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Human lab experiments have become an established method in information systems research for investigating user behavior, perception and even neurophysiology. The purpose of this paper is to facilitate experimental research by providing a practical guide on how to implement and conduct lab experiments in the freely available experimental platform Brownie.

Laying the groundwork of the tutorial, the paper first provides a brief overview of common design considerations for lab experiments and a generic session framework. Building on the use case of the widely used trust game, the paper then covers the different stages involved in running an experimental session and maps the conceptual elements of the study design to the implementation of the experimental software.

The paper generates findings on how computerized lab experiments can be designed and implemented. Furthermore, it maps out the design considerations an experimenter may take into account when implementing an experiment and organizing it along a session structure (e.g. participant instructions, individual and group interaction, state and trait questionnaires).

The paper reduces barriers for researchers to engage in experiment implementation and replication by providing a step-by-step tutorial for the design and implementation of human lab experiments.

The purpose of this paper is to address misconceptions about the design of experiments (DoE) usefulness, avoid bad practices and foster processes’ efficiency and products’ quality in a timely and cost-effective manner with this tool.

To revisit and discuss the hindrances to DoE usage as well as bad practices in using this tool supported on the selective literature from Web of Science and Scopus indexed journals.

A set of recommendations and guidelines to mitigate DoE hindrances and avoid common errors or wrong decisions at the planning, running and data analysis phases of DoE are provided.

Errors or wrong decisions in planning, running and analyzing data from statistically designed experiments are always possible so the expected results from DoE usage are not always 100 percent guaranteed.

Novice and intermediate DoE users have another perspective for developing and improving their “test and learn” capability and be successful with DoE. To appropriately plan and run statistically designed experiments not only save the user of DoE from incorrect decisions and depreciation of their technical competencies as they can optimize processes’ efficiency and products’ quality (reliability, durability, performance, robustness, etc.) in a structured, faster and cheaper way at the design and manufacturing stages.

DoE usefulness will be increasingly recognized in industry and academy and, as consequence, better products can be made available for consumers, business performance can improve, and the link between industry and academy can be strengthened.

A supplemental perspective on how to succeed with DoE and foster its usage among managers, engineers and other technical staff is presented.

The purpose of this research is to test how varying the numbers of attributes and alternatives affects the use of heuristics and selective information processing in discrete choice experiments (DCEs). The effects of visual attribute and alternative non-attendance (NA) on respondent choices are analyzed.

Two laboratory experiments that combined eye tracking and DCEs were conducted with 109 and 117 participants in the USA. The DCEs varied in task complexity by the number of product attributes and alternatives.

Results suggest that participants ignore both single attributes and entire alternatives. Increasing the number of alternatives significantly increased attribute NA. Including NA in choice modeling influenced results more in more complex DCEs.

The current experiments did not test for choice overload. Future studies could investigate more complex designs. The choice environment affects decision-making. Future research could compare laboratory and field experiments.

Private and public sectors often use DCEs to determine consumer preference. Results suggest that DCEs with two alternatives are superior to DCEs with four alternatives because NA was lower in the two-alternative design.

This empirical research examined effects of attribute and alternative NA on choice modeling using eye tracking and DCEs with varying degrees of task complexity. Results suggest that accounting for NA reduces the risk of over- or understating the impact of attributes on choice, in that one avoids claiming significance for attributes that might not truly be preferred, and vice versa.

The purpose of this paper is to demonstrate the power of experimental design as a technique to understand and evaluate the most important factors which influence teaching effectiveness for a postgraduate course in a higher education (HE) context.

The methodology involves the execution of a case study in the form of an experiment in a business school setting. The experiment was carried out with the assistance of over 100 postgraduate students from 26 countries. The data were collected over a two year period (2015 and 2016) from a postgraduate course offered by the same tutor for repeatability reasons.

The key findings of the experiment have clearly indicated that students’ perceptions of teaching effectiveness based on intuition and guesswork are not identical to the outcomes from a simple designed experiment. Moreover, the results of the experiment provided a greater stimulus for the wider applications of the technique to other processes across the case study HE sector.

One of the limitations of the study is that the experiment was conducted for a popular postgraduate course. It would be beneficial to understand the results of the experiment for less popular postgraduate courses in the university in order to drive improvements. Moreover, this research was conducted only for postgraduate courses and the results may vary for undergraduate courses. This would be an interesting study to understand the differences in the factors between undergraduate and postgraduate teaching effectiveness.

The outcome of this experiment would help everyone who is involved in teaching to understand the factors and their influences to improve students’ satisfaction scores during the delivery of teaching.

This paper shows how experimental design as a pure manufacturing technique can be extended to a HE setting.

Purpose – What role did economic experiments play in creating value in the commercial market for wireless Internet access? Rosenberg (1992, p. 181) defines such experiments broadly, “to include experimentation with new forms of economic organization as well as the better-known historical experiments that have been responsible for new products and new manufacturing technologies.”

Design/methodology/approach – The chapter provides an overview of the experience of a number of firms, focusing on the period between the late 1990s and early part of the 21st century, when the technology first blossomed in commercial markets. The chapter uses the experience of Lucent and Intel as primary illustrations of key concepts, and the chapter discusses how the framework generalizes beyond the experience of these two firms.

Findings – The distinction between directed and undirected experiments helps understand events in the evolution of Wi-Fi's value. They also bring new perspective to an extensive debate in communications policy about the best way to assign and allocate spectrum, focusing on the importance of the regulatory decision to provide space in which experiments can take place.

Originality/value – This framework has value for business history of the commercial Internet. This lens stresses the importance of preserving discretion to move business away from applications with low value, namely, away from allocations that used a conceptualization of the technology founded on a poor-use case, which later lessons showed had lower value than alternatives.

This paper reviews the current literature on theoretical and methodological issues in discrete choice experiments, which have been widely used in non-market value analysis, such as elicitation of residents' attitudes toward recreation or biodiversity conservation of forests.

We review the literature, and attribute the possible biases in choice experiments to theoretical and empirical aspects. Particularly, we introduce regret minimization as an alternative to random utility theory and sheds light on incentive compatibility, status quo, attributes non-attendance, cognitive load, experimental design, survey methods, estimation strategies and other issues.

The practitioners should pay attention to many issues when carrying out choice experiments in order to avoid possible biases. Many alternatives in theoretical foundations, experimental designs, estimation strategies and even explanations should be taken into account in practice in order to obtain robust results.

The paper summarizes the recent developments in methodological and empirical issues of choice experiments and points out the pitfalls and future directions both theoretically and empirically.

This study is concerned with the formal assessment of a Distance Learning Environment (DLE) created to deliver a course on UML sequence diagrams to university‐level students, divided into control and treatment groups. An ad‐hoc DLE was constructed to deliver instruction to the treatment group, while the control group was taught in a traditional face‐to‐face way. The main point of concern is whether a DLE can be as effective for the treatment group, as the faceto‐ face lecture is for the control group, in terms of gaining mastery on the domain. So, a controlled experiment was organized and executed, in order to measure the participants’ performance in both groups. The results have shown no statistically significant difference for both groups of students. So, it can be argued that in the context of this experiment and by following a DLE‐design close enough to the traditional face‐to‐face approach, one can obtain equally good results using distance learning as with the traditional system. However, a number of concerns remain and more work is needed to generalize the results of this work on other domains.

Based on previous inventories, the purpose of this paper is to extend the knowledge on public administration experiments by focusing on their experimental type, design, sample type and realism levels and external validity. The aim is to provide an overview of experimental public administration and formulate potential ways forward.

The authors examine the current state of experimental public administration, by looking at a systematic selection of ISI ranked experimental publications in major public administration journals (1992-2014) and recommend ways forward based on this review.

The review indicates a rise in experimentation in public administration in recent years, this can be attributed mostly to some subfields of public administration. Furthermore, most experiments in public administration tend to have relatively simple designs, high experimental realism and a focus on external validity. Experimental public administration can be strengthened by increasing diversification in terms of samples, experimental designs, experimental types and substantive scope. Finally, the authors recommend to better utilize experiments to generate usable knowledge for practitioners and to replicate experiments to improve scientific rigour.

This paper contributes to experimental public administration by drawing on a systematic selection of papers and assessing them in depth. By means of a transparent and systematic selection of publications, various venues or ways forward are presented.

We design experiments to jointly elicit risk and time preferences for the adult Danish population. The experimental procedures build on laboratory experiments that have used traditional subject pools. The field experiments utilize field sampling designs that we developed, and procedures that were chosen to be relatively transparent in the field with non-standard subject pools. Our overall design was also intended to be a general template for such field experiments in other countries. We examine the characterization of risk over a wider domain for each subject than previous experiments, allowing more precise estimates of risk attitudes. We also examine individual discount rates over six time horizons, as the first stage in a panel experiment in which we revisit subjects to test consistency and stability of responses over time. Risk and time preferences are heterogeneous, varying by observable individual characteristics. On a methodological level, we implement a refinement of existing procedures which elicits much more precise estimates, and also mitigates framing effects.

This paper discusses the factors to consider when designing studies to measure hiring discrimination against transgender job applicants.

The paper builds on academic literature related to hiring discrimination and transgender employment to build a detailed discussion of the numerous factors and issues inherent in hiring discrimination against transgender job applicants. By isolating and describing a number of relevant considerations, the paper aims to act as a guide for future studies to build upon.

Three types of hiring discrimination studies are discussed: correspondence tests, in-person experiments and student cohort experiments. Three main categories of factors relevant to an experiment’s design are then discussed: the legal context, industry/role factors and transgender population-specific factors. A flow-chart detailing the research design decision-making process is provided.

The discussion within this paper will act as a reference and a guide for researchers seeking to address the dearth of empirical studies in the literature. The list is not exhaustive; while a number of factors relevant to transgender-specific studies are identified, there may be more that could affect an experiment's design.

Hiring discrimination against transgender people has been recorded in many surveys, but there is little empirical measurement of this discrimination. To the author's knowledge, this paper is the first to examine the experimental design decisions related to transgender hiring discrimination. In doing so, it provides contributions for two primary audiences: those researching transgender employment issues but who have never conducted a study measuring hiring discrimination; and those who have previously conducted studies on hiring discrimination, but have not done so with reference to transgender job applicants.