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UBC Theses and Dissertations

An experimental study of the man-machine interface Masulis, Paul Stanton 1978

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EXPERIMENTAL STUDY OF THE MAN-MACHINE INTERFACE by PAUL STANTON MASULIS B.S., Carnegie-Mellon U n i v e r s i t y , 1976 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN BUSINESS ADMINISTRATION xn THE FACULTY OF GRADUATE STUDIES (Commerce and Business A d m i n i s t r a t i o n ) We accept t h i s t h e s i s as conforming to t h e r e q u i r e d standard: THE UNIVERSITY OF BRITISH COLUMBIA May, 1978 (c) Paul Stanton Masulis, 1978 In p r e s e n t i n g t h i s t h e s i s i n p a r t i a l f u l f i l l m e n t of the requirements f o r an advanced degree at the U n i v e r s i t y o f B r i t i s h Columbia, I agree that the L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r r e f e r e n c e and study. I f u r t h e r agree t h a t permission f o r e x t e n s i v e copying of t h i s t h e s i s f o r s c h o l a r l y purposes may be granted by the Head of my Department or by h i s r e p r e s e n t a t i v e s . I t i s understood t h a t copying or p u b l i c a t i o n of t h i s t h e s i s f o r f i n a n c i a l gain s h a l l not be allowed without my w r i t t e n p e r m i s s i o n . Paul Stanton Masulis F a c u l t y of Commerce and Business A d m i n i s t r a t i o n The U n i v e r s i t y of B r i t i s h Columbia 2075 wesbrook Place Vancouver, Canada V6T 1J6 Date: May 25, 1978 ABSTRACT In t h i s t h e s i s , the author pursued two o b j e c t i v e s . The f i r s t o b j e c t i v e was to present a working example of a convenient, " i d i o t - p r o o f " , i n t e r a c t i v e computer program (designed with the user - not the programmer - i n mind). The second o b j e c t i v e was t o i n v e s t i g a t e how v a r i o u s types of users i n t e r a c t with the computer, with the i n t e n t i o n of r e a c h i n g some c o n c l u s i o n s about which program i n t e r f a c e s were most a p p r o p r i a t e and convenient f o r v a r i o u s user types. I n a d d i t i o n , some t h e o r i e s about the e f f e c t s of v a r i o u s beha-v i o u r a l v a r i a b l e s were i n v e s t i g a t e d . The experimental t o o l used f o r t h i s r e s e a r c h was a simple i n t e r a c t i v e computer game i n which the p a r t i c i p a n t s searched f o r the optimum p r o f i t i n a t h r e e - d i m e n s i o n a l space, given a f i x e d time l i m i t . , Frequent p e r i o d i c measurements were a u t o m a t i c a l l y c o l l e c t e d on user performance, a t t i t u d e , r equests f o r r e p o r t s , u t i l i z a t i o n of s p e c i a l f e a t u r e s , and other v a r i a b l e s ; a l s o , the s o l u t i o n p r o t o c o l of each p a r t i c i p a n t was recorded. The users were c a t e q o r i z e d by c o q n i t i v e s t y l e ( h e u r i s t i c / a n a l y t i c ) , r i s k a t t i t u d e , and p r e v i o u s computer exper i e n c e as determined by a b a t t e r y of p r e - t e s t s and q u e s t i o n n a i r e s . In a n a l y z i n q the r e s u l t s , i t was found t h a t experience l e v e l was the dcminatinq f a c t o r on a l l dimensions: n o v i c e s were slower, f i n i s h e d l e s s f r e q u e n t l y , and were s i q n i f i c a n t l y l e s s c o n f i d e n t than experienced p l a y e r s . A h i q h l y s t r u c t u r e d proqram i n t e r f a c e was found t o be more a p p r o p r i a t e f o r these new use r s . , Experience was a l s o the dominating f a c t o r i n the use of r e p o r t s , although novices d i d show a marked l e a r n i n g e f f e c t over time - as d i d a l l users on most dimensions o f performance and behaviour. As p r e v i o u s l y hypothesized, a n a l y t i c - t y p e s and r i s k - t a k e r s played s i g n i f i c a n t l y f a s t e r and were more c o n f i d e n t than h e u r i s t i c - t y p e s and r i s k - a v e r t e r s , r e s p e c t i v e l y . Concerning u t i l i z a t i o n o f s p e c i a l program f e a t u r e s , i t was found t h a t i n p u t response d e f a u l t s i n f l u e n c e d users i n u n f a m i l i a r s i t u a t i o n s (ones which were new or d i d not have c l e a r - c u t r e s p o n s e s ) , and didn't a f f e c t them a t a l l i n f a m i l i a r circumstances. A n a l y t i c - t y p e s made l e a s t use o f d e f a u l t s . B i s k - a v e r t e r s were l e a s t l i k e l y t o a b b r e v i a t e commands. Al s o , the extent t o which commands were a b b r e v i a t e d depended much upon t h e i r l e n g t h , F i n a l l y , i n the area of s o l u t i o n p r o t o c o l s , i t was indeed found that h e u r i s t i c - t y p e s were much l e s s s t r u c t u r e d i n t h e i r approach t o s o l v i n g the problem than a n a l y t i c - t y p e s . . i v TABLE OP CjONTEIIS Chapter Page 1. I n t r o d u c t i o n .............. 1 2. L i t e r a t u r e Review ............... 3 3. The Computer Program 14 4. Data C o l l e c t i o n Methodology ..... ...................... 19 5. Hypotheses 25 6. A n a l y s i s of R e s u l t s 29 7. Conclus i o n s ........................................... 50 Footnotes ................................................. 57 B i b l i o g r a p h y ...... .................. .... .................. 60 Appendix A - Program L i s t i n g .............................. 62 Appendix B - Game I n s t r u c t i o n s 84 Appendix C - Sample I n t e r a c t i o n s .......................... 88 Appendix D - P r o f i t F u nction .............................. 99 Appendix E - Sample Program Output .,..,.........,....,,,.101 Appendix F - Sample P r o t o c o l s 103 Appendix G - Summary of R e s u l t s .......................... 107 V LIST OF TABLES Table Page 1. ANOVA - Game Enjoyment 31 2. ANOVA - Minutes/Period 32 3. ANOVA - Termination on Time ........................... 33 4. ANOVA - Confidence L e v e l 34 5. T-TESTS - Performance and S t r u c t u r e ................... 35 6., ANOVA - E r r o r Hate 38 7. ANOVA - Opening D e f a u l t s ............................. 39 8. ANOVA - Acceptance of D e f a u l t s ........................ 40 9. ANOVA - Extent of A b b r e v i a t i o n 41 10. ANOVA - A b b r e v i a t i o n by Length .....42 11. T-TESTS - Comparisons over Time ....................... 42 12. ANOVA - Use o f H i s t o r y Reports ........................ 45 13. ANOVA - Use of Ordered H i s t o r y Reports ................ 46 14. ANOVA - Use of Graphs .. 47 15. ANOVA - P r o t o c o l S t r u c t u r e 48 16. ANOVA - P r o t o c o l D i s p e r s i o n 48 v i ACKNOWLEDGEMENT I would l i k e t o take t h i s o p p o r t u n i t y to thank my a d v i s o r s . P r o f e s s o r s a l b e r t Dexter and Izak Benbasat, f o r t h e i r r o l e i n the establishment and completion of t h i s r e s e a r c h : f o r summer employment i n 1977 as r e s e a r c h a s s i s t a n t , doing systems programming f o r two i n t e r a c t i v e computer games (work which f i r s t i n s t i l l e d my d e s i r e to i n v e s t i g a t e more c a r e f u l l y the man-machine i n t e r f a c e ) ; f o r s u g g e s t i o n s regarding implementation o f t h i s r e s e a r c h ; f o r o c c a s i o n a l a d m i n i s t r a t i v e support; and e s p e c i a l l y f o r t h e i r c o n t r i b u t i o n s to t h i s f i n a l document, I a l s o thank my t h i r d committee member. Pr o f , Ronald T a y l o r , f o r h i s a d d i t i o n a l a s s i s t a n c e and enthusiasm. I n a d d i t i o n , I owe s i n c e r e thanks t o many of the students of S a i n t Andrew*s Residence H a l l f o r t h e i r p a r t i c i p a t i o n , support, encouragement, and f e l l o w s h i p during t h i s r e s e a r c h and throughout my two years at the U n i v e r s i t y of B r i t i s h Columbia. 1 Chapter One INTRODUCTION Even with the i n c r e a s i n g programming e f f o r t going i n t o the development o f i n t e r a c t i v e programs { e s p e c i a l l y s i m u l a t i o n s and games), design of the a c t u a l man-machine i n t e r f a c e has continued to be neglected. Many of the i n t e r a c t i v e programs which t h i s author has encountered tended to be q u i t e f r u s t r a t i n g t o use. For example, some r e q u i r e d t h a t e n t i r e commands be s p e l l e d out, when one or two l e t t e r s would be unambiquous. Others r e q u i r e d i n p u t i n a f i x e d format. S t i l l o t h e r s responded to an i l l e q a l i n p u t with an u n i n t e l l i g i b l e system e r r o r message. I t was not necessary t o look very f a r f o r examples; they were q u i t e p r o l i f i c i n the system program l i b r a r i e s o f every computer i n s t a l l a t i o n v i s i t e d . Some examples from the l i t e r a t u r e are i l l u s t r a t e d i n chapter two. A review of the l i t e r a t u r e i n d i c a t e d t h a t i n s u f f i c i e n t a t t e n t i o n seems to have been paid to t h i s i s s u e . Computer games abound as r e s e a r c h t o o l s , but few r e s e a r c h e r s appear to have considered whether t h e i r man-computer i n t e r f a c e s i g n i f i c a n t l y b i a s e d or discouraqed t h e i r s u b j e c t s . P e r u s a l of the standard t e x t s on man-computer communication was a l s o q u i t e f r u s t r a t i n q ; the t o p i c s covered were o f t e n too ge n e r a l or s o p h i s t i c a t e d f o r the designer of i n t e r a c t i v e programs f o r normal CRT t e r m i n a l s . Even i n the most u s e f u l c hapter, dozens o f i n t e r f a c e designs were l i s t e d and d e s c r i b e d , but few h i n t s were given as to when each was 2 a p p r o p r i a t e . A l s o , few r e f e r e n c e s were suggested f o r seeking f u r t h e r d e t a i l s ( r e i n f o r c i n g the notion that t h i s area had been f o r g o t t e n i n the l i t e r a t u r e ) . The goal of t h i s t h e s i s was t o examine a few i n t e r f a c e designs e x p e r i m e n t a l l y , with the i n t e n t i o n of determining the c o n d i t i o n s under which each was most a p p r o p r i a t e and i n d i c a t i n g any forms which may bias the user's behaviour. A l e s s e r g o a l was to a l s o study the e f f e c t s of some b e h a v i o u r a l v a r i a b l e s upon performance, a t t i t u d e , and s o l u t i o n p r o t o c o l . I n the f o l l o w i n g pages, t h e r e l e v a n t l i t e r a t u r e i s f i r s t b r i e f l y reviewed. Then, the a c t u a l program code i s presented and analyzed, with p a r t i c u l a r a t t e n t i o n paid t o the in p u t prompts, the methods f o r a c c e p t i n g i n p u t from the user, and the te c h n i q u e s f o r d e t e c t i n g and handling user i n p u t e r r o r s (often r e f e r r e d t o as " i d i o t - p r o o f i n g " ) . Next, the a c t u a l data c o l l e c t i o n and a n a l y s i s are d e s c r i b e d ; then the f o l l o w i n g chapter presents and d i s c u s s e s the r e s u l t s r e l a t i n g t o user performance, use of program d e f a u l t s and command a b b r e v i a t i o n s , behaviour over time, and the p a r t i c i p a n t s ' s o l u t i o n p r o t o c o l s . F i n a l l y , some p r a c t i c a l i m p l i c a t i o n s and f u t u r e d i r e c t i o n s f o r re s e a r c h are suggested i n the c o n c l u d i n g chapter. 3 Chapter Two LITERATURE REVI Eg J3§c&3round _nd M o t i v a t i o n In t h i s c h a p t e r , some of the l i t e r a t u r e r e l e v a n t to t h i s r e s e a r c h i s presented: background review, user e n g i n e e r i n g a r t i c l e s , p r e v i o u s computer experimentation i n v o l v i n g b e h a v i o u r a l v a r i a b l e s , l i t e r a t u r e concerning problem s o l u t i o n p r o t o c o l s , and t e x t s on the man-machine i n t e r f a c e are a l l d i s c u s s e d . Before l o o k i n g a t the l i t e r a t u r e which has d i r e c t bearing upon t h i s work, however, an i n d i r e c t l y r e l a t e d r e f e r e n c e i s mentioned. Although man-machine communication has on l y r e c e n t l y r e c e i v e d s e r i o u s a t t e n t i o n , i t has had a very i n t e r e s t i n g h i s t o r y . In h i s book Systems Psychology, 1 Kenyon B. DeGreene provided a very good summary of i t s h i s t o r y , from the i n t e n s i v e development of computer equipment i n the 1950s, to computer programs i n the e a r l y 1960s and man-machine i n t e r -r e l a t i o n s h i p s i n the l a t e 1960s. Chapter 10, e n t i t l e d ^Man-Machine I n t e r r e l a t i o n s h i p s , " was d e s c r i b e d very w e l l i n i t s own i n t r o d u c t i o n : T h i s chapter f i r s t reviews h i s t o r y and t r e n d s toward g r e a t e r computer s y s t e m a t i z a t i o n . Areas of s p a t i a l and temporal i n t e r f a c e between man and computer r e c e i v e s p e c i a l a t t e n t i o n . We then c o n s i d e r important s p e c i a l i z e d areas o f r e s e a r c h and a p p l i c a t i o n , which i n c l u d e means of d i r e c t , u s u a l l y dynamic man-computer communication by input and d i s p l a y d e v i c e s i n terms of given language s t r u c t u r e s , t i m e - s h a r i n g , and " s y m b i o t i c " problem s o l v i n g . Human f a c t o r s and managerial c o n s i d e r -a t i o n s i n computer systems f o l l o w . The chapter ends with an e v a l u a t i o n of the continued s o c i e t a l impact of computers. 2 4 I t a l s o i n c l u d e d a u s e f u l s e c t i o n on the main sources o f design and o p e r a t i o n a l e r r o r i n computer systems. O v e r a l l , t h i s r e f e r e n c e p r o v i d e s a good background f o r many of the ideas presented and p r a c t i c e d i n t h i s t h e s i s . More d i r e c t l y r e l a t e d to t h i s r e s e a r c h i s a d o c t o r a l t h e s i s by Peter G. W. Keen 3 a t Harvard U n i v e r s i t y ; i n f a c t , i t i s probably the s i n g l e major cause of t h i s r e s e a r c h . I n h i s t h e s i s . Keen suggested an i n t e r a c t i v e computer s i m u l a t i o n program which allowed the user n e a r l y complete freedom t o decide what he would l i k e to do next, i n s t e a d of the t r a d i t i o n a l 'request i n p u t - s i m u l a t e - d i s p l a y output-repeat* c y c l e . Hypothesizing t h a t t h i s concept had r a t h e r s t r o n g i m p l i c a t i o n s f o r ease of use by i n e x p e r i e n c e d computer users and p o s s i b l y by those who d i s p l a y a n o n - a n a l y t i c c o g n i t i v e s t y l e of problem s o l v i n g , t h i s author decided t o e x p e r i m e n t a l l y t e s t the i m p l i c a t i o n s of Keen's suggestions. I t should be mentioned, however, t h a t t h i s author a l s o c o n s i d e r s the concept o f l e s s s t r u c t u r e d computer i n t e r f a c e s very important - i n t h e proper environment. For i n s t a n c e , they would be a p p r o p r i a t e f o r programs which are run f r e q u e n t l y , by experienced u s e r s . {Since t h i s r e s e a r c h was begun, i t has been l e a r n e d that Botkin* found t h a t such an unst r u c t u r e d model was used with equal e f f e c t i v e n e s s by both a n a l y t i c and h e u r i s t i c d e c i s i o n makers). The Inventory Management Game, a resea r c h t o o l used q u i t e e x t e n s i v e l y by Benbasats and others a t the U n i v e r s i t y o f B r i t i s h Columbia, was another i n f l u e n t i a l cause o f t h i s 5 t h e s i s . Experience with the o r i g i n a l v e r s i o n of the computer game demonstrated t h a t i t u n n e c e s s a r i l y neglected the user. For i n s t a n c e , a i l responses - i n c l u d i n g * YES* and *NG» - had to be typed i n f u l l ; no reasonable d e f a u l t values were provided to minimize r o u t i n e t y p i n g ; some i n p u t reguests were ambiguous; and any t y p i n g e r r o r s were answered by the u n i n t e l l i g i b l e system message "ILLEGAL CHARACTER, ENTER REPLACEMENT NUMBER, OR RE-ENTER REST OF LINE FROM POINT OF ERROR, OR '8TS,»" A new v e r s i o n of the Inventory Management Game has c o r r e c t e d many o f these shortcomings, and the authors have provided some i n t e r e s t i n g r e s u l t s r e l a t i n g some c h a r a c t e r i s t i c s of an i n f o r m a t i o n system and a d e c i s o n maker t o the r e s u l t i n g d e c i s i o n making performance. For d e t a i l s , c o n s u l t the papers by Benbasat and S c h r o e d e r 6 , Benbasat and T a y l o r 7 , and Benbasat and D e x t e r 8 . Another e a r l y example o f a n o n - u s e r - o r i e n t e d i n t e r a c t i v e computer program appeared i n the June, 1969 i s s u e o f Management Science.» The authors s t a t e d i n the i n t r o d u c t i o n t h a t one m o t i v a t i o n f o r t h e i r r e s e a r c h was to answer the guestion "How should a problem environment be s t r u c t u r e d i n order t o e f f e c t i v e l y employ the a b i l i t i e s of both the manager and the o n - l i n e , r e a l t i m e computer?" They then proceeded to d e s c r i b e a job-shop s i m u l a t i o n program which r e g u i r e d the user to type such non-mnemonic commands as "F0RSIM=2*" to continue s i m u l a t i n g or "SRULE=3, HRULE=6, HRS=80, QZ=-201*" to change parameters, and which outputs a t a b l e with the ambiguous headings "L, M, J , I, NEXT, KACT, PROM, LEFT, CUSH, LIPR, 6 COMP, SETUP, IQ." To t h i s author, t h i s j u s t was not a c o n v i n c i n g e f f o r t to " e f f e c t i v e l y employ" the a b i l i t i e s of both manager and computer. User En£ineej_in_ Methods One of the outcomes of the 1973 N a t i o n a l Computer Conference was an e x c e l l e n t a r t i c l e by Anthony Wasserman, 1 0 e n t i t l e d "The Design o f ' I d i o t - P r o o f I n t e r a c t i v e Programs." According t o Wasserman, a program i s s a i d t o be i d i o t - p r o o f i f i t i s designed to a n t i c i p a t e any p o s s i b l e a c t i o n by i t s users and to respond i n such a manner as to minimize the chances of program or system f a i l u r e while s h i e l d i n g the user from the e f f e c t s of such a f a i l u r e . 1 1 Bearing i n mind Murphy's Law - anything t h a t can p o s s i b l y go wrong w i l l go wrong - Wasserman suggested f i v e p r i n c i p l e s : 1. Pr o v i d e a program a c t i o n f o r every p o s s i b l e type of i n p u t . 2. Minimize the need for t h e user t o l e a r n about the Computer System. 3. Provide a l a r g e number of e x p l i c i t d i a g n o s t i c s , along with e x t e n s i v e o n - l i n e user a s s i s t a n c e . 4. Provide program s h o r t - c u t s f o r knowledgeable users. 5. Allow the user to express the same message i n more than one way. These p r i n c i p l e s were a l l d e s c r i b e d i n d e t a i l i n the a r t i c l e , which then concluded with the f o l l o w i n g statement: There i s a s e r i o u s need f o r improved f a c i l i t i e s f o r the design o f i d i o t - p r o o f i n t e r a c t i v e programs, with a growing number of non-programmers using computers, development of comfortable man-machine i n t e r f a c e s w i l l outweigh many t r a d i t i o n a l c o n s i d e r -a t i o n s i n the o v e r a l l c r e a t i o n of i n t e r a c t i v e programs. 1 2 As s t a t e d e a r l i e r , the purpose o f t h i s t h e s i s was, i n f a c t , to provide a working example of an i d i o t - p r o o f program and t o perhaps make some c o n t r i b u t i o n to the above-mentioned need. 7 another e x c e l l e n t a r t i c l e about i d i o t - p r o o f i n g (or user e n g i n e e r i n g , or e r r o r engineering) came out of the 1971 F a l l J o i n t Computer Conference. In i t , W i l f r e d J. Hansen 1 3 suggested f o u r user e n g i n e e r i n g p r i n c i p l e s : Know the user Minimize memorization S e l e c t i o n not en t r y Names not numbers P r e d i c t a b l e behavior Access to system i n f o r m a t i o n Optimize o p e r a t i o n s Rapid execution of common o p e r a t i o n s D i s p l a y i n e r t i a Muscle memory Reorganize command parameters Engineer f o r e r r o r s Good e r r o r messages Engineer out the common e r r o r s R e v e r s i b l e a c t i o n s Redundancy Data s t r u c t u r e i n t e g r i t y 1 * Since some of these p r i n c i p l e s are q u i t e t e r s e , d e s c r i p t i o n s of a few o f t h e more vague ones f o l l o w : 'Names not numbers' suggests t h a t users be allowed t o enter a c t u a l names r a t h e r than a s s o c i a t e d number codes; ' P r e d i c t a b l e behavior' suggests t h a t the program have a " p e r s o n a l i t y " and be c o n s i s t e n t i n i t s output d i s p l a y and input requirements; 'Display i n e r t i a ' suqqests t h a t the t e r m i n a l d i s p l a y should change as l i t t l e as necessary i n c a r r y i n g out r e q u e s t s ; and 'Muscle memory' suqqests a need to desiqn a system so t h a t r e p e t i t i v e o p e r a t i o n s can be deleqated t o the lower part of the b r a i n ( i n the same way as many o f the o p e r a t i o n s i n d r i v i n q and t y p i n g ) . The a r t i c l e a l s o provided an e x c e l l e n t example of a user-engineered proqram. 8 P r e v i o u s Experimentation with B e h a v i o u r a l V a r i a b l e s another reason f o r t h i s r e s e a r c h was to r e l a t e some b e h a v i o u r a l a s p e c t s of users to t h e i r r e a c t i o n s to v a r i o u s program f e a t u r e s . T h i s was not a new concept; For i n s t a n c e , K. D. E a s o n 1 5 performed a study of "The Manager as a Computer Oser." In i t , the nature of management was presented, then a survey of 200 computer users was d e s c r i b e d , and, f i n a l l y , f our major causes f o r user d i s s a t i s f a c t i o n with computer systems were analyzed. The f o u r causes were: an inadeguate match t o the manager's needs, new problems caused by system advancement, changes i n user e x p e c t a t i o n s (as they r e a l i z e the computer's p o t e n t i a l ) , and l a c k of both time and d e s i r e t o l e a r n how to operate complex systems., Eason found t h a t computer programs would have to be more convenient and more f l e x i b l e i n the f u t u r e ; he concluded t h a t "unless i t i s p o s s i b l e to design forms of i n t e r a c t i o n acceptable to managers, t h i s r o l e f o r the manager may be very s h o r t l i v e d . " 1 * a s i m i l a r study i s d e s c r i b e d i n an a r t i c l e e n t i t l e d "Human F a c t o r s E v a l u a t i o n of a Computer Based I n f o r m a t i o n Storage and R e t r i e v a l S y s tem." 1 7 The authors e v a l u a t e d a government computer system c a l l e d the C e n t r a l Information Reference and C o n t r o l (CIRC) system, and found t h a t : In reviewing the r e s u l t s from the e v a l u a t i o n , there appeared to be three main f a c t o r s which i n f l u e n c e an i n d i v i d u a l ' s s a t i s f a c t i o n with the CIRC system: (1) t r a i n i n g and l e v e l o f p r o f i c i e n c y , (2) amount of i n f o r m a t i o n i n the system to meet task reguirements, and (3) the i n d i v i d u a l ' s t o l e r a n c e f o r i r r e l e v a n t m a t e r i a l . 1 8 9 The t h i r d p o i n t i s p a r t i c u l a r l y i n t e r e s t i n g . I t i s worthwhile to mention t h a t one of the advantages o f i n t e r a c t i v e systems i s the p o t e n t i a l to l e t the user choose what he needs - no more, no l e s s ; t h i s may be a j u s t i f i c a t i o n f o r p r o v i d i n g more uns t r u c t u r e d program i n t e r f a c e s which always a l l o w the user t o decide what he needs next. In an a r t i c l e from Data Base, Theodore J . Mock 1 9 d e s c r i b e d "A L o n g i t u d i n a l Study of Some Information S t r u c t u r e A l t e r n a t i v e s . " Mock s t u d i e d user performance with v a r i o u s Accounting Information System models, with the o b j e c t i v e o f c o n s i d e r i n g the impact of s e v e r a l b e h a v i o u r a l v a r i a b l e s and t e c h n i c a l i n f o r m a t i o n s t r u c t u r e v a r i a b l e s upon d e c i s i o n makers* p r o f i t performance and l e a r n i n g p a t t e r n s . In summary, the f i r s t s e t of experiments d i d demonstrate the f e a s i b i l i t y of e x p e r i m e n t a l l y i n v e s t i g a t i n g expected d i f f e r e n c e s i n i n f o r m a t i o n s t r u c t u r e s and the impact of c e r t a i n b e h a v i o u r a l v a r i a b l e s . . . Experimental data which i m p l i e s the s i g n i f i c a n c e o f b e h a v i o u r a l f a c t o r s i n c r e a s e s v a l i d i t y of suggesting t a i l o r i z e d i n f o r m a t i o n systems f o r decison makers e x h i b i t i n g d i f f e r e n t b e h a v i o u r a l c h a r a c t e r i s t i c s . 2 0 Another study, by Wynne and D i c k s o n , 2 1 looked a t "Experienced Managers* Performance i n Experimental Man-Machine D e c i s i o n System S i m u l a t i o n . " They were concerned with the e f f e c t i v e n e s s of Man-Machine D e c i s i o n I n f o r m a t i o n Systems (MHDIS), and ran experiments u s i n g an i n t e r a c t i v e s i m u l a t i o n program (which, u n f o r t u n a t e l y , was not e x p l i c i t l y d e s c r i b e d i n the a r t i c l e ) . A s a r e s u l t of t h e i r r e s e a r c h , Wynne and Dicksen reached two main c o n c l u s i o n s ; 10 F i r s t , the d i f f e r e n t i a l performance of s u b j e c t s i s r e l a t e d not only t o p e r s o n a l i t y v a r i a b l e s but a l s o t o i n f o r m a t i o n a c q u i s i t i o n and usaqe p a t t e r n s . . . I t appears from work thus f a r t h a t p e r s o n a l i t y and c o g n i t i v e s t y l e impact the e f f e c t i v e n e s s of HMDIS throuqh the s t r a t e g y o f system usage by the human. Second, the e f f e c t i v e n e s s of an 8MDIS must then be a f u n c t i o n of the ease (or d i f f i c u l t y ) with which the i n t e r a c t i v e computer program enables a d e c i s o n maker t o implement h i s p r e f e r r e d i n f o r m a t i o n handling s t r a t e q y . 2 2 In a paper e n t i t l e d "The Impact o f C o q n i t i v e S t y l e s on Information System D e s i q n , " 2 3 Benbasat and T a y l o r suqqested the f o l l o w i n g three g e n e r a l i z a t i o n s : 1. A n a l y t i c decison-maker types tend to p r e f e r d e c i s i o n a i d s and r e p o r t i n g systems which are q u a n t i t a t i v e i n nature with r e s u l t s supported with mathematical formulas. 2. H e u r i s t i c decision-makers need t o have more data search c a p a b i l i t i e s p r i o r to reachinq d e c i s i o n s . S i n c e they r e l y on feedback and t r i a l and e r r o r , an i n f o r m a t i o n system c a p a b i l i t y which can h i q h l i q h t t r e n d s and provide p e r i o d by p e r i o d comparisons would be s u i t a b l e f o r them. The i n f o r m a t i o n system should q i v e them c a p a b i l i t i e s to t r y a l t e r n a t i v e s o l u t i o n s and analyze the p o s s i b l e outcomes before they decide on t h e i r f i n a l approach to s o l v i n q the problem. 3. Decision-makers are a l s o d i f f e r e n t i n terms of t h e i r data g a t h e r i n g s t y l e s . The p r e c e p t i v e s would want a system which has c a p a b i l i t i e s of o r g a n i z i n g and aggregating data i n t o c a t e g o r i e s a c c o r d i n g to qiven parameters and e x c e p t i o n r e p o r t i n g a i d s , whereas the r e c e p t i v e s or maximal data u s e r s p r e f e r an i n f o r m a t i o n system which has access t o every p i e c e of h i s t o r i c a l d a t a . 2 * Turning now t o toward the area of r i s k a t t i t u d e , a paper by T a y l o r and D u n n e t t e 2 5 contained an i n t e r e s t i n g r e s u l t : Although r i s k - t a k i n g p r o p e n s i t y i n f l u e n c e d h e a v i l y both the amount of i n f o r m a t i o n processed and d e c i s i o n l a t e n c y , i t does not appear t h a t high r i s k -t a k e r s a t t a i n f a s t e r d e c i s i o n s by processing each item of i n f o r m a t i o n more r a p i d l y . . . Bather, i t would appear t h a t they are q u i t e d e l i b e r a t e i n 11 attempting to e x t r a c t as much va l u e as p o s s i b l e from the s m a l l e r s e t of i n f o r m a t i o n they examine. 2 * These r e s u l t s were from an i n d i v i d u a l l y and manually administered d e c i s i o n s i m u l a t i o n ; the research of t h i s t h e s i s provided an o p p o r t u n i t y t o consider the same hypotheses i n a computer environment. In another paper, on p s y c h o l o g i c a l determinants of bounded r a t i o n a l i t y , T a y l o r 2 7 p r o v i d e s examples c f more r i s k a t t i t u d e s t u d i e s , problem S o l u t i o n P r o t o c o l s In the area of s o l u t i o n p r o t o c o l s ( a l s o b r i e f l y c o n s i -dered i n t h i s t h e s i s ) , B a r r e t t ' s d e s c r i p t i o n of c o g n i t i v e s t y l e d e c i s i o n a p p r o a c h e s 2 9 was d i r e c t l y r e l e v a n t . B a r r e t t compared h e u r i s t i c and a n a l y t i c d e c i s i o n s t y l e s on f i v e dimensions. For example, with regard t o l e a r n i n g , i t was s a i d t h a t h e u r i s t i c s learned more by a c t i n g and placed emphasis on feedback; a n a l y t i c s l e a r n e d more by a n a l y z i n g and placed l e s s emphasis on feedback. In the area of search s t r a t e g y , h e u r i s t i c s used t r i a l and e r r o r , while a n a l y t i c s used f o r m a l r a t i o n a l a n a l y s i s . F i n a l l y , r e g a r d i n g approach t o a n a l y s i s , h e u r i s t i c s used common sense, i n t u i t i o n , and f e e l i n g s , whereas a n a l y t i c s developed e x p l i c i t models o f the s i t u a t i o n . The Jan-Machine I n t e r f a c e I t i s r e i t e r a t e d at t h i s time, that although t h i s t h e s i s looked at many of the concepts mentioned throughout t h i s c h apter, i t s o r i g i n a l purpose was to study the man-machine i n t e r f a c e a t a f a i r l y low l e v e l , with the o b j e c t i v e o f 12 r e a c h i n g some c o n c l u s i o n s about which i n t e r a c t i v e programming technigues are most h e l p f u l f o r users, and l e a s t l i k e l y to b i a s t h e i r behaviour. T h i s appeared to be an o r i g i n a l area of r e s e a r c h and, as s t a t e d e a r l i e r , was q u i t e untouched i n t h e l i t e r a t u r e . Many a r t i c l e s and books e x i s t e d which suggested the p h i l o s o p h i e s of v a r i o u s i n d i v i d u a l s and d e s c r i b e d working prototype systems, but few have e x p e r i m e n t a l l y t e s t e d the i m p l i c a t i o n s of t h e i r techniques f o r user performance and behaviour. The standard t e x t s , Han-Machine Communication by Meadow 2 9 and Design of Man-Computer Dialogues by M a r t i n , 3 0 provided some a s s i s t a n c e , although they were o f t e n too general or s o p h i s t i c a t e d t o be of d i r e c t a s s i s t a n c e i n normal, day-to-day s i t u a t i o n s . M a r t i n ' s t e x t , found by t h i s author t o be t h e more p r a c t i c a l of the two, d i d have one p a r t i c u l a r l y r e l e v a n t chapter: chapter seven e x p l i c i t l y c o n s i d e r e d d i s p l a y methods f o r alphanumeric computer t e r m i n a l s with T V - l i k e screens: In t a c k l i n g an a p p l i c a t i o n , the systems a n a l y s t must make some b a s i c d e c i s i o n s about the s t r u c t u r e of the s c r e e n conversation...Twenty-three techniques of c o n v e r s a t i o n are i l l u s t r a t e d below. They have been given the names: 1. Simple query 2. Mnemonic techniques 3. English-language technigues 4. Programming-like statements 5. A c t i o n code systems 6. M u l t i p l e a c t i o n code systems 7. B u i l d i n g up a r e c o r d 8. S c r o l l techniques 9. Simple i n s t r u c t i o n to operator 10. M u l t i p l e i n s t r u c t i o n t o operator 11. Menu s e l e c t i o n 12. M u l t i s c r e e n menu 13. Telephone d i r e c t o r y technique 14. M u l t i p a r t menu 13 15. Multianswer menu 16. Use of d i s p l a y e d formats 17. V a r i a b l e - l e n g t h m u l t i p l e e n t r y 18. M u l t i p l e - f o r m a t statements 19. Form f i l l i n g 20. O v e r w r i t i n g 21. Panel m o d i f i c a t i o n techniques 22. T e x t - e d i t t i n q techniques 23. Hybrid d i a l o g u e 3 1 Martin then proceeded t o d e s c r i b e each of these methods i n very good d e t a i l but, u n f o r t u n a t e l y , too seldom r e a l l y i n d i c a t e d when each was a p p r o p r i a t e . So the systems a n a l y s t f i n d s h i m s e l f barraged with twenty-three very simple to very complex methods of de s i g n i n q a t e r m i n a l i n t e r f a c e , and can only quess which i s most a p p r o p r i a t e f o r h i s s i t u a t i o n . Throughout the remainder of t h i s t h e s i s , a p e r s o n a l philosophy o f man-machine i n t e r f a c e design i s presented, and the e f f e c t s of a s m a l l s et of man-machine i n t e r f a c e techniques upon v a r i o u s user types are i n v e s t i g a t e d . 14 Chapter Three THE COMPOTES PROGRAM Program D e s c r i p t i o n The primary t o o l f o r t h i s r e s e a r c h was a simple i n t e r a c t i v e computer game.„ D e t a i l s of the game w i l l be provided i n the next chapter; the user e n g i n e e r i n g a s p e c t s of the computer program are d e s c r i b e d next. The computer game was completely w r i t t e n i n FORTRAN (a l i s t i n g of the code appears i n Appendix A). The a c t u a l game i s o n l y a s m a l l P a r t of the program; a s i g n i f i c a n t amount o f programming was necessary t o achieve the d e s i r e d user i n t e r f a c e and c o l l e c t a l l the re g u i r e d data. I t was a l s o necessary to use a few su b r o u t i n e s from the U n i v e r s i t y of B r i t i s h Columbia Computing Centre subprogram l i b r a r y $ i n c l u d i n g timing r o u t i n e s , f i l e c o n t r o l r o u t i n e s , and a ch a r a c t e r comparison r o u t i n e . The game had two d i s t i n c t v e r s i o n s , both d e s c r i b e d i n d e t a i l i n chapter f o u r . B r i e f l y , one v e r s i o n was h i g h l y s t r u c t u r e d and l e d the user through the s i m u l a t i o n by l o o p i n g through a s e t of que s t i o n s ; and the other v e r s i o n was r a t h e r unstructured and expected the user to l e a d t h e s i m u l a t i o n by e n t e r i n q commands i n any order he l i k e d . To f a c i l i t a t e t h i s d u a l v e r s i o n concept, the prcqram had to be h i q h l y modular. The proqram was made up of a b r i e f main proqram* which c a l l e d one of two " c o n t r o l " s u b r o u t i n e s {to qet the a p p r o p r i a t e v e r s i o n ) , which i n turn c a l l e d a number o f the remaininq t en su b r o u t i n e s . 15 One of the ten s u b r o u t i n e s , READPF, Was c a l l e d a t the program s t a r t u p to read i n the p r o f i t f u n c t i o n (a 30 by 70 matr i x ) . Another s u b r o u t i n e , SIMUL, performed the a c t u a l s i m u l a t i o n of another p e r i o d ( i . e . another t r i a l ) . Three o f the s u b r o u t i n e s , GETLIM, GETLIT, and GETNUM, handled a l l t e r m i n a l i n p u t . One s u b r o u t i n e , OUTMES, was j u s t a c o l l e c t i o n of a l l output messages needed throughout the program; by gath e r i n g them i n one p l a c e , only one r o u t i n e needed to be recompiled whenever the user i n t e r f a c e was r e f i n e d . Three more r o u t i n e s , HISTRY, SORTH, and SGRAPH, d i s p l a y e d the th r e e a v a i l a b l e r e p o r t s . , F i n a l l y , the remaining r o u t i n e , ZEND, performed a l l end-of-game cleanup. In the s t r u c t u r e d v e r s i o n o f the game, the c o n t r o l subroutine simply c a l l e d the a p p r o p r i a t e s u b r o u t i n e s i n a p r e s c r i b e d order, as a continuous l o o p . In the u n s t r u c t u r e d v e r s i o n , the program waited f o r a command from the user, decoded i t , and c a l l e d the s p e c i f i e d r o u t i n e . Hence, the onl y e x t r a programming e f f o r t r e g u i r e d i n order t o provide two v e r s i o n s l a y i n the two (quite s t r a i g h t f o r w a r d ) c o n t r o l r o u t i n e s . User Engineering Methods The remainder of t h i s chapter d e s c r i b e s the user e n g i n e e r i n g aspects of the program. Although the approach i s a p e r s o n a l one developed through years of experi e n c e , the reader w i l l note t h a t the methods s a t i s f y many of the c r i t e r i a and s u g g e s t i o n s of iasserman and Hansen presented i n chapter 16 two. In d e s i g n i n g the a c t u a l i n p u t of responses and commands from the user, ease of use was given top p r i o r i t y . F i r s t , the need f o r memorization by users was minimized. In t h e unstru c t u r e d v e r s i o n , a l i s t of a l l commands - and b r i e f d e s c r i p t i o n s of them - was a v a i l a b l e anytime. In both v e r s i o n s , a l l i n p u t prompts were of the same format, i l l u s t r a t e d by the f o l l o w i n g example: Enter d e s i r e d p r i c e l e v e l (1-30) [ 1 0 ] : As can be seen, f i r s t the g u e s t i o n was asked, then the allowed response range was i n d i c a t e d i n parentheses, then the d e f a u l t value was i n d i c a t e d i n b r a c k e t s , the d e f a u l t value was the value which the computer would assume the user wanted i f he entered nothing e l s e ( i n t h i s c a s e , the p r i c e which he had chosen i n the p r e v i o u s p e r i o d ) , and was i n c l u d e d i n order to reduce r o u t i n e t y p i n g . F i n a l l y , to f u r t h e r e l i m i n a t e the need f o r memorization, complete h i s t o r i e s of a l l p r e v i o u s a c t i v i t y were a v a i l a b l e t o the user a t anytime. The program a l s o handled a l l i n p u t p r o c e s s i n g i t s e l f . T h i s way, a l l user e r r o r s were i n t e r c e p t e d by the program ( " i d i o t - p r o o f i n g " ) before the system software could f i n d i t and respond with some i l l e g i b l e message or i n t e r r u p t . In t h i s game, e r r o r s were responded t o by the simple statement I n c o r r e c t Input. Please He-enter. T h i s was f o l l o w e d by a repeat of the o r i g i n a l prompt, which o f course reminded the user of the g u e s t i o n , the allowed responses, and the c u r r e n t d e f a u l t value. 17 The program read a l l input as a s t r i n g of alphanumeric c h a r a c t e r s (up to 60 of them). The s t r i n g was scanned, c h a r a c t e r by c h a r a c t e r * up to t h e f i r s t blank or comma, and t h a t s u b s t r i n g was c o n s i d e r e d to be the response. I f there were more c h a r a c t e r s f o l l o w i n g the blank (or comma), they would be used as the response to the next prompt(s) - a l l o w i n g experienced users to type ahead, and save time and f r u s t r a t i o n . I f the o r i g i n a l prompt wanted an a l p h a b e t i c response, then only the f i r s t c h a r a c t e r was used ( s i n c e a l l commands and responses i n t h i s game begin with with d i f f e r e n t l e t t e r s ) - thereby p e r m i t t i n g u n l i m i t e d a b b r e v i a t i o n . I f the o r i g i n a l prompt wanted a numeric response, then the program converted the s u b s t r i n g to a number; a d m i t t e d l y , t h i s was awkward i n F0BT8AN, but s t i l l i n e x p e n s i v e and w e l l worthwhile f o r the user. F i n a l l y , the user needed to be p r o t e c t e d not only from h i m s e l f , but from the computer and the environment i n g e n e r a l . In the event of a computer crash or other major problem, the program had a s a v e / r e s t a r t f a c i l i t y . As the program r a n , i t wrote out a simple f i l e . I f anything caused the program t o h a l t , a s p e c i a l run parameter allowed the user to r e s t a r t the program at e x a c t l y where he l e f t o f f - as i f nothing had happened. I t i s q u i t e apparent t h a t these f e a t u r e s d i d not come without a c o s t . However, t h e r e i s no reason why the i n p u t p r o c e s s i n g r o u t i n e s c o u l d not have been designed as a package 18 to be l i n k e d with a l l other a p p l i c a t i o n programs needing i n t e r a c t i v e c a p a b i l i t i e s - an approach which would l i k e l y save programming c o s t s i n the f u t u r e . A l s o , the r o u t i n e s c o u l d be w r i t t e n i n a more a p p r o p r i a t e language (probably assembler), to i n c r e a s e t h e i r e f f i c i e n c y . T h i s i s not to say t h a t e f f i c i e n c y i s a c r i t i c a l i s s u e . Indeed, i n most cases, the amount of time a program spends pr o c e s s i n g user i n p u t would l i k e l y be only a s m a l l p a r t of the t o t a l c o s t of running any program, while the s a v i n g s i n user's time and f r u s t r a t i o n c o u l d be g u i t e s u b s t a n t i a l . 19 Chapter Four DATA COLLECTION METHODOLOGY P r e - t e s t i n g and C l a s s i f i c a t i o n The a c t u a l data c o l l e c t i o n f o r t h i s r e s e a r c h i n v o l v e d o b t a i n i n g p a r t i c i p a n t s , p r e - t e s t i n g them, a r r a n g i n g f o r them to play the computer game, and a u t o m a t i c a l l y (by computer) c o l l e c t i n g data on them as they played. There were f i f t y p a r t i c i p a n t s i n the experiment, v i r t u a l l y a l l of whom were students, and a l l of them v o l u n t e e r s (some l u r e d by the p o s s i b i l i t y o f winning one of f i v e cash p r i z e s ) . As was d e s i r e d , the p a r t i c i p a n t s were q u i t e d i v e r s e : some were undergraduates, others sere graduates; some had e x t e n s i v e experience with computers, while others had never been near one; some were from commerce programmes, others from e n g i n e e r i n g , and s t i l l o t h e rs from a r t s . , The e x p e r i e n c e d i f f e r e n c e was a c r u c i a l one to t h i s experiment (necessary f o r t e s t i n g the main hypotheses). As pa r t of the p r e - t e s t i n g f o r the game, p a r t i c i p a n t s completed a s h o r t q u e s t i o n n a i r e about t h e i r h i s t o r y o f contact with computers. As a r e s u l t of t h i s q u e s t i o n n a i r e , which simply asked people t h e i r year, f a c u l t y , number of times they had used computers v i a punched c a r d s , and number of times they had used o n - l i n e computer t e r m i n a l s , they were c l a s s i f i e d as experienced or i n e x p e r i e n c e d (novice) users of o n - l i n e computer systems. However, bear i n mind throuqhout t h i s t h e s i s t h a t the experience e f f e c t may be somewhat confounded: 20 experienced computer users o f t e n a l s o had more advanced mathematical t r a i n i n g than novices. Next, the Group Embedded F i g u r e s T e s t , 3 2 a timed p e n c i l and paper t e s t , was administered to the p a r t i c i p a n t s . The score on t h i s t e s t p r o v i d e d a i n d i c a t i o n of whether the p a r t i c i p a n t s d i s p l a y e d h e u r i s t i c or a n a l y t i c c o g n i t i v e s t y l e s (see chapter two f o r d e f i n i t i o n s of these terms). For purposes of t h i s r e s e a r c h , the group was d i v i d e d a t i t s mean (15 on a s c a l e of 18). Since t h i s i s a r a t h e r high d i v i s i o n v alue, i t i s more a p p r o p r i a t e t o say t h a t t h i s r e s e a r c h compares low and high a n a l y t i c s r a t h e r than pure h e u r i s t i c s and a n a l y t i c s . ,. F i n a l l y , the p a r t i c i p a n t s completed the Kogan and wallach r i s k q u e s t i o n n a i r e . 3 3 T h e i r s c o r e on the q u e s t i o n n a i r e provided a measure of t h e i r r i s k a t t i t u d e ; again, the group was s p l i t at i t s mean (30 on a s c a l e of 60) and c l a s s i f i e d as r i s k - t a k e r s or r i s k - a v e r t e r s . A l l of the above p r e - t e s t i n g was administered to groups of about ten over a three day p e r i o d , and each s e s s i o n took j u s t over 30 minutes to complete. As each s u b j e c t l e f t the p r e - t e s t i n g s e s s i o n , he s e l e c t e d a convenient time to play the computer game during the f o l l o w i n g week. A l s o as they l e f t the p r e - t e s t i n g s e s s i o n , p a r t i c i p a n t s were given a b r i e f s e t of i n s t r u c t i o n s (see Appendix B). These i n s t r u c t i o n s d i d not e x p l a i n the nature of the computer game; r a t h e r , they provided d i r e c t i o n s f o r using the computer t e r m i n a l s and s p e c i a l program f e a t u r e s . There was a s e p a r a t e set of i n s t r u c t i o n s 21 f o r each of the two v e r s i o n s of the game to which the s u b j e c t s had been randomly assigned., A d f l e i s t e r i n g the Game The a c t u a l process o f p l a y i n g and a d m i n i s t e r i n g the game i s d e s c r i b e d i n t h i s s e c t i o n . Throughout t h i s d i s c u s s i o n , the sample i n t e r a c t i o n s which appear i n Appendix C may be co n s u l t e d f o r c l a r i f i c a t i o n o f any vague p o i n t s . The p a r t i c i p a n t s played the computer game i n groups of three over the course o f one week. The game had a maximum time l i m i t of 30 minutes, a f t e r which i t terminated a u t o m a t i c a l l y . About on e - h a l f o f the s u b j e c t s f i n i s h e d b e f o r e exceeding the time l i m i t . When p l a y i n g t h e game, each p a r t i c i p a n t was i n s t r u c t e d a t the beginning that he was the manager of a one-product company and that he was expected t o seek the optimum q u a n t i t y o f preduct t o manufacture and the p r i c e to s e l l i t f o r ; that i s , he was to maximize h i s company's p r o f i t . Hence, p l a y i n g the game i n v o l v e d r e p e a t e d l y s e t t i n g d i f f e r e n t < p r i c e , quantity> combinations and s i m u l a t i n g the next p e r i o d t o get the r e s u l t i n g p r o f i t . I f a s u b j e c t found the maximum p r o f i t w i t h i n 30 minutes, the game "rewarded" him by informin g him with a l l manner of b e l l s and w h i s t l e s : the t e r m i n a l screen f i l l e d up with d o l l a r s i g n s and c o n g r a t u l a t e d the p l a y e r , while the t e r m i n a l b e l l beeped u n t i l stepped by the game a d m i n i s t r a t o r . A c t u a l l y , the same b e l l s and w h i s t l e s announced an apology t o those who ran out of time. T h i s net only served to a t t r a c t the game 22 a d m i n i s t r a t o r * s a t t e n t i o n , but appeared t o both amaze and please the p a r t i c i p a n t s . As the game proceeded, the p a r t i c i p a n t s had access t o any or a l l of thr e e r e p o r t s . The f i r s t was a simple h i s t o r y o f t h e i r d e c i s i o n s and p r o f i t s f o r the pre v i o u s 25 p e r i o d s . The second was a l s o a h i s t o r y r e p o r t , except that i t was ordered by decreasing p r o f i t . F i n a l l y , the t h i r d was a 3-dimensional graph which d i s p l a y e d P r o f i t / 1 0 ( i . e . one d i g i t ) f o r each <price, guantity> p a i r simulated thus f a r . There are two sample game i n t e r a c t i o n s i n Appendix C, one f o r each of the two game v e r s i o n s . In the s t r u c t u r e d v e r s i o n , the user was e s s e n t i a l l y taken by the hand, and l e d through the game, step by st e p , i n a pr e d e f i n e d order. In the uns t r u c t u r e d v e r s i o n , the user had more freedom t o proceed as he wished by e n t e r i n g any of s i x commands (to s e t p r i c e o r g u a n t i t y , simulate another p e r i o d , or look at the r e p o r t s ) . I t should be mentioned t h a t none of the s u b j e c t s had any d i f f i c u l t y i n us i n g the s t r u c t u r e d game v e r s i o n . In a d d i t i o n , p a r t i c i p a n t s with p r e v i o u s computer experience had no problems with the un s t r u c t u r e d v e r s i o n . However, novice s u b j e c t s o f t e n needed v e r b a l a s s i s t a n c e from the game a d m i n i s t r a t o r i n order to get s t a r t e d with the un s t r u c t u r e d v e r s i o n . The p r o f i t f u n c t i o n which the users were attempting t o maximize appears i n Appendix D. The b a s i c p r o f i t f u n c t i o n was the same f o r everyone; however, the <price, guantity> p o s i t i o n of the optimum p r o f i t was generated randomly a t program s t a r t u p . Thus, f o r each p a r t i c i p a n t , the optimum p r o f i t 23 occured at a randomly set p r i c e between 5 and 25, and at a qu a n t i t y between 15 and 55. Si n c e the f u n c t i o n d i d not change shape, but o n l y moved, and s i n c e each person c o u l d search anywhere he wished, these s t e p s should not have made the game more d i f f i c u l t f o r some s u b j e c t s , a l s o , the p r o f i t v a l u e s were s c a l e d by another randomly generated c o n s t a n t t o values between 70 and 99. These steps e s s e n t i a l l y made the game d i f f e r e n t f o r each p a r t i c i p a n t and t h e r e f o r e e l i m i n a t e d any p o s s i b i l i t y of c o l l u s i o n . Data C o l l e c t i o n As the p a r t i c i p a n t s played the game, the program a u t o m a t i c a l l y c o l l e c t e d data about t h e i r performance and use of program f e a t u r e s . For each p e r i o d , i n f o r m a t i o n was recorded about: the amount of time taken t o complete i t ; the chosen p r i c e , q u a n t i t y , and r e s u l t i n g p r o f i t ; number o f commands executed; number o f d e f a u l t s taken; number of e r r o r s made; extent of i n p u t a b b r e v i a t i o n ; amount of use made of typeahead o p t i o n ; u t i l i z a t i o n of each r e p o r t ; and other a s p e c t s . A l i s t i n g of a sample output f i l e f o r one p a r t i c i p a n t appears i n Appendix E. A d d i t i o n a l data was c o l l e c t e d about each p a r t i c i p a n t ' s a t t i t u d e as he played. A f t e r p e r i o d s 5, 10, 15, ... (note l i n e code 2 i n the sample data f i l e i n Appendix E) , the normal flew of a c t i v i t y i n the game was i n t e r r u p t e d by a b r i e f q u e s t i o n n a i r e to get the us e r ' s c o n f i d e n c e l e v e l , r a t i n g o f the program u s a b i l i t y , and enjoyment l e v e l (see the sample i n t e r a c t i o n s i n Appendix C). 24 The data c o l l e c t e d a l s o contained a machine-readable s o l u t i o n p r o t o c o l f o r each p a r t i c i p a n t , i n d i c a t i n g e x a c t l y how each s u b j e c t moved through the two-dimensional space i n s e a r c h of the optimum p r o f i t . I t was found t h a t by p l o t t i n g the <price, guantity> p a i r s i n order of s i m u l a t i o n (as i n Appendix F) and then connecting the dots, one c o u l d d e t e c t whether users employed a random s e a r c h , a s t r u c t u r e d t r i a l and e r r o r , or a b i n a r y search or other w e l l - d e f i n e d a l g o r i t h m , a l l of which w i l l be d i s c u s s e d more completely i n chapter s i x . In the remainder o f t h i s t h e s i s , the output r e s u l t s f o r the f i f t y p a r t i c i p a n t s are presented and d i s c u s s e d . 25 Chapter J i v e THE HYPOTHESES I n t r o d u c t i o n Before the f i n a l r e s u l t s are analyz e d , t h i s chapter b r i e f l y i n t r o d u c e s the hypotheses which were being t e s t e d . Although the data from t h i s game provides numerous p o s s i b i l i t i e s f o r a n a l y s i s , the 26 hypotheses of t h i s c h a p t e r were the major m o t i v a t i o n s f o r t h i s r e s e a r c h and w i l l r e c e i v e most of the a t t e n t i o n throughout the remainder of t h i s t h e s i s . Since t h i s i s e x p l o r a t o r y r e s e a r c h , some of the hypotheses have no strong t h e o r e t i c a l b a s i s ; however, other hypotheses do attempt to v e r i f y the f i n d i n g s of ot h e r s , In t h i s c h a p t e r , the hypotheses w i l l simply be s t a t e d , with d e t a i l e d a n a l y s i s and c o n n e c t i o n t o pre v i o u s r e s e a r c h to f o l l o w i n the next chapter. In n e a r l y a l l of the hypotheses, t h e r e are fo u r independent v a r i a b l e s , each a t two l e v e l s : game v e r s i o n ( s t r u c t u r e d or u n s t r u c t u r e d ) , experience l e v e l (novice o r exp e r i e n c e d ) , c o g n i t i v e s t y l e (low a n a l y t i c or high a n a l y t i c ) , and r i s k a t t i t u d e ( r i s k - a v e r t e r or r i s k - t a k e r ) . For s i m p l i c i t y , these v a r i a b l e s w i l l be c a l l e d Mode, Exp, S t y l e , anc B i s k , r e s p e c t i v e l y . Ferformance and Game V e r s i o n The f i r s t category o f hypotheses i s r e l a t e d to g e n e r a l user performance and the two game v e r s i o n s . The f i r s t h y p o t h esis i s r a t h e r s p e c i a l , and i s assigned the number zero t o d i f f e r e n t i a t e i t from the r e s t . 26 S l £ 2_ k § _ i S 2. ~ Everyone w i l l enjoy p l a y i n g the game. Hypothesis J. - Mode, Exp, S t y l e , and Risk w i l l a l l a f f e c t the average t i n e spent p l a y i n g each p e r i o d . Hypothesis 2 - Mode, Exp, S t y l e , and Risk w i l l a l l a f f e c t whether the s u b j e c t s f i n i s h w i t h i n the 30 minute time l i m i t . Hypothesis 3 - Mode, Exp, S t y l e , and Risk w i l l a l l a f f e c t the average confidence l e v e l o f the p a r t i c i p a n t s . SXEothesis 4 - Uns t r u c t u r e d game v e r s i o n p l a y e r s w i l l be f a s t e r , f i n i s h more o f t e n , and be more c o n f i d e n t than s t r u c t u r e d v e r s i o n p l a y e r s . Hypothesis 5 - Experienced p l a y e r s w i l l be f a s t e r , f i n i s h more o f t e n , and be more c o n f i d e n t than n o v i c e s . Hypothesis 6 - High a n a l y t i c s w i l l be f a s t e r , f i n i s h more o f t e n , and be more c o n f i d e n t than low a n a l y t i c s . Hypothesis 7 - R i s k - t a k e r s w i l l be f a s t e r , f i n i s h more o f t e n , and be more c o n f i d e n t than r i s k - a v e r t e r s . Hypothesis 8 - The Mode/Exp i n t e r a c t i o n w i l l a f f e c t the e r r o r r a t e of the p a r t i c i p a n t s . S p e c i a l Program f e a t u r e s The next category of hypotheses i s r e l a t e d to the use of s p e c i a l program f e a t u r e s , such as d e f a u l t values and a b b r e v i a t i o n s . Hypothesis 9 - The d e f a u l t values f o r p r i c e and g u a n t i t y (at the beginning o f the game) w i l l i n f l u e n c e most users. Hypothesis _0 - S e t t i n g the d e f a u l t response f o r 27 qu e s t i o n s (about the user ' s d e s i r e t o see v a r i o u s r e p o r t s ) t o •yes 1 r a t h e r than 'no' s i l l not i n f l u e n c e the p a r t i c i p a n t ' s a c t u a l response. Hypothesis H - Exp, S t y l e , and Risk w i l l a l l a f f e c t whether users accept d e f a u l t values (when a p p r o p r i a t e ) . Hypothesis J2 - Mode, Exp, S t y l e , and Risk w i l l a l l a f f e c t the extent t o which users a b b r e v i a t e commands. Hypothesis 13 - The l e n g t h of commands w i l l be the main f a c t o r a f f e c t i n g the extent t o which they are a b b r e v i a t e d by users. Comparisons over Time The t h i r d c ategory o f hypotheses i s with regard t o comparisons over time, and i n d i c a t e s expected d i f f e r e n c e s between behaviour d u r i n g the beginning of the game and du r i n g the remainder of the game. HyjLothesis 14 - Average time spent p l a y i n g each p e r i o d w i l l decrease with time. Hypothesis 15 - User confidence w i l l i n c r e a s e with time. fllfifithesis 16 - User r a t i n g s of the u s a b i l i t y of the computer program w i l l improve with time. Hypothesis 17 - The extent o f a b b r e v i a t i o n by uns t r u c -tured game p l a y e r s w i l l i n c r e a s e with time. Hypothesis 18 - Usage o f H i s t o r y r e p o r t s w i l l decrease with time. Hypothesis 19 - Usage of Ordered H i s t o r y r e p o r t s s i l l decrease with time. fiy.J22th.esis 20 - Usage of Graphs w i l l i n c r e a s e with time. 28 Regort Usage and S o l u t i o n P r o t o c o l s These l a s t f i v e hypotheses concern e i t h e r the usage o f r e p o r t s or s o l u t i o n p r o t o c o l dimensions. Hypothesis 21 - Mode, Exp, and S t y l e s i l l a l l a f f e c t the use cf H i s t o r y r e p o r t s . Hypothesis 22 - Mode, Exp, and S t y l e w i l l a l l a f f e c t the use of Ordered H i s t o r y r e p o r t s . Hypothesis 23 - Mode, Exp, and S t y l e w i l l a l l a f f e c t the use c f Graphs. Hypothesis 24 - Exp, S t y l e , and Risk w i l l a l l a f f e c t whether users d i s p l a y e d a s t r u c t u r e d approach t o s o l v i n g the problem (with the emphasis on S t y l e ) . Hypothesis 25 - Exp, S t y l e , and Risk w i l l a l l a f f e c t the amount of d i s p e r s i o n d i s p l a y e d i n the search f o r the optimum (with the emphasis again on S t y l e ) . The l a s t two hypotheses are e x p l a i n e d i n more d e t a i l i n c h a p t e r s i x . The r e s u l t s of the t e s t s of a l l these hypotheses are presented and analyzed i n the next chapter, A summary of the r e s u l t s appears i n Appendix G. 29 Chapter -Six ANALYSIS OF RESULTS DaJS P r e p a r a t i o n Before the game data could be s t a t i s t i c a l l y a n a l y z e d , i t had t o be converted t o a more convenient form. Thus the output f i l e s f o r each of the 50 p a r t i c i p a n t s were compressed i n t o one l i n e each, y i e l d i n g one f i l e with 50 very l o n g l i n e s . To d e r i v e t h i s new f i l e , some v a r i a b l e s were simply copied d i r e c t l y from the o r i g i n a l f i l e , others were summations of o r i g i n a l data ( f o r example, t o t a l time p l a y i n g the game), others were averages (user c o n f i d e n c e ) , o t h e r s were e x t r a c t i o n s (minutes per perio d f o r the f i r s t 10 p e r i o d s ) , and s t i l l o thers were r e s u l t s normalized to 100 ( f o r example, the number c f graphs requested per 100 p e r i o d s ) . As mentioned i n chapter f i v e , n e a r l y a l l of the hypotheses i n v o l v e the f o l l o w i n g four t w o - l e v e l v a r i a b l e s : game v e r s i o n (1=structured, 2=unstructured), e x p e r i e n c e l e v e l (1=novice, 2=experienced), c o g n i t i v e s t y l e (1=low a n a l y t i c , 2=high a n a l y t i c ) , and r i s k a t t i t u d e ( 1 = r i s k - a v e r t e r , 2=risk-t a k e r ) . Again, f o r s i m p l i c i t y , these v a r i a b l e s w i l l be r e f e r r e d t o as Mode, Exp, S t y l e , and Bisk, r e s p e c t i v e l y . S t a t i s t i c a l A n a l y s i s Three b a s i c types of a n a l y s i s were Performed i n t h i s a n a l y s i s , a l l of them u s i n g the S t a t i s t i c a l Package f o r the S o c i a l S ciences (SPSS). 3* Since most hypotheses were concerned with determining which f a c t o r s (independent v a r i a b l e s ) most a f f e c t e d a given game outcome (dependent v a r i a b l e ) , an 30 a n a l y s i s of v a r i a n c e (ANOVA) was employed to t e s t these hypotheses, using the AUOVA r o u t i n e i n SPSS. In most ca s e s , e i t h e r a three-way or four-way c l a s s i f i c a t i o n (with three-way and four-way i n t e r a c t i o n s assumed to be zero) was used. The general model f o r the three-way c l a s s i f i c a t i o n was y = a • b1x1 *• b2x2 + b3x3 * d x 1 x 2 • c2x1x3 + c3x2x3 • e where y was the dependent v a r i a b l e , a was the o v e r a l l mean, xN were the independent v a r i a b l e s , bN were the main e f f e c t s , cN were the i n t e r a c t i o n e f f e c t s , and e was the e r r o r terra. The model f o r the four-way c l a s s i f i c a t i o n was the same, except with f o u r main e f f e c t s and s i x i n t e r a c t i o n e f f e c t s . Other hypotheses were concerned with how two groups of s u b j e c t s d i f f e r e d on an i n d i v i d u a l v a r i a b l e . In these c a s e s , two mean values were to be compared, so o n e - t a i l e d t - t e s t s were used to t e s t the hypothesized r e l a t i o n s h i p s . The SPSS T-TEST r o u t i n e , with cases c l a s s i f i e d i n t o two groups, was used t o perform the t e s t , using a pooled v a r i a n c e ( s i n c e the two p o p u l a t i o n v a r i a n c e s were assumed to be d i f f e r e n t ) . The remaining hypotheses ( a l l r e l a t e d to Comparisons over Time) i n v o l v e d the comparison of two v a r i a b l e s over a l l s u b j e c t s ; p a i r e d t - t e s t s were employed to t e s t these hypotheses. Again, the SPSS T-TEST r o u t i n e was used t o perform the t e s t ; however, t h i s time p a i r e d o b s e r v a t i o n s were s p e c i f i e d * In the a nalyses to f o l l o w , the SPSS r e s u l t s are reproduced i n t h e i r standard formats. The a n a l y s i s o f var i a n c e t a b l e s d i s p l a y the main e f f e c t s and the 2-way i n t e r a c t i o n s (expressed as " v a r i a b l e / v a r i a b l e " ) . 31 Hypotheses about Performance The r e s u l t s concerning hypothesis 0 - Everyone w i l l enjoy p l a y i n g the game - were e s p e c i a l l y encouraging. Throughout the e n t i r e game, the mean enjoyment l e v e l f o r a l l p l a y e r s was 7.0 on a s c a l e o f 1 to 9, where labored and 9=enjoying the game (see the sample a t t i t u d e q u e s t i o n n a i r e i n appendix C ) . T h i s was important because i t added credence t o the game r e s u l t s : s u b j e c t s d i d not j u s t go through the motions to get the game over with; they a c t u a l l y enjoyed the game and q u i t e probably "played t o win," An a n a l y s i s of v a r i a n c e was c a r r i e d out to see whether any p a r t i c u l a r user types enjoyed the game more than o t h e r s . , As can be seen i n t a b l e 1, none of the four independent v a r i a b l e s was s i g n i f i c a n t ; indeed, the o v e r a l l s i g n i f i c a n c e l e v e l was o n l y 0,71, < ^ SOURCE VAR. S.SQ. DF. , M. SQ. F SIGNXF. Mode 0.64 ! 0.64 0.33 0.57 Exp 0.04 1 0.04 0.02 0.89 S t y l e 0.35 1 0.35 0. 18 0.67 Bisk 0.47 1 0.47 0.24 0.63 Mode/Exp 0.40 1 . 0.40 0.20 0.65 Mode/Style 0.45 1 0.45 0.23 0.64 Mode/Bisk 0.06 1 0.06 0.03 0. 87 Exp/Style 2,08 1 2.08 1.06 0.31 Exp/Risk 9.08 1 9.08 4.64 0.04** S t y l e / R i s k 0.24 1 0.24 0. 12 0.73 Explained 13.82 10 1 .38 0.71 0.71 Re s i d u a l 76. 18 39 1.95 T o t a l 90.00 49 1.84 1 J Table 1. ANOVA - Game Enjoyment The a n a l y s i s o f hypothesis 1 - Mode, Exp, S t y l e , and Risk 32 w i l l a l l a f f e c t the average time spent p l a y i n g each p e r i o d -i s presented i n t a b l e 2. Nei t h e r game v e r s i o n nor c o g n i t i v e s t y l e impacted p l a y i n g speed. However, r i s k a t t i t u d e and experience both had a s i g n i f i c a n t e f f e c t upon the number of minutes spent p l a y i n g each peri o d (these two f a c t o r s w i l l be i n v e s t i g a t e d i n more d e t a i l i n hypotheses 5 and 7 ) . SOURCE VAR. S.SQ. DF. M.SQ. , F SIGNIF. Mode 112.68 1 . 112.68 0.14 0.71 Exp 2321.79 1 2321.79 2.96 0.09** S t y l e 255.99 1 255.99 0.33 0.57 Risk 3154.83 1 3154.83 4. 02 0.0 5** Mode/Exp 168.27 1 168.27 0.21 0.65 Bode/Style 572.40 1 572.40 0.73 0.40 Mode/Bisk 137. 12 1 137.12 0.18 0.68 Exp/Style 225.70 1 225.70 0.29 0.60 Exp/Risk 2152.16 1 2152 .16 2 .74 0.11* S t y l e / R i s k 348.61 1 348.61 0.44 0.51 Explained 10124.80 10 1012.48 1.29 0.27 Res i d u a l 30601.30 39 784.65 T o t a l 40726. 10 49 831.14 Table 2. ANOVA - Minutes/Period Table 3 d i s p l a y s the a n a l y s i s of variance f o r hypothesis 2 - Mode, Exp, S t y l e , and R i s k w i l l a l l a f f e c t whether the s u b j e c t s f i n i s h w i t h i n the 30 minute time l i m i t . Again, the r e s u l t s i n d i c a t e d t h a t game v e r s i o n had no e f f e c t whatsoever. C o g n i t i v e s t y l e and r i s k a t t i t u d e were a l s o i n s i g n i f i c a n t , while experience l e v e l was h i g h l y s i g n i f i c a n t , i n d i c a t i n g t h a t game t e r m i n a t i o n was almost completely determined by the experience l e v e l of the p l a y e r s (see hypothesis 5 f o r more d e t a i l s ) . T h i s suggests t h a t r e s e a r c h e r s should be extremely 33 wary of t h i s f a c t o r when c a r r y i n g out experiments using on-l i n e computer t e r m i n a l s . I ______ _ : -J SGOBCE VAB. S.SQ. DF. 1. SQ. Mode Exp S t y l e Bisk 0.00 3.28 0.29 0.21 0.00 3.28 0.29 0.21 0.00 16.75 1.46 1.08 Mode/Exp Mode/Style Mode/Bisk Exp/Style Exp/Bisk S t y l e / B i s k Explained B e s i d u a l T o t a l 0.05 0.08 0.06 0. 33 0.14 0.01 4.83 7.65 12.48 Table 3. 10 39 49 ANOVA -0.05 0.08 0.06 0.33 0.14 0.01 0.48 0.20 0.26 Termination 0.24 0.39 0.32 1.73 0.73 0.05 on Time SIGNIF. 1.00 0.00** 0.23 0.31 0.62 0.54 0. 57 0.20 0.40 0.83 2.46 0.02 Hypothesis 3 - Mode, Exp, S t y l e , and Bi s k w i l l a l l a f f e c t the average c o n f i d e n c e l e v e l of the p a r t i c i p a n t s - was t e s t e d next. As the a n a l y s i s ( t a b l e 4) demonstrates, game v e r s i o n was once again h i g h l y i n s i g n i f i c a n t ; r i s k a t t i t u d e had a weak l e v e l o f s i g n i f i c a n c e . , Experience again seemed t o have a str o n g i n f l u e n c e upon c o n f i d e n c e , and c o g n i t i v e s t y l e a l s o appeared as an important f a c t o r (hypothesis 6 w i l l i n v e s t i g a t e t h i s f u r t h e r ) . Having t e s t e d the th r e e general hypotheses about performance, the next four hypotheses i n v e s t i g a t e t h i s area a t a more d e t a i l e d l e v e l . The a n a l y s i s f o r hypotheses 4 through 7 appears i n t a b l e 5. While the previous ANOVAs i n d i c a t e d the r e l a t i v e importance of the f a c t o r s when considered t o g e t h e r . 34 SOURCE VAB. S. SQ. DF. M. SQ. F SIGNIF. Mode 1.60 1 . 1.60 0.01 0.95 Exp 2409.76 1 2409.76 6.99 0.01** S t y l e 1145.42 1 1145.42 3.32 0.08** Bisk 910.34 1 910.34 2.64 0,11* Mode/Exp 23.79 1 23.79 0.07 0.79 Mode/Style 101.21 1 101.21 0.29 0.59 Mode/Bisk 556.72 1 556.72 •1.62 0.21 Exp/Style 172.51 1 172.51 0.50 0.48 Exp/Risk 9.51 1 9.51 0.03 0.87 S t y l e / B i s k 448.38 1 448.38 1.30 0.26 Explained 6678.06 10 667.81 1. 94 0.07 B e s i d u a l 13437.94 39 344.56 T o t a l 20116.00 49 410.53 Table 4. ANOVA- Confidence L e v e l the t - t e s t s to f o l l o w w i l l t e s t t h e hypothesized d i f f e r e n c e s between groups on a s i n g l e v a r i a b l e , and the d i r e c t i o n s o f those d i f f e r e n c e s . In t a b l e 5, the v a r i a b l e Term, (termination) i s a t w o - l e v e l v a r i a b l e i n d i c a t i n g whether people f i n i s h e d on time (Term.=0) or not (Term. = 1). The p l a y i n g speed v a r i a b l e , Min/Per., i s the number of minutes spent p l a y i n g each p e r i o d . F i n a l l y , C o n f i d . (user confidence) i n d i c a t e s the number o f people (out o f 100) whom us e r s thought were performing b e t t e r than them (see the a t t i t u d e g u e s t i o n n a i r e example i n appendix C ). Hypothesis 4 - U n s t r u c t u r e d game v e r s i o n p l a y e r s w i l l be f a s t e r , f i n i s h more o f t e n , and be more c o n f i d e n t than s t r u c t u r e d v e r s i o n p l a y e r s - was t e s t e d by the f i r s t 3 t - t e s t s i n t a b l e 5, where i t was seen that t h i s h y p o t h e s i s was completely r e j e c t e d . C o n s i s t e n t w i t h the f i n d i n g s of the 35 1 J VARIABLE GROUPING # MEAN ST DEV. T PROB J Term. S t r u c t . U n s t r u c t . 24 26 0.50 0.46 0.51 0.51 0.27 0.34 I Min/Per. S t r u c t . Unstruct. 24 26 0.77 0.78 0.33 0.25 -0.22 0.41 j C c n f i d . i S t r u c t . Unstruct. 24 26 40.88 39.73 24.35 16.02 0.20 0.42 r - I Term. Novice Exper. 30 20 0.70 0.15 0.47 0.37 4.44 0.00** j Bin/Per. Novice Exper. 30 20 0. 84 0.69 0.33 0.19 1.84 0.04** | C c n f i d . Novice Exper. 30 20 46.57 30.85 17.48 20.89 2.88 0.00** J Term. High-anal. Lou-anal. 21 29 0. 38 0.62 0.49 0.50 -1.69 0.05** I Min/per. High-anal. Low-anal. 21 29 0.74 0. 83 0.24 0.35 -1.06 0. 15* | C o n f i d . High-anal. Low-anal. 21 29 34. 83 47.81 20.67 17.46 -2.34 0.01** I Term, R i s k - t a k e r R-averter 19 31 0.37 0.55 0.50 0. 51 -1.23 0. 11* I Min/Per. R i s k - t a k e r R-averter 19 31 0.67 0.84 0.18 0.32 -2.20 0.0 2** I C o n f i d . t — _ R i s k - t a k e r R-averter 19 31 33.42 44. 48 16.65 21.36 - 1.93 0.03** Table 5. T-TESTS - Performance and S t r u c t u r e ANOVAs, game v e r s i o n had no s i g n i f i c a n t impact upon the v a r i a b l e s speed, t e r m i n a t i o n , and con f i d e n c e . T h i s would seem to c o n t r a d i c t the c l a i m of the unstructured game v e r s i o n ' s s u p e r i o r i t y ; however, i t i s q u i t e l i k e l y t h a t t h i s game was j u s t too simple t o provide a s i g n i f i c a n t d i f f e r e n c e i n freedom 36 between the two v e r s i o n s . A c t u a l l y , o b s e r v a t i o n by the game a d m i n i s t r a t o r , problems with s t a r t i n g novice p a r t i c i p a n t s p l a y i n g , and v e r b a l comments from the p a r t i c i p a n t s a l l i n d i c a t e d a g r e a t e r d i f f e r e n c e than i m p l i e d i n t a b l e 5; novices appeared t o have more t r o u b l e with the u n s t r u c t u r e d game than experienced p l a y e r s . The f o u r t h through s i x t h rows i n t a b l e 5 t e s t e d hypothesis 5 - Experienced p l a y e r s w i l l be f a s t e r , f i n i s h more o f t e n , and be more c o n f i d e n t than n o v i c e s . As expected, t h i s h y p o thesis was s t r o n g l y supported, again i n d i c a t i n g that e xperience i s a f a c t o r which should be s e r i o u s l y accounted f o r i n a l l computer experiments. These r e s u l t s are s i m i l a r t o the f i n d i n g s of MacCrimmon, 3 S who concluded t h a t "experienced i n d i v i d u a l s seemed to be the most d e s i r a b l e s u b j e c t s t o u t i l i z e i n d e c i s i o n making experiments and r e s e a r c h . " 3 * The a n a l y s i s f o r hypothesis 6 - High a n a l y t i c s w i l l be f a s t e r , f i n i s h more o f t e n , and be more c o n f i d e n t than low a n a l y t i c s - was provided by t - t e s t s 7 through 9. The hypothesis was only weakly s i g n i f i c a n t on the speed v a r i a b l e , but t e r m i n a t i o n and co n f i d e n c e both d i s p l a y e d h i g h l y s i g n i f i c a n t d i f f e r e n c e s between groups. These r e s u l t s were g e n e r a l l y c o n s i s t e n t with the c o n c l u s i o n s o f Benbasat and T a y l o r {see chapter two). The t e s t of hypothesis 7 - R i s k - t a k e r s w i l l be f a s t e r , f i n i s h more o f t e n , and be more c o n f i d e n t than r i s k - a v e r t e r s was provided by the l a s t three t e s t s i n t a b l e 5. Speed and confidence showed very s i g n i f i c a n t d i f f e r e n c e s between groups. 37 while t e r m i n a t i o n was l e s s s i g n i f i c a n t . T h i s would seem t o c o n t r a d i c t the f i n d i n g s of T a y l o r and Dunnette, e s p e c i a l l y with r e s p e c t t o time per p e r i o d (see chapter two). T h i s , however, needs f u r t h e r i n v e s t i g a t i o n s i n c e t h e i r r e s e a r c h i n v o l v e d decision-making i n a ncn-computerized environment. The l a s t h y p o t h e s i s of t h i s s e c t i o n t e s t e d the b e l i e f t h a t novices would have d i f f i c u l t y with the u n s t r u c t u r e d game v e r s i o n , and would d i s p l a y i t through an i n c r e a s e d e r r o r r a t e . Hypothesis 8 - The Mode/Exp i n t e r a c t i o n w i l l a f f e c t the e r r o r r a t e of the p a r t i c i p a n t s - was analyzed by the ft NOVft i n f a b l e 6. C l e a r l y , t h e r e were no h i g h l y s i g n i f i c a n t v a r i a b l e s , and the hypothesis was r e j e c t e d ( i t may we l l be t h a t n o v i c e s compensated any d i f i c u l t i e s by devoting i n c r e a s e d thought and ca r e t o each move they made, a p o s s i b i l i t y which was supported by the p l a y i n g speed f i n d i n g s ) . The l a c k of s i g n i f i c a n t e f f e c t s was p o s s i b l y caused by the f a c t t h a t very few e r r o r s were made i n the game. Out of 50 p a r t i c i p a n t s , o n l y 11 made any e r r o r s anywhere i n the game; seven low a n a l y t i c s averaged l e s s than 5 e r r o r s per 100 p e r i o d s , and fou r high a n a l y t i c s averaged 2 e r r o r s per 100 p e r i o d s (where up to 6 i n p u t s were entered each p e r i o d ) . SlEotheses about the use o f S p e c i a l Program Features L i k e hypothesis 0, hypothesis 9 - The d e f a u l t v a l u e s f o r p r i c e and g u a n t i t y (at the beginning of the game) w i l l i n f l u e n c e most users - was t e s t e d by simple count. I t was found i n t h i s experiment that 28 of the 50 p a r t i c i p a n t s accepted at l e a s t one o f the opening d e f a u l t values (values 38 i 1 SCOECE VAR. S.SQ. , DF. M. SQ. F SIGJ3IF. Hode 15.34 1 15.34 0.63 0.43 Exp 20.77 1 20.77 0.85 0.36 S t y l e 66.26 1 66.26 2.70 0. 11* B i s k 5.76 1 5.76 0.24 0.63 Mode/Exp 4.96 1 4.96 0.20 0.66 Mode/Style 22.15 1 22.15 0.90 0.35 Bode/Risk 2.37 1 2.37 0.10 0.76 Exp/Style 41.06 1 41.06 1 .68 0.20 Exp/Risk 0.02 1 0.02 0.00 0.98 S t y l e / R i s k 6.47 1 6.47 0.26 0.61 Explained 221.78 10 22.18 0.90 0.35 Residual 872.64 39 22.37 T o t a l ., . , . , , , , 1096.42 49 22.38 Table 6. A NOV A - E r r o r r a t e which o r i g i n a l l y were a r b i t r a r i l y s e l e c t e d ) . , T h i s seemed t o i n d i c a t e that i n u n f a m i l i a r s i t u a t i o n s (where the user was u n c e r t a i n about e x a c t l y what to do n e x t ) , he was l i k e l y to accept d e f a u l t values r a t h e r than make h i s own d e c i s i o n s . To i n v e s t i g a t e whether any p a r t i c u l a r user types were more l i k e l y to accept these opening d e f a u l t s , an a n a l y s i s o f varia n c e was performed. As i n d i c a t e d i n t a b l e 7, there were no s i g n i f i c a n t sources of v a r i a n c e . Hypothesis 10 - S e t t i n g the d e f a u l t response f o r que s t i o n s (about the user's d e s i r e to see v a r i o u s r e p o r t s ) t o 'yes' r a t h e r than 'no* w i l l not i n f l u e n c e the p a r t i c i p a n t ' s a c t u a l response - was the next t o be t e s t e d . To do so, AMOVAs were performed f o r three v a r i a b l e s : use of H i s t o r y r e p o r t s , use of Ordered H i s t o r y r e p o r t s , and use of Graphs. The assumption was that p l a y e r s with *yes» d e f a u l t s would look a t 39 i : ' 1 | SOUBCE VAB. S.SQ. , DF. M. SQ. F SIGBIF. Mode 0. 63 1 0.63 0 .70 0.41 1 Sxp 0. 34 1 0.34 0 ,38 0.54 S t y l e 0. 65 1 0.65 0 .72 0.4C Bisk 0. 07 1 0.07 0 . 08 0.78 Mode/Exp •1. 59 1 1.59 1 .78 0.19 Mode/Style 0. 30 1 0.30 0 , 33 0.57 Mode/Bisk 0. 11 1 0.11 0 .12 0.73 Exp/Style 0. 51 1 0.51 0 .57 0.46 Exp/Bisk 0. 54 1 0.54 0 .61 0.4 4 S t y l e / B i s k 0. 52 1 0.52 0 .58 0.45 E x p l a i n e d 5. 18 10 0.52 0 . 59 0.75 B e s i d u a l 34. 50 39 0.90 T o t a l _ 39. 68 49 0,81 Table 7. ANOVA - Opening D e f a u l t s more r e p o r t s than p l a y e r s with "no* d e f a u l t s ; hence. D e f a u l t -value ( l ^ ' y e s * , 2=*no') was one of the independent v a r i a b l e s i n the t h r e e ANOVAs, Tc conserve space, the SPSS r e s u l t s are not p r o v i d e d , but i n a l l t h r e e cases D e f a u l t - v a l u e was found to be a very i n s i g n i f i c a n t source of v a r i a n c e {ranging from l e v e l 0.47 t c l e v e l 0.97). The i m p l i c a t i o n was t h a t i n f a m i l i a r circumstances (where the user was g u i t e sure of what to do n e x t ) , d e f a u l t v a l u e s had no i n f l u e n c e upon the user's d e c i s i o n s . The a n a l y s i s f o r hypothesis 11 - Exp, S t y l e , and B i s k w i l l a l l a f f e c t whether users accept d e f a u l t values - appears i n t a b l e 8. I t can be seen t h a t c o g n i t i v e s t y l e turned out to be very s i g n i f i c a n t , while n e i t h e r experience nor r i s k a t t i t u d e had any a f f e c t . S u r p r i s i n g l y , on average, high a n a l y t i c s made the l e a s t use c f the d e f a u l t v a l u e s ; i n f a c t * 40 out o f every 100 p e r i o d s they avoided 58 d e f a u l t s which they co u l d have accepted, while low a n a l y t i c s avoided only about 3. T h i s i s a very d i f f i c u l t r e s u l t t o e x p l a i n , and cou l d c e r t a i n l y use f u r t h e r i n v e s t i g a t i o n . i 1 SOURCE VSR. S.SQ. , DF. H.SQ. F SIGNIF. Exp 261.02 1 261.02 0.06 0.81 S t y l e 19007.54 1 19007.54 4 .38 0.0 5** Risk 6805.41 1 6805.41 1.57 0.23 Exp/Style 143.20 1 143.20 0.03 0.86 Exp/Risk 4515.86 1 4515.86 1.04 0.32 S t y l e / R i s k 8428.04 1 8428.04 1 .94 0.18 Explained 38531.88 6 6421.98 1 .48 0.24 Residual 73718.50 17 4336.38 T o t a l 1 12250.38 23 4880.45 Table 8. AHOVA - Acceptance of D e f a u l t s A n a l y s i s of v a r i a n c e was a l s o used t o t e s t h y p o t h e s i s Mode, Exp, S t y l e , and Risk w i l l a l l a f f e c t the extent to which users a b b r e v i a t e commands. The r e s u l t s ( t a b l e 9) showed game v e r s i o n and r i s k a t t i t u d e both t o be very s i g n i f i c a n t . The s i g n i f i c a n c e of the game v e r s i o n f a c t o r could be e x p l a i n e d by the p h y s i c a l d i f f e r e n c e between the two v e r s i o n s . The r i s k a t t i t u d e f a c t o r was more i n t e r e s t i n g : r i s k - a v e r t e r s a bbreviated to s i g n i f i c a n t l y l e s s extent than r i s k - t a k e r s (on average, t y p i n g 55 of every 100 c h a r a c t e r s p o s s i b l e , versus 35 of every 100 c h a r a c t e r s f o r r i s k - t a k e r s ) . T h i s may i n d i c a t e a f e a r of t r y i n g a f e a t u r e they do not understand, or a m i s t r u s t of the computer to i n t e r p r e t t h e i r a b b r e v i a t i o n s c o r r e c t l y . The l a s t h y p o thesis r e g a r d i n g program f e a t u r e s was 41 t — 1 SODBCE VAB. S. SQ. , DF. M.SQ. F SIGNIF. Mode 6726.46 1 . 6726.46 7.49 0.01** Exp 642.78 1 642.78 0.72 0.40 S t y l e 665.26 1 665.26 0.74 0.40 Bisk 3898.38 1 3898.38 4.34 0.0 4** Mode/Exp 1062.67 1062.67 •1.18 0.28 Mode/Style 336.99 1 336.99 0.38 0.54 Mode/Bisk 512.94 1 512 .94 0.57 0.45 Exp/Style 26.20 1 26.20 0.03 0.86 Exp/8isk 876.26 1 876.26 0.S8 0.33 S t y l e / B i s k 33.01 1 33.01 0.04 0.85 Explained 16453.66 10 1645.37 1.83 0.09 B e s i d u a l 35033.00 39 898.28 T o t a l 51486.66 49 1050.75 I — 1 Table 9. ANOVA - Extent o f A b b r e v i a t i o n hypothesis 13 - The l e n g t h o f commands w i l l be the main f a c t o r a f f e c t i n g the extent to which they are a b b r e v i a t e d by u s e r s . In the a n a l y s i s , the main e f f e c t Length was a t w o - l e v e l v a r i a b l e i n d i c a t i n g whether the game with s h o r t (3 t o 5 l e t t e r mnemonics) or long (5 to 8 l e t t e r ) commands was being played. The a n a l y s i s appears i n t a b l e 10 and v e r i f i e s the hy p o t h e s i s . Although experience and e x p e r i e n c e / c o g n i t i v e s t y l e are r e l a t i v e l y s i g n i f i c a n t sources o f v a r i a n c e , l e n g t h of commands was c l e a r l y the dominating f a c t o r . C o n c l u s i o n : i f commands are s h o r t , users w i l l tend t o type them i n f u l l ; i f long , users w i l l d e vise a b b r e v i a t i o n s . Hypotheses about Comparisons over Time These seven hypotheses were a l l r e l a t e d to user l e a r n i n g a f f e c t s . Each compared user behaviour over the f i r s t 10 periods t o behaviour over a l l remaining p e r i o d s , and was 42 | SOUBCE VAB. I E x P I S t y l e I Eisk ) le n g t h ) Exp/Style | Ixp/Bisk I Exp/Length I S t y l e / B i s k I S t y l e / L e n g t h J Bisk/Length | Explained J B e s i d u a l I T o t a l S. SQ. 1381.94 72. 14 16.93 8144.06 1359.23 606.73 94. 17 1091.69 102.56 261.68 17828.54 5927.01 23755.55 DF. M. SQ. F SIGMIF. ! . 1381.94 3.55 0.08** 1 72.14 0.18 0.67 1 16.93 0.04 0.84 1 8144.06 20.94 0.0 0** 1 . 1359.23 3.49 0.0 8** 1 606.73 1.56 0.23 1 94.17 0.24 0.63 1 1091.69 2.81 0. 12* 1 102.56 0.26 0.62 1 261 .68 0.67 0.43 10 1782.85 4.45 0.01 15 395.13 25 950.22 Table 10. ANOVA - A b b r e v i a t i o n by Length t e s t e d by a p a i r e d t - t e s t (shown i n t a b l e 11). VABIABLE GBOUPING # SEAN STDEV. , T PBOB Kin/Per. 10 Periods 45 0.70 0.21 Bemainder 45 0.53 0. 16 6.07 0.00** C c n f i d . 10 P e r i o d s 45 47. 16 24. 98 Bemainder 45 47.29 27.43 -0.03 0.49 U s a b i l i t y 10 P e r i o d s 45 5.24 2.35 Bemainder 45 5.71 2.86 - 1.38 0.09** Abbrev. 10 Periods 45 49.34 33.55 Bemainder 45 46.42 33.04 2.36 0.01** H i s t o r i e s 10 P e r i o d s 45 14.20 14. 40 Bemainder 45 7.38 10.28 3.26 0.00** C r d - H i s t . 10 P e r i o d s 45 9.58 11.06 Bemainder 45 8.78 11.76 0.38 0. 35 Graphs 10 P e r i o d s 45 19.16 22.63 Bemainder 45 25.80 24.64 -2.1 1 0.02** Table 11. T-TESTS - Comparisons over Time 43 Hypothesis 14 - Average time spent p l a y i n g each p e r i o d w i l l decrease with time - was c l e a r l y supported; user speed i n c r e a s e d from 0.70 minutes/period t o 0.53 minutes/period (an obvious, yet s t i l l encouraging, r e s u l t ) . On the other hand, hypothesis 15 - User confidence w i l l i n c r e a s e with time - was d e f i n i t e l y r e j e c t e d ; there was e s s e n t i a l l y no change i n user c o n f i d e n c e over time. Apparently, no matter how c l o s e they came t o the optimum, the users s t i l l f e l t t h a t everyone e l s e must be at t h e same s t a g e . I t may be d e s i r a b l e t o provide the user with some i n d i c a t i o n of comparative performance (reinforcement) whenever p o s s i b l e . A l s o supported was hyp o t h e s i s 16 - User r a t i n g s of the u s a b i l i t y of the computer program w i l l improve with time. As i n d i c a t e d i n t a b l e 11, t h e i r average r a t i n g s changed from 5.24 to 5.71 (on a s c a l e from 1 to 9), i n d i c a t i n g some higher a p p r e c i a t i o n of the program once they had a chance to t r y many of i t s f e a t u r e s . Hypothesis 17 - The extent of a b b r e v i a t i o n (by unstructured game v e r s i o n p l a y e r s ) w i l l i n c r e a s e with time -was a l s o v e r i f i e d , though l e s s d r a m a t i c a l l y . During the f i r s t ten p e r i o d s , 4 9 o f each 100 c h a r a c t e r s were typed; during the remainder of t h e game, 46 were typed. I t would seem t h a t people e i t h e r read i n the i n s t r u c t i o n s t h a t they could a b b r e v i a t e and d i d so from the s t a r t of the game, or they d i d not a b b r e v i a t e from the s t a r t and only a few l e a r n e d t o do so. The t e s t of hy p o t h e s i s 18 - Usage of H i s t o r y r e p o r t s w i l l decrease with time - was h i g h l y s i g n i f i c a n t . The average 44 number c f H i s t o r i e s requested per 100 pe r i o d s dropped from 14 to 7, presumably as people l e a r n e d the value of the Graphs. Hypothesis 19 - Osage c f Ordered H i s t o r y r e p o r t s w i l l decrease with time - was r e j e c t e d . T h e i r use remained q u i t e constant throughout the game; i n f a c t , they were never very popular. F i n a l l y , h ypothesis 20 - Osage o f Graphs w i l l i n c r e a s e with time - was supported by the r e s u l t s . I n i t i a l l y , o n l y 19 graphs were requested per 100 pe r i o d s ; a f t e r 10 p e r i o d s , though, n e a r l y 26 were requested. I t would appear t h a t users q u i c k l y l e a r n e d the value of a more p i c t o r i a l r e p o r t . I t should a l s o be mentioned t h a t f u r t h e r data a n a l y s i s r e v e a l e d that h e u r i s t i c s (low a n a l y t i c s ) were the only users who showed no s i g n i f i c a n t i n c r e a s e i n t h e i r use of Graphs. !22J:0.theses about Be port Osage a. n<| S o l u t i o n P r o t o c o l s The next three hypotheses a l l r e l a t e t o usaqe of r e p o r t s ; i n a l l of these, the dependent v a r i a b l e i s the number o f r e p o r t s looked at per 100 p e r i o d s . The two hypotheses f o l l o w i n g these both r e l a t e t o s o l u t i o n p r o t o c o l s . The f i r s t h y p o t h e s i s i n t h i s area i s hypothesis 21 Mode, Exp, S t y l e , and Bisk w i l l a l l a f f e c t the use of H i s t o r y r e p o r t s . The a n a l y s i s o f v a r i a n c e appears i n t a b l e 12, and shews only game v e r s i o n and experience as s i g n i f i c a n t f a c t o r s . Users of the s t r u c t u r e d v e r s i o n used H i s t o r i e s most o f t e n (presumably because they are c o n s t a n t l y reminded of t h e i r e x i s t e n c e ) , while experienced p l a y e r s used them l e a s t o f t e n ( p r e f e r r i n g t h e more i n f o r m a t i v e g r a p h i c a l r e p o r t ) , The 45 r e p o r t frequency by user type was: 18 r e p o r t s f o r s t r u c t u r e d game p l a y e r s and 6 f o r u n s t r u c t u r e d ; 16 r e p o r t s f o r experienced p l a y e r s and 7 f o r n o v i c e s . | SOUSCI VAR. S.SQ. DF. M. SQ. F SIGNIF. } | Mode 1639.96 1 1639.96 10. 17 0.00** | 1 Exp 808.12 1 808.12 5.01 0,03** J f S t y l e 1.82 1 1.82 0.01 0.92 | I Risk. 35.74 1 35.74 0.22 0.64 J | Mode/Exp 754.70 1 754.70 4.68 0.04** | | Mode/Style 71.52 1 71.52 0.44 0.51 | | Mode/Risk 1.03 1 1.03 0.01 0.94 | | Exp/Style 13.13 1 13.13 0.08 0.78 | I Exp/Risk 1.44 1 1.44 0.01 0.92 I I S t y l e / R i s k 2.60 1 2.60 0.02 0.90 | I Ex p l a i n e d 3583.96 10 358.40 2.22 0.04 | I R e s i d u a l 6287.26 39 161.21 | I T o t a l 9871.22 49 201.45 Table 12. ANOVA - Use of H i s t o r y Reports Hypothesis 22 - Mode, Exp, S t y l e , and Risk w i l l a l l a f f e c t the use c f Ordered H i s t o r y r e p o r t s i s analyzed i n t a b l e 13. The o n l y s i g n i f i c a n t f a c t o r i s game v e r s i o n (together with an experience i n t e r a c t i o n ) . Again, i t would seem t h a t s t r u c t u r e d v e r s i o n users, faced with repeated reminders of the r e p o r t ' s e x i s t e n c e , s e l e c t Ordered H i s t o r i e s more o f t e n than u n s t r u c t u r e d game users., No other s i n g l e f a c t o r had much impact ( r e c a l l from t h e a n a l y s i s of hypothesis 19 that t h i s r e p o r t was not very popular i n general) * The t e s t of Hypothesis 23 - Mode, Exp, S t y l e , and Risk w i l l a l l a f f e c t the use of Graphs had a p a r t i c u l a r l y i n t r i g u i n g r e s u l t . There were only weakly s i g n i f i c a n t sources 46 SOUfiCE VAR. S.SQ. DF. M.SQ. F SIGNIF. Mode 634.87 1 634.87 8.22 0.01** 121.95 1 121.95 1.58 0.22 S t y l e 29.10 1 29.10 0.38 0.54 Risk 63.46 1 63.46 0.82 0.37 Mode/Exp 343.67 1 343.67 4.45 0.04** Mode/Style 3.64 1 3.64 0.05 0.83 Mode/Risk 32.88 1 32.88 0.43 0.52 Exp/Style 37.64 1 37.64 0.49 0.49 Exp/Risk 7.47 1 7.47 0.10 0.76 S t y l e / R i s k 125.66 1 125.66 1.63 0.21 Explained 1618.67 10 161.87 2. 10 0.05 Re s i d u a l 3013.31 39 77.26 T o t a l 4631.98 49 94.53 Table 13. ANOVA - Use of Ordered H i s t o r y Reports of v a r i a n c e : experience and c o g n i t i v e s t y l e (see t a b l e 14). Experienced p l a y e r s reguested more Graphs than n o v i c e s (32 vs. 20) and high a n a l y t i c s reguested more than low a n a l t y i c s (31 vs. 21) . I SOURCE VAR. S.SQ.. DF. M. SQ. F SIGNIF. Mode 556.49 •j 556.49 1.05 0.31 Exp 1393.66 1 1393.66 2.62 0.11* S t y l e 1170.53 1 1170.53 2.20 0.15* Risk 268.33 1 268.33 0.51 0.48 Mode/Exp 0.02 1 0.02 0.00 0.99 Mode/Style 258.70 1 258.70 0.49 0.49 Mode/Risk 45.41 1 45.41 0.08 0.77 Exp/Style 59.37 1 59.37 0.11 0.74 Exp/Risk 674.95 1 674.95 1.27 0.27 S t y l e / R i s k 852.69 1 852.69 1 .60 0.21 Explained 5328.19 10 5328.19 1.00 0.46 R e s i d u a l 20734.72 39 531.66 T o t a l 26062.91 49 531.90 Table 14. ANOVA - Use of Graphs 47 The l a s t two hypotheses o f t h i s t h e s i s r e l a t e to users* s o l u t i o n p r o t o c o l s . By p l o t t i n g a l l of the < p r i c e , guantity> p a i r s i n the order i n which they were simulated, a p i c t u r e of each user's p r o t o c o l was obtained (see Appendix F f o r examples). By then "connecting the dots," one c o u l d get a good i d e a of what the o r i g i n a l p a r t i c i p a n t was attempting t o do. Some p a r t i c i p a n t s d i s p l a y e d h i g h l y s y s t e m a t i c a c t i v i t y , employing a b i n a r y search, a g r a d i e n t s e a r c h ( i . e . h i l l c l i m b i n g ) , a s p i r a l l i n g path, or other e x p l i c i t model. Other p a r t i c i p a n t s used a s t r u c t u r e d t r i a l and e r r o r ; they r o u t i n e l y t e s t e d every p o i n t i n the problem space (but with no apparent d e s i r e to zoom i n on the optimum when neared). F i n a l l y , seme p a r t i c i p a n t s showed no method a t a l l ; they j u s t wandered randomly through the problem space. To t e s t hypothesis 24 - Exp, S t y l e , and Bisk w i l l a l l a f f e c t whether users d i s p l a y e d a s t r u c t u r e d approach to s o l v i n g the problem (with the emphasis on S t y l e ) - the p r o t o c o l diagram f o r each user was traced manually, and the approach c l a s s i f i e d as s y s t e m a t i c or not. An ANOVA was then performed, y i e l d i n g the r e s u l t s i n t a b l e 15. Although experience was weakly s i g n i f i c a n t , c o g n i t i v e s t y l e was c l e a r l y the most s i g n i f i c a n t f a c t o r . Comprising 20 of the 27 s t r u c t u r e d p l a y e r s , high a n a l y t i c s were more f r e q u e n t l y s y s t e m a t i c and s t r u c t u r e d , s u p p o r t i n g B a r r e t t f s c l a s s i f i -c a t i o n s (see chapter two). One other measure was made upon the user p r o t o c o l s : based 48 SOURCE VAR. S. SQ. DF. M.SQ, F SIGNIF. Exp 1.62 1 1 .62 2.34 0. 13* S t y l e 3.31 1 3.31 4.78 0.03** Risk 0.01 1 0.01 0.01 0.94 Exp/Style 2.61 1 2.61 3.76 0.06** Exp/Risk 0.44 1 0.44 0.64 0.43 S t y l e / R i s k 0.16 1 0.16 0.24 0.63 Expl a i n e d 9.31 6 1.55 2.24 0.06** Re s i d u a l 29.81 43 0.69 T o t a l 39. 12 49 0.80 Table 15. ANOVA - Pr o t o c o l S t r u c t u r e SOURCE VAR. S. SQ. DF. M.SQ. F SIGNIF. Exp 0.79 1 0.79 3.36 0.07** S t y l e 0.08 1 0.08 0.32 0.57 Risk 0.03 1 0.03 0. 14 0.72 Exp/Style 0.27 1 0.27 1. 13 0. 29 Exp/Risk 0.75 1 0.75 3.18 0.08** S t y l e / R i s k 0.02 1 0.02 0.08 0.77 Expla i n e d 2. 15 6 0.36 1.51 0.20 Res i d u a l 10.17 43 0.24 T o t a l 12. 32 49 0.25 Table 16. ANOVA - P r o t o c o l D i s p e r s i o n upon the extent t o which p a r t i c i p a n t s searched the e n t i r e problem space, or j u s t c o n c e n t r a t e d upon one s m a l l area, the p r c t o c o l s were manually c l a s s i f i e d as d i s p e r s e d or not. Then hypothesis 25 - Exp, S t y l e , and Risk w i l l a l l a f f e c t t he amount cf d i s p e r s i o n d i s p l a y e d i n t h e i r search f o r the optimum {with the emphasis again on S t y l e ) - was t e s t e d . As i n d i c a t e d i n t a b l e 16, the onl y s i g n i f i c a n t f a c t o r was experience (together with a r i s k a t t i t u d e i n t e r a c t i o n ) ; a p p a r e n t l y , 49 experienced p l a y e r s were more f a m i l i a r with t h i s type of task and d i d not f i n d any need to " f e e l around" the e n t i r e problem space. N e i t h e r of the p s y c h o l o g i c a l v a r i a b l e s c o u l d e x p l a i n much of t h i s behaviour, As mentioned i n the previous chapter, a summary o f these r e s u l t s appears i n appendix G. 50 Chapter Seven CONCLUSIONS In t h i s t h e s i s , a new re s e a r c h t o o l ( i n the form o f an i n t e r a c t i v e computer program) has been i n t r o d u c e d . The motivation f o r t h i s has been d e s c r i b e d : t o present an exauple of a convenient, " i d i o t - p r o o f " computer program, and t o f a c i l i t a t e i n v e s t i g a t i o n o f some aspects of man-machine communication which c o u l d be of i n t e r e s t t o oth e r i n f o r m a t i o n systems r e s e a r c h e r s . , Seme of the r e l a t e d l i t e r a t u r e has been d i s c u s s e d ; then the user e n g i n e e r i n g of the computer program was de s c r i b e d i n d e t a i l . Next, the a c t u a l process of data c o l l e c t i o n f o r t h i s r e s e a r c h was presented. The p r e - t e s t i n g f o r t h i s r e s e a r c h was de s c r i b e d and shown t o be q u i t e convenient f o r both a d m i n i s t r a t o r and p a r t i c i p a n t , t a k i n g j u s t over one h a l f - h c u r . The d e t a i l s of the computer experiment were then presented. Again, the convenience aspect c o u l d not be over-emphasized: the qame l a s t e d only one h a l f - h o u r , making i t easy to admi n i s t e r and minimizing the p o s s i b i l i t y of s u b j e c t s g e t t i n g bored or needing t o hurry t o get i t over with. In the a c t u a l running of the experiments, two items are p a r t i c u l a r l y noteworthy. F i r s t , d e s p i t e heavy emphasis on the need t o c a r e f u l l y read the i n s t r u c t i o n s i n advance, i t was found t h a t some people j u s t d i d not do i t . T h i s suggests a need t o p e r s o n a l l y t u t o r every new user of a computer system or otherwise r e i t e r a t e the i n s t r u c t i o n s (perhaps cn the t e r m i n a l s c r e e n ) ; no matter how well the documentation may be 51 w r i t t e n , seme people j u s t w i l l not read i t or take the time t o pr o p e r l y understand i t - and the r e s u l t s can be d i s a s t r o u s (poor r e s u l t s now and l a c k of f a i t h i n computers i n the f u t u r e ) . The second problem observed while a d m i n i s t e r i n g the game i n v o l v e d g e t t i n g novice users s t a r t e d with the u n s t r u c t u r e d v e r s i o n of the game; the concept of a ge n e r a l command proces s o r appeared t o be j u s t too s o p h i s t i c a t e d f o r them. Personal a t t e n t i o n was needed t o e x p l a i n the task and sometimes demonstrate i t . I t seemed q u i t e c l e a r t h a t n o v i c e s would be happier with the s t r u c t u r e d qame v e r s i o n - a t l e a s t u n t i l they understood what was happening. To t h i s end, r e c a l l from chapter three that the e x t r a programming necessary t o wr i t e a proqram which c o u l d be run i n e i t h e r mode was q u i t e minimal. F i n a l l y , the process of data capture and c o n v e r s i o n was presented. I t was mentioned t h a t data about user performance, behaviour, a t t i t u d e , and even s o l u t i o n p r o t o c o l were a l l c o l l e c t e d by the computer proqram. This data was then analyzed with SPSS, using a n a l y s i s o f va r i a n c e and t - t e s t s (both n o r m a l - o n e - t a i l e d and p a i r e d ) . The r e s u l t s of these analyses are now reviewed, t h i s time i n a d i f f e r e n t order and with added d i s c u s s i o n . In r e l a t i n g p s y c h o l o g i c a l v a r i a b l e s to performance, i t was found t h a t c o g n i t i v e s t y l e had a strong e f f e c t upon whether people f i n i s h e d the game on time, and upon t h e i r c o n f i d e n c e l e v e l throughout the game. High a n a l y t i c s f i n i s h e d 52 more o f t e n and were more c o n f i d e n t than low a n a l y t i c s , i n d i c a t i n g t h a t t h i s game (and perhaps many mathematical tasks?) may favor high a n a l y t i c s . C o g n i t i v e s t y l e a l s o impacted r e p o r t usage: i t was found t h a t h e u r i s t i c s (low a n a l y t i c s ) were the only group which n e i t h e r decreased i t s use of h i s t o r y r e p o r t s nor i n c r e a s e d i t s dependence upon g r a p h i c a l r e p o r t s l a t e r i n the game (implying a preference f o r the l e s s s t r u c t u r e d and l e s s summarized feedback). The high a n a l y t i c s d i s p l a y e d a s i g n i f i c a n t tendency to avo i d a c c e p t i n g d e f a u l t responses (a r e s u l t r e q u i r i n g f u r t h e r i n v e s t i g a t i o n ) . F i n a l l y , i n the a n a l y s i s o f user s o l u t i o n p r o t o c o l s , i t was found that low a n a l y t i c s were s i g n i f i c a n t l y l e s s s t r u c t u r e d than high a n a l y t i c s i n t h e i r s e arch f o r the optimum (supporting the model suggested by Barrett) . Another p s y c h o l o g i c a l v a r i a b l e , r i s k a t t i t u d e , was found to s i g n i f i c a n t l y a f f e c t p l a y i n g speed and c o n f i d e n c e ; r i s k -t a k e r s spent l e s s time per move and were more c o n f i d e n t than r i s k - a v e x t e r s (guestionning t h e f i n d i n g s of T a y l o r and Dunnette). A l s o , r i s k - a v e r t e r s were found t o a b b r e v i a t e commands to much l e s s extent than other users. I f t h i s i s caused by a m i s t r u s t of the computer, e f f o r t s should be made to d i s p e l t h i s f e a r . However, the most dominant f a c t o r on a l l dimensions was the user's previous experience with o n - l i n e computer systems. Experienced p l a y e r s were much f a s t e r , f i n i s h e d more o f t e n , and were s i g n i f i c a n t l y more c o n f i d e n t than n o v i c e s . T h i s c l e a r l y i n d i c a t e s the importance o f e x p l i c i t l y r e c o g n i z i n g these 53 f a c t o r s i n any computer r e s e a r c h {ana probably any r e s e a r c h , f o r t h a t matter). Experienced users were happy with e i t h e r game v e r s i o n , w h i l e , as noted e a r l i e r , novices were i n i t i a l l y l o s t with the u n s t r u c t u r e d game. F i n a l l y , experienced p a r t i c i p a n t s made the l e a s t use of H i s t o r y r e p o r t s , and showed the l e a s t amount of d i s p e r s i o n i n t h e i r s o l u t i o n p r o t o c o l s , both i n d i c a t i n g an a b i l i t y to d e t e c t and d i s r e g a r d l e s s r e l e v a n t m a t e r i a l . Again, i t i s pointed out t h a t experienced s u b j e c t s seem t o have an advantage i n computerized r e s e a r c h . Game v e r s i o n , on the other hand, was found ( s t a t i s t i c a l l y ) t o be a very weak f a c t o r , a f f e c t i n g n e i t h e r speed, t e r m i n a t i o n , nor confidence. I t would seem t h a t , f o r reasonably simple t a s k s , both v e r s i o n s are e q u a l l y u s e f u l . The i d e a l , t h e r e f o r e , would be to provide the user with both a l t e r n a t i v e s , and l e t him choose whichever i s more comfortable f o r him (a c h o i c e which may change with time) . However, i t should be noted t h a t game v e r s i o n d i d a f f e c t some behaviour: users of the s t r u c t u r e d v e r s i o n (which reminds p l a y e r s of the a v a i l a b i l i t y of the r e p o r t s each period) requested s i g n i f i c a n t l y more H i s t o r y and Ordered H i s t o r y r e p o r t s . Another area which was c o n s i d e r e d b r i e f l y was e r r o r r a t e . No d i f f e r e n c e s were found among user types with r e s p e c t t o making t y p o g r a p h i c a l or range e r r o r s . In f a c t , there were very few e r r o r s made by any p a r t i c i p a n t s i n t h i s game; the user e n q i n e e r i n q aspects of the computer proqram {sinindzed memorization, i n d i c a t i o n of allowed responses, u n l i m i t e d a b b r e v i a t i o n , etc.) seem to have minimized the p o s s i b i l i t i e s 54 f o r e r r o r . The impact of d e f a u l t s under v a r i o u s circumstances was a l s o s t u d i e d . I t was found t h a t the opening p r i c e and q u a n t i t y d e f a u l t s were accepted by over one-half of the p a r t i c i p a n t s i n the f i r s t p e r i o d o f the game, whereas s e t t i n g the d e f a u l t response t o gu e s t i o n s about the user's d e s i r e to see a r e p o r t to *yes» r a t h e r than 'no' had no s i g n i f i c a n t e f f e c t upon whether they a c t u a l l y requested t h a t r e p o r t . Hence, i n l e s s w e l l - d e f i n e d s i t u a t i o n s , people appear to s e l e c t the d e f a u l t value r a t h e r than t h i n k f o r themselves. I t i s suggested t h a t d e f a u l t values not be provided i n these circumstances (as they may b i a s the r e s u l t s ) . On the ether hand, i n s i t u a t i o n s where the c h o i c e i s c l e a r , d e f a u l t s appear not to i n f l u e n c e the user, and are recommended as an a i d t o him (to minimize unnecessary typing) , The l a s t area examined concerning program usage was the e f f e c t of command l e n g t h upon the extent of a b b r e v i a t i o n by users. P l a y e r s o f the game with 3 t o 5 l e t t e r mnemonic commands a b b r e v i a t e d f a r l e s s f r e q u e n t l y than p l a y e r s of the game with 5 to 8 l e t t e r commands. There are two p o s s i b l e i m p l i c a t i o n s of t h i s : t o " f o r c e " users to a b b r e v i a t e commands (and presumably p l a y f a s t e r ) , i n t e n t i o n a l l y make the commands long; t o " f o r c e " users to remember the commands i n f u l l , make them reasonably s h o r t (but do not compromise t h e i r i n t e l l i g i b i l i t y ) . I t i s proposed t h a t the former i s more ap p r o p r i a t e when there are only a few commands and the l a t t e r i s best when the number of commands i s g u i t e l a r q e . 55 The l a s t area c o n s i d e r e d i n t h i s research compared user performance and behaviour i n the f i r s t 10 periods of the game to the remainder of the game. The a n a l y s i s of r e s u l t s r e v e a l e d t h a t p l a y i n g speed, extent of a b b r e v i a t i o n , and use of Graphs a l l i n c r e a s e d s i g n i f i c a n t l y over time, and use o f H i s t o r y r e p o r t s decreased over time ( a l l of which were d e s i r a b l e from a systems d e s i g n e r ' s point of view). Oser r a t i n g s of the u s a b i l i t y of the program a l s o i n c r e a s e d s l i g h t l y , i n d i c a t i n g t h a t users a p p r e c i a t e d the program more a f t e r they had time to get comfortable with i t . A s u r p r i s i n g r e s u l t was t h a t user c o n f i d e n c e d i d not change with time; i t would seem t h a t no matter how well people are d o i n g , i f they do not know how everyone e l s e i s doing, they assume t h a t they are performing only average. Perhaps some comparative performance feedback could remedy t h i s (when i t i s a v a i l a b l e , of c o u r s e ) . In summary, a very simple, yet e f f e c t i v e (and enjoyable!) r e s e a r c h t o o l has been d e s c r i b e d , and the r e s u l t s c f an experiment using i t have been presented. Some of the f i n d i n g s c f p r e v i o u s r e s e a r c h e r s have been confirmed; some new r e s u l t s have been provided about the man-machine i n t e r f a c e . C l e a r l y , many of these r e s u l t s have touched only the s u r f a c e , and much more resea r c h c o u l d be done i n t h i s area. For example; the use cf r e p o r t s c o u l d be i n v e s t i g a t e d f u r t h e r by having v e r s i o n s with and without g r a p h i c a l r e p o r t s and s t u d y i n g the impact upon performance, behaviour, a t t i t u d e , and s o l u t i o n 56 p r o t o c o l ; the comparisons over time could be a p p l i e d to the p e r i o d - b y - p e r i o d time s e r i e s data, r a t h e r than to two averages ( f i r s t 10 p e r i o d s versus remainder); the s o l u t i o n p r o t o c o l s c o u l d be s t u d i e d more c a r e f u l l y and s c i e n t i f i c a l l y ; or other aspects of s p e c i a l program f e a t u r e s c o u l d be i n v e s t i g a t e d , i n c l u d i n g u t i l i z a t i o n o f typeahead c a p a b i l i t i e s and i t s impact upon p l a y i n g speed, e r r o r r a t e , e t c . 57 FOOTNOTES 1 BeGreene, Kenyon B. "flan-Computer I n t e r r e l a t i o n s h i p s , " System Psychology, Kenyon E. DeGreene (ed.) , (New York: McGraw-Hill i o c k Company), 1970, pp. 281-336. 2 i b i d . , p. 282. 3 Keen, Peter G. W. "The I m p l i c a t i o n s of C o g n i t i v e S t y l e f o r I n d i v i d u a l D e c i s i o n Making," D.B.A. T h e s i s , Harvard U n i v e r s i t y (1973), Part I I I , Ch. 10, pp.28-31. * E o t k i n , J . W. "An I n t u i t i v e Computer System: A Cogni-t i v e Approach t c the Management Learning P r o c e s s , " D. E. A. T h e s i s , Harvard U n i v e r s i t y (1973). s Benbasat, Izak, and Soger Schroeder. "An Experimental I n v e s t i g a t i o n of Some MIS Design V a r i a b l e s , " MIS Q u a r t e r l y . V. 1, No. 1 (March, 1977), pp. 37-49. & i b i d . f Benbasat, Izak, and Bona Id N. T a y l o r . 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"An Experimental Study of the D e c i s i o n Making Behavior of Business E x e c u t i v e s , " Ph.D. T h e s i s , D n i v e r s i t y of C a l i f o r n i a , Los Angelas (1965). 3 * i b i d . , p. 207. 60 BIBLIPGRAPHY B a r r e t t , G erald V., C a r l 1. Thornton, and P a t r i c k A. Cabe. "Human Fa c t o r s E v a l u a t i o n of a Computer Based I n f o r m a t i o n Storage and R e t r i e v a l System," Human F a c t o r s , V. 10, Ho. 4 (august, 1968), pp. 431-436. Benbasat, Izak, and A l b e r t S..Dexter. "Value and Events Approaches to Accounting: An Experimental E v a l u a t i o n , " Working Paper No. 488, F a c u l t y of Commerce and Business A d m i n i s t r a t i o n , U n i v e r s i t y of B r i t i s h Columbia (1978). Benbasat, Izak, and Soger Sehroeder. "An Experimental I n v e s t i -g a t i o n of Seme MIS Design V a r i a b l e s , " iIS_ Q u a r t e r l y , V. 1, No. 1 (March, 1977), pp. 37-49. Benbasat, Izak, and Ronald N. T a y l o r . "The Impact of C o g n i t i v e S t y l e s on Information System Design," Working Paper No.,518, F a c u l t y of Commerce and Business a d m i n i s t r a t i o n . U n i v e r s i t y of B r i t i s h Columbia. B o t k i n , J . W. "An I n t u i t i v e Computer System: A Cogni-t i v e Approach to the Management Learning Process," D.B.A. T h e s i s , Harvard U n i v e r s i t y (1973)., Davis, Gordon B. Management Information Systems; Conceptual Found a t i o n S j t S t r u c t u r e , and Development. New York: McGraw-Hill Book Company, 1974. ~ DeGreene, Kenyon B. "Man-Computer I n t e r r e l a t i o n s h i p s , " System Psychology. Kenyon B. DeGreene (ed.), New York: McGraw-H i l l Book Company, 1970, pp. 281-336. Eason, K. D. "The Manager as a Computer User," a p p l i e d Ergonomics, V. 5, No. 1 (1974), pp. 9-14. Ferguson, Robert L., and C u r t i s H. Jones, "a Computer Aided D e c i s i o n System," Management Sc i e n c e , V. 15, No. 10 (June, 1969), pp. B-550 - B-561. Hansen, W i l f r e d J . "User E n g i n e e r i n g P r i n c i p l e s f o r I n t e r -a c t i v e Systems," Proceedings, 1971 AFIPS F a l l J o i n t Computer Conference, pp. 523-532. Keen, Peter G. W. "The I m p l i c a t i o n s of C o g n i t i v e S t y l e f o r I n d i v i d u a l D e c i s i o n Making," D.B.A. T h e s i s , Harvard Uni-v e r s i t y (1973) . Kogan, N. and M. A. Wallach. Risk - Taking.:, A Study i n Cognit^on and P e r s o n a l i t y , New York: H o l t , Rinehart, and Winston, 1964. 61 MacCrimmon, Kenneth R. "An Experimental Study of the D e c i s i o n Baking Behavior o f Business E x e c u t i v e s , " Ph.D. T h e s i s , U n i v e r s i t y of C a l i f o r n i a , Los Angelas (1965). Martin, James. Design of Man-Computer Di a l o g u e s . Enqlewood C l i f f s , New J e r s e y : P r e n t i c e - H a l l , Inc., 1973. Meadow, C h a r l e s T. Man-Machine Communication, New York: Wiley-I n t e r s c i e n c e , 1970. Mock, Theodore J . "A L o n g i t u d i n a l Study of Some Information S t r u c t u r e A l t e r n a t i v e s , " Data Base, V. 5, No. 2,3 € 4 ( S i n t e r , 1973), pp. 40-44. Nie, N. H., C. H. H u l l , J . G. J e n k i n s , K. St e i n b r e n n e r , and D. H. Bent. S t a t i s t i c a l Package f o r the S o c i a l S c i e n c e s , Second E d i t i o n , New York: McGraw-Hill Book Company, 1975. T a y l o r , Ronald N. " P s y c h o l o g i c a l Determinants of Bounded R a t i o n a l i t y : I m p l i c a t i o n s f o r Decision-Making S t r a t e g i e s , " D e c i s i o n S c i e n c e s , V. 6, No. 3 ( J u l y , 1975) , pp. 409-429." T a y l o r , Ronald N., and Marvin D. Dunnette. " R e l a t i v e C o n t r i b u t i o n of Decision-Maker A t t r i b u t e s to D e c i s i o n Processes," O r g a n i z a t i o n a l .Behavior and Human Performance. V. / l 2 . No. 2 (October, 1974), pp. 286-2S8., wasserman, Anthony I. "The Design o f ' I d i o t - P r o o f * I n t e r a c t i v e Programs," Proceedings, 1973 N a t i o n a l Computer Conference, V. 42, pp. M34-M38. Witkin, H.A., P. K. Oltman, E. Ruskin, and S. A. Karp. The Embedded f i g u r e s T e s t , P a l o A l t o , C a l i f o r n i a : C o n s u l t i n g P s y c h o l o g i s t P r e s s , Inc., 1971. Wynne, Eayard E. and Gary W. Dickson. "Experienced Managers* Performance i n Experimental Man-Machine D e c i s i o n System S i m u l a t i o n , " Academy o f Management J o u r n a l . V. 18, No. 1 (March, 1975), pp. 25-40. 62 Appendix A PROGRAM LISTING A l i s t i n g of the source code f o r the computer game appears on the next 21 pages. The program i s w r i t t e n e n t i r e l y i n FORTRAN and i s about 1000 l i n e s long. I t uses some subprograms ( f o r t i m i n g , f i l e c o n t r o l , and c h a r a c t e r comparison) which are s p e c i f i c t o the U n i v e r s i t y of B r i t i s h Columbia, and hence can probably only serve as an example f o r oth e r s . In the pages t o f o l l o w , the program comments should s u f f i c e as general documentation. 63 C CBT GAME FOB THESIS DATA COLLECTION C P. MASULIS -DECEASES,1977 C C TO BUN THIS GAME: C B *FTN SCARDS=PSM.FTN SFUNCH=PSM C B PSM*CPU:LIB PAB=CCC,YY¥,LLL,<USER»S NAME> C WHERE CCC=CYC * FOB STRUCTURED INPUT C =CMD FOB UNSTBUCTUBID INPUT C Y¥Y=YES * FOB *YES* DEFAULT/LONG COMMANDS C =NO FOB «NO» DEFAULT/SHOBT COMMANDS C LLL=L08 * FOB (10,25) INITIAL (PBICE,CTY) C =HI FOB (20, 45) INITIAL (PRICE,QTY) C * INDICATES DEFAULT VALUE C C IMPLICIT INTEGEB (A-Z) LOGICAL EQUC INTEGEB*2 MODE{30) ,CMD/»C0•/,NO/*NO*/,HI/*HI•/,BECOVB/* B E V 10GICAL*1 YNDEF,Y/*Y'/,E/*E*/,S/*S'/,N/*N*/,0/*0*/,BLANK/* */ LOGICAL*1 SDUM{10) INTEGEB*2 NAME(6) EQUIVALENCE (NAME (1) , MODE (9) ) INTEGEB*2 DATA,SAVE LOGICAL ATTN BEAL RZ COMMON PERIOD,PRICE,QTY,PBOFIT, DATA (30, 70) ,SAVE{ 100,4) ,ATTN COMMON MINPBF,HAXPBF,MAXPTR,YNDEF(4),REP(3),BUMNUM,NUBHLP COMMON NDMLIT,NUMGET,NUMCMD,NUMDEF,NUMERB,MAXCHR,NUMCHR,RZ COMMON NUMNCA,NUMNEN C C PRELOAD COMMON VARIABLES C RUMLIT-0 NUMGET=0 NUMCMD=0 NUMDEF=0 NUMEBB=0 MAXCHB=0 NUMCHB=0 NDMNUM=0 NUMHLP=0 NUMNDA-=0 »UMNDN=0 BEP(1)=0 BEP (2)=0 REP{3)=0 MAXPTR=1 PEBIOD=0 MAXPBF=0 MINPRF=999 SAVE(MAXPTB,4)=0 C C SETUP I/O C CALL ATNTEP (ATTN) LEN=7 , CALL CNTRL { * BAT E 10',LEN,6) 64 LEN=4 CALL CNTBL('BOLL', LEN,6) CALL FTNCMD(* DEFAULT 7=PSM# 1 1 ,16) CALL FTNCMD( 1DEFAOLT 8=IZAK:FUNCTION • ,24) C C CHECK FOB BECOVEBY BON C CALL PAB {MODE(3),NI,24,S6,S6) 6 IF{MODE(3) . NE.BECOVB) GOTO 19 BEAI(7,7) MX,MY,8Z 7 FOBMAT(24X,I3,5X,I3,5X,F5.3) CALL BEADPF (NAME,MX,MY,BZ) BEAD{7,8) MODE (3) , {YNDEF (I) ,1=1 ,3) 8 FOEMAT(10X,A2,26X,3A1) 11 BEAD(7,12,END=60) ICODE,PBICE,QTY,MTIM 12 FOaMAT<I2,3X,2l3,29X,I5) IF(ICODE.EQ.1) CALL SIMUL(.TfiUE.,MTIH) GOTO 11 C C CBEATE FILES IF NOT BECOVEBY BUN C 19 CALL DESTBY {*PSM#1 ») CALL CBEATE (* PSM# 1 «,1,0,256) CALL OUTMES {1) C C SELECT APPBOPSI ATE MODES C 30 IF {MODE <5) . EQ. NO) GOTO 35 YNDEF {1)=Y YNDEF (2) =E YNDEF (3) =S GOTO 40 35 YNDEF (1) =N YNDEF (2)=0 YNDEF (3) =BLANK 40 I F (MODE (7) * EQ. HI) GOTO 50 PBICE=10 QTY=25 GOTO 51 50 PBICE=20 QTY=45 C C BEAD IN PBOFIT FUNCTION, THEN CALL PBOPEB INPUT MONITCB C 51 MX=0 CALL BEADPF(NAME,MX,MY,BZ) 60 IF(MODE(3) .EQ.CMD) CALL GCMAND CALL GCYCLE STOP END 65 SUBROUTINE GCMAND C C INPUT MGRITOB - UNSTBUCTUBED INPUT C IMPLICIT INTEGEB(A-Z) LOGICAL EQUC LOGICAL*1 DOM ( 2 ) , CM B (10) , N/ • N»/# L NGCM D/ . TR U E. / INTEGEB*2 CMD2,G/» G'/,J/' J'/.S/' S»/#NULI/» •/ EQUIVALENCE (DUM (2) ,CMD{1) ) EQUIVALENCE (DUM (1) ,CMD2) 1GGICAL*1 YNDEF INTEGEB*2 DATA, SAVE LOGICAL ATTN BEAL RZ COMMON PEBIOD,PRICE,QTY,PBOFIT,DATA{30,70),SAVE{100,4),ATTN COMMON MINPBF, MAXPBF,M AXPTR, YNDEF (4) ,BEP (3) , NUMNUfl,NUMHLP COMMON NOMLIT,NUMGET,NUMCMD,NUMDEF,NUMEBR,MAXCHB,NUMCHB,BZ COMMON NUMNBA,NUMNDN C C INITIALIZATION C CMD2=NULL CALL TIME(O) IF (EQUC { YNDEF (1),N)) LNGCMD=.FALSE. IF{PEBIOD.GT.O) GOTO 10 CALL GETLIT(.TRUE.,CMD,LEN,10) »RITE{7,5) PRICE, QTY, (YNDEFfl) ,1= 1, 3) 5 FORMAT (* 0MODE=2 (CMD) PRICE=»,I2,» QTY= f,I2,» DEF-«,3A1) CALL OUTMES{2) GOTO 80 C C READ AND PROCESS COMMAND C 10 CALL GETLIT(.FALSE.,CMD,LEN,19) IF (IEN.EQ.0) GOTO 80 IF(CMD2.LT.S) GOTO 12 BTN=CMD2-S+13 GOTO 18 12 I F (CMD2.LT.3) GOTO 15 RTN=CMD2-J«-4 GOTO 18 15 RTN=CMD2-G*1 18 IF(RTN.LT.1 .OR. BTN.GT.13) GOTO 80 GOTO(70,50,80,80,80,80,80,80,60,20,30,80,40), BTN C C SET PRICE C 20 CALL GETNUM{.FALSE.,PRICE,1,30,11 ,12) MAXCHB=M AXCHR+5 NUMCHB=NUMCHB+LEN NUMCMD=NUMCMD+1 GOTO 10 C C SET QUANTITY C 30 CALL GETNUM(.FALSE.,QTY,1,70,13,14) MAXCHB=MAXCHB+3 IF (LNGCMD) MAXCHR=MAXCHR+5 NUMCH8= NUMCHR+LEN NUMCMD=NDMCMD«-1 GOTO 10 SIMULATE ANCTHEB PIB10 D CALL SI.1IUI. (. FALSE. ,0) MAXCH8=MAXCHl+3 IF (LNGCMD) MAXCBR=MAXCHR + 5 NUMCHR=NUMCHR+LEN NUMCMD=NUMCMD+1 GOTO 10 DISPLAY HISTORY REPORT CALL HISTRY MAXCHR=MAXCHR+4 IF (LNGCMD) MAXCHR=MAXCHR + 3 NUMCHR=NOMCHR+LEN NUMCMD=NUMCMD+1 GOTO 10 DISPLAY SORTED HISTOBY CALL SOBTH MAXCHR=MAXCHR+3 IF (LNGCMD) MAXCHR=M AXCHR+5 NUMCHR=NUMCHR*LEN NUMCMD=NUMCMD+1 GOTO 10 DISPLAY GRAPH CALL SGRAPH MAXCHR= MAXCHR + 5 NUMCHR=NUMCHR+LEN NUMCHD=NUMCMD*1 GOTO 10 USER COMMAND EBROR IHES=31 IF (LNGCMD) IMES=18 CALL OUTMES(IMES) IF (LEN. GT.O) NUMERR=NUMERR+ 1 CALL CLRSTR GOTO 10 END 67 SUBROUTINE GCYCLE C C INPUT MONITOR - STRUCTURED INPUT C IMPLICIT INTEGER (A-Z) LOGICAL EQUC LOGICAL*1 BOOL(10)/10*' •/,Y/'Y*/,N/'M*/ LOGICAL*1 YNDEF INTEGER*2 DATA,SAVE LOGICAL ATTN REAL RZ COMMON PERIOD,PRICE,QTY,PROFIT, DATA (30, 70), SAVE (100,4) ,ATTN COMMON MINPRF,MAXPRF,MAXPTR,YNDEF(4),REP(3),NUMNUH,NUMHLP COHBCN NUMLIT,NUMGET,NUMCMD,NUMDEF,NUMERR,MAXCHfi,NUMCHR,RZ COMKCN NUMNDA,NUMNEN C C INITIALIZATION C CALL TIME{0) IF (PERIOD. GT. 0) GOTO 10 CALL GETLIT (.TRUE. , EOOL,LEN, 10) HRITE(7,5) PRICE,QTY, (YNDEF (I) ,1= 1,3) 5 FORMAT {' 0MODE=1 (CYC) PRICE=',I2,* QTY=',I2,* DEF=»,3A1) CALL OUTMES{3) C C GET PRICE & QTY, AND SIMULATE C 10 CALL GETNUM(.TRUE.,PRICE,1,30,11,12) CALL GETNUM (.TRUE. , QTY, 1,70, 13, 14) NUMCMD=NUMCMD*2 CALL SIMDL{.FALSE. ,0) C C DISPLAY HISTORY REPORT - IF DE-SIRED C 2 0 CALL GETLIT (.TRUE. ,EOOL,LEN, 15) IF(EQUC(BOOL{1) ,N) .OR. (LEN.EQ.O . AND. EQUC (YNDEF (1) ,N) ) ) GOTO 30 I F (EQUC (BOOLJ1) , Y) .OR. (LEN. EQ. 0 .AND. EQUC (YNDEF (1) ,Y)) ) GOTO 25 CALL OOTMES (8) NUMERR=NUMERR+1 GOTO 20 2 5 CALL HISTRY IF(LEN.GT.0) HAXCHR=MAXCHR+1 30 IF (LEN.G-T. 0) MAXCHR=MAXCHR*2 IF(EQUC (BOOL (1) ,YNDEF (1) ).) NUMNDA=NUMNDA+1 NUMCHR=NUMCHR+LEN IF (LEN.EQ.O) NUMDEF=NUMDEF+ 1 C C DISPLAY SORTED HISTORY - IF DESIRED C CALL GETLIT (.TRUE. , BOOL,LEN, 16) IF(EQUC(BOOL(1) ,N) .OR. (LEN. EQ.O . AND. EQUC (YNDEF (1) , N) ) ) GOTO 40 IF(EQUC(BOOL(1) ,Y) .08, (LEN.EQ.O . AND. , EQUC (YNDEF (1) , Y) ) ) GOTO 35 CALL OUTMES(8) NUMERR=NUMERR+1 GOTO 30 35 CALL SORTH IF(LEN.GT.O) MAXCHR=MAXCHR+1 68 40 IF (LEN. GT. 0) HAXCHE^MAXCHR+2 IF (EQUC (BOOL (1) , YNDEF (1) j ) 8UMNDA=NUMNDA+1 NUHCHR=NUMCHR+LEN IF (LEN.EQ.O) NUMDEF=NUM DEF+ 1 C C DISELAY GRAPH - IF DESIRED C CALL GETLIT (.TRUE. , BOOL,LEN, 17) IF(EQUC(BOOL(1) ,N) .OR, (LEN.EQ.O * AND. EQUC (YNDEF (1) ,N))) GOTO 50 IF (EQUC { EOOL ( 1) ,Y).OR, (LEN.EQ.O .AND, EQUC (YN DEF (1) , Y) ) ) GOTO 45 CALL OUTMES{8) NUMERR=NUMERR+1 GOTO 40 45 CALL SGRAPH IF (LEN,GT,0) MAXCHR=HAXCHR+1 50 IF (LEN . GT. 0) HAXCHR = I3AXCHR«-2 IF{EQUC(BOOL(1),YNDEF(1))) NUHNDA=NUHNDA+1 NUMCHR=NUMCHR+LEN IF (LEN.EQ.0) NUMDEF=NUMDEF+1 N 0MCflD=N UMCMD+3 GOTO 10 END SUBROUTINE GETLIN(STBING,LENGTH) GET AN INPDT LINE FROM THE CRT IMPLICIT INTEGER (A-Z) LOGICAL EQUC 10GICAL*1 STBING(60), ELANK/* •/ LOGICAL*1 YNDEF INTEGEB*2 DATA,SAVE LOGICAL ATTN BEAL BZ COMMON PERIOD,PRICE,QTY,PROFIT,DATA(30,70),SAVE(100,4),ATTN COMMON MINPBF, MAXPRF, MAXPTR, YNDEF (4), BEP (3) , N UMNUM , NUMHLP COMMON NUMLIT,NOMGET,NUMCMD,NUMDEF,NUMEBB,MAXCHR,NUMCHR,RZ COMMON NUMNDA,NUMNDN READ A 60 CHARACTER STRING FROM USER NUMGET=NUMGET+1 LENGTH=0 HRITE(6,10) FOBMAT (*&: •) DO 15 1=1,60 STBING(I) = BLANK CONTINUE READ(5,20) (STRING (I) ,1=1,60) FORMAT(60A1) STBIP OFF TRAILING BLANKS DO 30 1=1,60 IF (EQUC (STRING (61-1) , BLAKK) ) GOTO 30 LENGTH=6 1-1 GOTO 40 CONTINUE BETUBN END SUBROUTINE GETLIT (NEWSTR, LIT,LITL EN ,PRO MPT) GET NEXT LITERAL IN INPUT STBING (UP TO 10 CHARS) LITEBAL IS DELIMITED BY SPACES OR A COMMA IMPLICIT INTEGER (A-2) LOGICAL EQUC,NEWSTR,ATTN LOGICAL*1 STRING (60) ,CHAB,LIT{10) , BLANK/1 «/#COMMA/»,'/ INTEGER S PT B/1/,LENGTH/0/ LOGICAL*1 YNDEF INTEGEB*2 DATA, SAVE BEAL RZ COMMON PERIOD,PRICE,QTY,PROFIT, DATA(30, 70) ,SAVE{100,4) ,ATTN COMMON MINPRF,MAXPRF,MAXPTR,YNDEF(4),REP{3),NUMNUM,NUBHLP COMBCN NUMLIT,NUMGET,NUMCMD,NUMDEF,NUHEBB,MAXCHR,NUMCHB,RZ COMMON NUMND A,NUMN DN INITIALIZATION NUMLIT= NUMLIT+1 LITLEN= 0 DO 5 1=1,10 LIT (I) =BLANK CONTINUE IF(.NOT.NEHSTR .AND. SPTR.LE.LENGTH) GOTO 10 CA1L OUT MES (PBO MPT) CALL GETLIN(STRING,LENGTH) IF(LENGTH.EQ. 0) GOTO 50 SPTR=1 STRIP OFF LEADING BLANKS IF{.NOT.EQUC(STBING(SPTR),BLANK)) GOTO 20 SPTB=SPTB+1 GOTO 10 BUILD ACTUAL LITEBAL, CHAB BY CHAB CHAR=STBING (SPTR) IF (EQUC (CHAB,BLANK) . OR. EQUC(CHAB, COMMA) ) GOTO 30 LITLEN=LITLEN*1 IF (LITLEN. LE. 10) LIT (LITLEN) =CHAR SPTR=SPTR+1 IF(SPTR.GT.LENGTH) GOTO 50 GOTO 20 STRIP OFF TRAILING BLANKS AND COMMAS IF(.NOT.EQUC(STBING(SPTB),BLANK)) GOTO 40 SPTB=SPTB+1 GOTO 30 IF (EQUC (STBING (SPTB),COMMA) ) SPTR=SPTR*1 RETURN ENTRY CIRSTR SPTR=100 RETURN END SUBROUTINE GETNUM(NEWNUM,NOMBER,LOW,HIGH,HELP,PROMPT) GET NEXT INTEGER NUMBER IN INPUT STRING IMPLICIT INTEGER (A-Z) LOGICAL*1 DUM (2) ,LITNUM (11) LOGICAL NEWNUM,BOOL INT£GER*2 NMCHR2,ZERO/* 0*/#NULL/« »/ EQUIVALENCE(DUM (1) ,NMCH82) LOGICAL*1 YNDEF INTEGER*2 DATA,SAVE LOGICAL ATTN REAL RZ COMMON PERIOD,PRICE,QTY,PROFIT,DATA(30,70),SAVE(100,4),ATT COMMON 8INPBF,MAXPRF,MAXPTR,YNDEF(4),REP(3),NUMNUM,NUMHLP COMMON NUMLIT,NUMGET,NUMCMD,NUMDEF,NUMERR,MAXCHR,NUMCHR,RZ COMMON NUMNCA,NUMNDN INITIALIZATION NUHNUM=NUMNUM+1 NMCHR2=NULL BEFALT=NUMBER BOCL=NEWNUM CALL GETLIT (BOOL,LITNUM,LITI-EN,PROMPT) IF (LITLEN. EQ. 0) GOTO 40 CONVERT STRING LITERAL TO INTEGER NUMBER=0 DO 20 I=1,LITLEN DUM(2)=LITNUM(I) DIGIT=NMCHR 2-ZERO IF(DIGIT.LT.Q .OR. DIGIT.GT.9) GOTO 30 NUMBER=NUMBEfi*10+DIGIT CONTINUE IF(NUMBER.LT.LOW .OR. NUMBER.GT.HIGH) GOTO 30 IF(NUMBER.EQ.DEFALT) NUMNDN=NOMNDN+1 GOTO 50 REQUEST USER TO RE-INPUT THE NUMBER CALL OUTMES (HELP) NUMHLP=NUMHLP+1 BOOL=.TRDE. GOTO 10 OSER TYPED JUST "RETURN", GIVE HIM THE DEFAULT - IF ANY I F (DEFALT.LT.0) GOTO 30 NUMBER=DEFALT NUHEEF=NUMDEF+1 RETURN END SUEBOUTINE HISTRY OUTPUT MOST BECENT GAME RESULTS IKELICIT INTEGEB (A-Z) LOGICAL*1 YNDEF INTEGER*2 DATA,SAVE LOGICAL ATTN REAL RZ COMMON PEBIOD,PRICE,QTY,PBOFIT,DATA(30,70) ,SAVE (100,4),ATTN COMMON MINPBF, M AXPBF, M AXPTB , YNDEF (4) ,BEP (3) ,NUMNUM, NUMHLP COMMON NUMLIT,NUMGET,NUMCMD,NUMDEF,NUMEBB,MAXCHR,NUMCHR,RZ COMMON NUMNDA,NUMNDN BEP (1)=BEP (1) +1 IF (PERIOD.GT.0) GOTO 5 CALL OUTMES{7) GOTO 30 K=25 IF (PERIOD.LT.K) K=PERIOD CALL OUTMES{28) DO 20 1=1,K J=PERIOD-K+I 8RITE(6,10) J,SAVE(J,1) ,SAVE(J,2) ,SAVE(J,3) FORMAT (IX,418) CONTINUE RETURN END 73 SOBROOTINE OUTMES (MSG) C C PRINT A MESSAGE ON THE CRT SCREEN C IMPLICIT INTEGER (A-Z) LOGICAL*1 YNDEF INTEGER*2 DATA,SAVE LOGICAL ATTN REAL RZ COMMON PERIQD,PRICE,QTY,PROFIT, DATA (30, 70) ,SAVE(100,4) ,ATTN COMMON MINPRF,MAXPRF,MAXPTR,YNDEF(4),REP<3),NUMNOM,NDMHLP COMMON NOMLIT,NUMGET,NUHCMD,NGMDEF,NOMEBR,MAXCHB,NOMCHB,RZ COMeCN NDMNEA,NOMNEN C GOTO(1,2,3,4,5,6,7,5,9,99,5,12,5,14,15, 16, 17,18,19,5,21, * 5,23,5,25,26,27,2 8,29,30,31,32,3 3,5,34,5,36,5,38,5,40, * 5,42,5,44,5,46,48,49), MSG 1 WRITE(6,1Q1) 101 FORMAT (10 {/) , * 'You are the General Manager f o r a sm a l l company ', * ' c a l l e d XYZ (name •/ * ' d i s g u i s e d ) , which manufactures and s e l l s one product, ', * 'Widgets (again •/ * ' d i s g u i s e d ) . I n your c o n t i n u i n g e f f o r t s to meet », * 'company o b j e c t i v e s »/ * ' - i . e . t o maximize p r o f i t (what e l s e ! ! ! ) - *, * 'you r e c e n t l y h i r e d an '/ * 'M.B.A. student, John Doe, to undertake some *, * • q u a n t i t a t i v e a n a l y s i s . * / / * 'John was i n s t r u c t e d t o develop a model and •, * 'computer program to help •/ * ' f i n d the optimal R e t a i l P r i c e and Production *, * 'Quantity f o r Widgets.'/ * ' A f t e r weeks of d i l i g e n t work he has produced a ', * 'very " s o p h i s t i c a t e d " ' / * 'WATFIV program t o do the job.'/) WRITE (6,201) 201 FORMAT(*It i s Monday morning, and John i s w a i t i n g *, * ' f o r you when you a r r i v e ' / * 'at the o f f i c e . He proudly presents h i s work t o you. *, * ' U n f o r t u n a t e l y , * / * 'being from a famous E a s t e r n B u s i n e s s School, ', * 'he never thought t o ' / * 'use the computer t c a c t u a l l y determine *, * 'the optimum a u t o m a t i c a l l y ; ' / * ' i n s t e a d , he designed a program with which ', * 'you c o u l d seek the o p t i - * / * 'mum y o u r s e l f (by spending p r e c i o u s time a t *, * *a computer t e r m i n a l , * / * ' s i m u l a t i n g the r e s u l t s of d i f f e r e n t *, * ' P r i c e / Q u a n t i t y combinations).*/) WRITE (6,301) 301 FORMAT(•You r e f r a i n from s t r a n g l i n g John, *, * 'and calmly thank him f o r h i s ' / * ' e f f o r t s (while making a mental memo ', * 'to h i r e o n l y 0.B.C. graduates'/ * ' i n the f u t u r e ) . You then proceed t o ', * 'the Computing Centre to t r y 1 / * 'out the new program.'// * ' fis you a r r i v e a t the t e m i n a l room, *, * 'you r e c a l l your marketing manager''s'/ * * r e p o r t i n d i c a t i n g t h a t your firm'»s demand', * * f u n c t i o n i s r a t h e r unusual.'/ * » You make a mental note net t o l e t your *, * ' i n t u i t i o n l e a d you a s t r a y , * / * ' and then s t a r t running the program...'/// * 'SPress BETUHN to continue.*} RETURN 2 WRITE(6,102) 102 FOBMAT (25 (/) ,» 1*** THE PBOGEAM •****// * * The .simulation i s d i r e c t e d by you, the user.*/ * ' When the word "COMMAND :" appears, e i t h e r e n t e r a command'/ * ' or j u s t press RETURN to get a l i s t of a v a i l a b l e commands.'/ * » Remember: A l l commands may be typed i n f u l l OR a b b r e v i a t e d »/ * • as you wish.•// * « Some h e l p f u l h i n t s : ' / * ' 1. The p o s s i b l e p r i c e range i s 1-30.*/ * * 2. The p o s s i b l e q u a n t i t y range i s 1-70.'/ * » 3. There i s one and only one maximum p o i n t . ' / * * 4. The qame w i l l a u t o m a t i c a l l y stop a f t e r 25 minutes.'/ * ' 5. The qame w i l l a l s c stop when you f i n d the optimum.'/ * * 6. The optimum values are d i f f e r e n t f o r everyone!'/ * ' 7. a f t e r a few p e r i o d s , be sure to t r y a l l r e p o r t s ' / * » i n order to l e a r n what they are...*///) RETURN 3 WBITE{6,103) 103 FOBMAT (25 {/) , ' 1 *** THE PROGRAM ***'// The proqram w i l l guide you through the s i m u l a t i o n , *, step by step. */ Simply answer a l l q u e s t i o n s as d i r e c t e d . ' / / Some h e l p f u l h i n t s : ' / 1. The p o s s i b l e p r i c e ranqe i s 1-30.'/ 2. The p o s s i b l e q u a n t i t y ranqe i s 1-70.'/ 3. There i s one and only one maximum p o i n t . ' / 4. The qame w i l l a u t o m a t i c a l l y stop a f t e r 25 minutes.'/ 5. The qame w i l l a l s o stop when you f i n d the optimum. */ 6. The optimum values are d i f f e r e n t f o r everyone!*/ 7. A f t e r a few p e r i o d s , be sure to t r y a l l r e p o r t s * / i n order to l e a r n what they are...'///) RETURN 4 WRITE (6,104) 10 4 FORMAT ( 15 (1X,78 (»$•)/) , 1X , 1 5 (• $ •) ,48 X ,1 5 {» $ «)/1 X , 15 (* $ *) , 2X , * *CONGRATULATIONS! YOU HAVE FOUND THE MAXIMUM!•,2X, * 15 ('$')/1X,15{»$') ,48X,15('$»)/1X, 15 (* $*) ,2X, * 'PLEASE TELL THE SUPERVISOR THAT YOU ARE DONE',2X, * 15 {'$'}/lX,15{'$') ,48X,15{»$') /15(1X,78('$») /)) RETURN 5 WRITE (6, 105) 105 FORMAT{»1*** ILLEGAL INPUT *** Try again...,?/) BETORN 6 WRITE{6,106) 106 FOBMAT (/'6ENTEB LOWEST PRICE TO BE DISPLAYED (1-26)') BETUBN 7 WRITE{6,107) * * * * * 75 107 FOBMAT(* ONo r e p o r t s u n t i l you have bequn p l a y i n q ! ! ' / ) BETUBN 9 WRITE(6,109) PERIOD,PRICE,QTY,PROFIT 109 FORMAT(//' P e r i o d ',13,* has been s i m u l a t e d . . . ' / / * 1 With PBICE= 9 ,13, » and QUANTITY=», 13,• your p r o f i t was $* * ,12///) RETURN 12 WRITE{6,112) PRICE 112 FOBMAT(/'SEnter d e s i r e d p r i c e l e v e l (1-30) [',I3,'1«) RETURN 14 WRITE (6,114) QTY 114 FOR MAT (/' S Enter d e s i r e d q u a n t i t y produced (1-7C) [',13,']*) RETURN 15 WRITE (6,115) (YNDEF (I) ,1=1,3) 115 FORMAT(/'&8ant to see H i s t o r y Report (YES or NO) J" * #3A1, » 3?•) RETURN 16 «RITE(6,116) (YNDEF (I),1=1,3) 116 FORMAT (/'SWant to see Ordered Report (YES or NO) [',3A1,»]?») RETURN 17 WRITE(6,117) (YNDEF (I) ,1=1,3) 117 FORMAT{/'&Want to see Summary Graph (YES or NO) [',3A1,*j?*) RETURN 18 WRITE(6,118) 118 FORMAT(//* ***** Only a v a i l a b l e commands a r e : * / / * 'PRICE Set r e t a i l p r i c e f o r t h i s p e r i o d * / * 'QUANTITY Set pr o d u c t i o n q u a n t i t y f o r t h i s p e r i o d ' / * • SIMULATE Simulate t h i s p e r i o d " s r e s u l t s * / * 'HISTORY Provide H i s t o r y Report'/ * 'ORDERING Provide Ordered H i s t o r y Beport'/ * * GRAPH Provide Summary Graph'//) RETURN 19 WRITE(6,119) 119 FORMAT(/'&COMMAND') RETURN 21 WRITE (6, 121) 121 FORMAT (» I f 100 other people were p l a y i n q t h i s qame r i q h t now,' * /'& how many would be c l o s e r to the optimum than you (0-100)?') RETURN 23 WRITE(6,123) 123 FORMAT {/' How would you r a t e the " u s a b i l i t y " of t h i s program;» * /••£ from 1 t o 9, where 1 = f r u s t r a t i n q , 9=convenient (1-9)?*) RETURN 25 WRITE (6, 125) 125 FORMAT{/• How would you d e s c r i b e your present a t t i t u d e * * /*S toward t h i s qame; 1=bored, 9=enjoying i t (1-9)?*} RETURN 26 WRITE(6,126) 126 FORMAT (/1X,65 (':')// * • Please CAREFULLY answer the f o l l o w i n q t h r e e questions;'/) RETURN 27 WRITE(6,127) 127 FORMAT {/1X,65 (':•)///) RETURN 28 HRITE(6,128) 128 FORMAT(32(/),' H i s t o r y Report f o r most re c e n t 25 p e r i o d s . ' / / * « PERIOD PRICE QTY PROFIT'/) BETUBN 76 PRICE,QTY*) 29 WHITE (6,12 9) 129 FORMAT (32 (/) ,' H i s t o r y Report - ordered by P r o f i t . ' / / * » PERIOD PRICE QTY PROFIT*/) RETURN 30 «RIIE(6,130) 130 FORMAT(///23X,'Graph of PEOFIT/10 V S . RETURN 31 WRITE (6,131) 131 FORMAT(//* ***** Only a v a i l a b l e commands a r e : * / / Set r e t a i l p r i c e f o r t h i s p e r i o d * / Set production q u a n t i t y f o r t h i s p e r i o d * / Simulate t h i s p e r i o d ' ' s r e s u l t s * / Provide H i s t o r y Report*/ Provide Ordered H i s t o r y Report*/ Provide Summary Graph*//) * 'PRICE * «QTY * *SIM * 'HIST * '08D * »GRAPH RETURN 32 BRITE{6,132) 13 2 FORMAT (15 (1X,78 {•$•)/) ,1X,15(*$») ,48X,15 (» $•)/1X ,15 (* $») ,2X, * »Y0U HAVE EXITTED WITH AN ATTENTION INTER UPT. » ,2X, * 15 (*$*)/lX,15(*$») ,48X, 15{*$*)/1X, 15 {* $») ,2X, * •PLEASE TELL THE SUPERVISOR THAT YOU ARE DONE*,2X, * 15 («$')/lX,15 {•$*) ,48X, 15{*$») /15(1X,78(«$*) /) ) RETURN 3 3 WRITE (6 ,133) 133 F08MAT(15{1X,78{*$*)/) ,1X,15('$') ,48X,15 (• $»)/1X, 15 (*$*) ,2X, * * SORRY, YOU HAVE EXCEEDED THE MAXIMUM TIME. «,2X, * 15 (»$')/lX,15{*$*) ,48X, 15 (»$*)/1X, 15 (« $') ,2X, * * PLEASE TELL THE SUPERVISOR THAT YOU ARE DONE»,2X, * 15 (»$')/1X,15 {*$») ,48X,15(*$»)/15(1X,78{«$')/)) RETURN 3 4 WRITE (6, 134) 134 FORMAT(/* I f 100 other people had played t h i s game, how many* * /*8 would have found the optimum i n fewer p e r i o d s RETURN 36 WRITE (6 , 136) 136 FORMAT(/* I f 100 other people had played t h i s game, how many' * /*& would have found the optimum i n l e s s time (0-100)?*) RETURN 38 WRITE (6,138) 138 FORMAT{//' *** For the next 3 q u e s t i o n s , "9" i s best ***•/ * /*SHow u s e f u l was the H i s t o r y Report, from 1 t o 9 (1-9)?') BETUBN 40 WRITE{6,140) 140 FORMAT(/*SHow u s e f u l was the Ordered H i s t o r y Report (1-9)?') BETURN 42 WRITE{6, 142) 142 FOB MAT {/'£How u s e f u l was the Graph Report (1-9)?*) RETURN 44 8RITE(6,144) 144 FOBMAT(/* I n your search f o r the optimum, about how many* * /» p e r i o d s d i d i t take you t o zoom i n on the general* * /•& v i c i n i t y of the optimum PBICE,QTY p a i r (1-50)?*) BETUBN 46 WBITE{6,146) 146 FORMAT(/* Would you d e s c r i b e your s e a r c h f o r the optimum as* * /• reasonably d i r e c t S s t r u c t u r e d (enter "1 M) or r a t h e r * * /*£ random&haphazard (enter "2") (1-2)?*) (0-100) ?») 77 BETUBN 48 WBITE(6,148) 148 FOBMAT{//////////////////* *** THANK YOU FOB PARTICIPATING ***»/ * ///* Please r e f r a i n frcm d i s c u s s i n g the game with others u n t i l * * //» a f t e r March 3 1st.*///) RETURN 49 8BITE(6,149) 149 FOBMAT{//*1 POST-GAME QUESTIONNAIBE*/» ========= =============»//) 99 BETUBN END SUBROUTINE READPF(NAME,MX,MY,RX) READS IN PROFIT FUNCTION (AFTER RANDOMLY SETTING LOCATION OF OPTIMAL POINT) IMPLICIT INTEGER (A-Z) INTEGER*2 NAME (6) LOGICAL*1 YNDEF REAL RX INTEGER*2 DATA, SAVE LOGICAL ATTN REAL RZ COMMON PERIOD,PRICE,QTY,PROFIT,DATA(30,70) ,SAVE (100,4),ATTN COMMON MINPBF,MAXPRF,MAXPTR, YNDEF (4) ,REP (3) , NUMNUM, NUMHLP COMMON NUMLIT,NUMGET,NUMCMD,NUMDEF,NUflERR,MAXCHR,NUMCHR,RZ COMMON NUMNDA,NUMNDN IF(MX.NE.O) GOTO 60 CALL TIME(2,0,MX) MX=-MX MX=IRAND (MX) MX=IRAND (0) MX=IEAND(10) IF(MX.GT.5) MX=MX*7 MY=IEAND (20) IF(MY.GT.10) MY=MY+7 MZ=IEAND{375) 8X= (MZ + 870. G) /1000.0 HRITE{7,55) (NAME (I) ,1=1,6) ,MX,MY,RX FORMAT (» 0NAME=» ,6A2,» MX=«,I3,» MY=»,I3,» RZ=*,E5.3) DO 61 1=1,MY READ(8,65) CONTINUE DO 66 J=1,70 READ(8,65) (DAT A ( I , 1) ,1=1,MX) , (DATA (I.J) ,1=1,30) FORMAT (4713) CONTINUE RETURN END 7 9 SUBROUTINE SGRAPH C C OUTPUTS & GRAPH OF PROFIT/10 VS. PRICE,QTY C IMPLICIT INTEGER (A-Z) INTEGER PRVPER/1/ INTEGER*2 SCR(30,70)/2100*1/ LOGICAL*1 SHADE (12)/* * , • 0' , • 1 * , » 2* , * 3* ,»4* ,• 5* , * 6* , * 7» , * 8* , * «9*,».*/,YNDEF,FIRST/.TRUE./ LOGICAL*1 PLABEL(30)/11*« X',* * , * E* , * C , * I * , * R * , * P * , * *,12* ,X*/ INTEGER*2 DATA, SAVE LOGICAL ATTN REAL RZ COMMON PERIOD,PRICE,QTY,PROFIT,DATA(30,70),SAVE(100,4),ATTN COMMON MINPBF,MAXPBF,MAXPTR,YNDEF (4) ,REP (3 ) , NUMNUM, NUMHLP COMMON NUMLIT,NUMGET,NUMCMD,NUMDEF,NUMERR,MAXCHR,NUMCHR,RZ COMMON NOMNDA,NUMNDN C C INITIALIZATION C BEP{3) =REP (3) *1 IF (PERIOD. GT. 0) GOTO 1 CALL ODTMES{7) GOTO 50 1 CALL OUTMES(30) IF (.NOT. FIRST) GOTO 6 DO 3 1=1,30 DO 2 J=5,70,5 SCR (I,J) = 12 2 CONTINUE 3 CONTINUE DO 5 1=5,30,5 DO 4 J=1,70 SCR(I,J) = 12 4 CONTINUE 5 CONTINUE FIRST=.FALSE. C C SET UP SCREEN MATRIX C 6 DO 10 I=PRVPER,PERIOD SCR (SAVE(I,1) ,SAVE(I,2) ) =SAVE (I,3) /10*2 10 CONTINUE C C NOB DRAW THE GRAPH C DO 40 J=1,30 I=31-J WRITE(6,30) I,PLABEL (I) , (SHADE (SCB(I,L) ) ,1=1 ,70) 30 F0RMAT(1X,I3,1X,71A1) 40 CONTINUE WRITE(6,45) 45 FORMAT(5X,31(*X»),* QUANTITY *,30 {»X*)//6X,«123456789*, * 10 (»1«) ,10 (*2») ,10 (*3«) , 10 (*4*) , 10 {* 5* ) ,10 <*6«) ,»7*/ * 15X,6 (»0123456789»),«0«) 50 BETURN END 80 SUBBOUTINE SIMUL (RECOVR,MTIM) C C SIMULATE ANOTHER PERIOD OF PLAY C IMPLICIT INTEGER(A-Z) LOGICAL RECOVR INTEGER TIMNEW»TIMGLD/0/,TOTTIM/0/ LOGICAL*1 YNDEF INTEGER*2 DATA,SAVE LOGICAL ATTN REAL RZ COMMON PERIOD,PRICE,QTY,PROFIT,DATA(30,70),SAVE{100,4),ATTN COMMON MINPRF,MAXPRF,MAXPTR,YNDEF (4),REP(3),NUMNOM,NUMHLP COMMON NUMLIT,NUMGET,NUMCMD,NUMDEF,NUMEBB,HAXCHfi,NUMCHB,RZ COMMON NUMNDA,NUMNDN C C CLEAR TYPEAHEAD AND RUN SIMPLE SIMULATION C CALL CLRSTR PEEI0D=PERI0D+1 fBOFIT=RZ*DATA(PRICE,QTY) IF (MINPRF.GT.PROFIT) MIN P B F= PRO FIT IF(MAXPRF.LT.PROFIT) M A X P RF = PR 0 FIT SAVE (PERIOD, 1)=PRICE SAVE (PERIOD,2) =QTY SAVE (PERIOD,3) = EROFIT IF(RECOVR) TOTTIM=TOTTIM+MTIM IF (RECOVR) GOTO 15 CALL OUTMES(9) C C GET CONNECT TIME OF USER C CALL TIME(2,0,TIMNEW) TIMNEB=TIMNEW/100 TIM-TIMNEW-TIMOLD TOTTI M=TOTTIM*TIM TIMOLD=TIMNEW WRITE (7,10) PERIOD,PRICE,QTY,PROFIT,NUMLIT,NUMGET,NUMCMD,NUMDEF, * NOMERR,MAXCHR,NUMCHB,NUHNUM,NUMHLP,NUMNDA,NUMNDN, * (REP(I) ,1=1,3) ,TIM 10 FORMAT (» 1«,413,1212,211,15) C C GET USER ATTITUDES (ONLY IN EACH 10TH PERIOD) C IF (MOD {(PERIOD-5), 10). NE.O) GOTO 15 I1=-1 I2=-1 I3=-1 CALL OUTMES(26) CALL GETNUM {.TRUE. ,11,0,100,20,21) CALL GETNUM (.TRUE.,12,1,9,22,23) CALL GETNUM (.TRUE. ,13, 1,9,24,25) WBITE(7,12) 11,12,13 12 FOBMAT (» 2' ,313) CALL OUTMES (27) CALL TIME(2,0,TIMOLD) TIH0LD=TIMOLD/100 81 C C PUT NEW RECORD INTO SORTED CHAIN C 15 IF(PERIOD.EQ. 1) GOTO 50 IF(PBOFIT.LT.SAVE(MAXPTR, 3) ) GOTO 20 SAVE(PERIOD,4)=HAXPTB MAXPTR=PERIOD GOTO 50 2 0 GLDITR=MAXPTR PTR=SAVE(OLDPTB,4) DO 30 1=1,499 IF(PTR.EQ.O) GOTO 40 IF(PROFIT. GE.SAVE(PTR, 3) ) GOTO 40 OLDPTR=PTR PTR=SAVE(QLDPT8,4) 30 CONTINUE 40 SAVE(PEEI0D,4) = PTR SAVE(OLDPTB,4) =PERIOD C C CHECK FOE END-OF-GAME C 50 IF (DATA (PRICE,QTY) .NE.8Q .AND. .NOT. ATTN .AND. TOTTIM.LT.15000) * GOTO 60 XF (DATA (PRICE,QTY) .EQ. 80) GOTO 52 IF(ATTN) GOTO 54 IF (TOTTIM. GE. 15000) GOTO 56 52 CALL OUTMES (4) IWAY=1 GOTO 58 54 CALL OUTMES(32) IWAY=2 GOTO 58 56 CALL OUTMES(33) IWAY=3 5 8 CALL ATNTRP (ATTN) CALL BELLWT (1) CALL ZEND(IWAY) C C RESET ALL COUNTERS C 60 NUMLIT=0 NUMGET=0 NUMCMD=0 NU8DEF=0 NUMERR= 0 MAXCHR=0 NUMCHR=0 SUMRUM=0 NUMHLP=0 NUMNDA=0 NUMNDN=0 REP (1) = 0 REP(2)=0 REP (3) = 0 70 BETURN END SUBROUTINE SGBTH OUTPUT SORTED RESULTS IMPLICIT INTEGEB(A-Z) LOGICAL*1 YNDEF INTEGEB*2 DATA,SAVE LOGICAL ATTN BEAD BZ COMMON PERIOD,PBICE,QTY,PROFIT,DATA(30,70),SAVE{100,4), ATTN COMMON MINPBF,MAXPBF,MAXPTB,YNDEF (4) , BEP (3 ) , NUMNUM ,NUMHLP COMMON NUMLIT,NUMGET#NUMCMD,NUMDEF,NUMEBB,MAXCHB,NUMCHB,BZ COMMON NUMNDA,NUMNDN SEP (2)=REP{2) +1 I F (PERIOD.GT.0) GOTO 5 CALL OUIHES(7) GOTO 30 K=2 5 IF (PERIOD. LT.K) K=PEBIOD PTB=MAXPTR CALL 0UTMES{29) DO 20 1=1,K WRITE(6,10) PTR,SAVE(PTR, 1) , S AV E { PTR,2) ,SAVE (PTE,3) FORMAT (1X,418) PTR=SAVE(PTB,4) CONTINUE RETURN END SUBROUTINE ZEND (IWA Y) END-OF-GAME CLEANOP IMPLICIT INTEGER (A-Z) INTEGER Q(9)/9*-1/ LOGICAL*1 YNDEF INTEGER*2 DATA,SAVE LOGICAL ATTN REAL RZ COMMON PERIOD,PRICE,QTY,PROFIT,DATA(30,70),SAVE(10Q,4),ATTN COMMON MINPRE,MAXPRE,MAXPTR,YNDEF(4) ,REP(3),NUMNUM,NOMHLP COMMON NUMLIT,NUMGET,NUMCMD,NOMDEF,NOMERR,MAXCHR,NOHCHR,BZ COMMON NUMNDA,NUMNDN CALL OUTMES (49) CALL GETNUM {.TRUE. ,Q (1) ,0,100,34, 35) CALL GETNUM{.TRUE.,Q(2),0,100,36,37) CALL GETNUM (.TRUE. ,Q(3) , 1,9,22,23) CALL GETNUM{.TRUE.,Q{4),1,9,24,25) CALL GETNUM (.TRUE. ,Q (5) , 1,9, 38,39) CALL GETNUM(.TRUE.,Q(6),1,9,40,41) CALL GETNUM (.TRUE. ,Q (7) , 1,9,42,43) CALL GETNUM (.TRUE. ,Q (8) ,1 ,50,44,45) CAIL GETNUM (.TRUE. ,Q (9) , 1 ,2, 46, 47) CALL OUTMES (48) »RITE(7,10) IWAY, (Q(I) ,1=1,9) 10 FORMAT(1 3»,413,1212,211,15) CALL RTHAIT{1500) CALL CMD('COPY PSM#1 TO IZAK: REPS (LAST* 1) • ,32) CALL CMD(«SIG » ,4) STOP END 84 Appendix B GAME INSTEUCTIONS L i s t i n g s of the pre-game i n s t r u c t i o n s appear on the next few pages; i n s t r u c t i o n s f o r the s t r u c t u r e d game v e r s i o n are on the next page, and i n s t r u c t i o n s f o r the uns t r u c t u r e d game are on the two pages f o l l o w i n g t h a t . As can be seen, the p a r t i c i p a n t i s only given d i r e c t i o n s f o r using the computer t e r m i n a l and s p e c i a l program f e a t u r e s ; the exact nature of the game i s d e s c r i b e d when the game i s a c t u a l l y played (see Appendix C). INSTRUCTIONS You w i l l soon be p l a y i n g a simple computer game {a " s i m u l a t i o n " ) . The nature of the game w i l l be d e s c r i b e d i n d e t a i l when you begin p l a y i n g . In the meantime, pl e a s e r e a d land understand!) the f o l l o w i n g i n s t r u c t i o n s - they are s h o r t , so please read them a t l e a s t a few times: 1) To en t e r i n p u t i n t o the computer, simply type on the computer t e r m i n a l keyboard as i f i t were a normal t y p e w r i t e r . A f t e r you have e n t e r r e d a l i n e , press the RETURN key t o ter m i n a t e the input, 2) I f you make a t y p i n g mistake i n the c u r r e n t l i n e , j u s t press the DEL LINE key (near the top r i g h t ) and then retype the l i n e . 3) A l l g u e s t i o n s asked by the game are of the same format; the f o l l o w i n g example i l l u s t r a t e s i t : Rant to see the H i s t o r y Report (YES or NO) [YES]? : As can te seen, f i r s t the a c t u a l guestion i s d i s p l a y e d , f o l l o w e d by the range of p o s s i b l e answers i n parentheses, f o l l o w e d - i n bracket s - by the answer which the computer w i l l assume you want i f you simply press the RETURN key. To answer NO t o the above g u e s t i o n , you c o u l d type NO or N - and then press RETURN. To answer YES, you co u l d type YES, Y, or nothing at a l l - and then press RETURN, 4) F i n a l l y , there i s one r e p o r t which must be e x p l a i n e d . I t i s a 3-dimensional graph, and i s best e x p l a i n e d with an example: I 2 0 I 3 I 2 i I 1 I 12345 QTY As can be seen, PRICE i s the v e r t i c a l a x i s , QTY i s the h o r i z o n t a l a x i s , and the PROFIT i s represented by a s i n g l e d i g i t (PROFIT/10 -no rounding!) a t the i n t e r s e c t i o n o f the a s s o c i a t e d PRICE,QTY p a i r PRICE QTY PROFIT 5 3 4 20 PRICE 3 5 2 23 2 1 2 17 ===> 1 4 1 33 5 4 08 86 INSTRUCTIONS You w i l l soon be p l a y i n g a simple computer game (a " s i m u l a t i o n " ) . The nature of the game w i l l be d e s c r i b e d i n d e t a i l when you begin p l a y i n g . In the meantime, please read (and understand!) the f o l l o w i n g i n s t r u c t i o n s - they are s h o r t , so please read them a t l e a s t a few times: 1) To en t e r i n p u t i n t o the computer, simply type on the computer t e r m i n a l keyboard as i f i t were a normal t y p e w r i t e r . A f t e r you have e n t e r r e d a l i n e , press the RETURN key t o ter m i n a t e the input. 2) I f you make a t y p i n g mistake i n the c u r r e n t l i n e , j u s t press the DEI LINE key (near the top r i g h t ) and then retype the l i n e . 3) You w i l l have to take the i n i t i a t i v e i n t h i s game; t h a t i s , you w i l l have to i n s t r u c t the computer what t o do next. To do t h i s , you must enter commands v i a the keyboard (the commands w i l l be d e s c r i b e d when you p l a y ) . When you enter commands, you can type the e n t i r e command, or any a b b r e v i a t i o n of i t . Thus, to e n t e r the command SIMULA! you c o u l d type SIMULATE, SIMUL, SIM, S, e t c . and then press RETURN. U) Some commands w i l l cause a gues t i o n t o be asked by the computer. A l l guestions asked w i l l be of the same format; the f o l l o w i n g example i l l u s t r a t e s i t : Enter p r i c e t o be charged next p e r i o d (1-30) £ 10] : As can be seen, f i r s t the a c t u a l q u e s t i o n i s d i s p l a y e d , followed by the ranqe of p o s s i b l e answers i n parentheses, f o l l o w e d - i n br a c k e t s - by the answer which the computer w i l l assume you want i f you simply press the BETURN key. To answer 2 0 to the above q u e s t i o n , you could type 20 - and then press BETUBN. To answer 10, you c o u l d type 10, o r no t h i n g at a l l - and then press BETUBN. 5) You may a l s o combine commands on one l i n e (separated by spaces!) i f you wish. For example, i f you knew t h a t the f o l l o w i n q sequence of events would occur (note t h a t a l l l i n e s end with a RETOBN): Command : PBICE Enter p r i c e to be charged next p e r i o d (1-30) [ 1 0 ] : 20 Command : SIMUL you could have j u s t typed ; Command : PBICE 20 Command : SIMUL or even: Command : PBICE 20 SIMUL 87 6) F i n a l l y , t h e r e i s one r e p o r t which must be e x p l a i n e d . I t i s a 3-dimensional graph, and i s best e x p l a i n e d with an example: PRICE QTY PROFIT 5 | 2 0 — — 4 I 3 3 4 20 PRICE 3 | 2 5 2 23 2 | 1 2 17 ===> 1 J 1 4 1 33 j 5 4 08 12345 QTY As can be seen, PRICE i s the v e r t i c a l a x i s , QTY i s the h o r i z o n t a l a x i s , and the PROFIT i s represented by a s i n g l e d i g i t (PROFIT/10 -no rounding!) a t the i n t e r s e c t i o n of the a s s o c i a t e d PRICE,QTY p a i r . 88 Appendix C SAMPLE INTERACTION The next pages provide examples of two s e s s i o n s o f the computer game (a s t r u c t u r e d v e r s i o n i n t e r a c t i o n appears on the f i r s t 6 pages, while an u n s t r u c t u r e d v e r s i o n i n t e r a c t i o n appears on the H pages f o l l o w i n g t h o s e ) , The opening i n s t r u c t i o n s , s e v e r a l periods o f s i m u l a t i o n , an a t t i t u d e q u e s t i o n n a i r e , and a l l three r e p o r t s are presented, (Note t h a t the graphs are much more readable on the computer t e r m i n a l where the dots are much f a i n t e r ) . 89 You are the General Manager f o r a s m a l l company c a l l e d XYZ (name d i s g u i s e d ) , which manufactures and s e l l s one product, Widgets (again d i s g u i s e d ) . In your c o n t i n u i n g e f f o r t s to meet company o b j e c t i v e s - i . e . t c maximize p r o f i t (what else!1!) - you r e c e n t l y h i r e d an M.E.A. student, John Doe, to undertake some g u a n t i t a t i v e a n a l y s i s . John was i n s t r u c t e d t o develop a model and computer program to help f i n d the o p t i m a l R e t a i l P r i c e and P r o d u c t i o n Q u a n t i t y f o r Widgets. A f t e r weeks of d i l i g e n t work he has produced a very " s o p h i s t i c a t e d " WATFIV program to do the job. I t i s Monday morning, and John i s w a i t i n g f o r you when you a r r i v e at the o f f i c e . He proudly presents h i s work to you. U n f o r t u n a t e l y , being from a famous Eastern Business School, he never thought t o use the computer t o a c t u a l l y determine the optimum a u t o m a t i c a l l y ; i n s t e a d , he designed a program with which you could seek the o p t i -mum y o u r s e l f (by spending p r e c i o u s time a t a computer t e r m i n a l , s i m u l a t i n g the r e s u l t s of d i f f e r e n t P r i c e / Q u a n t i t y combinations). You r e f r a i n from s t r a n g l i n g John, and c a l m l y thank him f o r h i s e f f o r t s (while making a mental memo t c h i r e o n l y 8.-B.C. graduates i n the f u t u r e ) . You then proceed t o the Computing Centre to t r y out the new program. As you a r r i v e a t the t e r m i n a l room, you r e c a l l your marketing manager r e p o r t i n d i c a t i n g t h a t your f i r m ' s demand f u n c t i o n i s r a t h e r unusual. You make a mental note not to l e t your i n t u i t i o n l e a d you a s t r a y , and then s t a r t running the program... *** THE PSOGRAM *** The program w i l l guide you through the s i m u l a t i o n , step by s t e p . , Simply answer a l l q u e s t i o n s as d i r e c t e d . Some h e l p f u l h i n t s : 1. The p o s s i b l e p r i c e range i s 1-30. 2. The p o s s i b l e g u a n t i t y range i s 1-70. 3. There i s one and o n l y one maximum p o i n t . 4. The game w i l l a u t o m a t i c a l l y stop a f t e r 25 minutes. 5. The game w i l l a l s o stop when you f i n d the optimum. 6. The optimum values are d i f f e r e n t f o r everyone! 7. A f t e r a few p e r i o d s , be sure to t r y a l l r e p o r t s i n order to l e a r n what they a r e . . . Enter d e s i r e d p r i c e l e v e l (1-30) f 10] : 15 Enter d e s i r e d q u a n t i t y produced (1-70) [ 25] : Peri o d 1 has been s i m u l a t e d . . , With PBICE= 15 and QU ANTITY= 25 your p r o f i t was $27 Bant t o see H i s t o r y Report (YES or NO) [NO ]? : NO Bant t o see Ordered Report (YES or NO) [NO ]? : N Rant to see Summary Graph (YES or NO) [NO ]? : Enter d e s i r e d p r i c e l e v e l (1-30) [ 1 5 ] : Enter d e s i r e d q u a n t i t y produced (1-70) [ 25] : 35 Period 2 has been s i m u l a t e d . , , with PRICE= 15 and Q0ANTITY= 35 your p r o f i t was $64 Want to see H i s t o r y Report (YES or NO) [ NO ]? : Want to see Ordered Beport (YES or NO) [NO ]? ; Want t o see Summary Graph (YES or NO) [NO ]? : Enter d e s i r e d p r i c e l e v e l (1-30) [ 15] : Enter d e s i r e d q u a n t i t y produced (1-70) [ 35] : 45 Pe r i o d 3 has been s i m u l a t e d . . . With PRICE= 15 and QUANTITY= 45 your p r o f i t was $77 Want to see H i s t o r y Report (YES or NO) [NO ]? z Want to see Ordered Beport (YES or NO) [NO ]? : Want t o see Summary Graph (YES or NO) [NO ]? : Enter d e s i r e d p r i c e l e v e l (1-30) [ 1 5 ] ; 10 Enter d e s i r e d q u a n t i t y produced (1-70) [ 45 ] : 35 91 Period 4 has been s i m u l a t e d . . . With PRICE= 10 and QUANTITY^ 35 your p r o f i t was $43 Want to see H i s t o r y Beport (YES or NO) f NO ]? : Want to see Ordered Beport (YES or NO) [NO j? : Want t o see Summary Graph (YES or NO) [NO ]? : Enter d e s i r e d p r i c e l e v e l (1-30) [ 1 0 ] ; 20 Enter d e s i r e d q u a n t i t y produced (1-70) [ 3 5 ] : Pe r i o d 5 has been s i m u l a t e d . . . With PBICE= 20 and QUANTITY= 35 your p r o f i t was $43 Please CAREFULLY answer the f o l l o w i n g three q u e s t i o n s : I f 100 other people were p l a y i n g t h i s game r i g h t now, how many would be c l o s e r to the optimum than you (0-100)? : 25 How would you r a t e the " u s a b i l i t y " o f t h i s program; from 1 to 9, where 1 = f r u s t r a t i n g , 9=convenient (1-9)? : 7 How would you d e s c r i b e your present a t t i t u d e toward t h i s qame; 1=bored, 9=enjoyinq i t (1-9)? : 9 Want to see H i s t o r y Report (YES or NO) [NO ]? : YES H i s t o r y Report f o r most re c e n t 25 p e r i o d s . PERIOD PRICE QTY PROFIT 1 2 3 4 5 15 15 15 10 20 25 35 45 35 35 27 64 77 43 43 Want to see Ordered Report (YIS or NO) [NO ]? : Y 92 H i s t o r y Report - ordered by P r o f i t . PERIOD PRICE QTY PROFIT 3 15 45 77 2 15 35 64 5 20 35 4 3 4 10 35 43 1 15 25 27 Want to see Summary Graph (YES or NO) [NO ]? : Y 30 29 28 27 26 25 24 23 22 Graph of PROFIT/10 vs. PRICE,QTY 21 X 20 X. 19 X 18 17 P 16 R 15 I . 14 C 13 E 12 11 X 10 X. 9 X 8 X 7 X 6 X 5 4 3 2 1 X. X X X X XX 1 • • • • • • • ft. • • • • XXXXXXXXXXXXXXXXXXXXXXXXXXXXX QOANTITY XXXXXXXXXXXXXXXXXXXXXXXXX 23456789111111111122222222223333 33333344444444445555 555555666666 01234567890123 4567890123 4567 89012345678 901234567890123 45 Enter d e s i r e d p r i c e l e v e l (1-30) [ 20] Enter d e s i r e d q u a n t i t y produced (1-70) [ 3 5 ] : 45 93 Period 6 has been s i m u l a t e d . . . . With PRICE= 20 and Q0ANTITY= 45 your p r o f i t was $39 Want t o see H i s t o r y Beport (YES or NO) [NO J ? : Want to see Ordered Report (YES or NO) {NO ]? : Want to see Summary Graph (YES or NO) [NO )? : Enter d e s i r e d p r i c e l e v e l (1-30) [ 20] : 10 Enter d e s i r e d q u a n t i t y produced (1-70) [ 45] : 45 Period 7 has been s i m u l a t e d . . . With PBICE= 10 and QOANTITY= 45 your p r o f i t was $55 Want to see H i s t o r y Report (YES or NO) [NO ]? : Want to see Ordered Report (YES or NO) [NO ]? : Want to see Summary Graph (YES or NO) [NO ]? : Enter d e s i r e d p r i c e l e v e l (1-30) [ 1 0 ] ; 15 Enter d e s i r e d q u a n t i t y produced (1-70) [ 4 5 ] ; 55 Period 8 has been s i m u l a t e d . . . With PRICE= 15 and QUANTITY= 55 your p r o f i t was $30 Want t o see H i s t o r y Report (YES or NO) [NO ]? : Want t o see Ordered Report (YES or NO) [NO ]? : Want to see Summary Graph (YES or NO) [NO ]? : Y 94 30 X 29 X 28 X 27 X 26 X 25 X•* • • Graph of PROFIT/10 vs PRICE,QTY * • 4 * * * 19 X 18 17 P 16 E 15 I. . . . 14 C 13 E 12 11 X 10 X... . 9 X 8 X 7 X 6 X 5 X • • • • 4 X 3 X 2 X I X XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX QO&NTITY XXXXXXXXXXXXXXXXXXXXXXXXX 12345678911111111112222222222333333333344444444445555£55555666666 012345678901234567890123 45678901234567890123 456789012345 Enter d e s i r e d p r i c e l e v e l {1-30) £ 15] : Enter d e s i r e d q u a n t i t y produced {1-70) [ 5 5 ] : 40 *. *« • •» » P e r i o d 9 has been s i m u l a t e d . with PRICE= 15 and QUAHTITX= 40 your p r o f i t was $70 95 You are the General Manager f o r a s m a l l company c a l l e d XYZ (name d i s g u i s e d ) , which manufactures and s e l l s one product, Widgets (again d i s g u i s e d ) . In your c o n t i n u i n g e f f o r t s t o meet company o b j e c t i v e s - i . e . to maximize p r o f i t (what else!!!) - you r e c e n t l y h i r e d an M.E.A. student, John Doe, to undertake some q u a n t i t a t i v e a n a l y s i s . John was i n s t r u c t e d to develop a model and computer program t o help f i n d the o p t i m a l R e t a i l P r i c e and Produ c t i o n Quantity f o r Widgets. A f t e r weeks of d i l i g e n t work he has produced a very " s o p h i s t i c a t e d " WATFIV program to do the job. I t i s Monday morning, and John i s w a i t i n g f o r you when you a r r i v e a t the o f f i c e . He proudly presents h i s work to you. U n f o r t u n a t e l y , being from a famous E a s t e r n Business School, he never thought t o use the computer to a c t u a l l y determine the optimum a u t o m a t i c a l l y ; i n s t e a d , he designed a program with which you could seek the o p t i -mum y o u r s e l f (by spending p r e c i o u s time at a computer t e r m i n a l , s i m u l a t i n g the r e s u l t s of d i f f e r e n t P r i c e / Q u a n t i t y combinations). You r e f r a i n from s t r a n g l i n g John, and calmly thank him f o r h i s e f f o r t s (while making a mental memo t o h i r e o n l y O.B.C. graduates i n the f u t u r e ) , You then proceed t o the Computing Centre t o t r y cut the new program. As you a r r i v e a t the t e r m i n a l room, you r e c a l l your marketing manager r e p o r t i n d i c a t i n g t h a t your f i r m ' s demand f u n c t i o n i s r a t h e r unusual. You make a mental note not to l e t your i n t u i t i o n l e a d you a s t r a y , and then s t a r t running the program... , *** THE PROGRAM *** The s i m u l a t i o n i s d i r e c t e d by you, the user. When the word "COMMAND :" appears, e i t h e r e n t e r a command or j u s t press RETURN t o get a l i s t of a v a i l a b l e commands., Remember; A l l commands may be typed i n f u l l OR abb r e v i a t e d as you wish. Some h e l p f u l h i n t s : 1. The p o s s i b l e p r i c e range i s 1-30. 2. The p o s s i b l e q u a n t i t y range i s 1-70., 3. There i s one and on l y one maximum p o i n t . 4. The game w i l l a u t o m a t i c a l l y stop a f t e r 25 minutes. 5. The game w i l l a l s o stop when you f i n d the optimum. 6. The optimum values are d i f f e r e n t f o r everyone! 7. A f t e r a few pe r i o d s , be sure t o t r y a l l r e p o r t s i n order t o l e a r n what they are... , ***** Only a v a i l a b l e commands are: QUANTITY SIMULATE HISTORY ORDERING GRAPH PRICE Set r e t a i l p r i c e f o r t h i s period Set production q u a n t i t y f o r t h i s p e r i o d Simulate t h i s p e r i o d ' s r e s u l t s Provide H i s t o r y Report Provide Ordered H i s t o r y Report Provide Summary Graph COMMAND : PRICE Enter d e s i r e d p r i c e l e v e l (1-30) [ 1 0 ] : 15 COMMAND : QUANTITY Enter d e s i r e d q u a n t i t y produced (1-70) [ 2 5 ] : COMMAND : SIMULATE Pe r i o d 1 has been s i m u l a t e d . . . With PRICE= 15 and QUANTITY= 25 your p r o f i t was $35 COMMAND : PRICE 15 QUANTITY 35 COMMAND : SIM Perio d 2 has been s i m u l a t e d . . . With PRICE= 15 and QUANTITY^ 3 5 your p r o f i t was $39 COMMAND : P 15 Q 45 S Period 3 has been s i m u l a t e d . . . With PRICE= 15 and QUANTITY= 45 your p r o f i t was $36 COMMAND : P 10 Q 35 S Period 4 has been s i m u l a t e d . . . With PBICE= 10 and QUANTITY= 35 your p r o f i t was $45 97 COMMAND : P 20 S Period 5 has been s i m u l a t e d . . . With PBICE= 20 and QUANTIT¥= 35 your p r o f i t was $12 Please CAREFULLY answer the f o l l o w i n g t h r e e q u e s t i o n s : I f 100 other people were p l a y i n g t h i s game r i g h t now, how many would be c l o s e r to t h e optimum than you (0-100)? : 25 How would you r a t e the " u s a b i l i t y " of t h i s proqram; from 1 to 9, where ^ f r u s t r a t i n g , 9=convenient (1-9)? : 7 How would you d e s c r i b e your present a t t i t u d e toward t h i s game; 1=bored, 9=enjoying i t (1-9)? : 9 COMMAND : ***** Only a v a i l a b l e commands a r e : PRICE Set r e t a i l p r i c e f o r t h i s period QUANTITY Set production q u a n t i t y f o r t h i s p e r i o d SIMULATE Simulate t h i s p e r i o d 1 s' r e s u l t s HISTORY Provide H i s t o r y Report ORDERING Pr o v i d e Ordered H i s t o r y Report GRAPH Pr o v i d e Summary Graph COMMAND : HIST H i s t o r y Report f o r most re c e n t 2 5 p e r i o d s . PEBIOD PBICE QTY PROFIT 1 15 25 35 2 15 35 39 3 15 45 36 4 10 35 45 5 20 35 12 98 COMMAND : GBDEBING H i s t o r y Beport - ordered by P r o f i t . PEBIOD PBICE QTY PBOFIT 4 10 35 45 2 15 35 39 3 15 45 3 6 1 15 25 35 5 20 35 12 COMMAND : G 30 29 X 28 X 27 X 26 X 25 24 X 23 X 22 X 21 X 20 19 X 18 17 p 16 B 15 14 c 13 E 12 11 X 10 9 X 8 X 7 X 6 X 5 4 X 3 X 2 X 1 X Graph o f PBOEIT/10 vs. PBICE,QTY [XXXXXXXXXXXXXXXXXXXXXXXXXXX QUANTITY XXXXXXXXXXXXXXXXXXXXXXXXX 12345678911111111112222 22222233333333 33444 44444445555555555666666 01234567890123456789012345678901234567890123456789012345 99 Appendix D PBOFJT FUNCTION A one-quarter p o r t i o n o f the p r o f i t f u n c t i o n (read i n by the computer game program) appears on the next page, with the highest p r o f i t i n each row u n d e r l i n e d . C l e a r l y , the f u n c t i o n i s simply a "winding mountain r i d g e . " To r e c r e a t e the e n t i r e p r o f i t f u n c t i o n , simply r e f l e c t the matrix on the next page along the l e f t edge and then along the bottom edge, y i e l d i n g a "four-arm mountain" with the peak a t 80. The p r o f i t f u n c t i o n i s , t h us, monotone i n c r e a s i n g i n two dimensions, with one g l o b a l maximum and no l o c a l maxima - yet i s s t i l l complex enough to keep each p a r t i c i p a n t t h i n k i n g . 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 3 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 3 4 4 5 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 3 4 5 6 7 0 0 0 0 0 0 0 0 0 0 0 1 1 2 3 4 5 6 6 7 8 0 0 0 0 0 0 0 0 0 1 1 2 2 3 4 5 6 7 8 9 10 0 0 0 0 0 0 0 0 1 1 2 3 3 5 6 7 8 9 10 11 10 0 0 0 0 0 0 0 1 1 2 3 4 5 6 7 9 10 11 12 11 10 0 0 0 0 0 0 1 1 2 3 4 5 7 8 9 11 12 13 12 1 1 10 0 0 0 0 0 1 1 2 3 4 6 7 9 10 11 13 14 13 12 1 1 9 0 0 0 0 1 2 2 3 5 6 8 9 11 12 14 15 14 13 11 10 9 0 0 0 1 2 2 4 5 7 8 10 11 13 15 16 15 14 12 11 9 8 0 0 0 1 2 3 4 5 7 9 10 12 14 16 17 16 14 12 10 9 7 0 0 1 2 3 4 6 7 9 11 13 15 17 18 17 16 14 12 10 9 7 0 0 1 2 3 4 6 8 10 12 14 16 18 19 18 16 14 12 10 8 6 0 1 2 3 5 6 8 10 12 14 16 18 20 19 18 16 14 12 10 8 6 0 1 2 3 5 6 9 11 13 15 17 19 21 19 17 15 13 11 9 6 5 0 1 2 3 5 7 9 11 14 16 18 20 22 20 18 16 14 11 9 7 5 1 2 4 5 7 9 12 14 17 19 21 23 22 20 18 16 14 11 9 7 5 1 2 4 6 7 10 12 15 17 20 22 24 22 20 17 15 12 10 7 6 4 1 3 4 6 8 10 13 15 18 21 23 25 23 21 18 15 13 10 8 6 4 3 4 6 8 11 13 16 19 21 24 26 25 23 21 18 15 13 10 8 6 4 3 4 6 8 11 14 17 19 22 25 27 25 22 19 17 14 11 8 6 4 3 3 4 6 9 11 14 17 20 23 26 28 26 23 20 17 14 11 9 6 4 3 3 4 7 9 12 15 18 21 24 27 29 27 24 21 18 15 12 9 7 4 3 5 7 9 12 15 18 22 25 28 30 29 27 24 21 18 15 12 9 7 4 3 5 7 10 13 16 19 22 25 29 31 29 25 22 19 16 13 10 7 5 3 2 5 7 10 13 16 20 23 26 30 32 30 26 23 20 16 13 10 7 5 3 2 5 8 10 14 17 20 24 27 30 33 30 27 24 20 17 14 10 8 5 3 2 5 8 10 14 17 21 24 28 31 34 31 28 24 21 17 14 10 8 5 3 2 5 8 11 14 18 22 25 29 32 35 32 29 25 22 18 14 11 8 5 4 2 9 11 15 19 23 27 30 34 37 36 33 30 26 22 18 15 11 8 6 4 2 9 12 16 20 24 28 32 36 39 36 32 28 24 20 16 12 9 6 4 2 0 9 13 17 21 25 29 34 38 41 38 34 29 25 21 17 13 9 6 4 2 0 10 13 18 22 26 31 35 40 <J3 40 35 31 26 22 18 13 10 7 4 2 0 14 18 23 28 32 37 42 45 44 41 36 32 27 23 18 14 10 7 5 2 0 14 19 24 29 34 39 43 47 43 3S 34 29 24 19 14 11 7 5 2 0 0 15 20 25 30 35 40 45 49 45 40 35 30 25 20 15 11 8 5 3 0 0 21 26 31 37 42 47 51 50 46 41 36 31 26 21 15 12 8 5 3 0 0 22 27 33 38 43 4 9 53 49 43 38 33 27 22 16 12 8 5 3 0 0 0 23 28 34 39 45 51 55 51 45 3S 34 28 23 17 13 8 6 3 0 0 0 30 36 42 48 54 58 56 52 46 40 34 29 23 17 13 9 6 3 0 0 0 31 36 4 4 50 56 61 56 50 44 38 31 25 19 14 9 6 3 0 0 0 0 39 46 53 59 64 62 57 51 45 38 32 25 19 14 10 6 3 0 0 0 0 41 48 55 62 67 62 55 48 11 34 27 21 15 10 7 3 0 0 0 0 0 50 57 65 70 68 63 56 49 42 35 28 21 16 10 7 3 0 0 0 0 0 60 67 73 71 66 58 51 44 36 29 22 16 11 7 4 0 0 0 0 0 0 70 76 74 68 61 53 46 38 30 23 17 11 8 4 0 0 0 0 0 0 0 80 78 72 64 56 48 40 32 24 18 12 8 4 0 0 0 0 0 0 0 0 101 Appendix E SAMPLE PROGRAM OUTPUT A l i s t i n g of the computer qame output f o r an i n d i v i d u a l player appears on the next page. On t h a t paqe, i f a l i n e i s preceded by a 0, i t i s i n t r o d u c t o r y i n f o r m a t i o n ; i f by a 1, i t i s the r e s u l t s of another p e r i o d o f s i m u l a t i o n ; i f by a 2, i t i s a s e t of a t t i t u d e q u e s t i o n n a i r e outcomes; and i f by a 3, i t i s t e r m i n a t i o n i n f o r m a t i o n . The l a b e l s a t the bottom of the output r e f e r t o the l i n e s preceded by a 1. The PERIOD, PRICE, QTY, PROFIT, #HISTORYS, #ORDERS, tGRAPHS, and SECS*10 (time) l a b e l s should be obvious. The other l i n e s a r e : #GETLITS - # o f s t r i n q l i t e r a l s i n p u t t e d from the user #GETLINS - # of times a new i n p u t l i n e was typed by the user #CCBEANDS - # of commands executed by the user #DEFAULTS - # of commands havinq d e f a u l t responses a v a i l a b l e TERRORS - # of e r r o r s mad by the user MAXCHARS - maximum # of c h a r a c t e r s the user c o u l d have typed NUMCHARS - a c t u a l # of c h a r a c t e r s the user d i d type #-GETNUMS - # of numbers i n p u t t e d from the user #HELPS - # of times a help messaqe was d i s p l a y e d #MCNADEF - # of a l p h a b e t i c d e f a u l t s net accepted by the user #NCWNDEF - # of numeric d e f a u l t s not accepted by the user OHAHI :=SAHPLE MX= 16 m ^ 9 BZ = 1 .196 OHODI ;=2 (CHD) P B I C E - 10 QTY=; 25 D E F = YES 1 1 1 1 1 9 7 3 0 020 3 2 0 •0 0 100 5379 1 2 3 3 2 5 1 3 0 021 3 2 0 0 0 000 342 1 3 5 5 2 5 1 3 0 021 3 2 0 0 0 000 287 1 4 9 9 1 5 1 3 0 021 3 2 0 0 0 000 138 1 5 13 13 15 5 1 3 0 02 1 3 2 0 0 0 000 626 2 50 5 9 1 6 15 35 60 7 3 4 0 026 4 2 0 0 0 001 1069 1 7 15 40 38 5 1 3 0 021 3 2 0 0 1 000 178 1 8 15 45 60 5 1 3 0 021 3 2 0 c 1 000 241 1 9 15 50 59 5 1 3 0 021 3 2 0 0 1 000 322 1 10 15 30 59 5 1 3 0 021 3 2 0 0 1 000 194 1 1 1 15 25 45 5 1 3 0 021 3 2 0 0 1 000 190 1 12 15 20 32 5 1 3 0 021 3 2 0 0 1 000 105 1 13 20 35 22 5 1 3 0 021 3 2 0 0 0 000 188 1 14 20 40 4 5 1 3 0 021 3 2 0 0 1 000 134 1 15 10 35 63 6 2 4 0 026 4 2 0 0 0 001 440 210 0 7 9 1 16 10 40 86 5 1 3 0 021 3 2 0 0 1 000 130 1 17 10 45 63 5 1 3 0 021 3 2 0 0 1 000 177 1 18 5 40 76 5 2 3 0 021 3 2 0 c 0 000 185 1 19 10 45 63 5 1 3 0 021 3 2 0 0 0 000 191 1 20 8 40 95 7 3 4 0 026 4 2 0 0 0 001 418 3 1 30 40 8 9 1 1 9 9 1 L P P Q P # . # # # # M H # # # # ### S I E B T B G G C D E A U G H N N HOG E N B I Y 0 E E 0 E B X «r E E 0 0 I B B C E I C E T T M F B C c T L N N SDA S C E I L L a A 0 H H N P A N T E P * C C T I I A 0 B A A 0 S 0 D OBH 1 0 T N N L S B B M E E ass 0 D S S D T S S S F F I E s S s S S 103 Appendix F SAMPLE PBCTOCOLS Three examples of user p r o t o c o l diagrams appear on the next t h r e e pages. On theses diagrams, •**» i n d i c a t e s the p o s i t i o n of the optimum p r o f i t ; below the diagram, i t i s i n d i c a t e d whether or not the s u b j e c t found the optimum. The 2 - d i g i t numbers i n d i c a t e the order i n which <price, quantity> p a i r s were si m u l a t e d (imagine p r i c e running from 1 t o 30 along the v e r t i c a l a x i s , and g u a n t i t y running from 1 to 70 along the h o r i z o n t a l a x i s ) . , By connecting the p o i n t s , one can get a good f e e l f o r what the o r i g i n a l p a r t i c i p a n t was up t o (see the end c f chapter s i x f o r f u r t h e r d e t a i l s ) . S A M P L E - ' S Y S T E M A T I C P R O T O C O L * F I N I S H E D o S A M P L E . " S T R U C T U R E D T R I A L A N D E R R O R " S O T F I N I S H E D 107 Appendix G SUMMARY OF RESULTS T h i s l a s t appendix summarizes the r e s u l t s o f the s t a t i s t i c a l t e s t s of the 26 hypotheses c o n t a i n e d i n t h i s t h e s i s (see chapter s i x f o r d e t a i l s ) . In the summary on the next pages, the hypotheses are broken down i n t o subparts whenever necessary. In the l a s t column, i t i s noted whether each hypothesis was r e j e c t e d or accepted { i . e . supported), based upon whether or not the n u l l hypothesis {of e q u a l i t y ) was accepted or r e j e c t e d , r e s p e c t i v e l y . HYP. DEPENDENT VARIABLE IND. VAR. SIGH. A/R 1. Minutes/Period Mode Exp S t y l e Risk ns 0.09 ns 0.05 Bel acc Re j acc 2. Termination Mode Exp S t y l e Risk ns 0.00 ns ns Eej Acc Ie j Re j 3. Confidence Mode Exp S t y l e Risk ns 0.01 0.08 0.11 Rej Acc Acc acc 4. Game Version Min/Per. Term, C o n f i d . ns ns ns Rej Rej Rej 5. Experience L e v e l Min/Per. Term. C o n f i d . 0.04 0.00 0.00 Acc Acc Acc 6. C o g n i t i v e S t y l e Min/Per. Term. C o n f i d . 0.15 0.05 0.01 Acc ACC Acc 7. Bisk a t t i t u d e Min/Per. Terra. C o n f i d . 0.02 0. 11 0.03 Acc Acc Acc 8. E r r o r Rate Mode/Exp ns Rej 9. Opening D e f a u l t s —, Acc 10. YES/MO De f a u l t s ns Acc 11. Acc, of D e f a u l t s Exp S t y l e Bisk ns 0.05 ns Be j Acc Rej 12. Extent o f Abbrev. Mode Exp S t y l e Risk 0.01 ns ns 0.04 Acc Eej Rej Acc 13. a b b r e v i a t i o n Length 0. 00 ACC 14. Ccmp. over Time Min/Per. 0.00 Acc 15. Comp. over Time C o n f i d . ns Rej 16. Ccmp. over Time U s a b i l i t y 0.09 Acc 17. Comp. over Time Abbrev. 0.01 Acc 18. Cemp. over Time H i s t o r i e s 0.00 Ace 19. Comp. over Time Grd-Hist ns R«1 20. , Comp. over Time Graphs 0.02 Acc 21. H i s t o r y Reports Mode 0.00 Acc Exp 0. 03 Acc S t y l e ns Bej Risk ns Bej 22. Ordered H i s t . Reports Mode 0. 01 Acc Exp ns Sej S t y l e ns Bej B i s k ns Bej 23. Graphs Mode ns Bej Exp 0. 11 Acc S t y l e 0. 15 Acc Bisk ns Bej 24. . P r o t o c o l s t r u c t u r e Exp 0. 13 Acc S t y l e 0.03 Acc B i s k ns Bej 25. , P r o t o c o l D i s p e r s i o n Exp 0. 07 Acc S t y l e ns Re j Bi s k ns Bej 

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