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The purpose of this chapter is to explore the differences in consumers’ willingness to pay for different types of design attributes due to different levels of specific anticipatory emotions evoked by them. The research aims to show how firms can benefit by leveraging the findings that different types of design attributes – that is, functionality, aesthetics, and environmental sustainability – affect profit margin per unit differently. Further, the chapter claims that design is a core competency that can pay dividends in terms of profit margins for firms. It is important for firms to develop expertise in understanding and leveraging relationships between the types of design attributes, specific emotions, and consumers’ willingness to pay.

The chapter uses the product categories of cell phones and laptop computers in the three experiments to test the hypothesized relationships between design attributes (functionality, aesthetics, and environmental sustainability), specific emotions, and willingness to pay.

The research finds that different attributes of design – functionality, aesthetics, and sustainability – evoke different types of emotions and different levels of willingness to pay.

The data were primarily collected via experiments in a behavioral laboratory.

Firms can leverage different attributes of design to position and price products according to emotional requirements of the target customer segment to match their willingness to pay and maximize profit margin per unit.

The research specifically measures willingness to pay in joint presentation – independent evaluation scenarios to assess differences in how functionality, aesthetics, and sustainability impact willingness to pay.

There have always been concerns about task complexity and respondent burden in the context of stated choice (SC) studies, with calls to limit the number of alternatives, attributes and choice sets. At the same time, some researchers have also made the case that too simplistic a design might be counterproductive given that such designs may result in issues of omitting important decision variables. This paper aims to take another look at the effects of design complexity on model results. Specifically, we make use of an approach devised by Hensher (2004)¹ in which different respondents in the study are presented with designs of different complexity, and look specifically at effects on model scale in a UK context, adding to previous Chilean evidence by Caussade et al. (2005). The results of our study indicate that the impact of design complexity may be somewhat lower than anticipated, and that more complex designs may not necessarily lead to poorer results. In fact, some of the more complex designs lead to higher scale in the models. Overall, our findings suggest that respondents can cope adequately with large number of attributes, alternatives and choice sets. The implications for practical research are potentially significant, given the widespread use, especially in Europe, of stated choice designs with a limited number of alternatives and attributes.

In this paper, we analyze statistical properties of stated choice experimental designs when model attributes are functions of several design attributes. The scheduling model is taken as an example. This model is frequently used for estimating the willingness to pay (WTP) for a reduction in schedule delay early and schedule delay late. These WTP values can be used to calculate the costs of travel time variability. We apply the theoretical results to the scheduling model and design the choice experiment using measures of efficiency (S-efficiency and WTP-efficiency). In the simulation exercise, we show that the designs based on these efficiency criteria perform on average better than the designs used in the literature in terms of the WTP for travel time, schedule delay early, and schedule delay late variables. However, the gains in efficiency decrease in the number of respondents. Surprisingly, the orthogonal design performs rather well in the example we demonstrated.

Quality Function Deployment (QFD) proposes to take into account the “voice of the customer,” through a list of customer needs, which are (qualitatively) mapped to technical requirements in House One. But customers do not perceive products in this space, nor do they not make purchase decisions in this space. Marketing specialists use statistical models to map between a simpler space of customer perceptions and the long and detailed list of needs. For automobiles, for example, the main axes in perceptual space might be categories such as luxury, performance, sport, and utility. A product’s position on these few axes determines the detailed customer requirements consistent with the automobiles’ position such as interior volume, gauges and accessories, seating type, fuel economy, door height, horsepower, interior noise level, seating capacity, paint colors, trim, and so forth. Statistical models such as factor analysis and principal components analysis are used to describe the mapping between these spaces, which we call House Zero.

This paper focus on House One. Two important steps of the product development process using House One are: (1) setting technical targets; (2) identifying the inherent tradeoffs in a design including a position of merit. Utility functions are used to determine feature preferences for a product. Conjoint analysis is used to capture the product preference and potential market share. Linear interpolation and the slope point formula are used to determine other points of customer needs. This research draws from the formal mapping concepts developed by Nam Suh and the qualitative maps of quality function deployment, to present unified information and mapping paradigm for concurrent product/process design. This approach is the virtual integrated design method that is tested upon data from a business design problem.

Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher & Puckett, 2007; Puckett et al., 2007; Puckett & Hensher, 2008), one significant empirical constraint was difficult in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Bliemer & Rose, 2006).

The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.

In this chapter, each of the completed models of curriculum is presented and evaluated using criteria from the attributes of effective curricula discussed in Chapter 1. Explanations of the design strategies that are used to demonstrate each attribute in a differentiated manner are included. The evaluation process provides an evaluation methodology to demonstrate the effectiveness of each model of a curriculum in a credible and trustworthy way.

In the previous chapters, the individual parts of the curricula were configured, aligned, and interconnected to deliver specific outcomes in each learning module. In this chapter, the components of each curriculum are assembled into one table to exhibit the order contained within each learning module within the overall curriculum. The standards for curricular attributes adopted at the beginning of the design process are the criteria for the evaluation of the completed curriculum. The strategies used to configure the components of each curriculum provide evidence of the curriculum’s characteristics, which demonstrate compliance with each criterion.

The evaluation of these attributes within a curriculum serves several purposes. First, they provide a checklist to guide the design process toward curricula that reflect these standards as developed by the profession of curriculum design in higher education. Second, they provide a measurement of the attributes of the curriculum to demonstrate the compliance of each curricular design with conventional standards. Third, these measurements can be compared with other institutional data to uncover correlations between the design assumptions and learner performance. These correlations often reveal unanticipated results, which inform the effectiveness of the instructional system.

These criteria are applied to the evaluation of the curriculum for each module to demonstrate the diverse manner in which each can be achieved in a discipline-specific manner. The compliance with these criteria is explained to be a matter of demonstration, as used in the discipline of qualitative research. These qualitative evaluations can then be compared with other operational data to understand the effectiveness of the design assumptions for each curriculum.

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.