IN FO RM ATIO N TO USER S The most advanced technology has been used to photograph and reproduce this manuscript from the microfilm rnastei, UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. U n iv e rs ity M icro film s In te rn a tio n a l A Bell & H o w e ll I n f o r m a t i o n C o m p a n y Order Number 9102651 A study of vice presidents'’ perceptions and uses of microcom pute for decision support in M ichigan’s four-year colleges and universities Alsohaim, Talal Ali, Ph.D. Michigan State University, 1990 Copyright © 1990 by A lsohaim , Talal A li. A ll rights reserved. UMI 300 N. ZeebRd. Ann Arbor, MI 48106 A STUDY OF VICE PRESIDENTS’ PERCEPTIONS AND USES OF MICROCOMPUTERS FOR DECISION SUPPORT IN MICHIGAN’S FOUR-YEAR COLLEGES AND UNIVERSITIES By Talal A. Alsohaim A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY D epartm ent of College and University Administration 1990 ABSTRACT A STUDY OF VICE PRESIDENTS’ PERCEPTIONS AND USES OF MICROCOMPUTERS FOR DECISION SUPPORT IN MICHIGAN’S FOUR-YEAR COLLEGES AND UNIVERSITIES By Talal A. Alsohaim P u rp o s e : This study sought to: (1) assess college and university vice presidents' perceptions and uses of microcomputers for decision support in M ichigan's four-year colleges and universities; and (2) determ ine the extent to which the use of microcomputers has lived up to the expectations of vice presidents. M eth o d o lo g y : A questionnaire was distributed to 192 selected vice presidents and 55% responded. The data analysis used included descriptive sta tistic s , P earso n correlation coefficient, ANOVA, and M ultiple Regression Analysis. The study consisted of six research questions and twenty null hypotheses. F in d in g s & Conclusions: College and university vice presidents have a positive perception toward the use of microcomputers for decision support, and th e ir expectations were m et. When com paring direct/indirect microcomputer use for decision support by vice presidents, direct users had a more positive perception. Direct microcomputer use for decision support by vice presidents is negatively correlated w ith their age, which may be attributed to younger vice presidents coming in already more familiar with computer use. Therefore, as older vice presidents retire and are replaced w ith younger executives, this may explain: (a) this study's detection of the increase in direct microcomputer use for decision support by executive adm inistrators as compared to previous studies; and (b) the expected continued increase in direct microcomputer use for decision support by vice presidents. More decisions of vice presidents were supported by data generated through the use of m ainfram es th a n m icrocomputers or minicomputers. Microcomputer software was most used to support vice presidents' budgeting and planning decisions. The highest selected reasons for not directly using microcomputers for decision support were "it is someone else's job," followed by the "lack of available time." R ec o m m e n d a tio n s: Among the recommendations emerged from this study were: (1) "MAD-CUE" Microcomputer Assisted Decisions for College and University Executives, a support group which should be organized and established nationwide to include executive administrators interested in the applications of microcomputer technology into college and university ad m inistratio n and decision making; and (2) More funds should be provided to introduce the use of microcomputer technology to support vice presidents' decision making, including suitable hardw are and software, adequate support services and training sessions. ©1990 TALAL A. ALSOHAIM All Rights Reserved Memory o f M y grandmother Whom I Love and Miss Meriam Issa M-Dossary (May g o d ‘F&st 9{er Sold In iHeaven) ACKNOWLEDGEMENTS Above all, I offer perpetual praise and thanks to God, Creator and Sustainer of the Universe, who taught humankind th a t which they knew not. The successful completion of this research could never have been completed without the assistance and motivation of many individuals. A few which I would like to mention. P articu lar appreciation is due to all of the vice presidents who participated in this research. Their responses were the essential pillars supporting this study. I am deeply indebted for the hours of professional assistance and guidance from Dr. K enneth L. Neff, my adviser and chairm an of my doctoral committee. His intellectual stimulation made my doctoral study a rewarding experience. Special thanks are also extended to Drs. Eldon R. Nonnamaker; Louis F. Hekhuis; and Jam es E. Snoddy, the members of my doctoral committee, whose constructive suggestions and teaching expertise substantially enhanced my knowledge during my years a t Michigan State University. My experiences will long be cherished and remembered. My sincere gratitude is extended to Dr. M artha L. Hesse, A ssistant Director of the Office of Planning and Budgets; and Dr. L. Patrick Scheetz, A ssistan t D irector of the Placem ent Services, both a t MSU. They professional^' supervised my internships and broadened my experience of the diverse management of a major research university. I am g ratefu l to Dr. E dw ard B. G ran t, Vice P re sid e n t for A dm inistration a t Central Michigan University, for his kind assistance and support in the early stages of this research. Special recognition, even if my gratitude cannot be fully expressed, is extended to Dr. Mike Kanitz and Dr. Donald O. Bush, both a t CMU, and Dr. B ernard M. Lall at Andrews University for their friendships, a constant source of motivation and encouragement. A respectful gratitude is reserved for sheikh O thm an Al-Saleh, in Riyadh, for his inspiration. His kindness and humbleness are admired by many individuals. A profound thanks goes to my colleagues a t work - Mr. Abdul Mohsin Al-Tamimi, Chairm an of the Board; Mr. Abdullah Al-Huraish, Technical D epartm ent Manager; and Mr. N ajdat Ja rrah , Attorney a t Law, all a t AL-TAMIMI Establishm ents in Alkhobar, Saudi Arabia — for th eir close friendships. I remember with fondness the successful time we spent in the lucrative world of business. No words adequately convey my supreme debt of gratitude to my father Ali M. A l-Suhaim , my m other, sisters, an d my b ro th ers: LtCol. M ohammad A. Al-Suhaim, and Engr. Najib A. Al-Suhaim, for th eir patience, m otivation, and consistent support which forever will be rem em bered. Ignoring Information Is R Turbulent Hoad To Institutional Destruction . . . T. JZCsofiaim TABLE OF CONTENTS Page LIST OF TABLES .................................................................................... xii LIST OF F I G U R E S ................................................................................... xvi C hapter I- IN T R O D U C T IO N ................................................................... 1 Statement of the P r o b l e m ................................................... 3 Purpose of the S t u d y .............................................................. 4 5 Importance of the S tu d y ......................................................... 6 Research Q u e s tio n s .............................................................. H y p o th eses.............................................................................. 8 D e lim ita tio n s ................................................................................11 L im itatio n s...................................... 11 A s s u m p tio n s ................................................................................12 Definitions of T e r m s .....................................................................12 Organization of the S t u d y ......................................................... 15 II- REVIEW OF RELATED L IT E R A T U R E ................................... 16 Microcomputer Applications in College and University A d m in is tr a tio n ....................................................16 Microcomputer A d v a n c e m e n t .........................................16 Microcomputer S o f t w a r e ....................................................20 Microcomputer A p p lic a tio n s .............................................. 21 Decision Support System (D SS)....................................................26 Microcomputer-Based D S S ....................................................29 Administrators' Attitudes and Perceptions Toward Computers as Decision S u p p o rt.............................................. 33 S u m m a r y ..................................................................................... 36 III- M E T H O D O L O G Y ..........................................................................38 P o p u la tio n ..................................................................................... 38 I n s tr u m e n ta tio n .......................................................................... 39 Validity of In stru m e n t...............................................................40 Reliability of I n s tr u m e n t......................................................... 44 D ata C o l l e c t i o n ..........................................................................45 Treatm ent of the D a t a ...............................................................47 S u m m a r y ..................................................................................... 47 ix Page IV- PRESENTATION AND ANALYSIS OF THE DATA . . 49 I n t r o d u c t i o n ............................................................................... 49 Descriptive A n aly ses.................................................................... 50 Research Questions and H y p o th e s e s........................................ 60 Research Question 1 ....................................................... 00 0 Hypothesis I .................................................................... 65 Research Question 2 ...............................................................G6 0 Hypothesis H a ...................... 06 0 Hypothesis l i b .................................................................... 69 0 Hypothesis l i e .................................................................... 69 0 Hypothesis l i d .................................................................... 70 71 0 Hypothesis H e .................................................. 0 Hypothesis I l f .................................................................... 72 Research Question 3 ...............................................................77 0 Hypothesis I l i a ...............................................................78 0 Hypothesis I l l b ...............................................................79 0 Hypothesis I I I c ...............................................................80 0 Hypothesis I l l d ...............................................................83 0 Hypothesis I l l e ...............................................................84 0 Hypothesis I l l f ...............................................................85 0 Hypothesis I l l g ...............................................................86 0 Hypothesis I l l h ...............................................................86 0 Hypothesis I l l i ...............................................................88 0 Hypothesis I l l j ...............................................................89 0 Hypothesis I l l k ...............................................................89 0 Hypothesis M l ...............................................................90 Research Question 4 ...............................................................93 0 Hypothesis IV . ...............................................................94 Research Question 5 ...............................................................95 Research Question 6 ............................................................. 102 Sum m ary ..................................................105 V- SUMMARY, FINDINGS, CONCLUSIONS, AND RECOM MENDATIONS............................................................. 107 S u m m a r y ....................................................................................107 F i n d i n g s ....................................................................................109 C o n c l u s i o n s ..............................................................................122 R e c o m m e n d a tio n s ....................................... 123 x Page APPENDICES ......................................................................................... 126 A. MICHIGAN'S FOUR-YEAR COLLEGES AND U N I V E R S I T I E S ........................................................................ 126 B. LETTER TO VALIDATION P A N E L .......................................127 C. LETTER OF HUMAN SUBJECT APPROVAL . . . . 128 D. THE SURVEY INSTRUMENT WITH COVER LETTER . 129 E. FOLLOW-UP LETTER TO THE VICE PRESIDENTS . 136 . F. SPSS-X COMMAND PROGRAM USED FOR DESCRIPTIVE ANALYSES....................................................... 137 G . SPSS-X COMMAND PROGRAM USED FOR INFERENTIAL A N A L Y S E S ..................................................140 H. COMMENTS BY VICE PRESIDENTS SUPPORTING THAT MICROS LIVED UP TO THEIR EXPECTATIONS . 146 I . COMMENTS BY VICE PRESIDENTS SUPPORTING THAT MICROS DID NOT LIVE UP TO THEIR EXPECTATIONS ....................................................... 149 J . OTHER REASONS BY VICE PRESIDENTS FOR NOT DIRECTLY USING MICROS FOR DECISION SU PPO R T....................... K. FREQUENCIES OF REPORTED PERCENT OF VICE PRESIDENTS’ DECISIONS SUPPORTED BY THE USE OF DIFFERENT TYPE OF COMPUTER UNITS . 150 . 151 L. FREQUENCIES OF REPORTED PERCENT OF MICROCOMPUTER GENERATED DATA BY VICE PRESIDENTS, SUPPORTIVE STAFF, AND EXTERNAL SOURCES TO SUPPORT DIFFERENT AREAS OF DECISION M A K IN G ...................................................................152 M . FREQUENCIES OF REPORTED PERCENT OF MICROCOMPUTER GENERATED DATA BY VICE PRESIDENTS AND SUPPORTIVE STAFF USING DIFFERENT TYPE OF MICROCOMPUTER SOFTWARE IN SUPPORT OF DECISION M A K IN G ................................. 155 BIBLIO G RA PH Y ......................................................................................... 157 xi LIST OF TABLES Table Page 3.1 Assigned Numerical Values for Choices in Both the Perception and Expectation S c a l e s ..............................................40 3.2 Calculated Face Validity of Each Item in the Perception and Expectation S c a l e s .................................................................... 44 3.3 Accumulated Number and Percent of Questionnaires Received by Number of Weeks L a p s e d ........................................ 46 4.1 Distribution of Respondents by P o s itio n ........................................51 4.2 Distribution of Respondents According to Length of E m p lo y m e n t .....................................................................................52 4.3 Distribution of Respondents According to Size of I n s t i t u t i o n .......................................................................................... 53 4.4 Distribution of Respondents by Type of Institution ( P u b lic /P r iv a te ) ............................................................................... 53 4.5 Distribution of Respondents by Type of Institution (Major/Non-Major Research I n s t i t u t i o n ) ...................................54 4.6 Distribution of Respondents by Age G r o u p ........................ 55 4.7 Distribution of Respondents by G e n d e r ..............................56 4.8 Distribution of Respondents by Highest Degree Held 4.9 Distribution of Respondents by Age of Highest Degree . . . . 56 . 59 4.10 Distribution of Respondents by Possession of Technical .................................................................... 60 Degree 4.11 Aggregated Means and Standard Deviations for the Perception and Expectation S c a l e s ..............................................62 4.12 Criteria Used to Interpret the Mean Scores of the Perception and Expectation S c a l e s ..............................................63 xii 4.13 Distribution of Respondents’ Opinions of Whether or Not Micros Lived Up to Their Expectations as Decision Support T o o l s ..................................................................................... 64 4.14 Pearson Correlation Coefficients of Perception With Expectation, Age and Age of Highest Degree (Two-Tailed Test) ......................................................... 67 4.15 Number, Mean, and Standard Deviation of Scores on the Perception Scale for Vice Presidents by Type of Institution (Major/Non-Major Research I n s t i t u t i o n ) ..................................68 4.16 ANOVA Results for Comparison of Scores on the Perception Scale for Vice Presidents by Type of Institution (Major/Non-Major Research I n s t i t u t i o n ) ..................................68 4.17 Number, Mean, and Standard Deviation of Scores on the Perception Scale for Vice Presidents by Highest Degree H e l d ..................................................................................... 70 4.18 ANOVA Results for Comparison of Scores on the Perception Scale for Vice Presidents by Highest Degree H e l d ..................................................................................... 70 4.19 Number, Mean, and Standard Deviation of Scores on the Perception Scale for Vice Presidents Who Hold Technical Degrees and Those Who Do N o t ................................................... 72 4.20 ANOVA Results for Comparison of Scores on the Perception Scale for Vice Presidents Who Hold Technical Degrees and Those Who Do N o t ...................................72 4.21 Number, Mean, and Standard Deviation of Scores on the Perception Scale for Vice Presidents Who are Directly and Indirectly Using Micros for Decision S u p p o r t ............................. 73 4.22 ANOVA Results for Comparison of Scores on the Perception Scale for Vice Presidents Who are Directly and Indirectly Using Micros for Decision Support . . . . 74 4.23 Multiple Regression Analysis of Relationship Between the Perception of Vice Presidents Toward the Use of Microcomputers for Decision Support and Independent V a r i a b l e s .................................................................... 76 4.24 Number, Mean, and Standard Deviation of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of P o s i t i o n .............................................. 79 xiii 4.25 ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of P o s i t i o n ............................................................................... 79 4.26 Pearson Correlation Coefficients of Direct Use of Micros by V.P. With Perception, Expectation, Length of Employment, Age, Age of Highest Degree, Size of Institution, and Total Number of Supportive Staff (Two-Tailed T e s t ) ............................. 81 4.27 Pearson Correlation Coefficients of Direct Use of Micros by V.P. With Perception, Expectation, Length of Employment, Age, Age of Highest Degree, Size of Institution, and Total Number of Supportive Staff (One-Tailed T e s t ) ............................. 82 4.28 Number, Mean, and Standard Deviation of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of Institution ( P r iv a te /P u b lic ) ............................................................................... 83 4.29 ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of Institution (Private/Public) ........................................ 84 4.30 Number, Mean, and Standard Deviation of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of Institution (Major/Non-Major Research I n s t i t u t i o n ) ...................................85 4.31 ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of Institution (Major/Non-Major Research I n s t i t u t i o n ) ...................................85 4.32 Number, Mean, and Standard Deviation of Vice Presidents’ Extent of Direct Micro Use for Decision Support by G e n d e r ....................................................... 87 4.33 ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support by Gender . 87 4.34 Number, Mean, and Standard Deviation of Vice Presidents' Extent of Direct Micro Use for Decision Support with Regard to Whether or Not They Hold Any Technical D e g r e e ....................................................................88 4.35 ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support with Regard to W hetner or Not They Hold Any Technical D e g r e e ............................................................................... 89 xiv 4.36 Multiple Regression Analysis of Relationship Between the Extent of Vice Presidents' Direct Use of Microcomputers for Decision Support and Independent V a r ia b l e s .................... 92 4.37 Pearson Correlation Coefficients of Perception With Direct and Indirect Use of Micro for Decision Support (Two-Tailed Test) ................................................... 94 4.38 Distribution of Respondents With Regard to Whether or Not They Have a Microcomputer in Their Own Office . . . 96 4.39 Distribution of Respondents With Regard to the Number of Microcomputer Units Operated by Supportive Staff . 96 . . 4.40 Number, Mean, and Standard Deviation of Percent of Microcomputer Direct and Indirect Use for Decision Support by Vice P re s id e n ts .............................................................. 97 4.41 Number, Mean, and Standard Deviation of Percent of Vice Presidents' Decisions Supported by Data Generated Through Different Type of Computer U n i t s ...................................98 4.42 Number, Mean, and Standard Deviation of Microcomputer Generated Data by Vice Presidents, Supportive Staff and Other Sources Used to Support Different Areas of Decision M a k i n g .............................................................100 4.43 Number, Mean, and Standard Deviation of Microcomputer Generated Data by Vice Presidents and Supportive Staff Using Different Type of Microcomputer Software to ....................................................... 101 Support Decision Making 4.44 Rank-Order of Respondents' Reasons for Not Directly Using Micros for Decision S u p p o r t ............................................ 104 xv LIST OF FIGURES F igure Page 1.1 Themes and Relationships to be I n v e s ti g a te d ....................... 7 2.1 Major Elements of a Decision Support S y s t e m ..............................30 4.1 Distribution of Respondents' Positions by Type of I n s t i t u t i o n ..........................................................................54 4.2 Distribution of Female Respondents by Position . . . . 57 4.3 Distribution of Male Respondents by P o s i t i o n ..............................57 4.4 Distribution of Respondents' Highest Degrees by Position . 58 4.5 Respondents' Opinions of W hether or Not Micros Lived up to Their Expectations as Decision Support Tools by Type of P o s i t i o n ............................................................................... 65 4.6 Distribution of Responses Regarding Reasons for Not Directly Using Micros for Decision Support by Type of I n s t i t u t i o n ........................................................................ 104 xvi CHAPTER I INTRODUCTION New technology can provide many solutions to many problems in our society. C ertain technology, such as computers, however, can be useful in achieving specific goals and objectives, and in many cases, in a minimal am ount of time. Further, the development of the microcomputer during the early 1970s attracted many organizations to adopt such technology in their daily operations. In higher education, increasing financial constraints and declining enrollm ent may hinder the survival of many colleges and universities. Keller (1983) reported th a t "between 10 percent to 30 percent of America's 3,100 colleges and universities will close their doors or merge with other institutions by 1995" (p. 3). These frightening statistics cannot be ignored by college and university executive adm inistrators, where m any sensitive decisions have to be m ade for th e survival of th e ir in stitu tio n s. W olotkiewicz (1980) agreed th a t college and u n iv ersity executive adm inistrators have recognized th a t "decisions are becoming increasingly interdependent and mistakes becoming increasingly unacceptable" (p. 240). Norris and Mims (1984) argued "although decision making will rem ain imperfect, the penalties for poor decision making will be extreme" (p. 707). Hence, executive adm inistrators are expected to produce better and more effective decisions, not to mention the tremendous pressure this may hold for these adm inistrators. 2 With these given conditions and perhaps more, it is obvious th a t many adm inistrators are seriously adopting more sophisticated m anagem ent support systems to improve their decision capacity. Yet because of the fact th a t "hard data" are becoming more vital for adm inistrative decision making in colleges and universities (Wyatt and Zechauser, 1975, p. 175), a potential candidate is the microcomputer, which can be a powerful decision support tool for college and university executive administrators. For the past decade microcomputers have "invaded" many operational areas in higher education institutions. This was largely due to the declining cost of microcomputer hardw are and software, the attractive discounts offered by microcomputer industries to institutions of higher education, and the need to adopt such technology. Although the use of microcomputers for decision support by executive a d m in istra to rs in colleges and u n iv e rsitie s is relativ ely a new phenomenon, some authorities on decision making agree th a t the "proper use of a microcomputer can greatly improve the quality of information for decision m aking and planning" (Tanner and Holmes, 1985, p. 9). I t is claimed th a t it can increase adm inistrative effectiveness (O'Danial, 1984), and productivity (Baldridge, Roberts and W einer, 1984; Brown and Droegemueller, 1983; Browne, 1985; H utten, 1984; Madron 1983). Microcomputers can also increase efficiency in the decision making process (Callamaras, 1984). While most colleges and universities continue to use mainframe computers to serve many adm inistrative functions, the application of microcomputers for financial analysis, planning, and m odeling can be more flexible and inexpensive for word processing (Madron, 1983). In general, microcomputers are more cost effective th an large m ainfram es and minicomputers (Baldridge, Roberts and Weiner, 1984; Compeau, 1984; Davis, 1988; Garmon, 1984; Madron, 1983). However, the lack of inform ation regarding the reliab ility of microcomputer systems make them vulnerable (Brown, 1983). While such a claim can be disputed by Evans (1983), it is indeed the decision maker as the "master" who plays a significant role in utilizing the microcomputer for his/her own advantages in the decision making process. As Lyon (1981) put it "good tools do not make good managers, but they can assist a good m anager in making better decisions" (p. 73). Since the decision m aker's perception may hinder or enhance the potential use of the microcomputer as a tool of decision support, this study will focus on the perceptions of college and university vice presidents with respect to the use of microcomputers for decision support in Michigan's four-year colleges and universities. Slatementj)l^heJrj3bkm. The decision making process may take many forms, but the basis on which many decisions are made can be labeled differently. Rational decision making, as one model of decision making, can be an essential element for institutional prosperity. Chaffee (1983) stated "when rational decisions and the conditions th a t m ake ratio n al decisions possible consistently characterize a college or university, th a t in stitu tio n experiences not only a high proportion of excellent decisions but also a high degree of confidence in itself, in its values, and in its administration" (p. 2). Based on previous studies, Chaffee explained the lack of rational decision making process in colleges and universities (p.2). While rational decision making, as Simon (1958) contended, "always requires the comparison of alternative means in term s of the respective ends to which they will lead" (p. 65), the "comparison" or perhaps the effective evaluation of alternatives m ay very well require both m ental and technological processing of information. As we advance further into the information age and to the twenty-first century, more information needs to be analyzed to effectively support rational decision making. The advancem ent in microcomputer technology has increased its potential use for information processing and as tools for decision support in many organizations. It has been claimed th a t using microcomputers can increase adm inistrative effectiveness and productivity. It can also increase efficiency in the decision making process. Hence "more, better and faster inform ation is associated w ith rationality" (Weisband 1987, p. 150). Interestingly enough, the extent of microcomputer use as decision support by college and university executives as well as w hether it m akes a difference to use them represent a concern th a t should be investigated. Exploring th e perceptions of M ichigan college and u niversity vice presidents toward the use of microcomputers, in addition to the application of such technology as decision support, should enable one to assess the extent to which adm inistrative effectiveness and productivity are increased by microcomputer use. Purpose of the Study The purpose of this study was to assess college and university vice presidents' perceptions and uses of microcomputers for decision support in M ichigan’s four-year colleges and u niversities. In assessing the perceptions, th e ex ten t to w hich m icrocom puters lived up to the expectations of the vice presidents was considered. In addition, several 5 independent variables were investigated for any relationship w ith the perception of vice presidents toward microcomputers, and the extent of their use of such technology. Importance of the Study Considerable research has been conducted on the applications of microcomputers as decision support which resulted in various decision models th a t can be applied to admini strati ve functions in colleges and universities. Several studies were also conducted to investigate the perceptions and attitudes of college presidents, deans and chairpersons toward computers, but they rarely included vice presidents, where many critical decisions lie within their hands. Interestingly enough, through the reviewed literature and to the best of the researcher's knowledge, there were no studies conducted to investigate vice presidents' perceptions and uses of microcomputers for decision support in Michigan's four-year colleges and universities. The results of this study may contribute to the two reasons for studying societal attitude toward computers as proposed by Mathews and Wolf (1983): 1. To better understand and correct the fallacious and often irrational attitudes toward this integral component of modem life. 2. To better understand the rational attitudes against computers and their uses so th a t individual and society may be protected, (p. 4) The introduction of computer technology into the hum an world has initiated and supported effective research related to such technology. Zemanek (1975) stressed "The computer creates by its existence and by the growing num ber of applications, a world of hum an decisions and choices which did not exist before; they deserve attention and study, investigation and publicity" (p. 10). Information stemming from this study could prove vital to college and university vice presidents for future planning of more suitable hardw are and software based on current composite applications of microcomputers as decision support. In addition, information from this study will provide college and u niversity vice presidents an inside look a t other vice p residents' utilizations of m icrocomputers in th e ir decision m aking process. O ther groups which may benefit from the results of this study are m icrocom puter designers and software developers. The results could provide up-to-date information for these groups to create more appropriate hardw are and software to serve college and university vice presidents' tasks more efficiently, flexibly and effectively. Thus, better application of microcomputers in the decision making process. To accomplish the purpose of this study, six research questions were developed to be explored as follows (for the following Arabic num erals and their sub-alphabetical letters, see their correspondences in Figure 1.1): 1. W hat are the perceptions tow ard the use of microcomputers for decision support by vice presidents at Michigan's four-year colleges and universities: a. P resent perceptions toward microcomputers. b. The extent to which microcomputers live up to the expectations of the vice presidents. 7 Figure 1.1--Themes and Relationships to be Investigated 1-a Perceptions Toward Microcomputers Microcomputer Applications Demographic Variables Expectations of Microcomputers 1 - b c. Relationships existing between vice presidents' perceptions and expectations of microcomputers. 2. W hat relationships exist between vice presidents' perceptions toward microcomputers as decision support and the type of their institution (major/non-major research institution), age, highest degree held, age of highest degree, possession of technical degree, and direct/indirect microcomputer use for decision support? 3. W hat relationships exist between the extent of microcomputer direct use for decision support by vice presidents and their positions, length of em ployment in cu rren t position, size of in stitu tio n , type of institu tio n , age, gender, age of highest degree held, possession of technical degree, perception, expectation, and the total number of supportive staff? 4. W hat relationships exist between vice presidents' perceptions and their direct and indirect use of microcomputers for decision support? 5. To w hat extent are microcomputers and related software used to generate data, as compared to other computer sources, to support areas of decision m aking by vice presidents (either directly or indirectly)? 6. W hat are the reasons, if any, for not directly using microcomputers for decision support by vice presidents? In order to answer the research questions presented in this study, the following hypotheses (sta te d in n u ll form) w ere form ulated for investigation: I. There is no significant relatio n sh ip betw een vice presidents' perceptions and expectations of microcomputers as decision support. II a. There is no significant difference between vice presidents from major research institutions and those who are not w ith regard to their perceptions toward the use of microcomputers for decision support. II b. There is no significant perceptions toward support and their age. the relationship between vice use presidents' of microcomputers for decision II c. There is no significant difference among the vice presidents toward the use of perceptions of microcomputers for decision support based on their highest degree held. II d. T here is no significant relationship betw een vice presidents' perceptions toward the use of microcomputers for decision support and the age of their highest degree. II e. There is no significant difference between vice presidents who possess technical degrees and those who do not with regard to their perceptions toward the use of microcomputers for decision support. II f. There is no significant difference between vice presidents who are directly using microcomputers and those who are not with regard to th e ir perceptions toward the use of microcomputers for decision support. I l i a . There is no significant difference among vice presidents for Academic, Business, Students or Public affairs with regard to the extent of their direct use of microcomputers for decision support. I llb . There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the length of employment in their current positions. IIIc. There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the size of institution. I lld . There is no significant difference between vice presidents from private institutions and those from public institutions with regard to the extent of their direct use of microcomputers for decision support. 10 I lle . There is no significant difference between vice presidents from major research institutions and those who are not with regard to the extent of their direct use of microcomputers for decision support. Illf. There is no significant relationship between the extent of vice presidents’ direct use of microcomputers for decision support and their age. I llg . There is no significant difference between male and female vice p resid en ts w ith reg ard to th e ex ten t of th e ir direct use of microcomputers for decision support. I l l h . There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the age of their highest degree held. I l l i . There is no significant difference between vice presidents who hold technical degrees and those who do not with regard to the extent of their direct use of microcomputers for decision support. I l l j . There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and their perceptions of microcomputer as decision support. I llk . There is no positive relationship between the extent of vice presidents' direct use of microcomputers for decision support and the degree of th eir expectation related to the use of microcomputer for decision support. III1. There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the total number of vice presidents' supportive staff. IV. There is no significant relationship betw een vice presidents' perceptions toward using microcomputers as decision support and 11 their direct and indirect use of microcomputers for decision support (through their supportive staff). This study was delimited to four selected types of vice presidents' positions at each of Michigan's four-year, baccalaureate granting colleges and universities th a t are accredited by the North Central Association of Colleges and Schools. This included both public and private institutions (see Appendix A). The four types of vice presidents' positions th a t this study focused on are as follows: 1. Vice President for Academic Affairs. 2. Vice President for Business Affairs. 3. Vice President for Students Affairs. 4. Vice President for Public Affairs. This study shall be restricted to investigate the selected vice presidents' perceptions and uses of microcomputers for decision support. liroiiatmns As w ith all questionnaire type research, where the researcher is dependent upon the validity of the responses, one should be cautious in generalizing the findings of such research. However, if the four-year institutions available in the state of Michigan approximate the variety of the four-year in stitu tio n s th a t are in the U nited S tates, to th a t extent, conclusions drawn from this study could be generalized to vice presidents in public and private four-year colleges and universities in the United States. 12 A ssum ptions It was assum ed in th is study th a t the response ra te to the questionnaires by the subjects might be relatively low. One possible factor is perhaps due to the fact th a t college and university vice presidents are extremely busy with their tasks. One attem pt by the researcher to elevate the response rate, was to keep the length and average tim e required to complete the questionnaire to a minimum and to display the questionnaire in an attractive fashion to procure the attention of the subjects. It was also assum ed in this study th a t many of the college and university vice presidents' tasks performed on computers are normally conducted by their supportive staff. IM unffiflnajifJernifl In th is study, some term s were used frequently. To clarify any am biguity th a t may relate to such term s, their intended meanings are described as follows: 0 H ypothesis: This refers to the null hypothesis which is, "merely the statistical and logical equivalent of the opposite of the research hypothesis" (Cates, 1985, p. 17). In this study all of the null hypotheses are introduced for a possible rejection. Age of Highest Degree: Number of years lapsed since respondents attained th eir highest academic degree. A ttitu d e : This term refers to "a relatively enduring and consistent set of opinions, often implying a value judgm ent, about particular persons or objects" (Gardner 1978, p. vi). Decision M aking: A process of formal or informal procedures th a t may be followed to arrive a t a final choice. 33 Decision Support; Any method or tool used to enhance decision making. Decision Support System (DSS): For the purpose of this study, this term refers to "a class of computerized aids th a t offer personalized facilities th at can be used to help an executive or m anager make decisions or process routine work" (Jamieson and MacKay, 1984, p. 56). Direct Micro Use: The extent of microcomputer use for decision support by vice presidents — m easured in percent of microcomputer total usage time directly by vice presidents for their decision support. Expectation: The extent to which microcomputers lived up to, fell short of, or exceeded vice presidents' expectations as tools for decision support. Indirect Mkm_.Hse: The extent of microcomputer use for vice presidents' decision support by th eir supportive staff — measured in percent of total microcomputer usage time by supportive staff for vice presidents decision support. M ainfram e C om puter: "The largest of the computers ... marked by fast CPU operating speed, almost limitless memories and storage, and a large number of users" (Hollander, 1986, p. 72). M ic ro c o m p u te r: "A complete tin y computing system, consisting of hardw are and software, whose m ain processing blocks are made of semiconductor integrated circuits" (Sippl and Sippl, 1980, p. 320). It is also called a Personal Computer(PC) or Micro. P erception: This term refers to "the interpretation of received stimuli in light of past experiences and knowledge" (Hopkins 1976, p. 90). Perception, in this study, is assessed by the degree of positive or negative feeling, as measured by a forced choice Likert-type scale, toward statem ents about the use of microcomputers for decision support. 14 Supportive Staff: Personnel including secretaries and persons who reports directly to the vice president and have at east some staff responsibilities. Vice President: In this study, this term refers to a deputy of a president of a college or university. Vice President for Academic Affairs: This term refers, but not exclusively, to a college or university "Chief Academic Officer" who "directs the academic program of the institu tio n . Typically includes academic planning, teaching, research, extensions and coordination of in te r­ departm ental affairs" (Torregrosa, 1990, p. xxi). The holder of this position may also be called the Provost. Vice President for Business Affairs: This term refers, but not exclusively, to a college or university "Chief Business Officer" who "directs business and financial affairs including accounting, purchasing, physical p lan t and property m anagem ent, personnel services, food services, auxiliary enterprises and related business matters" (Torregrosa, 1990, p. xxi). This position may also refer to the vice president for business and finance. Vice.. President for Students Affairs: This term refers, but not exclusively, to a college or university "Chief Student Life Officer" who directs "the student life program s including counseling and testing, housing, placem ent, stu d en t union, relationships w ith student organizations and related functions" (Torregrosa, 1990, p. xxi). At small institutions, this position may refer to the dean of students. Vice President for Public Affairs: This term refers, but not exclusively, to a college or university "Chief Public Officer" who "directs public relations program. May include alum ni relations, publication and development" (Torregrosa, 1990, p. xxi). 15 Organization of the Study This study was organized in five chapters. C hapter I included an introduction, statem ent of the problem, purpose of the study, importance of the study, research questions, hypotheses, delim itations, lim itations, assumptions, definition of term s and organization of the study. C hapter II consisted of reviewed related literatu re which included microcomputer applications in college and university adm inistration, decision support system (DSS), adm inistrators’ attitudes and perceptions toward computers as decision support, and a summary. Chapter III covered the methodology of the study which included the population, in stru m en tatio n —validity and reliability, d ata collection, treatm ent of the data, and a summary. Chapter IV revealed presentation and analysis of the data, which included the introduction, descriptive analyses, research questions and hypotheses, and a sum m ary. And finally, C hapter V presented a summary, findings, conclusions, and recommendations. CHAPTER II REVIEW OF RELATED LITERATURE The review of related literature to this study is organized and reported under three main headings, each of which included its own sub-headings. The three m ain headings are: (1) Microcomputer applications in college and university administration. (2) Decision Support Systems (DSS), and (3) A dm inistrators' attitudes and perceptions toward computers as decision support. Microcomputer ApplicationsJLoJMteae and University Administration In order to comprehend the potential application of microcomputers in adm inistrative function, recent trends and perhaps a brief highlight on the advancement of such technology may prove helpful. Microcomputer Advancement: Although it has taken the hum an race many years to develop the modern microcomputer, the basic idea of such a computing machine was traced all the way to China during the year 600 B.C. The following are selected development stages of the modern microcomputer from Gustafson (1985): 16 17 Dates Development 600 B.C. The abacus came into common use in China, allowing for faster more accurate numerical calculations... 1640s Blaise Pascal ... developed a mechanical adding and subtracting machine to help his tax-collector father perform his job faster. 1830s Charles Babbage ... developed the idea of a mechani­ cal digital computer. He called it the Analytical Engine ... Babbage is considered the father of the computer. 1930s Konrad Zuse ... built a simple computer from an Erector Set. This machine could perform a variety of calculations and was controlled by a program. 1936 IBM installed the first large tabulating machine at the U.S. Social Security Office. It used punch cards and could handle 120 million calculations per year. 1940s Alan Turing ... built a mechanical computer, Colossus I ... it was the first machine to use the binary, 0/1 num bering system. 1949 Maurice Wilkes ... built the first working, electronic digit, stored program computer. 1971 Ted Hoff, an Intel Corporation engineer, placed the whole central processing unit on one silicone chip. 1975 The first hobbyist microcomputer kit was marketed, the Altair 8800. It sold for $395. (pp. 177-79) Beyond 1975, microcomputers were increasingly m anufactured by many different enterprises in the United States, including manufacturers like IBM, Apple, Commodore, Radio Shack, Texas Instrum ents etc. Even though the United States Government imposed strict regulations on the export of American made microcomputers and microchips, it was not long before other countries in the F ar E ast began m anufacturing different 18 brands of microcomputers using the same basic idea of such technology. In fact, they began exporting to many countries in the world, including the United States. This contributed to a significant decrease in the price of microcomputers in the American m arkets as being more and more of a mass m arket product. McWethy and McKirgan (1977) stated th a t "The 1Million-dollar computer of 1950 costs $20 today, and computing speed is 100,000 times faster" (p. 65). The dramatic decrease in computer prices was deeply felt by Schwartz, of SRI International, a research facility in California, (qtd. in Sleight, 1980). As Schwartz stated "if cars and jets had been decreasing in price a t the same rate as microchips, cars would cost ju st $5 and a Boeing 707 jet would cost only $2,000" (p. H2). Davis (1988) supported a similar position: "in a period of roughly 30 years, computer technology moved from a condition where a room full of million dollar hardware was required to obtain 12,000 bytes of memory ... to the modem desktop microcomputers th a t provide two million bytes of main memory for under $10,000" (p. 3). The decrease in microcomputer prices was not the only relevant change occurring to such technology. With an increasing num ber of microcomputer manufacturers, the competition pressure to produce better and more powerful microcomputers increased as well. Smaller and faster microcomputers with more memory were being developed, as compared with the computers of the 1950s (Miller, 1988). Evans (1982) agreed with these rapid changes, stating "the units of which computers are made are getting sm aller and sm aller, shrinking beyond the range of ordinary microscopes into the infinities of the molecular world" (p. 54). This is not to say th a t the whole microcomputer as a u n it is shrinking beyond the recognition of hum an vision, but rath er w hat Evans referred to was the 19 "component u n its—central processor, program unit, memory, etc." of the m icrocom puter u n it, which is considered to be th e h e a rt of the microcom puter. Further, Simons (1985) wrote "Today computers are learning how to think about the information th a t they hold, how to draw conclusions from th eir knowledge in ways th a t are starting to outstrip hum an intellectual competence" (p. ix). W hether or not computers may compete with hum an capabilities is both debatable and yet to be seen. Bergley, Carey and Reese (1980) argued th a t "true intelligence involves elem ents of will, consciousness and creativity of which today's computers are incapable" (p. 52). While the overall changes of computer technology may enhance the quality and attractiveness of microcomputers for buyers, Dellow and Poole (1984) argued th a t such changes may perplex purchasers trying to determine the right time to acquire a microcomputer; the issue of whether to get it now or wait for better bargain. The choice of "waiting" may not be the answ er as Jam ieson and MacKay (1984) argued "waiting for the industry to settle down is not a choice ... development will continue at a fast pace for many years" (p. 61). Many computer experts agree th a t microcomputers "are here to stay." As Schwartz (qtd. in Sleight 1980) indicated: "Just as the steam engine brought unheard-of power to our hands in the Industrial Revolution, the microcomputers are bringing new dimensions of power to our brains." He further stressed th a t "The impact has barely begun to be felt. We're only a t the earliest stages, and the major applications of this new technology have not yet been realized" (p. H2). Evans (1982) reported th a t microcomputers will "become the most common pieces of technology in the world, and the 20 most useful" (p. 59). The recent development of the modern microcomputer made it more attractive to new and old users from diverse professions, including industry, governmental agencies, and the field of education in its various levels. Interestingly enough, the reviewed literatu re revealed positive views relevant to the future of microcomputers, both in term s of their application and their capabilities. However, reviewing the advancement of microcomputer technology seems to be incomplete without exploring some of the recent trends of its related software. Microcomputer Software: The microcomputer as an exclusive u n it is relatively w orthless without the use of software/programs; a microcomputer by itself is like having "a car w ithout gasoline." The recent development of the micro­ computer has created a demand in the m arket to develop and design different software using different kinds of program languages such as BASIC, FORTRAN, COBOL, PASCAL, C Language, and APL. While the microcomputer applications may vary depending on the intended purpose of the user, the goal of the microcomputer software industry is clear: to design a maximum user-friendly software th a t serves the maximum needs of the user while keeping the price as competitive as possible for the consum ers. The software industry has flooded the m arket w ith a variety of microcomputer software. Available software ranges from single purpose, such as wordprocessing to multi-purpose which includes, b u t is not limited to, D ata Base, Communication, Spreadsheet, Graphics and Statistics. The 21 selection of microcomputer software depends a great deal on the need of the user(s). However, for educational adm inistrators, G ustafson (1985) suggested th a t "efficiency" was the prime consideration. H utten (1984) argued th a t "ease of use, quality of documentation, integration, and support of hardw are devices," should all be considered when possible selection of software is sought (p. 48). While each software can be used to support a specific ta sk for the user, the prim ary purpose for adm inistrators is essentially to provide the needed information/decision model at the right time for adm inistrative decision making. Microcomputer Applications: D uring th e p a s t decade, th e advancem ent in m icrocom puter technology, the declining cost of hardw are and software, and the large discount offered by th e com puter in d u stry to in stitu tio n s of higher education has a t least, contributed to the "invasion" of microcomputers to assist in many areas in colleges and universities. Roskens (1974) argued th a t "no single force, with its myriad permutations, has had greater impact upon the style and operations of American Colleges and Universities in the last half century than computer technology" (p. 142). There is no doubt th a t microcomputers m anipulate data much faster than the hum an mind. In turn, data can be presented as vital information to decision makers in colleges and universities. As Jones (1982) stated, "in higher education, data are used by analysts to construct information for the use of adm inistrators and other-decision-makers" (p. 21). The development of computer technology has a great effect on the adm inistration of colleges an d u n iv ersities, W olotkiewicz (1980) rep o rted th a t "technology, 22 particularly computerization, has added a sophistication to adm inistration not only in control and m onitoring activities, b u t also in providing information and completing transactions for day to day operation" (pp. 3-4). Simon (1977), stressing the importance of computer technology for problem solving and decision making, wrote: A ssum ing new responsibilities is frequently painful. We see many difficulties in the world today th a t we did not see ten years ago. Sometimes we despair of dealing with all of the difficulties th a t confront us. We can take comfort, perhaps, in recognizing th a t th ere are really not more problems; th ere is ju s t an increasing awareness of w hat the problems are. The informationprocessing technology is playing a major role both in producing th a t recognition and in providing new alternatives for handling the problems (pp. 167-68). Use of microcomputers as tools for inform ation m anipulation and control is evident. As Madron (1983) contended, "Both microcomputers and large m ainfram es can assist accessibilities by allowing us to make more intelligent and selective use of the information produced" (p. 157), further stressing, "when m anagers can actually access required information for making decisions, then decisions will be better informed" (p. 160). As the use of information is expected to increase during this decade, similarly, "direct m anagem ent use of term inals and micros will increase from a relatively low rate to a very high rate" (Madron 1983, pp. 158-60). Many authors support the application of microcomputer technology to help college and university adm inistrators construct th eir own decision models using relevant information - H arris (1984), Cloutier and Hoffman (1985), O'Danniel (1984) etc. One successful application of microcomputers in adm inistrative function in higher education, was reported by Cloutier and Hoffman (1985). 23 At a time of budget reduction a t the University of Illinois a t Chicago, the Office of Campus Planning used a microcomputer and software called "VisiCalc" to develop a decision support system nam ed "Budgeting for University executives (BUX)." BUX which included a "what-if’ function and was designed to serve the Vice Chancellor for Administration as a decision support. One essential dimension of this system was to "test resource allocation alternatives" (pp. 22-28). Such creative applications of microcomputer technology into college and university m anagem ent were evident in the reviewed literatu re. However, in any microcomputer application as a tool for decision support, decision m akers m ust accept their own responsibility. As Tanner and Holmes (1985) wrote: Microcomputer and technology to be the servants of persons who are involved in planning, research, and decision making — not th eir m aster. Hence, the microcomputer is a sophisticated hireling, where the sophistication is dependent upon the m aster as well as the servant, (p. 8) The decision m akers are the "masters" who play significant roles in utilizing the microcomputer for th eir own advantages in the process of decision making. One incentive for microcomputer application as an expert system into problem solving and decision making is explained by Simons (1985): "the design of expert system is to enable computers to think about specialist knowledge in ways th a t help hum an experts, bring new insight in difficult areas, and provide a cost-effective way of tackling problems in fields where there m ight be a shortage of skilled (and sufficiently cheap) hum an experts" (pp. 174-75). Although only a small number of executive adm inistrators directly use microcomputers as reported in a study by Deel (1987), many have someone 24 else use microcomputers for them. Deel found in his study of Oklahoma college and university adm inistrators th a t "51.3 percent of the respondents have someone use the microcomputer on their behalf." Only "18.9 percent of the respondents personally use a microcomputer." In addition, among the results of Deel's study were: respondents expressed the need for training related to computer use, microcomputers were mostly used for wordprocessing, and respondents anticipated th a t microcomputer use would increase. While Deel found no significant relationship between the "overall use of microcomputers" by executive adm inistrators and their sex, position, type of in s titu tio n , in te re s t in le a rn in g m ore about m icrocomputers, graduation date, and adm inistrative experience; age however, was found to be negatively correlated (r = -.25) (p. 99). Baldridge, Roberts and W einer (1984) claimed th a t adm inistrators already heavily utilizing computer technology, especially in large institutions, they pointed out that: Large university campus adm inistrative applications generally absorb 50 to 60 percent of the computing usage. These activities cover a broad spectrum , including accounting, physical plant scheduling, payroll, stu d en t registration, reg istrar's records, budgeting analysis, and personal files. (1984, p.7) Microcomputers can be very useful tools for decision m akers a t the higher education level. Tanner and Holmes (1985) contended "proper use of a m icrocom puter can greatly improve the quality of inform ation for decision m aking and planning ... also operation w ithin an organization will improve" (p. 9). In supporting such a claim, Madron (1983) reported "one collective objective of any large organization is to make its staff more productive, and one way to make the people in the organization more productive is to provide them with b etter tools ... one of the major 25 productivity tools of the 1980s is the personal computer" (p.2). As such, efficiency is increased for decision makers familiar with the form of storage and retrieval of inform ation, either in or through the microcomputer. H arris (1984) argued th a t decision makers could then concentrate on the needed information rather than the "availability" of information (p. 23). To maximize the potential use of microcomputers by adm inistrators in the decision making process, the user(s) need to have enough knowledge on how to operate the hardware and execute the needed software. Baldridge, Roberts and Weiner (1984) suggested "It is probably more feasible for an adm inistrato r to gain a functional knowledge of a computer system's operation th a n for the com puter specialist to fully comprehend the adm inistrator's complex needs and concerns" (p. 27). While such concern was also supported by Mann (1979), a question can be raised a t this point as to why should adm inistrators in higher education adopt microcomputers for th e ir ta sk support. In response to th is concern, the "Big Ten" advantages of microcomputers reported by Baldridge, Roberts and Weiner (1984) should be listed: 1. Microcomputers have an excellent ratio between cost and usefulness. 2. Microcomputers can be quickly installed and people can be quickly trained to use them. 3. Microcomputers increase the flexibilities of existing campus networks. 4. Microcomputers software is cheap and readily available. 5. A decentralized microcomputer network does not go down when one unit fails. 6. Microcomputers maximize local control and decentralization. 7. Productivity increases when microcomputers are used. 26 8. The spread of microcomputers may slow the increase in computer specialists. 9. Microcomputers give excellent security for sensitive topics. 10. Microcomputers are "user-friendly." (pp. 35-37) In addition, microcomputers are portable which provide an easy way to be used in different locations w ithin an organization. All these advantages provide a threshold for executive adm inistrators to consider the application of microcomputers to assist in their decision making process. Madron(1983) said "micros should be welcomed as a major step forward in using our technology for greater productivity" (p. 15). IlQ.dd.oa.gu^ D uring th e early 1970s, a new phase from Inform ation and M anagem ent Science was developed to support decision m aking — the Decision Support System (DSS). Although DSS evolved from the concept of Management Information System (MIS), it was distinguished from MIS in its "flexibility, interactivness, discovery orientation, and ease of use for noncomputer decision makers" (Attaran and Bidgoli, 1986, p. 10). As there is no one definition of DSS (Keen, 1986), DSS may vary somewhat in the literature. Jamison and MacKay (1984) defined DSS as "a class of computerized aids th a t offer personalized facilities th a t can be used to help an executive or m anager make decisions or process routine work" (p. 56). Keen (1983) defined DSS as "a computer-based system (say, a data base m anagem ent system or a set of financial models) which is used personally on an ongoing basis by managers and their immediate staffs in 27 direct support of managerial activities - th a t is, decisions" (p. 326). DSS was considered "any type of computerized system used to provide information for m anagers and o th er decision m akers w ithin an organization" (Edmunds, 1987, p. 166). In general, however, DSS was used to solve many types of managerial decision making problems. Thierauf (1988) explained: Decision support systems allow the decision m aker to combine personal judgm ent w ith com puter output in a user-m achine interface to produce meaningful information for support in the decision-making process. Such systems are capable of solving all types of problems (structured, semistructured, and unstructured) and use query capabilities to obtain information by request. As deemed appropriate, they use quantitative models as well as database elements for problem solving, (p. 50) One m ust keep in mind th a t DSS does not replace the decision maker nor it does make the decision for the user; rather, it enhances the decision m aker's "judgement." King (1981) contended "by allowing experimentation with alternatives in ways th a t would never be feasible without the DSS" (p. 64). In short, the definition and purpose of DSS is clear — the use of com puter-based inform ation to construct p ath alternatives/m odels to support, if not improve, the user's decision making capacities. Graham (1983) concluded: An intelligent computer could help us with many of the personal, social, financial, and business problems we face every day. We would not w ant a computer to make all our decisions for us, of course, b u t we might well seek its advice, ju st as we might seek the advice of a knowlegable friend with no emotional stake in the issue at hand, (p.295) Since the creation of DSS, a few of its products have been developed as well — Executive Support System (ESS), Executive Information System (EIS), Expert Systems (ES), Artificial Intelligence (AI), etc. The la tte r two (ES and AI) may become more famous in the 1990s as reported by Stemp et. al. (cited in Towey 1989). While each of these DSS products is geared to 28 certain fields of application, th eir major purpose is the same as for the original DSS. somewhat. Interestingly, th eir unique characteristics m ay vary ES "examines and compares a given situation and its symptoms against the information stored in the knowledge base to help hypothesize: the likely outcome from a given set of circumstances; the cause of problem; or the best course of action" (Davis, 1988, pp. 64-65). Since ES draws its conclusions from stored information, the effectiveness of such a system depends heavily on the accuracy, relevancy, depth and currency of such information. AI, on the other hand, focuses more on the use of n atu ral language to communicate with the user. AI, as Thierauf (1988) reported, "relates fundamentally to the capability of the computer to reason, th a t is, to make inferences about known facts so as to reach logical conclusions" (p. 366). Future trends in AI seems to be both bright and challenging, because of the competition increase between Japanese and American computer scientists in the area of AI development. This may result in more advantages toward the use of DSS in the area of decision making, as Thierauf (1988) stated: The use of AI in future DSS focuses not on mere information but on knowledge of the highest quality th a t is pared, shaped, and tailored to the needs of the specific needs of the decision maker. This knowledge will be accessible to anyone, anywhere, at any tim e in an organization. It will be fast, powerful, and useful to decision makers, (p. 366) In th e p ast, m ost of DSS was im plem ented using m ainfram e computers. Present and expected future capabilities of microcomputers are diverting the application of DSS to smaller and more cost effective computer devices (Karon, 1986; Callamaras, 1984). It is interesting to note th a t much of the reviewed literature extensively covered the development of DSS, the different types of DSS and their applications as decision support 29 tools in the m anagem ent of business and industry with less focus on the managem ent of higher education institutions. Microcomputer-Based ■DSS: As the advancement in microcomputer technology continued, M artin (1989) noticed the increase use of microcomputer as a "workstation" for DSS. Callam aras (1984) argued th a t "most professionals can obtain the benefits of an MIS/DSS with a good microcomputer system" (p. 123). A parallel agreement was also supported by Davis and Sardinas (1985). Most of the reviewed related lite ra tu re , however, had relative agreem ent on the m ajor components or elements of DSS (A ttaran and Bidgoli 1986; Brown and Droegemueller, 1983; Gray and L enstra 1988; Sprague, 1986; Kassicieh et. al., 1986; Sprague and Watson, 1983; Stemp et. al. cited in Towey 1989; Tayagi et. al., 1988). If a DSS is to be implemented using a microcomputer unit, then these authors suggested th a t a well developed DSS m ust incorporate the following major elements: 1. Data Base. 2. Model Base. 3. User Interface. The links between the above three elements are displayed in Figure 2.1 (adopted and modified from Sprague and Watson, 1983, p. 21-23). Each of th e above elem ents rep rese n t a crucial p illar in th e appropriate development and application of most of the DSS and should be clarified as follows: 1. Data Base: The d a ta base is an im perative elem ent of any DSS as it is considered to be the "blood" of such a system. The data base m ust contain 30 Figure 2.1--Major Elements of a Decision Support System Information Control U ser Interface/ Microcomputer (Hardware & Software) Decision Maker/ User v sufficient data related to internal and external information th a t may affect the institution (Attaran and Bidgoli, 1986; Thierauf, 1988). The availability of external information within the data base of a DSS is crucial for executive decision m akers (Sprague, 1986). The data base is either "stored in the microcomputer ... or interface with data residing in a mainframe" (Karon, 1986, p. 101). The data base m ust be updated as required, with data organized and m aintained in a m anner th a t is easily accessed and 31 retrieved by authorized personnel. The data base should be capable of absorbing the needed data to effectively develop and implement the DSS. 2. Model Base: The objective of modeling within a DSS environment is to "represent and simulate segments of the decision-making process its e lf (Stemp et. al. cited in Towey, 1989, p. 53). To im plem ent a model elem ent on a microcomputer-based DSS, Brown and Droegemueller (1983) suggested various types of microcomputer software th a t can be used, such as "electronic spreadsheet, statistical packages, graphics and plotting packages, data base systems" (p. 14). However, Sprague (1983) stressed th at the model base of a DSS should include: - the ability to create new models quickly and easily; - the ability to catalog and m aintain a wide range of models, supporting all levels of management; - the ability to interrelate these models with appropriate linkages through the database; - the ability to access and integrate model "building blocks;" and - th e ability to m anage the model base w ith m anagem ent fu n ctio n s analogous to d a ta b a se m a n ag em en t (e.g., m echanism s for storing, cataloging, linking, and accessing models), (p. I l l ) 3. User Interface: The interface is considered the link between the user and the system (DSS), which includes both the hardw are and software. While the user normally controls DSS, Brown and Droegemueller (1983) argued th a t the decision m aker (user) was a "critical element in the microcomputer-based DSS ... he or she m ust be willing to use the available tools and routines and to weld the data, tools, and routines into models which will support and test various problem solutions and decisions"(p. 14). Thierauf (1988) stressed 32 "the focus of the user-m achine interface is on learning, creativity, and evaluatio n ra th e r th a n on replacem ent, autom ation, and routine procedures" (p. 42). The interface software "must be flexible, easy to use, reliable, reasonably self-explanatory, and responsive—ju st like a staff assistant" (Keen, 1983, p. 385). In general, however, the user interface element of a DSS should have the following capabilities: - the ability to handle a variety of dialogue styles, perhaps with the ability to shift among them a t the user’s choice; - the ability to accommodate user actions in a variety of media; - the ability to present data in a variety of formats and media; and - the ability to provide flexible support for the users' knowledge base. (Sprague, 1983, p. 113) Most of the reviewed literatu re supported a bright future for the previously discussed elements of DSS, as Sprague (1986) pointed out: New developments from artificial intelligence will make major contributions to all three of the DSS capability sets. D ata base managem ent will benefit from infusion of library science as well as AI to create b etter ways to organize and manage text-based data. Developments in model managem ent are leading to better ways of defining and m anipulating models. Dialog will profit significantly from the inclusion of n atu ral language processing techniques and voice recognition (p. 24) When implementing a Decision Support System, there are a few, but serious, considerations recom m ended by N orris and Mims (1984), especially for leaders in institutions of higher education: 1. The process for evaluating existing or planned systems m ust be carefully orchestrated, tak in g into consideration decision performance (how decision are made as well as their quality); 2. The full range of possibilities m ust be considered, including the use of microcomputers, m ain frames, and w hether to design unique systems or to purchase proprietary software packages; 3. U ntil users become acclimated to the potentials of decision support systems, researchers and planners may be necessary interm ediaries between decision m akers and data processing professionals; 33 4. There is a growing need for persons with knowledge of decision m aking as well as technical skills, persons who can speak m ultiple "languages" which cut across EDP, MIS, MS, and planning or other disciplines; 5. Finally, the process for developing decision support systems m u st have a significantly shorter development tim e and involve decision makers more effectively than has development for data processing systems, (p. 712) The reviewed literature supported the position th a t for a DSS to be an effective managem ent tool, the users (or perhaps the management) m ust have a positive attitude toward such technology. Thierauf (1988) argued th a t "the key to successful future decision support systems is organization personnel - from the highest level to the lowest level" (p. 369). Administrators.AtMtMg§^ndL£erjceplima_To-ward The reviewed literature revealed relatively few studies related to the attitudes and perceptions of college and university adm inistrators toward using microcomputers for decision support, and studies involving vice presidents were even more scarce. The study of attitude assessm ent of microcomputer users and their m anagers is essential to determ ine the successful application of such technology. Mann (1979) argued th a t "the g reatest problem s w ith com puting in higher education are people problems" (p. 74). While computers will not totally replace hum an jobs, many employees perceive computers as a th reat to job security (Blumenthal, 1982; O'Brien, 1982). Such perceptions may relate to how humans react to computeriza­ tion within th eir own environment. Fuhrm an and Buck (1986) indicated th a t m anagers and employees "may resist and, in some instances, almost 34 sabotage the im plem entation of a computerized system ... not only do employee resist com puterization, m anagers often react in th e same manner" (p. 417). Basically, if an organization monitor cost-effectiveness w ithin its operation, then many of its staff m ust be concerned with other competitors, even if one happens to be a microcomputer. Simon (1987) argued th a t "any technical device or machine th a t is supposed to increase productivity will presumably reduce the number of workers th a t are needed to tu rn out the product in question" (p. 7). The attitudes of individuals tow ard com puters were among the th ree necessities for adopting com puterization w ithin an organization. K iesler and Sproull (1987) stressed: To introduce new technology or modify old technology requires change in th ree areas: resources, behavior, and attitu d e s. Changing resources means changing the built technology and creating its necessary infrastructure. The necessary infrastruc­ ture of computing includes allocations of time and money, service people, teachers, physical space, computing procedures, and organizational units. Changing behavior means learning to use the new technology. It also means supporting and fostering new technology and acting to introduce it in specific areas. Changing attitu d es means coming to believe th a t the new technology is in stru m en tal to one's work and life. It also m eans holding symbolic beliefs in the legitim acy and value of computing, regardless of whether computers are actually used. (p. 30) At higher education institutions, registrars were the target population in some studies. Demarais (1987) examined the "interest, attitude, and experience" toward the use of microcomputer as a decision support by registrars. While 54% of the sample responded to D em arais' survey, among the study conclusions were those th a t registrars lack the familiarity w ith Decision Support Systems. A positive correlation was also found between the higher the degree held by reg istrars and th e ir perceived im portance of microcomputers. "Opinions of college and university 35 registrars toward computers" was one of the them es studied by Brewer (1987). He found some variables significantly correlated ip < .05), with the perception of re g istra rs tow ard com puters. These variables were "headcount enrollm ent group, ownership of a microcomputer, age group, and educational level." Behan (1985), interviewed 16 college executives regarding the use of a "computer-based inform ation system (CBIS)," in the area of "strategic planning." B ehan’s study revealed th a t the system was perceived as "essential to effective strategic planning," and the perceptions were "positively influenced by top management support of CBIS utilization." As such, managem ent support was also claimed as an essential requirem ent for successful microcomputer application into college and university adm inistrative functions (Smallen, 1988). H arris (1984) studied the use of computer-based modeling by decision makers to assist in their decision making capacity in 130 institutions. The results of his study revealed th a t a positive attitude of the users was a necessity to benefit from such tool. In addition, H arris also concluded th at "cause of habit, ease of access, or ju s t plain laziness," by the users were intervening in adapting com puter-based as a decision support tool. However, H arris did not find any correlation between successfully adapting computer-based modeling as a tool for decision support and the users' "educational emphasis or level, background in higher education, or even job classification" (p.23). Madron (1983) conducted a survey related to the use of microcomputers in organizations. One hundred thirty-six subjects responded to the survey from the Dallas Chapter of the D ata Processing M anagement Association. Of the respondents surveyed 3.2 percent were from the education 36 profession. The attitudes of the respondents toward using microcomputers revealed th a t "80.6 percent of the large organization users having micros rated themselves as either enthusiastic (41.8 percent) or positive (38.8 percent)" (pp. 131-43). This was parallel to the claim th a t micro users enjoyed the support in which microcomputers provided. As Ohles (1985) enthusiastically stated "To those of you with your fingers on the keyboard, introduce the microcomputer to education and educators, but please don't love it to death" (p. 53). The attitu d e s of educational ad m in istrato rs tow ard th e use of computers, w ithin the colleges of education located in the southeastern region of the United States, were the focus of a study by Conwill (1989). The results of Conwill's study included th at the use of computers in a decision m aking mode was conducted more by associate deans th an deans; also adm inistrators were significantly different in th e ir attitu d e s toward computers and the frequency of their computer use in a decision making mode, based on the size of their employed institution's students enrollment. The attitudes of college and university adm inistrators may relate to th eir direct use of computers, as Weisband (1987) indicated "To predict w hether an adm inistrator uses a computer, one ought to be able to ask the adm inistrator what he or she thinks of computers" (p. 155). Weisband also agreed th a t the "higher and more central the adm inistrator's position, the more positive the attitu d es th a t an adm inistrator will express about computing" (p. 157). Sum m ary The purpose of this chapter was to review pertinent literature to the topic of this study, in which three major areas were addressed. The first 37 was a review of the m ajor advancem ent in microcomputer technology w hich included th e tre n d in cost and power capabilities. Also, microcomputer applications in college and university adm inistration were reviewed and documented. Second, the Decision Support System (DSS) as a tool for decision support was investigated to its relevancy to increasing the user’s decision capacity. The m ajor elem ents of a microcomputer-based DSS were presented and discussed. Third, the importance of the attitudes of the decision m akers with regard to the use of computer technology as decision support tools was examined, which included variables found to have significant relationships with the attitudes of the users toward using such technology. No previous studies were found through reviewing the related literature which investigate the extent to which microcomputers have lived up to the expectations of top m anagem ent personnel a t institutions of higher education. This provided a major foundation for reasons to investigate an imperative issue with regard to the subject under study. In the following chapter, C hapter III, the methodology used in this study is presented. CHAPTER III METHODOLOGY The prim ary purpose of this study was to investigate vice presidents' perceptions and uses of microcomputers for decision support in Michigan's four-year colleges and universities. The purpose also included exploring the extent to which microcomputers have lived up to the expectations of vice presidents. To support the purpose, relevant views and information were needed from the subjects for the data analysis, therefore, a detailed discussion related to the subjects, instrum entation, data collection, and treatm ent of the data are essential as they are discussed in the following: Population The target population for this study included 192 vice presidents from 48 colleges and universities within the state of Michigan (see Appendix A). This included 15 public institutions and 33 private institutions. Of the 192 vice presidents identified, 48 were Vice Presidents for Academic Affairs, 48 were Vice Presidents for Business Affairs, 48 were Vice Presidents for Students Affairs and 48 were Vice Presidents for Public Affairs. The study considered both male and female vice presidents. The 1990 H igher Education Directory was used for the selection of colleges and universities, based on the following criteria: 38 39 1. Four-Year college or university, located within the state of M ichigan. 2. Baccalaureate granting institution. 3. Accredited by the North Central Association of Colleges and Schools. 4. Public or private institution. lng£mmgn£atifln The reviewed literature revealed no appropriate instrum ent to assess college and u n iv ersity vice p resid en ts' perceptions tow ard using microcomputers for decision support. Thus, such an in stru m en t was developed by the researcher. The instrum ent consisted of three parts (see Appendix D). The first p art of the instrum ent was intended to collect background data about the respondents and their institutions. P a rt II was designed to gather information regarding the respondents' use of microcomputers for decision support, by themselves and their supportive staff. P art III contained a number of statem ents with one scale on each side. The scale on the left of each statem ent (Perception Scale) is a forced-choice, four-point Likert-type scale for alternative responses ranging from strongly agree (SA) to strongly disagree (SD). This scale was designed to capture the respondents' perceptions tow ard using m icrocom puters for decision support. The scale on the right of each statem ent (Expectation Scale) was designed for the subjects to indicate the extent to which microcomputers fell short of, lived up to, or exceeded th eir expectancy level as decision support tools, ranging from less than expected (LTE) to more th an expected (MTE). As Table 3.1 shows, each of the choices in both the perception and 40 expectation scales were assigned a numerical value to facilitate the data analysis. At the end of p a rt III, two questions were included to secure inform ation regarding roasons for subjects not directly using the microcomputer for decision support, and an opportunity for the subjects to explain th eir concern on the extent to which microcomputers did/didn't live up to their expectation as decision support tools. Table 3.1—Assigned Numerical Values for Choices in Both the Perception and Expectation Scales Perception Scale Numerical Value Expectation Scale SD = 1 LTE D = 2 AE A = 3 SA = 4 = MTE The development of the instrum ent used in this study reflects (a) argum ents found through reviewing the related literatu re and (b) input from con su ltan ts w ith expertise in the area of methodology and m easurem ent, faculty members with interest in the topics investigated in this study, and a number of doctoral students in the college of education. Validity of Instrum ent: It is not necessary to m easure the validity of P a rt I and II of the instrum ent used for this study as it was intended to collect descriptive data related to the subjects' background and the extent of th eir direct and 41 indirect use of microcomputers for decision support. On the other hand, it was im perative to validate P art III of the instrum ent as it was designed to be used to assess th e respondents' perceptions tow ard th e use of m icrocom puters for decision su p p o rt and th e e x te n t to which microcomputers lived up to their expectations. Validity according to Nunnally (1978), "is a m atter of degree rath er th a n an all-or-none property" (p. 87). He further confirmed th a t content validity is "more ensured by th e plan of content and the plan for constructing items" (p. 111). This "plan" was relatively explained by Cates (1985), and it was followed to validate the content of p art III of the in strum en t: Researchers determ ine the content validity of a m easurem ent instrum ent by considering the content which might have been included, the use to which the instrum ent will be put, the ways in which items were selected to be included, and the ways in which the designer of the instrum ent confirmed th a t the included items cover the desired content adequately, (p. 123) As a result, prelim inary perception and expectation scale item s for P a rt III of the instrum ent were constructed which consisted of 31 item statem ents. The content validity test of these items was executed through the development stages of the entire instrument. It was determined th a t 11 item s should be omitted as being ambiguous and/or relatively redundant. The remaining 20 items were tested for their face validity as they related to the finishing quality of the instrum ent. Nunnally (1978) stressed th at "face validity can be considered as one aspect of content validity, which concerns an inspection of the final product to make sure th a t nothing went wrong in transform ing plans into a complete instrument" (p. 111). To te st the face validity of the perception and expectation scale, the instrum ent was sent with a cover letter, a five-point face validity scale, and 42 a copy of the study proposal to a panel of judges (see Appendix B). The panel consisted of six university adm inistrators from both public and private higher education institutions within the state of Michigan. The judges were chosen based on th e ir expertise in th e application of microcomputer technology into the decision making process and/or their adm inistrative functions within th eir institutions. All of the expected responses were returned, evaluated, and the formulas (3.1 & 3.2) of Ghods (1979) used to estim ate the face validity of each and all item s of the perception and expectation scales. In arriving at each item's face validity (F formula (3.1) was employed as follows: 6 F. = 2 R . / 2 4 J * t =i Where (3.1) F. = The face validity for each item estimated by all judges [ .=1, 2, 3, ..., 20 (number of items)] R. = The face validity for each item estimated by each judge (.= 1, 2, 3 ,4 ,5 ,6 ) 24 = The total possible score for any item estimated by all judges. While 0.000 <; F £ 1.000, Thus, the face validity of each item in the perception and expectation scale was calculated and recorded in Table 3.2. Further, in calculating the face validity of the whole perception and expectation scale (F), formula (3.2) was used as follows: Where F = The face validity for the whole perception and expectation scale, estimated by all judges. 20 = The total number of items in the perception and expectation scale. Therefore, the face validity of the whole perception and expectation scale (F) was calculated and recorded a t 0.767. Based on the Face Validity Scale used to evaluate all items by the panel of judges, F = 0.767 indicated a high face validity for the perception and expectation scale. A strong recom m endation was made by m ost judges to reduce the num ber of statem ent item s in the perception and expectation scales, to decrease the tim e required by vice presidents to complete the questionnaire, thereby increasing the response rate. Thus, the decision was made to delete items w ith face validity of less than 0.750 as they represented less than high face validity. In contrast, items with face validity of 0.750 or above were retained as they represented high to very high face validity. As a result, 8 items were retained to represent the final perception and expectation scales. The face validity of the final scale was computed a t F - 0.923 which designates a high to relatively very high face validity scale. In support of the judges' recommendation of minimizing the time spent in responding to the instrum ent by the subjects, the length and the average time required to complete the instrum ent was kept to a minimum. The instrum ent did not exceed six pages in length, excluding the cover letter, and the time required to complete the questionnaire was averaged at 14 minutes. 44 Table 3.2--Calculated Face Validity of Each Item in the Perception and Expectation Scales Item N um ber 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Face Validity (F) J 0.875 0.708 0.667 0.917 0.625 0.958 0.667 0.625 0.708 0.625 0.667 0.958 0.875 0.917 0.708 0.958 0.667 0.708 0.583 0.917 Reliability of Instrum ent: Reliability of a measure was defined by Lemke and Wiersma (1976) as "the degree to which a te st is consistent in its measurements" (p. 275). Similarly, Cates (1985) defined reliability as "the consistency with which an instrum ent produces equivalent scores" (p. 124). Cates indicated th a t a high consistency m easure should provide a high correlation coefficient (r). Thus, it was crucial to compute a reliability estimate for both the perception and expectation scales. 45 The internal-consistency estim ates of reliability for each of the perception and expectation scales were computed using Cronbach a (1951). As SPSS-X was used to compute both scores (see Appendix G), the degree of internal-consistency reliability for the perception scale was computed a t a = .93 and a = .65 for the expectation scale. While the expectation scale revealed a relatively lower a level than the perception scale, the judgment of th e appropriate use of such scale, should not have been hindered. Cronbach (1951) argued "A high a is therefore to be desired, but a test need not approach a perfect scale to be interpretable. Items w ith quite low intercorrelations can yield an interpretable scale" (p. 332). Hence, both a levels for the perception and expectation scales indicated fairly acceptable degrees of internal-consistency reliability. JXataJMection The 1990 Higher Education Directory was used to obtain the subjects' names, positions, and the addresses of the institutions a t which they were employed. The self-administered instrum ent was mailed directly to the subjects on January 2, 1990. An introductory cover letter accompanied each instrum ent, introducing th e researcher, the n atu re of the study, and expressed appreciation for the subjects' participation in this study (see Appendix D). Each survey instrum ent m ailing also included a self- addressed, stamped envelope. On January 10, 1990, follow-up letters were dispatched to all of the subjects (see Appendix E). This was conducted as a rem inder for subjects to complete the questionnaires, aiming to increase the response rate as much as possible. 46 Because th is study's subjects were college and university vice presidents, it was anticipated a t the initial stages of this study th a t the response rate to the questionnaire might be relatively low. This assumption was attributed to the notion th at the schedule of vice presidents is full and busy. Interestingly enough, the response rate was impressive, especially when given the time span in which a total of 59% of the num ber of questionnaires sent were received (see Table 3.3). Table 3.3—Accumulated Number and Percent of Questionnaires Received by Number of Weeks Lapsed Accumulated Weeks Lapsed After Questionnaires Were Sent Total N % 1 53 28% 2 76 40% 3 99 52% 4 112 58% 5 114 59% 5 114 59% As data were received through the mail from the respondents, they were organized and checked by the researcher for any error to determine their deletion or inclusion for this study. As a result, 8 of the total 114 received q u estio n n aires were om itted, due to th e ir incom pletion. R espondents were unavailable to complete these questionnaires, for 47 reasons such as vacant positions, travel or retirement. Hence, a total of 106 responses were judged as being usable, which yielded a usable response rate of 55%. Treatment of the Data After the data for this study were compiled and ready to be analyzed, data screening was conducted to assure th at the data were appropriate to be used for statistical analysis. The data were coded and entered into a data file using an IBM compatible microcomputer. A printout of the data file was retrieved and thoroughly compared with the original raw data to secure accuracy. The data were then uploaded to an IBM 3090-180 VF (Vector Facility) Mainframe Computer for statistical analysis. The Statistical Package for the Social Sciences, Version X (SPSS-X) was used to analyze the data for this study. The data analysis involved both descriptive and inferential statistical techniques. The descriptive analyses focused on demographic and background data, reporting frequencies and percentages. For testing the hypotheses and answ ering the research questions, means, standard deviations, the Pearson Correlation Coefficient, Analysis of Variance (ANOVA), and Multiple Regression Analysis were employed. The conventional 0.05 level of significance was set for testing the null hypotheses. Sum m ary It was determined th a t in order to accomplish the purpose of this study, an instrum ent should be constructed to collect the needed data related to the subjects under study. The instrum ent consisted of three 48 parts, the first p art was intended to collect background d ata about the subjects. The second related to the subjects’ direct and indirect use of microcomputers for decision support. The third p art of the instrum ent was designed to assess the subjects' perceptions and expectations of microcomputers as decision support tools. The third p a rt included two relatively open-ended questions for subjects to indicate their reason(s) for not directly using microcomputers for decision support, and the extent to which microcomputers lived up to their expectations as decision support tools. The instrum ent was validated through a panel of judges. While most of p art three of the instrum ent was intended to measure two single traits, the internal-consistency estimates of reliability was conducted, resulting in Cronbach a = .93 for the perception scale and a = .65 for the expectation scale. The 0.05 level of significance was set for testing all of the 20 null hypotheses. D ata were collected by mail during the month of January 1990. A usable response rate of 55% was attained. D ata were uploaded into an IBM 3090-180 VF m ainfram e computer for statistical analysis, which included frequencies, percentages, means, standard deviations, Pearson correlation coefficient, Analysis of Variance (ANOVA), and M ultiple R egression Analysis. The following chapter, C hapter IV, reveals presentation and analysis of the data. CHAPTER IV PRESENTATION AND ANALYSIS OF THE DATA Introduction In this chapter, the results based on data gathered from the responses of 106 vice presidents from Michigan's four-year colleges and universities, are presented in two stages: (1) Descriptive analyses, which include characteristics of respondents with respect to the demographic and general background, assumed to be related to the perception and expectation of vice presidents toward, and the extent of their use of, microcomputers for decision support; and (2) Testing the 20 null hypotheses under investigation to support answering the following research questions: 1. W hat are the perceptions tow ard the use of microcomputers for decision support by vice presidents a t Michigan's four-year colleges and universities: a. Present perceptions toward microcomputers. b. The extent to which microcomputers live up to the expectations of the vice presidents. c. Relationships existing between vice presidents' perceptions and expectations of microcomputers. 2. W hat relationships exist between vice presidents' perceptions toward microcomputers as decision support and the type of th eir institution (major/non-major research institution), age, highest degree held, age 49 50 of highest degree, possession of technical degree, and direct/indirect microcomputer use for decision support? 3. W hat relationships exist between the extent of microcomputer direct use for decision support by vice presidents and th eir position, length of employment in cu rren t position, size of in stitu tio n , type of institution, age, gender, age of highest degree held, possession of technical degree, perception, expectation, and the total num ber of their supportive staff? 4. W hat relationships exist between vice presidents' perceptions and their direct and indirect use of microcomputers for decision support? 5. To w hat extent are microcomputers and related software used to generate data, as compared to other computer sources, to support areas of decision m aking by vice presidents (either directly or indirectly)? 6. What are the reasons, if any, for not directly using microcomputers for decision support by vice presidents? Descriptive Analyses P a rt I of the questionnaire used in this study consisted of a set of questions designed to collect the raw data necessary to determ ine the categories of each of the following independent variables: C urrent position, length of employment in current position, total students' enrollment a t the employing institution, type of institution, age, gender, highest degree held, age of highest degree, and possession of technical degree. These variables, which relate to the respondents' characteristics, are presented in a systematic order, and when appropriate, tables and figures are used to enhance data presentation. 51 Position As indicated in Table 4.1, of 106 total respondents, 31 (29.2%) were vice presidents for academic affairs. Both vice presidents for students affairs and public affairs share an equal num ber of respondents (24 or 22.6%). While 22 (20.8%) of the respondents were vice presidents for business affairs, th e rem aining 5 (4.7%) respondents were from other vice presidents' positions. Table 4.1—Distribution of Respondents by Position Position Frequency V. P. for Academic Affairs V. P. for Business Affairs V. P. for Students Affairs V. P. for Public Affairs Other Positions Total Cum . Percent Percent 31 22 24 24 5 29.2 20.8 22.6 22.6 4.7 106 100.0 29.2 50.0 72.6 95.3 100.0 Length of Employment in the CurreniLPositmn For the purpose of descriptive analyses, the data representing this variable were classified into 5 categories. As revealed in Table 4.2, nearly 69% of the respondents served in their current/reported position for 5 years or less. While between 6-10 years of service was reported by almost 19% of the respondents, only 7 (6.6%) respondents reported serving between 11-15 years, and 5 (4.7%) reported serving between 16-20 years. Twenty-one years or more of service was reported by one or less than 1% of the respondents. 52 Table 4.2-D istribution of Respondents According to Length of Employment Length of Emp. As V.P. Frequency 5 Years or Less 6-10 Years 11-15 Years 16-20 Years 21 Years or More Total Cum. Percent Percent 73 20 7 5 1 68.9 18.9 6.6 4.7 .9 68.9 87.8 94.4 99.1 100.0 106 100.0 100.0 Size of Institution (Total Students' Enrollment) This variable represents the size of the respondents' institutions. It was classified into 5 categories. As indicated in Table 4.3, a large proportion of respondents (51 or 48.1%) were employed a t institutions with enrollm ent between 1000-4999 students. Two groups, comprised of 19 respondents each, had the same percentage of 17.9—one group was from institutions with enrollment of less than 1000 students, the other was from institutions with enrollments between 5000-14999 students. While 13 (12.3%) respondents were from institutions with enrollments of 25000 or more, only 4 (3.8%) reported enrollm ents between 15000-24999 students a t th eir institutions. Tvne of Institutions Table 4.4 shows th at the majority of respondents (71 or 67%) were from private institutions, with the rem aining respondents (35 or 33%) were from public institutions. Out of a total 71 respondents from private institutions, 19 (26.8%) were vice presidents for both academic affairs and 53 Table 4.3--Distributio» of Respondents According to Size of Institution Size of Institution ____________ 999 Students or Less 1000-4999 Students 5000-14999 Students 15000-24999 Students 25000 Students or More Total Frequency Cum. Percent Percent 19 51 19 4 13 17.9 48.1 17.9 3.8 12.3 17.9 66.0 83.9 87.7 100.0 106 100.0 100.0 students affairs, 16 (22.5%) were vice presidents for public affairs, and 14 (19.7%) were vice presidents for business affairs (see Figure 4.1). Of the total 35 respondents from public institutions, the highest num ber was 12 (34.3%) which were vice presidents for academic affairs, followed by 8 (22.9%) representing an equal number for both vice presidents for business affairs and public affairs, while 5 (14.3%) claimed to be vice presidents for students affairs (see Figure 4.1). Table 4.4-Distribution of Respondents by Type of Institution (Public/Private) Type of Institution______________________Frequency Public Private Total Cum. Percent Percent 35 71 33.0 67.0 33.0 100.0 106 100.0 100.0 54 Figure 4. l--Distribution of Respondents' Positions by Type of Institution P O S IT IO N S B Y IN S T IT U T IO N A L T Y P E !2 □ V.P.ACADAFF V.P.BUS AFF V.P. STUD AFF V.P.PUB AFF OTHER POS P 30 PRIVATE PUBLIC IN STITU TIO N AL TYPE Most of the respondents (96 or 90.6%) reported they were from non­ major research institutions; only 10 (9.4%) of the respondents were from major research institutions (see Table 4.5). Table 4.5-Distribution of Respondents by Type of Institution (Major/Non-Major Research Institution) Type of Institution ____________ Not Major Research Inst. Major Research Inst. Total Cum . Frequency Percent Percent 96 10 90.6 9.4 90.6 100.0 106 100.0 100.0 55 Aere The data for this variable were classified into 4 categories representing 4 age groups (see Table 4.6). As indicated in Table 4.6, nearly one-half of the respondents (51 or 48.1%) were between the age of 41-50 years, followed by 29 (27.4%) who were between the age of 51-60 years. Although 20 (18.9%) of the respondents were 40 years old or younger, on.'y 5 (4.7%) were 61 years or older, and less th an one percent of the respondents did not report their age. Table 4.6-D istribution of Respondents by Age Group Age Group Frequency 40 Years or Younger 41-50 Years 51-60 Years 61 Years or Older Total Valid cases 105 Cum . Valid Percent Percent Percent 20 51 29 5 1 18.9 48.1 27.4 4.7 .9 19.0 48.6 27.6 4.8 M issing 106 100.0 100.0 Missing cases 19.0 67.6 95.2 100.0 1 G ender Of the 106 respondents in this study, the m ajority were males representing about 70% of the respondents. Thirty two of the respondents (30.2%) were females (see Table 4.7). With respect to positions however, Figure 4.2 shows th a t the highest percent of female respondents (28.1%) were recorded a t the position of vice president for students affairs, followed by 25% a t the position of vice president for business affairs. On the other 56 hand, male respondents recorded highest a t the position of vice president for academic affairs (33.8%), followed by 24.3% a t the position of vice president for public affairs (see Figure 4.3). Table 4.7—Distribution of Respondents by Gender G ender________________________Value Male Female Frequency 0 1 Total ' Cum . Percent Percent 74 32 69.8 30.2 69.8 100.0 "106 100.0 100.0 HigkQ^-A,{^dfimi£j^.gi^£JMd As indicated in Table 4.8, slightly more th an 44% of th e respondents earned Doctoral degrees. Forty-two (39.6%) of the respondents reported having a M aster's as th eir highest degree held. Only 16 (15.1%) of the respondents reported a Bachelor's as their highest academic degree held. While no respondent reported a Specialist as being the highest earned degree, one respondent (0.9%) stated having other academic degree as being the highest earned. Table 4.8-Distribution of Respondents by Highest Degree Held Frequency Highest Degree Held________ Bachelor's Master's Doctorate Other Degrees Total Cum . Percent Percent 16 42 47 1 15.1 39.6 44.3 .9 15.1 54.7 99.1 100.0 106 100.0 100.0 57 Figure 4.2--Distribution of Fem ale Respondents by Position FEM ALE R E SP O N D E N T S B Y P O S IT IO N V.P. POSITIONS 9 .3 0 % 1 8 .8 0 % □ a □ ACAD AFF BUSAFF STUD AFF PUB AFF OTHERS 1 8 .8 0 % 2 5 .0 0 % 2 8 .1 0 % Figure 4.3-Distribution of Male Respondents by Position M A L E R E S P O N D E N T S B Y P O S IT IO N V.P. POSITIONS □ .8 0 % m □ ACAD AFF BUSAFF STUD A FF PUB AFF OTHERS 58 W ith respect to positions, no vice president for academic affairs reported a Bachelor's as being his/her highest degree held. As Figure 4.4 shows, h alf of the respondents reporting a Bachelor's as th eir highest degree held were a t the position of vice president for business affairs, followed by nearly 44% a t the position of vice president for public affairs. Respondents from the position of vice president for students affairs comprised the highest percent (33.3%) of the claimed Master's degree as the highest degree held, followed by 28.6% by vice presidents for business affairs. Respondents from the position of vice president for academic affairs shared more than h alf the reported Doctoral degrees (53.2%) as their highest degree held, while 23.4% were reported by vice presidents for public affairs. Figure 4.4-Distribution of Respondents' Highest Degrees by Position D ISTRIBU TIO N OF HIGHEST DEGREES B Y PO SITIO N iS BACHELOR’S H MASTER'S 'if'fiOCTORATES ACAD AFF BUS AFF STUD AFF V.P. P O SITIO NS PUB AFF OTHERS 59 Age of Highest Degree Although data for this variable were not provided by 4.7% of the respondents, a large portion of the respondents (47 or 44.3%) reported th a t their highest degrees were earned between 11-20 years in the past (see Table 4.9). While 25.5% of the respondents claimed earning their highest degrees within the previous 10 years, 19% reported th a t between 21-30 years had elapsed since they attained th eir highest degrees, followed by 6.6% whose highest degrees were earned more than 30 years ago. Table 4.9-Distribution of Respondents by Age of Highest Degree Age of Degree Frequency 10 Years or Less 11-20 Years 21-30 Years 31-40 Years Total Valid cases 101 V alid Cum . Percent Percent Percent 27 47 20 7 5 25.5 44.3 18.9 6.6 4.7 26.7 46.5 19.8 6.9 M issing 106 100.0 100.0 Missing cases 26.7 73.3 93.1 100.0 5 Possession of Technical Degree As Table 4.10 reveals, the majority of the respondents (95.3%) reported having no technical degrees, while 5 (4.7%) declared having technical degrees. eo Table 4.10--Distribution of Respondents by Possession of Technical Degree Technical Degree Frequency Don't Hold Tech. Degree Holder of Tech. Degree Total Cum . Percent Percent 101 5 95.3 4.7 95.3 100.0 106 100.0 100.0 B^S-£arcia_Qiiej;lio_ns_andHvPQtheses In this section, the presentation of the analysis of the data is divided into six sections to reflect the research questions for which answers were sought under the consideration of this study. Each research question is followed by its applicable hypothesis(es) and/or data needed to sufficiently answer the question presented. Each hypothesis was statistically tested and interpreted as presented, to contribute to the findings of the research questions under consideration. Research QuggMonl W hat are the perceptions tow ard the use of microcomputers for decision support by vice presidents at Michigan's four-year colleges and universities: a. Present perceptions toward microcomputers. b. The extent to which microcomputers live up to the expectations of the vice presidents. c. Relationships existing between vice presidents' perceptions and expectations of microcomputers. 61 The data used to answer p arts a and fe, of this research question are presented in Table 4.11. The raw scores for each item statem ent in the perception and expectation scales were aggregated by adding respondents' scores for each item to get an item average score on each scale. An overall mean on each scale was then calculated, using each respondent's average score on each scale. The overall mean on each scale is representative of respondents' perceptions and resulted expectations, respectively, toward the use of microcomputers as decision support. The mean aggregation was built on the resulted stren g th of the inter-item consistency reliability, performed for items in each scale which revealed an estim ated a = 0.9336 for the perception scale, and an a of 0.6522 for the expectation scale. To answer p arts & and & of this research question, a criterion was established in which the m ean score(s) can be evaluated and a conclusion draw n related to the perception of vice presidents tow ard the use of microcomputers for decision support, in addition to the extent of whether or not the use of microcomputers have lived up to the users' expectation. Thus, the criteria in Table 4.12 were used to evaluate the overall mean scores of both the perception and expectation scales. As Table 4.11 shows, the overall m ean for the respondents on the perception scale were calculated a t 2.956. Employing the criteria revealed in Table 4.12, this suggests th a t vice presidents have a "positive perception" toward the use of microcomputers for decision support. Table 4.11 also shows an overall m ean for respondents on the expectation scale of 1.948. implies th a t Given the criteria in Table 4.12, this score "microcomputers have m et the expectation of vice presidents" with regard to their application as decision support tools. Table 4.11--Aggregated Means and Standard Deviations for the Perception and Expectation Scales £______________ Perception Scale S.D. Expectation Scale Item Statements Mean Mean S.D. 1 . 1 make decisions that are more effective when I use microcomputers. 2.914 .761 1.929 .433 2. Microcomputers are cost-effective decision support tools in my operation. 3.202 .798 2.012 .450 3. Microcomputers are dependable machines for my decision making. 3.165 .719 1.915 .422 4. I make decisions that are more rational when I use microcomputers. 2.670 .761 1.939 .396 5. Microcomputers offer me good security for confidential data. 2.645 .829 1.952 .377 6. Productivity in my decision making increases when I use microcomputers. 2.956 .802 2.024 .415 7. Microcomputers offer me direct access to a greater range of stored data. 3.096 .804 1.918 .442 8. My decision making is more efficient when I use microcomputers. 3.011 .734 1.880 .425 Overall 2.956 .632 1.948 .226 63 Table 4.12-C riteria Used to Interpret the Mean Scores of the Perception and Expectation Scales Interpretation Scale Mean Score Perception 1.00 -1.49 1.50 - 2.49 2.50 - 3.49 3.50 - 4.00 Highly negative perception Negative perception Positive perception Highly positive perception Expectation 1.00 -1.66 Micros did not meet the expectation of their users Micros have m et the expectation of their users Micros have exceeded the expectation of their users 1.67 - 2.33 2.34 - 3.00 To further support addressing the issue of w hether or not microcomputers lived up to the expectation of their users, question 10 in p a rt III of the instrum en t gave respondents the opportunity to clearly indicate th eir position related to w hether or not microcomputers lived up to th eir expectations as decision support tools. As indicated in Table 4.13, 87.3% of the respondents answering question 10, clearly stated th a t microcomputers did live up to their expectations as decision support tools; only 12.7% or 10 of the respondents clearly stated th a t microcomputers did not live up to their expectations. Respondents were also provided w ith the opportunity to support their position w ithin the same question. As Appendix H shows, there were more positive and satisfactory comments made by respondents who believed th a t microcomputers did live up to th eir expectations, than those who did not (see Appendix I). 64 Table 4.13-Distribution of Respondents' Opinions of Whether or Not Micros Lived Up to Their Expectations as Decision Support Tools Micros Lived Up To Expectation Frequency Cum. Percent Percent Yes 69 87.3 87.3 No 10 12.7 100.0 79 100.0 100.0 Total Respondents to question number 10 in P art III of the instrum ent were cross-tabulated with their positions. As Figure 4.5 shows, excluding the category of other positions, 93% of the respondents from the position of vice president for public affairs indicated th a t micros have lived up to their expectations as decision support tools. Only 7% from the same position indicated th a t micros did not live up to their expectations. Although 91% of the respondents from the position of vice president for student affairs supported th a t micros lived up to their expectations, 25% of respondents from the position of vice president for business affairs felt th a t micros did not live up to their expectations, while the remaining 75% said th a t micros did live up to their expectations as decision support tools. P a rt £ of research question 1 raised the issue of whether or not any relationships exist between vice presidents' perceptions and expectations of microcomputers as decision support. For this reason Hypothesis I, in a null form, was introduced for investigation as follows: 65 Figure 4.5-Respondents’ Opinions of Whether or Not Micros Lived Up to Their Expectations as Decision Support Tools by Type of position MICROS LIVING UP TO EXPECTATIONS BYPOSITION ■ YES 03 n o 120.0 100.0 91',; 60-0 40.0 - 20.0 - 0.0 ACAD AFF STUD AFF PUB AFF OTHERS V.P. POSITIO NS 0 H y p o th e sis I : There is no significant relationship between vice presidents' perceptions and expectations of microcomputers as decision support. In testin g th is hypothesis, the Pearson C orrelation Coefficient (Pearson r) was used to compute the strength in relationship sought. While Pearson r, normally fails to detect any curvilinear relationship, and may affect the interpretation of the results (Khazanie, 1979; Mendenhall and Ott, 1976; N orusis, 1988), the m ean scores on both the perception and expectation scales were scatterplotted using the Plot command of the SPSSX program. The scatterplot revealed no sign of curvilinear relationship. G6 Thus, based on the results of the Pearson r te st (Table 4.14), there was a significant and positive relationship (r = .529, p < .001) between vice presidents' perception toward microcomputers as decision support and the extent of their expectations of microcomputers as decision support tools. Therefore, hypothesis I was rejected a t the .001 level of significance. Research Question 2 W hat relationships e^ist between vice presidents' perceptions toward microcomputers as decision support and the type of th eir in stitu tio n (major/non-major research institution), age, highest degree held, age of h ig h est degree, possession of technical degree, and direct/indirect microcomputer use for decision support? To answer this research question, six hypotheses, in null forms (XIa-f) were introduced for investigation: 0 H y p o th e sis II a : There is no significant difference between vice presidents from major research institutions and those who are not with regard to th eir perception toward the use of microcomputers for decision support. The data used to test this hypothesis are presented in Table 4.15. The independent variable of this hypothesis was "type of institution" which had two levels: those who were from major research institutions (group 1), and those who were not (group 2). The dependent variable was the "mean scores on the perception scale" for each of the two groups. A one-way analysis of variance was conducted to test the degree of association between the type of institution and the previously mentioned dependent variable. As shown in Table 4.16, the ANOVA test results did not indicate a significant Table 4.14--Pearson Correlation Coefficients of Perception With Expectation, Age and Age of Highest Degree (Two-Tailed Test) Variables Perception Perception 1.0000 Expectation .5293 ( 85) p = .000* 1.0000 Age -.0440 ( 93) p = .676 .0882 ( 84) p = .425 1.0000 Age of Highest Degree .0429 ( 90) p = .688 .1654 ( 82) p = .138 .7240 ( 100) p = .000* * Significant at .001 level Expectation Age Age of Highest Degree 1.0000 68 difference a t the .05 level among group 1 and group 2 with regard to their m ean scores on perception toward the use of microcomputers as decision support. Hence, the null hypothesis is not rejected. There is little or no difference among vice presidents who are from major research institutions and those who are not, with regard to their perception toward the use of microcomputers as decision support. Table 4.15 —Number, Mean, and Standard Deviation of Scores on the Perception Scale for Vice Presidents by Type of Institution (Major/Non-Major Research Institution) Type Of Institution N Mean Standard Deviation Not Major Research Inst. Major Research Inst. 84 10 2.976 2.788 .6328 .6293 Total 94 2.956 .6318 Table 4.16—ANOVA Results for Comparison of Scores on the Perception Scale for Vice Presidents by Type of Institution (Major/Non-Major Research Institution) Source of V ariation D.F. Sum of Squares M ean Squares Between Groups 1 .3184 .3184 W ithin Groups 92 36.8006 .4000 Total 93 37.1190 F Ratio .7960 F Prob. .3746 09 0 H ypothfisiS-ILh: There is no significant relationship between vice presidents' perceptions tow ard the use of microcomputers for decision support and their age. A Pearson Correlation Coefficient was used to test the relationship between the age of vice presidents and their perception. While the data under examination for both variables were scatterplotted, no curvilinear relationship was detected. Thus, based on the results shown in Table 4.14, the computed correlation was -.044 (p > .05), indicating no evidence for a significant relationship between the age of vice presidents and their perception toward the use of microcomputers for decision support. Thus, null hypothesis Il-b was not rejected. 0 H y p o th e s is II e : There is no significant difference among the perceptions of vice presidents tow ard the use of microcomputers for decision support based on their highest degree held. As shown in Table 4.17, the "highest degree held" served as the independent variable in this hypothesis, which consisted of three groups. Each of the three groups represented different educational levels. Group 1, consisted of vice presidents who reported a Bachelor's as th eir highest degree held, group 2 covered vice presidents who reported a M aster's as th eir highest degree held; and group 3 represented vice presidents who reported their highest degree to be a Doctorate. The dependent variable, on the other hand, was the "mean scores on the perception scale" for each of the three groups. A one-way analysis of variance was performed to test the degree of association between the highest degree held with the perception mean scores for the three groups. The ANQVA test results, shown in Table 4.18, indicated no significant mean difference between the three groups at 70 the .05 level of significance. Therefore, hypothesis II-c was not rejected. This reveals the lack of evidence to support any significant difference in vice presidents' perceptions toward the use of microcomputers for decision support based on their highest degree held. Table 4.17—Number, Mean, and Standard Deviation of Scores on the Perception Scale for Vice Presidents by Highest Degree Held Highest Degree Held N Mean Standard Deviation Bachelor's M aster's Doctorate 14 34 45 3.241 2.874 2.926 .4963 .6772 .6279 Total 93 2.954 .6349 Table 4.18—ANOVA Results for Comparison of Scores on the Perception Scale for Vice Presidents by Highest Degree Held Source of V ariation D.F. Sum of Squares M ean Squares F Ratio F Prob. Between Groups 2 1.4071 .7035 1.7745 .1755 W ithin Groups 90 35.6832 .3965 Total 92 37.0902 0 H y p o th e sis II d : There is no significant relationship between vice presidents' perceptions tow ard th e use of microcomputers for decision support and the age of their highest degree. 71 In testin g th is hypothesis, data related to both variables were scatterplotted. As a result, no apparent curvilinear relationship was detected. Further, a Pearson Correlation Coefficient was used to compute the strength in the relationship between the age of the highest degree held by vice presidents and their perceptions towards the use of microcomputers as decision support. The findings in Table 4.14 show a computed correlation of .04 (p > .05). This indicates th at no significant relationship between the age of the highest degree held by vice presidents and their perceptions toward the use of microcomputers for decision support was supported by the data. As a result, hypothesis XI-c was not rejected. 0 H y p o th e sis II e : There is no significant difference between vice presidents who possess technical degrees and those who do not with regard to their perceptions toward the use of microcomputers for decision support. Table 4.19 includes the data used to te st this hypothesis. The independent variable of this hypothesis was "possession of technical degree" which had two categories: those who hold technical degrees (group 1), and those who do not (group 2). The dependent variable was the "mean scores on the perception scale" for both groups. While the mean scores of vice presidents holding technical degrees (3.4) were higher than those with no technical degrees (2.93), a one-way analysis of variance was performed to investigate whether this difference was significant a t the .05 level. The results of the ANOVA test, revealed in Table 4.20, indicated no significant difference (p > .05). Thus, the null hypothesis was not rejected as there was no evidence to support any significant difference in vice presidents' perception toward the use of microcomputers for decision support w ith regard to whether or not they hold any technical degree. 72 Table 4.19--Number, Mean, and Standard Deviation of Scores on the Perception Scale for Vice Presidents Who Hold Technical Degrees and Those Who Do Not Technical Degree N Mean Standard Deviation Holder of Tech. Degree Do not Hold Tech. Degree 5 89 3.400 2.931 .3992 .6346 Total 94 2.956 .6318 Table 4.20-ANOVA Results for Comparison of Scores on the Perception Scale for Vice Presidents Who Hold Technical Degrees and Those Who Do Not Source of V ariation D.F. Sum of Squares M ean Squares F Ratio F Prob. Between Groups 1 1.0402 1.0402 2.6525 .1068 W ithin Groups 92 36.0788 .3922 Total 93 37.1190 0 H y p o th esis II f: There is no significant difference between direct and indirect use of microcomputers for decision support by vice presidents with respect to their perceptions toward microcomputers. The data used to test this hypothesis are presented in Table 4.21. The independent variable considered in this hypothesis was the "direct/indirect use of microcomputers for decision support," which was divided into two groups: group 1 consisted of vice presidents directly implementing or using 73 microcomputers for decision support, and group 2 which includes vice presidents im plem enting microcomputers for decision support through their supportive staff, including their secretaries. The dependent variable in this hypothesis was the perception m ean scores for vice presidents tow ard the use of microcomputers for decision support. As Table 4.21 shows, th e perception m ean score of vice presidents directly using m icrocom puters for decision support (3.16) was higher th a n those indirectly using microcomputers for decision support (2.61). To test for any statistical difference, a one-way analysis of variance was performed. As Table 4.22 shows, there was a significant difference (p < .001). Hence, the null hypothesis was rejected, which suggest th a t vice presidents directly using microcomputers for decision support have higher or more positive perceptions toward the use of microcomputers for decision support th an those indirectly using microcomputers for the same purpose. Table 4.21-Number, Mean, and Standard Deviation of Scores on the Perception Scale for Vice Presidents Who are directly and Indirectly Using Micros for Decision support Direct/Indirect Micro Users N Standard Mean_______ Deviation Direct Micro Users Indirect Micro Users 57 35 3.158 2.607 .4774 .7094 Total 92 2.948 .6331 74 Table 4.22-ANOVA Results for Comparison of Scores on the Perception Scale for Vice Presidents Who are Directly and Indirectly Using Micros for Decision Support Source of V ariation D.F. Sum of Squares M ean Squares F Ratio F Prob. Between Groups 1 6.5S98 6.5998 19.8808 .0000* W ithin Groups 90 29.8770 .3320 Total 91 36.4768 *Significant a t .001 level In contributing to th e answ er of research question 2, a Multiple Regression Analysis was employed to estim ate the proportion of the variance of the dependent variable, accounted for by a composite of independent variables considered in hypotheses I through Il-f. The m ultiple regression analysis was performed using the "Enter" method w ithin the SPSS-X environment (see Appendix G). The method "Enter," begins with no variables in the regression model/equation and forces pre­ specified independent variables one at a time, according to a predetermined level of significance, when controlling for any other independent variables already in the regression model. Independent variables found to be significantly correlated with the perception of vice presidents through testing hypotheses I through Il-f were forced into the regression equation one a t a time. The independent variables entered into the regression equation were in the order of their entry, A = Expectation, and B = Micro Direct Use vs. Indirect Use. The resu lts of th e regression analysis included a coefficient of m ultiple 75 correlation (r), coefficient of determination (r2), and an F probability for the significance of each model a t each step (see Table 4.23). The results in Table 4.23 shows th a t when the independent variable "Expectation" entered into the regression equation, it accounted for 26% (r2 - .2615) of the variation in the perception of vice presidents (dependent variable). The explained variation of the dependent variable was significantly increased by 6% when th e second independent variable "Micro D irect Use vs. Indirect Use" entered into the regression equation. As a result, independent variables A and B together in step number 2, accounted for 32% (r2 = .3179) of the total variation in the perception of vice presidents. F urther, based on the fundam ental m ultiple regression equation (formula 4.1), reported by Kerlinger (1986, p. 533), Y =fl + 6t I 1 1 + ... + 6X k. k. (4.1) W here Y = predicted value from a regression equation on the dependent variable, from an observed X. value. a = the constant of the Y-intercept; it is the Y value where the regression line crosses (or intercept) the Y axis. b. = the regression coefficient of X. X. = the observed score for variable X. used to predict the Y value of the dependent variable The computed regression equation a t step num ber 2 of the enter method of multiple regression analysis was: Y = .7524 + 1.0995 (X^ + .2531 (XJ W here Y ' = predicted average perception score for vice president i Table 4.23--Multipie Regression Analysis of Relationship Between the Perception of Vice Presidents Toward the Use of Microcomputers for Decision Support and Independent Variables (r) Coefficient of Multi Correlation ( r 2) Coefficient of Determination Increment in r2 Method Step No. Indepen. Variable Enter: 1 A .5114 .2615 .2615 .0000* 2 B .5638 .3179 .0564 .0000* * Significant at .001 level Variables' Definition: A = Expectation B = Direct Micro Use vs. Indirect Use Regression Equation: Y = .7524 + 1.0995 ( X ) + .2531 (X ) 1 £t F Probability 77 X = observed average expectation score for vice president i X 2= 1 or 0; 1 = vice president who were directly using micros for decision support, 0 = vice president who were indirectly using micros for decision support Thus, the above regression equation yielded a predictive model with a significant coefficient of determination (r2) at the .001 level. The r 2 for the two predictor model above was computed a t (0.3179). Research Question 3 W hat relationships exist between the extent of microcomputer direct use for decision support by vice presidents and their positions, length of employment in current position, size of institution, type of institution, age, gender, age of highest degree held, possession of technical degree, perception, expectations, and the total number of supportive staff? To answer this research question, 12 hypotheses (III a-1) in null form were investigated. The dependent variable for these hypotheses was the extent of microcomputer direct use by vice presidents for decision support. The data used to represent the dependent variable under study, were extracted from vice presidents' replies to question number 5 in part II of the instrum ent. Respondents were asked to break down the percentage of microcomputer use for their decision support, with regard to three groups of users; themselves, their supportive staff, or others. Respondents who reported themselves as users of microcomputers for decision support, by indicating any percent between 1 to 100, were selected and their responses used as the data to be analyzed to answer research question 3. This type of variable control was accomplished through the use of the "Select I f command in the SPSS-X program (see Appendix G). Each of the 12 78 hypotheses investigated different independent variables to reflect all variables raised in research question 3. All 12 hypotheses shared the same dependent variable which was the extent of direct microcomputer use by vice presidents for decision support. Each of the 12 hypotheses are presented in a null form, followed by its statistical test results. 0 H y p o th e sis III a : There is no significant difference among vice presidents for Academic, Business, Students or Public affairs with regard to the extent of their direct use of microcomputers for decision support. The data used to test this hypothesis are presented in Table 4.24. Four types of vice president positions were included in this hypothesis, each representing a separate group of vice presidents, as shown in Table 4.24. The group of vice presidents for students affairs had the highest average percent of direct microcomputer use for decision support (45.9%), whereas the lowest was for vice presidents 'or academic affairs (30.8%). A one-way analysis of variance was employed to te st if there was any significant difference between vice presidents' positions with regard to the extent of their direct use of microcomputers for decision support. The ANOVA test results, as revealed in Table 4.25, showed no significant difference at the .05 level. Hence, hypothesis Ill-a was not rejected. Thus, there is no evidence to suggest any statistical difference in the extent of vice presidents' use of microcomputers for decision support with regard to their positions. 79 Table 4.24-Number, Mean, and Standard Deviation of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of Position Standard Deviation Position Type N Mean V.P. V.P. V.P. V.P. 17 13 17 9 30.7647 41.6923 45.8824 38.8889 28.6128 31.1672 32.5593 25.5903 56 39.1964 29.8502 Academic Affairs Business Affairs Students Affairs Public Affairs Total Table 4.25—ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of Position D.F. Sum of Squares M ean Squares Between Groups 3 2050.3576 683.4525 W ithin Groups 52 46956.4816 903.0093 Total 55 49006.8393 Source of V ariation F Ratio .7569 F Prob. .5234 0 HyPflJfofiSigJaiJk: There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the length of employment in their current positions. In testin g this hypothesis, a Pearson Correlation Coefficient was conducted to compute the relationship between the extent of microcomputer use for decision support by vice presidents and the length of employment in their current positions. The use of scatterplot for the data of both variables 80 under examination revealed no curvilinear relationship and the test results of Pearson r (Table 4.26) showed a computed correlation of -.12 ip > .05) indicating no significant relationship between vice presidents' length of employment in their current positions and the extent of their direct use of microcomputers for decision support. Therefore, hypothesis Ill-b was not rejected. 0 H y p o th esis III c : There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the size of institution. This hypothesis was tested in two stages. F irst, the data for both variables under investigation were scatterplotted, revealing no curvilinear relationship. Secondly, a Pearson Correlation Coefficient was used to calculate the strength in relationship between the size of institutions a t which vice presidents were employed (represented by the total number of student enrollments), and the extent of their direct use of microcomputers for decision support. The test results of Pearson r, as shown in Table 4.26, indicated a computed correlation of -.25 ip = .06). Although, the results were not significant at the .05 level in the two-tailed test, it was found to be significant in the one-tailed test ip < .05, see Table 4.27). Since the null hypothesis was "non-directional," the resu lt of the tw o-tailed te st of significance should therefore be used w hether or not to reject the null hypothesis. Thus, hypothesis III-c was not rejected at the .05 level of the tw o-tailed te s t of significance. This study produced no evidence of relationship between vice presidents' size of employed institution and the extent of their direct use of microcomputers for decision support. Table 4.26--Pearson Correlation Coefficients of Direct Use of Micros by V.P. With Perception, Expectation, Length of Employment, Age, Age of Highest Degree, Size of Institution, and Total Number of Supportive Staff (Two-Tailed Test) Variables Extent of Direct Micro Use Extent of Direct Micro Use Perception Expectation Length of Employment Size of Institution Age Age of Highest Degree Number of Supportive Staff 1.0000 Perception .3348 (57) p = .011* Expectation .2500 ( 54) p = .068 .5080 (54) p = .000*** Length of Employment -.1177 (58) p = .379 -.1477 ( 57) p = .273 .1271 ( 54) p = .360 1.0000 Size of Institution -.2469 ( 58) p = .062 -.2020 ( 57) p = 132 -.1890 ( 54) p = .222 .0702 (58) p = .600 Age -.3665 (57) p = .005» -.0244 ( 56) p = .859 .2410 ( 53) p = .082 .3466 ( 57) p = .008** .1158 ( 57) p = .391 Age of Highest Degree -.1877 ( 56) p = .166 -.0249 (55) p = .857 .2358 (52) p = .092 .1970 ( 56) p = .146 .0948 ( 56) p = .487 .6323 ( 55) p = .000*** Number of Supportive -.0843 ( 58) p = .529 -.0656 ( 57) p = .628 .1290 ( 54) p = .352 -.0139 ( 58) p = .917 .2352 ( 58) p = .076 .1750 ( 57) p = .193 * Significant at .05 level 1.0000 1.0000 ** Significant at .01 level 1.0000 1.0000 1.0000 -.0040 ( 65) p =.979 *** Significant at .001 level 1.0000 Table 4.27--Pearson Correlation Coefficients of Direct Use of Micros by V.P. With Perception, Expectation, Length of Employment, Age, Age of Highest Degree, Size of Institution, and Total Number of Supportive Staff (One-Tailed Test) Variables Extent of Direct Micro Use Extent of Direct Micro Use Perception Expectation Length of Employment Size of Institution Age Age of Highest Degree Number of Supportive Staff 1.0000 Perception .3348 ( 57) p = .005** 1.0000 Expectation .2500 ( 54) p = .034* .5080 ( 54) p = .000*** Length of Employment -.1177 (58) p = .189 -.1477 ( 57) p = .136 .1271 ( 54) p = .180 1.0000 Size of Institution -.2469 ( 58) p = .031* -.2020 ( 57) p = .066 -.1690 ( 54) p = .lll .0702 (58) p = .300 Age -.3665 ( 57) p = .003** -.0244 ( 56) p = .429 .2410 ( 53) p = .041* 3466 ( 57) p = .004** .1158 ( 57) p = .195 1.0000 Age of Highest Degree -.1877 ( 56) p = .083 -.0249 ( 55) p = .428 .2358 ( 52) p = .046* .1970 ( 56) p = .073 .(©48 ( 56) p = .244 .6323 ( 55) p = .000*** Number of Supportive Staff -.0843 ( 58) p = .265 -.0656 ( 57) p = .314 .1290 ( 54) p = .176 -.0139 ( 58) p = .459 .2352 ( 58) p = .038* .1750 ( 57) p = .096 * Significant at .05 level 1.0000 ** Significant at .01 level 1.0000 1.0000 -.0040 ( 65) p = .488 *** Significant at .001 level 1.0000 83 0 H y p o th e sis III d : There is no significant difference between vice presidents from private institutions and those from public institutions with regard to the extent of th eir direct use of microcomputers for decision support. The data used to test this hypothesis are presented in Table 4.28. The independent variable tested in this hypothesis was the type of institution where vice presidents were employed, this variable consisted of two groups of vice presidents. Vice presidents employed at private institutions were in group 1, whereas group 2 included vice presidents employed a t public institutions. While group 1 had a higher mean percent of the extent of microcomputer direct usage time for decision support (41.5%) than group 2 (33%), a one-way analysis of variance was employed to te st for any significant difference between group 1 and group 2. The ANOVA test results, revealed in Table 4.29, indicated no significant difference at the .05 level. Therefore, hypothesis Ill-d was not rejected as there was no evidence to suggest any difference between vice presidents from private institutions and those from public institutions with regard to the extent of their direct use of microcomputers for decision support. Table 4.28—Number, Mean, and Standard Deviation of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of Institution (Private/Public) Type Of Institution____________ N__ Mean Standard Deviation Private Public 37 21 41.5405 33.0000 29.8985 29.0431 Total 58 38.4483 29.6258 84 Table 4.29-ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of Institution (Private/Public) Source of V ariation D.F. Sum of Squares M ean Squares F Ratio F Prob. 1.1156 .2954 Between Groups 1 977.1556 977.1556 W ithin Groups 56 49051.1892 875.9141 Total 57 50028.3448 0 H y p o th e sis III e : 'There is no significant difference between vice presidents from major research institutions and those who are not with regard to the extent of th eir direct use of microcomputers for decision support. The data used to test this hypothesis are presented in Table 4.30. The independent variable tested in this hypothesis was the "type of institution" where vice presidents are employed. This variable consisted of two groups of vice presidents; vice presidents from major research institutions were in group 1, w hereas group 2 included vice presidents from non-m ajor research institutions (Table 4.30). While group 2 had a higher mean percent of the extent of microcomputer direct usage time for decision support (40.5%) th an group 1 (23.6%), a one-way analysis of variance was performed to test for any significant difference between group 1 and group 2. The ANOVA test results (Table 4.31) revealed no significant difference at the .05 level. Therefore, hypothesis Ill-e was not rejected as there was no evidence to suggest any significant difference between vice presidents from 85 major research institutions and those who were not, with regard to the extent of their direct use of microcomputers for decision support. Table 4.30-Number, Mean, and Standard Deviation of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of Institution (Major/Non-Major Research Institution) Type Of Institution N Mean Standard Deviation Major Research Inst. Not Major Research Inst. 7 51 23.5714 40.4902 15.9985 30.5715 Total 58 38.4483 29.6258 Table 4.31—ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support by Type of institution (Major/Non-Major Research Institution) D.F. Sum of Squares M ean Squares F Ratio F Prob. Between Groups 1 1761.8854 1761.8854 2.0442 .1583 Within Groups 56 84266.4594 861.9011 Total 57 50028.3448 Source of V ariation 0 H y p o th esis III f: There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and their age. In testin g th is hypothesis, th e d ata for both v ariab les under consideration were scatterplotted to detect any curvilinear relationship. The results revealed no curvilinear relationship between the two variables 86 plotted. It was followed by a Pearson Correlation Coefficient to compute the strength in the relationship sought. The Pearson r test results (Table 4.26) revealed a significant relationship (r = -.37, p < .01) between vice presidents' age and the extent of th eir direct use of microcomputers for decision support. Hence, null hypothesis Ill-f was rejected a t the .01 level of significance. 0 H y p o th e sis III g: There is no significant difference between male and female vice presidents with regard to the extent of their direct use of microcomputers for decision support. The data used to test this hypothesis are shown in Table 4.32. The independent variable considered in this hypothesis was "gender," which had two categories: male vice presidents were in group 1, and female vice presidents were represented in group 2. As Table 4.32 shows, the mean percent on the extent of microcomputer direct usage by vice presidents was higher for females (47.9%) th an for males (34.5%). However, to test for any statistical differences, a one-way analysis of variance was employed. The ANOVA test results shown in Table 4.33 indicated no significant difference a t the .05 level. Therefore, hypothesis Hl-g was not rejected as there was no evidence to suggest any statistical difference in the extent of vice presidents' use of microcomputers for decision support with respect to their gender. 0 H y p o th e sis III fa: There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the age of their highest degree held. 87 A Pearson Correlation Coefficient was performed to compute the relationship between the extent of microcomputer use for decision support Table 4.32-Number, Mean, and Standard Deviation of Vice Presidents' Extent of Direct Micro Use for Decision Support by Gender G ender N Mean Standard Deviation Male Fem ale 41 17 34.5122 47.9412 29.0294 29.7415 Total 58 38.4483 29.6258 Table 4.33—ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support by Gender Source of V ariation D.F. Sum of Squares M ean Squares F Ratio F Prob. Between Groups 1 2167.1597 2167.1597 2.5357 .1169 W ithin Groups 56 47861.1851 854.6640 Total 57 50028.3448 by vice presidents and the age of their highest degree held. The use of scatterplot for the data of both variables under examination revealed no curvilinear relationship and the test results of the Pearson r (Table 4.26) showed a computed correlation of -.19 (p > .05). This finding indicated no significant relationship between the extent of vice presidents direct use of microcomputers for decision support and the age of their highest degree held. As a result, hypothesis Ill-h was not rejected. 88 0 H y p o th e sis I I L i: There is no significant difference between college and university vice presidents who hold technical degrees and those who do not w ith regard to the extent of their direct use of microcomputers for decision support. The data used to test this hypothesis are presented in Table 4.34. The independ en t v ariab le u n d er in v estig atio n in th is hypothesis was "possession of technical degree," which had two groups of vice presidents. Group 1 consisted of vice presidents holding technical degrees, while vice presidents who held no technical degree were aggregated in group 2. A one-way analysis of variance was performed to test for any significant difference between these groups in the extent of th eir direct use of microcomputers for decision support. The ANOVA test results (Table 4.35) revealed no significant difference between the two groups at the .05 level. Thus, hypothesis III-i was not rejected, which is an indication of the lack of evidence to support any statistical difference between the extent of direct use of microcomputers for decision support by vice presidents who held technical degrees and those who did not. Table 4.34—Number, Mean, and Standard Deviation of Vice Presidents' Extent of Direct Micro Use for Decision Support With Regard to W hether or Not They Hold Any Technical Degree Technical Degree ___________ N__ Mean Standard Deviation Holder of Tech. Degree Do not Hold Tech. degree 5 53 26.0000 39.6226 20.4328 30.2269 Total 58 38.4483 29.6258 89 Table 4.35--ANOVA Results for Comparison of Vice Presidents' Extent of Direct Micro Use for Decision Support W ith Regard to Whether or Not They Hold Any Technical Degree Source of V ariation D.F. Sum of Squares M ean Squares F Ratio F Prob. .9655 .3300 Between Groups 1 847.8920 847.8920 W ithin Groups 56 49180.4528 878.2224 Total 57 50028.3448 0 H y p o th esis III 1: There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and their perceptions of microcomputer as decision support. The data used to test this hypothesis were scatterplotted to detect any curvilinear relationship between the two variables under investigation. While the results of the plot did not reveal any curvilinear relationship, a Pearson Correlation Coefficient was employed to compute the strength in relationship between the two variables in this hypothesis. The test results of the Pearson r (Table 4.26) showed a significant relationship (r = .34, p < .05), between vice presidents' perception toward microcomputers and the extent of th eir direct use of microcomputers for decision support. The significant results a t the .05 level provide a basis for rejecting hypothesis 0 H y p o th esis III k : There is no positive relationship between the extent of vice presidents' direct use of microcomputers for decision support and the degree of th eir expectation related to the use of microcomputer for decision support. 90 In te stin g th is hypothesis, the d ata for both variables were scatterplotted to detect any curvilinear relationship between the two variables which could affect the test of relationship performed. As the scatterplot was evaluated, no sign of curvilinear relationship was noticed. While this hypothesis postulates no positive relationship between the two variables under study, a one-tailed test of Pearson Correlation Coefficient was performed to investigate, first, w hether or not there is a positive correlation, and secondly whether or not the correlation is significant a t the .05 level. As shown in Table 4.27, there was a significant relationship (r = .25, p < .05) between the degree of vice presidents’ expectation related to the use of microcomputers and the extend of their direct use of microcomputers for decision support. Thus, the results of the one-tailed test of significance was used to reject hypothesis IXI-k at the .05 level of significance. 0 H y p o th e sis III 1: There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the total number of vice presidents' supportive staff. After a scatterplot was performed for both of the variables considered in this hypothesis, revealing no sign of curvilinear relationship, a Pearson Correlation Coefficient was employed. The test results of Pearson r (Table 4.26) indicated a computed correlation of -.08 (p > .05) suggesting no significant relationship between the extent of vice presidents' direct use of microcom puters for decision support and the total num ber of th e ir supportive staff. Therefore, hypothesis III-l was not rejected. As research question 3 raises the issue of whether or not there is any relationship existing between the extent of vice presidents' direct use of microcomputers for decision support (dependent variable) and a number of 91 independent variables tested individually in hypotheses III a-1, it was logical enough to investigate w hether or not any composite of these independent variables can explain a significant proportion in the variation of the dependent variable under study. Hence, multiple regression analysis was employed. The "Enter" method of multiple regression analysis, within the SPSSX environment was used to force into a regression equation all variables found to have significant relationship with the dependent variable, one a t a time. The entry of each of the independent variables was in accordance to th eir bivariate relationship strengths with the dependent variable. As Table 4.36 shows, when the independent variable "age" entered the regression equation in step 1, it accounted for 9% of the variation in the dependent variable. In step 2, there was an increm ent of 13% in the coefficient of determ ination (r2) when the second independent variable "perception of vice president" entered the regression equation. As a result, 22% of the variance in th e dependent variable accounted for, by both independent variables A and B. The coefficient of determ ination (r2) increm ented by 4%, at step num ber 3, when the independent variable "expectation" entered the regression equation. As all of predeterm ined independent variables entered the regression equation, the m ultiple regression analysis yielded a predictive model with the following regression equation: Y = - 3.9429 -1.5046 (XJ + 15.4698 (XJ + 31.8867 (XJ W here Table 4.36--Multiple Regression Analysis of Relationship Between the Extent of Vice Presidents' Direct Use of Microcomputers for Decision Support and Independent Variables (r) Coefficient of Multi Correlation ( r 2) Coefficient of Determination Increment in r2 Method Step No. Indepen. Variable Enter: 1 A .3031 .0919 .0919 .0343* 2 B .4680 .2190 .1271 .0034** 3 C .5077 .2578 .0388 .0036** * Significant at .05 level **Significant at .01 level ***Significant at .001 level Variables' Definition: A = Age B = Perception C = Expectation Regression Equation: Y = - 3.9429 -1.5046 (X^ + 15.4698 (Xg) + 31.8867 (X^) F Probability 93 Y' = predicted average percent of direct micro use for decision support by vice president i X 1 = observed age, in years, for vice president i X = observed average perception score for vice president i X - observed average expectation score for vice president i The coefficient of determ ination (r2) represented by the regression equation above was significant at the .01 level. The above three predictor model yielded an r2 of .2578, which indicates th a t 26% of the variation of the dependent variable (extent of microcomputer direct use by vice president) was accounted for by the independent variables, age, perception and expectation. Research Question 4 W hat relationships exist between vice presidents' perceptions and their direct and indirect use of microcomputers for decision support? In answering this research question, hypothesis IV was tested. The dependent variable for this hypothesis was the extent of micro computers direct and indirect use (through their supportive staff) for decision support by vice presidents. The data for the dependent variable was extracted from respondents’ reply to question 5 in p art II of the instrum ent. Respondents were asked to break down the percentage of microcomputer use for their decision support by three groups of users: themselves, their supportive staff, and others. Respondents reporting between 1 to 100 percent for either themselves or their supportive staff were selected and the scores for both groups added to represent a score for each respondent. This, in turn, was used as the data needed for the dependent variable to be analyzed. The control over th e dependent variable was accomplished through the 94 "Compute" command in the SPSS-X program (see Appendix G). As the data for both variables under the consideration of this research question were ready to be analyzed, null hypothesis IV, was raised to be tested as follows: 0 H y p o th e s is IV: There is no significant relationship between vice presidents' perceptions toward using microcomputers as decision support and their direct and indirect use of microcomputers for decision support (through their supportive staff). A scatterplot was used to test for any curvilinear relationship between the two variables under investigation of this hypothesis, which revealed no sign of curvilinearity. As a result, a Pearson Correlation Coefficient was performed to calculate the strength of relationship sought. The Pearson r test result (Table 4.37) indicated a significant relationship (r = .52, p < .001) between the perception of vice presidents and the extent of their direct and indirect use of microcomputers for decision support. Thus, based on this finding, hypothesis IV was rejected at the .001 level of significance. Table 4.37—Pearson Correlation Coefficients of Perception With Direct and Indirect Use of Micro for Decision Support (Two-Tailed Test) Variables Perception Perception 1.0000 Direct & Indirect Use of Micros * Significant a t .001 level .5227 ( 92) p = .000* Direct & Indirect Use of Micros 1.0000 95 Research Question 5 To w hat extent are microcomputers and related software used to generate data, as compared to other computer sources, to support areas of decision making by vice presidents (either directly or indirectly)? This research question raises a number of issues. For the purpose of clarity, each issue was addressed separately. Hence, the above research question is fairly represented by the following four points: a. The extent of microcomputer direct and indirect use for decision support by vice presidents. b. The extent to which vice presidents' decisions were supported by data generated through the use of microcomputers, as compared to other computer sources. c. The extent th a t direct and indirect microcomputer generated data were used to support areas of decision making by vice presidents. d. The extent of microcomputer software use by vice presidents or th eir supportive staff to generate data to support th eir decision m aking. D ata used to address the above issues of research question 5 were derived from P a rt II of the instrum ent. The first issue (a) raises the concern of w hether or not vice presidents are using microcomputers for decision support, eith er directly or indirectly. For the purpose of responding to this concern, data were extracted from questions 3, 4, and 5 in pa rt II of the instrum ent. Prior to studying the extent of microcomputer direct and indirect use by vice presidents, it was im portant to find out the ratio of vice presidents w ith microcomputers in th e ir own offices, and the num ber of micro- 96 computers operated by their supportive staff. As a result, Table 4.38 shows th a t there were more vice presidents with microcomputers in th eir own offices (57.5%), as compared to those who did not (42.5%). With respect to vice presidents' supportive staff, Table 4.39 reveals th a t only 15.1% of the respondents reported th a t no microcomputers were operated by th eir supportive staff, while the rem aining respondents stated th a t th eir supportive staff operate on a t least one microcomputer unit. Table 4.38--Distribution of Respondents With Regard to Whether or Not They Have a Microcomputer in Their Own Office Having a Micro in Own Office Frequency Yes No Total Cum . Percent Percent 61 45 57.5 42.5 57.5 100.0 106 100.0 100.0 Table 4.39-Distribution of Respondents With Regard to the Number of Microcomputer Units Operated by Supportive Staff Number of Micro Units Frequency 0 1-3 4-6 7-9 10 or more Micro Units Total Cum . Percent Percent 16 57 24 5 4 15.1 53.8 22.6 4.7 3.8 15.1 68.9 91.5 96.2 100.0 106 100.0 100.0 97 As the extent of microcomputer direct and indirect use for decision support by vice presidents was pursued, Table 4.40 shows th a t vice presidents were directly using microcomputers for decision support on an average of 22% of the total microcomputer usage time in th eir offices. While th e ir supportive sta ff reserved an average of 55% of total microcomputer usage time to support vice presidents' decisions, only 9% were computed for other staff or sources. Table 4.40-Number, Mean, and Standard Deviation of Percent of Microcomputer Direct and Indirect Use for Decision Support by Vice Presidents U ser N Mean % Vice Presidents Supportive Staff Other Staff/Sources 98 98 98 22.30 54.45 8.91 Standard Deviation 29.48 35.61 21.36 Further, issue "b" of research question 5 raises the concern related to the extent of vice presidents' decisions supported by data generated through the use of microcomputers as compared to other computer sources. Hence, in response to this issue, data were extracted and analyzed from question 6 in P art II of the instrum ent. As a result, Table 4.41 reveals th a t the highest average percent (31%) of vice presidents' decisions were supported by data generated through the use of mainframe computer units, followed by a close m argin of 29% as an average percent of vice presidents' decisions supported by data generated through the use of microcomputer units. While d ata generated through the use of minicomputer units supported 96 only an average of 7% of vice presidents' decisions, less th an 3% of vice presidents' decisions were supported by data generated through the use of unknow n or other com puter units. To pursue the frequencies of percentages reported by respondents with regard to this issue, refer to Appendix K. Table 4.41—Number, Mean, and Standard Deviation of Percent of Vice Presidents' Decisions Supported by Data Generated Through Different Type of Computer Units Computer Unit N Microcomputer M inicom puter M ainframe Computer Unknown Computer Source Other Computer Unit(s) 101 101 101 101 101 Mean % Standard Deviation 28.98 7.19 30.57 1.63 0.94 25.49 20.17 29.77 7.71 7.37 Issue "c" of research question 5 was raised to explore the extent of direct and indirect microcomputer generated data used to support areas of decision making by vice presidents. Therefore, data from questions 7, 8, and 9 in p art II of the instrum ent were gathered and tabulated in response to this concern. As Table 4.42 shows, 18.17% was the highest average percent of data generated on microcomputers directly by vice presidents in support to their decision making in the area of Budgeting. Vice presidents’ decision m aking related to the area of Planning was supported by an average of 16.9% of the d ata generated through th eir direct use of microcomputers. On the contrary, the lowest average percent (0.94%) of data generated by vice presidents on microcomputers was used to support their decisions in the area of Facilities and Physical Plants. 99 The highest average (23.03%) of microcomputer generated d ata by supportive staff was used to support vice presidents' decisions in the area of Planning, followed by an average of 20.35% to support decisions in the area of Budgeting. While vice presidents' decisions related to the area of Accounting were supported by an average of 14.89% of data generated by supportive staff using microcomputers, only an average of 2.76% of data supported vice presidents' decisions in the area of Facilities and Physical Plants (see Table 4.42). D ata generated on microcomputers by other sources beside vice presidents and th eir supportive staff were also used to support vice presidents' decisions. An average of 23.86% of d ata from th is source supported vice presidents' decision in the area of Budgeting, followed by average percentages of 16.46, 15.66, 12.07, 10.53, 10.25, 9.02 and 2.2, in support of vice presidents' decisions, respectively, in th e areas of Accounting, P lanning, Personnel A dm inistration, Public R elations, Purchasing, Facilities and Physical Plants, and other tasks. To pursue the frequencies of reported percent of microcomputer generated data by vice presidents, supportive staff, and other sources to support different areas of decision making, refer to Appendix L. Issue "d” of research question 5, on the other hand, intended to gather inform ation on the extent of microcomputer softw are used by vice presidents or their supportive staff to generate data to support their decision making. For this reason, data from question 10 and 11 in P art II of the instrum ent were extracted, analyzed, and tabulated. As shown in Table 4.43, vice presidents used Word Processing/Text M anagement software to generate the highest average percent of data (26.61) to support th eir decisions, followed by an average of 12.83% of d ata generated on Table 4.42—Number, Mean, and Standard Deviation of Microcomputer Generated Data by Vice Presidents, Supportive Staff and Other Sources Used to Support Different Areas of Decision Making By Vice Presidents A rea N Mean % Standard Deviation Planning 101 16.90 26.16 Budgeting 101 18.17 Accounting 101 Purchasing By Supportive Staff By Other Sources Standard Deviation Mean % Standard Deviation 23.03 31.08 15.66 22.82 26.56 20.35 31.05 23.86 31.17 4.69 16.03 14.89 30.11 16.46 31.00 101 2.35 8.72 7.93 22.57 10.25 24.04 Facilities & Physical Plants 101 0.94 10.36 2.76 11.12 9.02 22.11 Personnel Administration 101 7.47 14.21 9.81 16.64 12.07 23.01 Public Relations 101 2.60 11.32 7.41 19.90 10.53 25.34 Other Tasks 101 5.45 16.85 5.75 19.32 2.21 11.32 Mean % Table 4.43--Number, Mean, and Standard Deviation of Microcomputer Generated Data by Vice Presidents and Supportive Staff U sing Different Type of Microcomputer Software to Support Decision Making By Vice Presidents Microcomputer Software N Standard Mean %__ Deviation Data Base 101 12.83 Spreadsheet 101 Graphics By Supportive Staff Mean % Standard Deviation 21.92 20.27 28.20 12.16 20.89 20.98 31.18 101 5.17 12.00 8.43 18.86 Word Processing / Text Manag. 101 26.61 31.84 45.45 35.09 Communication 101 7.80 20.48 10.00 23.03 Project Management 101 3.56 12.29 5.82 19.49 Other Micro Software 101 0.606 3.66 0.26 2.54 102 microcomputers using D ata Base software. Spreadsheet Software were used by vice presidents on microcomputers to generate an average of 12.16% of data used to support th eir decisions. O ther microcomputer software were used by vice presidents to generate only an average of less than one percent of data to support their decisions. Vice presidents' supportive staff were using Word Processing/Text M anagem ent software to generate the highest average percent of data (45.45) to support vice presidents' decisions, followed by the use of Spreadsheet and D ata Base software to generate an average of 20.98% and 20.27% of data, respectively, which was used to support vice presidents' decisions. Other microcomputer software were used by supportive staff to generate the lowest average percent of data (0.26) to support vice presidents’ decisions (see Table 4.43). However, to pursue the frequencies of percent of microcomputer generated data by vice presidents and supportive staff using different type of microcomputer software in support of th eir decision making, refer to Appendix M. Research Question 6 W hat are the reasons, if any, for not directly using microcomputers for decision support by vice presidents? D ata needed to answ er this research question were extracted from question 9 in P art III of the instrum ent. Question 9 provided respondents who were not directly using microcomputers for decision support to give the reason(s) for not doing so. While each respondent to this question may have more th an one reason for not directly using microcomputers, the "Multi Response" command of the SPSS-X was used to analyze and rank responses in order from the most frequent to the less frequent. As a result, Table 4.44 103 reveals th a t ju st a little over h alf of the respondents who replied to this question indicated th a t microcomputers were not directly used by them for decision support, because "it is someone else’s job." This reason was given for 30% (29.5) of the total responses and ranked to be the highest frequent reason selected. "Lack of available time" by respondents for not directly using microcomputers for decision support ranked second to the highest reason (20.5%), followed by the "lack of adequate training," ranking third, accounting for about 16% of the total responses. The fourth ranked reason was the "lack of available funds" (14.8%), followed by the "lack of interest" by respondents to directly use microcomputers for decision support (12.5%), while only 6.8% of the total responses reserved for other reasons (see Appendix J). It is interesting to note th a t when all of the responses were cross­ tabulated with the type of institutions at which respondents were employed, the results revealed th a t 92% of the respondents indicated th a t the "lack of available funds" was one reason hindering them from directly using micros for decision support were from private institutions (see Figure 4.6). Sixty-seven percent of the respondents who indicated th a t they "lack the available time" to directly use micros for decision support were from public institutions. As such, respondents from public institutions made up more th an half the responses indicating "the lack of interest" in using micros for decision support. Respondents from private institutions shared 79% of the responses claiming "lack of adequate training" as a reason for not directly using micros for decision support. In addition, more respondents from private institutions (65%) selected "it is someone else's job" as the reason for not directly using micros. 104 Table 4.44—Rank-Order of Respondents' Reasons for Not Directly Using Micros for Decision Support Rank-Order 1 2 3 4 5 6 Percent of Percent of Frequency Responses Respondents Reason It's someone else's job Lack of available time Lack of available training Lack of available funds Lack of interest Other reasons 26 18 14 13 11 6 29.5 20.5 15.9 14.8 12.5 6.8 52.0 36.0 28.0 26.0 22.0 12.0 Overall 88 100.0 176.0 Figure 4.6-Distribution of Responses Regarding Reasons for Not Directly Using Micros for Decision Support by Type of Institution REASO NS FOR N O T D IREC TLY USING MICROS no 100 79% SOMEONE ELSE'S NO TIME NO TRAINING REASONS NO INTEREST OTHERS ID Q] PUBLIC PRIVATE 105 Sum m ary The results of the data analysis were reported in this chapter. The major characteristics of subjects were tabulated and presented. Twenty null hypotheses were tested a t the .05 level of significance in support of most of the research questions under study. Statistical analyses used to test the null hypotheses included the Pearson correlation coefficient, Analysis of Variance, and Multiple Regression. As a result, six null hypotheses were statistically rejected. The regression analysis yielded two significantly predictive equations for two dependent variables. The first equation consisted of two predictors: (1) vice presidents' expectations of microcomputer use for decision support, and (2) direct/indirect use of microcomputers. These two variables accounted for 32% (r2 = .3179, p < .001) of the variation in vice presidents' perception tow ard the use of m icrocom puters for decision support (dependent variable). The second equation was comprised of three predictors: (1) age, (2) vice presidents' perception tow ard the use of microcomputers for decision support; and (3) vice presidents' expectations of microcomputers as decision support tools. These variables accounted for 26% (r2 = .2578, p < .01) of the variation in the extent of vice presidents' direct use of microcomputers for decision support (dependent variable). Each of the two yielded regression equations were also reported. A detailed report related to the use of microcomputers and related software by vice presidents and their supportive staff to support different areas of decision m aking was furnished. The num ber of vice presidents who were directly using microcomputers for decision support were provided. As such, reasons hindering vice presidents from not directly using m icrocom puters for decision support were rank-ordered and disclosed. A summ ary of the study, findings, conclusions based on the study findings, and recommendations are presented in Chapter V. CHAPTER V SUMMARY, FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS Summary The rapid advancem ent of microcomputer technology is evident not ju s t in the reviewed lite ra tu re , b u t also in our daily lives. Such advancem ent h as contributed to th e m ovem ent of m icrocom puter applications into college and university adm inistration. This movement was enhanced by the quality and advantages th a t microcomputers hold for th eir users. Nowadays, microcomputers are not ju s t devices for word processing and bookkeeping. With constructive applications they become essential analyses tools to support various executive decisions in colleges and universities. As more effective decisions are expected of executive adm inistrators, more sophisticated managem ent support systems serve as an alternative for more rational and perhaps better decision making. While microcomputers m ay increase adm inistrative effectiveness, productivity and efficiency, the potential use of such technology may be hindered or enhanced by the perception of the user. A positive perception of the user toward microcomputers is crucial to obtain more, if not complete, benefits from such technology. This constitutes a need to investigate the characteristics of those executive adm inistrators more likely to have positive perceptions tow ard, and using microcomputers for, decision support. Of equal importance is the exploration of whether or not the use of 107 108 microcomputers have lived up to the expectation of vice presidents. This could prove vital in determ ining the successful application of such technology to the executive adm inistration of colleges and universities. Hence, the purpose of this study was accomplished by answering six research questions and testing twenty null hypotheses. An instrum ent was developed to collect the needed data to support answering the research questions and testing the hypotheses presented. The instrum ent consisted of three parts: the first p a rt was intended to collect data related to the characteristics and background of the subjects. The second p art was used to gather information regarding the subjects' direct and indirect use of microcomputers for decision support, including different microcomputer software used to generate data to support vice presidents in different areas of decision making. The third p a rt of the instrum ent was used to assess the subjects' perceptions and expectations toward the use of microcomputers as decision support tools. The third part also included two questions for subjects to provide reasons, if any, for not directly using microcomputers for decision support, and to firmly indicate w hether or not microcomputers have lived to their expectations as decision support tools. The developed instrum ent was validated through a competent panel of judges. The reliability of the perception and expectation scales were computed, resulting in Cronbach a = .93 for the perception scale and a = .65 for the expectation scale. The final version of the instrum ent was mailed to 192 vice presidents from Michigan's four-year colleges and universities. A usable response rate of 55% was recorded. The data was thoroughly checked and screened for any detected errors. The Statistical Package for the Social Sciences, 109 version X (SPSS-X) was used on an IBM 3090-180 VF mainframe computer to analyze the data. The data analysis included frequencies, percentages, means, standard deviations, the Pearson correlation coefficient, Analysis of V ariance (ANOVA), and M ultiple Regression Analysis. To serve the purpose of this study, six research questions were investigated, including the testing of twenty null hypotheses a t the .05 level of significance. Emdinga This section reflects the findings of the study supported by the data analysis conducted to investigate the research questions and testing all of the null hypotheses. Research Question.! W hat are the perceptions tow ard the use of microcomputers for decision support by vice presidents a t Michigan's four-year colleges and universities: a. Present perceptions toward microcomputers. b. The extent to which microcomputers live up to the expectations of the vice presidents. c. Relationships existing between vice presidents' perceptions and expectations of microcomputers. In response to p art (a) of the above research question, the results of the data analysis revealed th a t the average perception score for vice presidents were recorded a t 2.956. Using the criteria in Table 4.12, it indicates th a t vice p resid e n ts have a "positive perception" tow ard th e use of microcomputers for decision support. This finding parallels Weisband's (1987) study w here she argued "the higher and more central the 110 ad m in istra to r's position, th e more positive the a ttitu d e s th a t an adm inistrator will express about computing" (p. 157). P art (b) of the above research question raised the issue of whether or not microcomputers have lived up to the expectations of vice presidents as decision support tools. The results showed th a t the average expectation score of vice presidents was computed at 1.948. Using the criteria in Table 4.12 this value fell w ithin the param eter th a t microcomputers use for decision support "have m et the expectations of their users." To confirm such findings, 87% of the respondents who replied to question 10 in p art III of the instrum ent, which asked "whether or not the use of microcomputers have m et their expectations as decision support tools," firmly indicated th a t "yes" they did. With respect to the different positions of vice presidents, the highest percentage of vice presidents w ithin the same position who indicated th a t microcomputers did not live up to th eir expectations as decision support, were from the position of vice presidents for business affairs. The results showed th a t the use of microcomputers did not m eet the expectations of 25% of vice presidents from the position of business affairs replying to question 10 in p art III of the instrument. For p art (c) of the above research question, the findings from testing the following null hypothesis were reported as follows: 0 H y p o th e s is I : There is no significant relationship between vice presidents' perceptions and expectations of microcomputers as decision support. This hypothesis was rejected at the .001 level of significance. There was a significant relationship (r = .529, p < .001) between vice presidents perception toward the use of microcomputers for decision support and the extent to w hether or not microcomputer use did live up to the expectations Ill of vice presidents. While the relationship appears to be linear and indeed positive, this finding implies th a t the higher or more positive the perception of vice presidents, the more likely the use of microcomputers for decision support have lived up to the expectations of vice presidents. Research W hat relationships exist between vice presidents' perceptions toward microcomputers as decision support and the type of th e ir in stitu tio n (major/non-major research institution), age, highest degree held, age of h ig h est degree, possession of technical degree, and direct/indirect microcomputer use for decision support? 0 H y p o th e sis II a : There is no significant difference between vice presidents from major research institutions and those who are not with regard to their perceptions toward the use of microcomputers for decision support. g jamathfiaiaJLli: There is no significant relationship between vice presidents' perceptions toward the use of microcomputers for decision support and their age. 0 H v fio th esis I I c : There is no significant difference among the perceptions of vice presidents toward th e use of microcomputers for decision support based on their highest degree held. 0 H y p o th esis II d : There is no significant relationship between vice presidents' perceptions toward the use of microcomputers for decision support and the age of their highest degree. 0 H y p o th e s is SI e : There is no significant difference between vice presidents who posses technical degrees and those who do not with regard to their perceptions toward the use of microcomputers for decision support. 112 Null hypotheses Ha through He were not rejected a t the .05 level of significance. This suggests th a t the following characteristics of vice presidents m ay n o t rela te to th e ir perceptions tow ard th e use of microcomputers for decision support: type of their employed institutions (major/non-major research institutions), age, highest degree held, age of their highest degree, and possession of technical degree. A previous study by Demarais (1987) found a positive correlation between the higher the degree held by college reg istrars and th eir perceived im portance of microcomputers. Different results were also reported by Brewer (1987), where significant correlations (p < .05) found between the perception of registrars tow ard computers and th eir age group as well as w ith th eir educational level. Such differences between present and previous findings may be due to the differences in the two populations, although both perform adm inistrative functions in institutions of higher education. 0 H ypothesis II f: There is no significant difference between direct and indirect use of microcomputers for decision support by vice presidents with respect to their perceptions toward microcomputers. This hypothesis was rejected at the .001 level of significance. There was a significant difference (p < .001) between direct and indirect use of microcomputers for decision support by vice presidents with respect to their perceptions tow ard microcomputers. The findings indicate th a t vice presidents directly using microcomputers for decision support had higher or more positive perceptions toward microcomputers th an vice presidents indirectly using microcomputers for decision support. A multiple regression analysis was used to estim ate the variation in the perception of vice presidents th a t can be accounted for by the extent of their expectations of microcomputers and whether or not they were directly 113 using microcomputers for decision support. The findings revealed th a t 32% (r2 = .3179) of the variation in the perception of vice presidents were accounted for or predicted by the extent of vice presidents’ expectations of microcomputers as decision support tools and by knowing w hether or not they were directly using microcomputers for decision support. While the regression equation was significant at the .001 level, it was computed and reported as follows: Y = .7524 + 1.0995 (XJ + .2531 (XJ W here Y ' = predicted average perception score for vice president i X 1 = observed average expectation score for vice president i X 2 = 1 or 0; 1 = vice president who were directly using micros for decision support, 0 = vice president who were indirectly using micros for decision support Research Question 3 W hat relationships exist between the extent of microcomputer direct use for decision support by vice presidents and th eir position, length of employment in current position, size of institution, type of institution, age, gender, age of highest degree held, possession of technical degree, perception, expectation, and the total number of supportive staff? 0 H y p o th e sis II? a : There is no significant difference among vice presidents for Academic, Business, Students or Public affairs with regard to the extent of their direct use of microcomputers for decision support. 0 H y p o th e sis 151 b : There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the length of employment in their current positions. 114 i: There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the size of institution. 0 H y p o th e sis H I d : There is no significant difference between vice presidents from private institutions and those from public institutions with regard to the extent of th e ir direct use of microcomputers for decision support. 0 H y p o th e sis III e : There is no significant difference between vice presidents from major research institutions and those who are not with regard to the extent of th eir direct use of microcomputers for decision support. Null Hypotheses Ilia through IHe were not rejected at the .05 level of significance. These findings indicate th a t the following characteristics of vice presidents m ay not relate to the extent of th eir direct use of microcomputers for decision support: positions, length of employment in current positions, size of institutions, and type of institutions. These findings are consistent with H arris (1984) who found no correlation between decision makers' use of computer-based tools for decision support and their "educational em phasis or level, background in higher education, or even job classification." Deel (1987) also found no significant relationship between the "overall use of microcomputers" by executive adm inistrators and their position, administrative experience, nor the type of institution. 0 H y p o th e sis III f: There is no significant relationship between the extent of vice presidents’ direct use of microcomputers for decision support and their age. This hypothesis was rejected a t the .01 level of significance. The findings, based on the results of the data analysis, indicated th a t there was 115 a significant relationship (r = -.37, p < .01) between the extent of vice presidents' direct use of microcomputers for decision support and th eir age. While the relationship was found to be negative, this indicated th a t the older th e vice p resid en t, th e less ex ten t h is/h er d irect use of microcomputers for decision support. This finding reveals a stronger negative correlation than Deel's (1987) study, where he found th a t age was negatively correlated (-.25) to the "overall use of microcomputer" by executive ad m in istrato rs. Such a difference in th e m agnitude of relationship could be due to the difference in m easurem ent technique and/or difference in time. 0 H yp oth esis III g: There is no significant difference between male and female vice presidents with regard to the extent of their direct use of microcomputers for decision support. 0 H ypothesis III h : There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the age of their highest degree held. 0 H ya.Q thesis_ni-i: There is no significant difference between college and university vice presidents who hold technical degrees and those who do not w ith regard to the extent of th eir direct use of microcomputers for decision support. Null Hypotheses Illg through IHi were not rejected at the .05 level of significance. These findings indicate th a t the following characteristics of vice presidents may not relate to the extent of th eir direct use of microcomputers for decision support: gender, age of highest degree held, possession of technical degree. Similar results were reported by Deel (1987) where no significant relationship was found between the "overall use of 116 microcomputers" by executive ad m in istrato rs and th e ir gender nor graduation date. 0 H y p o th e sis III i : There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and their perceptions of microcomputer as decision support. This hypothesis was rejected a t the .05 level of significance. This finding indicates th a t there was a significant relationship (r = .34, p < .05) between the extent of vice presidents' direct use of microcomputers for decision support and their perceptions toward the use of microcomputers. As the correlation was found to be positive, this indicated th a t the higher or more positive the perception of vice presidents tow ard the use of microcomputers, the more extent th eir direct use of microcomputers for decision support. This particular finding is consistent w ith W eisband’s study (1987), she argued "To predict w hether an adm inistrator uses a computer, one ought to be able to ask the adm inistrator w hat he or she thinks of computers" (p. 157). 0 H y p o th e sis III k : There is no positive relationship between the extent of vice presidents' direct use of microcomputers for decision support and the degree of their expectation related to the use of microcomputer for decision support. This hypothesis was rejected a t the .05 level of significance. The findings indicated th a t there was a significant relationship (r = .25, p < .05) between the extent of vice presidents' direct use of microcomputers for decision support and the degree of their expectations related to the use of microcomputer for decision support. The relationship was found to be positive which suggests th a t the closer microcomputers get to meeting the 117 expectations of vice presidents as tools for decision support, the more they will make direct use of microcomputers for decision support. 0 H y p o th e sis III ]: There is no significant relationship between the extent of vice presidents' direct use of microcomputers for decision support and the total number of vice presidents' supportive staff. Hypothesis III-l was not rejected a t the .05 level of significance. The findings, based on the results of the data analysis, indicate th a t vice presidents' total number of supportive staff may not relate to the extent of their direct use of microcomputers for decision support. After testing each of the null hypotheses Ill-a through III-l, multiple regression analysis was performed to estimate how much of the variation in the dependent variable (the extent of vice presidents' direct use of microcomputers for decision support) can be significantly predicted or accounted for by a composite of independent variables found to have significant relationships resulted from testing hypotheses Ill-a through III-l (age, perception, expectation). Hence, based on the results of the multiple regression analysis, a significant coefficient of determ ination was found (r2 = . 2578, p < .01). This indicates th at 26% of the variation in the extent of vice presidents' direct use of microcomputers for decision support was accounted for or predicted by their age, perception, and expectation. The predictive model from the multiple regression analysis yielded the following regression equation: Y = - 3.9429 -1.5046 (X ) + 15.4698 (X) + 31.8867 (X) 1 A O W here Y' = predicted average percent of microcomputer direct use for decision support by vice president i 118 X x = observed age, in years, for vice president i X &= observed average perception score for vice president i X - observed average expectation score for vice president i Research Question 4 W hat relationships exist between vice presidents' perceptions and their direct and indirect use of microcomputers for decision support? 0 H y p o th e sis IV: There is no significant relationship between vice presidents' perceptions toward using microcomputers as decision support and their direct and indirect use of microcomputers for decision support (through their supportive staff). This hypothesis was rejected a t the .001 level of significance. The findings revealed th a t there was a significant relationship (r = .52, p < .001) betw een vice presidents' perceptions tow ard using microcomputers as decision support and their direct and indirect use of microcomputers for decision support (through their supportive staff). The positive correlation suggests th a t the higher or more positive the perception of vice presidents, the more direct and indirect use of microcomputers for decision support by vice presidents and their supportive staff. Research Questkm_^ To w hat extent are microcomputers and related software used to generate data, as compared to other computer sources, to support areas of decision making by vice presidents (either directly or indirectly)? The results of the data analysis related to th is research question indicated th a t there were more vice presidents w ith microcomputers in 119 th e ir own offices (57.5%) th a n vice p resid en ts who did not have microcomputers in th e ir own offices (42.5%). The m ajority of vice presidents (84.9%) reported th a t their supportive staff operated on a t least one micro computer unit. While the present findings reveal about 58% of vice presidents have microcomputers in their own offices (and assuming for the purpose of th eir direct use), this was much higher th an Deel's findings (1987), where only 18.9% of the surveyed executive adm inistrators directly used microcomputers. This difference is perhaps due, as Deel pointed out, to the an ticipated increase use of microcomputers by adm ini stra to rs. Of the total microcomputer usage time for decision support in the offices of vice presidents, an average of 22% was conducted by vice presidents themselves, 55% by their supportive staff, and only 9% by other staff or sources. In term s of vice presid en ts’ overall decisions supported by data generated through the use of different com puter units, the findings indicated an average of 31% of vice presidents' decisions was supported by data generated through the use of mainframe computers, followed by 29% by data generated through the use of microcomputers. While 7% of vice presidents' decisions was supported by data generated through use of minicomputers, less than 3% was supported by data generated through the use of other or unknown computer sources. The highest average percent of microcomputer generated data directly by vice presidents (18%) was used to support their decisions in the area of Budgeting, followed by 17% of microcomputer generated d ata by vice presidents used to support their decisions in the area of Planning. The lowest average percent of microcomputer generated data (0.94%) by vice 120 presidents was used to support their decisions in the area of Facilities and Physical Plants. The highest average percent of microcomputer generated data by vice presidents' supportive staff (23%) was used to support vice presidents' decisions in the area of Planning, followed by 20% of microcomputer generated data by their supportive staff to support their decisions in the area of Budgeting. The lowest average percent of microcomputer generated data (3%) by vice presidents' supportive staff was used to support their decisions in the area of Facilities and Physical Plants. The highest average percent of microcomputer generated d ata by sources other than vice presidents and their supportive staff, was 24% to support vice presidents' decisions in the area of Budgeting, followed by 17% of data to support their decisions in the area of Accounting. The lowest average percent of microcomputer generated data (2%) by sources other than vice presidents and their supportive staff, was used to support their decisions in the area of Facilities and Physical Plants. Word Processing/Text M anagem ent softw are was used by vice presidents on microcomputers to generate the highest average of data (27%) to support their decisions, followed by an average of 13% of data generated using D ata Base software. While Spreadsheet software was used by vice presidents on microcomputers to generate an average of 12.16% of data used to support their decisions, less th an one percent of the data were generated using other microcomputer software. Vice p resid en ts' supportive sta ff used Word P rocessing/T ext Management software on microcomputers to generate the highest average of data (46%) to support vice presidents' decisions, followed by 21% of data generated by using Spreadsheet software, and 20% by using D ata Base 121 software all in support of vice presidents' decisions. The lowest average of d ata (less th a n one percent), was generated using other microcomputer software in support of vice presidents' decisions. Whereas in Deel's (1987) study, he in d icated th a t m icrocom puters were m ostly used for w ordprocessing. Research QuesttonJB W hat are the reasons, if any, for not directly using microcomputers for decision support by vice presidents? The findings based on the results of d ata analysis related to this research question indicated th at the highest selected reason for not directly using microcom puters for decision support by vice presidents, were because "it is someone else's job." This reason was given by more than half the vice presidents responding to question 9 of p art III in the instrum ent. "Lack of available time" by vice presidents ranked as the second highest reason (20.5%) for not directly using microcomputers for decision support, followed by a "lack of adequate training," ranking third, which accounted for about 16% of the total responses. The fourth ranked reason was a "lack of available funds" (14.8%), followed by a "lack of interest" by vice presidents to directly use microcomputers for decision support. Only 6.8% of the total responses cited "other reasons." Vice p resid en ts from private in stitu tio n s rep resen t 92% of the respondents indicating a "lack of available funds" was one of the reasons hindering them from directly using microcomputers for decision support, w hile vice presidents from public in stitu tio n s made up 67% of the respondents claiming th a t a "lack of available time" was a reason for not directly using microcomputers for decision support. The reasons "lack of 122 adequate training" and "it's someone else's job" for not directly using m icrocom puters for decision support were m ostly selected by vice presidents from private institutions, as they occupied respectively, 79% and 65% of the total respondents to each of the two reasons. Similar findings were disclosed by Deel (1987), as the surveyed subjects in his study expressed th a t the need for training related to computer use. On the contrary, th e p rese n t findings of reasons for not directly using microcomputers for decision support were not consistent with H arris (1984) who concluded th a t "cause of habit, ease of access, or ju st plain laziness" by decision makers were hindering their use of computers as decision support tools. The above discussed findings served as the foundation in which the following conclusions were drawn, and they should be considered in light of the study limitations noted in Chapter I. 1. College and university vice presidents have a positive perception tow ard th e use of microcom puters for decision support, and th e ir expectations were met. When comparing direct/indirect microcomputer use for decision support by vice presidents, direct users had a more positive perception ip < .05). This may be due to the notion th a t direct users are more exposed to the quality and advantages th a t microcomputers offer. 2. Direct microcomputer use for decision support by vice presidents is negatively correlated with their age ip < .01). This may be attributed to the inkling th a t younger vice presidents are coming in already prepared and oriented w ith expectations toward using computers for decision support. 123 Therefore, as older vice presidents retire and are replaced w ith younger executives, this could explain: (a) this study’s detection of the increase in direct microcomputer use for decision support by executive adm inistrators as compared to previous studies; and (b) the expected continued increase in direct microcomputer use for decision support by vice presidents. 3. More decisions of vice presidents were supported by data generated through the use of m ainfram es th a n microcomputers or minicomputers. This could be partially due to the fact th a t vice presidents' decisions are normally related to the entire institution and most of the data needed to support their decisions reside in the mainframe. It is highly likely th a t the needed data are downloaded from the mainframe to microcomputer units for treatm en ts and analyses. Microcomputer software, including word processing/text m anagem ent, data base and spreadsheet, were used the most to support vice presidents' decisions in the areas of budgeting and planning. 4. The highest selected reasons for not directly using microcom­ puters for decision support by vice presidents was because "it is someone else's job," followed by the "lack of available time." This could be due to the availability of supportive staff and the full schedule of vice presidents. Recommendations Based on study findings and conclusions, recom m endations for executive decision m akers in colleges and universities, and for further research related to this study, are listed below: 124 Recommendations for ExecutiyeDecision Makers in Colleges and Universities 1. "MAD-CUE" Microcomputer Assisted Decisions for College and U niversity Executives, a support group which should be organized and established nationwide to include executive administrators interested in the applications of microcomputer technology into college and university adm inistration and decision making. The advancem ent in electronic networks (including electronic mail and conferences) can positively serve the members of this group to efficiently communicate with each other to share experiences related to the applications of microcomputers in their operations. 2. More funds should be provided to introduce th e use of microcomputer technology to support vice presidents' decision making, including suitable hardw are and software, adequate support services, and train in g sessions. While th is suggestion is more focused on private institutions, public institutions should not be ignored. 3. Time should be spared by vice presidents to learn how to use microcomputer technology for th eir decision support. A special focus on young executives could prove to be beneficial in the long run. Recommendaticns_fnr^Furj;her„B-e>smrcli 1. Similar studies which account for randomly selected vice presidents from across the four-year colleges and universities in the United States, may prove helpful in confirming the generalizability of the current study. 2. While microcomputer technology is improving rapidly, a follow-up study of vice presidents from Michigan's four-year colleges and universities is suggested in a few years. This will serve the purpose of determining the 125 tren d and im pact of microcomputer technological advancement on th eir perceptions and uses of microcomputers as decision support tools. 3. Microcomputer software th a t are easy to learn and most effective for decision support m ight provide an interest to those vice presidents who claimed, "the lack of available time" as a reason for not directly using microcomputers for decision support. Hence, a study could be conducted to consider surveying vice presidents directly using microcomputers for decision support to gather such information. 4. Since the present study has revealed th a t more than h alf of the surveyed vice presidents use microcomputers, it would be of g reat significance to investigate the magnitude in effectiveness of the different type of microcomputer hardw are and software used by vice presidents. Such inform ation could prove to be helpful and tim e saving for vice presidents who will begin using microcomputers for decision support. 5. A study can be conducted to compare and contrast vice presidents from colleges and universities in different regions within the United States to determ ine w hether or not regional difference correlates to vice presidents' perception and/or extent of their use of computer technology for decision support. APPENDICES APPENDIX A MICHIGAN'S FOUR-YEAR COLLEGES AND UNIVERSITIES 126 Michigan's four-year; baccalaureate granting colleges and universities th a t are accredited by the North Central Association of Colleges and Schools: Private Adrian College Albion College Alma College Andrews University Aquinas College Baker College-Owosso Baker College-Flint Calvin College Center For Creative StudiesCollege Of Art And Design Cleary College Concordia College Davenport College Of Business Detroit College Of Business Grand Rapids Baptist College And Seminary Hillsdale College Hope College Kalamazoo College Kendall College of Art & De sign Lawrence Technological University Madonna College Marygrove College Mercy College Of Detroit Michigan C hristian College Muskegon College N azareth College Olivet Collage Sacred H eart Major Seminary/ College & Theologiate Saint Mary's College Siena Height College Spring Arbor College University of Detroit Walsh College of Accountancy & Business Administration William Tyndale College Source: Public Central Michigan University E astern Michigan University Ferris State University Grand Valley State University Lake Superior State University Michigan State University Michigan Tech. University Northern Michigan University Oakland University Saginaw Valley State University University Of MichiganAnn Arbor University Of MichiganDearborn University Of MichiganFlint Wayne State University W estern Michigan University 1990 Higher Education Directory, Constance Healey Torregrosa (ed.) Falls Church, VA: Higher Education Publications, Inc., 1990, pp. 161-72. APPENDIX B LETTER TO VALIDATION PANEL 127 (Date) (Name and Title) (Address) D ear Dr. _____ : I am in the process of constructing a proposal for my dissertation. The topic I have chosen is "A S tu d y o f Vice P residents' Perceptions a n d Uses o f M icrocom puters as D ecision S u pport in M ichigan's Four-Year Colleges a n d U niversities. " I sincerely need your help in completing this stage of my doctoral program at Michigan State University. Since there is no previously developed instrum ent to assess the perception of college and university vice presidents toward the use of microcomputers for decision support, I had to develop such instrum ent. Although I have completed the initial phase of the instrum ent, before it is finally used for data collection, the items in p art III of this newly developed questionnaire requires to be evaluated for their Face Validity by a panel of judges who are closely associated to the population I am studying. You have been selected as one potential candidate to judge my instrum ent as I am definite th a t your input will be crucial to shape the final product of the instrum ent. Please feel free to express the strength/weakness of each item in the Item Evaluation Form, by circling a point on the 5-point Face Validity Scale, and don't hesitate to comment on the content of my proposal. Enclosed please find the followings: a. Item Evaluation Form with numbered item statem ents and a 5point Face Validity Scale (0-4). b. A copy of my Instrum ent (consisting of three parts). c. A copy of my dissertation proposal to provide an idea of the study, if needed. Dr. ______, your assistance in this im portant inquiry will therefore be m ost valuable and greatly appreciated. Please use the enclosed selfaddressed stamped envelope for your returned evaluation. If you need to reach me, you may call me Collect a t (517) 355-6097. Thanks again for your assistance! Sincerely yours, Talal A. Alsohaim P.O. Box 6617 E. Lansing, MI 48826 E nclosures APPENDIX C LETTER OF HUMAN SUBJECT APPROVAL 128 MICHIGAN STATE UNIVERSITY UNIVERSITY COMMITTEE ON RESEARCH INVOLVING EAST LANSING • MICHIGAN • 4882411U HUMAN SUBJECTS (UCRIHS) 206 BERKEY HALL (517) 353-9738 Decem ber 5,1989 IRB# 88*489 Talal Alsohaim P.O. Box 6617 East Lansing, Ml 48826 Dear Mr. Alsohaim: RE: "A STUDY OF VICE PRESIDENTS’ PERCEPTIONS AND USES OF MICROCOMPUTERS AS DECISION SUPPORTS IN MICHIGAN’S FOUR-YEAR COLLEGES AND UNIVERSITIES IRB# 88-489" UCRIHS’ review of the above referenced project has now been completed. I am pleased to advise that the rights and welfare of the human subjects appear to be adequately protected and the Committee, therefore, approved this project at its meeting on December 4,1989. You are reminded that UCRIHS approval is valid for one calendar year. If you plan to continue this project beyond one year, please make provisions for obtaining appropriate UCRIHS approval one month prior to December 4.1990. Any changes in procedures involving human subjects must be reviewed by the UCRIHS prior to initiation of the change. UCRIHS must also be notified promptly of any problems (unexpected side effects, complaints, etc.) involving human subjects during the course of the work. Thank you for bringing this project to our attention. If we can be of any future help, please do not hesitate to let us know. Jphn K. Hudzik, Ph.D. Chair, UCRIHS JK H /sar cc: K. Neff APPENDIX D THE SURVEY INSTRUMENT WITH COVER LETTER 129 (Date) (Name and Title) (Address) Dear As a doctoral student at Michigan State University, I am in the process of collecting d ata for my dissertation. The attached questionnaire was developed to study vice presidents' perceptions and uses of microcomputers as decision support in Michigan's four-year colleges and universities. Your experience as an executive adm inistrator will contribute significantly to my study. Therefore, I sincerely hope you will agree to participate in this valuable study. Without the generous assistance of people such as you, this study cannot be conducted. The average time required for vice presidents to complete the attached questionnaire was 14 minutes. Would you please assist me by taking a few minutes to complete the attached questionnaire and retu rn it in the self-addressed, stamped envelope by January 20, 1990. I would like to assure you th at all your responses will be treated in strict confidence, and th a t all of the respondents will rem ain anonymous. If you have any questions, you may reach me a t (517) 355-6097. _________ your prompt response will be greatly appreciated. Sincerely yours, Talal A. Alsohaim Enclosures 130 PA R T I BACKGROUND DATA The questionnaire you are going to fill in does not demand your name nor your address. All your answers will be treated confidentially. Please answer the following questions by supplying the data required, or clearly m ark your answer with an ( X ) in the space provided. 1. Total students' enrollment in your college/university: _________ Students 2. Type of institution: P ublic_____ P riv a te_____ Major research institution: Y es_____ N o _____ 3. Title of your current position: Vice President for Academic Affairs Vice President for Business Affairs Vice President for Students Affairs Vice President for Public Affairs ______ ______ ______ ______ If other, please specify __ __________________________ 4. Years served in the above position: 5. Your sex? 6. Your age? Fem ale Years M ale_____ ____ Years Old 7. H ig h est degree of formal education you currently hold? Bachelor's M aster's S pecialist O th e r 8. Year highest degree obtained? 9. Do you hold any technical degree? ; D octorate ; Please specify_________________ _____ Yes No 131 PART II USES OF MICROCOMPUTERS This section focus on the uses of microcomputers by your office (the office of the vice president) including you and your supportive staff. The term "su p p o rtiv e s t a f f in this study refers to: personnel including secretaries and persons who reports directly to you and have a t least some staff responsibility. Please note the word " m ic ro c o m p u te r" in the following questions, if not otherwise stated, does not include its sole use as a medium between the user and other computer terminals. 1. How many secretarial staff do you have in your office? Secretarial Staff 2. How many other people serving as su pportive s ta ff in your office? Other su pportive s ta ff 3. How many microcomputer unit(s) do you have in your own office. (0 for none)? mi cro computer unit(s) 4. How many microcomputer unit(s) does you r su pportive s ta ff currently operate (0 for none)? microcomputer unit(s) 5. The use of microcomputer unit(s) for your decision support are normally conducted by: User % Of Decision-Sunnort Time Yourself % Your Supportive Staff % Others, please specify % % 100% 132 6. W hat percent of your decisions are based upon data generated through the following computer unit(s)? Computer Unit(s) M icrocomputer M inicom puter Mainframe Computer Unknown Computer Source Others, please specify % Of Your Decisions % % % _____ % % % To what extent, in terms of percent, are data generated hy y o u on microcomputers used in supporting your decisions in the following areas? % Of Microcomputer Area Generated Data P lanning Budgeting A ccounting Purchasing Facilities & Physical Plants Personnel A dm inistration Public Relations Other tasks, please specify % _____ % % % _____ % % % % % 8. To what extent, in terms of percent, are data genera ted by you r su pportive S taff on microcomputers used in supporting your decisions in the following areas? Area P lanning Budgeting A ccounting Purchasing Facilities & Physical Plants Personnel Adm inistration Public Relations Other tasks, please specify % Of Microcomputer Generated Data % % % _____ % % _____ % % % 133 9. To what extent, in terms of percent, are data generated through microcomputers by sources e x t e r n a l to your office used in supporting your decisions in the following areas? % Of Microcomputer Generated Data___ _____ % _____ % _____ % _____ % _____ % _____ % _____ % Area Planning Budgeting Accounting Purchasing Facilities & Physical Plants Personnel Administration Public Relations Other tasks, please specify % % 10. To what extent, in terms of percent, are y o u using the following microcomputer software to generate data to support your decisions? Microcomputer Software % Of Microcomputer Generated Data Data Base Spreadsheet G raphics Word Processing/Text Management Com m unication Project M anagement Other Micro. Software, please specify ______ _____ _____ _____ _____ _____ % % % % % % % % 11. To what extent, in terms of percent, are y o u r s u p p o r t i v e s t a f f using the following microcomputer software to generate data to support your decisions? Microcomputer % Of Microcomputer Software _____Generated Data Data Base Spreadsheet G raphics Word Processing/Text Management Com munication Project Management Other Micro. Software, please specify _____ _____ _____ _____ _____ _____ % % % % % % % % 134 PART m PERCEPTIONS TOWARD MICROCOMPUTERS The following statements were designed so you can indicate your opinion about microcomputers and their uses as decision support tools. For each statement, please circle of each statement. ONE choice from each o f the two scales provided at the left and right The scale on the left is designed to assess your perception toward microcomputers as decision support tools. The scale on the right is designed to indicate the extent to which microcomputers have lived up to your expectation as decision support tools. The choices in each of the two scales represent the following: LEFT (present perception): SA = you Strongly Agree with the statement. A = you Agree with the statement but not strongly so. D = you Disagree with the statement but not strongly so. RIGHT SCALE (related to expectation): LTE = your perception is Less Dian Expected. AE = your perception is As Expected. MTE = your perception is Afore Than Expected. SD = you Strongly Disagree with the statement SA A D SD 1. 1 make decisions that are more effective when I use microcomputers. LTE AE MTE SA A D SD 2. Microcomputers are cost-effective decision support tools in my operation. LTE AE MTE SA A D SD 3. Microcomputers are dependable machines for my decision making. LTE AE MTE SA A D SD 4. I make decisions that are more rational when I use microcomputers. LTE AE MTE SA A D SD 5. Microcomputers offer me good security for confidential data. LTE AE MTE SA A D SD 6. Productivity in my decision making increases when I use microcomputers. LTE AE MTE SA A D SD 7. Microcomputers offer me direct access to a greater range of stored data. LTE AE MTE SA A D SD 8. My decision making is more efficient when I use microcomputers. LTE AE MTE 135 9. If you personally don't use microcomputers as direct tools for decision support, please mark all that apply from the following reasons: a. b. c. d. e. f. Lack of adequate training. Lack of available time. Lack of available funds. Lack of interest. Because it is someone else’s job. Other, please specify: 10. Do you believe that microcomputers did live up to your expectation as decision support tools? Yes No If your response is Yes or No, please state your reason(s) to support your position: Thank you ., APPENDIX E FOLLOW-UP LETTER TO THE VICE PRESIDENTS 136 (Date) (Name and Title) (Address) D ear W ithin the past two weeks you should have received an envelope containing a cover letter, dated Ja n u ary 2, 1990. I personally asked for your kind participation in my doctoral dissertation study. A questionnaire and a selfaddressed stamped envelop was also included in the mentioned envelope so you can retu rn the completed questionnaire. The study, if you may recall, is related to Vice P resid en ts' P erceptions a n d Uses o f M icrocom puters as Decision Support in M ichigan's Four-Year Colleges a n d Universities. If you have already responded to the questionnaire and have sent it back, please disregard this letter. I am so obliged for your great assistant as your participation will contribute significantly to my study. If however, you have overlooked the questionnaire and you still have it, may I ask for your kind cooperation by taking a few m inutes from your valuable time to complete and return it to me in the provided envelope by January 20, 1990. I will sincerely rem ind you again, th a t your responses will be treated highly confidential and th a t all of the respondents will rem ain anonymous. Thanks again for your participation and I wish you a Happy New Year. Sincerely yours, Talal A. Alsohaim APPENDIX F SPSS-X COMMAND PROGRAM USED FOR DESCRIPTIVE ANALYSES 137 SPSS-X™ RELEASE 3.1 FOR IBM VM/CMS MSU COMPUTER LABORATORY IBM 3090-180 VF VM/SP HPO CMS For VM/SP HPO CMS M SU COMPUTER LABORATORY License Number 19626 TITLE "VICE PRESIDENTS’ SURVEY-ALSOHAIM" SUBTITLE 'DESCRIPTIVE ALL2.ASC DATA’ SET BLANKS =-1 FILE HANDLE ALL2 / NAME = "ALL2 ASC A" DATA LIST FILE = ALL2 FIXED RECORDS = 5 /I Q15-9 Q2.111 Q2.213 Q3 15 Q417-20(1) Q5 22 Q6 24-25 Q7 27 Q8 29-30 Q9 32 QHI.1 TO QIII.8 33-48 QIII.9 TO QIII.16 49-64 QII1 65-68(1) QIT2 69-72(1) QH3 73 QII4 74-75 /2 QII5.1 TO QII5.3 6-17 QII6.1 TO QII6.5 18-37 QII7.1 TO QII7.8 38-69 /3 TO QII8.8 6-37 QII9.1 TO QII9.8 38-69 /4 QniO.l TO QII10.7 6-33 QII11.1 TO QH11.7 34-61 ID 73-75 /5 COMPUTE PERCEP = MEAN (QIII.l TO QIII.8) COMPUTE EXPECT = MEAN (QIII.9 TO QIII.16) COMPUTE SS = SUM (QII1 TO QII2) VARIABLE LABELS Q1 'SIZE OF INSTITUTION’ Q2.2 'MAJ RESEARCH INST’ Q4 'LENGTH OF EMP AS V.P' Q6 'AGE' Q8 'AGE OF HIGHEST DEGREE’ Q in .l PERCEPTION IN 1’ QIII.3 'PERCEPTION IN 3' QIII.5 'PERCEPTION IN 5' Qin.7 'PERCEPTION IN 7’ QIII.9 'EXPECTATION IN 1' QIII. 11'EXPECTATION IN 3' QIII. 13EXPECTATION IN 5' QIII. 15'EXPECTATION IN 7’ QIII 'SECRETARIALS' QII3 'MICRO FOR SELF' QII5.1 'MICRO FR DS BY SELF’ QII5.3 'MICROS FR DS BY OTHERS' Q2.1 'PRIVATE / PUBLIC Q3 POSITION' Q5 'GENDER' Q7 EDUCATION’ Q9 'TECH DEGREE' QIII.2 'PERCEPTION IN 2’ QIII.4 'PERCEPTION IN 4’ QIII.6 'PERCEPTION IN 6' QIII.8 PERCEPTION IN 8' QIII. lO'EXPECTATION IN 2’ QIII. 12'EXPECTATION IN 4' QIII. 14'EXPECTATION IN 6' QIII. 16'EXPECTATION IN 8' QII2 'SUPPORTIVE STAFF' QII4 'MICROS FOR SS’ QII5.2 'MICROS FR DS BY SS' QII6.1 'DECISION BY MICROS' SPSS is a registered trademark of SPSS Inc. 138 QII6.2 'DECISION BY MINIS' QII6.4 'DECISION BY OTHR COMP' QII7.1 'SELF MICROS FR PLANNG' QII7.3 'SELF MICROS FR ACCOUN' QII7.5 'SELF MICROS FR FPP' QII7.7 'SELF MICROS FR P RELA' QII8.1 'SS MICROS FR PLANNG' QII8.3 'SS MICROS FR ACCOUN’ QII8.5 'SS MICROS FR FPP' QII8.7 'SS MICROS FR P RELA' QII9.1 'EXTR MICROS FR PLANNG' QII9.3 'EXTR MICROS FR ACCOUN' QII9.5 'EXTR MICROS FR EPP' QII9.7 'EXTR MICROS FR P RELA' QII10.1 'DATA BASE BY SELF' QII10.3 'GRAPHICS BY SELF’ QII10.5 'COMM BY SELF' QII10.7 'OTH SOFTWR BY SELF' QIII 1.2 'SPREADSHEET BY SS’ QII11.4 WORD PROCES BY SS' QII11.6 'PROJ MANG BY SS' ID 'CASE NUMBER' EXPECT EXPECTATION AVERAGE SCORE SS NUMBER OF SUPPORTIVE QII6.3 'DECISION BY MAINFRAME' QII6.5 'DECISION BY OTHERS' QII7.2 'SELF MICROS FR BUDGET' QII7.4 'SELF MICROS FR PURCH' QII7.6 'SELF MICROS FR P ADMN' QII7.8 'SELF MICROS FR OTHER' QII8.2 'SS MICROS FR BUDGET' QII8.4 'SS MICROS FR PURCH' QII8.6 'SS MICROS FR P ADMN' QII8.8 'SS MICROS FR OTHER' QII9.2 'EXTR MICROS FR BUDGET' QII9.4 'EXTR MICROS FR PURCH’ QII9.6 'EXTR MICROS FR P ADMN’ QII9.8 'EXTR MICROS FR OTHER’ QII10.2 'SPREADSHEET BY SELF' QII10.4 ’WORD PROCES BY SELF’ QII10.6 'PROJ MANG BY SELF’ QIII 1.1 'DATA BASE BY SS' QII11.3 ’GRAPHICS BY SS' QII11.5 'COMM BY SS’ QII11.7 'OTH SOFTWR BY SS' PERCEP 'PERCEPTION AVERAGE SCORE' F INCLUDING SECRETARIES' VALUE LABELS Q2.1 1 'PRIVATE' 0 'PUBLIC'/ Q2.2 1 'MAJOR RES INST' 0 ’NOT MAJOR RES INST'/ Q3 1 'V P FR ACADEMIC AFFAIRS' 2 V P FR BUSINESS AFFAIRS' 3 V P FR STUDENTS AFFAIRS’ 4 ’V P FR PUBLIC AFFAIRS' 5 'OTHER POSITIONS'/ Q5 1 'FEMALE' 0 'MALE'/ Q7 1 "BACHELOR'S" 2 "MASTER’S" 3 'DOCTORATE' 4 'SPECIALIST' 5 'OTHER DEGREES'/ Q9 1 'HOLDER OF TECH DEGREE' 0 "DON'T HOLD TECH DEGREE"/ QIII.l TO QIII.8 1 'STRONGLY DISAGREE’ 2 'DISAGREE' 3 'AGREE' 4 'STRONGLY AGREE'/ QIII.9 TO QIII.16 1 'LESS THAN EXPECTED' 2 'AS EXPECTED’ 3 'MORE THAN EXPECTED'/ MISSING VALUES ALL (-1) FREQUENCIES VARIABLES = ID /FORMAT = CONDENSE FREQUENCIES VARIABLES = Ql TO Q9 QIII TO QII9.8 PERCEP EXPECT SS /HISTOGRAM = NORMAL /STATISTICS = ALL RECODE Ql (0 THRU 999 = 1) (1000 thru 4999 = 2) (5000 THRU 14999 = 3) (15000 THRU 24999 = 4) (25000 THRU HI = 5) (ELSE = -1y Q4(0 THRU 5=1) (6 THRU 10 = 2) (11 THRU 15 = 3) (16 THRU 20 = 4) (20 THRU HI = 5) (ELSE = -1)/ 339 Q6 (0 THRU 40 = 1) (41 THRU 50 = 2) (51 THRU 60 = 3) (61 THRU HI = 4) (ELSE = -1)/ Q8 (0 THRU 10 = 1) (11 THRU 20 = 2) (21 THRU 30 = 3) (31 THRU 40 = 4) (ELSE = SS (0 = 1) (1 THRU 5 = 2) (6 THRU 10 = 3) (11 THRU 15 = 4) (16 THRU HI = 5) (ELSE = -1) -iy VALUE LABELS Ql 1 '999 STUDENTS OR LESS’ 2 ’1000-4999 STUDENTS’ 3 ’5000-14999 STUDENTS’ 4 ’15000-24999 STUDENTS' 5 '20 STUDENTS OR MORE'/ Q4 1'5 YEARS OR LESS* 2'6-10 YEARS’ 3 '11-YEARS' 4 '16-20 YEARS' 5 ’21 YEARS OR MORE’/ Q6 1 ’40 YEARS OR YOUNGER’ 2 '41-50 YEARS’ 3 ’51-60 YEARS’ 4 ’61 YEARS OR OLDER’/ Q8 1 ’10 YEARS OR LESS’ 2 ’11-20 YEARS’ 3 ’21-30 YEARS' 4’31-40 YEARS’/ SS 1 ’0’ 2 ’1-5’ 3 ’6-10’ 4 ’11-15’ 5 ’16 OR MORE'/ FREQUENCIES VARIABLES = Ql TO Q9 CROSSTABS TABLES = Q3 BY Q2.1 OPTIONS 4 CROSSTABS TABLES = Q3 BY Q5 OPTIONS 4 CROSSTABS TABLES = Q3 BY Q7 OPTIONS 4 FINISH APPENDIX G SPSS-X COMMAND PROGRAM USED FOR INFERENTIAL ANALYSES 140 SPSS-X™ RELEASE 3.1 FOR IBM VM/CMS MSU COMPUTER LABORATORY IBM 3090-180 VF For VM/SP HPO CMS VM/SP HPO CMS MSU COMPUTER LABORATORY License Number 19626 TITLE "VICE PRESIDENTS' SURVEY-ALSOHAIM" SUBTITLE 'HYPOTHESES ALL2.ASC DATA' SET BLANKS =-1 FILE HANDLE ALL2 / NAME = "ALL2 ASC A" DATA LIST FILE = ALL2 FIXED RECORDS = 5 /I Ql 5-9 Q2.111 Q2.2 13 Q3 15 Q417-20(1) Q5 22 Q6 24-25 Q7 27 Q8 29-30 Q9 32 QIII.l TO QIII.8 33-48 QIII.9 TO QIII.16 49-64 QIII 65-68(1) QII2 69-72(1) QII3 73 QII4 74-75 12 QII5.1 TO QII5.3 6-17 QII6.1 TO QII6.5 18-37 QII7.1 TO QII7.8 38-69 /3 QII8.1 TO QII8.8 6-37 QII9.1 TO QII9.8 38-69 /4 QII10.1 TO QII10.7 6-33 QII11.1 TO QII11.7 34-61 NOTRAING 63 NOTIME 64 NOFUND 65 NOINTRST 66 ELSEJOB 67 OTHERS 68 LIVEUP 70 ID 73-75 /5 COMPUTE PERCEP = MEAN (QIII.l TO QIII.8) COMPUTE EXPECT = MEAN (QIII.9 TO QIII.16) COMPUTE SS = SUM (QIII TO QII2) VARIABLE LABELS Ql 'SIZE OF INSTITUTION’ Q2.2 'MAJ RESEARCH INST' Q4 'LENGTH OF EMP AS V.P’ Q6 'AGE' Q8 'AGE OF HIGHEST DEGREE' QIII.l 'PERCEPTION IN 1' QIII.3 'PERCEPTION IN 3' QIII.5 'PERCEPTION IN 5' QIII.7 'PERCEPTION IN 7’ QIII.9 'EXPECTATION IN 1’ QIII. 11'EXPECTATION IN 3’ QIII. 13'EXPECTATION IN 5' QIII. 15'EXPECTATION IN 7’ QIII 'SECRETARIALS' QII3 'MICRO FOR SELF' Q2.1 'PRIVATE / PUBLIC' Q3 'POSITION' Q5 'GENDER' Q7 'EDUCATION' Q9 'TECH DEGREE’ QIII.2 'PERCEPTION IN 2’ QIII.4 'PERCEPTION IN 4' QIII.6 'PERCEPTION IN 6’ QIII.8 'PERCEPTION IN 8' QIII.IO'EXPECTATION IN 2' QIII. 12'EXPECTATION IN 4' QIII.14'EXPECTATION IN 6' QIII. 16'EXPECTATION IN 8' QII2 'SUPPORTIVE STAFF' QII4 'MICROS FOR SS' SPSS is a registered trademark of SPSS Inc. 141 QII5.2 'MICROS FR DS BY SS’ QII5.1 'MICRO FR DS BY SELF' QII5.3 'MICROS FR DS BY OTHERS’ QII6.1 'DECISION BY MICROS’ QII6.2 'DECISION BY MINIS' QII6.3 'DECISION BY MAINFRAME' QII6.4 'DECISION BY OTHR COMP' QII6.5 'DECISION BY OTHERS' QII7.1 'SELF MICROS FR PLANNG' QII7.2 'SELF MICROS FR BUDGET' QII7.3 'SELF MICROS FR ACCOUN' QII7.4 'SELF MICROS FR PURCH’ QII7.6 'SELF MICROS FR P ADMN’ QII7.5 'SELF MICROS FR FPP' QII7.7 'SELF MICROS FR P RELA’ QII7.8 'SELF MICROS FR OTHER' QII8.2 'SS MICROS FR BUDGET' QII8.1 'SS MICROS FR PLANNG' QII8.4 'SS MICROS FR PURCH' QII8.3 'SS MICROS FR ACCOUN' QII8.6 'SS MICROS FR P ADMN' QII8.5 'SS MICROS FR FPP' QII8.7 'SS MICROS FR P RELA’ QII8.8 'SS MICROS FR OTHER' QII9.1 ’EXTR MICROS FR PLANNG’ QII9.2 ’EXTR MICROS FR BUDGET’ QII9.3 'EXTR MICROS FR ACCOUN' QII9.4 'EXTR MICROS FR PURCH’ QII9.5 'EXTR MICROS FR EPP' QII9.6 'EXTR MICROS FR P ADMN' QII9.7 'EXTR MICROS FR P RELA' QII9.8 'EXTR MICROS FR OTHER’ QIII0.1 'DATA BASE BY SELF' QII10.2 'SPREADSHEET BY SELF’ QII10.4 'WORD PROCES BY SELF' QII10.3 'GRAPHICS BY SELF’ QII10.5 'COMM BY SELF’ QII10.6 'PROJ MANG BY SELF' QII10.7 'OTH SOFTWR BY SELF’ QII11.1 'DATA BASE BY SS' QII11.2 'SPREADSHEET BY SS' QII11.3 'GRAPHICS BY SS' QII11.4 WORD PROCES BY SS’ QIII 1.5 'COMM BY SS' QIII 1.6 'PROJ MANG BY SS’ QII11.7 'OTH SOFTWR BY SS’ NOTRAING 'LACK OF TRAINING' NOTIME 'LACK OF TIME’ NOFUND 'LACK OF FUND' NOINTRST 'NO INTEREST’ ELSEJOB "SOMONE ELSE'S JOB" OTHERS 'OTHER REASONS’ LIVEUP MICROS LIVING UP TO EXPECTATIONS’ ID 'CASE NUMBER’ PERCEP PERCEPTION AVERAGE SCORE' EXPECT EXPECTATION AVERAGE SCORE’ SS NUMBER OF SUPPORTIVE STAFF INCLUDING SECRETARIES' VALUE LABELS Q2.1 1 'PRIVATE' 0 'PUBLIC'/ Q2.2 1 'MAJOR RES INST' 0 'NOT MAJOR RES INST'/ Q3 1 V P FR ACADEMIC AFFAIRS' 2 V P FR BUSINESS AFFAIRS' 3 *VP FR STUDENTS AFFAIRS' 4 'V P FR PUBLIC AFFAIRS' 5 'OTHER POSITIONS'/ Q5 1 'FEMALE' 0 'MALE'/ Q7 1 "BACHELOR’S" 2 "MASTER'S" 3 'DOCTORATE' 4 'SPECIALIST' 5 'OTHER DEGREES'/ Q9 1 'HOLDER OF TECH DEGREE’ 0 "DON'T HOLD TECH DEGREE"/ QIII.l TO QIII.8 1 'STRONGLY DISAGREE’ 2 'DISAGREE' 3 ’AGREE’ 4 'STRONGLY AGREE'/ QIII.9 TO QIII.16 1 'LESS THAN EXPECTED' 2 'AS EXPECTED’ 3 'MORE THAN EXPECTED’/ NOTRAING 1 YES' 0 NO'/ NOTIME 1 YES' 0 NO'/ NOFUND 1 YES' 0 NO'/ NOINTRST 1 YES’ 0 NO'/ ELSEJOB 1 YES’ 0 NO’/ OTHERS 1 YES' 0 NO'/ LIVEUP 1 YES’ 0 NO'/ MISSING VALUES ALL (-1) DESCRIPTIVE VARIABLES = PERCEP EXPECT 142 RELIABILITY VARIABLES = QIII.l TO QIII.8 /SCALE (PERCEPT) = QIII.l TO QIII.8 /SUMMARY = TOTAL RELIABILITY VARIABLES = QIII.9 TO QIII.16 /SCALE (EXPECTA) = QIII.9 TO QIII.16 /SUMMARY = TOTAL MULT RESPONSE GROUPS = LIVINGUP 'MICROS LIVING UP TO EXPECTATIONS' (LIVEUP (0,1)) /FREQUENCIES = LIVINGUP MULT RESPONSE GROUPS = LIVINGUP 'MICROS LIVING UP TO EXPECTATIONS’ (LIVEUP (0,1)) /VARIABLES Q3 (1,5) /TABLES = Q3 BY LIVINGUP /CELLS = ROW PLOT TITLE = "RESPONDENTS’ PERCEPTION WITH EXPECTATION" /VERTICAL = 'PERCEPTION TOWARD USING MICROS FOR DS' /HORIZONTAL = "RESPONDENTS’ EXPECTATION OF MICROS" /FORMAT = REGRESSION /PLOT = PERCEP WITH EXPECT CORRELATIONS PERCEP EXPECT Q6 Q8 /PRINT = TWOTAIL ONEWAY PERCEP BY Q2.2 (0,1) /STATISTICS = ALL PLOT TITLE = "RESPONDENTS' AGE WITH PERCEPTION" /VERTICAL = 'AGE OF RESPONDENTS’ /HORIZONTAL = 'PERCEPTION TOWARD USING MICROS AS DS' /FORMAT = REGRESSION /PLOT = Q6 WITH PERCEP ONEWAY PERCEP BY Q7 (1,3) /RANGES = SCHEFFE /STATISTICS = ALL PLOT TITLE = "RESPONDENTS' AGE OF HIGHEST DEG WITH PERCEPTION" /VERTICAL = 'AGE OF HIGHEST DEGREE’ /HORIZONTAL = 'PERCEPTION TOWARD USING MICROS AS DS' /FORMAT = REGRESSION /PLOT = Q8 WITH PERCEP ONEWAY PERCEP BY Q9 (0,1) /STATISTICS = ALL TEMPORARY RECODE QII5.1 (0 = 1) (1 THRU 100 = 2) (ELSE = -1) VALUE LABELS QII5.1 1 'NONE OR INDIRECT USERS' 2 'DIRECT USERS' 143 ONEWAY PERCEP BY QII5.1 (1,2) /STATISCTICS = ALL TEMPORARY RECODE QE5.1 (0 = 0) (1 THRU 100 = 1) (ELSE = -1) VALUE LABELS QII5.1 0 ’NONE OR INDIRECT USERS’ 1 'DIRECT USERS' REGRESSION VARIABLES = PERCEP EXPECT QII5.1 /DEPENDENT = PERCEP /ENTER EXPECT /ENTER QII5.1 /CASEWISE = ALL DEPENDENT PRED RESID ZRESID DRESED MAHAL COOK TEMPORARY RECODE QII5.1 TO QII11.7 (0 = 1) (1 THRU 25 = 2) (26 THRU 50 = 3) (51 THRU 75 = 4) (76 THRU 100 = 5) (ELSE = -1) VALUE LABELS QII5.1 TO QII11.7 1 ’0’ 2 1-25%' 3 '26-50%' 4 '51-75%' 5 '76-100%' FREQUENCIES VARIABLES = QII5.1 TO QII11.7 COMPUTE USAGE = SUM (QII5.1 TO QII5.2) VARIABLE LABELS USAGE 'EXTENT OF MICRO USE BY SELF AND SUPP STAFF' PLOT TITLE = "RESPONDENTS' USE OF MICRO BY SELF AND SUPP STAFF WITH PERCEP" /VERTICAL = ’PERCEPTION TOWARD USING MICROS FOR DS* /HORIZONTAL = 'EXTENT OF MICRO USE BY SELF & SUPP STAFF' /FORMAT = REGRESSION /PLOT = PERCEP WITH USAGE CORRELATIONS USAGE PERCEP /PRINT = TWOTAIL SELECT IF (QII5.1 GE 1) ONEWAY QII5.1 BY Q3 (1,4) /RANGES = SCHEFFE /STATISCTICS = ALL PLOT TITLE = "RESPONDENTS’ LENGTH OF EMPLOY MENT WITH MICRO DIRECT USE" /VERTICAL = 'LENGTH OF EMPLOYMENT’ /HORIZONTAL = 'EXTENT OF MICRO DIRECT USE' /FORMAT = REGRESSION /PLOT = Q4 WITH QII5.1 144 CORRELATIONS QII5.1 PERCEP EXPECT Q4 Ql Q6 Q8 SS /PRINT = TWOTAIL CORRELATIONS QII5.1 PERCEP EXPECT Q4 Ql Q6 Q8 SS PLOT TITLE = "RESPONDENTS' SIZE OF INSTITUTION WITH MICRO DIRECT USE" /VERTICAL = "STUDENTS’ ENROLLMENT" /HORIZONTAL = ’EXTENT OF MICRO DIRECT USE' /FORMAT = REGRESSION /PLOT = Ql WITH QII5.1 ONEWAY QII5.1 BYQ2.1(0,1) /STATISCTICS = ALL ONEWAY QII5.1 BY Q2.2 (0,1) /STATISCTICS = ALL PLOT TITLE = "RESPONDENTS’ AGE WITH MICRO DIRECT USE" /VERTICAL = 'AGE OF RESPONDENTS' /HORIZONTAL = 'EXTENT OF MICRO DIRECT USE' /FORMAT = REGRESSION /PLOT = Q6 WITH QII5.1 ONEWAY QII5.1 BY Q5 (0,1) /STATISCTICS = ALL PLOT TITLE = "RESPONDENTS' AGE OF HIGHEST DEGREE WITH MICRO DIRECT USE" /VERTICAL = 'AGE OF HIGHEST DEGREE’ /HORIZONTAL = 'EXTENT OF MICRO DIRECT USE' /FORMAT = REGRESSION /PLOT = Q8 WITH QII5.1 ONEWAY QII5.1 BY Q9 (0,1) /STATISCTICS = ALL PLOT TITLE = "RESPONDENTS' PERCEPTION TOWARD MICROS WITH MICRO DIRECT USE" /VERTICAL = PERCEPTION TOWARD MICROS’ /HORIZONTAL = 'EXTENT OF MICRO DIRECT USE' /FORMAT = REGRESSION /PLOT = PERCEP WITH QII5.1 PLOT TITLE = "RESPONDENTS' EXPECTATION OF MICROS WITH MICRO DIRECT USE" /VERTICAL = 'EXPECTATION OF MICROS' /HORIZONTAL = 'EXTENT OF MICRO DIRECT USE’ /FORMAT = REGRESSION /PLOT = EXPECT WITH QII5.1 PLOT TITLE = "RESPONDENTS’NUMBER OF SUPP STAFF WITH MICRO DIRECT USE" /VERTICAL = NUMBER OF SUPP STAFF’ 145 /HORIZONTAL = 'EXTENT OF MICRO DIRECT USE' /FORMAT = REGRESSION /PLOT = SS WITH QII5.1 REGRESSION VARIABLES = QII5.1 Q6 PERCEP EXPECT /DEPENDENT = QII5.1 /ENTER Q6 /ENTER PERCEP /ENTER EXPECT /CASEWISE = ALL DEPENDENT PRED RESID ZRESID DRESID MAHAL COOK TEMPORARY RECODE QII3 (0 = 0) (1 THRU HI = 1) (ELSE = -1) QII4 (0=1)(1 THRU3 = 2)(4THRU6 = 3)(7THRU9 = 4) (10 THRU HI = 5) (ELSE = -1) VALUE LABELS QII3 1 YES’ 0 NO' QII4 1 'O’ 2 ’1-3’ 3 *4-6’ 4 ’7-9’ 5 ’10 MICROS OR MORE’ FREQUENCIES VARIABLES = QII3 QII4 DESCRIPTIVE VARIABLES = QII5.1 TO QII11.7 MULT RESPONSE GROUPS = NONUSERS ’REASONS FR NOT USING MICROS’ (NOTRAING TO OTHERS (1)) /FREQUENCIES = NONUSERS MULT RESPONSE GROUPS = NONUSERS ’REASONS FR NOT USING MICROS’ (NOTRAING TO OTHERS (1)) /VARIABLES Q2.1 (0,1) /TABLES = Q2.1 BY NONUSERS /CELLS = COLUMN FINISH APPENDIX H COMMENTS BY VICE PRESIDENTS SUPPORTING THAT MICROS LIVED UP TO THEIR EXPECTATIONS 146 —I have been able to manipulate the data and track donors. It holds financial information and is reliable on billing and pledge payments. —The use of a micro is now essential to good planning. There are rapid changes occurring th a t require the ability to make rapid decisions based upon correct input. The use of a micro has saved me many hours of time and has reduced margin for error substantially. -- Adequate information available when you need it. It helps with time m anagem ent. —My use of microcomputers has assisted the delivery of my responsibilities as I expected. —Expected them to reduce time to produce necessary data. Can more clearly see alternatives. Person with knowledge of computer files has more control. More alternatives can be generated. -- Microcomputers provide much readier access to data. They don't make my decisions, but they enable me to have w hat I need to make them. —Provide information. Efficient tool. Convenient for staff to use when located in office area. -- The programs we have are more related to training individuals to use microcomputers and to making decisions. —I found out th at they were as good as I was taught they would be! -- I use an intra-office network of Apple computers to link our fund-raising, alumni and communications programs. These microcomputers are used for word-processing, research, graphics, budgeting and other management tasks. The Apple Macintosh hardware and software is quite satisfactory. —It allows me to track activity over a period of time and see where my efforts and resources have been used most effectively. Information for decisions is timely and accurate. Causes those using computers to concentrate on w hat is im portant and gives them feedback. —Development is data and time sensitive/intensive business without the micros we couldn't manage the number of accounts we do with the number of staff we have. —We are able to be more efficient using microcomputers and get more done without increasing the number of personnel. 147 -- Quicker access to info. More ways to m anipulate info. Easier and more efficient storage of info. More cost efficient once on line. -- Microcomputers have saved time and made it possible to be productive during staff shortages. They make it possible for everyone to use the same database-for decision and planning. —Rapid access to stored data. Ability to do comparison calculations. —However, I find th at the time it takes for initial data entry is exorbitant; also down time is a problem. It is the manipulation of data which I find most useful, and could not be done efficiently by hand. —As reflected in some of the questions/statements on page 5 [p. 135]. —In previous jobs, yes. In current position —I am in meetings and others do support work. The electronic mail is great! —When we can generate the data we need, the microcomputers are excellent. In several areas this is possible. However, we have a long way to go before we can realize all the capabilities of our microcomputers. There ju st isn't enough time. —They are useful, bringing data to a machine th at can help analyze it. But there are many frustrations and people resist using them effectively. —I have found the technology very useful, relatively easy to access and implement, and most assuredly helpful in facilitating communication. We all have a common data set, even if we prefer to manipulate it for differing effects. -- Having worked with computers prior to this position and having been a programmer, I feel th at it did live up to my expectations. —Word processing support for my work has been very helpful, has done more than I ever hoped or dreamed it would, for all other applications my expectations have been minimal. -- They have provided adequate data for decision making and flexibility to develop new methods for decision making. —More useful than expected. —Mainframe - micro interface still cumbersome but equipment contributes to overall performance in a positive way. 148 —Decisions require data, without the use of microcomputers it would be very difficult to extract the information and prepare it for formal presentation. —As a product of the information age, I am accustomed to using micros and use them as p art of my daily experience. — Increase direct involvement in using, producing data. Increase speed of processing. Increase accuracy of word and data processing. -- The micro does, to an extent, "force me" into a more logical and conscious frame of mind. As I consider the need for certain data and the need to develop a certain structure for students for my decision making analysis. —My staff has made good use of microcomputers in developing data, but as an institution we are not yet making as much use of them as we could because we have not yet implemented mainframe systems th at will provide adequate data bases for uses in the microcomputer environment. —They allow for gathering sufficient information and placing it in a format th at is easily understood for decision making. APPENDIX I COMMENTS BY VICE PRESIDENTS CLAIMING THAT MICROS DID NOT LIVE UP TO THEIR EXPECTATIONS 149 H ard to collect and organize data necessary to support decision making environment. I have had some limited success but not as great as hoped for. Creating decision support systems is very time consuming and requires very good underlying data bases. Time and data are my major constraints. Not yet. Soon! APPENDIX J OTHER REASONS BY VICE PRESIDENTS FOR NOT DIRECTLY USING MICROS FOR DECISION SUPPORT 150 - Institution has long been committed to mainframe and network. Micro's are new on the scene here and well be used more during the 1990s. —I have used them a t other institutions. When the funding becomes available, it will be useful. —We don't use micro's yet. We use main frame with term inals directly to mainframe. We are in the process of converting to a system which will be a combination of mainframe and PC (with access to mainframe). —Technology is new on campus - will not be available in this office for sometime! -- We are a highly computerized college. Almost nothing is done manually. No adm inistrators have secretaries. We employ a word processing center. - N earest use in dept, research administration, computer systems, microsystems, program mgt. - Never had exposure over career. —Networking not completed in some areas. - We do not use micros in any p art of our operation-we are hooked up through an adm inistrative main frame system. Have considered use of micros—most of our needs are m et through m ain frame. —most data comes to me from mainframe source and it rarely has been processed by microcomputers. -- Because college mainframe holds all data. - Rely on supportive staff. APPENDIX K FREQUENCIES OF REPORTED PERCENT OF VICE PRESIDENTS’ DECISIONS SUPPORTED BY THE USE OF DIFFERENT TYPE OF COMPUTER UNITS --Frequencies of Reported Percent of Vice Presidents' Decisions Supported by the Use of Different Type of Computer Units % of Decisions Computer Unit 0 1-25 26-50 51-75 76-100 Missing Cases N (% of N) Microcomputers 16 (15.1) 45 (42.5) 24 (22.6) 13 (12.3) 3 (2.8) 5 (4.7) 106 (100%) 84 (79.2) 7 (6.6) 5 (4.7) 3 (2.8) 2 (1.9) 5 (4.7) 106 (100%) Mainframe 28 (26.4) 3G (28.3) 18 (17.0) 13 (12.3) 12 (11.3) 5 (4.7) 106 (100%) Unknown Computer Source 92 (86.8) 7 (6.6) 1 (0.9) 1 (0.9) 0 (0.0) 5 (4.7) 106 (100%) Other computer Sources 99 (93.4) 1 (0.9) 0 (0.0) 1 (0.9) 0 (0.0) 5 (4.7) 106 (100%) Minicomputers APPENDIX L FREQUENCIES OF REPORTED PERCENT OF MICROCOMPUTER GENERATED DATA BY VICE PRESIDENTS, SUPPORTIVE STAFF, AND EXTERNAL SOURCES TO SUPPORT DIFFERENT AREAS OF DECISION MAKING -Percent of Microcomputer Generated Data by Vice Presidents to Support Different Areas of Decision Making % of Microcomputer Generated Data 0 1-25 26-50 51-75 76-100 Missing Cases N (% of N) Planning 51 (48.1) 29 (27.4) 10 (9.4) 6 (5.7) 5 (4.7) 5 (4.7) 106 (100%) Budgeting 52 (49.1) 24 (22.6) 18 (17.0) 0 (0.0) 7 (6.6) 5 (4.7) 106 (100%) Accounting 87 (82.1) 8 (7.5) 3 (2.8) 0 (0.0) 3 (2.8) 5 (4.7) 106 (100%) Purchasing 90 (84.9) 7 (6.6) 4 (3.8) 0 (0.0) 0 (0.0) 5 (4.7) 106 (100%) Facilities & Physical Plants 91 (85.8) 9 (8.5) 0 (0.0) 0 (0.0) 1 (0.9) 5 (4.7) 106 (100%) Personnel Administration 71 (67.0) 16 (15.1) 13 (12.3) 1 (0.9) 0 (0.0) 5 (4.7) 106 (100%) Public Relations 90 (84.9) 8 (7.5) 2 (1.9) 0 (0.0) 1 (0.9) 5 (4.7) 106 (100%) Other Tasks 87 (82.1) 6 (5.7) 3 (2.8) 3 (2.8) 2 (1.9) 5 (4.7) 106 (100%) Area -Percent of Microcomputer Generated Data by Vice Presidents' Supportive Staff to Support Different Areas of Decision Making % of Microcomputer Generated Data 0 1-25 26-50 51-75 76-100 Missing Cases N (% of N) Planning 39 (36.8) 34 (32.1) 14 (13.2) 2 (1.9) 12 (11.3) 5 (4.7) 106 (100%) Budgeting 48 (45.3) 32 (30.2) 5 (4.7) 6 (5.7) 10 (9.4) 5 (4.7) 106 (100%) Accounting 66 (62.3) 21 (19.8) 1 (0.9) 3 (2.8) 10 (9.4) 5 (4.7) 106 (100%) Purchasing 80 (75.5) 12 (11.3) 1 (0.9) 2 (1.9) 6 (5.7) 5 (4.7) 106 (100%) Facilities & Physical Plants 88 (83.0) 9 (8.5) 3 (2.8) 0 (0.0) 1 (0.9) 5 (4.7) 106 (100%) Personnel Administration 60 (56.6) 26 (24.5) 11 (10.4) 3 (2.8) 1 (0.9) 5 (4.7) 106 (100%) Public Relations 80 (75.5) 12 (11.3) 4 (3.8) 3 (2.8) 2 (1.9) 5 (4.7) 106 (100%) Other Tasks 90 (84.9) 4 (3.8) 2 (1.9) 0 (0.0) 5 (4.7) 5 (4.7) 106 (100%) Area -Percent of Microcomputer Generated Data by Sources External to the Office of Vice Presidents Used to Support Different Areas of Decision Making % of Microcomputer Generated Data Area Missing Cases N (% of N) 0 1-25 26-50 51-75 76-100 Planning 49 (46.2) 29 (27.4) 14 (13.2) 7 (6.6) 2 (1.9) 5 (4.7) 106 (100%) Budgeting 45 (42.5) 21 (19.8) 19 (17.9) 4 (3.8) 12 (11.3) 5 (4.7) 106 (100%) Accounting 62 (58.5) 21 (19.8) 5 (4.7) 0 (0.0) 13 (12.3) 5 (4.7) 106 (100%) Purchasing 67 (63.2) 21 (19.8) 6 (5.7) 0 (0.0) 7 (6.6) 5 (4.7) 106 (100%) Facilities & Physical Plants 73 (68.9) 16 (15.1) 7 (6.6) 0 (0.0) 5 (4.7) 5 (4.7) 106 (100%) Personnel Administration 66 (62.3) 16 (15.1) 11 (10.4) 3 (2.8) 5 (4.7) 5 (4.7) 106 (100%) Public Relations 77 (72.6) 8 (7.6) 7 (6.6) 3 (2.8) 6 (5.7) 5 (4.7) 106 (100%) Other Tasks 95 (89.6) 2 (1.9) 0 (0.0) 4 (3.8) 0 (0.0) 5 (4.7) 106 (100%) APPENDIX M FREQUENCIES OF REPORTED PERCENT OF MICROCOMPUTER GENERATED DATA BY VICE PRESIDENTS AND SUPPORTIVE STAFF USING DIFFERENT TYPE OF MICROCOMPUTER SOFTWARE IN SUPPORT OF DECISION MAKING -Percent of Microcomputer Generated Data by Vice Presidents Using Different Type of Microcomputer Software in Support of Decision Making % of Microcomputer Generated Data 0 1-25 26-50 51-75 76-100 Missing Cases Data Base 60 (56.6) 24 (22.6) 12 (11.3) 2 (1.9) 3 (2.8) 5 (4.7) 106 (100%) Spreadsheet 99 (55.7) 25 (23.6) 12 (11.3) 4 (3.8) 1 (0.9) 5 (4.7) 106 (100%) Graphics 70 (66.0) 27 (25.5) 3 (2.8) 0 (0.0) 1 (0.9) 5 (4.7) 106 (100%) Word Processing/ Text Management 40 (37.7) 23 (21.7) 18 (17.0) 10 (9.4) 10 (9.4) 5 (4.7) 106 (100%) Communication 78 (73.6) 13 (12.3) 5 (4.7) 3 (2.8) 2 (1.9) 5 (4.7) 106 (100%) Project Management 83 (78.3) 15 (14.2) 2 (1.9) 0 (0.0) 1 (0.9) 5 (4.7) 106 (100%) Other Microcomputer Software 96 (90.6) 4 (3.8) 0 (0.0) 0 (0.0) 0 (0.0) 5 (4.7) 106 (100%) Microcomputer Software N (% of N) -P ercent of Microcomputer Generated Data by Vice Presidents' Supportive Staff Using Different Type of Microcomputer Software in Support of Decision Making % of Microcomputer Generated Data Microcomputer Software 0 1-25 26-50 51-75 76-100 Missing Cases Data Base 44 (41.5) 30 (28.3) 14 (13.2) 6 (5.7) 7 (6.6) 5 (4.7) 106 (100%) Spreadsheet 47 (44.3) 27 (25.5) 12 (11.3) 5 (4.7) 10 (9.4) 5 (4.7) 106 (100%) Graphics 64 (60.4) 29 (27.4) 5 (4.7) 0 (0.0) 3 (2.8) 5 (4.7) 106 (100%) Word Processing/ Text Management 25 (23.6) 11 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