Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
I propose that sunk costs of learning and the output over which these costs are spread determine the probability and depth of technology adoption. Depth of adoption describes the extent to which firms exploit the advantages of the technology. I find that plant size but not firm size predicts CAD and CNC adoption. Learning costs are lumpy, are closely connected to technology adoption and determine both the probability and depth of adoption. Depth of adoption is considerably more plant idiosyncratic than the decision to adopt.