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Using a large‐scale database of a major business services company, self‐reported usage volume data were compared with actual usage volume. Several business‐related factors were then examined in relation to the survey response bias. Survey response bias was found to be lower for clients that use the services more extensively, had been in business for a longer period of time, and have smaller number of employees. Survey response bias was also found to be lower when the level of involvement with the service (lower level of management in this study) was greater. Such response bias information would be useful for managers when making sales forecasts or market share estimations using survey responses.

The purpose of this study is to investigate the phenomenon of defensive biasing in work stress surveys, which occurs when employees trivialize potential stressors and strains due to fear of negative consequences from their supervisors or management. This study aims to better understand the factors that influence this behavior and to develop a scale to measure it.

The study used an online survey of 200 employees to investigate the factors influencing defensive biasing behavior. The researchers developed a scale for defensive biasing with the help of subject matter experts and derived possible factors from the literature. Participants were presented with a hypothetical scenario in which they imagined a work stress survey in their organization and were asked to answer related items. The data were analyzed using regression analysis.

The study found that defensive biasing behavior was significantly predicted by perceived anonymity and neuroticism. Participants who felt less anonymous and had higher levels of neuroticism were more likely to engage in defensive biasing. Job insecurity and trust in supervisors were not found to be significant predictors of defensive biasing.

This study contributes to the literature on work stress surveys by developing a scale for defensive biasing and investigating the factors that influence this behavior. The study highlights the importance of making the survey process more transparent to reduce defensive biasing and obtain trustworthy results.

Previous studies support the notion that corporate social responsibility (CSR) initiatives can have a positive effect on customers in the hospitality and tourism industry. However, most of these studies have ignored response biases and none have incorporated them into their analyses numerically. This study aims at closing this research gap.

The authors utilized a hybrid choice model to test for the hypothesized effects of social desirability (SD) and cynicism biases on reported purchase intention. The authors further compared the results with those of analyses that ignore these biases to demonstrate their distorting influence.

The results indicate that SD and cynicism biases have a moderating effect on reported purchase intention. Older generations and frequent travelers seem particularly prone to bias, and the biases have a distorting effect on the overall survey results.

Traditional analyses that exclude biases, incorrectly, suggest several aspects of CSR that are significant (or insignificant) to purchase intention, provide unreliable results. The authors did not generalize bias-prone respondent segments but urge future research to investigate this.

Hotel managers aspiring to gain competitive advantage through CSR investment must consider biases in their market research. Otherwise, they risk developing CSR initiatives that do not instigate positive customer behaviors, leading to the failure of the investment.

The authors quantified SD and cynicism as significant causes of response bias, which distorts survey results. Previous studies have conceptualized SD without quantifying its impact, while cynicism has been identified as a novel source of bias in the industry. This study further introduces hybrid choice modeling as a novel approach to address response bias that could extend itself beyond the industry studied here.

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.

Purpose — The purpose of the chapter is to make retrospective data from biographic surveys comparable with traditional cross-section travel surveys, by correcting some biases attached to the biographic collection method. This is applied to a biographic survey passed in France within the 2007–2008 national travel survey.

Methodology/approach — The methodology implemented deals with three specific biases: the general survey sampling and response rate, the survival bias, due to differential surviving rates according to generations, and the geographical bias, as biog‘raphies were not passed in all regions. All biases were corrected by computing specific weightings.

Findings — One main finding is that with these three corrections, biographic data can yield modal shares for commuting trips to work and for commuting trips to education that are similar to those derived from the historical cross-section surveys about regular trips.

Research limitations/implications — Though biographic collection suffers from the memory effect, this effect remains low and does not disturb the modal shares derived from biographies.

The most challenging issue is that of missing generations that contributed to past mobility. But they can be replaced by modeling with an age-period model.

Practical implications — The chapter provides methodology to correct biographic data to reconstitute historical behavior.

Social implications — Exploring the memory of living people is essential to save data about the past, that otherwise could be lost, although they may be useful to understand present behavior and future likely trends.

Originality/value of chapter — Investigating biographic surveys is a new topic in the field of transport survey methods.

The purpose of this paper is to examine the practice potential of self-administered drop-off as a survey mode for contingent valuation (CV) studies.

This paper conducts an empirical comparison of mode effects of two survey methods for improved ecological services in Beijing. Data were collected from a CV survey, which has two subsamples, one using face-to-face interviews and the other employing self-administered drop-off surveys.

There is some evidence of social desirability bias in the face-to-face interviews for the participation question; however, such effects do not carry over to subjects’ responses to the contribution decision. No difference is observed in sample demographics between modes. And satisficing effect is not observed in the drop-off survey in this study.

More well-controlled mode comparisons are warranted to test the robustness of the results; and collection time effects as well as the use of drop-off surveys for environmental valuation with different levels of complexity and familiarity are worthy of further study.

The authors find more similarities between drop-off and face-to-face surveys than differences therefore support the practice of self-administered drop-off surveys in CVM for environmental valuation.

This paper adds to the limited number of well-controlled mode comparisons in the CV surveys, and contributes to a better understanding of self-administered drop-off surveys, a potential low-cost alternative to face-to-face interviews in future CV applications.

Health care insurance companies often conduct sample surveys of health plan members. Survey purposes include: consumer satisfaction with the plan and members’ health status, functional status, health literacy and/or health services utilization outside of the plan. Vendors or contractors typically conduct these surveys for insurers. Survey results may be used for plans’ accreditation, evaluation, quality improvement and/or marketing. This article describes typical sampling plans and data analysis strategies used in these surveys, showing how these methods may result in biased estimators of population parameters (e.g. percentage of plan members who are satisfied). Practical suggestions are given to improve these surveys: alternate sampling plans, increasing the response rate, component calculation for the survey response rate, weighted analyses, and adjustments for unit non-response. Since policy, regulation, accreditation, management and marketing decisions are based, in part, on results from these member surveys, these important and numerous surveys need to be of higher quality.

The purpose of this paper is to investigate if and how firms approach debiasing and what determines its success. In particular, this study examines if debiasing is effective in reducing cognitive decision biases. This paper also investigates organizational characteristics that determine the effectiveness of debiasing.

This study uses survey data from German firms to answer the research questions. Target respondents are individuals in a senior management accounting function.

In line with the hypotheses, this paper finds that debiasing can reduce cognitive biases. Moreover, this study finds that psychological safety not only directly influences the occurrence of cognitive biases but is also an important factor that determines the effectiveness of debiasing.

This paper provides evidence that debiasing can serve as a powerful management accounting tool and discusses debiasing in the context of recent management accounting literature. This study also adds to the stream of research that investigates the role of psychological safety in organizations by highlighting its importance for successful debiasing.

This paper informs firms that use or intend to use debiasing about crucial determinants to consider when debating its implementation, i.e. psychological safety. This study also identifies risk management as a potential interface for the implementation of systematic debiasing.

While previous research primarily addresses specific cognitive biases and debiasing mechanisms using lab experiments, this is – to the best of the knowledge – the first study investigating cognitive biases and debiasing on a broad conceptual level using survey data.

The study of leisure consumption often involves estimating physical figures such as visits, attendance, and expenditures. However, the accuracy and reliability of such estimates are not adequately determined. It is evident that the misleading statistics arising from random variation may result into management mishaps. To address such a deficiency, this research first proposes that the information of the accuracy of estimation should be available. This study then presents appropriate ways of determining reliability and illustrates misconceptions of reliability measurement.