資訊專案具備高度不確定性，且在實務面上的效益難以衡量，這使得組織在資訊專案投資組合的選擇上面臨困難，為了支援資訊專案投資組合決策，過去研究多採用現代投資組合理論，透過衡量風險與報酬作為專案評估與選擇的依據；然而受限於資訊專案本身的特性，現有資訊專案投資組合模型多透過情境假設進行估計，導致投資組合風險估計產生偏誤。為了解決此一限制，本研究提出兩種替代性的估計方法──多時間點估計法與專家估計法，與傳統的三點估計法及整合所有歷史資料的基準模型進行比較，本研究使用美國聯邦資訊儀表板上公開的各項專案資料，在不同估計方法下建構專案價值與成本，並將其納入同時考量專案報酬與專案相依性的投資組合最佳化模型中；此外，本研究進一步針對不同規模組織建構效率前緣，以驗證各估計方法之投資組合在不同情境下的表現，實證結果顯示：相較於常用的三點估計法，本研究所提出估計法產生之效率前緣更接近基準模型，且能有效排除專案間的固定正向變動，在不同組織規模與資料條件下，為投資組合管理者提供更具彈性且可靠的決策支援。

Evaluating information technology (IT) investments is challenging due to the uncertainty and intangible nature of project outcomes. To support decision-making in IT portfolio management, prior studies have applied Modern Portfolio Theory (MPT) to evaluate projects based on their risk–return trade-offs. However, existing portfolio models often derive project-level distributions from predetermined estimation scenarios, which may artificially induce a consistently positive co-movement between projects and potentially bias portfolio risk estimation.
To address this limitation, this study proposes two alternative estimation approaches, namely Multi-Timestamp Estimation and Expert Estimation, and compares them with the traditional Three-Point Estimation approach and the full-data Baseline Model. Using project data from the U.S. Federal IT Dashboard, project cost and value distributions are constructed under each estimation method and incorporated into a portfolio optimization framework that accounts for both project returns and interdependencies. Efficient frontiers are then generated and compared across organizations of different sizes.
By incorporating historical temporal observations and expert-informed assessments, the proposed estimation approaches produce efficient frontiers that are closer to the baseline frontier than those generated by the traditional Three-Point Estimation approach. Nevertheless, when complete project information is unavailable, the traditional Three-Point Estimation approach remains a practical alternative for project evaluation. These findings provide portfolio managers with flexible decision support by enabling them to select estimation approaches that best match their data availability and organizational context.

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