Transaction costs, reinforcement rates and willingness to adapt: Empirical analysis of changes in activity-travel repertoires in response to hypothetical energy price policies

People are inclined to repeat their travel pattern daily, weekly or monthly. Such routines reflect the concept of inertia in travel choice, which has drawn considerable attention in travel behavior research (e.g., Schlich and Axhausen, 2003; Gärling and Axhausen, 2003). To avoid having to rethink daily activity-travel choices over and over again, individuals and households develop routines/scripts: particular activity-travel profiles are activated routinely with a high degree of consistency and temporal rhythm. Empirical studies have shown that once routines have been constructed, it is difficult to change them (Verplanken et al., 1994, 1997). Based on self-regulation theory, Garling (1998 & 2002) argued that behavioral change options are chosen on the basis of a psychological cost/benefit ratio, including increased planning efforts, activity suppression and increased time pressure. Inertia or habits play an important role as obstacles, which increase transaction costs for people to switch from current routines to new routines. Energy scarcity and traffic emissions are two major concerns in most countries, triggering the need to develop improved instruments for assessing sustainable urban development and transport management initiatives. Sustainable travel has motivated a plethora of research on travel behavior focusing on how to induce people to change to more sustainable travel. Thus, it is crucial to understand people’s activity-travel repertories or routines, and how to break these routines in stimulating them to adapt to more environmental friendly travel behavior. Therefore, in this study, our aim is to increase the understanding of how energy price policies affect individual scripts. We will examine: (1) the relations between transaction costs and resistance to adapt in reaction to various combinations of energy policies, (2) whether this relationship depends on current energy costs, the degree of costs increase generated by new policies, types of scripts or activities, etc., (3) whether heterogeneity exists in the way individuals adapt their scripts. Conceptually, we assume that every time a script is executed, it is reinforced and its activation level increased. Similarly, if a script is not executed over some period of time, its activation level is decreased. Reinforcement rate is thus a function of interval times (frequency) between executing the script. Reinforcement rates are positively correlated with increasing transaction costs in the sense that the more engraved the script, the more effort is required to change it and the more reluctant people are to actually adapt. On the other hand, there are limits to resistance to change. When faced with dramatically increasing energy costs, individual/household may face the trade-off between effort and the need to enforce changes to cope with the changing context. Increasing energy costs may have negative effects on reinforcement rates. Moreover, higher prices will require more efforts to compensate, which will increase the transaction costs. Therefore, in this study, we assume a trade-off between transactions costs and resistance to script change on the one hand and increasing need for behavioral change with increasing energy costs on the other. A structural equation model is formulated to estimate the relations between latent variables. Data for estimating this model were collected via a dedicated survey system (SINA). More specifically, data were collected on activity-travel repertories and the influence of hypothetical energy policies on adaptations of scripts. The data collection process includes four major stages. First, respondents are asked to complete their personal profile, which includes socio-demographic information, and detailed information related to their energy consumption, such as type of house, car type, fuel type, yearly electricity and gas expenditures and new energy facilities. Next, respondents are asked to detail their current scripts that are conducted at least once per month. These scripts are built in three steps. In step 1, respondents are asked to select all main activities that are conducting with some regularity. In step 2, they are prompted to provide details of the scripts related to the selected activities. In step three, the frequency of the script and the day(s) of the week, when it is executed, are solicited. After the relevant information about the current scripts is collected, respondents are asked to indicate how they would adapt these scripts under different hypothetical energy price policy scenarios. The energy costs as consequence of energy price policies were shown to respondents for assisting their adaptation decisions. The adaptation may include changes of one or more facets of a script and their frequency.