Exceptions of Diffusion of Innovation Theory During COVID-19 Pandemic and Health Policy Implications - A Viewpoint : Journal of Surgical Specialties and Rural Practice

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Exceptions of Diffusion of Innovation Theory During COVID-19 Pandemic and Health Policy Implications - A Viewpoint

Bhattacharya, Sudip; Sekhon, Harmehr; Sharma, Neha; Singh, Amarjeet1

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Journal of Surgical Specialties and Rural Practice 1(1):p 3-7, Sep–Dec 2020. | DOI: 10.4103/jssrp.jssrp_5_20
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Diffusion of Innovation expedites the health promotion through enhancement in behaviour changes. If the natural course is uninterrupted the DOI theory follows the normal curve but if dissemination component is supplemented with this, it can bring about change in the normal curve with faster adoption rate by the society. This new way of adoption will not only benefit the adopters but guide public health experts, policy makers and implementers amidst pandemic crises like COVID-19. Thus, the theory of dissemination should be clubbed with DOI theory for formulating any health policy for any country.


Public health is defined as, “the science and art of preventing disease, prolonging life, and promoting health and efficiency through organized community effort.” For assuring optimum public health there has to be community involvement.[1] It is understandable that for any worthwhile community effort and active involvement of people, a change in behavior of the public at large is required by the use of dissemination science or diffusion science. However, it is seen globally that in medical colleges there is minimal concentration on change-related behavior teaching.[2] Though, students are made to understand about the beliefs, attitudes, taboos, norms etc., under family medicine or social medicine or through medical sociology in their curriculum but they dearth in integrated teaching like how they can use statistics to expound the subject. Nevertheless, students are being told about “THE NORMAL DISTRIBUTION CURVE” by the statisticians under the department of Community Medicine/Family Medicine and Public Health but they are unable to illustrate its use in behavior change concept.[3]


To focus on persistent and looming applied problems which are in picture but not cleared out by the preeminent research disciplines like sociology, psychology and political science, has led to the emergence of “global science of dissemination” with advanced information and better communication technologies. This Dissemination science is being framed by the experts to be utilized in applied fields of education, communication, marketing, resource development, criminal justice, social work, health services and public health.[4]

Dissemination science basically refers to “regulate the utmost way for communicating evidence-based practice, policies and programs in to an interorganizational societal sector having probable adopters & implementers to yield persuasive results”. According to this definition, dissemination science covers the objectives of external validity i.e., reproducing & scaling up of positive effects in divergent settings and conditions (Moffitt, 2007).[5] A potential adopter is the individual who is aimed because of their decision -making role in investing resources for innovation, while an implementer refers to the individual who modifies their behavior using innovation. Generally, it is seen in complex organizations that user do not chose the innovations and implementers usually contravene the adopter's intentions. Furthermore, regarding consequential innovations, it is seen that adopters are larger than implementers in formal authorities and therefore knowledge with respect to magnitude & quality of implementation or the client response and are not precise. Thus, dissemination incorporates diffusion intervention with implementation intervention, for which many adopters are addressed, and the key objective is the quality of implementation. While, it can be quibbling that dissemination is extremely paramount type of diffusion study, which depicts implementers active aspect and passive aspects are taken over by the community.[4]

It is like imposing a curfew on or banning alcohol/tobacco and pushing for or forcing certain norms and behaviors where, generally, the legal component is inherently present. – like deodorant spray). Whereas in 'diffusion', the spread of the information is more natural, such as maintaining hand hygiene and practicing proper coughing etiquette can prevent against COVID-19. It is more of kind an opening up the cap of a scent bottle/lighting incense(agarbatti).


Behavior change in the target population is the ultimate focus of health education. This section plays a particularly important role specially for Family Medicine, Community Medicine, and Public Health. Occasionally, new idea which is promoted through health education does not get gel into target population with the equal speed, as idea is not accepted instantly. It takes time for dissemination and acceptance of any idea. Some people adopt quickly while rest takes some time for it.[36]

In this regard, Roger (1962) proposed the theory of “Diffusion of Innovation (DOI)” to illustrate the spread of an idea in a society with time.[7] Through this theory the behavior changes were delineated from what people used to do earlier. Like the change form cloth to sanitary napkin use by the females. The main concern is that people should accept and realize that innovative idea is beneficial for them. Greater the idea is perceived beneficial, the expeditious is the diffusion rate. According to Roger's theory, in a community, adopters of innovation are labelled as, only 2.5% innovators adopt the new idea at first instinct followed by 13.5% individuals who follow it which are called “early acceptors” and then comes the bulk of “early and late majority” which are 34% each respectively. Most of the people behaves in such a way because they do not to be counted under the category of first ones or last ones in adopting a new thing. Rest 16% are called as “laggards” who firstly do not acquire the idea or even if they do then already the time has passed [Figure 1].

Figure 1:
Profile and percentages of adopters/acceptors in any population

Hence two relevant dimensions are being explained through this DOI theory i.e., “time & numbers”.[7] Therefore, before introducing any innovation into the population, background characteristics of people should be considered beforehand as this might facilitate or delay the adoption. In term of numbers, in beginning the response is slow (2.5% +13.5%) but later it starts progressing (34% +34%).[6]


At Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India, a Multi-Purpose Behavior Therapy (MPBT) room is run under obstetrics & gynecology department [Table 1]. The emphasis is given on active collaboration and shared decision-making among patient & her family members and health care providers. From the main OPD, the patient is being referred for either face-face or through mobile phone/laptop-based counseling sessions on maintaining healthy lifestyle by means of exercise, yoga, dietary advices, behavior therapy and non-medicinal treatment. Adequate time is being devoted to each patient for resolving the queries of patient and of her family members. Mostly women that come are antenatal or have complaints related to menses, uterine prolapse, infertility, menopause, urinary incontinence and even osteoarthritis. This concept has also helped in women empowerment.

Table 1:

At the beginning, the obstetricians & gynecologists were hesitant in sending the patient to MPBT room, which led to just few patients who were receiving the counselling. But after few weeks due to positive feedback from the patients, there was rise in number of patients at the MPBT room and doctors also started referring the patients on regular basis.[89] Hence, this DOI theory can decrease the frustration level of implementers too if they do not receive or fail to elicit the desire response in early stages. It is reported that mostly (84%) people tend to have a positive attitude for innovation except for laggards, although acceptance rate may vary due to inherent fluctuation in people's behavior.[710] This case study strongly affirms the trend predictions of DOI theory.

The curve with percentages shown above corresponds to NC [Figure 1], with five accepted categories of adopters: First is Innovators (2.5%) who are passionate & adventurous about the innovation and does not resist in taking risks. They are the ones who firstly adopt any new idea or innovation. Second comes Early Adopters (13.5%) who are next to adopt the new idea. They are like opinion leaders who have carefree approach in adopting newer ideas. Next comes the Early Majority (34%) who do endorse the new idea before any typical person, but just they require is confirmation that the new idea will be helpful for them. They get influenced by the evidence and success stories of effectiveness of innovations. Fourth comes Late majority (34%) who adopt only when maximum number of people have already tried the new idea. They usually remain skeptical but information regarding benefit from idea attracts them. Last comes the Laggards (16%) who are generally cautious and orthodox and either do not adopt the innovation or do so very lately under the pressure of the people of adopter groups.[7]

In Figure 1, horizontal axis shows the categories with time while vertical axis depicts the number of acceptors. The curve represents the natural history of acceptance of a single innovation in a specific population. The adoption of idea or innovation hinges on five factors, first “Competitive advantage” i.e., amount of betterment through new specific idea than the previous idea or program. Secondly “Compatibility” with the traditional culture, values, and requirement of the target population. Thirdly “Complexity” in understanding and use of the innovation/idea. Fourthly “Testability” i.e., before committing for adoption at what level innovation can be tested and lastly “Observability” which refers to achieving tangible results through innovation. Apart from the successful use of DOI theory in the field of communication, agriculture, etc., and Community Medicine, and Public Health, it can also be used for formulating strategies for expedite change in health behaviour of the community or society.[7]

Normal curve can also be used for explaining the concept of “positive deviance”. This term was given by Marian Zeitlin in the 1990s. It is defined as “an approach to social change that enables communities to discover the wisdom they already have and then to act on it”. Zeitlin while working on nutrition section discovered that few children belonging to low socio-economic society were not malnourished as compared to others. She dig further and based on the theory of positive deviance, social change interventions were introduced across the globe, community wide issues were tackled and this eventually led to the improved nutritional status.[1112]

One considerable example is from the project of “Malaria Consortium piloting PD in Cambodia (Sampov Loun) [Table 1].”

This included meetings and storytelling for community orientation to explain the PD concept.

Through focus group discussions, participatory observation for identifying probable positive deviants, successful PD strategies & behaviours and through established normative behaviours, situational analysis was done. In-depth interviews were also conducted on potential PD candidates. To share about the identified PD behaviours and identified volunteers, a feedback session was conducted. A female migrant worker was chosen as a role model of PD, who was visiting malaria-prone villages for past 3–4 years never had malaria. She stated about her practices like using insecticide-treated net during sleep, she used to wear long-sleeved clothes, covered her legs and feet with scarf to prevent mosquito bites, and also used to get her blood test done during fever. These all practices were found to be quite useful for preventing malaria. Thus, regular formal trainings of volunteers were conducted with focus on developing communication skills and PD behaviors. This exercise consisted of on-the-jo training of volunteers and their acknowledgement, participatory monitoring using maps, monthly meetings and delivering the project to community.[13]

The key lessons that were learned were that through PD, within community, a strong sense of ownership can be developed. PD is a useful tool for enhancing communication with high-risk groups of the community. It also evokes tailor-made interventions for community. As it is culturally acceptable, it helps in behaviour change of the community at ease. Through PD, leadership qualities, capacity building can be established among volunteers. However, it requires soft skills with monitoring & supervision. Hence following recommendations were made that through Capacity building of volunteers, PD approach can be scaled up. With the help of mass media PD role models and their behavior can be highlighted and PD approach can be practiced in other areas.[13]


Now we will discuss the rate of adoption and the role of interventions which can alter the shape of the normal curve.

As we know, intrinsic factors and critical mass play an important role for the rate of adoption. Intrinsic factors are innate, inherent, and inseparable from the thing itself and essential. Healthy behavior like breastfeeding to a newborn is considered an intrinsic factor, appropriate intervention can increase the behavior change in a positive direction, that is, the increase in the rate of breast feeding. Whereas, a patient may not follow the same rate of positive advice in the case that a doctor becomes involved in a smoking cessation programme as this is not an intrinsic factor and the doctor and patient can be considered different entities altogether. Which means they are culturally and socio-economically unrelated to each other.

For example, in 2016, for screening breast cancer in early stages, a low cost, non- invasive device was established that was based on mapping body heat ingrained with artificial intelligence techniques. This device was developed by a Bangalore's health tech startup Niramai Health Analytics. This thermal sensing device uses “no touch technique” and does not require any technician or doctor. Initially the cases were low but gradually with mouth publicity, more cases started turning up.[14]

Due to the COVID-19 pandemic, the Government of India has imposed many restrictions like social distancing, maintaining hand hygiene, using gloves and face masks, etc.[15]

These hygienic practices have been taught for many years by public health professionals. Some of these practices have been a part of the normal lifestyle of some people. However, despite such knowledge and education, by and large, we can say that we had failed to achieve the desired behavior change among the public for improving their personal hygiene.[16]

Prior to this pandemic, our discussion and thinking surrounding behavior change was limited to the normal curve-related descriptions, in the context of diffusion of innovation theory.

There is a need to examine the recent example of changes in public behavior observed during the COVID-19 pandemic. Here, the rate of behavior change may not follow its usual normal curve-based predictions. Deviations are there in the shape of the course. Rather, we are now witnessing a sharp contrasting curve. If plotted again, this curve will look like a left skewed curve, in which it has an early initial peak, sharply rising upwards, followed by a plateau and then downward fall again to the base (as seen across the country when the lockdown was relaxed and the liquor shops opened; people stopped social distancing, maintaining hand hygiene, using gloves and face masks, etc.). The initial reaction can be explained by the strict pandemic law enforcement and the resultant fear of punishment as well as the fear of imminent death due to COVID-19 among the people [Figure 2].

Figure 2:
Variability in the rate of behaviour change

This alteration of behavior change pattern may be due to an increase in the critical mass, which refers to the number of people required to trigger a phenomenon [Figure 3].

Figure 3:
This graph shows the innovation curve and the tipping point, or critical mass

Ever since COVID-19 started in December 2019 in China, it has spread quickly across the world, due to faster communication technology (by 'dissemination' & not through 'diffusion'). Nowadays, most of the people have become well informed about the devastating situation facing well-developed countries like the USA, Italy and many more. As a result, most of the Indian population has come to realize that maintaining hygiene, i.e., preventive measures, are a lifesaving measure for this COVID-19 pandemic, as there is no alternative treatment available at the present time. Maybe this was the reason the advice acceptance rate increased rapidly and the DOI graph changed its normal course.

Now the question in India remains, that despite a high death toll of 1300 people in 4 months (Jan-April 2020) due to COVID-19, as compared to a similar TB and RTA mortality rate in India, which is 1000, why has the alteration of behavior change pattern happened? Why did people adopt this new behavior so quickly, which does not follow the natural course of DOI graph? One possible explanation is that it is due to dramatized death, which creates panic among the masses. We can compare this with a terrorist attack, it is observed that the number of people dying by Road Traffic Accidents round the year is 100 times more than the deaths due to terrorist attacks. But the main difference is the dramatization of terrorist related deaths, which compels people to behave differently.[17]

Another example is the September 11 terrorist attack in the World Trade Centre in the USA. Many of us still clearly visualize the panic this created. However, few of us can visualize the scene of the Pentagon attack in the USA, which occurred on the same day. Again, this incident reinforces the concept of “dramatization of deaths.”[17] We assume that similar things are happening in COVID-19 cases also.

Now coming to the shape of this graph, we assume that in COVID-19 cases, innovators, early adopters, and early majority, will constitute the majority, i.e., 95% of the cases, and the remaining 5% are late adopters and laggards. During this COVID-19 pandemic, it is observed that few percent of the people are not adopting the government imposed COVID-19 guidelines/measures, these people can be categorized as laggards (1-2%). Accordingly, the government has had to take strict actions like throwing tear gas and lathi charges to disperse the public from public places during “Public Curfew (Janata Curfew).”[18]

There is also a reverse side of the trend. DOI curve, which may be an inverted parabolic graph. It can be seen where the Government health initiatives are simply rejected by local people due to multiple reasons. In this graph, there will be a slight peak at the initial stage and later it becomes flat and parallel to the horizontal axis. Here, we propose a total of 5% will be innovators, early adopters, and early majority and the remaining 95% will be the later majority and laggards.

An example of this is latrine installation in tribal villages. It is evident that most of the tribal and rural people simply rejected this government initiative of “Swachh Bharat Abhiyan.”[19] This is because the majority felt that it is not culturally acceptable to install latrine in their homes and premises. In this case, the DOI plot will be the reverse of what we have seen in the COVID-19 pandemic.

Now our moot question is how we can speed up the DOI curve for adopting healthy behaviors? Do we recommend creating panic among the public for the forcible adoption of health behavior or should we adopt a more conventional style?


Till date, the health promotion/behavior change activities were mostly centered around the DOI theory. Our knowledge was limited to the normal curve only, but one important fact was ignored or missed which is that the DOI theory follows the normal curve if its natural course remains undisturbed.

If the dissemination component is added along (with inherent legislative components) with diffusion when the situation demands, the curve will change its natural course and take a different path as explained earlier and the rate of adoption will be faster (as evident from the COVID-19 pandemic), which can be beneficial for public health experts, policy makers and implementers during any humanitarian crisis like COVID-19 pandemic. It is essential to experiment and consider, with the theory of Dissemination along with DOI, the demand arises how to formulate a healthy public health policy for any country when the demand arises and the time to implement change in behavior is very limited.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


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COVID-19; diffusion of innovation; pandemic; theory of dissemination

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