Trud za MND DSS_01.2013 final_28.01.2013_BM

7/30/2019 Trud za MND DSS_01.2013 final_28.01.2013_BM

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UDK 005.53

Biljana MATEVSKA1,

Ljubica KANEVCE2,

Konstantin PETKOVSKI3

DECISION SUPPORT SYSTEMS

Abstract

Decision support systems are a specific class of information systems thathelp business people to make decisions. These systems work under thescheme of modeling information, facilitating the decision making process.Decision support systems allow for one to obtain relevant information froman unorganized set of data, which can be found in documents, spreadsheets,and even in the knowledge of people, which becomes the input to solvedecision-making problems, as well as generating strategies in companies.This paper critically analyses the nature and state of decision supportsystems (DSS) research. To provide context for the review and analysis, a

history of DSS is presented which focuses on the evolution of a number ofsub-groupings of research and practice: personal decision support systems,group support systems, negotiation support systems, intelligent decisionsupport systems, knowledge management based DSS, executive informationsystems/business intelligence, and data warehousing. To understand the stateof DSS research an empirical investigation of published DSS research ispresented.

1 Msc. Biljana Matevska - author of this paper is a doctoral student at the Faculty ofTechnical science, department of Industrial Management, Bitola, Republic of Macedonia,cell phone + 389 72 252 443, + 389 76 406 301, e-mail: [email protected],

[email protected] ;

2 PhD. Ljubica Kanevce - Faculty of Technical science, department of IndustrialManagement, Bitola, Republic of Macedonia, e-mail: [email protected];

3 PhD. Konstantin Petkovski - Faculty of Technical science, department of Industrial

Management, Bitola, Republic of Macedonia, e-mail: [email protected].

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mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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Key words: DSS Decision Support Systems, group support systems,

executive information systems, data warehousing, business intelligence,

information systems, research.

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1. INTRODUCTION

Decision support systems (DSS) are computer tools that help managers tomake decisions, and are responsible for obtaining, analyzing, reporting, andeven making decisions for themselves.DSS includes personal decision support systems, group support systems,executive information systems, online analytical processing systems, datawarehousing, and business intelligence. Over the three decades of its history,

DSS has moved from a radical movement that changed the way informationsystems were perceived in business, to a mainstream commercial ITmovement that all organizations engage.IS (Information systems), as an academic discipline, is currently at animportant stage of its development. It faces a significant downturn in ITactivity in commerce and government, which has led to serious decline instudent numbers in IS degree programs. At the same time there is agroundswell of concern about the nature and direction of IS research.DSS has been an important area of IS scholarship since it emerged in the

1970s.The current DSS industry movement of business intelligence (BI) is

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one of the most buoyant areas of investment despite the IT downturn of theearly to mid 2000s. The market in new BI software licenses grew 12% from2003 to 2004 and is expected to have compound growth of 7.4% to 2009.DSS is not a homogenous field and over its 35-year history a number ofdistinct sub-fields have emerged. The history of DSS reveals the evolution ofa number of sub-groupings of research and practice. The major DSS sub-fields are:

Personal Decision Support Systems (PDSS): usually Small-scalesystems that are developed for one manager, or a small number ofindependent managers, to support a decision task;

Group Support Systems (GSS): the use of a combination ofcommunication and DSS technologies to facilitate the effective workingof groups;

Negotiation Support Systems (NSS): DSS where the primary focus ofthe group work is negotiation between opposing parties;

Intelligent Decision Support Systems (IDSS): the application ofartificial intelligence techniques to decision support;

Knowledge Management-Based DSS (KMDSS): systems that supportdecision making by aiding knowledge storage, retrieval, transfer andapplication by supporting individual and organizational memory andinter-group knowledge access;

Data Warehousing (DW): systems that provide the large-scale datainfrastructure for decision support;

Enterprise Reporting and Analysis Systems: enterprise focused DSSincluding executive information systems (EIS), business intelligence(BI), and more recently, corporate performance management systems

(CPM). BI tools access and analyze data warehouse information usingpredefined reporting software, query tools, and analysis tools.

2. SOME HISTORICAL DEVELOPMENT OF DECISION SUPPORT

SYSTEMS

In the early 1960s, organizations were beginning to computerize many of theoperational aspects of their business. Information systems were developed toperform such applications as order processing, billing, inventory control,payroll, and accounts payable. The goal of the first management information

systems (MIS) was to make information in transaction processing systems

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available to management for decision-making purposes. Unfortunately, fewMIS were successful (Ackoff, 1967; Tolliver, 1971).

Figure 1: DSS Time Line (under construction)

The term decision support systems first appeared in a paper by Gorry andScott Morton (1971), although Andrew McCosh attributes the birth date ofthe field to 1965, when Michael Scott Mortons PhD topic, Using acomputer to support the decision-making of a manager was accepted by theHarvard Business School (McCosh, 2004). Gorry and Scott Morton (1971)constructed a framework for improving management information systemsusing Anthonys categories of managerial activity (Anthony, 1965) andSimons taxonomy of decision types (Simon, 1960/1977). Much of the earlywork on DSS was highly experimental, even radical (Alter, 1980; Keen andGambino, 1983). The aim of early DSS developers was to create anenvironment in which the human decision maker and the IT-based systemworked together in an interactive fashion to solve problems; the humandealing with the complex unstructured parts of the problem, the informationsystem providing assistance by automating the structured elements of thedecision situation. The emphasis of this process was not to provide the userwith a polished application program that efficiently solved the targetproblem. In fact, the problems addressed are by definition impossible, orinappropriate, for an IT-based system to solve completely. Rather, the

purpose of the development of a decision support system is an attempt toimprove the effectiveness of the decision maker. In a real sense, DSS is aphilosophy of information systems development and use and not atechnology. The decades indicated on the left hand side of the diagram referonly to the DSS types and not to the reference disciplines. Anotherdimension to the evolution of DSS is improvement in technology, as theemergence of each of the DSS types has usually been associated with thedeployment of new information technologies. The nature and development ofeach DSS type is discussed in detail below. DSS is not a homogenous field.

There are a number of fundamentally different approaches to DSS and each

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has had a period of popularity in both research and practice. Each of theseDSS types represents a different philosophy of support, system scale, levelof investment, and potential organizational impact. They can use quitedifferent technologies and may support different managerial constituencies.

Figure 2: The Evolution of the Decision Support Systems Field

3. WHAT IS ACTUALLY DSS

Class of computer programs whose purpose is to serve as consultantsduring the process of decision making. These programs use a collection offacts, rules and other knowledge for a limited field of action for the adoption

of effective conclusions for goal.Essential difference between expert systems and conventional computerprogram that targets expert systems may not have an algorithmic solution, soyou are forced make conclusions based on incomplete and assumed, but notproven information. The name expert system comes from that require humanexpert in making the final decision.

The passive decision support systems are responsible only for collectingand organizing information for its use by the people responsible for thedecision; therefore, these systems do not suggest any specific response. The

active systems are responsible for collecting information, upon which they

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base an explicit presentation of one or more solutions to the decisionproblem. A cooperative DSS is responsible for gathering the information,analyzing it, and then delivering it to the people responsible for decisionmaking, and is also tasked to revise or refine the information. The name"cooperative" is derived from the cooperative work between software andpeople, with the intention of making the best possible decision. Figure 2shows the classification of DSSs in the three families mentioned in theprevious paragraph, as well as their scope.

A communication-based model is based on the communication of severalpeople for making the decisions. The data-driven DSS is responsible forcollecting the information, which is then manipulated to meet the need of theperson responsible for the decision. The document-based DSS system usesvarious types of documents (pages of text, spreadsheets, database reports,

etc.) to solve decision problems, as well as to manipulate the information inan attempt to refine strategies. Knowledge-based systems analyze specificrules stored in a computer or rules used by a group of humans, which allowsone to establish whether a decision should be made. Finally, systems basedon models use statistic simulations and financial models to solve decisionproblems.The classification in Figure 2 is based on the interaction of the supportsystem with users. A classification based on the functioning of such toolsshows a separation into 3 types of families:

Figure 3: DSS classification and scope in decision making

It is important to note that although these are very good computer tools foranalyzing information, and even though they give options to be selected asthe best alternative, the final decision of the decision-making process mustbelong to people and not to software tools, which are inanimate machines

that are incapable of considering many external factors in the analysis and

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cannot contextualize the information they present, information which couldmodify the final decision.

Personal DSS (PDSS) are small-scale systems that are normally developedfor one manager, or a small number of independent managers, for onedecision task. PDSS are the oldest form of decision support system and foraround a decade they were the only form of DSS in practice. Theyeffectively replaced MIS (Management Information systems) as themanagement support approach of choice. The world of MIS was that of theCold War and the rise of the Multi-National Corporation. The majorcontribution of PDSS to IS theory is evolutionary systems development(Arnott, 2004). The notion that a DSS evolves through an iterative process ofsystems design and use has been central to the theory of decision support

systems since the inception of the field. Evolutionary development indecision support was first hinted at by Meador and Ness (1974) and Ness(1975) as part of their description of middle-out design. This was a responseto the top-down versus bottom-up methodology debate of the timeconcerning the development of transaction processing systems. Theimportance of this work was to give the concept a larger audience; Keen(1980) remains the most cited and thereby the most influential description ofthe evolutionary approach to DSS development. Amongst other contributorsto DSS development theory, Sprague and Carlson (1982) defined an

evolutionary DSS development methodology, and Silver (1991) extendedKeens approach by considering how DSS restrict or limit decision-makingprocesses.

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PDSS remains an important aspect of IT-based management support incontemporary practice.

Group Support Systems A GSS consists of a set of software, hardware,and language components and procedures that support a group of peopleengaged in a decision-related meeting (Huber, 1984). This definition can beexpanded to include communication and information processing (Kraemerand King, 1988). A number of frameworks have guided GSS research.Figure 5 below shows that the group decision making environment consistsof a combination of characteristics of the group (including group history,member proximity, group size, national culture, leadership behavior, andgroup cohesiveness), the task (including type of task, level of decision

making, phases of decision making, degree of task structure, difficulty, andtime synchronization), the group and organizational context (includingcorporate culture and behavior norms, maturity of the organization,organizational size, time frame of decision making, management style,recognition and reward systems), and the system (EMS, GDS, CSCW).These influence the group process which finally leads to a group outcome(including measures of efficiency, decision quality, group consensus, andsatisfaction) (Nunamaker et al., 1991).

Negotiation Support Systems (NSS) also operate in a group context but as

the name suggests they involve the application of computer technologies tofacilitate negotiations (Rangaswamy and Shell, 1997). As GSS weredeveloped, the need to provide electronic support for groups involved innegotiation problems and processes evolved as a focused sub-branch of GSSwith different conceptual foundations to support those needs.Intelligent Decision Support SystemsArtificial intelligence (AI) techniqueshave been applied to decision support and these systems arenormally calledintelligent DSS or IDSS (Bidgoli, 1998) although the term knowledge-basedDSS hasalso been used (Doukidis, Land, and Miller, 1989). Intelligent DSScan be classed into twogenerations: the first involves the use of rule-basedexpert systems and the second generation uses neural networks, geneticalgorithms and fuzzy logic (Turban et al., 2005). A fundamental tensionexists between the aims of AI and DSS. AI has long had the objective ofreplacing human decisionmakers in important decisions, whereas DSS hasthe aim of supporting rather than replacing humansin the decision task. As aresult the greatest impact of AI techniques in DSS has been embedded in thePDSS, GSS or EIS, and largely unknown to managerial users. This isparticularly the case in data mining and customer relationship management.

Knowledge Management-based Decision Support SystemsOrganizational

knowledge management (KM) has received a large amount of attention by

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executives and academics since the early 1990s. The action taken byorganizations to manage what they deem asknowledge is vital in its abilityto increase innovation and competitive advantage and support decision-making. KM affects the entire organization and involves the management ofseveral areasincluding IT, organizational behavior, organizational structure,economics and organizationalstrategy. It can support decision processes anddecision makers. Knowledge management as an information systemsmovement has also had an impact on DSS research with a major conferenceonthe topic being held in 2000 (Carlsson et al., 2000). Questions addressedin this research include(Carlsson and Lundberg, 2000).

Executive Information System (EIS) An application program specificallydesigned for use by the corporate executive. Presentation of material is oftenstructured after the board briefing book concept. Detailed information on

the summarized charts is often made available by using a concept known asdrilling. The EIS acts as a usable interface to a database of companyinformation. It automates high-level analysis and reporting, and typically hasa user-friendly graphical interface.

Business intelligence Its a classic question that has a classic answer companies need to translate data into information in order to make strategicbusiness decisions. Companies continuously create data whether they store itin flat files, spreadsheets or databases. These data are extremely valuable toyour company. Its more than just a record of what was sold yesterday, last

week or last month. It should be used to look at sales trends in order to planmarketing campaigns or to decide what resources to allocate to specific salesteams. It should be used to analyze market trends to ensure that yourproducts are viable in todays marketplace. It should be used to plan forfuture expansion of your business. It should be used to analyze customerbehavior. The bottom line is that your data should be used to maximizerevenue and increase profit. IT are the first for begin the process of creating areport. They need to extract the required data and pass it to the personcreating the report. That person then has to spend time manipulating the datato create the required report. This process can take many hours, even days,of effort. And this process needs to be carried out for each and every reportthat the company requires. Business Intelligence solutions automate theprocess of extracting data and producing reports thereby eliminating all ofthe manual effort of IT and the people creating the reports from raw data.

A Business Intelligence solution produces reports using data that has beenautomatically extracted from a cleansed data source (typically a database ordata mart) to produce accurate reports. In order to make important businessdecisions, for example, as to what new products to carry or what products todrop, it is vital that managers have accurate data in the reports on which they

base these decisions.

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Data security is a very real problem. As soon as data is extracted tospreadsheets the potential for abuse is greatly increased. Spreadsheets can belost, private corporate and sensitive data can be copied onto a number ofportable devices, and laptops can be stolen or misplaced. Cases whereprivate data is made public through negligence occur daily. Think Wiki.Business Intelligence solutions take advantage of existing securityinfrastructures to keep private data secure and within the company. Datawithin reports is typically presented to employees via the companys intranetand employees are given access to only the data they require to carry outtheir specific job functions.

Without a Business Intelligence solution companies may have to resort todumping vast amounts of data into spreadsheets from their databases. Thisin itself is a manual and, in most cases, an extremely time- consuming task.

The spreadsheets then have to be delivered to the person creating the report.Spreadsheets then have to be consolidated and the data manipulatedmanually to produce the desired reports. All this takes time and the datawithin the reports may be days or weeks old by the time the reports arecomplete and delivered to the manager.

Data warehousing In computing, a data warehouse or enterprise datawarehouse (DW, DWH, or EDW) is a database used forreporting and dataanalysis. It is a central repository of data which is created by integrating data

from multiple disparate sources. Data warehouses store current as well ashistorical data and are used for creating trending reports for seniormanagement reporting such as annual and quarterly comparisons.

The data stored in the warehouse are uploaded from the operational systems(such as marketing, sales etc., shown in the figure to the right). The data maypass through an operational data store for additional operations before theyare used in the DW for reporting.

The typical ETL-based data warehouse uses staging, data integration, andaccess layers to house its key functions. The staging layer or stagingdatabase stores raw data extracted from each of the disparate source datasystems. The integration layer integrates the disparate data sets bytransforming the data from the staging layer often storing this transformeddata in an operational data store (ODS) database. The integrated data arethen moved to yet another database, often called the data warehousedatabase, where the data is arranged into hierarchical groups often calleddimensions and into facts and aggregate facts. The combination of facts anddimensions is sometimes called a star schema. The access layer helps usersretrieve data. A data warehouse constructed from an integrated data source

system does not require ETL, staging databases, or operational data store

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databases. The integrated data source systems may be considered to be a partof a distributed operational data store layer. Data federation methods or datavirtualization methods may be used to access the distributed integratedsource data systems to consolidate and aggregate data directly into the datawarehouse database tables. Unlike the ETL-based data warehouse, theintegrated source data systems and the data warehouse are all integratedsince there is no transformation of dimensional or reference data. Thisintegrated data warehouse architecture supports the drill down from theaggregate data of the data warehouse to the transactional data of theintegrated source data systems. Data warehouses can be subdivided into datamarts. Data marts store subsets of data from a warehouse. This definition ofthe data warehouse focuses on data storage. The main source of the data iscleaned, transformed, cataloged and made available for use by managers and

other business professionals for data mining, online analytical processing,market research and decision support (Marakas & O'Brien 2009). However,the means to retrieve and analyze data, to extract, transform and load data,and to manage the data dictionary are also considered essential componentsof a data warehousing system. Many references to data warehousing use thisbroader context. Thus, an expanded definition for data warehousing includesbusiness intelligence tools, tools to extract, transform and load data into therepository, and tools to manage and retrieve metadata.

Figure 6:Data Warehouse

Benefits of a data warehouse

A data warehouse maintains a copy of information from the sourcetransaction systems. This architectural complexity provides the opportunityto:

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Maintain data history, even if the source transaction systems do not.Integrate data from multiple source systems, enabling a central view acrossthe enterprise. This benefit is always valuable, but particularly so when theorganization has grown by merger. Improve data quality, by providingconsistent codes and descriptions, flagging or even fixing bad data. Presentthe organization's information consistently. Provide a single common datamodel for all data of interest regardless of the data's source.

Data warehouses versus operational systems

Operational systems are optimized for preservation of data integrity andspeed of recording of business transactions through use of databasenormalization and an entity-relationship model.

Operational system designers generally follow the Codd rules ofdatabasenormalization in order to ensure data integrity. Codd defined fiveincreasingly stringent rules of normalization. Fully normalized databasedesigns (that is, those satisfying all five Codd rules) often result ininformation from a business transaction being stored in dozens to hundredsof tables. Relational databases are efficient at managing the relationshipsbetween these tables. The databases have very fast insert/update performancebecause only a small amount of data in those tables is affected each time atransaction is processed. Finally, in order to improve performance, older dataare usually periodically purged from operational systems.

4. DECISION SUPPORT SYSTEMS OFFER

On the international market, it is easy to find software suppliers who provideDSSs, due to their importance in helping companies. Table 1 shows a list ofDSS manufacturers reported by DSSResources.com, which is comprised ofmore than 80 manufacturers. This demonstrates the broad offer available forsuch software tools.

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Table 1. DSS design companies

These systems use analytical models to analyze information and to guide

those responsible for decision making. In addition, some of these models canuse both quantitative and qualitative variables simultaneously. Table 2presents some of the analytical methods for supporting the DSS processeswhich are most used. One of the most widely used software for decisionmaking in the industry is Expert choice, of the U.S. House of ExpertChoice software. This software is based on the Analytic Hierarchy Process(AHP) for selecting the best alternative in a process of decision analysis thatcan be multi-attribute, multi-objective, and that also may involve severalpeople in the analysis process.

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Table 2. Decision making analysis methods classification

The Current State of Decision Support Systems Arguably, the premierspecialist academic conference on DSS is the biannual IFIP Working Group8.3 Conference. This conference has been held continuously since 1983 andvirtually all leading DSS scholars have presented their ideas in this forum atsome time. In 2004 the conference (branded asDSS 2004) was held in Prato,

Italy (Meredith et al., 2004).DSS 2004 comprised 86 research papers; Table3 shows their breakdown according to the DSS types discussed above. Sevenarticles were classified as not DSS according to the definition adopted by thispaper.

Table 3.DSS 2004 Papers by DSS Type

There are no academically rigorous market statistics for EIS/BI/DW but

conversations with senior chief information officers indicate that almost all

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major commercial expenditure in decision support involves these DSS types.The industry research firm, Meta Group, estimates that the data warehousemarket is currently worth US $ 25 billion (Mills, 2004). IDC, anothercommercial research firm, believes that data warehousing and businessintelligence are central to contemporary IT investment and will remain so forsome time (Morris et al., 2003). Even allowing for serious overestimation bythe CIOs and the commercial researchers, the distribution of papers at DSS2004 shows a marked disconnect between the agendas of DSS researchersand senior IT professionals.

5. LITERATURE ANALYSYS

Previous analyses of information systems research have used a similar

sampling approach (Benbasat and Nault, 1990; Alavi and Carlson, 1992;Pervan, 1998). Alavi and Carlson (1992) used eight North American journalsfor their sample. However, Webster and Watson (2002) have criticized theover emphasis on North American journals in review papers. In response weincluded four European information systems journals (ISJ, EJIS, JIT, JSIS)in our sample. An alternative approach is to focus on a small number ofinfluential papers (Alavi and Joachimsthaler, 1992) or to aim for acomprehensive sample of all published research in the area including journalpapers, book chapters, and quality conference papers (Webster and Watson,2002).

Alternatively, if the journals Management Science and Decision Sciencesare removed (as both are generalist journals covering a much wider field ofwhich IS is a relatively small part), the proportion of DSS papers rises to21.2%. Each of thesedifferent measures indicate that DSS is an importantpart of the IS discipline.

The sample of articles analyzed is DSS research published between 1990and 2003 in 14 journals: Decision Sciences (DS); Decision Support Systems(DSS); European Journal of Information Systems (EJIS); Information and

Management (I&M); Information and Organization (I&O), formerlyAccounting, Management and Information Technologies; InformationSystems Journal (ISJ); Information Systems Research (ISR); Journal ofInformation Technology (JIT); Journal of Management Information Systems(JMIS); Journal of Organizational Computing and Electronic Commerce(JOC&EC); Journal of Strategic Information Systems (JSIS); GroupDecision and Negotiation (GD&N); Management Science (MS); and MISQuarterly (MISQ).

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6. RESEARCH ANALYSYS

Table 5 shows that around one-third (33.6%) of DSS research is non-empirical, with two-thirds (66.4%) empirical. Chen and Hirschheims (2004)analysis of overall IS research reported a significantly different split betweennon-empirical (40%) and empirical (60%). This means that DSS research hassignificantly more empirical research than general IS.

Table 5. Sample by Article Type

In addition, there are two major roles that managers can play in a DSS: clientand user. User is an obvious role. The client is the manager who pays for the

system and acts as a champion of the development with other managers. For

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small systems the client and user is often the same person. In papers wherethe clients and users were identified, DSS clients were most likely to beexecutives and users were most likely to be professionals followed by non-executive managers and executives. This means that professionals are oftenintermediaries between the IT-based applications and the ultimate decisionmakers.

The decision support focus of the sample shows a well-balanced mix ofdevelopment, technology, process and outcome studies. Importantly, DSSresearchers have maintained a strong recognition of the importance of the ITartifact in IS research. Studies that test theory are around one quarter of thesample; theory building dominates DSS research, while theory refinement isalmost non-existent.

7. KEY ISSUE

A number of information systems researchers are concerned that there is awidening gap between research and practice, particularly in the systemsdevelopment area Hirschheim and Klein, in a critical assessment of the ISdiscipline, identified major disconnects between IS researchers andexecutives, and between IS researchers and IS practitioners. Fundamentalto these disconnects is the perception that much IS research is of littlerelevance to the practice of these two vital constituencies. Benbasat andZmud identified five reasons why information systems research lacksrelevance. The first is an emphasis of rigor over relevance in order to gainthe respect of other academic disciplines; the second is the lack of acumulative tradition that yields strong theoretical models that act as afoundation for practical prescription; the third is the dynamism ofinformation technology, which means that practice inevitably leads theory;the fourth is a lack of exposure of IS academics to professional practice; andthe fifth is the institutional and political structure of universities which limitsthe scope of action of IS academics. An assessment of the practical relevance

of DSS articles is shown in Table 6. The assessment of the practicalrelevance of a journal paper is a subjective judgment. In judging relevancewe were informed by the aims and objectives of the paper, the nature of thediscussion, and in particular, the content of the concluding comments of eachpaper. The researchers spent considerable time in discussing and reviewingtheir coding of this factor to assist in calibrating the independent codingprocesses. Both authors have many years of DSS research experience andboth have been DSS practitioners; both maintain close links with industryand organizations and the judgment of relevance is based on this academic

and professional experience. Table 6 shows that overall, only 10.1% of

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research is regarded as having high or very high practical relevance. On theother hand, 49.2% of research was regarded as either having low practicalrelevance or none at all. Over time the relevance of DSS research has beenimproving. A one-way ANOVA of mean relevance scores over the threeanalysis eras shows significant improvement (pb0.01). Similar ANOVAs atthe DSS type level shows that only two types have had significantimprovement in relevance: PDSS (pb0.05) and IDSS (pb0.01). Theimprovement of relevance is driven by the large proportion of the samplethat is PDSS.

Table 6. The practical relevance of DSS types

Because DSS research has the mission of improving managerial decision-making, DSS articles should be grounded in quality judgment and decision-making research. In analyzing DSS papers, special care was taken todistinguish between merely citing reference theory in introductory passagesor focusing discussion and explicitly using reference theory in the design ofthe research and interpretation of results. Only the second, integral, use of

reference theory was coded in this paper. Surprisingly, 47.8% of papers didnot cite any reference research in judgment and decision-making in thisfashion. Further, the percentage of papers that explicitly used judgment anddecision-making reference research is relatively stable over time. Table 6shows the mean number of citations to judgment and decision-makingreference research per paper for each type of DSS.

Group and Negotiation Support, and Personal DSS have the most referencecitations, with the current professional mainstream of Data Warehousing

having the poorest grounding. One reason for this could be that GSS, NSS,

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and PDSS largely involve the application of technology to tasks that havebeen researched by other disciplines. As such it is relatively easy to select afoundation theory lens for DSS research. DW and BI are less mature DSStypes and current research is largely focused on technology and getting thedata right. It may be more difficult to find models of behavior to informresearch in these DSS types.

An important issue or tension in an applied field like DSS is the extent towhich the academic field leads or follows industry practice. One way ofidentifying where DSS lies on this continuum is to examine the publishing ofdifferent DSS types over time. Despite the lags in journal publishing, thisanalysis gives an indication of the level of conservatism of research agendas.At the start of our analysis period PDSS and GSS were the most important

DSS types; by the end of the period DW and Enterprise Reporting andAnalysis Systems were overwhelming dominant in practice.

PDSS research has evolved significantly over this time, driven bysustained improvement in information technologies and greater managerialknowledge and experience. It has however, waned considerably in perceivedimportance to industry.

Table 7. Number of cited judgment and decision-making references by DSStype

8. CONCLUSION

Decision makers relying on the internet to gather information face a difficulttask given the amount of irrelevant information that they must sort through inorder to find any relevant information. Unfortunately, some of thisinformation turns out to be inaccurate. Perceptions about deer and Lymedisease seem to have propagated through the internet based on public

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comments, but not based on scientific evidence. Certainly no academicresearchers will be surprised that the web contains much information that isinaccurate, but what may come as a surprise is the extent to which thisinformation is being used in making decisions. Presented an overview andthe analysis can be carried out the following conclusions: DSS have a veryquick and success historical development which indicates that they havemore practical application to support decision making at all levels necessaryfor proper decision making.Classification of DSS shown manner and conditions of their application:

arch

Personal DSS, Group Support Systems, Negotiation Support Systems,

Intelligent Decision Support Systems, Knowledge Management-based

Decision Support Systems, Executive Information System, Business

intelligence, Data warehousing, which Indicating varying degrees of

complexity and different capabilities of these classes of DSS. Analysis of themanner of functioning of the DSS show that these decision makers allowaccess properly organized and prepared data and give recommendations formaking final decisions. They today, in complex decision-making conditionsare necessary for a proper decision. A number of DSS that are offered to theinternational market from about 80 different manufacturers used approachesalso points to the fact that managers have a great benefit from their use.According to the analysis, papers that treat this problem it can be concludedthat there is about 14 magazines who published papers in this area. The

analysis of the research shows that they have predominantly empiricalcharacter and that are extremely important for decision-making theory.In conclusion, DSS, as an important field of information systems reseand practice, is at the crossroads; its future is both bright and troubled. Itsshare of IS research is declining but in industry it is growing significantlydespite the IT downturn.

. , . , . , - , . :

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. , . , . 80 , , . , , 14 .

. , , . ,.

9. APPENDIX

his annex contains the full list of references found in our survey from year

967) Management misinformation systems.Management

(1992) A review of MIS research and disciplinary

ation

(2001) Review: Knowledge management and

rt Systems: Current Practice and

A Framework for

) Decision support systems evolution: Framework, case

T

1970s to 2007.Ackoff, R.L. (1Science, 14(4), 147-156.Alavi, M. and Carlson, P.development. Journal ofManagement Information Systems, 8(4),45-62.

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Anthony, R.N. (1965) Planning and Control Systems:Analysis (Harvard University Graduate School of Business Administration,Cambridge, MA).

Arnott, D. (2004study and research agenda. European Journal of Information Systems, 13(4),247-259.

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Arrow, K.J. (1951) Social Choice and Individual Values (Wiley, NewYork). Basu, A. and Blanning, R.W. (1994) Metagraphs: A tool formodelling decision support systems. Management Science, 40(12), 1579-1600.

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http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S001273532012000300021&lng=pt&nrm=iso&tlng=pthttp://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S001273532012000300021&lng=pt&nrm=iso&tlng=pthttp://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S001273532012000300021&lng=pt&nrm=iso&tlng=pthttp://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S001273532012000300021&lng=pt&nrm=iso&tlng=pthttp://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S001273532012000300021&lng=pt&nrm=iso&tlng=pt

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