(Re) defining salesperson motivation: current status, main challenges, and research directions

The construct of motivation is one of the central themes in selling and sales management research. Yet, to-date no review article exists that surveys the construct (both from an extrinsic and intrinsic motivation context), critically evaluates its current status, examines various key challenges apparent from the extant research, and suggests new research opportunities based on a thorough review of past work. The authors explore how motivation is defined, major theories underpinning motivation, how motivation has historically been measured, and key methodologies used over time. In addition, attention is given to principal drivers and outcomes of salesperson motivation. A summarizing appendix of key articles in salesperson motivation is provided

Signal detection models for the same–different task

h i g h l i g h t s • Signal detection models for the same-different task are presented. • The models apply to the full four by two same-different table. • The approach allows one to consider models that recognize bias and other effects. • Two basic three-parameter signal detection models are presented. • It is shown how to fit the models with standard software for nonlinear mixed models. Signal detection models for the same-different task are presented. In contrast to the standard approach that only considers the proportion correct, the models apply to the full four by two same-different table. The approach allows one to consider models that recognize bias and other effects. Two basic signal detection models, associated with different decision rules, are presented. A version of the covert decisions rule is introduced that directly allows for same-different bias, in contrast to earlier versions. It is shown how to fit the models with standard software for nonlinear mixed models. The models are applied to data from a recent same-different study. a r t i c l e i n f o © 2013 Elsevier Inc. All rights reserved. The same-different task requires observers to decide if two presented events are the same, say AA or BB, or different, say AB or BA The standard approach to analyzing data from the samedifferent task is to collapse AA and BB presentations into a 'same' category, and AB and BA presentations into a 'different' category, and to then obtain 'hits' and 'false alarms' from the resulting two by two table (e.g., Two basic signal detection models for the same-different task are presented. The models follow from different decision rules that have been discussed in the literature, but differ from the standard approach in that they apply to the full four by two table and so they use all of the information available in the data. It is shown how to fit the models with standard software for nonlinear mixed models and with software for Bayesian estimation. The models are applied to data from a recent same-different study

The mirror effect and mixture signal detection theory.

The mirror effect for word frequency refers to the finding that low-frequency words have higher hit rates and lower false alarm rates than high-frequency words. This result is typically interpreted in terms of conventional signal detection theory (SDT), in which case it indicates that the order of the underlying old item distributions mirrors the order of the new item distributions. However, when viewed in terms of a mixture version of SDT, the order of hits and false alarms does not necessarily imply the same order in the underlying distributions because of possible effects of mixing. A reversal in underlying distributions did not appear for fits of mixture SDT models to data from 4 experiments