Int. J. Human-Computer Studies (2001) 55, 423}433doi:10.1006/ijhc.2001.0496Available online at http://www.idealibrary.com on

An approach to requirements analysis for decisionsupport systems

CAROLINE PARKER

HUSAT Research Institute, Loughborough University, Leicestershire LE11 3TU, UK.email: c.g.parker@lboro.ac.uk

(Received 26 November 2000, and accepted in revised form 31 May 2001)

The e!ectiveness of any system can only be measured in relation to its ability to supportthe user in the tasks they wish to carry out in those situations in which it has beendesigned to operate. Task analysis tools which break tasks down into component andmeasurable parts are used as a means of clarifying the support that a system shouldprovide. This paper uses the practical example of work carried out in the agriculturalsector to describe the potential of a speci"c approach to task analysis for the support ofcrop management decisions. The approach is based on the work of Arinze (1992), whoproposes that the basic cognitive element of the decision task is a question, a user enquiryof the environment or system. This paper describes how his approach to the collation andorganization of information was successfully used within the DESSAC project as a meansof ensuring that the decision support system (DSS) adequately supported the decisiontask and more recently within the requirements phase of three smaller projects. Theincorporation of the approach into a practical and #exible method for use within timeand cost constrained DSS development projects is outlined.

( 2001 Academic Press

KEYWORDS: user requirements; decision support; question approach; decision enquiries.

1. Introduction

This paper uses the practical example of work carried out in the agricultural sectorto describe the potential of a speci"c approach to task analysis for the support of cropmanagement decisions. The agricultural industry is under increasing pressure to maxi-mize pro"ts and reduce the impact on the environment of nutrients and pesticides. This,coupled with the increasing availability of new knowledge and new science, has led theindustry to investigate the potential of information management systems and in particu-lar decision support systems or DSS.

Decision support systems are computer-based systems designed to help users to makemore e!ective decisions by providing information in a way that actively supports thedecision process. Unlike expert systems, which are usually designed to supplant someaspect of an expert's role, DSS exist to complement and &&support'' decision-makersrather than to replace them. DSS have been developed on many platforms in manyindustries and for a wide variety of uses, for example medicine (e.g. Reisman, 1996),

1071-5819/01/100423#11 $35.00/0 ( 2001 Academic Press

424 C. PARKER

emergency services (e.g. Tufekci, 1995), utilities (e.g. Lindquist, McGee & Cole, 1996),"nancial services (e.g. Wong & Monaco, 1995) and agriculture. They are usually basedon a simulation model (i.e. a mathematical model describing the way an entity or groupof entities will react under given circumstances), or a rule base or both. In UK agricul-ture, as in other countries, DSS have been promoted as a means of revitalizing theknowledge transfer process in the wake of the removal of state funded advisory services.

A range of DSS have been produced in the UK agricultural sector over the past 15years to support a number of aspects of crop-based on-farm decision-making, e.g.enterprise planning, spray planning and crop development. However, despite the re-sources devoted to their development, and the apparent user demand for the supportthey potentially o!er (Parker, Francis & Cook, 1994), the technology has so far failed tomake an impact (Parker, 1999a; Newman, Lynch & Plummer, 1999). The author hasbeen working with agricultural DSS developers to try to understand the reasons behindthe lack of take up and to put in place mechanisms to ensure the technology has everychance of delivering its promise to the end-user.

As a human factors practitioner the starting point for the delivery of useful and usableDSS technology is the user, and the application of a user-centred design methodology tothe development process. User-centred design is taken to be the involvement of users atall stages of system development from initial planning, through requirements analysis,into development and evaluation. The application of user-centred design philosophy tothe development of DSS has, however, required a new approach to requirements'analysis and evaluation, which this paper explores.

The main case study for this paper is a MAFF LINK project called Decision SupportSystems for Arable Crops- or DESSAC. It is a 5-year project which aims to providea mechanism for delivering knowledge to the arable sector in a cost-e!ective and usableform. Decision support systems form a core component of this work. The author's role inthis #agship project has been to identify speci"c and generic requirements for DSS and toorganize the evaluation of the emerging software.

1.1. TASK ANALYSIS FOR DECISION SUPPORT SYSTEMS

One of the primary characteristics of a user-centred design approach is the use of taskanalysis to gather user requirements and to test task "t in evaluation. In the context ofhuman-based tasks, task analysis is the means by which details of the users' tasks, andinformation about the task environment, are collected so that the users' needs are wellunderstood.

The history of task analysis is rooted in the manufacturing industries and in machineuse at the start of the century, e.g. Gilbreth (1911) and Taylor (1911). Task analysismethods are concerned with formal ways of collecting information, organizing and usingit as the basis for design decisions, and for ensuring that tasks and functions areappropriately allocated within a new system (Kirwan & Ainsworth, 1993). A largenumber of techniques fall under the task analysis heading and it was the task of theauthor to "nd one that could be used within the DESSAC project. Hierarchical task

-MAFF LINK project P174.

DECISION SUPPORT SYSTEMS 425

analysis (HTA), for example, &&explores tasks through a hierarchy of goals indicating whata person is expected to do, and plans indicating the conditions when subordinate goalsshould be carried out'' (Shepherd, 1998). The speci"cation of tasks, operations and plansprovides raw material for the system design, and the ease with which the user can carryout the speci"ed tasks can be used as a measure of the design's success.

The majority of approaches (e.g. HTA and its hybrid forms) have been used tradition-ally to describe the physical aspect of a task, and the steps that are required to carry itout. They have made signi"cant contributions towards improving productivity in caseswhere the major elements of the task are observable, but it has been suggested that theyare less e!ective in the analysis of cognitive activities (e.g. Klein, Kaempf, Wolf, Thorsden& Miller, 1997). As a result of this debate and an increased emphasis on cognitive aspectsof work, cognitive task analysis or CTA techniques emerged (a thorough description ofthe evolution of task analysis is provided in Annett, 2000). Cognitive task analysis (CTA)concerns itself with the knowledge that people have, or need to have, in order tocomplete a task. Its approach is to describe and represent the cognitive elements thatunderlie decision-making, goal generation, judgements, etc. (Militello & Hutton, 1998).

While Shepherd (1998) has argued that HTA is appropriate for mapping behaviouraland cognitive activities if used correctly, other authors believe that distinct tools andapproaches are required (e.g. Seamster, Redding & Kaempf, 1997). In turn, while CTAwas developed to deal speci"cally with cognitive aspects it has been suggested that&&Arti"cially separating and focusing on the cognitive alone is likely to produce informa-tion that is not very useful in understanding, aiding, or training job performance''(Chipman, Scraagen & Shalin, 2000, p. 4). It would thus appear that the division betweenthe two approaches is blurred. However, at the time the DESSAC project requirementsprocess was underway, the particularly cognitive nature of the decision task appeared tosuggest a CTA approach would be useful.

CTA is considered to be &&appropriate for tasks that are cognitively complex (requiringan extensive knowledge base, complex inferences and judgement) and which take place ina complex, dynamic, uncertain, real-time environment'' (O'Hare, Wiggins, Williams& Wong, 1998). This description would seem to make CTA a highly appropriate choicefor inclusion in a design method for decision support systems. It would also seem to beparticularly true for the context in which the process described in this paper, cropproduction, is carried out. Decision-making in crop production can certainly be de-scribed as cognitively complex, requiring the manipulation of many variables, as thisquotation from Bartlett illustrates:

The decision-making process of farmers involves a range of factors that are taken intoaccount. Each farmer usually makes choices within the context of the household and isin#uenced by the households need&&s and goals as well as by the resources available to thehousehold. These resources include not only land, water, labour etc. but also social resourcessuch as information about agricultural methods or credit and any in#uence or politicalpower necessary in many areas to successful agricultural production. (Bartlett, 1980)

The chaotic nature of weather, the potential interactions between it and the crop and themyriad things that impact on its growth create a complex, dynamic and uncertainenvironment.

426 C. PARKER

2. Some problems with cognitive task analysis for DSS

While the stated approach of CTA seems to make it ideal for the task at hand, a numberof di$culties were observed when examining the use of traditional tools like GOMS andTAKD within the context of DSS development and evaluation. These are best seen fromthe context of the expected output of a cognitive task analysis (Preece, 1993).

f A description of knowledge required to carry out the task (declarative knowledge).f A representation of mental models used by individuals when carrying out the task.f A description of how the user carries out the task (&&how to'' knowledge).

While the "rst of these is very useful in de"ning the information needed within thesystem to support the decision process (for example within DESSAC, face-to-faceinterviews with 44 individuals involved in crop production resulted in a very detaileddescription of the knowledge used in carrying out the crop protection task) the other twoare not very helpful for the reasons described below.

2.1. A REPRESENTATION OF MENTAL MODELS USED BY INDIVIDUALS WHEN CARRYINGOUT THE TASK

Obtaining a mental model of a physical system within a traditional industrial contextmay be possible. It is also possible that the same mental model might be used bya number of people. If this is the case then that model might usefully be identi"ed andemployed, e.g. as a training aid or as part of a software navigation device. Within thecontext of decision-making in crop production, however, this approach is not so usefulbecause of the following.

f Models of how to make a decision can vary considerably between individuals. Forexample, in crop production decisions are based on di!erent levels of scienti"cknowledge and on the degree to which various factors (e.g. cost, environmentalconcerns, etc.) are considered important. One person may have an accurate perceptionof the crop development cycle and of pest development, and another may base theirdecision-making on a model of seasonal tasks handed down from his or her parents.

f Decision-making models are subject to constant change. In many complex environ-ments, including agriculture, the decision process is based on incomplete and constant-ly changing information. Crop development, for example, is only partially understoodand, as new science appears, mental models have to change to accommodate it. Thedegree to which individuals absorb the new material will also vary.

In summary, while the identi"cation of single or multiple mental models may be usefulas the basis on which to build a system speci"cation in some contexts, mental models aretoo diverse and changeable in this instance to be of practical use.

2.2. A DESCRIPTION OF HOW THE USER CARRIES OUT THE TASK (&&HOW TO'' KNOWLEDGE)

Decision- making does not lend itself to the type of analysis which yields useful &&how to''knowledge, as many real-life decisions are for ill-de"ned problems with several andcon#icting goals. In such circumstances there is simply no single correct &&how to'' andprobably as many examples of &&how to'' as there are decision-makers. Identifying an

DECISION SUPPORT SYSTEMS 427

optimum way of arriving at the best decision solution relies on knowing what the bestsolution will be, and that, in crop production, may not always be obvious. For example,a good "nancial outcome for one "eld/crop may have a negative impact on another moreimportant one, or it may be personally inconvenient to apply the optimum.

Decision-making is rarely the logical process we would like it to be (Stewart, 1994),and identifying &&how to'' data from decision-makers may not be a useful exercise.Psychological models of decision-making vary in the extent to which they attributerationality to human decision-making; but it has been shown, e.g. Tversky and Kahne-man (1974), that people are likely to adopt recency and frequency heuristics, rather thanlogical reasoning, to decide between options. In a crop production context this isre#ected in the behaviour of the farmer who, having su!ered badly from a disease in theprevious season, sprays heavily against it, even though the prevailing climatic conditionssuggest it will not pose a threat.

Identifying ways in which to support less irrational styles of decision-making may bea useful component of a decision support system, but the attempt to de"ne optimum&&how to'' approaches for decision-making would seem to be futile.

2.3. USE OF CTA IN AN AGRICULTURAL DSS DEVELOPMENT ENVIRONMENT

Another problem with many of the CTA methods examined at the time was cost (timeand resource). Most CTA methods have been developed and applied in the context ofresearch projects and require &&considerable time and resources'' to apply (Militello& Hutton, 1998). It has also been suggested that the cost (time and e!ort) of obtaininginformation for use in design is one of the main reasons for a designer's decision not toaccess it (Burns & Vicente, 2000). Time and funding are particularly important tosoftware development in UK agriculture, an industry which, particularly in the last 20years, has not been characterized by its wealth. In fact, the funds available for researchand development work from central government sources have been shrinking steadily.As a consequence, DSS project development teams in this sector are small, tightly fundedand with the main emphasis on biological rather than computer or human science. Thissuggests that, to succeed in this environment, a CTA method has to be cheap, #exible andeasy to use.

The rest of this paper describes the discovery and adoption of a cognitive task analysisand user requirements speci"cation tool, and its incorporation into a method to meet theneed for an appropriate, cost-e!ective and practical method for user-centred design indecision support systems development.

3. A CTA method for DSS: adopting the question approach

During early research into DSS uptake in the agricultural sector it became apparent tothe author that some DSS were not adopted because they did not answer &&the rightquestions''. For example, one system provided the user with an indication of growth ratefor a unit of a given crop when the most important question to that user group was &&howmany units will reach this size by this date?'' (the information was needed to plan salesand supply to major retailers). The science encapsulated in the model within the systemwas capable of answering the question but the system had not been developed to do so.

428 C. PARKER

The observation that system developers needed to pay more attention to the actualquestions their users wanted to ask of the system was re-enforced by data from otheragricultural projects. Investigation of the potential of this approach led to the discoveryand adoption of a taxonomy based on user questions formulated within managementscience by Arinze (1989).

3.1. QUESTIONS/DECISION ENQUIRIES

Arinze reasons that the key information #ow between the DSS and the user is the streamof requests from the user, i.e. the questions that the user asks of the system when using itto support decision-making, and that these should therefore be the key determinants ofthe shape and form of the DSS. The data from the DESSAC interviews suggested thatmuch of the crop protection decision task was indeed concerned with getting answers toquestions about the weather, disease levels, product e!ectiveness, etc. The focus on userquestions, both from the observations of system failure and the observations of thedecision-making process, seemed therefore to support the Arinze argument.

Where Arinze's work is particularly useful to task analysis and requirements speci"ca-tion is the division of these questions or &&decision enquiries'' (Arinze's term) intoa functional taxonomy (Arinze, 1992). He argues that when decision-makers interact witha DSS they will invariably make an enquiry of one of three main types labelled: state,action and projection enquiries.

3.1.1. State enquiries. State enquiries are made when the user is seeking informationabout the state of the world (or a model of it).

f Entities (e.g. products, diseases).f Processes (e.g. pest and disease lifecycles, market behaviour).f Attitudes (e.g. buyer attitudes, consumer attitudes).f Policies (e.g. legislation, buyer policies).f People (e.g. sta!, customers, suppliers).

The type of query that may be made about these items may be as follows.

f Purely descriptive (e.g. What is the current price of this product?).f Subjective (personal view e.g. Am I making enough pro"t?).f Temporal (describe when a state occurred e.g. When was the last disease risk period?).f Explanatory (why a situation exists e.g. Why is this crop doing so poorly?).f Normative (what should be in the normal run of things e.g. If I do nothing what is my

expected yield for this crop?).

Simple descriptive statistics, such as frequencies and means are also classed as Stateenquiries, although the data have already been manipulated.

3.1.2. Action enquiries. Action enquiries are requests for a plan of action to achievea speci"ed end-state. This is a reverse &&what if '' question, i.e. instead of what will happenif I do this, a projection enquiry asks how do I get to this pre-speci"ed end-state. Anaction enquiry might be very speci"c e.g. &&How should I plant this "eld to obtain thisspeci"c cropping pattern?'' or more general e.g. &&How do I achieve the best margin given

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this set of circumstances?''. In this type of query, it is the function of the DSS to generateactions in response to the user's goal setting.

3.1.3. Projection enquiries. Projection enquiries are more commonly known as &&what if ''enquiries. They are requests for an indication of outcome given a set of de"nedconditions e.g. &&How much will I lose if I delay the application of this spray for threedays?''. This type of enquiry also involves the assignment of probabilities to estimatedoutcomes, and will require risk and sensitivity analyses to be performed on potentialsolutions.

4. Use of the question approach in DESSAC

The DESSAC project was unique among the projects of its kind, at the time it wasinitiated, in that it provided speci"cally for a large-scale user-requirements analysis andspeci"cation phase. Detailed face-to-face interviews were conducted with 32 farmers and12 agronomic consultants over a period of 6 months followed up by a postal survey of750 arable farmers. A great deal of data relating to the questions decision-makers posed,and the information they used in decision-making, was gathered and a means ofmanaging it and translating it into a form that could be used as the basis for systemdesign was required. At this stage the utility of the taxonomy proposed by Arinze wasunknown.

As part of the exercise to generate smaller, more manageable requirements from overa 100 collated questions, the requirements team attempted to group them on a subjec-tively measured like-with-like basis. As an additional exercise the resultant groupingswere compared with the headings proposed by Arinze. The match between the groupingsgenerated by the exercise and the headings proposed by Arinze was striking (the authordid have prior, if limited, knowledge of these headings and this may have in#uenced hergrouping strategy). Table 1 shows the question headings set out under each of the maintaxonomic categories.

As a direct result of this discovery the project adopted the Arinze taxonomy. Its realvalue, however, was not in supporting the collation and re"nement of the question setbut in providing a means of moving from a statement of requirements to a functionalspeci"cation for the project.

Taking each of the three main headings as a starting point, the questions within thesurvey were analysed for common features and an overview emerged of the types ofsupport the system needed to provide. For each question the information needed toanswer it was identi"ed; this provided the project team with a list of data to (1) sourceand (2) provide means for displaying. Each question was also ranked by industry expertsthereby providing the project with a means of prioritizing requirements. The ability ofthe existing and proposed models to answer the questions posed in the Action andProjection categories were scrutinized, and alterations were made to the way in whichthese were being developed. For example, a simple model calculated the extent of diseasedamage on the crop in terms of yield; however, a key question was &&what impact will thishave on my margin over spray cost?'' (Parker, 1996), and the project therefore made surethat the "nancial aspects of the disease were also included. As part of the evaluationexercise the questions were used as the basis for the design of interface mock-ups.

TABLE 1Overview of summarized questions to emerge from DESSAC exercise

Category Explanation Questions

State Description of the current/potential state of the crop

Is there something about which I need totake action?Have I/will I have a problem with disease?What might it be?How serious is it/will it be?

Action Tell me how to get to X or What do I do?an optimum position

What chemicals will do the job?Which products are best for my speci"ccircumstances?What is the best way to use them?

Projection Tell me what will happen if What if ......?Y changes

I change any of my options?The weather changes?Disease levels change?

430 C. PARKER

Reference to questions ensured that design suggestions met the initial requirements andallowed the developers to evaluate the e!ectiveness of their design solutions well beforeany serious coding took place.

The use of the Arinze taxonomy in the DESSAC project provided the starting pointfor the design of the system: it provided a basis for the initial speci"cation of databasecontent and form, of model scope, and of interface features and functions. The taxonomydid not, however, provide guidance on the most appropriate way of delivering thisinformation at the interface or on navigation and support issues. These issues have beenaddressed by continual user involvement initially in workshops and as the projectprogressed within the context of laboratory- and "eld-based trials.

5. Use of the question approach elsewhere

Since this paper was initiated, the Arinze taxonomy has been used within the require-ments phase of three other projects. All three projects needed a rapid and economicmethod of identifying user requirements for decision support (for weed control, slugmanagement and pest and disease management in oil seed rape). In all three cases groupsof decision-makers were asked (within the context of half-day workshops) to list the typesof questions they asked themselves or the environment and the issues they consideredwhen making aspects of the decision (whether to act, what to act with and when to act).The approach was very fruitful and the participants found it interesting and thoughtprovoking. It also provided a means of focusing the participants' minds on the areas inwhich they felt they most needed additional support, a topic which formed the next

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exercise. The questions were summarized and allocated, by the author, into the Arinzecategories. The categories allowed the author to de"ne basic information and a list of themodel outputs that the system would need to provide. The allocation of questions to thestate category was very easy, but the distinction between the action and projectionqueries was a little blurred. Many of the decision-makers' questions had both an actionand a projection component to them and so the results from these two categories weresummarized together. While each project could only a!ord to run three or four suchgroups there was a remarkable consistency in the questions and issues raised, both withinthe groups and between them. The topics di!er but the types of questions that requiresupport appear to be common and will be the focus of another paper.

6. Discussion of this approach

Despite not being concerned with the identi"cation of mental models or procedures, theuser enquiry or question method can be seen as a form of cognitive task analysis in that itis concerned with the collection of details about a decision task and with understandingthe users' needs. It proposes (1) that the basic cognitive element of the decision task isa question, a user enquiry of the environment or system, and (2) that the strategy the userapplies is the asking of questions until su$cient information is obtained to permit theselection of one of possibly many decision alternatives. Whether or not it "ts into thebroader task analysis armoury rather than speci"cally into the cognitive toolkit is nota question this paper wishes to address.

The approach is a formal and practical means of gathering information about thecognitive task (either within an interview or workshop approach), of organizing it andusing it as the basis for design decisions; and it can be used to ensure that tasks andfunctions are appropriately allocated within a decision support system. By identifyingthe &&enquiries'' or questions inherent in a decision-making process, it becomes possibleto state the users' requirements for data and for mathematical models, the two maincomponents of the DSS. Because the designer knows that the system has to support theposing and answering of speci"c questions, this knowledge also guides the developmentof the interface. This makes the approach consistent with the Ecological Interface Designmethod of Vicente and Rasmussen (1992), in that the model of mechanisms which peoplehave for dealing with the complexity of the environment is provided by the questionsthey ask of it. While the exact form of the representation is not given by user questions,they provide a clear indication of the functions and features an interface should contain.(The apparent emergence of a set of &&generic'' questions from this research, and itsimplication for the development of interface features to support them, is an area ofresearch currently being pursued.) The availability of a comprehensive set of decisionenquiries also permits developers to test the emerging system against its requirements.Finally, a comprehensive set of decision enquiries permits the evaluation of one or moresystems against a clear set of criteria.

The Arinze taxonomy alone, while simple to apply, is not enough to provide agricul-tural DSS developers (or those with similar constraints) with a means of aligning theirsystem to meet the needs of the user. It has to be seen in the context of a structureduser-centred approach to design and development. As a result of the experience inmanaging user involvement in the DESSAC project, the author has developed a method

432 C. PARKER

which combines question-driven requirements and evaluation with workshop and proto-typing techniques (Parker, 1999b). The method is currently being used in other agricul-tural projects and is actively promoted within MAFF LINK. This tool is intended toprovide the time and cash-starved agricultural DSS developer with support for thecapture, prioritization and incorporation of user requirements. It has, to date been usedby the author with support from project partners but could, with more re"nement andwith the development of guidelines, be used by others. As a move in this direction two ofthe small project workshops were run by project team members, and while the quantityof data they generated was reduced the range of questions and issues appeared to be verysimilar. Problems appeared to be related to the experience of individuals in encouragingdiscussion rather than to the format of the event.

The assertion that the question or decision enquiry approach is key to the e!ectivedelivery of usable DSS may be criticized, justi"ably, on the grounds that this paper canonly o!er these four case studies, in addition to Arinze's original work, in its support. Theabsence of other such work in the literature, in the light of the method's undoubtedsuccess in DESSAC, is one of the motivations behind publication of this paper. Earlyresults from the requirements phase of three new agricultural DSS projects suggest thatthe DESSAC experience was not an isolated one, but it is to be hoped that this discussionwill encourage other researchers to evaluate the technique against their own needs. Twopossible tests for the e!ectiveness of this method may be (1) the extent to whichdevelopers "nd it useful and (2) the extent to which its application results in a usable anduseful product. Again initial feedback is encouraging but further research will be required(and is planned) to provide objective answers to these questions. There are now four setsof developers who can be questioned as to its utility and, as the DESSAC projectbecomes commercially available this year and the other modules will join it in 2003, theopportunity to obtain real feedback from users will soon be available.

The author would like to acknowledge the part played by her project colleagues from ADAS,Silsoe Research Institute, IACR Rothamstead, Farmplan and Morley Research Centre in the workwhich forms the basis of this paper, in particular to Robert Cook who unceasingly championsuser-centred design within DSS projects. She would also like to thank the Ministry of Agriculture,Fisheries and Food, the Home Grown Cereals Authority and the Biotechnology and BiologicalSciences Research Council for funding the DESSAC LINK and evaluation projects. Finally,thanks are due to the reviewers and particularly to the editor for helpful comments on earlier draftsof this paper.

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