List of Management Theories

This is a list of academic management theories which I find worth mentioning.
(work in progress)


Strategy

• Porter 5 Forces 
• BCG Matrix 
• Cost vs. Value Leadership 
• Resource-based view of the firm 
• Dynamic Capabilities 
• Strategic Ambiguity vs. Strategic Clarity 
• Strategic Renewal 
• Coopetition Theory

Management Theories 

• Scientific Management - Frederick Taylor 
• Adminstrative Management Theory - H. Fayol 
• Burocratic Management - Max Weber 
• Behavioural Theory of Management - Elton Mayo 
• Chaos Theory 
• Systems Management 
• Evolutionary Theory 
• Stakeholder Management 
• Contingency Theory 
• Principal Agent Theory 
• The Concept of Coopetition 
• Knowledge Management - Nonaka 
• Shareholder Value Management 

Organizational Behavior 

• Missionary Organization 
• Theory Z Organization 
• Holographic vs. Ideographic Organizations

Motivation 

• Maslow 
• Mintzberg 

Innovation / Business Model 

• Creative Destruction - Schumpeter 
• Disruptive Innovation - Christensen 
• BMI Canvas 
• Explorative vs. Exploitative Innovation 
• Pretotyping

Product Development / Operations 

• SCRUM and Agile Development/Agile Manifesto 
• Kanban 
• Lean Management 
• Complexity Management 

Leadership 

• Transactional vs. Transformational Leadership 
• Efficiency vs. Effectiveness - Drucker 
• Upper Echelons Theory 

Management & Sociology 

• Social Network Theory - Granovetter 
• Network Effects 

Group Dynamics 

• Irrational Exuberance 
• Nash Equilibrium 

Group Psychology

• Social Exchange Theory 
• Social Interdependence Theory 
• Social Identity Theory - Tajfel/Turner 
• Social Comparison Theory - Festinger 
• Competition & Collaboration - Morton Deutsch 
• Group Think 

reflective vs. formative models

Reflective Model 

Items <-- Construct 

e.g.

Construct: Drunkeness 

- uncoordinated walking 

- glossy eyes 

- vomiting 

(items are related) 

Formative  Model 

Items --> Construct 

- Beer 10 liter 

- Vodka 1 liter

- Wine 1 liter 

(unrelated )

Therefore, formative measures define, produce, or cause the construct rather than vice versa !

Basic Statistical Concepts

data 
nominal - male vs. female/  frequencies , percentages (non-parametric)
ordinal - e.g. Likert scale / first,second, third (non-parametric)
interval - discrete, parametric , continuous (eg temperature)
Ratio level - usually interval data, zero point reflects absence of characteristic

Discrete - adult/ non adult 
Continuous - angry to super angry 

Test Statistic = Systematic Variance / Unsystematic variance
We are comparing the amount of variance created by an experimental effect against the amount of variance due to random factors (such as differences in motivation, or intelligence)

t-value 
what is the probability that our samples are from the same population . You basically compare the means of two or more samples
it is a measure of unsystematic variance or variance not caused by the experiment

r-value (Effect Size)

is simply an objective and standardized measure of the magnitude of the observed effect. 

Pearson Correlation Coefficient

r = .1 (weak effect) 1% of variance between variables is explained

r = .3 (medium effect). 9% of variance between variables is explained

r = .5 (strong effect) 25% of variance is explained

p-value 
Significance - Chance of Error (being wrong), in other words the chance of a finding being due ot error
The chance of the null hypothesis to be rejected where it is actually true.
in Business this is accepted

p < .05

z-value 
are standard scores. it states the position of a raw score in relation to the mean of the distribution, using the standard deviation as the unit of measurement
z = raw score - mean / standard deviation

Standard Error 
the  standard deviation (or variability) of sample means. The higher the SE, the more the sample means differ from each other
The lower it is the more it accurately reflects the entire population


Mean: Sum / n
Median: right in the middle of samples
Mode: the most occuring

Standard Deviation 
Average distance of the values from the mean

Variance Extracted 
Summary measure of convergence among a set of items representing a latent construct.
It is the average % of variation explained among items



Type 1 Error (False Positive) 
Accepting effects that are in reality untrue

Type 2 Error (False Negative) 
Rejecting effcécts that are in reality true

Construct Validity (relationship betweeb measurement instrument and the construct)
Discriminant, Convergent, nomological validity

Discriminant Validity
Eg how good do the items of the construct of innovation differentiate from frome the construct of strategic validity

Convergent Validity
How good are the items for the innovation construct converging ?
If they do not converge the are likely not measuring the same phenomenon
- Cronbach Alpha, cut-off value > .70
- Composite reliability, cut-off value > .60
- AVE Average variance extracted, cut-off value AVE > .50
(AVE = average squared factor loading)

Indicator reliability / validity
- significant factor loadings of items >.70, t-values > 1.645

Multicollinearity
phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy

Solution:

Variance inflation factors (VIF) measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related.

Use to describe how much multicollinearity (correlation between predictors) exists in a regression analysis. Multicollinearity is problematic because it can increase the variance of the regression coefficients, making them unstable and difficult to interpret.


Parametric Tests 
Kolmogorov Smirnov Test 
if p > .05 distribution is probably normal 

Levene Test 
tests hypothesis that variances of two samples are equal
if p > .05 variances are more or less equal 

Anova 
Main Effect 
A “main effect” is the effect of one of your independent variables on the dependent variable, ignoring the effects of all other independent variables

Interaction Effect
A statistical interaction occurs when the effect of one independent variable on the dependent variable changes depending on the level of another independent variable

Independent T-Test of two samples 

Taken from https://statistics.laerd.com/statistical-guides/independent-t-test-statistical-guide.php

Independent t-test for two samples

Introduction

The independent t-test, also called the two sample t-test, independent-samples t-test or student's t-test, is an inferential statistical test that determines whether there is a statistically significant difference (variance in means for instance) between the means in two unrelated groups.

Null and alternative hypotheses for the independent t-test

The null hypothesis for the independent t-test is that the population means from the two unrelated groups are equal:

H₀: u₁ = u₂

In most cases, we are looking to see if we can show that we can reject the null hypothesis and accept the alternative hypothesis, which is that the population means are not equal:

H_A: u₁ ≠ u₂

To do this, we need to set a significance level (also called alpha) that allows us to either reject or accept the alternative hypothesis. Most commonly, this value is set at 0.05.

The concept of falsification is based on Popper's falsification theory. You cannot know scientific laws with absolute certainty. you can only falsify them --> Null hypothesis 

Cycle time, Velocity, Lead time

Definitions 

In Scrum, velocity is pretty much the same as cycle time.

Velocity measures what a development team is able to deliver in terms of developed product backlog items within a sprint.

From this blog https://leanandkanban.wordpress.com/2009/04/18/lead-time-vs-cycle-time/ I got this nice definition

Lead time clock starts when the request is made and ends at delivery. Cycle time clock starts when work begins on the request and ends when the item is ready for delivery. Cycle time is a more mechanical measure of process capability. Lead time is what the customer sees.

Lead time depends on cycle time, but also depends on your willingness to keep a backlog, the customer’s patience, and the customer’s readiness for delivery.

Another way to think about it is: cycle time measures the completion rate, lead time measures the arrival rate. A producer has limited strategies to influence lead time. One is pricing (managing the arrival rate), another is managing cycle time (completing work faster/slower than the arrival rate).

Model 
Sprint Retrospectives do increase velocity over time. Knowledge, insights are shared which leads to team learning which affects the velocity in a positive manner.
Hence, retrospectives act as a moderator between developing software and output.

Which KPI's should I focus on after a mobile app launch ?

These are some additional "quick&dirty" ideas around the question: What are the KPI's that I should take a look at after launching a mobile app (free-to-play in particular. btw, see article below on "Key Metrics for App Monetization." and "Healthy Retention Rates".

(free pic under CC)

1. Rolling Retention
In my short article on "Healthy Retention Rates", I primarily focused on rolling retention. This is: Take a look at a cohort and track their life time. Averaging this figure gives you the average life time.

2. DAU Frequency Retention
By that I mean a methodology which takes a snapshot from one specific day and takes a look at that daily cohort by measuring how many of those that have been active today have been active on a daily basis over the past 7 days. This also gives you an indication of recency. I have seen top mobile games achieve a 70% DAU Frequency retention. In other words, of that daily logged in users, 70% had logged each day for the past 5 out of 7 days! Of course, the DAU basis should be reduced by the number of new daily logins, so that you really have only loyal DAU.

3. Predictive LTV and Milestone Tracking

Define Milestones which could give you an indication as to whether your mobile game will succeed or not.
These two findings are from Tapjoys research on "predicting the future LTV of your your users."

• "Reaching a critical point of 1,000 users who make > 3 purchases is a good indication that an app will ultimately top $1MM in revenue. 84% of the apps with 1,000 or more users who completed three or more in-app purchases within the first 90 days broke that $1MM threshold.
• 35% conversion rate from 1st to 3rd purchase was the critical number for breaking the $1MM revenue threshold."

4. Recalibrate your own benchmarks constantly. 

I previously mentioned that D1 (40%), D7 (20%) and D30 (10%) is a good Western benchmark for midcore games. In my experience, East-Asians take a a different approach, which is  slightly more short term on this. I remember one Japanese executive mention the following retention rates as successful.

D1 > 50%
D3 25-30% !
D7 ~ 20%

In his talk, he stressed D3 which seems to empirically work for him to predict if a game is successful or not.

Anyway, these are just preliminary thoughts. Please comment below for further exchange.

How to compute a doubling with percentages

What does it mean when someone says: We are going to grow revenues

14% every year ? 

10% every year ?

5% every year ?

There is a very simple trick that allows you to quickly translate it in terms of doubling.

(free pic under CC)

Just take 70 and divide by the percentage number:


70/14 = 5 yrs

70/10 = 7 yrs

70/ 5 = 14 yrs


So when someone says:

We are going to grow 14% every year, this person is saying that revenues will double in 5 yrs.

 in short:

14% every year ? --> 2x in 5 yrs

10% every year ? --> 2x in 7 yrs


5% every year ? --> 2x in 14 yrs

Healthy Daily Retention Rates

What are healthy retention rates ? Below is a rolling retention benchmark.

(free pic under CC)

If your midcore f2p mobile title does not match any of these, forget it:

1 Day Retention: 40%
7 Day Retention: 20%
30 Day Retention: 10%

(for iOS 5.0 or 2.3 Android and higher)

Playstore Ranking and Revenues

Here are my guestimations for the Korean Market (Play Store)

Below figures reverse engineer the current mobile gaming revenue size of 1.5 billion US$ in Korea for Play Store (2013) figures.

Rank	Daily Revenues	Annual Revenues	Total Annual
1-5	$ 300.000	$109.500.000	$ 547.500.000
6-10	$ 100.000	$ 36.500.000	$ 182.500.000
11-30	$ 20.000	$ 7.300.000	$ 146.000.000
31-50	$ 10.000	$ 3.650.000	$ 73.000.000
			$ 949.000.000
	% of Total Revenues		66%
	Total Market Size		$1.437.878.788

Sounds Big

The sentence "sounds big" has a particular meaning to me.
 When I was 26 years old, Investment Analyst at T-Venture, I had the privilege to attend a Amadeus Capital Fund Board meeting.
 Pundits like Herrmann Hauser and Frank Bonsal (Founder NEA) were participating.
Someone was mentioning a new tech start-up (don't remember which) and Frank just said:

 Souunds BIIGG. (in a slightly southern accent)

(free pic under CC)

 So what was so particular about this comment in this particular context ?
It showed to me a lot about this gentleman:

•  He is aiming for huge markets 
•  He admits that he does not exactly know about the business/industry and is not ashamed of implicitly admitting it 
•  He seems to rely on intuition (could as well have said: Let's analyze the market and the product) 
•  It showed his positive mental attitude & optimism the way he said it.

Mochi Media is shutting down its services

Before the site completely shuts down on March 31, I am copying the CEO's blog post below.

I will write about MochiMedia's model at a later point in time

(free pic under CC)

Some Final Thoughts from Mochi

First off, thank you for working with us all of these years.

Mochi Media was founded in 2005 by Jameson and Bob with the mission of fueling the creativity of indie game developers. You focus on making a great game, and we’ll take care of the rest. At that time, Flash was a platform that held a lot of potential if developers could find ways to track, monetize and build better games. Together, Flash and Mochi provided an on-ramp to a career or business in game development.

In addition to furthering our services and business, Mochi assumed a role in growing the category. We organized and hosted FGS (aka Flash Gaming Summit) for five years to get this community out from behind computer screens and in person to talk game development. We supported developer meet-ups like Mochi London. And we addressed the “state of the union” with the Flash Game Market Survey. Moving forward, we expect that others will pick up the baton in advancing the indie cause.

I think I speak for everyone who has been a part of the Mochi team over the years in saying that the innovation from you developers inspired us. We take great pride in currently seeing Ninja Kiwi’s Bloons TD5, and Flipline Studios’Papa’s Freezeria To Go among the Top Games Charts on iOS. We love that at one time we shared a desk with Casual Collective which is now known as KIXEYE.

Today, there has never been a better time to be an indie game developer in terms of the platforms and audiences one can reach: Flash, iOS, Android, XBLA, PSN, Steam, HTML5, and the list goes on. If Mochi had a more meaningful position today beyond Flash, then there may have been a different path for the company going forward.

Though we won’t (as team Mochi) be a part of your future growth, we cannot wait to see what you create next. Best of luck.

Thank you,

Josh

LTV > CPI - a quick & dirty approach to calculate LTV

This is a five minute note about how to calculate mobile app LTV of a specific cohort








Number of total acquired users = 600

Number of churned users per day. This can be found out via Churnrate = 1- retention rate
Day 1 --> 100
Day 2 --> 200
Day 3 --> 300

We assume they churn at the end of the day and that every user that does not churn is active on each day.

         (free pic under CC)

Therefore number of days active or DAU is the following
Day 1 --> 100
Day 2 --> 400
Day 3 --> 900

Total days on which users have been active is therefore 1400 days (which equals DAU)

We assume that ehe sum of total revenues on those three days 140 €

Therefore ARPDAU = Revenues / DAU = 0.10cents

Average Life Time (LT) = Total days active / total users = 1400/600 = 2.3

LTV = average LT * ARPDAU = 2.3* 0.1cents = 23 cents

If we assume that 23 cents is excluding VAT (gross revenues) we have to deduct appstore fee
the net LTV is 16 cents

LTV > CPI (cost per install) is actually not quite ok, because we have to include a certain margin based on your costs

Based on current CPI's at ~ 2-3 $ or Euro, this seems unrealistic. What are solutions?

Virality
In case the game is viral or gets featured by Google / Apple by a factor of 6 , then LTV in theory goes to 1.40€. If you are able to meet a net LTV of 1€ then fine

Getting featured
This is a no brainer

Push Campaigns
Has been discussed many times. Problem is the incentivized installs f*ck your stats

Buying targeted
This means you are able to target those users which have a high LTV. There are certain companies that label this user acquisition strategy as "predictive LTV". We will see how it pans out

Key Mobile App KPI's to watch

Android Monetization still behind Apple

Apologies for the brevity of the post but that chart says it all:

Source: looks like Appannie (?)

Simple financial analysis

David An

e-commerce wonderworld

In a perfect scenario, contribution margin 1 (after COGS will be 50) leading to a contribution margin 2 of 25. If you are able to replicate that 4 times a period you run at a profit after 2 times, since customer acquisition costs are 50.

DNA of Goals

When you set goals they have to be:

Strategic thinking

This is linear business school stuff but helpful at times

Revenue Split of an iTunes Album

Below is an interesting chart I found on Neue Züricher Zeitung (NZZ - a leading Swiss national newspaper) on how revenues of an  iTunes album are split. 

NZZ researched a 17 Swiss Francs (sFR) download album. The title "Krümel für den Künstler" translates as "crumbs for the artist".

Here are the figures translated and in %:

Artist	SFr. 1,30	8%
Management	SFr. 1,50	9%
iTunes	SFr. 9,50	56%
Label/Producer/Distributor	SFr. 4,70	28%
	SFr. 17,00

Short Commentary:

• I was shocked when I saw these numbers. The artist only makes 8%. Compared to 30% fee of any app dollar that is made on the iOS Store, Apple takes 56%
• Label/Producer/Distributor would have to be specified. My guess is that the producer will get something between 2-3%. But check some more insights here: "Lessons from the music industry"
• Nobody remembers download services like Pressplay, Real, etc. anymore. Apple has a clear monopoly in the download sector which is currently being cannibalized by celestial jukebox streaming services like Rdio and Spotify. But as we already all know Apple is working on a music streaming service too. This space is really getting crowded with YouTube, Nokia, Sony, Samsung soon launching their own service. 

Oishii Karlsruhe - a new sushi business model

Sushi circle restaurants owners shiver everytime I enter their restaurant for the second or n time. That is because on "all you can eat days" nothing is left over. Especially, when I go there with another sushi aficionado, we almost abuse the right to order a la carte - Sashimi in particular, which does not stuff your stomach as much as normal sushi does.

Yesterday, I went to a new place called "Oishii Karlsruhe" - an "all you can eat sushi restaurant in Karlsruhe. Since there are not many good restaurants in Karlsruhe let alone Asian restaurants - this one caught my attention.

And since this blog is about innovation, I thought their business model is worthwhile discussing shortly. Disclaimer: I am not a restaurant expert, and the following thoughts are really a quick&dirty analysis.

Here is how it goes:

• You sit at a table, equipped with an iPad + an app that serves as a mobile-order machine. 
• You pay 22.90 Euro in total, excluding beverages. So you will end up at about 30 Euros. 
• You are are allowed to order 10 times x 5 dishes / 10 minutes. Each dish has one piece of sushi, e.g. Nigiri etc. 
• Each order can be made any time, but once an order is transmitted, you need to wait 10 minutes until the next order. In theory if you make use of 10 rounds, you sit there for 100 minutes continually eating.
• Lets say, you are 4 people. This allows you to order 20 pieces max per round  of order and you need to wait 10 minutes to order the next 20 pieces. 
• There is no price differentiation between sushi pieces. So an Amaebi, or Unagi will be the same price as a Tamago or a green tea ice cream. What matters is the number of units per order which is limited to 5 dishes per order per person.
• So you could potentially end up eating 50 dishes of sushi for a price of 22.9 Euro
• There is a penalty involved in case you do not finish ordered dishes. 

Pro/Cons for the user

• Some customers prefer eating less than 22.9 Euro. However, even sushi circle restaurants a la carte will soon hit 23 Euros unless you just want to go for a mini-snack for lunch. So 22.9. Euros is quite deal. And if you order entire menus a la carte, they often actually start at 20 Euros for a decent meal.
• You enjoy the freedom of choosing without calculating the effect of a sushi dish (grey??) on your maximum "meal-budget"  
• You can order a la carte and do not have to stare at the circling sushi dishes giving your friend full attention.
• You do not have to push the cook who is usually busy as hell with a begging expression on your hungry face for an a la carte order ("Can I have some salmon sashimi please"? "really, you already had 3"!)

 Pro/Cons for the restaurant

• The place was full and I could only see a few tables being empty. Enormously high table fill rate.
• People on average will not order more than 5 rounds which affects the the table turnover. I guess it will be quite predictable and have the shape of a normal curve with something like 1.5 hours in the middle scewing to 2 hours to the right and 1 hour to the left. 
• Same goes for average revenue per customer which will range between [ 26 to 30] including beverages. 
• I guess one could sum it up by concluding: high predictability regarding ARPU (average revenues per user) and table turnover. 
• What I would be worried about is material cost. Sushi is expensive. To do the analysis, one would have to look at the distribution of ordered sushi (stuff like amebi, unagi is really expensive)
• However as long as the number of restaurant visitors remains stable you have a good prediction of total revenues, thus can optimize profitability in a quite controlled manner. 

Conclusion

• Great advantage for the user who love to eat a lot of sushi at a fixed budget
• Therefore high fill rate, predictable table turnover rate and therefore stable total revenues
• Profitability to be optimized in purchase and production costs. The portfolio can be optimized upon that (for instance taking away super expensive sushi won't hurt the majority, maybe sushi freaks like me)
• Overall great idea which proves that innovation nowadays is about business model innovation.

Are mobile game developers overpaid ? Lessons from the music industry

What is a fair deal between a mobile game publisher and a developer? Of course, there are many ways to approach this question. So I thought why not compare numbers with adjacent industries, like the music business?

From: http://www.flickr.com/photos/jensview/ (CC licensed)

So I came across this book "All you need to know about the music business" from Donald Passman which seems to be some sort of industry standard literature. 


Passman takes the example of 500'000 units sold (gold album) and calculates revenues for the artist: 
 

Profit Statement Music Artist

(From Donald Passman, All you need to know about the music business, p.102)

• The artist makes about 34% of royalty revenues and 20% after paying his personal and business manager, that is $170'250 or $100'000 respectively
• However, there are additional costs to be covered by the artist. He/she has to pay a personal manager and a business manager (for managing financials). Normally, PM alone will get ~15% of gross revenues. Sometimes PM fee is capped at 50% of artists net income. So the figure is rather conservative.
• Recording costs are usually pre-financed by the record company, and is deducted in the form of a recoupment. 
• The producer gets about 3%, a star producer will get more 
• Free goods for physical CDs are a form of discount.

Conclusion

• The situation in the music industry is quite comparable to the mobile or online gaming industry
• Recording costs can be compared to development fee of a mobile game developer. These monies are paid upfront and recouped from the artists revenues but risk is carried by the publisher. In any case, the more is paid upfront to the artist/developer the better of course because of NPV (net present value)
• Overall, roughly 25-30% share for the content creator (artist, game developer) seems to be industry norm for creative industries. 

Any thoughts or comments? please comment below: