Journal of Vocational Behavior, 6, 109-120 (1975)

Subjective Evaluations of Employer Attributes by Administration Students’

ROBERT M. EDELSTEIN* and VITHALA R. RA03 Cornell University

The paper is an application of the conjoint measurement method- ology. An exploratory study was conducted of the trade-off relationships among four organizational attributes-dynamism, societal concern, functional/administrative emphasis, and relative size-as they relate to the selection of an employer by students of administration. Preference ranks of 36 employer concepts obtained from 86 graduate students of business, public, and hospital administration were analyzed according to the additive conjoint measurement model yielding implicit rank-ordering of the four organizational attributes as choice criteria. Irrespective of the program of study, dynamism and societal concern emerged as the most important determinants in employer selection, but differences existed among programs, year of study, and explicitly vs implicitly elicited weights.

Students entering management careers need to evaluate several potential employers in the course of their job decisions. An understanding of the evaluation process either implicitly or explicitly followed by the students is of interest to several individuals and organizations involved in the recruiting process. Knowledge in this area would assist employers in developing appropriate image-building strategies which, in turn, enable their recruiters in effectively identifying suitable candidates for employment.

It is extremely difficult for a student to articulate explicitly the process by which he arrives at a final evaluation of any employing organization. Thus direct questioning would not lead to a satisfactory description of the student’s evaluation process. However, a student’s evaluative judgments of a set of potential employers can be utilized in modeling the evaluation process implicitly followed by him. For this purpose each potential employer can be conceptualized as a multiattributed item with a profile of scores on a number

l We express our thanks to Mr. Charles C. McCord for his valuable advice in this study, to Mrs. Georgia Smith for assistance in field work, and to Messrs. Jorge Doehner and Geoffrey Soutar in data compilation and analysis.

2Now at Department of Industrial Engineering, Ben Curion University of the Negev, Beersheva, Israel.

3Requests for reprints should be sent to Vithala R. Rao, Graduate School of Business and Public Administration, Cornell University, Ithaca, New York 148.50.

109

Copyright @ 1975 by Academic Press, Inc. All rights of reproduction in any form reserved.

110 EDELSTEIN AND RAO

of relevant organtzational attributes. The goat then becomes one of finding an expression for the implicit model of employer evaluation in terms of these attributes. These expressions would permit a description of how the attributes of an organization are impLicitly traded off for one another, and also which attributes are impficitly considered more important than others by students in their evaluations.

This paper reports an exploratory study attempting to determine the trade-offs and importances of various factors in employer selection as viewed by a sample of students in the master’s programs of buisness, public, and hospital administration at Cornell University. One particular multiattribute evaluation model known as the additive conjoint measurement model was applied in this study. The objectives of this study were twofold: (1) to compare the trade-off relationships among four factors describing an organiza- tion for each of the three program areas, and (2) to compare these trade-offs among the three program areas.

The paper is organized into three broad sections: (I) a discussion of the conjoint measurement model as applied to employer selection, (2)a descrip- tion of the study design and method of analysis, and (3) results and some implications for several aspects of the recruiting process.

THE EVALUATION MODEL

Several multiattribute models of evaluation available in literature should be directly applicable to describe the implicit evaluation process of a student. A comprehensive discussion of these models is found in Coombs, Daws, and Tversky (1970), McCrimmon (1973), and Green and Wind (1973). These evaluation models can be broadly classified into two types: noncompensatory models and compensatory models. The noncompensatory models do not permit an analysis of relative trade-offs between attributes while compensatory models, as the name indicates, provide an assessment of how changes in one attribute can be compensated for by opposite changes in other attributes, i.e., trade-offs. The additive conjoint measurement model belongs to the class of compensatory models.

The theory of the additive conjoint measurement (ACM) model has been developed by mathematical psychologists (Lute & Tukey, 1964; Tversky, 1%7). An introductory discussion of this model is found in Green and Rao (1972). This model represents the evahration score of a multiattributed item as the sum of part-worth contributions of various attributes. The model is analogous to that of the analysis of variance (ANOVA) model with two major differences. The frost difference lies in the assumption of interaction terms. Except for random errors, the ACM model assumes that the evaluation score can be fully accounted for by the main effects alone without placing any

JUDGING EMPLOYER AlTRIBUTES 111

restriction on their functional form. The second difference is with respect to the assumption of the scale properties of evaluation scores. ‘lhe ACM model estimates the main effects from the ordinal evaluations of items. In the main-effects ANOVA model, the main effects are estimated such that their additive combination best reproduced the c&&I evaluation scores. In the ACM model the main effects are estimated such that their additive combina- tion best reproduces a monotone transformation of the original ordinul score. The estimated main-effects of the ACM model are known as the part-worth functions since each main effect represents the contribution the corresponding attribute makes to the item’s evaluation (or worth) score.

We can now apply the ACM model to the employer evaluation problem. Let us assume that each potential employer is described on k attributes. Further assume that each attribute has been divided into a predefmed number of levels. Let the jth attribute be represented by ni levels. Note that these levels can be either qualitatively or quantitatively described. Let the ith employer be represented by the levels (xir , xi2 ,. . . , x&, respectively, and on the k attributes. Let the original ranked score for the ith employer be Yi and the monotonely transformed score be denoted by Zi. The model then estimates the part-worth functions for the k attributes denoted respectively by CJr(xr), ~z(xz), . -. , U,(x,) such that

Zi = U,(Xjl) + Uz(Xi2) + . . . . + Uk(Xik) + error and Zi =M(Yi),

where M(Y) represents the monotone transformation. Computer programs exist for determining simultaneously the monotone

transformation and the part-worth functions. The program developed by Kruskal (1965) called MONANOVA suitable for analyzing data from factorial designs was employed in this study. This program estimates the part-worth U-functions such that a measure called stress (Kruskal, 1964) is minimized. The lower limit of this measure representing perfect fit is zero. No statistical significance tests are available for stress. However, on a judgment basis, values under 10% represent extremely good fits.

Since scaling of the resulting values of the U-functions is arbitrary, the MONANOVA program computes these functions such that the mean part- worth for each attribute is zero and the sum of squares of the U-functional values for all the attribute levels taken together equals the sum of the levels of all factors.

The part-worth function represents the relative emphasis a respondent places on an attribute at a given level. For example, for the attribute of size of a potential employer, if small size were considered desirable, and large size undesirable, the vahre of the part-wbrth function for large size would be lower than for small size. The numerical difference between these two values provides a measure of the trade-off in the utility between the large and small

112 EDELSTEIN AND RAO

sizes. The values of the part-worth functions can be used to estimate the utility assigned to each employer profde. The trade-offs between the levels of different attributes can be inferred by comparing the profiles with equal utilities.

The total sum of squares of the estimated utilities for different employers’ profdes can be decomposed into the corresponding sum of squares of the various attributes. The percentage sum of squares of each attribute provides a measure of the implicit importance assigned to the attribute in the evaluations by the respondent.

METHOD

Stimulus descriptions, To obtain results free of confounding with the images held by students of real employers, it was decided to use hypothetical descriptions as stimuli in this study. The? were generated according to a factorial design by varying four employer attributes. In the absence of any theory, these attributes were selected on the basis of preliminary discussions with the University’s placement officer and students. The attributes and levels used were: dynamism described in terms of the organization’s growth potential (2 levels), societal concern (3 levels), functional or administrative emphasis (3 levels), and size (2 levels). The semantic descriptions of the 36 potential employers thus generated are shown in Table 1. Descriptions were necessarily altered to reflect the program area of each responding student (business, public, or hospital administration). These employer descriptions differ essentially only with respect to the third factor, the functional or administrative emphasis of the organization. Specifically, it was described in terms of the functional emphasis of the firm for business students, in terms of administrative emphasis for public administration students, and in terms of the degree to which the job deals with specialists for hospital administration students.

Experimental task. The 36 descriptions of potential employers, each printed on a separate card, were presented in a random order to every respondent. The respondent was asked to assume that he had received an offer for a job with the same starting salary from each of these employers. His task was to rank them with respect to the subjective probability of his joining each one, considering the four organizational factors in the light of his own expectations and values. To simplify the ranking task, the respondent was first asked to classify the 36 cards into four categories, representing very high, high, low, and very low probability of accepting the offer. He then ranked the cards in each group from high to low probability. This procedure resulted in a complete preference ordering of all 36 employers by each student respondent.

To compare with implicit importance, each student was asked to explicitly rate the importance of the four organizational attributes used in the

113

Factor

JUDGING EMPLOYER ATTRIBUTES

TABLE 1

Semantic Descriptions of the 36 Potential Employers

Description Levels

Dynamism

Societal concern

Business: Public: Hospital:

Business:

Public:

Hospital:

Emphasis Business:

Public:

The company’s growth potential is: The organization’s growth potential is: The organization’s growth potential is:

The degree of societal concern of the company is: The degree of societal concern of the organization is: The degree of societal concern of the organization is:

Relative functional emphasis of the firm is:

Relative administrative emphasis of the organization is:

Hospital: Administration tends to deal with:

Size Business:

Public: Relative size of the organization is: Hospital: Relative size of the organization is:

Within the industry the company is relatively:

Low

High

High

Medium

Low

Marketing Production Finance

Federal State Metropolitan

Generalists Specialists Both Generalists

and Specialists

Small Large

study on an 1 l-point scale ranging from zero (least important) to 10 (most important). In this task respondents were also asked to indicate any other factors (e.g., salary) which they consider important in choosing an employer. Finally, background data, age, marital status, work experience, father’s education, and total monthly expenditure, were elicited.

Sample description. A sample of the 96 students in the business program and all students in the public (38) and hospital (55) programs of the Graduate School of Business and Public Administration were contacted by mail in this study. A total of 86 students responded to the questionnaire. The return rate was 46%. A description of the three respondent groups is given in Table 2.

Method of analysis. The preference ranks of the 36 employer concepts were analyzed for program areas in aggregate and for major subgroups. The subgroup analysis was done by year of study (first versus second) for each program area, and by functional concentration (marketing versus finance) for

114 EDELSTEIN AND RAO

TABLE 2

Sample Characteristics by Program

Program

Characteristic Business Public Hospital

Average age (years) 23.6 25 9 25.0 Percent married 53.9 39.1 45.8 Percent worked before 97.4 91.3 95.8 Percent full-time work experience 56.4 78.3 70.8 Father’s education (years) 155 14.3 13.8 Average monthly expenditure (S) 296.2 353.1 373.0 !hmpksize 39n 23 24 Fist year 20 18 15 Second year 19 5 9

Qcludirg 17 finance and I2 marketing majors.

the business program. At each level, the 36component average preference vector was analyzed using the MONANOVA algorithm. This analysis yielded part-worth limctions for each of the four factors of dynamism, societal concern, functional/administrative emphasis, and relative size.

For each program, the explicitly elicited rank order of importance of the four organizational factors was compared with the implicit rank order &rived from the MONANOVA analysis. The frequency of mention of additional factors considered salient in employer selection was also compared across the programs.

RESULTS

Progmm mear. The additive conjoint measurement model fitted the average preference data well as indicated by the low values of stress obtained for each program area. (These were, respectively, 0.05, 0.01, and 0.03 for the three programs.) The part-worth functions, shown in Fig. 1 describe the contribution to overall preference of each of the four factors. The estimated functions representing the implicit worth placed on the organizational attributes, permit comparison not only between factors within any program, but also between the three programs. For example, business program students tended to impute a decrement of 1.83 units of work preference to an organization described as low on the factor of dynamism, while assigning a positive contribution of 0.91 units to the one with a high level of societal concern. Public program students imputed a decrement of 1.09 units for a low level of dynamism, and an increment of 1.64 units for high societal concern.

JUDGING EMPLOYER ATTRIBUTES

I. DYNANISN 2. SOCIETIL CONCERN I I

3. FUNCTIONAL/ADMINlSTRAllVE

LO Public

-... .\ STATE YETnoPoUtAn

FEDERAL -)-----+

-1.0

f I.0 mspftal

/ .i

oEllE.plJsn . .!’ W,” , tyEcluJ5n.i’

I... .’ -4’

RELATIVE SIZE

-1.0

-2.0

115

L-1.0

Fig. 1. Part-worth functions for attributes by program.

The method of interpreting trade-offs between the attributes within a respondent group may be illustrated by using the estimated part-worth functions for the business program. For this purpose, the total utilities for each of the 36 potential employer profdes were computed according to the additive conjoint measurement model. These were plotted in descending order in Fig. 2. Employer profdes with almost equal utility (i.e., where the curve is almost horizontal) need to be compared to infer the nature of trade-offs. For

116

4

EDELSTEIN AND RAO

&j@&

Dynamism : I = LOW 2 = nigh

Societal I = Low Conorrn : 2 .= MadiUnl

3 = Hi@

Functional I = Horhoting 2 = Productial 3 = Flnanca

I s small

-4.0 ’ n 3 ‘I ’ 1 * ” 0 ” “‘I m ’ ” 1” 8 1” 0 “‘0 “I Employer

f&“k I 3 5 7 9 II 13 I5 I7 IS 21 23 25 27 29 31 33 35

Dymmirm22222222222222222 I I2 I I I I I I I I I I I I I I I I sooiotol

~~n333322322231ll2113313322322231112lll Funotionol ~,,,ph&s323132131123232113222132131II32321I2

Rdatke Sk* 211222112112211212112222112l2222l2I1

Fig. 2. Estimated utilities of the 36 employers for business program students.

example, compare the two profiles rank 4 (high, high, marketing, large) and rank 5 (high, medium, finance, large) described, respectively, on the four attributes of dynamism, societal concern, functional emphasis, and size with estimated utility of about 2.8. This revealed that a decrease of societal concern of the firm from high to medium levels was traded off for a change in the organization’s emphasis from marketing to finance functions. A similar inference could be drawn by looking at profiles 7 (high, high, marketing,

JUDGING EMPLOYER ATTRIBUTES 117

small) and 8 (high, medium, finance, small) whose utilities were around 2.7. Trade-offs between more than two factors could be inferred by comparing profile 15 (high, medium, production, small) with 16 (high, low, marketing, large), and 19 (low, high, production, large) with 20 (high, low, production, small). In the first pair, a change in functional emphasis from marketing to production needed to be compensated by an increase in societal concern and a decrease in relative size of the organization. In the second pair, a decrease in dynamism (or growth potential) of the firm was traded off for increases in both the attributes of societal concern and size.

Several comparisons may be made between the three programs. The worth for an organization was monotone with the qualitatively expressed level of the factors of dynamism and societal concern. In every case higher levels on these factors contributed more to overall preference than did lower level. The differences in the part-worths for these two factors between the three programs were striking. Business students showed higher utility for more dynamic organization than their public and hospital counterparts. On the other hand, public and hospital students showed higher worth for the more societally concerned organizations. It is clear that these two attributes were implicitly traded off for each other in reaching a total worth by students of all the three programs. These differences were consistent with the differences in the subject matter content of the three program areas.

Interesting patterns were associated with the functional/administrative attribute specific to each program. The business students expressed highest preference for employers emphasizing finance, less for marketing, and the least for production. For public administration students federal opportunities were more attractive than the two equally less appealing state or metropolitan jobs. The hospital program student expressed definite preference for jobs dealing with both specialists and generalists.

Measures of importance of the four attributes computed from the part-worth numbers are as follows for the three programs:

Attribute Business Public Hospital

Dynamism 75% 32% 29%

Societal Concern 14 65 63

Functional/Administrative Emphasis 11 2 8

Size 0 I 0

The principal factor determining overall preference was dynamism for business students, and societal concern for both the public and hospital students. The relative size of the employing organization consistently had the least contribu- tion to overall preference. The directional differences of the part-worth functions of the size factor among the program areas were minimal and of no pragmatic significance.

118 EDELSTEIN AND RAO

TABLE 3

Comparison of Explicit and Implicit Rank Orders of Importance of Four organizational Attributes

Attribute

Business

Implicit Explicit

Public

Implicit Explicit

Hospital

Implicit Explicit

Dynamism 1 2 2 3 2 2 Societal concern 2 4 1 1 I 1 Functional/Adminstrative

emphasis 3 1 3 2 3 3 Relative size 4 3 4 4 4 4

Rank correlation between explicit and implicit ranks 0.0 0.8 1.0

Year of srudy. Perceptual differences between first and second year students were observable with respect to the functional/administrative emphasis factor. Large differences existed only for business students who, in the second year, expressed a decreased preference for production-oriented employers, and an increased preference for marketing-oriented employers, accompanied by a moderate increase toward fmance jobs. The disparity between federal and other jobs was less manifest in second versus first year public students. The preference among hospital program students for jobs involving work with both specialists and generalists was more pronounced in the second year. Orientation toward employers with high societal concern decreased in the second year for public students.

Marketing and finance. Not surprisingly, the functional emphasis of preferred jobs was consistent with the student’s own area of concentration. The factor of societal concern was more important for students concentrating in marketing than in fmance. The preference for smah firms tended to be higher for marketing majors than for finance majors.

Explicit versus implicit weights. A comparison is provided in Table 3 between the explicitly stated rank order of importance of the four chosen organizational attributes and the implicit importances shown earlier for each program area. Least consistency may be noted between these two sets of ranks for business students. Furthermore, this divergence could be traced mainly to the two factors of societal concern and functional/administrative emphasis of the employer. For the business students, the factor of societal concern did not become salient except when they were forced to consider its trade-off with the other stated factors in the perceptual task. On the other hand, hospital students’ implicit evaluations are perfectly consistent with their explicit evaluations.

JUDGING EMPLOYER AlTRIBUTES

TABLE 4

Percent Respondents Mentioning Other Attributes of Importance

119

Attribute BlJsjpess

Program

Public Hospital

LocatiOn 14 51 71 Work environment 54 61 58 Advancement potential 41 48 42 Responsibility 23 52 54 Monetary factors 41 48 25

Sample base (39) (23) (24)

Additionul attributes. A content analysis of the openended responses on other attributes that the students considered relevant revealed some interesting insi&ts. Almost all the attributes mentioned tended to relate to the job within an organization rather than the characteristics of the organization itself. These job specific attributes included location, work environment, advance- ment potential, responsibility, and salary related considerations (Table 4). The infrequent mention of the responsibility attribute by the business students and of monetary attributes by the hospital students was the only striking difference.

DISCUSSION

This exploratory study clearly demonstrates the power of the additive conjoint measurement model in determining the tradeoffs implicit in the management students’ evaluations of potential employers. Based on a limited set of judgments collected from simple tasks, considerable information on the attribute trade-offs and importances could be distilled by application of the model. Further, it is clear from this study that the importances implicitly assigned to attributes were not always the same as those explicitly stated by the students. Thus, the inferences drawn from explicit statements can be misleading for strategy purposes.

Ex poti, the selection of the four organizational attributes appears to be reasonable. The additional attributes indicated by the students as salient in their employer consideration largely dealt with the specifications of the potential position within the organization rather than the organization itself. The empirical results, therefore, can be of value for organizations in evaluating their own images and perhaps developing strategies for altering them. To embark on image-building strategies, similar studies are necessary using actual firms as stimuli. An attempt in this direction using Coombsiam unfolding model was made by Hi and Pessemier (197 1).

120 EDELSTEIN AND RAO

Application of this model at the individual student level could generate preference data for input to an information system for counseling and placement of the type described by Holt and Huber (1969). Such a system would be incomplete without the complementary data of prospective employers on attributes considered relevant to student choice. The preferences for potential employers could be estimated utilizing the student’s idiosyncratic part-worth functions on these attributes, thereby identifying a set of worth- while alternatives for each student to consider. By limiting his set of considered alternatives to those perceptually consistent with his own prefer- ence structure, the student may more efficiently organize his task of choosing an employer. Employers might benefit from such a system by being able to better direct their recruiting effort to suitable institutions.

REFERENCES

Coombs, C. H., Dawes, R. M., & Tversky, A. Mathematical psychology, An elementary introduction. Englewood Cliffs, New Jersey: Prentice-Hall, 1970.

Green, P. E. & Rao, V. R. Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 1971,8, 355-363.

Green, P. E. & Wind, Y. Multiattribute decisions in marketing: A measurement approach. Hinsdale, Illinois: The Dryden Press, 1973.

Hill, R. E. & Pessemier, E. A. Multidimensional and unidimensional metric scaling of preference for job descriptions. Paper No. 308, Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of industrial Administration, Purdue University, April 1971.

Holt, C. C. & Huber, G. P. A computer aided approach to employment service placement and counseling. Management Science, 1969, 15, 573-594.

Kruskal, J. B. Analysis of factoral experiments by estimating monotone transormations of data. Journal of the Royal Statistic& Society, Series B, 1965.27, 25 I-263.

Kruskal, J. B. Multidimensional scaling by optimizing goodness of tit to a nonmetric hypothesis. Psychometrika, 1964, 29, l-27.

Lute, R. D. & Tukey, J. W. Simultaneous conjoint measurement: a new type of fundamental measurement. Journal of Mathematical Psychology, 1964, 1, l-27.

McCrimmon, K. R. An overview of multiple objective decision making. In J. L. Cochrane & M. Zeleney (Eds.), Multiple criteria decision making. Columbia, South Carolina: University of South Carolina Press, 1973.

Tverksy, A. A general theory of polynomial conjoint measurement. Journal of Mathe- matical Psychology, 1967, 4, I-20.

Received: November 11, 1972. Revised: July 10, 1974.