Project Network: Defining the Concepts
Michael Mauboussin argued that a dominant network becomes a source of sustainable competitive advantage that, once entrenched, is difficult to dislodge. Mike Buckley suggested that we explore network effects to develop a set of criteria for evaluating stocks that might then be used for a Project Network, modeled after our Project Hunt. Mike then offered a set of questions drawn from criteria for critiquing net stocks, hoping that we can improve both the criteria and the process as we go along.
Three models are significant here: (a) Rogers's Diffusion of Innovations Model (DIM), Moore's Technology Adoption Life Cycle (TALC), and Mauboussin's Network Effects Model (NEM). A glossary of definitions concerned with innovation, technology adoption, and network effects is introduced to help specify a common conceptual language. A few definitions of key Web traffic metrics are also appended.
If we use the same "words" in the same way, we can reduce errors both in usage and understanding. Definitions are not arbitrary when they are based upon a model or theory. A definition of a concept should faithfully translate the meaning that is specified or implied by the model. The following definitions attempt to faithfully translate or systematize the less familiar concepts in the DIM and NEM models. However, they reflect my good faith, best-effort interpretations, amendments, and additions. This is not a complete or final list; it remains open to your critiques, additions, and amendments.
Glossary of DIM and NEM Concepts.
Innovation: an idea, practice, or object that is perceived as new by the individual or other unit of adoption.
Diffusion of Innovation: the process by which an innovation is communicated through certain channels over time among the members of a social system.
Social System: a set of interrelated units that are engaged in joint-problem solving to accomplish a common goal.
Network: a netlike system of links, whether physical or virtual, that serves as a communication channel between elements, whether nodes, individuals, social units, or devices.
Connection: an active link in a network, for example, A speaks to B, whether by phone [physical connection] or in person [virtual connection].
Value: the value of an asset is determined by the expected benefits that it will generate.
Law of Expectancy: when predicting future values, investors use their expectations to predict the likelihood and magnitude of future benefits.
Network Value Law: the value of a network increases as the number of connected elements increase: the bigger, the better, then the more expected benefits.
NV Corollary: the [expected] value of connecting to a network depends on the number of already connected elements [people or devices that are expected to connect].
Metcalfe's Law: As the number of nodes [elements] increases arithmetically, the value of the network expands exponentially.
Network Effect: the value of a good increases because the number of connected people [the number of compatible devices] consuming the good increases.
Direct Network Effect: The value of a good increases as the number of people, who are connected by a physical network, consuming the good increase.
Virtual Network Effect: The value of a good increases as the number of people, who are connected by a non-physical social network, consuming the good increase.
Indirect Network Effect: The value of a good increases as the number of people using compatible devices [a variety of complementary devices] increase.
Law of Expanding Effects: Network effects have the potential to increase by steps through three degrees of magnitude, expanding [retracting] from: (a) positive [negative] feedback, to (b) increasing [diminishing] returns, to (c) virtuous [vicious] cycles.
Feedback: Feedback is a first-degree dynamic process that reduces the difference between the present state and a goal state to zero through an information loop. For positive [negative] feedback, the goal state is either fixed and positive [negative] or open-ended but directional, permitting continuing incremental advances [declines].
Increasing [Diminishing] Returns: A second-degree dynamic process in which positive [negative] outcomes produced by positive [negative] feedback reach critical points as the effects continue cumulating: how success [failure] begets success [failure] to create inflection [deflection] points of exponential change; how a business that is first to scale and establish critical mass [fails in the competition to establish critical mass] continues permanently to build [diminish] on its gains [losses] from positive [negative] feedback.
Virtuous [Vicious] Cycle: A third-degree dynamic process in which value drivers are impacted by increasing [diminishing] returns that reach economically critical points to produce interrelated effects: escalating demand increases revenues, while decreasing costs that push margins higher that yield more profits and/or lower prices that increase demand, restarting the cycle; all at the expense of its competitors who are caught in a vicious cycle of decreasing demand, revenues, and higher costs from attempting to stimulate demand that increasingly lower margins and profits.
Brian Arthur's Law: Of networks, there shall be few.
Attributes of Innovations: Individuals and other social units judge innovations by perceived attributes: (a) relative advantage: the degree to which an innovation is perceived as better than the idea [process, object] that is supersedes; (b) compatibility: the degree to which an innovation is perceived as consistent with the existing values, past experience, and needs of potential adopters; (c) complexity: the degree to which an innovation is perceived as relatively difficult to understand and use; (d) trialability: the degree to which an innovation may be experimented with on a limited basis; (e) observability: the degree to which the results of an innovation are visible to others.
Stages in Adoption: the innovation decision process occurs in five stages containing sixteen hierarchically ordered processes: (A) Knowledge Stage: (1) recalling information, (2) comprehending messages, (3) becoming knowledgeable or skillful; (B) Persuasion Stage: (4) liking the innovation, (5) discussing it with others, (6) accepting the message about the innovation, (7) forming a positive evaluation, (8) receiving support for he positive evaluation from the social system; (C) Decision Stage: (9) deciding to seek more info, (10) deciding to try the innovation; (D) Implementation Stage: (11) acquiring fresh information from using the innovation, (12) using the innovation regularly, (13) continued use; (E) Confirmation Stage: (14) recognizing benefits of use, (15) integrating innovation into ongoing routine, and (16) promoting the innovation to others.
Innovativeness (Adoptiveness; Susceptibility): the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas [processes, objects] than other members of a social system.
Rate of Adoption: the relative speed at which members of a social system adopt an innovation.
Adoption, Diffusion, or S-Shaped Curve: When plotted on the basis of frequency per unit/time, the curve of adoption creates a normal curve. When plotted as a cumulative curve, the S-shaped curve is created.
Adopter Categories: Based on a division of the normal curve, five ideal types were specified by Rogers [by Moore]: (a) Innovators [technology enthusiasts], (b) early adopters [visionaries], (c) early majority [pragmatists], (d) late majority [conservatives], and (e) laggards [skeptics].
The Temporal Dynamics of the S-Shape Curve: The adoption curve rises slowly at first, hits an inflection point at approximately 10% to 20% level of cumulative adoptions, becoming "impossible to stop" and rising rapidly to the 50%, the mean, where it declines at a rate that mirrors the heart of the adoption inflection, until it begins to subside slowly.
Clusters: small units within a larger local social system that are variously organized around shared goals or meeting member needs, that is, through shared goals that organize goal-oriented transactions or by social needs for inclusion, power, or affiliation that organize interpersonal interactions. Clusters vary in their degree of social structure, cohesiveness, and transience-permanence.
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Friendship Clusters: small cohesive, longer-lived social units based upon mutual liking, interests, and values.
Connectors: links between individuals in different clusters, including both local connectors with a community of clusters and long-distance connectors that create small-world effects.
Small-World Effect: the empirically demonstrated phenomenon that apparently distant individuals share social connections that are rarely separated by more than six degrees [six connectors].
Cascade Effect: the process of rapid diffusion [cascading] of innovation [rate of adoption] that occurs within cohesive clusters; the expression in action of direct or virtual network effects.
Connected Cascades: a count of the number of cascades that share at least one connection.
Cascade Spread: the fanning out of the cascade effect to new clusters through connectors; once a connection is formed, each new cluster has the potential to cascade, to adopt and spread the innovation.
Cascade Generations: The first generation is the origin cascade for diffusion; the second-generation cascades are descendants from first-degree connections to different cascades; the third-generation cascades descend from second-degree connections, etc.
Key Web Metrics: traffic measures from Web sites that have been empirically found to be value drivers, including reach, stickiness, and customer loyalty.
Reach: the number of unique individual who visit a site, stated as a percentage of the (active or total) web surfing population.
Stickiness: the ability to retain a visitor at the site once a customer has arrived, measure by time spent at the site and/or the number of pages viewed at the site.
Loyalty: the ability to generate repeat visits from earlier visitors, the frequency of return visits per unit of time.
I hope this helps. If you have read this far, it may have.
Don |
