Empirical Tests of a Model of Automobile Choice Incorporating Attitude

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Paper presented at the Urban Transport Systems conference, Lund University, Sweden, June 7-8 1999

Empirical Tests of a Model of Automobile Choice Incorporating Attitude, Habit, and Script§ Ole Boe1, Satoshi Fujii2, and Tommy Gärling1 1 Göteborg University, Sweden 2 Kyoto University, Japan Abstract In previous research habit has been found to play an important role for choices of automobile. We propose and test a model of the process of habit formation. In this model a positive attitude towards driving is assumed to lead to that the automobile is frequently chosen. We identify this with a habit which later leads to script-based choices, that is, choices that are made on the basis of scripts triggered by minimal information. In a questionnaire study we measure participants´ (n=60) attitudes towards driving, self-reported frequency of using automobile, and their choices of mode for 15 descriptions of scripts (e.g., going to the beach with friends). Using maximum-likelihood methods available in LISREL8, a causal model is estimated from the covariances between the obtained measures. The results confirm the hypothesis that a positive attitude causes frequent choices to drive which in turn lead to script-based choices. We obtain consistent results from an experiment in which participants (n=24) repeatedly made choices between driving and walking and choices of destinations. After repeated choices of driving, the results indicated as expected that choices of driving were made independent of destination choice.

Introduction Travel mode choice is frequently related to choices of activity, destination, departure time, and possibly other choices (Gärling et al., 1998). It is an example of everyday situations in which people face several choices at the same time which they must integrate. Such integration requires extensive information processing. Yet, people manage because they use simplifying heuristics (Boe & Gärling, 1998a, 1998b, 1998c; Gärling et al., 1997). Another reason which this article will focus on is that several choices are integrated in scripts (Abelson, 1981; Schank & Abelson, 1977), thus people only need to engage in the minimal information processing required by memory retrieval of script information. In the theory of reasoned action (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975) and its successor the theory of planned behavior (Ajzen, 1985, 1991), it is assumed that behavioral choice is based on deliberations taking into account several factors including attitude towards the choice alternatives. However, in the theory proposed by Triandis (1977) a reciprocal relationship between attitude and habit is recognized. Evidence supporting that past behavior or habit rather than attitude frequently predict future behavior has since then accumulated (e.g., Bagozzi, 1981; Bentler & Speckart, 1979; Fredricks & Dossett, 1983; Kahle, 1984; Kahle et al., 1981; Landis et al., 1978; Mittal, 1988; Wittenbraker et al., 1983). Yet, it is argued that habitual behavior sequences are perhaps frequently functional for obtaining certain goals or end states (Bargh & Gollwitzer,1994).

 §

This research was financially supported by grant #98-0148 to Tommy Gärling from the Swedish Transport and Communications Research Board. The article was written while Satoshi Fujii visited Department of Psychology, Göteborg University. His visit was made possible by grants from the Kyoto University Foundation. Address correspondence to Tommy Gärling, Department of Psychology, Göteborg University, P. O. Box 500, SE-40530 Göteborg, Sweden. E-mail: [email protected]

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Habit formation Positive attitude

Deliberate information processing

Choice

Outcome

Automatic information processing

Script-based choice Figure 1. A process model of how script-based choices develop. An observed effect of making the same behavioral choice over and over again is that people do not go through the same deliberate decision process any longer (Aarts et al., 1997; Oullette & Wood, 1998; Ronis et al., 1989). As a consequence, changes may go unnoticed resulting in that attitudes are not guiding the behavioral choices. Thus, behavioral choices that have become habitual may in fact not be adapted to the situational contexts. In this article we will demonstrate that such may be the case with habitual choices of driving. We assume that making the same choice of driving over and over again will lead to a gradually diminishing consideration of alternative choices. Each time a particular journey is made by car, the satisfaction of using the car reinforces the choice of driving, and thus, contributes to the strength of a car choice habit. With a diminishing motivation to evaluate alternatives, people will not compare pros and cons of alternatives and they will therefore search for less information, both internally and externally (Verplanken et al, 1994). The deliberate information processing is replaced by retrieval from memory of schemata or scripts (e.g., Squire, Cohen, & Nadel, 1984; Abelson, 1981) which match the information input and which directly suggest what choices to make. A measure of script-based mode choice has been developed and tested in several studies (Aarts, 1996; Aarts et al., 1997; Verplanken et al., 1994, 1996). However, this research has not investigated the transition from attitude to habit to script-based choice. A process model is shown in Figure 1. The aim of Study 1 was to use structural equation modeling (SEM) to test a causal model, derived from this process model, which posits that attitude affects the frequency of choices to drive which in turn affects the frequency of script-based choice. The estimates of the structural model are in Study 1 based on correlational data obtained at the same point in time. In Study 2 we study the process by which script-based choices are developed. In an experiment participants are asked to repeatedly make choices of destination and of mode (driving vs. walking). We assume that when the choices of driving become script-

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based, they will be less dependent on the choice of destination as they are demonstrated to do otherwise.

Study 1 Method Participants. Thirty male and 30 female undergraduates at Göteborg University participated in return for the approximate equivalent of USD 7.0. They were recruited from a pool of undergraduates who at the beginning of the semester volunteer to participate in experiments. Their mean age was 27.4 years. All of them had a driving license. Questionnaire and Procedure. Either in a class room after a lecture or in the laboratory after having participated in an unrelated experiment, participants answered a short questionnaire that consisted of three different parts. In the first part participants rated their attitude towards driving on three 7-point scales with endpoints defined by the adjective pairs negative-positive, bad-good, and dull-fun. In the second part participants indicated on two five-point scales ranging from never to daily how often they had had access to and how often they had driven a car during the last month. In the third part an adapted version of the script measure developed by Verplanken et al. (1994, 1997) was administered. Fifteen different everyday situations (e.g., visiting a friend on the other side of the city, going to the beach with friends, shopping in downtown) were listed and participants were asked to spontaneously write down what travel mode they would choose. Measures. The ratings on the three attitude scales were used as measures of attitude. As measures of habit the two frequency ratings of access to a car and driving a car were used. The measures of script-based choice were the responses to the 15 scripts scored as 1 if participants indicated that they would drive, otherwise 0.

Results The maximum likelihood method available in LISREL 8 (Jöreskog & Sörbom, 1993) was used to estimate the structural equation model in Appendix. The fit was excellent as indicated by the following fit statistics: χ2(df=150, N=60) = 150.10, p=.48, NNFI=0.973, CFI=0.979, and RMSEA=.054. All the expected parameter estimates were significant at conventional significance levels.

Study 2 Method Participants. Twenty-four high-school students (12 men and 12 women) participated in return for the approximate equivalent of USD 7.0. The students were approached in their classrooms where they filled out the same questionnaire as was administered in Study 1. On the last page of the questionnaire, they indicated that they were willing to take later part in an experiment. They were contacted again approximately a week later and were invited to the laboratory to participate in the experiment. The students were between 17 and 18 years old. None of them had a driving license. Procedure. Participants were randomly assigned to two experimental and one control group. In all groups the experimental task consisted of a series of fictitious joint choices between buying a specified consumer product at different prices in any of two stores located at different distances and whether to drive or walk to the chosen store. In one of the experimental groups the choice tasks were presented in two practice blocks where the product was always cheapest in the farthest store followed by a test block where the product was cheapest in the closest store (see Table 1). In the other experimental group the choice tasks were presented in four practice blocks followed by the same test block. Participants in the control group received four blocks of choice tasks. In these blocks the choice tasks in the practice blocks were

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presented twice mixed with the choice tasks in the test block presented twice. A test block then followed which was the same as in the experimental groups. In each block the order of the choice tasks was individually randomized. Table 1. Choice tasks presented to participants in Study 2.  Store 1 Store 2  Practice block

Test block

4800 meters/SEKb 450 4900 meters/SEK 350 5000 meters/SEK 400 5100 meters/SEK 300 5200 meters/SEK 600 5300 meters/SEK 500 5400 meters/SEK 650 5500 meters/SEK 550 800 meters/SEK 450 900 meters/SEK 350 1000 meters/SEK 400 1100 meters/SEK 300 1200 meters/SEK 600 1300 meters/SEK 500 1400 meters/SEK 650 1500 meters/SEK 550

800 meters/SEK 650 900 meters/SEK 550 1000 meters/SEK 600 1100 meters/SEK 500 1200 meters/SEK 800 1300 meters/SEK 700 1400 meters/SEK 850 1500 meters/SEK 750 4800 meters/SEK 650 4900 meters/SEK 550 5000 meters/SEK 600 5100 meters/SEK 500 5200 meters/SEK 800 5300 meters/SEK 700 5400 meters/SEK 850 5500 meters/SEK 750

 a

Sixteen different products which were easy to carry and possible to buy at the indicated prices (such as a ring, a jacket, pants, a sweater, a camera, etc.) were randomly assigned to each choice task in different ways for different participants. b A Swedish Crown (SEK) is approximately USD 0.15.

Participants who served individually were seated in a cubicle in the experimental room facing a computer screen. They were given general instructions indicating that their task would be to make choices of stores where to buy a consumer product. It was stressed that in each choice the consumer product was identical except for the price. In addition they were instructed to imagine that they intended to make a single trip to the store and back. The presentation of the choice problems on the computer was self-paced. The joint choices were presented simultaneously on the computer screen, the choice between driving or walking above the choice of store. In each choice task, participants were presented the following information: You have decided to buy a ring. It can be found in two stores which are located at different distances. You can choose whether you want to walk or drive there. Indicate which mode you would choose (A or B) and which store (C or D) you would choose. A. Car B. Walk C. The price is 650 SEK The distance is 800 meters. D. The price is 450 SEK The distance is 4800 meters

Participants were instructed to attend to all the information presented on the computer screen while making their choices. After having decided what to choose (AC, AD, BC, or BD), they were asked to press return and type their mode choice (A or B) followed by another return and their store choice (C or D). Across participants, which alternatives were presented as A or B or C or D were counterbalanced.

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Results The percentages of choices are shown in Tables 2 and 3. As may be seen, the experimental groups differ from the control group in both practice and test blocks. In the practice blocks the control group chose to walk to the closest store when the product was cheaper in that store whereas they chose to drive to the farthest store when the product was cheaper in that store. In contrast, the experimental groups almost always in practice blocks chose to drive to the farthest store where the cheaper product was available. In the test block which were identical for all participants, the experimental groups continue to choose to drive whereas the control group always chose to walk. Furthermore, the tendency to choose to drive increased with the number of practice blocks. Table 2. Percentages choices of driving and of closest stores in practice and test blocks.  Number of practice blocks  2 4 Control  Choice of driving Practice block 1 91.1 84.4 27.8 Block 2 96.4 89.1 34.7 Block 3 -96.9 62.5 Block 4 -100.0 59.7 Test block 39.3 53.1 0.0 Choice of closest store Block 1 0.0 0.1 63.9 Block 2 0.0 0.1 62.5 Block 3 -0.0 31.9 Block 4 -0.0 41.7 Test block 41.1 60.9 100.0 

Table 3. Percentages test-block choices of driving or walking to closest or farthest store.  Number of practice blocks  2 4 Control  Driving to closest store 19.7 26.5 0.0 Driving to farthest store 19.6 26.6 0.0 Walking to closest store 21.4 34.4 100.0 Walking to farthest store 39.3 12.5 0.0 

Discussion The results of Study 1 confirmed on the basis of correlational data that a positive attitude causes frequent choices to drive which in turn lead to script-based choices. In Study 2 the results suggested that frequent choices of driving resulted in changes in information processing leading to a choice of driving although walking otherwise would have been chosen. Thus, the results of Studies 1 and 2 are complementary in having bearings on the hypothetical process model. An important question for future research to address is what kinds of changes in information processing took place in the experimental groups in Study 2. It seems likely that

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participants first, in the practice phase, processed all available information, then in the test phase focused on subsets of this information. Since they almost always chose to drive in the practice phase, in line with what Verplanken et al. (1997) found, they might have stopped in the test phase to acquire any information relevant to this choice. In our Study 2 participants were requested to make a destination choice which might have counteracted such an effect. A plausible possibility is yet that the participants when making destination choices would ignore the distance information to a larger extent than information about the attractiveness of the destinations (the discounted prices of the products). If so, participants would in the test phase chose to drive to the shortest stores. In contrast, the control group would walk to the closest store because they are taking the distance information into account. The results showed that the latter was the case. However, although the experimental groups chose to drive, they did not invariably choose the closest location (Table 3). The reason for this is at present somewhat obscure and needs clarification. An alternative is that at least some participants learnt to connect a long distance with a low price, then simply continued to choose the long distance in the test block without considering the price. References Aarts, H. (1996). Habit and decision-making: The case of travel mode choice. Unpublished doctoral dissertation. University of Nijmegen, The Netherlands. Aarts, H, Verplanken, B., & Van Knippenberg, A. (1997). Habit and information use in travel mode choices, Acta Psychologica, 96, 1-14. Abelson, R. P. (1981). Psychological status of the script concept. American Psychologist, 36, 715-729. Ajzen I (1985) From intentions to actions: A theory of planned behavior. In: Kuhl J & Beckmann J (eds) Action-Control: From Cognition to Behavior (pp 11-39). Heidelberg: Springer. Ajzen I. (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50: 179-211. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall. Bagozzi, , R. P. (1981). Attitudes, intentions and behavior: A test of some key hypotheses. Journal of Personality and Social Psychology, 41, 607-627. Bargh, J. A. & Gollwitzer, P. M. (1994). Environmental control of goal-directed action: Automatic and strategic contingencies between situations and behavior. Nebraska Symposium on Motivation, 41, 71-124. Bentler, P. M., & Speckart, G. (1979). Models of attitude-behavior relations. Psychological Review, 86, 452-464. Boe, O., & Gärling, T. (1998a). Effects of causally relatedness and uncertainty on integration of outcomes of concurrent decisions (Göteborg Psychological Reports, 28, No. 6). Göteborg, Sweden: Göteborg University, Department of Psychology. Boe, O., & Gärling, T. (1998b). Loss sensitivity and integration of outcomes of concurrent risky decisions (Göteborg Psychological Reports, 28, No. 5). Göteborg, Sweden: Göteborg University, Department of Psychology. Boe, O., & Gärling, T. (1998c). Failures to integrate causally related outcomes of concurrent decisions (Göteborg Psychological Reports, 28, No. 7). Göteborg, Sweden: Göteborg University, Department of Psychology. Fishbein, M., & Ajzen , I. (1975). Belief, attitudes, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Fredricks, A. J. & Dossett, D. L. (1983). Attitude-behavior relations: A comparison of the Fishbein-Ajzen and the Bentler-Speckart models. Journal of Personality and Social Psychology, 45, 501-512. Gärling, T., Gillholm, R., Romanus, J., & Selart, M. (1997). Interdependent activity and travel choices: Behavioral principles of integration of choice outcomes. In D. Ettema & H. P. J. Timmermans (Ed.), Activitybased approaches to travel analysis (pp. 135-150). Oxford: Pergamon. Gärling, T., Laitila, T., & Westin, K. (1998). Theoretical foundations of travel choice modeling: An introduction. In T. Gärling, T. Laitila, & K. Westin (eds.), Theoretical foundations of travel choice modeling (pp. 1-32). Oxford: Pergamon. Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modelling with the SIMPLIS command language. Chicago, Scientific Software International.

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Kahle, L. R. (1984). Attitudes and social adaptation: A person-situation interaction approach. Oxford: Pergamon. Kahle, L. R., Klingel., D., & Kulka, R. A. (1981). A longitudinal study of adolescent attitude-behavior consistency. Public Opinion Quarterly, 45, 402-414. Landis, D., Triandis, H. C., & Adamopoulos, J. (1978). Habit and behavioral intentions as predictors of social behavior. The Journal of Social Psychology, 106, 227-237. Mittal, B. (1988). Achieving higher seat belt usage: The role of habit in bridging the attitude-behavior gap. Journal of Applied Social Psychology, 18, 993-1016. Oullette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124, 54-74. Ronis, D. L., Yates, J. F., & Kirscht, J. P. (1989). Attitudes, decisions, and habits as determinants of repeated behavior. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function. Hillsdale, NJ: Erlbaum. Schank, R. C., & Abelson, R. P. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Erlbaum. Squire, L. R., Cohen, N. J., & Nadel, L. (1984). The medial temporal region and memory consolidation: A new hypothesis. In H. Weingartner & E. Parker (Eds.), Memory consolidation. Hillsdale, NJ: Erlbaum. Triandis, H. C. (1977). Interpersonal behavior. Monterey, CA: Brooks/Cole. Verplanken, B., Aarts, H., & van Knippenberg, A., (1997). Habit, information acquisition, and the process of making travel mode choices, European Journal of Social Psychology, 27, 539-560. Verplanken, B., Aarts, H,. van Knippenberg, A., & Moonen, A. (1996). Habit versus planned behaviour: A field experiment. Unpublished manuscript, University of Nijmegen, The Netherlands. Verplanken, B., Aarts, H, van Knippenberg, A., & van Knippenberg, C. (1994). Attitude versus general habit: Antecedents of travel mode choice. Journal of Applied Social Psychology, 24, 285-300. Wittenbraker, J., Gibbs, B. L., & Kahle, L.R. (1983). Seat belt attitudes, habits, and behaviors: An adaptive amendment to the Fishbein model. Journal of Applied Social Psychology, 13, 406-421.

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APPENDIX: Parameter estimates for structural model

0.64 (2.84)

0.44 (1.17)

A1

1.69 (5.24)

A2

1.00

A3

1.36 (5.83)

0.63 (3.83)

0.47 (4.10)

0.19 (2.51)

0.15 (5.38)

H1

H2

S1

1.00

attitude

0.98 (11.01)

0.04 (4.43)

0.21 (5.46)

0.12 (5.72)

0.18 (5.40)

0.13 (5.72)

S3

S4

S5

S6

S2 1.00

1.28 (5.90)

0.62 (2.97)

0.84 (4.39)

habit 0.41 (2.60)

0.69 (3.43)

0.17 (5.55)

S7

0.89 (4.43)

S8

0.95 (4.34)

0.99 (4.67)

script

0.020 (2.63)

0.22 (6.64)

1.33 (3.46)

0.67 1.26 (3.18) (5.70)

1.48 (4.22)

1.24 (5.28)

0.81 (4.57)

0.92 (4.26)

S9

S10

S11

S12

S13

0.20 (5.51)

0.06 (5.16)

0.10 (4.94)

0.09 (5.38)

0.16 (5.33)

0.75 (3.93) S14

0.14 (5.66)

0.89 (4.26) S15

0.15 (5.39)

Estimates of covariances between error terms of measurement variables†



0.13 (5.33)

combination of variables

covariance estimates

t

combination of variables

covariance estimates

t

combination of variables

covariance estimates

t

A1 - S6 H2 - S3 S3 - S6 S4 - S13 S6 - S9 S9 - S10

0.095 -0.07 0.053 -0.039 0.050 -0.050

2.64 -2.05 2.88 -2.98 2.74 -3.41

A2 - S7 S2 - S6 S4 - S7 S4 - S14 S6 - S11 S10 - S14

0.15 -0.023 -0.044 0.078 0.032 0.25

2.88 -2.45 -3.39 4.42 2.31 3.01

H1 - S1 S2 - S11 S4 - S11 S5 - S7 S8 - S10 S12 - S15

0.15 -0.45 -0.032 0.046 -0.33 0.040

3.42 -4.17 -3.17 2.24 -2.92 2.43

Covariance between error terms of measurement variables is estimated in the case it is significant, otherwise it is fixed to be 0.