Decision making – Platform Business Firms

Far and near mindsets

Last month, I was at the Yale University and was listening to Prof. Nathan Novemsky on different mindsets. Of the various mindsets we discussed, psychological distance (and its impact on communication and marketing) caught my attention. In this blog post, I elaborate the concept of psychological distance, and why it is important in the context of entrepreneurship and multi-sided platform businesses.

Psychological distance: Basics

Prof. Novemsky’s (and his colleagues’) research indicates that as people get closer to the decision in terms of time, their mindset changes from a “far mindset” to a “near mindset”. When people engage with you on a far mindset, they are concerned about the “why” questions; whereas when they engage with you on a near mindset, they are concerned about the “how” questions.

Let me illustrate. When a customer downloads an Uber App for the first time, she is more concerned about how she is contributing to the environment by being part of the shared economy, and therefore is less concerned about issues like the minute features of the user interface/ user experience. On the other hand, a customer who is getting out of a day-long meeting with a demanding customer is worried more about the minute details of the ride, like the time taken for the car to arrive, type and cleanliness of the car, and driver’s credentials and behavior; as she is engaging on a near mindset.

Communication and marketing

The understanding of what mindset your customer is engaging with you is imperative to designing your communication. When you advertise a grocery home delivery service on television, you might want to appeal to the consumer’s far mindset … that talks about why he should choose your service rather than the neighborhood grocer/ vegetable market. For instance, the benefits of fresh produce straight from the farm (without middlemen) faster would make immense sense. However, when you communicate with your customer after he has decided to place an order, you might want to talk about specific discounts, receiving delivery at a convenient time, quantity changes, add-ons and freebies, and payment options.

What does this mean to start-ups/ entrepreneurs?

That’s simple, right. A founder communicating with a potential investor should talk to the “far mindset” rather than the “near mindset” if he has to raise money. However, a customer presentation has to appeal to the near mindset.

For instance, the home-health care start-up for pets (petzz.org) communicates convenience of all-day home-visits of veterinarians to its pet-owners; the specific plans available that pet-owners can choose from; and the significant increase in business for the veterinarian partners. However, when it runs camps to enroll pet-owners, it talks about “healthy pets are happy pets” communicating to the far-mindset.

However, the investor deck only appeals to the far mindset … how their business model leads to “healthy pets” and why this is a compelling value proposition for its pet-owners, veterinarians as well as other partners in its platform.

[Disclaimer: I advise petzz.org]

Implications for multi-sided platforms

Not so simple. I can envisage that there may different sides of a platform that may be operating at different mindsets and the MSPs may need to be continuously aware of. Take the example of the social-giving/ crowd-funding platform Milaap (milaap.org). The two sides of the platform are givers and fund-raisers.

Imagine a fund-raiser appeal … which one appeals to you most?

“help a school from rural Chattisgarh build toilets for girls”
“help support girls’ education”
“make sure girls like Shanti don’t drop out of school”

As you move down from option 1 to 2 to 3, you are increasingly operating from the far-mindset!

On the other hand, when Milaap attracts fund-raisers with the following messages

“you get the most socially-conscious givers at milaap”
“it’s easy to communicate with givers at milaap”
“it’s is easy to login, set up and free”

As you move down from option 1 to 2 to 3 here, you move towards a near-mindset!

It gets more complicated when the different sides of the platform are at different stages of decision-making. For instance, when a C2C used-goods marketplace platform like Quikr has a lot of buyers and lesser number of sellers; the messaging across the two sides has to be different! For the sellers who are yet contemplating joining the platform, the message has to be appealing to the far mindset (of decluttering their homes), whereas for the umpteen buyers who are looking for goods on the platform, the message has to appeal to the ease of transacting (near mindset).

Match the message to the mindset and the stage of the engagement

In summary, effective platforms have to communicate consistently across multiple sides of the platform, however keeping in mind the different mindsets of the respective sides. A cab hailing app has to communicate differently to its riders as well as drivers, while sustaining the same positioning. If the rider value offering was about speed of the cab reaching you, the driver communication has to be consistent – speed of reaching the rider. For the driver, it is near mindset (speed of reaching the rider is about efficiency), whereas for the rider, speed may be appealing to the far mindset (about not driving your own car and keep it waiting all day at an expensive parking place; or better still, reducing congestion in the city centers). And for sure, these messages also have to change over the various stages of consumer engagement, right!

Any examples of mismatched communication welcome!

Cheers from a rainy day in Nuremberg, Germany.

Hindsight and foresight in judgments under uncertainty

As I began reflecting on my earlier posts on You are intelligent: have you done something dumb? and Judgments, I rolled back to this classic article, so beautifully titled, Hindsight ≠ foresight. Written by B Fischhoff, way back in 1975, this paper provides two intuitive results (at least in hindsight): once the outcome of an event is known, people associate higher probability of its occurrence; and people were unaware that they have been influenced by hindsight (the knowledge of what actually happened).

Case class in a business school – hampered by hindsight bias

Take a business school case class for instance. As a case teacher, I face this a quite a lot of times. In a typical strategy cases, the primary question to the class is “what should the company do?” And since most cases are set a few years in the past, simple Internet search (by the students as part of their class preparation) would have informed them about what had actually happened. Given that students come into class with this hindsight, they try very hard to fit their preparation and theoretical arguments to the actual outcome, however irrational, or improbable it might have been. A good management teacher ought to therefore provide for this “hindsight bias” in students and ensure that a fair discussion happens in class on all possible outcomes.

Should I therefore, as a management teacher, provide my students with only cases for which the outcomes were not known? What therefore are my criteria to choose cases for a class? Do I fight to eliminate this “hindsight bias”? Let me come back to this later.

An air-crash investigation

Let us take another example. A special team has been tasked with investigating the cause of an air crash. Any investigation of an accident would inevitably entail putting together pieces of information to arrive at a causal relationship between the antecedent factors and the event, which is known to have happened. It is impossible to eliminate the source of bias here, the event. The investigation team has to be trained to create a counter-factual (good) outcome from the evidence at hand. They need to recreate the antecedents to the event in a manner that they evaluate if anyone in their place would have made the same decisions as the actors (pilots and crew who made certain decisions) did. Experimentally, it is akin to creating a control-group that knows all the facts leading up to the case, but not the actual outcome.

Investigating white-collar crime

In the case of white-collar crime, especially when it involves financial fraud, another significant factor interferes with hindsight bias, the size or impact. Larger frauds are fraught with more pronounced biases. Media coverage on “select” white-collar crimes are testimony to such biases. Nicholas Bourtin (read the article here), adds how armed with hindsight bias, financial crime investigators might ascribe malicious intent to even innocent mistakes or poor judgement.

As I was thinking about this issue, I just saw the breaking news of an earthquake of 7.2 magnitude hitting the Iraq-Iran border (Sunday, 12th November 2017). Hopefully, there isn’t much damage. And it triggered a thought.

Fighting hindsight bias – learning from geophysics

A great learning for fighting hindsight bias comes from geophysical studies. Imagine how geologists and geophysicists study earthquakes and volcanic eruptions. These are events that just “happen”, and then the scientists “reconstruct” the events through carefully collected data. Can we learn something from the way they fight hindsight bias? Sure.

Strategy #1: Conduct stability studies. Not just fault studies, but stability studies. Take the context of highly quake-prone areas, and go study why earthquakes aren’t happening! Such data would provide the ‘normal’ distribution of data with the occurance of earthquakes being the outliers.

Strategy #2: Broaden the search. Take all retrospective data for analysis. Study all the quakes that happened on a plate/ all eruptions of a volcano. Such events may occur very infrequently, and may be randomly distributed over time. However infrequent they may be, it would be worthwhile to study the antecedent conditions every time. Maybe, one can find a cause-effect relationship. Like concluding that most road accidents happen between 2.00am and 4.30am because there is a high likelihood of drivers sleeping behind the wheel (that is if they are still being driven by human drivers!).

Strategy #3: Combine the two, and seek patterns. Conduct stability studies and say why events do not happen, and conduct (with big data) longitudinal studies to infer why events do happen. Combine the two and create patterns. Such patterns can be immensely helpful in studying antecedents of events, and effectively fighting hindsight bias.

Fighting hindsight bias – applying it in managerial judgement

Straight, let us try and apply the learning from geo-physics to managerial judgement. First, consider prior probabilities of an event happening appropriately. Imagine an angry boss (no, I would like to believe that all bosses are not always angry!). In trying to understand what angered her today, use prior probabilities appropriately. She may be angry because she was being held accountable for something beyond her control (like your productivity), or just that she gets angry when she is frustrated about not being able to communicate or convince others. Strategy #1: ask yourself, when is she ‘not angry’? She is not angry when you complete your work on time, when you present your work properly (as she likes it), and when your work is of good quality. Then why is she angry today? You have the answer.

Second, stop thinking sample size and probability. Unless you have a really large sample size of such events, stop thinking about probability. Imagine predictions in sport or financial services. I was taught in my first finance classes, “past performance is not an indication of future performance”. And my brief indulgence with sports tells me that the law of averages is that “sustained good performance does not last long”. Would you be confident in predicting the goals scored by a football team if their prior performances were [4-1, 3-0, 5-2, and 1-0] or [1-4, 4-2, 2-2, 1-0] with the second number in each pair representing the goals scored by the opposition? Most would be confident of predicting the performance of the former scoring pattern than the latter. It might just happen that the next game is against the league leader (including someone with initials of CR7) and all these performances do not matter at all. You really need to collect loads of data on each team’s performance, including historical performances of all the opposition teams before you make any predictions.

Third, stay away from causal relationships (no I did not say casual relationships!), unless you have really “big data” on both the normal distribution of the event not happening, as well as the outlier chance of the event happening. Remember the wonder batsman, Pranav Dhanawade, the 17-year old kid who scored 1009* runs for his local cricket team. After a few years, his father has decided to return the scholarship he received, since he has not performed up to expectations (read it here). It was important that when an event of this nature (an extraordinary performance) occured, one needs to not just reward, but also invest in nurturing the talent. Without an adequate support structure to hone his talent, the financial reward was insufficient to sustain even acceptable performance.

So why do some firms perform better than others?

The answer may not lie in analyzing why those performed better, but in understanding what the others do that make them not perform as well as the high performers; longitudinal and cross-sectional (big) data on multiple firms’ performance; and being very cautious about making causal assertions. Isn’t this the core of strategy research, today?

Cheers!

You are intelligent: have you done something dumb?

One of my colleagues does her research on strategic thinking and in one of our conversations we discussed what is critical thinking, and how is it different from other concepts that are commonly used in management and leadership education. I chanced upon the research by Heather A. Butler (California State University Dominguez Hills) on the difference between intelligence and critical thinking. In this piece provocatively titled, Why do Smart People do Foolish Things?, she argues that intelligence and critical thinking are different. In this blog post, I will discuss how intelligent people should/ can acquire critical thinking skills.

Intelligence vs. critical thinking

Intelligence is measured through standardized tests like the IQ test that measures skills like visuo-spatial skills, calculations, pattern recognition, vocabulary and diction, and memory. Intelligence therefore, arms people with the ability to solve problems.

Critical thinking on the other hand is the ability to rationally think in a goal-oriented fashion, and a disposition to use those skills when appropriate. It has been defined as “the intellectually disciplined process of actively and skilfully conceptualizing, applying, analysing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action. In its exemplary form, it is based on universal intellectual values that transcend subject matter divisions: clarity, accuracy, precision, consistency, relevance, sound evidence, good reasons, depth, breadth, and fairness” (Michael Scriven & Richard Paul, 1987). In simple words, critical thinking is about questioning both information and beliefs, and using that questioning to guide behaviour. It is that ability to ensure the quality and consistency of information that forms the bedrock of critical thinking. While intelligence is about pattern-seeking and projection, critical thinking is about questioning the quality of information.

What intelligence creates is smartness. But that does not ensure that smart people don’t do stupid/ foolish things. Who hasn’t heard of intelligent Albert Einstein cutting two holes in the door for large cats and small cats? I am not going to tell any stories of all those stupid and foolish things I have done! If you were waiting for it, I am equally flattered (thank you for acknowledging that I am intelligent!), but I am going to disappoint you.

Developing critical thinking skills

Critical thinking creates rational thinking. The way a critical thinker would solve problems would be very different than someone with just intelligence. I conceptualize critical thinking as a skill over and above basic intelligence. Critical thinking encompasses “reflective and reasonable thinking” that is focused on defining “what to believe or do”. Defining “what to believe and do” requires three core skills – deduction, induction, and value-judging. Deduction is the process of making inferences based on a general statement, a set of hypotheses, and statements. The inference is based on a general theory of science, and is a top-down process. So, what is true of a class of things/ events in general, is true for each of the components. Therefore, if you were a German, you would be punctual.

Induction on the other hand, is bottom-up; where a set of data and observations from the ground will help make the inference. Data is collected, patterns sought, and from these patterns the theory is generalized. The key in inductive inference making is the collection of right quality and quantity of data to make good inferences.

The third and most important component of critical thinking skills is value judging. While it is easy to teach and train people on deductive and inductive reasoning, value judging is very difficult. Value judgement is an assessment of something good or bad, given one’s realities and priorities. At the definitional level, value judgements are made independent of data – these are value judgements. However, critical thinking as a competence integrates deduction (top-down inference making), induction (bottom-up inference making), and value judgements (assessment of good or bad).

As we define value judgements as “a choice of what we like or want” or “what is good or bad in this context”, it is very difficult to teach in a classroom, through any of traditional methods. In-situ experiential methods are required to train someone on making robust value judgements. Internships, externships, and apprenticeships are some useful methods for teaching/ training value judgements. Do you now realize why certain professions are called ‘practice’ – like law, consulting, or accounting?

Why should I learn critical thinking?

Critical thinking helps you in many ways. One, it helps you remain goal-directed. Armed with critical thinking skills, everyone will collect, collate, and make inferences based on what is good/ desirable for them. In the absence of critical thinking (all of deductive-inductive-value judgement or DIVj), one might not be able to make inferences in relation to the goal. For instance, if my objective is to choose an investment plan, I need to surely invest in a manner that matches my financial goals; and DIVj surely helps.

Two, critical thinking allows you to be flexible thinkers and evolve into amiable sceptics. It would not be easy to convince you in the absence of good theory (deductive), solid data (inductive), or goal-directed behaviour (value judgements). You are most unlikely to be swayed away by mob beliefs and unscientific arguments.

Three, critical thinking helps you be aware and accept your conscious and unconscious biases (including hindsight and confirmation biases).

Practising critical thinking

It is therefore important for you as a smart person to learn and practise critical thinking. Practising critical thinking is about consciously using deductive, inductive and value judgements. I know a lot of managers we train at our business schools with deductive and inductive skills, but much less of value judgements. One of my favourite arguments for the case method of learning is that it is the closest it gets to training people to make value judgements (read my earlier post on Judgements).

Some may argue that intelligence is part genetic and you may be born with or without intelligence. On the other hand, critical thinking is surely developed; albeit only through conscious efforts. Here’s calling all intelligent and smart people: to go out there and acquire/ practise critical thinking.

Judgments

This is that time of the year when Indian business schools welcome their new students. As a self-proclaimed proponent of the case method of learning, I am often invited by my school to teach a session on “case method of learning” to the first year students. And one of my key messages to one such group of students this year was this: “a lot of what you will learn in the business school in terms of content, can be read from a variety of sources; what you will learn in class through continuous, repeated practice is the ability to make sound judgments.” This post is an elaboration of my understanding of the role ‘judgments’ play in business and life.

Judgment: what is it, anyway?

In the legal world, a decision made by a ‘learned Judge’ after hearing out all the arguments from all parties involved. The judge makes up her mind after providing equal and fair opportunity to all concerned parties to present their points of view; a detailed analysis of the evidence presented; collating expert opinions; gleaning through precedents and cognizant of the opportunity of this particular decision setting a precedent; and keeping the law of the land as well as the changing (socio-economic) contexts. When a judge presents a judgement, it provides a guideline for what is good/ bad; preferable/ not-preferable; acceptable/ not-acceptable in that particular context. To that extent, there is a subjective evaluation of the options, given the specific context; and a specific preference for one course of thought/ action over another.

Is it different from decision-making?

At a basic level, a judgement is a decision. But it is more than a just a decision. A decision by definition is a choice. Professor William Starbuck famously distinguished policy making (where resource allocation is a continuous process) from decision-making as ” … the end of deliberation and the beginning of action” (for more details on this quote, and in general, a history of decision-making, read this classic HBR article).

I see the primary difference between decision-making and judgement as managing risk and uncertainty. In his classic book titled “Risk, Uncertainty, and Profit”, Frank Knight (1921) defined uncertainty in a situation where the outomes could not be comprehensively enumerated and the probabilities of their occurances cannot be estimated. On the other hand, risk is a situation where all possible outcomes could be listed and the probabilities may be calculated.

Instructions and advice

One of my favourite assertions in my case learning sessions is the difference between instructions and advice. Instructions as we all know are directions for performing an activity step-by-step, a sort of a standard operating procedure. No thinking involved here – just go ahead and do what is written up/ told to. Whereas advice is contextual. Someone tells you, “it worked for me/ others in a similar context, you may try it yourself”. Of course, this implies that if your context is different, feel free to ignore/ adapt. Isn’t that why advice is always free?!

I bring in an example of how a little boy is taught to cross the street. Imagine his mother’s instructions: “before you cross the street at the zebra crossing, look for the policeman at the intersection; and when he signals you to cross, run across the street as fast as you can!” Wonderful … as long as the context is fixed. What happens if at the intersection, there is no policeman … does the boy keep waiting? What happens if the policeman does not notice him waiting to cross the street? Of what happens when the policeman signals him to cross the street, but a car is speeding towards him? What happens if …. ? Here is where judgements come in handy. Instead of providing him instructions to cross the street, his mother should develop judgement skills in him.

Imagine how you cross the street … if any of you have tried crossing the street in India, you know it better. I distinctly remember when my German colleagues while attending a conference in India, had a harrowing time crossing streets! When you cross the street, you look both sides of the road, spot a car a fair distance away (163m farther), driving relatively slow (at 26 km per hour). You estimate that at your speed of walking (4.5 km per hour), you will be able to cross the 80ft wide street a well 29.5 seconds before the car crosses the point where you intend to cross. You get the point, right? Nobody does all these calculations, we know it. Decision scientists call it intuition, gut, judgements.

It is developed through practice, accumulated through experience, and through active experimentation. Acculturation through socialization and mentoring may help in developing judgements; but no guarantee that just by repeating an action again and again, one would develop judgement. Apart from this practice and experience, a critical component of judgement is intent. Plus, an ability to weigh the pros and cons (in almost real time), as is in decision-making.

Intent in Judgement

One needs to have a specific intent to learn from experience. It is very likely that someone can continue to do an activity repeatedly without developing a sense of judgement. Something like a rote learning or Pavlovian Conditioning. How many times have you experienced people doing the same activities again and again not knowing why they are doing it, and why that way? Inefficient bureaucracies are built on the separation of thinking from doing; the doers are refrained from thinking … they are told to just do, and suspend thinking. Imagine blue-collared workers in the Taylorian world, or even BPO workers, or some customer service executives in modern-day organizations. It requires concerted intent to learn judgement.

Will I lose my job to automation?

The question in most cases is not if, but when? Judgement has never been more important as it is today. Roles where judgements are not required, activities that can be codified into detailed processes (where all possible outcomes can be enumerated and probabilities calculated), automation will take over. Bots and robots dominate the internet world today. Almost every website that has a customer interface has a bot running … and sometimes the responses could be hilarious. For instance, an airline customer thanked an airline sarcastically for misplacing his luggage and the airline responded with a big thanks for his compliment. Obviously, the sarcasm was lost on the automated response. The machine could not “learn” enough. And the entire twitterati took over (read about it here).

We live in a world today where the buzzwords include “big data”, “analytics”, “business intelligence”, and “artificial intelligence”. I recently saw a cartoon on a blog (futurethink.com.sg) that I can relate to very well.

As machine learning, automation, robotics, and augmented reality dominate our industrial vocabulary, natural intelligence and human judgement should take centrestage in our discourse.

Learning judgement

My advice to budding managers, invest in learning judgement-making. Consciously, with intent. Practise, make mistakes, experiment. Define outcomes and build expertise. After all, what you want to make out of your life and career depends on your judgement, right?

Cheers.

Beware the stupid!

During one of my random browsing through the internet on my mobile device, I came across an interesting set of laws – the basic laws of human stupidity. Yes, you read it right, stupidity. By Carlo M. Cipolla (read the original article here), an Italian-born former professor emeritus of economic history at University of California Berkeley. This is simply genius. This post is to help you find how these laws apply to the start-up ecosystem of today. Read on.

The five laws

Let us first understand the five laws. The first law states:

Always and inevitably everyone underestimates the number of stupid individuals in circulation.

They are everywhere and appear suddenly and unexpectedly. Any attempt at quantifying the numbers would be an underestimation.

The second law states:

The probability that a certain person be stupid is independent of any other characteristic of that person.

There is serious diversity at act here. No race, gender, educational attainment, physical characteristics, psychological traits, or even lineage can explain the incidence of stupidity in a person. He says a stupid man is born stupid by providence, and in this regards, nature has outdone herself.

The third law is also labelled a golden law, and presents itself into a neat 2X2 matrix. It states:

A stupid person is a person who causes losses to another person or to a group of persons while himself deriving no gain and even possibly incurring losses.

This law classifies people in this world into four categories – the helpless, intelligent, the bandit, and the stupid. Organized on the two axes of gains for self and others, the helpless is fooled by others who gain at his expense; the intelligent creates value for himself as well as others; the bandit gains at the expense of others; whereas the stupid loses himself in the process of destroying others’ value.

While the actions of others are justifiable, it is the actions of the stupid that are so difficult to defend – no one can explain why he behaved that way.

While it is possible that people may behave intelligent one day, bandit another day, and helpless in another place and context; stupid people are remarkably consistent – they are stupid, irrespective. No rationality at all – just pure consistent. And that makes stupidity extremely potent and dangerous. For the simple reason that you cannot erect a rational defence against a stupid attack, as it comes as a surprise, and more importantly, there is no rational cause for the attack in the first place.

Which leads us to the fourth law, which states:

Non-stupid people always underestimate the damaging power of stupid individuals. In particular non-stupid people constantly forget that at all times and places and under any circumstances to deal and/or associate with stupid people always turns out to be a costly mistake.

Even intelligent people and bandits (who are rational) underestimate the probability of occurrence of stupid people, are genuinely surprised by the stupid attacks, and are at a loss to defend themselves effectively against stupidity. Given the inherent unpredictability of stupidity, it is both difficult to understand in the first place, and any attempts at defending against it may itself provide the stupid people with more opportunity to exercise his gifts!

Which leads to the fifth law, which states:

A stupid person is the most dangerous type of person.

And by corollary,

A stupid person is more dangerous than a bandit.

The danger of stupidity cannot be sufficiently understated than this law. Given the irrationality of stupidity, and the costs associated with stupid behaviour, a stupid person is far more dangerous than any other type of person. An intelligent person adds value to society, a helpless fool may transfer value from himself to others, a bandit may transfer value from others to himself; but the stupid erodes value to the society by executing a lose-lose strategy. There could be bandits who might border the stupid (someone who can kill a person for stealing $50 – the value they gain is lower than the value you lose; but the $50 for them is as valuable as life for you). But given the power of stupidity, they can create far more harm than one can even imagine.

The five laws of start-up world stupidity

Stupid business models are aplenty – they rear their head everywhere, every-time. Irrespective of the context, they are omni-present. No exceptions at all. Do you remember business models like Iridium (by Motorola) and the FreePC experiment? It exists even today … Casper Tucker wonders why he should make his own IP redundant (read here).
The probability of a stupid business model arising from a developed country, a venture of a large organisation, from the famed Silicon Valley (or Bangalore, Berlin, or Shanghai for that matter) is the same (and high). The start-up graves are littered with corpses of stupidity-induced deaths of both the firms, their investors, customers, and every other stakeholder you can think of. You think sandpaper for shaving or hair-removal is a bad idea, check this out!
Do I need to tell you the costs of stupidity in the start-up world? I have come across founders who in the first few months of the business taking off, begin talking valuation rather than growth. In the process, they have destroyed value squarely and truly for everyone around them, including themselves. Nothing can match the stupidity of a founder who sacrificed his employment to start-up a firm, acquire customers and force them to make asset-specific investments, make wonderful investor presentations and get a few to invest as angels, PE, or VC; and then instead of worrying about making the business profitable, chase valuation. I surely have mentored a few, and do not want to name them for obvious reasons.
The fourth law is the trick – stupid people thrive by their ability to surprise you by their conviction. And there are enough people who irrationally believe in them; but even the rational actors are unsure how to respond – till it all dawns on them. How many products listed in this article do you remember?
And they are just plain dangerous – they can bring the entire ecosystem down. Remember how the Real Value Vaccummizer brought the entire innovative company down (do you know the firm was the first to introduce a portable fire extinguisher by the brand name Cease Fire, which by the way stays a generic name for portable fire extinguishers)?

So, customers and investors, start-up founders and entrepreneurs, students and researchers, and everyone else, beware the stupid.

Cheers!

Problems, solutions, actors in a garbage can: how do we stir up the connections?

Research on decision making has been of interest to me for some time now. And, during my advise and consulting, I have come across a large number of entrepreneurs and managers struggling to make decisions, remain consistent in their decisions and make hard commitments to their decisions, as well as take ownership for the consequences of the decisions. I propose that such “under-decisiveness” (not being comfortable with their decision) is due to their inability to explain to themselves why and how their decisions are right (or appropriate). Some great research on this has been done in the past, and I would draw upon some insights from behavioural science research on decision making, some Indian philosophy, and some neuroscience. In this post, I introduce to my readers the concept of garbage can decision making, and its implications for managers and entrepreneurs.

Aha moments, first

In their recent article, David Rock and Josh Davis (Four steps to have more ‘Aha’ moments), urge decision makers to take breaks from the act of decision-making to make better decisions. In other words, sleep with your problems (no, I am not implying anything about your spouse!). The argument is that taking a break helps in (a) noticing quiet signals, (b) look inward, (c) take a positive approach, and (d) use less effort.

Quiet signals have been talked about in decision making literature in the past (almost the same as weak signals – I am not aware if quiet signals are any different). One of the best articles I have read recently about working with weak signals appeared at the MIT Sloan Management Review (How to make sense of weak signals). To summarise that article, Shoemaker and Day (the authors) urge us to follow nine approaches (see the exhibit in the article): 1) tap local intelligence, 2) leverage extended networks, 3) mobilise search parties, 4) test multiple hypotheses, 5) canvass the wisdom of the crowd, 6) develop diverse scenarios, 7) confront early, 8) encourage constructive conflict, and 9) trust seasoned intuition. If you would rather read a lighter article on how organisations can tap into weak signals, you may read what appeared in the McKinsey Quarterly (read here). The bottomline – listen more; listen to diverse sets of people; actively listen to conflicting views, and proactively build listening mechanisms and routines in your role/ function/ organisation.

To look inwards is easier said than done. Busy executives need to take their time easy. To quote my favourite analogy, which car needs more maintenance – the car that is been driven around between Whitefield and Bannerghatta Road in Bangalore, or the one that is being driven around a formula one track? The competitive formula one driver, driving at 300 kmph (or thereabouts) competing with other fast cars needs much more periodic pit stops than the car that is averaging about 6 kmph (okay, maybe 9 kmph), right? They busier you are, the more you need to take breaks. Taking breaks is not easy – you need to keep your mind active, right. That is where an active pursuit of another ‘activity’ is important. Build an alternative thing to do – I am not using the word ‘hobby’ deliberately. Build an activity that interests you, that you are passionate about. Something that motivates you enough to schedule your work and the ‘activity’ with relatively equal importance. One of my batchmates runs an internet aggregator, as well as competes in the triathlon. One another is a CEO by the day and a fiction writer by the evening. One another colleague of mine trains for marathons in the evening, is Dean for part of his time, and is Professor for the rest of the day.

I don’t need to elaborate about taking a positive approach. Enough research done about it. Using less effort is almost a summary of what is been said already. Take a break, do something else, listen to your own self, and then get back to the problem. You’ll be able to decide better. However, my thesis is that just these are not sufficient – it is like saying that by doing all of these (listening to more people, diverse people, yourself, and taking breaks) you will be able to improve your decision-making. I argue that it is also important to actively make the connections between data (collected through listening), insights (collected through listening to yourself), criteria (oops, we haven’t talked about it yet), and implementation plans (yes, yes, we will talk about this too).

Garbage can model of decision-making

Before we go into the process of what I call active decision-making, we need to understand the ‘garbage can’ model of decision making. Yes, you read it right, the garbage can! Way back in 1972, Cohen, March and Olsen wrote a classic article in the Administrative Science Quarterly, titled A garbage can model of organisational choice (read the abstract here). The primary argument is that decision-making is not as neat as it is taught in the first few sessions of your MBA curriculum, but it is much like a garbage can. In a garbage can, where actors (decision-makers) are looking for work; problems are looking for solutions; and solutions are looking for problems and decision-makers. Solutions are not created from ground up, but are available within the system; it is the active seeking by the decision-maker to match problems with solutions that matters most.

They label organisations as organised anarchies, characterised by problematic preferences, unclear technology, and fluid participation. In other words, organisations do not have clear priorities of projects and actions, unstable or immature processes, and there is a large (noncommittal) silent majority in every organisational decision making setting. Does it ring a bell? A lot of organisations have stated strategy, but the specific decisions made at the field do not reflect the organisation strategy; our organisational processes can do with a lot of discipline and consistency; and in every meeting there is only 20% people contributing to 80% of the voice.

Active decision-making

When we agree that organisations are indeed organised anarchies; and decision-making therefore reflects garbage cans, we need to work on making the connections actively, proactively.

Criteria is the first thing we need to focus on. Organisation’s strategy is one thing – every organisation claims to have one. Does the organisation actively translate the intent/ purpose and strategy into actionable criteria. I know of a variety or organisations where a lot of middle managers (and sometimes even senior managers) cannot translate the organisation’s purpose (or intent) into actionable priorities. when I ask them what their priorities in the next few years are, most of them cannot go beyond simple parameters like growth and profitability; and even if some of them do, very few of them understand why such actions are their priority. So, the first thing you need to is ensure that your organisation vision and strategy is translated into visible priorities and criteria for decision-making. In great organisations, every program, every decision, every initiative reflects their strategy and purpose.

Implementability is another under-rated aspect of decision-making. Every decision need to be implemented. One of my colleagues used to remark – effective decision making is when the decision-maker can take ownership for the consequences of the decision. Which means, the decision-maker should ensure that the decisions are implemented efficiently. Which means that the decision-making has to take into account the contextual realities right at the criteria and option-definition stages.

26-1-garbagecan

Summary

In summary, active decision-making requires the decision-makers to become both efficient and effective in their decision-making. Efficiency of decision-making is about the process of decision-making and effectiveness refers to the success of the decision. In order to ensure that good decisions balance efficiency and effectiveness, decision-makers need to pay sufficient attention to criteria/ options (effectiveness) as well as be aware of garbage can models, weak signals, sleeping over thoughts, active breaks (efficiency).

So, managers and entrepreneurs, even if you are muddling through, please balance efficiency and effectiveness of decision-making.

Obliquity – muddling through in entrepreneurship

Through my travel to the other side of the world last week, I read a couple of books. One of them was Obliquity by John Kay. The subtitle of the book reveals more than the title – why our goals are best achieved indirectly. In this post, I intend to build on my reading of the book, adapt a few ideas, and draw implications for platform-business startups. This is not an exhaustive review of the book – there are a lot of them available online; rather this is a summary of my notes from the book, which I thought was relevant for entrepreneurs.

What do you pursue?

The book builds on an intuitive understanding that relentless pursuit of anything does not take you where you want to go; as much as reaching there obliquely. Happiest people do not pursue happiness, and are happy because they do not actively pursue happiness. They enjoy what they do – their work, their roles, their chores, and are even not sure their activities will lead them to happiness. If you have not yet seen the movie, The Pursuit of Happyness, see it now. It is the experience that matters, not so much the outcome. Successful entrepreneurs startup to solve a world problem, at least something they faced themselves, and not make tons of money/ billions of dollars of valuation. Those that actively play the valuation game – yes I call it a game – do not optimize. Those who are constantly looking for exit options have not been successful. The book is replete with examples of firms whose intent to make money, and how they floundered.

Eudaimonia

Drawing on Aritstotle’s concept of Eudaimonia, Kay classifies three levels of purpose people and firms pursue. The lowest level is those of momentary happiness – like waving at a child smiling through the school bus window; the intermediate level may include a persistent sense of well-being, like a good holiday with family and friends in the Andamans (I have not been there, yet!); and the higher level of pursuit is what is referred to as Eudaimonia, something like the satisfaction of having a patent granted. Something that is fulfilling, achieving something that tells you that you have reached your potential. When I teach strategy introduction sessions, I draw upon vision and mission statements of a variety of (successful) firms to speak about how these statements are actually altruistic and ephemeral, conveying a larger sense of purpose. Consistently, firms that add shareholder value in their vision/ mission statements have faltered, to either rediscover themselves or bite the dust. So, entrepreneurs out there, what is your Eudaimonia? Appreciate that in ancient Greek philosophy (we are approaching the Olympics, right?), “the final end of action is realised in action, and is not a consequence of action. Eudaimonia is a goal set before each agent as soon as he starts to act; it is not chosen and cannot be renounced.” Define why you are in business, and what is your high-level pursuit?

Obliquity in problem solving

Okay, solve this brainteaser for me (cited in page 50-51 of the book). A man sets off walking a mile to his home from his work. As he starts, his dog sets off to meet him on the way, and when it finds him, licks his hand and returns back home. And continues do so (run towards the master, lick his hand and return back home) till the master and the dog reach home together. If the man walked at a speed of three miles per hour, and the dog at twelve miles per hour, how much distance did the dog cover?

You can calculate this distance using the principles of infinite series, but that would be a long-winded calculation. If any of you noticed, the dog was four times as fast as the master, it must have walked four times the distance the master walked in the same time, viz., four miles. This is what Kay refers to as oblique problem solving.

Oblique is simple, direct isn’t. What problems of your customers, partners, stakeholders are you solving? And how – directly, or obliquely? When Tally (www.tallysolutions.com) began selling computerised accounting solutions way back in the 1980s and 90s, their mantra was simple – keep the user experience simple, which translated into replicating the offline processes exactly in the online product. The trial balance looked the same, the ledger entries looked the same, and the end result was that every accountant was already familiar with Tally, when he finished his accounting degree. The “power of simplicity”, which incidentally is their corporate punchline, arose from their intent to not simplify the lives of the accountant, but to exactly replicate. Had they begun simplifying, I am not sure they would have attained this iconic status (and market share) amongst the millions of Indian small and medium businesses (SMBs).

Muddling through

People familiar with academic research on Organization Behavior would have heard of this term “muddling through”, first articulated by Prof. Charles E Lindblom in his seminal paper, “The Science of Muddling Through” (see the paper here). Kay concedes that obliquity is a (better) euphemism for muddling through, and elaborates on how goals, decisions, and actions are different across the direct and muddling through approach (see figure 7 in page 66-67 of the book). A quick summary for the not-so-academically inclined: muddling through represents a state where (a) goals are multi-dimensional and loosely defined; (b) goals evolve over time, in fact, even after the action has begun; (c) the external environment is complex – the structure of relationships is continuously evolving; (d) interactions amongst stakeholders is socially constructed; (e) the external environment is not known, and is uncertain; and (f) the range of events, and therefore the options available in front of the firm/ decision maker is unknown and uncertain. In such a complex and uncertain environment, decision makers engage in “successive limited comparisons of non-comprehensive actions”.

Entrepreneurs do engage in a variety of muddling through. We talked about pivoting and bricolage in an earlier post in this blog (Pelf). As the environment you encounter is uncertain and/ or complex, you are entitled to muddle through! However, do not lose sight of the higher level pursuit, your Eudaimonia. In the absence of the larger sense of purpose, muddling through will remain just that, and not lead you to your ultimate pursuit.

Ex post justification of random outcomes

Kay discusses in detail about how England footballer, David Beckham could “bend” the football, performing multi-variable physics calculations in matter of seconds as he takes a free kick (read a wonderful reporting about it in The Telegraph here). I am not convinced (like Kay) that David indeed did all those calculations, or did Wasim Akram and Waqar Younis, the early exponents of reverse swing in cricket seam bowling, or even exponents of the ‘legal’ doosra or carom-ball deliveries in spin bowling. They had some idea, tried something, experimented, experienced a difference, persisted, perfected and professed (subsequently). Ex post justifications, all of them. A lot of entrepreneurial successes and failures are also subject to the same phenomenon. Now that a famous startup firm has sold out, all the arm-chair analysts will bring out their own analyses on why they saw this was coming. Search on the Internet about the merger of Uber China with Didi Chuxing – you will find a lot of ex post justifications on why this was waiting to happen. There are relatively very few insights/ posts of what that means for other competitors, and how Lyft, Ola, or Grab would feel the impact; or even why Didi Chuxing decided to buy out a competitor so small in size and give its shareholders a share of their own pie. So, when you encounter ex post justifications, just concede to randomness, reflect to learn (you use the rear view mirror of the car to drive forward, right?), and continue forward.

So, in summary, entrepreneurs of today, define what is it that you pursue, what are your higher level goals, and what is your Eudaimonia. Appreciate that obliquity in decision making is here to stay and be prepared to muddle through the environment and indulge in some arm chair ex post justifications of performance.

Shameless self-promotion

By the way, if you are in Bangalore and are available on Saturday, the 6^th August 2016 forenoon (0950am onwards), you are invited to attend a panel discussion I am moderating on “Network Mobilization in Platform Business Firms” as part of the IIMB’s entrepreneurial summit, Eximius 2016. For more details, please visit http://eximius-iimb.com/4startupsnsrcel/. Free, mandatory registration at the website.

Reference class forecasting using pluralism: Fighting single parameter obsessions

Traveling around prestigious Universities and Business Schools in the US this week on an institutional assignment (this post comes from Chapel Hill, NC), one thing struck me in this society, pluralism. I read with interest my friend Suresh Satyamurthy’s piece in yourstory.com (link here) that uses a hangman metaphor for an investor review in the start-up world. In Suresh’s start-up world, the investor is hung-up on a single parameter – scale (pun intended). It set me thinking – any evaluation of performance (more importantly, assessment of future performance) needs to be grounded in as many parameters as possible. In this post, I will introduce Reference Class Forecasting (RCF) as a technique for fighting such biases like single parameter obsession. Drawing on research on behavioural economics, I attempt to provide guidelines for entrepreneurs and investors to make better forecasts of future performance.

Intent-outcome relationship

This is possibly the first and the most obvious starting point of any assessment. Start with what was the intent in the first place. If the stated intent of the platform was to transform the industry, please define what is industry transformation and measure those, and not start harping on profitability. Not every business needs to show the same kind of performance on the same parameters. Take the example of baby products company, firstcry.com. The founders’ motivation to start-up arose from the difficulty in finding products for their own children – availability, variety, poor quality, and certain international products/ brands not available in India (read their interview here). So, the best performance metric for assessing the performance of firstcry.com would be to see if they have been able to “make a wide variety of good quality international products and brands available to parents”. The performance metrics would therefore be (a) number of outlets – online and offline, (b) inventory size and variety, (c) number of brands, (d) number of products uniquely available at firstcry.com, at least in a specific geography, and (e) number of parents reached. Scale here would mean growth in number of customers, brands, products, and channels. Not GMV, not anything else. Yes, profitability is important, but not the first parameter of success.

Constructs, variables, and measures

Hmm, I may sound like a research methods teacher, but I think this is important to understand. Everyone (at least those reading this blog post) understands that everything could be measured in a variety of ways. A construct is an attribute of a person/ entity that cannot be observed or measured directly, but can be inferred using a number of indicators, known as manifest variables. For instance, entrepreneurial success is a construct that is measured by a variety of variables ranging from firm performance, firm growth, market power, firm’s influence in industry standard setting, pioneering innovation, to even investor wealth creation (or exit valuation) at sell-out to a large corporation. Each of these variables could be measured using different measures; see for instance, the number of measures we identified for firm growth in the context of firstcry.com in the last section. Can you see a decision–tree like structure here?

Indices

So, when I think of multiple parameters, I am reminded of indices. Indices like Human Development Index (HDI) as a measure of economic development, or a Consumer Price Index (CPI) as a measure of inflation. Each and every of these indices are prone to discussions and debates about what constitutes these indices and why; and in what proportion/ weights. Take for instance HDI that is a composite of life expectancy (personal well being), education (social well being), and income per capita (economic well being). Why only these? What about social and racial discrimination? What about ecological sustainability? Similar is the case with consumer price index (CPI), which is calculated using prices of a select basket of items, with price data collected weekly, monthly, or half-yearly for specific items. Again, why should tobacco products prices be included in CPI calculations? Or we could debate of how the housing price index is calculated for inclusion in the CPI. Does age composition of the household matter in calculating the CPI basket? For a relatively young family, would the basket of goods not be different than those families with more elders than children?

So, to cut my long argument short, please refrain from creating indices that just simply represent a mish-mash of parameters to evaluate a start-up.

My recommendation: Use reference class forecasting

Reference class forecasting (RCF), sometimes also referred to as comparison class forecasting is a method recommended to overcome cognitive biases and misplaced incentives. My favourite article on this appeared in The McKinsey Quarterly (see here). Let me elaborate the theory first.

Nobel laureate Daniel Kahneman and Amos Tversky’s work on theories of decision making under uncertainty is the starting point for understanding RCF. They described how people make decisions that are seemingly irrational while dealing with probabilities and forecasts using Prospect Theory (see an insightful class by Prof. Schiller, another Nobel Laureate, on YouTube here). Summary relevant to us: people are more concerned by smaller losses than equivalent gains; and people round off probabilities of occurrence to either zero or one, when it is close to either, and in between, exaggerate.

Let us understand how an entrepreneur could use this theory to manipulate his capital provider. She shows some initial success, and likens her business model to an already successful model somewhere else, in some other context; and gets the investor to exaggerate the probability of her success. For example, I know a friend wanted to build the Uber of toys in India. Why buy toys, just rent them, let the child play for a week, and return it back to the library next week to issue a new set of toys. Sounds exciting? Just that the economics did not work out the cost of damages to the toys small children could do, that would render it useless for the next borrower (like breaking one car wheel). The entrepreneur kept the rentals high enough to account for such losses, and soon her customers realised that the rentals were working out far more expensive than buying new toys, notwithstanding the child refusing to part with his toys at the end of the week. The entrepreneur continued to convince his investors to keep investing in her, luring them to wait for the economies of scale to kick-in and she could have enough bargaining power with toy manufacturers to directly import from the North of Himalayas, but that never happened and the investor exited the firm at its lowest valuation.

These biases manifest themselves in the form of delusional optimism, rather than a clear understanding and detailed evaluation of costs and benefits, even when hard data is available.

Steps in using RCF: A field guide

RCF helps forecasters and planners overcome these biases by situating the reference point outside of the subject being assessed. In order to forecast (or assess future performance) a business, investors need to identify a reference class of analogous businesses, estimate the distribution of the outcomes of those firms, and benchmark the enterprise at an appropriate point of the distribution. Firstly, the investors should identify appropriate reference class for the enterprise. These reference classes need to be identified using a variety of parameters that match the enterprise. The next step is to analyse the performance of the firms in the reference class and map them into a probability distribution. There may be clusters of firms that may emerge during this distribution-mapping exercise; there may be instances of only extremes of firm performance observed (say in winner-takes-all markets); or there could be continuous distributions.

The next task is to use pluralism in the parameters to position the enterprise in the distribution. Here is where multiple parameters would help in an reliable estimate of the position. For instance, an Uber for toys in India would only work when the marginal costs of renting out a car (wear and tear) is negligible compared to the fixed (sunk) costs of buying the car. Whereas in the toys market, the marginal costs of a child playing with the toy is a significant proportion of the market price of the toy, and therefore this enterprise would not be subject to the same evolutionary direction as Uber. However, if the enterprise was repositioned as a toy library (as my friend ultimately did), it would work – look at how the cost structures of library and toys work. It provided her a benchmark on only buying those toys that would be durable, held the customer’s attention for only short periods of time, and were very expensive to buy. Typical examples were multi-player games, which no child wanted to own independently (given the small size of families today), but would rent out during the weekends/ birthday parties for a small proportion of the cost of the game.

So, hers is calling entrepreneurs and investors to overcome such cognitive biases and forecast better.

Comments and feedback welcome.

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