WELL-BEING AT WORK IN PROCESSES OF ORGANIZATIONAL CHANGE: THE ROLE OF INFORMAL SOCIAL NETWORKS

AMORIM-RIBEIRO, ELISA M. B.; NEIVA, ELAINE R.; MACAMBIRA, MAGNO O.; MARTINS, LEONARDO F.

doi:10.1590/1678-6971/eRAMG220125

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Human and Social Management • RAM, Rev. Adm. Mackenzie 23 (01) • 2022 • https://doi.org/10.1590/1678-6971/eRAMG220125 copy

WELL-BEING AT WORK IN PROCESSES OF ORGANIZATIONAL CHANGE: THE ROLE OF INFORMAL SOCIAL NETWORKS

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Purpose:

This study evaluates the role of social networks of support, information, and trust in well-being at work, regarding the positive and negative affects and professional fulfillment of workers immersed in processes of organizational change.

Originality/value:

Organizational change is characterized as a dynamic process, constituted through relationships, immersed in a context of uncertainties. The mapping of relationships can help in understanding the information flows and the assessment of resource availability.

Design/methodology/approach:

151 professionals from a holding participated. This company undergoes changes in the scope of services offered and the organizational design. Links of support, information, and trust established according to the change processes were mapped. Associated with the network, the Well-Being at Work Scale was used. For data analysis, multiple regressions were used to construct explanatory models for well-being factors: fulfillment, positive and negative affects.

Findings:

Variables in support and information social network analysis (SNA) composed the predictive model of well-being in the three models. Among the researched ties, the support and information ties were part of the predictive model of well-being. The metrics that reveal how many times the employee is indicated and indicates others, proximity to highly cited neighbors, degree of participation in cohesive subgroups, the degree to which they assume a central position in the subgroups are indicators of actors’ positions capable of predicting well-being. The influence of the pattern of interaction between managers and employees should be considered in promoting well-being in organizations in the process of change.

KEYWORDS:
Change in organizations; Social network analysis; Well-being at work; Support network; Information network

Measure	Meaning
Indegree	Quantity of citations received by a certain actor (Hanneman & Riddle, 2011Hanneman, R. A., & Riddle, M. (2011). Concepts and measures for basic network analysis. In P. J. Carrington & J. Scott (Eds.), The Sage handbook of social network analysis (pp. 340–369). London: Sage.).
Outdegree	Quantity of citations given by a certain actor (Hanneman & Riddle, 2011Hanneman, R. A., & Riddle, M. (2011). Concepts and measures for basic network analysis. In P. J. Carrington & J. Scott (Eds.), The Sage handbook of social network analysis (pp. 340–369). London: Sage.).
Degree	Sum of Indegree and Outdegree (Hanneman & Riddle, 2011Hanneman, R. A., & Riddle, M. (2011). Concepts and measures for basic network analysis. In P. J. Carrington & J. Scott (Eds.), The Sage handbook of social network analysis (pp. 340–369). London: Sage.).
Bridging	Identifies actors who have an important linkage between two or more portions of a network (Everett & Valente, 2016Everett, M. G., & Valente, T. W. (2016). Bridging, brokerage and betweenness. Social Networks, 44, 202–208.).
Authority	More important actors in specific subgroups in the network (Lü et al., 2016Lü, L., Chen, D., Ren, X. L., Zhang, Q. M., Zhang, Y. C., & Zhou, T. (2016). Vital nodes identification in complex networks. Physics Reports, 650, 1–63.).
Eigenvector	The score of the actor is proportional to the sum of scores of the neighbors (Borgatti & Halgin, 2011Borgatti, S. P., & Halgin, D. S. (2011). Analyzing affiliation networks. In P. J. Carrington & J. Scott (Eds.), The Sage handbook of social network analysis (pp. 417–433). Thousand Oaks, CA: Sage.).
Modularity class	A measure of ego that informs the quantity of communities in which each actor takes part in (Newman, 2006Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582.).
Clustering coefficient	A measure of ego that informs the proportion of in which each node participates in densely tied subgroups (Katona, Zubcsek, & Sarvary, 2011Katona, Z., Zubcsek, P. P., & Sarvary, M. (2011). Network effects and personal influences: The diffusion of an online social network. Journal of Marketing Research, 48(3), 425–443.).

Control variable				Positive affect			Negative affect			Profissional fulfillment
Control variable		N	%	Mean	Median	Standard deviation	Mean	Median	Standard deviation	Mean	Median	Standard deviation
Sex	Missing	3	2%	0.6	1.2	1.18	0.5	1	1.22	-0.1	0.3	1.03
	Women	37	25%	-0.1	0.1	1.02	0.2	0.1	0.92	-0.3	0	1.2
	Men	110	73%	0.1	0.3	0.92	-0.1	-0.4	0.99	0.1	0.5	0.85
Education	Missing	3	2%	0.6	1.2	1.18	0.5	1	1.22	-0.1	0.3	1.03
	Esp/MBA/Ms.	17	11%	0.4	0.4	0.76	0.3	0.2	0.9	0.1	0	0.64
	Elementary	16	11%	0.3	0.3	0.96	-0.5	-0.8	0.8	0.4	0.8	0.8
	High school	66	44%	0.1	0.3	0.89	0	-0.3	1.02	0.2	0.5	0.74
	Higher education	29	19%	0	0.3	1.03	-0.3	-0.4	0.75	-0.3	0	1.27
	Incomplete higher education	19	13%	-0.4	-0.2	1.05	0.4	0.3	1.11	-0.5	-0.4	1.19
Company	A	28	19%	-0.2	0.1	1.12	0	-0.2	0.96	-0.3	0	1.3
	B	94	63%	0	0.1	0.92	0	-0.2	0.99	0	0.3	0.88
	C	5	3%	0.4	0.4	0.34	-0.7	-0.6	0.41	-0.3	0.1	0.78
	D	10	7%	0.5	0.5	0.58	0.3	0	1.04	0.3	0.7	0.71
	E	11	7%	0.5	1	1.08	-0.3	-0.8	1.15	0.7	0.9	0.34
	F	2	1%	-0.2	-0.2	0.21	-0.1	-0.1	0.18	0.5	0.5	0.68
Hierarchical level	Missing	1	1%	-0.8	-0.8	-	1.5	1.5	-	-1.3	-1.3	-
	Leader	38	25%	0.2	0.4	0.83	0.1	-0.2	0.97	0.3	0.5	0.6
	Subordinate	111	74%	0	0.3	0.98	-0.1	-0.3	0.97	-0.1	0.1	1.03
Geral		150	100%	0.06	0.27	0.95	-0.04	-0.31	0.98	0.03	0.29	0.96

Model term	Coefficient	Standard error	t	Sig.	95% confidence interval		Relevance
Model term	Coefficient	Standard error	t	Sig.	Lower	Higher	Relevance
Company - subset A	0.639	0.271	2.36	0.02	0.104	1.175	0.24
Company - subset B	-0.529	0.185	-2.862	0.005	-0.894	-0.163	0.24
Eigenvector - information	-7.522	2.006	-3.75	0	-11.488	-3.557	0.213
Coefficient of information clustering	3.602	1.051	3.427	0.001	1.524	5.68	0.178
Education subset A	-0.429	0.151	3.427	0.001	1.524	5.68	0.178
Coefficient of support clustering	-1.542	0.608	-2.537	0.012	-2.743	-0.34	0.098
Indegree - information	0.25	0.125	2.009	0.046	0.004	0.496	0.061
Leadership position	0.323	0.177	1.825	0.07	-0.027	0.672	0.051
Authority support	5.892	3.761	1.567	0.119	-1.543	13.328	0.037

Model term	Coefficient	Standard error	t	Sig.	95% confidence interval		Relevance
Model term	Coefficient	Standard error	t	Sig.	Lower	Higher	Relevance
Company subset A	0.516	0.195	2.652	0.009	0.131	0.901	0.24
Admission time	0	0	-2.578	0.011	0	0	0.227
Coefficient of authority - support	7.162	3.053	2.346	0.02	1.127	13.196	0.188
Bridging coefficient -information	7.162	3.053	2.346	0.02	1.127	13.196	0.188
Age	0.018	0.008	2.225	0.028	0.002	0.034	0.169

Model term	Coefficient	Standard error	t	Sig.	95% confidence interval		Relevance
Model term	Coefficient	Standard error	t	Sig.	Lower	Higher	Relevance
Education - subset A	0.481	0.183	2.628	0.01	0.119	0.843	0.51
Education - subset B	0.843	0.207	4.063	0	0.433	1.253	0.51
Degree of support	0.253	0.107	2.35	0.02	0.04	0.465	0.167
Eigenvector of support	-3.718	1.917	-1.939	0.054	-7.508	0.071	0.114
Sex	-0.341	0.176	-1.936	0.055	-0.69	0.007	0.113
Modularity class of information	-0.032	0.018	-1.788	0.076	-0.068	0.003	0.097

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[1] Elisa M. B. A. Ribeiro, Ph.D. from the Postgraduate Program in Psychology, Federal University of Bahia (UFBA); Elaine R. Neiva, Ph.D. from the Postgraduate Program in Psychology, University of Brasília (UnB); Magno O. Macambira, Ph.D. from the Postgraduate Program in Psychology, Federal University of Bahia (UFBA); Leonardo F. Martins, Ph.D. from the Postgraduate Program in Psychology, Federal University of Juiz de Fora (UFJF).

Elisa M. B. A. Ribeiro is now a professor at the Postgraduate Program in Social Psychology of Salgado de Oliveira University (Universo); Elaine R. Neiva is now an assistant professor 4 of Higher Education at the Postgraduate Program in Social, Work and Organization Psychology of UnB; Magno O. Macambira is now an assistant professor at the Department of Human Sciences and Philosophy (DCHF) of State University of Feira de Santana (Uefs); Leonardo F. Martins is now an assistant professor at the Postgraduate Program in Psychology of Universo.