The Change Function: Why Some Technologies Take Off and Others Flop

Overview

The Change Function (2006) by Pip Coburn arrives as a sharp corrective to a decade of excess. The dot-com bust had just laid bare a fundamental misunderstanding: technology companies and investors alike assumed that breakthrough technology — something genuinely new and useful — would automatically find a market. Coburn says no. The market does not care how clever your technology is. Markets care about a much simpler calculation running in every consumer's head.

Coburn's thesis is that technology adoption follows a change function — a ratio of perceived value to adoption anxiety. When perceived value exceeds anxiety, adoption happens. When it does not, the product fails, no matter how innovative it is or how much capital has been poured into it. This formula is deceptively simple, but it explains patterns that no amount of venture capital enthusiasm can override.

The Author

Pip Coburn worked as a technology analyst at UBS for many years, covering software and internet companies through the peak of the dot-com bubble and its catastrophic collapse. His experience during that period — watching analysts, CEOs, and investors systematically misjudge which technologies would succeed — shaped this book. Coburn styles himself "the frog," a reference to the fable of the frog in gradually heating water that does not notice rising temperature until it is too late. Under that analogy, Coburn sees himself as someone who stays in the boiling water long enough to understand how the companies actually behave, rather than jumping out at the first sign of heat.

His vantage point at UBS gave him access to thousands of investor meetings, product demonstrations, and management briefings across the technology sector. He watched brilliant engineers pitch products that made no commercial sense, watched companies burn hundreds of millions of dollars pursuing markets that did not exist, and watched the market punish "cool" technology with the same disregard it showed for any other commodity.

The Central Problem

The book opens by identifying the single most common mistake in technology investment and product strategy: confusing technological innovation with market demand. Coburn's core argument is that the two are not the same thing — and that founders, VCs, and corporate strategists routinely conflate them.

Technology innovation → Does not automatically → Market adoption

The gap between them is where Coburn's change function lives. It is not enough that a product is smart, or new, or technically superior. It must pass a consumer psychology test: will the receiver of the technology change enough of their existing behavior to obtain its benefits? If the answer is yes, the product can win. If the answer is no, it will not — no matter how much you spend on marketing.

The Change Function Formula

Coburn's framework is often summarized as:

flowchart LR
    A["Anxiety (A)"] -->|"Divides"| CF["Adoption Decision"]
    V["Perceived Value (V)"] -->|"Divides"| CF
    CF -->|"If V > A"| ADOPT["Adoption Happens"]
    CF -->|"If V < A"| REJECT["Product Fails"]

Anxiety is not a vague feeling. Coburn defines it as the sum of everything a consumer must change — habits, workflows, existing investments in learning, existing equipment, social relationships around the technology — to make use of the new product. Perceived value is the benefit the consumer believes they will receive. Adoption happens when V exceeds A. Period.

The VCR as Prototype

Coburn uses the VCR as his archetype for successful adoption. The VCR — videocassette recorder — arrived in the market with genuine consumer benefit: you could record television programs and watch them when you wanted. But launching it required nearly zero change in viewing behavior. You still sat in your living room. You still watched the same channels. You just got to choose the time. The change function was nearly pure benefit with near-zero anxiety.

This contrasts sharply with products that required massive behavior change to realize modest benefits. Coburn details examples throughout the book — technologies that worked technically but required consumers to rebuild entire habits around them, technologies that forced users to abandon investments in existing systems, technologies that promised convenience but delivered complexity.

Why "Cool" Does Not Win

One of the book's most provocative arguments is that being cool — being talked about in the media, praised by early adopters, or praised by technology journalists — predicts failure as often as it predicts success. Coburn observes that genuinely cool technology is often cool precisely because it requires behavior change. The people who adopt early do so because they are the kind of people who enjoy changing behavior. The mainstream market is not.

The pattern of "cool technology failing" recurs throughout history: early Wi-Fi devices, Tablet PCs before the iPad, WebTV, countless "information appliances" that made engineers excited and confused everyone else. Each of these required consumers to learn new workflows, abandon existing devices, and invest time in mastering interfaces that engineering teams had spent years perfecting. Coburn argues that this is not a market taste problem — it is math. The anxiety component of the change function is simply too high.

Dot-Com Bust Lessons

The book devotes substantial attention to the dot-com bust as a real-time case study in change function failure. The late 1990s produced an extraordinary number of products and business models that treated consumers as abstractions. Companies built platforms that required consumers to adopt entirely new shopping habits, communication patterns, and payment behaviors — all simultaneously — and expected investors to fund this because the long-term technology addressable market was enormous.

Coburn's counter: consumers do not care about addressable market size. They care about the next behavior change they have to make. The failure was not in the technology or the market size. It was in the absence of any analysis of the change function before billions of dollars were deployed.

Predicting Winners and Losers

A significant portion of the book is devoted to practical prediction. Coburn argues that, contrary to the popular belief that technology prediction is impossible, certain conditions make prediction reliable:

• When the change function is overwhelmingly positive (V >> A)
• When the product enables behavior the consumer already wants to do, more easily
• When the product degrades gracefully — consumers can use it partially while existing habits remain intact

These conditions are not rare. Coburn argues that analysts and investors routinely overlook them because they are too focused on technology rather than consumer behavior. Prediction failure is usually a focus failure, not a knowledge failure.

Convenience as the Hidden Variable

Convenience is not a separate concept in the book — it is embedded in both V and A. A convenient product lowers anxiety (it fits into existing habits more easily) and simultaneously increases value (the benefit arrives with less friction). Coburn treats convenience as the most underrated variable in product strategy because it acts on both sides of the equation simultaneously.

Place in the Genre

The Change Function occupies a distinctive position at the intersection of technology analysis, behavioral economics, and product strategy. It is more focused on consumer psychology than Crossing the Chasm, more empirical than most management books, and more skeptical of technology enthusiasm than the vast majority of technology writing from the era. It predates but anticipates much of the behavioral economics popularization that followed the 2008 financial crisis.

Key Ideas

| Idea | Description | |------|-------------| | The Change Function | V > A must hold for adoption; value divided by anxiety | | Anxiety as Behavior Cost | Every consumer behavior change has a real cost | | VCR as Prototype | Successful adoption = high value, near-zero anxiety | | Cool Technology Fails | Early adopter enthusiasm is a warning sign for mainstream | | Dot-Com Bust as CF Failure | Bust was predictable from change function analysis | | V > A is Sufficient | No marketing budget can overcome a negative change function | | Gradual Adoption Wins | Products that let consumers adopt incrementally succeed | | Convenience is Dual-Action | Lowers anxiety and raises value simultaneously | | When Crystal Balls Work | Prediction is reliable when CF is obviously positive | | Predictor vs. Backfill | Post-hoc rationalization is not prediction; real prediction works |

Core Concepts

The Change Function: C · P / V

Coburn arrives at the change function through consumer interviews, product launch analysis, and investor meeting observation. The formula he develops is:

Change Function = (C × P) / V

C = Consumer Anxiety
P = Pain of Change
V = Perceived Value

Adoption happens when V exceeds the product of C and P. The formula captures something subtle: anxiety and pain are multiplicative, not additive. A product that is moderately anxiety-inducing AND moderately painful to adopt will fail even if the value is high. A product with very low anxiety and very low pain can succeed with modest value.

The Variables Defined

C — Consumer Anxiety: The psychological resistance a consumer feels toward adopting something new. This is not rational. It includes fear of looking foolish, fear of obsolescence of current skills, fear of social disapproval, and the general human preference for the known. Coburn treats C as real — the model fails if you pretend consumers are rational actors.

P — Pain of Change: The actual logistical friction of adoption. Does the product require new equipment? Does it require learning new workflows? Does it require unlearning old ones? Does it require involvement from other people (spouses, colleagues, IT departments) who also need to change? Pain is measurable in time, money, and habit disruption.

V — Perceived Value: The benefit the consumer thinks they will receive. This is subjective — it is what the consumer believes, not what the product objectively delivers. A product that is objectively revolutionary will fail if consumers do not perceive the value, either because it is poorly communicated or because the benefit is too abstract to grasp before purchase.

The VCR Adoption Phenomenon

Coburn returns to the VCR as his defining case study because it demonstrates the change function operating at near-pure efficiency:

flowchart LR
    subgraph VCR["VCR Adoption Equation"]
        direction TB
        VCR_C["C: Low<br/>No social stigma,<br/>easy to understand"]
        VCR_P["P: Low<br/>Plug in device,<br/>load tape"]
        VCR_V["V: Clear<br/>Record TV,<br/>watch when you want"]
    end

    VCR_C -->|"×"| CP_VCR["C × P = Very Low"]
    VCR_P -->|"×"| CP_VCR
    CP_VCR -->|" divided by high V ="| ADOPT_VCR["Adoption: Fast and<br/>Widespread"]
    VCR_V -->|"Perceived Value"| ADOPT_VCR

    style ADOPT_VCR fill:#4CAF50,color:#fff
    style CP_VCR fill:#f44336,color:#fff

The VCR is not technically sophisticated — anyone could describe its function in one sentence. It fits cleanly into an existing behavior (television viewing). It requires no new skills. The value proposition — "watch what you want, when you want" — is immediately comprehensible. The result: VCR adoption went from zero to majority household penetration within roughly a decade, driven almost entirely by word of mouth and display in retail stores.

This is the benchmark. Most successful consumer technology approximates the VCR pattern: clear value, low behavior change, minimal social friction.

Why Consumers Do Not Adopt Products

The most important section of the book for product builders is Coburn's systematic refutation of what he calls "the marketing myth" — the belief that poor adoption is a marketing problem. Coburn argues it is almost never true. Consumers do not fail to adopt because they have not heard enough about the product. They fail to adopt because the change function is negative. No amount of advertising can make V > A when the consumer's C and P components are too high.

The Real Reasons for Non-Adoption

Habit Lock-In: Consumers have established patterns that work well enough. The cost of changing them is real even when the new product would be objectively better. Coburn cites examples of consumers who refused to switch to digital cameras for years, despite the superior image quality, because the habit of dropping film off at the photo lab was deeply embedded.

Social Anxiety: Some technologies make consumers feel old, out of touch, or uncool. Coburn observes that this is most acute for technologies that require visible behavior change — someone typing on a BlackBerry in 2004 looked productive; someone struggling with a Palm Pilot looked lost. The social cost of visible confusion is real.

Network Incompleteness: Some products require other people to also adopt before they become useful. A fax machine is useless if no one you know has one. Coburn treats this as a P component — the pain of being an early adopter in an incomplete network.

Complexity Stacking: The most lethal adoption killer is when a product requires multiple behavior changes simultaneously. Coburn points to early personal finance software as an example: to use it, consumers had to learn new software, reorganize their financial thinking, enter months of historical data, and trust a new piece of software with sensitive information — all at once. Most people did not.

The Dot-Com Bust Through the Change Function Lens

The dot-com bust of 2000–2002 becomes, in Coburn's analysis, the largest real-world demonstration of change function failure in history. Hundreds of billions of dollars were deployed into business models that violated the change function consistently:

flowchart LR
    subgraph DOTCOM["Dot-Com Bust: CF Violations"]
        B2B["B2B E-Commerce<br/>High P — retailers had to<br/>rebuild entire operations"]
        PORTALS["Web Portals<br/>High C — consumers did not want<br/>a new homepage"]
        INFRA["Infrastructure Plays<br/>Low V — value too abstract<br/>for most customers"]
        CONSUMER["Consumer Dot-Coms<br/>High P — new shopping,<br/>payment behavior required"]
    end

    B2B -->|"V < A"| FAIL1["Investor Losses"]
    PORTALS -->|"V < A"| FAIL2["Investor Losses"]
    INFRA -->|"V < A"| FAIL3["Investor Losses"]
    CONSUMER -->|"V < A"| FAIL4["Investor Losses"]

    style FAIL1 fill:#f44336,color:#fff
    style FAIL2 fill:#f44336,color:#fff
    style FAIL3 fill:#f44336,color:#fff
    style FAIL4 fill:#f44336,color:#fff

The common thread: in almost every case, the technology was genuinely innovative and the long-term market was real. The failure was in assuming that consumers or businesses would be willing to change enough behavior to access the value. Most were not. Coburn's prediction: the bust was not surprising once you applied the change function to the typical dot-com business plan.

Why "Cool" Does Not Win

Coburn's second major corrective is to the enthusiasm of early adopters and the technology press. He documents a recurring pattern: technology that generates intense excitement among engineers and technology enthusiasts consistently fails in the mainstream market. The reason is precisely the change function dynamic: technologies that excite early adopters tend to be technologies that require behavior change. Why? Because early adopters are the people who enjoy changing behavior. Mainstream consumers are not.

Case: Tablet PCs (pre-iPad). Multiple companies released tablet computers before Apple's iPad in 2010. Each was technically sophisticated, praised by reviewers, and eagerly adopted by technology professionals. None reached mainstream adoption. The reason: using a tablet required abandoning the keyboard-and-mouse paradigm that most workers had spent years mastering. The anxiety and pain outweighed the value for the mainstream market.

Case: WebTV (1996). WebTV allowed consumers to browse the internet on their television sets. It was technically clever, well-funded, and received significant media coverage. It failed. Consumers who wanted the internet used computers. Consumers who watched television did not want to browse the internet on a screen designed for viewing from across a room at low resolution.

Examples Across Technology Domains

Mobile Phones

The mobile phone industry provides the clearest long-running demonstration of the change function. Early mobile phones (1980s–1990s) were large, expensive, and required consumers to abandon their home phones. The change function was close to neutral. Adoption was slow and confined to business users. The breakthrough came when technology evolved to the point where V increased dramatically — the phone became a multi-purpose device — and P decreased — it shrank, became cheaper, and integrated into existing habits. The iPhone's success in 2007 was not primarily a technology breakthrough; it was a change function optimization. The touch interface lowered P (easier to use than buttons) while raising V (internet, apps, media in one device). C stayed low because it felt familiar.

Financial Services

Online banking provides an early example of change function dynamics. Early online banking required consumers to trust software with their finances, learn new interfaces, and abandon the ritual of visiting a branch or calling a phone representative. Coburn documents that early adoption was glacial precisely because C was high (trust) and P was high (new workflow). The breakthrough came when value shifted: online bill pay, real-time balance checking, and transaction history made the old behavior (filing paper statements, writing checks) seem obviously painful by comparison. V rose fast enough to overcome C and P.

ATMs provide an even earlier example. Initial resistance was high — consumers did not trust machines with their money. But the value (24-hour access, no wait, no teller interaction) was so high relative to anxiety that adoption accelerated once the first adopters demonstrated that the machines were safe.

Consumer Technology

Digital cameras illustrate a gradual change function shift. Early digital cameras were expensive, had poor image quality, and required consumers to learn new software and workflow. The change function was negative for most consumers. Film cameras worked well enough. Over time, as prices fell, quality improved, and printing services emerged (lowering P), the change function crossed zero. The crossover was not a single event — it was a gradual process where V climbed and P fell until adoption became inevitable.

The CPV Framework in Practice

Coburn provides a practical framework for applying the change function to product strategy:

The Three Levers

Lower C (Anxiety):

• Reduce the visible difference from current behavior
• Use familiar interfaces and metaphors
• Let consumers try the product with minimal commitment
• Social proof from people like the target consumer
• Graduated exposure — do not ask consumers to switch entirely on day one

Lower P (Pain of Change):

• Reduce learning requirements
• Make the product backward-compatible or additive
• Minimize the number of simultaneous changes required
• Offer support, training, and assistance at the moment of transition
• Design for partial adoption — consumers should get value before they have fully switched

Raise V (Value):

• Focus on the benefit, not the feature
• Make value visible before purchase (free trials, clear demonstrations)
• Address the comparison directly: "This is better than what you already do"
• Reduce the gap between promised value and delivered value
• The best value propositions map directly to existing consumer desires, not new ones you are trying to create

How to Predict Winners and Losers

Coburn's practical contribution is a prediction methodology rooted in the change function. He argues that accurate prediction is possible when analysts apply the framework rigorously rather than relying on technology enthusiasm or market size projections.

Conditions for Reliable Prediction

The change function is most predictive when:

1. V is obvious and immediate. The consumer can understand the benefit before purchase
2. C is low. There is no social stigma or fear associated with adoption
3. P is minimal. The product fits into existing habits or requires minimal new learning

Products meeting these conditions have historically succeeded at very high rates. Products violating one or more of them have failed at very high rates.

The Predictor vs. Backfill Distinction

Coburn makes a sharp distinction between real prediction and what he calls "backfill" — the practice of explaining why a technology succeeded or failed after the fact, using language that sounds predictive but was constructed in hindsight. Genuine prediction requires stating the change function outcome before the market result is known. Coburn argues that most technology analysts engage in backfill, not prediction, and that this is why their track records are poor.

The Role of Convenience

Convenience is the hidden variable that appears throughout the book even though it is not formally part of the C/P/V notation. Coburn treats convenience as an amplifier that acts on both sides of the equation simultaneously:

• Convenience lowers P — a convenient product requires less behavior change
• Convenience raises V — the same benefit arrives faster, with less effort

The result is that convenience improvements shift the change function in consumers' favor more powerfully than either a value increase or a friction reduction alone. Coburn argues that convenience is consistently the underrated driver in technology adoption and that most product strategists focus on features while convenience — the way the product fits into existing lives — is the actual competitive advantage.

Analysis

Strengths

• The formula is genuinely predictive. The C/P/V framework is not a post-hoc storytelling device. Coburn applies it systematically to the dot-com bust in real time, showing how it could have been anticipated. The framework's predictive power is its strongest feature — it gives analysts and founders something concrete to work with rather than vague "market timing" language.

• Timely and grounded in real failure. Unlike many technology books written during the dot-com boom that celebrated the bubble, The Change Function arrived with the wreckage still visible. Coburn's analysis is not theoretical — he watched the failures happen from inside the analyst community that enabled them.

• Democratizes good technology analysis. Coburn's formula requires no specialized financial education. Any founder, product manager, or investor can apply it. This accessibility — the book's ability to make sophisticated consumer psychology available to non-specialists — is a genuine contribution.

• Severe critique of the analyst profession. Coburn is unusually honest about the failures of his own profession. He documents how technology analysts at major firms systematically overrated companies with weak change functions, motivated by investment banking relationships rather than client interests. This candor was rare in 2006 and remains rare.

• The VCR as anchor case study is excellent. Using a familiar, universally understood technology as the prototype for all adoption analysis grounds the book. Readers immediately grasp the framework because they can map it onto a product they have lived through.

• Convenience as dual-action variable is sharp. Recognizing that convenience acts on both anxiety and value simultaneously (rather than as a third variable) is analytically elegant and practically useful. Most frameworks miss this compounding effect.

Weaknesses

• The formula is more descriptive than mathematical. Coburn presents C/P/V as a formula, but it is not operationalized — there is no unit of measurement for anxiety or pain. The book offers qualitative guidance but no quantitative framework. This limits its utility for rigorous market research.

• Limited cross-cultural evidence. The examples are largely US-centric. Consumer adoption patterns in Japan, Europe, and emerging markets can differ dramatically due to infrastructure, culture, and technology access. Coburn acknowledges this briefly but does not deeply address it.

• The dot-com bust dominates the narrative. Roughly half the book is devoted to analyzing the bust. While relevant, this means other technology cycles (the PC revolution, the rise of the internet in the 1990s, mobile phone adoption outside the US) receive less attention than they deserve.

• Pain quantification is undertheorized. Coburn identifies "pain of change" as a variable but does not develop a robust methodology for measuring it across different types of technology. What makes software pain different from hardware pain? The book gestures toward the distinction but does not work it out fully.

• Post-2006 technology cycles untested. The book's major test cases (dot-com, VCR, early mobile, pre-iPod consumer electronics) all predate the social media era, app store economy, and platform businesses. Whether the framework holds for products that primarily change social behavior (Facebook, TikTok, Instagram) rather than functional behavior (recording TV, managing finances) is not addressed.

• Ignores network effects. Coburn's framework is built for individual consumer psychology. Network-effect products — where value depends on other people adopting — require analysis beyond the individual change function. SMS, social media, and two-sided marketplaces have adoption dynamics that the basic C/P/V formula does not fully capture.

Comparison to Similar Books

| Book | Author | Key Difference | |------|--------|----------------| | Crossing the Chasm | Geoffrey Moore | Moore focuses on market segmentation and marketing strategy. Coburn focuses on consumer psychology and the individual adoption decision. They are complementary — Moore describes what to do after you identify the change function; Coburn explains why the chasm exists in the first place. | | The Innovator's Dilemma | Clayton Christensen | Christensen explains why incumbent firms fail to adopt disruptive technology. Coburn explains why consumers fail to adopt technology. Both address technology adoption but at different levels of analysis. | | Diffusion of Innovations | Everett Rogers | Rogers provides the academic sociology of adoption. Coburn provides the applied psychology of adoption. Rogers describes the adopter categories; Coburn explains the calculation that determines which category a consumer falls into. | | The Four Steps to the Epiphany | Steve Blank | Blank focuses on customer development methodology for startups. Coburn focuses on the psychological framework that determines whether customer development will succeed. | | Predictably Irrational | Dan Ariely | Ariely documents systematic cognitive biases. Coburn's change function is largely compatible with behavioral economics but is more focused specifically on technology adoption rather than general decision-making. |

Practical Applicability

• For startup founders: Directly applicable at the product-strategy stage. Before building or pitching, running the change function analysis on your target consumer is cheaper and more informative than most market research. Coburn gives founders permission to kill ideas that look good on paper but will fail in practice — this saves more capital than any pitch advice.

• For technology investors: The framework is most valuable as a sanity check during the excitement phase of a cycle. When every analyst is bullish on a category, running the change function against the most-lauded company provides a sobering check. Coburn's analysis of the dot-com bust demonstrates this clearly.

• For enterprise technology vendors: The framework explains why enterprise adoption cycles are long and why "land and expand" strategies work. Enterprise software buyers have high C (risk aversion) and high P (process change). Products that reduce P through integration with existing tools and reduce C through gradual rollout succeed more reliably than products requiring rip-and-replace.

• For product managers: The three levers — lower C, lower P, raise V — provide a direct prioritization framework. When deciding among feature proposals, ask which lever each feature affects. Coburn's emphasis on convenience as a dual-action variable is particularly relevant for product design decisions.

• For technology journalists and analysts: The predictor vs. backfill distinction is a direct challenge to the profession. Coburn's framework demands that analysts state their change function prediction before the market outcome and accept being wrong when the formula fails — a discipline that would improve the quality of technology coverage.

Omissions

• Quantitative modeling. The book never attempts to assign numerical values to C, P, or V. A scoring rubric or self-assessment tool would have made the framework more actionable for practitioners who need to make go/no-go decisions.

• Disruption from the consumer side. Coburn analyzes how consumers resist change. He does not address how consumer change functions evolve over time — how a generation raised with digital technology has a structurally lower C and P for new products, fundamentally shifting the adoption landscape.

• B2B adoption specifics. While Coburn touches on enterprise markets, the book primarily analyzes consumer behavior. B2B adoption has additional dimensions — procurement processes, multi-stakeholder decisions, IT department gatekeeping — that the basic change function does not fully address.

• Platform and ecosystem dynamics. Products whose value comes from a network (social media, marketplaces, communication tools) have adoption dynamics that differ from standalone products. Coburn does not address how network effects interact with the C/P/V formula.

• Post-2008 technology cycles. The 2010s saw a fundamentally different technology market — social media dominance, mobile-first design, app store economics, the platform business model. Coburn's examples predate all of these. The framework may apply, but the book does not demonstrate it.

• International market differences. The book draws primarily on US consumer behavior. Technology adoption in markets with different infrastructure, cultural attitudes toward novelty, and economic conditions may follow different patterns. Coburn addresses this only briefly.

Verdict

The Change Function is a quietly important book that did not receive the attention it deserved at publication. Published in the shadow of the dot-com bust, it appeared at a moment when the technology industry was least interested in hearing that consumer adoption is hard and that most new products fail. The book's core insight — that adoption is a function of perceived value relative to adoption anxiety — is simple, memorable, and durable.

The framework's greatest strength is also its greatest limitation: it is a model of individual consumer psychology, not a comprehensive theory of technology markets. It does not address network effects, platform dynamics, culturally specific adoption patterns, or the way that C and P themselves evolve as technologies become familiar. For those contexts, the framework needs supplementation. But as a first-order tool for asking "will this product be adopted?" it remains one of the most useful frameworks available.

For founders, investors, and product strategists, the book earns its place on the shelf. The C/P/V formula is worth internalizing. The VCR case study is worth revisiting when evaluating new product categories. The dot-com bust analysis is worth returning to during every technology cycle's peak enthusiasm phase. Coburn's frog perspective — staying in the water long enough to understand how things actually work rather than reacting to temperature changes — is a stance that serves any technology participant well.

Rating: 8/10 — Durable framework, strong case studies, timely but underappreciated. Read it alongside Crossing the Chasm for a complete picture of technology adoption: Moore explains the market structure; Coburn explains the consumer psychology underneath it.