application development is au area of explosive growth, providing dramatic ... computer aided design and drafting (CADD) systems make it possible to more ...
Mathl. Comput. Modelling Vol. 15, No. 7, pp. 83-93, Printed in Great Britain. All rights reserved
AN AHP
APPLICATION
08%7177191 83.99 + 0.00 Copyright@ 1991 Pergamon Press plc
1991
TO COMPUTER
RAVI CHAND
VELLORE
AND DAVID
SYSTEM
SELECTION
L. OLSON
Department of Business Analysis & Research, Texas A&M University College Station, TX 77843
(Received
October
1990)
Abstract--Computer application development is au area of explosive growth, providing dramatic new capabilities to do additional work faster and more accurately. In the construction industry, computer aided design and drafting (CADD) systems make it possible to more completely specify work to be done. Some project owners require drawings be submitted in magnetic form. Therefore, it is necessary for construction and engineeringfhms to acquire and develop expertise in state of the art computer systems. Because many system configurations are available, selection of CADD systems is an important decision, requiring consideration of a number of objectives. AHP is used to combine cost factors with subjective factors. AHP also is used to consider the impact on end users as a group and au central data processing.
1. INTRODUCTION Computer development has yielded many tools which aid almost every aspect of human endeavor. Computer aided design is one of the most impressive application developments. Manual drafting methods, requiring untold manhours of tedious effort, have been replaced by more precise and much faster electronic means of developing engineering drawings. As new technology develops and demonstrates strong superiority over past technology, it soon permeates the way operations are conducted throughout an industry. Another feature of new technology is that system acquisition is complicated. In an environment of rapid technological development, new systems have obvious benefit for organizations. However, there are many options available, with diverse compensating advantages. Further, the impact on organization wide performance is complicated, as new techniques outdate the procedures that employees throughout the organization have developed with experience. New computer technology often allows end users to operate system applications. This can create new factors of complexity, in that the impact of system acquisition affects many aspects of organization operations. End user computing (EUC) involves the operation of computer applications by individuals EUC has proven very beneficial in many organizations performing specified functional tasks. (Guimaraes and Hamanujam [l] and Hivard and Huff [2]). B u t control problems develop with new systems, as there are a variety of computer experience levels within an organization. EUC activity is expected to be over 70% of organizational computing in the 1990s (Benjamin [3] and Greenberg [4]). While EUC activity is expected to reap great benefits, it creates a challenge for rational management, as many new proposals can be expected. This paper presents an analysis to determine the best investment in a computer aided design and drafting (CADD) decision. CADD is an area of rapidly growing technology, with many new products being developed, resulting in a dynamic environment (Sawyer [5]; Stover [S]). The decision involved a number of incommensurate criteria, requiring analysis capable of reflecting the relative value of available options. Further, the decision involves impact on two groups-end users of computing (EUC) and central data processing/management information systems (DP/MIS). Analytic hierarchy process (Saaty [7]) can be a valuable tool in identifying criteria of importance, in systematically considering the impact of the decision on a number of organizational systems, and in aiding the decision maker to reach a more educated and rational decision. Typeset by A&-‘&X 83
84
R.C. VELLORE. D.L. OLSON
2. PROBLEM
BACKGROUND
The company is a leading contractor executing works in the area of roads, housing projects, industrial buildings, airports, tunnels, underpasses, etc. The design drawings needed for the construction are provided by a consultant working for the client. These drawings need to be recast in a manner suitable for use by project construction staff and later for obtaining payments from the client. These additional sets of drawings are produced by a team of draftsmen. A few years ago the government, a major client, issued a directive that all drawings should be in magnetic form and be produced by computer aided design and drafting (CADD) equipment. A transition period was allowed during which both consultants and contractors could change over from a manual to the CADD set up. Being a leading player in the contracting business, the company decided to introduce CADD technology, train people and move into a production environment within the changeover period allowed by the government. During the transition, some consultants continued to submit design drawings in hard copy format. Thus, the design drawings had to be re-input by the company at its own expense and then translated for use by the project staff. The CADD department was started with a minimum configuration consisting of: A state of the art graphics workstation and a low-end mini-computer model manufactured by a leading CADD vendor. Early objectives were: (1) create awareness of CADD in the company; (2) undertake small projects and treat them as a period of learning; and (3) ensure that the investment in hardware and software could be integrated with the final full scale system selected by the company. The Case A CADD production environment in a contracting company involves creating a large number of shop drawings and as-built drawings. Shop drawings are translations of current plans according to project schedules and consultant design drawings, that can be used by project staff in their construction activity. Depending on nature of the project and its size, required shop drawings could run anywhere from tens to hundreds or more. Once the project enters the finishing stages, another set of drawings, called as-built, are produced to help the company get payments from the client. The client releases payments to the contractor after verifying the as-built drawings and making sure that they conform to the initial design plus any modifications intimated formally by them. Thus, the contractor is involved in the production of a large number of drawings during a project’s construction phase. The end-user, putting forth his case for scanning equipment to augment the initial start-up CADD environment, focused on three aspects: (1) manpower; (2) time; and (3) hardware and software price. The scanner would assist in inputting hard copy drawings submitted by clients into the machine much faster than would be possible manually. The reduction of manpower costs was estimated to be 3.41. Additionally, productivity improvement was estimated to be 6 times or better over manual operations. Given these enormous savings in manpower costs and time, the end-user felt that the scanner would pay for itself many times over. Although the scanning equipment was available from the vendor of an earlier purchased machine, the end-user recommended another vendor whose prices were lower. Some of the factors not considered by the end-user were: Maintenance of the scanning equipment; reliability of the equipment and its subsequent effect on productivity; and vendor support. 3. THE
AHP
ANALYSIS
A hierarchy of criteria considered in the decision was developed. Estimated cost of alternative systems was of course important. Other qualitative features need to be considered as well. The factors contributing to EUC success (Cheney et al. [8]; Rivard and Huff [2]) can be classified into the following major categories: DP/MIS interaction factors, tool (hardware/software) perceptions, adaptability to change, and attitudes. In the past the effectiveness of an IS has been measured by the surrogate User Information Satisfaction (UIS) (Bailey & Pearson, [9]). UIS as an IS instrument has been validated and applied by several researchers in the past (Ives et al. [lo]; Doll & Torkzadeh [ll]). Th e scales associated with UIS in these studies have been investigated
85
An AHP application
and those relevant include: DP/MIS
to the broad
EUC classifications
mentioned
above have been chosen.
These
Interaction Factors:
(1) Relationship with DP/MIS staff (2) Means of input/output with DP/MIS (This relates to the area of EUC data exchange CIS) (3) Competition with DP/MIS (C oncerns over power and responsibility) (4) Timeliness of output (5) Vendor support (Standardization of hardware and software) (6) Training end-users
with
Tool Perceptions: (1) (2) (3) (4) (5) (6)
Confidence in systems Users’ understanding of systems Response/turnaround time Volume of output (Impact of new applications Job effects (Impact on job performance) Flexibility of system
on the overall system)
Adaptability to Change: (1) Technical competence (Of end user) (2) Personal control of EUC systems (3) Computer knowledge (Of end user) Attitudes: (1) Environment (General office attitude (2) Top management support (3) Personal (Psychological attitude)
toward
EUC)
Note that almost all of these factors are qualitative. One would expect end-users to have their view of interaction factors, and the DP/MIS department to have a different view. The model we propose would allow each organizational actor or group to make their own assessment of of organizational performance on those factors. The end user would evaluate the effectiveness tool performance, adaptability to change, and attitudes. We would expect top management (or the managerial level faced with a particular decision) to reconcile expected differences in perceived organizational performance, and to weigh benefits against the cost of providing them. This would enable the decision maker to consider not only cost factors, but to identify problems in computer support provided end users, as well as burdens on the DP/MIS department. Further, the model could be used to evaluate new end user proposals by evaluating the expected impact on both quantitative and qualitative factors. Project proposals to improve performance on factors where current performance is unacceptable could be weighed against their cost. Following these classifications of qualitative factors, an analysis was conducted for the case. A consultant conducted the analysis, and used AHP rating relative advantages from the perspective of the different groups involved. In addition to cost, the other four factors were the impact on interaction with the current system, the development of new CADD tools to effectively aid company operations, the impact of change on personnel, and the impact on personnel attitudes. An overall rating of these five factors was made from the perspective of top management. Then, the relative proportion of impact due to end user computing (EUC) and data processing/management information systems operations (DP/MIS) was made for each of the four qualitative factors. Each
86
R.C. VELLORE, D.L. OLSON OVERALL
I COST
I INTERACTION EUC
VALUE
EUC
RELATIONSHIP
CONFIDENCE
MEANS of I/O
UNDERSTANDING
COMPETITION
TURNAROUND
TIMELINESS VENDOR SUPPORT END-USER TRAIN DP/MIS
SUPPORT
PERSONAL
PERSONNEL
CONTROL COMPUTER KNOWLEDGE
FLEXIBILITY DP/MIS
COMPETITION
TURNAROUND
TRAIN
TOP MGMT
COMPETENCE
VOLUME
UNDERSTANDING
END-USER
ENVIRONMENT
TECH
JOB EFFECTS
MEANS of I/O
SUPPORT
EUC
EUC
TIME
CONFIDENCE
VENDOR
ATTITUDES
CHANGE
OUTPUT
RELATIONSHIP
TIMELINESS
I
I
I TOOLS
DP/MIS
DP/MIS TECH
TOP MGMT
PERSONAL
SUPPORT
CONTROL
TIME OUTPUT
ENVIRONMENT
COMPETENCE
PERSONNEL
COMPUTER
VOLUME
KNOWLEDGE
JOB EFFECTS FLEXIBILITY
Top management provided the relative importance of the five major criteria. End User Computing and DP/MIS
relative emphasis for each of the four qualitative criteria (Interaction, Tools, Change,
and Attitudes) were also evaluated by top management. Then the relative importance of subfactors below each of these four qualitative criteria were rated from the perspectives of both End User Computing and DP/MIS. Finally, the performance of each ahernative decision (system with scanning and system without scanning) on each of these eighteen subcriteria were evahrated from the perspective of End User Computing as well as from the perspective of DP/MIS. Figure 1. Heirarchy of factors.
of these factors were then analyzed in greater depth, with relative impact considered independently. Figure 1 presents the hierarchy of objectives.
on EUC and DP/MIS
Two alternative systems were considered, after screening for technical capability, vendor support, and compatibility with existing systems. Alternative 1 included scanning capability, expediting electronic development of existing drawings created manually. Alternative 2 involved less investment, but did not have scanning capability. The hierarchy is presented in the appendix. Cost estimates were developed for the life cycle of the project. Cost categories considered included hardware and software acquisition expenses, personnel costs, and costs of operating each system. A common time frame reflecting equipment life expectency was used. Because AHP considers the ratio of relative contribution to value, the ratio of these cost estimates was used to identify relative alternative performance. Interaction with the current system was evaluated on six subfactors. These subfactors were the relative impact on the relationship between end users and the central DP/MIS group, the impact on input/output operations, competitive advantage acquired, impact on the time required to create or reproduce drawings, vendor support, and end user training. As with the other three
87
An AHP application Evaluations by top management OVERALL
VALUE COST
INTERACTION
1
COST INTERACTION TOOLS
TOOLS
CHANGE
ATTITUDES
3
2
7
3
1
113 1
2 5
l/3 3
1
112 1
CHANGE ATTITUDES a9444
.29085
.05683
.15cQO
EUC
33333
.50000
.16667
.66667
DP/MIS
A6667
.50000
.83333
.33333
40788
eigen vector
consistency index = .0498 RELATIVE
IMPORTANCE
Figure 2. Top level pairwise comparisons.
non-cost factors, pairwise comparison evaluations were conducted from the perspective of end user operations as well as from the DP/MIS perspective. Tool perceptions were subdivided into six subelements. Confidence in systems reflected the perceived accuracy of the alternative systems to produce quality output. Understanding of output reflected the ability of the system to generate output that users would understand. Turnaround time referred to the amount of time between job submission and user receipt of output. Output volume considered the impact of generated output on operations. Job effects reflected the impact of the proposed systems on the ability of end users to perform their job effectively. Flexibility of the system considered the ability to adjust the system to user or organizational operations. As with the other noncost factors, these subelements were evaluated with respect to both end user and DP/MIS perceptions. Adaptability to change considered three aspects. Technical competence focused on the ability of end users to understand the new technology. Personal control referred to the ability of individual end users to have control over their system operations. The last subelement in this group was the degree of end user computer knowledge required to operate the system. The impact of the proposed alternative systems on attitudes was the last factor considered. The expected impact on the end user and DP/MIS environment was included, as well as the amount of top management support and attitude of personnel. 4. PAIRWISE
COMPARISONS
The ratio of relative importance of the five primary factors was obtained through pairwise comparisons. Cost was considered the most important factor, and was rated as moderately more important than interaction and attitudes, between moderately more important and equivalent to tools, and having a very strong relative importance with respect to change. Tools were rated as essentially more important than interaction factors and change factors, and moderately more important than attitude factors. Attitude factors were rated as moderately more important than interaction factors, and slightly more important than change factors. Interaction factors were rated as slightly more important than change factors. These pairwise comparisons yielded a normalized set of ratio weights: Overall value = .40788 COST
+ .09444 INTERACTION
+ .29085 TOOLS HCM15:7-G
+ .05683 CHANGE
+ .15000 ATTITUDES.
R.C. VELLORE, D.L. OLSON INTERACTION
weight .09444
End-User weight 33333 system
with scanning
RELATICNSHIP
.06580
.33333
.00069
a6667
.00138
MEANS of I/O
.18458
.759cJo .00436
.25660
.00145
COMPETITION
.17062
.33333
.00179
66667
.00358
TIMELINESS
.25648
.83333
.00673
.16667
.00135
VENDOR
.16909
.50990
.00252
56996
.00252
.16243
.33333
.00170
66667
.00341
SUPPORT
END-USER TRAINING subtotals TOOLS
system
with scamkg
.01770
.01369
weight .29085
End-User weight .50096 CONFIDENCE
.15747
.66667
.01527
33333
.00763
UNDERSTANDING
.05741
.50990
.00418
.59cm
.00418
TURNAROUND
.12652
.a3333
.01533
.16667
.00307
.07933
.75660
.00866
.25099
.00280
OUTPUT
TIME
VOLUME
JOBEFFECTS
.36169
.66667
.03507
33333
.01753
FLEXIBILITY
.21757
.75690
.02373
.25069
.00701
subtotals
.10224
CHANGE
.04321
weight .05683
End-User weight .16667 TECH COMPETENCE
.25838
.66667
.00163
33333
.00082
PERSONAL
.63699
.75990
.00452
.25066
.00151
.16473
.75090
.00074
.25069
.00025
COMPUTER
CONTROL KNOWLEDGE
subtotals
.00689
ATTITUDES
.00258
weight .15000
End-User weight 66667 ENVIRONMENT
.36900
.66667
.02060
33333
.01030
TOP MGMT SUPPORT
.58155
.66667
.03877
33333
.01930
PERSONNEL
.16945
.75990
.00821
.25066
-00274
subtotals
a06758
.03243
End-User total scores
.19450
.09191
(Final product of weights given in bold) Figure 3. Evaluations of Alternative Performance on each subcriterion-End Perspective.
User
The consistency index for this set of pairwise comparisons was .0498, which is less than the test limit of .112 for five factors. This indicates acceptable consistency. Pairwise comparisons were made comparing the relative importance of interaction, tools, change, and attitude factors with respect to impact on end user perceptions and on DP/MIS perceptions. It was expected that these two groups would have different perceptions of the impact of the proposed system on operations, and that each group would rate relative importances differently. Decision impact on DP/MIS operations was rated as twice as important as on EUC operations with respect to interaction factors. Impact on tool factors was rated as equal for both groups. Impact on change factors for DP/MIS was rated as five times as strong as on EUC. The impact on attitudes for EUC was rated as double that on DP/MIS. Figure 2 presents the
An AHP application INTERACTION DP/MIS
weight .09444
weight .66667 system
system
with scanning
with scanning
RELATIONSHIP
33595
66667
.01410
x3333
.00705
MEANS of I/O
.12694
33333
.00266
.66667
.00538
COMPETITION
.11662
66667
.00489
33333
.00245
TIMELINESS
.17040
.66667
.00715
33333
.00858
VENDOR SUPPORT
.13483
33333
.00288
.66667
.00566
END-USER TRAINING
.11526
.75000
.00544
.25000
.00181
subtotals TOOLS
.03707
.02588
weight .29085
DP/MIS
weight .50000 CONFIDENCE
.14839
.66667
.01439
33333
.00719
UNDERSTANDING
.06488
.50000
.00472
.50000
.00472
TURNAROUND
.26583
.75000
.02899
.25000
.00967
.36328
.75000
.03062
.25000
.01221
JOB EFFECTS
.03943
.33333
.OOlQl
66667
.00382
FLEXIBILITY
.11819
.66667
.01146
33333
.00572
OUTPUT
TIME
VOLUME
subtotals
10109
CHANGE
.04433
weight .05683
DP/MIS
weight .83333 TECH COMPETENCE
.64833
.66667
.02047
33333
.01023
PERSONAL
.12202
.66667
.00385
33333
.00193
.22965
.66667
.00725
33333
.00863
COMPUTER
CONTROL KNOWLEDGE
subtotals
.03157
ATTITUDES DP/MIS
.01679
weight .15000
weight .33333 ENVIRONMENT
.2Q696
.66667
.OOOQO
33333
.00495
TOP MGMT SUPPORT
.53961
.66667
.01799
33333
.00890
PERSONNEL
.16342
.66667
.00545
33333
.00272
subtotals DP/MIS
89
total scorea
.03334
.01666
.20307
.10273
(Final product of weights given in bold) Figure 4. Evaluations of alternative performance on each subcriterion-DP/MIS perspective.
pairwise comparisons of top level factors, as well as relative importances of EUC and DP/MIS perspectives for each of the top level qualitative factors. Detailed evaluations of subfactors was conducted from the perspective of EUC as well as from the perspective of DP/MIS. The matrices of pairwise comparisons for this step are given in the appendix. Figure 3 presents the weight development for subfactors from the EUC perspective, as Figure 4 does from the DP/MIS perspective. Also included are the contributions to total score for each of the alternative systems. From the EUC perspective, the system with scanning had a ratio advantage of 2.12 on qualitative factors over the system without scanning (.19450/.09191). This ratio was 1.98 from the DP/MIS perspective.
R.C. VELLORE, D.L. OLSON
90 COST RATIOS
Life cycle costs of Alternative with scamkg
cost is 1.68108 greater than
Alternative without scamdng Normalized weights are therefore: Alt-tive
with scanning
.37298
Alternative
without scanning
.62702
FINAL
COMPARATIVE
RESULTS: Weight
Alternative
Alternative
with scenning
without scanning
(EUC & DP/MIS COST
‘INTERACTION TOOLS
.29085
CHANGE
.05683
ATTITUDES
TOTAL
.40788
.15213
.25575
EUC
.33333
.03148
.01779
.01369
DP/MIS
66667
.06296
.03707
.02588
EUC
.50000
.14543
.10224
.04321
DP/MIS
.50000
.14543
.10109
.04433
EUC
.16667
.00947
.00689
.00258
DP/MIS
.83333
.047x
.03157
.01579
EUC
.66667
.lOOOO
.06758
.03243
DP/MIS
33333
.05000
.03334
.01666
.54970*
.45032
.40788 .09444
.15000
1.0
scores from following pages)
1.0
Figure 5. Development of final scorea.
The last step of the AHP procedure was to use pairwise comparisons of the relative advantage of the two alternative systems on each factor. Figure 5 presents the final scores. As discussed above, the cost estimates for the two alternative systems were included by obtaining the ratio of costs, with the most attractive alternative receiving this ratio as a relative advantage. This analysis resulted in identification of the ratio of relative advantage of alternative I (the system with scanning capability) over alternative 2 (the system without scanning capability) of .54970 to .45032 (implying alternative 1 is 1.22 as valuable relative to cost as alternative 2). This analysis clearly shows an overall advantage for system 1, and the system with scanning capability was recommended. 5. CONCLUSIONS The use of AHP in this application provided a means to evaluate available CADD alternatives considering all factors deemed important in the decision. Because two distinct groups, with different perceptions of value, were involved, the pairwise comparisons were conducted from the perceptions of both end users and the central DP/MIS department. These groups were given differential weights of importance for different hierarchy elements, depending upon top management’s perceived importance of each factor on these two groups. Cost elements were estimated on a monetary unit scale, and thus did not require pairwise comparisons within cost subelements. The relative cost advantage of system 2 over system 1 was included in the overall AHP analysis through the top level pairwise comparisons of factors. Analytic hierarchy process enabled a consistent and thorough study of all factors involved in this decision. The decision to automate work activities, such as the adoption of computer aided design and drafting, provides organizations the opportunity to harness a highly valuable technological capability. Cost analysis alone will not adequately support this decision, because the potential benefits (and, as in this case, future requirements) are not easily quantifiable in monetary terms. Cost estimation was included in the analysis. As was demonstrated in this example, AHP easily supports including cost terms, as well as reflecting other factors of importance. Through identification of the relative ratio importance of factors, AHP provides a sound methodology to support complex decision making.
91
An AHP application REFERENCES
1. T. G uimaraes and V. Ftamanujam, Personal computing trends and problems: An empirical study, Managemenl Informalion Systems Quarterly 10:2, 179-187 (1986). 2. S. Rivard and S.L. Huff, Factors of success for end-user computing, Communications of ihe ACM 31:5, 552-561 (1988). 3. RI. Benjamin, Information technology in the 1980s: A long range planning scenario, Management Informalion Syslems Quarterly 6:2, 1131 (1982). 4. E.R. Greenberg, The rise of end users, Management Review 78:9, 57-90 (1989). 5. P. Sawyer, Drawings on the move, The Chemical Engineer 30:2, 30-32 (1989). 6. R.N. Stover, Improved scanners challenge manual digitizers, Machine Design, 151-155 (April 21, 1988). 7. T.L. Saaty, A scaling method for priorities in b.ierarchicalstructures, Jovmal of Malhemaiical Pq~hology 15, 234-281 (1977). 8. P.H. Cheney, R.I. Mann and D.L. Amoroso, Organizational factors affecting the success of End-User cornputing, Journal of Management Informalion Syaiems 3:1, 65-80 (1986). 9. J.E. Bailey and S.W. Pearson, Development of a tool for measuring and analyzing computer user satisfaction, Management Science 19:5, 530-545 (1983). 10. B. Ives, M.H. Olson and J.J. Baroudi, The measurement of user information satisfaction, Communicafions of the ACM 26:10, 785-793 (1983). 11. W.J. Doll and G. Torkzadeh, The measurement of end-user computing satisfaction, Management Znformation Systems Quarlerly 12:2., 259-273 (1988).
APPENDIX INTERACTION END-USER
PERSPECTIVE Relationship 1
Relationship M-
of I/O
I/O
Competition
Timeliness
Vendor
l/2 l/2
l/3 1
l/2 314
l/4 2
1
l/2
314
1
l/3 1
Competition Timeliness
1
Vendor support Training End-Users eigen vector
.06580
Training
2
2
1
314 1
.18458
.17062
.25648
.16009
.16243
I/O 4
Competition
Timeliness
Vendor
Training
2
3
2
3
1
2
l/3
314
2
1
l/2
314
2
1
314
1
314 1
.13483
.11586
consistency index = .0685 DP/MIS
PERSPECTIVE Relationship
Relationship
1
Means of I/O Competition Timeliness
1
Vendor support Training End-Users eigen vector
33595
.12694
consistency index = .1184
i .124 limit for six factors
.11662
.17040
92
R.C. VELLORE, D.L. OLSON TOOL
PERCEPTIONS
END-USER
PERSPECTIVE Confidence
Understanding
Response
Volume
Effects
1
2 1
2
3
l/2
l/3
l/2
l/3
l/4
l/4
1
2
l/3
1
1
l/5 1
l/3 3
Confidence in systems User understanding
Flexibility
of output Response/Turnaround Volume of output Job effects System flexibility eigen vector
1 .15747
.05741
.12652
.07933
36169
.21757
Confidence
Understanding
Response
Volume
Effects
Flexibility
1
2
l/3
l/3
5
2
1
l/5
l/5
2
l/2
1
l/2
5
2
1
6
3
1
l/4 1
.03943
.11819
consistency index = .0913 DP/MIS
PERSPECTIVE
Confidence in systems User understanding of output Response/Turnaround Volume of output Job effects System flexibility eigen vector consistency index = .0913
.14839
.06488
.26583
.36328
An AHP application ADAPTABILITY
CHANGE
TO
END-USER
PERSPECTIVE COMPETENCE
TECHNICAL PERSONAL
COMPETENCE
1
CONTROL
COMPUTER
93
CONTROL 113 1
KNOWLEDGE
eigen vector
KNOWLEDGE 3 5 1
.25838
m473
consistency index = .0193 DP/MIS
PERSPECTIVE
TECHNICAL
COMPETENCE
COMPETENCE PERSONAL
1
CONTROL
COMPUTER
CONTROL
3
1
Ii2
KNOWLEDGE
eigen vector
KNOWLEDGE
5
1 .64833
.12202
.22965
consistency index = .0018
ATTITUDES END-USER
PERSPECTIVE ENVIRONMENT
ENVIRONMENT TOP MANAGEMENT
1 SUPPORT
TOP SUPPORT 112 1
PERSONNEL 3 5 1
PERSONNEL eigen vector
.30900
.58155
.10945
consistency index = .0018 DP/MIS
PERSPECTIVE ENVIRONMENT
ENVIRONMENT TOP MANAGEMENT
1 SUPPORT
TOP SUPPORT
PERSONNEL 2
112 1
3
.53961
.16342
PERSONNEL eigen vector consistency index = .0046
.29696
