Driving Outcome-Based IT Contracts with Automation
Background & Rationale -Outcome-based contracts (OBCs) in IT outsourcing are contracts that deliver assured cost reduction or increased revenue at the customer business through the offered application. Such applications are enabled by automation and digital transformation which reduce uncertainties by better prediction and reduce revenue leakages by improving efficiencies. Examples are neighborhood retail demand prediction, drug development, prediction of tax-evading transactions, online monitoring of equipment for diagnostics and prognostics, joint ventures for product development and marketing, etc.
Such contracts face the following challenges. It is difficult to describe the outcome before signing a contract since the future is evolutionary, can show surprises, and comprises uncertainties – both within contracting parties and the external environment. The parties to the contract often fight over who has created the value.
Parties may not be willing to spend on costly unforeseen contingencies which lead to contract maladaptation. It involves both 10 parties blurring their organization boundaries by sharing information and business plans raising conflict of interest. These challenges force customer firms to perform most services within themselves (make) and outsource only the IT assets at the edge to external contractors.
Despite these challenges, some firms enter into the OBCs as a source of new value, efficient utilization of their investments in AI and digital, and delink their business model from human effort-based models. This paper explains how firms overcome these challenges in the OBCs by leveraging the characteristics of technology assets, appropriate ownership forms, and firm reputations.
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Our paper develops a framework by using the lens of contract (theories of incomplete contracts) to analyse the contract challenges in three dimensions – pre-contract, contract implementation, and contract renewal. The framework is used to analyse the uncertainty-reducing and productivity-improving capabilities of automation and digital transformation (hereafter, “the AA” and “the DT”) in these dimensions.
The paper develops propositions on how the ownership of the aforementioned technology assets, ownership, and reputations can be leveraged to overcome contract challenges. Propositions are used to answer the following questions often raised by product development, marketing, and strategy teams in technology organizations.
This paper solves the implications of reputation over the ownership of automation and digital transformation assets. It also explains the reasons of two firms enter into a relationship for OBC although the theory suggests internalization (make) and also describes intrafirm and inter-firm reputation interaction and its influence on OBC services.
Methodology and Main Findings
We developed functional forms in probabilistic temporal logic to represent the capability of AA and DT as players in a multi-agent concurrent stochastic game. We used PRISM-games, a probabilistic model checking and multi-agent reinforcement learning platform of the Department of Computer Science, Oxford University to explain the OBC phenomenon, test the propositions, and develop insights.
Group Discussion Topics
The reputation is modelled by using Fermi distribution. Testing revealed that AA and DT are complementary, and a joint ownership form of contract is desirable under reputation effects. Reputation transfers occur between the contracting parties in the same direction, and continuous reallocation of ownership is required to maintain joint ownership. Rapid technological advancement and industry maturity can gravitate the transaction towards common ownership.
Implications to Practice
This paper demonstrates how AA and DT can be combined to form a virtuous cycle of ever-increasing economic benefits in an inter-firm contract. The reputation function developed in this paper can be used to learn the realizable outcome from the current realized outcome. It can be used as a contract tracker and relationship dashboard.
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Our approach of viewing the OBC from the perspectives of pre-contract and post-contract efficiencies can guide investment prioritization on an ongoing basis such that relevance for the service provider is intact. The paper derives equilibrium conditions wherein the outcome based contract transitions to a traditional contract, joint venture, or merger. Our contract theoretic framework can be used for the design and development of multi-agent reinforcement learning (MARL) systems in competitive and cooperative task models.
The full research paper can be accessed here Shanmugam, R. K., & Dhingra, T. (2023). Outcome-based contracts–Linking technology, ownership, and reputations. International Journal of Information Management, 70, 102624.
DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102624