Heuristics in Open Innovation Activities. An Explorative Attitude Survey


Bachelor Thesis, 2016

50 Pages, Grade: 1,7


Excerpt

Table of contents

Abstract

Table of contents

List of illustrations

List of tables

1. Introduction
1.1 The adaptive toolbox
1.2 Motivation
1.3 Outline

2. Background
2.1 Open laboratories and open innovation activities
2.2 The adaptive toolbox
2.3 Assignment of the concept to visions of rationality

3. Methodological approach
3.1 The research question
3.2 Formalised research design

4. Results of the data analysis
4.1 Profiling of the sample
4.2 Evaluation of the environment
4.3 Classification of the heuristics

5. Discussion – the significance of this explorative attitude survey
5.1 Limitations
5.2 Opportunities for future research

References

Appendices
Questionnaire (German version)
Bundled information from the transcribed interviews

Abstract

In contemporary innovation management, companies increasingly dissolve their internal boundaries to leverage potentials from the outside world by creating synergies for the development of new technical solutions, for example through the reception of key incentives provided by lead users. Such processes of open innovation have remained relatively unexplored from the part of various authors and their theories on problem-solving. One such example serving as the basis for this thesis is the adaptive toolbox by Gerd Gigerenzer, focusing on individual behaviour under limited knowledge in a certain environment and cognitive mechanisms called heuristics, used daily by human beings to avoid or overcome complications.

In this context, the centrepiece of this work is the answer to the question which heuristics do individuals deploy in an open innovation environment and how these techniques can be categorised. Therefore, a qualitative attitude survey was realised for the accomplishment of this task, what additionally involved the conduction of semi-structured interviews at the FabLab laboratory for open innovation and an extensive content analysis afterwards.

In terms of the results, five different approaches were detected in the given setting and specifically classified according to the individual motives mentioned by the subjects from the sample. In addition, the categories “heuristic” and “frequency” support the categorisation by allowing a listing and a preliminary quantification of the heuristics.

Annotation: For the purpose of both gender equality and simplicity in expression, this paper includes only female pronouns. However, each of the concerned phrases refers contextually to both sexes.

List of illustrations

Figure 1: Forms of collaboration in open innovation incubators (own elaboration, adapted from O’Hern & Rindfleisch (2008))

Figure 2: The visions of rationality (Source: adapted illustration from Gigerenzer & Todd (1999))

Figure 3: Stage model of the qualitative content analysis (Source: own illustration based on Mayring (2000))

List of tables

Chart 1: Ways to acquire heuristics (Source: own chart based on Gigerenzer (2003))

Chart 2: The mission and vision of the FabLab subsidiary in Nuremberg

Chart 3: Questions compiled for the semi-structured interviews (own elaboration)

Chart 4: Suggested classification of heuristics (own chart and elaboration)

Chart 5: Profiling of the interviewees (self-provided)

Chart 6: Evaluation of the environment (self-provided)

Chart 7: Basic information for the derivation of the heuristics (self-provided)

Chart 8: Derivation of the heuristics (self-provided version in German)

Chart 9: Derivation of the heuristics (self-provided version in English)

1. Introduction

Regardless of an organisational viewpoint of an enterprise or an individual one of a customer, the human actions are determined by one’s decisions made in a problematic situation. According to Gigerenzer et al. (2001; 2011), each individual faces generally uncertain situations in everyday life, where a person has limited knowledge of all alternatives, consequences and probabilities linked with every solution scenario. Considering this setting, Gigerenzer et al. (2001; 2011) remark that an individual would rather rely on her own intuition and cognitive mechanisms called heuristics to overcome such situations, whereas a computation would result in a highly time-absorbing, slowly developing search for an alternative. This realistic connection between the mind and the environment embodies the fundament on which Gigerenzer’s adaptive toolbox approach is built.

1.1 The adaptive toolbox

Based on the aforementioned characteristic, the adaptive toolbox contains numerous concrete instructions a person needs to adapt cognitively to certain social and physical environments (Gigerenzer, 2003). Because of the fast and frugal character of the applied heuristics, the individual usually does not conduct complex computations and look for all the information about the surrounding to assimilate quickly (Gigerenzer, 2003). Moreover, the toolbox metaphor implies that parts of an acquired heuristic, the building blocks, can be recombined to develop new heuristic tools (Gigerenzer & Todd, 1999).

This changeability highlights a substantial difference between the adaptive toolbox and the optimisation. According to Gigerenzer and Selten (2001), the usage of heuristics does not rely on the achievement of a mathematical maximum benefit, resulting in a perfect or even universal answer to at least one problem. Moreover, another reason specified by Gigerenzer and Selten (2001) reveals that the feasibility of optimisation strategies is only reasonable in a limited number of simplified cases.

For example, the search for a universal tactic to win at board games of the likes of chess or go already overrides the basis for an optimisation, because there is none in existence. Instead, a chess or go player would leverage specific, adaptive strategies to her advantage (Gigerenzer & Todd, 1999).

1.2 Motivation

The rational, simplistic thinking behind a flexible toolbox containing acquired heuristics makes Gigerenzer’s theory promising in view of the fact that the most useful incentives resulting from open innovation activities also originate from a practical, less complex way of thinking due to limited available resources.

Despite this basic similarity, the potential of the adaptive toolbox has remained underestimated as well as relatively unexplored in the context of innovation management up to date. According to Gigerenzer and Kruglanski (2011), especially the heuristics are confronted with three essential misunderstandings in problem solving, which they enumerate as follows:

- Heuristics are only the second-best option regarding the accuracy-effort trade-off.
- Complex solutions are the best answer to complex problems.
- Heuristics tend to be error-prone and unconscious.

At the same time, these postulates not only represent the main problem affecting the reception of heuristics in general, but also the thesis topic focused on the domain of open innovation activities.

Therefore, this initial situation leads to two particular intentions pursued by this thesis. Firstly, the outcome of this explorative attitude survey is supposed to display a selection of detected and categorised heuristics applied in a realistic, practical environment. The proposed classification of these problem-solving techniques serves the purpose to underline the realism featuring heuristics and the usefulness of the adaptive toolbox, which should falsify the aforementioned misconceptions on problem solving approaches.

Secondly, the results of this thesis should complement the current research fundament reasonably, as well as provide clues for further qualitative and quantitative investigations taking the usage of heuristics in open innovation activities into account. Along with such future studies, the inferences of this thesis shall motivate the management of enterprises to include open innovation concepts and at least let them recognise the potential role of heuristics in their R&D strategy.

1.3 Outline

The thesis is structured as follows. In the second chapter, it gives an overview of open innovation activities and open laboratories, as well as of Gigerenzer’s model of the adaptive toolbox and its perspective on the mind and the environment. For complementary purposes, this theory is compared with other rational visions on decision-making. The third chapter includes the posed research question and introductions to the adequate method, namely the semi-structured interviewing followed by transcription techniques and the qualitative content analysis. Subsequently, the fourth chapter presents the outcome of the research conducted at the FabLab, what lets conclude this paper afterwards with an evaluating discussion in the fifth and last chapter.

2. Background

This section serves as an introduction into the notions of open innovation laboratories and the adaptive toolbox, the most vital parts of the further practical research presented in the later part of this thesis. Beginning with a sub-chapter about open laboratories, their role is briefly outlined along with the variety of possible activities happening there. The second subchapter not only describes Gigerenzer’s approach in general, but also reveals details about the fast and frugal heuristics as its main component. Additionally, it explains the term “ecological rationality” as a connecting link between an individual’s mind and the environment, whose conditions influence her process of decision-making. Subsequently, the third subchapter consists primarily of a scientifically sound assignment of the presented theory to others in order to stress its functionality in a real-life setting. In this context, it insinuates the scope of the research determining the next chapters. All this information serves as the theoretical framework for the research question, which is explicitly phrased in the beginning of the third chapter on the methodology.

2.1 Open laboratories and open innovation activities

Generally, open innovation laboratories can be characterised as publicly accessible spaces, offering a platform for the realisation of design projects, the conduction of collective research or the development of solutions to particular problems (Fritzsche & Möslein, 2015). Usually situated in central, industrial and/or academic districts of larger cities (Fritzsche, 2015), these institutions involve many people with a higher educational background and institutional actors, who fulfil a function of a host, an organiser of events or as an advisor interacting with the visitors (Roth et al., 2014).

Depending on its focus on technical or societal problems, an open innovation laboratory like a FabLab can rely on the construction of technical prototypes with the help of state-of-the-art machinery like 3D printers and laser cutters (Gershenfeld, 2005; Hatch, 2013), whereas a European Living Lab emphasises the human interaction in collaboration with economic and political institutions (Leminen, Westerlund, & Nyström, 2012).

Nevertheless, both of them share the same ambition to produce sustainable, knowledge-holding artefacts through a common process of creative, but also rational problem-solving (Fritzsche & Möslein, 2015).

Basically, the communication happens between two types of participants, the engineer-innovator and the user-innovator (Fritzsche, 2015), who are active on their own without depending on company regulations, contracts or other agreements (Fritzsche, 2015). Consequently, this free, bilateral diffusion of knowledge during the phase of planning or construction is also called open innovation (Chesbrough, 2003).

However, as diverse as the possibilities are to dedicate oneself to the materialisation of an idea in an open innovation laboratory, so are the forms of collaboration between the visitors or the visitor and a machine, as figure 1 below demonstrates.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1 : Forms of collaboration in open innovation incubators (own elaboration, adapted from O’Hern & Rindfleisch (2008) )

Having this complex network of different actors and activities, a detailed study is necessary to explore how people actually proceed in these spaces. This thesis provides such a study, based on the concept of heuristic problem solving.

2.2 The adaptive toolbox

As hinted in the introduction, the theory behind the adaptive toolbox concerns itself with a set of numerous heuristics, whose task is to support individuals at adapting themselves to the social and physical circumstances of an environment (Gigerenzer & Todd, 1999).

In principle, one can imagine the mentioned heuristics as shortcuts simplifying the trains of thought in a situation where resources like time and knowledge are scarce and a quick decision is forced (Gigerenzer, 2003). According to Johnson & Payne (1985; Gigerenzer & Todd, 1999), the pursuit of success also belongs to the influential external factors.

Provided by these heuristics, the adaptive toolbox follows the principles of search, stopping and decision rules (Gigerenzer & Todd, 1999), which pave the way to the determined solution.

During the first stage, namely the search, an individual has two possibilities to look for the required information. On the one hand, she might follow Simon’s approach of satisficing and advance by identifying only particular alternatives, which exceed a personal aspiration level (Gigerenzer & Selten, 2001; Simon, 1955). On the other, she could act using fast and frugal heuristics, as proposed by Gigerenzer and Selten (2001). In this case, the person would just look for convincing cues related to an already known alternative of a situation.

The search procedure itself may occur either individually in a random or ordered manner, or by imitating that process through social interaction with fellow human beings (Gigerenzer & Selten, 2001).

Once this period has been stopped, what is an equivalent to the stopping rule itself, and the appropriate information has been found, the individual makes use of a decision rule. In opposite to computational methods including probabilities or biases, the resulting decision, based on heuristics applied during the entire event, relies only on one special alternative or cue and is, according to Gigerenzer (2001), neither supposed to be less accurate nor irrational.

2.2.1 Fast and frugal heuristics

The fast and frugal heuristics extend the suggestions of the adaptive toolbox and embody a specific manifestation of the general heuristics explained earlier.

Composed of building blocks based on the search, stopping and decision rule, they can prove themselves effective in an adequate environment (Gigerenzer, 2003). Furthermore, the fundament of the building blocks may be either cognitive or emotional. In other words, even emotions are in no way inferior to the rather neutral cognition. They also play a supportive role in fashioning heuristics of an adaptive toolbox as well as influence the determination of the respective block elements. Thus, an individual can after all be equipped with the same capability of avoiding a costly, meticulous search for all the information outweighing the action’s benefits (Gigerenzer, 2003).

Additionally, Gigerenzer deepens the aspect of the acquisition of fast and frugal heuristics in the same work (2003), distinguishing between four kinds of obtainment while illustrating them through the examples shown in the following chart 1.

Abbildung in dieser Leseprobe nicht enthalten

Chart 1 : Ways to acquire heuristics (Source: own chart based on Gigerenzer (2003) )

Firstly, heuristics can already be coded genetically. In reference to a study conducted by Dugatkin and Godin (1998), mate copying demonstrates a classic example of genetically prepared heuristics. As explained by Gigerenzer (2003), female guppies, for instance, reduce their decision for a male conspecific to only one, main reason, namely the male’s popularity with other females. The more a male guppy meets this requirement, the higher is the chance of a female candidate to reproduce with his help.

Secondly, an individual can acquire heuristics by learning through observation, imitation and instruction over the course of her life. With this in mind, the development of skills like steering an aircraft is strongly connected with the prevention of dangers such as collisions (Gigerenzer, 2003). Therefore, pilots especially learn to internalise heuristics referring to spatial relations between their vehicle and the traffic around, for example by telling through a scratch in the windscreen if the own plane should be dived away when another approaching airplane is moving disproportionally to the mark (Gigerenzer, 2003).

Thirdly, people are able to design their own fast and frugal heuristics. At this point, it is worth mentioning a completely new heuristic created by Green and Mehr (1997), accelerating the diagnosis of acute heart diseases in a more understandable and accurate way than the established methods of the likes of the Heart Disease Predictive Instrument (HDPI).

Lastly, new heuristics can be generated from the building blocks of already existing ones (Gigerenzer & Selten, 2001; Gigerenzer & Todd, 1999). In this context, the diagnosis heuristic by Green and Mehr can also be assigned to this category. The reason for that is its basis on the Take the Best heuristic by Gigerenzer and Goldstein (1996) and its premise of making a decision in favour of one out of two alternatives, for example of a binary value like “yes” or “no”, which would apply better to a set cue.

Regardless of the way a person obtained her heuristics, all of them share a commonality in terms of the selection of a particular problem-solving approach. As stated by Gigerenzer (2003), before deciding in favour of one heuristic, the individual usually compares the appropriateness of various ones with each other by reference to own past experiences, the knowledge about the cues of the given situation and the domain-specific task to solve.

To sum up, fast and frugal heuristics, which are made of building blocks and can be acquired in different ways, serve to make quick decisions in order to adapt oneself to a real environment. Neither do they imply a search for all the information about an environment, nor do their stopping and decision rules involve complex probability or utility calculations for every possible scenario to find out the perfect solution (Gigerenzer & Todd, 1999).

In the context of the adaptive toolbox, these heuristics are rather comparable with bets placed on a special environment, concluded merely from the own experiences or short, rough probing (Gigerenzer & Todd, 1999; Gigerenzer & Selten, 2001).

The reason for this behaviour is closely related to the coherence between the domain-specificity of heuristics and the environments, an aspect discussed in the following subchapter.

2.2.2 Ecological rationality and domain-specificity

In general, the adaptive toolbox incorporates two intertwined aspects based on Darwin’s model of human functioning, namely the ecological rationality on the one hand, and the domain-specificity on the other (Gigerenzer, 2003).

Regarding the first characteristic, Gigerenzer and Selten mean by “ecological rationality” the match between an environmental structure and the heuristic, which, in the best case, is supposed to work out well (2001; Gigerenzer et al., 2012). In other words, a heuristic proves to be ecologically rational if it is adaptable to the limiting circumstances an environment provides (Gigerenzer & Todd, 1999). In a problematic scenario, it ought to lead to the sufficient and not the best solution.

Therefore, the adaptive toolbox does follow an ecological principle instead of a purely logical one (Gigerenzer, 2003), what becomes visible thanks to the second notion of the “domain-specificity” concerning the mentioned adaptive toolbox as well as the heuristics. In fact, an individual does not exploit them as all-purpose tools for every kind of problem she encounters in her lifespan, but only for a set of similar complications where they could contribute best to overcoming the hurdles (Gigerenzer, 2003). This fact confirms the realism of Gigerenzer’s approach, and it especially explains the self-evidence of a human being’s need to acquire numerous heuristics in different ways. She pursues the goal to gain control of her environment to perform in it as good as possible.

A third, indirect premise complementing this theoretical system is the psychological plausibility, which focuses on understanding the cognitive, emotional and social facets of human behaviour under limited resources of time, knowledge, memory etc. (Gigerenzer & Todd, 1999; Gigerenzer & Selten, 2001).

Taking the ecological rationality, the domain-specificity and the psychological plausibility into account, all of these three factors are going to play a crucial role in the identification and classification of heuristics, what is further explained in the methodology chapter.

2.3 Assignment of the concept to visions of rationality

Closing up the theoretical basis of this thesis, this subchapter covers the contextualisation of Gigerenzer’s adaptive toolbox and the fast and frugal heuristics from a wider perspective and highlights the differences and the similarities with other forms of rationality.

As depicted in figure 2, rationality itself can be perceived in two ways.

Abbildung in dieser Leseprobe nicht enthalten

Figure 2 : The visions of rationality (Source: adapted illustration from Gigerenzer & Todd (1999) )

On the one hand, decisions can be taken under the presumption that the individual is not affected by any limited access to the decisive resources of time, knowledge, money etc. Simply said, she would mentally embody an omniscient, demonic creature, analysing a situation and every consequence resulting from a decision in its entirety (Gigerenzer & Todd, 1999).

In contrast, by realising the provided restrictions of an environment regarding the remaining time, the amount of available knowledge or the budget, a person rather tends to apply the search, stopping and decision rules typical of the satisficing and heuristic techniques. In such a case, she would be already aware of the imposed boundaries in her rational thinking and pursue finding the best balance possible within the trade-off of time, money, competencies and other necessary resources (Gigerenzer & Todd, 1999).

In order to complete this understanding and the role of heuristics, the comparison with particular kinds of demons and bounded rationality is continued in the following paragraphs.

2.3.1 Comparison with demons

In general, there are two forms of demonic rationality, namely the unbounded rationality and the optimisation under constraints (Gigerenzer & Todd, 1999).

As a matter of fact, it is the unbounded rationality which reflects the less realistic approach towards problem solving. This weakness becomes evident due to the disregard of the costs of a search and every other constraint a problematic situation usually involves. Moreover, it replaces all probabilities and the respective computations by a settled certitude in all human actions, as suggested in the deeds of a God or in Laplace’s vision of a secular superintelligence (Gigerenzer & Todd, 1999). Taking the previous descriptions of the heuristics and the adaptive toolbox into account, it is acceptable to say that the unbounded rationality stands for an opposite approach, especially because of the lack of commonalities with the methods described by Gigerenzer.

As opposed to this, the second demonic form known as optimisation under constraints conveys a similar idea of the searching process and respects the limitation of a human mind (Gigerenzer & Todd, 1999). However, its standard of the empirical modelling of the environment might allow a precise analysis of the structure by calculating the optimal stopping point (Gigerenzer & Selten, 2001; Stigler, 1961), but in the face of real and fast decision-making it suffers from a particular weakness. Especially in terms of an enterprise’s financial and strategic activities, the benefits of a determined solution can be surpassed by first-order and additional second-order costs of the prior search, emerging from the dedication of an even higher amount of time for the analysis of every possibility and the respective cost-benefit-calculations (Gigerenzer & Todd, 1999; Gigerenzer & Selten, 2001; Vriend, 1996; Winter, 1975).

Therefore, the optimisation under constraints seems to be adequate only in a certain number of problems, given that the issue would not necessarily lead to a costly mathematical elaboration of a less beneficial solution (Gigerenzer & Selten, 2001).

In a direct comparison, it is worth noting that fast and frugal heuristics limit computations of this kind (Gigerenzer, 2003) and substitute them by a link with the ecological rationality to provide a realistic alternative (Gigerenzer & Todd, 1999).

With this in mind, the adaptive toolbox lets exploit the structure of a dynamic environment from a simplified point of view and on a trial-and-error basis to achieve a sufficient outcome (Gigerenzer, 2003).

2.3.2 Comparison with bounded rationality

Gigerenzer and Selten explain that the models of bounded rationality describe “how a judgment or decision is reached rather than merely the outcome of the decision, and they describe the class of environments in which these heuristics will succeed or fail.” (2001: 4).

Actually, their fundament can be traced back to the theories of Herbert Simon, which summarise the logic behind bounded rationality with the scissors metaphor, consisting of one blade symbolising an individual’s cognitive capacities and another one embodying an environment’s structure (Gigerenzer, 2003). When these two elements interlock, the models of bounded rationality can decisively contribute to find a simple and fast solution by circumventing an extensive search for information and accepting only a necessary amount. This strategy can turn out as useful in real-life situations, where an optimal alternative can be either unknown or even non-existent (Gigerenzer & Todd, 1999; Simon, 1987).

As remarked in the introduction of this chapter and depicted in figure 1, the satisficing is one model following this principle, apart from the fast and frugal heuristics. Determined by searching, stopping and decision rules, its distinctive feature is the aspiration level related to the minimum quality an alternative should meet. Moreover, this attribute is set individually and remains flexible to further adjustments until the stopping rule comes into effect and signalises that an option exceeds the designated aspiration level (Gigerenzer & Todd, 1999; Simon, 1956b, 1990).

However, setting an aspiration level beforehand and comparing every relation between the effectiveness of every solution and the general aspiration level afterwards might become additional, time-consuming processes (Gigerenzer & Todd, 1999; Simon, 1956a).

[...]

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Details

Title
Heuristics in Open Innovation Activities. An Explorative Attitude Survey
College
Friedrich-Alexander University Erlangen-Nuremberg  (Fab Lab Region Nürnberg e.V.)
Grade
1,7
Author
Year
2016
Pages
50
Catalog Number
V1010166
ISBN (eBook)
9783346403209
ISBN (Book)
9783346403216
Language
English
Tags
innovation management, heuristics, qualitative analysis, open innovation, schumpeter, adaptive toolbox, gigerenzer, nuremberg, nürnberg, germany, multiple case study, r&d, research, development, research and development
Quote paper
Axel Capalbo (Author), 2016, Heuristics in Open Innovation Activities. An Explorative Attitude Survey, Munich, GRIN Verlag, https://www.grin.com/document/1010166

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