Abstract:Background: There are thousands of research articles in the field of neuroblastoma that have been published over the past few decades. However, the heterogeneity and variable quality of scholarly data may challenge scientists or clinicians to survey all published articles. However, holistic measuring and analyzing of neuroblastoma related literature by sophisticated mathematical methods can provide a unique opportunity to gain deep insights into the global research performance and collaborative architectonical… Show more
“…Finally, we retrieved the metadata for these 4,488 articles which included author names, titles, country of corresponding author, total number of publications, citation counts (i.e., total citations, average article citations, and number of citing articles with and without selfcitations), journal sources, keywords, countries and regions, and author-level metrics (c.f., Martynov et al, 2020).…”
This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data‐driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross‐fertilization of theories used across fields, and neglected research topics.
“…Finally, we retrieved the metadata for these 4,488 articles which included author names, titles, country of corresponding author, total number of publications, citation counts (i.e., total citations, average article citations, and number of citing articles with and without selfcitations), journal sources, keywords, countries and regions, and author-level metrics (c.f., Martynov et al, 2020).…”
This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data‐driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross‐fertilization of theories used across fields, and neglected research topics.
“…As a tool widely used for qualitative and quantitative analysis of global scientific literature, bibliometric analysis can effectively show the knowledge structure and possible development trend of specific fields through information visualization [16][17][18]. Bibliometric analysis has been performed in the research field of nervous system diseases [19,20]. However, the specific mechanism of autophagy in ischemic stroke is still unknown [21], and the role of autophagy in ischemic stroke cannot be ignored.…”
Autophagy plays a key role in ischemic stroke, but its mechanism remains to be elucidated. In order to explore the effect of autophagy on ischemic stroke, bibliometric analysis and view tools are used to identify the directions of the global research trends and construct full view of the autophagy in ischemic stroke from 2006 to 2022. The research hotspots of autophagy related to ischemic stroke are visually analyzed and generated various visual maps to display publications, authors, sources, countries, organizations, and keywords. By bibliometric analysis, it can be seen that the investigations of autophagy in ischemic stroke is focused on both brain injury and neuroprotection. The impact of a variety of inflammatory factors and signaling pathways on autophagy following an ischemic stroke is also studied. Autophagy plays an important role in all phases of ischemic stroke. It is of great significance to guide the development of treatment plans for ischemic stroke.
“…Internationally, it was estimated that 4% of the world's research output was devoted to the coronavirus in 2020, but 2020 also observed an exponential increase in publications on all subjects submitted to scientific journals, perhaps many researchers had to stay at home and focus on writing up papers rather than conducting science [ [17] , [18] , [19] ]. Bibliometric studies provide interesting methods for measuring the scientific value of a particular field over a specific time [ [12] , [13] , [14] ].…”
Section: Discussionmentioning
confidence: 99%
“…The known increase in COVID-19 pandemic research productivity worldwide, requires attention to bibliometric analysis of the local publication patterns to shed light on where we stand. Bibliometric studies or scientometric assessment has been utilized to assess the scientific output of different world regions in several scientific fields [ [12] , [13] , [14] ]. Noteworthy, GCC countries bibliometric indicators suggested general paucity in productivity and reduced visibility compared to other countries [ 12 , 15 ].…”
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