Unlearning Ineffective or Obsolete Technologies

Often, before they can learn something new, people have to unlearn what they think they already know. That is, they may have to discover that they should no longer rely on their current beliefs and methods. This paper describes eight viewpoints that can help people to do this.Information Systems Working Papers Serie

KNOWLEDGE AND INFORMATION WORK IN ORGANIZATIONS

Since the turn of the century, the United States, Canada, and Western Europe have been moving toward service and information economies and away from an agricultural and manufacturing economies (Euromonitor, 1990; Machlup, 1962; Rubin and Huber, 1986; Porat, 1977). The fraction of workers using information to produce economic value has been rising, and the fraction working with their hands in factories or on farms has been declining. In the United States, the percentage of jobs in manufacturing fell from 27 percent in 1920 to 17 percent in 1990. In the European Community, the value-added by manufacturing grew at an average annual rate of 6.2 percent from 1960 to 1970, but this growth rate was only 0.7 percent from 1980 to 1985. Among white-collar workers, the fastest growing occupations have been clerical, professional, and technical workers, and managers and administrators (Wolff and Baumol, 1987). Six factors have been involved in this shift. First, third-world and developing societies have become centers of manufacturing, while the so-called advanced societies have shifted toward services. In Europe, the telecommunications sector has been growing about 9 to 11 percent annually, and the software and computing services sector has been growing 15 to 20 percent annually (Sema Group, 1991). Second, knowledge-intense and information-intense products and services have grown rapidly, and the production of traditional products has also been using knowledge more intensively. Third, business has invested heavily in equipment to support information work. In the United States, information-related equipment accounted for 20 percent of capital investment in 1979; this figure had become 40 percent of capital investment by 1986. Fourth, knowledge workers and information workers have replaced manual production workers within the manufacturing sectors. Machine-tool operators, for instance, have often been replaced by technicians who monitor computer-controlled machine tools. Fifth, workers have increased education and information-processing skills (Howell and Wolff 1991). Sixth, new kinds of knowledge-intense and information-intense organizations have emerged that are devoted entirely to the production, processing, and distribution of information. These new kinds of organizations employ millions of people (Office of Technology Assessment, 1988). As early as 1976, the value of information-sector products and services had already exceeded that of the manufacturing sector in the U. S. By 1990, the information sector (including services) accounted for $3 out of every$4 of GNP, and over half of the U. S. workers were doing some type of information work (Howell and Wolff, 1993; Roach, 1988). The U. S., however, represents an extreme case. For instance, in the software and computing services sector, the United States has about 55 percent of the world market, the European Community has about 25 percent, and Japan has about 8 percent (Sema Group, 199 1). This article surveys information work, information workers, and the computer systems that support such work. It then examines the organizations that are most dependent on knowledge and information work -- knowledge-intensive firms.Information Systems Working Papers Serie

Opening Pandora's Box: Studying the Accuracy of Managers' Perceptions

Both researchers and managers depend on the accuracy of managers' perceptions. Yet, few studies compare subjective with "objective" data, perhaps because it is very difficult to do well. These difficulties also muddy interpretations of results. On one hand, studies suggest that managers' perceptions may be very inaccurate. On the other hand, the observed errors in managerial perceptions may arise from research methods instead of managers. Because perceptual data are so significant for both researchers and managers, researchers need to understand both the potential contaminants of perceptual research and the determinants of perceptual errors and biases. This article reviews studies of the accuracies of managers' perceptions, points out hazards in such research, and suggests various ways to improve studies of perceptions. The suggestions encompass improvements in gathering more valid subjective data, locating more appropriate "objective" data, finding appropriate respondents, and using statistical methods that provide accurate and reliable estimates with small samples.Information Systems Working Papers Serie

Unlearning Ineffective or Obsolete Technologies

Often, before they can learn something new, people have to unlearn what they think they already know. That is, they may have to discover that they should no longer rely on their current beliefs and methods. This paper describes eight viewpoints that can help people to do this.Information Systems Working Papers Serie

Opening Pandora's Box: Studying the Accuracy of Managers' Perceptions

Both researchers and managers depend on the accuracy of managers' perceptions. Yet, few studies compare subjective with "objective" data, perhaps because it is very difficult to do well. These difficulties also muddy interpretations of results. On one hand, studies suggest that managers' perceptions may be very inaccurate. On the other hand, the observed errors in managerial perceptions may arise from research methods instead of managers. Because perceptual data are so significant for both researchers and managers, researchers need to understand both the potential contaminants of perceptual research and the determinants of perceptual errors and biases. This article reviews studies of the accuracies of managers' perceptions, points out hazards in such research, and suggests various ways to improve studies of perceptions. The suggestions encompass improvements in gathering more valid subjective data, locating more appropriate "objective" data, finding appropriate respondents, and using statistical methods that provide accurate and reliable estimates with small samples.Information Systems Working Papers Serie

Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Collegial nests can Foster Critical Thinking, Innovative Ideas, and Scientific Progress.

How can management and strategy scholars organize to generate more productive, more innovative, and more impactful research? With appropriate cultures and leaders, small and egalitarian discussion groups that we call “collegial nests” can become powerful generators of innovative ideas and creators of extraordinary scholars. Collegial nests need cultures that free participants to think critically, to cherish new viewpoints, and to speak freely without fear of ridicule. They also need leaders who model such cultures and facilitate frequent discussions. Two case examples illustrate how productive collegial nests can create better science and better scientists. To generate scientific innovation and progress on a large scale, many autonomous groups tackling related issues are desirable. Modern communication technology is making it feasible for groups to operate over large distances and to coordinate with each other at very low cost. Collegial nests offer greater potential for enhancing scholarly productivity and innovation than do attempts to regulate scholarship via hierarchical structures. Multiplicity can lower the probability of wasting resources on low-yield paths, egalitarian control can reduce the influence of vested interests, and a combination of shared goals and partial autonomy can integrate enthusiasm with sensible risk taking