Fuzzy Network Morphology Analysis: A Method for Future Research in the Media Industry

Objective
Both public and private media organizations operate in a highly complex and uncertain environment. The advancement of digital technologies and mobile communications has resulted in fundamental changes in this industry, and anticipated developments in communication technologies, digital convergence, stakeholder participation in value production and distribution, new forms of media consumption, and other issues have made the industry's future direction unclear and complicated. In such a situation, senior executives and media policymakers must find ways to forecast the future and devise strategies to address the opportunities and threats that lie ahead. Many experts have emphasized the significance of future research in media management. emphasized strategic thinking in media managers in the sense of seeing the future and understanding the dynamics of the environment, and they saw the idea of the future as the origin of the media organization's actions and thus the drivers of current actions. As a result, managers try to understand not only what is happening, but also what might happen, has the potential to happen, or will happen in certain circumstances in the future. Among the various methods presented to predict the future, such as "valuable" and "exploratory" or "quantitative", "pseudo-quantitative", and "qualitative", one of the less used methods is the "Morphology Analysis," which analyzes and processes existing and future structures using set theory, topology, and random functions (Mozuni and Jonas 2017). This method identifies a vision of future drivers (Prashar, Tortorella and Fogliatto 2022) and analyzes the possibility of occurrence and its effect on the organization using matrix logic (Krauss, et al. 2022, Miklautsch, Hoffelner and Woschank 2022). Despite its solid theoretical foundations, this method's analytical dimensions have yet to be fully developed. As a result, given the importance of this method in future research studies in general, and future research of media organizations in particular, the current study seeks to identify the method's flaws as well as its theoretical and practical development in the media industry.
Research Methodology
To analyze the morphology with a network-fuzzy approach, there are eight steps as follows: 1) defining a problem and formulating it; 2) Determining the future drivers of the media industry; 3) drawing a graph of the effects of propellants; 4) extracting the weight vector of the thrusters; 5) Verbal-fuzzy evaluation of the uncertainty of drivers; 6) Multidimensional matrix drawing; 7) evaluation of the outputs, based on the two factors of the place of occurrence and the degree of proximity to the desired goal; 8) A deeper analysis of the possible answers is suggested. The Dematel model was proposed to draw a graph of the effect of propellants, and a matrix was calculated based on the mathematical relationships of the final step of Dematel to extract the weight vector of the propellants (importance factor of the propellants). In this study, a fuzzy number was used to show the linguistic value of the k-th expert, and the fuzzy average operation was used to combine the k-th expert's fuzzy numbers, and finally, the uncertainty of the drivers was calculated as a fuzzy number. In the final section, the multidimensional morphological matrix was developed and mathematically modeled based on the two elliptic fuzzy vectors described in the verbal-fuzzy evaluation and the center of gravity of the propellant in the media industry. According to the obtained model, the weight of the propellant will increase as the decentralization in the verbal-fuzzy evaluation decreases and the distance of the center of gravity of the propellant in the media industry from the origin point of the coordinates increases.
Findings
In this section of the study, an attempt was made to present a numerical example based on five media industry drivers based on the opinions of seven experts. The communication matrix between the drivers was extracted first based on verbal concepts, and the fuzzy matrix was calculated next. The calculations yielded L, M, and U values for two vectors a (lack of concentration in importance factor) and b (lack of concentration in event uncertainty). According to the findings, the second driver has the highest level of concentration, while the first driver has the lowest level of concentration. Finally, the value of V or the weight of each propellant was calculated using equation 9. The weight vector obtained shows that propellants 4, 1, 5, and 2 have the greatest amount of weight. The network-phase morphology matrix of media industry drivers can be extracted further in this study using the diagram below.
Discussion & Conclusion
As stated in the n article, future studies have been proposed as one of the study trends in the media industry due to the increasing complexities of the environment and the rapid changes in the media industry. Future research is conducted using a variety of methods that are classified according to the nature of the data. One of the less introduced qualitative methods is morphology, which evaluates future drivers based on the qualitative judgment of experts based on the two dimensions of "effect on the future" and "probability of occurrence". Because human speech is composed of two parts, mental and objective, verbal evaluation is challenged by degrees of abstraction and concreteness, resulting in ambiguity in the truth of the meaning. Therefore, verbal evaluations, especially in humanities studies, have multiple states of degrees of truth instead of the dual state of truth-untruthfulness. On the other hand, this method is developed based on the mutual relations of the drivers, which is assumed to be linear in the previous studies of this interaction. Based on this, as discussed in the article, future studies have emerged as one of the study trends in the media industry by using fuzzy logic, the possibility of multiple valuations of degrees of truth, and using the Dematel technique, the possibility of network analysis and non-independence in future engines and analysis of morphology. Future research will take the form of various methods that are classified based on the nature of the data. One of the less introduced qualitative methods is morphology, which evaluates future drivers based on the qualitative judgment of experts based on the two dimensions of "effect on the future" and "probability of occurrence". Because human speech consists of two parts, mental and objective, verbal evaluation is confronted with degrees of abstraction and concreteness, causing ambiguity in the truth of the meaning. At the end of the findings, the proposed model is a kind of applied qualitative classification based on two fuzzy vectors "effect on the future" and "probability of occurrence" and the calculation of two variables "lack of concentration" and "center of gravity" in the evaluation and classification of future drivers in the media industry. Following the theoretical development of the morphology matrix's fuzzy-network mathematical relationships, it was simulated in the MATLAB software environment and tested using a numerical example. The morphology of organizational social media was done through discrete valuation and with a nominal scale in Viravali and Viayalakshmi's (2019) article, and the relationship between the dimensions was not considered, nor was the precedence between the studies done. However, in this article, the valuation was done using a fuzzy and continuous distance scale, and because the studied dimensions were not independent, the relationships between them were studied using network logic, and finally, precedence Propellers were obtained by combining the value of two developed morphology vectors. Furthermore, according to the research findings of Prashar et al. (2022), Krauss et al. (2022), and Miklautsch et al. (2022), the morphological analysis method has a high capability in the typology of all kinds of qualitative concepts and rank classification, which is still applicable after the development of the network-fuzzy model. This feature has been preserved for this method. At the end of the findings, the proposed model is a kind of applied qualitative classification based on two fuzzy vectors "effect on the future" and "probability of occurrence" and the calculation of two variables "lack of concentration" and "center of gravity" in the evaluation and classification of future drivers in the media industry.