Search results

The purpose of this paper is to provide a comprehensive review of the respondents’ fraud phenomenon in online panel surveys, delineate data quality issues from surveys of broad and narrow populations, alert fellow researchers about higher incidence of respondents’ fraud in online panel surveys of narrow populations, such as logistics professionals and recommend ways to protect the quality of data received from such surveys.

This general review paper has two parts, namely, descriptive and instructional. The current state of online survey and panel data use in supply chain research is examined first through a survey method literature review. Then, a more focused understanding of the phenomenon of fraud in surveys is provided through an analysis of online panel industry literature and psychological academic literature. Common survey design and data cleaning recommendations are critically assessed for their applicability to narrow populations. A survey of warehouse professionals is used to illustrate fraud detection techniques and glean additional, supply chain specific data protection recommendations.

Surveys of narrow populations, such as those typically targeted by supply chain researchers, are much more prone to respondents’ fraud. To protect and clean survey data, supply chain researchers need to use many measures that are different from those commonly recommended in methodological survey literature.

For the first time, the need to distinguish between narrow and broad population surveys has been stated when it comes to data quality issues. The confusion and previously reported “mixed results” from literature reviews on the subject have been explained and a clear direction for future research is suggested: the two categories should be considered separately.

Specific fraud protection advice is provided to supply chain researchers on the strategic choices and specific aspects for all phases of surveying narrow populations, namely, survey preparation, administration and data cleaning.

This paper can greatly benefit researchers in several ways. It provides a comprehensive review and analysis of respondents’ fraud in online surveys, an issue poorly understood and rarely addressed in academic research. Drawing from literature from several fields, this paper, for the first time in literature, offers a systematic set of recommendations for narrow population surveys by clearly contrasting them with general population surveys.

Despite the growing availability of scanner-panel data, surveys remain the most common and inexpensive method of gathering marketing metrics. The purpose of this paper is to explore the size, direction and correction of response errors in retrospective reports of category buying.

Self-reported purchase frequency data were validated using British household panel records and the negative binomial distribution (NBD) in six packaged goods categories. The log likelihood theory and the fit of the NBD model were used to test an approach to adjusting the errors post-data collection.

The authors found variations in systematic response errors according to buyer type. Specifically, lighter buyers tend to forward telescope their buying episodes. Heavier buyers tend either to over-use a rate-based estimation of once-a-month buying and over-report purchases at multiples of six or to use round numbers. These errors lead to overestimates of penetration and average purchase frequency. Adjusting the aggregate data for the NBD, however, improves the accuracy of these metrics.

In light of the importance of purchase data for decision making, the authors describe the inaccuracy problem in frequency reports and offer practical suggestions regarding the correction of survey data.

Two novel contributions are offered here: an investigation of errors in different buyer groups and use of the NBD in survey accuracy research.

Longitudinal investigations are often suggested but rarely used in operations and supply chain management (OSCM), mainly due to the difficulty of obtaining data. There is a silver lining in the form of existing large-scale and planned repeated cross-sectional (RCS) data sets, an approach commonly used in sociology and political sciences. This study aims to review all relevant RCS surveys with a focus on OSCM, as well as data and methods to motivate longitudinal research and to study trends at the plant, industry and geographic levels.

A comparison of RCS, panel and hybrid surveys is presented. Existing RCS data sets in the OSCM discipline and their features are discussed. In total, 30 years of Global Manufacturing Research Group data are used to explore the applicability of analytical methods at the plant and aggregate level and in the form of multilevel modeling.

RCS analysis is a viable alternative to overcome the confines associated with panel data. The structure of the existing data sets restricts quantitative analysis due to survey and sampling issues. Opportunities surrounding RCS analysis are illustrated, and survey design recommendations are provided.

The longitudinal aspect of RCS surveys can answer new and untested research questions through repeated random sampling in focused topic areas. Planned RCS surveys can benefit from the provided recommendations.

RCS research designs are generally overlooked in OSCM. This study provides an analysis of RCS data sets and future survey recommendations.

Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected.

Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention.

Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention.

Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.

Purpose — The paper aims at an improvement of the understanding, how mobility is reported in longitudinal surveys and to develop ideas how to assess the completeness of the reported mobility.

Methodology/approach — Analyses of data quality and completeness are performed on the multiday and multiperiod data of the German Mobility Panel. Distinctions are made between differing reporting behaviours of individuals who either reported three times, two times or only once.

Findings — It can be shown that the reporting behaviours are different depending on the number of repetitions. The results illustrate that on the one hand individuals who repeat the survey in a consecutive wave tend to report with greater motivation, endurance and accuracy. On the other hand, participants who have not reported completely and accurately are more likely to drop out. These effects positively influence the quality and completeness and therefore the reliability of recorded mobility figures in multiperiod mobility surveys.

Practical implications — The analytical possibilities of combined multiday and multiperiod data in terms of the assessment of data quality will be demonstrated. Hints to identify such types of survey artefacts are presented.

Across Europe, on average more than 95% of all passenger cars and half of all light commercial vehicles are permanently available to a household. This includes both privately owned vehicles and company cars. The profiles of vehicle use can be specified as average annual distance driven per vehicle and for the fleet as a total, purpose of travel (trip destination), infrastructure use (urban, interurban or motorway road transport) and also fuel consumption together with data on CO₂ emissions. Indicators on vehicle use can be tracked in various ways:

• self-administered panels of households, which permit their vehicles to be followed for several years;

• national or local household travel surveys (with a seven-day trip diary);

• official vehicle inspection and vehicle registration files;

• ‘vehicle surveys’ based on vehicle registry data;

• traffic counts;

• data collected for road-charging purposes.

self-administered panels of households, which permit their vehicles to be followed for several years;

national or local household travel surveys (with a seven-day trip diary);

official vehicle inspection and vehicle registration files;

‘vehicle surveys’ based on vehicle registry data;

traffic counts;

data collected for road-charging purposes.

The paper will present a review of mainly vehicle-based survey methods used in France, Germany, Finland, the United Kingdom, the United States and Canada, describing existing sampling frames to their scope, advantages and limitations, as well as their costs. Issues addressed in this context will be further examined in terms of their methodological challenges as well as their purpose.

The leading questions underlying this paper as well as the corresponding workshop are: why is it necessary to have data on passenger travel or transportation; and, looking at international experience, how good are vehicle-based surveys in delivering the required information? In discussing problems experienced in the different countries with data collection and evaluation methods, emphasis will be put on potential strategies for methodological and technological improvement and problem solving. One example is the potential use, benefits and constraints of new survey technologies presented by vehicle tracking techniques.