Posts Tagged ‘problem solving’

Risk and Creeping Conservatism – The Death of a Company

November 16, 2010

Some readers have asked me to expand a bit on what I meant by “Conservatism” as being part of what leads to corporate death – they were worried that I meant that their political views were being cast in a negative light. While it certainly would be interesting to see if political Conservatism was related to risk aversion, in decision-making the term “conservatism” relates to risk aversion and how that winds up leading to paralysis and some very bizarre behaviour under stress.

It’s all about the price of eggs

Some years ago Daniel Putler of the USDA noticed that customer behaviour towards egg prices was asymmetrical in what he called a “reference price effect”(Putler 1992).
When egg prices rose, as predicted by standard economical theory, demand dropped, but what was inexplicable by economic theory was that when egg prices subsequently dropped, the purchasing did not respond in equal measures – people reacted more strongly to the price rise than they did to price drop.

This response asymmetry turns out to be the result of a deep-seated, probably evolutionary, cognitive bias towards risk. As a species, the behavioural economics theory goes, we are more attentive and react more strongly to risk than reward and this plays out whenever we have already set an expectation or reference (Chen, Lakshminarayanan et al. ; Tversky and Kahneman 1991; McDermott, Fowler et al. 2008).
It’s easy to imagine how this could develop evolutionarily – the sound of tall savannah grass moving might be just the wind, it could be somebody bringing us a nice melon, or it might be somebody or something creeping up on us. If one typically reacted to it as a threat and were wrong it would just mean we burned up a little energy and appeared jumpy. If on the other hand we typically assumed it was something nice and were wrong, then we would more often end up as lunch.

Jumpy but living people leave more offspring than relaxed but dead people.

Nightmare Auctions

There are some amusing experiments that show how this works in everyday life, one is the Bazerman auction in which non-rationally escalating commitments result from loss-aversion (Bazerman and Neale 1992; Bazerman 2001). Prof. Max Bazerman has routinely carried out the following piece of trickery on unsuspecting students.

Bazerman offers an amount of money out of his wallet for auction, say $20. The rules of the auction are atypical but fairly straightforward: bids must be increments of a certain minimum value ($1), and the winner is obviously the person with the highest bid.

So far so good.

The payout however is a little different – the runner-up also pays their own bid, but only the winner gets the prize.

Behavior is fairly typical, bids come in thick and fast until the bids approach a significant proportion of the prize, at which point most players drop out, leaving the leader and runner-up alone in the game.
Bids slow but keep climbing until the fateful point is reached where the next bid will be $21 for a $20 prize. At this point, rather than capitulate, the typical outcome is further furious bidding as each player ups the ante and tries to avoid loss – In many trials reaching $180 bids for the $20 prize.

Before you think that this is all rather contrived and of no importance to your firm, consider Nick Leeson.

Because Leeson was biased in this same way, he piled debt upon debt trying to recover an initial loss, and the result of this put Barings Bank out of business (Nicholson 1998; Hoch and Kunreuther 2001; Goto 2007).

The phenomenon has been seen in countless examples ranging from big lies to cover small lies (President Clinton?) to people who sell their good shares and hold onto those that are plummeting – a trait shared by monkeys.

It even overpowers actual gains.

Take exchange rates: Some time ago the Australian dollar was at 0.96 to the US dollar against a typical exchange rate of closer to 0.7. A person holding AUD could thus make a tidy profit by paying the $20 fee and moving a large quantity of AUD into USD. However, in many cases, when the rate dropped to 0.94, people, having pegged expectations to 0.96, now regarded 0.94 as a loss, and instead of selling and getting the benefit of 0.94 against a typical 0.7, held onto their AUD in the hopes that it would climb back to 0.96.

However, it proceeded to drop further, triggering even more angst, and resulting in those people tending to perceive and even big loss, and even more desire to see it climb back up before they wanted to sell, … and so on.

This is the way people lose great fortunes on the stock market, in gambling, and in business ventures in general.

Risk aversion also has a twin brother – Aversion to Change

Change Reluctance

Since most random change is harmful, risk aversion equates to a reluctance to change what is tried and true, and herein lies the real rub – while good professional practices can limit the harm done due to Bazerman Auction situations and can embed safeguards against scenarios such as Barings Bank, there is another problem that no amount of procedural interlocks and policies can prevent – external changes.

Even if a company has an absolutely perfect market approach, externalities cause unpredictable changes in the business terrain that require adaptation and course corrections that will inevitably require novelty and innovation.

This inverse link between risk-aversion and innovation has been the subject of books (Hunt and Hazel 2003) and has even spurred some research suggesting that it is tied to low cognitive ability (Dohmen, Falk et al.) We might in a snide moment say that corporations that are risk averse are just stupid, but that is unkind and untrue.

Which brings us back to the kind of Conservatism that leads to corporate death.


Even though in mature companies the cultural & existential narratives tend to be “onwards and upwards” in aspiration, the more mundane “How things are done here” or ground truths tend to show that there is often a gap between declared vs operant behaviour and goals, and that actual behaviour is risk averse rather than innovative.

The difficulty is that to gain the benefits of experimentation without the risks that catastrophic failure might bring, one has to build an environment in which frequent small risks can be taken without jeopardizing the survival of the organization. In order to do this, one has to be open to experimentation and wilfully expend resources to experiment. In order to have that, one has to have a mindset and corporate culture in which “playing” is allowed, and as companies shed their youthful “go-go” character during their entrepreneurial stage, caution and change reluctance grow.


Matthew Loxton is a Knowledge Management professional and holds a Master’s degree in Knowledge Management from the University of Canberra. Mr. Loxton has extensive international experience and is currently available as a Knowledge Management consultant or as a permanent employee at an organization that wishes to put knowledge to work.


Bazerman, M. and M. Neale (1992). “Nonrational escalation of commitment in negotiation.” European Management Journal
10(2): 163-168.

Bazerman, M. H. (2001). Smart money decisions: why you do what you do with money (and how to change for the better), Wiley.

Chen, K., V. Lakshminarayanan, et al. “The evolution of our preferences: Evidence from capuchin monkey trading behavior.”

Dohmen, T., A. Falk, et al. “Are risk aversion and impatience related to cognitive ability?” The American Economic Review
100(3): 1238-1260.

Goto, S. (2007). “The Bounds of Classical Risk Management and the Importance of a Behavioral Approach.” Risk Management and Insurance Review
10(2): 267-282.

Hoch, S. J. and H. C. Kunreuther (2001). “A complex web of decisions.” Wharton on making decisions: 1-14.

Hunt, B. and G. Hazel (2003). The Timid Corporation: why business is terrified of taking risk, J. Wiley.

McDermott, R., J. H. Fowler, et al. (2008). “On the evolutionary origin of prospect theory preferences.” The Journal of Politics
70(02): 335-350.

Nicholson, N. (1998). “How hardwired is human behavior?” Harvard Business Review
76: 134-147.

Putler, D. S. (1992). “Incorporating reference price effects into a theory of consumer choice.” Marketing Science
11(3): 287-309.

Tversky, A. and D. Kahneman (1991). “Loss aversion in riskless choice: A reference-dependent model.” The Quarterly Journal of Economics
106(4): 1039-1061.


Please contribute to my self-knowledge and take this 1-minute survey that tells me what my blog tells you about me. – Completely anonymous.


Matthew Loxton is a Knowledge Management professional and holds a Master’s degree in Knowledge Management from the University of Canberra. Mr. Loxton has extensive international experience and is currently available as a Knowledge Management consultant or as a permanent employee at an organization that wishes to put knowledge to work.


Death, Learning, and Corporate Survival

October 26, 2010

Why do mature companies die or grow frail and get eaten?

After all, once they have passed through the helter-skelter of childhood and have attained stability after the hectic days of early formation, why don’t they just live on forever?
This was a topic that interested Arie de Geuss of Royal Dutch Shell and he asked a similar question to one that led to a breakthrough in medical science almost four centuries ago – could the same hold for how we look at corporations?

Death as a subject

In 1662, John of Graunt built tables of mortality for the city of London, listing for each year the numbers of deaths by cause. This required not just the collection of data about death, itself a valuable exercise, but also required him to think in terms of categories of causes of death. Although many of the categories have changed over time, this process of thinking once set in motion, led to steady revision and improvement.

For example, from the year 1632, Graunt lists these as the top five causes of mortality:

Chrisomes*, and infants        2268
**                     1797
Fever                                    1108
Aged                                     628
s†, and the small Pox    531

*Infant mortality before 1 month of age
†Means “sediment”, but it is unclear what Graunt meant by this in conjunction with Smallpox

This systematic approach paved the way for tracking and intervention, and gave birth to the science of demographics and enabled epidemiology to develop.
You could say that Graunt was a necessary and key player in the development of modern medicine.

The Mortality of Companies

In his analysis of companies in terms of mortality, de Geuss created categories from the data that led him eventually to conclude that companies die because they develop learning disabilities – they became deaf and blind, and stopped learning – and therefore eventually succumbed to external forces that they were unable to notice or against which to marshal an appropriate response in time.

I view this in terms of Organizational Learning (OL) – which is why I describe my occupation as “Knowledge Management and Organizational Learning”, and I break it into five major components:

  1. Stimulus-Response Learning
  2. Vicarious and Promiscuous Learning
  3. Scenario Planning
  4. Ongoing Professional Development
  5. Innovation Intent

Stimulus-Response Learning

This is the kind of thing that even an earthworm can do, but which many organizations seem to lack.

If an earthworm touches an electrified wire, it eventually learns to avoid the wire, no matter which part of its body did the touching. In contrast, some companies will repeat the same mistake over and over again, seemingly needing to reiterate the same mistake several times with each and every business unit and team before the message finally gets through and becomes part of its adaptive repertoire.

Being smarter than an earthworm should not be that difficult for a corporation made up of smart people, but it means that internal communications and repositories are done in such a way that if one part of the organization makes a mistake or encounters something that poses a risk, that all other units and geographies have access to that same information in a way that they can actually use (and do!).

This turns out to be more difficult than one might assume and the “plumbing” side of providing email, portals, knowledge-bases, and content management are only about a third of the solution. The remainder is a corporate culture that is able to learn across divisional boundaries, and for this you need both leadership and vibrant Communities of Practice

Many organizations never get this far, and die because the rock that they stubbed their toe on last year, came back and hit them in the head this year.

Vicarious and Promiscuous Learning

Once one has evolved past the realm of Annelids, the next big advantage is to learn from other people rather than needing to take the lumps yourself. This saves money and time, and is therefore a direct competitive advantage.
Rome learnt from Carthage, apprentices learn from their tradesmen, and hopefully a company can actively look for examples of what to do and what not to do by observing others. Except where patents and copyright are an obstacle, the keyword is to “shamelessly borrow” ideas and then modify them to fit localized conditions.

This is best done by the leadership team, and by the Communities of Practice who can effortlessly dig their roots into the pool of expertise and experience that lies outside the organization but within their domain of excellence. When an SME comes back from attending a trade show or seminar they can mutate the ideas to suit the organization and spread them throughout the organization via the interdepartmental CoP structure.

Just achieving this stage will provide a significant competitive advantage and add decades of life-expectancy.


So far we have dealt with the past and the present, and the next evolutionary phase is to consider the future beyond the next departmental quarterly review. Scenario-planning is a toolset that attempts to break at least partially free from the learned helplessness and practiced defensiveness that Chris Argyris outlines as part of “Single-Loop Learning“. By posing “what if” scenarios, there is the possibility, if you are nimble, to catch yourself before the auto-protective blinds come down and to notice the stealthy approach of a hidden predator, or surprise yourself with an outcome that was unexpected.

This is the playground of the giants mainly, because everyone else is too busy “just surviving” to look several years down the pike and try to make out the fuzzy shapes on the horizon or in the shadows. The irony is that it can lead to complacency (look at BP and the recent gulf of Mexico debacle), in the same way that seatbelts and airbags led to less careful driving in some people.

Scenario-planning requires a mix of dogged fact-finding and logical step-wise thinking, systems-thinking, and imaginative brainstorming. Plenty of DIY books exist on the topic, but usually a firm needs external help at least in the beginning. It also requires a mix of culture and technique that is frankly beyond most firms. After producing various scenarios and plotting the likely outcomes, and then working back to find solutions, it requires a very peculiar kind of management culture to stare the scenarios in the face and put money and executive sponsorship behind remedial action.

Although this is a critical component of achieving and maintaining longevity, its very success is a risk, since dodging future bullets makes a firm more likely to become complacent and also to value the process less. People in westernized countries are less likely have their children immunized because they have forgotten or have never experienced the real diseases – dodging them makes them seem less like the killers they are.

Ongoing Professional Development

Another dimension in successfully competing is simply having better skills and intellectual assets than your competitors. This runs the gamut of identifying people with better SKAs than your competition, to acquiring and keeping them, to putting them to work more efficiently and effectively than the next company in your market space. However, time passes, things change, tools rust, and if you want to keep ahead of the competition, having a workforce composed of people who actively pursue their own ongoing professional development is surely the best.

This is also the key element in forming a CoP, and without a culture of ongoing learning, the intellectual assets of a company will slowly gather dust and be buried.
The absence of a vibrant and concerted effort to maintain professional expertise is an early sign of cognitive degeneration in a firm, and a harbinger of senescence. If your staff don’t actively pursue their own ongoing professional development, you are already a dead-man walking.

Innovation Intent

The final dimension is the desire for change, and perhaps the hardest of all to achieve.

As companies age, like people, they tend to grow more conservative in outlook and more comfortable with the tried and true over the new and exciting.

This is a perfectly logical risk-aversive approach since most novelty, most innovation either fails or is deleterious. Mutations, for example, seldom produce an improvement – usually they just result in cancer. So sticking to what has already proven to work adequately is a very safe bet – in the short term.

However, this leads inevitably to rigidity in the face of change and decreased ability to formulate new solutions when the old ones no longer apply. Think of this in terms of bacteria – over time bacteria will acquire resistance to existing medications no matter how effective they were originally, and unless novel attacks are discovered, eventually the bacterium starts gaining ground and flourishes.
For this reason one has to have a deliberate intent to innovate, to test out new approaches and ideas before the old ones are exhausted and overrun.

However, this requires a cultural environment in which experimentation is supported, controlled, and encouraged. An early warning sign is if mistakes are typically punished rather than treated as learning opportunities – If punishment is the first and foremost reaction, then you have a safe bet that there is little innovation and the firm is already gathering moss and accumulating risk.

A word of caution is appropriate here – Major innovations don’t typically come from individual work, nor from steady evolutionary refinement over time, but from importing mature ideas from other domains and collaboration between people and across domains and organisations.
If individual work is rewarded and there is a winner-take-all culture, you already have a massive handicap.


Studying the causes of death in firms serves two valuable purposes – knowing the facts of death itself, and the formation of a classification on which to build remedial efforts. This provides a framework against which to take preventative and generative action, and with careful action, a firm can greatly extend its productive lifespan.

Most of the steps require a cultural component, and all require leadership and executive support that can look beyond the next quarterly earnings. But for those companies that have the character and desire, the processes listed can provide not just a new lease on life, but significant competitive advantage.


Matthew Loxton is a Knowledge Management professional and holds a Master’s degree in Knowledge Management from the University of Canberra. Mr. Loxton has extensive international experience and is currently available as a Knowledge Management consultant or as a permanent employee at an organization that wishes to put knowledge to work.

Aspects of Questionnaire Design

August 19, 2010

I have blogged before about using executive presentations and other artifacts as part of organization-wide organizational learning (see below), and in this blog I will cover some aspects of questionnaire design.

I will assume that the reader either already knows or will research the basic questionnaire-design constructs like having clear agent of action, avoiding double-barreled questions, speaking in the active voice, and the like.

Why Create a Quiz?

The objective of a quiz is to perplex and mystify the reader, or so you have been led to believe over the years of taking them yourself and being mystified at the purpose of some questions that seem to delve the depths of irrelevancy, and perplexed by the minutes of your life ebbing away as you answer them.

However, this is not at all the purpose of setting questions.

Setting questions achieves the following:

  • A second shot at highlighting to the audience what you regard as the important take-away points. You should set questions only on the things you want the reader to know are the most important bits – by posing a question you are saying to the audience “…and this is the important part
  • Finding out if there is something inside your material that is systematically misleading the audience. If significant numbers of people get the wrong answer, then you have misled them someplace and you need to fix that.
  • Finding out if there is a bias of some kind in the audience population. If only one department, or a specific age-group, or only people over six feet tall get certain answers wrong, or pick a specific incorrect answer from a list, then something is going on that you need to look into – which is probably something you told them previously.
  • Finding out if what you said made a lick of sense.
  • Discovering if the person felt confident about their answer or not

Of course this gets a tiny bit more complicated, but then that is why you are in this business – you like complicated things.

Which bring us to How.


  1. Only ask questions that test understanding on something you regard as a vital point – don’t waste your time and theirs on setting questions on irrelevant material.

  2. Never offer frivolous alternatives in a multiple-choice question, each alternative should be something the person is likely to pick due to a misunderstanding that you have already discovered.

  3. If in doubt, leave it out.

  4. Test and retest before launching.

  5. You need the SME to be involved in building a questionnaire because only they can know which questions are significant, and which answers are valid.

How to Create a Quiz

I have a book on my shelf that is written by the guru on questionnaire design, A.N. Oppenheim (Oppenheim1998) and one of the few books exclusively focused on the topic of designing questions. The preface to the 2nd edition starts off with the following:

The world is full of well-meaning people who believe that anyone who can write plain English and has a modicum of common sense can produce a good questionnaire. This book is not for them”

The basic drift is that it isn’t that simple to construct a good questionnaire, and boy, isn’t it in spades!

Ask a bad question, and you will get nonsensical answers and be left wondering what the audience thought you meant.
You will also have wasted your chance, and have wasted the respondent’s time – for which there is no excuse whatsoever.

There are plenty of texts (such as published by O’Reilly) dealing with the technical side of questionnaire tools both SaaS ( Survey Monkey, etc.) or embedded within Learning Management ( Moodle, WebCT, etc.), Trouble-Ticketing ( Remedy, OTRS, etc.), and other suites.
But that’s the easy part, albeit the part with the thickest manuals.

What I am going to cover here is the more tricky part of how to build the dialogue involved in asking questions in an eLearning context.
You cannot see the puzzled look on your respondent’s face in an eLearning situation, so you will have to plan for it when you design your questions.

Step 1 – Critical Elements

Identify the critical concepts or facts that you want the audience to understand and retain, jot these down.
If you get past 15 or 20, consider breaking your course into more than one part – a tutorial with more than a dozen critical points is starting to get really big, and unlikely to stick. Five is a good number, try to keep it that focused.
Keep it tight, keep it light, and rather build more tightly-focused courses than trying to solve the world’s problems in one fell swoop.

Step 2 – “By George She got it”

For each question, consider what supportive information you can give for a correct answer.
You are getting another shot at contextualizing and once more to drive a point home, don’t waste it.
You should present the respondent with a text of your choice and you should conform to the dialectic form of “
yes, and …”.
Affirm the correct answer and then provide the context of why that answer is right, and drive the point home a little deeper.

Step 3 – “um… no, because …”

For each incorrect answer you provide or which might occur (you will enable them to pick a wrong answer, right?), you need to furnish targeted corrective information.
Try to present wrong alternatives not to confuse, but to identify what you think are common mistakes or potential mistakes you want to address, so that once more you can drive your point home and provide a context.
The idea is to provide them with enough information (including referring or linking to other sources), so that you get the issue cleared up in their mind before they move on.

Step 4 – Concluding Summary

Many questionnaire tools will give you the option (which you will naturally take) to provide a feedback statement after they have finished answering it.

This is once again, an opportunity to provide additional context or remind them of the facts.
It allows you to place the question and answer in perspective in the broader picture, and provide the respondent with an additional link in why this is important and how to picture it.

Step 5 – Confidence

There is a big difference between getting something wrong when you are taking your best guess and being wrong and simultaneously being very confident about your answer, and it is very useful to know which is the case.
Consider constructing your questionnaire to add a rider to each question to measure how confident the respondent is – a simple five-point
Likert Scale should be fine.


By now you can see another reason why asking irrelevant questions is a waste of effort – for each question you need a comment for a right answer, comments for wrong answers, and a comment to put the whole question into a meaningful perspective.
A whole bunch of work that you only want to do if the question is worth the effort.

Remember, you are in an asynchronous dialogue with the respondent, and the objective is to pass on not just facts, not merely information, but knowledge – and you can only do that by also providing perspective and context.

Good Luck!

Some previous posts on Organizational Learning:

“How to get added value from corporate presentations”

“Knowledge Transfer”

“Niche Mastery – How KM can add a few hundred million dollars to corporate worth”

“The Corporate College is dead, long live the Corporate College!”

“Corporate blogging and web2.0 – training wheels first

Please contribute to my self-knowledge and take this 1-minute survey that tells me what my blog tells you about me. – Completely anonymous.


[Oppenheim1998] Oppenheim A. Questionnaire design, iterviewing and attitude measurement. . Pinter, 1998.


Matthew Loxton is a Knowledge Management professional and holds a Master’s degree in Knowledge Management from the University of Canberra. Mr. Loxton has extensive international experience and is currently available as a Knowledge Management consultant or as a permanent employee at an organization that wishes to put knowledge to work.

Knowledge Management in Practice: Service Center Operations.

March 25, 2010

One criticism of Knowledge Management is that its practitioners are reputed to be a cloaked brethren steeped in arcane practices and secret terminology.
Well not really, I haven’t actually ever heard any such criticism, but I am sure it sometimes crosses people’s minds when they wonder just what the practical benefit of KM is.

The truth is that KM is very much about the fine details of daily work performed in a way that the big picture says they should be (supporting the corporate mission), and good KM practices interlock across many disciplines in an organization to help an organization deliver on its objectives.

One place where this comes together very visibly is at the call-center where an organization’s customers go for help.

The Business Issue

Poor service at the call center can drive down a customer’s willingness to be a good reference for sales prospects, makes retention of maintenance contracts more difficult, reduces the likelihood that they will increase their use of the product portfolio, and drives up the cost to keep and maintain the customer.

It comes down to putting knowledge to work in the service of organizational objectives.

However whilst people love to know things, they find learning quite hard, and except for when fear is involved, learning takes a lot of effort, repetition, understanding and reflection, as well as needing a context.  Learning is also very expensive to bring about and to maintain – people forget, and memories are subject to mutation, combination, and simple fabrication.

The Approach

A principle in KM is therefore not to teach something unless you really need to – It is far more effective to present the person with the right information in an ergonomic and timely manner, than to teach it to them and hope they will remember it correctly in the future.
Instead of forcing people to remember transaction codes (which might change anyway), or procedures (which may be amended), or details of what products a customer uses (which you hope will change), you could rather present that information in its latest version, if and when they need it.

That means you might want to make judicious use of dropdowns, help texts, or a knowledge base to present the right information at just the right time – In fact, a major source of trouble in call-centers arises from staff who having struggled to find information, will keep a cache of private printouts, cheat-sheets, and code-lists (and then use those rapidly obsolescent versions forever).
At best, private stashes lead to inconsistent experience for the customer, or even outright harm (or at least a black mark on a quality audit).

Elimination of the perception of a need for hoarded information is thus a major objective in applying good Knowledge Management practices, so the information must be reliably at hand and useful to purpose.

Old Habits Die Hard

Eliminating knowledge hoarding is one of those places where learning is key, and needs to be built into the ongoing training of support/service staff.

One way to do this is to regularly hold open-book quizzes that walk staff through a given process or a scenario, and in which the first assessment question tests if they have the right document at hand. This enables you to detect when caches are being used and to provide quiz feedback through eLearning that points them to where the correct information is to be found. It also lets you see where the documentation is misleading or unclear.

Obviously to support this you will be best served by having a Learning Management System to deploy the eLearning material and to automatically grade quizzes etc., and in turn an Electronic Content Management System of some kind to help you keep document versioning and editing under wraps.

It also means that you need to know:

(a) What information is critical to operations, and

(b) Who has that knowledge.

Getting your SMEs to produce the documentation, the cheat-sheets, and the help texts and Job Aids, is the start of building and managing your intellectual assets, which allows you to recognize SMEs, and to reward knowledge-sharing behavior – You can of course limp along just fine without all this, and many companies do but there is a price to pay in ability to execute and in higher costs.

Higher costs can of course be reduced by off-shoring to lower-cost geographies, but then  you will need to quickly ramp up the capabilities of the offshore team and avoid simply creating a “your mess for less” situation in which they simply learn all your habits both good and bad.

The Presentation Layer

The second place that KM practices play a role in this scenario is the targeted use of Knowledge Bases – and that is plural. Unless you implement clever filtering, you will need to keep different-purposed KBs separate but linked.

Here are two principles applicable in a call-center/customer-support environment:

–          The level-one people are the arbiters of whether an article is a good one or not because “good” is whatever allows them to close a call at level one, and “bad” is one that may be technically perfect, but doesn’t put information in their hands in a form that they could use.

–          The level-one staff are also determine the template format and the structure of KB articles for their use, and decide what fits ergonomically in their environment.

Level 2 and 3 support should therefore produce KB articles for use by level 0/1 that would:  

  1. Enable them to close a case at level one, or failing that
  2. Gather the necessary information for use by other levels

The corollary to this is a principle that the higher levels should be trying to only get cases that are either exotic/complex or dangerous, by empowering level 0/1 to increasingly deal with everything else. They do so by giving them information that is ergonomically suited to level 0/1 operational needs.

A good practice to determine content requirements at level s 2-3 is to Pareto the cases being passed on by level 0/1

–          Firstly, by simple description frequency: to find repetitions that could potentially be addressed with a KB article and have a high hit-rate

–          Secondly, by the potential impact: to make sure that there are articles to deal with situations that could lead to a disaster if incorrectly handled.

At level one, a similar Pareto exercise can be carried out to look at cases that caused embarrassment or escalation, or which simply occur frequently and thus unnecessarily drain resources at higher levels.

Where the solution and its resultant KB article is not producible by level 0/1 staff, a Lean Principle approach is appropriate.
In this method the knowledge article topics are determined by level 0/1 and requested from levels 2 and 3 for fulfillment. Rewarding the best submission is likely to drive attention and response to these requests without producing too much “money for junk” behavior. However, it is wise to bear Kerr’s dictum in mind – don’t reward one thing and expect something else.

I hope that this article showed you how very practical and vital good knowledge-management practices are in a function as specific as customer-support.

That’s my story, and I’m sticking to it


Matthew Loxton is the director of Knowledge Management & Change Management at Mincom, and blogs on Knowledge Management. Matthew’s LinkedIn profile is on the web, and has an aggregation website at
Opinions are the author’s and not necessarily shared by Mincom, but they should be.

Knowledge Management Issues: Dealing with Failure.

March 13, 2010

Professionals often excel at dealing with expediencies, but perform quite poorly when it comes to deeper root-causes. This may be a side-effect of their expectations of success – having rarely failed throughout their educational and professional backgrounds and careers. When single-loop strategies do not perform as expected, these persons often become defensive and seek a ‘scapegoat’. This is discussed in relation to the broader concept of organisational learning.


Both Science and Business systematically pay more attention to successful outcomes than unsuccessful outcomes due to structural mechanisms which drive this behaviour. One particular author noting the bias towards success and who is cited across many domains of practice is Chris Argyris, whose depiction of “double-loop learning” involving learning about learning has had great influence.
Argyris details the psychological tendency for people to remain cemented in “single-loop” strategies and the risks that  poses.
A recent trend to correct the “winner” bias can be seen in various domains where efforts are underway to use failures as warning signals to trigger the double-loop learning and Model-II strategies as described by Argyris. The greatest benefits of this reflexive corrective action, and the focus on what does not work are perhaps less wasted effort, and a protection against systematically faulty reasoning.


Fulmer reports a study undertaken by the Dutch oil giant, Shell, which showed an average expected corporate lifespan of less than 40 years – caused in their view by corporate “learning disabilities” (Fulmer, 1998:8).

Among the common properties they discerned in those companies surviving beyond that average is the ability to tolerate novelty and innovation.

This creates a tension since with novelty and innovation comes a high risk of failure, and studies indicate that businesses are failure averse.

Aversion to failure

As a norm, we pay a lot of attention to success – we admire those who succeed, we publish those research projects which were successful, and those papers that describe successful experiments or findings. This amounts to a bias towards only documenting things that are successful.
An intolerance for failure (or admission of failure) may however prevent us from gaining new insights or saving us from future failure. This can become institutionalized and prevent leaders especially from seeing their own failing methods for what they are because they are unused to failure, and also because they are surrounded by people and structures that continue to obscure both the causes and results of maladaptive behaviour. (Burke, 2006)

This is not restricted to business, but is also present in science. For example The Royal Society of Chemistry which justifiably claims to be “the largest organisation in Europe for advancing the chemical sciences” states in its guide to authors that “In general there is no need to report unsuccessful experiments”[1].


In his earlier work on “Action Science”, Argyris noted a tendency of people for seeking out and selecting data to fit or confirm what they already believe, and are “predisposed to attribute the behavior of others (but not their own) to dispositional traits[2] (Argyris, 1985:96), this he tied to what he coined as embedded “Single Loop Learning” strategies.

In this Model-I archetype, he lists what he perceives as the operant rules or “theory-in-use” vs “espoused theory”:

–          Remain in unilateral control

–          Win, don’t lose

–          Suppress negative feelings

–          Act as rationally as possible

He describes the “Single Loop” process in terms of a thermostat in a heating system. Information is not solicited, nor is the system capable of self-awareness or of changing the control inputs or norms. If the temperature drops below a set threshold, action is initiated to return the temperature to nominal, but the nominal setting itself is persistent and the heating mechanism unchangeable.

In this sense, objections or errors in Model-I actions operating under single-loop learning processes “create defensiveness, self-fulfilling prophecies, self-sealing processes, and escalating error” (Argyris, 2000:5).

Not only are the errors unmentionable, but the very un-mentionability is unmentionable also, hence “self-sealing”. (Argyris, 1999:58)

Argyris poses this as a defense mechanism against embarrassment and feelings of threat (Argyris, 1990:10) which is deeply entrenched, finely practiced, and in which the individual is highly skilled to the degree that the mechanisms are automatic, instantaneous, and spontaneous. (Argyris, 1990 ch2)

When people are challenged about these self-sealing actions or an unmentionable is mentioned, they “become defensive , screen out criticism, and put the ‘blame’ on anyone and everyone but themselves” (Argyris, 1999:127), and thus inclined to scapegoat rather than engage in either self-reflection or analysis of the “tried and true” methods of  received wisdom.

This externalization of blame or “an enemy out there” attitude is echoed in a parallel view given by Senge in his description of management teams and how disagreement with expectations it is usually demonstrated in a fashion that “lays blame, polarizes opinion, and fails to reveal the underlying differences …” (Senge, 1990:24).

To address these second-order problems where the norms or approaches need to be changed, Argyris proposes his Model-II “Double Loop Learning” in which he lists a new set of governing values (Argyris, 2000:98) :

–          Valid Information Seeking

–          Free and Informed Choice

–          Internal Commitment

This would seem like the solution and the end of the process, but drawing on his concept of “System Domain Defenses” Bain speaks of how organizations “… avoid change wherever possible…” and have a tendency for regression over time back to faulty operant behaviour even after corrective changes had been put into place. He attributes this to the fact that organizations are typically part of bigger communities of practice that support the original behavioural models, and that like organizations, they too are averse to change. (Bain, 1998:416).

This reluctance to change brings us back to the “Learning Disabilities” that de Geus of Royal Dutch Shell articulated with regard to failed companies.

If the first-order actions of Model-I Single Loop Learning are thus unable to solve second-order problems, and we require second-order Model-II “Double Loop Learning” but are also averse to the change and the effort cost, then clearly we would need a mechanism to trigger Double Loop Learning when needed, and cultural attitude to act on it.

This discussion suggests an approach using external informational input to break through the organizational system domain fabric and to embrace failure as a “signal from nature” that the mechanism itself is in error. This approach is already mature in the sciences under the framework of theoretical falsifiability and critical tests

The Sciences

One of the foundations of modern science is the concept of falsifiability as articulated by Sir Karl Popper and embodied by the logical form of modus tollens (Kemerling, 2002).
In this form we discover a truth from the combination of a critical test (Thornton, 2006) and the failure of an assertion.

If my car is white, then no number of white objects in my parking bay can enable me to conclude that what is there must be my car, however if what is parked in my spot is not white, then I am sure that it cannot be my car.[3]


This allows nature to dictate and to break theories that are in error by providing information external to our system domain.

Looking for false outcomes is thus foundational to science, but we may ask if it is common amongst the scientists themselves?

In an experimental study, Kerns, Mirels, and Hinshaw demonstrated that a large proportion of career scientists were unable to identify valid propositional logic statements. (Kerns 1983) and were frequently unable to use modus tollens correctly.

We can see therefore how science itself is structured to combat this conformational bias and how it seeks external information and has an operant culture of reacting to falsification as suggested earlier, but that Bain’s regression process described earlier is driving this back into Model-I “skilled incompetence” (Argyris, 2006:41).

Several attempts are being made to address this by organizations in many different communities of practice ranging from Oncology (Kern, 2007), Biomedicine (Olsen, 2007), Computer Science (Prechelt, 2006), ecology and evolutionary biology (Blank et al, 2007), Natural Language Processing and Machine Learning (Dale et al, 2007), and Qualitative and Quantitative Results in the Social Sciences (Biesma et al, 2007).


Model-I behaviour is our natural highly learned and skilled state, and it may be difficult or impossible to maintain Model-II behaviour over long periods of time. When confronted with evidence of Model-I failures, our natural reaction will be defensive and to seek external agents to blame in order to maintain systemic homeostasis and avoid embarrassment and feelings of loss of control. It is however possible to use Double-Loop Learning to make systematic changes to address Model-I problems that Single-Loop Learning simply entrenches. One mechanism to engage Model-II activity is to place deliberate triggers in our processes either with quality procedures or with exposure to external thinking.

Further study is necessary into how effort-reduction may play a role in regression to Model-I behaviour archetypes and to bring neuropsychology and behavioural models into synch with Argyris’s views on Single Loop Learning, and with Bain’s observations that organizational changes tend to regress in time back to the dysfunctional but protective Domain Fabric.[1]

Further Reading

1.       “Does double loop learning create reliable knowledge?” 
Author(s):Deborah Blackman, James Connelly, Steven Henderson
The Learning Organization; Volume: 11   Issue: 1; 2004 Research paper

2.       “Transcending organisational autism in the UN system response to HIV/AIDS
in Africa”
Author(s):John G.I. Clarke
Kybernetes; Volume: 35   Issue: 1/2; 2006 Conceptual paper

3.       “The effect of downsizing strategy and reorientation strategy on a
learning orientation”
Author(s):Mark Farrell, Felix T. Mavondo
Personnel Review; Volume: 33   Issue: 4; 2004 Research paper

4.       “Towards a new approach to understanding service encounters: establishing,
 learning from and reconciling different views” 
Author(s):Mark N.K. Saunders, Christine S. Williams
Journal of European Industrial Training; Volume: 24   Issue: 2/3/4; 2000 

5.       “Circular organizing and triple loop learning”
Author(s):A. Georges L. Romme, Arjen van Witteloostuijn
Journal of Organizational Change Management; Volume: 12   Issue: 5; 1999 

6.       “A supplier development programme: the SME experience”
Author(s):Sharon Williams
Journal of Small Business and Enterprise Development; Volume: 14   Issue: 1;

7.       “We will teach you the steps but you will never learn to dance”
Author(s):Jane Turner, Sharon Mavin, Sonal Minocha
The Learning Organization; Volume: 13   Issue: 4; 2006 Case study

8.       “Narratives in ERP systems evaluation”
Author(s):Jonas Hedman, Andreas Borell
Journal of Enterprise Information Management; Volume: 17   Issue: 4;
2004 General review

9.       “Grief and educative leadership” 
Author(s):R.J.S. Macpherson, Barbara Vann
Journal of Educational Administration; Volume: 34   Issue: 2;
1996 Case study


  1. Argyris, 1985 “Action Science”, Chris Argyris, Robert Putnam, Diana McLain-Smith, Published 1985 Jossey Bass.
  2. Argyris, 1990 “Overcoming Organizational Defenses”, Chris Argyris, Prentice Hall 1990.
  3. Argyris, 1999, “On Organizational Learning”, 2nd edition, Chris Argyris, Blackwell 1999
  4. Argyris, 2000, “Flawed Advice and the Management Trap”, Oxford Press 2000
  5. Argyris, 2004 “Reasons and Rationalizations: The Limits to Organizational Knowledge”, Chris Argyris, Oxford Press 2004
  6. Bain, 1998 “Social defenses against organizational learning”, Human Relations, vol 51, no. 3, pp. 413-429
  7. Biesma et al, 2007 : Biesma, Regien et al website “The Journal of Spurious Correlations last accessed 5 Apr 07
  8. Blank et al, 2007 : Jochen Blank, Michael J. Stauss, Jurgen Tomiuk, Joanna Fietz and Gernot Segelbacher  “Journal of Negative Results” last accessed 5 Apr 07
  9. Burke, 2006, “Why leaders fail: exploring the darkside”, Ronald J. Burke
  10. Dale et al, 2007 : Dale, Robert  website “Natural Language Processing and Machine Learning last accessed 5 Apr 07
  11. Fulmer, 1998 “The second generation learning organizations: new tools for sustaining competing advantage”, Fulmer RM, Gibbs P, Keys JB, Organizational Dynamics, vol. 27, no.2, pp. 7-20
  12. Gough, 2006, “Women See Friends, Men See Foes”,Nancy Gough
  13. International Journal of Manpower; Volume: 27   Issue: 1; 2006
  14. Kemerling, 2002,  “Philosophy Pages Last accessed 5 Apr 07
  15. Kern 2007, Website “Journal of Negative Observations in Genetic Oncology” at last accessed 5 Apr 07
  16. Kern, 1993, “Scientists’ Understanding of Propositional Logic: An Experimental Investigation “ Leslie H. Kern, Herbert L. Mirels, Virgil G. Hinshaw
  17. Olsen, 2007 : Olsen, Bjorn Website “Journal of Negative Results in Biomedicine” last accessed 5 Apr 07
  18. Prechelt, 2006 : Prechelt, Lutz Website “Forum for Negative Results” last accessed 5 Apr 07
  19. Sabrina M. Tom, Craig R. Fox, Christopher Trepel, and Russell A. Poldrack Science 26 January 2007 315: 515-518 [DOI: 10.1126/science.1134239]
  20. Science 2 June 2006 312: 1281 [DOI: 10.1126/science.312.5778.1281c]
  21. Senge, 1990 “The Fifth discipline: the art and practice of the learning organization”, Doubleday 1990, pp. 19-25
  22. Social Studies of Science, Vol. 13, No. 1 (Feb., 1983), pp. 131-146
  23. Thornton , 2006, “Karl Popper”, Thornton, Stephen, In The Stanford Encyclopedia of Philosophy (Winter 2006 Edition), Edward N. Zalta (ed.), At, accessed last April 20, 2007.
  24. Tom et al, 2007 “The Neural Basis of Loss Aversion in Decision-Making Under Risk



  It may be interesting to examine why gossip or “informal social communication” is mostly about negative outcomes, and why traffic accidents get our attention. This is perhaps an evolutionary byproduct of attention to danger since ignoring one true danger can be fatal, whereas running away from a false alarm is usually not. This is evident by the asymmetry in the neurology of risk evaluation (Tom et al, 2007).

[1] See RCS author’s guidelines at Last accessed 7 March 2010

[2] This is also known as the “fundamental attribution bias”

[3] Sadly, Popper was undone in part by the Duhem-Quine thesis which showed that rejecting an hypothesis in this way was not foolproof, since other reasons may exist why it failed. In this case, perhaps somebody painted my car!


Matthew Loxton is the director of Knowledge Management & Change Management at Mincom, and blogs on Knowledge Management. Matthew’s LinkedIn profile is on the web, and has an aggregation website at
Opinions are the author’s and not necessarily shared by Mincom, but they should be.

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