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APPENDIX: Scientific and Legal Guidelines on Employee Selection Procedures— Checklists for Compliance Both scientific and legal guidelines for selecting employees are available to HR professionals. The purpose of this appendix is to present both sets of guidelines in the form of questions to be answered. Obviously the relevance of each question will vary with the context in which it is asked. Taken together, both sets of guidelines represent key issues to address in any selection situation and, more broadly, with respect to any HR decision. SCIENTIFIC GUIDELINES—SUMMARY CHECKLIST1 PREMISE The essential principle in the evaluation of any selection procedure is that evidence must be accumulated to support an inference of job relatedness. Selection procedures are demonstrated to be job related when evidence supports the accuracy of inferences made from scores on, or evalu- ations derived from, those procedures with regard to some important aspect of work behavior (e.g., quality or quantity of job performance, performance in training, advancement, tenure, termination, or other organizationally pertinent behavior) (SIOP, 2003, p. 4). PLANNING AND ANALYSIS OF WORK 1. Is there a clear statement of the proposed uses of the selection procedures being consid- ered, based on an understanding of the organization’s needs and rights and of its present and prospective employees? 2. Has the user identified the sources of evidence most likely to be relevant to the validation effort—that is, relationships to measures of other variables, content-related evidence, and evidence based on the internal structure of the test? 3. Has the design of the validation effort considered (a) existing evidence, (b) design features required by the proposed uses, (c) design features necessary to satisfy the general require- ments of sound inference, and (d) the feasibility of particular design features? 4. Has there been a systematic analysis of work that considers, for example, work complexity; work environment; work context; work tasks, behaviors, and activities performed; or worker requirements [e.g., knowledge, abilities, skills, and other personal characteristics (KSAOs)]? 5. Does the analysis of work identify worker requirements, as well as criterion measures, by assembling information needed to understand the work performed, the setting in which the work is accomplished, and the organization’s goals? 6. In the analysis of work, is the level of detail appropriate for the intended use and the avail- ability of information about the work? Sources of Validity Evidence 1. Does the user understand the construct the selection procedure is intended to measure? 2. If criteria other than job performance are used, is there a theory or rationale to guide the choice of these other variables? 1 Source: Based on materials found in Society for Industrial-Organizational Psychology, Inc. (2003). Principles for the Validation and Use of Personnel Selection Procedures (4th ed.). Bowling Green, OH: SIOP. For more information on the checklist items, consult the subject index. From Appendix A of Applied Psychology in Human Resource Management, 7/e. Wayne F. Cascio. Herman Aguinis. Copyright © 2011 by Pearson Education. Published by Prentice Hall. All rights reserved. 445

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance CRITERION-RELATED EVIDENCE OF VALIDITY 1. Is the choice of predictors and criteria based on an understanding of the objectives for test use, job information, and existing knowledge regarding test validity? 2. Are standardized procedures used? That is, are there consistent directions and procedures for administration, scoring, and interpretation? Feasibility 1. Is it possible to obtain or develop a relevant, reliable, and uncontaminated criterion measure(s)? 2. Is it possible to do the research on a sample that is reasonably representative of the popula- tion of people and jobs to which the results are to be generalized? 3. Does the study have adequate statistical power—that is, a probability of detecting a signif- icant predictor–criterion relationship in a sample if such a relationship does, in fact, exist? 4. Has the researcher identified how design characteristics might affect the precision of the estimate of predictor–criterion relationships (e.g., sample size, the statistic computed, the probability level chosen for the confidence interval, the size of the relationship)? 5. Is the design, predictive or concurrent, appropriate for the population and purpose of the study? DESIGN AND CONDUCT OF CRITERION-RELATED STUDIES Criterion Development 1. Are criteria chosen on the basis of work relevance, freedom from contamination, and reliability rather than availability? 2. Do all criteria represent important organizational, team, and individual outcomes, such as work-related behaviors, outputs, attitudes, or performance in training, as indicated by a review of information about the work? 3. Do adequate safeguards exist to reduce the possibility of criterion contamination, deficiency, or bias? 4. Has criterion reliability been estimated? 5. If ratings are used as measures of performance, is the development of rating factors guided by an analysis of the work? 6. Are raters familiar with the demands of the work, as well as the individual to be rated? Are raters trained in the observation and evaluation of work performance? Choice of Predictors 1. Is there an empirical, logical, or theoretical foundation for each predictor variable chosen? 2. Is the preliminary choice among predictors based on the researcher’s scientific knowledge rather than on personal interest or mere familiarity? 3. Have steps been taken to minimize predictor contamination (e.g., by using standardized procedures, such as structured interviews)? 4. If judgment is used in weighting and summarizing predictor data, is the judgment itself recognized as an additional predictor? 5. Has predictor reliability been estimated? Choice of Participants 1. Is the sample for a validation study representative of the selection situation of interest? 2. If a researcher concludes that a variable moderates validity coefficients, is there explicit evidence for such an effect? 446

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance Data Analysis for Criterion-Related Validity 1. Has the method of analysis been chosen with due consideration for the characteristics of the data and the assumptions involved in the development of the method? 2. Has the type of statistical analysis to be used been considered during the planning of the research? 3. Does the data analysis provide information about effect sizes and the statistical signifi- cance or confidence associated with predictor–criterion relationships? 4. Have the relative risks of Type I and Type II errors been considered? 5. Does the analysis provide information about the nature of the predictor–criterion relation- ship and how it might be used in prediction (e.g., number of cases, measures of central tendency, characteristics of distributions, variability for both predictor and criterion vari- ables, and interrelationships among all variables studied)? 6. Have adjustments been made for range restriction and/or criterion unreliability, if appro- priate, in order to obtain an unbiased estimate of the validity of the predictor in the popula- tion in which it will be used? 7. If adjustments are made, have both adjusted and unadjusted validity coefficients been reported? 8. If predictors are to be used in combination, has careful consideration been given to the method used to combine them (e.g., in a linear manner, by summing scores on different tests, or in a configural manner, by using multiple cutoffs)? 9. If a researcher combines scores from several criteria into a composite score, is there a rationale to support the rules of combination, and are the rules described? 10. Have appropriate safeguards been applied (e.g., use of cross-validation or shrinkage for- mulas) to guard against overestimates of validity resulting from capitalization on chance? 11. Have the results of the present criterion-related validity study been interpreted against the background of previous relevant research literature? 12. Are unusual findings, such as suppressor or moderator effects, nonlinear regression, or the benefits of configural scoring, supported by an extremely large sample or replication? EVIDENCE FOR VALIDITY BASED ON CONTENT 1. If a selection procedure has been designed explicitly as a sample of important elements in the work domain, does the validation study provide evidence that the selection procedure samples the important work behaviors, activities, or worker KSAOs necessary for perform- ance on the job or in training? 2. Are the work and worker requirements reasonably stable? 3. Are qualified and unbiased subject matter experts available? 4. Does the content-based procedure minimize elements that are not part of the work domain (e.g., multiple-choice formats or written content when the job does not require writing)? 5. Has each job content domain been defined completely and described thoroughly in terms of what it does and does not include, based on, for example, an analysis of work behaviors and activities, responsibilities of job incumbents, or KSAOs required for effective per- formance on the job? 6. Has the researcher described the rationale underlying the sampling of the content domain? 7. Is the selection procedure based on an analysis of work that defines the balance between work behaviors, activities, or KSAOs the applicant is expected to have before placement on the job and the amount of training the organization will provide? 8. Does the specificity–generality of the content of the selection procedure reflect the extent to which the job is likely to change as a result of organizational needs, technology, or equipment? 447

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance 9. Has the researcher established guidelines for administering and scoring the content-based procedure? 10. Has the reliability of performance on content-based selection procedures been determined? 11. Is the job content domain restricted to critical or frequent activities or to prerequisite knowledge, skills, or abilities? EVIDENCE OF VALIDITY BASED ON INTERNAL STRUCTURE 1. Does the researcher recognize that evidence of internal structure, by itself, is insuffi- cient to establish the usefulness of a selection procedure in predicting future work performance? 2. Are relevant analyses based on the conceptual framework of the selection procedure (typi- cally established by the proposed use of the procedure)? 3. If evidence of validity is based on internal structure, did the researcher consider the relationship among items, components of the selection procedures, or scales measuring constructs? 4. Is the inclusion of items in a selection procedure based primarily on their relevance to a construct or content domain and secondarily on their intercorrelations? 5. If scoring involves a high level of judgment, does the researcher recognize that indices of interrater or scorer consistency, such as generalizability coefficients or measures of inter- rater agreement, may be more appropriate than internal consistency estimates? Generalizing Validity Evidence 1. If a researcher wishes to generalize the validity of inferences from scores on a selection procedure to a new situation, based on validation research conducted elsewhere, is such transportability based on job comparability (in content or requirements) or similarity of job context and candidate group? 2. If synthetic or job component validity is used as a basis for generalizing the validity of inferences from scores on a selection procedure, has the researcher documented the rela- tionship between the selection procedure and one or more specific domains of work (job components) within a single job or across different jobs? 3. If meta-analysis is used as a basis for generalizing research findings across settings, has the researcher considered the meta-analytic methods used, their underlying assumptions, the tenability of the assumptions, and artifacts that may influence the results? 4. Are reports that contribute to the meta-analytic research results clearly identified and available? 5. Have researchers fully reported the rules they used to categorize jobs, tests, criteria, and other characteristics of their studies? Have they reported the reliability of the coding schemes used to categorize these variables? 6. Are there important conditions in the operational setting that are not represented in the meta-analysis (e.g., the local setting involves a managerial job and the meta-analytic data- base is limited to entry-level jobs)? 7. If the cumulative validity evidence in a meta-analysis is relied on for jobs in new settings or organizations, are the following conditions met? a. Is the selection procedure to be used as a measure of the trait, ability, or construct studied? Is it a representative sample of the type of selection procedure included in the meta-analysis? b. Is the job in the new setting similar to, or a member of, the same job family as the job included in the validity generalization study? 8. Is the researcher attempting to generalize on the basis of a method in general (e.g., interviews, biodata) rather than on the basis of a specific application of the method? 448

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance Fairness and Bias 1. Does the researcher recognize that fairness has no single definition, whether statistical, psychometric, or social? 2. Has the researcher tested for predictive bias (consistent nonzero errors of prediction for members of a subgroup) when there are compelling reasons to question whether a predic- tor and a criterion are related in a comparable fashion for specific subgroups, given the availability of appropriate data? 3. If a test of predictive bias is warranted, has the researcher tested for it using moderated multiple regression? 4. Do tests for predictive bias meet the following conditions: use of an unbiased criterion, sufficient statistical power, and homogeneity of error variances? 5. Has the researcher conducted an item sensitivity review, in which items are reviewed by individuals with diverse perspectives for language or content that might have differing meaning for members of various subgroups and for language that could be demeaning or offensive to members of various subgroups? Operational Considerations INITIATING A VALIDATION EFFORT 1. Have all aspects of the research been performed in compliance with the ethical standards of the American Psychological Association? 2. In defining an organization’s needs, objectives, and constraints, have the researcher and the organization’s representative taken into account the desires of various stakeholders and determined the relative weights to be given to each point of view? 3. Have researchers considered the legal and labor environments when deciding on validation approaches or selection instruments? 4. In choosing a validation strategy, has the researcher considered the number of individuals who currently perform the work and their similarity to the applicant population? 5. Has the researcher considered alternative sources of information for the validation effort, such as workers, managers, supervisors, trainers, customers, archival records, databases, and internal and external reports? 6. Has the researcher explained to decision makers the issues underlying the acceptability of a selection procedure as part of the initial planning effort? 7. Do managers and workers understand in general terms the purpose of the research, the plan for conducting the research, and their respective roles in the development and validation of the selection procedure? Understanding Work and Worker Requirements 1. In cases where traditional jobs no longer exist, has the researcher considered important requirements for a wider range or type of work activity? 2. Does the sampling plan for data collection take into account the number of workers and their locations, their characteristics (experience, training, proficiency), their shift or other work cycles, and other variables that might influence the analysis of work? 3. In documenting the work-analysis process, has the researcher described the data-collection methods, analyses, results, and implications for the validation effort? Requirements SELECTING ASSESSMENT PROCEDURES FOR THE VALIDATION EFFORT 1. Is the researcher familiar with research related to the organization’s objectives? 2. In choosing components of a selection battery, has the researcher considered the overall con- tribution of each component, its relative contribution, and potential construct redundancy? 449

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance 3. Has the researcher ensured that administration and scoring tasks can be completed consis- tently across all locations and administrators? 4. Has the researcher carefully considered the format (e.g., multiple-choice, essay) and medium (i.e., the method of delivery) of the content of the selection procedure? 5. Have researchers considered approaches designed to minimize negative perceptions of a selection procedure and to enhance its acceptability to candidates? 6. If alternate forms of a selection procedure are developed, has the researcher taken steps to ensure that candidates’ scores are comparable across forms? SELECTING THE VALIDATION STRATEGY 1. Is the strategy selected feasible in the organizational context, and does it meet project goals within the constraints imposed by the situation? 2. When individual assessment is used (one-on-one evaluations), does the assessor have a rationale for the determination and use of selection procedures? SELECTING CRITERION MEASURES 1. Has the researcher considered the psychometric characteristics of performance-oriented criteria (those that represent work activities, behaviors, or outcomes, such as supervisory ratings)? 2. Are all criteria representative of important work behaviors, outcomes, or relevant organiza- tional expectations regarding individual behavior or team performance? DATA COLLECTION 1. Has the researcher communicated relevant information about the data-collection effort to all those affected, including management, test takers, those who provide criterion data, and those who will use the test? 2. Has the researcher determined the extent to which pilot testing is necessary or useful? 3. Have participants in the validation research been given confidentiality unless there are per- suasive reasons to proceed otherwise? 4. Have all data been retained at a level of security that permits access only for those with a need to know? DATA ANALYSES 1. Have all data been checked for accuracy? 2. Is there a documented rationale for treating missing data or outliers? 3. Are data analyses appropriate for the method or strategy undertaken, the nature of the data (nominal, ordinal, interval, ratio), the sample sizes, and other considerations that will lead to correct inferences from the data? 4. If selection procedures are combined, have the algorithm for combination and the rationale for the algorithm been described? 5. Have the rationale and supporting evidence for the use of multiple hurdles or a compensa- tory model been presented? 6. In recommending the use of a rank-ordering method or a cutoff score, does the recommen- dation take into account labor-market conditions, the consequences of errors in prediction, the level of a KSAO represented by a chosen cutoff score, and the utility of the selection procedure? 7. If test-score banding is used, has the researcher documented the basis for its development and the decision rules to be followed in its administration? 8. Has the researcher presented normative information relevant to the applicant pool and the incumbent population? 450

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance Communicating the Effectiveness of Selection Procedures 1. Has the researcher used expectancy or utility analyses to communicate the effectiveness of selection procedures? 2. Has the researcher identified the results of utility analyses as estimates based on a set of assumptions? 3. Have minimal and maximal point estimates of utility been presented to reflect the uncer- tainty in estimating various parameters of the utility model? Appropriate Use of Selection Procedures 1. Has the researcher produced evidence of validity to support individual components as well as the combination of selection procedures? 2. Are selection procedures used only for the purposes for which there is validity evidence? 3. Are the recommendations based on the results of a validation effort consistent with the objectives of the research, the data analyses performed, and the researcher’s professional judgment and ethical responsibilities? TECHNICAL VALIDATION REPORT 1. Do all reports of validation research include the name of the author and date of the study, a statement of the purpose of the research, a description of the analysis of work, and docu- mentation of any search for alternative selection procedures? 2. Are the names, editions, and forms of commercially available selection instruments described? For proprietary instruments, has the researcher described the items, the con- struct(s) that are measured, and sample items, if appropriate? 3. Does the report describe the methods used by the researcher to determine that the selection procedure is significantly related to a criterion measure or representative of a job content domain? 4. Does the report provide a detailed description of criterion measures; the rationale for their use; data-collection procedures; and a discussion of their relevance, reliability, and free- dom from bias? 5. Does the report describe the research sample and the sampling procedure relative to the interpretation of results? Does it provide data regarding restriction in the range of scores on predictors or criteria? 6. Are all summary data available that bear on the conclusions drawn by the researcher and on his or her recommendations? 7. Are the methods used to score items and tasks described fully? 8. Are norm or expectancy tables presented to help guide relevant interpretations? 9. Does the report provide recommendations for implementation and the rationale supporting them (e.g., rank ordering, score bands, cutoff scores)? 10. Have all research findings that might qualify the conclusions or the generalizability of results been reported? 11. Are complete references provided for all published literature and available technical reports (some of which may be proprietary and confidential)? ADMINISTRATION GUIDE 1. Does the administration guide document completely the information needed to administer the selection procedure, score it, and interpret the score? 2. If the selection procedure is computer based or in a form other than paper and pencil, does the guide include detailed instructions on the special conditions of administration? 3. Is the information developed for users or examinees accurate and complete for its purposes and not misleading? 451

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance 4. Does the writing style meet the needs of the likely audience? 5. Does the guide include an introduction to inform the reader of the purpose of the assess- ment procedure and an overview of the research that supports the procedure? 6. Does the guide include contact information, a thorough description of the selection proce- dures, and an indication of persons to whom the procedure is applicable, and does it state any exceptions to test requirements? 7. Does the administration guide state the necessary qualifications of administrators and the training required to administer the procedures described in the guide? 8. Does the guide provide detailed instructions regarding the actual implementation of the selection procedures, as well as rules and tips for providing an appropriate testing environ- ment and for ensuring the candidate’s identity? 9. Does the guide include detailed instructions for scoring and interpreting the results of the selection procedure? 10. Have quality control checks been implemented to ensure accurate scoring and recording? 11. If computer-based test interpretation (CBTI) is used to process responses to a selection procedure, did the researcher provide detailed instructions on how CBTI is to be used in decision making? 12. Does the guide provide detailed information regarding recordkeeping and test-score databases? 13. Does the guide communicate how selection-procedure scores are to be reported and used and who has access to them? 14. Does the guide include information about how to provide feedback to candidates? 15. Does the guide communicate general principles about how persons with disabilities or how deviations from normal procedures (e.g., sessions disrupted by power failures or illness of a candidate) are to be handled? 16. Does the guide explain whether candidates may be reassessed and how reassessment will take place? 17. Does the administration guide emphasize the importance of safeguarding the content, scor- ing, and validity of the selection procedure, and does it identify practices for ensuring the security of selection-procedure documents? OTHER CIRCUMSTANCES REGARDING THE VALIDATION EFFORT AND USE OF SELECTION PROCEDURES 1. If advised of changes in organizational functioning, does the researcher examine each situ- ation on its own merits and make recommendations regarding the impact of the change on the validation and use of any selection procedure? 2. Does the researcher periodically review and, if necessary, update selection procedures and their technical or administration guides? 3. For candidates with disabilities, does the user make special accommodations to minimize the impact of a known disability that is not relevant to the construct being assessed? 4. Are researchers and individuals charged with approving accommodations knowledgeable about the availability of modified forms of the selection procedure, psychometric theory, and the likely effect of the disability on selection-procedure performance? 5. Although most employers have too few cases for extensive research, are the principles set forth in this document followed to the extent possible in the preparation of modified selec- tion procedures for candidates with disabilities? 6. Is there documentation of the modifications made, the psychometric characteristics of the modified selection procedures, and the performance of candidates with disabilities on the original form of the procedure (if available)? 7. Does the test user take steps to ensure that a candidate’s score on the selection procedure accurately reflects his or her ability rather than construct-irrelevant disabilities? 452

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance A “yes” answer to each question in the checklist, while an ideal to strive for, is somewhat unrealistic to expect. This raises the question of relative stringency in adhering to the individual principles. It is important to recognize that this document constitutes pronouncements that guide, support, or recommend, but do not mandate specific approaches or actions . . . inde- pendent of the professional judgment of those with expertise in the relevant area. (SIOP, 2003, p. 2) LEGAL GUIDELINES ON EMPLOYEE SELECTION PROCEDURES2 1. Adverse Impact A. Records Relating to Adverse Impact 1. What is the race, sex, or ethnic group of each applicant or candidate who has applied for, or is eligible for, consideration for each job? Sec. 4B, 15A 2. How are data gathered for those who appear in person? Sec. 4B 3. How are data gathered for those who do not appear in person? Sec. 4B 4. What are the operational definitions of “hires,” “promoted,” or “otherwise selected” and “applicant” or “candidate” used for computing the selection rate? Sec. 16R 5. Where records of race, sex, or ethnic background are kept on a sample, how is the sample selected? Sec. 4A 6. For a user with more than 100 employees what, for the past year, is the adverse impact of the selection procedures for groups that constitute more than 2 percent of the labor force or applicable work force? Sec. 15A(2)(a) B. Special Record-Keeping Provisions 1. Is the user exempted from keeping records on a race or ethnic group because it consti- tutes less than 2 percent of the labor force? Sec. 15A(1) 2. Does the user, by virtue of having fewer than 100 employees, qualify for simplified record-keeping procedures? Sec. 15A(1) 3. Where adverse impact has been eliminated, what is the adverse impact for the two suc- ceeding years? Sec. 15A(2)(b) C. Four-Fifths Rule 1. What is the distribution by race, sex, and ethnic group of applicants, candidates and those hired or promoted for each job for the period in question? Sec. 4B 2. Is the selection rate of any racial, ethnic, or sex group less than four-fifths of that of the group with the highest rate? Sec. 4D 3. Where the total selection process has an adverse impact, what is the adverse impact of the components? Sec. 15A(2)(a) D. Adverse Impact When User Meets Four-Fifths Rule 1. Does a statistically significant difference in selection rate have a practically significant impact on the employment of members of a race, sex, or ethnic group, even when it does not meet the four-fifths rule? Sec. 4D 2. Is the sample of candidates for promotion used in determining adverse impact restricted by prior selection on a selection procedure that is the same as, similar to, or correlated with, the procedure in question? Sec. 4C 3. Is the selection procedure a significant factor in the continuation of discriminatory assignments? Sec. 4C(1) 2 Zetetic for Testers II, © Richard S. Barrett, 1978, is used with the author’s permission. Checklist items are keyed to sections in the federal Uniform Guidelines on Employee Selection Procedures (1978). 453

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance 4. Does the weight of court decisions or administrative interpretations hold that the selec- tion procedure is not job related? Sec. 4C(2) 5. What data are there in the literature or available unpublished sources that bear on the differences in test scores of candidates from different races, sexes, or ethnic groups? Sec. 4D E. Qualifying Circumstances Relating to Adverse Impact 1. What procedures are used to recruit minorities and women, and what was their effect on the applicant population? Sec. 4D 2. How does the user’s general, long-term posture toward fair employment affect the conclusions regarding adverse impact? Sec. 4E 3. What safeguards are adopted to assure that recorded information about sex, race, or ethnic background is not used adversely against protected minorities and women? Sec. 4B 2. Validation A. General Information Regarding Validity 1. What is the purpose of the selection procedure? Sec. 15B(2), 15B(10), 15C(2), 15C(7), 15D(2), 15D(9) 2. What is the rationale for the choice of the validation strategy that is used? Sec. 5A, B, C, 14B(1), 14C(1), 14D(1) 3. How is it determined that specific jobs are included or excluded from the study? Sec. 14B(1), 14C(1), 14D(2), 15B(3), 15D(4) 4. What are the existing selection procedures, and how are they used? Sec. 15B(2), 15C(2), 15D(2) 5. What reasons are advanced, if any, that a criterion-related validity study is not techni- cally feasible? Sec. 14B(1) 6. What reasons are advanced, if any, that a test cannot, or need not, be validated? Sec. 15A(3)(v) 7. Does the user have, or has the user had since the Civil Rights Act applied to the user, records of data that can be or could have been used as predictors or criteria for a criterion- related validity study? Sec. 14B(1) 8. What has been done to change an informal or unscored selection procedure to one which is formal, scored, and quantifiable? Sec. 6B(1) B. Identifying Information 1. What are the names and addresses of the contact person or of the researchers who pre- pared any report on the selection procedure that is used in establishing its job related- ness? Sec. 15B(12), 15C(8), 15D(12) 2. What are the locations and dates of the validity study(ies)? Sec. 15B(1), 15C(1), 15D(1) 3. For each published selection procedure, manual, and technical report, what is the name, author, publisher, date of publication or revision, or form? Sec. 15B(1), 15C(4), 15D(6) 4. What is the content and format of each unpublished selection procedure? Sec. 15B(1), 15C(4), 15D(6) C. Job Analysis 1. What job analysis procedure is used? Sec. 14A, 14B(2), 14C(2), 14D(2), 15B(3), 15C(3), 15D(4) 2. When and for what purposes was the job analysis prepared and last revised? Sec. 14A, 14B(2), 14C(2), 14D(2), 15B(3), 15C(3), 15D(4) 3. How does the job analysis describe the work behaviors, their relative frequency, criti- cality or importance, level of complexity, or the consequences of error? Sec. 14A, 14B(2), 14C(2), 14D(2), 15B(3), 15C(3), 15D(4) 454

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance 4. How are the relative frequency, criticality or importance, level of complexity, and the consequences of error in job performance determined? Sec. 14A, 14B(2), 14C(2), 14D(2), 15B(3), 15C(3), 15D(4) D. Professional Control 1. What professional control is exercised to assure the completeness and accuracy of the collection of the data? Sec. 5E, 15B(13), 15C(9), 15D(10) 2. What professional control is exercised to assure the accuracy of the data analyses? Sec. 5E, 15B(13), 15C(9), 15D(10) 3. Was the analysis planned before examination of the data? If not, what changes were made, and why? Sec. 15B(8) 3. Criterion-Related Validity A. Sample 1. What is the definition of the population to which the study is to be generalized, and how is the sample drawn from it? Sec. 14B(4), 15B(6) 2. How does the departure, if any, from a random sample of applicants or candidates affect the interpretation of the results? Sec. 14B(4), 15B(6) 3. If any members of the population are excluded from the sample, what is the reason for their exclusion? Sec. 14B(4), 15B(6) 4. If any data on any members of the sample were eliminated after they were collected, what is the reason for their being eliminated, and how does their omission affect the conclusions? Sec. 14B(4), 15B(6), 15B(13) 5. What are the pertinent demographic data on the sample such as age, sex, education, training, experience, race, national origin, or native language? Sec. 14B(4), 15B(6) 6. Is the sample used in the validation study representative of the candidates for the job in age, sex, education, training, job experience, motivation, and test-taking experience, or other pertinent characteristics? Sec. 14B(4), 15B(6) 7. Where samples are combined, what evidence is there that the work performed and the composition of the samples are comparable? Sec. 14B(4) B. Criterion Measures 1. What is measured by each criterion? Sec. 14B, 15B(5) 2. How was criterion performance observed, recorded, and quantified? Sec. 15B(5) 3. What forms were used for each criterion measure? Sec. 14B(3), 15B(5) 4. What instructions are given to those who provide the criterion data, and how is it established that the instructions are followed? Sec. 14B(3), 15B(5) 5. Where an overall measure or a measure of an aspect of work performance is used, what steps were taken to make sure that it measures relevant work behaviors or work outcomes, and not irrelevant information? Sec. 14B(3), 15B(5) 6. Where several criteria are combined into one overall measure, what is the rationale behind the procedure for combination? Sec. 15B(5) 7. Where measures of success in training are used as the criterion, what showing is there of the relevance of the training to performance of the work, and of the relation- ship of performance on the training measures to work performance? Sec. 14B(3), 15B(5) 8. How is opportunity bias taken into account in the use and interpretation of objective measures? Sec. 14B(3), 15B(5) 9. Where measures other than those of job performance are used, such as tenure, regularity of attendance, error rate, or training time, what is the utility of predicting performance on these measures? Sec. 14B(3), 15B(5) 10. Where a paper-and-pencil test is used as a criterion, how is its relevance established? Sec. 14B(3), 15B(5) 455

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance 11. Where criterion measures are couched in terms that tend to define the subject matter covered by the test, what is the job relatedness of the measures? Sec. 14B(3), 15B(5) 12. What precautions are taken to make sure that judgments of employee adequacy are not contaminated by knowledge of performance on selection procedures? Sec. 14B(3), 15B(5) 13. What are the data bearing on leniency, halo, and reliability of measures of job performance? Sec. 15B(5), 15B(8) C. Fairness of Criterion Measures 1. What steps are taken to eliminate or take into account possible distortion in perform- ance measures as the result of conscious or unconscious bias on the part of raters against persons of any race, sex, or ethnic group? Sec. 14B(2), 15B(5) 2. Do minorities and women have equivalent assignments, materials, and quality control standards? Sec. 14B(2), 15B(5) 3. Do minorities and women have equal job experience and access to training or help from supervisors? Sec. 14B(2), 15B(5) 4. What comparison is made of rating results, broken down by race, sex, or ethnic group of raters and race, sex, or ethnic group of the workers who are rated? Sec. 15B(11) D. Results 1. What methods are used for analyzing the data? Sec. 14B(5), 15B(8) 2. What are the validity coefficients for all comparisons between predictors and criteria for all major subgroups? What is the number of cases and significance level associated with each validity coefficient? Sec. 14B(5), 15B(8) 3. For each measure of the selection procedure or criterion, what is the mean and stan- dard deviation for each major group? What is the reliability and standard error of measurement? Sec. 14B(5), 15B(8) 4. When statistics other than the Pearson product moment correlation coefficient (or its derivatives) or expectancy tables or charts are used, what is the reason that they are preferred? Sec. 14B(5) 5. Are validity coefficients and weights verified on the basis of a cross-validation study when one is called for? Sec. 14B(7) 6. How much benefit would accrue to the employer if it were possible to select those who score highest on the performance measure and how much actually accrues through the use of the selection procedure? Sec. 15B(10) 7. What does item analysis show about the difficulty of the items, the effectiveness of distractors (answers keyed as incorrect), and the relation between the items and the test or between the items and the criterion? Sec. 15B(5), 15C(5) *** E. Corrections and Categorization 1. Where a validity coefficient is corrected for restriction in range of the selection proce- dure, how is the restriction in range established? Are there any reasons why its use might overestimate the validity? Sec. 15B(8) 2. Where a validity coefficient is corrected for unreliability of the criterion, what is the rationale behind the choice of the reliability measure used? Are there any reasons why its use might overestimate the validity? Sec. 15B(8) 3. What are the levels of significance based on uncorrected correlation coefficients? Sec. 15B(8) 4. Where continuous data are categorized, and particularly where they are dichotomized, what is the rationale for the categorization? Sec. 15B(8) 456

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance F. Concurrent Validity 1. Where concurrent validity is used, how does the researcher take into account the effect of training or experience that might influence performance of the research subjects on the selection procedure? Sec. 14B(2), 14B(4), 15B(5) 2. Where concurrent validity is used, what account is taken of those persons who were considered for employment but not hired, or if hired, who left the job before their work performance was measured as part of the research study? Sec. 14B(4), 15B(6) G. Prediction of Performance on Higher-Level Jobs 1. Where proficiency on the higher-level job is used as a criterion, are the knowledges, skills, and abilities developed by training and experience on that job? Sec. 5I 2. Where proficiency on the higher-level job is used as a criterion, do a majority of the employees advance to the higher level job in less than five years? Sec. 5I H. Fairness 1. How is fairness defined? Sec. 14B(8), 15B(8) 2. How is the fairness of the selection procedure established? Sec. 14B(8), 15B(8) 3. What steps are taken to eliminate unfairness in performance measurements, and what is the evidence that they were successful? Sec. 14B(8), 15B(8) 4. Where the performance on a selection procedure is relatively poorer for minorities or women than their performance on the job, how is the selection procedure modified to eliminate the disparity? Sec. 14B(8), 15B(8) 4. Content Validity A. Relevance of a Content Validity Strategy 1. Are the applicants or candidates expected to be trained or experienced in the work? Sec. 14C(1) 2. Are the knowledges, skills, or abilities measured by the selection procedure learned on the job? Sec. 14C(1) 3. Does the selection procedure require inferences about the psychological processes involved? Sec. 14C(1) B. Relation between Selection Procedure and Work Behaviors 1. Is the selection procedure a representative sample of work behaviors? Sec. 14C(1), 14C(2) 2. How is it shown that the behaviors demonstrated in the selection procedure are repre- sentative of the behaviors required by the work? Sec. 14C(4) 3. Does the selection procedure produce an observable work product? Sec. 14C(2) 4. How is it shown that the work product generated by the selection procedure is repre- sentative of work products generated on the job? Sec. 14C(4) 5. What is the reliability of the selection procedure, and how is it determined? Sec. 14C(5) C. Knowledge, Skills, and Abilities 1. What is the operational definition of the knowledge, skill, or ability measured by the selection procedure? Sec. 14C(4) 2. How is it established that the knowledge or skill or ability measured by the test is a necessary prerequisite to successful performance? Sec. 14C(1), 14C(4) D. Adequacy of Simulation 1. Is that part of the work content represented by each item identified so that it is possible to determine whether the behavior required by the selection procedure is a sample of the behavior required by the job? Sec. 15C(5) 457

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance 2. Does the test question require a response that implies identifiable behavior? Sec. 14C(4) 3. Is the behavior identified by the keyed answer correct and appropriate to the job and the specific situation described? Sec. 14C(4) 4. Is the behavior identified by the test question accurately perceived by the test taker? Sec. 14C(4) 5. Is it likely that the actual job behavior will conform to the behavior described by the candidate’s response? Sec. 14C(4) 6. Does the level of difficulty of the question correspond to the level of difficulty of the work behavior required for satisfactory performance? Sec. 14C(4) 7. Can journey workers who are performing satisfactorily pass the test? Sec. 14C(4) E. Training 1. Is a requirement for a specified level of training, education, or experience justified on the basis of the relationship between the content of the work and of the training, edu- cation, or experience? Sec. 14C(6) 2. Where a measure of success in training is used as a selection procedure, how is it shown that the performance evaluated by the measure is a prerequisite to successful work performance? Sec. 14C(7) 5. Construct Validity 1. What is the operational definition of the construct measured by the test? Sec. 14D(2) 2. How is it determined that the constructs covered by the test underlie successful perform- ance of frequent, important, or critical duties of the job? Sec. 14D(2) 3. What is the psychological or other reasoning underlying the test? Sec. 14D(2) 4. What is the evidence from studies conducted by the user and by other researchers that shows that the selection procedure is validly related to the construct? Sec. 14D(3) 5. What evidence shows that the construct, as it is measured by the selection procedure, is related to work behaviors? Sec. 14D(3) 6. Validity Generalization 1. Where criterion-related validity studies performed elsewhere are used to show the job relatedness of a test that has not been validated locally, what showing is there that: • All reasonably accessible studies useful for establishing the weight of evidence of validity are included in the bibliography? (Copies of unpublished studies, or studies reported in journals that are not commonly available, should be described in detail or attached.) Sec. 15E(1)(e) • The studies are reasonably current and current research methods are used? Sec. 15E(1)(e) • The population and the sample drawn from it, the performance measures and job behaviors and other significant variables are sufficiently similar to permit generalization? Sec. 7B(2), 7D, 8B, 15E(1)(a), 15E(1)(b), 15E(1)(c) • The selection procedures are fair and valid for the relevant races, sexes, or ethnic groups? Sec. 7B(1), 7B(3), 7C, 15E 2. Where validity data come from an unpublished source, does the representative of the source assert that there is no evidence from other studies that failed to demonstrate validity or that shows the test to be unfair? Sec. 15E(1)(e) 3. What sources of unpublished research who were contacted indicated a) that they had no relevant information, b) that they had relevant information but would not communicate it, and c) that they communicated some or all of the relevant data? Sec 15E(1)(e) 4. Where validity studies incorporate two or more jobs that have one or more work behaviors in common, how similar are the work behaviors, and how was the similarity established? Sec. 14D(4)(b), 15E(1) 458

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance 7. Application A. Use of Selection Procedures 1. How is each of the selection procedures used in the selection decision? Sec. 14B(6), 14C(8), 14C(9), 15B(10), 15C(7), 15D(9), 15E(1)(d) 2. Does the use made of the validated selection procedures conform to the findings of the validity study? Sec. 5G, 14B(6) 3. What is the rationale for the weight given to each element in the employment procedure, including tests, interviews, reference checks, and any other sources of information? Sec. 15B(10) 4. How is it determined that rank ordering, if used, is appropriate for selecting employees? Sec. 14B(6), 14C(9), 15B(10), 15C(7), 15D(9) 5. How is it determined that the passing score, if used, is reasonable and consistent with normal expectations of acceptable proficiency of the work force employed on the job? Sec. 5H, 14B(6), 14C(8), 15B(10), 15C(7), 15D(9) 6. If the passing score is based on the anticipated number of openings, how is the score related to an acceptable level of job proficiency? Sec. 5H B. Test Administration 1. Under what conditions is the test administered with respect to giving instructions, per- mitting practice sessions, answering procedural questions, applying time limits, and following anonymous scoring procedures? Sec. 9B 2. What precautions are made to protect the security of the test? Is there any reason to believe that the test is not secure? Sec. 12 3. What steps were taken to assure accuracy in scoring, coding, and recording test results? Sec. 9B, 15B(13), 15C(9), 15D(10) 4. What procedures are followed to assure that the significance of guessing, time limits, and other test procedures are understood? Sec. 9B 5. Are the test takers given practice, warm-up time, and instructions on the mechanics of answering questions? Sec. 9B 6. Do all candidates have equal access to test preparation programs? Sec. 11 7. Under what conditions may candidates retake tests? Sec. 12 C. Selection Decisions 1. What are the qualifications of those who interpret the results of the selection proce- dure? Sec. 9B, 14B(6), 14C(8) 2. How are HR department receptionists and interviewers selected, trained, and super- vised? Sec. 9B 3. What questions do interviewers ask, what records do they keep, and what decision rules do they follow in making recommendations? Sec. 15B(7), 15C(4), 15D(6) 4. What control is exercised, and what records kept, regarding the decisions of supervi- sors to hire or promote candidates? Sec. 15B(7), 15C(4), 15D(6) 5. What are the procedures used to combine the information collected by the selection process for making the selection decision? Sec. 15B(7), 15C(4), 15D(6) D. Reduction of Adverse Impact 1. What adjustments are made in selection procedures to reduce adverse impact and to eliminate unfairness? Sec. 13B, 14B(8)(d) 2. Is the job designed in such a way as to eliminate unnecessary difficulties for minorities and women? Sec. 13A 3. In determining the operational use of a selection procedure, how are adverse impact and the availability of other selection procedures with less of an adverse impact taken into account? Sec. 13B 459

Appendix: Scientific and Legal Guidelines on Employee Selection Procedures—Checklists for Compliance 4. What investigation was made to identify procedures that serve the user’s legitimate interest in efficient and trustworthy workmanship and have less adverse impact? What are the results? Sec. 3B, 15B(9) 5. Has anyone with a legitimate interest shown the user an alternative procedure that is purported to have less adverse impact? If so, what investigation has the user conducted into its appropriateness? Sec. 3B 6. Have all races, sexes, and ethnic groups of applicants or candidates been subjected to the same standards? Sec. 11 7. Where validation is not feasible, what procedures are used to establish that the selec- tion procedures are as job related as possible and will minimize or eliminate adverse impact? Sec. 6A, 6B 8. Is the person who scores the tests or other selection procedure directly aware of, or able to, infer the sex, race, or national origin of the applicants? Sec. 9B E. Currency, Interim Use 1. Does a user who is using a test that is not fully supported by a validity study have sub- stantial evidence of validity, or have a study under way? Sec. 5J 2. When was the validity study last reviewed for currency of the validation strategy and changes in the labor market and job duties? Sec. 5K 460

APPENDIX: An Overview of Correlation and Linear Regression THE CONCEPT OF CORRELATION The degree of relationship between any two variables (in the employment context, predictor and criterion) is simply the extent to which they vary together (covary) in a systematic fashion. The magnitude or degree to which they are related linearly is indicated by some measure of correla- tion, the most popular of which is the Pearson product moment correlation coefficient, r. As a measure of relationship, r varies between –1.00 and +1.00. When r is 1.00, the two sets of scores are related perfectly and systematically to each other (see Figure 1). Bivariate plots of predictor and criterion scores, as in Figure 2, are known as scatterplots. In the case of an r of +1.00, high (low) predictor scores are matched perfectly by high (low) criterion scores. When r is –1.00, however, the relationship is inverse, and high (low) predictor scores are accompanied by low (high) criterion scores. In both cases, r indicates the extent to which the two sets of scores are ordered similarly. Needless to say, given the complexity of variables operating in applied settings, rs of 1.00 are the stuff of which dreams are made! If no relationship exists between the two variables, then r is 0.0, and the scatterplot is circular in shape. If r is moderate (positive or negative), then the scores tend to cluster in the shape of a football or ellipse (see Figure 2). Obviously the wider the football, the weaker the relationship, and vice versa. Note that in pre- dicting job success the sign of the correlation coefficient is not important, but the magnitude is. The greater the absolute value of r, the better the prediction of criterion performance, given a knowledge of predictor scores. In fact, the square of r indicates the percentage of criterion variance accounted for, given a knowledge of the predictor. Assuming a predictor–criterion correlation of .40, r2 = .16 indicates that 16 percent of the variance in the criterion may be determined (or explained), given a knowledge of the predictor. The statistic r2 is known as the coefficient of determination. As an overall measure of relationship, r is simply a summary statistic, like a mean. In fact, both predictor and criterion variables can be put into standard score form: x-x y-y zx = sx and zy = , sy High High Job performance criterion y Low High Low High Low Low Predictor Predictor score score x x FIGURE 1 Perfect positive and negative relationships. From Appendix B of Applied Psychology in Human Resource Management, 7/e. Wayne F. Cascio. Herman Aguinis. Copyright © 2011 by Pearson Education. Published by Prentice Hall. All rights reserved. 461

(a) High Appendix: An Overview of Correlation and Linear Regression (b) (c) Job performance criterion y r = 0.0 r = +0.50 r = – 0.50 High Low Predictor score Low x Predictor score Predictor score x x FIGURE 2 Examples of correlations varying in magnitude and direction. where sx and sy are population standard deviations, usually estimated using their sample-based counterparts Sx and Sy. Then r can be interpreted as a mean. It is simply the average of the sum of the cross products of zx and zy: a zxzy (1) r= n Of course, r is only one type of correlational measure. Sometimes the scatterplot of x and y values will indicate that the statistical assumptions necessary to interpret r—namely, bivariate normality, linearity, and homoscedasticity—cannot be met. Under these circumstances, other, less restrictive measures of correlation may be computed (cf. Guilford & Fruchter, 1978), but, like r, each is a measure of relationship between two variables and may be interpreted as such. THE CONCEPT OF REGRESSION Although correlation is a useful procedure for assessing the degree of relationship between two variables, by itself it does not allow us to predict one set of scores (criterion scores) from another set of scores (predictor scores). The statistical technique by which this is accomplished is known as regression analysis, and correlation is fundamental to its implementation. The conceptual basis for regression analysis can be presented quite simply by examining a typical bivariate scatterplot of predictor and criterion scores, as in Figure 2(b). The scatterplot yields several useful pieces of information. The predictor–criterion relationship obviously is pos- itive, moderately strong (r = +.50), and linear. In order to predict criterion scores from predictor scores, however, we must be able to describe this relationship more specifically. Prediction becomes possible when the relationship between two variables can be described by means of an equation of the general form y = f(x), read “y is a function of x.” In other words, for every value of x, a value of y can be generated by carrying out appropriate mathematical operations on the value of x. In short, if x is the predictor, y (the criterion) can be predicted if we can specify the function f, which serves to relate x and y. Perhaps the most familiar of all functional relationships is the equation for a straight line: yN = a + bx. Since r always measures only the degree of linear relationship between two vari- ables, the equation describing a straight line (the basis for the general linear model in statistical theory) is especially well suited to our discussion. The interpretation of this equation (in this con- text termed a regression line) is straightforward. For every unit increase in x, there is an increase in y that may be determined by multiplying x by a regression coefficient b (the slope of the 462

Appendix: An Overview of Correlation and Linear Regression 100 Job 90 y performance 80 70 y^ criterion y y 60 50 r = 0.50 0 25 50 75 100 Predictor score x FIGURE 3 Prediction of job performance from predictor scores. straight line, Δy/Δx, which indicates the change in y observed for a unit change in x) and adding a constant a (indicating the point at which the regression line crosses the Y-axis). When this functional relationship is plotted for all individuals in the sample, the result will be a straight line or linear function, as in Figure 3. The goodness of fit of the regression line to the data points can be assessed by observing the extent to which actual scores fall on the regression line as opposed to falling either above it or below it. In Figure 3, for example, note that for a predictor score of 50 we predict a job performance score of 77 for all individuals with predictor scores of 50. This y value may be determined by extending a projection upward from the X-axis (predictor score) until it inter- sects the regression line and then reading off the predicted y value from the Y-axis (criterion score). As the scatterplot in Figure 3 demonstrates, however, of those individuals with the same predictor score of 50, some score above 77 on the criterion and some score below 77. Since the correlation between predictor and criterion is less than 1.00, prediction will not be perfect, and some errors in prediction are inevitable. The regression line, therefore, is simply a moving average or mean, which summarizes the predictor–criterion relationship at each x value. The difference between observed (y) and predicted 1yn2 job performance scores at each x value is the amount by which the regression line prediction is in error. By extension, the average error in prediction from the regression equation for all individuals could be summa- rized by g(y - yn)/n. But, since the regression line is a moving average or mean and since one property of a mean is that deviations above it are exactly compensated by deviations below it (thereby summing to zero), such an index of predictive accuracy is inappropriate. Hence, devi- ations from the regression line (y - yn) are squared, and the index of predictive accuracy or error variance is expressed as s2y.x = a (y - yn)2/n (2) Note the subscripts y.x in Equation 2. These are important and indicate that we are predicting y from a knowledge of x (technically we are regressing y on x). In correlation analysis, the order of the subscripts is irrelevant, since we only are summarizing the degree of relationship between x and y and not attempting to predict one value from the other. That is, rxy = ryx. In regression analysis, however, by.x ordinarily will not be equivalent to bx.y (unless rxy = 1.00). Since the aim is to predict, the designation of one variable as the predictor and the other as the criterion is important (Landis & Dunlap, 2000); so also is the order of the 463

Appendix: An Overview of Correlation and Linear Regression subscripts. For any given problem in bivariate linear regression, therefore, there are two regression lines: yn = a + bx and xn = aœ + b œy A logical question at this point is “Okay, we know how to measure how accurate our regression line is, but how can we plot it so that it provides the best fit to the data points?” Statisticians generally agree that a line of best fit is one that is cast in such a way that the average error of prediction, g (y - yn)2/n is a minimum. When this condition is satisfied, we have achieved a least-squares solution of our regres- sion equation yn = a + bx. Although in principle the number of possible values of b that will yield a linear equation is infinite, only one value will produce a line of best fit (in the least- squares sense), since the average error of prediction will be minimized at that value. How can such a value be determined? Mathematically, the optimum value of b is directly related to r: sy (3) by.x = rxy sx That is, b represents the slope of the regression line. The slope is affected by two parameters: (1) rxy, the correlation coefficient; and (2) the variability of criterion scores about their mean (sy), relative to the variability of predictor scores about their mean (sx). If both x and y are in standard (z) score form, then both sx and sy are equal to 1.0, and the slope of the regression line is equal to rxy. For example, suppose Jerry scores 75 on an aptitude test whose validity with respect to a certain criterion is .50. The mean test score is 60, and the standard deviation of the test scores is 15. Therefore, Jerry’s zx score is (75 - 60) 15 = = 1.00 15 15 Since the test-criterion relationship is .50, Jerry’s predicted criterion score is zyn = rxyz x = (.50)(1.0) = .50 or half a standard deviation above the mean criterion score. Since all scores are in standardized form, a = 0; but, when x and y are in raw score (unstandardized) form, then a Z 0. The value of a may be obtained, however, by the following formula: a = qy - bqx (4) Assume that in Figure 3 the regression line crosses the Y-axis at a value of 50 (that is, a = 50). Assume also that for every unit increase in x there is a half-unit increase in y (that is, b = 0.5). The regression equation yn = a + bx then may be expressed as yn = 50 + .5x For any given x value, we now have a regression equation that allows us to predict a y value corresponding to it. For example, if x were 80, then yn = 50 + (.5)(80) = 50 + 40 = 90 464

Appendix: An Overview of Correlation and Linear Regression Let us pause for a moment to answer a question that probably is perplexing you by now: “If we already know the criterion scores of a group, why do we need to predict them?” The answer is that, when we set out initially to determine the degree of predictor–criterion relation- ship, we do need both sets of scores; otherwise, we could not assess the relationship in the first place. If the relationship is strong, then we may want to use the predictor to forecast the criterion status of all new applicants for whom no criterion data exist, and we probably can do so quite accurately. Accuracy also may be increased by adding one or more predictors to our single pre- dictor. The problem then becomes one of multiple prediction, and we shall consider it further in the next section. MAKING PREDICTIONS BASED ON MULTIPLE PREDICTORS Geometrically, the amount of bivariate predictor–criterion association may be visualized in terms of Venn diagrams—that is, in terms of the amount of overlap between two circles that rep- resent, respectively, the total variances of x and y (see Figure 4). Since there still exists a fair amount of potentially predictable criterion variance, a stronger relationship (and, therefore, appreciably more accurate criterion prediction) is likely to result if additional valid predictors can be found and incorporated into the regression equation (see Figure 5). Such a conception is much more representative of real-world job success prediction, since decisions generally are made on the basis of multiple sources of information. This more complex state of affairs presents little problem conceptually, representing only a generalization of bivariate correlation and linear regression to the multivariate case. For a more rigorous treat- ment of these topics, consult any one of several excellent texts (e.g., Cohen, Cohen, West, & Aiken, 2003; Pedhazur, 1982). In the case of multiple regression, we have one criterion variable, but more than one predictor variable. Their combined relationship is called a multiple correlation and is symbol- ized by R. Likewise, R2, the coefficient of multiple determination, analogous to r2, indicates the proportion of criterion variance that may be explained using more than one predictor. In practice, the degree to which prediction can be improved (i.e., the amount of additional crite- rion variance that can be accounted for) depends on several factors. A crucial one is the degree of intercorrelation among the predictors themselves. Compare the situation in Figure 5 with that of Figure 6. When the predictors are uncorrelated, as in Figure 5, R2 may be computed simply by adding together the individual squared correlation coefficients, r2: R 2y.x1x2x3 Á xn = r 2 + r 2 + r 2 + Á + r 2 (5) x1y x2y x3y xny When the predictors are correlated with one another, however, the computation of R2 becomes a bit more involved. In examining Figure 6, note that the amount of overlap between the criterion and each predictor can be partitioned into two components: (1) that which is unique to a given predictor and (2) that which is shared with the other predictors. In computing R2, we are concerned only with determining the amount of unique criterion variance explainable by the predictor composite. YX FIGURE 4 Bivariate predictor/criterion covariation. 465

Appendix: An Overview of Correlation and Linear Regression X1 Y X2 X3 FIGURE 5 Predictor/criterion covariation, given uncorrelated predictors. Therefore, for each predictor, that portion of predictor–criterion overlap that is shared with the other predictors must be removed. This can be accomplished (in the two-predictor case) as follows: r 2 + r 2 - 2rx1x2rx1yrx2y x1y x2y Ry2.x1x2 = (6) 1 r 2 - x1x2 Consider two extreme cases. If rx1x2 = 0, then Equation 6 reduces to Equation 5. On the other hand, if x1 and x2 are perfectly correlated, then no additional criterion variance can be accounted for over and above that which is accounted for using bivariate correlation. As a general rule of thumb then, the higher the intercorrelation between predictors, the smaller the increase in R2 as a result of adding additional predictors to the selection battery. In the employment context, we are concerned primarily with generating predictions of job suc- cess (using the multiple linear regression model), given knowledge of an individual’s standing on several predictor variables. As with bivariate regression, certain statistical assumptions are necessary: linearity, homoscedasticity, and normality. In addition, it is assumed that errors are random (with a mean value of zero and a population variance equal to se2) and that any pair of errors will be inde- pendent (i.e., the errors corresponding to two observations, y1 and y2, do not influence one another). The multiple-regression model is simply an extension of the bivariate regression model. The general form of the model is as follows: y = a + byx1.x2Áxnx 1 + byx2.x1Áxn + Á + byn.x1Á xn x n (7) - 1 X1 Y X2 X3 FIGURE 6 Predictor/criterion covariation in the case of correlated predictors. 466

Appendix: An Overview of Correlation and Linear Regression The a and b coefficients are interpreted as in bivariate regression, except that byx1.x2Áxn is the regres- sion coefficient for the x1 values and byx2.x1Áxn is the regression coefficient for the x2 values. The value of byx1.x2Áxn (known as a partial regression coefficient) indicates how many units y increases for every unit increase in x1 when the effects of x2 . . . have been held constant. Likewise, the value byx2.x1Áxn indicates how many units y increases for every unit increase in x2 when the effects of x1 . . . xn have been held constant. In short, each partial regression coefficient indicates the unique contribution of each predictor to the prediction of criterion status. As in bivariate regression, the b weights are optimal (in the least-squares sense) and guarantee the maximum possible correlation between predicted and obtained y values. Calculation of the optimal b weights requires the simulta- neous solution of a set of linear equations (known as normal equations) in which there are as many normal equations as there are predictors. This is a rather complex procedure, but, in view of the wide availability of statistical software programs, it is less of an obstacle today than it once was. The con- stant a can be computed readily in the multiple regression two-predictor case from a = yn - qx 1byx1.x2 - qx 2byx2.x1 (8) Likewise, R2 = s 2 Á xn y.x1x2 s2y and indicates the proportion of total criterion variance that is accounted for by the predictor variables. The implementation of the multiple regression model is straightforward, once we have derived our prediction rule (i.e., determined the optimal b weights). Assume we have data on 200 persons hired over a six-month period in a large, expanding manufacturing operation. The data include scores on an aptitude test (x1) and a work sample test (x2), as well as job performance measures after the six-month period. After analyzing these data to determine the values of a, byx1.x2, and byx2.x1 that best describe the relationship between predictors and criterion, suppose our multiple-regression equation assumes the following form: yn = 8 + .3x 1 + .7x 2 This equation says that the most likely criterion score for any new applicant (assuming the appli- cant comes from the same population as that on whom the equation was derived) is equal to 8 plus .3 times his or her aptitude test score plus .7 times his or her work sample score. If a new applicant scores 60 on the aptitude test and 70 on the work sample test, his or her predicted job performance score six months after hire would be yn = 8 + (.3)(60) + (.7)(70) = 8 + 18 + 49 = 75 PREDICTIVE ACCURACY OF MULTIPLE REGRESSION The best-fitting regression line may be considered a kind of moving average or mean, but there will be some dispersion of actual criterion scores both above and below those predicted by the regression line. These scores tend to distribute themselves normally (see Figure 3), with the preponderance of actual criterion scores falling on or near the regression line and fewer scores falling farther away from it. A distribution of these deviations for all individuals would provide a useful index of how far off we are in predicting y from x. The wider the dispersion, the greater the error of prediction. (Conversely the smaller the dispersion, the smaller the error of prediction.) Since the standard deviation is a convenient measure of dispersion, we can use it as an index of the extent of our errors in prediction. 467

Appendix: An Overview of Correlation and Linear Regression Equation 2, sy2.x = g (y - yn)2/n, which we referred to earlier as our index of predictive accuracy, is a variance indicating the amount of variability about the regression line. The square root of this expression is a standard deviation—the standard deviation of the errors of estimate— more commonly known as the standard error of estimate (SEE). Although the SEE is computed based on sample data and, therefore, is a statistic, we are interested in the population estimate, sym- bolized with sy.x. It can be shown (Ghiselli, Campbell, & Zedeck, 1981, p. 145) that sy.x = C a (y - yn)2/n is equivalent to sy.x = sy 41 - r 2 xy or, in the case of two predictors (which can easily be extended to more than two), sy.x1x2 = sy 41 - Ry2.x1x2 (9) The standard error of estimate (sest) is interpreted in the same way as any standard devia- tion. It is a most useful measure, for it allows us to create confidence limits around a predicted criterion score within which we would expect some specified percentage of actual criterion scores to fall. Thus, on the average, 68 out of 100 actual criterion scores will fall within ; 1sest of predicted criterion scores, and 95 out of 100 actual criterion scores will fall within ; 1.96 sest of predicted criterion scores. To illustrate, suppose the standard deviation of a sample of job per- formance scores for recent hires is 8.2 and the multiple R between a battery of three tests and a criterion is .68. The sest for these data may be computed as follows: sest = 8.2 21 - .682 = 6.0 for all applicants with predicted criterion scores of 86. For example, the limits 80 and 92 (86 ; 6.0) will contain, on the average, the actual criterion scores of 68 percent of the applicants. Likewise, the limits 74.2 and 97.8 (86 ; 1.96 sest) will contain, on the average, the actual criterion scores of 95 percent of the applicants. Suppose R2 = 0 for a given predictor–criterion relationship. Under these circumstances, the slope of the regression line is zero (i.e., it is parallel to the X-axis), and the best estimate of crite- rion status for every value of the predictor is equal to qy. In such a situation, sest equals sy.x1x2 = sy 41 - Ry2.x1x2 sest = sy 11 - 0 sest = sy Thus, even if R2 = 0, criterion status for all individuals still can be predicted with sest = sy if sy is known. Therefore, sy serves as a baseline of predictive error from which to judge the degree of improvement in predictive accuracy by any regression equation with R2 7 0. As R2 increases, sest decreases, thereby demonstrating enhanced predictive accuracy over base- line prediction. 468

APPENDIX: Decision Trees for Statistical Methods Do you wish to test a hypothesis? YES NO Estimation problem Test of hypothesis (see Fig. 2) Is there more than one variable? NO YES Univariate methods Multivariable procedures Confidence intervals point estimates Is there more than one measurement variable? NO YES Regression Are there more than Are two measurement relationships estimated linear variables? in their parameters? NO YES NO YES Partial r Nonlinear Linear regression Can you regression Multiple linear assume a linear regression relationship between the variables? NO YES The correlation Can you ratio (eta) assume normality? Note: Measurement variable = a variable for which we record responses, readings, or observations. NO YES Spearman's rho Pearson r Kendall's tau Biserial r Point-biserial r Kendall's partial Tetrachoric r rank correlation FIGURE 1 Decision tree for estimation problems. From Appendix C of Applied Psychology in Human Resource Management, 7/e. Wayne F. Cascio. Herman Aguinis. Copyright © 2011 by Pearson Education. Published by Prentice Hall. All rights reserved. 469

Appendix: Decision Trees for Statistical Methods Test of hypothesis Are you interested in testing for relationships? NO YES Is the Are measurement variable the data quantitative? quantitative? NO YES NO YES Is the Is there χ2, Contingency Tests of coefficients independent more than coefficient of correlation variable quantitative one variable? and normally Fisher's exact test Test of regression distributed? YES Binomial test, estimates Phi coefficient NO Discriminant analysis NO YES Fisher's exact test Multinomial proba- Can Is there you assume more than one bility rule normality? measurement χ2, Phi coefficient variable? NO YES NO YES Mann-Whitney t test Can you Multivariate U test χ2 test assume procedures normality? (see Fig. 3) NO YES Friedman ANOVA Analysis of variance Kruskal-Wallis Analysis of covariance ANOVA FIGURE 2 Decision tree for hypothesis testing. 470

Appendix: Decision Trees for Statistical Methods All multivariate methods Are some of the variables dependent on others? YES NO Dependence Interdependence methods methods How many Are variables are inputs dependent? quantitative? One Several YES NO Is Are it they quantitative? quantitative? YES NO YES NO Multiple Multivariate Factor Cluster Metric regression analysis analysis analysis multidimensional of variance scaling Multiple Canonical Nonmetric Latent discriminant analysis scaling structure analysis analysis FIGURE 3 A classification of multivariative methods. Source: Reprinted from J. N. Sheth. The multivariative revolution in marketing research. Journal of Marketing, 1971, 35, 15. Published by the American Marketing Association. 471

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Index Page references followed by \"f\" indicate illustrated Agencies, 13-14, 16-17, 19-21, 24, 27-29, 37, availability, 30, 104, 197, 200, 204, 231, 244, 246, 267, figures or photographs; followed by \"t\" indicates a 171-172, 201-203, 217, 246, 262, 269, 282, 307, 345, 402, 445-446, 449, 452, 459, table. 407-408, 411 467 A Agencies, advertising, 203 testing, 30, 267, 282, 452 agenda, 254, 281, 405 Available, 4, 9, 13, 16, 21, 25, 29-30, 39, 45, 51-52, Abilities, 1-3, 32, 45, 47, 56-57, 79, 95, 112, 123, 129, Agent, 205, 209, 273, 375 134, 137, 145, 169-170, 178-180, 183-186, Agents, 56, 58, 62, 196-197, 261, 268, 302 55, 73, 77, 79-80, 90, 101, 117-118, 191, 193, 208, 214, 216-218, 222, 228-236, 126-128, 135, 138, 141, 144, 147-149, 237-238, 244, 250, 257, 261-262, 265, 270, life insurance, 56, 62, 261, 302 152-154, 159-161, 163-164, 166-167, 170, 276, 286-287, 296, 302, 307, 312-313, 315, Aggregate demand, 246 176-177, 181-185, 189, 192-194, 196, 318, 319, 328-329, 352, 361-362, 405, 407, aggression, 53, 263 198-199, 201-202, 208, 213, 221, 225, 420, 433, 445, 448, 457 agreement, 5, 13, 36, 78, 81-84, 90, 93, 98, 115, 123, 229-232, 236, 238, 243, 245-246, 255-258, 260, 263, 267, 270, 273, 275-276, 282, Absenteeism, 60-63, 261, 263, 379 130-131, 140, 142, 158, 175, 221, 226-227, 286-289, 294, 296, 302-303, 306-308, age and, 263 238, 279, 289, 383, 430, 442, 448 317-318, 319-321, 325, 328, 330, 332, 336, Agreements, 5, 23, 130, 347, 400, 421 339, 347-349, 359, 361, 374, 378, 380-383, Abstract, 30, 144-145, 156 AIDS, 24, 86, 222-224, 275, 315, 369 387-388, 396-397, 399, 402, 404, 408, 415, Abstraction, 42, 144 AIG, 248 430, 443, 445, 447-448, 451-452, 454, 458 Abuse, 53, 55, 263, 365, 402 Aircraft, 214, 229 Avatars, 203 Accountability, 96, 98, 339, 400-402, 413 Alternative media, 272, 280 avoidance, 295, 421, 428-429, 440 Accountants, 56, 230 American Management Association, 198, 409 Awareness, 3, 43, 101, 105, 122, 312, 315, 335, 360, Accounting, 56, 68-69, 338, 379, 439 American Society for Training and Development, 348 389, 404, 436-437 Accounting rules, 439 Americans with Disabilities Act, 17, 23, 25 accuracy, 56, 76, 83, 86, 88, 92-93, 98, 101-102, 1991, 17, 25 B anecdotes, 91, 229 106-108, 117, 132, 134, 147, 154, 168, 175, Anger, 49, 410 Back-translation, 430 181, 193, 221, 232, 250, 267, 269, 277, 279, Animation, 376 bad news, 441 281, 289-290, 301, 308, 311, 319-320, 323, announcements, 34, 403 325, 335-336, 340-341, 346, 410, 413-414, antecedents, 156, 415 communicating, 441 445, 450, 455, 459, 463, 465, 467-468 anticipate, 40, 193, 238, 253, 421 Balance sheets, 406 of information, 93, 311, 325, 410, 445, 459, 465 appearance, 20, 61, 166, 270, 272, 274-276 Bankruptcy, 28, 256 Achievement, 9, 31, 54, 92, 117, 120-121, 126, 139, Application, 17, 46, 102, 114, 118-119, 137, 139-140, Banks, 95, 102, 216 144, 148, 150, 209, 256, 265-266, 290-291, 153-154, 162, 164, 167, 183, 196, 203, 207, Bargaining, 4, 36, 227, 348, 411 295, 298-299, 301, 361-362, 367-368, 379, 255, 257-259, 261-262, 266-268, 270, 275, Behavior, 2-4, 7, 33, 48, 51, 55, 59-64, 68-71, 73, 79, 388, 401, 422, 439 278, 282, 297, 331, 338, 341, 349, 355, 360, Acquisitions, 5, 240, 243, 335 362-365, 379, 385, 389, 399, 408, 424, 448, 82-83, 87-91, 94-97, 100, 103-108, 113, Action plans, 36, 45, 196, 237-239, 243-245, 250, 459 118-119, 121, 123, 140, 141-142, 144-145, 253-254, 375 Application forms, 46, 118, 257-259, 261-262, 266, 147-148, 151, 156-157, 159, 161, 176, 214, Adaptation, 437 278, 282, 331 223-224, 229, 234, 245, 249, 255, 258-261, addresses, 34, 57, 160, 186, 286, 335, 405, 422, 437, application service providers, 207 264, 270-274, 278, 280-281, 286-288, 293, 454 Applications, 46, 197, 199, 201, 203, 207, 213, 216, 295-296, 300-303, 305, 307-308, 310-311, Adjustment process, 438 234, 236, 258-259, 265, 267, 273, 308-309, 313-315, 324-325, 339, 347-349, 353, adjustments, 13, 175, 218, 342-343, 447, 459 334-335, 365, 386, 438 355-358, 361-365, 367-371, 374, 376-377, Administrators, 282, 304, 450, 452 Arab countries, 428 379-380, 386-387, 394, 396, 399-401, Advances, 33, 195, 231, 266, 280, 282, 293, 412 Argentina, 435 406-407, 410, 413, 415-416, 419-421, 424, Advantages, 6, 74, 81, 88, 90, 106, 108, 160, 165, arguments, 36, 67-68, 188, 194, 295, 424 427-430, 436, 438-439, 443, 445, 450, 167-168, 169, 194, 212, 221, 236, 266-267, logical, 295 457-458 276, 282-283, 287, 300, 318, 322, 326, 336, Art, 427 consistency in, 60 346, 366, 382, 389, 397 Asia, 5 in groups, 4, 90, 97 Advertising, 20, 27, 63, 69, 196, 200-203, 246, 417, Assertiveness, 158, 288 morale and, 63 424 Assets, 23, 42 Behavioral objectives, 358, 361 Co-op, 202 attention, 41, 48, 56-57, 59-60, 71, 81, 87, 91, 104, Behaviorally anchored rating scales, 91, 279 corporate, 424 129, 140, 143, 147-148, 166, 191, 195, 204, Belgium, 429, 435 defined, 201 212, 273, 281, 289, 300, 307, 315, 317, 347, Benefits, 4, 9, 16, 19, 22-23, 27-29, 33, 35, 37, 64, 77, e-mail, 424 359, 362, 365, 367, 369, 375-376, 385, 402, 167, 193, 200, 211, 217, 243, 253, 267, 279, evaluating, 63 405, 407-408, 413, 432, 436 295, 300, 335-336, 338, 340-341, 343-344, local, 20, 201 Attitudes, 47, 60, 94, 96, 98, 121, 123, 150, 209, 216, 360, 363, 376, 384, 402-403, 405, 414, 417, online, 203 233, 249, 259, 263, 272, 274, 296, 299, 308, 419, 421, 424, 428, 437, 439, 447 product, 69 348-351, 353, 355-359, 362-364, 368, 381, payment of, 19 types of, 200-202 389, 395, 406, 427-428, 430, 446 service, 4, 9, 23, 27-28, 267 Affect, 3, 20, 34, 43, 48-49, 51, 58, 61-62, 68, 71, consistency in, 60 Best practices, 403 82-83, 95-96, 98, 103, 105, 108, 117, 119, of employees, 363, 428 biases, 66, 71, 73, 79, 83, 85-87, 89, 95, 101, 108, 124, 129, 140, 150-151, 156, 158, 168, Attorneys, 13, 266 232, 264, 273, 280, 282, 288, 310-312, 315, 179-180, 196-197, 202, 212, 221, 238, 250, Attribute, 63, 107, 118-120, 130, 145, 216, 260, 387, 392, 441 266, 272, 276-277, 280-282, 292-294, 297, 344-346, 363, 416, 430 cultural, 441 302, 304, 307, 315, 323, 340, 342-346, attributes, 97, 107, 112, 140, 144, 200, 204, 216-217, bibliography, 458 349-350, 355, 359, 364, 368, 380, 383-386, 221, 233-234, 247, 254, 260, 262, 274, 289, Bid, 27 389, 392, 394, 396, 401, 405-406, 408, 413, 291, 336, 349, 363, 386, 428, 438 Big Five model, 293 415, 418, 439-440, 446, 454-455 audience, 98, 217, 378, 452 Blending, 238 Affirmative action programs, 20, 29, 36 needs of, 452 blogs, 427 Africa, 428 Audits, 252 business, 427 African Americans, 18, 29, 31, 36, 96, 119, 178-179, Australia, 441 company, 427 183, 185, 290, 303, 313, 389, 417 Austria, 429 board of directors, 249, 254 Age, 1, 3, 6, 13, 16-17, 22-23, 25-26, 29, 34-35, 37, authentication, 410 Boards of directors, 45, 248 62-63, 96-98, 137, 150, 166, 170, 208, 245, Authority, 7, 14, 55, 84, 214, 262-263, 297-298, 418 Body coordination, 228 249, 252, 260, 263, 268, 272, 287, 290, 306, civil, 262 Body language, 428 316, 342, 348, 350-351, 356, 387, 409, 428, authorization, 23, 409-410 Bonds, 2 455 Autocratic, 7 performance, 2 Age Discrimination in Employment Act, 17, 22 Autocratic style, 7 Brand, 154, 196, 212, 214, 306-307, 404 brand equity, 196 decisions about, 214 473

Brand equity, 196 dynamic, 99 constructive feedback, 360, 369, 377 Brand names, 404 Collective bargaining, 4 Consumer behavior, 245 Breakdown, 365 Collectivism, 427-429, 444 Consumer groups, 42 Break-even analysis, 340 Colleges, 202, 246, 265 Consumers, 5, 82, 353, 402-403, 416 Bridges, 213, 228 Columbia, 185, 435 Consumption, 289 Britain, 347, 441 Columns, 83, 134 Contacts, 198, 203-204, 206, 208, 210, 222, 439 Budget, 122 Commitment, 6, 24, 48-49, 62, 81, 106, 108, 254, Content, 19, 31, 43, 49, 60, 89, 95, 99, 102, 112, 118, Budgeting, 69, 243, 319, 337, 339, 341 350-351, 355, 359, 367-368, 394, 406, 414, 120, 122-123, 125-126, 128, 131-132, 140, capital, 69, 319, 337, 339, 341 421, 431-432 141, 144-148, 155-156, 166-168, 188, 214, Bureau of Labor Statistics, 246 committees, 17, 413 220-221, 224, 228, 233-236, 260, 263, 273, Business environment, 432 Communication, 4, 7, 10, 49, 74, 76, 99, 107, 113, 280-281, 286, 300, 306, 309, 314, 347, 350, Business operations, 5, 250, 426 196-197, 207-208, 217, 270, 296, 312, 314, 352-354, 357-358, 362, 364-367, 370, 374, Business plan, 242 360, 363, 376, 392, 394, 405, 436, 440-441 377-381, 384, 395-397, 400, 405, 432, 437, Business planning, 45, 238, 243, 246, 250 barriers to, 76 439, 443, 445, 447-452, 454, 457-458 business plans, 45, 238-240 Communication process, 270 Contingency theories of leadership, 7 business processes, 243 communication skills, 217 path-goal theory, 7 Business strategy, 195, 210, 242, 250, 253, 289, 350, Communism, 426 Continuity, 183, 248 Communist Party, 22 Continuous improvement, 350, 439 403 Companies, 6, 8-10, 19, 22, 24, 27, 30, 42, 62, 85, Contract, 5-6, 11, 16, 27, 29, 37, 193, 213, 402 198, 200, 203-207, 209-211, 238, 240-242, Contracts, 10, 18, 21, 27-28, 30, 249, 268, 428 C 244, 246, 248, 266, 298, 308, 347-350, 374, Contrast effect, 273 378, 401-403, 405, 408-410, 426-427, Control, 7, 10, 17, 23-24, 43, 45, 48-49, 59, 63, 67, 71, Canada, 405, 434-435 432-433, 436-437, 442-443 73, 77, 81, 87-88, 90, 93, 107, 191, 196, Capabilities, 3, 47, 197, 209, 240-241, 250, 353-354, Company culture, 9 207, 221, 223, 226, 228-229, 237-239, 243, company policy, 94 246, 252-254, 259, 274, 287-288, 290, 294, 365, 370, 393 Compensation, 4, 9, 13, 19-20, 24, 48, 192, 211, 238, 296, 299, 303-306, 345, 359-361, 363, 365, Capacity, 298, 310, 365, 404 240, 243, 245, 248, 256, 263, 340, 405, 374, 376-378, 381, 383-397, 402-403, 409, Capital, 2, 5-6, 9, 42, 69, 202, 240-241, 246, 248, 441-443 418-419, 429, 438, 442, 452, 455-456, 459 as motivation, 4 Control systems, 252 252-253, 319, 337, 339, 341, 345, 348, 382, of employees, 238, 245, 248, 340, 405 Controllers, 8 402-403, 427 Compensatory damages, 18, 25 Controlling, 17, 20, 30, 54, 80, 277-278, 400, 427 customer, 9, 240-241, 348 Compete, 1, 3, 5, 7, 9, 11, 32, 239-243, 249, 254 Conventions, 202 fixed, 339 Competition, 5, 10, 87, 195, 201, 210, 241, 243-244, Convergence, 141, 228, 314 growth, 241, 248, 345, 348 288, 293, 298, 347, 353 Conversation, 215, 225, 279 human, 2, 5-6, 9, 69, 202, 240, 246, 252, 319, 339, Competitive advantage, 9, 195, 203, 238-239, 252 conversations, 408 Competitive intelligence, 196-197 conversion, 40, 440 341, 402-403 Competitive strategy, 213, 233, 241 Cooperation, 9, 90, 121-122, 226, 256, 288, 293, 302, intellectual, 240-241, 248 choosing, 233 360 requirements, 2, 9, 202, 253, 339, 348 Competitiveness, 36 Coordination, 3, 55, 125, 161, 228, 358, 360, 374-375, structural, 5 Competitors, 9, 42, 46, 75, 138, 195, 200, 202, 206, 392, 440, 442 working, 2, 6, 9, 253, 382, 403 240-241, 353, 370 manufacturing, 228 Capital budgeting, 69, 319, 337, 341 complaints, 15, 19, 28-29, 33-35, 53, 61, 91, 411, 413 systems, 374 Capitalism, 426-427 customer, 35, 61 Copyright, 1, 13, 39, 51, 70, 73, 100, 111, 116, 141, capitalization, 160, 345, 447 Compliance, 16, 19, 21, 23, 27, 29-30, 37, 84, 155, 169, 195, 198, 213, 227, 237, 255, 285, Career, 1, 5-6, 24, 33, 54, 62, 66, 71, 200, 203-204, 180, 182, 193, 237, 403-404, 410, 421, 298-300, 319, 324-325, 339, 347, 352, 373, 445-460 399, 422, 425, 445, 461, 469 206, 215-216, 233, 236, 238, 244-247, 251, Component parts, 41 overview of, 13, 461 265, 287-288, 297, 299, 301, 304-305, 308, compromise, 107, 415 Core beliefs, 240 311-312, 350-351, 357, 367, 413-414, 426, computer programmers, 318, 341-342 Core competencies, 234, 240 438-439, 442-444 Computer software, 370 Corporate culture, 349, 439 Career development, 71, 308, 426, 443 Conditions, 7, 18, 20, 32, 41-42, 45, 59-62, 68, 71, 83, Corporate governance, 405 career planning, 216, 245 103, 118, 122-124, 126, 128, 136-137, Corporate responsibility, 403, 424 Careers, 9-10, 117, 298, 308, 411, 429, 431 149-150, 154, 165, 167, 173, 179-180, 186, Corporate social responsibility, 403-404 Case law, 30 198-200, 204, 210, 212, 217, 221-222, 238, Corporate strategy, 43, 404 Case study, 412 242, 244-246, 259, 273, 277, 280, 288-290, Corporate tax rates, 341 Catalogs, 258, 353 293-295, 303, 320, 323-324, 333, 343-344, Corporate web sites, 206 Census data, 197 346, 349, 354, 358-359, 370, 377, 385, 389, corporation, 8-9, 108, 203, 242, 246-247, 280, 343, Certainty, 245, 288, 320, 405 391-392, 401, 410, 423, 438, 441, 444, 408, 426 CFA, 84-85 448-451, 459 definition of, 343 change management, 376 Confidence, 62, 70, 94, 96, 117, 130, 132-134, 149, disadvantages of, 108 Character, 42, 255 164, 183, 187, 273, 299, 304-305, 312, 331, Corporations, 5, 8, 81, 241, 288, 360, 400-402, 407, Cheating, 126, 267-268 336, 355, 365, 369, 387, 390, 403, 446-447, 415, 423, 442 checklists, 88-90, 226, 315, 445-460 468, 469 domestic, 402 Checkout, 56 confidentiality, 55, 82, 192, 400, 407, 413, 415-416, foreign, 442 chief compliance officer, 403 418-421, 450 professional, 400, 407, 415 Chief executive officer, 34, 247-248 Configuration, 99 corrections, 65, 80, 135, 153-155, 163-164, 166, 168, Chief information officer, 408 manufacturing, 99 295-296, 390, 456 Children, 27, 121, 249, 260, 415, 439, 442 Confirmation, 255 Corrective action, 99, 252, 375 China, 5, 347, 402, 429 Conflict, 5, 14, 107, 115, 189, 270, 279, 296, 369, 396, cost accounting, 69 Chinese, 10, 438 399, 421-422 Cost leadership, 239, 243 Civil rights, 13, 17-19, 25-26, 28, 30-31, 33, 36-37, Conflict resolution, 279, 296, 369 Cost of living, 200 175, 185, 262, 400, 454 connotation, 176 Cost-benefit analysis, 417-418 Civil Rights Act, 17-19, 25-26, 30-31, 33, 36, 175, 262, Consideration, 1, 19, 23, 26, 47, 49, 59, 61, 68, 93, Costs, 2, 13, 30, 39-41, 46-47, 49, 77, 122, 199-200, 454 117, 122, 129-130, 132, 140, 142, 148-150, 208, 237, 241, 251-253, 255, 267, 280, 321, Claims, 22, 24, 29, 71, 195, 228, 263, 269, 423 169, 171, 176-177, 186, 189-190, 193, 204, 330, 334-343, 348, 358, 363, 382-384, 387, investigation, 29 207, 232-233, 262, 293, 295-296, 312, 395, 414, 417, 427-428, 436 Classical approach, 320 314-315, 327, 337, 342, 346, 355, 363, 385, conversion, 40 Classification, 21, 113-114, 118, 120, 192, 207, 412-413, 416, 420, 423, 434, 441, 447, 453 distribution, 330, 337, 340, 384 214-215, 222, 233, 235, 259, 264, 290, 334, Consistency, 59-60, 78, 92, 98, 123-128, 130-131, labor costs, 241, 341 471 143, 156, 187, 216, 248, 265, 267, 271, 277, product and, 348 Climate, 37, 62, 75, 166, 200, 211, 235, 252, 267, 281, 297, 300, 316, 448 sales and, 200 350-351, 364-365, 380, 431 Constitutional law, 410 Countries, 5, 25, 47, 247, 403, 426-431, 433-435, 437, clothing, 112, 224 Constraints, 32, 61, 76, 122, 148, 160, 167, 195, 197, 439, 443-444 Cluster analysis, 63, 234, 471 217, 242-243, 292, 326, 352, 355, 389, 414, courtesy, 94 Clusters, 58, 216, 232, 315 426, 439, 449-450 Creativity, 5, 7, 10, 113, 261, 272, 427 Coaching, 75-76, 105, 108, 127, 248, 276, 291, 313, implementing, 160 developing, 5, 10 335, 376, 441 NULL, 148, 167 credibility, 164, 202, 342, 378, 384, 392, 435 Code of conduct, 412 Construction, 19, 59, 76, 89, 121, 147, 156, 218, 228, Credit, 47, 145, 232, 261-262, 419-420, 441 coding, 53, 167, 177, 223, 225, 273, 310, 394, 418, 246, 290, 297, 314, 411, 430, 439 Critical incidents, 80, 88, 91, 93, 95, 229, 265, 357, 448, 459 Coercion, 344, 348, 417, 419, 421 Coercive power, 156 Cognition, 369 Cognitive component, 184 collaboration, 10, 99, 107, 348, 421, 427 474

381, 437-438 Delegation, 305 Economic variables, 68 criticism, 40, 105, 107-108, 290-291, 336, 344, 432 Demand, 10, 19, 25, 45, 107, 112, 123, 196, 199-200, Economies of scale, 5 Economy, 5-6, 9, 36, 42, 356, 400, 407, 427, 431 giving, 105, 108 221, 234, 237-239, 242, 244-246, 249-251, CRM, 360 253-254, 328, 336, 339, 347, 402 downsizing and, 5 Cross-training, 9, 360 aggregate, 246 team, 5 CSR, 9 derived, 45, 123 Ecuador, 429 Cultural awareness, 436 for labor, 238, 250 Education, 1-2, 7, 9, 13, 19, 30, 32, 35, 39, 51, 73, 78, Cultural differences, 427-429, 431-432, 437-438, 444 Demand conditions, 246 Cultural distance, 439 Demand for products, 347 96-98, 111, 118, 141, 146, 150, 169, 195, Cultural factors, 290 Democracy, 7, 422 201, 213, 218-220, 231, 235-236, 237, 244, Cultural training, 47, 426, 435, 437, 443 Demographics, 8, 246 246, 255, 258, 262, 281, 285, 288, 290, 316, Cultural values, 262, 384, 437 Denmark, 429 319, 347, 373, 399, 406, 412, 425, 434, 439, Cultural variables, 439 Department of Justice, 27, 29 442, 445, 455, 458, 461, 469 Culture, 5, 8-9, 85, 241, 251, 267, 288, 349, 406, Department of Labor, 27-29, 178, 215, 223, 234, 246 Education level, 96 Dependent variables, 95, 394 Efficiency, 7, 68, 117, 120-121, 123, 129, 190, 193, 426-431, 435, 437-439, 441-443 Depression, 49, 234 201, 208, 212, 214, 225, 267, 317, 319-320, adjustment and, 438, 443 Derivatives, 456 322, 325, 346, 360, 392 definition of, 349, 428 design, 4, 6, 8-10, 41, 45-47, 49, 69, 75, 77, 101, 103, El Salvador, 402 high-context cultures, 428 109, 129-130, 135, 147-148, 150, 165-166, Electronic mail, 408 national, 9, 406, 427, 431 182, 214-215, 218, 232, 234-235, 240-241, electronic media, 280 organizational culture, 8 243, 261, 276, 290, 302, 304, 309, 312, 315, Eligibility, 27 research on, 267, 438 347-355, 357-371, 376-377, 380-381, ellipses, 176 Culture shock, 428, 442 384-397, 406, 414, 416-417, 420, 422, 427, E-mail, 6, 207, 209-211, 214, 399, 407-409, 424 Currency, 439-440, 460 437, 443, 445-446 advertising, 424 globalization of, 439 elements of, 77, 437 emotions, 192, 427 Currency conversion, 440 principles of, 347, 351, 359, 362, 370, 376, 384 Empathy, 270, 276, 415 Curves, 119 Determinant, 9, 76, 149, 231, 354 emphasis, 4, 8, 48, 60, 69, 74, 85, 87, 105-106, 108, Customer needs, 217 Developed countries, 426-427 164, 205, 234, 238, 241, 243, 253, 259, 280, Customer orientation, 287, 441 diagrams, 120, 172, 218, 358, 465 286-287, 290, 319, 332, 348, 357, 359, Customer satisfaction, 9, 353 types of, 218 389-390, 396, 399-400, 405, 428 Customer service, 9, 48, 68, 257, 357, 397 Dictionary of Occupational Titles, 234 Employee benefits, 4 Customers, 6, 9, 33, 42, 53, 55, 68, 79, 91, 94, 100, Differentiation, 77, 157, 403 Employee engagement, 405 product, 403 Employee involvement, 348, 364 122, 186, 212, 241-243, 257, 264, 302, 348, Digital divide, 267 Employee selection, 29, 141, 161, 170, 176, 337, 346, 353-354, 357, 370, 378, 401-402, 405, 442, Direct investment, 11, 426 445-460 449 Directories, 206 Employee theft, 261, 263 as partners, 402 disabilities, 8, 17, 23-25, 28, 38, 46, 97, 266, 276, 281, Employee training, 348, 404 Customs, 23, 268, 289, 428, 437, 439, 441-442 452 Employees, 6-10, 16, 18-20, 22-29, 32-35, 37, 42, defined, 289 Disability, 3, 13, 16, 23-27, 29, 33, 36-37, 97, 170, 44-45, 47-50, 52, 56, 64, 73-78, 87-88, 91, 276, 452 97, 99, 102, 104-107, 150-152, 155, 159, D definition of, 33, 170 178-179, 196-197, 199-206, 211, 213, 215, total, 25 217-219, 223, 229-230, 238-239, 241, Damage, 53, 185, 224 Discipline, 4, 35, 42, 74, 79, 409, 422, 436, 440 243-246, 248, 251-253, 256-258, 261-263, property, 53 Discount rate, 341, 383 265, 268-270, 287, 291-292, 297-298, Discrimination, 16-37, 78, 119, 125, 169-170, 175, 308-309, 311, 313, 329, 337-341, 345, Damages, 18, 25-26, 33 183, 185, 190, 192, 228, 237, 278, 314, 400, 348-349, 353-354, 357, 359, 362-365, compensatory, 18, 25 407, 413, 417 367-368, 375, 379, 382-383, 397, 399-403, general, 33 Distance, 6, 88, 96, 115-116, 138-139, 273, 365, 405, 407-411, 413-414, 416-417, 419, 423, punitive, 18, 25, 33 428-429, 439 428, 433, 437-438, 442-443, 445, 453, 457, special, 33 cultural, 428-429, 439 459 distractions, 118, 125, 129 benefits for, 23, 27-28, 341, 402 data, 46, 56, 58, 60, 63-67, 70, 73-74, 80, 83, 86-88, Distribution, 85-86, 88-89, 115, 120, 124, 132-134, loyalty, 6, 199, 354 97, 99, 102, 104, 107, 113, 120, 130, 132, 137-138, 140, 152, 154-155, 162, 164, 167, selection of, 152, 433 135, 137-138, 140, 141, 143, 145-153, 155, 171, 177, 183, 245, 294, 328-330, 333, 337, selection process for, 433, 459 158-159, 161-164, 166-167, 170, 178-179, 340, 370, 384, 394, 415, 433, 438, 453, 467 Employment, 1, 3, 5-6, 10-11, 13-33, 35, 37, 39-40, 181, 188-189, 191, 197-200, 207-208, 213, Distributive justice, 186 43-50, 52, 64, 69, 72, 73-75, 79, 82-83, 216-217, 219, 221-224, 226, 231, 234, 236, Diversity, 1, 10-11, 35, 37, 185, 187-189, 196-197, 87-89, 108, 122, 138, 140, 141, 144, 240, 244-245, 248, 250, 253, 255, 257-259, 204, 212, 245, 252, 328, 335, 345, 405-406 147-148, 155, 162-163, 168, 169-194, 195, 261, 263, 266, 268, 282, 286-287, 290, 293, Division of labor, 2, 217 197, 201-203, 214, 217, 221-222, 234, 237, 297, 301, 308, 310-312, 314, 316-317, Documentation, 23, 106, 167, 213, 233, 328, 414, 255, 257-258, 261, 263, 267-271, 273-274, 319-320, 322-326, 329, 331, 335, 339-341, 451-452 276, 280, 282-283, 290, 316, 320, 326, 329, 344-345, 355, 359, 363, 375-376, 379-380, documents, 305, 307, 452 335, 379, 400, 407, 412-414, 417-418, 420, 384-385, 388-392, 394-397, 405-406, security of, 452 453-454, 457, 459, 461, 466 408-410, 412-414, 418-420, 422-423, 428, Dollar, 40, 48, 52, 63-64, 68-70, 87, 123, 208, 263, family and, 17, 26 432, 434, 443, 446-447, 449-451, 453-456, 297, 336-345, 379, 382 full, 3, 23, 31, 45, 186, 412 458, 463-465, 467-468, 470 SDR, 340 employment interviews, 255, 270-271, 273, 282, 316 Dollars, 29, 44, 64, 68, 258, 313, 319, 338-340, 342, Employment opportunities, 16, 20, 29 Data collection, 64-65, 167, 240, 244, 255, 257, 324, 345, 378, 382 Employment settings, 144, 169, 191, 263 406, 420, 449-450 Dominance, 113, 157 Employment tests, 329, 414 Downsizing, 5-6, 10-11, 48, 86, 103, 254, 347 Empowerment, 9-10, 214 data source, 83 Downstream, 306 skills, 9-10, 214 Database, 165, 180, 206, 231, 233, 235-236, 244, Draft, 40, 219 encryption, 410, 419 drawings, 218 Endowments, 402 403, 428, 448 Drug testing, 268-269, 283 England, 258, 270 characteristics of, 231, 233, 428, 448 Drugs, 24, 268, 271, 410 English, 35, 37-38, 118-119, 435 systems, 233, 428 Duties, 106-107, 120, 162-163, 215, 218, 221-223, Enhancement, 151, 251, 354 uses, 244 225, 235, 257, 292, 300, 364, 410, 428, 458, Enron, 402, 410 databases, 154, 237, 244, 271, 348, 449, 452 460 Entities, 102 dates, 201, 454 legal, 162, 257, 458, 460 Entrepreneur, 299 Deadlines, 78 Duty, 27, 78, 215, 224 social, 299 Death, 419 Dynamics, 103, 241, 280, 436 Entrepreneurs, 9, 297 deception, 269, 388, 418 Entrepreneurship, 5 Decision criteria, 414 E Environment, 2, 4, 7-8, 33, 37, 39, 41, 43, 55, 58, 60, Decision makers, 39, 47-49, 233, 253, 289, 320, 322, 76, 200, 206, 209, 211, 217, 221, 244-245, Earnings, 52 254, 256-257, 282, 288, 291, 296-297, 306, 325, 328, 333, 345, 383-384, 449 Eastern Europe, 5 347, 349-351, 353, 355-356, 358-359, Decision making, 56, 67, 69, 99, 117, 185-187, 189, E-business, 247 361-362, 364, 370-371, 376, 389, 401, 412, Economic development, 439 416, 432-433, 438-441, 445, 452 197, 227, 243, 271-272, 277, 294, 305, Economic environment, 211, 245 ethics and social responsibility, 412 319-346, 348, 355, 378, 380-381, 405, 407, Economic factors, 341-343 natural, 401 426, 441, 452 Decision-making, 86, 186, 272, 322, 335-336, 346, 358, 360, 381, 385, 393, 406, 414 group, 186, 360, 381, 393, 414 Decision-making process, 186, 272, 336 Deductive reasoning, 228 defamation, 256 Defendant, 18, 29 Degree of relationship, 461-463 475

Environmental factors, 439-440 Fads, 423 goal congruence, 76 Equal Employment Opportunity Commission, 16, 19, Failure, 23, 32, 34, 56, 59, 67, 105, 113, 149, 151, Goals, 19, 27, 30, 32, 36, 49, 51-52, 54, 65, 69, 73-74, 28, 37, 192 174, 226, 256, 295, 327-329, 334, 336, 76, 78-79, 87, 105-108, 162, 186, 189-190, Equal Pay Act, 17-19 353-354, 368, 392 197, 204, 213-214, 233, 237-239, 243-244, Equilibrium, 196 fairness, 48, 77, 81, 102-103, 147, 161, 169-194, 196, 246, 250, 252, 300, 309, 320, 328, 345, 349, 204, 240, 269, 313, 411, 449, 456-457 352, 354-355, 360, 365, 367-369, 375, 378, labor market, 196 False advertising, 417 390, 395-396, 401-402, 405, 421, 423, Equity, 190, 196, 422 Family, 17, 26-27, 38, 62, 206, 215, 233-234, 240, 428-429, 436, 439, 445, 450 249, 365, 367, 437-440, 448 definition of, 186, 233, 349, 401, 428 internal, 196 employment and, 17, 27 Goal-setting, 107-108, 368, 428 ETC, 2, 39, 54, 63, 81, 176, 200, 217, 219, 222, 227, of expatriates, 437, 439-440 Goal-setting theory, 368 Family and Medical Leave Act, 17, 26, 38 Goods, 2, 5, 9, 39, 55, 64, 78, 426-427 237, 277, 302, 309, 329, 340, 394, 410 Family businesses, 249 complementary, 78 Ethical behavior, 399-400, 410, 415 FAST, 40-41, 207, 229, 265, 363, 400, 427 free, 5, 427 ethical dilemmas, 399, 415-416, 421-422, 424 Favors, 33, 179 private, 5 Ethical issues, 399-424 Feature, 8, 40, 89, 203, 306, 444 public, 5 Federal government, 13-14, 20-21, 29, 341 Government, 1, 13-14, 17, 20-21, 29-30, 77, 172, 217, ethical behavior, 399-400, 410, 415 feedback, 39, 42-43, 45, 48, 73, 75-76, 79-81, 86, 249, 268, 308, 341, 408-409, 414, 416, 438 Ethical standards, 9, 415, 449 89-91, 95, 101-102, 104-108, 118, 192, 195, intervention, 341 Ethics, 399-401, 403, 412-413, 415-416, 422, 424 232, 237-239, 247, 252, 274, 277, 281, 296, Government agencies, 217 302, 310-311, 315, 348, 350, 352, 357-358, Government regulations, 249, 268, 414 Confidential information, 415, 424 360-363, 366, 369-371, 375-378, 380-381, Governmental organizations, 402 Internet, 401 384, 390, 397, 428, 431-432, 439, 441, 452 grammar, 125 Laws, 400, 413 constructive, 76, 101, 106, 360, 369, 377 Grants, 18, 408 Value conflicts, 422 destructive, 105, 107-108 Graphic rating scales, 88, 91-93, 95 Ethnicity, 18, 170, 172, 175-177, 179-180, 184-185, giving, 76, 86, 104-105, 108, 192, 281, 428 Graphs, 236, 361 Field studies, 389, 423 Great Britain, 441 189, 193, 275, 307 Fields, 10, 42, 178, 234, 420 Great Depression, 234 Europe, 5, 433 Filtering, 201 Greece, 303, 429, 438 European Community, 5, 433-435 Finance, 5, 10, 341, 386 grooming, 248, 276 Evaluation, 4, 39, 43-45, 51-72, 75, 77, 79, 90, 94, Financial analysis, 10, 251 Gross sales, 64 Financial crisis, 403 Group, 3-4, 7, 10, 13, 16-17, 23, 26, 31, 34, 36, 57, 99-100, 117, 121, 130, 142, 144, 146-147, financial reporting, 405 60, 62, 66-67, 73, 78-80, 82-83, 88-89, 91, 196-197, 200, 207, 215-216, 237-239, Fines, 23 93-94, 97, 101, 103-104, 109, 112-114, 116, 252-254, 255, 265, 271, 274, 277, 280, Fire, 2, 9, 35, 37, 91, 218, 224, 277 119-121, 124, 127, 130, 132-134, 137-138, 293-294, 310, 315, 335, 341, 350-352, 357, hostile, 37 141, 146-147, 150-152, 155-157, 169-183, 364, 376-378, 380-381, 384, 388, 390-392, Firm performance, 249 185-186, 190-191, 193, 197, 200, 204, 207, 394, 396, 399-400, 406-407, 410-411, Firms, 2, 23, 56, 86, 197, 201-203, 206-207, 209, 213, 214-215, 217, 221-222, 225, 233, 258, 262, 413-414, 424, 437, 439, 441, 445-446 234, 239, 241, 243, 245-249, 252, 257, 269, 274, 276, 279-281, 286-287, 290, 293, evidence, 11, 16, 25, 31-32, 35, 37, 49, 55, 57-58, 60, 297, 309, 345-346, 347-348, 374, 376, 396, 296-298, 300, 302-304, 309, 311, 314-315, 69-71, 81, 84-85, 95, 97, 101-102, 107-108, 404, 431, 439, 443 317, 319, 321, 327, 329-330, 334, 337-339, 120, 130, 140, 141, 143-151, 154-158, organizational processes, 243 343-345, 352-353, 355, 360, 363, 365, 160-162, 167-168, 169-172, 174-176, first impressions, 97, 273-274 368-370, 375-376, 380-383, 386-395, 400, 178-179, 188, 192-194, 203-206, 208-209, Flexibility, 9-10, 65, 187, 233, 250, 255, 286, 308, 310, 404, 414-415, 421, 428-429, 434, 436, 438, 212, 214, 220, 225, 228, 236, 250, 254, 256, 322, 345, 376, 431 448, 453, 456, 465 261-264, 266, 269, 275-277, 280-282, Flood, 353 behavior in, 7, 60, 62, 157, 303, 400 294-296, 302-303, 307, 310-311, 314, 317, Focus groups, 46 Group behavior, 79 321, 325, 346, 350, 359-361, 366-367, 370, following up, 211 Group cohesiveness, 62, 296 374, 378-379, 397, 402, 404, 406, 412, Food, 2, 363, 438 group dynamics, 103 414-416, 419, 424, 435, 438, 443, 445-448, Forecasting, 140, 233, 237, 243, 245, 250, 291, 297, Group membership, 66 450-451, 455, 457-458, 460 308, 320, 341 Group performance, 57, 178 supporting, 55, 450-451 sales, 245, 297 groups, 4, 7, 9-10, 13, 16-18, 21, 29-32, 35-37, 42, exaggeration, 178, 206, 431 Forecasts, 45, 175, 196, 237-239, 244-246, 249-250, 45-46, 60, 63-64, 66, 73, 76, 80, 83, 88-90, Exchange, 103, 271, 360, 417, 438 253-254 97, 99, 103-104, 108, 119, 134, 137, 144, Exchange relationships, 103 Foreign direct investment, 11, 426 154, 157, 166, 169-177, 179-183, 185-189, Exchanges, 102 nature of, 11 192-193, 201, 204-205, 219, 221, 226, 230, Exclusion, 16, 108, 187, 320, 335, 455 Foreign-service premium, 443 232-234, 256, 258-259, 262, 274, 278, 280, Expansion, 202, 234, 237, 245 forms design, 129 288, 290, 297, 302, 304, 329-330, 333, 337, Expatriate managers, 439 Foundations, 218 341-342, 348, 358, 361, 364, 375, 383, Expatriates, 428, 431, 435, 437-440, 442-444 France, 303, 347, 435 386-389, 391-395, 401-402, 405-406, 417, perspective of, 439 Free market, 5 421, 428, 433-435, 453-454, 458, 460 expect, 6, 31, 37, 60, 63, 84, 119, 193, 203, 213, 220, Free markets, 5 development of, 63, 137, 435 226, 248, 267, 272, 282, 295, 300, 328, 337, Free rider, 99 Guatemala, 429, 441 345, 364, 368, 387, 390, 412, 416, 423, 453, Free trade, 5 Guidelines, 13-14, 29, 32-33, 118, 122, 141, 161, 468 Freedom, 47, 79, 92-93, 123, 302, 322, 363, 400, 407, 170-171, 176, 182, 185, 217, 236, 251, 281, Expectancy theory, 300 418, 422, 446, 451 310, 328, 407, 409-412, 416, 419-420, 423, Expectations, 5, 9, 53, 74, 77, 94, 97, 209, 211-212, Frequency, 53, 90, 95, 113-114, 119-120, 216, 430, 445-460 254, 270, 272, 274, 288-289, 301, 308, 361, 221-222, 226, 263, 270, 310, 333, 336, 361, Ethics, 412, 416 368, 386, 389, 399-402, 404, 414, 416-417, 428, 454-455 420-422, 442-443, 450, 459 H expected loss, 186 G Expenditures, 200 hacking, 206 Expenses, 54-55, 200-201, 241, 348 Gambling, 263 handshake, 274 Experience, 32, 40, 46, 49, 57, 63, 75, 79, 82, 96, 98, Gatekeepers, 364 Hazards, 222, 393 102, 104, 114, 121, 123, 145-146, 148, 150, Gender, 3, 13, 16, 19, 25-26, 32, 73, 96-98, 169, 157, 162, 176, 187, 197-198, 200-201, 203, physical, 222 206-208, 218-220, 226, 230, 232, 235-236, 175-176, 178-180, 190, 193, 202, 204, 225, Health care, 2, 290, 360 238, 244-245, 247, 252, 255, 258-259, 263, 268, 273-274, 279, 288, 303, 313, 329, Health insurance, 27 261-262, 265-266, 270, 272, 274, 276-283, 356, 411, 416, 424, 428-429 helping others, 429 289-293, 296, 302, 304-305, 307, 311, 316, gender bias, 279 Hierarchy, 7, 42, 104, 199, 216, 235, 308, 416, 427 329, 349, 355, 359, 361-362, 364, 368, Germany, 402, 434-435 High-context cultures, 428 386-387, 392, 414, 431-432, 435, 437, gestures, 428 Hiring practices, 417 442-443, 449, 455-458 Global economy, 5-6, 9, 431 hiring process, 201, 205 expertise, 56, 64, 130, 299, 311, 364, 412, 420, 422, Global organization, 402 Hispanic Americans, 176 453 Global talent, 203 Home country, 431, 438 Explanations, 167, 290, 381, 385, 397 Globalization, 5, 426-428, 430, 439, 443 Honduras, 438 Explosion, 5 definition of, 428 Hong Kong, 429 Exporting, 39 dimensions of, 426-428, 430, 439, 443 Hospitals, 1 External environment, 355 markets, 5, 443 Host country, 431 External stakeholders, 401-403, 405 pace of, 5 Host-country nationals, 438 Eye contact, 156, 273, 428 summary, 439 HRM, 3-4, 9, 13, 37, 49, 51, 73-74, 340, 399-401, F Facebook, 206, 208 Face-to-face interviews, 271, 280 Facilitators, 8, 292 476

404-407, 424 Ingratiation, 271 Job description, 71, 78, 217-218, 257 HTML, 118, 181-184, 408 Inheritance, 419 job descriptions, 201, 213, 217 HTTP, 118, 122, 149, 181-184, 235-236, 263, 407 Initiating structure, 293, 295-296 Job design, 215, 232, 243 Human capital, 6, 202, 252 Initiative, 47, 92-93, 244, 270, 345, 354, 440 Job involvement, 351, 359, 367 Human resource management, 1, 3, 13-38, 39, 49, 51, Injury, 402 Job loss, 49, 211 Innovation, 233, 238-239, 243, 247, 267, 287 job offer, 196, 201, 209, 211-212, 270, 276 73, 111, 141, 169, 195, 213, 237, 242, 246, Innovations, 267 255, 267, 285, 319, 347, 373, 399-424, 425, instant messaging, 203 accepting, 211, 270 445, 461, 469 Insurance, 27, 53, 56, 58, 62, 95, 204-205, 232, 261, Job performance, 2, 4, 30-31, 33, 47-48, 51-52, 54, process of, 51, 73, 399, 424 technology and, 419 273, 302 56, 59-69, 71-72, 73, 79, 81, 83-85, 87, 92, Human resources, 1-3, 9, 62, 65, 137, 170, 187, 193, applications, 273 95, 98, 134, 143, 148, 156, 158, 161, 163, 195, 236, 237-239, 242-243, 245, 290, 415, availability, 204 166-167, 169, 172-175, 178, 180, 183, 186, 424 contracts, 27 188-190, 205, 209, 221-223, 229, 231, 255, labor relations, 243 score, 205, 273 261, 265, 268, 270-271, 281-282, 289-293, human resources management, 187 types of, 204, 261 296, 303-305, 307, 310, 312-313, 316-317, Human rights, 400 Insurance agents, 56, 58, 261, 302 320-322, 330, 333, 338-339, 342-344, hyperlinks, 365 Intangibles, 440 350-351, 354, 358, 361, 363-364, 382-383, hypothesis, 60, 82, 117, 144, 147-148, 154, 162, 164, Integration, 5, 43, 77, 162, 237, 250, 310, 312, 315, 387, 390, 405-406, 433-434, 438-441, 443, 170, 175, 177, 277, 297, 303, 308, 344, 387, 445, 455-456, 461-463, 467-468 390, 392-393, 395, 469-470 337, 339, 341, 412 conscientiousness and, 292-293, 317, 434 Integrity, 165-166, 263-265, 282, 287, 316, 410-411, defined, 33, 47, 52, 54, 56, 61, 64-65, 85, 92, 156, I 415-416, 430-432, 441 158, 166, 223, 289, 293, 304, 317, 433 Ice, 23, 306 Integrity tests, 165, 263-265, 282, 316, 430 Job rotation, 376 III, 14 Intellectual capital, 241, 248 Job satisfaction, 97, 209, 211, 296, 350, 406 Illegal activities, 263 intelligence, 3, 30, 96, 116-117, 156, 161, 196-197, job search, 208-210, 276 illustration, 46, 108, 134-135, 137, 158, 167, 188, 218, Jobs, 1-3, 5-7, 10, 19, 24, 27, 31-32, 35-36, 39, 41, 264, 289, 291, 297, 306, 419 261, 263, 306, 359, 383, 392-393 emotional, 291 43-45, 47-49, 59-61, 65, 69, 78-79, 82, 87, Image, 185, 196, 200, 209, 267, 345, 362, 367, 370 Interdependence, 5, 43, 426, 471 91, 94-95, 97, 144, 149-151, 160-162, Immigration, 17, 23 Interest, 3-4, 37, 43, 54, 64, 91, 99, 103, 121, 146, 164-165, 167, 170, 174, 178, 185, 192-193, Immigration Reform and Control Act, 17, 23 195-197, 199, 201-202, 204-210, 212, Impairment, 16, 23-24 156-158, 182, 188-189, 192, 196, 204, 212, 213-236, 238, 246, 248-250, 253, 256-257, Implementation, 48, 75-77, 99, 101, 103-104, 108, 214, 229-231, 238, 245, 262, 291, 330, 362, 268, 271, 275, 277-280, 283, 289, 292-293, 385, 392, 400, 415, 423, 446, 460 299-300, 303-304, 306, 316, 319-320, 326, 154, 166, 175, 185, 242, 258, 266-267, 282, credit, 262 339, 342, 347-348, 355, 361, 364, 366-367, 304, 319, 346, 350-351, 354, 361-362, Internal customers, 68 377-378, 382, 407, 414-415, 433-435, 442, 373-397, 404, 406, 415-416, 421, 424, 439, International business, 203 446, 448-449, 454, 457-458 451-452, 462, 467 International experience, 244, 443 age and, 97 Importing, 39 International law, 402 attitudes and, 233, 249 Impression, 46, 86, 206, 212, 259, 270-272, 274, 295, International markets, 195 job performance and, 2, 69, 268, 433 310 Internet, 6, 11, 46, 121, 195, 199-202, 205-206, levels, 49, 60-61, 78-79, 95, 165, 185, 197, 199, Inc., 23, 33, 131, 136, 198, 230-231, 250, 343, 209-210, 212, 213-214, 255, 266-268, 282, 351-352, 381, 408, 445 347-348, 375, 401-402, 409, 419, 427, 443 206, 214, 220-221, 226, 228, 230, 234, Incentives, 202, 242-243, 363, 382 defined, 201, 401 246, 253, 256, 277, 279, 292, 299-300, Income, 230, 267, 297, 306, 341 focus groups, 46 304, 319-320, 348, 355, 361 increase in, 267 search engines, 209 measuring, 2, 178, 222, 257, 306, 377-378, 382, market, 341 Interpersonal skills, 7-8, 56, 270, 301, 310, 360, 431, 448 Income tax, 230 441 promotion and, 217, 304 Incorporation, 164, 423 feedback, 310, 360, 431, 441 redesigning, 378 independent contractors, 205 Interviews, 31, 37, 46, 73-74, 76, 90-91, 104-106, service, 5-7, 24, 27, 48, 65, 78, 91, 206-207, 209, Independent variables, 327, 396, 430 108-109, 113, 117, 157, 198, 203, 208-209, 214, 219, 246, 248, 257, 268, 304, 326, Indexes, 115, 266, 287, 339, 384 211, 215, 225, 227, 231-232, 236, 255-256, 348, 442 Index.html, 181-184 266-267, 270-273, 276-283, 291, 294, 306, Jobs, Steve, 195 India, 347, 439 308, 313, 316, 324-325, 400, 411, 430, 441, Joint venture, 439 currency, 439 446, 448, 459 Jordan, 35 Individualism, 427-429, 444 Intranet, 214, 359, 375-376 journals, 200, 202, 430, 458 Indonesia, 402 Intranets, 203 attributes, 200 Inductive reasoning, 145, 289 Inventories, 5, 46, 60, 120-122, 127, 150, 185, 196, field, 200 Industry, 9, 41-42, 62, 76, 118, 127, 206-207, 241, 202, 226, 237, 245, 255, 257, 260, 262-265, Justice Department, 15 244, 250, 263, 289, 403 286-287, 289, 291-295, 301, 318, 324 Inequality, 16, 429 when used, 263, 301 K infer, 98, 113, 164, 295, 349, 358, 460 Inventory, 45, 150, 202, 226, 228, 232, 237-239, Inflation, 221, 232, 314, 382, 442 244-245, 254, 259, 261-263, 276, 290, 293, Knowledge, 4, 7, 31-32, 34, 45, 47, 51, 54, 62, 66, 71, Information, 1-2, 4-7, 9, 11, 21, 24, 26, 29, 31-32, 34, 301, 315, 324, 348, 417, 435 73, 79, 81, 84-85, 97, 103, 107, 112, 117, 37, 39-40, 42-43, 45-48, 59, 66-67, 69, 74, Inventory control, 239 122, 125, 137, 140, 142-147, 150-151, 197, 77-83, 86, 88-91, 93, 96-99, 101, 103-107, Investment, 5, 11, 46, 57, 229, 241, 267, 341, 344, 208, 214, 216, 218, 222, 225, 227-228, 109, 112, 114, 117, 121-125, 129-131, 364, 381-382, 396, 426, 432, 440, 442 230-231, 234-235, 250, 276-278, 281, 286, 134-135, 137, 140, 142, 146, 149, 152-156, government, 341 288-289, 291, 296, 302-305, 307, 310, 316, 159, 161-164, 166, 171, 175, 177, 183, gross, 440 323, 328-329, 336, 347-349, 352, 355-357, 187-189, 192, 195-198, 200-206, 208-212, net, 341 359-362, 364-365, 369-370, 376-377, 214-217, 220-223, 225-227, 229-236, 244, private, 5 383-384, 390, 392, 396, 404, 407, 409, 247, 253, 255-259, 261-262, 270-271, Investments, 2, 39, 240, 311, 340, 382, 385 412-413, 416-418, 421-422, 431-434, 273-275, 277-278, 281-282, 292, 296, Investor relations, 403 439-440, 442, 445-446, 448, 456-457, 461, 300-302, 305-306, 308, 310-312, 315-316, Investors, 401 463, 466 319-320, 322, 324-326, 330-331, 334, Iran, 438 336-337, 339, 343-346, 348, 354-355, 357, Ireland, 42 sharing, 7 360, 362-368, 374-375, 377, 379-380, Israel, 201, 392, 429 Korea, 435, 441 383-384, 388, 390, 394-397, 400-402, 405, 407-410, 413-415, 417-420, 424, 426-427, J L 433, 436, 438, 440, 442, 445-447, 449-452, 454-455, 458-459, 462, 465 Japan, 347, 429, 435 Labor, 1-2, 4-5, 10-11, 17-20, 27-30, 45, 178, 195-201, imperfect, 189, 270 expatriates, 435 203, 212, 215, 217, 223, 227, 234-235, Information gathering, 29 238-239, 241, 243-246, 250, 253, 288, 338, Information management, 214 Japanese language, 436 341-343, 345, 403, 410-411, 434, 439, information overload, 221 Jargon, 215, 344 449-450, 453, 460 Information processing, 89, 98, 196-197, 310 Job analysis, 4, 31, 39, 43-45, 49, 70-71, 77-78, 95, Information search, 231, 306 corporate social responsibility, 403 Information system, 234, 244, 408 118, 145, 147, 161, 213-217, 220-226, foreign direct investment, 11 Information systems, 4 228-229, 231, 233-234, 236, 262, 278-279, labor relations, 215, 243 Information technology, 5, 11 281-282, 293, 320-321, 328, 414, 454 Labor costs, 241, 341 Infrastructure, 5-6, 201 job applicants, 24, 113, 115, 145, 150, 152, 159, 165, labor force, 453 190, 197, 201, 208-211, 227, 259, 264-265, Labor market, 1, 5, 10, 45, 196, 198-200, 235, 246, 268, 278, 290, 294-295, 303, 306, 320, 325, 334, 399, 405, 408, 411 460 job boards, 206-207 equilibrium, 196 Labor productivity, 253 477

Labor relations, 215, 243 278, 283, 286-292, 295-300, 302-303, 305, Natural environment, 401 Labor supply, 238, 245 310-311, 313-315, 318, 342-344, 346, Natural resources, 289 Labor unions, 203, 411 354-355, 359, 361-364, 367, 370, 378, Need for achievement, 209 Lags, 58, 343 383-386, 389, 391, 394, 397, 402, 408-409, Need for power, 298-299 Language, 35, 78, 93, 118, 211, 231, 234-235, 244, 412, 416, 422-423, 431-435, 437-439, negative information, 107, 274, 363 441-442, 444, 449 Negative relationship, 183 262, 264, 336, 348, 413, 428, 430, 436-438, Manufacturers, 348 Negotiation, 196-197 441, 443, 449, 455 Manufacturing, 64, 99, 202, 228, 232, 269, 402, 467 online, 235, 244 Margins, 401 Objectives, 197 style, 428, 441 Marijuana, 260, 268 Planning, 196-197 Layoffs, 16, 22, 28, 35-36, 49, 75, 203, 237 Market changes, 345 Process, 196-197 Leader, 247, 249, 296, 300, 304, 368, 392 Market potential, 63 Questions, 197 Leadership, 4, 7, 10, 63, 80, 84, 96, 103, 135, 190, Market share, 42, 63, 345, 440 Netherlands, 303, 429 239, 243-244, 246-249, 254, 262, 265, Market value, 341 Networking, 195-197, 206, 208, 210, 408, 427, 435, 286-287, 289, 292, 295-296, 298-302, 317, Marketing, 10, 211, 378, 380-381, 394, 471 360, 376, 379, 381, 392, 394, 415, 426, 430, global, 10 441 441, 443 ideas, 211 New products, 427 communication in, 4 needs and, 378 Newspapers, 200, 202 Leadership ability, 286, 295, 317 people, 211 NGOs, 401-402 Learning, 5, 7-9, 23, 40, 54, 67, 79, 121, 149, 226, place, 10, 380 230, 241, 243, 248, 277, 280, 291, 347-354, return on investment (ROI), 381 nongovernmental organizations, 401 357-363, 365-367, 369-371, 374, 376-377, Marketing research, 471 Nigeria, 429 380, 384, 396, 427, 431-432, 437-438 Marketplace, 7, 41, 200, 238, 243, 249 Nongovernmental organizations, 401 legal aspects, 13, 15 Markets, 4-5, 9, 11, 19, 195-196, 199, 201, 212, Nonprofit organizations, 308, 412 Legal issues, 189, 262 238-240, 242-244, 249-250, 254, 288, Nonverbal cues, 107, 272-273 Legal liability, 37, 256 342-343, 403, 436, 443 Norms, 55, 121, 137-138, 140, 230, 252, 328, 379, Legislation, 13, 18-19, 243, 407, 413 Massachusetts, 21 letters, 52, 256, 273, 305 Matrices, 314, 430, 433 384, 399-400, 416, 421-422, 427-428, 430, follow-up, 273 meaning, 61, 91, 114, 116, 141-144, 155-156, 161, 433, 437 of recommendation, 256 170, 177, 192, 233, 237, 239, 291, 297, 312, North America, 8 Liability, 33-34, 37, 256-257 369, 407, 430, 449 North American Free Trade Agreement, 5 absolute, 257 understanding of, 155 Norway, 429 basis of, 256 Measurement, 51-72, 73-74, 76-77, 80, 82, 84-85, 91, business, 34 95, 99, 102, 108-109, 112-117, 119, 122-126, O employer, 33-34, 256 128-138, 140, 143-145, 147-148, 156-158, law, 33-34, 37, 256 163, 165, 168, 182, 187, 189-190, 192, 207, Objectives, 1, 3-4, 7, 19, 30, 40-41, 47-49, 51, 55, 69, libel, 256 240, 252, 269, 281, 295-296, 303, 317, 71, 74, 79, 82, 91-92, 101, 108, 113, 148, Licenses, 222, 244 319-320, 324-326, 328-329, 332, 335-336, 187, 197, 204, 237-240, 243, 246, 248, 250, Licensing, 413 340, 346, 362, 373-397, 403, 405-406, 252-253, 295, 309, 337, 341, 345, 347, Life insurance, 56, 62, 261, 273, 302 412-413, 424, 426, 430, 443, 456, 469-470 349-353, 355-361, 369-370, 377, 379, 396, group, 62, 302 measurements, 51, 56-57, 69, 99, 112, 117-118, 124, 401-402, 415, 422, 428, 436, 439, 441-442, need for, 302 148, 229-230, 286, 336, 379, 386, 457 444, 446, 449, 451 term, 56 mechanics, 96, 137, 151, 215, 246, 259, 335, 366, Lifestyle, 400, 419, 442 434, 459 accounting, 69, 379, 439 LinkedIn, 195, 206, 208, 210 Media, 45-46, 206, 246, 272, 280, 282, 351, 396 objectivity, 85, 130, 281 listening, 107, 270, 348 median, 34, 60, 115, 297, 304, 311-312 Obligation, 205, 419 effective, 270 Mediation, 29 Occurrence, 61, 336, 384, 421 Literacy, 236 medium, 149, 300, 350, 362, 396, 400, 433-434, 450 Offer, 24, 27, 38, 80, 82, 93, 196-199, 201, 205-212, Loading, 411 selecting, 300, 433, 450 Loans, 443 written, 300 228, 257, 264, 270, 276, 328, 342, 375, 400, Locus of control, 294 meetings, 34, 94, 202, 232, 305, 419, 441 411, 435 Logistics, 160, 223, 276, 353, 392 formal, 441 Offset, 41, 125, 211, 311, 319 London, 83, 101, 104-106, 131, 263, 311, 411-412 online, 419 Offshoring, 10 long-term memory, 106 purpose of, 441 Oil, 16, 34, 162, 246, 249-250, 280, 304 Loss, 5, 49, 62, 135, 150, 186-188, 211, 249, 312, types of, 34, 202 One-to-one relationship, 47 317, 322-323, 335, 339, 386, 441-443 Memory, 105-106, 145, 275, 291 On-the-job training, 20, 374-376, 414 assessment, 317, 443 memos, 305 Open systems, 39, 42, 50, 196, 320 control, 49, 386, 442 Mergers, 5, 210 Operational planning, 243 direct, 323 Mergers and acquisitions, 5 Operations, 5, 64, 113, 115, 139, 195-196, 201-202, expected, 49, 186 message, 37, 196-197, 385, 433 205, 207, 211, 223, 229-230, 236, 237, indirect, 441 competing, 196 249-250, 310, 354-355, 358, 385, 412, 426, ratio, 322, 339 positive, 385 431, 439, 441-442, 462 reduction, 322 sales, 385, 433 Opportunities, 10-11, 16, 20, 22, 29, 37, 61-62, 78, 82, Low-context cultures, 428 scope of, 433 192, 200, 202, 208, 217, 238, 240-241, 253, Lying, 52, 269 Metrics, 180, 207, 212, 242, 341 301, 312, 345, 348, 350, 358-360, 370, 377, Mexico, 185 382, 422, 431-432, 436-437, 443 M Middle managers, 87, 245, 290, 305, 394 Optimism, 298-299 Mobility premium, 443 Oracle, 234 Malaysia, 429 modifiers, 90 Organization, 1-2, 4, 6, 10, 20, 24, 29-30, 33, 39-41, Management, 1, 3-4, 6, 9, 13-38, 39-44, 48-49, 51-52, Money, 39, 45, 55, 75, 150, 182, 197, 251, 255, 335, 43-50, 54-55, 58, 64-70, 73-75, 79, 81-82, 341, 348, 350, 353, 377, 402, 405, 414, 417, 85, 87, 91, 99, 103-104, 108-109, 138, 151, 62-64, 66, 73-109, 111, 113, 141, 150, 159, 429 162, 164-165, 167, 172, 174, 186, 189-192, 169, 180, 185, 187, 192, 195, 198-199, Monte Carlo simulation, 179-180 196-197, 200-202, 204-205, 207, 209, 201-202, 204, 206-207, 210, 212, 213-215, Mortgage, 248, 257 211-212, 213-215, 217, 222, 232-234, 228-230, 237-244, 246-250, 252-253, 255, Motivation, 3-4, 13, 32, 52, 56, 77, 81, 86, 102, 107, 237-240, 243, 245-246, 248-249, 252-254, 261, 267, 271, 285-288, 290, 292, 297-299, 122, 126, 150, 186, 226, 261-262, 277, 258, 270, 287-289, 292, 296, 301, 305, 310, 301-302, 305, 308-311, 313-314, 318, 319, 286-287, 292, 294-300, 309, 317, 347, 312, 314-315, 321, 328, 330, 332, 335-338, 321, 342-346, 347, 349-350, 353, 356-358, 350-351, 354, 359, 361, 367-370, 406, 408, 341, 343-345, 348-355, 357, 364, 367, 370, 360, 364-366, 371, 373, 376, 379, 384-385, 426, 430, 443, 455 375, 377-381, 389-390, 392-394, 396, 389, 392, 394, 396, 399-424, 425-426, 429, theories of, 32, 430, 443 400-403, 405-407, 409, 415-417, 420, 424, 431-432, 438-444, 445, 450, 461, 469 multimedia, 374-376, 397 429, 432, 435, 440, 443, 445, 447, 449 activities of, 28 Multinational corporations, 402, 442 control systems, 252 functions of, 23-24 Multinational enterprises, 441 definition of, 33, 186, 233, 343, 349, 401 sexual harassment and, 416 Music, 137 Organization development, 375, 379 Management by objectives, 357 Organizational behavior, 300, 339, 430, 443 Management issues, 401 N Organizational commitment, 351, 359, 367, 406 Managerial roles, 8 Organizational culture, 8 Managers, 3-4, 6-8, 10-11, 23-24, 29, 32, 34, 39, National Science Foundation, 246 learning, 8 42-44, 47-48, 56, 62, 69, 74, 76, 81-82, National security, 22, 36 Organizational objectives, 3-4, 19, 48, 55, 79, 240 84-88, 102-105, 107-108, 122, 137, 197-198, Nations, 426 Organizational politics, 76 202, 204, 206-208, 210, 233, 238-245, Organizational processes, 49, 243, 354, 379, 405 247-248, 250-253, 261, 265-266, 268, 275, Organizations, 1-11, 17, 19-20, 24, 37, 39, 41-45, 47, 49-50, 61, 63-66, 71, 73-76, 78, 81-82, 87-88, 90, 94, 99-100, 103-104, 107, 131, 141, 160, 162, 167-168, 181, 185, 190, 195-196, 199-202, 204, 209-210, 213-217, 478

231-232, 236, 238-240, 243-244, 246, 251, 319 Production costs, 358, 395 254, 257-258, 266-267, 289, 294-295, 301, Person-organization fit, 204 external, 395 303, 308-311, 315-316, 319-320, 332, 335, Persuasion, 228, 368, 440 343-345, 347-350, 352-354, 357, 362, 371, Productivity, 57, 60, 84, 108, 199-200, 238-239, 241, 376, 379, 388, 392, 396, 400-410, 412, personal, 228, 440 244, 251-253, 287, 341-342, 350, 363, 402, 415-416, 419-420, 422-424, 431, 433, 435, Philanthropy, 407 423, 434, 440 437, 448 Orientation, 7, 24, 34, 47, 62, 68, 185, 192, 196, 211, Management, 407 labor, 199-200, 238-239, 241, 244, 253, 341-342, 215, 219, 223-224, 228, 287, 289, 293, 299, Philippines, 429 434 351, 367, 376, 428-429, 431, 437-438, 441 Physical environment, 217 future, 7, 185, 211, 293, 376, 429, 431 PILOTs, 32, 217, 355 Products, 2, 5-6, 39, 55, 68-69, 120, 127, 145, 161, long-term vs. short-term, 429 Place, 3, 8, 10-11, 36, 39-40, 73, 76, 85-87, 94, 104, 226, 231, 240, 243, 247, 268, 347-348, performance, 34, 47, 62, 68, 192, 215, 219, 223, 353-354, 357, 402, 405, 427, 457, 462 113-114, 148, 153-155, 172, 191, 201, 209, 228, 287, 289, 293, 299, 351, 367, 376, 217, 223, 230, 234, 236, 240, 243, 247-248, defined, 55, 457 428, 437-438, 441 257, 288, 307, 310, 318, 342, 348-349, development of, 69 Outdoor advertising, 202 376-377, 380, 387, 390, 396, 401, 411, industrial products, 161 Output, 52, 60, 76, 78, 183-184, 227, 339-341, 345, 417-418, 422-423, 430, 435, 438, 440, 452, levels of, 226, 240, 243, 348 383 465 Professional development, 376, 441 potential, 60, 78, 183 plagiarism, 419-420 Professional fees and services, 200 Outsourcing, 5, 348 Plaintiff, 18, 25, 31, 33 Professional standards, 407, 414 overhead, 200 planning stage, 416 Professionals, 5, 10, 13, 17, 39, 45, 47, 58, 60, 76, Ownership, 345, 401, 404 Plans, 27, 36, 45, 58, 80, 102, 106, 132, 149, 175, 195-196, 198, 202-204, 218, 237-240, 253, 265, 267, 290, 332, 341, 383, 407, 413, P 242-245, 248, 250, 253-254, 357, 360, 371, 415, 422, 434, 445 375 Profit, 65, 121, 289, 301, 345, 380, 382, 401-402 PACE, 5-6, 41, 214, 267, 282, 436, 443 approaches to, 237 definition of, 401 Packaging, 60 business, 27, 45, 195, 202-203, 237-240, 242-245, Profits, 9, 239, 358, 363, 382, 401, 407, 440 Pakistan, 429 Programmer, 341 Panama, 429 248, 250, 253-254 projection, 297, 463 Parameter, 119, 158, 328, 339-340 Policies, 10, 13, 16, 36-37, 47, 55, 62, 65-66, 102, Promotion, 13, 16, 18, 26, 28-29, 31, 35, 53, 62, 66, parentheses, 149 69, 72, 79, 81-82, 105, 115, 155, 192, 202, Partnering, 24 185, 192, 202, 237, 240-242, 289, 311, 350, 214, 217, 238, 245-246, 251, 270-271, 287, passwords, 410 399-402, 404-405, 408-410, 415, 423 297, 299, 302-304, 309, 312-313, 336, 366, Patents, 245 limited, 36-37, 102, 415 443, 453 Path-goal theory, 7 participating, 311 communication process, 270 Patio, 291 valued, 47, 202, 423 prompting, 278 paychecks, 9, 47 Political parties, 1 Property, 53, 115-116, 141, 144, 410, 463 Payoff matrix, 40 Politics, 76, 210 damage, 53 payroll, 198 Polygraph Protection Act, 269 proposals, 341 Perceived value, 253 Pooling, 307 Protection, 2, 9, 18, 20, 23, 25-26, 34-36, 105, 269, percentages, 114, 138-139, 219, 223, 230, 338 Population, 30, 82, 118, 133, 135, 144, 148-149, 409, 418-419, 421 Perception, 77, 80, 105, 107, 161, 228, 257, 274, 280, 154-155, 159-160, 163-165, 175-177, Prototype, 274 179-184, 290, 322, 328, 331, 342, 393, 397, Prototypes, 274 418-419, 432 446-447, 449-450, 454-455, 458, 462, Psychology, 1-11, 13, 39, 43, 51-52, 58-59, 61, 63, 70, influences on, 419 466-468 73, 94, 111-112, 118-119, 127, 131, 134-135, Perceptual speed, 289 Portfolio, 382 137, 141-142, 162, 165, 169, 178, 180-181, Performance, 2-4, 13, 22, 30-34, 39, 43-44, 46-49, Portugal, 303, 429 184-185, 187, 190, 195-196, 213, 219-220, Posttesting, 193 235, 237, 255, 260, 269, 274, 279, 285, 289, 51-72, 73-109, 112, 117-118, 120-125, 130, posture, 273, 454 291, 297-299, 311, 316, 319, 347, 361, 373, 132-134, 136-137, 139-140, 141, 143-144, Power, 4, 7, 14-15, 18, 27, 29-30, 90, 103, 120-121, 375-376, 399, 404, 406-407, 412, 415, 420, 146-151, 156-159, 161-164, 166-167, 169, 148-149, 154, 156, 165, 170, 175-176, 425-444, 445, 461, 469 172-180, 183, 186-193, 198, 201, 205-206, 179-183, 193, 217, 224, 240, 246, 248, 258, Public health, 246 208-209, 215-219, 221-223, 226, 228-229, 261, 264, 268, 297-299, 322, 348, 389-390, Public opinion, 13 231, 233, 237, 239, 242-244, 246-254, 415, 420, 422, 427-429, 446, 449, 452 Public policy, 13, 28, 192, 264 255-257, 261-268, 270-272, 274, 276, 278, Power distance, 428-429 Public relations, 122, 270, 378, 401, 407 280-282, 287-297, 299-308, 310-317, preemployment testing, 180-181 campaign, 401 320-323, 326, 328-330, 333, 335-340, prejudice, 174 defined, 401 342-345, 348-368, 370, 376-377, 379-388, Premium, 443 objectives, 401 390, 392, 394, 399, 401-407, 410-411, 415, Premiums, 200, 442 Publicity, 203, 205 418, 424, 426, 428, 432-441, 443-444, gross, 200 punctuality, 260 445-448, 450, 452, 455-458, 461-463, presentations, 217, 423 Punitive damages, 18, 25, 33 467-468 Pretesting, 126-127, 389, 414 Purchasing, 24, 68, 82, 123, 365 firm performance, 249 Price, 19, 60, 87, 122, 199, 241, 268, 330, 358, 382 definition of, 123 Performance appraisal, 48, 73-74, 76-79, 81, 86-87, Prices, 5, 268, 348 purpose, 2, 19, 28, 68, 74, 81, 84, 86, 89, 99, 106, 90-91, 93, 96, 98, 100, 102-103, 105, maximum, 348 117-118, 123-124, 137, 144, 147, 149, 209, 108-109, 215, 233, 244, 253, 257, 357, mergers and, 5 213, 215-216, 223, 225-226, 232-233, 236, 440-441, 444 Principal, 9, 19, 51, 64, 144, 146-147, 362 238, 240-241, 281, 288, 308-309, 322, 335, Performance evaluations, 77, 103 Principles, 17, 30, 37, 39, 42, 64, 78, 102, 108, 116, 351, 354-355, 357-358, 360, 374-376, 378, Performance feedback, 73, 104, 108, 311, 363, 428, 162, 177, 192, 218, 227, 234-235, 241, 258, 380-381, 387, 391, 397, 401, 413-414, 416, 441 265, 328, 347, 351, 358-360, 362, 364, 418, 420, 441-444, 445-446, 449, 451-452, Performance management system, 64, 73-74, 76-77, 369-370, 376-377, 379, 384, 400, 409, 454 79, 86, 90, 102-103, 109, 311, 405 411-413, 415, 423, 445, 452-453 defining, 118, 216, 236, 240, 387, 449 Performance measures, 31, 56, 58-59, 67, 81, 87, privacy, 6, 245, 258, 267, 283, 399-400, 407-411, 413, general, 2, 68, 81, 84, 86, 118, 209, 216, 223, 226, 132, 150, 243, 291, 306, 330, 456, 458, 467 417-419 Performance orientation, 438 invasion of, 258, 283, 407, 409-411, 413, 417 233, 238, 322, 354, 360, 374, 380, 387, performance reviews, 254, 441 of information, 6, 400, 407, 409-410, 418 397, 418, 445, 449, 452, 454 Permits, 18, 33, 147, 158, 202, 206, 223, 234, 241, Privacy Act, 409 of research, 84, 86, 137, 209, 416, 418, 420 305, 450 Private sector, 403 specific, 86, 99, 106, 144, 216, 225, 232, 236, 238, Personal contacts, 210 Probability, 84, 119, 133, 146, 148-149, 173-174, 185, 309, 354, 357-358, 374, 387, 391, 413, Personal services, 228 329, 333, 390, 446, 470 449, 454 Personality, 3, 32, 46, 56, 60, 62, 74, 91, 96-98, a priori, 146 statement of, 238, 358, 445, 451 105-106, 108, 116-117, 121-122, 126-127, objective, 148, 333 Pygmalion effect, 368 129, 134, 137, 145, 150, 158, 166, 178, 185, problem solving, 7, 278, 312-314, 355, 369 191, 196, 212, 216, 231-233, 236, 248, 255, Problem-solving skills, 279, 288 Q 257, 259-261, 263-265, 270, 276, 282-283, Procedural justice, 49, 102, 202 286-287, 290-295, 297, 301, 304-305, 307, Procurement, 232 Quality, 9, 19, 29, 53, 61, 67, 69, 76, 81, 84, 86-87, 90, 314-315, 317-318, 319-320, 324, 331, Product development, 394 92-93, 96, 106, 112-113, 118, 121, 137, 140, 350-351, 361-362, 367, 388, 406, 415, 428, Product differentiation, 403 145, 192, 195, 199, 201, 205, 207-208, 226, 430, 434-435, 443 Product line, 240 231, 237, 239, 241, 243, 247, 250, 252-253, personality tests, 257, 293-294, 331 Product quality, 61 261, 272, 274, 279, 288, 291, 294, 307, 315, Personality traits, 117, 129, 166, 216, 231-232, 259, Production, 6, 9, 46, 60, 63, 73, 78, 87, 99, 151, 214, 337, 339, 342, 350, 353, 376, 393, 407, 410, 264, 270, 291-293, 295, 297, 307, 314, 318, 226, 261, 265, 358, 379, 394-395, 401, 427 414, 429, 437, 443, 445, 452, 456 national, 9, 265, 427 quality control, 67, 93, 452, 456 Quality management, 76 quality of hire, 199 479

Quality standards, 92 113, 116, 118, 122, 130-132, 135-137, 141, interpersonal, 7, 56, 62, 65, 76, 100, 109, 296, 301, Quantitative research, 406 143, 146, 148-152, 154, 159-162, 164-167, 306, 369 Questionnaires, 129, 207-208, 225-226, 232, 271, 170, 175-176, 179-182, 189-190, 193, 196, 199, 201, 204, 206, 208-211, 214-215, 217, managerial, 62, 65, 238, 274, 290, 293, 296-299, 308, 375, 388, 392-393, 428, 430 221, 227-230, 234-236, 239, 241, 254, 256, 301, 305-307, 315, 412, 438 Quota, 36, 54, 332-333 258, 261, 264, 267, 269-272, 274-277, Quota system, 36 280-281, 286, 290-293, 295-296, 300, Role ambiguity, 399, 421 Quotas, 30, 37 302-304, 308-309, 312, 315-316, 325, 330, Rumors, 55 335, 337, 340, 343-344, 354, 358-359, 362, Russia, 347 R 366, 368-370, 375, 379-380, 384-387, 390, 396, 399-401, 404-407, 409, 412, 414-424, S Race, 3, 13, 16, 18, 20-22, 25-27, 29, 31, 37, 60, 73, 427, 430-431, 433, 437-439, 442-444, 96-98, 171-172, 175-176, 179, 190-191, 225, 446-452, 457-458, 471 sabotage, 53, 365 263, 268, 272, 275, 279, 281, 303, 307, 329, conducting, 61, 118, 137, 148, 161-162, 164-166, Salaries, 44, 46, 200, 203, 340 361, 453-456, 460 Salary, 9, 53, 76, 81, 94, 201, 205, 208, 211, 246, 252, 180, 215, 227, 236, 272, 275, 280-281, Racial discrimination, 18, 20 286, 344, 385, 404, 415-417, 422-424, 261, 287-288, 298-299, 302, 308, 312-314, Rates, 27, 30, 33, 60, 73, 84, 95, 100, 130, 154, 449 339-340, 344, 366, 434 planning, 39, 59, 122, 148-149, 196, 201, 215, 227, Sales, 9, 35, 48, 52-54, 56, 60-61, 63-65, 70, 79, 82, 164-165, 171, 180, 209, 245, 253, 287, 290, 234, 239, 241, 254, 296, 302, 308-309, 87, 89, 91, 94, 122, 199-200, 202, 230, 245, 297, 311, 334, 341, 379, 385, 417 312, 375, 385, 396, 399, 405, 416, 442, 247, 253, 261, 273, 292, 297, 301, 336, 340, definition of, 33 447, 449 353, 357, 379, 383-385, 387, 394-395, 427, discriminatory, 30 primary, 65, 74, 141, 190, 196, 214, 221, 267, 290, 433 excessive, 417 293, 418, 442 Sales potential, 60 reasonable, 30, 33, 385 purpose of, 81, 309, 335, 354, 375, 401, 414, 416, Sales records, 54, 379 Rating, 48, 59, 64, 66, 73-74, 79-80, 82-96, 98, 444, 446, 449, 451-452 Sales training, 61, 384 100-102, 108, 117, 130, 135, 146-147, 151, secondary, 189-190, 241, 420 Training programs, 384 179, 220, 226-229, 257, 265, 271, 274, 276, Research and development, 39, 47, 74, 427 Salespeople, 57, 60, 94, 261, 353 278-279, 281, 288, 302, 304, 307, 309-311, cost of, 47 Motivation, 261 323-326, 340, 364, 370, 386-387, 431-432, human, 39, 74 Samples, 13, 52-53, 56, 58, 60, 77, 88, 113, 125-126, 446, 456 Research design, 69, 130, 135, 165, 182, 384, 386, 130, 136, 150-151, 159-160, 162, 165, 170, Ratios, 46, 116, 154, 169, 171, 197-200, 212, 253, 396, 406, 416, 420 176, 179-180, 182, 186, 210, 261, 265, 268, 294, 332, 334, 343 research process, 399, 424 271, 286-287, 290-291, 295, 300-304, Raw materials, 2, 55, 345, 427 Resources, 1-3, 9, 62, 65, 127, 137, 160, 167, 306-307, 313, 316-318, 322, 325, 342, 361, Reach, 3, 53, 83, 104, 154, 191, 206, 227, 250, 272, 169-170, 187, 193, 195, 212, 236, 237-240, 385, 389, 395, 432-434, 447, 455 297, 325, 330, 336, 350, 369, 376, 438 242-243, 245, 247-248, 250-252, 287, Sampling, 52, 65, 123, 126-128, 131-133, 140, 144, readability, 228 289-290, 297, 303, 341, 343, 345, 350, 354, 147, 152, 154, 159, 163-165, 230, 252, Readiness, 246, 350 356, 377, 382-383, 402, 414-416, 424 287-288, 296, 301, 303, 312, 403, 447, 449, Realistic job preview, 209, 212 Responsibility, 10, 18, 30, 44, 62, 66, 78, 94, 104, 137, 451 Recession, 5 214, 222-223, 243, 247, 252-253, 270, 287, Sampling distribution, 154 Reciprocity, 422 289-290, 300, 302, 326, 348, 358, 399-424, Sanctions, 23, 25, 29-30, 34, 37, 298, 370 recommendations, 78, 103, 212, 218, 220, 248, 253, 432, 435, 439 SAP, 234 255-257, 266, 278, 282-283, 315, 328, 409, Restricted, 82, 125, 153-155, 199, 218, 269, 331, 448, Scanning, 211 451-452, 459 453 Schema, 355 Records, 32, 34, 37, 54, 75, 197, 256, 258, 265, 379, Restrictions, 133, 440 scope, 5, 17, 45, 244-245, 326, 390, 412, 431, 433 399, 408-410, 418, 449, 453-454, 459 Retail stores, 427 screening interviews, 267 Recruiting, 2, 24, 43, 45-46, 185, 193, 195-209, 212, Retailers, 353 SD, 14, 162, 383-384 246, 252, 267, 270, 320, 333, 335, 417 Retention, 4, 261, 362-363, 366-367, 369, 385, 409 SDR, 340 Recruitment, 1, 4, 10, 13, 27, 29, 39, 41, 43-46, 48-49, Retention processes, 369 Search engines, 209 52-53, 69, 185, 192, 195-212, 215, 217, Retirement, 1, 10, 23, 48-49, 245-246 Second Life, 203 237-238, 243, 245, 250, 257-258, 319, 339, Return on investment, 381, 440 Security, 1, 10-11, 22-23, 36, 244, 269-270, 283, 408, 343, 345 Revenue, 199, 249, 340 410, 414, 421, 450, 452, 459 redundancy, 312, 324, 363, 449 Revenues, 250, 403, 426-427, 440 Selection, 4, 13, 15-16, 26-27, 29-31, 35-37, 39-41, Reference group, 137 Reverse culture shock, 442 43-50, 52, 54, 58, 61, 64, 66, 68, 72, 85, 90, Referrals, 201, 203-205 revision, 454 113-114, 117, 120, 123, 128, 138-139, 141, Reform, 17, 23 Reward systems, 428 143-145, 151-155, 159, 161-162, 168, Social Security, 23 Rewards, 5, 75, 103, 105, 108, 296, 362, 366, 438 169-181, 183-193, 198, 205, 208-209, 211, Regression analysis, 63, 233, 312, 330, 462-463 Risk, 10, 13, 32, 48, 95, 105, 187, 246, 261-262, 287, 214-219, 232-233, 237-238, 245, 250-251, Regulation, 14, 364-365, 400 305, 341, 350, 368, 382, 403, 405, 417, 255-283, 285-318, 319-346, 351-352, 357, federal, 14 421-422, 432 374, 377-378, 382, 386, 388, 390-391, 393, reasons for, 365 asset, 305 395, 400, 405, 412, 414, 423, 426, 432-433, state, 14 business, 10, 32, 246, 287, 305, 350, 403, 405, 432 435, 437, 441, 444, 445-460, 466 Regulations, 29, 37, 55, 218, 249, 268, 412, 414-415 enterprise, 350 of human resources, 237, 245 Relational database, 235 financial, 10, 246, 287, 341, 382, 403, 405 selection interviews, 113, 209, 400 Relationships, 2, 6, 10, 30, 60-61, 69, 71, 80, 98, 100, in society, 10 Selection process, 154-155, 187, 190, 192, 205, 211, 103, 115, 138-140, 150, 154, 157, 159, insurance and, 261 251, 255, 257, 262, 281-282, 290, 293, 312, 167-168, 195, 205-206, 218, 226-227, 233, market, 10, 246, 341 317-318, 319-321, 331-332, 341, 346, 253, 261, 276, 282, 291-293, 298, 300-301, objective, 48, 246, 287, 368 432-433, 437, 453, 459 303, 317, 326-327, 348, 357, 378-380, operational, 403 validity and, 317 399-400, 403, 412, 416, 424, 428-429, 436, personal, 10, 32, 261-262, 287, 421-422 Selective attention, 273 445-447, 461-462, 469-470 strategic, 246, 287, 403, 405 Self-concept, 105-106, 108 Relative efficiency, 325 subjective, 287 Improving, 108 Reliable test, 124 Risk management, 246, 403 Self-efficacy, 107, 192, 276, 293, 350-351, 359, 365, Religion, 3, 13, 20-22, 25-27, 29, 175, 259 benefits of, 403 367-368, 370, 394 Reorganization, 49, 348 financial, 246, 403 Self-esteem, 49, 104, 107, 200 Repatriation, 426, 440-444 objectives, 246 Self-interest, 189, 415 employees, 442-443 Risk taking, 10, 105, 287, 305, 350, 368, 432 Self-maintenance, 437 Repatriation of profits, 440 Risks, 32, 400-401, 411, 417, 419, 421, 431-432, 447 Self-managed teams, 214 Repetition, 387 minimization of, 421 Self-monitoring, 81, 96 Replication, 167, 273, 388, 419-420, 447 patterns of, 400-401 Self-promotion, 105, 270-271 reports, 35, 54-55, 90-91, 198, 206, 218, 231, 259, Robbery, 91, 222 Self-reliance, 6, 8, 11 276, 295, 298, 306, 381, 406, 409, 420, 438, Role, 1, 4, 7, 30, 33, 48, 53, 56, 62, 65, 73, 76-77, Self-serving bias, 191 448-449, 451 100, 104, 106, 109, 121, 147, 155, 166, 192, Sensitivity, 63-64, 71-72, 228, 412-413, 418, 449 checklists for, 448-449, 451 208, 215, 220, 238, 241, 253, 257, 265, sentences, 120 components of, 448-449 274-277, 290, 293, 296-299, 301, 305-307, September 11, 2001, 408 distributing, 35, 55 311, 314-315, 325, 336, 344, 346, 353, 361, Service organizations, 78 documentation of, 451 368-369, 375, 399, 404, 406-407, 412, Services, 2, 5-6, 9-10, 17, 28, 39, 55, 82, 122, 200, length of, 35 420-422, 426, 429, 435, 438 202, 211, 228, 230, 246, 269, 308, 347-348, online, 206 in groups, 4 354, 357, 402-403, 413-415, 426-427, 437, types of, 54, 206, 218, 231, 276, 406, 409 informational, 344 439, 441-443 Representativeness, 133, 147 defined, 55 research, 31, 39, 46-47, 51, 56-57, 59, 61, 65, 67, 69, differentiation, 403 74, 76, 78-84, 86-87, 95, 98-99, 102-108, levels of, 228, 230, 348 480

Sexual harassment, 25, 32-34, 37-38, 407, 416-418 Stereotyping, 32 systems development, 215 Shareholder, 402 Stock, 197 planning, 215 Shareholders, 9, 402 Stockholders, 42 Shopping centers, 200 Stories, 236, 423 T Shortage, 246 Strategic management, 159, 239, 242 Shrinkage, 159-160, 168, 236, 290, 447 strategic objectives, 40, 345 Tables, 96, 98, 149, 236, 332, 337, 391, 451, 456 Signaling, 196-197 Strategic planning, 237, 240-243, 254, 385, 405 Tacit knowledge, 291 Silence, 272 Strategies, 39-41, 43, 46-49, 76, 95, 99, 125, 127, Taiwan, 81, 429, 441 SIMPLE, 7, 10, 40, 69, 88-89, 93, 95, 115, 122, 131, Tangible products, 2 141, 144, 149, 155-156, 160, 167-168, Tax rates, 341 144, 185-186, 192, 198-200, 205-207, 212, 181-182, 185, 189, 194, 196-197, 200, 203, Taxation, 341 217, 223, 225, 229-231, 245, 258, 269, 301, 208, 239-243, 254, 276, 294-295, 304, Taxes, 342-343, 383 304, 319, 322, 327-328, 333, 336, 349, 353, 319-320, 324-326, 330, 337, 339, 346, 368-369, 377, 382, 389, 413, 427 347-348, 355, 357, 360, 365-366, 368, 370, sales, 383 Singapore, 300, 303, 429, 435 382, 387, 399, 414, 420-421 taxonomy, 88, 157, 216, 228, 231, 234, 260, 272, 291, SIR, 419 competitive, 196-197, 203, 239-241 Situational interview, 279 corporate, 43, 243 324, 380 situational interviews, 279 functional, 254 teams, 5, 7-11, 48, 73, 79, 99-100, 103, 108-109, Size, 3, 9, 25, 28, 40, 60, 62-63, 86, 100, 108, 112, Strategy, 40-41, 43-44, 46-47, 50, 67, 76, 126, 145, 116, 132-133, 139, 141, 147-149, 151-152, 148, 159, 161, 173, 181-182, 185-186, 188, 213-214, 248, 311, 315, 335, 348-349, 355, 159-161, 163, 168, 175, 177, 179-183, 209, 190, 195, 197, 200, 210, 213, 233, 238-242, 359-360, 364, 375, 380, 392, 426, 435 215, 249, 264, 309, 319, 322-323, 326, 364, 248-250, 252-254, 259, 275, 289, 294, 306, disadvantages of, 108 382-383, 385, 390, 401, 403, 420, 446 323-326, 328, 333, 335-336, 350, 365, 394, effective, 7, 48, 73, 79, 103, 108, 349, 355, Skilled labor, 434 396, 403-405, 413, 421-422, 437, 449-450, Skills, 1, 5, 7-10, 22, 30, 40, 43, 45, 47-49, 53-54, 56, 454, 457, 460 359-360, 426, 435 65, 68, 70, 75, 79, 84, 99, 101, 112, 135, combination, 46, 67, 161, 185, 249, 306, 323-326, types of, 99-100, 103, 213 145, 158, 197, 202-203, 206-208, 213-214, Teamwork, 241, 360, 434-436, 441 216-218, 222, 227, 230-236, 237-238, 394, 450 Technical competence, 423, 433 243-246, 250-251, 266, 270, 276-279, 281, corporate social responsibility, 403-404 Technical skills, 65, 433 286, 288-289, 292, 296, 301-303, 310, 312, defined, 47, 126, 185, 233, 289, 457 Technological advances, 266, 280, 282 328-329, 347-349, 351-352, 356-361, differentiation, 403 Technological progress, 267 364-367, 369-370, 376-378, 381, 383, 395, focus, 46, 145, 173, 213, 233, 242, 248, 306, 413, Technology, 4-6, 8-11, 24, 62, 99, 162, 195, 201, 203, 407, 427, 431-433, 437-438, 441, 445, 448, 457 437 206, 217, 231-232, 241-242, 244-245, Skills training, 359, 376 global, 238, 248, 326, 403, 437 249-250, 255, 266-267, 280-283, 288, 307, slander, 256 stability, 126, 145 347-348, 350, 353-354, 361, 365, 370, slides, 187 Stress, 10, 47, 49, 62, 97, 192, 304, 350, 365-366, 376-377, 382, 403, 419, 427, 442, 447 Slope, 177-181, 193, 338, 392, 462, 464, 468 advances in, 195, 231 Smoke, 224, 260 368, 388, 397, 417, 437 culture and, 267 Sociability, 293, 326 environmental, 62 information technology, 5, 11 Social behavior, 4, 347-349, 427-428 structured interviews, 266, 271, 276, 278, 283, 291, Telecommunications, 99, 137, 295-296, 427 Social environment, 217 Teleconferencing, 214 Social factors, 272 324, 446 Telemarketing, 292 Social loafing, 99 Students, 37, 81-82, 86, 88, 165, 170, 185-186, 203, telephone, 114, 178, 200, 256-257, 266-267, 271, 280, Social network, 210 305 Social networking, 196-197 210, 259, 263, 274, 277, 279-280, 290, 311, Telephone interviewing, 280 Social norms, 399-400, 422 344, 358, 410, 412-413, 420, 423 Telephone interviews, 256, 271, 280 Social responsibility, 400, 403-404, 412 Subgroups, 31, 163, 169, 175, 179-183, 225, 262, Tenure, 54, 62, 73, 97, 114, 249, 262, 338, 341-343, Social Security, 23 356, 427, 449, 456 445, 455 reform, 23 Subsidiaries, 428, 439, 441 Termination, 21, 27, 35, 48-49, 185, 408-409, 416, Socialization, 263 wholly owned, 439 421, 445 Societies, 427, 429, 441, 443 Substitution, 150, 327, 342 Terminology, 13, 207, 215, 431 Society, 1, 3, 5, 10, 26-27, 32, 49, 122, 181, 186, 190, Success, 6, 17, 34, 39, 41, 47-48, 51-52, 54, 56, 62, Territory, 63, 87 192, 195, 202, 237, 242, 246, 267, 269, 289, 66-68, 71, 77, 90, 102, 108, 118, 157-158, Terrorism, 5, 269, 408 298, 319-320, 348-349, 399-400, 406-407, 170, 172-174, 183, 195, 207, 213, 225, 237, Test performance, 121, 150, 169, 186, 191-192, 290 412, 417, 428, 438, 441, 445 239, 252-253, 258-259, 261-262, 267, 274, Thailand, 438 summary, 181, 192, 445 276, 281, 286-290, 292, 295-299, 301-303, Theft, 53, 261-263, 410-411 software, 10, 122, 157, 201, 205-208, 211, 223, 231, 305-306, 308, 317-318, 320-322, 324, employee, 53, 261, 263, 410-411 266, 282, 370, 374, 390, 402, 427, 467 326-328, 332-336, 346, 348, 350, 352-354, Third-country national, 431 application service providers, 207 358, 361, 368, 370, 378, 405, 415-416, Threats, 105, 107, 240, 269, 385-386, 388, 394, 397, evaluation of, 207 423-424, 433-435, 437-439, 443, 455, 458, 409 tracking, 282 461, 465-466 Time requirements, 200 South America, 5 summarizing, 223, 288, 358, 434, 446, 463 Time value of money, 341 Spain, 303, 402, 431, 435 Supply, 19, 45, 55, 196, 199, 234, 237-239, 243-246, Timing, 48-49, 196-197, 199, 379, 438 Spam, 6 250-251, 253-254, 256 tone, 107 Specialization, 218 aggregate, 246 objective, 107 Specialized skills, 246, 395 of labor, 199, 234, 244-246, 253 Top managers, 7, 233, 238, 242, 253, 288, 290 Specific performance, 293, 323 Supply and demand, 19, 45, 196, 237-239, 245, Tort law, 410 specific purpose, 99, 216 250-251, 254 Total cost, 200 Speculation, 61, 412 Support, 13, 19, 49, 57, 60, 71, 76, 78, 81, 95, 99, Total quality management, 76 spelling, 116 103, 144-145, 157, 162, 164, 167, 176, 178, Trade, 5, 47, 150, 183, 185, 202, 207, 317-318, 347, Stakeholders, 74, 103, 289, 345, 399-406, 421-422, 202, 204-205, 213, 216, 228, 231, 234, 242, 389, 426 449 247, 253, 256, 259, 261, 263, 273, 277, 290, domestic, 347 Stamina, 228 303, 311, 314, 321, 343-344, 348, 350, 352, Trade barriers, 426 Standard deviation, 86, 104, 133-134, 138, 140, 153, 354-355, 359, 364-365, 377, 404, 414-415, Trade-offs, 47, 207, 318 163, 180, 187-188, 259, 264, 294, 338-340, 438-440, 445, 447, 451, 453 Training, 1-2, 4, 9-10, 13, 19-21, 24, 27-30, 34, 36-37, 350, 382-383, 456, 464, 467-468 Surplus, 245, 251 39-41, 43-50, 57, 61, 65, 67, 69, 73-75, SD, 383 consumer, 245 82-83, 91, 94, 99, 101-102, 105, 108-109, Standardization, 121-122, 258, 266, 272, 277, 312 total, 245, 251 121, 123, 126, 147, 149, 151-152, 157, 167, Static strength, 228 Survey feedback, 252, 375 178, 187, 192, 202, 204, 211, 215, 219, 222, statistics, 114, 130, 143, 145, 182, 187, 226, 246, 384, surveys, 74, 226, 231, 349, 380-381, 403, 410, 428, 225-226, 230, 232, 235-236, 237-238, 412, 456 430 243-245, 248, 250-253, 255, 258, 261, 265, analyzing, 226, 456 Sweden, 429, 435 272, 274, 277-279, 281-283, 288, 296, misleading, 246 system, 2, 9, 13-14, 16, 26, 31, 35-36, 40-44, 48-50, 300-301, 304-306, 308-311, 315-318, 319, Status, 20, 27, 37, 148, 162, 166, 170, 196, 207, 212, 60, 62, 64, 73-77, 79-81, 86, 89-90, 102-104, 326, 328, 330-331, 335-336, 345, 347-371, 271, 276, 290, 292, 319, 321, 323-324, 326, 108-109, 120, 127, 156, 178, 186, 189-192, 373-397, 404-406, 412, 414, 424, 426, 428, 330, 404, 417, 420, 432, 465, 467-468 195, 205, 207, 216, 231-232, 234-235, 433-439, 441, 443, 445-447, 449, 452, Status quo, 148, 432 237-239, 244, 252, 254, 280-282, 287, 298, 455-458 Steam, 6 308, 311, 320, 328, 335, 340, 342-346, 347, methods for, 2, 37, 73, 99, 236, 265, 378, 396 steering committee, 232 365, 379, 405, 408-410, 415-416, 420-424, Training programs, 20, 24, 43, 47-48, 65, 74, 91, 99, 434-435 101, 108, 149, 352, 354-355, 357, 359, System design, 232 363-365, 378, 383-384, 390, 394, 396-397, systems analysis, 37, 41 404, 406, 441 systems analysts, 364 effectiveness of, 47, 65, 390, 406 Systems approach, 39-50 Transfers, 45, 48, 53, 201, 203, 215, 243, 251, 361 481

Transformation process, 42, 55 115, 118, 125, 127, 129, 132, 135, 142-143, 195-197, 199, 204, 215, 237-254, 308, 335, transitions, 248, 361, 371 150, 152-155, 157, 162-163, 166-167, 170, 348-349, 355, 378, 405, 427 Translation, 288, 430-431 176-178, 233, 245, 267, 273, 275-276, 281, diversity in, 204 288, 290, 292, 296, 298-299, 303, 313-314, Workforce diversity, 35, 197, 335 language, 430 322-324, 327, 330, 340, 345, 350, 361, 370, Workforce Management, 244 Translations, 93 374, 378, 384-385, 387, 391, 394, 396, 406, Work-life balance, 203 Transportation, 23, 36, 95, 236, 365 415-416, 424, 430, 438-439, 445, 447-449, workplace, 1, 7, 9, 24, 28, 33-35, 37-38, 55, 103-104, Trends, 9-10, 57, 185, 198, 208, 249, 348, 431, 437 458, 461-462, 466-467, 469, 471 204, 214, 268-269, 348-349, 364, 408, Triple bottom line, 289, 399, 401-402, 424 Variance, 62, 64-65, 68, 80-82, 85, 95-97, 102, 115, 410-411 TRIPS, 200-201 122, 124, 126, 128, 130-133, 137, 142-143, changing, 7, 9, 214 Trucks, 9, 282 146, 149, 151-158, 162-165, 177, 179-180, Workplace deviance, 55 Trust, 9, 31, 49, 81, 103, 199, 210, 217, 240, 295, 354, 182, 188, 203, 234, 261, 267, 288-289, 294, World, 1-2, 5-6, 9-11, 43, 48, 113, 214, 231-232, 234, 301, 304-306, 312-314, 323-324, 330, 332, 241, 261, 267, 308, 400, 403, 412, 426-427, 360, 403, 408 334, 364, 367, 370, 389, 410, 436, 461, 463, 443, 465 Turkey, 438 465-468, 470-471 foreign direct investment, 11, 426 Turnover, 9, 33, 53, 62-63, 87, 153, 205, 209-210, Venezuela, 429 World War, 261, 308 Verbal comprehension, 290 First, 308 238, 245, 249, 252, 258, 261, 268, 311, 335, video clips, 223 Second, 308 338, 379-380, 384, 406, 434, 443 video interviews, 203 World Wide Web, 231 Twitter, 208 Videoconferencing, 255, 267, 272, 280, 282 Written reports, 90 videos, 279, 375 Written tests, 31, 46, 118, 192 U Vietnam, 17, 28-29, 444 WWW, 118, 149, 207, 246, 263, 352, 408 Violence, 256 Uncertainty avoidance, 428-429 Virtual offices, 6 Y underscores, 435 virtual teams, 214 Understudy assignments, 376 virus, 24 Yen, 124 Unemployed, 40, 208 Vision, 7, 74, 125, 217, 233, 241, 245, 282, 360, 394, YouTube, 402 Unemployment, 238 404 Unions, 17, 42, 203, 356, 411, 416 visualization, 290 Z United Kingdom, 403, 434-435 Visualize, 43, 112 voice mail, 407-408, 424 Zoning, 218 corporate social responsibility, 403 Volume, 16, 48, 52, 54, 63, 70, 87, 92, 113, 189, 206, United States, 5, 14-15, 18, 23, 32, 108, 142, 170, 245, 253, 261, 297, 379, 385, 440 Voting rights, 19 173, 185, 247, 267, 295, 303, 347, 402, vulnerabilities, 413 407-408, 426, 429-430, 433-435, 441-443 foreign direct investment, 426 W North American Free Trade Agreement, 5 Universities, 37, 81, 202, 344, 402, 412 Wages, 19, 33, 45, 192, 212, 225, 235, 340, 343, 401 Upward shift, 295 differences in, 340 U.S, 5, 10, 14-15, 17-18, 20, 23, 25-29, 33, 36, 103, efficiency, 212, 225 178, 197, 206, 211, 215, 223, 234, 246, minimum, 19, 33 265-266, 268-269, 278, 308, 348, 359, 374, 383, 407, 431, 433-434, 438, 442 Waiver, 23 U.S., 5, 10, 14-15, 17-18, 20, 23, 25-29, 33, 36, 103, War, 18, 261, 308 178, 197, 206, 211, 215, 223, 234, 246, Warrants, 15 265-266, 268-269, 278, 308, 348, 359, 374, Water, 83, 197, 224 383, 407, 431, 433-434, 438, 442 Weaknesses, 48, 73, 75, 77, 87-89, 118, 167, 169, U.S. Air Force, 265-266 U.S. Census Bureau, 197, 266 210, 240, 247, 437 U.S. Department of Energy, 269 Wealth, 206, 212 U.S. Department of Labor, 27-29, 178, 215, 223, 234, Weather conditions, 59 246 Web, 6, 10, 195-196, 201, 203, 205-206, 211-212, U.S. economy, 36 U.S. Postal Service, 268 213, 231-232, 236, 266-267, 286-287, Utilities, 41, 104, 250, 335-337, 383 353-354, 365, 376, 401-402, 409-410 Utility, 39-41, 46-47, 49-50, 51, 63-65, 67, 69, 77, 99, Web site, 203, 206, 211-212, 213, 231, 236, 401 102, 117, 124, 149, 187-190, 262, 272, 281, Web sites, 195, 203, 206, 402, 410 294, 313, 316-317, 319-320, 324, 330, Wireless communications, 214 332-333, 335-346, 359, 374, 379-385, Women, 8, 10, 17-19, 22, 29-30, 33, 36, 51, 60, 98, 450-451, 455 119, 178-179, 183, 189, 192, 197, 250, 252, 264, 274, 276, 288, 313, 348, 355, 368, 408, V 415, 417, 437, 454, 456-457, 459 Won, 13, 19, 22, 29, 249 Valid test, 341, 392 Work, 1-11, 13, 18-19, 23-24, 26, 29, 32-35, 39-40, 43, Validity, 32, 57, 59-60, 63, 66, 68, 70-71, 77, 79, 46-47, 49, 51, 53-55, 57-62, 64, 67-69, 71, 74-75, 77, 79, 86, 88, 92, 95-100, 107, 109, 82-86, 95, 120, 122-124, 132-133, 139, 117, 119, 122, 131, 144-145, 149-151, 157, 141-168, 169-176, 180-186, 188-189, 161, 167, 178-179, 186, 189, 192, 198-199, 193-194, 201, 214, 219-220, 228, 233, 236, 201-204, 206-212, 213-236, 239, 243-244, 256, 258, 261-267, 269, 271, 273, 275, 279, 253-254, 256, 258-266, 270, 279, 281, 281-282, 286, 289-290, 292-297, 301-305, 286-287, 292-293, 296, 298-299, 302-304, 307, 309, 311-318, 319-323, 328, 330-339, 306-308, 313, 316-318, 328, 336, 339, 342, 342-346, 352, 359, 385, 392-394, 396, 400, 347-348, 350-351, 355, 359-365, 367-370, 412-413, 428, 432-435, 443, 445-448, 375, 378, 380-382, 385, 392-393, 402, 451-452, 454-460, 464 407-408, 410-412, 416, 420, 424, 426-427, Value, 5, 7, 9, 19, 40, 44, 54, 65, 67-68, 70, 80, 86, 90, 429-431, 433-435, 437, 439-442, 445-451, 93-94, 105, 117, 119, 132-133, 135-136, 453-455, 457-459, 467 141-142, 146, 154, 177, 179, 187-188, 196, attitudes toward, 96, 98, 150, 209, 263, 355 204, 212, 221, 229, 231, 236, 241, 245, 253, work products, 145, 226, 457 256, 266, 293, 332, 337-343, 345-346, 354, Work schedules, 24 364, 377, 379, 382, 389, 401-403, 405, 413, Work teams, 7, 9, 79 418, 420, 422, 435, 442, 461-464, 466-468 global, 7, 9 building, 70, 117, 196, 337 Workers, 1, 3-11, 18, 23-24, 26-27, 29, 34-35, 37, 45, defined, 7, 54, 65, 93, 136, 177, 293, 337, 401 47, 64, 75-77, 90, 93, 95, 97, 115, 133, 150, market value, 341 190, 192, 195, 197, 199, 204, 213-215, 217, Value added, 266, 346 219, 223, 225-226, 230, 232, 234, 238, 241, Value creation, 403, 405 245-246, 251, 256, 263, 265, 268, 347-348, Value-added, 231 355, 357, 366, 379, 382-383, 393, 402, 429, Vanguard Group, 207 433, 443, 449, 456, 458 Variability, 1-2, 59, 63, 81, 101, 113, 115, 132-134, skilled, 6, 10, 433 149, 154, 162-163, 233, 275, 296, 319, 328, unskilled, 348 338, 340, 342-343, 359, 361, 367, 382, 416, workforce, 1, 5-6, 9-10, 21, 25, 35-36, 39, 44-45, 49, 438, 447, 464, 468 Variable costs, 342-343, 383 Variables, 41, 49, 60-63, 68-69, 80, 87, 95-98, 102, 482