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The link between business orientation of the entrepreneur
with effectuation and causation
Auteur: BSc. V.L.C. Meub
Studentnummer: 1545833 Telefoonnummer: 06-52493883 E-Mail: Vincent_meub@hotmail.comAdres: Jan de hartogplein 34 2353LL, Leiderdorp
Begeleider: Prof. A.J. Groen
Datum: August 2013
Rijksuniversiteit Groningen
Faculteit Economie & Bedrijfskunde
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Abstract: this paper attempts to provide a richer understanding on when certain
entrepreneurial logics (causation & effectuation) are most likely to be used by
entrepreneurs. Therefore the 4 dimensions of Quinn’s competing values model are used to determine the orientation of the entrepreneur, this is linked to causation and
effectuation in using statistical analyses. Data has been collected by the VentureLab of the University of Twente. The main findings Indicate that in a high-tech start-up context, effectual logic indeed is more likely to be used by the entrepreneur.
Key words: entrepreneurship, entrepreneur, effectuation, causation, quantitative study
Word count: 8,203
Acknowledgements: Special thanks go out to Professor A.J. Groen who supervised the
master thesis project and helped me envision my goal during this project. Furthermore, special thanks go out to Professor J. Kraaijenbrink, head of the VentureLab of the
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Index
Introduction………. 4
Theoretical framework……….5
- Causation & effectuation………. 5
- Quinn’s competing values model……… 10
- The high-tech start-up context……….11
Methodology………..14
- Data collection………....15
- Sample……….15
- Methods………..16
- Hypothesis testing………21
- Validity & reliability of the analyses……….22
Results………24
- Factor analysis………24
- Cronbach’s alpha………...24
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Introduction
The use of effectual and causal entrepreneurial logic is a developing theme in contemporary literature (Fisher, 2012). Since Sarasvahty (2001) coined the terms effectuation and causation, attempts have been made to further refine the meaning and applicability of these concepts. However, the literature does not provide a thorough insight in which situations either concept is more appropriate. Data has been collected by VentureLab Twente which might be able to provide a more comprehensive view of which entrepreneurial logic seems to be the preferred choice of entrepreneurs under certain circumstances. Therefore, the researcher sets out to provide an insight in these phenomena which can contribute to a better comprehension of when these
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Theoretical framework
Causation and effectuation (Sarasvathy):
The seminal work on causation and effectuation by Sarasvathy (2001) provides a useful definition of what these terms actually entail and furthermore, provides a
comprehensive example to illustrate what the differences are between them. The following definition is given: “Causation processes take a particular effect as given and focus on selecting between means to create that effect. Effectuation processes take a set of means as given and focus on selecting between possible effects that can be created with that set of means”(Sarasvathy, 2001).
The well-known example that has been cited many times since is that of the chef that has to create a meal in either a causal or effectual way. “Imagine a chef assigned the task of cooking dinner. There are two ways the task can be organized. In the first, the host or client picks out a menu in advance. All the chef needs to do is list the ingredients
needed, shop for them, and then actually cook the meal. This is a process of causation. It begins with a given menu and focuses on selecting between effective ways to prepare the meal. In the second case, the host asks the chef to look through the cupboards in the kitchen for possible ingredients and utensils and then cook a meal. Here, the chef has to imagine possible menus based on the given ingredients and utensils, select the menu, and then prepare the meal. This is a process of effectuation. It begins with given
ingredients and utensils and focuses on preparing one of many possible desirable meals with them” (Sarasvathy, 2001).
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Furthermore she states that: “The distinguishing characteristic between causation and effectuation is in the set of choices: 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. Both causation and effectuation are integral parts of human reasoning that can occur simultaneously, overlapping and intertwining over different contexts of decisions and actions” (Sarasvathy, 2001).
Causation and effectuation (Fisher):
Fisher (2012) illustrates the differences between causation and effectuation by
answering 4 major questions relating to the process, these questions are: What factors form the explanation? How are the factors identified related to outcomes of interest?
Why can we expect the proposed relationships to exist? Who, Where, When? The
assumptions and limitations underlying the theory. He also illustrates the answers to these questions for entrepreneurial bricolage which is however, irrelevant for this paper. As this provides us with more in-depth information about both effectuation and causation from a business perspective then the example mentioned by Sarasvathy, we will now sum up the key aspects relating to these questions for both processes. The following is all part of Fishers theoretical overview with additional explanations of the factors of interest in this study:
Causation:
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In other words, the factors forming part of the explanation using causal logic are:
opportunity identification, opportunity recognition, opportunity exploitation and taking account of the environment that forms the context of the opportunity. As you can see this is a process with a clear structure starting with a perceived opportunity before actually acquiring the resources needed to exploit the opportunity.
How Are the Factors Identified Related to Outcomes of Interest? “In the causal view
of entrepreneurship, markets are rarely created. Markets, and entrepreneurial opportunities within those markets, are assumed to preexist (Casson, 1982; Shane & Venkataraman, 2000). The goal of the entrepreneur is to grab as much of the existing market universe as possible (Kotler, 1991). Therefore, in identifying “how” causation factors are related to new venture outcomes, it is argued that identifying opportunities, focusing on goals, analyzing returns, competitive analysis, and controlling the future will be more strongly related to outcomes for later entrants into an established industry (Sarasvathy, 2001).”
As you can see causal logic has been regarded as an entrepreneurial logic most suited for use in more stable environments by the literature. This makes sense if you look at the factors that form part of the explanation. The factors mentioned like opportunity recognition presume that there is an opportunity that can be perceived by an
entrepreneur, who may decide on exploiting it or not. In effectual logic this process seems to be inverted as entrepreneurs evaluate their strengths and capabilities and use these to create an opportunity.
Why Do the Proposed Relationships Exist? “The explanation for “why” causation logic
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Gartner). “By helping firm founders to make decisions, to balance resource supply and demand, and to turn abstract goals into concrete operational steps, business planning reduces the likelihood of venture disbanding and accelerates product development and venture organizing activity” (Delmar & Shane, 2003, p. 1165).”
The most important part to distinguish on is the formal business planning process mentioned above, this is something that will be less likely to be used under effectual logic. Moreover, formal planning may be greatly less effective in settings where uncertainty is high.
Boundary Conditions: “For the causal process to be applicable, the market for a
product or service needs to exist prior to exploitation, and historic information must be available to evaluate opportunities and assess means to exploit those opportunities (Sarasvathy, 2001). The existence of a market, and the existence of information about the market, is therefore a boundary condition for this approach.”
In short, the available information mentioned above to evaluate opportunities and assess means to exploit those opportunities will be scarce if at all there when entering a new market or even creating new markets. It is these conditions where effectual logic should be more suitable than causal logic (Sarasvathy).
Process: The process underlying the causal approach to entrepreneurship is reflected
in Figure 1. The process flow diagram shows how the recognition and evaluation of opportunities allow for opportunity identification that leads to the establishment of goals and a plan to exploit the identified opportunity. Thereafter, the entrepreneur raises resources to develop and market a solution and, in turn, engages in the processes of creating something to address the opportunity identified, which hopefully results in entry into the marketplace, allowing for feedback that leads to further refinements of the product or service” (Fisher, 2012).
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Effectuation:
What Factors Form Part of the Explanation? “The key factors that are part of the
explanation for the role of effectual logic in entrepreneurship include (1) starting with means as opposed to establishing end goals; (2) applying affordable loss instead of expected return when evaluating options; (3) leveraging relationships instead of competitive analysis when assessing relationship with other individuals and
organizations; and (4) exploiting and not avoiding contingencies (Sarasvathy, 2008). Starting with means describes how entrepreneurs make important decisions by
focusing on the resources under their control—asking “Who am I?”; “What do I know?”; and “Whom do I know to uncover opportunities?” (see Figure 2)—rather than focusing on a predefined end goal. They engage in activities and allow goals to emerge and change as they exploit the means under their control, thereby engaging in an ongoing process of exploration (March, 1991) to uncover options for what they can do. Chandler, DeTienne, McKelvie, and Mumford (2011) operationalized this as follows: Affordable loss entails making decisions based on what one is willing to lose, and committing a specific amount of resources to an endeavor with the understanding and acceptance that such resources may be lost. The alternative is to make resource allocation decisions based on probabilities and expected returns (Latene, 1959). Leveraging strategic
relationships suggests that entrepreneurs can focus their attention on building
partnerships rather than doing systematic competitive analysis. Competitive analysis has traditionally been a key input into strategy formulation (Porter, 1979), yet
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Although the above largely speaks for itself, it becomes quickly apparent that the key input to effectual logic is the entrepreneur himself, and not some perceived opportunity in the environment. However, changes in the environment can spark the entrepreneur to use his skillset in new ways which may or may not lead to future returns. The outcome on forehand is not estimated or measured, because there rarely are any reference points using this effectual logic.
How Are the Factors Identified Related to Outcomes of Interest? “It is argued that
effectual processes are related to uncovering and exploiting opportunities in new markets with high levels of uncertainty (Sarasvathy, 2001). Because of this, Sarasvathy suggests that successful early entrants into a new industry are more likely to have used effectuation processes than causation processes. Because effectuation does not involve elaborate planning and prediction, the costs associated with such activities are reduced (Bhide, 2000; Mintzberg, 1994). Sarasvathy (2001) argues that if new firms created through processes of effectuation fail, they will fail early and/or at lower levels of investment than those created through processes of causation.”
The affordable loss principle of effectual logic is highlighted in the above. This is the major explanation that firms using effectual logic fail early and at lower levels.
Why Do the Proposed Relationships Exist? Effectuation builds on the decision theory
literature that suggests that if decision makers believe they are dealing with relatively unpredictable phenomena, they will try to gather information about future trends through experimental and iterative learning (e.g., Ries, 2011). The elements of effectuation are enactments of experimental and iterative learning techniques that enable entrepreneurs to discover information about the future as time passes (Sarasvathy, 2001).
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Boundary Conditions: “Effectuation is relevant in dynamic, nonlinear, and ecological
environments (Sarasvathy, 2001) where the future is unknowable and, therefore, not measurable. The theory of effectuation assumes that entrepreneurial opportunities are subjective, socially constructed, and created by an entrepreneur through a process of enactment in which “managers construct, rearrange, single out, and demolish many ‘objective’ features of their surroundings, and literally create their own
constraints”(Weick, 1979, p. 243).
Process: “The process flow of an effectual approach to entrepreneurship is captured in
Figure 2. The entrepreneurial process is initiated with an examination of the means available to an entrepreneur. The questions “Who am I?”, “What do I know?”, and
“Whom do I know?” allow for an examination of the means available to an entrepreneur, which allows him or her to consider what he or she can do (Sarasvathy & Dew, 2005). Through interacting with others and engaging with stakeholders, the entrepreneur discovers new means and establishes new goals that allow for revaluation of means and possible courses of action (Sarasvathy & Dew).”
Figure 2, effectuation process:
Quinn’s competing values model:
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paradoxical nature of effective management, which must be at once be adaptable and flexible yet stable and controlled, and which must be strategic and goal-oriented while also behaving pragmatically, attending to human resources and managing risk”
(Wilkinson, 2010). Furthermore, “Quinn’s model identifies eight leadership roles (facilitator, mentor, innovator, broker, producer, director, coordinator, and monitor) that are required of managers, in varying degrees, in any type of business or operation. Quinn’s model is based on two dimensions—focus (internal focus or external focus) and structure (stability or flexibility)” (Wilkinson, 2010).
One could argue that the dimensions themselves can be split up in pairs of dimensions leaving the following 4 dimension (Internal, external, flexibility and control). For the purpose of this paper, distinguishing these separate dimension will hopefully contribute to a better understanding under what leadership profile an entrepreneur chooses to use effectual or causal logic in running his day to day business. Whether entrepreneurs are internally or externally oriented is something that is prone to change and evolve over time. Emphasis on both the internal and external dimension simultaneously is also possible. So neither of the dimensions are mutually exclusive meaning that an entrepreneur showing strong internal orientation can also show strong external orientation.
The following part is another paraphrase from Wilkinson (2010): “Fisher and Muller (2005) defined leadership as “the art and science of continuous achievement and notable advancement in accomplishing the vision, goals, and objectives of a passionate constituency with the informed consent and willful support of that constituency over a sustained period of time”. They also stated that “leaders look outside as well as inside their organizations, while others, such as managers and administrators, are focused only on the mechanics of the organization, not its direction”.
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interested to see if this stronger emphasis for either has any connection with a stronger preference for using causal or effectual logic.
Figure 3, Quinn’s competing values framework
The high-tech start-up context:
Although there are various and sometimes inconsistent definitions given for high-tech, or high-tech industries, for this paper the following definition will be used: “High-tech industries or companies engage in production by the use of resources that are state-of-the-art and show at least some degree of newness to the market.” Some definitions focus on the output of the process like high-tech products, although this can be
considered high-tech this definition offers no contribution to this research because we are looking at the process and not at outputs.
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The fact that we are looking at companies that are operating in a high-tech context would make it plausible to assume that moving into uncertain, dynamic and new industries (or creating a new industry) would lead to a preference for effectual approaches. This leads to the formulation of the first hypothesis of this paper:
Hypothesis 1: Entrepreneurs that start a business in a high-tech context prefer to use effectual approaches rather than causal approaches.
Please note that preference will be operationalized by what the entrepreneurs actually have done so far, or are currently still doing. They are not asked what they would rather do, but they are asked what they have done so far to determine whether they show a preference for either entrepreneurial logic.
Internal emphasis focuses the well-being and development of people and resources within the organization while an external emphasis focuses on the well-being and development of the organization. How the choice for either emphasis relates to the preference for an effectual or causal approach is not easy to predict and the literature is somewhat unclear about this, therefore an exploration hereof is warranted. This leads to the formulation of the second hypothesis.
Hypothesis 2: Internal or external emphases do not influence the preference of the entrepreneur for either an effectual or causal approach.
The control- flexibility dimension has a clearer relation to the preference for either effectual or causal approaches because causation shows a lot of characteristics of
control while effectuation seems to be more connected to flexibility. Therefore our third hypothesis will test whether or not this assumption is accurate.
Hypothesis 3a: An entrepreneur emphasizing control will show a preference for causal approaches.
Hypothesis 3b: An entrepreneur emphasizing flexibility will show a preference for effectual approaches.
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the following sentence: “Under what circumstances which types of processes provide particular advantages and disadvantages is an issue to be resolved through future empirical studies” (Sarasvathy, 2001).
Although there will be no direct answer to this stemming from this research, it will provide some insight in how the choice for either is affected by dimensional orientation of the entrepreneur.
Methodology
Data collection
Data has been collected by VentureLab Twente, which is an initiative of the University of Twente (Netherlands) to provide help for entrepreneurs who are in the start-up phase of a high-tech venture. This help is provided in the form of coaching and trainings. In exchange for this help, the entrepreneurs filled in a number of surveys provided by the VentureLab Twente. As the VentureLab is an ongoing initiative, more entrepreneurs have been joining and are still joining since the beginning of VentureLab Twente. There are multiple surveys that the entrepreneurs have to fill out. Upon joining the VentureLab the entrepreneurs are asked to fill out an intake survey. This survey is particularly focused to gain an insight into the personal profile of the participant. After filling out the intake survey the entrepreneurs are asked to respond to follow-up surveys on a 4-monthly basis. The purpose of these 4-monthly surveys is to keep track of the progress, questions and plans the entrepreneurs have concerning their personal, team and business development. At the end of the program, the entrepreneurs are asked to fill out an end-evaluation survey. During this end-evaluation, comparable questions regarding their attitudes, businesses and experiences are asked as in the intake and 4-monthly surveys. Furthermore, an assessment of the VentureLab itself was added.
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is stated that the answers will be used to conduct research on how to continually improve the program. It is then stressed that for these reasons it is important that the entrepreneurs fill out the surveys as honestly as possible. Furthermore the remarks state that in the view of VentureLab, very different persons with very different backgrounds can be successful entrepreneurs. For that reasons there are no right or wrong answers in these questionnaires. Lastly it is explained that the questionnaires are not an assessment and do not affect the decision whether or not someone will be accepted to the program.
The confidentiality issue is addressed by ensuring that the answers of the
entrepreneurs will be treated as strictly confidential and be treated anonymously. The entrepreneurs are ensured that any report that might be written would typically report only on aggregated and not individual results.
Furthermore the participants are instructed that the questionnaire consists of multiple choice questions, and then the duration is mentioned. The participants are advices to take a short break after part 3 of the survey. Lastly the participants are asked to fill out the questions as honestly and quickly as possible, and to fill out the first answer that comes to mind.
All surveys were conducted in English.
Sample
All participants in the sample are entrepreneurs who have Dutch backgrounds, at least in the sense that they have started a venture in the Netherlands. The University of Twente is situated in the mid-eastern region of the Netherlands and all ventures of the participants of VentureLab are located within geographical proximity to the University. A prerequisite to be accepted to this VentureLab program is that the venture should be part of the high-tech industry. In the literature various different definitions are given for high-tech industry but there does not seem to be a consensus on what this term should actually entail. Within the sample of the VentureLab participants are active in the high-tech industry in one of the following ways: they either use high-high-tech production
state-of-the-17
art goods or services. Therefore, both of these examples refer to high-tech industry throughout this paper.
The data collected from the sample was delivered to the researcher in two separate excel sheets, one containing the answers to the intake survey, the other containing the answers to the 4-monthly evaluations and the end evaluation. In total, 185 respondents filled out the intake which was delivered to the researcher. Unfortunately, not all
respondents who participated in the intake participated in all other evaluations (4-monthly and end evaluation). This became apparent when the researcher sorted the responses of the different evaluations on the basis of the respondents ID. Only
respondents that participated in the separate evaluations are included in the sample. Therefore, some of the responses had to be eliminated from the sample which reduced the usable sample size to 165 respondents.
Methods
In order to encapsulate the different aspects of Quinn’s separate orientations (internal, external, flexibility and control) as proposed in the competing values model (Quinn & Rohrbaugh, 1983), the answers to multiple questions were included to grasp the proposed concepts as well as possible. The questions had to relate to the key aspects that these orientations entail. Data was exported to SPSS Statistics 20, a suitable program to manipulate the data in order to create meaningful results for this paper. Measurement scales for Quinn’s dimensions
4 separate scales will be created including the selected responses to questions that are linked to the separate concepts on the basis of the literature used by the researcher. For each of these scales a factor analysis will be performed. If the initial eigenvalues total table shows only one factor with a value above 1, all items can be included in the scale for Cronbach’s alpha testing. In case there is more than 1factor, we will need to
determine on what factor each of the variables load. We will do this by looking at the component matrix in SPSS. Only variables that score the highest on the same factor or component should be combined into the scale (Pallant, 2010).
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values of alpha, ranging from 0.70 to 0.95. A low value of alpha could be due to a low number of questions, poor interrelatedness between items or heterogeneous
constructs. For example if a low alpha is due to poor correlation between items then some should be revised or discarded” (Tavakol & Dennick, 2011). However, “If alpha is too high it may suggest that some items are redundant as they are testing the same question but in a different guise. A maximum alpha value of 0.90 has been
recommended” ( Tavakol & Dennick, 2011). Therefore, Cronbach’s alpha values between 0.70 and 0.90 will be deemed acceptable throughout this paper.
All questions included in the separate scales were to be answered on a 5-point Likert scale ranging from low to high. The questions included in each scale will be presented beneath.
Internal
The following questions have been included in the internal scale:
Consider how you have worked in your business (-unit) the last year and indicate the degree to which you agree or disagree with each of the following statements. In case you are on your own, you can substitute ‘I’ for ‘We’:
- We were careful not to commit more resources than we could afford to lose.
- We were careful not to risk more money than we were willing to lose with our initial idea.
- We were careful not to risk so much money that the company would be in real trouble financially if things didn't work out.
- We allowed the business to evolve as opportunities emerged. - We adapted what we were doing to the resources we had.
External
The following questions have been included in the external scale:
Consider how you have worked in your business (-unit) the last year and indicate the degree to which you agree or disagree with each of the following statements. In case you are on your own, you can substitute ‘I’ for ‘We’:
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best returns.
- We researched and selected target markets and did meaningful competitive analysis. - We had a clear and consistent vision for where we wanted to end up.
- We used pre-commitments from customers and suppliers as often as possible. - We developed a strategy to best take advantage of resources and capabilities.
Flexibility
The following questions have been included in the flexibility scale:
To what extent are you confident that you can perform the following tasks successfully? - See new market opportunities for new products/services.
- Discover new ways to improve existing products/services. - Identify new areas for potential growth.
- Tolerate unexpected changes in business conditions.
Consider how you have worked in your business (-unit) the last year and indicate the degree to which you agree or disagree with each of the following statements. In case you are on your own, you can substitute ‘I’ for ‘We’:
- We were flexible and took advantage of opportunities as they arose.
Control
The following questions have been included in the control scale:
Consider how you have worked in your business (-unit) the last year and indicate the degree to which you agree or disagree with each of the following statements. In case you are on your own, you can substitute ‘I’ for ‘We’:
- We designed and planned business strategies.
- We organized and implemented control processes to make sure we met objectives. - We designed and planned production and marketing efforts.
- We used a substantial number of agreements with customers, suppliers and other organizations and people to reduce the amount of uncertainty.
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The degree to which entrepreneurs have engaged in either effectual and/or causal business logic is measured by predetermined pairs of questions in the survey. Each pair contains a question relating to effectual logic and a question relating to causal logic. These pairs have been adapted from Wiltbank et al. (2009) by the researchers of VentureLab. Still, the same process of factor analysis and Cronbach’s alpha testing as with the 4 dimensions will be applied to check for internal consistency. Each pair consists of 2 sub questions, A and B. Which of these sub questions refers to effectuation or causation is assigned randomly, as to avoid recognition by the respondents.
Furthermore, it is relevant to point out that each respondent answered to each question, whether it was about using effectual or causal logic.
The questions relating to the measurements scales of both effectuation and causation will now be presented.
Effectuation
The following questions have been included in the effectuation scale:
There are various ways to start and run a business (-unit) and we would like to know the approach that you have followed so far. Please indicate the degree to which you agree or disagree with each of the following statements. In case you have not been involved in particular activities, please imagine how you would perform them: As you assemble information on your business, you:
- Talk with people you know to enlist their support in making this become a reality. As you develop a marketing approach for your product you:
- Imagine possible courses of action based on your prior experience.
When you think about the uncertainty of a market for your idea, you move forward anyway because:
- Your expertise allows you to influence that uncertainty. As you manage product development, you are driven by:
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If you look at predictions for where potential markets are heading, you: - Discount them as they do not incorporate the impact of your innovation. In setting up your business (-unit), you base your strategy on:
- What you are capable of, given the means available to you.
As you learn about the expectations other people have for this industry, you:
- Imagine ways your venture will change aspects of the situation they are forecasting.
Causation
The following questions have been included in the causation scale:
There are various ways to start and run a business (-unit) and we would like to know the approach that you have followed so far. Please indicate the degree to which you agree or disagree with each of the following statements. In case you have not been involved in particular activities, please imagine how you would perform them: As you assemble information on your business, you:
- Study expert predictions of where the market is “heading”. As you develop a marketing approach for your product you: - Research the competitors’ approaches.
When you think about the uncertainty of a market for your idea, you move forward anyway because:
- Your actions can create a future you value.
As you manage product development, you are driven by:
- Comparing your progress against the development of competitors. If you look at predictions for where potential markets are heading, you:
- Use them to create forecasts of what your business(-unit) might accomplish over time. In setting up your business (-unit), you base your strategy on:
- Relevant forecasts and analyses.
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Non-responses on single items in the scale
Whenever a scale is created from multiple items in the above mentioned way, one major drawback becomes apparent. Whenever a participant failed to respond to one of the questions in the scales, the scale measure will show a non-response for that participant. Whenever using one scale to sort the responses of another scale, the non-response becomes even higher. because the chance of failure to answer on of the items included in either scale becomes higher. Although this will lead to some more non-responses, the researcher embraces this approach over the alternative, which would be using the average scores on the other items included in the scale to estimate the value of the non-response item.
Comparing the high and low scores of Quinn’s dimensions in relation to effectuation and causation
Although not explicitly mentioned in the hypotheses, the researcher aims to get a better understanding of the entrepreneurial logic used (effectuation or causation) for the highest and lowest scores on each of the 4 dimensions of the competing values model. How this will be done will now be illustrated by an example:
We want to know whether scoring high or low on the internal dimension affects the scores on the effectuation scale. We then use the scores on the internal dimension to sort the respective score on the effectuation scale. A new variable is created in SPSS which includes both the high and the low score (top 50% vs. low 50% scores). A grouping variable is created assigning the value of 0 to the high scoring group, and assigning the value 1 to the low scores. Using the independent sample t-test will now allow us to compare the mean scores on the effectuation scale of both groups.
Furthermore, the significance levels of found scores will be reported by the SPSS output as well.
Hypothesis testing
Independent sample t-test
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(internal vs. external and flexibility vs. control) will be used. This approach is chosen because we are interested in the differences between those that clearly lean towards either of the dimensions.
It is important to note that (e.g.) people that score high on the internal dimension, could also be among the top scores on the external dimension. This could lead to a
comparison of the same respective score on either the effectuation or causation scale. For that reason, the respondents ID will be checked in each situation where this problem could occur. Then the respective scores on the relevant dimensions will be compared, the respondent will then be assigned to the group on which he has the highest score. In case of an identical score on both relevant dimensions, the respondent will be withdrawn from both groups, as it would be unjustified to assign him to either group.
For means comparisons like these, the independent sample t-test is adequate. As the test allows the researcher to form two groups based on his own selection criteria. This paper focuses on testing whether scoring high on certain dimensions shows a
relationship with the scores on certain entrepreneurial logics used by the respondents. Means comparison at the very least will gain some insight whether the hypothesized relationships exist or not.
Other means comparison methods can be considered less adequate because they either refer to a pre- and post-measurement (paired sample t-test) to isolate the effect of a certain event, which is not the case in this paper. Or they don’t allow the sorting by high scoring groups based on the chosen criteria (t-test).
For all of the independent sample t-tests significance levels of 95% will be used to ensure statistical soundness of the results.
Validity & reliability of the analyses
Validity
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not set any standards to measure the dimensions of the competing values model (to the best of the researcher’s knowledge) the conceptualization of these aspects has been done very diligently. It has been attempted to convert and connect the concepts with the available data as adequate as possible. Nonetheless, the researcher refrains from
proclaiming that no improvements could be made. However, using statistical sound methods to combine relating questions into scales should definitely increase the internal validity to nearly the highest extent the researcher can influence. Those attempts will be made.
External validity refers to the data’s ability to be generalized across people, settings and times (Cooper & Schindler, 2006). As mentioned before in this paper, the data was collected from a very specific setting, namely from a high-tech business start-up context in the east of the Netherlands. Keeping this in mind the results of this research might have been completely different in in a different setting. This is a major drawback. However, this paper is not aimed at creating an empirical theory. It is aimed at testing the theoretical foundations in a specific setting. Considering a completely different setting, the hypotheses could have been dramatically different. For the purpose of testing the theoretical foundation in this particular setting, the setup of this research seems to be adequate.
The content validity of a measuring instrument is the extent to which it provides
adequate coverage of the investigative question guiding the study (Cooper & Schindler, 2006). For the above mentioned reasons about testing the theoretical foundations in a specific setting, content validity of this study can be considered high.
Reliability
Reliability refers to the degree in which the measurement instrument supplies consistent results (Cooper & Schindler, 2006). To the best of the researcher’s
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Results
Factor analysis
Factor analysis of the separate item scales was performed to ensure that all the
preselected items could be included in the scales. Factor analysis of all 4 scales from the dimensions of the competing values model showed the following result. In the
eigenvalues total table for each scale only one factor had a value above one. This indicates that all variables should be included in the scale (Pallant, 2010).
The effectuation and causation scales showed something slightly different. When looking at the eigenvalues total tables there were 2 items showing a score above 1 on both scales. Theory dictates that whenever this is the case that only variables that score the highest on the same factor or component should be combined into the scale. The component matrix however showed that all variables loaded most heavily on the same factor (Pallant, 2010). Therefore inclusion of all items in the scale was warranted.
Cronbach’s Alpha
The results of the Cronbach’s alpha tests for the 4 dimensions of the competing values model are presented in the table below:
Table 1:
Scale: Number of items: Cronbach’s alpha:
Internal 5 0.840
External 5 0.776
Flexibility 5 0.776
Control 4 0.773
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The results of the Cronbach’s alpha test for the effectuation and causation scale are presented in the table below:
Table 2:
Scale: Number of items: Cronbach’s alpha:
Effectuation 7 0.729
Causation 7 0.761
Both scales fall into the acceptable range of 0.70 to 0.90, therefore continuing the analyses with these scales is warranted. Furthermore, all items included in both scales contributed to a higher value of Cronbach’s alpha.
High vs. low scores on competing values dimensions in relation to effectuation
and causation
This section of the paper was mainly explorative in nature and reports on how high or low scores on each of the 4 dimensions influence the scores on effectuation and
causation. Following tables reports the mean scores on effectuation and causation for the high and low scoring groups of the dimensions.
The * symbol will be used throughout the results section to indicate the statistical significance of at least 95% of the result.
Table 3: high/low scores in relation to effectuation
Scale/Score Mean effectuation Significance
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Table 4: high/low scores in relation to causation
Scale/Score Mean causation Significance
Internal/high 3.54 .726 Internal/low 3.50 .726 External/high 3.78 .000* External/low 3.25 .000* Flexibility/high 3.68 .006* Flexibility/low 3.36 .006* Control/high 3.83 .000* Control/low 3.21 .000*
Higher scores on each of the 4 dimensions lead to higher scores on effectuation and causation with one exception, high or low scores on the internal dimension do not affect the scores on causation.
Hypotheses
Hypothesis 1: in a high-tech start-up context in general effectual logic is more likely to be used.
The following table presents the results of the independent sample t-test where group 0 represents the overall scores on the effectuation scale compared to the overall scores on the causation scale (group 1).
Table 5:
Scale Mean score Significance
Effectuation 3.74 .005*
Causation 3.52 .005*
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Hypothesis 2: Internal or external emphases do not influence the preference of the entrepreneur for either an effectual or causal approach.
Below, 2 tables will be presented. The first represents the high scores of both the internal and external scale in relation to effectuation.
Table 6: high scores of internal vs. external orientation in relation to effectuation
Scale Mean score Significance
Internal 3.89 .900
External 3.90 .900
The second table represents the high scores of both the internal and external scale in relation to causation.
Table 7: high scores of internal vs. external orientation in relation to causation
Scale Mean score Significance
Internal 4.38 .004*
External 4.14 .004*
Hypothesis 2 is partially supported!
Whether an entrepreneur is more internally or externally oriented has no effect on the use of effectual logic. However, more internally oriented entrepreneurs seem to have a slightly but significantly higher preference for causal approaches than externally oriented entrepreneurs.
Hypothesis 3a: An entrepreneur emphasizing control will show a preference for causal approaches.
29 Table 8:
Scale Mean score Significance
Causation 3.81 .239
Effectuation 3.93 .239
Hypothesis 3a is not supported!
Hypothesis 3b: An entrepreneur emphasizing flexibility will show a preference for effectual approaches.
The table that is presented below shows the scores on causation and effectuation in relation to flexibility.
Table 9:
Scale Mean score Significance
Effectuation 4.04 .001*
Causation 3.68 .001*
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Discussion
Findings
Whether or not certain entrepreneurial orientations influence the use of effectual or causal logic by the entrepreneur is a question not addressed before in the literature. The major contribution of this paper is the attempt to address this issue for the very first time. Furthermore, creating a link between existing theory and the phenomena of effectuation and causation has been attempted.
It was hypothesized that entrepreneurs who own a starting business which is active in the high-tech industry would prefer to use effectual logic rather than causal logic for running their day to day business. The findings of this paper support that notion. An attempt has been made to link the 4 dimensions of Quinn’s competing values framework (internal, external, flexibility and control) to the theory of effectuation and causation by Sarasvathy. The hypothesis that internal or external orientation has no connection to the usage of effectual or causal logic by the entrepreneur was only
partially supported. Internally oriented entrepreneurs indeed showed no preference for using either entrepreneurial logic. However, externally oriented entrepreneurs showed a slight but significant preference for using effectual logic.
The hypothesis that entrepreneurs showing an orientation towards control would prefer to use causal logic is not supported. However, entrepreneurs who are oriented towards flexibility indeed are more likely to use effectual logic.
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Implications
The results of this research show that in the context of a high-tech start-up, which is characterized by high levels of uncertainty, effectual logic seems to be the most
frequently used entrepreneurial logic. Whether this also leads to better performance is a question beyond the scope of this paper. However, one can conclude that causal logic seems to be less desirable in these highly uncertain contexts as the one under
investigation in this study.
For entrepreneurs entering highly uncertain markets, or even not yet existing markets this implicates that planning key courses of action is more likely to lead to
disappointment then success. Rather, knowing the strengths and capabilities of themselves and the organization as a whole allows entrepreneurs to set courses of action as opportunities emerge. Whether this holds truth in more stable environments remains an open question, and would offer an interesting addition to this research. The finding that causal logic was not the preferred logic on any dimension of Quinn’s framework is interesting by itself. The researcher and theory seemed to agree that entrepreneurs that are highly control oriented should show a preference for causal logic; this however was not the case in the high-tech start-up context. Therefore, it would be interesting to see how these results would have been in a more stable and predictable environment.
Limitations
As with most studies, this study contains some major limitations which limit the generalizability and applicability of the findings. The first major limitation is the
generalizability of the results. Because of the highly specific context from which the data was derived, it is not unthinkable that the results of this study would have been
different in a different context. The high-tech start-up context is inherently
characterized by unstable and uncertain environments. As mentioned before, a more holistic picture can emerge if the study were conducted across several different
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study remains an open question and will hopefully be addressed in future research. The third limitation is the data collection method. The questionnaires used by VentureLab Twente were not specifically designed to measure the concepts of interest of this study. Hopefully, this work will inspire future researchers to create a data collection method directly aimed at capturing the concepts in a more holistic manner. The last important limitation is the possibility of reversed causality. In this research it is proposed that showing a strong orientation for either of Quinn’s dimensions would lead to the usage of either effectual or causal logic by the entrepreneur. Whether the use of either logic has a reversed relationship with a stronger orientation towards any of Quinn’s dimensions is another question that will hopefully be resolved in future research.
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