The Application of Social Network Analysis to Accounting and Auditing

Kacanski, Slobodan; Lusher, Dean

doi:10.6007/ijarafms/v7-i3/3286

Cited by 12 publications

(9 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since the publication of the book "Who Shall Survive" by the Romanian-American psychiatrist, psychosociologist, and educator Jacob Levy Moreno in 1934, research and development in the field of social network analysis has been exponentially increasing in a wide range of different areas [43][44][45][46][47][48][49]. It spans social and behavioral sciences [50], agriculture [51] health care [52] environment [53], law, national safety, criminology and terrorism [54], political science [55], organizational science industry, management and leadership [56], communication, and learning and media [57], just to name a few. J. Clyde Mitchell defined social network analysis as, "A specific set of linkages among a defined set of persons, with the additional property that the characteristics of these linkages as a whole may be used to interpret the social behavior of the persons involved" [58].…”

Section: History and Evolution Of Social Network Analysismentioning

confidence: 99%

Applying Social Network Analysis to Identify Project Critical Success Factors

Nunes

Abreu

2020

Sustainability

View full text Add to dashboard Cite

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.Sustainability 2020, 12, 1503 2 of 32 competency and training are important factors, it is almost always the network factor that is the big key predictor of high performance in organizations and are usually characterized by having broader and diverse problem-solving networks fueled with positive energy [20]. But a broader network does not necessarily mean larger in size. Furthermore, some authors argue that when it comes to effective networks of collaboration, bigger does not always means better, but rather, superior quality means better [7,8]. However, creating a broad and diverse problem-solving network usually requires an extra mile from the employees of an organization, essentially because they need to be more flexible, accountable, and empowered, and proactively search and maintain such networks. This also means that more work will be done through informal networks of relationships, removing to a certain extent, the role of the formal organizational structure in several ways [8,21]. In fact, building a broader and more diverse problem-solving network requires a proper organizational structure that enables people to strategically create the necessary connections in an energized way. Usually, due its rigid nature, a formal organizational structure [6] is not able to provide for the needs of building such problem-solving networks [8,22]. However, either under the pressure or when highly motivated to get the work done, employees of an organization naturally engage in informal networks of collaboration, in order to overcome the natural constraints of the formal organizational structure to get the work done [8,22,23]. Very often these emerging informal networks are not ruled by the rational-legal authority system that the formal organizational structure provides ba...

show abstract

Section: History and Evolution Of Social Network Analysismentioning

confidence: 99%

Applying Social Network Analysis to Identify Project Critical Success Factors

Nunes

Abreu

2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…The beginning of the use of graph theory in analyzing dynamic relationships between entities (persons) is credit to the Romanian American psychiatrist, psychosociologist, and educator Jacob Levy Moreno (1889-1974), as his work "Who Shall Survive" was published in 1934 [45][46][47]. Nowadays the application of social network analysis covers a wide range of different areas such as organizational science industry, management and leadership [48], political science [49], behavioral sciences [50], communication, learning and media [51], law, national safety, criminology and terrorism [52], just to name a few. Social network analysis (SNA) is the process of studying and analyzing social structures data with a variety of measures developed based on graph theory, that contributes to explain how social structures evolve across time, and how they impact the environment where they do exist [53].…”

Section: Social Network Analysis In Organizationsmentioning

confidence: 99%

Managing Open Innovation Project Risks Based on a Social Network Analysis Perspective

Nunes

Abreu

2020

Sustainability

View full text Add to dashboard Cite

In today’s business environment, it is often argued, that if organizations want to achieve a sustainable competitive advantage, they must be able to innovate, so that they can meet complex market demands as they deliver products, solutions, or services. However, organizations alone do not always have the necessary resources (brilliant minds, technologies, know-how, and so on) to match those market demands. To overcome this constraint, organizations usually engage in collaborative network models—such as the open innovation model—with other business partners, public institutions, universities, and development centers. Nonetheless, it is frequently argued that the lack of models that support such collaborative models is still perceived as a major constraint for organizations to more frequently engage in it. In this work, a heuristic model is proposed, to provide support in managing open innovation projects, by, first, identifying project collaborative critical success factors (CSFs) analyzing four interactive collaborative dimensions (4-ICD) that usually occur in such projects—(1) key project organization communication and insight degree, (2) organizational control degree, (3) project information dependency degree, (3) and (4) feedback readiness degree—and, second, using those identified CSFs to estimate the outcome likelihood (success, or failure) of ongoing open innovation projects.

show abstract

“…In accounting research, modelling real-world problems is a strength, but data preprocessing and preparation are weaknesses (Amani and Fadlalla, 2017). The process of transforming raw data into a data set appropriate for research is not always a simple task (Kacanski and Lusher, 2017). Forensic accountants need to understand the limits of computational techniques, and some of these limits are directly related to data (Taylor, 2011).…”

Section: Data Processingmentioning

confidence: 99%

Identifying financial patterns of money laundering with social network analysis: a Brazilian case study

Lima

Serrano

Imoniana

et al. 2021

JMLC

View full text Add to dashboard Cite

Purpose This study aims to understand how forensic accountants can analyse bank transactions suspected of being involved with money laundering crimes in Brazil through social network analysis (SNA). Design/methodology/approach The methodological approach taken in this study was exploratory. This study cleaned and debugged bank statements from criminal investigations in Brazil using computational algorithms. Then graphs were designed and matched with money laundering regulations. Findings The findings indicated that graph techniques contribute to a range of beneficial information to help identify typical banking transactions (pooling accounts, strawmen, smurfing) used to conceal or disguise the movement of illicit resources, enhancing visual aspects of financial analysis. Research limitations/implications Research found limitations in the data sets with reduced identification of originators and beneficiaries, considered low compared to other investigations in Brazil. Furthermore, to preserve restrict information and keep data confidential, data sets used in research were not made available. Practical implications Law enforcement agencies and financial intelligence units can apply graph-based technique cited in this research to strengthen anti-money laundering activities. The results, grounded in analytical approaches, may offer a source of data to regulators and academia for future research. Originality/value This study created data sets using real-life bank statements from two investigations of competence by the Brazilian Federal Justice, including real-data perspectives in academic research. This study uses SNA, which is a popular approach in several areas of knowledge.

show abstract

The Application of Social Network Analysis to Accounting and Auditing

Cited by 12 publications

References 63 publications

Applying Social Network Analysis to Identify Project Critical Success Factors

Applying Social Network Analysis to Identify Project Critical Success Factors

Managing Open Innovation Project Risks Based on a Social Network Analysis Perspective

Identifying financial patterns of money laundering with social network analysis: a Brazilian case study

Contact Info

Product

Resources

About