modeling decision-specific stress: some methodological considerations

Modeling Decision-Specific Stress: Some Methodological ConsiderationsAuthor(s): Morris B. Holbrook and Michael J. RyanSource: Administrative Science Quarterly, Vol. 27, No. 2 (Jun., 1982), pp. 243-258Published by: Sage Publications, Inc. on behalf of the Johnson Graduate School of Management,Cornell UniversityStable URL: http://www.jstor.org/stable/2392302 .

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Modeling Decision- Specific Stress: Some Methodological Considerations

Morris B. Holbrook and Michael J. Ryan

? 1982 by Cornell University. 0001 -839218212702-02431$00.75

We wish to thank Johannes M. Pennings for his helpful comments on an earlier draft of this paper. The work was supported by a grant from the NAFA Foundation, Inc. We gratefully acknowledge the assistance provided throughout the project by that organization's directors and staff.

Arguments are advanced for a reoriented approach to the study of managerial stress. Specifically, (1) managerial stress should be investigated as a decision-specific phenomenon that varies across decision areas; (2) the determinants of such decision-specific stress may usefully be studied at the individual level of analysis; and (3) at the aggregate level, the pattern of relationships underlying stress can be elucidated by using multidimensional scaling (MDS) techniques to create spatial representations that clarify complex associations among stress components and facilitate their interpretation. These points are investigated in a study of stress in the role of automotive fleet administrator. Based on a sample of 324 fleet administrators, general support isfound for an individual-level model of the determinants of decision-specific stress (R=.65,p<.0001) but not for the specific hypothesized effects of decision- specific ambiguity. Apparent inconsistencies in these findings are resolved by an aggregate-level spatial representation obtained through MDS analysis.

INTRODUCTION

Recent years have witnessed a veritable explosion of research on managerial stress. Since the pioneering work by Kahn et al. (1964), considerable energy has been devoted to the empirical investigation of stress determinants. Excellent reviews of these studies have been provided by, among others, Kahn (1973), McGrath (1976), Cooper and Marshall (1976,1978), Van Sell, Brief, and Schuler (1976, 1981), Cox (1978), Harrison (1978), and McLean (1979). These reviews present exhaustive summaries of research findings concerning the antecedents, consequences, and moderators of managerial stress. The emerging conclusions need not be repeated here except to note that their importance to issues of private and public health is self-evident.

Less attention has been devoted to some key methodological issues that arise in studying the determinants of managerial stress. The present paper addresses this latter set of questions and pursues three specific objectives: (1) to urge the usefulness of stress research that seeks to establish relationships across decision areas in intra-individual analysis; (2) to test a simple individual-level model in which decision-specific stress is determined by the amount of ambiguity, conflict, and overload associated with each decision area; (3) to illustrate the usefulness of aggregate-level multidimensional scaling techniques (MDS) in clarifying the complex patterns of stress-based relationships among decision areas.

Inter-Individual versus Intra-Individual Measures

Most research on stress has been conducted within the framework of cross-sectional survey designs in which stressors and stresses are measured at the level of the job or role. Pepitone (1967: 182-183) attributed this tendency to "social psychology's commitment to a relatively large unit of analysis. Thus," he added, "from a social psychological point of view, stressor variables are typically seen as imbedded in certain kinds of global social situations." Such research might hope to demonstrate, for example, that Mr. X, whose role is poorly structured, taxing, and full of interpersonal conflicts,

243/Administrative Science Quarterly, 27 (1982): 243-258



experiences more job-related tension and anxiety than Mr. Y, who occupies a highly routinized, unchallenging, and insular role and is therefore relatively free from stress.

Methodologically, however, tests of such propositions are on the same philosophical footing as statements of the form: "John loves Mary more than Paul loves Patricia." The invalidity of such inter-individual utility comparisons has long been rec- ognized by economists and mathematical psychologists (Luce and Raiffa, 1957; Becker and McClintock, 1967). Logically, it would seem that analogous comparisons of perceived stressors and experienced stresses between individuals are equally invalid.

This problem has been handled with some success by behavioral scientists working in other research traditions. Consumer researchers, for example, have reacted to the difficulty of inter-individual utility comparisons by adopting the analysis of brand preferences at an intra-individual level, asking respondents to rate or rank their relative liking for several items within a set of offerings (Bass and Wilkie, 1973). These preference orderings have the same logical status as statements of the form: "John loves Mary more than he (John) loves Patricia." Such statements, however damaging to Patricia, involve only intra-individual utility comparisons and thus appear to be less susceptible to measurement ambiguities than are inter- individual comparisons.

A similar issue arises in the research on expectancy models of job motivation. As formulated byVroom (1964) or Graen (1976), for example, such models have represented a particular individual's choice among a range of alternative behaviors. Mitchell (1974: 1056-1057), however, has lamented the tendency of expectancy researchers to employ cross-sectional levels of analysis so that "all the tests to date have been across subject rather than within subject." This substitution of inter- for intra-individual analysis incurs the interpretive difficulties described by Campbell and Pritchard (1976: 93): If a between subjects analysis is to be used, then the meaning of a variable must be the same across subjects.... Differing underlying utility functions would confuse the between people comparisons and confuse the observed relationships.

McGrath (1976: 1360) appears to be alone in emphasizing that the same problem arises in research on organizational stress, where it may not be legitimate to make inter-individual comparisons in perceived levels of stressors or stresses, but where intra-individual comparisons concerning their relative presence across a variety of tasks or situations may be appropriate: A proper test ... requires some rather demanding conditions. There must be some basis for identifying situations which differ in "stressfulness" or "demand." What is needed is ... a set of situations which can at least be ordered in the degree of demand which they impose.... Furthermore,... the stressfulness of situations varies ... from one task to another for the same individual. Thus, a reasonable test ... requires placing the same persons in a series of situations, which vary in degree of demand.

McGrath's argument may be adapted to the present purposes by regarding an organization member's role as composed of a large number of different types of decisions. In some decision areas, such as calculating tax withholdings or ordering office

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Decision-Specific Stress

supplies, stress may be minimal. In others, such as settling labor disputes or purchasing a mainframe computer, it may reach painful proportions. In these circumstances, it therefore appears feasible to test propositions concerning differences in stress in individual-level analysis across decision areas, thereby preserving the psychological validity of such comparisons.

Forthis reason, data on stress components used in the present study are standardized across decision areas separately for each individual (Bass and Wilkie, 1973; Mitchell, 1974). Thus, measures of stressors and stresses are placed on a relative basis in which a given decision area is scored for each partici- pantas more or less stressful in comparison with othercdecision areas. Working with measures standardized across decision areas at the individual level, one may investigate the depen- dence of stress on such stressors as ambiguity, conflict, and overload in both individual-level and aggregate-level analyses.

A Simple Model of Decision-Specific Stress atthe Individual Level

Cooper and Marshall (1976, 1978) have provided an exhaustive catalog of stressors such as intrinsic job factors (e.g., bad working conditions, overload), organizational role (e.g., ambiguity, conflict, responsibility), relationships at work (e.g., with superiors, subordinates, or peers), career development (e.g., job insecurity), organizational structure and climate (e.g., lack of participation in decision making), extraorganizational sources (e.g., marital problems), and individual characteristics (e.g., Type A behavior patterns). To this list, McLean (1979) has added various stressful events such as being subjected to perform- ance evaluation, being threatened by institutional practices that violate personal values, and being forced to retire because of age. Meanwhile, Poulton (1978) has elaborated the set of poor working conditions to include such stressful environmental factors as bad visibility, noise, vibration, adverse climate, pollu- tion, heavy exertion, perceived danger, and nighttime hours.

A more general conceptualization of stressor variables was developed in French, Rogers, and Cobb's (1974) notion of "person-environment fit." This concept is described by Harri- son (1978: 178):

P-E fit can be used to define job stress. A job is stressful to the extent that it does not provide supplies to meet the individual's motives and to the extent thatthe abilities of the individual fall below demands of the job which are prerequisite to receiving supplies.

According to Cox (1978), examples of stressors arising from poor P-E fit would include internal factors (e.g., failure to fulfill personal needs), external factors (e.g., unemployment), environmental factors (e.g., noise), task-inherent factors (e.g., repetitiveness), and role-related factors (e.g., conflict, ambiguity, responsibility).

The present study follows Kahn (1973), McGrath (1976), Van Sell, Brief, and Schuler (1976, 1981), and Biddle (1979) by focusing primarily on three interrelated stressor variables. Spe- cifically, our model of stress argues that, for any given individual, the relative stress (Si) associated with a particular decision area (i) depends on its relative levels of ambiguity (Ai), conflict (C1), and overload (Of). It is proposed that decision-

245/ASQ, J une 1 982



specific stress can be represented by a weighted sum of the form

Si=wa Ai + wc Cj + w00Oi + e1

Where the w's are intra-individual standardized regression coefficients and ei is an error term.

This model has the advantage of focusing attention on the decision-specific nature of stress within the range of decision areas that pertain to a particular organizational role. Since all analysis occurs at the individual level, no difficulties arise concerning the problem of inter-individual comparisons. There may be cases, however, when one does wish to examine general relationships at the aggregate level of analysis. Here, the previously mentioned intra-individual standardization of data becomes useful, particularly when combined with MDS techniques.

Multidimensional Scaling of Correlational Data in Aggregate-Level Analysis

Once one acknowledges the variety of decision areas constitut- ing any given type of organizational role, the need to investigate the aggregate pattern of relationships among decision-specific stressors and stresses becomes clear. Indeed, G raen (1 976: 1 230) points out that when roles are regarded as complex sets of systematically related variables, it becomes essential to demonstrate "consistent patterns of integrated and significant results." A powerful apparatus for studying, representing, and interpreting such complex patterns of relationships among decision-specific stressors and stresses at the aggregate level is provided by the techniques of multidimensional scaling (MDS). In the proposed application of MDS, all analysis is performed on data that have been standardized across decisions for each individual so as to place comparisons on the appropriate relativistic basis. This point will become more clear in the discussion that follows.

As reviewed, for example, by Kruskal and Wish (1978) or Green and Wind (1973), MDS has most often been used to study perceptions of objects where some measure of pairwise similarity is available and preferences among objects where estimates of the affective affinity between individuals and objects can be obtained. The usefulness of these approaches in such areas as consumer research (Green and Wind, 1973) and experimental aesthetics (Berlyne, 1974) cannot be overesti- mated. Yet one should note that MDS techniques are basically general methods for representing structure in data and are therefore potentially useful in a wide variety of other data- analytic applications. MDS techniques normally used for mapping perceptions can be applied in any case where one has measures of proximity or strength of association between items and wishes to represent these complex interrelation- ships in a diagrammatic display that facilitates understanding. Similarly, techniques normally used for mapping preferences can be readily extended to generate visual representations of other kinds of relationships between items and correlative variables.

More specifically, MDS techniques can be directly extended to create clearly interpretable spatial representations of the aggregate-level patterns and determinants of decision-specific

2461ASQ, June 1982




stress. Two such extensions appear in this paper. The first represents the aggregate pattern of associations among decision areas in relative degrees of experienced stress. The second represents the relationships of relative decision- specific stress to underlying role stressors such as relative ambiguity, conflict, and overload. Together, these extensions of MDS techniques represent relationships in a way that sheds considerable light on systematic patterns of stress-related variables when these are first measured relatively at the intra-individual level and then analyzed at the aggregate level.

The first extension creates a stress space containing a number of decision areas. Distances between decision areas reflect the degree to which relative stress experienced in one area tends to be associated with that felt in the other. Specifically, if one has an m x n array of stress measures form individuals inn decision areas (standardized for each individual across decisions), the correlations of standardized stress between decision areas and across individuals will produce an n x n matrix of associations that can be submitted to MDS so as to generate a spatial pattern of distances among decision areas. In such a space, the proximity of two decision areas indicates the extent to which individuals who experience relatively high stress in one tend also to feel relatively high stress in the other. If two decision areas are close together in the space, the relative stress felt in one tends to be high (low) when that in the other is high (low). Conversely, if two decision areas are far apart, association between their relative stress levels may be nonexistent or even negative. If the dimensions of the MDS space can be meaning- fully named, they may provide clues concerning the reasons for the overall pattern of stress correlations. Such MDS analyses of correlational data have been suggested in the cases of intellec- tual abilities (Guttman, 1 966), political preferences (Weisberg and Rusk, 1970), brand purchases (Lehmann, 1972), aesthetic tastes (Holbrook and Huber, 1979), and leisure activities (Hol- brook, 1980; Holbrook and Lehmann, 1981). The present study extends the method of multidimensionally scaled correlations to the analysis of relationships between the relative stressfulness of various decision areas.

The second extension of MDS techniques involves the representation of stressor variables associated with relative stress and adapts a method proposed by Carroll (1972) in the context of preference mapping (where, of course, the need for intra- individual comparisons is also a problem). Specifically, standardized stressor variables are represented bystressorvectors in the previously created stress space in such a way that the perpendicular projections of decision areas onto a particular vector are maximally correlated with their mean standardized scores on the relevant stressor. The method for positioning such vectors involves computing each decision area's mean standardized score on the stressor in question and then regress- ing these standardized stressor means on the coordinates of the decision areas in the stress space. Vector orientations are determined by interpreting the resulting regression coefficients as direction cosines (Carroll, 1972). Fits of the vectors may be gauged by their associated multiple-correlation coefficients. Vector interpretations are clear and straightforward: the closer a decision area's perpendicular projection is to the positive (negative) end of a given stressor vector, the hig her (lower) is its

2471ASQ, June 1982



mean standardized score on the relevant stressor variable. Where mean standardized scores are high (low), one may conclude that- relative to other decision areas-the decision area in question involves a large (small) degree of that particular stressor for many respondents.

The two extensions of MDS techniques just described sound more complicated than they turn out to be in practice. Indeed, the authors contend that, far from being excessively burden- some or confusing, the extended MDS approach is actually a helpful simplifying device for representing complex patterns of relationships in a straightforward visual display that facilitates comprehension and interpretation. In that sense, as well as in some of its mathematical details (Torgerson, 1958), the technique resembles factor analysis. As in the case of factor analysis, some subjectivity may be required in the interpretation of results. But, like factor analysis, the potential for clarifying patterns of association is enormous.

The third objective of the present paper, therefore, is to indicate the usefulness of MDS in representing the aggregate pattern of associations involved in the relationship of relative decision- specific stress to its underlying determinants.

METHOD

Research Setting

In accordance with the three purposes outlined above, the present paper reports an illustrative application of the above- mentioned technique to the study of stress in automotive fleet administration. Fleet administrators are organization members whose role involves the acquisition, maintenance, and disposal of cars and other small corporate vehicles. In this capacity, they account for approximately 12 percent of U.S. car sales and are therefore a segment of the market that exerts a major eco- nomic impact on the fortunes of the domestic automobile industry. Their role thus provides an interesting setting for the methodological considerations discussed above.

The fleet administrator's role typically involves numerous types of decisions varying in complexity, interdependence with others, and importance to the organization. This position is therefore well-suited to the kinds of intra-individual analysis advocated earlier. Based on (1) a perusal of texts on fleet management (Botzow, 1968; Phelps, 1969; Larkin, 1975; Ruegg, 1976), (2) informal discussions with experienced offi- cials of the National Association of Fleet Administrators (NAFA), (3) a pre-test questionnaire sent to 1 2 New York Chapter members (eight of whom subsequently participated in a focus-group session), and (4) a pilot study using an independent sample of 135 respondents from the NAFA organization, the fleet administrator's acquisition decisions were broken down into the following 17 key areas: OWN/LEASE - whether to own or lease fleet vehicles; TYPE LEASE -type of lease (finance, net, maintenance); LESSOR/DEALER -choices of specific lessor(s) or dealer(s); SIZE- size of fleet cars (small, midsize, large); TOP/BOTTOM - choice among fleet cars at top, middle, and bottom of line; MAKE- make of fleet cars (Ford, Chevy, etc.); OPTIONS- what options to include at company expense;

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CHOICES - what choices to offer drivers (color, transmission, etc.); TIMING - timing of new car acquisitions; REPLACEMENT- age or mileage at which to replace fleet vehicles; DISPOSAL- how to dispose of fleet cars (trade-in, auction, etc.); PERSONAL USE- the degree of personal use of fleet cars to permit the driver and his or her family; REIMBURSEMENT- method of reimbursement for personal use of cars; MAINTENANCE - arrangements for maintenance and repairs; REPAI R/REPLACE - repair or replacement after an accident; SURPLUS - how to handle surplus vehicles; INSURANCE - how best to insure vehicles and drivers.

These 1 7 decision areas served as the basis for the study reported here.

Sample

Questionnaires were sent to 750 randomly selected American NAFA members (none of whom had participated in the pilot study) and were returned by 324 respondents for a response rate of 43 percent. Due to the sensitive nature of the questions on stress, it was necessary to promise respondents complete anonymity in order to secure their cooperation. This factor made it virtually impossible to compare respondents and nonrespondents in order to assess nonresponse bias. However, in the aforementioned pilot study on an independent sample of 135 NAFA members, which also asked sensitive questions on stress, demographic and organizational characteristics were collected from those returning the questionnaire. These corre- sponded well to independent assessments of the nature of the NAFA membership and thus suggested informally that nonresponse exerted no serious distorting effect. Moreover, the fact that key parts of the present analysis were based on scores standardized for individuals and on relationships based on these standardized scores - rather than on simple group averages -

makes it unlikely that conclusions would have been affected by the inclusion of nonrespondents.

Questionnaire Items

The components of the decision-specific stress model were measured using a series of rating scales similar to those pre-tested in the pilot study. First, all decision areas were evaluated, in four 1 7-item sets of 5-position checkmark scales, from "very false" to "very true" with respect to their levels of (1) ambiguity ("I know exactly what is expected of me with regard to. . ."), (2) intersender conflict ("I receive incompatible requests from two or more people with regard to. . ."), (3) overload ("I frequently have to do more work than I can handle with regard to. . .", and (4)stress ("I work undera greatcdeal of tension with regard to. . ."). Each scale was scored from 1 to 5 or from 5 to 1, depending on the wording of the question.

For every respondent, considered separately, each of the four scales was standardized across decision areas to a mean of 0.0 and standard deviation of 1.0. As noted previously, this data standardization places all comparisons on an intra-individual basis (Bass and Wilkie, 1973; Mitchell, 1974). This step makes little difference in correlational analysis at the individual level but is crucial in aggregate-level analysis.

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Analysis

Individual-level decision-specific stress model. The decision-specific stress model was tested by intra-individual regressions of decision-specific stress (Si) on decisional ambiguity (Ai), conflict (Ci), and overload (O) across the 17 decision areas for each respondent considered separately. The average multiple correlation (computed using Fisher's r-to-z transformation) and mean standardized regression coefficients across these individual regressions served to indicate the general fit of the model and the overall relative contributions of its three components.

As a point of comparison, the results from the approach advocated here were contrasted with those that would have been obtained from more conventional cross-sectional analysis using raw data in regressions run separately for each decision area across respondents. Such cross-sectional regressions are subject to spurious effects due to scale-response tendencies in which those respondents who tend to mark high (low) on one scale also tend to mark high (low) on another. Thus, the cross-sectional results provide a base line against which individual-level analysis can be compared to ascertain whether the latter, conceptually more appropriate approach matters empirically.

Aggregate-level decision-specific stress model. As a further check on the individual-level results, the stress model may be estimated using (1) regression-a&cross decisions based on mean standardized stress and stressor scores and (2) regression across role occupants based on multi-item indices composed of raw scores for stress and stressors summed across decisions. The first procedure is an aggregate-level decision-specific analogue of the individual-level approach advocated here, while the second is the appropriate cross-sectional basis for comparison. This cross-sectional approach has the advantage of using multi-item indices likely to be high in reliability, but it incurs the danger of generating spurious relationships (or nonrelation- ships) based on the effects of yea-saying, scale-response tendencies, or other instrument-related artifacts.

Aggregate-level stress space. In orderto obtain astress space in which the distances between decision areas reflect their

degrees of correlation on the stress scale- decision-specific stress scores were first standardized for each individual across decision areas. The standardized stress scores were then correlated between decision areas to generate proximity measures that were, in turn, submitted to metric multidimensional scaling analyses in from one to four dimensions (Torgerson, 1958). Fits of the MDS solutions in each dimensionality were judged on the basis of the correlation across the 136 decision pairs between input proximities and output squared distances. This correlational index of fit differs from-Kruskal's (1964) stress formula, but resembles a measure (based on the correlation between input and output scalar products) that has been widely used in other metric MDS applications (cf. Carroll and Chang, 1970; Carroll, 1972). The authors prefer the measure of fit based on squared distances because, unlike the scalar products correlation, it is not upwardly biased in cases where the rank of the input proximities matrix exceeds the dimensionality of the output MDS space.

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Aggregate-level stressor vectors. For each decision area, average standardized stressor levels (ambiguity, conflict, and overload) were computed on the basis of scores standardized for each individual across decision areas. As noted above, this data standardization permits the removal of methods variance due to response-set bias. For each stressor separately, mean levels were then regressed across decision areas on the coordinates of the decision areas in the stress space. The resulting regression coefficients indicated the direction of the vectorthat provided an optimal fit between decision areas with higher standardized stressor means at one end and lower standardized stressor means at the other. These vectors therefore indicate the direction of maximal increase for each stressor across the 1 7 decision areas. The farther a given decision area lies in the positive direction of a particular vector, the higher its mean standardized score tends to be on the stressor represented by that vector. The degree of fit of such a vector representation is indicated by the multiple correlation obtained in the regression of mean standardized scores on decision coordinates.

RESULTS

Individual-Level Decision-Specific Stress Model

The individual-level decision-specific stress model was supported by a moderately strong and highly significant mean multiple correlation between decision-specific stress and the three stressorvariables: R-..65 (computed using Fisher's r-to-z transformation wherez=.78, uj=.06, p<.0001). Judged by their mean standardized regression coefficients, significant positive contributions to the prediction of stress were made by conflict (Tv,=. 1 1, wC= .02, p<.0001) and by overload (W0=.29, rWO==.03, p<.000 1), but not by ambiguity (Wa= .02, Wa= .02,

n.s.). As suggested by the relatively low standard errors of the WC and wo coefficients, most individuals showed positive effects of conflict and overload. It should be noted, however, that respondents were not completely homogeneous in this respect. Thus, 22 percent and 6 percent showed negative effects of conflict and overload, respectively. By contrast, heterogeneity was greater for ambiguity, with 33 percent of the coefficients negative in direction. This result highlights one advantage of individual-level analysis in revealing heterogeneity of stress responses across respondents. Though of potential interest in some applications, this heterogeneity was not considered further in the present analysis.

In sum, the mean standardized weights (W) reflect the average relative strength of each stressor variable in contributing to the explanation of stress (when controlling for the contributions of the other two stressors). The analysis therefore suggests that, whereas decisional conflict and overload play demonstrable roles in determining decision-specific stress for fleet administrators, perceived decisional ambiguity exerts no such clear effect.

Comparison with Disaggregated Cross-Sectional Stress Model

This result may be compared with the analogously disaggregated cross-sectional analyses, performed separately for each d ecision area across respondents. Here, the corresponding

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statistics were: RH.60 (computed using Fisher's r-to-z transformation wherez=.70, oj=.02, p<.0001), a= .03 (ara = .01, p =.001), -c= .12 (ocr= .01, pa<000 1), and W,= .53 (oW =.0 1, ~~~~~~~~~~~ ~~~~~~~~~~01 p<.0001). These suggest strong overall relationships, again with highly significant positive effects on stress of conflict and overload. Now, however, there is a weaker but still significant positive effect of ambiguity.

Thus, the conventional cross-sectional approach produces results that appear to be spurious in the case of ambiguity. This misleading finding could result from an artifact in which people who check the high (low) end of one scale also tend to check the high (low) end of another. Accordingly, the resulting spurious finding does not appearwhen analyses are performed on the more appropriate intra-individual basis.

Aggregate-Level Decision-Specific Stress Model

The aggregate-level regression across decisions on mean standardized stress and stressor scores produced extremely strong results: R=.98 (F3,13=95.5, p<.00 1), wa=-. 25 (F1,13=17.6, p=.001 ), wc=. 1 6 (F1,13=5.3, p<.05), w0=.81 (F1,13=1 38.2, p<.001). Here, conflict and overload continue to exert their anticipated positive effects. Surprisingly, however, ambiguity actually exerts a negative effect when the stress relationship is viewed at the aggregate level across decision areas. This finding will be further clarified by the MDS results discussed below.

Comparison with Aggregate Gross-Sectional Stress Model

Again, the results obtained by the proposed decision-specific approach may be compared with those found by an analogous cross-sectional procedure. Here, as might be expected given the possible operation of response-set artifacts, the role- specific multi-item stress and stressor indices were highly reliable statistically, with alpha coefficients of .90 (ambiguity), .94 (conflict), .94 (overload), and .93 (stress). However, the cross-sectional regression of these multi-item stress and stressor indices produced results considerably weaker than those found in the aggregate-level decision-specific analysis described in the last section: R=.69 (F3,320=97.3, p<.001), Wa =.03 (F1 ,320=0.53, n.s.), wc= .04 (F1 ,320=0.829, n.s.), w0 = .65 (F1,320= 179.5, p<.001). Explained variance fell from 96 to 48 percent; adjusted R2 dwindled from .95 to .47. Moreover, the positive effect of conflict dropped from significance while the negative effect of ambiguity disappeared. In short, it appears that the aggregate cross-sectional approach loses information by combining responses across decision areas at the level of the job (rather than maintaining the distinctions among different types of decisions). Specifically, in the case of ambiguity, it is likely that the aforementioned tendency toward a spurious relationship due to scale-response artifacts cancels out what would otherwise be a negative relationship (as revealed by the aggregate-level decision-specific analysis). Thus, whatever the aggregate cross-sectional approach gains in reliability from its use of multi-item indices with high coefficient alphas, it appears to sacrifice in the strength and validity of its findings.

Aggregate-Level Stress Space

The correlational fits of stress spaces (in from one to four dimensions) were as follows: (1) .55, (2) .72, (3) .76, and (4) .80.

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Externally-Oriented

OWN/LEASE TYPE LEASE

INSURANCE

REIMBURSEMENT DISPOSAL LESSOR/DEALER

MAINTENANCE PERSONAL USE REPAI R/REPLACE

SURPLUS

Ca 0 C

a REPLACEMENT v

TIMING

CHOICES

I MAKE OPTI ONS

SIZE

TOP/BOTTOM

Internally-Oriented

Figure 1. Stress space based on metric MDS analysis of correlations in standardized stress ratings across individuals between decision areas.

Since there were diminis hing returns after the extraction of two dimensions, the two-dimensional solution was retained for purposes for further analysis.

This two-dimensional stress space is shown in Figure 1. Here, a reasonable pattern of associations between decision areas is evident. For example, those who felt relatively hig h (low) stress in OWN/LEASE decisions also tended to feel relatively high (low) stress in TYPE LEASE decisions (r=.61). Those who felt stress in TOP/BOTTOM decisions also experienced stress in SIZE decisions (r=.42). And so on.

The dimensions of the stress space were named judgmentally according to the relative positions of decision areas along the two axes. Thus, decision areas at the left of the horizontal axis appeared to be relatively more routine (e.g., whether to repair or replace a car after an accident) and those toward the right more unique (e.g., whether to own or lease fleet vehicles). Mean- while, the vertical axis appeared to distinguish between decision areas oriented more toward internal bargaining (e.g., whether to give drivers top-, middle-, or bottom-of-the-line cars) and those oriented more toward external boundary relations (e.g., where to obtain insurance or how to dispose of fleet cars).

In this light, the horizontal dimension of the stress space supports the distinction made by organizational buying- behavior theorists between "rebuy" and "new task" purchasing decisions (Robinson, Faris, and Wind, 1967; Webster and Wind, 1972). The vertical dimension accords with the emphasis often placed on the importance of boundary relations in engen- dering stress (Adams, 1976; Miles, 1976,1977; Miles and Perreault, 1976). Thus, the "internally-oriented" decision areas in the lower part of the stress space are likely to involve

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corporate sales managers and company comptrollers in conten- tion over how luxurious a car to provide. Caught in the middle, the fleet administrator is likely to experience an unusually high degree of intersender conflict concerning such decision areas. The role of the fleet administrator therefore reverses the pattern sometimes found elsewhere in which boundary roles present the greatest degree of conflict (Adams, 1976; Miles, 1976, 1977; Miles and Perreault, 1976; Rogers and Molnar, 1 976).

In sum, these impressions are reflected in the names assigned to the axes in the stress space shown in Figure 1 . These names are intended to suggest the underlying reasons for the overall pattern of associations in stressfulness between decision areas. Admittedly, the names of the dimensions reflect the author's jdugment (as would also be true in the case of factor analysis). However, this judgment was generally corroborated by the literature cited in the last paragraph, as well as by several fleet administrators with whom we shared the results.

Stressor Vectors

Further clarification of the results found in testing the decision-specific stress model appears in Figure 2, which presents vectors based on mean standardized scores for decision areas on the various stressorvariables. All stressor vectors but one attained correlational fits at or above R=.69 (F2,14= 6.5,

Average Ambiguity

(.57)

OWN/LEASE TYPE LEASE

INSURANCE / REIMBURSEMENT

DISPOSAL ~~LESSOR/DEALER

MAI NTENANCE PERSONAL USE REPAI R/REPLACE

SURPLUS

REPLACEMENT

TIMING

CHOICES

Average Overload MAKE

(.76) OPTIONS SIZE

Average T Stress TOP/BOTTOM (.81)

Average Conflict

(.69)

Figure 2. Vectors representing mean stress components plotted by re- gressing average standardized scores on spatial coordinates across decision areas.

2541ASQ, June 1982




p=.01), as indicated by the parenthetical multiple correlation coefficients shown in the figure. The fit (RH= .57) of the remain- ing vector (ambiguity) reached marginal significance (F2,14=3.4, p<.10).

This diagram indicates that average relative overload increases in the direction of routine decisions concerning maintenance and replacement. These, in other words, are the humdrum decisions that apparently make the greatest demands on the fleet administrator's time, effort, and energy. Meanwhile, average relative conflict increases toward decision areas that involve internal bargaining with vehicle users over options, size, and relative luxury of the car. Viewed in this light, average relative stress rises in a direction that is almost the resultant of the overload and conflict vectors, with a somewhat greater contribution coming from overload. Thus, this representation accounts for the aforementioned positive contributions of conflict and overload to the prediction of decision-specific stress, as well as for the greater contribution of the latter in individual-level and aggregate-level decision-specific stress models. Furthermore, the otherwise puzzling results for ambiguity are explained by this variable's marginally significant tendency to increase in a direction opposite to the other stressors, reaching its highest levels in more unique and externally-oriented decisions concerning insurance, reimbursement, and type of lease. It follows, therefore, that relative ambiguity makes no positive contribution to explaining decision-specific stress at the individual level of analysis. Indeed, at the aggregate level, the contribution of ambiguity actually becomes significantly negative when controlling for other stressor variables. Thus, the MDS analysis helps to explain the unexpected results for ambiguity. This stressor makes no positive contribution to the explanation of decision- specific stress precisely because it increases in a direction counter to overload and conflict in the stress space.

DISCUSSION

The substantive interpretation of these results must be ap- proached with some caution. First, it would be dangerous to generalize beyond the case of fleet administration to other organizational roles that might be of a primary interest to different researchers. Second, even within the general area of fleet administration, one should avoid drawing conclusions about decision areas omitted from the study (though we believe these to be few in number). Among those decision areas studied for these particular organizational members, however, it does appear that the most ambiguous decisions (e.g., OWN/LEASE, TYPE LEASE, INSURANCE) are the least overloading (in terms of responsibility or time pressure, for example) and, therefore, the least stressful. Apparently, at least in the case investigated here, ambiguity does not contribute to decision-specific stress when it is not accompanied by relatively taxing demands on the decision maker's time and effort. This conceptually interesting interactive relationship is one that might fruitfully be investigated in future studies of decision- specific stress.

Because these conclusions are highly situation-bound and apply to one type of decision maker in a particular organizational context, comparable research on other organizational roles

255/ASQ, J une 1982



would need to start from scratch with pilot studies and pre- tests, like those described earlier, to gain some understanding of the specific decision areas involved, as a prelude to further data collection and analysis. Such careful preliminary steps are prerequisites to any investigation that adopts the decision- specific viewpoint. They impose a cost that appears to be justified in light of the analytic gains to be obtained.

This state of affairs is consistent with the predominantly methodological focus of the present paper. In this connection, the three objectives set forth at the outset appear to have been largely accomplished by this application of MDS techniques to the study of decision-specific stress in fleet administration. First, the study has demonstrated the feasibility and usefulness of regarding stress as a decision-specific phenomenon that varies across the set of decision areas addressed by any particular role occupant. Second, for the case of fleet management, a stress model based on this decision-specific view was supported by both individual-level and aggregate-level analyses, which produced strong overall relationships but sur- prising results concerning the effect of ambiguity. Third, an aggregate MDS analysis helped to elucidate the overall pattern of relationships underlying these results. For example, the stress space provided a vivid visual representation of the pattern of associations in relative stress levels between decision areas and suggested that such associations depend primarily on whether decision areas are routine or unique and internally- or externally-oriented. Moreover, vectors in this stress space indicated why relative decisional ambiguity failed to contribute positively to the prediction of decision-specific stress.

The major theoretical contribution of the present study is its suggestion that stress may be usefully modeled as a decision- specific phenomenon. Otherwise, its contribution is primarily methodological in nature. In this latter connection, the study warns that there are potential dangers in the way research on managerial stress is usually conducted - namely, cross- sectionally with the job or role as the unit of analysis - and suggests that some problems may be avoided and insights gleaned by conducting analyses across decision areas using both straightforward correlational procedures and more elabo- rate MDS techniques.

REFERENCES

Adams, J. Stacy 1976 "The structure and dynamics of

behavior in organizational boundary roles." In M. D. Dun- nette (ed.), Handbook of Indus- trial and Organizational Psychology: 1175-1199. Chicago: Rand McNally.

Bass, Frank M., and William L. Wil- kie 1973 "A comparative analysis of at-

titudinal predictions of brand preference." Journal of Market- ing Research, 10:262-269.

Becker, Gordon M., and Charles G. McClintock 1967 "Value: Behavioral decision

theory." Annual Review of Psychology, 18:239-286

Berlyne, Daniel E., ed. 1974 Studies in the New Experi-

mental Aesthetics: Steps To- ward an Objective Psychology of Aesthetic Appreciation. New York: Wiley.

Biddle, B. J. 1979 Role Theory: Expectations,

Identities, and Behaviors. New York: Academic Press.

Botzow, Herman S. 1968 Auto Fleet Management. New

York: Wiley.

Campbell, John P., and Robert D. Pritchard 1976 "Motivation theory in industrial

and organizational psychology." In M. D. Dunnette (ed.), Handbook of Industrial and Or- ganizational Psychology: 63- 130. Chicago: Rand McNally.

256/ASQ, June 1982




Carroll, J. Douglas 1972 "Individual differences and

multidimensional scaling." In Roger N. Shepard, A. Kimball Romney, and Sara Beth Ner- love (eds.), Multidimensional Scaling: Theory and Applica- tions in the Behavioral Sci- ences: 105-155. New York: Seminar Press.

Carroll, J. D., and J. J. Chang 1970 "Analysis of individual differ-

ences in multidimensional scaling via an n-way generalization of Eckart-Young decomposi- tion." Psychometrika, 35:283-319.

Cooper, Cary L., and Judi Marshall 1976 "Occupational sources of

stress: A review of the literature relating to coronary heart disease and mental ill health." Journal of Occupational Psychology, 49:11 -28.

1978 "Sources of managerial and white collar stress." In Cary L. Cooper and Roy Payne (eds.), Stress at Work: 81 -105. New York: Wiley.

Cox, Tom 1978 Stress. London: Macmillan.

French, J. R. P., W. Rogers, and S. Cobb 1974 "A model of person-

environment fit." In G. V. Coelho, D.A. Hamburgh,andJ. E. Adams (eds.), Coping and Adaptation. New York: Basic Books.

Graen, George 1976 "Role-making processes within

complex organizations." In M. D. Dunnette (ed.), Handbook of Industrial and Organizational Psychology: 1201 -1245. Chicago: Rand McNally.

Green, Paul E., and Yoram Wind 1973 Multiattribute Decisions in

Marketing: A Measurement Approach. Hinsdale, IL: Dryden Press.

Guttman, Louis 1966 "Order analysis of correlational

matrices." In R. B. Cattell (ed.), Handbook of Multivariate Ex- perimental Psychology: 438- 458. Chicago: Rand McNally.

Harrison, R. Van 1978 "Person-environment fit and

job stress." In Cary L. Cooper and Roy Payne (eds.), Stress at Work: 175-205. New York: Wiley.

Holbrook, Morris B. 1980 "Representing patterns of as-

sociation among leisure activities: A comparison of two techniques." Journal of Leisure Research, 12:242-256.

Holbrook, Morris B., and Joel Huber 1979 "The spatial representation of

responses toward jazz: Applica- tions of consumer esthetics to mapping the market for music." Journal of Jazz Studies, 5, No. 2:3-22.

Holbrook, Morris B., and Donald R. Lehmann 1981 "Allocation of discretionary

time: Assessing complemen- tarity among activities." Jour- nal of Consumer Research, 7:395-406.

Kahn, Robert L. 1973 "Conflict, ambiguity, and over-

load: Three elements in job stress." Occupational Mental Health, 3, No. 1:2-9.

Kahn, Robert L., D. M. Wolfe, R. P. Quinn, J. D. Snoeck, and R. A. Ro- senthal 1964 Organizational Stress: Studies

in Role Conflict and Ambiguity. New York: Wiley.

Kruskal, Joseph B. 1964 "Nonmetric multidimensional

scaling: A numerical method." Psychometrika, 29:115-129.

Kruskal, Joseph B., and Myron Wish 1978 Multidimensional Scaling. Be-

verly Hills, CA: Sage.

Larkin, James J. 1975 Vehicle Leasing. New York: At-

com.

Lehmann, Donald R. 1972 "Judged similarity and brand-

switching data as similarity measures." Journal of Market- ing Research, 9:331 -334.

Luce, R. Duncan, and Howard Raiffa 1957 Games and Decisions. New

York: Wiley.

McGrath, Joseph E. 1976 "Stress and behavior in organi-

zations." In M. D. Dunnette (ed.), Handbook of Industrial and Organizational Psychology: 1351 -1395. Chicago: Rand McNally.

McLean, Alan A. 1979 Work Stress. Reading, MA:

Addison-Wesley.

Miles, Robert H. 1976 "Role requirements as sources

of organizational stress." Jour- nal of Applied Psychology, 61:172-179.

1977 "Role-set configuration as a predictor of role conflict and ambiguity in complex organizations." Sociometry, 40:21-34.

Miles, Robert H., and William D. Perreau It, Jr. 1976 "Organizational role conflict: Its

antecedents and consequences." Organizational Be- havior and Human Perform- ance, 17:19-44.

Mitchell, Terence R. 1974 "Expectancy models of job

satisfaction, occupational preference, and effort: A theoretical, methodological, and empirical appraisal." Psychological Bulletin, 81:1053-1077.

Pepitone, Albert 1967 "Self, social environment, and

stress." In M. H. Appley and R. Trumbull (eds.), Psychological Stress: Issues in Research: 182-199. New York: Appleton-Centu ry-Crofts.

Phelps, Clyde W. 1969 The Role of Fleet Leasing in

Motor Vehicle Fleet Plans of Business Firms. Baltimore: Commerical Credit Co.

Poulton, E. Christopher 1978 "Blue collar stressors." In Cary

L. Cooper and Roy Payne (eds.), Stress at Work: 51 -79. New York: Wiley.

Robinson, P. J., C. W. Faris, and Y. Wind 1967 Industrial Buying and Creative

Marketing. Boston: Allyn & Ba- con.

Rogers, David L., and Joseph Mol- nar 1976 "Organizational antecedents of

role conflict and ambiguity in top-level administrators." Ad- ministrative Science Quarterly, 21 :598-610.

Ruegg, Rosalie T. 1976 The Police Patrol Car: Eco-

nomic Efficiency in Acquisition, Operation, and Disposition. Washington, DC: National Bureau of Standards.

Torgerson, Warren S. 1958 Theory and Methods of Scal-

ing. New York: Wiley.

257/ASQ, June 1982



Van Sell, Mary, Arthur P. Brief, and Randall S. Schuler 1976 "Role conflict and role am-

biguity: A review and synthesis of the literature." Working Paper No. 76-30, College of Business Administration, Uni- versity of Iowa.

1981 "Role conflict and role ambiguity: Integration of the literature and directions for future research. " Human Relations, 34:43-71.

Vroom, Victor H. 1964 Work and Motivation. New

York: Wiley.

Webster, Frederick E., Jr., and Yoram Wind 1972 Organizational Buying Behav-

ior. Englewood Cliffs, NJ: Prentice-Hall.

Weisberg, H. F., and J. G. Rusk 1970 "Dimensions of candidate

evaluation." American Political Science Review, 64:1167- 1185.

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modeling decision-specific stress: some methodological considerations

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