{"id":2249,"date":"2023-09-19T11:36:26","date_gmt":"2023-09-19T11:36:26","guid":{"rendered":"https:\/\/illuminated-text.com\/cb\/?p=2249"},"modified":"2025-01-10T14:03:12","modified_gmt":"2025-01-10T14:03:12","slug":"where-does-cb-knowledge-come-from","status":"publish","type":"post","link":"https:\/\/illuminated-text.com\/cb\/where-does-cb-knowledge-come-from\/","title":{"rendered":"Where does CB knowledge come from?"},"content":{"rendered":"\n

CB is very eclectic in where it draws knowledge from. This is probably inevitable given that exchange is central to so many different parts of our human experience. People from nearly every walk of life are consumers, in ways that differ from moment to moment, and from person to person, and from place to place. Many of us also have jobs or hobbies that place us on the selling side too.<\/span><\/p>\n\n\n\n

Within academia CB draws from nearly every other big discipline that is primarily about people: psychology, sociology, anthropology, and (increasingly) neuroscience. <\/p>\n\n\n\n

At times consumer behaviour researchers have argued about whether they have any of their own theories or simply draw them from elsewhere, <\/span>. How strongly they wish to feel about this debate is left as an exercise for the reader.<\/p>\n\n\n\n

CB borrows not only a lot of its key ideas from neighbouring fields but a lot of its research methods toolkit too. CB researchers do a lot of interviews, experiments, participant observation, surveys, analysis of ‘big data’ sets from the ‘real world’, etc.<\/p>\n\n\n\n

practitioners<\/h2>\n\n\n\n

Unlike many other academic fields, though, CB also has a large number of ‘practitioners’ – people who are not academics, but spend their professional lives trying to sell things in marketplaces. These people might not share their academic colleague’s enthusiasm for big explanations that describe whole classes of phenomena (they just want to sell their screwdrivers), and they may not test their understandings in ways that are quite as systematic (again, their main focus is on driving the right kind of exchanges into happening). And perhaps this means they might be a little more prone to falling into a ‘confirmation bias’ where they stick to what they think they know, without fully considering the alternatives#up. <\/p>\n\n\n

\n
#down. Academics aren't immune from confirmation biases either. As the famous physicist Max Planck once observed (with a little help to make the phrasing pithier<\/a>): \"Science advances one funeral at a time\"<\/pre>\n<\/div><\/div>\n\n\n
Practitioners are, though, engaged in a steady feedback loop between trying things out, and then seeing if it makes money or not. They do this with huge volumes of time, money, and effort, and a very high degree of motivation to arrive at creative, good, and useful answers. That, historically, has been an excellent recipe for learning a lot of useful things – even if there is a tendency to rediscover the same things quite a few times over.<\/p>\n\n\n\n
When practitioners do find cool new things, sometimes they hide it as an industrial secret, but so much of marketing happens in public that this can be quite hard, so more commonly they talk about it, and occasionally write books. The best of these are deeply insightful, rigorous, and widely read (we will discuss a few of them!). To be fair it can be hard to find these best options amidst the walls of breathless accounts of how failing businesses were transformed into multi-million dollar successes by a heroic CEO who applied this one simple trick that can be summarized in a short enough series of bullet points that you can buy it at an airport bookstore, and have it fully digested by the time you land wherever else#up.<\/p>\n\n\n
\n
#down. Then it is usually repackaged under a buzzy new names a few years later, and re-marketed all over again; There is a lot of money to be made from advising (and reassuring) people who make money<\/pre>\n<\/div><\/div>\n\n\n
All cynicism aside, practitioners’ (literal) industrial-scale contact with customers, combined with their strong incentive to drive results, has given practitioners an active laboratory that over the years has been a rich source of insight, and academics (especially ones with more of a strategy orientation) have not been shy about tapping into it.<\/p>\n\n\n\n
Academics vs. practitioners<\/h2>\n\n\n\n
The relationship between CB academics and practitioners is a strange one, compared to many other academic fields. Most academic fields don’t have any specific area of business practitioners (e.g., philosophy, French, sociology#up). The fields that do have such links tend to be quite tightly bound to them. Aspiring young practitioners of medicine, legal practice, accounting, and engineering are usually required to start their careers with a relevant academic credential, that is explicitly oriented towards teaching them to be doctors, lawyers, accountants, or engineers. They are legally prohibited from practising their craft until they pass a qualifying exam. Academic researchers in these fields spend a lot of their time writing about what their corresponding practitioners do (and should do) in various situations. For example, nursing academics might study how nursing practitioners make decisions in stressful contexts, and how this is shaped by their group dynamics.<\/p>\n\n\n
\n
#down. And yet they are useful in the world of business! Many jobs draw on the critical thinking skills they teach, along with more specific skills like deciphering dense texts or speaking different languages.<\/pre>\n<\/div><\/div>\n\n\n
CB lies somewhere in the middle between these extremes. There are many jobs in the business world with titles like ‘consumer insight’, but there is no specific academic qualification that you must hold to be given one of these jobs (indeed, you can start your own business tomorrow and give yourself that title if you want). It’s not unusual for people with these types of jobs to have studied consumer behaviour, but many have not – they simply learn on the job, and\/or by watching people closely, and by interacting with them. They can be very successful, either way, if they are good at what they do.<\/p>\n\n\n\n
CB academics and practitioners have plenty in common, and plenty to learn from each other – after all, academics are trying to understand how people relate to markets, and practitioners are directly engaged in those markets, trying to relate to people! Sometimes the two groups have useful dialogues with each other about these, but probably more often they don’t.<\/p>\n\n\n\n
You might wonder why these conversations don’t happen constantly, but there are many reasons they don’t. One is that practitioners and academics often have different types of goals. Practitioners tend to ask very narrow questions like: “How do people in the North-West of England choose snacks,” and “what would get them to choose our biscuits brand over specific alternatives X and Y?” An academic might be interested in that same study, but only as an example of how people regulate their emotions with food, or to find general tactics that programs can be used elsewhere to encourage healthier food consumption. Or, a practitioner might find out what factors would help a gambling website grow in a specific population, whereas an academic might ask how harmful gambling websites might be (what is the role of impulsivity?), and whether they work in similar ways to other potentially harmful things like drinking and smoking. The bottom line is that where the practitioner just wants to sell packs of biscuits, academics are looking to sell understanding; drawing audiences to their work with the broader or deeper understanding that it creates. <\/p>\n\n\n\n
Your own personal goals may fall at various steps along this spectrum at different times. Some of you reading this book will be motivated to learn how to sell things, and others more motivated to unravel the mysteries of the world for its own sake. CB is for both of you, and this book will strive to accommodate both.<\/p>\n\n\n\n
Academics vs. Academics<\/h2>\n\n\n\n
Within CB academia there are many different flavours of researchers. We will not do a deep exploration of the ontology and epistemology of varying schools of thought#up, as this is not a methodology book. <\/p>\n\n\n
\n
#down. Philosophers of science do seem curiously drawn to long words<\/pre>\n<\/div><\/div>\n\n\n
That said, there is one broad split that is so wide that we will see it at times even in this introductory text, so let’s take a moment to recognize it. That is the difference between positivist vs interpretivist paradigms.<\/p>\n\n\n\n
Positivist thought<\/h4>\n\n\n\n
Postivism is about defining ideas as tightly as possible, and carefully testing them against observations of the world to see if they can be shown to be wrong. It may seem weird to try to show that your ideas are wrong, but the notion is if your idea can survive really convincing attempts to murder them, then even their critics might have to grudgingly accept that they might be right. One way to think of positivism is as a big attempt to be sceptical about everything and yet to still find things worth believing. <\/p>\n\n\n\n
Positivists frown on ideas that can’t be ‘falsified’ (i.e., there is no way to show they might be wrong). For example, if your belief was that “in our heart of hearts, we’re all basically good people,” then that might be a pretty healthy and functional approach to living your life, but a positivist would put on their sceptic hat, and argue that this was a terrible idea. First, we can’t really define what “heart of hearts” means (not in any rigorously measurable fashion), and second, there’s no way to prove that this idea is wrong. You could demonstrate any amount of people behaving badly, and still say “oh, but they didn’t really mean it! Not in their heart-of-hearts!”  <\/p>\n\n\n\n
Positisim is a rigorous taskmaster, if possibly not an ideal guide to optimal human living in our every waking moment.<\/p>\n\n\n\n
How true are all those results? The “Replication Crisis”<\/h4>\n\n\n\n
A report in 2011 claimed to show evidence for ESP (“extra sensory perception”). This might not be remarkable except that it was published in a social psychology journal so elite that it rejects nearly every article submitted to it, the author was a well-known psychologist, and the 9 experiments it described met the technical standards of its day. But the results it described were hard to believe; One purported to show students performing better on a test when they studied after the test was already over<\/strong>#up. <\/p>\n\n\n
\n
#down. Experiment 8 of Bem <\/span> - the test was recognizing words, and the 'studying' was further exposure to those words after the test was over<\/pre>\n<\/div><\/div>\n\n\n
Unfortunately for students everywhere, sceptical scientists later confirmed that these findings don’t hold up to critical scrutiny (see here<\/a> for a somewhat technical rundown). Numerous scientists became highly alarmed though: If such an obviously dodgy ‘finding’ could appear to reach the high standards of proof demanded by elite journals, then what else might be slipping through? How could this even happen#up?<\/p>\n\n\n
\n
#down. One theory that is popular in some parts of social media suggests that scientists engage in a big conspiracy to \"discover\" made-up findings. Science is far from perfect, but the number one reason to doubt this is that you have heard of Albert Einstein. Albert got famous by showing other sceintists that they were wrong about very big things. The road to superstardom for any ambittious young scientist (of which there are plenty) is replacing their colleague's wisdom, not confirming it<\/pre>\n<\/div><\/div>\n\n\n
The resulting investigation sent waves crashing through scientific fields as far-flung as economics and medicine. Some scientists paused their attempts to learn new things in favour of trying to create large-scale re-creations of widely accepted older works. Some were confirmed, others were not. <\/p>\n\n\n\n
Initially, this created a great deal of debate and uncertainty. If big-name theories like “ego depletion” and “stereotype threat” could turn out to be hollow, despite the hundreds of papers behind them, then what did we really know at all. This is what we call the “replication crisis”.<\/p>\n\n\n\n
Eventually the fields adjusted, identifying the QRMs (Questionable Research Methods) that fueled the crisis, developing statistical methods to detect their use in past work, and formalizing better standards to guard against them in future.<\/p>\n\n\n\n
What exactly were these Methods, and what made them Questionable?<\/h5>
\n
Feel free to skip this section if you aren’t interested in the mechanics of how so much research could go so wrong.<\/p>\n\n\n\n
Imagine you are hired as an assistant professor at a research-intensive university. If you don’t publish a steady stream of papers in ‘good’ journals your employer will probably not keep you on the payroll, and other ‘big’ universities won’t want to hire you either. Publications are the ‘coin of the realm’ in academia- they get you employment, fame, and grant money to do more research.<\/p>\n\n\n\n
You can’t succeed at this if your findings are too boring. A big study showing that “people enjoy shopping”, would be greeted with yawns. Everyone already knows this, and no good journal will accept it. So you need to find a steady stream of things that are counter-intuitive or surprising enough to make them interesting.<\/p>\n\n\n\n
Let’s play out an example: say your idea is people will shop differently if you make them think about money first. You ask a bunch of research participants to shop online, and as they do this tell some of them to keep flipping a coin (to remind them of money). When you analyse the data your idea doesn’t work; There’s nothing to indicate that coin-flipping changed people’s buying. Oh no! But you don’t give up (your career and job are important to you)! You roll up your sleeves and dive deep into the data, looking at it very<\/strong> carefully for anything else that might be interesting.<\/p>\n\n\n\n
Have you ever played the game of finding shapes in the clouds in the sky?  “This one looks like a face. That one looks like a weasel”. It’s amusing, and humans are good at finding patterns even in random objects like clouds.<\/p>\n\n\n\n
Numbers, like clouds, are often pretty random, and you can find patterns in them too if you stare long enough; Especially if you are clever, inventive, and sufficiently motivated. If you, keep trawling through your hypothetical coin flipping data, you might find yourself saying: “Wait a minute! The people who got a lot of ‘heads’ on their coin flips bought less stuff. Ah ha! That’s cool!” If you think about it hard enough you can probably come up with some kind of story that explains it pretty well. Maybe seeing all those heads makes people think that they should ‘use their heads’ and so they buy less impulsively! All you need to do is dress that up in some fancier language, add a couple of citations, and it starts to look a lot like an interesting theory!<\/p>\n\n\n\n
So you run more studies in which people shop while you remind them of heads in different ways, and most of them don’t ‘work’ (i.e., produce your “heads make people sensible” pattern), but if you run enough, you’ll eventually chance on a few that do work. Why? Think of it this way: Roll a dice 3 times, and there is less than a 1% chance that it will land on ‘6’ every time. That’s pretty unlikely! But if you keep rolling that dice all day, over and over and over, and don’t stop until you hit a streak of three 6’s in a row, it won’t take you that long to find the result you are looking for. It will be even quicker if you hit a string of three 1’s and decide that this could be interesting too, or hit a ‘6, 1, 6’ combination and decide that this is going from extreme to extreme so quickly that it is interesting! The point is, the more times you roll the dice, and the more patterns you might paint as revealing, the less time it will take to get a streak that you want to describe as meaningful. <\/p>\n\n\n\n
Our scientist could be doing a bigger fancier version of this same exercise, sifting through dozens of studies, discarding the ones that don’t work as “pretests” that merely refined their methodology, and reporting only the results that show interesting-looking patterns that they can describe (in isolation) as “very unlikely due to chance alone”.<\/p>\n\n\n\n
Maybe you look closely at one data set that didn’t find your ‘head are for thinking’ result, and notice that it does work, but only among the people who were considering impulse-buy type items (such as chocolate bars). So you announce that this is what you predicted all along (because it makes so much sense that you should<\/strong> have predicted it, right), and now your theory is confirmed in even more detail!<\/p>\n\n\n\n
So they throw out the studies that didn’t work (there must be something wrong with those, right), and write up the ones that did, give your paper a snappy title like “Reminder: don’t lose your head!”, and ‘lo’ it is accepted and you keep your job and become better-known in the scientific community.<\/p>\n\n\n\n
Other labs try to do studies on your ‘keep your head on’ effect, and mostly these too don’t work, but they all conclude that they must have done something wrong, and they give up. But a few of them will also chance on data where it works (especially given a bit of creative data interpretation), and those are the studies that get published, and we build a “literature” in which the ‘effect’ is seen as well-known widely-supported, and you become Dr. famous as “Dr. keep-your-head-on”.<\/p>\n\n\n\n
<\/p>\n<\/div><\/div>\n\n\n\n
Changes to safeguard against questionable research were identified, and are often now asked for by good journals. These include things like:<\/p>\n\n\n\n