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The difference between qualitative and quantitative research is subjectivity and objectivity. Subjectivity is associated with qualitative research, while objectivity is associated with quantitative research. 

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• BMJ Glob Health
• v.4(Suppl 1); 2019

Synthesising quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes, designs and outlining some methods

1 School of Social Sciences, Bangor University, Wales, UK

Andrew Booth

2 School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK

Graham Moore

3 School of Social Sciences, Cardiff University, Wales, UK

Kate Flemming

4 Department of Health Sciences, The University of York, York, UK

Özge Tunçalp

5 Department of Reproductive Health and Research including UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), World Health Organization, Geneva, Switzerland

Elham Shakibazadeh

6 Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Associated Data

bmjgh-2018-000893supp001.pdf

bmjgh-2018-000893supp002.pdf

bmjgh-2018-000893supp003.pdf

bmjgh-2018-000893supp005.pdf

bmjgh-2018-000893supp004.pdf

Guideline developers are increasingly dealing with more difficult decisions concerning whether to recommend complex interventions in complex and highly variable health systems. There is greater recognition that both quantitative and qualitative evidence can be combined in a mixed-method synthesis and that this can be helpful in understanding how complexity impacts on interventions in specific contexts. This paper aims to clarify the different purposes, review designs, questions, synthesis methods and opportunities to combine quantitative and qualitative evidence to explore the complexity of complex interventions and health systems. Three case studies of guidelines developed by WHO, which incorporated quantitative and qualitative evidence, are used to illustrate possible uses of mixed-method reviews and evidence. Additional examples of methods that can be used or may have potential for use in a guideline process are outlined. Consideration is given to the opportunities for potential integration of quantitative and qualitative evidence at different stages of the review and guideline process. Encouragement is given to guideline commissioners and developers and review authors to consider including quantitative and qualitative evidence. Recommendations are made concerning the future development of methods to better address questions in systematic reviews and guidelines that adopt a complexity perspective.

Summary box

• When combined in a mixed-method synthesis, quantitative and qualitative evidence can potentially contribute to understanding how complex interventions work and for whom, and how the complex health systems into which they are implemented respond and adapt.
• The different purposes and designs for combining quantitative and qualitative evidence in a mixed-method synthesis for a guideline process are described.
• Questions relevant to gaining an understanding of the complexity of complex interventions and the wider health systems within which they are implemented that can be addressed by mixed-method syntheses are presented.
• The practical methodological guidance in this paper is intended to help guideline producers and review authors commission and conduct mixed-method syntheses where appropriate.
• If more mixed-method syntheses are conducted, guideline developers will have greater opportunities to access this evidence to inform decision-making.

Introduction

Recognition has grown that while quantitative methods remain vital, they are usually insufficient to address complex health systems related research questions. 1 Quantitative methods rely on an ability to anticipate what must be measured in advance. Introducing change into a complex health system gives rise to emergent reactions, which cannot be fully predicted in advance. Emergent reactions can often only be understood through combining quantitative methods with a more flexible qualitative lens. 2 Adopting a more pluralist position enables a diverse range of research options to the researcher depending on the research question being investigated. 3–5 As a consequence, where a research study sits within the multitude of methods available is driven by the question being asked, rather than any particular methodological or philosophical stance. 6

Publication of guidance on designing complex intervention process evaluations and other works advocating mixed-methods approaches to intervention research have stimulated better quality evidence for synthesis. 1 7–13 Methods for synthesising qualitative 14 and mixed-method evidence have been developed or are in development. Mixed-method research and review definitions are outlined in box 1 .

Defining mixed-method research and reviews

Pluye and Hong 52 define mixed-methods research as “a research approach in which a researcher integrates (a) qualitative and quantitative research questions, (b) qualitative research methods* and quantitative research designs, (c) techniques for collecting and analyzing qualitative and quantitative evidence, and (d) qualitative findings and quantitative results”.A mixed-method synthesis can integrate quantitative, qualitative and mixed-method evidence or data from primary studies.† Mixed-method primary studies are usually disaggregated into quantitative and qualitative evidence and data for the purposes of synthesis. Thomas and Harden further define three ways in which reviews are mixed. 53

• The types of studies included and hence the type of findings to be synthesised (ie, qualitative/textual and quantitative/numerical).
• The types of synthesis method used (eg, statistical meta-analysis and qualitative synthesis).
• The mode of analysis: theory testing AND theory building.

*A qualitative study is one that uses qualitative methods of data collection and analysis to produce a narrative understanding of the phenomena of interest. Qualitative methods of data collection may include, for example, interviews, focus groups, observations and analysis of documents.

†The Cochrane Qualitative and Implementation Methods group coined the term ‘qualitative evidence synthesis’ to mean that the synthesis could also include qualitative data. For example, qualitative data from case studies, grey literature reports and open-ended questions from surveys. ‘Evidence’ and ‘data’ are used interchangeably in this paper.

This paper is one of a series that aims to explore the implications of complexity for systematic reviews and guideline development, commissioned by WHO. This paper is concerned with the methodological implications of including quantitative and qualitative evidence in mixed-method systematic reviews and guideline development for complex interventions. The guidance was developed through a process of bringing together experts in the field, literature searching and consensus building with end users (guideline developers, clinicians and reviewers). We clarify the different purposes, review designs, questions and synthesis methods that may be applicable to combine quantitative and qualitative evidence to explore the complexity of complex interventions and health systems. Three case studies of WHO guidelines that incorporated quantitative and qualitative evidence are used to illustrate possible uses of mixed-method reviews and mechanisms of integration ( table 1 , online supplementary files 1–3 ). Additional examples of methods that can be used or may have potential for use in a guideline process are outlined. Opportunities for potential integration of quantitative and qualitative evidence at different stages of the review and guideline process are presented. Specific considerations when using an evidence to decision framework such as the Developing and Evaluating Communication strategies to support Informed Decisions and practice based on Evidence (DECIDE) framework 15 or the new WHO-INTEGRATE evidence to decision framework 16 at the review design and evidence to decision stage are outlined. See online supplementary file 4 for an example of a health systems DECIDE framework and Rehfuess et al 16 for the new WHO-INTEGRATE framework. Encouragement is given to guideline commissioners and developers and review authors to consider including quantitative and qualitative evidence in guidelines of complex interventions that take a complexity perspective and health systems focus.

Designs and methods and their use or applicability in guidelines and systematic reviews taking a complexity perspective

Supplementary data

Taking a complexity perspective.

The first paper in this series 17 outlines aspects of complexity associated with complex interventions and health systems that can potentially be explored by different types of evidence, including synthesis of quantitative and qualitative evidence. Petticrew et al 17 distinguish between a complex interventions perspective and a complex systems perspective. A complex interventions perspective defines interventions as having “implicit conceptual boundaries, representing a flexible, but common set of practices, often linked by an explicit or implicit theory about how they work”. A complex systems perspective differs in that “ complexity arises from the relationships and interactions between a system’s agents (eg, people, or groups that interact with each other and their environment), and its context. A system perspective conceives the intervention as being part of the system, and emphasises changes and interconnections within the system itself”. Aspects of complexity associated with implementation of complex interventions in health systems that could potentially be addressed with a synthesis of quantitative and qualitative evidence are summarised in table 2 . Another paper in the series outlines criteria used in a new evidence to decision framework for making decisions about complex interventions implemented in complex systems, against which the need for quantitative and qualitative evidence can be mapped. 16 A further paper 18 that explores how context is dealt with in guidelines and reviews taking a complexity perspective also recommends using both quantitative and qualitative evidence to better understand context as a source of complexity. Mixed-method syntheses of quantitative and qualitative evidence can also help with understanding of whether there has been theory failure and or implementation failure. The Cochrane Qualitative and Implementation Methods Group provide additional guidance on exploring implementation and theory failure that can be adapted to address aspects of complexity of complex interventions when implemented in health systems. 19

Health-system complexity-related questions that a synthesis of quantitative and qualitative evidence could address (derived from Petticrew et al 17 )

It may not be apparent which aspects of complexity or which elements of the complex intervention or health system can be explored in a guideline process, or whether combining qualitative and quantitative evidence in a mixed-method synthesis will be useful, until the available evidence is scoped and mapped. 17 20 A more extensive lead in phase is typically required to scope the available evidence, engage with stakeholders and to refine the review parameters and questions that can then be mapped against potential review designs and methods of synthesis. 20 At the scoping stage, it is also common to decide on a theoretical perspective 21 or undertake further work to refine a theoretical perspective. 22 This is also the stage to begin articulating the programme theory of the complex intervention that may be further developed to refine an understanding of complexity and show how the intervention is implemented in and impacts on the wider health system. 17 23 24 In practice, this process can be lengthy, iterative and fluid with multiple revisions to the review scope, often developing and adapting a logic model 17 as the available evidence becomes known and the potential to incorporate different types of review designs and syntheses of quantitative and qualitative evidence becomes better understood. 25 Further questions, propositions or hypotheses may emerge as the reviews progress and therefore the protocols generally need to be developed iteratively over time rather than a priori.

Following a scoping exercise and definition of key questions, the next step in the guideline development process is to identify existing or commission new systematic reviews to locate and summarise the best available evidence in relation to each question. For example, case study 2, ‘Optimising health worker roles for maternal and newborn health through task shifting’, included quantitative reviews that did and did not take an additional complexity perspective, and qualitative evidence syntheses that were able to explain how specific elements of complexity impacted on intervention outcomes within the wider health system. Further understanding of health system complexity was facilitated through the conduct of additional country-level case studies that contributed to an overall understanding of what worked and what happened when lay health worker interventions were implemented. See table 1 online supplementary file 2 .

There are a few existing examples, which we draw on in this paper, but integrating quantitative and qualitative evidence in a mixed-method synthesis is relatively uncommon in a guideline process. Box 2 includes a set of key questions that guideline developers and review authors contemplating combining quantitative and qualitative evidence in mixed-methods design might ask. Subsequent sections provide more information and signposting to further reading to help address these key questions.

Key questions that guideline developers and review authors contemplating combining quantitative and qualitative evidence in a mixed-methods design might ask

Compound questions requiring both quantitative and qualitative evidence?

Questions requiring mixed-methods studies?

Separate quantitative and qualitative questions?

Separate quantitative and qualitative research studies?

Related quantitative and qualitative research studies?

Mixed-methods studies?

Quantitative unpublished data and/or qualitative unpublished data, eg, narrative survey data?

Throughout the review?

Following separate reviews?

At the question point?

At the synthesis point?

At the evidence to recommendations stage?

Or a combination?

Narrative synthesis or summary?

Quantitising approach, eg, frequency analysis?

Qualitising approach, eg, thematic synthesis?

Tabulation?

Logic model?

Conceptual model/framework?

Graphical approach?

• WHICH: Which mixed-method designs, methodologies and methods best fit into a guideline process to inform recommendations?

Complexity-related questions that a synthesis of quantitative and qualitative evidence can potentially address

Petticrew et al 17 define the different aspects of complexity and examples of complexity-related questions that can potentially be explored in guidelines and systematic reviews taking a complexity perspective. Relevant aspects of complexity outlined by Petticrew et al 17 are summarised in table 2 below, together with the corresponding questions that could be addressed in a synthesis combining qualitative and quantitative evidence. Importantly, the aspects of complexity and their associated concepts of interest have however yet to be translated fully in primary health research or systematic reviews. There are few known examples where selected complexity concepts have been used to analyse or reanalyse a primary intervention study. Most notable is Chandler et al 26 who specifically set out to identify and translate a set of relevant complexity theory concepts for application in health systems research. Chandler then reanalysed a trial process evaluation using selected complexity theory concepts to better understand the complex causal pathway in the health system that explains some aspects of complexity in table 2 .

Rehfeuss et al 16 also recommends upfront consideration of the WHO-INTEGRATE evidence to decision criteria when planning a guideline and formulating questions. The criteria reflect WHO norms and values and take account of a complexity perspective. The framework can be used by guideline development groups as a menu to decide which criteria to prioritise, and which study types and synthesis methods can be used to collect evidence for each criterion. Many of the criteria and their related questions can be addressed using a synthesis of quantitative and qualitative evidence: the balance of benefits and harms, human rights and sociocultural acceptability, health equity, societal implications and feasibility (see table 3 ). Similar aspects in the DECIDE framework 15 could also be addressed using synthesis of qualitative and quantitative evidence.

Integrate evidence to decision framework criteria, example questions and types of studies to potentially address these questions (derived from Rehfeuss et al 16 )

GIS, Geographical Information System; RCT, randomised controlled trial.

Questions as anchors or compasses

Questions can serve as an ‘anchor’ by articulating the specific aspects of complexity to be explored (eg, Is successful implementation of the intervention context dependent?). 27 Anchor questions such as “How does intervention x impact on socioeconomic inequalities in health behaviour/outcome x” are the kind of health system question that requires a synthesis of both quantitative and qualitative evidence and hence a mixed-method synthesis. Quantitative evidence can quantify the difference in effect, but does not answer the question of how . The ‘how’ question can be partly answered with quantitative and qualitative evidence. For example, quantitative evidence may reveal where socioeconomic status and inequality emerges in the health system (an emergent property) by exploring questions such as “ Does patterning emerge during uptake because fewer people from certain groups come into contact with an intervention in the first place? ” or “ are people from certain backgrounds more likely to drop out, or to maintain effects beyond an intervention differently? ” Qualitative evidence may help understand the reasons behind all of these mechanisms. Alternatively, questions can act as ‘compasses’ where a question sets out a starting point from which to explore further and to potentially ask further questions or develop propositions or hypotheses to explore through a complexity perspective (eg, What factors enhance or hinder implementation?). 27 Other papers in this series provide further guidance on developing questions for qualitative evidence syntheses and guidance on question formulation. 14 28

For anchor and compass questions, additional application of a theory (eg, complexity theory) can help focus evidence synthesis and presentation to explore and explain complexity issues. 17 21 Development of a review specific logic model(s) can help to further refine an initial understanding of any complexity-related issues of interest associated with a specific intervention, and if appropriate the health system or section of the health system within which to contextualise the review question and analyse data. 17 23–25 Specific tools are available to help clarify context and complex interventions. 17 18

If a complexity perspective, and certain criteria within evidence to decision frameworks, is deemed relevant and desirable by guideline developers, it is only possible to pursue a complexity perspective if the evidence is available. Careful scoping using knowledge maps or scoping reviews will help inform development of questions that are answerable with available evidence. 20 If evidence of effect is not available, then a different approach to develop questions leading to a more general narrative understanding of what happened when complex interventions were implemented in a health system will be required (such as in case study 3—risk communication guideline). This should not mean that the original questions developed for which no evidence was found when scoping the literature were not important. An important function of creating a knowledge map is also to identify gaps to inform a future research agenda.

Table 2 and online supplementary files 1–3 outline examples of questions in the three case studies, which were all ‘COMPASS’ questions for the qualitative evidence syntheses.

Types of integration and synthesis designs in mixed-method reviews

The shift towards integration of qualitative and quantitative evidence in primary research has, in recent years, begun to be mirrored within research synthesis. 29–31 The natural extension to undertaking quantitative or qualitative reviews has been the development of methods for integrating qualitative and quantitative evidence within reviews, and within the guideline process using evidence to decision-frameworks. Advocating the integration of quantitative and qualitative evidence assumes a complementarity between research methodologies, and a need for both types of evidence to inform policy and practice. Below, we briefly outline the current designs for integrating qualitative and quantitative evidence within a mixed-method review or synthesis.

One of the early approaches to integrating qualitative and quantitative evidence detailed by Sandelowski et al 32 advocated three basic review designs: segregated, integrated and contingent designs, which have been further developed by Heyvaert et al 33 ( box 3 ).

Segregated, integrated and contingent designs 32 33

Segregated design.

Conventional separate distinction between quantitative and qualitative approaches based on the assumption they are different entities and should be treated separately; can be distinguished from each other; their findings warrant separate analyses and syntheses. Ultimately, the separate synthesis results can themselves be synthesised.

Integrated design

The methodological differences between qualitative and quantitative studies are minimised as both are viewed as producing findings that can be readily synthesised into one another because they address the same research purposed and questions. Transformation involves either turning qualitative data into quantitative (quantitising) or quantitative findings are turned into qualitative (qualitising) to facilitate their integration.

Contingent design

Takes a cyclical approach to synthesis, with the findings from one synthesis informing the focus of the next synthesis, until all the research objectives have been addressed. Studies are not necessarily grouped and categorised as qualitative or quantitative.

A recent review of more than 400 systematic reviews 34 combining quantitative and qualitative evidence identified two main synthesis designs—convergent and sequential. In a convergent design, qualitative and quantitative evidence is collated and analysed in a parallel or complementary manner, whereas in a sequential synthesis, the collation and analysis of quantitative and qualitative evidence takes place in a sequence with one synthesis informing the other ( box 4 ). 6 These designs can be seen to build on the work of Sandelowski et al , 32 35 particularly in relation to the transformation of data from qualitative to quantitative (and vice versa) and the sequential synthesis design, with a cyclical approach to reviewing that evokes Sandelowski’s contingent design.

Convergent and sequential synthesis designs 34

Convergent synthesis design.

Qualitative and quantitative research is collected and analysed at the same time in a parallel or complementary manner. Integration can occur at three points:

a. Data-based convergent synthesis design

All included studies are analysed using the same methods and results presented together. As only one synthesis method is used, data transformation occurs (qualitised or quantised). Usually addressed one review question.

b. Results-based convergent synthesis design

Qualitative and quantitative data are analysed and presented separately but integrated using a further synthesis method; eg, narratively, tables, matrices or reanalysing evidence. The results of both syntheses are combined in a third synthesis. Usually addresses an overall review question with subquestions.

c. Parallel-results convergent synthesis design

Qualitative and quantitative data are analysed and presented separately with integration occurring in the interpretation of results in the discussion section. Usually addresses two or more complimentary review questions.

Sequential synthesis design

A two-phase approach, data collection and analysis of one type of evidence (eg, qualitative), occurs after and is informed by the collection and analysis of the other type (eg, quantitative). Usually addresses an overall question with subquestions with both syntheses complementing each other.

The three case studies ( table 1 , online supplementary files 1–3 ) illustrate the diverse combination of review designs and synthesis methods that were considered the most appropriate for specific guidelines.

Methods for conducting mixed-method reviews in the context of guidelines for complex interventions

In this section, we draw on examples where specific review designs and methods have been or can be used to explore selected aspects of complexity in guidelines or systematic reviews. We also identify other review methods that could potentially be used to explore aspects of complexity. Of particular note, we could not find any specific examples of systematic methods to synthesise highly diverse research designs as advocated by Petticrew et al 17 and summarised in tables 2 and 3 . For example, we could not find examples of methods to synthesise qualitative studies, case studies, quantitative longitudinal data, possibly historical data, effectiveness studies providing evidence of differential effects across different contexts, and system modelling studies (eg, agent-based modelling) to explore system adaptivity.

There are different ways that quantitative and qualitative evidence can be integrated into a review and then into a guideline development process. In practice, some methods enable integration of different types of evidence in a single synthesis, while in other methods, the single systematic review may include a series of stand-alone reviews or syntheses that are then combined in a cross-study synthesis. Table 1 provides an overview of the characteristics of different review designs and methods and guidance on their applicability for a guideline process. Designs and methods that have already been used in WHO guideline development are described in part A of the table. Part B outlines a design and method that can be used in a guideline process, and part C covers those that have the potential to integrate quantitative, qualitative and mixed-method evidence in a single review design (such as meta-narrative reviews and Bayesian syntheses), but their application in a guideline context has yet to be demonstrated.

Points of integration when integrating quantitative and qualitative evidence in guideline development

Depending on the review design (see boxes 3 and 4 ), integration can potentially take place at a review team and design level, and more commonly at several key points of the review or guideline process. The following sections outline potential points of integration and associated practical considerations when integrating quantitative and qualitative evidence in guideline development.

Review team level

In a guideline process, it is common for syntheses of quantitative and qualitative evidence to be done separately by different teams and then to integrate the evidence. A practical consideration relates to the organisation, composition and expertise of the review teams and ways of working. If the quantitative and qualitative reviews are being conducted separately and then brought together by the same team members, who are equally comfortable operating within both paradigms, then a consistent approach across both paradigms becomes possible. If, however, a team is being split between the quantitative and qualitative reviews, then the strengths of specialisation can be harnessed, for example, in quality assessment or synthesis. Optimally, at least one, if not more, of the team members should be involved in both quantitative and qualitative reviews to offer the possibility of making connexions throughout the review and not simply at re-agreed junctures. This mirrors O’Cathain’s conclusion that mixed-methods primary research tends to work only when there is a principal investigator who values and is able to oversee integration. 9 10 While the above decisions have been articulated in the context of two types of evidence, variously quantitative and qualitative, they equally apply when considering how to handle studies reporting a mixed-method study design, where data are usually disaggregated into quantitative and qualitative for the purposes of synthesis (see case study 3—risk communication in humanitarian disasters).

Question formulation

Clearly specified key question(s), derived from a scoping or consultation exercise, will make it clear if quantitative and qualitative evidence is required in a guideline development process and which aspects will be addressed by which types of evidence. For the remaining stages of the process, as documented below, a review team faces challenges as to whether to handle each type of evidence separately, regardless of whether sequentially or in parallel, with a view to joining the two products on completion or to attempt integration throughout the review process. In each case, the underlying choice is of efficiencies and potential comparability vs sensitivity to the underlying paradigm.

Once key questions are clearly defined, the guideline development group typically needs to consider whether to conduct a single sensitive search to address all potential subtopics (lumping) or whether to conduct specific searches for each subtopic (splitting). 36 A related consideration is whether to search separately for qualitative, quantitative and mixed-method evidence ‘streams’ or whether to conduct a single search and then identify specific study types at the subsequent sifting stage. These two considerations often mean a trade-off between a single search process involving very large numbers of records or a more protracted search process retrieving smaller numbers of records. Both approaches have advantages and choice may depend on the respective availability of resources for searching and sifting.

Screening and selecting studies

Closely related to decisions around searching are considerations relating to screening and selecting studies for inclusion in a systematic review. An important consideration here is whether the review team will screen records for all review types, regardless of their subsequent involvement (‘altruistic sifting’), or specialise in screening for the study type with which they are most familiar. The risk of missing relevant reports might be minimised by whole team screening for empirical reports in the first instance and then coding them for a specific quantitative, qualitative or mixed-methods report at a subsequent stage.

Assessment of methodological limitations in primary studies

Within a guideline process, review teams may be more limited in their choice of instruments to assess methodological limitations of primary studies as there are mandatory requirements to use the Cochrane risk of bias tool 37 to feed into Grading of Recommendations Assessment, Development and Evaluation (GRADE) 38 or to select from a small pool of qualitative appraisal instruments in order to apply GRADE; Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) 39 to assess the overall certainty or confidence in findings. The Cochrane Qualitative and Implementation Methods Group has recently issued guidance on the selection of appraisal instruments and core assessment criteria. 40 The Mixed-Methods Appraisal Tool, which is currently undergoing further development, offers a single quality assessment instrument for quantitative, qualitative and mixed-methods studies. 41 Other options include using corresponding instruments from within the same ‘stable’, for example, using different Critical Appraisal Skills Programme instruments. 42 While using instruments developed by the same team or organisation may achieve a degree of epistemological consonance, benefits may come more from consistency of approach and reporting rather than from a shared view of quality. Alternatively, a more paradigm-sensitive approach would involve selecting the best instrument for each respective review while deferring challenges from later heterogeneity of reporting.

Data extraction

The way in which data and evidence are extracted from primary research studies for review will be influenced by the type of integrated synthesis being undertaken and the review purpose. Initially, decisions need to be made regarding the nature and type of data and evidence that are to be extracted from the included studies. Method-specific reporting guidelines 43 44 provide a good template as to what quantitative and qualitative data it is potentially possible to extract from different types of method-specific study reports, although in practice reporting quality varies. Online supplementary file 5 provides a hypothetical example of the different types of studies from which quantitative and qualitative evidence could potentially be extracted for synthesis.

The decisions around what data or evidence to extract will be guided by how ‘integrated’ the mixed-method review will be. For those reviews where the quantitative and qualitative findings of studies are synthesised separately and integrated at the point of findings (eg, segregated or contingent approaches or sequential synthesis design), separate data extraction approaches will likely be used.

Where integration occurs during the process of the review (eg, integrated approach or convergent synthesis design), an integrated approach to data extraction may be considered, depending on the purpose of the review. This may involve the use of a data extraction framework, the choice of which needs to be congruent with the approach to synthesis chosen for the review. 40 45 The integrative or theoretical framework may be decided on a priori if a pre-developed theoretical or conceptual framework is available in the literature. 27 The development of a framework may alternatively arise from the reading of the included studies, in relation to the purpose of the review, early in the process. The Cochrane Qualitative and Implementation Methods Group provide further guidance on extraction of qualitative data, including use of software. 40

Synthesis and integration

Relatively few synthesis methods start off being integrated from the beginning, and these methods have generally been subject to less testing and evaluation particularly in a guideline context (see table 1 ). A review design that started off being integrated from the beginning may be suitable for some guideline contexts (such as in case study 3—risk communication in humanitarian disasters—where there was little evidence of effect), but in general if there are sufficient trials then a separate systematic review and meta-analysis will be required for a guideline. Other papers in this series offer guidance on methods for synthesising quantitative 46 and qualitative evidence 14 in reviews that take a complexity perspective. Further guidance on integrating quantitative and qualitative evidence in a systematic review is provided by the Cochrane Qualitative and Implementation Methods Group. 19 27 29 40 47

Types of findings produced by specific methods

It is highly likely (unless there are well-designed process evaluations) that the primary studies may not themselves seek to address the complexity-related questions required for a guideline process. In which case, review authors will need to configure the available evidence and transform the evidence through the synthesis process to produce explanations, propositions and hypotheses (ie, findings) that were not obvious at primary study level. It is important that guideline commissioners, developers and review authors are aware that specific methods are intended to produce a type of finding with a specific purpose (such as developing new theory in the case of meta-ethnography). 48 Case study 1 (antenatal care guideline) provides an example of how a meta-ethnography was used to develop a new theory as an end product, 48 49 as well as framework synthesis which produced descriptive and explanatory findings that were more easily incorporated into the guideline process. 27 The definitions ( box 5 ) may be helpful when defining the different types of findings.

Different levels of findings

Descriptive findings —qualitative evidence-driven translated descriptive themes that do not move beyond the primary studies.

Explanatory findings —may either be at a descriptive or theoretical level. At the descriptive level, qualitative evidence is used to explain phenomena observed in quantitative results, such as why implementation failed in specific circumstances. At the theoretical level, the transformed and interpreted findings that go beyond the primary studies can be used to explain the descriptive findings. The latter description is generally the accepted definition in the wider qualitative community.

Hypothetical or theoretical finding —qualitative evidence-driven transformed themes (or lines of argument) that go beyond the primary studies. Although similar, Thomas and Harden 56 make a distinction in the purposes between two types of theoretical findings: analytical themes and the product of meta-ethnographies, third-order interpretations. 48

Analytical themes are a product of interrogating descriptive themes by placing the synthesis within an external theoretical framework (such as the review question and subquestions) and are considered more appropriate when a specific review question is being addressed (eg, in a guideline or to inform policy). 56

Third-order interpretations come from translating studies into one another while preserving the original context and are more appropriate when a body of literature is being explored in and of itself with broader or emergent review questions. 48

Bringing mixed-method evidence together in evidence to decision (EtD) frameworks

A critical element of guideline development is the formulation of recommendations by the Guideline Development Group, and EtD frameworks help to facilitate this process. 16 The EtD framework can also be used as a mechanism to integrate and display quantitative and qualitative evidence and findings mapped against the EtD framework domains with hyperlinks to more detailed evidence summaries from contributing reviews (see table 1 ). It is commonly the EtD framework that enables the findings of the separate quantitative and qualitative reviews to be brought together in a guideline process. Specific challenges when populating the DECIDE evidence to decision framework 15 were noted in case study 3 (risk communication in humanitarian disasters) as there was an absence of intervention effect data and the interventions to communicate public health risks were context specific and varied. These problems would not, however, have been addressed by substitution of the DECIDE framework with the new INTEGRATE 16 evidence to decision framework. A d ifferent type of EtD framework needs to be developed for reviews that do not include sufficient evidence of intervention effect.

Mixed-method review and synthesis methods are generally the least developed of all systematic review methods. It is acknowledged that methods for combining quantitative and qualitative evidence are generally poorly articulated. 29 50 There are however some fairly well-established methods for using qualitative evidence to explore aspects of complexity (such as contextual, implementation and outcome complexity), which can be combined with evidence of effect (see sections A and B of table 1 ). 14 There are good examples of systematic reviews that use these methods to combine quantitative and qualitative evidence, and examples of guideline recommendations that were informed by evidence from both quantitative and qualitative reviews (eg, case studies 1–3). With the exception of case study 3 (risk communication), the quantitative and qualitative reviews for these specific guidelines have been conducted separately, and the findings subsequently brought together in an EtD framework to inform recommendations.

Other mixed-method review designs have potential to contribute to understanding of complex interventions and to explore aspects of wider health systems complexity but have not been sufficiently developed and tested for this specific purpose, or used in a guideline process (section C of table 1 ). Some methods such as meta-narrative reviews also explore different questions to those usually asked in a guideline process. Methods for processing (eg, quality appraisal) and synthesising the highly diverse evidence suggested in tables 2 and 3 that are required to explore specific aspects of health systems complexity (such as system adaptivity) and to populate some sections of the INTEGRATE EtD framework remain underdeveloped or in need of development.

In addition to the required methodological development mentioned above, there is no GRADE approach 38 for assessing confidence in findings developed from combined quantitative and qualitative evidence. Another paper in this series outlines how to deal with complexity and grading different types of quantitative evidence, 51 and the GRADE CERQual approach for qualitative findings is described elsewhere, 39 but both these approaches are applied to method-specific and not mixed-method findings. An unofficial adaptation of GRADE was used in the risk communication guideline that reported mixed-method findings. Nor is there a reporting guideline for mixed-method reviews, 47 and for now reports will need to conform to the relevant reporting requirements of the respective method-specific guideline. There is a need to further adapt and test DECIDE, 15 WHO-INTEGRATE 16 and other types of evidence to decision frameworks to accommodate evidence from mixed-method syntheses which do not set out to determine the statistical effects of interventions and in circumstances where there are no trials.

When conducting quantitative and qualitative reviews that will subsequently be combined, there are specific considerations for managing and integrating the different types of evidence throughout the review process. We have summarised different options for combining qualitative and quantitative evidence in mixed-method syntheses that guideline developers and systematic reviewers can choose from, as well as outlining the opportunities to integrate evidence at different stages of the review and guideline development process.

Review commissioners, authors and guideline developers generally have less experience of combining qualitative and evidence in mixed-methods reviews. In particular, there is a relatively small group of reviewers who are skilled at undertaking fully integrated mixed-method reviews. Commissioning additional qualitative and mixed-method reviews creates an additional cost. Large complex mixed-method reviews generally take more time to complete. Careful consideration needs to be given as to which guidelines would benefit most from additional qualitative and mixed-method syntheses. More training is required to develop capacity and there is a need to develop processes for preparing the guideline panel to consider and use mixed-method evidence in their decision-making.

This paper has presented how qualitative and quantitative evidence, combined in mixed-method reviews, can help understand aspects of complex interventions and the systems within which they are implemented. There are further opportunities to use these methods, and to further develop the methods, to look more widely at additional aspects of complexity. There is a range of review designs and synthesis methods to choose from depending on the question being asked or the questions that may emerge during the conduct of the synthesis. Additional methods need to be developed (or existing methods further adapted) in order to synthesise the full range of diverse evidence that is desirable to explore the complexity-related questions when complex interventions are implemented into health systems. We encourage review commissioners and authors, and guideline developers to consider using mixed-methods reviews and synthesis in guidelines and to report on their usefulness in the guideline development process.

Handling editor: Soumyadeep Bhaumik

Contributors: JN, AB, GM, KF, ÖT and ES drafted the manuscript. All authors contributed to paper development and writing and agreed the final manuscript. Anayda Portela and Susan Norris from WHO managed the series. Helen Smith was series Editor. We thank all those who provided feedback on various iterations.

Funding: Funding provided by the World Health Organization Department of Maternal, Newborn, Child and Adolescent Health through grants received from the United States Agency for International Development and the Norwegian Agency for Development Cooperation.

Disclaimer: ÖT is a staff member of WHO. The author alone is responsible for the views expressed in this publication and they do not necessarily represent the decisions or policies of WHO.

Competing interests: No financial interests declared. JN, AB and ÖT have an intellectual interest in GRADE CERQual; and JN has an intellectual interest in the iCAT_SR tool.

Patient consent: Not required.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data sharing statement: No additional data are available.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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• Published: 22 August 2024

Geopolitics and energy security: a comprehensive exploration of evolution, collaborations, and future directions

• Qiang Wang   ORCID: orcid.org/0000-0002-8751-8093 1 , 2 ,
• Fen Ren 2 &
• Rongrong Li 1 , 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1071 ( 2024 ) Cite this article

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• Development studies
• Environmental studies
• Politics and international relations

The intersection of geopolitics and energy security is a critical area of study that has garnered increasing interest from scholars around the globe. This paper employs bibliometric theory and methodologies to explore the research trajectory concerning the influence of geopolitical dynamics on energy security. Our findings, derived from both quantitative and qualitative analysis of relevant literature, reveal several key insights. Firstly, there is a notable upward trend in publications on this topic, reflecting a widespread recognition of the intricate link between geopolitics and energy security. This growing body of research aligns with the exponential growth law observed in scientific literature, showcasing a novel pattern of geographical distribution centered around energy issues. Secondly, an examination of collaboration networks at the national, institutional, and individual levels identifies China as the leading country in terms of research partnerships, positioning Chinese institutions and scholars at the forefront of this field. Lastly, our analysis delineates the research evolution within this domain through three distinct phases—pre-, mid-, and post-development stages. It highlights the shifting focus of global researchers towards the energy transition process, energy policy formulation, the stability of energy markets, and the environmental impacts of energy production and consumption. This study not only maps the current landscape of research on geopolitics and energy security but also signals the critical areas of interest and collaboration that shape this vital field of inquiry.

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Introduction.

Energy, as a productive resource, is essential to ensuring the productive lives of the country’s citizens, it is also a strategic and politically attributed resource and plays an important role in ensuring national security and socio-economic stability (Yang et al., 2022 ). As the world’s industrialization process accelerates, technological advances and industrial expansion continue to drive social development, the extensive demand for energy resources has triggered global concerns about energy security. The concept of energy security is initially concerned with ensuring an uninterrupted and reliable supply of energy to meet a country’s or region’s production needs. However, despite the importance of this issue, there is still no consensus among academics on a definition of energy security. This is because the concept depends on the contextual background and the different national settings (Kruyt et al., 2009 ). The scope of energy security is not limited to energy supply but also encompasses the stability of energy markets, the connectivity of global energy supply chains, and the sustainability of energy resources. Energy security is of paramount importance to the economic stability and growth of countries and regions. A stable energy supply is the foundation for sustaining industrial production, transportation, and daily life. Any disruption in energy supply or sharp price fluctuations will result in higher production costs and increased inflation, thereby affecting economic growth and social stability. In addition, energy security is an important component of national security. Disruptions or shortfalls in energy supplies can lead to social unrest and affect national security (Sivaram and Saha, 2018 ). Therefore, energy security is the key to sustaining economic growth, ensuring political stability, and promoting social well-being (Lee et al., 2022 ). The factors affecting energy security are multifaceted, among which the impact of geopolitical risks on energy security cannot be ignored. Geopolitics is defined as the risks associated with war, terrorism, and inter-State tensions that affects the normalization of international relations and the peace process (Lee and Wang, 2021 ). First of all, the political stability of energy-supplying countries has a direct impact on the reliability of their energy exports. Factors such as political instability, civil unrest, and war can lead to disruptions in energy production and transportation, thus threatening the stability of the global energy supply chain. For instance, instances of political unrest and conflict in the Middle East frequently resulted in disruptions to the oil supply, which in turn gave rise to pronounced fluctuations in the price of oil on the international market (Ben Cheikh and Ben Zaied, 2023 ). The ongoing conflict between Russia and Ukraine has also resulted in significant fluctuations in the prices of oil and gas (Zhao et al., 2023 ). Secondly, the establishment and maintenance of diplomatic relations between countries also have a significant impact on energy security. International sanctions, trade disputes, and diplomatic conflicts may restrict energy imports or exports, thereby exposing countries that are dependent on imported energy to the risk of supply shortages and price increases (Zhang et al., 2024 ). The relationship between Russia and the West served as an illustrative example of the manner in which geopolitical tensions can give rise to increased uncertainty regarding the supply of gas, which in turn affected Europe’s energy security (Slakaityte et al., 2023 ). In addition, geopolitical risks include the security of energy transportation corridors, such as security threats to maritime transportation routes (Desogus et al., 2023 ). A significant disruption to the global energy market would result from the threat or actual blocking of important transportation corridors, such as the Strait of Hormuz or the Strait of Malacca (Meza et al., 2022 ). Furthermore, in the global transition to renewable energy, the deployment of renewable energy is also influenced by geopolitical risks. Countries experiencing geopolitical turmoil exhibited lower levels of domestic consumption and reduced government investment in renewable energy-related infrastructure and technology (Alsagr and van Hemmen, 2021 ). Despite research suggesting that geopolitics contributes to the deployment of renewable energy competition for fossil energy sources, such as oil, leads countries to seek out alternative energy sources (Ben Cheikh and Ben Zaied, 2023 ). The intrinsic link between geopolitics and energy security needs to be urgently addressed as countries grapple with the complexities of conserving energy resources in an environment of uncertainty.

Researches on geopolitical risk and energy security in global studies are multifaceted, and most studies used different empirical methods to shed light on the complex relationship between them. Using panel GMM and VAR models, Bin Zhang et al. empirically analyzed the impact of geopolitical risk on China’s energy security from 1994 to 2021. Their findings explained the dynamic relationship between geopolitical risk and energy security, geopolitical risk didn’t necessarily harm energy security and confirmed the existence of a bidirectional causal relationship between the two. In this context, the establishment of stable and fluid international relations was essential for the maintenance of national energy security (Zhang et al., 2023a ). Similarly, in a recent study, Chien-Chiang Lee et al. also identified a two-way impact of geopolitical risk on energy security (Lee et al., 2024 ). Khalid Khan et al. investigated the causal relationship between geopolitical risk and energy security using a full-sample analysis of time series. They assessed the interaction between the two in the time dimension in conjunction with graphs of changes in geopolitical risk indicators, demonstrating that geopolitical risk was inextricably linked to energy security (Khan et al., 2023 ). Geotao Hu et al. used the natural discontinuity grading method to classify 102 countries around the world into energy security levels and studied the game relationship between energy security and geopolitical risk, and their study revealed the focus of the game between them (Hu et al., 2022 ). Indra Overland et al. addressed the geopolitical impacts that countries around the globe were likely to experience as a result of their energy transitions, proposing indicators to measure the geopolitical gains or losses of countries after the transition, and predicting the geopolitical impacts of countries after they have realized their energy transitions (Overland et al., 2019 ). Since the emergence of the topic of energy security and geopolitics, a considerable number of research studies have been conducted, and the number of literature reviews synthesizing the research findings has gradually increased. Early in the publication, Benjamin K. Sovacool et al. discussed definitions and metrics for energy security (Sovacool and Mukherjee, 2011 ). And definitions, dimensions, and metrics of energy security were examined by B.W. Ang et al. Their study identified 83 definitions of energy security that have emerged from previous literature as well as seven major themes in the field of energy security, which need to be further constructed to provide an in-depth measure of energy security (Ang et al., 2015 ), similarly, Abdelrahman Azzuni and colleagues conducted a comprehensive review of the literature on the definition and dimensions of energy security. Their analysis identified and categorized 15 distinct dimensions and related parameters of energy security (Azzuni and Breyer, 2018 ). C.J. Axon and colleagues approached the subject from the standpoint of sustainability versus risk in their examination of the role of risk in energy security assessments (Axon and Darton, 2021 ), Mathieu Blondeel et al. attempted to consider the energy system transition through a “whole-system” perspective, encompassing both the “high-carbon energy transition” and the “low-carbon energy transition”. They also addressed geopolitical considerations pertinent to the energy system transition (Blondeel et al., 2021 ). The findings of research on the two subjects failed to yield consistent results. The current research lacks a structural understanding of the overall research topic. The research sub-directions are diverse and dynamic, and it is not possible to grasp the future direction of research and the emerging trends. Therefore, it is crucial to grasp the main lines of this research direction among the many studies and to reveal the focus between the different studies, this requires a systematic review of published scholarly work using a comprehensive study. The bibliometric approach is based precisely on the cross-citation relationships between literature, through emergence detection, spectral clustering, and other techniques, the conceptual trends, thematic evolution, and future development trends of the research field can be further analyzed and the pioneering achievements and key research groups in the research field can be objectively identified. Academic papers are scarce in the subject area that use bibliometric methods to explore hotspot preambles, Wei Zhou et al. conducted a bibliometric analysis of publications on energy security from 2000 to 2017, and their findings revealed the composition of research at the time, identified early features of research in the field, and suggested future research directions (Zhou et al., 2018 ). In a recent study, Yuyan Jiang and colleagues employed data from 2005 to 2023 to ascertain the present state and projected trajectory of recent research in the field of energy security (Jiang and Liu, 2023 ). Their study critically examined the content structure of scholarly publications on energy security over the timeframe of their research, and although energy security often appeared alongside geopolitical risk, their study didn’t explicitly include geopolitical risk in the framework of their research, but evaluating scholarly movements following the linkage between the two. Therefore, our research employed a systematic methodological paradigm aimed at comprehensively integrating and analyzing scientific publications related to energy security and geopolitics. It was not limited to traditional bibliometric analysis, but the systematic integration and analysis of a large amount of literature through data retrieval and deep text mining techniques. Specifically, the innovations and contributions of this study are as follows. Firstly, we collected and organized scientific publications on energy security and geopolitics globally, establishing a sample literature database closely related to the research topic. Based on this sample database, we conducted a compositional analysis of the research content in this field, deeply exploring the level of scientific contributions of different research subjects (such as academic institutions, countries, research teams, etc.). This analysis revealed the research focus and academic influence of each subject in this field. Secondly, we conducted a detailed analysis of topic flows and citation networks in the literature through the use of advanced text mining and topic modeling techniques. This analysis revealed important knowledge sources and core literature within the field of energy security and geopolitics, as well as demonstrating the process of knowledge iteration. By analyzing current research trends and the dynamic changes in the citation network, it is possible to scientifically foresee the new research directions and hot issues that may emerge in the field, which provides a reference for academics and policymakers and helps to guide future research and policy development.

The remaining parts of this study are organized as follows. Section “Research method” and section “Research design” provide the research methodology and research framework of the study, which focuse on the theories used in the study along with the important steps of the study. Section “Results” analyzes the results of the study, and Section “Conclusions, implications, and limitations” summarizes the full text, pointing out the shortcomings of the study and making suggestions for future research.

Research method

Bibliometrics.

Bibliometrics is a comprehensive analytical technique that combines various disciplines such as statistics, informatics, and mathematics (Andrade-Valbuena et al., 2019 ), and it has been widely used to assess the social and intellectual roots of disciplines (Wang et al., 2021 ). It has been argued that, if used properly, bibliometrics can determine research funding allocations, set research priorities, map scientific developments, and reward performance. Lotka’s Law, Bradford’s Law, Zipf’s Law, Price’s Law, the law of literature aging, and the law of literature citation laid the theoretical foundations for the bibliometric development (Venable et al., 2014 ). This study mainly applied the six basic laws of Price’s Law, Lotka’s Law, and Bradford’s Law to explore trends in literature growth, core author productivity, and core journals in the field.

Performance analysis

Performance analysis in bibliometric research examines the important contribution of research components to the field of study (Donthu et al., 2021 ). Performance evaluation of individuals, institutions, and countries by counting the number of publications owned by different subjects. The number of publications measures scientific productivity, and a high number of publications maps to high scientific productivity (Caputo et al., 2021 ). Furthermore, to assess the quality of publications, the total number of citations received by a publication is employed as a measurement indicator. Publications with a high number of citations are deemed to be widely recognized within the industry and to exert a considerable influence. This study first summarized the publication production patterns of geopolitical studies on energy security by calculating the annual distribution of publication levels and predicting the growth trajectory of future publications, then followed by computational analysis of trends in the geographical distribution of national publications, institutional publications and authors’ publications, evaluating the research contributions to the field from macro, meso, and micro perspectives.

Collaborative network analysis

Collaborative research is an important form of scientific research, a behavioral activity undertaken by researchers to achieve the goal of producing new scientific knowledge, it facilitates cross-fertilization of different disciplines and promotes the generation and development of new knowledge (Lee and Bozeman, 2016 ). Collaborative research is usually presented in the form of co-authored papers, where researchers affiliates with different countries and institutions work together to produce knowledge (He et al., 2021 ). Scientific collaboration enhances the quality of research outputs, as evidenced by studies indicating that collaborative publications are cited more often than those created alone, especially for highly internationalized research papers (Adams et al., 2018 ; Gorraiz et al., 2012 ). In other words, a research paper will be more widely recognized in the field if it is co-authored by multiple countries and multiple authors. This study examined the structure of research based on the static attributes of the research scholars, which reflected the identity attributes of the researchers within the academic field, including the researchers’ institutions and countries (Liu et al., 2024 ). Consequently, both national and institutional collaboration are founded upon the basis of author collaboration, which represents the most fundamental unit of collaboration. The visualization of collaboration between research scholars, research institutions, or countries is presented through the collaboration network. Collaborative network is an undirected network used to describe inter-subjective collaborative relationships and patterns based on collaboration conducted by different researchers, nodes in a network represent research individuals, such as nodes in a country collaboration network represent country attributes. Node size represents the number of publications, and the connecting lines of the nodes usually indicate the collaboration between different subjects, and the thickness of the connecting lines correspondingly indicates the intensity of collaboration, if the collaboration between two subjects is more frequent, then it is represented as a thicker connecting line (Jin et al., 2020 ). The process by which scientific research collaboration is formed is illustrated in Fig. 1 .

This figure shows the process of collaboration formation: on the far left is the number of authors in the article, followed by the authors’ affiliations, then followed by a collaboration matrix based on the authors’ collaborations in the article, and on the far right is the collaboration network based on the matrix.

This study mapped country collaborative networks, institutional collaborative networks, and author collaborative networks to explore whether differences in geographic location played a role in international collaborative behavior, as well as to reveal the number and characteristics of institutional and author collaborative groups in the area.

Keywords analysis

In bibliometric studies, article keywords are often used to identify the main research and hot topics, for keywords are important textual elements that summarize the main research content of a scholarly publication (Li et al., 2016 ), the frequency of occurrence of a keyword reflects the importance of the word in the text, high-frequency keywords often represent important topics. The distance between keywords reflects the relevance of different keywords, with higher-relevance keywords clustered closer to each other and forming keyword clusters (Huang et al., 2019 ). Different clusters of keywords map different topics in the research field. Therefore, to identify the distribution of core themes in the study of geopolitical impacts on energy security and their evolutionary paths, we used the keyword co-occurrence method to analyze the co-occurrence of keywords from all the collected literature and explored the resulting keyword clusters in depth to identify future research directions and research focus in the field.

Science mapping analysis

Data visualization can intuitively express important node information such as group structure in a network, and is an important characterization method for processing large amounts of data. VOSviewer provides visualization of the similarity of node distances, allows users to create networks of countries, institutions, and author collaborations, and provides three network graph representations: clustering view, time view, and density view (van Eck and Waltman, 2010 ), and it can handle large amounts of literature data (Van Eck and Waltman, 2007 ). In this study, we used VOSviewer to map collaborative network, literature citation network, and keyword co-occurrence network, during the threshold setting process, we chose the full-count method, in which a paper co-authored by two subjects is attributed to each author in the paper, and the smallest unit in the network was also set to be 1, which can fully demonstrate the structure of knowledge collaboration and actors in the research field of this topic, and then clustering view and temporal view of collaborative network were formed. Gephi was used to map the performance networks of institutions and journals, it offers several layout methods to display network graphs according to their weights (Bastian et al., 2009 ). In addition, we used a bibliometric package in the Rstudio programming (Aria and Cuccurullo, 2017 ) to obtain accurate information on the distribution of literature. Also, the statistical analysis of this study was calculated by Microsoft Excel.

Research design

Data sources and processing.

In this study, the basic bibliographic information was obtained from the core collection of the Web of Science (WOS), Science Citation Index Expanded (SCI-EXPANDED), Social Science Citation Index (SSCI), Arts and Humanities Citation Index (AHCI), Conference Proceedings Citation Index (CPCI-S), and Emerging Sources Citation Index (ESCI) are included in the core collection, which is widely used in bibliometric studies. The definition of energy security is of great importance in identifying search terms, as it delineates the crucial aspects of energy security and its scope. However, the definition of energy security is context-dependent and subject-dependent and has not yet resulted in a concept that is uniformly used in the industry (Kruyt et al., 2009 ). The historical definitions of energy security have initially focused on the stability of access to fossil fuels, particularly oil (Strojny et al., 2023 ). The increased use of natural gas and other fuels, such as coal, has also expanded the scope of energy security. The distribution of fossil fuels has led to the gradual inclusion of economic attributes in the attributes of energy security, as oil has become a globally traded commodity (Jenny, 2007 ; Wang et al., 2022 ). Energy prices, energy trade, and the stability of energy markets all play a crucial role in energy security. Secondly, the energy trading process is susceptible to the risk of supply chain disruptions due to the inherent vulnerability of energy supply chains to transportation risks, particularly given the considerable distances over which energy is transported (Scheepers et al., 2006 ; Spanjer, 2007 ). Security of energy supply has also become an important part of energy security concerns. Finally, in the process of energy transition, the transition from fossil energy to clean energy requires ensuring the stability and continuity of clean energy supply. At the same time, based on geopolitical considerations of energy security, energy cooperation may be effective in minimizing geopolitical conflicts due to the competition for energy resources and in ensuring the security of energy supply. Accordingly, the selection of keywords in this section was comprehensive and aligned with the fundamental elements of the conceptual framework of energy security, including “energy security”, “energy risks”, “energy supply risks”, “energy cooperation”, “energy transition”, “energy transportation”, “energy markets”, “energy price”, “energy trade” as search keywords. Subsequently, we broke down the term “energy” in “energy security” according to the nature of the energy source, subdividing it into “coal”, “oil”, “natural gas”, “electricity”, “wind”, “nuclear”, “water energy”, while adding “renewable energy” and “clean energy” on this basis. The combination of these two subsections of keywords constituted a searchable formula for the retrieval of academic results that were closely related to the topic of “energy security”. The second section concerned subject words related to geopolitical risk, as investigated by Jiangli Yu and Ahmet Faruk Aysan et al. (Aysan et al., 2023 ; Yu et al., 2023 ), the keywords of geopolitical risk were set as “geopolitical risk”, “geopolitics”, “international conflict”, “international geopolitics”, and “geopolitics”. To retrieve data, the search field designated as “Topic” was utilized, which means a topic search is conducted within the article’s title, abstract, keywords, and keywords plus. Data was accessed on January 7, 2024, and the period was set to all years. To obtain a high-quality data source, we first restricted the publication types, conference papers, editorial materials, letters, notes, book chapters, and book review types of articles were excluded, and only articles and review articles were included in the study, followed by restricting the language to English. Then we analyzed the titles and abstracts of the retrieved papers, and in some cases, even the entire contents of some papers, to determine whether each paper focused on the topic. It’s worth mentioning that even though we tried to find the most relevant papers through the search strategy described above, there were still some irrelevant papers because different authors have their own styles to highlight their articles. Ultimately, we obtained 429 papers for the bibliometric analysis.

Research framework

The occurrence of geopolitical events has had a significant impact on global energy activities, economic trade, and cooperative exchanges. This study utilized data from literature titles included in the Web of Science core collection to examine the impact of geopolitical risk on energy security. Breaking away from the traditional method of organizing a literature review, this study provided an in-depth analysis of the impact of the presence of geopolitical risks on the research field of energy security in terms of the historical development of publications, the geographical distribution, the scientific collaboration, the evolution of the knowledge base and research hotspots in this research field.

The traditional literature review is a method of summarizing and evaluating the existing literature in a particular field of study. This is typically conducted by a researcher who selects, reads, and summarizes relevant literature based on their research experience and expertise (Cronin, 2011 ). Its purpose is to provide background information on a research topic, demonstrate the progress of research in the field, and identify major research findings, theoretical perspectives, and problems, thereby providing references and insights for further study (Li and Wang, 2018 ; Rozas and Klein, 2010 ). The absence of strict procedural constraints in a systematic and standardized process may result in the researcher’s subjective bias influencing the selection and evaluation of literature, thereby reducing the reliability and comprehensiveness of the results of the review. In contrast, the bibliometric method is founded upon the external characteristics and internal connections of the literature. It is based on a series of rigorous procedures for the inclusion and exclusion of literature, as well as general research steps, which are employed to study the temporal distribution, quantitative characteristics, and patterns of change of a given topic. It incorporates a greater quantity of literature, employing mathematical and statistical methods to analyze the research profile of a given topic at a macro level (Kirby, 2023 ). Furthermore, bibliometric offers a significant advantage in the analysis of citation relationships among literature, which is not feasible within a limited timeframe with a traditional literature review. The bibliometric builds citation-coupling networks, co-citation networks, collaborative networks, and co-occurrence networks in the literature, which can predict future research directions in the forward direction, analyze the knowledge base underlying the subject area in the backward direction, and dynamically present the thematic evolution of the research field, as well as identify outstanding contributors and important literature in a particular field (McBurney and Novak, 2002 ; Ninkov et al., 2022 ). In conclusion, the traditional literature review is concerned with the analysis of the research content and findings presented in the literature, to summarize and analyze previous research and identify future research directions. Instead, bibliometric is more concerned with the analysis of the distribution and change of research results in a given field. The research results in a certain field can be assessed regarding the number of research and citation relationships. This allows for the impact of academic research to be evaluated, the academic frontiers and hotspots to be discovered, and research management and decision-making to be facilitated. Therefore, this paper referred to the methodology of F. De Felice et al. using hierarchical analysis for the analysis and discussion of the bibliometric study (De Felice et al., 2018 ), specifically, structured modeling was carried out according to the following four steps:

First, identify the research objectives and the research questions to be addressed. During this stage, the research perspective was further focused on the field of energy security through extensive reading on the impact of geopolitical risks on global economic trade, energy activities, education, and scientific research cooperation.

Second, select the research methodology. By breaking down the research questions and research objectives, the appropriate research methodology was selected, along with the time and scope of the study.

Third, identify keywords and construct a search formula. In this stage, by discussing with experts and scholars and reading the basic research about the field, we extracted the representative key phrases of the research field, constructed the search formula, searched in the database, and de-weighted and cleaned the data.

Fourth, data visualization and analysis. After data collection and data cleaning, the data were calculated, and through various data visualization tools, the collected literature data were visually characterized and analyzed to visualize and understand the development trend, distribution range, and research status of the research field. The roadmap of the research conducted in this study is shown in Fig. 2 .

This figure depicts the research roadmap of this paper. The right side of the figure illustrates the research content of this paper while the left side depicts the research process corresponding to the research content of this paper.

Descriptive statistics of literature information

The basic information about the literature data used in this study is given in Table 1 . The study period runs from 2003 to 2023 and involves a total of 429 publications from 135 journals, with an average half-life of publications of 4.04 years, 19,847 references are cited in these publications. In addition, the author’s keywords and keywords plus used to conduct topic exploration are identified 1136 and 732 respectively, through which the article analyzed the main research trends in this research area. In publications studying the impact of geopolitics on energy security, 1001 authors are involved in the process of knowledge creation, of which 73 authors conducte their research independently.

Publication trend

Thomas Kuhn in The nature of scientific revolutions proposed that the process of scientific development is a “primitive science” to “conventional science” transformation, as well as the transition from one “conventional science” to another “conventional science” process. It was divided into several stages: the scientific development of the pre-scientific, conventional science, scientific crises, scientific revolutions and the new conventional science. The formation of a discipline has undergone a theoretical accumulation of the formation of the paradigm to the paradigm of paradigm change, and then produce a new paradigm of the process of the entire process of scientific development under the impetus of scientific revolutions, the entire scientific development process of the continuous cycle of development (Kuhn, 1970 ). Price’s proposed literature growth curve is consistent with Thomas Kuhn’s theory of scientific development, he believed that the growth of the literature shows a logical growth trend of the “S” curve, but the growth of the literature is not endless and will eventually stop at a certain K (Price, 1963 ). The mathematical expression for the theoretical model of the literature growth by the logistic curve is shown below:

where \(F\left(t\right)\) is the literature accumulation for the year, \(t\) is the time, \(k\) is the literature accumulation when the time tends to infinity, and is the maximum value of the literature accumulation, and \(a,{b}\) are the conditional parameters.

To examine trends and forecast future developments in the growth of publications related to geopolitics and energy security, and to test whether the growth of the literature in this area conforms to a logistic growth curve, we fit a logistic to the annual cumulative publications. The trend in annual cumulative publication growth was first fitted using Excel, and it was found that the cumulative literature was optimally fitted according to the exponential, which got \({R}^{2}=0.9873\) . Subsequently, according to the curve trend to take k  = 90,000, to determine \(a=1.9\) when the most consistent with the cumulative curve, at this time to get \(b=0.2576\) , and ultimately got the logistic growth curve as shown in Fig. 3 , the cumulative annual growth in the number of publications in the field of research in line with \(y=1.9{{\rm {e}}}^{0.2576t}\) . Comparison with the logistic growth curve reveals that the growth of literature in the field is currently in the pre-growth phase of the logistic curve and may reach the horizontal phase of the logistic curve after the next few decades. In the pre-growth phase, the annual number of publications increases significantly in 2022–2023, from 65 to 135, probably due to the impact of the Russia–Ukraine conflict in 2022, which has redirected people’s attention to the study of geopolitics and energy security.

This figure illustrates the growth trend of literature in the study area, with the horizontal axis representing time and the vertical axis indicating the cumulative number of publications. The smaller part of the graph depicts the detailed trend of annual and cumulative numbers of articles published.

Geographical spatial distribution

Spatial analysis of geographic distribution can reveal collaborative networks related to the geographic distribution of publications. Therefore, Scimago and VOSviewer were combined to map the geographic collaborative network of national issuance volumes. A geo-visualization network of the distribution of publications and the collaboration between countries is shown in Fig. 4a and b . The area of the circles in the graph indicates how many publications there are, with larger circles representing more publications, and the connecting lines between the nodes of the different circles indicating the collaboration between countries. In terms of the geographical distribution of publications, countries in Asia, Europe, Australia, and the Americas make the greatest contribution to this field. Among Asian countries, China coveres 168 publications and have the highest number of publications in this field, followed by the United Kingdom (60), the United States of America (43), Germany (26), and Turkey (24). Most of the countries in Europe are involved in research outputs in this area, in addition to countries in the Middle East, which may be attributed to the increased interest in research related to oil security in the region due to resource abundance.

a Global geographic distribution of publications and collaboration networks. b Localized zoomed-in view of the collaboration network. c Chord map of the intensity of country collaboration. This figure illustrates a geographic network of collaboration in the field of geopolitics and energy security. Nodes indicate countries, with size indicating the number of country postings. Connecting lines indicate collaborations between countries. a indicates the global collaboration network of countries, b indicates the detailed collaboration networks in Europe, northern Africa, and western Asia, and c indicates the country collaboration chord map.

Nevertheless, an exclusive emphasis on the number of national publications to assess a country’s scientific output is inadequate. The quantity of publications in a country merely reflects its quantitative capacity, without incorporating the quality of these publications into the evaluation. Therefore, considering the availability of data, we counted the total number of citations of the countries through VOSviewer, ranked the two indicators, the number of publications of the countries and the total number of citations by entropy-weighted TOPSIS, and evaluated them using SPSSAU (project. T S, 2024 ), which evaluates the 67 countries that participated in the publications. The entropy-weighted TOPSIS initially identifies the positive and negative ideal solution values (A+ and A−) for the evaluation indexes. Thereafter, the distance values D+ and D− are calculated for each evaluation object concerning the positive and negative ideal solutions, respectively. Finally, the proximity of each evaluation object to the optimal solution ( C ) is determined, and the C is ranked. The final ranking of the top 10 countries is presented in Table 2 .

As illustrated in the accompanying table, the composition of the top ten countries differes when considering both the quantity and quality of publications. China retains its position at the top of the list, with 168 publications garnering 3608 citations from scientists across the globe. The reasons may be explained in the following ways. Firstly, as the world’s largest energy consumer, China’s rapid economic growth has led to an ever-increasing demand for energy, which has driven a significant number of studies and publications on energy security and geopolitics. Secondly, the Chinese government attached great importance to energy security and geopolitics and has formulated a series of policies and strategies, as well as provided strong support and funding to promote research and development in related fields. Furthermore, China is a highly active participant in international collaboration and academic exchanges. With the advancement of the Belt and Road Initiative, China’s influence in the global energy market is increasing, which has led to a significant increase in the international attention and citation value of its research results. The second-ranked country is the United Kingdom, which has a total of 60 publications with a total of 2139 citations, and the third-ranked country is Pakistan, which has 22 publications with a total of 1407 citations.

In the national collaboration on publications, the study of geopolitics on energy security involves a total of 67 countries around the world, of which 59 countries have collaborative relationships. From the chord diagram of international research collaboration, the depth of the color of the connecting lines between countries indicates the intensity of their collaboration. In Fig. 4c , the color of the connecting line between China and the United Kingdom, the United States, Romania, Saudi Arabia, Turkey, Spain, and Vietnam is red, which indicates that the intensity of collaboration between China and these countries is higher than that between other countries and that China has more partners and higher collaboration credits in this field of research. In addition, it is found that the geographic distribution of articles in studies of geopolitics and energy security shows a clear energy-oriented country or geopolitical risk-oriented country, unlike previous academic research, the main geographic distribution of publications in this subject area is concentrated in energy-rich or geopolitically risk-intensive areas, gradually moving away from the geographic distribution trend where the level of economic development leads to the distribution of scientific research.

Contribution of institution

In terms of meso-institutional collaboration, a total of 686 institutions around the world are involved in the research, forming a large network of institutional collaboration. The number of publications and the collaboration between them is shown in Fig. 5 . As can be seen from Fig. 5 , Qingdao University (China) has an outstanding research performance in this field, with 23 publications and a total of 782 citations. Meanwhile, Qingdao University has formed collaborative relationships with 33 domestic and foreign organizations, and the intensity of collaboration is 53. These institutions include the Lebanese American University, the Central University of Punjab, and the University of Southampton. The organizations within China are Qilu University of Technology, Southwest Jiaotong University, and Anhui University of Finance and Economics. The study of geopolitical impacts on energy security has resulted in 27 collaborative groups, which have worked together on a wide range of research topics.

This figure depicts a collaborative network of institutions. Nodes represent institutions, and lines between nodes indicate collaborative relationships between institutions. Nodes of the same color indicate similar research content.

Contribution of author

Core author distribution.

Lotka’s Law describes the distribution of the frequency of scientific productivity: in a given field of study, the number of authors writing \(n\) papers are approximately \(\frac{1}{{n}^{2}}\) of the number of authors writing 1 paper. The proportion of all authors writing 1 paper to the total number of authors is approximately 60% (Lotka, 1926 ; Tsai, 2015 ). To test whether Lotka’s Law applies to this field of study, we analyzed it using Lotka’s Law and verified the reliability of the law using nonparametric hypothesis testing. The K–S test is a useful nonparametric hypothesis testing method that is primarily used to test whether a set of samples comes from a certain probability distribution. We followed the following steps to test.

Firstly, the data used for the calculations were prepared according to Table 3 , which shows the number of authors with \(x\) publications, the total number of publications, the cumulative number of publications and the cumulative number of authors, as well as the cumulative percentage.

Secondly, the data in Table 4 were used to calculate the exponent of Lotka’s Law, which was calculated from the least squares formula:

Thus, the absolute value of the exponent \(n\) is between 1.2 and 3.8, in accordance with Lotka’s Law.

Subsequently, \(c\) and critical value were calculated by the following equation:

Calculated to get c  = 0.7907, \({{\rm {critical}}\; {\rm {value}}}=0.3781\) .

Finally, a nonparametric hypothesis test K–S test in Table 5 was conducted to test the reliability of Lotka’s Law.

Therefore, the absolute value \({D}_{\max }=0.0839\, < \,0.3781\) was calculated by the above steps, and hence it can be concluded that Lotka’s Law is valid in this subject area.

Co-author network

From the above analysis, it is clear that the author-output pattern of geopolitical impact on energy security is consistent with Lotka’s Law, to further explore patterns of author collaboration in this area, we used VOSviewer to map the network of author collaborations.

As shown in Fig. 6 , there are 13 author collaborations in academic publications that examine the impact of geopolitics on energy security. One of the outstanding contributing authors in the field is Su Chi-Wei, who has contributed 14 scholarly publications and forms a collaborative cluster with 40 other authors. This is followed by Khan, Khalid (11 publications) with collaborative links with 32 authors, Umair, Muhammad (10 publications) with academic collaborations with 28 authors, and Qin, Meng, and Ma, Feng who have the same number of publications, both contributing 7 articles to the academic community. But Ma, Feng has more collaborations with other researchers, collaborating with 23 researchers, while Qin, Meng has collaborations with 21 authors. As shown in (a) of Fig. 6 , among the top 5 authors in terms of number of publications, three authors are from China. In addition, from the time plot of the authors’ publication volume and collaborative networks, the node colors are dark to light indicating that the authors published their research papers from far to near. The collaborative cluster of authors led by Ma, Feng has a long-standing interest in this research area, with their research focusing on the market impact of uncertainty in geopolitical risk and volatility in crude oil prices. Su chi-wei, Khan, Khalid, Umair, Muhammad, and Qin, Meng are late researching this area. Their team published papers between 2021 and 2023 that examined the interactions between renewable energy, the energy transition, oil prices, and geopolitical risks. These contributions have helped to advance the field. It can also be seen in Fig. 6 that in the fringe group of the author collaboration network, the fringe authors tend to be publishers of recent publications and have not yet formed larger collaborative clusters and these fringe authors may be transformed into center authors in future studies.

a Collaboration network of the top 5 authors in terms of number of publications. b Author collaborative evolutionary networks. This figure depicts the authors’ collaborative network and its temporal evolution. Nodes represent authors, and connecting lines between nodes indicate collaborative relationships between them. Nodes of the same color indicate similar research content.

Contribution of journals

The geopolitical impact on energy security cuts across multiple disciplinary areas and has been analyzed from multiple publications, with the contribution of journals to the field assessed through the number of articles published in them. Information on the types of journals that ranks among the top 10 by the number of articles published in the field is shown in Table 6 . Resources Policy has the highest focus on the topic of geopolitical influences on energy security, publishing 66 articles, and as can be seen from Fig. 7 , Resource Policy shows a sharp increase in the number of articles published after 2022, possibly due to the increased global energy risks resulting from the Russia-Ukraine conflict, which has become a popular topic of choice for the journal. This is followed by Energy Policy (33 articles), Energy Economics (27 articles), Energy Research & Social Science (17 articles), and Energy (16 articles). Among the top 5 journals, journals in the field of energy and resources receive more attention than other fields. In addition, the co-citation network of journals (Fig. 8 ) shows the common citation relationships between publications published in different journals, with the thickness of the connecting line indicating the strength of the citation. Resources Policy and Energy Economics are the journals with the highest strength of connectivity, and articles in these two journals have the highest number of citations, suggesting that the content of articles published in Resources Policy and Energy Economics are highly similar in terms of research direction.

This figure illustrates the annual publication trend for the top 10 journals in terms of the number of articles published. The horizontal axis represents the year while the vertical axis depicts the number of articles published by the journal.

This figure depicts the journal citation network, where nodes represent journals, and connecting lines indicate citation relationships between papers published in the journals.

To further clarify the distribution of core journals in this subject area of geopolitical impact on energy security, the Bradford distribution of core journals was mapped using the Rstudio. Bradford’s Law describes the uneven distribution of scientific articles across journals due to differences in closeness between specialized disciplines (Bradford, 1934 ). Journals can be classified into three categories based on the number of articles published. The ratio of the number of journals in each group is \(1:a:{a}^{2}\) (Yang et al., 2016 ), which indicates that a large number of specialized papers are first concentrated in a few core journals, with some papers appearing in other journals related to the specialty. Bradford’s Law has been widely used to study different subject trends. Based on the information provided in the data in Table 7 , the journals are categorized into three regions, each of which carries approximately the same number of articles. As can be seen in Fig. 9 , the core journals in this subject area are mainly Resources Policy , Energy Policy , Energy Economics , Energy Research & Social Science . Journals in the core zone account for 2.96% of all journals and publish 33.33% of the articles in the field. Journals in the relevant journals account for 14.07% of the total number of journals and publish 33.8% of the articles in the field, while journals in the discrete journals account for 82.96% of the total number of journals and publish 32.87% of the articles in the field as shown in Table 8 . The four journals, Resources Policy , Energy Policy , Energy Economics , and Energy Research & Social Science , are more concerned with geopolitics and energy security. Researchers engaged in this field may therefore consider these journals as a source of knowledge.

This figure illustrates the distribution of core journals within the field of study. The horizontal axis represents the journal category, the vertical axis represents the number of journal publications, and the shaded area represents the range of core journals.

Contribution of core literature

We used VOSviewer to map the literature coupling network of geopolitical impact studies on energy security to explore the most influential academic literature in the field, as shown in Fig. 10 , where the node size indicates the total number of citations to the article and the connecting lines indicate the coupling relationships. Concurrently, the academic literature that has been cited the most is highlighted, and the detailed information of the top 10 most cited articles is listed in Table 9 , including the title of the article, the first author, the country of affiliation, publication year, the total number of citations, the journal of publication, and the DOI of the literature. As illustrated in it, the literature with the greatest number of citations is Lynne Chester’s article Conceptualizing Energy Security and Making Explicit Its Polysemic Nature , published in Energy Policy in 2010. This article has been cited a total of 310 times since its initial publication, and it is widely recognized within the industry as a highly cited document in this subject area. This article presented an early research explanation of the conceptualization of energy security. It addressed the multifaceted connotations of energy security, the market paradigm, and its multidimensional nature from a theoretical perspective that informed subsequent studies (Chester, 2010 ). The second most frequently cited article is Renewable Energy and Geopolitics: A Review by Roman Vakulchuk, published in 2020. This review article presented a comprehensive analysis of the geopolitical literature related to renewable energy. The study revealed that many publications on renewable energy and geopolitics employed limited research methodologies, failed to delineate geopolitical periods, and lacked in-depth discussions. Furthermore, the analysis indicated that most relevant articles focused on oil-producing countries, while ignoring coal-dependent countries (Vakulchuk et al., 2020 ). Moreover, it is notable that almost half of the top 10 cited literature originates from China, which serves to corroborate China’s research production level in this area.

This figure represents the literature coupling network, the nodes represent the literature, the node size represents the number of citations, the node connecting lines represent the coupling relationship of the literature, and the node color represents the time distribution.

Thematic distribution

Thematic keywords.

Keywords can provide information about the core content of the article (Wang et al., 2024b ). The frequency of keyword occurrences over time can reflect research trends in the field of study. We used Rstudio programming techniques to draw keyword heat maps and cumulative keyword heat maps in the research area of geopolitical impact on energy security. As shown in Fig. 11 , which demonstrates the top 20 high-frequency keywords in the study of geopolitical impact on energy security. From the keyword heat map and the cumulative keyword heat map, it can be seen that “Natural gas” and “Oil” are the first to appear in the heat map, and both of them have a significant heat in 2006, and the heat lastes for a long time. It shows that the geopolitical impact on energy security is first and foremost reflected in the impact on natural gas and oil and that geopolitics has a significant long-term impact on hydrocarbons. In addition to “natural gas” and “oil” having significant heat in the keyword heat map, other keywords that appear earlier and have significant heat include “Russia” and “China”. In addition, in terms of sudden heat, “Climate change” receives huge attention in 2016. “Energy policy”, “Energy”, “Uncertainty”, “Natural gas” and “Oil” have a sudden increase in heat in 2021. The following is an in-depth analysis of the featured keywords.

This figure depicts the distribution of keyword frequency and cumulative keyword frequency. The horizontal axis represents the year, the vertical axis represents the keyword category, and the color represents the heat value of the keyword.

Natural gas and oil

The co-occurrence mapping of natural gas and oil linked to other keywords is shown in Fig. 12 . “Natural gas” co-occurs with several keywords such as “energy security”, “consumption”, “market”, “crude oil”, “oil”, “policy”, “risk”, “China”, “Russia”, “EU”, and so on. “Oil” co-occurs with several keywords such as “energy policy”, “renewable energy”, “market”, “natural gas”, “vulnerability”, “return”, “price”, “cooperation”, “consumption”, “China” and “Russia”. Natural gas and oil are important energy components and occupy a prominent place in the global energy landscape. Natural gas is a vital source of electricity generation, and natural gas-fired power plants can provide backup and grid stability for intermittent renewable energy sources such as solar and wind power (Baldick, 2014 ; Mac Kinnon et al., 2018 ), their ability to increase or decrease rapidly complements the variability of renewable energy production. Natural gas is highly efficient, flexible, and low-emission compared to other fossil fuels, and natural gas produces fewer carbon emissions and less pollution when burned (Safari et al., 2019 ). At the same time, natural gas is an important source of energy to support industrial production and social life. Oil is a key feedstock for the petrochemical industry (Keim, 2010 ). It provides raw materials for the production of a wide range of products, including plastics, synthetic rubber, solvents, fertilizers, and chemicals, and is an important driver of global trade and economic activity. The geopolitical impact on energy security is the first thing that prompts global scientists to discuss natural gas and oil, given their wide-ranging and important international status, for geopolitical factors play a crucial role in determining the global distribution of natural gas reserves and oil. Countries with rich hydrocarbon reserves often have important strategic advantages that influence regional political alliances, trade relations (Gu and Wang, 2015 ). And geopolitical tensions could disrupt oil and gas supplies and affect global oil and gas markets. Armed conflict and political instability in natural gas regions increase the risk of gas supply disruptions and hinder the construction of projects such as gas pipelines.

This figure shows the co-occurrence network for the keywords “natural gas” and “oil”, where different nodes represent different keywords and the lines between the keywords represent co-occurrence relationships.

Russia and China

The connection between Russia and China in the keyword co-occurrence diagram is shown in Fig. 13 . Russia has co-occurring relationships with the keywords “energy security”, “gas”, “oil”, “cooperation”, “Ukraine”, “Europe”, “renewable energy”, “China”, “policy”. In the co-occurrence mapping of the keyword China, there are co-occurrence relationships for several keywords such as “economic growth”, “energy security”, “energy transition”, “oil price”, “cooperation”, “return”, “demand”, and “consumption”. Russia has the world’s largest natural gas reserves and is one of the largest producers of crude oil, as well as being the world’s largest producer and exporter of natural gas (Karacan et al., 2021 ). In view of the geographical advantages, a number of European countries have formed close energy cooperation with Russia, and the rich energy reserves have become an important tool for Russia’s strategic negotiations and energy diplomacy (Bilgin, 2009 ). Russia is located in a geopolitical risk zone, with armed conflict with Ukraine in 2022 having a huge impact on Russian and global energy markets (Rokicki et al., 2023 ). Several European countries have restricted Russian energy imports, leading to an energy supply crisis in Europe (Kuzemko et al., 2022 ). China is the world’s largest energy consumer, and the diversification of China’s energy mix has made it more concerned about global energy security conditions (Boute, 2019 ). This is because China’s energy demand is fueled by rapid economic growth and accelerated industrialization. Whereas China is heavily dependent on energy imports, the impact of regional conflicts and political tensions on global energy supplies could also affect China’s energy import trade. China actively engages in energy cooperation with countries in Central Asia (Zhou et al., 2020 ) and Africa (Bradshaw, 2009 ), putting forward the “Belt and Road” initiative, and significant investment in global energy infrastructure was done to increase China’s influence in major energy-producing regions, ensure access to key resources and enhance the country’s energy security (Duan and Duan, 2023 ).

This figure shows the co-occurrence network for the keywords “Russia” and “China”, where different nodes represent different keywords and the lines between the keywords represent co-occurrence relationships.

Climate change

As shown in Fig. 14 , climate change is closely related to the keywords “environment”, “energy security”, “energy transition”, “carbon emissions”, “renewable energy”, and “cooperation”. Climate change has been an important global issue, and its involvement in the discussion of geopolitical influences on energy security is notable. On the one hand, geopolitical factors have led to changes in global energy consumption patterns, and the deterioration of inter-State relations could re-exacerbate dependence on fossil fuels such as coal, oil, and gas. The “Escalation effects” of geopolitical risks reduce renewable energy consumption and lead to higher carbon emissions (Anser et al., 2021 ). Geopolitical decisions related to the development of energy infrastructure may affect the integration of renewable energy into national or regional energy systems, slowing down clean energy deployment plans and increasing global greenhouse gas emissions. On the other hand, favorable geopolitical policies and international cooperation can drive investment in clean energy technologies and increase opportunities for international R&D cooperation. In conclusion, the implications for climate change under the geopolitical discussion of energy security are complex.

This figure shows the co-occurrence network for the keywords “Climate change”, where different nodes represent different keywords and the lines between the keywords represent co-occurrence relationships.

Energy policy and uncertainty

As shown in Fig. 15 , energy policy is closely related to the keywords “renewable energy”, “price”, “oil”, “climate change”, and “country”. In the keyword co-occurrence mapping of “uncertainty”, the terms “market,” “price,” “return,” and “economic growth” appear more frequently. Energy policy and uncertainty are key themes influencing the discussion of geopolitical implications for energy security. Government intervention is an important response to energy security issues, and governments around the world develop energy policies as a strategic framework to address the complex interplay of domestic and international factors that seek to enhance energy security and reduce uncertainty in the energy sector (Youngs, 2009 ). The formulation of energy policy is influenced by factors such as national energy structure and energy consumption (Li et al. 2024 ). Uncertainty about geopolitical risks also affects national energy policies, and it is important for national policymakers to combine measures to address geopolitical risks with the maintenance of national energy security and to reduce the vulnerability of global energy prices, energy trade, and energy supply to geopolitical risks. Uncertainty in the geopolitical landscape poses a challenge to energy policymakers. Sudden geopolitical events, changes in international relations, or changes in the dynamics of energy markets can threaten energy security, and the development of effective energy policies has become an important tool for addressing geopolitical threats to energy security.

This figure shows the co-occurrence network for the keywords “Energy policy” and “Uncertainty”, where different nodes represent different keywords and the lines between the keywords represent co-occurrence relationships.

Thematic evolution path

This section mapped the timeline of keyword co-occurrence from the perspective of the temporal evolution of keyword co-occurrence. As shown in Fig. 16 , the transition from cold to warm indicates the time from far to near, and the average occurrence time of keywords can be identified by the time color band in the graph. The research phases can be categorized into three distinct phases according to the average year in which the keywords appeared. The average year of emergence of the first stage is 2018–2020, with a focus on the energy sector, which means objects that geopolitics may threaten. The main objects of energy security risks that can be extracted from typical words are “natural gas”, “oil”, “power”, “hydropower”, “nuclear power”, “fossil fuels”, “energy trade”, and they form the core of the global energy infrastructure. The identified energy security risks are multifaceted, encompassing not only traditional concerns related to fossil fuels but also reaching into the complex dynamics of the “energy trade”. The interconnected nature of energy resources and their global distribution necessitate a thorough review of trade relationships to assess potential vulnerabilities in energy supply chains. In the geopolitical area, certain countries play a pivotal role, directly affecting or being affected by developments in the energy sector, “China”, “Russia”, “EU”, “United States”, “India”, “Germany”, “Japan”, “Turkey”, “Central Asia”, “Middle East”, “Ukraine”, “Pakistan”, “Poland” are in the spotlight at this stage. Each of these countries faces a unique set of challenges and opportunities in terms of energy security. As mentioned previously, China is a rapidly growing consumer and producer of energy, influencing the global energy market (Odgaard and Delman, 2014 ). Russia is rich in energy reserves and plays an important role in regional and global energy dynamics. The EU, as a collective entity, plays a central role in the development of energy policies and in promoting cooperation among its member States. India’s economy is booming and it seeks to ensure a stable and continuous supply of energy to support its growth trajectory (Kumar and Majid, 2020 ). Germany, Japan, and Turkey represent industrialized countries with special energy needs and dependencies (Cherp et al., 2017 ; Kilickaplan et al., 2017 ). A comprehensive look at countries and regions provides a comprehensive understanding of the interconnected network of energy security issues, including supplier and consumer countries in the global energy landscape. As the research continues, it aims to unravel the intricate relationships, dependencies, and potential hotspots that will shape the future of global energy security.

This figure depicts the temporal evolution of keyword co-occurrences, with colors ranging from cool to warm to indicate time from far to near.

The average year of occurrence of the second stage is 2020–2022, which is a light warm color on the clustered time plot. During this period, the keywords “geopolitical risk”, “renewable energy”, “energy transition”, “crude oil”, “price”, “crude oil price”, “uncertainty”, “return”, “demand”, “policy uncertainty”, “growth”, “oil price shocks”, “volatility”, “price volatility”, “markets”, “gold price”, “stock market” are found to be more frequent. Popular keywords provide a comprehensive overview of key themes and concerns in the energy industry and related markets. The emergence of the term “geopolitical risk” as a focal point indicates an acute awareness of the impact of geopolitical events on energy markets and the wider global economy, as well as a heightened sensitivity to geopolitical tensions, conflicts, and geopolitical strategies that could disrupt energy supplies and markets. “Renewable energy” and “energy transition” continue to feature prominently, highlighting the growing emphasis on sustainable and clean energy. This period has been characterized by growing interest and discussion around the global shift to renewable energy, reflecting a concerted effort to address environmental concerns and reduce dependence on traditional fossil fuels. The constant references to “crude oil”, “price” and “crude oil price”, together with terms such as “oil price shocks”, “volatility”, “price fluctuations”, “market”, “gold price” and “stock market”, highlight the energy industry’s continued interest in and scrutiny of the intricate relationship between geopolitical risks and global energy markets. Conflicts, political tensions, or disruptions in the oil supply chain in the world’s major oil-producing regions could lead to unpredictable and dramatic fluctuations in oil prices. Such sharp fluctuations create uncertainty for both producers and consumers, affecting investment decisions and market dynamics (Mei et al., 2020 ). In conclusion, this stage of research focuses on the fluctuations of geopolitics in the energy economy market and the financial market, and it is gradually recognized that geopolitics produces dramatic fluctuations in the energy economy market, while the sensitivity of the crude oil price, oil price to geopolitical risks promotes the exploration of measures to resist the geopolitical risks.

The average year of occurrence of the third stage is 2022–2023, which appears in red on the clustered time plot. “GDP”, “financial development”, “natural resources”, “green finance”, “determinants”, “empirical analysis”, “utility testing”, “regression analysis”, “impulse response analysis”, “time series”, “wavelet correlation”, and other keywords frequently appear. It is worth noting that the interconnection between the financial system and the energy market has received extensive attention from researchers and scholars in the context of the geopolitical impact on energy security, as indicated by keywords such as “GDP”, “financial development” and “green finance”. The keywords “determinants,” “empirical analysis,” “utility testing,” “regression analysis,” “impulse response analysis,” “time series,” and “wavelet correlation” collectively indicate a methodological shift toward rigorous quantitative analysis at this stage. Researchers seem to have employed advanced statistical tools and econometric techniques to explore the determinants and effects of various factors on energy-related phenomena. The methodological shift suggests that the field is moving toward evidence-based policymaking and a desire to build a solid empirical foundation. The diversity of keywords in this phase implies a multidimensional exploration, integrating economic, financial, and environmental factors, in addition to multiple keywords on research methodology suggesting that research is moving towards more advanced analytical tools and empirical frameworks.

Thematic clustering

Keyword clustering analysis is able to explain the main hotspots in the research field, which was mapped by VOSviewer and Scimago. As shown in Fig. 17 , hotspot clusters are distributed in a two-dimensional rectangular coordinate system, and different colors indicate different clusters. The distribution of colors and the legend in Fig. 17 show that the main hotspots in this research area are distributed in six clusters. We obtained cluster labels from the keywords contained in the clusters and discussed with experts to determine the keyword labels that best summarize the nature of the clusters and labeled them in Fig. 17 . The size of a clustering cluster is determined by the number of keywords contained in the cluster. The cluster with the largest number of keywords is the green cluster, which focuses on keywords such as “fossil energy”, “clean energy”, “renewable energy” and “energy transition”, it is therefore reasonable to name the green cluster “energy transition”. And then the purple cluster, which is identified through keyword analysis as being closely related to the natural environment, and is therefore identified as being labeled “natural environment”. Similarly, based on the keyword categories, the blue cluster is labeled “energy policy”, and the red and pink clusters, which cover a sparse number of keywords and tend to be similar in nature to the orange clusters, are combined and labeled “energy market”. It is worth noting that the horizontal and vertical axes in the 2D cartesian coordinate system have no obvious data meaning, but merely indicate the relative positions of the keywords and their clusters in the 2D space. Subsequently, our study further explored for the identified keyword clusters.

This figure illustrates keyword clustering, wherein nodes represent keywords and different nodes are colored to indicate distinct clusters. The horizontal and vertical axes represent the relative positions of the nodes.

Green cluster: energy transition

Energy transition refers to a change in the way energy is utilized, a reduction in the share of fossil energy in the energy mix, and a transition from traditional fossil energy consumption to clean energy consumption (Rasoulinezhad et al., 2020 ). Geopolitical risk works both ways for energy transition, with major changes in international energy markets under the Russia–Ukraine conflict. European countries, opposed to Russia’s military conflict over Ukraine and determined to reduce energy trade with Russia, have resumed coal- and oil-fired power generation amid gas shortages (Wang et al., 2023 ), higher geopolitical risk also increases the cost of renewable energy deployment (Shirazi et al., 2023 ), slows down the energy transition and inhibits the transition to renewable energy. Meanwhile, “high-risk” countries at geopolitical centers may face obstacles in seeking foreign investment, inhibiting the development of renewable energy infrastructure (Fischhendler et al., 2021 ). On an optimistic note, studies have demonstrated the positive contribution of geopolitical risk to the development of renewable energy, with high geopolitical risk spurring countries to consume more renewable energy (Sweidan, 2021 ), which could be an important tool to facilitate the clean energy transition (Liu et al., 2023 ). The complex relationship between geopolitical risk and renewable energy has been subjected to multiple argumentative studies, and thus energy transition is one of the important research directions for researchers and scholars in various countries in the context of geopolitical risk affecting energy security.

Purple cluster: natural environment

The three themes of geopolitical risk, energy security, and climate change have become popular topics for researchers and scholars around the world. Geopolitical tensions not only bring political and economic uncertainty but also harm the natural environment (Acheampong et al., 2023 ). The direct impact of geopolitical risk on the environment is manifested in the control of and access to valuable natural resources, such as oil, gas, minerals, and water, competition for which can lead to overexploitation, environmental degradation, and ecosystem destruction (Li et al., 2023 ). International conflicts and armed struggles also have a greater impact on the surrounding environment, and conflicts can lead to increased air pollution and destruction of green facilities in the region, and the production and manufacture of military equipment can increase atmospheric carbon dioxide (Ullah et al., 2020 ). Furthermore, geopolitical risks act on the natural environment by affecting the consumption structure of the energy sector. The previous analysis showed that the process of energy transition was negatively affected by geopolitical risks, the decline in the consumption of renewable energy sources, and the reduction of clean energy infrastructure were not conducive to the suppression of carbon emissions. In addition, unfriendly relations between countries can hamper global cooperation in addressing climate change and environmental issues, and prolonged hostilities can impede the conclusion of bilateral or multivariate agreements, which in turn affects sustainable development (Zhao et al., 2021 ).

Red, pink, and orange cluster: energy market

Geopolitical risks have historically played an important role in influencing global energy prices. One study summarized three channels through which geopolitical risk affected energy prices: the threat of conflict acting on energy conversion resulting in lower oil prices, the impact on energy prices of rising negative investor sentiment due to the threat of conflict, and the role of geopolitical uncertainty on energy supply and demand (Li et al., 2020 ). Additionally, geopolitical tensions and conflicts in major oil- and gas-producing regions could disrupt the production and transportation of energy resources. For example, conflicts in the Middle East involving major oil-producing countries such as Iraq or Saudi Arabia had the potential to result in supply disruptions and subsequent increases in oil prices (Cunado et al., 2019 ; Su et al., 2019 ). Then, geopolitical events have affected national foreign trade policies, leading to the imposition of sanctions or embargoes on certain countries, restricting their ability to export or import energy resources, and reducing the global supply of oil and natural gas, resulting in higher prices. Thus, the complex relationship between geopolitical risks and global energy markets has led to a strong interest in this direction among researchers and scholars in various countries.

Blue cluster: energy policy

As Governments grapple with the dual challenge of meeting growing energy demand and addressing climate issues, the energy policy landscape has changed significantly and is often influenced by geopolitical risks. Energy policy is an integrated strategic framework for managing the production, consumption, and sustainability of a country’s energy resources and plays an important role in economic development, national security, and environmental stability (Chen, 2011 ). The multidimensional objectives of energy policy underscore its centrality to national interests: ensuring reliable and affordable energy supplies, promoting economic growth, reducing environmental impacts, and enhancing energy security (Doukas et al., 2008 ). Energy policy is undergoing transformative changes in the contemporary geopolitical landscape, driven by an intricate interplay of technological advances, environmental imperatives, and geopolitical risks (Wang et al., 2024 ). The geopolitical landscape brings a layer of complexity to energy policy, as countries must navigate an intricate web of alliances, rivalries, and resource dependencies. Geopolitical risk manifests itself in the energy sector in a variety of ways, including disruptions in the global energy supply chain due to conflicts in major oil-producing regions, and trade disputes affecting energy trade (Golan et al., 2020 ; Zhang et al., 2023b ). In the face of these risks, there is a need for a nuanced energy policy that requires a comprehensive understanding of how global geopolitical dynamics can affect energy markets and, in turn, a country’s energy security. Therefore, as the world faces continued geopolitical uncertainty, energy policy will continue to evolve, reflecting the need to balance energy security, economic development, and environmental sustainability in an increasingly interconnected and dynamic global environment.

Conclusions, implications, and limitations

Geopolitics has a profound impact on the energy sector, and the threat in particular is global energy security. Using a systematic literature review and bibliometric analysis, we analyzed more than 400 articles published in the Web of Science core collection qualitatively and quantitatively, and identified the historical development trend, the distribution of research power, the overview of the international collaboration, the research hotspots, and the evolution path of the research. The main findings of this study are as follows:

Researches in geopolitics and energy security is under development, the subject area has moved away from economic factors in the distribution of scientific research to a greater reliance on the global distribution of energy sources. In other words, the distribution of literature output in this subject area no longer follows the trend of distribution between developed and developing countries but is distributed in energy-rich countries or regions, such as oil and gas resources.

The macro-, meso- and micro-networks of scientific collaboration show a more connected group of collaborators, with China as an important research force in the field, and strong links with a number of countries in the Americas, the Middle East and Europe. A total of 27 collaborative groups are generated globally in the institutional collaborative network (ICN). Among them, Qingdao University (China), which has formed the largest collaborative network with a number of institutions at home and abroad, represents the collaborative institutions of the center. Chi-Wei Su is identified as an important co-occurring author in the author collaboration network (ACN), with a large number of collaboration clusters center on him. The K–S test verifies the validity of Lotka’s Law for the distribution of authors in this field and the application of Bradford’s Law identifies the core journals in this research area as Resource Policy , Energy Policy , Energy Economics , Energy Research & Social Science .

The keyword heat map of the thematic analysis shows that the first keywords to be hit in this area are natural gas and oil, and that there is a long-term impact on hydrocarbons, and keywords such as climate change, energy policy, and uncertainty have received sudden attention additionally. The evolutionary path of the thematic analysis shows the three main stages of the development of this research topic, while the keyword clustering shows that the research on this topic focuses on the areas of energy transition, energy markets, energy policy, and the natural environment.

Our research prompts global policymakers to pay further attention to the uncertain risks posed by geopolitics to energy security, and endeavor to promote scientific research collaboration and international goodwill among countries to solve practical problems together. Concurrently, it is imperative to rectify the principal research direction, accelerating the transformation of the country’s energy structure, maintaining the stability of the energy market, and formulating rational energy policies, while paying attention to the impact on the natural environment. In addition, our research has certain advantages in terms of identifying overall trends and future directions of a research topic, however, there are still some limitations in data collection, data processing and tool application. First, our data were obtained from the Web of Science core collection, and by manually reading the titles, abstracts, and bodies, we screened the academic papers that best fit the topic for inclusion in the subsequent analysis, but we still failed to immune to omissions. The homogeneity of the database selection may result in the omission of gray literature in the field, as we initially focused on high-quality literature published in high-quality journals. Second, in addition to academic papers, which can represent a country’s research priorities, other categories of academic activities such as research projects, conference papers, and books can also reflect research trends to a certain extent, however, our paper data excluded this information, and it is possible for future research to collect and process the results of the different academic categories to enrich the field’s research. Finally, the systematic limitations of the bibliometric approach may have produced errors in the results of the statistical and bibliometric analysis of the articles, and future research could further improve the research methodology to reduce systematic errors.

Data availability

The datasets publicly available should be through https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DYCRUR .

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Acknowledgements

This work is supported by the “Youth Innovation Team Project” of the Higher Education Institutions under the Shandong Provincial Department of Education (No. 2023RW015), and the National Natural Science Foundation of China (No. 71874203).

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QW: Conceptualization, methodology, software, data curation, writing—original draft preparation, supervision, writing—reviewing and editing. FR: Methodology, software, data curation, investigation writing—original draft, writing-reviewing and editing. RL: Conceptualization, methodology, data curation, investigation writing-original draft, writing—reviewing.

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Wang, Q., Ren, F. & Li, R. Geopolitics and energy security: a comprehensive exploration of evolution, collaborations, and future directions. Humanit Soc Sci Commun 11 , 1071 (2024). https://doi.org/10.1057/s41599-024-03507-2

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• Volume 14, Issue 8
• Validation of a quantitative instrument measuring critical success factors and acceptance of Casemix system implementation in the total hospital information system in Malaysia
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• Noor Khairiyah Mustafa 1 , 2 ,
• http://orcid.org/0000-0002-4741-5970 Roszita Ibrahim 1 ,
• Zainudin Awang 3 ,
• Azimatun Noor Aizuddin 1 , 4 ,
• Syed Mohamed Aljunid Syed Junid 5
• 1 Department of Public Health Medicine , Universiti Kebangsaan Malaysia Fakulti Perubatan , Cheras , Federal Territory of Kuala Lumpur , Malaysia
• 2 Ministry of Health Malaysia , Putrajaya , Malaysia
• 3 Faculty of Business Management , Universiti Sultan Zainal Abidin , Kuala Terengganu , Malaysia
• 4 International Casemix Centre (ITCC) , Hospital Universiti Kebangsaan Malaysia , Cheras , Kuala Lumpur , Malaysia
• 5 Department of Public Health and Community Medicine , International Medical University , Kuala Lumpur , Federal Territory of Kuala Lumpur , Malaysia
• Correspondence to Dr Roszita Ibrahim; roszita{at}ppukm.ukm.edu.my

Objectives This study aims to address the significant knowledge gap in the literature on the implementation of Casemix system in total hospital information systems (THIS). The research focuses on validating a quantitative instrument to assess medical doctors’ acceptance of the Casemix system in Ministry of Health (MOH) Malaysia facilities using THIS.

Designs A sequential explanatory mixed-methods study was conducted, starting with a cross-sectional quantitative phase using a self-administered online questionnaire that adapted previous instruments to the current setting based on Human, Organisation, Technology-Fit and Technology Acceptance Model frameworks, followed by a qualitative phase using in-depth interviews. However, this article explicitly emphasises the quantitative phase.

Setting The study was conducted in five MOH hospitals with THIS technology from five zones.

Participants Prior to the quantitative field study, rigorous procedures including content, criterion and face validation, translation, pilot testing and exploratory factor analysis (EFA) were undertaken, resulting in a refined questionnaire consisting of 41 items. Confirmatory factor analysis (CFA) was then performed on data collected from 343 respondents selected via stratified random sampling to validate the measurement model.

Results The study found satisfactory Kaiser-Meyer-Olkin model levels, significant Bartlett’s test of sphericity, satisfactory factor loadings (>0.6) and high internal reliability for each item. One item was eliminated during EFA, and organisational characteristics construct was refined into two components. The study confirms unidimensionality, construct validity, convergent validity, discriminant validity and composite reliability through CFA. After the instrument’s validity, reliability and normality have been established, the questionnaire is validated and deemed operational.

Conclusion By elucidating critical success factor and acceptance of Casemix, this research informs strategies for enhancing its implementation within the THIS environment. Moving forward, the validated instrument will serve as a valuable tool in future research endeavours aimed at evaluating the adoption of the Casemix system within THIS, addressing a notable gap in current literature.

• quality in health care
• public health
• health economics

Data availability statement

No data are available.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-082547

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STRENGTHS AND LIMITATIONS OF THIS STUDY

The rigorous validation process of the questionnaire, including initial validation, translation, pre-testing and exploratory factor analysis using pilot test data, followed by confirmatory factor analysis using field data, enhances the reliability and validity of the instrument used for data collection.

The use of statistical techniques such as the Kaiser-Meyer-Olkin (KMO) measure, Bartlett’s test of sphericity, factor loadings, Cronbach’s alpha and various validity tests (unidimensionality, construct validity, convergent validity, discriminant validity) ensures the robustness of the analysis.

While the large sample size enhances generalisability to some extent, the study was conducted in only five selected hospitals in Malaysia; thus, the findings may not be representative of all hospitals in the country or other healthcare systems.

This study does not include other professional roles, such as paramedics, medical record officers, information technology officers and finance officers because the knowledge and involvement of these roles in the Casemix system are not comparable to that of medical doctors.

The findings of the study may be specific to the healthcare context in Malaysia and may not be directly applicable to other countries or healthcare systems with different sociocultural, organisational or technological characteristics.

Introduction

The global healthcare landscape is witnessing profound evolution driven by an array of challenges, including the rise of non-communicable diseases, the resurgence of communicable diseases, demographic shifts and escalating healthcare costs. 1 Governments and healthcare authorities worldwide are under mounting pressure to navigate these complexities while optimising operational efficiency and ensuring equitable access to quality healthcare services. 1 Within this context, Malaysia has emerged as a proactive player, spearheading innovative strategies to streamline healthcare delivery and bolster system performance. The Ministry of Health (MOH) Malaysia’s proactive stance is exemplified by its robust efforts to standardise and enhance the quality of healthcare services through the implementation of clinical standards and pathways based on international best practices. 2 Notably, initiatives such as the hospital information system (HIS) and the Casemix system have been instrumental in revolutionising healthcare management practices and fostering a culture of continuous improvement. 3–6

Background of Hospital Information System (HIS)

The HIS stands as a cornerstone of technological innovation in healthcare management, offering a comprehensive platform for efficient data collection, storage and processing. 7 HIS responsibilities include managing shared information, enhancing medical record quality, overseeing healthcare quality and error reduction, promoting institutional transparency, analysing healthcare economics and reducing examination and treatment durations. 8–13 In Malaysia, the adoption of HIS, categorised into total hospital information system (THIS), intermediate hospital information system (IHIS) and basic hospital information system, has paved the way for seamless integration of patient data, administrative tasks, and financial transactions and appointment management into a single system within a hospital. 14–19 The pioneering implementation of a fully integrated paperless system as a THIS facility at Hospital Selayang underscores Malaysia’s commitment to embracing cutting-edge technology to enhance healthcare delivery. 20–22 Today, 19 out of 149 Malaysian hospitals have IT facilities. 23 24 Despite challenges during implementation, the overall advantage of using a comprehensive system is priceless. 22 25–29

Background of Casemix system

The Casemix system is a global system that categorises patient information and treatments based on their types and associated costs, aiming to identify patients with similar resource needs and treatment expenses. 30 31 It is widely used globally such as in the USA, Western Europe, Australia, Eastern Europe and Asia, playing a crucial role in hospital financing. 32 33 Originating from Australia, it optimises resource utilisation, improves cost transparency and enhances healthcare service efficiency. 34 35 However, its adoption in developing nations like Malaysia faces challenges due to technological constraints and resource limitations. 23 36 37 The Malaysian diagnosis-related group (MalaysianDRG) Casemix system categorises patients based on healthcare costs, improving efficiency and resource allocation. 38–40 This system enhances provider payment measurement, healthcare service quality, equity and efficiency, and assists policymakers in allocating cash for hospitals. 24 41 The information from the MalaysianDRG is integrated into the executive information system, providing access to system outputs such as DRG, severity of illness, average cost per disease and Casemix Index. 38–40

Integration of Casemix within HIS

The integration of Casemix within HIS frameworks represents a paradigm shift in healthcare management, offering a unified platform for data-driven decision-making, performance monitoring and quality improvement initiatives. 42 In the USA, there is a need to evaluate existing HIS against advanced hardware and software. 42 As hospitals face public opposition due to rising medical expenses, governments are under pressure to manage healthcare costs more effectively. 42 Casemix-based reimbursement policies aim to compensate medical expenses based on Casemix rather than the number of services provided. 42 By consolidating clinical, administrative and financial data within a single system, Casemix-based systems are multifaceted and require organisational restructuring and educational initiatives for successful implementation. 33 Strategies such as providing feedback to clinicians and integrating decentralised databases into HIS are crucial for ensuring data credibility and accuracy. 33 Transitioning from traditional medical record management to health information management requires careful planning and adjustments due to the lack of automation in the current HIS. 33

Theoretical and conceptual framework

Multiple frameworks are commonly used to evaluate technology systems’ acceptance and success attributes. There are noteworthy frameworks, such as the technology acceptance model (TAM), the DeLone and McLean Information Systems Success Model (ISSM), the HOT-Fit Evaluation Framework and the Unified Theory of Acceptance and Use of Technology (UTAUT). The TAM is a widely used framework for assessing the acceptability and success of technology systems, particularly in HIS. 43–47 It suggests that user perceptions of ease of use, usefulness and intention to use significantly impact system usage. 43–47 The DeLone and McLean ISSM evaluates the effectiveness of information systems by examining relationships between system quality, information quality, user happiness, individual impact, organisational impact and overall system success. 48 49 The HOT-Fit Evaluation Framework, evolved from the ISSM, evaluates the congruence of persons, organisations and technology within an information system, considering technological variables, organisational factors and human factors. 12 50 The UTAUT enhances the TAM by incorporating additional elements such as social impact, enabling situations, and behavioural intentions. 43 51 52

By integrating these frameworks within the context of Casemix implementation within THIS, the investigators aim to assess critical success factors and address barriers to adoption and acceptance, facilitating seamless integration and maximising the potential of healthcare modernisation efforts. Hence, the investigators opted to integrate HOT-Fit and TAM frameworks as this study’s conceptual framework to achieve the research’s specific objectives, scope and contextual considerations (see figure 1 ). HOT-Fit offers a comprehensive framework for examining the alignment between human, organisational and technological factors, while TAM provides a focused lens on individual-level technology acceptance dynamics. 12 44–47 Based on the current study’s conceptual framework, the HOT-Fit framework focuses on technological constructs like system, information and service quality, while the TAM framework covers human dimensions like perceived ease of use, usefulness, intention to use and acceptance. The integration of these frameworks is crucial for achieving the study’s specific and general objectives. Thus, these two frameworks are suitable and deemed appropriate for this study. On the other hand, UTAUT does not appear suitable for the current investigation due to the broad scope and complexity of existing TAM with additional external variables and ISSM was also not selected due to its simplicity. 43 51 52

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Conceptual framework.

This current study aims to evaluate the critical success factors (CSFs) and doctors’ acceptance of Casemix implementation within the THIS environment to understand the issues MOH Malaysia facilities experience better, fill a research gap on Casemix implementation and help shape plans for modernising healthcare. A comprehensive tool, such as a questionnaire, was created to meet the study objectives. This paper aims to examine a multidimensional instrument that was created to meet the study objectives. Consequently, the exploratory factor analysis (EFA) is instrumental in uncovering underlying factors within observed variables to ensure precision and robustness, while confirmatory factor analysis (CFA) was needed to verify the measurement model’s linkages and confirm that the theoretical model was valid, reliable and suitable for data collection, thereby yielding valuable insights. 53–56 Given its merits, the current study used CFA to evaluate the measurement model’s validity. After validation processes, structural equation modelling (SEM) was employed to analyse how exogenous, mediating and endogenous constructs interrelate and determine parameters into a structural model to analyse direct, mediating and moderating effects on the study’s goals and hypotheses. While the technology evaluation frameworks offer crucial insights, it is essential to note that Casemix is designed to organise patient data and treatment costs rather than analyse the acceptability and success of technology systems. Moreover, meeting the study objectives for evaluating Casemix adoption in THIS can be done without a separate instrument for each system. It can assist healthcare organisations and policymakers in understanding CSFs facilitating the implementation and acceptance of the Casemix system, and guiding the development of targeted strategies for seamless implementation, enhancing patient care, work efficiency and resource allocation. Therefore, a reliable and valid quantitative instrument is required to achieve these goals.

Methodology

Study design and ethical approval, study design.

This study employed a sequential explanatory mixed-methods design. Nevertheless, the researchers in the present article solely highlight the exploration and development of items, as well as the reliability and validation of the quantitative study. The data collection for the quantitative pilot study was from 1–14 February 2023, the quantitative phase was from 1 April to 31 June 2023, the qualitative pilot study was on 15 September 2023 and the qualitative field study was from 17 October 2023 to 4 January 2024. This paper highlights on the development of instruments for quantitative phase procedures and findings of the validation of quantitative study only. The quantitative phase used a cross-sectional study design to gather data throughout a specified duration. 53 57 58

Ethical approval

This study has obtained ethical approval from:

The Medical Research Ethics Committee of the Faculty of Medicine, Universiti Kebangsaan Malaysia (JEP-2022–777), see ( online supplemental file 1 ), and

The Medical Research Ethics Committee of the Ministry of Health Malaysia (NMRR ID-22–02621-DKX), see ( online supplemental file 2 ).

Supplemental material

Study instrument.

This study used a self-administered questionnaire to collect data on the CSF and acceptance of Casemix in THIS environment. The instrument was developed in Malay and English for a better understanding of the respondents due to the geographical areas of the study where Malay is the national language of Malaysia. The questionnaire comprised 60 items divided into three sections, each with a limited number of constructs. Section 1a consists of 8 questions that collected demographic information such as age, gender, educational background and work experience in the MOH Malaysia and current hospital. Section 1b assessed the comprehension/knowledge level of the Casemix system using 10 items. Meanwhile, Section 2 represented the perceived Critical Success Factors of Casemix implementation in the THIS context, consisting of 37 items within six constructs: system quality (SY)—4 items, information quality (IQ)—5 items, service quality (SQ)—5 items, organisational factors (O)—9 items, perceived ease of use (PEOU)—5 items, perceived usefulness (PU)—4 items and intention to use (ITU)—5 items. Section 3 encompasses the outcome of the study which is the user acceptance (UA) construct, which contains 5 items.

The study incorporates and modifies existing scholarly works rooted in the Human Organisation Technology (HOT-Fit) and TAM frameworks for sections 2 and 3. The two sections, each evaluated using a 10-point interval Likert scale. The 10-point interval scale offers respondents a greater range of response possibilities that align with their precise evaluation of a question. 55 56 59 60 A score of 1 represents ‘strongly disagree’, while a score of 10 represents ‘strongly agree’. The constructs and components of the instrument were derived from previous research. 12 43 44 48 50 61–64 These items represented eight constructs: SY, IQ, SQ, ORG, PEOU, PU, ITU and User Acceptance.

The constructs described in sections 2 and 3 underwent initial validation, reliability testing and EFA, using pilot data. CFA was also performed using field data. Details regarding the development validation and reliability procedures of the instrument are provided in subsequent sections. Hence, to facilitate transparency and reproducibility, a blank copy of the measurement instrument developed and validated in this study has been included as a supplementary file (see online supplemental file 3 : Blank Copy of Quantitative Instrument).

Independent variables

A few constructs have been examined in this study as mentioned in Subsection 1.6, the conceptual framework comprising technology, organisation and human dimensions.

Technological factors

Constructs such as system quality (SY), information quality (IQ) and service quality (SY) constitute the technological factors. Addressing system quality issues is imperative for fostering user acceptance and realising system benefits. 43 Reliable and accurate systems with dependable functionality enhance user acceptance, while a user-friendly interface and seamless performance enhance user experience. Integration with existing systems promotes acceptability and interoperability. 43 44 Conversely, information quality, encompassing data security and privacy, is crucial in safeguarding patient data, bolstering user confidence and fostering system adoption. 65 Service quality encompasses the support and assistance provided during and after system implementation, with practical training, responsive helpdesk support, and ongoing maintenance contributing to user satisfaction and system success. 51 66 67 Hence, these three constructs encompassing technological dimensions were adapted from the HOT-Fit framework. 12 50

Organizational characteristics

Organisational dimensions, such as an organisational structure and environment, can limit or facilitate the acceptance or implementation of technical advancements. 68 The elements of organisational dimension were the most generally surveyed attributes in IT adoption in organisations. 69 Previous research has identified relative benefit, centralisation, formalisation, top management support and perceived cost as essential organisational elements influencing any organisation’s decision to embrace current information systems technologies. Management barriers are defined as a lack of efficient planning, a lack of trained people, and limits linked to training courses, according to Abdulrahman and Subramanian. 70 The management, technological, ethical-legal and financial barriers were all integrated into the organisational factor category in this study. Previous research has found that technology adoption rates are related to preparedness and impediments to readiness. 71 Along with several other studies, senior leaders play a critical role in using information systems at the organisational level. 72 Direct involvement of senior executives in IS operations demonstrates the importance of IS and ensures their support and involvement in the overall performance of IS efforts in the organisation. 73 Organisational environment and structure can influence user acceptance of information technology, underscoring the importance of organisational improvement initiatives to enhance user acceptance. 74–77 Hence, this primary construct encompassing organisational dimensions was adapted from the HOT-Fit framework. 12 50

Human factors

The TAM is a framework that consists of five fundamental elements: PEOU, PU, ITU, actual system use and external Variables. 78–81 PEOU is a subjective evaluation of a technology’s ease of use, influenced by usability, training and user assistance. 78–81 PU quantifies the level of usefulness attributed to technology, influenced by factors such as usefulness and compatibility with user needs and responsibilities. 78–81 Intention to Use (ITU), External factors, such as organisational regulations, access and availability, can also influence the interactions within the model. 78–81 External variables, such as individual variances, cultural influences and supportive environments, can either amplify or reduce the impact of perceived ease of use and usefulness on behavioural intention and actual use. 78–81 The TAM has been a crucial paradigm for understanding technology acceptance and has significantly impacted research in information systems and technology adoption. The HOT-Fit Evaluation technique, which focuses on system use and user satisfaction, is suitable for this study. 12 50 These two constructs are interconnected to PEOU and PU, delineated by the TAM framework. 78–81 For successful implementation of an information system, medical doctors perceive it as easy to use (PEOU) through adequate training, user-friendly interfaces and intuitive system design. 78–81 Healthcare providers should also perceive the system as useful (PU) to ensure successful implementation, highlighting its benefits such as improved efficiency, quality of care and cost control. 78–81

Dependent variable

The only dependent variable in this study is acceptance which is adapted from the TAM. 44 45 82 The study presents a pragmatic taxonomy of eight different implementation outcomes, including acceptability/acceptance, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration and sustainability. 64 Acceptability is a crucial aspect of implementation, referring to the acceptance of a specific intervention, practice, technology or service within a specific care setting. 64 It can be measured from the perspective of various stakeholders, such as administrators, payers, providers and consumers. 64 Ratings of acceptability are assumed to be dynamic and may differ during pre-implementation and throughout various stages of implementation. In similar literature, Proctor et al delineated examples of measuring provider and patient acceptability/acceptance including case managers’ acceptance of evidence-based procedures in a child welfare system and patients’ acceptance of alcohol screening in an emergency department. 64 The terms acceptability and acceptance are interchangeably used to describe implementation outcomes. Therefore, in this study, the researchers would like to explore the acceptance of the Casemix system in the MOH’s THIS facilities.

Patients and public involvement

Participants in this study were medical doctors and this study did not involve any patients or the public. Hence, there was no patient or public involvement in this study.

Initial validation processes

The initial validation procedures were conducted to establish the content, criteria and face validity/pre-test of the instrument for the field study.

Content validity

Content validity is significant when developing new measurement tools because it links abstract ideas with tangible and measurable indicators. 83 This involves two main steps: identifying the all-inclusive domain of the relevant content and developing items that correspond to this domain. 83 The Content Validity Index (CVI) is often used to measure this validity. 84–86 Recent studies have demonstrated the content validity of assessment tools using the CVI. 87–90 The best method for calculating the CVI, suggesting that the number of experts reviewing an instrument should range from 2 to 20. 84–86 91 92 Typically, the number of experts varies from 2 to 20 individuals. 93 For the current study, two experts from the Hospital Financing (Casemix Subunit) at MOH Malaysia were selected. This is coherent with the number of experts that are recommended by a few literature in online supplemental file 4A . 84 There are two types of CVI: I-CVI for individual items and S-CVI for overall scales. 84–86 91 92 S-CVI can be calculated by averaging the I-CVI scores (S-CVI/Ave) or by the proportion of items rated as relevant by all experts (S-CVI/UA). 84–86 91 92 Before calculating CVI, relevance ratings are converted to binary scores. The relevance rating was re-coded as 1 (scale of 3 or 4) or 0 (scale of 1 or 2), as indicated in online supplemental file 4B . Online supplemental file 4C reveals two experts’ item-scale relevance evaluations to exhibit CVI Index calculation. In this study, the experts validated the questionnaire contents, achieving perfect scores of 3 or 4 for all items, resulting in S-CVI/Ave and S-SCVI/UA scores of 1.00. In conclusion, a thorough methodological approach to content validation, based on current data and best practices, is essential to confirm the overall validity of an evaluation.

Criterion validity

Criterion validity denotes to the degree of correlation between a measure and other established measures for the same construct. 62 88 89 94 95 An academic statistics expert and an expert in questionnaire development and validation procedures reviewed criterion validity. This can be reviewed in online supplemental file 5 . Subsequently, a certified translator translated the instrument from English to Malay back-to-back precisely.

Face validity

A face validity assessment was undertaken to evaluate the questionnaire’s consistency of responses, clarity, comprehensibility, ambiguity and overall comments. Before commencing the pilot study and fieldwork, the researchers acknowledged and resolved the concerns that were previously mentioned. 62 90 96 Following the validation process, 11 respondents were purposefully selected for face validity also known as pre-testing to accomplish the prerequisite for face validation. Furthermore, they must meet exclusion criteria like those stipulated for participants in the field study. Subsequently, these respondents were excluded from participation in the quantitative field study. The study population will be described further in Subsection 2.6.2. The objective of this pre-test or face-validation process was to assess the consistency of responses, and clarity, ambiguity and overall design of the questionnaire. 97 This will be done through the evaluation from the online Google Form of the Questionnaire. Before conducting the pilot study and fieldwork, the researchers took into consideration the concerns that had been raised. 97 The face validity result has been uploaded as online supplemental file 6 .

Quantitative pilot test and EFA

The pilot study was conducted at a Federal Territory hospital in Malaysia, Hospital W. The pilot study population also possess similar characteristics to the participants/samples involved in the subsequent quantitative field study. Additionally, these respondents were excluded from participation in the quantitative field study. This study used a minimum of 100 samples to ensure valid results for the EFA. 97 98 Hence, since the current pilot study is using EFA, the minimal sample size of 100 is therefore supported by a few studies and books experienced in research and validation procedures. 54–56 97 99 Therefore, to account for a projected drop-out rate of 20%, the minimum sample size for this preliminary pilot study was determined to be 125 medical doctors. 100 The research was conducted without participant or public involvement in the design, conduct, reporting or dissemination strategies. The data collection method was also like the field study. It was employed using an online Google Form Questionnaire. Participants were asked to scan a Google Form link or QR code to access information sheets, consent forms and online questionnaires. Each participant was notified that their information would be kept private their anonymity would be retained solely for the study, and they could withdraw at any time.

The pilot study will use EFA to measure data from a collection of hidden concepts. EFA is a method that generates more accurate results when each shared component is represented by many measured variables, either exogenous or endogenous constructs. 54–56 97 98 101–103 The collected data will be used to identify and quantify the dimensionality of items that assess the construct. 53–56 59 60 104 EFA is essential to determine whether items in a construct produce distinct dimensions from those found in previous studies. 53–56 59 60 104 Factors’ dimensionality may change as they are transported from other domains to a new research topic, and fluctuations in the population’s cultural heritage, socioeconomic status and passage of time might affect dimensionality. The EFA methodology uses principal component analysis (PCA) to decrease the amount of data, but it fails to discern between common and unique changes efficiently. 97 98 PCA is indicated when there is no known theoretical framework or model, and it is used to create the first solutions in EFA. Four requirements of PCA included (1) components with eigenvalues more than one, (2) factor loadings greater than 0.60 for practical relevance, (3) no item cross-loadings greater than 0.50 and (4) each factor has at least three items to be retained. 97 98 The data’s eligibility for factor analysis was determined using the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) of >0.6 and Bartlett’s test of sphericity. 55 56 105–107 The effectiveness of Bartlett’s test for factor analysis hinges on the significant result, with a value near p<0.001 (p<0.05) indicating acceptability. 53–56 107 The scree plot also determined the best number of constructs to keep. 53–56

Quantitative field study

Study location.

The present study gathered data from five hospitals situated in various Malaysian zones—South, North, West, East and East Malaysia—that are outfitted with the Total Hospital Information System (THIS) and Casemix system. The study used cluster sampling to select study sites in Malaysia, dividing the country into five distinct clusters. Five hospitals that had successfully implemented Casemix for at least 3 years were chosen to represent different regions of Malaysia. Hospital N was selected for the northern region, Hospital E for the eastern region, Hospital S for the southern region and Hospital W for the central/western region. Hospital EM was chosen for East Malaysia. Cluster sampling is suitable when the research encompasses a vast geographical expanse.

Target population for the study

The target population for this study was medical doctors by profession working in hospitals under MOH in 2023. The study collectively obtained a sampling frame of 3580 medical doctors by profession, encompassing hospital directors, deputy directors (medical division), consultants/specialists, medical officers and house officers from the five selected hospitals. These doctors should fulfil the inclusion and exclusion criteria of this study as follows:

Inclusion criteria

Permanent/ contract of service medical doctors who were posted to current participating hospital.

Has working experience in the current participating hospital for at least 3 months.

Agree to participate in the study.

Exclusion criteria

Attachment medical doctors.

Refuse to participate in the study.

The study population of face validation/pre-test and pilot test has characteristics similar to those of the study population in the field study. The pre-test and pilot-test samples will also be excluded from samples in the field study. Participants were given surveys to complete at their own pace, without fear or pressure.

Sample size and sampling method

The target population was selected using proportionate stratified random sampling, dividing the total population into homogeneous groups. 16 108–110 Proportionate stratified random sampling is a probability sampling method that includes separating the entire population into similar groups (strata) to conduct the sampling process.

The authors are concerned about the sample size needed for CFA validation of the measurement model. However, current studies do not have a consensus on the appropriate sample size. For small indicators, a minimum sample size of 100–150 respondents is often needed, 111–113 whereas, precise analysis for CFA may require 250–500 respondents. 114 115 Some authors suggested the following suggestions for the sample size requirement: (a) a sample size to parameter ratio of 5 or 10, (b) ten cases per observation/indicator and (c) 100 cases/observations per group for multigroup modelling. 116–118 In conclusion, the researchers opted to employ five times the number of indicators in the questionnaire because the number of indicators for latent variables is large. 116 119 The final questionnaires contain 59 items, requiring a total sample size of 295. However, there is an additional 20% anticipated dropout rate. The sample size was estimated using the formula n=n/1-d (n=total samples, n=minimum required samples and d=drop out rates), yielding a minimum sample size of 369. 100 This is also corroborated by other research, which states that because the conceptual framework in this study consists of eight constructs, each with at least four items, the required sample size is 300, with an additional 20% expected drop-out rate, the calculated sample size was 375. 56 97 100 102 As a result, the researchers opted to distribute questionnaires to the 375 participants using proportionate stratified random sampling depending on their professional roles as suggested. 56 97 102 116

Data collection methods

The data collection method for the quantitative field study is similar to the techniques used in the quantitative pilot study. This data collection method was elaborated in Subsection 2.4.4. However, the link for the participant information sheet (PIS) and informed consent forms was included on the first page of the questionnaire which is https://bit.ly/3F8IF2e . The participant’s information sheet and informed consent forms are attached as online supplemental files 7 and 8 , respectively. Similarly to the quantitative pilot study, respondents may do so freely without losing their data if they withdraw from the survey midway. Participants were assured that their information would be kept confidential and that their anonymity would be strictly protected during the field study. Participants who wish to participate must first consent and complete all survey questions. They were also instructed to contact the lead investigator with any questions. The participants have up to 2 weeks to complete and submit the online questionnaire. All survey information was linked to a research identification number. For example, study identifications 001 to 375 on the subject data sheets will be used instead of the subject’s name. The appropriate senior management and Casemix System Coordinators (CSCs), the department’s Casemix Coordinator and Heads of Department will be contacted 3 days before the data gathering session concludes. All measures were taken to safeguard participants’ privacy and anonymity.

Data analysis using Confirmatory Factor Analysis (CFA)

Once the EFA technique has been completed, these constructs and emerging components of the revised conceptual framework were used in the field study. Hair et al and Awang et al described two distinct models in the field study: the measurement model used in the CFA technique and the structural model used to estimate paths using the SEM. 54–56 97 99 This study paradigm has the features of a confirmatory form of research, with a focus on behavioural components. This type of SEM is known as covariance based-SEM (CB-SEM) and exhibits theory testing or theory-driven research that integrates existing theories to replicate an established theory into a new domain, confirming a pre-specified relationship. 54–56 97 99

The SPSS Analysis of Moment Structures (AMOS) V.24.0 software was used in CFA to evaluate the unidimensionality, validity and reliability of the measurement model. 53 54 56 The instrument’s normality is also achieved using CFA. 53 54 56 There are two ways to validate measurement models: pooled and individual CFA. 54–56 120 121 Pooled-confirmatory factor analysis’ (Pooled-CFA) higher degree of freedom enables model identification even when some constructs have fewer than four components. 54–56 120 121 The missing data will be omitted/discarded from the analysis. To ensure unidimensionality, the permissible loading factor for each latent construct is calculated, and items that cannot fit into the measurement model due to low factor loading are excluded. 53 55 56 97 122–125 The cut-off value for acceptable factor loading varies depending on the research goal. However, this study used a threshold value of 0.5 to minimise item deletion. 53 55 56 97 121 122 126 Convergent validity is assessed by calculating the average variance explained (AVE) for each construct. 53 55 56 97 111 122 Meanwhile, composite reliability (CR) assesses how often a construct’s underlying variables are used in structural equation modeling. 53 55 56 97 122 A latent construct’s CR must be 0.6 to achieve composite reliability. 53 55 56 97 122

Several fitness indicators were reported among scholars. Some recommendations are to report fit indices as absolute fit (chi-squared goodness-of-fit (Χ 2 ) and standardised root mean square residual, or SRMR), parsimony-corrected fit (root mean square error of approximation, or RMSEA), Comparative Fit Index (CFI) and comparative fit (Tucker-Lewis Fit Index (TLI)). 54–56 99 123 124 126–129 They advised using at least one index from the three fitness categories: absolute fit, incremental fit and parsimonious fit. 54–56 123 124 126–129 A model fit was indicated using a set of cut-off values: RMSEA values from 0.05 to 1.00, CFI >0.90 and Chisq/df<5.00, which would imply a reasonable fit. 53–56 126 129–131

Findings for the pilot test through exploratory factor analysis

Out of the required minimum sample size of 125, a total of 106 participants took part in the quantitative pilot study, resulting in an 84.8% response rate. According to Hair et al and Awang et al , in order to conduct an EFA, at least 100 samples are needed. 54–56 97 However, considering a potential drop-out rate of 20%, the minimum required sample size for this pilot study is 125. Researchers performed an EFA to find the primary dimensions from a wide set of latent constructs represented by 42 items before conducting the CFA. EFA uses PCA as the extraction method to reduce data and create a hypothesis or model without pre-existing preconceptions about the variables’ quantity or nature. 54–56 97 132 The EFA deemed indicators above 0.60 significant, and indicators loading into the same component were combined to match the measurement model. 97 The measurement model (for CFA) and structural model (for path estimation) of SEM will use EFA results. 54–56 97 99 EFA was used to evaluate and appraise the items measuring the construct, while CFA was used to validate the measurement. 12 43 44 50 61 EFA and CFA used pilot and field study data, respectively. EFA is a method used to select factors for retention or removal, using PCA and varimax rotation. It is a popular orthogonal factor rotation approach that clarifies factor analysis. 53 55 56 97 122 The extraction technique reduces the organisational factors (O) from nine to eight items, with one item, ‘Organisational competency to provide the resources for the implementation of the Casemix system in THIS setting,’ not reaching the factor loading of 0.6, hence it was 55 97 see table 1 .

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Factor loading of EFA with PCA and varimax rotation

To prepare for the next stage, the researcher reorganises the objects into their respective components and begins data collection in the field study. The EFA results also reveal that the two components of the organisational characteristics (O) construct were later named organisational structure (STR) and organisational environment (ENV). 53 55 56 97 122 The instrument was used for 41 items in the field study and analysed with Cronbach’s alpha, ensuring its internal reliability for the field study, 53–56 97 133 see table 2 below.

The number of items for each construct before and after EFA and Cronbach’s alpha

Consolidating correlated variables was EFA’s primary goal. EFA established eight constructs from the pilot study data and according to the researcher’s conceptual framework (See figure 1 ). 53–55 The overall results of KMO and Bartlett’s sphericity test for all constructs, see table 3 . The KMO value was 0.859, which is larger than 0.6. The result of Bartlett’s test of sphericity shows that p value <0.001 yielded statistically significant findings, which is p value <0.05. 53 55 56 97 122 Therefore, it is appropriate to proceed with further study.

Results of the KMO and Bartlett’s test of sphericity

The amount of variance accounted for, referred to as total variance explained (TVE), 53–56 97 see table 1 ( online supplemental file 9 ). Each component had an eigenvalue larger than 1 and the TVE was 84.07%, exceeding 60%. 53–56 97 The researcher should contemplate incorporating more items to assess the structures as it indicates that the existing items are inadequate for accurately assessing the constructs if the TVE is less than 60%. However, this does not occur in the present study.

The EFA approach also includes the scree plot. The researcher can ascertain the number of components by observing the distinct slopes in the scree plot. 53–56 97 The scree plot exhibits nine distinct slopes, as shown in figure 1 ( online supplemental file 9 ). Hence, the EFA identifies a total of nine components.

Cronbach’s alpha would calculate measuring each item’s internal reliability. Internal reliability assesses how well the selected items measure the same construct. 53–56 97 133 All constructs topped 0.7 Cronbach’s Alpha. Hence, this instrument is reliable for use in field study.

Findings for the field study through the confirmatory factor analysis

The ultimate measurement tool for field study comprises 41 elements from the EFA procedure. To adequately address the intricacy of the quantitative instrument for the field study, the researchers determined that a minimum of 300 samples was necessary to implement CFA. 97 An additional 20% drop-out rate resulted in a minimum sample size of 375 individuals for the field study. Hence, out of this sample, only 343 participants answered, indicating a response rate of 91.5%. 100 No missing data was reported.

CFA validates factor loading and assessment in this study. The researcher tests a theory or model using CFA. Unlike EFA, CFA is a form of structural equation modelling that makes assumptions and expectations about the number of factors and which factor theories or models best suit prior theory. 53–56 97 EFA relied mainly on outer loading; however, factor loadings and fitness indices are now considered. Researchers must confirm that both folds meet standards. CFA also lets academics test financial literacy indicators and measurement models. Thus, a proper measuring model helps researchers interpret their data.

Validity, unidimensionality and reliability were necessary for all latent construct assessment models. 53 55 56 97 122 The latent construct measurement model needed convergent, construct and discriminant validity. 53 55 56 97 122 AVE assesses convergent validity, while measurement model fitness indicators determine construct validity. 54–56 On the other hand, composite reliability (CR) was used to calculate instrument reliability since it was better than Cronbach’s alpha. 54–56 133

Figure 2 shows that Pooled-CFA validated all latent constructs in the measurement model simultaneously. These constructs were aggregated using double-headed arrows to execute a Pooled-CFA. Pooled-CFA’s increased degree of freedom allows model identification even when some constructs have fewer than four components. 54–56 Pooled-CFA was employed in this investigation since only one construct has two components.

Result from Pooled-CFA procedure.

Uni-dimensionality

Unidimensionality is a set of variables that can be explained by one construct. 7–9 Unidimensionality is achieved when all construct-specific measuring items have acceptable factor loading. 54–56 Remove CFA components with low factor loadings from the measurement model until fit indices are met. 53–56 97 134 Table 4 summarises the build items with factor loadings >0.6. 54–56

Factor loading of all items, composite reliability (CR) and average variant extracted (AVE) and normality testing

Convergent validity

Convergent validity is a group of indicators that measures a construct. 54–56 97 135 It assesses the strength of correlations between items that are hypothesised to measure the same latent construct. 56 97 The average variance extracted (AVE) statistic can be used to verify the convergent validity of a construct. If the concept’s AVE is more than 0.5, it possesses convergent validity. 53 56 97 136 Table 4 shows that the AVE for all structures was more than 0.5. Organisational characteristics/factors (ORG) AVE shows the highest AVE, which was 0.857, and environment component, the lowest AVE, which is 0.699. The model is, therefore, convergently valid.

Construct validity

When all model fitness indices met the criteria, construct validity was attained. 55 56 97 Construct validity was established using absolute, incremental and parsimonious fit indices. 55 56 97 Some researchers recommend using one fitness index from each model fit category. 55 56 97 This study employed RMSEA, CFI and normed chi-square (x2)/df as its main indicators. According to table 5 , this instrument met all three fitness indices: (1) the RMSEA value was below the threshold of 0.08 (0.054), confirming the absolute fit index; (2) the instrument achieved the incremental fit index category by obtaining a CFI value above 0.90; and (3) the parsimonious fit index, measured using Chisq/df, yielded a value of 2.014, which is below the accepted value of 3.0. 55 56 97 This study proved the instrument’s construct validity.

Fitness index summary

Discriminant validity

The survey’s discriminant validity was tested to ensure no redundant constructs were found in the model. The model is discriminant when the square root of the average variance extracted (AVE) for each construct is greater than its correlation value with other constructs. 55 56 136 Table 6 summarises the discriminant validity index, which showed that all constructs met the threshold. 55 56 136 The diagonal values (bold font) in this table were greater than all other values in their row and column, suggesting discriminant validity for all constructs. 55 56 136

Discriminant Validity Index

Composite reliability

Estimating model reliability uses composite reliability (CR). 55 56 97 CR between 0.6 and 0.7 is acceptable. 55 56 97 Table 4 above shows that the instrument’s composite reliability exceeded 0.6 for all structures. The environment component had the lowest CR (0.903), while the information quality construct had the highest (0.954). Therefore, this instrument’s composite reliability is accomplished.

Normality assessment

Each item evaluating the construct’s distributional normality was assessed. All skewness values must be within the usual range. 56 97 Skewness between −1.5 and 1.5 is considered acceptable. All model components’ skewness values are between −1.5 and 1.5, indicating their normal distribution. 56 97 The instrument’s data distribution met the normality condition, as shown in table 4 .

This study focused on redeveloping and validating an instrument to gauge medical doctors’ intent to use and accept the Casemix system within the Total Hospital Information System (THIS) context. The EFA and CFA indicated that the instrument was well-designed and validated for assessing medical practitioners’ acceptance of the Casemix system in THIS setting. 55 56 97 The acceptance of the Casemix system among medical physicians in hospital information systems was found to be influenced by various factors including system and service quality, perceived ease of use, usefulness, relevance to clinical practice, training and good organisational support, impact on efficiency and productivity, and confidence in information quality involving data accuracy and security. Healthcare organisations must address these components to gain physician acceptance. 43 44 137 They can optimise Casemix system use, improving patient care and results. 137

Principal findings

Findings of exploratory factor analysis (efa).

The pilot test data was analysed using EFA, which helps researchers understand complex datasets and discover observed variable correlations. 55 56 97 EFA reduces variable dimensions by identifying common patterns, shaping fundamental factors that influence observable variables and grouping related variables. 122 126 138 It simplifies model design by computing factor loadings, which indicate the intensity and direction of factor-observable variable interactions. EFA also finds underlying components in a dataset, while CFA analyses and confirms an EFA-proposed factor structure. 55 56 97

All structures underwent KMO and Bartlett’s sphericity tests, with all structures having KMO values over 0.6. 55 56 105–107 The scree plot, part of EFA, was used to count components and found nine constructs on 42 items. 55 56 105–107 The study found that one construct should now have two parts, mainly due to demographic changes, particularly socioeconomic status and education. Component 1 explained 14.115% of construct variance, while component 9 explained 6.610%. All constructs had 84.07% total variance Explained (TVE), exceeding the minimum threshold of 60%. 55 56 60 112 129

The EFA discovered nine components, including O1-O9 for organisational factors. 43 45 50 139 41 of 42 items had factor loadings above 0.6, requiring item O1 to be eliminated. 53 55 56 97 122 Only organisational factors (O) had nine items reduced to eight following extraction. The remaining seven constructs had only one component and no additional components, resembling HOT-Fit and TAM framework organisational constructs.

The study stresses tool dependability and internal consistency, using markers such as Cronbach’s alpha (α), person reliability, person measure and valid responses. 133 140 A Cronbach’s alpha coefficient of 0.7 or above is acceptable in social science and other studies. 53 138 141 142 Internal reliability is measured by how well-selected items measure the same idea. 53–56 97 98 133 143 The researcher reordered questionnaire items for the field investigation, and CFA authenticated and confirmed all eight constructs on field data, which is elaborated further in the next Subsection 4.1.2.

Findings of Confirmatory Factor Analysis (CFA)

Once the pilot data was assessed and the EFA was commenced, the final questionnaire will be used in the quantitative field study. Eventually, another procedure will be conducted to validate the questionnaire, also known as CFA, based on the field study data. The CFA will validate the instrument’s convergent, construct and discriminant validity. Unidimensionality, composite reliability and normality evaluations are also needed to reveal whether the instrument’s items are valid. 53–56 97 Therefore, the findings of this study demonstrate that the quantitative instrument has been validated and proven reliable for assessing medical practitioners’ intention to use and accept the Casemix system within the context of THIS. Using EFA and CFA is imperative for ensuring the instrument’s validity, reliability and trustworthiness. 53–56 97

By using EFA, the organisational factors (O) emerged into two components. The organisational factors (O) construct was renamed as organisational characteristics (ORG) in the measurement model, and the newly emerged components were named organisational structure (STR) and organisational environment (ENV). Measurement models refer to the implicit or explicit models that relate the latent variable to its indicators. 55 56 97 The organisational characteristics (ORG) construct is assessed as a second-order construct due to the emerged components. When dealing with a complex framework, researchers can choose to do the CFA individually for each second-order construct, and then followed by Pooled-CFA, through item parcelling or straight away employ Pooled-CFA. 55 56 The use of Pooled-CFA is beneficial because of its improved efficiency, effectiveness and ability to address identification difficulties. 55 56 However, although there are many constructs in this study, this measurement model only includes one second-order construct, which is the (ORG) construct with two emerged components. The other seven constructs are made up exclusively of first-order constructs, each consisting of a maximum of five items. Therefore, a direct Pooled-CFA was employed. 55 56

This study uses CFA to validate factor loading and assessment in a theory or model. 53–56 97 CFA is a form of structural equation modelling that makes assumptions and expectations about the number of factors and which factor theories or models best suit prior theory. 53–56 97 According to Baharum et al in their few studies, they measured success factors in newly graduated nurses’ adaptation and validation procedures. 129 144 145 Likewise, for example, CFA also allows academics to test financial literacy indicators and measurement models, ensuring that a proper measuring model helps researchers interpret their data as elaborated in a few studies. 146–148

Validity, unidimensionality and reliability were necessary for all latent construct assessment models. 53 55 56 97 122 The latent construct measurement model needed convergent, construct and discriminant validity. 53 55 56 97 122 Convergent validity is assessed using the average variance extracted (AVE) statistic, while construct validity is determined by measurement model fitness indicators. 54–56 Composite reliability (CR) was used to calculate instrument reliability since it was better than Cronbach’s alpha. 54–56 133

Unidimensionality is a set of variables that can be explained by one construct. 7–9 Unidimensionality is achieved when all construct-specific measuring items have acceptable factor loading. 54–56 Convergent validity is a group of indicators that are considered to measure a construct. 54–56 97 135 Convergent validity is achieved when the concept’s AVE is more than 0.5, and the highest AVE for all structures was 0.857. 53 56 97 136 Normality assessment was conducted on each item evaluating the construct’s distributional normality, with skewness values within the usual range (–1.5 to 1.5). 56 97 The instrument’s data distribution met the normality condition.

Construct validity is attained when all model fitness indices meet the criteria, using absolute, incremental and parsimonious fit indices. 55 56 97 The instrument met all three fitness indices, confirming the absolute fit index with RMSEA=0.054 (aim<0.1), achieving the incremental fit index category by obtaining a CFI value above 0.90 and yielding a parsimonious fit index of 2.014 (aim<5.0). 55 56 97

Discriminant validity was tested to ensure no redundant constructs were found in the model. 55 56 136 The model obtained discriminant validity since each construct’s square root of average variance extracted (AVE) is bigger than its correlation value with other constructs. 55 56 136 The summary discriminant validity index showed all constructs met discriminant validity.

The instrument’s composite reliability exceeded 0.6 for all structures, with the environment component having the lowest CR (0.903) and the information quality construct having the highest (0.954). 55 56 136 Calculating model reliability with composite reliability (CR). 55 56 97 Acceptable CR is 0.6–0.7. 55 56 97 As shown in table 1 , the instrument’s composite reliability exceeded 0.6 for all constructs. The environment component (ENV) had the lowest CR (0.903), while information quality had the highest (0.954). Thus, this instrument’s composite reliability is achieved.

Therefore, all necessary procedures to determine validity, reliability and normalcy were conducted, and no items were excluded. As a result, the total number of items remained at 41. Construct, convergent, discriminant validities and composite reliability have all been attained. All things satisfied the criteria of normality.

Strengths and weaknesses of the study

There are various ways in which this study could benefit the medical community and policymakers. 149 150 The research assesses important success elements that affect physicians’ adoption of the Casemix system in hospitals that have a THIS. Policymakers and hospital administrators may find it easier to pinpoint the critical elements influencing the Casemix system’s effective deployment with the aid of the study’s findings. 151 To successfully implement clinical pathway/case management programmes, policymakers may find the study to help understand the significance of ongoing clinician support and acceptance, top management leadership and support, and a committed team of case managers, nurses and paramedical professionals. 151 152 Policymakers can potentially use the findings to impact admissions decisions, thereby increasing clinical practice openness. 152–154

Strengths and limitations exist in this research. One of the strengths of the study was that it employed a sequential explanatory mixed-method approach to investigate the CSFs and acceptance of the Casemix system among medical practitioners in THIS. 58 155 156 The findings revealed that there might be unnoticed CSFs in the quantitative phase, suggesting the need for a qualitative method to identify more CSFs, perceptions and challenges/barriers. Quantitative data support hypothesised associations, but qualitative data provide in-depth data to supplement quantitative conclusions. 157 The mixed-method approach is expected to improve research design and yield more valid results.

Additionally, another strength of this study is that it uses a strict methodological approach to instrument development and validation. It uses both EFA with pilot test data and CFA using field data, which makes the instrument used for data collection more reliable and valid. Many statistical tests were used to make sure the instrument worked well and the analysis was accurate. These included the KMO measure, Bartlett’s test of sphericity, systematic deletion of items based on factor loadings, Cronbach’s alpha and different validity tests such as unidimensionality, construct validity, convergent validity and discriminant validity. 55 56 105–107

Although the study had a large sample size, it was only conducted in five selected hospitals in Malaysia. Therefore, the findings may not accurately represent all THIS hospitals in the country or other healthcare systems. Other professional positions, including paramedics, medical record officers, information technology officers and finance officers, are not included in this study since their involvement and level of understanding in the Casemix system are not similar to that of medical practitioners, despite being relatively involved in the Casemix system. Hence, this may limit the generalisability of the findings could be a potential weakness of the study. The study’s findings are likely to be distinctive/unique to the healthcare setting in Malaysia and may or may not be directly transferable to other nations or healthcare systems that have distinct sociocultural, organisational or technological characteristics. While this study’s findings are rooted in Malaysia’s healthcare setting, where the Casemix system and THIS are prevalent, their applicability to other countries or healthcare systems with different sociocultural, organisational or technological characteristics should be carefully considered. Despite this, there are potential avenues through which the insights gained from this research could benefit other nations or healthcare systems. For example, the principles of efficiency and effectiveness in healthcare management highlighted in this study could be adapted and implemented in various settings. Additionally, the lessons learnt from the challenges faced in Malaysia’s healthcare system could serve as valuable guidance for other countries looking to improve their systems.

Strengths and weaknesses concerning other studies

Compared with previous studies, this research contributes to the field by providing a validated instrument tailored to assess the acceptance of the Casemix system within the THIS environment. Prior literature has examined various aspects of Casemix implementation in Malaysia as well as in other countries. However, no one has investigated Casemix in THIS or even in HIS. Thus, this study offers a comprehensive evaluation tool that addresses critical success factors influencing medical doctors’ acceptance, filling a significant research gap. Given the absence of prior research in this area, the newly created quantitative tool would be advantageous in achieving the study objectives and serve as a point of reference for future investigations.

However, previous literature by Beth Reid describes the importance of developing Casemix-based hospital information system management. 33 The Casemix-based hospital information system is a comprehensive approach to healthcare management that involves estimating costs per diagnosis-related group (DRG), building a Casemix-based system and addressing organisational design and education issues for successful implementation. 33 It is crucial to provide Casemix reports to hospital staff and clinicians to identify errors in data. Improving the quality of data is essential for both hospitals and universities. To ensure the credibility of the HIS, it must tap into decentralised databases to ensure common input data for each patient’s diseases and procedures. 33 Sharing data is beneficial for clinicians as it allows them to avoid investing time and effort in ensuring database accuracy to discover that the data used for Casemix activities, such as funding, is obtained from the medical record. 40 This approach is essential for ensuring the accuracy and efficiency of healthcare management. 33

Additionally, a study by Saizan showed that THIS hospital showed the lowest Casemix performance in terms of accuracy of the main diagnosis, the completeness of other diagnoses, and the coding of main and other diagnoses. 16 This article outlines two themes with three subthemes, each theme based on why the performance is the lowest. These two themes are the poor commitment of clinicians and obstacles in the work process. Furthermore, another study revealed that one THIS hospital in Malaysia had the lowest Casemix performance in terms of main diagnosis accuracy, other diagnosis completeness, and main diagnosis and other diagnostic coding accuracy. 16 This article presents two overarching themes, each consisting of three subthemes based on the qualitative, in-depth interview findings. These themes are centred around the underlying reasons behind the lowest Casemix performance. The two main themes identified are the lack of dedication among professionals and the challenges encountered in the workflow.

Meaning of the study: possible explanations and implications

The validated and reliable instrument developed in this study holds implications for clinicians, policymakers and healthcare organisations aiming to optimise Casemix system implementation within HIS. Identifying critical factors influencing acceptance, such as system, information and service quality, is imperative to meet study objectives. Organisational characteristics such as environment and structure, as well as human factors such as perceived ease of use and perceived usefulness, the findings offer actionable insights for enhancing system adoption, utilisation and success. Policymakers and hospital administrators can use these findings to streamline Casemix deployment strategies, improving patient care outcomes and operational efficiency within the THIS.

First, while the specific details of the findings may not directly translate to other contexts, the underlying principles and methodologies employed in this study can serve as a valuable template for researchers in different settings. By adapting and contextualising the research methods and instruments used in this study, researchers in other countries can conduct similar investigations tailored to their healthcare environments. 158 159

Second, the identification and evaluation of critical success factors for implementing healthcare information systems, such as the Casemix system, are universal challenges healthcare organisations face worldwide. 33 158 160 Because of this, the conceptual framework and analytical methods created in this study can help us understand what makes people accept and use these kinds of systems in different situations. Researchers and policymakers in other countries can leverage these insights to inform their strategies for implementing and optimising healthcare information systems.

Additionally, while the contexts and details of the Casemix system and THIS may vary across different countries, the broader goals of improving resource allocation, clinical decision-making and quality of care are shared objectives across healthcare systems globally. Therefore, the findings of this study, particularly regarding the factors influencing system acceptance and success, have the potential to resonate with stakeholders in other countries who are working towards similar goals. 151 161 162

Overall, while recognising the contextual specificity of the study’s findings, there is potential for the insights generated to contribute to the broader body of knowledge on healthcare information systems and inform practices in other countries or healthcare settings with distinct characteristics. Through collaboration and adaptation, the lessons learnt from this research can be extrapolated and applied to diverse healthcare contexts, ultimately contributing to advancing healthcare delivery worldwide. 33 158 160 By sharing best practices and lessons learnt, healthcare systems around the world can benefit from the findings of this study and improve their information systems. This collaborative approach can lead to more efficient and effective healthcare delivery on a global scale.

Unanswered questions and future research

The current study proposes employing this instrument in future research, broadening the target population to include more professional occupations and increasing the sample size for more robust results. The novelty of this research lies in its comprehensive analysis of the direct and indirect effects of these parameters on user acceptance of implementing Casemix within THIS environment. SEM was employed to investigate the proposed model. Apart from that, mediating effects have been examined in this study involving a few critical constructs, such as PEOU, PU and ITU, using similar analysis methods. Additionally, more information on moderating characteristics, including age, gender, professional positions, degree of education, years of experience in MOH Malaysia and current THIS hospital and Casemix system knowledge, could improve the instrument. These moderating effects were examined using SEM as well.

The innovation of this study is that it examines the CSFs that influence the acceptance of the Casemix system in the THIS environment, specifically in MOH hospitals in Malaysia. The immediate findings have clear significance for healthcare organisations and policymakers in Malaysia, and even globally. However, the more significant implications for readers in other countries are also relevant. First and foremost, recognising CSF in implementing the Casemix system provides valuable information that can be applied to healthcare systems, especially those equipped with THIS facility universally. Gaining insight into these aspects can provide valuable strategic decision-making guidance in other nations seeking to implement or improve similar systems within their healthcare infrastructure.

Furthermore, the study uses a methodological approach that involves the use of a mixed-methods approach. The quantitative phase, elaborated on in this article, employs a reliable quantitative instrument that validates exploratory and confirmatory factor analyses and reliability testing. Moreover, semi-structured, in-depth interviews were conducted with the Deputy Directors representing the top management and the CSCs of 5 participating hospitals. Hence, these mixed-methods studies provide a strong foundation for evaluating the adoption of the Casemix system within healthcare information systems. Readers from different countries might use and modify these approaches to conduct comparable investigations in their specific circumstances, enhancing the comprehension of healthcare informatics worldwide.

Moreover, the study highlights the significance of interdisciplinary collaboration among healthcare practitioners, technology specialists and policymakers in facilitating the practical application of the Casemix system as one of the clinical and costing modules essential in healthcare settings, especially in facilities equipped with HIS. This interdisciplinary approach to tackling issues in healthcare informatics is generally applicable and can be implemented in various countries and healthcare systems.

To summarise, this study’s immediate findings may address the CSF of the Casemix system implementation within THIS of the healthcare system in Malaysia. However, its broader significance lies in providing valuable insights, methodological frameworks and interdisciplinary approaches that can be applied globally to adopt the Casemix system within the realm of the HIS in other countries, and it is not only applicable locally in the Malaysian setting.

In summary, this research has comprehensively evaluated the fundamental principles outlined in the conceptual framework. Various methodological approaches, including content validity, criterion validity, translation, pre-testing for face validity, pilot testing using EFA and field study employing CFA, have been employed to assess the validity of the items. 12 43 44 50 61 The EFA analysis computed KMO, Bartlett’s test for sphericity and Cronbach’s alpha values, all meeting the criteria for sample adequacy, sphericity and internal reliability. 53–56 97 Additionally, the CFA analysis tested for unidimensionality, construct validity, convergent validity, discriminant validity, composite reliability and normality, further confirming the validity and reliability of the instrument used to evaluate critical success factors and the acceptance of the Casemix system within the THIS context. 53–56 97

Consequently, this validated instrument holds promise for future quantitative analyses, including covariance-based structural equation modeling (CB-SEM) or variance-based structural equation modeling (VB-SEM). In this study, CB-SEM, in conjunction with SPSS-AMOS V.24.0, was used to explore the direct, indirect, mediating and moderating effects among the constructs outlined in the conceptual framework. The findings from these quantitative analyses will be presented in forthcoming articles, providing further insights into the Casemix system’s applicability within the current healthcare landscape. Moreover, the instrument’s demonstrated statistical reliability and validity position is a valuable tool for future research endeavours concerning the Casemix system in the THIS context, addressing an existing research gap. With the establishment of the instrument’s normality, validity and reliability, it can now be considered operational and validated for use in subsequent studies. This research holds the potential to enhance our understanding of the critical success factors and acceptance of the Casemix system, thereby facilitating its improved implementation within the THIS setting. Moving forward, the instrument will be instrumental in conducting further research initiatives to assess the adoption and effectiveness of the Casemix system in THIS environment, addressing a current scarcity of literature.

Ethics statements

Patient consent for publication.

Consent obtained directly from patient(s).

Ethics approval

This study was approved by both the Medical Research Ethics Committee from the Ministry of Health and the Medical Research Ethics Committee from the Faculty of Medicine, Universiti Kebangsaan Malaysia with the reference numbers: NMRR ID-22-02621-DKX and JEP-2022-777 respectively. Informed consent was obtained from all participants through the Google form with a statement that all data would be confidential. All methods were carried out under the ethical standards of the institutional research committee and conducted according to the Declaration of Helsinki. All methods were performed based on the relevant guidelines and regulations. This study was not funded by any grants. The authors declare there were no conflicts of interest concerning this article.

Acknowledgments

In recognition of their involvement and contributions to this study, the authors would like to express their gratitude to the respondents. In addition, the authors would like to express their gratitude to all content and criterion validators of this study: Dr. Fawzi Zaidan and Dr. Nuratfina from the Hospital Financing (Casemix) Unit of the Ministry of Health Malaysia, and Prof. Dr. Zainudin Awang from Universiti Sultan Zainal Abidin. Their remarks and recommendations made a significant contribution to the advancement of this instrument.We express our appreciation to the Casemix System Coordinators, as well as the Hospital and the Deputy Directors from Hospitals W, E, S, N, and EM, for their great collaboration in distributing the questionnaire link and for actively engaging in this study.

Additionally, for their suggestions on improving this paper, the authors would like to express their gratitude to the reviewers. Finally, we also want to express our appreciation to Associate Professor Ts. Dr. Mohd Sharizal for proofreading this article.

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Contributors All authors, NKM, RI, ZA, ANA and SMASJ, have substantial contributions to the conception or design of the work; the acquisition, analysis or interpretation of data for the work; and drafting the work or reviewing it critically for important intellectual content. NKM carried out the pilot test and fieldwork, prepared the literature review, extensive search of articles, critical review of articles, performed the statistical analysis, interpretations, and technical parts, and designed the organization of this paper and original draft write-up. RI advised and supervised the overall write-up and conducted the final revisions of the article. ZA checked and validated the statistical analysis and interpretation of the results. ANA and SMASJ co-supervised the study, the manuscript preparation and the article revision. All authors have read and agreed to the final draft of the manuscript, hence, obtaining a final approval of the version to be published. Additionally, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. RI is responsible for the overall content as guarantor, since she is a corresponding author for this study. The guarantor accepts full responsibility for the finished work and/or the conduct of the study, has access to the data and controls the decision to publish.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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History and future of business ecosystem: a bibliometric analysis and visualization

• Open access
• Published: 27 August 2024

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• Xia Zhang 1 ,
• Yue Yang 1 &
• Yun Chen   ORCID: orcid.org/0000-0001-5123-1624 2  

The business ecosystem theory has developed rapidly in recent years and has become a hot topic in the field of business and management. However, the use of this concept is controversial. This study systematically reviewed literature published spanning nearly three decades from 1993 to 2022. In this paper, researchers designed an improved traceability method to retrieve literature based on data sources form Web of Science. VOSviewer and CiteSpace are adopted as two scientific atlas tools for information processing and visualization to evaluate the relationship between sub fields of business ecosystem. The findings show that the four branches of business ecosystem, i.e., innovation, platform, entrepreneurship and service, absorb theoretical ideas to varying degrees. Among them, the theoretical inheritance relationship of innovation branch is most clear, and gradually grows into the backbone of ecosystem research. Major contribution of this study is reflected in three aspects: Firstly, the improved traceability method provides a repeatable quantitative description process on the basis of significantly reducing researchers’ subjective participation. Secondly, from perspective of bibliometrics, the branch direction and key nodes of theory development are identified. Thirdly, the study helps identify the future development directions of business ecosystem, including innovation, digitalization, entrepreneurship, self-organization and the strategic transformation guided by emerging technologies.

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1 Introduction

Open innovation in business models is as impactful as technological innovation (Chesbrough, 2007 ). At the end of the last century, companies like Apple and Wal-Mart achieved significant success through disruptive innovations based on open platform models. Their achievements have inspired managers and researchers to understand that, in today’s business environment, companies must transcend traditional organizational boundaries to tackle innovation challenges. They need to incorporate external supplements into their governance systems to overcome key bottlenecks that might lie outside organizational control (Adner, 2006 ). In this multi-faceted interaction structure, a system regarded as complex at one level can function as a component in a more extensive system (Christensen & Rosenbloom, 1995 ). Simple bilateral relations cannot fully explain the intricate value relationships among network members in such nested systems, necessitating a shift from existing linear value theories.

In this context, business ecosystem theory emerged. The term “ecosystem” originally described the interactions between organisms and their physical environment (Tansley, 1935 ). This concept has since expanded to encompass complex connections and dynamic evolution beyond natural sciences, profoundly influencing social science research. Business ecosystem theory, a product of interdisciplinary linkages, metaphorically bridges natural and social sciences, offering a groundbreaking business perspective: companies should be viewed not as isolated industry members but as part of a cross-industry business ecosystem (Moore, 1993 ). This theory provides a framework to bridge the gap between reality and theoretical understanding. Surprisingly, it did not gain significant research attention for a long time. Entering the new century, the concept of business ecosystems regained researchers’ interest, with the term appearing sporadically in business research fields. Significant milestones were reached a decade later with two influential studies published in Harvard Business Review (Iansiti & Levien, 2004 ; Adner, 2006 ), leading researchers to recognize the potential of business ecosystems to develop into a comprehensive theoretical knowledge system.

Business ecosystems are not naturally occurring; they are partially shaped by experimental and engineering design from various perspectives (Jacobides et al., 2018 ), reflecting the intentions of system designers. In these systems, each member occupies a unique niche, developing capabilities aligned with goals set by designers, collectively creating value for the entire network (Moore, 2016 ; Iansiti & Levien, 2004 ). System designers are typically one or more core enterprises, referred to as cornerstone companies or focal actors. They simplify complex connections among network participants by creating service, tool, or technology platforms, leveraging platform leadership to influence the innovation direction within the system (Cusumano & Gawer, 2002 ). Non-core companies usually do not rely on a single ecosystem; they benefit from cross-ecosystem operations and diversification strategies. Participation in ecosystems extends the operational scope of non-core firms, equipping them with the management capabilities and technical resources essential for innovation (Selander et al., 2013 ).

Given the diversity of stakeholders, ecosystem structures may represent some of the most extensive network structures in management research (Autio & Thomas, 2014 ). The broad membership facilitates the integration of ecosystem theory with other theoretical paradigms, evolving various ecological branches tailored to different application scenarios. This trait aided the dissemination of concepts in the early stages of theory development. However, with the rapid expansion of terminology usage and fine-grained theoretical applications, the notion of business ecosystems has shifted from being a premium to a discount, similar to a diversified entity (Khanna & Yafeh, 2007 ). Chaotic usage scenarios and blurred theoretical boundaries undermine the theory’s core values, threatening its legitimacy. Some scholars have sharply criticized this trend in recent years (Oh et al., 2016 ; Bogers et al., 2019 ), suggesting that “ecosystems” function more as a “conceptual umbrella” covering various viewpoints rather than a coherent scientific theory (Spigel, 2017 ).

The interdisciplinary nature of business ecosystem theory results in research being widely distributed across various disciplines and fields. This distribution leads to significant subjectivity in the literature review process. Consequently, our study reflects on the limitations of mainstream literature retrieval methods and proposes an improved “traceability method” for collecting literature. Our research focuses on the following three issues:

What is the main scope of relevant research on business ecosystem theory?

What is the logical relationship between the fields of ecological branching?

What are the theoretical development trends and future research directions?

The rest of this paper is structured into four parts. Section 2 introduces the research and data acquisition methods used in this study. Section 3 reveals fundamental information about the retrieved literature, such as growth trends and the distribution of disciplines and journals. Section 4 analyzes and interprets data concerning the three questions above using keyword co-occurrence, co-citation analysis, cluster distribution, burst detection, and timeline trends. Section 5 compares the traceability method used in this study with traditional search techniques and conducts cluster analysis for findings ; The final part, Sects. 6 & 7 summarizes the study, discussing future research directions in this field.

2 Methodology and data sources

The reasonable selection and filtration of literature are crucial factors that enable smooth and accurate research analysis. Traditionally, the data collection process in existing research comprises two main components: conditional restrictions (such as databases, core terms, subject areas, journals, ratings, etc.) and manual review. This study adheres to this approach for the initial phase of data collection and identifies two opposing challenges:

Subject area restrictions or stringent journal designations can compromise the integrity of research on the periphery.

Removing these restrictions risks limiting the scope to the direction of natural ecology.

This issue partly stems from the metaphorical nature of the business ecosystem concept itself.

To address this challenge, conventional methods often rely on a manual screening process, which increases the subjectivity of the investigator. A horizontal comparison of previous data collection methods highlights the prevalence of this issue. Even with the most stringent double restriction method (Tsujimoto et al., 2018 ), the screening rate for manual review exceeds 50% (see Table  1 below). Such intensive screening can introduce researchers’ personal biases, undermining the credibility of discussions on theoretical boundaries.

2.1 Improved traceability method

To address the challenges in the data collection process, this study developed a literature retrieval method based on concept traceability, using two key literatures as foundational points: (1) Moore’s article “Predators and Prey: A New Ecology of Competition” published in 1993, and (2) the monograph “The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems” published in 1996. The former marks the birth of the business ecosystem concept, while the latter provides the first comprehensive explanation of the theory. Given the expanding scope of ecosystem logic, traceability helps distinguish research based on the business ecosystem concept from those that are not. When an article cites these foundational works, it indicates that the author acknowledges a logical connection between their research and the business ecosystem concept, whether positively or critically. The data samples thus obtained form a necessary subset strongly related to business ecosystem theory.

Building on this foundation, researchers employed VOSviewer and CiteSpace for information processing and visualization. Both programs are designed to construct and view bibliometric maps (Eck & Waltman, 2010 ). VOSviewer excels in speed when handling large-scale maps and balances expressive drawing and functionality, while CiteSpace offers greater operability with a unique timeline view and burst detection function. Bibliometric maps provide a systematic method for researchers to understand the evolution of scientific fields and integrate various information to capture the latest technologies (Chen, 2017 ). This study combines the advantages of both tools to mine and expand information, ensuring that gaps in the sample are filled to meet the literature combing sufficiency requirements.

In summary, this research identifies the shortcomings of traditional methods in handling literature related to business ecosystems and proposes an improved traceability method to address the challenges of the manual review process in data collection.

2.2 Adopted data sources

This study uses the Web of Science (WOS) database as the primary data source. WOS is the leading platform for scientific citation search and analysis, supporting a wide range of scientific tasks across different knowledge areas and serving as a data set for large-scale, data-intensive research. When comparing different databases, WOS is typically regarded as the most stable (Harzing & Alakangas, 2016 ; Mongeon & Paul-Hus, 2016 ; Li et al., 2018 ). Although WOS lacks coverage of social science books (Waltman, 2016 ), this does not impact the study’s content.

Using the WOS citation function “Cited References,” 1106 items were retrieved that cited the 1993 baseline literature. Standard restrictions were applied to refine the target scope: selecting the “Social Sciences Citation Index (SSCI)” and “Science Citation Index Expanded (SCIE)” qualification levels to enhance literature quality, restricting subject headings to include “ecosystem*” to ensure relevance, and selecting only “article” types, excluding “early access” articles. As of March 1, 2022, a total of 400 papers met these requirements. The 610 works citing the 1996 baseline monograph were similarly screened, resulting in 189 retained articles. The two literature sets were combined and deduplicated, yielding a final sample of 488 articles. Each document in the sample focuses on ecosystems and is influenced by Moore’s business ecosystem theory to varying degrees, identifying the sample as research “established on the basis of business ecosystem thinking.”

This information query process is general and traceable. For further review, two experts in related fields were invited to examine the samples and list any doubtful literature. If both experts had doubts about the same literature, it was excluded; if they disagreed, consensus was reached through discussion. The results showed that all sample documents successfully passed the review process.

3 Fundamental information of retrieved business ecosystem literatures

This section presents fundamental information about the retrieved literature to outline the contours of the business ecosystem field. It includes the distribution of publications by year, country and region, WOS field, journal, and research institution. Among these indicators, only the distribution ratios for years and journals sum to 1, while other items have cross-connections.

Figure  1 illustrates the growth trend of articles citing Moore’s foundational literature in the WOS database. The earliest related article appeared in 2004, confirming a decade-long period of relative silence for the theory. The research field entered an explosive growth phase around 2012, with the number of published papers continuing to rise after a brief fluctuation. Overall, more than half of the total published papers have been produced in the last three years. Currently, the research concept appears to have reached the mature stage of its life cycle, with the publication growth rate stabilizing.

The growth trend of articles in the field of business ecosystem

Figure  2 illustrates the distribution of documents across different countries and regions, segmented into three time periods represented by different colors. Prior to 2019, the top three countries by the number of articles were the USA, England, and China. In the subsequent two years, China’s share of published articles increased significantly, propelling it to the top rank. As of 2022, the top four countries in terms of total published documents are China, England, the USA, and Finland, with a significant gap between these and the following countries and regions.

Distribution of the sample articles in different countries and regions

Figure  3 demonstrates distribution of literature by different subject areas. “Management” and " Business” categories are the main research fields of this theory. At the same time, there are also a large number of research works involving this theory in the fields such as “Regional Urban Planning”, “Environmental Studies”, “Environmental Sciences” and “Green Sustainable Science Technology”. This suggests that ecosystem theory extends beyond stereotypes and builds bridges between multidisciplinary fields. This echoes our concern that “subject area restrictions or more aggressive designated journal restrictions undermine the integrity of the research fringes”.

The top 11 WOS categories by number of articles

In terms of journal distribution, the 488 articles in the sample are spread across 195 journals. Among these, Technological Forecasting and Social Change and Sustainability have notable quantitative advantages, with 47 and 37 papers published, respectively, accounting for 9.63% and 7.58% of the total. From the perspective of research institutions, the University of Cambridge and Tsinghua University are tied for the highest number of publications, although the University of Cambridge holds a more central position within the knowledge network.

4 Main scope of Business Ecosystem Literatures

This section further analyzes the commonalities and connections between the sample literature, describing the main scope of business ecosystem research using the bibliometric indices “co-occurrence” and “co-citation”.

4.1 Keywords co-occurrence

The full record information of 488 documents was imported into VOSviewer to analyze the co-occurrence of keywords. According to the bibliometric data, 2251 keywords were involved in the sample. To achieve better visualization, the co-occurrence threshold for keywords was set to 6 times, resulting in a visualization map with 135 items, as shown in Fig.  4 below:

Co-keyword network visualization on business ecosystem research

The 135 keywords formed 6 clusters, and the top eleven words sorted by “Total Link Strength” covered all six categories, as shown in Table  2 .

Researchers integrated high-order words calculated by frequency and centrality, categorizing them into three groups:

Initial Search Terms and Derivatives: This includes terms like ecosystem, business ecosystem, network, and business model. Here, the network is related and similar to the ecosystem, with the former being relationship-based and the latter purpose-based. An interesting distinction is that two companies within the same network structure can have vastly different business ecosystems due to differing value propositions (Adner, 2017 ).

Nominalized Verbs: This category includes words such as innovation, value creation, competition, evolution, and cooperation. These terms are highly expressive, reflecting the core of business ecosystem thought. Innovation is the most prominent word, indicating that all business ecosystem projects revolve around innovation. The concept encompasses both dynamic processes and outcomes compared to traditional ecological studies. Notably, “value creation” appeared 88 times, while terms like value distribution or value sharing were scarcely used, highlighting a preference and imbalance in theoretical development. The term competition, particularly in the context of Moore’s “death of competition,” refers to a shift from enterprise to ecosystem competition, often resulting in more intense conflicts between ecosystems.

Generic Terms: This includes words like strategy, performance, technology, knowledge, and framework. Strategy here implies a meso-level perspective, often higher than individual enterprises or industries but below the macro societal level. Performance emphasizes the effective output of ecosystem members, echoing the focus on value creation and reflecting a pursuit of research quantification by scholars.

4.2 Co-citation analysis

A key feature of science mapping is co-citation analysis. When two articles appear together in the bibliography of a third article, they form a co-citation relationship (Chen, 2006 ). The co-citation function identifies significant works in the study of inheritance relationships, isolating weakly related or unrelated literature. This process expands our focus from the 488 documents to those within their citation networks, allowing researchers to identify key research results that connect knowledge networks. Conclusions drawn from this approach are significant for discussions on boundary and genre divisions in business ecosystem theory.

According to bibliometric data, 9,725 citation sources were involved in the sample. By setting a minimum citation threshold of 20, 221 entries were included in the visual map, shown in Fig.  5 . This map highlights journals with significant attention in the field, briefly introduced as follows:

4.2.1 Harvard business review

Known for being forward-looking, it is the origin and cradle of business ecosystem theory, publishing significant works by Moore, Iansiti, and early Adner.

4.2.2 Strategic management journal

Known for outstanding works by Adner and Kapoor ( 2010 ), Jacobides et al. ( 2018 ), and Hannah and Eisenhardt ( 2018 ), these works are frequently cited and remain foundational.

4.2.3 Research policy

Notable for the number of articles published on ecosystems, significantly outperforming other journals in this index.

Using CiteSpace, co-citation analysis was conducted on key nodes. Full record information of 488 documents was imported, with the network clipping method set to “Pathfinder.” In the co-citation graph, node size represents the frequency of occurrences, and line thickness indicates co-occurrence frequency. Figure  6 shows two visualization perspectives of co-citation analysis:

Author Perspective: This map shows the shapers of theoretical foundations, key bottleneck breakthroughs, and continuous investment builders, emphasizing the historical significance of researchers.

Literature Perspective: This map observes field connections and sustained influence, emphasizing the importance of recent research results and depicting a more complex relationship structure between literature.

Co-citation analysis maps from the perspective of author (above) and literature (below)

Among the top ten authors with total citations, Moore, Iansiti, Adner, Jacobides, and Autio have been previously mentioned. Gawer and Nambisan will be introduced in clustering information and burst detection later. Porter and Teece, masters in strategic management and competitive strategy, also provide intellectual value for business ecosystem theory. Porter’s concept of creating shared value aligns with business ecosystem ideas (Porter & Kramer, 2011 ), focusing on value shared within the ecosystem. Teece’s most co-cited work explores innovative support for the digital platform ecosystem (Teece, 2018 ). Additionally, Eisenhardt stands out as a prominent node in the citation network, with her work improving the case study method being frequently cited (Eisenhardt, 2007 ).

This sector explored the scope of business ecosystem literature using co-occurrence and co-citation analyses. The analysis revealed the evolution of business ecosystem research and its integration with strategic management, highlighting the importance of shared value and digital platform ecosystems, and underscoring the historical and ongoing contributions to the field. In the following sector, we will compare the method used in this study with traditional search techniques.

5 Findings and discussion

5.1 comparison between new traceability method and traditional search techniques.

The traceability method proposed in this study offers significant advantages over traditional search techniques. Firstly, it aligns closely with the trajectory of business ecosystem theory, which has a well-documented origin and a ten-year quiescent period, effectively minimizing interference from multiple sources. Secondly, the literature sourced through this method directly links to the theoretical origin, aiding in excluding: 1) Passive fuzzing usage, where researchers use ecological concepts merely as a backdrop without engaging with the theoretical source; 2) Actively blurred usage, where authors may avoid acknowledging the theory’s historical importance for various reasons; 3) Same disciplinary usage, where the concept of ‘ecosystem’ is used differently within the same field, such as the interaction between businesses and natural ecology, without a significant inheritance relationship.

Thirdly, this method mitigates the impact of subjective biases, providing highly discriminative samples that help address contentious issues more effectively.

Although the proposed traceability method has certain limitations compared to traditional search techs, the study has effectively addressed these limitations. One limitation is that it omits documents without citation information, such as articles in the Harvard Business Review, which cannot be retrieved using citation data. Another limitation is the potential overemphasis on certain authors and their research teams, beyond the method’s intended scope. To address the first limitation, this study used bibliometrics to expand the sample and complete the knowledge network. Bibliometric methods employ quantitative approaches to describe, evaluate, and monitor published research, introducing a systematic, transparent, and repeatable review process, thereby enhancing review quality (Zupic & Čater, 2015 ). The second limitation regarding author prominence was addressed by analyzing work from Google Scholar, showing that most of Moore’s ecosystem-related work is independent, with the chosen base points having clear advantages in timelines and citation counts, suggesting that the influence of authorial weight is within acceptable limits.

This study also incorporated a control data set, applying traditional domain constraints like “Management or Business or Economics” and restricting the level to SSCI and SCIE, excluding articles with “early access”. The sample was manually reviewed, resulting in 579 out of 952 articles passing the review. Researchers further validated the new method’s unique advantages by conducting lexical clustering analysis on co-cited documents and comparing these with samples obtained via traditional searches. The analysis, supported by CiteSpace software, confirmed that clusters with a modularity (Q) value above 0.3 and a silhouette (S) value above 0.7 are considered structurally sound and efficient. The new method achieved Q values of 0.926 and S values of 0.952, surpassing traditional methods in creating more coherent and interconnected clusters. The traditional method resulted in scattered clusters with sparse connections, whereas the traceability method produced tightly integrated clusters, enhancing cross-disciplinary linkages and producing distinct cluster labels, which are illustrated in Figs.  7 and 8 .

Clustering comparison of traditional retrieval methods

Clustering comparison of traceability retrieval methods

Comparing the cluster profiles of the two groups of samples, the researchers found significant discrepancy. The clustering modules obtained under the traditional retrieval method are obviously scattered, and the connections between nodes are relatively sparse, while the modules are closely combined under the traceability method, covering more node in the intersection area. These articles serve as a key link between different fields. At the same time, the cluster labels extracted by the two methods are quite different. Tables  3 and 4 respectively list the clustering information of both two samples. The serial numbers are arranged according to the number of members in the group, and the correlation depends more on location of the cluster. With 25 members as the boundary, traceability samples form 7 categories above the scale, and this indicator is 8 in traditional samples. LSI and LLR represent two label extraction algorithms, which are carried out after the clustering ends and do not affect the shape of the clusters.

The results indicate that traditional clustering labels cover a broader range and include general terms like business model and digital platform, suggesting a less precise focus on the research field. New technology hotspots, such as digitization and the Internet of Things, have become central concepts in this theory. The traditional retrieval method often extends literature too far into adjacent disciplines. For example, the semantics of “service-dominant logic” overshadow “service ecosystem,” making it a key clustering label, while entrepreneurship literature is overrepresented, splitting the concept into “Entrepreneurial Ecosystem” and “Value Capture.” Additionally, “digital service” forms a loosely connected category, making it challenging to determine a stable relationship with business ecosystem theory. These issues highlight the negative impact of stringent field restrictions and intensive manual review on the scientific quality of literature samples.

5.2 Relationship between ecological branches and cluster analysis

Despite significant differences, both sample groups agree on basic concepts. They clearly delineate four ecosystem sub-concepts: innovation, platform, entrepreneurship, and service, aligning with mainstream business ecosystem reviews. Business, innovation, and platform clusters hold central positions, while entrepreneurship and service are relatively peripheral. The entrepreneurial ecosystem consistently forms an independent module with a stable member association structure. The following example will analyze the clusters generated according to the traceability method.

Cluster 0 is named as the entrepreneurial ecosystem, and this category has the most group members, and the top three papers with co-citation index are Spigel, 2017 ; Acs et al., 2017 ; Audretsch & Belitski, 2017 . Entrepreneurial flow is an incomplete ecosystem, which is generally limited by geography, and more consideration is given to analysis and research in conjunction with local cultural backgrounds and social systems. There are also barriers in the exchange of entrepreneurial ecosystems and external resources. Entrepreneurs often do not compete for market share, but sell an expectation to attract capital. Therefore, the entrepreneurial ecosystem is likely to lack a dominant player.

On a larger map scale, entrepreneurial ecosystems are connected to knowledge ecosystems, but their value propositions and relational structures are fundamentally different. The centers of the knowledge ecosystem are universities and public research institutions, and value flows mainly linearly along the value chain; the cornerstone of the business ecosystem is the leading company that provides key resources and business infrastructure, and the value creation process adopts an integrated approach (Clarysse et al., 2014 ). It can also be seen from the co-citation relationship that the logical connection between the two concepts is estranged and does not form a major clustering structure. It is worth noting that the process of converting knowledge to business value is still included in the field of business ecosystem research.

The label of cluster 1 is the subject word business ecosystem, and the top three documents in the co-citation index are Adner, 2017 ; Gawer & Cusumano, 2014 ; Oh et al., 2016 . According to Moore’s ( 2016 ) definition, business ecosystem is an economic community of suppliers, major producers, consumers, competitors, and other stakeholders whose members collectively develop their capabilities and tend to align with the direction set by one or more central companies. Iansiti and Levien ( 2004 ) summarized the roles of companies in the business ecosystem as cornerstone, dominant and niche; and constructed three health indicators for evaluating business ecosystems: productivity, robustness and niche creation. As can be seen from the two core literatures of business flow, the school starts from the role of stakeholders, studies the behavior and activities of the participants, and finally boils down to the value proposition of the system. Adner ( 2017 ) reads this process in reverse, starting with a value proposition, considering the activities needed to materialize it, and ending with actors that need to be adjusted. A logical deepening develops between the two schools, the former emphasizing roles and structural relationships, the latter emphasizing value propositions and changing processes. From the perspective of operational effects, starting from the value proposition helps to establish connections with potential participants and achieve multilateral interaction.

Cluster 3 is named platform ecosystem or two-sided marketplace. The top three articles in the co-citation index are Gawer, 2014 ; McIntyre & Srinivasan, 2017 ; Reuver et al., 2018 . Platform may be the fastest growing of all research streams. Under the trend of the Internet of Everything, any business form can be built on the platform, but only by focusing on platform behavior can it be regarded as a platform genre literature. Gawer ( 2014 ) defines an external platform as a product, service or technology, that is the ecological basis for an organization’s external innovators to develop their own complementary products, technologies or services. We also noticed that the platform is in a crossover zone, and its S value is only 0.852, which is in a low range. This means that its composition is more complex.

Cluster 4 is named Innovation Ecosystem, with an S-value of 0.965 being the highest in the list. This indicates a high homogeneity of the set. The top three papers in this cluster are Jacobides et al., 2018 ; Gomes et al., 2018 ; Hannah & Eisenhardt, 2018 . Jacobides et al., ( 2018 ) believes that the mainstream of ecological literature includes business flow, innovation flow and platform flow. The above-mentioned schools of business ecosystem theory have inherited the commonalities of ecosystem research. The ecological characteristics that have been agreed upon are modularity, complementarity, multilateral market relationships and common value proposition. This work by Jacobides is also the most recent explosive literature (Fig.  8 ). What deserves special attention is that the outbreak period of this document has not yet ended, and its second-ranked intensity score still has a large room for improvement.

The label of cluster 6 is service ecosystem, and the top three co-citation literatures are Vargo & Lusch, 2016 ; Lusch & Nambisan, 2015 ; Vargo et al., 2015 . Compared with the logic deepening of “role” to “structure” in the business school, the service school tends to transform from “product” to “service”. In this process, the service-dominant (S-D) logic is the core. Humorously, the research positions of Vargo and Lusch, the founders of S-D logic, may still be slightly different. Moore’s work is almost never cited in Vargo’s literature, while Lusch describes in detail the process of combining S-D logic and ecosystems: a relatively independent and self-regulating system consisting primarily of loosely coupled social and economic actors linked together by shared institutional logic and exchange of services to create common value (Lusch & Nambisan, 2015 ).

The top three co-citation literatures of other two clusters are Tsujimoto et al., 2018 ; Rong et al., 2015 ; Russell & Smorodinskaya, 2018 (cluster 3); and Adner & Kapoor, 2010 , Adner, 2012 ; Basole & Karla, 2011 (cluster 5). Due to space limitations, the introduction will not be carried out. Readers can read and refer to it by themselves. In particular, digitization has been inserted into multiple research streams and has the potential to develop into an independent digital ecosystem school. From the perspective of cohesion, the concept is only lack of landmark literature from the perspective of ecosystem.

6 Development trends and future research directions in business ecosystem

The burst detection function in CiteSpace is used to investigate the phenomenon of sudden increases in the frequency of research topics over a short period, with intensity indicating the level of attention to these hotspots. In the field of business ecosystem research, 43 outbreak literature nodes were initially identified using default parameters. By adjusting the criteria, researchers narrowed this down to the nine most significant pieces of literature.

As shown in Fig.  9 , these nine articles play a crucial role in the evolution of research directions. Business ecosystems and innovation ecosystems exhibit contrasting logical structures, forming at the intersection where a role-based perspective transitions to a structural perspective (Adner & Kapoor, 2010 ; Kapoor, 2018 ). The independence of the innovative school signifies a shift in ecosystem research from a metaphorical ecological relationship to the fundamental logic of business activities. Another critical aspect is examining the value creation and value capture processes as interconnected components (Ritala et al., 2013 ), which helps bridge the research gap resulting from an overemphasis on value creation.

The top 9 literatures by burst strength

Nambisan ( 2013 ) discussed the innovation ecosystem and entrepreneurial environment within the context of central platforms. Due to the overlapping meanings of “business ecosystem” and “innovation ecosystem,” this article serves as a bridge connecting the four main modules. The mixing of terms is common in platform research. In this context, Moore and Iansiti’s work is recognized for their research on platform-based business ecosystem innovation (Gawer & Cusumano, 2014 ). One of the figures summarizes literature related to the platform ecosystem and compares it with the literature flow of other platforms (Thomas et al., 2014 ).

Figure  10 illustrates the time axis map of the 13 main research lines. Solid lines indicate that a line has formed an emerging research area, while dotted lines suggest a cooling trend. Analysis shows that the two-sided market route transitioned to the innovation ecosystem route around 2018, with the business ecosystem branch completing this shift earlier. The convergence of these paths has fostered the growth of the innovation branch into a mainstream research line. The service path has developed steadily for a long period, though its popularity has waned in the past two years. The digital technology research series draws from multiple branches, with its influence steadily expanding, making it the route with the most development potential. Generally, the life cycles of Routes 2, 9, 10, 12, and 15 are relatively short and have been out of the spotlight for a long time. Conceptual fields such as entrepreneurship, innovation, Internet of Things, digitalization, and self-organization continue to release energy, with innovation and digitization leading the way.

Timeline map of main research routes

The research findings indicate that future development in the ecological domain will predominantly focus on innovation, digitization, entrepreneurship, self-organization, and strategic transformations driven by technologies such as the Internet of Things. Due to extensive digital scene construction and industrial digital transformation, digital ecosystem theory is well-grounded in practice and has the potential to evolve into a distinct research domain. Business ecosystem theory effectively captures the dynamic evolutionary process of value logic through three critical links: value creation, value capture, and value sharing. While there is substantial work on integrating value creation with value capture, research that intricately weaves these with value sharing remains scant.

Following the model proposed in our paper, relevant literature in the field has emerged. Consequently, we have adopted this traceable method to identify and review 17 documents published since March 2022, aiming to examine recent research developments. The key findings from this review are discussed below.

Yoon et al. ( 2022 ) examined the connection between business and biological ecosystems, suggesting that a key specie, a leader within a business ecosystem, can enhance its success by strategically managing symbiotic relationships; Shou et al. ( 2022 ) deconstructed business ecosystems into four aspects: complementarity, capabilities, co-creation, and co-evolution, noting that many of the world’s largest and most valuable companies adopt this ecosystem approach. The lack of a unified understanding of business ecosystem features and characteristics complicates the ability of business leaders to formulate and implement effective strategies; Hoeborn et al. ( 2022 ) developed a morphological framework describing all value systems and applied it to business ecosystems, linking its characteristics with ongoing inter-organizational research to aid practitioners in implementing ecosystem concepts; Chandrasekharan and Titov ( 2022 ) explored the business models within the ÜlemisteCity ecosystem to understand the conceptualization of business models and the factors influencing their creation or transformation from an ecosystem perspective, developing a conceptual framework to enhance organizational participation and value processes within ecosystems. Cui et al. ( 2022 ) explored how key enterprises govern their business ecosystems under conditions of resource abundance and resource scarcity.

Further studies have linked business ecosystems to various industries, exploring structural dimensions and standards for assessing industries. Chang et al. ( 2022 ) used fuzzy hierarchical analysis, fuzzy decision-making methods, and experimental laboratory methods to construct five evaluation dimensions and thirty-one evaluation criteria to explore the open data service industry from the perspective of the business ecosystem. Winkler et al. ( 2023 ) demonstrated how knowledge misalignment, knowledge gaps, cultural differences, insufficient building codes, frequently changing regulations, and the implementation of highly embedded innovations disrupt ecosystem coordination, by studying the challenges faced by business ecosystem coordination when implementing solar PV systems in the Swedish built environment. Zhao et al. ( 2022 ) explored the structure of the business ecosystem required for companies to achieve sustainable performance and investigated the open innovation that can be promoted on this basis. Mann et al. ( 2022 ) introduced orchestration as a concept to pursue this research opportunity, using it to observe digital transformation in business ecosystems. Fort ( 2023 ) studied productivity and fairness in the U.S. financial market from the perspective of the business ecosystem. Wei and Li ( 2023 ) researched the impact of platform strategies and niche strategies on corporate growth based on the perspective of business ecosystem positioning. Suuronen et al. ( 2022 ) revealed the significant impact of digital business ecosystems on the industry through a systematic literature review of the prerequisites, challenges, and benefits of manufacturing DBEs. Yi et al. ( 2022 ) examined stakeholder relationships, organizational learning, and business model innovation based on the perspective of business ecosystem research systems. Burström et al. ( 2022 ) integrated business and digital ecosystem literature to study the present and future of software ecosystems. Kokkonen et al. ( 2023 ) studied digital twin business ecosystems based on qualitative data collected from six case companies in the manufacturing industry. Marques-McEwan et al. ( 2023 ) investigated the transition to CE in the chemicals manufacturing industry, revealing the rules for creating circular business ecosystems. Zhu and Du ( 2023 ) investigated the impact on the value of existing business ecosystems when new innovations are introduced, through an event study of Google’s self-driving car announcement.

Collectively, these insights not only deepen academic understanding of business ecosystems but also guide enterprises in formulating and implementing effective strategies in today’s complex business landscape. As digital scene construction and industrial digital transformation continue to solidify the practical foundation for integrating digitalization with ecosystem theory, the direction is poised to evolve into an independent branch of study. However, research methodologies still require further refinement to broaden theoretical applicability. Facing these challenges, coupling business and social ecosystems offers a viable direction. Developing standards and regulatory frameworks to guide sustainable business ecosystem constructions and prevent capital-driven changes in cornerstone enterprises’ nature remain critical future research topics.

7 Conclusion

This paper designed an improved traceability method to retrieve literature related to business ecosystem theory in the WOS database, aiming to avoid interference from the stringent field restrictions and intensive manual screening typical of traditional retrieval methods. Co-occurrence, co-citation, and cluster analyses were used to outline the context of knowledge production, with research results visualized using two scientific mapping tools, VOSviewer and CiteSpace.

This study provides several key insights. Firstly, innovation, platform, entrepreneurship, and service, as main ecological branches, inherit business ecosystem theory to varying degrees. The innovation branch has a clear inheritance relationship and has become a new backbone of ecosystem research. The platform branch has a relatively loose association structure with extensive cross-links to other branches. The entrepreneurial branch’s unique theoretical application scenarios make it easily distinguishable. The service branch combines S-D logic with business ecosystem theory, but research progress on this branch’s ecosystem preference is slow due to S-D logic’s prominence. We identified the shapers of theoretical foundations, breakthroughs of key bottlenecks, and builders of continuous investment in each branch, focusing on nine key literatures that bridge different fields and play a significant role in ecosystem research development.

Although the study offers valuable references for scholars as discussed above, some limitations should be noted and addressed in future research. Firstly, the sample data is sourced from a single database, limiting journal coverage. Secondly, early literature citations are inconsistent, compounded by the impact of journal literature without citations, creating obstacles for vertical logical context and visual analysis. Finally, this article proposes a literature retrieval strategy based on the genealogy of concepts, using James Moore’s seminal works as temporal benchmarks, i.e. his1993 article “Predators and Prey: A New Ecology of Competition,” marking the inception of the business ecosystem concept; and his 1996 book, ‘The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems,’ which provided the first comprehensive interpretation of the theory. However, Moore’s introduction of the concept in 1993 did not gain academic acceptance until a decade later, with significant studies emerging only in 2022. This highlights the unique aspects of studying this concept. While the traceability method is suitable for historical research of business ecological theory, its application in other research domains may introduce noise, requiring careful judgment by researchers regarding specific circumstances. Therefore, discussing the limitations and applicability of this method to other fields is essential.

Data availability

The data that support the findings of this study are derived from public domain resources, which are available in Web of Science.

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The research leading to these results received funding from Chongqing Education Commission, under Grant Agreement No.: 23SKGH138: “Research on the relationship between the ecological dominance of chain owner enterprises, supply chain integration and supply chain innovation performance”.

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Zhang, X., Yang, Y. & Chen, Y. History and future of business ecosystem: a bibliometric analysis and visualization. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05318-6

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