Paul Gray
email: paulgray@cgu.edu

Anat Hovav
email: anatzh@korea.ac.kr

Methods for Studying the Information Systems Future

Keywords: Modeling the future, environmental scanning, Delphi, scenarios, role of futures in academic research 2 Futures Research

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

A research method is a strategy of inquiry which moves from the underlying philosophical assumptions to research design and data collection.

Michael Myers (1977) Historically, the study of the human and organizational aspects1 of management information systems (MIS) adopted the methodologies of social science. This view of research permeated IS as early as the Minnesota experiments [START_REF] Dickson | Research in Management Information Systems: The Minnesota Experiments[END_REF]. The majority of work published at the time used surveys or quasi-experiments. Over time, the field recognized the need for a Apluralism of methodologies@ [START_REF] Robey | Research Commentary: Diversity in Information Systems Research: Threat, Promise, and Responsibility[END_REF][START_REF] Walsham | The Emergence of Interpretivism in IS Research[END_REF]. At the IFIP 8.2 working conference in 1997, Markus pointed out that qualitative research methods were becoming acceptable in MIS research. These methods, including case studies, action, ethnographic, and grounded research, are now a routine part of the IS tool kit. These methodologies encompass positivist, and critical work. One cannot say that a given methodology is better than another [START_REF] Markus | The Qualitative Difference in Information Systems Research and Practice[END_REF]). The choice depends on the research question and the context being studied.

The Conference Theme

The theme of this conference is AResearching the Future.@ The future is a dynamic environment and is fraught with uncertainty. Because the future will differ from the present, simple extrapolation of current trends into the years ahead is not enough.

Policies, organizational business practices, science and technology, and tastes can and will change Information Systems and their use. The occurrence of events and the direction of trends are unknown in advance. Often the best that can be done is assigning probabilities to outcomes. As a result, conventional methods of research are no longer sufficient when considering the long-term future. Consider, for example, the future of the Internet and its security. At this writing, the United States government proposes to allow the President to shut down domestic Internet providers in an emergency (e.g., foreign cyber-attack). What is the probability this law will pass in 2011? If passed, will the authority be used? How severe must an attack be to declare an emergency? If an emergency is declared, how long will the Internet be down in the United States? In other countries? What will be the effect on the economy as a function of shutdown period?

Another example: The conventional wisdom is that the trend of security incidents is rising. Will this trend continue? Stabilize? Decrease? Alternate between increasing and decreasing? Are there inflection points? How will security be improved by specific policies? By enforcement? By government investment? By private sector investment? For each of these alternatives, when (if ever) will they be introduced? If one of them is introduced, how much will it affect other options? Will its security be perceived so poor that significant numbers will stop using the Internet? What are the economic implications if the Internet collapses? If alternative future situations are possible, what is the probability of each? If a particular unfavorable outcome is highly likely under one set of assumptions, are there policies, actions, or technologies that can ameliorate or eliminate that outcome?

Note that in these examples the scope is much broader than the IS field=s conventional studies of individual organizations or industries. The impact of IS is moving from the organizational context to society and beyond. Furthermore, the research questions being asked by the MIS discipline have expanded from the operational and strategic to societal policies and issues. To address these questions, MIS researchers need to expand their methodology toolkit.

Although futures research methodologies are used extensively in such disciplines as policy analysis, strategy, competitive intelligence, and forecasting, and are widely used by industry, government, and centers for studying the future, they are rarely used in IS research. Studies about the future of information technology itself have appeared from time to time. Herbert [START_REF] Simon | The Sciences of the Artificial[END_REF] forecast design science and artificial intelligence. [START_REF] Dahlbom | Going to the Future[END_REF][START_REF] Dahlbom | The Future of Our Profession[END_REF] discussed the future of IT in conceptual terms but did not use a futures methodology to address ICT research questions.

What This Paper Is About

This paper introduces three well-established futures research methodologies, and shows how these methodologies can be applied to rigorous IS academic research. The goal of this paper is technology transfer; that is, to familiarize IS researchers with these methodologies and explore some of the institutionalization mechanisms needed to incorporate them into the IS research tradition. The discussion is exploratory to initiate dialogue among IS researchers. Section 2 briefly describes three standard futures methodologies (environmental scanning, Delphi, and scenarios) and illustrates how they can be incorporated into IS research. Using scenarios as an example, Section 3 discusses futures methodology as an academic method for IS research. This section includes epistemology, research design (research questions, unit of analysis, setting propositions and criteria for interpreting results), and issues such as construct and internal validity and generalizability. The conclusions (Section 4) present our views of what can best be accomplished with conventional and futures methods. Appendices A and B expand the descriptions of the Delphi and scenarios methodologies, Appendix C suggests guidelines for futures research in information systems, and Appendix D lists 37 methods used in futures research.

Assumptions of Futures Research

The three methods and their variations described in this section share a common set of assumptions. First, the future is not prewritten nor is it wholly determined by the past. Second, present conditions and trends will continue along evolutionary lines unless natural limits are approached (e.g., speed of light) and/or extreme external changes occur (e.g, PC, Internet, iPhone, iPad). External changes are inherently uncertain. Some aspects cannot yet be explained scientifically and some aspects (e.g., policy) depend on human choice. Attempts to guide the future through policy are based on (1) assessment of present and past conditions, (2) possible future changes, and (3) the extent to which future conditions can be altered by social resources. The desirability of social conditions is a value judgment, not based on fixed criteria of economic rationality.

Futures research methods should be undertaken when2

1. Uncertainty about the future is high 2. Historical data is limited 3. The number of stakeholders (e.g., IT and communications vendors, users, companies, government) is large 4. The issue is important for decision makers 5. Major impact on IS is involved and the issue is more than just technical 6. The analysts involved are knowledgeable about the problem

Environmental Scanning/Collective Intelligence

Environmental scanning3 involves observing the present and using the results to infer the future. [START_REF] Glenn | Futures Research Methodology, Version 3.0[END_REF] trace the method back to the crow=s nest on ancient sailing ships. Environmental scanning is also a way to gain the input needed for SWOT (strength, weaknesses, opportunities, threat) analysis. 4Environmental scanning is used to create databases that serve as the foundation for futures studies. The objective is to distinguish among constant, slow, continuous or potentially disruptive change. Scanning involves analyzing multiple sources of information (public and private) and identifying early change indicators. Such indicators are used to provide managers the required lead-time necessary to respond to future events and trends. Public sources include database searches, executive speeches and interviews, papers at meetings, and employment advertisements. Much analysis and skill are involved in filtering information (i.e., deciding what is important and what is irrelevant). Of particular interest, for example, are finding disruptive technologies and obtaining reliable estimates of when they will reach the market. Environmental scanning is often used in business intelligence.

Delphi

The Delphi method [START_REF] Linstone | The Delphi Method: Techniques and Applications[END_REF] combines the wisdom of crowds [START_REF] Surowiecki | The Wisdom of Crowds[END_REF]) to estimate, as a function of time, the probabilities of events (e.g., an invention) and the evolution of trends (ongoing phenomena) when there is no source of factual data and a basis for opinion exists. The information sought usually affects decisions and policies.

In Delphi, questions are presented to a panel of subject experts on a topic of interest. For example, what is the earliest, most likely,and latest time when the Google.com market share for businesses equals Microsoft=s? How many PCs will be sold in Africa in 2015? In 2020? In 2025? (Input provided to the respondents might include 2000B2009 sales.) Respondent anonymity prevents undue influence, groupthink, or biased judgment under uncertainty [START_REF] Tsohou | Process-Variance Models in Information Security Awareness Research[END_REF]). The panelists are typically dispersed in space and time, and communication is often facilitated by information and communication technologies (e.g., email, GDSS, Web 2.0 applications).

A Delphi survey is administered in loop-back rounds, usually by a facilitator. Each round=s responses are tabulated and fed back to the group. Panelists who provided extreme estimates (smallest and largest values) are asked to state their reasons. Their opinions are also used as inputs to the next round. The rounds stop when either consensus or dissensus occurs. Consensus is measured with traditional statistical tools (cluster analysis or distance from the mean). Desired betas5 are predetermined. Experimental results indicate that good forecasts can be obtained and that accuracy can be improved by asking people to state their expertise for each item. Dissensus on a question indicates that expert opinion on the subject is divided. At present, Delphi is the most commonly used futures research methodology in published IS research. Appendix A explains the method further.

The cross-impact model (e.g., [START_REF] Glenn | Futures Research Methodology, Version 3.0[END_REF] is the next step of sophistication beyond Delphi. Delphi assumes events and trends are mutually independent and all other things are equal (i.e., ceteris paribus). These assumptions are rarely valid in the real world. For example, the probability of occurrence of a particular innovation may be more likely if another innovation occurs or if a trend reaches a threshold level. That is, event probabilities and trends do interact with one another. An additional set of inputs is therefore required: the estimated change (i.e., the impact) in the probabilities of every other event if a particular event occurs.

Cross-impact analysis is run as a multi-period stochastic simulation. The first period (e.g., 2 years) is examined. A random number generator is used to determine which events Aoccurred.@ That is, if the random number for an event is less than or equal to the probability of an event during that period, the event is declared occurred. For each occurred event, its impact on events that have not yet occurred is used to update the future probabilities of events with which it interacts. Given the updated probabilities, the next period is run. The process is repeated until the entire time being studied is completed. To keep the updated probabilities in the range 0 to 1, the calculations involve the use of likelihood ratios. The output of a single run is a scenario that has a low probability of occurrence. The aggregate of all the runsCthat is, the complete simulationCprovides expected values and variances.

If the foregoing sounds complex, it is. Cross-impact requires many more inputs and calculations than Delphi. In terms of physical effort by researchers and expert panels, it takes much longer to perform than a Delphi. However, the results obtained provide a much richer understanding of the problem studied.

Scenarios

Scenarios are a way of communicating about the future. They are stories that describe the future in terms that managers and other nonspecialists can understand. That does not mean that they are arbitrary. They are carefully developed to reflect the logical implications of assumptions and forecasts about the future [START_REF] Gray | The IS Organization of the Future: Four Scenarios for 2020[END_REF]. Scenarios are an end product of a prudent analysis, not the workings of the imagination of a novelist or a science fiction writer. Scenario building is a discipline that involves a number of stakeholders and typically describes future events and trends within a given set of assumptions and constraints. Why Scenarios? Organizations use scenarios to forecast alternative futures they may face. Scenarios examine the issues to be resolved, time relations, interactions, and logical consequences. Scenarios are most powerful when several are used to present alternative views of the future as seen from the present. They are often used as a policy analysis tool [START_REF] Boroush | Scenarios for the Defense Industry after 1995[END_REF]. For example, if a scenario indicates a high probability of an undesirable future, actions can be taken to reduce that probability (e.g., lobbying, merging, changing business practices, innovating).

Scenarios are most valuable when: (1) the external environment is highly uncertain, (2) the environment could take plausible alternative trajectories, or (3) specific indicators measure the driving forces and their likely impacts if they occur.

Scenario Development. Six principles in creating high quality scenarios are described in [START_REF] Boroush | Scenarios for the Defense Industry after 1995[END_REF] and [START_REF] Morrison | The Future Tool Kit[END_REF].

1. Establish assumptions and constraints in advance. 2. Identify the critical (or possible) choices likely to be faced. 3. Identify key drivers. 4. Model or create the scenario space (see below). Select the values to be associated with each variable in the scenario space. The values should lead to a range of outcomes that cover the scenario space. 5. Analyze the implications of alternative contingency policies and strategic plans that cope with each scenario. 6. Use Delphi, qualitative discussion, or cross-impact analysis as inputs.

To use scenarios for long-term strategic planning, iterate steps 3, 4, and 5 to identify the impact of various actions.

Scenario Principles. Scenarios must adhere to three principles. They must be possible, plausible, and internally consistent .

Possible: No barriers to the events described can occur (e.g., speeds faster than light) Plausible: The described events are believable (e.g., isolating all corporate computers from the Internet to ensure privacy is possible but not plausible) Internally consistent: All parts of the scenario are consistent with one another (e.g., that a firm relies on law enforcement for cyber protection but personnel rarely report hacking attempts is internally inconsistent)

The Scenario Space. Scenarios are embedded in a scenario space. The space is defined by dimensions with prespecified values. For example, the scenario space in Figure 1 is based on two dimensions: technical software security and identity theft and privacy breaches, with each assigned a high and a low. These two dimensions create four scenarios, one for each combination.

The practicalities of scenarios and an example from our previous work are presented in Appendix B.

Futures as an Academic Method

The theme of this conference indicates that investigating the future of ICT and ICT-based practices is ready to become a new, viable part of mainstream IS research.

Researchers who want to establish new paradigms generally overcome the inevitable gatekeeper objections by mobilizing a community of supporters to achieve the convergence of opinions required to introduce new ways of thinking. To gain mainstream acceptance, futures research methodologies should have the characteristics of a scientific methodology so they can be applied with academic rigor. This section presents exploratory ideas for making futures research methodologies viable for IS. The section is framed in terms of a specific methodology, scenarios, discussed in Section 2.4 and Appendix B. It is also framed in terms of the sequence of steps usually followed in IS research. The initial step is to explore whether existing IS research designs can incorporate established futures research methods to create rigorous and relevant results.

Epistemology

The epistemology of futures research methods can be either positivistic or interpretive.

In positivistic research, the assumptions lead to propositions which are then investtigated using, for example, Delphi or cross-impact analysis. Alternatively, they could be interpretive, where subjective processes lead to various outcomes (scenarios). In both qualitative and interpretive research, the role of the researcher is instrumental.

The researchers= values and beliefs help shape their investigation. It is rare for two futures studies to arrive at identical solutions. Yet, by determining the necessary dimensions and driving forces in advance, the researchers can justify their assumptions and the logical progression of their arguments. [START_REF] Markus | Information Technology and Organizational Change: Causal Structure in Theory and Research[END_REF] termed Aprocess theories.@7 Process theories are longitudinal and dynamic. Process theories are defined as Acausation consists of necessary components in sequence@ (Markus and Robey 1988, p. 590) where chance and random events play a role. Future methodologies can be used to incorporate interactions (e.g., through cross impact) and unexpected events (e.g., through scenarios). In process theories, the dependent variable is a set of discrete outcomes rather than a continuous variable. Outcomes depend on the nature of the process as well as the levels of the predictors. Conventional variance theory, on the other hand, assumes that a change in the independent variables will result in a predicted change to the dependent variable. In addition, process models are event-driven [START_REF] Tsohou | Process-Variance Models in Information Security Awareness Research[END_REF]. Process theories assume that the dependent variable can change over time and that the sequence and duration of events matter.

Theoretical Foundations and

Process studies aim to answer research questions such as, AHow does a particular issue emerge, develop, increase/decrease/cease over time?@ [START_REF] Tsohou | Process-Variance Models in Information Security Awareness Research[END_REF]. Futures methodologies are well positioned to answer such questions. For example, a scenario can describe the process by which an issue will emerge and the events that could lead to its development. Cross impact analysis can be used to estimate the probability distribution that an issue becomes salient over time or ceases to exist.

Setting Propositions and Linking Data to Propositions. [START_REF] Gregor | The Nature of Theory in Information Systems[END_REF] defines five types of IS theories based on their structure. Theories for predicting (Gregor=s Type III), address the question Awhat will be@ rather than why something is.

Such theories should be used to predict outcomes from a set of relevant factors. Predictive theories can be used to define a set of alternative propositions. Unlike in traditional research, the assumption is that the future cannot be predicted and that propositions should be formulated as discussed above, under AResearch Design Components@ and their formulation depends on the methodology used. For example in a study of the future of academic e-journals [START_REF] Hovav | Electronic Publishing: A Framework[END_REF], the authors used Eisenhard=s (1994) framework to develop two propositions: the future of academic e-journals depends on user acceptance and on economic viability. A subsequent paper developed a scenario space and created four possible scenarios [START_REF] Hovav | Future Penetration of Academic Electronic Journals: Four Scenarios[END_REF]. The scenarios were used to link the data collected about six types of e-journals in support of the two propositions.

Gregor also states that predictive theories, although used in econometrics and finance, are rarely used in IS. She suggests using traditional research methods designed for studying the present and the past (e.g., correlations, regression, and data mining) to address Type III problems. We disagree. Futures research methodologies are more suitable to the study of these type of problems.

Unit of Analysis. Most futures studies tend to be broad, looking at topics that span societies, countries, organizations, and technologies.

Selecting Criteria for Interpreting Results. The methods illustrated in this paper depend on the selection of a set of assumptions. In their classification of current and future information systems, [START_REF] Ein-Dor | A Classification of Information Systems: Analysis and Interpretation[END_REF] argued that selecting the dimensions for classifying the systems is crucial and difficult. A different set of dimensions can lead to different results. Sommer and Sommer=s (1991) guideline for ethnographic research also highlights the need to separate the selection of criteria from data collection. Separation is especially important due to the involvement of the researcher and his/her possible subjectivity in data collection. These assertions also apply to futures research. By determining the dimensions along which the future is described in advance, research subjectivity can be lessened. For example, one of the critical and more difficult steps in scenario building is identifying the driving forces that shape one potential future from others [START_REF] Morrison | The Future Tool Kit[END_REF].

Quality of the Design. Construct, internal, and external validity are important in research design. Quantitative research has well-defined mathematical methods to measure internal and external validity. Qualitative research also has a set of requirements (e.g., [START_REF] Yin | Case Study Research Design and Method[END_REF], although these guidelines are less defined and are often subjective. The following subsections explore the content, internal, and external considerations for futures research methodologies.

Construct Validity. Construct validity refers to the ability to ensure that the measures used correlate with the theorized construct. Quantitative research uses methods such as convergent and discriminant analysis. Qualitative research uses triangulation, multiple sources of information, chain of evidence, and participant review [START_REF] Yin | Case Study Research Design and Method[END_REF]. Futures research methodologies could use multiple sources of data and methods, converging expert review and the Awisdom of crowds.@ The implementation depends on the specific method used, the question studied, and the availability of data. Consider a simple example. A research project investigating the long-term relationships between Internet security and e-commerce might use both environmental scanning (Section 2.2) and Delphi (Section 2.3). If the two methods lead to similar forecasts, the validity of the relations found between security and e-commerce are assumed to be stronger.

Internal Validity. Internal validity refers to the validity of the causal relationships between constructs. In traditional quantitative research, internal validity is measured statistically. In futures studies as in qualitative research, internal validity guidelines need to be developed. These guidelines are likely to vary among methodologies. For example, in scenarios, the relationships among various outcomes need to be plausible, possible, and internally consistent (Section 2).

Generalizability. Generalizability (i.e., external validity) to a range of situations is the hallmark of a good theory. However, generalizability has to be examined from two aspects: (1) the methods and (2) the problems. The methods of futures research are generalizable in that they can be used in most studies about the Afuture ofY@ including both the future and the impact of IS. However, the results obtained from these methods rarely extend beyond the specific problem being considered.

Comparison of Methodologies. Table 1 shows an initial comparison of a futures research methodology (the creation of scenarios) with conventional qualitative and quantitative methodologies. This comparison provides illustrative examples and is intended to stimulate discussion.

Conclusions

Information Systems is a field that changes rapidly. It is subject to disruptions in both technologies and social norms. If IS researchers are to do more than speculate about change, they need to ensure that their analyses about the future rest on solid ground. Fortunately, by learning about existing, well-established methodologies, such as the ones presented in this paper and in [START_REF] Coates | Scenario Planning[END_REF], [START_REF] Glenn | Futures Research Methodology, Version 3.0[END_REF], [START_REF] Gray | From Hindsight to Foresight: Applying Futures Research Techniques in Information Systems[END_REF], and [START_REF] Linstone | The Delphi Method: Techniques and Applications[END_REF], researchers do not need to reinvent them. Rather, they can use them to gain insight into what lies ahead.

The results of applying any of the futures research methods cannot and does not forecast the future precisely. Because they do reduce the uncertainly, they can improve decision-making at the firm, country, and societal levels. The relatively straight forward steps in a perfect Delphi are8

1. Specify the subject and the objective 2.

Specify all outcomes you want to know about 3.

Specify how the results will be used if achieved 4.

Include only precisely phrased judgmental questions that cover topics of interest 5.

Assemble respondents who can answer questions creatively, in depth, and on schedule 6.

Provide respondents with relevant historical data so that they anchor their answers based on reality 7.

Collate the group=s responses consistently and promptly 8.

Make sure respondents are explicit about the reasons for their estimates, interpretations, and recommendations 9.

Analyze the data 10.

Identify any important needed improvements in methodology and results 11.

Write and publish a report

Research Results. Experiments were undertaken by Delphi co-inventors [START_REF] Dalkey | An Experimental Application of the Delphi Method to the Use of Experts[END_REF]. UCLA graduate students and RAND analysts were asked questions, (e.g., number of telephones in Uganda) for which correct answers were available. However, only a few subjects were likely to know the answers. In general, the subjects made reasonable Aestimates@ for these questions. The major findings were 1.

The spread of opinion narrows after the second round and the median usually shifts toward the true answer.

2.

Delphi interactions generally produce more accurate estimates than face-to-face meetings.

3.

Usually, the narrower the range of opinion, the more accurate the answer. 4.

Self-appraised expertise is a powerful indicator of accuracy.

Variations on Delphi. Variations on the standard Delphi process include $ Round 0. In round 0, the panel members define the subject, using open-ended questions, rather than the organizer selecting the questions. This approach expands and refines the range of topics covered (e.g., in a Delphi on computer security, a panelist introduces a new physical form of identification or a different encryption algorithm). $ Self-rating. Rather than accepting all responses as equal, panelists rate their own expertise on each question. These ratings become weights when tabulating responses. Self-ratings are not perfect (e.g., a small fraction rates themselves low on almost everything but their specialty whereas others claim expertise on almost everything). $ Permanent Panel. For running many Delphi sessions over time, recruiting panelists for each inquiry becomes a major task. Helmer (personal communication) proposed setting up a large permanent panel (called D-net) from which people are chosen for a particular Delphi. Such a panel might include broadly knowledgeable, computer-literate, retired people.

$ Mini Delphi. Distributed technologies used in real time allow the panel to work simultaneously. For each round, the participants enter their responses anonymously into the computer and give reasons for their values. For each question, two or more participants (typically those with extreme answers) are asked to discuss their views briefly. Panelists then revise their estimates anonymously. This approach is particularly useful for gaining closure.

Delphi Advantages. Delphi brings together knowledgeable people to work on a problem and maintains their attention on the issues. The set of questions provides a framework in which to work. Anonymity minimizes barriers to communication. Delphi provides people the opportunity to be heard as individuals rather than as labels (e.g., affiliation, field, reputation). It produces precise, documented records.

Delphi Disadvantages. Anonymity and separation from other panelists removes the face-to-face stimulation of interactions. When invented, the process was paper and pencil. A round took considerable time because questionnaires and results of previous rounds involved reproduction, mailing, and waiting for responses. Panel size decreased with each round because of a lack of timely response. Much changed with email and the Internet. Although rounds run more quickly, the attrition problem still exists.

Conclusions on Delphi. Delphi is well established. It incorporates expert opinion into forecasts, providing insights for scenarios and input to cross-impact analysis. Occasionally, Delphi is easy to discount given the history of expert opinion failure and the many pitfalls in running it. Yet, the use of the wisdom of crowds, where the crowd knows about the subject, commends it where other approaches fail.

In creating scenarios, it is import to discuss (1) the stakeholders involved, (2) the effects on the stakeholders, and (3) the assumed values of the parameters.

The parameter values must be consistent with the scenario space element being discussed. For example, a scenario for coping with security issues by making software an outsourced service results in a different role, size, and technical composition of the IS department than one based on technological advances and preventive tools.

Number of Scenarios. Because each scenario takes considerable time to create, only a few can be generated in a particular study. Select a small number of scenarios (e.g., four or six) from a scenario space which together represent diverse future outcomes. Present an even number of scenarios; an odd number leads managers to select the middle scenario as most likely and avoid considering the others [START_REF] Morrison | The Future Tool Kit[END_REF]. With two scenarios, managers select only the more favorable one.

Scenario Pitfalls. [START_REF] Coates | Scenario Planning[END_REF] suggests five pitfalls scenario builders should avoid:

1. Confusing the scenario and the outcome 2. Confusing the scenario with a forecast 3. Assuming that scenarios are static 4. Relying on extrapolation as THE scenario 5. Creating scenarios where everything is maximum or minimum or average

Example: In the 1990s, companies like Digital Equipment Corporation disappeared because they assumed the mainframe or minicomputer would continue to be the only future. They did not plan for microcomputers or Intel architecture.

Conclusions on Scenarios. Scenarios are stories used for communicating and are a basis for policy discussions. They help in understanding the implications of different outcomes and the range of possibilities as well as in developing alternatives that can react to or influence whichever future occurs. Scenario building is both art and science. Although it involves experts that follow the processes described here, rarely do two scenario builders create the same scenarios for a given problem [START_REF] Coates | Scenario Planning[END_REF].

Example: Figure B1 and Table B1 summarize the findings of a study published by the authors in Information Systems Frontiers in 1999. Four scenarios were considered in a twodimensional scenario space. The details are shown in Table B1. The shadings show the outcomes as of 2011. Much of the status quo remains but only three of the Dystopian elements exist and many of the Utopian and Technological outcomes came true. Recognize that scenarios rarely occur as forecast.

Social

Table 1 .

 1 Comparison of Quantitative, Qualitative and Scenarios Methodologies

	Item	Scenarios	Quantitative	Qualitative*
		Dynamic	Static	Static
	Time	Longitudinal	Cross sectional	Mixed
	Theory type	Process	Variance	Variance/process
	Research question	Prediction	Analysis	Analysis
			Explanation	Description
	Construct and	Possible, plausible,	Statistical	See Tsohou et al. 2008
	internal validity	internally consistent
	Antecedents	Past events, trends	Quantifiable dependent	Descriptive or quantifiable
			variables	dependent variables
	Consequences	Alternative or	Quantifiable	Descriptive or qualitative
		unforeseen outcomes	independent variables	independent variables
	Postulations	Scenario space	Hypotheses	Propositions
	Unit of analysis	Society, policy,	Individual, group,	Individual, group,
		organization	organization	organization
	Generalizability	Classification, analy-	Statistical, classifica-	Analytical but not
		tical, bounded by cases	tion	statistical
	Role of the	Researcher beliefs and	Neutral, objective	Weak or strong
	researcher	values help shape the		constructionist views
		investigation
	Role of theory	Dimensions of the	Define variables and	Define variables and
		scenario space, driving	quantifiable relation-	descriptive relationships
		forces and possible	ships, suggest models
		directions
	*Although qualitative methodologies range from positivistic case studies to critical theory building, the
	entries in the table refer to positivistic case studies, the most common qualitative method used in MIS
	research.

Table B1 .

 B1 Scenarios on the Future of IS***

	Variable	Utopian	Dystopian	Status Quo	Technology
	Technological
	Internet diffusion*	Internet III**** Internet II**** Internet II**** Internet III****
	Rate of technolo-gical change*	Constant	Decreasing	Constant	Increasing
	Y2K problem*	Solved	Unsolved	Partly solved	Solved
	Moore=s law*
	Acceptance	Limited Telecommunications	Ubiquitous Telecommunications
	High	Status Quo		Utopian
	Low	Dystopic		Technology
	Figure B1. Scenario Space for 1999 Study

Although IS research is primarily concerned with systems, people, and the policies involved in managing IS, those aspects change as the technology changes. Hence IS research also needs to consider the impact of future technologies.

These criteria were enumerated by Olaf Helmer (personal communications).

Also called collective intelligence, futures scanning systems, and early warning systems.

SWOT analysis is taught in business schools as a way of assessing present and future competition.

That is, the acceptable probability of Type II error.

Although, this list is based on[START_REF] Yin | Case Study Research Design and Method[END_REF], who centers his research on case studies, the basic steps also apply to other methodologies.

This contention is an example and does not negate the use of futures research methodologies to answer other type of research questions.

Source: Personal communication with Olaf Helmer.

About the Authors

Paul Gray is Professor Emeritus and Founding Chair of the School of Information Systems and Technology at Claremont Graduate University. His interests in studying the future started in the 1970s when he was a professor at The University of Southern California. He was previously a professor at Stanford University, the Georgia Institute of Technology, the University of Southern California, and Southern Methodist University. Prior to his academic career, he worked for 16 years in research and development organizations including nine years at SRI International. He received his Ph.D. from Stanford University.

Anat Hovav is an associate professor at Korea University Business School in Seoul, South Korea. She holds a Ph.D. in Management and Information Systems from Claremont Graduate University and has over 15 years of industry experience in information systems management and strategic planning. Her research interests include the socio-technical aspects of organizational information security, studies of the future, risk assessment, Internet standards, and electronic scholarship.

Appendix A. Delphi

In the 1950s, the Air Force regularly convened expert civilian panels to obtain technical and/or policy advice about future directions. They found the advice they received was typically from one or two dominant participants, rather than the whole group. The dominant individuals were the most respected or most vocal. The Air Force hired RAND Corporation to develop a method to overcome group-think. The RAND researchers, led by Olaf Helmer and Norm Dalkey, decided the problem was the face-to-face interaction because inevitably the dominant voices prevailed. Therefore, they recommended the panels should be anonymous and unaware of who favored which policy.

The Approach. Delphi is a method that uses anonymity of opinion to obtain forecasts from a group of experts. A group is needed if the scope of the problem is too broad for any one expert or so new that there are few experts. The group cross-fertilizes ideas and ensures that all aspects of the problem are covered.

Delphi involves multiple rounds. During the first round, the experts are asked to give their estimates and their reasons for making those estimates. The resulting data is analyzed. If there is consensus on a particular question, there is no need to continue on that question. However, if there is dissensus, the distribution of answers (without attribution to individual respondents) is fed back to the group as are the rationales given for the outliers. The group is asked to re-estimate. This is round 2. Usually a number of rounds is needed to reach consensus or to conclude that there is dissensus.

Appendix B. Scenarios

Practicalities. The sequence of steps in scenario development are 1. Select system variables of interest 2. Forecast system parameters 3. Develop scenario space 4. Perform a consistency check 5. Develop measurements 6. Write the scenarios 7. Analyze the scenarios and develop relevant policies Scenarios for a given time horizon can be written from three points of view [START_REF] Coates | Scenario Planning[END_REF]:

1. A newspaper story written on some date in the future (e.g., July 1, 2020) describing the situation that day with no history or motivation. Assumes everyone knows what happened previously. 2. A history written on say, July 1, 2020, that describes the events that led to the future situation. 3. A forecast that starts in the present and shows the evolutionary path which results from the assumptions in the scenario space. This approach leads forward into the future rather than working backward from the end-point. This evolutional approach is the one most often used for strategic planning and is the one the authors recommend. [START_REF] Gray | Using Scenarios to Understand the Frontiers of IS[END_REF] *See [START_REF] Gray | Using Scenarios to Understand the Frontiers of IS[END_REF] for assumptions. **Mix implies both options included. ***These scenarios were created in 1999. The shaded areas show which elements were correct as of 2011. ****The term Internet II refers to a next generation Internet infrastructure using IPv6. Internet III refers to the version after IPv6. This terminology is not to be confused with Web 2.0, which refers to existing 2011 Web-based services.

Appendix C. Suggested Guidelines for Futures Research in Information Systems

1. Proper process: Define the philosophical assumptions for the study based in theory, research design, data collection, data analysis based on preset criteria, and conclusions. Each step needs to be well defined, self-contained, and justified. 2. Research design: Use a structured research design, following the five steps described in Section 3. 3. Triangulation: Use several methods or several sources of data. Triangulation is particularly difficult when data is scarce. For example, a study can incorporate and compare results from a Delphi study and environmental scanning in selecting events and trends (i.e., multiple sources of data). Similarly, researches can use cross impact and scenario analyses when estimating the probability of future outcomes (i.e., multiple methods). This process is similar to the triangulation used in case studies that involve multiple cases and multiple investigators (Yin 1994). 4. Content validity: The definition of content validity in futures studies differs from the one used in other IS methodologies. However, much as in traditional research, content validity can be obtained by using a panel of experts. For example, the discussion of scenario building in Section 2.3 stresses that events described in a scenario should be possible, plausible, and internally consistent. The results of a well-designed Delphi can be used to achieve these criteria (Section 2.2). 5. Standard formats of reporting [START_REF] Markus | Writing Up AIntensive@ Research in the AStandard Article Format@ Enablers or Constraints[END_REF]) result in making the reviewing process easier and in engaging the reader. 6. Consistency in the unit of analysis: Avoid mixing units of analysis in defining events and trends. It is too easy to shift from one level of analysis (e.g., the organization) to another (e.g., society). For example, if the unit of analysis is the level of technology over time, the Delphi questions presented to the experts should first ask them to provide not only a baseline estimate of the technology over time but also estimates of the technology=s impact on society and suggested policies. 7. The set of characteristics or dimensions should be selected prior to the data collection or analysis [START_REF] Sommer | A Practical Guide to Behavioral Research: Tools and Techniques[END_REF]. Researchers should not confuse the subject of the analysis with its attributes.

Appendix D. Futures Research Methodologies

Table D1 lists 37 futures research methodologies described in great detail in [START_REF] Glenn | Futures Research Methodology, Version 3.0[END_REF].

Table D1. Futures Research Methodologies


Eleanor Wynn
email: eleanorwynn3@gmail.com

The Present as Future: The Problem of Translation in Corporate Science Projects

Keywords: Corporate research, virtual teams, machine learning, desktop information management, 3D user interface, innovation

The paper reviews a set of research projects intended to plan for the future. It uses an actor-network construct from Latour=s work on science practice, translation. The paper contrasts normal science practice with science in a corporate context. Both situations occur in actor networks, require translation and contain arbitrary decisions. The corporate context can be more problematic because of the greater diversity of actors and agendas. The longer the work goes on, the more actors there are. Research per se is a sustained effort toward emergent goals; in the context of fluid corporate networks, these goals can become lost in mis-translation from one phase to the next. Results that would otherwise build up science findings miss their mark, are delayed, or are overtaken by other developments. The findings exist, but the outcomes for which they were intended may not materialize.

Introduction

Latour emphasizes the role of translation from one step of scientific process to another. The results are transportable abstractions that allow the original facts on the ground to be progressively formalized and encoded for dissemination. In Pandora=s Hope, Latour follows this process through several chapters on the classification of soils from the Brazilian jungleBsavannah boundary. The research question was whether the jungle or the savannah was advancing; the answer would be based on soil and plant types. The interdisciplinary team of scientists literally took dirt out of the ground, placed it into a grid of small boxes, and progressively abstracted its form from there, with iterations of process to ensure fidelity: that what came out the other end as Ascience@ actually represented the categories and locations of dirt where it was found. He calls this process translation. At each step the content changes its substance but should be traceable back to the original substance due to fidelity of translation. Therefore, any generalizations made from the abstractions ought to be true of the original material. [START_REF] Latour | We Have Never Been Modern[END_REF] includes and even privileges anthropology in the sciences; so this discussion does not engage the quantitativeBqualitative distinction.

When science is carried out in a corporation, the translation process easily becomes distorted by other agendas. The context is normally science for the production of some good, either basic processes for products and inventions, or internal uses like manufacturing. More recently, workforce and end users have become subjects for examination. In this engagement, the researcher is likely a well qualified person trained in methods. This person will, therefore, have faithful translation as a basic unstated goal. The difference between corporate and academic science is done with the results. This can change with changing actors. So even though each step of the process may be science, the end-to-end process itself may not. The predilections of actors at a senior level and their long-range views are critical in this fidelity. Since these actors and agendas change, the corporation introduces more variability than our sense of science normally allows.

Actors in the network take the form of human players, management objectives, reward systems, technology capabilities, research methods, and externally sourced solutions, to name a few. The translation lens helps us understand what should happen even in the fragile process of constructing academic science, and what the expectation of a research undertaking is. The violation of this principle explains obstacles in researching the future in a complex situation with a broad range of changing actors.

The present Acase@ is a series of research projects carried out over 8 years. The purpose of the projects was to guide IT decisions about key features of virtual collaboration tools for the company workforce. Collaboration tools were seen as problematic because the workforce had come to operate in an increasingly distributed fashion based on various policies. Each project led to a new question. A variety of methods came into play depending on the nature of the questions.

It is worth spending a moment to address the question of Aproblems@ and their relationship to researching the future. A problem is a form of breakdown or anticipated breakdown (Dreyfus 1984;[START_REF] Zimmerman | Heidegger=s Confrontation with Modernity: Technology, Politics, and Art[END_REF]. Even though a problem may be Aexperienced,@ its causes and details remain unanalyzed until it is reflected upon. The present tends to be taken for granted even when problems are experienced. The process of coping with problems was characterized in [START_REF] Hammer | Reengineering the Corporation[END_REF] as Aworkarounds@; this characterization still applies. If the present is ready-to-hand [START_REF] Dreyfus | What Computers Can't Do: The Limits of Artificial Intelligence[END_REF] then why analyze it and why think of changes for the future? Workarounds can build up to a point where eventually they cause a break-down. Then the present is experienced as problematic or present-at-hand, and leads to thoughts about a different future. This requires a different perspective on what is actually happening in the present.

The dilemma in an actor network is that what is the future to one actor may be the present to another. Failure to note this difference is part of the problem of translation. These problems are more likely to arise for end user and organizational research than in computer science and engineering because people already have perceptions and opinions about the former, but the latter are not exempt. Many innovations in software development have idled while stakeholders attempt to grasp the science, the usage model, or both. This series of projects contained software innovations that did not translate accurately to higher level actors, or ran into organizational problems like short-term agendas of individual human actors. The people working on a project see the project as fully present and even perhaps behind the current capabilities. Others who lack the same context may see it as so far into the future that it is not yet practical to execute. This becomes self-fulfilling.

Before researching the future, we must locate the future. [START_REF] Latour | We Have Never Been Modern[END_REF] claims that it starts in a new understanding of the present. Innovative solutions rely upon insights about present problems or breakdowns and in most cases are paired with new technologies or methods for addressing them. Since the present is mostly ready-tohand [START_REF] Dreyfus | What Computers Can't Do: The Limits of Artificial Intelligence[END_REF]) and taken for granted, seeing and analyzing those problems is a challenge. Different stakeholders, therefore, see them differently or have different member analyses based on everyday reasoning.

Latour casts the reframing back into the past.

As soon as we direct our attention simultaneously to the work of purification and the work of hybridization, we immediately stop being wholly modern, and our future begins to change. At the same time we stop having been modern, because we become retrospectively aware that the two sets of practices have always already been at work in the historical period that is ending (Latour 1993, p. 11).

The future and the past are always part of the present. The calendar shows a difference between yesterday and tomorrow but trajectories of events are entangled with concurrent presents, pasts, and futures. Just as a topic like global warming is concurrently political, scientific, and narrative, yet artificially divided [START_REF] Latour | We Have Never Been Modern[END_REF], the topic also contains within it different time horizons. Past events led to the present, current events affect the future, and, within any given moment, people are acting out different pasts, presents, and possible futures according to their position in a network, a perspective they are committed to, or how they are affected. [START_REF] Escobar | Encountering Development: The Making and Unmaking of the Third World[END_REF] refers to this as hybrid cultures where national and regional cultures include traditional and contemporary stances.

Floods in Pakistan and drying mountain lakes in the Andes are the present for local people, but to people far away, they represent only a possible and tentative future. The researcher is often in a position of having either imagined or seen the possible future as an imminent reality, and then must convey or translate that to others for whom some other scenario occupies the overwhelming present and immediate future. A researcher of informatics in particular is always bridging present and future.

Structure

The paper has four major parts: (1) a discussion of researching the future;

(2) applicable constructs;

(3) thumbnails and rationales for a series of research projects; and (4) a discussion of where things went off track. All the projects together make the case. The case is a process of (1) becoming aware of questions in the environment;

( Researching the future becomes a sociology of knowledge; so we have also in mind if not in focus the work of [START_REF] Foucault | The Archaeology of Knowledge and the Discourse on Language[END_REF] and of [START_REF] Knorr-Cetina | Epistemic Cultures: How the Sciences Make Knowledge[END_REF].

3

The Case (A Set of Projects) [START_REF] Zimmerman | Heidegger=s Confrontation with Modernity: Technology, Politics, and Art[END_REF] describes Heidegger=s concern for the technological mindset where technology is seen as dominating, segmenting, and imposing instead of Auncovering.@ In this set of studies we adopted the latter, less deterministic process.

We uncovered new problems in the search for answers to other questions. Unlike a conventional plan of research, we chose new methods opportunistically as questions (how virtual are we?), new facts or factants (most employees work on multiple teams), translation devices (demo), technical elements (an existing software solution), and methods (machine learning) emerged.

The goal of the projects was to provide better tools for collaboration, and to address discovered problems of multiple team memberships, multitasking and cognitive flow. The paper presents a summary of the research efforts and the emergent nature of discovery as each new finding gives rise to a new question. The collection of projects uses different methods over time to discover collaboration obstacles of a globally distributed workforce and potential software tools.

The actorBnetwork construct and the notion of translations helps to explain the divergent outcomes of this research and related development efforts. It shows how research in a corporate environment differs from research in science because the translation mechanisms are damaged or under different pressures. Fidelity of translation is overtaken by other priorities.

The effort came from an IT organization because of IT=s role in supplying workforce software tools. At the start of this work, the company had over 80,000 employees worldwide, functionally distributed with substantial proportions collaborating across the globe. This means that a single department had members in different parts of the globe and many of those members were on teams that regularly met periodically. Functional distribution is a strength in terms of creating communication channels and robust social networks across the company, but it poses a technological challenge to support information sharing and productive interaction.

Since we are looking at both the process of discovery and the issues of translation from an actor network perspective, below is an outline of the main actors.

$ a core set of human actors interacting over time $ disciplinary backgrounds of the human actors $ agendas arising from the backgrounds $ agendas arising from success criteria for individuals $ software tools for discovery and for development into user interface designs $ other research methods such as survey, interview and machine tracking $ factants or things discovered in the research $ present and future capabilities of software tools $ attraction factors of research findings and software tools $ organizational agendas that supercede the individual human actors and constrain them $ variation over time in all of these

4

The Research Projects

In this section, we review the projects. Each project arose to answer one or more questions and some questions arose from previous results. The first question was the generalizability of the work practices of a team organized to define the problems and recommend a solution to the virtual collaboration needs of the company. In his writings on theorizing, sociologist Alan [START_REF] Blum | Theorizing: On the Beginning of Social Inquiry[END_REF] points out that theory arises from everyday experience, but takes this as present-at-hand and thus seeks to form an analysis. The group described below engaged in theorizing about the nature of globally distributed teams and their needs. It then became important to test the informal hypotheses that were recurrently expressed as assumptions in the conversation. The expression Awe=re so virtual@ led to a question as to what proportion of employees actually collaborated in ways similar to the team members.

The method for this question was a demographic survey. The second question arose from the challenge of how to represent the implications of the survey results so that more senior management could grasp them. This led to a video mock-up of a proposed design and included a three-dimensional representation of the desktop with interlinked personal and team workspaces. The third question arose from a specific result of the survey, that two-thirds of the workforce were members of many teams (three to five and more). This led to a series of machine learning studies to understand how people managed the information flow from these teams.

The research questions did not come directly from management stakeholders. Senior management created umbrella agendas leading to self-directed work. In most cases, the questions became evident from observed gaps in knowledge. The projects were carried out with normal scholarly process, where the assumed eventual audience would be peers in the academic community. Validity was assumed adequate within the internal audience, and at times the results were widely used by others. At other times, finer points were lost, or the research could not be processed as relevant due to translation gaps.

The Virtual Collaboration Research Team

A cross-functional team was organized by two senior individual contributors. Members included a workplace anthropologist, a product planner, two engineers, a software trainer, a collaboration product manager, and an engineering manager. Membership was by invitation and voluntary. The topic had a high profile on a vision/mission roadmap for IT. The company had experienced a period of expanded overseas growth and was functionally distributed. This means that all of the service organizations (information technology, finance, human resources) were globally distributed; most of the core product design teams were organized by geographic specializations around the globe but had to periodically coordinate design sections; manufacturing is site-focused but shared best practices and expertise and is highly standardized methodologically; and software development is often sourced from offshore; sales is highly distributed and typically standalone by region. Each division has different proportions and periodicity of distributed collaboration, but all have it.

Virtuality Survey

As the VCRT talked about global team collaboration, members periodically exclaimed Awe=re so virtual!@ meaning that the company operates extensively in a Avirtual@ collaboration mode. But who are Awe@ and what is Avirtual@? What does Awe=re so virtual@ mean? It meant different things to different people. It had many confounded constructs embedded in it. To provide definition, extent, and impact of the different elements of virtual collaboration, we took the starting point of theorizing about our own behavior and undertook a survey to see how much of the workforce had the same experience.

A literature review paper [START_REF] Watson-Manheim | A New Perspective on >Virtual=: Analyzing Discontinuities in Work Environments[END_REF] had identified five factors in common within the IS literature on the use of the term virtual to describe corporations. In consultation with the authors, we defined a survey that would measure a composite index of virtuality along the dimensions of discontinuities in time, geography, culture, business unit, and software tools. We added the discontinuity of multi-teaming because we noted that software products often contained assumptions of single team membership [START_REF] Chudoba | How Virtual Are We? Measuring Virtuality in a Global Organization[END_REF]. This demographic survey was conducted using a stratified random sample of all employees in all job types and all geographies [START_REF] Lu | Understanding Virtuality in a Global Organization: Toward a Virtuality Index[END_REF].These results provided a reliable measure of how virtual the company was and which dimensions made a difference. There was a high level of virtuality, with approximately two-thirds of employees being engaged in a virtual team and working across more than one of the discontinuities. The job profiles of people engaged in virtual teaming closely matched the job types within the company. Given these proportions, we could say that virtual teaming was a way of life and obstacles to it would be barriers to everyday productivity. (In a translation not discussed in the paper, the leap from these results to a finance-recognized metric did not materialize.)

The survey included a measure of performance that could work across the wide array of job types and regions: the shared company values. These values represented common knowledge that could provide a reliable self-assessment of team performance. This metric held up well over the 7 years the survey was conducted in terms of consistency of response and ability to track a trend. We correlated the virtuality variables with the performance variables to determine the aggravating factors in virtual teaming. These turned out to be consistency of software tools, confirming that this was a problem within the scope of IT. Geography was a factor only when time separation was added, even though geography had been the most visible dimension of virtuality to decision makers. Within the United States, geographic location was neutral to team performance.

The survey thus disconfirmed the supposed negative effects of location along a North-South axis. East-West axis adds the dimension of time separation and a separate study was conducted on that [START_REF] Cummings | Crossing Spatial and Temporal Boundaries in Globally Distributed Projects: A Relational Model of Coordination Delay[END_REF]. Social variables like trust and friendship were the highest rated performance metrics for teams. This could be because virtual teaming was routine and made use of telephone bridges rather than conference rooms. This places everyone on an equally remote status. Incremental improvements in both internal and vendor supplied software over time smoothed out many hitches in coordination of meeting logistics. One likely organizational effect of virtual teaming was the tendency for people to participate in multiple (three to five) projects as location becomes a neutral factor.

The survey became an actor as the creator of factants that established the need for collaboration solutions and identified the types of need. It led to the development of a collaboration scenario that included multi-teaming as a core way of working and added the requirement for both separation of spaces on a display with coordination of content that might be shared across teams. That scenario was expressed in a mock-up that incorporated features of a technically sophisticated user interface that had been abandoned by the company product research labs before the IT collaboration effort began. The existence of this elegant technology became an actor and a pawn in the trajectory of events.

4.3

The Demo

The mock-up was an essential boundary object for translating the intentions the team developed into actions that could be taken by the organization. Many slide presentations were drafted and used for interim presentations; but without a visual display of what the solution would look like and act like, conversations with others elicited comparisons to standalone features of existing software tools. To the team members, those tools were incomplete because they lacked several things: the ability to address multiple team membership, location and cultural references for context, and the integration of core design features. Since key problems identified had been Atoo many tools@ and Adifferent tools,@ the solution of assembling yet another suite of off-the-shelf products was antithetical to providing coordination. The mock-up performed the role of translation device. At the same time it introduced a new functional element: 3D. This technology had its own history; it became the platform for engaging new actors, securing new funding, and acquiring a higher level of visibility. While these developments at the time seemed highly positive, they eventually led the translation process off track. While the demo communicated well as a simple mock-up, once the more sophisticated technology of the 3D source metaphor became funded, some of its technical virtuosity distracted from the original intent. The narrative of the survey data became a backdrop to why the 3D solution was desirable and together these made a great story and presentation. If they had remained together as rationales, the translation process would have continued along a normal scientific path as Latour describes it.

3D User Interface for Information Management

Once 3D was introduced as the graphic metaphor for the mock-up, it became obvious that it also contained a solution to one of the key problems encountered in the survey: the fact that two-thirds of employees work on three to five teams at any given point in time. With 3D as the user interface, users could keep all of their teams in mind (termed Aall my teams in one place@) as they toggled back and forth. This would solve the problem of multiple open and minimized windows that by the 2000s made the virtual desktop no different than a physical desktop with piles of paper on it. The system that inspired the metaphor for the mock-up had the following history and properties.

In the late 1990s, a small group of lab researchers worked on software developments in sense-making. The goal was to break out of file structures to more intuitive ways of organizing and finding information and associated objects. The solution involved the development of a three-dimensional graphical space as the desktop context, whereby documents and other screen objects could be pushed to the Aback,@ retaining their dimensions and appearance but becoming much smaller. The software was intended to incorporate object properties and drag-and-drop links between visual objects (documents, programs, and people) that would then retain their associations. Since it did not depend on file hierarchy, any object could be related to as many other objects as necessary. The purpose was to create a contextual order that was independent of file structure and more naturally followed human associative reasoning. This group was disbanded in 2001 during a reorganization and the software development discontinued. Some of the work that had gone into navigation, zooming, snap-in-place, and 3D rendering and linking was truly pioneering and remains technically impressive [START_REF] Light | Miramar: A 3D Workplace[END_REF].

This software informed the mock-up that was presented to IT staff at a strategic long range planning meeting. The sponsor who was present at the meeting told the team that the demo had Amoved the cause of collaboration ahead by five years in the space of five minutes.@ There was a great deal of expressed excitement; a lab and group were funded to pursue this and other advanced collaboration solutions including video conferencing. The group had about 10 members, significant for an exploratory group in IT. The group hired one of the original 3D developers and a number of application scenarios were populated to illustrate how this solution would work in different parts of the company. This went along well for a few years until a company-wide reorganization affected the entire department.

Multi-Teaming and Multitasking

Consistent with the thesis that each new discovery leads to another question, we took the opportunity to work with faculty and students in the Department of Electrical Engineering and Computer Science at Oregon State University to try to understand the effects of multi-teaming and multitasking using two software programs developed at the Center for Machine Learning. What the 3D software lacked at the time it was originally developed was a compelling usage scenario, which we might call another actor in the system. Although it looked good, and the developers passionately believed it solved a problem of information management, no such problem had been credibly documented, either for the individual user or for sets of users working on multiple teams. The virtuality survey uncovered the structural situation that provided a strong rationale for the use of 3D. Although any given person might be aware of having to work across multiple teams, heretofore nobody had any notion of the extent of this company-wide. Even once it was known it was difficult to say exactly why it was a problem. Eventually the machine learning study revealed the kind of hyperactivity that this widespread and even organizationally functional behavior produced, and uncovered further problems hidden in everyday behavior.

Here several actors/agendas converged: the findings from the survey, the availability of the machine learning project, the ability to fund external research, and the 3D desktop design. The fact that the design was a technical artifact gave it added influence. It helped significantly that it was visually engrossing.

The Oregon State University projects used two different machine learning programs to instrument people=s desktops for extended periods. No content was recorded for reasons of information security and end-user privacy. The software tracked a range of activities within the primary office application suite. Between the two programs, the software tracked window and applications switches, interruptions, and operations like cut/paste, file save/save-as, attach, open, etc. [START_REF] Jensen | The Life and Times of Files and Information: A Study of Desktop Provenance[END_REF].

The original pilot with TaskTracer [START_REF] Stumpf | Interacting Meaningfully with Machine Learning Systems: Three Experiments[END_REF]) was meant to create project-based context retention by labeling and then storing together related documents so that they would all be visibly associated and therefore linked together regardless of file structure. This would address one of the issues revealed in the survey: if you are working on multiple teams using various collaboration software programs, how do you track what you are doing with each team? The file hierarchy is not quite adequate since many documents can be used across teams. With a traditional file hierarchy and even with shared storage, a version control problem immediately presents itself.

The first machine learning program, TaskTracer, required human mediation in order to Alearn@ meaningful associations. Results showed how much window and task-switching was going on, but users quickly tired of trying to train the program. The practical goal of associating content was not realized. There were already so many categories to sort into and so much new information arriving hourly, mostly via e-mail, containing project content [START_REF] Bellotti | Taking Email to Task: The Design and Evaluation of an Email Centered Task Tool[END_REF]) that it became overwhelming to establish the basic sorting framework for the program.

The hyperactivity traced with the program (window switches more than once a minute, 90 percent of file searches repeated in a session, etc.) arises from a scenario like a user participating in a phone bridge, concurrently looking at a document on a shared screen, answering e-mails, looking up information to answer the e-mails, and looking up other information pertinent to the meetingCall in the course of a half-hour. These behaviors are part of the pressures of work and the capability of instant information access and response. It becomes central to a real-time multi-tasking life-world that is efficient in many ways even while it has high cognitive costs.

Cross-Application Information Flow and Provenance

We funded the research to understand what users were doing on the client. Switching can be a benefit. The windows capability replaced modal computing in the 1980s and was considered an advantage [START_REF] Tesler | The Smalltalk Environment[END_REF]. But the amount of switching driven by new models of work without the underlying software model keeping up has become frenetic. A new concept of information management is therefore critical. Because TaskTracer had a high adoption hurdle even as an experiment, we continued with a passive software program, TaskTrail, that solved a different problem, did not require user intervention, and applied pattern-matching after the fact rather than in real time.

TaskTrail tracked operations like opening and closing windows, switching windows, copy/paste, file save/save-as, file attach, and more to find out where information comes from. The work falls into the domain of provenance studies and was the largest such study to date. More than 20 users were tracked over a 2-month period, with initial and halfway interviews to provide the context of practice: Awhat were you trying to do?@ The program, again recording no content, ran in the background. Computer Science/HCI graduate students at Oregon State University performed post hoc analysis. These data revealed patterns of information flow on the desktop and highlighted a user focus on information as contrasted with a vendor and IT emphasis on applications. The results show a long information half-life and heavy reuse patterns as users recontextualize and refine information objects available from various sources.

Significant percentages of information on a given user=s desktop were copied from one document and/or application to another. This showed users create original documents by reusing large amounts of information from themselves and from others. They continually recontextualize information they have saved on the client, received in an attachment, or downloaded from a shared file or the Internet. These findings strongly support the informatization construct [START_REF] Zuboff | In the Age of the Smart Machine: The Future of Work and Power[END_REF]. Each person recreates information relevance in light of a new context. But they are doing this through a maze of operations inconsistent with the model of use. We contend that software tools can make it much easier to coordinate and manage information through the visual interface.

Outcomes

The findings of the machine learning studies created more urgency for the use of the 3D environment to manage information, even as the latter motivated the machine learning studies. Supporting external research on hedonic and flow concepts [START_REF] Reeves | The Media Equation: How People Treat Computers, Television and New Media Like Real People and Places[END_REF] was brought in to justify 3D. Managers had resisted the notion that work should be like play, but the work on heart rate in first-person shooter videogames and on the effect of visual imagery on neurological flow states [START_REF] Csikszentmihalyi | Flow and the Psychology of Discovery and Invention[END_REF] were factants that became part of the internal marketing message for the 3D program. Although these findings were consistent with the trajectory of translations, they also led to a divergent path.

The research results could have had an impact on how IT influenced major vendors of office software. But new translation problems arose. The obviousness of the finding that people are focused on information, while vendors focus on applications, was not obvious to the engineering managers who implement new tools. Further, the research findings were overwhelmed by the sudden currency of the hedonic constructs: Awork as play@ and 3D visualization. Previously rejected in IT, these notions were reintroduced from another quarter at a higher level. The machine learning project ought to have supported an agenda of maximizing cognitive flow, which is the same neurological state for both productive and hedonic purposes. 3D is one way of doing that in the context of multi-teaming and the software was deemed technically first class.

But its technical brilliance and visual appeal gave the 3D piece of the network a life of its own. There had been a circularity of reinforcement. Once the virtuality survey findings justified the 3D user interface, that interface then justified further study of problems on the desktop arising from multi-teaming. This led the research trajectory back in the direction of the original intent of the 3D software effort from the 1990s, even though for a while the software was merely an accessory to illustrate some intentions about collaboration. Having the software and an original developer gave momentum to the 3D effort (along with the lab funding). Then it created a compelling reason to understand more about user behavior than could be provided in the survey or, for that matter, by any other means. This led to the machine learning work and could have led to new information management models.

Instead, shorter-term agendas requiring less faithful translation arose. A senior executive urgently needed materials for a keynote speech on the topic of Aplay.@ The 3D UI and its arguments were ready for that. Then the adrenaline of being on a senior management radar narrowed down the scope of participants and voices. Points that distract from the focus of an executive presentation, while essential, may seem like clutter. Once the senior manager was engaged, other managers were encouraged to push the play agenda farther. The 3D UI was now sufficiently developed to launch a joint venture with a company that was creating a virtual collaboration environment in the image of a game.

At this point, translation in Latour=s sense became completely lost in other agendas and we switch over to a Foucauldian scenario. Play turned into Agame@ and game turned into Avirtual environment with avatars and realistic rooms.@ The intellectual underpinnings of the agenda lost out to the shiny parts. In the end, the keynote was short-lived and did not lead to funding; a joint development project with another large company to use this software in an ambitious way fell apart due to decisions at their end; the third-party venture with the small company eventually fizzled (for lack of a compelling usage model). After large-scale organizational changes, the remaining core developer has seen the tide go out on an innovative idea for a second time and is seeking new funding.

It can always come back again once the future catches up to the past. There are organizational data, a usage model based on those data, constructs of cognitive flow, and task switching that have been measured. The research flow still adds up. And the world still needs this solution. All of this, both research and development, and the inspiration that guided them, became enmeshed in other agendas.

Analysis: The Translations and Trajectories

A network is both contemporary and historical. There were networks interacting across all of the projects discussed here to greater and lesser degrees. At any point in time, one or another actor had motivations and rewards in carrying the projects forward. Without a historically extended network, there never would have been a cohesive vision since there were temporal gaps in execution. Each person had different skills and levels of investment in the concepts and prototypes. One person was an original developer on the team that created the Labs version. Another focused on collaboration design options, workforce distribution data, and had a spatial (next cube) connection to a different original 3D developer. A third person was contracted to do a visual scheme for a concept presentation and remembered working on the 3D application. Others became persuaded of the 3D option. The availability of the machine learning research agenda led to supporting evidence that no survey or ethnographic method could have captured. The cognitive research on flow supported the general direction and created a back argument against the notion that work and play are two different things. Many translation processes here added up in a reasonable way. The agenda that cross cuts the translation is the need and urge to move the work up to a higher level of visibility and sponsorship. It is a need but it translates differently. The translation became as follows:

Questions about collaboration and the generalizability of team experience led to virtual team demographics. This led to the discovery of approximately two-thirds virtual teaming and the newly identified phenomenon of multi-teaming, which in turn identified shortcomings in software offerings. (Team repositories were created as standalone entities. If a user was on several teams, related documents had to be separately uploaded and version control was lost). This led to the need to translate into a compelling management message to gain funding, which took the form of the demo. The demo borrowed the 3D concept from an earlier abandoned project. The 3D software brought its own question: Just how bad is the information management problem? This gave rise to the machine learning projects. These had two roles, one of them just to understand information management on the desktop and the other to validate a compelling need for 3D as a solution. These things all had a logical sequence, even with the inclusion of the translation for management in the form of the demo.

The departure from translation came from the dynamics of a second round of translation for management. That first translation brought funding but no intervention. The second translation failed to carry along research justifications that went into the forward momentum of the whole effort. It turned 3D into show material and eventually lost its scientific and technological force.

In the rush of opportunity that surrounded the 3D development project as a potential commercial product, the chain of translation that Latour describes as working well in science through the care of human actors broke down. The most compelling representation can easily win out over the most faithful or most useful. The emphasis of the 3D desktop had shifted gradually away from information management: first to the service of a high quality public demo, and then to the allure of a product possibility involving a meeting space with sophisticated graphics. Because at each phase some of the actors and agendas changed, the information management concept dropped out of sight, even as the need for it was further emphasized by the machine learning research. Advocates for the information management usage model either were no longer in the conversation or were kept busy by technical challenges of navigation and visualization in the 3D space.

Instead of visual content management there were visual representations of spatial and physical objects that had game-like realism. Instead of an interface that provides a simplified window into something highly complex, the momentum went into something that was simple but fancy-looking.

Summary

This case shows how the future had to be understood by making the present presentat-hand. This happened through a series of steps, each informed by the previous one, with new elements coming in opportunely. Research into the implications of the multi-teaming phenomenon revealed in the survey confirmed that this behavior introduces an information management and design challenge. A solution was readily available in the form of an abandoned software project that merited revival. However, due to the lack of translation and the power of certain inscriptions, the whole package of rationales failed to progress together.

The research remains as a finding about a possible future. This could serve as guidance for those who do science for any large institution. It is easy to be caught up in the seemingly intrinsic merits of an approach and findings, and to be unaware of power agendas and the need for two kinds of translation. One is translating not just through stages of science but up through a power hierarchy. Latour=s philosophical resources provided the counterpoint of what makes science a method as a human process of faithful translation. The notion of translation, therefore, is precisely that element that can become lost when science is done for other audiences and with other actors. Much credit goes to Latour for his deep vision of anthropology and its capability as the science that pulls together the artificially sundered threads of pastBpresentBfuture, scienceBnatureBpolitics, and narrative.

About the Author

Eleanor Wynn holds a Ph.D. in Linguistic Anthropology from the University of California at Berkeley. Her dissertation research at Xerox Palo Alto Research Center was the first anthropological study of office work in the computer era, using conversation analysis to show cognitive and social processes embedded in group knowledge. She has worked as a researcher senior scientist at Xerox and Bell-Northern and as a Principal Engineer at Intel Corporation, as well as consulting on workforce information system requirements to other large companies. Her academic work includes visiting teaching engagements, scholarly publications, and service as conference chair. Dr. Wynn=s current research interest is in organizations as complex systems and the application of complexity modeling methods to enterprise processes at all scales.