research paper soft systems methodology

An Introduction to Soft Systems Methodology

Soft systems methodology (SSM) is the outcome of a real-world action research program that uses the idea of systems to improve poorly defined, so-called soft problem areas. Theory and practice of SSM arouse interest and encourage discussions from various backgrounds by academics and practitioners. In order to introduce SSM for use in the real world, this chapter begins with different definitions and methodologies of systems thinking. Then, SSM defines the seven technical analysis steps, including the soft systems thinking and the necessary techniques such as rich picture, CATWOE analysis, root definition, and conceptual modeling. SSM has organizational analysis and practical applications in the industry sector that are reviewed and classified.

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Autobiographical retrospectives: Learning your way to ‘action to improve’ – the development of soft systems thinking and soft systems methodology

Collaborative design.

Within the context of a three year applied research project conducted from 2003-2006 in a North American university library, staff were encouraged to reconsider organizational assumptions and design processes. The project involved an organizational leader and an external consultant who introduced and collaboratively applied Soft Systems Methodology (SSM) practice. Project results suggest the efficacy of using ‘soft’ systems thinking to guide interaction (re)design of technology-enabled environments, systems, and tools. In addition, participants attained insights into their new roles and responsibilities within a dynamically changing higher education environment. Project participants also applied SSM to redesign ‘in house’ information systems. The process of employing systems thinking practices to activate and advance organizational (re)learning, and initiating and elaborating user-centered interaction (re) design practices, culminated in a collaborative design (co-design) approach that readied participants for nimble responsiveness to continuous changes in the dynamic external environment.

Soft Systems Methodology for Conceptual Modeling

Varieties of systems thinking: the case of soft systems methodology, modeling the design team as a temporary management structure: reality versus theory.

The focus of the cost management literature is almost exclusively on technical issues, with scant attention to its social, political and organisational dimensions. In this paper the authors document research examining the design team as a temporary management structure, with emphasis on the efficacy of the cost management system as a vehicle for attaining client objectives with respect to time, cost and quality. Soft systems methodology is used to explore the perceptions of stakeholders to the cost management system, thus developing conceptual models of the theory and practice of cost management. Significant differences were found to exist between the perceptions of individual stakeholders concerning design team participants, participants’ roles, and the very purpose of the cost management system. Recommendations are made for structural, attitudinal and procedural changes to the cost management system in order to facilitate its effective functioning in the achievement of the client’s needs and objectives.

Systems Thinking and Soft Systems Methodology

A bibliographic and visual exploration of the historic impact of soft systems methodology on academic research and theory.

Soft systems methodology (SSM), an analytic method commonly employed in engineering and business research, produces models focused on human activities and relevant structures used to explain complex, engineered systems. The original version of SSM involves seven stages; five address real-world aspects and observable data, while two stages leverage a systems thinking viewpoint. This approach allows the development of a simplified depiction of complex systems representative of the multi-perspective lenses used to comprehend the systemic complexity of a problem and provide a clearer picture to analysts and decision makers. This bibliometric meta-analysis of 286 relevant publications in engineering, business, and other social sciences fields explores the historic impacts of SSM on academic research and systems thinking in relevant publications that described or employed SSM for research from 1980–2018. This study produced descriptive narrative outcomes and data visualizations including information about top SSM authors, author citation impacts, common dissemination outlets for SSM work, and other relevant metrics commonly used to measure academic impact. The goal of this piece is to depict who, what, why, when, and where SSM had the greatest impact on research, systems thinking, and methodology after nearly 40 years of use, as we look towards its future as a methodological approach used to comprehend complex problem situations.

Re-energising the way we manage change in healthcare: the case for soft systems methodology and its application to evidence-based practice

Abstract Background Updating, improving and spreading the evidence base for healthcare practices has proven to be a challenge of considerable magnitude – a wicked, multi-dimensional problem. There are many interlinked factors which determine how, why and whether any particular implementation effort or intervention succeeds. Soft Systems Methodology (SSM), strongly grounded in systems ideas and complexity science, offers a structured, yet flexible process for dealing with situations that are perceived as problematical and in need of improvement. The aim of this paper is to propose the use of SSM for managing change in healthcare by way of addressing some of the complexities. The aim is further to illustrate examples of how SSM has been used in healthcare and discuss the features of the methodology that we believe can be harnessed to improve healthcare. Discussion SSM is particularly suited for tackling real world problems that are difficult to define and where stakeholders may have divergent views on the situation and the objectives of change. SSM engages stakeholders in a learning cycle including: finding out about the problematical situation, i.e. the context in which the problem exists, by developing a rich picture of the situation; defining it by developing conceptual models and comparing these with the real world; taking action to improve it by deciding on desirable and feasible improvements; and implementing these in an iterative manner. Although SSM has been widely used in other sectors, it has not been extensively used in healthcare. We make the case for applying SSM to implementation and improvement endeavours in healthcare using the example of getting clinicians at the hospital level to use evidence-based guidelines. Conclusion Applying SSM means taking account of the multi-dimensional nature of care settings, and dealing with entrenched and unique contexts, cultures and socio-political ecosystems – precisely those that manifest in healthcare. There are gains to be made in appreciating complexity and facilitating contextualization of interventions, and by approaching improvements in an iterative learning cycle.

MODEL PENGELOLAAN KESELAMATAN KERJA NELAYAN DI PALABUHANRATU, KABUPATEN SUKABUMI

Proses keselamatan dan kesehatan kerja seperti proses manajemen pada umumnya adalah penerapan berbagai fungsi manajemen, yaitu perencanaan, pelaksanaan dan pengawasan.Tujuan dari penelitian ini adalah menginventarisasi dan mengidentifikasi aspek-aspek yang terkait dengan manajemen keselamatan kerja nelayan serta membangun model manajemen keselamatan kerja nelayan dari kondisi yang terjadi.  Pada penelitian ini digunakan metode berfikir secara sistem (systems thinking) dengan pendekatan metodologi sistem lunak (soft systems methodology). Hasil inventarisasi menunjukkan bahwa permasalahan dalam manajemen keselamatan kerja nelayan di Palabuhanratu umumnya terkait pengorganisasian dan pengelolaan secara terpadu. Pengelolaan manajemen keselamatan kerja nelayan menunjukkan ciri-ciri tidak sistemik dan ciri-ciri organisasi yang mengalami ketidakmampuan belajar.Model Konseptual Pengorganisasian Pengelolaan Sistem Manajemen Keselamatan dan Kesehatan Kerja (SMK3) dapat diimplementasikan dengan kondisi adanya perencanaan dan kebijakan yang mengatur tugas dan wewenang lembaga yang terlibat, adanya mekanisme komunikasi yang mudah dipahami, dibentuknya sistem pengawasan yang terukur, serta komitmen dari semua bagian yang terlibat. Model konseptual pengelolaan secara terpadu SMK3 dapat diimplementasikan dengan kondisi adanya komitmen dari semua lembaga yang terlibat, mekanisme komunikasi, koordinasi, dan keterbukaan informasi, dilakukannya pengawasan bersama dan kesetaraan kelembagaan dan kewenangan serta pelayanan yang terukur.

Theory and practice of soft systems methodology: a performative contradiction?

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Mapping the use of soft systems methodology for change management in healthcare: a scoping review protocol

Hanna augustsson.

Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia

Kate Churruca

Jeffrey braithwaite, associated data, introduction.

It is notoriously challenging to implement evidence-based care and to update and improve healthcare practices. One reason for the difficulty is the complexity of healthcare and the powerful influence of context on implementation and improvement efforts. Thus, there is a need for multifaceted, flexible change methods that takes these complexities into consideration. One approach that has the potential in this regard is soft systems methodology (SSM). However, little is known about how SSM has been applied in healthcare settings, making it difficult to assess the usefulness of SSM for implementation science or improvement research. The aim of the proposed scoping review is to examine and map the use and outcomes of SSM in healthcare.

Methods and analysis

The review will adapt the framework outlined by Arksey and O’Malley (2005). Citations will be uncovered through a comprehensive database search of the peer-reviewed literature. Two reviewers will conduct a two-stage review and selection process where the titles/abstracts are examined followed by a screening of full texts of the selected citations. Reference lists of included citations will be snowballed to identify potential additional citations. Inclusion criteria are English language, peer-reviewed empirical papers focusing on the application of SSM in a healthcare setting. Both general information about the citations and information related to the objective of the review will be extracted from the included citations and entered into a data charting form. The extracted information will be reported in diagrams and tables and summarised to present a narrative account of the literature. The proposed review will provide information on the potential for using SSM to affect change in healthcare.

Ethics and dissemination

No primary data will be collected, and thus ethical permission is unnecessary. Dissemination of results include peer-reviewed publications and conference presentations.

Strengths and limitations of this study

• The review will be limited to the peer-reviewed and English-speaking literature.
• It will not provide a definitive account of the effectiveness of soft systems methodology (SSM).
• The scoping methodology will allow information from a broad range of studies, using different designs and methods, to be included and synthesised.
• The review will provide a comprehensive overview of the application of SSM in healthcare and synthesise information that can inform assessment of the feasibility and usefulness of SSM in healthcare.
• The findings may highlight future directions for research on SSM in healthcare.

Healthcare organisations are continuously required to implement new evidence and to improve their practices. However, improvement is exceedingly difficult. For instance, despite efforts to implement evidence-based care, only 55%–60% of patients receive recommended care in Australia 1 2 and the USA. 3 These difficulties are related at least in part to the complexity of healthcare and the pervasive influence that context has on implementation and improvement efforts. Context includes the attitudes, perceptions and actions of the individuals involved, their collective cultural attributes and features of the inner and outer setting. 4–6

Contextual factors manifest on multiple levels, are interwoven and interlinked and interact in unpredictable ways in influencing implementation. This makes it difficult to plan, execute and then predict how an intervention will be adopted and taken-up in any specific setting. 7 8 Indeed, interventions that have been shown to be effective in one setting can fail to produce results in another setting. For instance, a meta-analysis comparing the effectiveness of internationally adopted interventions, culturally adapted interventions and novel interventions showed that novel and adapted interventions were more effective than adopted interventions. 9 One explanation for this is that such interventions provide a better fit to the organisation’s needs, culture, processes and structures and therefore are more likely to be embraced, implemented and sustained. 5 10 11

An important aspect of intervention fit is related to the individuals and groups that are involved in, and affected by, the intervention. They can ultimately contribute to the intervention’s success or failure. 4 5 12 For the intervention to succeed, relevant individuals and stakeholder groups must recognise the problem and need for change, 13 agree on solutions and put the intervention into practice. 5 12 All-in-all, these issues underscore that improvement interventions cannot be separated from the context in which they are implemented. 4 5 14 15

This suggests that there is a need for more flexible, multifaceted and participatory change approaches that takes complexity into consideration rather than trying to simplify problems, interventions and contexts. 6 16–18 One approach that has been proposed to be useful in facilitating change in complex settings is soft systems methodology (SSM). 19–21 SSM has several features that have been emphasised as important for responsive implementation and improvement interventions such as involving stakeholders in the change process, 8 17 factoring in the local context, 4 5 11 facilitating adaptation and ongoing learning 4 8 10 22 and taking a systems approach to change rather than trying to control all dependent variables or striving to affect change in one part of the system without recognising the interconnections with other parts. 23 24

SSM in brief

SSM builds on systems theory. It is a methodology designed for tackling real-world problems that may be hard to define and where people may have divergent views on the problem in focus, and the objective of change, or both. SSM is described as a learning process that engages relevant stakeholders in a staged inquiry into a problematic situation with the aim of developing a purposeful model of activity that can be used for learning about the real-world and facilitating improvement of the problem. 19–21 The learning cycle ( figure 1 ) involves four activities: (1) Finding out about the problematic situation, including cultural and political dimensions . In this activity, the context of the problem situation and the interlinks between different contextual factors are explored. A rich picture is developed to illustrate this. (2) Formulating relevant purposeful activity models , that is, modelling how the activities in an improved situation could look. (3) Debating the situation, using the models to find changes that are desirable as well as contextually and culturally feasible, and seeking agreement between disparate views. (4) Taking action to bring about improvement by identifying opportunities for gain and progress based on the prior three activities, and testing changes as a basis for further learning. 19

A generic SSM learning cycle. Source: Checkland and Poulter. 19 Permission granted by John Wiley and Sons for use of this image. Licence number: 4403900665359.

SSM in healthcare

A review conducted in 2007 showed that SSM had been used in a variety of areas, including healthcare. However, the majority of studies on SSM had been conducted in relation to development and implementation of information and communication technology, and environmental and ecological problem situations. 25 A more recent review focused on the methodological aspects of the use of SSM in healthcare up to 2014. 26 This review showed that SSM had been applied in various ways, including being modified and used in combination with other methods. However, there is, to the best of our knowledge, no recent review mapping the use of SSM in healthcare, especially in identifying the type of problems to which SSM has been applied, or the types of interventions and outcomes that have been reported following the use of SSM.

We propose a review to investigate these issues. Due to the likelihood of varying study designs and outcomes of studies describing the use of SSM, we consider a scoping review to be the optimal format. This will provide information about settings, the purpose of SSM use and an overview of the types of interventions that have been proposed and implemented as well as their reported outcomes. The findings will illustrate the extent to which SSM can be useful for the kinds of problematical situations healthcare is facing, and particularly it should unlock value in understanding contextually adapted change and improvement strategies.

We propose adapting for our purpose the framework of Arksey and O’Malley 27 for conducting scoping reviews. The framework includes five stages: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; and (5) collating, summarising and reporting the results. The study protocol is outlined according to these five stages.

Stage 1: identifying the research question

The objective of the review is to examine and map the use and outcomes of SSM in the context of healthcare. The review will be guided by secondary questions: (1) In which countries and healthcare settings has SSM been applied? (2) How has SSM been applied, for example, for problem structuring, or for proposing or implementing interventions? (3) For what type of problems has SSM been used? (4) To what degree have stakeholders been involved and consulted in the SSM process? (5) What kinds of interventions have been proposed or implemented using SSM? (6) What kinds of outcomes have been reported following the use of SSM?

Eligibility criteria

Citations will be assessed against the following inclusion criteria: English-language, peer-reviewed, empirical research articles published in scholarly journals where the full text is available. The content of the citations should be on the application of SSM in a healthcare setting, including primary care, mental health, hospital care, residential age care, rehabilitation and community health facilities. Studies claiming to apply one or several elements from SSM will be included even if SSM has not been applied in its entirety. Studies using SSM, or parts of SSM, in combination with other methods will also be included. Citations focusing on the use of SSM in settings other than healthcare, for example, educational settings (including healthcare education) will be excluded. No date limit will be applied.

Stage 2: identifying relevant studies

The review will focus on peer-reviewed literature. The main identification strategy will be to search key electronic databases: Scopus, MEDLINE, Web of Science, CINAHL, EMBASE and PsycINFO. These databases were selected because they include a broad range of literature from different disciplines such as biomedicine, psychology, health services research and nursing.

The search strategy ( table 1 ) will use the term ‘soft systems method*’ to identify citations referring to SSM. Search terms will be used to limit the search to the healthcare context, for example, health* and ‘acute care’. The wildcard character, representing one or more other characters, allows variable endings of keywords, for example, healthcare, health system and healthcare organisation. In addition to the database search, the reference lists from the included citations will be snowball searched to identify additional citations. To reduce the likelihood that relevant articles are overlooked, we will also hand search reference lists of key methodological papers and review papers.

Search strategy

Because of the focus on a specific and named methodology, the search strategy can be defined well in advance, enabling the identification of relevant citations and minimising citations not related to the scope of the review. The database searches will be made by one researcher (HA) and sample citations by another (KC) and include all citations published before the study cut-off date.

Stage 3: study selection

After duplicates have been removed in a structured process, 28 all references will be imported into Rayyan, a web and mobile app, that organises and facilitates the initial screening of titles and abstracts. 29 Two reviewers (HA and KC) will apply the inclusion and exclusion criteria to all the citations, both in the title and abstract review and in the full text review. To test the inclusion and exclusion criteria and ensure consensus on included citations, titles and abstracts from 10% of the identified references will be assessed by the two reviewers. Interrater agreement rates will be calculated using Cohen’s kappa. 30 Any discrepancies between authors concerning the inclusion or exclusion of citations will be resolved through discussion and, if necessary, a third researcher (JB) will be consulted. After this initial test, any adjustments or clarifications needed will be made to the inclusion and exclusion criteria. An agreement rate of 0.8 will be used as a target to ensure that the criteria are properly defined. The researchers will then review the remaining titles and abstracts. In the next step, the reviewers will assess the full texts of the included citations for final inclusion.

Stage 4: charting the data

An electronic data charting form will be developed to guide data charting from included citations. The form will be used to collect data relating to both general information about the citations such as publication year and authors as well as information related to the objective of the review ( box 1 ). Charting the results in a scoping review can often be an iterative process since the review method may reveal additional data that may be relevant to extract. 27 31 The data charting form will be piloted by the two reviewers using a random selection of the citations and any changes needed will be made prior to data charting from the remaining citations. One of the reviewers (HA) will then independently chart the data from the remaining citations, with sample validation by KC and JB.

Overview of data items for charting

Information to be charted:.

• Publication year.
• Country of origin.
• Type of healthcare setting(s).
• Methods (design, data collection, participants).
• Way of using soft systems methodology (SSM) (for problem structuring, for proposing/implementing interventions).
• Type of problem that SSM has been applied to.
• Degree of stakeholder participation (eg, number of stakeholder groups that have been consulted in the different SSM activities).
• Type of intervention (if applicable).
• Type of outcomes reported (if applicable).

Stage 5: collating, summarising and reporting the results

A numerical overview of the extent, nature and distribution of the included studies will be summarised and reported in diagrams and tables to provide a synthesis of the literature on the use of SSM in healthcare. This may include in which countries and healthcare settings that SSM has been used and in what ways SSM has been used (eg, as a problem structuring method, for proposing/implementing interventions or other applications). The extracted information will also be summarised to present a narrative account of the literature. Themes and categories will be both deductively developed, based on the research questions, and inductively developed, based on the empirical data and determined during the analytical process. 32 Examples include a narrative description of what type of problems SSM has been used to address, which interventions that have been proposed and/or implemented and reported outcomes.

Patient and public involvement

There was no patient or public involvement in the design of this scoping review protocol.

The complexity of the healthcare system and the challenge of implementing new evidence and improving care practices calls for multifaceted, flexible approaches to facilitate change that take these complexities into consideration. SSM is an approach that has the potential to facilitate change in complex settings and situations. However, how SSM has been used in healthcare, including the type of changes for which it may be useful, and outcomes reported following application of SSM, is not clear. As such, the proposed review aims to map the use of SSM and to explore the potential for using SSM to affect health systems change.

Supplementary Material

Contributors: JB conceptualised the study. HA and JB developed the study design and methodology. HA drafted the initial manuscript, assisted by KC and JB. All authors had the opportunity to contribute to the final manuscript, edited it into its final form and approved the final submission.

Funding: This work is supported by the National Health and Medical Research Council Partnership Centre grant in Health System Sustainability (ID:9100002).

Competing interests: None declared.

Ethics approval: As no primary data will be collected, no ethical permission will be required. Dissemination of results include peer-reviewed publications and conference presentations.

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

Patient consent for publication: Not required.

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Introduction to Soft Systems Methodology

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Abstract

The purpose of this chapter is to introduce Soft Systems Methodology (SSM). The paper does not include a comprehensive description of SSM, but does outline its history, its fundamental nature and discuss how it might be used in practice. The origins of SSM date back to Lancaster University in the UK in the 1960s. It was the output of an action research programme which applied systems engineering methodology to the type of problems normally faced by managers. SSM should be viewed as an experiential learning cycle rather than a decision-making approach or an engineering approach. There are four main elements to the learning cycle of SSM. The first is a real-world situation which is perceived as problematical by stakeholders. The aim is to express the situation “as is” as best we can; to take a holistic view of the situation, capture alternative viewpoints and identify key issues. The tool used is Rich Picturing. The second element involves purposeful activity modeling. These models are driven by the assumption all situations will contain people at all levels trying to take purposeful action.The third element of SSM is a structured discussion between participants, which is informed by elements one and two. This element seeks to develop ideas, agreement and action plans to take the situation forward. The fourth element of SSM is action to improve the situation. SSM requires action to be taken – it is an ongoing process of experiential learning. SSM can be difficult for inexperienced users because it represents a general set of principles, rather than a specified method. Also, the modeling language of SSM is unusual within the field of applied systems thinking. It is highly flexible and takes time to master.

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4 Systems Thinking and Soft Systems Methodology

Peter Checkland is Emeritus Professor of Systems at Lancaster University. After fifteen years in industry he started teaching and researching at Lancaster. Seeking a better approach to complex management problems, he led the thirty‐year programme of action research which yielded Soft‐Systems Methodology and the distinction between ‘hard’ and ‘soft’ systems thinking. His work has received many honours, including four honorary doctorates, the Beale Medal of the OR Society, the gold medal of the UK Systems Society, the ‘Pioneer’ award of the International Council on Systems Engineering, and a Fellowship from the American Systems Engineering Honor Society.

• Published: 02 September 2011
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This article presents the seminal work on systems thinking and soft systems methodology (SSM), which has greatly influenced the development of the information system (IS) field. It examines basic systems ideas and what it means to do ‘systems thinking’ and marks a distinction between ‘hard’ and ‘soft’ systems thinking, both of which are relevant in the creation of an information system in a real-life situation. This is done through an account of the development of the approach, which led to recognition of the ‘hard/soft’ distinction between these two linked, but different ways of using systems ideas, namely SSM. It shows how these ideas are relevant to the IS field. Finally it summarizes the implications of these ideas for illuminating and making sense of the field of IS as a whole.

Introduction

Every organized process carried out to achieve some desirable end (of which the creation of an information system is an example) will take its form and content from some framework of ideas which are taken as given, and make the process meaningful for those who carry out the process or make use of its end product. This chapter examines ideas which underpin work on information systems (IS) namely systems ideas. It examines basic systems ideas and what it means to do ‘systems thinking’, and marks the distinction between ‘hard’ and ‘soft’ systems thinking, both of which are relevant to creating any real‐world ‘information system’ in an organization. This is done through an account of the development of the approach which led to recognition of the ‘hard/soft’ distinction between these two linked but different ways of using systems ideas, namely Soft Systems Methodology (SSM). Finally it summarizes the implications of these ideas for illuminating and making sense of the field of IS as a whole.

Firstly, however, it may be useful to illustrate the degree of complexity of (human) situations in which information systems are developed.

During the joint Anglo‐French effort to design, make, and sell the world's first supersonic passenger‐carrying aircraft, Concorde, my group at Lancaster University were invited by a director of what was then the British Aircraft Corporation to take a ‘systems engineering’ approach to the Anglo‐French Concorde Project. Our aim was to give advice on how the management of the project could be improved. This was at a time when the two pre‐production aircraft, one in Bristol, one in Toulouse, were nearing completion, but had not yet flown. The Concorde project was, at the time, the subject of much public debate in the UK, since it was by then very apparent that it was going to take years longer to develop than originally thought and would cost many millions more than originally estimated. As we started the work, we took it as completely obvious, not to be questioned, that this was clearly an engineering project; and it seemed obvious too that this project was ‘a system to create a supersonic passenger aircraft according to a defined technical specification, within a certain time, at a certain cost, and under the constraints that it must gain the certificate from the Civil Aviation Authority which will enable the public to fly in it, and that it must not unacceptably damage the environment’. We had the idea that we could model this activity, and work out from the models the information systems which would be required to support that activity. Then we could, in the light of the models, examine the project's real‐world activity and information support, and work out how to improve them.

In the event we were amazed when our models seemed to bear no relation at all to real‐world structures and activities and, most importantly, engendered little interest in the engineers and managers working on ‘the Concorde project’. Our eventual hard‐won learning was that there was in fact no ‘project’ in the accepted sense of the way the word is used in the management literature. Although the phrase ‘the Concorde project’ was on everyone's lips, used many times each day, project management as such was not at all in evidence; that is not how Concorde was created. It was created by a functional structure in which all the hydraulic engineers were in one department, all the electrical engineers in another, etc.

Realizing our mistake provided valuable learning for us. We were beginning to learn that there are great difficulties in an ill‐formed and conceptually confused field like management (of which IS is a part) which stem from the fact that there is no agreed language available for serious discussion which is separate from everyday language. Physical chemists know exactly what they mean by ‘entropy’, or ‘the Q‐branch of an infra‐red spectrum’. Would‐be scholars in the management field, on the other hand, have no shared precise meaning for many of their relevant concepts, for example ‘role’, ‘norm’, ‘culture’, or ‘information system’; all these terms are fuzzy as a result of their unreflective use in everyday chat. Serious work in management and in IS needs always to be aware of that.

In the Concorde work our initial mistake was due to the fact that we were thinking that when people used the phrase ‘the Concorde project’ they were actually referring to something in the British Aircraft Corporation which corresponded to an organized project in the full sense of the word. We were too ready to accept the everyday language phrase ‘the Concorde project’ as indicating that there actually was a project in existence, and that we could model it as a system and hence work out its necessary information support.

This example of the misleading use of the word ‘project’ in the British Aircraft Corporation at that time is an example of the untrustworthiness of casual everyday language in the management field. Unfortunately the problem is very much worse in that part of the broad field of management which uses the language of ‘systems’. Casually, in everyday talk we speak of ‘the education system’, ‘the prison system’, the ‘transport system’, etc., as if these were integrated coordinated wholes with each part contributing coherently to the performance of the whole. (To use the phrase ‘the transport system’ is, in the UK at the present time, almost to make a joke. The UK has no coherent system of transport, only an aggregate of structures and processes which deliver transport services in an uncoordinated way.) The word ‘system’ has been captured by everyday language to refer, without precision, to any large more‐or‐less connected entity.

For serious intellectual and scholarly purposes we need to separate ‘system’ from everyday language and use the word as a technical term; ‘system’ is, truly, the name of a concept: that of a complex whole which can adapt or be adapted to a changing environment and so remain viable through time.

With the proviso that the word ‘system’ be used in this serious sense, systems thinking can help to provide understanding and clarification of the whole field of IS. It is in order to try to dispel some of the confusion which results from the casual use of the word ‘system’ in everyday language, that this chapter will examine: the origins of systems thinking and the core ideas behind the concept ‘system’; the development of ‘soft’ systems thinking in Soft Systems Methodology; and the implications of this for work in IS. It draws on earlier accounts including Checkland ( 1981 , 1988 , 1996 ), Checkland and Scholes ( 1990 ), Checkland and Haynes ( 1994 ), Checkland and Poulter ( 2006 ). (Checkland, 1996 , an encyclopaedia article, includes an annotated bibliography.)

Systems Thinking: Origins

Systems ideas emerged as a generalization of ideas about organisms which were developed within biology in the first half of the twentieth century. Where classical physics developed its core conceptualizations in the Newtonian revolution of the seventeenth century (to be modified by Einsteinian physics in the twentieth) and chemistry developed its core concepts (‘elements’, ‘compounds’, etc.) in the eighteenth and nineteenth centuries, biology was later in emerging as a science with an accepted conceptual framework. As the new science of biology was developed, its concern with living things led to many controversies over the nature of the living, and hence over the proper concerns of the new discipline. Much debate concerned the issue of vitalism : did living things, which to many were, intuitively, clearly ‘more than the sum of their parts’, possess some mysterious non‐material component or directing spirit which characterized the living? (The spirit was even given a name, entelechy !) In the twentieth century the elucidation of molecular genetic mechanisms based upon DNA replication has finally resolved these debates. But within biology itself there was from the second half of the nineteenth century a strand of holistic thinking within the subject which argued that the degree of organization was the crucial characteristic of living organisms, rather than the presence of some metaphysical directing spirit.

The school of thought associated with these so‐called ‘organismic biologists’, based on such work as Woodger's Biological Principles of 1929, focused upon the organism as the unit of analysis in biology, and developed ideas about the processes which characterize metabolism and self‐reproduction in organisms. It was one of these organismic biologists, the Austrian Ludwig von Bertalanffy, who founded the systems movement by beginning to argue, in the late 1940s, that these ideas about organisms could be extended to complex wholes of any kind: to ‘systems’ (von Bertalanffy 1968 ; Gray and Rizzo 1973 ).

Since it was ideas about physically existing organisms which were generalized as ideas about ‘systems’ in general, it is perhaps not surprising that most people, taking their cue from everyday language, simply assume unquestioningly that systems exist in the world. It is the later history of the systems movement which has painfully established that system is, truly, the abstract concept of a whole which may or may not turn out to be useful as a descriptive device for making sense of real‐world wholes. Unfortunately, from the very start of the work in the systems movement, Bertalanffy himself used the word promiscuously both as an abstract idea (i.e. epistemologically) and as a label‐word (i.e. ontologically). (It may be remarked at this point that this chapter's title has the phrase ‘Systems Thinking’—i.e. the process of thinking using systems ideas—rather than ‘Systems Theory’ precisely because the latter title would be taken by many to mean ‘the theory of systems’, taking as given the status of systems as ‘things in the world’.)

But in spite of this confusion the concept system has been found to be useful as an explanatory device in many subject areas: including for example ecology, engineering, economics, anthropology, sociology, psychology, geography, as well as the natural sciences. In fact systems thinking has emerged as a meta‐discipline and as a meta‐language which can be used to talk about the subject matter of many different fields.

An example of the status of systems thinking as a meta‐subject is shown by the emergence of cybernetics, which occurred at about the same time that Bertalanffy was advocating the development of general system(s) theory. The mathematician Norbert Wiener worked on mechanical/electrical computing machines to aim and fire anti‐aircraft guns, the work representing an attempt to automate what the hunter after a moving quarry does intuitively. Wiener, with Bigelow, was developing feedback systems in which information about current performance modifies future performance. Excess feedback in such systems leads to oscillatory ‘hunting’ about the desired performance, which cannot then be tightly controlled. Wiener also worked with a Harvard medical scientist Rosenblueth, who was familiar with the pathological condition ‘purpose tremor’ in which the patient, in trying to pick up a simple object, overshoots and goes into uncontrollable oscillation. The similarity between the human and electro‐mechanical cases suggests that these are two different manifestations of a notional control system which can be described as a general case applicable to many different embodiments. Wiener ( 1948 ) launched cybernetics as the general (meta‐level) science of ‘communication and control in man and machine’, though Plato had already made the same point more than 2,000 years earlier in making an analogy between a helmsman steering a ship and a statesman steering the ‘ship of state’ (Checkland 1981 : 82–6). Nowadays cybernetics is a subset of the broad area covered by systems thinking.

From its emergence in organismic biology, systems thinking has both developed core systems ideas and itself been developed in a number of different ways in different areas. The next sections summarize these developments.

Systems Thinking: Core Concepts

In the journal Philosophy of Science in 1955 it was announced that ‘A Society for the Advancement of General Systems Theory is in the process of organization’. Behind this initiative were the biologist Bertalanffy, an economist (K. E. Boulding), a physiologist (R. W. Gerard), and a mathematician (A. Rapoport). Their idea was that there could be developed meta‐level theory and models ‘applicable to more than one of the traditional departments of knowledge’—just as, in the example given above, a single model of a feedback control system might give an account which can describe both the behaviour of electro‐mechanical servo‐mechanisms made by engineers and the behaviour called ‘purpose tremor’ in human patients. Surely the engineers and medical scientists would find this both intriguing and useful? The society referred to was the Society for General Systems Research, and it still exists, with a small membership, as the International Society for the Systems Sciences. But its original project, the development of a meta‐level systems theory into whose language problems within many disciplines could be translated for solution—leading to an anticipated greater unification of the sciences—cannot be declared a success. The kind of meta‐level problem solving envisaged by the pioneers has not occurred.

Rather, systems ideas have made contributions within many different subject areas. In this the original ideas of the protagonists of general system(s) theory have been vindicated to some extent. There is now a set of systems ideas which find application in many fields.

At the core of systems thinking is a concept which clearly derives very directly from our intuitive or casual knowledge of organisms: the concept of a whole entity which can adapt and survive, within limits, in a changing environment. This notion of ‘the adaptive whole’ is the central image in systems thinking, and the systems movement can be regarded as the attempt to explore the usefulness of this particular concept in many different fields. In order to understand and use this concept we need a handful of further ideas which, together with the idea of the adaptive whole, constitute the bedrock of systems thinking. (For more detailed discussion, see Checkland 1981 : chs. 3 and 4.)

First, for an observer to choose to see some complex entity as a whole, separable from its environment, it must have properties which (for that observer at least) are properties of it as a single entity: so‐called emergent properties . These are the properties which make the whole entity ‘more than the sum of its parts’. This is not a mysterious concept, except to those New Age mystics who are drawn to the systems movement by a yearning for an elusive ill‐defined holism. The parts of a bicycle, in a sack, are simply an aggregate. When assembled in the particular structure we call ‘a bicycle’, that entity has vehicular potential, which is an emergent property of the whole. In the structure of DNA, the laws of physical chemistry allow any sequence of the amino‐acid residues which join the double helix. In order to explain experimental findings we have to invoke the idea that certain sequences constitute a ‘code’ which, in biological reproduction, results in our having red hair or a large nose. The ‘genetic code’ is an emergent property of the amino‐acid sequences. Another example: university degrees are awarded not by departments or courses but by the university as a single entity. The power to confer degrees is an emergent property of the institution as a whole, one which the institution itself invents.

Secondly, wholes having emergent properties may well have smaller wholes with their own emergent properties; for example, it is meaningful to think of a department of a university as having autonomous emergent properties (the resources and authority to put on a particular course, for example). Equally, the larger whole (the university) may be only a part in a yet larger whole (the university sector of higher education) with its own emergent properties…and so on. In other words systems thinking includes the idea of layered structure .

Thirdly, if our entity is to survive in environments which change, it must have available to it ways of finding out about its environments and ways of responding internally to them; it must have processes of communication and control , which may be automatic (control of core temperature in our bodies for example) or created by human beings (rules within a university, for example), depending on the kind of entity being considered.

These four ideas: emergent properties, layered structure, processes of communication, and control, are the ideas needed to describe the basic concept of an adaptive whole. They are the core ideas of systems thinking, and have been used in different ways in many different fields, from engineering to economics, from geography to jurisprudence.

Broadly speaking, systems ideas have been used in three different areas: the study of wholes which Nature creates (often called ‘natural systems’); the study of wholes designed by human beings (‘designed systems’) such as watches, radios, suspension bridges; and finally the study of human affairs, including such activities as governing, managing, designing, educating, etc. Obviously the three areas show many links. A landscape gardener, for example, enacts a human system of activity which interacts with, to modify, a natural system.

In general, work involving ‘natural’ and ‘designed’ wholes, are ones in which there is usually good mapping between the systems concepts and the observed real world. Not surprisingly these are areas in which the everyday use of the word ‘system’ does not lead to too many intellectual difficulties. It is in these cases possible to forget that systems are always, fundamentally , abstract concepts which may or may not map real entities. Here there are reasonable grounds for equating an entity in the real world (e.g. ‘the natural drainage system of the Rhine Valley’) with the systems description of it. It is from this kind of work that the use of system as a mere label for something in the real world derives.

But such easy mapping is much more problematical in the third broad area of application, that of human affairs. In the 1960s the main development of systems thinking in this area skirted round and managed to avoid this problem by focusing on arrangements to meet goals or objectives declared in advance to be desirable. If a carefully defined objective could be taken as given then a ‘system’ to meet the objective could be engineered . This adoption of the notion of goal seeking was the approach which characterized the post‐war developments arising from the success of operational research in the Second World War. Bell Telephones formalized their approach to the development of new technology in ‘systems engineering’ (Hall 1962 ); RAND Corporation developed ‘systems analysis’ (Optner 1973 ). As mainframe computers were developed the same thinking was adopted to provide an approach to designing and establishing a computer system. (This was reasonable in the early days when computers were used only to transfer to a machine the transaction processing previously done by clerks; but the thinking behind early computer systems analysis became increasingly inappropriate as the technology developed, as this book illustrates.)

This thinking about achieving a declared objective characterizes the first major use of systems thinking within human affairs. It is essentially systematic in character. The approaches just referred to consist of examining and selecting one among a number of alternative systems to achieve an objective which is defined as desirable at the start and is taken as given. It is thus limited to the subset of situations in which objectives are undisputed, so that the problems are those of ‘how to do it?’ not problems of ‘what to do?’ This is mainly the case in situations which relate to technology.

This idea of creating systems to meet a desirable objective, now thought of as ‘Hard’ Systems Thinking, has been very successful, and it is the characteristic core of most post‐Second World War ‘management science’. However, a great many managerial problem situations cannot be reduced to goal‐seeking. The reduction does work well in technological situations, and Systems Engineering is now a well‐developed field with its own journals, professional associations, methodology, and techniques. But for most managers, at every level, most of the time, the managerial task is to cope with ill‐defined, dynamic, multifaceted situations in which objectives are unclear, and many different interests clamour for attention. Such situations entail problems often described as ‘wicked’, in that just when you think you are on top of them they reveal new aspects which demand attention and call for rethinking.

It was in seeking a way of coping with this type of situation that a different, broader way of using systems ideas emerged, different, that is, from assuming that the world out there contains systems whose objectives can be defined, enabling the systems to be engineered to achieve them. The broader way of using the basic systems ideas (now known as ‘Soft’ Systems Thinking) came out of a programme of action research in real‐world situations which was carried out over a period of thirty years at Lancaster University. It broadened the scope of systems thinking and also led to useful ways of thinking about the field of information systems as a whole. These developments are described in the next sections of this chapter.

The Development of Soft Systems Thinking and Soft Systems Methodology

The research context.

The context which enabled the development of Soft Systems Thinking was the creation of a postgraduate Department of Systems Engineering at Lancaster University in the late 1960s. The Department was founded by Professor Gwilym Jenkins (the well‐known statistician), and the present writer joined him in 1969. After fifteen years as first a technologist, then a manager of research in the new industry of making wholly synthetic fibres, I decided to start a new career when Jenkins explained to me that he intended to interpret the word ‘engineering’ in the Department's name in the broader sense in which the word can be used. In the English language you can ‘engineer’ a meeting with someone, or ‘engineer’ the release of hostages, as well as ‘engineer’ a chemical plant. Also, after an interesting and illuminating time in a giant corporation (ICI Ltd) I was intrigued by—and thoroughly agreed with—Jenkins's idea that the way to further develop systems engineering was to work in real‐world problem situations outside the University, in organizations large and small in both public and private sectors. This strategy became possible because we found ourselves recruiting Masters students of average age around 30, mature people who were at a point in their lives when they were rethinking who they were and what future direction their careers might take. Jenkins and I also agreed that just as whatever ideas we have in our head influence the experience we acquire in the world, ultimately it is the experience of the world which is the source of the ideas. In the relation between the two (as shown in Figure 4.1 ) what is most important is neither the ideas nor the experience but the arrows: the cycle between the two , the mutual influence which leads to each creating the other. Asked by Professor Jenkins to take over the lead in establishing the Department's research programme, I felt that, given mature students, we could use them in a programme of action research in organizations in ways which would not be possible with fresh graduates. After a few years we supplemented this effort by persuading the University to allow us to establish a university‐owned consultancy company, ISCOL Ltd. This enabled us to undertake projects at any time and of any length, i.e. not restricted by the timings of the one‐year Masters degree. Some of our best students stayed on after the Masters course to work for a year or two as ISCOL consultants. The Company structure was simple. Each year ISCOL Ltd. gave its surplus to a charity, namely its owner Lancaster University! Over the twenty years in which the Company existed the University received nearly £500,000 from ISCOL. The size of the surplus was not crucially important from our point of view, as long as there was a surplus to ensure continuity. What was most important was that we had here an overall mechanism in which each and every research project—whether from the Masters course or from ISCOL—was one unit within an overall coherent strategy. Also, in the action research approach (Checkland and Holwell 1998a ) researchers do not simply observe or collect data from outside organizations, they take part in the work going on to solve problems or to bring about improvements. This ensured that each of the 300+ projects carried out during the life of the programme had the potential to make its own contribution to the research learning as a whole, adding to the cumulative experience and helping to develop the ideas.

Ideas about the world and experience of it steadily create each other

The Research Process

The initial research idea was to take Systems Engineering (SE) as developed by Bell Telephone Laboratories, and described in Arthur Hall's classic book A Methodology for Systems Engineering (1962), and try to apply it outside the area for which it had been developed, namely situations in which desirable new technical systems were developed. Our aim was to test it in messy general management situations, ones in which the option of taking some defined objective as given was not available. Whatever happened, we would learn from the experience gained as we cycled many times round the cycle shown in Figure 4.2 . This shows a cycle of three elements: methodology; use; and learning from use, which itself may then modify the declared methodology. We were breaking into that cycle by taking SE as given.

We quickly found that classic SE was not rich enough to cope with the complexity found in problematical management situations. Two early experiences illustrate this. In the Anglo‐French work on Concorde mentioned in the Introduction to this chapter, we were initially thinking, like systems engineers, that we could work from a tightly defined objective related to the work going on in Bristol and Toulouse: creating the world's first supersonic passenger‐carrying jet aircraft. This was not wrong but it was naive. As well as being an engineering project Concorde had a significant political aspect. Britain and France signed a treaty (no less) to do the joint work at a time when the French President was vetoing British entry into the European Common Market. From the British perspective this work constituted a political as well as an engineering project, one which aimed to show what a good European partner the UK could be. To engage richly with the situation it was necessary to embrace both the engineering and the politics. This realization was seen eventually as a key experience in the development of Soft Systems Methodology.

The concept behind the development of Soft Systems Methodology

Also in the early years of the programme, on a very different scale, in a small carpet‐making company, the owner/managing director said at the start of the work ‘I know I need to do more than live from day to day in this Company; I want you to make me plan.’ Seeking to impress him quickly with our practicality, work began in an area which we thought could rather easily be improved, namely production planning. But no, he did not want that; nor did he want to take up other suggestions we made. This was very frustrating. Eventually it was possible to make sense of this situation by recognizing the managing director's unstated fear: that his firm might become more profitable and begin to grow—which would change both it and his lifestyle. In saying ‘make me plan’ he really wanted to ensure that his firm remained viable but did not change. He did not look for greater wealth; he wanted to make sure he preserved a pleasant lifestyle which enabled him to play golf on two mid‐week afternoons every week. Such is the real complexity of human affairs, something which often surprises graduates of MBA courses.

Learning from experiences of this kind brought the realization that SE methodology would not survive unscathed in the cycle of Figure 4.2 when applied to typical management, rather than technical situations. In the end the experiences led to a methodology so enriched that it acquired its own name: Soft Systems Methodology (SSM). As SSM emerged it was also realized that the modelling of activity systems, which had become a feature of SSM, also led to a highly relevant approach to work on information systems (IS), as indicated in Figure 4.3 .

The outcome of the Action Research Programme

This arose from the fact that for each activity in an activity model you can ask: ‘What information would be required to do this activity; and what information would be generated by doing it?’ Thus activity models can be a coherent (arguable, discussable) source of information models. This has led to much use of SSM in the IS field, a development which was not anticipated from the start of the research; it was a welcome bonus from the action research experience.

The Nature of SSM

Doing research is always a confusing experience when you are in the middle of it, and the research which established SSM and Soft Systems Thinking was no exception. However, the Action Research process, in which the research object is the real‐world experience itself, requires reflection upon the experience, and this supplies a structure through which the learning can emerge to be captured explicitly (Checkland and Holwell 1988a ).

Over a period of some years it became clear that the methodology emerging from reflection on dealing with ‘wicked’ management problem situations took a more subtle view of human affairs than the goal‐seeking view which underlies classic SE and most Management Science. This new view will be set out succinctly at this point rather than being left to emerge later in this discussion. (This will better serve the present purpose, which is to provide a clear account of the shape of SSM, explain the difference between ‘hard’ and ‘soft’ systems thinking, and, finally, argue the relevance of SSM to work in the IS field.)

Every human situation will always contain people who interpret the perceived real world differently from each other. Every such situation will contain different world‐views, that is to say different mental structures which cause the world to be seen differently by different people—one observer's ‘terrorist’ being another's ‘freedom fighter’. In the Concorde work the absence of organized project management stemmed from a senior management world‐view that world‐class engineering was best ensured by organizing professional engineers in peer groups: hydraulic engineers in one department, electrical engineers in another, etc.; hence the functional organizational structure.

However, world‐views, though often hard to change, are never completely static; they change over time, often remarkably quickly, as a result of changing experience or sudden crisis. The ‘British Aircraft Corporation’ which developed Concorde is now ‘BAE Systems’, a company in which project management is currently the unquestioned norm. Similarly, when we wished to set up ISCOL Ltd. in Lancaster University there was much opposition to the idea. Had the decision been in the hands of Senate we would not have been allowed to go ahead, since the idea of bringing in money for services rendered was thought by some academics to be somehow improper. Luckily the decision was in the hands of the University Council, which contains people from outside the University, and that body allowed us to establish ISCOL Ltd. Nowadays, although universities are not—by private sector standards—noticeably dynamic organizations, university Deans urge academics to think of anything they could do which would bring in money, and encourage them to do it vigorously. World‐views do change!

Finally, human situations will always contain people trying to act purposefully—deliberately, consciously, with intent—this being one of the core characteristics which makes us human. Whether we work in giant corporations or in small firms, and whether in the private or the public sector, it is always the case that people try to take purposeful action, though the purpose might well be in dispute and the intentional action may well be mixed with random thrashing about. There is never any shortage of that in human affairs. These three characteristics: different world‐views; changing world‐views; would‐be purposeful action, provide an image of human situations which underpins SSM. They lead to the shape of the approach.

In making sense of the emergence of SSM from the action research experience four crucial points of learning can be identified. They established both the image of human situations just described and the overall shape of SSM. First came the realization that every research situation was characterized by would‐be purposeful action. This led to the idea of making models of purposeful activity, and a way of doing this was worked out based on the basic systems ideas (emergent properties, a layered structure, and processes of communication and control). Not much progress could be made with this, however, until it was accepted that any such model could be built only on the basis of a declared world‐view, since a number of world‐views will be relevant to any contemplated purposeful action. For example a number of different models relevant to capturing the purposeful activity of a hospital could be built. Their content would depend upon the world‐view selected, which could range among those attributed to doctors, nurses, managers, patients, government, the local community, etc.

These thoughts in combination led to the basic shape of the SSM process, which is shown in Figure 4.4 . In this the concern is not with trying to define ‘a problem’, and certainly not ‘a system’ but lies in expressing ‘a situation considered by some to be problematical’. Models of purposeful activity considered relevant to exploring the situation are built.

SSM's inquiring/learning system

The models are used as a source of questions to ask of the situation addressed. This provides structure to a debate focused on some possible change which is both desirable and feasible—desirable, that is, given the models used, and feasible culturally for these particular people in this particular situation with their particular history and perspectives. The debate about change might be expected to find consensus, but in our experience that is the occasional special case. The norm in the human tribe is the need to find accommodation s among world‐views in conflict. This entails finding versions of the situation which different people with different outlooks could nevertheless live with . The whole process is one of learning one's way to useful practical change; it needs to be constituted as a learning system. SSM provides many techniques to help in traversing this learning cycle. They will be mentioned in outline below, but first it is important to make some general points about the cycle as a whole:

the process, which is action‐oriented, is in principle ongoing , although developed in time‐limited projects it can also be seen more generally as a way of going about the task of managing;

the models, unlike most models in management science do not purport to describe anything in the real world, they are only intellectual devices which help bring structure to debate about change; they are models of ways of thinking about the real situation each based on a particular world‐view thought relevant to the particular situation;

traversing the cycle is not a step‐by‐step process; rather, experienced users will be found doing work at many or all stages of the cycle during a use of the approach: for example, doing more finding out, building new models, comparing models with real‐world activity, finding more relevant world‐views; in general, reacting to the debate engendered, letting it speak, rather than confining it to any particular template;

since each model is built according to a declared world‐view, the use of the models to question the real situation raises the focus of debate from current specifics to the meta level: What world‐views are being taken as given here? What alternatives are there? This is eye‐opening in most situations; really sophisticated users of SSM use Figure 4.4 , once it is internalized, as itself a sense‐making model to bring clarity and understanding to the engagement with the situation; the best uses usually reveal the approach to be in the hands of people in the situation addressed, with facilitating help from someone with detailed knowledge of SSM.

As far as techniques are involved in SSM, they facilitate the activities surrounding the learning cycle in Figure 4.4 . They all emerged in research experience and have been found to be consistently helpful. It is not relevant here to describe them in detail. (The most accessible source of detail is Checkland and Poulter ( 2006 ), an account of SSM written for people wishing to use the approach rather than agonize about its theory or its history.)

Finding out about the problematical situation is helped by representing it pictorially in ‘Rich Pictures’ and in three analyses. The rationale for drawing Rich Pictures is that the complexity in real life is always a nexus of interacting relationships, and relationships are better represented in pictures than in linear prose. The three analyses (One, Two, and Three) supplement the pictures, covering: examination of the intervention in the situation (One); the social flavour of the situation in terms of roles, norms, and values (Two); and the ‘commodities’ which embody power in the situation—how they are acquired, used, defended, relinquished etc. (Three). This finding out is a source both of ideas for models likely to be insightful and for an understanding of what kind of change might be feasible in the situation. (In the Concorde work described above, the introduction of a project management approach was culturally infeasible at that time in the British Aircraft Corporation.)

The model building in SSM, since it entails forming only concepts of a defined purposeful activity (not descriptions of part of the real world), is a straightforward application of logical thinking, once a well‐formulated account of the activity (a ‘Root Definition’ in SSM) has been made. The core of this kind of model building is the realization that any purposeful activity can be expressed as a transformation process T which converts an input into an output. (At one level: ‘unpainted garden fence’ into ‘painted garden fence’, at another: ‘health care needs of the citizens of Oslo’ into ‘those needs met’). Model building is then a matter of assembling the activities necessary to obtain the input to T, the activities to transform it, and the activities to dispose of T's output. These activities, linked by arrows which express the dependency of one activity upon another, represent the model's operational activities. To this we add activities of monitoring the operational activities in order to take control action if necessary. This meets the requirements of a system as an adaptive whole and gives a structure like that in Figure 4.5 .

The form of an activity model as an adaptive whole entity (i.e. ‘a system’)

Figure 4.6 shows an activity model built using the IKEA mission statement as if it were a Root Definition.

It is not by chance that Figure 4.5 has seven activities in the operational part of the model. It has been found very useful always to represent the transformation process initially in around ‘7±2’ activities, this expression coming from George Miller's well‐known 1968 paper in cognitive psychology (‘The Magical Number 7±2’). This suggests, from laboratory experiments, that the human brain may have the capacity to cope with this many concepts simultaneously. Any of that initial cluster of activities in a model can always itself become the source of a Root Definition and a more detailed model. (For example a single activity: ‘Obtain raw material X’ could itself be expanded into a model in its own right whose activities would include such items as ‘Find suppliers’, ‘Ascertain their reliability’, ‘Compare prices’, etc. This stems from the layered nature of the core systems concept of an adaptive whole.)

An SSM‐style activity model built from the concept in the IKEA mission statement: marketing home furnishing items of good function and style at prices low enough to enable a majority of people to buy them

In order to help create purposeful models of this kind SSM provides several guidelines from experience. Having thought of a combination of world‐view and purposeful activity relevant to exploring the problem situation, express it in the PQR formula: do P, by Q, in order to contribute to the higher‐level aim R—for example ‘paint the garden fence, by hand painting, in order to improve the appearance of the property’. This answers the questions, What?, How?, and Why? (Note that in Figure 4.6 the Root Definition used is of the kind: do P to help achieve R, missing out the ‘how’ and with R only implied as ‘run a successful business’.) Then expand consideration of the purposeful concept using the CATWOE mnemonic, where T and W are the transformation process and world‐view, and the other elements enrich this further. C (for the metaphor ‘customer’) answers the question: who would be most affected by the outcome of this transformation process? A (for ‘actor(s)’) addresses the question: who would carry out these activities? O (for ‘owner(s)’) covers: who could stop this transformation, and E (‘environmental constraints’ on T) asks: what does this process have to take as ‘given’ from outside itself—for example, is there a finite budget or timescale, or perhaps legal constraints which have to be met? Answering these questions ensures a rich Root Definition from which the operational part of the model can be constructed. It also allows the other part of the model—the monitoring and control activity—to be completed. (Note that the IKEA mission statement (Figure 4.6 ) could be enriched by using CATWOE and PQR.)

Since no two human situations are ever identical, a particular model might have some highly specific measure of performance, for example ethics or aesthetics. However there are three criteria which are always relevant, and defining them for a particular model always leads to insights. In asking what criteria enable performance to be monitored, we are asking how might T fail, and we need criteria for each of three kinds of failure. There might be failure to deliver the output from the input—we need a criteria for efficacy . Quite separately the process might be using too much resource, even if it were efficacious—we need a criteria for efficiency . Thirdly, even if efficacious and efficient the process might not be contributing to some higher level or longer‐term aim, since it is possible to do well something which is actually not worth doing at all—it might be ineffective . So we need to decide how efficacy, efficiency, and effectiveness (the ‘3 Es’ in SSM) defined in this way, would be judged for the particular purposeful transformation in question.

Finally, it was realized in the course of the research that Root Definitions can be of two types, now called ‘Primary Task’ or ‘Issue‐based’ (IB). PT definitions are models in which the purposeful activity model maps, in principle, some real‐world structure—for example in a company with a functional organization structure we might make a model related to, say, production, in which case the model boundary would map onto the organization boundary of the production department. An IB definition on the other hand is a model of an activity which is relevant and important but is not itself captured in an organizational entity. For example, when working in the Petrochemical Division of ICI Ltd, it was found useful to make a model of ‘a system to innovate in the petrochemical industry’. There was no ‘innovation department’ as such. Many of the activities in the model did in fact exist in the real situation but were spread between Research, Production, and Marketing Departments: the model boundary cut across the organizational boundaries. The general rule is: do not use only PT or IB models, use both. Not surprisingly, IB models stir up interest precisely because they cut across organization boundaries and hence impinge upon political realities in the organization in question. Figure 4.7 summarizes all of these guidelines which support the modelling process in SSM.

When it comes to using models to structure debate, exactly how this is done will depend very much on the norms and attitudes in the situation addressed, making generalization difficult. In a project to rethink the role, structure, and information systems of a 600‐strong Head Office department in the Shell Group, for example (described in detail in Checkland and Scholes 1990 ) the work was carried out in a sequence of two‐day management workshops, making use of a standard way of tackling issues within the Shell culture. Day One aimed to end up with some relevant models, Day Two used the models to question the real world of the Head Office department and its ongoing interactions with its internal ‘clients’ in the Company.

Guidelines for building models of concepts of purposeful activity in Soft Systems Methodology

Technically, comparing the models with the real situation is done by using the models as a source of questions to ask of the real world. It is of course also possible to ask, at a higher level, how the world‐views associated with the models compare with the (normally unexamined) assumptions about the perceived world which characterize the situation being explored. The questioning can be informal—putting models on flip charts, for example, and having a discussion with reference to them—or can be carried out systematically. In this latter case it is a matter of asking, for each activity and link in a model questions such as: Does this exist in the real situation?, How is it manifest?, Is it regarded as satisfactory?, Is it essential?, By what criteria is it judged?, etc., etc.

As this kind of comparison proceeds the aim is always to think in terms of possible desirable change which is also feasible in the culture in question—change which different interests could live with and is acceptable within the local politics, or changes them. The change itself may be structural, procedural, or attitudinal, or a mix of all three. As ideas for a particular change emerge, it can be scrutinized in terms of what, who, how, when, etc., not forgetting an always relevant question: what enabling activity would be needed to bring about this change? In the UK's National Health Service, for example, it used to be the case that hospital consultants were very powerful indeed, though that has changed in recent years. When work in the NHS using SSM started, no change in a UK hospital could be made if powerful consultants did not support it. The enabling activity was: gain consultant support.

The aim of this necessarily brief account of SSM is to show that it is a well‐established learning approach to real‐world complexity. Based on the notions of usually unexamined, but in principle changeable world‐views, together with the ubiquity in human affairs of an intention to act purposefully, it explores situations thought to be problematical, and learns its way to ‘action to improve’. Its content was honed over thirty years in several hundred projects which were thought of as both practical interventions and as pieces of methodological research. A slow maturation led to a way of engaging with real situations which was very far from the classic systems engineering process of the 1960s with which it had started. It was only gradually recognized that a very significant intellectual step separated SSM from classic SE, a step which marks and establishes the distinction between ‘hard’ and ‘soft’ systems thinking. This is explained in the next section and leads to a final section which applies these ideas to the field of information systems.

‘Hard’ and ‘Soft’ Systems Thinking

Whenever you observe someone taking purposeful action in the everyday world it is always interesting to ask yourself: what ideas must they be taking as given if they assume that what they are doing is a sensible thing to do? Similarly, of any organized process of intervention in real situations we can ask ourselves: what must be the accepted assumptions behind this process? This question can be asked at two levels. There will be an assumed philosophical position reflected in the form of the intervention process, and there will be an assumed sociology reflecting a particular view of the nature of human society.

As SSM developed into a mature process it was gradually realized that it assumed both a different philosophy and a different sociology from those assumed at the start, namely the assumptions behind the classic Bell Telephone systems engineering process (Hall 1962 ) which had itself been taken as given at the start of the research. In common with most of the systems work done in the 1950s and 1960s, classic SE uses the word ‘system’ in the same way that it is used in everyday language. It is assumed to be a label‐word for reified ‘systems’ which exist out there in the real world. The world is taken to contain multiple interacting systems which could be engineered to work better. (This is the same assumption found in RAND Corporation systems analysis (Hitch 1955 ),classic system dynamics (Forrester 1961 ), classic operational research (Rivett and Ackoff 1963 ) and the viable systems model (Beer 1972 ).) All of this work thus assumes a philosophy of positivism, the philosophy of natural science, and a process of goal‐seeking/optimizing. Along with this goes a sociology of functionalism, in which each part of the system addressed plays its part in contributing to the achievement of system objectives. The viable systems model, for example, consists of a set of linked functional subsystems, each with an explicit role in the system as whole. This model is then mapped onto real‐world organizations as if functionalism is the only way to conceptualize an organization. The world‐view of this strand of thinking is thus that illustrated in the upper half of Figure 4.8 in which the observer sees systems in the real world which can be engineered to achieve their objectives.

Once SSM had embraced the concept of alternative world‐views—which can themselves change over time—together with the ubiquity in human settings of would‐be purposeful action, its concept of social reality was that this was in a process of continuously being constructed and reconstructed in human talk and action (Checkland 1981 : ch. 8). Positivism and functionalism do not cover this perception. Rather it is based on a Husserlian philosophy of phenomenology, in which what is prime is not the external world itself but our mental activity which engages with that reality, together with an interpretive sociology of the kind which Alfred Schutz derived from Husserl's philosophy (Schutz 1967 , originally published in German in 1932). The lower half of Figure 4.8 illustrates the intellectual stance in which SSM sits. The observer sees in the world complexity and confusion, but can consciously organize the process of engaging with the world as a learning system. The difference between the upper and lower parts of Figure 4.8 lies in their attributions of systemicity (systemness). The upper observer assumes the world to be systemic, a set of linked systems. The other observer assumes that he or she can make the process of inquiry into the world systemic. This different attribution of systemicity is the crucial distinction between ‘hard’ and ‘soft’ systems thinking. (For more detailed discussion, see Checkland and Holwell 2004; and Checkland and Poulter 2006 : Appendix A: SSM's Theory.)

Hard and Soft system thinking (the former being a special case of the latter)

Expressed in a different way: hard systems thinking uses the word ‘system’ ontologically; soft systems thinking, as in SSM, uses the word epistemologically, SSM providing an epistemology for making sense of experienced social reality. This defines the relation between ‘hard’ and ‘soft’. The two are not necessarily separate. Within soft systems thinking the user has the freedom to decide that the chosen action‐to‐improve might be, in a particular case, to design and implement in the real situation a system to do something or other, with all of the work from the 1950s and 1960s available to help with this. Thus soft systems thinking does not throw away the earlier thinking: the hard stance is an occasional special case within the soft.

Implications of Soft Systems Thinking and Soft Systems

Methodology for information systems, basic ideas.

The provision of information was rarely absent from the issues involved in the several hundred projects of the SSM research programme. This is not surprising; information has long been a compelling issue in human affairs. The first printed book in Europe, the Gutenberg Bible, appeared in 1455, and by 1501 the Pope was suggesting to bishops that the control of printing should be considered in order to preserve the purity of the faith (Roberts 1980 ). Information is always potentially subversive, and control of it is a very common way of exercising power in modern organizations. So it is to be expected that the opening up of the mind entailed in soft systems thinking and SSM should be relevant to work in the field of information systems.

We can ignore the so‐called ‘information theory’ developed from work done in the 1920s by communication engineers. Their concern as engineers was signal coding, transmitting, receiving, and decoding. The context of the messages sent was not of interest. To them ‘I have just changed my socks’ has the same status as ‘I have just pressed the nuclear button.’ One of the pioneers of the theory, Warren Weaver (in Shannon and Weaver 1949 ), describes as ‘disappointing and bizarre’ the fact that the theory ‘has nothing to do with meaning’. But ‘having meaning’ is the usual connotation of the admittedly ambiguous concept: ‘information’. Although there is no universally agreed sharp definition of ‘information’, there is broad agreement in the literature that ‘information’ is what you get when human beings attribute meaning to data in a particular context.

In fact we can make sense of the world of IS via a familiar but usually unremarked sequence of mental processes (Checkland and Holwell 1998b ). At the start of the sequence are the uncountable millions of facts which we call data (from the Latin ‘dare’, to give). These are the world's givens. In the first process we pay attention to a very small subset of all the items of data, the ones of interest to us in our contexts. Unfortunately we do not have a word for this subset of items of data which we focus on, have an interest in, or pay attention to. The word capta suggests itself—from the Latin ‘capere’, to take—and would be useful since the selection of capta from a mass of data is a crucial process both for individuals and organizations. It shapes their creation of meaning. For example a random item of data which could no doubt be obtained quickly from the internet is: the temperature today in Tucson, Arizona. I have no current interest in this. But if I were travelling to Tucson tomorrow and was packing my bag, I might well promote this item to the category capta, and find out the current temperature in Tucson. This item of capta would immediately be processed into the next category, information , since it would have acquired meaning in terms of the context of my packing of clothes for the trip. Over time the next process operates. Collections of structurally linked data or capta attain a more permanent status and can survive for a period (possibly a long period) over time: they constitute structures of knowledge ; for example: plate tectonics, or the history of rock climbing in Colorado. Finally we may add a process in which some knowledge can achieve the higher status we call ‘wisdom’. This has no agreed definition but implies a combination of experience‐based knowledge which can provide insightful judgements.

Understanding these processes is the business of the IS field, and we may note that at the heart of the sequence: data/capta/information/knowledge/wisdom is the creation of what are usually called ‘information systems’. This is a rather loose everyday‐language expression. More precisely, in terms of the above discussion, information systems are actually ‘capta‐processing systems’ which—the designer intends—yield desired information. Users of the system, of course, may attribute a meaning to the processed capta different from that intended by the system designer. For example, a system tabulating student marks on a university degree course, intended to help classify degree grades at an Examination Board meeting, may be seen by lecturers as revealing unequal and unfair teaching loads. In this lies much of the complexity, and the interest, of work in the IS field. But the starting point at least is clear: define the capta which will yield the desired information.

Creating information systems

When it comes to thinking about the process of designing and implementing information systems some very basic systems thinking is helpful. Suppose that some purposeful human action is conceptualized in an SSM‐style activity model, model A. Suppose that this action is served, supported, or helped by some other purposeful activity which could be conceptualized in another model, model B. Now it is clear that model B could not be built until model A is available. This is because what counts as the service, support, or help provided by B would entirely depend upon how A has been described. In general: if one system serves another then the system which serves can only be conceptualized when an account of the system served is available. Now, no one creates information systems for their own sake. They are service systems, created to serve or support people taking purposeful action. Thus an information system can be conceptualized properly only after prior careful conceptualization of the purposeful activity served. Therefore well‐thought‐out work to create an information system has to begin by defining the real‐world purposeful action which the information system will support, not by considering data, capta, or information, and especially not by considering IT.

Subtlety is then added to this process by the fact that a real‐world purposeful action can always be looked at according to various different world‐views. For example, the information systems needed to support a manufacturing operation will be very different if that operation is conceptualized in terms of maximizing production efficiency, rather than in terms of meeting a market need. Also, additional subtlety comes from the fact that the sophistication of modern IT makes it possible to think of new purposeful activities, ones not possible without the technology, and also new kinds of information system which would not be possible without the capabilities of today's technology. The general situation is therefore that shown in Figure 4.9 . Expression of intended purposeful action makes it possible to define the needed IS support, which is realizable through IT. The dotted feedback then acknowledges the role of modern IT in extending the possibilities both of real‐world action and its information support. System design logically calls for iteration among the elements in Figure 4.9 .

Back in the real world, every reader of this book will have experienced instances in which organizations have purchased computers and/or off‐the‐shelf systems and only then asked themselves what they are going to use them for. Once that is decided they usually wish they had bought a rather different system. Sometimes major problems of this kind become public knowledge, for example: the failure of the £75 million Taurus project of the London Stock Exchange; the abandonment of the London ambulance system after twenty‐four hours of chaos; the criticism by the UK Audit Commission of the expenditure of £106 million on would‐be integrated hospital support systems which yielded identifiable benefits of £3.3 million. Such failures emphasize the need for clear basic thinking relevant to the provision of information systems.

The concept of IT‐based information support of purposeful action

The argument here is that systems thinking, especially soft systems thinking and SSM can provide a way of conceptualizing the social processes in which, in a particular organizational context, a particular group of people can conceptualise their world and hence the purposeful action they wish to undertake. That provides the basis for ascertaining what informational support is needed by those who undertake that action. Then it becomes appropriate to ask how modern information technology can both provide that support, and also enrich the thinking about possible action and possible information systems. (For more extensive discussion of the relation between SSM and IS see Checkland and Holwell 1998b .)

One of the strongest impulses of human beings is to dispel uncertainty by attributing meaning to what they perceive—even if this entails believing (temporarily) that the earth is flat. We are meaning‐creating animals, which means that at the core of the IS field is the human process of attributing meaning to what we perceive, in our contexts. We continuously create information in order to feel more comfortable, more in control of our perceived world. Sometimes this process is captured in formal ‘information systems’ involving, nowadays, sophisticated IT. But the technology can never take over the human act of attributing meaning. This means that IS can never become a purely technical field. Work within it—whether practice or research—will always exhibit a steady symbiotic interaction between human aspects and technical support.

This chapter argues that the human/social/technical interaction can be conceptualized and helped by Soft Systems Thinking and Soft Systems Methodology. The latter embodies a view of the nature of human groups: not only organizations, but also less formal groups such as teams, task forces, or groups of friends. It sees them as information‐creating‐and‐exchanging groups who create an ongoing story or narrative which makes sense of them and their unfolding history. This has been expressed, as below, for a group of family doctors working in a medical practice, but it applies equally to all organizations and reasonably stable human groups:

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Soft Systems Thinking and Soft Systems Methodology offer a means of uncovering the narratives which sustain human groups, and hence enable us to grasp their (changing) ideas about their relevant purposeful action and the information support it will require. This makes both approaches highly relevant to the related human, social, and technical issues which characterize, and will continue to characterize, the IS field.

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Re-energising the way we manage change in healthcare: the case for soft systems methodology and its application to evidence-based practice

• Hanna Augustsson   ORCID: orcid.org/0000-0001-6203-0676 1 ,
• Kate Churruca 1 &
• Jeffrey Braithwaite 1  

BMC Health Services Research volume  19 , Article number:  666 ( 2019 ) Cite this article

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Updating, improving and spreading the evidence base for healthcare practices has proven to be a challenge of considerable magnitude – a wicked, multi-dimensional problem. There are many interlinked factors which determine how, why and whether any particular implementation effort or intervention succeeds. Soft Systems Methodology (SSM), strongly grounded in systems ideas and complexity science, offers a structured, yet flexible process for dealing with situations that are perceived as problematical and in need of improvement. The aim of this paper is to propose the use of SSM for managing change in healthcare by way of addressing some of the complexities. The aim is further to illustrate examples of how SSM has been used in healthcare and discuss the features of the methodology that we believe can be harnessed to improve healthcare.

SSM is particularly suited for tackling real world problems that are difficult to define and where stakeholders may have divergent views on the situation and the objectives of change. SSM engages stakeholders in a learning cycle including: finding out about the problematical situation, i.e. the context in which the problem exists, by developing a rich picture of the situation; defining it by developing conceptual models and comparing these with the real world; taking action to improve it by deciding on desirable and feasible improvements; and implementing these in an iterative manner. Although SSM has been widely used in other sectors, it has not been extensively used in healthcare. We make the case for applying SSM to implementation and improvement endeavours in healthcare using the example of getting clinicians at the hospital level to use evidence-based guidelines.

Applying SSM means taking account of the multi-dimensional nature of care settings, and dealing with entrenched and unique contexts, cultures and socio-political ecosystems – precisely those that manifest in healthcare. There are gains to be made in appreciating complexity and facilitating contextualization of interventions, and by approaching improvements in an iterative learning cycle.

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Healthcare organisations are required to continuously update their practices to ensure that the best available care is provided to patients. However, the gap between research evidence on effective practices and practice itself is well known [ 1 , 2 , 3 , 4 ]. This signals a core problem: that it is notoriously difficult to update, improve and spread the evidence base for healthcare practices [ 5 ].

The success of implementation and improvement efforts depends on a myriad of factors related to the intervention itself, the process by which the intervention is being implemented, and the context in which the intervention is situated [ 2 , 6 ]. However, the complexity of implementing interventions does not stop with the involvement of multifarious factors at different levels. The contextual factors that matter are likely to differ between settings as well as between interventions [ 7 ]. Making things even more difficult, these factors are interlinked and affect each other, often in unpredictable ways [ 8 ]. From this follows that every intervention, even a seemingly straight forward one, influences the overall system in which it is implemented, and the overall system influences every intervention [ 9 ]. Despite this, influencing factors are often assessed individually, assuming a linear relationship between them and the outcomes, and ignoring possible interactions between factors [ 8 ].

The intense interconnectedness of factors influencing improvement interventions calls for simultaneous consideration of all parts of the system when attempting to implement improvements—in contrast to studying or intervening in isolated parts of the system [ 10 ]. Thus, problems must be considered as they exist in the ‘real world’ [ 11 ]. In short, context matters [ 2 , 6 , 12 , 13 , 14 ], and rich, multi-faceted, structured approaches are therefore necessary for successful improvement efforts. The goal of this paper is to propose the use of SSM for managing change in healthcare by way of addressing some of these complexities. We use the example of getting clinicians at hospital level to use evidence-based guidelines, a common issue in healthcare that has proven to be challenging and largely influenced by contextual factors [ 15 , 16 ]. An additional aim is to present some illustrative examples of how SSM has been used in healthcare and to discuss the features of the methodology that we believe can be harnessed to improve healthcare.

Soft systems methodology - a systems approach to improvement

A systems perspective assumes that systems are wholes, not readily decomposable, comprised of interdependent components with flexible, porous boundaries. The interacting components (artefacts, buildings, equipment, individuals and groups) combine in unanticipated ways over time, behaving and interacting dynamically [ 11 ]. Designed to encapsulate such complex stochastics, Checkland and colleagues [ 17 , 18 ] developed Soft Systems Methodology (SSM). SSM is based on systems ideas and is described as a structured, yet flexible, process for dealing with situations that are perceived as problematical and in need of actions to improve the situation [ 19 ]. SSM has a broad application but may be particularly suited to messy problems where the problem situation is hard to define, where stakeholders have divergent views about the situation and the objective of change [ 19 ] and when attempts to improve things have failed [ 20 ]. SSM is a comprehensive methodology and has a number of concepts and tools developed explicitly for it. Table  1 provides an overview and descriptions of these.

A fundamental idea behind SSM is that the process of inquiry into the complexity of the ‘real world’ can be simulated as a learning process. The learning process goes from finding out about the problematical situation to defining it, and taking action to improve it. ‘Real world’ in SSM language refers to the unfolding and interacting flux of events and ideas experienced as everyday life [ 21 ] and this is distinguished from the system thinking world in which conceptual models to learn about the ‘real world’ and how to improve the situation are created. An important aspect of SSM is that it recognizes peoples’ diverging underlying assumptions about the world, i.e. their disparate worldviews. These different worldviews affect their understanding of the problematical situation and potential solutions. Thus, any one-size-fits-all solution, or even a uni-dimensional view of what the problem is, will never approximate the complexity of the real world. For SSM, individuals will always try to act purposefully but will proceed from their own perspective and thus will behave differently from other actors [ 18 , 19 ].

SSM invites relevant stakeholders in a given context to participate in the process of improvement, taking account of their differing perspectives. This, in turn, has the effect of engaging them in change processes and moving towards a model predicated on continual improvement rather than treating stakeholders as the implementation arm of a change project, the subjects in an intervention, or barriers to, or resistors of, change. The SSM learning and change management approach is well defined by Checkland and colleagues (e.g. [ 18 , 19 ]), but we summarise it here into the four activities of the SSM process:

Finding out about the problem, including culturally and politically

As a first step, the focus should be on understanding the problematical situation, i.e. the circumstances in which the problem may exist, rather than the problem itself. The SSM process starts with at least one stakeholder perceiving that things could be better than they are or that there is some perceived problem requiring attention. This does not necessarily mean that all relevant stakeholders perceive it to be a problem or perceive the situation in the same way. Thus, in SSM it is important to gain perspectives from different stakeholder groups, e.g. different clinical staff groups, managers and patient representatives.

Different methods and information sources may be used to gain an understanding about the situation. More interactive methods such as focus groups or workshops can facilitate the creation of a common understanding about the situation and the objective of change. However, focus groups, especially if performed with mixed stakeholder groups, pose a challenge when it comes to power structures. Groups of members with differing levels of power (e.g. care providers and patients) imply the risk of individuals with lower levels of power participating to a lesser extent [ 22 ]. A basis of this activity, and in SSM in general, is that no perspective is more or less important and that minority opinions or opinions not in agreement with the official line should not be disregarded. Also, participation from all relevant stakeholders should be facilitated and encouraged. Thus, attention needs to be paid to the power structures in the SSM process just as in focus groups. This requires a skilled facilitator and sometimes other methods to collect stakeholders’ views about the situation, e.g. interviews, may be better even if this decreases the possibilities for debate.

In addition to eliciting the perspectives from different stakeholders, it is also important to investigate different perspectives of the problematical situation. This involves analyses of: 1. the intervention, including the actors involved, 2. the socio-cultural context including roles, norms and values and 3. existing power structures.

This activity helps to define the problematical situation, allowing different perspectives to be considered. The gathered information, e.g. from interviews, focus groups and documents, is used to develop a rich picture, which describes the problematical situation in drawings or diagrams and helps to elucidate the links between different factors, processes and structures. We have come to think of this as the “a picture tells a thousand words” activity (Fig.  1 : Example of a rich picture).

Example of a rich picture. Legend: The picture illustrates the interlinked relationships influencing implementation of evidence-based guidelines in a hospital. The picture is based on Figure 3.2 in Greenhalgh [ 10 ] and the authors’ own experience in implementation science. N.B. all conceptualisations are a simplification of the real world and we do not claim that all potentially important factors are illustrated in the picture

Formulating relevant purposeful activity models (PAMs)

This activity involves creating a conceptual model of one or more aspects of the problematical situation outlining a set of purposeful activities relevant to the situation. A model can only be based on a single declared worldview and thereby represents one way of looking at a complex reality. The model is not intended to be a perfect model to be implemented but used as a basis for discussion and learning about the problem situation and potential ways to improve it. This, we label the “simulation-modelling of the world” activity.

SSM theory articulates several tools for use by its adherents (see Table  1 for terms) in order to facilitate the formulation of PAMs. One such tool is the root definition, which is a statement describing the activity system to be modelled. Formulation of root definitions can be helped by using the PQR formula which answers the questions: what should be done (P), how should it be done (Q) and why should it be done (R). In SSM language, the task here is to do P by Q in order to achieve R.

A PAM, and the learning and discussions based on the model, should include a specific set of information in order to be comprehensive enough to guide further work. This is facilitated by another tool in SSM, summarised by the CATWOE mnemonic. The C stands for customer (e.g. in our case, this might typically be patients) and represents the group of beneficiaries, or victims, who are affected by the system’s activities. The A is for the actors (e.g. healthcare professionals) that are responsible for carrying out the main activities of the system, i.e. to make the envisaged change. The T depicts for transformation and represents the process by which inputs are converted to outputs. The W stands for worldview and represents key stakeholders’ underlying assumptions about why the transformation is important. The O is the owner of the system and includes people and roles that can change or stop the transformation process (e.g. healthcare professionals, administrators, policymakers) and the E represents the environmental constraints and enablers , i.e. contextual factors, that influence the PAM. It is worth noting that people and roles can fall into more than one group.

It is useful to think about how to assess the outcomes of the PAMs and formulate criteria for efficacy, efficiency and effectiveness (the three Es in SSM language). This helps to guide continuous monitoring of the progress of an intervention which in turn provides information enabling relevant control actions to be taken to improve the system activities and the outcomes. Altogether, the information gained from developing the root definition, PQR, CATWOE and the three E’s (Table  2 : An illustrative example of the application of SSM tools) is used to create a relevant PAM (Fig.  2 : An illustrative example of a purposeful activity model).

An illustrative example of a PAM. Legend: The PAM outlines a generic process for implementation of evidence-based guidelines into practice in a hospital setting

Debating the situation, using the models

In this third activity, the information gained from developing the rich picture together with the PAM is used to organise a discussion about potential improvements. The simulated model of the world helps illuminate differences between the way the stakeholders are constructing the world (the PAM), and the problem situation, which enables the questions that will ultimately lead to change. The simulated model should not be viewed as a perfect model but simply as a device to structure discussion about improvements. The focus should be on both:

changes which would improve the current situation that are both desirable and culturally feasible

accommodations between conflicting interests amongst stakeholders which will enable improvements to be made

The aim of this third activity is to find changes that can lead to improvements and that are contextually and culturally feasible in the specific situation. It also aims to acknowledge the conflicting views in health care – doctors, nurses, allied health practitioners, managers, patients and policymakers all differ in their perspectives from each other, and to accommodate these divergent views. This third activity, drawing on the idea from resilient health care [ 23 ], we name the “bridging the world-as-imagined so it is in line with the world-as-done” activity.

Taking action to bring about improvement

This activity involves identifying opportunities for improvement based on the previous activities. It then proceeds to testing changes as a basis for further learning amongst stakeholders involved in the change.

The testing is done iteratively to challenge and adapt the improvement intervention. This iterative testing is facilitated through monitoring of progress and by taking control actions based on this. We call this the “change-in-context, realised” stage.

In SSM thinking, the process does not stop when the fourth activity is “completed”—because there is no such thing as being finished in a complex system such as that which delivers care to patients. SSM is a continuous learning process and since services and organisations are under continuous development and variables are in constant motion, problem-solving processes and improvement efforts must be flexible and accommodating to real world fluidity and dynamism Fig.  3 . Illustrates a generic SSM learning cycle with all four activities outlined.

A generic SSM learning cycle. Legend: Source: Checkland and Poulter [ 19 ]. Permission granted by John Wiley and Sons for use of this image. Licence number: 4390591134436

Application of SSM in healthcare

SSM has been used in a range of different fields [ 24 ]. However, it has lagged in healthcare, for reasons that are not completely clear. We found 871 articles on SSM in the multidisciplinary database, Scopus, but only 21 empirical studies conducted in healthcare in PubMed, the health and medical database.

The identified papers show that when SSM has been applied in healthcare, it has been used as a structured way of analysing problematical situations alone (e.g. [ 25 ]), for a combination of problem analysis and suggestion for and/or development of improvement interventions (e.g. [ 26 , 27 , 28 , 29 , 30 ]) and policies (e.g. [ 31 , 32 ]), as well as for the evaluation of interventions (e.g. [ 33 , 34 ]). When it has been used, it has been applied in several different healthcare settings including: acute care, community care, child and adolescent care, emergency care, mental health, and palliative care. Table  3 provides some specific examples of how SSM has been used for healthcare improvements. The identified studies illustrate SSM’s flexibility and versatility—it can be useful for a range of different problems in healthcare as well as in a range of different healthcare contexts. Furthermore, the examples show that SSM has been applied in different ways, e.g. using different data collection methods and SSM tools and involving stakeholder groups to a varying extent.

However, the studies also highlight limitations in the empirical evidence for the use of SSM in healthcare. SSM has most often been used to structure a problem and to make recommendations for improvements but to a lesser extent to take action to improve and evaluate the outcomes from this. Of the identified studies, only three [ 29 , 30 , 36 ], mentioned implementation of the proposed improvements and of those only two presented the outcomes of the implementation in subsequent papers [ 35 , 37 ]. It seems that SSM has been considered most useful in the initial stages of an improvement process, when defining the problem situation and exploring potential solutions and less useful in the process of putting these improvement suggestions in place. Without the next step of evaluating the implementation and outcomes of the improvements it is difficult to fully assess the usefulness and effectiveness of SSM for healthcare improvement.

How can SSM be harnessed to improve healthcare?

With growing understanding of healthcare as a complex adaptive system [ 38 , 39 ] not amenable to linear, top-down change strategies [ 11 ], it is timely to revisit the potential importance, and utility, of SSM. Because of failures of the past (many change strategies fail, and many more fall short of their sponsors’ intentions) most change experts will agree that we must move towards a learning system—one that applies more multi-faceted, systems-receptive change models, and evaluates progress across time (e.g. [ 10 , 38 , 40 , 41 , 42 , 43 ]). We propose that using SSM as this structured, multifaceted approach has the potential to facilitate contextually adapted improvements in healthcare by: involving stakeholders affected by change and with expertise about the local context, facilitating contextualization of improvement interventions to the local context, taking a systems approach to assess and address the nominated situation, and by approaching improvements in an iterative learning cycle. Below we outline our proposed key principles for the use of SSM in healthcare in future.

Participation

Any successful intervention requires individuals to change behaviour in some way [ 44 ]. As Greenhalgh et al. [ 2 ] expressed it, “People are not passive recipients of innovations. Rather (and to a greater or lesser extent in different persons), they seek innovations, experiment with them, evaluate them, find (or fail to find) meaning in them, develop feelings (positive or negative) about them, challenge them, worry about them, complain about them, “work around” them, gain experience with them, modify them to fit particular tasks, and try to improve or redesign them—often through dialogue with other users”.

This means that the individuals involved can make or break an intervention and that it is vital to include them in the process. We must not treat them as subjects but participants. This is in line with the emergence of partnership research [ 45 ] and models of collaboration, and co-production of knowledge in healthcare which emphasise that knowledge is generated within its context of use [ 46 , 47 , 48 ]. A core component of SSM is that it proposes a collaborative approach to problem solving and change management. It explicitly seeks to collect different views of a problematical situation (activity 1), as well as involving stakeholders in improving the situation (activities 2–4). This helps to highlight varying views of the situation, the dimensions of the intervention, and to take different perspectives into consideration. By highlighting individuals’ beliefs, perceptions and attitudes, levels of readiness for change can be detected and addressed to improve the likelihood of successful outcomes [ 49 , 50 ].

Contextualization and taking a systems approach

In the case we make above, context matters, and an intervention that is adapted to fit the local circumstances is more likely to be successful and sustained [ 2 , 40 , 51 , 52 ]. Thus, there are good reasons to consider how improvement interventions could be contextualized. SSM facilitates this in two ways. First, the participatory approach involves different stakeholders with unique context knowledge who use this knowledge to analyse the problematical situation and contribute to change management. Second, the systems thinking associated with SSM implies that the whole system is taken into consideration rather than looking at individual components in isolation. This facilitates alignment between different parts of the systems and decreases the risk of making changes that have unintended and unwanted consequences for other parts of the system. Similarly, it can help to illuminate the processes and systems that are already in place and working, in order to take advantage of these when making improvements, e.g. by linking the improvements to these processes and systems [ 42 ].

Continuous adaptations and learning

The dynamic and changing nature of healthcare organisations and the context in which they subsist necessitate continuous adaptation and refinement of interventions [ 40 ]. Yet another argument for continuous adaptation is that it is often impossible to take every potential problem and influencing factor into consideration prior to implementation. This calls for a move away from the traditional methods of evaluating interventions where processes and outcomes are evaluated months or years after initial implementation, towards the use of rapid feedback loops to assess intervention progress [ 40 , 41 , 42 ]. SSM addresses this by engaging participants in an iterative process of assessing their local context, making improvements and then doing things again. Within the SSM paradigm, the learning process is continuously monitored to assess progress and problems so that relevant control actions can be taken to refine or change the implementation and the intervention.

The process of SSM also has the potential to facilitate organisational learning. By involving different stakeholders, knowledge sharing and knowledge creation as well as the development of shared meaning and understanding across individuals and groups are enabled [ 53 , 54 ]. The involvement of organisational members in analysing, developing and testing improvements can facilitate a culture that supports experimentation, where people are comfortable with questioning current practices and encouraged to explore new ideas and innovations [ 53 ]. Finally, by engaging stakeholders in the improvement process they learn about how to use a structured approach to making improvements, which can be applied in future improvement efforts.

SSM in relation to other change management and implementation approaches

SSM entails both similar and unique features when compared with other approaches to organisational improvement. One example is the investigation of the context in which the problem situation is located, an important first activity in SSM. In this sense it is similar to implementation determinant frameworks (e.g. [ 2 , 6 ]) and process models (e.g. [ 55 ]). However, these approaches generally provide guidance, e.g. in the form of lists, for what factors may be important and should be assessed, which is not specified in SSM. SSM on the other hand uses pictures or diagrams to explore the context so that links between different parts can be identified. This may help avoid seeing influencing factors and parts of the system as separate from each other.

SSM also has similarities to other approaches when it comes to managing change in an iterative learning cycle. For instance, Plan-Do-Study-Act [ 56 ], Dynamic Sustainability Framework [ 40 ] and Normalization Process Theory [ 57 ] all entail this component and few scholars or practitioners dealing with change in healthcare, believe that it is a straightforward process. What distinguishes SSM is that it uses system thinking to create models that can be used to learn about the situation in need of improvement and helps to explore and decide on feasible and desirable changes.

Another difference is that while implementation approaches are focused on describing or guiding the implementation process, understanding influences of implementation and evaluating implementation [ 8 ], SSM is more focused on the problem structuring. As such, SSM may be especially suited for ill-defined problems and can help assist in defining the intervention to be implemented and therefore contribute to the step before actual implementation. Thus, it may be used to complement implementation approaches.

Limitations

We have argued that SSM can be used to engage stakeholders in a collaborative process of making contextualized improvements and have outlined key principles for this. As to limitations, while SSM involves aspects that are important for implementation, e.g. participation, consideration of contextual factors and continuous evaluation [ 6 ], it provides little guidance for how to perform the last step, i.e. taking action to improve except for making improvements in an iterative way. This may be one reason why the identified studies mainly applied SSM as a way to structure problems and come up with suggestions for improvements and to a much lesser extent for implementation of the improvement actions. Another limitation is the relatively low number of empirical studies which makes it challenging to draw conclusions about the impact of SSM in healthcare.

The technicalities of SSM can make it difficult to appreciate and apply, especially for people who are not used to systems modelling or SSM language. Application often requires facilitation by an SSM expert (from inside or outside of the organization) who is familiar with the process and SSM tools and mechanisms [ 58 , 59 ]. Thus, SSM application will often require experience or technical support. Furthermore, since it is a participatory approach it requires the organisation and the individuals in it to be invested in the process for it to be worthwhile. To ensure support and build trust and understanding with involved practitioners it is important to secure allocated time, arenas for interactions as well as skills in project management and communication [ 60 ]. Finally, we do not provide a detailed guide for how to use SSM. For this we refer to the books by Checkland and colleagues on the topic (e.g. [ 19 ]).

Complex systems like healthcare require multi-faceted solutions. The time for attempting change via unsophisticated, linear, top-down means in complex health settings is surely over. We have put forward the case for using SSM to re-energise the way we manage change in healthcare and highlighted participation, contextualization, taking a systems approach, factoring in complexity thinking, and embracing continuous adaptation and learning as key principles for change which can be facilitated by applying SSM logic, tools and approaches.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

Customer, actor, transformation, worldview, owner, environmental constraints and enablers

Purposeful activity model

What, how, why

Soft Systems Methodology

Efficacy, efficiency and effectiveness

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Acknowledgements

The authors would like to thank Claire Boyling and Meagan Warwick for assistance with formatting the manuscript.

This work is supported by funding by the National Health and Medical Research Council Partnership Centre Grant in Health System Sustainability (ID:9100002) and other funding (CI Braithwaite). The funder had no role in the design and conduct of the study, including the collection, analysis and interpretation of the data or the writing of the manuscript.

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Encyclopedia of Operations Research and Management Science pp 766–770 Cite as

Soft systems methodology

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INTRODUCTION

Soft Systems Methodology (SSM) is an approach to tackling the kind of problematical situations with which managers of all kinds and at all levels have to deal in their professional lives. As its name implies, SSM is based upon systems ideas, and “a systems approach,” but not in the conventional sense in which those phrases are usually used. Normally those words imply taking parts of the real world to be systems, and improving the performance of those systems in meeting their declared objectives. This is the approach found, for example, in systems engineering, systems analysis and classical OR. SSM was developed in the 1970s and 80s in management situations in which objectives were themselves problematical and the engineering/optimizing of the approaches developed in the 1950s and 1960 could not be used unchanged. It is thus complementary to the earlier methods.

SSM was developed in a program of action research in the kinds of real-world situations which are often referred...

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8 Soft systems methodology

Learning Outcomes

• Contextualise systems as a ‘Wholistic’ project management method approach.
• Compose the requirements for a Systems Lens application.
• Formulate Soft Systems Methodology frameworks.

This module will explore a systems approach to integrating all the different components within the project environment, to create a comprehensive approach to solving the problem.

Broadly speaking, a systems approach is used to create an understanding of the interrelationships between different components within the environment, the project, and the stakeholders. Through a generalisation of the different components, the project team is better able to understand the interdependent nature of the factors (Cleland 1997; Meredith and Mantel 2011; Kerzner 2017). Additionally, the systems approach allows the project team to understand the situation in its entirety, including resources, materials, market conditions, organisational needs, stakeholders and so forth. By understanding these factors, the project is better able to meet the project objectives and keep the end-state in mind throughout, to ensure that the approach is the most efficient and effective process possible.

This is a disciplined way to view the environment and identify potential solutions to problems while being open to opportunities. These opportunities can be realised through understanding that everything is related to everything else in the environment or organisation.

A system is a composition of numerous related and dependent components which, through interactions with one another, create a whole. Therefore, a system is a compilation of distinct factors or components which form a complex whole. Although this definition is general, a key element of a system is how the collection of factors or components come together to produce an outcome (INCOSE 2015). This outcome is not attainable by the individual elements – an outcome can only be created  through the interactions between and across the components and factors.

By applying a systems approach to project management, the view of the project changes from a set of tasks and activities to a combination of sub-systems which work together to make a broader system (Cleland 1997; Meredith and Mantel 2011; Kerzner 2017). The broader system’s effectiveness and performance is impacted by the corresponding performance of the sub-systems of which it is comprised. Therefore, by viewing the project management process as a system which operates as an entity comprised of sub-systems, project managers can identify areas within the project which could lead to success or failure. However, the sub-systems which comprise the project are not limited to internal factors within the organisation – external components or factors play a significant role within the systems approach.

Through a systems approach, a project manager, project team and the broader project organisation are empowered to consider the impacts of the environment when implementing changes or projects. The context surrounding the project should be established at the outset as this will provide a viewpoint of the system. This viewpoint will support decision-making throughout the project, encourage realignment of resources as needed and trigger changes in response to the environment.

Considerations

Before a project manager considers applying a systems approach (Cleland 1997; Meredith and Mantel 2011; Kerzner 2017), there are several components which need to be considered:

• How all tasks, activities, processes, and deliverables within the project depend on one another needs to be documented. However, consideration is needed to understand the properties of the individual components outside of their dependencies.
• Project goals need to be clear; each component of the project should be working towards those goals.
• Resources supporting the project should be consistent throughout. Where additional resources are required, the impacts on the outcome need to be considered. This includes impact on quality, scope, budget, and schedule.
• Uncertainty is expected within a project. Consideration is required to provide support in managing and responding to uncertainty as it arises (for example, risk and issues management processes).
• Resources should be allocated roles and responsibilities based on their skills and experience. These resources can work together as part of a sub-project team, to support the development of different deliverables. For each deliverable, a different approach may be required to manage the needs and complexities.
• Visualisation can be used to support documenting the complexities.

Through a systems approach, project managers are supported to ensure they are aiming for the project’s goals and objectives.

Let’s watch the following video by Systems Innovations which explains the primary differences between analytical methods of reasoning and systems thinking

Video [5 mins,  41 sec]   Note: Closed captions are available by selecting the CC button in the video below.

How to apply a systems approach

In addition to using traditional project management methodologies, the systems approach can be used to effectively manage a project. Based on systems theory, there are 4 primary tools and principles which can be applied from the Systems Thinking Iceberg, recreated in Figure 28.

Figure 28. Systems Thinking Iceberg, by Carmen Reaiche and Samantha Papavasiliou, licensed under CC BY (Attribution) 4.0

Based on Figure 28, below are 4 principles and tools which can be applied to projects to support the systems approach to project management (Cleland 1997; Meredith and Mantel 2011; Kerzner 2017).

•  a detailed problem statement
•  triggers, causes and side-effects
•  the reactions of the different stakeholders
•  links between problems and solutions previously attempted.
•  when it occurs (frequency)
•  who has been impacted
•  steps taken to rectify
•  interactions between the event and other factors or events
•  identifying potential causes
•  testing potential solutions.
•  environmental elements within the system
•  causes of the behavioural patterns
•  stakeholders within the system
•  underlying interactions between stakeholders, environment, and causes.
•  what supports the underlying structure
•  the values, expectations, and beliefs within the system and broader environment
•  how the problem is understood
•  the proposed solutions and how will they be implemented and analysed.

Systems approaches can be applied through a cyclical method which considers the relationships between each component of a project phase. See Figure 29 for examples.

Figure 29.  Examples of the cyclical approach that can be used to support systems approaches to project management, by Carmen Reaiche and Samantha Papavasiliou, licensed under CC BY (Attribution) 4.0

By applying the systems approach, organisations can understand the interactions between different areas, documents, and tasks and activities. By using a systems approach:

• Project managers are able to realise the need for a holistic approach to prepare, plan, and implement a project.
• The multidimensional components which have an impact on the outcomes of a project (for example, technological, financial, resources, cultural, etc.) can be documented.
• Project managers can understand how different dimensions or structural components will influence the stakeholders and their expectations, and how the market and environment can change swiftly and significantly. This is commonly in response to economic factors, ecological issues, stakeholder values, news cycles and so forth.
• The end-to-end interactions between tasks, activities, resources, stakeholders and so on, are considered and work together to reach the common goals and objectives of the broader system (or the project).

Therefore, when the systems approach is applied to a project, project managers are better able to respond to the conditions outside of their control, and create efficiencies within their projects boundaries to maximise outcomes.

Soft Systems Method

Soft Systems Methodology (SSM) is an approach which is used to create structure in complex problems and develop changes which are both feasible for and wanted by all the stakeholders. These stakeholders include internal stakeholders (employees, developers) and external stakeholders (users, clients, competitors). As a result, everyone provides different insights into and solutions to solve a problem (Checkland and Scholes 1990; Checkland 2000; Checkland and Poulter 2006).

To support the understanding of SSM, a soft system can be defined as a human activity system (HAS). This HAS is purposeful and organised in that groups of people work collectively to achieve a purpose or outcome.

SSM was designed to allow each heterogeneous group of stakeholders the opportunity to provide their insights into the problem. Each group or stakeholder can document the problem in their own way and provide their insights into feasible or desirable outcomes or solutions (Checkland 2000).

Through collaboration, a solution can be created that is agreed upon by all stakeholders. It supports quicker decision-making through consensus. The approach is used to show the links between the real world and the considerations and components documented within the systems world.

The 7 steps to SSM

There are 7 steps to SSM (see Figure 30). These steps are not necessarily carried out in linear order and some steps may not need to be completed. These steps should be used to support collaboration, decision-making and problem-solving.

Figure 30. SSM 7 steps, by Carmen Reaiche and Samantha Papavasiliou, licensed under CC BY (Attribution) 4.0

Step 1. Identify the problem situation  

This step involves gathering relevant information to understand the problem situation. There are several tools which can be used to support information gathering (Checkland and Scholes 1990; Checkland 2000; Checkland and Poulter 2006), including:

•  interviews
•  brainstorming sessions
•  historical and current data
•  news articles
•  document analysis
•  organisational structure
•  control policies
•  observation sessions.

Through the information gathered, analysis should support understanding the possible components and factors which could influence or impact the problem situation.

Let’s go through the rest of the steps using a sample organisation: Lugano. Lugano is a financial firm that offers digital services to clients. This organisation is experiencing decreased overall use of digital services and significant increases in the need for support provided by frontline employees by telephone. It is unclear what is causing this increased need for support. Information is gathered via employee and user feedback, data and document analysis. Lugano will  be used as an example in the following step.

Step 2. Describe the problem situation

From Step 1, the analyst has sufficient information to understand the problem space and document the situation through pictures or diagrams. The tool recommended in SSM is the rich picture diagram (Checkland and Scholes 1990; Checkland 2000; Checkland and Poulter 2006). This diagram outlines the problem situation using a graphical representation of the different relationships, communication mechanisms, processes, structure, people, concerns, conflict, and climate. A rich picture can incorporate images, text, symbols, and icons.

Figure 31 provides an example of part of a rich picture. This example highlights Lugano’s relationships between the digital services provided to users, and the support mechanisms in place to provide guidance when needed. The problem situation Lugano is the increased requirement for support and the decreased use of digital services. The problems highlighted in Lugano’s example include the need for skills development and training for users, accuracy and relevance of information and records provided, and access to services.

Figure 31.  Example rich picture from a digital service offering perspective, by Carmen Reaiche and Samantha Papavasiliou, licensed under CC BY (Attribution) 4.0

Step 3. Develop key definitions

Once the rich picture has been created, the next step is to determine the best way for the system to function. This process starts with creating root definitions which provide an ideal view of the key systems and structures (Checkland and Scholes 1990; Checkland 2000; Checkland and Poulter 2006). This commonly follows the CATWOE elements (Checkland and Scholes 1990; Checkland 2000; Checkland and Poulter 2006). Using the sample organisation:

Customers: Who are Lugano’s clients, and the users, stakeholders, and key players within a system?

Actors: Who are the employees within the organisation who support the transformation process?

Transformation: Which process will be transformed by Lugano, specifically considering what the output is and how the problem will be solved?

Worldview/Weltanschauung: What is the bigger picture or the environmental view of the situation, specifically the stakeholders within the environment who can influence the transformation?

Owners: Who within Lugano can make the changes or has the power to approve the start and end of the project or transformation?

Environmental constraints: What are the elements within the environment which influence Lugano and have the capacity to impact the system negatively, and how should they be managed?

CATWOE supports the creation of the root definition, which is defined as the representation of the problem situation to be addressed. Therefore, a root definition is defined as a statement which concisely and clearly describes the system of interest (or under review). It commonly starts with a single sentence that begins with ‘A system to’ followed by ‘all key elements of the system’.

Table 7. A CATWOE example using Lugano

Table 8. A root definition example using Lugano

Tables 7 and 8 provide an example of CATWOE and creating the root definition for the digital service example. This example shows the key players and the aim of the transformation within the root definition. Through this approach, the problem became clearer, and the system of interest became the digital service and surrounding environment.

When applying SSM its important to understand the transformation component correctly, especially in relation to inputs and outputs (Checkland and Scholes 1990; Checkland 2000; Checkland and Poulter 2006). This is outlined in Figure 32, which shows that Input (I) should support the transformation and lead to the Output (O). A common mistake is incorrectly identifying the system input (the entity change) with the resources required to implement the change.

Figure 32. Inputs create transformation which leads to outputs, by Carmen Reaiche and Samantha Papavasiliou, licensed under CC BY (Attribution) 4.0

Forbes and Checkland (1987) provided some definitions and rules to support the documentation of the transformation:

•  (T) transforms the Input (I) into Outputs (O).
•  The input must be present in the output; however, it will be in a different or changed state.
•  An abstract (intangible) input will create an abstract (intangible) outcome.
•  A tangible (concrete) input will create a tangible (concrete) output.

Step 4: Create conceptual models

This step requires creating a conceptual model which is used to analyse the activities which need to occur to undertake the transformation. The activities outlined should only be based on actions taken by actors (internal to the organisation). These activities need to link back to the root definition and be limited to a project group to control (Wilson 2001). All activities need to achieve the objectives of the transformation, and activities must include monitoring the transformation and providing feedback. It should consider what is meant by success, how it is measured and who will measure it.

The key activities required for the digital services example include:

• Determine what factors influence digital service use.
• Assess actions required to improve these.
• Take action.
• Measure behavioural change.
• Measure impact of change on the environment.
• Report results.
• Monitor and manage the system performance, recommend improvements.

Figure 33. Example draft of the digital services conceptual map, by Carmen Reaiche and Samantha Papavasiliou, licensed under CC BY (Attribution) 4.0

As outlined in Figure 33, there are clear operational activities which need to be taken to activate the transformation. Each activity should be monitored to ensure it is easy to follow and that there is a clear process in place. The conceptual model outlined in Figure 33 is in draft state – it shows a starting point for developing a complete model.

Within a conceptual model, Forbes and Checkland (1987) recommended:

•  having 7+/-2 activities of the same size
•  describing each activity using a verb
•  using arrows to show logical dependencies
•  numbering activities to reaffirm the dependencies.

Conceptual models are made to document HAS, which are softer models (Tavella and Hjortso 2012). This is because it is difficult for human behaviour to repeat and reproduce the same actions repeatedly with the same results. Therefore, there is an innate variability in the human activities and performances outlined within the conceptual models. These still require monitoring and controlling to support the transformation and ensure that changes are made as required. The overarching structure of a HAS is outlined in Figure 34, and this approach can be used to support improvements to the conceptual model. This calls out the operational system within the organisation’s control (operational subsystem) and the elements which occur outside of the direct control of the organisation, this being the response to the implemented change. These are tracked and monitored and as changes are required, they are implemented.

Figure 34. HAS overarching structure, by Carmen Reaiche and Samantha Papavasiliou, licensed under CC BY (Attribution) 4.0

This monitoring and controlling process should follow the 3Es: effective, efficient, efficacy (Wilson 2001; Checkland 2000). When planning, the transformation needs to consider:

•   Effective : Is the system acting in the way it should be? Does the system contribute to the broader organisational goals?
•   Efficient : Does the system use the least number of resources? Does it use the resources appropriately?
•   Efficacy : Does the system provide the expected results?

Using the 3Es, a project manager is better equipped to determine what level of monitoring and controlling is required and how it could be completed.

Another critical component of a conceptual model is the use of feedback loops (Checkland 2000; Wilson 2001). Within conceptual models, there are commonly two forms:

•  Internal feedback loop. This loop highlights how the actors (or the individual completing the work) need to alter how they work to meet the transformation.
•  External feedback loop. This loop looks at the links between the inputs and the outputs, specifically interested in how the system is performing.

Therefore, an effective project manager needs to clearly define their success measures for the transformation and ensure that they are built into the system.

Step 5. Compare conceptual models to reality

Conceptual models are developed through applying theory; however, they are not necessarily representative of reality. Therefore, Step 5 requires an understanding of how much these models reflect the real world (Checkland 2000; Wilson 2001). This requires an analysis of the gaps, to determine whether the provided solution will meet the needs. This analysis is required to understand:

•  conceptual model activities
•  the real world
•  what can be completed.

Table 9 is an example of the analysis for the digital services transformation, using 3 columns based on the above analysis questions.

Table 9. Example conceptual model vs. real world comparison (digital services example)

Step 6. Assess feasibility and define changes

Based on the results of Step 5, a feasibility assessment is required of the suggested changes (Checkland 2000; Wilson 2001). The changes are normally classified as a change in:

•  procedures and processes
•  attitudes or behaviours.

This requires an analysis of 3 primary elements: feasibility, priorities and risk analysis.

Feasibility

Feasibility requires understanding how the different activities will be undertaken. A feasibility analysis will need to consider whether something is achievable (Checkland and Scholes 1990; Checkland 2000; Checkland and Poulter 2006), based on:

•  Cultural feasibility: Will the employees or actors involved be able to complete the work?
• Technical feasibility: What is the required support or modern technology required to implement the change?
•  Dependencies: Are there links between the organisational and technological systems? What order do updates need to go in?
•  Win-Win: Do the recommended changes make it easier for the organisation, employees, and clients?

This is a vital component; the changes need to be prioritised based on what impact they will have on the desired transformation, what risks they pose and how difficult they will be to implement. This can follow Kaplan and Norton’s (1993) balanced scorecard approach – an example of factors is outlined in Figure 35.

Figure 35. Example of the balanced scorecard for the digital services example, by Carmen Reaiche and Samantha Papavasiliou, licensed under CC BY (Attribution) 4.0

According to Kaplan and Norton (1993) there are 4 primary elements within the balanced scorecard and successful organisations, projects, and transformation find a balance between each of these components.  Each component provides a different view of the organisation to operate efficiently and effectively. These components are:

• Financial perspective: outlines the different cost measures involved in the organisation, project and or change.
• Client perspective: outlines how client satisfaction, retention and market share will be measured and improved.
• Internal processes perspective: outlines what the change will cost and how it will impact the quality of the internal business processes.
• Innovative perspective: outlines measures of employee satisfaction, knowledge management, improvement rates and number or percentage of employees included in the improvement.

These 4 components or perspectives are interlinked – they do not function in isolation. Using the scorecard approach, the factors within the perspectives need to consider:

•  objectives: organisational objectives and strategies
•  measures: following the objectives, how you will measure progress
•  targets: what is the objective aiming to achieve
•  initiatives: actions taken to meet the objectives.

Risk analysis

The third tool to support feasibility assessment is the completion of a risk assessment. Risk analysis is the process which determines the likelihood and impact of a risk occurring. The assessment considers how the risk will impact the project schedule, quality, budget, and scope. The analysis technique recommended in SSM is the risk analysis matrix.

The risk analysis matrix assesses the likelihood of a risk occurring and the overall severity if it were to occur. These are classified by importance and impact. Likelihood and consequence (impact) are measured as low, medium, high, or very high (Vose 2008), as shown in Figure 36.

Figure 36. Example of a risk analysis matrix, by Carmen Reaiche and Samantha Papavasiliou, licensed under CC BY (Attribution) 4.0

Each risk should be identified, analysed, and considered as part of the feasibility assessment.

To complete the assessment, the project manager should understand the potential feasibility of the changes, the priority of each change and the level of risk associated. This should be used as a guide to help determine which changes should be implemented.

Step 7. Take action to implement proposed changes

The final step is to implement the proposed and agreed upon changes (as outlined in Step 6). The implementation should follow the required steps outlined within the conceptual model (and reality analysis). Once implemented, there is a potential for the system changes to provide new opportunities and problems that require responses. As a result, the process would need to start again.

Advantages of SSM

There are several advantages to applying SSM, including:

• provides a structure for complex problems or situations
• easy to follow steps
• rigorous testing required
• encourages multiple iterations.

Disadvantages of SSM

There are several potential disadvantages to applying SSM, including:

• requires organisational change, which can be difficult to convince stakeholders of
• solutions can be narrowed down too early
• rich pictures are challenging to create, due to their lack of structure
• actions are expected quickly; however, the process can be time consuming.

In sum, SSM provides an analysis tool and technique which outlines the different requirements for the system transformation. This module outlines the 7 primary steps required to implement the methodology. SSM is a systems approach which can be used to undertake problem-solving and analysis of complex situations. Therefore, a cycle of research, learning and reflection is recommended based on the perceptions of all the stakeholders to better provide solutions for the problem space.

Test your knowledge

Key Takeaways

• A system is composed of numerous related and dependent components which, through interactions with one another, create a whole. Therefore, a system is a compilation of distinct factors or components which form a complex whole.
• By viewing the project management process as a system which operates as an entity comprised of sub-systems, project managers can identify areas within the project which could lead to success or failure.
• Systems approaches can be applied through a cyclical approach which considers the relationships between each component of a project phase.
• SSM is used to create structure in complex problem and then develop changes which are both feasible for and wanted by all the stakeholders.

Checkland P and Poulter J (2006) Learning for action: a short definitive account of soft systems methodology and its use, for practitioners, teachers, and students , John Wiley and Sons Ltd, United States.

Checkland P (2000) ‘Soft systems methodology: a thirty year retrospective’, Systems Research and Behavioral Science , 17(S1):S11–S58.

Checkland P and Scholes J (1990), Soft systems methodology in action , vol. 7, Wiley, Chichester.

Cleland DI (1997) ‘Defining a project management system’, Project Management Quarterly , 8(4):37–40.

Forbes P and Checkland PB (1987) ‘Monitoring and control in systems models’, Internal Discussion Paper 3/87, Department of Systems, University of Lancaster.

INCOSE (2015) INCOSE systems engineering handbook: a guide for system life cycle processes and activities , 4th edn, Wiley, United States.

Kaplan RS and Norton DP (1993) ‘Putting the balance scorecard to work’, Harvard Business Review Magazine , Sep/Oct, accessed 3 August 2022. https://hbr.org/1993/09/putting-the-balanced-scorecard-to-work

Kerzner H (2017) Project management: a systems approach to planning, scheduling, and controlling , 12th edn, Wiley, United States.

Meredith JR and Mantel Jr SJ (2011) Project management: a managerial approach , John Wiley & Sons.

Tavella E and Hjortsø C (2012). ‘Enhancing the design and management of a local organic food supply chain with soft systems methodology,’ International Food and Agribusiness Management Review ,  15(2): 47–68.

Vose D (2008) Risk analysis: a quantitative guide , 3rd edn, Wiley, United States.

Wilson B (2001) Soft systems methodology conceptual model building and its contribution , Wiley, United States.

Management Methods for Complex Projects Copyright © 2022 by Carmen Reaiche and Samantha Papavasiliou is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Soft systems methodology: a thirty year retrospective

• Soft systems methodology: a thirty year retrospective File type PDF File size 382.67 KB

Soft Systems Methodology (SSM) can be used to gain understanding of complex relationship drive situations and their contexts. It was developed by the author of this research paper, Peter Checkland, as a comprehensive process which tried to ensure that both whole and parts of a situation were continually honed and refined in cycles of action.

"We use systems models because our focus is on coping with the complexity in everyday life, and that complexity is always, at least in part, a complexity of interacting and overlapping relationships." (Checkland 2000 p29)

This research paper explains how SSM is used to achieve sharp definition of a situation by:

• Finding out about a problem situation, including culturally/politically;
• Formulating some relevant purposeful activity models;
• Debating the situation, using the models, seeking from that debate both
• (a) changes which would improve the situation and are regarded as both desirable and (culturally) feasible, and
• (b) the accommodations between conflicting interests which will enable action- to         improve to be taken;
• Taking action in the situation to bring about improvement. Source: Checkland 2000 p21

Specific methods mentioned

• Rich Picture Building  p22
• Building Purposeful Activity Models p 26

Checkland P (2000) Research Paper. Soft Systems Methodology: A Thirty Year Retrospective, in Systems Research and Behavioral Science Syst. Res. 17, S11–S58 

'Soft systems methodology: a thirty year retrospective' is referenced in:

• Rich pictures

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