There is ongoing interest in understanding how capabilities are developed by various actors in platform ecosystems. While much of the existing research has focused on the capabilities of platform owners, we know relatively little about how complementors build the capabilities necessary for their participation in ecosystems. In this paper, we argue that some platform ecosystems involve outsourcing contractual relationships between platform owners, complementors, and clients. This introduces a distinct setting—platform-based outsourcing ecosystems—which invites insights from the IS outsourcing literature. Traditionally, the IS outsourcing literature has assumed that vendors develop capabilities primarily through experiential learning in dyadic and mediated settings, with evidence for learning between vendors in outsourcing ecosystems. Building on this foundation, we conducted a case study of a complementor operating on the AliCloud platform to examine how capability development unfolds in such ecosystems. Our analysis reveals a learning process shaped by changing contractual relationships: the complementor begins with vicarious learning under subcontracting from the platform owner, transitions to experiential learning under direct contracting with clients, and finally adopts reciprocal experiential learning as a sub-platform owner working with other complementors. In each phase, the complementor assumes a distinct role that reflects its contractual relationships with other actors in the ecosystem. We theorise this learning sequence in a process model that explains how shifts in contractual positioning trigger corresponding shifts in learning mechanisms and capability formation. The paper offers implications for both the IS outsourcing and platform ecosystem literatures, highlighting how complementor roles and learning approaches evolve in response to contractual dynamics.
Agentic AI promises operational speed, yet in finance, opaque decision paths hinder regulatory auditability. We address this autonomy paradox with a knowledge-enabled explainability (KEE) design theory that makes compliance an intrinsic, explicitly governed system property. KEE decouples probabilistic learning from an explicit, rule-governed reasoning layer, relocating epistemic uncertainty from opaque model inference to a semantic structure that can be inspected, contested, and updated. This enables ante-hoc structural control through a semantic, programmable graph that encodes regulatory mandates as executable, inspectable constraints. The theory specifies three design principles: structural knowledge codification, topological reasoning scaffolding, and adaptive synchronization. It also introduces six guidelines that operationalize regulator-acceptable, path-based audit trails. A longitudinal study of a multinational fintech platform provides naturalistic evidence that KEE can support high-velocity operations while producing path-based audit trails and accommodating controlled changes to regulatory logic. The study contributes to design science by theorizing auditable autonomy as ante-hoc structural control, specifying the entity-concept-event paradigm as its ontological mechanism, and formalizing this mechanism as a prescriptive design theory for regulated agentic AI.
Digital change (DC) broadly entails a radical reconfiguration of an organization’s modus operandi through the deployment of a growing and often untried set of digital technologies. Prior research has extensively examined DC in terms of outcomes, antecedents, and mechanisms. However, one critical aspect remains underexplored: the role of ambiguity arising from unknown technologies, non-linear change processes, and the coexistence of multiple organizational goals. Existing DC literature commonly attributes failure to an inability to remove ambiguity – that is, the contested and fluid meanings, goals, and means of DC – and to align clear strategic intent with well-defined technology functions. We challenge this view by conceptualizing ambiguity not as a problem to be eliminated, but as an inherent and generative condition of DC that must be actively reckoned with. Drawing on March’s distinction between goal ambiguity and technology ambiguity, we develop a framework of four DC configurations based on the presence or absence of ambiguity in strategic intent and in the articulated role of digital technologies. We analyze four DC cases, treating each as a progression through shifting configurations of goal and technology ambiguity. Using three longitudinal empirical cases and one baseline alignment case from prior literature, we identify four distinct DC pathways, each characterized by a specific sequence of configurations. We theorize how ambiguity shapes DC trajectories and outcomes and show that practitioners may need to deliberately increase or decrease ambiguity in goals and/or technology functions to propel DC forward.
Process complexity is a widely used concept for analyzing and improving socio-technical processes, but the term is applied in inconsistent ways, and its conceptualizations and operationalizations are often ambiguous. As a result, the same process can appear highly complex under one operationalization and comparatively simple under another, fragmenting insights across research streams. To map and structure the various approaches to process complexity, we conduct a scoping review across three domains in which process complexity has been studied: information systems, organization science, and computer science. We identify 21 distinct operationalizations of process complexity and organize them along six levels of analysis (process, between-sequence, sequence, partial pattern, action pair, and single action). By expressing these operationalizations in a unified notation and characterizing each by its underlying computation and level of analysis, we provide a means for measuring, relating, and comparing different forms of process complexity. This multi-level understanding allows researchers to investigate trade-offs, tipping points, and optimal degrees of complexity across processes. We conclude with a reflection on the implications for information systems research and practice.
Blockchain-based smart contracts embed agreement enforcement within executable code, constraining the partner discretion that inter-organisational relationships (IORs) have traditionally coordinated through trust, communication, and relational governance. This raises a question that popular accounts tend to foreclose: whether automated enforcement makes inter-organisational trust unnecessary, or whether trust instead changes in function and object. Drawing on expert consultation with 28 blockchain specialists and 25 in-depth interviews with executives, we conduct an inductive study of how IORs transform as organisations adopt smart contracts. We develop a framework in which intensive relational exchange enables organisations to build two inter-organisational capabilities, Distributed Transparency Capability and Algorithmic Coordination Capability, through which they actualise the affordances that smart contracts offer. Through this capability development a distinctive inter-organisational trust form emerges, System-based Trust, defined as warranted reliance on coordinating infrastructure that partner organisations jointly construct. System-based Trust complements rather than replaces relational trust: it carries operational coordination and frees relational trust for strategic collaboration and exception handling, producing Hybrid Inter-organisational Relationships as the outcome of successful transformation. The framework applies to small and medium-sized enterprise ecosystems with limited numbers of trading partners; larger-scale consortia and power-asymmetric networks fall outside its scope. Our findings reposition smart contracts as occasions for capability development rather than substitutes for inter-organisational trust.
