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As we approach the millennium, we find ourselves in a world that places ever greater weight and significance on the outcome of polls, surveys, and market research. The advent of modern polling began with the use of scientific sampling in the mid‐1930s and has progressed vastly beyond the initial techniques and purposes of the early practitioners such as George Gallup, Elmo Roper, and Archibald Crossley. In today's environment, the computer is an integral part of most commercial survey work, as are the efforts by academic and nonprofit enterprises. It should be noted that the distinction between the use of the words “poll” and “survey” is somewhat arbitrary, with the mass media seeming to prefer “polling,” and with academia selecting “survey research.” However, searching online systems will yield differing results, hence this author's inclusion of both terms in the title of this article.

This study aims to highlight the system-wide potential relationships between forms of human bias, selected Lean tools and types of waste in a manufacturing process.

A longitudinal single-site ethnographic case study using digital processing to make a material receiving process Lean was adopted. An inherent knowledge process with internal stakeholders in a stimulated situation alongside process requirements was performed to achieve quality data collection. The results of the narrative analysis and process observation, combined with a literature review identified widely used Lean tools, wastes and biases that produced a model for the relationships.

The study established the relationships between bias, Lean tools and wastes which enabled 97.6% error reduction, improved on-time accounting and eliminated three working hours per day. These savings resulted in seven employees being redeployed to new areas with delivery time for products reduced by seven days.

The single site case study with a supporting literature survey underpinning the model would benefit from testing the model in application to different industries and locations.

Application of the model can identify potential relationships between a group of human biases, 25 Lean tools and 10 types of wastes in Lean manufacturing processes that support decision makers and line managers in productivity improvement. The model can be used to identify potential relationships between forms of human biases, Lean tools and types of wastes in Lean manufacturing processes and take suitable remedial actions. The influence of biases and the model could be used as a basis to counter implementation barriers and reduce system-wide wastes.

To the best of the authors’ knowledge, this is the first study that connects the cognitive perspectives of Lean business processes with waste production and human biases. As part of the process, a relationship model is derived.

A large‐scale random sample is used to empirically examine the relationships between adoption of computer‐aided design (CAD) and five organisational characteristics that are likely to affect the probability of a firm adopting an information technology. The organisational characteristics tested are bureaucratic control, internal communication, external communication, organisational innovation, and the firm’s size. Results indicate that bureaucratic control, internal communication, and external communication do affect the likelihood of a firm adopting CAD but organisational innovation and organisational size do not. These results suggest there are differences and similarities between the organisational influences associated with classic adoption models developed with emerging technologies and the organisational influences associated with CAD adoption and possibly other mature information technologies.

The purpose of this paper is to consider the concepts of individual and complete statistical power used for multiple testing and shows their relevance for determining the number of statistical tests to perform when assessing non-response bias.

A statistical power analysis of 55 survey-based research papers published in three prestigious logistics journals (International Journal of Physical Distribution and Logistics Management, Journal of Business Logistics, Transportation Journal) over the last decade was conducted.

Results show that some of the low complete power levels encountered could have been avoided if fewer tests had been used in the assessment of non-response bias.

The research offers important recommendations to scholars engaged in survey research as they assess the effects of non-respondents on research findings. By following the recommended strategies for testing non-response bias, researchers can improve the statistical power of their findings.

While industrial marketing managers have long been concerned about the representativeness of sample information, few direct measures have been available of how accurately the sample represents the population. Suggests the use of characteristics available from sources external to the survey process as a basis for such assessment. Presents procedures for gathering, analysing and interpreting such surrogate measures.