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The purpose of this research is to test how varying the numbers of attributes and alternatives affects the use of heuristics and selective information processing in discrete choice experiments (DCEs). The effects of visual attribute and alternative non-attendance (NA) on respondent choices are analyzed.

Two laboratory experiments that combined eye tracking and DCEs were conducted with 109 and 117 participants in the USA. The DCEs varied in task complexity by the number of product attributes and alternatives.

Results suggest that participants ignore both single attributes and entire alternatives. Increasing the number of alternatives significantly increased attribute NA. Including NA in choice modeling influenced results more in more complex DCEs.

The current experiments did not test for choice overload. Future studies could investigate more complex designs. The choice environment affects decision-making. Future research could compare laboratory and field experiments.

Private and public sectors often use DCEs to determine consumer preference. Results suggest that DCEs with two alternatives are superior to DCEs with four alternatives because NA was lower in the two-alternative design.

This empirical research examined effects of attribute and alternative NA on choice modeling using eye tracking and DCEs with varying degrees of task complexity. Results suggest that accounting for NA reduces the risk of over- or understating the impact of attributes on choice, in that one avoids claiming significance for attributes that might not truly be preferred, and vice versa.

This chapter reviews models of decision-making and choice under conditions of certainty. It allows readers to position the contribution of the other chapters in this book in the historical development of the topic area.

Bounded rationality is defined in terms of a strategy to simplify the decision-making process. Based on this definition, different models are reviewed. These models have assumed that individuals simplify the decision-making process by considering a subset of attributes, and/or a subset of choice alternatives and/or by disregarding small differences between attribute differences.

A body of empirical evidence has accumulated showing that under some circumstances the principle of bounded rationality better explains observed choices than the principle of utility maximization. Differences in predictive performance with utility-maximizing models are however small.

The chapter provides a detailed account of the different models, based on the principle of bounded rationality, that have been suggested over the years in travel behaviour analysis. The potential relevance of these models is articulated, model specifications are discussed and a selection of empirical evidence is presented. Aspects of an agenda of future research are identified.

It has long been recognised that humans draw from a large pool of processing aids to help manage the everyday challenges of life. It is not uncommon to observe individuals adopting simplifying strategies when faced with ever increasing amounts of information to process, and especially for decisions where the chosen outcome will have a very marginal impact on their well-being. The transactions costs associated with processing all new information often exceed the benefits from such a comprehensive review. The accumulating life experiences of individuals are also often brought to bear as reference points to assist in selectively evaluating information placed in front of them. These features of human processing and cognition are not new to the broad literature on judgment and decision-making, where heuristics are offered up as deliberative analytic procedures intentionally designed to simplify choice. What is surprising is the limited recognition of heuristics that individuals use to process the attributes in stated choice experiments. In this paper we present a case for a utility-based framework within which some appealing processing strategies are embedded (without the aid of supplementary self-stated intentions), as well as models conditioned on self-stated intentions represented as single items of process advice, and illustrate the implications on willingness to pay for travel time savings of embedding each heuristic in the choice process. Given the controversy surrounding the reliability of self-stated intentions, we introduce a framework in which mixtures of process advice embedded within a belief function might be used in future empirical studies to condition choice, as a way of increasingly judging the strength of the evidence.

There is extensive evidence that decision-makers, faced with increasing information load, may simplify their choice by reducing the amount of information to process. One simplification, commonly referred to as attribute non-attendance (ANA), is a reduction of the number of attributes of the choice alternatives. Several previous studies have identified relationships between varying information load and ANA using self-reported measures of ANA. This chapter revisits this link, motivated by recognition in the literature that such self-reported measures are vulnerable to reporting error.

This chapter employs a recently developed modelling approach that has been shown to effectively infer ANA, the random parameters attribute non-attendance (RPANA) model. The empirical setting systematically varies the information load across respondents, on a number of dimensions.

Confirming earlier findings, ANA is accentuated by an increase in the number of attribute levels, and a decrease in the number of alternatives. Additionally, specific attributes are more likely to not be attended to as the total number of attributes increases. Willingness to pay (WTP) under inferred ANA differs notably from when ANA is self-reported. Additionally accounting for varying information load, when inferring ANA, has little impact on the WTP distribution of those that do attend. However, due to varying rates of non-attendance, the overall WTP distribution varies to a large extent.

This is the first examination of the impact of varying information load on inferred ANA that is identified with the RPANA model. The value lies in the confirmation of earlier findings despite the evolution of methodologies in the interim.

This paper surveys corporate officers responsible for greenhouse gas (GHG) reporting and assurance to determine the attributes that influence their choice between an accounting and a non-accounting GHG assurance provider. Differences in the relative importance of these attributes between those selecting accounting and non-accounting assurers are also explored.

A survey questionnaire was completed by 25 corporate officers responsible for reporting and voluntarily assurance of GHG emissions in Australia. The questionnaire asked the respondents to indicate the relative importance of 41 company and assurer attributes in influencing their assurance provider choice.

Results indicate that attributes related to the assurance provider, such as team and team leader assurance knowledge, reputation, objectivity and independence, are more influential than attributes related to the nature of the company or the nature of the GHG emissions. Attributes such as geographical dispersion of operations were found to be differently important to this decision between companies purchasing assurance from accounting and non-accounting firms.

The study’s main limitation is the small number of participants. Future research may extend this study by exploring the conditions under which companies voluntarily assure GHG emissions as well the motivations of responsible officers in their assurer choice.

This paper provides valuable insights to GHG assurers to assist their understanding of the attributes that are important to potential GHG assurance clients.

The study makes unique contributions to the assurer choice literature by not only addressing this issue in the context of the dichotomous GHG assurance market but also by addressing it from the perspective of the assurance purchaser.

The purpose of this study is to examine the impact of key decision-making attributes on consumers’ choice of accommodation among and between hotels and Airbnb.

The study used a choice-based conjoint approach using 21 key decision-making factors that impact consumers’ choice of accommodation across five segments ranging from economy to luxury. Latent class estimation was used to identify segments of respondents who tend to have similar preferences for accommodation.

The results showed the presence of a consistent pattern of decision-making across the five accommodation segments, culminating in a hierarchy of importance in accommodation choice. The 21 key decision-making attributes comprised three tiers in order of decreasing importance: quality and service, amenities, and accessibility and safety. Further, latent class analysis indicated the presence of a hotel group and an Airbnb group of customers, which allowed us to identify how both types of providers might maximize the value of their offers to encourage customer switch.

The accommodation landscape is extremely dynamic (particularly as the COVID-19 pandemic unfolds) and complex. The present study cannot capture all of its intricacies but provides an invaluable foundation for future research on the topic of consumer choice in an evolving and competitive accommodation market.

Extant research on accommodation choice has focused on hotels or Airbnb only. Moreover, research that has considered both types of accommodation simultaneously is limited in its conceptual and methodological scope. The present study synthesizes the fragmented literature on consumers’ accommodation choices and offers a holistic and coherent schematic – the hierarchy of importance in accommodation choice – that can be used by future researchers and practitioners alike.

This paper reviews the current literature on theoretical and methodological issues in discrete choice experiments, which have been widely used in non-market value analysis, such as elicitation of residents' attitudes toward recreation or biodiversity conservation of forests.

We review the literature, and attribute the possible biases in choice experiments to theoretical and empirical aspects. Particularly, we introduce regret minimization as an alternative to random utility theory and sheds light on incentive compatibility, status quo, attributes non-attendance, cognitive load, experimental design, survey methods, estimation strategies and other issues.

The practitioners should pay attention to many issues when carrying out choice experiments in order to avoid possible biases. Many alternatives in theoretical foundations, experimental designs, estimation strategies and even explanations should be taken into account in practice in order to obtain robust results.

The paper summarizes the recent developments in methodological and empirical issues of choice experiments and points out the pitfalls and future directions both theoretically and empirically.

Much of the research conducted in logistics/SCM has focused on satisfaction/retention of customers. This has left a critical gap for managers: before customers can be satisfied and ultimately retained, a purchase choice of logistics services has to occur. To date, very little research has addressed how logistics customers make purchase choice decisions about logistics services. The purpose of this paper, using logistics research methods, is to introduce adaptive choice modelling (ACM) to address this gap and put forth a research method that is useful for academic researchers and logistics/SCM managers.

This paper provides an overview of ACM, along with a discussion of its important research advantages, limitations, and practical applications. Additionally, an empirical demonstration of this research technique is provided to illustrate how academic researchers and logistics managers can use ACM to better understand the decision‐making process of customers when selecting logistics services.

In order to demonstrate this research technique, a research project was designed and implemented that analyzed the choice process of consumers selecting parcel carriers to ship a textbook. The results show that price, speed of delivery, and tracking are the three most important variables in the selection decision. The results also show that consumers are not homogeneous, but can be divided into five distinct need‐based segments. Recognizing and understanding the nature of these segments should help managers better meet the needs of parcel shippers.

The main research limitation with this study is that it is based on a convenience sample; thus future research will need to replicate this study to confirm the research findings. However, the ultimate purpose of the study is to present a new research method and discuss how to apply this method, so that logistics/SCM practitioners and academic researchers can better understand customers of logistics/SCM services. Thus, while the nature of the sample is a limitation, it should be viewed in this context.

While conjoint analysis has existed for decades, this technique has rarely been implemented by logistics/SCM researchers and practitioners. Instead, logistics/SCM researchers and practitioners have focused more on retention methods and have virtually ignored modelling the actual purchase choice of logistics/SCM services. New advancements in conjoint analysis, specifically the ACM approach, have many important and unique advantages and applications for logistics/SCM researchers and practitioners. ACM has not been used in a logistics/SCM context.

The purpose of this paper is to assess the brand image of Cape Town as a tourist destination using a progressive method of unstructured and structured techniques such as word association and free association.

A mixed method study was designed incorporating two phases. Phase one involved in‐depth interviews with a convenience sample of 85 international visitors to Cape Town. Phase two consisted of a survey, which resulted in 585 useable questionnaires that incorporated both open and closed‐ended questions.

The results indicate the strengths and weaknesses of each technique used. For example, word association is effective at eliciting positive images and holistic impressions but weak at identifying affective images. The free‐choice technique offers a more balanced perception of positive, negative, cognitive and affective images of a brand.

It is possible through the use of unstructured and structured techniques together to identify commonality in image perceptions but also differences in such perceptions on the basis of visitors' demographic and travelling characteristics.

The findings highlight the strengths and weaknesses of techniques such as word association and free association. The results indicate that some image attributes may not always adequately differentiate the brand from its competitors.

The paper contributes to understand the relationship between three components of brand knowledge namely, image, differentiating attributes and choice factors in the context of an African city brand.