Two decades ago, Vargo and Lusch (2004) challenged the traditional goods-centred paradigm by introducing Service-Dominant (S-D) logic, a relational, co-creative view of value creation. S-D logic reframes exchange as the reciprocal provisioning of service – the application of knowledge and skills for the benefit of others – and depicts value as co-created through interactions among firms, customers, and other actors. This perspective proved influential in marketing and management by shifting focus from tangible product outputs to intangible service processes, collaborative ecosystems, and customer experiences. S-D logic is built on the insight that operant resources (e.g. knowledge and competencies) rather than operand resources (physical goods) are the fundamental source of competitive advantage. By treating customers as co-creators of value rather than passive recipients, S-D logic offered a more dynamic, customer-centric worldview than a Goods-Dominant (G-D) logic that preceded it.
However, as the business landscape undergoes a dramatic transformation driven by big data, machine learning (ML), generative AI, and algorithmic decision-making, it is imperative to ask whether the dominant logic of value creation must evolve once again. We are now in an era where artificial intelligence (AI), data analytics, and algorithms permeate organisational processes. Firms like Amazon, Google, Alibaba, and Uber coordinate value creation and capture not just through service exchanges, but through millions of automated decisions – recommendation algorithms, dynamic pricing engines, real-time logistics optimisations, fraud detection models, and more – executed every minute across their operations. In short, the primary source of competitive advantage appears to be shifting toward the capacity to make superior decisions at scale and speed by leveraging AI and vast data.
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I propose Decision-Dominant (D-D) logic as the emerging paradigm to describe this shift. In essence, I argue that decisions – the choices that allocate resources, personalise offerings, set prices, detect risks, and configure operations – are becoming the fundamental unit of value creation and capture in modern firms. In an era of ubiquitous data and powerful algorithms, superior value creation or capture emerges from the quality, speed, and scalability of the decisions made. Firms that can consistently make high-quality decisions quickly and execute them widely (often via automation) hold a substantial advantage over those that cannot.
I label this perspective “decision-dominant” logic to emphasise that intelligent decision processes, powered by analytical and AI capabilities, now dominate value creation and capture much as service interactions did in the S-D logic paradigm.
This article series, written from a strategic management perspective, develops the conceptual foundations of Decision-Dominant logic as a complement – not a replacement – to the existing goods-dominant and service-dominant logics.
In this first article, I review the core principles of Goods-Dominant (G-D) and Service-Dominant (S-D) logic, highlighting their limitations in explaining new mechanisms of value creation and capture in the algorithmic age. Next, drawing on decision science and emerging phenomena like algorithmic management, I articulate why decision-making competence should be elevated to the centre of understanding competitive advantage. I then propose several foundational premises of Decision-Dominant logic, illustrating how it builds upon but also extends beyond G-D and S-D logic. Throughout, I anchor my arguments in established theories – including dominant logic in strategy, dynamic capabilities, organisational learning, organisational information processing, and the resource-based view – to ensure conceptual rigour and connection with extant management theory. I also anticipate potential criticisms and challenges, such as concerns regarding the definitional scope of “decision,” the novelty of this perspective in relation to existing decision theories, and the ethical implications of algorithmic decision systems, and discuss how Decision-Dominant logic addresses these issues. In closing, I argue that Decision-Dominant logic offers a timely and complementary lens for understanding value creation and capture in the algorithmic economy, enriching our theoretical toolkit alongside G-D and S-D logic.
