## 1.1 Phenomenon or Problem 2025-06-09 | Paragraph | Title | Key Sentence | | --------- | --------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **P1** | **The Entrepreneurial Sequencing Dilemma** | Entrepreneurs face a critical sequencing dilemma when building ventures – whether to secure resource partners first or validate customer demand before committing resources. | | **P2** | **Costly Failures: Infrastructure-First Disasters** | Better Place built extensive battery-swapping infrastructure before securing EV adoption, burning $850M; Webvan scaled supply across 26 cities assuming demand would follow, losing $1B+. | | **P3** | **Strategic Success: Contrasting Approaches** | Tesla initially focused on proving market demand while scrambling to finalize supply chains; Redwood prioritized processing technology before scaling customers, achieving market dominance. | | **P4** | **Sequencing Impact: Beyond Individual Actions** | These contrasting outcomes reveal that sequencing decisions – beyond individual action merits – significantly influence venture trajectories and survival rates. | | Element | Details | | ------------------------------- | ----------------------------------------------------------------------------------------------------------- | | 🫀 **Intent** | Show the real-world importance of stakeholder sequencing using startup failure data. | | 👀 **Look for** | Reports or articles showing that premature supply investment or scaling without demand leads to failure. | | 🔑 **Keywords** | "startup premature scaling", "stakeholder sequencing failure", "Webvan case study", "product before market" | | 📚 **Sources** | CB Insights, Startup Genome, Harvard Business Review, MIT Sloan Management Review | | 💸 evaluator's acceptance<br> | [[💸(📝🪢) investor(poms)]] feels stakeholder sequencing problem are urgent and important<br><br> <br> | Early-stage ventures often face a **critical sequencing decision**: whether to secure supply-side resources (technology, capacity, partners) before demand materializes, or vice versa. Missteps in this prioritization can be fatal. **Webvan**, for example, spent **$1 billion** building automated warehouses and fleets in anticipation of online grocery demand[d3.harvard.edu](https://d3.harvard.edu/platform-rctom/submission/webvans-demise-or-when-technology-fails-to-meet-operations/#:~:text=I%20would%20argue%20that%20the,Not%20only%20were%20the%20warehouses). Its facilities ran at barely one-third capacity as customer adoption lagged[d3.harvard.edu](https://d3.harvard.edu/platform-rctom/submission/webvans-demise-or-when-technology-fails-to-meet-operations/#:~:text=after%20the%20warehouses%20began%20operating,xv), leading to over $1 billion in losses and bankruptcy by 2001[d3.harvard.edu](https://d3.harvard.edu/platform-rctom/submission/webvans-demise-or-when-technology-fails-to-meet-operations/#:~:text=sluggish%20growth%20of%20the%20consumer,acquire%20a%20new%20customer%2022)[d3.harvard.edu](https://d3.harvard.edu/platform-rctom/submission/webvans-demise-or-when-technology-fails-to-meet-operations/#:~:text=Although%20some%20analysts%20argue%20that,realized%20that%20people%20find%20too). Conversely, **Better Place** invested **$850 million** in electric vehicle battery-swapping infrastructure up front[reuters.com](https://www.reuters.com/article/business/environment/electric-car-company-better-place-shuts-down-after-burning-through-850m-idUS3895319175/#:~:text=Better%20Place%20raised%20about%20%24850,Stanley%20and%20VantagePoint%20Capital%20Partners), but fewer than 1,000 cars ultimately used the network[reuters.com](https://www.reuters.com/article/business/environment/electric-car-company-better-place-shuts-down-after-burning-through-850m-idUS3895319175/#:~:text=The%20bet%20was%20risky%20because,from%20adopting%20any%20one%20standard)[reuters.com](https://www.reuters.com/article/business/environment/electric-car-company-better-place-shuts-down-after-burning-through-850m-idUS3895319175/#:~:text=Better%20Place%20became%20one%20of,from%20Israeli%20President%20Shimon%20Peres). These failures illustrate _overage-cost_ disasters – heavy resource investments with insufficient demand. On the other hand, emphasizing demand first can also be risky: Tesla’s first model (Roadster) amassed eager customers without an established manufacturing base, forcing costly last-minute logistics to deliver on promises (an _underage-cost_ problem). The initial Roadster supply chain spanned three continents and 10-week lead times, so when early units needed design fixes, Tesla had to scramble and incur millions in expedited shipping and rework. Why do entrepreneurs struggle to prioritize action? Given the premise that early stage entreprenuers will fail if they cannot find ANY pair of resource partner AND cutomer who accept proposed value proposition ([[📜🟦_fine+22_integrate(om-theory, ent-practice)]]), we analyze three core challenges: Table 1 summarizes these core challenges. As illustrated, early-stage entrepreneurs face a fundamental challenge of sequencing commitments to different stakeholders under extreme uncertainty (Knight, 1921). A venture must decide whether to first invest in **supply-side** capabilities (e.g. developing product and operational capacity) or to secure **demand-side** commitments (e.g. acquiring early customers or market partners). Committing too heavily to one side without signals from the other can lead to wasted resources or missed opportunities – a tension analogous to the classic newsvendor problem in operations management that balances overstocking versus stockouts (Arrow et al., 1951). We introduce the **STRAP (Stakeholder Prioritization)** framework to formalize this sequencing decision using a newsvendor-style analytic logic. STRAP provides a decision-analytic approach for entrepreneurs to determine whether to “build supply first” or “create demand first,” by modeling stakeholder commitments as analogous to inventory decisions under uncertainty and optimizing the expected payoff trade-offs of each sequence. LP: Context & Prevalence of the Sequencing Dilemma The stakeholder sequencing dilemma represents a systemic failure mode that claims the majority of entrepreneurial ventures, transcending industries and geographies. Industry data consistently reveals that premature scaling—committing resources before achieving product-market fit—ranks as the primary cause of startup failure, with estimates suggesting over 70% of failed startups scaled prematurely by building capacity before validating demand. This pattern manifests across diverse sectors: from Webvan's $1.2 billion collapse after building warehouse infrastructure before confirming customer willingness-to-pay, to countless health-tech startups that invested in regulatory compliance before validating clinical demand. The sequencing challenge proves particularly acute in platform businesses where entrepreneurs must solve "chicken-and-egg" problems of attracting both sides of a two-sided market. Recent analysis of startup post-mortems reveals a consistent pattern: entrepreneurs systematically misjudge whether to prioritize customer acquisition or resource partner development, leading to what the Lean Startup framework calls exhausting "runway"—the finite time before a startup must "achieve lift off or fail". This isn't merely poor execution; it reflects a fundamental decision-making challenge where the wrong sequencing choice creates irreversible commitment to a failing path. --- Prior research suggests that entrepreneurship as a discipline has lacked unifying theories or frameworks to guide such decisions. This stands in contrast to other fields—e.g. Porter’s Five Forces in strategy or the CAPM in finance—and leaves founders without clear principles for _decision-making under uncertainty_. Scholars have begun calling for an “**operations for entrepreneurs**” approach, noting that while traditional operations management teaches inventory, supply chain, and capacity planning for established firms, it offers little guidance for new ventures where both demand and supply are unknown. At the same time, emerging research on **Bayesian entrepreneurship** emphasizes that startups must engage in systematic experimentation, learning, and adaptation. Founders should iteratively test hypotheses about their market and technology—effectively treating venture development as a series of experiments—while judiciously managing the costs of failure. However, even with a philosophy of “experiment and learn,” entrepreneurs still need actionable models to decide _which_ experiments to run (e.g. invest in product development or in market development first). Notably, the burden is not only on the founder’s learning; stakeholders (customers, investors, partners) must also be convinced and aligned through these experiments. Thus, a comprehensive framework must account for how early sequencing choices signal and influence other stakeholders in the venture’s ecosystem. **STRAP**—the Stakeholder Prioritization framework proposed in this paper—addresses these gaps. We draw an analogy to the classic _newsvendor problem_ in operations, which balances the cost of ordering too much inventory (overage) versus too little (underage). In a startup context, the “inventory” is not physical stock but organizational commitments—building a product or operations capability in advance versus waiting to secure customers. We model the venture’s two pivotal stakeholder classes (supply-side resource partners and demand-side customers) with binary states (absent (0) or present (1)) and associated payoffs. This model yields a simple, rigorous decision rule based on the ratio of underage to overage costs. By framing stakeholder sequencing as an optimization of expected stakeholder _conversion_ value, STRAP offers an intuitive yet formal tool to guide entrepreneurs. It speaks directly to the root causes: providing a unifying decision logic for rigorously comparing complex sequencing scenarios (addressing C1); incorporating uncertainty via probabilities to equip entrepreneurs with actionable evaluation metrics (addressing C2); and extending to multi-stakeholder interdependencies and institutional-level generalizability (addressing C3). In what follows, Section 2 develops the STRAP model in increasing levels of complexity (deterministic, stochastic, and interdependent cases). Section 3 then translates the model’s insights into a practical decision framework, including visual decision maps and case illustrations (Tesla, Better Place, Webvan) that demonstrate the cost consequences of sequencing errors and how STRAP could have averted them. Section 4 discusses theoretical and managerial implications, mapping each component of STRAP back to the core challenges and highlighting how innovation ecosystem approaches (e.g. MIT’s _The Engine_, Flagship Pioneering) embody similar principles. We conclude in Section 5 with a summary of STRAP’s contributions to entrepreneurship theory and practice as a rigorous, adaptable, yet intuitive framework for early venture decision-making. ## 1.2 Literature Review: Nature of Need and Solution Direction (3+3 paragraphs) 2025-06-09 spectrum of too operational and too strategical with substitutive utility (instead of product market fit) lowers false positive probability, offers flexible strategy and operations (easier to pivot if there's better matching between rp and c), and is more meaningful (reverse engineer given perceptual dimension). compared to product and market fit. to construct the meaning of startup strategy and operations, it should be fit around stakeholders, rather than abstract product market fit | Paragraph | Title | Key Sentence | | --------- | --------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **P5** | **Nature of the Need: Multi-Dimensional Decision Problem** | Entrepreneurs face a constrained optimization problem with freedom to choose stakeholder engagement order, resource limitations allowing only sequential engagement, uncertainty in acceptance probabilities, and noisy learning from stakeholder interdependence. | | **P6** | **Strategic Need: Operationalizing Bayesian Entrepreneurship** | While Bayesian entrepreneurship advances heterogeneous belief theory, its focus on "venture success probability" as a random variable creates operationalization barriers for entrepreneurs (who cannot control others' beliefs) and false positive problems for social planners (due to unmeasurable, stochastic, interdependent belief structures). | | **P7** | **Operational Need: Integrated Operations Framework** | Existing entrepreneurship literature lacks quantitative frameworks that integrate operations management principles with strategic stakeholder engagement under uncertainty and resource constraints. | | P7.5 | explain integrating strategy and operations | In our framework, entrepreneurs face a two-stage decision process that bridges strategic and operational dimensions. The first stage encompasses strategic "what and why" decisions—establishing value propositions through production specifications, pricing, and quality levels that determine stakeholder acceptance probabilities. The second stage addresses operational "how and when" decisions—optimizing stakeholder engagement sequence under resource constraints using newsvendor logic to balance the overage costs of idle capacity against the underage costs of missed opportunities. This integrated approach transforms the abstract challenge of managing heterogeneous beliefs into a concrete state transition system with measurable binary variables, providing entrepreneurs with actionable decision-making tools for navigating the complex path from idea to market validation. | | **P8** | **Strategic Solution: Acceptance as Measurable State Variable** | We reframe entrepreneurial validation as a two-stage decision process where entrepreneurs first specify value propositions that directly affect resource partners' and customers' acceptance probabilities, transforming heterogeneous beliefs into operationalizable binary variables through stakeholder commitment verification. | | **P9** | **Operational Solution: Inventory model (why Newsvendor?)** | We apply newsvendor logic to the second-stage decision of stakeholder prioritization, where entrepreneurs optimize engagement timing by reacting to forecasted demand of self-fulfillment needs, balancing the 'overage costs' of idle capacity against the 'underage costs' of missed opportunities.<br><br>[[📜SO_johnston02(caution startup)]]<br><br> | **SUBGOAL: given we accept first BE 🏛️pillar assumption “heterogeneous belief”, I explain two problems of second BE 🏛️pillar, focusing on its choice of venture success as random variable to analyze the effect of heterogeneity  - problem1: entrepreneurs can’t operationalize - problem2: social planners can’t prevent high false positive problem  - cause: heterogeneous belief on “probability of success” is unmeasurable, stochastic, interdependent solution: framing end-of-nail test as finding feasible solution of three dimension decision vector: value proposition, customer, resource partner (i.e. checking whether (v,c,r) exists for “min 0 s.t. A(v,c,r) =b”). [Correctness of solution can be verified](https://en.wikipedia.org/wiki/NP-completeness) quickly by asking two questions: “resource partner accepted value proposition? [yes/no]”, “customer accepted value proposition? [yes/no]” solution is operationalizable to entrepreneurs as 1) value proposition is variable (unlike one and the other’s belief) and 2) acceptance given this variable is measurable. solution can also lower false positives (=(1-p)s2) as commitment from stakeholders increases probability of success (p) and multiple testing decreases sensitivity (s2).**