Choice of Travel Mode in the Theory of Planned Behavior: The Roles of Past Behavior, Habit, and Reasoned Action

Bamberg, Sebastian; Ajzen, Icek; Schmidt, Péter

doi:10.1207/s15324834basp2503_01

Cited by 962 publications

(699 citation statements)

References 30 publications

(30 reference statements)

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“…This applies to various circumstances, such as travel mode choice or environmental conservation activities (e.g. Bamberg et al 2003;Kaiser et al 2005; for a meta-analysis see Armitage & Conner, 2001).…”

Section: Personal Capabilitiesmentioning

confidence: 99%

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Disentangling household and individual actors in explaining private electricity consumption

Seebauer

Wolf

2016

Energy Efficiency

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Previous research often regard household and individual as synonymous actors, although the overall household electricity consumption is the aggregate of diverging actions by individual household members. We disentangle the impact of actor-specific predictors on household and individual electricity consumption, employing regression models to data of 204 Austrian multi-person households. Predictors add more to the explained variance of household and individual electricity consumption if they are located at the same actor level as the dependent variable. While household electricity consumption is best predicted by the household context and value/knowledge factors, individual electricity consumption depends foremost on habit and whether a person stays at home during the day. The study exemplifies that future research and interventions need to decompose actor levels to better understand and target the drivers of private electricity consumption. Methodological challenges in measuring individual and household consumption behaviour are discussed.

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Section: Personal Capabilitiesmentioning

confidence: 99%

“…T h e w o r d i n g w a s h e l d c l o s e t o c o m m o n operationalizations of PBC in the previous literature (e.g. Abrahamse & Steg, 2009;Bamberg et al, 2003;Kaiser et al, 2005). PBC items were aggregated to a mean index, with Cronbach's α = .53.…”

Section: Contextual Factorsmentioning

confidence: 99%

Disentangling household and individual actors in explaining private electricity consumption

Seebauer

Wolf

2016

Energy Efficiency

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“…The stronger the intention, the more likely the behavior will be performed. Further, TPB has been used previously to explain behavior in the transportation domain (Bamberg, Ajzen and Schmidt, 2003;Chaney, Bernard and Wilson, 2013;Schniederjans and Starkey, 2014;Chen et al, 2016). If intention to purchase can predict if customers will purchase ancillary services, then airlines do not need to rely on actual purchase data, providing airlines the freedom to collect intention data from various sources.…”

Section: Methodsmentioning

confidence: 99%

Data-driven insights: Assessment of airline ancillary services

Leon

Uddin

2017

JTM

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Airlines increasingly rely on ancillary service fees for revenue generation. As a result, many ancillary services have been conceived and implemented. However, each customer does not desire to purchase every ancillary service. This research examines the heterogeneity among U.S. international airline passengers and their willingness to pay for assorted ancillary services. Antecedents to purchase intention and actual purchase behavior were evaluated using Amazon Mechanical Turk for data collection. Our results show that there are differences in airline passenger preferences when purchasing ancillary services on international flights. The number of times a passenger flies in a year and the reason for travel are found to be consistently significant. Occasionally, age and income are found to be significant. These findings will be very useful to airline marketing executives and could help to assure consumers receive the services they want at the price levels they are willing to pay.

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“…Panel data are common in transportation research (Golob et al; and habits are often observed in travel behavior (Gärling and Axhausen;, in particular in route choice (Aarts and Dijksterhuis;2000;Bamberg et al;Thøgersen;2006;Eriksson et al;Verplanken et al;Gardner;2009;Schwanen et al; and in car ownership (Jong et al;2004). In Markov models of destination choices, transition matrix represents the probability of choosing a destination given the choice of destination at the previous stop.…”

Section: Location Choice Modelsmentioning

confidence: 99%

Location choice with longitudinal WiFi data

Danalet

Tinguely

Lapparent

et al. 2016

Journal of Choice Modelling

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While moving from diary survey to location-aware technologies, recent data collection techniques provide new insights about location choices. Only few dynamic models of location choice exist in the literature, and none of them to our knowledge correct for serial correlation. In this paper, we apply a method proposed by Wooldridge (2005) to deal with the initial values problem on the choice of catering locations on a campus using WiFi traces. Cross-validation, price elasticity and simulation of a scenario predicting the opening of a new catering location are presented. Predicted market shares of the new catering location correspond to point-of-sale data of the first week of opening.

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Choice of Travel Mode in the Theory of Planned Behavior: The Roles of Past Behavior, Habit, and Reasoned Action

Cited by 962 publications

References 30 publications

Disentangling household and individual actors in explaining private electricity consumption

Disentangling household and individual actors in explaining private electricity consumption

Data-driven insights: Assessment of airline ancillary services

Location choice with longitudinal WiFi data

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