DEVELOPMENT AND CONVERGENT VALIDATION

JOURNAL OF BUSINESS AND PSYCHOLOGY Volume 8, No. 1, Fall 1993

DEVELOPMENT AND CONVERGENT VALIDATION OF A METHODOLOGY FOR ESTIMATING CROSS-JOB RETRAINING TIMES Charles E. Lance The University of Georgia

Michael J. Kavanagh SUNY-Albany

R. Bruce Gould Armstrong Laboratory, Human Resources Directorate

ABSTRACT: Cross-job transferability of skills is defined in terms of the ease with which individuals can apply knowledge and skills acquired in a previous job in learning to perform a new job. This study reports the development of a prototype methodology for estimating retraining times based on analyses of transferability of skills. Using this methodology which assesses interjob similarity in task content and task learning time, times to retrain across 41 differ-

Research sponsored by the Air Force Office of Scientific Research/AFSC, United States Air Force, under contract F49620-87-R-0004. The United States Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation hereon. This research was completed while the first author was a Summer Faculty Research Fellow, and while the second author was Visiting Senior Scientist, in the Manpower and Personnel Division, Air Force H u m a n Resources Laboratory, Brooks AFB, TX. Views expressed in this article are those of the authors and do not necessarily reflect those of the United States Air Force. An earlier version of this paper was presented at the August, 1989 meeting oi the American Psychological Association, New Orleans, LA. The authors wish to t h a n k Vida Scarpello, Bob Vandenberg. Karl Kuhnert, Wayne Archer Michelle Lynskey, and two reviewers for their helpful comments on an earlier version of this paper. We would also like to thank David Mayfield, Phil Davis, and Wayne Archer for their help in various aspects of this work. The first author also would like to acknowledge the support of the Dublin Business School, Dublin City University in the preparation of this manuscript. , r» , r Address correspondence concerning this article to Cbarles E. Lance, Department ol Psychology, University of Georgia, Athens, GA 30602. 67

® 1993 Human Sciences Press, Inc.

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ent U.S. Air Force enlisted jobs were estimated. Convergent validities of retraining time estimates were tested in terms of correlations with differences in jobs': (a) general job leaming difficulty, and (b) aptitude requirements. Results supported predictions tbat cross-job retraining time would be longer (a) into jobs that generally are more difficult to leam, (b) from jobs that generally are less difficult to learn, and (c) across jobs having different, ratber tban similar, aptitude requirements. Implications for training, buman resources planning, and coping witb increasingly rapid technological cbanges in tbe workplace are discussed. Transferability of skills can be defined as "the continuous use of acquired knowledge and abilities when moving from one job to another" (Fine, 1957a, 1957b, p. 938), or in terms ofthe ease with which individuals trained to proficiency in one job can apply acquired knowledge and skills in learning another job. Although transferability of skills across jobs within the same occupational or career family has been studied within the career planning literature (Anderson, Milkovich, & Tsui 1981; Burak & Mathys. 1980; Hall, 1976), little work exists on the transferability of skills across jobs in different occupational or career families, where very different task requirements may require correspondingly different skills of job incumbents. In this article, we briefly review the literature relating to transferability of skills across jobs. We then describe a new approach to the empirical estimation of cross-job retraining times across dissimilar jobs. Next, we present convergent validity evidence which provides initial empirical support for the estimation of cross-job retraining times. Finally, we discuss theoretical and practical implications of tbe estimation of cross-job retraining times for human resources management.

BACKGROUND • The topic of the transferahility of skills has arisen in the contexts of civilian occupational mobility (Byrne, 1975; Fine, 1957a, 1957b), transitions from military to civilian occupations (Mangum & Ball, 1987), the design of formal educational curricula (Altman, 1976; Dillon & Horner, 1968; Pratzner, 1978), and personal and work role development (Brett,' 1984). Also, large bodies of literature exist relating to (a) transfer of training to on-the-job performance (Baldwin & Ford, 1988- M Feldman 1981; Kelly, 1982; Royer, 1979), (b) resocialization and adjustment fol-' lowing job transfer (Brett, 1982, 1984; D. Feldman, 1976- Pinder & Schroeder, 1987; Pinder & Walter, 1984), (c) patterns of career mobility (Anderson et al., 1981; Forbes, 1987; Louis, 1980; Vardi, 1980), and (d) human resource planning for staffing and training needs (Dyer, 1982, 1985; Milkovich, Dyer, & Mahoney, 1983); but, surprisingly little literature exists on cross-job family transferability of skills.

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Cross-job family transferability of skills is however, an emerging concern for human resources management (HRM) in the light of increasingly rapid technological changes in the workplace (Downs, 1985; Dyer, 1982; Fossum, Arvey, Paradise, & Robbins, 1986; Kozlowski & Farr, 1988; Rumberger, 1981). Some of the HRM concerns created by technological innovation are skills obsolescence (Fossum et al., 1986), and the creation of imbalances between current workforce skill mixes and skills needed to support the new technology. Private sector employers usually have several HRM options for coping with new personnel requirements and employee skill mix imbalances. These include various workforce attrition or reduction strategies for areas of personnel surplus (Greenhalgh, Lawrence, & Sutton, 1988), external recruitment for personnel shortages (Guion, 1976; Scbwab, 1982), and retraining and redeployment of current employees (Downs, 1985). However, in more "closed" personnel systems such as in the public sector, or in situations in wbich strong job security provisions limit workforce reduction options, "retooling" and making optimal use of the current workforce may be one of tbe primary mechanisms for coping with technological innovation. That is, in situations of limited human resources pools, these resources must be utilized in the most effective way, and redeployment of current employees often may be more efficient than relying upon external labor markets. Interjob Similarity and the Transferability of Skills The training literature supports the idea that training transfer "is maximized to the degree that there are identical stimulus and response elements in the training and transfer settings" (Baldwin & Ford, 1988, p. 66). Similarly, Fine (1957a, 1957b) has argued that cross-job family transfers should be more successful to the extent that the old and new jobs are similar in terms of the work performed. The limited empirical research on transferability of skills supports tbe idea that interjob similarity relates to the ease with which a new job is learned and performed. For example, Gordon and Fitzgibbons (1982) found that interjob similarity (i.e., between the old and new job) related positively to performance in the new job, and Gordon, Cofer, and McCullough (1986) found that interjob similarity related negatively to training time required on the new job. However, Gordon et al. (1986) indexed interjob similarity as a simple dichotomous variable indicating whether the new job was in tbe same, or a different, department of a textile mill. Gordon and Fitzgibbons (1982) indexed interjob similarity based on a more formal job evaluation system, but all research participants (rt ^ 162) were sewing machine operators. Thus, past research on transferability of skills has been limited to a

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small number of jobs within the same job family. Below, we describe the development of a prototype methodology, based on analyses of transferability of skills, for estimating cross-job family retraining time for a large number of jobs (41). The general rationale for the development of these retraining time estimates is that transfer of skills across jobs should be easier to the extent that the old and new jobs' task contents are similar. , • . Additional Predictors of Cross-Job Retraining Time We tested the convergent validity of the retraining time estimates developed here against two other predictors of retraining ease; old versus new job differences in (a) job learning difficulty and (b) aptitude requirements. Job Learning Difficulty. Learning to perform a simple job should be easier than learning to perform a complex one. Work can be difficult for many reasons (Madden, 1962), but becoming proficient in a new job should be more difficult to the extent that the job tasks are generally more difficult to learn relative to other jobs. Task learning difficulty has been defined in terms of the time required for a typical incumbent to learn to perform a task satisfactorily (Burtch, Lipscomb, & Wissman, 1982; Weeks, 1984). Aggregating across tasks, job learning difficulty can be defined in terms of the average learning difficulty of tasks performed within a job in reference to the relative time spent performing them (Burtch et al., 1982). Recently, Mumford, Weeks, Harding, and Fleishman (1987) have provided evidence for the construct validity of job (occupational) learning difficulty as involving the complexity of a job's cognitive demands. Thus, irrespective of the old-new job similarity in task content, the transferability of previously acquired knowledge and skills into a new job should be more difficult to the extent that tbe new job generally is difficult to learn. Thus, our first hypothesis was of a positive relationship between the general job learning difficulty of the new job and cross-job retraining time estimates, which reflect both old - new job differences in task content as well as task learning times. In addition, skills should be more generally transferable from jobs that are more difficult to learn, than from less difficult jobs. Despite the fact that some difficult jobs (e.g., Russian - Bengali Cryptographer) may require very narrowly specialized and nontransferable skills, this idea is based on the general rationale that difficult jobs are more likely to require skills (e.g., problem solving, goal setting) that are more broadly applicable to performance in other jobs, than are simpler jobs (Pratzner, 1978). Thus oiu- second hypothesis was of an inverse relationship be-

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tween cross-job retraining time estimates and the learning difficulty of the old job. Interjob Similarity in Aptitude Requirements, Skills also should be generally more transferable across jobs to the extent that the old and new jobs draw upon similar, rather than different, aptitudes (Peterson & Bownas, 1982). For example, retraining from a job drawing heavily upon verbal ability (Office Manager) into a second verbally-oriented job (Sales Representative) should be easier than into a new job requiring primarily mechanical abilities (Heating and Air Conditioning Maintenance). Since higher values on cross-job retraining time estimates described below indicate longer retraining times, our third hypothesis was of an inverse relationship between cross-job retraining time estimates and interjob similarity in aptitude requirements. Finally, the learning difficulty of the new job should have a larger impact on transferability of skills if the new job draws upon different, rather than similar, employee aptitudes. For example, the learning difficulty of a new job requiring primarily mechanical abilities may play a larger role in determining retraining times from verbally-oriented jobs, than from other mechanically-oriented jobs. Thus, our fourth hypothesis was for stronger positive relationships between cross-job retraining time estimates and the occupational learning difficulty of the new job for situations in which the old and new jobs require different, rather than similar, aptitudes. METHOD Study Context The present study was part of an ongoing research effort on transferability of skills to support retraining and strategic human resource planning in the U. S. Air Force (USAF). The sample for the present study consisted of a subset of USAF enlisted jobs. Eacb year, over 10,000 USAF enlisted personnel retrain into one of nearly 300 different enlisted jobs in order to balance staffing levels, to fill personnel requirements, and to allow enlisted personnel to further their career goals by retraining in additional skills (Emerson, 1987). Task Taxonomy Development of the retraining time estimates described below required a common task taxonomy to facilitate cross-job comparisons of task content differences and task learning times (Dunnette, Hough, & Rosse, 1979; Fleishman, 1984). This need to conduct cross-job task con-

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tent comparisons was addressed as part of a Job Categorization Project completed earlier by the USAF Occupational Measurement Center (OMC) (Bell & Thomasson, 1984). Bell and Thomasson (1984) used a "retranslation" technique (Smith & Kendall, 1963) to categorize thousands of tasks performed by enlisted personnel (Christal, 1974) into 26 rationally defined taxonomic task categories. These task category names and definitions are shown in Table 1. We based our analyses of interjob task content differences on this taxonomic system because of its (a) specific applicability to tasks performed in USAF enlisted jobs, (b) comprehensiveness (Bell & Thomasson, 1984), and (c) usefulness for allocating USAF enlisted jobs' tasks into its categories (Gould, Archer, Filer, Short, & Kavanagh, 1989). Skills/Knowledge Questionnaire The taxonomy in Table 1 formed the basis for developing a Skills/ Knowledge Questionnaire (SKQ) for the purposes of the present study. The SKQ was developed to assess the task content and skill requirements of a number of Air Force enlisted jobs. SKQ instructions first directed subject matter experts (SMEs) to complete a standard background information form, and to review the 26 task category names and definitions shown in Table 1. Next, SMEs were directed to make three judgments for each of the 26 taxonomic task categories: (a) a binary "Part-of-job" rating, (b) Relative Time Spent performing tasks within categories endorsed as being part of the job ("1 - Very small amount," to "9 - Very large amount"), and (c) average Months to Proficiency on tasks included in each category for a newly assigned person who had just completed formal training ("1 - 0-1 Months" to "9 - 9 or more months"). Rating instructions directed SMEs to consider a typical journeymanlevel incumbent's job duties in completing each of the three ratings. Data Collection In May 1988, the SKQ, along with a cover letter, detailed rating instructions, and rating category definitions, was mailed to 1356 supervisors in 47 enlisted Air Force jobs. The 47 jobs which, over the prior 2 years, had the highest rates of movement, either "out of - to another job (the new, or *To-Job"), or "into" - from another job (the old, or "FromJob") were targeted for data collection. Potential survey participants were identified by randomly selecting 30 supervisors' names in each job from personnel records. For jobs in which there were fewer than 30 supervisors Air Force-wide, all were selected as potential respondents. Study participation was voluntary.

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Table 1 Task Categories and Definitions Clerical - Performing secretarial or clerk type functions, such as filing, preparing forms, or answering phones. May involve understanding and application of rules, manuals, or regulations. Computational - Performing basic math computations, such as adding, multiplying, dividing, or computing simple averages. May involve understanding and application of rules, manuals, or regulations. Also includes the operation of adding machines or calculators. Office Equipment Operation - Operating general office equipment such as typewriters, copy machines, or stenographs. Also includes minor maintenance such as changing fluid, changing ribbons, clearing jams, or replacing bulbs. Mechanical - Tasks which involve the manual manipulation of tools or equipment. Also involves those tasks which require an understanding ofthe mechanical principles and/or actual mechanical works of machinery or its components. Simple Mechanical Equipment/Systems Operation - Operation of simple equipment, machinery, or systems (other than office equipment) requiring only basic knowledge or understanding of the equipment. Complex Mechanical Equipment/Systems Operation - Operation of equipment, machinery, or systems (other than office equipment! requiring advanced or in-depth knowledge, complex skills, or significant manual coordination. Mechanical Electrical - Tasks involving hoth mechanical and electrical knowledge or skills but with the primary emphasis of the task being mechanical in nature. (Note: Tasks which appear to have approximately equal emphasis should be grouped according to the more important or critical aspect). Mechanical-Electronic - Tasks involving both mechanical and electronic knowledge and skills bui with the primary emphasis ofthe task being mechanical in nature. (Note: Tasks which appear to have approximately equal emphasis should be grouped according to the more important or critical aspect). These tasks may also involve some incidental electrical knowledge. Electrical - Tasks which involve systems and equipment that produce or transmit electrical power; including transformers, generators, motors, and associated power lines and wiring. May involve small amounts of other components such as mechanical, electronic, or administrative, but the primary aspect is electrical. Electronic • Tasks which involve devices, circuits, or systems that conduct or transmit complex electrical signals, such as transistors, resistors, diodes, or printed circuit boards; included wiring, such as coaxial cables which carry coded signals. Requires understanding of principles cf electronics and/or the functioning of components. May involve small amounts of mechanical, electrical, or administrative components but the primary aspect is electronic. Electrical-Mechanical • Tasks which involve both electrical and mechanical skills but the primary aspect is electrical. Electrical-Electronic - Tasks which involve both electrical and electronic skills and knowledge. Does not Involve significant amounts of mechanical skills. Electronic-Mechanical - Tasks which involve both electronic and mechanical skills and/or knowledge, but the primary aspect is electronic.

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Table 1 {Continued) Simple Physical Labor - Tasks involving simple manual lahor, such as sweeping, lifting, carrying, or cleaning. Cleaning tasks would ordinarily be included in this category if no technical knowledge is involved or required. Medical Patient Care - Tasks whose predominant aspects involve physical or verbal interaction with patients. Medical-Equipment Oriented • The primary aspect of these tasks involves the use or operation of some type ot medical equipment, instruments, or supplies. May involve some degree of patient interaction. Usually, medical X-ray or medical lab tasks would be grouped under this category. Medical Procedures - The primary aspect of these tasks involves some procedure in a medical lah or operating room, etc. May involve some degree of patient interaction. Simple Nontechnical Procedures - These tasks are usually simple in nature, somewhat procedural and do not require a great deal of knowledge, training, or experience to perform; require only simple instructions or directions; may involve following a checklist. Communicative-Oral - Tasks whose primary aspect is communicative in nature; may involve the operation of communication devices, such as radios or telephones, when the primary emphasis of the task is to communicate something rather than strictly the operation of the device. Communicative-Written • Tasks that involve communicating in a written manner; more than just a preparation of a standard form or standard report requiring filling in blanks. General Tasks or Procedures - Any general task or technical procedure that does not involve signtficant amounts of mechanical, electrical, or electronic skills or knowledge and is not primarily administrative in nature, yet does require some detailed knowledge to be performed. (Note: If a task involves some mechanical skill, or requires the individual to know electrical or electronic principles, it should be categorized under those categories). Reasoning/Planning/Analyzing - Tasks whose primary aspects involve reasoning or interpretive skills. May include coordinating when it involves reasoning problems or answering inquiries. (Note: Does not involve normal supervisory planning such as assigning work, evaluating performance, interpreting regulations, etc.). Scientific Math Reasoning or Calculations • These tasks involve more than simple arithmetic computations; may involve using or applying formulas, using or preparing tahles or charts; may require knowledge of physics, chemistry, geography, etc.; may involve use of equipment such as gauges, slide rules, plotters, or calculators. Special Talents - Tasks which involve skills which cannot be completely taught, such as playing musical instruments, drawing or composing. Usually involves some elements of creativity. Supervisory - Tasks whose primary aspects involve supervision of others, including assigning individuals to work load, generating schedules, assessing performance, etc. Training - Tasks associated with the giving of job-oriented training.

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Respondent Characteristics Surveys were returned by mail in June 1988 from 675 respondents for a response rate of 50%. Data were obtained from five or more respondents in 46 ofthe 47 jobs surveyed. However, five of these jobs had been determined as belonging to more than one aptitude requirement area (e.g., Mechanical and Electronics, see below). Since in the present study we were interested in job differences in aptitude requirements as related to estimates of cross-job retraining time, we retained data only from respondents in the 41 jobs that were assigned to a single aptitude requirement area. This resulted in the loss of 70 respondents' data. Another 45 respondents' records were deleted due to missing data or because the respondent's reported Air Force Specialty Code (AFSC) did not match the list of AFSCs targeted for data collection. The 41 jobs included in the present study, along with their AFSCs, assigned aptitude requirement area (MAGE Area, see below) and job learning difficulty indices {JLDs, see below) are shown in Table 2. The typical respondent in the data reported here (n ^ 560) was male (91.2%), had some college education (mean education 13.56 years, SD = 1.51), supervised four others (mean = 4.11, SD - 8.03), had been in their job over 3 years (mean = 38.09 months, SD = 33.22), and in the Service over 15 years (mean Total Active Federal Military Service = 182.81 months, SD = 57.47). None of the respondent characteristics in the sample reported here differed significantly from those excluded from the complete set of respondents. Descriptive Statistics Descriptive statistics for SKQ ratings are shown in Table 3. As expected, there were large differences in the extent to which task categories were endorsed as being part of tbe job. Many respondents endorsed clerical-, communicative-, and supervisory-related activities, while smaller percentages endorsed mechanical-, electronic-, and medicalrelated activities. These results are consistent with the mix of 41 jobs included in this study (see Table 2). Table 3 also shows Months to Proficiency (MTP) rating category means and standard deviations. MTP responses that were "missing" because a task category was not endorsed as part of the job were coded "0" to indicate "Zero months to proficiency." Interrater reliabilities (intraclass correlations - ICCs) for MTP ratings also are shown in Table 3. ICC (1, k) indexes the reliability ofthe mean of k judges' ratings (Lahey, Downey, & Saal, 1983; Shrout & Fleiss, 1979), and in the present study, there was a mean of 13.7 re-

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Table 2 U. S. Air Force Enlisted Jobs AFSC 113x0c 122x0 207x1 241x0 242x0 251x0 272x0 274x0 275x0 304x0 305x4 306x0 411x0c 411xla 426x2 431x1 431x3 451x4 451x5 451x6 454x3 456x1 472x4 491x1 491x2 492x1 493x0 496x0 603x0 645x1 645x2 651x0 661x0 702x0 705x0 732x0 733x1 751x1 811x0 811x2 903x1

U. S. Air Force Job Title • • -

Flight Engineer Aircrew Life Support Morse Systems Operator Safety Specialist : -. Disaster Preparedness Weather Specialist Air Traffic Control Operator Command and Control Specialist i' Tactical Command and Control Spec Wideband Communication Equip Spec Elec Component & Switching Sys Spec Elec Commun & Crypto Equip Sys Spec Missile Systems Maintenance Spec Missile Maintenance Specialist Jet Engine Mechanic ] Tactical Aircraft Maintenance Spec Airlift Aircraft Maintenance Spec F-15 Avionics Test Stn & Comp Spec F-16/A-10 Avionics Test Stn & Comp Spec F/FB-lll Avionics Test Stn & Comp Spec Fuel Systems Maintenance Specialist Electronic Warfare Systems Specialist Vehicle Maintenance Con & Analysis Tech Communication-Computer Systems Operator Communication-Computer Systems Progr. Information Systems Radio Operator Communication-Computer Systems Control Comm-CompSystems Program Mgt Spec Vehicle Operator/Dispatcher Materiel Storage & Distribution Spec Supply Systems Analysis Specialist Contracting Specialist Logistics Plans Specialist Administration Specialist Legal Services Specialist Personnel Specialist Manpower Management Training Systems Specialist Security Specialist Law Enforcement Specialist Nuclear Medicine Specialist

MAGE Area G-55 G-30 A-45 G-53 G-58 G-64 G-43 G-48 G-48 E-67 E-67 E-67 E-67 M-51 M-44 M-51 M-51 E-67 E-67 E-67 M-51 E-67 A-45 G-43 G-53 A-45 E-56 G-58 M-44 G-30 A-51 A-56 A-61 A-32 A-45 A-45 G-64 G-56 G-35 G-35 G-43

JLD

91 81 80 105 105 83 98 95 92 122 120 125 126 130 154 120 125 124 124 124 131 119 80 91 91 80 126 91 78 70 91 93 125 67

77 80 121 96 92 94 79

Note. AFSC refers to Air Force Specialty Code. MAGE refers to Armed Services Vocational Aptitude Battery selector aptitude area composites used by the U. S. Air Force (M = Mechanical, A = Administrative, G = General, E = Electronic); numbers reflect minimum percentile aptitude area cutoff scores. JLD refers to Job Learning Difficulty indices. See text for complete explanation.

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Table 3 SKQ Rating Descriptive Statistics and Intraclass Correlations Category 1. 2. 3. 4. 5. 6, 7. 8. 9. 10. 11. 12.

13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

23. 24.

25. 26.

Clerical Computational Office Equip Operation Mechanical Simple Mechanical Complex Mechanical Mechanical-Electrical Mechanical-Electronic Electrical Electronic Electrical-Mechanical Electrical-Electronic Electronic-Mecbanical Physical Labor Med-Patient Care Med-Equip Orient Med-Proced ures Simple Nontech Proc Communicative-Oral Comm unicati ve-Written General Tasks Reasoning/Planning Science/Math Special Talents Supervisory Training

Part of Job

Mean

SD

ICC (l,k)

84% 73% 73% 61% 53% 39% 28% 27% 29% 35% 24% 26% 29% 69% 5% 4% 6% 68% 76% 68% 63% 66% 37% 22% 66% 91%

2.59 2.16 1.81 2.04 1.71 2.17 1.29 1.32 1.30 2.34 1.23 1.45 1.48 1.07 0.15 0.16 0.17 1.71 3.23 3.30 3.58 3.93 1.86 1.28 4.23 5.87

2.27 2.30 2.05 2.72 2.30 3.17 2.47 2.53 2.42 3.52 2.50 2.75 2.74 1.36 0.88 0.94 0.94 1.97 3.02 3.22 3.21 3.61 2.95 2.80 3.73 3.07

.768 .784 .689 .853 .777 .822 .901 .766 .851 .966 .801 .890 .901 .459 .630 .813 .722 .396 .870 .842 .709 .821 .825 .678 .376 .367

Months to Proficiency

spondents from each of the 41 jobs (i.e., k = 13.7). ICCs were high for nearly all the MTP ratings, indicating that SMEs made reliable judgments ahout the nature of their jobs. Exceptions were categories 18 (Simple Nontechnical Procedures), 25 (Supervisory), and 26 (Training). However, all rating categories were retained for the purposes of the present study so that potentially important joh differences in task content would not he omitted in calculating cross-job retraining time estimates. The large standard deviations in Tahle 3 are due to the positive skew of the MTP ratings when calculated across all jobs. This skew occurred because many respondents indicated that incumbents of their job do not perform tasks within these categories (e.g., Administration Specialists who do not perform electronics-related tasks). When calculated separately for each job, however, the standard deviations are much lower as suggested by the relatively high ICCs. Overall, results in Table 3 indi-

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cate that SMEs successfully used the USAF OMC taxonomy (Bell & Thomasson, 1984) in making reliable judgments about their jobs' task content and learning times. Calculation of Cross-Job Retraining Time Estimates Cross-job retraining time estimates were calculated from SKQ Months to Proficiency (MTP) ratings. Mean MTP ratings were computed across raters within each of the 41 jobs listed in Table 2. Next, MTP mean vectors (e.g., XI and X2 for Jobl and Job2) were compared element-wise (i.e., across the 26 SKQ rating categories) for all jobs to calculate two sums: (a) differences between XI and X2 values for which XI values were larger (i.e., S (Xlj - X2i), only if Xlj > X2i, and zero otherwise, indicating task skills to be acquired in moving from Job2 to Jobl), and (b) differences between XI and X2 values for which X2 values were larger (i.e., 2 (X2i - Xlj), only if X2j > Xlj, and zero otherwise, indicating task skills to be acquired in moving from Jobl to Job2 ). For example, Figure 1 illustrates a h5T)othetical comparison between two jobs' mean MTP vectors across five task categories (A through E). Comparisons in categories A and C each indicate one "unit" of skills Figure 1 Calculation of Cross-Job Retraining Time Estimates

c n CI

B

Task Category Job 1

Retraining Estimates:

^H

Job

2

Job 1 —> Job 2 = 2 units Job 2 " > Job 1 s 4 units

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to be acquired in moving from Job 1 to Job 2 (Figure 1 shows that Job 1 incumbents do not perform tasks in Category C). Comparisons in Categories B and E indicate that moving from Job 2 to Job 1 would require the acquisition of 1 "unit" of Category B skills, and 3 "units" of Category E skills. Summing these differences yields a retraining time estimate of 2 units for moving from Job 1 to Job 2, and an index of 4 units for moving from Job 2 to Job 1. A total of 1640 retraining time estimates {i.e., 41^ — 41) were calculated from the 41 vectors of MTP rating means across the 26 SKQ task categories. Note that these retraining time estimates (a) represent the relative difficulty of cross-job retraining, since they represent task skills to be acquired in moving from one job to another, (b) are asymmetric with respect to retraining between Job, and Jobj, and (c) are intended to represent only the relative difficulty of retraining, that is, no absolute metric of cross-job retraining time was assumed to have been established. Thus, these 1640 comparisons led to the generation of a 41 x 41 asymmetric matrix of retraining time estimates containing zeros along the principal diagonal. Job Learning Difficulty Indices Since 1971, the USAF has maintained a program of research on the measurement of the learning difficulty of tasks, and entire jobs (Burtch et al., 1982; Garcia, Ruck, & Weeks, 1985; Lecznar, 1971; Mead & Christal, 1970; Ramage, 1987; Weeks, 1984). Task learning difficulty indices are derived from SMEs' relative judgments ofthe length of time required for a typical incumbent to learn to perform a task satisfactorily. Job learning difficulty indices (JLDs) are job-level indices ofthe degree of general job learning difficulty. JLDs are aggregates of jobs' task difficulties and indicate the average task difficulty per unit time spent performing them. Note that whereas the cross-job retraining time estimates are designed to reflect task learning times only with respect to task content that is dissimilar between jobs, JLDs index general job learning difficulty, irrespective of the job learner's previous job experiences. JLDs recently have been benchmarked across jobs on a common 25point reference scale (see Mumford et al., 1987; Ramage, 1987) and range, theoretically, from a low of 10 to a high of 250 (Davis, Archer, Gould, & Kavanagh, 1989). Burtch et al. (1982) have reported interrater agreement coefficients in the .90s and test-retest reliability estimates in the .80s for SME learning difficulty judgments, and Mumford et al. (1987) recently reported "strong support for the construct validity" of the JLD index for a sample of 48 jobs compared against training criteria. JLDs for the jobs included in the present study are shown in the

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rightmost column of Table 2, where higher JLDs indicate greater job learning difficulty. Aptitude Requirements The U. S. military bases accession and classification decisions, in part, on applicants' Armed Services Vocational Aptitude Battery (ASVAB) scores (Department of Defense, 1984). Each Service uses a somewhat different configuration of ASVAB selector composites. The USAF uses four aptitude composites: Mechanical, Administrative, General, and Electronic (MAGE), for the selection and classification of enlistees (Department of Defense, 1984), and each job is classified as drawing primarily upon one (or more) of these aptitude areas (Alley, Treat, & Black, 1988). As mentioned earlier, five of the jobs surveyed had multiple MAGE area assignments and were dropped from data reported here. MAGE area assignments, and selector area percentile cutoff scores for each of the jobs included in the present study are shown in Table 2. For example. Flight Engineer (AFSC 113xOc) has an entry cutoff at the 55th percentile on the General ASVAB composite. Design of the Input Data Matrix A 1640 X 6 input data matrix was constructed with the number of rows corresponding to the number of elements in the 41 x 41 matrix of retraining time estimates (excluding the diagonal), and columns corresponding to (a) cross-job retraining time estimates computed from the MTP mean vector comparisons (see Calculation of Cross-Job Retraining Time Estimates, above), (b) the job learning difficulty (JLD) of the "From-Job," or the job referenced in moving from Job, to Jobj ("FromJob JLD"), (c) JLD of the "To-Job," or the job referenced in moving from Jobj to Jobj ("To-Job JLD"), (d) an arbitrary number representing the From-Job's MAGE aptitude area assignment ("From-MAGE," coded M = 1, A = 2, G - 3, and E - 4), (e) an identical code number representing the To-Job's MAGE area assignment ("To-MAGE"), and (f) a dummy-coded variable ("MAGE-F/T") indicating whether the FromJob's MAGE aptitude area assignment and the To-Job's was the same {= 1) or different (= 2).

RESULTS Correlational results for the first three hypotheses are shown in Table 4. Our first hypothesis was of a positive relationship between job learning difficulty ofthe new job (the "To-Job") and cross-job retraining

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Table 4 Descriptive Statistics and Correlations With Cross-Job Retraining Time Estimates Indices

Mean

1. To-Job JLD 2. From-JobJLD 3. MAGE-F/T

102.34 102.34 1.71

Standard Deviation

Correlation With Ease-of-Movement .389** -.147** .190**

20.96 20.96 0.45

**p < .01

time estimates. A significant positive correlation (r = .389, p < .01, see Table 4) confirmed the prediction that retraining into jobs that generally are more difficult to learn (higher JLDs) tended to be those that had higher retraining time estimates. Our second hypothesis predicted an inverse relationship between the leaming difficulty of the old job (the "From-Job") and cross-job retraining time estimates. This hypothesis too was confirmed (r = - .147, p < .01), indicating a tendency for retraining from more difficult jobs to be estimated as easier than from less difficult jobs. Our third hypothesis was that cross-job retraining would be estimated to be easier to the extent that the new job entails similar, rather than different, aptitude requirements. We tested this hypothesis first by correlating cross-job retraining time estimates with the binary MAGEF/T variable which indicated whether retraining was within (= 1) or across (= 2) MAGE aptitude area assignments. Correlational evidence in Table 4 (r = .190, p < .01) supported the hypothesis. Another way in which we evaluated this hypothesis was by testing for a significant From-MAGE x To-MAGE interaction effect on retraining time estimates in a 4 x 4 ANOVA design {MAGE area assignment of the "From-Job" x MAGE area of the "To-Job"). Results in Table 5 confirmed significant From-MAGE and To-MAGE main effects, as well Table 5 Analysis of Variance Results for Cross-Job Retraining Time Estimates Source

DF

Mean Square

F

1. 2. 3. 4.

3 3 9 1624

1758.635 10352.945 1961.922 113.616

15.479** 91.122** 17.268**

From-Job MAGE (F) ToJoh MAGE (T) FxT Residual **p < .01

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Figure 2 Analysis of Variance Results for Cross-Job Retraining Time Estimates

35 0) 30 -H

25

0)

W E 20 15

2 % a

M

A G To-Job Aptitude Area

as a significant From-MAGE x To-MAGE interaction. Cross-job retraining time estimate cell means are plotted in Figure 2. Figure 2 shows that (a) retraining/rom Administrative and General (A & G) jobs into other jobs was indicated to be generally more difficult than from Mechanical and Electronic (M & E) jobs, (b) retraining into Mechanical and Electronic jobs from other jobs was indicated to be generally more difficult than into Administrative and General jobs, and (c) with the exception of the Administrative area, retraining time estimates were lower within, rather than across, MAGE areas. This latter fmding provides general support for the third hypothesis. Our final hypothesis was that interjob similarity in aptitude requirements would moderate the relationship between learning difficulty ofthe new job (the "To-Job") and retraining time estimates. Specifically,

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we predicted a stronger relationship between retraining time estimates and JLDs for retraining across, versus within, MAGE aptitude areas. We tested this hypothesis using hierarchical moderated regression. First, we regressed retraining time estimates on the new jobs' JLDs (ToJob JLDs) and the binary MAGE-F/T variable which indicated whether the From-Job's aptitude area and the To-Job's were the same, or different. Next, we added the To-Job JLD x MAGE-F/T cross-product. The cross-product added significantly to the prediction of retraining time estimates (AR^ = .03 F = 64.25, p < .01) indicating a significant To-Job X MAGE-F/T interaction. Figure 3 shows the form of this interaction in a plot of subgroup

Figure 3 Subgroup Regression Results for Relation Between Cross-job Retraining Time Estimates and Job Learning Difficulty

35 30 Different From-Job/To-Job Aptitude Areas

CO

E

• ^

m

25

g

u o 20 15

^

10

Same From-Job/To-Job Aptitude Areas

67

To-Job JLD

154

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regression lines. Consistent with the fourth hypothesis, there was a significant relationship between retraining time estimates and the learning difficulty of the To-Job for retraining across (r = .43, p < .01), but not within (r = - .02, p > .10) MAGE areas.

DISCUSSION The present study supports the feasibility of estimating cross-job retraining time using a methodology that focuses on the analysis of job differences in task content and learning times. Results indicate that first-level supervisors can make reliable judgments of jobs' task contents and task learning times which can be used to estimate cross-job retraining time. Results also supported the convergent validity of cross-job retraining time estimates. Specifically, cross-job retraining time was estimated to be more difficult into new jobs which (a) generally are more difficult to leam, and (b) have different aptitude requirements compared to the old job. Results also support the idea that the general learning difficulty of a new job plays a larger role in determining cross-job retraining time if the old and new jobs draw on different sets of aptitudes. The immediate value of these results is the use of estimates of cross-job retraining times to optimize personnel reallocations in situations of workforce imbalances. For example, one simple "internal" solution to workforce imbalances is to retrain "surplus" employees to perform jobs in which there are needs for additional personnel. However, this solution could easily be nonoptimal if, for example, an organization has too many surgeons and not enough file clerks (or vice versa!). Analyses ofthe tightness/looseness ofthe external labor market along with the transferability of the current workforce's skills across the organization's joh families, can help determine the appropriateness of alternative retrenchment, recruiting, staffing, and training strategies for reducing skill mix imbalances, along with their anticipated costs and payoffs (Steffy & Maurer, 1988). Cross-job retraining is likely to become more of a concern as a result of (a) the changing age structure of the U. S. and European labor markets (Ahlburg & Kimmel, 1986), (b) declining numbers of qualified workers entering the workforce (Sherman, 1987), (c) increasingly rapid adoption of evolutionary and revolutionary technological advances in the workplace (Kozlowski & Farr, 1988), and (d) increasing strategic, long-range, planning on the part of human resource managers (Dyer, 1985). The present study demonstrates that the estimation of cross-job family transferability of skills to support longer-range human resource planning is feasible, but it is also clear that more research on this topic is needed.

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Study Limitations Although encouraging, results from this study should be viewed within the context ofthe following study limitations: (a) the validity of the task taxonomy supporting cross-job retraining time estimates, (b) the gross nature ofthe additional predictors of cross-job retraining time, (c) the lack of evidence for retraining time estimates as predictors of actual retraining ease, and (d) the military context in which the present study was conducted. One of the key requirements for estimating cross-job retraining time on the basis of job differences in task content is a taxonomy of tasks for the system of jobs being compared (Dunnette, et al., 1979; Fleishman, 1984). Dozens of work taxonomies have heen proposed (see, for example, Fleishman & Quaintance, 1984). Some taxonomies are intended to be broadly applicable to the world of work (e.g.. Farina, 1973; Wheaton, 1973) while others are intended to be appropriate for more circumscribed work domains and/or classificatory purposes (e.g., Bennett, 1971; Dowell & Wexley, 1978; Ramsey-Klee, 1979). The taxonomy used in the present study is closer to the latter since it was developed for the specific purpose of classifying tasks performed by USAF enlisted personnel. Results corroborated the usefulness of this taxonomy for assessing cross-job task content differences, and additional research supports its usefulness for classifying individual tasks performed in USAF enlisted jobs (Gould et al., 1989). However, the overall validity of this (or any) work taxonomy should be regarded as tentative in the light of (a) future research findings, (b) the purpose for which the taxonomy is to be used, and (c) future changes in the nature of work performed in the relevant work domain. Second, convergent validity evidence was obtained for the cross-job retraining time estimates developed here, but this evidence should be viewed in the context ofthe gross nature ofthe alternative predictors of retraining ease. The aptitude area assignments for the jobs studied here represent broad-grained classifications of the types of general abilities required for successful job performance, rather than a detailed specification of specific skill requirements (Peterson & Bownas, 1982). Also, JLDs indicate general job learning difficulty, rather than differences in the leaming difficulties of different jobs' specific job components. Thus, convergent validation evidence presented here most probably underestimated the validity of the cross-job retraining time estimates of actual retraining ease. This points to a third limitation to this study: the validity of the retraining time estimates derived here was assessed only in terms of their convergence with other putative predictors of retraining ease. Further assessment of the validity of retraining time estimates against ac-

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tual retraining ease would provide stronger evidence of their usefulness. However, this would require extensive longitudinal data collection, and this was simply beyond the scope of this study. Naturally, we recommend this as a high-priority topic for future research. Finally, the generalizability of the present findings could be questioned due to the military context of the study and the fact that some of the jobs studied here do not have common counterparts in the civilian sector. Nevertheless, the methodology described here for estimating cross-job retraining times could be readily applied in other organizational contexts by (a) identifying an appropriate taxonomy to facilitate comparisons among jobs in the system under study, either from among existing taxonomies (Fleishman & Quaintance, 1984), or empirically, from job analysis data (e.g., the Occupational Analysis Inventory [Cunningham, 19881), (b) obtaining SME learning time estimates for each job on each of the taxonomic categories (e.g., Months to Proficiency judgments described above), and (c) assessing differences in jobs' content and learning times across taxonomic categories (as in Development of CrossJob Retraining Time Estimates, above). However, since most private sector employers do not maintain personnel data bases as rich as the U. S. military's, validation evidence for retraining time estimates may be more difficult and costly to obtain. Thus, the generalizability of the results of this study may be more limited by practical and cost factors, rather than scientific concerns. Future Research Needs

.

'

One ofthe most pressing research needs is for work that links crossjob retraining time predictors to the actual ease with which employees can draw upon previously leamed knowledge and skills in learning a new job. In particular, prospective research is needed which tracks employee job proficiency prior to, during, and following job retraining, and which links retraining time predictors to measures ofthe amount of job learning required to become proficient in a new assignment. Future research also should integrate existing findings on social and motivational determinants of job transfer success (e.g., Brett, 1984; Pinder & Schroeder, 1987; Pinder & Walter, 1984; Wanous, Reichers, & Malik, 1984) with research relating to job characteristic and individual difference determinants. For example, characteristics of the social environment, including situational constraints (O'Connor, Eulberg, Peters, & Watson, 1984; Peters & O'Connor, 1980) could neutralize or enhance (Howell, Dorfman, & Kerr, 1986) relationships between cross-job transferability of skills and individual aptitude and interjob similarity determinants. Research also is needed to identify appropriate cross-level linkages

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(Dansereau & Markham, 1987; Rousseau, 1985) between transferability of skills and cross-job retraining time determinants. Theoretically, transferability of skills can be considered an individual-level variable (the transferability of an individual's skills to various jobs) or a job-level variable (the general transferability of skills between Jobj and Jobj). At the individual level, retraining time determinants may exist at the individual level (e.g., cognitive abilities, willingness to relocate), the level of the job {e.g., task content similarity) and the organization (e.g., supportive versus constraining climate). Job-level transferability of skills, on the other hand, may be determined quite differently. Dansereau and Markham (1987) and Lance, Hedge, and Alley (1989) have shown how relations among variables can vary as a function ofthe level of analysis. The need here is to determine cross-level linkages between transferability of skills defined at different levels of analysis, and between determinants of retraining ease. Finally, the generalizability of the methodology and results of the present study needs to be evaluated in other organizational contexts. Understanding the costs involved, it still may be practical for organizations to undertake this research, particularly if cost savings from improved reassignment procedures accrue. REFERENCES Ahlburg, D. A., & Kimmel, L. (1986). Human resources management implications ofthe changing age structure of the U.S. labor force. Research in Personnel and Human Resources Management, 4, 339-374. Alley, W. E.. Treat, B. R., & Black, D. E. (1988), Classification of Air Force jobs into aptitude clusters. (AFHRL-TR-88-14). Air Force Human Resources Laboratory, Manpower and Personnel Division, Brooks AFB, TX. Altman, J. W. (1976). Transferability of vocational skills: Review of literature and research. Center for Vocational Education, Ohio State University, Information Series No. 103. Anderson, J. C, Milkovich, G. T., & Tsui, A. A. (1981). A model of intra-organizational mobility. Academy of Management Review, 6, 529-538. Baldwin, T. T., & Ford, J. K. (1988). Transfer of training: A review and directions for future research. Personnel Psychology, 41, 63-105. Bell, J., & Thomasson, M. (1984). Job categorization project. Randolph AFB, TX: Occupational Analysis Program, United States Air Force Occupational Measurement Center. Bennett, C. A. (1971). Toward an empirical, practicable, comprehensive task taxonomy. Human Factors, 13, 229-235. Brett, J. M. (1982). Job transfer and well being. Journal of Applied Psychology, 67, 450-463. Brett, J. M. (1984). Job transitions and personal and role development. Research in Personnel and Human Resources Management, 2, 155-185. Burak, E. H., & Mathys, N. J. (1980). Career management in organizations: A practicat human resource planning approach. Lake Forest, IL: Brace-Park. Burtch, L. D,, Lipscomb, M. S., & Wissman, D. J. (1982). Aptitude requirements based on task difficulty: Methodology for evaluation. (AFHRL-TR-81-34). Air Force Human Resources Laboratory, Manpower and Personnel Division, Brooks AFB, TX. Byrne, J. J. (1975). Occupational mobility of workers. Monthly Labor Review, 53-59.

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