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Abstract

The influence of interdisciplinary research has become a trending topic in library and information science. However, research examining the influence of interdisciplinarity on scholars is still limited. This current study examines the interdisciplinarity from the author co-citation network approach. We focus on the field of social networking sites (SNS) research, namely studies about online platforms that people use for networking (e.g., Facebook), and examine whether interdisciplinarity would promote academic impact. About 2,489 articles in the field of SNS published from 2014 to 2016 were retrieved, with 611 first authors cited by those articles identified. The interdisciplinarity of the scholar was assessed in the author co-citation networks using two indexes: betweenness centrality and constraint. We identified four primary disciplines of SNS research in 2014 to 2016. Interdisciplinarity indexes were used to predict the scholarly impact in this field in the next 3-year interval (2017–2019), measured by citation counts. We found that citation number was predicted by betweenness centrality but not by constraint. Theoretical and practical implications of interdisciplinarity in research activities are discussed.

Keywords

interdisciplinarity author co-citation network citation analysis social networking sites scientometrics

Introduction

In recent years, leading science foundations, such as the National Science Foundation in the US, have been promoting interdisciplinary research to coordinate research efforts and solve complex scientific problems (Nichols, 2014). Interdisciplinarity sometimes becomes the criteria for assessing research journals and institutions (Dworkin et al., 2019; Zhu & Fu, 2019). Most previous research agreed on the positive influence of interdisciplinarity on the impact of academic articles (Larivière et al., 2015; Leahey et al., 2017; Yegros-Yegros et al., 2015; Zhang et al., 2021), given that proper strategies of interdisciplinary research were adopted. Amid the attention to journal articles, the impact of interdisciplinarity on scholars has not been adequately examined. Hence, this research investigates the interdisciplinarity of scholars from a network analysis approach. We aim to answer whether the interdisciplinarity in author co-citation networks will boost academic impact in the following years. This study focuses on social networking sites as the research context—a burgeoning research field that draws attention from multiple subjects, such as social science and mathematics. Interdisciplinarity is measured by two indexes in the network—betweenness centrality and constraint. To the best of our understanding, this study offers the first examination of scholar interdisciplinarity’s influence on academic impact in SNS research.

Literature Review

Scientific Discipline and Interdisciplinarity

“Discipline” is a commonly used word in the discourse of education and science, referring to a particular branch of learning or knowledge (Bourdieu & Strand, 2007). Traditionally, discipline refers to the narrowness of focus or boundaries between different knowledge specializations. A discipline is defined as a synonym of “research fields” and operationalized as “subject categories” (Hammarfelt, 2018). In science metrics, subject categories are the most accepted form of academic discipline (Chi & Young, 2013; Zhu & Fu, 2019). However, established subject categories might not be precise enough to address the evolutional nature of knowledge. Discipline evolves and differentiates as scholarly efforts continue to understand the environment more penetrating and comprehensively (Bourdieu & Strand, 2007). According to the social constructivist’s view, the evolution of discipline is shaped by social processes, including teaching, mentoring, and knowledge production (Krishnan, 2009). In this sense, subject categories are not ideal for mapping the development of science disciplines over time.

Academic disciplines can also be assessed based on institutions, referring to tightly-knit groups of scholars whose internally agreed-upon methods and knowledge (Krishnan, 2009). Institutions are not always tangible but can also be “invisible colleges,” defined as “a set of informal communication relations among scientists or other scholars who share a specific common interest or goal” (Lievrouw, 1989, p. 622). The informal communication practices include sharing the same knowledge, addressing research problems, and determining publishing standards (Trowler, 2014). Mapping these invisible colleges shaped by academic interactions hints at the development of academic disciplines. Bibliometric data that includes citation and authorship information has long been utilized to study informal science disciplines since the late 20th century (for a review, see Zitt et al., 2019).

The interdisciplinary approach means to “integrate separate disciplinary data, methods, tools, concepts, and theories in order to create a holistic view or common understanding of a complex issue, question, or problem” (Wagner et al., 2011, p. 16). Therefore, it is to be noticed that collaborations between established subject categories are not sufficient to be named interdisciplinarity research. On the other hand, the same research perspectives, tools, and data are more likely to be reflected by the recognition of subsequent scholars. For example, research on information science shows that scholars working on the same emerging knowledge base are likely to be co-cited by the following research (Hou et al., 2018). Hence, it is more plausible to assess interdisciplinarity based on citation practices, such as the pattern of being co-cited, rather than based on subject categories.

Assessing Discipline and Interdisciplinarity: A Citation Network Perspective

The citation network offers a solution to measure informal academic disciplines, or “informal colleges.” Rather than approaching disciplines based on established categories, the citation-based approach assesses the knowledge structure of previous scholars based on their successors. The theoretical foundation for citation analysis is based on insights into the development of science. Small (1973) proposes the concept of co-citation relationship, defined as the frequencies of two documents that are cited together. In another article, Small (1980), p. 183) states that citation networks mark the “consensus structure of the concepts in the field,” while the clusters in citation networks marked the consensual usage of documents formed by scholarly communications. Highly cited concepts and methods were usually associated with highly cited documents. Small (1980)’s research identifies research paradigms based on citation networks and the most influential works in each cluster. Build upon this idea, citation network analysis has already been adopted to identify research disciplines, subdisciplines, or subfields in co-citation networks (Bhatt et al., 2020; Jeong et al., 2014).

Interdisciplinarity and Research Impact

We suggest that two bodies of theory, namely structure holes and social capital, can be used to explain how interdisciplinarity affects performance. Burt (1992) proposed the concept of “structural holes” in the context of the entrepreneurial process. This concept suggests that gaps between players in networks create opportunities for information access, timing, referrals, and control. In this situation, the role of the tertius, or third person, becomes essential. The tertius benefits in two ways: information and control. The tertius has early access to a broader diversity of information. Exposure to diverse thoughts and information can help generate new ideas (Burt, 2004). Structural holes also allow the tertius to organize cooperation and set terms (Burt, 1992). The role of the tertius was later conceptualized as “brokerage.” Network brokers connect nodes and communities and transfer knowledge and resources (Burt, 2000; Stovel & Shaw, 2012). Hence, interdisciplinary scholars can serve as brokers of knowledge that connect different research disciplines, which might help knowledge integration and innovation.

The social capital theory describes the aggregation of resources by maintaining less institutionalized relationships. Social capital can be considered resources brought by “durable networks” connected with social actors based on mutual recognition and acquaintances (Bourdieu, 1986). Network science studies discussed the roles of brokers and triadic closures of the network as extensions. Social capital was further developed into bonding and bridging social capital. Bonding capital lies within a homogenous community; it brings together people who already know each other and benefits those with internal access. Bridging capital emerges in a heterogeneous network by bringing together people who do not know each other (Leonard, 2004; Putnam, 2000). In terms of operationalization, a broker’s role in a social network is to possess bridging social capital. Burt (2000) suggested that brokers’ social capital compensates for structural holes in the network. The social capital theory implies that interdisciplinary scholars can benefit from learning novel knowledge from other disciplines and producing more innovative ideas.

However, previous studies on the impact of interdisciplinary research yield mixed findings. Numerous studies have analyzed the interdisciplinary influence based on scholars (Chi & Young, 2013), journals (Zhu & Fu, 2019), articles (Larivière & Gingras, 2010), and institutions (Cassi et al., 2014). For communication studies, citing interdisciplinary articles beyond social science will increase research impact, whereas citing founding disciplines does not contribute to impact (Zhu & Fu, 2019). We proposed two factors that might explain confusion: levels and characteristics of interdisciplinarity.

First, as suggested before, interdisciplinarity can be studied at multiple levels: article, author, journal, and institution. Journals, teams, and institutions seem to benefit more from interdisciplinarity. A study on a Canadian research collaboration network showed that a team’s productivity was associated with the diversity of disciplines included in that team (Mo, 2016). Bibliographic research on journals in communication studies has shown that citing across disciplines increases the citation’s impact (Zhu & Fu, 2019). Studies investigating the article and individual levels suggest interdisciplinarity is more of a double-edged sword. Cross-discipline research finds fewer citations in the medium run but more in the long run (Rafols et al., 2012; Rinia et al., 2001). The second important finding concerns the characteristics of interdisciplinarity. Highly distal interdisciplinarity seems to jeopardize the citation impact of research because the result is too provocative. In contrast, interdisciplinary research involving similar disciplines received the most citations (Leahey et al., 2017; Yegros-Yegros et al., 2015). A meta-analytic review concluded that three factors are crucial for increasing the citation numbers of journal articles: variety, disparity, and balance. Variety refers to the number of disciplines involved in research, while balance and disparity refer to the evenness of distribution and degree of difference, respectively (Yegros-Yegros et al., 2015). Previous findings provide two implications for future research. First, as interdisciplinarity seems to benefit research at more aggregate levels (journals, research teams), more research is needed at individual levels to provide more robust empirical evidence. Second, interdisciplinarity needs to be examined by considering the disciplines’ variety, evenness, and differences. Being co-cited with a closely related and distal discipline cannot be considered equal. Similarly, there might also be differential effects when being co-cited with several disciplines and with one discipline only.

Research Significance and Objectives

From the previous review, measuring discipline and interdisciplinarity from an author network perspective is conceivable and yields advantages compared to previous studies. First, we contribute to current interdisciplinarity research by focusing on individual scholars working on Social Networking Sites (SNS) research. To our knowledge, previous studies on this topic mainly focus on other subjects. In contrast, no relevant studies have been conducted on SNS, a research focus that lies in the intersection of communication studies and computer science. In such a way, our work helps accumulate more empirical evidence about the influence of interdisciplinarity. Second, the author co-citation network as the methodology is an implicit way of measuring interdisciplinarity. The previous network approaches to interdisciplinarity mainly construct network edges based on tangible social relations (e.g., co-authorship). This study, however, constructs the network based on co-citation relations among authors that reflect implicit knowledge relations. Lastly, we compare two indexes of interdisciplinarity, namely betweenness centrality and constraint. Such comparison allows us to determine how differential approaches to interdisciplinarity affect the prediction of academic impact. We can also gauge the best practice to boost academic impact through interdisciplinarity in the real world by interpreting the definition of betweenness centrality and constraint. Previous research has not made efforts on this front.

The objective of this research is two-fold. First, we expect the disciplines mapped by citation networks to differ from the subject categories. Hence, we aim to identify these implicit disciplines embedded in author co-citation networks in SNS research. Second, we aim to assess whether the interdisciplinarity of a scholar influences academic impact. Based on these objectives, we propose two research questions:

RQ1: What disciplines can be identified based on the co-citation network of SNS research?

RQ2: Does interdisciplinarity promote scholars’ academic impact in the short term?

Methodology

Author Co-Citation Network to Disciplinarity

The author co-citation network is an extension of co-citation analysis. It is constructed similarly to an article-based co-citation network. Two articles are linked in the article co-citation network if a single paper cites them. In the author co-citation network, the first author of each paper replaces the article as a node (vertex) in the article co-citation network. Previous studies on author co-citation analysis focused on mapping the structures of literature based on citation data, such as identifying major research paradigms, changes in knowledge structures, bridging positions of specific authors, or research subfields (Acedo & Casillas, 2005; Nerur et al., 2008; Zhao et al., 2018). Following this line of analysis, we use the author co-citation network for the current research to identify the academic disciplines. Further, we also assess an author’s interdisciplinarity—his or her intellectual linkage to diverse disciplines from a network perspective. Especially, interdisciplinary scholars must have more cross-boundary linkages and shorter distances to other disciplines (Zhu & Fu, 2019).

Measurement of Interdisciplinarities

Previous research has suggested two indicators for interdisciplinarity measurement based on networks: betweenness centrality and constraint. The betweenness centrality measures the extent to which a node is “standing” in between the other nodes (Freeman, 1977). Considering that the shortest path exists for every two nodes in the graph, betweenness centrality is measured by the number of shortest paths that pass through the node. Therefore, a node with high betweenness centrality is considered to have more network communication control. Owing to this feature, several bibliographic studies have measured the interdisciplinarity of journals by using the betweenness centrality (Leydesdorff, 2007; Silva et al., 2013). In operationalization, the betweenness centrality of a node calculates the sum of times it can interrupt the shortest paths between every pair in the graph (Freeman, 1977).

The constraint is considered another indicator of interdisciplinarity. Burt (2004) proposes the constraint to measure the time and energy an ego node has to invest in his/her network. The network constraint measures brokerage in network and factors in three elements—network size, density, and hierarchy (Burt, 1992). Studies have used this concept to measure the brokerage in collaboration networks for knowledge production (Ahuja, 2000; C. Wang et al., 2014). Based on the previous review, the present study detailed RQ2 as the following research hypotheses:

H1: The betweenness centrality of a scholar is positively associated with his/her subsequently received citation numbers.

H2: The constraint of a scholar is positively associated with his/her subsequently received citation numbers.

Based on the approach of citation network, this research attempts to assess the discipline and interdisciplinarity of a scholar from the subsequent publications about the topic, rather than subject categories. The research articles on social networking sites are selected as the research object. Social networking sites refer to the services that have the four following features: (1) interactive based on web 2.0 applications; (2) allow user-generated content; (3) user-create service-specific profiles; and (4) facilitate the development of social networks (Obar & Wildman, 2015). In recent years, there has been a surge of common social networking platforms, such as Twitter, Facebook, and LinkedIn. Academic inquiries into social networking sites are becoming more diverse and multidisciplinary. For example, scholars focused on network dynamics on social networking sites often refer to theories in mathematics and network analysis methodologies (Michaël et al., 2015). The anthropologist can also explore how the usage of social networking sites based on digital anthropology methods (Miller, 2012). The two examples showed that social networking sites provide scholars from various disciplines to explore this topic. Thus, “social networking site” is appropriate for interdisciplinarity research.

Data Collection

The citation network data was imported from the Web of Science (WoS) search engine. Using the keyword “social networking site,” this research retrieved all2,489 articles published from 2014 to 2016 in three mainstream databases for scientific publications- Science Citation Index Expanded (SCI-Expanded), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). SCI-Expanded database mainly includes articles about research in natural and engineering science. Similarly, SSCI and A&HCI correspond to research articles in social science and humanities. Based on the subject categories of WoS, the data acquired includes literature from more than 100 subjects. The five subjects with the most number of articles are Psychology Multidisciplinary (342), communication (305), Psychology Experimental (280), Information Science Library Science (247), and Computer Science Information Systems (199). Traditional engineering (e.g., Computer Science Information Systems) and social science subjects (e.g., communication) share considerable portions of the sample. Such features suggest that social networking site research appears to be multidisciplinary and worth studying.

This study also acquired3,321 articles published between 2017 and 2019 from SCI-Expanded, SSCI, and A&HCI indexed journals from the WoS database using the same keyword and method as the previous retrieving. The academic performance of cited scholars will be assessed based on the times 2017 to 2019 articles cited them.

Network Construction

This research constructed a co-citation network based on the authors cited by 2014 to 2016 articles. Articles published more than 8 years before were not considered to narrow down this research’s scope. In this way, only authors of articles published after 2006 were included for network construction. The 8-year threshold is also determined considering the time when social networking sites got popular. The first authors of cited papers constructed the nodes in the author co-citation networks, while co-citation relationships between two authors served as links. The authors were selected based on the built-in function of Citespace to split out most influential authors in each year’s network based on g-index (k = 25). Six hundred eleven most highly cited authors were selected. The network formed by 611 scholars is disconnected with a diameter of 9 and an average path length of 3.598. The density and transitivity are 0.038 and 0.098, respectively.

This research constructed the network using the citation analysis software Citespace. Citespace is the software that was developed to process citation data and visualize citation networks (Chen et al., 2010). The commonly used network analysis software, Pajek, was utilized to transfer the network output from Citespace to the adjacency matrix. The adjacency matrix generated by Pajek and citation data were imported into R for data analyses. Citation network analysis usually requires normalization and standardization when computing indexes to rule out the effect of the size of the network (Marian Gabriel & Matjaž, 2016). This research utilized the built-in function of Citespace to normalize the graphs based on the cosine similarity algorithm (Chen, 2006, 2016). In the original function, the weights of edges after the normalization range from 0 to 1. This research multiplied the weight of each edge, making edges’ weights in the network range from 0 to 10.

Results

Descriptive Findings

The author co-citation network identified four clusters of authors that marked four major internal disciplines of SNS research (see Figure 1). The clusters are partitioned when nodes are strongly connected within the cluster, but are relatively loosely connected with nodes from other clusters. Publications from key authors at the core of each sub-network are specifically focused on to determine the themes of clusters. The four subdisciplines are identified as follows.

Figure 1.

Four internal disciplines identified in the author co-citation network (2014–2016).

The first subdiscipline focused on network theories and measures and the implications of networks to real life, such as how networks are related to the concept of social capital. For example, core authors have analyzed how subjective well-being is assortative with online social networks (Bollen et al., 2011). Another example is user-generated content that gained social influence (Bakshy et al., 2009). The second subdiscipline focused on SNS in public health and health behavior. This subdiscipline focused on using SNS services to promote health knowledge and awareness, such as providing health information in community centers (Martinez et al., 2008). The third subdiscipline focused on SNS in political participation. Research topics include local administration using social media or SNS in social movements. The fourth subdiscipline focused on youth and SNS use (Lenhart & Madden, 2007). This subdiscipline mainly discusses the usage of social media by younger generations and the potential impacts on youngsters. Topics related to developmental aspects of youngsters and mental health are mainly focused on (e.g., Coulthard & Ogden, 2018).

Interdisciplinarity and Academic Impact

The study proceeds to discuss the academic impact of interdisciplinarity measures. First, we conduct Spearman rank correlation to explore the relationship among centrality measures, efficiency, and constraint. The Spearman rank correlation is a non-parametric correlation test for ranked variables (see Table 1). The test is also frequently adopted by previous network-based analyses of science metrics (Abbasi & Altmann, 2011; Abbasi et al., 2011).

Table 1.

Spearman Rank Correlation for Five Measures in the 2016 to 2016 Networks.

	Variables	1	2	3
1	Betweenness centrality	—
2	Constraint	−.845**	—
3	Citation counts	.471**	−.441**	—

Correlation is significant at the .05 level (two-tailed).

Correlation is significant at the .01 level (two-tailed).

From the result of the Spearman rank correlation analysis, betweenness and degree centrality, constraint, and efficacy appear to be correlated with citation counts at a high significance level. The eigenvector centrality appears to be deviated from the other measures for not showing any correlations with them.

To predict interdisciplinarity’s influence, this research then used Poisson multiple regression model to test the effect of each variable on the citation counts. This research used Poisson regression due to the nature of citation numbers as a counted variable. Before conducting the Poisson multiple regression, the possibility of overdispersion was checked. Overdispersion refers to the situation in which the variable’s observed variance is larger than theoretical assumptions. The results show that the ratio of observed variance and theoretical variance is 40.095, with a statistical significance (p < .05). Therefore, this research uses Poisson regression based on quasi-likelihood models. The regression results are summarized below (see Table 2).

Table 2.

Poisson Multivariable Regression Results in Predicting Citation Counts.

Parameters	B	SE	t	p
Intercept	2.666	0.733	3.639	.000**
Betweenness centrality	24.924	10.608	2.350	.019*
Constraint	.001	0.344	0.005	.996

Correlation is significant at the .05 level (two-tailed).

Correlation is significant at the .01 level (two-tailed).

For H1, a statistically significant relationship is found for betweenness centrality and citation counts (β = 24.92, SE = 10.61, t = 2.35, p < .05; H1 supported). However, for H2, there is no statistically significant relationship between (β = .00, SE = 0.34, t = 0.01, n.s.; H2 not supported). In other words, the betweenness centrality of a scholar is positively associated with his/her subsequently received citation numbers. However, the constraint of a scholar is not found to be associated with his/her subsequently received citation numbers.

Discussion and Conclusion

Our findings suggest the viability of determining science disciplines from scholars focusing on a single research topic. Four subdisciplines are identified in research about SNS. It should be noted that the subdiscipline list is not exhaustive (there might be other less popular research focuses, such as SNS in crisis communication). Amid the limitation, we provide evidence that scientific disciplines can be identified based on networks of citation practices formed by scholars. Some discipline identification efforts are already based on author co-citation networks (Liu et al., 2015; Zhao et al., 2018). Our result is consistent with some previous findings, suggesting that several disciplines can be on a single research topic (Liu et al., 2015). However, we did not find a dominant hegemonic discipline in the network of SNS research (Zhao et al., 2018). Future research can discuss how the topic and context of research influence the discipline structure of citation networks.

The second finding is a positive relationship between betweenness centrality and academic impact, compared to the non-significant relationship between constraint and academic impact. The finding related to betweenness centrality is consistent with previous research in library and information science (Yan & Ding, 2009). When a scholar “interrupts” a greater portion of links among other nodes in a co-citation network, the scholar is expected to be cited more frequently in the short-term (3 years) future. In an author co-citation network, when a scholar is co-cited with scholars from different disconnected disciplines, his/her academic impact might have the potential to grow in the near future.

It is also worth discussing why constraint as another indicator of “brokerage” was not associated with academic impact. Constraint as a local measure—meaning that the calculation of constraint only considers the adjacent nodes to the ego, while the rest nodes in the graph are not considered. However, the interdisciplinarity of a scholar should be examined in relation to his/her position in the entire research spectrum and should not be limited to his/her adjacent scholars. Moreover, the constraint is also influenced by three dimensions of the network: size, density, and hierarchy. Specifically, small network size, high density, or hierarchical structure contribute to higher constraints (Burt, 1992). Previous research noted that the size of author clusters in co-citation networks on a specific research topic could range from 4 to 30 (Markus, 2003; Schildt et al., 2006). Thus, different cluster sizes might influence the validity of constraints in measuring interdisciplinarity.

Several practical implications can be given based on current findings. Firstly, individual scholars should make efforts to increase the interdisciplinarity of their works. Based on the network-based discipline of this research, interdisciplinarity is not limited to synthesizing and doing research based on knowledge across different subject categories. In the current study, a scholar’s interdisciplinarity can also be based on the author co-citation networks that he/she is embedded in. We suggest that a scholar deliberately engages with different types of science disciplines to be impactful and co-cited with other disciplines. Moreover, we can also provide some practical implications from the differential focuses of betweenness centrality and constraint. Betweenness centrality emphasizes a scholar’s brokerage position in the whole network. In comparison, constraint as a local measure only considers the adjacent nodes. As constraint turned out to be unrelated to impact, one scholar should not only consider synthesizing ideas from various his/her “adjacent” scholars. In other words, one should not aim to be co-cited with many scholars that are not associated with each other. Even though such an act can be considered interdisciplinary, it does not contribute to academic influence. Rather, a scholar should consider his/her position in the entire network, meaning the overall image consisting of all research disciplines related to one topic. A scholar who aims to be influential should pay attention to engaging with distinct ideas from all different disciplines.

Limitations and Future Research

This study’s findings should be interpreted with limitations considered. Our datasets with 3-year intervals enable us to make inferences about interdisciplinarity in the short run. However, the data constrain the understanding of interdisciplinarity in the long run. As previous research showed, the delay in the positive effect of interdisciplinarity might be as long as 13 years (Rinia et al., 2001). Since the gap between the two datasets used in this study is only 3 years, the long-term effect of interdisciplinarity might be missed. Future research might consider tracking the influence of interdisciplinarity with different time intervals (e.g., 1–20 years).

There are more independent and control variables to be considered in this research. Our study only includes betweenness centrality and constraint as independent variables. As noted by a recent review, Cluster Coefficient (CC) and Average Similarity (AS) have also been proposed as indicators of interdisciplinarity (Q. Wang & Schneider, 2020). Future research can consider incorporating these two and even more measures to assess interdisciplinarity. Future research can also consider controlling size, density, and hierarchy when predicting academic impact based on interdisciplinarity.

Future research can also consider extending the samples into every node in the network (to analyze all authors in the author co-citation network). Due to the limitation of computing power, we only focused on highly cited scholars in this research. Less cited scholars are often on the edge positions of networks and are less connected with other authors. Less cited scholars might have fewer social resources and receive limited attention from subsequent scholars. It would be worthwhile to investigate whether interdisciplinarity plays a similar role for less cited scholars.

Lastly, we call for efforts to discuss comprehensively whether and how interdisciplinarity will affect academic impact. Currently, numerous empirical studies have been done in this direction, while the operationalization and measurements of some key concepts vary greatly, such as academic institutions, collaborations, and academic impact. Some summarizing works are highly desirable. For example, future research can compare the strengths and weaknesses of different indexes about interdisciplinarity aided by the theoretical explication of interdisciplinarity as a concept. Moreover, it would be ideal for conducting comparative research on how interdisciplinarity increases academic impact in different contexts (e.g., subjects or countries).

Footnotes

None.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This research was funded by 2023 Interdisciplinary Research Team Project of Young Scholars in Humanities and Social Sciences of Soochow University “Reflexive Regulation of Algorithmic Marketing Governance of Platforms” (Project No.: 5033720423).

Ethics Statement

Not applicable.

ORCID iD

Ke Zhang

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Research Interdisciplinarity and Citation Impact: A Network Analysis of Social Networking Sites Research

Abstract

Keywords

Introduction

Literature Review

Scientific Discipline and Interdisciplinarity

Assessing Discipline and Interdisciplinarity: A Citation Network Perspective

Interdisciplinarity and Research Impact

Research Significance and Objectives

Methodology

Author Co-Citation Network to Disciplinarity

Measurement of Interdisciplinarities

Data Collection

Network Construction

Results

Descriptive Findings

Interdisciplinarity and Academic Impact

Discussion and Conclusion

Limitations and Future Research

Footnotes

Declaration of Conflicting Interests

Funding

Ethics Statement

ORCID iD

References