4.2.1. Threshold Definitions

So, what are the adopter categories? This is a bell-shaped curve which shows the individual innovativeness and percentages in each category. There are 5 aspects as we discussed: the innovators at the front, followed by early adopters, early majority, late majority, and laggards at the end. This is a kind of bell-shaped curve.

The adopter categories help us understand how people adopt new technologies or innovations. The bell-shaped curve represents the spread of innovation among individuals, from the first to adopt (the innovators) to the last (the laggards). Innovators are those who are willing to take risks on new ideas, while laggards are more resistant to change. The early adopters and majority groups fall somewhere in between, usually adopting after seeing that the innovation has been proven feasible and beneficial.

Consider a new smartphone. Innovators are the early users who pre-order it before it even hits the shelves. Early adopters see their friends' positive experiences and quickly purchase the phone themselves. The early majority waits to see reviews and how the device performs in the real world, while the late majority only gets it when they notice almost everyone owning one. Finally, laggards stick to their old phones until their model is no longer functional.

And with the micro level or the neighborhood level, as I told you that there are very low threshold, low threshold, high threshold, and the laggards, so you have the personal network threshold defined as an adoption network exposure at the time of adoption.

Threshold levels refer to the varying degrees of openness an individual has to adopt new innovations, influenced by their social connections. For example, someone with a very low threshold might adopt an innovation quickly as soon as a few people in their network do, while those with high thresholds might need more assurance and a higher proportion of their peers adopting the innovation before they decide to try it themselves. The concept illustrates how peer influence can drive the rate of adoption.

Think of a new diet fad as an innovation. A person with a very low threshold might start following the diet as soon as a few friends try it and share their success. In contrast, another person may not adopt it until a significant number of people in their circle have had success and they feel convinced that it's worth it. This dynamic shows how social groups can influence personal decisions about adopting new trends.

Now, if we take the perception of the most innovative and the conservative, if you see an example now, here a person A who has a group of 5 friends and he is the one who has actually taken the risk of starting it, and whereas in person D, he is still in a conservative mindset but his surroundings... Despite other friends, he is being stable.

This chunk emphasizes the contrast between individuals who are more innovative and those who are conservative. Person A takes the initiative to try new things, inciting his friends to follow suit. Meanwhile, person D remains resistant to change, influenced by his conservative mindset. This illustrates how social dynamics affect individual decisions regarding innovation adoption. It suggests that innovators can inspire their peers to take risks, while conservative attitudes can prevent change even within a close-knit group.

Imagine a group of friends deciding whether to try out a new restaurant. One friend who loves exploring new cuisines (Person A) encourages the group to join him. If most friends are indifferent, Person A's enthusiasm might sway at least one or two of them to venture out. Conversely, a friend (Person D) who always prefers the familiar might resist, sticking to their favorite local diner despite hearing rave reviews. This scenario highlights how some people drive change while others hesitate.

There are two reference points... A has an exposure of 60% around him who are using these tanks and B has none. We call it time phase 2, so by looking at his case now, A has adopted that, and B still has not.

The concept presented here examines how the levels of exposure to an innovation among peers can change over time and influence adoption decisions. At an initial point, Person A has many users in his network contributing to his decision to adopt. Meanwhile, Person B, who does not have a similar level of exposure, has yet to adopt the innovation. As time progresses, the networks' dynamics can shift to encourage or discourage adoption, showing how timely peer influence plays a critical role in the adoption process.

Think of a new social media app. Initially, only a few of your friends have it, resembling Person A's experience. You see them posting about it and enjoying it, which influences you to join in. On the other hand, if you start with a group of friends that hasn't yet adopted the app (like Person B), you may be less likely to try it until clearer peer validation arises. Over time, as more friends adopt the app, you may feel compelled to join as well.

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