Atttudes and behavior: Implications of attitudes toward behavioral alternatives

JOURNAI

OF ESPERIMFNTAI.

SOCIAL

PSYCHOLOGY

17,

2X6-307

( 1981 t

Attitudes and Behavior: Implications of Attitudes toward Behavioral Alternatives JAMES JACCARD

Received

February

19. 1980

The relationship between attitudes and behavior was considered. An attitudinal model of behavioral alternatives was presented and its applied and theoretical implications explicated. Three investigations were reported that tested the initial viability of the approach. Support for the model was observed in all three investigations.

Early studies of the relationship between attitudes and behavior have generally demonstrated a weak relationship between these variables (Wicker, 1969; Ehrlich. 1969; McGuire, 1969). Wicker (1969) concluded his review of attitude-behavior studies by noting that there is “little evidence to support the postulated existence of stable underlying attitudes within the individual which influence . . his actions” (p, 75). More recently, investigators have attempted to specify variables that moderate the attitude-behavior relationship and to identify those situations under which attitudes will predict behavior. One such line of research has been the study of situation and personal factors that render motivational (and, hence, attitudinal) variables irrelevant to behavioral prediction (Fishbein & Jaccard. 1973). These have included the study of such variables as the ddity of the person to perform a behavior (i.e.. whether or not his or her abilities allow the individual to perform a behavior he is motivated to perform), and the extent to which behavioral performance depends upon other people or events. Weigel and Newman (1976) and Fishbein and Ajzen (1974) have attempted to analyze the attitude-behavior discrepancy in terms of the nature of the behavioral criterion measure. According to these theorists, general attitude measures The author would like to thank George King, Richard Knox, and David Brinberg for their assistance in this research, and Saul Kassin for his comments on portions of the manuscript. Requests for reprints should be sent to James Jaccard, Department of Psychological Sciences, Purdue University, West Lafayette. IN 47907.

OO?‘-1031181/031~?86-?7%07.OOiO Copyright All rights

(5 1981 by Academtc Press. Inc of reproduction m any form reserved.

ATTITUDES

AND

BEHAVIOR

287

should predict broad-based behulioral patterns, but may not be relevant to the prediction of specific behaviors. In attempting to predict specific behaviors, Ajzen and Fishbein (1977) have suggested the need to measure specific attitudes, namely, the attitude toward performing the behavior. Their review of the literature generally indicates that more specific measures of attitude lead to increased prediction of behavior typically studied in attitude-behavior research. However, even when specific measures of attitude are employed, the attitude-behavior relationship may still be weak (e.g., Jaccard, King, & Pomozal, 1977). This state of affairs has led to the development of theories that include variables other than attitude in the prediction of behavioral intentions and behavior. For example. Fishbein and Ajzen ( 1975) have suggested that norms may also influence a person’s decision to perform a behavior. Triandis (1977) has developed a theory that includes norms, roles, and self-concept considerations in the prediction of behavior. Models have also been presented by Wicker (1971), Rokeach and Kliejunas (1972), Sheth (1972), and others. Generally speaking, these models are based on the general linear model such that behavior (or behavioral intent) is said to be a weighted linear function of a set of predictor variables (attitudes, norms, etc.). The present paper considers an alternative model of the attitude-behavior relationship. Unlike previous theories, it is not based on the general linear model and consequently does not use weighting parameters in its derivation (Bentler & Speckart, 1979). In addition, it specifies a set of “additional” predictor variables that are distinct from those previously presented. Finally, the theory is based on principles derived from decision theory, although its conceptualization has been altered slightly. A Beha\,iorul

Alternati~~e Model

of Social Behaljior

The present model is concerned with situations in which an individual has the opportunity to perform one of n alternative behaviors, B,, B, . . . B,. These behaviors are assumed to be mutually exclusive and exhaustive such that performance of one precludes performance of another. On the simplest level, one can always speak of two behavioral alternatives, (I) performing a behavior (e.g., signing a petition), and (2) not performing that behavior (e.g., not signing a petition). The present theory is concerned with how an individual decides to perform one of the II behavioral alternatives. It is assumed that this behavioral decision will, in general, result in behavioral performance. However, it is also recognized that additional factors may influence behavioral performance, as discussed by Jaccard (1975) and Fishbein and Ajzen (1975). Attitude theory and models of choice behavior. A number of models of choice behavior have been explicated in experimental psychology (Lee. 1971). One of the more extensively studied models is that of sub-

288

JAMES

JACCARD

jective expected utility (SEU) theory, as derived from economics. Essentially, this theory holds that each behavioral alternative has associated with it a given SEU. This SEU is a value representing the likelihoods and worths of the various outcomes associated with a given behavioral alternative. Formal applications of SEU theory have generally been conducted in the context of contrived laboratory settings. The model has served as the basis of several less formal theories of behavioral decision making in social and organizational psychology. For example, Vroom ( 1964) has argued that an individual chooses to perform acts on the basis of the strength of an expectancy that the act will be followed by a given outcome and the value or attractiveness of that outcome. This “SEU” for a given behavioral alternative is thus defined as SEU, = $&,I/,. I

(1)

I

where SEU, = the subjective expected utility. SEU. associated with behavioral alternative i. E,, = the strength of the expectancy that act i will be followed by outcome j. and V, = the valence of outcome j. The model states, as does traditional decision theory, that the individual will choose to perform that behavior with the highest SEU. Applications of SEU-type models have been controversial (e.g., Heneman & Schwab, 1972; House, Shapiro. & Wahba, 1974; Mitchell, 1974; Mitchell & Biglan. 1971). Major problems exist with the measurement of expectancies and utilities. especially in the context of applied research. Major issues concern (1) the need for ratio scales of subjective probabilities and evaluations, and (2) the problem of deciding exactly what outcomes to include in the equation with the resultant possibilities of (a) including irrelevant outcomes and/or (b) excluding relevant outcomes. No solution to these issues is currently available. In addition, there is considerable variation in how the expectancies and utilities are conceptualized. These measurement problems and conceptual differences have typically restricted the applicability of the SEU model outside of laboratory settings. Equation (1) is highly similar to models of attitude formation developed in the attitude literature. Fishbein and Ajzen (1975) for example, hold that an individual’s attitude toward an action is an additive function of his or her subjective probabilities that the action leads to a set of outcomes weighted by the evaluations of those outcomes: (7-j where A,,, = the attitude toward the act, h, = the subjective probability that the act will lead to outcome “i.” C, = the evaluation of outcome 6,.I, 17 and n = the number of beliefs. A similar model was originally presented by Rosenberg (1956).

ATTITUDES

AND

BEHAVIOR

289

Equations (1) and (2) are highly similar in structure. The expectancy component in Eq. (1) is directly analogous to the belief component in Eq. (2) and the valence component in Eq. (1) is directly analogous to the evaluation component in Eq. (2). If the two equations are, in fact, comparable, then it may be possible to circumvent the measurement problems in deriving an estimate of an SEU for a given behavioral alternative. One way of conceptualizing an individual’s SEU for a given behavioral alternative is the extent to which he or she positively or negatively evaluates that behavioral alternative. Each behavioral alternative can be located on an affective dimension and it can be argued that the individual will decide to perform that behavior toward which the most positive affect is held. High SEU scores would probably imply positive affect associated with the behavioral alternative whereas low SEU scores would imply negative affect. This conceptualization of SEU would be identical to current definitions of attitude, i.e., the location of an attitude object (behavioral alternative) on a bipolar affective dimension. If such a conceptualization accurately reflects the notion of SEU, then standard attitude scaling procedures (see Green, 1954; Summers, 1970) should provide reasonable estimates of global SEUs. These procedures do not require (at least in the sense that the SEU model does) that all relevant and no irrelevant outcomes be specified with respect to the behavioral alternative, nor do they require the assunption of ratio scales. The focus of these techniques is nor on understanding the determinants of attitude (i.e., the information that has gone into influencing the attitude) but rather with simply measuring the attitude. This shift in focus allows the adoption of several strategies and criteria not directly applicable in the traditional SEU model. For example, semantic differential scales that load on an evaluative dimension can be used to assess the attitude toward performing each behavioral alternative. Such attitudinal measures do not require knowledge of relevant outcomes yet still would provide estimates of the overall SEU associated with an alternative. If SEU and attitude are equivalent, then traditional SEU theory should have direct implications for the attitude-behavior relationship. An integration of these approaches would suggest the following: In a given situation, an individual has a set of behavioral alternatives which he or she may perform. The individual may be said to possess an attitude toward performing each of these behavioral alternatives. An attitude is conceptualized as the location of the alternative on a bipolar affective dimension and is directly measurable using standard attitude-scaling techniques. The individual will decide (i.e., intend) to perform that alternative toward which the most positive attitude is held, and this decision should in turn, influence the behavior of the individual. This conceptualization of the determinants of behavioral decisions has important implications for current theories of the attitude-behavior relationship. These will now be considered.

290

JAMES

JACCARD

Theoretical implications. As noted earlier, several theorists have attempted to explain attitude-behavior discrepancies by suggesting that variables other than attitudes may influence behavioral decisions (or behavioral intentions as they are called in most theories). These theories are typically stated in the format of a multiple regression equation such that BI = ~3, ,Y, + \I‘~ X, .

. + \t‘P X,.

(3)

where BZ = the intention to perform the behavior: X, through X,, = the relevant predictor variables, of which the attitude toward performing the behavior = X,; and ~1, through M’,, = empirically determined regression weights which are said to reflect the importance of the respective variable. According to these models, discrepancies between traditional measures of attitude and behavior can be explained in the context of Eq. (3) since (I) the traditional attitude measured is usually not specific to the behavioral criterion (i.e.. it is an attitude toward an object and not an attitude toward a behavior), (2) there may be other factors (e.g.. normative beliefs) determining the behavior such that attitude is completely irrelevant to the behavior, and (3) attitude may only determine intentions to perform a behavior and thus should only be predictive of the action to the extent that these intentions are highly related to the behavioral criterion. Several important differences exist between the models represented in Eq. (3) and the behavioral alternative model. One major difference between the two models is the emphasis on behavioral predictability from a between-subject versus within-subject perspective. This difference may best be illustrated by considering only the first component of Eq. (3) as compared with the behavioral alternative model (i.e., we will assume that A,,, is the only important factor in determining intentions and set the remaining ~7’s in Eq. (3) to zero).’ Equation (3) would involve measuring different people’s attitudes toward performing a behavior (e.g., toward voting for the Democratic candidate) and correlating these measures with a behavioral criterion (e.g., whether or not the person voted for the Democratic candidate). It is assumed that the more favorable the attitude, the more likely it is the behavior will be performed. Table I presents a hypothetical example of three individuals and their attitudes toward voting for the Democratic candidate (column 1). According to Eq. (3). individual 1 should be most likely to vote for the Democratic candidate, followed by individual 2, and individual 3 should be least likely to vote for the Democratic candidate. In contrast, the behavioral alternative model suggests behavioral prediction requires knowledge of ’ The setting of these weights to zero is done purely some of the differences between the Iwo approaches. are nonzero will be considered shortly.

for expositional reasons to highlight The case where the remaining II’

ATTITUDES

1 .--

2 3

13 ___~

10 7 ___~._____~

291

AND BEHAVIOR

.~

19 12 5 ~

-6 3 ~~~

~-.

~~~

2

~~

~~~ ~~~~~

Nore. A, = Attitude toward voting for the Democratic candidate Ion a O-20 scale). A, = Attitude toward voting for the Republican Candidate (on a O-20 scale).

the distribution of attitudes across behavioral alternatives. If a person possesses a positive attitude toward performing h, and an even more positive attitude toward performing h2, it is unlikely he will perform h, even though he has a positive attitude toward the behavior. By taking the difference between the attitudes toward the two alternatives in Table 1. it can be seen that the behavioral alternative model makes the reverse prediction of Eq. (3): Individual 3 should be most likely to vote for the Democratic candidate, while individual I should be least likely to vote for the Democratic candidate. In Eq. (3). then, it is assumed that the more positive the attitude relative to other people’s attitudes. the more likely it is the person will perform the behavior. In the behavioral alternative model, however, it is assumed that the more positive the attitude relative to the person’s attitudes toward other behavioral alternatives, the more likely it is the person will perform the behavior. A second difference in the two approaches concerns the specification of factors other than attitudes that are relevant to behavioral prediction. A large number of social scientists (e.g., Wicker, 1969: Triandis, 1977) have argued that a person’s attitude toward an act (A,,.,) is only one of a number of variables that influence behavior and for adequate prediction must be taken into account. Accordingly, a these “other variables” number of models have been proposed which investigate such additional factors as attitudes toward the situation (Rokeach & Kliejunas, 1972), habit (Triandis. 1977). and normative beliefs about what important others think he should do (Fishbein, 1972). In the behavioral alternative model. however, the “other variables” consist of attitudes toward the remaining behavioral alternatives. Thus, the models differ in where they direct the theorist to look for an understanding of attitude-behavior discrepancies. A third difference between the two approaches concerns the way in which the “other variables” are utilized within the theoretical network. In Eq. (3), the crucial predictor variables (X, through X,) are given weights (~1, through it!& representing the importance of a variable in determining intentions. The size of these weights is said to vary as a function of the behavior under study and individual differences. Generally, these weights are estimated via multiple regression procedures.

292

JAMES

JACCARD

The weighted predictor variables are summed to yield an index of predicted behavior. In the behavioral alternative model. no such weighting parameters are employed nor is there a summative relation among the weighted predictor variables. Rather. the attitudes toward performing each of the behavioral alternatives are compared with one another and that alternative toward which the most positive attitude is held represents the predicted behavior. One might be tempted to extend models of behavioral intention based on Eq. (3) to include behavioral alternatives thus resulting in a more “complete” model of decision behavior. Such an approach was suggested by Ajzen and Fishbein (1969). In the context of Fishbein’s model, this would involve measuring an A,,, arzd an SN (subjective norm) toward each behavioral alternative and regressing an intention measure for each alternative onto the appropriate A,,, and SN. The combination of A:,,, and SN would then be dictated by the results of the various regression equations. There are several problems with this approach relative to the present one. First, the behavioral alternative model, as currently conceptualized, is idiographic in nature and allows prediction of an individual’s behavior knowing only that individual’s attitudes toward the behavioral alternatives. The regression approach, in contrast, is not idiographic. It requires that a group of individuals be studied so that least-squares estimates of the weights (M’, and Map)can be obtained for any given alternative. This approach requires that all individuals within the group be homogeneous in terms of how they weight A;,,, and SN for a given alternative and also requires some rather stringent measurement assumptions (e.g., all individuals interpret the rating scales in the same way). Thus, deriving accurate estimates of how to weight the multiple predictors is problematic from the standpoint of psychological explanation. (see Gordon (1968) for additional issues in this respect.) These assumptions are not requisite in the behavioral alternative model, since no weighting parameters are involved.’ Second, if behavioral predictions are made on the basis of A,,, and SN (rather than BI). the regression approach would require that the standard errors of estimate be low and homogeneous across all alternatives. If the regression analysis yielded a poor goodness-of-fit for just one alternative, the entire analysis could be jeopardized. Finally, there is no empirical evidence to indicate that normative factors, when used in the context of the behavioral alternative model. will allow for increased behavioral prediction over and above the measures of A,,,. Initial attempts to study the model might therefore best employ the more parsimonious approach of focusing on just the attitude construct. This 2 A\ problem. relative

presented. It simply importance

this criticism states that of ALL, and

I\ there SN

really more of a methodological are no unambiguous procedure< in influencing behavior or behavioral

than a theoretical for determining the decision?.

ATTITUDES

AND

BEHAVIOR

293

would be consistent with the formal hterature on decision making and makes the assumption that the influence of these other variables on behavioral decisions is mediated by the attitudinal variables.’ Empiriml

Research

The results of three investigations are presented that test the initial viability of the model as outlined above. No attempt is made to competitively test the model with theories such as those proposed by Fishbein. Triandis, Sheth. and others. The experimental strategies required to do so are complex, especially since the various theories involve different numbers of variables, different parameter estimation procedures, different measurement procedures. and different combinatorial rules. The prediction strategies of the present investigations are not conducive to such competitive tests. However, it does seem reasonable to test the initial viability of the SEU model when translated into an attitudinal context. Thus, the present investigations were designed to establish the generalizability of the behavioral alternative model in terms of its predictive validity. The studies investigate three different behavioral domains: (I) voting behavior, (2) decisions to have children. and (3) signing up for an alcoholic treatment program. Each behavioral domain differed in terms of the number of alternatives available to the individual (3, 5, and 2). One of the investigations used noncollege adults living in a Midwestern community, one used college students, and one used individuals in a hospital who were diagnosed as alcoholics. In two of the investigations, data concerning the attitude-behavior link were available. In the other investigation, only data for the attitude-decision (i.e., intention) relationship were gathered. Strictly speaking, the behavioral alternative model, like Fishbein’s model, should only predict an individual’s decision to perform a behavior and not necessarily behavior per se. However, in the two investigations using behavioral criteria, a strong decision-behavior link was observed, and hence, behavioral measures were used as the predictive criterion. INVESTIGATION

The first investigation” constitutes voting behavior in the 1976 elections.

1

a prediction strategy relative to The model was tested at each of

’ Some researchers have observed that SN contributes independently to BI over and above A,,, when these variables are studied in a regression format (i.e.. across subjects) and with respect to only one behavioral alternative. However. these studies have not addressed the predictive power of SN over and above A,,, when used in an idiographic manner as suggested by decision theory. J Other portions of the same data base were reported by Jaccard. Knox, and Brinberg t 1979).

294

three levels, elections

JAMES JACCAKD

namely,

the presidential.

senatorial,

and congressional

Method Rr~pondrnfs.Respondents were 119 males and females living in a moderate-sized Midwestern community. Sampling procedures were designed to obtain a heterogeneous sample in terms of education with an approximately equal number of Democrats. Republicans, and Independents. Respondents were initially contacted by phone and administered a brief phone survey. Upon completion. respondents were asked if they would be willing to complete a longer survey in their home. It should be emphasized that the goal of the sampling procedures was not representativeness, but rather heterogeneity. so as to achieve a more stringent test of the generality of the theory. The final sample consisted of 54 males and 65 females who ranged in age from 23 to 56 with a mean age of 34.6. Education level varied from less than a high school education to a professional degree (e.g.. Ph.D.1 with 34 respondents having not completed high school and 85 respondents with a high school degree or more. A total of 36 Democrats. 48 Republicans, and 34 Independents were interviewed tone individual did not complete the party identification question). Refusals to participate in the final survey were relatively low, being less than 10% of those initially contacted. Only registered voters were contacted and the survey was conducted I week prior to the elections. Maferictls. The home interviews took approximately I hour to complete. During the first stage of the interview. the interviewer presented an oral introduction to the study and a standardized set of instructions concerning the questionnaire. The respondent then filled out a practice section. This section contained examples of each type of scale and served to anchor the endpoints and eliminate any “warm-up” effects from the data. In addition. the interviewer could detect any misunderstanding of scales prior to actual data collection. The entire interview schedule was self-administered. Hopefully. this minimized any effect of “interviewer” on the subject’s responses. The interviewer did. however. remain in the house while the respondent was completing the questionnaire to answer any questions the respondent might have. The measure of behavioral decision was obtained by simply asking respondents who they intended to vote for in the election. For the concept of voting decision. as well as all others to be discussed, the question was asked with respect to each of three races: the presidential race, the senatorial race, and the congressional race. The measurement of the attitudinal variables used standard attitude-scaling techniques that have been developed and refined through previous research (e.g.. Ajzen & Fishbein. 1972: Fishbein & Ajzen. 1975; Jaccard. Weber, & Lundmark, 1975). For a given election (e.g.. presidential) attitude\ toward each of three voting alternatives were measured: (I l voting for the Democratic candidate. (2) voting for the Republican candidate. and (3) not voting. Each attitude was assessed on a 7-point Guilford self-rating scale with endpoints unfavorable-favorable (e.g.. If I were to rate my attitude toward voting for Jimmy Carter 1 would say that it is [scale]). This method of attitude measurement has been shown to yield reliable measures which correlate highly with multi-item scales, such as Thurstone scales and Likert scales (Jaccard et al., 1975). A large number of demographic, attitudinal. and opinion items were also assessed in the questionnaire. The measure of the behavioral decision and the attitude measures were separated by these items to reduce demand characteristics and artificiality of responses. These items are described in Jaccard, Knox, and Brinberg (1975). who also present data indicating that responses were not due to social desirability or demand characteristics. Voting behavior was measured one day after the election, when respondents were recontacted and asked if they voted. and if so. for whom. These self-reports served as estimates of voting behavior. Research in political science (e.g.. Campbell. Converse, Miller. &

ATTITUDES

AND BEHAVIOR

295

Stokes, 1960; Fishbein & Coombs. 1974) has indicated that such estimates of voting behavior tend to be highly accurate.

Results Knowledge of candidates. Prior to completing the questionnaire all respondents were asked to name the Democratic and Republican candidates who were running for the presidential, senatorial, and congressional offices in the election. All respondents correctly named the candidates at the presidential level. At the senatorial level, 72.4% correctly named the Democratic candidate (the incumbent), while 86.2% correctly named the Republican candidate. At the congressional level, 81.9% correctly named the Democratic candidate (the incumbent), while 75.0% correctly named the Republican candidate. After completing this section, the names of all candidates were identified and respondents completed the remainder of the questionnaire. Intention-behavior relationship. Given that decisions were measured 1 week prior to the election a relatively strong relationship would be expected between the stated voting decisions and voting behavior. The data bore this expectation out. For the presidential election, measures of decision accurately predicted 87% (103/118) of the respondents, for the senatorial election the hit rate was 85% (100/l 18), and for the congressional election the hit rate was 76% (90018). Most of the errors in prediction focused on people who intended to vote for a given candidate but who failed to vote at all on election day. In fact, when these people are eliminated and only those who actually voted are considered the hit rates were 98, 97, and 86%, respectively. These data suggest that an analysis of the relationship between the attitudinal measures and voting behavior (rather than voting decisions) is appropriate. Test of the behavioral alternative model. The relationship between attitudes toward performing each voting alternative and voting behavior was examined as follows: For each individual a predicted voting decision was derived by selecting that voting alternative toward which the most positive attitude was held. This predicted voting decision was then compared with the observed voting behavior in a two-way frequency table. Table 2 presents the analysis for each of the three elections. For the presidential election, no prediction could be made for 10 respondents since the most positive attitude toward a given voting alternative was the same as that toward at least one other alternative. The corresponding number of “no predictions” for the senatorial and congressional races was 13 and 18, respectively. For the remainder of respondents, the present theory accurately predicted 86.1% (93/108) of the behaviors in the presidential election, 88.5% (93/105) in the senatorial election, and 86.0% (86/100) in the congressional election. This is higher than either chance or base-rate predictions and well within the constraints

296

JAMES

JACCARD

TABLE FREQUENCY

ANALYSIS

OF VOTING

BEHAVIOR

2

AND

ATTIT~JDE

TOWARD

Observed

voting

PresidentialJ

Predicted Voting Behavior

Dem Rep No vote

Dem

Rep

36 4 0

5 56 I

Nore. Dem = Democratic vote. d No prediction made for b No prediction made for c No prediction made for

VOTING

behavior

Senatorial”

No vote 7 3 I

candidate. IO respondents I3 respondents 18 respondents

Rep

Dem

Rep

I’) 7 I

3 710

= Republican due to equal due to equal due to equal

ALTERNATIVES

Congressional’

No vote 4 I 2 candidate.

Dem

Rep

ho 5 0

5 14 I

No vote

No vote 3 0 2 = did not

attitudes. attitudes. attitudes.

of measurement error. The corresponding hit rates for predicting behavioral decisions (which is the most appropriate criterion for the model) were 87.3% (96/110) in the presidential election, 86.0% (921107) in the senatorial election, and 80.4% (82/102) in the congressional election. The data for behaviors and behavioral decisions were thus comparable and represent reasonable degrees of goodness of fit. Compurison tvith single mensurc of attitude. Traditional research on the attitude-behavior relationship would attempt to predict an individual’s behavior based only upon his or her attitude toward performing that behavior. In contrast, the behavioral alternative model requires measures of attitudes toward each of the behavioral alternatives in order to predict behavior. In order to test whether consideration of attitudes toward the behavioral alternatives increased behavioral prediction, a comparison with predictions based upon a single attitude measure was performed .? Strictly speaking, the traditional attitude-behavior approach is not concerned with choices among alternatives, but rather with the proposition that performance of a specijc behavior (e.g., voting for the Democratic candidate) will be influenced by a person’s attitude toward performing that behavior. In the context of voting behavior, the crucial behavioral criterion would seem to be the specification of that candidate the individual will cast his vote for. The behavioral alternative model is well suited to such predictions. The traditional attitude model, however, requires that three behavioral criteria be established, namely, (I) whether or not the person voted for the Democratic candidate, (2) whether or not the person voted for the Republican candidate, and (3) ( These data must be interpreted with caution due to potential differences of the measures used to make predictions in the two approaches.

in the reliability

ATTITUDES

AND TABLE

PROPORTION

OF ACCURATE

Behavioral alternative model Single attitude model

3

BEHAVIORAL PREDICTIONS AND SINGLE ATTITUDE

Presidential Dem

Rep

.90

.88

.85

.84

297

BEHAVIOR

FOR BEHAVIORAL MODEL

ALTERNATIVE

Senatorial No vote

MODEL

Congressional

Dem

Rep

No vote

Dem

Rep

No vote

.94

.90

.93

.94

.87

.x9

.96

.92

.X0

.X8

.90

.71

.70

.Yl

whether or not the person voted. In comparing the two approaches, each of these behavioral criteria will be dealt with separately. Given the above behavioral criteria, it was necessary to derive a predicted score for each approach which indicates whether or not a given individual will perform the behavior. In the behavioral alternative model, such a score was obtained as follows. For the Democratic behavioral criterion, an individual was predicted to have voted for the Democratic candidate if his attitude toward voting for the Democratic candidate was more positive than his attitude toward voting for either of the remaining two candidates. Otherwise, it was predicted that the individual did not vote for the Democratic candidate. Similar predicted votes were derived for the other two behavioral criteria using identical procedures. In deriving predicted scores for the traditional attitude model it was necessary to convert a continuous measure (A,,,) into a dichotomous prediction. This required the following standard cross-validation procedures: First, the sample was randomly divided into two groups, A and B. consisting of 59 subjects each (half of the sample in each group). Within sample A, a cutoff score was determined whereby any attitude score equal to or above the cutoff score was classified as a “vote” and any attitude score below the cutoff score was classified as a nonvote. The cutoff score was determined empirically by examination of the observed behavioral distribution within the sample. For example, in sample A, if 45% of the respondents voted for the Democratic candidate, the 55th percentile of the attitude scores was used as the cutoff score. The cutoff score derived in sample A was applied to sample B, and the cutoff score derived in sample B was applied to sample A. (This procedure represents a form of double cross-validation, see Mosier. 1951; Norman, 1965.F ’ Comparison data were also generated using a procedure other than the empirical approach outlined here. This involved predicting that the individual would perform the behavior if his or her A,,, was positive. and otherwise predicting nonperformance of the behavior. Hit rates using this approach were extremely low and suggested that this \tratepy was simply not viable.

298

JAMES JACCARD

Table 3 presents the proportion of accurate predictions for the behavioral alternative model and the traditional attitude model for each behavioral criterion in each of the three races. Both the behavioral alternative model and the traditional attitude model had relatively high hit rates. In all cases, the proportion of hits for the behavioral alternative model exceeded that of the traditional attitude model. However, this difference was only significant at the Congressional level (-< = 2.81. p < .Ol for Democratic candidate, z = 3.36, p < .Ol for Republican candidate. z = 1.45, ns for no vote). Reasons for this are discussed later.’ INVESTIGATION

2

The second investigation constitutes a study of the number of children college students intend to have in their completed family. Method Respondw~f.s. Respondents were 100 introductory psychology students who participated in the research as part of a course requirement. Fifty respondents were males and fifty were females. MafrriaO. Respondents were administered a questionnaire designed to elicit relevant outcomes for each of 5 behavioral alternatives. namely, to have 0.1.1,3, or 4 or more children in their completed family. The questionnaire was based upon elicitation procedures developed by Rosenberg and Oltman (1962). For a given alternative. respondents were asked to list “what you think would be the advantages or disadvantages for you if you were to have children (no more nor less) in your completed family.” Respondents were told to list these serially. one per line (a series of lines was provided). and to list “whatever comes to mind.” They were told to list “as many as comes natural to you.” The order of the behavioral alternatives was randomized individually for each respondent. Before completing this section, one half the respondents indicated the number of children they intended to have in their completed family. The other half of the respondents completed this question after the elicitation procedure. In addition, all respondents completed a set of semantic differential scales designed to measure the attitude toward performing each behavioral alternative. Specifically. each alternative was rated on three 7-point bipolar scales with adjectives of good-bad. wise-foolish, and pleasant-unpleasant. These scales were taken from Fishbein and Raven’s (1962) AB scales and have been demonstrated to be reliable and valid indicants of attitude. An attitude score for a given alternative was defined as the average of responses to the three scales (and hence could range from I to 7). These items were also counterbalanced such that one half of the respondents completed them prior to the elicitation questions and one half of the respondents completed them after the elicitation questions. At all times the decision items and the attitudinal items were separated by the elicitation questions. Before completing the questionnaire. all respondents were provided with instruction? as to how to use the scales they would encounter and were given several examples and practice items. In addition to the above measures, a number of additional demographic and opinion variables were assessed. ’ Hit rates for the single attitude model in the former but not was used in all three

decision model excluded subjects with ties while hit rates for the included all subjects. It is theoretically meaningful to exclude ties the latter. hence the present comparison procedures. This procedure investigations reported in this article.

ATTITUDES

AND BEHAVIOR TABLE

FREQUENCY

Predicted family size decision

ANALYSIS

0 1 2 I 4+

299

4

OF FAMILY SIZE DECISION AND ATTITLTDE TOWARD FAMILY SIZE ALTERNATIVES

Observed family Size Decision ~~~ ~-~~~~ ~-~ 0 I 2

3

4+

2 0 0 0 0

0 0 2 I4 2

0 0 0 I 2

0 3 0 0 0

0 2 58 4 0

Results For each respondent, a predicted intended family size was derived by selecting that behavioral alternative toward which the most positive attitude was held. Of the 100 respondents, 10 had equal “highest” attitudes and hence, a prediction could not be made. For all 10 of these individuals, the tie concerned the alternatives of having two children and having three children. Table 4 presents a two-way frequency analysis of predicted versus obtained decisions for the remaining 90 subjects. A strong relationship between predicted and obtained decision was observed. The hit rate was 79 out of 90 or 87.7% correct predictions. This was significantly higher than either chance or base-rate predictions. Order of measurement of the attitudinal measures did not affect hit rates of the model. Comparison with single measure of attitude. A comparison of the behavioral alternative model with the traditional attitude model was performed using the same procedures discussed in Investigation 1. Table 5 presents the proportion of correct predictions for each approach. Again, the model based upon behavioral alternatives achieved higher prediction in all cases, of which two were statistically significant (Z = 2.45, p < .05 for two-child alternative, ; = 1.96. p < .0.5 for three-child alternative). TABLE PROFQRTION

OF ACCURATE

5

BEHAVIORAL PREDICTIONS FOR BEHAVIORAL AND SINGLE ATTITUDE MODEL

ALTERNATIVE

MODEL

Family size decision

Behavioral alternative model Single attitude model

0

I

2

3

4+

1.00 .98

.98 .92

.91

.90 .80

.9J

.J8

.96

NOW. The differences in proportion of correct hits for the options of two and three children (comparing the two models) is statistically significant. p < .05.

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INVESTIGATION 3 The third investigation is based upon data reported in McArdle (1972). The study concerned decisions on the part of alcoholics to sign up for a special treatment program being administrated in veterans hospitals. Method Rrspondenr.\. Respondents were I?0 males who had been admitted to a VA hospital within the past 7 days for drinking problems. Criteria for inclusion in the study were that at least one of the following conditions be met: (1) a primary or secondary diagnosis of alcoholism: (1) excessive drinking being mentioned in a psychological evaluation, a social service report. or the nursing notes written within the last 7 years: (3) arrival at the admission office in an intoxicated state. Mutrrids. The data for the present analyse\ were collected in the context of a large1 questionnaire designed for other purposes. This questionnaire is described in detail by McArdle tl972). The major dependent variable of interest is whether or not the respondent signed up for a special treatment program. Two behavioral alternatives are thus relevant: t I J signing up for the program and (2) not signing up for the program. The attitude toward each of these behavioral alternatives was measured on a 7-point bipolar semantic differential scale with endpoints of good-bad. The phrasing for the attitude toward signing up for the program was “Do you think that signing up for the Alcoholic Treatment Unit during your present stay in this hospital is: “[scale]. A similar phrasing was used for the not-signing alternative. but with appropriate wording changes. After the entire questionnaire was administered (covering a number of attitudinal. personality. and demographic items) respondents were given the opportunity to sign up for the treatment program. Ptmrdrrrc. Respondents were administered the questionnaires in groups of approximately 30. After completing the questionnaire the questionnaires were collected and a signup sheet passed among the respondents.

Results

A predicted behavior score was derived by selecting that behavioral alternative toward which the most positive attitude was held. For 21 of the 120 respondents, equal attitude scores were obtained and no predictions could be made. Table 6 presents a frequency analysis of the predicted and obtained scores for the remaining respondents. The model accurately predicted 88 of 99 respondents, or a hit rate of 88.9%. This was significantly higher than either change or base-rate predictions. It is interesting to note that of those 21 respondents who had equal attitude scores toward the two behavioral alternatives, none actually TAHLE FREQLIEK~

ANALYSIS

OF SIGNISG

DECISIONS

A&II

6 ATIITL-DI:

TOWARE)

Observed Sign Predicted score

Sign Not Sign

hX 0

SIGNING

AI TERNAT)\

score Not

sign II 10

t\

ATTITUDES

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301

signed up for the program. Thus, there was a clear bias in people with equal attitudes not to sign up for the program. One possible explanation for this has been presented by Davidson and Beach (in press), who suggest that when SEUs of behavioral alternatives are functionally equivalent, individuals will simply continue to perform that behavior which they have been performing in the past and will not undertake any “new” or “novel” behaviors. Comparison with single measure of attitude. A comparison of the behavioral alternative model with the traditional attitude model was performed using the same procedures discussed in the previous investigations. Since there were only two alternatives, it was possible to derive a single proportion of hits based upon the single measure of A,,,. The hit rate using this measure was .84 as contrasted with the hit rate of .89 observed for the behavioral alternative model. Both approaches predicted behavior relatively well. The difference in hit rate was not statistically significant (Z = .97, ns). DISCUSSION The three investigations reported are generally supportive of the behavioral alternative model as applied in an attitudinal context. The model achieved relatively high hit rates for three different types of decisions using heterogeneous groups. The linkage of the SEU decision-theory approach to approaches on the attitude-behavior relationship raise a number of issues for future research that have not been explored by attitude theorists. Generally speaking, the decision process can be divided into three stages: (1) the generation of behavioral alternatives, (2) the evaluation of these alternatives, and (3) making a choice among the alternatives, i.e., making a behavioral decision. We will consider the data of the present investigations in this context. Generation of behavioral alternath’es. Relatively little research has been conducted on the first stage of the decision process, namely, generation of behavioral alternatives. Individuals may be expected to differ in the kinds of alternatives they perceive as being available to them in a given situation. Depending upon the type and number of alternatives perceived, different behavioral predictions might be made. Thus, the alternative generation process could potentially have important implications for analyses of the attitude-behavior relationship. At least two different strategies can be used when specifying and conceptualizing the behavioral alternatives that are relevant in a given situation. First, individuals can be directly asked the different behavioral options that they perceive are available to them. Second, based upon an analysis of the situation, the investigator can specify those alternatives that he or she believes are relevant in the situation, without directly asking the individual. This latter strategy was used in the present series

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of studies, in which the specification of alternatives seemed fairly straightforward. However, this will not always be the case, and in situations where the number and type of alternatives may differ as a function of individuals, care must be taken to ensure that the relevant set of alternatives are studied. Obviously, the fit of the model could be drastically affected by the failure to study alternatives that are meaningful to the individual’s under investigation. SEU models have typically been applied in situations where the behavioral alternatives are mutually exclusive and exhausitive. in practice, it should be possible to specify such a set of alternatives for any given situation at a given point in time. It may be the case that a particular option involves the blending together of two other behavioral alternatives. For example, when choosing a major in college, the student may opt for a dual major rather than selecting between psychology and mathematics. This dual major would represent a behavioral alternative in its own right that has certain advantages and disadvantages associated with it. perhaps independent of those associated with ma.joring in psychology or majoring in mathematics. Although it should be possible to specify a set of perceived mutually exclusive and exhausitive behavioral alternatives at any given point in time, this is not to say that such a set of alternatives will remain unchanged over time. New alternatives may suggest themselves in the future and the viability of old alternatives may change such that they are dropped from the set of behavioral options. In addition, people will sometimes choose the option of collecting more information before acting, and this could, in turn, influence the nature of the alternative set eventually considered prior to behavioral performance. The process of discovering new alternatives and deleting old ones is of psychological interest in its own right and has obvious implications for the analysis of the attitude-behavior relationship. The above analysis also suggests several strategies for behavior change that have not been formally developed in the attitude and persuasion literature. Specifically, changes in behavior can potentially be induced by suggesting new behavioral alternatives that the individual may not have considered before (and thus changing the distribution of attitudes across alternatives) or by ruling out the viability of alternatives already perceived as being relevant. E~duation of ultemati~~cs. The second stage of the decision process, evaluation of the set of behavioral alternatives. focuses upon the attitude formation process and how people come to evaluate an alternative in a positive or negative fashion. The present paper did not address this issue and focused only upon predicting behavior from measures of attitudes toward behavioral alternatives. The SEU model of Eq. ( 1) and the expectancy-value model of Eq. (2) represent one approach to specifying

ATTITUDES

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303

the psychological factors underlying the attitude formation process. It is doubtful that these models are descriptive of attitude formation for all people and all kinds of behavioral alternatives. Nevertheless, the extensive empirical base underlying these models suggest that they are applicable in many important applied settings. Behavioral choice. The behavioral alternative model is explicit regarding the third stage of the decision process, i.e., behavioral choice. It states that a person will choose to perform that alternative among a set of alternatives toward which the most positive attitude is held. A reasonable degree of empirical support for this proposition was observed in the present series of studies. However, it is not expected that this principle will hold universally. Classical decision theory distinguishes between principles of optimizing and satisficing. The decision to perform that behavioral alternative toward which the most positive attitude is held represents an optimizing strategy. In contrast. a satisficing strategy would state that an individual evaluates sequentially each member of a set of alternatives, and chooses to perform the first alternative that satisfies a threshold point on the affective dimension (e.g.. the first alternative that is positively evaluated). The factors that influence the ordering of alternatives in terms of the sequential evaluations as well as factors that influence the threshold point remain as topics for empirical inquiry. Generally speaking, it would seem that individuals will adopt an optimizing strategy for decisions that are of major importance to them, but may adopt a satisficing strategy for less important decisions. The behavioral alternative model states that it is necessary to measure attitudes toward all of the relevant behavioral alternatives in order to make accurate behavioral predictions. In some situations, this will not be necessary. In the two alternative cases, for example, if the attitude toward performing one of the alternatives is highly negatively correlated with the other alternative, then knowledge of one of the attitudes should yield just as accurate behavioral predictions as knowledge of both. This is because the attitudinal information is essentially redundant and the process depicted in Table 1 cannot operate. However, when the attitudes toward the behavioral alternatives are not negatively correlated, the behavioral alternative model will probably yield more accurate predictions than the measure of a single attitude. These factors can account for the comparative data observed in the present series of studies. In all cases, the behavioral alternative model predicted as well or better than a singleattitude model. In cases where hit rates were similar (studies 1 and 3) the correlation between the relevant alternatives was strong and negative (mean r = - .79 and - .77, respectively). This was not true of the second study, where the correlation between the attitude toward having two children and that toward having three children was - .18, and the hit rates differed significantly, nor for the Congressional election in study

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1, where the correlation between attitudes for the two candidates was - .31. Even if behavioral prediction is equivalent for the two approaches, the analysis of factors underlying attitudes toward all the alternatives may yield important psychological insights that might not otherwise be apparent. The types of advantages and disadvantages perceived to result from each of the separate behavioral alternatives may differ considerably and these may suggest behavior change strategies that would not necessarily follow from the single attitude approach. A more complete understanding of the psychological dynamics underlying a behavioral decision may thus be possible by studying the entire set of relevant alternatives. Finally, an additional strength of the behavioral alternative model relative to the single attitude approach concerns the manner in which behavioral predictions are made. Given an attitude profile, the behavioral alternative model provides a rational basis for determining which behavioral alternative will be selected. This is not contingent on any population-dependent parameter estimates. On the other hand, the single attitude approach does not provide such an a priori basis for establishing a cutoff point on the attitude scale to distinguish the behavers from the nonbehavers. With the single attitude approach, that cutoff point must be statistically estimated.8 One problem with the behavioral alternative model from the standpoint of behavioral prediction is the case where two or more alternatives are tied in terms of having the highest attitude score: Under such circumstances. no behavioral prediction can be made. This problem is, in some respects, a strength of the model. Given equal attitudes, a person’s behavioral decision is likely to be unstable over time and to fluctuate considerably. Under these conditions, accurate long-range prediction of behavior from intention and attitudinal measures is unlikely. The behavioral alternative model is useful in that it helps to identify those individuals for whom this will be the case. It is clear that people with truly equal attitudes toward behavioral alternatives make a decision on some basis other than the attitudes in question. Further research is needed to identify how such people make their decisions and the kinds of factors that influence this. Davidson and Beach (in press) suggest that in such cases. people are likely to perform that alternative which is consistent with their past behavior, due to an inertia effect involved with ” It should be noted that research in consumer psychology. which attempts IO predict purchasing behavior from attitudes toward different brands, does not apply to the behavioral alternative model. In this research. the attitude typically measured toward the brand (e.g.. Rolls Royce) rather than the brhurirwd alrt~~urir~e in question (e.g., purchasing a Rolls Royce). As noted earlier, this distinction between attitude toward an object and attitude toward

a behavior

is crucial

with

respect

to behavioral

prediction.

ATTITUDES

AND

BEHAVIOR

305

changing established behavioral patterns. Some support for this explanation was observed in the present series of studies. Applied implications. Although the behavioral alternative model is interesting mainly in terms of the psychological issues it raises for past studies of the attitude-behavior relationship, it also has a number of important applied implications which further indicates its utility. Consider the case where a consumer psychologist is attempting to influence a group of individuals to perform one of four behavioral alternatives (purchase product A from the class A. B, C, and D). For the population of interest, the attitude toward each of these behavioral alternatives can be measured. Depending upon the distribution of these attitudes, different influence strategies would be dictated. First, it may be found that the attitude toward alternatives C and D is quite low and, for all intent and purposes, the psychologist does not need to concern himself/herself with these alternatives. In general, three strategies are then available to the psychologist: (1) make the attitude toward alternative A more positive, (2) make the attitude toward B more negative, or (3) some combination of the above. The mean attitude scores for the two alternatives will, in part, dictate the strategy used. If the attitude toward alternative B is already highly positive, the psychologist would be ill-advised to simply make the attitude toward alternative A more positive. There would probably be a “ceiling effect” and the only effective strategy would be to first lower the attitude toward alternative B to some degree. In contrast, if the mean attitude score toward alternative B was only moderately positive (and the mean attitude score toward A was slightly less positive), then any of the three strategies discussed above could potentially be effective. If the decision were made to strengthen the decision to perform A (i.e., make the decision more resistant to change) then this would be accomplished by maximizing the discrepancy between alternative A and alternative B. Again, this could be accomplished by using any of the three influence strategies outlined above. The behavioral alternative model also has implications for the selection of target populations. If the psychologist is trying to increase the number of individuals who perform A, then those individuals most likely to change their behavior will be those whose attitudes toward alternatives A and B are roughly equal (i.e., those who are only slightly more positive to B than A). In contrast, individuals who have much more positive attitudes toward B and A should be relatively difficult to influence since the discrepancy between attitudes is large. Thus, initial change efforts might be focused on those individuals where the influence efforts are most likely to pay off, i.e., the former as opposed to the latter group. The kinds of change strategies one would use for one group might differ considerably from the kinds of influence strategies used for the other group.

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All of these strategies can be derived independent of knowledge of the specific outcomes and psychological processes underlying an attitude toward a given alternative. Ultimately, however, the understanding of the attitude formation process is critical. As noted in the introduction, models bearing on the attitude-behavior relationship have dealt with discrepancies between attitudes and behavior by specifying factors other than attitude that can influence behavior. These other variables have generally been used in a regression equation format to predict behavioral intent. An application of decision theory to this body of literature suggests that the examination of all of the behavioral alternatives available to the individual is a crucial step in understanding attitude-behavior discrepancies. Few attitude theorists have recognized this. with the exception of Ajzen and Fishbein (1969, 1980). and even these theorists have not adequately developed the implications and different approaches possible within a behavioral alternative context. The present paper will hopefully stimulate additional research in this respect. REFERENCES Ajzen,

1.. & Fishbein. M. The prediction of behavioral intentions in a choice situation. Jo1unul ofE.rprrimenfa/ Sociul Psychology. 1969, 5. 400-416. Ajzen, I., & Fishbein, M. Attitude-behavior relations: A theoretical analysis and review of empirical research. Psvcho/o~!iccr/ Bltllerin. 1977. 84, 888-919. Ajzen. I.. & Fishbein. M. Understanding uttitude~ und predicfing social behuvior. Englewood Cliffs, N.J.: Prentice-Hall, 1980. Bentler. P. M.. & Speckart, G. Models of attitude-behavior relations. Ps~cho/o,qic.cr/ Hrr,ievt,. 1979. 86, 452-464. Campbell. A., Converse, P. E.. Miller, W. E.. & Stokes, D.. UZE ericun l’oter. New York: Wiley. 1960. Davidson. A. R.. & Beach, L. Error patterns in the prediction of fertility behavior. Poptclation und En\%-onment: Behavioral and Sock1 Is.sue.v, in press. Edwards. A. Techniques of‘uttitude scale construction. New York: Appleton-Century-Crofts, 1957. Ehrlich, H. J. Attitudes. behavior. and intervenmg variables. Americun Sociologixt. 1969. 4, 29-34. Fishbein. M. Toward an understanding of family planning behavior. Journal of Applied Social Psychology. 1972. 2, 219-227. Fishbein, M.. & Ajzen. 1. Attitudes toward objects as predictors of single and multiple behavioral criteria. Psycho/o&d Revien,. 1974. 81, 59-74. Fishbein. M.. & Ajzen, 1. Beliqf, uttitudr, intenfion untf hehu~?ort An inrrodrrcrion to rlteorv trntf research. Reading. Mass.: Addison-Wesley, 1975. Fishbein. M.. & Coombs, F. S. Basis for decision: An attitudinal analysis of voting behavior. Journal of‘App/ied Sociul Psychology. 1974. 4, 95-124. Fishbein, M.. & Jaccard, J. Theoretical and methodological issues in the prediction of family planning intentions and behavior. Representative Research in Sociul PsycholOgJ. 1973. 4, 37-52. Fishbein, M.. & Raven. B. The AB scales: An operational detinition of belief and attitude. Hnmun Relutions, 1961, 15, 35-44. Gordon. R. A. Issues in multiple regression. Amerktrn Joltrnul of’ Sociolog?;. 1968, 73. 592~616.

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Green. B. F. Attitude measurement. In G. Lindrey (Ed. 1. Mtrr~dhook ot‘.soc.irr/ ~.c~c~/~o/oc~. Vol. I, Reading. Mass.: Addison-Wesley. 1954. Pp. 335-369. Green, P. E., & Srinivasan, V. Conjoint analysis in consumer research: Issues and outlook. Journal

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1978, 5, 103-173.

Heneman, H. G., & Schwab. D. P. An evaluation of research on expectancy theory predictions of employee performance. Psycho/ogiw/ Bulletin. 1977. 78, l-9. House, R. J.. Shapiro. H. J.. & Wahba, M. A. Expectancy theory as a predictor of work behavior and attitude: A reevaluation of empirical evidence. Decision Scienws. 1974. 5. 54-77. Jaccard. J. A theoretical analysis of selected factors important to health education 5traegies. Health Education MonoRruphs. 1975. 3, 151-167. Jaccard. J., King, G. W.. & Pomazal. R. Attitudes and behavior: An analysis of specificity of attitudinal predictors. Numan Relations. 1977. 30, 817-824. Jaccard. J.. Knox, R.. & Brinberg, D. Prediction of behavior brom beliefs: An extension and test of a subjective probability model. Journal c!f‘ Personulity und Soc,itr/ Psxcho/o~~. 1979. 37, 1339-1248. Jaccard. J.. Weber. J.. & Lundmark, J. A multitrait-multimethod analysis of four attitude assessment procedures. Journal ofE.uperimentu/ Sock/ P.s~c~~o/o~~. 1975, I I. 149-154. Lee, W. Decision theory and human behar,ior. New York: Wiley. 1971. McArdle. J. B. Positive and ne,qatib,e c,omtnrrriications und subsequent uttitude und hrhu\Gor change in ulcohoks. Ph.D. dissertation, University of Illinois, 1972. McGuire, W. J. The nature of attitudes and attitude change. In G. Linkzey and E. Aronson (Eds.), Handbook oj‘sociu/ psychology. Reading, Mass.: Addison-Wesley, 1969. Mitchell. T. R. Expectancy models of job satisfaction. occupational preference. and effort: A theoretical, methodological, and empirical appraisal. Psycho/o~ic~u/ Bulletin. 1974. 81, 809-826. Mitchell, T. R.. & Biglan. A. H. Instrumentality theories: Current uses in psychology. P.rycho/ogica/ Bulletin, 1971, 76. 432-4.54. Mosier. C. I. Problems and designs of cross-validation. Educutiontrl und P.svc~hc~/~~gictr/ Meusurement, 1951, 11, 5-l I. Norman, W. T. Double-split cross-validation: An extension of Mosier’s design, two undesirable alternatives. and some enigmatic results. Journal of’ Applied Psycho/o,qy. 1965, 49, 348-357. Pomazal. R., & Jaccard. J. An informational approach to altruistic behavior. Jolrrncll c!f Personality and Sociui Psychology. 1976. 33, 3 17-376. Rokeach. M.. & Kliejunas, P. Behavior as a function of attitude-toward-object and attitudetoward-situation. Journal ofPersonu/ity and Social Psycho/o,~~. 1973, 22, 194-X1 Rosenberg. M. J. Cognitive structure and attitudinal affect. Jowntrl of Abnormtrl und Sociul Ps~cho/og~. 1956. 53, 367-372. Rosenberg. M. J. & Oltman. P. K. Consistency between attitudinal affect and spontanrou\ cognitions. Jorrrncll 0f P.vyc~hc~/oy~. 1967. 54, 485-490. Sheth, J. N. A field study of attitude structure and the attitude-behavior relationship.in J. Sheth (Ed. ). Models t?f b/dyer hehuLior: Conceptrtul. yctnntitutive. cmpiricul. New York: Harper & Row, 1972. Summers. D. Attitude meusrcrement. Chicago: Rand McNally, 1970. Triandis, H. C. lnterpersoncd behutior. Monterey, Calif.: Brooks-Cole, 1977. Vroom, V. H. Work und motivation. New York: Wiley, 1964. Wicker. A. W. Attitudes vs. actions: The relationship of verbal and overt behavioral responses to attitude objects. Journul of Social ISSI~ES. 1969. 25, 41-78. Wicker. A. W. An examination of the “other variables” explanation of attitude-behavior inconsistency. Journal of Personulity and Social Is.srre.r. 1971, 19. 18-30. Weigel. R., & Newman, L. Increasing attitude-behavior correspondence by broadening the scope of the behavioral measure. Journal of Personality und Sociu/ P.y~(.ho/oy~. 1976. 33, 793-802.