1. Introduction
In today’s world, rapidly developing digital technologies and increasingly adopted innovative and competitive business models have compelled many businesses to undergo digital transformation (DT) in recent years. DT refers to businesses of all sizes leveraging online resources and technology to improve the efficiency of their products, services, customer experiences, workflows, and decision-making processes [
1,
2]. Virtual reality, digital literacy, the Internet of things, digital employees, big data, machine learning, cloud services, and blockchain have increased the adoption of DT, allowing organizations to improve service quality, efficiency, profitability, and customer satisfaction [
1,
3]. With the increase in online resources and the acceleration of technological developments, DT has become a strategic process that closely concerns businesses [
2].
This process has resulted in radical shifts in the strategic decision-making paradigms, business models, and services of businesses in every country and industry. Emerging digital technologies such as artificial intelligence, robotics, big data analytics, the Internet of things, and blockchain play an important role in increasing company efficiency and lowering costs by enabling data-driven decision-making processes [
4]. Furthermore, the problems encountered during the COVID-19 pandemic process clearly demonstrated the need for DT in many public and industrial areas. This case has accelerated the process of organizational DT. According to Statista, spending on DT was USD 1.18 trillion in 2019, with a projected increase to USD 1.8 trillion by 2022. This figure is expected to rise to USD 2.8 trillion by 2025 [
5]. To be successful in global competition, businesses must adapt quickly to competitive business strategies and be constantly renewed. The most significant of these innovations is DT. The strategic significance of DT in today’s global competitive environment is growing by the day [
6].
Academic research on DT is becoming increasingly important as the importance of DT for businesses and societies grows. DT research has recently increased as a result of the increased interest in this field [
4]. Academic research on DT covers a wide range of research topics and draws important conclusions about business success in DT processes and global competition [
3]. Academic research on the applications and effects of DT in various fields includes efforts to investigate how this transformation affects social, cultural, economic, and technological developments. Furthermore, numerous studies on DT processes and applications have been conducted in the context of various countries, governments, regions, organizations, or industries [
7,
8]. While the increase in DT studies provides important inferences and insights into the field, the exponential increase makes understanding the themes and focal points of the field-specific research landscape from a holistic perspective difficult [
1]. Understanding DT research trends, themes, and application areas will make it easier for new researchers to recognize this field and identify potential research gaps [
4]. Understanding these trends, issues, and changes is also important for developing a vision for the future of DT. Scientific literature is a rich and important source of information in this context. Using automated text mining procedures based on machine learning is one of the most effective ways to analyze the scientific literature [
9,
10]. Because automated text mining efficiently summarizes a large number of documents, it enables the discovery of hidden semantic topics in that collection [
10,
11,
12].
Studies based on a DT literature analysis, on the other hand, are mostly of the systematic review and bibliometric analysis variety, with a relatively small number of studies. These studies primarily concentrated on the DT of various industries, including education [
13], construction [
14], and manufacturing [
15]. It has been observed that studies dealing with DT in general have a small number of articles, such as systematic literature reviews [
16] or bibliometric analysis [
17]. Automated text mining research focuses on the temporal effects of DT [
18] and its industrial applications [
19].
Despite these commendable efforts, no study based on automated text mining has addressed DT as a whole. This study’s goal is to identify the research and implementation topics and their temporal trends by analyzing research on DT over the last decade using automated text mining and topic modeling procedures. The field of DT has been approached from a broad perspective for this purpose, and themes and trends have been identified using a machine learning-based topic modeling algorithm. A systematic taxonomy map for DT associated with the obtained topics has been created. Furthermore, the discovered topics’ trends and temporal developments are revealed. Because current reviews for DT have focused on a small number of articles or on specific industries, it is expected that this study will contribute to the literature with more specific and deep insights than the others. Answers to the following research questions (RQs) will be sought in the context of evaluating the previous decade of DT.
RQ1: What are the bibliometric characteristics of DT research?
RQ2: What are the main topics of DT research?
RQ3: How do DT topics fit into a taxonomy?
RQ4: What are the temporal trends in DT research topics?
4. Results
4.1. Descriptive Analysis (RQ1)
In order to answer the RQ1 question, this section provides bibliometric characteristics of DT research.
Table 1 shows the numbers and percentages of articles by year. The low number of studies conducted in the first four years (2013–2016) is highlighted in
Table 1. Despite an increase in the number of studies conducted during the process, the majority of the studies (80.32%) were conducted during and after the pandemic period (2020–2022).
Table 2 lists the most productive journals for DT. The majority of the DT articles were published in the journal
Sustainability Switzerland, as shown in
Table 2. This journal is followed by
Technological Forecasting and Social Change and
IEEE Access.
Table 3 shows which subject areas are covered by the DT studies. Because the articles cover a wide range of topics, the sum of the distribution percentages exceeds 100% in
Table 3. Further, this table demonstrates the multidisciplinary nature of DT. According to this table, the fields of “Business, Management, and Accounting”, “Social Science”, and “Computer Science” stand out in DT studies.
The keywords most frequently used in DT research are shown in
Table 4. “Digital Transformation” is the most frequently used keyword, according to
Table 4. “Digitalization” is the second most frequently used keyword, and “Industry 4.0” is the third.
Table 2 lists the most productive journals for DT. The majority of the DT articles were published in the journal
Sustainability Switzerland, as shown in
Table 2. This journal is followed by
Technological Forecasting and Social Change and
IEEE Access.
Table 3 shows which subject areas are covered by the DT studies. Because the articles cover a wide range of topics, the sum of the distribution percentages exceeds 100% in
Table 3. In addition, this table demonstrates the multidisciplinary nature of DT. According to this table, the fields of “Business, Management, and Accounting”, “Social Science”, and “Computer Science” stand out in DT studies.
The keywords most frequently used in DT research are shown in
Table 4. “Digital Transformation” is the most frequently used keyword, according to
Table 4. “Digitalization” is the second most frequently used keyword, and “Industry 4.0” is the third.
4.2. Topic Modeling Analysis (RQ2)
The topics identified by LDA and their citation rates are presented in this section. The LDA topic model was applied to the DT experimental corpus, and 34 topics were discovered, as shown in
Table 5. Topic ratios expressed as percentages indicate the frequency with which they occur in articles.
Appendix A,
Table A1 lists the descriptive keywords for each topic. The keywords for each topic are listed in descending order of frequency in
Table A1. The top three topics with the highest percentages, as shown in
Table 5, are “Management”, “DT in Economy”, and “SDCL” (software development life cycle). “CIO-CDO Roles in DT”, “Smart City”, and “Cultural Entrepreneurship” received the lowest percentages.
Table 6 also shows the citation rates (CR) of the discovered topics. The “Management” topic with the highest percentage is also the most cited topic, according to
Table 6. This is followed by the “Business Model” and “Industry 4.0” topics. Although the percentages of both topics are low (
Table 5), the citation rates are high. Similarly, “Digital Technologies”, “Innovation Capability”, and “COVID-19” topics have high citation rates despite having a low percentage. “Cultural Entrepreneurship” and “DT in Tourism” have the lowest citation rates as the topics with the lowest percentages. “Digital Literacy”, on the other hand, stands out as the topic with the lowest citation rate.
4.3. Taxonomy of Digital Transformation Topics (RQ3)
The topics were classified, and a taxonomy map was created in this section, which included four major categories: implementation, process, technology, and people (see
Table 7). As shown in
Table 7, the first category is implementation (45.76%). The implementation category includes specific areas where digital transformation is used.
This category contains 16 topics, including “DT in Economy”, “DT in Education”, “DT in Health”, “DT of SME”, “E-Government”, “Industry 4.0”, “Sustainable Energy”, “E-Commerce”, “COVID-19”, “Smart Grid”, “DT in Construction”, “Supply Chain”, “DT in Finance”, “DT in Tourism”, “Smart City”, and “Cultural Entrepreneurship”. The second category, process (26.08%), includes elements such as developing, understanding, improving, implementing, and managing organizational business processes for a successful DT. The process category contains six topics: “Management”, “SDLC”, “Performance Analysis”, “Economic Policy”, “Business Model”, and “Innovation Capability”. The third category is technology (22.48%). This category is about DT technologies and tools. “Social Media”, “Digital Technologies”, “Big Data”, “Internet of Things”, “Service Provider”, “Artificial Intelligence”, “Cybersecurity”, “Cloud Computing”, and “Virtual Reality” are all topics in the technology category. People (5.63%) are the fourth category, and they play an important role in DT. Topics in the people category cover the roles of human expertise in organizational DT. “Digital Literacy”, “HRM” (Human Resources Management), and “CIO-CDO Roles in DT” are the topics.
4.4. Time-Based Trend Analysis of the Topics (RQ4)
To reveal the changes in trends and evolutions of 34 topics reflecting the DT research landscape over time, annual changes in percentages of topics were identified, and trend values for each topic were calculated.
Appendix A,
Table A2 displays the percentages, trend values, and trend directions of the topics by year. The topics in this table are listed in descending order (from green to red) based on the overall trend values in the last column (see
Appendix A,
Table A2). Annual percentages for the last five years were used to calculate the overall trend shown in the last column, because in the first five years, the percentage weight of all the topics has not been formed yet.
Appendix A,
Table A2 is presented in the form of a colorized heatmap table. Each row of this table demonstrates the annual percentage changes for a specific topic as well as descriptive information about how that topic has evolved over time. In this table, the green colors indicate percentages above the topic’s average in a row, the red colors indicate percentages below the topic’s average, and their colors approach white as these annual values approach the average value (see
Appendix A,
Table A2).
Figure 2 also depicts the top ten topics with the highest increasing and decreasing trends. According to
Figure 2, “Sustainable Energy” appears to be the topic with the highest increasing trend. This topic is followed by “DT in Health”, “E-Government”, “DT in Education”, and “Supply Chain”, respectively. “Big Data”, “Digital Technologies”, and “Social Media” have the most decreasing trends. On the other hand, despite the low percentage of topics (see
Table 4) and citation rate, the topic “Sustainable Energy” shows an increasing trend (see
Table 5). “Social Media” is another fascinating topic in this context. The topic “Social Media” has the most decreasing trend, despite having a relatively high percentage of topics and citations.
6. Conclusions
This research aims to reveal the research and application topics of DT, as well as their temporal trends and hierarchical taxonomy. Semantic content analysis was performed on 5350 peer-reviewed journal articles specific to the field for this purpose, using the LDA topic modeling algorithm, which is commonly used in text mining. The research themes of the field, their taxonomy, and their evolution over time were determined using this analysis. According to the topic percentages and citation rates of topics in our study, transformation management holds an important place in DT research. As another finding, four subcategories of DT were identified: implementation, process, technology, and people. More specifically, our taxonomy findings revealed that nearly half of the studies covered DT implementations in various fields. Furthermore, contrary to expectations, our research found that there is little interest in virtual reality technology, which is one of the most popular technologies. Furthermore, when the number of topics, the frequency of occurrence of the topics, and citation rates are considered, it is possible to conclude that the human component of DT has not yet received sufficient attention. Our findings also show that internal stakeholders are generally focused on the human component, while research trends for external stakeholders are low. Furthermore, our research shows that in the last five years, researchers have become more interested in industrial applications of DT. In this respect, “Sustainable Energy”, “DT in Health”, “E-Government”, “DT in Education” and “Supply Chain” were identified as topics with the most increasing trend. Another important finding from our research is that DT research, particularly in energy, is still in its early stages and evolving rapidly.
This study, like all others, has limitations. In this study, only peer-reviewed journal articles were used. The study can be repeated in future studies by including different types of studies, such as books and conference papers from various databases. Furthermore, different topic modeling algorithms can be used to perform analyses, and the results can be compared. More experimental research is needed for comparison and validation of our results in the field of DT. It is expected that our methodology and findings will be useful in future research. Furthermore, this study is expected to serve as a guide for researchers to investigate potential opportunities in the field. One of the potential benefits of this study is that it will aid strategic decision-making processes by increasing practitioners’ awareness of DT.