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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.

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 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.

Patients and health professionals often make decisions which involve a choice between discrete alternatives. This chapter reviews the econometric methods which have been developed for modelling discrete choices and their application in the health economics literature. We start by reviewing the multinomial and mixed logit models and then consider issues such as scale heterogeneity, estimation in willingness to pay space and attribute non-attendance.

The purpose of this study is to decompose the effects of country-of-origin labeling (COOL) into multiple dimensions—macrolevel image, related to the country image, and microlevel image, related to dairy industry/product attributes—and investigate how each dimension affects Chinese consumers' evaluation of imported milk.

This study adopted the Becker–DeGroot–Marschak (BDM) auction mechanism to elicit consumers' willingness to pay (WTP) for milk from different countries (New Zealand, Australia, Germany, France and China). The experiment was conducted with 348 shoppers at supermarkets in three major cities of China (Hangzhou, Wuhan and Shijiazhuang). The study subject was ultrahigh-temperature processing (UHT) milk (200 mL Tetra Pak aseptic brick package).

The results show that Chinese consumers are willing to pay a premium for UHT milk from New Zealand, Australia, Germany and France compared to domestic milk, and the premiums are 59.4, 58.9, 57.9, and 52.9% respectively. Both microlevel and macrolevel images exert a substantial influence on consumers' WTP, and the microlevel image has a greater impact on consumers' evaluation of milk than the macrolevel image. Particularly, the macropolitical, microtechnology/quality and microdesign/package dimensions have a positive influence on WTP for milk.

This study contributes to the existing literature in introducing the country-of-origin image (COI) construct with different dimensions to get in-depth knowledge about the country-of-origin (COO) effect in food or agricultural economics.

This study aims to explore the degree of Chinese consumers' trust and confidence in the Chinese dairy products supply chain and the relationships between trust and overall confidence in dairy products safety and quality.

This study collected data from 1,278 respondents by field survey from five provinces of China. The data were analyzed using ordered logit model.

This study shows the following results: (1) Chinese consumer confidence in domestic dairy products and trust in actors of the dairy chain are at a moderate-to-low level. (2) Government regulators are considered to take the most responsibility, with both an optimism-enhancing and a pessimism-reducing effect (the former effect is greater), while perceived trust in dairy farmers and retailers has little effect. (3) Perceived care has both an optimism-enhancing and a pessimism-reducing effect, and the former effect is stronger. Competence and openness have an optimism-enhancing effect and a pessimism-reducing effect, respectively. (4) The importance of the three dimensions of trust related to optimism-increasing and pessimism-reduction is limited, except in the case of government regulators.

This study contributes to a better understanding of consumer trust in food safety and also help demonstrate to the actors and institutions involved in the dairy supply chain the best way to improve the performance of their duties to meet the consumers' needs for safe and quality dairy products.

Purpose — The main objective of this survey is to collect data for the development of six models in a freight modeling framework. The framework aims to simulate the interactions between shippers and carriers in a freight market.

Methodology/approach — A web-based survey was designed using stated preference methods and experimental auctions, to collect information about shipper and carrier behavior when facing hypothetical situations. Hypothetical situations were constructed using information collected during the survey.

Findings — The modeling results are available for one model, the carrier selection model. In this model, data were collected using stated preference (SP) methods. Nine SP designs were developed using D-designs and an approach to minimize the nonattendance problem. A multinomial probit model was used. No bias was found due to the position of alternatives on the screen, signs of the parameters are as expected, and level of service attributes are relevant in the carrier selection process.

Research limitations/implications — The final response rate was small (about 9%) which is not uncommon in surveys with freight managers. This response rate might result in nonresponse bias of the estimates, which is the subject of future research.

Practical implications — Since freight transport is the output of a freight market, the application of the freight modeling framework presented in this chapter has potential to improve forecasts of freight flows.

Originality/value of chapter — To the best of our knowledge, the survey presented in this chapter consists of an innovative data collection procedure for the development of an original freight modeling framework.

The purposes of this study are to identify the reasons that prevent students from attending lectures, to examine the impact of absenteeism on their academic performance and to explore the role of gender in their attendance.

A questionnaire survey made up of statements was distributed to accounting students. Descriptive measures, a five-point Likert scale, Pearson's chi-square test and phi test were employed to achieve the study's objectives.

The study shows that most of the accounting students surveyed are aware of the importance of attending lectures, since they believe that attendance will have a positive impact on their understanding of course material and on their academic performance. The study indicates that overall academic performance and student attendance are related. It also indicates that there is a statistically significant association between the overall academic performance of accounting students and their gender.

To gain greater insight into educators, academic institutes, researchers and parents concerning the reasons that lie behind students not attending accounting lectures in public higher education institutions in Kuwait and the effect of attendance on academic performance.

Most prior studies have been conducted in developed countries and Western contexts. Especially, with the potential perceived differences in cultures and norms, this empirical study is expected to contribute to fill the gap in this research field.

This paper tests a research model that examines work engagement (WENG) as a mediator between green human resource management (GHRM) and absenteeism and green recovery performance.

To gauge the aforesaid linkages, the authors used data collected from employees and their supervisors in the international five-star chain hotels in Istanbul, Turkey. One-hundred and eighty-two respondents completed the surveys.

GHRM fosters WENG. As predicted, WENG alleviates absenteeism and triggers green recovery performance. Employees' favorable perceptions of GHRM (cognitive evaluation) give rise to higher WENG (emotional response), which in turn directs their behaviors such as reduced absenteeism and higher green recovery performance (behavioral response).

No empirical study has gauged the linkage between GHRM and WENG and nonattendance behavior or absenteeism so far. Moreover, there are few empirical pieces in the current literature that have tested the mechanism through which GHRM is associated with green and/or nongreen outcomes.