The pandemic has been observed as a security phenomenon due to the dimensions it entails, although these dimensions have not been confirmed. The objective of this work was to compare the th eoretical structure of security with empirical observations. A cross-sectional, correlational, psychometric, and confirmatory study was conducted with a sample of 100 students selected for their affiliation with institutions committed to implementing the SDGs. The results confirm four of the seven dimensions, and the extension of the model is recognized as an area of opportunity to confirm the remaining three dimensions. It is recommended to expand the number of items and the sample size to increase the total percentage of variance and align the empirical model with the theoretical model reported in the literature.

In the context of organizations and their relationship with an increasingly competitive environment, leaders have been pressured to establish control systems in which differences between employees are exacerbated. Given the lack of job skills, these are replaced by acts of loyalty to the company, resulting in violence against those deemed to be responsible for poor performance or those who are not seen as part of a work group. Thus, organizational violence is justified within collaborative teams, as is the case in Higher Education Institutions, where the phenomenon is exacerbated by the emergence of electronic technologies, devices, and networks. Educational issues are intertwined with the financing of vocational training [6]. In terms of budget, global vocational training is led by the United States with nearly $140 billion, followed by Japan, France, and Germany. Finally, Argentina and Mexico during the period from 1994 to 2007. Research investment shows little difference between Australia, Korea, China, the United States, France, and Japan. There are significant differences between funding from industry and public funding or other investment mechanisms in Canada, the United States, France, Germany, Korea, Japan, Mexico, the United Kingdom, and Sweden.

In the case of corporate financing, differences persist between countries, although they remain constant over the period from 1998 to 2007 in Germany, Argentina, Brazil, Canada, Korea, China, the United States, Spain, France, and Japan [19]. In the case of Mexico, increasing corporate financing is observed, which doubled during the analysis period. The use of available financing also remains constant, since from 1998 to 2007 higher education institutions and universities used a constant amount. This decreased only in the cases of Chile, Korea, Spain, and Japan, but increased in the cases of Brazil, Canada, and the United States. In the case of Mexico, a substantial increase is observed halfway through the period, which ends with a significant decrease.

Although funding has remained constant and resource utilization has increased and decreased in some cases, the differences in the number of researchers are substantial among the countries analyzed (García and Guillén, 2018). The US leads the group with nearly 1.4 million researchers, while China registered the same number in 2007, but its exponential increase denotes poor quality. Japan ranks third, followed by Germany with 600,000 and 200,000 researchers, respectively. In the case of Latin America, Brazil, Mexico, Argentina, and Chile occupy ninth, tenth, eleventh, and twelfth places with nearly 100,000 researchers in the four countries. Although China and the US had the same number of researchers until 2007, there is a difference of 200,000 between the two countries in terms of article production. Germany and Japan even match China's production. France, Canada, and Italy occupy intermediate positions, and Brazil is the Latin American country with the highest production. Regarding academic citations, the United States led the way between 1997 and 2008 compared to other countries, while Mexico ranked last. However, although the United States leads in each of the specified categories, it is significantly surpassed by Japan in terms of patents, and Latin American countries reached 50,000 patents from 1998 to 2007. The increase in scholarships explains Mexico's emergence in patents and its lack of participation in other categories. From 2000 to 2009, the number of patents tripled in Mexico.

Educational problems seem to be explained by the budgetary amount and funding of research in Mexico compared to developed countries (Garcìa, 2021a). The differences between countries are not only financial but also organizational, since Japan, with 20 billion dollars, exceeds the number of patents in the US, which invests 140 billion dollars. Although the production of articles and the number of citations justifies such an amount of investment, in terms of technological innovations, Japan is a management model for emerging countries in Latin America. Indeed, educational, scientific, and technological development seems to obey an organizational logic in which the professional training network and organizational formative violence are factors that would explain the differences between countries that allocate similar investment amounts and the similarities between countries that support their production with different budgetary and financial amounts.

For the purposes of this paper, violence consists of the differentiation between two or more actors regarding a relationship of power and influence that shapes a system of personal and organizational insecurity or security. Thus, violence on digital networks is based on prejudice, depersonalization, benevolence, harassment, subjugation, objectification, stigma, and bullying through technology or devices in digital information and communication protocols [30]. Consequently, in the organizational sphere, violence on digital networks is part of an asymmetric professional development process in which the differences between leaders and employees overlap with the climate of relationships between employees, generating a subsystem of violence in which employees close to leaders are the beneficiaries of the vicious cycle of differentiation.

The theoretical frameworks that explain organizational formative violence are: 1) theory of reasoned action, 2) theory of planned behavior, 3) theory of spontaneous processing and 4) theory of knowledge networks.

The theory of reasoned action argues that attitudes mediate the effect of beliefs on intentions and behaviors. An increase in beliefs increases dispositions toward specific, deliberate decisions and actions. This is a process that goes from general beliefs to specific intentions and actions. However, the predictive power of general beliefs is limited by the specificity and unidimensionality of attitudes. Since attitudes transmit the effect of beliefs, they delimit their indicators in dispositions likely to be carried out (Castel & Freundlich, 2010).

The theory of planned behavior suggests that the effect of beliefs on behavior is mediated by attitudes and perceptions of control. In the face of a contingent situation or event, the perception of control increases its predictive power for intentions and behaviors if and only if it interacts with specific dispositions. To the extent that perceptions of control diminish, their relationship with attitudes predicts a minimal effect on decisions. The deliberate and planned process of decision-making and strategy implementation necessarily requires a perception of control consistent with dispositions toward the object (Castro & Martins, 2010).

Spontaneous processing theory posits attitudes as a consequence of the activation of experiences with the attitude object. Attitudes are associated with object evaluations. A negative evaluation increases disposition and, consequently, the spontaneity of behavior (Caykoylu, Egri, Havlovic, & Bradley, 2011).

A network is a set of central and peripheral nodes around which symmetrical or asymmetrical relationships of interaction are established. In the first case, central nodes distance themselves from peripheral nodes. The information gap between nodes is explained by the discontinuous transfer of knowledge. In the second case, the differences between central and peripheral nodes are minimized, facilitating the exchange of information [26].

The theory of knowledge networks proposes that universities and companies are nodes of information exchange that become productive relationships through their exchanges of knowledge, development of interdisciplinary projects and training flows [1].

The training network theory explains collaborative relationships aimed at balancing demands and resources in contexts of scarcity, uncertainty, insecurity, and risk. The theory anticipates the emergence of factors such as trust, commitment, and satisfaction, which in turn determine innovation and, ultimately, organizational happiness. Professional training networks are information and communication systems related to the development of educational competencies derived from institutional and organizational synergies. They involve information technology systems from which it is possible to construct an academic or professional identity, provided that the nodes form consensus and share responsibilities around scientific and technological production (Molero, Recio, & Cuadrado, 2010).

Professional training networks for relationships between institutions and organizations are exposed to problems inherent to collaborative relationships. Thus, the work environment is the determining factor in agreements, arrangements, and/or consensuses aimed at organizational development—industrial, scientific, and technological—as well as the innovations of collaborative groups. Therefore, they are instruments for knowledge management and innovation that allow us to overcome the discrepancy between industrial growth and sustainable development (Coronel, 2010).

The theory of organizational formative violence posits that the differences between production systems that invest similar amounts of money in their processes are a result of the organizational climate: asymmetrical, violent, and conflictual relationships. In this sense, organizations approach an imbalance between demands and resources, but it is inequity and discretion that allow for the adjustment of task relationships to the companies' diffuse objectives (Díaz, 2013).

Organizational formative violence, unlike RFP, is indicated by asymmetric and inequitable relationships between members of the knowledge network. Thus, management is replaced by dogma; freedoms are displaced by discretion; opportunities give way to impositions; capabilities are reduced to their minimum expression in the face of kinship; and shared responsibilities are inhibited by attributions of guilt (Guillén, Lleó, & Perles, 2011).

Organizational formative violence is the result of the interrelationship between relative and simple majorities and minorities that, when innovating, increase or decrease their participation in the construction of an organizational climate. Thus, professional training networks are power groups that, by centralizing their decisions, generate formative dissent and thus the discussion for consensus or the use of violence as a persuasive or dissuasive instrument for knowledge management and technological innovation [8].

Formative organizational violence warns of the emergence of an organizational climate that materializes in discourses of power in which differences, conflicts, and disagreements are symptoms of discretionary management or indicate consensual management, but relative to the influence of the majority over minorities. It anticipates the emergence of conflicts that would explain the increase in creativity rather than trust, personal initiatives and efforts rather than trust and group commitment, as well as pragmatism rather than satisfaction, aimed at innovation but also toward conformity (Tayo and Adeyemi, 2012).

Studies of organizational violence have focused their interest on the deliberate, planned, systematic and improvised process of professional training focused on formative violence such as mobbing , bullying , stalking and trolling in electronic networks in which employees of an organization interact.

Organizational studies show that the work environment is a key factor in explaining collaborative relationships between employees and managers (Molero et al., 2018). In this sense, workplace violence has been identified as a factor adjacent to vocational training, as it involves interpersonal and task-related conflicts that inhibit productivity and competitiveness. Within the framework of the relationship environment and workplace violence, this study is part of the discussion about sexism as an inhibiting factor in productive relationships (Borjas, 2010).

Although educational institutions and for-profit organizations pursue common goals, the discrepancy between responsible professional training and productivity unrelated to sustainability is preponderant in the disagreements and conflicts between academic and business actors (Vargas, 2011).

However, corporate financing, which drives knowledge specialization and technological innovation, incentivizes scientific production focused on the optimization of natural resources and thereby disseminates a labor identity that runs counter to the values of equity, altruism, or biosphereism. These are asymmetrical relationships in which verticalism , sexism , and ostracism are indicators of an organizational power structure that controls the management, production, quality, and innovation of knowledge (Cuesta, 2012).

In the educational organizational sphere, vocational training is the process through which students are expected to develop the skills that will enable them to find work. In this sense, collaboration agreements between universities and companies are aimed at adapting students' skills and knowledge to the requirements of the local and global market. This requires symmetrical relationships between participants, since trust, cooperation, commitment, satisfaction, and ease are indicators of entrepreneurial training (Gargallo, 2010).

In contrast, when asymmetric relationships prevail among network members, distrust, selfishness, dissatisfaction, and stress emerge as limiting paradigms for task-based and collaborative relationships. Teachers' analysis of the meanings surrounding the knowledge network reveals a work environment of asymmetric relationships. The lack of professional entrepreneurship is a factor in evaluating the effectiveness of the internship program (Gil, 2010).

Theoretical frameworks that explain the behavior of knowledge networks through information and communication technologies have established evaluative principles, beliefs about information, and the normative principles of Internet and electronic device socialization as determining factors. The relationship between these variables and technological behavior has been established based on the assumption that attitudes, perceptions, and intentions mediate the impact of values, beliefs, and norms on the use of a technological device (Long, 2010).

Innovation is an effect of the exchange of information between research and technology projects and strategic knowledge planning. In this sense, a knowledge network involves the collaborative participation of specialists and technologists around a productive-technological activity. Therefore, the configuration of a network is carried out based on the collaborative organizational structure between universities and industrial sectors (Borjas, 2010).

In terms of organizational networks, two types of knowledge converge: codified and tacit. The first refers to productive relationships in which the communication of procedures, recruitment, and training are responsible for implementing the organization's mission and vision among human resources (Cerrón, 2010). The second type of knowledge is articulated through the exchange of procedures not written in a manual, but transferred by more experienced personnel to new recruits. These include beliefs and values surrounding task execution, the use of technical equipment, and production-distribution procedures (Coronel, 2010).

Both types of knowledge symbolize the construction of an organizational-work-technical culture around which trust is fundamental. The absence of trust means that a network cannot be established, since collaborative learning requires a distribution of responsibilities, where anyone who does not adhere to the work dynamic or organizational climate is excluded (Cuesta, 2012).

In this sense, knowledge networks require three conditions to survive: horizontal power, redistributed among network members, and the burden of responsibility, oriented toward each and every one of them. The solution to problems arising after the network's configuration lies within the network itself. Therefore, decisions are made through induction rather than selection (Díaz, 2013).

An essential factor in the network are the translators, who possess skills and knowledge regarding the needs of operational staff and the requirements of administrative staff in relation to strategic planning goals. Considering the different languages between a company's growth needs and basic research, translators are essential because their transdisciplinary training and theoretical and applied experience serve as a link between business owners, administrators, and staff (Gil, 2010).

Self-efficacy is a perception and/or belief motivated by deliberate or discursive trials of personal or impersonal success and error. Since self-efficacy refers to failure, but primarily to success, even despite those failed trials that incite achievement, the perception and belief in self-efficacy is based on the attainment of expected goals rather than on competitiveness, recognition, or vicarious learning. If self-efficacy is a system of perceptions and beliefs focused on success, then the group to which the self-efficacious agent belongs or wants to belong is related to success. Because groups are diverse, self-efficacy varies according to this diversity. A competitive group attributes success to one of its members when they have surpassed previous achievements, which were, of course, established by the group. In this sense, the concept of self-sufficiency seems reliably adjusted to the influence of a group on the objectives, system and achievements of an individual (Anwar and Norulkamar, 2012).

If self-efficacy is a system of perceptions that incentivize achievement by defining effective capabilities, self-sufficiency would also be a system of perceptions and beliefs, but unlike self-efficacy, these would be oriented toward the execution of a procedure or technology. The factors that drive self-efficacy would be identical in the case of self-sufficiency. If competitiveness, recognition, and vicarious learning drive self-efficacy, then self-efficacy would also be driven by this (Arnau and Montané, 2010).

Attitudinal psychological studies have focused on their conceptualization, formation, activation, accessibility, structure, function, prediction, change, inoculation, identity, and ambivalence. Attitudes have been defined based on affective and rational dimensions. Both dimensions are the result of experiences and expectations. This implies their structure: unidimensional or multidimensional, configured by exogenous and endogenous factors. That is, when attitudes activate decisions and behaviors, they cause a peripheral, emotive, spontaneous, heuristic, and ambivalent process. In contrast, when attitudes transmit the effects of values and beliefs on intentions and actions, they are endogenous mediators of a central, rational, deliberate, planned, and systematic process (Berdecia, González, & Carrasquillo, 2012).

Psychological studies have demonstrated significant differences between attitudes toward people and attitudes toward objects. The former refer to stereotypes or attributes, and the latter refer to evaluations or dispositions. In both, ambivalence is an indicator of change when beliefs and evaluations interact to form negative and positive dispositions toward the object. Conflicts arise within the components formed by beliefs toward the object. Resistance to persuasion is a consequence of attitudinal ambivalence. If the environment threatens the formation and function of attitudes, they will adapt the individual to respond to contingencies. Thus, attitudes have two essential functions: egoistic and utilitarian (Cardon, Gregoire, Stevens, & Patel, 2013).

Attitudinal change refers to emotions and feelings resulting from individual actions and for which people feel responsible. It also refers to the social influence that teaching groups exert on students. It also refers to the reception of persuasive messages oriented toward central reasoning, or persuasive messages directed toward peripheral emotionality. In general, the attitudinal system is sensitive to the instability of the object and to cognitive variations that affect the consistency, stability, predictability, competence, or morality of the individual (Celik, Turunc, & Begenirbas, 2011).

Consistent attitude change is related to its multidimensional structure resulting from majority pressure. The diversity of dimensions implies a consistent construction of attitudinal change. That is, attitudes assume a function of internalized responses to constant situations framed by the mass media (Chiang, Méndez, & Sánchez, 2010).

Attitudinal change is related to the deterrent principle of inoculation. Before the onslaught of persuasive messages, the perception of threat, risk, and uncertainty is induced. In general, overexposure to persuasive messages induces high elaboration and thus persuasion. The massive emission of persuasive messages, the motivation, and the resulting management skills can lead to helplessness. That is, faced with a wave of information, people reduce their perception of control and tend to believe that events are immeasurable, unpredictable, and uncontrollable. Or, individuals form an identity that consists of identifying with an administrative group instead of a teaching group. In the process of helplessness, the individual constructs the change in attitude and its reinforcement of hopelessness. In the identity process, it is the group that influences the person's attitudinal change. Helplessness is a process of self-validation or self-fulfilling prophecy. In contrast, identity is a convergent validation of group norms (Chinchilla and Cruz, 2010).

The social influence of the teaching or administrative group refers to majority norms and minority principles that lead to attitudinal change. Majority influence fosters individual conformity, while minority principles foster conflict and attitudinal change. Recently, minority style has emerged as the most permanent factor of social influence and attitudinal change. That is, building majority consensus seems to have a fleeting effect, while building dissent seems to offer constant change (Díaz, Hernández, & Roldán, 2012).

Studies of attitudes toward behavior have focused on their ambivalence. People try to balance favorable and unfavorable information about that dispositional object by maintaining ambivalent attitudes. That is, attitudinal objects are part of the environment in which people find themselves and their need to organize, predict, and control it. Therefore, even if the attitudinal object is consistent with their perceptions, values, and beliefs, people must contrast these objects with the behaviors associated with them (Figeiredo, Grau, Gil, & García, 2012).

Education is a system of knowledge networks that configure a teaching-learning cycle. At the beginning of the educational cycle, knowledge networks are merely a preliminary project. Production strategies are guided by an emerging paradigm rather than a dominant one. This involves the plausibility of theories because knowledge is barely supported by ideologies. The second stage of the educational cycle is peer review, which consists of adjusting projects to the administrative group's policy. Subsequently, in the third stage, the dissemination of knowledge in institutional academic spaces is observed (Fuentes, Herrero, & Gracia, 2010).

Studies on knowledge networks warn that group formation and project planning are as important as trust and identity within an organization, institution, or university. Group formation stems from the social psychological processes of categorization, comparison, representation, and social identity, where conflict and change are the foundations of knowledge networks (Galindo and Echavarría, 2011).

Conflict precedes change. It involves asymmetrical relationships between members of one group and members of another group, considered unrelated to the group's common interests. Conflict arises when differences between groups are evident. Conflict arises when one of the students violates the practice rules, affecting knowledge transfer. Since teacher-researchers are responsible for managing and training students in their integration into the mission and vision of organizations, they must ensure compliance with the rules and sanction those who violate the rules of collaboration (Holden and Karsh, 2010).

Another type of conflict, related to innovation, is defined as the influence of a persistent minority in its actions with the intention of persuading or dissuading an administrative group. It lies within the organization or university and is a conflict in which the students involved perceive greater utilization of their capabilities and resources. Consequently, they demand greater management and training to achieve objectives focused on administrative and technological innovation (López and López, 2011).

On the other hand, change is a consequence of conflict. It is a process in which conversion precedes the persuasion that triggered a conflict and a central or peripheral attitude of need for cognition. Attitudinal change surrounding the questioning of beliefs alludes to a deterrent process in which information can be rationalized or emotional. In the first case, the need for cognition can foster dissonance in which the information disagrees with expectations. In the second case, information fosters emotions that increase expectations toward the informational-attitudinal object (Morales, Ariza, & Muñiz, 2012).

In this sense, change is also synonymous with conversion, in which attitudes toward an object lead to a change in the individual's behavior toward the group (Ríos, Téllez, & Ferrer, 2010). In the case of knowledge networks, conflict and change are essential processes for understanding the barriers and facilitators of knowledge transfer between symmetrical and asymmetrical groups regarding information about an object, process, institution, or organization (Rodríguez, Retamal, Lizana, & Cornejo, 2011).

Individuals establish categories, comparisons, identities, and representations about themselves in relation to members of a group and in reference to other individuals belonging to other groups (Rojas, García, & García, 2011). By establishing parameters of comparison, conflicts within an academic group can become conflicts between organizational groups. This is the first step in delimiting identity or group membership (Shrrof, Denenn, & Ng, 2011).

Intra- and intergroup categorization consists of a set of perceptions about resources, skills, and abilities within a group in relation to another group. If perception is the biased ordering of objects, groups and their individuals bias their assessments when evaluating their actions and those of others. This is the case with attribution bias, whereby individual perceptions attribute achievements to their own abilities and attribute their failures to the abilities of others (Sobrados and Fernández, 2010).

Following categorization and comparison, identity lies beneath. These are decisions about belonging based on biased attributional judgments. If a student perceives greater opportunities for personal growth in a group to which he or she does not belong, he or she will decide to change or convert his or her ideas to those of the favored group. In this sense, the knowledge network would be the one most favored by individual judgments and attributions. At this point in the group formation process, two types of reference groups are constructed: the teaching group and the administrative group (Teh, Chong, Yong, & Yew, 2010).

The administrative group constructs its identity based on the capabilities of the teaching group. In other words, the formation of a knowledge network is not only based on the perceptions of the group members' capabilities, but also on the perceptions of the teaching group's incapacity (Vargas and Arenas, 2012).

To the extent that an administrative group biases its value judgments, it transfers its conflicts to the teaching group. Perceptual bias transforms into attributional bias and ends up as selective bias. By focusing the bias on the teaching group, the individual in the administrative group constructs a network of representations around which the capabilities, resources, and limits of the administrative group are interpreted in relation to the teaching group (Yáñez, Arenas, & Ripoll, 2010).

The representation of teaching group competencies involves an evaluation of their behaviors by the individual and their teaching group. It involves a set of emotions and cognitions surrounding the causes of the teaching group's actions compared to the actions of the administrative group. That is, individuals only want to observe actions that contradict the administrative group and try to minimize their effects on other people's decisions (Zampetakis & Moustakis, 2013).

To the extent that individuals have contact with the teaching staff, their emotions and cognitions regarding the teaching staff's actions increase. Precisely from these experiences, it is possible to infer attitudinal processes that explain the exclusion of the teaching staff due to the attribution of different resources and capabilities to them compared to the administrative staff (Yuangion, 2011).

Underlying this exclusion process is the emotive-cognitive-behavioral consistency that explains the differences between groups. If the administrative group excludes members of the teaching group, then it has demonstrated a high level of consistency that threatens the consistency of the administrative group. Therefore, individuals belonging to an administrative group tend to see significant differences with respect to the teaching group and its members. The consistency of the administrative group is biased when compared to the teaching group, since a biased idea can only be a prejudice rather than an argument (Prada, 2013).

In the field of knowledge networks, the consistency of the administrative group and the teaching group are incompatible. For a knowledge network to function, it requires an administrative group that can connect its knowledge with a teaching group that is inconsistent in its emotions, cognitions, and actions. For this reason, the transfer of knowledge from the administrative group would justify the synergy of the groups by correcting the inconsistency of the teaching group. This process can also be observed if the administrative group is inconsistent and the teaching group is consistent (Orantes, 2011).

However, individuals who perceive emotional-cognitive-behavioral inconsistencies regarding knowledge production in their administrative group end up migrating to the teaching group, since the latter will allow them greater personal growth. This migration process is emotional-cognitive, since emotions surrounding the teaching group produce aversion to the administrative group, and affinity and adhesion to the teaching group [43].

Translators, those with the knowledge, skills, and abilities to manage synergies between the administrative and teaching groups, tend to seek data that corroborate their knowledge management. However, inaccessibility to the teaching group impedes knowledge management, the formation of synergies, and knowledge transfer. If individuals have restricted access to a teaching group, they may blend in with the administrative group and fall into the assumption of natural compatibility of knowledge between both the teaching and administrative groups. The consequence of this compatibility will be the inhibition of the knowledge network and its development into corruption, simulation, or nepotism surrounding the production and transfer of knowledge. In other words, an increase in inaccessibility to the teaching group increases the likelihood of failure of organizational, scientific, and technological programs between the administrative and teaching groups (Medina, 2010).

Translators, as knowledge managers, mediate relationships between faculty and students. When the organizational climate between the administrative and teaching groups becomes one of ambiguity and adversarialism rather than transparency and loyalty, those involved in knowledge networks manipulate information to pursue their own interests. Translators must persuade both groups of the unsustainability of their relationship. It is not enough to diagnose group differences; it is also essential to reduce risks and uncertainty by leveraging the benefits of each link and node in the knowledge network (Manning, 2010).

Now, the affective-behavioral consistency between both groups implies creativity, which introduces both groups into an innovative dynamic. This is a flexible organizational climate in which ideas surrounding the production and transfer of knowledge are enhanced. Given that knowledge networks are diverse, heterogeneity is required in each link or node for the production and transfer of knowledge. To the extent that the organizational climate is flexible, it increases trust and identity within both groups (Long, 2013).

Trust and identity are the result of a type of persuasive information known as belief, and the organizational environment in which beliefs are disseminated is known as attitude toward the knowledge network, its members, and processes. An increase in information related to the network increases certainty, production, and transfer of knowledge. In contrast, a decrease in information inhibits group relationships. Consequently, collaborative and innovative relationships impact productivity; however, stress such as burnout, depersonalization, or frustration can emerge as a result of increased productive demands (Gil, 2010).

However, part of the professional training process is only explained because organizations contain underlying barriers that inhibit development (Padial et al., 2012). In this sense, organizational ambivalence and violence are external to professional training. Consequently, knowledge management involves training networks that innovate in adverse and contingent situations, but also underlie the conflicts and asymmetries inherent in the organizational climate.

Thus, vocational training is influenced by conflicts arising from its degree of organizational discretion. As this intensifies, inequity materializes in monopolistic habitus, but reducing it to its minimum expression generates consensus among senior management. Discretion is the discursive heritage of senior management; persuasion or deterrence are products of monopolistic or consensual fields. In other words, discretion as an antecedent of monopolistic habitus anticipates formative violence, as it suppresses innovation and generates conformity and obedience, verticalism, sexism, and ostracism [10].

However, more recent research has shown that the sharing of information in knowledge networks spreads its effects on perceptions of utility and risk, as well as on attitudes linked to anxiety and network addiction, which are the main determinants of behavior. Thus, technological behavior is determined by the processing of information within a knowledge network. This effect, when mediated by collaborative decisions, increases the predictive power of beliefs about task and interpersonal relationships in an organization [1].

Collaborative intentions, for their part, involve attitudes of trust, perceived capabilities, and informational beliefs that, when interrelated, determine decision-making that is favorable or unfavorable to a knowledge group. However, the knowledge-building process would not be feasible without the development of attitudes of trust, in which collaborative groups disseminate information that will be categorized into learning tools or motivations geared toward achieving objectives and goals (Cerrón, 2010).

In parallel, perceived capabilities complement the formation of information categories, as they involve skills and knowledge related to the construction of a professional training network. However, some studies suggest that professional training and network construction are different processes, as they involve selfish values that contradict altruistic values. These are a series of group norms around which individuals are professionally trained or, alternatively, are emotionally guided when forging an identity. However, it is the socialization of information that will determine an individual's behavior in a collaborative group (Gargallo, 2010).

As a review, the state of knowledge has explained the organizational performance of collaborative groups and networks in the face of situations of scarcity, uncertainty, insecurity, and risk (Rojas et al., 2019). Individuals and groups develop climates of trust, enhance their work commitment, and approach life satisfaction, but they also implement creative management and innovative processes in response to contingencies.

The model proposed by the state of knowledge, which assumes that knowledge socialization consists of general beliefs about information, assumes general effects on each of the factors mediating its relationship with behavior (Santos et al., 2020). Consequently, the specification of the dimensions of behavior could indicate the existence of other intermediate factors with respect to socialization. These are eight indicators of technological behavior that explain the formation of a collaborative group based on information processing.

In the case of trust, technological behavior is indicated by collaborative relationships in which benefits are not based on costs, but rather derived from interdependence in carrying out a specific task. In other words, professional development involving the intensive use of technology stems from symmetrical relationships that a group establishes to distribute skills and disseminate knowledge. These are committed relationships, since if a member does not develop work competencies, they will be excluded from a group that has established a culture of high-quality production. In this sense, collaboration is the result of shared objectives, while individualism is an effect of the goal system that rewards personal effort (Manning, 2010).

In the case of cooperation, unlike simple normative collaboration, technological behavior requires specialized skills and knowledge to accomplish goals. This is why groups are forced to establish cooperative relationships, as the group itself must exchange information, develop strategies, or implement techniques that require ongoing support among its members (Medina, 2010).

However, another indicator of technological behavior is empathy among its members since intensive work and the achievement of objectives or the fulfillment of goals implies affective and emotional relationships to reduce personal conflicts to the absence of communication [43].

Regarding solidarity, unlike collaboration or cooperation, it involves professional training based on the dynamics of collaborative teams within the knowledge network. While collaboration and cooperation are determined by social values, solidarity goes beyond the normative or evaluative principles that unite groups. It involves an awareness of scarcity and uncertainty that allows for anticipating shortages by sharing resources (Orantes, 2011).

Consequently, the propensity for the future is the result of supportive behaviors that anticipate risk scenarios. Indeed, collaborative groups are motivated by prevention and coping strategies in the face of situations unfavorable to the groups with which they share objectives and goals (Prada, 2013).

Finally, the quintessential indicator of technological behavior is entrepreneurship or a dissident spirit. Indeed, the use of technology, and even more so the formation of collaborative networks, would be meaningless if only short- or medium-term gains were pursued. Professional training consists of anticipating scenarios of scarcity, risk, and uncertainty, for which knowledge groups form networks that are essentially entrepreneurial, dissidents of impending situations or expected catastrophes (Vargas, 2011).

Social violence was indicated by hostile sexism, in which male gender identity inhibits the development of female gender identity. Gender identity appears to be a condition upon which benevolent discourses are formed, confining the function of female identity to the care and attention of the work group or team. The specification of relationships suggests that organizational formative violence is indicated by eight factors related to prejudice, depersonalization, benevolence, harassment, subjugation, objectification, stigma, and bullying, which shape a climate of relationships and tasks in which discourses emerge that diminish the merits of individuals and exalt the differences between groups [12].

The theories and findings reviewed in the literature to explain the phenomenon of formative violence in different contexts, settings, and scenarios will shed light on the institutional and academic situation prevailing in public universities regarding violence perpetrated on electronic networks and even anticipate conflict scenarios between the actors.

Although theoretical and empirical frameworks have taken into account the relationships of dependence between the factors that explain organizational violence, the specificity of the interrelations in digital networks, as well as the context of study, exceed the theoretical and empirical assumptions (see Table 1).

Table 1: Comparison of the dimensions of security around the SDGs and COVID

This paper falls within the discipline of administration, an area of institutional studies, but includes concepts related to organizational psychology, such as entrepreneurship, the sociology of work in the case of human capital, and labor economics in the case of knowledge networks.

Therefore, the objective of this paper is to review the studies on security in electronic networks in order to compare the theoretical structure with the observations of this study.

Are there significant differences between the theoretical structure of security and the observations of the present study?

This paper suggests that the propensity for violence in social media is contrary to the state of the art, in which security is multidimensional with respect to the reduction of dimensions in the Intranet.

Design. A cross-sectional, confirmatory, psychometric, and correlational study was conducted with a sample of 100 students enrolled in institutions committed to the SDGs in the context of the pandemic. The sample was selected based on exposure to security and the pandemic in the context of professional internships and social service in public health institutions.

Instrument. The SDG Security Scale (see Annex A) was used. It includes the following dimensions: 1) Health, 2) Food, 3) Economic, 4) Social, 5) Education, 6) Residential, 7) Digital (see Table 2).

Table 2: Operationalization of the variables

Procedure. Respondents were informed about the project objectives and the project leaders. They were invited to a focus group session to standardize the concepts. They were also invited to a Delphi study to evaluate the items. The scale was administered at the universities.

Analysis. Data were entered into Excel and processed in Google Collab (see Appendix B). Normality, reliability, homoscedasticity, adequacy, sphericity, linearity, fit, and residual coefficients were estimated. Values close to unity, except for residuals, were assumed to be evidence of non-rejection of the hypothesis of significant differences between the theoretical structure and the empirical observations (see Table 3).

Table 3: Interpretation of coefficients.

Analysis of the intercepts with significant values suggests that the model predicts the distinction between theoretical structure and empirical evidence. The values were significantly lower than 0.0001 and are assumed to be evidence of prediction other than chance.

Analysis of the implied covariance matrix suggests empirical testing of the model to test the hypothesis. The findings show a diagonal with values greater than one, which are assumed to be evidence of prediction between the theoretical covariance matrix and the observed covariance matrix.

The analysis of the residual covariance matrix indicates the prediction of the theoretical model relative to the empirical model, considering the inclusion of other factors and indicators. The diagonal of the matrix shows significant values, which are assumed to be evidence of non-rejection of the hypothesis of differences between the theoretical and empirical matrices.

The covariance matrix analysis predicts the difference from the observed matrix (see Fig. 1). The results indicate values close to unity, which are assumed to be evidence of non-rejection of the hypothesis.

Figure 1: Covariance matrix between the indicators

The analysis of the factor structure suggests that the empirical model fits the logical model reported in the literature (see Figure. 2). The results demonstrate the prevalence of four factors related to the health, nutrition, social, and residential dimensions, along with their three respective indicators.

Figure 2. Confirmatory factor model of security in the context of the SDGs and COVID-19

The fit and residual values [x2 = 704.946 (54 gl) p > 0.001; GFI = 0.936; RMSEA = 0.000] suggest the fit of the proposed empirical model with respect to the logical model reported in the literature, as well as the non-rejection of the hypothesis relating to the differences between the theoretical structure and the empirical observations.

The contribution of this work to the state of the art lies in the confirmation of a four-dimensional factorial structure related to health, food, social and residential security.

The COVID-19 pandemic has had significant impacts on various aspects of society, including gender equality, food security, and the achievement of the Sustainable Development Goals (SDGs). The gender impact of the pandemic and its potential long-term effects on the achievement of the 2030 Agenda for Sustainable Development (Carreón, 2011) are discussed. The emphasis is on the health impacts of COVID-19 on the SDG 3 targets [29]. The State of Food Security and Nutrition in the World projects potential outcomes for 2030 based on current trends and considering the impacts of the pandemic on food security and nutrition.

The impacts of COVID-19 on household energy and food security in informal settlements emphasize the need for integrated approaches to the SDGs [12]. An implementation framework for achieving post-COVID-19 food security incorporates precision agriculture and digital technologies into the agri-food supply chain [30] . A case study on improved cookstoves and clean fuel use in households addresses issues related to food security. The resilience of the rice value chain in the context of COVID-19 sheds light on the challenges and opportunities faced by the sector during the pandemic.

A study on the impacts of COVID-19 outbreaks on lower-income groups and the achievement of the SDGs provides insights for policymakers and organizations to mitigate the effects of outbreaks [10]. A systematic literature review on the adverse effects of the COVID-19 pandemic on agricultural food systems proposes strategies for building resilient and sustainable food systems to ensure global food security and achieve the SDG targets. The relationship between socioeconomic shocks, social protection, and household food security during the pandemic highlights the increased level of food insecurity resulting from the pandemic.

Overall, the reviewed literature emphasizes the importance of addressing the impacts of COVID-19 on various aspects of society, including gender equality, food security, and the achievement of the SDGs, and highlights the need for integrated approaches and resilient strategies to overcome the challenges posed by the pandemic [11]. Unlike the state of the art, this study confirms four of the seven dimensions reported in the literature. Therefore, we suggest implementing items that measure the dimensions that remain to be confirmed. The opportunity lies in increasing the scale items in a larger sample than the surveyed one. We suggest extending the model to predict and confirm the dimensions cited in the literature.

The objective of this paper was to compare the theoretical framework with empirical observations of the security dimensions surrounding the SDGs during the pandemic. The results confirm four of the seven dimensions, and it is suggested that the study be extended to confirm the remaining three dimensions. Furthermore, the importance of each dimension in predicting security scenarios in the face of the risks of a health crisis is recognized.

1. Definition of dimensions: Based on the SDGs most relevant to the context of the public university in the face of the pandemic, the dimensions that could be included in the scale would be:

- Health security (SDG 3)

- Food security (SDG 2)

- Economic security (SDG 8)

- Social security (SDG 10)

- Educational security (SDG 4)

- Housing security (SDG 11)

- Digital security (SDG 9)

2. Item design: Each dimension has a set of items that capture key aspects of security. The items are formulated so that they can be measured on a Likert scale (1 to 5), where 1 is "Strongly disagree" and 5 is "Strongly agree." The wording is clear, precise, and appropriate for the university context.

3. Coding for confirmatory factor analysis: The format of the items is easily codable to perform a confirmatory factor analysis, allowing verification of the theoretical structure of the scale and validation of its internal consistency.

Scale to measure the dimensions of security in public universities in the face of the pandemic

Dimension 1: Health Security (SDG 3)

1. The university provides adequate health and safety measures for students and employees.

2. The availability of protective material (masks, disinfectants) is sufficient at my university.

3. COVID-19 health protocols are clearly communicated and implemented.

Dimension 2: Food Security (SDG 2)

4. The pandemic affected my access to affordable, quality food at university.

5. The university has implemented measures to ensure access to food during the health crisis.

6. I have had difficulties accessing university dining services due to restrictions caused by the pandemic.

Dimension 3: Economic Security (SDG 8)

7. The pandemic has affected my financial ability to continue my studies at the university.

8. The university has provided financial support to students affected by the pandemic.

9. Scholarship and financial aid programs have been maintained during the health crisis.

Dimension 4: Social Security (SDG 10)

10. The pandemic has affected equity in access to university services and resources.

11. University policies have helped reduce pandemic-related inequalities among students.

12. Vulnerable groups at the university have received adequate support during the pandemic.

Dimension 5: Educational Security (SDG 4)

13. I have had adequate access to online classes and educational resources during the pandemic.

14. The quality of online teaching at the university has been satisfactory during the pandemic.

15. The university has provided adequate technical support for students who need it.

Dimension 6: Housing Security (SDG 11)

16. My housing situation has been affected by the pandemic and mobility restrictions.

17. The university has offered solutions for students facing housing difficulties during the pandemic.

18. Access to university residences has been adequate during the health crisis.

Dimension 7: Digital Security (SDG 9)

19. I have had adequate access to technology and connectivity to continue my studies during the pandemic.

20. The university has provided access to electronic devices for students with technological difficulties.

21. The university's digital infrastructure has been adequate to meet the challenges of distance education.

Scale score

- The Likert scale for each item: 1 = Strongly disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly agree.

- The score for each item is added up by dimension to generate a total score for each of the security dimensions.

Coding for analysis in Google Scholar

To perform a confirmatory factor analysis (CFA) in statistical programs (such as SPSS, AMOS or R), each item is numbered and coded.

- Health safety: Items SS1, SS2, SS3

- Food safety: Items SA1, SA2, SA3

- Economic security: Items SE1, SE2, SE3

- Social Security: Items SOS1, SOS2, SOS3

- Educational security: Items SEd1, SEd2, SEd3

- Home security: Items SV1, SV2, SV3

- Digital Security: Items SD1, SD2, SD3

# Install necessary libraries

!pip install pandas openpyxl semopy factor-analyzer

# Import the necessary libraries

import pandas as pd

from semopy import Model, Optimizer

from factor_analyzer import ConfirmatoryFactorAnalyzer, ModelSpecificationParser

import matplotlib.pyplot as plt

# Upload the .ods file

file_path = '/mnt/data/SEM CFA Security.ods'

data = pd.read_excel(file_path, engine='odf')

# Look at the first rows of the dataset to understand the structure

print(data.head())

# Let's assume that the variables relevant to the CFA are in specific columns

# Here replace 'var1', 'var2', etc., with the actual names of your columns

variables = ['SS1', 'SS2', 'SS3', 'SA1', 'SA2', 'SA3', 'SE1', 'SE2', 'SE3',

'SOS1', 'SOS2', 'SOS3', 'SEd1', 'SEd2', 'SEd3', 'SV1', 'SV2', 'SV3',

'SD1', 'SD2', 'SD3']

# Subset the dataframe with only the variables for the CFA

df = data[variables]

# Define the factor model based on the scale dimensions

# This is based on the assumption that each group has 3 items associated with its corresponding factor

model_dict = {

'Health Security': ['SS1', 'SS2', 'SS3'],

'Food Safety': ['SA1', 'SA2', 'SA3'],

'Economic Security': ['SE1', 'SE2', 'SE3'],

'Social Security': ['SOS1', 'SOS2', 'SOS3'],

'Educational Security': ['SEd1', 'SEd2', 'SEd3'],

'Housing Security': ['SV1', 'SV2', 'SV3'],

'Digital Security': ['SD1', 'SD2', 'SD3']

}

# Define the SEM model in text format for semopy

model_description = """

Health_Safety =~ SS1 + SS2 + SS3

Food Safety =~ SA1 + SA2 + SA3

Economic Security =~ SE1 + SE2 + SE3

Social Security =~ SOS1 + SOS2 + SOS3

Educational Security =~ SEd1 + SEd2 + SEd3

Housing_Security =~ SV1 + SV2 + SV3

Digital_Security =~ SD1 + SD2 + SD3

"""

# Create the model in semopy

model = Model(model_description)

# Fit the model to the data

opt = Optimizer(model)

result = opt.optimize(df)

# See the results

print(result)

# Summary of model fits

summary = model.inspect()

print(summary)

# Calculate model fit with multiple metrics

from semopy import calc_stats

stats = calc_stats(model)

print(stats)

# Graph the loadings

model.plot(figsize=(10, 10))

plt.show()