Building a Knowledge Base for the Knowledge Worker System
by Beverly E. Thomas and Wayne J. Schmidt
Every daily activity of the U.S. Army depends in part on the work of knowledge workers-action officers who gather, process, and pass on information essential for mission accomplishment. The effectiveness of knowledge workers is often impaired by constantly shifting schedules, information overload, lack of standard automated tools, and the requirements of accomplishing necessary-but-mundane tasks such as scheduling, coordinating with others, and reporting. The U.S. Army Construction Engineering Research Laboratories (USACERL) is developing a computer-based performance support environment (PSE) called the Knowledge Worker System (KWS), which is intended to substantially reduce the problems and inefficiencies inherent in knowledge work. The objective of this phase of the research was to identify the information that must be collected to establish the rich database required to support KWS applications. It has been found that any knowledge work task may be characterized by one (or more) of 13 attributes. Similarly, the work of any functional knowledge work group may be characterized by some or all of these 13 task attributes. The information to be collected depends on the objectives of the specific application-not all applications will require data pertaining to all 13 task attributes.
The work described in this report will conclude with development of a cost-effective methodology for building a KWS database.
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FOREWORD
This work was conducted for the Directorate of Military Programs, Programming and Execution Support Office, Headquarters, U.S. Army Corps of Engineers (HQUSACE), under Project 4A162784AT41, "Military Facilities Engineering Technology"; Work Unit SA-AS1, "Knowledge Worker Task Characterization." The HQUSACE technical monitor was Mr. John Sheehey, CEMP-P.
The research was performed by the Facility Systems Division (FF) of the Infrastructure Laboratory (FL), U.S. Army Construction Engineering Research Laboratories (USACERL). The USACERL principal investigator was Ms. Beverly Thomas. Mr. Wayne Schmidt is the team leader, Management and Knowledge Integration Team, CECER-FFK. Mr. Alan W. Moore is Chief, CECER-FF. Dr. Michael J. O'Connor is Chief, CECER-FL. The USACERL technical editor was Gordon L. Cohen, Information Management Office.
The authors wish to acknowledge the considerable role played by Mr. Sheehey in describing the need for, and refining the concept of, the Knowledge Worker System.
COL Daniel Waldo Jr. is Commander and Director of USACERL, and Dr. L.R. Shaffer is Technical Director.
1 INTRODUCTION
Background
Every day-to-day activity of the U.S. Army relies at some stage
on the work of action officers who gather, process, and pass on
information essential to mission accomplishment. Much of their
time is spent on mundane but necessary chores such
as scheduling, coordinating with others, and reporting. The daily
activities of these action officers, also referred to as
knowledge workers, are frequently driven by continuously
shifting events and dates, and ever-increasing volumes of information.
Timely readjustment and response to this dynamic environment is
critical, even if the knowledge worker is new on the job and does
not completely understand the process or the task. In the Army
this problem is compounded because turnover is high, as is the
amount of information to be analyzed.
An office chief at Headquarters, U.S. Army Corps of Engineers
(HQUSACE) described to researchers from the U.S. Army Construction
Engineering Research Laboratories (USACERL) the specific problems
of one work group. The productivity of these knowledge
workers, members of a functional work group involved in the Military
Construction, Army (MCA) budgeting process, was impeded by information
overload, conflicting deadlines, loss of vital institutional knowledge
due to turnover, and "islands of automation" serving
one functional area while inaccessible to others. As USACERL studied
this environment it became clear that such problems were likely
to be common anywhere (in the Army or elsewhere) that a large
group of knowledge workers was linked by a complex system of organizational
tasking. The study led to a set of findings and principles
that may apply to knowledge work groups in general. The
problems common to all knowledge work groups might be substantially
reduced with the help of a new category of computer software:
the performance support environment (PSE).
USACERL is now developing an automated PSE, the Knowledge Worker
System, to guide Army action officers through their maze of daily
tasks by helping them organize, set priorities, and execute their
work efficiently and effectively, in better coordination with
fellow workers. This system may be used to enhance the performance
of any functional work group linked by a network of microcomputers.
To be effective, however, this PSE will require a rich database
of procedural information, and obtaining such information is likely
to be expensive. Two tasks that must precede actual development
of a PSE for knowledge work, therefore, are (1)
identifying the information needed to support the systezn,
and (2) establishing a cost-effective methodology for collecting
that information.
The overall objective of this research is to develop a cost-effective
means of building the information base required for the Knowledge
Worker System (KWS) performance support environment. The objective
of this phase of the research was to identify the information
that must be collected to support such a PSE.
Approach
The activities of knowledge workers involved in the formulation
and justification of the MCA budget were analyzed. As problems
were identified, the objectives for an MCA-specific PSE were formulated
and revised. From this initial research and a study of the literature,
characteristics common to all knowledge work environments were
inferred. A general model for knowledge work was formulated, and
the overall objectives for a universal knowledge worker PSE were
defined. Thirteen specific task attributes were identified as
a means of characterizing activities common to all knowledge work
groups. These task attributes were then associated with the overall
objectives of a knowledge work PSE.
While this research was originally spurred by the needs of a specific
work group, the work is now directed toward the development of
an automated system with potentially universal application to
any knowledge work environment. Nevertheless, the needs of the
MCA-related work group discussed above are still of central importance
to this research. This group serves as the first real-world environment
for refining and validating the PSE concept. However, because
very little writing or research has been done on this topic,
a number of different knowledge work environments ultimately will
need to be analyzed for the broader premises of a knowledge work
PSE to be validated with certainty.
Mode of Technology Transfer
No U.S. Army Corps of Engineers (USACE) or Army guidance documents
will be impacted by the results of this study. It is anticipated
that KWS will be supported and maintained by USACERL's technical
assistance center or another Corps technical assistance center.
Technology transfer will be accomplished through system documentation,
field demonstrations, and user training activities.
The Need for Performance Support
The Army's administrative infrastructure is supported by action
officers who define requirements to accomplish goals, allocate
resources, review programs, analyze data, provide guidance, and
gather and disseminate information. These action officers, or
knowledge workers, add value to the information they process by
using their acquired skills in concert with the leveraging power
of automation.
The tasks that Army knowledge workers perform are driven by Department
of Defense (DOD) deadlines, work group coordination, and ad
hoc events. These knowledge workers must cope with large volumes
of diverse information from many sources to effectively do their
jobs.
Job performance is adversely affected by the nature of knowledge
work. Knowledge workers are often distracted from their real goals
due to the repetitive, labor-intensive chores related to accessing
information and producing reports. The sheer mass of data characteristic
of the work is often overwhelming.
Adding to this problem, the Army has a high personnel turnover
rate. This may result from upward personnel mobility, reorganization,
downsizing, or other causes. As employees are reassigned to new
positions, procedural and situation-contingent knowledge leaves
with them, which necessitates costly retraining of new personnel.
Objectives of a PSE for MCA Knowledge Workers
Study of the need for a knowledge work PSE initially focused on
the needs of a functional work group involved in MCA budgeting
activities. The major needs that emerged were taken as the objectives
for a PSE targeted at MCA knowledge workers, The following seven
general objectives were identified:
It should be noted that the original objective of solving a problem
for a specific work group was subsequently enlarged as described
in Chapter 1. The list of objectives above is included in the
broader system objectives later formulated to apply to all knowledge
work PSEs, but it is not identical in all respects. Not all knowledge
work groups have identical performance support needs, but it was
determined that a broad set of objectives could address all problems
likely to affect the performance of any given work group.
The Knowledge Worker System (KWS) is proposed as a solution to
the information overload of knowledge workers. KWS is conceived
as a PSE that will guide Army action officers through their daily
tasks by helping them organize, prioritize, and execute their
work efficiently and effectively.
The purpose of a PSE is to take better advantage of the cognitive
processing strengths of two entities: the computer, which works
best on iterative processes, and the human mind, which is most
effective in judgment-and creativity-based tasks, One purpose
of KWS is to flatten the learning curve for newly assigned knowledge
workers. The system will provide a new employee with institutional
knowledge captured from action officers who previously worked
in the new employee's position. With information culled from their
job predecessors, new employees will more effectively be able
to prioritize tasks and assemble the information necessary to
complete them in a timely matiner. KWS will store and access the
information that represents the processes and knowledge related
to a specific job description.
KWS is an example of groupware-software designed for use by collaborative
work groups. The program is designed to be driven by a master
calendar that lists milestones and automatically links the assignments
of every employee working on each project. Dynamic scheduling
capabilities will enable KWS to provide users a list of activities
that should be underway on a given day. KWS will link members
of a functional work group so they can send electronic mail, route
documents, and query common databases.
KWS will generate an on-screen window that presents a list of
a user's key task assignments or critical deadlines. When the
user selects a task, another window will display the steps required
to cornplete that activity. The user can access forms, routing
sheets, or other supporting documentation relevant to the task.
In this way, KWS will explain to employees what, when, and how
tasks must be done. It will also automatically execute repetitive
tasks. capture institutional knowledge, perform dynamic scheduling,
assist in workload leveling, and support management reporting
and analysis.
Scheduling
All aspects of KWS will be linked with a distributed schedule.
This master schedule will keep track of the tasks for which each
knowledge worker is responsible. Information about the tasks will
be maintained in the database. A knowledge worker can use the
system to view the progress of others upon whose work his or her
tasks are based. The knowledge worker can also determine which
other members of the work group depend on his or her work.
The KWS schedule database maintained at the work-group level may
also be made available to other related work groups through the
Corps of Engineers Automation Plan (CEAP) environment. This extra
avenue for connectivity will improve the capability of Hequarters
or other related work groups to track projects by functional area
or organizational element.
Schedule information can be viewed in a variety of ways. It will
be presented to the knowledge worker as a list of tasks for which
the individual is responsible. A supervisor can view the same
information supplemented by various criteria to get a better overview
of what the work group is doing. This information can be used
to assess impact of personnel shifts, manage and
implement projects, and plan and monitor resource changes.
Schedule information can be used for planning and to ensure that
all work is progressing as scheduled. As dates within the schedule
change, the affected knowledge workers will be electronically
notified. All documents, files, and executable programs
associated with the tasks affected by the schedule shift
will be carried along. This linkage will ensure that all information
pertinent to task performance is quickly md logically accessible
to the knowledge worker to whom the task is transferred.
Forthermore, when a supervisor uses KWS to reassign a task to
another member of the work group, all associated information will
be transferred with it. In this way, the newly assigned or temporarily
tasked knowledge worker will benefit from the institutional knowledge
of previous performers of the task, including their productivity
tools and relevant documentation.
Automatic Execution
KWS will provide an environment for work performance. That is,
KWS will offer a unified interface that ties together the whole
set of productivity tools used by knowledge workers. These tools
are the executable programs used by knowledge workers on a daily
basis. KWS will associate the tools with the task for which they
are required.
As previously discussed, one major goal of KWS is to perform many
of the repetitive tasks currently done by individual knowledge
workers. Since many knowledge work tasks are repetitive in nature,
KWS will provide a facility for the automatic invocation of
programs. This facility will allow a knowledge worker to launch
external programs and supporting software from within the KWS
environment
In addition, KWS will provide methods for automating the processes
of entering the steps required to perform a task and producing
documents.
Information Flow
KWS will coordinate the exchange of information between itself
and other programs. It will facilitate the flow of information
throughout the organization, from worker to worker, program to
program, and system to system.
KWS will use a distributed database server/host machine that can
be accessed at multiple sites by different knowledge workers with
personal computers. The communications capability provided by
KWS will allow knowledge workers to access all of the database
information and communicate with any other member of the work
group. The users will be able to communicate with one another
via an electronic message facility.
In addition, a notification "daemon," or message, will
inform the knowledge worker when a remote job is completed or
an important task is added to the user's list of assignments.
User Interface
KWS will provide an intuitive and consistent graphical user interface
(GUI). Human factors engineering research has proven that a GUI
decreases user training requirements (The Benefits of the Graphical
User Interface: A Report on New Primary Research [Temple,
Barker & Sloan, Inc., Lexington, MA, Spring 1990]). KWS will
take advantage of this technology to increase the user's effectiveness
on the system, which ultimately will boost the user's job performance.
Knowledge Capture and Access
Knowledge workers can electronically "jot" down hints
for improving a process while using KWS. The system will also
provide a simple way to attach references to important documents.
KWS will provide the facility to relate any document to any task
in the system. Besides providing a more flexible interrelated
document structure, KWS will also provide automatic version control
whenever a new version of an existing one is created. KWS will
also provide the ability to search for any document in the system
based on a keyword. A document search by similarity to the current
task and the ability to archive documents are also provided.
University of Minnesota Model
Few models of the knowledge work process exist. One model has
been developed by Davis, Collins, Eierman. and Nance at
the University of Minnesota. This model describes a conceptual
framework for research on productivity within the knowledge work
arena (G. Davis, R.W. Collins, M. Eierman. and W.D. Nance, Conceptual
Model for Research on Knowledge Work, Management Information
Systems Research Center (MISRC)-WP-91-10 [Univenity of Minnesota,
February 1991]). The model, referred to here as the University
of Minnesota model (Figure 1), depicts an environment for knowledge
work that includes the organizational, professional. and social
context in which knowledge workers operate.
In this model, knowledge work may be viewed in an environmental
context-as one overall construct within which a knowledge worker
operates to achieve various goals. Alternatively, the model offers
a rnicroanalysis context that provides a view of three subconstructs
of knowledge work.
One subconstruct, work management, addresses the
self-regulatory feature of knowledge work. For some knowledge
workers, work management is performed at an individual and intuitive
level. Other knowledge workers rely upon an explicit system that
drives task design, planning, and scheduling. Goal setting, activity
selection and sequencing, information resource selection, and
task planning are included as key work management variables.
Another subconstruct, task motivation, focuses on
the forces that affect the intensity, direction, and persistence
of a knowledge worker's effort. The University of Minnesota model
cites task structure, deadlines, interruptions, and individual
biological differences as factors that influence task motivation.
The third subconstruct, task execution, incorporates
attentional information processing (focused problem solving),
automatic information processing (work that does not require conscious
thought because the performer has overlearned the task), and physical
processing (operation of mechanisms that facilitate task performance).
In addition to these subconstructs, there are three major front-end
inputs to the knowledge work process: task characteristics, personal
resources, and information resources.
Task characteristics consist of variables such as task
activities, time frame, task formalization, task ambiguity, task
complexity, and task significance.
Personal resources are personal characteristics that a
knowledge worker draws upon when performing work, such as domain
knowledge, personality traits, individual goals, and time availability.
Information resources include external factors, such as
technologies, procedural tools, and data items.
Davis et al discuss task outcomes as tangible results of
the knowledge work process. Examples of mask outcomes include
decisions, analyses, reports, lessons, plans, and other physical
products.
As stated earlier, much of the research to date has focused
on the processes of knowledge work in general. This
work has led to the formulation of a USACERL model of the
knowledge work process. The USACERL model was originally based
on study of Army knowledge workers involved in the MCA budget
formulation and justification process known as planning, programming,
and budgeting. However, the authors consider it to be a valid
model for any other knowledge work environment as well.
The foundation for the USACERL model of knowledge work
is the Stimulus-Organism-Response (S-0-R) paradigm, which
has its basis in classical psychology. The S-0-R premise
holds that all human activity can be understood in terms of the
following sequence: a stimulus acts upon an organism (i.e., person)
to evoke a response (E.J. McComick, Human Factors
in Engineering and Design [McGraw-Hill, 1976]). The
USACERL knowledge work model is further influenced by the definition
of knowledge workers as professionals who (1) collect information,
(2) analyze or otherwise add value to the information,
and (3) produce further information. A description of the
knowledge work process has been refined as depicted in Figure
2. Each component and subcomponent of the model is described in
the following sections.
Prerequisite Information
This component of the model pertains to the function of gathering
information. Four elements compose the prerequisite information
component.
Task Origin. Information about the source of the tasking
is communicated through this element. It conveys details such
as whether the task is part of the organization's mission statement,
whether the task has been delegated by a supervisor to a subordinate,
who retains ultimate responsibility for the task, and what skill
level is required to perform the task.
Institutional Knowledge. Tips, hints, procedural details,
and other expert information comprise this element. In particular
it refers to the "how-to" information that is accumulated
through experience and often lost when a knowledge worker leaves
the position.
References and Guidance. This element refers to manuals,
regulations, help files, tutorials, books, instruction guides,
pamphlets, guidance letters, memoranda, directives, templates,
examples of previous versions of a product, and other miscellaneous
documents and files.
Information and Data. The supporting facts, figures, statistics,
and other types of knowledge necessary to make the calculations,
reviews, judgments, and other decisions that knowledge workers
generate comprise this element.
Analysis
As mentioned previously, analysis is the value-added component
of knowledge work. This component corresponds with the S-0-R paradigm
in terms of its emphasis on the organism. Two elements are included
within this component.
Enabling Tools. Technologies such as expert systems, spreadsheets,
word processors, decision support tools, and custom-designed software
are examples of typical enabling tools a knowledge worker uses
in performing the value-adding function.
Work Execution. This element denotes performance of the
task. Knowledge work tasks entail processes such as analysis,
review, planning, decisionmaking, resource allocation, policy
making, and communication of information.
Product
The final component of the knowledge work process, as understood
by the USACERL model, generates an informadon-based product such
as coordination between knowledge workers, a decision, or a report.
These products may be transmitted orally, printed on paper, or
transmitted and stored in an electronic medium.
Compare the two models discussed here with the traditional Human
Information Processing (HIP) model which represents the primary
functions of human/machine systems (See, for example, E.J.
McCormick). The HIP model describes four basic functions: sensing
(information reception), information storage, information processing
and decision, and action (physical operation and communication
links).
Both the University of Minnesota and USACERL models address all
four of these HIP functions. A key distinction, however, is that
the two knowledge work models emphasize the individual worker's
ability to influence the final results of the process. Further
distinguishing the USACERL model from both the Minnesota and HIP
models is the importance placed on institutional knowledge. The
other two models address institutional knowledge only indirectly,
under "Information Resources" (Minnesota) or "Information
Storage" (HIP), for example. In the USACERL model, however,
institutional knowledge is considered a main driver of the process.
Therefore, it is considered important that any performance support
environment developed for knowledge workers should facilitate
the capture, retention, and accessibility of institutional knowledge-especially
to assist people who are new in a position. Design and development
of KWS will include a special focus on this aspect of the system.
The model that drives USACERL research into the knowledge work
process was presented in Chapter 3. USACERL research into knowledge
work performance support to date has focused on the ability to
characterize tasks for the purpose of identifying specific information
to collect when building a knowledge base. In this chapter, task
attributes are proposed as a way of summarizing the information
critical to the performance of knowledge work.
The task attributes outlined below were the product of a series
of debriefings of MCA knowledge workers as well as a literature
search. The review of literature pertaining to task analysis,
decision making, and human-computer interfaces revealed that no
classification system for knowledge work tasks currently exists.
Early drafts of a system of task attributes were amended and refined
by members of the targeted MCA work group and USACERL researchers.
The product of this work was a list of 13 task attributes that
pertain to knowledge work in general.
Thirteen Essential Task Attributes
Thirteen attributes have been identified to represent essential
information that must be collected in the characterization of
any task. These attributes are:
Title
This attribute identifies the task by name. The content
of the knowledge work task should be conveyed in an abbreviated
manner via the task title.
Date Due
This item states when the task must be finished. The optimal
start date can be calculated using this attribute
with the one following, duration.
Duration
Duration refers to the length of time required to complete the
task. Duration includes three elements:
Estimated. This element projects the time required for
task completion. The estimate is used when historical data are
incomplete or unreliable.
Actual. The amount of time used to complete the task is
recorded by this element.
Suspense vs Quality. This element records task duration
in situations where a short suspense (inadequate time between
notification of the task assignment and the date due) is in effect.
In such a case the quality of the product may be compromised.
Dependencies
This attribute describes a task's linkage to tasks that either
precede or follow it. This information is conveyed via two elements:
Predecessor. This element identifies tasks that must precede
the given task.
Successor. This element identifies tasks that logically
follow the task.
Priority
Tbis item refers to the relative importance of the task.
It may convey either a numeric ranking order or a narrative ranking
such as "critical," "important," "normal,"
or "low importance."
Status
This attribute describes the degree of task completion. The task
slot may be filled with an indicator such as "not started,"
"in progress," "finished," or "dropped."
Personnel Assignments
This attribute designates who is responsible for the task. The
following elements are included:
Assigned By. This element records the name of the individual
who originated the task.
Skill Level. This element describes what type of personnel
can perform the task. This information may take the form of a
position title such as "Branch Chief," a personnel wage
rating scale such as "GS-7 or above," or a descriptor
such as "General Engineer," "Clerical Worker,"
or "Budget Analyst." This information will vary according
to the nature of both the organization and the task.
Assigned To. This element records who is responsible for
ensuring that the task is completed.
Performed By. This element records to whom performance
of the task is delegated. This information is relevant when the
person who completes the task and the person responsible for the
task are different.
Motivation
This attribute conveys the reason for performing the task.
The task may be motivated by a mission statement,
regulation, managerial mandate, personal initiative, etc.
Frequency of Recurrence
The frequency with which the task must be repeated is reflected
in this attribute. Its elements include:
Ad hoc. Indicates a one-time, special-purpose task not
expected to reoccur.
Cyclic. Indicates a task that requires repetition on an
annual, quarterly, monthly, daily, or other periodic basis.
Cyclic tasks can be further broken down to indicate whether they
occur on a fixed date, e.g., beginning or end of the cycle.
References
This attribute records the books, manuals, tutorials, help systems,
regulations, job aids, letter, memoranda, reports, opinions,
diagrams, and other documents that are specifically relevant to
performance of the, task. The attribute is divided into
two major elements:
Attachments. This element includes examples of what the
task previously produced, either partial or complete.
Guidance. This element consists of explanatory
material that provides instructions about the task. Guidance should
not be confused with steps, the latter being procedural
information that decomposes the task into a discrete sequence
of activities.
Tools
This attribute refers to any automated aids or information technology
that help the worker accomplish task execution. Four elements
are included:
Software. This element records the full name of any software
programs pertaining to the task, including version numbers.
Data. This element lists the source of data to be processed
by the software named above.
Access Path. This element lists the linkages required among
workstations, communications packages, networks, databases, printers,
or other devices needed to use the tools.
Authorizations. Any logins, passwords, codes, or
other authorizations needed to operate the tools should be
recorded under this element.
Outputs
The output attribute links products of the task, whether
tangible (e.g., a report, graph, updated database, letter) or
intangible (e.g., notification, denial, authorization, coordination,
plan, decision). Two elements are included:
Media. This element indicates whether output is verbal,
a telephone call, printed copy, electronic, a specific form, etc.
Target. This element records where the product is sent
(e.g., a specific knowledge worker, office, printer, or work station).
Steps
This attribute refers to the decomposition of the task
into a sequence of steps. It is the procedural information that
indicates how to perform the task. This procedural information
includes the tips, lessons learned, and other institutional
knowledge. For a surnrnmy of decomposifion techniques,
see D. Diaper, Task Analysis for Human-Computer Interaction
(Ellis Horwood, 1989).
The following discussion provides considerations for selecting
a set of task attributes relevant to the overall objective of
the knowledge base for any KWS application. The entire set of
knowledge work task attributes, as outlined in Chapter 4, may
not be necessary for all knowledge bases, depending on the specific
application.
Overall KWS System Objectives
For members of a work group linked by a common organizational
tasking, KWS has the following major objectives:
Cost Justification as an Objective
The inherent purpose of KWS is to serve as a productivity multiplier
for Army knowledge workers. Ultimately, KWS must be able to enhance
the knowledge worker's performance of key tasks if its development
costs are to be justified. The actual demonstration of KWS effectiveness
must await system development and testing. At this stage of the
research, however, two possible approaches to cost justification
appear promising.
The first approach is based on the hedonic wage model as applied
to the cost justification of an office information system (OIS).
The hedonic model assumes that an OIS can both decrease the amount
of time required to complete a given task and facilitate the restructuring
of work assignments. Both of these factors are postulated to
result in higher efficiency. Professionals have more time to
perform work in their various specialties and spend less time
on routine and nonproductive tasks. The combination of the OIS
and restructuring within an office can correct the misallocation
of time spent by professionals on lower-value activities (Peter
G. Sassone and A. Perry Schwartz. "Office Information Systems
Cost Jusffication," IEEE Aerospace and Electronic System
Magazine, Vol 1 [August 1986], pp 21-26.). The premise
of this hedonic wage model closely matches the purposes of a performance
support environment, and would seem to apply to the KWS concept
in particular.
A second possible approach to cost justification of KWS computes
the amount of time spent on direct work versus indirect work.
Direct work is defined as activities required to generate
mission-related products. Indirect work includes tasks
that support personnel accomplishing mission-related work. In
the knowledge work setting, indirect work includes such activities
as copying, upward reporting, personnel management, filing,
and responding to requests for information. Both types of work
are necessary, but most of a worker's time should be spent
on direct work. One advocate of this approach suggests that professionals
should strive to spend approximately 60 percent of their time
doing direct work. Comparison of the percentage of time professionals
spend on direct work versus indirect work before and after introduction
of an OIS may yield a useful indicator of the system's effectiveness
(Ray B. Helton, "Achieving White-Collar Whitewater Performance
by Organizational Alignment," National Productivity Review
(Spring 1991), pp 227-244.). Since this is exactly the kind
of performance support that KWS will address, investigation of
potential cost justification along these lines also seems promising.
KWS objectives, including cost justification, are summarized in
Table 1. The task attributes that satisfy each objective
are listed. Again, it must be noted that any specific application
of KWS may not require the collection and tracking of the 13 task
attributes in the knowledge base. Tailoring KWS to a work group
will require careful formulation of application-specific objectives,
then identifying which task attributes are necessary to fulfill
those objectives.
KWS Objectives and Associated Task Attributes
Army action officers who process information for the use of
others belong to a category of professionals called
knowledge workers. Job performance is adversely affected by the
nature of knowledge work due to the distractions created by the
many necessary but mundane administrative chores such as scheduling,
coordinating with others, and reporting. The work environment
is also adversely affected by information overload, conflicting
deadlines, loss of institutional knowledge due to personnel turnover,
and a collection of automated tools not able to address all task
areas of the group. A computer-based system called a performance
support environment could automate many routine administrative
tasks, freeing the knowledge worker to spend more time
using his or her professional skills directly on intellectual
mission-oriented tasks.
Initial research on a PSE for knowledge work has concentrated
on identifying all of the major attributes of knowledge work tasks.
These attributes may be handled as the analogues of fields in
a database: each one represents a point at which to record key
information needed for tracking and executing projects from start
to finish, and recording results for future reference. The integration
of this information in an automated system may form the
basis for a PSE applicable to any knowledge work environment.
Thirteen task attributes applicable to any kind of knowledge work
have been identified. Not all attributes will apply to the task
characterization of all functional work groups, however. Therefore,
before beginning the construction of a PSE knowledge base for
a particular work group, these task attributes should be analyzed
in light of specific application objectives. Only data supporting
the relevant task attributes need be collected.
On the basis of the general objectives for a knowledge
work PSE, two approaches to justifying development costs seem
applicable: the hedonic wage model and the direct work/indirect
work criterion. T'he approach for measuring the cost effectiveness
of the Knowledge Worker System should be decided upon and included
in the list of overall system objectives.
Objective
Scope
2 THE PERFORMANCE SUPPORT ENVIRONMENT CONCEPT
The Knowledge Worker System as a Performance Support Environment
Functional Description of KWS
3 MODELS OF THE KNOWLEDGE WORK PROCESS
Figure 1. University of Minnesota Model of Knowledge Work.
(Source: Davis et al., 1991. Used with permission.)
USACERL Model
Figure 2. USACERL Model of Knowledge Work.
Knowledge Work Models vs Traditional Model
4 TASK CHARACTERIZATION BY TASK ATTRIBUTE DEFINITION
The following sections define each attribute.
5 TASK ATTRIBUTES REQUIRED FOR A KWS KNOWLEDGE BASE
Relationship of Objectives to Task Attributes
System Objective Relevant Task Attributes
Task Management Title, Date Due, Duration, Priority,
Personnel Assignment, Motivation, Status
Information Flow Title, References, Guidance, Outputs
Scheduling Title, Date Due, Duration, Priority,
Personnel Assignment, Motivation, Status,
Dependencies, Degree of Reoccurrence
Information Linkage Title, References, Guidance, Tools
Institutional Knowledge Title, Steps
Automatic Execution Title, Tools, Outputs
Cost Justification Title, Duration, Personnel Assignment,
Motivation, Outputs
6 SUMMARY
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