O.D . AND SOME OF ITS PRACTICAL TECHNIQUES

ORGANIZATION DEVELOPMENT
the management of change

Robert H. Rouda & Mitchell E. Kusy, Jr.

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(C) copyright 1995 by the Technical Association of the Pulp and Paper Industry.

This is the third in a series of articles which originally appeared in Tappi Journal in 1995-96, to introduce methods addressing the development of individuals and organizations through the field of Human Resource Development. (The article has been updated, and is reproduced with permission of the copyright owner.)

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WHAT IS OD?

Beckhard (1) defines Organization Development (OD) as "an effort, planned, organization-wide, and managed from the top, to increase organization effectiveness and health through planned interventions in the organization's processes, using behavioral-science knowledge." In essence, OD is a planned system of change.

• Planned. OD takes a long-range approach to improving organizational performance and efficiency. It avoids the (usual) "quick-fix".
• Organization-wide. OD focuses on the total system.
• Managed from the top. To be effective, OD must have the support of top-management. They have to model it, not just espouse it. The OD process also needs the buy-in and ownership of workers throughout the organization.
• Increase organization effectiveness and health. OD is tied to the bottom-line. Its goal is to improve the organization, to make it more efficient and more competitive by aligning the organization's systems with its people.
• Planned interventions. After proper preparation, OD uses activities called interventions to make systemwide, permanent changes in the organization.
• Using behavioral-science knowledge. OD is a discipline that combines research and experience to understanding people, business systems, and their interactions.

We usually think of OD only in terms of the interventions themselves. This article seeks to emphasize that these activities are only the most visible part of a complex process, and to put some perspective and unity into the myriad of OD tools that are used in business today. These activities include Total Quality Management (an evolutionary approach to improving an organization) and Reengineering (a more revolutionary approach). And there are dozens of other interventions, such as strategic planning and team building. It is critical to select the correct intervention(s), and this can only be done with proper preparation.

WHY DO OD?

• Human resources -- our people -- may be a large fraction of our costs of doing business. They certainly can make the difference between organizational success and failure. We better know how to manage them.
• Changing nature of the workplace. Our workers today want feedback on their performance, a sense of accomplishment, feelings of value and worth, and commitment to social responsibility. They need to be more efficient, to improve their time management. And, of course, if we are to continue doing more work with less people, we need to make our processes more efficient.
• Global markets. Our environments are changing, and our organizations must also change to survive and prosper. We need to be more responsible to and develop closer partnerships with our customers. We must change to survive, and we argue that we should attack the problems, not the symptoms, in a systematic, planned, humane manner.
• Accelerated rate of change. Taking an open-systems approach, we can easily identify the competitions on an international scale for people, capital, physical resources, and information.

WHO DOES OD?

To be successful, OD must have the buy-in, ownership, and involvement of all stakeholders, not just of the employees throughout the organization. OD is usually facilitated by change agents -- people or teams that have the responsibility for initiating and managing the change effort. These change agents may be either employees of the organization (internal consultants) or people from outside the organization (external consultants.)

Effective change requires leadership with knowledge, and experience in change management. We strongly recommend that external or internal consultants be used, preferably a combination of both. ("These people are professionals; don't try this at home.")

Bennis (2) notes that "external consultants can manage to affect ... the power structure in a way that most internal change agents cannot." Since experts from outside are less subject to the politics and motivations found within the organization, they can be more effective in facilitating significant and meaningful changes.

WHEN IS AN ORGANIZATION READY FOR OD?

There is a formula, attributed to David Gleicher (3, 4), which we can use to decide if an organization is ready for change:

Dissatisfaction x Vision x First Steps > Resistance to Change

This means that three components must all be present to overcome the resistance to change in an organization: Dissatisfaction with the present situation, a vision of what is possible in the future, and achievable first steps towards reaching this vision. If any of the three is zero or near zero, the product will also be zero or near zero and the resistance to change will dominate.

We use this model as an easy, quick diagnostic aid to decide if change is possible. OD can bring approaches to the organization that will enable these three components to surface, so we can begin the process of change.

OD IS A PROCESS

Action Research is a process which serves as a model for most OD interventions. French and Bell (5) describe Action Research as a "process of systematically collecting research data about an ongoing system relative to some objective, goal, or need of that system; feeding these data back into the system; taking actions by altering selected variables within the system based both on the data and on hypotheses; and evaluating the results of actions by collecting more data." The steps in Action Research are (6, 7):

1. Entry. This phase consists of marketing, i.e. finding needs for change within an organization. It is also the time to quickly grasp the nature of the organization, identify the appropriate decision maker, and build a trusting relationship.
2. Start-up and contracting. In this step, we identify critical success factors and the real issues, link into the organization's culture and processes, and clarify roles for the consultant(s) and employees. This is also the time to deal with resistance within the organization. A formal or informal contract will define the change process.
3. Assessment and diagnosis. Here we collect data in order to find the opportunities and problems in the organization (refer to DxVxF>R above.) For suggestions about what to look for, see the previous article in this series, on needs assessment (8). This is also the time for the consultant to make a diagnosis, in order to recommend appropriate interventions.
4. Feedback. This two-way process serves to tell those what we found out, based on an analysis of the data. Everyone who contributed information should have an opportunity to learn about the findings of the assessment process (provided there is no apparent breach of anyone's confidentiality.) This provides an opportunity for the organization's people to become involved in the change process, to learn about how different parts of the organization affect each other, and to participate in selecting appropriate change interventions.
5. Action planning. In this step we will distill recommendations from the assessment and feedback, consider alternative actions and focus our intervention(s) on activities that have the most leverage to effect positive change in the organization. An implementation plan will be developed that is based on the assessment data, is logically organized, results- oriented, measurable and rewarded. We must plan for a participative decision-making process for the intervention.
6. Intervention. Now, and only now, do we actually carry out the change process. It is important to follow the action plan, yet remain flexible enough to modify the process as the organization changes and as new information emerges.
7. Evaluation. Successful OD must have made meaningful changes in the performance and efficiency of the people and their organization. We need to have an evaluation procedure to verify this success, identify needs for new or continuing OD activities, and improve the OD process itself to help make future interventions more successful.
8. Adoption. After steps have been made to change the organization and plans have been formulated, we follow-up by implementing processes to insure that this remains an ongoing activity within the organization, that commitments for action have been obtained, and that they will be carried out.
9. Separation. We must recognize when it is more productive for the client and consultant to undertake other activities, and when continued consultation is counterproductive. We also should plan for future contacts, to monitor the success of this change and possibly to plan for future change activities.

It would be nice if real OD followed these steps sequentially. This rarely happens. Instead, the consultants must be flexible and be ready to change their strategy when necessary. Often they will have to move back and repeat previous steps in light of new information, new influences, or because of the changes that have already been made.

But for successful OD to take place, all of these steps must be followed. It works best if they are taken in the order described. And, since learning is really an iterative, not a sequential process, we must be prepared to re-enter this process when and where appropriate. 

If you would like to know more about OD, we highly recommend the books by Cummings and Worley (9), and by Rothwell, Sullivan and McLean (10)

• These are some of interventions that OD practitioners choose from in partnering with organizational leaders to create "planned change."

Applying criteria to goals

Here the leadership establishes objective criteria for the outputs of the organization's goal-setting processes. Then they hold people accountable not only for stating goals against those criteria but also for producing the desired results.

Establishing inter-unit task forces

These groups can cross both functional parts of the organization (the "silos") as well as employee levels. They are ideally accountable to one person and are appropriately rewarded for completing their assigned task effectively. Then they disband.

Experimentation with alternative arrangements

Today organizations are subject to "management by best-seller." The OD practitioner attempts to get leaders to look for changes that may take 3-5 years to work through. The meta-goal in these interventions is to create what is being called a "learning organization," one that performs experiments on organizational structure and processes, analyzes the results, and builds on them.


Identifying "key communicators"

The OD professional here carefully determines who seems to be "in the know" within the organization. These people often do not know that they are, in fact, key communicators. This collection of individuals are then fed honest information during critical times, one-on-one and confidentially.


Identifying "fireable offenses"

This intervention deepens the understanding of and commitment to the stated values of the organization. The OD professional facilitates the work of the organization's leaders to answer the critical question, "If we're serious about these values, then what might an employee do that would be so affrontive to them that he/she would be fired?"


In-visioning

This is actually a set of interventions that leaders plan with OD's help in order to "acculturate" everyone in the organization into an agreed-upon vision, mission, purpose, and values. The interventions might include training, goal setting, organizational survey-feedback, communications planning, etc.

Team Building

This intervention can take many forms. The most common is interviews and other prework, followed by a one- to three-day offsite session. During the meeting the group diagnoses its function as a unit and plans improvements in its operating procedures See J. E. Jones & W. L. Bearley, TEAMBOOK, published by HRDQ, for a catalog of team-building interventions.

Intergroup Problem Solving

This intervention usually involves working with the two groups separately before bringing them together. They establish common goals and negotiate changes in how the groups interface. [See J. E. Jones & W. L. Bearley, Intergroup Diagnostic Survey, published by HRDQ, for a catalog of intergroup interventions.

Management/leadership training

Many OD professionals come from a training background. They understand that organizations cannot succeed long term without well-trained leaders. The OD contribution there can be to ensure that the development curriculum emphasizes practical, current situations that need attention within the organization and to monitor the degree to which training delivery is sufficiently participative as to promise adequate transfer of learnings to the job.


Setting up measurement systems

The total-quality movement emphasizes that all work is a part of a process and that measurement is essential for process improvement. The OD professional is equipped with tools and techniques to assist leaders and others to create measurement methods and systems to monitor key success indicators.

Studies of structural causes

"Root-cause analysis" is a time-honored quality-improvement tool, and OD practitioners often use it to assist organizational clients to learn how to get down to the basis causes of problems.

Survey-feedback

This technology is probably the most powerful way that OD professionals involve very large numbers of people in diagnosing situations that need attention within the organization and to plan and implement improvements. The general method requires developing reliable, valid questionnaires, collecting data from all personnel, analyzing it for trends, and feeding the results back to everyone for action planning.

"Walk-the-talk" assessment

Most organizations have at least some leaders who "say one thing and do another." This intervention, which can be highly threatening, concentrates on measuring the extent to which the people within the organization are behaving with integrity.


This article covers the most common OD interventions. Every practitioner augments this list with both specially designed interventions that meet the precise needs of clients and with other, more complex interventions such as large-group sessions, and other popular programs. It is important, however, that all OD professionals be completely grounded in these basic interventions.

SAMPLING TECHNIQUE

Sampling (statistics)

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Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. Each observation measures one or more properties (weight, location, etc.) of an observable entity enumerated to distinguish objects or individuals. Results from probability theory and statistical theory are employed to guide practice.

The sampling process consists of 7 stages:

• Definition of population of concern
• Specification of a sampling frame, a set of items or events that it is possible to measure
• Specification of sampling method for selecting items or events from the frame
• Determine the sample size
• Implement the sampling plan
• Sampling and data collecting
• Review of sampling process

Contents [hide] 1 Population definition 2 Sampling frame 3 Sampling method 3.1 Quota sampling 3.2 Simple random sampling 3.3 Stratified sampling 3.4 Cluster sampling 3.5 Random sampling 3.6 Matched random sampling 3.7 Systematic sampling 3.8 Mechanical sampling 3.9 Convenience sampling 4 Sample size 5 Types of data 5.1 Categorical and numerical 6 Sampling and data collection 7 Review of sampling process 7.1 Non-response 8 Weighing of samples 9 History of sampling 10 See also 11 Graduate degree programs specializing in sampling/survey methods 11.1 Doctoral and Masters Degrees 11.2 Masters Degrees only 12 Notes 13 References

[edit] Population definition

Successful statistical practice is based on focused problem definition. Typically, we seek to take action on some population, for example when a batch of material from production must be released to the customer or sentenced for scrap or rework.

Alternatively, we seek knowledge about the cause system of which the population is an outcome, for example when a researcher performs an experiment on rats with the intention of gaining insights into biochemistry that can be applied for the benefit of humans. In the latter case, the population of concern can be difficult to specify, as it is in the case of measuring some physical characteristic such as the electrical conductivity of copper.

However, in all cases, time spent in making the population of concern precise is often well spent, often because it raises many issues, ambiguities and questions that would otherwise have been overlooked at this stage.

[edit] Sampling frame

In the most straightforward case, such as the sentencing of a batch of material from production (acceptance sampling by lots), it is possible to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not possible. There is no way to identify all rats in the set of all rats. There is no way to identify every voter at a forthcoming election (in advance of the election).

These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory.

As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. For example, in an opinion poll, possible sampling frames include:

• Electoral register
• Telephone directory
• Shoppers in Anytown, High Street on the Monday afternoon before the election.

The sampling frame must be representative of the population and this is a question outside the scope of statistical theory demanding the judgment of experts in the particular subject matter being studied. All the above frames omit some people who will vote at the next election and contain some people who will not. People not in the frame have no prospect of being sampled. Statistical theory tells us about the uncertainties in extrapolating from a sample to the frame. In extrapolating from frame to population its role is motivational and suggestive.

There is however, a strong division of views about the acceptability of representative sampling across different domains of study. To the philosopher, representative sampling procedure has no justification whatsoever because it is not how truth is pursued in philosophy. 'To the scientist, however, representative sampling is the only justified procedure for choosing individual objects for use as the basis of generalization, and is therefore usually the only acceptable basis for ascertaining truth'. (Andrew A. Marino) [1]. It is important to understand this difference to steer clear of confusing prescriptions found in many web pages.

In defining the frame, practical, economic, ethical and technical issues need to be addressed. The need to obtain timely results may prevent extending the frame far into the future.

The difficulties can be extreme when the population and frame are disjoint. This is a particular problem in forecasting where inferences about the future are made from historical data. In fact, in 1703, when Jacob Bernoulli proposed to Gottfried Leibniz the possibility of using historical mortality data to predict the probability of early death of a living man, Gottfried Leibniz recognised the problem in replying:

Nature has established patterns originating in the return of events but only for the most part. New illnesses flood the human race, so that no matter how many experiments you have done on corpses, you have not thereby imposed a limit on the nature of events so that in the future they could not vary.

Having established the frame, there are a number of ways of organizing it to improve efficiency and effectiveness.

[edit] Sampling method

Within any of the types of frame identified above, a variety of sampling methods can be employed, individually or in combination.

[edit] Quota sampling

In quota sampling, the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgment is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.

It is this second step which makes the technique one of non-probability sampling. In quota sampling the selection of the sample is non-random. For example interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for many years.

[edit] Simple random sampling

In a simple random sample of a given size, all such subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection. The frame is not subdivided or partitioned, in contrast to the more complex methods below..

[edit] Stratified sampling

Where the population embraces a number of distinct categories, the frame can be organized by these categories into separate strata. A sample is then selected from each stratum separately, producing a stratified sample. The two main reasons for using a stratified sampling design are [1] to ensure that particular groups within a population are adequately represented in the sample, and [2] to improve efficiency by gaining greater control on the composition of the sample. In the second case, major gains in efficiency (either lower sample sizes or higher precision) can be achieved by varying the sampling fraction from stratum to stratum. The sample size is usually proportional to the relative size of the strata. However, if variances differ significantly across strata, sample sizes should be made proportional to the stratum standard deviation. Disproportionate stratification can provide better precision than proportionate stratification. Typically, strata should be chosen to:

• have means which differ substantially from one another.
• minimize variance within strata and maximize variance between strata.

[edit] Cluster sampling

Sometimes it is cheaper to 'cluster' the sample in some way e.g. by selecting respondents from certain areas only, or certain time-periods only. (Nearly all samples are in some sense 'clustered' in time - although this is rarely taken into account in the analysis.)

Cluster sampling is an example of 'two-stage sampling' or 'multistage sampling': in the first stage a sample of areas is chosen; in the second stage a sample of respondent within those areas is selected.

This can reduce travel and other administrative costs. It also means that one does not need a sampling frame for the entire population, but only for the selected clusters.

Cluster sampling generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between themselves, as compared with the within-cluster variation.

[edit] Random sampling

In random sampling, also known as probability sampling, every combination of items from the frame, or stratum, has a known probability of occurring, but these probabilities are not necessarily equal. With any form of sampling there is a risk that the sample may not adequately represent the population but with random sampling there is a large body of statistical theory which quantifies the risk and thus enables an appropriate sample size to be chosen. Furthermore, once the sample has been taken the sampling error associated with the measured results can be computed. With non-random sampling there is no measure of the associated sampling error. While such methods may be cheaper this is largely meaningless since there is no measure of quality. There are several forms of random sampling. For example, in simple random sampling, each element has an equal probability of being selected. It may be infeasible in many practical situations. Other examples of probability sampling include stratified sampling and multistage sampling.

[edit] Matched random sampling

A method of assigning participants to groups in which pairs of participants are first matched on some characteristic and then individually assigned randomly to groups. (Brown, Cozby, Kee, & Worden, 1999, p.371).

The Procedure for Matched random sampling can be briefed with the following contexts,

a) Two samples in which the members are clearly paired, or are matched explicitly by the researcher. For example, IQ measurements on pairs of identical twins.

b) Those samples in which the same attribute, or variable, is measured twice on each subject, under different circumstances. Commonly called repeated measures. Examples include the times of a group of athletes for 1500m before and after a week of special training; the milk yields of cows before and after being fed a particular diet. Babu H.M

[edit] Systematic sampling

Selecting (say) every 10th name from the telephone directory is called an every 10th sample, which is an example of systematic sampling. It is a type of probability sampling unless the directory itself is not randomized. It is easy to implement and the stratification induced can make it efficient, but it is especially vulnerable to periodicities in the list. If periodicity is present and the period is a multiple of 10, then bias will result. It is important that the first name chosen is not simply the first in the list, but is chosen to be (say) the 7th, where 7 is a random integer in the range 1,...,10-1. Every 10th sampling is especially useful for efficient sampling from databases.

[edit] Mechanical sampling

Mechanical sampling is typically used in sampling solids, liquids and gases, using devices such as grabs, scoops, thief probes, the coliwasa and riffle splitter.

Care is needed in ensuring that the sample is representative of the frame. Much work in this area was developed by Pierre Gy.

[edit] Convenience sampling

Sometimes called grab or opportunity sampling, this is the method of choosing items arbitrarily and in an unstructured manner from the frame. Though almost impossible to treat rigorously, it is the method most commonly employed in many practical situations. In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample.

[edit] Sample size

Where the frame and population are identical, statistical theory yields exact recommendations on sample size^[1]. However, where it is not straightforward to define a frame representative of the population, it is more important to understand the cause system of which the population are outcomes and to ensure that all sources of variation are embraced in the frame. Large number of observations are of no value if major sources of variation are neglected in the study. In other words, it is taking a sample group that matches the survey category and is easy to survey. Bartlett, Kotrlik, and Higgins (2001) published a paper titled Organizational Research: Determining Appropriate Sample Size in Survey Research Information Technology, Learning, and Performance Journal that provides an explanation of Cochran’s (1977) formulas. A discussion and illustration of sample size formulas, including the formula for adjusting the sample size for smaller populations, is included. A table is provided that can be used to select the sample size for a research problem based on three alpha levels and a set error rate.

[edit] Types of data

[edit] Categorical and numerical

There are two types of random variables: categorical and numerical. Categorical random variables yield responses such as 'yes' or 'no'. Categorical variables can yield more than two possible responses. For example: 'Which day of the week are you most likely to wash clothes?' Numerical random variables yield numerical responses, such as your height in centimeters.

There are two types of numerical variables: discrete and continuous. Discrete random variables produce numerical responses from a counting process. An example is 'how many times do you visit the cash machine in a typical month?' Continuous random variables produce responses from a measuring process. Height is an example of a continuous variable because the response takes on a value from an interval. Precision of the measurement instrument(s) may lead to tied observations. A tied observation occurs when the measuring device is not sensitive or sophisticated enough to detect incremental differences in the experimental or survey data.

[edit] Sampling and data collection

Good data collection involves:

• Following the defined sampling process
• Keeping the data in time order
• Noting comments and other contextual events
• Recording non-responses

[edit] Review of sampling process

After sampling, a review should be held of the exact process followed in sampling, rather than that intended, in order to study any effects that any divergences might have on subsequent analysis. A particular problem is that of non-responses.

[edit] Non-response

In survey sampling, many of the individuals identified as part of the sample may be unwilling to participate or impossible to contact. In this case, there is a risk of differences, between (say) the willing and unwilling, leading to selection bias in conclusions. This is often addressed by follow-up studies which make a repeated attempt to contact the unresponsive and to characterize their similarities and differences with the rest of the frame.

[edit] Weighing of samples

In many situations the sample fraction may be varied by stratum and data will have to be weighted to correctly represent the population. Thus for example, a simple random sample of individuals in the United Kingdom might include some in remote Scottish islands who would be inordinately expensive to sample. A cheaper method would be to use a stratified sample with urban and rural strata. The rural sample could be under-represented in the sample, but weighted up appropriately in the analysis to compensate.

[edit] History of sampling

The idea of random sampling by the use of lots is an old one, mentioned several times in the Bible. In 1786 Pierre Simon Laplace estimated the population of France by using a sample, along with ratio estimator. He also computed probabilistic estimates of the error. These were not expressed as modern confidence intervals but as the sample size that would be needed to achieve a particular upper bound on the sampling error with probability 1000/1001. His estimates used Bayes' theorem with a uniform prior probability and it assumed his sample was random.The theory of small-sample statistics developed by William Sealy Gossett put the subject on a more rigorous basis in the 20th century. However, the importance of random sampling was not universally appreciated and in the USA the 1936 Literary Digest prediction of a Republican win in the presidential election went badly awry, due to severe bias. A sample size of one million was obtained through magazine subscription lists and telephone directories. It was not appreciated that these lists were heavily biased towards Republicans and the resulting sample, though very large, was deeply flawed.

[edit] See also

• Sampling (case studies)
• Sample size rule of thumb for estimate of population mean

[edit] Graduate degree programs specializing in sampling/survey methods

[edit] Doctoral and Masters Degrees

• Program in Social Statistics (Survey Methodology) - University of Southampton
• Joint Program in Survey Methodology (JPSM) - University of Maryland-College Park and University of Michigan-Ann Arbor
• Survey Research and Methodology - University of Nebraska-Lincoln
• Program in Survey Methodology - University of Michigan-Ann Arbor
• Advanced Research and Methodology - New Bauhaus Chicago

[edit] Masters Degrees only

• Master in Official Statistics - University of Southampton, UK
• Graduate Program in Survey Research - University of Connecticut
• Diploma in Official Statistics - Hebrew University, Israel
• Methodology and Statistics for the Social and Behavioral Sciences - Utrecht University, the Netherlands

[edit] Notes

1. ^ Mathematical details are displayed in the Sample size article.

[edit] References

• Brown, K.W., Cozby, P.C., Kee, D.W., & Worden, P.E. (1999). Research Methods in Human Development, 2d ed. Mountain View, CA : Mayfield. ISBN 1-55934-875-5
• Bartlett, J. E., II, Kotrlik, J. W., & Higgins, C. (2001). Organizational research: Determining appropriate sample size for survey research. Information Technology, Learning, and Performance Journal, 19(1) 43-50.
• Chambers, R L, and Skinner, C J (editors) (2003), Analysis of Survey Data, Wiley, ISBN 0-471-89987-9
• Cochran, W G (1977) Sampling Techniques, Wiley, ISBN 0-471-16240-X
• Deming, W E (1975) On probability as a basis for action, The American Statistician, 29(4), pp146-152.
• Flyvbjerg, B (2006) "Five Misunderstandings About Case Study Research." Qualitative Inquiry, vol. 12, no. 2, April 2006, pp. 219-245. [2]
• Gy, P (1992) Sampling of Heterogeneous and Dynamic Material Systems: Theories of Heterogeneity, Sampling and Homogenizing
• Kish, L (1995) Survey Sampling, Wiley, ISBN 0-471-10949-5
• Korn, E L, and Graubard, B I (1999) Analysis of Health Surveys, Wiley, ISBN 0-471-13773-1
• Lohr, H (1999) Sampling: Design and Analysis, Duxbury, ISBN 0-534-35361-4
• Sarndal, Swenson, and Wretman (1992), Model Assisted Survey Sampling, Springer-Verlag, ISBN 0-387-40620-4
• Stuart, Alan (1962) Basic Ideas of Scientific Sampling, Hafner Publishing Company, New York
• ASTM E105 Standard Practice for Probability Sampling Of Materials
• ASTM E122 Standard Practice for Calculating Sample Size to Estimate, With a Specified Tolerable Error, the Average for Characteristic of a Lot or Process
• ASTM E141 Standard Practice for Acceptance of Evidence Based on the Results of Probability Sampling
• ASTM E1402 Standard Terminology Relating to Sampling
• ASTM E1994 Standard Practice for Use of Process Oriented AOQL and LTPD Sampling Plans
• ASTM E2234 Standard Practice for Sampling a Stream of Product by Attributes Indexedby AQL

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Category: Sampling (statistics)

Talent management

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This article or section may need to be wikified to meet Wikipedia's quality standards. Please help improve this article, especially its section layout, and relevant internal links. (help) This article has been tagged since March 2007.

Talent management is a professional term that gained popularity in the late 1990s. It refers to the process of developing and fostering new workers through onboarding, developing and keeping current workers and attracting highly skilled workers at other companies to come work for your company. Talent management in this context does not refer to the management of entertainers.

Companies that are engaged in talent management (human capital management) are strategic and deliberate in how they source, attract, select, train, develop, promote, and move employees through the organization. This term also incorporates how companies drive performance at the individual level (performance management).

The term talent management means different things to different people. To some it is about the management of high-worth individuals or "the talented" whilst to others it is about how talent is managed generally - i.e. on the assumption that all people have talent which should be identified and liberated.

This term is usually associated with competency-based human resource management practices. Talent management decisions are often driven by a set of organizational core competencies as well as position-specific competencies. The competency set may include knowledge, skills, experience, and personal traits (demonstrated through defined behaviors).

Older competency models might also contain attributes that rarely predict success (e.g. education, tenure, and diversity factors that are illegal to consider in many countries).

In the late 1990s, technology companies engaged in a 'war for talent'.

The term was coined by McKinsey & Company following a 1997 study and then it was the title of a book by Ed Michaels, Helen Handfield-Jones, and Beth Axelrod.

[edit] External links

• Talent Management: What's It Mean? - Article from Reliable Plant magazine

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Human resource management

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Human resource management (HRM) is the strategic and coherent approach to the management of an organization's most valued assets - the people working there who individually and collectively contribute to the achievement of the objectives of the business.^[1] The terms "human resource management" and "human resources" (HR) have largely replaced the term "personnel management" as a description of the processes involved in managing people in organizations.^[2] Human Resource management is evolving rapidly. Human resource management is both an academic theory and a business practice that addresses the theoretical and practical techniques of managing a workforce.

Contents [hide] 1 Synonyms 2 Academic theory 2.1 Critical Academic Theory 3 Business practice 4 Careers 5 Professional organizations 6 See also 7 References

[edit] Synonyms

Its Synonyms include:

• Personnel administration
• Personnel management
• Manpower management
• Industrial management^[3][4]

But these traditional expressions are becoming less common for the theoretical discipline. Sometimes even industrial relations and employee relations are confusingly listed as synonyms,^[5] although these normally refer to the relationship between management and workers and the behavior of workers in companies.

The theoretical discipline is based primarily on the assumption that employees are individuals with varying goals and needs, and as such should not be thought of as basic business resources, such as trucks and filing cabinets. The field takes a positive view of workers, assuming that virtually all wish to contribute to the enterprise productively, and that the main obstacles to their endeavors are lack of knowledge, insufficient training, and failures of process.

HRM is seen by practitioners in the field as a more innovative view of workplace management than the traditional approach. Its techniques force the managers of an enterprise to express their goals with specificity so that they can be understood and undertaken by the workforce, and to provide the resources needed for them to successfully accomplish their assignments. As such, HRM techniques, when properly practiced, are expressive of the goals and operating practices of the enterprise overall. HRM is also seen by many to have a key role in risk reduction within organistions.^[6]

Synonyms such as personnel management are often used in a more restricted sense to describe activities that are necessary in the recruiting of a workforce, providing its members with payroll and benefits, and administrating their work-life needs.

[edit] Academic theory

The goal of human resource management is to help an organization to meet strategic goals by attracting, and maintaining employees and also to manage them effectively. The basic premise of the academic theory of HRM is that humans are not machines, therefore we need to have an interdisciplinary examination of people in the workplace. Fields such as psychology, industrial engineering, industrial and organizational psychology, industrial relations, sociology, and critical theories: postmodernism, post-structuralism play a major role. Many colleges and universities offer bachelor and master degrees in Human Resources Management.

One widely used scheme to describe the role of HRM, developed by Dave Ulrich, defines 4 fields for the HRM function^[7]:

• Strategic business partner
• Change agent
• Employee champion
• Administration

However, many HR functions these days struggle to get beyond the roles of administration and employee champion, and are seen rather as reactive than strategically proactive partners for the top management. In addition, HR organizations also have the difficulty in proving how their activities and processes add value to the company. Only in the recent years HR scholars and HR professionals are focusing to develop models that can measure if HR adds value.^[8]

[edit] Critical Academic Theory

Postmodernism plays an important part in Academic Theory and particularly in Critical Theory. Indeed Karen Legge in 'Human Resource Management: Rhetorics and Realities' posses the debate of whether HRM is a modernist project or a postmodern discourse (Legge 2004)? In many ways, critically or not, many writers contend that HRM itself is a movement away from the modernist traditions of personnel (man as machine) towards a postmodernist view of HRM man as individuals. Critiques include the notion that because 'Human' is the subject we should recognise that people are complex and that it is only through various discourses that we understand the world. Man is not Machine, no matter what attempts are made to change it i.e. Fordism / Taylorism, McDonaldisation (Modernism).

Critical Theory also questions whether HRM is the pursuit of "attitudinal shaping" (Wilkinson 1998), particularly when considering empowerment, or perhaps more precisely pseudo-empowerment - as the critical perspective notes.

[edit] Business practice

Human resources management comprises several processes. Together they are supposed to achieve the above mentioned goal. These processes can be performed in an HR department, but some tasks can also be outsourced or performed by line-managers or other departments.

• Workforce planning
• Recruitment (sometimes separated into attraction and selection)
• Induction and Orientation
• Skills management
• Training and development
• Personnel administration
• Compensation in wage or salary
• Time management
• Travel management (sometimes assigned to accounting rather than HRM)
• Payroll (sometimes assigned to accounting rather than HRM)
• Employee benefits administration
• Personnel cost planning
• Performance appraisal

[edit] Careers

The sort of careers available in HRM are varied. There are generalist HRM jobs such as human resource assistant. There are careers involved with employment, recruitment and placement and these are usually conducted by interviewers, EEO (Equal_Opportunity_Employment) specialists or college recruiters. Training and development specialism is often conducted by trainers and orientation specialists. Compensation and benefits tasks are handled by compensation analysts, salary administrators, and benefits administrators.

[edit] Professional organizations

Professional organizations in HRM include the Society for Human Resource Management and the Chartered Institute of Personnel and Development (CIPD).

[edit] See also

• Human resources

[edit] References

1. ^ A Handbook of Human Resource Management Practice, 10th edition, Armstrong. M. (Kogan Page) London 2006 ISBN 0 -7494 - 4631 -5
2. ^ A Handbook of Human Resource Management Practice, 10th edition, Armstrong. M. (Kogan Page) London 2006 ISBN 0 -7494 - 4631 -5
3. ^ Columbia Encyclopedia: "personnel management" - see "industrial management".
4. ^ Encyclopædia Britannica: "Personnel administration is also frequently called personnel management, industrial relations, employee relations."
5. ^ Encyclopædia Britannica
6. ^ http://www.towers.fr/essays/hrm.html
7. ^ Ulrich, Dave (1996) "Human Resource Champions". Harvard Business School Press
8. ^ Smit, Martin E.J.H. (2006), "HR, Show me the money; Presenting an exploratory model that can measure if HR adds value", available online

- Wilkinson, A. 1998. Empowerment: theory and practice. Personnel Review. [online]. Vol. 27(1): 40-56. Available from: Emerald on the World Wide Web: http://hermia.emeraldinsight.com/vl=2601464/cl=84/nw=1/fm=docpdf/rpsv/cw/mcb/00483486/v27n1/s3/p40 [Accessed 16.02.2004].

- Legge, K. 2004. Human Resource Management: Rhetorics and Realities. Anniversary Ed. Hampshire: Palgrave Macmillan

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Categories: Human resource management | Organizational studies and human resource management