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Friday, 3 September 2021

A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

 Source: https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/acm2.13394

RADIATION ONCOLOGY PHYSICS
Open Access

A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

First published: 28 August 2021

All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Abstract

Purpose

Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation.

Methods

This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates.

Results

The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers.

Conclusions

Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.

 

How to cite

Zhang, B, Rahmatullah, B, Wang, SL, Zhang, G, Wang, H, Ebrahim, NA. A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis. J Appl Clin Med Phy. 2021; 1- 21. https://doi.org/10.1002/acm2.13394

 

Saturday, 14 August 2021

Trends and patterns in digital marketing research: bibliometric analysis

 Source: https://link.springer.com/article/10.1057/s41270-021-00116-9#auth-Nader-Ale_Ebrahim

Trends and patterns in digital marketing research: bibliometric analysis

Abstract

In today’s digital era, the importance of digital marketing has increased from one year to another as a way of providing novel properties for informing, engaging, and selling services and products to clients. The research’s aim is to investigate trends and patterns in the area of digital marketing research from 1979 to June 2020 through a bibliometric analysis technique. A total of 924 articles published were obtained from the Scopus database for the analysis. In this paper, we examine variant bar charts including the year of publication, writer, publication, keyword, and country to provide more insights. Results indicated that digital marketing research steadily increased during the study period and the maximum publications occurred in the year 2019 that reach to 163 documents. The trend of publications is still growing. The top 20 documents based on the times cited per year (TCpY) were qualitatively analyzed. The largest number of multiple (MCP) and single (SCP) publications was from the USA, followed by the UK and China. The top 20 most repeated authors’ keywords out of 1909 with their trends illustrated. The “real-time bidding”, “machine learning”, “big data”, “social media marketing”, and “influencer marketing” are the emerging keywords in the digital Marketing area. This bibliometric study generally provides the whole image of the field and suggests that researchers focus on novel areas to add new findings and knowledge in the literature if they conduct digital marketing research.

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References

  1. Abedini, A., R. Rahman, H.S. Naeini, and N. Ale Ebrahim. 2017. The 100 most cited papers in’industrial design’: A bibliometric analysis. Exacta – EP, São Paulo 15 (3): 515–526.

  2. Aghaei Chadegani, A., H. Salehi, M. Yunus, H. Farhadi, M. Fooladi, M. Farhadi, and N. Ale Ebrahim. 2013. A comparison between two main academic literature collections: Web of Science and Scopus databases. Asian Social Science 9 (5): 18–26.

    Google Scholar 

  3. Aksnes, D.W. 2003. Characteristics of highly cited papers. Research Evaluation 12 (3): 159–170.

    Article  Google Scholar 

  4. Ale Ebrahim, S., A. Ashtari, M.Z. Pedram, N. Ale Ebrahim, and A. Sanati-Nezhad. 2020. Publication trends in exosomes nanoparticles for cancer detection. International Journal of Nanomedicine 15: 4453.

    Article  Google Scholar 

  5. Aria, M., and C. Cuccurullo. 2017. Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics 11 (4): 959–975. https://doi.org/10.1016/j.joi.2017.08.007.

    Article  Google Scholar 

  6. Barnes, S.J., and E. Scornavacca. 2004. Mobile marketing: The role of permission and acceptance. International Journal of Mobile Communications 2 (2): 128–139.

    Article  Google Scholar 

  7. Baumgartner, H., and R. Pieters. 2003. The structural influence of marketing journals: A citation analysis of the discipline and its subareas over time. Journal of Marketing 67 (2): 123–139.

    Article  Google Scholar 

  8. Bethu, S., V. Sowmya, B.S. Babu, G.C. Babu, and Y.J.N. Kumar. 2018. Data science: Identifying influencers in social networks. Periodicals of Engineering and Natural Sciences 6 (1): 215–228.

    Article  Google Scholar 

  9. Bharadwaj, V., P. Chen, W. Ma, C. Nagarajan, et al. 2012. Shale: An efficient algorithm for allocation of guaranteed display advertising. Paper presented at the Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 1195–1203. https://doi.org/10.1145/2339530.2339718.

  10. Brodie, R.J., and B. Juric. 2018. Customer engagement: Developing an innovative research that has scholarly impact. Journal of Global Scholars of Marketing Science 28 (3): 291–303.

    Article  Google Scholar 

  11. Cai, H., K. Ren, W. Zhang, K. Malialis, J. Wang, Y. Yu, and D. Guo. 2017. Real-time bidding by reinforcement learning in display advertising. Paper presented at the Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 661–670. https://doi.org/10.1145/3018661.3018702.

  12. Cavallo, R., R.P. McAfee, and S. Vassilvitskii. 2015. Display advertising auctions with arbitrage. ACM Transactions on Economics and Computation (TEAC) 3 (3): 1–23.

    Article  Google Scholar 

  13. Diodato, V. 1994. Dictionary of bibliometrics psychology press. Binghamton: The Haworth Press.

    Google Scholar 

  14. Ferreira, M.P. 2011. A bibliometric study on Ghoshal's managing across borders. Multinational Business Review 19 (4): 357–375.

  15. Ferreira, M.P., J.C. Santos, M.I.R. de Almeida, and N.R. Reis. 2014. Mergers & acquisitions research: A bibliometric study of top strategy and international business journals, 1980–2010. Journal of Business Research 67 (12): 2550–2558.

    Article  Google Scholar 

  16. Fierro, I., D.A. Cardona Arbelaez, and J. Gavilanez. 2017. Digital marketing: A new tool for international education. Pensamiento & Gestión 42: 241–260.

    Google Scholar 

  17. Franceschini, F., and D. Maisano. 2011. Regularity in the research output of individual scientists: An empirical analysis by recent bibliometric tools. Journal of Informetrics 5 (3): 458–468.

    Article  Google Scholar 

  18. Fujita, M., P. Harrigan, and G. Soutar. 2017. A netnography of a university’s social media brand community: Exploring collaborative co-creation tactics. Journal of Global Scholars of Marketing Science 27 (2): 148–164.

    Article  Google Scholar 

  19. Ghanbari Baghestan, A., H. Khaniki, A. Kalantari, M. Akhtari-Zavare, E. Farahmand, E. Tamam, and M. Danaee. 2019. A crisis in “open access”: Should communication scholarly outputs take 77 years to become open access? SAGE Open 9 (3): 2158244019871044.

    Article  Google Scholar 

  20. Ghosh, A., P. McAfee, K. Papineni, and S. Vassilvitskii. 2009. Bidding for representative allocations for display advertising. Paper presented at the International workshop on internet and network economics, 208–219.

  21. Ghosh, A., B.I.P. Rubinstein, S. Vassilvitskii, and M. Zinkevich. 2009. Adaptive bidding for display advertising. Paper presented at the 18th International World Wide Web Conference, WWW 2009, Madrid, 251–260.

  22. Gordhamer, S. 2009. Ways social media is changing business. New York: Mashable. Retrieved from http://Mashable.ComMedia-Business/2009/09/22/Social-Media-Business/.

  23. Guercini, S., P.M. Bernal, and C. Prentice. 2018. New marketing in fashion e-commerce. Journal of Global Fashion Marketing 9 (1): 1–8.

    Article  Google Scholar 

  24. Han, S.-L., T. Thao Nguyen, and V. Anh Nguyen. 2016. Antecedents of intention and usage toward customers’ mobile commerce: Evidence in Vietnam. Journal of Global Scholars of Marketing Science 26 (2): 129–151.

    Article  Google Scholar 

  25. Hay, A.M., and K.S. Beavon. 1979. Periodic marketing: A preliminary graphical analysis of the conditions for part time and mobile marketing. Tijdschrift Voor Economische En Sociale Geografie 70 (1): 27–34.

    Article  Google Scholar 

  26. Järvinen, J., and H. Karjaluoto. 2015. The use of Web analytics for digital marketing performance measurement. Industrial Marketing Management 50: 117–127.

    Article  Google Scholar 

  27. Jayawardhena, C., A. Kuckertz, H. Karjaluoto, and T. Kautonen. 2009. Antecedents to permission based mobile marketing: an initial examination. European Journal of Marketing 43 (3/4): 473–499.

  28. Kalantari, A., A. Kamsin, H.S. Kamaruddin, N.A. Ebrahim, A. Gani, A. Ebrahimi, and S. Shamshirband. 2017. A bibliometric approach to tracking big data research trends. Journal of Big Data 4 (1): 1–18.

    Article  Google Scholar 

  29. Kamal, Y. (2016). Study of trend in digital marketing and evolution of digital marketing strategies. International Journal of Engineering Science 6 (5): 5300–5302.

  30. Kaplan, A.M., and M. Haenlein. 2010. Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons 53 (1): 59–68.

    Article  Google Scholar 

  31. Kargaran, S., M. J. Pour, and H. Moeini. 2017. Successful customer knowledge management implementation through social media capabilities. VINE Journal of Information and Knowledge Management Systems, 47 (3): 353–371.

  32. Karjaluoto, H., and T. Alatalo. 2007. Consumers’ attitudes towards and intention to participate in mobile marketing. International Journal of Services Technology and Management 8 (2–3): 155–173.

    Article  Google Scholar 

  33. Khodabandelou, R., N. Aleebrahim, A. Amoozegar, and G. Mehran. 2019. Revisiting three decades of educational research in Iran: A bibliometric analysis. Iranian Journal of Comparative Education 2 (1): 1–21.

    Google Scholar 

  34. Kim, J. 2018. Social dimension of sustainability: From community to social capital. Journal of Global Scholars of Marketing Science 28 (2): 175–181.

    Article  Google Scholar 

  35. Kim, E.Y., and K. Yang. 2018. Self-service technologies (SSTs) streamlining consumer experience in the fashion retail stores: The role of perceived interactivity. Journal of Global Fashion Marketing 9 (4): 287–304.

    Article  Google Scholar 

  36. Ko, E. 2019. Bridging Asia and the world: Global platform for the Interface between marketing and management. New York: Elsevier.

    Google Scholar 

  37. LABS, X. 2017. How is big data influencing digital marketing strategy? Retrieved from https://www.xcubelabs.com/blog/how-is-big-data-influencing-digital-marketing-strategy/.

  38. Lamberton, C., and A.T. Stephen. 2016. A thematic exploration of digital, social media, and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing 80 (6): 146–172.

    Article  Google Scholar 

  39. Levy, S., and Y. Gvili. 2015. How credible is e-word of mouth across digital-marketing channels?: The roles of social capital, information richness, and interactivity. Journal of Advertising Research 55 (1): 95–109.

    Article  Google Scholar 

  40. Li, S., J.Z. Li, H. He, P. Ward, and B.J. Davies. 2011. WebDigital: A Web-based hybrid intelligent knowledge automation system for developing digital marketing strategies. Expert Systems with Applications 38 (8): 10606–10613.

    Article  Google Scholar 

  41. Maghami, M.R., M.E. Rezadad, N.A. Ebrahim, and C. Gomes. 2015. Qualitative and quantitative analysis of solar hydrogen generation literature from 2001 to 2014. Scientometrics 105 (2): 759–771.

    Article  Google Scholar 

  42. Martín-Consuegra, D., M. Faraoni, E. Díaz, and S. Ranfagni. 2018. Exploring relationships among brand credibility, purchase intention and social media for fashion brands: A conditional mediation model. Journal of Global Fashion Marketing 9 (3): 237–251.

    Article  Google Scholar 

  43. Nerur, S.P., A.A. Rasheed, and V. Natarajan. 2008. The intellectual structure of the strategic management field: An author co-citation analysis. Strategic Management Journal 29 (3): 319–336.

    Article  Google Scholar 

  44. Perlich, C., B. Dalessandro, R. Hook, O. Stitelman, T. Raeder, and F. Provost. 2012. Bid optimizing and inventory scoring in targeted online advertising. Paper presented at the Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 804–812. https://doi.org/10.1145/2339530.2339655.

  45. Poushter, J., C. Bishop, and H. Chwe. 2018. Social media use continues to rise in developing countries but plateaus across developed ones. Retrieved from https://www.pewresearch.org/global/2018/06/19/social-media-use-continues-to-rise-in-developing-countries-but-plateaus-across-developed-ones/.

  46. Ramos-Rodríguez, A.R., and J. Ruíz-Navarro. 2004. Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal, 1980–2000. Strategic Management Journal 25 (10): 981–1004.

    Article  Google Scholar 

  47. Ren, K., W. Zhang, K. Chang, Y. Rong, Y. Yu, and J. Wang. 2017. Bidding machine: Learning to bid for directly optimizing profits in display advertising. IEEE Transactions on Knowledge and Data Engineering 30 (4): 645–659.

    Article  Google Scholar 

  48. Ren, K., W. Zhang, Y. Rong, H. Zhang, Y. Yu, and J. Wang. 2016. User response learning for directly optimizing campaign performance in display advertising. Paper presented at the Proceedings of the 25th acm international on conference on information and knowledge management, 679–688. https://doi.org/10.1145/2983323.2983347.

  49. Saura, J.R., P. Palos-Sánchez, and L.M. Cerdá Suárez. 2017. Understanding the digital marketing environment with KPIs and web analytics. Future Internet 9 (4): 76.

    Article  Google Scholar 

  50. Shafique, M. 2013. Thinking inside the box? Intellectual structure of the knowledge base of innovation research (1988–2008). Strategic Management Journal 34 (1): 62–93.

    Article  Google Scholar 

  51. Taiminen, H.M., and H. Karjaluoto. 2015. The usage of digital marketing channels in SMEs. Journal of Small Business and Enterprise Development 22 (4): 633–651. https://doi.org/10.1108/JSBED-05-2013-0073.

    Article  Google Scholar 

  52. Taylor, C.R., Y.-N. Cho, C.M. Anthony, and D.B. Smith. 2018. Photoshopping of models in advertising: A review of the literature and future research agenda. Journal of Global Fashion Marketing 9 (4): 379–398.

    Article  Google Scholar 

  53. Van Eck, N.J., and L. Waltman. 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84 (2): 523–538. https://doi.org/10.1007/s11192-009-0146-3.

    Article  Google Scholar 

  54. Wang, J., W. Zhang, and S. Yuan. 2016. Display advertising with real-time bidding (RTB) and behavioural targeting. arXiv:1610.03013.

  55. Weinberg, T. 2009. The new community rules: Marketing on the social web. Journal of Applied Communications 96(2): 11.

  56. Whiting, A., and D. Williams. 2013. Why people use social media: A uses and gratifications approach. Qualitative Market Research 16 (4): 362–369.

    Article  Google Scholar 

  57. Willett, P. 2007. A bibliometric analysis of the Journal of Molecular Graphics and Modelling. Journal of Molecular Graphics and Modelling 26 (3): 602–606.

    Article  Google Scholar 

  58. Woodside, A.G., and P. Bernal Mir. 2019. Clicks and purchase effects of an embedded, social-media, platform endorsement in internet advertising. Journal of Global Scholars of Marketing Science 29 (3): 343–357.

    Article  Google Scholar 

  59. Yang, Z., Y. Shi, and B. Wang. 2015. Search engine marketing, financing ability and firm performance in E-commerce. Procedia Computer Science 55: 1106–1112.

    Article  Google Scholar 

  60. Yin, C., S. Ding, and J. Wang. 2019. Mobile marketing recommendation method based on user location feedback. Human-Centric Computing and Information Sciences 9 (1): 1–17.

    Article  Google Scholar 

  61. Yodel, G. 2017. What is influencer marketing. Huffington Post. https://www.huffpost.com/entry/what-is-influcner-marketing_b_10778128#:~:text=Influencer%20marketing%20is%20simply%20the,the%20character%20of%20a%20brand.&text=Anyone%20with%20internet%20access%20can,well%20enough%2D%20become%20an%20influencer.

  62. Yuan, S., J. Wang, and X. Zhao. 2013. Real-time bidding for online advertising: measurement and analysis. Paper presented at the Proceedings of the Seventh International Workshop on Data Mining for Online Advertising.

  63. Zarrella, D. 2009. The social media marketing book. Sebastopol: O’Reilly Media Inc.

    Google Scholar 

  64. Zhang, M., and N. Dholakia. 2018. Conceptual framing of virtuality and virtual consumption research. Journal of Global Scholars of Marketing Science 28 (4): 305–319.

    Article  Google Scholar 

  65. Zhang, W., Y. Rong, J. Wang, T. Zhu, and X. Wang. 2016. Feedback control of real-time display advertising. Paper presented at the Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 407–416. https://doi.org/10.1145/2835776.2835843.

  66. Zhang, W., and J. Wang. 2015. Statistical arbitrage mining for display advertising. Paper presented at the Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1465–1474. https://doi.org/10.1145/2783258.2783269.

  67. Zhang, W., Yuan, S., and Wang, J. (2014). Optimal real-time bidding for display advertising. Paper presented at the Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 1077–1086. https://doi.org/10.1145/2623330.2623633.

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Correspondence to Zahra Ghorbani.

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Ghorbani, Z., Kargaran, S., Saberi, A. et al. Trends and patterns in digital marketing research: bibliometric analysis. J Market Anal (2021). https://doi.org/10.1057/s41270-021-00116-9

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Keywords

  • Digital marketing
  • Electronic commerce marketing
  • Search engine marketing
  • VoSviewer
  • Bibliometric

Tuesday, 3 August 2021

How to Increase the Visibility and Impact of your Research

 Source: https://doi.org/10.6084/m9.figshare.15101871.v1

 Why would a researcher or a research department want to increase the visibility of their research? It can be as simple as wanting to improve their university ranking or academic impact by gaining more views and citations for their research articles. However, others may wish to increase their visibility to attract more opportunities for collaboration or even to highlight their research impact on a larger society.

Tuesday, 27 July 2021

A bibliometric study of medical tourism

 Source: https://doi.org/10.1080/13032917.2021.1954042

Research Article

A bibliometric study of medical tourism

Received 15 Sep 2020, Accepted 07 Jul 2021, Published online: 22 Jul 2021
ABSTRACT

This bibliometric study analyzes 1535 publications from 1952 to 2020 on medical tourism. The aim of the study is to portray medical tourism publication trends from different perspectives such as the main subjects, word dynamics, authors’ originations and finally research gaps and future directions. The study shows that concepts like “perceived value” and “destination image” are among the emerging concerns in recent studies. The themes of research were grouped into six themes of marketing, economic & political, social & cultural, ethical, technological and governmental within these categories, factors affecting emotional aspects of tourists’ decision-making were found to be under-studies, compared to factors feeding the cognitive side of their choice of destinations.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated and/or analyzed during the current study are available in the ZENODO repository http://doi.org/10.5281/zenodo.5025655 .

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Notes on contributors

Alireza Habibi

Alireza Habibi holds a PhD in Marketing from UKM-Graduate School of Business, Malaysia. He is an assistant professor at Ahlul Bayt International University, IRAN. His research interests include tourism, service, Islamic marketing.

Maryamossadat Mousavi

Maryamossadat Mousavi holds a Ph.D. in Human Resource Management from University of Tehran, Iran. Her research interest include tourism, cultural studies and human resource management

Seyedh Mahboobeh Jamali

Seyedh Mahboobeh Jamali holds PhD in science from USM, Malaysia. She is a researcher at Ministry of Education, Iran. Her research interests include educational management studies and research tools.

Nader Ale Ebrahim

Nader Ale Ebrahim holds a PhD in Technology Management from Faculty of Engineering, University of Malaya, Malaysia. His research interests are research visibility and research tools.