@inproceedings {Simula.simula.520,
	title = {Does the Prioritization Technique Affect Stakeholders{\textquoteright} Selection of Essential Software Product Features?},
	journal = {5th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement},
	year = {2011},
	pages = {261{\textendash}270},
	publisher = {ACM},
	type = {Conference},
	abstract = {Context: Being able to select the essential, nonnegotiable product features is a key skill for stakeholders of software projects. Such selection relies on human judgment, sometimes supported by structured prioritization techniques and associated tools. Goal: Our goal was to investigate whether certain attributes of prioritization techniques affect stakeholders\&$\#$39; threshold for judging product features as essential. The four investigated techniques reflect four combinations of granularity (low, high) and cognitive support (low, high). Method: In one experiment, 94 subjects in four treatment groups indicated the features (from a list of 16) that would be essential in their decision to buy a new cell phone. With a similar setup in a controlled field experiment, 44 domain experts indicated the software product features that were essential for the fulfillment of the project\&$\#$39;s vision. The effects of granularity and cognitive support on the number of essential ratings were analyzed and compared between the experiments. Result: With lower granularity, significantly more features were rated as essential. The effect was large in the first experiment and extreme (Cohen\&$\#$39;s d=2.40) in the second. Added cognitive support had medium effect (Cohen\&$\#$39;s d=0.43 and 0.50), but worked in opposite directions in the two experiments, and was not statistically significant in the second. Implications: The results of the study imply that software projects should avoid taking stakeholders\&$\#$39; judgments of essentiality at face value. Practices and tools should be designed to counteract potentially harmful biases; however, more empirical work is needed to obtain more insight into the causes of these biases.},
	doi = {10.1145/2372251.2372300},
	author = {Hans Christian {Benestad} and Jo Erskine {Hannay}}
}
