Introduction

In the social sciences, fixed effects meta–analytical models have gained special relevance due to their ability to predict a scenario, context and process, although they have focused on the estimation and prediction of simple variables, avoiding the effects of diffuse variables such as emerging ones. In training and research processes [1]. In the sciences of complexity, the analysis of fuzzy logic has been used to observe the emergence of emerging entities such as university governance in which new actors seem to define the quality of academic processes and products, such as managers, producers and knowledge transfers [2].

Fuzzy logic is due to mathematical and computational algorithms applied to the orientation of aerospace or vehicular technologies to face the imponderables of air or land traffic, avoiding coalitions and facilitating the movement of people or goods [3]. In educational and behavioral sciences, fuzzy logic has been used to address emerging issues such as corporate reputation [4]. It is estimated that in the last three years, new jobs are related to new skills such as comprehensive data analysis [5]. The self–concept of an institution regarding its academic training is linked to professional and labor training, even when the distance between the educational curricula and the demands of the labor market are distant.

In that tenor, the investigation; the management, production and transfer of knowledge have been involved in complex, random and diffuse processes that affect the formation of human capital in general and intellectual capital [6]. For this reason, a systematic review of the educational, academic, scientific and technological systems is necessary to establish training, education and training paths for those interested [7]. Systematic reviews and meta–analyses have shown that factors are grouped into determinants of behavior as long as their relationships are systematic [8]. In this sense, the emergence of corporate reputation can be meta–analyzed as a factor that the literature had not addressed, but whose random and heterogeneous effects can reveal it as an axis of academic training.

However, traditional fuzzy logic studies have been built from disturbances, contingencies and disturbances in which the gradients (corruption, catastrophes, collisions) are fuzzy determinants of the distribution of the population, its capacities and resources [9]. Fuzzy logic studies have not established the relationship of homogeneous random effects and have not inferred the emergence of new determinants of behavior.

In the case of the social sciences, fuzzy logic models warn of the emergence of actors such as the cases of managers, producers and disseminators of knowledge who, in interrelation with repositories and technologies, make up the metrics of the quality of the processes and scientific and technological knowledge. products of institutions in alliances with knowledge–creating organizations [10]. In this sense, academic training is no longer just collateral to corporate reputation as indicators of fuzzy logic [11]. In addition, academic training is a process that reveals corporate reputation considering the relationship with its determinants.

Budsankon et al. [12], carried out a systematic review of the studies that brought effects of the environment on analytical, critical and creative thinking skills, establishing as predictors that the classroom environment and intellectual skills explain 96% of the variance total. That is, the emergence of corporate reputation is less likely to emerge than academic training, but it is possible to infer from a systematic and meta–analytic review if this relationship persists in other similar studies during an observation period.

Payborji & Haghighi [13], carried out a meta–analysis on the total effects of intellectual capital management on the productivity of companies, finding a positive and significant relationship between management with respect to knowledge production, profitability and reputation. corporate. The research explained the emergence of indicators of a fuzzy logic in academic training, but the study design was aimed at demonstrating the random effects of factors stated in the literature rather than the appreciation of random and heterogeneous events.

Basyith A [14], found in his review that a high percentage of Indonesian companies are family–owned, consequently, such a situation would be expected to influence the profitability of companies by not having an intellectual capital formation system, but the listing law. on the stock market by imposing hiring standards and the quality of employees, led to nepotism, affecting the hiring of talent. The fuzzy logic of family decisions could have been addressed from the meta–analysis of those factors that generate trust, but nepotism or influence peddling clouded the analysis of the determinants of profitability.

In summary, the formation of intellectual capital ranges from traditionalist nepotism to transparency in the hiring of intellectual capital, measuring its performance from management in its academic, professional and labor training, as well as its consolidation encrypted in the conversion of intangible assets. By the degree of impact on the value of companies that create knowledge [15]. From the fuzzy logic of academic training, knowledge networks that make possible the risk threshold reflected in an opaque or transparent curriculum are concomitant to corporate reputation as long as it is derived from objectives, tasks and knowledge creation goals.

It is precisely in this phase where the management, production and transfer of codified knowledge coincide in the formation of intellectual capital; professional service and labor practice established by alliances between institutions and knowledge–creating organizations [16]. A meta–analytic review of those institutional factors that allow the formation of intellectual capital will allow us to anticipate inhibition or facilitation scenarios that define a corporate reputation.

Therefore, the objective of this work will be to establish the dissipative trajectories of the investigative training process in order to be able to prospectively observe the decision–making of the managers, producers and diffusers of specialized and updated investigative knowledge as required by the indexing systems.

Are there significant differences between the fuzzy logic gradients related to academic training and corporate reputation reported in the literature with respect to the meta–analytic observations of the present work?

The premises that guide this work warn: 1) Corporate reputation and professional training can be explained from the fuzzy logic scale because they are emerging gradients of complexity; 2) The relationships between the variables suggest that the homogeneous random effects can be compared with the findings reported in the literature; 3) The risk threshold that allows interpreting the fuzzy logic scale and the meta–analysis of structural equations will allow testing the hypothesis; 4) The homogeneous random effects parameters will facilitate the understanding of the fuzzy relationships between the variables, as well as their structure.

Theoretical psychological dimensions—values, beliefs, perceptions, motives, attitudes, norms, intentions, and experiences—fit the weighted dimensions [26]. Theoretical psychological dimensions are different from the weighted dimensions.

Material and methods

This section presents the phased description of the risk and impact assessment methodology developed.

Phase I: Comprehensive Population Monitoring to determine management, production and transfer strategies. A direct follow–up was carried out, which gives a detailed population count and a measure of the works that are of interest for management, production and transfer, such as types of studies, paradigm, theory, model, construct and variables (see Table 1). Phase II: Identify threats that inhibit the formation of human capital. Perturbation gradients are identified from terminal efficiency classification, participation in academic events such as conferences, and scientific and technological production published in repositories such as Copernicus, Dialnet, Ebsco , Latinex , Publindex , Redalyc, Scieco , Scopus, WoS and Zenodo. This helps identify threats, areas of opportunity and competitive advantages. Phase III: Formation of the Expert Evaluation Team (EA). The team includes 10 experts in management, production and information transfer. His responsibilities include: Scoring and grading of questionnaires; and opine to ensure the reliability of the data (see Table 2).

Sample

Preliminary interviews were conducted to explore norms, values, perceptions, beliefs, attitudes, motives, intentions, and actions around the request for induced abortion. Once the conceptual dimensions were established, the items were constructed. Subsequently, it was massively applied, and the items were excluded. Once the scales were established, their final application was carried out. At the time of handing in the questionnaires, they were informed that their responses would not have an indirect or direct, negative or positive impact on their grades. Subsequently, the data was processed in the Statistical Package for Social Sciences (SPSS) and Analysis of Structural Moments (AMOS).

Analysis

Dependency relationships were established between each of the variables following the established hypotheses. Once a significant relationship between each of the variables was verified, the model and its adjustment with indices and residuals were estimated.

Normality

The normal distribution was estimated with the multivariable kurtosis parameter assuming that a value less than five is evidence of normality and the Bootstrap sampling and significance statistic with a value close to zero.

Reliability

Internal consistency was estimated with the subscale item correlation for which an alpha value greater than 0.60 and less than 0.90 was assumed as evidence of reliability. Items that lowered the required threshold were discarded.

Validity

The Kayser Meyer Olkin (KMO) parameters and the Bartlet test were weighted to establish adequacy and sphericity, while the factor–item correlation from an Exploratory Factor Analysis of principal axes with promax rotation and obliquity criteria was considered as evidence of construct validity if the value is greater than 0.300.

Structure

An exploratory factor analysis was carried out, considering values below 0.90 and above 0.40 as evidence of a dependent relationship, while values close to zero were assumed to be spurious relationships. In contrast, values greater than 0.90 were considered as evidence of collinearity and multicollinearity.

Adjust

The hypothesis contrast was performed with the chi square statistic whose value and level of significance close to zero were assumed as evidence of acceptance of the null hypothesis [27]. On the contrary, values greater than 0.05 were considered as evidence of acceptance of the alternative hypothesis. However, since the sample consisted of 210 students, the chi–square parameter turned out to be sensitive to the size of the sample. This is how the Goodness of Fit Index (GFI) and the Mean Square Error of Approximation (RMSEA) were included.

Results

Table 2 shows the normal distribution, reliability and validity required for the contrast of the model of relationships specified in seven hypotheses. That is, the kurtosis values indicate the distribution of the responses of the respondents in such a way that it allows inferring the consistency of these same results in other samples or latitudes in which the eight factors will emerge forming a structure of dependency relationships. Precisely, the empirical test of these hypotheses is presented below.

The experiences related to the termination of pregnancy were determined by the expectations surrounding the request for assisted abortion, although these perceptions were determined to a lesser extent by the values. In other words, the values seem to reduce the influence of a psychological factor such as perception in relation to the experience of requesting an induced abortion (Figure 1).

The fit and residual parameters ⌠χ2 = 356.46 (67df) p = 0.067; GFI = 0.990; CFI = 0.975; RMSEA = 0.000⌡ show the acceptance of the null hypothesis regarding the adjustment of the theoretical psychological–cultural relationships with respect to the estimated relationships.

Discussion

The contribution of this study to the state of knowledge and the literature consulted lies in the establishment of an exploratory factorial structure of dependency relationships between indicators and psychological–cultural factors as determinants of intentions and experiences related to the interruption of pregnancy.

However, the findings of this research:

• Were carried out in a context of high youth population density and high birth rate, as is the case of Huehuetoca, State of Mexico.
• It was carried out under a non–experimental design and with a non–probabilistic selection of students from a public university.
• Was weighted under the criteria of obliquity, main axes and promax rotation of an exploratory factorial analysis without considering the measurement errors inherent to the context, the sampling and the conceptualization–operationalization of the variables.
• Therefore, it is necessary to empirically test the specified and weighted models with a confirmatory factor analysis of principal components, variable rotation and least squares technique to establish the dependency relationships between indicators and factors considering The size of the sample and
• The specification of relationships between cognitive variables without considering cultural variables such as values and norms.
• The incidence of measurement errors.

Conclusion

The contribution of this work to the state of the question lies in the establishment of a trajectory model in which the measurement of cognitive factors determines the relationship between sociocultural and behavioral factors. In this way, lines of studies related to the mediation of sociocultural and sociocognitive factors in the relationship between environmental factors such as insecurity about the request for interruption of pregnancy. Public policies and the local agenda can benefit from the established findings. Crime prevention strategies and programs can be defined based on the findings related to the determinants of the experiences of pregnancy interruption.

Acknowledgements

None.

Conflict of Interest

Author declares there is no conflict of interest.

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