Encyclopedia of Decision Making and Decision Support Technologies Frédéric Adam University College Cork, Ireland Patrick Humphreys London School of Economics and Political Science, UK
Volume II In-Z
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[email protected] Web site: http://www.igi-global.com/reference and in the United Kingdom by Information Science Reference (an imprint of IGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 0609 Web site: http://www.eurospanonline.com Copyright © 2008 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Encyclopedia of decision making and decision support technologies / Frederic Adam and Patrick Humphreys, editors. p. cm. Summary: "This book presents a critical mass of research on the most up-to-date research on human and computer support of managerial decision making, including discussion on support of operational, tactical, and strategic decisions, human vs. computer system support structure, individual and group decision making, and multi-criteria decision making"--Provided by publisher. ISBN-13: 978-1-59904-843-7 ISBN-13: 978-1-59904-844-4 1. Decision support systems. 2. Decision making--Encyclopedias. 3. Decision making--Data processing. I. Adam, Frédéric. II. Humphreys, Patrick. HD30.213.E527 2008 658.4'03--dc22 2007047369 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this encyclopedia set is new, previously-unpublished material. The views expressed in this encyclopedia set are those of the authors, but not necessarily of the publisher.
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A Support System for the Strategic Scenario Process Hannu Kivijärvi Helsinki School of Economics, Finland
Kalle Piirainen Lappeenranta University of Technology, Finland
Markku Tuominen Lappeenranta University of Technology, Finland
Samuli Kortelainen Lappeenranta University of Technology, Finland
Kalle Elfvengren Lappeenranta University of Technology, Finland
Introduction In modern day business, management of uncertainty in the environment has become a vital part in building success. The increasing speed of changes in the field of business and shortening product lifecycles are being discussed right up to the point where these concepts are becoming clichés (e.g., Teece, Pisano, & Shuen, 1997; Wiggins & Ruefli, 2005). The problem of uncertain conditions boils down to the question: how can a business develop reasonable strategies for steering the company in the long run (Mintzberg, 1994)? Strategic planning and decision making in some form or another is seen as an important part of modern corporate management. Traditional techniques and tools have been criticized for being too rigid from the perspective of managing the environment (Mintzberg, 1994; Schoemaker, 1995). In many instances, the analysis that fuels the development of corporate strategies is a snapshot of the surrounding world and does not perceive possible anomalies in the development of situations. In the traditional sense, management is all about knowing the relevant decision parameters and forecasting the result of each decision. In contrast, scenario planning has gained attention as a structured method for interfacing strategic planning with the evolving operating conditions (e.g., Mintzberg, Ahlstrand, & Lampel, 1998; Walsh, 2005). Scenarios are not a single point prediction of a defined time-space in some point of future, and multiple scenarios have conventionally been used to map the borders of plausible futures (Schwartz, 1996; Schoemaker, 1995; van der Heijden, Bradfield, George, Cairns, & Wright, 2002), which aims at avoiding problems that arise if carefully conducted forecast of future business proves to be faulty.
This article presents the concept of a supported scenario process that can be used in strategic decision making. The purpose is to illuminate the conceptual background of scenario planning, the methods for formulating scenarios, and the way this scenario process can be supported to make it more efficient. The main contribution is the description of the supported process that has been developed and tried in several sessions, ranging from student exercises to technology scenarios in an intersection of industries. Finally, this article will present some future prospects for this field of research and practice.
Conceptual Background Defining Scenarios Starting from the very beginning, Kahn and Wiener (1967, p. 33) define scenarios as “hypothetical sequences of events constructed for the purpose of focusing attention to causal processes and decision points”, with the addition that the development of each situation is mapped step by step and the decision options of each actor are considered along the way. The aim is to answer the questions “What kind of chain of events leads to a certain event or state?” and “How can each actor influence the chain of events at each time?” Schwartz (1996) describes scenarios as plots that tie together the driving forces and key actors of the environment. In Schwartz’ view, the story gives a meaning to the events, and helps the strategists in seeing the trend behind seemingly unconnected events or developments. Schoemaker (1991, 1993, 1995) writes that scenarios simplify the infinitely complex reality to
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a finite number of logical states by telling how the elements of a scenario relate with each other in a defined situation. In Schoemaker’s view, scenarios as realistic stories might focus the attention to perspectives which might otherwise end up overlooked. Chermack (2004) adds that scenarios as a process is a way to enhance decision making processes in an organization, as a result of knowledge convergence experienced in a successful scenario process. Some writers (e.g., Blanning & Reinig, 2005; Schwartz, 1996) use the concept of “drivers of change” to describe such forces as influential interest groups, nations, large organizations, and trends, which shape the operational environment of organizations. The interpretation used in this study is that these drivers create movement in the operational field, which can be reduced to a chain of related events. These chains of events are, in turn, labeled as scenarios, leading from the present status quo to the defined end state during the time span of the respective scenarios, as seen in Figure 1. From these definitions, one can derive that scenarios are a set of separate, logical paths of development, which lead from the present to a defined state in the
future. Furthermore, it can be deduced that scenarios are not descriptions of a certain situation some time in the future, nor are they a simple extrapolation of past and present trends. As of this point, a single scenario is referred to as a scenario, and multiple scenarios developed as a set are referred to as scenarios.
Support Methods Group support systems (GSS) are a potential set of methods that can be used to support scenario processes. By definition, GSSs are a collection of applications, similar to groupware, aimed to facilitate group work and communication (Jessup & Valacich, 1999; Turban, Aronson, & Liang, 2005). In the general hierarchy of decision support systems (DSS), GSS is placed in the branch of communication driven DSSs (Power, 2002). Without going into too much detail, GSS implementations generally feature tools for idea generation, prioritization, commenting, and discussion, packaged into a software suite (Turban et al., 2005). Generally, GSS tools are perceived as an effective way to mediate meetings, share information, and achieve consensus on
Figure 1. The relationship of drivers, events and scenarios (a single scenario highlighted, driver relations depicted with the gray arrows)
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Table 1. Benefits and challenges of using GSSs, (adapted from Jessup & Valacich, 1999; Power, 2002; Turban et al., 2005; Weatherall & Nunamaker, 1995) GSS features
Process structuring
Keeps the group on track and helps them avoid diversions: - clear structure of the meeting; improved topic focus; systematical handling of meeting items
Goal oriented process
Aids a group to reach its goals effectively: - process support facilitates completing the tasks; discussion seen to be concluded; electronic display makes the commitments public
Parallelism
Enables many people to communicate at the same time: - more input in less time; reduces dominance by the few; opportunity for equal and more active participation; participation and contribution at one’s own level of ability and interest; electronic display distributes data immediately
Group size
Group memory
Anonymity
Access to external information
Data analysis
Different time and place meetings
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Description and advantages
Allows larger group sizes: - makes it possible to use tools for the effective facilitation of a larger group; enhances the sharing of knowledge Automatically records ideas, comments and votes: - instantly available meeting records; records of past meetings available; complete and immediate meeting minutes Members’ ideas, comments and votes not identified by others: - a more open communication; free anonymous input and votes when appropriate; less individual inhibitions; focus on the content rather than the contributor; enhanced group ownership of ideas Can easily incorporate external electronic data and files: - integration with other data systems; effective sharing of needed information Automated analysis of electronic voting: -the voting results focus the discussion; software calculates, for example, the average and standard deviation of the voting results Enables members to collaborate from different places and at different times: - offers means for remote teamwork
Outcome
Challenges
Shorter meetings
Improved quality of results Greater commitment Immediate actions
Learning through commitment and collaboration
Shorter meetings Improved quality of results
Sufficient amount of detail
Greater commitment
Relevant and coherent scenarios
Better documentation Immediate actions
More/better ideas Greater commitment
Implementation to decision making
Better trustworthiness of scenarios and process
Easier to justify the acquisition of the system
Shorter meetings Better documentation
Reduced travel costs Time savings
Efficient communication for knowledge creation
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decisions concerning unstructured or seimstructured problems (Aiken, Hawley, & Zhang, 1994; Power, 2002; Turban et al., 2005). It has been suggested in some of the more recent studies that GSSs would particularly enhance “exchange of unshared information” (Garavelli et al., 2002), which could be interpreted so that GSS facilitates communicating also tacit knowledge. Despite the positive overtone in most studies, Fjermestad and Hiltz (1999, 2001) conclude that studies concerning the efficiency of GSS as a whole would indicate that the difference compared to unsupported face-to-face meetings is insignificant or inconclusive. Limayem et al. (2005) explain this by noting that the usual mode of GSS research takes the actual group process as a “black box“ and focuses on varying and describing the inputs and studying the ex post attitudes toward the process. The benefits of using GSS are listed along with the challenges of the scenario process in Table 1. Weighing the benefits and challenges in using GSS, it seems that the research findings support the possibility of
facilitating the scenario process effectively by means of a GSS. In many instances, GSS has been deemed effective in facilitating communication and, to some extent, improving group cohesion and idea generation (e.g., Benbunan-Fich, Hiltz, & Turoff, 2002; Huang, Wei, Watson, & Tan, 2002). Other benefits might be commitment and consensus creation through anonymity and information sharing, and when the participants’ roles outside the session are not present with the input seen by the group, the focus would turn to the substance more than in a traditional face-to-face situation. Of course, vested interests are not unavoidable when dealing with humans, but in an anonymous system, the power distance and relations will presumably not have as great an effect as in unmediated face-to-face communication. In some sense, this would indicate that electronically mediated work methods might not be ideal for knowledge creation. On the other hand, there are also contradicting views, claiming that, due to effective information sharing and consensus creation, the use of a GSS would in fact be beneficial to learn-
Figure 2. An illustration of different map types
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ing or knowledge creation in a group (Garavelli et al., 2002; Kwok & Khalifa, 1998). On the subject of scenario process, little has been said directly about mediating the scenario process with electronic means. Perhaps the best known example is Blanning and Reinig’s method, which has been described in multiple instances (Blanning & Reinig, 2005). Studies that are better known are the strategic planning exercises in an USAF fighter wing reported by Adkins, Burgoon, and Nunamaker (2002) and the experiences in the early stages of GroupSystems at IBM by Nunamaker, Vogel, Heminger, Martz, Grohowski, and McGoff (1989). Among others, Kwok and Khalifa (1998) claim that GSS enhances group learning through active participation and cooperative working. In scenario literature, it is sometimes claimed that one of the major benefits of scenario process is the process itself, in the sense that it opens the decision makers up to consider effects of change, also in ways that are not written down in the actual scenarios (Bergman, 2005; Chermack, 2004; Schoemaker, 1995). In this perspective, it would be feasible that the GSS could add value to both the process and the final scenarios. Scenario processes can be supported by a number of other methodologies as well. If the scenario process is considered as a learning experience, there might be room
and demand for techniques enhancing knowledge representation. For some time now, there have been many suggestions, but rather limited research, about maps of different flavor. The most widely featured types of maps are the Mind Map, which have even been registered as a trademark, the concept map, the cognitive map, and the causal map (Figure 2). The main differences are that a mind map pictures a central concept and the up-springing branches of relating matters, where the other maps can be used to describe multiple concepts with intertwining relations and causalities. Despite their differences, maps are generally used as elementary knowledge models or repositories. The advantage of concepts formed in maps is the relatively easy and quick understandability, due to the graphical representation and immediately observable relations between the elements (Perusich & MacNeese, 1997). Supposedly, the characteristics offer improved sense making to the user. The value of maps in general would be that with mapping techniques, relatively large volumes of complex data could be presented in an illustrative manner. When it comes to the scenario process, it can be proposed that, for example, the drivers and their relations can be formed into a map fairly easily, and perhaps the information value and usability of such a map would be higher than a written document of the same subject.
Table 2. Different scenario processes (adapted from Bergman, 2005) Intuitive approach Key elements
Heuristic approaches
Statistical approach
Schwartz (1996)
van der Heijden et al. (2002)
Schoemaker (1991, 1995)
Godet (1993)
Defining the problem and scope
1. Exploration of a strategic issue
1. Structuring of the scenario process
1. Framing the scope 2. Identification of actors and stakeholders
1. Delimitation of the context 2. Identification of the key variables
Analyzing the key elements of scenarios
2. Identification of key external forces 3. Exploring the past trends 4. Evaluation of the environmental forces
2. Exploring the context of the issue
3. Exploring the predetermined elements 4. Identification of uncertainties
3. Analysis of past trends and actors 4. Analysis of the interaction of actors and the environment
3. Developing the scenarios 4. Stakeholder analysis 5. System check, evaluation
5. Construction of initial scenarios 6. Assessment of initial scenarios 7. Creation of the final learning scenarios 8. Evaluation of stakeholders
5. Creation of the environmental scenarios 6. Building the final scenarios
6. Action planning
9. Action planning 10. Reassessment of the scenarios and decision-making
7. Identification of strategic options 8. Action planning
Constructing the scenarios
Implications
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5. Creation of the logic of initial scenarios 6. Creation of final scenarios 7. Implications for decisionmaking 8. Follow-up research
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Figure 3. A generic scenario process Problem setting
Identification of the drivers of change
Composition of preliminary scenarios
Evaluation of results
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Implementation
Iteration
As for the question of which mapping technique to use, there is hardly any comparative research that would enlighten the possible differences in the acceptance and intelligibility of maps for different audiences. Causal maps, which are basically cognitive maps with the strength of the interaction marked down as a number, offer a chance to use correlation coefficients and such quantitative techniques as reinforcement. Plain cognitive maps suit the illustration on systems thinking and offer a way to identify feedback loops and such from the data and concept maps offer a touch of qualitative spice with free use of verbal descriptions in the linking phrases. As for mind maps, they perhaps suit best the purpose of data abstraction or summarizing.
Scenario Process The literature on scenario planning describes a multitude of techniques, methods, and processes. Table 2 describes in detail some of the most cited models, according to Bergman (2005). Starting from the first column on the left, Schwartz (1996) exemplifies the intuitive approach, which largely relies on logical thinking in constructing scenarios. In the middle are two examples of heuristic methods that are more structured than the intuitive, but less than the statistical ones. On the right is presented the statistical approach by Godet (1993), which is built on modeling the environment and estimating the development on mathematical grounds. The process and method adopted in this study represents the intuitive-heuristic side of the practice after Kahn and Wiener (1967) and Schoemaker (1991, 1995). Despite obvious differences in the approaches, there are common elements across the field of scenario planning. These characteristic elements, which are used as the base for the remainder of this study, are: (1) definition of the problem, (2) analyzing the key ele-
ments, that is, the drivers of change and uncertainties, (3) developing (preliminary) scenarios, (4) evaluation of results and revision, (5) creating the final scenarios, and (6) implementing the scenarios in decision making. Figure 3 illustrates the generic scenario process adopted for this study. The detailed content of the process is described in section three.
Experiences with the Supported Scenario Process This section illustrates the supported scenario process phase by phase through an actual case example from a medium-sized university. The process followed the general steps described in Figure 3. For details see Piirainen, Tuominen, Elfvengren, and Kortelainen (2007). The participants of the sessions were research, teaching, and administrative staff from different departments and administration of the university. The main material used in the scenarios was gathered in two GSS-sessions, the first of which was a pilot session and the second was used to form the final scenarios.
Problem Setting In the problem setting, a clear statement of the goal and scope helps to steer the process and keeps the discussions relevant. In the opening session, special emphasis was given to questions such as what is the goal of the process, what information is needed, who will (need to) participate in the process, what methods are to be used, what is the schedule for the process, what questions the scenarios aim to answer, what is the time span, and so on. In the example, the objective was to map out the future position and the operational environment of the university during the next 10 years.
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Identification of the Drivers After the opening session where the problem was defined, the actual work started by brainstorming the key external uncertainties and drivers of change. The phase comprised a defined period of time for idea generation with brainstorming or a similar tool, followed by a period for writing comments on the ideas and clarifica-
tion of the proposed drivers, so that there would not be ambiguity about the meaning of the inputs. After the categorization of the drivers, the brainstorming was followed by a discussion where ambiguous items were clarified between the participants by verbal explanations and additions to the system. Unclear items were rephrased or explained by comments, and overlapping items were removed or merged. After the discussion,
Table 3. The most important drivers of change in the order of importance 1st Session
Avg. (Std. dev.)
2nd Session
Avg. (Std. dev.)
Strong concentration of universities in Finland
8.22 (1.79)
Specialization of universities to achieve high quality
8.86 (0.90)
Call for centralization of research to achieve critical mass
8.11 (1.45)
Role of top tier research gains weight as a competitive advantage
8.00 (1.53)
Ministry of Education reduces funding for universities
7.89 (1.36)
Competition between universities tenses and the role of image increases
7.86 (0.38
Intensifying competition on research project funding
7.78 (0.97)
Cooperation between the university and the industry grows
7.86 (2.41)
Cooperation with the polytechnic
7.78 (1.39)
Demand for combination of technology and economics in society
7.86 (1.57)
Linking of business and technological studies
7.78 (2.39)
Globalization demands more for survival
7.43 (1.62)
Furthers shift from budget funding to research services
7.67 (0.71)
The workings of university finance changes
7.43 (2.15)
Merger of universities and polytechnics
7.67 (2.06)
Government’s role as the financier of universities decreases
7.43 (2.23)
Mission of the university: quality research or degree factory
7.56 (1.88)
Quality and amount of available student material
7.43 (2.57)
Increasing demand for research on welfare technology
7.56 (2.01)
Amount and importance of outside funding increases
7.29 (1.98)
Quality and amount of available student material
7.44 (1.94)
Importance of schooling and research as a part of national competitiveness increases
7.29 (2.56)
Decreasing competitiveness of traditional industries
7.22 (1.72)
Shifts in demand of technologies
7.14 (1.68)
(continued on following page)
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Table 3. continued Decreasing appreciation of university degrees
7.22 (1.92)
Increasing understanding of market structure
7.14 (1.86)
Requirements of innovative university
7.22 (1.99)
Ever-increasing internationalization
7.14 (1.95)
Opportunities for long-term productive cooperation with the industry
7.11 (1.45)
Capacity to absorb new technologies
7.14 (2.67)
Teaching of mathematical subjects in elementary and high schools
7.11 (1.90)
Russia requires more attention from the university
7.00 (2.16)
Engineering work shifts to third world countries
7.00 (1.50)
Forest cluster and basic technology keep their importance
7.00 (2.52)
Effect of regional development planning
6.89 (1.96)
Economic growth in Asia increases knowledge and knowhow
7.00 (3.16)
Increasing unemployment of graduate engineers in Finland
6.89 (2.03)
Importance of business ventures as users of technology increases
6.86 (1.35)
Focus and amount of EU research funding
6.89 (2.15)
Shift from handing out degrees to knowledge diffusion
6.86 (1.46)
the drivers were prioritized by voting. Table 3 presents the drivers from sessions in the order of rank of importance after voting.
Preliminary Scenarios The drivers presented in Table 3 form the backbone of the scenarios for the university in case. After working out the relevant drivers, there is an array of possibilities to work the preliminary scenarios. The literature describes various methods, but in this case the method proposed by Blanning and Reinig (2005) was adopted. The phase comprises brainstorming of events which will or could be triggered by the drivers. Thus, after the identification of the drivers and discussion, the participants were asked to identify concrete events that are consequent to the identified drivers. In the first of the two sessions, the participants started with a blank screen and a printed list of the identified drivers, but in the second session there were three base categories: internal, interest groups, and micro and macro environment. The resulting event sets were once again discussed and commented on, and overlapping events were merged or removed.
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The point of this exercise is to have a large set of events, which are derived from the drivers. When there is a sufficient number of events, say 50-100, the events can be again commented on and prioritized, leaving the most insignificant out if there is a need to lessen the bulk. The catch in this approach is that the group votes for (or otherwise assigns) a subjective probability and impact factor for each event. At this stage, Blanning and Reinig (2005) propose that the events are projected to a scatter plot where probability 1-100% forms the x-axis and impact from very negative to very positive forms the y-axis. The original proposition is that the scenarios are formed by selecting three groups of 10-20 events, so that the most probable events form a realistic scenario, medium to high probability events with a positive impact form a positive scenario, and the events with medium to high probability and a negative impact form a negative scenario. In the present case, the scenarios were formed on the basis of the voting by grouping them graphically from the scatter plot, so that in the final stage of the workshop the results were subjected to discussion with the concern that the set was logical and coherent and that the events were grouped in approximate chronological order. In the case, how829
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ever, the expectation-maximization clustering method, which is based on iterative use of the k-means algorithm (Witten & Frank, 2005, p. 265), was used instead of the originally proposed graphical clustering, to adjust the final grouping of the event to scenarios. The GSS workshop phase of the process ended in the evaluation of the events and the graphical grouping, from which the data is moved for the rest of the process in a GSS-log file. The log contains the input items and voting results, that is, the drivers, events, and their ranks, impacts, and probabilities. The first task after the workshop is to examine the log, clean the data, and start the mapping of the drivers and the events. Experience from scenario writing suggests that the concept map seems to tell a story of what happens and how, whereas the latter cognitive map provides a better answers to questions beginning with “why.” With this in mind, the approach taken in the mapping was to use the principles of systems thinking to ponder about the cause and effect between the drivers and events inside the scenarios. Using this analogy, the drivers of the scenarios form a system with feedback relations. Figure 4 illustrates the feedback loops identified from a cognitive map drawn from the drivers in the case in question. After the scenario sets were formed by clustering the events, and the drivers were examined, the logic behind the scenarios was manifested in concept maps
based on the event items and their comments. The mapping as a process went so that the events were fed to a cognitive mapping program, IHMC cMapTools, and the links between the events were drawn. The primary source of links were again the comments from the GSS log, and secondarily the reasoning based on the driver map and common knowledge. The names for the scenarios were picked after examining the general theme in the scenarios. In this case, after the initial maps were drawn, they were presented to some of the closer colleagues familiar with the sessions as a sort of focus group interview, to test the reactions and validate the logical structure of the maps. Figure 5 shows one of the final concept maps of the case, used to illustrate the logic and the paths of the scenarios.
Final Scenarios The final phase in the scenario process before implementation is evaluation of the results and formulation of the final scenarios. The objective in the evaluation is to judge whether the scenarios cover the intended scope and timeframe adequately and whether they are logical enough for the final stories. The final scenario stories are written around the logics in the concept maps. During the writing the maps are subject to some minor adjustment. Otherwise, the writing is a fairly straightforward process of tying the
Figure 4. An abstracted cognitive map of the drivers in the case 1 More private sector funding, closer cooperation
2 As technology advances, new ventures will take their place beside basic industry
3 Research gains importance as a factor of national competitiveness, which encourages more profound teaching instead of mass degrees 4 Due to higher demand and competition, the research scope deepens and the perspective widens
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Figure 5. An example of a scenario map
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events together as a logical story from the present to a defined state in the future. Previous work, such as publications by government bureaus, research organizations, and similar instances, offers the opportunity to test and challenge the writer’s own perspectives. Careful examination of the drivers aids considerably in forming the scenario logics and reverberates in the stories, as well as in the scenario maps. One might characterize the process as iterative, as a resonance between the drivers and the scenario maps conducted by the writer. The detailed contents of the four created scenarios are provided in Piirainen et al. (2007). Figure 6 provides a window to the final results.
Conclusion and Discussion This study has contributed to the existing body of research by integrating decision support concept to the scenario concept and providing some practical insight to the execution of the scenario process. The actual case suggests that the concept of using GSSsupported scenarios as an aid in decision making is a promising and feasible concept. The workshops utilizing the described process have generally reached their goals, the resulting scenarios have been rated as fairly trustworthy, and the process as a whole has been well accepted. Also based on the data collected
Figure 6. Overview of the scenarios
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in the workshops, the use of a GSS has had a positive impact in the sessions through mediating the work efficiently, by providing anonymity when needed, and offering tools such as voting. Table 4 summarizes the challenges met, as well as the different tools used during the scenario process. As can be observed, the challenges and requirements vary through the process, which creates its own problems, as one single software solution or technique cannot effectively mediate the whole process. It can be tentatively concluded that the supported scenario process provides an effective way to compile scenarios to be used as a decision aid in strategic management problems. The GSS with other tools enable efficient information gathering from participants from different organizations and lower the barriers of communication, resulting in what could be called as respectable scenarios. The limiting factor in GSS or any other support method is often the experience of the users. The facilitated decision room session is often the safe choice if the participants are not used to working with computer mediation or the systems have not been tested in different setting. As Bradfield, Wright, Burt, Cairns, and van der Heijden (2005) noted, the general trend in scenario practice has been toward more analytical and sophisticated methods, such as morphological analysis, field anomaly relaxation, cross and trend impact analysis, and methods aimed at raising the methodological integrity of
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scenarios. The line of research presented in this article is how to make scenarios more convenient and efficient as a process, which contributes to the possibility of bringing the benefits of using scenarios closer to operational management from strategic and long range planning. There is also a growing body of literature on the subject of broadening the scope of utilizing scenarios, especially in the fields of management of technology and new product development (Drew, 2006; Kokkonen, et al., 2006; Naumanen, 2006). Together, these efforts push the envelope of making scenarios more usable as a method for decisions support. However, what is still lacking is research on the impact of scenario process to the organizations implementing the scenario process, which is brought forward by Chermack (2004). The other missing corner is research concerning the effect of the scenario method to the reliability of scenarios as a method for forecasting/foresight and the financial and other tangible effects scenarios are promised to have. These kinds of research questions are hard to undertake, and the possibility to retrieve definitive results is slim, but nevertheless they are important to scenario planning as a management discipline.
References Adkins, M., Burgoon, M., & Nunamaker, J. F., Jr. (2002). Using group support systems for strategic planning with the United States Air Force. Decision Support Systems, 34, 315-337. Aiken, M., Hawley, D., & Zhang, W. (1994). Increasing meeting efficiency with a GDSS. Industrial Management & Data Systems, 94, 13-16 Benbunan-Fich, R., Hiltz, S. R., & Turoff, M. (2002). A comparative content analysis of face to face vs. asynchronous group decision making. Decision Support Systems, 38, 457-469. Bergman, J.-P. (2005). Supporting knowledge creation and sharing in the early phases of the strategic innovation process. Acta Universitatis Lappeenrantaesis, 212, 180. Blanning, R. W., & Reinig, B. A. (2005). A framework for conducting political event analysis using group support systems. Decision Support Systems, 38, 511527.
Table 4. Challenges and support tools in the scenario process
Challenges
Elements of the scenario process Problem setting
Drivers
Preliminary Scenarios
Reasonable and clear objectives and scope
Cropping the relevant drivers
Recognizing relevant and significant events
Fusing the collective knowledge of the participants
Elucidation of events and underlying logic prior to voting
Choosing the participating group
Gaining the trust of the participants Choosing the (right) methods and means
Support tools
Covering all aspects in the scope of the scenarios
GSS
Interfacing the events logically to the drivers Getting the causalities and timeline right GSS, Mapping tools, Clustering,
Evaluation
Final scenarios
Implementation
Preserving the underlying logic of the group Assurance of the participants
Getting the attention of the group to actually validate the results
Compromising between the level of detail, length and style
Making the scenarios realistic and interesting, without alienating the readers
Instituting the scenarios in strategizing and/or daily management
Mapping tools, possibly GSS
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Key Terms Causal Map: Causal maps include elements called nodes, which are allowed to have causal relationships of different strengths of positive or negative loading depicted with a number, usually in the range of from 1 (weak) to 3 (strong). The relationships of the nodes are depicted with arcs or links labeled with the assumed polarity and loading factor or strength of causality, Links with positive polarity refer to dependency (when A increases B increases proportionally to the loading factor) and negative to inverse dependency (when A increases, B decreases). Cluster Analysis, Clustering: Generally, cluster analysis, or clustering, comprises a wide array of mathematical methods and algorithms for grouping similar items in a sample to create classifications and hierarchies through statistical manipulation of given measures of samples from the population being clustered.
Cognitive Map: The map consists of nodes similar to causal map, but the relationships do not have specified strength, just the polarity is included. The relations are illustrated with arrows or connectors, which are assigned with either a plus or a minus sign to depict the polarity of the relationship (see Causal map). Concept Map: Concept maps are built of nodes connected by connectors, which have written descriptions called linking phrases instead of polarity of strength. Concept maps can be used to describe conceptual structures and relations in them and the concept maps suit also aggregation and preservation of knowledge (see Causal map, Cognitive map). Driver(s) of Change, Driver(s): The drivers create movement in the operational field, which can be reduced to a chain of related events. It is not assumed that a driver has one defined state, but multiple possible states in different scenarios. Thus, a driver can influence multiple events, which may or may not be inconsistent in a given set of scenarios, but according to the definition of a scenario, not in a single scenario (see: A scenario). Group Support System (GSS): By definition, group support systems are a collection of applications aimed at facilitating group work and communication similar to groupware. In the general hierarchy of decision support systems (DSS), GSS is placed in the branch of communication driven DSSs. GSS implementations generally feature tools for idea generation, prioritization, commenting, and discussion, packaged into a software suite. Groupware: Both groupware and GSS are computerized systems designed for facilitating group work. This study adopts the narrow definition of GSS as a system that is used to aid decision making in a defined situation, between certain individuals assembled for a particular task, during a specified time, and groupware as a system that is used to mediate and facilitate the workflow of a wider audience in an undisclosed timeframe (see: Group Support System). Mind Map: A mind map consists of a central concept which acts as a headline for the map and the branches that represent the aspects of the main concept. A mind map allows summarizing and decomposition of the key aspects of a complex problem or issue.
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A Scenario, Scenarios: Scenarios are a set of separate, logical paths of development, which lead from the present to a defined state in the future. These separate scenarios are chains of events leading from the present status quo to the defined end state during the defined time span. Furthermore, scenarios are not descriptions of a certain situation some time in the future, nor are they a simple extrapolation of past and present trends.
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Scenario Method, Scenario Methodology: The scenario method contains the set of assumptions, rules, and tools, including but not limited to the process outline, which govern the process of scenario planning. The scenario method describes the basic assumptions and process model, how the future is to be captured in the scenarios, and the method through which the scenarios are formed, including the recommended support systems, modeling techniques, and data sources (see: A scenario, Driver(s) of change).
