social norms
Posted in Behavioral theories message framing

How social norms shape our behavior – I

You must have been into a library; you stay silent. That is a classic social norm example. Another common one is littering. As we all know social norms can either be socially desirable or socially undesirable.

Every day we adhere to social norms that are an array of unwritten sets of rules we follow. In a way, these play a crucial role in the smooth functioning of a society. Once we deviate from it, we can expect negative consequences.

Robert Cialdini deconstructs the concept further; he describes two types of social norms: Descriptive and injunctive.

Descriptive (popular) social norms = What is being done

The descriptive social norm refers to actions that others seem to be doing. Irrespective of its impact – either socially desirable or socially undesirable – social norms exhibit a “contagious effect”. Take the example of littering; seeing others maintaining a clean environment persuades us to keep those places clean; in contrast, if we see others litter a place, we tend to follow it.

Need evidence?

Robert Cialdini’s 1991 report provides evidence. He together with his team demonstrated how “littering begets more littering”. Not only that, they further showed when a norm began to change; in a perfectly clean environment, the subjects adhered to that social norm even with one litter; with the increase of the number of litters, the “slippery slope began”. We tend to change our perceived descriptive social norm from the ” no one litters here” to “everyone litters here”.

This certainly goes beyond littering, even to policy making!

The interesting thing here is that we, most of the time inadvertently, promote socially undesirable norms with our statements highlighting socially undesirable behaviours as the norm. It results in a dangerous “boomerang effect”.

David Halpern in his excellent book memoir – “Inside the Nudge Unit” (David Cameron’s unit where he worked) writes, ” I have lost count of the number of examples of Robert Cialdini’s “big mistake” that I have seen”.

Injunctive social norms = what ought to be done

in contrast to the descriptive social norms, injunctive social norms refer to actions that people either approve or disapprove of (as we perceive). It could either be a displaying “do not litter” notice, the presence of a designated place to dispose of garbage, or what we see that another person removes and properly disposes of the litter.

As I understand, there is a very important difference between the descriptive social norms and the injunctive social norms; the former can be situation-specific while the latter’s influence can be very robust. For example, Cialdini and his team’s research demonstrated seeing that someone picking up and removing litter from a clean environment (social disapproval) lead others to imitate that behavior not only at that particular place but in other settings also.

How can we apply the above concepts usefully?


  • Using descriptive social norms is like a double-edged sword; it becomes effective only when most people engage in socially desirable behaviors; if most engage in socially undesirable behaviors it will backfire.
  • If we suspect most people engage in socially undesirable behaviors, Cialdini suggests using injunctive social norm focus.

I will write in a later post how descriptive and injunctive social norms influence, for better or worse, during this COVID 19 pandemic.

Posted in Behavioral theories

Goodhart’s Law: When the metric is the target…

This is a very common trap the decision-makers fall into. Find out how?

I will begin with two analogies: “Nail production” and the “cobra effect”.

The famous “nail production” analogy

A company announced to its employees that they were going to assess their productivity by the number of nails manufactured. The result was a large number of useless small nails; then, the managers came up with a different metric: the weight of nails manufactured. The result was few but heavy, again useless nails”.

This story itself could be a manufactured one; but, it is a good introduction to the concept.

However, Charles Goodhart, a British economist who served at the Bank of England monetary committee articulated the concept and first wrote about it in 1975.

The following sentence is the common reference used by experts to explain Goodhart’s law.

“When a metric becomes the target, it fails to become a good measure”

Charles goodhart; source:

I found another interesting analogy to the concept.

Cobra effect

This story relates to India’s British colonialism era. At that time, the rulers wanted to get rid of cobras prevalent on the streets. As a strategy, the rulers offered financial rewards to those who brought dead cobras; it was an instant success. However, with time, people started to breed cobras to claim more money. When the rulers found out their folly, they gave up the reward. As a result, they experienced more cobras on the streets because people released their “harvest”. (I do not know how far it was true).

Let us take another metric now:

Number of calls per hour

Suppose you want to expand your service and you decide to give incentives to your employees at the call center based on the number of calls made per hour. And you also set a target: 10 calls per hour. Now, the metric becomes the target. If you do not add another related metric/s with regard to quality, your employees will definitely achieve the measure at the expense of the quality of the call because they might be tempted to end a call within six minutes disregarding to meet the callers’ need.

However, I find, Drs. Gregg Fonarow and Boback Ziaeian expand it a little further in their editorial comment to the Journal of the American College of Cardiology. They say that this could happen when the metric is attached to financial incentives.

Metric attached with incentives! What incentives?

Here, the keyword is the incentive. Goodhart’s law comes into play when the metric becomes the target and it is attached with incentives – usually, but not always, with financial incentives.

Hospital readmission rates

Drs. Gress Fonarow and Boback Ziaeian captures this law in their editorial to the American College of Cardiology about hospital readmission rates with the following sentence;

“Metric of performance attached with financial incentives does not serve its original purpose.”

Drs. Gregg Fonarow and Boback Ziaeian

According to them, sometime back, healthcare researchers observed higher readmission rates associated with some selected conditions of heart failure within 30 days of discharge. They wanted to reduce the 30-day hospital readmission rate by giving financial incentives to residents as well as financial penalties to the management if their hospitals failed to meet the target.

As expected they achieved the target, however not without unintended consequences.

A group of researchers led by Dr. Ankur Gupta in 2018 reported at JAMA Cardiology that hospital readmission reduction program was associated with reduced 30 – day and 1-year readmission rates but at the same time associated with increased 30-day and 1-year mortality rates.

The law can come into play not only with financial incentives. Consider the following example.

Number of research papers published

In the academic field, the number of research papers published is the standard metric in the advancement of science, economy, or any other academic discipline.

However, if the ultimate goal of this metric is either the expertise of the subject or the technical know-how of the paper publishing is, I think, Goodhart’s law does not come into play. It matters only when we consider this metric as a measure of personal academic advancement because it may lead to aspiring researchers to play around with data sets to show statistically significant results and spinning with the writing of abstracts and conclusions.

That means the operation of Goodhart’s law depends not only on the metric when it is attached with the incentives – either financial or otherwise – but with the target or goal that is expected to measure.

This is an introduction to Goodhart’s law. Those who have explored the concept further have come up with its variants. One such expert is David Manheim; those interested can listen to one of his interviews through the following link:

The interview:

Posted in Behavioral theories

Stages of Self-Change

Although behavior change occurs in a continuum, researchers have identified stages of self-change in some behaviors such as quitting smoking. Let us embark on this journey starting from 37 years ago: 1983.

1983: “Stages of change”

Since the early 1980s, two researchers – James Prochaska and Carlos DiClemente – had been working with smokers who wanted to quit. They observed that some quitted without any outside help. In order to explore the observation further, they recruited 872 middle-aged volunteers who were smoking from Rhode Island and Texas through a newspaper advertisement. After classifying them into five groups, the study participants were followed for two years. Prochaska and DiClemente published their findings in 1983.

In this study, they conceptualized quitting as a process (a journey) someone goes through in several stages – not as a one-time event. They considered the quitters would journey through four stages: from pre-contemplation to contemplation, action, and maintenance. Based on that premise, the participants were grouped into the following categories:

  • Pre-contemplators (Immotives): those who smoked but no intention of quitting; “there is nothing that I need to change”.
  • Contemplators: those who smoked during the year before the study but seriously thinking about quitting; “I know that I have a problem that needs to address”; “But, I am not ready yet”; “I have other priorities at the moment”.
  • Recent quitters: those who quit smoking less than 6 months
  • Long-term quitters: those who had not smoked more than 6 months
  • Relapsers: those who attempted during the year before the study but failed

Their study did not limit just to describe the stages only; they wanted to find out how these volunteers “processed their cognitive and emotional challenges” throughout the stages.

“Processes of change”

Based on their prior knowledge, the researchers defined ten processes of change. These consisted of five cognitive and affective constructs while the rest were behavioral. The following were those according to their published paper with a sample item for each variable.

Cognitive and affective processes

  • Consciousness-raising (getting the facts): “I look for information related to smoking”.
  • Social liberation (notice public support): “I notice public places have set aside places for non-smokers”.
  • Self-reevaluation (creating a new self-image): “My dependence on smoking makes me disappointed in myself”.
  • Environmental reevaluation (noticing your effect on others): “I stop thinking that my smoking is polluting the environment”.
  • Dramatic relief (paying attention to feelings): “Warnings of health hazards move me emotionally”.

Behavioral processes

  • Self-liberation (making a commitment): “I tell myself that I am able to quit if I want to”.
  • Counter-conditioning (substituting): “I do something else instead of smoking when I want to relax”.
  • Reinforcement management (rewarding): “I am rewarded by others when I do not smoke”.
  • Stimulus control (managing the environment): “I remove things from my workplace that remind me of smoking”.
  • Helping relationships (getting support): “I have someone who listens when I need to talk about my smoking”.

How they assessed the change processes

The researchers created a 40-item self-report questionnaire to measure the change processes; and, the respondents answered the questions on a 5-item Likert scale (1 = not at all; 5 – repeatedly).

In addition, their smoking status was also assessed and the respondents’ saliva was tested to validate self-reporting of the smoking status. During the two years of follow-up, the researchers interviewed them every six months.

Their findings: Four stages of change

I depicted their findings in the following graphic; the integration of the four stages – pre-contemplation, contemplation, action, and maintenance – into the processes of change variables.


Let us go through the above graphic.

The pre-contemplators were not interested in seeking information in contrast to contemplators. Making a commitment – for those in the action stage – seemed to be the most highly rated activity. The self-reevaluation became important for both groups who were in the contemplation and action stages. And, those in the action stage valued high being rewarded and getting outside help too. Finally, those in the maintenance stage rated high in coping skills such as substitution and stimulus control but, interestingly, not public support and being rewarded. Both maintenance stage strategies seemed to be useful in the action stage as well.

They also added more evidence to their previous percentage distributions of different stages in a population: 40% each in pre-contemplation and contemplation stages, and less than 20% in the preparation stage. These proportions seem to vary by country; for example, 70% of Germany’s smokers were in the pre-contemplation stage.


Self-efficacy, temptation, and stages of change

Our perceived ability to do a certain task will increase the chances of doing that task, according to Albert Bandura’s self-efficacy theory which was formulated in 1977. Research has shown this self-efficacy construct even predicts the likelihood of engaging in a certain behavior/task including smoking. This association does not change with the type of measuring scale indicating how robust and powerful this construct is.

With regard to the stages of change, prior to 1985, Prochaska, DiClemente, and Michael interviewed 957 volunteers who were smoking with a self-efficacy scale. In addition to self-efficacy question items, it included questions related to temptation level for smoking. The researchers hypothesized self-efficacy and temptation as a critical dimension – a mediator – in the behavior change journey. They measured these constructs using a 31-item scale: the self-efficacy by asking how confident they were to avoid smoking at the home, office, etc, and the temptation by asking how tempted they were to smoke in those situations.

This study found that the study participants traveled through different stages of change at different speeds and different directions. For example, while some contemplators moved forward to the action stage, another group stepped backward to the pre-contemplative stage – even some stalled at the same stage. I drew the following graph using mean scores shown in their paper; we can appreciate how temptation and self-efficacy mean scores changed with the stage of change among smokers.


Since the publication of the smokers’ two-year journey in 1983, a plethora of studies appeared in research journals on this subject. A decade later, in 1992, Prochaska and his research colleagues reviewed those and expanded their original four-stage model into a five-stage one; they added “preparation” as a separate stage in-between contemplation and action stages.

“Preparation” as a stage of change

The authors seemed to have been convinced that people tried (rehearsed?) some minor behavioral activities before they embarked on real action; for example, reducing the number of cigarettes, delaying the first one (DiClemente et al., 1991, etc.

In contrast to the preparation stage, those in the action stage stayed abstinent and maintained the new state for a period of one to six months according to the authors.

Maintenance stage

Some of those who are in this stage may march forward to the maintenance stage abstaining from smoking beyond six months; they may adopt some more actions – such as counterconditioning and stimulus control – to prevent relapse. Research shows that those who enter into this stage shuttle between this stage and other previous stages 3 – 4 times before stabilizing in the maintenance stage, according to the authors.

From a linear pattern to a spiral pattern

Prior to 1992, Prochaska and others considered that the shuttling through the stages follows a linear pattern; however, with the emergence of new evidence, they conceptualized the journey through the stages much like a spiral pattern – not linear.

Why spiral?

According to the new evidence, in the field of addiction, although we travel upward from pre-contemplation to maintenance through several intermediate stages, many may spiral all the way down up to the pre-contemplation stage. Or else, they might stay at any stage sometime – sometimes even years. However, some of them may again move upwards armed with new attempts. Research has shown, according to the authors, about 15% of smokers regressed to the pre-contemplation stage; however, 85% of them climbed again either to the contemplation or preparation stage.

Spiral pattern (Source: American Psychologist, September 1992, p.1104: link:

1994: Weighing pros and cons: Decisional balance

In a paper published in 1994, Prochaska discussed another aspect of the stages of change model: the decisional balance or weighing pros and cons. He postulated that this construct was key to move from one stage to another. In fact, he expanded Janis and Mann’s decision-making model introduced in 1977. The model argues that we make decisions after weighing four major consequences:

  • Anticipated gains or losses to the self
  • Anticipated gains or losses to the significant others
  • Approval or disapproval from the significant others
  • self-approval or disapproval

Prochaska reviewed twelve problem- behavior that tested stages of change model and the above four constructs; in fact, eight because each variable consisted of two opposing ends – gains or losses; and approval or disapproval. The study participants responded to the questions on a 5-point Likert scale ranging from (1) not important to (5) extremely important.

Then came the most interesting part; those in the pre-contemplative stage scored higher standard T scores for the costs of changing behaviours than its benefits and the scores of those in the action stage reversed. In other words, pre-contemplators’ anticipated costs of changing behaviours were higher than their anticipated benefits of changing the behaviour; however, the pattern reversed among contemplators and those in the action stage; they weighed higher the anticipated benefits of behaviours than its costs of changing the behaviour. And, he reported another interesting finding; those in the action stage weighed lower the anticipated costs of changing the behaviour than the contemplators.

How can we translate into action?

First of all, I believe the above findings are very strong because Prochaska drew his suggestions by analyzing studies addressing 12 different problem behaviors.

Although the above findings were from cross-sectional studies, we can theorize, as Prochaska did, that, first, we should raise the target audience’s perceived benefits of changing the behavior to transport them from the pre-contemplation stage to the contemplation stage. Then, we need to reduce their perceived costs of changing into the new behavior.

Interestingly, he further claimed that the rate of increase in the benefits was “at least twice as great as” a decrease in the costs when actors move along the stages from pre-contemplation to action.

Strong and weak principles

Based on the above results, Prochaska published another paper in 1994, confidently introduced two principles: strong principle and weak principle.

The strong principle

The strong principle states that progressing from the pre-contemplation stage to the action stage requires an increase of one standard deviation in the pros (benefits) of the healthier behaviour; the increase equates to a minimum of 20% increase of variance of the expected change. It also includes its own mirror images: increase in cons (costs) of not making a healthier change – either of failure to acquire healthy behaviour or failure to stop the unhealthy behaviour.

The 20% increase in the variance of either quitting benefits or costs of not changing the habit is not easy. How can we achieve that level? Prochaska opined that in addition to individual-level interventions, public health policies also need to be aligned. What are the individual level interventions? They include raising the consciousness about the benefits of quitting and the costs of smoking and self-reevaluation skills. Assisting them to digest the benefits of quitting may be adequate, according to Prochaska. Obviously, we can raise its perceived benefits only.

Prochaska argues that public health interventions can be used to raise its actual benefits. how? It is by raising taxes; smokers have to pay more to buy cigarettes. He suggests another interesting method that is still valid – raising their insurance premium and reducing insurance benefits!

However, no such interventions were available when he wrote this paper – in 1994.

The weak principle

Acquiring new behaviour carries some costs too. This weak principle addresses that problem; it says moving from pre-contemplation to action stage requires half a decrease of a standard deviation (0.5) in the costs of acquiring a new (healthy) behaviour. Unlike in the case of strong principle, this equates to a 5% reduction in the variance of the costs considered. It also has its mirror image: the benefits of not making the behaviour change.

However, Prochaska observed that the weak principle was not as consistent as the strong principle across the problem behavior.


Fourteen years after the above study, researchers – Kara Hall and Joseph Rossi – tested these strong and weak principles for more problem behaviors. Quite remarkably, they found the same findings to the point of one standard deviation with regard to the strong principle and 0.5 standard deviations for the weak principle. They meta-analyzed data from studies covering as many as 48 problem behaviors published between 1983 and 2003. The Preventive Medicine journal published the study in 2008.

Posted in Behavioral theories

What is the Prospect Theory?

Choose one of the following two options;

  • Option 1: a sure gain (100% chance) of gaining $ 240,
  • Option 2: 25% chance of gaining $ 1000 or a 75% chance of gaining nothing

Which one do you choose?   

Now, choose one of the following two options.

  • Option 1: a sure loss (100%) of $750,
  • Option 2: 75% chance of losing $1000 or 25% chance of losing nothing.

Which one do you choose?

According to Daniel Kahneman and Amos Tversky, two renowned American Psychologists and the authors of the Prospect theory, 84% chose the first option in the first scenario and 87% chose the second option in the second scenario.    

This is one of the major tenets of the Prospect Theory of Kahneman and Tversky; when we are confronted with a sure gain, we do not take any risks (we averse the risk); however, when we are confronted with a sure or almost sure loss, we take the riskier alternative (we seek the risk to averse the loss).  

Think of this situation now:

You have three bowls; the middle one with water at room temperature, the left one with warm and the right one with cold water; now, immerse your left hand into the warm water and the right hand into cold water; now immerse both hands into the middle bowl. You will experience warm in the right hand and cold in the left hand.

Reference point

So, the above interpretation varies relative to the reference point which is the middle bowl in this case.

Kahneman and Tversky say that the same applies to financial and other situations too. In other situations the reference point is usually the status quo; it can also be the future goal.

So, anything above the reference point is considered as gains and anything below the reference point is considered as losses. We also can change the reference point at any time. 

Now, think of a situation that turning on a weak light in a dark room and turning on the same light in a brightly lighted room. You see the difference. The weak light has a larger impact in the darkroom. Similarly, Kahneman and Tversky say that the perceived difference between $100 and $200 is larger than the difference between $900 and $1000. 

Our differential responses to gains and losses

What is the nature of the relationship in increments of gains and losses relative to the reference point?

To me, this is the most interesting part of this theory; it is S-shaped which is succinctly explained by the following graph.


The top-down line is the neutral reference line which is our perceived psychological value. Anything towards the right side from you is considered gains and anything towards the left side from you is considered as losses.  There is a very interesting point here; this S-shaped curve is not symmetrical to either side of gains and losses.

Observe closely; the loss curve is steeper than the gain curve. How do you interpret that? Kahneman and Tversky say that we respond more strongly to losses than the gains even when the corresponding differences are the same. They name this phenomenon as loss aversion. 

Posted in Behavioral theories

Diffusion of Innovation Theory and the First Follower

Derek Siver’s first follower: 

One evening, a crowd numbering about 100 were sitting and chatting on a greenery garden in front of a beach. Suddenly a young lad stood out, marched forward, and started to dance according to the tune of the music playing behind. He was without his shirt. A little while later, another guy joined him. Another few minutes later, about 4 – 5 both boys and girls joined them. Soon, another 10 – 20 more stood and were them; all were dancing and enjoying together; later almost all were there; few of them did not join the massive crowd – they were still sitting. This is all about the Diffusion of Innovation Theory and leadership.

The above short description is from the super famous Derek Sivers’ short video clip that I watched on YouTube. You can watch it through this link. Since 2010, about 5.3 million views have been recorded to date!. Or, you can listen to his TED talk on the same video clip through this link.

It was all about leadership! His TED talk was about how to start a movement.

When we announce a novel idea to an audience, how might they respond to it? How many may grasp the idea? How many will reject it? How the rest of the crowd will react?

Diffusion of Innovation Theory

We can explain the behavior of this group of people with the help of the famous “Diffusion of Innovation Theory”. This theory was a brainchild of the late Everett Rogers. He published its first edition in 1962. It followed four more editions until 2003 before his demise in 2004. 

According to the theory, whenever a new idea, product or service is introduced, we can identify five groups of people in regard to different ways we respond to.

S – curve

So, a pattern exists whenever a new idea, product, service or whatever comes to light in front of an audience. As I mentioned earlier, we can recognize five groups according to Rogers.


Innovators are risk-taking futurists; they promptly understand the usefulness of the innovation and willing to take the risk. Not only that, they are willing to be partners of the project. They constitute 2.5% of the target population. The leading dancer and his first followers in Derek’s video are innovators. 

Early adopters

This segment of the audience steps out only if they are convinced about the benefits of the innovation. They are economically and socially well off, hence not much cost-sensitive. They constitute 13.5% of the target population. Moreover, this group possesses certain crucial characteristics – they are opinion leaders, role models, prefer to be trend-setters, and even adventurers.

The early majority (Pragmatists)

If we can forge really strong connections with the previous two groups – innovators and early adopters – the group next in line are the early majorities. They are larger in numbers than the previous group, as we can appreciate, about 34% of our target population. They are “pragmatists” because they do not take action without solid evidence; they are cost-sensitive; they do not take risks; they prefer simple tasks; And, they do not want to “waste” time in studying about this product in detail. However, they need an external push to take action, likely to depend on early adopters.   

The late majority (Conservatives)

This group is as much larger as the early majority group consisting of 34%. However, they are cautious and will not go with the crowd until they are convinced with others who are around them must have joined the new trend and also subject to heavy peer pressure. They are very cost-sensitive. Reluctantly, they might join with the rest of the crowd because they fear they will not be fitting into the mainstream. However, they could easily be influenced by laggards – the last group. 

Laggards (Skeptics)

Although, very few in number, this is a very critical group that influences – negatively – the movement or project’s success. They are unlikely to join the rest; instead, they will be very critical and likely to influence the late majority. They tend to say this is not the way we have always been doing things. They are suspicious of new things. They need much more support, mentoring. This group is lesser in numbers – 16% – than the early and late majority groups – sometimes not worth spending time on them.

Posted in Behavioral theories

Health Belief Model – III: 60 years later

In my previous two posts on the Health Belief Model, I discussed its origin and a review of the model conducted a decade later. 

In 1974, Rosenstock found that perceived susceptibility to a problem and perceived benefits were the most powerful components of the model. However, he pointed out that the published studies by that time did not address cues to action and perceived accessibility (in other words perceived barriers). Then, Janz and Becker, a decade later found that perceived barriers as the most powerful variable.

Health Belief Model -III: 60 years later

In 2010, more than 60 years after the introduction of the Health Belief Model (HBM), Christoper J. Carpenter evaluated its effectiveness using meta-analysis which more advanced and powerful method for analyzing results because unlike in Janz and Becker’s method, in which they counted number of statistically significant results in studies whilst Carpenter calculated mean effect sizes considering whole suitable samples as one sample. However, his review involved only 18 studies that assessed the model’s components (constructs) at two time periods – at the beginning and then sometime later. This is unique since the review included longitudinal studies only.

A summary of the Carpenter review:

His chosen studies covered mammogram screening, quitting smoking, drug-taking, dental care, condom use, cervical smear testing, and program attendance. As we can see, these studies have focused on our responses to screening services, medication compliance and repetitive behavior such as condom use.

The following were his findings: 

  • Overall, perceived barriers found to have the strongest correlation with the whole sample. (This is consistent with previous two reviews).
  • Perceived benefits had the second-strongest correlation with the whole sample.
  • Perceived severity and perceived susceptibility had the weakest correlation with the whole sample.
  • Perceived benefits found to have the strongest correlation with preventive behaviors.

The updated Health Belief Model

The updated model consists of six constructs. It is as follows;

constructThe belief of,
perceived susceptibilitythe chance of affecting
perceived severitythe belief of the seriousness of the problem
perceived benefitsthe effectiveness of the recommended action
perceived barriersthe perceived physical as well as psychological barriers
cues to action triggers to take action
self-efficacythe ability to take action

What are the practical applications of these findings?

Whenever we design a promotional material or a program, we need to highlight on HBM constructs in the following order in priority order.

  1. How to overcome barriers that the target population would perceive in engaging in the recommended behavior
  2. How to highlight benefits in ways that the target population would perceive
  3. How to inform about susceptibility and severity in ways that the target audience would perceive
Posted in Behavioral theories

Health Belief Model – II: a decade later

A decade after the empirical testing of the Health Belief Model by Becker et al. in 1974, Janz and Becker published a review in 1984 in the Health Education Quarterly Journal.

What studies they reviewed

They have reviewed 46 Health Belief Model (HBM) – related studies: 18 prospective and 28 retrospectives. It included both preventive health behaviours and sick role behaviours.

Studies on healthy role behaviours

It included 3 studies of vaccination behaviour against Swine Flu, one vaccination study against influenza, one study related to screening for genetic disease (Tay-Sachs disease), four studies related to breast self-examination, and one study on high blood pressure screening.

Studies on drug compliance behaviours

Not only studies on health promotion behaviours, they also looked at medicine compliance behaviour studies too. It included anti-high blood pressure compliance, adherence to dietary changes among individuals with diabetes and end-stage renal disease, mother’s compliance with physician advice on child’s health, and clinic attendance.


Overall, one important finding they mentioned (p.41) that findings from the prospective study designs (which is superior design than retrospective ones), were either similar or better than the findings from retrospective study designs.  This is great news. 

According to their comparison of pre-1974 and post-1974 findings, “perceived susceptibility” topped as the most powerful variable in pre-1974 research whereas “perceived barriers” became the most powerful variable in post-1974 studies irrespective of the study design either prospective or retrospective and in the preventive and sick role behaviours. However, “perceived susceptibility” remains more important in preventive health behaviours than sick role behaviours. “Perceived severity” earned much lower significant score (36% in the significant ratio) in preventive health behaviours whereas it rose up to 85% in sick role behaviours such as drug compliance.

A major limitation

The reviewers have correctly identified that the absence of standardised measuring tools for each HBM variable renders significant difficulties in the model appraisal process.

Posted in Behavioral theories

Health Belief Model – I

Historical context:

In the 1950s, pulmonary tuberculosis became rampant in some neighborhoods in the US. To catch the bacteria as early as possible, the US health authorities had been X-raying people’s chest to detect lung changes that are caused by this micro beast.

However, there was a problem; even though the service was free of charge, not all people attended those clinics. They wanted to find out why.

Geoffrey Hochbaum and his team designed a multi-site research project to find answers to the following questions.

  1. What factors influenced community members in deciding either to attend or not to attend the clinic?
  2. What factors influenced them to obtain the service after deciding to attend the service?
How did they conduct the study? 

They interviewed random samples from 3 cities totaling to 1,200 individuals; 450 in Boston, 450 in Cleveland, and 300 in Detroit. They adopted a random sampling approach because they wanted to generalize their findings to study populations. Each interview lasted more than an hour and covered themes related to participants’ beliefs, attitudes, and feelings regarding the activity including services’ administrative aspects.

Question types: 

In addition to traditional question items with close and open-ended questions, they included projective questions that lead participants to awaken their subconscious level of thinking.

What did they find? 
  • 42% attended the service voluntarily without any symptoms.
  • Another 16% attended due to their suspicion of having symptoms,
  • Another 14% attended due to their significant others’ influence.
  • Another 10% who attended did not show any consistent pattern.
  • 17% did not attend.

Perhaps, the most crucial finding was that only 35% of those who knew that tuberculosis can exist without having symptoms obtained a chest X-ray. This signifies that there were some other factors that influenced their decision making for attending the service apart from awareness. This holds true even today – not all who are aware of the usefulness of screening programs such as mammography and colonoscopy, do not attend those services.

However, moving beyond the above findings, Godfrey Hochbaum in the article named, ” Why people seek diagnostic X rays?”  published in the Public Health Reports in 1956 demonstrated how the participants’ beliefs/perceptions interfered in translating awareness into actions. I am highlighting here two important beliefs/perceptions: susceptibility and usefulness.

Perceived susceptibility

Group 1: Those who perceived that they were susceptible.

Of those who believed that they would contract the disease (perceived susceptibility), 82% attended the service. In contrast, of those who did not believe so, fewer than 50% attended the service.

Perceived benefits

Group II: Those who perceived that a X-ray would help having better outcomes.

Of those who believed that having an X-ray without symptoms would help to have better outcomes, 90% attended the service.

In this study, the researchers wanted to find out why those with a higher risk of contracting the disease did not attend the service – individuals with lower socio-economic backgrounds and older people. They found that the above findings held true regardless of study participants’ socio-economic status, age, and gender.

Methodological limitations

As pointed out by Rosenstock, the major methodological limitation is the study’s retrospective nature in that both existing beliefs and behavior (having an X-ray) were inquired at the same time. This is because people tend to change their previous perceptions in accordance with their later decisions as shown by Festinger’s cognitive dissonance theory.

Kegeles's study

In contrast to the Godfrey Hochbaum’s retrospective study, Kegeles conducted a two-phase prospective study with individuals who had a pre-paid dental care plan and their subsequent preventive health visits: first, he inquired about their beliefs and second, three years later, about their visits to a dental clinic. Then he compared these findings with a control group from the same company. Both groups were stratified by age and marital status for analyses.

Following were his findings:

  • 58.2% who felt susceptible made dental visits whereas 41.9% who did not feel susceptible made dental visits. This finding was statistically significant,  p* (X2 = 5.18) < .05.
  • 47.5% who perceived benefits of dental visits, made dental visits whereas 44.6% who did not perceive benefits of dental visits made dental visits. However, this finding was not statistically significant.
  • 67.3% who felt susceptible and perceived benefits if such visits made dental visits whereas only 38.1% of those who did not feel susceptible and perceive the benefits of such visits made dental visits. This finding was statistically significant,  p (X2 [Yates]t = 5.42) < .01.(page 169).
Questions they used.

The question they used to assess perceived susceptibility was, “how likely do you think that your worst dental problem or such a problem will happen to you again? The responses were likely and unlikely.

However, it is important to note here that they had used open-ended questions during the first phase and close-ended questions during the second phase.

As summarised by Rosenstock, Kegeles found when both perceived susceptibility and perceived benefits exist together, more participants took action than those beliefs considered separately.

An important critique by Rosenstock

Rosenstock pointed out two important factors that needed to be addressed at that time: cues to action and perceived access to the services. However, He did not cite specific studies to support his claim. Instead, he referenced the following model presented by Becker (1974) tested in his study. I will discuss his methodology in a later post.

Graphical presentation of the Health Belief Model (Becker, 1974)

Following is the model suggested by Becker in 1974 and then Rosenstock cited the model in his critique. 

Becker's Health Belief Model 1974