change within public organizations.
• Digital transformation utilizes e-government to improve economic prosperity, sustainable development, and citizens’ living standards [ 15 , 23 ]. However, governments must overcome various transformation challenges.
• Researchers are skeptical of claims that digitization alone will improve public services and performance without meaningful citizen engagement [ 25 , 28 ]. Empirical data does not always support this.
• Service Accessibility and Inclusiveness: Assessing how broadly and equitably services are available to different population groups, including those with disabilities or those living in remote areas [ 14 ].
• Efficiency and Response Times: Measuring the time it takes to deliver services to citizens and businesses, and how digital transformation has reduced these times [ 23 ].
• Customer Satisfaction: Using surveys, feedback forms, and social media analytics to gauge citizen satisfaction with digital services [ 25 ].
• Digital Skills and Literacy: Evaluating the impact of digital services on citizens’ digital literacy and skills development, which can lead to a better quality of life [ 11 ].
• Economic Indicators: Analyzing economic data such as employment rates, business start-ups, and growth in the digital economy as measures of business climate improvement [ 23 ].
• Quality and Relevance of Services: Assessing the fit between the services provided and the actual needs and preferences of citizens and businesses [ 26 ].
• Transparency and Trust: Evaluating how digital transformation has improved transparency in government operations and how this affects trust in public institutions [ 13 ].
• Health and Social Outcomes: Analyzing the impact of digital services on health, education, and social inclusion, which are direct indicators of quality of life improvements [ 17 ].
• Innovation and Competitiveness: Measuring the extent to which digital transformation has fostered innovation, both in the public sector and in the wider marketplace, thereby impacting the business climate.
• Data Utilization: Evaluating how effectively data is being used to continuously improve services and make evidence-based decisions [ 18 ].
• Customer Feedback and Engagement: Actively soliciting and responding to customer feedback can help tailor services to meet user needs effectively.
• User-Centric Design: Designing digital services with the user experience in mind, including ease of use, accessibility, and intuitiveness, prioritizes customer satisfaction.
• Analytics and Data-Driven Insights: Using data analytics to understand customer behaviors and preferences allows for the optimization of services and personalization of customer interactions.
• Continuous Improvement: Implementing a cycle of ongoing evaluation and improvement ensures that services remain relevant and effective over time.
• Cross-Functional Teams: Collaboration between IT, customer service, and other departments helps align digital initiatives with customer expectations.
• Training and Support: Providing adequate training and support to customers when new systems or services are introduced helps ease the transition and improves adoption.
• Change Management: Strategic change management practices that address the cultural and organizational aspects of transformation can facilitate smoother integration of digital initiatives.
• Performance Metrics: Setting and tracking key performance indicators related to customer experience helps measure the impact of digital strategies and identify areas for improvement.
• Transparency: Being transparent about how customer data is used and giving customers control over their information can enhance trust and satisfaction.
• Regulatory Compliance and Security: Ensuring that digital solutions comply with regulations and provide robust security for customer data is crucial for maintaining trust and a positive experience.
• Technological Investment: Investing in up-to-date and scalable technologies can support efficient and responsive customer service.

3.4. Implications for Digital Transformation Practices among Local Governments

• Ref. [ 11 ] emphasizes the importance of a data-driven discussion on the impact of digital transformation in the public sector and its relationship to knowledge management practices. This underscores the significance of utilizing knowledge management to drive effective digital transformation initiatives in local governments.
• Ref. [ 24 ] examines the factors driving digital transformation among local governments, providing valuable insights for smart city development in China. Understanding these drivers is crucial for local governments to plan and implement digital initiatives effectively.
• Ref. [ 13 ] presents a quantitative report on the influence of municipality population size on digital maturity in urban municipalities in Slovenia. This study offers valuable insights into the digital maturity of local governments and provides implications for digital transformation practices based on population dynamics.
• Ref. [ 27 ] provides evidence of digital transformation based on citizen evaluations of public service delivery in China. This study offers theoretical and practical implications for understanding the impact of digital transformation on public service ratings. Local governments can use this information to improve their service delivery based on citizen evaluations.
• Ref. [ 12 ] discusses the emerging trends of digital transformation and artificial intelligence in public administration. The author emphasizes the need for significant structural changes to reduce bureaucracy and enhance the quality, productivity, accessibility, and transparency of public institutions. This highlights the potential for local governments to incorporate artificial intelligence into their digital transformation initiatives.
• Ref. [ 23 ] explores e-government as a key driver of economic prosperity and sustainable development. Their research offers insights into how digital technologies can enhance the efficiency of public services and contribute to economic prosperity. This study provides implications for local governments to leverage the e-government in order to achieve sustainable development goals.
• Ref. [ 17 ] explores big data governance in public administration, highlighting the role of big data technologies in managing technological processes to deliver more efficient public services to citizens. This provides implications for local governments to effectively govern and utilize big data in their digital transformation efforts.
• Ref. [ 14 ] focuses on shifting from bureaucracy to citizen centricity, with emphasis on adopting digital capabilities to improve customer experience in public service organizations. This provides implications for local governments to prioritize citizen-centric digital transformation strategies.
• Ref. [ 18 ] outlines the possibilities, pitfalls and governance considerations associated with improving public services using artificial intelligence, offering insights into the responsible application of AI in public administration. It provides implications for local governments to address the challenges and governance aspects of integrating AI into their digital transformation initiatives.

3.5. Implementing Privacy-Preserving Measures in Cloud-Assisted Vehicular Networks

4. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Latupeirissa, J.J.P.; Dewi, N.L.Y.; Prayana, I.K.R.; Srikandi, M.B.; Ramadiansyah, S.A.; Pramana, I.B.G.A.Y. Transforming Public Service Delivery: A Comprehensive Review of Digitization Initiatives. Sustainability 2024 , 16 , 2818. https://doi.org/10.3390/su16072818

Latupeirissa JJP, Dewi NLY, Prayana IKR, Srikandi MB, Ramadiansyah SA, Pramana IBGAY. Transforming Public Service Delivery: A Comprehensive Review of Digitization Initiatives. Sustainability . 2024; 16(7):2818. https://doi.org/10.3390/su16072818

Latupeirissa, Jonathan Jacob Paul, Ni Luh Yulyana Dewi, I Kadek Rian Prayana, Melati Budi Srikandi, Sahri Aflah Ramadiansyah, and Ida Bagus Gde Agung Yoga Pramana. 2024. "Transforming Public Service Delivery: A Comprehensive Review of Digitization Initiatives" Sustainability 16, no. 7: 2818. https://doi.org/10.3390/su16072818

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Public Perceptions of the Public Service- Findings from the Ceylinco General Insurance Limited

Proceedings of the 7th International Research Conference on Humanities & Social Sciences (IRCHSS) 2021

Posted: 22 Mar 2021

Ekanayake E.A

University of moratuwa, liyanage p.m.t.s.k.s, university of ruhuna.

Date Written: March 21, 2021

In the context of rapid globalization and the rebalancing of the world economy towards developing countries, world recognizes that relationship of public sector and private sector is essential to ensure the continuing ability of businesses to compete in the international marketplace. Private sector companies are potentially the main beneficiaries when the public sector provides productive services; equally, they can be seriously held back from innovating themselves and from being profitable when public services do not deliver. The main objective of this study is to identify the private Sector Employees’ (PSE) Perception towards Public service in Sri Lanka. This study was carried out selecting a random sample of one hundred and fifty PSE representing Ceylinco Insurance Pvt Ltd. Data were gathered based on interviewed questionnaire method. Descriptive analysis, Multivariate analysis and Factor analysis were used. A specific composite index construction methodology-based weights on multiple corresponding analysis was applied to measure the level of people’s Idea about Public Sector (IPS), Effectiveness and Efficiency of Public Sector (EEPS), Knowledge about Public Sector (KPS). Results indicated that 52% use government services because they do not have any other option. 83% believe outdated rules and regulations are barriers to government organizations while 76% believe low IT penetration and 68% inefficiency employees. The workers who previously worked in government sector have better perception towards the public sector more than those who did not. Data were analysed using quantitative methods. All indices EEPS, KPS and IPS of the PSE were negatively skewed respectively with median values of 78.63, 68.32 and 67.79. Factor analysis was performed with 14 statements related to perception towards the public sector. The Cronbach alpha was 0.8631 and significant supported the use of factor analysis in order to extract independent variables associated with perception towards the public sector. The degree of common variance among the fourteen variables is “mediocre” which reflects if a factor analysis is conducted, the factors extracted will account for the fair amount of variance but not a substantial amount. Factors which decide the perception towards the public sector are efficiency and effectiveness problems, technological barriers, absence of user friendliness, personal attitudes and time consumption. Finally, strengthening the customer relationship, improving effectiveness and efficiency, removing technological barriers, creating user friendly environment, changing the personal attitudes of public servants can be given as recommendations.

Keywords: Private sector, Public sector, Perception, Effectiveness, Attitudes

Suggested Citation: Suggested Citation

Ekanayake E.A (Contact Author)

Matara Sri Lanka

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