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This paper aims to demonstrate how consumer clickstream data from a leading hotel search engine can be used to validate two hidden information processing stages – first eliminate alternatives, then choose – proposed by the revered information processing theory of consumer choice.

This study models the two hidden information processing stages as hidden states in a hidden Markov model, estimated on consumer search behavior, product attributes and diversity of alternatives in the consideration set.

First, the stage of information processing can be statistically characterized in terms of consumer search covariates, including trip characteristics, use of search tools and the diversity of the consideration set, operationalized in terms of: number of brands, dispersion of price and dispersion of quality. Second, users are more sensitive to price and quality in the first rather than the second stage, which is closer to purchase.

The results suggest practical implications for how search engine managers can target consumers with appropriate marketing-mix actions, based on which information processing stage consumers might be in.

Most previous studies on validating the information processing theory of consumer choice have used laboratory experiments, subjects and information display boards comprising hypothetical product alternatives and attributes. Only a few studies use observational data. In contrast, this study uniquely uses point-of-purchase clickstream data on actual visitors at a leading hotel search engine and tests the theory based on real products, attributes and diversity of the consideration set.

In this chapter, we describe how random utility maximization (RUM) discrete choice models are used to estimate the demand for commodity attributes in quality-differentiated goods. After presenting a conceptual overview, we focus specifically on the conditional logit model. We examine technical issues related to specification, interpretation, estimation, and policy use. We also discuss identification strategies for estimating the role of price and non-price attributes in preferences when product attributes are incompletely observed. We illustrate these concepts via a stylized application to new car purchases, in which our objective is to measure preferences for fuel economy.

The purpose of this paper is to explore the effect of the product attribute beliefs of ready-to-drink (RTD) coffee beverages (i.e. content sensory, packaging and branding, and content functional attributes) on perceived value (i.e. utilitarian and hedonic value) and repurchase intention.

In the study survey, 401 participants who had purchased RTD coffee beverages within the previous month were included. Structural equation modelling was employed to analyse the survey data.

The findings indicate that consumers of RTD coffee beverages form utilitarian and hedonic value perceptions through content sensory, packaging and branding, and content functional attribute beliefs. Furthermore, utilitarian value is one of the most crucial determinants of repurchase intentions. The findings further reveal that whereas content functional attribute beliefs have a dominant influence on utilitarian value, content sensory attribute beliefs have a greater influence on hedonic value.

The findings of this study can serve as a reference for RTD coffee beverage companies to develop new products and communication strategies.

This research aims to investigate the role of brand types (value brand vs premium brand) in determining review content. Specifically, this research focuses on attribute-based reviews for an innovative product and suggests that consumers of value brands tend to discuss more attributes in their product reviews than those of premium brands. Also, this research suggests that review valence and time have moderating effects on the relationship between brand types and the number of attributes discussed in a review.

This research employed a data set of online consumer reviews (N = 106,980) for wireless earbuds from Amazon.com. It extracted products' attributes from review text using Bigram analysis and measured the number of attributes discussed in a review. It then analyzed the effect of brand types (value brand vs premium brand) on the number of attributes and the moderating effect of review valence and time.

The estimation results of Poisson models reveal that reviews for value brands tend to contain more product attributes than reviews for premium brands. Interestingly, the tendency is stronger among positive (vs negative) reviews, and it decreases over time as more reviews are accumulated.

While existing studies focused on the outcomes of the review content, there was not enough investigation into the determinants of the review content. This study focuses on the number of attributes discussed in a review, a key content characteristic, and provides the first empirical evidence that review content differs by brand types of an innovative product.

The purpose of this paper is to investigate how necessary changes in a manufacturing system can be determined based on a new product specification. It proposes a formal modelling approach, enhancing the utilization of changeability of a manufacturing system given a set of changes in a product.

To develop the proposed modelling approach, a design science research method is used to iteratively frame an issue, develop a solution and evaluate it in a relevant environment. Evaluation is carried out through a case study.

A stepwise method is introduced, facilitating the creation of a model describing the relations between product characteristics within a product family and the changeability of a manufacturing system. Limitations of each manufacturing system module are evaluated to determine permittable changes in the product domain. This establishes clear relations between product attributes and manufacturing capabilities. Through this, users receive feedback on which parts of the manufacturing system must change, depending on changes in product attributes.

Testing has been carried out in an academic learning factory setting. Products and processes are thus less complicated than an industrial setting. The system used for validation is highly modular by design.

The proposed approach could be used during product development, when determining characteristics and variety of new products, evaluating the consequences of changing the solution space. This implies a shorter time-to-market and lower product costs.

Faster product development and shorter time-to-market would give manufacturers increased agility to track market needs, and ultimately lead to greater fulfilment of customer requirements.

The current body of literature focus on modelling either products or manufacturing systems. Little literature addresses both, but does not touch on identifying changes within parts of the manufacturing system, nor supports the high changeability proposed in this research.

Integrating new technological attributes into existing products is a common way to innovate and is supposed to meet consumers’ functional needs. This paper aims to demonstrate how adding such attributes also increases willingness to pay (WTP) a premium for a product by activating consumers’ social need to feel unique.

The data were collected through a quantitative survey based on a nationally representative sample (N = 345). A choice-based conjoint analysis was used to estimate the perceived value of the new technological attribute and WTP a premium.

The perceived value of the new technological attribute has a positive effect on WTP a premium only for consumers with a high degree of social innovativeness (linked to their need for uniqueness) because they interpret this innovation as an opportunity to differentiate themselves from others.

When companies innovate by introducing new technological attributes, their communication should emphasize and trigger these attributes’ high performance and uniqueness. Thus, consumers seeking social differentiation through innovation will be much less sensitive to price and will be more prone to pay a premium for these products.

The main contribution of this article is to show that integrating and emphasizing a new technological attribute can increase consumers’ WTP a premium beyond that of the attribute’s functional value. Thus, new technological attributes will decrease the price sensitivity of consumers high in social innovativeness and increase their WTP a premium for the product, because they consider it as a means to stand out from others.

This article analyzes farmers' preferences for different nonindexed crop insurance alternatives, using discrete choice experiment data on cocoa farmers from southern Ghana. We examine farmers' attendance to attributes by comparing self-reported attribute nonattendance (ANA) to the behavior inferred from the choices.

We utilize the latent class endogenous attribute attendance (EAA) model to address potential endogeneity by jointly modelling farmers' attribute processing strategies with their choice of attributes of the insurance products.

The results show that premium levels, mode and length of indemnity payouts tend to influence farmers' preferences for crop insurance products. The findings also reveal that credit-constrained farmers attend more to premium and payment mode attributes of the crop insurance products and that credit-constrained farmers tend to exhibit lower willingness-to-pay estimates for the crop insurance attributes.

The findings from the study suggest that credit constraints do not only limit input use, but also tend to have statistically significant impact on farmers' cocoa insurance participation decisions.

The study examines the impact of credit constraints on farmers' crop insurance preferences while accounting for ANA.

Customer acquisition and retention methods are the most critical issues for any enterprise. By identifying potential customers and targeting them through marketing activities, enterprises can minimize marketing costs and maximize transaction probability. However, because market surveys are labor- and time-consuming, and data mining is ineffective for obtaining competitor data, enterprises may be unable to understand real-time changes in market trends and consumer preferences. The paper aims to discuss these issues.

This study developed a mechanism that automatically searches for potential customers in virtual communities. In addition, a common product attribute (CPA) model was developed based on the five dimensions of the theory of consumption values and a questionnaire survey was conducted to verify the corresponding relationships. Subsequently, the authors quantified and applied the relationship between the proposed CPA model and consumption values theory.

During the experiment, functional and social values yielded more accurate predictions. Contrary to our expectations, emotional value yielded an inaccurate prediction of potential customers. The overall precision was 0.74, with a threshold of 0.5.

Due to each industry including the distinctive characteristics and attributes regarding its products, the methods and models were only adopted in food industry for testing effectiveness.

Considering the food industry as an example, this study adopted the case study method to screen potential customers based on 400 articles from virtual communities, and combined a latent semantic analysis method with a backpropagation neural network to verify the effectiveness of the proposed method.

By adopting the proposed enterprise-product profile model, enterprises can compile basic information related to their products and industry. The proposed system can be used by enterprises to identify potential customers in areas with potential for market development.

Since imprecision, vagueness and ambiguity are often innate in human semantics, a flexible and tolerant method is needed to decode the voice of customer (VoC), so that the essential customer requirements can be identified and duly addressed. Quality function deployment (QFD) is a well‐known methodology for projecting the customer requirements onto the relevant design and production requirements and actions plan. This paper proposes an intelligent approach which extends the applications of QFD beyond its conventional boundary. The fuzzy inference technique is adopted to accommodate the possible imprecision and vagueness during VoC interpretation. The resulting model maps the customer requirements onto the relevant product attributes, taking into consideration their relationships and correlation during the inference process. The sub‐conclusions drawn from the fuzzy inference process are aggregated and defuzzified to yield the crisp design targets which can be used to guide the downstream manufacturing planning and control activities.

Many consumer-focused corporate social responsibility (CSR) studies suggest a positive link between the responsibility demonstrated by a company and consumers’ intention to favor the company in their purchases. Yet an analogous causal effect between corporate social and financial performances is not evident. This chapter conceptualizes how social desirability and cynicism contribute to the discrepancy between consumers’ attitudes and their actual purchase behavior, and analyzes why consumer choices indicated in surveys do not consistently convert into actions.

We develop a conceptual framework based on hybrid choice modeling to estimate the impact of two new variables, Corporate Social Desirability and Corporate Social Cynicism, on CSR research. The model presented synthesizes research findings from the fields of CSR and psychology with a discrete choice methodology that allows inclusion of psychological aspects as latent variables.

The goal of the framework is to bridge the gap between choices stated by consumers in CSR surveys and their actual choices by quantifying and extracting the effects of biases that otherwise threaten the validity of such survey results. As the next step, the practical value of the model must be evaluated through empirical research combining a CSR choice study with social desirability and cynicism measurement.

The framework proposes a novel way of controlling CSR surveys for potential biases created by social desirability and cynicism and enables quantification of this impact, with potential application to other fields where psychological aspects may distort research results. Future empirical evidence based on the framework may also offer new insights into the mechanisms by which the two biases distort findings.