Experimental analysis of choice

Experimental choice analysis continues to attract academic and applied attention. We review what is known about the design, conduct, analysis, and use of data from choice experiments, and indicate gaps in current knowledge that should be addressed in future research. Design strategies consistent with probabilistic models of choice process and the parallels between choice experiments and real markets are considered. Additionally, we address the issues of reliability and validity. Progress has been made in accounting for differences in reliability, but more research is needed to determine which experiments and response procedures will consistently produce more reliable data for various problems.Sawtooth SoftwareDecision Research     

Representing Heterogeneity in Consumer Response Models 1996 Choice Conference Participants

We define sources of heterogeneity in consumer utility functions relatedto individual differences in response tendencies, drivers of utility, formof the consumer utility function, perceptions of attributes, statedependencies, and stochasticity. A variety of alternative modelingapproaches are reviewed that accommodate subsets of these various sourcesincluding clusterwise regression, latent structure models, compounddistributions, random coefficients models, etc. We conclude by defining anumber of promising research areas in this field.     

A Cross-Validity Comparison of Conjoint Analysis and Choice Models at Different Levels of Aggregation

Several (ratings-based) conjoint analysis and experimental choice (choice-based conjoint) models are compared on their ability to predict both aggregate choice shares among the sample and individual choices in an availability validation task. While there was a weak relationship between validations at the individual and aggregate levels, several models stand out. In general, models capturing individual differences validated well at both the individual and aggregate level. The hierarchical Bayes choice and conjoint models validated particularly well.Among choice models, the hierarchical Bayes choice model had the highest aggregate and individual level-validations. It was followed by the hybrid and seven segment latent segment choice models. Overall, the highest validating ratings-based conjoint model was the hierarchical Bayes model. However, the seven segment latent segment conjoint model produced better aggregate choice share validations than any other conjoint model. These results indicate that validations can be improved either by using benefit segment models and/or merging different types of data to estimate more individualized models.In most cases, rescaling improved the ratings-based, but not the choice-based choice share validations. This suggests that one might adjust for differences between ratings and choice tasks before making choice share predictions.     

Estimating the effect of brand beliefs on brand evaluations when beliefs are measured with error

We consider the internal validity of estimates of the effects of brand beliefs on brand evaluations when beliefs are measured with error. Consumer research suggests numerous errors that may impact belief measures. However, the literature has not determined precisely why and how myriad types of error matter for the evaluation-belief relationship. Furthermore, the literature has not explicitly considered what is necessary and sufficient to control for different types of belief error when using the latent general factor approach. We show that the important distinction for empirical research is not the origin of the error per se but its relationship to affective evaluation. Error related to brand evaluation has an inflationary effect on estimates of the evaluation-belief relationship while error unrelated to brand evaluation has an attenuating effect. We use a bifactor structural equations model to decompose belief measures into general and specific dimensions. The model uses bias free variation in specific beliefs to identify effects on brand evaluation while controlling for a general belief dimension correlated with evaluation. Compared to models that do not adjust for the bias, estimates of the bias corrected marginal effects are smaller but positive and significant.     

The Internet of Things and new business opportunities

Since the Internet of Things (IoT) is an emerging phenomenon, there is a lack of holistic understanding of what IoT is and what business opportunities it can offer for entrepreneurs and existing companies. This article has three main parts. First, it introduces IoT as a broad, socio-technical phenomenon. As a part of this goal, the article covers various elements within the technological, physical, and socioeconomic environments that comprise IoT. Second, this article proposes two approaches for creating new business models using IoT: a sustaining approach and a disruptive approach. The article concludes with a brief reflection on the extent to which the future of IoT can be predicted. This discussion brings up the limitations of the approach for creating new business models outlined in this article and provides guidelines on how this approach should be used. The ultimate goal of this article is to stimulate thinking, creativity, and entrepreneurship in relation to the IoT.     

Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009)

Bove, Pervan, Beatty, and Shiu [Bove, LL, Pervan, SJ, Beatty, SE, Shiu, E. Service worker role in encouraging customer organizational citizenship behaviors. J Bus Res 2009;62(7):698-705.] develop and test a latent variable model of the role of service workers in encouraging customers' organizational citizenship behaviors. However, Bove et al. [Bove, LL, Pervan, SJ, Beatty, SE, Shiu, E. Service worker role in encouraging customer organizational citizenship behaviors. J Bus Res 2009;62(7):698-705.] claim support for hypothesized relationships between constructs that, due to insufficient discriminant validity regarding certain constructs, may be inaccurate. This research comment discusses what discriminant validity represents, procedures for establishing discriminant validity, and presents an example of inaccurate discriminant validity assessment based upon the work of Bove et al. [Bove, LL, Pervan, SJ, Beatty, SE, Shiu, E. Service worker role in encouraging customer organizational citizenship behaviors. J Bus Res 2009;62(7):698-705.]. Solutions to discriminant validity problems and a five-step procedure for assessing discriminant validity then conclude the paper. This comment hopes to motivate a review of discriminant validity issues and offers assistance to future researchers conducting latent variable analysis.     

The characteristic function of rough Heston models

It has been recently shown that rough volatility models, where the volatility is driven by a fractional Brownian motion with small Hurst parameter, provide very relevant dynamics in order to reproduce the behavior of both historical and implied volatilities. However, due to the non‐Markovian nature of the fractional Brownian motion, they raise new issues when it comes to derivatives pricing. Using an original link between nearly unstable Hawkes processes and fractional volatility models, we compute the characteristic function of the log‐price in rough Heston models. In the classical Heston model, the characteristic function is expressed in terms of the solution of a Riccati equation. Here, we show that rough Heston models exhibit quite a similar structure, the Riccati equation being replaced by a fractional Riccati equation.     

A Nonlinear Model of the Term Structure of Interest Rates

We present an economically motivated two–factor term structure model that generalizes existing stochastic mean term structure models. By allowing a certain parameter to acquire dynamical behavior we extend the two–factor model to obtain a nonlinear three–factor model that is shown, in a deterministic version, to be equivalent to the Lorenz system of differential equations. With reasonable parameter values the model exhibits chaotic behavior. It successfully emulates certain properties of interest rates including cyclical behavior on a business cycle time scale. Estimation and pricing issues are discussed. Standard PCA techniques used to estimate HJM type models are observed to be equivalent to dimensional estimates commonly applied to 'spatial data' in nonlinear systems analysis.
It is concluded that techniques commonly used in the analysis of nonlinear systems may be directly applicable to interest rate models, offering new insights in the development of these models. Tests of nonlinearity in interest rate behavior may need to focus on long cycle times.     

An adaptive nonlinear approach for estimation of consumer satisfaction and loyalty in mobile phone sector of India

To facilitate business growth assessment of customer׳s, satisfaction and loyalty levels in mobile sector are two important issues which need in-depth investigation. These two levels of customers are nonlinearly related to their corresponding attributes. The past studies have mostly assumed linear relation and have mostly used regression based models for estimation of these levels and the results are not encouraging. To overcome this limitation, the present study has developed simple nonlinear models for accurate estimation of these two parameters using their related key factors and results obtained are shown to be much better. This paper has also observed the positive effect of satisfaction on the loyalty estimation of customers. Employing the proposed nonlinear adaptive models, the service provider can also predict the satisfaction and loyalty levels of each of its customers which help the organization to determine the number of possible future churners.     

基于验证性因子分析的并购价值创造影响因素研究

伴随着中国并购数量的增多和规模的扩大，企业如何掌握并购价值创造影响因素的问题愈加至关重要。以中国A股上市公司并购案例为研究对象，根据官方网站采集并购样本基本信息加以研究表明：并购价值创造影响因素大体归纳为：直接利益相关者、上市公司未来发展、并购方案的具体实施、上市公司的运营、行业和社会环境等六类。以这六类因素为潜变量，对每一类因素提出三个测量变量并建立假设模型，通过验证性因子分析证明模型成立。影响因素反映正确，对于确立并购过程中潜在的需求量和测量变量的结构关系和差额作用具有重要意义。     

Do consumer perceptions matter in measuring choice variety and variety seeking?

This research marries scanner panel choice data of consumers in single member households to their survey-based perception data and draws upon behavioral and modeling research to contribute to the variety seeking literature. It presents a new consumer-based measure of choice variety based on inter-product similarity perceptions and illustrates that others-based (i.e., researchers, managers, and retailers) measures of choice variety such as number of brand switches underestimate choice variety. Moreover, incorporating the consumer-based measure instead of others-based measures in latent class models improves their efficiency of classifying households into high versus low variety seeking. The consumer-based measure of choice variety is valid in terms of its positive relationship with consumer's intrinsic or trait-based variety seeking tendency. Theoretical (market structure, consideration set) and managerial implications (targeting of price promotions to reduce spending, product design, cross-price elasticity) of the findings are offered.     

A multi-category customer base analysis

Customer base analysis is an essential tool to measure and develop relationships with customers. While various models have been proposed in a noncontractual setting, they focus primarily on analyzing transactional patterns associated with a single product category or a firm-level activity, such as the times at which purchases are made at a particular retailer. This research proposes a modeling framework for customer base analysis in a multi-category context. Specifically, we model the time between a customer's purchases at the firm and the product categories that comprise her shopping basket arising from multi-category choice decisions. The proposed model uses a latent space approach that parsimoniously captures the dynamics of multi-category shopping behavior due to the interplay between purchase timing and shopping basket composition. We also account for interdependence among multiple categories, temporal dependence across category choices, and latent customer attrition. Using category-level transaction data, we show that the proposed model offers excellent fit and performance in predicting customer purchase patterns across multiple categories. The forecasts and inferences afforded by our model can assist managers in tailoring marketing efforts across categories.     

Corporate Governance Reform and CEO Compensation: Intended and Unintended Consequences

Recent scandals allegedly linked to CEO compensation have brought executive compensation and perquisites to the forefront of debate about constraining executive compensation and reforming the associated corporate governance structure. We briefly describe the structure of executive compensation, and the agency theory framework that has commonly been used to conceptualize executives acting on behalf of shareholders. We detail some criticisms of executive compensation and associated ethical issues, and then discuss what previous research suggests are likely intended and unintended consequences of some widely proposed executive compensation reforms. We explicitly discuss the following recommendations for reform: require greater independence of compensation committees, require executives to hold equity in the corporation, require greater disclosure of executive compensation, increase institutional investor involvement in corporate governance (including executive compensation), and require firms to expense stock options on their income statements. We provide a brief summary discussion of ethical issues related to executive compensation, and describe possible future research.     

Dissecting the Random Component of Utility

We illustrate and discuss several general issues associated with the random component of utility, or more generally unobserved variability. We posit a general conceptual framework that suggests a variance components view as an appropriate structure for unobserved variability. This framework suggests that unobserved heterogeneity is only one component of unobserved variability; hence, a more general view is required. We review a considerable amount of empirical research that suggests that random components are unlikely to be independent of systematic components, and random component variances are unlikely to be constant between or within individuals, time periods, locations, etc. We also review evidence that random components are functions of (elements of) systematic components. The latter suggests considerable caution in the use and interpretation of complex choice model specifications, in particular recently introduced forms of random parameter models that purport to estimate distributions of preference parameters. Several areas for future research are identified and discussed.     

Characteristics of a Logistics-Based Business Model

In companies where excellence in logistics is decisive for the outperformance of competitors and logistics has an outspoken role for the strategy of the firm, there is present what we refer to here as a “logistics-based business model.” Based on a multiple case study of three Nordic retail companies, the purpose of this article is to explore the characteristics of such a logistics-based business model. As such, this research helps to provide structure to logistics-based business models and identifies a way forward for companies for whom logistics and distribution are a major concern for the overall strategy of the firm.     

Leveraging Big Data to Develop Supply Chain Management Theory: The Case of Panel Data

Increased data availability is poised to shape both business practice and supply chain management (SCM) research. This article addresses an issue that can arise when trying to use big data to answer academic research questions. This issue is that distilled data often have a panel structure whereby repeated measurements are available on one or more variables for a substantial number of subjects. Thus, to fully leverage the richness of big data for academic research, SCM scholars need an understanding regarding the different types of research questions answerable with panel data. In this article, we devise a framework detailing different types of research questions SCM scholars can answer with panel data. This framework provides a basis to categorize how SCM scholars have examined the services supply chain setting of health care with public data regarding hospital‐level patient satisfaction. We extend prior research by testing a series of three questions not yet examined in this area by fitting a series of structured latent curve models to seven years of hospital‐level patient satisfaction for nearly 4,000 hospitals. The discussion highlights theoretical and methodological challenges SCM scholars are likely to encounter as they use the panel data in their research.     

Customer satisfaction study via a latent segment model

The aim of this study is to apply a new conceptual model, and a new technique as an approach to the modelling of customers’ satisfaction, and to develop an overall satisfaction index (OSI). This study evaluates customers’ satisfaction of a certain public organization service, and argues that in order to estimate the global customers’ satisfaction measure we must appeal to methodologies recognizing that satisfaction must be understood as a latent variable, quantified through multiple indicators. Thus, it is natural that we consider the latent segment models (LSM) approach to proceed to the evaluation of customer's service satisfaction. As a result of these models estimation, we selected a three latent segment model, that is to say, the latent variable customer satisfaction has three classes: segment 1, with 50.4 percent of the customers, that represents “The Very Satisfied”, for those to whom everything is very well in the organization service; a segment 2, with 33.4 percent of the customers, representative of the “The Well Satisfied”, not totally satisfied with the quality of the organization, and a segment 3, with 16.2 percent of the customers, “Satisfaction Demanders”, thinking that organizational quality can be improved. Finally, we developed an overall satisfaction index which is important to show how the company as a whole is performing.     

基于协同理论的复合型高技能电子商务人才培养模式创新与实践

以培养复合型高技能人才作为高职高专电子商务专业的目标已经成为了社会各界的共识,但是很多院校在培养复合型电子商务人才时,普遍出现了"复"而不"合"的问题。本文基于协同理论提出了复合型高技能电子商务人才培养的新框架,并从如何协同信息技术和经管知识以形成复合型知识结构、如何协同校企合作以形成复合型能力结构、如何协同专业教育和创新创业教育以形成复合型素质结构等角度进行培养方案设计,以期在理论和实践上为相关院校创新复合型高技能电子商务人才培养模式提供参考。     

A latent structure factor analytic approach for customer satisfaction measurement

The linkage of customer satisfaction, customer retention, and firm profitability has been well established in the marketing literature, and provides ample justification as to why customer satisfaction measurement (CSM) has been a focal point in marketing decision making. Although aggregate market level research on understanding the determinants of customer satisfaction is abundant, CSM decisions at segment level are possible only if the individual or market segment differences in the formation of overall satisfaction judgments and subsequent heterogeneity in the role these various determinants play are understood. Based on expectancy-disconfirmation theory in customer satisfaction, we propose a maximum likelihood based latent structure factor analytic methodology which visually depicts customer heterogeneity regarding the various major determinants of customer satisfaction judgments involving multiple attributes, and provides directions for segment-specific CSM decisions. We first describe the proposed model framework including the technical aspects of the model structure and subsequent maximum likelihood estimation. In an application to a consumer trade show, we then demonstrate how our proposed methodology can be gainfully employed to uncover the nature of such heterogeneity. We also empirically demonstrate the superiority of the proposed model over a number of different model specifications in this application. Finally, limitations and directions for future research are discussed.     

A dimensional versus attribute approach for disaggregate choice models

A fundamental issue facing choice modelers is to make a decision on what kind of independent variables to include in a choice model. With survey data, the two immediate options are: actual product attributes or underlying latent dimensions (factor scores). Using behavioral logic we argue that heterogeneity of consumer perceptions of variables and their saliences should be the key items moderating such a decision. We present empirical evidence to support our theory that dimensional (factor score) based models do better in terms of predictions than attribute based models in more heterogeneous populations. Empirical analysis shows that in segments (where consumer heterogeneity is lower) the predictive performance of attribute based models improves relative to the factor score model and may actually have a better predictive fit when the respondents are relatively homogeneous with respect to attribute ratings and saliences.