Tolerance for Failure and Incentives for Collaborative Innovation

Hutchison‐Krupat, Jeremy; Chao, Raul O.

doi:10.2139/ssrn.1921550

Cited by 3 publications

(3 citation statements)

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“…Since the selection of innovation projects and the choice of an adequate portfolio are often conducted by decision makers without widespread application of quantitative models, this lack of behavioral investigations is surprising. The use of experimental methods in NPD research is promising but not common (Kavadias, 2014) and one of the very few exceptions is the study by Hutchison-Krupat and Chao (2014), who conduct controlled experiments aimed at understanding how incentives and tolerance for failure impact the decisions of individuals engaged in a collaborative innovation initiative. Our experimental approach to address project portfolio selection can be positioned in relation to two areas of literature focusing on human behavior in resource allocation settings as well as financial portfolio settings.…”

Section: Related Literaturementioning

confidence: 99%

“…In financial portfolio selection, the information to estimate unknowns is usually acquired from prediction-based strategies while in the project portfolio context also control-based strategies, focusing on shaping unknowns via proactive behavior, are applied. While risk might be reduced by internal actions like increasing the data integrity (Cooper et al, 1998), selecting adequate teams that increase the speed to market (Sarin & McDermott, 2003), or increasing the probability of success by allocating more resources (Hutchison-Krupat & Chao, 2014), risk in NPD will persist . Behavioral finance has had some success in explaining how investors act and, in particular, what kinds of portfolios they tend to choose (Barberis & Thaler, 2003).…”

Section: Related Literaturementioning

confidence: 99%

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Human Behavior in Project Portfolio Selection: Insights from an Experimental Study

2018

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Choosing the right subset from a set of candidate projects is a key driver of success and failure in new product development (NPD). In many cases, managers make these decisions based on intuition or simple rules. We investigate how human decision makers act in the context of project portfolio selection conducting experimental studies based on the knapsack problem. We address the question about which decision rules people apply to select a portfolio and how cognitive limitations influence their selection process. Grounded in portfolio selection practice, we investigate subjects' adherence to four heuristics. Decision making is partially explained by adherence to one simple decision rule, but decision makers' cognitive capacity limits the application of this heuristic. Subjects' cognitive deficits can be avoided by offering basic decision support, thereby increasing their heuristic adherence and their performance.

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Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Human Behavior in Project Portfolio Selection: Insights from an Experimental Study

2018

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“…MTurk has become a common way of conducting experiments in marketing and behavioral economics, and researchers in behavioral operations have also begun using MTurk as a subject pool (see, for example, Dixon et al 2017, Hutchison-Krupat and Chao 2014, Lau et al 2014, de Vericourt et al 2013. Given this recent increase in the popularity of online experiments, studies in various academic disciplines have looked at the validity of the resulting data.…”

Section: Introductionmentioning

confidence: 99%

Running Behavioral Operations Experiments Using Amazon's Mechanical Turk

Lee¹,

Seo

Siemsen

2018

Production and Operations Management

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Mechanical Turk (MTurk), an online labor market run by Amazon, provides a web platform for conducting behavioral experiments; the site offers immediate and inexpensive access to a large subject pool. In this study, we review recent research about using MTurk for behavioral experiments and test the validity of using MTurk for experiments in behavioral operations management. We recruited subjects from MTurk to replicate the inventory management experiment from Bolton and Katok (), as well as the procurement auction experiment from Engelbrecht‐Wiggans and Katok (), and the supply chain contracting experiment from Loch and Wu (). We successfully replicate individual biases in the inventory management and procurement auction experiments, but learning in the individual tasks occurs more slowly on MTurk compared to the original studies. Further, we find that social preference manipulations in the supply chain experiment are ineffective in changing the behavior of MTurk subjects, in contrast to the original study. We conducted an additional replication study of the supply chain contracting experiment using student subjects in a standard laboratory. Results from this laboratory replication also fail to replicate the original laboratory study, indicating that the effect of social preferences on supply chain contracting may not be robust to alternative subject pools. We conclude that factors potentially influencing the differences observed on MTurk are less related to the online environment, but more related to the diversity and characteristics of subject pool on MTurk. Overall, MTurk appears to be an important and relevant tool for researchers in behavioral operations, but we caution researchers about slower learning of the MTurk subjects and the use of social preference manipulations on MTurk.

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