Delphi Expert Elicitation Dataset — MDP Framework for Expert Knowledge Management in Software Industry

This dataset supports the paper "Optimizing Expert Knowledge Investments in the Software Industry: A Markov Decision Process Approach to Innovation in Knowledge Management" (submitted to Journal of Innovation & Knowledge, Elsevier).
The dataset contains the complete results of a three-round modified Delphi expert elicitation study conducted to calibrate the transition probabilities and model parameters of a Markov Decision Process (MDP) framework for expert knowledge management. Thirteen domain experts (5 Chief Technology Officers and 8 Senior Engineering Managers) from the Argentine software industry participated across three rounds over six weeks (combined experience: 147 years; M = 11.3, SD = 2.9).
The file comprises seven sheets: (1) expert panel demographics and selection criteria; (2) the complete Delphi questionnaire instrument covering 47 parameters across six categories (transition probabilities, reward function coefficients, fatigue model, knowledge decay rates, cost structure, and discretization thresholds); (3–5) individual expert responses for Rounds 1, 2, and 3, with convergence statistics per parameter (median, mean, SD, IQR, CV); (6) convergence analysis demonstrating full consensus by Round 3 — Mean IQR converged from 0.28 to 0.12 (threshold < 0.15), the count of probability parameters with IQR > 0.20 decreased from 23 to 0 out of 31 (threshold < 5), and Mean CV decreased from 0.41 to 0.18 (threshold < 0.25); and (7) final adopted parameter values with full traceability to the MDP transition function and reward specification, distinguishing between Round 3 median values (adopted for transition probabilities P1–P20 and cost parameters P34–P38) and Round 3 mean values (adopted for reward function coefficients P21–P24, where right-skewed distributions favored the mean as the more informative central tendency estimator).
This dataset enables complete reproducibility of all model parameters from raw expert judgment to final adopted values (47 parameters × 13 experts × 3 rounds = 1,833 data points).