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How to conduct systematic literature reviews in management research: a guide in 6 steps and 14 decisions

• Review Paper
• Open access
• Published: 12 May 2023
• Volume 17 , pages 1899–1933, ( 2023 )

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• Philipp C. Sauer   ORCID: orcid.org/0000-0002-1823-0723 1 &
• Stefan Seuring   ORCID: orcid.org/0000-0003-4204-9948 2  

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Systematic literature reviews (SLRs) have become a standard tool in many fields of management research but are often considerably less stringently presented than other pieces of research. The resulting lack of replicability of the research and conclusions has spurred a vital debate on the SLR process, but related guidance is scattered across a number of core references and is overly centered on the design and conduct of the SLR, while failing to guide researchers in crafting and presenting their findings in an impactful way. This paper offers an integrative review of the widely applied and most recent SLR guidelines in the management domain. The paper adopts a well-established six-step SLR process and refines it by sub-dividing the steps into 14 distinct decisions: (1) from the research question, via (2) characteristics of the primary studies, (3) to retrieving a sample of relevant literature, which is then (4) selected and (5) synthesized so that, finally (6), the results can be reported. Guided by these steps and decisions, prior SLR guidelines are critically reviewed, gaps are identified, and a synthesis is offered. This synthesis elaborates mainly on the gaps while pointing the reader toward the available guidelines. The paper thereby avoids reproducing existing guidance but critically enriches it. The 6 steps and 14 decisions provide methodological, theoretical, and practical guidelines along the SLR process, exemplifying them via best-practice examples and revealing their temporal sequence and main interrelations. The paper guides researchers in the process of designing, executing, and publishing a theory-based and impact-oriented SLR.

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1 Introduction

The application of systematic or structured literature reviews (SLRs) has developed into an established approach in the management domain (Kraus et al. 2020 ), with 90% of management-related SLRs published within the last 10 years (Clark et al. 2021 ). Such reviews help to condense knowledge in the field and point to future research directions, thereby enabling theory development (Fink 2010 ; Koufteros et al. 2018 ). SLRs have become an established method by now (e.g., Durach et al. 2017 ; Koufteros et al. 2018 ). However, many SLR authors struggle to efficiently synthesize and apply review protocols and justify their decisions throughout the review process (Paul et al. 2021 ) since only a few studies address and explain the respective research process and the decisions to be taken in this process. Moreover, the available guidelines do not form a coherent body of literature but focus on the different details of an SLR, while a comprehensive and detailed SLR process model is lacking. For example, Seuring and Gold ( 2012 ) provide some insights into the overall process, focusing on content analysis for data analysis without covering the practicalities of the research process in detail. Similarly, Durach et al. ( 2017 ) address SLRs from a paradigmatic perspective, offering a more foundational view covering ontological and epistemological positions. Durach et al. ( 2017 ) emphasize the philosophy of science foundations of an SLR. Although somewhat similar guidelines for SLRs might be found in the wider body of literature (Denyer and Tranfield 2009 ; Fink 2010 ; Snyder 2019 ), they often take a particular focus and are less geared toward explaining and reflecting on the single choices being made during the research process. The current body of SLR guidelines leaves it to the reader to find the right links among the guidelines and to justify their inconsistencies. This is critical since a vast number of SLRs are conducted by early-stage researchers who likely struggle to synthesize the existing guidance and best practices (Fisch and Block 2018 ; Kraus et al. 2020 ), leading to the frustration of authors, reviewers, editors, and readers alike.

Filling these gaps is critical in our eyes since researchers conducting literature reviews form the foundation of any kind of further analysis to position their research into the respective field (Fink 2010 ). So-called “systematic literature reviews” (e.g., Davis and Crombie 2001 ; Denyer and Tranfield 2009 ; Durach et al. 2017 ) or “structured literature reviews” (e.g., Koufteros et al. 2018 ; Miemczyk et al. 2012 ) differ from nonsystematic literature reviews in that the analysis of a certain body of literature becomes a means in itself (Kraus et al. 2020 ; Seuring et al. 2021 ). Although two different terms are used for this approach, the related studies refer to the same core methodological references that are also cited in this paper. Therefore, we see them as identical and abbreviate them as SLR.

There are several guidelines on such reviews already, which have been developed outside the management area (e.g. Fink 2010 ) or with a particular focus on one management domain (e.g., Kraus et al. 2020 ). SLRs aim at capturing the content of the field at a point in time but should also aim at informing future research (Denyer and Tranfield 2009 ), making follow-up research more efficient and productive (Kraus et al. 2021 ). Such standalone literature reviews would and should also prepare subsequent empirical or modeling research, but usually, they require far more effort and time (Fisch and Block 2018 ; Lim et al. 2022 ). To achieve this preparation, SLRs can essentially a) describe the state of the literature, b) test a hypothesis based on the available literature, c) extend the literature, and d) critique the literature (Xiao and Watson 2019 ). Beyond guiding the next incremental step in research, SLRs “may challenge established assumptions and norms of a given field or topic, recognize critical problems and factual errors, and stimulate future scientific conversations around that topic” (Kraus et al. 2022 , p. 2578). Moreover, they have the power to answer research questions that are beyond the scope of individual empirical or modeling studies (Snyder 2019 ) and to build, elaborate, and test theories beyond this single study scope (Seuring et al. 2021 ). These contributions of an SLR may be highly influential and therefore underline the need for high-quality planning, execution, and reporting of their process and details.

Regardless of the individual aims of standalone SLRs, their numbers have exponentially risen in the last two decades (Kraus et al. 2022 ) and almost all PhD or large research project proposals in the management domain include such a standalone SLR to build a solid foundation for their subsequent work packages. Standalone SLRs have thus become a key part of management research (Kraus et al. 2021 ; Seuring et al. 2021 ), which is also underlined by the fact that there are journals and special issues exclusively accepting standalone SLRs (Kraus et al. 2022 ; Lim et al. 2022 ).

However, SLRs require a commitment that is often comparable to an additional research process or project. Hence, SLRs should not be taken as a quick solution, as a simplistic, descriptive approach would usually not yield a publishable paper (see also Denyer and Tranfield 2009 ; Kraus et al. 2020 ).

Furthermore, as with other research techniques, SLRs are based on the rigorous application of rules and procedures, as well as on ensuring the validity and reliability of the method (Fisch and Block 2018 ; Seuring et al. 2021 ). In effect, there is a need to ensure “the same level of rigour to reviewing research evidence as should be used in producing that research evidence in the first place” (Davis and Crombie 2001 , p.1). This rigor holds for all steps of the research process, such as establishing the research question, collecting data, analyzing it, and making sense of the findings (Durach et al. 2017 ; Fink 2010 ; Seuring and Gold 2012 ). However, there is a high degree of diversity where some would be justified, while some papers do not report the full details of the research process. This lack of detail contrasts with an SLR’s aim of creating a valid map of the currently available research in the reviewed field, as critical information on the review’s completeness and potential reviewer biases cannot be judged by the reader or reviewer. This further impedes later replications or extensions of such reviews, which could provide longitudinal evidence of the development of a field (Denyer and Tranfield 2009 ; Durach et al. 2017 ). Against this observation, this paper addresses the following question:

Which decisions need to be made in an SLR process, and what practical guidelines can be put forward for making these decisions?

Answering this question, the key contributions of this paper are fourfold: (1) identifying the gaps in existing SLR guidelines, (2) refining the SLR process model by Durach et al. ( 2017 ) through 14 decisions, (3) synthesizing and enriching guidelines for these decisions, exemplifying the key decisions by means of best practice SLRs, and (4) presenting and discussing a refined SLR process model.

In some cases, we point to examples from operations and supply chain management. However, they illustrate the purposes discussed in the respective sections. We carefully checked that the arguments held for all fields of management-related research, and multiple examples from other fields of management were also included.

2 Identification of the need for an enriched process model, including a set of sequential decisions and their interrelations

In line with the exponential increase in SLR papers (Kraus et al. 2022 ), multiple SLR guidelines have recently been published. Since 2020, we have found a total of 10 papers offering guidelines on SLRs and other reviews for the field of management in general or some of its sub-fields. These guidelines are of double interest to this paper since we aim to complement them to fill the gap identified in the introduction while minimizing the doubling of efforts. Table 1 lists the 10 most recent guidelines and highlights their characteristics, research objectives, contributions, and how our paper aims to complement these previous contributions.

The sheer number and diversity of guideline papers, as well as the relevance expressed in them, underline the need for a comprehensive and exhaustive process model. At the same time, the guidelines take specific foci on, for example, updating earlier guidelines to new technological potentials (Kraus et al. 2020 ), clarifying the foundational elements of SLRs (Kraus et al. 2022 ) and proposing a review protocol (Paul et al. 2021 ) or the application and development of theory in SLRs (Seuring et al. 2021 ). Each of these foci fills an entire paper, while the authors acknowledge that much more needs to be considered in an SLR. Working through these most recent guidelines, it becomes obvious that the common paper formats in the management domain create a tension for guideline papers between elaborating on a) the SLR process and b) the details, options, and potentials of individual process steps.

Our analysis in Table 1 evidences that there are a number of rich contributions on aspect b), while the aspect a) of SLR process models has not received the same attention despite the substantial confusion of authors toward them (Paul et al. 2021 ). In fact, only two of the most recent guidelines approach SLR process models. First, Kraus et al. ( 2020 ) incrementally extended the 20-year-old Tranfield et al. ( 2003 ) three-stage model into four stages. A little later, Paul et al. ( 2021 ) proposed a three-stage (including six sub-stages) SPAR-4-SLR review protocol. It integrates the PRISMA reporting items (Moher et al. 2009 ; Page et al. 2021 ) that originate from clinical research to define 14 actions stating what items an SLR in management needs to report for reasons of validity, reliability, and replicability. Almost naturally, these 14 reporting-oriented actions mainly relate to the first SLR stage of “assembling the literature,” which accounts for nine of the 14 actions. Since this protocol is published in a special issue editorial, its presentation and elaboration are somewhat limited by the already mentioned word count limit. Nevertheless, the SPAR-4-SLR protocol provides a very useful checklist for researchers that enables them to include all data required to document the SLR and to avoid confusion from editors, reviewers, and readers regarding SLR characteristics.

Beyond Table 1 , Durach et al. ( 2017 ) synthesized six common SLR “steps” that differ only marginally in the delimitation of one step to another from the sub-stages of the previously mentioned SLR processes. In addition, Snyder ( 2019 ) proposed a process comprising four “phases” that take more of a bird’s perspective in addressing (1) design, (2) conduct, (3) analysis, and (4) structuring and writing the review. Moreover, Xiao and Watson ( 2019 ) proposed only three “stages” of (1) planning, (2) conducting, and (3) reporting the review that combines the previously mentioned conduct and the analysis and defines eight steps within them. Much in line with the other process models, the final reporting stage contains only one of the eight steps, leaving the reader somewhat alone in how to effectively craft a manuscript that contributes to the further development of the field.

In effect, the mentioned SLR processes differ only marginally, while the systematic nature of actions in the SPAR-4-SLR protocol (Paul et al. 2021 ) can be seen as a reporting must-have within any of the mentioned SLR processes. The similarity of the SLR processes is, however, also evident in the fact that they leave open how the SLR analysis can be executed, enriched, and reflected to make a contribution to the reviewed field. In contrast, this aspect is richly described in the other guidelines that do not offer an SLR process, leading us again toward the tension for guideline papers between elaborating on a) the SLR process and b) the details, options, and potentials of each process step.

To help (prospective) SLR authors successfully navigate this tension of existing guidelines, it is thus the ambition of this paper to adopt a comprehensive SLR process model along which an SLR project can be planned, executed, and written up in a coherent way. To enable this coherence, 14 distinct decisions are defined, reflected, and interlinked, which have to be taken across the different steps of the SLR process. At the same time, our process model aims to actively direct researchers to the best practices, tips, and guidance that previous guidelines have provided for individual decisions. We aim to achieve this by means of an integrative review of the relevant SLR guidelines, as outlined in the following section.

3 Methodology: an integrative literature review of guidelines for systematic literature reviews in management

It might seem intuitive to contribute to the debate on the “gold standard” of systematic literature reviews (Davis et al. 2014 ) by conducting a systematic review ourselves. However, there are different types of reviews aiming for distinctive contributions. Snyder ( 2019 ) distinguished between a) systematic, b) semi-systematic, and c) integrative (or critical) reviews, which aim for i) (mostly quantitative) synthesis and comparison of prior (primary) evidence, ii) an overview of the development of a field over time, and iii) a critique and synthesis of prior perspectives to reconceptualize or advance them. Each review team needs to position itself in such a typology of reviews to define the aims and scope of the review. To do so and structure the related research process, we adopted the four generic steps for an (integrative) literature review by Snyder ( 2019 )—(1) design, (2) conduct, (3) analysis, and (4) structuring and writing the review—on which we report in the remainder of this section. Since the last step is a very practical one that, for example, asks, “Is the contribution of the review clearly communicated?” (Snyder 2019 ), we will focus on the presentation of the method applied to the initial three steps:

(1) Regarding the design, we see the need for this study emerging from our experience in reviewing SLR manuscripts, supervising PhD students who, almost by default, need to prepare an SLR, and recurring discussions on certain decisions in the process of both. These discussions regularly left some blank or blurry spaces (see Table 1 ) that induced substantial uncertainty regarding critical decisions in the SLR process (Paul et al. 2021 ). To address this gap, we aim to synthesize prior guidance and critically enrich it, thus adopting an integrative approach for reviewing existing SLR guidance in the management domain (Snyder 2019 ).

(2) To conduct the review, we started collecting the literature that provided guidance on the individual SLR parts. We built on a sample of 13 regularly cited or very recent papers in the management domain. We started with core articles that we successfully used to publish SLRs in top-tier OSCM journals, such as Tranfield et al. ( 2003 ) and Durach et al. ( 2017 ), and we checked their references and papers that cited these publications. The search focus was defined by the following criteria: the articles needed to a) provide original methodological guidance for SLRs by providing new aspects of the guideline or synthesizing existing ones into more valid guidelines and b) focus on the management domain. Building on the nature of a critical or integrative review that does not require a full or representative sample (Snyder 2019 ), we limited the sample to the papers displayed in Table 2 that built the core of the currently applied SLR guidelines. In effect, we found 11 technical papers and two SLRs of SLRs (Carter and Washispack 2018 ; Seuring and Gold 2012 ). From the latter, we mainly analyzed the discussion and conclusion parts that explicitly developed guidance on conducting SLRs.

(3) For analyzing these papers, we first adopted the six-step SLR process proposed by Durach et al. ( 2017 , p.70), which they define as applicable to any “field, discipline or philosophical perspective”. The contrast between the six-step SLR process used for the analysis and the four-step process applied by ourselves may seem surprising but is justified by the use of an integrative approach. This approach differs mainly in retrieving and selecting pertinent literature that is key to SLRs and thus needs to be part of the analysis framework.

While deductively coding the sample papers against Durach et al.’s ( 2017 ), guidance in the six steps, we inductively built a set of 14 decisions presented in the right columns of Table 2 that are required to be made in any SLR. These decisions built a second and more detailed level of analysis, for which the single guidelines were coded as giving low, medium, or high levels of detail (see Table 3 ), which helped us identify the gaps in the current guidance papers and led our way in presenting, critically discussing, and enriching the literature. In effect, we see that almost all guidelines touch on the same issues and try to give a comprehensive overview. However, this results in multiple guidelines that all lack the space to go into detail, while only a few guidelines focus on filling a gap in the process. It is our ambition with this analysis to identify the gaps in the guidelines, thereby identifying a precise need for refinement, and to offer a first step into this refinement. Adopting advice from the literature sample, the coding was conducted by the entire author team (Snyder 2019 ; Tranfield et al. 2003 ) including discursive alignments of interpretation (Seuring and Gold 2012 ). This enabled a certain reliability and validity of the analysis by reducing the within-study and expectancy bias (Durach et al. 2017 ), while the replicability was supported by reporting the review sample and the coding results in Table 3 (Carter and Washispack 2018 ).

(4) For the writing of the review, we only pointed to the unusual structure of presenting the method without a theory section and then the findings in the following section. However, this was motivated by the nature of the integrative review so that the review findings at the same time represent the “state of the art,” “literature review,” or “conceptualization” sections of a paper.

4 Findings of the integrative review: presentation, critical discussion, and enrichment of prior guidance

4.1 the overall research process for a systematic literature review.

Even within our sample of only 13 guidelines, there are four distinct suggestions for structuring the SLR process. One of the earliest SLR process models was proposed by Tranfield et al. ( 2003 ) encompassing the three stages of (1) planning the review, (2) conducting a review, and (3) reporting and dissemination. Snyder ( 2019 ) proposed four steps employed in this study: (1) design, (2) conduct, (3) analysis, and (4) structuring and writing the review. Borrowing from content analysis guidelines, Seuring and Gold ( 2012 ) defined four steps: (1) material collection, (2) descriptive analysis, (3) category selection, and (4) material evaluation. Most recently Kraus et al. ( 2020 ) proposed four steps: (1) planning the review, (2) identifying and evaluating studies, (3) extracting and synthesizing data, and (4) disseminating the review findings. Most comprehensively, Durach et al. ( 2017 ) condensed prior process models into their generic six steps for an SLR. Adding the review of the process models by Snyder ( 2019 ) and Seuring and Gold ( 2012 ) to Durach et al.’s ( 2017 ) SLR process review of four papers, we support their conclusion of the general applicability of the six steps defined. Consequently, these six steps form the backbone of our coding scheme, as shown in the left column of Table 2 and described in the middle column.

As stated in Sect.  3 , we synthesized the review papers against these six steps but experienced that the papers were taking substantially different foci by providing rich details for some steps while largely bypassing others. To capture this heterogeneity and better operationalize the SLR process, we inductively introduced the right column, identifying 14 decisions to be made. These decisions are all elaborated in the reviewed papers but to substantially different extents, as the detailed coding results in Table 3 underline.

Mapping Table 3 for potential gaps in the existing guidelines, we found six decisions on which we found only low- to medium-level details, while high-detail elaboration was missing. These six decisions, which are illustrated in Fig.  1 , belong to three steps: 1: defining the research question, 5: synthesizing the literature, and 6: reporting the results. This result underscores our critique of currently unbalanced guidance that is, on the one hand, detailed on determining the required characteristics of primary studies (step 2), retrieving a sample of potentially relevant literature (step 3), and selecting the pertinent literature (step 4). On the other hand, authors, especially PhD students, are left without substantial guidance on the steps critical to publication. Instead, they are called “to go one step further … and derive meaningful conclusions” (Fisch and Block 2018 , p. 105) without further operationalizations on how this can be achieved; for example, how “meet the editor” conference sessions regularly cause frustration among PhDs when editors call for “new,” “bold,” and “relevant” research. Filling the gaps in the six decisions with best practice examples and practical experience is the main focus of this study’s contribution. The other eight decisions are synthesized with references to the guidelines that are most helpful and relevant for the respective step in our eyes.

The 6 steps and 14 decisions of the SLR process

4.2 Step 1: defining the research question

When initiating a research project, researchers make three key decisions.

Decision 1 considers the essential tasks of establishing a relevant and timely research question, but despite the importance of the decision, which determines large parts of further decisions (Snyder 2019 ; Tranfield et al. 2003 ), we only find scattered guidance in the literature. Hence, how can a research topic be specified to allow a strong literature review that is neither too narrow nor too broad? The latter is the danger in meta-reviews (i.e., reviews of reviews) (Aguinis et al. 2020 ; Carter and Washispack 2018 ; Kache and Seuring 2014 ). In this respect, even though the method would be robust, the findings would not be novel. In line with Carter and Washispack ( 2018 ), there should always be room for new reviews, yet over time, they must move from a descriptive overview of a field further into depth and provide detailed analyses of constructs. Clark et al. ( 2021 ) provided a detailed but very specific reflection on how they crafted a research question for an SLR and that revisiting the research question multiple times throughout the SLR process helps to coherently and efficiently move forward with the research. More generically, Kraus et al. ( 2020 ) listed six key contributions of an SLR that can guide the definition of the research question. Finally, Snyder ( 2019 ) suggested moving into more detail from existing SLRs and specified two main avenues for crafting an SLR research question that are either investigating the relationship among multiple effects, the effect of (a) specific variable(s), or mapping the evidence regarding a certain research area. For the latter, we see three possible alternative approaches, starting with a focus on certain industries. Examples are analyses of the food industry (Beske et al. 2014 ), retailing (Wiese et al. 2012 ), mining and minerals (Sauer and Seuring 2017 ), or perishable product supply chains (Lusiantoro et al. 2018 ) and traceability at the example of the apparel industry (Garcia-Torres et al. 2019 ). A second opportunity would be to assess the status of research in a geographical area that composes an interesting context from a research perspective, such as sustainable supply chain management (SSCM) in Latin America (Fritz and Silva 2018 ), yet this has to be justified explicitly, avoiding the fact that geographical focus is taken as the reason per se (e.g., Crane et al. 2016 ). A third variant addresses emerging issues, such as SCM, in a base-of-the-pyramid setting (Khalid and Seuring 2019 ) and the use of blockchain technology (Wang et al. 2019 ) or digital transformation (Hanelt et al. 2021 ). These approaches limit the reviewed field to enable a more contextualized analysis in which the novelty, continued relevance, or unjustified underrepresentation of the context can be used to specify a research gap and related research question(s). This also impacts the following decisions, as shown below.

Decision 2 concerns the option for a theoretical approach (i.e., the adoption of an inductive, abductive, or deductive approach) to theory building through the literature review. The review of previous guidance on this delivers an interesting observation. On the one hand, there are early elaborations on systematic reviews, realist synthesis, meta-synthesis, and meta-analysis by Tranfield et al. ( 2003 ) that are borrowing from the origins of systematic reviews in medical research. On the other hand, recent management-related guidelines largely neglect details of related decisions, but point out that SLRs are a suitable tool for theory building (Kraus et al. 2020 ). Seuring et al. ( 2021 ) set out to fill this gap and provided substantial guidance on how to use theory in SLRs to advance the field. To date, the option for a theoretical approach is only rarely made explicit, leaving the reader often puzzled about how advancement in theory has been crafted and impeding a review’s replicability (Seuring et al. 2021 ). Many papers still leave related choices in the dark (e.g., Rhaiem and Amara 2021 ; Rojas-Córdova et al. 2022 ) and move directly from the introduction to the method section.

In Decision 3, researchers need to adopt a theoretical framework (Durach et al. 2017 ) or at least a theoretical starting point, depending on the most appropriate theoretical approach (Seuring et al. 2021 ). Here, we find substantial guidance by Durach et al. ( 2017 ) that underlines the value of adopting a theoretical lens to investigate SCM phenomena and the literature. Moreover, the choice of a theoretical anchor enables a consistent definition and operationalization of constructs that are used to analyze the reviewed literature (Durach et al. 2017 ; Seuring et al. 2021 ). Hence, providing some upfront definitions is beneficial, clarifying what key terminology would be used in the subsequent paper, such as Devece et al. ( 2019 ) introduce their terminology on coopetition. Adding a practical hint beyond the elaborations of prior guidance papers for taking up established constructs in a deductive analysis (decision 2), there would be the question of whether these can yield interesting findings.

Here, it would be relevant to specify what kind of analysis is aimed for the SLR, where three approaches might be distinguished (i.e., bibliometric analysis, meta-analysis, and content analysis–based studies). Briefly distinguishing them, the core difference would be how many papers can be analyzed employing the respective method. Bibliometric analysis (Donthu et al. 2021 ) usually relies on the use of software, such as Biblioshiny, allowing the creation of figures on citations and co-citations. These figures enable the interpretation of large datasets in which several hundred papers can be analyzed in an automated manner. This allows for distinguishing among different research clusters, thereby following a more inductive approach. This would be contrasted by meta-analysis (e.g., Leuschner et al. 2013 ), where often a comparatively smaller number of papers is analyzed (86 in the respective case) but with a high number of observations (more than 17,000). The aim is to test for statistically significant correlations among single constructs, which requires that the related constructs and items be precisely defined (i.e., a clearly deductive approach to the analysis).

Content analysis is the third instrument frequently applied to data analysis, where an inductive or deductive approach might be taken (Seuring et al. 2021 ). Content-based analysis (see decision 9 in Sect.  4.6 ; Seuring and Gold 2012 ) is a labor-intensive step and can hardly be changed ex post. This also implies that only a certain number of papers might be analyzed (see Decision 6 in Sect.  4.5 ). It is advisable to adopt a wider set of constructs for the analysis stemming even from multiple established frameworks since it is difficult to predict which constructs and items might yield interesting insights. Hence, coding a more comprehensive set of items and dropping some in the process is less problematic than starting an analysis all over again for additional constructs and items. However, in the process of content analysis, such an iterative process might be required to improve the meaningfulness of the data and findings (Seuring and Gold 2012 ). A recent example of such an approach can be found in Khalid and Seuring ( 2019 ), building on the conceptual frameworks for SSCM of Carter and Rogers ( 2008 ), Seuring and Müller ( 2008 ), and Pagell and Wu ( 2009 ). This allows for an in-depth analysis of how SSCM constructs are inherently referred to in base-of-the-pyramid-related research. The core criticism and limitation of such an approach is the random and subjectively biased selection of frameworks for the purpose of analysis.

Beyond the aforementioned SLR methods, some reviews, similar to the one used here, apply a critical review approach. This is, however, nonsystematic, and not an SLR; thus, it is beyond the scope of this paper. Interested readers can nevertheless find some guidance on critical reviews in the available literature (e.g., Kraus et al. 2022 ; Snyder 2019 ).

4.3 Step 2: determining the required characteristics of primary studies

After setting the stage for the review, it is essential to determine which literature is to be reviewed in Decision 4. This topic is discussed by almost all existing guidelines and will thus only briefly be discussed here. Durach et al. ( 2017 ) elaborated in great detail on defining strict inclusion and exclusion criteria that need to be aligned with the chosen theoretical framework. The relevant units of analysis need to be specified (often a single paper, but other approaches might be possible) along with suitable research methods, particularly if exclusively empirical studies are reviewed or if other methods are applied. Beyond that, they elaborated on potential quality criteria that should be applied. The same is considered by a number of guidelines that especially draw on medical research, in which systematic reviews aim to pool prior studies to infer findings from their total population. Here, it is essential to ensure the exclusion of poor-quality evidence that would lower the quality of the review findings (Mulrow 1987 ; Tranfield et al. 2003 ). This could be ensured by, for example, only taking papers from journals listed on the Web of Science or Scopus or journals listed in quartile 1 of Scimago ( https://www.scimagojr.com/ ), a database providing citation and reference data for journals.

The selection of relevant publication years should again follow the purpose of the study defined in Step 1. As such, there might be a justified interest in the wide coverage of publication years if a historical perspective is taken. Alternatively, more contemporary developments or the analysis of very recent issues can justify the selection of very few years of publication (e.g., Kraus et al. 2022 ). Again, it is hard to specify a certain time period covered, but if developments of a field should be analyzed, a five-year period might be a typical lower threshold. On current topics, there is often a trend of rising publishing numbers. This scenario implies the rising relevance of a topic; however, this should be treated with caution. The total number of papers published per annum has increased substantially in recent years, which might account for the recently heightened number of papers on a certain topic.

4.4 Step 3: retrieving a sample of potentially relevant literature

After defining the required characteristics of the literature to be reviewed, the literature needs to be retrieved based on two decisions. Decision 5 concerns suitable literature sources and databases that need to be defined. Turning to Web of Science or Scopus would be two typical options found in many of the examples mentioned already (see also detailed guidance by Paul and Criado ( 2020 ) as well as Paul et al. ( 2021 )). These databases aggregate many management journals, and a typical argument for turning to the Web of Science database is the inclusion of impact factors, as they indicate a certain minimum quality of the journal (Sauer and Seuring 2017 ). Additionally, Google Scholar is increasingly mentioned as a usable search engine, often providing higher numbers of search results than the mentioned databases (e.g., Pearce 2018 ). These results often entail duplicates of articles from multiple sources or versions of the same article, as well as articles in predatory journals (Paul et al. 2021 ). Therefore, we concur with Paul et al. ( 2021 ) who underline the quality assurance mechanisms in Web of Science and Scopus, making them preferred databases for the literature search. From a practical perspective, it needs to be mentioned that SLRs in management mainly rely on databases that are not free to use. Against this limitation, Pearce ( 2018 ) provided a list of 20 search engines that are free of charge and elaborated on their advantages and disadvantages. Due to the individual limitations of the databases, it is advisable to use a combination of them (Kraus et al. 2020 , 2022 ) and build a consolidated sample by screening the papers found for duplicates, as regularly done in SLRs.

This decision also includes the choice of the types of literature to be analyzed. Typically, journal papers are selected, ensuring that the collected papers are peer-reviewed and have thus undergone an academic quality management process. Meanwhile, conference papers are usually avoided since they are often less mature and not checked for quality (e.g., Seuring et al. 2021 ). Nevertheless, for emerging topics, it might be too restrictive to consider only peer-reviewed journal articles and limit the literature to only a few references. Analyzing such rapidly emerging topics is relevant for timely and impact-oriented research and might justify the selection of different sources. Kraus et al. ( 2020 ) provided a discussion on the use of gray literature (i.e., nonacademic sources), and Sauer ( 2021 ) provided an example of a review of sustainability standards from a management perspective to derive implications for their application by managers on the one hand and for enhancing their applicability on the other hand.

Another popular way to limit the review sample is the restriction to a certain list of journals (Kraus et al. 2020 ; Snyder 2019 ). While this is sometimes favored by highly ranked journals, Carter and Washispack ( 2018 ), for example, found that many pertinent papers are not necessarily published in journals within the field. Webster and Watson ( 2002 ) quite tellingly cited a reviewer labeling the selection of top journals as an unjustified excuse for investigating the full body of relevant literature. Both aforementioned guidelines thus discourage the restriction to particular journals, a guidance that we fully support.

However, there is an argument to be made supporting the exclusion of certain lower-ranked journals. This can be done, for example, by using Scimago Journal quartiles ( www.scimagojr.com , last accessed 13. of April 2023) and restricting it to journals in the first quartile (e.g., Yavaprabhas et al. 2022 ). Other papers (e.g., Kraus et al. 2021 ; Rojas-Córdova et al. 2022 ) use certain journal quality lists to limit their sample. However, we argue for a careful check by the authors against the topic reviewed regarding what would be included and excluded.

Decision 6 entails the definition of search terms and a search string to be applied in the database just chosen. The search terms should reflect the aims of the review and the exclusion criteria that might be derived from the unit of analysis and the theoretical framework (Durach et al. 2017 ; Snyder 2019 ). Overall, two approaches to keywords can be observed. First, some guides suggest using synonyms of the key terms of interest (e.g., Durach et al. 2017 ; Kraus et al. 2020 ) in order to build a wide baseline sample that will be condensed in the next step. This is, of course, especially helpful if multiple terms delimitate a field together or different synonymous terms are used in parallel in different fields or journals. Empirical journals in supply chain management, for example, use the term “multiple supplier tiers ” (e.g., Tachizawa and Wong 2014 ), while modeling journals in the same field label this as “multiple supplier echelons ” (e.g., Brandenburg and Rebs 2015 ). Second, in some cases, single keywords are appropriate for capturing a central aspect or construct of a field if the single keyword has a global meaning tying this field together. This approach is especially relevant to the study of relatively broad terms, such as “social media” (Lim and Rasul 2022 ). However, this might result in very high numbers of publications found and therefore requires a purposeful combination with other search criteria, such as specific journals (Kraus et al. 2021 ; Lim et al. 2021 ), publication dates, article types, research methods, or the combination with keywords covering domains to which the search is aimed to be specified.

Since SLRs are often required to move into detail or review the intersections of relevant fields, we recommend building groups of keywords (single terms or multiple synonyms) for each field to be connected that are coupled via Boolean operators. To determine when a point of saturation for a keyword group is reached, one could monitor the increase in papers found in a database when adding another synonym. Once the increase is significantly decreasing or even zeroing, saturation is reached (Sauer and Seuring 2017 ). The keywords themselves can be derived from the list of keywords of influential publications in the field, while attention should be paid to potential synonyms in neighboring fields (Carter and Washispack 2018 ; Durach et al. 2017 ; Kraus et al. 2020 ).

4.5 Step 4: selecting the pertinent literature

The inclusion and exclusion criteria (Decision 6) are typically applied in Decision 7 in a two-stage process, first on the title, abstract, and keywords of an article before secondly applying them to the full text of the remaining articles (see also Kraus et al. 2020 ; Snyder 2019 ). Beyond this, Durach et al. ( 2017 ) underlined that the pertinence of the publication regarding units of analysis and the theoretical framework needs to be critically evaluated in this step to avoid bias in the review analysis. Moreover, Carter and Washispack ( 2018 ) requested the publication of the included and excluded sources to ensure the replicability of Steps 3 and 4. This can easily be done as an online supplement to an eventually published review article.

Nevertheless, the question remains: How many papers justify a literature review? While it is hard to specify how many papers comprise a body of literature, there might be certain thresholds for which Kraus et al. ( 2020 ) provide a useful discussion. As a rough guide, more than 50 papers would usually make a sound starting point (see also Paul and Criado 2020 ), while there are SLRs on emergent topics, such as multitier supply chain management, where 39 studies were included (Tachizawa and Wong 2014 ). An SLR on “learning from innovation failures” builds on 36 papers (Rhaiem and Amara 2021 ), which we would see as the lower threshold. However, such a low number should be an exception, and anything lower would certainly trigger the following question: Why is a review needed? Meanwhile, there are also limits on how many papers should be reviewed. While there are cases with 191 (Seuring and Müller 2008 ), 235 (Rojas-Córdova et al. 2022 ), or up to nearly 400 papers reviewed (Spens and Kovács 2006 ), these can be regarded as upper thresholds. Over time, similar topics seem to address larger datasets.

4.6 Step 5: synthesizing the literature

Before synthesizing the literature, Decision 8 considers the selection of a data extraction tool for which we found surprisingly little guidance. Some guidance is given on the use of cloud storage to enable remote team work (Clark et al. 2021 ). Beyond this, we found that SLRs have often been compiled with marked and commented PDFs or printed papers that were accompanied by tables (Kraus et al. 2020 ) or Excel sheets (see also the process tips by Clark et al. 2021 ). This sheet tabulated the single codes derived from the theoretical framework (Decision 3) and the single papers to be reviewed (Decision 7) by crossing out individual cells, signaling the representation of a particular code in a particular paper. While the frequency distribution of the codes is easily compiled from this data tool, the related content needs to be looked at in the papers in a tedious back-and-forth process. Beyond that, we would strongly recommend using data analysis software, such as MAXQDA or NVivo. Such programs enable the import of literature in PDF format and the automatic or manual coding of text passages, their comparison, and tabulation. Moreover, there is a permanent and editable reference of the coded text to a code. This enables a very quick compilation of content summaries or statistics for single codes and the identification of qualitative and quantitative links between codes and papers.

All the mentioned data extraction or data processing tools require a license and therefore are not free of cost. While many researchers may benefit from national or institutional subscriptions to these services, others may not. As a potential alternative, Pearce ( 2018 ) proposed a set of free open-source software (FOSS), including an elaboration on how they can be combined to perform an SLR. He also highlighted that both free and proprietary solutions have advantages and disadvantages that are worthwhile for those who do not have the required tools provided by their employers or other institutions they are members of. The same may apply to the literature databases used for the literature acquisition in Decision 5 (Pearce 2018 ).

Moreover, there is a link to Step 1, Decision 3, where bibliometric reviews and meta-analyses were mentioned. These methods, which are alternatives to content analysis–based approaches, have specific demands, so specific tools would be appropriate, such as the Biblioshiny software or VOSviewer. As we will point out for all decisions, there is a high degree of interdependence among the steps and decisions made.

Decision 9 looks at conducting the data analysis, such as coding against (pre-defined) constructs, in SLRs that rely, in most cases, on content analysis. Seuring and Gold ( 2012 ) elaborated in detail on its characteristics and application in SLRs. As this paper also explains the process of qualitative content analysis in detail, repetition is avoided here, but a summary is offered. Since different ways exist to conduct a content analysis, it is even more important to explain and justify, for example, the choice of an inductive or deductive approach (see Decision 2). In several cases, analytic variables are applied on the go, so there is no theory-based introduction of related constructs. However, to ensure the validity and replicability of the review (see Decision 11), it is necessary to explicitly define all the variables and codes used to analyze and synthesize the reviewed material (Durach et al. 2017 ; Seuring and Gold 2012 ). To build a valid framework as the SLR outcome, it is vital to ensure that the constructs used for the data analysis are sufficiently defined, mutually exclusive, and comprehensively exhaustive. For meta-analysis, the predefined constructs and items would demand quantitative coding so that the resulting data could be analyzed using statistical software tools such as SPSS or R (e.g., Xiao and Watson 2019 ). Pointing to bibliometric analysis again, the respective software would be used for data analysis, yielding different figures and paper clusters, which would then require interpretation (e.g., Donthu et al. 2021 ; Xiao and Watson 2019 ).

Decision 10, on conducting subsequent statistical analysis, considers follow-up analysis of the coding results. Again, this is linked to the chosen SLR method, and a bibliographic analysis will require a different statistical analysis than a content analysis–based SLR (e.g., Lim et al. 2022 ; Xiao and Watson 2019 ). Beyond the use of content analysis and the qualitative interpretation of its results, applying contingency analysis offers the opportunity to quantitatively assess the links among constructs and items. It provides insights into which items are correlated with each other without implying causality. Thus, the interpretation of the findings must explain the causality behind the correlations between the constructs and the items. This must be based on sound reasoning and linking the findings to theoretical arguments. For SLRs, there have recently been two kinds of applications of contingency analysis, differentiated by unit of analysis. De Lima et al. ( 2021 ) used the entire paper as the unit of analysis, deriving correlations on two constructs that were used together in one paper. This is, of course, subject to critique as to whether the constructs really represent correlated content. Moving a level deeper, Tröster and Hiete ( 2018 ) used single-text passages on one aspect, argument, or thought as the unit of analysis. Such an approach is immune against the critique raised before and can yield more valid statistical support for thematic analysis. Another recent methodological contribution employing the same contingency analysis–based approach was made by Siems et al. ( 2021 ). Their analysis employs constructs from SSCM and dynamic capabilities. Employing four subsets of data (i.e., two time periods each in the food and automotive industries), they showed that the method allows distinguishing among time frames as well as among industries.

However, the unit of analysis must be precisely explained so that the reader can comprehend it. Both examples use contingency analysis to identify under-researched topics and develop them into research directions whose formulation represents the particular aim of an SLR (Paul and Criado 2020 ; Snyder 2019 ). Other statistical tools might also be applied, such as cluster analysis. Interestingly, Brandenburg and Rebs ( 2015 ) applied both contingency and cluster analyses. However, the authors stated that the contingency analysis did not yield usable results, so they opted for cluster analysis. In effect, Brandenburg and Rebs ( 2015 ) added analytical depth to their analysis of model types in SSCM by clustering them against the main analytical categories of content analysis. In any case, the application of statistical tools needs to fit the study purpose (Decision 1) and the literature sample (Decision 7), just as in their more conventional applications (e.g., in empirical research processes).

Decision 11 regards the additional consideration of validity and reliability criteria and emphasizes the need for explaining and justifying the single steps of the research process (Seuring and Gold 2012 ), much in line with other examples of research (Davis and Crombie 2001 ). This is critical to underlining the quality of the review but is often neglected in many submitted manuscripts. In our review, we find rich guidance on this decision, to which we want to guide readers (see Table 3 ). In particular, Durach et al. ( 2017 ) provide an entire section of biases and what needs to be considered and reported on them. Moreover, Snyder ( 2019 ) regularly reflects on these issues in her elaborations. This rich guidance elaborates on how to ensure the quality of the individual steps of the review process, such as sampling, study inclusion and exclusion, coding, synthesizing, and more practical issues, including team composition and teamwork organization, which are discussed in some guidelines (e.g., Clark et al. 2021 ; Kraus et al. 2020 ). We only want to underline that the potential biases are, of course, to be seen in conjunction with Decisions 2, 3, 4, 5, 6, 7, 9, and 10. These decisions and the elaboration by Durach et al. ( 2017 ) should provide ample points of reflection that, however, many SLR manuscripts fail to address.

4.7 Step 6: reporting the results

In the final step, there are three decisions on which there is surprisingly little guidance, although reviews often fail in this critical part of the process (Kraus et al. 2020 ). The reviewed guidelines discuss the presentation almost exclusively, while almost no guidance is given on the overall paper structure or the key content to be reported.

Consequently, the first choice to be made in Decision 12 is regarding the paper structure. We suggest following the five-step logic of typical research papers (see also Fisch and Block 2018 ) and explaining only a few points in which a difference from other papers is seen.

(1) Introduction: While the introduction would follow a conventional logic of problem statement, research question, contribution, and outline of the paper (see also Webster and Watson 2002 ), the next parts might depend on the theoretical choices made in Decision 2.

(2) Literature review section: If deductive logic is taken, the paper usually has a conventional flow. After the introduction, the literature review section covers the theoretical background and the choice of constructs and variables for the analysis (De Lima et al. 2021 ; Dieste et al. 2022 ). To avoid confusion in this section with the literature review, its labeling can also be closer to the reviewed object.

If an inductive approach is applied, it might be challenging to present the theoretical basis up front, as the codes emerge only from analyzing the material. In this case, the theory section might be rather short, concentrating on defining the core concepts or terms used, for example, in the keyword-based search for papers. The latter approach is exemplified by the study at hand, which presents a short review of the available literature in the introduction and the first part of the findings. However, we do not perform a systematic but integrative review, which allows for more freedom and creativity (Snyder 2019 ).

(3) Method section: This section should cover the steps and follow the logic presented in this paper or any of the reviewed guidelines so that the choices made during the research process are transparently disclosed (Denyer and Tranfield 2009 ; Paul et al. 2021 ; Xiao and Watson 2019 ). In particular, the search for papers and their selection requires a sound explanation of each step taken, including the provision of reasons for the delimitation of the final paper sample. A stage that is often not covered in sufficient detail is data analysis (Seuring and Gold 2012 ). This also needs to be outlined so that the reader can comprehend how sense has been made of the material collected. Overall, the demands for SLR papers are similar to case studies, survey papers, or almost any piece of empirical research; thus, each step of the research process needs to be comprehensively described, including Decisions 4–10. This comprehensiveness must also include addressing measures for validity and reliability (see Decision 11) or other suitable measures of rigor in the research process since they are a critical issue in literature reviews (Durach et al. 2017 ). In particular, inductively conducted reviews are prone to subjective influences and thus require sound reporting of design choices and their justification.

(4) Findings: The findings typically start with a descriptive analysis of the literature covered, such as journals, distribution across years, or (empirical) methods applied (Tranfield et al. 2003 ). For modeling-related reviews, classifying papers against the approach chosen is a standard approach, but this can often also serve as an analytic category that provides detailed insights. The descriptive analysis should be kept short since a paper only presenting descriptive findings will not be of great interest to other researchers due to the missing contribution (Snyder 2019 ). Nevertheless, there are opportunities to provide interesting findings in the descriptive analysis. Beyond a mere description of the distributions of the single results, such as the distribution of methods used in the sample, authors should combine analytical categories to derive more detailed insights (see also Tranfield et al. 2003 ). The distribution of methods used might well be combined with the years of publication to identify and characterize different phases in the development of a field of research or its maturity. Moreover, there could be value in the analysis of theories applied in the review sample (e.g., Touboulic and Walker 2015 ; Zhu et al. 2022 ) and in reflecting on the interplay of different qualitative and quantitative methods in spurring the theoretical development of the reviewed field. This could yield detailed insights into methodological as well as theoretical gaps, and we would suggest explicitly linking the findings of such analyses to the research directions that an SLR typically provides. This link could help make the research directions much more tangible by giving researchers a clear indication of how to follow up on the findings, as, for example, done by Maestrini et al. ( 2017 ) or Dieste et al. ( 2022 ). In contrast to the mentioned examples of an actionable research agenda, a typical weakness of premature SLR manuscripts is that they ask rather superficially for more research in the different aspects they reviewed but remain silent about how exactly this can be achieved.

We would thus like to encourage future SLR authors to systematically investigate the potential to combine two categories of descriptive analysis to move this section of the findings to a higher level of quality, interest, and relevance. The same can, of course, be done with the thematic findings, which comprise the second part of this section.

Moving into the thematic analysis, we have already reached Decision 13 on the presentation of the refined theoretical framework and the discussion of its contents. A first step might present the frequencies of the codes or constructs applied in the analysis. This allows the reader to understand which topics are relevant. If a rather small body of literature is analyzed, tables providing evidence on which paper has been coded for which construct might be helpful in improving the transparency of the research process. Tables or other forms of visualization might help to organize the many codes soundly (see also Durach et al. 2017 ; Paul and Criado 2020 ; Webster and Watson 2002 ). These findings might then lead to interpretation, for which it is necessary to extract meaning from the body of literature and present it accordingly (Snyder 2019 ). To do so, it might seem needless to say that the researchers should refer back to Decisions 1, 2, and 3 taken in Step 1 and their justifications. These typically identify the research gap to be filled, but after the lengthy process of the SLR, the authors often fail to step back from the coding results and put them into a larger perspective against the research gap defined in Decision 1 (see also Clark et al. 2021 ). To support this, it is certainly helpful to illustrate the findings in a figure or graph presenting the links among the constructs and items and adding causal reasoning to this (Durach et al. 2017 ; Paul and Criado 2020 ), such as the three figures by Seuring and Müller ( 2008 ) or other examples by De Lima et al. ( 2021 ) or Tipu ( 2022 ). This presentation should condense arguments made in the assessed literature but should also chart the course for future research. It will be these parts of the paper that are decisive for a strong SLR paper.

Moreover, some guidelines define the most fruitful way of synthesizing the findings as concept-centric synthesis (Clark et al. 2021 ; Fisch and Block 2018 ; Webster and Watson 2002 ). As presented in the previous sentence, the presentation of the review findings is centered on the content or concept of “concept-centric synthesis.” It is accompanied by a reference to all or the most relevant literature in which the concept is evident. Contrastingly, Webster and Watson ( 2002 ) found that author-centric synthesis discusses individual papers and what they have done and found (just like this sentence here). They added that this approach fails to synthesize larger samples. We want to note that we used the latter approach in some places in this paper. However, this aims to actively refer the reader to these studies, as they stand out from our relatively small sample. Beyond this, we want to link back to Decision 3, the selection of a theoretical framework and constructs. These constructs, or the parts of a framework, can also serve to structure the findings section by using them as headlines for subsections (Seuring et al. 2021 ).

Last but not least, there might even be cases where core findings and relationships might be opposed, and alternative perspectives could be presented. This would certainly be challenging to argue for but worthwhile to do in order to drive the reviewed field forward. A related example is the paper by Zhu et al. ( 2022 ), who challenged the current debate at the intersection of blockchain applications and supply chain management and pointed to the limited use of theoretical foundations for related analysis.

(5) Discussion and Conclusion: The discussion needs to explain the contribution the paper makes to the extant literature, that is, which previous findings or hypotheses are supported or contradicted and which aspects of the findings are particularly interesting for the future development of the reviewed field. This is in line with the content required in the discussion sections of any other paper type. A typical structure might point to the contribution and put it into perspective with already existing research. Further, limitations should be addressed on both the theoretical and methodological sides. This elaboration of the limitations can be coupled with the considerations of the validity and reliability of the study in Decision 11. The implications for future research are a core aim of an SLR (Clark et al. 2021 ; Mulrow 1987 ; Snyder 2019 ) and should be addressed in a further part of the discussion section. Recently, a growing number of literature reviews have also provided research questions for future research that provide a very concrete and actionable output of the SLR (e.g. Dieste et al. 2022 ; Maestrini et al. 2017 ). Moreover, we would like to reiterate our call to clearly link the research implications to the SLR findings, which helps the authors craft more tangible research directions and helps the reader to follow the authors’ interpretation. Literature review papers are usually not strongly positioned toward managerial implications, but even these implications might be included.

As a kind of normal demand, the conclusion should provide an answer to the research question put forward in the introduction, thereby closing the cycle of arguments made in the paper.

Although all the works seem to be done when the paper is written and the contribution is fleshed out, there is still one major decision to be made. Decision 14 concerns the identification of an appropriate journal for submission. Despite the popularity of the SLR method, a rising number of journals explicitly limit the number of SLRs published by them. Moreover, there are only two guidelines elaborating on this decision, underlining the need for the following considerations.

Although it might seem most attractive to submit the paper to the highest-ranking journal for the reviewed topic, we argue for two critical and review-related decisions to be made during the research process that influence whether the paper fits a certain outlet:

The theoretical foundation of the SLR (Decision 3) usually relates to certain journals in which it is published or discussed. If a deductive approach was taken, the journals in which the foundational papers were published might be suitable since the review potentially contributes to the further validation or refinement of the frameworks. Overall, we need to keep in mind that a paper needs to be added to a discussion in the journal, and this can be based on the theoretical framework or the reviewed papers, as shown below.

Appropriate journals for publication can be derived from the analyzed journal papers (Decision 7) (see also Paul and Criado 2020 ). This allows for an easy link to the theoretical debate in the respective journal by submitting it. This choice is identifiable in most of the papers mentioned in this paper and is often illustrated in the descriptive analysis.

If the journal chosen for the submission was neither related to the theoretical foundation nor overly represented in the body of literature analyzed, an explicit justification in the paper itself might be needed. Alternatively, an explanation might be provided in the letter to the editor when submitting the paper. If such a statement is not presented, the likelihood of it being transferred into the review process and passing it is rather low. Finally, we want to refer readers interested in the specificities of the publication-related review process of SLRs to Webster and Watson ( 2002 ), who elaborated on this for Management Information Systems Quarterly.

5 Discussion and conclusion

Critically reviewing the currently available SLR guidelines in the management domain, this paper synthesizes 14 key decisions to be made and reported across the SLR research process. Guidelines are presented for each decision, including tasks that assist in making sound choices to complete the research process and make meaningful contributions. Applying these guidelines should improve the rigor and robustness of many review papers and thus enhance their contributions. Moreover, some practical hints and best-practice examples are provided on issues that unexperienced authors regularly struggle to present in a manuscript (Fisch and Block 2018 ) and thus frustrate reviewers, readers, editors, and authors alike.

Strikingly, the review of prior guidelines reported in Table 3 revealed their focus on the technical details that need to be reported in any SLR. Consequently, our discipline has come a long way in crafting search strings, inclusion, and exclusion criteria, and elaborating on the validity and reliability of an SLR. Nevertheless, we left critical areas underdeveloped, such as the identification of relevant research gaps and questions, data extraction tools, analysis of the findings, and a meaningful and interesting reporting of the results. Our study contributes to filling these gaps by providing operationalized guidance to SLR authors, especially early-stage researchers who craft SLRs at the outset of their research journeys. At the same time, we need to underline that our paper is, of course, not the only useful reference for SLR authors. Instead, the readers are invited to find more guidance on the many aspects to consider in an SLR in the references we provide within the single decisions, as well as in Tables 1 and 2 . The tables also identify the strongholds of other guidelines that our paper does not want to replace but connect and extend at selected occasions, especially in SLR Steps 5 and 6.

The findings regularly underline the interconnection of the 14 decisions identified and discussed in this paper. We thus support Tranfield et al. ( 2003 ) who requested a flexible approach to the SLR while clearly reporting all design decisions and reflecting their impacts. In line with the guidance synthesized in this review, and especially Durach et al. ( 2017 ), we also present a refined framework in Figs.  1 and 2 . It specifically refines the original six-step SLR process by Durach et al. ( 2017 ) in three ways:

Enriched six-step process including the core interrelations of the 14 decisions

First, we subdivided the six steps into 14 decisions to enhance the operationalization of the process and enable closer guidance (see Fig.  1 ). Second, we added a temporal sequence to Fig.  2 by positioning the decisions from left to right according to this temporal sequence. This is based on systematically reflecting on the need to finish one decision before the following. If this need is evident, the following decision moves to the right; if not, the decisions are positioned below each other. Turning to Fig.  2 , it becomes evident that Step 2, “determining the required characteristics of primary studies,” and Step 3, “retrieving a sample of potentially relevant literature,” including their Decisions 4–6, can be conducted in an iterative manner. While this contrasts with the strict division of the six steps by Durach et al. ( 2017 ), it supports other guidance that suggests running pilot studies to iteratively define the literature sample, its sources, and characteristics (Snyder 2019 ; Tranfield et al. 2003 ; Xiao and Watson 2019 ). While this insight might suggest merging Steps 2 and 3, we refrain from this superficial change and building yet another SLR process model. Instead, we prefer to add detail and depth to Durach et al.’s ( 2017 ) model.

(Decisions: D1: specifying the research gap and related research question, D2: opting for a theoretical approach, D3: defining the core theoretical framework and constructs, D4: specifying inclusion and exclusion criteria, D5: defining sources and databases, D6: defining search terms and crafting a search string, D7: including and excluding literature for detailed analysis and synthesis, D8: selecting data extraction tool(s), D9: coding against (pre-defined) constructs, D10: conducting a subsequent (statistical) analysis (optional), D11: ensuring validity and reliability, D12: deciding on the structure of the paper, D13: presenting a refined theoretical framework and discussing its contents, and D14: deriving an appropriate journal from the analyzed papers).

This is also done through the third refinement, which underlines which previous or later decisions need to be considered within each single decision. Such a consideration moves beyond the mere temporal sequence of steps and decisions that does not reflect the full complexity of the SLR process. Instead, its focus is on the need to align, for example, the conduct of the data analysis (Decision 9) with the theoretical approach (Decision 2) and consequently ensure that the chosen theoretical framework and the constructs (Decision 3) are sufficiently defined for the data analysis (i.e., mutually exclusive and comprehensively exhaustive). The mentioned interrelations are displayed in Fig.  2 by means of directed arrows from one decision to another. The underlying explanations can be found in the earlier paper sections by searching for the individual decisions in the text on the impacted decisions. Overall, it is unsurprising to see that the vast majority of interrelations are directed from the earlier to the later steps and decisions (displayed through arrows below the diagonal of decisions), while only a few interrelations are inverse.

Combining the first refinement of the original framework (defining the 14 decisions) and the third refinement (revealing the main interrelations among the decisions) underlines the contribution of this study in two main ways. First, the centrality of ensuring validity and reliability (Decision 11) is underlined. It becomes evident that considerations of validity and reliability are central to the overall SLR process since all steps before the writing of the paper need to be revisited in iterative cycles through Decision 11. Any lack of related considerations will most likely lead to reviewer critique, putting the SLR publication at risk. On the positive side of this centrality, we also found substantial guidance on this issue. In contrast, as evidenced in Table 3 , there is a lack of prior guidance on Decisions 1, 8, 10, 12, 13, and 14, which this study is helping to fill. At the same time, these underexplained decisions are influenced by 14 of the 44 (32%) incoming arrows in Fig.  2 and influence the other decisions in 6 of the 44 (14%) instances. These interrelations among decisions to be considered when crafting an SLR were scattered across prior guidelines, lacked in-depth elaborations, and were hardly explicitly related to each other. Thus, we hope that our study and the refined SLR process model will help enhance the quality and contribution of future SLRs.

Data availablity

The data generated during this research is summarized in Table 3 and the analyzed papers are publicly available. They are clearly identified in Table 3 and the reference list.

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Characterizing Community Forests in the United States

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Reem Hajjar, Kathleen McGinley, Susan Charnley, Gregory E Frey, Meredith Hovis, Frederick W Cubbage, John Schelhas, Kailey Kornhauser, Characterizing Community Forests in the United States, Journal of Forestry , Volume 122, Issue 3, May 2024, Pages 273–284, https://doi.org/10.1093/jofore/fvad054

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Research on community forests (CFs), primarily governed and managed by local forest users in the United States, is limited, despite their growth in numbers over the past decade. We conducted a survey to inventory CFs in the United States and better understand their ownership and governance structures, management objectives, benefits, and financing. The ninety-eight CFs in our inventory are on private, public, and tribal lands. They had various ways of soliciting input from, or sharing decision-making authority with, local groups, organizations, and citizens. Recreation and environmental services were the most important management goals, but timber production occurred on more than two-thirds of CFs, contributing to income on many CFs, along with a diversity of other income sources to fund operations. We discuss the difficulties in creating a comprehensive CF inventory and typology given the diversity of models that exist, reflecting local social and environmental conditions and the bottom-up nature of community forestry in the United States.

Study Implications: Despite their small footprint in the United States, community forests are a rapidly developing model of forest ownership, governance, and management that helps protect forestlands and open space and demonstrates how market and nonmarket forest goods and services can be produced for broad and enduring community benefits. This study inventories and characterizes community forests in the United States to increase understanding of this model, its prevalence, and its potential. It provides a baseline of information that serves as a foundation for further exploration and research on the impacts and contributions of community forests.

Over the past few decades, many countries have increasingly promoted community forests (CFs) as a way to conserve forests, enhance rural livelihoods, and recognize the traditional and customary rights of local forest users to access, use, and manage forests ( Hajjar et al. 2021 ; Lund et al. 2018 ). CFs are delineated forest areas where community members have access to natural resources, are engaged in their governance, and receive indirect and direct benefits from their management ( Charnley and Poe 2007 ; McDermott and Schreckenberg 2009 ). Although CFs have existed in many forms across the globe for centuries, these more recent efforts are typically formal, government-sanctioned, and often government-sponsored. A total 14% of the world’s forests, and 28% of forests in low- and middle-income countries, are currently owned or managed by Indigenous peoples and local communities ( Rights and Resources Initiative 2018 ). Internationally, CF initiatives span a broad range of tenure regimes, institutional arrangements, relationships between communities and higher levels of government, activities, and outcomes that have evolved in line with local contexts, conditions, needs, and goals ( Charnley and Poe 2007 ; Hajjar and Molnar 2016 ).

In the United States, CFs have also existed in diverse forms for centuries ( Baker and Kusel 2003 ; McCullough 1995 ), although as elsewhere, formally designated community forests have been increasing in number since the 1990s. This relatively recent trend is likely driven by several factors. First, vertically integrated forest products companies nationwide have been divesting of their industrial timberlands since the late 1980s for economic reasons ( Zhang 2021 ), causing a large-scale shift in timberland ownership from industrial to institutional investors ( Zhang 2021 ). To prevent residential development, maintain access to local forests, conserve forest resources, and keep working forests working to provide economic opportunities for local residents, initiatives to acquire industrial timberland and manage it as CFs have proliferated ( Belsky 2008 ). Second, private family forest owners are aging; the average age of the primary decision-maker over family forestlands is 65 and, for about 20% of these ownerships, 75 or older ( Butler et al. 2021 ). Keeping their family forestland intact for future generations is a top concern for family forest owners ( Butler et al. 2021 ). If their descendants are uninterested or unable to keep this land in the family, community groups or municipalities may wish to acquire it as a CF to prevent its subdivision and fragmentation and provide community benefits.

Third, Indigenous peoples in the United States have regained greater control over ancestral lands, including forestlands, both on and off tribal trust lands over the past several decades ( McGinley et al. 2022 ). Some tribes have acquired forestland through fee simple purchase, including with funding designated for CF creation, and established CFs on those lands ( McGinley et al. 2022 ). Fourth, the 1990s saw a dramatic increase in citizen participation in decision-making about the management of public forestlands ( Baker and Kusel 2003 ; Charnley and Poe 2007 ). This trend has persisted, with community-based organizations, community members, forest collaborative groups, and other stakeholders playing a greater role in management decision-making and collaborative forest stewardship on federal lands ( Davis et al. 2020 ). In some cases, these arrangements may exhibit the characteristics of a CF.

Simultaneously, several programs providing funding for land acquisition to create CFs have arisen in the past two decades ( McGinley et al. 2022 ). Access to funding along with the emergence of supportive policies, organizations providing technical assistance, and practitioner networks have fostered a more favorable environment for CF creation since the 2000s ( Frey et al. forthcoming ). These trends have played out somewhat differently in different locations, but together they have contributed to a nationwide rise in CFs in the United States.

Unlike many other countries around the world with communal property systems, CFs in the United States do not exist as a distinct land tenure or ownership class. CFs have been established on a variety of public, private, and tribal lands and have diverse land tenure arrangements ( McGinley et al. 2022 ). Furthermore, there is no universally accepted definition of a CF in the United States ( Frey et al. forthcoming ). These two facts make studying CFs in the United States, as a distinct form of forest tenure, management, and governance, challenging. Literature on US CFs, most of it published since the 1990s, has primarily been descriptive in nature, relying on limited numbers of case studies to elaborate on the various motivations for creating CFs and the institutional and political context that pushed them forward ( Belsky 2015 ; McCarthy 2006 ); development of mechanisms and institutional arrangements for governance ( Abrams 2023 ; Abrams et al. 2015 ; Belsky and Barton 2018 ); and their potential benefits ( Christoffersen et al. 2008 ; Lyman et al. 2014 ). Belsky (2008) proposed a typology of CFs defined by who owns the CF—Indigenous groups, towns or municipalities, or community-based conservation organizations. A key message of the scientific literature is that a vast diversity of CFs exists in the United States, reflecting the diverse social, economic, and ecological contexts in which they occur.

To our knowledge, no prior research has attempted to document or characterize the full suite of CFs in the United States. Thus, the goals of this paper are to (1) identify, inventory, and characterize CFs in the United States; (2) enhance understanding of their ownership and governance structures, management objectives, and sources of income; (3) extend the discussion of the variability in forms of CFs and build on previous work to refine a CF typology; and (4) problematize how we recognize CFs in the United States (i.e., what is included, what is not, and why).

Defining CFs 1

A common but broad premise of CFs internationally is that place-based communities have some role in determining how local forests are to be managed for community benefit ( Hajjar et al. 2021 ). In the United States, communities associated with CFs are frequently not only place-based but also communities of interest and practice or some combination of these ( McGinley et al. 2022 ), complicating the notion of “community” and “local” (see Brosius et al. [2005 ] for a discussion). For purposes of deciding what to include in this study, we considered the following attributes of CFs, which are prevalent in the literature on US CFs (see Frey et al. forthcoming ): (1) ownership or tenure by a local governmental or nongovernmental organization (NGO) on behalf of the community; (2) communities are substantively involved in forest management and governance; (3) communities have secure rights to access and benefit from the forest; (4) social and economic benefits for local communities are a management priority; and (5) forest conservation values are permanently protected.

Creating a CF Inventory

To catalogue and characterize CFs in the United States, we first undertook an inventory of existing CFs, aiming to be as comprehensive as possible. Given the lack of a consistent definition or model of CFs, we used a hybrid approach to identify them ( Frey et al. forthcoming ). This meant first searching for entities that self-identify their property or initiative as a CF and for those that have participated in programs or policies related to CFs. Then we overlaid a series of inclusion criteria based on the attributes of CFs outlined above. Therefore, to be included in our study, local communities had to have rights of access and use and some form of management responsibility or decision-making authority (beyond consultation) over local forests. Additionally, these forests were managed to promote ecological sustainability and contribute to conservation while creating tangible local community benefits as a management priority.

We began by compiling a list of CFs and related information from a US Endowment for Forestry and Communities study ( Christoffersen et al. 2008 ) and a previous exploratory project ( Hovis et al. 2022 ). We then added to this list, drawing from CF lists provided by organizations that work with and support them, such as the Ford Foundation, the Northwest Community Forest Coalition, the Northern Forest Center, the Trust for Public Lands, the Open Space Institute, and the USDA Forest Service (Forest Service) Community Forest and Open Space Conservation Program. We also used Google Search Engine to identify any additional CFs not already included in our list. Search terms included: state name AND community forest OR community managed forest OR community-based forest OR town forest. We further consulted with various professionals in our networks involved with CFs (e.g., via the Northwest Community Forest Coalition annual meeting) to ensure the comprehensiveness of our list. Finally, we consulted with two project advisory committees that we set up at the start of the funded project under which this research was undertaken: one, a research advisory committee consisting of CF professionals across select government agencies, CF coalitions, and networks; the other, a tribal forestry advisory committee consisting of representatives of tribes with CFs and tribal natural resources networks.

We also used Google Search Engine to record any information on the identified CFs, usually landing on the websites of CF owners or their supporters. This information typically included the group name, forest location, acreage, landowner, governance, management objectives, history, URL, and contact information. Searches and consultations took place between 2019 and 2023, with more CFs identified and added continually as we heard of cases that were missed in our searches or that were being newly created. We examine the limitations of this approach in the Discussion section.

We initially located 136 possible CFs in the United States using these methods. Of these, thirty-two clearly did not meet our criteria, and we were unable to find additional information or contacts for eleven. To the remaining ninety-three CFs that met our inclusion criteria and for which we had contact information, we sent an internet-based survey using Qualtrics. We requested that a CF manager or other person familiar with the CF fill out the survey. The survey included questions about the CF, such as size, forest type, ownership, decision-making, who is involved in day-to-day management, management priorities, rules of access and use, and financing. Although most survey questions were designed to capture objective characteristics of the CF (i.e., size, ownership, etc.), we acknowledge that answers to a question asking about “management priorities” may not reflect the diversity of priorities a community may have for its forests. Rather, we expected that a CF manager responding to the survey would choose priorities that were being explicitly managed for, consistent with their management plan or mission statement.

To increase response rates ( Dillman et al. 2014 ), we followed up by sending reminder emails after 2 and 4 weeks and then through phone calls where phone numbers were available. Following this, for all nonresponses or cases where contact information could not be located, we filled out the survey ourselves to the extent possible using CF websites and other available resources. Not all survey questions had responses readily available from website sources, and so these surveys were not as complete. This resulted in some topic areas having smaller sample sizes, as displayed in the Results section. We also followed this protocol for newly identified CFs throughout the time period of the research (either newly created CFs or CFs discovered through our networks that met our criteria), for a combined total of ninety-eight CFs recorded up to April 2023. Survey responses were tabulated in SPSS, where descriptive statistics (frequencies and crosstabs) were used to show patterns across various CF characteristics.

We refer to three regions in discussing our results based on the Forest Service Resources Planning Act Assessment (RPA) regions: the West, combining the Pacific Coast and Rocky Mountain RPA regions, including CFs in Montana, Idaho, Washington, Oregon, California, and Arizona; the North, which includes CFs found in Maine, Vermont, New Hampshire, Massachusetts, New York, Connecticut, Michigan, and Wisconsin; and the South, which includes CFs in Georgia, North and South Carolina, Virginia, and Puerto Rico.

We collected data on ninety-eight CFs across the United States, constituting the sample used for this study ( SI Table 1 ). The survey response rate was 87% (eighty-five of ninety-eight); for the remaining thirteen survey nonresponses, we gathered information from internet sources. We expect the number of CFs to continue to grow in the coming years: after closing the survey in April 2023, we learned of at least four additional initiatives that were close to acquiring CF lands and nine that were seeking funds to purchase CF land. We believe that ninety-eight is close to the current total number of self-identifying CFs in the United States but acknowledge that it is likely an undercount of actual CFs that meet our inclusion criteria. We discuss the difficulties in accurately capturing all US CFs in the Discussion section. Rather than thinking of our sample as a complete inventory of all US CFs, we consider it sufficient for characterizing different types of CFs in the United States.

Location, Year Established, and Size

The greatest number of CFs per state were found in West Coast states ( figure 1 ; Washington, fourteen CFs; Oregon, twelve; California, nine); northeastern states (Maine, twelve CFs; Vermont, nine; New Hampshire, eight); and the upper midwestern states of Michigan and Wisconsin (five each). Fewer were located in southern states, with a handful spread across Georgia, North and South Carolina, and Virginia. The earliest recorded CFs in our sample were created in the 1930s and 1940s ( figure 2 ), mostly city and county forests in the northwestern United States (Montesano Community Forest, Hood River County Forest, Ashland Forestlands, Arcata Community Forest), and two town forests that self-describe as CFs in the Northeast (Gorham Town Forest, Mendon Town Forest). Most CFs in our sample were created after 2010 when there was a sharp increase in the number of CFs in all regions. This time period corresponded with new legislative support for CFs in some states (e.g., Washington State’s 2011 Community Forest Trust legislation) and at the federal level (e.g., the Forest Service's 2011 Community Forest Program), which have helped tribes, local governments, and nonprofit organizations acquire land at risk of development to establish CFs.

Location of CFs in our database. In this article, we refer to three regions in discussing our results: the West, which includes CFs found in Montana, Idaho, Washington, Oregon, California, and Arizona; the North, which includes Maine, Vermont, New Hampshire, Massachusetts, New York, Connecticut, Michigan, and Wisconsin; and the South, which includes Georgia, North and South Carolina, Virginia, and Puerto Rico.

Number of CFs in the United States since 1930.

The total area covered by CFs in our inventory is 436,411 acres (ac). Of this total, 87% of CFs were smaller than 5,000 ac ( figure 3 ), and 63% were less than 1,000 ac. By region, median sizes of CFs were: 1,360 ac in the West, 375 ac in the North, and 334 ac in the South. Nine CFs in the West were 5,000 ac or larger, compared to four in the North and none in the South. The majority of CFs less than 1,000 ac (thirty-nine of sixty-one CFs) were located in the North, with over half of those being between 100 and 500 ac (twenty-five of thirty-nine CFs). A total of 76% of reporting CFs said their forests were located on one contiguous parcel and 24% were on multiple unconnected parcels (varying from two to seventeen parcels).

Acreage of CFs across regions.

Ownership, Decision-Making Authority, and Management

As indicated in figure 4 , CFs in our sample were primarily owned by either a local government body (town, city, or county government) or by an NGO (e.g., a community-based organization, land trust, or other nonprofit). In the West, CFs were mostly purchased from private corporate owners (industrial timber companies, timber investment management organizations [TIMOs], or real estate investment trusts). In the North, CF lands were mostly acquired from private family forest owners. Ownership types included CFs of various sizes, although CFs larger than 1,000 ac tended to be held by a government body, whereas the majority of NGO-held CFs were smaller than 1,000 ac ( SI Table 2 ).

Ownership of CFs: (a) current landowner of forestlands designated as CFs and (b) previous landowners from whom the current landowner acquired the CF land. “Joint ownership” in (a) were parcels jointly owned by a local government body and a land trust ( n  = 3), a private utilities firm ( n  = 1), or a university ( n  = 1); a tribe and a conservancy ( n  = 1); and a land trust and private equity firm ( n  = 1). “Other” in (b) were parcels that were pieced together from multiple ownerships.

Land ownership largely corresponded with the entity with ultimate decision-making authority over management, access, and use of the CF ( SI Figure 1 ). Government agencies largely had authority over government-owned CFs, tribes over tribally owned CFs, and NGOs over the land they owned. These entities had various ways of soliciting input from, or sharing decision-making authority with, local groups, organizations, and citizens. In some cases, this was institutionalized through formal joint decision-making processes. For example, there were eleven cases of local government ownership (town, city, or county-owned forests) where decision-making authority was jointly held by both that government body and formal citizen councils or committees established for this purpose. In other cases, although respondents did not describe decision-making as “joint,” they involved community members through mechanisms such as advisory committees and boards made up of local citizens, formal community and public consultation processes (mostly for city or town government ownerships), or various events, regular meetings, and other formal and informal mechanisms that sought community input (mostly for NGO ownerships). Local groups and volunteers contributed to day-to-day management of CFs across most ownerships ( SI Figure 2 ). In particular, various recreation-related volunteer groups helped to maintain trail systems. Otherwise, in many cases, forest consultants or forestry professionals from government agencies or NGOs contributed to forest planning and stewardship.

Management Goals and Allowed Activities

Survey respondents were asked to select the top four goals, from a list of options, that the CF was managed for ( figure 5 ). Across the country, the vast majority of CFs stated that recreation was a top management goal (82% of ninety-five reporting CFs). Collectively, conservation-oriented goals (watershed, habitat or open space protection, biodiversity conservation and restoration, and carbon sequestration, totaling 98% of reporting CFs), as well as other nonextractive goals (education, recreation, and cultural heritage protection, totaling 93% of reporting CFs) were much more prominent than extraction-oriented goals (timber production, nontimber forest products (NTFPs) management, agroforestry, and livestock grazing, totaling 47% of reporting CFs). However, timber production was among the top four management goals reported across the sample as a whole.

Primary management goals. Respondents were asked to list top four management goals for their CF.

There were few strong patterns between ownership type and management goals ( SI Table 3 ). Recreation was listed as one of the top management goals in over 75% of cases across CF ownership types, except for tribal (two of five CFs) and private corporate (one of two) owned CFs. All but two CFs owned by local governments listed a conservation-oriented goal. Half of local government-owned (twenty-two of forty-four CFs) and half of NGO-owned CFs (fifteen of thirty-two CFs) listed an extractive-oriented goal. Of the five tribal-owned CFs in our sample, only one listed an extractive-oriented goal (agroforestry) as a primary management goal and only one indicated that timber was produced but not as a primary goal. Local government and NGO-owned CFs reported slightly more often that producing timber was a primary goal (government: nineteen CFs listed it as a primary goal, twelve as a nonprimary goal, and twelve do not produce timber; NGO: thirteen, ten, and six, respectively).

Although timber production occurred on 70% of reporting CFs (sixty-five of ninety-three reporting CFs; figure 6A ), in almost half of those cases (twenty-eight of sixty-five cases) timber production was not one of the top four primary management goals of the CF. Geographically, no CFs in the southern region produced timber as a primary goal. In the North, slightly more CFs produced timber as a primary goal than not as a primary goal (twenty versus sixteen CFs), with only nine reporting no timber production. In the West, seventeen CFs reported producing timber as a primary goal, with ten producing but not primary and ten not producing. Timber production occurred across all acreages ( figure 6B ), including on almost two-thirds of the smallest CFs in our study (<1,000 ac) and on all CFs larger than 5,000 ac (although not always as a primary goal). Similarly, timber production occurred across all ownership types ( figure 6C ), whether as a primary goal or not. Of those engaged in timber production, a private consulting forester was used to oversee timber sales in 43% of fifty-one reporting cases, and internal staff from the CF owner in 26% of cases. Across ownerships, the entity that did the logging was most often a private contracting company (70% of fifty reporting cases). These entities were located at a place within 25 miles of the CF in 52% of forty-two reporting cases, or 26–50 miles in 33% of cases.

Status of timber production across CFs (a) by region, (b) CF size, and (c) ownership type.

Community forests had a variety of rules related to which activities were allowed and whether permits from CF owners were needed if allowed ( SI Figure 3 ). Motorized recreation, camping, and commercial uses of firewood or NTFPs were only allowed in a handful of CFs, often with the requirement of a free or paid permit. Hunting and fishing, in accordance with state regulations, were allowed in more than half of the reporting cases (69% of sixty-five reporting cases and 78% of sixty reporting cases, respectively) and rarely required a permit from CF owners. Personal use of firewood and other NTFPs were allowed in 22% and 40% of sixty-three reporting cases, respectively, although firewood use often required a permit. Altogether, 85% of sixty-six reporting cases allowed some nontimber extractive activities for personal use (either firewood, other NTFPs, hunting, or fishing). Only one CF did not allow recreation and four allowed it only with a free permit. In almost all cases, the same rules applied to the local community as to the general public, except for a few instances where NTFP and firewood use were limited to local community members.

Income Generation and Budgetary Support

A number of CFs across ownerships generated revenue from forest products and services (80% of forty-nine reporting CFs), mostly from timber sales ( figure 7 ; SI Table 4 ), although two-thirds of those reporting revenue generation stated that timber contributed to less than 30% of their budget. The few instances of revenue from hunting leases and payments for ecosystem services (mainly carbon offsets) were mostly reported in CFs owned by private nonprofits, whereas grazing permits or agriculture revenue were only reported in three state or local government-owned CFs ( SI Table 4 ). Timber revenue was reported across all ownerships where timber harvest occurred, except for the two cases of state government ownership, where it is anticipated in the future, once the forest regains commercial value following harvest by the previous owner.

Main sources of revenue generated from forest activities in 48 reporting CFs by region.

Grants from federal or state governments were the most frequently cited sources of annual budgetary support from 2018 to 2020, the period we asked about (70% of fifty-three reporting CFs; figure 8 ; SI Figure 4 ), although almost two-thirds of those CFs stated that grants contributed to less than 30% of their budget. Unsurprisingly, government-owned CFs more often reported (federal, state, or local) government sources for budgetary support. Local government-owned CFs were more reliant on local government funds: 71% of twenty-four reporting local government CFs stated they received funding from local governments (50% of them stating that they received more than 60% of their budget from this source), with only a handful of nongovernment owned CFs reporting support from this source. The NGO-owned CFs reported relying on donations from local community members and fundraiser events much more often than government-owned CFs (in three cases, community donations made up more than 60% of the budget). We did not track sources of funds used for acquiring forestlands in our survey.

Sources of budgetary support 2018–2020 by ownership type. Public ownership includes federal, state, and local governments, and private ownership includes both corporate and nonprofits. Polygons indicate largest differences between private and public ownerships.

As the results indicate, there are a variety of ownership and governance forms that CFs currently take in the United States, a variety of benefits that they provide, and a diversity of income sources that they rely on. As stated above, one goal of this study was to discuss the variability in CFs and develop a robust typology of them. Although Belsky (2008) proposed a CF typology based on ownership types, given the diversity of CFs we encountered in our survey (including within ownership types), we intended to develop a typology based on key characteristics, including ownership, decision-making, operational management, goals, size, and income sources. Two-step cluster analyses and Pearson’s χ 2 tests were performed to assess whether the CFs in our dataset could be empirically grouped according to various combinations of these characteristics. However, limited patterns emerged for creating definitive statistical typologies. Instead, we discuss here some emergent qualitative patterns based on the descriptive statistics reported in the Results section, reflect on the diversity of CFs in the United States, and propose a basic typology for practical purposes. Finally, we discuss the difficulties in creating a comprehensive CF inventory for the United States, given this diversity.

Ownership type emerged as a factor that seemed to shape some key functions of a CF—specifically, decision-making authority and sources of budgetary support. Publicly owned CFs (mostly by local city or town government) more often reported having either a government entity as ultimate decision-making authority or joint authority between local government and citizen councils or other local groups. They were also more reliant on government funding for budgetary support, either through federal or state grants, local government funds, or combinations of these. Privately owned CFs (mostly community-based organizations and local land trusts) more often reported having those same owners make decisions about the CF and less often reported that they formally engage in joint decision-making (although it is difficult to ascertain actual community participation in governance with our survey research design). They also more often reported relying on community donations and fundraiser events than local government funds. All five tribally owned CFs in our dataset were run by tribes themselves, including decision-making authority and operational management. Besides these basic characteristics, however, ownership type seems to have little influence on the size of CFs, management goals, allowed activities, timber production (equally present in public and private CFs), or earned income sources.

We saw moderate regional differences in ownership and size (more government ownership and larger sizes in the West), and who the CF owner bought their forestland from. Ownership history may help explain why the median size of CFs in the West was considerably larger than in the North. The majority of CF lands in the West were purchased from private corporate forest owners, whose holdings are often in the hundreds of thousands of acres ( Sass et al. 2021 ), and from TIMOs in particular, which typically sell land every 10 to 15 years ( Zhang 2021 ). In contrast, the majority of CF lands in the North were purchased from family forest owners; approximately 90% of these ownerships in the United States are under 50 ac ( Butler et al. 2021 ). Yet CFs larger than 5,000 ac occur in both the North and the West.

In both these regions, timber production often occurred across CFs of all sizes and was a primary management goal in roughly equal frequency, although not in our small sample of southern CFs. Timber production was not limited to any particular ownership type, or size class, of CF; rather, the potential to harvest timber as a management goal and source of revenue generation is likely influenced by the nature of the forest assets contained in a particular CF. Those with productive timberlands are presumably more likely than those lacking them to have timber production as a primary management goal. However, it may take years for this goal to be realized if the former owner recently harvested a substantial amount of commercial timber. All CFs across regions emphasized conservation goals, but forest restoration (phrased in the survey as “forest restoration, including wildfire management”) was cited more often in the West. Almost all CFs allowed public access for recreation and many for nontimber extractive activities for personal use. It is likely that some CFs regulate access more than others, but we could not capture this variation in our survey.

The difficulty in creating a typology of CFs is unsurprising given that, by definition, CFs reflect the values and priorities of the communities in which they are situated. Other historical, social, economic, and environmental factors also likely influence their characteristics. Additionally, policies and programs that provide funding opportunities to support CFs and their operations vary by state, influencing their sources of budgetary support. Investigating underlying factors that lead to the diversity in CF models and characteristics is a rich area for further research.

The second phase of our research (a larger project than reported here, aiming to better understand how CFs contribute to conservation and rural prosperity in the United States) uses a case-study sampling approach based on two characteristics that we postulated would be important distinguishing features of a typology: ownership of the CF and whether timber production is a primary management goal of the CF ( Table 1 ). We acknowledge that our survey results do not show that these two characteristics are statistically related to many other factors examined here but reasoned that ownership can influence CF governance and financing mechanisms, and that the role (or lack thereof) of timber production reflects the CF’s management goals, forest resources, financing mechanisms, and benefit streams. We recognize that CFs produce a host of benefits for communities beyond timber production. However, whether a CF prioritizes timber, harvests timber but does not prioritize it, or does not harvest timber emerged as an effective way to distinguish groups of CFs from each other in terms of their management priorities and resulting benefit streams. Otherwise, most CFs shared recreation and conservation-related goals.

A basic typology based on ownership and whether timber is a primary management goal of the CF. Percentages (in parentheses) reflect percentage of eighty-two CFs in our inventory that reported on timber status and ownership.

The diversity of CFs in the United States also reflects the grassroots nature of community forests across the country, making them somewhat unique relative to community forests globally. In many low- and middle-income countries, community forests are forests managed using a top-down model imposed and defined by national CF policies or land reforms and extensive financial and technical support from external donor organizations (e.g., national or international NGOs, multilateral/bilateral aid agencies), with communities receiving some rights and many responsibilities for forest management ( Charnley 2023 ; Hajjar et al. 2021 ; Ribot et al. 2006 ). In contrast, in the United States, CF establishment is typically driven from the bottom up, in most cases through local governments, locally based NGOs, or groups of citizens that come together to protect their local forests. There is no distinct CF tenure category at the national level and few national or state-level policies associated with community forests in the United States. Exceptions include Washington and New York states, where there are legislatively approved funding sources 2 to support CF acquisition and associated policy requirements once established, and the national-level Forest Service Community Forest and Open Space Conservation Program, which has supported the acquisition of numerous CFs in our inventory. This more grassroots approach results in a broad range of ownership, management, governance types, and rights and responsibilities among community members relative to many other countries. It also makes CFs somewhat hard to pinpoint in the United States, posing challenges for efforts to inventory them.

Stemming from this diversity in CFs, a key difficulty we faced in undertaking this inventory was determining what to include. Our approach to including CFs that self-identify as such or had participated in a program or policy related to CFs and met our criteria was naturally limiting. Although this approach was necessary to make an inventory possible, we acknowledge that many more CFs potentially exist than we included here, depending on how a CF is defined. In particular, our inventory captured many town forests and land trust forestlands, some tribal forests, and some state and federal forests. Yet these general ownership categories need further examination.

Town forests are local government-owned forests common across much of New England and the Northeast and in many cases may be considered CFs. They have long been established to generate income from timber and other resources for town budgets or specific projects and public services, to protect water, soil, and wildlife habitat, and to provide recreation and education opportunities for local community members and others ( Baker and Kusel 2003 ; Brown 1941 ; Hovis et al. 2022 ; McCullough 1995 ). The local ownership, management, and benefits of many town forests fulfill most of the criteria of CFs as laid out above. However, the acquisition and designation of a town forest does not guarantee its long-term protection from sale or development, and depending on how much the community participates in governance, it may or may not fulfil the governance criterion of CFs ( McGinley et al. 2022 ).

Similarly, many land trusts own forestlands that could be considered CFs, depending on how these forests are governed and managed, potentially increasing the number and extent of CFs in the United States. However, land trusts may not provide access for local communities or the general public to their forested land, may not provide for local community participation in decision-making, or may not manage their forests specifically for local benefits.

The extent to which tribal forests should be considered CFs is also complicated. Most tribal lands are trust lands, with about 56 million acres of land held in trust for tribes by the federal government (2.3% of US land area; DOI 2023 ). Although these lands are managed for the benefit of individual tribes, forest management activities take place under the direction of forest management and integrated resource management plans developed under the federal Bureau of Indian Affairs (BIA) guidelines and are subject to BIA approval. Since the passage of the Indian Self-Determination and Education Assistance Act of 1975 (Public Law 93-638), an increasing number of tribes have established contracts, known as 638 contracts, with the BIA by which tribal government forestry departments assume management responsibilities for forests on trust lands. These contracts are initiated by a formal request by a tribe to the BIA. By 2011, 112 tribes had taken advantage of these self-determination/self-governance opportunities for forest management, compared to 187 that relied on BIA to manage their lands directly ( Gordon et al. 2013 ). Given this complexity in governance, it is unclear to what extent the trust lands of individual tribes meet the criteria of CFs; such classification should be undertaken by tribes themselves. Tribes can also purchase and own fee lands to which they hold title. The five tribally owned CFs in our sample (they self-identify as such) were purchased this way from private landowners. Further research on tribal forests could explore the variations in ownership, benefits, and management of these forests on trust and fee lands.

Our inventory includes two CFs owned by Washington State and one that occurs on federal lands in California. These cases may appear to contradict our defining attributes of a CF, namely that they have local, long-term ownership or tenure, and that communities have significant decision-making authority. We included the state and federal CFs in our inventory primarily because they self-identified as CFs. However, they also display several attributes of a CF. The two state-owned CFs were acquired through Washington’s 2011 Community Forest Trust Program ( WA DNR, n.d. ). The legislation that created the program stipulated that CFs acquired with program funds (from state budget appropriations) be state-owned, and that state agencies have ultimate decision-making authority. But the legislation also stipulated that state-owned CFs have an advisory committee composed of roughly twenty members representing diverse stakeholder interests to inform those decisions and co-develop forest management plans with citizen input, and that CF management objectives should reflect the values of local communities ( WA Legislature 2011 ).

Regarding the federally owned case, Weaverville CF, the community manages the CF through a 10-year renewable cooperative stewardship agreement between the Forest Service and Bureau of Land Management (who own and administer different parts of the CF), and the local county resource conservation district (RCD) ( Frost 2014 ; Kelly 2018 ). The RCD is responsible for implementing forest management activities and is governed by a board of directors that oversees CF management, with input from a steering committee composed of ten to fifteen members, including local citizens and agency and RCD staff. Local residents have opportunities to provide input at community meetings that occur once or twice annually. The CF is managed to meet local community needs and priorities, such as wildfire risk reduction, habitat improvement for fish and wildlife, and recreation ( Frost 2014 ; Kelly 2018 ).

The question of whether CFs in the United States that self-identify as such should be considered CFs if they occur on land that is state- or federally owned—with the government retaining ultimate decision-making authority—deserves more attention and is a matter of debate among some practitioners and scholars (see Frey et al. forthcoming ). The international literature recognizes CFs that occur on national government-owned land where communities have concessions to manage the forests for a specified time period (e.g., several CFs in Canada [ Teitelbaum et al. 2006 ], Cameroon [ Piabuo et al. 2018 ], Guatemala [ Taylor 2010 ]); and CFs on national government land that are comanaged by the state and local communities (e.g., Tanzania; Blomley and Ramadhani, 2006 ). This highlights the importance of taking into account the governance criterion in defining CFs in the United States—the level of community involvement in decision-making—just as with town forests and land trusts, and opens the door for potential additional CFs on public lands that might fit the criteria but were not captured here.

The CFs we identified comprise less than 0.1% of all forests in the United States but are a rapidly developing model of forest ownership, governance, and management that provides local community benefits. They take a creative approach to funding and managing local forestlands through public, NGO, or tribal structures, generated income sources, and grant and donor fundraising. They have continued long-standing town and tribal forest ownership and management, helped protect forestlands and open space from imminent development, and offered innovative ways to form explicit community partnerships to manage existing public and private landscapes. As they solidify income sources and management capability, they also might serve as a new model of how market and nonmarket goods and services can be produced on forestlands for broad and enduring community benefits.

We have likely not included all individual CFs in the United States in this study and may have significant undercounts of certain types of CFs. Potential undercounts stem largely from ambiguity over which town, tribal, and private (e.g., land trust-held) forests meet our CF definition and criteria and lingering questions over whether CFs exist on federal lands. Nevertheless, the inventory will increase continually as communities develop proposals for CFs and obtain acquisition funding each year and new research is carried out. To help address this research limitation, we plan to create a centralized, publicly accessible repository that can serve as a living inventory to be updated as more CFs are either acknowledged as such or created. Although incomplete, our current inventory captures a fair representation of the variety of CF models in the United States, reflecting a diversity of ownerships, governance structures, management goals, benefit streams, and more.

This initial research to inventory and describe US CFs provides a sound base for further exploration. Future research could further explore levels of local participation in forest management and governance and when and how these variables would qualify a forest as a CF on public, private, or tribal lands. More in-depth research could also help refine our CF typology to include characteristics hard to ascertain from a survey instrument, such as level of community involvement or capacity and organizational development stage (e.g., incipient or mature). Furthermore, as more NGO-owned and town-owned forests self-identify with the label “community forest,” the consequences, advantages, and disadvantages of using that label will need further examination.

Future research could also compare CF models with traditional (noncommunity based) private and public forest ownerships to highlight their relative differences, advantages, and disadvantages. For example, some CF models share similarities but also have important differences with private family forest ownerships in terms of priority management objectives and timber production ( Butler et al. 2021 ; Shanafelt et al. 2023 ), warranting a systematic comparison of ownership types. Finally, we began this exercise of inventorying CFs in the United States to better understand their contributions to conservation and rural prosperity. Better understanding the ability of communities to capture CF monetary and nonmonetary benefits (and to do so equitably) can help inform the design of policies, programs, and actions to best support CFs.

Supplementary data are available at Journal of Forestry online.

This study was funded in part by the USDA National Institute of Food and Agriculture, Agriculture and Food Research Initiative (AFRI) Award number 2021-67023-34426. Partial funding was also provided by the USDA Forest Service’s Southern Research Station, Pacific Northwest Research Station, and International Institute of Tropical Forestry as well as Oregon State University and North Carolina State University.

The authors are developing a publicly accessible repository of community forests. In the meantime, the data used in this study will be made available upon reasonable request.

Abrams , Jesse. 2023 . Forest Policy and Governance in the United States . New York, NY : Routledge .

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Community forests, community forestry, and community-based forestry are terms that are often used interchangeably in the U.S. literature; however, see Frey et al. (forthcoming) and Belsky (2008) for a discussion of important differences.

The Washington State Community Forests Program was established by the state legislature in 2019 to provide grant funding for CF acquisition ( https://rco.wa.gov/grant/community-forests-program/ ). The New York Community Forest Conservation Grant program similarly funds municipal land acquisitions for community forests ( https://www.dec.ny.gov/lands/124345.html#:~:text=and%20contact%20information-,Program%20Overview,Leadership%20and%20Community%20Protection%20Act ).

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McKinsey Global Private Markets Review 2024: Private markets in a slower era

At a glance, macroeconomic challenges continued.

McKinsey Global Private Markets Review 2024: Private markets: A slower era

If 2022 was a tale of two halves, with robust fundraising and deal activity in the first six months followed by a slowdown in the second half, then 2023 might be considered a tale of one whole. Macroeconomic headwinds persisted throughout the year, with rising financing costs, and an uncertain growth outlook taking a toll on private markets. Full-year fundraising continued to decline from 2021’s lofty peak, weighed down by the “denominator effect” that persisted in part due to a less active deal market. Managers largely held onto assets to avoid selling in a lower-multiple environment, fueling an activity-dampening cycle in which distribution-starved limited partners (LPs) reined in new commitments.

About the authors

This article is a summary of a larger report, available as a PDF, that is a collaborative effort by Fredrik Dahlqvist , Alastair Green , Paul Maia, Alexandra Nee , David Quigley , Aditya Sanghvi , Connor Mangan, John Spivey, Rahel Schneider, and Brian Vickery , representing views from McKinsey’s Private Equity & Principal Investors Practice.

Performance in most private asset classes remained below historical averages for a second consecutive year. Decade-long tailwinds from low and falling interest rates and consistently expanding multiples seem to be things of the past. As private market managers look to boost performance in this new era of investing, a deeper focus on revenue growth and margin expansion will be needed now more than ever.

Perspectives on a slower era in private markets

Global fundraising contracted.

Fundraising fell 22 percent across private market asset classes globally to just over $1 trillion, as of year-end reported data—the lowest total since 2017. Fundraising in North America, a rare bright spot in 2022, declined in line with global totals, while in Europe, fundraising proved most resilient, falling just 3 percent. In Asia, fundraising fell precipitously and now sits 72 percent below the region’s 2018 peak.

Despite difficult fundraising conditions, headwinds did not affect all strategies or managers equally. Private equity (PE) buyout strategies posted their best fundraising year ever, and larger managers and vehicles also fared well, continuing the prior year’s trend toward greater fundraising concentration.

The numerator effect persisted

Despite a marked recovery in the denominator—the 1,000 largest US retirement funds grew 7 percent in the year ending September 2023, after falling 14 percent the prior year, for example 1 “U.S. retirement plans recover half of 2022 losses amid no-show recession,” Pensions and Investments , February 12, 2024. —many LPs remain overexposed to private markets relative to their target allocations. LPs started 2023 overweight: according to analysis from CEM Benchmarking, average allocations across PE, infrastructure, and real estate were at or above target allocations as of the beginning of the year. And the numerator grew throughout the year, as a lack of exits and rebounding valuations drove net asset values (NAVs) higher. While not all LPs strictly follow asset allocation targets, our analysis in partnership with global private markets firm StepStone Group suggests that an overallocation of just one percentage point can reduce planned commitments by as much as 10 to 12 percent per year for five years or more.

Despite these headwinds, recent surveys indicate that LPs remain broadly committed to private markets. In fact, the majority plan to maintain or increase allocations over the medium to long term.

Investors fled to known names and larger funds

Fundraising concentration reached its highest level in over a decade, as investors continued to shift new commitments in favor of the largest fund managers. The 25 most successful fundraisers collected 41 percent of aggregate commitments to closed-end funds (with the top five managers accounting for nearly half that total). Closed-end fundraising totals may understate the extent of concentration in the industry overall, as the largest managers also tend to be more successful in raising non-institutional capital.

While the largest funds grew even larger—the largest vehicles on record were raised in buyout, real estate, infrastructure, and private debt in 2023—smaller and newer funds struggled. Fewer than 1,700 funds of less than $1 billion were closed during the year, half as many as closed in 2022 and the fewest of any year since 2012. New manager formation also fell to the lowest level since 2012, with just 651 new firms launched in 2023.

Whether recent fundraising concentration and a spate of M&A activity signals the beginning of oft-rumored consolidation in the private markets remains uncertain, as a similar pattern developed in each of the last two fundraising downturns before giving way to renewed entrepreneurialism among general partners (GPs) and commitment diversification among LPs. Compared with how things played out in the last two downturns, perhaps this movie really is different, or perhaps we’re watching a trilogy reusing a familiar plotline.

Dry powder inventory spiked (again)

Private markets assets under management totaled $13.1 trillion as of June 30, 2023, and have grown nearly 20 percent per annum since 2018. Dry powder reserves—the amount of capital committed but not yet deployed—increased to $3.7 trillion, marking the ninth consecutive year of growth. Dry powder inventory—the amount of capital available to GPs expressed as a multiple of annual deployment—increased for the second consecutive year in PE, as new commitments continued to outpace deal activity. Inventory sat at 1.6 years in 2023, up markedly from the 0.9 years recorded at the end of 2021 but still within the historical range. NAV grew as well, largely driven by the reluctance of managers to exit positions and crystallize returns in a depressed multiple environment.

Private equity strategies diverged

Buyout and venture capital, the two largest PE sub-asset classes, charted wildly different courses over the past 18 months. Buyout notched its highest fundraising year ever in 2023, and its performance improved, with funds posting a (still paltry) 5 percent net internal rate of return through September 30. And although buyout deal volumes declined by 19 percent, 2023 was still the third-most-active year on record. In contrast, venture capital (VC) fundraising declined by nearly 60 percent, equaling its lowest total since 2015, and deal volume fell by 36 percent to the lowest level since 2019. VC funds returned –3 percent through September, posting negative returns for seven consecutive quarters. VC was the fastest-growing—as well as the highest-performing—PE strategy by a significant margin from 2010 to 2022, but investors appear to be reevaluating their approach in the current environment.

Private equity entry multiples contracted

PE buyout entry multiples declined by roughly one turn from 11.9 to 11.0 times EBITDA, slightly outpacing the decline in public market multiples (down from 12.1 to 11.3 times EBITDA), through the first nine months of 2023. For nearly a decade leading up to 2022, managers consistently sold assets into a higher-multiple environment than that in which they had bought those assets, providing a substantial performance tailwind for the industry. Nowhere has this been truer than in technology. After experiencing more than eight turns of multiple expansion from 2009 to 2021 (the most of any sector), technology multiples have declined by nearly three turns in the past two years, 50 percent more than in any other sector. Overall, roughly two-thirds of the total return for buyout deals that were entered in 2010 or later and exited in 2021 or before can be attributed to market multiple expansion and leverage. Now, with falling multiples and higher financing costs, revenue growth and margin expansion are taking center stage for GPs.

Real estate receded

Demand uncertainty, slowing rent growth, and elevated financing costs drove cap rates higher and made price discovery challenging, all of which weighed on deal volume, fundraising, and investment performance. Global closed-end fundraising declined 34 percent year over year, and funds returned −4 percent in the first nine months of the year, losing money for the first time since the 2007–08 global financial crisis. Capital shifted away from core and core-plus strategies as investors sought liquidity via redemptions in open-end vehicles, from which net outflows reached their highest level in at least two decades. Opportunistic strategies benefited from this shift, with investors focusing on capital appreciation over income generation in a market where alternative sources of yield have grown more attractive. Rising interest rates widened bid–ask spreads and impaired deal volume across food groups, including in what were formerly hot sectors: multifamily and industrial.

Private debt pays dividends

Debt again proved to be the most resilient private asset class against a turbulent market backdrop. Fundraising declined just 13 percent, largely driven by lower commitments to direct lending strategies, for which a slower PE deal environment has made capital deployment challenging. The asset class also posted the highest returns among all private asset classes through September 30. Many private debt securities are tied to floating rates, which enhance returns in a rising-rate environment. Thus far, managers appear to have successfully navigated the rising incidence of default and distress exhibited across the broader leveraged-lending market. Although direct lending deal volume declined from 2022, private lenders financed an all-time high 59 percent of leveraged buyout transactions last year and are now expanding into additional strategies to drive the next era of growth.

Infrastructure took a detour

After several years of robust growth and strong performance, infrastructure and natural resources fundraising declined by 53 percent to the lowest total since 2013. Supply-side timing is partially to blame: five of the seven largest infrastructure managers closed a flagship vehicle in 2021 or 2022, and none of those five held a final close last year. As in real estate, investors shied away from core and core-plus investments in a higher-yield environment. Yet there are reasons to believe infrastructure’s growth will bounce back. Limited partners (LPs) surveyed by McKinsey remain bullish on their deployment to the asset class, and at least a dozen vehicles targeting more than $10 billion were actively fundraising as of the end of 2023. Multiple recent acquisitions of large infrastructure GPs by global multi-asset-class managers also indicate marketwide conviction in the asset class’s potential.

Private markets still have work to do on diversity

Private markets firms are slowly improving their representation of females (up two percentage points over the prior year) and ethnic and racial minorities (up one percentage point). On some diversity metrics, including entry-level representation of women, private markets now compare favorably with corporate America. Yet broad-based parity remains elusive and too slow in the making. Ethnic, racial, and gender imbalances are particularly stark across more influential investing roles and senior positions. In fact, McKinsey’s research  reveals that at the current pace, it would take several decades for private markets firms to reach gender parity at senior levels. Increasing representation across all levels will require managers to take fresh approaches to hiring, retention, and promotion.

Artificial intelligence generating excitement

The transformative potential of generative AI was perhaps 2023’s hottest topic (beyond Taylor Swift). Private markets players are excited about the potential for the technology to optimize their approach to thesis generation, deal sourcing, investment due diligence, and portfolio performance, among other areas. While the technology is still nascent and few GPs can boast scaled implementations, pilot programs are already in flight across the industry, particularly within portfolio companies. Adoption seems nearly certain to accelerate throughout 2024.

Private markets in a slower era

If private markets investors entered 2023 hoping for a return to the heady days of 2021, they likely left the year disappointed. Many of the headwinds that emerged in the latter half of 2022 persisted throughout the year, pressuring fundraising, dealmaking, and performance. Inflation moderated somewhat over the course of the year but remained stubbornly elevated by recent historical standards. Interest rates started high and rose higher, increasing the cost of financing. A reinvigorated public equity market recovered most of 2022’s losses but did little to resolve the valuation uncertainty private market investors have faced for the past 18 months.

Within private markets, the denominator effect remained in play, despite the public market recovery, as the numerator continued to expand. An activity-dampening cycle emerged: higher cost of capital and lower multiples limited the ability or willingness of general partners (GPs) to exit positions; fewer exits, coupled with continuing capital calls, pushed LP allocations higher, thereby limiting their ability or willingness to make new commitments. These conditions weighed on managers’ ability to fundraise. Based on data reported as of year-end 2023, private markets fundraising fell 22 percent from the prior year to just over $1 trillion, the largest such drop since 2009 (Exhibit 1).

The impact of the fundraising environment was not felt equally among GPs. Continuing a trend that emerged in 2022, and consistent with prior downturns in fundraising, LPs favored larger vehicles and the scaled GPs that typically manage them. Smaller and newer managers struggled, and the number of sub–$1 billion vehicles and new firm launches each declined to its lowest level in more than a decade.

Despite the decline in fundraising, private markets assets under management (AUM) continued to grow, increasing 12 percent to $13.1 trillion as of June 30, 2023. 2023 fundraising was still the sixth-highest annual haul on record, pushing dry powder higher, while the slowdown in deal making limited distributions.

Investment performance across private market asset classes fell short of historical averages. Private equity (PE) got back in the black but generated the lowest annual performance in the past 15 years, excluding 2022. Closed-end real estate produced negative returns for the first time since 2009, as capitalization (cap) rates expanded across sectors and rent growth dissipated in formerly hot sectors, including multifamily and industrial. The performance of infrastructure funds was less than half of its long-term average and even further below the double-digit returns generated in 2021 and 2022. Private debt was the standout performer (if there was one), outperforming all other private asset classes and illustrating the asset class’s countercyclical appeal.

Private equity down but not out

Higher financing costs, lower multiples, and an uncertain macroeconomic environment created a challenging backdrop for private equity managers in 2023. Fundraising declined for the second year in a row, falling 15 percent to $649 billion, as LPs grappled with the denominator effect and a slowdown in distributions. Managers were on the fundraising trail longer to raise this capital: funds that closed in 2023 were open for a record-high average of 20.1 months, notably longer than 18.7 months in 2022 and 14.1 months in 2018. VC and growth equity strategies led the decline, dropping to their lowest level of cumulative capital raised since 2015. Fundraising in Asia fell for the fourth year of the last five, with the greatest decline in China.

Despite the difficult fundraising context, a subset of strategies and managers prevailed. Buyout managers collectively had their best fundraising year on record, raising more than $400 billion. Fundraising in Europe surged by more than 50 percent, resulting in the region’s biggest haul ever. The largest managers raised an outsized share of the total for a second consecutive year, making 2023 the most concentrated fundraising year of the last decade (Exhibit 2).

Despite the drop in aggregate fundraising, PE assets under management increased 8 percent to $8.2 trillion. Only a small part of this growth was performance driven: PE funds produced a net IRR of just 2.5 percent through September 30, 2023. Buyouts and growth equity generated positive returns, while VC lost money. PE performance, dating back to the beginning of 2022, remains negative, highlighting the difficulty of generating attractive investment returns in a higher interest rate and lower multiple environment. As PE managers devise value creation strategies to improve performance, their focus includes ensuring operating efficiency and profitability of their portfolio companies.

Deal activity volume and count fell sharply, by 21 percent and 24 percent, respectively, which continued the slower pace set in the second half of 2022. Sponsors largely opted to hold assets longer rather than lock in underwhelming returns. While higher financing costs and valuation mismatches weighed on overall deal activity, certain types of M&A gained share. Add-on deals, for example, accounted for a record 46 percent of total buyout deal volume last year.

Real estate recedes

For real estate, 2023 was a year of transition, characterized by a litany of new and familiar challenges. Pandemic-driven demand issues continued, while elevated financing costs, expanding cap rates, and valuation uncertainty weighed on commercial real estate deal volumes, fundraising, and investment performance.

Managers faced one of the toughest fundraising environments in many years. Global closed-end fundraising declined 34 percent to $125 billion. While fundraising challenges were widespread, they were not ubiquitous across strategies. Dollars continued to shift to large, multi-asset class platforms, with the top five managers accounting for 37 percent of aggregate closed-end real estate fundraising. In April, the largest real estate fund ever raised closed on a record $30 billion.

Capital shifted away from core and core-plus strategies as investors sought liquidity through redemptions in open-end vehicles and reduced gross contributions to the lowest level since 2009. Opportunistic strategies benefited from this shift, as investors turned their attention toward capital appreciation over income generation in a market where alternative sources of yield have grown more attractive.

In the United States, for instance, open-end funds, as represented by the National Council of Real Estate Investment Fiduciaries Fund Index—Open-End Equity (NFI-OE), recorded $13 billion in net outflows in 2023, reversing the trend of positive net inflows throughout the 2010s. The negative flows mainly reflected $9 billion in core outflows, with core-plus funds accounting for the remaining outflows, which reversed a 20-year run of net inflows.

As a result, the NAV in US open-end funds fell roughly 16 percent year over year. Meanwhile, global assets under management in closed-end funds reached a new peak of $1.7 trillion as of June 2023, growing 14 percent between June 2022 and June 2023.

Real estate underperformed historical averages in 2023, as previously high-performing multifamily and industrial sectors joined office in producing negative returns caused by slowing demand growth and cap rate expansion. Closed-end funds generated a pooled net IRR of −3.5 percent in the first nine months of 2023, losing money for the first time since the global financial crisis. The lone bright spot among major sectors was hospitality, which—thanks to a rush of postpandemic travel—returned 10.3 percent in 2023. 2 Based on NCREIFs NPI index. Hotels represent 1 percent of total properties in the index. As a whole, the average pooled lifetime net IRRs for closed-end real estate funds from 2011–20 vintages remained around historical levels (9.8 percent).

Global deal volume declined 47 percent in 2023 to reach a ten-year low of $650 billion, driven by widening bid–ask spreads amid valuation uncertainty and higher costs of financing (Exhibit 3). 3 CBRE, Real Capital Analytics Deal flow in the office sector remained depressed, partly as a result of continued uncertainty in the demand for space in a hybrid working world.

During a turbulent year for private markets, private debt was a relative bright spot, topping private markets asset classes in terms of fundraising growth, AUM growth, and performance.

Fundraising for private debt declined just 13 percent year over year, nearly ten percentage points less than the private markets overall. Despite the decline in fundraising, AUM surged 27 percent to $1.7 trillion. And private debt posted the highest investment returns of any private asset class through the first three quarters of 2023.

Private debt’s risk/return characteristics are well suited to the current environment. With interest rates at their highest in more than a decade, current yields in the asset class have grown more attractive on both an absolute and relative basis, particularly if higher rates sustain and put downward pressure on equity returns (Exhibit 4). The built-in security derived from debt’s privileged position in the capital structure, moreover, appeals to investors that are wary of market volatility and valuation uncertainty.

Direct lending continued to be the largest strategy in 2023, with fundraising for the mostly-senior-debt strategy accounting for almost half of the asset class’s total haul (despite declining from the previous year). Separately, mezzanine debt fundraising hit a new high, thanks to the closings of three of the largest funds ever raised in the strategy.

Over the longer term, growth in private debt has largely been driven by institutional investors rotating out of traditional fixed income in favor of private alternatives. Despite this growth in commitments, LPs remain underweight in this asset class relative to their targets. In fact, the allocation gap has only grown wider in recent years, a sharp contrast to other private asset classes, for which LPs’ current allocations exceed their targets on average. According to data from CEM Benchmarking, the private debt allocation gap now stands at 1.4 percent, which means that, in aggregate, investors must commit hundreds of billions in net new capital to the asset class just to reach current targets.

Private debt was not completely immune to the macroeconomic conditions last year, however. Fundraising declined for the second consecutive year and now sits 23 percent below 2021’s peak. Furthermore, though private lenders took share in 2023 from other capital sources, overall deal volumes also declined for the second year in a row. The drop was largely driven by a less active PE deal environment: private debt is predominantly used to finance PE-backed companies, though managers are increasingly diversifying their origination capabilities to include a broad new range of companies and asset types.

Infrastructure and natural resources take a detour

For infrastructure and natural resources fundraising, 2023 was an exceptionally challenging year. Aggregate capital raised declined 53 percent year over year to $82 billion, the lowest annual total since 2013. The size of the drop is particularly surprising in light of infrastructure’s recent momentum. The asset class had set fundraising records in four of the previous five years, and infrastructure is often considered an attractive investment in uncertain markets.

While there is little doubt that the broader fundraising headwinds discussed elsewhere in this report affected infrastructure and natural resources fundraising last year, dynamics specific to the asset class were at play as well. One issue was supply-side timing: nine of the ten largest infrastructure GPs did not close a flagship fund in 2023. Second was the migration of investor dollars away from core and core-plus investments, which have historically accounted for the bulk of infrastructure fundraising, in a higher rate environment.

The asset class had some notable bright spots last year. Fundraising for higher-returning opportunistic strategies more than doubled the prior year’s total (Exhibit 5). AUM grew 18 percent, reaching a new high of $1.5 trillion. Infrastructure funds returned a net IRR of 3.4 percent in 2023; this was below historical averages but still the second-best return among private asset classes. And as was the case in other asset classes, investors concentrated commitments in larger funds and managers in 2023, including in the largest infrastructure fund ever raised.

The outlook for the asset class, moreover, remains positive. Funds targeting a record amount of capital were in the market at year-end, providing a robust foundation for fundraising in 2024 and 2025. A recent spate of infrastructure GP acquisitions signal multi-asset managers’ long-term conviction in the asset class, despite short-term headwinds. Global megatrends like decarbonization and digitization, as well as revolutions in energy and mobility, have spurred new infrastructure investment opportunities around the world, particularly for value-oriented investors that are willing to take on more risk.

Private markets make measured progress in DEI

Diversity, equity, and inclusion (DEI) has become an important part of the fundraising, talent, and investing landscape for private market participants. Encouragingly, incremental progress has been made in recent years, including more diverse talent being brought to entry-level positions, investing roles, and investment committees. The scope of DEI metrics provided to institutional investors during fundraising has also increased in recent years: more than half of PE firms now provide data across investing teams, portfolio company boards, and portfolio company management (versus investment team data only). 4 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023.

In 2023, McKinsey surveyed 66 global private markets firms that collectively employ more than 60,000 people for the second annual State of diversity in global private markets report. 5 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023. The research offers insight into the representation of women and ethnic and racial minorities in private investing as of year-end 2022. In this chapter, we discuss where the numbers stand and how firms can bring a more diverse set of perspectives to the table.

The statistics indicate signs of modest advancement. Overall representation of women in private markets increased two percentage points to 35 percent, and ethnic and racial minorities increased one percentage point to 30 percent (Exhibit 6). Entry-level positions have nearly reached gender parity, with female representation at 48 percent. The share of women holding C-suite roles globally increased 3 percentage points, while the share of people from ethnic and racial minorities in investment committees increased 9 percentage points. There is growing evidence that external hiring is gradually helping close the diversity gap, especially at senior levels. For example, 33 percent of external hires at the managing director level were ethnic or racial minorities, higher than their existing representation level (19 percent).

Yet, the scope of the challenge remains substantial. Women and minorities continue to be underrepresented in senior positions and investing roles. They also experience uneven rates of progress due to lower promotion and higher attrition rates, particularly at smaller firms. Firms are also navigating an increasingly polarized workplace today, with additional scrutiny and a growing number of lawsuits against corporate diversity and inclusion programs, particularly in the US, which threatens to impact the industry’s pace of progress.

Fredrik Dahlqvist is a senior partner in McKinsey’s Stockholm office; Alastair Green  is a senior partner in the Washington, DC, office, where Paul Maia and Alexandra Nee  are partners; David Quigley  is a senior partner in the New York office, where Connor Mangan is an associate partner and Aditya Sanghvi  is a senior partner; Rahel Schneider is an associate partner in the Bay Area office; John Spivey is a partner in the Charlotte office; and Brian Vickery  is a partner in the Boston office.

The authors wish to thank Jonathan Christy, Louis Dufau, Vaibhav Gujral, Graham Healy-Day, Laura Johnson, Ryan Luby, Tripp Norton, Alastair Rami, Henri Torbey, and Alex Wolkomir for their contributions

The authors would also like to thank CEM Benchmarking and the StepStone Group for their partnership in this year's report.

This article was edited by Arshiya Khullar, an editor in the Gurugram office.

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