Diffusion of technology

Extracted Attributes

• Popularizer

  Everett Rogers

• Core Elements

  Innovation, Adopters, Communication Channels, Time, Social System

• Key Influencer

  Social Capital

• Marketing Chasm

  The gap between niche appeal and mass self-sustained adoption

• Foundational Text

  Diffusion of Innovations (1962)

• Adopter Categories

  Innovators, Early Adopters, Early Majority, Late Majority, Laggards

• Measurement Metric

  Level of adoption (xj ct) across countries and years

• Critical Mass Point

  The boundary between early adopters and the early majority

• AI Diffusion Strategy

  Platform strategy focused on AI copilots and autonomous agents

Timeline

• Earliest technology diffusion data recorded in the Cross-country Historical Adoption Technology (CHAT) dataset. (Source: Web Search: The Spatial Diffusion of Technology)

  1825-01-01

• Everett Rogers publishes the first edition of 'Diffusion of Innovations', establishing the core theory. (Source: Wikipedia)

  1962-01-01

• Consultants at Regis McKenna, Inc. theorize the 'marketing chasm' as the barrier to mass adoption. (Source: Wikipedia)

  1989-01-01

• Research published on the factors affecting technology diffusion in agricultural extension. (Source: Web Search: FACTORS AFFECTING DIFFUSION OF TECHNOLOGY)

  2019-03-01

• Satya Nadella discusses the global diffusion of AI and Microsoft's platform strategy at the Davos fireside chat. (Source: Document 4e50eb82-56c2-4d20-910f-9a43912c1cd7)

  2024-01-15

Diffusion of innovations

Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread. The theory was popularized by Everett Rogers in his book Diffusion of Innovations, first published in 1962. Rogers argues that diffusion is the process by which an innovation is communicated through certain channels over time among the participants in a social system. The origins of the diffusion of innovations theory are varied and span multiple disciplines. Rogers proposes that five main elements influence the spread of a new idea: the innovation itself, adopters, communication channels, time, and a social system. This process relies heavily on social capital. The innovation must be widely adopted in order to self-sustain. Within the rate of adoption, there is a point at which an innovation reaches critical mass. In 1989, management consultants working at the consulting firm Regis McKenna, Inc. theorized that this point lies at the boundary between the early adopters and the early majority. This gap between niche appeal and mass (self-sustained) adoption was originally labeled "the marketing chasm". The categories of adopters are innovators, early adopters, early majority, late majority, and laggards. Diffusion manifests itself in different ways and is highly subject to the type of adopters and innovation-decision process. The criterion for the adopter categorization is innovativeness, defined as the degree to which an individual adopts a new idea.

Web Search Results

• [PDF] The Spatial Diffusion of Technology - Princeton University

  0.2). Hence, a technology-specific pattern of omitted variables would be necessary to account for the geographic patterns of technology dynamics that we uncover. Even though it is hard to imagine what this set of omitted variables could be, we acknowledge that our methodology cannot rule out this possibility. Our goal is to describe, for what we believe is the first time, the spatial patterns of technology diffusion across countries and to make the case that these patterns can be parsimoniously rationalized by models of spatial technology diffusion. [...] These other flows have also been shown to decline with distance due to transport cost and other migration restrictions. However, in clear contrast with technology, for people, goods, and investment flows, the effect of distance does not dissipate over time. Once technology is diffused, distance does not matter because ideas and innovations only need to be conveyed to each individual once and can then be used repeatedly afterwards. This particular characteristic of technology, which distinguishes it from other flows, is very much present in the data, both in our purely empirical specification, and in the estimated structural model. [...] Our empirical methodology is based on the following econometric specification: xj ct = βj 1cIj c + βj 2tIj t + βj 3yct + βj 4xj −ct + βj 5y−ct + ϵj ct (1) The dependent variable, xj ct, is the level of adoption of a technology j in country c in year t. Technol-ogy adoption measures come from the cross-country historical adoption technology (CHAT) dataset (Comin and Hobijn, 2004, 2009, and 2010). To maximize the country representation of the sample, we focus on 20 major technologies, listed in Table 1.13 Broadly speaking, the technologies studied belong to three sectors, transportation, communication, and industry. They cover, in an unbalanced way, technology diffusion in 161 countries going back until 1825. For each technology measure, (e.g.

• [PDF] Technology Diffusion: Measurement, Causes, and Consequences

  2.2.2 Traditional Measures of Technology Diffusion It is possible to extend extensive measures of technology diffusion to more disaggregated levels to study how producers have access to a technology once it has arrived to a country. Let’s suppose that potential adopters have a binary choice of whether to incur in a sunk cost of adopting the technology. After they incur in such a cost, they can use the technology indefinitely at no extra cost. Let’s define Yt as: Yt = mt M , (2.1) Technology Diffusion: Measurement, Causes, and Consequences 569 where M is the (fixed) number of potential adopters and mt is the number of producers that have adopted the technology at time t. This is how the diffusion literature has measured diffusion traditionally. [...] To explore the empirical importance of these mechanisms,Comin et al. (2013) (CDR, henceforth) measure how far a country is from the high-density points in the distribution of technology adoption in the other countries. They denote this measure of the spatial distance from other country’s technology SDT. A negative correlation between SDT and adoption, after controlling for country and time fixed effects, implies that countries that are further away from those where the technology diffuses faster tend to experience a slower adoption of the technology. [...] World technology frontier—At a given instant of time, t, the world technology frontier is characterized by a set of technologies and a set of vintages specific to each technology. To simplify notation, we omit time subscripts, t, whenever possible. Each instant, a new technology,τ,exogenously appears.We denote a technology by the time it was invented. Therefore, the range of invented technologies is (−∞, t].

• [PDF] FACTORS AFFECTING DIFFUSION OF TECHNOLOGY IN ...

  ISSN 2348-1218 (print) International Journal of Interdisciplinary Research and Innovations ISSN 2348-1226 (online) Vol. 7, Issue 1, pp: (311-313), Month: January - March 2019, Available at: www.researchpublish.com Page | 311 Research Publish Journals FACTORS AFFECTING DIFFUSION OF TECHNOLOGY IN AGRICULTURAL EXTENSION RADHIKA KRISHNAN ASSISTANT PROFESSOR IN ECONOMICS, NSS COLLEGE, PANDALAM, KERALA, INDIA Abstract: The technology dissemination in agriculture depends on several factors. The article states about diffusion studies conducted at various parts of the world. Rogerian theory analysed about the various factors affecting adoption and is considered as pioneering work in the field of diffusion studies. Farmers adopt a technology if it is compatible with their condition. Education, [...] EG (2018) agricultural development is a subset of rural development. Sustainable agricultural development also comprises safeguarding and maintaining productive capacity for the future and increasing productivity without damaging the environment or endangering natural resources. 2. LITERATURE REVIEW The studies on diffusion found out that the curve of diffusion of technology appears to be s-shaped. The extent of adoption of technology depends on several factors according to Rogers (1995) and he had proposed different stages in the process of diffusion. The first stage is the knowledge about the innovation. The farmers are depending more on indigenous knowledge and it is a bit difficult to convince them that the adoption of new technology is beneficial to them. People are divided into five [...] of African Economies, Vol. 20, number 4, pp. 562–595 Dasgupta Satadal (1989) The Diffusion of Agricultural Innovations In Village India, Wiley Eastern Limited, New Deihi Gathecha Christine Wangari, Bowen Michael, Silim Said and Kochomay Samuel (2012) The Diffusion of Agricultural Innovations: The Effectiveness of Communication Channels used in the Improved Pigeon pea Varieties in Makueni County, Kenya, Paper presented in International Conference on Agriculture, Chemical and Environmental Sciences (ICACES'2012) Oct. 6-7, Dubai (UAE) Reghunath Namitha(2016),Innovations in Technology Dissemination in Kannur District Thesis submitted to Kerala Agricultural University,Thiruvananthapuram Rogers, E.M. (2003). Diffusion of innovations (5th ed.). New York: Free Press. Röling Niels(2004)

• Diffusion of innovations - Wikipedia

  ## Failed diffusion [edit] Failed diffusion does not mean that the technology was adopted by no one. Rather, failed diffusion often refers to diffusion that does not reach or approach 100% adoption due to its own weaknesses, competition from other innovations, or simply a lack of awareness. From a social networks perspective, a failed diffusion might be widely adopted within certain clusters but fail to make an impact on more distantly related people. Networks that are over-connected might suffer from a rigidity that prevents the changes an innovation might bring, as well. Sometimes, some innovations also fail as a result of lack of local involvement and community participation. [...] Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread. The theory was popularized by Everett Rogers in his book Diffusion of Innovations, first published in 1962. Rogers argues that diffusion is the process by which an innovation is communicated through certain channels over time among the participants in a social system. The origins of the diffusion of innovations theory are varied and span multiple disciplines. [...] 31. ^ Eveland, JD (1986). "Diffusion, Technology Transfer and Implementation". Knowledge: Creation, Diffusion, Utilization. 8 (2): 303–322. doi "Doi (identifier)"):10.1177/107554708600800214. S2CID "S2CID (identifier)") 143645140. 32. ^ Rogers, EM (1995). Diffusion of Innovations. New York: Free Press. 33. ^ Greenhalgh, T.; Robert, G.; Macfarlane, F.; Bate, P.; Kyriakidou, O. (2004). "Diffusion of Innovations in Service Organizations: Systematic Review and Recommendations". The Milbank Quarterly. 82 (4): 607–610. doi "Doi (identifier)"):10.1111/j.0887-378x.2004.00325.x. PMC "PMC (identifier)") 2690184. PMID "PMID (identifier)") 15595944.

• [PDF] Factors Affecting Technological Diffusion Through Social Networks

  Jackson et al. (2017) pointed out that the impact of homophily on technology diffusion depends on the net effects of two countervailing forces. On one hand, the existence of homophily implies higher density within communities and—as dis-cussed earlier—that it could strengthen technology diffusion. It could particularly be the case when a complex contagion process is involved and high homophily can help incubate—within a group—adoption behaviors that might not take hold otherwise. On the other hand, high homophily often means that there are less cross-community ties, which could slow or even prevent widespread adoption of a technology. [...] Connecting Empirical Evidence on How Networks Shape Technology Diffusion Method of Review This survey of literature collects and reviews empirical studies that determine what factors affect technology diffusion through social networks. Following Phelps et al. (2012), this paper narrows its scope to empirical studies as they are rigorously tested and replicable, giving them—from the policy-making perspective—more credibility than untested theoretical insights. Moreover, convincing insights from theoretical or conceptual work are often explored in empirical research, so excluding the former should not significantly alter conclusions drawn from the review of the latter. [...] In contrast, the traditional economics literature tends to view the process of technology diffusion as the cumulative result of a series of calculations of rational individuals who each weigh his or her own incre-mental benefits of adopting a new technology against the costs of change, in a setting that is typically characterized by uncertainty, limited information, and financial constraints (Hall 2006; Brown et al. 2013; Jaffe 2015). 2. This paper adopts a broad definition of technology: A mapping of labor and capital inputs into de-velopment outcomes. This definition covers technologies such as artificial intelligence and 3D-printing, as well as financial innovations and advancement in management practices.