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The Usability of graphical user interfaces of mobile computing devices designed for construction foremen : icons and pr...

University of Florida Institutional Repository
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Permanent Link: http://ufdc.ufl.edu/UFE0013729/00001

Material Information

Title: The Usability of graphical user interfaces of mobile computing devices designed for construction foremen : icons and predefined text lists compared
Physical Description: Mixed Material
Language: English
Creator: Qu, Tan ( Dissertant )
Hinze, Jimmie W. ( Thesis advisor )
Hasell, Mary J. ( Thesis advisor )
Jones, Pierce ( Dissertant )
Akers, Ronald ( Reviewer )
Wetherington, Leon E. ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Design, Construction, and Planning thesis, Ph.D
Dissertations, Academic -- UF -- Design, Construction, and Planning
Genre: bibliography   ( marcgt )
theses   ( marcgt )

Notes

Abstract: Field documentation by construction foremen traditionally has been done through the use of pen and paper. The drawbacks of the traditional method and the need to computerize the field documentation process have long been recognized by researchers of construction management. Mobile computing devices provide an excellent hardware platform for addressing this need. Unfortunately, the past research efforts and technological developments in this area have not provided solutions with good usability. This study examined past research from a usability point of view and focused on the graphical user interface usability aspect of the problem. The inefficiency associated with the data input method though stylus and touch sensitive screen was examined. The focus of the study was on construction foremen, but other participants in the construction industry were also included as a basis of comparison. The study investigated the experience of the research participants with computers, personal digital assistants (PDA's) and other touch sensitive screen devices. The study evaluated the usability properties of icons and pre-determined text lists as potential candidates for automated data entry on mobile computing devices in the construction field. The views of participants on the standardization of the content of the field documentation, importance of quick data entry in the field, and the inefficiency associated with a stylus writing data input method were explored. Thirty-five construction foremen employed by sitework contractors, 37 construction professionals, and 28 university students were selected to complete a specially designed computer visual search game that consisted of an icon visual search interface and a text visual search interface. Each subject completed 14 visual search tasks in each interface. Results showed foremen and construction professionals performed visual search tasks faster with icons than with pre-determined text lists. Study results also showed comparable levels of accuracy of data input and also good satisfaction ratings when using the icon interface when compared with the text interface. The results also suggested a strong positive correlation between the task completion time and task errors (fewer errors when task times were short). A strong negative correlation was noted between the construction experience of the research participant and the task errors; i.e., participants with less experience made more errors.
Subject: automated, computing, construction, foremen, graphical, icons, mobile, sitework, usability
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 220 pages.
General Note: Includes vita.
Thesis: Thesis (Ph. D.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 003589386
System ID: UFE0013729:00001

Permanent Link: http://ufdc.ufl.edu/UFE0013729/00001

Material Information

Title: The Usability of graphical user interfaces of mobile computing devices designed for construction foremen : icons and predefined text lists compared
Physical Description: Mixed Material
Language: English
Creator: Qu, Tan ( Dissertant )
Hinze, Jimmie W. ( Thesis advisor )
Hasell, Mary J. ( Thesis advisor )
Jones, Pierce ( Dissertant )
Akers, Ronald ( Reviewer )
Wetherington, Leon E. ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Design, Construction, and Planning thesis, Ph.D
Dissertations, Academic -- UF -- Design, Construction, and Planning
Genre: bibliography   ( marcgt )
theses   ( marcgt )

Notes

Abstract: Field documentation by construction foremen traditionally has been done through the use of pen and paper. The drawbacks of the traditional method and the need to computerize the field documentation process have long been recognized by researchers of construction management. Mobile computing devices provide an excellent hardware platform for addressing this need. Unfortunately, the past research efforts and technological developments in this area have not provided solutions with good usability. This study examined past research from a usability point of view and focused on the graphical user interface usability aspect of the problem. The inefficiency associated with the data input method though stylus and touch sensitive screen was examined. The focus of the study was on construction foremen, but other participants in the construction industry were also included as a basis of comparison. The study investigated the experience of the research participants with computers, personal digital assistants (PDA's) and other touch sensitive screen devices. The study evaluated the usability properties of icons and pre-determined text lists as potential candidates for automated data entry on mobile computing devices in the construction field. The views of participants on the standardization of the content of the field documentation, importance of quick data entry in the field, and the inefficiency associated with a stylus writing data input method were explored. Thirty-five construction foremen employed by sitework contractors, 37 construction professionals, and 28 university students were selected to complete a specially designed computer visual search game that consisted of an icon visual search interface and a text visual search interface. Each subject completed 14 visual search tasks in each interface. Results showed foremen and construction professionals performed visual search tasks faster with icons than with pre-determined text lists. Study results also showed comparable levels of accuracy of data input and also good satisfaction ratings when using the icon interface when compared with the text interface. The results also suggested a strong positive correlation between the task completion time and task errors (fewer errors when task times were short). A strong negative correlation was noted between the construction experience of the research participant and the task errors; i.e., participants with less experience made more errors.
Subject: automated, computing, construction, foremen, graphical, icons, mobile, sitework, usability
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 220 pages.
General Note: Includes vita.
Thesis: Thesis (Ph. D.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 003589386
System ID: UFE0013729:00001


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THE USABILITY OF GRAPHICAL USER INTERFACES OF MOBILE COMPUTING
DEVICES DESIGNED FOR CONSTRUCTION FOREMEN:
ICONS AND PRE-DEFINED TEXT LISTS COMPARED













By

TAN QU

















A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY


UNIVERSITY OF FLORIDA


2006
































Copyright 2006

by

Tan Qu




























This dissertation is dedicated to my family for their loving support over the years to
finish this important chapter of my academic goals. My wife Wei Sun, who also received
her Ph.D. at the University of Florida, has been a wonderful counselor and gave me moral
support whenever I needed it. This dissertation is especially dedicated to my son, Tan,
who is coping every day with the learning disabilities associated with his autism. His
inquisitiveness and persistence in learning has taught me a completely new perspective
about the meaning and the privilege of the higher education. He was the very first and the
most enthusiastic reviewer of the icons that were designed for this study. This dissertation
is also dedicated to my daughter Victoria, who has been the joy of my life and who was
the constant inspiration to me with her loving energy. Last but not the least, this
dissertation is dedicated to Isabella, my infant daughter, who unconditionally shares her
love.















ACKNOWLEDGMENTS

I am grateful to many individuals for their support in this research effort. Without

their guidance and assistance, this study would not have been possible.

My supervisory committee was an excellent source of direction, both during the

stage of preparing a feasible and sound proposal for the study and throughout the actual

research and dissertation writing phases. I am much indebted to my committee chair Dr.

Jimmie Hinze, who has exemplified his true scholarship and mentorship and guided me

through individual steps of the study. Dr. Hinze has devoted countless hours from his

busy schedule in critiquing the study design and data analyses. His detailed review of the

working drafts was extremely beneficial. Dr. Pierce Jones provided many valuable

suggestions and directions in refining the research apparatus. His knowledge and

expertise in computer visual communications proved indispensable in this area. Dr.

Ronald Akers provided the needed scrutiny in statistical methods used in the study and

offered many good suggestions. Dr. Mary Jo Hasell provided considerable assistance

during the initial proposal development and offered many thoughtful insights. Her

encouragement to me in finishing the Ph.D. program was much appreciated. Dr. Leon

Wetherington gave helpful advice in refining the survey questionnaire and the experiment

apparatus, and he provided a very practical perspective to this study.

Many other individuals who contributed to this study must also be acknowledged.

Paul Ridilla, my best friend, also a very knowledgeable construction management

consultant, has provided great support in completing this research effort through his









candid and practical views from his over 50 years of experience in the construction

industry. I cannot forget to mention Jimmy Flores and many other foremen, construction

professionals, and students at the M. E. Rinker, Sr. School of Building Construction of

the University of Florida for their participation in the study. I cannot name each one of

them here but their valuable time in completing the study was much appreciated. I also

would like to give thanks to the individuals at the Florida Department of Transportation

and the principals of the firms that participated in this study.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES .................................................. ................ ....... xi

LIST OF FIGURES ......... ....................... .......... ....... ............ xvi

ABSTRACT ........ .............. ............. ...... ...................... xix

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

P problem Statem ent................. .................. ........ ................ ....... ........... ..
Field Information Documentation in Construction ..........................................2
Problems Associated with Paper-based Documentation Method..........................3
Computerizing Field Information Documentation ..............................................4
R research O bjectives.......... ..................................................................... ....... . ....

2 L IT E R A TU R E R E V IE W .................................................................. .....................9

Human-Computer Interface/Interaction (HCI), Graphic User Interface (GUI), and
U sab ility ........................ ............ .............. ..... .... ................. 9
Past Research Examined from A HCI and Usability Perspective ..............................11
Foremen and Their Role in the Information Communication Process.....................13
Graphic User Interface on Pen-Based Mobile Computing Devices ...........................16
Icons, Signs and Symbols A Brief Historical Review........................ ..................18
Signs, Symbols and Icons in Construction and the Possibility of Using Icons as
Automated Data Entry in Graphic User Interface ............................................23
Icons vs. Pre-defined Text .......... ........... ......................... ......... ...............25
Effect From Interface Implementation Differences ........................................26
Visual Appeal Factor Associated With Iconic Interfaces...................................27
Abstract Vs. "Concrete" Icons And Icons As Computer Command Vs. As
Inform action U nits .......... ........ .......................... .. ..... .... ... .. ...... .... 27
Subject Characteristics ........................................ ...............................27
S u m m ary .......................................................................................... 2 8









3 RESEARCH M ETHODOLOGY ........................................ .......................... 29

R research Q questions ................. .. ...... .. ... .. ................ .... ........ ..... .... 3 1
Do Construction Foremen Perform Computer Tasks Faster Using Icons Than
Using Predefined Text Lists Or Vice Versa?.......................... ................31
Do Construction Foremen Experience Fewer Errors Using Icons Or Pre-
D defined Text List? .......................................................... ...... .. .................32
Do Construction Foremen Have A Preference Between Predefined Text Lists
A nd Icons? ............................................... ............... .... .................... 33
What Is The Ranking Order Of The Above Three Usability Aspects From
The Point Of View Of Construction Foremen? ..........................................34
What Are The Views Of Construction Foremen About The Concept Of The
Icon Based Mobile Field Documentation Applications? ..............................34
What Is The General Knowledge And Experience Of Construction Foremen
On M obile Com putting D evices? .............................. ... .................... ....34
What Percentage Of The Information In Current Field Documentation Do
Foremen Think Can Be Standardized For Use With The "Click And
Select" C concept? ................................................ .... .. .. .. ........ .... 35
S am p le s ..................................................... 3 5
M eth o d s .........................................................................3 7
Visual Searching Task Experim ent .......................................... ............... 38
A pparatus/M materials .............................................................................. 39
Icon training session ........... ........................................ .... .... .............. 40
Icon visual search test .......................... .. .. ................ ........ .. ........ .. 41
Test platform ........................................43
Icons and Pre-defined Text Lists ............... ........................ ............... 44
D ata collection m ethod........................................... .......................... 46
R response V ariables .............. ................................ ............. ............... 47
Visual Search Game Design considerations..................... .................48
Sample Icon-Based Mobile Equipment Usage Documentation Application ......50
P ro cedu res ...............................5 4............................
Research Hypotheses ........... ........................ .................. .......... .... 55
Task Completion Tim e ....................................... ...............................55
T a sk E rro rs ................................................................5 7
U ser Satisfaction ........ .................................................................. .... .... .... 57
Survey Q questionnaire D esign ............................................. ............................ 58
Foremen Demographics................. .. .. ..........................59
Foremen's Experience with Touch Sensitive Screen Devices and Mobile
C om putting D evices.......................... ............ ... ................... ............ 59
Foremen's View on Standardization of the Content of Field Documentation ....60
Foremen's Preference Between Icons and Pre-defined Text List.....................60
Foremen's View about Icon-based Field Information Documentation Tools.....61

4 PILOT STUDY ....................... .... ... ............ ............... .. ...... 63

Icon Design and Recognition Quality Testing .............. ................................. 63
Test Platform D difference Study ................................ ............................................ 68









D a ta ................................................................................................................ 6 8
H ypotheses T testing ............. .. ........ .......... .................. ...... ................. 72
Hypotheses testing about variances of the populations on Fujitsu and
N on-Fujitsu platform s............................. ....................... 72
Hypotheses testing about the difference between the means of the data
collected on the Fujitsu and Non-Fujitsu platforms ..............................76
Icon Learning Curve A nalysis......................................................... ............... 81
Learning Curve R egression ........................................ ........................... 81
Long Term Effect of Icon Training ................................ .. ..... ............... 86
Number of Training Sessions Required for the Final Study ............................91
Establishing Training Session Time Baseline...............................................91
Lessons Learned During the Pilot Study .................................. ............... 92
Experim ental environm ent ............................................... ............... 93
V erbal instructions ............................................ .............. ..............93

5 RESULTS AND DISCU SSION S......................................... .......................... 94

Sam ple D em graphics ..... ............................................................ ............... 94
A g e ..............................................................................9 4
E d u c a tio n ....................................................................................................... 9 5
Construction Experience .................................. .....................................96
Foreman's Crew size ................... ............................ ........97
Foremen Categorizations .......................................................................... 98
Occupations of the Construction Professionals.................................... 99
Student Status ................. ......... ........ .... ........100
Com puter Experience ................................................ ..... ..... ................ 100
Experiences of the Research Subjects with Touch Sensitive Screen Devices
(T S S D 's) ................ ..... .......... .... .... .................................. 1 0 1
Experiences of the Research Subjects with Personal Digital Assistants (PDA's)....104
Views of The Research Subjects about the Efficiency of the Data Entry
M echanism by Handwriting Recognition .................................. ............... 105
Forem en and PD A Efficiency ........................................ ........ ............... 106
Construction Professionals ................................................... ............... ... 107
Stu dents ................................................................ 109
C ross-groups .............................. ...............................110
The Views of Subjects on the Importance of Quick Data Entry on Mobile
C om putting D devices .................................. .. .. ... ...... .... .. ........ .. ........... .. 112
The Views of Foremen and Construction Professionals about the Standardization
of the Field D ocum entation Content...................................... ......... ............... 113
The Views of Foremen and Construction Professionals about the Percentage of
the Field Documentation Content That Could be Standardized..........................114
Satisfaction Ratings of the Subjects with the Icon Visual Search Game and Text
V isual Search G am e ................... ........... ... ....... ... .. ............... ................ 115
Hypothesis Testing on Subjects' Satisfaction Ratings with the Icon Visual
Search Game and Text Visual Search Game..............................................118
Wilcoxon matched pairs signed rank test.............................119
Paired D difference t-test..................................... ............................ ........ 120









Ranking Order of the Three Usability Factors (Task Time, Task Errors, and
Satisfaction Level) ..................................................................... .. ... ....... 121
Ranking Order of the Three Usability Factors by Foremen.............................121
Ranking Order of the Three Usability Factors by Construction Professionals .122
Ranking Order of the Three Usability Factors by Students ...........................123
The Views of Subjects about the Icon-based Field Documentation Systems on
M obile C om putting D evices ................. .................... .. ..................... .... 124
Readiness of the Foremen to Use Field Documentation Systems on Mobile
C om putting D evices ............... ........ ...... .... ............ ...... ................ ...... 126
Visual Search Game Results Analyses and Hypotheses Testing ...........................127
Average Task Time ......................... ....... .............................. 127
Average Task Instruction Reading Tim e..........................................................129
A average Task Search Tim e ........................................ .......................... 131
T ask E rrors ................ ................................................... 133
Error R education in Training Sessions.................................................. ................ 135
One-Way ANOVA (Analysis of Variance) of Visual Search Game Results and
Subject Types ............... ........... ........ ........................................... 137
Correlation Analysis between Construction Experience and the Average Icon
S each T im e ................... .... ............................... .. ... ... .... ............... 14 0
Correlation Analysis between Construction Experience and the Task Errors..........141
Correlation Analysis of the Average Task Search Time and Task Errors.............. 142
One-Way ANOVA (Analysis of Variance) of Visual Search Task Time of
Foremen with Computer Usage as Factor Levels ...........................................143

6 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ..........................145

S u m m ary ......................... ......... ................... .......... ... ..... ..................... 14 5
Are Computer Tasks Performed Faster When Using Icons Than When Using
Predefined T ext L ists? ................... ... ..... ............................ .... 145
Are Textual Instructions Processed Faster Than The Iconic Instructions? .......146
Are Icons Located Faster Than Text? ................... ................................... 146
Errors With Icons Versus Errors With Pre-Defined Text List ..........................146
Preferences of Pre-defined Text Lists Versus Icons............... .... ..............147
Ranking Order of the Three Usability Factors ............................. .................. 147
Views about Using Icon Based Mobile Field Documentation Applications.....147
Views about the Standardization of Information Contained in Field
Docum entation ............................. ...... ... ...... ...... .. .............. 147
Experience of Foremen with Mobile Computing Technologies .....................148
C o n clu sio n s.................................................... ................ 14 8
R research Lim stations .............................................. ... .... ....... ........ 149
R ecom m endations....... .............. .......................... .. .. .. ....... .. ............ 150
Future Research Recom m endations .............................. .......... ............... .... 151
Other Sectors of the Construction Industry and Other Geographical Areas .....151
Intelligent Data Validation in Data Input Process............................................151
Modeling of the Cognitive Activities of the Visual Search Process Through
the Use of Eye-tracking Technologies................................ ................. 152










APPENDIX

A PILOT STUD Y RESULTS D A TA .................................. ...................................... 153

B FINAL STUDY RESULT DATA...................................... .......................... 159

C SURVEY QUESTIONN AIRE ........................................................ .... ........... 190

L IST O F R E FE R E N C E S ......................................................................... ................... 192

B IO G R A PH IC A L SK E T C H ........................................... ...........................................200













































x
















LIST OF TABLES


Table p

3-1 Icons and Pre-defined Text Lists Used in the Visual Search Tests..........................44

4-1 Icon Recognition Quality Testing Results .................................... ............... 64

4-2 Icon Recognition Evaluation Results Organized by Evaluator.............................68

4-3 Example of Excluding Outliers in the Computation of the Average Task Time .....70

4-4 Correlation Between the Mean Task Time and Task Errors on the Fujitsu
P platform ................ .......... ........................... ...........................7 1

4-5 Correlation Between the Mean Task Time and Task Errors on the Non-Fujitsu
P platform ................ .......... ........................... ...........................7 1

4-6 Platform Difference Study Sample Variance F Values...................................76

4-7 Platform Difference Study t Test for Equality of Means ................................80

4-8 Platform Difference Study Sample Group Statistics ............................................81

4-9 Learning Rate on the Average Task Time Per Icon for Each Training Session ......84

4-10 Group Statistics of Learning Rates...................................... ......................... 85

4-11 Session Tim e D ata Statistics ............................................................................. 92

5-1 Mean and Median Ages of the Foremen, Construction Professionals, and
S tu d en ts ............................................................................. 9 4

5-2 Mean and Median Construction Experience Durations of the Foremen,
Construction Professionals and Students ...................................... ............... 96

5-3 Levene Test of Homogeneity of Variances on the Total TSSD's Scores............103

5-4 LSD Test of the Means of Total TSSD's Scores ...........................104

5-5 Levene Test of Homogeneity of Variances on the Ratings of Foremen with and
without PDA Experience...... .................... .. ........................... 107









5-6 One-Way ANOVA of the Means of the Numeric Ratings of Foremen with PDA
Experience and Foremen without PDA experience ............................................ 107

5-7 Levene Test of Homogeneity of Variances on the Ratings of Construction
Professionals with and without PDA Experience ...............................................108

5-8 One-Way ANOVA of the Means of the Numeric Ratings of Construction
Professionals with and without PDA experience.............................109

5-9 Levene Test of Homogeneity of Variances on the Ratings of Students with and
w without PD A Experience ......................................................... ............... 109

5-10 One-Way ANOVA of the Means of the Numeric Ratings of Students with and
w without PD A experience ......................................................... .............. 110

5-11 LSD Test of the Means of Numeric Ratings by Foremen, Construction
Professionals, and Students with Prior PDA Use Experience.............................111

5-12 Percentages of the Field Documentation Content that Could be Standardized As
Estimated by Foremen and Construction Professionals .............. ... ...............114

5-13 Wilcoxon Signed Ranks -Satisfaction Rating Differences between the Icon
Visual Search Game and the Text Visual Search Game .....................................119

5-14 Wilcoxon Signed Ranks Test Statistics-Satisfaction Rating Differences between
the Icon Visual Search Game and the Text Visual Search Game ........................120

5-15 Paired Samples Differences t-Tests Statistics- Subjects' Satisfaction Ratings
with the Icon Visual Search Game and the Text Visual Search.............................121

5-16 Paired Samples t-Tests Importance Ratings of the Foremen on Shorter Task
Time, Fewer Task Errors and Higher User Satisfaction .....................................122

5-17 Paired Samples t-Tests Importance Ratings of the Construction Professionals
on Shorter Task Time, Fewer Task Errors and Higher User Satisfaction............123

5-18 Paired Samples t-tests Students' Importance Ratings on Shorter Task Time,
Fewer Task Errors and Higher User Satisfaction............................124

5-19 LSD Test Results Responses of Foremen vs. Their Computer Usage................125

5-20 Paired Samples t-Tests -Average Task Time in the Icon User Interface vs. Text
U ser Interface .................................... ........................................ 129

5-21 Paired Samples t-tests -Average Task Time in the Icon User Interface vs. Text
U ser Interface ....................................................... .... ...................... 13 1

5-22 Paired Samples t-tests -Average Task Search Time in the Icon User Interface vs.
Text U ser Interface ......................................... ...... ........ .. ........ .... 133









5-23 Paired Samples t-Tests Mean Task Errors in the Icon User Interface vs. Text
U ser Interface ..................................................................... .........135

5-24 Correlation Between Training Session Errors and Construction Experience ........136

5-25 Levene Homogeneity of Variance Tests on the Visual Search Game Results
Between Foremen, Construction Professionals, and Students............................137

5-26 One-way ANOVA of the Average Task Time, Average Task Instruction
Reading Time, and Average Task Search Time By Subject Types ....................138

5-27 Post-Hoc LSD Test Results on Task Search Time in the Icon Visual Search
G a m e ........................................................................... 1 3 9

5-28 Tamhane's T2 Test on the Task Errors in The Icon Visual Search Game And
Text Visual Search Game Subject Type As Factor Levels ........... ................ 140

5-29 Correlation Analysis on the Construction Experience and Icon Search Time.......141

5-30 Correlation Analysis between the Construction Experience and Icon Search
E errors ................ .................................... ...........................14 1

5-31 Correlation Analysis between the Construction Experience and Text Search
E errors ................ .................................... ...........................142

5-32 Correlation Analysis between the Icon Search Time and Icon Search Errors .......142

5-33 Correlation Analysis between the Text Search Time and Text Search Errors.......143

5-34 Levene Homogeneity of Variance Tests Task Time of Foremen with Different
C om puter U sage .................................................. .. .... ... .. ........ .... 143

5-35 One-way ANOVA of the Task Time of Foremen Computer Usage as Factor
L levels ............................................................... .... ...... ......... 144

A-i Average Task Time for Each Training Session..........................................153

A-2 Number of Errors for Each Training Session....................................................... 154

A-3 Subject 1(Homebuilder Superintendent) Icon Training Session Data ...................155

A-4 Subject 2 (Engineer) Icon Training Session Data..................................................156

A-5 Subject 3 (Framing Foreman) Icon Training Session Data.............................157

A -6 Session Tim e for Each Training Session..................................... ..................... 158

B -l Forem en D em graphics ................................................................................... 159









B-2 Construction Professionals Demographics.................................. ...... ............ ...160

B -3 Student D em graphics ............................................ ......................................... 16 1

B-4 Foremen's experience with common touch sensitive screen devices ..................162

B-5 Construction professionals' experience with common touch sensitive screen
devices .............. .......... ....... ......... ......................................... 163

B-6 Students' experience with common touch sensitive screen devices ....................164

B-7 Foremen' experience with PDA's ...............................................165

B-8 Construction Professionals' experience with PDA's ........................ ...............166

B-9 Student Subjects' Experience with PDA's..... ..............................167

B-10 Foremen's Ratings of the Efficiency of the Data Entry Mechanism by Stylus
Handwriting on M obile Computing Devices ................................. ... ................ 168

B-11 Construction Professionals' Ratings of the Efficiency of the Data Entry
Mechanism by Stylus Handwriting on Mobile Computing Devices.................169

B-12 Students' Ratings of the Efficiency of the Data Entry Mechanism by Stylus
Handwriting on M obile Computing Devices ............................... ............... .170

5-13 Foremen Subjects' Ratings of the Importance of Being Able to Input Data
Quickly on Mobile Computing Devices................................... ....... .......... 171

B-14 Construction Professionals' Ratings of the Importance of Being Able to Input
Data Quickly on M obile Computing Devices .................................. ................. 172

B-15 Student Subjects' Ratings of the Importance of Being Able to Input Data
Quickly on Mobile Computing Devices.................. ......................... 173

B-16 Foremen's View about Whether Most Content of Their Field Documentation
C ou ld b e Stan dardized ............................................ ......................................... 174

B-17 Construction Professionals' View about Whether Most Content of the
Construction Foremen's Field Documentation Could be Standardized ...............75

B-18 Foremen's Estimate of the Percentage of the Information in Their Field
Documentation Could be Standardized............................... ............ ............. 176

B-19 Construction Professionals' Estimate of the Percentage of the Information in
Construction Foremen's Documentation that Could be Standardized................... 177

B-20 Foremen's Satisfaction Ratings with Icon Visual Search Game and the Text
V isual Search G am e .............. ........................... ... ...... ................. .. 178









B-21 Construction Professionals' Satisfaction Ratings with Icon Visual Search Game
and Text V isual Search G am e ...................................................... .............. 179

B-22 Student Subjects' Satisfaction Ratings with Icon Visual Search Game and Text
V isual Search G am e .......... ... .................. ..... ............ .. ...... .. ........ .. 180

B-23 Foremen's Importance Ratings on Shorter Task Time, Fewer Task Error and
H higher U ser Satisfaction ............................................... ............................. 181

B-24 Construction Professionals' Importance Ratings on Shorter Task Time, Fewer
Task Error and Higher User Satisfaction .................................... ............... 182

B-25 Students' Importance Ratings on Shorter Task Time, Fewer Task Error and
H higher U ser Satisfaction ............................................... ............................. 183

B-26 Foremen's Views About Whether the Icon-based Field Documentation Systems
W would Help Do Their Jobs ............ ... ................................ 184

B-27 Construction Professionals' Views About Whether the Icon-based Field
Documentation Systems Would Help Foremen Do Their Jobs ...........................185

B-28 Student Subjects' Views About Whether the Icon-based Field Documentation
Systems W would Help Foremen Do Their Jobs................................................... 186

B-29 Foremen Subjects' Average Task Time, Average Task Instruction Reading
Time, Average Task Search Time, and Average Task Errors.............................187

B-30 Construction Professionals' Average Task Time, Average Task Instruction
Reading Time, Average Task Search Time, and Average Task Errors .................188

B-31 Students' Average Task Time, Average Task Instruction Reading Time,
Average Task Search Time, and Average Task Errors ............... ................189
















LIST OF FIGURES


Figure pge

2-1 Illustration of the Evolution of Human-Computer Communication Process ..........23

3-1 Extended Stages of the Information Processing Model (Preece at al. 1994) ..........30

3-2 Sample Screen Shot of the Icon Training Session ................................................41

3-3 Sample Screenshot of the Icon Visual Search Session ............... .... ........... 42

3-4 Screenshot of the Text Visual Search Session ........................ ... ..................... 43

3-5 Visual Search Response Variable Definitions ............................... ............... .48

3-6 Main Screen of the Sample Icon-based Field Documentation Application
(shown running on Handspring Treo 270 Model)...................................... 51

3-7 Equipm ent Selection Screen.......................................................... ............... 52

3-8 Scraper Selection Screen ........... ......... ...... ................................. ............... 52

3-9 Scraper Time Information Entry Screen ...................................... ............... 53

3-10 Scraper W ork Production Input Screen......................................... ................... 53

3-11 Schematic Diagram of the Human Eye, With the Fovea at the Bottom Courtesy
from Wikipedia, http://en.wikipedia.org/wiki/Optic fovea, February 7, 2006 .......56

4-1 Mean Task Time and Search Errors Observed in the Platform Difference Study ...71

4-2 Learning Effect of the Mean Average Task Time.................................................82

4-3 M ean Learning Rate Scatter Plot ........................................ ......................... 86

4-4 Average instruction reading time, search time, and task time (Subject 1)...............87

4-5 T ask E errors (Subject 1) ......... ................. .................................. ..........................88

4-6 Average instruction reading time, search time, and task time (Subject 2)...............88

4-7 Task E errors (Subject 2) .................................. ......... ...................................89









4-8 Average instruction reading time, search time, and task time (Subject 3)...............89

4-9 Task Errors (Subject 3) .............................. ............. ...................................90

5-1 Age Group Distributions of the Research Subjects ...... ...................................... 95

5-2 Education Levels of the Research Subjects..... ..............................96

5-3 Construction Experience of the Research Subjects............................................... 97

5-4 C rew Sizes of Forem en ................................................. ............................... 98

5-5 Forem en Specializations ................................................ .............................. 99

5-6 Occupations of the Construction Professionals................ ...... ..............99

5-7 Computer Use Experience of Foremen ..... ..................... ............101

5-8 Subjects' experience with common TSSD's......................................103

5-9 Experience of Research Subjects with PDA Devices ............... ..... .......... 105

5-10 Efficiency Ratings of Foremen on the Stylus Writing Method on PDA Devices.. 107

5-11 Efficiency Ratings of Construction Professionals on the Stylus Writing Method
on PD A D devices ......... ............ ............. ............. .. .. ........ .. ............ 108

5-12 Stylus Writing Input Method Efficiency Ratings by Foremen, Construction
Professionals, and Students Who Had PDA Use Experience ...............................111

5-14 The Importance Ratings on Being Able to Enter Information on Mobile
Computing Devices Quickly ............................ ..... ...................................... 113

5-14 Responses of Foremen and Construction Professionals on Whether The Content
of The Field Documentation Could be Standardized...........................................114

5-15 Satisfaction Ratings of Foremen on the Icon Visual Search Game and Text
V isual Search G am e ................ ................................. ....... .. ........ .. 116

5-16 Satisfaction Ratings of Construction Professionals on the Icon Visual Search
Game and Text Visual Search Game ....................................... ...............116

5-17 Satisfaction Ratings of Students on the Icon Visual Search Game and Text
V isual Search G am e .............. ........................... ... ............... .. 117

5-18 Subjects' Equivalent Numeric Satisfaction Ratings on the Icon Visual Search
Game and Text Visual Search Game ....................................... ...............118









5-19 Views of Subjects About Whether the Icon-based Field Documentation Systems
would Help Foremen Do Their Jobs ........................................... ............... 125

5-20 Responses of Foremen on Whether They Would Use a Field Documentation
System on M obile Computing Devices ............................. ..... .... ............. 127

5-21 Mean Average Task Time Observed on the icon interface vs. text interface for
each sam p le ...................................................... ................ 12 8

5-22 Mean Average Task Instruction Reading Time Observed during the Icon Visual
Search Gam e vs. the Text Visual Search Game.....................................................130

5-23 Mean Average Task Search Time Observed during the Icon Visual Search Game
vs. the Text Visual Search Gam e ..................................... ........... ........ ....... 132

5-24 Mean Task Errors Observed during the Icon Visual Search Game vs. the Text
V isual Search G am e .................. ............................ .. ...... .. ............ 134

5-25 Task Errors Observed during the Icon Training Sessions.................................... 136


xviii















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

THE USABILITY OF GRAPHICAL USER INTERFACES OF MOBILE COMPUTING
DEVICES DESIGNED FOR CONSTRUCTION FOREMEN:
ICONS AND PRE-DEFINED TEXT LISTS COMPARED

By

Tan Qu

May 2006

Chair: Jimmie W. Hinze
Cochair: Mary J. Hasell
Major Department: Design, Construction, and Planning

Field documentation by construction foremen traditionally has been done through

the use of pen and paper. The drawbacks of the traditional method and the need to

computerize the field documentation process have long been recognized by researchers of

construction management. Mobile computing devices provide an excellent hardware

platform for addressing this need. Unfortunately, the past research efforts and

technological developments in this area have not provided solutions with good usability.

This study examined past research from a usability point of view and focused on

the graphical user interface usability aspect of the problem. The inefficiency associated

with the data input method though stylus and touch sensitive screen was examined. The

focus of the study was on construction foremen, but other participants in the construction

industry were also included as a basis of comparison. The study investigated the









experience of the research participants with computers, personal digital assistants

(PDA's) and other touch sensitive screen devices.

The study evaluated the usability properties of icons and pre-determined text lists

as potential candidates for automated data entry on mobile computing devices in the

construction field. The views of participants on the standardization of the content of the

field documentation, importance of quick data entry in the field, and the inefficiency

associated with a stylus writing data input method were explored.

Thirty-five construction foremen employed by sitework contractors, 37

construction professionals, and 28 university students were selected to complete a

specially designed computer visual search game that consisted of an icon visual search

interface and a text visual search interface. Each subject completed 14 visual search tasks

in each interface. Results showed foremen and construction professionals performed

visual search tasks faster with icons than with pre-determined text lists. Study results also

showed comparable levels of accuracy of data input and also good satisfaction ratings

when using the icon interface when compared with the text interface. The results also

suggested a strong positive correlation between the task completion time and task errors

(fewer errors when task times were short). A strong negative correlation was noted

between the construction experience of the research participant and the task errors; i.e.,

participants with less experience made more errors.














CHAPTER 1
INTRODUCTION

The value and importance of information acquisition, transfer, organization, and

utilization are well accepted in the construction industry. "In a profound sense, the

management of a construction project is about managing the project information flow"

(Winch 2002, pp. 339). A construction project from inception to completion involves a

multitude of varied participants and the whole construction process generates vast

amounts of information. Effectively managing such an immense volume of information

to ensure its accuracy and availability in a timely manner is crucial to the successful

completion of any project (Cox et al. 2002).

Problem Statement

A construction project is a unique, complex, custom-built response to a client's
needs. (Russell 1993)

It is not only a process whereby information from the participants in the form of

building or site plans, specifications, construction schedules, and various other

documents are implemented, but also a process where new information is created. This

process takes on physical and time dimensions and often generates a mammoth amount

of information of varying interest to the various participants. As the time dimension

grows, the volume of the information also increases, providing new data for the spatial,

time, resource and cost variables of the project.









Field Information Documentation in Construction

Many aspects of the construction process require accurate documentation of site

conditions, including progress, quality, quantity, change, conflicts, and as-built

information on the project. Documentation, communication, and analysis of construction

field data are beneficial to all participants of a construction project (Hwang et al. 2003).

For example, field data are needed for the project owners to verify and approve

construction payment requests. Engineers and architects rely on field data to verify their

design assumptions and improve the designs. Contractors require up-to-date field

information to have a good understanding of the project status. In the construction

industry, where disputes and litigation are almost commonplace, accurate documentation

not only minimizes the possibility of disputes and claims, but also facilitates construction

innovations and improvements (Liu 2000). The importance and legal ramifications of

accurate vs. poorly documented construction information are well cited by the

practitioners and academicians in the construction industry (Kangari 1995, O'Brien

1998).

Field information documentation is especially important to contractors. Russell

(1993) pointed out that the collection of field information is important to

* Record the values of various context variables (weather conditions and work-force
parameters) that are helpful in explaining reasons behind the current status of a
project.

* Assess the current status of activities, extra work orders, and back charges in terms
of active state (postponed, started, ongoing, idle, and finished), work scope
completed, and problems encountered and their immediate consequences (man-
hours and/or time lost).

* Measure resource consumption rates and their allocation to ongoing activities









Besides having these functions, information collected in the field is often kept by

contractors as historical data for preparing future estimates and schedules (Fayek et al.

1998).

Problems Associated with Paper-based Documentation Method

Traditionally, field information documentation has been done through the use of

paper forms. This practice remains the same with few changes over the years for the

majority of the construction industry (McCullouch and Gunn 1993, Fayek et al. 1998,

Cox et al. 2002). With the paper-based documentation method, information is manually

entered in notebooks or pre-printed forms. These notebooks (sometimes called "logs")

and forms are periodically sent to the main offices for top management review and for

archival purposes. Sometimes the pre-printed forms can be further processed by copying

desired information from multiple forms into one form and even into a computer

spreadsheet.

Unfortunately, such systems are based on a large number of paper documents and

have numerous drawbacks, especially when the need arises for accessing and retrieving

the information that has been collected. Fayek et al. (1998) identified some of the

problems with paper documentation as follows:

* Inconsistent procedures for collecting data on different types of resources (labor,
equipment, materials, and subcontractors);

* Inaccurate assignment of hours to cost codes;

* Lack of data on site conditions, schedule progress, and problems associated with
activities which lead to cost and schedule overruns;

* Multiple entry of the same data;

* Lack of timely feedback on project performance.









With these deficiencies, it is difficult to obtain timely information on potential

problems with schedules, resources and safety issues and to initiate the appropriate

corrective actions. Incomplete/inadequate and inaccurate documentation, as a result of

poor recordkeeping, are often considered inferior evidence documents in litigation or

arbitration procedures (Kangari, 1995).

The use of inaccurate information in project bidding and project resource allocation

often results in significant economic consequences that are manifested as construction

delays and business losses (Cox et al. 2002). As in any competitive industry, nothing

could be more devastating to construction companies than making important decisions on

unreliable information.

Computerizing Field Information Documentation

With the apparent problems associated with the paper-based documentation

method, the need and importance to computerize the field information collection process

have long been recognized in the construction industry (Russell 1993, McCulloch 1993,

Condreay 1997, Elzarka et al. 1997, Liu 2000, Cox et al. 2002, Hwang et al. 2003). As an

industry-wide practice, the use of computer technology in fulfilling this need has not yet

come to reality.

Computer use in the offices of the construction companies is no longer considered

"high-tech" business "things" that only a few privileged ones can have access to. The use

of desktop computers and desktop applications is an essential part of business operations

that include accounting, word processing, project estimating, project scheduling, and e-

mail communications. However, computer use by field personnel for documenting field

data is still not a common practice in the construction industry. Communications between

field personnel and office management, to a large extent, continue to be verbal









communications through the use of telephones (wired or wireless) and two-way radios.

Although this part of the communication channel between the field and the office has

been greatly improved by the advancement of wireless communication technologies, the

transient nature of verbal communications leaves few means by which the information

can be conveniently stored and retrieved.

This obstacle in computerizing construction field communications is perceived in

the industry to be due to various forms of barriers (Toole 1998, Davis and Songer 2003,

Flood et al. 2003). Flood et al. summarized the barriers as follows:

1. Lack of application development: various computing models and concepts have
been developed through years of research. However realizing and fine-tuning that
concept into a workable application often requires financial and time commitments
for research and development that are not readily available.

2. Institutional and individual barriers: these include old beliefs and resistance to
change and to the adoption of new technologies; lack of understanding of the
potential of a tool; lack of resource commitment to its proper implementation;
concerns about possible legal ramifications in the use of a new technology; and
lack of confidence in the integrity of the output from a new technology.

3. Quality issues as "user-friendliness" and "integrity of the software:" these include
issues such as the ease with which an application can be learned by its users, the
ease of which output and results can be interpreted, the convenience of data input,
the convenience with which the application can be tailored to work for each
specific problem, etc.

The "barriers" or problems described by Flood et al. are inter-related. For example,

the institutional barriers exist because although the computer technologies advance

rapidly there has not yet been any stabilized system of solutions that fully considered the

differing characteristics of various potential construction field users. To clarify the point,

the lack of a unified operating system standard in the mobile computing technologies has

resulted in many different mobile computing devices that are available commercially and

these technologies cannot be considered as stabilized as they are under constant patch,









upgrade and refinement. Second, existing software applications for mobile computing

devices to be used for construction field documentation are scarce and often the end-user

characteristics and working environment was not considered when they were developed.

All these factors along with the old beliefs have made it difficult for construction

companies to invest in these technologies for their field personnel. Since construction

companies have not universally adopted the mobile computing technologies for their field

supervisors, there has been little enthusiasm from the software developers to address this

application.

The third category of barriers, discussed above, seems to be the root problem. The

ability to conveniently input data in the construction field has been a challenge and a

driving force for the research in computerizing the construction field documentation

process. Many ideas and directions exist in providing solutions for this need. However,

there have not been any studies taking on the system usability point of view in examining

the problems. Usability refers to how easily a system can be learned and used by its

intended end users, how fast the users can complete the required tasks, how much the

system is prone to errors, and how much the users like to use the system. Among the

major aspects of the system usability, hardware usability issues are generally addressed

by the computer industry on a continuing basis while the software usability issues

constitute the primary interest to researchers in the construction industry. This study will

undertake a system usability approach to examine the problems existing in the computer

software interfaces designed for use in the construction industry and evaluate possible

alternative solutions.









Research Objectives

With respect to the third category of the problem as summarized by Flood et al.

(2003), existing research efforts have mainly focused on the hardware aspect of the issue.

Research in the past has mostly revolved around the approach of exploring commercially

available mobile computing devices and their suitability for construction field

information communications. Small-sized mobile computing devices equipped with

touch sensitive screens have now been accepted as a basic platform for computer use in

the construction field environment; however, the graphical user interface aspect has not

been extensively investigated.

This study will introduce the concept of the usability of the computer graphical user

interface into the construction research world and use this concept to provide a new

perspective on how past research on mobile computing in construction has progressed.

The problems related to the inefficiency of the pen/stylus handwriting input method on

mobile computing devices will be examined. Existing studies on alternative automated

data collection technologies to augment the pen/stylus data input method for mobile

computing systems in construction will also be reviewed and discussed.

As the main objective of this study, icons (graphical or illustrative representations

of concepts or items) as a possible alternative mechanism for automated data entry on

mobile computing systems will be investigated. This study will focus on construction

foremen as the real field information providers and the validity of icons as the main

mechanism in the graphical user interfaces designed for them. From the usability

approach, icons and pre-defined text lists will be compared in evaluating their relative

effectiveness and efficiency in construction field data input processes. A user interface

experiment with the participation from sitework construction foremen in Central Florida









will be conducted to determine which mechanism results in better usability, e.g., shorter

task times, fewer user errors and higher user satisfaction. The priority order assessment

by foremen for these three important usability factors (task completion time, task errors,

and user satisfaction) will be surveyed. The effect of foremen demographics on the

resulting data will also be analyzed and discussed.

The experience of construction foremen with mobile computing devices will be

explored. This study will also investigate through face-to-face interviews their opinions

about using icon-based mobile documentation tools. As potential end users, they will be

asked questions related to the computerization of field documentation.














CHAPTER 2
LITERATURE REVIEW

This chapter will discuss some general concepts related to human-computer

interaction/interface (HCI), graphical user interface (GUI), icon, and usability theories.

Past research on computerizing construction field communications will be reviewed from

a system usability perspective. Construction foremen and their role in the information

communication process on construction sites will be examined. Limitations associated

with the pen/stylus handwriting-based data input method will be discussed as well. This

chapter will also provide a brief historical review on icons, signs and symbols. In the later

part of this chapter, the concept of using icons as an automated data entry mechanism in

graphical user interfaces designed for construction foremen will be discussed.

Human-Computer Interface/Interaction (HCI), Graphic User Interface (GUI), and
Usability

Barker (1989) informally defined a human-computer interface (HCI) as a

mechanism which facilitates the flow of information between a computer and a human.

The Association for Computing Machinery's Special Interest Group on Computer-

Human Interaction (ACM SIGCHI) described human-computer interaction as a field with

intertwined roots in computer graphics, operating systems, human factors, ergonomics,

industrial engineering, cognitive psychology, and computer system engineering.

Redmond-Pyle and Moore (1995) stated that in typical information systems and office

systems the human-computer interface includes the following:

* The parts of the computer hardware that the user interacts with, e.g., screen,
keyboard, mouse, on/off switch, etc.









* The images or data that are visible on the screen, e.g., windows, menus, messages,
help screens.

* User documentation such as manuals and reference cards.

The second component in Redmond-Pyle and Moore's definition of the HCI

structure is often referred as the graphic user interface (GUI). GUI provides the

uppermost presentation layer for the communications (visual input and output) between

the users and the computers.

The term "usability," in simple words, defines how usable a product or system is

when it is put to use by the users to perform the intended activities or tasks. In other

words, a product with high usability is easier to learn and use than a product with low

usability. Therefore it is easy to understand that if a product has a low "usability," it will

have less probability to be accepted by its intended customers or users.

The definitions most cited by the researchers in the usability world are from

Shackel (1990) and Nielsen (1994). Shackel's definition of usability and Nielsen's

definition share many common aspects and the main components and characteristics of

their definitions are summarized as follows:

* Effectiveness: for a specified range of tasks and group of users in a particular
environment, how effectively can the tasks be performed using the interface? What
are the frequency and seriousness of the user errors? This is sometimes referred to
as "productivity" or efficiency of use once the system has been learned, as it
includes how fast the user can correctly perform tasks.

* Learnability and retention of knowledge and skills learned: how much training and
how much practice do users require before they become proficient with the system?
If use is intermittent, how much relearning time do users need to re-gain the
required knowledge and skills to use the system?

* Flexibility: to what extent is the interface still effective if there are changes in the
task or environment?









* Attitude or subjective user satisfaction: do people who use the system find it
stressful and frustrating, or do they find it rewarding to use, and feel a sense of
satisfaction? Do users like the system?

Since the 1980's, usability theory is widely recognized as an important software

quality alongside technical aspects such as functionality, internal consistency, reliability

etc. Most of the major information technology companies maintain their own usability

divisions to investigate potential usability pitfalls in their products and systems before

they are released to the market. Usability engineering is a crucial part of the computer-

related business to survive in today's customer/user-driven market where user acceptance

is critical to success when launching new products or systems. To the end users, a system

with good usability can help improve their productivity, reduce the quantity or frequency

of user errors, and require less training for those who will use the new system (Redmond-

Pyle and Moore 1995).

Past Research Examined from A HCI and Usability Perspective

Complex technical systems do not evolve fully formed, but rather in fits and starts
as the combination of technical possibility and economic advantage encourages
localized development. (Winch 2002, pp. 341)

In a retrospective point of view, the past research in the construction industry on

computerizing field information communication has mainly focused on usability issues of

the hardware and functionality aspects of the Human-Computer Interface. The approach

adopted by most researchers consisted of taking the technologies and computing devices

commercially available and evaluating their appropriateness and functionalities in various

types of field information documentation/communication tasks. Examples of such include

research on pen computers (McCullouch 1993, Coble and Kibert 1994, Songer et al.

1995, Elzarka et al. 1997, Liu 2000), research on bar code technology (Coble and Elliott

1995, Condreay 1997), research on Radio Frequency Identification (RFID) technology









(McCullouch 1991, Jaselskis and El-Misalami 2000), research on wireless

communication technology used in conjunction with handheld PC's (De La Garza and

Howitt 1997), and more recently research on pocket PC's (Repass et al. 2000, Bowden et

al. 2002, Cox et al. 2002, Williams 2003). In addition to these adaptive approaches in

finding the ideal computing device suitable for construction field needs, there are also

some innovative research studies such as Digital Hardhat (Liu 1997, a system employing

a hardhat-mounted video camera and pen computer that is capable of capturing textual,

sound, pictorial information) and Gator Communicator (Alexander 1996, a handheld

computer prototype that is based on the OS-9000 real-time operating system and includes

a global position receiver (GPS), digital compass, digital stereo camera, and digital two-

way wireless radio functions).

While these research efforts provided many valuable insights and lessons as to the

characteristics of the ideal mobile computing platform that would be suitable for

construction field settings, the graphic user interface or software aspect of the system

usability has unfortunately often been neglected. The characteristics of an effective

graphical user interface for field users was seldom considered. It should be recognized

that usability of the graphical user interfaces has considerable importance. A good

example to illustrate such a point was a study conducted by Tektronix Laboratories on the

effect of user interface design upon user productivity (Bailey et al 1988). In that study, a

Tektronix 11000 series laboratory oscilloscope was compared to its predecessor 7000

series. The 7000 series interface was a dedicated physical control system while the 11000

system employed a rich graphical user interface that included icons, popup menus,

assignable controls and a touch panel. The study results showed that the 11000 series had









a 77% performance gain over the 7000 series and the researchers attributed the

performance gain to the benefit from the better cognitive factors of strategy selections

and recall of operational details associated with the 11000 series's user interface.

Foremen and Their Role in the Information Communication Process

Recognizing the users and their particular needs is the first step in the process of

successful usability engineering. There are various groups of existing and potential field

computer users on construction sites. For general contractors and construction managers

their field personnel are typically project superintendents, field engineers, and often

project managers on some larger projects. For self performed work, the general

contractors are also similar to subcontractors or specialty contractors where their field

personnel include construction workers and foremen.

According to the Household Data Annual Averages statistics for 2002 released

from the Bureau of Labor Statistics of the U.S. Department of Labor, there are 6.774

million workers employed in the U.S. construction industry. A foreman in the

construction industry usually supervises two to more than twenty workers, with a crew

size of six to eight workers being most typical (Borcherding 1977a, Elliot 2000). Based

on this ratio, it therefore can be estimated that there are approximately one million

foremen in the U.S. construction industry. Therefore, research on foremen and

computerizing their documentation tasks can have significance in improving the

computer use and possibly improving the productivity and the product quality in the

construction industry.

Foremen are key individuals on the construction site. Research work on foremen

and their roles in the construction process occur primarily in literature published in the

1970's and 1980's, with a few studies in the 1990's. For example, Borcherding, who









perhaps contributed most in the research work related to construction foremen, defined

foremen as the "key link between management and individual workmen" (Borcherding

1977a). In an effort to identify and clarify the functions and information needs of various

construction management personnel, Tenah (1986) defined the primary functions of

foreman as one who "organizes and coordinates employees engaged in a specific craft or

function on a construction project; reads and interprets drawings, blue prints, and

specifications; allocates, assigns and inspects work; administers union agreements and

safety enforcement; hires and trains employees." Hinze and Kuechenmeister stated that

(1981) foremen as first-line supervisors are responsible for directing, guiding, and

managing crew members to achieve quality workmanship within budget and on schedule.

Senior (1996) observed the efficient foremen devote a substantial proportion of their time

to planning the job.

With the challenging characteristics of their job and a busy work schedule, foremen

often devote most of their attention to field problem solving, issuing work orders to their

crews, coordinating with other contractors, and performing other functions of their job

responsibilities. Field documentation such as daily field activity reports, accident

investigations, daily safety reviews, and other company internal report forms (Coble and

Baker 1993) are often relegated to the bottom of their priority list. As a result, these field

documentation tasks are either completed haphazardly or deferred to whenever their

schedules allow the time for such activities. Consequently these field documentation

efforts often contain incomplete information or inaccurate information that makes it

difficult for management to fully exploit its value. As previously discussed, management

often relies on the information collected in the field for making essential business









decisions such as preparing a bid for a new project or allocating manpower and

equipment resources among ongoing projects. The deficiency in the information collected

in the field, though clearly desirable to be rectified, is often sacrificed by management as

a trade-off to a smooth running project that is on schedule and within budget. This

dilemma has been long recognized in the construction industry (Borcherding 1977a,

Coble and Baker 1993).

Coble and Baker (1993) stated "construction foremen are clearly the missing link to

fully computerizing a construction company." Coble (1994) further pointed out that in

order to successfully computerize them, the research effort must take into consideration

the foremen's background, characteristics, and job concerns. It was generally believed the

majority of the construction foremen have no education beyond high school and this was

supported in a study conducted at Stanford University (Borcherding 1977) and another

study conducted at the University of Florida (Elliott 2000). Elliott's study also indicated a

mean foreman age of 40.0 and an average of 9.5 years of experience for the construction

foremen included in the study sample (N=119). In the construction industry, foremen

typically advance to their position through many years of experience from craft workers

in crews to positions of leadership, primarily as foremen. Foremen must be willing to

accept responsibility, possess the ambition to lead others, and the desire to achieve goals

(Borcherding 1977a). The feeling of threatened job security, diminished social status, or

reduced self-esteem is usually understood as the driving force for the individual

resistance to the changes brought forth by new technologies and was considered as a

factor in foremen's resistance towards the idea of using computers in their realm (Coble

1994). This paradigm may seem to have changed some in the recent years with the









increasing indispensability of computers in the society and is somewhat indicated in

Elliott's study. In fact, 79.9% of the foremen Elliott surveyed indicated that handheld

computing systems may have the potential on helping them do their jobs. While this

possible trend is encouraging, the fundamental characteristics of construction foremen as

being efficient and productive individuals will still require handheld/mobile computing

systems designed for them to be efficient and easy to use. Unfortunately, as previously

mentioned, this area historically has not made much progress.

Graphic User Interface on Pen-Based Mobile Computing Devices

Most research on the mobile computing systems in the construction field revolve

around the basic concept of using pen and touch sensitive screens as the main input

platform regardless of their sizes/categories (e.g., tablet PC's, palmtop computers, pocket

PC's, etc.) or operating systems. It is widely accepted that in the construction field the

use of a physical keyboard is not practical for mobile users such as construction foremen

on a busy and rugged construction site (Coble et al. 1996, Alexander et al. 1997). Yet

manual data entry through a pen (sometimes called stylus) is not a faster or more reliable

way than using a keyboard either. To input a character the user has to make a series of

hand strokes with the stylus across the touch sensitive screen and it requires extensive

practicing for a user to become proficient in using a stylus. A few research studies in the

construction industry on pen computing technologies have recognized this limitation

(e.g., Rojas and Songer 1996, Bowden et al. 2002). This problem of inconvenient data

entry is inherent with such use of a pen or stylus (Masui 1998).

As a result, alternative automated data entry technologies such as bar codes, radio

frequency identification, etc. as previously discussed, were explored by some

construction researchers to augment the manual data entry limitations associated with









pen/stylus technology. Particularly, the advancement of speech recognition technologies

in recent years brought construction researchers' interest into this area to explore the

potential uses of speech recognition technologies as an automated data entry method on

construction sites. Sunkpho and other researchers at Carnegie Mellon University explored

such technologies and have prototyped a framework for developing audio-centric

(namely speech recognition) interfaces in field data collection applications (Sunkpho et al

2000, Sunkpho and Garrett 2003). Speech technologies hold great potential in providing

automated data entry in computer applications as they are considered one of the "natural"

communication mechanisms between humans and computers and as a general rule

speaking is faster than typing or writing. However, this technology has its limitations in

the construction field as well. First, noise interference on the construction site is a major

problem in the reliability of data entry (speech input) process and this problem is

unfortunately inherent with the construction environment and cannot be eliminated.

Secondly, as Sunkpho and others recognized, integrating a speech interface into

application is not a trivial feat as this is a complex technology (Sunkpho and Garrett

2003). Moreover, speech recognition is not the most efficient method in actuating the

computer commands in the graphical user interfaces. Querying and database

manipulation on the collected voice data are even more complicated tasks.

Experts in the construction research field have accepted that predefined drop-down

menus and text lists in the graphic user interface may be a more efficient and easy-to-

implement method to automate the data entry process in the construction field. Many

researchers believe a substantial portion of the information documented in the field is

repetitive from project to project and can easily be standardized (e.g., McCullouch 1993,









Rojas and Songer 1996, Cox et al. 2002, Bowden et al. 2002). Using a pen/stylus to click

and select items in the graphic user interface is a relatively quick and effortless process

therefore the user efforts in performing computer tasks seem to be trivial.

As a user industry in information technologies, the construction industry has not

studied the graphical user interfaces in much detail compared to the computer industry.

This is probably a result of the unique nature of the construction industry that is not

generally understood by those in the computer industry. Yet graphic user interface can

play an important role in determining the overall usability of a computer system. For

example, older adults usually have a difficult time in using the graphics user interfaces

designed for average users as a result of the normal effects of aging including some

decline in cognitive, perceptual and other abilities. Studies have found using area cursors

(larger sized cursors) and sticky icons (feature of icons that eases the selection process)

can improve their performance in basic selection tasks (Worden et al. 1997). In addition,

even different operating system user interfaces on similar types of personal digital

assistant (PDA) devices can result in significantly different user performance (Teresa et

al. 2001).

Icons, Signs and Symbols A Brief Historical Review

Before words there were sounds and intonation, before writing there were symbols.
Speech splintered into different languages, different symbols developed into
various writing systems. Writing systems separated into the symbolic and the
phonetic, but symbolic iconographies persisted from earliest writing to the present
day. Only the symbols changed. As the computer replaced the pen and the brush, so
iconography, with today's symbols, prepares for tomorrow. (pp. 63, Sassoon, R.
and Gaur, A. 1997)

Icons, signs and symbols exist everywhere in our lives and workspaces. Because of

their communicative power, icons are used in a wide variety of situations to inform

people about particular conditions or to give instruction. For example, symbols or









pictographs are widely used in the Olympic games to depict various sports; they are used

on product packaging cartons and in instructional manuals to inform people how to

properly handle, transport, store and use products; they are used in public places such as

airports and train stations worldwide to provide directions and identification of important

facilities (e.g., luggage claim areas, telephone booths, currency exchanges, escalators,

etc.); they are used in equipment instrumentations such as the instrument clusters in

automobiles to indicate malfunctions and warnings (e.g., low fuel reserve, engine

malfunction, etc.) when illuminated; they are used on the roadways to alert drivers of

road conditions, allowable speeds, recreational interest areas, general service facilities at

exits, etc.; they are used at workplaces to caution of safety perils, hazardous materials and

required safety equipment and measures, etc. This list can go on and on.

The general philosophy that icons, signs and symbols are used instead of the

character-based representations is that they are more intuitive and effective in conveying

the intended information. In fact, using pictorial representations by humans to

communicate non-verbally dates back to primitive times (Sassoon and Gaur 1997). In

early times, the need for written communication was simple and character-based written

languages did not exist. Yet, our ancestors used pictographs carved on rocks and other

objects to document information or communicate intellectual thoughts between one

another. Later when the need for written communication became more sophisticated and

more specific, the pictographs gradually broke down into smaller information units and

by the use of conventions they have evolved into today's written languages which are

totally based on abstract characters or radicals (radical are used in Chinese, Japanese and

other Asian written languages). In his book "The Alphabet: An Account of the Origin and









Development of Letters," Taylor (1991) illustrated an example that how the picture of the

owl was conventionalized into today's letter "M." In the old Egyptian language the name

of the owl was mulak. The picture of the owl is believed to have been primarily used as

an ideogram to denote the bird itself, secondly as a phonogram standing for the name of

the bird. It then became a syllabic sign used to express the sound mu, the first syllable of

the name, until ultimately it was employed simply to denote m, the initial sound of that

syllable. In his book "The Icon Book: Visual Symbols for Computer Systems and

Documentation," Horton (1994) also listed similar illustrations on how the letters "A"

and "0" have evolved from the ancient Egyptian hieroglyph, Sinai script, Moabite stone,

and early Phoenician to Greek and Roman characters. In Chinese, words denoting the

objects such as sun, moon, and mountains were also evolved from the early actual

graphic representations of these objects. Therefore, in one sense, the use of signs,

symbols and icons in today's society may be regarded as an effort of reverse engineering

of the human written communication evolution process.

The twentieth century has seen quite a few systemized research efforts in

developing visual communication systems using signs and symbols. Otto Neurath (1882-

1945) developed a method of visual presentation of statistical information as an

educational medium using pictograms that later became well known as the "International

System of Typographic Picture Education" (ISOTYPE). The basic principle of the

ISOTYPE system is that each symbol represents both a topic and a designated quantity,

and symbols can be "compounded" (McLaren 2000). For example, 'man' + mining' =

mine worker (McLaren 2000) or 'shoes' + 'factory' = shoe factory (Horton 1994). In the

1960's Charles Bliss developed a system called "Semantography" which consists of an









"alphabet" of 100 fundamental symbols that can be juxtaposed or superimposed to

represent even richer concepts. The fundamental set of symbols includes numbers,

mathematical symbols and simple geometric shapes and many of these shapes are easily

recognizable because they are abstractions of familiar objects or are already used

internationally (Horton 1994). The International Organisation for Standardisation (ISO)

and the International Electrotechnical Commission (IEC) also developed approximately

1,450 standardized symbols for international use. These are compiled respectively in ISO

7000 'Graphical Symbols for Use on Equipment Index and Synopsis' and IEC 417

'Graphical Symbols for Use on Equipment Index, Survey and compilation of the Single

Sheets" (McLaren 2000).

Icons, and other terms including signs, symbols, signets, ideograms, index,

phonograms, and pictograms/pictographs are closely related and are often confusing to

ordinary people. From the semiotics (defined as the science of signs," Eco 1976) point

of view, Marcus summarized (2003) the definitions of these terms as follows (pp. 38):

* Signs: perceivable (or conceivable) objects that convey "meaning."

* Symbols: signs that have meaning by convention and are often abstract, like the
letters of this sentence or the national flag.

* Icons: Signs that are self-evident, "natural," or "realistic" for a particular group of
interpreters, like a photograph of a person, a "realistic" painting, or a right-pointed
arrow to indicate something should move to or is located to the right.

* Index: a special semiotics term for signs that are linked by the cause-and-effect in
space and time, like a photograph representing a scene, or a fingerprint on the
coffee mug at the scene of the crime.

* Ideograms: symbols that stand for ideas or concepts, for example, the letter "i"
standing for "information," help desk," or "information available."

* Phonograms: symbols that stand for sounds, for example, the letter "s."









* Pictogram: an icon (or sometimes symbol) that has clear pictorial similarities with
some object, like the person or men's room sign that (for some interpreters) appears
to be a simplified drawings of a (specially, male) human being.

Despite such seemingly detailed linguistic delineations of these terms, the nuances

between icons and other terms and the significance of the nuances often diminish when

they are used in various disciplines. The interchangeable use of some of the terms is

common in today's society where graphically enriched software application user

interfaces are flourishing. In the computer applications, icons can be referred to

anything, not just the easy-to-recognize pictographs but also the abstract images or

symbols which can be totally unrelated but arbitrarily assigned to represent certain

computer commands. More interestingly, there are also studies on audible icons or

"earcons" which have taken the definition of icons to a new dimension (Brewster et al.

1993).

The use of icons in computer graphical user interface was early incorporated in the

design of Xerox's 8010 "Star" Office Workstation (Bewley et al., 1983) and has become

the main component in software applications to allow the user to easily navigate through

the programs. The motivation of using icons in computer graphical user interfaces is

similar to other applications (e.g., public information displays, equipment labeling, traffic

controls, etc.) to facilitate the communication process between the human and

computer. As shown in Figure 2-1, in the early days that computers technology, users of

computers communicated with them by means of simple 'binary state' switches, buttons

and numeric octall or hexadecimal) key pads. As interface technology improved, the

mode of interaction was superseded by the use of QWERTY keyboards which enabled

the construction of command line interfaces. As the complexity of these grow, they

became more difficult to learn and remember. The introduction of graphical user









interface in the Xerox 'Star' workstation and later on Microsoft Windows took away the

complexity of the command line interface through the use of 'dialogue boxes.' With the

graphical user interface, the need for a user to type is substantially reduced. Instead, users

use mouse to point to objects (such as icons and pictures) on the screen to execute desired

commands. In some sense, much of the "ease to use" of a computer system often depends

upon the power of the metaphors embedded in the end-user interfaces. Since icons are

more visually distinctive than abstract words, it is generally believed that it is easier to

identify an icon than a word from a group of screen objects in a graphical user interface.

Icons can represent a considerable amount of information in very little space and space is

often at a premium on computer display screens (Hemenway, 1982).


SWITCHES, BUTTONS + KEYPADS


COMMAND LINE
INTERFACES


HUMAN DIALOGUE BOXES COMPUTER


GRAPHICAL USER
INTERFACES



ICONS INTERFACE + ICONIC LANGUAGES


Figure 2-1. Illustration of the Evolution of Human-Computer Communication Process

Signs, Symbols and Icons in Construction and the Possibility of Using Icons as
Automated Data Entry in Graphic User Interface

Signs and symbols are widely used in the construction industry. Pictorial symbols

based signs are commonly used on construction sites to convey various safety warnings









and messages. Construction plans by nature are graphical representations of the

construction process of buildings via conventionally accepted symbols and rules. For

example, in site utility plans, straight or curved lines stand for various types of pipes with

the size information either directly noted near the lines or indirectly noted by means of a

pipe schedule. Different symbols are used to show various fittings or structures (e.g., gate

valves, bends, fire hydrants, backflow preventers, sanitary manhole, etc.). This system is

also used in drawings for virtually all other trades (e.g., plumbing, fire sprinklers, HVAC,

electrical, etc.). Construction foremen, whose main job functions include reading the

construction plans and then issuing work orders to their crews, have considerable

experience working with symbols-based graphical communication systems in this regard.

Using the "click and select" concept as previously discussed, Coble and Elliott

(1996) proposed the idea of using icons as the basic means not only for computer

commands but also for data entry in the graphic user interfaces designed for construction

field users. Unfortunately this idea never got to the stage of being implemented into a

working system and therefore was never tested in real settings to assess the usability.

Coble B. (1997) at the University of Florida tested 56 icons with 59 respondents

consisting primarily of construction project managers, superintendents, foremen and field

engineers for icon recognition response and 41 icons were matched successfully to their

descriptions by the respondents with a 90% or better concurrence rate. These results

indicate that if designed properly, icons can be used for automated data entry in the

graphic user interface designed for construction foremen.

Both icons and pre-defined text have the potential benefit of reducing the data input

effort by construction foremen as the intended end users. Therefore it would be









interesting to know if there is a difference between these two options in terms of

usability. Usability comparison between icons and pre-defined text lists in the graphic

user interface needs to be studied in order to validate or invalidate the concept of using

icons to automate the data entry process in the mobile computing systems designed for

construction foremen.

Icons vs. Pre-defined Text

Existing empirical studies have equivocated on the issue of whether there is a

difference in terms of task completion time and user errors between textual

representations and iconic representations in the computer user interfaces. A few earlier

studies suggested there is little or no performance gain that iconic representations have

over textual representations. For example, Rohr and Keppel (1984) compared icons and

text as computer commands in word processing and reported no improvement for icons

over text in terms of task completion time and error rates. Kacmar (1989) compared text,

icons and text+icon combination in matching programming concepts and labels and

found combined labels most accurate and no difference in all three mechanisms in terms

of task time. Whiteside et al. (1985) did a comparison of different interface design

approaches and their effect on different types of computer users (novice, transfer, and

expert). Whiteside et al. (1985) found there was no significant performance improvement

for iconic interfaces and novice and transfer users actually performed worse with iconic

interfaces. Egido and Patterson (1988) studied effects of icons on navigation through a

catalogue and the study results showed the search time for icons was slower than text or

text plus labels. The study results by Egido and Patternson also indicated that icon users

took fewer steps but spent more time on each step than those with labels. Benbasat and

Todd (1993) conducted an experimental investigation under two factor levels where icons









versus text and direct manipulation versus menu-based were paired into four different

interface types. Benbasat and Todd concluded that there were no difference between the

icon and text-based interfaces for the time taken to complete the task and the number of

errors made. On the other hand, a more recent study by Staggers and Kobus (2000)

indicates icon-based graphical user interface has a shorter response time, fewer errors and

higher user satisfaction than text-based user interface. In Staggers and Kobus's study, 98

randomly selected male and female nurses completed 40 tasks using a text-based

interface and an icon-based graphical interface. Overall, nurses had a significantly faster

response time (P<0.01) and fewer errors (P<0.01) using the graphical interface than the

text-based interface. The icon-based graphical user interface was also rated significantly

higher for satisfaction than the text-based interface, and the graphical user interface was

faster to learn (P<0.01). Given these seemingly contradicting conclusions of previous

studies, a reliable statistical inference could not be drawn that there is no difference

between icon and text-based interfaces for construction foremen in terms of task

completion time, number of errors, and level of user satisfaction. The reasons are further

discussed below.

Effect From Interface Implementation Differences

Many of the earlier empirical studies did not preclude the effect of interface

implementation differences on the study results. Factors such as font size, icon size,

spacing and layout might have influenced the study results but were not counterbalanced

to minimize their effects on the study results. Therefore these study results were not

totally conclusive.









Visual Appeal Factor Associated With Iconic Interfaces

Visual appeal refers to the phenomena that users tend to spend more time on icon-

based user interfaces because of their visual attractions. Therefore, if the visual appeal

factor was not counterbalanced in the study, the results may not be conclusive to show if

the longer task time associated with icon-based user interface was a result from longer

processing and recognition time or from the visual appeal factor associated with the

iconic interface. Many earlier studies did not take this factor into consideration.

Abstract Vs. "Concrete" Icons And Icons As Computer Command Vs. As
Information Units

Icons in existing empirical studies are generally abstract icons and used for

denoting computer commands. Icons used for computer command are often abstract in

concept and arbitrarily assigned to a particular computer command and require extensive

usage for a user to acquire the relationship association. Icons of interest in this study are

"concrete" icons, which means they are on the lesser abstract end of the scale and are

primarily used for information units. Therefore there is clearer association between the

icons and the objects/activities represented.

Subject Characteristics

The subjects included in many of the earlier studies were often college students or

people who had considerable computer experience. The specific advantages and

disadvantages associated with icons may vary from novice users to intermediate users to

expert users. Therefore, advantages associated with icon-based interfaces may not be as

remarkable to expert users as to novice users. Previous studies have addressed little in

this area.






28


Summary

Computerization of the field documentation tasks of construction foremen has not

become a prevalent practice to date. Of the efforts done in the past, little has been focused

on the usability of the graphical user interface for the data collection systems designed for

construction foremen. The comparison of the use of icons and pre-determined text lists as

automated data input mechanisms for construction foremen in particular was non-

existent.














CHAPTER 3
RESEARCH METHODOLOGY

This chapter introduces the research questions that this study attempted to address.

It also discusses the methods used to accomplish the research objectives stated in the

previous chapters. The chapter is organized in the following sections: (1) research

questions, (2) methods, (3) sample selection criteria and techniques, (4) study design, (5)

survey questionnaire design, and (6) statistical procedures for analysis of the results.

As stated in the previous chapters, it has not been determined whether icons have

better usability than pre-defined text lists in the graphical user interfaces designed for

construction foremen. This was the main question that this study attempted to answer. In

the field of cognitive psychology, humans are characterized as information processors -

everything that is sensed (sight, hearing, touch, smell and taste) is considered as

information that the mind processes (Preece et al. 1994). With the information processor

theory, information enters and exits the human mind through a series of ordered

processing stages (Lindsay and Norman 1977). As summarized in Figure 3-1,

information from the environment is encoded into some form of internal representation in

Stage 1; in Stage 2 the internal representation of the stimulus is compared with

memorized representations that are stored in the brain; in Stage 3 a response is

formulated to the encoded stimulus; when an appropriate match is made the process

passes on to stage 4, which deals with the organization of the response and the necessary

action (Preece et al. 1994). Based on this theory, it can be conjectured that the human

brain would process text and graphic information differently in these four stages and the









difference would be dependent on the predominant information processing mode

(graphical or textual) that one is accustomed to. Larkin and Simon (1987) pointed out that

textual and pictorial information differs in terms of the effort associated with making

inferences. Jacob (1995) stated that the problem of human-computer interaction could be

viewed as two powerful information processors (human and computer) attempting to

communicate with each other via a narrow-bandwidth, highly constrained interface.

Therefore to address the human-computer interaction problem, more natural and more

convenient means need to be provided for users and computers to exchange information

easily and reliably. Addressing this problem can also help the researchers understand

better whether construction foremen as information processors may process the graphic-

based information faster and more accurately than the text-based information. The answer

may depend on their extensive experience working directly in the field and dealing with

construction plans as highly graphic-based communication media.

---------------------
Attention


Input output
or lp 1.Encoding 2.Comparison 3.Response 4.Response O or
Sor Selection Execution or
Stimuli I II Response
I----------------------
Memory


Figure 3-1. Extended Stages of the Information Processing Model (Preece at al. 1994).

Determining whether foremen process icons better than text is important to the

information technology sector providing IT solutions to the construction industry because

the computer programming to implement the graphical user interface typically takes 40-

90% of the entire program code in today's software applications (Chalmers 2003). It

takes a great amount of time and effort to develop quality icons and often requires many









trial and error processes and refinements before finalizing an icon that perfectly serves

the design intent. Therefore it is important first to know whether or not icons can actually

improve the usability of the graphical user interfaces designed for construction foremen,

otherwise time and efforts invested in designing icons and implementing iconic user

interface are not guaranteed to reap the intended benefits.

Research Questions

In the computer field, the usability of a system is typically measured by collecting

and analyzing the following data: time required for using the system to complete a given

task; number of errors and type of errors experienced by using the system to perform the

task; time required to learn the system to perform the task; retention quality of the

knowledge learned to use the system; and the user's subjective assessments of the system

(Chin et al. 1988, Roberts and Engelbeck 1989, Jeffries et al. 1991, Nielsen and Philips

1993).

From the usability point of view, there are several questions of primary interests in

this study and they are discussed below.

Do Construction Foremen Perform Computer Tasks Faster Using Icons Than Using
Predefined Text Lists Or Vice Versa?

More specifically, do construction foremen tend to find the correct choice faster

using icons or pre-defined text lists? User tasks in computer graphical user interfaces

usually take two basic steps: first locating the correct screen target (e.g., button, menu

item, etc.) that can be the most time-consuming, and then performing the desired action

on the chosen component. One salient trend in the human-computer interaction research

field in recent years has focused on studying the visual searching or location learning

aspect of user tasks in the graphical user interfaces to gain more understanding about the









cognitive models of human-computer interaction and subsequently to find ways to

improve the usability of the human-computer interface (Salvucci 1999, Byrne et al. 1999,

Ehret 2002, Hornof and Halverson 2003). Being able to quickly find the screen objects

can reduce a user's task time, errors, and frustration (Ehret 2002). Investigating this

question is especially meaningful to construction researchers because although the

timesaving in visual searching or location learning for each individual task may appear to

be small, the aggregated effect can be significant in the entire software application over a

sustained period of time. For construction foremen, computer systems need to be efficient

and effective to use. Therefore, any effort to achieve this goal is significant in the process

of computerizing the field documentation tasks of construction foremen.

Do Construction Foremen Experience Fewer Errors Using Icons Or Pre-Defined
Text List?

Frequency and seriousness of the errors in user computer tasks also account for an

important aspect of the usability in a system. It is easy to understand that if users

experience more errors on one system then it is likely for them to become more easily

frustrated with the said system than any other competing system. The frustration could in

turn lower their motivations to use the system. If provided with choices, users would

naturally reject the error-prone system and adopt the one with fewer errors. User errors in

graphical user interface generally can be grouped into the following three categories: 1)

identification errors (observed errors are clearly the results of incorrect identification), 2)

selection errors (observed errors are accidental mouse/pen selection errors although the

user has identified the correct choice), 3) experimenter interventions, both subject

initiated and experimenter initiated. Although it is desirable to analyze all types of user

errors, this study will specifically focus on the identification type of user errors as these









types of errors are directly related to how the interfaces are implemented. Selection errors

as a result of pen/touch screen sensitiveness and pen-using skills are considered as

hardware related and therefore would generally have equal effects on icon-based user

interface and text-based user interface (provided the icon-represented screen objects and

text-represented screen objects are comparable in size). Selection errors will not be

explored in this study.

Do Construction Foremen Have A Preference Between Predefined Text Lists And
Icons?

Using a 7-step Likert ranking scale, what are typical foremen satisfaction ratings

with the icon interface and the text interface (1. Very Dissatisfied; 2. Dissatisfied; 3.

Slightly Dissatisfied; 4. No Opinion; 5. Slightly Satisfied; 6. Satisfied; 7. Very Satisfied)?

Users' satisfaction with a system is not only closely related to the efficiency and efficacy

of the system that is directly translated to task time and users errors, but also is affected

by the psychological effect (disorientation, anxiety, etc.) and the cognitive load (how

much mental effort is required) of the user interface. Computer anxiety and computer

related anxiety was estimated to affect 30% of the United States workforce (Logan 1994).

Computer related distress is commonly believed to yield increases in mistakes,

debilitating thoughts, self-depreciating thoughts, irrational beliefs and absenteeism

(Ramsay 1997). Rozell and Garden (2000) further noted that researchers have observed

that motivation, or level of effort, is one of the primary variables affecting individual

performance in general and computer-related performance in particular. Recognizing and

developing systems towards user preference and user satisfaction is the key in today's

user-driven information technology market.









While the above three questions comprise the main focus of this study, the

following questions are also to be explored.

What Is The Ranking Order Of The Above Three Usability Aspects From The Point
Of View Of Construction Foremen?

Which aspect do they perceive as the most important? Text-based user interface

and icon-based user interface both have their advantages and disadvantages. Although it

is desirable to have a system that is far superior to its competition in all aspects, reality

shows it is not always the case. Therefore, when making system selections, it is important

to know which factors are viewed as most important to construction foremen.

What Are The Views Of Construction Foremen About The Concept Of The Icon
Based Mobile Field Documentation Applications?

In other words, how do construction foremen as the end users perceive the icon

based mobile documentation applications for automating the field documentation

process? Would they perceive that this kind of applications would help do their jobs

better?

What Is The General Knowledge And Experience Of Construction Foremen On
Mobile Computing Devices?

How do they perceive the difficulty and inefficiency associated with handwriting

input using pen/stylus on current mobile computing devices? Elliott's study (2000)

indicated the 84.0% of the construction foremen in his sample (N=119) did not use

computers to perform any part of their jobs, but 50.4% of the sampled foremen did use

computers in their homes. Elliott's study also showed 79.9% of the foremen responded

positively that they thought mobile computing devices would help them do their jobs.

Mobile computing devices such as the personal digital assistants (PDA's) are

commonplace nowadays, and affordability no longer seems to be the issue as it was a few









years ago. They are more widely used, not only for work-related tasks, but also as a tool

for better organization of personal business. Therefore, the knowledge and experience of

foremen with mobile computing devices need to be investigated to understand their

exposure to this type of technology. Difficulty associated with handwriting input using

pen/stylus has long been perceived by the researchers as a roadblock in the

implementation process of mobile computing devices in the construction field. However

it is not known whether construction foremen as the end users have the same perception.

Therefore this issue needs to be explored.

What Percentage Of The Information In Current Field Documentation Do Foremen
Think Can Be Standardized For Use With The "Click And Select" Concept?

There always exist non-standard and miscellaneous information items in foremen's

field documentation. But is the percentage of the items that can potentially be

standardized significant enough to justify the use of automated information entry either

by icons or pre-determined text lists?

Last, how do construction foremen demographics (age, construction experience,

computer use experience, etc.) affect the results for the first three research questions? Are

there any correlations between these user parameters and the observed user performance

variables in terms of task time and error rates?

Samples

Although it was desirable to include all the foremen from all construction trades in

the sample universe, the study was specifically focused on construction foremen in the

sitework trades (e.g., clearing and excavating, underground utilities, and paving) in the

greater Orlando area in the state of Florida. The declaration on the sample universe was

important to minimize the effect on the study results by potential external factors such as









1) subjects' individual knowledge differences on trade specific construction activities,

procedures, and equipment, i.e., construction foremen in the sitework trades may not be

very familiar with knowledge pertaining to the mechanical or electrical trades, 2)

geographical location factor (although the influence from this factor may likely be very

small as the construction workforce in U.S. are generally very mobile).

The Blue Book, a business directory widely used in the U.S. construction industry,

was used to identify the potential sitework contractors in the greater Orlando area that

could be included in the study. The management personnel of these firms were contacted

to seek their permission to solicit the participation of their foremen in the study. When a

foreman agreed to participate in the study, the visual searching experiment was arranged

and conducted in an indoor environment (e.g., contractors' main offices, and job site

trailers) and the questionnaire survey was given subsequently.

The inclusion criteria for this study were as follows:

* Firm management agreed to allow their foremen to be included in the study

* The foreman was willing to voluntarily participate in the study

* The foreman was able to readily read and write in English fluently

* The foreman had normal vision or corrected vision and was able to read the 14 pt
text without any difficulties when seated 12 to 18 inches in front the experiment
apparatus.

Although only the foremen sample was initially planned, two additional samples

were taken in the final study phase. The initial sample included thirty-five foremen who

were selected from eight different sitework construction companies. A total of twelve

companies were contacted but eight actually participated in the final study. These eight

companies were not the same ones that participated in the pilot study phase. The second

sample included thirty-seven subjects whose professions were closely related to civil









engineering/sitework construction. This sample included twelve project managers, five

superintendents, four project engineers, one construction inspector, one construction

estimator, eight civil engineers, four CAD technicians employed by civil engineering

firms, and two construction management consultants. Subjects in this sample were

employed by seventeen different firms, with six subjects employed by the state

transportation department and thirty-one by private companies. Additionally, these

subjects were located in the U.S. except for one who was in the U.K. The subject from

the U.K. was a research consultant in the field of mobile computing technologies for the

construction industry. The third sample included twenty-six graduate students and two

undergraduate students in the School of Building Construction at the University of

Florida. These students were selected because they would likely be in various

construction supervision/management positions when they graduate from the university.

Their views about the research questions were of interest as well.

Methods

Two methods were employed in this study to collect data to answer the above

stated research questions. The first method was essentially a computer visual search game

that each subject was required to play. The computer game contained the code to track

various user interface events (screen targets pressed, mouse cursor locations, time stamps

for various events, etc.) that provided the quantitative data to answer the first two

research questions (user task completion time and error rate). Such information was

recorded in a simple text format data file that was then imported to a spreadsheet

application for data formatting and initial processing.

For the more qualitative questions (Research questions 3 through 8), a survey

questionnaire was used to obtain the subjects' views/answers to these questions. The









survey questionnaire also included the section for the subjects' demographic information,

their knowledge and experience in mobile computing devices equipped with touch

sensitive screens, their subjective evaluations on the experimental apparatus, and other

research questions.

For research question #5, a sample icon-based application operating on the Palm

OS for documenting equipment usage was demonstrated to the subjects. The sample

equipment usage tracking application was designed to work with the stylus and the touch

sensitive screen and required no typing or handwriting to input the key information. A

user would only need to use the stylus to select different icons to navigate between the

screens and to input the equipment usage information (equipment number, operating

hours, idle time, downtime (if any), and the quantities of the work completed). The more

detailed discussion on the sample icon-based equipment usage tracking application can

be found in the later part of the chapter. After the icon application demonstration, the

subjects were then asked to describe their views about the icon-based mobile field

documentation application. Data collected through the computer experiment and survey

questionnaires were imported into a statistics program for analysis.

Visual Searching Task Experiment

The visual searching task experiment was designed to collect the data for a user's

task time and error rate response variables under the two different factor levels (icons or

pre-defined text lists). The visual searching task required a subject to identify and select

either an icon screen object or a pre-defined text screen object from a group of screen

objects to match the instruction given at the top of the screen. The instruction was given

in a different format from the screen objects, i.e., textual instruction for icon interface and

iconic instruction for textual interface. Each visual search task basically consisted of









three steps: reading the instruction, and locating an icon or pre-defined text list that

matched the instructions, and selecting the correct screen target. The computer game

tracked the time used for reading the instruction and the time for locating and selecting

the screen objects for each search task.

Apparatus/Materials

The apparatus used in the visual searching task experiment was a custom developed

icon/text matching computer game. The computer game was tested in a pilot study phase

and underwent several iterations and refinements to incorporate the findings learned

during the pilot study phase. The computer game recorded users' mouse movements and

actions on the screen during each visual search game session. The recorded information

provided data on the time and user errors variables for each visual searching task. The

visual search game included three icon-training sessions, one text-icon visual search

session, and one icon-text visual search session. Each subject was required to complete

three icon-training sessions before the text-icon visual search session or the icon-text

visual search session could begin. The order of the text-icon visual search session and

the icon-text visual search session in each game was randomly determined by the code

in the computer game. Three training sessions were given to each subject as the pilot

study (see Chapter 4) showed this to be needed for a test subject to adequately learn the

icons. In the event that a subject's overall time for any icon training session was longer

than 91 seconds (a threshold level determined in the pilot study that 90% of tested

subjects were able to complete 3 sessions), a dialog box would pop up on the screen to

alert the test subject that the session time was longer than the baseline and they needed to

endeavor to do better in the next training session. In addition, an elapsed time meter was









also displayed at the corner of the computer screen to remind the test subject of the time

and to motivate them to complete the game quickly.

Icon training session

Figure 3-2 shows the screenshot of a typical icon training session. Fifteen icons

were displayed in 3 rows and 5 columns. The locations of the icons were randomly

determined by the code in the computer program. In a typical icon training session, a

textual instruction for the target screen object was displayed near the top of the screen.

When the screen was completely displayed, the clock would start counting the elapsed

time. The test subjects were required to read the textual instruction and then try to find

the correct icon matching the textual description, e.g., "Excavator Laying Pipe" as shown

in Figure 3-2. When a correct icon was selected, that particular icon would be removed

from the screen and then the next visual search task would begin. If an incorrect icon

were selected, a dialog box would pop up on the screen to prompt the test subject to retry.

A total of 4 retries were allowed before that visual search task was called unsuccessful.

Each test subject was required to complete three icon training sessions before being

allowed to move on the subsequent text-icon or icon-text visual search test sessions.



































Figure 3-2. Sample Screen Shot of the Icon Training Session

Icon visual search test

After completing three icon-training sessions, each test subject was considered to

have acquired the knowledge on the corresponding matching relationships between the

icons and the text descriptions. The test subjects were then given either the icon visual

search test or the text visual search test based on the random number generated by the

computer (icon visual search test first if the random number was an odd number and visa

versa). Figure 3-3 shows a typical icon visual search test interface. The icon visual search

test screen is essentially identical to the icon-training interface except that after each

successful match the full screen would be re-generated with all the icons at completely

different locations from the previous visual search task. Figure 3-4 shows a typical text

visual search interface. The text visual search interface utilized the same principle as the


I









icon visual search interface (screen re-generated after each visual search task). In the text

visual search interface, the target instruction was given in icon format with screen objects

consisting of a pre-determined text description list. Fourteen text objects were used in the

text visual search game and they were organized in 7 rows and 2 columns.


Figure 3-3. Sample Screenshot of the Icon Visual Search Session




































Figure 3-4. Screenshot of the Text Visual Search Session

Test platform

The computer game was designed to run on any computer with Windows 2000 or

other later Micorsoft Windows operating systems and supporting at least 800-pixel by

600-pixel screen resolution. The computer game was designed with the capability to

capture the system time stamps accurate to one millisecond (1/1000 second). Data

obtained in the pilot study phase (see Chapter 4) showed the differences in the results

from tests conducted on various computer platforms were not significant at a confidence

coefficient of 95% (a = 0.5). The computers used in this study were a Fujitsu Stylistic

3400 pen tablet PC and an IBM 600E Laptop computer.









Icons and Pre-defined Text Lists

Icons used in the computer game were designed to depict various construction

activities/operations. Thirty-five icons were initially designed with the guidance from a

university professor in the construction management research field. After the preliminary

icon recognition testing and pilot study, fifteen icons with the highest recognition success

rates were selected and used in the computer game for this study. These icons and their

corresponding text descriptions are listed in Table 3-1.

Table 3-1. Icons and Pre-defined Text Lists Used in the Visual Search Tests
# Icon Pre-defined Text List



1 3-wheel Steel Roller Compacting



2 Traffic Roller Compacting Asphalt



3 Asphalt Paving Operation



4 Dozer Grading Dirt



5 Mobilizing Equipment


Excavator Backfilling trench









Table 3-1. Continued
# Icon



7



81



9 E



10


I
11



12



13



14


N*1J


Pre-defined Text List




Excavator Excavating Trench



Excavator Installing Structure



Excavator Laying Pipe



Excavator Loading Truck



Pouring Concrete



Loader Moving Dirt



Loader Moving Pipe



Material Delivery


Motor Grader Fine Grading









Data collection method

The tools used to evaluate system usability in the computer industry have changed

greatly over the last two decades. When the field of usability was first formed, simple

prototyping tools such as HyperCard were often used to create the scenario task user

interfaces. Primitive data collection methods such as paper and pencil were often used to

log user event information and the test administrator's observations. As the usability

discipline have gained more importance in the computer industry, the usability study

tools also have become more sophisticated to explore deeper usability issues in computer

hardware and software products. The usability divisions in most major information

technology companies have dedicated usability testing labs that are well equipped to

allow the evaluators to observe and analyze the potential users' task behaviors in a

controlled environment. The study platforms in simulated manner or actual product form

are often assisted with special computer programs that can capture the user interface

events (e.g., time spent on each task, time paused, time that a subject stayed on a

particular user interface object) and store this information in an event log file that can be

retrieved later and analyzed in detail. One example of such sophisticated usability study

tools used in recent years is the eye-tracking system (e.g., Salvucci 1999, Byrne et al.

1999, Hornof and Halverson 2003) that can record the participant's eye movement

information during a task scenario. Eye-tracking can identify the patterns of visual

activities that subjects exhibit while interacting with computer graphical user interfaces.

Such tools are highly desirable in most usability studies. However the complexity and

costs associated with procuring, setting up, calibrating, and operating them often makes it

prohibitive to use the eye-tracking system on small-scaled and incidental research

projects.









Nonetheless, the main feature of these laboratory usability tools is no more than

tracking and collecting the user interface event data such as menu selections, keystrokes,

cursor location and movement, etc. The same concept is used in the data collection

method designed in this study. As discussed before, the visual search game was

programmed to capture the time stamps and mouse/pen event information and log the

collected data in a tab delineated text file that could be imported into a Microsoft Excel

spreadsheet program for initial data formatting and processing.

Response Variables

The visual search game captured and recorded the system time stamps at the

following user interface events: screen displayed/elapsed time meter started, search

instruction displayed, mouse cursor enters the search object panel, and screen object

selected. It also recorded information such as the target object names and the name of

each screen object selected.

As shown in Figure 3-5, the instruction reading time treading was derived as the

difference between the time stamp "screen displayed/search instruction displayed" and

the time stamp "mouse cursor entered the search object panel." Similarly, the time used

to search for the target object was obtained by subtracting the time stamp "mouse cursor

entered the search object panel" from the time stamp "the "Nth screen object selected" for

that particular search task. The number of search errors was counted as N-l, namely the

number of attempts before the correct target was selected at the Nth try. The session time

was also obtained by finding the difference between the time stamp "elapsed time meter

started" and the time stamp of the last selected object in the session.











2nd Target Nth Target
Selected if Selected if
Search Mouse Cursor 1st Target Previous Previous
Instruction Enters Search Selected Choice Choice
Displayed Panel Incorrect _. Incorrect



I I I TIME SCALE
Reading Search
Target Instruction Number of Search Errors

Instruction Reading
Time treadnq Search Time


Figure 3-5. Visual Search Response Variable Definitions

Visual Search Game Design considerations

Special considerations were taken in the design of the visual searching experiment

and are discussed below.

Instruction format. Verbal instructions were initially contemplated in the pilot

study phase with the intent of precluding the potential bias that might exist in the user

interface for the pre-defined text lists in situations where study participants might attempt

to locate the choice by word matching. However it was evident that this was not a

concern that needed to be addressed and on the contrary it could possibly introduce the

"noise" in the result data from other factors, such as instructor accent,

environment/background distractions, etc. As a result, the verbal instruction mode was

dropped from the later versions of the visual search game. Instead, the iconic interface

was designed with textual instruction and the pre-determined text list interface was

designed with iconic instruction. This modification in instruction format/mode also

facilitated comparing the time used for reading textual instruction with the time used for

reading iconic instruction to see whether there was a difference in processing the iconic

information and textual information by the test subjects.









Randomly-sequenced screen objects layout scheme. In order to reduce any bias

that might exist when participants tried to locate the screen object (icon or pre-defined

text list) by remembering its location in the previous task screen, the layout sequences of

the screen objects in all the experiment graphical user interfaces were randomly re-

assigned for each visual search task.

Font size. The font and size of the text used in the pre-defined text user interface

was Arial 14 point which is approximately 0.15 inch in height when shown on the Fujitsu

Stylistic 3400 screen. The Arial 14 point size of text as in the pilot study was deemed

adequate for most people when the Fujitsu Stylistic 3400 screen was held approximately

12 to 18 inches in front of the eyes.

Colors and contrast. Previous research by Nasanen and Ojanpaa (2003) showed

that with increased the levels of contrast or sharpness, the search time, the number of eye

fixations per search and fixation duration decreased. As colors and contrast are not of

particular interest in this study, the screen objects (text lists and icons) in this study were

designed in monochrome (the highest contrast) to eliminate or minimize the potential

effect of the color and contrast factors on the study results.

Size of the icons. Prior research (Lindberg and Nasanen 2003) found that the size

of icons has a strong effect on the speed of icon processing in the human vision system.

Lindberg and Nasanen's study showed that icons smaller than a 0.70 view angle resulted

in significantly longer search times. As the study of the size of icons was not within the

scope of this study, icons used in this study were designed to have a view angle

significantly greater than 0.70 as observed by Lindberg and Nasanen. Icons used in this

study were designed in 64-pixel by 64-pixel which is approximately 0.67 inch by 0.67









inch when shown on the Fujitsu Stylistic 3400 screen. This icon size translates to 3.190

and 2.130, respectively, when the test subjects are seated 12 to 18 inches in front of the

screen.

Repeated Measures. In order to maximize the information obtained from each

study participant, repeated measures were used as the residual effects between the two

user interfaces is generally believed negligible. Each participant completed 14 visual

searching tasks (matching icons or text to the specified tasks) in the icons user interface

and 14 tasks in the pre-defined text lists user interface during the test sessions.

Sample Icon-Based Mobile Equipment Usage Documentation Application

To obtain the foremen's views on icon-based field information documentation

tools, a sample icon-based construction equipment timesheet application running Palm

OS was developed for the study. Equipment usage is a piece of information commonly

tracked by almost all sitework contractors. This information is typically used for prepare

the billing and also to measure productivities. Often foremen have to write on the pre-

printed paper form to record the equipment use time and the activities that the equipment

is used for. Figures 3-6 to 3-10 show the major screenshots of the icon based mobile

application. To document equipment usage time information, a foreman would select the

equipment timesheet icon in the first screen (Figure 3-6). For example, to document the

equipment usage on scrapers, the foreman would select the scrapers icon in the second

screen as shown in Figure 3-7 (shows various types of construction equipment). The third

screen (Figure 3-8) showed all scrapers that had been mobilized onto the project site. The

foreman then would select the particular scraper for which the time information was to be

logged. In the fourth screen (Figure 3-9), the foreman would enter the equipment hour









meter reading, equipment operating time, idle time and downtime information by clicking

the icons and the soft keypad displayed on the touch sensitive screen. In the fifth screen

(Figure 3-10), the foreman would enter the completed work information by also selecting

appropriate icons. The application was designed in such a way that the foremen would

not have to write with stylus or use the hard keypad and combination keys to enter the

above information.








Mobilize Demobilize Material tri Mobilize Demo [ Material
Equipment ~ t Deliveries Equipment Equipr[ Delveries
CORNERST O F T LOG --Equipment Time
,, --Scraper, [] __
WE F tNl N I974F F 19 1E3










Figure 3-6. Main Screen of the Sample Icon-based Field Documentation Application
(shown running on Handspring Treo 270 Model)































Figure 3-7. Equipment Selection Screen


Figure 3-8. Scraper Selection Screen































Figure 3-9. Scraper Time Information Entry Screen


Figure 3-10. Scraper Work Production Input Screen









Procedures

Based on the experience from the pilot study phase, it was found that the total time

required for each subject to complete the visual search game and fill out the questionnaire

survey needed to be limited to ten minutes or less. Otherwise, the interest to participate

by the potential subjects, and their employing firms, tended to be very low. The final

version was designed to be completed in the ten minutes time frame with six to eight

minutes allotted for the visual search game and two to three minutes for filling out the

survey forms. Generally the manager of a company would first be contacted to obtain the

permission to interview the foremen and also to obtain their assistance in making

arrangements for the interviews. The actual interviews were usually held in conjunction

with the companies' weekly or monthly meeting at the home offices but some were held

at the jobsite offices.

Once a potential subject had agreed to participate in the study, a short introduction

was provided on the visual search game and the survey. For the subjects who had never

used a computer or mouse before, the test administrator provided additional guidance

through the first training session to ensure they could efficiently use the mouse and

understood the game protocols. As stated before, the subject started with either the icon

visual search test or the text visual search test based on the random number generated by

the computer code. At the end of the visual search experiment, the subjects were asked to

complete the questionnaire survey. Subjects either verbally gave their answers to the

questions that were recorded on the survey form by the experiment administrator or filled

out the questionnaire by themselves.









Research Hypotheses

From the research questions 1, 2 and 3 discussed earlier in this chapter, the

following hypotheses were formulated:

Task Completion Time

Regarding the task completion time between icon-based user interface and text-

based user interface, the null hypothesis and the alternative hypothesis are stated as

follows:

H10: There is no difference in the task completion time for icon-based user

interface and text-based user interface.

H1a: There is a difference in the task completion time for icon-based user

interface and text-based user interface.

For the purpose of this study, a meaningful difference between the task completion

time for icon-based user interface and text-based user interface on the per-task level was

defined as 1,000 milliseconds (or one second). This number is selected because according

to the theories in the physiology and psychology of eye movements, the vision acuity is

not distributed uniformly across the visual field (Jacob 1995). Instead, the highest acuity

is concentrated on the fovea that covers approximately one degree of the field of view. As

shown in Figure 3-11, fovea is a spot located near the rear center of the human eye that is

responsible for sharpest central vision. Outside the fovea, the peripheral vision acuity

ranges from 15 to 50 percent of that of the fovea. The peripheral vision is generally

inadequate to see an object clearly; therefore, in order to see an object (e.g., a word or an

icon) clearly, one must move the eyeball to make that object appear directly on the fovea.

During a typical visual search process, when the target appears in the peripheral vision,

the eyes make sudden movements (called saccades, typically 30-120 milliseconds) to







56


make the target appear in the fovea vision range and then a fixation (a period of relative

stability during which an object can be viewed) follows. Fixations typically last between

200 and 600 milliseconds. There is also a 100-300 milliseconds delay before the saccade

occurs. It is estimated that a complete fixation process could take 330-1,020 milliseconds.

Therefore, 1,000 milliseconds (translated to one to three fixation periods) could be used

as a suitable meaningful difference. A significance level of 0.05 (. = 0.05) was chosen

for this hypothesis.


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ZoRaLtE fin M





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Figure 3-11. Schematic Diagram of the Human Eye, With the Fovea at the Bottom
Courtesy from Wikipedia, http://en.wikipedia.org/wiki/Opticfovea, February
7, 2006









Task Errors

Regarding the number of identification errors between icon-based user interface

and text-based user interface, the null hypothesis and the alternative hypothesis are stated

as follows:

H20: There is no difference in number of identification errors for icon-based

user interface and text-based user interface.

H2a: There is a difference in the number of identification errors for icon-based

user interface and text-based user interface.

The meaningful difference in identification errors is defined as 1 (one error). As

factors such as screen brightness, screen object sizes, color, contrast, and newness to the

icons are muted to the extent that their effects on the resultant data are minimal, the

number of identification errors are expected to decrease significantly. In the pilot study,

the maximum number of identification errors in all icon visual search tests and text visual

search tests was two. A significance level of 0.05 (a = 0.05, for Type I error) was chosen

for this hypothesis.

User Satisfaction

Research question #3 concerns the satisfaction rating of the icon-based interface as

compared to the text-based interface. The response to this question would generally be an

ordinal variable. However, if the rankings of the satisfaction rating scale can be evenly

placed on a -1 to +1 scale, the response variable then could be treated as a numeric

variable and therefore more information would be available from the resultant data. With

this treatment, the rating "Not at all" was assigned a value of-1.0, "Did not like it" -

0.67, "Slightly disliked it" -0.33, "No opinion" 0, "Liked it a little" +0.33, "Liked it"







58


+0.67, and "Liked it very much" +1.0. The null hypothesis and the alternative hypothesis

were formulated as follows:

H30: There is no difference in construction foremen's satisfaction rating for

icon-based user interface and text-based user interface.

H3a: There is a difference in construction foremen's satisfaction rating for icon-

based user interface and text-based user interface.

The meaningful difference in foremen's satisfaction rating was defined as 0.165

(1/2 step on the satisfaction rating scale). A significance level of 95% was also chosen for

this hypothesis (a = 0.05).

Survey Questionnaire Design

Research questions 4 through 9 as stated earlier in the chapter were to be answered

from the information collected through the survey questionnaire. The questionnaire used

in this study was designed to facilitate an organized and consistent method of gathering

data during personal interviews. Questions pertinent to the research were developed and

then refined in the pilot study. For many questions, a Likert scale or semantic differential

scale was deemed appropriate and scaled answers were developed. Several variations of

the Likert scale were used and are listed as following:

* Agreement Scale:

1 2 3 4 5 6 7
strongly disagree slightly no slightly agree strongly
disagree disagree opinion agree agree

* Importance Scale

1 2 3 4 5
not important of little Fairly important very
at all importance important important










* Efficiency Scale

1 2 3 4 5 6 7
very inefficient slightly no slightly efficient very
inefficient inefficient opinion efficient efficient

* Satisfaction Scale

1 2 3 4 5 6 7
not at all did not like slightly no liked it a like it liked it
disliked it opinion little very much

There were also open-ended questions in the survey questionnaire because the

possible answers to some of the questions could not be anticipated. The answers to the

open-ended questions were sorted and grouped in the results analysis phase.

Foremen Demographics

The first section of the questionnaire gathered demographic information about the

individual participant and the participant's employer. This included the company name,

years in business, the foreman's specific trade, the duration of the foreman's construction

experience, the foreman's average crew size, the foreman's education level, and the

foreman's age.

Foremen's Experience with Touch Sensitive Screen Devices and Mobile Computing
Devices

The survey questionnaire was also intended to obtain general information of the

construction foremen's experience and use of touch sensitive screen devices and mobile

computing devices. Many of today's touch sensitive systems in use already have

incorporated icons to some extent. A foremen's experience on such systems might have

an effect on the foreman's answer on the preference between text based interface and

icon based interface.

* Which of the following touch-sensitive screen devices have you used? (check all
that apply)









a. ATM Machines

b. Information Kiosks

c. Store checkout services

d. Other, please specify

* Have you ever used a mobile computing device (for example, Palm Pilot or pocket
PC's)? (Yes/No)

* Do you use a mobile computing device for work or for your own personal
business? (Yes/No)

* If "Yes," what do you mainly use it for?

a. Work b. Personal Business c. Both

* If "Yes," how much time do you use it for on a weekly basis?

minutes

* How efficient do you think it is to enter the field information on computers using
the stylus writing method? [Efficiency Scale]

* How important do you think it is to be able to enter the field information on
computers in a quick and efficient manner? [Importance Scale]

Foremen's View on Standardization of the Content of Field Documentation

Foremen were asked whether they thought the information content of their typical

field documentation could be standardized. If the answer was "Yes," then they were

asked to estimate a percentage of the amount that could be standardized.

* Do you think most information in your field documentation can be standardized on
the computer so you can pick and choose on the computer screen? [Agreement
Scale]

* If the answer is "Yes," what is the percentage of information that you think can be
standardized? %

Foremen's Preference Between Icons and Pre-defined Text List

As one of the important factors in usability, foremen satisfaction ratings with text-

based interface and icon-based interface were assessed. Foremen were asked how much









they liked the icon visual search game and the text visual search game using a 7-step

Likert scale as previously mentioned. This study was more interested in knowing which

interface had a higher user satisfaction and to what extent the difference varied.

* How much did you like the icon game? [Satisfaction Scale]

* How much did you like the text game? [Satisfaction Scale]

* Please rank the importance of the following three usability factors ("1" being the
lowest and "10" being the highest):

a. shorter task completion time

b. fewer errors

c. satisfaction

Foremen's View about Icon-based Field Information Documentation Tools

After the visual search game, the subjects were shown how to use the sample icon-

based mobile equipment usage documentation application. The subjects were then asked

their opinions as to whether they thought the icon-based mobile computing system could

help them better fulfill their field documentation responsibilities.

* Do you think the icon-based mobile computer tools like the one shown to you
would help you do your daily log? [Agreement Scale]

Please comment on the answer:

* If you were given an icon-based mobile computer tool just as the one shown to you
for your field documentation, would you use it?

a. Yes

b. No, please explain reason

As the survey questionnaire was originally designed for foremen, it was used for

the "other construction professionals" sample and the subjects were asked to fill out the

survey as best applicable to them. A shortened and modified questionnaire survey form






62


was used for the student sample with most questions the same as the foremen

questionnaire.














CHAPTER 4
PILOT STUDY

This chapter documents the preliminary studies conducted during the process of

designing the visual search game. The chapter is organized into the following sections:

icon design and recognition quality testing, sample size preliminary estimation for

hypothesis testing, visual search game initial testing, test platform effect study, and icon

learning curve analysis.

Icon Design and Recognition Quality Testing

Icons included in this study were designed in the Axialis AX-Icons 4.5 program.

A total of 35 (see Table 4-1) 64-pixel by 64-pixel icons were developed to represent

various sitework construction activities and operations. These icons were first evaluated

by a university professor in the construction management research field and went through

several iterations before a recognition quality testing by other construction related

professionals was conducted. In the icon recognition quality-testing phase, a total of 18

persons whose professions were directly related to sitework construction participated in

the evaluation:

* 6 construction foremen

* 3 equipment operators

* 2 superintendents for a sitework contractor

* 1 project engineer

* 2 superintendents for general contractors

* 3 construction inspectors for a civil engineering firm









* 1 construction surveyor

A printout sheet with the 35 icons was shown to the 18 participants and they were

asked to identify the construction activity that each icon represented. If a participant's

description of the icon matched the design intent then the recognition of that icon was

deemed successful. All 18 participants completed the recognition quality evaluation and

the results are shown in the Table 4-1. Recognition success rate for an icon was defined

as the percentage of evaluators who successfully identified that particular icon at the

verbal prompt over the total number of evaluators. Table 4-2 shows the number of icons

successfully recognized by each evaluator.

Table 4-1. Icon Recognition Quality Testing Results
Icon Icon Image Icon Description Recognition
No. Success Rate

1 Excavator Excavating Trench 16/18 (or
MWE_ 88.89%)


,17/18 (or
2 Excavator Laying Pipe 4/18 (r
944415/18 (or

3 Excavator Setting a Structure 15/18 (or
83.33%)


4 Excavator Loading Truck 17/18 (or
17944418 (or)


5 Pouring Concrete 17/18 (or
94.44%)


S16/18 (or
6 Dozer Clearing Trees 88189%)









Table 4-1. Continued
Icon Icon Image
No.


Icon Description


Recognition
Success Rate


Excavator Clearing Trees



Dump Truck Unloading Materials



Flat Belly Pan Loading Material



Peddle Pan Loading Material



Dozer Cutting Trench



Dozer Grading Dirt



Motor Grader Fine Grading


14



15



16


Survey and Layout



Maintenance of Traffic



Loader Grading Dirt


Loader Moving Dirt


14/18 (or
77.78%)


17/18 (or
94.44%)


10/18 (or
55.56%)


8/18 (or
44.44%)


7/18 (or
38.89%)


17/18 (or
94.44%)


17/18 (or
94.44%)


15/18 (or
83.33%)


17/18 (or
94.44%)


13/18 (or
72.22%)


16/18 (or
88.89%)









Table 4-1. Continued
Icon Icon Image
No.


Icon Description


Recognition
Success Rate


Loader Moving Pipe



Mixer Mixing Subgrade



Box Blade Grading Dirt



Single-drum Roller Compacting



Maintenance of Traffic



Self-elevating Scraper Loading Material



Double-drum Roller Compacting
Asphalt



Paving Asphalt



Traffic Roller Compacting Asphalt



3-Wheel Steel Roller Compacting



Small Double Roller Compacting
Asphalt


17/18 (or
94.44%)


9/18 (or
50.00%)


16/18 (or
88.89%)


16/18 (or
88.89%)


17/18 (or
94.44%)


15/18 (or
83.33%)


17/18 (or
94.44%)


17/18 (or
94.44%)


17/18 (or
94.44%)


17/18 (or
94.44%)


17/18 (or
94.44%)









Table 4-1. Continued


Icon Description


Recognition
Success Rate


Plate Tamp Compacting Dirt



Broom Tractor Sweeping



Excavator Backfilling Trench



Mobilize Equipment



Material Delivery



Construction Accident


Dewatering Operation


Icon
No.


Icon Image


17/18 (or
94.44%)


15/18 (or
83.33%)


17/18 (or
94.44%)


17/18 (or
94.44%)


17/18 (or
94.44%)


16/18 (or
88.89%)


15/18 (or
83.33%)









Table 4-2. Icon Recognition Evaluation Results Organized by Evaluator
Evaluator Evaluator Job Function # of Icon Successfully
# Recognized
1 Construction Inspector 30
2 Construction Inspector 32
3 Earthwork Foreman 35
4 Earthwork Foreman 33
5 Construction Surveyor 32
6 Earthwork Foreman 31
7 Equipment Operator 29
8 Equipment Operator 30
9 Earthwork Foreman 34
10 Construction Inspector/P.E. 29
11 Equipment Operator 33
12 General Superintendent 29
13 Superintendent 33
14 Project Engineer 30
15 Project Superintendent (General Contractor) 32
16 Underground Utilities Foreman 32
17 Earthwork Foreman 34
18 Project Superintendent (General Contractor) 31

Based on the icon recognition quality test results, fifteen icons with relatively high

recognition success rates were selected from the 35 icons and used in the visual search

game computer program.

Test Platform Difference Study

As it was likely that the visual search game would be administered on different

computer platforms during the final testing phase, it was important to know whether

different computer platforms could cause differences in the test results. To answer this

question, a test platform difference study was conducted to investigate the potential

influence of the platform differences.

Data

Two independent samples with 15 subjects per sample were drawn from a local

civil engineering firm. Each subject completed five (5) icon-training sessions. The









subjects completed 15 visual search tasks in each session. None of the subjects had

previously seen the icons or the visual search game before the test. The first sample test

was taken on a Fujitsu 3400 Tablet PC. The second sample test was taken on various

types of computers that the test subjects used daily. The results for the average task time

and task errors from the icon-training sessions are shown in Table A-i in Appendix A.

The individual task time was defined as the time used by a subject to read the textual

instruction and subsequently find the corresponding icon. The average task time for each

session was defined as the average of the individual task times in that session. To reduce

the bound of error in the results, the two highest and two lowest task times were excluded

and only the remaining eleven (11) observations were used to compute the average time

for each session. For example, Table 4-3 illustrates how Subject l's average task time in

Session 3 was calculated. Two lowest individual task time observations 500 ms and 1,407

ms and two highest observations 13,172 ms and 10,859 ms were considered as the

outliers and were not included in the computation of the average search task time for

Session 3. The average task time for Session 3 was computed with the remaining eleven

individual task time observations. Based on this method, the average task time by Subject

1 in Session 3 was computed to be 3,966 milliseconds. The reason for excluding the

outliers was mainly to reduce the bound of error due to either excessively long or short

incidental task times. The excessively long task times usually occurred where the visual

search process was halted on one particular icon with which the subject had great

difficulty. The excessively short task times typically occurred when the mouse cursor was

right on the target icon when the next task started. The shortest task time also frequently

occurred with the last visual search task where only one icon remained on the screen.









Table 4-3. Example of Excluding Outliers in the Computation of the Average Task Time
Search Task # Task Time (in Milliseconds)
1 6,937
2 14-859
3 3,156
4 2,156
5 13,72
6 3,031
7 4,953
8 6,172
9 3,625
10 5,562
11 4-407
12 3,281
13 1,578
14 3,172
15 500

Figure 4-1 shows the mean task time and search errors in each of the five icon

training sessions for the Fujitsu sample and Non-Fujitsu sample. As it is evident in Figure

4-1, the mean task time decreased as the training sessions progressed and the task errors

also reduced in a similar fashion on both of the Fujitsu and Non-Fujitsu platforms.

Pearson correlation coefficients between the mean task time and task errors on both of the

Fujitsu platform and Non-Fujitsu platform, as shown in Tables 4-4 and 4-5, indicate that

the correlation is significant at 0.01 level on both platforms. In other words, task time and

task errors were highly correlated. Longer task time was generally correlated to more task

errors while shorter task time correlated to fewer errors.






















1 2 3 4 5
Session No.


--- Fujitsu Task Time
SFujitsu Session Errors


-u-Non-Fujitsu Task Time
-e- Non-Fujitsu Session Errors


Figure 4-1. Mean Task Time and Search Errors Observed in the Platform Difference
Study

Table 4-4. Correlation Between the Mean Task Time and Task Errors on the Fujitsu
Platform
Fujitsu Task Non-Fujitsu
Time Task Errors
Fujitsu Task Time Pearson Correlation 1 0.999
Sig. (2-tailed) 0.01
N 5 5
Fujitsu Task Errors Pearson Correlation 0.999 1
Sig. (2-tailed) 0.01
N 5 5

Table 4-5. Correlation Between the Mean Task Time and Task Errors on the Non-Fujitsu
Platform
Non-Fujitsu Non-Fujitsu
Task Time Task Errors
Non-Fujitsu Task Time Pearson Correlation 1 0.998
Sig. (2-tailed) 0.01
N 5 5
Non-Fujitsu Task Errors Pearson Correlation 0.998 1
Sig. (2-tailed) 0.01
N 5 5









Hypotheses Testing

In determining the potential differences in study results that might be caused by

different computer platforms, the hypotheses about the differences between the means of

the two populations and the hypotheses about the variances of the two populations were

tested.

Hypotheses testing about variances of the populations on Fujitsu and Non-Fujitsu
platforms

It was important to compare the variability in the results data from the Fujitsu and

Non-Fujitsu platforms. This was because for the small sized samples (N<30) the

assumption of equal variance would be required to calculate the pooled sample variance

to test the hypotheses about the means from these two different populations.

With a confidence level of 95% (a = 0.05), the following Hypotheses were stated:

Hypothesis Conclusion
If Ho is accepted equal variance likely exists in
the average task times in Session 1 between the
Fujitsu platform and Non-Fujitsu platform. Test
H0: 2 Ft= 2llNFujits platform is unlikely a factor that causes different
o 0 T1Fujitsu -- T1Non-Fujitsu
variability in test results. It is appropriate to
calculate pooled sample variance from ST1Fujitsu and
ST1Non-Fujitsu-
T1 If Ho is rejected there is insufficient evidence to
support that equal variance exists in the average
task times in Session 1 between the Fujitsu
2 2 platform and Non-Fujitsu platform. Test platform
Hi: a TIFujitsu C2T1Non-Fujitsu is likely a factor that causes different variability in
test results. It is not appropriate to calculate
pooled sample variance from ST1Fujitsu and ST1Non-
Fujitsu.
If Ho is accepted equal variance likely exists in
the average task times in Session 2 between the
Fujitsu platform and Non-Fujitsu platform. Test
T2 Ho: o2T2Fujitsu 2T2Non-Fujitsu platform is unlikely a factor that causes different
variability in test results. It is appropriate to
calculate pooled sample variance from ST2Fujitsu and
ST2Non-Fulitsu.














Hi: a T2Fujitsu # 7 T2Non-Fujitsu


Ho: a2T3Fujitsu a= 2T3Non-Fujitsu








HI: 2 T3Fujitsu 7 a2T3Non-Fujitsu


Ho0 C2T4Fujitsu a= 2T4Non-Fujitsu







2 2
Hi: a T4Fujitsu T4Non-Fujitsu


T5 HO: 2 T5Fujitsu =- 2T5Non-Fujitsu


If Ho is rejected there is insufficient evidence to
support that equal variance exists in the average
task times in Session 2 between the Fujitsu
platform and Non-Fujitsu platform. Test platform
is likely a factor that causes different variability in
test results. It is not appropriate to calculate
pooled sample variance from ST2Fujtsu and ST2Non-
Fujitsu.
If Ho is accepted equal variance likely exists in
the average task times in Session 3 between the
Fujitsu platform and Non-Fujitsu platform. Test
platform is unlikely a factor that causes different
variability in test results. It is appropriate to
calculate pooled sample variance from ST3Fujtsu and
ST3Non-Fujitsu-
If Ho is rejected there is insufficient evidence to
support that equal variance exists in the average
task times in Session 3 between the Fujitsu
platform and Non-Fujitsu platform. Test platform
is likely a factor that causes different variability in
test results. It is not appropriate to calculate
pooled sample variance from SFujitsu and ST3Non-
Fujitsu.
If Ho is accepted equal variance likely exists in
the average task times in Session 4 between the
Fujitsu platform and Non-Fujitsu platform. Test
platform is unlikely a factor that causes different
variability in test results. It is appropriate to
calculate pooled sample variance from ST4Fujitsu and
ST4Non-Fujitsu-
If Ho is rejected there is insufficient evidence to
support that equal variance exists in the average
task times in Session 4 between the Fujitsu
platform and Non-Fujitsu platform. Test platform
is likely a factor that causes different variability in
test results. It is not appropriate to calculate
pooled sample variance from ST4Fujitsu and ST4Non-
Fulitsu*
If Ho is accepted equal variance likely exists in
the average task times in Session 5 between the
Fujitsu platform and Non-Fujitsu platform. Test
platform is unlikely a factor that causes different
variability in test results. It is appropriate to
calculate pooled sample variance from S5Fujitsu and
ST5Non-Fujitsu-













Hi: 2 T5Fujitsu C7 T5Non-Fujitsu


2 2
Ho: 2 E1Fujitsu = C2E1Non-Fujitsu



El


HI: 2E1Fujitsu 2E1Non-Fujitsu






Ho: 0 E2Fujitsu = 7 E2Non-Fujitsu



E2


H1: 2 E2Fujitsu 2 a E2Non-Fujitsu






E3 Ho: 7 E3Fujitsu = 7 E3Non-Fujitsu


If Ho is rejected there is insufficient evidence to
support that equal variance exists in the average
task times in Session 5 between the Fujitsu
platform and Non-Fujitsu platform. Test platform
is likely a factor that causes different variability in
test results. It is not appropriate to calculate
pooled sample variance from ST5Fujits and ST5Non-
Fujitsu.
If Ho is accepted equal variance likely exists in
the task errors in Session 1 between the Fujitsu
platform and Non-Fujitsu platform. Test platform
is unlikely a factor that causes different variability
in test results. It is appropriate to calculate pooled
sample variance from SE1Fujtsu and SE1Non-Fujtsu.
If Ho is rejected there is insufficient evidence to
support that equal variance exists in the task
errors in Session 1 between the Fujitsu platform
and Non-Fujitsu platform. Test platform is likely
a factor that causes different variability in test
results. It is not appropriate to calculate pooled
sample variance from SE1Fujtsu and SE1Non-Fujtsu.
If Ho is accepted equal variance likely exists in
the task errors in Session 2 between the Fujitsu
platform and Non-Fujitsu platform. Test platform
is unlikely a factor that causes different variability
in test results. It is appropriate to calculate pooled
sample variance from SFujitsu and SE2Non-Fujitsu
If Ho is rejected there is insufficient evidence to
support that equal variance exists in the task
errors in Session 2 between the Fujitsu platform
and Non-Fujitsu platform. Test platform is likely
a factor that causes different variability in test
results. It is not appropriate to calculate pooled
sample variance from SFujitsu and SE2Non-Fujitsu
If Ho is accepted equal variance likely exists in
the task errors in Session 2 between the Fujitsu
platform and Non-Fujitsu platform. Test platform
is unlikely a factor that causes different variability
in test results. It is appropriate to calculate pooled
sample variance from SEFuitsu and E3Non-Fujitsu-













Hi: 2 E3Fujitsu C2 2E3Non-Fujitsu






Ho: C E4Fujitsu = 2 E4Non-Fujitsu






Hi: C2E4Fujitsu C 2E4Non-Fujitsu






Ho: 2 E5Fujitsu = 7 E5Non-Fujitsu






Hi: C2E5Fujitsu C 2E5Non-Fujitsu


If Ho is rejected there is insufficient evidence to
support that equal variance exists in the task
errors in Session 3 between the Fujitsu platform
and Non-Fujitsu platform. Test platform is likely
a factor that causes different variability in test
results. It is not appropriate to calculate pooled
sample variance from SE3Fujit and SE3Non-Fujitsu
If Ho is accepted equal variance likely exists in
the task errors in Session 4 between the Fujitsu
platform and Non-Fujitsu platform. Test platform
is unlikely a factor that causes different variability
in test results. It is appropriate to calculate pooled
sample variance from SE4Fuitsu and SE4Non-Fujitsu
If Ho is rejected there is insufficient evidence to
support that equal variance exists in the task
errors in Session 4 between the Fujitsu platform
and Non-Fujitsu platform. Test platform is likely
a factor that causes different variability in test
results. It is not appropriate to calculate pooled
sample variance from SE4Fujtsu and SE4Non-Fujitsu.
If Ho is accepted equal variance likely exists in
the task errors in Session 5 between the Fujitsu
platform and Non-Fujitsu platform. Test platform
is unlikely a factor that causes different variability
in test results. It is appropriate to calculate pooled
sample variance from SEFujitsu and sENon-Fujitsu
If Ho is rejected there is insufficient evidence to
support that equal variance exists in the task
errors in Session 5 between the Fujitsu platform
and Non-Fujitsu platform. Test platform is likely
a factor that causes different variability in test
results. It is not appropriate to calculate pooled
sample variance from SE5Fultsu and sENon-Fujitsu


To test the above stated hypotheses, F values (F=S12/S22, note Population 1 was

denoted as the population providing the largest sample variance) for each set of

hypotheses were calculated and listed in Table 4-6. F /2 (a/2 = 0.025) value with nl-1

(15-1=14) degrees of freedom for the numerator and n2-1 (15-1=14) degrees of freedom


for denominator is also shown in Table 4-6.









Table 4-6. Platform Difference Study -

Sample S12
Variable
Ti 47,779,430.471
T2 17,371,668.271
T3 14,929,980.804
T4 13,533,569.440
T5 12,102,513.284
El 31.360
E2 5.444
E3 3.004
E4 2.778
E5 1.960


Sample Variance F Values

S22 F=S12/S22

34,221,720.004 2.119
19,017,158.084 1.603
14,415,690.240 1.101
13,647,113.640 1.364
11,557,280.160 2.099
27.738 1.795
9.000 1.340
5.138 1.446
3.484 1.655
2.778 1.111


As shown in Table 4-6, the calculated F values for T1, T2, T3, T4, T5, Ei, E2, E3, E4,

and E5 are all less that than F0.025 values. Therefore, at a confidence level of 95% (a =

0.05), the Ho hypotheses for T1, T2, T3, T4, T5, Ei, E2, E3, E4, and E5 cannot be rejected.

This led to the conclusion that at a confidence level of 95% (a = 0.05) different computer

test platforms (Fujitsu or Non-Fujitsu computers) did not introduce the difference in the

variability in the average task time and task errors for each of the five icon training

sessions. Therefore, it is assumed with confidence that data to be collected on different

computer platforms would have the equal variances.

Hypotheses testing about the difference between the means of the data collected on
the Fujitsu and Non-Fujitsu platforms

It is important not only to analyze the variability of the data collected on the Fujitsu

and Non-Fujitsu platforms but also to compare the means of the data from these two

independent samples. If different platforms do result in different means, then the data in

the final study should all be collected on the same platform to avoid the unwanted bias in

the data that may exist because of the platform difference factor. For this, the following

hypotheses were stated with a confidence level of 95% (a = 0.05):


F0.025
(ni- = 14,
n2-1=14)
2.983
2.983
2.983
2.983
2.983
2.983
2.983
2.983
2.983
2.983









Hypothesis


Ho: T1Fujltsu T1Non-Fujltsu 0


Ti



H1: TiFujltsu T1Non-Fujitsu 0





Ho: T2Fultsu T2Non-Fujitsu 0


T2



H1: T2Fujitsu- T2Non-Fujitsu 0





H : T3Fujltsu T3Non-Fujitsu 0


T3



H1: T3Fujitsu T3Non-Fujitsu 0





T4 Ho: T4Fultsu T4Non-Fulitsu 0


Conclusion
If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the average task
times of the Fujitsu population and Non-Fujitsu
population for Session 1. Test platform is
unlikely to be a factor that causes a difference in
test results.
If Ho is rejected there is a difference between
the means of the average task times collected on
the Fujitsu platform and Non-Fujitsu platform
for Session 1. Test platform is likely to be a
factor that causes a difference in test results.
If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the average task
times of the Fujitsu population and Non-Fujitsu
population for Session 2. Test platform is
unlikely to be a factor that causes a difference in
test results.
If Ho is rejected there is a difference between
the means of the average task times collected on
the Fujitsu platform and Non-Fujitsu platform
for Session 2. Test platform is likely to be a
factor that causes a difference in test results.
If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the average task
times of the Fujitsu population and Non-Fujitsu
population for Session 3. Test platform is
unlikely to be a factor that causes a difference in
test results.
If Ho is rejected there is a difference between
the means of the average task times collected on
the Fujitsu platform and Non-Fujitsu platform
for Session 3. Test platform is likely to be a
factor that causes a difference in test results.
If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the average task
times of the Fujitsu population and Non-Fujitsu
population for Session 4. Test platform is
unlikely to be a factor that causes a difference in
test results.












H1: T4Fujltsu T4Non-Fujitsu # 0





H0: T5FultsuT- T5Non-Fujitsu= 0






H1: T5Fuljtsu- T5Non-Fujitsu 0





Ho: ElFujitsu E1Non-Fujitsu 0






HI: EFujlitsu E1Non-Fujitsu : 0





Ho: E2Fujitsu E2Non-Fuitsu 0






H1: E2Fujtsu E2Non-Fujitsu 0


If Ho is rejected there is a difference between
the means of the average task times collected on
the Fujitsu platform and Non-Fujitsu platform
for Session 4. Test platform is likely to be a
factor that causes a difference in test results.
If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the average task
times of the Fujitsu population and Non-Fujitsu
population for Session 5. Test platform is
unlikely to be a factor that causes a difference in
test results.
If Ho is rejected there is a difference between
the means of the average task times collected on
the Fujitsu platform and Non-Fujitsu platform
for Session 5. Test platform is likely to be a
factor that causes a difference in test results.
If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the task errors
of the Fujitsu population and Non-Fujitsu
population for Session 1. Test platform is
unlikely to be a factor that causes a difference in
test results.
If Ho is rejected there is a difference between
the means of the task errors collected on the
Fujitsu platform and Non-Fujitsu platform for
Session 1. Test platform is likely to be a factor
that causes a difference in test results.
If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the task errors
of the Fujitsu population and Non-Fujitsu
population for Session 2. Test platform is
unlikely to be a factor that causes a difference in
test results.
If Ho is rejected there is a difference between
the means of the task errors collected on the
Fujitsu platform and Non-Fujitsu platform for
Session 2. Test platform is likely to be a factor
that causes a difference in test results.













Ho: E3Fultsu- E3Non-Fuitsu= 0





H1: E3Fujltsu E3Non-Fujitsu 0





H0: E4Fltsu E4Non-Fujitsu 0





H1: E4Fujltsu E4Non-Fulitsu f 0





H0: E5Fujitsu- E5Non-Fujitsu= 0





H1: E5Fujltsu ES5on-Fulitsu # 0


If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the task errors
of the Fujitsu population and Non-Fujitsu
population for Session 3. Test platform is
unlikely to be a factor that causes a difference in
test results.
If Ho is rejected there is a difference between
the means of the task errors collected on the
Fujitsu platform and Non-Fujitsu platform for
Session 3. Test platform is likely to be a factor
that causes a difference in test results.
If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the task errors
of the Fujitsu population and Non-Fujitsu
population for Session 4. Test platform is
unlikely to be a factor that causes a difference in
test results.
If Ho is rejected there is a difference between
the means of the task errors collected on the
Fujitsu platform and Non-Fujitsu platform for
Session 4. Test platform is likely to be a factor
that causes a difference in test results.
If Ho is accepted sample evidence is not
sufficient to support the conclusion that there is a
difference between the means of the task errors
of the Fujitsu population and Non-Fujitsu
population for Session 5. Test platform is
unlikely to be a factor that causes a difference in
test results.
If Ho is rejected there is a difference between
the means of the task errors collected on the
Fujitsu platform and Non-Fujitsu platform for
Session 5. Test platform is likely to be a factor
that causes a difference in test results.


As the sample sizes of the Fujitsu sample and Non-Fujitsu sample were both less

than 30, the t distribution with 28 degrees of freedom (n+n2-2 = 15+15-2=28, nl=15,

n2=15) was used to develop the critical values for the test. The following assumptions

were made for the test: 1) Fujitsu population and Non-Fujitsu population both have

normal distributions; 2) population variances in the Fujitsu population and Non-Fujitsu










population are equal. Pooled estimates of the population variances were calculated from

the variances of the Fujitsu sample and Non-Fujitsu sample. For a = 0.05, t /2 with 28

degrees of freedom is 2.048. Critical values (lower value 0 2.048*Spooled, upper value 0

+ 2.048*Spooled) for the test were also calculated and the resultant data are listed in Table

4-7.

Table 4-7. Platform Difference Study t Test for Equality of Means
(Sample 1- Fujitsu Platform, Sample 2- Non-Fujitsu Platform)
Pooled
le Sample Sample Difference Sample Critical Values
1 2 ofSample S1 $Sample
Variable 1 2 ofSample SV S2 Variance
Mean Mean Means (S2/nl+ Lower Upper
S22/n2)0 5
T1 6,912.27 5,849.93 1,062.33 3,199.987 2,198.060 1,002.377 -2,052.868 2,052.868
T2 4,167.93 4,360.87 -192.93 1,084.059 1,372.435 451.572 -924.820 924.820
T3 3,863.93 3,796.80 67.13 746.550 711.389 266.259 -545.299 545.299
T4 3,678.80 3,694.20 -15.40 1,050.892 899.898 357.229 -731.606 731.606
T5 3,478.87 3,399.60 79.27 1,012.110 698.636 317.538 -650.318 650.318
El 5.60 5.27 0.33 4.763 3.555 1.535 -3.143 3.143
E2 2.33 3.00 -0.67 3.063 2.646 1.045 -2.140 2.140
E3 1.73 2.27 -0.53 1.387 1.668 0.560 -1.147 1.147
E4 1.67 1.87 -0.20 1.759 2.264 0.740 -1.516 1.516
E5 1.40 1.67 -0.27 1.056 1.113 0.396 -0.811 0.811

As shown in Table 4-7, the observed sample mean differences for T1, T2, T3, T4, T5,

Ei, E2, E3, E4, and E5 were all located between the lower and upper critical values.

Therefore, at a confidence level of 95% (significance level a = 0.05), the Ho hypotheses

for T1, T2, T3, T4, T5, E1, E2, E3, E4, and E5 cannot be rejected.

Based on the results from the platform difference study, there is not sufficient

evidence to reject the hypotheses that data collected on the different platforms do not

differ in terms of the population means and population variance. Therefore, risk of

introducing bias in the results by administering the visual search game on different

computers can be considered to be low.