As an illustration of the need to develop additional design principles for effectuation, consider Myra, who reads Sarasvathy’s (
) book on effectual entrepreneurship carefully before setting out to open an Indian restaurant by effectuating her means. To begin, Myra tries to determine her means: She has no prior experience in the restaurant business, and her cooking is not great. Still, several of her friends are known for their good home cooking and have expressed interest in working as chefs. Myra also knows a few restaurant owners who might lend her a hand. Myra wants to innovate but is not sure how to integrate high-tech into her restaurant idea. Still, these angel investors could introduce her to other investors who are more interested in restaurants. Last, but not least, Myra knows that one of her most important means is her charming personality, which can easily land her a temporary job in other restaurants to gain experience.
As Myra jots down her available means, she realizes she has more than one available means, so must decide which means are good for effectuating with. Is it better to effectuate with few or many means? As she ponders further, increasingly more questions arise, causing her to feel unguided on how to use effectuation to design a new venture. She learned about (stakeholder) self-selection of means, as well as the affordable loss principle in constraining how means are put to work, but would these principles be sufficient in her case? As the new venture evolves, how should she select which variations to retain and which to put aside? As means are not static, how can she facilitate change?
5.1 Initialization: effectuate from what you have (what is good to have?)
The effectuation process begins with the first principle of effectuation of “who you are, what you know, and whom you know” (Sarasvathy
, p. 258), resembling the means at hand. Starting with these means a general aspiration is formed that takes shape through co-creation with committed stakeholders (the third effectuation “crazy quilt” principle) resulting in the convergence of ideas and the emergence of more specific goals (Sarasvathy and Dew
). Yet, as entrepreneurs often have a repertoire of contacts and knowledge, they must discern which means serve as good starting points, among all means they might procure (Fisher
). Although effectuation theory highlights the key role of means, it does not specify the composition of the set of means. Still, entrepreneurs evaluate alternative means and adjust their means in relation to environmental constraints (Sarasvathy
; Sarasvathy and Dew
). Therefore, guidance on what means would be good to have in the first place may be helpful (cf. Arend et al.
; Chiles et al.
; Read and Dolmans
Viewed through the lens of genetic algorithms, effectuation begins with the building blocks of which entrepreneurs have control (Sarasvathy (
) also uses the term of building blocks). The key building blocks include knowledge and experience-related elements, such as ideas, beliefs, assumptions, values, interpretative schema, and know-how, as well as available and potential relationships and pre-commitments (for a review of potential building blocks or “units of selection,” see Breslin
). The building blocks of earlier start-ups and other career experiences (Engel et al.
) are passed on and recombined through the experience and knowledge of founders, employees, customers, investors, and other stakeholders.
Building blocks, therefore, are critical design elements. Yet, in both genetic algorithms and effectuation, designers cannot predict how good the building blocks will perform because of the uncertainty about both the environment and the fit with the building blocks. There is no deterministic landscape against which an effectual designer can pitch the fitness of the (combinations of) building blocks. At the same time, the initial set of means and pre-commitments in combination with the effectuator’s aspiration give a rough direction to the venture; this aspiration also provides initial coherency in the collection of building blocks. The unpredictability of the environment makes it impossible to pick building blocks deterministically. In such a situation, the genetic algorithm instead suggests creating a diverse population of building blocks (Goldberg
). Some of the most diverse building blocks in the population will undoubtedly fail, but they may contribute no less to the ultimate satisfying solutions by creating crossovers. Emphasizing effectuation notions of experimentation and failure, the creation of variety adds to the tendency in effectual thinking to go with existing, comfortable means and relationships.
Good building blocks are not static but rather serve as dynamic seeds for creating new and better building blocks, similar to biological seeds that easily break themselves to become plants. Following this logic, effectual “crazy quilting” with partners and stakeholders’ pre-commitments serves as a good design principle to seed building blocks. In line with effectuation theory, the set of building blocks needs to be developed through trusted partnerships, which will practically limit the set of building blocks to what someone can handle in terms of collaborations. Some building blocks are more complementary than others. Good building blocks may evolve into valuable parts of business models and ventures, even if they do not pay off immediately (Sarasvathy et al.
). A genetic algorithm-based design process can begin without a minimum standard for any building block (Chattoe-Brown
); rather, this minimum standard refers to building blocks’ diversity. As Dew et al. (
) observed, expert entrepreneurs, in collaboration with their stakeholders, create more and more novel variations than experienced managers. Thus, based on insights from genetic algorithms as well as the findings of Dew et al. (
), in effectuation, the variety of means might be more important than the sheer number of means.
Returning to our hypothetical example, Myra can benefit from starting with a varied set of means from her and her stakeholders: reaching out and experimenting with different contacts (friends who love to cook, investors, other restaurant owners), gaining a little experience with other restaurants that would lend her a hand, and using her personality to attract clients. Starting with such a diverse set of means is likely better than venturing with a set minimum standard for one particular means (e.g., find a restaurant job to learn for a predefined number of years).
An easy way to increase the diversity of building blocks is to increase the population size of those building blocks. According to the genetic algorithm, when populations are too small, they can lead to premature convergence and substandard solutions (Goldberg
), whereas when populations are too large, they can waste valuable time and resources. For effectuation, insufficient building blocks produce a highly localized search, whereas an excessively large population can result in thinly spread resources, scattered efforts, and the starvation of resources for good building blocks. Thus, we expect an inverted U-shaped relationship between the size of the population of building blocks and design fitness, which is, however, unique for each venture. In the same population, diverse and complementary building blocks work better than overlapping ones. In Myra’s situation, although she has no prior restaurant experience and is not a good cook herself, starting with all her available means would be too much, so the resulting effectual design would suffer. If she began with just one means—for example, a particular friend who loves cooking—she may end up with a restaurant far from the best she could have developed.
Design principle 1: Gather more than just a few means; at the same time, it is more important to have more diverse means than merely more means.
5.2 Selection: selection criteria for building blocks
Because building blocks are not static, design principles must be in place to ensure that the better building blocks grow and take over a dominant share of the population in the effectuation process. Whereas in biological evolution selection is mainly attributed to environmental forces (Hodgson
), in genetic algorithms and effectuation, the selection is predominantly driven by decision-makers. In organizational settings, building blocks compete for the attention of human beings (Ocasio
), implying that humans move on from certain building blocks if they stop noticing their public expressions, internalizing them, or reproducing them (Weeks and Galunic
). For example, new ideas or new procedures are put down if they are not remembered and enacted.
In effectuation theory, the entrepreneur is the “pilot” who makes selection decisions in collaboration with committed stakeholders (Sarasvathy
). Thus, in this selection, the fitness of any particular means is subjective or intersubjective, not an objective given (Chiles et al.
; Dolmans et al.
). For effectual entrepreneurs, selection is an internal process of co-adaptation, involving their relevant stakeholders (Dew et al.
). Thus, the primary selection mechanisms are the direction set out by working with the set of means in view of a general aspiration and the co-creation processes with stakeholders. Yet, entrepreneurs might need more guidance in making selections, and thus, it is valuable to specify useful selection mechanisms that account for the uncertainty of the situation and lack of a predefined market environment that would determine the right fit.
The sense of whether something works or not in an individual’s mind is explained in psychology by self-regulation theories, in particular control theories (e.g., Eccles and Wigfield
). These theories posit that individual entrepreneurs create a perception of the state of a variable of interest and compare the perception to a referent, which represents the desired state. The discrepancies between these two states determine the (lack of) fitness. The desired state is known in effectuation as a general aspiration (Sarasvathy
). In many cases, while it is hard to devise an algorithm for calculating the fitness of a design, it is relatively easy to tell the relative change in fitness (i.e., if a change increases or decreases the fitness). Similarly, Myra has a hard time to pinpoint the exact fitness score of a dish, say curry mutton, but she can tell relatively easily whether changing curry mutton to curry fish pleases more customers, and thus, she would be able to tell whether building blocks are fitness-reducing versus fitness-enhancing. As in genetic algorithms, what matters is the relative performance improvement instead of the exactitude of performance.
More importantly, because it is impossible to define perfect criteria to evaluate and select building blocks, due to dynamic uncertainty (Schmitt et al.
) about their future performance, designers of genetic algorithms focus on designing fast-and-frugal tests. Such fast-and-frugal tests are also essential for effectuation (Sarasvathy
) and widely recognized by practitioners, such as in the “lean start-up” movement (Ries
). Effectuation has one main design principle to guide selection, namely
, which prescribes that an entrepreneur should decide on the basis of what he or she can afford to lose (Dew et al.
). Along this principle, we can identify other principles that guide selection based on genetic algorithm research, for example: running out of time, achieving sufficient quality, substantial convergence in the population (implying a decrease in diversity), and time elapsed since the last improvement.
If, for instance, we apply these design principles to choose alternative business model elements, we note that
substantial convergence in the population could happen if, after some time, one or a few of these alternatives take off while others do not really gain traction. In practice, the elements that take off will attain consistent attention from the key stakeholders over time, and thus convergence will occur. In Myra’s case, she starts with a little bit of everything—reaching out and experimenting with different contacts, gaining a little experience with other restaurants, and using her personality to attract clients for catering services. After doing this for 3 months, feedback from her main partners and clients points to the catering concept as critical. As such, the experimental trials converge into the catering concept as a building block to effectuate with, and as the catering consumes most attention, the other building blocks become less important. Thus, she has—unintentionally—reached substantial convergence in the population of building blocks.
Responding to calls to include time in the affordable loss principle (Dew et al.
), we point at the
running out of time
criterion which refers to the time someone can afford to test a certain solution, for instance allowing 2 months as an affordable period to validate an idea through customers. Using such rules, Myra tests the idea of working with a “wanna-be-chef” friend for 2 months, at the end of which Myra uses the market’s responses to decide on the idea. Using the principle of
achieving sufficient quality
, entrepreneurs can similarly create simple rules, such as “Can we find two major buyers for the current service or product in three months?” Can Myra fill 50% of her restaurant tables in half a year? Finally, the
time elapsed since last improvement
criterion is again based on the time someone can afford, this time to come up with an improvement. It could lead to a simple rule, such as “If we cannot improve a certain technology after working on it for a year, we move on to other technologies” or “If we cannot find another key customer segment in two months, we have to change our approach.” Practicing entrepreneurs, consciously or subconsciously, use such guiding principles, but these have not been recognized as design principles for guiding effectual decisions, nor have they been studied explicitly.
The preferences indicated by these tests do not require global knowledge and are not deterministic in nature (Sastry et al.
; Sastry and Goldberg
). Instead, these nondeterministic fast-and-frugal selection principles help to maintain a necessary level of diversity under conditions of fundamental uncertainty. These fast-and-frugal tests do not assume existing markets or customer preferences but are low fidelity design principles for situations where entrepreneurs did not figure out their market yet, or in cases where entrepreneurs actually transform markets themselves (e.g., Dew et al.
; Sarasvathy and Dew
Design principle 2: Use one or more fast-and-frugal tests (i.e., substantial convergence in the population, sufficient quality, time elapsed since last improvement) to select appropriate building blocks for new venture creation.
5.3 Variation: facilitating changes by decomposing
The creation of variation, a crucial process in effectuation, occurs in collaboration with stakeholders, as specified in the third effectuation principle (Sarasvathy
; cf. Dew et al.
). As such, variations are not just a blind try but often guided by previous experience in a co-creating process (cf. Alvarez et al.
; Engel et al.
). New building blocks are introduced along the way by leveraging contingencies (the fourth effectuation principle “lemonade”). Dew et al. (
) find that expert entrepreneurs create a greater range of variations than experienced managers do. These expert entrepreneurs also create variations based on their previous experience in addition to random variations (Sarasvathy et al.
). In a study on the creation of novelty through effectual transformations, Dew et al. (
) found inductively that expert entrepreneurs mentioned a myriad of processes to introduce variations: deletion and supplementation, composition and decomposition, exaptation, manipulation, deformation, localization, prototyping, stereotyping, and free association. These variation processes, from an evolutionary view, are fundamentally generated by mutation and crossover of building blocks. Crossing over properly requires decomposition (Goldberg
), which reduces structural interdependencies and thereby allows for easy evolution (Simon
). Thus we emphasize here the process of composition, which is seen as an essential prerequisite of designing high-performing, enduring artifacts in effectuation theory (Sarasvathy
). Without decomposition, variation is limited, as faced by novice entrepreneurs (Sarasvathy et al.
). However, the role of iterative and continued decomposition is less obvious to nondesigners, remains underspecified (Sarasvathy and Dew
), and is often neglected (Sarasvathy
), so we elaborate specifically on the process of decomposition in effectuation.
Decomposition helps in two main ways. First, it helps to identify building blocks that can be changed, reused, and recombined, as described by the logic of exaptation that existing elements get co-opted for new roles (Dew et al.
). Different decompositions will surface distinct building blocks and thus enable distinct exchanges, leading to distinct exaptations. Second, decomposing helps to reveal the relationships and hence the interconnectedness among the building blocks in which the whole is greater than the sum of its parts.
To decompose better, genetic algorithms can provide some guidance. Different decompositions should be considered—as a thought exercise—to identify distinct sets of building blocks, some of which may evolve toward competent solutions better than others (Goldberg
). Empirical evidence confirms that good designers are those with the ability to find various representations of a design issue and the multitude of representations help them to solve hard problems (Goldberg
). Theoretically, such representations might include economic–financial representations (as found in accounting textbooks), real options theory (Kogut and Kulatilaka
; Vassolo and Anand
; Zhang and Babovic
), and dynamic capability views (Teece et al.
). The multitude of representations enable searches for new decompositions; those new decompositions can then help to reveal building blocks that are hard to find, deep, complex, integrated, and difficult to separate. Many capabilities, routines, and imprint effects are tough to reveal because they have developed tacitly, through years of experience. Without attempts to facilitate decomposition, designers can rely only on good luck to find good building blocks.
Myra thus might try a financial decomposition, which helps to reveal the costs, revenues, and margins and which, in the end, helps her realize that starting a conventional restaurant is too expensive. Therefore, she decides to start with an Indian-themed catering service, using the help from her friends who cook at home. Within a few weeks, the business has gained some success, especially catering to office parties that want to try something different. With such clients in mind, Myra drafts a simple flow diagram, drawn from a business model canvas, to decompose her business into its key building blocks: creating the menu, buying food and materials, preparing the food with key people, and serving the food. Finally, Myra asks a friend for input, who helps her decompose the capabilities in her new venture. Adding these different decompositions to her drawing board gives Myra different sets of building blocks that she can change to iterate her new venture. For example, she identifies that the process and capability of buying food and materials can be broken down and improved, and therefore, she engages more actively with a friend who knows a great deal about food and materials.
Design principle 3: Decompose by constructing multiple representations to identify building blocks and subsequently explore new combinations by exchanging building blocks.
5.4 Retention: speed of updating building blocks
Retention means deciding which new means, generated through variation, are kept as the new basis. For example, Myra may try varying her dishes, either intentionally or accidentally, and then decide on whether to update the dish with those variations. Effectuation theory states that the entrepreneur, as the pilot-in-the-plane, makes these updating decisions based on the available information at that time. Genetic algorithms teach us about the speed of updating processes and indicate that the decisions on building blocks should be neither too fast nor too slow. A decision that takes too long would imply a waste of resources on building blocks that fail to produce eventually, likely at the cost of investing in those that are productive. However, weeding out (combinations of) building blocks too quickly fails to provide the time for the building blocks to develop and become productive. Furthermore, building blocks cannot be put away deterministically with respect to the rate of introducing new ones, because if so, the diversity needed to create improvements disappears, and the success rate falls into local improvement. Many seasoned entrepreneurs clearly know the importance of allowing time and patience before making final decisions about the experimentation in which they engaged. Thus, we anticipate an inverted U-shaped relationship between the time taken for selection and design fitness.
As soon as Myra started a restaurant, alongside her catering business, she chose to focus on drawing in discovery-driven clients. She plans to introduce a new dish every other day, and if the new dish does not sell well in those 2 days, she will remove it. In reality though, if a dish does not perform well, Myra often takes weeks to adjust it and changes a dish only if it fails consistently for 2 weeks. The reason for this lengthened elimination decision is that only one chef works at the restaurant, so the speed of generating new dishes is relatively slow. However, to draw in clients, Myra realizes she has a large collection of Indian music and can experiment with different songs; she does not hesitate to stop the song if it fails to appeal to customers, even after just a few seconds. Therefore, she adjusts the speed of her selection decisions to the time required, such as the period needed to develop a new dish.
Yet the speed of updating the building blocks is interdependent with the severity of these decisions and is dependent on the amount of variation created. Thus, selection must align with the rate of introducing changes and variations. Change is valuable for innovation and entrepreneurship, so a higher frequency of change might appear preferable. Moreover, a high mutation rate early on is helpful to get out of local maxima. However, mutation rates also can be too high, as research in both genetic algorithms and biology shows (e.g., Domingo-Calap and Sanjuán
; Elena and Sanjuán
; Gordo and Sousa
). The occurrences of mutation and recombination that increase fitness are far less frequent than those that decrease it. If the rate of change is too high and many changes occur together, entrepreneurs cannot effectively remove underperforming building blocks (i.e., abort and change some of their venturing experimentation) in favor of better ones. Because the detrimental changes cannot be weeded out in time if the rate of change is too high, the selection mechanism needs to be adjusted. In contrast, if the selection is too fast with respect to the rate of change, new ideas are not given enough time to evolve before they are judged, likely leading to biases against new and risky changes. Ventures with such unbalanced mechanisms tend to develop a culture contrary to changes (Denrell and March
For Myra’s discovery-driven clients, the ambience of her new restaurant, including visual and acoustic effects, is a key design factor. To alter the acoustic effect, Myra simply changes her CDs; to alter the visual effect, she would need to redecorate, which represents a much slower process. Therefore, if a song is not generating its desired effect, Myra quickly pulls out another option from her large collection of Indian music. Conversely, Myra would only change the decor if she becomes totally convinced, from feedback from many clients, of the need for change, because it takes much longer to get the decoration right.
Design principle 4a: Align selectivity with the rate of change and severity of change.
Design principle 4b: Align selection decision speed with the rate of change and severity of change.
contains an overview of all the design principles related to the genetic algorithm steps, linking the new and existing effectual principles and illustrating the new ones with examples. In the iterative genetic algorithm-based design process, these design principles become inherently interrelated, consistent, and collectively independent (Romme and Endenburg
). That is, the design principles are interrelated because they cooperate iteratively in the four-step evolutionary process (e.g., without decomposition, there is no population of building blocks). Some design principles coordinate with other design principles, such as principle 4.