Research examined both the Position Analysis Questionnaire (PAQ) and the possible analyst sources for gathering job analysis information. 25 state government jobs were examined using job incumbents, supervisors, job analysts, and a comparison group of college students. Reliability and validity (judge convergence and prediction of present pay levels) information was determined for each of the four analyst categories. Results indicate that there is little difference between analyst sources, including students, in terms of their ability to reliably analyze a job using the PAQ. Convergent validity results showed a high degree of agreement among all judge categories when summing item frequencies across all 25 jobs. The prediction of present pay levels was significant for all judge categories but was noticeably smaller than previously reported studies. This seemed to be primarily due to the restriction in salary range of the present study. An analysis of judge response bias showed that supervisors and incumbents rate all or most PAQ items higher than their analyst counterparts. These findings suggest that who furnishes responses to a job analysis inventory makes little practical difference. The exception is that the determination of pay levels and human requirements for test construction purposes should be viewed with caution when different analyst sources are utilized for different jobs.THE Position Analysis Questionnaire (PAQ) (McCormick, Jeanneret, Mecham, 1972) is a structured job analysis questionnaire consisting of 189 elements of a "worker oriented" nature. Jobs are analyzed into meaningful and quantifiable "units" of job information Requests for reprints should be sent to Milton D. Hakel, The

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Creating industrial policy and programmes, especially in technology, is fraught with high levels of uncertainty. These programmes target the development of products that will not be sold for several years; therefore, one of the risks is that the products will no longer be in demand or will have been overtaken by more advanced technologies. In several cases, the authors have seen these programmes fail because the intended targets of the programme did not apply to use the programme funds (poorly targeted program) or did not use the programme properly. An integrated programme involving foresight, competitive intelligence and business analytics assists in decreasing the probability of the risks and problems described above, resulting in better designed and more successful industrial policy. These techniques can also be used to create a dashboard for monitoring programme use so that any problems can be corrected early on. The dashboard uses advanced analytics to assess programme applications and programme inquiries to assess whether the programme is being used properly. Via an integrated intelligence process, it monitors the external environment to ensure that programme assumptions in terms of what technologies are most appropriate remain valid. The dashboard relies on information available in open sources and available to the government.

This paper reports on an integrated research program involving three related studies that examined successful foresight programs. It analyzes the key factors that appear to determine whether or not foresight, once launched by a government, can be successful. The study was performed by a team of researchers in Canada in the period 2005-2007. It found eight key factors, beyond the usual ones associated with the application of leading edge methods. The overall conclusion is that the methodology, appropriate budget and techniques alone are insufficient factors to explain the success of foresight programs. The interview results indicate that success is ultimately defined as the impact of the foresight exercise on government policy, and as the growth of the foresight function. Taken together, the results should help organizations establish the parameters for a successful foresight program.