A bibliometric analysis of systematic review research in business model scholarship

The purpose of this paper is to present a rigorous bibliometric analysis of the deployment of systematic review methodologies in business model research, with a focus on identifying prominent contributors, assessing their impact and uncovering key thematic areas. To achieve this, through statistical analysis of bibliographic data, we adopt various bibliometric methods–a specific form of systematic review–that allow us to rigorously investigate the intellectual structure of the field (Garfield, 1972). A business model describes how a firm creates, delivers and captures value (Teece, 2010), and since the early 2000s, the concept has gained significant traction in business and management literature, as well as in practitioner discourse, culminating in a substantial body of research mainly across strategic management, entrepreneurship, marketing and innovation fields. Arguably, the surge in scholarly interest was partly stimulated by various journal calls for special issues on business model research approximately ten to fifteen years ago (see Baden-Fuller et al., 2010; Demil et al., 2015; Ehret et al., 2013; Gatignon et al., 2017). However, as Foss and Saebi (2017) highlight, in their highly cited systematic review of 150 peer-reviewed articles, the business model literature remains fragmented and theoretically immature. More recent critiques argue that the field is marked by inconsistencies in construct definition, limited empirical generalizability and a lack of cumulative theorizing (Budler et al., 2021). The tendency to reiterate dominant paradigms – rather than propose disruptive or novel constructs – has constrained opportunities for innovation and interdisciplinary synthesis (Fjeldstad and Snow, 2018; Ritter and Lettl, 2018). As a result, we argue the field risks intellectual stagnation unless future research interrogates underlying assumptions and seeks more integrative, empirically grounded approaches. Foss and Saebi (2017) therefore advocate for the development of a systematic research agenda that addresses antecedents, contingencies and outcomes through comparative and theory-driven inquiry. Parallel to this growth in business model research, there has also been increasing use of systematic review methodologies within the business and management disciplines. Initially rooted in biomedical research (Mulrow, 1994; Tranfield et al., 2003), these methodologies are now being used to investigate the structure and evolution of knowledge across diverse domains. Despite their relevance, a preliminary search of articles in the Journal of Business Models – a publication dedicated to business model research – revealed only three papers using systematic review methods (see Ghoreishi, 2023; Luoma et al., 2021; Van de Ven et al., 2023). This finding suggests that a more extensive review across the broader business and management literature publishing business model research is both warranted and timely.

Literature reviews are critical to academic advancement, as they build on existing knowledge and delineate where the boundaries of inquiry currently lie (Coombes and Nicholson, 2013). These reviews may be conducted through subjective qualitative synthesis or objective systematic techniques. While interpretivist scholars may critique the positivist leanings of systematic reviews, we argue that both approaches are complementary. Together, they can illuminate trends in authorship, publication and thematic development (Acedo and Casillas, 2005; Verma and Gustafsson, 2020). Recent studies (see for instance Martinez-Lopez et al., 2018) have increasingly applied systematic methods to analyze content published in specific journals. Elsbach and van Knippenberg (2020, p. 1227) assert that systematic reviews are “among the most useful vehicles for advancing knowledge and furthering research.” However, when applying bibliometric analysis, it is essential that the literature search be transparent and reproducible (Block and Fisch, 2020). For this reason, this paper respects the Bibliometric Analysis Procedure and Best Practice Guidelines (Donthu et al., 2021), which consist of four steps: (1) define the aims and scope of the paper, (2) select bibliometric techniques, (3) gather relevant data and (4) conduct and report the analysis. For Step 1 of the guidelines, our aims for this paper are hence expressed through the following three research questions:

Drawing on a corpus of over 350 journal articles (see Supplementary Material), this paper is among the first that analyzes objectively the deployment and impact of systematic review methodologies in business model research hitherto by applying a rigorous bibliometric analysis. The paper contributes to the business model literature by identifying areas where assimilation has taken place and hence how the field can make distinctive contributions to this literature in the future by identifying key authors’ impact, revealing influential journals and publication trends over time and by highlighting foundational and emerging works. A theoretically grounded framework to guide this paper and future inquiry is also proposed. The paper also represents a call to action for business model researchers to engage further with contemporary systematic review methodologies to understand the breadth and depth of existing bodies of work and hence identify gaps in knowledge that can be explored further. The paper is organized as follows: following the introductory section which introduces and justifies the paper; a review of the evolution of business model literature is presented which is followed by a review of relevant literature on systematic review methods; this is followed by the data collection process to produce the sample dataset (or corpus) of business model literature for subsequent bibliometric analysis; a presentation and discussion of the results follows; and finally, conclusions and implications for future research as well as the methodological limitations are presented.

The concept of the business model has undergone a profound transformation over the last two decades from a static representation of organizational architecture to a dynamic narrative of understanding value co/creation, delivery and capture in firms (Kumar et al., 2025; Teece, 2010). Initially conceived as a structural template delineating how firms convert inputs into economic outputs, early business model concepts emphasized operational efficiency, resource configuration and transactional logic, which appeared to gain increasing importance since the Link to the website era in the late 1990s (Wirtz et al., 2016), with the term now widely embedded in everyday business vocabulary. According to Teece (2010, p. 172), a business model describes a network’s “design or architecture of value creation, delivery and capture mechanism”. Magretta (2002, p. 3), using a metaphor for explaining what a business model does, describes them as “stories–stories that explain how enterprises work”, while others describe them as mental models (Storbacka and Nenonen, 2011) and recipes (Baden-Fuller and Morgan, 2010). Contemporary discourse accordingly treats the business model as both artefact and process: a living schema that mediates between strategy and execution.

As the concept has evolved, scholars have increasingly recognized the inherent complexity of business models. This complexity stems from the multiplicity of interdependent elements–value propositions, resource bases, stakeholder relationships, governance structures and revenue mechanisms–that interact in non-linear, contextually contingent ways. The rise of digitalization and artificial intelligence (Di Vaio et al., 2020), circular and sharing economy models (Centobelli et al., 2020) and platform ecosystems (Kapoor et al., 2021) have further expanded the conceptual boundaries. These developments illustrate how business models must adapt to technological turbulence, multi-sided interdependencies and evolving forms of value co/creation, rendering them increasingly fluid, multi-layered and dynamic. Consequently, the business model has shifted from a relatively stable representation of firm activity to a complex system of activities and relationships that must be continually configured and reconfigured.

However, despite this growth in conceptual richness, business model literature remains fragmented and theoretically immature (Foss and Saebi, 2017). More recent studies argue that the field is marked by inconsistencies in construct definition (Budler et al., 2021), with scholars variously positioning the business model as a description, a cognitive frame, a strategic tool or a holistic system of activities. Such plurality reflects the construct’s multidimensional nature, yet it also creates obstacles to developing a widely accepted definition. Differences in disciplinary origins, methodological approaches and underlying assumptions have produced competing conceptualizations, making consensus difficult. Furthermore, the continual emergence of new technological and organizational forms means that the boundaries of what constitutes a business model are constantly being stretched. As a result, the field risks intellectual stagnation if definitional ambiguity persists, highlighting the need for greater theoretical integration while still acknowledging the complex and evolving nature of business model research.

Systematic reviews are a complement to the traditional qualitative and interpretive approach to literature reviews utilizing quantitative tools and techniques and, through the identification of citation counts and patterns, systematic reviews provide greater objectivity concerning the classification of publications related to a particular research field (Coombes and Nicholson, 2013) and are regarded as a “fundamental scientific activity” (Mulrow, 1994, p. 597). The traditional qualitative-based narrative literature review is arguably the most common type of review, often being the least rigorous and costly in terms of time and resources (Tranfield et al., 2003). However, a systematic literature review serves a different purpose than a traditional narrative literature review by providing a more organized, complete and rigorous review of academic literature. Such quantitative-based review methodologies are manifested of several types with the popular nomenclature including bibliometric analysis/reviews, citation/co-citation analyses and meta-analyses increasingly being adopted in business and management literature (see Block and Fisch, 2020; Donthu et al., 2021; Tranfield et al., 2003 for a more detailed discussion of different systematic review approaches) and offer an alternative to the traditional qualitative and interpretive approach to undertaking literature reviews (Okoli, 2015).

Bibliometric reviews consist of a set of methods that can be deployed to evaluate the social and structural relationships between different research constituents (i.e. authors, countries, institutions, topics) through statistical analysis of large amounts of bibliographic data. Such quantitative-based literature reviews also provide greater objectivity concerning the analysis of articles (Zupic and Cater, 2015) and aim to minimize bias through extensive literature searches of published studies with their recent popularity being attributed to both the accessibility of free bibliometric software, such as Gephi and VOS Viewer, for constructing and visualizing bibliometric networks, as well as to academic databases, such as Clarivate’s Web of Science, Elsevier’s Scopus and Google’s Scholar, which have made obtaining and downloading large amounts of bibliographic data relatively easy (Donthu et al., 2021; Van Eck and Waltman, 2010). Such bibliographic data includes the authors, titles, abstracts and keywords of published documents, as well as publication sources and related citation data. The two main objectives in systematic reviews relate to (1) the number of articles that measure productivity and (2) the number of citations that measure influence and popularity. By reviewing academic literature, one aims to understand the breadth and depth of existing bodies of work and hence identify author networks, literature gaps, topic clusters and academic silos that can be explored further (Block and Fisch, 2020). By systematically analyzing, synthesizing and summarizing bodies of related literature, hypotheses can be tested and/or new theories developed, and according to Snyder (2019, p. 333), systematic reviews can “address research questions with a power that no single study has” because such reviews “integrate findings and perspectives from many empirical findings.”

For Step 2 of the guidelines, which involves selecting the techniques for bibliometric analysis, research disciplines are characterized by patterns of communication between scholars. These patterns of communication manifest themselves in various ways, but foremost among these are citations from one author’s work to another. Citation analysis is a commonly used method to support empirical investigations of the intellectual structure of various disciplines (Garfield, 1972). A citation is the acknowledgement that one article receives from another and generally implies a relationship between parts or the whole of the cited article and parts or the whole of the citing article (Smith, 1981). Citation analysis may focus on either or both articles and their authors (Osareh, 1996). The raw data that citation counts provide are appealing for analysis as they are “unobtrusive measures that do not require the co-operation of a respondent and do not themselves contaminate the response” (Smith, 1981, p. 84). The basic assumption underlying citation analysis is that authors cite their influences, so that citations act as surrogates for the influence of the cited work (Acedo and Casillas, 2005). Therefore, the total citations to a certain article, author or journal offer an acceptable surrogate of that article’s, author’s or journal’s impact on a corresponding research field (Culnan, 1986). Citation analysis is a way of measuring relative importance or impact, and recognized approaches for ranking the impact of an author, an article or a journal publication include Eigenfactors (Link to the website) and the h-index (Hirsch, 2005). Gross and Gross (1927) were the first to use citation counts to evaluate the importance of scientific work. Since then, citation analyses have been undertaken for assessment of national science policies and disciplinary development (Bornmann and Daniel, 2008). On the other hand, co-citation analysis provides a forward-looking assessment of article similarity can be used to reveal the intellectual structure (we use the term knowledge base) of a research field (Small, 1973). This analysis involves tracking pairs of articles that are cited together in the source articles. When the same pairs of articles are co-cited by many authors, clusters of research begin to form and the articles in these clusters tend to share some common theme (Hjorland, 2013). In contrast, bibliographic coupling provides a retrospective assessment of conceptual similarities (Kessler, 1963; Weinberg, 1974). Bibliographic coupling occurs when two articles reference a common third article in their bibliographies, and the analysis provides a picture of an established research area (we use the term research front). However, combining both methods can offer a more comprehensive view of a research field, capturing both established and emerging trends. The co-word – or co-occurrence of author keywords analysis – can be used to enhance our understanding of the thematic clusters derived from co-citation analysis because the themes formed through the commonalities in articles tend to be relatively general (Chang et al., 2015), and the co-occurrence of keywords analysis can help scholars to interpret the meanings of each thematic cluster. Besides helping to uncover seminal publications and knowledge foundations, the analysis can be used to provide a preview of future research in the field (Donthu et al., 2021). Finally, co-authorship analysis explores the relationships between scholars within a given research domain. As a structured form of intellectual collaboration (Acedo et al., 2006), co-authorship offers valuable insights into how researchers engage and collaborate with one another. Next, we introduce a conceptualization of citation versus co-citation and bibliographic coupling analysis that will serve as a theoretically grounded framework to guide the remainder of this paper, which is illustrated in Figure 1.

Next, we present our approach to the search and collection of bibliographic data from the academic database.

For Step 3 of the guidelines, which involves a thorough search of one or more academic databases to identify and gather all relevant studies on the topic, it is extremely important that clear search criteria are established to set the boundaries to determine which studies are included in the review. To address the first research question and determine the extent to which systematic review methodologies have been deployed in business model research, an initial search using the Elsevier Scopus academic database was undertaken for the period between 1970 and 2024 inclusive to create our first corpus. The initial search covered a wide range of years because bibliometric “snapshots” for analysis are arguably inadequate, even periods of five years are not long enough (Van Raan, 1996). A summary of the methodological approach adopted is presented in Table 1.

This initial search using Scopus was undertaken using the search string “business model*” AND “systematic review*” OR “systematic literature review*” OR “citation analys*” OR “bibliometric*” OR “meta-analys*” OR “meta analys*” in the Article Title / Abstract / Keywords of the publication. This initial search revealed just 1,516 documents which consisted of journal articles, conference papers and editorial material published in various languages between 1998 and 2024. We have assumed here that journal articles represent the frontiers of business model research (Coombes and Nicholson, 2013), and these are sufficient to capture all systematic review type research in the context of business models to create our sample corpus. The search was then further refined to include only journal articles in the Scopus subject area of Business, Management and Accounting. This search revealed 709 journal articles. This search was then refined further to include only journal articles published in English language to avoid wrong interpretations. This search revealed 703 journal articles. This search was then refined again to include only articles published in journals. This search revealed only 352 articles published in 180 journals between 2011 and 2024. After manually screening the corpus for any duplicate or irrelevant articles – specifically those that mentioned “business model” but were not systematic review papers or vice versa – we established the final sample corpus for subsequent analysis. No results were revealed prior to 2011. The results and discussion from our analysis are presented next.

For Step 4 of the guidelines, to undertake the bibliometric analysis and report on the findings, Figure 2 presents a line graph illustrating the distribution of the 352 journal articles from our corpus published in business and management journals between 2011 and 2024. From the graph, it is evident that the number of systematic review articles published initially remained weak for the first five years and only since 2016 started to increase. Since then, the number of systematic review articles published has increased substantially, as indicated by the dotted trendline, which clearly evidences the increasing interest in systematic review methodologies in business model research.

Table 2 presents a summary of the 180 business and management journals publishing articles between 2011 and 2014. Representing eight percent of the overall number of articles, the Journal of Cleaner Production (n = 28) clearly leads the table. Representing 11.4% of the overall number of articles, the journals Business Strategy and the Environment (n = 20) and Journal of Business Research (n = 20) appear in joint second place. Representing 4.3% of the overall number of articles, the journal Technological Forecasting and Social Change (n = 15) appears in fourth place. Representing 2.6% of the overall number of articles, the journal Management Review Quarterly (n = 9) appears in fifth place. Representing 0.9% of the overall number of articles, the Journal of Business Models (n = 3) appears in joint tenth place.

Based on this initial analysis, it is evident that research on the deployment of systematic review methodologies in business model research appears nascent, albeit growing rapidly in a variety of intra-disciplinary business and management journals.

To address the second research question and identify the most prominent authors of the journal articles in our corpus that have deployed systematic review methodologies in business model research and their impact, we next conducted an author citation analysis. In our citation analysis, the relative importance or impact of an author’s article is measured by counting the number of times that author’s article has been cited by other authors’ studies. Table 3 presents a summary of the journal articles that have reached a minimum of 100 citations hitherto from an overall total of 22,333 citations in the corpus. However, it is important to acknowledge that citation analysis is retrospective in nature and hence continually becomes updated after some time has passed (Coombes and Nicholson, 2013). The citation counts reveal a pronounced clustering of research around themes of business model innovation, circular economy, digitalization and sustainability. Foss and Saebi (2017) clearly dominate the field with over (n = 1,500) citations, evidencing their article’s central role in framing and evaluating the trajectory of business model innovation research. Similarly, Linnenluecke (2017) with (n = 1,000) citations offers a contribution on resilience, reflecting the expanding overlap between sustainability and strategic management. Methodological pluralism is increasingly embraced, notably through techniques such as fuzzy-set qualitative comparative analysis (Kraus et al., 2018), further legitimizing complexity-informed inquiry. Digital transformation and artificial intelligence are recurrent topics, intersecting with business model debates across multiple sectors. Works by Di Vaio et al. (2020) with (n = 662) citations and Caputo et al. (2021) with (n = 378) citations exemplify this techno-centric pivot, while others focus on sector-specific transformations, such as the fashion industry (Todeschini et al., 2017), manufacturing (Luz Martín-Peña et al., 2018) and construction (Chen et al., 2022). The sharing economy and platform logic are also focal points, especially through Cheng (2016) with (n = 689) citations and Hossain (2020) with (n = 253) citations, who synthesize fragmented perspectives and frame future agendas. Circular economy-related literature reflects a multidimensional growth in relevance, with Centobelli et al. (2020) with (n = 444) citations, Bressanelli et al. (2019) with (n = 420) citations and Ferasso et al. (2020) with (n = 373) citations outlining both conceptual foundations and operational challenges. Several reviews also investigate the interplay between innovation systems, digitalization and sustainability transitions, often calling for more empirical work to ground emerging conceptual frames.

Next, to address the third research question and identify the key topics of the journal articles that have deployed systematic review methodologies in business model research in our corpus, we conducted a co-occurrence of author keywords analysis. The intention, here, is to generate a more detailed understanding of the knowledge components and knowledge structure of a particular academic field. This analysis assumes that keywords that frequently appear together (i.e. co-occur) in the same articles bear a relationship to one another. Using the VOS Viewer bibliometric software (Van Eck and Waltman, 2010), Figure 3 presents an illustration showing the results of the co-occurrence of all keywords analysis of journal articles between 2011 and 2024. Seven key clusters can be identified. Each cluster provides a preview of different facets of business model research, from sustainability to innovation and technology. For instance, the most prominent keywords: “business model” (Cluster 5) and “circular economy” (Cluster 4) have the highest total link strengths, indicating their significant influence and connectivity within their clusters. Multiple clusters (Clusters 3, 4, 6 and 7) emphasize sustainability, indicating a strong research interest in sustainable development and business practices. Clusters 2 and 5 highlight the importance of innovation and emerging technologies, such as digital transformation and artificial intelligence. In Cluster 1, colored red, the keywords with the highest total link strength are bibliometric analysis (n = 73), business models, bibliometrics, sharing economy, bibliographic coupling, co-citation analysis, collaborative consumption, VOS viewer, content analysis and blockchain. This cluster seems to focus on bibliometric methods and their applications in various fields, including business models and the sharing economy. In Cluster 2, colored green, the keywords with the highest total link strength are business model innovation (n = 48), digital transformation, artificial intelligence, bibliometric, review, technology, digital technology, internet of things and disruptive innovation. This cluster is centered around innovation, digital transformation and emerging technologies. In Cluster 3, colored blue, the keywords with the highest total link strength are literature review (n = 52), digitalization, industry 4.0, supply chain management, servitization, ecosystem and sustainable development goals. This cluster emphasizes literature reviews, digitalization and industry 4.0, with a focus on sustainability. In Cluster 4, colored yellow, the keywords with the highest total link strength are circular economy (n = 85), systematic review, circular business model, open innovation, meta-analysis and supply chain. This cluster is focused on circular economy and systematic reviews, highlighting the importance of sustainability and innovation. In Cluster 5, colored purple, the keywords with the highest total link strength are business model (n = 97), innovation, value creation, big data and value capture. This cluster revolves around business models, innovation and value creation. In Cluster 6, colored cyan, the keywords with the highest total link strength are sustainability (n = 86), entrepreneurship, sustainable development and resilience. This cluster is dedicated to sustainability and entrepreneurship. Finally, in Cluster 7, colored orange, the keywords with the highest total link strength are systematic literature review (n = 59) and sustainable business models. This cluster focuses on systematic literature reviews and sustainable business models.

While each author keyword is formally assigned to a single cluster based on its strongest co-occurrence patterns, thematic overlaps are evident both within and across clusters. Certain keywords – particularly those associated with broad or evolving discourses – appear in semantically related forms in more than one cluster. For instance, within Cluster 6, both sustainability and sustainable development co-occur, reflecting internal conceptual cohesion. However, related keywords such as sustainable business models appear in Cluster 7, while sustainable development goals appear in Cluster 3. These cross-cluster keywords suggest that although clustering imposes a thematic partitioning, the underlying thematic landscape remains interconnected, with key concepts bridging distinct but adjacent research domains.

The recurrence of keywords such as sustainability, sustainable development, sustainable business models and sustainable development goals across Clusters 3, 6 and 7 signals a convergence around sustainability-oriented business model innovation. This aligns with foundational literature on value creation and capture in sustainable contexts (see for instance Bocken et al., 2014) as well as emerging frameworks that integrate circular economy principles (Cluster 4) and resilience (Cluster 6) into strategic design. Similarly, the prominence of digital transformation, artificial intelligence and industry 4.0 in Clusters 2 and 3 reflects the growing influence of technological affordances on business model reconfiguration (see for instance Di Vaio et al., 2020; Li, 2020; Marcon et al., 2022). The presence of bibliometric and systematic review methodologies (Clusters 1, 4 and 7) further suggests a meta-analytical turn in the field, enabling scholars to map intellectual structures and trace the evolution of key themes (Coombes, 2023; Coombes and Nicholson, 2013). Arguably, these interlinked clusters illustrate how business model research is increasingly situated at the intersection of sustainability transitions, digital innovation and methodological pluralism.

Next, we conducted a bibliographic coupling analysis to complement the previous co-citation analyses. Again, using the VOS Viewer bibliometric software, Figure 4 presents an illustration showing the results from 2011 to 2024. Where the size of circles (nodes) indicates citation volume; larger circles represent more citations. The connections (edges) between articles signify relationships, with strength values indicating referenced links. Nine key clusters can be identified. Cluster 1, colored red, has a mix of high link strength and moderate citation counts, suggesting a network of collaborative researchers rather than singular dominant figures. Cluster 2, colored green, exhibits strong citation figures and dense networking, making it a hub of impactful scholarship. Cluster 3, colored blue, has fewer high-citation authors, indicating a research niche where connectivity is crucial for influence rather than individual citation impact. Clusters 4 to 9 exhibit a blend of emerging researchers with increasing citations, reflecting areas of business model research with growing academic discourse. Certain authors are evidenced that exhibit exceptionally high citation counts, reflecting their influence within business model research. For instance, Foss is a leading scholar, both highly cited and well-connected within their research network (n = 1,557 citations, n = 787 total link strength). Despite lower connectivity, Belussi’s high citation count suggests foundational contributions to a niche research domain (n = 1,108 citations, n = 57 total link strength). Appearing several times, Di Vaio’s strong interdisciplinary presence across clusters showcases broad influence. This pattern suggests that citation impact can be independent of collaboration levels–some scholars produce highly cited work with less engagement in collaborative research networks. There are also authors appearing across multiple clusters that indicate interdisciplinary contributions. For instance, Di Vaio appears in Clusters 1, 2 and 4, suggesting expertise spanning different thematic areas, whilst Zhang also appears in multiple clusters, indicating research spanning multiple domains. In contrast, the lower citation counts with moderate network strength indicate emerging scholars. For instance, Secinaro (n = 102 citations, n = 204 total link strength), Lim (n = 51 citations, n = 171 total link strength) and Latino (n = 43 citations, n = 137 total link strength). These scholars are actively engaged in networks, indicating potential future citation growth as their work gains traction.

Finally, through a textual analysis of the 352 abstracts drawn from our corpus, using the Pro Word Cloud application, we present a word cloud analysis which represent visually the textual data drawn from the abstracts where the size, weight or color of each word reflects its frequency within the corpus. This word cloud is illustrated in Figure 5.

Drawing from thematic patterns identified in the word cloud, the prominence of terms like social (n = 204), business (n = 980), entrepreneurship (n = 76), startup (n = 36) and capital (n = 25) – human, intellectual and venture – suggests a dynamic intersection between societal impact and entrepreneurial finance. This entrepreneurial thread points to a growing emphasis on socially responsible investment and the role of business startups in addressing global challenges. Surrounding this is an academic research thread with terms such as research (n = 799), analysis (n = 395), literature (n = 561) and knowledge (n = 148), which anchor the word cloud in scholarly rigor. Their presence implies a methodological foundation–involving systematic reviews, bibliometric mapping or empirical synthesis – used to explore and validate the concepts at play. The sustainability thread is woven throughout, with sustainable (n = 202), decarbonization (n = 20) and environmental (n = 128) highlighting ecological concerns and the strategic alignment with global goals such as SDG8 and SDG17. These references to the UN Sustainable Development Goals indicate a commitment to inclusive economic growth and cross-sector partnerships. The digital environment thread is also evident through the cloud via terms like digitalization (n = 50) and metaverse (n = 30), suggesting a forward-looking orientation that embraces digital transformation and emerging technologies.

Business model research has gained traction in recent years, but its development appears to have stalled more recently due to conceptual ambiguity. Therefore, to investigate the current intellectual structure and evolution of business model research, rooted in bibliometrics, the purpose of this paper was to present a rigorous bibliometric analysis of the deployment of systematic review methodologies in business model research and to identify the prominent authors engaging with this method of research, their impact and the key topics arising from these studies. With the use of Scopus and VOS Viewer and drawing on a corpus of over 350 journal articles, this paper is among the first that has analyzed objectively the deployment of systematic review methodologies in business model research by applying a rigorous bibliometric analysis from a longitudinal perspective, with a particular emphasis on the period between 2011 and 2024, as a complementary approach to a traditional subjective literature review, for clarifying theoretical ambiguities and guiding future business model research. The paper contributes to the business model literature by identifying areas where assimilation has taken place and hence how the field could make distinctive contributions to this literature in the future. Despite its increasing adoption across business and management disciplines, systematic reviews have been underutilized in business model research, suggesting missed opportunities to consolidate and advance knowledge in the field. Hence, the results from the preceding longitudinal bibliometric analysis have addressed the three research questions and (1) with the use of Scopus searched for and created a bibliographic dataset (or corpus) that identified the extent that systematic review methodologies have been deployed in business model research hitherto, (2) with the use of the corpus identified the most prominent author(s) that have deployed systematic review methodologies in business model research, as well as stating their impact, and (3) with the use of the corpus and VOS Viewer, discovered and visualized the key topics of these author(s)’ studies that have deployed systematic review methodologies in business model research. A theoretically grounded framework to guide future inquiry is also proposed. Finally, the paper also represents a call to action for business model researchers generally to engage further with contemporary systematic review methodologies to understand better the breadth and depth of existing bodies of work and hence identify gaps in knowledge that can be developed further with potentially new and exciting contributions being made in the future. It is important to be aware that systematic review methodologies also have some limitations due to the methodological constraints that result from the research designs deployed and from the corpus extracted from the academic databases. The results from the foregoing analyses have, therefore, been limited due to the bibliometric constraints that resulted from our corpus. The first constraint relates to the nature of the database searches of published journal articles. Scopus is constantly being updated with new literature as it becomes published; therefore, our corpus represents a “snapshot” of data during the period of data collection. The second constraint relates to the bibliometric analysis of articles published in business and management journals contained in our corpus. We acknowledge that business model research has also been published in other interdisciplinary journals which were excluded from our corpus, that is those published in other languages. The third constraint relates to the nature of the search strings. Alternative keyword search strings and variations in inclusion and exclusion criteria could have yielded different results in the Scopus searches. However, given the objective nature of our search protocol, we deliberately avoided making subjective judgments about what should or should not be classified as business model research. Our approach was to analyze and report on the corpus as retrieved, based strictly on the predefined search parameters. We considered it beyond the scope of this paper to interrogate the disciplinary boundaries or conceptual validity of individual articles that met those criteria. The fourth constraint relates to the citation analysis of our corpus. Citation analysis is retrospective in nature, and the citation data only appear after some time has passed (Coombes and Nicholson, 2013). Nevertheless, we suggest that the findings presented in this paper will be very helpful for researchers interested in obtaining insights from the results of previous papers adopting systematic review methodologies in business model research.

The supplementary material for this article can be found online.