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 Machine-tool operators, for instance, have often been replaced by technicians who monitor computer-controlled machine tools.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

Why Baseline Modelling is Better than Null-Hypothesis Testing: Examples from International Business Research

• Purpose – This chapter reports on a rapidly growing trend in data analysis – analytic comparisons between baseline models and explanatory models. Baseline models estimate values for the dependent variable in the absence of hypothesized causal effects. Thus, the baseline models discussed in this chapter differ from the baseline models commonly used in sequential regression analyses. Baseline modeling entails iteration: (1) Researchers develop baseline models to capture key patterns in the empirical data that are independent of the hypothesized effects. (2) They compare these patterns with the patterns implied by their explanatory models. (3) They use the derived insights to improve their explanatory models. (4) They iterate by comparing their improved explanatory models with modified baseline models. • Methodology/approach – The chapter draws on methodological literature in economics, applied psychology, and the philosophy of science to point out fundamental features of baseline modeling. Examples come from research in international business and management, emerging market economies, and developing countries. • Findings – Baseline modeling offers substantial advantages for theory development. Although analytic comparisons with baseline models originated in some research fields as early as the 1960s, they have not been widely discussed or applied in international management. • Practical implications – Baseline modeling takes a more inductive and iterative approach to modeling and theory development. • Originality/value of paper – Because baseline modeling holds substantial potential, international-management scholars should explore its opportunities for advancing scientific progress

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

A Call for Openness in Research Reporting: How to Turn Covert Practices Into Helpful Tools

Research articles often give inaccurate information about how researchers developed hypotheses, analyzed data, and drew conclusions. Published articles sometimes report only some hypotheses that researchers tested, or some statistical analyses that researchers made. Articles often imply that researchers formulated all hypotheses before they examined their data, when in fact they added or deleted hypotheses after they made some data analyses. Indeed, such covert practices are so common that new entrants into management research may think they are correct behavior. Yet, these practices create false impressions about the validity of research and they undermine the openness that ought to create trust among researchers. Researchers have tried to halt these practices by labeling them “unethical,” but their continued prevalence questions the effectiveness of wholly critical approaches. We propose a constructive path toward reform: advocating honesty about actual research processes by adding discussions of inferences drawn after data analyses. Post hoc data analyses can stimulate important theoretical ideas; running alternative statistical models can deepen understanding of empirical patterns; lack of support for hypotheses can identify incorrect or incomplete theories. The management research culture should encourage these practices. Their negative effects result from the lack of explicit reporting about them

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

Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests

Null-hypothesis significance tests (NHSTs) have received much criticism, especially during the last two decades. Yet, many behavioral and social scientists are unaware that NHSTs have drawn increasing criticism, so this essay summarizes key criticisms. The essay also recommends alternative ways of assessing research findings. Although these recommendations are not complex, they do involve ways of thinking that many behavioral and social scientists find novel. Instead of making NHSTs, researchers should adapt their research assessments to specific contexts and specific research goals, and then explain their rationales for selecting assessment indicators. Researchers should show the substantive importance of findings by reporting effect sizes and should acknowledge uncertainty by stating confidence intervals. By comparing data with naïve hypotheses rather than with null hypotheses, researchers can challenge themselves to develop better theories. Parsimonious models are easier to understand and they generalize more reliably. Robust statistical methods tolerate deviations from assumptions about samples

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