Shopping Context and Consumers' Mental Representation of Complex Shopping Trip Decision Problems

Depending on the shopping context, consumers may develop different mental representations of complex shopping trip decision problems to help them interpret the decision situation that they face and evaluate alternative courses of action. To investigate these mental representations and how they vary across contexts, the authors propose a causal network structure that allows for a formal representation of how context-specific benefits requirements affect consumers’ evaluation of decision alternative attributes. They empirically test hypotheses derived from the framework, using data on consumers’ mental representations of a complex shopping trip decision problem across four shopping contexts that differ in terms of opening hour restrictions and shopping purpose, and find support for the proposed structure and hypotheses.retailing;consumer decision-making;context effects;mental representations;shopping trip decisions

Estimating the parameters of a dynamic need-based activity generation model

Several activity-based models made the transition to practice over the last decade. However, modeling dynamic activity generation and especially, the mechanisms underlying activity generation are not well incorporated in the current activity-based models. This paper describes a first step in estimating the parameters of a need-based activity generation model. A survey was carried out to collect activity data for a typical week and a specific day among a relatively large sample of individuals. The diary data includes detailed information about activity history and future planning. Furthermore, person-level needs on relevant dimensions were measured using Likert scales. Estimation of the model involves a range of shopping, social, leisure and sports activities, as dependent variables, and socioeconomic, day preference, and need variables, as explanatory variables. The results show that several person, household and dwelling attributes have an influence on activity-episode timing decisions in a longitudinal time frame and, thus, on frequency and day choice of conducting the social, leisure and sports activities

Representing and estimating interactions between activities in a need-based model of activity generation

Although several activity-based models made the transition to practice in recent years, modelling dynamic activity generation and especially, the mechanisms underlying activity generation are not well incorporated in the current activity-based models. For example, current models assume that activities are independent, but to the extent that different activities fulfil the same underlying needs and act as partial substitutes, their interactions/dependencies should be taken into account. This paper describes the parameter estimation of a need-based activity generation model, which includes the representation of possible interaction effects between activities. A survey was carried out to collect activity data for a typical week and a specific day among an adequate sample of individuals. The diary data contain detailed information on activity history and future planning. Estimation of the model involves a range of shopping, social, leisure and sports activities, as dependent variables, and socioeconomic, day preference, and interaction variables, as explanatory variables. The results show that several person, household, and dwelling attributes influence activity-episode timing decisions in a longitudinal time frame and, thus, the frequency and day choice of conducting the social, leisure and sports activities. Furthermore, interactions where found in the sense that several activities influence the need for other activities and some activities affect the utility of conducting another activity on the same day

Multi-day activity scheduling reactions to planned activities and future events in a dynamic agent-based model of activity-travel behavior

Modeling multi-day planning has received scarce attention today in activity-based transport demand modeling. Elaborating and combining previous work on event-driven activity generation, the aim of this paper is to develop and illustrate an extension of a need-based model of activity generation that takes into account possible influences of pre-planned activities and events. This paper describes the theory and shows the results of simulations of the extension. The simulation was conducted for six different activities and parameter values. The results show that the model works well and that the influences of the parameters are consistent, logical and have clear interpretations. These findings offer further evidence of face and construct validity to the suggested modeling approach

Eliciting needs underlying activity-travel patterns and their covariance structure: results of multi-method analyses

Modeling dynamic activity generation is high on the research agenda in activity-based transport demand modeling. The concept of dynamic needs has been put forward as such a mechanism. The aim of this paper is to investigate which needs underlie the generation of discretionary activities such as social, recreational and sports activities. Three surveys were conducted to elicit, establish and analyze the needs. We carried out qualitative face-to-face interviews based on a laddering technique to reveal need dimensions using an exhaustive classification of discretionary activities. Quantitative approaches were then used to determine which needs are equivalent in terms of their effects on activities and, hence, can be merged. Finally, a questionnaire-based survey involving a large sample of individuals is used to measure personal levels of the needs identified and to correlate these measures with socio-economic and behavioral characteristics. In total, six independent needs emerged from this research, namely Physical exercise, Social contact, Relaxation, Fresh air / being outdoors, New experiences, and Entertainment. Many-to-many relationships between activities and needs support the hypothesis that substitution relationships may play a significant role in activity generation. This implies that current practice in activity-based modeling of focusing on activities may produce biased results when developing dynamic models of transport demand. Furthermore, the results show that personal levels on these needs correlate with various socio-economic as well as behavioral variables

Toward personalised and dynamic cultural routing: a three-level approach

This paper introduces the concept of “smart routing” as a recommender system for tourists that takes into account the dynamics of their personal user profiles. The concept relies on three levels of support: 1) programming the tour, i.e. selecting a set of relevant points of interests (POIs) to be included into the tour, 2) scheduling the tour, i.e. arranging the selected POIs into a sequence based on the cultural, recreational and situational value of each, and 3) determining the tour’s travel route, i.e. generating a set of trips between the POIs that the tourist needs to perform in order to complete the tour. The “smart routing” approach intends to enhance the experience of tourists in a number of ways. The first advantage is the system’s ability to reflect on the tourists’ dynamic preferences, for which an understanding of the influence of a tourist’s affective state and dynamic needs on the preferred activities is required. Next, it arranges the POIs together in a way that creates a storyline that the tourist will be interested to follow, which adds to the tour’s cultural value. Finally, the POIs are connected by a chain of multimodal trips that the tourist will have to make, also in accordance with the tourist’s preferences and dynamic needs. As a result, each tour can be personalised in a “smart” way, from the perspective of both the cultural and the overall experience of taking it. We present the building blocks of the “smart routing” concept in detail and describe the data categories involved. We also report on the current status of our activities with respect to the inclusion of a tourist’s affective state and dynamic needs into the preference measurement phase, as well as discuss relevant practical concerns in this regard

Shopping Context and Consumers' Mental Representation of Complex Shopping Trip Decision Problems

Depending on the shopping context, consumers may develop different mental representations of complex shopping trip decision problems to help them interpret the decision situation that they face and evaluate alternative courses of action. To investigate these mental representations and how they vary across contexts, the authors propose a causal network structure that allows for a formal representation of how context-specific benefits requirements affect consumers’ evaluation of decision alternative attributes. They empirically test hypotheses derived from the framework, using data on consumers’ mental representations of a complex shopping trip decision problem across four shopping contexts that differ in terms of opening hour restrictions and shopping purpose, and find support for the proposed structure and hypotheses