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Jonathan A . Morell
Industrial Technology Institute, Ann Arbor, Michiga n
Data is reported regarding the impact of persona l
computer use in the Naval Military Personnel Command .
The emphasis is on furthering knowledge of variable s
which may be affected by the introduction of offic e
automation, and of how those variables might be measured .
Particular attention was paid to the problem of abstractin g
generalizable measures from descriptions of what people di d
with their computers .
Analysis of results revealed six general categories of
impact, each with several specific, measurable indicators .
The general categories include : budgeting, information fo r
planning or administration, amount of time saved, uses of
time saved, "information creation", and communication .
Examples of measurable indicators of each category are :
budgeting - percent of allocated budget spent within a
fiscal year ; planning and administration - currency of
available data ; use of time saved accomplishment of
new tasks ; information creation - development of
"private" data bases ; and communication - developmen t
of "private" data bases ; and communication expectations concerning data that should be available .
The intent is to demonstrate that an effort to measur e
the impact of office automation can be successful, and to
urge others to undertake similar analyses with other dat a
sets. Together, such efforts would lead to much improve d
understanding of how office automation affects activity
within organizations .
Keywords : office automation, measurement, impact ,
evaluatio n
Common sense, everyday observation and man y
research studies tell us that office automation (OA) i s
having a marked impact on office work . Despite this, th e
"impact" of OA has proved to be a slippery concept .
Benefits have been difficult to quantify, as has the nature of
specific categories of impact which might require specia l
measurement techniques .
This paper is an effort to further the art and science o f
measuring the organizational impact of OA . Its approach i s
an analysis of OA's impact as perceived by a group of end users in one particular organizational setting . By carefull y
analyzing interview data, it became possible to derive th e
types of specific impacts that can be expected, and t o
estimate their importance to the organization where th e
changes occurred . The data also provided a foundation fo r
directing data gathering efforts in other contexts, and th e
16 DATA BASE Fall/Winter 1988
beginning of efforts at quantification . By showing how th e
specific impacts of OA can be discerned in one setting, th e
intention is to stimulate similar efforts by others who hav e
access to other data sets .
Identification of relevant variables is only one of man y
important methodological issues in assessing the impact o f
OA . Others include : comparison groups, measuremen t
scales, and validity checks on people's reports, to nam e
only the most prominent . "Variable specification" ,
however, is the most fundamental of all these concerns . I f
the right concepts are not measured, no amount of researc h
will yield any useful information about OA .
To set the context, this article begins with a few brief
examples illustrating the range of previous efforts to asses s
the organizational impact of OA . The discussion the n
moves to an explanation of the setting in which thi s
research took place, and from there to a discussion o f
impact .
Efforts to assess the impact of OA can be convenientl y
grouped by means of a two-way table in which rows refer t o
OA technology, and columns refer to impact . In each case ,
the crucial distinction is whether variables are dealt with i n
general or in specific terms . This framework is show n
schematically in Figure 1 .
Although the terms "global" and " specific " actuall y
represent two ends of a continuum, they provide usefu l
distinctions for understanding the range of studie s
concerning OA . With regard to OA technology, th e
important differentiator is whether a study focuses on wel l
identified types of hardware, software, or application . As a n
example, a study which asked respondents about their use o f
word processing and spreadsheets would be "specific ." If th e
study referred only to the "use of microcomputers," it woul d
be "global ." With respect to impact, a "specific" stud y
might ask about changes in well identified aspects of work ,
such as decision making or communication . A "global "
version might ask for a summary judgement about "impact
on work life . "
While none of the categories in Figure 1 represent pur e
forms, they do reflect the basic approaches taken t o
measuring the impact of OA . (Except for the "global global" cell, which is so general that it does not appear i n
the literature .) The following are brief examples of ho w
each category is manifest in a variety of research studies .
Reference to impac t
Reference to
office automation :
Specifi c
Globa l
Specifi c
Figure 1 . Schematic representation of efforts to assess the impact of Office Automatio n
beneficial impact, 3- some beneficial impact, 4- sligh t
beneficial impact, 5- no impact, and 6- negativ e
consequences have outweighed benefits .
Finally, research of this type often substitute s
"frequency of use" for global estimates of impact . Laudo n
[1986] for instance, used this approach in his effort t o
assess the influence of networking on the use of persona l
computer (PC) technology . His study dealt with a variety o f
specific offices within 25 firms in the financial service s
industry . Data were collected by means of both open-ende d
interviews and through structured questionnaires which wer e
filled out by the researchers . One aspect of the study aske d
respondents to estimate how often (never . monthly .
weekly, daily) they used PCs for a variety of purposes :
searching data bases, external communications, learnin g
aids, and the like .
In an effort to determine the impact of OA o n
managers, Fleischer and Morcll [1986, 1987] surveyed 16 8
managers in 15 organizations . One part of their forced choice mailed questionnaire asked respondents to use a fiv e
point scale to rate how OA (defined in general terms )
influenced the amount of time they spent doing 11 commo n
business activities . Examples of elements of the lis t
include : "talking on the telephone," "preparin g
presentations, " "using data to make decisions," an d
"business travel ." (The scale points were 1- much mor e
time, 2- more time, 3- same time, 4- less time, and 5 much less time . A "don't know" category was als o
included . )
Another example demonstrates how the "globa l
technology - specific impact" approach can be used with a
very different methodology . Bikson, Stasz and Manki n
[1985] conducted a series of s t r uctured interviews in order to
obtain an in-depth understanding of how computer-mediate d
work (defined only as "computer-based information an d
communication systems") affected one large corporat e
headquarters . An important part of their data analysis was to
determine how the technology affected a series of variable s
related to organizational behavior : 1- work changes
(enrichment . demands, reinvention, management style) ; 2 communication ; 3- ability to return to old ways o f
accomplishing tasks ; 4- productivity (time savings) ; 5 physical and psychological complaints ; 6- formal jo b
changes ; and 7- job satisfaction .
One part of the Fleischer and Morell [1986, 1987 ]
study was an effort to ascertain how OA affects managers '
decision making . In pursuit of that knowledge, one part o f
their instrument defined three types of problem : "cut an d
dried," "analytic" and "unstructured ." These problem types
form a decreasing scale of the extent to which specifi c
information can help one find a well defined solution .
Respondents were then asked to use a five point scale t o
rate how helpful various aspects of OA were in findin g
solutions for each type of problem .
Over and above illustrating basic approaches t o
understanding impact, these examples demonstrate tha t
research on OA can embody a wide variety o f
methodologies . Researchers can have different levels o f
contact with respondents (personal interview, phon e
interview, questionnaire) . Different dimensions of impac t
can be measured (e .g ., frequency of use, direct estimate o f
change) . Respondents can vary (e .g ., end-users or peopl e
who have observed end users) .
One example of this approach is provided by DeLon g
and Rockart [1986], whose effort to study executive suppor t
systems involved telephone interviews of knowledgeable
information systems personnel in 45 Fortune 50 0
companies . Part of their analysis involved imposing thre e
categories of impact (extensive, moderate, and low), o n
three elements of executive support - office automation ,
status access, and query and analysis . Their definitions o f
those terms were such that each represented a step in a n
ascending scale of end-users' ability to access an d
manipulate information .
A second example is again provided by Fleischer an d
Ivlorell's [1986, 1987] study of managers . One section o f
their instrument was a list of specific OA application s
(word processing, spreadsheets, statistical analysis, etc .) ,
each of which had to be rated on a six point scale of impac t
on work life : 1- profound change for the better, 2- major
In spite of all the research using these varie d
approaches, our understanding of OA ' s impact o n
organizations is less than fully satisfying . Quantification o f
"bottom line" impact (money, time, market share, etc . )
remains elusive, probably because of the myriad of factor s
that intervene between actual use of OA and "ultimate "
consequences . Quantification of impact on work life o r
organizational behavior is equally elusive, perhaps becaus e
those concepts in themselves are so difficult to measure .
DATA BASE Fall/Winter 1988 17
The difficulties of assessment were summed up ver y
well by Peter Keen [1986] during his effort to develop a n
approach for valuing decision support systems . As part o f
that work he identified twelve possible benefits of th e
technology, and rated each on "ease of measurement" and
" quantifiability in terms of bottom line " impact . Hi s
assessment appears in Table 1 . (Quoted from page 77 of hi s
work . )
Table 1 . Difficulty in Measuring the Impact of Office Automation *
10 .
11 .
12 .
Increase in number of alternatives examined
Better understanding of the business
Fast response to unexpected situations
Ability to carry out ad hoc analysis
New insights and learning
Improved communication
Cost savings
Better decisions
More effective teamwork
Time savings
Making better use of data resource
*From Keen, 1986 .
Two types of information are necessary in order to
breach this impasse . First, one needs a better understandin g
of how OA actually changes the routine activities of end users and their work groups . If that information were
known, it might be possible to devise specific measures o f
those changes .
The second step is to discern how changes in work group functioning ripple through an organization . That
knowledge would provide a sense of the range o f
organizational activities that are affected, and the magnitude
of OA related changes as they become continually dilute d
by the great number of other factors which affect a n
organization's performance . Thus, we would know how fa r
removed from end-use we could look and still detect impact s
directly attributable to OA . We would also begin t o
understand how widespread use of OA might, in th e
aggregate, affect an entire organization .
The Naval Military Personnel Command (NMPC) i s
the personnel department for the military employees o f
United States Navy . The Command's 1,500 personne l
oversee a workforce of 507,000 enlisted people and 73,00 0
officers, each of whom changes billets (positions) ever y
two to three years . (All statistics presented in this sectio n
are approximations .) Within NMPC is a group known a s
N4, whose purpose is to actually make assignments o f
personnel . (Other sections are responsible for tasks such a s
policy development, occupational standards, and financia l
planning .) The core of N4 are groups of detailers, each o f
which has unique responsibility for assignments to a
particular subset of the numerous job classifications withi n
the Navy .
Detailers make their assignments based on knowledg e
of which personnel are near the end of their rotations, wha t
billets need to be filled, and the job requirements of each
billet . A typical group of detailers might comprise fiv e
18 DATA BASE Fall/Winter 1988
Easy to
Quantifiable i n
"bottom line" term s
people . Despite the large number of personnel in the Navy ,
the highly differentiated structure of billeting means tha t
detailers know their constituents fairly well . It is commo n
for detailers to have personal conversations with a
constituent, and to make considerable efforts to place tha t
constituent in a job or location he or she desires . Detailers
see their work as contributing to "readiness" by getting th e
right people in the right positions, and by trying to plac e
those people in jobs they want .
Although there are well defined guidelines for th e
billeting process, creativity and initiative are often required .
If a billet opens up and nobody is precisely qualified, it ma y
be necessary to find a person who is almost qualified, a s
might be the case with a competent electronics technicia n
who is not trained on a specific piece of equipment . Suc h
placements may entail arranging for training, or discussion s
with a commanding officer concerning priorities for th e
expertise that officer is trying to obtain . All of this activity
must be done within budgetary constraints, which limit th e
money available to move and train people .
In sum, detailing is a job that requires creativity an d
individualized, personalized decision making which must b e
carried out within a rule-bound, highly bureaucrati c
organizational context . To assist with these efforts ,
detailing groups have recently been acquiring large numbers
of personal computers to supplement their large, automated ,
centralized information systems .
The study consisted of 23 in-depth structured interviews
with a variety of personnel who had recently received a PC .
Fifteen respondents were interviewed shortly after the y
received their PC . Eight were then re-interviewe d
approximately six months later in order to assess an y
changes which may have taken place . Respondents were
asked to quantify their answers whenever possible . Nin e
respondents worked in groups that were primarily involve d
with detailing, while the other six worked within th e
detailing bureaucracy, with a variety of planning an d
oversight responsibilities .
The basic unit of analysis for the study was the work
group of the person being interviewed . If for example, th e
respondent worked in a detailing group that represente d
surface ship officers, data were analyzed so as to understan d
the relationship between PC use and the functioning of tha t
entire work group . In addition, some data were collected
relevant to the respondent in person, as with for example ,
questions about prior knowledge of computers .
In terms of the classification scheme presented i n
Figure 1, this study falls into the "specific technology specific impact" category . Respondents were asked t o
provide detailed information on their use of hardware an d
software, and to link that use (as much as possible) t o
specific consequences .
Table 2 . Consequences of PC Us e
quantitative brief verba l
estimate of description o f
impact (a) impac t
Three cases : Detailing groups use spreadsheets to keep track of their budgets . Examples of benefits
include knowing when particular types of moves must be stopped or can begin, and how many more move s
can be made within a year.
Corrected costs for a move were often added to a manual sequential list, not substituted for the incorrect value .
Money was "lost" to the shop .
Budget projections are made by analyzing patterns of past moves . Better estimates of how many moves (o f
particular types) should be planned each quarter .
A specialized data base is kept on 5,000 people .
Spreadsheets help keep track of historical patterns of moves .
Planning/Administratio n
Three cases : Review boards are given faster and more fine-grained information .
Two cases : In-house files supply more current information : I-match of billeting requirements wit h
available assignments, 2- lag between assignment and order writing may cause double assignment .
Three cases : PCs allow better detailing . Examples : 1- Electronic files allow more comprehensive sortin g
of people against job requirements . 2- Billets are matched against available personnel for small subgroups .
3-Computerized preference card data makes it easier to make difficult assignments .
Four Cases : PCs allow better workforce planning .
Examples : 1- studies of personnel distribution, 2- projections of workforce, 3- tracking service histories and ,
4- anticipate training needs .
Three cases : PCs allow better information and presentation at briefings .
Review board decisions are analyzed to help detailers counsel their constituents .
A specialized data base is kept on 5,000 people.
The PC allows better information on correspondence to be kept .
Personnel activities are tracked .
Training is coordinated .
Tim e
Savings (b )
16 hrs/mo
82 hrs/mo
160 hrs/mo
40 hrs/mo
1 r"lE
A PC is used to keep track of funds for moving and training .
General word processing .
Standard Navy correspondence.
A PC organizes information to determine allocation of moving funds .
Reports compare current, authorized and projected workforce needs .
Three cases : Word processing allows more revision of documents and facilitates correspondence .
PC allowed faster analysis of implications of deferring moves between fiscal years .
Summary of detailer activity is kept in order to answer question s
a- When provided by respondent.
b- Many of the uses reported here involve information for both fiscal and non-fiscal planning . These cases are listed
under "time savings" if the respondent indicated that the information would have been generated in any case, whether o r
not a PC was available .
DATA BASE Fall/Winter 1988 19
Presentation of findings will begin with a n
enumeration of users ' specific applications of PC
technology . based on that data, two other types of impac t
will be discussed - adding value to work, and trackin g
impact throughout the organization . The overall structure
for information in this section can be outlined as follows :
Specific uses for personal computer s
• Budgetary information
• Planning and administratio n
• Time savings
Adding value to work
• Use of tim e
• Information creation
Tracking impact throughout the organizatio n
• Locus of information us e
Organizational consequences of information us e
Respondents' statements showed a clear difference i n
emphasis which allowed their OA uses to be classified i n
one of three ways :
1 . new or better information for budgetary plannin g
and administration ;
1 new or better information for planning an d
administration of nonmonetary issues ; an d
3 . time savings .
Table 2 summarizes this information . (In order to
convey a sense of how PCs are actually used, response s
here are presented in some detail .) The most obviou s
finding reflected in Table 1 is that although respondent s
were articulate about the uses of their PCs, the onl y
quantifiable estimates they could provide were for tim e
savings . Careful inspection of each use category, "
however, provides many leads for developing other
measures of impact .
Budgetary Information
In the "budget" category, all uses except the last touc h
directly on the coordination of spending money wit h
definable activities - moves of personnel, training sessions ,
and the like . This leads to several specific questions whos e
answers might be supplied by end-users, by others affecte d
by OA use, or through the examination of archival data . 1 What percentage of allocated budgets are spent within a
fiscal year? 2- How "well-paced " are those activities
throughout the fiscal year? 3- Were adequate funds allocated ?
4- Were estimates of expenditure adequate? 5- How often
was the pace of activity altered because of unexpected level s
of funding? Each of these are likely consequences whic h
would be expected to occur if in fact OA helped sprea d
better budgeting to more groups throughout th e
organization .
Planning and Administratio n
The "planning and administration" category presents
added difficulties because unlike fiscal matters, there is n o
20 DATA BASE Fall/Winter 1988
common variable (such as money) that figures in al l
measures . Even so, some underlying themes can be found.
First is the speed of information retrieval ; many of the use s
in this category echo the theme of getting informatio n
faster . A second theme is the advantage of obtaining more
current information than would otherwise be available .
Third is the range of data that is available ; man y
respondents talked about PCs in terms of help wit h
obtaining data elements that would not otherwise b e
available . Finally, there is the ability to interrelate sets o f
data elements, which many respondents saw as a valuabl e
use of their PC technology .
There are several advantages to abstracting themes fro m
responses as was done in the above categories . First, i t
removes the unit of analysis from the unique use of an y
given end-user . By so doing, it becomes possible t o
aggregate information across contexts, or to compar e
settings . As an example, most computer users do not hav e
to generate information for review boards . Many users ,
however, understand pressures to quickly provide fine grained information . Second, these themes are close enoug h
to the experience of end-users (and those they deal with) that
proper questioning technique still has the potential to elici t
meaningful data . Finally, there may be opportunities t o
measure these factors through archival means . As examples ,
one might find a paper trail relating to the adequacy o f
budget projections, or to the number of requests for ad ho c
data analysis .
Time Saving s
In one sense, the "time" category presents the fewes t
problems, as it is the only situation where respondents fel t
comfortable in providing quantitative estimates . Cautio n
must be exercised, however, because there may be a ver y
large difference between the gross time saved by using a P C
and the net amount. Table 2 contains the net savings, afte r
allowances were made for the very considerable amount o f
time that many respondents spent entering data into, an d
maintaining, their PC based information systems .
Although estimates of time saved are interesting, the y
provide little sense of how information technology ha s
added value to the work of the organization . To learn that, i t
becomes important to find out what people did with their
newfound "free" time.
Use of Tim e
A question to that effect yielded 17 cases wher e
respondents clearly felt that new work was done as a resul t
of efficiencies induced by PC use . These cases can b e
divided into four distinct categories .
Better Analysis . Ability to Make a Case .
Respondents spent more time obtaining information an d
doing cogent analyses, thus being able to make a better cas e
for a position they wished to argue .
Example : We have to continually submit and update
plans . We used to contradict ourselves a lot, but no w
that we have more accessible data we do better analyses
and are more consistent in what we say .
Locus of Information Us e
New Tasks/Projects . Respondents were able to d o
additional tasks which they previously did not have time t o
do, or do well .
Examples : 1- We were given a new task of preparin g
certain briefings . Luckily we recently obtained a PC ,
which allowed us to do our old work much faster .
Without the time savings, it would have been ver y
difficult to do a good job with both the briefings an d
the work we used to do . 2- I have the time to ru n
special projects on the Officer Distribution Informatio n
System .
Finishing Work on Time . Work that used t o
require people working overtime is now accomplishe d
during regular working hours .
Example : We were never able to get our assignment s
out on time without people working after hours . No w
things get clone on time.
Doing Work Better . Many respondents claime d
that the time savings due to microcomputers allowed the m
to increase the quality of their work .
Examples : 1- The microcomputer lets us keep muc h
better track of the assignments we make . 2- There wa s
never enough time for me to explain why I had to as k
for certain budgetary information . Now that I can make
those explanations there is less need to go back t o
people and ask for clarifications .
Information Creatio n
There is also evidence for a second type of valu e
adding, over and above the amount of work that is done . I
refer to a process of "information creation" that may b e
analogous to the process of "job creation" within th e
economy . Just as a new technology may create jobs that di d
not previously exist, information technology may caus e
new information to exist within an organization . Twenty two cases of "information creation" were identified . Thes e
are situations where it is clear that the PC made new type s
of information available, and that the respondent appreciate d
the value of that information . Six of these cases involve d
budget related information . Nine were concerned wit h
nonfinancial planning or administration, four with detailin g
efficiency, and three with ad-hoc decision support needs .
Significantly, only one of these categories - detailin g
efficiency - is so unique to this particular study that it s
measurement would be difficult to generalize across divers e
settings .
All of the findings discussed so far focused on change s
within individual work groups, thus assisting with the firs t
requirement set out earlier - a better understanding of how
information technology may affect end-users and activitie s
within their work groups . We now turn to the secon d
realm that must be explored - tracing how changes in wor k
group activity ripple throughout organizations .
As a first step, it was determined how much of th e
information created by PC users was directly communicate d
outside of the group where it was created . This data i s
important because it yields an estimate of how muc h
immediate impact can be expected . If for example, 9 0
percent of the information generated were transmitte d
outside the group, the pattern of impact might be fa r
different than if that 90 percent were used internally, to hel p
the group function better . In the former case, the search fo r
impact could follow information . In the latter, the searc h
for impact must begin with assessments of how the group' s
general functioning affected its environment .
The communication question was analyzed by askin g
respondents to state who received the reports, briefings, an d
informal communications that were based on PC generate d
information . Respondents indicated that about 25 percent o f
the information was for the primary use of the group tha t
generated it . (Examples of "internal use" would be a grou p
keeping its own microbudget, or an internal list of know n
changes that have not yet worked their way into th e
mainframe information system . )
Organizational Consequences of information Use
The study also touched on the organizationa l
consequences of the communication that took place . It di d
so by collecting data on whether PC use had any impact o n
changing people's expectations about what informatio n
should be available for use . It is reasonable to expect suc h
changes because of PC technology's ability to affect th e
currency, content and form of information that i s
transmitted . A question to this effect was asked of the eigh t
respondents included in the follow-up phase of th e
interviewing . Seven of them agreed that their PC use ha d
resulted in changed expectations by their audience s
concerning information availability . Responses fell int o
two categories - information for decision making, and th e
format of reports and briefings . Table 3 summarizes thi s
information . (The table contains paraphrases of respondents '
answers . )
Unfortunately it was beyond the scope of this study t o
interview the recipients for information in order to verif y
respondents' reports, or to see how the PC base d
information affected their work . The examples in Table 3 ,
however, seem specific and clear enough to form the basi s
for such secondary interviews . It seems feasible then, to
trace impact at least one step from the work group wher e
the technology is put to use .
This article demonstrated how analysis of an existing ,
largely open-ended data set can yield a variety of measurabl e
factors which can greatly enhance our understanding of OA' s
impact on organizations . (A summary of these factor s
appears in Table 4 .) These factors are fine-grained elements
of the "specific impact" section of Figure 1, and represen t
variables which exhibit several important characteristics . 1 Their use would improve our understanding of how O A
impacts organizations . 2- Many of the variables can b e
quantified, either directly, or through the estimates o f
informed observers . 3- The variables represent a level o f
DATA BASE Fall/Winter 1988 21
Table 3. Changed Expectations Due to PC Us e
Information for decision makin g
Detailers liked the new information so much that they asked for similar but more detailed reports, broken down into eve n
finer categories .
The study of move patterns went over so well that we were asked for another, related analysis .
My new monthly report was received so well by my bosses that now they expect to see it . It's a problem in a way because
generated it for my internal use, and now I have to make sure it gets out on a regular basis .
A host of people are beginning to ask for more information because they have seen what we are doing because of ou r
briefings and the materials we produced . Now more people are asking for tailored information and we do n ' t have tim e
to do it all .
Format for briefings
The graphics were so well received that now they expect to see it that way . They will not accept how things were done in th e
past .
My boss is so impressed he is ordering hardware and software so that good graphics will become the standard way of doin g
things .
People are recognizing that the data is presented better because the graphs are better . As a result they are asking for mor e
better displays of the data .
precision not previously seen in research on OA . 4Measurement is possible at a level of abstraction which
allows cross-setting comparison, but is also specifi c
enough to provide practically useful information abou t
individual settings .
Both the data from this study, and the process of obtaining that data, can assist two audiences - researcher s
with an academic interest in OA ; and people who must plan
for OA or justify its acquisition . (Such people may reside
in user organizations, or be consultants brought in to help
plan or justify .) The work reported here may assist in three
ways, First, the findings may help people formulate mor e
precise instruments . As an example, the data on
"information creation " may prompt others to seek such
effects in their own contexts .
A second use is to provide a direction for extracting
more useful information from data that has already been
collected . Experience shows that many organizations do in house studies of OA use . Perhaps the approach
demonstrated here will increase the value of that existing
data .
Finally, the study demonstrates that a relatively small
scale effort at collecting open-ended data on the use of O A
may lead to instruments which arc relevant to specific
setting, and which also allow aggregation of data across
organizational units . This demonstration may lead to the
efficient collection of powerful information that will ad .
vance our knowledge of OA, and which will assist
organizations who must deploy their OA resources to best
This particular study is too limited to allow a
comprehensive statement about all the important variable s
which should be included in studies of OA . To give just
two examples, the data say almost nothing about the
quality of people's working lives, or, about productivity in
traditional clerical settings . An understanding of these and
many other realms of impact could certainly profit from the
type of analyses applied here . The important point is tha t
such analyses can be done . In the aggregate, those analyse s
22 DATA BASE Fall/Winter 1988
would lead to better research on OA, and to increase d
understanding of the consequences of the technology .
Bikson, T .K ., Stasz C ., and Mankin, D ., "Computermediated work : Individual and organizational impact in on e
corporate headquarters, " The Rand Corporation, Sant a
Monica, Calif. (R-3308-OTA) 1985 .
DeLong, D .W . and Rockart, J .F ., "A survey of curren t
trends in the use of executive support systems," chap . 10 ,
pp 190 - 208 in : J .F. Rockart and C .V . Bullen (eds .), Th e
Rise of Managerial Computing Dow Jones-Irwin ,
Homewood, Ill. 1986 .
Fleischer, M . and Morell, J .A ., "Managers as informatio n
technology end users," Proceedings of the 198 6
International congress on technology and technology
exchange . pp 239 -243, Oct. 6 - 8, Pittsburgh Penn . 1986 .
Fleischer, M . and Morell, J .A ., "Survey of offic e
automation use by managers, " manuscript in preparation ,
draft available from authors, 1987 .
Keen, P .G . Value analysis : Justifying decision suppor t
systems, Chap . 4, pp . 69 - 90 in J .F . Rockart and C .V .
Bullen (Eds .) The rise of managerial computing . Do w
Jones-Irwin, Homewood, Ill . 1986 .
Lauden, K .C ., "From PCS to managerial workstations :
Organizational environment and management policy in th e
financial industry," chap 8 p 87 - 115 in M . Jarke ,
Managers . micros and mainframes : Integrating systems fo r
end-users. , Wiley, N .Y .,1986 .
Table 4 Summary of Factors Affected by Office Automatio n
General category
Specific facto r
New/better information fo r
budgetary planning or
1.percent of allocated budget spent within a fiscal yea r
2. "pacing" of expenditures throughout a fiscal yea r
3. adequacy of funding projections and estimate s
4. accuracy of funding projections and estimate s
5. alterations in activity levels due to unexpected amount of availabl e
New/better information fo r
nonbudgetary planning o r
1.speed of information retrieva l
2. currency of available informatio n
3. range of available dat a
4. ability to interrelate data elements
Use of "free" time made
available by OA efficiency
1.performance of more cogent data or problem analysi s
2. accomplishment of new tasks or project s
3. finishing work on tim e
4. doing work better (fewer mistakes, etc . )
5. "information creation" within the organizatio n
1. patterns of information transmissio n
2. expectations concerning information that should be available
Jonathan A . Morel l
Industrial Technology Institute, Center for Social and Economic Issues .
PO Box 1485, Ann Arbor MI, 48106, (313) 769-4000
Thanks go to Linda Berry, Pete Bengtson and an anonymous reviewer for their comments on earlier drafts of this paper .
This research was sponsored by the Naval Military Personnel Command under inter-agency agreement # 40-1284-82 .
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