Dimensions of information systems design: A framework for a long-range research program

DIMENSIONS OF INFORMATION SYSTEMS DESIGN: A FRAMEWORK FOR A LONG-RANGE RESEARCH PROGRAM Jii~i.4~1

University

of Oulu. Institute

I~VARI

of Data Processing Science, Linnanmaa.

SF-90570 OULU

57. Finland

Abstract-Even though the two lFlP WG8.I working conferences on the comparative review and feature analysis of information systems methodologies (ISDMs) have made an import~lnt cuntribu(ion. there is a need for supplemen!ary longer-term comparative research rthich might graduallv produce empirically-based knowledge about what are to be regarded as sound principles of IS design-in various circumstances. Due to its scope. this research should take the form of broad international cooperation based on a reasonably common research framework, one close in spirit to that of Ives. Hamilton and

Davis. A research framework of this kind should include as one element a profound conceptual model for 1s design as a process. an area which is left relatively unanalyzed in the model of Ives et al. For that purpose the paper outlines a framework based on the sociocybernetic metamodel for IS design as a starting point for discussion and hopefully a more elaborate framework.

I. PROLOGUE

There is general agreement on one thing in our field: that the state of practice and research is unsatisfactory. Bubenko, for instance, states that “SUBSTANTIAL progress in the theory and methodology of designing, analyzing, implementing and operating formalized, computer based information systems is needed more than ever” ([If, p. IO). It is quite easy to agree with this statement, as also with his opinion that we should be prepared to invest more in “substantial” longer-term research ([II, p- 27). The problem is, of course. what are the best means of ensuring this progress. Bubenko expresses his belief in the importance of information modelling as a key to such progress ([I], p. IO) and emphasizes throughout his paper the significance of a firm mathematical foundation as a prequisite for this. We do not want to argue against these claims, but in our opinion these form only a part of the whote story:. We wish to point out in particular that the “soundness” of information systems design methodologies is ultimately an empirical question, and consequently empirical research should have a prominent role in this kind of research program. 1.2 Problems of empirirnl resmrth

There are, of course, several well-known problems entailed in empirical research into IS design t We are not insisting that Bubenko considers these to tell the whole story. On the contrary, he emphasizes the mult~d~scipiinary nature of the area (cf. f6tj, p. 6). We are worried, however, about the risk that mathematical formality may be regarded as a principal indicator of the “scientific” status of research into IS design methodologies.

methodologies (ISDMs, e.g. Verrijn-Stuart in [7], p. vi). First, there is the question of the criteria to be used in the evaluation. In our opinion. methodologies do not describe any external reality, and consequently have no truth value([3]). Their scientific merit should be evaluated on the basis of their practical ~~11ue;that is, to what extent they promote the achievement of certain utilitarian goals and on the basis of their ~7i.~~o~j~f~l wltw. recognizing their potential advantages and drawbacks compared with existing alternative methodologies. Practical value suggests that LSD&is are value dependent, and in this way emphasizes the importance of explicating the underlying value assumptions of the methodologies. Historical value. on the other hand, underlines the need for comparative research into ISDMs. Even though the CRIS effort has made important contributions in this respect. we can still state that we do not have very much comparative knowledge about the relative benefits and drawbacks of alternative methodologies. Secondly, there is great unanimity about the situation dependency of methodologies. This means that we cannot expect to find “a ‘best’ methodology in any general sense” (Verrijn-Stuart in [2]. p. vi). Thirdly, and perhaps most importantly, the social and human behavioural element of information systems and their design raises several fundamental research problems concerning the controllability and repeatability of research and thus the generalizability and publicity of its results. The phenomenal level of reality related to the idea of IS design and use as a conscious, intentional human and social action also has certain important implications concerning the research methods, as noted by Nissen([4]), referring to Bergman([j]). It also seems obvious for us that provided we do not exclude the social and human aspects from consideration. the

general state of research into ISDMs is bound to the state of the related human and social sciences.+ I.?

Prospecfs

forcampclrflti~~e

empirical

resenrch

is, of course. justified to question the prospects for empirical and particularly empirical and comparative. research into ISDhls. The CRIS effort([2. 491) thus far has been a short-term activity with relatively modest goals. but should we strive for more ambitious, longer-term comparative research which might gradually accumulate empirically founded evidence on the relative benefits and drawbacks of different ISDMs or rather of different principles of IS design’? The easiest. way to answer this may be to consider the alternatives. Is the only alternative to rely on ‘natural selection’: that is. only good methodologies and principles are able to survive? It is quite easy to put forward several objections to this latter strategy and we therefore do not see any reasonable alternative to the former. This is without doubt a rocky path (we shall put forward some further complications in the next section), and we cannot expect rapid progress with any definite results. Yet it would seem quite pessimistic to think that we cannot make any progress which would in the long-run, say in IO-15 years, considerably increase our empirically founded understanding of fS design. Due to the scope and time span of this kind of research. it would be important for it to be placed on a broad international basis, within IFIP for instance, concentrating on relatively time-invariant features of 1s design. Cooperation of this kind obviously requires some paradigmatic agreement concerning the object of the research, the problems, existing knowledge and methods([6]) and a more concrete research framework (close in spirit to that of[7]). In the follo~ving section we outline a longrange program for empirical research into information systems design methodologies. It

I .? An outline

for

the resecwiz

program

The program consists of four major activities I. Taking stock of major paradigms 2. Developing more concrete research frameworks (corresponding to each major paradigm) 3. Inspiring and supporting empirical research based on the paradigms and research frameworks identified 4. ~~onitoring the fruitfulness of the paradigms and research frameworks. $ Referring to the previous footnote. we are afraid that the problems OF empirical research and the “scientific under-development” of the human and socjal sciences may lead to overemphasis of mathematical formality as a “scientific” surrogate criterion for research. This may result in a state of ‘formal perversion’. if we are allowed a slight modification of A&offs expression ‘technical perversion’ in the context of OR(l63, l8])+a field not very far from ours. Observe that this technical orientation is regarded as a major explanation of the current crisis in OR even by many operations researchers (e.g. [64, 181).

Without delving into the semantics of the concept ‘paradigm’ we assume that even in our field of Information Systems Science (ISS) it is possible to have fundamentally differing ontological, epistemological and methodological assumptions related to our research and its scope and aims(cf. [8, 9]), even though we have not encountered any clear and convincing exposition of the alternative paradigms of IS research. Our requirement for each paradigm is that it should lead to an essentially different concrete research framework from the alternative paradigms. Due to this close relationship, the first two activities should be carried out visually in parallel. It is also in these two activities that international COoperation is required in the identification of the paradigms and frameworks, in reconciling them into a few representative ones and in authorizing and marketing them. The research mentioned in the third activity should take place in individual research institutes as a part of their research programs. Our conviction is that the very existence of internationally recognized and accepted. relatively concrete research frameworks should inspire research of this kind. Such common frameworks would facilitate interpretation of the results and also increase the possibility of cumulative research. Compared with the CRIS effort. we do not see any need to arrange international conferences for the presentation and publication of the results. In this respect, the existing channels, e.g. scientific journals, are sufficient. Instead it is necessary to monitor now and then the fruitfulness of the paradigms and frameworks on the basis of the experience gained and to make necessary modifications. This, of course, requires internation~ll cooperation.

The main purpose of this paper is to outline a research framework as a concretization of the proposal for a long-range research program presented in the previous section, and as a starting point for discussion and hopefully for a more elaborate framework. Our framework is based on the sociocybernetic metamodel for IS design which is presented formally in [3] and [IO] and was also briefly introduced at the CRIS conference in York([ll]). The framework is highly consistent with the wellknown scheme of Ives, ~amiIton and Davis(l71) who identify nine principal components in their model, ‘The External Environment’, ‘The Organizational Environment’, ‘User Environment’, ‘IS Development Environment’, ‘IS Operations Environment’, ‘The Use Process’, ‘The Development Process’, ‘The Operation Process’ and ‘The Information Subsystem’. Our proposal can be interpreted as a refinement of the ‘Information Subsystem’ and particularly the ‘Development Process’ components which remain quite unanalyzed in [7]. To our understanding, this sociocybernetic

187

Dimrnsions of information systems kign

framework could provide some elements or suggestions for a conceptual framework for IS design which could support broader international research cooperation, especially in the commensurability and cumulativeness of its results. The framework also tries to identify dimensions of potential general principles for IS design, principles which may not be directly recognized in actual methodologies, but may underline them as similarities, differences or omitted possibilities([~2]). We believe that these principles form a relatively time-invariant level of IS design, a level to which we should pay special attention in our future research. Our paper consists of three principal parts. In section 2 we shall discuss IS design in general terms as information production for the decision making involved in IS development, concluding it with a brief presentation of the paradigm undertying this paper, and in sections 3 and 4 we shall analyze the concept of information system and the information system design process as the two central constituents of the research framework to be presented in this paper.

Y

Exaqenous

2. INFOR\I.ATIO?

SYSTEMS

DESIGN .AND DECISIOS

MUUSG

The sociocybernetic metamodel regards IS design as an inquiry process supporting the unit which decides on the principal alternatives for the information/data system and controls the design process. The situation is depicted in Fig. 1. We do not impose any requirements upon the composition of the decisionmaking unit. It may be a unit of representatives from diverse interest groups with quite differing values or it may be a more homogeneous group and in the extreme case only one person. Its composition is largely a politico-cultural question. The structure of Fig. I emphasizes that the principal alternatives concerning the information/data system should be made explicit for consideration and negotiation (cf. the principle of alternative designs in [ 131). This is due to the fact that the values to be taken into account in the decisions concerning the information/data system are difficult for their “owners” to explicate. and still more difficult to

Y-

,

L

J ”

Dependent

Actlan

quslity

u~troiieble fEtors

fC!CiOfS

Fig. 1. IS design and decisionmaking

interaction.

188

J.

hVARl

combine in the case of multiple interest groups with conflicting goals or to communicate to designers. The structure does not exclude all communication of values to the designers as guidance (with constraints, etc.) for the search alternatives and for the more minor decisionmaking needed in the refinement of alternatives (cf. [lo]).: 2.2 Free and informed choice We have suggested free and informed choice (see [14]) as an ideal for the interaction of IS design and the related organs of decision making([3]). This is an ideal in the sense that it evidently can never be satisfied completely, but approximations can be made as far is desired (cf. e.g. [ 151). One complication in this interaction is the fact that the same persons may be involved in both the design and the decisionmaking (see [3] for a discussion of some other obstacles). This is evident in the case of user participation in IS design, when the users or their representatives may participate in the decisionmaking, too. The same situation may hold in the case of professional IS designers, who may also participate (as persons) in a more neutral sense, e.g. as arbitrators in conflict situations. Irrespective of these overlaps, we consider it very important to emphasize that in the designer’s role the user and professional IS designers, at least on the whole, should aim at supporting the ideal of free and informed choice instead of pursuing only their own vested interests. 2.3 The role of IS design methodologies It is time to return to the role of ISDMs in the process depicted in Fig, 1. First, we assume that they are systems of prescriptive statements (requirements, recommendations, guidelines, rules, instructions, . . .) which guide different features of IS design. From the viewpoint of research into ISDMs, it is important to observe that they only partially define the information/data system as a product of the whole process, since the structure of Fig. 1 emphasizes the importance of the decisionmaking process as a determinant of the result. On the basis of our discussion above we can also conclude that ISDMs should support free and informed choice by the decisionmaking organs. This suggests that the quality of ISDMs could be evaluated in terms of the ‘effectiveness’ and ‘efficiency’ of the IS design process as a knowledgeproducing inquiry system. We find here a striking similarity with Checkland’s idea of ‘soft systems methodology’ as “a means of structuring debate” ([16], p. 150). It must be observed, however, that ISDMs only partially define the IS design process. We have put forward elsewhere([3, IO]) the idea of a reflective IS design, in which the detailed prot We refer in the following to the decison-making unit when speaking about decisionmaking without any qualification.

cedures, techniques and tools of ISDMs are applied in a flexible, situation-dependent way. 2.4 The effectiveness of IS design The idea of IS design as an inquiry process suggests that its ‘effectiveness’ could be evaluated in terms of the quality of the information produced for the decisionmaking unit, according to the following classification* 1. Information on the interest groups and their values 2. Information on the exogenous factors and ‘causalities’ co-producing the impacts of the information/data system 3. Information on the IS alternatives 4. Information on the internal (formal) quality of the IS alternatives. 5. Information on the feasibility of the IS alternatives with respect to previous choices at higher levels of abstraction (see part 3) and special constraints 6. Information on the external quality of the information/data system (cf. e.g. [ 171). It is interesting to observe that Hildebrandt suggested quite similar ideas in the context of OR some years ago([l8]). He distinguishes three kinds of information: objective, activity and state information, roughly corresponding to our first, third and second categories in that order ([ 181, pp. 125- 126). He also proposes that these information constituents could be evaluated using a uniform framework of criteria, information quantity (= intensity and extent) and quality (completeness, topicality, correctness and precision) ([18], p. 127). We are somewhat doubtful about this possibility in our context, as we see that each of these six constituents ,has a slightly different information character([ 191). Especially in the case of IS alternatives, the evaluation framework is difficult to apply, because these alternatives ‘create new reality’ rather than describing the existing reality.? Our points 4-6 above roughly correspond to the concept of ‘model validation’ in OR (cf. [20]), a subject which is not clearly visible in Hildebrandt’s treatment. Referring specifically to point 4, we can interpret the internal quality criterion as including various formal criteria which are deemed to be relevant for IS models (concerning their formal correctness, structuredness, various independences, etc.). Whatever these relevant formal criteria may be, they are not in our opinion sufficient for evaluating the effectiveness of the IS design process, since IS design should also facilitate informed choice between IS alternatives; that is, it should also be able to identify substantively (see part 3) relevant IS alternatives and to provide information $ Information on alternatives concerning the IS design process and their value, costs, premises etc (see [31) is omitted here. ?I Observe the tradition of representativeness as a validation criterion for OR models (cf. [?O]).

Dimensions

of information

about their external quality. about exogenous factors affecting this quality, about alternative ways of structuring the IS design process and about the suitability of these alternatives. There are, of course, several possibilities for operationalizing the idea of the effectiveness of IS design discussed above. One alternative is simply to measure the overall degree of informativeness as experienced by the decisionmakers. The discussion of these alternatives and their characteristics (validity and reliability) is beyond the scope of this paper. 2.5 A paradigm

for

IS research

Referring to the research program proposed in section 1.4 we have explicated our fundamental assumptions about IS research as a paradigm statement in Table 1 (cf. [31). This statement is largely a summary of our discussion thus far, even though some of its constituents are explained more completely later (the ontology of an information system). In order to clarify our paradigmatic position in a larger context, let us briefly compare it with the framework of paradigms in sociology and organization theory suggested by Burrell and Morgan([9]).

Table 1. A paradigm for IS research.

I. Disciplinary view of ISS ISS is an applied science which should reflect the prob-

l

l

l

lems and evolution of the practice to be supported. ISS produces and studies methods (methodologies. principles, techniques, tools etc) relevant for information &terns and their development. The ideal aim of ISS is to oroduce nublic and eeneralizable knowledge applicable in practice, while being aware of the limitations imposed by the human and social element of IS design and its situational dependency.

2. The ontology of ISS l IS development is intentional social and human action and IS design information production supporting this action. l Information systems are multilevel systems of quite differing ontological natures, varying from a technical system to a formal linguistic system and an ‘informal’ social system. 3. The epistemology of ISS Methods of IS design do not have any truth value, since they do not describe any outside reality but rather create a new one. Instead their scientific merit should be evaluated on the basis of their practical and historical value. Their evaluation requires comparative and empirical de. scriptive research. The methodology of ISS The ontological diversity of ISS favours a pluralistic methodology, with research methods ranging from formal methods and methods obtained from the natural sciences to the special interpretive methods of the human and social sciences.

The ethics of ISS l

The aim of IS research and IS desrgn IS to support tree and informed choice on the part of the decision-makers involved in IS development.

shst~rns design

189

They suggest that the paradigms reflect two principal dimensions, the first one being the subjectiveobjective dimension and the second one the dimension of radical change contra social regulation. These two dimensions define four paradigms, which the authors label ‘functionalist’, Ynterpretive’. ‘radical humanist’ and ‘radical structuralist’. The subjective-objective dimension in [9] is furthermore an aggregate of ontological (nominalism contra realism), epistemological (positivism contra antipositlvism) and methodological (nomothetic contra idiographic) positions and of an assumption about human nature (voluntarism contra determinism). Even though our paradigm contains subjective elements in its ontological, epistemological. methodological and human nature constituents. we consider the paradigm to be closest to ‘functionalist’. Our assumption here is that the position of ISS and IS design on the dimension of radical change contra social regulation is determined by their role in the social practice which ISS aims at supporting and that this role tends to be regulative or functionalist. even though in terms of Kilmann’s framework([Zl]) ISDMs represent structure-oriented rather than process-oriented approaches to organizational design or development. Since the dimensions in [9] are ordinal rather than interval scales, our ‘functionalistic position’ should be interpreted in relative rather than absolute terms. Referring to Checkland’s discussion ([l6], pp. X0-283). we could argue that the idea of ‘free and informed choice’ has a certain affinity with ‘Radical humanism’, and in particular with ‘Critical theory’ (the idea of ‘public unrestricted discussion, free from domination, of the suitability and desirability of action-orienting principles and norms. .‘). On the other hand, the existing organization research in general and that underlying this paper is almost exclusively confined to the ‘functionalist’ paradigm (cf. [9]). This means that inasfar as we are not going to develop our reference disciplines (such as organization theory), these disciplines tend to restrict our paradigm choices. We do not insist that the paradigm of Table I leads uniquely to the research framework to be presented in the following two parts. It illustrates only that paradigmatic discussions tend to be abstract, and therefore we consider research frameworks, which are more concrete, to be more important elements of the research program from the practical point

of view.

3. THE CONCEPT OF .&S INFOR~I.~TION/DATA SYSTEJI

3. I Introduction Thus far we have used the term information or data system quite freely, although interest has been focused throughout on systematized: that is, formal and organized, information/data systems in a specified organizational context. The adjective formal

190

J. II~ARI

emphasizes that the information/data included is expressed in a formal language with a specified syntax and semantics and that the derivation performed by the system is based on formal rules. Organized means that the resources ‘implementing’ the information/data system have been selected and organized and the necessary execution rules are specitied. Due to our restriction to formal information data processing, the information/data systems to be discussed in this paper can in principle be computerized. but this is not by any means a necessity. We shall return to the importance of the organizational context after a while. At this level, however, it is possible to consider the information/data system as a special type of communication system with specified input and output users (roles performed by certain persons). Irrespective of the “technical” organization of the system (adpimanual/hybrid) communication naturally takes place by means of physical signs; that is, data. Since the term ‘information’ has many more human-oriented connotations, e.g. information is the additional knowledge in the mind of the interpreter of data (e.g. [22. 23]), the term ‘data system’ would in our opinion be more appropriate (cf. e.g. [24. 41). In deference to the current terminology we nevertheless use the terms ‘information system’ and ‘data system’ interchangeably. More formally, we define a data system as a pair (model component, real ustern) in which the model component defines some part of systemized information/data processing (organizational communication) and the real system is a system of the data to be processed and communicated and the resources (human beings, adp equipment, software products) to be used in this, a system which is able to exhibit the behaviour determined by the model component. 3.2 Syntactic informationidata

and substantive

features

of

systems

Reflecting the decomposition into model component and real system, the features concerning the information/data system can be divided into syntactic and substantive features. Syntactic features concern the conceptual structure of the information/data system and its linguistic description (cf. [26]). The features related to the conceptual structure can further be divided into those concerning its syntax and its comprehensiveness (cf. [l I]). For a fixed conceptual structure there are an infinite number of imaginable linguistic representations which may differ with respect to their typegraphical, lexical, tabular, etc.-or in _notational details (see [27, 281). On the other hand. we should observe, especially in IS design, that many description techniques do not only differ linguistically but also have different underlying conceptual structures. The evaluation of linguistic representations is an empirical question, of course. IMost of the experimental research into languages within “soft-

ware engineering” is concentrated on programming (see [29] for a bibliography), and seems to indicate the need for additional research and the complexity of the subject due to the many contingencies involved (see [27. 281). Even if it is questionable to what extent experiences gained from programming languages can be generalized to cover the whole area of IS design. it seems quite likely that complexity is increasing rather than decreasing, due to the greater variety in the use of models and in the persons participating in the process. ISDMs may include implicit substantive assumptions/restrictions and/or explicit recommendations concerning the real data system and data processing. Implicit substantive assumptionsirestrictions are imposed by the conceptual structure of the metamodel for an information/data system. for example. The conceptual structure may be restricted in the sense that it is possible to define only some subset of all conceivable or imaginable data processing. Explicit substantive recommendations suggest direct solutions for the information/data system concerning its real component. 3.3 Levels of abstraction

for

information/data

systems

It is very important to observe that there is growing agreement in our field about the need to distinguish (at least) three levels of abstraction in the model component of a data system and about the fruitfulness of doing so (cf. e.g. [30]). I. Model A: This level defines the organizational context of the information/data system 2. Model B: This level defines the “technical implementation”-independent (conceptual/infological .) specification for the information/ data system 3. Model C: The technical solution for the information/data system. To our knowledge there are not very many ISDiMs which cover all these three levels. The ISAC methodology is evidently the most famous in this respect. Its ‘change analysis’ corresponds to level A, its ‘information analysis’ to level B and ‘data system design’ and ‘equipment adaptation’ to the level C[23]). ‘Activity studies’ in our opinion is a combination of levels A and B.+$ The levels are important in several respects: 1. We have genuine design alternatives at each level (cf. also [31]). The existence of alterna-

t Observe that we use the names of the phases here to denote the models to be designed, since the levels are not explicitly recognized in ISAC (being embedded in the description languages). It is very important to keep the levels of modelling and the phases separate, since alternative ways exist for combining them; that is, the one-toone correspondence of linear life-cycle models (e.g. ISAC) is only one possibility (see 4.2). $ This is a rough description. We omit the question of how “pure” ISAC is in this respect.

Dimensions of information s>stems dcG:n

tives. particularly at the first two levels. is often neglected (cf. [31. 131). The design is essentially different at these three levels. ;\t level .A we are actually dealing with organizational design or development (OD).‘; If we restrict ourselves to human organizations we are not designing formal systems at this level in the sense of formal language (see section 3. I ). even though consciously designed aspects of organizations are usually called formal organizations.4 At level B we are designing formal systems, although it is important to observe that the information requirements are usuall) quantitatively very numerous and qualitative11 fuzzy. When confining ourselves to adp systems we are designing at level C technical artifacts which are clearly “hard systems”. to use this metaphorical expression of Checkland([ 161). At the former levels the systems are essentially softer (in decreasing order’?). Different evaluation criteria for information/ data systems can be expected to be relevant or meaningful at the three levels (cf. [ 17.3 I]). This is due to the fact that the levels imply a priority of values (cf. [3]) in addition to a priority of action (cf. [3?]). 3.4 On t/le complexity of the concept o/ informationldatn system We have been involved during the last six years in the development of the PIOCO model (e.g. [3]) which has some of its roots in the PSC model developed in the early seventies([33]). In the PIOCO metamodel for a data system([34, 31) we distinguish three levels, the pragmatic (P) metamodel corresponding to level A, the input-output (l/O) metamodel corresponding to B and the constructive-operative (C/O) metamodel corresponding to level C. On the basis of this work we can conclude that the ‘information/data system’ is conceptually a very complex entity even at the metalevel. Our original proposal([34]) identified over I50 modelling concepts, for instance, and the total number is consid-

erably increased in the refined versions for the I/O metamodel([35. IO]) and the C/O metamodel (in preparation).

These modelling concepts are design parameters (cf. [36]) which must be fixed in each information/ data system. This, of course, means that IS design, even though interpreted narrowly, omitting its diagnostic aspects, (cf. e.g. [37]), is really a complexity management process([37]) which requires effective means of promoting this management. In the PIOCO metamodel for a data system we have

‘: See [?I] for a discussion on the differences between these two traditions. 5 It could be insisted that this (OD) level falls beyond ISDIMS, but we are well aware of evidence, especially in implementation research, that it should be an integral part of methodologies (cf. [65-681).

191

assumed that, in addition to the levels of modelling described above. levels of detail supporting topdown design and partial in-built views are important+. although there is some disagreement. especially about the soundness of the top-down approach in IS design (cf. [69]). J.

THE INFOR>I.ATIOS SYSTE.\I DESIGN PROCESS

4. I Introduction It is assumed in the sociocybernetic metamodel for IS design that IS design as a process can be structured by means of IS design (or systemeering) acts([3]). The formal background to the framework and to systemeering acts based on the sociocybernetic theory of action([38]) and information economics([391) has been described elsewhere(l3. IO]) and will not be repeated here. Systemeering acts have the general structure depicted in Fig. 2. P chnmcteristics

of svstemeering

acts

At the pragmatic level (P) we can identify the intended users of the systemeering acts and the usage of the information produced. The intended users are any participants involved in the IS design process or the related decisionmaking. In an extreme case it may also be the designer himself performing the act. The interpretation of a systemeering act as information production for certain users naturally brings into focus the fit between the intended users and the other characteristics of the systemeering act. This fit is naturally an empirical problem. and there is growing interest in this type of empirical research, even though most research up to now seems to have been confined to programming (see [29]). In IS design we must, of course, recognize the broader category of people participating in different roles with different frames of reference and other personal characteristics (cf. [40]). II0 chnracteristics of systemeering acts At the input-output level (I/O) we can recognize the semantic content; that is, the object and target systems, the information production methods and the syntactic form of the systemeering act. It was suggested in [I I] that the semantic content of systemeering acts, and especially their object systems, can lead to the recognition of many interesting classifications of ISDMs, e.g. organicisociotechnicalimechanistic views of information/data objective/subjective/mixed explanation systems, information requirements, reality/communication t Observe that the complexity of the PIOCO metamodel for a data system is partly due to the levels of abstraction. The in-built views are manifested as decompositions of the I/O metamodel into ‘information’. ‘information process’, ‘interaction’ and ‘object system’ models and of the C/O metamodel into ‘data’, ‘data process’, ‘control and supporting action’ and (technical) ‘organization’ models([341).

sTiLYr

ing

A_..

I/O characteristics

P characteristics / ./’ Users

/?A Semantic

Inte\ded “Sage

fi

Informag:

Target Object systems system3

Fig.

2. Characteristics

equipment-independent/ modelling approaches. equipment-dependent technical design approaches for adp systems, data:oriented/processoriented data base (file) design, data-orientediprocessoriented program design, etc. The important point in the projection of these classifications to the object systems of systemeering acts is to illustrate that they can be studied in the form of the question: ‘Does the methodology really reflect or support such an approach?‘, rather than ‘What does it explicitly report‘?’ In fact there may be some discrepancy between the ‘reality’ and what is reported in the model. The information production rules comprise various observation (and prediction) methods including ‘interpretative’ methods (cf. 191). and refinement rules used in systemeering acts which correspond quite closely to cognitive tools/aids in (411. ‘Syntactic form’ refers to the conceptual and linguistic structure of the data/information produced by the systemeering act (cf. notational tools in [41]). We have already discussed in some detail the syntactic form of the models for the information/data system to be designed (section 3.2). In this context we wish to point out once again that it is at least as important for the syntactic form to support the IS design process, e.g. its creative aspects([37]), as to support the maintenance and use of the finished information/data system. Cl0 characteristics ofsysterneering clcts The C/O characteristics of systemeering acts include the technical methods used and the control of these. Technical methods closely correspond to ‘augmentative tools’ as discussed in [4l] and include various techniques and tools which’make the systemeering act possible or more efficient. Control defines the organization, planning and supervision of systemeering acts. Our distinction between information production methods and the organization of systemeering acts points to the analytical nature of our framework.

C/O characteristics

Syntacp,

//\xi_ Techni%c&

ZF-

Conceptu- Linguisal struttic deture scription of systemerring

acts.

Many examples from group innovation methods, expert groups (see [42]) and, more specifically in the field of IS design, testing methods (see [431) show the close relationship between these features, but our starting point was that all the characteristics (P. I/O, C/O) are inherent aspects of systemeering acts and that they may be related to a greater or lesser extent, a fact which should, of course, be recognized in recommendations concerning systemeering acts.

order ofIS design The IS design process consists of sequential and parallel systemeering acts, the context and temporal order of which is depicted in Fig. 3. Each lifecycle includes an IS design and implementation process of its own. The design process may be divided into a number of (main) phases, each of which consists of a number of sub-phases determined by the learning dynamics in the course of IS design process. The sub-phases consist of individual systemeering acts which have their own component structure and order. We shall analyse the dynamics of the IS design process in some detail in the following. The tempord

4.2 Decisionmaking

an informationldata

dynamics for the selection of system

The decision-making dynamics involved in the selection of an information/data system determine the life-cycle and main-phase dynamics of the system and the IS design process.

Lije-cycle

dynamics

In order to allow a conceptually flexible evolution of data systems we extend and modify the traditional concept of life cycle as follows: the life cycle of an operating data system at the level of abstraction i is the time span between changes in the data system model at that level of abstraction. Evolution can take place at a different tempo at different levels of abstraction, but is synchronized

Dimensions

of information

systems design

IS design procc%

Learning

Decision-mskinq dynamic3 concerning

dynamics

Life-cycle

t-l&n-phase

dynamics

dynamo

I Lifecycles

Subphases

Order

structllre

I

I

t-kinptMses

Fig. 3. Component

Comment

SyJtemeering act structure

and order of LS design process

in the sense that a change at upper levels inevjtabiy imposes changes at the lower levels. The evolutionary approach is a learning mechanism which allows a ‘long-run’ feedback from the real operation and use of the data system. Some of the philosophical background to this approach is discussed in [Xl, for example. Without going into the details of this background and various manifestations of the evolutionary approach, we are apt to think that there is growing agreement about the importance of this evolution as a factor that merits special attention in the conceptual structuring Df information/data systems and the IS design process.

The main-phase.dynam~cs in a hierarchic~~l design process can be defined in a simple way. il tnaitl phnse is an IS design process located between the selection points of the data system at two consecutive levels of abstraction. This definition does not require any distinction to be made between decisionmaking by the decisionmaking unit and that implicit in the IS design, whereas in the case of the iterative and decomposition methods we must make this distinction. A rnnin ~~~~~.~~ is thus an IS design process located between two consecutive selections of the data system by the decisionmaking unit. If we take a historical view of main-phase dynamics, there is a noticeable trend toward hierarchical design based on more and more refined meMnin-phnse dyttamics tamodels for a data system, models reflecting the principle of levels of Abstraction. Due to increased We concentrate on two aspects of the mainagreement on the ‘reasonable’ levets of abstraction phase dynamics in this context, the underlying prinfor information/data system’s, a certain converciple of the dynamics and the linear/nonlinear strucgence can also be expected to take place in the ture of the main phases. main-phase dynamics of different ISDMs (cf. secThe main-phase dynamics of ISDiMs are mainly tion 3.3). based on (131) Even though we recognize the value of these soI. A hierarchical design or problem-solving prophisticated models for increasing our understanding cess which applies to some levels ofabstraction in the model of the data system. The order of of data systems and the IS design process. and as useful practical guidelines, it is important to be the main phases is det~rmjned by the hieraraware of their shortcomings as determinants of the chical order of the levels of abstraction and the main-phase dynamics. First, we have already recdata system is fixed at the corresponding level ognized that the levels of abstraction may be too of abstraction after each main phase.? detailed for the design of alternatives and the re2. An iterative method which aims at constraining lated decisionmakjn~, so that it is reasonable to the data system (at all levels of abstraction) at combine consecutive levels. Secondly. we have the some specified level of detail (cf. hierarchical value-dependency problem in the hierarchical dedecomposition), precision (cf. ‘attribute design process as observed in section 3.3. Thirdly, the scriptions’ in [44]) or importance (cf. ‘most critthree basic principles of decisjonmaking dynamics ical components’ in 1451) identified above stress different reasons for phasing 3. A decomposition method, which decomposes the IS design process. These shortcomings lead to the data system into subsystems after each the potential need for treating main phase dynamics main phase. This decomposition and the inter-. in terms of contingency theory. Such an analysis action between the subsystems will be subject lies beyond the scope of this paper, however. to selection. Returning to the linear/nonlinear structure of the main phases, it is by no means necessary for the IS t The relationship between the levels of abstraction design process to follow strictly the linear order of and main phases is not stipulated as being a bijection. Two the levels of abstraction even in the case of hieror more consecutive levels of abstraction may be comarchical design. This fact has been clearly recogbined in a main phase.

19-l

J.

llVARl

nized e.g. by Swat-tout and Balzer([-l6]) and Tausworth([47]). In the PIOCO model for the IS design process([48. 31. cf. [33]) the reason for this nonlinear structure is extremely simple. We assume that the decisionmaking unit making the choices must have a sufficient belief in the realizability of the candidate models (cf. ‘realizability’ in [50], ‘satisfactory realizability’ in [51]) and sufficient knowledge about the expected costs of alternatives even in the earliest phases. In conclusion, we see the nonlinear structure as a logical necessity if these assumptions about the decisionmaking are accepted. An interesting question is, of course, to what extent the actual IS design process, applying some linear ISDIM, in fact realizes a nonlinear structure due to the various iterations. One of our aims in the case of the PIOCO model has been to make ‘inevitable iterations’ explicit constructs, because their treatment even as natural but unplanned iterations leads to a distorted view of the IS design process (see section 4.3). 4.3 Learning dynamics involved in IS design In this section we shall discuss learning in the sense of ‘short-run’ learning during the construction of a data system, in contrast to the ‘long-run’ learning of the evolutionary approach to IS design. An IS design process is a complex Qhenomenon of human and social action requiring cons’cious control, that is, planning and supervision. This control may reflect the idea of the blueprint mode of planning, “the production of glossy plans and the unswerving execution of proposals they contain. . .” or the process mode of planning “whereby programmes are adapted during their implementation as and when incoming information requires such changes.” ([52], pp. 131-132). Generally speaking, the blueprint mode of planning is suitable in situations where there is not much uncertainty involved or the uncertainty can be controlled at reasonable cost or ignored without unreasonable loss, while the process mode of planning is appropriate when the above conditions are not fulfilled to a sufficient degree([52]). The short-run learning dynamics are not explicitly dealt with in most ISDMs. They are in a sense passed over by allowing unplanned iterations in which one can return to an earlier main phase or repeat the same main phase or the same activity. Yet unplanned iterations are considered exceptional situations which disturb the blueprint model for the main course of the IS design process.? This short-run learning is an explicit construct in the PIOCO model for the IS design process and the learning dynamics are also clearly recognized in the

+ Observe that learning, as an increase in the knowledge of the participants, is recognized in some ISDMs (e.g. ISAC.[23]). The ISAC model nevertheless deals with it as a prerequisite for IS design, and its impact upon the dynamics of IS design is reduced to unplanned iterations.

experimental (prototype) approaches to IS design. Even so. this short-run learning should not be confused with the mode of information production (prototypes contra abstract models. see [ 191. We must remember, however, that IS design situations differ quite markedly in their uncertainty, and consequently different learning strategies are justified. It is also significant that the uncertainty may vary from one main phase to another, and this justifies flexible learning dynamics in the whole IS design process. The learning strategy also has repercussions for the control of the IS design process (its efficiency, schedules, etc.). A common procedure here is to prepare prescriptive plans (including a time schedule and costs) for the main phases, and maybe for the whole IS design and implementation process, and to use this prescriptive plan as a yardstick for the IS design process. This procedure naturally does not work, however. if one’s plans change greatly and frequently during the process, but alternative, evidently ‘softer’ means of control are required. There are various contingencies related to the applicability of these means and consequently we can conclude that the learning dynamics are to a high degree a management problem, and can be used in a flexible way in different IS design processes, and even in one process, recognizing the situational factors affecting the relative benefits and the drawbacks of the process contra the blueprint mode of planning. 4.4 Systemeering

acts

In the introductory section of this part we dealt with the inherent characteristics of systemeering acts. In this section, we devote our main attention first to the component structure and order of systemeering acts defining various procedures of IS design and then to the classification of systemeering acts. IS design procedures

In section 3.4 we discussed the complexity of information/data systems as design objects (target systems) and mentioned some syntactic means for managing this complexity. These mechanisms suggest various potential procedures for IS design, the number of which may be quite large. In order to illustrate this, let us take the decomposition of the I/O metamodel (that is, level B in section 3.3) into ‘information’, ‘information process’, ‘interaction’ and ‘object system’ models in the PIOCO metamodel for a data system([34]). There are certain implicit or explicit suggestions in the literature that we could start the I/O design 1. by specifying the information the users are interested in (cf. precedence analysis[53, 23)) 2. by identifying the functions (information process types) to be included in the information/ data system (cf. activity diagrams in SADT,[54, 551)

Dimensions of information systems design 3. by outlining

195

the user-system interaction (cf. pies. Our only remark is that the classification proposed can be applied in the context of IS design V61) 4. by defining the structure and behaviour of the (and testing) at all three levels A. B and C (see secobject system (UoD) about which the users tion 3.3). Due to the differences in the nature of the wish to have information (e.g. SYSDOC,[S7]). design at these levels, it may be that different prinIf the Ii0 design proceeds in linear order according ciples are reasonable at each (cf. Checkland’s disto the four sub-models, we have 4! = 24 alternatinction between ‘hard systems methodology‘ and tives. In many methodologies some of the sub‘soft systems methodology’Jl61). models are merged, however, e.g. the functional analysis in EDM(I581) and NIAM([.591) includes as- 4.5 On the complexity of the IS design process pects of the first two submodels. The situation is IS design is a very complex process of human to some extent similar in ISAC([60]), even though and social action. To continue our quantitative anit is important to observe that the precedence an- alysis, we can recognize that the detailed ISDtMs alysis makes it more information-oriented than currently available (e.g. ISAC and EDM[23, 581) EDM and NIAM. We should also recognize that include some 100 basic systemeering acts. Recalling most ISDMs do not make any distinction between that each systemeering act has in principle around the realm of UoD and the realm of information (cf. 10 inherent characteristics (which may not be doc[22]).$ Consequently these two sub-models are umented by the methodologies), we have approx. combined in many methodologies (e.g. in RE1000 features, even though these two, like most curMORA,[61]). If we take all these combinations and rent methodologies, have many simplifying charavailable permutations into account, we obtain 75 acteristics (cf. [ 1I]): l they describe the IS design process as a linear possible I/O design procedures. We use the term ‘design’ in a restricted sense in structure this calculation, omitting verification, testing and l the treatment of the diagnostic aspects of IS deevaluation acts. Also, our analysis proceeds in quite sign is superficial aggregate terms within the four sub-models. Conl they are ‘one-valued approaches omitting altersequently there are several conceivable alternatives native procedures which may be reasonable in for the design of each sub-model (e.g. top-down and different situations bottom-up approaches to each). l many ‘micro’-features of IS design are not treated Not all these 75 alternatives may be reasonable l they do not include substantive recommendaones, but it is beyond the scope of this paper to tions concerning the information/data system. make such an evaluation. We do, however, have It is, of course, justified to question whether ISDMs to take into account the diagnostic aspect of IS de- should cover all these aspects. From the practical sign in this context (e.g. [37]) which we have totally viewpoint, it should be observed that a detailed omitted thus far. This means that in evaluating treatment of IS design may make the methodology these alternatives we should assess how well they more operational, while from the viewpoint of rehelp the users to determine their (substantive) re- search it is very difficult to deem problems related quirements concerning the I/O specifications. Rec- to any one of these features irrelevant in advance. ognizing the variation amongst users and information/data systems it may be unrealistic to expect s. EPILOGUE there to be one ‘best’ approach to I/O design among One of the aims of this paper has been to illusthe alternatives. trate that we have a very rich object of research in both the quantitative and the qualitative sense. Our Classification of systemeering acts tenet has also been that the “soundness” of inforSystemeering acts can be classified into interest mation systems design methologies, methods, techgroup analysis, goal analysis, situation analysis, niques and tools is ultimately an empirical question generation and refinement of alternatives, verifiand inasfar as this empirical basis is deficient, our cation, feasibility evaluation and quality assessment acts (cf. [ 1I]). This classification makes it pos- discipline remains a speculative science. The overall purpose has been to give an impetus sible to evaluate certain underlying principles for the development of a long-range program for concerning the scope of the interest group analysis (formal/political/comprehensive), the mode of goal empirical research into ISDMs, preferably based on co-operation. The program outlined analysis (functional/normative), the temporal di- international includes the expectation that there may be several mension of situation analysis (reactive/preactive). the search strategies (inductive/deductive) and the alternative paradigms for the research, paradigms which lead to essentially differing concrete research comprehensiveness of the search (satisficing/optimframeworks. In order to concretize the proposal, a izingimixed) etc. specific research framework is presented, a refineWe shall not go into the details of these princiment of the earlier framework proposed by Ives. Hamilton and Davis([7]), and its underlying para$ The reasons for the strict distinction between these digm is explicated and related to a more general submodels are explained in [3] and [35].

J. IIVARI

196

model for paradigms in sociology and organization theory. The research framework which forms the principal contribution of this paper is based on the sociocybernetic metamodel for IS design and tries to identify the guiding principles and invariant structures of IS design, principles which may manifest themselves as similarities, differences or omitted possibilities in existing methodologies, and to define the contexts for details and structures which may recur at many points in the IS design process. The need for this kind of structural framework is evident when one takes into account the diversity of features in IS design and it is aggravated by the everincreasing number of ISDMs. It is also important that the research frameworks should not be limited to any specific ISDM, but should have a wider applicability. Therefore they cannot include any specific exotic details, but should be metalevel frameworks of more general ideas and principles of IS design. The applicability of the frameworks presented in this paper has partly been tested in the use of its background, the sociocybernetic metamodel for IS design, for a feature analysis of eight quite differing ISDMs. Acknoivledgemenrs-This work was supported by the Academy of Finland. The author is also grateful to his colleague Erkki Koskela for his cooperation in the development of the PIOCO model and anonymous referees for their helpful comments. REFERENCES

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