Download pdf - Budgetary control and demographics in employee performance evaluation

OMEGA Int. J. of Mgmt Sci.. Vol. 17, No. 3, pp. 263-272, 1989 0305-0483/89 $3.00 + 0.00 Printed in Great Britain. All rights reserved Copyright ~ 1989 Pergamon Press pk

Budgetary Control and Demographics in Employee Performance Evaluation

D W F I N N

Texas Tech University, USA

L S E A T O N J R

T P C R O N A N

University of Arkansas, USA

(Received January 1988; in revised form November 1988)

This study examined employee performance data and found that evidence exists which supports the linkage between worker etEciency and a set of demographic factors. One may generalize from these results that budgetary control and plmming could be enhanced by including an evaluation of potential employee's projected performance levels using demographic factors in the employee selection and evaluation process. These demographic factors are identified and an employment decision model is presented to improve the potential employment decision.

Key words--budgeting, budgetary control, demographics, employment evaluation, performance evaluation

INTRODUCTION

MODELS OF PRODUCTIVITY, COSt reduction, and quality improvement are generally designed to help improve productive output. However, effective use of managerial information is more than recognition that one should consider all relevant data when determining productivity and profitability [6]. Profitability and productivity of a firm may be enhanced by improving the quality of the workforce through the use of more sensitive selection procedures. The focus of this study is to add to the understanding and the more effective use of selected variables in the initial employee selection process, particularly to that of a study by Finn [l].

Productivity is often thought to be influenced more by the elements within the work-space (i.e. management style, work-space environment, etc.) than elements outside of the work-space. However, there is evidence in the literature that additional factors or determinants may be indirectly related to productivity and job

tenure [1, 2, 5, 7, 10, 12]. Productivity of an organization depends upon worker efficiency; but, organizational productivity may further be dependent on nonwork-space elements. Organi- zational productivity will increase (decrease) as overall worker performance increases (de- creases). Consequently, organizational productivity is compromised when management does not optimize determinants of worker productivity when hiring workers and/or budgeting labor requirements for a participant. An organization can effectively improve worker performance and productivity and organizational productivity by consideration of the importance of nonwork- space determinants in the hiring process.

Study of the relationship of performance planning, standard setting, evaluation processes, and worker productivity could identify predictor variables that could be used to enhance selection of the most productive workers. In a study by Finn [l], five demographic factors were identified in explaining employee performance. The study compared the efficiency

263

OME 17 .'~-E

264 Finn et al.--Employee Performance Evaluation

ratings of workers obtained over a 12 year period and a set of demographic characteristics identified from employee employment application forms.

The purpose of this study is to further inves- tigate whether a relationship exists for performance during training using worker selection variables commonly appearing on an employee application form and those used in the study by Finn. The potential benefit from using specific variables to predict worker performance during training should aid the organization in two ways: first, the organization could improve the hiring process through the development of generalized 'optimal' worker profiles to be used in the selection process; second, task specific worker profiles could be developed. Such profiles would enable management to more effectively place workers in specific jobs requiring varying degrees of skill within the organization. Both results would improve planning and should result in more effective labor force selection and budgetary planning.

RELATED STUDIES

1. The role o f standards in budgetary control

Conventional cost control typically focuses upon differences or variations which arise after completion of tasks. Conversely, effective budgetary control involves planning so that differences between planned and actual results are minimal. Otley [11] described the role of budgetary data:

• .. a budget can be used to represent standards of both effectiveness and efficiency. It repre- sents a standard of effectiveness insofar as it

' Potential determinants of worker performance not examined in the study were not selected for the following reason. Research indicates that hygienic factors appear to have little positive effect toward motivation and higher performance levels of employees [Waters and Waters (1972); King (1970); Schwab, DeVitt and Cum- mings (1971)]. Studies comparing job success and per- sonality testing have had little significance in predicting job success [Hedberg and Baxter (1957); Ghiselli and Barthos 0953)]. A search for supportive economic factor studies relating to employee performance yielded little evidence. While economic factors may influence performance of employees, the scope of this study precluded the use of economic factors in the analysis. Specifically, the study examined employee performance during the training period and, as such, employees were not trained at the same point in time and did not share common economic externalities. Thus, an analysis of economic factors was not included in the present study•

specifies a set of desired outputs and a standard of efficiency to the extent that it details the inputs deemed necessary to produce the specified outputs.

Cost control through the evaluation of worker performance data, in terms of comparisons of effectiveness and efficiency, is an effective method of budgetary control. Such control is enhanced when one is able to improve upon the prediction of outcomes in establishing the control parameters.

2. Performance changes for workers

Generally, performance standards are ad- justed as technological and other work related changes take place in the production process. When there is an absence of technological and/or other work related changes, the activity may be characterized as a steady state production cycle. Research indicates that some performance standards may decay over time. Such changes are frequently due to 'unknown' causes [3, 13]. Thus, worker performance does change over time without any known technological or other work related changes and such change may be explained by elements other than work related factors. When non-work related identifiable elements contributing to changes in performance may be programmed and be used in standard setting and measurement the process results will be enhanced.

3. Demographic factors for worker performance I

The role of the relationship of demographic factors and worker performance has been the subject of numerous studies. Kirchner and Dun- nette [8] found a significant relationship between personal (demographic) traits and long tenured female employees. In a study by Wernimont [14], positive predictive performance tests were developed for use with office personnel, but the predictive ability of the test deteriorated over time. The authors suggested that the test be revised and reweighted after three to five years in order to retain its predictive characteristics.

In a survey of 21 studies, Schuh [12] reported at least one significant factor that related demographic variables and employee tenure in all but two of the studies. Schuh's findings did not identify factors which management could use to identify specific worker characteristics, i.e. job stability, high performance, long tenure, etc. In

Omega, Vol. 17, No, 3 265

another study, Finn [1] reported that five demographic variables were significantly related to employee efficiency for post-training production employees.

Little research has been completed to evaluate the predictive value, if any, of indirect or non- job elements or characteristics on worker performance. As noted above, the objectives of this study are to identify demographic factors which may be used to predict productivity of new employees. Specifically, we will test the relationship of selected variables and performance during the training period. Further, the data will be analyzed to determine if a cycle is present that could lead to an improvement of the budgeting and standard setting process through timely revision of standards coinciding with the cycle. Finally, the results will be compared with those of the earlier study by Finn to determine which characteristics, if any, are consistent for the two studies. These objectives will be accom- plished by examining and analyzing worker performance data from an international firm that utilizes labor performance standards in budgeting labor costs and in evaluating worker performance.

METHODOLOGY

1. Sample

Experimental group. A sample of 73 workers was selected for study. The firm produces business forms for government and private business clients. 2 The manufacturing process requires several intermediate steps, and upon completion of each step of the process, another department receives the partially completed order and per- forms the next required production step. This process is repeated until the order has been completed. Thus, the process and worker tasks are clearly differentiated and lend themselves to a study such as this.

'Characteristics of the specific firm from which the data are taken make this firm uniquely appropriate and representative for identifying factors related to worker performance for this study since the production processes have remained virtually unchanged over the test period. Plant layout, machinery, and formal management objectives are, essentially, the same and have existed in substan- tially the same manner for the past 12 years. The production environment of the same firm is representative of worker tasks of other production environments which also have similar assembly manufacturing processes.

The group selected for study consists of all employees in the Press department. The study involves 73 workers hired for the Press department over a period of 12 years. The production process, i.e. machinery and required skill levels, d i d not change during this time frame. The 'average' Press department worker, when hired, was 21 years of age, married, and resided in a rural area. Individual Press department employment application records and production performance records during training were used to obtain the data base. During the period studied, a learning curve was used to set standards and evaluate employee production performance for the Press department. Thus, individual performance standards varied as a function of the individual's time on the job. For example, production performance efficiency is illustrated in Fig. 1. Average employee performance for the first 12 months on the job deviated from 67% efficiency to 90% efficiency. Average employee performance during the last 12 months of training varied less as the range of efficiency was between 92% and 102%. As illustrated in Fig. l, average employee performance during the final months of training were consistent with management's training objectives of achieving 100% efficiency of the standard. The old standard efficiency curve had previously existed before the present study was conducted. The new standard efficiency curve had also been statistically validated as being more representative of employee performance than the old efficiency curve [2].

Production process. Standard time units for completing each production step were developed and become an integral part of the evaluation process for each employee. The number of standard time units allowable for each step on a given order is based upon the complexity and size of the order. Standard time units were determined by the industrial engineering department using, essentially, time and motion obser- vations and historical performances of similar employees. Standards are set at attainable levels and are not considered by management or the union to be 'too rigorous'. The standards which are illustrated in Fig. 1 were developed using this approach. The actual standard measurement process is discussed in more detail in a subsequent section.

Employee training. The training period for new employees was 32 months and actual

266 Finn ¢t al.--Employ¢¢ Performance Evaluation

Table I. Independent variables for regression analysis

Variable name' Variable description

DEM I' DEM 2' DEM 3' DEM 4" DEM s DEM 6" DEM 7 DEM 8-10

DEM I I DEM 12 DEM 13 DEM 14--18

DEM 19-23

DEM 8 = 1 DEM 9 = I DEM 10 = I

DEM 14 = I DEM 15 = I DEM 16= I DEM 17=1 DEM 18 = 1

DEM 19 = I DEM 20 = I DEM 21 = I DEM 22 = I DEM 23 = I

--Age of employee when starting work --Marital Status-- --Number of dependents --Number of years in school --Own home--- --Relatives employed at this plant-- --Length of preceding job in months --Willingness to work different shifts before starting work

(shift selections are not mutually exclusive) if employee is willing to work shift I, 0 if otherwise if employee is willing to work shift 2, 0 if otherwise if employee is willing to work shift 3. 0 if otherwise --Number of years in local area --Commuting distance to job --Residence with relatives--I if live with relatives, 0 if otherwise --Immediate preceding job type if factory work {assembly), 0 if otherwise if general labor, 0 if otherwise if farm related labor, 0 if otherwise if military service. 0 if otherwise if non-manual, 0 if otherwise --Reason given or reported for leaving last job if dismissed (disciplinary action), 0 if otherwise if laid off (other than disciplinary action). 0 if otherwise if salary was too low. 0 if otherwise if advancement not likely. 0 if otherwise if still employed. 0 if otherwise

I if married, 0 if otherwise

I if own home, 0 if otherwise I if yes, 0 if otherwise

=Statistically significant in previous studies investigating job tenure [see Schuh (1967); Kirchner and Dunnett 0957); Vernimont (1962); Tiffin. Parker and Habersat (1947)].

monthly efficiency ratings for each month of service were developed by reference to a company standard. Standards = F (month in training, size of order, complexity of order). Performance evaluations were undertaken monthly during the training period. Evaluations were made for all employees used in the study. The data set of the study included all employee efficiency ratings for the 32 months of training. All workers in the department studied were matched with their respective month of training (months I to 32) and an arithmetic mean for efficiency of all employees with equal length of service performing the same task were com- puted.

lOO

9 5

9 0

s5

eo

7O ~ ss

6O 55 5O

Fig.

/ / ~ OLd standard efficiency

. / / - - - A c ~ .ff,0,en0y r ~

~ . - - - New st=nderd efficiency

I I I I I I I I 4 8 1 2 1 6 2 0 2 4 2 8 3 2

Months of trolnlng

Standard efficiency performance and employee efficiency performance.

Monthly e~iciency rating. In accordance with organizational norms for all other departments, management evaluated productivity by measuring the actual time required by the employee to complete a production step and compared this to the standard time allowed--the learning curve value for the specific function at this point in the employee's experience curve. The monthly efficiency rating is calculated as follows:

STUn MERi = - - (I)

Z ATUn

where:

MERi = Monthly Efficiency Rating for ith employee.

STUn = Standard time units required to complete the nth job for the month.

ATUn=Actual time units required to complete the nth job for the month.

n -- number of jobs.

Thus, the monthly efficiency rating, as a per- centage, is based on the sum of the standard time allowed compared to actual time required for each job. Individual monthly efficiency ratings were compared to standard requirements to evaluate current individual performance and

Omega, Vol. 17, No. 3 267

lOO

95

- 9 0

85

~ 8oI - - 7 5 ~

65 6O 55

/

/ . . . . /

/ / /

/ / Stondord efficiency rote

- - Hlghest, l tOwest sml~Loyee efficiency record

I I I I I I t J 4 8 12 t 6 20 24 28 52

Months of training

Fig. 2. Actual employee efficiency compared to the standard efficiency curve.

progress during the training period. Productiv- ity of new pressmen is expected to increase to 100% of previously defined efficiency standards over the 32 month training period. The increase in efficiency is based on a learning 'rate' derived from a 90% learning curve. Examples of 'best' and 'worst' worker efficiency results are illustrated in Fig. 2.

2. Selection of independent variables

Demographic factors for study were selected using two methods. First, factors were identified

3A possible limitation of the study is related to the measurement and classification of demographic characteristics for each employee. The measurement of demographic variables was made at the beginning of the training program. Subsequent analysis of employee factors was made using data collected over the entire training program. Of the 23 initial demographic variables, 14 variables may have changed during the training period. If changes occured at different rates for different employees for a significant number of the variables during the training period, the results may be less applicable. The data is based on personnel applications and may include self-report bias. Such self-report bias may not be consistent among the applicants.

*Five of the demographic factors included in the model have been statistically identified in previous research as being related to employee tenure [Schuh (1967); Kirchner and Dunnett (1957); Wernimont (1962); Tiffin, Parker and Habersat (1947)]. Thus, it seems reasonable to postulate that a relationship between these and other factors and employee performance exists.

5Because of the necessary assumptions that are required for regression analysis, it was determined that the variance of the error terms is constant for all employees. Finally, autoeorrelation was found to exist in the efficiency data and independence of the residuals was obtained using an iterative method [Neter, Waserman and Kutner (1983)]. The demographic variables were determined to be drawn from a normal distribution (Kolmogorov-Smimov D- statistic), the variance of the error terms were constant (plot of residuals), and independence of the residuals existed (Durbin-Watson test).

through a review of previous research relating to the subject. The second method involved selecting items routinely appearing on employment questionnaires used by personnel departments. Selection of factors, without prior statistical support, was made on an a priori basis. 3

The demographic data, to be used as independent variables for the study, were gathered from company personnel applications and identified as either interval or nominal variables. 4 These variables are shown in Table 1.

3. Model and hypothesis

Results of previous research suggest that demographic factors may have a significant impact on explaining employee performance or efficiency [1, 12]. The notion that demographic factors may be used to predict employee performance led to the hypothesis stated in the alter- nate form:

H,: Demographic factors are useful in predicting employee worker performance

A regression model was used to test the hypothesis using the monthly efficiency rating (MER) as the dependent variable with the demographic factors as independent variables) Should one or more of the demographic variables be found to be significant in the regression model to employee efficiency, the null hypothesis will be rejected. The results would indicate that demographic factors do help explain and thus predict employee performance over time. If the null hypothesis is rejected, then an indication for evaluating specific demographic characteristics of prospective employees will be supported and employee selection processes allowing for the variables to be identified. Conversely, should the null hypothesis not be rejected, evidence will then exist which indicates that demographic factors examined in the study have little value in predicting worker performance.

RESULTS

Analysis of the worker efficiency records to determine how workers performed compared with the established company standard indicated that workers consistently performed near the desired efficiency standard (Table 2). Each worker's efficiency records for the 32 month training period were grouped from highest to

268 Finn et al.--Employee Performance EL'aluation

lowest for each month of training. Rankings of employees were based on average performance over the training period. Each month of training was examined separately and equal groupings (above average, average, below average) were made for all employees. Employees who performed in a specific group more often than the other two groups were classified into that cate- gory. Thus, 23 individuals were classified as above average, 24 employees average, and 26 employees performed at a below average efficiency level. Management stressed that the performance ratings that the employees received were all within acceptable limits for employees working in this firm.

Tests for muiticollinearity and multiple correlation [9] were performed prior to calculating the regression statistics. A Pearson Product- Moment correlation analysis was conducted and significant multicollinearity was not de- tected. One variable, "reason for leaving last job" (DEM 19) was deleted from the analysis, since no one reported that they were dismissed from their last job as a result of disciplinary action. Furthermore, the analysis to test for multiple correlation indicated no significant levels of correlation when compared to the remaining group of variables.

The stepwise regression was performed (see Table 3) and eight variables were selected for the model 6. The model was significant (<0.001) with 40% of the variation in employee efficiency being identified by the eight demographic variables. Of the eight variables selected, only the variables "commuting distance to job" and "employed elsewhere while interviewing" (DEM 12 and DEM23) were significant at greater than a 0.10 level and neither variable was greater than a 0.20 level of significance. The results indicate that efficiency rating (MER) is a function of demographic factors (DEMs).

6Additionally, a factor analysis procedure (principal compo- nents method specifying a minimum eigen value of one) was used to determine if the remaining 22 variables could be reduced to fewer number of factors for the study. The results of the factor analysis indicated that nine factors had been selected. The nine factors only accounted for 72% of the total variance and, in normal applications, would indicate a satisfactory reduction of the number of variables for the study. However, none of the factors contained more than one variable with a high factor loading and as this procedure was used for data reduction and summarization it was determined that the 22 variables would be used and coded for the analysis intact.

Table 2. Employee performance ratings during training

Performance rating

Employee Months Months identification above Months below Overall

number average(%) average(%) average(%) rating

I0 37.9 24.2 37.9 2" I I 6.3 56.3 37.4 2 12 35.7 42.9 21.4 2 13 44.4 29.7 25.9 3 14 77.3 13.6 9.1 3 15 59. I 27.3 13.6 3 16 46.2 30.8 23.0 3 17 25.8 48.4 25.8 2 18 76.2 23.8 0.0 3 19 3.2 19.4 77.4 I 20 32.3 16.1 51.6 I 21 19.4 58.1 22.5 2 22 35.7 46.4 17.9 2 23 71.4 17.9 10.7 3 24 0.0 18.8 81.2 I 25 20.0 32.0 48.0 I 26 24. I 34.5 41,4 1 27 13.3 53.3 33.4 2 28 17.2 34.5 48.3 I 29 35.7 14.3 50.0 1 31 35,7 50.0 14.3 2 32 I0,0 46.7 43.3 2 33 28. I 37.5 34.4 2 34 9.4 59.4 31.2 2 35 3.1 40.6 56.3 3 36 60.0 30.0 I0.0 3 37 12.5 34.4 53. I I 38 0.0 28. I 71.9 I 39 62.5 25.0 12.5 3 40 0.0 9.4 90.6 I 41 20.0 53.3 26.7 2 42 25.8 45.2 29.0 2 43 43.8 46.9 9.3 2 44 6.7 60.0 33.3 2 45 46.7 33.3 20,0 3 46 9.4 21.9 68.9 I 47 16. I 32.3 51.6 I 48 3.4 31.0 65.5 I 49 52.9 35.3 I 1.8 3 50 91.4 4.3 4.3 3 51 6.2 43.8 50.0 1 52 6.2 40.6 53.2 I 53 17.2 55.2 27.6 2 54 72.0 20.0 8.0 3 55 26.7 50.0 23.3 2 56 59.4 34.4 6.2 3 57 I00.0 0.0 0.0 3 58 40.6 31.3 28. I 3 59 40.6 43.8 15.6 2 60 33.3 41.7 25.0 2 61 18.8 56.2 25.0 2 62 96.7 0.0 3.3 3 63 12.9 45.2 41.9 2 64 9.4 56.3 34.4 2 65 46.7 40.0 13.3 3 66 57.7 34.6 7.7 3 67 33.3 22.2 44.4 I 68 23. I 46.2 30.7 2 69 I00,0 0.0 0.0 3 70 71.4 28.6 0.0 3 71 I0.0 I0.0 80.0 I 72 10.5 42. I 47.4 I 73 63,2 31.6 5.2 3 74 72.2 16.7 I I.I 3 75 5.6 44.4 50.0 I 77 17.6 29.4 52.9 I 78 76.5 17.6 5.9 3 79 53.3 33.3 13.3 3 80 26.7 26.7 46.6 I 89 40.0 20.0 40.0 2" 95 12.0 28.0 60.0 I 97 22.7 36.4 40.9 I 99 67.7 19.4 12.9 3

"Performance radngs were equal for above avera~ and below average. The overall rating was classified as average ratings.

Omega, Vol. 17, No. 3

Table 3. Regression results for employee efficiency and demographic variables

269

Dependent variable: (Employee performance) Independent variables: (Demographic variables)

Regression Error Total

R square ffi 0.4005 C(P) ffi 4.0685

DF Sum of Mean squares square

8 3330.08 416.26 62 4983.70 80.38 70 8313.78

F Prob > F

5.18 0.0001

SigtdJictmt imdtpendent Variables Variable description Variable name B-value Std error Type II SS F Prob > F

Employee efficiency Intercept 90.3199 Marital status (1 if married, 0 if other) 8 DEM 2 4.7680 2.5672 277.2712 3.45 0.0680 Willingness to work Shift 2 DEM 9 -9.7469 4.0704 460.9070 5.73 0.0197 Commuting distance to job DEM 12 -0.2083 0.1289 210.0727 2.61 0.1110 Residence is with relatives DEM 13 4.8913 2.8030 244.7700 3.05 0.0859 Preceding job type was general labor DEM 15 9.0837 2.6460 947.3762 11.79 0.0011 Preceding job type was military service DEM 17 11.0360 3.2455 929.4179 11.56 0.0012 Left prior job because of low salary DEM 21 7.6151 3.8707 311.1227 3.87 0.0536 Employed elsewhere while interviewing DEM 23 -3.8048 2.8122 147.1466 1.83 0.1810

Therefore, regression analysis results were significant and the null hypothesis was rejected. Thus, it can be concluded that demographic factors are useful predictors of employee performance.

The regression results indicated that eight demographic factors were significant (Table 3) in predicting employee performance for this organization and the null hypothesis (H0) was rejected. 7 The specific variables that were significant indicated positive influence on the intercept (90.3199) for employees who reported that they (a) were changing jobs to increase their wage, (b) were previously working at a job requiring manual labor, (c) had prior military service, (d) were married, and (e) lived with parents when employed. The results indicate higher performance levels for employees indi- cating a positive response to each respective question. The results would further suggest that factors other than traditional devices should be used in the evaluation of prospective employee performance.

7An inherent question may exist when either internal or external validity is not high. Discussions with management concerning the operating procedure o f this firm indicated that the procedures used by this plant were representative o f the firm as a whole. The s tandard setting procedure was highly centralized for the firm and it seems reasonable to conclude that the internal validity for this study is high and that the results can be generalized for the company as a whole. Because o f the exploratory nature of this study and the results, a comparison with related research was not possible and it must be concluded that the external validity is unknown and, therefore, a possible limitation o f the study.

The variables which exhibited negative influence on the intercept were (f) workers with greater commuting distance to work, (g) workers who were currently employed elsewhere (workers holding two jobs), and (h) employees who were willing to work the second shift.

The effect of commuting distance on employee performance suggests that employees who commute long distances will be more inefficient than employees who commute shorter distances. Furthermore, the analysis indicated that workers who are currently employed elsewhere will impact negatively on their projected employee performance when compared to other applicants. Finally, employees who reported that they would only work the second shift (4: 00 pm to 12: 00) would negatively effect overall projected work performance. These results were somewhat perplexing until a review of the employee data revealed that most of the employees working the second shift were often farm owners and employment with the firm studied would be their *second' job. This result was consistent with the finding relating to commuting distance. It would seem reasonable to postulate that workers in this firm who have a second job would be less efficient than those workers with only one job because of fatigue, lack of motivation for the company job, and similar reasons.

COMPARISONS WITH PREVIOUS RESEARCH

The results of this study compare with those of Finn in his study which compared demo-


graphic factors and post training worker efficiency. Of the five significant variables reported in that study, four of the variables were significant in the present study. They were marital status (DEM 2), residence is with relatives (DEM 13), preceding job type was general labor (DEM 15), and preceding job type was military service (DEM 17).

The R ~ of 38.54% in the previous study is comparable with the current study of 40.05%, an unusually high R 2. However, it should be noted that much of the variation in employee efficiency has not been explained. Again, other factors may significantly impact upon worker performance.

It should also be noted that two limitations of the current study exist: First, by using performance data of the same workers in the current study as well as in the earlier cited study, it seems reasonable to assume that if the efficiency data for post-training is significant with demographic factors then the efficiency data during training will also be significant with those demographic characteristics. Further, the study is 'firm specific', it only examined the efficiency data and demographic data for employees from one business entity.

DISCUSSION OF RESULTS

The significant demographic variables that were identified in the present study enable management to develop a worker profile for potential employees of the firm. Positive profile attributes for employees would include the following characteristics in order of importance:

(l) Previous work experience which was related to manual labor was very important. This attribute would normally be expected because the job description for the workers in this study were involved in manual labor.

(2) Prior military service was also highly significant. The individuals involved in the study that were prior military service were all classified as enlisted personnel. While not a certainty, most of the workers were probably involved in manual tasks while in the military. The manual labor variable and prior military service variable were highly correlated.

(3) A willingness to work the second shift was important. The second shift began at midnight and ended at eight am. Conversations with management revealed that, generally, workers wanted to work the second shift because they would be able to continue working on their farms. Again, according to management, workers who would work the second shift often were unable to meet standard efficiency levels because of fatigue from working during the day in another job.

(4) Potential workers who were trying to increase their economic wealth. While this attribute may identify potential employees who would frequently change jobs if they could increase their salary, the employees in the current study were all long-term employees who had been employed with the firm for more than three years.

(5) Applicants who were married were also positive significant factors in the results of this study. These individuals probably exhibit a greater degree of economic responsibility than do individuals who are not married.

(6) Potential employees who lived with their parents were also positive significant factors in the study. This characteristic was somewhat surprising since all of these individuals were single. This characteristic is somewhat conflicting with the previous characteristic; however, the individuals who live with their parents may elicit a very strong desire to suport themselves and be self-sufficient.

Thus, the positive profile characteristics that management would want to search for when hiring prospective workers, at least for this company, would be individuals who had strong experience in manual labor and/or miltary service, wanted to get a better paying job, and finally, were married or lived with their parents. Negative profile characteristics would include individuals wanting to work the second shift

Performance evaluation:

(I) Personnel applications

(2) Analysis of demographic data

No

Omega, VoL 17, No. 3

Table 4. Employment decision model

- - , Y e s - - , testing and --.Yes --, Interviews evaluation

No ~ No

Yes Employment

decision

271

because they were working at another job during the day. The results of the current and past studies would seem to suggest a need to include an analysis and evaluation of other factors, such as demographic profiles, in the employment process. The employment decision model present in Table 4 includes such an evaluation in the selection of potential employees. Existing selection criteria, along with an evaluation of demographic profile data would be the first step in screening potential employees. If these criteria are not favorable, then a negative employment decision would be made at that point. The second step in the employment process should involve the evaluation of specific skills necessary to complete the job responsibilities that will be required. Thus, if the results of this testing are not favorable, the employment decision will be made at that time. Finally, interviews would be conducted to complete the employment decision process and if favorable results are obtained through this step, the prospective employee would receive an offer for employment.

CONCLUSIONS

The results of this study can aid management in more effective selection, training, and retention of employees. Using factors identified in the study, coupled with specific job skill data (especially physical ability to complete the task) should enable management to more effectively select new employees for the job. Worker profiles can be developed on a prospective basis using methods similar to those used in this study. Better selection of employees, as illustrated in Table 4, should result in increased productivity of the group. Overall, all performance standards and performance should be enhanced by better screening of new employees in that employees with "poor performance predictors" would then be eliminated from the employment process.

Evidence already exists which links demographic factors to production worker performance after training has been completed. The objective of this study was to determine if evidence existed linking demographic factors to production worker performance during training, a criteria that could provide more timely information to the firm. We examined performance data from a single firm and found that similar evidence does exist to support this linkage, as it did in the earlier study. Thus, from a budgetary control perspective in both a pre- and post-employment sense, one may generalize from these results that budgetary control and planning should include an evaluation of prospective employee's projected performance levels. Demographic factors may be used to predict some of the variations in employee performance.

The measurement and continual review of worker efficiency, along with the evaluation of the determinants of worker performance such as demographic factors and their impact upon projected performance levels of workers, will enable firms to more effectively budget labor requirements and the subsequent costs for such labor. A performance evaluation revision plan based on a periodic evaluation of performance standards will enable management to better evaluate worker performance and ensure that standards are more current and, if appropriate, attainable. The results of this study suggest that further study of demographic variables could prove worthwhile in identifying specific high (or low) potential workers and thereby enhance firm employee selection processes. In addition, the information provided to the organization selecting the measurement of worker effectiveness and efficiency will provide supple- mental information to facilitate traditional budgetary control methods employed within the firm.


REFERENCES

1. Finn DW (1984) Productivity predictors of assembly workers using standards based on learning curves. Omega 12(6), 569-574.

2. Finn DW and Munter P (1983) On the usefulness of learning curves in determining productivity standards. SWAIDS Annual Meeting, Houston, Texas, March 9-1 !, 1983.

3. Finn SW, Seaton L and Cronan TP (1984) An Investigation of Standard Setting and Productivity Using Learning Curves, American Accounting Associa- tion Annual Meeting, Toronto, Canada, August 15-18, 1984.

4. Hanson EI (1966) The budgetary control function. Accg Rev. 41(2), 239-243.

5. Herzbcrg F (1968) One more time: How do you moti- vate employees? Hart. Bus Re~'. 46(1), 53-62.

6. Kaplan RS (1983) Measuring manufacturing performance: A new challenge for managerial accounting research. Acctng Rev. 58(4), 686-705.

7. Kerr WA (1974) Labor turnover and its correlates. J. appl. Psychol. 31(4), 366-371.

8. Kirchner WK and Dunnett MD (1957) Applying the weighted application blank technique to a variety of office jobs. J. appl. Psychol. 41(4), 206-208.

9. Neter J, Wasserman W and Kutner MH (1983) Applied Linear Regression Models. Irwin, Illinois.

10. Oldham GR (1976) The motivational strategies used by supervisors: Relationships to effectiveness indicators. Organl Bebav. Human Perform. 15(1), 66-86.

I I. Otley, DT (1978) Budget Use and Managerial Perfor- mance. J. Acctng Res. 16(2), 122-149.

12. Schuh AJ (1967) The predictability of employee tenure: A review of the literature. Personn. Psychol. 20(2), 133-152.

13. Towill DR and Kaloo U (1978) Productivity drift in extended learning curves. Omega 6(4), 295-304.

14. Wernimont PF (1962) Re-evaluation of a weighted application blank for office personnel. J. appL Psychol. 4~(6), 417-419.

ADDRESS FOR CORRESPOND£NCF.: Professor DW Finn, Texas Tech University, COBA-Box 4320, Lubbock, TX 79409, USA.