## 3 Results and Discussion
### Co-Word Analysis  
[PERSON] et al. were the first to propose a co-word analysis approach [18]. Researchers have since utilized the co-word analysis to discover the patterns of the bibliometric data of various fields [8; 10; 15], so the co-word analysis is accepted as a successful approach for text mining as well as thematic content analysis [13]. A high frequency of keywords (co-occurrence) that appear together suggests that the articles are more closely related and have a stronger association [19; 20; 21]. The occurrence of the keywords in scholarly publications is revealed via keyword (co-word) analysis. \"Author Keywords\" and \"Keywords Plus\" are the main two types of keywords for bibliometric research. \"Author Keywords\" are phrases that researchers choose to apply in their works, and \"Keyword Plus\" also contains key terms from the pre-set list of linked research fields that was developed by the journals' editors and WoS specialists. Table 1 displays the top 10 most pertinent terms found in the publication records related to the Internet of Things with Business Model Innovation domains. The keyword distribution is found as Keyword Plus (ID) (\(n=258\)) and Author's Keywords (DE) (\(n=293\)). The most frequent Author's Keywords included \"internet of things (IoT)\", \"business model/models\", \"industry 4.0\", \"business model innovation\", \"digital transformation\", and \"digitalization\". The most frequent Keyword Plus terms included \"innovation\", \"internet\", \"technology\", \"challenges\", \"management\", and \"value creation\". A \"word cloud\", commonly referred to as a \"tag cloud,\" is a graphic representation of textual data [22] derived from different keywords or from any given text corpus [23]. The Web of Science database structure states four essential elements when analyzing a word cloud: the abstract, the paper's title, the Author's Keywords (DE), and Keyword \begin{table}
\begin{tabular}{c c c c c} \hline  & **Author Keywords (DE)** & **Article Counts** & **Keywords Plus (ID)** & **Article Counts** \\ \hline
1 & internet of things & 39 & innovation & 30 \\ \hline
2 & business model/models & 26 & internet & 12 \\ \hline
3 & industry 4.0 & 24 & technology & 12 \\ \hline
4 & business model innovation & 15 & challenges & 11 \\ \hline
5 & digital transformation & 8 & management & 11 \\ \hline
6 & digitalization & 8 & value creation & 10 \\ \hline
7 & value creation & 5 & future & 8 \\ \hline
8 & artificial intelligence & 4 & smart & 8 \\ \hline
9 & circular economy & 4 & transformation & 8 \\ \hline
10 & industrial internet of things & 4 & business model innovation & 7 \\ \hline \end{tabular}
\end{table} 
Table 1: Top 10 keywords. Plus (ID). The significant terms and phrases are emphasized; typically, words or phrases that are given specific font sizes and colors based on their relevance and depending on the frequency of the text data relating to the primary categories. Bold and larger-sized words denote a word's greater significance and rising level of interest among researchers in the topic they belong to [8]. In order to apply a co-word analysis and create a word cloud to depict the influence of key terms based on their frequency in research domains, it is common that both keywords among the dataset should be gathered from the articles to complete the clear picture of the science mapping according to the search strategies [15; 24]. Figure 1 demonstrates the most frequent words in each of the categories. The term \"innovation\" has the highest use among the corpus text in Keyword Plus, followed by \"internet\", \"technology\", \"management\", \"challenges\", and \"value creation\". Similarly, the term \"business model innovation\" has significantly higher use than others, followed by \"business model/models\", \"digitalization\", and \"digital transformation\". The terms \"internet of things\" and \"industry 4.0\" have various text forms in the corpus (Figure 2) such as \"internet of things\", \"iot\", \"internet of things (iot)\" and \"industry 4\", \"0\", and \"industry 4.0\".