series of ‘Psychology and Physiology are deeply connected’, this is the final
is popularly known amongst psychologists as ‘The Love Bridge’ study, named
after the bridge in Capilano Canyon, Vancouver where the experiment took place.
The suspension bridge spans 450 feet and hovers 230 feet above the ground,
causing it to sway as the wind blows. There was another bridge in the area that
was a small but sturdy wooden bridge bordered by guardrails, just ten feet off
At various times
throughout the day, researchers Donald Dutton and Arthur
Aron, had a young female assistant approach men between 18-35, as they
stepped off the end of each bridge with a scripted story – that she was a
psychology student conducting a study on the effects of exposure to scenic
attractions on creative expression. The assistant would then ask each man to
fill out a short survey. When done, she would offer to tell him about the study
when she a little bit more time. Then she would write down her name and number
and hand it over to the men. Most men happily accepted it and walked off.
As expected the
female assistant started getting calls from the men. While only two of sixteen
men who crossed the small sturdy wooden bridge called, half of the eighteen men
who crossed the suspension bridge called. Why did she miraculously become more
attractive to the men who crossed the suspension bridge than to the men who
crossed the small sturdy wooden bridge?
Turns out that
for the men who crossed the suspension bridge, anxiety and adrenaline
translated into a heightened romantic interest in the assistant. Their
physiological reactions affected their perceptions and behaviour.
But could the men
who took the suspension bridge be more courageous and daring and therefore more
likely to take a chance on calling the assistant?
To test the
possibility, the researchers went back to Capilano to conduct a follow-up
study. This time the female assistant was stationed only at the end of the
suspension bridge. She approached some of the men right after they crossed and
others, ten minutes after they had finished crossing.
More men who met
the assistant just after they crossed called, than the ones who were approached
ten minutes later. The latter’s anxiety had subsided and their adrenaline
levels had gone down.
No wonder going
for a roller-coaster ride on a date makes sense.
Source: Attraction under conditions of high anxiety – Donald Dutton and Arthur Aron – Journal of Personality and Social Psychology 30 (1974): 510-17.
For years vehicle makers in India have promoted fuel efficiency by communicating how many kilometres per litre their vehicle delivers. But can people who care about fuel efficiency be able to easily calculate the cost of fuel over the lifetime of the vehicle, or over a long period of, say, five years, when fuel efficiency is measured in kilometres per litre (kmpl)? Consider this. If you were to buy a car that gave 15kmpl and cost ₹5 lakh, and were comparing it to another car that gave 12kmpl and cost ₹4.5 lakh, how would you make the decision? Most people think that the fuel efficiency between a car that gives 15kmpl is not that significantly different from a car that gives 12kmpl. Behavioural science studies find that making such calculations is not intuitive for most people. So they tend to use simple rules of thumb to make quick decisions, which leads to biases and errors.
For example, if you cover a distance of 100km using a car that gives 15kmpl, you’ll be consuming 6.67litres of fuel. If you cover a distance of 100km using a car that gives 12kmpl, you’ll be consuming 8.33litres of fuel. This doesn’t seem like a big difference, right? Now imagine you were comparing litres per kilometre (lpkm) expressed as litres per 100,000km. Then at 15kmpl, you’ll need 6,667litres compared with 8,333litres for the 12kmpl car. That’s a difference of 1,666litres and at ₹100 a litre, it comes to an additional fuel cost of ₹166,600.
Now the same facts, when
reframed in terms of litres per 100,000km, look substantial even though it’s the same fact as 15kmpl compared with 12kmpl. Even if we assume you drive about 10,000km a year, then at 15kmpl, you’ll need 667litres against 833litres for the 12kmpl car. That’s a difference of 166litres and at ₹100 a litre, it comes to an additional fuel cost of ₹16,660 per year. So measuring fuel efficiency in a more tangible manner as lpkm can change the way consumers perceive fuel costs and has the potential to alter their choice altogether. Keeping in mind that fuel costs mostly increase every year, kmpl as a measurement of fuel efficiency becomes even less accurate.
Duke University professors Richard P. Larrick and Jack B. Soll wrote about this way back in 2008. They called it the mpg or miles per gallon illusion. For ease of understanding, we’ll use the metric system used in India and illustrate the mpg illusion in terms of kmpl.
Consider a decision between two cars—a current vehicle and a new vehicle that is more efficient. Which improvement do you think will save the most fuel over 10,000km—(a) an improvement from 10 to 11kmpl; (b) an improvement from 16.5 to 20kmpl; (c) an improvement from 33 to 50kmpl? In most likelihood your answer will be c. But surprisingly, all options save the same amount of fuel over 10,000km: about 100litres.
Equal increases in kmpl are not equal in gas savings. Kilometres per litre can be confusing when thinking about the benefits of improving kmpl. For example, an increase from 10kmpl to 20kmpl produces more savings than does an increase from 20kmpl to 40kmpl.
Behavioural science studies have shown that most consumers do not understand the non-linear nature of the kmpl measure. They tend to interpret kmpl as linear with fuel costs. People tend to underestimate the fuel cost differences among low-kmpl vehicles and to overestimate fuel costs among high-kmpl vehicles.
As a result, buyers may well underestimate the benefits of trading a low-kmpl car for one that is even slightly more fuel-efficient. At the same time, they may overestimate the benefits of trading a high-kmpl car for one that has even higher kmpl.
Consumers don’t have that much motivation, time and attention to understand this issue. Even hardcore auto enthusiasts are likely to be making intuitive comparative judgements based on kmpl and are likely to make poor recommendations.
That’s why the researchers Larrick and Soll came up with the behavioural design solution of gpm (gallons per mile) or, in our case, litres per kilometre measured over meaningful distances. Think about which is more useful to know: How far you can drive on a litre of fuel? Or, how much fuel will you use while owning a car?
Kmpl answers the first question and lpkm answers the second question. Also, gpm or lpkm gets directly translated to cost of fuel for the consumer.
In US, the Environmental Protection Agency and department of transportation revised the fuel economy label to also include gpm (gallons per 100miles) and fuel cost over five years compared to the average, in addition to mentioning the mpg. In Europe, fuel consumption labels communicate litres/100km. In India, we’re still following kmpl, but it is difficult to compare kmpl of one car with another that costs a bit more, but has a higher kmpl number or compare a car that costs a bit less and has lower kmpl number.
What matters is how much money we will be spending on fuel over the lifetime of driving the car. And that can directly be calculated by lpkm. Not just that, if appliances like refrigerators can have behavioural design nudges like the energy consumption star ratings, why can’t vehicles get a fuel consumption star rating based on lpkm?
We owe this article to not just Duke University professors Richard P. Larrick and Jack B. Soll, but also to behavioural economist stalwarts Richard Thaler and Cass Sunstein because of whom we chanced upon the work of the former professors.
Recently it was reported that a 9W 697 Mumbai-Jaipur flight was turned back to Mumbai after take off as, during the climb the crew forgot to select the bleed switch to maintain cabin pressure. This resulted in the oxygen masks dropping. Thirty out of 166 passengers experienced nose and ear bleeding, some also complained of headache.
Aviation safety experts say such an incident was “extremely rare” as turning on the bleed switch is part of a check-list that pilots are expected to mandatorily adhere to. If turning on a switch that regulated cabin pressure is part of standard protocol, how could the pilots make such a simple, common-sensical error. And more importantly how can such errors be avoided in the future?
Traditional thinking suggests increasing the training of the pilots so that it makes them better and thereby avoid such errors. But training is not a full-proof method of ensuring human errors don’t get repeated. That’s because as long as humans need to rely on their memory to ensure the cabin pressure switch is turned on, errors are bound to happen. Sure check lists work. But that’s still a manual method of ensuring that the switch is turned on. And after repeatedly performing the tasks on the checklists over multiple flights, checklists themselves become routine habitual tasks done without much thinking. Also given that there are multiple tasks pilots need to perform in the 3-4 minutes after taking off, the chance of errors happening during those critical moments becomes high.
So instead of the pilot having to rely on their memory or routine check-lists, the answer to avoid such human errors lies in implementing simple behavioural design nudges. For example, if there was a continuous audio-visual reminder that the bleed switch had not been turned on, it would draw the pilot’s attention and it would be highly likely they would have turned it on. Such an audio-visual reminder was not present in this kind of an older generation of aircraft, and therefore the chance of human error increased.
The Japanese have a term for such error-proofing – poka yoke. This Japanese word means mistake proofing of equipment or processes to make them safe and reliable. These are simple, yet effective behavioural design features that make it almost impossible for errors to occur. The aim of error-proofing is to remove the need for people to think about the products or processes they are using. Some examples of behaviourally designed products used in everyday life are the microwave oven that doesn’t work until the door is shut or washing machines that start only when the door is shut and remains shut till the cycle is over. Elevator doors now have sensors that cause them to not close when there is an obstruction. This prevents injury to someone trying to enter as the doors are closing.
Human behaviour cannot be trusted to be as reliable as a machine. In fact, human behaviour is far from perfect. Yes, the people who operate expensive and complicated machines may be the best trained, but human errors in the form of simple error, lapse of judgment or failure to exercise due diligence are inevitable. According to Boeing, in the early days of flight, approximately 80 percent of accidents were caused by the machine and 20 percent were caused by human error. Today that statistic has reversed. Approximately 80 percent of airplane accidents are due to human error (pilots, air traffic controllers, mechanics, etc.) and 20 percent are due to machine (equipment) failures.
Another instance of how systems could be made safe by applying behavioural design is of airplane emergency evacuations. During the emergency landing of the Emirates flight EK521 at the Dubai airport in 2016, passengers were running to get their bags from the overhead cabins, instead of evacuating the plane. Only when the airplane staff began yelling at them to leave their bags and run, did the passengers finally pay heed to their calls and evacuate. Just a few minutes after the evacuation, the plane caught fire. It was a near miss situation. Had even a few passengers waited to get their bags from the overhead cabins, many of them would have got engulfed in fire. Again the natural instinct to correct such a situation would be to train people to evacuate and get them to listen to the flight’s safety instructions. But behavioural science studies have proven that such efforts are time-consuming, money-draining, unscalable and most importantly ineffective at changing human behaviour. In such an emergency situation, if the overhead cabins were automatically locked, with a label “Locked due to emergency”, passengers would not waste time trying to open them. That would in turn get passengers to behave in the desired manner and evacuate faster.
Sometimes behavioural design nudges are intuitive. Other times they are counter-intuitive. In a fire-drill experiment by behavioural scientist Daniel Pink, he found that placing an obstacle like pillar in the middle of a doorway got people to exit a hall 18% faster than without the pillar. The pillar was an obstacle but it split up people into two streams at the exit. That got people to use each side of the door, which in turn made the flow of people exiting the hall a lot smoother and faster. When the pillar wasn’t there to separate them at the exit, people bottle-necked at the door making the exit slower. Likewise, behavioural design could go a long way to design safer buildings, machines and systems and reduce human errors.
We found the connection between Psychology and Physiology so intriguing that we did more research and found more studies done on it. Intuitively it makes sense – the mind affects the body and the body affects the mind, but chancing upon hard scientific evidence makes it even more convincing.
So Becca Levy and colleagues at Yale got senior citizens over the age of 70 to take a special hearing test. A sequence of three ascending pitches for each ear was played. Each time a senior citizen heard a tone, they were supposed to raise their hand. The average score was 3.53 out of 6.
Next the seniors were asked to write the first five words that came to mind when they thought of an old person. The researchers noted how each senior responded and categorized each answer. The first category was from very positive (e.g. compassionate) to very negative (e.g. feeble). The second category was from external (e.g. white hair) to internal (e.g. experienced). The researchers got two sets of data – hearing test and attitude profile of each senior.
Three years later, the same seniors were invited to take the same hearing test again. This time the average score dropped. But not all participants’ hearing deteriorated equally. Those seniors who used negative and external descriptors to describe old age were worse off. Even after isolating other factors that would diminish hearing e.g. medical condition, the researchers found that the negative and external descriptors were responsible for a 0.7-point drop in a senior’s score – amounting to eight years of normal aging – in just three years. Even participants who scored a full 6 in the first round, and had used negative and external descriptors, experienced worse off diminished hearing.
This proves that negative and external feelings about old age can actually make people physically age faster. The effect is not limited to hearing alone, but to memory loss, cardiovascular weakness and even a reduction in overall life expectancy by an average of 7.5 years.
Hearing decline predicted by Elders’ stereotypes – Becca Levy, Martin Slade, Thomas Gill – Journal of Gerontology: Psychological Sciences 61B (2006): 82-88
Longevity increased by positive self-perceptions of aging – Becca Levy, Martin Slade, Suzanne Kunkel and Stanislav Kasl – Journal of Personality and Social Psychology 83 (2002): 261-70
Longitudinal benefit of positive self-perceptions of aging on functional health – Becca Levy, Martin Slade and Stanislav Kasl – Journal of Gerontology: Psychological Sciences 56B (2002): 409-17
Improving memory in old age through implicit self-stereotypes – Becca Levy – Journal of Personality and Social Psychology 71 (1996): 1092-1107
How do you feel when someone tells you how beautiful/handsome you look? Doesn’t it change your self-perception even if for few minutes or hours? But guess what, it has effects beyond your imagination.
In one of the most fascinating studies we’ve read, fifty-one women were made to have a short conversation on the phone with randomly selected men, thanks to researchers Mark Snyder, Elizabeth Tank and Ellen Bercheid. The women chitchatted about ordinary things – what they did for a living, their background – things you’d normally chat with strangers. But unlike the women, each man had received a bio and a snapshot of her. The bio was accurate, but the photos were fake. Half were of very pretty women, and other half of less attractive women.
As expected the men glanced through the bios, but they gave a hard look at the photos. Before talking to the women, each man was asked to rate his expectations of her. First group of men who saw photos of pretty women expected to interact with sociable, poised, humorous and socially adept women. Second group of men who saw photos of less attractive women expected to interact with unsociable, awkward, serious and socially inept women.
This where the experiment really began. The researchers recorded the calls and created clips of the women’s voices only. These clips were played out to a third group of random men, who knew nothing about the experiment. Listening to just the women’s side of the conversations, this third group of men were asked to rate their expectation of the women. Guess what, they attributed the same traits to the women that men of the first and second group had attributed to them, based on their fake photos.
How did this happen? The researchers explained – Once the men of the first and second group, formed their opinion of the women, it affected every aspect of how they interacted with them. The men talking on the phone with someone who they believed to be pretty, listened more actively and were more engaged. These “pretty” women on the other end unconsciously picked up on cues the men were sending them and took on the characteristics that the men expected them to have. Being thought of as beautiful made the women actually think of themselves as beautiful and exhibit their ‘beauty’ in their conversations. The third group in turn picked up on the cues from the voices of the “pretty” women and rated them as sociable, poised, humorous and socially adept women! Similar was the outcome for women’s voices based on the second group of men’s opinions.
Source: ‘Social perception and Interpersonal behavior: On the Self-fulfilling nature of social stereotypes’ – Journal of Personality and Social Psychology 35 (1977): 656-66.
That’s what happened in South Africa when a bank wanted to push personal loans to fifty thousand of its customers. In a field experiment conducted by Bertrand, Karlan, Mullainathan, Shafir and Zinman, the bank crafted several variations of the loan offer letter.
They tested lots of variations in features of a direct mailer sent to 53,000 potential customers with formal jobs in urban and semi-urban parts of South Africa. Some of the features varied were proposing uses of the loan, presenting more examples of loans – like loan amount, tenure, rate, payable amount, etc.; displaying interest rates in different ways, showing competitors rates and showing a picture of a pretty woman.
The letters included different interest rates (ranging from 3.25% to 7.75% per month); some featured comparison to a competitor’s rate; others a lucky draw – ten cell phones up for grabs each month; still others a photo of either a man’s or a woman’s pleasant, smiling face. The versions were randomly assigned and mailed off.
To start with the obvious one – customers were significantly more likely to apply for low-rate loans. But two other factors were influential in getting customer response, though they had nothing to do with the terms of the loan. One – the number of loan examples. Mailers with four examples of loans attracted far fewer applicants than mailers with just one example. Presenting more options drove away customers. Showing one loan example instead of four attracted as many additional applicants as dropping the interest rate by about a third!
Second, adding a picture of a pleasant, smiling face of a woman had the same effect on men as lowering the loan’s interest rate by 25%. Surely no customer would say that his decision to borrow boiled down to the picture in the corner of the mailer, but the data was there to prove it. Having a picture of a pretty woman logically doesn’t make for a better financial offer, but what happened is that the men were attracted to the woman and therefore signed up for the loan. And interestingly customers (in South Africa) didn’t respond any differently when the race of the woman was varied. The effect of a woman’s photo on women didn’t make much of a difference as it did on men.
No man would consciously sign up for a higher interest loan just because the offer letter had a picture of a woman on it, right? But male customers made errors in evaluating the attractiveness of the loan because they didn’t focus on the important data. Instinct took over. That’s why its best to A/B test Behavioural Design solutions to know which ones work. Without testing, you will never know what works, what doesn’t and which could be the best solution.
Source: Marianne Bertrand, Dean Karlan, Sendhil Mullainathan, Eldar Shafir and Jonathan Zinman – What’s advertising content worth? A field experiment in the consumer credit market – Quarterly Journal of Economics 125 (1), February 2010.
All economic transactions have a buyer and seller. As consumers we are buyers. As marketers, we are sellers. So, in all economic transactions, buyers and sellers have a conflict of interest. If you are selling a soap to consumers, you are making money from consumers. Consumers get the soap in exchange. If the seller is unhappy, he can raise the price and make more money or target different buyers. If the buyer is unhappy he can choose another seller of soap for better value at a lower or higher price. Both sellers and buyers have some advantages and some disadvantages.
Let’s examine sellers and buyers in other markets. For example, wealth. The seller is either a company or an agent who sells a financial product and the buyer is the end consumer. When sellers sell a financial product, it’s not usually as simple to understand and buy as a soap is. The information is usually in favour of sellers. Also, sellers try and hide the actual price of the product and fees are not disclosed upfront or not mentioned clearly. The amount of money is also a relatively large part of the savings for purchase of a financial product, whether it is insurance or mutual funds or shares in an IPO. Think of a typical insurance agent or mutual fund adviser or relationship manager. Whichever company and product gives her a higher commission, she usually tries to sell that product to the buyer, irrespective of whether the buyer has any knowledge about it, needs that product or knows how much it costs. In such a case, sellers take advantage of buyers.
Another example is health. The seller is a hospital or a doctor. The information is in favour of the doctor because the patient is almost completely dependent on her. The conflict of interest is immense here between sellers (doctors) and buyers (patients), because the seller wants to make money off the buyer, while the buyer has very limited information and not much choice. One may think that the seller here is likely to have consideration for the buyer to a greater degree because of the nature of the relationship and the nature of the service, but does that really hold true? Do doctors sell only what the buyers need or do they take advantage of the lack of knowledge of buyers, just like insurance agents? Doctors too have goals of covering huge overhead costs and fixed costs like education to recover their money. So how do you think this kind of seller behaves with buyers? Surprisingly such conflict of interest doesn’t usually catch the attention of buyers.
Another type of conflict of interest, is by stockbrokers. The broker may claim to have “inside” information about impending news on a stock and may urge buyers to buy the stock quickly. Investors buy the stock, which creates a high demand and pumps up the prices. This entices more buyers to believe the hype and buy shares. Stockbrokers then dump their shares. The price drops, and other investors are left holding stocks that are worth nothing compared to what they paid for it.
We live in a world full of information asymmetry and caveat emptor (buyer beware). Sellers always put themselves first. As a solution, policies mandate disclosure. But disclosures like “insurance is a subject matter of solicitation”, assuming that buyers are being made aware of conflicts of interest, so they would discount the seller’s pitch. But this works only in theory. Behavioural science studies show that it makes no difference to the real behaviour of sellers or buyers. For example, calorie labelling on packaged foods does not have the intended effect of decreasing calorie purchasing or consumption.
Conflicts of interest are everywhere, and their fundamental nature leads to a change in people’s view of the world in important ways, causing them to give biased advice and behave in dishonest ways. Conflict of interest pushes sellers into the direction of what is not good for buyers financially. And disclosures, the way they are currently framed, don’t fix the problem. That’s why policy makers need to recognise the size of the conflict and the depth of their influence, and try to create behaviourally designed disclosures, so that buyers are not taken advantage of. Policy makers need to understand that buyers are not necessarily rational; they have limited attention, limited cognitive bandwidth, suffer from biases and use rules of thumb to make decisions. That’s why policy makers need to rethink how disclosures are consumed by buyers and understand their actual effect on the behaviour of buyers. It would be best if disclosures are made intuitive with simple visuals and plain language that’s easy to read and understand, and are placed at prominent locations, so that they become part of the buyer’s decision-making process.
This article first appeared in afaqs, a leading Indian marketing and advertising publication. Afaqs shared it as a “mustread, brilliant article” on social media.
The mutual fund industry has been running an ambitious investor awareness campaign ‘mutual funds sahi hai’. At the same time, the AUM of the mutual fund industry touched a record level of Rs23 trillion by end of 2017—up from Rs16.46 trillion at the end of December 2016. But correlation does not mean causation. The main causes of the rise in mutual fund industry’s AUM is the combination of the effect of demonetization, decline in interest rate on fixed deposits, gold and real estate’s lackluster performance, flow of FII investment in Indian markets and the historical fact that retail investors are the last to jump into equity markets.
The ‘mutual fund sahi hai’ campaign has had messaging like ‘life mein risk, toh mutual funds mein kyon nahi’ (if there’s risk in life, then why not in mutual funds’), ‘thoda thoda karke bhi invest kiya ja sakta hai’ (one can invest small sums too), ‘planning long-term karni ho ya short-term’, ‘mutual funds mein patience rakhna zaroori hai (one needs patience in mutual funds). The campaign has poured crores of the industry’s money into such communication. However, the campaign reminds me of the story of the blind men and the elephant. According to the story, none of the blind men were aware of the shape and form of an elephant. So they inspected it by touching it. The blind man whose hand landed on the trunk, thought the elephant was like a thick snake. The one who touched its ear, thought it was like a fan. The one who touched its leg, thought it was like a tree-trunk. The one who touched its side thought it was like a wall. Another who felt its tail, described it as a rope. The last felt its tusk and described it as a spear. None of the blind men had the complete context.
Similarly, the ‘mutual fund sahi hai’ campaign creates limited perception by describing ‘stand-alone features’ of a mutual fund. It doesn’t describe what a mutual fund is. Without the complete context, each blind man saw the elephant as something other than what it was. Likewise, without explaining what a mutual fund really is, how would a first time investor understand the concept of a mutual fund? And without understanding the concept of a mutual fund, how would a first time investor trust it?
Most investor awareness campaigns include heavy doses of complicated financial jargons like power of compounding, equity, debt, hybrid, etc. But these words are alien for first time investors. Plus, campaigns include wishful thinking like be a disciplined investor and execute goal-based plans. A behavioural science study by Fernandes, Lynch, & Netemeyer – a meta-analysis of over 200 financial programs on educating investors – has found that the largest effect any of them had was a mere 0.1%. Research amongst first time investors by Briefcase shows that the only thing they recall about mutual funds is ‘mutual funds are subject to market risks, please read the scheme documents carefully before investing’, without even knowing what it really means. Leave alone the cognitive challenge of choosing a fund from over a thousand of them, they don’t even get the concept of a mutual fund. The ‘mutual fund sahi hai’ campaign doesn’t address this problem.
But behavioural science can help. Behavioural science involves using powerful principles to create intuitive communication. One example is the principle of familiarity. In an experiment by behavioural scientist Bornstein et al, faces of individuals were flashed on a screen so that quickly that participants couldn’t recall having seen those people. Yet, when these participants met those people, they liked them and were persuaded by them to a greater extent. So to get first time investors to adopt mutual funds faster, the communication needs to make the unfamiliar, familiar. Mutual funds need to draw heavily from what people are already familiar with – banks, savings, fixed deposits, recurring deposits, provident funds, etc. But the latest ‘mutual fund sahi hai’ campaign does exactly the opposite and talks about mutual funds being a new way of investing.
The behavioural science principle of cognitive overload shows that too much choice and information results in indecision and lower sales. Behavioural scientists Sheena Iyengar and Mark Lepper set up a display at a supermarket in which passersby could sample a variety of jams that were made by a single manufacturer. Either 6 or 24 flavors were featured at the display at any given time. Results – only 3% of those who approached the 24-choice display actually purchased any jam. In comparison 30% bought when the choice was between 6 flavors. In an experiment on retirement funds, behavioural scientists Sheena Iyengar, Huberman and Jiang analyzed retirement programs of 8,00,000 workers in the US and found that when only 2 funds were offered, the rate of participation was around 75%, but when the 59 funds were offered, the participation rate dropped to about 60%. Likewise, the concept of mutual funds needs to be made simple and easy to understand, without any jargons, ensuring there’s no cognitive overload for first time investors.
In our research with first time investors, when we asked them to illustrate ‘income’, they drew cash and cheque. When asked to illustrate ‘savings’, they drew a bank branch with its signage. But when they were asked to illustrate a ‘mutual fund’, they drew a blank. But the powerful principles of behavioural science can help create that image for a mutual fund – intuitive and persuasive. Because only if the first time investor gets what a mutual fund is, will she/he trust it and invest in it.
Drinking water is essential to human health. The amount one should drink varies from person to person based on gender, age, height, weight, physical activity, sweat levels, metabolism level, body temperature, humidity levels, external temperature, altitude, quantity and quality of food intake, quantity and quality of other fluids’ intake and host of other details. When you don’t get enough water, every cell of your body is affected. You lose a lot of electrolytes, including sodium, potassium and chloride, which are essential to your body’s functions. Pretty much all of your cellular communications revolve around sodium and potassium, including muscle contractions and action potentials. Fatigue, lethargy, headaches, inability to focus, dizziness and lack of strength are all signs of dehydration. Nature has given us a powerful alert system – thirst. But in our busy chaotic lives we often ignore it and forget to drink water.
Behavioural Design vs awareness
There is enough information about why we should drink more water, yet most people feel they don’t drink enough. Education doesn’t change behaviour.
Behavioural change requires a different approach. Drinking water regularly is a good habit. Habits are essentially automatic in nature, where one does not consciously think about the action. In other words, habits are auto-pilot behaviours. For a behaviour to become a habit, it requires three things to come together – trigger, action and reward. When the loop gets completed, the habit sets into place. For example, over a period of time we have gotten used to waking up in the morning (trigger), brushing our teeth (action) and feeling fresh (reward). To create good habits, initially conscious effort is required. However, we humans are lazy, so the lesser the effort to get the habit started, the better. Eg. We forget to drink water during the day. So if there’s a trigger like a reminder from the water bottle, we’re likely to drink water. Over time the action of opening the water bottle because of the reminder can become auto-pilot i.e. become a habit. This approach led us to create a water bottle that glowed and beeped that gently nudged people to drink water 16% more.
We chose to do an experiment in an office of one of our corporate clients. The administration department of that company would keep filled-water-bottles on the desk of each employee every morning and refill it once every evening. So we bought the same type of water bottles for our experiment so as to not draw any suspicion amongst participants. And we created two versions of caps. In the first version of the cap, we fitted a chip which recorded the number of times the water bottle was opened. In the second version of the cap, we fitted a chip which recorded the number of times the water bottle was opened and in addition, the cap now glowed and beeped once after every two hours of the water bottle being opened. If the bottle wasn’t opened, then the cap would glow and beep after an hour. When the water bottle was opened, the cap would sense it and stop glowing. In both versions the chip was hidden inside the caps.
Creating prototypes of both versions of water bottle caps took longer and was costlier than we expected (planning fallacy). We could only produce a total of 70 water bottle caps over more than a year. Thirty-five pieces of each version – first version with recording chip without glow and beep and second version with recording chip with glow and beep. Because of being able to produce 70 water bottle caps we chose to randomly select thirty-five participants from the office employees who wished to participate in our experiment.
In week 1 we gave them our similar looking water bottles with the first version of the cap with recording chip hidden in it. In week 2 we replaced the caps with the second version of the cap with the recording chip with the glow and beep. We accounted for data from Monday morning to Friday night in both weeks. We then compared the data of how many times the water bottle was opened with the numbers of hours the employees had spent in office on each day of Week 1 (no glow and beep) and Week 2 (glow and beep). Had we been able to conduct the experiment amongst a larger set of sample, we would have chosen the typical control group and treatment group, but due to the above mentioned capacity, time and money constraints we did a before-and-after format for this experiment.
In week 2 employees opened the water bottles 16% more than in week 1. It means the employees were not sufficiently hydrated with regular water bottles even though they were kept on their desk right in front of their eyes. The simple Behavioural Design of glow and beep water bottle caps got employees to drink 16% more frequently than without the Behavioural Design nudge.
Frequently asked questions
Q. How much water does one need?
A. Scientific studies are inconclusive on the amount of water required by an adult. Some say its 3 litres. Some say 2.5 litres. Some (Mayo clinic) say for men its 3 litres and for women its 2.2 litres. But fact is that calculating how much water you need depends upon your gender, age, height, weight, physical activity, sweat levels, metabolism level, body temperature, humidity levels, temperature, altitude, quantity and quality of food intake, quantity and quality of other fluids intake and host of other reasons. It’s extremely difficult to calculate real time hydration levels accurately.
Q. Why didn’t we create a bottle that could calculate how much water each individual person needed?
A. To do that we’d need to know people’s gender, age, height, weight, physical activity, sweat levels, metabolism level, body temperature, humidity levels, temperature, altitude, quantity and quality of food intake, quantity and quality of other fluids intake and host of other details. It’s extremely difficult to calculate real time hydration levels accurately. Sensors and software that can capture all of the above seamlessly are very expensive as of date. Measuring only some of the inputs would lead to an inaccurate result that would be misleading. So we used a simple rule of thumb of drinking water every two hours to stay hydrated.
Q. What’s the best way to judge whether you are hydrated or dehydrated?
A. The most scientific and simplest way to judge whether you are hydrated or dehydrated is to look at the colour of your urine. If your urine is crystal clear it means you’re probably drinking too much water. If its light or mild yellow it means your drinking an adequate amount of water. If its proper yellow or darker it means you need to drink more water. If its brown you need to visit a doctor.
Mild Dehydration Affects Mood in Healthy Young Women – Lawrence E. Armstrong, Matthew S. Ganio, Douglas J. Casa, Elaine C. Lee, Brendon P. McDermott, Jennifer F. Klau, Liliana Jimenez, Laurent Le Bellego, Emmanuel Chevillotte and Harris R. Lieberman – The Journal of Nutrition – 21 December, 2011.
Mild dehydration impairs cognitive performance and mood of men – Matthew S. Ganioa, Lawrence E. Armstronga, Douglas J. Casaa, Brendon P. McDermotta, Elaine C. Lee, Linda M. Yamamotoa, Stefania Marzano, Rebecca M. Lopez, Liliana Jimenez, Laurent Le Bellego, Emmanuel Chevillotte and Harris R. Lieberman – British Journal of Nutrition – Volume 106 / Issue 10 / November 2011, pp 1535-1543
Lawrence E. Armstrong – an international expert on hydration who has conducted research in the field for more than 20 years (professor of physiology in UConn’s Department of Kinesiology in the Neag School of Education)
‘We’re only human’ is a term associated with humans of course, but more so with accidents. But if that were our attitude we wouldn’t be able to learn much on how to prevent them in the future. And thankfully that’s not what happened after the train accident on 6th March, 1989 in Glasgow, Scotland.
That afternoon the train driver, pulled out of Bellgrove station and within half a mile, ploughed head-on into a train travelling in the opposite direction. The driver of the other train died along with another passenger. The driver who caused the accident had to be cut free from the wreckage and lost a leg in the accident.
So how and why did the accident happen?
It was the guard’s responsibility to check that all passengers were either on or off the train and that the signal on the station indicated that it was safe for the train to proceed. The guard admitted that he had not checked the signal, partly because it wasn’t easy from his position at the back of the train and he knew the driver would be able to see it clearly from the front. He rang the usual two bells to give a ready-to-start signal. But the signalman confirmed that the signal was red during the whole time. On the other hand, for the driver, the red signal would have been visible for another 13 or 14 seconds, even after pulling away, but he still didn’t notice it. In the final investigation report, the driver got the majority of the blame for the accident, with the guard cited as a contributory factor, because ultimately it is the driver’s responsibility to check that it is safe to proceed.
The accident happened because the driver had built up a simple habit. When he heard the two bells, he acknowledged it and set off without checking the signal himself.
A Behavioural Design solution was used later to prevent such accidents from happening. A reminder switch was put in the driver’s cabin that cut power to the train, when it was activated. Drivers were made to turn it on when they stopped at a station as an extra safety check. Now if they heard the two bells and tried to apply power immediately, the train wouldn’t move. They had to turn off the reminder switch, and that prompted them to check the signal first. But a system, which halts the train automatically, if the driver jumped the red signal, would be an even better Behavioural Design solution.
Source: Making Habits Breaking Habits by Jeremy Dean