Philosophy:Case-based evidence

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Case-based evidence is a scientific method based on the supposition that certain human behavioural patterns, also including basic attitudes and stances, and with particular reference to the acceptance of systems, technical devices and procedures, can be transferred from a series of given problems, the 'analogical sources', to another, current problem, the 'analogical target'. The term "case-based evidence“ and the procedure described in the following was first used and coined in work carried out by the Information Management Institute (IMI) of the Aschaffenburg University of Applied Sciences.[1] (Professor Georg Rainer Hofmann) in 2009.


The case-based evidence method involves a number of steps[2]. Analogies form the core of the method; the findings thus supplied comprise mechanisms that are (presumed to be) transferable from analogical sources to a current case. These mechanisms are then presented in a synoptic model and ultimately tested in a series of qualified expert interviews.

Formulating the research question

Outlining the problem can be seen as the most important fundamental factor determining the process of locating suitable analogies. Only when the research question precisely addresses the most urgent knowledge gap that is relevant for gaining acceptance will it be possible to search for and find suitable analogous cases in which this knowledge gap can be closed as exactly, precisely and appropriately as possible.

Identifying relevant analogical components

In order to find suitable analogies, it is first necessary to closely examine the case at hand, the analogical target, and the problem to be solved. This consists of locating those components that are presumed to have the greatest influence on the problem to be solved. Relevant analogical components can be found in:

  • The characteristics of the case under examination,
  • The relationships between characteristics,
  • The user group, or
  • The relationship between the user group and the components of the case.

There is as yet no known algorithm-based solution for accurately locating a feasible analogy. However, an analogy will only prove to be feasible once it is based on relevant analogical components.

Identifying analogous cases

Based on these found analogical components and the abstract formulation of the problem, a search can be made for analogous cases – the analogical sources. The search for analogous cases can be performed from two perspectives:

  • A structural analogy places the focus on detecting characteristics and structures from the outset situation in the analogy. This form of analogy is preferred for existing products and services as well as for ongoing projects. The starting point of the search is based on those factors of the analogical target that are seen as critical for acceptance. The search should therefore focus on cases that display a similarity in terms of the stated critical factors and for which the resulting, expected acceptance problems have already solved. The acceptance of a product, service or project can depend on several attributes, depending on the individual circumstances; it is therefore advisable to search for a separate analogy for each case component considered relevant to such acceptance.
  • A target analogy focuses on the goal to be attained in the given case. The search aims at finding a 'role model', i.e. it has already achieved the goal that is to be attained with the given case.[3] This goal should be described at different levels of abstraction, to allow both close and distant analogies to be addressed. This form of analogy is particularly useful in projects that are currently in their planning phase or in innovative product developments.

Research-based activities

The point in the case-based evidence process at which further research is appropriate depends on the expertise that is available ad hoc with respect to the analogical goal and the analogical sources. The aim of the research and theoretical preconsiderations is to research and document both the analogical target, which is predetermined, and all the information relevant to the analogical sources that is 'unproblematically researchable'. As for determining the extent of the research, there is no real guide value, but a pragmatic approach would be to avoid trivial questions being asked in the subsequent expert interviews, answers to which may be found by a simple query in an Internet search engine.

Drawing analogical conclusions

The components of analogical conclusions in the case-based evidence method can be described as follows:

  1. Current case – analogical target. The starting point is a given case in which the motives leading the persons involved to behave in a certain way cannot be clearly identified or are undetermined and therefore constitute the research question. Moreover, it is not clear what parameters lead to a change in behaviour, e.g. an improvement in acceptance.
  2. Comparative cases – analogical sources. Similarly structured, or isomorphic, cases that can be found either historically or currently in 'other' thematic fields and economic areas are identified. In these comparative cases, the parameters that lead to particular cognitive processes and behaviours are known (or better known).
  3. Isomorphically based conclusion. Conclusions are drawn regarding the analogical target, based on the findings from analogical sources.

The fine art consists of locating precisely these feasible analogies[4] and transferring the attitude and behaviour schemas thus identified to the problem of the current case, such as the market acceptance of an innovative IT system. The connection between the analogical source and the analogical target is admittedly not a causal one, as they are 'really' independent of each other. However, it can be observed in many examples that certain mechanisms, such as people's behavioural patterns, can be transferred from one case to another. In cognitive psychology, the ability to perceive analogies and transfer found isomorphs from analogical source to analogical target is a central process, and is even deemed an absolutely fundamental cultural achievement of mankind.[5] This circumstance is currently the subject of intensive discussion in modern popular scientific literature.[6] It should nevertheless be stated that from a scientific-theoretical point of view, the formation of analogies has no causal-methodical basis whatsoever. Here, the principle of cause and effect stands back in favour of the means-to-an-end principle.

Synoptic modelling

Synoptic modelling, according to the encyclopaedic guidelines of Jürgen Mittelstraß[7], has to satisfy the following criteria:

  1. Abstract: the model, as a restricted mapping of reality, must be 'simpler' than the sum of observations.
  2. Relevant: the model must 'have something to do' with the research object under investigation.
  3. Predictive: the model must allow statements to be made about the research object, especially and in particular about its future behaviour.
  4. Communicable: the model must be such that it can be communicated in the scientific community; it must not be 'arbitrarily incomprehensible'.

A further factor is the aspect of deficiencies in the model, such as redundancies, tautologies and contradictions. It has not gone unrecognised that synoptic modelling has a certain 'degree of creativeness'.

Series of qualified expert interviews

In a third step, to verify the evidence, the conclusions are assessed by means of structured interviews with selected experts (analogical source). Rather than questioning a large number of 'representative' people, a comparatively (or even very) small group is subjected to qualified and structured interviewing. The selection of those questioned presumes the so-called 'expert assumption'[8] and attempts to include as fully as possible the expertise to be covered. A certain degree of dismissiveness has established itself in the context of empirical findings, when empirical research is based on a small 'n', i.e. the findings are based on a small number of interviews. This is inexplicable taking into consideration the small overall number of qualified persons who can be questioned.


Case-based evidence has proven itself particularly well when it comes to the investigation of acceptance and trust in products and processes. In this area, forecasts of the probable acceptance of new products, services, processes, or similar can often be made with particular success and indications extracted from isomorphic cases as to how the probability of acceptance can be increased in particular cases. These approaches take into consideration a close cooperation with other academics – both scientists and practitioners – with regard to the following points:

  • Identification and expert analysis of isomorphic analogies and scenarios,
  • Due professional planning and appropriate psychosocial implementation of interviews,
  • Modelling in accordance with the findings of the research and interviews.

Classification: the case-based evidence method and the middle range theory

As the field of business information systems developed over the thirty-year period between around 1980 and 2010, it took on an interface function that places it between the technically based field of (core) computer science and the application-oriented field of business management. These two central questions, the one of a technical (concerning engineering design) and the other of a business management (concerning the useful value of the applications) nature, together form one of the focuses of business informatics in the German-speaking world. The method of case-based evidence is based on analogy, in contrast to learning through inductive reasoning and deductive reasoning. In business informatics, drawing inductive conclusions from observed phenomena and applying them to more general knowledge ('economic theory') is a widespread way of evaluating technical and economic systems. In turn, (predictive) deductions are made from 'theory' onto new or future phenomena. It is the subject of heated discussion ('based on scientific theory') what precise form inductive conclusions and the deduction process should have; one expression of this is that of design science research.[9] In particular, critical rationalism along the lines of Karl Popper rejects induction as an illusion and disputes the possibility of objective knowledge progress, in distinct contrast to the objective progress of knowledge in Hegel's dialectic. Regarding the observation of personal behaviour – in the social sciences – inductive conclusions are often difficult, because they frequently involve quantitative, ambiguous statements ('half and half' statements). Hence, the formulation of generally valid laws of social behaviour is often dispensed with in favour of a 'quantifying' – as it were prosaic – presentation. One way out of this hardly satisfying situation is to do away with spatially and temporally unlimited 'natural scientific' theories ('grand theories'), in favour of the middle range theory. This term was established by Robert K. Merton in 1949 and further elaborated on in the 1960s. The middle range theories go beyond the mere empirical description of social behavioural modes and pursue a subjective-interpretative approach which is rooted in the synoptic modelling that is based on historical-empirical observation; local, spatially and temporally restricted statements are then sufficient. The statements of the theories of middle range should be regarded as neither highly complex nor trivial.

Examples from applied research

The examples referred to in the following refer to work carried out at the Information Management Institute of the Aschaffenburg University of Applied Sciences.

Acceptance of cloud computing

The study into the acceptance of cloud computing[10] by IMI and EuroCloud Deutschland_eco e. V.[11] aimed at developing practicable measures that are useful when it comes to alleviating the deficient market acceptance of cloud computing. In turn, the reason why market acceptance seemed lacking appeared to stem from deficient operational and data security as well as legal considerations. As shown by a comparison of other, isomorphic cases (acceptance of premium motor cars, bank products, DATEV eG), several aspects, such as technical features or purchase price, which are currently regarded as significant in the cloud computing discussion, can be deemed here as non-decisive purchasing factors. It would be far more conducive to reinforce the trust of buyers and the usefulness of the product, by means of the following essential factors:

  • Potential information transparency – the customer is able to obtain information about the product and the manufacturing process,
  • The customer is informed as to the maturity of the technology – which requires consistent customer training for a product,
  • Formation of closed customer groups – a certain level of qualification is required before a person can become a customer; in other words, not just anybody can become a customer,
  • Setting up public warranties and liability shifts – such as are already familiar from savings banks (Sparkassen) and cooperatives,
  • Psychology of the 'transfer' of sympathy and responsibility to the IT system,
  • Reduction of the discussion concerning system prices and technical details.

For the cloud computing industry, the building up of a 'culture of trust' to gain the acceptance of private and commercial customers will be indispensable. This undertaking will doubtlessly take a certain amount of time and will not respond to any attempt at forcing; however, it does lend itself to positive influencing and correct orientation by applying the measures identified in the project.[12]

Acceptance of IT terminal equipment recycling

The work being done at the IMI on the acceptance of recycling IT terminal equipment pursues the basic idea of addressing the attitude towards recycling IT terminal equipment, for example, discarded mobile phones, on the one hand by analysing isomorphic scenarios and on the other by conducting expert interviews. The isomorphic scenarios analysed were the recycling of drinks bottles and cans (including those with a single-use deposit or drink can deposit), second-hand clothing, and the return and recycling of waste oil in the mineral oil industry. In addition, the technical problems encountered in disposing of and reconditioning mobile telephones were discussed. The results obtained were compiled into an action framework for shaping the process of introducing recycling systems for IT terminal equipment. However the business foundation for the operative implementation was withdrawn following a change in the regulatory position (municipal 'notification requirement') in mid-2012.

Other examples

An analogical conclusion from the historical development of automobility can be drawn for the acceptance of electromobility.[13] Accordingly, the spread of two-wheeled automobiles was a precursor to that of the four-wheeled automobile. This suggests that it would be advisable to devote particular attention to the market development of electric bicycles and motorcycles. The debate on net neutrality calls on the one hand for a network that does not distinguish between communications on the basis of their content and in which data is treated the same irrespective of the sender and receiver. The aim is to avoid competition-distorting measures that would promote the formation of a monopoly. In the case of a data bottleneck in the Internet, no distinction is made in terms of the content being transported. On the other hand, the debate also calls for an egalitarian net that does not admit differences in service class. This means in turn that there is no way of ensuring the service quality of a particular transmission. In this example, knowledge can be enhanced by drawing analogical conclusions from public road traffic: mechanisms such as special lanes for buses or bicycles in cities, special rights for emergency vehicles of the rescue services, from regulations, such as those controlling oversize transports or convoys, and from a drop in or absence of marginal costs, represented by an Internet flat rate. Each of these displays interesting isomorphic analogies.


  1. Aschaffenburg University of Applied Sciences.
  2. Schumacher, Meike; Hofmann, Georg Rainer: „Case-based Evidence - Grundlagen und Anwendung“, Springer Vieweg, 2016.
  3. Horton G: Bessere Ideen finden mit der Zielanalogie. Abrufbar unter:
  4. Kalogerakis, Katharina: „Innovative Analogien in der Praxis der Produktentwicklung“, Gabler Verlag, 2010.
  5. Kalogerakis, Katharina: „Innovative Analogien in der Praxis der Produktentwicklung“, Gabler Verlag, 2010, S. 14 ff.
  6. Hofstadter, Douglas; Sander, Emmanuel: „Die Analogie: Das Herz des Denkens“, Klett-Cotta, 2014.
  7. Jürgen Mittelstraß (Hrsg.): „Enzyklopädie, Philosophie und Wissenschaftstheorie“, J. B. Metzler, 2004.
  8. Bredl, Klaus; Lehner, Franz; Gruber, Hans; Strasser, Josef: Kompetenzerwerb von Consultants in der Unternehmensberatung. In: Hofmann, Georg Rainer; Alm, Wolfgang (Hrsg.): Management der Mitarbeiter-Expertise in IT-Beratungsbetrieben – Grundlagen, Methoden und Werkzeuge: Tagungsband zur Multikonferenz Wirtschaftsinformatik 2002. Information Management Institut, Hochschule Aschaffenburg, 2002
  9. Österle, Hubert; Winter, Robert; Brenner, Walter (Hrsg.): „Gestaltungsorientierte Wirtschaftsinformatik: Ein Plädoyer für Rigor und Relevanz“, book-on-demand, 2010.
  10. Hofmann, Georg Rainer; Schumacher, Meike: „Studie zur Akzeptanz von Cloud Computing“, EuroCloud Deutschland_eco e. V., EuroCloud Austria, 2012, Köln, Wien
  11. Eurocloud Deutschland_eco e.V..
  12. Hofmann, Georg Rainer; Schumacher, Meike: „Abschätzung der Akzeptanz von IT-Systemen mittels Methoden der Case-based Evidences und Qualifizierten Experteninterviews – ein Metathema der Integration und Konnexion“ in: Integration und Konnexion, Tagungsband zur AKWI 2013, Verlag News und Media, Berlin, 2013.
  13. Lessing, Hans-Erhard: Automobilität – Karl Drais und die unglaublichen Anfänge. Maxime Verlage, 2003