FCM as the method for exploratory modeling

58 Overall, three qualities make FCM suitable choice for exploratory modeling and this study:

first, as a system modeling technique, it is a good match when the system is viewed as non-

linear because there are multiple interactions in between variables, feedbacks,

feedforwards, loops, and therefore mediation effects. Second, it is relatively easy for a non-

familiar reader to understand the system components of an FCM model and how it works,

which makes it easy to update, validate or expand the model. Third a large number of

scenarios can be run for different sets of parameters (in the case of FCM: different initial

values and weights) in order to find patterns and formulate new hypotheses.

However, to my knowledge, FCM has neither been used for organizational research on

ambidexterity, which is dominated by regression analysis as the quantitative research

method of choice, nor for exploratory modeling, which is often done with system

dynamics. The following paragraphs compare both methods to FCM to explain how FCM

is a suitable candidate for fulfilling the objectives of the proposed research.

a) FCM vs. regression analysis. A system of independent variables, mediators and

dependent variables can be modeled using regression models. However, when working

with regression methods, it is possible to over-simplify the system to a set of direct

correlations and overlook the mediators, or real causes, as actual independent variables.

Instead, FCM employs a holistic view when modeling reality. It starts with a bigger picture

by collecting all the relationships before prematurely trying to prove or disprove the

existence of any given causalities. This disparity in practicing FCM and regression analysis

is partially rooted in the different approaches to theory development that accompany these

59 certain constructs and hypotheses are proposed and then the researcher tests them against

the target phenomenon, research is theory first. In contrast, in theory later approach, the

phenomenon of interest is observed first, and then relevant components to the participants

are identified, and final theory is proposed (Goel et al. 1997; Zenobia and Weber 2012).

The cognitive mapping phase of FCM modeling is a powerful means to discover different

aspects of phenomena and key concepts within the system and eventually formulate the

relationship in between them. Regression analysis starts from an already established

hypothesis of the relations between two or more variables and tries to statistically reject or

accept the hypotheses in a deductive approach. Therefore, while FCM in the proposed

research is used in an inductive setting to observe the phenomenon of ambidexterity from

hundreds of perspectives (peer-reviewed articles from multiple research streams) and then

develops a theory, regression analysis in the context of ambidexterity is most often used to

test a theory against the sample data (De Clercq, Thongpapanl, and Dimov 2014; He and

Wong 2004b; Jansen et al. 2009; Patel, Messersmith, and Lepak 2013; Yang, Zheng, and

Zhao 2014).

b) FCM vs. system dynamics (SD). Although system approaches and particularly

system dynamics (SD) potentially fulfill similar objectives, FCM is slightly more adaptable

to the nature of this research for two reasons. First, since SD is represented based on the

stocks and flows of variables, maintaining the compatibility in between dimensions is a highly crucial matter in which any violation puts the validity of the model at risk (Senge

1980; Oliva 1996; Qudrat-Ullah 2005). FCM, on the other hand, is a more conceptual and

therefore dimensionless modeling technique that makes it more adept at representing

60 studies that have adopted system dynamics to model qualitative or conceptual systems

(Richardson 1991; Barlas 1996; Coyle 2000; Luna-Reyes and Andersen 2003), but it

highlights the fact that non-dimensionality of the FCM gives it a natural compatibility for

representing the conceptual and cognitive models. Second, when compared with system

dynamics, FCM is a relatively easier method to be comprehended by non-familiar readers.

On the surface, FCM is a causal diagram that could be presented to an expert panel with

none or minimal knowledge of the method for the purpose of validation or future

executives for the sake of simulations, updates, and expansions. Some (Isaacs and Senge

1992; J. D. Sterman 1994) have discussed a risk to the simulations, so-called Video-Game

Syndrome, where the model is perceived as being too complex to be understood by the user. In such scenarios, like playing a video game, instead of reflecting on why their actions

failed to produce the intended results, users simply keep experimenting until their score

improves. The high degree of readability of FCM will decrease the risk of videogame

syndrome when the simulation model is used as an interactive decision support system for

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4. Research Design

The starting point for any FCM modeling project is a cognitive map, which is subsequently

translated into a quantitative model that is calibrated, tested, and refined. Multiple

frameworks for FCM modeling exist in the literature that are similar in principle:

Overarching steps often include preparation (clarification of objectives and information

needs, plans for knowledge elicitation), knowledge capture in the form of cognitive maps,

translation of cognitive maps into FCM models that show concepts and positive or negative

causal links between them, FCM calibration (i.e. weight assignment) and testing, and

model use and interpretation (Muhammad Amer, Antonie Jetter, and Tugrul Daim 2011;

Antonie J. Jetter and Kok 2014). Similar processes are also used in related fields. For

example, Nadkarni and Shenoy (Nadkarni and Shenoy 2004) analyze texts to create system

models with Bayesian networks. They employ the following steps: data elicitation,

extracting model concepts and causal relationships to construct causal maps, modifying the

causal maps to create Bayesian networks, and deriving parameters for the Bayesian map

model (Nadkarni and Shenoy 2004). The research design for the current study borrows

from these best practices but puts more emphasis on the test and analysis phase to satisfy

the requirements of an exploratory modeling approach.

While Figure 7 represents all the steps and flow of this research in a graphical and concise

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