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We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

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.

While it is widely recognized that firms in an era of technological ferment exist under conditions of significant uncertainty and ambiguity, little is known about the exact processes through which firms explore their ideas and resolve uncertainty. Arguing that our understanding of the era of ferment is much less developed than other aspects of the technology life cycle, we examine the micro-dynamics of technology-based entrepreneurial firms during this period. We focus on the role of purposeful experimentation as a key form of learning for start-ups firms in the era of ferment. Our approach contrasts with the prevailing view in the literature in which the era of ferment is characterized by extensive experimentation across firms, with each firm representing a single data point in an industry-level experiment. It also extends the learning literature by focusing on start-ups and taking the perspective that learning can encompass purposeful experimentation as well as local search and chaotic adaptation in the era of ferment. Building on the literature on experimental design, we propose a definition and taxonomy of purposeful experimentation. The taxonomy defines the experimental landscape as having three domains – technological, product and business model; and two dimensions – degree of simultaneity and degree of parameter manipulation. We examine this framework using data from a technology-based start-up and find evidence for purposeful experimentation as a key element of the firm’s learning strategy. We also highlight the organizational constraints and challenges that are associated with experimentation. Our findings emphasize the importance of entrepreneurial action, choice and internal experimentation processes.

This chapter examines the external validity of lab experiments on corruption by evaluating the extent to which experimental results are robust to the degree of field context included in the experimental design. To do so, we follow Harrison and List (2004) and partition corruption experiments into four classes depending on their field context. A comparison of the results obtained within each class reveals that similar treatment effects tend to emerge. Although a definitive answer to the external validity question has yet to be provided, these preliminary results provide some support to the external validity of lab experiments on corruption.

This chapter analyzes a geographic quasi-experiment embedded in a cluster-randomized experiment in Honduras. In the experiment, average treatment effects of conditional cash transfers on school enrollment and child labor were large – especially in the poorest experimental blocks – and could be generalized to a policy-relevant population given the original sample selection criteria. In contrast, the geographic quasi-experiment yielded point estimates that, for two of three dependent variables, were attenuated. A judicious policy analyst without access to the experimental results might have provided misleading advice based on the magnitude of point estimates. We assessed two main explanations for the difference in point estimates, related to external and internal validity.

We review the existing laboratory experimental studies on corruption that have generated results with clear policy implications. We present and discuss experimental findings on the role that both monetary incentives and nonmonetary motivations may play in corruption decision-making, and, hence, in the fight against corruption.

In this article, we survey experiments that are directly related to monetary policy and central banking. We argue that experiments can also be used as a tool for central bankers for bench testing policy measures or rules. We distinguish experiments that analyze the reasons for non-neutrality of monetary policy, experiments in which subjects play the role of central bankers, experiments that analyze the role of central bank communication and its implications, experiments on the optimal implementation of monetary policy, and experiments relevant for monetary policy responses to financial crises. Finally, we mention open issues and raise new avenues for future research.

If we are to examine the role of “controls” in different experimental settings, it is appropriate that the word be defined carefully. The Oxford English Dictionary (Second Edition) defines the verb “control” in the following manner: “To exercise restraint or direction upon the free action of; to hold sway over, exercise power or authority over; to dominate, command.” So the word means something more active and interventionist than is suggested by it’s colloquial clinical usage. Control can include such mundane things as ensuring sterile equipment in a chemistry lab, to restrain the free flow of germs and unwanted particles that might contaminate some test.

Since quality cannot be manufactured or tested into a product but must be designed in, effective product design is a prerequisite for effective manufacturing. However, the concept of effective product design involves a number of complexities. First, product design often overlaps with such design types as engineering design, industrial design and assembly design. Second, while costs are key variables in product design, costing issues often arise that add more complexities to this concept.

The management accounting literature provides activity-based costing (ABC) and target costing techniques to assist product design teams. However, when applied to product design these techniques are often flawed. First, the product “user” and “consumer” are not identical as often assumed in target costing projects, and instead of activities driving up the costs, managers may use budgeted costs to create activities to augment their managerial power by bigger budgets and to protect their subordinates from being laid off. Second, each of the two techniques has a limited costing focus, activity-based costing (ABC) focusing on indirect costs and target costing on unit-level costs. Third, neither technique accounts for resource interactions and cost associations.

This paper applies the new method of associative costing (Bayou & Reinstein, 2000) that does not contain these limitations. To simplify the intricate procedures of this method, the paper outlines and illustrates nine steps and applies them to a hypothetical scenario, a design of a laptop computer intended for the college-student market. This method uses the well-known statistical techniques of clustering, Full Factorial design and analysis-of-variance. It concludes that in product design programs, the design team may need to make tradeoff decisions on a continuum beginning with the design-to-cost point and ending at the cost-to-design extreme, as when the best perceived design and the acceptable cost level of this design are incongruent.