Keyword Co-Occurrence Analysis

Keyword co-occurrence networks are the mutual interconnectedness of terms according to their paired presence within the literature base. In knowledge mapping, each node represents a keyword, and each link represents the co-occurrence of a pair of words. The weight of a link connecting each pair represents the number of times these words co-occur in multiple articles. Therefore, the co-occurrence network effectively represents the cumulative knowledge of a domain, in terms of its crucial knowledge components and insights, as established by patterns and strength of links between keywords that appear in the literature (Radhakrishnan et al., 2017; Wu et al., 2019).

In Figure 9, depicting the keyword  co-occurrence network  for  AI  in marketing, the lower-left corner pertains to studies on neural networks and their tight connections with studies of market segmentation, model development, and behavioral research. Similarly, we find strong co-occurrences across studies that focus on sales, WOM, user- generated content, and quality. In  the  center,  the  highest  co-occurrence  values  link “big data” with “model,” which in turn  are  connected  with  a  multiplicity  of  other nodes. Performance and impact considerations are central too, reflecting the outcome- oriented nature of these studies. On the right side, keyword co-occurrences indicate a strong presence of AI studies linking big data analytics with capabilities, competitive advantage, or firm performance. Then at the bottom, we find a  strong  presence  of studies that connect firm management and strategy with data mining, analytics, and innovation through AI.

 

Figure 9: Keyword co-occurrences

 
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