Community-Driven Reconstruction and Recovery: A Systematic Review of Successes and Challenges

Community-Driven Reconstruction and Recovery: A Systematic Review of Successes and Challenges | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Community-Driven Reconstruction and Recovery: A Systematic Review of Successes and Challenges khaled Alosaimi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9001942/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This article examines the potential of community-driven approach in post-conflict recovery through a systematic review of 41 peer-reviewed and grey literature sources published between 2000 and 2024. The review searched Scopus, Web of Science, JSTOR, Google Scholar, and institutional repositories, and applied predefined inclusion criteria. All included studies were appraised using the Mixed Methods Appraisal Tool (MMAT 2018) to ensure methodological quality. Guided by theories of collective action and resilience, the review applies Reflexive Thematic Analysis (RTA) to address three key questions: the types of activities undertaken by CDR programmes; the core components that differentiate CDR from broader community-based interventions; and the ways decision-making, trust, ownership, and collective action interact to shape its logic and outcomes. It also highlights challenges, including social division, elite capture, weak accountability, and limited empowerment strategies. The findings advance debates on bottom-up reconstruction by demonstrating how CDR can strengthen community resilience when supported by inclusive frameworks, while also identifying conditions that may undermine its sustainability. Social Work systematic review post-conflict reconstruction governance collective action social cohesion Figures Figure 1 Introduction Community-Driven Development (CDD) is an aid delivery approach that emphasises community control over decision-making and investment resources (Fearon et al. 2008). It promotes engagement of beneficiaries in the design and management of development initiatives (Kyamusugulwa 2013 a). CDD produces two primary types of results: “more and better-distributed assets and stronger, more responsive institutions” (Humphreys et al., 2014 :2). Beath, et al. ( 2013 ) argue that CDD promotes inclusive development, empowerment, and governance strengthening; several evaluations remain cautious (Casey 2018 ). For example, Fearon et al. ( 2011 ) found limited evidence that CDD programs in Sierra Leone and Liberia significantly improve collective action or local institutional capacity. Similarly, Mansuri and Rao (2004) highlight both the potential and pitfalls of CDD, warning against assuming automatic transformation of social relations without attention to power dynamics and local context. Over the past three decades, Community-Driven Reconstruction (CDR) emerged from the CDD framework refers to post-conflict or post-disaster recovery approaches in which communities play a leading role in identifying priorities, managing resources, and implementing reconstruction activities (Bennett and D’Onofrio 2015; Lizanne and Patel 2007). Unlike general CDD, CDR is recognised for its potential to support recovery efforts, social cohesion, and promote cooperation in conflict-affected communities (World Bank 2006 ; Koyabu 2005 ). Moreover, the United Nation identify CDR as a key component in broader disarmament, demobilisation, and reintegration (DDR) programs, particularly in addressing the challenges of post-war reintegration (Del Castillo 2008 ). However, applying the community-driven approach to conflict contexts presents significant challenges, including the presence of returnee populations, ex-combatants and vulnerable populations, as well as a lack of a history of good governance and breakdown in institutions (Van Leeuwen 2010 ). Furthermore, concerns have been raised about the overlap between top-down and bottom-up approaches in reconstruction initiatives (William 2020 ), alongside ambiguity regarding the goals and expected outcomes of CDR. For example, in Sierra Leone, Casey et al. ( 2011 ) evaluated the GoBifo - a CDD initiative implemented between 2005 and 2009 that aimed to strengthen local institutions, improve decision-making processes, and finance small-scale public goods. Their findings show that while GoBifo delivered substantial improvements in local infrastructure and public goods, it did not significantly shift social cohesion or collective action in the short term. However, later follow-up studies of the same program report more positive long-term impacts, particularly in civic participation and public goods provision (White et al. 2018; Casey 2018 ). Similarly, Fearon et al. ( 2009 a) illustrate that while CDR programs in Liberia, such as small-scale infrastructure projects and community-managed resource initiatives, were generally improved infrastructure and local services, their effectiveness was limited by differences in community members’ willingness to cooperate. This review is distinct in its exclusive focus on post-conflict, its inclusion of both peer-reviewed and grey literature, and its use of reflexive thematic analysis across multiple study designs. This makes it different from previous syntheses, which have not combined these elements within a single review. For example, Samii ( 2023 ) examines the CDR effectiveness in post-war settings, focusing on Afghanistan, and highlights both successes and limitations. King ( 2013 ) assesses CDR outcomes in conflict-affected regions, reporting mixed and often disappointing results. These contributions remain context-specific and fragmented. White et al., (2018) synthesise evidence on 23 CDD programs in various contexts, but their scope extends beyond post-conflict settings, making it less relevant to my review. Given these complexities, a systematic review dedicated to community-driven approach is therefore essential to consolidate existing knowledge, identify patterns, and inform future research and policy interventions. This review aims to synthesise the existing literature on community-driven in post-conflict settings. Specifically, it explores the types of activities undertaken in community-driven programs, the contexts in which they operate, and the reported effects on communities recovering from conflict. The review is guided by McBride and Patel's (2007) definition of community-driven approach, which emphasises community ownership, governance, and accountability, providing a foundation for the subsequent analysis. Therefore, three key research questions guided this systematic literature review: Q1 What activities do community-driven programs undertake during reconstruction operations? Q2 What are the core components that make community-driven different from general community-based interventions? Q3 How do decision-making, collective action, trust, and local ownership interact to form the logic of community-driven? This review follows Khan et al.'s ( 2003 ) framework, which outlines five steps framework for conducting systematic reviews: Frame the review questions, Identify relevant studies, Assess the quality of the included studies, Summarise the evidence, and interpret the findings. The first step framing the review questions has already been completed in the previous section. The following subsections outline how the remaining four steps were systematically applied to ensure transparency and rigour throughout the review process. Identifying Relevant Publications To search for relevant empirical studies, broad keywords and search terms were used in the specialised databases to capture relevant studies written in English on the topic of community-driven approach in post-conflict settings, using all variations of relevant phrases. To include a broader range of studies, I focused on literature from 2000 to 2024 as community-driven approach gained prominence in post-conflict recovery during the early 2000s, particularly through international development initiatives. This period captures evolving debates, policy shifts, and long-term program impacts. Study design and methodological terminology, as well as terms relating to research outcomes, were excluded to avoid bias. The search strategy includes three categories: population “who”, event and intervention (See Table 1 : Search strategy). Table 1 Search strategy Who "Community" OR "leaders" OR "women" OR "youth" OR "IDPs" OR "ex-combatant" OR "survivor" OR "leader" OR "refugees" Events "post-conflict" OR "armed conflict" OR "internal war" OR "civil war" OR "peace transition" OR "ceasefire" OR "truce" Interventions "Reconstruction" OR "rehabilitation" OR "community-led" OR "community-driven" OR "empowerment" OR "collective action" OR "rebuilding" OR "reintegration" OR "reconciliation" OR "peacebuilding" OR "recovery" In addition, to identify the relevant publications based on the above broad keywords and search terms, I used academic databases and search engines, as illustrated in Table 2 : Databases searched for the systematic review. Table 2 Databases searched for the systematic review 1. ProQuest Accessibility Statement 2. ASSIA: Applied Social Sciences Index and Abstracts 3. EBSCOhost Academic Search Complete 4. Science Direct 5. EBSCOhost Research Databases 6. JSTOR digital library and platform 7. Scopus 8. Google Scholar Furthermore, bibliographies and reference lists of included articles were reviewed to identify additional relevant studies and journals not found in database searches. Including both primary studies and grey literature provides a broader and more comprehensive understanding of existing knowledge (Mahood et al., 2014 ). The grey literature search involved identifying relevant organisations, authorities, and stakeholders on Google, as well as reviewing evaluation reports, policy documents, and publications from databases such as UN Data and the World Bank Open Data. A full list of grey literature databases is provided in Table 3 : Sources of grey literature searched for the systematic review. Keywords from the earlier search strategy were also used to locate relevant reports and policies. Table 3 Sources of grey literature searched for the systematic review Name of Organisation Website National Bureau of Economic Research | NBER https://www.nber.org World Bank Open Knowledge https://openknowledge.worldbank.org ResearchGate https://www.researchgate.net SSRN (Social Science Research Network). https://papers.ssrn.com Columbia University http://www.columbia.edu International Growth Centre https://www.theigc.org Criteria for Inclusion and Exclusion The inclusion and exclusion criteria were established to ensure an unbiased selection of the most relevant articles (Siddaway et al. 2019 ). Following Mahood et al. ( 2014 ), articles were selected based on explicit and reproducible methods, with criteria clearly stated in Table 4 : Inclusion and Exclusion List of Systematic Review. Human-generated hazards, such as war and civil unrest, were included, while naturally generated hazards, such as floods and earthquakes, were excluded. Consequently, papers in non-conflict settings were excluded, as the dynamics and security challenges in post-conflict environments differ substantially from those found in purely natural disaster–related contexts (Peters and Kelman 2020). However, studies situated in mixed conflict–disaster environments such as Crawford and Morrison’s (2020) work on the Nepal earthquake were included where security challenges were a defining feature of the post-conflict context. Despite efforts to gather a wide range of relevant publications, challenges in accessing resources may have led to the oversight of some articles. Table 4 Inclusion and Exclusion List of Systematic Review Category Inclusion Exclusion Hazard type Human made or mixed conflict-disaster contexts Natural hazards such as disasters, floods, earthquakes, and volcanoes Research type Qualitative and quantitative Conceptual, narrative/anecdotal Audience Community members, community-led/driven/based reconstruction and development initiatives Government-led or top-down initiatives without community leadership, participation, or involvement Language English language only Language other than English Stage Transition to peace, post-conflict, recovery stage Emergency or normal situation Audience Community members Governmental Institutions Period From 2000 to 2024 Before 2000 Results Consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al. 2020), a flow diagram of the results is displayed below in Fig. 1 : PRISMA Flow Diagram. During the identification stage, 839 articles were discovered via database searches, including 677 peer-reviewed articles and 162 non-peer-reviewed articles. After removing 83 duplicates, 756 articles remained for eligibility screening. Among these, 643 were excluded during the review of title and abstract for the following reasons: 213 were off-topic (not about community-driven or led or participation); 167 focused on the emergency phase; 87 were conceptual considerations of preparedness or natural hazards; 59 were governmental-focused; and 117 were agency-focused rather than community-focused. The full texts of the remaining 113 were carefully screened, resulting in the exclusion of 72 papers that did not meet the eligibility criteria due to reflecting an author's viewpoint, being humanitarian aid-focused as they do not involve community-led decision-making or reconstruction activities, which are essential features of CDR. In the end, 41 articles met the inclusion criteria and were fully analysed, as outlined in Appendix A. Descriptive characteristics of included studies Most studies included in this review were conducted in post-conflict settings across various regions, including Africa, the Balkans, the Middle East, Northern Ireland, Asia and Latin America. Grey literature accounted for six papers (15%) of the sample. These included a working paper using a randomised field experiment in rural Afghanistan to examine whether elected local councils improve governance and service delivery (Beath et al., 2013 ), a World Bank working paper synthesising evidence from Sub-Saharan Africa and other fragile states to assess whether community-driven approach and radical decentralisation enhance governance and development outcomes (Casey et al., 2018), and a working paper presenting field experimental evidence from Sierra Leone on how external aid reshapes local institutions and collective action (Caseyet al., 2011 ). Other examples are an impact assessment working paper from Lofa County, Liberia, examining how CDR influences participation, social cohesion, and local collective action (Fearon et al., 2008), an APSA Annual Meeting paper reporting a field experiment in post-conflict Liberia on the effect of democratic institutions on collective action and community participation (Fearon, Humphreys, & Weinstein, 2011 ), and a World Bank evaluation report analysing water resources in the Arab world, highlighting regional challenges, management strategies, and future projections for sustainable water use in the Middle East and North Africa (Dasgupta et al., 2009 ). Additionally, the sample included preliminary research insights from the Myanmar Institute of Politics and Public Policy (Hansen 2019 ), a revised draft with lessons learned from an IRC-funded project (Lizanne and Patel 2007), and a Columbia University presentation exploring the effects of community participation in school governance (Burde 2004 ). The review also included 35 peer-reviewed articles (85%) that met the outlined criteria and were deemed relevant and valuable. Together, these sources provide a comprehensive understanding of CDR, balancing deep theoretical with practical evidence. The included studies employed a range of designs and sampled participants across diverse geographic and demographic contexts. This diversity reflects variations in community-driven approach implementation and the range of research approaches used to study it. According to Patten (2016), empirical studies are based on observed and measured phenomena, deriving knowledge from experience rather than belief. The final corpus of 41 studies included: 19 qualitative studies, 15 quantitative studies, and seven mixed-methods studies. The quality of the selected articles was assessed using the Mixed Methods Appraisal Tool (MMAT) (Hong et al. 2018 ), developed for systematic reviews that include qualitative, quantitative and mixed-methods studies. It consists of two rounds of questions: the first round included two screening questions regarding the research question and the data collected. In the second round of questions, each study is assigned to one of five types (qualitative studies, quantitative randomised control trials, quantitative non-randomised studies, quantitative descriptive studies and mixed-methods studies), with five distinct questions answered for each study type. Table 5 provides details of the study designs and methods, including the range of specific methods used. They varied from in-depth or semi-structured interviews to narrative analysis. The first column represents the research design, the second represents the number of articles using each design, the third represents the methodology type, and the fourth represents the number of articles using methods. As this review formed part of my PhD research on community empowerment in post-conflict reconstruction, my supervisory team was involved in the process. We discussed the inclusion and exclusion criteria for the review, and any disagreements were resolved through supervisory consultation. A detailed description of the MMAT tool, along with the results of its application, is provided in Appendix B. Table 5 Details of research methods used across the studies. Research design Number Method Number Qualitative studies 19 Baseline data include longitudinal design 6 Randomised controlled trial 13 Grounded theory 1 Non-randomised studies 2 Ethnographic 3 Mixed methods 7 In-depth/semi-structured interviews 28 Surveys 10 Focus groups 5 Observation 2 Case study 3 Secondary data analysis 13 Qualitative Studies A substantial portion of the reviewed studies (n = 19) employed qualitative research designs to explore the lived experiences, perceptions, and socio-political dynamics shaping CDD/R. These studies prioritised depth and context, aligning with Creswell's (2017) assertion that qualitative approaches are well-suited for investigating complex, meaning-laden processes such as post-conflict reconstruction. Common methodologies included semi-structured interviews, Focus Group Discussions (FGDs), ethnography, and case studies, all of which facilitated deep engagement with participants. The quality of these studies was assessed by the MMAT, which confirmed that most had articulated research questions and selected appropriate methods (e.g., in-depth interviews, participant observation) to address their research objectives. A key strength of these studies lies in their ability to generate rich, context-specific insights into the mechanisms and challenges of CDD/R in diverse settings. Ethnographic research added significant value by engaging researchers in the field. For instance, Kyamusugulwa, Hilhorst, and Van Der Haar (2014) conducted an in-depth ethnographic study in the Democratic Republic of Congo (DRC), participating as observers in CDR initiatives across 34 villages. Their triangulation with an independent research team enabled a nuanced understanding of how CDR influenced local governance structures. Similarly, Kyamusugulwa ( 2013 a) addressed potential bias by including both direct and indirect beneficiaries in his interviews, providing a more balanced account of CDR’s perceived effectiveness. However, the qualitative body of work also revealed several methodological challenges. One recurring issue concerned the researcher's positionality and the need to build trust in conflict-affected environments. Dawar and Ferreira (2021), for example, reported difficulties in establishing connections in communities in Pakistan, where local suspicion impeded open dialogue. Likewise, Koyabu ( 2005 ) recognised the risk of bias stemming from her dual position as both a gender and development expert within the Afghan CDR programme and an evaluator of its outcomes. These cases underscore the complexity of maintaining objectivity in participatory research where professional roles and research aim may overlap. Another notable limitation was inconsistency in methodological transparency. Three studies failed to provide sufficient information on their data analysis procedures, making it difficult to evaluate the credibility and rigour of their findings. For example, although Kyamusugulwa ( 2013 b) employed interviews to explore community perceptions, the lack of clarity around data interpretation weakened the analytic depth of the study. Similarly, McBride and Patel’s ( 2007 ) cross-country comparative research, which involved Afghanistan, Azerbaijan, Rwanda, and Kosovo, was hindered by a lack of methodological detail, ultimately limiting its ability to draw firm conclusions about the sustainability of CDR outcomes across contexts. Despite these shortcomings, the diversity of qualitative approaches ranging from ethnographic immersion to thematic and comparative analysis provides a complex yet insightful picture of how CDR is conceptualised and implemented. Among the reviewed studies, ten achieved the highest appraisal score (MMAT level 5) for their coherent methodological designs, robust data collection, and thorough analysis. Seven studies received a level 4 rating, often due to external constraints such as limited access or community mistrust, while three studies were rated level 3 due to inadequate methodological justification or analytic rigour. In summary, qualitative research has been instrumental in illuminating the nuanced realities of CDR, particularly in post-conflict settings where context and relationships are central to recovery. While methodological challenges persist, the evidence generated from these studies significantly enriches our understanding of community agency, governance, and the socio-political dynamics of reconstruction. Quantitative Studies This section provides quality indicators for the 15 articles that employed quantitative research designs, including 13 randomised controlled trials and 2 non-randomised studies. These studies utilised data collection tools such as surveys and questionnaires to test hypotheses and maximise objectivity, replicability, and generalisability of findings (Table 5 ). The following two sections demonstrate how the MMAT appraisal tool was applied to the selected quantitative studies. Randomised Controlled Trials Thirteen studies employed Randomised Controlled Trial (RCT) designs to evaluate the impact of community-driven programs. The methodological quality of these RCTs varied, although several demonstrated notable strengths, including transparent random allocation to treatment and control groups. In some cases, randomisation was conducted through publicly observed selection processes, enhancing baseline comparability and supporting credible causal inference. Of the thirteen RCTs reviewed, six were rated as level 5 in terms of methodological quality. These studies were characterised by robust research designs, including transparent randomisation procedures, well-balanced comparison groups, and appropriate statistical analyses. In addition, in these six studies, researchers were not directly involved in program implementation, which reduced the risk of bias and further strengthened internal validity. While RCTs are designed to support causal inference, this is only achieved when randomisation is properly implemented and analytically reinforced, as demonstrated in these studies. Although participant blinding was not feasible given that communities were necessarily aware of their intervention status, several studies took steps to mitigate associated biases. Nonetheless, a recurring concern across the RCTs was the potential for response bias in self-reported outcomes. For instance, in Sudan, Avdeenko and Gilligan ( 2015 ) acknowledge that respondents might have tailored their responses to align with perceived expectations or social desirability, thereby complicating impact assessment. In Liberia, King ( 2013 ) identified attrition between baseline and endline data collection as a significant limitation, raising the possibility of selection bias. Similarly, Fearon et al. ( 2015 ) were not blinded to group assignments, increasing the risk of researcher influence on outcomes. In Afghanistan, Beath et al. ( 2013 ) did not specify whether the research team was aware of intervention allocation. Their findings, partly based on respondents’ perceptions, raised questions about measurement validity. Further concerns were evident in Liberia-based studies by Fearon (2006) and Casey et al. ( 2012 ), where both treatment and control groups engaged in collective activities. However, the absence of detailed information on randomisation procedures and group comparability at baseline weakened the internal validity of these trials. Notably, Björkman and Svensson ( 2009 ), in Uganda, found that decentralising decision-making to community actors led to improvements in health and education outcomes, respectively. A particularly exemplary study was Casey et al. (2018), which received a level 5 rating. This meta-analysis synthesised evidence from high-quality RCTs across multiple countries, assessing both material and institutional outcomes of CDD programs. The authors explicitly addressed core methodological challenges, including the lack of blinding and issues of external validity, while maintaining rigorous inclusion criteria. Their comprehensive discussion of decentralisation, elite capture, and the tension between participatory processes and program performance positions this study as a methodological benchmark in the field. Four RCTs were rated level 4 due to limitations in methodological transparency, particularly about blinding procedures and the potential for survey bias. For example, Beath et al. ( 2013 ) employed rigorous data collection methods but relied heavily on subjective outcome measures without clarifying the extent of researcher blinding. Two studies, such as Casey et al. ( 2012 ) and Fearon (2006), were rated level 3. Both suffered from unclear reporting on group comparability at baseline and lacked information on implementation fidelity. Fearon (2006), in particular, did not present evidence that treatment and control communities were equivalent before the intervention, which limits the interpretability of findings. Non-Randomised Studies Two quasi-experimental studies, Choi et al. ( 2020 ) in Cambodia and D’Exelle et al. (2017) in the Democratic Republic of Congo (DRC), provided robust evidence despite the absence of randomisation. Both studies were rated level 4 and exemplify strong design features for non-randomised controlled trials, including transparent sampling procedures, clear inclusion and exclusion criteria, and careful consideration of potential confounding factors. Choi et al. ( 2020 ) sampled 1,805 households across 60 villages in Cambodia, with 911 households from 30 treatment villages and 904 from 30 control villages. Control households were deliberately selected to reside at least five kilometres away from treatment villages to minimise contamination. To assess program impact and reduce bias, the authors employed three regression models per outcome: a baseline model without controls, a model with household- and community-level controls, and a third model including district fixed effects. This layered approach strengthened the credibility of their estimates by systematically accounting for potential confounders. D’Exelle et al. (2017) examined differences between beneficiaries and non-beneficiaries of a CDR intervention. The two groups were largely similar, with the main difference being that only one group received intervention support. To control for potential bias, the authors accounted for social proximity by including predicted cooperation as a covariate. They further addressed time-consistent confounders by controlling for all variables that significantly differed between the two groups at baseline, thereby enhancing internal validity. Both studies demonstrated strong efforts to mitigate bias and ensure validity, including through matching, statistical controls, and sensitivity analyses. However, the absence of random assignment limits the strength of causal claims compared to RCTs. Despite this, their design rigour and analytical strategies provide credible and valuable insights into CDR outcomes. A detailed methodological appraisal of these studies is provided in Appendix B using the Mixed Methods Appraisal Tool (MMAT). Mixed Qualitative and Quantitative Four studies employed mixed methods designs: Rautanen and Koppen (2014), Higashida et al. ( 2017 ), Hansen ( 2019 ), and Crawford and Morrison (2020) were rated level 3. While all provided clear explanations for adopting mixed methods approaches and demonstrated partial integration of qualitative and quantitative data, they fell short in reporting how discrepancies between data types were addressed, a key criterion in the Mixed Methods Appraisal Tool (MMAT). Despite these limitations, the use of mixed methods enriched the understanding of both perceptions and measurable outcomes. For instance, Crawford and Morrison (2020) investigated gender and caste dynamics in Nepal using surveys, interviews, and FGDs. Their triangulation strategy enhanced the credibility of the findings by combining attitudinal data with in-depth qualitative insights. Notably, they shifted from interviews to surveys to capture quantifiable patterns across a broader population, an approach that helped identify general trends which qualitative methods alone might not have revealed. Similarly, Higashida et al. ( 2017 ) used surveys and FGDs in Sri Lanka to examine the effects of community-based rehabilitation for people with disabilities. While their study revealed important findings, it did not include perspectives from government stakeholders and acknowledged the potential influence of NGOs on participant responses. Furthermore, the quantitative data collected may not represent the full range of experiences among all individuals with disabilities in the targeted communities, limiting external validity. Across these studies, the rationale for methodological integration was clearly articulated. For example, Crawford and Morrison (2020) systematically compared findings from different data sources, interviews, FGDs, and surveys to build a comprehensive account of social dynamics in post-conflict settings. However, none of these studies explicitly discussed inconsistencies or divergent results between data strands, thereby limiting the transparency of their analytical processes. Overall, while these studies effectively employed mixed methods to address their research questions, their moderate quality ratings reflect gaps in integration transparency and representativeness. Full methodological assessments are presented in Appendix B. Discussion Data analysis followed Braun et al.'s ( 2023 ) reflexive thematic analysis guidelines. Coding was conducted inductively, allowing patterns to emerge from the data, and focusing on underlying meanings and assumptions rather than only surface-level descriptions (Olmos-Vega et al. 2023 ). Reflexivity was enacted throughout the process by critically examining my positionality (e.g., a PhD researcher based in the UK with a Yemeni background) underlying assumptions, and expectations, and by discussing interpretive decisions with my supervisory team. The unit of analysis was the selected studies' descriptions and evaluations of post-conflict reconstruction interventions, including community-driven, community-led, implemented in partnership with local actors, or led by INGOs. Key findings were extracted into a Word document, and the full dataset was read repeatedly to generate initial codes, which were then grouped and refined into broader themes. Varity in the studies including differences in design, outcomes, and context was handled by focusing on shared conceptual patterns across studies while preserving variations through sub-themes where relevant. Themes were evaluated for their coherence and alignment with the research goals and representativeness in relation to the full dataset. The analysis revealed three interrelated themes: (1) Rebuilding Infrastructure, (2) Everyday Livelihoods, and (3) Governance and Community Participation. Social cohesion did not appear as a standalone theme; instead, it functions as an analytical axis that cuts across and informs the interpretation of all three themes (see Table 6 ) that define CDR activities. These correspond to the review questions and align with three core CDR factors: equity, inclusiveness, efficiency, and governance (Lawson 2011). Counts of how many articles referenced each theme are presented in Table 6 for transparency only; these numbers do not indicate importance or weighting within the interpretive framework. For quantitative studies, including RCTs, findings were summarised narratively with attention to the direction and general magnitude of the most policy-relevant outcomes, and a distinction was made between perception-based measures and behavioural or institutional indicators when interpreting results. Table 6 Community-Driven Approach Themes – Benefits and Obstacles Theme Number of Articles Potential Benefits Potential Obstacles Governance and Community Participation 36 - Democratic practices (Fearon et al. 2008) - Community decision-making (Bakonyi et al. 2015 ) - Promote accountability, transparency (Kyamusugulwa et al. 2018) - Cooperation with local institutions (Ratner et al. 2014) - Enhance trust in formal institutions (Van den Boogaard and Santoro 2021 ) - Citizen-government engagement/CSOs (Casey et al. 2018) - Elite capture (Vervisch et al. 2013) - Undermines formal institutions through the creation of parallel structures (Bakonyi et al. 2015 ) (Rautanen and Koppen 2014) Everyday Livelihoods 28 - Promote inclusion of vulnerable populations (Crawford and Morrison 2020) - Responds to community needs (Higashida et al 2017 ) - Welfare improvement (King 2013 ) - Improve social cohesion (Fearon et al. 2008) - Create divisions (Findley 2018 ) - Elite or one-group capture (Dawar and Ferreira 2021) - level of embezzlement is not changed. (Beath et al 2013 ) Rebuilding Infrastructure 24 - Improve public services (Dawar and Ferreira 2021) - Increase resilience (Ratner et al 2014) - Reduces intra-group disputes (Fearon et al 2015 b) - Increases interpersonal trust (Fearon et al. 2009 ) - Elite capture or dominance by one group may limit effective targeting (Vervisch et al. 2013) - Not appropriate for all communities (Higashida et al. 2017 ) - Lower participation rates (Humphreys, 2014 ) Rebuilding Infrastructure Access to basic services and infrastructure, such as water, electricity, healthcare, and education, is consistently cited as a visible and tangible outcome of community-driven efforts. Across 24 studies, these developments are often framed as core priorities articulated by local communities themselves. While such outcomes are frequently celebrated as indicators of success, closer analysis reveals that the relationship between infrastructure provision and broader social transformation remains uneven and under-theorised. Some studies suggest that infrastructure contributes to social cohesion, yet often without clearly unpacking the mechanisms involved. For example, in Afghanistan, Beath et al. ( 2015 ) found that improved access to water and electricity under a community-driven programme was associated with reduced intra-village disputes and higher interpersonal trust among male villagers. However, the study did not elaborate on the nature or resolution of these disputes. This omission limits our understanding of whether improved services directly reduce tensions or whether the participatory processes embedded in community-driven initiatives are the real drivers of trust and social order. Similarly, in Liberia, Fearon et al. (2008) found that community-driven programmes improved access to education and promoted transparency and accountability in local decision-making. Trust in community leaders increased, particularly among marginalised groups such as ex-combatants and the poor. Yet the programme had only a limited impact on citizens' sense of political efficacy and showed weak outcomes in terms of economic empowerment. Although the programme helped reduce the likelihood of tensions escalating into violence, its limited impact on shifting local power dynamics suggests that community-driven approach may stabilise rather than transform existing social orders. This raises a normative and strategic question: should community-driven be expected to challenge entrenched power relations, or is its role better understood as reinforcing existing governance structures while making them more transparent? However, some studies show that reconstruction interventions implemented top-down, without proper participation or consideration of local needs and grievances, can be ineffective. For example, Dawar and Ferreira (2021) found that military interests and local influencers in Pakistan excluded people from decision-making in the community-driven approach. Critics as Rautanen and Koppen (2014) argue that top-down is less likely to yield positive outcomes and fails to address the immediate needs of vulnerable communities. Similarly, in Sudan, despite communities selecting infrastructure projects, Avdeenko and Michael (2015) found no measurable effect on social capital, suggesting that participation in project selection alone is insufficient to generate cohesion. This aligns with Kyamusugulwa’s ( 2013 a) broader critique: infrastructure provision, while vital, does not inherently produce social transformation. Active, inclusive engagement in decision-making processes is often the missing link. Taken together, the reviewed evidence indicates that infrastructure and services can serve as key enablers of recovery and enhanced well-being in post-conflict settings. Their potential to foster social cohesion and empowerment depends less on the physical outputs delivered and more on the process through which decisions are made, ownership is built, and legitimacy is conferred. Everyday Livelihoods Economic recovery is a key pillar of community-driven approach, particularly in post-conflict and post-disaster settings where livelihoods are frequently destroyed. Much of the literature positions economic recovery as a technical goal - improving income, assets, or employment – but the relationship between economic improvement and broader social outcomes such as cohesion, trust, and well-being is less straightforward and, at times, contradictory. Many studies show that economic interventions, such as microfinance, livestock restocking, and public goods provision, can improve short-term welfare perceptions, particularly among vulnerable groups like women. For instance, Wulandari et al. ( 2018 ) found that in post-disaster Sinabung, income, rather than social identification with the community, was a more robust predictor of individual well-being. This suggests that in contexts of acute deprivation, material security may be a precondition for psychosocial recovery or social participation, rather than an outcome of it. Bauer et al. ( 2016 ) highlight that economic stability is crucial for fostering other social factors, such as trust and cooperation, suggesting that it may need to be established before other social factors can contribute to overall community resilience and cohesion. Similarly, King ( 2013 ) notes that while community-driven initiatives aim to improve material conditions, they can also contribute to social cohesion when implemented inclusively. However, the direction of causality remains difficult to establish: do people trust more because their material needs are met, or are effective collective actions only possible in already cohesive communities? Evidence from Liberia further complicates this picture. Fearon et al. (2008) found that while access to livelihoods improved through community-driven, this did not necessarily translate into increased asset holdings or long-term perceptions of economic security. This distinction between short-term opportunity and long-term stability is crucial and highlights that these outcomes are specific to community-driven interventions, rather than other livelihood programs. It underscores the need for caution in interpreting livelihood gains as indicators of sustainable welfare or resilience. Several studies reported that community-driven projects contributed to the quality and quantity of local public goods. For example, Collins et al. ( 2014 ) found that community-driven approach improved basic infrastructure and contributed to perceived improvements in local economic conditions. Yet again, evidence on whether such improvements translate into structural changes in household-level welfare is mixed. In Somalia, Bakonyi et al., ( 2015 ) noted that while community-driven livelihood initiatives enhanced access to small-scale public goods, these did not appear to yield significant household welfare improvements. Indeed, several evaluations, including Casey ( 2018 ) in Sierra Leone and Koyabu ( 2005 ) in post-tsunami Indonesia, underscore the utility of community-driven approach in delivering tangible public goods, but stop short of claiming deeper socioeconomic transformation. This suggests that community-driven interventions are mainly community-level, participatory projects. Evaluating them based on their ability to create system-wide change may therefore be misleading. Most community-driven initiatives focus on stabilising communities, empowering local actors, and supporting local recovery, rather than transforming entire governance or economic systems. Importantly, the distribution of economic benefits within communities is not always equitable and can generate new forms of exclusion or resentment. In Sri Lanka, Higashida et al. ( 2017 ) documented how community-driven approach that linked social trust-building to economic support risked creating disappointment among committee members who did not receive material benefits. This perceived unfairness led to disengagement from participatory processes, echoing the latest critiques (e.g., Findley 2018 ) that aid can inadvertently deepen social divisions. The lesson here is that economic assistance must be designed with “conflict sensitivity”. Hamming ( 2011 ), ensuring that benefits do not reinforce existing inequalities or produce new tensions. In Cambodia, Choi et al. ( 2020 ) found that community-driven approach was more effective in restoring livelihoods and social cohesion, particularly when marginalised groups were included in the process. Similarly, Fearon et al. ( 2009 ) observed that post-conflict community cohesion can be strengthened through well-structured external support, particularly when such interventions are transparent and widely perceived as fair. Overall, the reviewed studies highlight both the promise and the pitfalls of linking economic recovery to social reconstruction. Socio-economic gains can foster optimism and local engagement, but if unevenly distributed or poorly integrated into participatory processes, they may undermine social trust. This suggests that community-driven economic strategies must not only aim to improve livelihoods but must do so in a way that is inclusive, transparent, and mindful of local power dynamics. Otherwise, economic assistance risks becoming a source of division rather than cohesion. Governance and Participation Community-driven has been widely promoted as a mechanism to strengthen local governance and community participation in post-conflict settings. Evidence from various contexts suggests that while community-driven can reshape local governance structures and norms, its effects are uneven and highly context-dependent. Several studies show that community-driven has supported the formation of community-based governance institutions and encouraged democratic practices at the local level. For instance, Fearon et al. (2008) found that community-driven in Liberia increased trust in local decision-making processes and reduced the likelihood of violence by promoting inclusion of marginalised groups such as the poor and ex-combatants. Similarly, in Sierra Leone, community-driven facilitated inter-village collaboration and increased contact between communities and local authorities, contributing to the institutionalisation of local governance (Casey, 2018 ). In Afghanistan, community-driven interventions fostered greater collaboration across ethnic groups and more positive attitudes toward female participation in governance, reflecting a degree of social cohesion (Beath et al. 2013 ; 2015 ). In other contexts, such as Somalia, youth and women’s participation remained limited, though interaction with clan authorities enhanced citizens’ capacity to express their needs (Bakonyi et al., 2015 ). Some community-driven programmes appear to challenge patriarchal structures, but broader institutional support and local acceptance remain critical for such transformations to take root. These examples highlight that the quality of participation shapes both the distribution of benefits and the potential for strengthening social cohesion and trust within communities. In contexts where the state’s presence is limited, community-driven may also foster forms of hybrid governance. In the Democratic Republic of the Congo, Ho et al. (2015) report that partnerships between local communities and health systems enhanced transparency, participation, and access to care. Yet, the study leaves open a crucial conceptual question: were frontline health workers perceived as extensions of the state or as part of the local social fabric? This ambiguity matters, as it affects whether such partnerships strengthen state legitimacy or foster more locally rooted forms of governance and accountability. Other evidence points to the potential of participatory, decentralised approaches to enhance both access and local ownership. In Nepal, Rautanen and Koppen (2014) describe how water management projects rooted in community values and traditional knowledge bolstered the resilience of Water User Committees. Nevertheless, even in these participatory contexts, underlying political affiliations and social cleavages within civil society limited collective responsibility and cohesion. This underscores a critical tension: decentralisation may empower local actors, but it does not necessarily overcome pre-existing power asymmetries or exclusions. It raises a broader analytical question: under what social and political conditions does participatory governance lead to equitable and cohesive outcomes? Similarly, in Sierra Leone, newly elected committees were largely inactive, which researchers attributed to insufficient capacity building (Kyamusugulwa et al. 2018). These findings raise concerns about whether local governance structures can be sustained once international support is withdrawn. The influence of elites in community-driven processes is another recurrent theme. In some cases, elite involvement was instrumental in resource mobilisation (Fearon et al. 2015 ), while in others, it led to elite capture, undermining participatory goals (Bakonyi et al. 2015 ). The distinction between beneficial elite support and harmful elite domination remains under-explored, particularly in contexts where traditional leaders retain significant authority. For instance, while Beath et al. ( 2013 ) noted that democratic councils in Afghanistan were responsible for distributing aid, misuse persisted, and traditional power structures continued to shape outcomes. Community-driven has also been linked to broader state-building objectives, although evidence here is mixed. In Afghanistan, some studies report higher electoral participation among individuals who participated in or benefited from community-driven initiatives (Beath et al. 2015 ), whereas in Liberia, community-driven programmes were associated with improved trust in national institutions (Fearon et al. 2009 b). However, other research suggests that these effects are limited or conditional. Casey ( 2018 ) argues that community-driven did not substantially change political attitudes toward central government, and parallel governance structures established by NGOs may hinder vertical integration between communities and the state (Rautanen and Koppen 2014). A recurring argument in the literature is that the success of community-driven in supporting governance and state legitimacy depends on the meaningful involvement of state institutions. Projects that engaged local and national authorities, such as those in Somalia and Afghanistan, were more likely to foster coordination and institutional trust (Hamming 2011 ; Koyabu 2005 ). Conversely, initiatives led solely by INGOs sometimes created gaps in state legitimacy and accountability (Burde, 2004 ). Several authors (e.g., Lizanne and Patel 2007; Van den Boogaard and Santoro 2021 ) emphasise that transitional governments, rather than NGOs, were often best positioned to lead community-driven processes in fragile contexts such as Kosovo, Rwanda, and Azerbaijan. In sum, while community-driven can influence local governance, its success depends on how effectively it is embedded within existing institutional and socio-political contexts. Top-down interventions that neglect these dynamics often fall short of their governance goals. Further comparative research is needed to understand how different configurations of local and state engagement shape the outcomes of participatory reconstruction efforts. Conclusion and Research Priorities This review demonstrates that community-driven approach plays a significant, though uneven, role in supporting post-conflict recovery. Across the literature, outcomes vary widely in relation to service delivery, infrastructure rehabilitation, economic welfare, local governance, political attitudes, and social norms. Nevertheless, the evidence provides a clearer understanding of what community-driven entails, how it operates in practice, and the conceptual logic that distinguishes it from broader community-based approaches. It typically engages communities in identifying priorities and planning and implementing reconstruction projects, such as rehabilitating essential infrastructure and improving access to basic services, while also contributing to stronger local governance. These activities are designed not simply to deliver material improvements but to embed communities at the centre of recovery processes. In doing so, community-driven differs from general participatory interventions by granting communities substantive decision-making authority over resources, priorities, and implementation. This redistribution of authority generates stronger forms of local ownership, as communities are not merely consulted but actively design, manage, and oversee reconstruction efforts. The review identifies several interrelated elements, such as community decision-making power, collective action, local ownership, trust and social cohesion, and internally rooted accountability that together define the conceptual logic of community driven. These elements operate as a mutually reinforcing system: trust enables communities to act collectively; collective action strengthens and ownership; and strong ownership fosters internal accountability. When these dynamics function effectively, community-driven can support both the rebuilding of physical infrastructure and the restoration of social relationships, which in turn strengthens social cohesion. However, the strength of this system depends heavily on contextual conditions. Evidence shows that community-driven tends to generate more sustainable outcomes where pre-existing social cohesion, community capacities, and supportive institutional environments are present. Conversely, elite capture, weak local governance, and unresolved intergroup tensions frequently undermine participation and limit alignment with community priorities. These findings highlight the importance of designing reconstruction programmes that are sensitive to local dynamics, particularly in divided or fragile contexts. The review also reveals notable gaps in the existing literature. Research has focused on post-conflict settings, with limited understanding of how community-driven functions in communities affected by both conflict and disaster. The broader impacts of community-driven on economic welfare, especially for women and youth, remain underexplored, as do its potential unintended consequences for equity and inclusion. Some community-driven programmes appear to challenge power structures, but meaningful, lasting change depends on broader institutional support and local acceptance. This study highlight that the quality of participation whether tokenistic or empowered shapes both the distribution of benefits and the potential for strengthening social cohesion and trust within communities. This review has several limitations. First, only English-language studies were included, which may have introduced language bias. Second, database coverage may be incomplete, as not all relevant studies are indexed in the databases searched. Third, there is a risk of publication bias, as peer-reviewed journals may preferentially publish positive findings. The inclusion of grey literature helped to mitigate some of these biases by capturing reports, evaluations, and program documents not available in academic journals, although some gaps may remain. Further work is also needed to understand how community-driven shapes community–state relations, supports trust in governance, and contributes to long-term political and institutional stability. Community-driven initiatives should be closely integrated into state systems to avoid creating parallel structures that could undermine local governance. Safeguards are needed to prevent elite capture and ensure that benefits are distributed fairly. Transparency in decision-making, resource allocation, and participation should be maintained, and interventions should be designed in a conflict-sensitive way to engage marginalised groups without increasing local tensions. Abbreviations ASSIA (Applied Social Sciences Index and Abstracts) CDR (Community-Driven Reconstruction) CDD (Community-Driven Development) CCR (Centre for Conflict Resolution) DRC (Democratic Republic of Congo) FGDs (Focus Group Discussions) IDPs (Internally Displaced People) MMAT (Mixed Methods Appraisal Tool) PCRD (Post-Conflict Reconstruction and Development) PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) RTA (Reflexive Thematic Analysis) RCT (Randomised Controlled Trial) SCCR (Scottish Centre for Conflict Resolution) Declarations Ethics approval and consent to participate This study is a systematic review of existing literature and does not involve human participants. Therefore, ethical approval and informed consent were not required. Consent for Publication Not applicable. This study is a systematic review of publicly available literature and did not involve human participants. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9001942","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":598809049,"identity":"74d5f74f-8625-4a9d-beee-52a8c636d2e9","order_by":0,"name":"khaled Alosaimi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGElEQVRIie3RsUrDQBjA8S8etMtB1iuR5BW+cJAp6KskBM6liqNgwUChLkLWin0O55ODdPEBAhkFpwwpLh06eJoqGC5gN5H7Dwnk8rvcRwBstr/Y+OOCgK6+yd7ayEzInkzyb0J+QzRC2X80RNw5Ue328pHz6vxJwixOC68o3yicBMBEYiJMjcTkDusoqi4SCaVI71eKPFDIwpyJ/nBdikZAsY6jaorSyRXHKiOEAkmAneUmESjKNztN+HJPTjtyM0hQUfT0VyJkHfGRfRKliflgoZ7FO8aas+cGZVIKn1UZd1a4Dhf01Ti+v56rTbOrw+J2ytt2FlN3mb5Ac3UduGOBxvF/9LXrEcXBHzmQsz3odZvNZvvnvQPDLFeJMxUDcwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8953-1726","institution":"Centre for Peace and Security, Coventry University","correspondingAuthor":true,"prefix":"","firstName":"khaled","middleName":"","lastName":"Alosaimi","suffix":""}],"badges":[],"createdAt":"2026-03-01 14:02:13","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9001942/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9001942/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103844964,"identity":"5d5b8774-bfcc-43bb-93d7-11a8182d8ae9","added_by":"auto","created_at":"2026-03-03 15:29:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29408,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flow Diagram\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9001942/v1/7b5af161847a59dbbe953893.png"},{"id":104401038,"identity":"a44d3a7c-048e-45bb-b3b5-2e2ab5a2d8f6","added_by":"auto","created_at":"2026-03-11 12:11:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":846263,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9001942/v1/7337ccbc-49c1-4c5a-bd95-6d6b0be4f8e7.pdf"},{"id":103844963,"identity":"5bd3d9f3-8b49-4764-b314-6a02243ac91c","added_by":"auto","created_at":"2026-03-03 15:29:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40690,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixDataextractfromtheselectedarticles.docx","url":"https://assets-eu.researchsquare.com/files/rs-9001942/v1/9d8fdeb2f83db963c8853847.docx"},{"id":103844966,"identity":"e0817c77-fa15-461b-bdaa-ef3b223cd732","added_by":"auto","created_at":"2026-03-03 15:29:56","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":71456,"visible":true,"origin":"","legend":"","description":"","filename":"MMATforsystematicreview.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9001942/v1/04d36558388f6b7a1336ba33.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCommunity-Driven Reconstruction and Recovery: A Systematic Review of Successes and Challenges\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCommunity-Driven Development (CDD) is an aid delivery approach that emphasises community control over decision-making and investment resources (Fearon et al. 2008). It promotes engagement of beneficiaries in the design and management of development initiatives (Kyamusugulwa \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003ea). CDD produces two primary types of results: \u0026ldquo;more and better-distributed assets and stronger, more responsive institutions\u0026rdquo; (Humphreys et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e:2). Beath, et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) argue that CDD promotes inclusive development, empowerment, and governance strengthening; several evaluations remain cautious (Casey \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, Fearon et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found limited evidence that CDD programs in Sierra Leone and Liberia significantly improve collective action or local institutional capacity. Similarly, Mansuri and Rao (2004) highlight both the potential and pitfalls of CDD, warning against assuming automatic transformation of social relations without attention to power dynamics and local context.\u003c/p\u003e \u003cp\u003eOver the past three decades, Community-Driven Reconstruction (CDR) emerged from the CDD framework refers to post-conflict or post-disaster recovery approaches in which communities play a leading role in identifying priorities, managing resources, and implementing reconstruction activities (Bennett and D\u0026rsquo;Onofrio 2015; Lizanne and Patel 2007). Unlike general CDD, CDR is recognised for its potential to support recovery efforts, social cohesion, and promote cooperation in conflict-affected communities (World Bank \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Koyabu \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Moreover, the United Nation identify CDR as a key component in broader disarmament, demobilisation, and reintegration (DDR) programs, particularly in addressing the challenges of post-war reintegration (Del Castillo \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, applying the community-driven approach to conflict contexts presents significant challenges, including the presence of returnee populations, ex-combatants and vulnerable populations, as well as a lack of a history of good governance and breakdown in institutions (Van Leeuwen \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Furthermore, concerns have been raised about the overlap between top-down and bottom-up approaches in reconstruction initiatives (William \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), alongside ambiguity regarding the goals and expected outcomes of CDR. For example, in Sierra Leone, Casey et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) evaluated the GoBifo - a CDD initiative implemented between 2005 and 2009 that aimed to strengthen local institutions, improve decision-making processes, and finance small-scale public goods. Their findings show that while GoBifo delivered substantial improvements in local infrastructure and public goods, it did not significantly shift social cohesion or collective action in the short term.\u003c/p\u003e \u003cp\u003eHowever, later follow-up studies of the same program report more positive long-term impacts, particularly in civic participation and public goods provision (White et al. 2018; Casey \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, Fearon et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003ea) illustrate that while CDR programs in Liberia, such as small-scale infrastructure projects and community-managed resource initiatives, were generally improved infrastructure and local services, their effectiveness was limited by differences in community members\u0026rsquo; willingness to cooperate.\u003c/p\u003e \u003cp\u003eThis review is distinct in its exclusive focus on post-conflict, its inclusion of both peer-reviewed and grey literature, and its use of reflexive thematic analysis across multiple study designs. This makes it different from previous syntheses, which have not combined these elements within a single review. For example, Samii (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) examines the CDR effectiveness in post-war settings, focusing on Afghanistan, and highlights both successes and limitations. King (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) assesses CDR outcomes in conflict-affected regions, reporting mixed and often disappointing results. These contributions remain context-specific and fragmented. White et al., (2018) synthesise evidence on 23 CDD programs in various contexts, but their scope extends beyond post-conflict settings, making it less relevant to my review. Given these complexities, a systematic review dedicated to community-driven approach is therefore essential to consolidate existing knowledge, identify patterns, and inform future research and policy interventions. This review aims to synthesise the existing literature on community-driven in post-conflict settings. Specifically, it explores the types of activities undertaken in community-driven programs, the contexts in which they operate, and the reported effects on communities recovering from conflict.\u003c/p\u003e \u003cp\u003eThe review is guided by McBride and Patel's (2007) definition of community-driven approach, which emphasises community ownership, governance, and accountability, providing a foundation for the subsequent analysis. Therefore, three key research questions guided this systematic literature review: Q1 What activities do community-driven programs undertake during reconstruction operations? Q2 What are the core components that make community-driven different from general community-based interventions? Q3 How do decision-making, collective action, trust, and local ownership interact to form the logic of community-driven?\u003c/p\u003e \u003cp\u003eThis review follows Khan et al.'s (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) framework, which outlines five steps framework for conducting systematic reviews: Frame the review questions, Identify relevant studies, Assess the quality of the included studies, Summarise the evidence, and interpret the findings. The first step framing the review questions has already been completed in the previous section. The following subsections outline how the remaining four steps were systematically applied to ensure transparency and rigour throughout the review process.\u003c/p\u003e\n\u003ch3\u003eIdentifying Relevant Publications\u003c/h3\u003e\n\u003cp\u003eTo search for relevant empirical studies, broad keywords and search terms were used in the specialised databases to capture relevant studies written in English on the topic of community-driven approach in post-conflict settings, using all variations of relevant phrases. To include a broader range of studies, I focused on literature from 2000 to 2024 as community-driven approach gained prominence in post-conflict recovery during the early 2000s, particularly through international development initiatives. This period captures evolving debates, policy shifts, and long-term program impacts. Study design and methodological terminology, as well as terms relating to research outcomes, were excluded to avoid bias. The search strategy includes three categories: population \u0026ldquo;who\u0026rdquo;, event and intervention (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Search strategy).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSearch strategy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"Community\" OR \"leaders\" OR \"women\" OR \"youth\" OR \"IDPs\" OR \"ex-combatant\" OR \"survivor\" OR \"leader\" OR \"refugees\"\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEvents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"post-conflict\" OR \"armed conflict\" OR \"internal war\" OR \"civil war\" OR \"peace transition\" OR \"ceasefire\" OR \"truce\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterventions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"Reconstruction\" OR \"rehabilitation\" OR \"community-led\" OR \"community-driven\" OR \"empowerment\" OR \"collective action\" OR \"rebuilding\" OR \"reintegration\" OR \"reconciliation\" OR \"peacebuilding\" OR \"recovery\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, to identify the relevant publications based on the above broad keywords and search terms, I used academic databases and search engines, as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Databases searched for the systematic review.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDatabases searched for the systematic review\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. ProQuest Accessibility Statement\u003c/p\u003e \u003cp\u003e2. ASSIA:\u0026nbsp;Applied Social Sciences Index and Abstracts\u003c/p\u003e \u003cp\u003e3. EBSCOhost Academic Search Complete\u003c/p\u003e \u003cp\u003e4. Science Direct\u003c/p\u003e \u003cp\u003e5. EBSCOhost Research Databases\u003c/p\u003e \u003cp\u003e6. JSTOR digital library and platform\u003c/p\u003e \u003cp\u003e7. Scopus\u003c/p\u003e \u003cp\u003e8. Google Scholar\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, bibliographies and reference lists of included articles were reviewed to identify additional relevant studies and journals not found in database searches. Including both primary studies and grey literature provides a broader and more comprehensive understanding of existing knowledge (Mahood et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The grey literature search involved identifying relevant organisations, authorities, and stakeholders on Google, as well as reviewing evaluation reports, policy documents, and publications from databases such as UN Data and the World Bank Open Data. A full list of grey literature databases is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Sources of grey literature searched for the systematic review. Keywords from the earlier search strategy were also used to locate relevant reports and policies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSources of grey literature searched for the systematic review\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName of Organisation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWebsite\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Bureau of Economic Research | NBER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nber.org\u003c/span\u003e\u003cspan address=\"https://www.nber.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorld Bank Open Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openknowledge.worldbank.org\u003c/span\u003e\u003cspan address=\"https://openknowledge.worldbank.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearchGate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSRN (Social Science Research Network).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://papers.ssrn.com\u003c/span\u003e\u003cspan address=\"https://papers.ssrn.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColumbia University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.columbia.edu\u003c/span\u003e\u003cspan address=\"http://www.columbia.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational Growth Centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.theigc.org\u003c/span\u003e\u003cspan address=\"https://www.theigc.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCriteria for Inclusion and Exclusion\u003c/h2\u003e \u003cp\u003eThe inclusion and exclusion criteria were established to ensure an unbiased selection of the most relevant articles (Siddaway et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Following Mahood et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), articles were selected based on explicit and reproducible methods, with criteria clearly stated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Inclusion and Exclusion List of Systematic Review. Human-generated hazards, such as war and civil unrest, were included, while naturally generated hazards, such as floods and earthquakes, were excluded. Consequently, papers in non-conflict settings were excluded, as the dynamics and security challenges in post-conflict environments differ substantially from those found in purely natural disaster\u0026ndash;related contexts (Peters and Kelman 2020). However, studies situated in mixed conflict\u0026ndash;disaster environments such as Crawford and Morrison\u0026rsquo;s (2020) work on the Nepal earthquake were included where security challenges were a defining feature of the post-conflict context. Despite efforts to gather a wide range of relevant publications, challenges in accessing resources may have led to the oversight of some articles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInclusion and Exclusion List of Systematic Review\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHazard type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman made or mixed conflict-disaster contexts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural hazards such as disasters, floods, earthquakes, and volcanoes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResearch type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQualitative and quantitative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConceptual, narrative/anecdotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAudience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunity members, community-led/driven/based reconstruction and development initiatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGovernment-led or top-down initiatives without community leadership, participation, or involvement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLanguage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish language only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLanguage other than English\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransition to peace, post-conflict, recovery stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmergency or normal situation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAudience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunity members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGovernmental Institutions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeriod\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrom 2000 to 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBefore 2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eConsistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al. 2020), a flow diagram of the results is displayed below in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e: PRISMA Flow Diagram. During the identification stage, 839 articles were discovered via database searches, including 677 peer-reviewed articles and 162 non-peer-reviewed articles. After removing 83 duplicates, 756 articles remained for eligibility screening. Among these, 643 were excluded during the review of title and abstract for the following reasons: 213 were off-topic (not about community-driven or led or participation); 167 focused on the emergency phase; 87 were conceptual considerations of preparedness or natural hazards; 59 were governmental-focused; and 117 were agency-focused rather than community-focused. The full texts of the remaining 113 were carefully screened, resulting in the exclusion of 72 papers that did not meet the eligibility criteria due to reflecting an author's viewpoint, being humanitarian aid-focused as they do not involve community-led decision-making or reconstruction activities, which are essential features of CDR. In the end, 41 articles met the inclusion criteria and were fully analysed, as outlined in Appendix A.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDescriptive characteristics of included studies\u003c/h3\u003e\n\u003cp\u003eMost studies included in this review were conducted in post-conflict settings across various regions, including Africa, the Balkans, the Middle East, Northern Ireland, Asia and Latin America. Grey literature accounted for six papers (15%) of the sample. These included a working paper using a randomised field experiment in rural Afghanistan to examine whether elected local councils improve governance and service delivery (Beath et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), a World Bank working paper synthesising evidence from Sub-Saharan Africa and other fragile states to assess whether community-driven approach and radical decentralisation enhance governance and development outcomes (Casey et al., 2018), and a working paper presenting field experimental evidence from Sierra Leone on how external aid reshapes local institutions and collective action (Caseyet al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Other examples are an impact assessment working paper from Lofa County, Liberia, examining how CDR influences participation, social cohesion, and local collective action (Fearon et al., 2008), an APSA Annual Meeting paper reporting a field experiment in post-conflict Liberia on the effect of democratic institutions on collective action and community participation (Fearon, Humphreys, \u0026amp; Weinstein, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and a World Bank evaluation report analysing water resources in the Arab world, highlighting regional challenges, management strategies, and future projections for sustainable water use in the Middle East and North Africa (Dasgupta et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the sample included preliminary research insights from the Myanmar Institute of Politics and Public Policy (Hansen \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a revised draft with lessons learned from an IRC-funded project (Lizanne and Patel 2007), and a Columbia University presentation exploring the effects of community participation in school governance (Burde \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The review also included 35 peer-reviewed articles (85%) that met the outlined criteria and were deemed relevant and valuable. Together, these sources provide a comprehensive understanding of CDR, balancing deep theoretical with practical evidence.\u003c/p\u003e \u003cp\u003eThe included studies employed a range of designs and sampled participants across diverse geographic and demographic contexts. This diversity reflects variations in community-driven approach implementation and the range of research approaches used to study it. According to Patten (2016), empirical studies are based on observed and measured phenomena, deriving knowledge from experience rather than belief. The final corpus of 41 studies included: 19 qualitative studies, 15 quantitative studies, and seven mixed-methods studies.\u003c/p\u003e \u003cp\u003eThe quality of the selected articles was assessed using the Mixed Methods Appraisal Tool (MMAT) (Hong et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), developed for systematic reviews that include qualitative, quantitative and mixed-methods studies. It consists of two rounds of questions: the first round included two screening questions regarding the research question and the data collected. In the second round of questions, each study is assigned to one of five types (qualitative studies, quantitative randomised control trials, quantitative non-randomised studies, quantitative descriptive studies and mixed-methods studies), with five distinct questions answered for each study type. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides details of the study designs and methods, including the range of specific methods used. They varied from in-depth or semi-structured interviews to narrative analysis. The first column represents the research design, the second represents the number of articles using each design, the third represents the methodology type, and the fourth represents the number of articles using methods. As this review formed part of my PhD research on community empowerment in post-conflict reconstruction, my supervisory team was involved in the process. We discussed the inclusion and exclusion criteria for the review, and any disagreements were resolved through supervisory consultation. A detailed description of the MMAT tool, along with the results of its application, is provided in Appendix B.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of research methods used across the studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQualitative studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaseline data include longitudinal design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandomised controlled trial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrounded theory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-randomised studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEthnographic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn-depth/semi-structured interviews\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurveys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFocus groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary data analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eQualitative Studies\u003c/h3\u003e\n\u003cp\u003eA substantial portion of the reviewed studies (n\u0026thinsp;=\u0026thinsp;19) employed qualitative research designs to explore the lived experiences, perceptions, and socio-political dynamics shaping CDD/R. These studies prioritised depth and context, aligning with Creswell's (2017) assertion that qualitative approaches are well-suited for investigating complex, meaning-laden processes such as post-conflict reconstruction. Common methodologies included semi-structured interviews, Focus Group Discussions (FGDs), ethnography, and case studies, all of which facilitated deep engagement with participants. The quality of these studies was assessed by the MMAT, which confirmed that most had articulated research questions and selected appropriate methods (e.g., in-depth interviews, participant observation) to address their research objectives.\u003c/p\u003e \u003cp\u003eA key strength of these studies lies in their ability to generate rich, context-specific insights into the mechanisms and challenges of CDD/R in diverse settings. Ethnographic research added significant value by engaging researchers in the field. For instance, Kyamusugulwa, Hilhorst, and Van Der Haar (2014) conducted an in-depth ethnographic study in the Democratic Republic of Congo (DRC), participating as observers in CDR initiatives across 34 villages. Their triangulation with an independent research team enabled a nuanced understanding of how CDR influenced local governance structures. Similarly, Kyamusugulwa (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003ea) addressed potential bias by including both direct and indirect beneficiaries in his interviews, providing a more balanced account of CDR\u0026rsquo;s perceived effectiveness.\u003c/p\u003e \u003cp\u003eHowever, the qualitative body of work also revealed several methodological challenges. One recurring issue concerned the researcher's positionality and the need to build trust in conflict-affected environments. Dawar and Ferreira (2021), for example, reported difficulties in establishing connections in communities in Pakistan, where local suspicion impeded open dialogue. Likewise, Koyabu (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) recognised the risk of bias stemming from her dual position as both a gender and development expert within the Afghan CDR programme and an evaluator of its outcomes. These cases underscore the complexity of maintaining objectivity in participatory research where professional roles and research aim may overlap. Another notable limitation was inconsistency in methodological transparency. Three studies failed to provide sufficient information on their data analysis procedures, making it difficult to evaluate the credibility and rigour of their findings. For example, although Kyamusugulwa (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003eb) employed interviews to explore community perceptions, the lack of clarity around data interpretation weakened the analytic depth of the study. Similarly, McBride and Patel\u0026rsquo;s (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) cross-country comparative research, which involved Afghanistan, Azerbaijan, Rwanda, and Kosovo, was hindered by a lack of methodological detail, ultimately limiting its ability to draw firm conclusions about the sustainability of CDR outcomes across contexts.\u003c/p\u003e \u003cp\u003eDespite these shortcomings, the diversity of qualitative approaches ranging from ethnographic immersion to thematic and comparative analysis provides a complex yet insightful picture of how CDR is conceptualised and implemented. Among the reviewed studies, ten achieved the highest appraisal score (MMAT level 5) for their coherent methodological designs, robust data collection, and thorough analysis. Seven studies received a level 4 rating, often due to external constraints such as limited access or community mistrust, while three studies were rated level 3 due to inadequate methodological justification or analytic rigour. In summary, qualitative research has been instrumental in illuminating the nuanced realities of CDR, particularly in post-conflict settings where context and relationships are central to recovery. While methodological challenges persist, the evidence generated from these studies significantly enriches our understanding of community agency, governance, and the socio-political dynamics of reconstruction.\u003c/p\u003e\n\u003ch3\u003eQuantitative Studies\u003c/h3\u003e\n\u003cp\u003eThis section provides quality indicators for the 15 articles that employed quantitative research designs, including 13 randomised controlled trials and 2 non-randomised studies. These studies utilised data collection tools such as surveys and questionnaires to test hypotheses and maximise objectivity, replicability, and generalisability of findings (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The following two sections demonstrate how the MMAT appraisal tool was applied to the selected quantitative studies.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRandomised Controlled Trials\u003c/h2\u003e \u003cp\u003eThirteen studies employed Randomised Controlled Trial (RCT) designs to evaluate the impact of community-driven programs. The methodological quality of these RCTs varied, although several demonstrated notable strengths, including transparent random allocation to treatment and control groups. In some cases, randomisation was conducted through publicly observed selection processes, enhancing baseline comparability and supporting credible causal inference.\u003c/p\u003e \u003cp\u003eOf the thirteen RCTs reviewed, six were rated as level 5 in terms of methodological quality. These studies were characterised by robust research designs, including transparent randomisation procedures, well-balanced comparison groups, and appropriate statistical analyses. In addition, in these six studies, researchers were not directly involved in program implementation, which reduced the risk of bias and further strengthened internal validity. While RCTs are designed to support causal inference, this is only achieved when randomisation is properly implemented and analytically reinforced, as demonstrated in these studies.\u003c/p\u003e \u003cp\u003eAlthough participant blinding was not feasible given that communities were necessarily aware of their intervention status, several studies took steps to mitigate associated biases. Nonetheless, a recurring concern across the RCTs was the potential for response bias in self-reported outcomes. For instance, in Sudan, Avdeenko and Gilligan (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) acknowledge that respondents might have tailored their responses to align with perceived expectations or social desirability, thereby complicating impact assessment. In Liberia, King (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) identified attrition between baseline and endline data collection as a significant limitation, raising the possibility of selection bias. Similarly, Fearon et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) were not blinded to group assignments, increasing the risk of researcher influence on outcomes. In Afghanistan, Beath et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) did not specify whether the research team was aware of intervention allocation. Their findings, partly based on respondents\u0026rsquo; perceptions, raised questions about measurement validity. Further concerns were evident in Liberia-based studies by Fearon (2006) and Casey et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), where both treatment and control groups engaged in collective activities. However, the absence of detailed information on randomisation procedures and group comparability at baseline weakened the internal validity of these trials.\u003c/p\u003e \u003cp\u003eNotably, Bj\u0026ouml;rkman and Svensson (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), in Uganda, found that decentralising decision-making to community actors led to improvements in health and education outcomes, respectively. A particularly exemplary study was Casey et al. (2018), which received a level 5 rating. This meta-analysis synthesised evidence from high-quality RCTs across multiple countries, assessing both material and institutional outcomes of CDD programs. The authors explicitly addressed core methodological challenges, including the lack of blinding and issues of external validity, while maintaining rigorous inclusion criteria. Their comprehensive discussion of decentralisation, elite capture, and the tension between participatory processes and program performance positions this study as a methodological benchmark in the field. Four RCTs were rated level 4 due to limitations in methodological transparency, particularly about blinding procedures and the potential for survey bias. For example, Beath et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) employed rigorous data collection methods but relied heavily on subjective outcome measures without clarifying the extent of researcher blinding. Two studies, such as Casey et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Fearon (2006), were rated level 3. Both suffered from unclear reporting on group comparability at baseline and lacked information on implementation fidelity. Fearon (2006), in particular, did not present evidence that treatment and control communities were equivalent before the intervention, which limits the interpretability of findings.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNon-Randomised Studies\u003c/h3\u003e\n\u003cp\u003eTwo quasi-experimental studies, Choi et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Cambodia and D\u0026rsquo;Exelle et al. (2017) in the Democratic Republic of Congo (DRC), provided robust evidence despite the absence of randomisation. Both studies were rated level 4 and exemplify strong design features for non-randomised controlled trials, including transparent sampling procedures, clear inclusion and exclusion criteria, and careful consideration of potential confounding factors. Choi et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) sampled 1,805 households across 60 villages in Cambodia, with 911 households from 30 treatment villages and 904 from 30 control villages. Control households were deliberately selected to reside at least five kilometres away from treatment villages to minimise contamination. To assess program impact and reduce bias, the authors employed three regression models per outcome: a baseline model without controls, a model with household- and community-level controls, and a third model including district fixed effects. This layered approach strengthened the credibility of their estimates by systematically accounting for potential confounders. D\u0026rsquo;Exelle et al. (2017) examined differences between beneficiaries and non-beneficiaries of a CDR intervention. The two groups were largely similar, with the main difference being that only one group received intervention support. To control for potential bias, the authors accounted for social proximity by including predicted cooperation as a covariate.\u003c/p\u003e \u003cp\u003eThey further addressed time-consistent confounders by controlling for all variables that significantly differed between the two groups at baseline, thereby enhancing internal validity. Both studies demonstrated strong efforts to mitigate bias and ensure validity, including through matching, statistical controls, and sensitivity analyses. However, the absence of random assignment limits the strength of causal claims compared to RCTs. Despite this, their design rigour and analytical strategies provide credible and valuable insights into CDR outcomes. A detailed methodological appraisal of these studies is provided in Appendix B using the Mixed Methods Appraisal Tool (MMAT).\u003c/p\u003e\n\u003ch3\u003eMixed Qualitative and Quantitative\u003c/h3\u003e\n\u003cp\u003eFour studies employed mixed methods designs: Rautanen and Koppen (2014), Higashida et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Hansen (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Crawford and Morrison (2020) were rated level 3. While all provided clear explanations for adopting mixed methods approaches and demonstrated partial integration of qualitative and quantitative data, they fell short in reporting how discrepancies between data types were addressed, a key criterion in the Mixed Methods Appraisal Tool (MMAT). Despite these limitations, the use of mixed methods enriched the understanding of both perceptions and measurable outcomes. For instance, Crawford and Morrison (2020) investigated gender and caste dynamics in Nepal using surveys, interviews, and FGDs. Their triangulation strategy enhanced the credibility of the findings by combining attitudinal data with in-depth qualitative insights. Notably, they shifted from interviews to surveys to capture quantifiable patterns across a broader population, an approach that helped identify general trends which qualitative methods alone might not have revealed.\u003c/p\u003e \u003cp\u003eSimilarly, Higashida et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) used surveys and FGDs in Sri Lanka to examine the effects of community-based rehabilitation for people with disabilities. While their study revealed important findings, it did not include perspectives from government stakeholders and acknowledged the potential influence of NGOs on participant responses. Furthermore, the quantitative data collected may not represent the full range of experiences among all individuals with disabilities in the targeted communities, limiting external validity. Across these studies, the rationale for methodological integration was clearly articulated. For example, Crawford and Morrison (2020) systematically compared findings from different data sources, interviews, FGDs, and surveys to build a comprehensive account of social dynamics in post-conflict settings. However, none of these studies explicitly discussed inconsistencies or divergent results between data strands, thereby limiting the transparency of their analytical processes. Overall, while these studies effectively employed mixed methods to address their research questions, their moderate quality ratings reflect gaps in integration transparency and representativeness. Full methodological assessments are presented in Appendix B.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eData analysis followed Braun et al.'s (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) reflexive thematic analysis guidelines. Coding was conducted inductively, allowing patterns to emerge from the data, and focusing on underlying meanings and assumptions rather than only surface-level descriptions (Olmos-Vega et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Reflexivity was enacted throughout the process by critically examining my positionality (e.g., a PhD researcher based in the UK with a Yemeni background) underlying assumptions, and expectations, and by discussing interpretive decisions with my supervisory team. The unit of analysis was the selected studies' descriptions and evaluations of post-conflict reconstruction interventions, including community-driven, community-led, implemented in partnership with local actors, or led by INGOs. Key findings were extracted into a Word document, and the full dataset was read repeatedly to generate initial codes, which were then grouped and refined into broader themes. Varity in the studies including differences in design, outcomes, and context was handled by focusing on shared conceptual patterns across studies while preserving variations through sub-themes where relevant. Themes were evaluated for their coherence and alignment with the research goals and representativeness in relation to the full dataset. The analysis revealed three interrelated themes: (1) Rebuilding Infrastructure, (2) Everyday Livelihoods, and (3) Governance and Community Participation. Social cohesion did not appear as a standalone theme; instead, it functions as an analytical axis that cuts across and informs the interpretation of all three themes (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) that define CDR activities. These correspond to the review questions and align with three core CDR factors: equity, inclusiveness, efficiency, and governance (Lawson 2011). Counts of how many articles referenced each theme are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e for transparency only; these numbers do not indicate importance or weighting within the interpretive framework. For quantitative studies, including RCTs, findings were summarised narratively with attention to the direction and general magnitude of the most policy-relevant outcomes, and a distinction was made between perception-based measures and behavioural or institutional indicators when interpreting results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab6\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCommunity-Driven Approach Themes – Benefits and Obstacles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eNumber of Articles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePotential Benefits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePotential Obstacles\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGovernance and Community Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e- Democratic practices (Fearon et al. 2008)\u003c/p\u003e \u003cp\u003e- Community decision-making (Bakonyi et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e- Promote accountability, transparency (Kyamusugulwa et al. 2018)\u003c/p\u003e \u003cp\u003e- Cooperation with local institutions (Ratner et al. 2014)\u003c/p\u003e \u003cp\u003e- Enhance trust in formal institutions (Van den Boogaard and Santoro \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e- Citizen-government engagement/CSOs (Casey et al. 2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e- Elite capture (Vervisch et al. 2013)\u003c/p\u003e \u003cp\u003e- Undermines formal institutions through the creation of parallel structures (Bakonyi et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Rautanen and Koppen 2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEveryday Livelihoods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e- Promote inclusion of vulnerable populations (Crawford and Morrison 2020)\u003c/p\u003e \u003cp\u003e- Responds to community needs (Higashida et al \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e- Welfare improvement (King \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e- Improve social cohesion (Fearon et al. 2008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e- Create divisions (Findley \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e- Elite or one-group capture (Dawar and Ferreira 2021)\u003c/p\u003e \u003cp\u003e- level of embezzlement is not changed. (Beath et al \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRebuilding Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e- Improve public services (Dawar and Ferreira 2021)\u003c/p\u003e \u003cp\u003e- Increase resilience (Ratner et al 2014)\u003c/p\u003e \u003cp\u003e- Reduces intra-group disputes (Fearon et al \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003eb)\u003c/p\u003e \u003cp\u003e- Increases interpersonal trust (Fearon et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e- Elite capture or dominance by one group may limit effective targeting (Vervisch et al. 2013)\u003c/p\u003e \u003cp\u003e- Not appropriate for all communities (Higashida et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e- Lower participation rates (Humphreys, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRebuilding Infrastructure\u003c/h2\u003e \u003cp\u003eAccess to basic services and infrastructure, such as water, electricity, healthcare, and education, is consistently cited as a visible and tangible outcome of community-driven efforts. Across 24 studies, these developments are often framed as core priorities articulated by local communities themselves. While such outcomes are frequently celebrated as indicators of success, closer analysis reveals that the relationship between infrastructure provision and broader social transformation remains uneven and under-theorised. Some studies suggest that infrastructure contributes to social cohesion, yet often without clearly unpacking the mechanisms involved. For example, in Afghanistan, Beath et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that improved access to water and electricity under a community-driven programme was associated with reduced intra-village disputes and higher interpersonal trust among male villagers. However, the study did not elaborate on the nature or resolution of these disputes. This omission limits our understanding of whether improved services directly reduce tensions or whether the participatory processes embedded in community-driven initiatives are the real drivers of trust and social order.\u003c/p\u003e \u003cp\u003eSimilarly, in Liberia, Fearon et al. (2008) found that community-driven programmes improved access to education and promoted transparency and accountability in local decision-making. Trust in community leaders increased, particularly among marginalised groups such as ex-combatants and the poor. Yet the programme had only a limited impact on citizens' sense of political efficacy and showed weak outcomes in terms of economic empowerment. Although the programme helped reduce the likelihood of tensions escalating into violence, its limited impact on shifting local power dynamics suggests that community-driven approach may stabilise rather than transform existing social orders. This raises a normative and strategic question: should community-driven be expected to challenge entrenched power relations, or is its role better understood as reinforcing existing governance structures while making them more transparent?\u003c/p\u003e \u003cp\u003eHowever, some studies show that reconstruction interventions implemented top-down, without proper participation or consideration of local needs and grievances, can be ineffective. For example, Dawar and Ferreira (2021) found that military interests and local influencers in Pakistan excluded people from decision-making in the community-driven approach. Critics as Rautanen and Koppen (2014) argue that top-down is less likely to yield positive outcomes and fails to address the immediate needs of vulnerable communities. Similarly, in Sudan, despite communities selecting infrastructure projects, Avdeenko and Michael (2015) found no measurable effect on social capital, suggesting that participation in project selection alone is insufficient to generate cohesion. This aligns with Kyamusugulwa’s (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003ea) broader critique: infrastructure provision, while vital, does not inherently produce social transformation. Active, inclusive engagement in decision-making processes is often the missing link. Taken together, the reviewed evidence indicates that infrastructure and services can serve as key enablers of recovery and enhanced well-being in post-conflict settings. Their potential to foster social cohesion and empowerment depends less on the physical outputs delivered and more on the process through which decisions are made, ownership is built, and legitimacy is conferred.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEveryday Livelihoods\u003c/h2\u003e \u003cp\u003eEconomic recovery is a key pillar of community-driven approach, particularly in post-conflict and post-disaster settings where livelihoods are frequently destroyed. Much of the literature positions economic recovery as a technical goal - improving income, assets, or employment – but the relationship between economic improvement and broader social outcomes such as cohesion, trust, and well-being is less straightforward and, at times, contradictory. Many studies show that economic interventions, such as microfinance, livestock restocking, and public goods provision, can improve short-term welfare perceptions, particularly among vulnerable groups like women. For instance, Wulandari et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that in post-disaster Sinabung, income, rather than social identification with the community, was a more robust predictor of individual well-being. This suggests that in contexts of acute deprivation, material security may be a precondition for psychosocial recovery or social participation, rather than an outcome of it. Bauer et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) highlight that economic stability is crucial for fostering other social factors, such as trust and cooperation, suggesting that it may need to be established before other social factors can contribute to overall community resilience and cohesion. Similarly, King (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) notes that while community-driven initiatives aim to improve material conditions, they can also contribute to social cohesion when implemented inclusively. However, the direction of causality remains difficult to establish: do people trust more because their material needs are met, or are effective collective actions only possible in already cohesive communities? Evidence from Liberia further complicates this picture. Fearon et al. (2008) found that while access to livelihoods improved through community-driven, this did not necessarily translate into increased asset holdings or long-term perceptions of economic security. This distinction between short-term opportunity and long-term stability is crucial and highlights that these outcomes are specific to community-driven interventions, rather than other livelihood programs. It underscores the need for caution in interpreting livelihood gains as indicators of sustainable welfare or resilience.\u003c/p\u003e \u003cp\u003eSeveral studies reported that community-driven projects contributed to the quality and quantity of local public goods. For example, Collins et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that community-driven approach improved basic infrastructure and contributed to perceived improvements in local economic conditions. Yet again, evidence on whether such improvements translate into structural changes in household-level welfare is mixed. In Somalia, Bakonyi et al., (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) noted that while community-driven livelihood initiatives enhanced access to small-scale public goods, these did not appear to yield significant household welfare improvements. Indeed, several evaluations, including Casey (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) in Sierra Leone and Koyabu (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) in post-tsunami Indonesia, underscore the utility of community-driven approach in delivering tangible public goods, but stop short of claiming deeper socioeconomic transformation. This suggests that community-driven interventions are mainly community-level, participatory projects. Evaluating them based on their ability to create system-wide change may therefore be misleading. Most community-driven initiatives focus on stabilising communities, empowering local actors, and supporting local recovery, rather than transforming entire governance or economic systems.\u003c/p\u003e \u003cp\u003eImportantly, the distribution of economic benefits within communities is not always equitable and can generate new forms of exclusion or resentment. In Sri Lanka, Higashida et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) documented how community-driven approach that linked social trust-building to economic support risked creating disappointment among committee members who did not receive material benefits. This perceived unfairness led to disengagement from participatory processes, echoing the latest critiques (e.g., Findley \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) that aid can inadvertently deepen social divisions. The lesson here is that economic assistance must be designed with “conflict sensitivity”. Hamming (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), ensuring that benefits do not reinforce existing inequalities or produce new tensions. In Cambodia, Choi et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that community-driven approach was more effective in restoring livelihoods and social cohesion, particularly when marginalised groups were included in the process. Similarly, Fearon et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) observed that post-conflict community cohesion can be strengthened through well-structured external support, particularly when such interventions are transparent and widely perceived as fair.\u003c/p\u003e \u003cp\u003eOverall, the reviewed studies highlight both the promise and the pitfalls of linking economic recovery to social reconstruction. Socio-economic gains can foster optimism and local engagement, but if unevenly distributed or poorly integrated into participatory processes, they may undermine social trust. This suggests that community-driven economic strategies must not only aim to improve livelihoods but must do so in a way that is inclusive, transparent, and mindful of local power dynamics. Otherwise, economic assistance risks becoming a source of division rather than cohesion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGovernance and Participation\u003c/h2\u003e \u003cp\u003eCommunity-driven has been widely promoted as a mechanism to strengthen local governance and community participation in post-conflict settings. Evidence from various contexts suggests that while community-driven can reshape local governance structures and norms, its effects are uneven and highly context-dependent. Several studies show that community-driven has supported the formation of community-based governance institutions and encouraged democratic practices at the local level. For instance, Fearon et al. (2008) found that community-driven in Liberia increased trust in local decision-making processes and reduced the likelihood of violence by promoting inclusion of marginalised groups such as the poor and ex-combatants. Similarly, in Sierra Leone, community-driven facilitated inter-village collaboration and increased contact between communities and local authorities, contributing to the institutionalisation of local governance (Casey, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Afghanistan, community-driven interventions fostered greater collaboration across ethnic groups and more positive attitudes toward female participation in governance, reflecting a degree of social cohesion (Beath et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). In other contexts, such as Somalia, youth and women’s participation remained limited, though interaction with clan authorities enhanced citizens’ capacity to express their needs (Bakonyi et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Some community-driven programmes appear to challenge patriarchal structures, but broader institutional support and local acceptance remain critical for such transformations to take root. These examples highlight that the quality of participation shapes both the distribution of benefits and the potential for strengthening social cohesion and trust within communities.\u003c/p\u003e \u003cp\u003eIn contexts where the state’s presence is limited, community-driven may also foster forms of hybrid governance. In the Democratic Republic of the Congo, Ho et al. (2015) report that partnerships between local communities and health systems enhanced transparency, participation, and access to care. Yet, the study leaves open a crucial conceptual question: were frontline health workers perceived as extensions of the state or as part of the local social fabric? This ambiguity matters, as it affects whether such partnerships strengthen state legitimacy or foster more locally rooted forms of governance and accountability. Other evidence points to the potential of participatory, decentralised approaches to enhance both access and local ownership. In Nepal, Rautanen and Koppen (2014) describe how water management projects rooted in community values and traditional knowledge bolstered the resilience of Water User Committees. Nevertheless, even in these participatory contexts, underlying political affiliations and social cleavages within civil society limited collective responsibility and cohesion. This underscores a critical tension: decentralisation may empower local actors, but it does not necessarily overcome pre-existing power asymmetries or exclusions. It raises a broader analytical question: under what social and political conditions does participatory governance lead to equitable and cohesive outcomes?\u003c/p\u003e \u003cp\u003eSimilarly, in Sierra Leone, newly elected committees were largely inactive, which researchers attributed to insufficient capacity building (Kyamusugulwa et al. 2018). These findings raise concerns about whether local governance structures can be sustained once international support is withdrawn. The influence of elites in community-driven processes is another recurrent theme. In some cases, elite involvement was instrumental in resource mobilisation (Fearon et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), while in others, it led to elite capture, undermining participatory goals (Bakonyi et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The distinction between beneficial elite support and harmful elite domination remains under-explored, particularly in contexts where traditional leaders retain significant authority. For instance, while Beath et al. (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) noted that democratic councils in Afghanistan were responsible for distributing aid, misuse persisted, and traditional power structures continued to shape outcomes.\u003c/p\u003e \u003cp\u003eCommunity-driven has also been linked to broader state-building objectives, although evidence here is mixed. In Afghanistan, some studies report higher electoral participation among individuals who participated in or benefited from community-driven initiatives (Beath et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), whereas in Liberia, community-driven programmes were associated with improved trust in national institutions (Fearon et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003eb). However, other research suggests that these effects are limited or conditional. Casey (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) argues that community-driven did not substantially change political attitudes toward central government, and parallel governance structures established by NGOs may hinder vertical integration between communities and the state (Rautanen and Koppen 2014). A recurring argument in the literature is that the success of community-driven in supporting governance and state legitimacy depends on the meaningful involvement of state institutions. Projects that engaged local and national authorities, such as those in Somalia and Afghanistan, were more likely to foster coordination and institutional trust (Hamming \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Koyabu \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Conversely, initiatives led solely by INGOs sometimes created gaps in state legitimacy and accountability (Burde, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). Several authors (e.g., Lizanne and Patel 2007; Van den Boogaard and Santoro \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) emphasise that transitional governments, rather than NGOs, were often best positioned to lead community-driven processes in fragile contexts such as Kosovo, Rwanda, and Azerbaijan.\u003c/p\u003e \u003cp\u003eIn sum, while community-driven can influence local governance, its success depends on how effectively it is embedded within existing institutional and socio-political contexts. Top-down interventions that neglect these dynamics often fall short of their governance goals. Further comparative research is needed to understand how different configurations of local and state engagement shape the outcomes of participatory reconstruction efforts.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion and Research Priorities","content":"\u003cp\u003eThis review demonstrates that community-driven approach plays a significant, though uneven, role in supporting post-conflict recovery. Across the literature, outcomes vary widely in relation to service delivery, infrastructure rehabilitation, economic welfare, local governance, political attitudes, and social norms. Nevertheless, the evidence provides a clearer understanding of what community-driven entails, how it operates in practice, and the conceptual logic that distinguishes it from broader community-based approaches. It typically engages communities in identifying priorities and planning and implementing reconstruction projects, such as rehabilitating essential infrastructure and improving access to basic services, while also contributing to stronger local governance. These activities are designed not simply to deliver material improvements but to embed communities at the centre of recovery processes. In doing so, community-driven differs from general participatory interventions by granting communities substantive decision-making authority over resources, priorities, and implementation. This redistribution of authority generates stronger forms of local ownership, as communities are not merely consulted but actively design, manage, and oversee reconstruction efforts.\u003c/p\u003e\u003cp\u003eThe review identifies several interrelated elements, such as community decision-making power, collective action, local ownership, trust and social cohesion, and internally rooted accountability that together define the conceptual logic of community driven. These elements operate as a mutually reinforcing system: trust enables communities to act collectively; collective action strengthens and ownership; and strong ownership fosters internal accountability. When these dynamics function effectively, community-driven can support both the rebuilding of physical infrastructure and the restoration of social relationships, which in turn strengthens social cohesion. However, the strength of this system depends heavily on contextual conditions. Evidence shows that community-driven tends to generate more sustainable outcomes where pre-existing social cohesion, community capacities, and supportive institutional environments are present. Conversely, elite capture, weak local governance, and unresolved intergroup tensions frequently undermine participation and limit alignment with community priorities. These findings highlight the importance of designing reconstruction programmes that are sensitive to local dynamics, particularly in divided or fragile contexts.\u003c/p\u003e\u003cp\u003eThe review also reveals notable gaps in the existing literature. Research has focused on post-conflict settings, with limited understanding of how community-driven functions in communities affected by both conflict and disaster. The broader impacts of community-driven on economic welfare, especially for women and youth, remain underexplored, as do its potential unintended consequences for equity and inclusion. Some community-driven programmes appear to challenge power structures, but meaningful, lasting change depends on broader institutional support and local acceptance. This study highlight that the quality of participation whether tokenistic or empowered shapes both the distribution of benefits and the potential for strengthening social cohesion and trust within communities.\u003c/p\u003e\u003cp\u003eThis review has several limitations. First, only English-language studies were included, which may have introduced language bias. Second, database coverage may be incomplete, as not all relevant studies are indexed in the databases searched. Third, there is a risk of publication bias, as peer-reviewed journals may preferentially publish positive findings. The inclusion of grey literature helped to mitigate some of these biases by capturing reports, evaluations, and program documents not available in academic journals, although some gaps may remain. Further work is also needed to understand how community-driven shapes community–state relations, supports trust in governance, and contributes to long-term political and institutional stability. Community-driven initiatives should be closely integrated into state systems to avoid creating parallel structures that could undermine local governance. Safeguards are needed to prevent elite capture and ensure that benefits are distributed fairly. Transparency in decision-making, resource allocation, and participation should be maintained, and interventions should be designed in a conflict-sensitive way to engage marginalised groups without increasing local tensions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASSIA (Applied Social Sciences Index and Abstracts)\u003c/p\u003e\n\u003cp\u003eCDR (Community-Driven Reconstruction)\u003c/p\u003e\n\u003cp\u003eCDD (Community-Driven Development)\u003c/p\u003e\n\u003cp\u003eCCR (Centre for Conflict Resolution)\u003c/p\u003e\n\u003cp\u003eDRC (Democratic Republic of Congo)\u003c/p\u003e\n\u003cp\u003eFGDs (Focus Group Discussions)\u003c/p\u003e\n\u003cp\u003eIDPs (Internally Displaced People)\u003c/p\u003e\n\u003cp\u003eMMAT (Mixed Methods Appraisal Tool)\u003c/p\u003e\n\u003cp\u003ePCRD (Post-Conflict Reconstruction and Development)\u003c/p\u003e\n\u003cp\u003ePRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)\u003c/p\u003e\n\u003cp\u003eRTA (Reflexive Thematic Analysis)\u003c/p\u003e\n\u003cp\u003eRCT (Randomised Controlled Trial)\u003c/p\u003e\n\u003cp\u003eSCCR (Scottish Centre for Conflict Resolution)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a systematic review of existing literature and does not involve human participants. Therefore, ethical approval and informed consent were not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study is a systematic review of publicly available literature and did not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to support the preparation of this manuscript. This research was conducted as part of a PhD project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAvdeenko, Alexandra, and Michael J. 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Sinabung. \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e 158 (1): 012035. https://doi.org/10.1088/1755-1315/158/1/012035\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Centre for Peace and Security, Coventry University IV5 Innovation Village, Cheetah Road, Coventry, CV1 2TL ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"systematic review, post-conflict, reconstruction, governance, collective action, social cohesion","lastPublishedDoi":"10.21203/rs.3.rs-9001942/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9001942/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article examines the potential of community-driven approach in post-conflict recovery through a systematic review of 41 peer-reviewed and grey literature sources published between 2000 and 2024. The review searched Scopus, Web of Science, JSTOR, Google Scholar, and institutional repositories, and applied predefined inclusion criteria. All included studies were appraised using the Mixed Methods Appraisal Tool (MMAT 2018) to ensure methodological quality. Guided by theories of collective action and resilience, the review applies Reflexive Thematic Analysis (RTA) to address three key questions: the types of activities undertaken by CDR programmes; the core components that differentiate CDR from broader community-based interventions; and the ways decision-making, trust, ownership, and collective action interact to shape its logic and outcomes. It also highlights challenges, including social division, elite capture, weak accountability, and limited empowerment strategies. The findings advance debates on bottom-up reconstruction by demonstrating how CDR can strengthen community resilience when supported by inclusive frameworks, while also identifying conditions that may undermine its sustainability.\u003c/p\u003e","manuscriptTitle":"Community-Driven Reconstruction and Recovery: A Systematic Review of Successes and Challenges","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 15:29:46","doi":"10.21203/rs.3.rs-9001942/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4db5c43-effb-4e80-b6d5-2da0c2491b3a","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63721367,"name":"Social Work"}],"tags":[],"updatedAt":"2026-03-03T15:29:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 15:29:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9001942","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9001942","identity":"rs-9001942","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}