Purpose:The outbreak of the COVID-19 forced companies to transform from the on-site work model to the remote or hybrid ones. This paper analyzes threats and challenges resulting from changes in the network structures before and during the pandemic-driven emergence of new work models. Design/Methodology/Approach: This study compares the structure of the social networks in three different work models, (a) office work model before (b) remote work model after the outbreak of coronavirus, and (c) hybrid work model during some of the pandemic restrictions loosening. Research is based on series of experiments conducted in the frame of strategic business simulations. In our approach, the primary source of information where people with their knowledge, behaviors, and points of view. Findings: Our findings confirm the early stage of remote and hybrid work model proficiency among managers and shed light on emerging threats for organizational transformation and innovation capability in such distributed work models.Practical Implications: New work models need to be deeply verified and improved. In the reality of distributed work models, we believe that analyzing social networks will become a critical approach used in organizations to understand weak ties forming their innovation and transformation capabilities. Originality/Value: We used the opportunity of ongoing longitudinal research during which the COVID-19 outbreak occurred. We recorded and analyzed disruptive changes in the social networks of competing teams during pandemic-caused transformations. We found the importance and threats of fragile social ties for organizations operating under distributed work models for innovation and transformation capabilities.
Every organizational change is considered a big challenge. Even having assured enough of the resources needed to drive the change successfully, there is also the necessity to choose the people who would be able to properly lead the change. Organizational network analysis provides some techniques and methods that help in visualizing the informal organizational structure. In the following paper some of these will be presented with the emphasis on showing the potentially key persons for the change that is planned to be developed. In the presented case study one large company was examined. The aim of the article is to analyse the problem of choosing the right people to drive change. In order to select potential change leaders there is an algorithm proposed which takes into consideration two aspects. Namely, the intensity of the archetypical leadership value of the actor, and the actor's position in the informal network. The results confirm that a relatively small group of change leaders can directly reach the majority of employees, which is one of the crucial factors for the change to succeed. The visualizations used in the study can shorten the time needed to find the right people to drive the change, and also reduce the probability of wrong guesses provided by the intuition.
Purpose: Organizational network analysis is usually a long and complex process, consisting of many steps and generally using many analytical tools. Design/Methodology/Approach: Numerous papers have been written about the analysis itself, especially about network measures, concepts, and visualizations used in research. This is unfortunately not the case for the data collection process. Findings: The practice of conducting research shows that effective data collection is often a barrier to network analysis in organizations. The current situation related to COVID-19 has become an additional difficulty in assessing the occurrence of relationships between employees based on observations. Practical Implications: An analysis of the literature and especially years of conducted studies in different organizations show that researchers still lack deeper insights into that kind of issue, such as identifying the main challenges and potential ways to improve the data collection process. This is a research gap we strive to address within this paper. Originality/Value: In the addition to the effort made towards structuring the entire process of data collection in the organizational network analysis research contribution of this paper include our proposals of potential improvements that could be made to the data collection process in analyzing organizational networks.
Umiejętność szybkiego zlokalizowania potencjalnie istotnych osób w organizacji stale nabiera znaczenia. Brokerzy, określani w analizie sieciowej mianem kluczowych graczy, bez wątpienia stanowią osoby, na których stratę żadne przedsiębiorstwo nie chce i w warunkach nieustająco rosnącej konkurencji nie może sobie pozwolić. W organizacjach liczących nierzadko setki, tysiące, a nawet dziesiątki tysięcy pracowników znalezienie osób pełniących funkcję pośredników tradycyjnymi metodami należy do niemałych wyzwań. W niniejszym artykule zaprezentowano jedno z podejść pozwalających skrócić czas i ograniczyć prawdopodobieństwo wytypowania niewłaściwych osób. Celem artykułu jest analiza problemu identyfikacji potencjalnych brokerów przy wykorzystaniu wizualizacyjnych algorytmów opartych na grafach, a szczególnie zaprezentowanie zmodyfikowanej wersji znanego algorytmu Fruchtermana-Reingolda, który zakłada uwypuklenie położenia potencjalnych brokerów w sieci nieformalnej.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.