research topics on stakeholder management

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• Published: 11 July 2018

How to engage stakeholders in research: design principles to support improvement

• Annette Boaz   ORCID: orcid.org/0000-0003-0557-1294 1 ,
• Stephen Hanney 2 ,
• Robert Borst 3 ,
• Alison O’Shea 1 &
• Maarten Kok 4  

Health Research Policy and Systems volume  16 , Article number:  60 ( 2018 ) Cite this article

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Closing the gap between research production and research use is a key challenge for the health research system. Stakeholder engagement is being increasingly promoted across the board by health research funding organisations, and indeed by many researchers themselves, as an important pathway to achieving impact. This opinion piece draws on a study of stakeholder engagement in research and a systematic literature search conducted as part of the study.

This paper provides a short conceptualisation of stakeholder engagement, followed by ‘design principles’ that we put forward based on a combination of existing literature and new empirical insights from our recently completed longitudinal study of stakeholder engagement. The design principles for stakeholder engagement are organised into three groups, namely organisational, values and practices. The organisational principles are to clarify the objectives of stakeholder engagement; embed stakeholder engagement in a framework or model of research use; identify the necessary resources for stakeholder engagement; put in place plans for organisational learning and rewarding of effective stakeholder engagement; and to recognise that some stakeholders have the potential to play a key role. The principles relating to values are to foster shared commitment to the values and objectives of stakeholder engagement in the project team; share understanding that stakeholder engagement is often about more than individuals; encourage individual stakeholders and their organisations to value engagement; recognise potential tension between productivity and inclusion; and to generate a shared commitment to sustained and continuous stakeholder engagement. Finally, in terms of practices, the principles suggest that it is important to plan stakeholder engagement activity as part of the research programme of work; build flexibility within the research process to accommodate engagement and the outcomes of engagement; consider how input from stakeholders can be gathered systematically to meet objectives; consider how input from stakeholders can be collated, analysed and used; and to recognise that identification and involvement of stakeholders is an iterative and ongoing process.

It is anticipated that the principles will be useful in planning stakeholder engagement activity within research programmes and in monitoring and evaluating stakeholder engagement. A next step will be to address the remaining gap in the stakeholder engagement literature concerned with how we assess the impact of stakeholder engagement on research use.

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Closing the gap between research production and research use is a key challenge for the health research system. Stakeholder engagement is being increasingly promoted across the board by health research funding organisations, and indeed by many researchers themselves, as an important pathway to achieving impact [ 1 ]. The literature is diverse, with a rapidly expanding but still relatively small number of papers specifically referring to ‘stakeholder engagement’, and a larger number of publications discussing issues that at least partly overlap with stakeholder engagement. Several of the papers explicitly analysing stakeholder engagement come from the field of environmental research (e.g. Jolibert and Wesselink [ 2 ], Phillipson et al. [ 3 ]). However, stakeholder engagement is also gaining traction in the health field. A recent supplement in this journal consolidated learning relating to tools and approaches to stakeholder engagement within the United Kingdom Department for International Development’s Future Health Systems research consortium [ 4 ]. In particular, in health, there is an important stream of analysis from North America. A review for the United States Agency for Healthcare Research and Quality drew on papers from a range of fields [ 5 ].

This opinion piece provides a short conceptualisation of stakeholder engagement, followed by ‘design principles’ that we put forward based on a combination of existing literature and new empirical insights from our recently completed longitudinal study of stakeholder engagement in research. We have drawn on a systematic literature search conducted to inform the wider study and in particular to conceptualise stakeholder engagement (Additional file  1 ).

Literature review

Conceptualising stakeholder engagement: what does the literature say.

Stakeholders have been defined as “ individuals, organizations or communities that have a direct interest in the process and outcomes of a project, research or policy endeavor ” ([ 6 ], p. 5). In seeking to conceptualise stakeholders, Concannon et al. [ 7 ] developed the 7Ps Framework to identify stakeholders in Patient-Centered Outcomes Research and Comparative Effectiveness Research in the United States of America. The 7Ps are patients and the public, providers, purchasers, payers, public policy-makers and policy advocates working in the non-governmental sector, product makers, and principal investigators. The seven categories signal an overlap with the large literature on patient and public involvement (PPI) in research. However, our focus here is on multi-stakeholder engagement, where diverse groups of stakeholders take part in the research process. Deverka et al. [ 6 ] define engagement as “ an iterative process of actively soliciting the knowledge, experience, judgment and values of individuals selected to represent a broad range of direct interest in a particular issue, for the dual purposes of: creating a shared understanding; making relevant, transparent and effective decisions ” ([ 6 ], p. 5).

Roles, activities and phases of stakeholder engagement: What does the literature say?

There are additional issues about the definition of stakeholder engagement when the nature of the engagement activities is considered. For example, there are issues about how far co-creation/participatory action research approaches can be considered to be stakeholder engagement or something so far beyond the usual stakeholder engagement that they are really in a different category [ 8 ]. Similarly, there is a large and currently distinct literature on PPI in research [ 9 ], including the development of reporting guidelines such as GRIPP2 [ 10 ]. There are a number of parallels in the issues discussed in these literatures as well as some interesting differences (particularly in terms of power inequalities). However, herein, we conceptualise PPI as a subset of stakeholder engagement in-line with most of the literature, including Concannon et al. [ 7 ].

Most of the stakeholder engagement literature highlights the broad range of activities in which stakeholders can engage depending on their own skills and attributes and the capacity and wishes of the researchers conducting specific studies. At the broadest level of a research system, or research funding body, Lomas [ 11 ] claimed there were many activities in which stakeholders could be engaged in a ‘linkage and exchange’ approach for health services research. These were setting priorities, funding programmes, assessing applications, conducting research and communicating findings. The importance of engaging a wide range of stakeholders in priority-setting has often been emphasised. The pioneering study by Kogan and Henkel [ 12 ] analysed both the importance of engaging policy-makers in setting research agendas to meet their needs, and the obstacles to making the process work well. These obstacles included issues around how far the assessment of needs-based research should focus on the relevance and practical impact of the research as well as its scientific merit. Many of the more recent studies explicitly examining stakeholder engagement also set out a range of activities in which stakeholders may be involved. These are often related to phases of the research processes. Concannon et al. [ 7 ] provide a list of roles related to stages and used the identified roles in a subsequent review [ 13 ].

Knowledge translation (KT) is one of many terms used to describe efforts to ensure research evidence is used to inform decision-making [ 14 ]. Although the importance of engaging stakeholders in KT is recognised, it has been acknowledged that stakeholder engagement is often overlooked in favour of more conventional dissemination strategies [ 15 ]. Integrated KT has been developed as an approach to collaborative research in which researchers work with stakeholders who identify a problem and have the influence and sometimes authority to implement the knowledge generated through research [ 16 , 17 ]. Grimshaw et al. [ 14 ] argue that different groups of stakeholders are likely to be engaged depending on the type of research that is being translated.

Assessing the impact of stakeholder engagement: What does the literature say?

A final consideration about the nature of the body of literature specifically on stakeholder engagement is that not only is it still quite limited in total, but there are also notable areas where authors claim it is particularly sparse. In particular, Hinchcliff et al. [ 18 ] examined the literature on multi-stakeholder health services research collaborations in an attempt to address the question of whether it was worth investing in them. They identified very few studies (Harvey et al’s. [ 19 ] 2011 evaluation of a Collaboration for Leadership in Applied Health Research and Care being one exception) and concluded that their generalisability was questionable. They therefore suggested that “ The lack of reliable evidence compels implementers to rely largely on trial and error, risking variable success ” ([ 18 ], p. 124).

The nature of engagement activity is less contentious than the arguments about its potential impact. Research impacts on non-academic audiences are defined by the United Kingdom Higher Education Funding Council as: “ benefits to one or more areas of the economy, society, culture, public policy and services, health, production, environment, international development or quality of life, whether locally, regionally, nationally or internationally ” [ 20 ]. Various studies have attempted to assess a range of impacts of research (especially health research) and/or attempted to identify facilitators and barriers of research use in policy-making. There are also a growing number of reviews of such studies [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. While these are not explicitly studies of stakeholder engagement, many of them have identified some form of collaboration between researchers and users as one of the factors most likely to lead to the research making an impact. However, this wider range of literature does not go into detail in terms of analysing the nature of the processes of stakeholder engagement that leads to impact.

Studies specifically focusing on the impact of stakeholder engagement are less common, although it is a growing area of interest [ 28 , 29 ]. Jolibert and Wesselink [ 2 ] found a few examples of impact, but suggested ways to increase impact through what they describe as sustained interactions. Concannon et al. concluded that approximately 20% of their study participants “ reported that stakeholder engagement improved the relevance of research, increased stakeholder trust... enhanced mutual learning by stakeholders, and researchers about each other, or improved research adoption ” ([ 13 ], p. 1697), whereas 6% reported improved transparency and 9% increased understanding of research processes. Also, while Forsythe et al. referred to a lack of evidence about impact, they also observed that “ Commonly reported contributions included changes to project methods, outcomes or goals; improvement of measurement tools; and interpretation of qualitative data ” ([ 30 ], p. 13). In the United States, the Center for Medical Technology Policy website makes a strong statement about the impact of stakeholder engagement: “ Including the perspectives of all key stakeholders has powerful benefits, enhancing both the short- and long-term relevance of clinical research efforts ” [ 31 ].

Assessing the impact of stakeholder engagement: a new study

Given the diversity of stakeholder engagement and the thin evidence base for its impact, our study set out to identify a set of indicators that might be used to identify stakeholder engagement with potential for impact. We identified a study called the European study on Quantifying Utility of Investment in Protection from Tobacco (EQUIPT) and then conducted our own study, Stakeholder Engagement in EQUIPT (SEE-Impact) as a prospective study of stakeholder engagement running alongside. EQUIPT, a major European Commission (EC) – funded project, aimed to achieve impact through extensive stakeholder engagement. Both studies are briefly described in Box 1.

The results of the EQUIPT study have now been published [ 32 , 33 ] and a full account of the main methods from SEE-Impact have been submitted for publication. Papers on the full findings are being finalised. Herein, our aim is to address the statement in our original funding proposal in 2013 that it should be possible to identify aspects of the stakeholder engagement (and perhaps other features of the processes) that might be viewed as intermediate indicators of the eventual impact achieved.

Our analysis of the complex and nuanced process of stakeholder engagement has resulted not in a list of indicators, but in a set of design principles. We hope that these design principles will help to inform the future development of stakeholder engagement as a mechanism for promoting research impact. These principles, rooted in both the existing literature and in the findings from our prospective study of stakeholder engagement, are intended to inform the planning and delivery of stakeholder engagement activities. It is anticipated that they will also provide a structure for building a narrative account of stakeholder engagement as part of an evaluation of an individual project or programme. They might also provide a starting point for the development of future indicators.

Design principles for stakeholder engagement in research

The project team (comprising members of the SEE-Impact research team and collaborators from EQUIPT) met for a 2 day analysis workshop. One aim of the workshop was to begin to build a consensus among the team on what seemed to be the key design principles emerging from the SEE-Impact data and the on-going literature review. SEE-Impact data included observational data, interviews and document analysis. The research team continued to develop the principles through an ongoing period of deliberation, informed by the impact study and the literature. As part of this process, the principles were categorised into three groups, namely organisational, values and practices.

In this section, we first present empirical evidence from the SEE-Impact study that informed our development of the design principles. We then briefly summarise published evidence for each group of design principles in order to situate them in the wider literature.

Design principles and empirical evidence from the stakeholder engagement in EQUIPT for impact (SEE-impact) study

The stakeholder engagement study (SEE-Impact) and the project being studied (EQUIPT) are described in Box 1. In terms of the organisational level principles, the EQUIPT project objectives for stakeholder engagement were clear, as set out in the proposal, protocol and project documents [ 34 ]. The key aims of stakeholder engagement activity were to access the knowledge and skills (described in the protocol as co-creation innovation in the working space) and to increase influence and impact (described in the protocol as dissemination innovation in the transfer space through stakeholder engagement).

In terms of values, the commitment to stakeholder engagement was more clearly demonstrated by some of the EQUIPT project team members than others. For some team members, previous successful experience of an interactive form of working with stakeholders had built a commitment to this particular way of working. It also provided experience of practical elements of working with stakeholders, but perhaps most importantly lived experience of the practical benefits of engagement. For other members of the team, too, working with stakeholders fitted closely with their ethos and values. For example, the Hungarian team talked about their pragmatic approach to research and the need to conduct useful and usable research, with stakeholder engagement being a key component. However, a small group within the wider project team did not seem committed to ensuring stakeholder engagement remained a core element of the project. They favoured a particular, individualised approach to stakeholders and, over time, partially reshaped the stakeholder engagement activities to something more akin to research participation (that is, taking part in a research study as a means of generating specific data as determined by researchers, rather than as co-producers of research). Finally, not all stakeholders identified by the project team were interested in engaging with the project. In particular, the lack of policy priority given to smoking cessation (the focus of the return on investment (ROI) tool) made engagement of policy stakeholders in the Netherlands very difficult to achieve.

In terms of practices, while the EQUIPT project protocol did set out how the stakeholder engagement would operate [ 34 ], there was not as much flexibility as the investigators would have liked in terms of the project plan and this had an impact on the nature of the stakeholder engagement activities. In particular, time intensive methods of engagement originally proposed in the protocol (particularly the large number of face-to-face meetings) began to look unrealistic to members of the team. The lack of flexibility came in part from the funder. The EC told the project team at an early point that there was no scope for negotiation around the project end date. Thus, initial delays in the project put a strain on the project timetable and deliverables. Members of the team proposed a shift from face-to-face meetings with stakeholders to Skype meetings in an effort to ‘catch up’. The technical team producing the new version of the ROI tool for roll out in Europe added to a sense of urgency in ‘speeding up’ the stakeholder engagement work with their need for data to feed into their work. Nevertheless, despite the practical difficulties, in EQUIPT, a significant amount of consideration had been given to stakeholder engagement, including planning how the input provided by stakeholders might be gathered, collated, analysed and used. Vokó et al. highlight that it is important to “ fully analyse several aspects of stakeholder engagement in research ” ([ 32 ], p. 15) and note that there is a tendency to ignore the value of early stakeholder engagement when it comes to development and transferability in the work of economic evaluation. EQUIPT’s careful consideration and the methods adopted facilitated a much more rigorous approach to stakeholder engagement than is often experienced.

Design principles and supporting literature

The design principles for stakeholder engagement are organised into three groups, namely organisational, values and practices, albeit with some inevitable overlaps. We look at each category in turn, alongside a consideration of some of the relevant literature.

Organisational

Clarify the objectives of stakeholder engagement

Embed stakeholder engagement in a framework or model of research use

Identify the necessary resources for stakeholder engagement

Put in place plans for organisational learning and rewarding of effective stakeholder engagement

Recognise that some stakeholders have the potential to play a key role

Some examples from the literature

It is desirable to have a conceptual framework that situates stakeholder engagement as part of a plan for promoting research use in practice. Deverka et al. [ 6 ] proposed an ‘analytic-deliberative’ conceptual model for stakeholder engagement which “ illustrates the inputs, methods and outputs relevant to CER [comparative effectiveness research]. The model differentiates methods at each stage of the project; depicts the relationship between components; and identifies outcome measures for evaluation of the process ” ([ 6 ], p. 1). Furthermore, having a clear evaluation plan is considered critical. Concannon et al. recommended conducting “ evaluative research on the impact of stakeholder engagement on the relevance, transparency and adoption of research ” ([ 13 ], p. 1698). Esmail et al. argue that evaluations of stakeholder engagement should be “ designed a priori as an embedded component of the research process ” ([ 35 ], p. 142). They suggest that, where possible, evaluations should use predefined, validated tools. Jolibert and Wesselink [ 2 ] point out that linking stakeholders’ contributions with specific research objectives is important in order to establish when and how to engage and with whom. They argue that, at the recruitment stage, stakeholders should be made aware of, for example, their role/s, what they could contribute, costs in terms of time and effort, and benefits. Concannon et al. also conclude that funding is needed “ to account for the costs of implementing meaningful engagement activities ” ([ 7 ], p. 989).

In a Canadian study looking at stakeholder involvement in KT as a means of leading to more evidence-informed healthcare, Holmes et al. [ 36 ] identify a range of complexities which, they argue, need to be taken into account by funding schemes in order to meet funders’ and stakeholders’ expected ROI. Stakeholder involvement in research and implementing its findings is complex and time consuming, and the authors recommend an advocacy role where funders support a range of activities to address barriers to effective KT. These include carrying out an assessment of stakeholders’ KT needs “ to identify gaps and opportunities and avoid duplication of efforts ” ([ 36 ], p. 6). Kramer et al. [ 37 ] looked at the involvement of intermediary organisations as research partners on three interventions across four sectors, namely manufacturing, transportation, service and electrical utilities sectors. The authors describe the difficulties, benefits and challenges from the perspectives of both researchers and research partners and stress the importance of allowing the design of the protocol to be collaborative and flexible. Researchers need to honour, trust and respect their partners’ knowledge and expertise, and take into account their needs and priorities. Failure to meet these criteria will significantly dampen stakeholders’ enthusiasm. They also point out the importance of having a model of collaborative research with clear guidelines of how to conduct partnership research projects in order to further facilitate the use of research by practitioners. There would be an invested interest in “ the research question, design and findings, and this would prove to be very valuable as a knowledge transfer strategy ” ([ 37 ], p. 330).

The main literature on stakeholder analysis of policy-making is also useful for highlighting that some stakeholders have more potential to play a key role in the policy deliberations than others. For example, as part of their review of stakeholder analysis of health policy-making, Brugha and Varvasovszky [ 38 ] described an example in which the Hungarian Ministries of Finance and Industry were non-mobilised, high-influence, low-interest stakeholders in debates about public health interventions, but might, in some circumstances, become mobilised high-interest actors.

Foster shared commitment to the values and objectives of stakeholder engagement in the project team

Share understanding that stakeholder engagement is often about more than individuals

Encourage individual stakeholders and their organisations to value engagement

Recognise potential tension between productivity and inclusion

Generate a shared commitment to sustained and continuous stakeholder engagement

Concannon et al. [ 7 ] stress that researchers and stakeholders should be committed to the processes from the outset. Hinchcliff et al. [ 18 ] argue that it is important to define expectations and roles and provide time. Hering et al.’s [ 39 ] global study of water science and technology used stakeholder involvement in the objectives and approaches of the research for the co-production of knowledge as part of transdisciplinary research. Key aspects of particular value to the research included early identification and involvement of stakeholders, continuous engagement with stakeholders, and availability to stakeholders of supporting materials and in multiple languages. Mallery et al. recommend continuing to build trust with stakeholders “ throughout the engagement process ” ([ 5 ], p. 27).

Plan stakeholder engagement activity as part of the research programme of work

Build flexibility within the research process to accommodate engagement and the outcomes of engagement

Consider how input from stakeholders can be gathered systematically to meet objectives

Consider how input from stakeholders can be collated, analysed and used

Recognise identification and involvement of stakeholders is an iterative and ongoing process

Forsythe et al. [ 30 ] highlight the importance of careful and strategic selection of stakeholders. As part of evidence and experience-based guidance to researchers and practice personnel about forming and carrying out effective research partnerships, Ovretveit et al. [ 40 ] developed a guide to categorise and describe types of partnerships or approaches to collaborative working. The guide sets out a framework for the roles and tasks, and the allocation of responsibilities for each partner involved. Roles and tasks are assigned to three main categories, namely questions, design and data, reporting and dissemination, and implementation and integration into organisation or policy. Concannon et al. [ 13 ] suggest the need to develop (and validate) stakeholder engagement tools to support engagement work. Forsythe et al. also stress the importance of “ establishing ‘parameters and expectations for roles’, giving stakeholders guidance, and allowing time for stakeholders to ‘get comfortable with their roles’ as important tasks ” ([ 30 ], p. 19).

The review of methods of stakeholder engagement conducted by Mallery et al. [ 5 ] identified a range of innovative methods and stressed the potential for engaging stakeholders at different points in the research process. The five methods highlighted for consideration were online collaborative forums, product development challenge contests, online communities, grassroots community organising and collaborative research. Jolibert and Wesselink [ 2 ] explored levels and types of stakeholder engagement in 38 EC-funded biodiversity research projects and the impacts of collaborative research on policy, society and science. They looked at how and when stakeholders were involved and the roles played, and argue that greater engagement throughout the whole of the research process, rather than, for example, at the dissemination stage, tends to lead to improved assessment of environmental change and effective policy proposals. Jolibert and Wesselink suggest, following Huberman’s [ 41 ] work in education, that it is desirable to have ‘sustained interactivity’ between researchers and users. Concannon et al. suggest that “ General principles can be drawn from community-based participatory research, which underscores that engagement is a relationship-building process ” ([ 7 ], p. 988). They found that, if bi-directional relationships are sustained over time, stakeholders can serve as ambassadors for high-integrity evidence even where the findings are contrary to generally accepted beliefs. Hinchcliff et al. point to the importance of “ building respect and trust through ongoing interaction ” ([ 18 ], p. 125). Forsythe et al. flag up the importance of continuous involvement and using in-person contact to build relationships [ 30 ]. They also stress the value in having a flexible approach that can adapt to the practical needs of stakeholders. A recent supplement of this journal edited by Paina et al. [ 4 ] also highlighted the importance of flexibility in making space for stakeholder engagement in research processes.

Based on the literature and the application of initial principles to our study, we have developed the elaborated design principles presented in Box 2.

Conclusions

There is a growing interest in stakeholder engagement as a potentially promising approach to promoting research impact. There is also a developing literature mapping out who potential stakeholders might be (the ‘who’), considering approaches to stakeholder engagement (the ‘how’) and identifying rationales for stakeholder engagement (the ‘why’). In this paper, evidence from the literature around these dimensions has been combined with the findings from our study of stakeholder engagement in an EC-funded project to develop a set of design principles to inform future stakeholder engagement in research. The design principles encompass organisational factors, values and practices. We hope that the principles will be useful in planning stakeholder engagement activity within research programmes and in monitoring and evaluating stakeholder engagement. Active engagement of stakeholders may well shift our understanding of what research use looks like [ 39 ]. A next step will be to address the remaining gap in the stakeholder engagement literature concerned with how we assess the impact of stakeholder engagement on research use.

Box 1: Studying stakeholder engagement in tobacco control policy

EQUIPT: the European-study on Quantifying Utility of Investment in Protection from Tobacco

The EQUIPT study set out to work with stakeholders to develop a tool to help government officials, policy-makers and healthcare providers across Europe examine the cost effectiveness and impact of anti-smoking initiatives. The tool was developed as part of a €2 million European Commission grant. An earlier version had already been piloted with local authorities around the United Kingdom, with users able to draw on specific circumstances, statistics and data to predict the impact of tobacco control in their particular regions. The successful stakeholder engagement in the United Kingdom work encouraged the research team to fully integrate stakeholder engagement into the European study. In this study, the following stakeholders were identified: National and European stakeholders consisting of policy-makers, academics, health authorities, insurance companies, advocacy groups, ministries of finance, national committees, clinicians and health technology assessment (HTA) professionals, and experts on smoking cessation and HTA. Ninety three stakeholders took part. They were engaged in a variety of ways, including through one-to-one interviews, Skype meetings and events. Much of the engagement activity focused on the development of the return of investment tool for application in different countries.

SEE-Impact: Stakeholder Engagement in EQUIPT for Impact

SEE-IMPACT was a 3-year prospective study awarded £157,000 from the United Kingdom’s Medical Research Council funding as part of their joint Methodology Programme with the National Institute for Health Research, earmarked to boost understanding of the impact of health-related studies on society and the economy. The study compared and contrasted the way the EQUIPT decision support tool was taken up in a further four European countries – Germany, Hungary, the Netherlands and Spain. The SEE-Impact study focused in particular on the ways in which stakeholders were engaged throughout the EQUIPT study. The study used a range of methods including interviews, surveys, observations and reviews of documents to develop a detailed understanding of how stakeholder engagement might work as a mechanism for promoting impact. An initial literature review on stakeholder engagement was used to distil a set of propositions for testing. Further details about the project can be found on the website of the MRC (now under United Kingdom Research and Innovation).

Box 2 Design principles for stakeholder engagement

1) Clarify the objectives of stakeholder engagement

The objectives might be one or more of accessing knowledge and skills; supporting interpretation of the results and drafting recommendations; supporting future influence and impact on policy and practice; increasing recruitment/enabling research; supporting transferability. The objectives need to be shared then among all parties.

2) Embed stakeholder engagement in a framework or model of research use

There are a number of models and frameworks designed to show how stakeholders might be engaged in a way that helps increase the chances of research being used in policy and practice, for example, the linkage and exchange model [ 9 ]

3) Identify the necessary resources for stakeholder engagement

Resources to consider are budget, time, skills and competences to manage engagement

4) Put in place plans for organisational learning and rewarding of effective stakeholder engagement, for example, through appropriate evaluation of stakeholder engagement

5) Recognise that some stakeholders have the potential to play a key role

Identify those stakeholders who are particularly interested in being engaged and those who are likely to be influential. Depending on the objective of stakeholder engagement, they may provide the most useful input, and are most likely to play a key role in using the results; their engagement should be especially encouraged

6) Foster shared commitment to the values and objectives of stakeholder engagement in the project team

Ideally, make sure the commitment is there from the outset [ 6 ]

7) Share understanding that stakeholder engagement is often about more than individuals

Consideration needs to be given to stakeholders’ roles where they act as representatives – their power and influence within organisations and networks they represent and how these change over time

8) Encourage individual stakeholders and their organisations to value engagement

Support and build capacity for stakeholders and their organisations to engage

9) Recognise potential tension between productivity and inclusion

Engagement may lead to greater relevance and impact, but may have implications for productivity in meeting project objectives (for example, in a timely fashion). Engaging stakeholders, taking into account their needs and inputs and adjusting elements of the research project based on their feedback takes time and can slow down the research process

10) Generate a shared commitment to sustained and continuous stakeholder engagement

Project teams and stakeholders see the value of links between research producers and research users to build ongoing collaborations in order to meet the objectives

11) Plan stakeholder engagement activity as part of the research programme of work

This should be built into the project protocol or plan (see Pokhrel et al. [ 34 ])

12) Build flexibility within the research process to accommodate engagement and the outcomes of engagement

It will also be important to build in mechanisms to allow researchers to have the independence to articulate what is out of scope

13) Consider how input from stakeholders can be gathered systematically to meet objectives

The importance of some face-to-face contact and interactions should be considered

14) Consider how input from stakeholders can be collated, analysed and used

This important aspect of stakeholder engagement needs to be considered earlier than often happens

15) Recognising identification and involvement of stakeholders is an iterative and ongoing process

Ongoing interaction will be fostered by taking the time and creating the structures to build trustful relationships ([ 6 , 12 ])

Abbreviations

European Commission

European-study on Quantifying Utility of Investment in Protection from Tobacco

Knowledge translation

Patient and public involvement

Return on investment

Stakeholder Engagement in EQUIPT for Impact

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Acknowledgements

The authors would like to acknowledge the contribution of the EQUIPT team, in particular, the Principal Investigator Subhash Pokhrel.

The SEE-Impact study (Stakeholder Engagement in EQUIPT for Impact), received funding from the United Kingdom Medical Research Council to explore the engagement of stakeholders in the EQUIPT project.

The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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AB and SH conceived of the study. All authors contributed to the design, data collection and analysis. An initial draft of the paper was produced by AB, with all authors contributing significantly to its development and revision. The final version has been approved by all authors.

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Stakeholder theory and management: Understanding longitudinal collaboration networks

Roles Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Management Studies, Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon

Roles Conceptualization, Supervision, Writing – review & editing

Affiliation School of Project Management, Faculty of Engineering, The University of Sydney, Sydney, Australia

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Affiliation School of Engineering and IT, University of New South Wales (UNSW), Canberra, Australia

• Julian Fares, 
• Kon Shing Kenneth Chung, 
• Alireza Abbasi

• Published: October 14, 2021
• https://doi.org/10.1371/journal.pone.0255658
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This paper explores the evolution of research collaboration networks in the ‘stakeholder theory and management’ (STM) discipline and identifies the longitudinal effect of co-authorship networks on research performance, i.e., research productivity and citation counts. Research articles totaling 6,127 records from 1989 to 2020 were harvested from the Web of Science Database and transformed into bibliometric data using Bibexcel, followed by applying social network analysis to compare and analyze scientific collaboration networks at the author, institution and country levels. This work maps the structure of these networks across three consecutive sub-periods ( t 1 : 1989–1999; t 2 : 2000–2010; t 3 : 2011–2020) and explores the association between authors’ social network properties and their research performance. The results show that authors collaboration network was fragmented all through the periods, however, with an increase in the number and size of cliques. Similar results were observed in the institutional collaboration network but with less fragmentation between institutions reflected by the increase in network density as time passed. The international collaboration had evolved from an uncondensed, fragmented and highly centralized network, to a highly dense and less fragmented network in t 3 . Moreover, a positive association was reported between authors’ research performance and centrality and structural hole measures in t 3 as opposed to ego-density, constraint and tie strength in t 1 . The findings can be used by policy makers to improve collaboration and develop research programs that can enhance several scientific fields. Central authors identified in the networks are better positioned to receive government funding, maximize research outputs and improve research community reputation. Viewed from a network’s perspective, scientists can understand how collaborative relationships influence research performance and consider where to invest their decision and choices.

Citation: Fares J, Chung KSK, Abbasi A (2021) Stakeholder theory and management: Understanding longitudinal collaboration networks. PLoS ONE 16(10): e0255658. https://doi.org/10.1371/journal.pone.0255658

Editor: Ghaffar Ali, Shenzhen University, CHINA

Received: November 24, 2020; Accepted: July 21, 2021; Published: October 14, 2021

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

Data Availability: All relevant data are within the paper and its Supporting information files.

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

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

Introduction

The emergence of research collaboration networks has largely contributed to the development of many scientific fields and the exponential increase in research publications [ 1 ]. Scientific collaboration is described as the interaction occurring between two or more entities (e.g. authors, institutions, countries) to advance a field of knowledge by uncovering scientific findings in more efficient ways that might not be possible through individual efforts [ 2 , 3 ]. Collaborative relationships affect research performance by disseminating the flow of knowledge, improving research capacity, enhancing innovation, creating new knowledge sources, reducing research cost through economies of scope, and creating synergies between multi-disciplinary teams [ 2 , 4 – 7 ]. Therefore, understanding the status quo of a scientific discipline requires understanding the social structure and composition of these collaborative relationships [ 1 , 8 , 9 ].

Social network analysis (SNA) is one of the most utilized methods for exploring scientific collaboration networks. SNA can quantify, analyze and visualize relationships in a specific research community, identify central opinion leaders that are leading collaborative works as well as evaluate the underlying structures that are influencing collaboration. Usually in a scientific collaboration network, the authors, institutions, and countries are referred to as “actors” or “nodes” and the collaborative relationships between them as “ties”. Indeed, there are a plethora of studies that used SNA to examine scientific collaboration networks of co-authors in various disciplines [ 2 , 10 – 18 ]. However, the findings of the above studies remain inconclusive regarding the longitudinal associations between structures of co-authorship networks and research performance across different sub-periods [ 18 – 20 ], and particularly in the “stakeholder theory and management” (STM) field, there is paucity of evidence. The value of the STM discipline in scientometrics and scientific collaboration research lies in its cross-disciplinary nature, i.e., having been applied in various business [ 21 , 22 ] and non-business domains [ 23 – 25 ], interconnecting different scientific disciplines that were once considered dispersed. The stakeholder theory is considered by many as a “living Wiki”- that is continuously growing through the collaboration of various scholars from different research fields. In light of the above argument, the aims of this study are to:

• explore the evolution of research collaboration networks of each of the authors, institutions, and countries in the STM discipline and across three consecutive sub-periods ( t 1 : 1989–1999; t 2 : 2000–2010; t 3 : 2011–2020),
• identify the key actors (authors, institutions, and countries) that are leading collaborative works in each sub-period, and
• understand the longitudinal effect of co-authorship networks on research performance measured by research productivity (i.e. the number of published papers) and citation counts of the entities [ 26 ].

Certainly, scholars can collaborate in a multitude of different ways ranging from faculty-based administrative works, conference participations, meetings, seminars, inter-institutional joint projects and informal relationships [ 27 ]. However, this study uses co-authorship analysis–as a widely used and reliable bibliometric method that explores co-authorship relationship on scientific papers between different actors (nodes) being authors, institutions or countries. Therefore, the analysis in this paper is carried out at three level: the micro level–authors of the same or different institutions; the meso level–inter-institutional strategic alliances (universities and departments); and the macro level–international partnerships entailing the authors and institutions, all of which are major spectrums of research collaboration [ 7 , 28 ].

To do so, the web of science (WoS) database is used to extract the bibliometric data of 6127 journal articles published in the last 32 years (1989–2020). This data was analyzed using Bibexcel as a package program for bibliometric analysis, UCINET for further SNA, and VOSviewer for visualizing the networks. The results provide important insights for allocating governmental funding, maximizing research output, improving research community reputation and enhancing cost savings that all should be directly or indirectly piloted by the most suitable scientists that can influence and lead collaborative research in their networks [ 29 , 30 ].

This paper starts with a brief history of STM research, followed by an overview of network theories most relevant to this study. Then, the methodology for data collection, refinement and analysis is described. Descriptive and SNA results are presented for each of the examined networks across the three sub-periods, followed by the findings of the association testing between different social network measures (ego-density, degree centrality, betweenness centrality, closeness centrality, efficiency, constraint and average tie strength) and each of the citation counts and research productivity metrics. Lastly, the conclusions and the theoretical and practical implications are provided.

Literature review

Origins of stm.

The stakeholder concept was first originated in the Stanford Research Institute in the 1960s, and then more formally introduced by Freeman [ 31 ] as a new theory of strategic management that aims to create value for various organizational groups and individuals to achieve business success. The stakeholder theory aims to define and create value, interconnect capitalism with ethics and identify appropriate management practices [ 32 ]. A stakeholder is best defined as “any group or individual who can affect or is affected by the achievement of the organization’s objectives” [ 31 ]. Freeman emphasized on the relationships between the organization and its stakeholders as the central unit of analysis and a point of departure for stakeholder research. Accordingly, Rowley [ 33 ] was the first to introduce social networks to STM to understand the mechanism of such relationships. In particular, he argued that a focal firm’s response to stakeholder pressure is based on the interplay between the centrality of the focal firm and the density of stakeholder alliances. There have been many seminal works that put stakeholder theory on a solid managerial science footing, such that of Donaldson and Preston’s [ 34 ] that conceptualized the theory from a descriptive, instrumental and normative approach, followed by Mitchell et al. [ 35 ] who proposed a framework for identifying stakeholder salience using the attributes of power, legitimacy and urgency, and so on [ 36 – 39 ].

Expansion of STM

From the early 2000s, stakeholder theory has shown to be a class of management theory rather than an exclusive theory, per se, by its applicability in various business domains such as business ethics [ 40 – 42 ], finance [ 43 – 45 ], accounting [ 46 , 47 ], marketing [ 22 , 48 , 49 ] and management [ 21 , 50 , 51 ]. Afterwards, the interest has moved to stakeholder analysis—a main systematical analytical process for stakeholder management that involves identifying and categorizing stakeholders, and identifying best practices for engaging them [ 52 ]. Even some scientific disciplines, such as project management, has considered stakeholder management as one of its core knowledge areas for achieving project success [ 53 ]. This exponential growth of the field has resulted in more than 55 stakeholder definitions [ 54 ] and numerous frameworks for stakeholder identification [ 35 , 55 , 56 ], categorization [ 57 , 58 ], and engagement [ 59 – 62 ]. However, the enlargement of the stakeholder analysis body caused ambiguousness in its concepts and purpose [ 34 , 56 , 63 ], where it turned into an experimental field for different methods to be explored. Jepsen and Eskerod [ 64 ] revealed that the tools used for stakeholder identification and categorization were not clear enough for project managers to use, being referred to as theoretical [ 65 ].

The theoretical debates seemed to have alleviated between 2010 and 2020, where researchers focused instead on the applicability of stakeholder theory in the real world cases [ 66 , 67 ]. Empirical studies mainly examined the behavior of firms and their stakeholders towards each other, such as how firms manage stakeholders [ 68 , 69 ] and how stakeholders influence a firm [ 70 ]. Once again, the scientific paradigm of STM has mostly been uncovered in the domains of strategic management [ 71 , 72 ] and project management [ 73 – 75 ]. Therefore, it is evident that growth of STM has continued on a much larger scale than in the previous years, but little is known about the structure of collaboration networks that have contributed to its development and diversification.

Social network theories and measures

A social network is a web of relationships connecting different actors together (e.g., individuals, organisations, nations). The purpose of analyzing networks in scientific research is to evaluate the performance of certain research actors through the structure and patterns of their relationships, as well as to guide research funding and development of science [ 76 ]. Following previous works [ 52 , 77 ], SNA can be conducted through a variety of metrics such as ego-density at the network level; degree, betweenness and closeness centrality, efficiency and constraint at the actor level; and tie strength at the tie level [ 78 , 79 ].

At the network level, density is the most basic network concept which measures the widespread of connectivity throughout the network as a whole [ 80 ]. In other words, it explains the extent of social activity in a network by determining the percentage of ties present [ 81 ]. On the other hand, ego-network density is used to describe the extent of connectivity in an ego’s surrounding neighborhood [ 82 ]. In this study, the ego is either an author, institution or country. A dense network allows the dissemination of information throughout the network [ 83 ] and reflects a trustworthy environment for different actors [ 84 ]. However, a dense network is a two-edged sword where it might obstruct the ability of actors to access novel information outside their closely knitted cliques.

Actor level analysis was first pioneered through the “Bavelas–Leavitt Experiment” which involved five groups of undergraduate students, each had to communicate using a specific network structure (i.e. visualized as a ‘star’, ‘Y’, ‘circle’) to solve puzzles [ 85 , 86 ]. It was found that the efficiency of information flow between group members was the highest in the centralized structures (‘star’ and ‘Y’), leading to the formation of the network ‘centrality’ concept. Accordingly, Freeman [ 87 ] identified three measures of centrality which are degree, betweenness and closeness. Degree centrality that denotes the number of relationships a focal node has in the network. In other words, it is the number of co-authors associated with a given author. Degree centrality is mostly considered as a measure of ‘immediate influence’ or the ability of a node to directly affect others [ 88 , 89 ]. Betweenness centrality is the number of shortest paths (between all pairs of nodes) that pass through a certain node [ 52 ]. Betweenness centrality is a good estimate of power and influence a node can exert on the resource flow between other actors [ 87 , 90 , 91 ]. A node with high betweenness centrality can be considered as an actor that regularly plays a bridging role among other actors in a network. On the other hand, closeness centrality measures the distance between a node and others in a network and reflects the speed in which information is spread across the entire network [ 87 ]. An actor with high closeness centrality is considered independent and can easily reach other actors without relying on intermediaries [ 81 ].

Another important actor level theory is Burt’s [ 92 ] structural hole theory which highlights the importance of having holes (absence of ties) between actors to prevent redundant information. Otherwise, an actor can have redundant relationships by being connected to actors that themselves are connected, where maintaining these relationships could be costly and time consuming in which might constrain the performance of network actors. Burt proposed using ‘efficiency’ and ‘constraint’ to represent the presence of structural holes and redundant relationships, respectively.

Regarding tie level analysis, Granovetter [ 93 ] introduced the ‘strength of weak ties’ theory. He argued that individuals with weak relationships can obtain information at a faster rate than those with strong relationships. This is because individuals who are strongly connected to each other tend to share information most likely within their closely knitted clique than to transfer it to outsiders. In contrast, Krackhardt et al. [ 94 ] stressed on the importance of ‘strong ties’ to create a trustworthy environment, facilitate change and accelerate task completion. Additionally, Hansen [ 95 ] showed that strong ties rather than week ties can enhance the delivery of complex information.

Materials and methods

Data collection.

This paper used co-authorship information to explore collaborative networks. The ‘Web of Science’ database was utilized with the search being restricted to journal articles with strings of ["stakeholder management" or "stakeholder analysis" or "stakeholder identification" or "stakeholder theory" or "stakeholder engagement" or "stakeholder influence"] in their title, abstract or keywords. These are the most frequently used themes in stakeholder research to describe the concept of STM. Other types of documents such as conference proceedings, and books were excluded. The year 1989 was chosen as the outset date of our research because the results of Laplume et al. [ 96 ] and the web of science search showed that the first stakeholder-based scientific article was published in 1989.

In order to have a better understanding of the evolution of collaboration networks, different datasets were required. Therefore, the overall time period of 32 years was split into three consecutive sub-periods, that being t 1 : 1989–1999), t 2 : 2000–2010 and t 3 : 2011–2020. The bibliometric data for each phase was extracted independently in plain text format (compatible with Bibexcel package program for bibliometric analysis) and involved manuscript titles, authors’ names and affiliations, journal titles, institutional names, identification numbers, abstracts, keywords, publication dates, etc. Out of 21,173 authors, 3115 were duplicates, so 19,058 authors were sent for further analysis. The number of articles extracted was 85 for t 1 , 885 for t 2 and 5157 for t 3 , counting for a total number of 6127 articles.

Data refinement

The bibliometric datasets for the three sub-periods were imported into Bibexcel package program [ 97 ] for data preparation and co-occurrence analysis. Fig 1 summarizes the entire methodological process used for extracting and analyzing the data. The first issue encountered was to resolve name authority control problems (i.e. different entities with same names, or same entities with different names [ 27 ]. For instance, some journal articles were the same but had different titles (e.g., ‘Moving beyond dyadic ties’ and ‘Moving beyond Dyadic Ties: A Network Theory of Stakeholder Influences’). Therefore, a standardization process was conducted by removing duplicates (i.e., articles with same DOI were considered as one source). Moreover, it was important to convert upper and lower cases (e.g., WICKS AC, Wicks AC) of all records to a standard lower-case format (Wicks AC) to avoid duplication of records that might impact network structure. For some of the records, especially that of institutions and countries, it has been shown that co-occurrence has occurred between the same institutions and the same countries. In this case, the names were not brought together but kept apart due to the fact that collaboration has happened between authors of the same institution, or between institutions of the same country. In other words, self-loops were not excluded from our analysis. Using Bibexcel, we extracted social network data for each of the authors, institutions and countries networks and for each sub-period, that involved information about the presence and absence of relationships between the actors. Then, the data was imported into excel and manually scrutinized to correct possible spelling errors.

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https://doi.org/10.1371/journal.pone.0255658.g001

Social network analysis

The matrices were imported into an SNA program used by many network scholars—“UCINET 6.0” [ 98 ] to calculate the social network measures for each matrix. UCINET is a SNA software mainly used for whole network studies, which features a large number of network metrics to quantify patterns of relationships. Centrality measures were calculated for the authors, institutions and countries to determine those that are leading collaborative works in their networks. However, further network measures such as ego-density, efficiency, constraint and average tie strength were only calculated for the authors to cohesively understand the longitudinal effect of co-authorship networks on research performance.

Ego-density, degree centrality, betweenness centrality and closeness centrality, efficiency and constraint were calculated for each author, institution and country and for each sub-period.

To construct and visualize the collaboration networks of authors, institutions and countries, bibliometric data from WoS was directly imported into VOSviewer–a specialized software tool that visualizes networks based on scientific publications [ 103 ].

Data analysis

To understand the association between social network measures and research performance, the extracted social network measures from UCINET were imported into SPSS with the number of citations and documents published for each author. Correlation and T-tests determined whether a positive or a negative association exists between the explored variables.

Results and discussion

Descriptive results.

A total of 6127 articles were obtained from different journals between 1989 and 2020. As seen in Table 1 and Fig 2 , there is an exponential increase in the number of published articles. 85 articles were published in t 1 , 885 in t 2 and remarkably 5157 in t 3 . This shows that the majority of collaborative endeavors have occurred in the last decade with a 482% increase in the number of articles from t 2 to t 3 . The number of articles written by multi authors (three or more authors) in the last 32 years is 3590 (58.5%) which is much higher than double author articles (1603 articles, 26.16%) and single author articles (934 papers, 16.2%). Fig 2 shows that the number of published articles increased gradually from 2 to 44 articles between 1989 and 2004, with an exponential increase in 2005 and onwards (i.e., the number of publications in 2004 has been doubled in 2005). The period from 2014 and 2019 experienced the highest number of published articles, indicating the increased interest of the academic community in STM research.

https://doi.org/10.1371/journal.pone.0255658.g002

https://doi.org/10.1371/journal.pone.0255658.t001

Regarding institutional co-occurrence, it is evident that t 3 has witnessed the highest number of collaborative institutions (3778) than t 2 (879) and t 1 (132). Similarly, the number of collaborating countries was the highest (155) in t 3 and the lowest (16) in t 1 . Given that a scientific field might require 45 years to mature [ 104 ], the overall results show that the STM field moved from incubation ( t 1 ) to incremental growth ( t 2 ) to maturity ( t 3 ), reflected by the dramatic increase in the number of articles, institutions, countries and in the number of citations (106,466 in total) especially in t 3 (61,942).

Social network analysis results

Using SNA, the 10 most prolific and influential actors for each network (authors, institutions, countries) in each sub-period ( t 1 , t 2 , t 3 ) were identified.

Each node/circle represents a researcher who have published in the STM field. The size of each node size is proportional to the number of citations. A line connecting two nodes indicates an, at least, one published paper between two authors in STM field.

https://doi.org/10.1371/journal.pone.0255658.g003

https://doi.org/10.1371/journal.pone.0255658.t002

This indicates that collaboration is in the form of sub-networks of closely knitted authors each forming their own collaborative clique. It is evident that collaboration is still premature with only 156 authors not well connected in the network. t 1 is known as the discovery period of stakeholder theory where it first appeared in management journals (e.g. Academy of Management Review) [ 32 ].

In t 2 , the collaboration network consists of 1957 authors and has become larger and more condensed than in t 1 . However, it is important to note that Table 1 earlier shows that 62% of articles (547 out of 885 articles) are single and double authored and only 38% (338 articles) are multi-authored. This finding can be noted in Network B, Fig 3 with the emergence of more than 1000 single and dyadic authors that have further fragmented the collaboration network as a whole. This disintegration of the stakeholder domain is expected because the stakeholder theory has a wide scope of interpretations and the term ‘stakeholder’ can mean different things to different people [ 105 ]. With the increase in stakeholder theoretical disputes between the moral justifications [ 41 ] and managerial implications of the theory [ 38 , 66 , 105 ], numerous solo, dyadic and triadic have risen, detaching from both the mainstream stakeholder theory research [ 34 , 35 ], and the large network cliques [ 106 , 107 ]. Perhaps, a reason why most of the prolific actors in t 1 did not make the list in t 2 is because new research areas have emerged, such as stakeholder engagement [ 108 , 109 ], stakeholder social network analysis [ 56 , 110 ], stakeholder involvement in policy decision making [ 111 ] and many more.

As it can be interpreted from the graphical visualization in Fig 3 , that the scenario observed in t 3 is very similar to that in t 2 , but with a larger network of 16,905 authors (763% increase in number of authors from t 2 ). In particular, the number of components has increased to 88 and expanded to include 12 actors. In contrast, network density–the percentage of existing ties over the total number of possible ties–has decreased from 1.8% in t 1 to 0.08% in t 3 . Although it seems intuitive that density would increase with new researchers entering the field, this did not seem to be the case where density decreased with further fragmentations that reduced the number of connections as the number of nodes increased. This finding is supported by a study [ 18 ] that found a decrease in network density of author collaboration networks from 0.026 in the 1980s to 0.003 in the 2000s. In the presence of 16905 authors with different research interests, it is nearly impossible to connect the majority of the nodes and achieve a high network density. The overlay color range in Network C, Fig 3 also shows that the majority of publications have occurred between 2014 and 2018 with few co-authorships noted in the last two years.

The SNA results presented in Table 2 show that Tugwell P is the most influential author in the network, followed by Graham ID, Newman PA, Dawkins JS and Walker CE who all have higher degree centrality scores than the rest. Remarkably, the findings of betweenness centrality in t 3 show an increase in the importance of the intermediary role, as all prominent actors (see Table 2 ) have a higher betweenness centrality score compared to that of t 1 and t 3 . The brokerage role is significant in t 3 with the decrease in degree and closeness scores. Therefore, the collaboration network has become more dependent on authors with a brokerage role in t 3 .

The evolution of the collaboration network across three decades shows that the STM authors do not belong to the same network. This observation has also been reported in the Network Meta-Analysis field where collaborating authors belonged to different network clusters [ 113 ]. Therefore, the collaboration network can be best described as involving a high number of authors with different research interests that have pursued different research areas by either being a part of a sub-network of three or more actors or by working alone or in pairs. Evidence for radical changes in network structures from t 1 to t 3 , other than the increase in component sizes and fragmentation, have not been demonstrated, where this is still considered an important and unexpected finding. The findings show that the stakeholder concept is a multidisciplinary theory applied in various research domains such as in health care management [ 114 – 119 ], marine policy [ 120 , 121 ], agriculture [ 24 , 122 ], applied geography [ 123 , 124 ], engineering and architecture [ 23 , 125 ], marketing [ 126 – 128 ], public affairs [ 25 , 129 – 131 ], project management [ 73 , 132 – 134 ] and tourism [ 135 – 137 ]. In other words, the stakeholder concept has been developed mainly by multidisciplinary teams of both experienced and emerging scientists. Therefore, this finding contradicts what has been recently speculated that STM is still at an early stage and that published studies are still limited [ 138 ].

Institutions.

Institutional collaboration enables the sharing of unique resources and improves research visibility and contribution [ 16 ]. The results show that the first period contained 88 institutions that have participated in stakeholder research. Surprisingly, 8 out of the 10 most collaborative central institutions are from the United States (see Table 3 ) and are Health Management Link (Indianapolis, USA), Indiana University, University of Iowa, Kings Daughters Hospital, Penn State University, Washington State University, Colorado State University and Boston University. Similar to the author collaboration network in t 1 (Network A, Fig 3 ), the institutional network (Network A, Fig 4 ) shows that the collaboration network doesn’t constitute a main component but is disseminated into several small size components (3 to 5 nodes). This shows that the above institutions are only influential in their own cliques.

Each node/circle represents an institution that has been involved in STM research. The size of each node size is proportional to the number of connections. A line connecting two nodes indicates a collaborative relationship between two institutions in the STM field.

https://doi.org/10.1371/journal.pone.0255658.g004

https://doi.org/10.1371/journal.pone.0255658.t003

In contrast to t 1 , t 2 has witnessed a wider international collaboration where 8 out of the 10 most prolific institutions are from outside the US (see Table 3 ), also being the top 5 institutions and are Erasmus University (Netherlands) which has the highest degree centrality (0.028) and being the most influential intermediary with York University (Canada) (Betweenness centrality = 0.05), University of London (UK), University of Queensland (Australia), University of East Anglia (UK); followed by two US institutions–University of North Carolina and Harvard University, and then Autonomous University of Barcelona (Spain), Utrecht University (Netherlands) and Aarhus University (Denmark). This result is interestingly surprising as it challenges previous studies that showed that most published papers, in general, are from USA, UK and Canada, which also are the most central in collaboration networks [ 1 , 16 , 139 ].

Regarding the network structure and contrary to the institutional network in t 1 , the result show the emergence of a main component in t 2 that is well connected and highly centralized by constituting a nucleus of all of the above prolific institutions, but surrounded by numerous institutions that are isolates (i.e. nodes disconnected from the main component). However, a deeper inspection reveals that an institution can also be considered highly influential without being embedded in the main component, such as in the case of Autonomous University of Barcelona (placed between the main component and the isolates in Fig 4 , Network B). Autonomous University of Barcelona is connected to 16 other institutions present in its own clique, such as Queen Mary University of London, Medical University of Vienna and Illinois state university. This analysis reinforces the important role of cliques in facilitating collaborating processes. The findings overall place STM research on the global radar by being in favor of the most prestige universities worldwide such as University of London, Harvard University and University of Queensland.

The results for t 3 show University of Leeds being the most prominent institution with the highest degree, betweenness and closeness centrality, followed by the University of Toronto, University of Washington, University of Calgary, University of Oxford, University of Otawa, University of Oxford, University of British Colombia, University of Melbourne, University of Sydney and Harvard University. Most of these institutions do not belong to the same components and therefore, it can be argued that collaboration is led by highly central actors disseminitated across the entire network. This has facilitated the connection of detached neighbourhoods as reflected by the increase in density from 0.003 in period 2 to 0.014 in period 3 (367% increase in density). This finding is contrary to Koseoglu [ 20 ] who found that collaboration network density in strategic management research did not increase across 34 years despite the increase in network size.

For this reason, each period is characterised by having a very distinct list of prolific actors that change with the change in network size and structure. Moreover, the number of vertices has dramatically increased from 1201 in period 2 (879 nodes) to 12833 in period 3 (3778 nodes). It can be argued that interesting patterns were observed in the institutional network for t 3 , especially with the reduction of isolates, the increased density and the enlargement of the main component in t 2 to include other large cliques that reached 31 nodes (158% increase in clique size). This finding contradicts previous research in strategic management that showed that large institutional cliques did not emerge with the enlargement of collaboration network [ 20 ]. The overlay color range in Network C in Fig 4 shows that the majority of institutions have published between 2014 and 2018 with a continual rise in 2019 and 2020.

Table 4 provides interesting observations where USA and England are the most prolific actors that are leading collaborative research in the last 32 years. This finding is also supported by previous studies that showed that countries in North and South America, with Europe, are the best-connected countries to faciliate international research collaboration [ 20 , 139 – 141 ]. The collaboration network in t 1 only exists because of the brokerage roles performed by USA and England (see Network A, Fig 5 ). USA stands out by having the most direct relationships (degree centrality = 0.4), brokerage position (betweenness centrality = 0.142) and being the closest to all other actors (closeness centrality = 0.454). USA and England are considered ‘cutpoints’ that if removed would disconnect the entire two networks. For this reason, the rest of the countries (Australia, Canada, Scotland, etc) are considered prolific only because of their only single relationship with either USA or England. A number of isolates are also noted and are Wales, Israel, Belgium, Sweden, Spain and New Zealand.

Each node/circle represents a country that has been involved in STM research. The size of each node size is proportional to the number of connections. A line connecting two nodes indicates a collaborative relationship between two countries in the STM field.

https://doi.org/10.1371/journal.pone.0255658.g005

https://doi.org/10.1371/journal.pone.0255658.t004

Unlike the scenario in t 1 , a significant involvement of new countries in the collaboration network is observed in t 2 while still having USA and England as the most central actors. An interesting finding is that the majority of countries that followed USA and England were not among the prolific actors in t 1 , such as Germany, Italy, Belgium, Spain and Denmark. On other hand, some countries that existed in t 1 , such as Australia, Cananda and Netherlands, have taken a more significant role in the collaboration network in t 2 , while Scotland, Hungary, Thailand, Jamaica and Ireland have dissappeared from the prolific radar for t 2 and t 3 . Remarkable, the network density of the country contribution network in t 2 and t 3 are 11.2% and 10% which are considered the highest compared to all of the previous networks in most decades. Fig 5 shows that the collaboration network of countries started by being uncondensed, fragmented and highly centralised with 16 countries controlling the marjority of connections, to a highly dense, less fragmented network of 74 countries in t 2 , to a larger network of 141 countries and 1059 vertices counting for a 10% density in t 3 . Network 3, Fig 5 shows that the majority of countries emerged between 2014 and 2017.

To our knowledge, a well connected network of collaborative countries as observed in t 2 and t 3 is not occasional. Geographic, linguistic and cultural distances between scientists of different countries may significantly impact collaboration prevalence [ 142 , 143 ]. According to Li et al. (2016), it is more often for collaboration to occur within the same country or same institution due to many reasons including the ease of communication, low intra-competition and low funding opportunities. For example, a study on how higher educations perceive stakeholder salience was possible due to the collaboration of Benneworth and Jongbloed [ 144 ] who both were researchers at the University of Twente in the Netherlands. However, the findings in this study allowed us to observe cross country collaboration since the origin of stakeholder theory in the 1980s. Perhaps a contributing reason for this global collaboration, at least in part, is the presence of several funding agencies, such as the Economic and Social Research Council (ESRC), that supported many stakeholder research studies which brought together many scientist from different countries such as Wales, England, Spain and Sweden [ 145 , 146 ].

Effect of co-authorship networks on research productivity and citation-based performance

A preliminary investigation of the associations involved exploring the correlations between actors’ network attributes and research performance for each period. Since the assumption of normality has been violated, non-parametric tests of Spearman correlation and Mann-Whitney U Test were conducted. The results in Table 5 show that the correlations varied differently across the three sub-periods with regards to magnitude, direction and significance. Research productivity is shown to have the strongest correlation with tie strength in t 1 (r = -0.39, p < 0.01), betweeness centrality in t 2 (r = 0.67, p < 0.01) and ego-density in t 3 (r = -0.563, p < 0.01). On the other hand, citation counts is mostly correlated with tie strength in t 1 (r = 0.49, p < 0.01) and t 2 (r = 0.48, p < 0.01).

https://doi.org/10.1371/journal.pone.0255658.t005

Remarkably, the correlations between research productivity and each of degree centrality (r = -0.19, p < 0.01) and tie strength (r = -0.04, p < 0.01) in t 3 , have shifted its direction as opposed to the positive correlations in t 1 and t 2 . The results overall show that all social network variables (ego-density, betweenness, closeness, efficiency, contraint, tie strength) are either negatively or positively correlated with research performance (i.e., citation counts, research productivity) (see Table 5 for more information).

To explore the association between ego-density and research performance, the median for ego-density index was chosen as a cut point to segregate the participants into two groups: authors with ego-density scores above the median and are considered as “high ego-density group” and authors with ego-density scores lower than the median and are considered as “low ego-density group”. The results of the Mann-Whitney U test (U = 2658, z = -2.86, p = 0.04) summarized in Table 6 show a positive association in t 1 with higher research performance scores observed in the high-density group (Mdn = 83) than the low density group (Mdn = 75). Similarly, the results (U = 443079, z = -6.6, p = 0.00) show a positive association in t 2 with higher research performance scores observed in the high density group (Mdn = 1015) than the low density group (Mdn = 973). Accordingly, we argue that it was essential to have highly dense collaborative clusters in the first decade to publish scientific papers that can bring awareness to stakeholder theory as a newly developed theory of management and ethics.

https://doi.org/10.1371/journal.pone.0255658.t006

The results show that degree centrality is positively associated with both research productivity andcitation counts in t 2 while no association in t 1 . In particular, authors with numerous collaborative relationships in t 2 had higher citation counts (Mdn = 1042) and research productivity (Mdn = 1011) than those with fewer relationships (Mdn = 925 and Mdn = 977 respectively); U = 411370, p = 0.00 and U = 449944, p = 0.03 respectively. In t 3 , a positive association is shown between degree centrality and citation counts (U = 2738017, p = 0.00) where authors with numerous collaborative relationships having higher citation counts (Mdn = 2656) than those with fewer relationships (Mdn = 2347). In contrast, authors with numerous collaborative relationships in t 3 had lower research productivity (Mdn = 2404) than those with fewer relationships (Mdn = 2594); U = 2887576, p = 0.00. Therefore, we can infer that individual collaborative relationships are no longer effective in the last decade in enhancing research performance compared to the periods of stakeholder theory origin and development ( t 1 and t 2 ) that required joint efforts to advance the field.

Regarding betweenness centrality and research performance, the results show that authors that lie on the shortest path between other authors had better research performance in t 2 in terms of research productivity (Mdn = 1939), U = 1704, p = 0.00; and citation counts (Mdn = 1623), U = 20655, p = 0.00, than those who are not considered intermediaries (Mdn = 969, Mdn = 979 respectively). Similar results are shown in t 3 between the low betweenness group in terms of research productivity (Mdn = 2445), U = 119157, p = 0.00; and citation counts (Mdn = 2463), U = 586781, p = 0.00; and the high betweenness group (Mdn = 4585, Mdn = 3897 respectively). The absence of a positive association in t 1 can be explained by the low number of authors (n = 156) that disabled the formation of large cliques, in which its structures prompt brokerage salience.

With respect to closeness centrality, the overall results show a positive association in all periods, where authors with low closeness centrality in t 1 had lower research productivity (Mdn = 75) that those with high closeness centrality (Mdn = 83), U = 2658, p = 0.04). In t 2 , the results show that authors with low closeness centrality had low research productivity (Mdn = 978) and citation counts (Mdn = 922) than those with high closeness centrality (Mdn = 1010, 1042 respectively; U = 445944, p = 0.05 for research productivity, U = 405711, p = 0.00 for citation counts. A positive association is observed in t 3 regarding citation counts between low closeness group (Mdn = 2286) and high closeness group (Mdn = 2727); U = 2572243, p = 0.00. The only exception is in t 2 with research productivity where a negative association is observed where low closeness group having higher research productivity (Mdn = 2525) than the low closeness group (Mdn = 2474); U = 3058094, p = .037. Hence, the findings infer that the closeness of authors to each other, (i.e. being separated by few network steps) was important for all periods in enhancing research performance except for research productivity in t 3 which relied more on authors with high degree and betweenness centrality as shown by the above results.

Efficiency is positively associated with research productivity and citation counts for all periods. For t 1 , authors who were surrounded by non-redundant ties had higher citation counts (Mdn = 89) and research productivity (Mdn = 80) than those who have a less efficient network position (Mdn = 63 and Mdn = 76, respectively); U = 1977, p = 0.00 for citation counts, U = 2742, p = 0.05 for research productivity. Similarly, authors who were surrounded by non-redundant ties had higher citation counts (Mdn = 1052) and research productivity (Mdn = 1015) than those who have a less efficient network position (Mdn = 929, Mdn = 977 respectively); U = 429965, p = 0.00 for citation counts, U = 472013 p = 0.01 for research productivity. Similarly, efficient authors had higher citation counts (Mdn = 2548) and research productivity (Mdn = 2722) than those who were less efficient (Mdn = 2387, Mdn = 2209 respectively); U = 2848639, p = 0.00 for citation counts, U = 2414066, p = 0.05 for research productivity. These findings indicate that authors surrounded by structural holes–being connected to a primary co-author in a group and receiving novel information–had good research performance. Moreover, it can be argued that expansion of the STM field relied on novel information flowing between efficient authors of different disciplines.

The findings show that constraint is positively associated with research performance in t 1 and t 2 while in t 3 a negative association is shown instead. In particular, authors with redundant ties had higher research productivity in t 1 (Mdn = 83; U = 2658, z = -2.8, p = .004) and citation counts in t 2 (Mdn = 1028; U = 469269, z = -2.2, p = .03) than those that are less constrained (Mdn = 75, Mdn = 970 respectively). This finding contradicts previous research which showed that constraint is negatively associated with research performance before year 2010 [ 147 ]. However, in t 3 , a negative association is shown were highly contrained individuals (i.e. those with redundant ties) had lower citation counts (Mdn = 2275) than those that are less constrained (Mdn = 2716), U = 25726787, p = 0.00). Therefore, research productivity in t 2 and citation counts in t 3 have been mainly enhanced via authors with redundant relationships that lead back to same group of co-authors. We argue that with the wide expansion of the collaboration network in t 3 , that had witnessed the emergence of many scholars, it is difficult for authors to establish relationships with all members of a clique, and therefore, must rely on relationships established with primary actors, reflected by the salience of structural holes.

With respect to tie strength, the findings show a positive association with research performance in t 1 and t 2 . With regards to t 1 , the results show that authors, who had strong relationships with other authors, had better citations (Mdn = 101) and research productivity (Mdn = 83) than those with weaker ties (Mdn = 56, Mdn = 74 respectively). Similarly, in t 2 , authors with strong ties had higher citations (Mdn = 1269) and research productivity (Mdn = 1064) than those with weak ties (Mdn = 778, Mdn = 945 respectively). Therefore, the theory of “strong ties” [ 94 ] in ehancing productivity is supported by our analysis. Strong relationships between co-authors are essential for increasing citation and publication counts.

Conclusion and implications

This study descriptively analyzed the evolution of research collaboration networks of authors, institutions and countries, in the STM discipline and identified key actors that are leading collaborative works. In addition, this study examined the longitudinal effect of co-authorship networks on research performance by exploring the associations between collaborative social network variables and each of citation counts and research productivity.

The findings of the authors’ collaboration network revealed a premature and fragmented network in t 1 , where collaboration has happened in the form of sub-networks or cliques of closely knitted actors. In t 2 , the network increased in size by the emergence of mostly single and dyadic authors which further disintegrated the network. In t 3 , a larger network and a higher number of cliques emerged, with the most prolific actors having a strong brokerage role (betweenness centrality). The overall results show that stakeholder theory has a wide scope of interpretations and lacks universal consensus on its concepts and frameworks [ 34 , 35 , 148 , 149 ].

The findings of the institutional collaboration networks revealed that the collaboration network in t 1 is fragmented into several small size cliques controlled mostly by US institutions. In contrast, a wider international collaboration was witnessed in t 2 , with the emergence of non US-institutions. The results for t 3 showed that the most prolific universities (University of Leeds, University of Washington, University of Toronto) did not belong to the same components, therefore, indicating that the collaboration is led by highly central actors disseminated across the entire network.

The collaboration network of countries originated by being uncondensed, fragmented and highly centralised in t 1 , with only 16 countries where USA and England being the most prolific actors in STM research. The collaboration network became highly dense and less fragmented in t 2 with 74 countries joining the scene. A larger network of 141 countries was observed in t 3 with high density and less fragmentation.

Regarding the impact of co-authorship networks on research performance, efficiency was found to be the only network measure positively associated with both citation counts and research productivity in all of the three periods (see Table 6 ), indicating the importance of structural holes in enhancing research performance. In summary, STM research performance is influenced by authors (1) in highly dense collaborative clusters (ego-density), are (2) close to all other actors in the network, (3) efficient (those that present novel research information); (4) constrained by repetitive relationships and (5) that have strong ties with other authors.

This paper contributes to STM reseach by showing the evolvement of the field and the dynamic changes in its structures. The findings demonstrate that STM is indeed a multi-disciplinary discipline, reflected by fragmented co-authorship network from t 1 to t 3 and the emergence of a high number of single and dyadic author representing disunity in STM research interest. This heeds the growing calls to explore the structural composition of STM [ 150 ]. Fig 6 supports this notion which illustrates keyword co-occurrence networks in STM discipline in t 1 , t 2 , t 3 . The main keywords with the highest co-occurrence in t 1 are ‘stakeholder analysis’, ‘stakeholder’ and ‘stakeholder theory’, which all were fundamental and related concepts in STM but each belonging to a different clique. This indicates that STM had not received profound universal consensus at that time and had various comprehensions. However, the application of STM in other disciplines was on the rise, especially with ‘stakeholder analysis’ coinciding with ‘strategic planning’, ‘climate change’ and ‘participatory research’. In t 2 , new major keywords appeared such as ‘corporate social responsibility’, ‘business ethics’ and ‘corporate governance’, all belonging to the same cluster (all having a red color) indicating wide acceptance of stakeholder theory as a theory of management and ethics. Other non-related STM keywords (‘climate change’, ‘health’, ‘, ‘resource-based view’, ‘governance’, ‘networks’, etc.) had also emerged, indicating that STM is a “living Wiki” that is continuously growing through the collaboration of stakeholder scholars from different research fields [ 32 ].

Each node/circle represents a keyword in STM research. The size of each node size is proportional to the number of connections. A line connecting two nodes indicates an affiliation between two keywords. Node color represents related clusters of keywords.

https://doi.org/10.1371/journal.pone.0255658.g006

This study provides practical contributions to scientists in the STM field and educational managements worldwide. First, the concrete findings from the association testing can help stakeholder scientists improve their research performance by altering the configuration of their collaborative relationships, especially degree, betweenness, and closeness centralities. Institutions can benefit from these results to increase citations rates and research productivity. Second, this study provides empirical evidence regarding the structure of collaboration networks and central actors, that if acted upon, can directly or indirectly lead the allocation of government funding, maximization of research outputs, improvement of research community reputation and the enhancement of cost savings [ 29 , 30 ], that can all improve collaboration and developing coordinated research programs that can advance the field.

Supporting information

https://doi.org/10.1371/journal.pone.0255658.s001

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Stakeholder Management: Proposal for Research—Do Successful Project Managers Employ ‘Interest-Based Negotiation’ to Create Successful Project Outcomes?

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• John Heathcote 4 ,
• Colin Butlin 4 &
• Hadi Kazemi 4  

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Increasingly the management of stakeholders is reported, by research on the management of projects, as being critical to the successful development of projects. Current research into the management of stakeholders charts a move from: classifying who stakeholders might be, to one of determining whether and how to manage them, to one of recommending ‘engagement’. Stakeholders are seemingly important players in the project’s environment because they are able to both significantly influence the project’s delivery and because they may well be the arbiters of whether the project can be considered successful or not. This latter point indicates the role that stakeholders and those stakeholders that are beneficiaries of the project can have in determining how ‘value’ is interpreted. This research proposal identifies a gap in existing literature; that gap is in the final process of stakeholder management. Aligned to a risk management process, stakeholder management ends with the idea that the stakeholder will be managed . As writers show that ‘engagement’ might be beneficial, then ‘interest-based negotiation’ (IBN) allows for a project manager to engage with these groups through IBN. Anecdotal evidence shows that elements of IBN might be unconscious components of successful project managers’ interactions with stakeholders. This paper proposes a study design that will allow for the hypothesis H1 ‘Successful stakeholder engagement can be correlated with project managers employing elements of interest-based negotiation’ to be tested.

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Heathcote, J., Butlin, C., Kazemi, H. (2020). Stakeholder Management: Proposal for Research—Do Successful Project Managers Employ ‘Interest-Based Negotiation’ to Create Successful Project Outcomes?. In: Scott, L., Dastbaz, M., Gorse, C. (eds) Sustainable Ecological Engineering Design. Springer, Cham. https://doi.org/10.1007/978-3-030-44381-8_19

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Applying a stakeholder management approach to ethics in charitable fundraising

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Stakeholder Management: Volume 1

Table of contents, five challenges to stakeholder theory: a report on research in progress.

This chapter suggests that there are at least five main challenges to the development of stakeholder theory as it currently stands. We need more research on understanding what counts as the total performance of a business; accounting for stakeholders rather than accounting only for investors; explaining real stakeholder behavior; formulating smart public policy given stakeholder theory; and rethinking the basics of ethical theory. The chapter explains the issues involved in each challenge and suggests ways to meet the challenge. It is a preliminary report of research in progress as well as a blueprint for how others may join the conversation to develop a more useful stakeholder theory.

Stakeholder Theory Classification, Definitions and Essential Contestability

Stakeholder theory has been accused of being an umbrella concept rather than a distinct theory per se. Recognizing the stakeholder concept as an essentially contested concept subject to multiple competing interpretations, this chapter presents a systematic meta-level conceptual analysis. This chapter aims to contribute to the optimal development of stakeholder theory by clarifying the conceptual confusion surrounding its central construct to help prevent stakeholder theory from developing into an accumulation of disparate ideas. Multi-contextual contributions to stakeholder theory are analysed via an unparalleled bounded systematic review of 593 stakeholder definitions. Determinants of the stakeholder concept have been deconstructed and analysed to establish how definitional variables relate to variants of stakeholder theory. These determinants have been sorted, filtered and ordered to produce a comprehensive, multi-dimensional classification of stakeholder theory based on four hyponyms which relate to 16 definitional categories. The classification was then subjected to empirical testing with positive results. This evaluation of the stakeholder concept illustrates how contributions are aligned and interrelated, thereby prescribing what is acceptable (unacceptable) as inclusion within stakeholder theory. An invaluable overview of what we know about stakeholder theory is presented within a single model, drawing the conclusion that stakeholder theory is indeed a single theory.

Normative Stakeholder Theory

Theories of management require normative justification; that is, they rely on some conception of what is morally good, right, and just. This chapter examines some of the normative reasons for adopting a stakeholder theory of management and for rejecting the once, and perhaps still, “dominant” shareholder-centric approach. This chapter then surveys some of the prominent “normative cores” that are used to ground stakeholder theory, that is, Kantian, contractarian, feminist ethics, and ethical pragmatism, and the moral obligations that each normative approach generates. Some pressing questions are raised with respect to each normative approach. To what extent ought we to recognize imperfect obligations to shareholders? Are contractarian hypernorms morally substantive? How exactly should we care about stakeholders, and is care even an appropriate attitudinal response? Without some commitment to objective ethical standards, how can pragmatists resolve stakeholder conflict?

Value Creation Theory: Literature Review and Theory Assessment

This chapter assembles the key literature on value creation for consideration in relationship to stakeholder theory. The literature review identifies and explains the core topics concerning value creation and related ideas. The purpose is to stimulate research into the theory, practice, and social consequences of value creation in a stakeholder management framework. The construct of “value” lacks theoretical precision and empirical verification. The most fundamental and disputed question addressed is which value approach for the firm best contributes to overall (aggregate) social welfare. The vital issue is whether the managerial stakeholder theory is superior, at long-run value creation for multiple stakeholders including society at large, to the conventional agency theory. Business executives and directors are the ones who choose between agency and stakeholder approaches to management. Their actions influence organizational and social outcomes. Research is limited to a literature review, followed by a discussion of the likely role of value creation theory in future stakeholder research. The chapter first defines value. The basic approach is then to focus on key topics in the relevant literature. The last section addresses the role of value creation theory in future stakeholder research.

The Power of and in Stakeholder Networks

The argument that applications of social network research tools and theories to stakeholder research will advance our understanding of how organizations should and do interact with their stakeholders and how stakeholders influence organizations has been well known for over 15 years. However, the integration of social network analysis and stakeholder research has been limited to date. To motivate stakeholder network research, I illustrate the similarities and complementarities between these research streams, arguing that the social network perspective tackles weaknesses in stakeholder models supporting the creation of more fruitful models of organization–stakeholder environments. I illustrate how stakeholder power and legitimacy, and focal organization obligations can be better modeled theoretically and measured empirically using social network concepts and techniques.

Stakeholder Prioritization Work: The Role of Stakeholder Salience in Stakeholder Research

In this chapter, we update stakeholder salience research using the new lens of stakeholder work: the purposive processes of organization aimed at being aware of, identifying, understanding, prioritizing, and engaging stakeholders. Specifically, we focus on stakeholder prioritization work — primarily as represented by the stakeholder salience model — and discuss contributions, shortcomings, and possibilities for this literature. We suggest that future research focus on stakeholder inclusivity, the complexity of prioritization work within intra-corporate markets, the integration of stakeholder prioritization with other forms of stakeholder work, and the development of managerial tools for multiobjective decision making within the strategic management context.

Challenging Stakeholder Salience: Lessons from Dormant Local Stakeholders

Stakeholder thinking has contributed considerably to the organizational literature by demonstrating the significance of the environment in managing organizations. Stakeholders affect and are affected by organizations’ daily operations and decisions. They have varied and often conflicting interests, making it necessary for managers and organizations to know who they are as well as their attributes. Consequently, Mitchell et al. (1997) developed the stakeholder salience theory to help managers and organizations identify the power of certain stakeholders and their salience to the organization. With a few exceptions, the mainstream stakeholder salience theory is in many ways still largely static, short-term oriented, and firm-centered. The aim of this paper is to revisit certain conformist assumptions concerning the role of marginalized stakeholders, or “dormant” stakeholders, in stakeholder thinking. Overall, this chapter is a call to a new conceptualization of stakeholders that reintroduces stakeholder dynamics at the core of stakeholder thinking to overcome its restrictive shortcomings. We argue that managing stakeholder relationships is not simply meeting stakeholder demands but also involves taking into account the long-term dynamics of stakeholder interactions.

Regarding Marginal Stakeholders

Stakeholders are typically described as those who may affect or be affected by the actions of a firm. The purpose of this chapter is to present an argument that stakeholder theory should pay specific regard to what I term marginal stakeholders , that is, parties affected by a firm’s actions but who nevertheless have no actual or foreseeable influence to shape its strategic goals. Several key proponents of stakeholder theory maintain that these groups are not legitimate stakeholders and therefore do not warrant consideration. For example, marginal groups are routinely excluded from discussions of stakeholder fairness. Alternatively, theorists presume that advocates with leverage will protect these groups, or appeals to human rights will be sufficient. In contrast, I contend that there are cases where the firm has benefitted, but identifiable and discrete stakeholders have been negatively affected by corporate action in an environment where rights are ignored or there is no significant legal recourse. Drawing on foundational literature on fairness and insights from social psychology, I conclude that fully realized stakeholder theory means that a corporation has to consider its duties to all those affected by the impact of a firm, including the powerless.

Stakeholder Action: Predictors of Punitive and Prosocial Stakeholder Behaviours

Stakeholders often engage in actions aimed at either benefitting or punishing firms for their behaviour. Such behaviours can have very serious implications for various types of firm performance, including financial performance. Though one might expect that the investigation of possible precursors of such “stakeholder action” would be a priority of researchers in stakeholder theory, to date research within the stakeholder literature directed towards understanding stakeholder behaviour has been somewhat scarce. In this chapter, I present common themes and assumptions that prevail in the existing research on stakeholder action, identify certain important questions concerning such assumptions and suggest avenues for future research on stakeholder behaviour.

Toward a More Productive Dialogue between Stakeholder Theory and Strategic Management

This chapter highlights some of the tensions and most promising points of convergence between the strategic management and stakeholder theory literatures. We briefly examine the early development of both areas, identifying some of the background assumptions and choices that informed how the fields evolved, and how these factors led the two fields to engage in scholarly pursuits that seldom intersected for a period of years, followed by a renewal of interest among strategists in themes that are central to stakeholder theory. From this discussion, we develop a larger agenda with specific topics as examples of areas that offer promise for integrative research that can advance knowledge in both fields. Our vision of the future is one in which the larger aspirations of scholars in strategy and stakeholder theory are more fully realized with human purposes, broadly defined, as the focal point.

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Stakeholder theory and management: Understanding longitudinal collaboration networks

Julian fares.

1 Department of Management Studies, Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon

Kon Shing Kenneth Chung

2 School of Project Management, Faculty of Engineering, The University of Sydney, Sydney, Australia

Alireza Abbasi

3 School of Engineering and IT, University of New South Wales (UNSW), Canberra, Australia

Associated Data

All relevant data are within the paper and its Supporting information files.

This paper explores the evolution of research collaboration networks in the ‘stakeholder theory and management’ (STM) discipline and identifies the longitudinal effect of co-authorship networks on research performance, i.e., research productivity and citation counts. Research articles totaling 6,127 records from 1989 to 2020 were harvested from the Web of Science Database and transformed into bibliometric data using Bibexcel, followed by applying social network analysis to compare and analyze scientific collaboration networks at the author, institution and country levels. This work maps the structure of these networks across three consecutive sub-periods ( t 1 : 1989–1999; t 2 : 2000–2010; t 3 : 2011–2020) and explores the association between authors’ social network properties and their research performance. The results show that authors collaboration network was fragmented all through the periods, however, with an increase in the number and size of cliques. Similar results were observed in the institutional collaboration network but with less fragmentation between institutions reflected by the increase in network density as time passed. The international collaboration had evolved from an uncondensed, fragmented and highly centralized network, to a highly dense and less fragmented network in t 3 . Moreover, a positive association was reported between authors’ research performance and centrality and structural hole measures in t 3 as opposed to ego-density, constraint and tie strength in t 1 . The findings can be used by policy makers to improve collaboration and develop research programs that can enhance several scientific fields. Central authors identified in the networks are better positioned to receive government funding, maximize research outputs and improve research community reputation. Viewed from a network’s perspective, scientists can understand how collaborative relationships influence research performance and consider where to invest their decision and choices.

Introduction

The emergence of research collaboration networks has largely contributed to the development of many scientific fields and the exponential increase in research publications [ 1 ]. Scientific collaboration is described as the interaction occurring between two or more entities (e.g. authors, institutions, countries) to advance a field of knowledge by uncovering scientific findings in more efficient ways that might not be possible through individual efforts [ 2 , 3 ]. Collaborative relationships affect research performance by disseminating the flow of knowledge, improving research capacity, enhancing innovation, creating new knowledge sources, reducing research cost through economies of scope, and creating synergies between multi-disciplinary teams [ 2 , 4 – 7 ]. Therefore, understanding the status quo of a scientific discipline requires understanding the social structure and composition of these collaborative relationships [ 1 , 8 , 9 ].

Social network analysis (SNA) is one of the most utilized methods for exploring scientific collaboration networks. SNA can quantify, analyze and visualize relationships in a specific research community, identify central opinion leaders that are leading collaborative works as well as evaluate the underlying structures that are influencing collaboration. Usually in a scientific collaboration network, the authors, institutions, and countries are referred to as “actors” or “nodes” and the collaborative relationships between them as “ties”. Indeed, there are a plethora of studies that used SNA to examine scientific collaboration networks of co-authors in various disciplines [ 2 , 10 – 18 ]. However, the findings of the above studies remain inconclusive regarding the longitudinal associations between structures of co-authorship networks and research performance across different sub-periods [ 18 – 20 ], and particularly in the “stakeholder theory and management” (STM) field, there is paucity of evidence. The value of the STM discipline in scientometrics and scientific collaboration research lies in its cross-disciplinary nature, i.e., having been applied in various business [ 21 , 22 ] and non-business domains [ 23 – 25 ], interconnecting different scientific disciplines that were once considered dispersed. The stakeholder theory is considered by many as a “living Wiki”- that is continuously growing through the collaboration of various scholars from different research fields. In light of the above argument, the aims of this study are to:

• explore the evolution of research collaboration networks of each of the authors, institutions, and countries in the STM discipline and across three consecutive sub-periods ( t 1 : 1989–1999; t 2 : 2000–2010; t 3 : 2011–2020),
• identify the key actors (authors, institutions, and countries) that are leading collaborative works in each sub-period, and
• understand the longitudinal effect of co-authorship networks on research performance measured by research productivity (i.e. the number of published papers) and citation counts of the entities [ 26 ].

Certainly, scholars can collaborate in a multitude of different ways ranging from faculty-based administrative works, conference participations, meetings, seminars, inter-institutional joint projects and informal relationships [ 27 ]. However, this study uses co-authorship analysis–as a widely used and reliable bibliometric method that explores co-authorship relationship on scientific papers between different actors (nodes) being authors, institutions or countries. Therefore, the analysis in this paper is carried out at three level: the micro level–authors of the same or different institutions; the meso level–inter-institutional strategic alliances (universities and departments); and the macro level–international partnerships entailing the authors and institutions, all of which are major spectrums of research collaboration [ 7 , 28 ].

To do so, the web of science (WoS) database is used to extract the bibliometric data of 6127 journal articles published in the last 32 years (1989–2020). This data was analyzed using Bibexcel as a package program for bibliometric analysis, UCINET for further SNA, and VOSviewer for visualizing the networks. The results provide important insights for allocating governmental funding, maximizing research output, improving research community reputation and enhancing cost savings that all should be directly or indirectly piloted by the most suitable scientists that can influence and lead collaborative research in their networks [ 29 , 30 ].

This paper starts with a brief history of STM research, followed by an overview of network theories most relevant to this study. Then, the methodology for data collection, refinement and analysis is described. Descriptive and SNA results are presented for each of the examined networks across the three sub-periods, followed by the findings of the association testing between different social network measures (ego-density, degree centrality, betweenness centrality, closeness centrality, efficiency, constraint and average tie strength) and each of the citation counts and research productivity metrics. Lastly, the conclusions and the theoretical and practical implications are provided.

Literature review

Origins of stm.

The stakeholder concept was first originated in the Stanford Research Institute in the 1960s, and then more formally introduced by Freeman [ 31 ] as a new theory of strategic management that aims to create value for various organizational groups and individuals to achieve business success. The stakeholder theory aims to define and create value, interconnect capitalism with ethics and identify appropriate management practices [ 32 ]. A stakeholder is best defined as “any group or individual who can affect or is affected by the achievement of the organization’s objectives” [ 31 ]. Freeman emphasized on the relationships between the organization and its stakeholders as the central unit of analysis and a point of departure for stakeholder research. Accordingly, Rowley [ 33 ] was the first to introduce social networks to STM to understand the mechanism of such relationships. In particular, he argued that a focal firm’s response to stakeholder pressure is based on the interplay between the centrality of the focal firm and the density of stakeholder alliances. There have been many seminal works that put stakeholder theory on a solid managerial science footing, such that of Donaldson and Preston’s [ 34 ] that conceptualized the theory from a descriptive, instrumental and normative approach, followed by Mitchell et al. [ 35 ] who proposed a framework for identifying stakeholder salience using the attributes of power, legitimacy and urgency, and so on [ 36 – 39 ].

Expansion of STM

From the early 2000s, stakeholder theory has shown to be a class of management theory rather than an exclusive theory, per se, by its applicability in various business domains such as business ethics [ 40 – 42 ], finance [ 43 – 45 ], accounting [ 46 , 47 ], marketing [ 22 , 48 , 49 ] and management [ 21 , 50 , 51 ]. Afterwards, the interest has moved to stakeholder analysis—a main systematical analytical process for stakeholder management that involves identifying and categorizing stakeholders, and identifying best practices for engaging them [ 52 ]. Even some scientific disciplines, such as project management, has considered stakeholder management as one of its core knowledge areas for achieving project success [ 53 ]. This exponential growth of the field has resulted in more than 55 stakeholder definitions [ 54 ] and numerous frameworks for stakeholder identification [ 35 , 55 , 56 ], categorization [ 57 , 58 ], and engagement [ 59 – 62 ]. However, the enlargement of the stakeholder analysis body caused ambiguousness in its concepts and purpose [ 34 , 56 , 63 ], where it turned into an experimental field for different methods to be explored. Jepsen and Eskerod [ 64 ] revealed that the tools used for stakeholder identification and categorization were not clear enough for project managers to use, being referred to as theoretical [ 65 ].

The theoretical debates seemed to have alleviated between 2010 and 2020, where researchers focused instead on the applicability of stakeholder theory in the real world cases [ 66 , 67 ]. Empirical studies mainly examined the behavior of firms and their stakeholders towards each other, such as how firms manage stakeholders [ 68 , 69 ] and how stakeholders influence a firm [ 70 ]. Once again, the scientific paradigm of STM has mostly been uncovered in the domains of strategic management [ 71 , 72 ] and project management [ 73 – 75 ]. Therefore, it is evident that growth of STM has continued on a much larger scale than in the previous years, but little is known about the structure of collaboration networks that have contributed to its development and diversification.

Social network theories and measures

A social network is a web of relationships connecting different actors together (e.g., individuals, organisations, nations). The purpose of analyzing networks in scientific research is to evaluate the performance of certain research actors through the structure and patterns of their relationships, as well as to guide research funding and development of science [ 76 ]. Following previous works [ 52 , 77 ], SNA can be conducted through a variety of metrics such as ego-density at the network level; degree, betweenness and closeness centrality, efficiency and constraint at the actor level; and tie strength at the tie level [ 78 , 79 ].

At the network level, density is the most basic network concept which measures the widespread of connectivity throughout the network as a whole [ 80 ]. In other words, it explains the extent of social activity in a network by determining the percentage of ties present [ 81 ]. On the other hand, ego-network density is used to describe the extent of connectivity in an ego’s surrounding neighborhood [ 82 ]. In this study, the ego is either an author, institution or country. A dense network allows the dissemination of information throughout the network [ 83 ] and reflects a trustworthy environment for different actors [ 84 ]. However, a dense network is a two-edged sword where it might obstruct the ability of actors to access novel information outside their closely knitted cliques.

Actor level analysis was first pioneered through the “Bavelas–Leavitt Experiment” which involved five groups of undergraduate students, each had to communicate using a specific network structure (i.e. visualized as a ‘star’, ‘Y’, ‘circle’) to solve puzzles [ 85 , 86 ]. It was found that the efficiency of information flow between group members was the highest in the centralized structures (‘star’ and ‘Y’), leading to the formation of the network ‘centrality’ concept. Accordingly, Freeman [ 87 ] identified three measures of centrality which are degree, betweenness and closeness. Degree centrality that denotes the number of relationships a focal node has in the network. In other words, it is the number of co-authors associated with a given author. Degree centrality is mostly considered as a measure of ‘immediate influence’ or the ability of a node to directly affect others [ 88 , 89 ]. Betweenness centrality is the number of shortest paths (between all pairs of nodes) that pass through a certain node [ 52 ]. Betweenness centrality is a good estimate of power and influence a node can exert on the resource flow between other actors [ 87 , 90 , 91 ]. A node with high betweenness centrality can be considered as an actor that regularly plays a bridging role among other actors in a network. On the other hand, closeness centrality measures the distance between a node and others in a network and reflects the speed in which information is spread across the entire network [ 87 ]. An actor with high closeness centrality is considered independent and can easily reach other actors without relying on intermediaries [ 81 ].

Another important actor level theory is Burt’s [ 92 ] structural hole theory which highlights the importance of having holes (absence of ties) between actors to prevent redundant information. Otherwise, an actor can have redundant relationships by being connected to actors that themselves are connected, where maintaining these relationships could be costly and time consuming in which might constrain the performance of network actors. Burt proposed using ‘efficiency’ and ‘constraint’ to represent the presence of structural holes and redundant relationships, respectively.

Regarding tie level analysis, Granovetter [ 93 ] introduced the ‘strength of weak ties’ theory. He argued that individuals with weak relationships can obtain information at a faster rate than those with strong relationships. This is because individuals who are strongly connected to each other tend to share information most likely within their closely knitted clique than to transfer it to outsiders. In contrast, Krackhardt et al. [ 94 ] stressed on the importance of ‘strong ties’ to create a trustworthy environment, facilitate change and accelerate task completion. Additionally, Hansen [ 95 ] showed that strong ties rather than week ties can enhance the delivery of complex information.

Materials and methods

Data collection.

This paper used co-authorship information to explore collaborative networks. The ‘Web of Science’ database was utilized with the search being restricted to journal articles with strings of ["stakeholder management" or "stakeholder analysis" or "stakeholder identification" or "stakeholder theory" or "stakeholder engagement" or "stakeholder influence"] in their title, abstract or keywords. These are the most frequently used themes in stakeholder research to describe the concept of STM. Other types of documents such as conference proceedings, and books were excluded. The year 1989 was chosen as the outset date of our research because the results of Laplume et al. [ 96 ] and the web of science search showed that the first stakeholder-based scientific article was published in 1989.

In order to have a better understanding of the evolution of collaboration networks, different datasets were required. Therefore, the overall time period of 32 years was split into three consecutive sub-periods, that being t 1 : 1989–1999), t 2 : 2000–2010 and t 3 : 2011–2020. The bibliometric data for each phase was extracted independently in plain text format (compatible with Bibexcel package program for bibliometric analysis) and involved manuscript titles, authors’ names and affiliations, journal titles, institutional names, identification numbers, abstracts, keywords, publication dates, etc. Out of 21,173 authors, 3115 were duplicates, so 19,058 authors were sent for further analysis. The number of articles extracted was 85 for t 1 , 885 for t 2 and 5157 for t 3 , counting for a total number of 6127 articles.

Data refinement

The bibliometric datasets for the three sub-periods were imported into Bibexcel package program [ 97 ] for data preparation and co-occurrence analysis. Fig 1 summarizes the entire methodological process used for extracting and analyzing the data. The first issue encountered was to resolve name authority control problems (i.e. different entities with same names, or same entities with different names [ 27 ]. For instance, some journal articles were the same but had different titles (e.g., ‘Moving beyond dyadic ties’ and ‘Moving beyond Dyadic Ties: A Network Theory of Stakeholder Influences’). Therefore, a standardization process was conducted by removing duplicates (i.e., articles with same DOI were considered as one source). Moreover, it was important to convert upper and lower cases (e.g., WICKS AC, Wicks AC) of all records to a standard lower-case format (Wicks AC) to avoid duplication of records that might impact network structure. For some of the records, especially that of institutions and countries, it has been shown that co-occurrence has occurred between the same institutions and the same countries. In this case, the names were not brought together but kept apart due to the fact that collaboration has happened between authors of the same institution, or between institutions of the same country. In other words, self-loops were not excluded from our analysis. Using Bibexcel, we extracted social network data for each of the authors, institutions and countries networks and for each sub-period, that involved information about the presence and absence of relationships between the actors. Then, the data was imported into excel and manually scrutinized to correct possible spelling errors.

Social network analysis

The matrices were imported into an SNA program used by many network scholars—“UCINET 6.0” [ 98 ] to calculate the social network measures for each matrix. UCINET is a SNA software mainly used for whole network studies, which features a large number of network metrics to quantify patterns of relationships. Centrality measures were calculated for the authors, institutions and countries to determine those that are leading collaborative works in their networks. However, further network measures such as ego-density, efficiency, constraint and average tie strength were only calculated for the authors to cohesively understand the longitudinal effect of co-authorship networks on research performance.

Ego-density, degree centrality, betweenness centrality and closeness centrality, efficiency and constraint were calculated for each author, institution and country and for each sub-period.

Ego-density is number of actual ties not involving the ego divided by the number of possible ties in an ego network:

where n refers to the number of alters the ego is connected to, Z ij is the tie strength between actors i and j and (n (n − 1))⁄2 refers to hightest possible number of ties.

Degree centrality is the count of contacts a focal node has in a network [ 99 ]. It is not reasonable to compare a node with a centrality score of 20 in a network of 50 nodes with a node of same centrality score but in a smaller network of 15 nodes. Therefore, in order to understand the extent to which authors are central in a network and compare their centrality across different networks that vary in size, Freeman’s [ 100 ] normalized measures (n-1) for degree, betweenness and closeness centrality are used. Normalized degree centrality:

Where i is the focal node, j is any other actor and z ij = 1 for an existing tie between i and j .

Normalized betweenness centrality is calculated as the proportional number of times a focal node lies on the shortest path between other actors [ 101 ]:

where i is the focal node, j and q are any other two actors, z jq is the total number of shortest paths from j to q , and z jq ( i ) is the contribution of i to those paths.

Normalized closeness centrality is the total number of distances between the focal node and all other nodes:

where z ( pj , pq ) is the shortest distance between node pj and node pq in the network.

Efficiency is measured by dividing the number of non-redundant actors divided by network size:

where i is the focal node, j and q are any other two actors, p iq is the tie strength between i and j and m jq is the tie strength between j and q. N is the number of alters in the ego network.

Conversely, network constraint measures the extent to which an actor’s time and energy are invested in contacts who are themselves are connected to one another [ 102 ]:

where i is the ego having a strong tie with j (represented by p ij ), j is another alter having a strong tie with I (reprenseted by p iq ) and q is also an another alter having a strong tie with j (represented by p qj ).

Mean tie strength is the sum of the strength of all ties of an ego (outgoing and ingoing), each tie strength ranging from 1 to 4, divided by the number of alters in a network:

where j is the ego, q is the alter, S qj is the tie strength between j and q, and N q is the number of alters in an ego’s network.

To construct and visualize the collaboration networks of authors, institutions and countries, bibliometric data from WoS was directly imported into VOSviewer–a specialized software tool that visualizes networks based on scientific publications [ 103 ].

Data analysis

To understand the association between social network measures and research performance, the extracted social network measures from UCINET were imported into SPSS with the number of citations and documents published for each author. Correlation and T-tests determined whether a positive or a negative association exists between the explored variables.

Results and discussion

Descriptive results.

A total of 6127 articles were obtained from different journals between 1989 and 2020. As seen in Table 1 and Fig 2 , there is an exponential increase in the number of published articles. 85 articles were published in t 1 , 885 in t 2 and remarkably 5157 in t 3 . This shows that the majority of collaborative endeavors have occurred in the last decade with a 482% increase in the number of articles from t 2 to t 3 . The number of articles written by multi authors (three or more authors) in the last 32 years is 3590 (58.5%) which is much higher than double author articles (1603 articles, 26.16%) and single author articles (934 papers, 16.2%). Fig 2 shows that the number of published articles increased gradually from 2 to 44 articles between 1989 and 2004, with an exponential increase in 2005 and onwards (i.e., the number of publications in 2004 has been doubled in 2005). The period from 2014 and 2019 experienced the highest number of published articles, indicating the increased interest of the academic community in STM research.

Regarding institutional co-occurrence, it is evident that t 3 has witnessed the highest number of collaborative institutions (3778) than t 2 (879) and t 1 (132). Similarly, the number of collaborating countries was the highest (155) in t 3 and the lowest (16) in t 1 . Given that a scientific field might require 45 years to mature [ 104 ], the overall results show that the STM field moved from incubation ( t 1 ) to incremental growth ( t 2 ) to maturity ( t 3 ), reflected by the dramatic increase in the number of articles, institutions, countries and in the number of citations (106,466 in total) especially in t 3 (61,942).

Social network analysis results

Using SNA, the 10 most prolific and influential actors for each network (authors, institutions, countries) in each sub-period ( t 1 , t 2 , t 3 ) were identified.

Table 2 shows that Bair JD is considered the most prolific author in t 1 with the most direct connections (degree centrality = 0.045) (all centrality measures are normalized) and the largest betweenness centrality ( 8 10 - 3 ) and is considered the closest to all other actors in the network (closeness centrality = 0.343). Bosse GC, Driskill JM and Fottler MD are next in line with same centrality scores, followed by Friedman R, Jones TM, Berman SL, Agle BR and Sonnenfeld JA. Fig 3 shows the evolution of collaborative networks of co-authors by sub-period. Surprisingly, it is shown that some of these authors share the same clique, especially for Bair JD, Bosse GC and Driskill JM, but the majority of the authors in Table 2 do not belong to a single integral clique.

Each node/circle represents a researcher who have published in the STM field. The size of each node size is proportional to the number of citations. A line connecting two nodes indicates an, at least, one published paper between two authors in STM field.

This indicates that collaboration is in the form of sub-networks of closely knitted authors each forming their own collaborative clique. It is evident that collaboration is still premature with only 156 authors not well connected in the network. t 1 is known as the discovery period of stakeholder theory where it first appeared in management journals (e.g. Academy of Management Review) [ 32 ].

In t 2 , the collaboration network consists of 1957 authors and has become larger and more condensed than in t 1 . However, it is important to note that Table 1 earlier shows that 62% of articles (547 out of 885 articles) are single and double authored and only 38% (338 articles) are multi-authored. This finding can be noted in Network B, Fig 3 with the emergence of more than 1000 single and dyadic authors that have further fragmented the collaboration network as a whole. This disintegration of the stakeholder domain is expected because the stakeholder theory has a wide scope of interpretations and the term ‘stakeholder’ can mean different things to different people [ 105 ]. With the increase in stakeholder theoretical disputes between the moral justifications [ 41 ] and managerial implications of the theory [ 38 , 66 , 105 ], numerous solo, dyadic and triadic have risen, detaching from both the mainstream stakeholder theory research [ 34 , 35 ], and the large network cliques [ 106 , 107 ]. Perhaps, a reason why most of the prolific actors in t 1 did not make the list in t 2 is because new research areas have emerged, such as stakeholder engagement [ 108 , 109 ], stakeholder social network analysis [ 56 , 110 ], stakeholder involvement in policy decision making [ 111 ] and many more.

Larger cliques are observed which some reaching to 16 authors and with the emergence of numerous small to medium size sub-networks. For t 2 , a totally new set of influential authors have emerged but being less central than those in t 1 with lower degree and closeness centrality scores but with higher betweenness in general. This indicates that collaboration endeavors are mainly driven by clique members rather than by highly central actors. Similarly, another study showed that key authors are more likely to form a well-connected group that collaborates frequently and diversely [ 112 ], rather to collaborate solely through central actors. Among the most influential actors are Boitani I and Turner W who have the same centrality scores, followed by Barnett J, Brown K, then Freeman RE and Grant T who have a lower degree centrality (0.004) but are still considered highly central by occupying a strong brokerage position (betweenness centrality is 1.22 10 - 5 a n d 1.2 10 - 5 respectively). Bloom G, Berron P, Robert A and Andersson I are less central but still considered highly influential.

As it can be interpreted from the graphical visualization in Fig 3 , that the scenario observed in t 3 is very similar to that in t 2 , but with a larger network of 16,905 authors (763% increase in number of authors from t 2 ). In particular, the number of components has increased to 88 and expanded to include 12 actors. In contrast, network density–the percentage of existing ties over the total number of possible ties–has decreased from 1.8% in t 1 to 0.08% in t 3 . Although it seems intuitive that density would increase with new researchers entering the field, this did not seem to be the case where density decreased with further fragmentations that reduced the number of connections as the number of nodes increased. This finding is supported by a study [ 18 ] that found a decrease in network density of author collaboration networks from 0.026 in the 1980s to 0.003 in the 2000s. In the presence of 16905 authors with different research interests, it is nearly impossible to connect the majority of the nodes and achieve a high network density. The overlay color range in Network C, Fig 3 also shows that the majority of publications have occurred between 2014 and 2018 with few co-authorships noted in the last two years.

The SNA results presented in Table 2 show that Tugwell P is the most influential author in the network, followed by Graham ID, Newman PA, Dawkins JS and Walker CE who all have higher degree centrality scores than the rest. Remarkably, the findings of betweenness centrality in t 3 show an increase in the importance of the intermediary role, as all prominent actors (see Table 2 ) have a higher betweenness centrality score compared to that of t 1 and t 3 . The brokerage role is significant in t 3 with the decrease in degree and closeness scores. Therefore, the collaboration network has become more dependent on authors with a brokerage role in t 3 .

The evolution of the collaboration network across three decades shows that the STM authors do not belong to the same network. This observation has also been reported in the Network Meta-Analysis field where collaborating authors belonged to different network clusters [ 113 ]. Therefore, the collaboration network can be best described as involving a high number of authors with different research interests that have pursued different research areas by either being a part of a sub-network of three or more actors or by working alone or in pairs. Evidence for radical changes in network structures from t 1 to t 3 , other than the increase in component sizes and fragmentation, have not been demonstrated, where this is still considered an important and unexpected finding. The findings show that the stakeholder concept is a multidisciplinary theory applied in various research domains such as in health care management [ 114 – 119 ], marine policy [ 120 , 121 ], agriculture [ 24 , 122 ], applied geography [ 123 , 124 ], engineering and architecture [ 23 , 125 ], marketing [ 126 – 128 ], public affairs [ 25 , 129 – 131 ], project management [ 73 , 132 – 134 ] and tourism [ 135 – 137 ]. In other words, the stakeholder concept has been developed mainly by multidisciplinary teams of both experienced and emerging scientists. Therefore, this finding contradicts what has been recently speculated that STM is still at an early stage and that published studies are still limited [ 138 ].

Institutions

Institutional collaboration enables the sharing of unique resources and improves research visibility and contribution [ 16 ]. The results show that the first period contained 88 institutions that have participated in stakeholder research. Surprisingly, 8 out of the 10 most collaborative central institutions are from the United States (see Table 3 ) and are Health Management Link (Indianapolis, USA), Indiana University, University of Iowa, Kings Daughters Hospital, Penn State University, Washington State University, Colorado State University and Boston University. Similar to the author collaboration network in t 1 (Network A, Fig 3 ), the institutional network (Network A, Fig 4 ) shows that the collaboration network doesn’t constitute a main component but is disseminated into several small size components (3 to 5 nodes). This shows that the above institutions are only influential in their own cliques.

Each node/circle represents an institution that has been involved in STM research. The size of each node size is proportional to the number of connections. A line connecting two nodes indicates a collaborative relationship between two institutions in the STM field.

In contrast to t 1 , t 2 has witnessed a wider international collaboration where 8 out of the 10 most prolific institutions are from outside the US (see Table 3 ), also being the top 5 institutions and are Erasmus University (Netherlands) which has the highest degree centrality (0.028) and being the most influential intermediary with York University (Canada) (Betweenness centrality = 0.05), University of London (UK), University of Queensland (Australia), University of East Anglia (UK); followed by two US institutions–University of North Carolina and Harvard University, and then Autonomous University of Barcelona (Spain), Utrecht University (Netherlands) and Aarhus University (Denmark). This result is interestingly surprising as it challenges previous studies that showed that most published papers, in general, are from USA, UK and Canada, which also are the most central in collaboration networks [ 1 , 16 , 139 ].

Regarding the network structure and contrary to the institutional network in t 1 , the result show the emergence of a main component in t 2 that is well connected and highly centralized by constituting a nucleus of all of the above prolific institutions, but surrounded by numerous institutions that are isolates (i.e. nodes disconnected from the main component). However, a deeper inspection reveals that an institution can also be considered highly influential without being embedded in the main component, such as in the case of Autonomous University of Barcelona (placed between the main component and the isolates in Fig 4 , Network B). Autonomous University of Barcelona is connected to 16 other institutions present in its own clique, such as Queen Mary University of London, Medical University of Vienna and Illinois state university. This analysis reinforces the important role of cliques in facilitating collaborating processes. The findings overall place STM research on the global radar by being in favor of the most prestige universities worldwide such as University of London, Harvard University and University of Queensland.

The results for t 3 show University of Leeds being the most prominent institution with the highest degree, betweenness and closeness centrality, followed by the University of Toronto, University of Washington, University of Calgary, University of Oxford, University of Otawa, University of Oxford, University of British Colombia, University of Melbourne, University of Sydney and Harvard University. Most of these institutions do not belong to the same components and therefore, it can be argued that collaboration is led by highly central actors disseminitated across the entire network. This has facilitated the connection of detached neighbourhoods as reflected by the increase in density from 0.003 in period 2 to 0.014 in period 3 (367% increase in density). This finding is contrary to Koseoglu [ 20 ] who found that collaboration network density in strategic management research did not increase across 34 years despite the increase in network size.

For this reason, each period is characterised by having a very distinct list of prolific actors that change with the change in network size and structure. Moreover, the number of vertices has dramatically increased from 1201 in period 2 (879 nodes) to 12833 in period 3 (3778 nodes). It can be argued that interesting patterns were observed in the institutional network for t 3 , especially with the reduction of isolates, the increased density and the enlargement of the main component in t 2 to include other large cliques that reached 31 nodes (158% increase in clique size). This finding contradicts previous research in strategic management that showed that large institutional cliques did not emerge with the enlargement of collaboration network [ 20 ]. The overlay color range in Network C in Fig 4 shows that the majority of institutions have published between 2014 and 2018 with a continual rise in 2019 and 2020.

Table 4 provides interesting observations where USA and England are the most prolific actors that are leading collaborative research in the last 32 years. This finding is also supported by previous studies that showed that countries in North and South America, with Europe, are the best-connected countries to faciliate international research collaboration [ 20 , 139 – 141 ]. The collaboration network in t 1 only exists because of the brokerage roles performed by USA and England (see Network A, Fig 5 ). USA stands out by having the most direct relationships (degree centrality = 0.4), brokerage position (betweenness centrality = 0.142) and being the closest to all other actors (closeness centrality = 0.454). USA and England are considered ‘cutpoints’ that if removed would disconnect the entire two networks. For this reason, the rest of the countries (Australia, Canada, Scotland, etc) are considered prolific only because of their only single relationship with either USA or England. A number of isolates are also noted and are Wales, Israel, Belgium, Sweden, Spain and New Zealand.

Each node/circle represents a country that has been involved in STM research. The size of each node size is proportional to the number of connections. A line connecting two nodes indicates a collaborative relationship between two countries in the STM field.

Unlike the scenario in t 1 , a significant involvement of new countries in the collaboration network is observed in t 2 while still having USA and England as the most central actors. An interesting finding is that the majority of countries that followed USA and England were not among the prolific actors in t 1 , such as Germany, Italy, Belgium, Spain and Denmark. On other hand, some countries that existed in t 1 , such as Australia, Cananda and Netherlands, have taken a more significant role in the collaboration network in t 2 , while Scotland, Hungary, Thailand, Jamaica and Ireland have dissappeared from the prolific radar for t 2 and t 3 . Remarkable, the network density of the country contribution network in t 2 and t 3 are 11.2% and 10% which are considered the highest compared to all of the previous networks in most decades. Fig 5 shows that the collaboration network of countries started by being uncondensed, fragmented and highly centralised with 16 countries controlling the marjority of connections, to a highly dense, less fragmented network of 74 countries in t 2 , to a larger network of 141 countries and 1059 vertices counting for a 10% density in t 3 . Network 3, Fig 5 shows that the majority of countries emerged between 2014 and 2017.

To our knowledge, a well connected network of collaborative countries as observed in t 2 and t 3 is not occasional. Geographic, linguistic and cultural distances between scientists of different countries may significantly impact collaboration prevalence [ 142 , 143 ]. According to Li et al. (2016), it is more often for collaboration to occur within the same country or same institution due to many reasons including the ease of communication, low intra-competition and low funding opportunities. For example, a study on how higher educations perceive stakeholder salience was possible due to the collaboration of Benneworth and Jongbloed [ 144 ] who both were researchers at the University of Twente in the Netherlands. However, the findings in this study allowed us to observe cross country collaboration since the origin of stakeholder theory in the 1980s. Perhaps a contributing reason for this global collaboration, at least in part, is the presence of several funding agencies, such as the Economic and Social Research Council (ESRC), that supported many stakeholder research studies which brought together many scientist from different countries such as Wales, England, Spain and Sweden [ 145 , 146 ].

Effect of co-authorship networks on research productivity and citation-based performance

A preliminary investigation of the associations involved exploring the correlations between actors’ network attributes and research performance for each period. Since the assumption of normality has been violated, non-parametric tests of Spearman correlation and Mann-Whitney U Test were conducted. The results in Table 5 show that the correlations varied differently across the three sub-periods with regards to magnitude, direction and significance. Research productivity is shown to have the strongest correlation with tie strength in t 1 (r = -0.39, p < 0.01), betweeness centrality in t 2 (r = 0.67, p < 0.01) and ego-density in t 3 (r = -0.563, p < 0.01). On the other hand, citation counts is mostly correlated with tie strength in t 1 (r = 0.49, p < 0.01) and t 2 (r = 0.48, p < 0.01).

Remarkably, the correlations between research productivity and each of degree centrality (r = -0.19, p < 0.01) and tie strength (r = -0.04, p < 0.01) in t 3 , have shifted its direction as opposed to the positive correlations in t 1 and t 2 . The results overall show that all social network variables (ego-density, betweenness, closeness, efficiency, contraint, tie strength) are either negatively or positively correlated with research performance (i.e., citation counts, research productivity) (see Table 5 for more information).

To explore the association between ego-density and research performance, the median for ego-density index was chosen as a cut point to segregate the participants into two groups: authors with ego-density scores above the median and are considered as “high ego-density group” and authors with ego-density scores lower than the median and are considered as “low ego-density group”. The results of the Mann-Whitney U test (U = 2658, z = -2.86, p = 0.04) summarized in Table 6 show a positive association in t 1 with higher research performance scores observed in the high-density group (Mdn = 83) than the low density group (Mdn = 75). Similarly, the results (U = 443079, z = -6.6, p = 0.00) show a positive association in t 2 with higher research performance scores observed in the high density group (Mdn = 1015) than the low density group (Mdn = 973). Accordingly, we argue that it was essential to have highly dense collaborative clusters in the first decade to publish scientific papers that can bring awareness to stakeholder theory as a newly developed theory of management and ethics.

+Positively association

-Negative association

The results show that degree centrality is positively associated with both research productivity andcitation counts in t 2 while no association in t 1 . In particular, authors with numerous collaborative relationships in t 2 had higher citation counts (Mdn = 1042) and research productivity (Mdn = 1011) than those with fewer relationships (Mdn = 925 and Mdn = 977 respectively); U = 411370, p = 0.00 and U = 449944, p = 0.03 respectively. In t 3 , a positive association is shown between degree centrality and citation counts (U = 2738017, p = 0.00) where authors with numerous collaborative relationships having higher citation counts (Mdn = 2656) than those with fewer relationships (Mdn = 2347). In contrast, authors with numerous collaborative relationships in t 3 had lower research productivity (Mdn = 2404) than those with fewer relationships (Mdn = 2594); U = 2887576, p = 0.00. Therefore, we can infer that individual collaborative relationships are no longer effective in the last decade in enhancing research performance compared to the periods of stakeholder theory origin and development ( t 1 and t 2 ) that required joint efforts to advance the field.

Regarding betweenness centrality and research performance, the results show that authors that lie on the shortest path between other authors had better research performance in t 2 in terms of research productivity (Mdn = 1939), U = 1704, p = 0.00; and citation counts (Mdn = 1623), U = 20655, p = 0.00, than those who are not considered intermediaries (Mdn = 969, Mdn = 979 respectively). Similar results are shown in t 3 between the low betweenness group in terms of research productivity (Mdn = 2445), U = 119157, p = 0.00; and citation counts (Mdn = 2463), U = 586781, p = 0.00; and the high betweenness group (Mdn = 4585, Mdn = 3897 respectively). The absence of a positive association in t 1 can be explained by the low number of authors (n = 156) that disabled the formation of large cliques, in which its structures prompt brokerage salience.

With respect to closeness centrality, the overall results show a positive association in all periods, where authors with low closeness centrality in t 1 had lower research productivity (Mdn = 75) that those with high closeness centrality (Mdn = 83), U = 2658, p = 0.04). In t 2 , the results show that authors with low closeness centrality had low research productivity (Mdn = 978) and citation counts (Mdn = 922) than those with high closeness centrality (Mdn = 1010, 1042 respectively; U = 445944, p = 0.05 for research productivity, U = 405711, p = 0.00 for citation counts. A positive association is observed in t 3 regarding citation counts between low closeness group (Mdn = 2286) and high closeness group (Mdn = 2727); U = 2572243, p = 0.00. The only exception is in t 2 with research productivity where a negative association is observed where low closeness group having higher research productivity (Mdn = 2525) than the low closeness group (Mdn = 2474); U = 3058094, p = .037. Hence, the findings infer that the closeness of authors to each other, (i.e. being separated by few network steps) was important for all periods in enhancing research performance except for research productivity in t 3 which relied more on authors with high degree and betweenness centrality as shown by the above results.

Efficiency is positively associated with research productivity and citation counts for all periods. For t 1 , authors who were surrounded by non-redundant ties had higher citation counts (Mdn = 89) and research productivity (Mdn = 80) than those who have a less efficient network position (Mdn = 63 and Mdn = 76, respectively); U = 1977, p = 0.00 for citation counts, U = 2742, p = 0.05 for research productivity. Similarly, authors who were surrounded by non-redundant ties had higher citation counts (Mdn = 1052) and research productivity (Mdn = 1015) than those who have a less efficient network position (Mdn = 929, Mdn = 977 respectively); U = 429965, p = 0.00 for citation counts, U = 472013 p = 0.01 for research productivity. Similarly, efficient authors had higher citation counts (Mdn = 2548) and research productivity (Mdn = 2722) than those who were less efficient (Mdn = 2387, Mdn = 2209 respectively); U = 2848639, p = 0.00 for citation counts, U = 2414066, p = 0.05 for research productivity. These findings indicate that authors surrounded by structural holes–being connected to a primary co-author in a group and receiving novel information–had good research performance. Moreover, it can be argued that expansion of the STM field relied on novel information flowing between efficient authors of different disciplines.

The findings show that constraint is positively associated with research performance in t 1 and t 2 while in t 3 a negative association is shown instead. In particular, authors with redundant ties had higher research productivity in t 1 (Mdn = 83; U = 2658, z = -2.8, p = .004) and citation counts in t 2 (Mdn = 1028; U = 469269, z = -2.2, p = .03) than those that are less constrained (Mdn = 75, Mdn = 970 respectively). This finding contradicts previous research which showed that constraint is negatively associated with research performance before year 2010 [ 147 ]. However, in t 3 , a negative association is shown were highly contrained individuals (i.e. those with redundant ties) had lower citation counts (Mdn = 2275) than those that are less constrained (Mdn = 2716), U = 25726787, p = 0.00). Therefore, research productivity in t 2 and citation counts in t 3 have been mainly enhanced via authors with redundant relationships that lead back to same group of co-authors. We argue that with the wide expansion of the collaboration network in t 3 , that had witnessed the emergence of many scholars, it is difficult for authors to establish relationships with all members of a clique, and therefore, must rely on relationships established with primary actors, reflected by the salience of structural holes.

With respect to tie strength, the findings show a positive association with research performance in t 1 and t 2 . With regards to t 1 , the results show that authors, who had strong relationships with other authors, had better citations (Mdn = 101) and research productivity (Mdn = 83) than those with weaker ties (Mdn = 56, Mdn = 74 respectively). Similarly, in t 2 , authors with strong ties had higher citations (Mdn = 1269) and research productivity (Mdn = 1064) than those with weak ties (Mdn = 778, Mdn = 945 respectively). Therefore, the theory of “strong ties” [ 94 ] in ehancing productivity is supported by our analysis. Strong relationships between co-authors are essential for increasing citation and publication counts.

Conclusion and implications

This study descriptively analyzed the evolution of research collaboration networks of authors, institutions and countries, in the STM discipline and identified key actors that are leading collaborative works. In addition, this study examined the longitudinal effect of co-authorship networks on research performance by exploring the associations between collaborative social network variables and each of citation counts and research productivity.

The findings of the authors’ collaboration network revealed a premature and fragmented network in t 1 , where collaboration has happened in the form of sub-networks or cliques of closely knitted actors. In t 2 , the network increased in size by the emergence of mostly single and dyadic authors which further disintegrated the network. In t 3 , a larger network and a higher number of cliques emerged, with the most prolific actors having a strong brokerage role (betweenness centrality). The overall results show that stakeholder theory has a wide scope of interpretations and lacks universal consensus on its concepts and frameworks [ 34 , 35 , 148 , 149 ].

The findings of the institutional collaboration networks revealed that the collaboration network in t 1 is fragmented into several small size cliques controlled mostly by US institutions. In contrast, a wider international collaboration was witnessed in t 2 , with the emergence of non US-institutions. The results for t 3 showed that the most prolific universities (University of Leeds, University of Washington, University of Toronto) did not belong to the same components, therefore, indicating that the collaboration is led by highly central actors disseminated across the entire network.

The collaboration network of countries originated by being uncondensed, fragmented and highly centralised in t 1 , with only 16 countries where USA and England being the most prolific actors in STM research. The collaboration network became highly dense and less fragmented in t 2 with 74 countries joining the scene. A larger network of 141 countries was observed in t 3 with high density and less fragmentation.

Regarding the impact of co-authorship networks on research performance, efficiency was found to be the only network measure positively associated with both citation counts and research productivity in all of the three periods (see Table 6 ), indicating the importance of structural holes in enhancing research performance. In summary, STM research performance is influenced by authors (1) in highly dense collaborative clusters (ego-density), are (2) close to all other actors in the network, (3) efficient (those that present novel research information); (4) constrained by repetitive relationships and (5) that have strong ties with other authors.

This paper contributes to STM reseach by showing the evolvement of the field and the dynamic changes in its structures. The findings demonstrate that STM is indeed a multi-disciplinary discipline, reflected by fragmented co-authorship network from t 1 to t 3 and the emergence of a high number of single and dyadic author representing disunity in STM research interest. This heeds the growing calls to explore the structural composition of STM [ 150 ]. Fig 6 supports this notion which illustrates keyword co-occurrence networks in STM discipline in t 1 , t 2 , t 3 . The main keywords with the highest co-occurrence in t 1 are ‘stakeholder analysis’, ‘stakeholder’ and ‘stakeholder theory’, which all were fundamental and related concepts in STM but each belonging to a different clique. This indicates that STM had not received profound universal consensus at that time and had various comprehensions. However, the application of STM in other disciplines was on the rise, especially with ‘stakeholder analysis’ coinciding with ‘strategic planning’, ‘climate change’ and ‘participatory research’. In t 2 , new major keywords appeared such as ‘corporate social responsibility’, ‘business ethics’ and ‘corporate governance’, all belonging to the same cluster (all having a red color) indicating wide acceptance of stakeholder theory as a theory of management and ethics. Other non-related STM keywords (‘climate change’, ‘health’, ‘, ‘resource-based view’, ‘governance’, ‘networks’, etc.) had also emerged, indicating that STM is a “living Wiki” that is continuously growing through the collaboration of stakeholder scholars from different research fields [ 32 ].

Each node/circle represents a keyword in STM research. The size of each node size is proportional to the number of connections. A line connecting two nodes indicates an affiliation between two keywords. Node color represents related clusters of keywords.

This study provides practical contributions to scientists in the STM field and educational managements worldwide. First, the concrete findings from the association testing can help stakeholder scientists improve their research performance by altering the configuration of their collaborative relationships, especially degree, betweenness, and closeness centralities. Institutions can benefit from these results to increase citations rates and research productivity. Second, this study provides empirical evidence regarding the structure of collaboration networks and central actors, that if acted upon, can directly or indirectly lead the allocation of government funding, maximization of research outputs, improvement of research community reputation and the enhancement of cost savings [ 29 , 30 ], that can all improve collaboration and developing coordinated research programs that can advance the field.

Supporting information

Funding statement.

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

Data Availability

• PLoS One. 2021; 16(10): e0255658.

Decision Letter 0

30 Dec 2020

PONE-D-20-37035

Understanding Collaboration Networks in Stakeholder Theory and Management: A Longitudinal Approach

Dear Dr. Fares,

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Ghaffar Ali, PhD

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Reviewer #2: Yes

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Reviewer #1: I enjoyed reading the article, the authors presented a longitudinal approach to unveil Collaboration Networks in Stakeholder Theory and Management. I have several comments which must be addressed before considering this article for publications.

English editing of the manuscript is required.

The abstract in the system and manuscript are not similar, please check carefully.

Line 243: UCINET is not explained, full form could be presented on its first appearance in the manuscript.

All abbreviation / acronyms could be thoroughly checked in the revised version.

ED, the ego-density could be mentioned in preceding paragraphs.

Discussion with reference to other studies and findings could have been added to strengthen the inferences derived from the analysis.

Fig 2 the sharp dip in 2020 indicates that authors did not consider the whole year, I would suggest either remove 2020 or add the latest papers as well. Though, there is a possibility of decrease in number of publication due to the pandemic.

Overall, the paper is well written and could be considered for publication after minor revision.

Reviewer #2: 1. This study has some interesting points which carry potential of publication. However, in its current form, some revisions are required in few areas before any final decision. I have few yet significant comments on this paper and analyses. Kindly find my comments and suggestions below:

2. Abstract needs revision in terms of methodology and possible practical policy implications at regional and global levels and in terms of language.

3. Arrange keywords in alphabetical order.

4. Brief history section should be literature review section. And more sophisticatedly arranged.

5. How to validate data or verify because no specific source except WoS provided?

6. Why social network analysis is used? How about other methods?

7. Table captions are always provided at top of the table. Correct all.

8. Conclusions and policy implications is way long section. Reduce it to 1.5 page only please. Only provide the most significant things.

9. Fig. 4, network B, contains very minute words inside. Please use better font size.

10. Try not to use references older than 2012. And also add some citations from this journal PLOS One, to link your paper with this.

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Reviewer #1: No

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Author response to Decision Letter 0

Response to Reviewers

The authors highly appreciate the reviewers’ insightful and helpful comments to improve the manuscript, and they would like to thank them for this great opportunity. Please see below our detailed response to the changes required. All line numbers in this document refer to the manuscript file while track changes are showing.

Editors Comments:

1. Comment: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

The manuscript has been revised according to the guidelines listed in the above two links. For instance, see the updated affiliations at the beginning of the manuscript (lines 6-14). Also, the heading font size has been changed to 16 for heading 1, 14 to heading 2, etc.

2. Comments: We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly.

The dataset has now been submitted during the second revision and will be available.

3. Comment: In your revised cover letter, please address the following prompts:

The above points have been included in the cover letter where there are no legal restrictions to share the data.

4. Comment: Please ensure that you refer to Figure 1 in your text as, if accepted, production will need this reference to link the reader to the figure.

Figure 1 is now referred to in the text. Kindly see lines 244-245 (track changes showing). All figures now are referred to in the text.

5. Comment: Please include a separate caption for each figure in your manuscript.

A separate caption is now included for each figure.

Reviewer #1:

The authors would like to thank Reviewer 1 for the insightful comments. Indeed, the comments helped improved the paper a lot.

1. Comment 1: English editing of the manuscript is required.

The authors agree that English language is wordy in some sections, and therefore, english editing has now been done throughout the manuscript. For instance, kindly see:

� The Abstract

� Introduction

� Lines 365-369

� Lines 376-382

� Lines 391-394

� Lines 405-411

� Lines 424-428

� Conclusion and Implication part

� And other sections of the document.

2. Comment: The abstract in the system and manuscript are not similar, please check carefully.

They are updated now to be the same

3. Comment: Line 243: UCINET is not explained, full form could be presented on its first appearance in the manuscript.

Surprisingly, there is no full form for “UCINET”. UCINET is known as a windows software package for social network analysis (see https://sites.google.com/site/ucinetsoftware/home ). The authors now included an explanation for UCINET in the manuscript (see lines 264-266)

4. Comment: All abbreviation / acronyms could be thoroughly checked in the revised version.

The main abbreviations are STM and SNA:

The abbreviation used for “Stakeholder theory and management” is “STM”. Now it has been used to replace the full word in line 33.

The abbreviation used for “social network analysis” is “SNA”. Now it has been used to replace the full word in lines:

114, 122, 179, 263, 264,802

There are no other abbreviations in the text.

5. Comment: ED, the ego-density could be mentioned in preceding paragraphs.

Kindly note that “ego-density” is mentioned in the abstract (line 44), introduction (124), literature (line 179) and in the remaining sections of the paper.

If it is requested that the abbreviation “ED” for “ego-density” to be used instead, the authors would like to kindly note that this can be done, however, they prefer that the full term “ego-density” to be used in conjunction with the other social network measures that have no abbreviations, such as betweenness, closesess, efficiency, etc. Otherwise, the authors have to find an abbreviation to all of these, which perhaps, might make it harder for the reader to memorize all of these terms while reading the paper.

6. Comment: Discussion with reference to other studies and findings could have been added to strengthen the inferences derived from the analysis.

The results of this study are divided into 3 main section – authors, institutions and countries. Therefore, we have now referenced studies that support/contradict our findings for each of the three sections.

For authors, see: (the numbers of references changes, see again)

� Reference number 115, line 394

� Reference number 18, line 409

� Reference number 116, line 427

For institutions, see:

� Reference 16 line 470

� Reference 20, line 492

� Reference 20, line 503

For countries:

See references 20,142,144 in line 509

7. Comment: Fig 2 the sharp dip in 2020 indicates that authors did not consider the whole year, I would suggest either remove 2020 or add the latest papers as well. Though, there is a possibility of decrease in number of publication due to the pandemic.

The authors would like to thank the reviewers for this unintentionally overlooked mistake. The authors would like to note that they submitted this paper on November 24, and haven’t taken into consideration the published papers in December. After a review of all the papers published in 2020 (see picture below), the number increased from 256 to 356. So the final number is 356. The authors believe that the drop in number from 1163 to 356 is due to the Covid – 19 pandemic, which they personally experienced and has also impacted their publication performance overall as well as that of others.

Reviewer #2:

The authors would like to thank Reviewer 2 for the insightful comments. Indeed, the comments helped improved the paper a lot.

1. Comment: Abstract needs revision in terms of methodology and possible practical policy implications at regional and global levels and in terms of language.

These changes have now been made:

For revision in terms of methodology, see lines 28-35 (track changes showing)

For revision in terms of practical policy, see lines 45-53

Language has been improved by reducing the number of words and using more straightforward terminologies across the entire abstract, and also across the entire manuscript.

2. Comment 3. Arrange keywords in alphabetical order

3. Comment: Brief history section should be literature review section. And more sophisticatedly arranged.

� “Brief history” is now replaced by” Literature Review”.

� The subsection “Origins of STM” has been included below “Literature Review”

� New subsection “Expansion of STM” has been included with new ideas about stakeholder analysis expansion (lines 151 and 155).

� Subsection “social network Theories” has been included within the “literature review” section

4. Comment: How to validate data or verify because no specific source except WoS provided?

The authors would like to kindly note that data, in such form of research, cannot be validated using traditional research validation techniques. The data is obtained from Web of Science (WoS) which is a trusted public source and is widely regarded by scholars as fairly comprehensive and robust. The same approach used in this study can be used to extract data from other scholarly databases like Scopus, Dimensions etc. However, there is overlaps among such data sources and WoS is the pioneer and most trusted source for meta-data analysis of academic articles although it is coverage may be less than other, focusing mainly on prominent journals (publishers).

5. Comment: Why social network analysis is used? How about other methods?

The reason why social network analysis research has been used in this study is because relationships are of paramount importance in explaining behavior, and in particular, how research performance is affected by the structure of authors’ relationships. We rely on a “networks perspective” that uses individual relations to explain individual outcomes. Also, social network analysis has been the main tool for exploring scientific collaboration networks in many studies (kindly see reference numbers 1,2,4,6,12,18,20 etc)

Regarding other methods, there is a wealth of social network models that can be utilized, but the choice boils down to which method addresses the papers’ aims:

(i) explore the evolution of research collaboration networks, (ii) identify the key actors (authors, institutions and countries) that are leading collaborative works in each sub-period, and (iii) understand the longitudinal effect of co-authorship networks on research performance

These aims require social network analysis to be used in order to quantify the structure of relationships

There are many social network methods in literature, and therefore, the authors have identified a number of well-known models that have been used but cannot be operationalised for this study. Among the popular network models is Exponential Random Graph Models (ERGM) that explores if a network deviates significantly from chance. ERGM requires that the social network is treated as the dependent variable, where in our study, the network constructs were treated as the independent variables and research performance as the dependent variables. Nevertheless, ERGMs requires incorporate nodal attributes in model estimation, where in our study, the characteristics of scientists were excluded. With respect to models that treat time more explicitly in the sense that they model what happens at the micro (individual) level at each point in time is network exposure which explores the social influence in diffusion, a topic that also falls out of scope. Kindly note that there are also other models that cannot be operationalized in this study, but the authors are afraid that discussing them would diverge the study away from its objective.

5. Comment: Table captions are always provided at top of the table. Correct all.

Done now for all 6 Tables.

6. Comment: Conclusions and policy implications is way long section. Reduce it to 1.5 page only please. Only provide the most significant things.

The authors reduced the number pages from 6 to less than 2.5 pages. The authors would like to kindly ask, if possible, to have 2.5 pages instead of 1.5, because we have too many results that need to be summarized (author, institution, countries, association testing) and there is also the theoretical and practical implications that need to be included as well.. Because it is a long manuscript so to speak, and has different research objectives, its better to have a conclusion that discusses briefly each objective:

� the evolution of collaboration networks for authors, instituions and countries.

� associated testing between network variables and research performance.

� we also include a paragraph about key words which is important, and associated figures.

� practical contribution.

7. Comment: Fig. 4, network B, contains very minute words inside. Please use better font size.

Larger font size is used now. Old figure deleted.

8. Comment: Try not to use references older than 2012. And also add some citations from this journal PLOS One, to link your paper with this.

The authors would like to kindly note that since we are covering an era between 1989 and 1999, and between 2000 and 2010, we had to include studies published in that era. However, these have been minimized now, and other new references, related to PLOS one and other journals, have been included/

References added (some are PLOS ONE)

� Jappe A. Professional standards in bibliometric research evaluation? A meta-evaluation of European assessment practice 2005–2019. PloS one. 2020;15(4):e0231735.

� Walker, J., Chaar, B. B., Vera, N., Pillai, A. S., Lim, J. S., Bero, L., & Moles, R. J. (2017). Medicine shortages in Fiji: A qualitative exploration of stakeholders’ views. PLoS One, 12(6), e0178429.

� Naseem MA, Lin J, Rehman RU, Ahmad MI, Ali R. Moderating role of financial ratios in corporate social responsibility disclosure and firm value. PloS one. 2019;14(4):e0215430

� Jensen MC. Value maximisation, stakeholder theory and the corporate objective function. Unfolding Stakeholder Thinking: Routledge; 2017. p. 65-84. Sartas, M., Van Asten, P., Schut, M., McCampbell, M., Awori, M., Muchunguzi, P., ... & Leeuwis, C. (2019). Factors influencing participation dynamics in research for development interventions with multi-stakeholder platforms: A metric approach to studying stakeholder participation. PloS one, 14(11), e0223044.

� LeClair, A. M., Kotzias, V., Garlick, J., Cole, A. M., Kwon, S. C., Lightfoot, A., & Concannon, T. W. (2020). Facilitating stakeholder engagement in early stage translational research. PloS one, 15(7), e0235400.

� Zweigenthal, V. E., Pick, W. M., & London, L. (2019). Stakeholders’ perspectives on Public Health Medicine in South Africa. PloS one, 14(8), e0221447

� Political stakeholder theory: The state, legitimacy, and the ethics of microfinance in emerging economies. Business Ethics Quarterly. 2017;27(1):71-98.

� Freeman RE, Dmytriyev S. Corporate social responsibility and stakeholder theory: Learning from each other. Symphonya Emerging Issues in Management. 2017(1):7-15.

Old References removed:

� Katz JS, Martin BR. What is research collaboration? Research policy. 1997;26(1):1-18.

� De Haan J. Authorship patterns in Dutch sociology. Scientometrics. 1997;39(2):197-208.

� Smith HJ. The shareholders vs. stakeholders debate. MIT Sloan Management Review. 2003;44(4):85-90.

� Wallace JS. Value maximization and stakeholder theory: compatible or not? Journal of Applied Corporate Finance. 2003;15(3):120-7.

� Zingales L. In search of new foundations. The journal of Finance. 2000;55(4):1623-53.

� Orts EW, Strudler A. The ethical and environmental limits of stakeholder theory. Business Ethics Quarterly. 2002:215-33.

� Laband DN, Tollison RD. Intellectual collaboration. Journal of Political economy. 2000;108(3):632-62.

� Vučković-Dekić L. Authorship-coauthorship. Archive of Oncology. 2003;11(3):211-2.

� Berman SL, Wicks AC, Kotha S, Jones TM. 1999. Does stakeholder orientation matter? The relationship between stakeholder management models and firm financial performance. Academy of Management journal

� Jones TM. Instrumental stakeholder theory: A synthesis of ethics and economics. Academy of management review. 1995;20(2):404-37.

� Jones TM, Wicks AC. Convergent stakeholder theory. Academy of management review. 1999;24(2):206-21.

� Borgatti SP. Centrality and AIDS. Connections. 1995;18(1):112-4

� Boiko PE, Morrill RL, Flynn J, Faustman EM, Belle Gv, Omenn GS. Who holds the stakes? A case study of stakeholder identification at two nuclear weapons production sites. Risk Analysis. 1996;16(2):237-49

� Chubin D, Friedman R, Kemp K, Fainberg A, Linsenmeyer J. Policy analysis at the US Office of Technology Assessment. International Journal of Technology Management. 1996;11(5-6):589-603.

� Harrison JS, Freeman RE. Stakeholders, social responsibility, and performance: Empirical evidence and theoretical perspectives. Academy of management Journal. 1999;42(5):479-85.

9. Comment: Lastly, English language editing required. Please avoid use of “we”, “our”, “us” in n

English language has been edited throughout the paper and all “We”s have been removed (see lines: 25, 30, 111, 119, 126, 223, 282, 312, etc.

To see the English editing in some sections, kindly see below:

� Abstract

Submitted filename: Response to Reviewers.docx

Decision Letter 1

22 Jul 2021

Stakeholder Theory and Management: Understanding Longitudinal Collaboration Networks

PONE-D-20-37035R1

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Acceptance letter

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REVIEW article

Project stakeholder management in the clinical research environment: how to do it right.

• 1 Department of Population Health, New York University Medical Center, New York, NY, USA
• 2 Department of Management, Zicklin School of Business, Baruch College, New York, NY, USA
• 3 District Mental Health Programme, Madurai Medical College, Madurai, India
• 4 Division of Community Psychiatry, M. S. Chellamuthu Trust and Research Foundation, Madurai, India
• 5 University of Virginia Darden School of Business, Charlottesville, VA, USA
• 6 Department of Neuropsychiatry and Behavioral Science, University of South Carolina School of Medicine, Columbia, SC, USA
• 7 University of South Carolina, Columbia, SC, USA

This review introduces a conceptual framework for understanding stakeholder management (ShM) in the clinical and community-based research environment. In recent years, an evolution in practice has occurred in many applicants for public and non-governmental funding of public health research in hospital settings. Community health research projects are inherently complex, have sought to involve patients and other stakeholders in the center of the research process. Substantial evidence has now been provided that stakeholder involvement is essential for management effectiveness in clinical research. Feedback from stakeholders has critical value for research managers inasmuch as it alerts them to the social, environmental, and ethical implications of research activities. Additionally, those who are directly affected by program development and clinical research, the patients, their families, and others, almost universally have a strong motivation to be involved in the planning and execution of new program changes. The current overview introduces a conceptual framework for ShM in the clinical research environment and offers practical suggestions for fostering meaningful stakeholder engagement. The fifth edition of PMBOK ® of the Project Management Institute, has served as basis for many of the suggested guidelines that are put forward in this article.

A true architect is not an artist but an optimistic realist. They take a diverse number of stakeholders, extract needs, concerns, and dreams, and then create a beautiful yet tangible solution that is loved by the users and the community at large. We create vessels in which life happens

– Cameron Sinclair ( 26 )

In recent years, a revolution in thinking about organizational management and decision making has taken place. Increasingly, programs have been incorporated into organizations, typically private sector corporations or government agencies, which have sought to involve “stakeholders” in management decision making. Stakeholders are the customers, suppliers, the general public, and any other group, which are likely to be affected by the organization’s ultimate decisions. The process of incorporating the ideas and input from these groups has been termed “stakeholder engagement.” It reflects an increasingly accepted attitude that organizations not only have an ethical obligation to involve the participation of stakeholders in their collective activity but also in so doing their overall organizational effectiveness will be enhanced. While certain generalizations in the application of this philosophy are constant, minor variations also exist, which reflect the specific goals that the organization is pursuing. In this review, the application of stakeholder engagement in clinical research settings, particularly in hospitals or university research centers, is considered.

According to the Institute of Medicine (IOM), the purpose of comparative effectiveness research (CER) is, “to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve healthcare at both the individual and population level” ( 1 ). The Kellogg Commission report defines engagement as follows: “By ‘ engagement ’ we refer to institutions that have redesigned their teaching, research, and extension and service functions to become even more sympathetically and productively involved with their communities, however, community may be defined” ( 2 ). Hospitals and research centers are increasingly taking deliberate steps to include their broader constituencies in project management decision making and to seek their input at an early stage of the research or program implementation process. The term “ community engagement ,” can be defined as, “the process of working collaboratively with and through groups of people affiliated by geographic proximity, special interest, or similar situations to address issues affecting the well-being of those people” [( 3 ), p.3]. It has been noted that traditional models of research which view study subjects or targets of program development as passive audiences may result in research findings that are poorly aligned with the information needs of real-world decision makers ( 4 , 5 ). An additional impetus for this interest has been the Patient Protection and Affordable Care Act of 2010, which was enacted to promote patient engagement. The purpose of the act has been to help patients, clinicians, purchasers, and policy makers make better informed health decisions by “advancing the quality and relevance of evidence about how to prevent, diagnose, treat, monitor, and manage diseases, disorders, and other health conditions.”

The key focus in the process of stakeholder engagement is of course the stakeholder. Freeman ( 6 ): 46 defined stakeholder as, “any group or individual who can affect or is affected by the achievement of the organization’s objectives.” According to the project management institute (PMI), the term stakeholder refers to, “an individual, group, or organization, who may affect, be affected by, or perceive itself to be affected by a decision, activity, or outcome of a project” ( 7 ). In other words, almost any individual or group of individuals with an interest or stake in a consensus-building process thereby the outcome of the project and/or an ability to exert a positive or negative influence by the execution or completion of a project or being affected by the work or its deliverables, outputs, or results.

In clinical research, researchers are often faced with questions about the choices that must be made by patients. Research can also be focused on assisting the process of program development. In either instance, the underlying motivation remains the same: to healthcare delivery, to become aware of dysfunctionalities that may exist in healthcare, and to improve the outcomes of proposed changes. It is essential then that research and program processes are assisted by those who are most directly affected by proposals, i.e., the patients themselves. Central to the process of encouraging stakeholder involvement therefore is a basic assumption that patients have the right to make the best decisions about their own health care.

Stakeholder engagement versus stakeholder management (ShM): in recent years, the term “ stakeholder engagement ” (ShE) has become widely used in applied clinical research and new program development. An important reason for this is that it has been repeatedly shown that critical health issues, which are often known to the patients or research subjects themselves, may not have been addressed in the original research or program proposals ( 8 ). Stakeholder engagement is a bidirectional process. It begins when the researcher communicates and interacts with stakeholders, and ultimately results in informed decision-making concerning the selection, conduct, as well as dissemination of research findings in order to achieve a desired outcome and enhance accountability ( 9 , 10 ). Stakeholder engagement is thus differentiated from one-way communication processes that seek to influence groups to agree with a decision that has already been made.

The obligation to serve all stakeholder interests is often called stakeholder management ( 11 , 12 ). The main distinction between stakeholder management and stakeholder engagement largely rests on the extent to which stakeholders are involved in the decision-making processes. The process of engagement varies across different research programs, but is highly noticeable in complex, multidisciplinary research.

A stakeholder analysis is a process, which provides insights into, and understanding of, the interaction between a project and its stakeholders. In other words, the process of listing, classifying, and assessing the influence of these stakeholders in a project is termed a stakeholder analysis. Stakeholder analysis systematically gathers and analyzes both qualitative and quantitative information thereby to determine whose interest should be taken into account throughout the project. One of the first tasks that a clinical project manager must undertake is to identify how stakeholders can make the greatest impact on the research project or program change, which is being contemplated. The function of stakeholder analysis is to produce an awareness of who will be affected by the project and who can contribute to making the project more successful. The stakeholder analysis, which is usually undertaken at an early stage of planning, is an integral part of risk and reward assessment activities.

It is essential for maximal project effectiveness that managers be committed to the basic philosophy of stakeholder involvement. Project managers must communicate and impart what they see as their goals but also seek to encourage participation by stakeholders so that their perspectives are included in decision making.

The process of identifying, engaging stakeholders must begin well in advance so that dialog is seen to play an important part of project implementation; no decisions should be already made before commencing stakeholder engagement on project-related issues.

Benefits of Stakeholder Engagement

Well managed projects, although long and complex, create long-term economic gain and social values meaning that proper use of taxpayer’s money. When done correctly, stakeholder engagement provides opportunities to further align clinical research practices with societal needs, values, and expectations, helping to drive long-term sustainability and stakeholder interests.

Stakeholder engagement is intended to help administrators fully realize the benefits of applying community and patient interest in hospital programs, and to ensure that research and program changes benefit those who are most directly affected.

The stakeholder focus group is a communication medium through which the opinions of individuals or groups of individuals who are impacted by the research can be elicited. Focus groups can also serve to clarify each stakeholder’s role and responsibilities, as well as promoting an overall understanding of the project requirements. Such processes also provide stakeholders with an environment in which they can express their opinions and feel that they have been heard.

In a series of related manuals the Patient-Centered Outcomes Research Institute (PCORI) ( 13 ) has provided a group of examples of how hospitals and medical clinics can encourage stakeholder involvement, in various research projects or programs whose aim was to improve the quality of medical services.

It can be seen from one of our case studies (see Appendix) that stakeholders can make meaningful contributions to a project when opportunities are structured to encourage their participation. The process of encouraging stakeholder participation is referred to as stakeholder management.

Requirements for Stakeholder Management

Stakeholder management involves the processes of identifying (both internal as well as external) stakeholders; assessing stakeholders’ skills, knowledge, and expertise; determining stakeholders’ requirements; determining stakeholders’ interests and expectations; determining stakeholders’ communication needs; addressing stakeholders’ issues and concerns as they occur; maintaining a positive relationship and communicating with stakeholders throughout the project; identifying stakeholders’ influence-controlling strategies; making sure that stakeholders are involved in the project at the required level throughout the project; and confirming continuous interactions with the stakeholders. In the area of clinical research patients and other stakeholders such as physicians, clinicians, nurses, and others have critical roles to play. Clinical researchers at the outset of research need to ask for patient participation in the development of research questions. Researchers need to find out the exact characteristics of study participants and to define what the nature of the research outcomes should be. In this process, contributions from patients are helpful and often critically important for project success. The process of carrying out research also involves measuring the results of research interventions and monitoring the progress of the research, especially in terms of whether or not it is being directed toward the initial intentions of the research. Finally, patients, who are often very closely connected with the target populations of the research, have a direct perspective on how the targets of the research will respond to the research recommendations, and therefore, can provide useful inputs for insuring its relevancy.

Project Stakeholder Management Processes

The PMI identifies four key processes that are associated with the stakeholder management knowledge area in initiating, planning, executing, and monitoring and controlling process groups ( 7 ) (Table 1 ).

Table 1 . Four project stakeholder management processes and key outputs .

Identify Stakeholders

This entails identifying all people or organizations impacted by the project and documenting relevant information regarding their interests, expectations, involvement, and influence on project success. In the hospital setting, the stakeholders are usually the patients, but can also be healthcare professionals and the families of patients. Examples of stakeholders are given in Table 2 .

Table 2 . The stakeholders can be categorized or classified in many different ways for different purposes .

Throughout the project the following critical tasks should be carried out.

All internal and external stakeholders should be identified. These will usually be the patients but often will include the patients’ family members, healthcare providers, or program administrators.

Stakeholders’ interests, requirements, and expectations should be identified. Obviously, patients are interested in the effects of proposed program changes or research outcomes on their health and well-being, but may have additional interests such as hoping to improve their employment prospects, or expanding their range of capabilities. Clinical researchers and administrators should be alert to these concerns and take appropriate steps to address them. It has been found, for instance, that stakeholder views at the beginning of a program evaluation process may be provisional or may change as a result of additional information. Additionally, stakeholders’ interests may change over time. In one study, the results of pre-workshop and final workshop voting often differed, suggesting that prioritization efforts relying solely on requests for topics from stakeholder groups without in-person discussion may provide different research priorities ( 14 ). Thus, efforts should be made to audit the evolving nature of stakeholders’ expectations and preferences through structured methods.

All stakeholders’ levels of influence should be determined. It is often the case that patients and other beneficiaries of program development have talents and skills that may not be reflected in records of formal education or social standing. Certain personal traits, which patient stakeholders may possess, such as communication skills or life experience, could nevertheless prove invaluable for achieving project goals.

A communication plan for the stakeholders should be determined. Patient stakeholders may not always be familiar with or comfortable in using traditional channels of communication in large organizations. As noted by Lavallee et al. ( 15 ), the increasing availability of mobile technology, social media, internet venues, and electronic devices has multiplied the communication options for many, but carries with it the risk of increasing the quantity of participants while reducing the depth of involvement. Often, the use of focus groups or small informal meetings can be used to increase the quality of communication or to elicit participation from those who might otherwise be reticent about expressing their views. Reviews of methods of communication for engaging stakeholders have concluded that a combination of approaches probably yields the best results. Methods such as voting or using ranking procedures such as the analytic hierarchy process ( 16 ) and other structured techniques are best for establishing research priorities, whereas in-person methods are best for clarifying ideas and generating ideas ( 17 ). Repeated exposure to these experiences be useful for identifying patient stakeholders’ core concerns and for acclimatizing them to organizational communication.

Stakeholders’ expectations and influence over the project should be managed. Reality checks are important for balancing patients’ idealistic expectations and the necessity of dealing with the challenges of getting things done through institutions. Program administrators must identify patient stakeholders’ strengths and channel these for optimal organizational impact.

Depending on their complexity, size, and type, most projects have a diverse number of internal and external stakeholders at different levels of the organization with different authority levels.

Stakeholder identification is a dynamic and sometimes difficult process, and the influence of a stakeholder may not become evident until later stages of a project. And, sometimes projects evolve so that solving unseen problems emerges as a critical task. It is essential to identify as many as stakeholders as possible at the beginning of the project and classify them according to their level of interest, influence, importance, and expectations at the earliest stages of the project as much as possible (Table 3 ). The identification of the relevant stakeholders is not only a core necessity but also poses a significant challenge. For example, under cost constraints, it might not be possible to identify all external stakeholders ( 18 ). On the other hand, stakeholders who are missed out during the identification process might have special requests to be fulfilled. This could potentially delay the project completion or escalate the cost as their requirement needs to be fulfilled. Additionally, as Bryson ( 19 ) pointed out that the failure to attend to the information concerns of stakeholders clearly is a kind of flaw in thinking or action that too often and too predictably leads to poor performance outright failure or even disaster.

Table 3 . Stakeholder management strategy .

As per the PMBOK ® , the “Identify Stakeholders” process has the following inputs, tools and techniques, and outputs:

Project Charter

The project charter gives an overall picture of the project as well as describing some of the stakeholders and their interest in the project along with their requirements.

Procurement Documents

If a project is based on an established contract or the result of a procurement activity, the parties in that contract are key project stakeholders. Other relevant parties such as suppliers, legal parties, and people who will execute the contract should also be considered as part of the project stakeholders list.

Enterprise Environmental Factors

Hospital culture and structure, and other factors may influence the identify stakeholders process.

Organizational Process Assets

To benefit from previous experience those in charge of developing proposals should carefully review the efforts of earlier projects. The stakeholder register template, lessons learned, and the stakeholder registers from previous projects may influence the identify stakeholders process.

Stakeholder Analysis

It is not possible to treat all stakeholders equally in the project, and they are given different priorities with respect to their interests, expectations, and influence on the project. Stakeholder analysis is a process of systematically gathering and analyzing all relevant quantitative and qualitative information about the stakeholders in order to prioritize them and determine whose interests should be taken into consideration throughout the project.

As per PMI, stakeholder analysis is performed by the following steps:

Step 1: all potential project stakeholders and their relevant information, such as their roles, interests, knowledge levels, expectations, and influence levels should be identified.

Step 2: the potential impact or support each stakeholder can contribute should be identified. As per the PMBOK ® , there are several classification models below:

• Power/interest grid: this is based on the level of authority or power and the level of concern or interest that a stakeholder has regarding the project outcome (Figure 1 ).

• Power/influence grid: this is based on the level of authority or power and active influence a stakeholder has.

• Influence/impact grid: this groups stakeholders based on their involvement or influence and their ability to affect changes to planning or execution (impact).

• Salience model: this addresses a stakeholder’s power or ability to impose their will, urgency, or need for immediate attention from the team and legitimate involvement in a project.

Step 3: in order to influence the stakeholders to enhance their support and to mitigate potential negative impacts, the way in which key stakeholders are likely to react or respond in various situations should be assessed.

Figure 1. Stakeholder mapping: the power versus interest grid . The gird shows stakeholders on a two-by-two matrix showing the strategies to be employed to engage and manage them. Power/interest grid model shows the grouping of the stakeholders based on their level of authority (“power”) and their level or concern (“interest”) regarding the project outcomes. Identifying and classifying the stakeholders is pivotal as it helps to develop appropriate strategies to effectively engage and manage all the stakeholders involved in a particular project. This also provides a clear-cut strategy and action-oriented and workable plan to interact with the all the stakeholders in an effective manner so as to minimize the resistance and maximize the support. A project is as successful as the stakeholders think it is. The details of power versus interest grids are found elsewhere ( 25 ).

Stakeholders who have greater power or influence and a strong interest in the project should be managed closely and continuously updated. Stakeholders who have significant power but low interest in the project should be kept informed about the project. Stakeholders who have low power and low interest should be monitored, and stakeholders who have low power and high interest should be kept satisfied.

Expert Judgment

Judgment and expert opinions can be gathered to identify stakeholders, usually from the senior management. These resources can include project team members, project managers from similar projects, subject matter experts, industry groups and consultants, and other units within the hospital or research setting.

Profile analysis meetings with team members and the sponsor will be beneficial for identifying stakeholders and their knowledge, potential roles, importance, impact, interest, and expectations in the project.

Stakeholder Register

This contains all details related to the identified stakeholders including but not limited to

• Stakeholder classification: stakeholders can be classified in many different ways. For example, primary (users of the products, services, or results) or secondary (may not be the direct users, but have some influential relationship), Internal/external, neutral/resistor/supporter/hard to hear, and so on.

• Identification information: name, title, location, organization, role in the project, position, and contact information.

• Assessment information: key requirements and expectations, potential impact, importance, and influence on the project.

A project manager may publish the stakeholder register with other project documentation or keep it in reserve for personal use only (Table 4 ).

Table 4 . A snapshot of a stakeholder register: stakeholder register is a project document, which is an output of identify stakeholders process .

Plan Stakeholder Management

The plan stakeholder management process defines an approach for managing stakeholders throughout the entire project life cycle as per their interest, impact, importance, and influence over the project. It defines the strategies for building close relationships with stakeholders who can benefit the project and for minimizing the influence of stakeholders who may have a negative impact on the project.

This process is iterative and should be reviewed on a regular basis as the required level of engagement of the stakeholders’ changes in the project.

As per the PMBOK ® , the Plan Stakeholder Management process has the following inputs, tools and techniques, and outputs:

Project Management Plan

Components of the project management plan (PMP) such as the human resource management plan, staffing management plan, communications management plan, change management plan, and others are used in developing the stakeholder management plan (SMP).

This contains all details related to the identified stakeholders, including identification information, assessment information, and classification.

All environmental factors within the hospital or clinical research facility, including its culture and history of the organization, are used.

All organizational process assets, especially lessons learned and historical information are used.

Judgment and expert opinions can be gathered from senior management, project team members, identified stakeholders, project managers from similar projects, subject matter experts, industry groups and consultants, other units within the organization, and other people to identify the level of involvement required from each stakeholder at various stages of the project. However, it is possible that expert judgment can be mistaken when possible expert judgment must be balanced with input from the stakeholders themselves.

Meetings with team members and the sponsor will be beneficial for identifying the level of engagement required from each stakeholder.

Analytical Techniques

Various analytical techniques are used for identifying the required level of stakeholder engagement. These techniques take into consideration stakeholder sensitivity to project goals and personal orientations such as being unaware, resistant, neutral, supportive, or providing leadership.

Stakeholder Engagement Assessment Matrix

The stakeholder engagement assessment matrix (SEAM) is used to assess the current and desired state of engagement of a stakeholder for the current phase of the project (Table 5 ).

Table 5 . Stakeholder engagement assessment matrix (SEAM): please note that the current and desired engagement level of key stakeholders expect to change as the project progresses and develops .

The SEAM illustrates that only Stakeholder 4 is engaged in the project at the desired state. The project manager should consider additional communication and further actions to bring all other stakeholders to the supportive and leading states.

Stakeholder engagement is critical to project success; thus, required actions and communication should be planned to minimize the gap between the desired level of engagement and the actual level of engagement.

Stakeholder Management Plan

Stakeholder management plan, which is a subsidiary plan of the PMP that defines the processes, procedures, tools, and techniques to effectively engage stakeholders in project decisions and execution on the analysis of their needs, interests, and potential impact ( 7 ). The SMP can be formal, informal, highly detailed, or broadly framed based on the needs of the project. The SMP typically describes the following:

• information needs of each stakeholder or stakeholder group;

• stakeholder communication requirements;

• format, method, time frame, and frequency for the distribution of required information to the stakeholders;

• person responsible for communicating the information to the stakeholders;

• methods of refining the SMP;

• required engagement level of the stakeholders at various stages of the project;

• stakeholder management strategy that defines an approach to manage stakeholders throughout the entire project life cycle. It defines the strategies to increase the support of the stakeholders who can impact the project positively and minimize the negative impacts or intentions of the stakeholders who can negatively impact the project.

The portion of the plan that contains sensitive information such as stakeholders’ personalities and attitudes, negative impact that stakeholders may cause, or other factors is not usually published and is kept in reserve by the project manager for personal use only.

Project Documents Updates

Project documents such as the project schedule, stakeholder register, and others may be updated.

Manage Stakeholder Engagement

The Manage Stakeholder Engagement process is focused on meeting and exceeding the stakeholders’ expectations by continuously communicating with them, clarifying and resolving their issues, addressing their concerns, and improving the project performance by implementing their change requests.

As per PMI, the project manager is responsible for managing the stakeholders’ expectations. Meeting the stakeholders’ expectations increases the probability of project success by enabling the stakeholders to be active supporters of the project, drastically reducing unresolved stakeholder issues, and limiting disruptions in the project.

As per the PMBOK ® , the Manage Stakeholder Engagement process has the following inputs, tools and techniques, and outputs:

Within the research context, the SMP identifies information needs, communication requirements, required engagement level at various stages of the project, stakeholder management strategy, and other factors to identify and manage stakeholders throughout the entire project life cycle.

Communications Management Plan

The communications management plan is a subsidiary of the PMP. It can be formal, informal, highly detailed, or broadly framed based on the needs of the project. The communications management plan typically describes the following: purpose for communication; Information needs of each stakeholder or stakeholder group; stakeholder communication requirements; format, method, time frame, and frequency for the distribution of required information; person responsible for communicating the information; methods for updating the communications management plan; persons or groups who will receive the information; glossary of common terms; issues/concerns escalation procedures.

A change log is used to document changes that occur during a project. A lot of these changes can impact different stakeholder interests; thus, the change log is reviewed in this process.

Organization communication requirements, issue management procedures, change control procedures, and historical information are used.

Communication Methods

According to the needs of the project, the methods of communication identified for each stakeholder in the communications management plan are utilized during the manage stakeholder engagement process.

Interpersonal Skills

The project manager applies appropriate interpersonal skills or soft skills to manage stakeholder expectations by building trust and resolving conflict.

Management Skills

Management skills such as presentation skills, negotiation skills, writing skills, and public speaking skills used by the project manager can greatly influence how stakeholders feel about the project.

An issue is an obstacle that threatens project progress and can block the team from achieving its goals. An issue log is a written log document to record issues that require a solution. It helps monitor who is responsible for resolving specific issues by a target date. There should be one owner assigned for each issue reported within the project.

Change Requests

Change requests can include a new change to the product or the project, corrective or preventive actions, and other items.

Project Management Plan Updates

The SMP portion of the PMP is updated as new stakeholders’ requirements are identified, existing requirements are changed, or as a result of addressing concerns and resolving issues of the stakeholders.

Project documents that may be updated include, but are not limited to, the following:

• Issue log: this will be updated as resolutions to the current issues are implemented and new issues are identified.

• Stakeholder register: this is updated as stakeholders’ statuses change, new stakeholders are identified, registered stakeholders are no longer involved or impacted by the project, and other factors.

Organizational Process Assets Updates

Lessons learned from managing stakeholders, feedback from stakeholders, project records, causes of issues, and reasons for corrective actions chosen may be updated.

Control Stakeholder Engagement

The control stakeholder engagement is the process of evaluating and monitoring overall stakeholder relationships and ensuring stakeholders’ appropriate engagement in the project by adjusting plans and strategies as required. As the project progresses and its environment changes, this process will maintain or increase the efficiency and effectiveness of stakeholder engagement activities.

As per the PMBOK ® , the Control Stakeholder Engagement process has the following inputs, tools and techniques, and outputs:

Components of the PMP such as the human resource management plan, staffing management plan, communications management plan, change management plan, and others are used in controlling stakeholder engagement.

An issue is an obstacle that threatens project progress and can block the team from achieving its goals. An issue log is a written log document to record issues that require a solution. A modified issue log is developed as a result of identifying new issues and resolving current issues.

Work Performance Data

Work performance data such as resource utilization, deliverables status, schedule progress, percentage of work completed, number of defects, number of change requests, technical performance measures, costs incurred, quality updates, and other factors are used in this process.

Project Documents

Project documents such as issue logs, the stakeholder register, the project schedule, the change log, and others are used in this process.

Information Management Systems

An information management system is an automated system that can serve as a repository for information, a tool to assist with communication, and a system for tracking documents and deliverables. An information management system also supports the project from beginning to end by collecting and distributing information about cost, schedule, and performance for the stakeholders. Several reporting techniques such as spreadsheet analysis, table reporting, presentations, graphics for visual representations, and others may be consolidated from various systems and communicated to the stakeholders.

Judgment and expert opinions can be gathered from senior management, project team members, identified stakeholders, project managers from similar projects, subject matter experts, industry groups and consultants, other units within the organization, and other people to identify new stakeholders, reassess the current stakeholders, and figure out the level of involvement required from each stakeholder at various stages of the project.

Status review meetings with the team, sponsor, and other stakeholders will be beneficial for reviewing information about stakeholder engagement.

Work Performance Information

Work performance information such as deliverables status, change request implementation status, and forecasted estimates to completion are distributed through communication processes.

These are recommended corrective actions for bringing the imminent performance of the project as per the expectations in the PMP and recommended preventive actions for reducing the probability, and impact of future negative project performance will generate a lot of change requests.

Most of the components of the PMP may be updated to reflect changes in the stakeholder management strategy and the approach to effectively control stakeholder engagement in the project.

Project documents such as the issue log, the stakeholder register, and others may be updated.

Lessons learned from managing stakeholders, feedback from stakeholders, project records, causes of issues, reasons for corrective actions chosen, project reports, stakeholder notifications, and other items may be updated.

While the burden of disease is growing rampantly and disproportionately, the challenge to global health outreach efforts is to prioritize those illnesses, which require immediate attention. The global health equity sorts to prioritize on improving health care and achieving equity in health of people around the world. In this context, researchers from high income countries often study the existing diseases and/or emerging challenges in low income countries in order to gain expertise on the health care needs ( 20 ). In this regard, it is essential for overall program effectiveness that representatives of local communities, the stakeholders who will be most impacted by health outreach programs, be invited to provide their insights into which health needs are greatest. The encouragement of ShE and ShM often has a secondary benefit inasmuch as organization’s reputation is subsequently enhanced, which further facilitates organizational effectiveness. The very presence of stakeholders may foster an organizational environment, which encourages relevancy of program objectives to stakeholders’ expectations, a coupling, which in turn contributes to achievement of the project’s goals. Additionally, stakeholders can provide reality checks, which aid in the prioritizing of research objectives, in identifying potentially difficult political issues, and in providing the means to navigate around or to overcome challenges. The experience of stakeholders is thus invaluable for guiding research and achieving program objectives from their early stages in the laboratory to their final clinical application.

Although the process of partnering with stakeholders in clinical research settings is still in its nascent stages, it is anticipated that it will increasingly become accepted and implemented by project managers. In tandem with this process, greater efficiency and transparency will develop in working with stakeholders to meet targets ( 21 ). Part of the function of stakeholder analysis is to promote an understanding of stakeholders and to ensure that their expectations are being met. It is anticipated that project heads will increasingly encourage an awareness (ensuring transparency) of who will be affected by the project and who can contribute to making the project more successful.

Stakeholders have unique perspectives and often possess a number of capabilities which they have acquired from life experience. Program developers can derive the maximum benefit from stakeholders if the proper context is established for drawing out this experience. Alternatively, barriers to effective participation by stakeholders can occur if managers remain unaware of stakeholders’ skills, or if they believe that they do not have appropriate knowledge to contribute.

By increasing the acceptability of programs, stakeholders increase the likelihood of their success. Stakeholders play pivotal roles as healthcare advocates or healthcare ambassadors, partners, and/or agents of change. Although stakeholders differ considerably in their expertise and interests, their involvement is pivotal inasmuch as it can facilitate the successful completion of projects. Stakeholder participation can (a) improve relevance; (b) promote visibility and research transparency; (c) accelerate and translate the research findings to real-world challenges; (d) enhance greater project acceptance as confidence derived in the decisions made during the project’s milestone developments. Similarly, the project’s final outcome can only be considered successful when it is acknowledged by its key stakeholders.

Due to the broad range of ways in which stakeholders can influence program development, it is essential that their behavior be closely monitored, and modulated if necessary. One of the advantages of the described system of viewing the management of stakeholder engagement is that it documents many processes that have taken place. Future efforts to manage this type of engagement can therefore benefit from established experience.

In a nutshell, as Wheeler et al. ( 22 ) pointed out, “a truly stakeholder-responsive approach demands the acceptance of multiple stakeholders and requires that an organization develop a tolerance for ambiguity together with the sensitivities and capabilities needed to inspire trust with diverse and sometimes completing interests.”

A balanced assessment recognizes that certain caveats apply in the establishment of stakeholder engagement and management in clinical and research settings. These relate to the unique nature, demands, resources, and implementation issues which every organization has and how these demands can interact with the unique skills and abilities which stakeholders bring to it.

Many investigators lack clear or a basic understanding and/or training concerning the stakeholder framework as well as terminologies (Figure 2 ).

Figure 2. Project stakeholder management summary .

Often a reactive approach is favored over a proactive one for dealing with stakeholder issues is favored over a proactive one. As suggested by Greenwood, the “glorified depiction of stakeholder engagement gives way to the murky reality that engagement of stakeholders can mean many things to many people” ( 23 ). In this conceptualization, stakeholders may be viewed merely as actors with whom project managers form arms-length transactions rather making a tightly knitted partnership. This limited perspective may result in a failure to assess, understand the social, spiritual, and environmental needs of stakeholders.

Not every project can require or afford to have a full ShE or ShM system in place as outlined in this review. The main barrier here is that stakeholder involvement, and the fostering of attitudes that encourage this process, require a degree of organizational change (i.e., additional paperwork, more meetings, and more communication), which can be expensive. Hence, the benefits of managing a full program of stakeholder engagement, analysis, and management are only applicable to high impact projects. As Cennamo et al. ( 24 ) have noted a stakeholder-committed organization may still act out of self-interest. Thus, while localized programs may recognize and incorporate contributions from stakeholders and the larger community, these activities may continue to serve institutional objectives, which are narrowly focused or even possibly inimical to broader community interests. Additionally, stakeholder preferences are not absolute, but relative, and may also be evolving. Hence, the salience might change frequently across time. Another issue is that there is no generic “one size fits all” strategy for ShM and ShE, rather, the strategy and its execution depend very much on the local stakeholder landscape, as well as the problems that are being addressed. These include the stakeholder assets that are available and the opportunities that exist for their cultivation. Additionally, a barrier to effective participation and the subsequent consensus-building process is that the identified stakeholders may lack appropriate knowledge or skill sets, or believe, correctly or incorrectly, that they do not have appropriate knowledge to contribute and/or the investigators have the knowledge and experience to identify it. Stakeholders might have multiple perspectives and conflicting views, needs, and priorities. These may eventually result in identifying what they perceive to be the “best” or “appropriate” solution in any given situation, although the course of action might completely differ from that of investigators. This, in turn, results in potential “conflicts” and “trade-offs” in terms of project objectives. For example, the conflicting interest among the stakeholders with varied levels of power, importance, interest, and agenda must be managed efficiently. This poses a challenge to novice project managers to experienced investigators. These may potentially limit control mechanisms, and thus impede organizational performance. On the contrary, it is difficult to generate interest and involvement in projects, which are perceived to have little or immediate relevance. Finally and most importantly, while most of the ShE literature emphasizes the positive benefits of stakeholder engagement, it less frequently addresses the potential costs and risks with the adoption of a stakeholder perspective.

Above all, it is impossible to engage with stakeholders and to do stakeholder management in an authentic and effective way without dealing with the multiplicity of ethical issues that arise. These issues arise, first of all, because the stakeholders’ interests can conflict along key ethical dimensions. Therefore, engaging with them must be sensitive to the rights of the parties involved, as well as to the overall harms and benefits, which accrue from managerial action. Second, it is not always apparent when there may be conflicts of interests or hidden advantages or hidden disadvantages among key stakeholders and decision makers. Obviously, these conflicts must be disclosed, and many organizations have specific procedures for handling such conflicts. However, given the nature of the decisions that are to be made, managers must be willing to accept that effective stakeholder management places them squarely in the realm of ethical decision making.

It is not always possible to anticipate all of the ethical issues and conflicts, which may develop in such a multi-stakeholder environment. Therefore, the traditional method of assigning responsibility for solving these problems to an ethics committee does not always work. Clinical project managers must be willing to make choices based on both good ethics (based on PMI’s code of ethics and professional conduct) and on the overall purpose and values of their institution that is best for all stakeholders ( 27 ). While some ethical issues can be anticipated at the start of the project, all should be subjected to discussion among the project stakeholders to find the best possible course of action.

Assuming that the challenges reviewed above can be overcome, additional “higher order” issues will emerge. These will consist of how to best promote the operational adaptability, viability, and implementation of the changes in an acceptable timeframe. Project managers will need to ask if the benefits of managing a full stakeholder analysis are really greater than the costs associated with it. Efforts will also need to be directed toward retrospective analysis, i.e., did real cases go badly because the stakeholder views were not sought out? The difficulty of these questions varies in different clinical settings but it is essential that they be resolved for maximum project effectiveness.

In summary, the concept of promoting stakeholder engagement and management is a relatively recent one in the clinical research arena; hence, there are many lessons to be learnt in the coming years. As this is an iterative process, although the current efforts from funding agencies such as PCORI are necessary but are insufficient to respond to the above challenges. All indications are that attempts to meet these challenges will nevertheless provide significant benefits for project management effectiveness.

From a clinical standpoint, stakeholder engagement and management is pivotal to the development and deployment of community-oriented national and global health initiatives. The ultimate purpose of such engagement is the efficient use of time, money, and resources thereby positively impact existing and/or emerging healthcare challenges.

For the purpose of our review, we have followed the guidelines of PMBOK ® ( 7 ), which provides a common vocabulary to guide the processes. In doing so, our review outlines a systematic model for planning, managing, and implementing stakeholder engagement based on PMBOK ® guidelines. Further, the application of the project management knowledge, skills, tools, and techniques can augment the chances of success, even in complex projects.

This review has drawn on the experience of stakeholder engagement in private organizations and government agencies and has argued that the process is equally viable in hospital program development and in clinical research. It has taken the view that the concept of stakeholder engagement and a proper stakeholder management framework is more than a useful adjunct to pursuing project or program goals and is actually pivotal for enhancing organizational success.

These guidelines have been broken down into a number of component parts. It emphasizes that the stakeholders should first be identified, that their interests and expectations should be understood, and that their level of power and influence should be understood as well. A plan for communicating with stakeholders has been outlined and techniques for encouraging their participation and management have been laid out.

Author Contributions

All authors intellectually contributed to the design, analysis, and interpretation of the results and to drafting the critical review of manuscript. All authors reviewed and approved the final version of the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to sincerely thank Professor Philip Kotler, S. C. Johnson & Son Distinguished Professor of International Marketing, Kellogg School of Management, Northwestern University, USA for his time, advice, and his valuable comments on the earlier versions of our manuscript, which enormously improved the quality of the final manuscript. We are also grateful for the comments from the anonymous reviewers. We gratefully acknowledge the National Institutes of Health; National Institute of Mental Health; Duke Endowment; South Carolina Department of Health and Human Services. As of the end of 2012, the Duke Endowment had provided more than $7.25 million in support of the program. Additional funding has come from the South Carolina Department of Health and Human Services, the National Institute of Mental Health (NIMH), and the National Institutes of Health (NIH). NIMH and NIH awarded two grants (one in 2009 and one in 2012) totaling $3.04 million to support evaluation of project outcomes (on quality, economic impact, utilization, and sustainability). In addition to grant funding, user fees and third-party insurance cover some program-related costs; for example, beginning in 2013, BlueCross BlueShield of South Carolina began covering use of telemedicine in mental health. However, the funders had no involvement in study design, data collection, data analysis and interpretation, preparation of the manuscript, or in the decision to submit the manuscript for publication.

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Appendix: Examples of Stakeholder Engagement

Statewide telepsychiatry initiative.

In one of our landmark study by the author (MN), the South Carolina Department of Mental Health partnered with the University of South Carolina School of Medicine, Department of Health and Human Services and 18 predominantly rural hospitals from the South Carolina Hospital Association to establish the statewide telepsychiatry initiative. In this public-private-academic partnership, the psychiatrists were available via teleconference 16 h a day, 7 days a week, to assess and treat patients with mental health issues at hospital emergency departments.

The clinicians called the psychiatrist on duty whenever they have a patient who needed a mental health assessment and/or counseling and provide relevant medical records and details. Through a secure video link, the psychiatrists were able to assess the patient and makes recommendations about needed treatment and follow-up, including referrals to community-based resources.

The advantage of such program was that, it drastically reduced emergency department wait times, inpatient admissions, as well as costs; increased attendance at follow-up outpatient appointments; and generated higher level of satisfaction among patients and clinicians. Additional evidence includes post-implementation surveys that gauge the satisfaction of patients and clinicians with the program.

In such an innovative approach, the stakeholder (State Department of Mental Health, University of South Carolina, Department of Neuropsychiatry and Behavioral Science, Hospital, Physicians and Nurses from the South Carolina Hospital Association, and Patients) engagement proved to be feasible, necessary, and beneficial in clinical environment.

Outcome of the Initiatives – Did it Work?

The program has reduced emergency department (ED) wait times, inpatient admissions, and costs; increased attendance at follow-up outpatient appointments; and generated high levels of satisfaction among patients and clinicians.

Shorter ED Wait Times: From March 2009 through 2014, the average waiting time for patients with mental health problems at the 18 participating EDs fell by roughly 50%. Fewer hospitalizations: during the same timeframe, 11% of ED patients assessed by a psychiatrist were hospitalized, half of the 22% admission rate among similarly cared-for patients in South Carolina EDs not offering this program.

Health care utilization: Telepsychiatry consultations resulted in an estimated $1,400 less per mental health patient compared to patients seen in ED’s that did not have telepsychiatry due primarily to the reductions in hospital admissions.

Greater attendance at follow-up appointments: About 46% of patients served by the program attended an outpatient follow-up appointment within 30ădays of the initial ED visit, well above the 16% attendance rate among similar ED patients cared for in South Carolina hospitals not offering the program. Similarly, 54% of patients served by the program attended a follow-up appointment within 90ădays versus 20% among ED patients in hospitals not offering the program.

High satisfaction among patients and staff: More than 80% of patients served by the program reported being satisfied with the process and services received. In addition, 84% of ED physicians and staff believe that the program has improved patient care; 91% report being satisfied with program-related procedures; and 84% express satisfaction with the technology used.

How We Did It: Planning and Development Process Key Steps Included the Following:

Decision to focus on telepsychiatry : The increasing popularity of telemedicine and its potential to bring services to underserved geographic areas made telepsychiatry a logical initial target for these discussions. After reviewing the use of telemedicine in South Carolina and other States, the partners collectively decided to create and implement a telepsychiatry program in hospital EDs.

Securing of funding for demonstration project : In 2007, program leaders contacted the Duke Endowment (a non-profit foundation that supports innovative health care programs) for funding a demonstration project. In 2008, the Duke Endowment agreed to provide a $3.7 million, 3-year grant for this purpose.

Project planning : Four hospitals agreed to participate in the demonstration project, which launched in March 2009. In advance of this date, the partners and participating hospitals worked together to hire and train six psychiatrists, install and test the video equipment in EDs, and set up an EMR system linking the psychiatrists with the EDs.

Program expansion : After the demonstration project went smoothly, program leaders decided in June 2009 to expand the program to three additional sites and have continued to add more sites gradually since that time. From March 2009 through August 2014, more than 20,000 telepsychiatry ED consults have taken place. As of August 2014, 20 hospitals participate, with plans to add 6 more in 2014.

Standardization of training : As the initiative expanded, program leaders standardized the training process for psychiatrists and ED staff, as outlined below:

Psychiatrist training : Participating psychiatrists complete 6ăh of clinical training by viewing videos prepared by University of South Carolina School of Medicine faculty. Supplemented with handouts, the videos cover child and adolescent psychiatry, adult psychiatry, geriatric psychiatry, addiction psychiatry, risk assessment, and legal issues. Newly hired psychiatrists also undergo peer review every 2ăweeks during their first 3ămonths of employment. In addition, during this initial 3-month period, the supervising psychiatrist meets with other hospital physicians to review and discuss consultations performed by newly hired psychiatrists.

ED staff training : ED staffs in participating hospitals watch a video that explains the videoconferencing system and reviews the goals of the program and the training and credentials of the participating psychiatrists. A member of the project leadership team visits each participating ED to discuss questions or concerns that staff might have about the program.

Ongoing meetings to resolve problems, plan expansion : Representatives of the partnering organizations and the participating hospitals meet on a quarterly basis to share program-related experiences, resolve any issues or problems that arise, and discuss and plan for expansion to other EDs. Typically, hospital administrators, providers, researchers, and information technology staff come to these meetings; representatives of non-participating hospitals are also welcome to attend to learn more about the program.

Resources Used and Skills Needed

Staffing : Six full-time psychiatrists and one part-time psychiatrist staff the program, under the supervision of a lead psychiatrist. The program includes several stakeholders namely a director, coordinator, fiscal technician, programmer, and two information resource consultants (subject matter experts; SMEs). Faculty and staff members from the University of South Carolina and Emory University, staff members from the Department of Mental Health and from the South Carolina Office of Research and Statistics also provide support to the program.

Costs : Hard data on the program’s total annual cost are not available. Major program expenses consist of staff salaries and the upfront and ongoing expenses associated with the videoconferencing and EMR technologies.

Keywords: PCORI, PMBOK, PMI, clinical research, code of ethics, professional conduct, project stakeholder management

Citation: Pandi-Perumal SR, Akhter S, Zizi F, Jean-Louis G, Ramasubramanian C, Edward Freeman R and Narasimhan M (2015) Project stakeholder management in the clinical research environment: how to do it right. Front. Psychiatry 6:71. doi: 10.3389/fpsyt.2015.00071

Received: 14 December 2014; Accepted: 27 April 2015; Published: 18 May 2015

Reviewed by:

Copyright: © 2015 Pandi-Perumal, Akhter, Zizi, Jean-Louis, Ramasubramanian, Edward Freeman and Narasimhan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Seithikurippu R. Pandi-Perumal, Department of Population Health, Division of Health and Behavior, New York University Medical Center, Center for Healthful Behavior Change (CHBC), Clinical and Translational Research Institute, 227 East 30th Street (between 2nd and 3rd Avenue), Floor # 6 – 632E, New York, NY 10016, USA, pandiperumal2015@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Project Management

• What Is a Stakeholder Analysis? Importance, Benefits and Steps in 2024

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Stakeholder analysis helps project managers identify and communicate with individuals who can influence a project’s outcome. By following this guide, you can better position yourself to win stakeholders over and guide your team to success.

Last Updated: 13 May'24 2024-05-13T15:58:22+00:00

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• The stakeholder analysis process allows project managers to identify the key stakeholders who have influence in, power over or input to give on a project.
• Determining who the key stakeholders are and how much influence they have will lead to improved buy-in, better communication, higher product quality and more positive project outcomes.
• The analysis process generally involves four steps: identifying stakeholders, stakeholder mapping, engaging with stakeholders and understanding stakeholders. However, this can vary depending on the project and company.

Facts & Expert Analysis About Stakeholder Analysis in Project Management:

• Communication trends: A 2022 survey showed that 62% of product managers host meetings to present information to internal stakeholders, but only 14% have one-on-one stakeholder conversations. Additionally, 11% refer stakeholders to roadmaps and 10% create ad-hoc documentation when asked. 1
• Communication preferences: The same survey showed that 45% of product managers would like to continue with in-person meetings, 35% would like to refer internal stakeholders to project roadmaps, 10% would like to have one-on-one meetings and 6% would create documents when requested. 1
• Methodologies: Stakeholder analysis is a vital part of project management for those using traditional methods such as Waterfall, PRINCE2 or Critical Path, and for those using Agile frameworks such as Scrum or Kanban.

Creating project charters before a project begins is one of the most important steps a project manager can take to set up for success. However, equally important is the less-talked-about stakeholder analysis process. This process can ensure team alignment and can be carried out in many of the best project management software platforms.

Proper stakeholder analysis ensures that the project manager, scrum masters and product owners understand the project stakeholders and their influence before a project begins. It may seem trivial, but failing to grasp internal and external stakeholders’ motivations, needs and wants can quickly bring a project to its knees.

In this stakeholder analysis guide, you’ll learn about the types of stakeholders you’ll interact with and the four steps in stakeholder analysis. You’ll also discover stakeholder analysis methods and why it’s vital to get to know your stakeholders. If you’re looking for a broader overview, our article on the characteristics of project management may be just what you need.

What Is a Stakeholder Analysis?

Stakeholder analysis is the process of identifying all internal and external individuals a project will involve or affect before work on the project begins. As a project manager, scrum master or product owner , you should group key stakeholders by levels of participation, interest in the project and levels of influence, and then determine how to communicate with them.

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After identifying stakeholders and conducting stakeholder analysis, project managers can determine who needs to be engaged, how often communication should occur and which information should be shared. When performed correctly, the stakeholder analysis process can increase buy-in and support throughout the project life cycle .

Stakeholder Analysis Theory

When conducting research into stakeholder analysis, you’ll likely run into the stakeholder analysis theory. This theory, which first appeared in 1984, states that organizations should deliver value to all employees, investors, suppliers and other related parties rather than only prioritizing shareholders.

The theory has since become a major topic in business ethics. Many champion the idea that shareholder value should not be the major objective of conducting business, as maximizing it at the expense of everything else is simply not sustainable.

The Four Types of Stakeholders

The types of important stakeholders you interact with will vary based on the company’s organizational structures and the nature of the project itself. However, regardless of these factors, there are generally four types of stakeholders who will have business interests in a project. They are:

• Influencers: This group includes those who have the power to influence decisions and potentially change the direction of a project. You might encounter influencers from unions or lobby groups, or they might be individuals in your company who strongly agree or disagree with the project.
• Governance: This group of stakeholders includes company owners, senior executives, board members, regulators and auditors. They typically take great interest in how a project is being run, the processes being used and the overall quality of the project. They’re also the most likely group to offer guidance and advice during the project.
• Providers: This category includes contractors, suppliers, temporary staffing agencies and anyone else who supplies resources to the project. This group will generally be interested in the financial side of the project, health and safety, resource allocation and how they can directly impact project processes and operations.
• Users: This group comprises individuals who will make use of the products and services created during the project. They will want regular updates on project progress and may expect to take part in sprint reviews so they can inspect product iterations, provide feedback and request new features or changes.

The Four Steps of Stakeholder Analysis

Conducting an analysis of various stakeholders is a straightforward process that only requires four steps, but don’t let this fool you. A stakeholder assessment is one of the most important tasks a project manager or product owner can do outside of creating a project scope (a detailed project plan). The four steps of stakeholder analysis are:

• Identifying stakeholders: Before the project begins, project leaders should list every stakeholder with whom they may need to communicate.
• Stakeholder mapping: Stakeholders should be mapped with a power grid or modified RACI chart . They can be high power , high interest (must keep them happy); high power, low interest (keep them satisfied); low power, high interest (keep them informed); or low power, low interest (share project details sporadically).
• Engaging with stakeholder groups: Come to a consensus regarding how, when and where communication will occur with each stakeholder group.
• Understanding stakeholders: Communicate with stakeholders and ask how they feel about the project. Discuss the vision, plans and goals to gauge interest levels, and to determine who will be an ally and who may raise concerns.

What Is the Purpose of Stakeholder Analysis?

Stakeholder analysis identifies stakeholders before a project begins and helps project leaders determine each stakeholder’s impact, which can increase the likelihood of buy-in and support. Managing stakeholders can help you execute projects that meet or exceed expectations. Below, we’ll look more closely at why you should trust the process.

Meeting with key stakeholders (internal and external) before the project starts will help you better understand what each individual wants and determine whether you can use their knowledge to help complete the project. Meeting and chatting with stakeholders will also build relationships, which could help you gain support for the project.

Once you have completed your stakeholder analysis and determined where the stakeholders fall, you can gather all parties for a project kickoff meeting to discuss plans and strategies. Involving stakeholders in a kickoff meeting will ensure that everyone understands the project, what’s expected of them and how they can contribute.

Communicating with stakeholders during the analysis process will help you understand their interests. You can identify issues each stakeholder might raise, decisions that have significant meaning to them, and what they want to see and accomplish by the end of the project.

Stakeholder engagement is a fantastic way to preemptively mitigate internal risk and avoid potential conflicts. By listening to stakeholders and correctly identifying their power, legitimacy and influence, you can address issues that could cause disruptions throughout the project life cycle before the work begins.

Determining how to communicate with each stakeholder is vital to staying in their good graces. Ask how each stakeholder likes to communicate (email, phone, message services like Slack) and make a note of it. It may seem trivial, but having a stakeholder engagement plan and communication strategy will help you gain their support.

Benefits of Stakeholder Analysis

Whether you use Agile frameworks or traditional project management methods, taking the stakeholder analysis process seriously can set a firm foundation for success in any project management career . As you can imagine, many benefits can come from this process, including:

• Gaining support: The better you analyze stakeholders and understand their needs, the more support you will have.
• Optimized resources: By understanding what internal stakeholders and external stakeholders bring to the table, you can influence how and when resources are delivered, optimize supply chains and more.
• Better relationships: Knowing the project stakeholders and having an idea of what makes them tick can boost professional relationships, which in turn will lead to free-flowing, non-toxic work environments.
• Enhanced communication: Understanding how each stakeholder likes to be contacted can ease tensions and encourage transparent communication.
• Clear expectations: Going into a project, whether simple or complex, with everyone on the same page will make everything run more smoothly.
• Higher-quality products: Perhaps the biggest benefit to come from the stakeholder analysis process is the prospect of higher-quality deliverables. With everyone on the same page and all expectations and goals aligned, project teams know precisely what’s expected of them.

Stakeholder Identification Methods

Now that you know what the stakeholder analysis process is and the benefits of getting to know stakeholders, it’s time to look at a few popular stakeholder analysis tools and methods for identifying who the power and bit-part players in your project are. The four most popular stakeholder analysis methods are:

• Power grid: This is used to analyze stakeholders via a quadrant grid that determines each individual’s power within the organization and level of interest in the project. The X and Y axes in the power grid represent interest and power, respectively.
• Power-predictability matrix: Similar to the power grid, the power-predictability matrix uses a quadrant to determine how much power a stakeholder has and how predictable they are. The X and Y axes in the power-predictability matrix represent predictability and power, respectively.
• Salience model: The salience model uses Venn diagrams to help determine the power, legitimacy and urgency of all project stakeholders.
• Stakeholder knowledge base chart: This method uses a quadrant grid to identify stakeholders. It helps project leaders determine stakeholders’ awareness levels and whether they approve of or disagree with the project. The quadrant grid has X and Y axes that denote stakeholder attitude and knowledge, respectively.

Stakeholder Analysis Template

Creating and maintaining tools that can help you conduct stakeholder analysis can be challenging, especially if you make them yourself. Fortunately, plenty of the best free project management software solutions and many paid options offer templates you can use as-is or customize to your liking.

You should use templates whenever possible, as they will help you quickly conduct a stakeholder analysis meeting. Many out-of-the-box templates can track preferred engagement methods and roles, whether stakeholders are internal or external, and their levels of impact and involvement.

This monday.com template and this ClickUp template do all of that and more. To find out more about what monday.com can do, check out our monday.com review . Alternatively, learn more about ClickUp in our ClickUp review .

How Do You Conduct a Stakeholder Analysis?

Every stakeholder analysis process will look different based on the project you’re working on, the project management methodology you use and the organizational structure in place. However, as a general rule, the process will look similar to the stakeholder analysis example below.

• Identify the Project Stakeholders The project leadership team should meet to identify every stakeholder (internal and external) who will impact or influence the project in any way, shape or form. Everyone from suppliers to executives, from providers to end users, and from marketers to finance managers should be listed.
• Group Stakeholders Together After brainstorming and identifying every stakeholder, it’s time to categorize each individual stakeholder based on their influence , how involved they will be, their level of interest and the power they hold via one of the analysis methods discussed earlier.
• Determine Communication Methods to Win Stakeholder Favor After prioritizing stakeholders, the next step is to determine how to gain support from each individual. Brainstorm with your team, using mind maps to note what motivates each stakeholder and how the project can be aligned to match their priorities.
• Develop a Communication Plan Once you have figured out what motivates each stakeholder, it’s time to create a communication plan . Discover how each stakeholder likes to communicate and devise a plan that will allow stakeholders to attend project review meetings so they can share feedback and request new features.

After you have developed a RACI chart or used a template that allows you to note down all of your findings, it’s time to start working on the project charter and hold a kickoff meeting to get all the key project players on the same page and on your side.

Final Thoughts

No matter which project management methodology you use, you should conduct a stakeholder analysis before every new project. You’ll get a good idea of who you’ll be interacting with, whether you’ll face any opposition and who will be an ally from the start. Determining this upfront will help you plan better and lead to a more smoothly running project.

What’s your favorite project management methodology, and what does the process of analyzing stakeholders look like to you? Have you found that the process makes influencing and winning over stakeholders easier, or do you fear the stakeholder management process due to potential pushback? Let us know in the comment section and, as always, thanks for reading.

FAQ: Stakeholder Identification

The four steps of stakeholder analysis are identifying stakeholders, stakeholder mapping, engaging with stakeholders and understanding stakeholders.

The four types of stakeholders are influencers, governance, providers and users.

The stakeholder analysis theory states that employees, project investors and suppliers should be valued just as much as shareholders.

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