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Lesson#21
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Collecting and Analyzing Diagnostic information-1
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Collecting and Analyzing Diagnostic information
Table 4:
A Comparison of Different Methods of Data Collection
A Comparison of Different Methods of Data Collection
Method Major Advantages Major Potential Problems
Questionnaires
•
Responses can be quantified
and easily summarized
• Easy to use with large
samples
• Relatively inexpensive
• Can obtain large volume
of
data
• non-empathy
• Predetermined
questions/missing issues
• Over-interpretation of
data
• Response bias
Interviews •
adaptive-allows data
collection on a range of
possible subjects
1. Source of “rich”
data
2. Empathic
3. Process of
interviewing
can build rapport
• Expense
• Bias in interviewer
responses
• coding and
interpretation
difficulties
• self-report bias
Observations •
collects data on behavior,
rather than reports of
behavior
• Real time, not
retrospective
• Adaptive
• coding and
interpretation
difficulties
• Sampling inconsistencies
• Observer bias and
questionable reliability
• Expense
Unobtrusive measures •
Non-reactive- no response
bias
• High face validity
• Easily quantified
• Access and retrieval
difficulties
• Validity concerns
• Coding and
interpretation
difficulties
Sampling:
Before discussing how to analyze data, the issue of sampling
needs to he emphasized. Application of the
different data-collection techniques invariably raises the
following questions: “How many people should be
interviewed and who should they be?” “What events should be
observed and how many?” “How many
records should be inspected and which ones?”
Sampling is not an issue in many OD cases. Because practitioners
collect interview or questionnaire data
from all members of the organization or department in question,
they do not have to worry about whether
the information is representative of the organization or unit.
Sampling becomes an issue in OD, however, when data are
collected from selected members, behaviors, or
records. This is often the case when diagnosing
organization-level issues or large systems. In these cases, it
may be important to ensure that the sample of people, behaviors,
or records adequately represents the
characteristics of the total population. For example, a sample
of fifty employees might be used to assess the
perceptions of all three hundred members of a department.
A
sample of production data might be used to
evaluate the total production of a work group. OD practitioners
often find that it is more economical and
quicker to gather a sampling of diagnostic data than to collect
all possible information. If done correctly,
the sample can provide useful and valid information about the
entire organization or unit.
Sampling design involves considerable technical detail, and
consultants may need to become familiar with
basic references in this area or to obtain professional help.
The first issue to address is sample size, or how
many people, events, or records are needed to carry out the
diagnosis or evaluation. This question has no
simple answer: the necessary sample size is a function of
population size, the confidence desired in the
quality of the data, and the resources (money and time)
available for data collection.
First, the larger the population (for example, number of
organization members or total number of work
outcomes) or the more complex the client system (for example,
the number of salary levels that must he
sampled or the number of different functions), the more
difficult it is to establish a “right” sample size. As
the population increases in size and complexity, the less
meaning one can attach to simple measures, such
as an overall average score on a questionnaire item. Because the
population comprises such different types
of people or events, more data are needed to ensure an accurate
representation of the potentially different
subgroups. Second, the larger the proportion of the population
that is selected, the more confidence one
can have about the quality of the sample. If the diagnosis
concerns an issue of great importance to the
organization, then extreme confidence may be needed, indicative
of a very large sample size. Third, limited
resources constrain sample size. If resources are limited but
the required confidence is high, then
questionnaires will be preferred over interviews because more
information can be collected per member per
dollar.
The second issue to address is sample selection. Probably the
most common approach to sampling
diagnostic data in OD is a simple random sample, in which each
member, behavior, or record has an equal
chance of being selected. For example, assume that an OD
practitioner would like to select fifty people
randomly out of the three hundred employees at a manufacturing
plant. Using a complete list of all three
hundred employees, the consultant can generate a random sample
in one of two ways. The first method is
to use a random number table printed in the back of almost any
statistics text; the consultant would pick
out the employees corresponding to the first fifty numbers under
three hundred beginning anywhere in the
table. The second method is to pick every sixth name (300/50 =
6) starting anywhere in the list.
If the population is complex or many subgroups need to be
represented in the sample, a stratified sample
may be more appropriate than a random one. In a stratified
sample, the population of members, events, or
records is segregated into a number of mutually exclusive
subpopulations and a random sample is taken
from each subpopulation. For example, members of an organization
might be divided into three groups
(managers, white-collar workers, and blue-collar workers), and a
random sample of members, behaviors, or
records could be selected from each grouping to reach diagnostic
conclusions about each of the groups.
Adequate sampling is critical to gathering valid diagnostic
data, and the OD literature has paid little
attention to this issue. OD practitioners should gain
rudimentary knowledge in this area and use
professional help if necessary.
The Implementation of Data Collection:
Data collection begins with a decision about who to obtain data
from and how many respondents there
should be. The use of interviews may limit the number of
respondents, whereas the use of a questionnaire
may increase the number. Data should be collected from several
levels and departments in the organization,
but different questions may be needed for each of them. The
results of a survey of OD practitioners about
the methods they use to gather data are reported by Burke, Lark,
and Koopman. The one-to-one interview
is the most common data-gathering method, used by 87 percent of
the respondents. Other methods
include observation (60) percent), group interviews (52
percent), questionnaires (45 percent), and existing
documents (37 percent).The survey also shows that practitioners
normally rely on a variety of datagathering
methods.
Once an appropriate technique has been selected, the actual
data-collection program must be
accomplished. This includes the operational aspects of
designing, printing, distributing, and collecting the
data-collection instrument. Outside data-collection agents are
more effective than internal personnel. The
use of outside data-collection agents is recommended because it
apparently makes respondents feel more
secure and trusting that candid answers will not be used against
them. There are companies that develop
data-collection instruments, test them and make them available
commercially. The disadvantage is that such
instruments may be too generalized and not focused enough for a
specific organization to get reliable and
useful data.
Once again, confidentiality of data is a critical issue. A small
pilot study or beta test of the data-collection
instrument is also a good idea. This should include a practice
analysis before the large-scale data collection
begins to ensure that every possible problem is corrected.
The Analysis of Data:
The techniques for analyzing data vary from relatively
straightforward, simple methods to highly
sophisticated statistical techniques. Several important
questions must be considered before a data-collecting
method is selected: How are the data to be analyzed? Are they to
be analyzed statistically, and if so, what
type of analysis is to be used? Will the data be processed by
hand or by computer? Will they be coded, and
if so how? These questions must be taken into account prior to
data collection so that the data can be used
to draw inferences and conclusions. This is especially true with
large-scale surveys or interviews, because
the large amount of data makes processing a difficult task. The
analysis may include comparisons of
different divisions within the organization. Management levels
can also be compared. To make
comparisons, however, it is necessary to properly code the
surveys or interviews. “If you can’t measure it,
you can’t control it,” says Meg Whitman, CEO of eBay Inc.
Evaluating the Effectiveness of Data Collection:
A systematic data-collection program has to establish some
criteria for how well the data meet the
objectives in terms of quantity and quality. Obviously, the
sample has to be large enough sample to enable
generalization of results. The accuracy of the data, that is,
the degree to which the data deviate from the
truth, is also an important factor.
A number of criteria may be used to compare data-collection
techniques. There is necessarily a trade-off
between data quantity and accuracy, on the one hand, and
collection cost and time spent collecting, on the
other. Naturally the practitioner wants to obtain the best
available data that can be generated within the
given cost and time constraints. The following criteria lay out
some guidelines.
The Validity of the Data:
Probably the most important question is: Are we measuring and
collecting data on the dimensions that we
intend to measure? OD programs frequently have to deal with
difficult subjective parameters such as
attitudes and values.
The Time to Collect Data:
How long will it take to gather the data using any given
technique? How much time is available? Experience
suggests that data collection usually takes longer than planned.
The Cost of Data Collection:
How much do the data cost? A large-scale interviewing program
costs a great deal of time and money. The
practitioner and the client must determine how much money can be
spent in the data-gathering stage. They
should also consider the problem of diminishing returns: What is
the minimum number of interviews
needed for a reliable measure?
The Organization Culture and Norms:
The practitioner has to decide what techniques are best suited
to a given organization’s culture and will
yield the most valid data given these constraints. For example:
Are people likely to be open and candid, or
hidden and resistant? Does the climate call for open
confrontation and questions or a more indirect form
of data gathering?
The Hawthorne Effect in Data Collecting:
One of the most difficult factors to eliminate is the so-called
Hawthorne effect—
the
effect the observer
has on the subject. The very act of investigating and observing
may influence the behavior of those being
investigated.
One characteristic of successful change programs is that they
gather data about organizational problems
before initiating a change effort. An effective data-collection
process enables the change effort to focus on
specific problems rather than rely upon a generalized program.
The data-collection stage provides managers
and organization members with hard data that can he compared
with intuitive, subjective problem
awareness.
Techniques for Analyzing Data:
Data analysis techniques fall into two broad classes:
qualitative and quantitative. Qualitative techniques
generally are easier to use because they do not rely on
numerical data. That fact also makes them easier to
understand and interpret. Quantitative techniques, on the other
hand, can provide more accurate readings
of the organizational problem.
Qualitative Tools:
Of the several methods for summarizing diagnostic data in
qualitative terms, two of the most important are
content analysis and force-field analysis.
Content Analysis:
A popular technique for assessing qualitative data, especially
interview data, is content analysis, which
attempts to summarize comments into meaningful categories. When
done well, a content analysis can
reduce hundreds of interview comments into a few themes that
effectively summarize the issues or
attitudes of a group of respondents. The process of content
analysis can be quite formal, and specialized
references describe this technique in detail. In general,
however, the process can be broken down into three
major steps.
First, responses to a particular question are read to gain
familiarity with the range of comments made and
to determine whether some answers are occurring over and over
again.
Second, based on this sampling of comments, themes are generated
that capture recurring comments.
Themes consolidate different responses that say essentially the
same thing. For example, in answering the
question “What do you like most about your job?” different
respondents might list their co-workers, their
supervisors, the new machinery, and a good supply of tools. The
first two answers concern the social
aspects of work, and the second two address the resources
available for doing the work.
Third, the respondents’ answers to a question are then placed
into one of the categories. The categories
with the most responses represent those themes that are most
often mentioned.
Force-Field Analysis:
A second method for analyzing qualitative data in OD derives
from Kurt Lewin’s three-step model of
change. Called force-field analysis, this method organizes
information pertaining to organizational change
into two major categories: forces for change and forces for
maintaining the status quo or resisting change.
Using data collected through interviews, observation, or
unobtrusive measures, the first step in conducting
a force-field analysis is to develop a list of all the forces
promoting change and all those resisting it. Then,
based either on the OD practitioner’s personal belief or perhaps
on input from several members of the
client organizations a determination is made of which of the
positive and which of the negative forces are
most powerful. One can either rank the order or rate the
strength of the different forces.
Figure 27 illustrates a force-field analysis of the performance
of a work group. The arrows represent the
forces, and the length of the arrows corresponds to the strength
of the forces. The information could have
been collected in a group interview in which members were asked
to list those factors maintaining the
current level of group performance and those factors pushing for
a higher level. Members also could have
been asked to judge the strength of each force, with the average
judgment shown by the length of the
arrows.
This analysis reveals two strong forces pushing for higher
performance: pressures from the supervisor of
the group and competition from other work groups performing
similar work. These forces for change are
offset by two strong forces for maintaining the status quo:
group norms supporting present levels of
performance and well-learned skills that are resistant to
change. According to Lewin, efforts to change to a
higher level of group performance shown by the darker band in
Figure 27 should focus on reducing the
forces maintaining the status quo. This might entail changing
the group’s performance norms and helping
members to learn new skills. The reduction of forces maintaining
the status quo is likely to result in
organizational change with little of the tension or conflict
typically accompanying change caused by
increasing the forces for change.
Figure 27: Force-Field Analysis of Work Group
Performance:
An example of how force-field analysis can be used may be
helpful. The general manager of a hospital
employing 300 workers and her immediate subordinates identified
the 6 percent daily absentee rate as an
area of concern. They determined that a 3 percent absentee rate
would be much more acceptable. In other
words, they found a “performance gap.” After going over the
survey results with the OD practitioner, it
was decided to use force-field analysis to gain an improved
diagnosis of this problem. In a brainstorming
session, the work team listed all of the forces tending to
restrain and increase absenteeism. (figure28)
The managers made the length of the arrows proportionate to the
strength of the forces. They had a choice
of several strategies to reduce the performance gap. They could
decrease the strength of the restraining
forces; increase the strength of the driving forces, or a
combination of both. Generally, if the forces that
put pressure on people (such as fear of losing their job) are
increased, the tension within the system will
also increase, possibly bringing about stronger resistance and
unpredictable behavior, It is often better to
increase forces that do not put pressure on people (for
instance, a promotion policy that is more closely
tied to an employee’s absentee rate), to reduce restraining
forces, or to add new driving forces.
Fig 28: Example of the Use of Force-Field Analysis
Quantitative Tools:
Methods for analyzing quantitative data range from simple
descriptive statistics of items or scales from
standard instruments to more sophisticated, multivariate
analysis of the underlying instrument properties
and relationships among measured variables. The most common
quantitative tools are means, standard
deviations, frequency distributions, scattergrams, correlation
coefficients, and difference tests. These
measures are routinely produced by most statistical computer
software packages. Therefore, mathematical
calculations are not discussed here.
Means, Standard Deviations, and Frequency Distributions:
One of the most economical and straightforward ways to summarize
quantitative data is to compute a
mean and standard deviation for each item or variable measured.
These represent the respondents’ average
score and the spread or variability of the responses,
respectively. These two numbers easily can be
compared across different measures or subgroups. For example,
Table 5 shows the means and standard
deviations for six questions asked of one hundred employees
concerning the value of different kinds of
organizational rewards. Based on the five-point scale ranging
from one (very low value) to five (very high
value), the data suggest that challenging work and respect from
peers are the two most highly valued
rewards. Monetary rewards, such as pay and fringe benefits, are
not as highly valued.
Table 5. Descriptive Statistics of Value of Organizational
Rewards.
Descriptive Statistics of Value of Organizational Rewards
Organizational Rewards Mean Standard Deviation
Challenging work
Respect from peers
Pay
Praise from supervisor
Promotion
Fringe benefits
4.6
4.4
4.0
4.0
3.3
2.7
0.79
0.81
0.71
1.55
0.95
1.14
Number of respondents = 100
1 = very low value, 5 = very high value
But the mean can be a misleading statistic. It only describes
the average value and thus provides no
information on the distribution of the responses. Different
patterns of responses can produce the same
mean score. Therefore, it is important to use the standard
deviation along with the frequency distribution
to gain a clearer understanding of the data. The Frequency
distribution is a graphical method for displaying
data that shows the number of times a particular response was
given. For example, the data in Table 5
suggest that both pay and praise from the supervisor are equally
valued with a mean of 4.0. However, the
standard deviations for these two measures are very different at
0.71 and 1.55, respectively. Table 6 shows
the frequency distributions of the responses to the questions
about pay and praise from the supervisor.
Employees’ responses to the value of pay are distributed toward
the higher end of the scale, with no one
rating it of low or very low value. In contrast, responses about
the value of praise from the supervisor fall
into two distinct groupings: twenty-five employees felt that
supervisor praise has a low or very low value,
whereas seventy-five people rated it high or very high. Although
both rewards have the same mean value,
their standard deviations and frequency distributions suggest
different interpretations of the data.
Table 6: Frequency Distribution of Responses to “Pay” and praise
from Supervisor” items.
Frequency Distributions of Responses to “Pay” and “praise from
Supervisor” Items
Pay (Mean = 4.0)
Response Number checking each response Graph
(1) Very low vlue
(2) Low value
(3) Moderate value
(4) High value
(5) Very high value
0
0
25
50
25
Xxxxx
Xxxxxxxxxx
xxxxx
Praise from Supervisor (Mean = 4.0)
Response Number checking each response Graph
(1) Very low value
(2) Low value
(3) Moderate value
(4) High value
(5) Very high value
15
10
0
10
65
Xxx
Xx
Xx
xxxxxxxxxxxx
In general, when the standard deviation for a set of data is
high, there is considerable disagreement over the
issue posed by the question if the standard deviation is small;
the data are similar on a particular measure.
In the example described above, there is disagreement over the
value of supervisory praise (some people
think it is important but others do not), but there is fairly
good agreement that pay is a reward with high
value.
Scatter grams and Correlation Coefficients:
In addition to describing data, quantitative techniques also
permit OD consultants to make inferences
about the relationships between variables. Scattergrams and
correlation coefficients are measures of the
strength of a relationship between two variables. For example,
suppose the problem being faced by an
organization is increased conflict between the manufacturing
department and the engineering design
department. During the data-collection phase, information about
the number of conflicts and change
orders per month over the past year is collected. The data are
shown in Table 7and plotted in a
Scattergrams in Fig 29.
Table 7: Relationship between Change Orders and Conflicts
Relationship Between Change Orders and Conflicts
Month Number of Change Orders Number of Conflicts
April
May
June
July
August
September
October
November
December
January
February
March
5
12
14
6
8
20
10
2
15
8
18
10
5
4
3
2
3
5
2
1
4
3
4
5
A Scattergram is a diagram that visually displays the
relationship between two variables; it is constructed by
locating each case (person or event) at the intersection of its
value for each of the two variables being
compared. For example, in the month of August, there were eight
change orders and three conflicts, whose
intersection is shown on Figure 29as an X.
Three basic patterns can emerge from a Scattergram, as shown in
Fig 30. The first pattern is called a
positive relationship because as the values of x increase, so do
the values of y. The second pattern is called
a negative relationship because as the values of x increase, the
values of y decrease. Finally, there is the
“shotgun” pattern wherein no relationship between the two
variables is apparent. In the example shown in
Figure 29, an apparently strong positive relationship exists
between the number of change orders and the
number of conflicts between the engineering design department
and the manufacturing department. This
suggests that change orders may contribute to the observed
conflict between the two departments.
Figure 29: Scattergram of change order versus conflict
Figure 30: Basic Scattergram Patterns
The correlation coefficient is simply a number that summarizes
data in a scattergram. Its value ranges
between +1.0 and -1.0. A correlation coefficient of +1.0 means
that there is a perfect, positive relationship
between two variables, whereas a correlation of -1.0 signifies a
perfectly negative relationship. A correlation
of 0 implies a “shotgun” scattergram where there is no
relationship between two variables.
Difference Tests:
The final technique for analyzing quantitative data is the
difference test. It can be used to compare a sample
group against some standard or norm to determine whether the
group is above or below that standard. It
also can be used to determine whether two samples are
significantly different from each other. In the first
case, such comparisons provide a broader context for
understanding the meaning of diagnostic data. They
serve as a “basis for determining ‘how good is good or how bad
is bad.” Many standardized questionnaires
have standardized scores based on the responses of large groups
of people. It is critical, however, to choose
a comparison group that is similar to the organization being
diagnosed. For example, if one hundred
engineers take a standardized attitude survey, it makes little
sense to compare their scores against standard
scores representing married males from across the country. On
the other hand, industry-specific data are
available; a comparison of sales per employee (as a measure of
productivity) against the industry average
would be valid and useful.
The second use of difference tests involves assessing whether
two (or more) groups differ from one
another on a particular variable, such as job satisfaction or
absenteeism. For example, job satisfaction
differences between an accounting department and a sales
department can be determined with this tool.
Given that each group took the same questionnaire, their means
and standard deviations can be used to
compute a difference score (t-score or z-score) indicating
whether the two groups are statistically different.
The larger the difference score relative to the sample size and
standard deviation for each group, the more
likely that one group is more satisfied than the other.
Difference tests also can be used to determine whether a group
has changed its score on job satisfaction or
some other variable over time. The same questionnaire can be
given to the same group at two points in
time. Based on the group’s means and standard deviations at each
point in time, a difference score can be
calculated. The larger the score, the more likely that the group
actually changed its job satisfaction level.
The calculation of difference scores can be very helpful for
diagnosis but requires the OD practitioner to
make certain assumptions about how the data were collected,
these assumptions are discussed in most
standard statistical texts, and OD practitioners should consult
them before calculating difference scores for
purposes of diagnosis or evaluation.
Feeding Back Diagnostic Information:
Perhaps the most important step in the diagnostic process is
feeding back diagnostic information to the
client organization. Although the data may have been collected
with the client’s help, the OD practitioner
usually is responsible for organizing and presenting them to the
client. Properly analyzed and meaningful
data can have an impact on organizational change only if
organization members can use the information to
devise appropriate action plans. A key objective of the feedback
process is to be sure that the client has
ownership of the data.
As shown in Figure 31, the success of data feedback depends
largely on its ability to arouse organizational
action and to direct energy toward organizational problem
solving. Whether feedback helps to energize the
organization depends on the content of the feedback data and on
the process by which they are fed back to
organization members.
We now discuss criteria for developing both the content of
feedback information and the processes for
feeding it back. If these criteria are overlooked, the client is
not apt to feel ownership of the problems
facing the organization. A flexible and potentially powerful
technique for data feedback that has arisen out
of the wide use of questionnaires in OD work is known as survey
feedback. Its central role in many largescale
on efforts warrants a special look.
Figure 31: Possible Effects of Feedback
Determining the Content of the Feedback:
In the course of diagnosing the organization, a large amount of
data is collected. In fact, there is often more
information than the client needs or could interpret in a
realistic period of time. If too many data are fed
back, the client may decide that changing is impossible.
Therefore, OD practitioners need to summarize the
data in ways that enable clients to understand the information
and draw action implications from it. The
techniques for data analysis described earlier can inform this
task. Additional criteria for determining the
content of diagnostic feedback are described below.
Several characteristics of effective feedback data have been
described in the literature. They include the
following nine properties:
1.
Relevant
.
Organization members are likely to use feedback data for problem solving when
they
find the information meaningful. Including managers and
employees in the initial data-collection
activities can increase the relevance of the data.
2.
Understandable.
Data must be presented to organization
members in a form that is readily
interpreted. Statistical data, for example, can be made
understandable through the use of graphs
and charts.
3.
Descriptive.
Feedback data need to be linked to
real organizational behaviors if they are to arouse
and direct energy. The use of examples and detailed
illustrations can help employees gain a better
feel for the data.
4.
Verifiable.
Feedback data should be valid and accurate
if they are to guide action. Thus, the
information should allow organization members to verify whether
the findings really describe
the organization. For example, questionnaire data might include
information about the sample of
respondents as well as frequency distributions for each item or
measure. Such information can help
members verify whether the feedback data accurately represent
organizational events or attitudes.
5.
Timely.
Data should be fed back to members as
quickly as possible after being collected and
analyzed. This will help ensure that the information is still
valid and is linked to members’
motivation to examine it.
6.
Limited.
Because people can easily become
overloaded with too much information, feedback data
should be limited to what employees can realistically process at
one time.
7.
Significant.
Feedback should be limited to those
problems that organization members can do
something about because it will energize them and help direct
their efforts toward realistic changes.
8.
Comparative.
Feedback data can be ambiguous without
some benchmark as a reference.
Whenever possible, data from comparative groups should be
provided to give organization
members a better idea of how their group fits into a broader
context.
9.
Un-finalized.
Feedback is primarily a stimulus for
action and thus should spur further diagnosis
and problem solving. Members should be encouraged, for example,
to use the data as a starting
point for more in-depth discussion of organizational issues.
Characteristics of the Feedback Process:
In addition to providing effective feedback data, it is equally
important to attend to the process by which
that information is fed back to people. Typically, data are
provided to organization members in a meeting
or series of meetings. Feedback meetings provide a forum for
discussing the data, drawing relevant
conclusions, and devising preliminary action plans. Because the
data might include sensitive material and
evaluations about organization members’ behaviors, people may
come to the meeting with considerable
anxiety and fear about receiving the feedback. This anxiety can
result in defensive behaviors aimed at
denying the information or providing rationales. More
positively, people can be stimulated by the feedback
and the hope that desired changes will result from the feedback
meeting.
Because people are likely to come to feedback meetings with
anxiety, fear, and hope, OD practitioners need
to manage the feedback process so that constructive discussion
and problem solving occur. The most
important objective of the feedback process is to ensure that
organization members own the data.
Ownership is the opposite of resistance to change and refers to
people’s willingness to take responsibility
for the data, their meaning, and the consequences of using them
to devise a change strategy. If the feedback
session results in organization members rejecting the data as
invalid or useless, then the motivation to
change is lost and members will have difficulty engaging in a
meaningful process of change.
Ownership of the feedback data is facilitated by the following
five features of successful feedback
processes:
1.
Motivation to
work with the data.
People need to
feel that working with the feedback data
will have beneficial outcomes. This may require explicit
sanction and support from powerful
groups so that people feel free to raise issues and to identify
concerns during the feedback
sessions. If people have little motivation to work with the data
or feel that there is little chance
to use the data for change, then the information will not be
owned by the client system.
2.
Structure for
the meeting.
Feedback meetings need
some structure or they may degenerate
into chaos or aimless discussion. An agenda or outline and a
discussion leader can usually
provide the necessary direction. If the meeting is not kept on
track, especially when the data
are negative, ownership can be lost in conversations that become
too general. When this
happens, the energy gained from dealing directly with the
problem is lost.
3.
Appropriate
attendance.
Generally, people who have
common problems and can benefit
from working together should be included in the feedback
meeting. This may involve a fully
intact work team or groups comprising members from different
functional areas or
hierarchical levels. Without proper representation in the
meeting, ownership of the data is lost
because participants cannot address the problem(s) suggested by
the feedback.
4.
Appropriate
power.
It is important to clarify the
power possessed by the group. Members
need to know on which issues they can make necessary changes, on
which they can only
recommend changes, and over which they have no control. Unless
there are clear boundaries,
members are likely to have some hesitation about using the
feedback data for generating action
plans. Moreover, if the group has no power to make changes, the
feedback meeting will
become an empty exercise rather than a real problem-solving
session. Without the power to
address change, there will be little ownership of the data.
5.
Process help.
People in feedback meetings require
assistance in working together as a group.
When the data are negative, there is a natural tendency to
resist the implications, deflect the
conversation onto safer subjects, and the like. An OD
practitioner with group process skills
can help members stay focused on the subject and improve
feedback discussion, problem
solving, and ownership.
When combined with effective feedback data, these features of
successful feedback meetings enhance
member ownership of the data. They help to ensure that
organization members fully discuss the
implications of the diagnostic information and that their
conclusions are directed toward relevant and
feasible organizational changes.
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