UNDERSTANDING CONSUMER INTENTIONS TO ... - ANZMAC

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The study was conducted in a field setting using survey methodology. A web-based questionnaire was used to collect data. Development of the scales to measure each of the constructs in the model proceeded through a series of steps, based on the procedures suggested by Ajzen and Fishbein (1980) and Ajzen (1985, 1991). The questionnaire was then subjected to two rounds of pretesting on the web prior to sending out the final instrument. Results A total of 956 e-mail letters were sent to staff at a New Zealand university and local research organisations. From this sample pool, 323 useable responses were received, for an effective response rate of 34%. Before commencing analysis of the theoretical models, all variables were examined for reliability, validity and normality, and were deemed adequate for further analysis. Each of the two models presented above was then subjected to path analysis testing, using AMOS 3.6 software for structural equation modeling. A path analysis approach was used because all variables were manifest rather than latent. Introducing latent variables in addition to the manifest variables would have required a larger sample size. The data did not prove to be a good fit for the proposed TRA model. The best model, achieved had a Chi square of 160.12, 11df. Although 41% of the variance in carpool behavioural intention was explained in the model, the root mean square error of .21, p=.00 indicated the poor fit of the model to the data. Carpooling intentions were better explained when the data were tested using the TPB model. However, the model did not fit exactly as hypothesised. The best fitting model, presented in Figure 3, achieved a Chi square of 14.95, 5df, with a rmsea of .08, p=.13. SRA PRA COMPAT EXPER INT_NORM .18 .23 .30 .22 0, 1 err1 .78 .40 ATTITUDE .13 .20 SUBNORM .37 0, 1 Err4 .70 .50 INTENT Hypothesised relationship (not significant) Hypothesised relationship (significant at p ≤ .05) Significant relationship (p ≤ .05), not hypothesised Significant covariance relationship (p ≤ .05) EXT_NORM FAC_RES SELF_EFF .40 .19 Err2 0, 1 PB_CONT .87 0, 1 Err3 .25 .29 Chi square = 14.95 df = 5 p = .01 cmindf = 2.99 rmsea = .08 p of close fit = .13 Figure 3. Carpooling Behaviour as Explained by the Theory of Planned Behaviour
Discussion and Conclusion The intent of this study was to compare two models of consumer intentions to participate in carpooling behaviour, based on the TRA and the TPB. Both models are based on a theoretical foundation but also incorporate many of the factors that have been found to be related to many environmental behaviours. Although the TRA model has been useful in a number of other settings (Sheppard et al 1988), this model does not do an adequate job of explaining carpooling behaviour. This finding supports the results of other studies that have used the TRA to understand environmental behaviours (Bagozzi and Dabholkar 1994; Goldenhar and Connell 1993; Taylor and Todd 1997). This suggests that carpooling behaviour is not purely volitional and other factors, such as perceived behavioural control, need to be incorporated to augment the TRA model. The TPB does provide a stronger basis for exploring the relationships and predicting carpooling behaviour. First, Attitude is the key determinant of Behavioural Intentions, suggested by the strong statistical link between ATTITUDE and INTENT. Thus, the more convenient, flexible and pleasant a respondent finds carpooling, the more likely s/he is to engage in carpooling behaviour. In turn, Attitude towards carpooling is positively influenced by Personal Relative Advantages, as predicted. That is, the more a respondent felt that s/he could save money, reduce wear and tear on the car, find a carpark more easily, or interact with others, the more favourable his/her attitude towards carpooling. Compatibility is also positively related to Attitude, as expected. This means that to the extent that carpooling does not compromise a respondent’s flexibility, independence, work hours/schedule, or other commitments, the more favourable his/her attitude towards carpooling. Additionally, Compatibility is also directly related to Behavioural Intention, as indicated by the significant link between COMPAT and INTENT. This had not been hypothesised from theory and is an important finding in understanding carpool behaviour because of the direct effect that incompatibility may have on one’s carpool intentions. Attitude is also positively influenced by having previous carpool experience, as predicted. This relationship had not been suggested in the TPB, but has been suggested in previous research. Thus, the addition of this variable to the theoretical model has helped provide additional explanatory power to the model. Surprisingly, Facilitating Resources were also found to be positively related to Attitude, as this had not been foreseen in the theoretical model based on the TPB. However, it does make intuitive sense that to the extent someone has the resources to engage in carpooling (eg, having access to others who want to carpool, an ‘arrangeable’ work schedule, and time to plan for carpooling arrangements), the more positive one would feel about carpooling. Thus, the relationship between Facilitating Resources and Attitude is an important finding. Second, Subjective Norm (as measured directly, or through internal and external influences) is not related to carpooling intentions . This finding is contrary to the Theory of Planned Behaviour, but is an important finding in carpool research in New Zealand. Third, Perceived Behavioural Control is directly related to Behavioural Intentions, as predicted. This means that non-motivational factors such as the amount of control one perceives over
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