Effectuation in decision-making to respond to market...

Effectuation in

decision-making to respond

to market uncertainty in

high technology industries

Master’s Thesis 15 credits

Department of Business Studies

Uppsala University

Spring Semester of 2017

Date of Submission: 20170530

Nataly I. Quiroga Tadayuki Hohyama Loi Tran Supervisor: Philip Kappen

ACKNOWLEDGEMENTS

The work was carried out at Department of Business Studies, at Uppsala University, under the

supervision of Professor Philip Kappen. We would like to express our sincere thanks for his

support, invaluable feedback and guidance.

We are grateful for valuable assistance and sharing of information with the staff assigned to

this program, in particular to professor Ivo Zander - the Program Coordinator.

Nataly and Loi would like to thank the Swedish Institute since this thesis has been produced

during our scholarship period at Uppsala University.

Sweden, May 30, 2017

Nataly, Tada and Loi

http://www.fek.uu.se/

ABSTRACT

Uncertainty is inherent in the process of entrepreneurial activities and has caused a high failure

rate of startups. In fact, 46% of new ventures run out of business within 4 years of operation,

according to Statistic Brain Research Institute. On the other hand, a type of uncertainty that

entrepreneurs need to prioritize varies depending on the industry. In high technology industries,

severe problems are frequently caused especially by market uncertainty due to continuous

technological developments and industries’ volatile characteristic. In entrepreneurship

research, Sarasvathy introduced the concept of effectuation in 2001. Since then, the theory of

effectuation has been studied by a number of researchers, as successful entrepreneurs have

incorporated this theory. However, empirical evidence of effectual processes covering

the applicability in high technology industries has not been testified yet. Therefore, the main

purpose of this research is to fill this gap and find an answer to our research question, how do

entrepreneurs effectuate in decision-making to respond to market uncertainty in high

technology industries? We implemented a quantitative investigation by conducting an online

survey of entrepreneurs in high technology industries. The main findings and conclusions are

that entrepreneurs in high technology industries apply both causation and effectuation.

However, causation is slightly more implemented than effectuation. Additionally, we found

that experimentation-driven approach helps entrepreneurs in high technology industries deal

with market uncertainty as supplementation of effectuation.

Keywords: Effectuation, Causation, Experimentation-driven approach, Market uncertainty,

High technology industries.

TABLE OF CONTENTS

1. INTRODUCTION ................................................................................................................................ 1

2. THEORETICAL FRAMEWORK ............................................................................................................ 4

2.1. UNCERTAINTY .............................................................................................................................. 4

2.1.1. UNCERTAINTY IN ENTREPRENEURSHIP RESEARCH .................................................................. 4

2.1.2. MARKET UNCERTAINTY ............................................................................................................ 6

2.1.3. MARKET UNCERTAINTY IN HIGH TECHNOLOGY INDUSTRIES .................................................. 7

2.2. EFFECTUATION AND CAUSATION ................................................................................................ 7

2.2.1. EFFECTUATION AND CAUSATION UNDER UNCERTAINTY ........................................................ 7

2.2.2. EFFECTUATION AND CAUSATION THEORY IN DECISION-MAKING .......................................... 8

2.3. EXPERIMENTATION-DRIVEN APPROACH ...................................................................................13

3. METHODOLOGY .............................................................................................................................19

3.1. RESEARCH DESIGN .....................................................................................................................19

3.2. RESEARCH SAMPLING ................................................................................................................19

3.2.1. SAMPLE & SAMPLE SIZE .........................................................................................................19

3.2.2. THE SELECTION OF SAMPLING ...............................................................................................20

3.3. DATA COLLECTION .....................................................................................................................20

3.3.1. SURVEY QUESTIONNAIRE .......................................................................................................20

3.4. ANALYSIS PROCESS.....................................................................................................................25

4. RESULTS AND DISCUSSIONS ...........................................................................................................27

4.1. DISCUSSION ................................................................................................................................27

4.1.1. EXPECTATION 1 ......................................................................................................................27

4.1.2. EXPECTATION 2 ......................................................................................................................32

4.1.3. EXPECTATION 3 ......................................................................................................................35

5. CONCLUSION ..................................................................................................................................39

6. LIMITATIONS AND FURTHER RESEARCH ........................................................................................41

6.1. LIMITATIONS ..............................................................................................................................41

6.2. FURTHER RESEARCH...................................................................................................................41

7. REFERENCES ...................................................................................................................................43

8. APPENDIX .......................................................................................................................................51

APPENDIX 8.1: QUESTIONNAIRE ............................................................................................................51

APPENDIX 8.2: SURVEY RESULT .............................................................................................................58

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1. INTRODUCTION

According to Marmer (2011), startups are temporary organizations designed to scale into large

companies and are planned to search for product/market fit under conditions of uncertainty.

Despite the hard work and investment that involve a startup, 46% of new ventures run out of

business within 4 years of operation (Statistic Brain Research Institute, 2017). According to the

statistic provided by Forbes in 2013, 8 out of 10 European businesses ‘crash and burn’ within

18 months and the remaining 20 percent are unlikely to survive past year five. Those significant

numbers raise a lot of concern for many companies and researchers to investigate the reasons

behind.

CB Insights (2014), a technology market intelligence platform service, studied 101 post-

mortem essays from startup founders, about the reason why one’s own startup company ended

up being a failure. According to the analysis, there are many reasons why startups fail, from a

lack of product-market fit to disharmony on the team. However, the most significant reason

comes from market uncertainty. 42% of the founders considered “No Market Need” to be one

of the reasons for their failures, literally meaning that customers did not need what they

produced. In addition, a firm might not understand both current consumer needs and future

consumer needs (Mohr, Sengupta and Slater, 2005). Furthermore, “Get Outcompeted”,

“Pricing/Cost Issues” are also regarded as reasons for the failures by 19%, 18% of the founders

respectively. The mutual ground of these factors is that these negative consequences are

attributed to external market variation, thus being categorized as market uncertainty (Mohr et

al., 2005).

According to a research from COMPUSTAT (2013), the level of uncertainty is different among

industries. Many high technology industries such as Medical Equipment, Computers, Computer

Software suffer the most from uncertainty. High technology industries are often unstable and

unpredictable. Therefore, the uncertainty of how fast the technology will spread becomes a

crucial matter because the characteristic of a Product Life Cycle of high technology products

and services tends to be short (Mohr, Sengupta and Slater, 2009), due to continuous

technological developments and its volatile characteristic (Sjöberg and Wicén, 2008). In such

a complex and fast changing business environment, entrepreneurs face with a multitude of

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decisions every day. They have to make decisions even if they are unwilling to do so (Pearce

II and Robinson, 1989). Furthermore, several intellectual subjects of entrepreneurship

highlighted the importance of decision-making (Arrow, Kamien, Olson, Sexton, Simon and

Venkataraman, 1999), (Saravasthy, 2001).

In 2001, Sarasvathy introduced the concept of effectuation as the dominant model for

entrepreneurial decision-making, which is opposed to the causal model. While causation is

consistent with planned strategy approaches (Mintzberg,1978), (Ansoff, 1988), (Brews and

Hunt, 1999), effectuation processes are consistent with emergent or non-predictive strategies

(Wiltbank et al., 2006).

The distinctive characteristic between causation and effectuation is the set of choices (Diemer,

2010), choosing between means to create a particular effect, versus choosing between many

possible effects using a particular set of means. Whereas causation models consist of many-to-

one mappings, effectuation models involve one-to-many mappings (Sarasvathy, 2001). To

clarify the difference, Sarasvathy (2001) applied a simple metaphor: a chef asked to cook

dinner for a host. The causational process would mean that the host chooses a specific menu,

upon which the chef shops for the necessary ingredients and cooks the required meal. Hence,

the outcome is given and predictable, and the focus is on acquiring and selecting between, the

means to achieve the end. The effectual process would mean that the host asks the chef to

imagine possible menus based on the available means in the kitchen: available ingredients and

utensils. Hence, the means are given and the focus is on what can be achieved with them.

Over the last decade, as the effectuation continues to gain more foothold in the field of

entrepreneurship research, few empirical studies have been performed on the consequences of

thinking effectually rather than causationally (Diemer, 2010). Causation and effectuation

theoretical principles has been described by different authors, such as Sarasvathy (2001),

Sarasvathy and Kotha (2001), Dew, Sarasvathy (2005b), Read and Sarasvathy (2005), Kupper

and Burkhart (2009), Karri and Goel (2006), Dew, Wiltbank, Read, Dew and Sarasvathy

(2009), Chadler, Tienne, McKelvie and Mumford (2011), Maine and Dos Santos (2012).

According to Sarasvathy (2001) and Sarasvathy (2008), entrepreneurs empirically use both

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causation and effectuation processes and they can be applied simultaneously depending on the

context. In order to answer our research question. However, recent studies on entrepreneurial

decision-making and subsequent Sarasvathy’s studies suggest effectuation as the most

appropriate approach for entrepreneurs whose ventures faced with high uncertainty (Chandler

et al., 2011), (Chesbrough, 2010), (Perry, Chandler and Markova, 2011), (Dew et al., 2009),

(Sarasvathy, 2001), (Read and Sarasvathy, 2005).

The empirical proof of effectuation processes leading to advantages in startups has just begun

to be gathered. While empirical data have been used extensively to build, refine and reinforce

the theory of effectuation, proving that successful entrepreneurs use effectual processes (Dew

et al., 2009), (Read, et al., 2009), (Sarasvathy, 1998, 2001), (Sarasvathy and Dew, 2005),

(Sarasvathy and Kotha, 2001), empirical evidence of effectual processes covering

the applicability or practical implications in high technology industries has not been gathered

yet. Similarly, effectuation has been frequently linked to market uncertainty (Chandler et al.

2011). Therefore, the aim of this thesis is to study, how do entrepreneurs effectuate in

decision-making to respond to market uncertainty in high technology industries?

The practical implications are that entrepreneurs can benefit from the results obtained by

gaining a deeper knowledge to deal with market uncertainty. From the entrepreneurial

perspective, the study hopes to provide valuables ideas with those who are working in or

considering entering high technology industries in the future.

This thesis is structured into 6 chapters. Chapter 1 aims to provide an overall introduction to

our research project including its significance, ambitions, methodology and limitations.

Chapter 2 focuses on a theoretical framework with literature reviews of the main concepts.

The research methodology is presented in Chapter 3. Our empirical data are presented together

with our discussions and analysis of the expectations in Chapter 4. Chapter 5 includes a

summary and conclusion on the research project. Finally, Chapter 6 provides further research

suggestion on the given topic.

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2. THEORETICAL FRAMEWORK

The aim of this chapter is to determine the theoretical framework to support and answer the

research question. There are 3 sections in this chapter: Section 2.1 provides the literature review

about uncertainty and market uncertainty, followed by its relationship with high technology

industries. Section 2.2 describes the main characteristics and describes the relationship between

the process of effectuation and causation, and decision-making under uncertainty. Section 2.3

presents an overview of experimentation-driven approach, its relationship with

the effectuation process and the main methods applied by startups in dealing with uncertainty.

2.1. UNCERTAINTY

2.1.1. UNCERTAINTY IN ENTREPRENEURSHIP RESEARCH

Uncertainty is defined as an unknown, unmeasured and unpredictable risk by Knight (1921),

and it applies to situations where we need to expect adequately but cannot do so (Peter Dizikes,

2010). However, even if the word of “risk” was used to depict uncertainty, he clearly

differentiates it from risk. Risk-involved circumstances make the probability distribution

knowable, according to his study. As a result, the likelihood of certain risk-oriented things can

be calculated. In contrast, uncertain conditions make the probability distribution unknowable.

Thus, the likelihood of incidents in an uncertain situation cannot be calculated but only

estimated (Sarasvathy, 2001), (Read and Sarasvathy, 2005), (Sarasvathy, 2008).

Soh and Maine (2013) depict entrepreneurs and startup companies as actors temporally

embedded in evolving and multifaceted contexts with the uncertain future. Jalonen (2012) also

claims that the role of entrepreneurs is the realization of innovation, and uncertainty is inherent

in the process of realizing entrepreneurial innovation. Therefore, bearing uncertainty can be

regarded as an inevitable task especially in the entrepreneurship field (Praag, 1999). However,

the impact of circumstances caused by uncertainty on entrepreneurial activities is huge

inasmuch as entrepreneurs receive profits as a reward for the tolerance to uncertainty (Knight,

1921). In addition, entrepreneurial activities require frequent changes due to variations in

business environment (Feinleib, 2012), and it causes new uncertainties. Therefore, less

attention or a lack of methodologies and managerial ability to deal with uncertainty has led

entrepreneurial projects to failures.

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On the other hand, uncertainty cannot be asserted that it only causes negative consequences.

One of the biggest positive effects of uncertainty is that entrepreneurs are able to increase

knowledge and experience through handling uncertainty. Jalonen (2012) also emphasizes that

this process (bearing uncertainty, increasing knowledge and experience, and making use of

them for the achievement of innovation) is important because not only are many uncertainties

caused by a lack of knowledge or information, but entrepreneurs are also able to become more

proactive with the increased knowledge and experience of bearing uncertainty. Thus, the

increase in knowledge about uncertainty provides them with new insights for their innovation.

Moreover, distinguishing different types of uncertainty benefits entrepreneurs to choose

appropriate strategies for coping with uncertainty (Wernerfelt and Karnani 1987). Jalonen

(2012) describes the main types of uncertainty and their manifestations as shown in table 2.1.

Table 2.1. Types of Uncertainty

UNCERTAINTY MANIFESTATION OF UNCERTAINTY

Technological

Uncertainty

Due to the novelty of technology, its details are unknown

Uncertainty regarding knowledge required to use new

technology

Market Uncertainty

Unclear customer needs

Lack of knowledge about the behavior of competitors

Difficulties in predicting the price development of raw

materials and competing products and services

Regulatory/institutional

Uncertainty Ambiguous regulatory and institutional environment

Social/political

Uncertainty

Diversity of interests among stakeholders of innovation

processes - a power struggle

Acceptance/legitimacy

Uncertainty

Necessary skills and knowledge contradict existing skills

and knowledge possessed by perceived users of innovation

Innovation threatens individual’s basic values and/or

organization’s norms

Managerial Uncertainty Fear of failure

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Lack of requisite tools to manage Risk inherent in

innovation process

Timing Uncertainty

Lack of information in the early phases of innovation

Ambiguity of information in the late phases of innovation

Temporal complexity

Consequence Uncertainty

Indirect consequences

Undesirable consequences

Unintended consequences

2.1.2. MARKET UNCERTAINTY

The CB Insights, a technology market intelligence platform service, declared that three main

causes which most frequently happen for the low success rate of startup projects based on their

study of 101 startup post-mortems. No Market Need, Get Outcompeted and Pricing/Cost Issues

are listed as the main reasons for startup failures by 42% of founders, 19% and 18%

respectively. The mutual ground of these factors is that these negative consequences are

attributed to market uncertainty caused by external market variation. According to The CB

(2014), Insights’ study, “No Market Need” can be paraphrased as demand uncertainty because

it literally means that entrepreneurs did not understand both current consumer needs and future

consumer needs, and produced what they did not need (Mohr, Sengupta and Slater, 2009). "Get

Outcompeted" happens due to the competitive uncertainty caused when rivals' competitive

offensive affects a firm. Wiersema and Bantel (1993) claim that with the concentration ratio of

the industry, this sort of market uncertainty has increased. Thus, a lack of attention to

competitors’ behavior causes critical circumstances. Finally, “Pricing/ Cost Issues” can be

interpreted as input cost uncertainty according to McGrath (1997). This is caused by the fact

that a number of firms have only weak influence towards their supplier’s prices. Thus, they

have struggled to manage or reduce the level of input cost uncertainty. Furthermore, The

Statistic Brain Research Institute (2017) also claim that pricing issue is caused by “No

Knowledge of pricing” and “Emotional Pricing” which refers to the art of basing prices for a

product/service on a feeling or mood rather than good solid information (Benun, 2011). In short,

the assessment of changes in customer needs, input costs, and competitive climate is difficult.

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2.1.3. MARKET UNCERTAINTY IN HIGH TECHNOLOGY INDUSTRIES

The characteristics of high technology startups can be interpreted as the high level of

uncertainty, as high technology startup firms are required to involve complicated resources,

complex environments and sophisticated resource integration requirements (Blanco, 2007).

Furthermore, Sjöberg and Wicén (2008) claim that the characteristic of market uncertainty in

high technology industries can be defined as its short-term volatility. Morh. J et al (2009)

emphasize that the uncertainty of how fast the technology will spread in high technology

markets becomes a crucial matter. This is because the characteristic of Product Life Cycle of

high technology products and services tends to be short, as technological developments spurred

by competitions occur one after another. Therefore, our technology might struggle to satisfy

the market if other competitors’ technology has already permeated into the market. As a result,

a high technology company is required to accomplish the target in a short period of time because

of the uncertain potential time that a market will be available.

2.2. EFFECTUATION AND CAUSATION

An explanation of the relationship between the process of effectuation and causation, in

decision-making under uncertainty will be gathered. It follows the description of the theory and

principles of effectuation and the differentiation with causation. This framework will form the

basis for the research instrument.

2.2.1. EFFECTUATION AND CAUSATION UNDER UNCERTAINTY

Simon (1981) and Sarasvathy (2001) introduced the effectuation theory based upon Knightian

uncertainty, as the dominant model for entrepreneurial decision-making, contrary to the

causation process. Therefore, if decision makers are dealing with a measurable or relatively

predictable future, they will probably tend to make use of information gathering and

information analysis methods which is in line with the causation approach, “if we can predict

the future, we can control it” (Sarasvathy, 2001). If decision makers are dealing with a relatively

unpredictable future, they will try to gather information to control aspects through experimental

techniques aimed at discovering the underlying distribution of this unpredictable future

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(Sarasvathy, Dew, Read and Wiltbank, 2008). This is in line with the effectuation theory, “to

the extent that we can control the future, we do not need to predict it” (Sarasvathy, 2001).

2.2.2. EFFECTUATION AND CAUSATION THEORY IN DECISION-MAKING

A number of theories regarding entrepreneurial decision-making processes are an important

part of the literature on entrepreneurship (Pot, 2013). Arrow et al. (1999) and Sarasvathy (2000,

2001, 2005, 2008) highlighted several intellectual issues of interest in entrepreneurship,

including the importance of decision-making under uncertainty.

Recent studies on entrepreneurial decision-making and subsequent Sarasvathy’s studies suggest

that the process of effectuation is the most appropriate for entrepreneurs whose ventures faced

with uncertainty (Chadler, Tienne, McKelvie and Mumford, 2011), (Chesbrough, 2010),

(Perry, Chandler and Markova, 2011), (Dew, Wiltbank, Read, Dew and Sarasvathy, 2009),

(Sarasvathy, 2001), (Read and Sarasvathy, 2005), (Sarasvathy, Dew, Read, Wiltbank, 2008).

Sarasvathy supported her theory based on intellectual lineage of the ideas from Peirce (1878),

James (1912), Lindblom (1959), Simon (1959), Vickers (1965), Allison (1969), Weick (1979),

Nystrom and Starbuck (1981), March (1982), Buchanan and Vanberg (1991), Burt (1992), and

Mintzberg (1994), to the most important influence Knight (1921) with the Knightian

uncertainty as a notion of a fundamentally unknown future. In contrast, causation theory has a

very old and venerable lineage in philosophy, from Aristotle’s ideas, causation has developed

through Schwenk, (1988), Mackie (1998), Rosen (1991), Schwenk and Shrader, (1993) and

Shane (2003).

Over the last decade, as effectuation continues to gain more foothold in the field of

entrepreneurship research, few empirical studies have been performed on the consequences of

thinking effectually rather than causationally (Diemer, 2010). Causation and effectuation

theoretical principles have been described by different authors, such as Sarasvathy (2001),

Sarasvathy and Jotha (2001), Dew and Sarasvathy (2002, 2005), Read and Sarasvathy (2005),

Kupper and Burkhart (2009), Karri and Goel, (2006), Wiltbank et al. (2009), Dew et al. (2009),

Chadler , Tienne, McKelvie and Mumford (2011), Maine and Dos Santos (2012).

9

The theory of effectuation was expanded upon by Sarasvathy (2001), Sarasvathy et al. (2005),

and Sarasvathy (2008). The theory suggests that under conditions of uncertainty, entrepreneurs

adopt a decision logic that is different to that explicated by a traditional, more rational model

of entrepreneurship called causation (Fisher, 2012). Sarasvathy (2001) explains that analytical

decision-making processes depend on knowing the causal relations between means or choices

and effects or goals. Effectuation as a decision-making logic focuses on exploiting unexpected

events and encounters contingencies in an uncertain and dynamic environment, where effects

change and are shaped and constructed over time, and are sometimes formed by chance. Thus,

effectuation takes over a set of means or choices to control and create opportunities from the

contingencies which arise unexpectedly.

In contrast, causation describes the traditional way that entrepreneurship is created. Causation

is consistent with planned strategy approaches and based on project objective (Ansoff, 1988),

(Brews and Hunt, 1999), (Minzberg, 1978), (Sarasvathy, 2001). Thus, causation focuses on

achieving the desired effect (goal) through a specific set of given means. As shown in Figure

2.1, “causation consists of many-to-one mappings, whereas effectuation involves one-to-many

mappings”. (Sarasvathy, 2001).

Figure 2.1

Causal Vs Effectual Reasoning

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Effectuation process, taking a set of means, which is a function of who the entrepreneurs are,

what they know and whom they know, which are manifested in their individual personalities,

abilities and social networks (Sarasvathy, 2001). Then, entrepreneurs work within these means

to create the effects. According to Chandler et al. (2011), Dew (2009), Sarasvathy (2001), there

are four core principles of the theory of effectuation, which are described below. Similarly, to

generally differentiate entrepreneurs applying causation process and effectuation process,

Sarasvathy et al. (2005) developed a list which was subjected to several revisions in which she

contrasted between processes of causation and effectuation in terms of decision criteria, as

shown in Table 2.2. This framework and multidimensional construct (Chandler et al. 2011) –

explained below in numeral 2.3- will support the basis for the research instrument.

i. Affordable loss rather than expected returns: while causation focuses on

maximizing the potential returns for a decision by selecting optimal strategies.

Effectuation focuses on the downside potential, trial, and errors, learning, and

improvement while doing.

The amount of investment is determined by how much the stakeholders can lose. The

effectuator prefers options that create more options in the future over those that

maximize returns in the present.

ii. Strategic alliances rather than competitive analysis: Causation, emphasize detailed

competitive analysis (Porter, 1980). Effectuation emphasizes building partnerships with

strategic alliances, collaborating with people who can complement the skills needed and

create pre-commitments from stakeholders to reduce and/or eliminate uncertainty and

to erect entry barriers.

iii. Exploitation of contingencies rather than exploitation of preexisting knowledge:

When preexisting knowledge, such as expertise in a particularly new technology, forms

the source of competitive advantage, causation models might be preferable.

Effectuation, however, would be better for exploiting contingencies that arose

unexpectedly over time.

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iv. Controlling an unpredictable future rather than predicting an uncertain one:

Causation reasoning is useful in domains where future is predictable, goals are known

and external environmental factors serve as the ultimate selection mechanism. But it

does not provide useful criteria for action in domains where these three characteristics

are absent (Knight, 1921), (March, 1982), (Weick, 1979). Effectuation, however,

focuses on the controllable aspects of an unpredictable future. Therefore, short-term

experiments create opportunities to offsets any surprise that comes along.

Although the definitions described above – effectuation and causation - may be somewhat

difficult to understand for the reader, to exemplify and clarify the effectuation and causation

processes, Sarasvathy (2001) illustrates with “Curry in a Hurry” imaginary Indian restaurant

example. Firstly, we will describe the general characteristics of each type of decision process -

causation and effectuation - and then describe how entrepreneurs might usually proceed in both

scenarios.

In the example, causation process would start with a given goal (the Indian restaurant), then

entrepreneur will focus on the literature and research, and might apply the traditional STP –

segmentation, targeting and positioning process, which involves considerable amounts of time

and analytical effort. The investigations probably will start with the universe of all potential

customers and then will be segmented considering different variables (demographics, ethnic

origin, income level, patterns of eating) of similar restaurants in the market. Through the

implementation of questionnaires, interviews or group discussions among other methodologies,

the entrepreneur will arrive at the target segment and finally identify the type the menu choice,

decoration, hours of service and other relevant operational details. Finally, the entrepreneur will

design the marketing and sales campaign to induce the chosen market target to try the

restaurant.

In effectuation processes, however, entrepreneurs try to stay flexible and open to changes as

the future is yet to be shaped and depends on alliances with the entrepreneurs’ stakeholders

(Hermes, 2016). Moreover, considering that most of the entrepreneurs have limited monetary

resources, they will have to creatively bring the idea to market with as close to zero resources

as possible. In that sense, entrepreneurs might begin to convince an established restaurant to

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become a strategic partner or do just enough market research to convince a financier to invest

the money needed to start the restaurant. Several other courses of effectuation can be imagined

by continually listening to the customer and building an ever-increasing network of customers

and strategic partners. Entrepreneur can then identify a workable segment profile, for example,

contact friends or colleagues to taste the food and start a catering or delivery service instead, or

as the business grows, entrepreneurs will be able to launch their own restaurant. This means

that the original idea is going to change in the process, allowing to create several possible effects

irrespective of the generalized end goal.

Table 2.2

Contrasting Effectual against Causal reasoning. Sarasvathy and Dew (2005)

ISSUE CAUSAL POSITION EFFECTUAL POSITION

View of the future Prediction. The future is a

continuation of the past; can

be acceptable predicted

Design. The future is contingent on

actions by willful agents

Constructs pertaining to individual decisions

Givens Goals are given Means (Who I am, what I know, and

whom I know) are given

Decision agenda Resources. What resources

ought I to accumulate to

achieve these goals?

Effects. What effects can I create with

the means I have?

Basis for taking action Desired worlds. Vision of a

desired world determines

goals; goals determine sub-

goals, commitments and

actions

Possible worlds. Means and stakeholder

commitments determine possible sub-

goals – goals emerge through

aggregation of sub-goals

Basis for commitment Should. Do what you ought to

do – based on analysis and

maximization

Can. Do what you are able to do – based

on imagination and satisficing

Stakeholder

acquisition

Instrumental view of

stakeholders. Project

objectives determine who

comes on board

Instrumental view of objectives. Who

comes on board determines project

objectives

Constructs in terms of responses to the environment

Predisposition toward

risk

Expected return. Calculate

upside potential and pursue

(risk adjusted) best

opportunity

Affordable loss. Calculate downside

potential and risk no more than you can

afford to lose

Predisposition toward

contingencies

Avoid. Surprises may be

unpleasant. So, invest in

techniques to avoid or

neutralize them.

Leverage. Surprises can be positive. So,

invest in techniques that are open to

them and leverage them into new

opportunities.

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Attitude toward

success/failure

Outcomes. Success and failure

are discrete outcomes to be

sought after or avoided,

respectively.

Process. Successes and failures are

inputs into a process that needs to be

managed such that failures are outlived

and successes are accumulated

Attitude toward

probability estimates

Update beliefs. Estimates are

used in a Bayesian fashion – to

update one’s beliefs about the

future.

Manipulate conditionals. Estimates

signal which conditionals may reified or

falsified so the future can be skewed

through action.

Attitude toward others Competition. Constrain task

relationships with customers

and suppliers to what is

necessary.

Partnership. Build your market together

with customers, suppliers and even

prospective competitors.

Underlying logic To the extent, we can predict

the future, we can control it.

To the extent, we can control the future,

we do not need to predict it.

2.3. EXPERIMENTATION-DRIVEN APPROACH

The concept of effectuation introduced by Sarasvathy (2001) has been investigated by many

researchers in the field of entrepreneurship in recent years. A few of them attempt to empirically

analyze and measure effectuation in the startup context. In his study about the construct

underlying effectuation, Chandler et al. (2011) found that effectuation forms a

multidimensional construct composed of four sub-dimensions: (1) affordable loss, (2)

experimentation, (3) flexibility and (4) pre-commitment.

i. Affordable loss is an effectuation sub-construct that entails managers deciding what they

are willing to risk by following a particular strategy (Dew et al., 2009). In other words,

managers evaluate an investment according to whether the business could absorb the loss

from the total failure of a venture.

ii. Experimentation has been conceptualized as a series of trial-and-error changes pursued

along various dimensions of strategy, over a relatively short period of time, in order to

develop a competitive advantage (Sarasvathy and Venkataraman, 2011), (Van de Ven and

Polley, 1992).

iii. Flexibility specifies that effectuators tend to remain flexible since the structure of the

emerging organization is dependent on contingent opportunities and the particular

investments made by the stakeholders. Thus, the need for prediction is greatly reduced

(Sarasvathy, 2001). As firms become established and grow, they must implement policies,

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procedures and routines (Mintzberg, 1978), whereas entrepreneurs (and especially

effectuators) are able to remain flexible and experiment.

iv. Pre-commitment is concerned with establishing pre-commitments and alliances with

customers, suppliers and other strategic partners which help reduce the uncertainty

associated with ventures. Diversifying risk among multiple stakeholders allows the

effectuator to constrain the potential loss, thus making it more affordable (Sarasvathy,

2001). According to Chandler et al. (2011), causation and effectuation can occur

overlapping, where the pre-commitment dimension is shared with causation.

Among the sub-dimensions of effectuation, experimentation as a driver for innovation and new

business development has been surfacing in the management and business literature (Anderson

and Simester, 2011), (Ries, 2011), (Chesborough, 2010), (McGrath, 2010), (Barthélemy, 2006).

Experimentation-driven approach is related to effectuation investigated by many entrepreneurs

(Sarasvathy, 2001). Both effectuation and experimentation-driven approach emphasize action

in the present as opposed to planning for the future (Hassi and Tuulenmäki, 2012). According

to Ries (2011), instead of making complex plans that are based on assumptions,

experimentation allows entrepreneurs to make constant adjustments based on accurate data,

rooted in customer’s feedback. Experimentation-driven approach is characterized by open-

ended outcomes and a living process. It means that the outcome which we have in mind at the

beginning might not be the best, and it is, therefore, subject to change at any time. Furthermore,

the process of execution is not fixed but decided based on the best available information.

Experimentation-driven approach is illustrated by an iterative process of trial-and-error with

four-step learning cycle presented in figure 2.2. It consists of setting a hypothesis, planning an

experiment, executing it and analyzing the results. The process begins with hypothesis formed

by entrepreneurs based on previous experience, learning and guess on problem or solution.

Then such hypothesis is developed into execution plan consists of experiment tests. In the next

phase, entrepreneurs execute such experiments and plan with metric of measurement that

followed by analysis and learning in the last phase.

15

Figure 2.2:

Iterative learning process for experimentation, adapted from Thomke (1998)

Experimentation is a method for developing and learning. It helps generate new information,

new ideas, and may even lead to the identification of entirely new opportunities (Hassi and

Tuulenmäki, 2012). According to Sarasvathy (2001), entrepreneurs do not focus on analyzing

their operating environment, but proceed through action that creates new information, revealing

opportunities. Learning by experimentation applied trial-and-error begins with the selection or

creation of one or more possible solutions. Throughout a series of iterations with testing and

learning, the idea or solutions will be revised and refined. Consequently, progress is constantly

made.

Experimentation-driven approach is the most suited for situations where uncertainty is high,

especially in high technology industries. Experimentation would help entrepreneurs constantly

examine what works and what does not work with their venture business. Thus, they can

continuously improve their startup performance while dealing with many unforeseeable

situations. In recent decades, many entrepreneurs apply experimentation-driven approach in

their business when developing and launching a new venture. Among many methods and tools

that reflect the approach of effectuation, Agile, The Lean Startup and Design Thinking are the

16

ones applied by many startups in dealing with uncertainty. While Agile methodology has

become popular in startup community for long, The Lean Startup methodology is just a few

years old and its concepts have quickly taken root in the startup world. Business schools have

already begun adapting their curricula to teach The Lean Startup Methodology (Blank, 2013).

Blank (2013) also emphasized that Design Thinking provides the tactical day-to-day process of

how to turn ideas into products. To bring the general concepts of those aforementioned

experimental methods, we briefly illustrate their main characteristics before coming up with a

comparison among the methods.

i. Agile methodology: Agile is a framework for delivering products quickly and efficiently.

It refers to an iterative, incremental method of managing the design and build activities

of engineering, information technology and other business areas that aim to provide new

product or service development in a highly flexible and interactive manner; an example

is its application in Scrum, an original form of agile software development.

ii. The Lean Startup: The Lean Startup methodology provides entrepreneurs with a

scientific approach to build successful new business ventures. There are five principles of

the Lean Startup methodologies, “entrepreneurs are everywhere”, “entrepreneurship is

management”, “validated learning”, “build-measure-learn” and “innovation accounting”.

The core component of the Lean Startup is the build-measure-learn feedback loop that

helps entrepreneurs deal with the context of uncertainty. The father of the Lean Startup

method - Ries (2011) also introduces new concepts of Minimum Viable Product (MVP)

and Pivot to illustrate how the method works.

iii. Design Thinking: Design thinking can be described as a discipline that uses the

designer’s sensibility and methods to match people’s needs with what is technologically

feasible and what a viable business strategy can convert into customer value and market

opportunity (Brown, 2008).

17

Table 2.3

Agile – The Lean Startup – Design Thinking in Comparison

Comparision Agile The Lean Startup Design Thinking

Methodology

Adapting to changes

easier to execute tasks

faster

Improving virtually

everything by

eliminating anything

that doesn’t bring

value to the

customer

Creating valuable

solutions by using

customer empathy and

creativity

Objective Product Vision Product Market Fit User needs

Action loop

Product backlog –

sprint backlog –

iteration (sprints) –

potentially shippable

result

Build-measure-learn Empathy-Define-Ideate-

Prototype-Test

Method for

demonstrating

progress

Definition of ‘done’ Validated learning The A-ha moment

Leaning &

Change Sprint Review Pivot Reframing

Toolkit

Sprints, boards,

Scrum Master,

acceptance tests, user

story mapping etc.

Hypotheses, split

(A/B) tests,

customer interviews,

MVP, Customer

Success Manager

etc.

Shadowing, qualitative

interview, paper

prototyping,

brainstorming, emotional

value proposition,

Synthesis, etc.

In conclusion, effectuation plays an important role in entrepreneur’s decision-making. As a

sub-dimension of effectuation, experimentation which is reflected by many experimental

methods such as Agile, The Lean Startup and Design Thinking has been wisely applied by

many entrepreneurs in dealing with market uncertainty.

In this study, to answer the research question: how do entrepreneurs effectuate in decision-

making to respond to market uncertainty in high technology industries, the three following

expectations will also be investigated:

Expectation 1. High technology entrepreneurs prefer to apply effectuation rather than

causation in decision-making.

18

Expectation 2. Experimentation-driven approach can help high technology entrepreneurs deal

with market uncertainty.

Expectation 3. Entrepreneurs apply different experimental methods to deal with market

uncertainty.

19

3. METHODOLOGY

In this chapter, the methodology used in this study will be explained and discussed. There are

four sections in this chapter: In section 3.1, the appropriate research design will be described.

Section 3.2 defines the research sampling. Section 3.3 describes data collection. Finally, this

chapter ends with a description of the methodology used for analyzing the data in Section 3.4

3.1. RESEARCH DESIGN

In order for this research to figure out the relation among entrepreneurs in high technology

industries, effectuation, causation, experimentation-driven approach and market uncertainty,

we adopted a research design involving a quantitative survey. Accordingly, we conducted an

online survey which focused on studying entrepreneurs in high technology industries to

identify, analyze and depicts our research question, how entrepreneurs effectuate in decision-

making to respond to market uncertainty in high technology industries.

3.2. RESEARCH SAMPLING

3.2.1. SAMPLE & SAMPLE SIZE

Our sample was selected from entrepreneurs in high technology industries. The criteria for this

research to select suitable entrepreneurs were: i) entrepreneurs who have ever established a

startup company ii) whose products and/or services belong to high technology industries, iii)

who have already had customers and iv) who have already generated revenues. The reason for

setting i) is that we wanted to investigate not only active entrepreneurs, but also people who

used to be active entrepreneurs but currently doing something not entrepreneurial. This is

because we deemed that whether a respondent was an active entrepreneur or not would not

matter, as our study would investigate entrepreneurs’ experience that how they dealt with

market uncertainty. Furthermore, iii) and iv) were set as criteria in order to pursue a quality

data collected from entrepreneurs who have already experienced market uncertainty practically

in the process of developing their businesses. In other words, we avoided studying people

considering themselves to be as entrepreneurs, but have neither established companies nor

generated revenues. Furthermore, as Holloway (1997) states that sampling process tends to

continue until enough number of studies can be collected, we aimed at gathering 100 studies

20

from the online survey and approached and spread the survey to as many entrepreneurs as we

could.

3.2.2. THE SELECTION OF SAMPLING

As Burns and Grove (2003) clarify that the selection of a group of humans, events, behaviors

for research is the fundamental of sampling, we selected and contacted several institutions

where our target entrepreneurs belong in order to ask to spread our survey to the entrepreneurs.

The institutions that we approached are startup incubators, accelerators, co-working spaces,

venture event organizations, entrepreneur communities and an educational institution. In order

to spread our survey, at first, we contacted each institution’s management member such as a

founder, a manager, or an event director who can access to his or her own forum to inform our

request for the survey or can send our survey to entrepreneurs in each institution. The reason

why we took this way was that we had already had some connections with the management

members. Consequently, we deemed that it would be efficient for us to contact entrepreneurs.

However, after the execution of this way, we found it not to be effective, as we could not receive

any response from entrepreneurs. Therefore, we changed to directly sending either emails or

messages to entrepreneurs via alumni network, LinkedIn startup groups and Facebook startup

communities. As we expected that the frequency of checking SNS, Social Networking Service,

was high in general and this way would let entrepreneurs notice our messages more effectively,

it turned out to be a better response rate. In addition to these methods, we also used individual

network. Not only did we ask entrepreneurs whom we had already known to answer the survey,

but we also asked our friends to introduce us to entrepreneurs whom they had known.

3.3. DATA COLLECTION

3.3.1. SURVEY QUESTIONNAIRE

A primary data collection for this study has been collected through the survey questionnaire

method. The survey has been developed based on the analysis of the literature review of

effectuation and causation dimensions, experimentation-driven approach and market

uncertainty defined in Chapter 2.

21

The survey questionnaire method has been divided into three parts: firstly, we applied the same

method and measures proposed by Chandler et al. (2011) to measure the effectuation and

causation constructs – described in the numeral 2.3 -, and thus answering the expectation 1,

high technology entrepreneurs prefer to apply effectuation rather than causation in decision-

making. Secondly, trial and error process has been tested to deal with the three variables for

market uncertainty - Validating Market Need, Responding to Competitors' Strategies and

setting the price -, to answer the expectation 2, experimention-driven approach can help high

technology entrepreneurs deal with market uncertainty. Finally, we explored which

experimental methods – Agile Method, The Lean Startups, Design Thinking and others - are

applied the most to deal with market uncertainty variables - Validating Market Need,

Responding to Competitors' Strategies and setting the price -, to answer the expectation 3,

Entrepreneurs apply different experimental methods to deal with market uncertainty. More

information regarding the variables used, is provided below.

There are several methods for collecting survey data. Considering our sample type described in

the numeral 3.2.1, we applied online survey in this research. We saw the advantages of this

method, as Entrepreneurs can click on a URL send by e-mail and be transported to the web

survey tool (Evans and Mathur, 2005). The survey tool applied was the Google Doc. On the

other hand, considering the limited time to develop the research the Google Form Surveys gave

us the advantage of being administered in a time-efficient manner, reducing the period it takes

to get the respondents, compared to face-to-face interviews (Kannan, Chang and Whinston,

1998).

To prevent the surveys being perceived as junk mail or spam, personal messages were sent with

the link to the survey. Similarly, a periodic reminder has been sent and follow-up forms were

maintained to track respondents.

As we mentioned above, the structure of the Survey questionnaire was divided into three parts:

i. Effectuation and Causation.

The operationalization of causation and effectuation measures, has been taken from the

research article “Causation and effectuation process: A validation study” by Chandler,

DeTienne, McKelvie and Mumford, 2011. The research study, shows that effectuation

22

is a multi-dimensional formative construct, divided into four sub-constructs- defined in

the numeral 2.3 - Experimentation, Affordable loss, Flexibility and Pre-commitments,

and Causation is a single dimensional construct. The Chandler et al. (2011) research

validated the proposed measures with an acceptable Cronbach’s alpha levels, which

ensures the validity and reliability of the measures applied in this study. Table 3.2.

shows the measures employed in the survey and the constructs for effectuation and

causation.

The authors decided to follow, the same scales applied by Chandler et al. (2011). The 5

points Likert Rating Scale was adopted (Likert, 1932) in the survey. The scale applied

was ranging from: Fully disagree – Disagree- Neutral - Agree and Fully Agree.

Appendix 8.1 shows the survey questionnaire applied for this study.

Table 3.1

Effectuation and Causation measures Chandler et al. 2011

ITEMS CONSTRUCT

We experimented with different products and/or business models Experimentation

The product/service that we now provide is essentially the same as originally

conceptualized. Experimentation

The product/service that we now provide is substantially different than we first

imagined. Experimentation

We tried a number of different approaches until we found a business model that

worked. Experimentation

We were careful not to commit more resources than we could afford to lose. Affordable Lost

We were careful not to risk more money than we were willing to lose with our

initial idea. Affordable Lost

We were careful not to risk so much money that the company would be in real

trouble financially if things didn't work out. Affordable Lost

We allowed the business to evolve as opportunities emerged. Flexibility

We adapted what we were doing to the resources we had. Flexibility

We were flexible and took advantage of opportunities as they arose. Flexibility

We avoided courses of action that restricted our flexibility and adaptability. Flexibility

We used a substantial number of agreements with customers, suppliers and other

organizations and people to reduce the amount of uncertainty. Pre-commitment

We used pre-commitments from customers and suppliers as often as possible Pre-commitment

We analyzed long run opportunities and selected what we thought would provide

the best returns Causation

We developed a strategy to best take advantage of resources and capabilities Causation

23

We designed and planned business strategies Causation

We organized and implemented control processes to make sure we met objectives Causation

We researched and selected target markets and did meaningful competitive

analysis Causation

We had a clear and consistent vision for what we wanted to do Causation

We designed and planned production and marketing efforts Causation

ii. Experimentation-driven approach

Sarasvathy and Venkataraman (2011) stated that Experimentation has been

conceptualized as a series of trial-and-error process. Therefore, the

authors operationalized experimentation-driven approach in terms of determining the

applicability of trial and error process to deal with market uncertainty.

Following the theoretical framework presented in the numeral 2.1.2 (Market

Uncertainty), the CB Insights (2014), demonstrated that there are three main reasons for

startups failures.

The first reason was “No market need”, meaning that the customer did not need what

they produced. Consequently, the authors set the first variable for the analysis of market

uncertainty: Validating Market Need.

The second reason was "Get Outcompeted", which happens due to the competitive

uncertainty created when rivals' competitive offensive affects a firm. Thus, the second

variable for the analysis of market uncertainty was: Responding to Competitors'

Strategies.

The third reason for startup failure, according to the CB Insights was “Pricing/ Cost

Issues” can be interpreted as input cost uncertainty according to McGrath (1997). This

is caused by the fact that a number of firms have only weak influence towards their

supplier’s prices. Thus, they have struggled to manage or reduce the level of input cost

uncertainty. Thus, the authors set the third variable for the analysis: Setting the Price.

The mutual ground of these factors is that the negative consequences are attributed to

external market variation, thus being categorized as market uncertainty.

24

Experimentation is often carried out adopting simplified measures (Thomke,

von Hippel and Franke, 1998). As the aim is to determine if the trial and error process

is applied to deal with market uncertainty – validating market need, setting the price

and respond to competitors’ strategies-, and according to Hannan and Freeman (1984),

experimentation could be perceived with unequal frequencies. Therefore the 5

points Likert Rating Scale have been applied, ranging from: Fully disagree – Disagree-

Neutral - Agree and Fully Agree. Table 3.2 shows the measures and scales applied in

the survey for experimentation-driven approach.

Table 3.2

Experimentation-driven approach measurement

Experimentation-driven approach

Q21. I applied trialanderror process to validate the market need. * *Mark only one oval.

Q22. I applied trialanderror process to set the right price for my product/service.

*Mark only one oval.

Q23. I applied trialanderror process to quickly respond to competitors' strategies. * *Mark only one oval.

iii. Experimental methods

According to numeral 2.3 different tools like Agile, the Lean Startup, Design Thinking,

among others, are adopted by many startups in dealing with uncertainty. Therefore, in

order to answer the expectation 3, the authors applied the same variables for market

uncertainty mentioned above - validating market need, setting the price and respond to

competitors’ strategies- to determine which techniques are the most used. The “other”

Fully Disagree

Fully Agree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Disagree

Neutral

Agree

Fully Disagree

25

category was also included, allowing the respondents to provide their own

technique. Table 3.3 shows the measures applied for Experimental methods.

Table 3.3

Experimental methods measurement

Experimental Methods

Q24. What experimental methods did you apply to validate market need? *Check all that apply.

Q25. What experimental methods did you apply to set the right price for your

product/service? * *Check all that apply.

Q26. What experimental methods did you apply to quickly respond to competitors'

strategies? * *Check all that apply.

3.4. ANALYSIS PROCESS

The main aim of this analysis was to validate the given expectations, based on entrepreneur’s

feedbacks by the online survey in order to answer the main research question. The data gathered

by the online survey were first analyzed by Google Docs with the graph summary report to

provide initial insights. Then SPSS software was used to process the raw data from online

survey to explore the data and do statistical calculations. In addition, we reflected the theory

The Lean Startup Methodology Design Thinking Methodology

Agile Methodology

Other:


Agile Methodology

Other:


Agile Methodology

Other:

26

during the data collection and analysis process to validate expectations as well as studying the

main research question.

All the measures of effectuation, causation, experimentation-driven approach in responding to

market uncertainty are converting from the five scale (Fully disagree – Disagree- Neutral -

Agree and Fully Agree) into 5 point Likert Rating Scale (1,2,3,4 and 5). Then important

statistical numbers such as mean, Std. deviation, % average etc. were calculated by SPSS.

Consequently, we formed descriptive statistics to observe the relationships among causation,

effectuation as well as sub-dimensions of effectuation to find the answer for the first

expectation. On the other hand, the similar statistical numbers were calculated for

experimentation-driven approach in responding to market uncertainty that was measured by 3

variables (Validating market need, setting price, responding to competitors' strategies)

developed from theoretical framework. The second expectation was studied based on the

descriptive statistics.

In terms of data processing for expectation 3, we collected information about the tools that high

technology entrepreneurs used to validate market need, set the right price and respond to

competitor’s strategies. We calculated the frequency statistic to identify the most preferable

method that entrepreneurs used to deal with market uncertainty.

27

4. RESULTS AND DISCUSSIONS

In this section, the results of the data are given. The distribution of the survey data is presented

in Tables 4.1, 4.2 and 4.3. The variables in the table are the variables which are constructed

from the Likert items. The averages are recalculated into percentages to give an explicit

description.

Overall, we got 41 respondents in the online survey. The respondents’ detail information

illustrates that there are various high technology industries that the entrepreneurs are working

on. Therefore, the data that we got can generalize our finding when study effectuation in high

technology industries in general. Furthermore, almost 71% the respondents expected to have

our final research report that reflects the practical implication of this research study. The results

that we had analyzed were presented in the following parts respectively to the order of

expectations we have made.

4.1. DISCUSSION

4.1.1. EXPECTATION 1

HIGH TECHNOLOGY ENTREPRENEURS PREFER TO APPLY EFFECTUATION

RATHER THAN CUASATION IN DECISION-MAKING.

As shown in Table 4.1 the means show higher rates of causation (3.78), compared

with effectuation (3.58). Looking at the percentages, causation gives a total degree of 51.39%

and 48.61% for effectuation. It could be observed that contrary to our expectation 1,

entrepreneurs have a preference in Causation rather than Effectuation.

Table 4.1

Descriptive Statistics for Effectuation and Causation

Descriptive statistics for survey data - Effectuation/Causation

N Mean Std. deviation Average

Effectuation 41 3.58 0.33 48.61%

Causation 41 3.78 0.17 51.39%

28

Observing the effectuation sub-dimensions - Experimentation, Affordable loss, Flexibility, and

Pre-commitments-, as illustrated in table 4.2, the means are 3.43, 3.45, 4.08, 3.37

respectively. The percentage of each sub-dimension are as follows, Experimentation (23,93%),

Affordable Loss (24.07%), Flexibility (28.49%) and Pre-commitments (23,51%).

Table 4.2

Descriptive Statistics for Effectuation and Causation

Descriptive statistics for survey data - Effectuation sub-dimensions

N Mean Std. deviation % of construct

Effectuation

Experimentation 41 3.43 0.42 23.93%

Affordable loss 41 3.45 0.14 24.07%

Flexibility 41 4.08 0.23 28.49%

Pre-commitments 41 3.37 0.00 23.51%

EFFECTUATION

Flexibility

Sarasvathy (2001) stated that effectuation processes are characterized by Flexibility.

This can be demonstrated with the results in the table 4.2 Flexibility is the highest

percentage 28.49% among effectuation sub-dimensions. Analyzing individually the

questions 8 to 11 (see Appendix 8.2), the results show that from question 8, (95.1%) of

entrepreneurs prefer to be flexible and allow the business to evolve to the opportunities

that arise. This is corroborated from the results obtained in question 10, 95.1% of

entrepreneurs prefer to be flexible and took advantage of opportunities as they arose.

Regarding the question 9, 84.6% of entrepreneurs, adapt their processes to the resources

they have. Lastly, concerning question 11, 68.3% of entrepreneurs, avoid situations that

restricted their flexibility and adaptability.

29

Affordable loss

The Affordable loss becomes the second important criterion among effectuation sub-

dimensions. From table 4.2, Affordable loss is closer to Experimentation, rather than

Flexibility. Sarasvathy (2001) stated that “Effectuation predetermines how much loss is

affordable and focuses on experimenting with as many strategies as possible with the

given limited means”.

Taking into consideration the results from question 5 to 7 (see Appendix 8.2), the results

show that regarding question 5, 61.0% of entrepreneurs consider not to commit more

resources than they could afford to lose, thus, experiments that would cost more than

the entrepreneurs can afford to lose are rejected in favor of affordable experiments

(Chandler et al. 2011), contrary to the 19.6% that might prefer to maximize their returns,

committing resources in the present by selecting optimal strategies. 19,5% of

entrepreneurs were neutral.

In respect to questions 6 and 7, the results show that entrepreneurs do not want to

compromise resources. From question 6, 51.3% of entrepreneurs do not want to

compromise the resources more than they are willing to lose. In contrast, 31.7% of

entrepreneurs prefer to risk more money than they are willing to lose, 17.1% of

entrepreneurs were neutral under this concern. Regarding question 7, 56.1% of

entrepreneurs do not want to risk so much money than the company is willing to lose.

In contrast, 36.6% of entrepreneurs prefer to risk more money than the company is

willing to lose, 7.3% of entrepreneurs were neutral under this concern.

Experimentation

Flexibility and experimentation are conceptually closely related, implying that

flexibility can be expressed through experimentation. Empirically, from table 4.2,

23.93% experimentation construct was lower than 28.49% flexibility, and closer to

24.07% affordable loss.

30

Reviewing the results from questions 1 to 4 the results show that relating to question 1,

75.6% of entrepreneurs prefer to experiment with different products and/or business

models, 9.8% of entrepreneurs were neutral and 14.6% were disagree. Similar results

come from question 4, 73.2% of entrepreneurs prefer to try different approaches to find

a business model that works, 17.1% were neutral and 9.7% were disagree.

Regarding question 2, 53.7% of entrepreneurs consider that the product they provide is

essentially the same as originally conceptualized, 43.9% of entrepreneurs were disagree

and 2.4% were neutral. From question 3, 41.5% of entrepreneurs think that the product

they provide now is substantially different from the original conception. The Same

percentage 41.5% of entrepreneurs disagreed and 17.1% were neutral. Although the

questions 2 and 3 are opposite, the results were not as significant as questions 1 and 4.

Which leads us to think that the questions could be confusing for the readers, especially

question 3 that obtained 17.1% Neutral, thus, a better result would have been achieved

by applying another scale to avoid this central tendency.

i. Pre-commitments

From table 4.2, pre-commitment with 23.51%, is the last criterion to be considered by

entrepreneurs among effectuation sub-dimensions. If we look strictly at the counts in our

empirical results in Appendix 8.1, from question 12, 51.2% of entrepreneurs used a

substantial number of agreements with customers, suppliers and other organizations to

reduce the amount of uncertainty, 26.8% of entrepreneurs were not agree and 22.0%

were neutral. Regarding question 13, 51.2% of entrepreneurs used pre-commitments

from customers and suppliers as often as possible, 29.2% of entrepreneurs were disagree

and 19.5% were neutral. Therefore, both questions confirm that entrepreneurs use

alliances with customers, suppliers, and other strategic partners to reduce the uncertainty

while spreading responsibilities to other stakeholders. “This process of diversifying risk

among multiple stakeholders also allows the effectuator to constrain the potential loss,

thus making it more affordable” (Chandler et al. 2011). Considering the percentage

obtained in “neutral”, we would probably have better results if another scale had been

applied in order to avoid central tendency in the survey.

31

We could conclude that entrepreneurs apply effectuation process, by being flexible to take

advantage and allow the business to evolve to the opportunities that arise. Moreover,

entrepreneurs adapt the process to the resources available, and do not want to risk so much

money than the company is willing to lose. Finally, entrepreneurs prefer to experiment with

different products and/or business models to find one that works.

CAUSATION

From table 4.2, causation gives a total degree of 51.39% compared with the 48.61% of

effectuation. Taking in consideration the findings from question 14 to 20 (see Appendix

8.2), the results show that considering the question 14, 70.8% of entrepreneurs would

rather analyze long run opportunities and select the options that provides the best returns,

(19.5%) of entrepreneurs were disagree and (9.8%) were neutral.

Regarding question 15, 78% of entrepreneurs prefer to develop a strategy to best take

advantage of resources and capabilities, while 7.3% of entrepreneurs were disagree and

14.6% were neutral. Question 15, is also supported with question 16, 70.7% of

entrepreneurs are agree with designing and planning business strategies, 17.1% of

entrepreneurs were not agree and 26.8% were neutral.

With respect to question 17, 53.7% of entrepreneurs prefer to organize and implement

control processes to meet objectives, 17.1% of entrepreneurs were disagree and 29.3%

were neutral.

From question 18, the results show that 68.3% of entrepreneurs chose to research and

select target markets and elaborate a meaningful competitive analysis. The findings are

also supported with the results from question 20, 70.8% of entrepreneurs choose to

design and plan production and marketing efforts, while 12.2% of entrepreneurs were

disagree and 17.1% were neutral.

Lastly, regarding question 19, 75.6% of entrepreneurs prefer a clear and consistent vision

for where they want to end up, whereas 9.8% of entrepreneurs were disagree and 14.6%

were neutral.

32

The findings demonstrated that the way high technology entrepreneurs apply causation process

is to prefer long-term opportunities, selecting the options that provide the best returns rather

than using pre-commitments from customers, suppliers and other strategic

stakeholders. Entrepreneurs intended to design and plan business strategies, research and select

target markets and elaborate competitive analysis. Finally, implement control processes to meet

objectives while having a consistent vision.

The authors decided to follow the same scales applied by Chandler et al. (2011) - the 5 points

Likert Rating Scale -, but there were some situations where the percentage of the “neutral”

option was 20%, therefore, it might be suggested to applied different scale to avoid central

tendency.

What catches our attention are the results of the expectation 1 - high technology entrepreneurs

prefer to apply effectuation rather than causation in decision-making. One would have thought

that entrepreneurs in high technology industries would rather be effectual instead of causal, due

to the characteristics of high technology industries, which involves complex environments, and

a higher level of uncertainty. The study demonstrated that entrepreneurs in high technology

industries apply both processes, but mainly causation. Thus, entrepreneurs have adapted their

business processes and even their way of thinking to deal with market uncertainty. Moreover,

it may be necessary further researchers emphasizing causation implications in the

entrepreneurial field.


THE EXPERIMENTAL-DRIVEN APPROACH CAN HELP HIGH TECHNOLOGY

ENTREPRENEURS DEAL WITH MARKET UNCERTAINTY.

The results of experimentation-driven approach in responding to market uncertainty has been

illustrated in the following table 4.3. The overall mean is 3.52 while its sub-constructs,

validating market need, setting price and responding to competitor’s strategies, are 3.83, 3.51

and 3.22 respectively. It is clear that validating the sub-construct market need experienced the

highest mean in comparison to other sub-constructs. These numbers help prove the

33

effectiveness of experimentation-driven approach in dealing with the most serious reason

causing startup failures – “no market need”.

Table 4.3

Descriptive Statistics for Experimentation-driven approach

N Mean Std. deviation Average

Experimentation respond to Market Uncertainty 41 3.52 0.31 100.00%

Validating market need 41 3.83 1.07 36.26%

Setting price 41 3.51 1.05 33.26%

Responding to competitors' strategies 41 3.22 1.21 30.48%

Based on the positive results from the survey, we can re-confirm our expectation that

entrepreneurs applied experimentation-driven approach to deal with market uncertainty.

Particularly, in validating customer need and setting the right price, which we studied in

question 21, 22 of the online survey, a majority of entrepreneurs agree or fully agree that they

can deal with market uncertainty by applying experimentation-driven approach. However, such

approach did not help them much when they faced uncertainty in dealing with competitor’s

strategies (the result of question 23).

As can be clearly seen from the chart 4.1, that 76% of high technology entrepreneurs

agree or fully agree that trial-and-error helps them validate the market need.

34

Chart 4.1

Applied trial-and-error process to validate market need

To deal with market uncertainty that comes from setting product or service price, more

than half of the entrepreneurs applied trial-and-error to experiment and find the right

price. To be more detailed, the chart 4.2 shows that 56% of respondents agree or fully

agree that they applied trial-and-error to set the right price while only 20% of

respondents disagree or fully disagree.

Chart 4.2

Applied trial-and-error process to set the right price for product/service

35

The chart 4.3 illustrates that high technology entrepreneurs applied different methods

and approaches to deal with uncertainty coming from competitor’s strategies. While

32% respondents agree that such approach help them deal with competitors, a

significant percentage of respondents (24%) neither agree or disagree.

Chart 4.3

Applied trial-and-error process to quickly respond to competitor’s strategies

In short, as a dimension of effectuation, experimentation-driven approach is applied by many

high technology entrepreneurs in decision-making to deal with market uncertainty.


ENTREPRENEURS APPLY DIFFERENT EXPERIMENTAL METHODS TO DEAL

WITH MARKET UNCERTAINTY.

Table 4.4 shows the respondent’s applied methods to deal with market uncertainty. In this table,

N and % (N) shows the number of respondents who applied each method and its percentage

respectively. Looking at “Validate market need”, 68.29% of the respondents applied The Lean

Startup Method. This was followed by Design Thinking (46.34%), Agile Method (39.02%) and

Other (9.76%). Turning to “Set pricing”, also The Lean Startup Method has the highest figure

36

(56.10%). This was followed by Agile Method (26.83%), Design Thinking (24.39%) and Other

(14.63%). Finally, “Respond to competitors’ strategies” shows The Lean Startup as the highest

(46.34%), and this was followed by Design Thinking (31.71%), Agile Method (29.27%) and

Other (12.20%).

Table 4.4

Descriptive Statistics for Experimental Methods

Descriptive statistics for survey data - Experimental Methods

Experimentation respond to Market Uncertainty N Result % (N)

Validate market need 41

Agile Methodology 41 16 39.02%

The Lean Startup Methodology 41 28 68.29%

Design Thinking Methodology 41 19 46.34%

Other 41 4 9.76%

Set pricing



Design Thinking Methodology 41 10 24.39%

Other 41 6 14.63%

Respond to competitors' strategies



Design Thinking Methodology 41 13 31,71%

Other 41 5 12.20%

The survey results for experimental methods also help us verify our expectation that

entrepreneurs apply different experimental methods deal with Market uncertainty.

The Chart 4.4 Applied Method shows the result of the experimental methods that our

respondents applied to validate market need, to set pricing and to respond to competitors’

strategies.

37

Chart 4.4

Applied Method

Overall Trend

It can be clearly seen that a method applied the most for dealing with market uncertainty was

The Lean Startup at the average rate of 56.8%, sharply contrasting with others (Agile Method:

31.7%, Design Thinking: 34%, Other: 12.3%). 68% of the respondents applied The Lean

Startup to validate market need, 56% of them applied it to set pricing and 46% of them did it to

respond to competitors’ strategies. This can be an evidence that recently The Lean Startup has

become the most common and essential method among entrepreneurs in high technology

industries. It is significantly effective especially for dealing with market uncertainty.

To validate market need

The remarkable point is that entrepreneurs tend to apply different experimental methods to

validate market need the most. For instance, 46% of respondents applied also Design Thinking

to validate market need while only 24% of respondents applied it to set pricing and 32% of

them applied it to respond to competitors’ strategies. This can be paraphrased that entrepreneurs

pay their attention to market need the most, thus introducing different methods to validate it

more carefully and certainly. The respondents who chose Other also mentioned their own

39%

27% 29%

68%

56%

46%46%

24%

32%

10%15%

12%

TO VALIDATE MARKET NEED TO SET PRICING TO RESPOND TO COMPETITORS’ STRATEGIES

Applied Method

Agile Method The Lean Startup Design Thinking Other

38

methods such as survey, user test session, interview and literature review. Therefore, the

application of different methods is thought to be vital for the customer need validation.

To set pricing

The result of “to set pricing” shows that the most applied method is The Lean Startup. However,

the figures of the application of both Agile Method and Design Thinking are relatively low.

15% of respondents chose Other and they listed interview and A/B testing as their methods.

Therefore, methods to set pricing vary depending on the company.

To respond to competitors’ strategies

The application rate of The Lean Startup is way lower (46%) than it “to validate market need”

(68%). This shows that when responding to competitors’ strategies, entrepreneurs do not regard

The Lean Startup as important as when they validate market need. Instead, Agile Method and

Design Thinking are relatively considered to be as effective methods. In addition, 12% of

respondents chose Other and they used analysis of benchmarks, analysis of market condition

changes as their own effective methods, or they did not apply anything to respond to

competitors’ strategies.

To sum up, the trend of the experimental methods in this study was that entrepreneurs utilize

different methods at the same time but mostly based on The Lean Startup. The Lean Startup

itself can help entrepreneurs in high technology industries the most deal with market

uncertainty. However, especially for validating market need, the combination of the different

methods is expected to be more effective. Therefore, this result verified our expectation 3,

entrepreneurs apply different experimental methods to deal with market uncertainty.

39

5. CONCLUSION

High technology startups become a major factor influencing the world economy (Tadmor,

1997). However, entrepreneurs developing high technology products face particular problems.

Previously we mentioned that high technology startups involve complex environments, greater

degree of turbulence, higher level of uncertainty and shorter expected life cycle. Therefore,

high technology industries are required to accomplish the target in a short period of time

because of the uncertain potential time that a market will be available.

In addition, according to Sarasvathy (2001), under conditions of uncertainty, entrepreneurs

should adopt effectuation as a decision-making process. Therefore, the authors expect that

entrepreneurs in high technology industries will prefer to apply effectuation process, rather than

causation. However, the study has demonstrated that empirically, entrepreneurs in high

technology industries use both processes, but they prefer mainly apply causation process.

entrepreneurs have adapted their business processes and even their way of thinking to deal with

different situations. Thus, future studies on the implications and consequences of being causal

in the entrepreneurial field will be required.

The main objective of this research was to answer the research question: How do entrepreneurs

effectuate in decision-making to respond to market uncertainty in high technology

industries?

The findings demonstrate that entrepreneurs effectuate mainly being flexible, this is consistent

with the characteristics of high technology industries, where entrepreneurs are required to adapt

easily to changes, and take advantage of opportunities that arise. Furthermore, entrepreneurs

prefer to adapt their processes to the resources they have, thus entrepreneurs do not want to

compromise on the resources more than they are willing to lose. Lastly, entrepreneurs prefer to

experiment with different products and/or business models, as well to try different approaches

to find a business model that works.

The study proves that entrepreneurs in high technology industries applied experimental-driven

approach, which is a dimension of effectuation process. Therefore, the findings demonstrate

40

that the entrepreneurs effectuate through the trial-and-error process to deal with market

uncertainty. Previously, we mentioned that entrepreneurs face three main reasons for startup

failures, Validating Market Need, Responding to Competitors' Strategies and Setting the

Price, as variables of market uncertainty. The study demonstrates that 76% of entrepreneurs

apply the trial-and-error process to validate the market need, 56% of entrepreneurs choose trial-

and-error to set the price and 32% of entrepreneurs agree that trial-and-error help them deal

with Competitors’ strategies. Therefore, entrepreneurs effectuate applying experimental-driven

approach, through the trial-and-error process, mainly to validate the market need.

Different experimental methods are currently known, but little is known about how empirically,

entrepreneurs in high technology industries deal with market uncertainty. The

results demonstrate that 56.8% of entrepreneurs apply the Lean Startup to deal with market

uncertainty, followed by Agile Method (31.7%) and Design Thinking (34%). 68% of

entrepreneurs consider that the Lean Startup help them validate the market need, 56% of

respondents applied it to set the price and 46% of entrepreneurs choose the Lean Startup to

respond to competitors’ strategies. It is clearly seen that the Lean Startup becomes the most

preferred among entrepreneurs to deal with market uncertainty in high technology industries,

especially to validate the market need and setting the price.

Considering the limitations described below, the authors conclude that this study contributes a

theoretical and empirical knowledge to entrepreneurs in high technology industries. It provides

different methods that entrepreneurs currently use to deal with market uncertainty, especially

to address the three most relevant reasons for startup failures - Validating Market Need,

Responding to Competitors' Strategies and Setting the Price.

41

6. LIMITATIONS AND FURTHER RESEARCH

We did continuously improve the quality of this research throughout the two months of

investigation, however, our work is not without limitations. It is due to the time shortage and

limited resources of this topic in the field of entrepreneurship research. We acknowledge that

the limitations could be the opportunities for us, and other researchers as well, to conduct further

studies in the future.

6.1. LIMITATIONS

The biggest limitation of this research is its sample size. We planned to conduct at least 100

surveys of high technology entrepreneurs in order to have sufficient data to analyze. However,

we got 41 valid responses. The number of responses is not large enough to sufficiently

generalize the research results. In terms of geographical location of respondents, although the

authors tried to approach entrepreneurs in all over the world via different social networks that

they involve. The results that we got are mostly from Sweden, European and Asia countries.

Therefore, it is also the limitation of this study considering generalizations of the research

findings.

In this study, only basic analysis methods – descriptive statistics and frequency distribution

were applied to investigate the data while more advanced statistical methods could have been

applied to draw inferences from the collected data. It is partly because the main purpose of this

study is to identify which approach and tools high technology entrepreneurs applied to deal

with market uncertainty rather than studying the correlation among variances of effectuation

and experimentation-driven approach. However, if we could develop that further, we would

have more insights on how entrepreneurs effectuate in specific circumstances under sub-

dimensions of effectuation.

6.2. FURTHER RESEARCH

In the future longitudinal studies, it would be important to conduct in-depth studies about this

topic by applying more advanced research methodologies and analysis tools in bigger samples

of respondents. Therefore, future scholars can observe and provide more significant findings

42

on the correlation between effectuation and causation as well as their sub-dimensions in the

context of market uncertainty for startups.

As we mentioned in the theory and method parts, effectuation and causation have been

empirically measured by some researchers. However, there is a gap on studying the

measurement of experimentation-driven approach and market uncertainty in research literature

that we have developed by theoretical framework to investigate in this study. Thus, for future

research, it would be interesting to conduct study on measurement of experimentation-driven

methods and market uncertainty.

Furthermore, based on the finding of this study, major studies about the implications of

causation process and the consequences of being causal and effectual in the entrepreneurial

field will be needed.

Empirical research literature about effectuation in startup environment is still in its infancy,

thus there is a broad range of topics for scholars to explore in the future. In this research, we

investigate the topic in high technology industries only, however, it is highly possible that

effectuation could also help entrepreneurs deal with different types of uncertainty in different

industries.

43

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51

8. APPENDIX

APPENDIX 8.1: QUESTIONNAIRE

Effectuation in decision-making to respond to market

uncertainty in high technology industries

As a part of our Master’s thesis in the Entrepreneurship Program, at Uppsala University, we are conducting a survey that investigates how high technology entrepreneurs deal with market uncertainty. This survey consists of 25 multiple-choice questions that may take you around 10 minutes to complete. We highly appreciate your help on answering this survey. Any information obtained in connection with this study will remain confidential.

Due to the scope of the study, please carry out this survey only if you are an entrepreneur who

started a business in high technology industries such as Computers, Computer Software,

Medical Equipment, Aerospace, Automotive, Artificial Intelligence, Biotechnology, Bioinformatics, Robotics, Telecommunications, Machinery & equipment etc.

* Required

52

Respondents Details

1. What is your company's product? *

2. What industry does your company belong

to? *

3. Does your company already have customers? *

Mark only one oval.

Yes

No

4. Your email if you would like to receive our

final research paper for this topic (Optional)

53

Effectuation and causation in decision-making

Please answer the following questions from “Fully disagree” to “Fully agree”, to what extent do you agree? Please answer this questionnaire on the basis of reflecting on your own company.

Q1. We experimented with different products and/or business models *

Mark only one oval.

Q2. The product that we now provide is essentially the same as originally conceptualized


Q3. The product that we now provide is substantially different than we first imagined *


Q4. We tried a number of different approaches until we found a business model that worked


Q5. We were careful not to commit more resources than we could afford to lose


Q6. We were careful not to risk more money than we were willing to lose with our initial idea


Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

54

Q7. We were careful not to risk so much money that the company would be in real trouble

financially if things didn't work out


Q8. We allowed the business to evolve as opportunities emerged *Mark only one oval.

Q9. We adapted what we were doing to the resources we had


Q10. We were flexible and took advantage of opportunities as they arose


Q11. We avoided courses of action that restricted our flexibility and adaptability *


Q12. We used a substantial number of agreements with customers, suppliers, other

organizations and people to reduce the amount of uncertainty


Q13. We used precommitments from customers and suppliers as often as possible *


Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

55

Q14. We analyzed long run opportunities and selected what we thought would provide the best

returns


Q15. We developed a strategy to best take advantage of resources and capabilities


Q16. We designed and planned business strategies *


Q17. We organized and implemented control processes to make sure we met objectives


Q18. We researched and selected target markets and did meaningful competitive analysis


Q19. We had a clear and consistent vision for where we wanted to end up


Q20. We designed and planned production and marketing efforts


Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

56

Experimentationdriven approach

In this section, we will ask you questions related to experimentation that are simply defined as follows:

Experimentation is illustrated by an iterative process of trialanderror with learning. It means that the process of building the business is not fixed but decided based on the best available information.

Please answer the following questions from “Fully disagree” to “Fully agree”, to what extent do

you agree? Please answer this questionnaire on the basis of reflecting on your own company.

Q21. I applied trialanderror process to validate the market need. *


Q22. I applied trialanderror process to set the right price for my product/service. *


Q23. I applied trialanderror process to quickly respond to competitors' strategies. *


Experimental methods

In this section, we will ask you questions related to experimental methods that are simply defined as follows:

Agile methodology is a framework for delivering products quickly and efficiently. It refers to an iterative, incremental method to build any product in a highly flexible and interactive manner. An example is its application in Scrum, an original form of agile software development.

The Lean startup is the method that iterates the buildmeasurelearn feedback loop to help entrepreneurs deal with the context of uncertainty in building their business. Minimum Viable Product (MVP) and Pivot are the important concepts of The Lean startup methodology.

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

Disagree

Neutral

Agree

Fully Agree

Fully Disagree

57

Design thinking can be described as a discipline that uses the designer’s sensibility and methods to match people’s needs with what is technologically feasible and what a viable business strategy. It creates valuable solutions by using customer empathy and creativity.

Please answer the following questions on the basis of reflecting on you and your company.

Q24. What experimental methods did you apply to validate market need?

*Check all that apply.

Other:

Q25. What experimental methods did you apply to set the right price for your

product/service? *


Other:

Q26. What experimental methods did you apply to quickly respond to competitors' strategies?*


Other:

We have done the survey! Thank you so much for your help!

Agile Methodology



Agile Methodology


Agile Methodology

58

APPENDIX 8.2: SURVEY RESULT

EFFECTUATION IN DECISION-MAKING TO RESPOND TO MARKET

UNCERTAINTY IN HIGH TECHNOLOGY INDUSTRIES

EFFECTUATION AND CAUSATION IN DECISION-MAKING

Q1. We experimented with different products and/or business models.

Q2. The product that we now provide is essentially the same as originally conceptualized.

59

Q3. The product that we now provide is substantially different than we first imagined.

Q4. We tried a number of different approaches until we found a business model that worked.

Q5. We were careful not to commit more resources than we could afford to lose.

60

Q6. We were careful not to risk more money than we were willing to lose with our initial idea.

Q7. We were careful not to risk so much money that the company would be in real trouble

financially if things didn't work out

Q8. We allowed the business to evolve as opportunities emerged

61

Q9. We adapted what we were doing to the resources we had

Q10. We were flexible and took advantage of opportunities as they arose.

Q11. We avoided courses of action that restricted our flexibility and adaptability.

62

Q12. We used a substantial number of agreements with customers, suppliers, other organizations

and people to reduce the amount of uncertainty.

13. We used pre-commitments from customers and suppliers as often as possible.

Q14. We analyzed long run opportunities and selected what we thought would provide the best

returns.

63

Q15. We developed a strategy to best take advantage of resources and capabilities.

Q16. We designed and planned business strategies.

Q17. We organized and implemented control processes to make sure we met objectives.

64

Q18. We researched and selected target markets and did meaningful competitive analysis.

Q19. We had a clear and consistent vision for where we wanted to end up.

Q20. We designed and planned production and marketing efforts.

65

EXPERIMENTATION-DRIVEN APPROACH

Q21. I applied trial-and-error process to validate the market need.

Q22. I applied trial-and-error process to set the right price for my product/service.

Q23. I applied trial-and-error process to quickly respond to competitors' strategies.

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EXPERIMENTAL METHODS

Q24. What experimental methods did you apply to validate market need?

Q25. What experimental methods did you apply to set the right price for your product/service?

Q26. What experimental methods did you apply to quickly respond to competitors' strategies?

Effectuation in decision-making to respond to market...

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