The importance of selected critical issues in the field of management information systems as perceived by MIS managers and MIS faculty

Scope and method of study. During the spring of 1989, the extent of difference between what MIS managers and MIS faculty consider the perceived level of importance of 31 selected critical MIS issues was conducted for both today and five years from today. In addition to data collection for the 31 issues, demographic data for both groups was collected in order to build a profile of the individual groups. The MIS critical issue data was analyzed using Pearson's chi-square statistic and the demographic data for each group was tabulated and measured based on frequency of responses. Those critical MIS issues found significant were supplemented with Cramer's statistic.; Findings and conclusions. In the 1989 study, the following critical MIS issues were found not significant by both groups for the present and for five years from today. These were: Management Information System (MIS) strategic planning; Executive Information System (EIS) strategic planning; understanding the role and contribution of EIS to general management; aligning the MIS organization with that of the parent organization; promoting effective use of the data resource; developing an information architecture; facilitating and managing end-user computing; planning, implementing, and managing telecommunications; measuring MIS effectiveness and productivity; improving the effectiveness of software development; and managing the applications portfolio; selecting and integrating packaged applications software; improving information security and control; MIS ethics; the impact of personal computers in an institutional environment; and, the impact of decision support systems. The following critical MIS issues were found significant by both groups for the present and five years from today. These were: specifying, recruiting, and developing MIS human resources; planning, implementing, and managing factory automation; determining appropriate MIS funding levels; managing the impact of artificial intelligence/expert systems; promoting management of the data resource; and, communicating with upper-level management.