Evaluation and Ranking of Knowledge Workers

Experience of Pakshoo

GIG Business Excellence Manager, Iran

ir.excellence@excellence

&

Azizeh Teimouri

Pakshoo QC Manager, Iran

azizeht@yahoo.com

Abstracts: This paper presents performance appraisal of knowledge workers in R&D department. A survey was

deployed to determine if organization structure is in conformity with world class standards. The results

showed that leadership and systems and methods in GIG compatible with world class standards but there

is a gap between GIG HR Systems and world class organizations.

One of the most important requirements was compiling appraisal of knowledge workers. In this system by

using evaluating theory, competency models, and experiences of managers has been developed new

approach. In this paper presents performance measurements, relationship between performance

measurements and job position and payments and performance measurements techniques and objectives.

Pay attention to knowledge workers causes empowerment of people in knowledge and skills and finally

profitability of organization.

Knowledge worker definition Knowledge worker, a term coined by Peter Drucker in 1959, is one who works primarily with information

or one who develops and uses knowledge in the workplace.1 Due to the constant industrial growth in

North America and globally, there is increasing need for an academically capable workforce. In direct

response to this, Knowledge Workers are now estimated to outnumber all other workers in North America

by at least a four to one margin2. A Knowledge Worker's benefit to a company could be in the form of

developing business intelligence, increasing the value of intellectual capital, gaining insight into customer

preferences, or a variety of other important gains in knowledge that aid the business.

It has been further defined as work that involves analyzing information and applying specialized expertise

to solve problems, generate ideas, teach others, or create new products and services.3 It is difficult to

define knowledge work in more detail because knowledge work is primarily invisible. It is hidden in the

head of the knowledge worker. Because of the difficulty of measuring knowledge worker production,

dissatisfied knowledge workers may take advantage of the situation. This dissatisfaction may produce

behavior in which personnel seek more financial satisfaction by giving themselves a "stealth raise", i.e.,

cutting back the effective hours in which they perform knowledge work at the office. They may dedicate

more mental effort to another activity that is not job-related that brings them more satisfaction4. This

contradicts Frederick Taylor's main philosophy of a fair day's work for a fair day's pay. Even though there

should be no expectation of blind company loyalty as was expected in the past, companies should expect

good work and some form of commitment to productivity from their knowledge workers while they are

on the job.5

Knowledge workers work in an environment described as a knowledge network. There is always an

increasing need for knowledge to grow and progress continually, whether tacit or explicit. Knowledge

grows like organisms, with data serving as food to be assimilated rather than merely stored. All

knowledge workers, particularly R&D project managers, need to easily access and search internal and

external knowledge bases.

Toffler observed that typical knowledge workers in the age of knowledge economy and knowledge

society must have some system at their disposal to create process and enhance their own technological

knowledge.

Knowledge workers are believed to produce more when empowered to make the most of their deepest

skills; they can often work on many projects at the same time; they know how to allocate their time; and

they can multiply the results of their efforts through soft factors such as emotional intelligence and trust.

Organizations designed around the knowledge worker (instead of just machine capital) are thought to

integrate the best of hierarchy, self-organization and networking rather than the worst. Each dictates a

different communications and rewards system, and requires activation of knowledge-sharing and action

learning. A basic pattern rule of human systems is that when you mix them you will get the worst of each

unless you contextually and carefully attend to connecting the best6.

In the Knowledge Age, 2% of the working population will work on the land, 10% will work in Industry

and the rest will be Knowledge Workers.

Classes of Knowledge workers Knowledge work, ranges from tasks performed by individual knowledge workers to global social

networks. This framework spans every class of knowledge work that is being or is likely to be

undertaken. There are seven levels or scales of knowledge work.

1. Knowledge work, (e.g., writing, analyzing, advising) is performed by subject-matter specialists

in all areas of an organization.

2. Knowledge functions (e.g., capturing, organizing, and providing access to knowledge) are

performed by technical staff, to support knowledge processes projects.

3. Knowledge processes (preserving, sharing, and integration) are performed by professional

groups, as part of a knowledge management program.

4. Knowledge management programs link the generation of knowledge (e.g., from science,

synthesis, or learning) with its use (e.g., policy analysis, reporting, program management) as

well as facilitating organizational learning and adaptation in a knowledge organization.

5. Knowledge organizations transfer outputs (content, products, services, and solutions), in the

form of knowledge services, to enable external use.

6. Knowledge services support other organizational services, yield sector outcomes, and result in

benefits for citizens in the context of knowledge markets.

7. Social networks enable knowledge organizations to co-produce knowledge outputs by leveraging

their internal capacity with massive social networks.

Productivity Measurement History Measurement requires collecting data. Categorizes three basic ways to collect data about a given

phenomenon or organizational system: inquiry, observation, and collecting system data or documentation.

This data gathering is the essential part of measurement. It is the process by which productivity

benchmarks are established. In the simplest form, the outputs are evaluated against the inputs, but even at

this simple level terminology may be a problem. Some writers include no quantitative indicators such as

quality in their definition of "output," but others confine the discussion of productivity to I/O. The

definition affects the type and amount of data gathered.

USACERL has identified the need to measure productivity among knowledge workers to recognize any

gains that can be attributed to implementation of KWS. They developed a measuring knowledge worker

productivity model, and discussed which methodologies may work best in specific knowledge work

environments7. An extensive search of work measurement literature was conducted. More than 100

journal articles, papers, and books were reviewed. Topical areas reviewed included work measurement,

productivity, organizations, psychology, decision theory, and quality improvement. Several

methodologies were examined for applicability to the kinds of environments in which Army knowledge

workers operate, and the most promising were identified.

There is a distinct difference in the productivity of an organization and the productivity of a single work

unit of that organization. A research group indicated this difference by use of its third objective-to

establish measures that reflect an organization's degree of success in meeting its established goals. The

goals for each level of the organization should differ to represent the contribution that specific level

expects to make toward overall organizational goals. Therefore, each level's productivity evaluation

should be different, reflecting its unique goals.

Historically, work has been separated into blue-collar and white-collar categories. This view can be

expanded to include knowledge work as a third category. Knowledge work is all work whose output is

mainly intangible, whose input is not clearly definable, and that allows a high degree of individual

discretion in the task. This difference in work content requires different approaches to productivity

evaluation. The difficulty of measuring something that is not clearly defined has been noted. An expanded

definition of work that includes a category for knowledge work is a first step in the evaluation of

knowledge worker productivity. Other models can be fined in the same reference.

Some writers offer several suggestions to make measurement simpler and acceptable to the KW:

� The KWs must participate in the establishment and evaluation of the measures of their

productivity. The more they are involved, the less likely they will feel threatened.

� Any process that seems too complex to measure is likely to have less complex sub processes,

which are more practical to measure.

� Always use the best measure for the job, even if several different measures must be pursued for

different processes.

� Do not expect absolute accuracy, but try for the best that is economical.

� Regardless of the shortcomings, measuring is better than not measuring.

The literature review shows that productivity measurement is discussed from a wide variety of

viewpoints. A variety of implementation methodologies have been developed for different applications.

What is lacking is a concept that unifies these diverse views. This section discusses several aspects of

such a unifying concept. In addition, productivity measurement is most valuable as a dynamic measure,

not as a static measure.

The authors propose categorizing work by eight components, as detailed in Table 1. Figures 1-4 show the

components of work arrayed on a horizontal scale. Each characteristic is represented by a horizontal line,

and is scaled from high to low.

The graph is set up so inversely related components are at opposite ends and strongly related components

are grouped together. For example, "Decision making" and "Knowledge Use" are directly related to

"Complexity" by definition. "Structured" is inversely related to "Complexity," so these two components

are at opposite ends of the graph. There is not a lot of "Complexity," as defined, in a very structured job-

the amount of decision making and the knowledge used is low. This means that "Structured" is also

inversely related to "Knowledge Use" and "Decision making." "Volume" is directly related to "Time per

Job" and partially related to "Repetitive," "Structured" and "Complexity." Table 1 defines all eight

components in more detail.

Figure 1 shows two examples of knowledge-intensive work plotted on the graph. Figure 2 shows two

examples of what typically has been called "blue-collar" work. Figure 3 demonstrates the area into which

very knowledge-intensive work would plot and Figure 4 does the same for "blue-collar" work.

Figure1. Two Examples of Knowledge Work Figure2. Two Examples of Blue-Collar Work8

These relationships account for the general slope of the lines in Figures 1 and 2. One would expect

knowledge-intensive work to have a negative slope, i.e., the value of the components of work will

decrease down the list. One also would expect that the skilled work, or blue-collar work, would have a

positive slope. Neither of these slopes is expected to be perfect; rather, they are expected to indicate the

knowledge or skill level of the work being examined.

Figure3.Expected Graph Area of Knowledge Work Figure4. Expected Graph Area of Blue-Collar Work9

The graph is set up so inversely related components are at opposite ends and strongly related components

are grouped together. For example, "Decision making" and "Knowledge Use" are directly related to

"Complexity" by definition. "Structured" is inversely related to "Complexity," so these two components

are at opposite ends of the graph. There is not a lot of "Complexity," as defined, in a very structured job-

the amount of decision making and the knowledge used is low. This means that "Structured" is also

inversely related to "Knowledge Use" and "Decision making." "Volume" is directly related to "Time per

Job" and partially related to "Repetitive," "Structured" and "Complexity." Table 1 defines all eight

components in more detail.

Where knowledge work is involved, work becomes more important than outputs in calculating

productivity. While knowledge workers may be using expensive equipment, the budgets for their areas

usually consist primarily of salaries and benefits. As difficult as it may be to directly link knowledge

work to outputs, it is even more difficult to link the knowledge worker's equipment to the same outputs.

The knowledge work itself is often used to tie equipment use to outputs. This further increases the

importance of the work in calculating productivity. Measurement, as discussed here, is a determination of

the labor involved in the tasks performed by the work group.

Evaluation measures are not all alike. They differ in complexity, accuracy, adaptability, and applicability.

Table1. Table of Work Component Descriptions Component Description

Decision

making

The application of knowledge in the determination of how to process the work. This

application of knowledge differentiates decision making from simple choices such as

"stamp" or "do not stamp."

Complexity The difficulty of the job. This component involves the number and difficulty of

decisions, and the amount of knowledge needed.

Knowledge Use The amount and complexity of information required to do the work.

Structured

Structure involves constraints on how, when, where, and what is done. Both complex and

simple work can be very structured. The assembly-line job is usually fairly simple, but

very structured. A legal case can be very complex, but it also is very structured.

Repetitive A function done the same way every time, and will always be done the same way. If the

job changes each time, then it is not repetitive.

Volume

The number of times the profiled activity will occur in a given time cycle. This can be

expressed in many ways, which will affect the gauge of high-low. Volume will be based

on the number of completed actions per year.

Time per Job The total time spent completing the job, from start to finish.

Skilled

Activity

The physical difficulty of performing the work. This inversely relates to the mental

difficulty or complexity. There are activities that require both skilled physical and mental

activity-surgery, for example.

There are many specific methods, but this discussion will focus on the categories that can be constructed

to classify measuring techniques.

Many tags might be used for classification. These ranges from who performs the measurement, to how it

is done, to how long it will take. The purpose here is to categorize the measurement techniques in a way

that allows matching them to work based on content. The complexity of the measurement technique is

often a good indicator of the type of work it is best suited to measure. Note that these are not absolute

matches. Sometimes the best method is different than expected. This does not invalidate the general

approach to categorization because it is intended primarily as a guideline.

Very simple measures usually produce less accurate results, but are simple to implement and require less

time. Their use is justifiable where it is impossible or not cost-effective to use more precise measures.

These techniques can be used for any type of work, but are best reserved for complex tasks that occur

infrequently, at random times, and at different levels of complexity. Table 2 groups the techniques,

starting with the most complex and ending with the simplest. The groupings are based on the complexity

of setting up the analysis and conducting the evaluation.

Table2. Table of Work Measurement Categories Group Description Techniques

1 Complex setup Complex implementation Predetermined time-motion studies, Stop-watch studies, Logging

2 Complex setup Simple implementation Self-logging, Sampling, Counting

3 Simpler setup Moderate implementation Committee, Estimation

The more complex techniques require more expertise to design and implement. The techniques in

Category 1 usually require extensive preparation. The work has to be analyzed and described. Data must

be gathered on frequencies and volumes. A measurement plan has to be devised-one that fairly represents

the work being evaluated. The implementation for Category 1 usually requires an analyst with a high

level of expertise in the techniques being used. Techniques in Category 2 can be simpler to implement

because setup involves simple measures designed to be performed by those involved in the normal

workflow. But the preparation in Category 2 is difficult: the work must be understood so valid measures

can be designed. Category 3 is simpler to set up because the process is a continuous one, and much of the

setup difficulty in Categories 1 and 2 can be spread over time. Implementation is only moderately

complex because it is a continuation of the initial setup process. Recall that the inclusion of the workers

and management in the design of any work analysis project in a knowledge work area is essential to the

project's acceptance and correct result10

.

In the knowledge work environment it is important to understand that an individual's performance can

vary over time, and that the difference in performance of the same work by two different individuals can

be substantial. It is also important to remember that the apparent inability to apply measurement

techniques can often be attributed to the perception that the job is simply too large or complex to measure.

Sometimes looking at individual parts of the job can make measurement easier. Some people suggest

starting with a definition of the group's products and working backwards to the lowest logical division of

the work. Others recommend examining the responsibilities for the work performed.

How to measure and what to measure is a complex decision. Taking a single measure is not necessarily

the best solution. The best way to measure depends on the cost, effort, and need. Lower levels in an

organization require more detail than higher levels in the same organization. At a departmental or work

group level, detail is needed, but cost and available resources may dictate the use of a less than perfect

measurement mix. Categorizing work by its content components decision making, complexity,

knowledge use, time, volume, structure, repetition, and skill level-facilitates understanding the work and

how to best measure it. Picking a measurement technique appropriate to work content is only part of the

job. Cost and accuracy must also be factored into the decision.

Measurement techniques vary in implementation costs and accuracy. Costly, accurate techniques are most

appropriate for work whose content allows for accurate measurement and justifies the costs. Less

expensive, less accurate techniques are available for work whose content prohibits a high degree of

accuracy and where cost is a major deterrent to measurement. Table 3 recaps this information.

Table3. Summary of Measurement Technique Effort, Accuracy, and Cost Measurement Technique Setup Implementation Accuracy Cost Predetermined time-motion, stopwatch, and logging Complex Complex High High

Self-logging, sampling Complex Simple Moderate Moderate

Committee evaluation estimation Simple Simple Low Low

Far too many people in business still don't get it. They continue to believe that technology is the answer to

pretty much any problem in their organization. They aren't completely understood or given proper credit,

nor do they themselves always contribute up to their potential because of organizational hurdles.

It's time for all organizations to completely embrace the concept that people, not technology, are their

most important asset. A researcher has done a nice job of combining original research with excellent case

studies with his own insight into what he calls "a manual on corporate thinking for the 2000s."11

In early 2005, detailed the impact of globalization on the white-collar workforce in developed countries.

Many U.S. jobs thought to require knowledge economy skills, and therefore secure, he reported, are now

being exported to nations like India and China that have good telecommunications infrastructures, an

overabundance of skilled workers, and, compared to the U.S., a very low wage scale12

.

Is the educational establishment addressing this trend? The article Education and the Changing Job

Market contains this remarkable graph titled Trends in Tasks done by the US Workforce 1969-199813

(1969 = 0) (Figure 5). But what exactly do we mean when we say expert thinking and complex human

communications? What separates these job skills from routine cognitive work? And are there skill sets

which students must master before being able to considered complex communicators or expert thinkers?

I would posit that there is a Maslovian-type Hierarchy of Knowledge Worker Skills, skills that need be

mastered prior to the acquisition and application of higher order skills. I will categorize these as: Basic

Skills, Discipline/Profession Specific Skills, Technology Skills, Information Problem-Solving/HOT

Skills, and Conceptual Skills. Each is described below (Figure 6). The ability to read for understanding,

interpret visual information, write comprehensibly and persuasively, and solve numeric problems are, and

will remain, the foundations on which all other knowledge work skills rest. To this end, the United States

has ambitiously devised systems of testing to help assure that all students have these illiteracies. Much of

this testing, which varies by state, tests only basic reading comprehension, simple composition and low

level arithmetic skills.

The danger many educators perceive in an emphasis on the basics is that if only the basics are tested (and

thereby valued), schools will ignore the affective, creative, and problem-solving sides of education and

give student few chances to apply these skills in meaningful ways. Basic skills, in other words, are an

important bar to set for students, but an exceeding low one. Yet, as a primary, if not sole measure of

school effectiveness, school leaders are establishing goals and improvement plans addressing student

performance on very basic skills.

The term knowledge worker is used here much the way Robert Reich uses symbolic analyst — to include

professionals, upper-middle managers and above, and others who create, modify, and synthesize

knowledge14. The incorrect premises are15

:

Figure5. Trends competencies Figure 6. Pyramids of the Basics Skills

� That knowledge workers primarily create new knowledge

� That demand for knowledge work will grow in pace with the increased supply and rising

productivity of knowledge workers.

� That we can make meaningful assessments of future labor markets by projections from today's

labor markets.

� That productivity increases, by themselves, will lead to increases in income.

Now we can turn to the potential supply of knowledge products. This will depend on the number of

adequate workers offering to work, the number of hours that each knowledge worker seeks to work, and

the productivity of their labor. We can assume that the number of workers seeking employment as

knowledge workers will increase due to both push and pull factors. The major push factor will be the

disappearance of other types of employment. A major pull factor is the interesting and pleasant nature of

much knowledge work compared to other jobs. And, unless there are significant increases in the wage

rates for knowledge labor, it is not likely that the number of hours each one seeks to work will decrease.

As the productivity of farmers and farm workers grew faster than the demand for agricultural products,

the number of farmers and farm workers had to fall. When the productivity of manufacturing workers

grew faster than our demand for manufactured products, the number of manufacturing workers

necessarily shrunk. But there are two important differences. Employment in agriculture fell as

employment in manufacturing was growing; employment in manufacturing fell as employment in the

service sector was growing. And in both agriculture and manufacturing the slow pace of change made it

easier for the growing sector to absorb the labor that was being cast out of the shrinking sector. The pace

of technological change is much faster now. And there is no apparent sector that can absorb the labor that

the knowledge sector casts off or the labor cast off by other sectors that the knowledge sector fails to

absorb. When we finally get around to asking "What comes after knowledge work?" we have to admit

that there is no answer (Figure 7).

As knowledge workers, we like to think that most of our work involves the creation of new knowledge of

knowledge that would not exist without our mental efforts. Unfortunately, this is actually a fairly small

part of our work. When we examine the work pattern of knowledge workers, we find six more or less

distinct types of work (Figure 8):

1. Routine work that is hard to separate from knowledge work. Formatting an article, for example,

is work that might be done by a typist, but would be done by the knowledge worker when that

takes less time than preparing the document and formatting instructions for the typist.

2. Networking, promoting, socializing.

3. Finding the data needed to produce the knowledge.

4. Creating what others have probably already created when this would take less time than to

search, find, and appropriate what has been produced by others.

5. Truly original knowledge work creating what has not been created before.

6. Communicating what has been produced or learned.

The most important distinction is between 4 and 5. We may spend much of our time creating knowledge

that is new to us, but is the same or similar to products that have been created by other knowledge

workers. The fate of most knowledge workers is a work life in which we are constantly reinventing the

exam question, the flood insurance clause, the advertising copy for a sweater ad producing goods and

services that are new to us but not new to the greater society. Such work may well be pleasant and

fulfilling. The knowledge worker is indeed engaged in the creation of knowledge. But the knowledge

worker is only being paid for it because creating knowledge that exists elsewhere is presently cheaper

than finding it. Finding the intelligence in the network that we once hoped to develop as artificial

intelligence and expert systems software doesn't depend on some unrealized technological breakthrough.

It can come from the continuation of present trends in speed, interconnectivity, and search routines.

Knowledge work has always been important in knowledge creation, but in recent history knowledge

creation has begun to correlate much more obviously with wealth creation. As a result, the business

minded have increasingly applied to knowledge work practices that were developed to manage industrial

work. In general, this has worked rather badly. 16

Fig7. Knowledge Work Supply, Demand, and Productivity Fig8. What Do Knowledge Workers Do

Traditional Models of Supervision--A Poor Fit with Knowledge Work Consider state-of-the-art economic models that underlie traditional notions of supervision, the so-called

"agency models." To be fair, not all management scholars approve of these models. Behavioral scientists

dislike them particularly. Nevertheless, agency models embody the assumptions most common in the

design of modern supervisory arrangements.

Agency models contains a "principal" (the employer), an "agent" (the employee), and the relationship

between them17. The motivations of these actors are simple. The agent dislikes expending effort but likes

getting paid. The principal dislikes paying the agent but likes the valuable work that the agent does. The

objectives of principal and agent are thus diametrically opposed: The agent wants to be paid as much as

possible for doing as little work as possible, whereas the principal wants to get as much work as possible

from the agent while paying as little as possible. Contracting is complicated by the fact that the principal

can't directly see how much effort the agent is expending but can only observe a result of the agent's

work, which also depends on random factors. The primary conclusion of agency theory is the importance

of tying pay to performance, to provide employees with appropriate incentives.

The basic elements and assumptions of traditional supervisory practice are all present in this simple

model. You can see a rationale for current trends toward measurement and accountability in everything

from research funding to educational testing in public schools. You can see the rationale behind the

modern passion for "incentivizing" performance. Yet there are several obvious cracks in the model as it

applies to knowledge work:

� The difficulties in observing knowledge work are more profound than those assumed by the

model. Not only can't a supervisor observe effort directly in knowledge work, sometimes the

supervisor can't understand what the worker is doing and may not be qualified to judge results..

� "Agent" motivations are inconsistent with those of knowledge workers. Knowledge workers are

often interested in their work and motivated by a desire to do it well. Agency models suggest no

way of leveraging these worker motivations.

� In traditional agency models, effort is a single dimension. The productivity of knowledge work,

in contrast, often has to do with how effort is allocated across multiple dimensions. By

definition, knowledge work is more about how smart you work and less about how hard you

work. Incentive schemes intended to extract more effort from knowledge workers often distort

their effort allocations, forcing them to apply effort in the wrong places.

Problems in Measuring Knowledge Work Evaluating productivity is never more difficult than when evaluating knowledge work. Consequently, this

type of productivity evaluation is poorly understood. There are several reasons why knowledge work is so

hard to evaluate. Productivity measurement is an indicator of how well the goals of a work group are

being met. Whether a tight or loose definition of productivity is used, the validity of the results will

depend on the validity of the input. In contrast with hourly manufacturing and service workers, the

productivity of salaried knowledge workers such as engineers can be difficult to measure. In particular,

hourly workers may practice physical absenteeism, while salaried engineers may practice mental

absenteeism. An important aspect of this issue is what specifically causes engineers to mentally depart

from their jobs before they physically leave. This phenomenon is labeled "cognitive turnover" (CT)18

.

First is the problem of inertia. If work is being measured and rewarded, those reaping the rewards will

want it to stay the same. The areas and the types of work that have been measured in the past continue to

get attention today. Problems associated with measuring new areas of work are seen as roadblocks rather

than challenges. Planning and work measurement in the knowledge worker areas is not conducted as

scientifically as it has been in other areas. However, this inertia is diminishing as increasing numbers of

studies show how to evaluate knowledge work, and as the potential benefits continue to grow.

A related problem is that individual productivity increases do not transfer to the productivity of higher

levels of organization. This makes it seem as if measuring the productivity of knowledge workers will not

change anything. But this does not mean knowledge workers should not be measured at all. It seems that

the work group is the proper level at which to evaluate knowledge worker productivity.

Most of the remaining problems in applying productivity measures to knowledge work result either from

the intrinsic complexity of the work or from disagreements about what to evaluate. The complexity of

knowledge work arises from several factors. It is not routine, involves much independent judgment, and

requires several people to work together. Furthermore, a considerable amount of knowledge is required to

do the work.

The no routine nature of knowledge work means that it is very difficult to measure a norm. There is no

obvious average to observe and record, so any measure will be somewhat inaccurate. The degree of

independent judgment involved in knowledge work means that the "norm" may vary from individual to

individual. Each person can accomplish the work in his or her own way, further complicating

measurement of a norm. The dependency of one worker on another can mean that, although one worker is

performing very well, the problems of another worker determine the overall performance.

The question of what to evaluate also stems in part from the complexity of knowledge work. Productivity

measures applied to white-collar workers often concentrate on the countable results of the work rather

than the work itself, which is information. The work is so complex that an artificial indicator is evaluated

rather than the actual work. Often, the indicator is chosen because it is easily quantified. This approach

ignores potentially important aspects of the output, such as quality. The value of the output, which

includes its quality, is very important in knowledge work. This value is the primary output.

Managing Knowledge Workers The agency model presumes the employer is able to accurately measure employee performance.

Measuring performance is always difficult, and in knowledge work it is especially difficult. If you have

no real chance of observing, understanding, or attributing the results of employee work, you become

much more dependent on employees' willingness to openly communicate the meaning of their work.

Fortunately, knowledge workers often have a commitment to the work itself that makes them inclined

toward information sharing. Strong, agency-theory based incentives typically interfere with open

communications by giving knowledge workers reasons to compete with their colleagues and to horde

information about how to perform well on official performance measurements. Best practice calls for

emphasis on relationships, collaboration, and professionalism, and for de-emphasis of formal

performance measures.

The cost structure that drives physical work toward linear, sequential work processes is not inherent in

knowledge work. "Retooling" in intellectual domains is often much less costly than it is in physical work,

and there are fewer "scrap costs." Knowledge work is therefore less constrained than traditional physical

work by the need to get it right the first time and can instead be more iterative and more oriented toward

exploring, experiencing, trying, and trying again. In knowledge work, rapid experimentation can

substitute for detailed planning.

Successful knowledge work processes often iterate frequently. They are characterized by alternating

periods of unstructured work by individuals and small groups and structured "pulling in the reins" by

managers to integrate work. Such processes often look messy, even when healthy and productive. Team

size needs to be controlled, because the complexity of the "reining in" process can become overwhelming

if there are too many people involved. Two main principles of knowledge-worker management can be

summarized as follows19

:

� Emphasize collaboration and professionalism; de-emphasize incentive schemes and performance

measures. Play up knowledge workers' natural tendencies to be committed to their work and its

overall objectives.

� Emphasize iterative work structures rather than linear, sequential ones. Don't over plan. Rotate

between unstructured individual experiences and structured integration of individual work.

Note that not all knowledge work has the characteristics that make these more modern trends applicable.

You have to make sure that your workers are indeed committed to their work before relying on that

commitment in collaboration. You must make sure the cost of iteration is in fact low before you structure

work iteratively rather than linearly. If the prerequisites are satisfied, though, you can manage knowledge

workers in a way that is not only more effective but also more humane and pleasant for all involved.

Good research managers understand this implicitly: that relationships based on professionalism and

mutual respect work far better than scales of accountability and incentive schemes in most knowledge

work settings.

Specific traits of knowledge workers are:

� primarily identify themselves with their profession rather than workplace; more sensitive to the

kudos and esteem they receive from their peers than those they receive from management

� highly mobile and quick to change jobs

� driven primarily by the pride of accomplishment

� have strong believes/ personalities; they respond much better to being pulled than being pushed

� informal networking with peers, inside and outside their own company, helps them benchmark

their personal efforts and their company's competitiveness

An individual effectiveness of KWs is based on results and credibility, perceived reputation, and network

of relationships rather than formal authority, job description, or position in the hierarchy. And these are

the main attributes of a knowledge worker20

:

� connect people with people, connect people with ideas

� are good networkers, network for results

� do not follow the rules, do not conform

� feel good about themselves

� motivate others

� demonstrate integrity, are self reliant

� are goal oriented

� are able to identify critical knowledge

� add value to the organization

� have strong subject expertise in a specific area

� trustworthy - can be trusted and trusts others

� make decisions

� push the boundaries

� are informal active leaders, assume authority - ask for forgiveness, not permission

� strong belief in the value of knowledge sharing

� take a holistic view, see the wider picture

� are catalysts, facilitators and triggers

� do not need praise, walk the talk

� do not have a 'knowledge is power' attitude

� prepared to experiment with technology

Job Satisfaction Needs of Knowledge Workers can describe as:

� Challenge, above all

� Continuous training and coaching

� To know the organization's mission and to believe in it

� The need to see results

Meeting Specific Requirements of Knowledge Workers must be:

� consider and treat them as professional partners

� respect their expertise, support them in its application, and help them extend it further

� give them influence in decisions that determine where and how their expertise is applied to

specific innovation initiatives, as well as how it contributes to the overall business strategy

Finally, the knowledge workers management is a complicated context that we can describe it by 3

main components: management, leadership and coaching (Figure9).

Fig9 .balanced manager in new world

Case Study:

Introduce Pakshoo Pakshoo Chemical & Manufacturing Co. was established in 1966. Having employed the latest technical

knowledge along with specialized and concerned associates, PAKSHOO Co. is currently being known as

a pioneer and innovative company in detergent industries in Iran and can be mentioned as the biggest and

most reputable companies of Iranian private sectors in the line of detergents, sanitary ware and cosmetics.

The products of this company, namely Golrang, AVE, Softlan, Homeplus, Cloritex and Merit have a

special position in Iranian market and for Iranian consumers, and have found their way into the market of

other countries in the world at the same time.

From its early year's products, we may refer to dishwashing liquid, whitening, towel & clothes softener,

window cleanser, detergents, carpet shampoo, to which some types of powder, shampoo, hand-washing

liquid, toothpaste, hair softener in various colors and packages were added, in the later years through

extended efforts. Pakshoo defines the quality of production as the satisfaction of consumers and considers

it as the key to its success.

Methodology Knowledge worker in Pakshoo organization is somebody who has minimum B.S. with one year work

experience in organization in right job& right position that organization required.

Formulation of knowledge worker's salary: Premium + main salary = total salary

Main salary = A* (B + C + D + E + F + G + H) * M

A: Organization class or guild

B: Level of academic degree

C: Relation between academic degree and job

D: Important of university for academic education

E: Norm/average of last academic degree

F: Precedence of work in and out of organization (correlate and uncorrelated) (Table 4)

G: Grant of education courses meanwhile work correlate with job and position

H: Grant of academic potency/ability and effectiveness of person

Table4. Grants of Job Experience Grant for each year record of work out of firm Grant for each year record of work in firm

Uncorrelated Correlate after 5

years

Correlate in first

5 years Uncorrelated

Correlate after 5

years

Correlate in first

5 years

Grants

10 100 50 20 200 100 PHD

8 70 35 16 150 70 MS/GP

6 60 30 12 120 50 BS

M: A coefficient that recommends by HR manager and nominate by MD of firm every year

Notice: the TOTAL GRANT for H is 4000 and Maximum grant for a person is 10000. In the table 5 there

is explanation of job titles and job level, minimum grant, etc.

Minimum grant and work criteria (Table 5):

Experience

(Year)

Second

case

Experience

(Year)

First case

(degree)

Minimum

grant Job level Job title

8 MS/GP 4 PHD 10000 1 Member of board

6 MS/GP 2 PHD 8000 2 Senior researcher/unquestionable

10 BS 3 MS/GP 6000 3 Researcher I

8 BS 2 MS/GP 5000 4 Researcher II

6 BS 1 MS/GP 4000 5 Senior expert

3 BS - MS/GP 3000 6 Expert I

-- --- 2 BS 2000 7 Expert II

Frame of specialty grant Different parameters that grant are given:

1- Any independent applicable project (A, B and C classes)

� Grant is distribute between these parameters :

� Design of formulation

� experimental work

� Primary packaging

� Secondary packaging

� color

� Fragrance/ flavor

� Analysis

2- Any successful/useful recommend or improving the current products

3- Any creative idea that helps for progress of firm

4- Any article as oral presentation or poster in conferences

5- Any published article in journals

6- Any presented academic report on academic meeting

7- Any report that accepted by department manager

Different kinds of projects that is important: 1- Create new product

2- Review of current product (Improving of current product)

3- Create a new analysis method

4- Codify the standard of products

5- Codify the standard of packaging material

6- Designing packaging and supervision for its implementation

Distribution of grant between members of a project : 50 – 70 % for head of project

30 – 10% for rest

Maximum grant for different projects: 1- 100% of grant for projects that finalized lab , pilot plant and mass production steps and manufacture of

product is going.

2- 80% of grant for projects that finalized lab , pilot plant and mass production steps but manufacture of

product is not going.

3- 60% 0f grant for projects that finalized lab& pilot plant but mass production is not done.

Grant distribution for papers: - 100 for presented paper in international conferences as oral

- 100 for presented paper in international conferences as poster

- 50 for presented paper in national conferences

- 100 for published paper in international journals

- 40 for published paper in national journals (compilation, essay)

- 20 for published paper in national journals (translate)

* If the team is 2 people: 60% of grant is belonging for main author/writer and 40% is for further

** If the team is 3 people: 50% of grant is belonging for main author/writer and 30% and 20 % are for

subsequent.

Deployment of model 1- Creation expert committee for evaluation of performance & quality indexes of knowledge workers

2- Creation general committee for evaluation of general & quantity indexes of knowledge workers

DOCUMENTATION We prepare a form and distributed between knowledge workers to fill it for keeping the records for

parameters that were indexes for evaluation.

Results For deploying this model, organization must change organizational chart of R&D and define new jobs

with new parameters. The results of this evaluation finalized as illustrated in table 6.

Table6. Results of R&D evaluaton Equal Organization Level Result Percentage of total employee

Researcher I 7.7 Intermediate Manager

Researcher II 7.7

Administrator Senior expert 38.5

Expert I 7.7

Expert II 31

Expert

Normal expert 7.7

Lessons learned

1. Key points: 1. It is very important to prepare and involve employee before and when the plan is running

2. In each case some of employee would be dissatisfied

3. Leader team has a key role in these kinds of projects

4. Organization must committed to this plan all over the plan

5. It must assess and review continually

6. Accuracy of data is very important and has a critical role in these kinds of projects

7. Knowledge workers Evaluation Models are unique for each organization. Although

experience of past projects can help teams for next projects but in each organization and

each culture a special model can be effective.

2. Outcomes: 1. Defining employee evaluation measurement

2. Aligning objectives of organization and individuals

3. Increase job satisfaction

4. Clearing situation of individuals for organization and themselves

5. Define career path

References:

1 Wikipedia, the free encyclopedia, Knowledge worker

2 Haag, S., Cummings, M., McCubbrey, D., Pinsonneault, A., & Donovan, R. (2006). Management

Information Systems For the Information Age (3rd Canadian Ed.). Canada: McGraw Hill Ryerson

3 Evans J.R., and W.M. Lindsay, The Management And Control Of Quality, West Publishing Company,

1993

4 Barber, Luke, and Matt Weinstein, Work Like Your Dog: Fifty Ways to Work Less, Play More, and

Earn More, Villard, 1999

5 Jones, Erick C, A Methodology for Measuring Engineering Knowledge Worker Productivity,

Publication: Engineering Management Journal, March 1 2006

6 Wikipedia, the free encyclopedia, Knowledge worker

7 Beverly E. Thomas and John P. Baron, USACERL Interim Report FF-94/27, Evaluating Knowledge

Worker Productivity: Literature Review, June 1994

8 Beverly E. Thomas and John P. Baron, USACERL Interim Report FF-94/27, Evaluating Knowledge

Worker Productivity: Literature Review, June 1994

9 Beverly E. Thomas and John P. Baron, USACERL Interim Report FF-94/27, Evaluating Knowledge

Worker Productivity: Literature Review, June 1994

10 Salemme, Tom, "Measuring White Collar Work," White Collar Productivity Improvement, American

Productivity Center, 1986, pp 1524.

11 www.kmworld.com/Articles

12 Doug Johnson ,Skills for the Knowledge Worker, Teacher-Librarian, December 2005

14 Kit Sims Taylor, The Brief Reign of the Knowledge Worker: Information Technology and

Technological Unemployment

15 The Same

16 Robert Austin, Managing Knowledge Workers, United States, 21 July 2006

17 www.Gurteen.com

18 Jones, Erick C, A Methodology for Measuring Engineering Knowledge Worker Productivity,

Publication: Engineering Management Journal, March 1 2006