Background: When it comes to how Supply Chain (SC) works, it can often be viewed as a chain linking entities together. The linking process can often be viewed as collaboration between two or more SC partners. Without a connection, or collaboration, goods cannot circulate between the beginning of the chain all the way to the end of the chain. This is why collaborating with your partner and being aligned on the same level is important, which is often not the case, as struggles typically appear in the process. When it comes to solutions for these struggles, Artificial Intelligence (AI) tools could be an answer. This study aims to understand this relationship, as there is little to no academic attention towards AI solutions for collaboration in the SC. Purpose: The purpose of this study is to investigate how AI technologies can influence collaboration between SC partners, as well as to understand which could be the outcomes of an AI mediated SC collaboration. Furthermore, the intent is to summarize the findings into a framework for better visualization. Method: To fulfill the purpose of the study, an exploratory qualitative study has been conducted using 13 qualitative interviews with managers and highly skilled individuals in SC, while using an inductive approach. Finally, the data has been analyzed and interpreted using a thematic analysis, which resulted in five dimensions: information automation, AI aided human based decisions, AI based decisions, incentive alignment automation and outcomes of AI mediated collaboration. Conclusion: The study presented a new framework that explains the new dynamic relationship between the AI affected collaboration elements, with information automation sitting at the roots and coordinating the process. Furthermore, by automating information flows, forecasts and analyses could also be automated, making the incentive automation and decision making elements faster and better focused on real data, thus strengthening the outcomes of the collaboration. Lastly, an AI mediated collaboration could affect the trust among partners, as well as how power dynamics work in a partnership.

本研究主要探討高階經理人之選擇權與公司績效之關聯性，研究發現，公司發給高階經理人之選擇權價值與Tobin’s Q呈正向關係。為區分誘因調整（the incentive alignment perspective）與利益榨取（the rent extraction perspective）兩觀點，本研究將選擇權預測值分為經濟因素、公司治理與殘值三部分。研究結果顯示依據經濟因素所預測出之選擇權價值與Tobin’s Q呈正向關係，其符合誘因調整觀點，即發放選擇權可減少代理問題。而依公司治理所預測出之選擇權價值，則與Tobin’s Q 呈負向關係，此亦與利益榨取觀點相符，即當公司治理較差時，發給高階經理人之選擇權往往較股東最適程度為高。此外，本研究亦發現選擇權與Tobin’s Q有一非線性關係。 / This study examines the association between employee stock options (ESOs) and future firm performance (Tobin’s Q). The evidence shows that the value of ESOs granted to CEOs in the current and past five years are positively associated with Tobin’s Q. To test the incentive alignment perspective and the rent extraction perspective, this study predicts the value of ESOs granted to CEOs due to economic determinants, governance quality and residual value. I find that the predicted component of ESO grants due to economic determinants are positively related with Tobin’s Q, consistent with the incentive alignment perspective that ESOs are granted to reduce the agency problem. Further, the predicted component of ESO grants attributable to the governance factors are negatively associated with Tobin’s Q, indicating that for firms with poor governance, the actual level of incentives executives receive may go well beyond the optimal level for shareholders. The negative association is consistent with the rent extraction perspective. Moreover, this study also finds a non-linear association between the Tobin’s Q and the ESO grant values.