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Realistic digital data entry and data handling is the key to unlocking the power of modern computer tools for all forms of building management functions. A survey system is needed to get the vital data about property collected and entered into any management database. Three discrete usage areas for computer technology are identified: data collection, manipulation and presentation. The key differences between the various types of building survey are outlined. The conceptual and actual challenges in each survey system area are explored. Past problems and mistakes in applying technology are discussed. Innovative automated, efficient, and integrated solutions are suggested. A new multi‐media and completely digital approach is recommended for applying new computer‐based tools to all kinds of property inspection. The future tools for super‐efficient building surveys in the next century are considered and predicted.

Purpose — In this paper we describe a total design data collection method (expanding the definition of the usual “total design” terminology used in typical household travel surveys) to emphasize the need to describe individual and group behaviors embedded within their spatial, temporal, and social contexts.

Methodology/approach — We first offer an overview of recently developed modeling and simulation applications predominantly in North America followed by a summary of the data needs in typical modeling and simulation modules for statewide and regional travel demand forecasting. We then proceed to describe an ideal data collection scheme with core and satellite survey components that can inform current and future model building. Mention is also made to the currently implemented California Household Travel Survey that brings together multiple agencies, modeling goals, and data collection component surveys.

Findings — The preparation of this paper involved reviewing emerging transportation modeling approaches and paradigms, policy questions, and behavioral issues and considerations that are important in the multimodal transportation planning context. It was found that many of the questions being asked of policy makers in the transportation domain require a deep understanding of the interactions and constraints under which individuals make activity-travel choices, the learning processes at play, and the attitudes and perceptions that shape ways in which people adjust their travel behavior in response to policy interventions. Based on the work, it was found that many of the traditional travel survey designs are not able to provide the comprehensive data needed to estimate activity-based model systems that truly capture the full range of behavioral considerations and phenomena of importance.

Originality/value of paper — This paper offers a review of the emerging transportation modeling approaches and behavioral paradigms of importance in activity-based travel demand forecasting. The paper discusses how traditional travel survey designs are inadequate to meet the data needs of emerging modeling approaches. Based on a review of all of the data needs and new data collection methods that are making it possible to observe a full range of human behaviors, the paper offers a total survey data collection design that brings together many different surveys and data collection protocols. The core household travel survey is augmented by a full slate of special purpose surveys that together yield a rich behavioral database for activity-based microsimulation modeling. The paper is a valuable reference for transportation planners and modelers interested in developing data collection enterprises that will feed the next generation of transportation models.

This book provides an international perspective on improving information to support transportation decision making. It comprises a selection of papers plus workshop syntheses from the 9th International Conference on Transport Survey Methods in Chile in November 2011. The conference was organized into 14 workshops with both paper presentations and discussions in the workshops forming the majority of the conference activity. The papers reported primarily on research pertaining to continuous improvement in transport survey methods — the backbone of the transportation data pipeline in most countries. But some papers also addressed the new ways in which innovation — notably technological innovation — is being applied to the capture and analysis of data to produce necessary information faster, better, and less expensively. The conference program built on a rich legacy of intellectual pursuits spanning the past two decades, and it is anticipated that the conference will continue into the future. Thus, the contents of this book represent a 5–10 year view through a moving window on the international state of the practice and concerns in transport survey methods.

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.

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.

Researchers now have more ways than ever before to capture information about groups of interest. In many areas, these are augmenting traditional survey approaches – in others, new methods are potential replacements. This paper aims to explore three key trends: use of nonprobability samples, mobile data collection and administrative and “big data.”

Insights and lessons learned about these emerging trends are drawn from recent published articles and relevant scientific conference papers.

Each new trend has its own timeline in terms of methodological maturity. While mobile technologies for data capture are being rapidly adopted, particularly the use of internet-based surveys conducted on mobile devices, nonprobability sampling methods remain rare in most government research. Resource and quality pressures combined with the intensive research focus on new sampling methods, are, however, making nonprobability sampling a more attractive option. Finally, exploration of “big data” is becoming more common, although there are still many challenges to overcome – methodological, quality and access – before such data are used routinely.

This paper provides a timely review of recent developments in the field of data collection strategies, drawing on numerous current studies and practical applications in the field.

The purpose of this paper is to focus on the case where – by design – one needs to impute cross-country cross-survey (CCCS) data (situation typical for example among multinational firms who are confronted with the need to carry out comparative marketing surveys with respondents located in several countries). Importantly, while some work demonstrates approaches for single-item direct measures, no prior research has examined the common situation in international marketing where the researcher needs to use multi-item scales of latent constructs. The paper presents problem areas related to the choices international marketers have to make when doing cross-country/cross-survey research and provides guidance for future research.

Multi-country sample of real data is used as an example of cross-sample imputation (292 New Zealand exporters and 302 Finnish ones) the international entrepreneurial orientation (IEO) data. Three variations of the input data are tested: first, imputation based on all the data available for the measurement model; second, imputation based on the set of items based on the invariance structure of the joint items shared across the two groups; and third, imputation based both on examination of the invariance structures of the joint items and the performance of the measurement model in the group where the full data was originally available.

Based on distribution comparisons imputation for New Zealand after completing the measurement model with Finnish data (Model C) gave the most promising results. Consequently, using knowledge on between country measurement qualities may improve the imputation results, but this benefit comes with a downside since it simultaneously reduces the amount of data used for imputation. None of the imputation models leads to the same statistical inferences about covariances between latent constructs than as the original full data, however.

Considering multiple imputation, the present exploratory study suggests that there are several concerns and issues that should be taken into account when planning CCCSs (or split questionnaire or sub-sampling designs). Even if there are several advantages available for well-implemented CCCS designs such as shorter questionnaires and improved response rates, these concerns lead us to question the appropriateness of the CCCS approach in general, due to the need to impute across the samples.

The combination of cross-country and cross-survey approaches is novel to international marketing, and it is not known how the different procedures utilized in imputation affect the results and their validity and reliability. The authors demonstrate the consequences of the various imputation strategy choices taken by using a real example of a two-country sample. The exploration may have significant implications to international marketing researchers and the paper offers stimulus for further research in the area.

This paper provides an extensive review of surveys and data-collection programmes focused on urban goods movement (UGM). Surveys investigating passenger urban travel have a decades-long tradition. The same is not true for UGM. The first specific UGM surveys appeared about 10 years ago in response to the rapid growth of car traffic, congestion, pollution and lack of space. Most of the time, these surveys have been carried out to resolve specific, local problems concerning traffic. Only a few of them have taken a global approach to urban logistics by including all logistics operators (own-account and carriers), all delivery vehicles (heavy and light vehicles), all deliveries and pickups (from express to full payload) and an entire metropolitan area and surroundings. Due to various European programmes, an inventory has been created to analyse urban goods data collection according to spatial level and methodology of capture. With this inventory, European urban freight indicators can be described, along with the units in which they are measured and their purposes. The relevance of urban goods transport surveys lies in their capacity to give decision-makers an account of urban freight transport functioning, ratios and data, so as to help in formulating planning, regulation and forecasting. It appears that focusing on the movement (delivery/pick-up), as the unit of analysis in establishment-driver surveys is the most efficient approach to describe the generation of vehicular flow in the city. This fact is revealed in the French UGM surveys, which take into account the complexity of urban logistics.