scholarly journals Identifying Project Corporate Behavioral Risks to Support Long-Term Sustainable Cooperative Partnerships

2021 ◽  
Vol 13 (11) ◽  
pp. 6347
Author(s):  
Marco Nunes ◽  
António Abreu ◽  
Célia Saraiva
Keyword(s):  
Social Network ◽  
Project Management ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Building Blocks ◽  
Research Literature ◽  
Management Tools ◽  
Dynamic Interactions ◽  
Major Factors

Projects are considered crucial building blocks whereby organizations execute and implement their short-, mid-, and long-term strategic visions. Projects are thought, developed, and implemented to solve problems, drive change, satisfy unique needs, add value, and exploit opportunities, just to name a few objectives. Although existing project management tools and techniques aim to deliver projects with success, according to the latest reviewed literature, projects still keep failing at an impressive pace. Among the extensive list of factors that may threaten project success, several articles from the research literature place particular importance on a still underexplored factor that may strongly lead to unsuccessful project delivery. This factor—usually known as corporate behavioral risks—usually emerges and evolves as organizations work together to deliver projects across a bounded period of time, and is characterized by the mix of formal and informal dynamic interactions between the different stakeholders that constitute the different organizations. Furthermore, several articles from the research literature also point out the lack of proper models to efficiently manage corporate behavioral risks as one of the major factors that may lead to projects failing. To efficiently identify and measure how such corporate behaviors may contribute to a project’s outcomes (success or failure), a heuristic model is proposed in this work, developed based on four fundamental fields ((1) project management, (2) risk management, (3) corporate behavior, and (4) social network analysis), to quantitatively analyze four critical project social networks ((1) communication, (2) problem-solving, (3) advice, and (4) trust), by applying the theory of social network analysis (SNA). The proposed model in this work is supported with a case study to illustrate its implementation and application across a project lifecycle, and how organizations can benefit from its application.

Identify project corporate behavioral risks to support long-term sustainable cooperative partnerships.

2021 ◽  
Author(s):  
marco nunes ◽  
Antônio José de Abreu Pina
Keyword(s):  
Social Network ◽  
Project Management ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Building Blocks ◽  
Two Dimensions ◽  
Corporate Behavior ◽  
Proposed Model ◽  
Project Lifecycle

Projects can be seen as the crucial building blocks whereby organizations execute and implement their short, and long-term strategic vision. Projects are thought to solve problems, drive change, satisfy unique needs, add value, or exploit opportunities, just to name a few. In order to successful deliver projects, project management tools and techniques are applied throughout a project´s lifecycle, essentially to efficiently and in a timely manner, identify and manage project risks. However, according to latest reviewed literature, projects keep failing at an impressive rate. Although research in the project management field argues that such failure rate is due to a huge variety of reasons, it highlights particular importance to a still underexplored and not quite well understood (regarding how it emerges and evolves) risk type, that may lead projects to failure. This risk type, called as corporate behavioral risks, usually emerge, and evolve as organizations work together across a finite period of time (for example, across a project lifecycle) to deliver projects, and is characterized by the mix of countless formal and informal dynamic interactions between the different elements that constitute the different organizations. Understanding the extent to which such corporate behavior influences project´s outcomes, is a breakthrough of high importance that positively impacts two dimensions; first, enables organizations that deliver projects (but not only), to increase the chances of project success, which in turn is a driver of sustainable business, because it allows the development and implementation of effective, and timely corrective measures to project´s tasks and activities, and second, it contributes to the scientific community (on the organizations field), to generate valuable and actionable new knowledge regarding the emergence and evolution of such cooperative risks, which can lead to the development of new theories and approaches on how to manage them. In this work, we propose a heuristic model to efficiently identify and analyze how corporate behavioral risks may influence project´s outcomes. The proposed model in this work, lays its foundations on four fundamental fields ((1) project management, (2) risk management, (3) corporate behavior, and (4) social network analysis), and will quantitatively measure four critical project social networks ((1) communication, (2) problem-solving, (3) advice, and (4) trust) that usually emerge as projects are being delivered, by applying the theory of social network analysis (SNA), more concretely, SNA centrality metrics. The proposed model in this work is supported with a case study to illustrate its implementation across a project lifecycle, and how organizations can benefit from its application.

scholarly journals Identify project corporate behavioral risks to support long-term sustainable cooperative partnerships.

2021 ◽  
Author(s):  
marco nunes ◽  
Antônio José de Abreu Pina
Keyword(s):  
Social Network ◽  
Project Management ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Building Blocks ◽  
Two Dimensions ◽  
Corporate Behavior ◽  
Proposed Model ◽  
Project Lifecycle

Projects can be seen as the crucial building blocks whereby organizations execute and implement their short, and long-term strategic vision. Projects are thought to solve problems, drive change, satisfy unique needs, add value, or exploit opportunities, just to name a few. In order to successful deliver projects, project management tools and techniques are applied throughout a project´s lifecycle, essentially to efficiently and in a timely manner, identify and manage project risks. However, according to latest reviewed literature, projects keep failing at an impressive rate. Although research in the project management field argues that such failure rate is due to a huge variety of reasons, it highlights particular importance to a still underexplored and not quite well understood (regarding how it emerges and evolves) risk type, that may lead projects to failure. This risk type, called as corporate behavioral risks, usually emerge, and evolve as organizations work together across a finite period of time (for example, across a project lifecycle) to deliver projects, and is characterized by the mix of countless formal and informal dynamic interactions between the different elements that constitute the different organizations. Understanding the extent to which such corporate behavior influences project´s outcomes, is a breakthrough of high importance that positively impacts two dimensions; first, enables organizations that deliver projects (but not only), to increase the chances of project success, which in turn is a driver of sustainable business, because it allows the development and implementation of effective, and timely corrective measures to project´s tasks and activities, and second, it contributes to the scientific community (on the organizations field), to generate valuable and actionable new knowledge regarding the emergence and evolution of such cooperative risks, which can lead to the development of new theories and approaches on how to manage them. In this work, we propose a heuristic model to efficiently identify and analyze how corporate behavioral risks may influence project´s outcomes. The proposed model in this work, lays its foundations on four fundamental fields ((1) project management, (2) risk management, (3) corporate behavior, and (4) social network analysis), and will quantitatively measure four critical project social networks ((1) communication, (2) problem-solving, (3) advice, and (4) trust) that usually emerge as projects are being delivered, by applying the theory of social network analysis (SNA), more concretely, SNA centrality metrics. The proposed model in this work is supported with a case study to illustrate its implementation across a project lifecycle, and how organizations can benefit from its application.

scholarly journals Applying Social Network Analysis to Identify Project Critical Success Factors

2020 ◽  
Vol 12 (4) ◽  
pp. 1503 ◽  
Author(s):  
Marco Nunes ◽  
António Abreu
Keyword(s):  
Social Network ◽  
Project Management ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Critical Success Factors ◽  
Success Factors ◽  
Dynamic Interactions ◽  
Critical Success ◽  
Project Lifecycle ◽  
Collaboration Types

A key challenge in project management is to understand to which extent the dynamic interactions between the different project people—through formal and informal networks of collaboration that temporarily emerge across a project´s lifecycle—throughout all the phases of a project lifecycle, influence a project’s outcome. This challenge has been a growing concern to organizations that deliver projects, due their huge impact in economic, environmental, and social sustainability. In this work, a heuristic two-part model, supported with three scientific fields—project management, risk management, and social network analysis—is proposed, to uncover and measure the extent to which the dynamic interactions of project people—as they work through networks of collaboration—across all the phases of a project lifecycle, influence a project‘s outcome, by first identifying critical success factors regarding five general project collaboration types ((1) communication and insight, (2) internal and cross collaboration, (3) know-how and power sharing, (4) clustering, and (5) teamwork efficiency) by analyzing delivered projects, and second, using those identified critical success factors to provide guidance in upcoming projects regarding the five project collaboration types.

scholarly journals Social Network Analysis across Healthcare Entities, Orange County, FL, 2016

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Danielle Rankin
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Disease Control ◽  
Acute Care ◽  
Orange County ◽  
Acute Care Hospitals ◽  
Patient Movement

Objective: To create a baseline social network analysis to assess connectivity of healthcare entities through patient movement in Orange County, Florida.Introduction: In the realm of public health, there has been an increasing trend in exploration of social network analyses (SNAs). SNAs are methodological and theoretical tools that describe the connections of people, partnerships, disease transmission, the interorganizational structure of health systems, the role of social support, and social capital1. The Florida Department of Health in Orange County (DOH-Orange) developed a reproducible baseline social network analysis of patient movement across healthcare entities to gain a county-wide perspective of all actors and influences in our healthcare system. The recognition of the role each healthcare entity contributes to Orange County, Florida can assist DOH-Orange in developing facility-specific implementations such as increased usage of personal protective equipment, environmental assessments, and enhanced surveillance.Methods: DOH-Orange received Centers for Medicare and Medicaid Services data from the Centers for Disease Control and Prevention Division of Health Care Quality Promotion. The dataset contains the frequency of patients transferred across Medicare accepting healthcare entities during 2016. We constructed a directional sociogram using R package statnet version 2016.9, built under R version 3.3.3. Node colors are categorized by the type of healthcare entity represented (e.g., long-term care facilities, acute care hospitals, post-acute care hospitals, and other) and depict the frequency of patients transferred with weighted edges. Node sizes are proportional to the log reduction of the total degree of patients transferred, and are arranged with the Fruchterman-Reingold layout. We calculated standard network indices to assess the magnitude of connectedness across healthcare entities in Orange County, Florida. Additionally, we calculated node-level indices to gain a perspective of the strength of each individual entity.Results: A total of 48 healthcare entities were included in the sociogram, with 44% representing Orange County, Florida. Although the majority of the healthcare entities are located in nearby counties, 90% of patient movement occurred across Orange County entities. The range of patient movement was 1 to 5196 with a median of 15 patients transferred in 2016. The network in Orange County is sparse with a density of 0.05, but the movement of patients across the healthcare entities is predominately symmetric (reciprocity=97%). The sociogram is centralized (degree centrality= 0.70) and contains a vast amount of entities that serve as connectors (betweenness centrality=0.53). The node-level indices identified our acute care hospitals and long term acute care hospitals are the connectors of our county health system.Conclusions: The SNA of patient movement across healthcare entities in Orange County, Florida provides public health with knowledge of the influences entities contribute to the county healthcare system. This will contribute to identifying changes in the network in future research on the transmission risks of specific diseases/conditions, which will enhance prioritization of targeted interventions within healthcare entities. In addition, SNAs can assist in targeting disease control efforts during outbreak investigations and support health communication. A SNA toolkit will be distributed to other local county health departments for reproduction to determine baseline data and integrate county-specific SNAs.

scholarly journals Mapping the field of educational administration research: a journal citation network analysis

2016 ◽  
Vol 54 (3) ◽  
Author(s):  
Yinying Wang ◽  
Alex J. Bowers
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Educational Administration ◽  
Design Methodology ◽  
Citation Network ◽  
Research Literature ◽  
Citation Network Analysis ◽  
Theory Research ◽  
Journal Citation

Purpose The purpose of this study is to uncover how knowledge is exchanged and disseminated in the educational administration research literature through the journal citation network. Design/methodology/approach Drawing upon social network theory and citation network studies in other disciplines, we constructed an educational administration journal citation network by extracting all 157,372 citations from 5,359 journal articles in 30 educational administration journals from 2009 to 2013. We then performed social network analysis to visualize the network structure by journal clusters, and quantified journal prominence and interdisciplinarity by calculating Freeman indegree and betweenness, respectively. In addition to journal-to-journal citations, we examined the sources of non-journal citations by citation counts. Findings The results of journal prominence, interdisciplinarity, and eight journal clusters in the citation network indicate that educational administration, as a porous field, intimately interacts with the sub-fields of education (e.g., urban education and teacher education), other disciplines (e.g., economics, human resources, sociology, and psychology), and the research internationally. In addition to journals as the knowledge source (45.29%), we also found books (31.08%) and reports (14.98%) are important citation sources in the educational administration research literature. The most cited books and reports shed light on the knowledge base in the theory, research, and practice of educational administration. Originality/value The results of this by far the largest-scale study of educational administration journals present abundant evidence that educational administration is a porous field. This study also presents social network analysis as an alternative method to evaluate journal influence in the educational administration field.

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