5.2. Dynamics of Influence Parameters

Here, we are talking about I found this really awesome tool because they will just tried it but then you know at this stage I wish I do try it using this earlier, it is great so you know, they sometimes repent, better we have not tried it before it has been a successful you know, so like that these laggards, this is how there is also we talked about the micro level network which has to do with the personal and direct diffusions. And the second one is with a macro level networks which has an indirect networks, how from what we are using in Delhi and how it spreads to different cities or different communities across and this is where the macro level networks, it goes along with a very different indirect networks as well.

This section introduces the concept of different adoption categories in the diffusion of innovations. It mentions 'laggards' as individuals who are often hesitant to adopt new technologies or ideas until they see others successfully using them. The discussion highlights two levels of network influence: micro-level, which refers to personal and direct interactions among individuals, and macro-level, which encompasses broader, indirect networks influencing communities such as how a new technology in Delhi can spread to other cities. Understanding these two levels helps in analyzing how information and innovations are disseminated.

Imagine a new smartphone that is launched in a city. Some individuals (laggards) hesitate to buy it because they want to see how their friends or family members like it first, whereas others might jump on it right away. The local tech enthusiasts might discuss it over coffee (micro-level), while news articles, ads, and social media posts spread the word to other cities (macro-level), influencing even more people eventually.

So, similarly when you are looking at a larger picture that is again the macro level networks whether it has been implemented in Delhi, whether implemented in Roorkee you know so this is how we looked at it. Now, if we take the perception of the most innovative and the conservative, if you see an example now, here a person A who have a group of 5 friends and he is the one who have actually taken the risk of starting it. And then, whereas in the person D, he is still in a conservative but his; all his surroundings still relying on his own ways of thinking though his network have started erupting, he takes time to think about it, so that is where we talk about who is more innovative, the one who immediately you know takes that risk to test it and the person B and person C again they comes in you know here again, in this case, it is also looked at how other people have already adopted and either this person have influenced others, so that gradually changes.

This chunk focuses on the spectrum of innovation within community networks, contrasting innovative individuals who embrace change and take risks against more conservative individuals who prefer to stick to familiar approaches. It highlights that even within a group of friends, the adoption of new ideas varies; some might quickly adopt new trends while others need more time to adapt. This dynamic showcases how social networks can influence individual decision-making regarding new technologies.

Think of a group of friends deciding whether to try a new restaurant. Friend A is adventurous and decides to go immediately, while Friend D is cautious and waits to hear feedback. Friends B and C might decide based on what they hear from A or D. This reflects how personal networks influence decisions; A's enthusiasm might encourage B and C, while D's hesitation keeps him from jumping in until he feels more comfortable.

There is also the second argument of it is how in time, how the exposure and the threshold you know how it varies, in time 1, like you have that there are 2 reference points which we are referring in this small example, one is A and one is B. And A has again the 5 friends and B has 5 friends and they have their own networks and this we can see by this diagram, we can see it is a kind of a community network. And this A has an exposure of 60% around him who are using these tanks and B has none so, we call at time phase 2, so by looking at his case now, A has adopted that and B still has not but when you look at the time 5, now A have adopted and it also which has spread it to the largest community but now B has adopted.

This section discusses how the factor of time impacts the adoption of innovations. It uses the example of two individuals, A and B, who start with different social circles. At first, A is exposed to 60% of peers already using a new tool, leading him to adopt it quickly, while B is isolated and does not adopt it. However, over time, B eventually adopts the innovation as A's influence spreads to the larger community. This illustrates how exposure and timing can play critical roles in the adoption process.

Consider the rollout of electric vehicles (EVs). At first, driver A is surrounded by friends who have already made the switch, encouraging him to get an EV. Meanwhile, driver B hasn't noticed many EVs among his social circle and sticks to gas-powered cars. Over time, as more people in the community start switching to EVs and share their experiences, driver B eventually decides to make the leap as well. This story highlights the importance of time and social exposure in adopting new technologies.

So, what are the adopter categories, this is a bell shaped curve which shows the individual innovativeness and percentages in each category, there has 4, 5 aspects as we discussed the laggards at the end the innovators on the front and then you have the early adopters, early majority and the late majority so, this is a kind of bell shaped curve.

This segment explains the categorization of individuals based on their adoption behavior, which is often visualized as a bell curve. This curve illustrates how different groups, such as innovators, early adopters, early majority, late majority, and laggards, relate to the speed and timing of their adoption of new innovations. The innovators are the first to adopt, while laggards, who adopt much later, are at the end of the spectrum. Understanding these categories can help us analyze how innovations spread through societies.

Visualize a new fitness trend, like yoga. The first few people to practice yoga in a community are the innovators. They share their experiences and gradually influence early adopters who try it next. As more people embrace yoga, the early majority gets involved, followed by the late majority who join in once a community buzz is generated. The last to adopt may be seen as laggards—those who only try yoga once they see others consistently benefiting from it.

And similarly, and who are these early adopters; these at a macro level or regional level these are the individuals whose time of adoption was greater than one standard deviation earlier than the average time of adoption. so these are referred as early adopters and early and late majority adopters which is the central phase, or the individuals whose time of adoption was bounded by one standard deviation earlier and later than the average.

In this chunk, the distinction between categories of adopters is explored further. Early adopters are those who adopt innovations significantly earlier than the average person, while early and late majority adopters fall within one standard deviation of the average. This classification helps researchers understand how quickly different segments of society embrace new ideas and products.

Consider a new digital payment app launched in your city. Those who download and start using the app within the first week are the early adopters, while those who join in over the next few months fall into the early majority. The laggards, however, may only start using the app a year later when they see most people in their community using it comfortably.

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