Structural metrics for decision points within multiple-domain matrices representing design processes

Kreimeyer, Matthias; Gürtler, Matthias; Lindemann, Udo

doi:10.1109/ieem.2008.4737906

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…There was only one publication in 2000, 2003, 2004, and 2006, which shows an extremely low compounded annual growth rate of research articles in this field. Whereas no articles were published in 2001, 2002, 2005, 2008, 2009, and 2013 articles were published over the period 2017 to 2021. This shows that there is a growing interest in this topic.…”

Section: Annual Scientific Productionmentioning

confidence: 99%

“…To ensure transparency and replication, the study precisely outlined the specific search words and filters utilized in the search procedure. According to Kreimeyer et al (2009), there are a number of Boolean operators, including AND, NOT, and OR, to find specific topics. Hence in the current study, the keywords such as ''digital AND transformation AND financial AND services'' were typed to search in the article title, abstract, and keywords of the articles published in the Scopus database.…”

Section: Data Pre-processingmentioning

confidence: 99%

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Embracing Digital Transformation in Financial Services: From Past to Future

Thottoli,

Islam,

Yusof

et al. 2023

Sage Open

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Financial services are significantly impacted by digital transformation, which has been believed as one of the primary trends reshaping society and industry. A comprehensive understanding of digital transformation from an academic and management perspective is required, and it is crucial to close the knowledge gap using bibliometric and qualitative research techniques. Hence, the purpose of this paper is to identify the extensive research themes in digital transformation in financial services from 2000 to 2021, their significance and interdependencies, to determine which trending topics seem to be the most influential, to trace the evolution of digital transformation in the financial service, and to address new and under-researched field of research that is intriguing for future research. This study used a bibliometric analysis of 288 peer-reviewed research articles published over the last 22 years in digital transformation in financial services. The data were extracted from the Scopus database and used various bibliometric analyses such as conceptual structure analysis, co-citation network analysis, social structure, and keyword analysis using R software. The findings show an overall increasing trend in journal publications, author productivity, collaborative research by institutions, and countries’ collaboration from the year 2019, as well as major insights from co-citation analysis. According to Lotka’s law, most authors (769, or 95.77%) contributed only one article. In addition, the study assesses multiple research areas, such as the effects of digital transformation in financial services, applied technology and insights, and digitalization processes, comprising the latest trending topics in this research stream that extensively cover up in financial services. One of the limitations of this study is that it used only a single database. The author recommends that the findings of this research be reinforced with more targeted and extensive qualitative literature in this field of study. This study establishes a framework for policymakers, academics, and researchers to investigate the areas where digital technologies influence financial services. This study also highlighted several cutting-edge digital technologies used for different functions of the financial services of an organization. This study illustrates the bibliometric structure of the digital transformation literature in financial services and provides insights into the literature’s growth year over year. It provides a comprehensive understanding of this evolving field, guiding future research and facilitating informed decision-making in the financial industry.

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Section: Annual Scientific Productionmentioning

confidence: 99%

Section: Data Pre-processingmentioning

confidence: 99%