Participations and Communications of Myanmar Academicians on Research Gate among Differences Disciplines

The purpose of this paper is to know the participating and communication of different disciplines among Myanmar academicians in ResearchGate (RG). The data were manually collected by visiting the profile pages of all members who had an account with the Institution of Myanmar in RG. In total, 1035 RG members and 59 participants' communications were analyzed by using the statistic method — Kruskal-Wallis H test under the five disciplines. The results show that Engineering and Technology disciplines massively participated than other disciplines on ResearchGate, while Natural science disciplines are more in research items. Life Science and Medicine disciplines have the most scholarly communication, respectively. There is no RG metric significant in social science disciplines. But, different disciplines of Myanmar academicians show varying levels of interest in being involved in RG with different significance

to know the correlation between RG scores and profile metrics.  studied the impact of institutional differences on RG reputational metrics found that RG metrics serve as indicators of research activities among US institutions. Some studies investigated the impact of ASNS metrics on users (Hoffmann et al., 2016, Thelwall and Kousha, 2015, Shrivastava & Mahajan, 2015, & 2017, Yan, Zhang, & Bromfield, 2018. With regards to the user interaction approach, Li, Huang, Ye & Zhang, (2019) explored the scholar's question post to investigate the answer quality that found answer quality had not affected the question, but, get the academic resources. Prior studies on communication and interaction performance of the User in ASN Q &A site have investigated different information inquires and answering types. Kim, (2018) investigate biological scientists in the USA for academicians' article sharing mode. The study of Salahshour Rad, Nilashi, Mohamed Dahlan & Ibrahim, (2017) conducted the Malaysian researchers in an academic social network to know the individuals' behavioral intention and use of ASNSs. Lee, Oh, Dong, Wang & Burnett, (2019) assessed how were the motivations for self-archiving research items on academic, social networking sites by randomly selected ResearchGate users. Other user interaction approach studies were Goodwin, Jeng & He, 2014, Li, He & Zhang, 2016& 2019, Jeng, DesAutels, He & Li, 2017, Ostermaier Grabow & Linek, 2019, Li, He, Zhang, Geng & Zhang, 2018 The vast majority of the studies above did not found on the activities of Myanmar academicians on participating and communication of ResarchGate. To address the above resaech gaps, this study explores each profile of Myanmar academicians and each scholar's communication in ResearchGate. Specifically, this research aims to describe how the statement of RG metric (such as RG score, research items, citation, read, following, and follower) and also Interaction (Information seeking, Discussion seeking, and suggestion seeking)in ResearchGate among Myanmar academicians.  Vol.11, No.2, 2021 the related data of members automatically. It provided the Question and Answering section for members than to the job vacancy. ResearchGate also provides browsing features where one can search the need through keyword s search and provide the filters: projects, publication, funding, questions, jobs, institutions, and departments. ResearchGate has an interface with other diffused social networks such as Facebook, Twitter, and Friend Feed of LinkedIn, so you can connect through yet existing profiles (ResearchGate, 2014).

Data Collection
When exploring the profiles of RG's members, there are over 16 million members in RG. Among those, over 2000 profiles with Myanmar Name are found in 2019. We start with the name of Myanmar institutions to set up an initial data set. We collected each institutional URL to get the information of each member, totaling 1684 profiles are found. Each user profile page with a series of indicators is collected manually. Then, the data of the study are clean with 2 criteria: user profiles must be with the Myanmar Institution in ResearchGate, but duplicate profiles are deleted, and users must have at least 10 followers or followings. After cleaning the profiles with 2 criteria, there choose 1035 profiles for this study. Within 1035 profiles, there are communications with 146 question posts and 294 answer posts of 59 participants, which are chosen for communications. The detail of the University list can be seen in Table 2.

Coding
For the classification of discipline, the coding scheme followed the study of Vaughan, Tang & Yang, (2017). The disciplines were divided into five disciplines: Engineering and Technology, Life Science and Medicine, Natural Science, Social Science, and Humanity.
For the Information of RG members, the coding scheme ultimately included the following categories in relation to options for self-presentation and related functionality on RG: Institution, Name, position, degree, specialized discipline, research interest, research items (article, conference paper, full text, poster, etc.), number of follower and following and number of communications. This information of each member is noted in the excel sheet manually.
For the interaction, the coding scheme adapted from the study of Ostermaier Grabow & Linek, (2019). The categories were established for the main elements of the communications: Characteristics of communication types and language used in communication types.
Characteristics of interaction types: the factual description of posting for their academic works and organized into information-seeking, discussion-seeking, suggestion-seeking and socio information. Informationseeking questions and Discussion-seeking questions are adapted from the study of Jeng, DesAutels, He & Li, (2017) and suggestion seeking questions and socio information are adapted from the study of Deng, Tong & Fu (2018) (see table 9. in Appendix).
Used language of interaction post is the length of the communication post and is arranged into a short Diffusion distance of rice root exudates? I woud like to know the diffucusion distance of rice root exudates. How far can rice roor exudates affect the microorganisms in bulk soil? I donot mean for soil depth, just horizontal distance. I will be really appreciated for your answers. Long sentence over several lines What is the best method to determine the phosphate solubilizing activity of bacteria? We are using Vogel method, in which sodium molybdate and sulphuric acid are used to form a blue color complex to determine the solubilized P in culture broth with UVvis spectrophotometer at 830 nm. In this method, we are using cation exchange resin. But, I would like to use another method that doesn't need to use cation exchange resin. Which method are you all using to determine the Phosphate solubilizing activity of bacteria?

Data Analysis
In the study, 1035 profiles of Researchgate members were classified under their interest disciplines. These disciplines are calculated with the RG metrics: (a) RG score, (b) publication metrics such as number of publications, reads, and citations, (c) Social interaction metrics (number of followers, number of followings, and communications) to know the relativities of each discipline. The statistic method-Kruskal-Wallis H test was applied in the data analysis of the study . The test is a nonparametric technique (distribution-free test) that can be used for both continuous and ordinal-level dependent variables (Pallant, 2005, StatisticsSolutions, 2017. The test enabled finding statistical differences between several nonparametric samples. User communications of 59 participants of user communication in ResearchGate among Myanmar academicians were analyzed manually.

Finding
In this section, results related to three questions of this study will be presented. Data are analyzed and summarized concerning RG metrics: (a) RG score, (b) publication metrics such as number of publications, reads, and citations (c) social interaction metrics: number of followers, number of followings, and communication. Table 4 and 5 showed the result of the demographic information of academicians. The study was conducted on 1035 Myanmar academicians in Researchgate.   Table 6 shows the computed variables for RG Score on five different disciplines. The results for RG scores show that Humanity users have the highest value (mean=5.46, SD= 9.80), while engineering and technology users have the lowest value (mean= 0.77, SD= 2.78). RG score is (mean=1.38, SD= 3.86) between discipline. All the means of RG score metrics fall within their respective 95% confidence intervals. With the Kruskal-Wallis H test, this study shows that RG scores among different disciplines were relatively large (x 2 = 62.91, df = 4, Sig = 0.000).

Research Metrics
Research metrics include the number of publications that a user uploaded onto RG, indicating a user's participation status and academic output, and the total number of reads and citations of these publications, revealing academic quality and impact. As shown in Table 7, the number of publications per User, numbers of citations per publication, and the number of reads per publication and per interactions.  SD= 4684.37) between discipline (n=1035). Research metrics fall within their respective 95% confidence intervals and significant between differences discipline of researchitems (x 2 = 18.21, df = 2, Sig = 0.001) citations (x 2 = 30.69, df = 4, Sig = 0.000) and reads (x 2 = 16.46, df = 4, Sig = 0.002) in Kruskal-Wallis H test.

Social Interaction Metrics
Social interaction metric is a metric that is measured on the number of following and followers and communication of academicians in RG. Follower, number of ResearchGate users who follow the author (those ResearchGate will receive notifications when the author uploads new materials to ResearchGate).
Following is the number of ResearchGate users the author follows (the author will receive notifications when those users upload new material to ResearchGate) Information and Knowledge Management www.iiste.org ISSN 2224-5758 (Paper) ISSN 2224-896X (Online) Vol.11, No.2, 2021  Follower are difference with (mean=15.84, SD=20.85) between discipline (n=1035). Following (social interaction metric) results found that engineering and technology is highest value (mean= 24.26, SD= 38.68), while Humanity have the lowest value (mean= 10.70, SD=20.12). Follower are difference with (mean=15.84, SD=20.85) between discipline (n=1035) within their respective 95% confidence intervals. The Kruskal-Wallis H test results also indicate that there are the significant between differences disciplines of number of follower (x 2 = 11.18, df = 4, Sig= 0.25*) and number of the following (x 2 = 21.61, df = 4, Sig= 0.00*). Interaction Interaction includes the number of postings when the academicians ask, discuss and suggest the information for their academic works.   Information  suggestion  Engineering and  Technology  26  12  12  19  6  49  35  47  59  141   Life Science and  Medicine  32  27  23  38  8  96  37  51  64 Vol.11, No.2, 2021 41 posts) and information seeking (37 posts among 294 posts), respectively. Social science disciplines have only one information seeking. The most active answer post used discipline is a Life science and Medicine disciplines, followed by Engineering and Technology disciplines and Social Science disciplines. There is no activity in Natural science and Humanity. Table 10 Used language in interaction Table 10 shows the results of used language in Questions and Answers. Engineering and Technology disciplines use the most Medium language types (32 posts among 146 posts), short language types (9 posts among 146 posts), and long language types (8 posts among 146 posts), respectively. Life science and medicine disciplines use the most Medium language types (57 posts among 146 posts), short language types (25 posts among 146 posts), and long language types (14 posts among 146 posts), respectively. Social science disciplines have only short language types of question posts.

Discussion
The study showed disciplinary differences in the relationships between RG metrics.
Answering the first question, the results highlight disciplinary differences in the use of ResearchGate and show the different populations of this site. Academicians from the Engineering and Technology disciplines are more active to participate in ResearchGate and more following other scholars in ResearchGate. The finding is in harmony with those of Ostermaier Grabow & Linek, (2019), who had found that the majority of participants are engineering and technology disciplines (engineering and computer science), and it is different from the study of Elsayed, (2016).
Answering the second question, RG metrics are divided into two groups from the aspects of motivation. The one group represents the motivation of scholar reputation. The other group represents the motivation of information seeking values. RG metrics for motivations of scholar reputation are RG score, research items, citations, reads, followers, and communications. Humanity disciplines are large participate in research activity with the proof of RG score more and citations more and also Natural Science with high uploading of Research items that results, on the other hand, showed that effects more increased in followers (social interaction metric). But, Engineering and Technology disciplines are getting more reads from other scholars that is the action of communications (social interaction metric). RG research metrics (reads) is a complex metric that tie-up with research items read and communication read. The result is the same of the  , which means these disciplines positively tie to academic influence as reflected in RG score, citation score, research items, and follower. In addition, more RG metrics: RG score, research items, citation and follower tend to improve scholarly reputation. Life Science and Medicine and social science disciplines users show that they intend to use the RG by the motivation of information seeking values as indicated in followings to other researchers. Thus, the results show that there is the relativity of each RG metric between disciplines.
Answering the third question, the most active users in communication are from Life science and Medicines disciplines and followed by Engineering and Technology disciplines and Social science disciplines. When the academicians more preferred to use the characteristic of information suggestion for question post (social interaction metric) by adding the statement of problems for their works, the study found that the characteristics of answer posts use the information suggestion characteristic, too. Socio information use less when comparing the Interaction of posts because most of the users do not use polite usage and farewell words at the beginning or end of their post (Ostermaier Grabow & Linek, 2019). Used language of Interaction is the most in the medium sentence in which combines the negative and negative statements by senior researchers who experts in research knowledge.

Conclusion
Targeting the research questions, the study is sought the initial insights of the participating and communication natures of different disciplines among Myanmar academicians in ResearchGate. The study found that disciplines in Engineering and Technology are more participating to explore the Information in RG, but Natural Science and Humanities disciplines intend to share more their research activities in RG. Life Science and Medicine are more participating in discussion threads, respectively. On the other hand, the results concluded that different disciplines of academicians show varying levels of interest in being involved in RG with difference significant. The study is a primary work to explore the activities of Myanmar academicians in RG. In the future, more studies should attempt to consider the motivation and behavior of Myanmar academicians in RG when they sought an insight of knowledge by using academic, social network sites.