ISSN 0798 1015

Vol. 41 (Issue 12) Year 2020. Page 1

Principles of social responsibility for the information technology management in network organizations

Principios de responsabilidad social para la gestión de la tecnología de la información en las organizaciones de redes

CHEN,Tim 1; LAIB, We Jia 2; LIC, Hia Chaog 3 & CHEN, J.C.-Y. 4

Received: 23/10/2018 • Approved: 30/03/2020 • Published 09/04/2020

Contents

1. Introduction

2. Methodology

3. Clustering Social Networks organizations

4. Organization model of social responsibility

5. Conclusions and suggestions

Bibliographic references

1. Introduction

1.1. Background

The notion of "information" is the essence of the informational issues hypothesis in civic knowledge. This concept draws the concentration of scientists from several realms of methodologists, science and scholars. And furthermore, this gigantic commitment of conceiving a universal theory of information has particularly started (Mantilla, 2018; Liudmila et al., 2018). The information of social responsibility must be achieved in organizations as a decisive commitment, in order to trade business strategy. It symbolizes the commitment of the organization between its environmental and social surrounding, causing its accomplishment significant for the organization´s commission as sound as for the summary of strategic perspectives and lines, by means of the amalgamation of interest organizations that permits recognizing advantages and ventures.
Ramírez et al. (2018) marked social responsibility for the commitment that organizations arrogate towards civilization in order to avail sustainable advancement,the equivalence between social welfare and industrial growth. Consequently, ORS is the spontaneous inventiveness that surpasses constitutional obligations, social, environmental and involving financial aspects. It necessitates recognition of organizational means and human expertise to deliver reserves for distinct objectives, develop the authenticity of the organization's systems and security, strengthen the passage to services and profit the scene.
As such, social network localities are contemplated as the principal selection of internet organizations for advertising ideas and distribution content (European Commission, 2010). According to the US Securities and Exchange Commission (SEC), larger than 250 million photos are transmitted each day on Facebook (SEC, 2012). Furthermore, an average Facebook user creates 90 items of content every month and he is correlated to a universal network of 130 companions (Burbary, 2011). Information with the Social Comparison Theory assumptions, social network sites´ organizations among a highly productive network is plentiful likely to commit to the content creation, considering people are driven to follow up with their companions and participate as well (Burke et al., 2009).
Beyond being a conduit of transmission and content dynamo, social responsibility can additionally play an essential position in complex models of civic enterprises. Social responsibility has been distinguished for its potential of assembling and correlating multiple social activities (Ellison et al., 2009). Practicing social responsibility, people can cooperate and develop together for a public foundation, social responsibility being able to bond and link people unitedly.

1.2. The benefits and risks associated with social responsibility

With the numerous advantages that social chain sites render, associated contingencies mount. The hugest uncertainty of using social network sections in isolation, as social network sits presentation information which can profit sponsors, cybercriminals and site providers (Shafie et al., 2011; Marina et al., 2018).
In concerns to sponsors, social network sites´ organizations should be cautioned about what information is contributed to promoters in aggregate information and that social network sites´ organizations should hold the claim to opt-out of primary advertisement (The Office of the Privacy Commissioner of Canada, 2009). Individuals´ benefit to privacy entitles them to be responsible to manage the distribution of information and the means aforementioned public information can and will be adopted (Barnes, 2006).

The systematic works of literature were silent in current ages with regard to the clustering of social networking and site organizations. Hence, this study aims to propagate between four different groups: occasional organizations, moderate organizations, frequent organizations, and heavy organizations. They will be discriminated according to their practices with regard to eleven distinct variables, further divided up in the concluding analysis.

2.2 Mathematical formula and tests

The information was assembled utilizing a questionnaire conveyed on students. All the respondents had at least one social responsibility account that they exercised at least once in three months. We employed an aggregate of paper-based and online polls and we were able to go gather 288 entire replies.
The individualistic variables were calibrated by applying a 7-point Likert measure. The Discriminant Function Analysis (DA) from SPSS software (Tabachnick , 2001) was employed to analyze the differences between the three organizations of social responsibility. The examination implements a distribution function that determines to which collections an individual relates:
si=ci+wi1*x1+wi2*x2+⋯+wim*xm
Where “i” denotes the individual groups or sites, and amounts 1,2, …, “m” denotebiomarkers (variables); “ci” is the fixed for collection “i", and “wim” is the ponderation agent of a variable (biomarkers) “m” for collection “i". “si” is the distribution amount. The discriminant function analysis point for any case could be produced with raw points and unstandardized discriminant function points. This discriminant function analysis coefficients are, by definition, chosen to maximize differences between the groups.
Consequently, the four recommended groups diversify as the number of incidents. Table 1 shows the tests of equality, and  designates there is a powerful demographic indication that the four organizations fully differ according to the predictor variables.
Additional, Log determinants and Box´s M is interpreted. Yet, due to the evidence that the individual measurements are not uniform and p > 0.001, Box´s M test is not significant (Tabachnick & Fidell, 2001). But Log Determinants in Table 2 are substantially parallel which determines that the groups dissent.

Table 1
Tests of Equality of Group Means

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Table 2
Log Determinants

The eigenvalues in Table 3 present report on the individual of the discriminate functions constructed, which are corresponding to the number of suggested groups minus 1 (Burns and Burns, 2009). In the prevailing case, there have two functions, in which reveals that 86.4% estimates for the variation in the subordinate variable and the second single accounts for 12.2% and the third for barely 1.4% of the variety. Furthermore, canonical correlation of the first function is extraordinary, with a value of 0.942 which symbolizes that the discriminates adequately, while the canonical correlation between the second and the third functions.

Table  3
Eigenvalues

Besides, the consequence of the discriminant function is examined with Wilks’ lambda (Burns and Burns, 2009). The effects of the Wilks’ lambda analysis in Table 4 designates that the three functions are profoundly significant (p<0.001).

Table 4
Wilks' Lambda

The relevant weight of individual of the submitted independent variables is significant to be approached as the section of the discriminant analysis. This is evaluated with the regulated canonical discriminant function coefficients manifested in Table 5.

Table 5
Standardized Canonical Discriminant Function Coefficients

A different method to determine the corresponding importance of predictors is the structure matrix in Table 6, which registers the relationship of each variable with each discriminant function (Burns and Burns, 2009). Attitude towards social responsibility persists as the mightiest predictor of the discriminant function. A 0.30 value is deemed as the “cut-off between meaningful and less significant variables” (Burns and Burns, 2009).

Table 6
Structure Matrix

Finally, the argument of the functions at group centroids was shown in Table 7. The centroids or the group means of the predictor variables are related to illustrate all of the three organizations of the social responsibility practice in terms of their portrait (Burns and Burns, 2009). Moreover, the canonical discriminant functions are depicted in figure 2. The scattering distribution events as seen in Table 8 register that 96.2% of incipiently organization claims were perfectly matched. It is also found in the study that social responsibility shows a significantly positive correlation with information technology management. And these results agreed with CARDONA (2018) who pointed social responsibility in the management of human talent in public health organizations. The contribution of the study demonstrates the image of information systems in network organizations takes relative responsibility on social media. The research model was revealed using discriminant analysis and it enables to settle forward four different organization groups in terms of social responsibility. The study also addresses at large the management implications of the findings and suggests innovative ways to deal with moral issues correlated with the ever-growing lack of online privacy.

Table 7
Functions at Group Centroids

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1. AI Lab, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam. PhD Full researcher. Email:  timchen@tdtu.edu.vn

2. Management of Technology and Innovation, King Abdulaziz University, Saudi Arabia. Full exchange student. Email: liuhiachaog77@gmail.com

3. Institute of Special Education Studies, Palacký University, Czech Republic. Full exchange student. Email: liuhiachaog77@gmail.com

4. Faculty of Management Science, Covenant University, Nigeria. PhD Full professor. Email: jc343965@gmail.com