Development of an agile business strategy in an uncertain market environment

Table of Content

Abstract

Keywords

Preface

Index Figures

Index Table

Index Abbreviations

Executive Summary

1. Introduction
1.1 Problem description and research question
1.2 Aim of the paper
1.3 Methodology
1.4 Structure of the paper

2. Literature review and the general term definition
2.1 General Term Definition
2.1.1 Strategy
2.1.2 The market and competitive space
2.1.3 Uncertainty
2.1.4 Causation versus Effectuation
2.1.5 Prediction versus Control and Influence
2.1.6 Systemic Knowledge Management
2.2 Literature Review on different strategy approaches
2.2.1 Positioning as strategy formation
2.2.2 Construction as strategy formation
2.2.3 Agility as strategy formation

3. Framework to select elements to build an agile strategy
3.1 Evaluating the degree of control
3.1.1 Data set
3.1.2 Questions to evaluate the elements
3.1.3 Evaluation results
3.2 Elements of an agile strategy framework
3.2.1 Evaluating status quo and competitive space
3.2.2 Transfer into a business model
3.2.3 The experimenting phase

4. A case study at “Connect”
4.1 Who is “Connect”?
4.1.1 The Founders
4.1.2 Description of the “Connect” business
4.1.3 Challenges and current status
4.2 Applying the agile strategy framework to “Connect”
4.2.1 Analysing status quo
4.2.2 Analysing competitive space
4.2.3 Analysing current business model
4.3 Analysing innovation accounting measures
4.4 Summary and next step proposal

5. Conclusion

6. Reference List

Appendix List

Appendix 1: Example: Evaluating predictability

Appendix 2: Example: Evaluating controllability

Appendix 3: Example: Evaluating dimensions of the competitive space

Appendix 4: Data analysis of “Connect”

Abstract

Strategic decision-making is a key task for every senior manager of any sized company. Strategic decisions are characterized by highly uncertain conditions. Managing this uncertainty and anticipating the future, is seen as key to success and the measure for being a successful manager. In the opposite direction, if firms fail to anticipate how the future will be, it is then often called “bad luck”. However, if “failing to anticipate the future” were correlated with bad luck, this would mean that “anticipating the future right” should be directly linked with good luck. However, good luck is often ignored and seen as excellent management performance instead.

To reduce the influence of luck, this master thesis deals with the different approaches on strategy-formation and decision-making, to identify to what extent they differ in their methodology on how they manage the uncertainty and how they try to get control over the future. The aim is to identify best practice approaches that should lead to better performance on reduced risk and cost.

Keywords

Agile Business, Decision-Making, Start-up, Strategic Thinking, Strategy Development, Strategy Management, Uncertainty, Effectuation, The Lean Startup,

Preface

“There is surely nothing quite so useless as doing with

great efficiency what should not be done at all”

Peter F. Drucker

Index Figures

Figure 1: Strategy as a link between the firm and its environment; adapted from Grant (2010)

Figure 2: Pattern of strategic decisions, adapted from Mintzberg (1998)

Figure 3: The structure of modern exchange markets; adapted from Kotler (2012)

Figure 4: A simple market system, adapted from Kotler (2012)

Figure 5: Three-dimensional competitive space; adapted from Duhaime et al., (2012)

Figure 6: Nature of industry change; adapted from Duhaime et al. (2012)

Figure 7: Levels of uncertainty; adapted from Courtney et al. (1997)

Figure 8: Causal methods vs effectuation; adapted from Sarasvathy (2008)

Figure 9: Framework of prediction and control; adapted from Wiltbank et al. (2006)

Figure 10: Basic systemic ribbon

Figure 11: Connection between resources, capabilities and competitive advantage; adapted from Grant (2010)

Figure 12: Typical elements of the causal strategy process; adapted from Angwin (2014)

Figure 13: Elements of the dynamic model of effectuation; adapted from Sarasvathy (2008)

Figure 14: Build-Measure-Learn feedback loop, adapted from Ries (2011)

Figure 15: Innovation-accounting trinity, adapted from Maurya (2013)

Figure 16: Innovation-accounting metrics, adapted from Maurya (2013)15

Figure 17: Innovation-accounting measure linked with value and growth elements; adapted from Maurya (2013)

Figure 18: Lean canvas, adapted from Osterwalder (2004) and Maurya (2013)

Figure 19: Basic steps for experiment loop, adapted from Maurya (2013)

Figure 20: Experiment stages; adapted from Maurya (2013)

Figure 21: Uncertainty level at competitive space; total view

Figure 22: Uncertainty level at competitive space; detail

Figure 23: Control level input, total

Figure 24: Controllability level input; competitive space

Figure 25: Controllability level outcome; total

Figure 26: Controllability level outcome; competitive space

Figure 27: Uncertainty level vs Control Level; Input

Figure 28: Control level over outcome

Figure 29: Elements of an agile strategy framework

Figure 30: Customer relationship chain

Figure 31: Positioning within the competitive space

Figure 32: Current business model “Connect”

Figure 33: Total view of hypothesis “Cost saving” along the innovation accounting measures

Index Table

Table 1: Levels of Predictability/uncertainty adapted from Knight (1921)

Table 2: Taxonomy of future phenomena: adapted from Sarasvathy (2008)

Table 3: Levels of controllability

Table 4: Number of elements categorized related to competitive space

Table 5: Evaluation matrix uncertainty levels; adapted from Knight (1921)

Table 6: Evaluation matrix control levels

Table 7: Decision matrix

Table 8: Example of series of split experiments

Index Abbreviations

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Executive Summary

Strategic decision-making is the key task of every senior manager irrespective of the size of the company. Many decisions are important, but not every important decision is of strategic nature. Strategic decision-making is characterized by a high degree of uncertainty. Therefore, this master thesis is concentrating on how to manage uncertainty in the decision-making process.

The handling of uncertainty in strategic decision-making has been a key topic for many decades. Frank Knight wrote in 1921 about this topic and distinguished between three types of uncertainty; Predictable with Risk, Uncertainty and Unknowable. Since then, the race for the best tools and methods to manage the uncertainty is still ongoing. It is the breeding ground for countless scholars and consulting work.

There are different approaches how this control can be achieved. Mainstream paper and textbooks on strategic management, led by Michael E. Porter, strongly focus on industrial analysis with particular emphasis on prediction (Wiltbank et al., 2006). The idea behind the so-called causal methods is, that future goals can be defined based on analysing and focusing on the exogenous environment. To achieve such goals plans can be derived from past experience then extrapolated and forecast into the future. By regular review and further analysis deviations from plans can be detected and adapted to achieve the predefined goal.

In contrast to the causal approach, a more recent entrepreneurial approach called “Effectuation”, has been developed by Saras Sarasvathy (2008). She studied the behaviour in the decision-making process of experienced entrepreneurs and came to the conclusion that entrepreneurs do not predict at all, but put the focus on the endogenous environment under their control. This emphasis is underpinned by the premise that to the extent of being able to achieve control over something, it is not necessary to predict it.

The third and youngest approach on strategic decision-making is called “The Lean Startup” by Eric Ries (2011). He is pairing the premises from the Toyota lean management system with agile elements from the modern software development projects. The premise of his approach is that you can only achieve control by building the hypothesis of the expected future and prove them by systemic experimenting.

The aim of this master thesis is to develop a framework to build an agile business strategy. Therefore the above-mentioned three approaches are critically reviewed and compared with each other, to identify advantage but also the limitation of each approach. The common basis of the causal and effectual approaches is to achieve control. While the causal method puts the emphasis on predicting exogenous events, the effectual approach puts the emphasis on controlling endogenous events only. However, to find out how well each approach is able to deliver the promised result, the performance of their ability to achieve control and their ability to manage the different levels of uncertainty was tested.

In spite of being the mainstream approach, the causal approach could not deliver on both, the level of uncertainty, but could also not prove the ability to achieve control. The effectual approach delivered a better result in both, in ability to achieve control but also on the level of uncertainty for the required data. However, although the effectual approach delivers a better chance for control, it does not deliver total control at all, and a way to manage uncertainty is still required. That said, the agile elements are the ideal combination to cover the missing part of the effectual world. Based on that knowledge, a framework based on elements of the effectual and “The Lean Startup” approach was developed. To prove the usability of this framework, a case study of a young startup company has been performed.

The conclusion from this master thesis is that entrepreneurial thinking has an advantage over predictive thinking in uncertain or unknowable business situations. And entrepreneurship is nothing mystical for genius visionaries only. It is a management task that can be learned by everyone, better to say should be part of every manager toolbox! Successful managers have to be able to play of both games; the right one at the right time. Meaning they have to learn to distinguish, understand and accept when the environment for a business decision is predictable with risk or variance, and when it is unknown or unknowable. The level of uncertainty shall be thereby the only trigger, because all situations can be important, difficult and full of complexity. It might be sometimes hard to admit not to know how the future will be, but it will be the way to future success.

Almost all job profiles for mid or senior managers ask for candidates with the capability of “entrepreneurial thinking and acting”. Taking this research under consideration, they hopefully mean the ability to apply agile elements and not just searching for a hidden genius that is able to see the future with his magic crystal ball. Nevertheless, it would be interesting to see more research of that subject in future.

The basic requirement for the idea of an agile strategy framework is, that it needs to be applicable independent from company size or companies life-cycle. The developed framework shows one possibility to fulfil this requirement. The basic structure cannot only be used from Top-Management, but also in business units, sub-divisions, or even in a single R&D or Sales project. The only requirement is a strategic decision in an uncertain or unknown environment. So far the framework has been challenged only by one single real case, but it would be nice to see more research papers and applications for further proof.

Finally, the tricky thing with strategies is the fact that you only know whether it is right or wrong after you have tried it. The agile strategy framework does not claim to be the only way to success. It is also not a guarantee of success, but it is definitely one of the fastest and most cost efficient ways to fail and to find out if going on the right path.

1. Introduction

1.1 Problem description and research question

Senior managers are experiencing increasing difficulty in coordinating decisions and maintaining control at companies that are growing in size and complexity. Most people would agree that the root cause for this phenomenon is the currently increasing globalization, but the above statement was already made in the 1950s and 1960s (Grant, 2010). The 1950s and 1960s were the start date for corporate planning to serve this purpose. The typical format was a five-year document where goals and objectives were described and economic trends were forecast. But unfortunately, these plans also failed to anticipate the oil crisis in the mid 70s and as a result the shift from “strategy planning” towards “strategy making” had been started (Grant, 2010). Since then a run and huge effort on finding the holy grail of strategy formation has been started. Led by Michael E. Porter, a new era with a strong focus on industry analysis - from analysing profitability, competitive advantage or market share etc - grew. The tools and methods had been further developed until today. However, since these tools and methods failed to anticipate not only the burst of the dot.com bubble in the early of the 21st century, but also the recent global financial crisis (GFC) the holy grail on strategy formation seems not to be found yet. Maybe the quality and effectiveness of such methods and theories should not only be assessed only on occasionally occurring big negative events. Therefore a valid question might be to assess their effectiveness on how well they are able to support regular business decisions, too. To find an answer to this question is one key driver for the author of this master thesis.

With more than 15 years experience in strategically relevant sales positions, the author did not only participate in countless strategy planning meetings, but had also to write a similar number of business plans to justify future sales-strategies and related R&D activities. The natures of these plans are usually that they have to display the future in many aspects, some are known, others best guessed of past experience, and others are total unknowable, and just based on gut feeling. Of course a certain level of market intelligence and visionary inspiration is necessary to look above the current borders, but how to manage things that remain uncertain or even unknowable at all? However, in many cases, companies are flexible enough to recognize deviations from the actual situation compared to earlier plans in time and they are able to adapt to new plans quickly. However, experience also taught that often deviations are neglected in the hope, that in the longer term, a vision comes true. Retrospectively seen, the authors experience is that the more turbulent and dynamic the market environment, the more focus was put on the best guess and the factor “hope” and if someone failed, the excuse that it was unforeseeable, was always valid. Is it then just misfortune if plans do not pay off or are there other methods available to manage the uncertain or unknowable elements of the future better?

The discipline of scientific strategy management offers an enormous selection of books and scientific papers on different tools and methods. The race for the holy grail of strategy formation seems not only fascinating to scholars, but is also the ground for countless demands for strategy consulting firms. The suggested strategy processes are often to a certain extent contradictory, which might lead to the assumption that the truth might be somewhere in the middle and that the successful manager, will need to be aware of all the different kinds of tools and methods, and additionally be able to implement the right one at the right time.

The nature of business strategy is the aspiration to achieve control over the environment for the sake of personal benefit. While the so-called causal approach claims to achieve control by focusing on predicting the future, the so-called effectuation approach claims that things that can be controlled do not need to be predicted and focusing therefore only on what can be controlled (Wiltbank et al., 2006). However, both approaches still don’t answer the question on how to manage the existing uncertain or unknowable elements. Since the agile approach of ”The Lean Startup” (Ries, 2011) claims to manage the uncertainty and by proving the assumptions through experiments, this might deliver the answer. Therefore, the approach from Ries will be analysed and challenged how it can be combined with either the causal or effectual strategy formation approach.

With this master thesis these three current major streams on strategy formation shall be critically reviewed, assessed and compared by a qualitative content analysis. Aim is to identify relevant elements for building an agile business strategy.

The underlying research question for this master thesis is:

How to develop an agile business strategy in an uncertain market environment?

1.2 Aim of the paper

The aim of the Master Thesis is to develop a framework for an agile business strategy. Each business strategy is consisting partly of predictable but also uncertain and even unknowable elements (Knight, 1921). Based on literature review appropriate tools and methods to manage these three types of uncertainty shall be identified and a framework for an agile business strategy shall be developed.

In addition, to show the applicability of the concept of the framework, the author will take the role of a management consultant and apply this framework to the real case of a start-up company.

1.3 Methodology

This master thesis is built on three pillars. First, a textbook and literature research on different strategy formation approaches, where the strengths and weaknesses of the different approaches got identified. Second, the evaluation of the different approaches in terms of their ability to manage the uncertainty of future events and the ability to achieve control and the development of an agile business strategy framework. Finally, analysing the real case of a start-up company tests this framework.

In the theoretical part three different methods and approaches will be critically reviewed. First, the classical causal approach, which is based on the premise of an existing market, its drivers for strategy are exogenous events and control is achieved by predicting the future as best as possible. The second approach Effectuation (Sarasvathy, 2008), can be seen as the counter approach to causal methods. Effectuation is based on the premise that markets have to be constructed, and that the drivers for strategy are endogenous events and are therefore controllable. The future is seen as unknowable and therefore as not predictable. The third approach is called the “Lean Startup method” (Ries, 2011). This method is based on agile elements and is neither based on predicting nor non-predicting future events, but on proving hypothesises in the way of strategy formation.

To answer the research question, the causal and the effectual approach will be evaluated and tested against their quality on predictability and controllability in existing or non-existing markets in the context of the Knightian definition of uncertainty (Knight, 1921). Therefore, a tool^[1] to structure the collected information will be developed to evaluate the different categories of uncertainty and controllability. Predictability (see also Table 1) will be linked by each parameter with questions (Knight, 1921):

a) Are historic data available?

Answers: yes or no

b) Is the future distribution;

Answers: known or unknown or unknowable

c) Is the future draw (probability);

Answers: unknown or unknowable

The controllability (see also Table 3) will be evaluated by the question whether it is possible to influence or affect, and on how to achieve control, meaning to have power over, both on the input and outcome. Following questions will be used:

a) Is it possible to affect the variable^[2] ?

Answers: yes/no

b) Is it possible to get power over it?

Answers: yes/no

The result of the evaluation shall be the decision base for the selection of going for either the causal or the effectual approach. To build finally an agile strategy framework, either the causal or the effectual approach will be matched with the agile elements of “The Lean Startup” approach to manage the still existing uncertain or unknowable variables.

The agile strategy framework will be finally first applied at the real life example of “Connect”^[3], by analysing the current business model according the elements of the agile strategy framework. The author accompanied the start-up between January and June 2014 and closed with a common workshop in June 2014. The application of the agile strategy framework at “Connect” will be performed by the method of qualitative content analysis. Thereby the collected data from multiple meetings, calls or other conversations gets compiled and evaluated according the developed agile strategy framework approach. The result was finally discussed and further developed during the common workshop.^[4]. The aim is to assess whether this framework is usable in a real life case, and whether the outcome would lead to identify potential weakness of the current setup and to prepare the organization for improved strategic decisions in future.

1.4 Structure of the paper

The first chapter will include a detailed description and introduction, including problem description and research questions.

Chapter two will include a general term definition and the literature review of used tools and method. The chapter will discuss the advantages but also the limitations of the different approaches.

In chapter three the evaluation of the different approaches and the development of a framework to build an agile business strategy will be described.

In chapter 4, the current status of “Connect”, will be described and the framework for an agile business strategy will be applied.

The final chapter closes the thesis with a general conclusion and contemplation.

2. Literature review and the general term definition

One of the major differences between causal and effectual strategy formation methods is the view, how an organization can manage its future. The more traditional causal view is to be able to control the future, you need to predict the uncertainty of the future, while the effectual approach says that if you are able to control the future, then there is no need to predict the uncertain future (Sarasvathy, 2008).

To align the understanding on how the different terms are used in this master thesis, the literature review starts with the definition of the relevant terms.

2.1 General Term Definition

2.1.1 Strategy

The term strategy has its origin from the field of military and was related to managing conflicts and winning battles. The literature can be traced back to ca. 500 B.C. when the Chinese philosopher and military general Sun Tzu wrote his book the Art of war (Sun Tzu & translated by Sawyer, Ralph 1994). It is one of the most popular study of strategy ever written and has had a huge influence on the history of warfare. His developed strategy principles influenced great Asian war leaders as Mao Tse-tung, Giap, and Yamamoto but also western warlords form Napoleon, the German Generals in World War II until the recent wars in the gulf area. However, the principles of Sun Tzu found their way also into the modern business environment. Many Asian companies make this book a required reading for key executives and also many Western businesspeople are using it for inspiration and advise to manoeuvre companies through competitive situations of all kinds (Sun Tzu & translated by Sawyer, Ralph 1994).

In the business context, strategy is seen as the link between the firm and its environment and as the determination of long-run goals and objectives and adoption of action and allocation of resources (Chandler 1962; Grant 2010)..

Figure 1: Strategy as a link between the firm and its environment; adapted from Grant (2010)

A further popular view of business strategy was set by Henry Mintzberg. He defined strategy as a pattern on sequentially decisions. In his view, a strategy is in a constant move from an intended or planned strategy to the final realized strategy. On the move, the final realized strategy is influenced by deliberate strategies and emergent strategies. The part of the original strategy plan, which was not executed is called the unrealized strategy (Mintzberg et al. 1998).

Figure 2: Pattern of strategic decisions, adapted from Mintzberg (1998)

But what makes strategy finally “strategic? Every day executives must make a variety of decisions. Most of them will respond to daily routine issues, while some of them will have the potential to affect the organization fundamentally. Such critical decision are characterized by three elements; Strategic decisions are important, they come along with allocation of significant resources and they usually involve more than one department (Duhaime, Irene et al. 2012).

2.1.2 The market and competitive space

A huge portion of the strategy literature is about markets; mainly whether they already exist is the first challenge. The traditional description of markets has been a physical place where buyers and sellers came together to exchange goods for other goods or money. Therefore, the markets have been divided into three categories: demand, supply and institutions. Hence, people who are willing and are able to pay for a product or service characterize demand. People who are willing and are able to supply a product or service at a price people are willing to pay characterize supply. Third, institutions like distribution-mechanism or legislation authorities are required to complete a market (Kotler & Keller, 2012).

However, in modern societies, markets are much more complex than in the past. Manufacturers need to go to resource markets, which include, but is not limited to raw material, labour, finance etc., to buy resources and turn them into products and services and sell them either directly or via intermediary markets to consumers. Global, national or regional government markets guide these exchange processes. The structure of a modern exchange economy is shown in Figure 3.

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Figure 3: The structure of modern exchange markets; adapted from Kotler (2012)

The lines between the different elements are symbolizing the exchange streams between the single markets. The outer circle shows the exchange from goods and services for money. The exchange towards “the inner box” representing the government can additionally be done by paying taxes (Kotler & Keller, 2012).

In strategy formation literature, the term “Market”, is also often used to cover different groups of customers. While a collection of sellers are called an “Industry”. Figure 4 shows such a simple seller-buyer system. The lines indicate the exchange between goods and services for money but also the important communication and information cycle where buyers are indicating their demand and willingness to buy a product; and sellers are indicating their willingness to supply products to match with buyers demand (Kotler & Keller, 2012).

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Figure 4: A simple market system, adapted from Kotler (2012)

But this illustration gives only very static information about markets and ignores the dynamic in pace and direction of markets. Traditionally perspectives of such market systems assume them to be relatively stable, and remain in equilibrium. However, this is nowadays rarely the case, because the nature of such systems is a constant, underlying change from customer demographic changes, new emerging technologies and new innovative products and services (Duhaime et al., 2012). Further, while markets are striving for equilibrium by the “invisible hand”^[5], competition, at both seller and buyer markets, are striving to break the equilibrium for the own benefit. The existence of incomplete information is also often called the ultimate nature of innovation, new products and strategic opportunity (Denrell et al., 2003).

To cover the dynamic in markets, Derrick Abell (1980) suggested to see the markets or industries, independent whether they assume to be already existing or assumed to be constructed, as a place where rivals are competing in various dimensions. These dimensions are the “what” industry participants are willing to supply the “who”, the ones are able and willing to pay for a product/service and generating demand and the “how” such products or services are provided which includes both the external environment, but also the internal environment. This competitive space can be visualized as a three-dimensional space outlined by customers, see Figure 5 (Duhaime et al., 2012; Molz, 2013).

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Figure 5: Three-dimensional competitive space; adapted from Duhaime et al., (2012)

The orange arrows in above figure are representing the potential dynamics in both directions on each dimension. Which means that at a certain point of time the trend on the “What?” dimension can go towards the direction of adding additional high sophisticated features or in the opposite way of reducing features and increasing the easy usability^[6]. The source of the dynamic change can be based on customer demographic change, emerging of new technologies or new products with competing space but also new substitute products from so far not competing industries. Moves within these three dimensions are creating unsupported space or room for new markets to be served (Duhaime et al., 2012).

However, moves along the different dimensions can come not only from own actions, but are also initiated by other market participants, which is one source of uncertainty. Especially, the existing threat from outsiders are questioning the effectiveness of the best planning techniques, because an organization can find itself in a relative stable environment for a long time and can be faced with a suddenly turbulent environment (Mintzberg et al., 1998). And it might happen that an organization finds itself quite quickly in a new competitive space with new customers, new technologies and new rival products or substitutes, see Figure 6 (Duhaime et al., 2012). However, to be seen through positive eyes, it might very quickly be faced with new opportunities, since the unknown and incomplete information of such a new competitive space is the ultimate source of new business opportunities.

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Figure 6: Nature of industry change; adapted from Duhaime et al. (2012)

The model of the three-dimensional competitive space is independent whether an organization is aiming to offer a product in a so-called existing market or whether it is aiming to invent something total new and has to create a new market. In both cases, customers and a process or technology is needed to bring demand and supply together. The three dimensions not only co-exists, but can be seen as one system with mutual influence. A change in the product dimension could have an impact on the customer preferences and opening access to new customers but might also bear the risk of losing existing ones. However, vice versa, a change in customer preferences, induced by the firm itself or by rivals, might have an impact on the product specification or business model etc. Therefore, the decision cannot be made on assessments of a single dimension, but need to be done in the context of one system (Senge, 2006)^[7].

2.1.3 Uncertainty

An early definition of uncertainty was made by Frank Knight (1921), he distinguished between the three types of uncertainty: the first where a future is drawn on past experience with known distribution and an unknown draw; the second consisting of both an unknown distribution and an unknown draw and the third with “unknowable” future in principle (Sarasvathy, 2008), or in Knights own words,

"no valid basis of any kind for classifying instances" (Knight, 1921, p.225).

Knightian uncertainty is referring to real innovation, where neither past experience exists, nor human behaviour can be predicted at all. Therefore is the Knightian definition of prediction and control conceptually at odds, because an environment based on human creative action is actually producing a non-existent, not just a hard-to-predict future (Wiltbank et al., 2006).

The Knightian definition leads to three levels of uncertainty, “Predictable with “Risk”, Uncertainty/Not predictable and Unknowable (see Table 1).

Table 1: Levels of Predictability/uncertainty adapted from Knight (1921)

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The categories are defined as follow. If the distribution is known, for any given draw, the probability can be precisely calculated and the risk can be managed by analytical techniques. If the distribution and the draw is unknown, which is usually meant by uncertainty, the variable can only be estimated. If knowledge about distribution is non-existent and knowledge of the draw is not classifiable, the category is named unknowable.

A later definition of uncertainty was distinguishing between four categories; the first: a single view on a clear enough future; the second: the future with a limited set of alternatives; the third: with a range of possible outcomes and the fourth: named the true ambiguity (Courtney et al., 1997).

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Figure 7: Levels of uncertainty; adapted from Courtney et al. (1997)

Their definitions are often used in techniques and tools usually used by the strategy planner to improve strategic decision-making based on single-point forecasting, multi-point forecasting or scenario-planning, etc. (Angwin et al., 2011; Angwin, 2014). These strategy tools are all assuming that the future is predictable, that there is a certain probability of a single view, multiple alternatives or a certain range of outcomes, which is again in contrast to the Knightian view. These methods are based on assumptions that a firm knows which alternatives might arise, or where the borders for potential scenarios are. However, this might work to prepare an important decision, but is not applicable to strategic decisions. As an example, a firm might be able to assume or predict a certain market demand for a particular product and can prepare their production capacity for a few variants or for a few potential volume scenarios. This is of course a very important decision, but it is not of strategic nature. A strategic decision would be to develop the product with the right features at all. But knowing the right features in future and the customer need is uncertain or even unknowable.

Therefore strategists have to question if there is enough information available, especially in new competitive spaces, to predict only one single view or predict the alternatives or boundaries of the range, or is it serendipity to find it well ahead? However, several scholars assess this imbalance in availability of information as the key source for strategic opportunity (Denrell et al., 2003). To sum up, future phenomena can be classified as shown in the following table (Sarasvathy, 2008):

Table 2: Taxonomy of future phenomena: adapted from Sarasvathy (2008)

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Real life examples of predictability would be forecasting demand for well-established brands and products and for estimation techniques, variables like environmental issues including air-pollution, global warming, demographic development, etc.. New and innovative products and markets would fall into the third category Table 2.

2.1.4 Causation versus Effectuation

In this paper two major models and methods of strategy formation will be compared. The classical view is the so-called “Causation”, which starts with defining a goal as an effect to be created. In the following required means to achieve the preselected ends gets identified. In contrast, the effectual model starts with given means and is based on the view to find new ends. Effectuation is based on the premise that human beings cause the future and, therefore, the future can be controlled and/or created through human action (Sarasvathy, 2008), or as Sarasvathy wrote

‘A causal logic is based on the premise: “To the extent we can predict the future, we can control it.” An effectual logic is based on the premise: “To the extent we can control the future, we do not need to predict it’ (Sarasvathy, 2008, p.17).

Comparing effectuation with the causal procedure, the STP process starts with the definition of a market and potential customer as base for a potential product specification (Kotler & Keller, 2012). Information is collected by using techniques like focus groups and surveys, etc.. As a next step market gets segmented and assessed on revenue potential. Based on competitive analysis a generic strategy is developed and a product adequately positioned (Sarasvathy, 2008).

A comprehensive analysis of the causal and the effectual approach are made in chapter 2.2.1.2. and 2.2.2.2.

As shown in the below figure, the effectuation model can be seen as the inverse to the causal model (Sarasvathy, 2008).

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Figure 8: Causal methods vs effectuation; adapted from Sarasvathy (2008)

2.1.5 Prediction versus Control and Influence

In the following chapters different strategy approaches will be discussed. The aim of each strategy approach is to achieve a certain level of “control” in a market, environment, organization, etc., by direct control, prediction or influence. Therefore, these terms will be used in the following definition according Merriam-Webster Online dictionary (“Merriam-Webster Dictionary”).

To assess the level of control in the context of strategy formation, each variable of the single elements from the causal and from the effectual strategy approach, will be crossed with two questions “Is it possible to affect the variable” and “Is it possible to get power over the variable”^[8]. Based on these answers, three levels of controllability will be identified: controllable, influenceable and uncontrollable (see Table 3).

Table 3: Levels of controllability^[9]

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Since the future remains uncertain, many different approaches to manage the future have emerged. The different approaches can be mapped in a 2x2 matrix on “emphasis on prediction” versus “emphasis on control” (Wiltbank et al., 2006), see Figure 9. The term prediction will be used in the following according to the definition at Merriam-Webster Online dictionary (“Merriam-Webster Dictionary”).

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Figure 9: Framework of prediction and control; adapted from Wiltbank et al. (2006)

The traditional causal models are based on high emphasis on prediction. Typically a powerful visionary leader, often an entrepreneur or manager, who shapes and defines and imagines future corporate strategy (Hamel & Prahalad, 1991; Courtney et al., 1997), supported by strategy teams who plan the strategy execution based on industry and competitive analysis (Ansoff, 1979; Porter, 1980; Kotler & Keller, 2012). The aim is on the one hand to use generic strategies to generate a sustainable competitive advantage and on the other to reach control via precise planning. In case of a discrepancy between the expected future and reality, the focuses are on predicting harder and plan more accurately the next time.

The adaptive approach negates the theory of predicting the future. Instead, it supports the idea of adapting fast to the changing environment. Therefore corporates do need to be able to manage emergent strategies (Mintzberg et al., 1998) and put a strong focus on corporate and dynamic capabilities (Teece et al., 1997).

Typical representatives for the transformative approaches are theories about value curves (Kim & Mauborgne, 1997) and effectuation from Sarasvathy (2008). A further method has been introduced recently, The “Lean Startup” (Ries 2011), which is based on agile methods. These methods are well known in software development and project management, under the name scrum. ”The Lean Startup” was designed for start-up companies, but can be used in principle also in more mature companies or projects, too. The key principle of this method is not to generate complex plans based on the best guess assumptions, or quick adapting to new situations, but getting control by setting up a hypothesis and test it e.g. by the Build-Measure-Learn cycle. Based on such feedback loops, decisions made can be adjusted, persevered or pivoted into a new direction (Ries, 2011).

2.1.6 Systemic Knowledge Management

The ultimate goal of strategic management is to prepare an organization for the future. Hence, a strategist has to have management skills to manage the known, the unknowable and the uncertainty (Willke, 2011).

Systemic knowledge management distinguishes between data, information and knowledge, which has an important impact in the daily practice. The difference between data and information is thereby that data are the observed differences, while information a system intrinsic pre-processed data, and are defined as the system relevant differences. Information is the intermediate step towards knowledge. However, that means that depending on the relevance criteria, different information can be derived. A successful information exchange can only be achieved with properly aligned relevance criteria. Finally, knowledge is generated if information is brought into a second context of relevancies. Thereby the second context does not consist of relevance criteria, but of a pattern of significant experience (Willke, 2011).

Figure 10 shows the visualized process for hypothesis testing and consists of the following steps; which could be again be processed and tested in a systemic loop.

1. Data collection, analysing past and current status
2. Building hypothesis, linking collected data with relevance
3. Planning experiments to test the hypothesis
4. Performing hypothesis test
5. Reviewing / Contemplation of experiments

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Figure 10: Basic systemic ribbon

2.2 Literature Review on different strategy approaches

In the following chapters the different approaches on strategy formation are reviewed. It will be structured in two main categories; emphasis on positioning within an exogenous environment and emphasis on constructing within an endogenous environment. The first chapter, positioning, includes the classical planning method based on the design-planning-positioning school and the adaptive methods suggested by the learning school. The second part, construction, will focus on entrepreneurial methods based on visionary and transformative approaches.

2.2.1 Positioning as strategy formation

The emphasis on the exogenous environment already has a long tradition since the early 1950’s. Since then this approach in strategy formation made some evolution steps until today. The below chapter will describe the classical design-planning-positioning School approach and will close with a detailed description of a today’s typical causal strategy-planning process.

2.2.1.1 Classical Design-, Planning- and Positioning School

Most mainstream studies on strategic formation are reduced to two prescriptive approaches how firms can prepare decision making. These are rational decisions, executed as incremental steps to either improve the predictions or to adapt faster to the upcoming changes in the environment (Fuqua et al., 1999). Which way a firm can go is finally based on the ability of a firm to predict changes and on the dynamic of the markets in which the firm is active. Both approaches having their emphasis on positioning their firm in a given uncertain environment and they differ mainly in how they cope with that given situation (Wiltbank et al., 2006).

The supporter of the rational planning view might argue, that under increased uncertainty the ones who work hard to analyse the situation and to predict more accurately the environment, will outperform the ones who don’t (Wiltbank et al., 2006). They argue that other methods suffer from personal and group bias, and that individual judgement is bounded to rationality and that decision-making by explaining the real situation will find more acceptance (Bazerman & Moore, 2013).

Nevertheless, there is awareness that prediction is not perfect and difficult, but it is the most effective way to keep aligned with the environment (Hough & White, 2003). Igor Ansoff argues that despite the inaccuracy of prediction, it is nevertheless useful for strategy formation under uncertainty, because rational planning delivers a valuable framework to create and evaluate emergent strategies (1991).

The most influential approach in strategy formation is based on the Design school, which originated in the 1950s and 1960s. A strategy gets designed around the classical SWOT analysis^[10] to design a fit between external threat and opportunities and internal strength and weakness. The design school promotes, that organizations structure should follow strategy and be determined by it, and the responsibility for strategy formation has to be with the CEO (Chandler, 1962).

The second predictive school was originated around the same time as the design school and is built around similar premises. The so-called planning school emerged, but the planning process was stronger when formalized and separated and executed in distinct steps and supporting frameworks, planning routines and planning cycles. The planning school has a strong focus on formalized procedures and quantified goals of the organization. Objectives are set and measured for both internal and external conditions (Mintzberg et al., 1998).

The chief executive is still in charge of strategy formation, but supported directly by highly educated planners in specialized strategic planning departments. A huge number of checklists and techniques have been invented to predict and forecast future conditions with the goal to get control over it. Besides simple return-on-investment calculations, techniques to calculate shareholder values have been introduced. To improve prediction – or control - the strategy has to be broken down to sub-strategies for different business units and parallel strategies to cover budget, objectives, projects, etc.. Altogether being the companies “master plan” consisting of long-term, mid-term and short-term plans to achieve a successful implementation (Mintzberg et al., 1998). The basic idea was to produce a strategy blueprint, filled with specified components, assemble them and get a ready made strategy, which just has to be executed as planned.

This development got a further boost into the direction of industry and competitive analysis with Michael Porter’s famous book: Generic Competitive Strategies (1980). The strategy offices usually not only delineating one but several strategies with the idea behind, that the executives just need to select the right one for execution. Officially the CEO remains the architect of strategy formation, but in practice he has the role to approve the strategy designed by the strategy office (Mintzberg et al., 1998).

To overcome the uncertainty in the planning process, the scenario technique has been developed. It is a tool,

‘predicated on the assumption that if you cannot predict the future, then by speculating upon a variety of them you might open up your mind and even, perhaps, hit upon the right one.’ (Mintzberg et al., 1998, p.75).

However, scenarios are not bad per se. Since world change managers need to share a common view and a range of the potential future world. Therefore the scenario technique is a helpful tool to share and to articulate a common understanding (Mintzberg et al., 1998).

The third main contributor to the predictive strategy formation is the positioning school. It was introduced in the early 1980s. The positioning school accepts most of the premises and fundamental models that underlies the planning and design school, but puts the focus towards content of strategy and techniques to perform competitive and industry analysis (Mintzberg et al., 1998).

While the planning and design school credo was to put no limitation to the strategy in any given situation, the positioning school argued that only a few key strategies, namely the positioning within an economic marketplace, can be defended against existing and future competitors. Meaning that companies, which are able to reach a competitive advantage, will enjoy higher profit than others and with the additional profit they will be able to enlarge as well as consolidate this position. This logic ended up with a limited number of generic strategies e.g. differentiation and focused market scope (Mintzberg et al., 1998).

Since the positioning school has a strong focus on analysis, a set of analytical tools dedicated to match, identify and select the right strategy to the conditions at hand has been created. Similar as to the other prescriptive schools, strategy management remained perceived as a control process that produces deliberate strategies. Similar to planning school, the CEO remained in the role of key strategist, supported by the strategy planning officer, now called Analyst (Mintzberg et al., 1998).

Each of these approaches is based on the assumption that even the future is unknown; it is predictable to a certain degree. The decision-maker knows what they want to achieve and they have clear and well-ordered preference. Further they assume that the environment is exogenous to human actions (Sarasvathy, 2008).

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^[1] Aim of the master thesis is not to evaluate the tool itself. To develop a generic tool, a separate master thesis would be needed.

^[2] Variables are the single elements and questions of the different approaches

^[3] The start-up company prefers to keep the company name confidential, therefore will be referred to as “Connect” for the purpose of this study.

^[4] See Appendix Nr. x

^[5] Methaphor credited to Adam Smith and his renowned book „The Wealth of Nations“.

^[6] This trend was e.g. seen in the white goods or video recorder business. After a period where each new generation came along with even more features, a new trend for better usability replaced the run for features. The suppliers who where able to change the structure of the market beat the established firms.

^[7] The approach of „System Thinking“ as suggested by Senge is definitely of high of importance in strategic decision making and execution of strategies. But a further and deeper dive into this topic would go beyond the scope of this master thesis.

^[8] Example see Appendix 1

^[9] The theoretical available fourth level cannot happen in real case scenario and was therefore excluded

^[10] Developed in the 1960s, and credited to Albert Humphrey at Stanford internal environment (now SRI International)