Forecasting corporate performance
| dc.contributor.author | Harrington, Robert P. | en |
| dc.contributor.committeechair | Leininger, Wayne E. | en |
| dc.contributor.committeemember | Killough, Larry N. | en |
| dc.contributor.committeemember | Brown, Robert M. | en |
| dc.contributor.committeemember | Bonham, T. W. "Hap" | en |
| dc.contributor.committeemember | Johnson, Dana J. | en |
| dc.contributor.department | Business | en |
| dc.date.accessioned | 2015-07-10T20:00:19Z | en |
| dc.date.available | 2015-07-10T20:00:19Z | en |
| dc.date.issued | 1985 | en |
| dc.description.abstract | For the past twenty years, the usefulness of accounting information has been emphasized. In 1966 the American Accounting Association in its State of Basic Accounting Theory asserted that usefulness is the primary purpose of external financial reports. In 1978 the State of Financial Accounting Concepts, No. 1 affirmed the usefulness criterion. "Financial reporting should provide information that is useful to present and potential investors and creditors and other users..." Information is useful if it facilitates decision making. Moreover, all decisions are future-oriented; they are based on a prognosis of future events. The objective of this research, therefore, is to examine some factors that affect the decision maker's ability to use financial information to make good predictions and thereby good decisions. There are two major purposes of the study. The first is to gain insight into the amount of increase in prediction accuracy that is expected to be achieved when a model replaces the human decision-maker in the selection of cues. The second major purpose is to examine the information overload phenomenon to provide research evidence to determine the point at which additional information may contaminate prediction accuracy. The research methodology is based on the lens model developed by Eyon Brunswick in 1952. Multiple linear regression equations are used to capture the participants’ models, and correlation statistics are used to measure prediction accuracy. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.extent | x, 247 leaves | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/10919/54515 | en |
| dc.language.iso | en_US | en |
| dc.publisher | Virginia Polytechnic Institute and State University | en |
| dc.relation.isformatof | OCLC# 13719989 | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject.lcc | LD5655.V856 1985.H376 | en |
| dc.subject.lcsh | Business planning | en |
| dc.subject.lcsh | Accounting -- Economic aspects | en |
| dc.subject.lcsh | Managerial accounting | en |
| dc.subject.lcsh | Quality control -- Statistical methods | en |
| dc.title | Forecasting corporate performance | en |
| dc.type | Dissertation | en |
| dc.type.dcmitype | Text | en |
| thesis.degree.discipline | Business | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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