Recent developments in information technology, along with the expansion of social networks and digital media, have transformed the traditional boundaries of psychological operations, making cyberspace the primary platform for many such operations (Taghipour & Jafari,2021). In today’s complex and rapidly changing world, psychological operations have gained a prominent position as one of the most influential tools in political, military, cultural, and social arenas. These operations, which aim to influence the attitudes, beliefs, and behaviors of target audiences through cognitive and communicative tools, have increasingly become a central concern of states and organizations (Nazari, 2017). Numerous studies indicate that neglecting the cultural and mental context of target audiences can render psychological operations ineffective or even lead to counterproductive outcomes (Paddock, 2002). Therefore, the design of effective psychological operations requires the use of frameworks and models based on a comprehensive analysis of local, media-related, and psychological conditions of the audience. In this regard, Shahbazi and Shahabi (2011), in their study titled “Examining the Role of New Media in the Enemy’s Psychological Warfare,” emphasized the necessity of developing media literacy and establishing cognitive barriers against psychological operations.

In the military domain, the concept of “morale” has always been regarded as a decisive factor in success. Leo Tolstoy refers to an X factor or the “spirit of the army,” while Mengelsdorf defines it as vitality, confidence, and the voluntary willingness to undertake difficult missions (Elyasi,2003). The destruction of this morale is one of the core objectives of the enemy’s psychological warfare. Given the importance of this issue, the founder of the Islamic Revolution, Imam Khomeini (RA), stated: “The weapon of propaganda is more powerful than the weapon of war.” Moreover, the use of psychological operations dates back to ancient times, such as Gideon’s battle against the Midianites, where victory was achieved without direct confrontation by creating an illusion regarding the number of forces.

A review of the research literature reveals that although valuable studies have been conducted on psychological operations within society, there remains a clear research gap in presenting a comprehensive model for preparing combat forces against psychological operations and for their effective utilization by the forces themselves. Accordingly, the main objective of the present study is to identify the influential factors and propose a conceptual model for psychological operations in the domain of combat forces. The main research question is: What are the factors affecting psychological operations of combat forces, and how are the structural relationships among these factors configured?  Addressing this issue is considered a strategic necessity for enhancing the psychological resilience of forces and increasing the effectiveness of future operations.

Literature Review

Psychological operations, as an interdisciplinary field of study, have been examined from various perspectives. In a general classification, previous studies can be grouped into two main domains:

ü Content and media-oriented studies: A large number of studies, such as those by Mirsamiei et al. (2015) and Paddock (2002), have focused on the analysis of propaganda content, media techniques, and the effects of messages on public opinion. These studies generally emphasize the role of tools and messages in psychological operations.

ü Audience and preparedness oriented studies:  Another group of studies, such as Elyasi (2003) and Rezaei (2019), have emphasized the psychological characteristics of the audience, morale-enhancing factors, and the necessity of training. However, few studies have systematically addressed the relational patterns among these factors within the context of combat forces, with the aim of presenting an operational model for force preparedness. For example, Nazari (2017) emphasized the need to adapt psychological operations to socio-cultural structures. Taghipour and Jafari (2021) also highlighted the irreplaceable role of cyberspace as a new platform for such operations. As noted, the present study seeks to integrate these perspectives, focus them specifically on the domain of combat forces, and, by employing a mixed-methods approach (qualitative quantitative), address the existing research gap by proposing an indigenous and practical model.

 Methodology

This study is applied in terms of purpose and employs an exploratory mixed-methods design (qualitative → quantitative) in terms of data collection. The methodological process of the research was conducted in the following three phases:

Phase 1: Identification of Factors (Qualitative Method)

In this phase, using an interpretive approach and thematic analysis, influential factors were extracted from qualitative data. The statistical population consisted of experienced experts and commanders in the armed forces and the field of military psychology, who were selected through purposive sampling based on the criterion of theoretical saturation. Data were collected through 15 in-depth semi-structured interviews. Data analysis involved a three-stage coding process: open coding (extraction of 108 initial codes), axial coding (classification into 29 concepts), and selective coding (integration into 5 main themes). To ensure reliability and validity, techniques such as member checking, constant comparison with the literature, and the use of feedback from two external reviewers were employed.

 Phase 2: Instrument Design and Distribution (Qualitative Quantitative Bridge)

The factors extracted in Phase One (29 components within five categories) were structured into a matrix-based questionnaire using the DEMATEL method. In this questionnaire, experts were asked to determine the degree of direct influence of each factor on the others on a scale ranging from zero to four.

 Phase 3: Relationship Analysis (Quantitative DEMATEL Method)

In this phase, the DEMATEL technique was employed to identify the internal and structural relationships among the factors. This technique determines cause–effect relationships between factors through matrix calculations and classifies

them into two groups: influencing (cause) factors and influenced (effect) factors. Data from 12 expert-completed questionnaires were analyzed, and the standard DEMATEL procedures including the calculation of the direct influence matrix, the indirect influence matrix, and the drawing of the causal relationship network were carried out. The results of the interview analyses ultimately led to the extraction of five main themes as the key factors influencing psychological operations of combat forces.

Research Findings: Qualitative Findings (Thematic Analysis)

The content analysis of 15 semi-structured interviews conducted with experts from the armed forces, based on the thematic analysis method, resulted in the identification of five main themes (categories), 29 concepts (components), and 108 initial codes. These themes were identified as the key factors influencing the design and implementation of psychological operations in combat forces. The thematic network of the extracted themes is presented in Table 1. Thematic Network of Factors Affecting Psychological Operations of Combat Forces.

 Prioritization of Factors from Experts’ Perspectives

To determine the level of importance and priority of these factors, the frequency of extraction of each theme from all interviews was calculated. The factor of psychological operations training, with an absolute frequency of 15 out of 15, was identified as the most fundamental pillar of success in the field of psychological operations of combat forces.

Table 2. Prioritization of Psychological Operations Factors Based on Expert Interviews

Presentation and Explanation of the Final Model

By integrating the qualitative findings (thematic network) and quantitative results (prioritization), a conceptual model of the factors affecting psychological operations of combat forces was developed. This model has the following characteristics:

Figure 1. Model of Factors Affecting Psychological Operations of Combat Forces (Goodarzi, 2025)

Model Foundation (Causal Factor):

Psychological operations training is positioned at the foundation of the model as the primary pillar and essential prerequisite. This factor provides the necessary human capacity for the effective implementation of all subsequent stages of psychological operations.

 Interactive Operational Cycle:

Three factors cyberspace management, psychological operations design, and use of psychological operations tools form an interactive operational cycle. This cycle indicates that the design of operations determines the selection of tools and cyberspace channels, while feedback received from cyberspace contributes to revising and improving subsequent operational designs.

 Context and Ultimate Objective (Effect Factor):

Societal psychological factors constitute both the main context of influence and the ultimate objective of all psychological operations (whether aimed at strengthening friendly forces or weakening the enemy). This factor lies at the center of the cycle, and all operational activities are ultimately designed and executed to influence this context.

 Causal Relationships and Feedback:

The bidirectional arrows among the components indicate dynamic relationships and continuous feedback. For example, changes in the societal psychological context necessitate revisions in operational design and cyberspace management mechanisms.

 DEMATEL Technique (Decision Making Trial and Evaluation Laboratory)

The DEMATEL technique was first developed by Fontela and Gabus in 1972. As a multi-criteria decision-making method based on pairwise comparisons, it utilizes expert judgments to extract system factors and systematically structure them using graph theory principles. The outcome of this process is a hierarchical structure of system factors accompanied by bidirectional causal relationships, with the strength of these relationships quantified through numerical scores.

The DEMATEL method is primarily used to analyze complex systems and to structure sequences of information. By scoring the intensity of relationships, examining feedback effects, and considering non-transitive relationships, this method enables a transparent representation of interactions within systems composed of numerous components. Such transparency allows experts to more accurately express their views regarding the direction and magnitude of influences among factors. The main objective of the DEMATEL technique is to identify the pattern of causal relationships among a set of criteria.

In research studies employing the DEMATEL method, the following steps are typically undertaken:

ü  Identification of relevant factors based on expert opinions and literature review.

ü  Design of the direct-relation matrix using pairwise comparisons.

ü  Normalization of the direct-relation matrix.

ü  Computation of the total-relation matrix (direct and indirect effects).

ü  Calculation of prominence (D + R) and relation (D − R) indices.

ü  Classification of factors into cause and effect groups.

ü  Visualization of causal relationships through a causal diagram.

 Step 1: Using one of the idea generation methods among experts, such as brainstorming, brainwriting, Delphi technique, or conference, a list of existing and effective factors in the problem under study should be extracted from the perspective of the expert group.

Step 2: Based on the factors and criteria extracted in the previous step, a pairwise comparison survey matrix is created, where the rows and columns of this matrix are formed by the same criteria. Experts are asked to fill out the initial (empty) matrix by conducting pairwise comparisons to determine the effect of each criterion on the others. The assigned numbers should have the following meanings:

ü  Factor A has no effect on Factor B.

ü  Factor A has a low effect on Factor B.

ü  Factor A has a moderate effect on Factor B.

ü  Factor A has a relatively high effect on Factor B.

ü  Factor A has a very high effect on Factor B.

 Step 3: The matrices resulting from Step Two are collected, and a decision regarding the existence or absence of a relationship between any two factors is made based on the majority vote of the experts.

Step 4: For each relationship confirmed in the previous step, the average score given by the experts to the direct influence of row factor A on column factor B is determined.

Step 5: Considering Steps Three and Four, matrix Z is formed, representing the intensity of influence governing the direct relationships within the system.

Step 6: The row sums of the entries in matrix Z are calculated. Matrix Z is then multiplied by the "reciprocal of the maximum value of the obtained row sums" to derive matrix X, which represents the relative intensity of influence governing the direct relationships within the system.

Step 7: Matrix T, representing the relative intensity of influence governing both direct and indirect relationships within the system, is formed. Based on graph theory principles, the total of direct and indirect influences of vertices on each other, considering all possible feedback loops, is calculated as the sum of the terms of an infinite geometric progression.

Step 8: The possible intensity of indirect effects among the existing elements is calculated.

Step 9: In matrix T, the sum of the row entries is D and the sum of the column entries is R. For each factor constituting the system, (D + R) indicates the importance (prominence) of that factor in the system, representing the total degree of influence given and received. The net effect (relation) of each factor on the other factors in the system is obtained from the difference (D - R).

Step 10: A Cartesian coordinate system is established where the horizontal axis is scaled by (D+R) and the vertical axis by (D-R). The position of each existing factor is determined by a point with coordinates (D+R, D-R) in this system. The diagram drawn in the previous steps is transferred to this coordinate system to obtain a simple graphical representation of the system's final structure. To guide the DEMATEL method, after gathering potential factors, expert opinions regarding these factors must be utilized. The sample size in most studies based on the DEMATEL method is 10 to 12 selected experts. It is crucial to note that in this process, the quality of expert judgment is the more important factor.

An important point to consider when collecting expert opinions is that scores should only be given for the direct influence relationship of row factor A on column factor B; the inverse relationship should not be considered. Also, the indirect influence of row factor A on column factor B through other existing factors should be disregarded.

The DEMATEL Method in a Structural Model

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is one of the most widely used techniques for identifying and analyzing causal–effect relationships among criteria. To develop a structural model composed of causal and effect factors, the DEMATEL method is employed. To address the ambiguity inherent in human judgment, the linguistic variable “influence” is used along with several linguistic terms, including very high influence, high influence, low influence, very low influence, and no influence. These linguistic terms are represented by positive fuzzy numerical values. As demonstrated by the characteristics and outcomes of the DEMATEL technique, this method not only enables the ranking of components and determination of their relative importance but also identifies the degree to which each component influences or is influenced by other factors.

To identify the relationships among N criteria, an N× N matrix is constructed. This matrix is referred to as the direct-relation matrix and is denoted by X. Experts are then asked to assess the degree of influence of each criterion on the others using a scale ranging from 0 to 4 (Fazel et al.,2013).

Table 3. Scoring Scale Used in the DEMATEL Questionnaire

Modeling Relationships among Variables Using DEMATEL

To reflect the mutual interactions among the main criteria, the DEMATEL technique is applied. This method enables experts to express their judgments more accurately regarding both the direction and intensity of effects among factors. It should be noted that the matrix derived from the DEMATEL technique (i.e., the internal relation matrix) simultaneously illustrates causal relationships among factors as well as their influencing and influenced roles.

Based on the completed questionnaires and the collected matrices, and in accordance with the maximum rule, the relationships among approaches, criteria, and sub-criteria were identified. Subsequently, the median values of expert judgments for these relationships were calculated. Finally, a directed graph (digraph) representing the relationships among approaches, criteria, and sub-criteria was constructed based on weighted scores ranging from 0 to 4. According to the influence levels identified by the experts, the corresponding matrices were derived. For this purpose, the scoring table was provided to the experts, and they were asked to evaluate the degree of influence of each factor on the others. Initially, twelve experts were requested to schematically illustrate the influence relationships among the factors based on their professional experience. The term experts refer to specialists who completed the DEMATEL questionnaire.

After identifying the research factors, expert opinions regarding the five identified items (research factors) were collected using the DEMATEL questionnaire. Following data collection, the arithmetic mean of expert judgments was calculated. Since twelve experts participated in the study, the values in each matrix cell were summed and divided by the number of experts (12). The resulting values represent the average expert judgment matrix.

 Table 4. Average Expert Judgment Matrix

The scoring matrix is ​​completed based on the opinions of the experts who responded to the questionnaire

Dematel Technique Calculation Formula

Calculation of the Direct-Relation Matrix (M)

After collecting the DEMATEL questionnaires, the first step is to calculate the arithmetic mean of the experts’ judgments. When the opinions of several experts are used, the simple arithmetic mean of their evaluations is applied to form the direct-relation matrix (M).

At this stage, based on the relationships specified in the graph, the matrix M is constructed. For example, the relationship between training in psychological operations and management of cyberspace is equal to 3.95.

After collecting the DEMATEL questionnaires from 12 experts, the arithmetic mean of their responses was calculated, and the average direct-relation matrix was formed.

This matrix represents the intensity of the direct influence of each row factor on each column factor. The research criteria were labeled as follows.

symbols of research criteria

DEMATEL Technique Calculations and Results Analysis

Step 1: Construction of the Average Direct-Relation Matrix

The values of this matrix are obtained by calculating the arithmetic mean of the experts’ opinions using a 0-4 scale.

 Calculation of the α Value

According to the relevant formula, α is obtained as the inverse of the largest row-sum value. Then, the obtained value is multiplied by all elements of the matrix to derive matrix N (this process is referred to as normalization of matrix M).

To normalize the direct-relation matrix, the formula N=X / K (i.e., Equation 2 shown in the figure below) is used. To calculate K, the sums of all rows and columns are first computed. The largest resulting value is denoted by K, which is equal to 15.66 in the normalized direct-relation matrix table.

Step 2: Normalization of the Direct-Relation Matrix

Table 6. Normalized Direct-Relation Matrix

Calculation of the Total-Relation Matrix

To calculate the total-relation matrix, the identity matrix I is first constructed. Then, the normalized matrix is subtracted from the identity matrix, and the resulting matrix is inverted. Finally, the normalized matrix is multiplied by the inverse matrix.

First, the sums of all rows and columns are calculated. The inverse of the largest row or column sum forms the value k. Based on the table, the largest value is 15.66, and all values in the table are multiplied by the inverse of this number in order to normalize the matrix. (This process is referred to as normalization of matrix M.)

At this stage, the direct (effect) relationship matrices are constructed based on the identified relationships and the mean scores obtained in the previous steps, and the normalized direct-effect matrix at the level of approaches is calculated.

The total-relation matrices (T) indicate the relative intensity of both direct and indirect relationships among approaches, criteria, and sub-criteria. The table presents the total-relation matrix for the approaches.

Network Relationship Map (NRM) Visualization

To determine the Network Relationship Map (NRM), the threshold intensity must first be calculated. This method allows minor relationships to be disregarded and a meaningful network of relationships to be drawn. Only the relationships whose values in matrix T are greater than the threshold value are displayed in the NRM.

To calculate the threshold value of relationships, it is sufficient to compute the average of the values in matrix T. After the threshold intensity is determined, all values in matrix T that are smaller than the threshold are set to zero; that is, the corresponding causal relationships are not considered.

The output of the DEMATEL method consists of four indices: R, D, R + D, and R − D.

The R index represents the row sum of the values and indicates the influence of a factor on other factors, reflecting the degree of influence of a variable. Based on the DEMATEL index analysis, psychological operations training, cyberspace management, societal psychological aspects, psychological operations design, and the use of psychological operations tools influence one another, indicating their level of impact as influencing variables.

In the table, the D index, which is the column sum of the values, represents the degree to which a criterion is influenced by other criteria in the model. Accordingly, psychological operations training has the highest level of influence, followed by cyberspace management.

The horizontal vector (D + R) indicates the overall prominence of each variable and the extent to which the factor both influences and is influenced within the system. In other words, the larger the value of D + R, the greater the interaction of that factor with other system factors. Based on this, psychological operations training and cyberspace management show the highest levels of interaction with the other criteria under study. The R + D horizontal axis represents the importance of the variables and is interpreted as their level of prominence or centrality.

The vertical vector (D − R) represents whether a variable is influential or influenced, as well as the strength of its causal power. In general, if D − R is positive, the variable is considered a causal factor, whereas if it is negative, the variable is considered an effect (resultant) factor. Based on the DEMATEL index analysis, the causal group includes psychological operations training and cyberspace management, while the effect group includes societal psychological aspects, psychological operations design, and the use of psychological operations tools.

The vertical axis (R − D) classifies factors into two groups: causal and effect. Factors with positive values belong to the causal group, whereas those with negative values belong to the effect group. Moreover, if this value is zero for a given factor, it can be considered both causal and effect simultaneously (Hung, 2011).

Based on the pattern of relationships, a causal diagram can be drawn according to the table.

Finally, a Cartesian coordinate system was constructed in which the horizontal axis represents D + R values and the vertical axis represents D − R values. The position of each factor was determined by a point with coordinates (D + R, D − R) in this system. In this way, a graphical representation was obtained.

A summary of the graphical representation is presented in the diagram.

According to the figure, psychological operations training and cyberspace management, which are positioned above the horizontal axis, have a positive net effect intensity and are therefore categorized as causal, driving, or influencing indicators. In contrast, the indicators located below the horizontal axis exhibit a negative net effect intensity and are clustered as dependent indicators. Accordingly, societal psychological factors, psychological operations design, and the use of psychological operations tools are positioned below the horizontal axis, indicating a higher degree of susceptibility to influence.

The higher the position of an indicator in the diagram, the greater its degree of influence; conversely, the lower its position, the greater its degree of being influenced. Moreover, as indicators move toward the right side of the diagram, they gain greater importance, because the sum of their influencing and influenced effects increases. In other words, an indicator that has greater interaction with other indicators possesses higher importance. Therefore, psychological operations training and cyberspace management are of greater importance compared to the other indicators. Based on the results, it was determined that the most influential components are psychological operations training and cyberspace management.