{"id":52732,"date":"2021-10-15T14:48:00","date_gmt":"2021-10-15T18:48:00","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=52732"},"modified":"2021-10-28T16:18:58","modified_gmt":"2021-10-28T20:18:58","slug":"diffusion-of-innovation-model","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/diffusion-of-innovation-model\/","title":{"rendered":"The Diffusion of Innovation Model (aka TALC) Explained"},"content":{"rendered":"\n<p>What is the <a href=\"https:\/\/www.shortform.com\/blog\/diffusion-of-innovation-theory\/\">Diffusion of Innovation<\/a> (aka TALC) model? How many categories of customers are there in the TALC framework? <\/p>\n\n\n\n<p>The Diffusion of Innovation model (also known as the <a href=\"https:\/\/www.shortform.com\/blog\/technology-adoption-life-cycle-chasm\/\">Technology Adoption Life Cycle<\/a>) was developed by Beal and Bohlen, two agricultural extension agents working for Iowa State College in the 1950s. They developed the model based on studies of when farmers started using new agricultural innovations, such as fertilizer and hybrid seed corn. Others soon generalized the model to technological innovations outside of agriculture.<\/p>\n\n\n\n<p>In this article, we&#8217;ll take a look at how the Diffusion of Innovation Model explains the <a href=\"https:\/\/www.shortform.com\/blog\/adoption-of-new-technology\/\">adoption of new technology<\/a> throughout the five stages of its maturity. <\/p>\n\n\n\n<!--more-->\n\n\n\n<h2 class=\"wp-block-heading\">The Diffusion of Innovation Model Explained<\/h2>\n\n\n\n<p>The Diffusion of Innovation model predicts that as a technology matures, the number of potential new buyers first increases (as the technology starts to catch on) and then decreases (as you run out of potential customers who haven\u2019t already bought it), following the profile of a bell curve. <\/p>\n\n\n\n<p>The area under the curve represents the total number of customers for the new technology. This area is divided into five psychographic categories (classifications by psychological and demographic criteria) of prospective customers, as shown in the figure below.&nbsp;<\/p>\n\n\n\n<p><img loading=\"lazy\" decoding=\"async\" width=\"624\" height=\"275\" src=\"https:\/\/lh5.googleusercontent.com\/uC3TgiKpBUqvTPpcXID4UhrsDhv7uCIk24N0QqoVLDQM_TTzlFw82stYZ7hiIzcXnyDhcsjkdoIpNhUNs6QVkccM-uqz6oAZHzfIFzC2mQd6puYU13vyAnzFr1hCHayOYfxk7l2i\"><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Psychographic Categories of Customers<\/strong><\/h3>\n\n\n\n<p>The Diffusion of Innovation model divides customers into five categories based on their \u201cpsychographics,\u201d the combination of psychology and demographics that dictates their purchasing behavior. These categories are:<\/p>\n\n\n\n<ul class=\"wp-block-list\" id=\"block-20450541-bddf-40ba-8677-d600047b8104\"><li><strong>Innovators<\/strong> are the first to buy new technology. According to Moore, they love new technology just because it\u2019s new technology, but they\u2019re often on a limited budget because they usually work highly technical jobs, rather than positions in upper management.<\/li><li><strong>Early Adopters<\/strong> are the second group to buy new technology. According to Moore, early adopters are usually visionary business managers: They don\u2019t value new technology for its own sake, but they have enough technical insight to identify the strategic advantages that new technology can provide.<\/li><li>The <strong>Early Majority<\/strong> are the third group to adopt new technology. According to Moore, they are pragmatic people, who are interested in leveraging new technology to improve their business, but also averse to the risks of unproven technologies. They are less technically inclined than innovators or early adopters, and tend to measure a high-tech product more by industry standards and the product\u2019s reputation than by direct assessment of the underlying technology.<\/li><li>The <strong>Late Majority<\/strong> are the fourth group to adopt new technology. According to Moore, they are more conservative and less technically competent than the early majority: They\u2019re not interested in getting ahead with technology, but they don\u2019t want to fall behind.<\/li><li><strong>Laggards<\/strong> do not adopt new technology willingly. If they adopt it at all, it will be old technology by the time they do.&nbsp;<\/li><\/ul>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Category Boundaries<\/strong><br><br>According to Geoffrey Moore, the author of <em>Crossing the Chasm<\/em>, the boundaries between the five psychographic categories of the TALC lie approximately at the <a href=\"https:\/\/en.wikipedia.org\/wiki\/Standard_deviation\">standard deviation<\/a> intervals on the<a href=\"https:\/\/en.wikipedia.org\/wiki\/Normal_distribution\"> bell curve<\/a>. (Most of the area under a <a href=\"https:\/\/www.shortform.com\/blog\/the-bell-curve\/\">normal bell curve<\/a> lies within three standard deviations of the center, so in this case, the <a href=\"https:\/\/en.wikipedia.org\/wiki\/Standard_deviation\">standard deviation<\/a> intervals essentially divide the graph into six equally spaced regions along the horizontal axis.)<br><br>However, the graph that Moore presents in <em>Crossing the Chasm<\/em> depicts the boundaries considerably further from the center of the curve than standard deviation intervals would be. This makes the early and late majorities appear to represent a larger proportion of the overall population. Moore provides no explanation for the discrepancy in the graph, so presumably his graphic artist simply didn\u2019t draw it to scale.<br><br>In our figure above, the boundaries are marked at the standard deviation intervals, consistent with Moore\u2019s assertion. However, other analysts have challenged the proportions of the population allotted to each category. Some argue that with the accelerating pace of technological innovation, <a href=\"https:\/\/scholarlykitchen.sspnet.org\/2009\/08\/11\/a-decade-of-crossing-the-chasm-the-mountain-has-moved\/\">the peak of the bell curve has shifted to the left<\/a>, meaning that today there are proportionately more people in the early majority and <a href=\"https:\/\/www.shortform.com\/blog\/early-adopters-model\/\">early adopter<\/a> categories, and fewer people in <a href=\"https:\/\/www.shortform.com\/blog\/the-late-majority\/\">the late majority<\/a>.<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>What is the Diffusion of Innovation (aka TALC) model? How many categories of customers are there in the TALC framework? The Diffusion of Innovation model (also known as the Technology Adoption Life Cycle) was developed by Beal and Bohlen, two agricultural extension agents working for Iowa State College in the 1950s. They developed the model based on studies of when farmers started using new agricultural innovations, such as fertilizer and hybrid seed corn. Others soon generalized the model to technological innovations outside of agriculture. In this article, we&#8217;ll take a look at how the Diffusion of Innovation Model explains the<\/p>\n","protected":false},"author":7,"featured_media":11965,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[45,103,25],"tags":[512],"class_list":["post-52732","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business","category-sales","category-statistics","tag-crossing-the-chasm","","tg-column-two"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.3 (Yoast SEO v24.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Diffusion of Innovation Model (aka TALC) Explained - Shortform Books<\/title>\n<meta name=\"description\" content=\"The diffusion of innovation model was developed to chart technology adoption in agriculture. 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