The subsequent sections delve into the diverse maps generated by VOSviewer, initiating with a meticulous examination of keyword co-occurrence and progressing to a comprehensive bibliographic coupling of countries and authors.
3.3.1. Keyword Co-Occurrence Analysis
A powerful method for identifying patterns and emerging research domains in a particular field is co-occurrence analysis. This technique involves examining the concurrent occurrences of two terms in text, thereby revealing the conceptual and thematic structure of a scientific topic. An essential element of this method is co-word analysis, which investigates the occurrences of keywords in titles, abstracts, and keywords, providing insights into their prevalence in scholarly articles. Employing VOSviewer for keyword co-occurrence analysis in this study yielded a network representation that illustrates the strength and quantity of connections. The size of each circle, linked to an item, signifies the relevance of associated terms. Cluster analysis revealed established areas in auditing, digital accounting, and digital business research, offering insights into potential future trends.
Figure 4 visually depicts keyword co-occurrences and network relationships within the semantic structure of Accounting and Corporate Sustainability. This visualization tool aids in assessing keyword frequency and the robustness of relationships. Circles represent keyword clusters, and connecting lines signify relationships, with shorter distances indicating stronger connections.
Cluster analysis, rooted in the co-occurrence of phrases, unveiled four principal clusters indicative of noteworthy research trends. These clusters were categorized based on their quantity and strength, resulting in the identification of four distinct clusters, encompassing 1517 links with a total link strength of 4255.
The red cluster comprises key terms like "digital transformation," "digital business model," and "digital accounting," signifying their high frequency and significance. This cluster explores digital transformation broadly, emphasizing the impact of the digital economy on professions like digital accounting and auditing during the technological revolution. It is closely connected to the Yellow cluster, collectively focusing on studies that delve into future services enabled by modern technologies in the context of business digitization, including the examination of big data and its analytical strategies.
Furthermore, the blue cluster investigates the impact of social media on e-commerce and customer behavior, with a particular emphasis on the post-COVID-19 era, during which digital strategies gained global prominence.
Figure 4 depicts the emergence of the green cluster at the top of the map, featuring keywords such as "firm performance," "management," "information technology," and "digital business transformation." This cluster primarily explores the relationship between information technology and corporate management, aiming to enhance performance within the framework of business digital transformation.
3.3.2. Investigating Trends in Auditing and Digital Accounting Research within the Digital Business Environment
The researchers employed WoS data and the VOSviewer program (accessible at
www.vosviewer.com; van Eck and Waltman 2010) to create four co-occurrence networks. These networks were established using all keywords identified in the titles and abstracts of papers, as well as in citation contexts associated with auditing and digital accounting research in the digital business environment. The text-mining feature of VOSviewer was utilized to extract keywords from titles, abstracts, and citation contexts, producing a comprehensive map of keyword co-occurrence. Keywords that appeared together in titles, abstracts, or citation contexts were considered to co-occur, and the proximity between keywords indicated their level of similarity in terms of co-occurrence. Terms with a higher co-occurrence frequency were more likely to be found together. Additionally, VOSviewer provided a clustering tool that grouped keywords based on their co-occurrence patterns (Van Eck and Waltman 2017).
Table 1.
Network parameters with the highest reliance on the study's keywords.
Table 1.
Network parameters with the highest reliance on the study's keywords.
Keywords |
Links |
Total Links Strength |
Occurrences |
Cluster Color |
Innovation |
66 |
524 |
122 |
Red |
Technology |
64 |
372 |
78 |
Yellow |
Digital Transformation |
60 |
263 |
69 |
Red |
Firm Performance |
58 |
259 |
61 |
Green |
Digitalization |
63 |
225 |
59 |
Red |
Strategies |
60 |
277 |
58 |
Yellow |
Management |
62 |
240 |
57 |
Green |
Model |
60 |
226 |
57 |
Blue |
Information-Technology |
45 |
180 |
42 |
Green |
Digital Business |
47 |
132 |
41 |
Yellow |
Table 7 displays network parameters with the highest reliance on the study's keywords. To pinpoint the distinctive theme of each cluster, the study conducted an in-depth analysis of the keywords within each cluster. This involved scrutinizing the specific topics represented by the keywords in each cluster.
Cluster 1 (Red) emerges as highly significant and deserving of comprehensive investigation in the future. It prominently occupies the left side and bottom of the map, extending from the central point. Notably, it establishes direct connections with other clusters, including the green and yellow clusters. These findings emphasize the imperative for comprehensive research on this specific cluster, aiming to attain a more profound understanding of its implications and repercussions. In the digital economy, characterized by the widespread integration of digital technologies, traditional business models are being rapidly supplanted by more agile and tech-savvy approaches (Sibanda et al., 2020). Digital accounting, as an integral component of this transformation, witnesses a fundamental evolution. Automation, artificial intelligence, and advanced data analytics are becoming pivotal elements in financial processes. Routine transactions , such as data entry and reconciliation, are increasingly automated, freeing up professionals to focus on higher-value strategic activities. Real-time data access and analysis enable more accurate and timely financial reporting, enhancing decision-making capabilities for businesses operating in the digital sphere (Ahmed et al., 2022).
Concurrently, the realm of auditing is undergoing a significant transformation. The rise of digital systems necessitates auditors to develop expertise in assessing complex technological infrastructures and data security measures (Otia, & Bracci, 2022). Auditing methodologies are evolving to assess not only financial statements but also the resilience of digital systems and the integrity of data flows. The integration of machine learning algorithms and predictive analytics into auditing processes improves the detection of anomalies and potential risks. (Roszkowska, 2021). However, these advancements also bring forth challenges. Professionals in digital accounting and auditing must grapple with issues related to data privacy, cybersecurity, and the ethical use of emerging technologies. The need for continuous learning and upskilling becomes imperative as these fields evolve at an unprecedented pace (Busulwa, & Evans, 2021).
Cluster 2 (Yellow) is a pivotal area for future in-depth exploration. The digitization of business is driving a wave of future services powered by modern technologies, ushering in a transformative era with far-reaching implications (Lang, & Lang, 2021). At the core of this evolution is the substantial impact of big data and its analytical strategies, shaping the course of business operations (Grover et al., 2018). A significant consequence is the enhanced capability for informed decision-making. As businesses undergo heightened digitization, they generate extensive data, and big data analytics allows organizations to extract meaningful insights. Consequently, this facilitates a nuanced understanding of customer behavior, market trends, and operational efficiency, empowering businesses to make accurate and forward-thinking strategic decisions. (Awan et al., 2021).
Future services in the realm of business digitization emphasize personalized customer experiences. Big data analytics enables businesses to customize services based on individual preferences, purchasing history, and behavior, enhancing satisfaction and fostering loyalty in the competitive digital landscape (Sestino et al., 2020). However, these advancements prompt considerations about data privacy and security. The collection and use of vast datasets require robust measures to protect sensitive information. Balancing the benefits of big data analytics with ensuring data protection poses a critical challenge in the pursuit of future services in business digitization (Ogbuke et al., 2022). The consequences of this shift extend beyond individual businesses, impacting broader economic landscapes. Industries adept at harnessing big data analytics gain a competitive advantage, contributing to economic growth and innovation. Conversely, businesses slow to adopt these technologies may face a disadvantage in an increasingly digitized global marketplace (Morabito, 2015).
Cluster 3 (blue) highlighted in blue on the map, comprises a complex ensemble of ideas that merit further investigation. The synergy of social media, e-commerce, and evolving consumer behavior has become a pivotal force, accentuated by the profound changes catalyzed by the COVID-19 crisis. This pandemic not only expedited the adoption of digital strategies but also fundamentally transformed the e-commerce landscape and consumer interactions, notably through the amplified role of social media (Nanda et al., 2021; Al-Qudah et al., 2022).
The immediate consequence of the crisis was a surge in online activities, driven by lockdowns and social distancing measures, compelling businesses and consumers to shift towards digital platforms (Amankwah-Amoah et al., 2021). Social media platforms emerged as central hubs for e-commerce transactions, offering innovative avenues for businesses to engage with customers. The traditional customer journey underwent a significant transformation, with social media serving as a primary touchpoint for discovery, research, and purchasing decisions (Varadarajan et al., 2022).
The implications are diverse. Firstly, social media functions as a potent tool for brand promotion and product visibility, allowing businesses to strategically reach a global audience and foster brand awareness. Secondly, The interactive character of social media facilitates instantaneous communication, empowering businesses to promptly respond to customer concerns and customize offerings based on immediate feedback. (Dwivedi et al., 2021). However, this shift also presents challenges. The vast reach of social media necessitates effective navigation of online reputation management and customer feedback complexities. Negative reviews or viral incidents can substantially impact a brand's image. Additionally, the data-driven nature of e-commerce on social media raises privacy concerns, demanding a delicate balance between personalized marketing and respecting customer privacy (Rauschnabel et al., 2022).
Cluster 4 (green) emerges as significant and deserving of comprehensive investigation in the future. The relationship between information technology and corporate management has profound implications for business performance, particularly within the framework of digital transformation. As organizations increasingly integrate advanced technologies into their operations, the impact on corporate management becomes a critical determinant of overall success (Hanelt et al., 2021).
One significant implication is the potential for streamlined operations and increased efficiency. Information technology provides tools and systems that automate routine tasks, facilitate data analysis, and enhance communication within the organizational structure (Al Hbabi et al., 2012). This automation empowers corporate management to allocate resources more strategically, emphasizing high-value tasks and strategic decision-making (Dewett & Jones, 2001). Consequently, operational efficiency frequently improves, costs diminish, and the business environment becomes more agile. Additionally, integrating information technology into corporate management practices facilitates data-driven decision-making (Brynjolfsson & Hitt, 2000). Management teams can gain comprehensive insights into various facets of the business utilizing sophisticated analytics tools and real-time reporting capabilities (Saggi & Jain, 2018). This data-driven approach enables more informed and timely decision-making, fostering proactive rather than reactive management. As a result, businesses can swiftly adapt to evolving market conditions, customer preferences, and industry trends (Tseng et al., 2022).
However, the growing dependence on information technology in corporate management presents certain challenges. The introduction of new technologies often requires substantial investment, both financially and in employee training. Additionally, there are concerns about data security, privacy, and the potential for technological disruptions. Managing these challenges effectively becomes crucial for organizations aiming to optimize the advantages of digital transformation (Saarikko et al., 2020).