An Empirical Study on Memory Bias Situations and Correction Strategies in ERP Effort Estimation

摘要

An Enterprise Resource Planning (ERP) project estimation process often relies on experts of various backgrounds to contribute judgments based on their professional experience. Such expert judgments however may not be bias-free. De-biasing techniques therefore have been proposed in the software estimation literature to counter various problems of expert bias. Yet, most studies on de-biasing focus on systematic bias types such as bias due to interdependence, improper comparisons, presence of irrelevant information, and awareness of clients' expectations. Little has been done to address bias due to experts' memory. This is surprising, knowing that memory bias retrieval and encoding errors are likely to affect the estimation process outcome. This qualitative exploratory study investigates the memory bias situations encountered by ERP professionals, and the possible coping strategies to problems pertaining to those situations. Using interviews with 11 practitioners in a global ERP vendor's organization, we explicate how experts retrieve and encode stored memory, what kind of errors they experience along the way, and what correction techniques they were using. We found that both errors due to memory retrieval and due to memory encoding seemed to lead to project effort underestimation. We also found that the most common memory correction strategy was the use of mnemonics.