originality and value of the research paper

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What does originality in research mean? A student's perspective

Affiliation.

1 University of South Wales Cardiff, UK.
PMID: 25059081
DOI: 10.7748/nr.21.6.8.e1254

Aim: To provide a student's perspective of what it means to be original when undertaking a PhD.

Background: A review of the literature related to the concept of originality in doctoral research highlights the subjective nature of the concept in academia. Although there is much literature that explores the issues concerning examiners' views of originality, there is little on students' perspectives.

Review methods: A snowballing technique was used, where a recent article was read, and the references cited were then explored. Given the time constraints, the author recognises that the literature review was not as extensive as a systematic literature review.

Discussion: It is important for students to be clear about what is required to achieve a PhD. However, the vagaries associated with the formal assessment of the doctoral thesis and subsequent performance at viva can cause considerable uncertainty and anxiety for students.

Conclusion: Originality in the PhD is a subjective concept and is not the only consideration for examiners. Of comparable importance is the assessment of the student's ability to demonstrate independence of thought and increasing maturity so they can become independent researchers.

Implications for research/practice: This article expresses a different perspective on what is meant when undertaking a PhD in terms of originality in the doctoral thesis. It is intended to help guide and reassure current and potential PhD students.

Keywords: PhD; Student perspectives; doctoral research; originality.

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Measuring originality in science

Open access
Published: 11 November 2019
Volume 122 , pages 409–427, ( 2020 )

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originality and value of the research paper

Sotaro Shibayama ORCID: orcid.org/0000-0002-6701-9828 1 &
Jian Wang 2

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Originality has self-evident importance for science, but objectively measuring originality poses a formidable challenge. We conceptualise originality as the degree to which a scientific discovery provides subsequent studies with unique knowledge that is not available from previous studies. Accordingly, we operationalise a new measure of originality for individual scientific papers building on the network betweenness centrality concept. Specifically, we measure the originality of a paper based on the directed citation network between its references and the subsequent papers citing it. We demonstrate the validity of this measure using survey information. In particular, we find that the proposed measure is positively correlated with the self-assessed theoretical originality but not with the methodological originality. We also find that originality can be reliably measured with only a small number of subsequent citing papers, which lowers computational cost and contributes to practical utility. The measure also predicts future citations, further confirming its validity. We further characterise the measure to guide its future use.

How to design bibliometric research: an overview and a framework proposal

Literature reviews as independent studies: guidelines for academic practice

How to Write and Publish a Research Paper for a Peer-Reviewed Journal

Avoid common mistakes on your manuscript.

Introduction

As science progresses through discoveries of new knowledge, originality constitutes one of the core values in science (Gaston 1973 ; Hagstrom 1974 ; Merton 1973 ; Storer 1966 ). As such, originality is highly regarded in the recognition system of science and is relevant for critical science decisions such as funding allocation, hiring, tenure evaluation, and scientific awards (Dasgupta and David 1994 ; Merton 1973 ; Stephan 1996 ; Storer 1966 ). Despite its importance, originality of scientific discoveries is hard to measure. In practice, originality is often evaluated by means of peer reviews (Chubin and Hackett 1990 ), which is feasible only in a small scale, whereas assessing originality in a large scale poses a formidable challenge. Though bibliometric studies have recently made a considerable advancement in measuring various aspects of scientific discoveries (Boudreau et al. 2016 ; Foster et al. 2015 ; Lee et al. 2015 ; Trapido 2015 ; Uzzi et al. 2013 ; Wang et al. 2017 ), originality per se has rarely been measured. This study proposes a new measure of originality building on the network betweenness centrality concept (Borgatti and Everett 2006 ; Freeman 1979 ) and measures the originality of a scientific paper based on the directed citation network between its references and subsequent papers citing the focal paper. To validate the proposed originality measure, we conducted a questionnaire survey and demonstrate that the proposed measure is significantly correlated with the self-assessed theoretical originality but not with the methodological originality. The result also shows that the measure can predict the number of citations that the focal paper receives in the future, which further confirms the validity of the measure. We also find that the originality can be reliably measured with only a small number of subsequent citing papers. This substantially lowers computational cost compared to previous related bibliometric measures and facilitates practical use of the measure.

Literature review

Though originality is one of the core values in science, there is not yet a clear consensus on what it exactly means (Dirk 1999 ; Guetzkow et al. 2004 ). In a broad sense, originality could mean anything new (e.g., new method, new theory, and new observation) that adds to the common stock of scientific knowledge. To differentiate the degree of newness, the sociology of science literature has argued that scientific discoveries can either conform to the tradition or depart from it, and only the latter is considered to be original (Bourdieu 1975 ; Kuhn 1970 ). Following this stream of thoughts, we define originality as the degree to which a scientific discovery provides subsequent studies with unique knowledge that is not available from previous studies. As further explained, this definition is in line with the network betweenness centrality concept and allows a straightforward operationalisation.

Existing measures of originality

Despite the theoretical interest in and practical relevance of originality, how to measure originality is under-developed. In small scales, a few studies explored the aspects in which research must be new in order to be perceived by scientists as original (Dirk 1999 ; Guetzkow et al. 2004 ). Dirk ( 1999 ) conducted a questionnaire survey to evaluate three dimensions (hypotheses, methods, and results) of newness, finding that life scientists consider research with unreported hypotheses as original rather than research with new methods. Through in-depth interviews of social scientists, Guetzkow et al. ( 2004 ) also identified various dimensions of newness associated with perceived originality: approach, theory, method, data, and findings. They also found that relevant dimensions of originality can differ across scientific fields.

Bibliometric techniques for science decisions have been rapidly developing thanks to the advanced computing power and enriched bibliometric data (Hicks et al. 2015 ). A few approaches to measure the newness (originality, novelty, creativity, etc.) of a study are worth noting, although they are not necessarily labelled as “originality.” The first approach considers originality as a quality established only through reuse of a study by subsequent studies or the collective evaluation by peer scientists, but not as an intrinsic quality of a study (Merton 1973 ). For example, Wang ( 2016 ), following the definition of creativity (Amabile 1983 ), argues that forward citation counts can be viewed as peer recognition of novelty and usefulness, and therefore is a proxy for creativity.

The second approach is more recently developed and views originality as an inherent quality of a scientific paper that can be measured at the time of publication, irrespective of subsequent use of the paper. This approach has several nuanced conceptualisation strategies. One strategy focuses on the newness of a study based on the introduction of a new concept or object. For example, Azoulay et al. ( 2011 ) measured the novelty of an article based on the age of keywords assigned to the article. Within the field of biochemistry, Foster et al. ( 2015 ) also measured the novelty on the basis of new chemical entities introduced in a study. This approach is intuitively straightforward but requires a reliable and up-to-date dictionary encoding all existing concepts and objects, which is not always the case.

Another strategy is based on the assumption that integrating a broader scope of knowledge is a sign of newness. For example, the originality of a patent is operationalised as the diversity of technological domains it cites, where diversity is measured using the Herfindahl-type index of patent classes that the focal patent cite (Hall et al. 2001 ; Harrigan et al. 2017 ; Trajtenberg et al. 1997 ). A similar approach is used to measure the interdisciplinarity of scientific papers (Stirling 2007 ; Wang et al. 2015 ; Yegros–Yegros et al. 2015 ), though it conceptually features diversity rather than originality.

A third strategy, building on the combinatorial novelty perspective, views novelty as making new or unusual combinations of pre-existing knowledge components, where knowledge components can be operationalised by keywords (Boudreau et al. 2016 ), referenced articles (Trapido 2015 ), referenced journals (Uzzi et al. 2013 ; Wang et al. 2017 ), and chemical entities (Foster et al. 2015 ). An obvious limitation of this approach is that it captures only the combinatorial novelty but not other types of novelty.

Our proposed conceptualisation of originality lies between these two approaches. We consider originality to be rooted in a set of information included in a focal scientific paper. However, we argue that the value of the paper is realised through its reuse by other scientists, and that its originality is established through its interaction with other scientists and follow-on research (Latour and Woolgar 1979 ; Merton 1973 ; Whitley 1984 ). A few recently developed measures, though not conceptualised as originality, are in line with this approach. For example, Funk and Owen-Smith ( 2017 ) assess whether an invention destabilises or consolidates existing technology streams, by examining the pattern of forward citations to a focal patent and its references. This measure is adopted by Wu et al. ( 2019 ) to evaluate the disruptiveness of scientific papers. Similarly, Bu et al. ( 2019 ) measure the independent impact of papers based on the co-citation and bibliographic coupling between a focal paper and its citing papers.

Proposed measure of originality

Base measure.

We propose to measure the originality of an individual scientific papers based on its cited papers (i.e., references) and citing papers (i.e. follow-on research). We draw on subsequent papers that cite the focal paper to evaluate whether the authors of these subsequent citing papers perceive the focal paper as an original source of knowledge (Fig. 1 A). Suppose that the focal paper X cites a set of prior papers ( reference set R ) and is cited by a set of subsequent papers ( citing set C ). If X serves as a more original source of knowledge, then the citing papers (i.e., papers in citing set C ) are less likely to rely on papers that are cited by X (i.e., papers in reference set R ). In contrast, if X is not original but an extension of R , then C will probably also cite R together with X . In other words, we exploit the evaluation by the authors of follow-on research to measure the originality of the focal paper.

Directed network of papers and citations. Note: Papers are the nodes, and citation links are the directional edges, where nodes with arrowheads cite nodes with arrow tails

This idea is operationalised as follows. Suppose that the focal paper X cites N references and is cited by M subsequent papers. For the n -th reference ( $ n \in \left\{ {1, \ldots ,N} \right\} $ ) and the m -th citing subsequent paper ( $ m \in \left\{ {1, \ldots ,M} \right\} $ ), define $ x_{nm} $ as follows:

In Fig. 1 A, for example, $ x_{11} = 1 $ and $ x_{12} = 0 $ . If a subsequent citing paper m cites few references of X , it implies that X provides original knowledge for m that is not provided by reference set R . In contrast, if the subsequent citing paper m cites many references of X , it implies that the author of m perceives the focal study X as being unoriginal. Thus, the originality score of the focal study X evaluated by the author of m is the share of papers cited both by X and by m :

This calculation is repeated for M citing papers, and the mean value is used as the originality score for X :

This measure corresponds to the proportion of 0’s in the citation matrix (i.e., missing citation links) between the cited and citing papers of X . This measure ranges from 0 to 1, and a higher value implies a higher level of originality.

To add a theoretical basis to the proposed measure, we draw on the network centrality framework (Borgatti and Everett 2006 ; Freeman 1979 ). In short, Eq. ( 3 ) is equivalent to the normalised betweenness centrality of X in the directed citation network (see Appendix 1 ). Betweenness centrality is defined as the number of the shortest paths that pass through the focal node among every pair of nodes in a connected network (Freeman 1979 ). Betweenness centrality has been used as a measure of mediation and brokerage in various networks, such as transportation flow and employee interaction in organisations (Flynn and Wiltermuth 2010 ; Gomez et al. 2013 ; Puzis et al. 2013 ). The intuition is that a directed network represents the flow of information from origins (e.g., cited papers) to destinations (e.g., citing papers), and that a node with a high level of betweenness centrality plays an important intermediary role in passing information in the network. This is consistent with the derivation of our measure. High values in the proposed originality measure indicate that the focal paper X cannot be bypassed in the flow of knowledge from old studies to recent studies; in other words, X provides original knowledge.

Prior bibliometric studies have used betweenness centrality to analyse citation networks, but most studies have treated citation networks as undirected. Because the information of citation direction is lost, the interpretation of betweenness centrality has been concerned more with connectedness than with information flow (Leydesdorff 2007 ). Previous studies have also found that papers with high betweenness centrality tend to receive more citations in the future (Shibata et al. 2007 ; Topirceanu et al. 2018 ).

Note that the proposed measure restricts the scope of network to the immediate neighbours of a focal node, whereas most previous studies draw on the whole network. Although this constraint overlooks information about remote nodes and links, using the whole network is not without limitation. Importantly, the computation with the whole network causes “double counting” because many paths between remote nodes can share the same subsets of links (Borgatti and Everett 2006 ; Brandes 2008 ). It also incurs substantial computational burden especially for large networks. A few variant betweenness centrality operationalisations have been proposed to address these issues (Borgatti and Everett 2006 ). Among others, k -betweenness centrality (or bounded-distance betweenness centrality) considers only paths with length of k or shorter, where k is a positive integer (Freeman et al. 1991 ). Two-betweenness centrality is a special case, which considers only immediate neighbours to focal nodes (Gould and Fernandez 1989 ), as our proposed measures do. Previous studies have found that k -betweenness centrality with small k ’s can reasonably predict the betweenness centrality of the whole network (Ercsey-Ravasz et al. 2012 ), while it substantially reduces the computational cost.

Weighted measure

A potential weakness of the base measure in Eq. ( 3 ) is that it can be biased by the number of references cited by papers in citing set C , as well as the number of citations received by papers in reference set R . Namely, subsequent papers with many references are more likely to cite papers in reference set R , and highly cited papers in reference set R are more likely to be cited by subsequent papers in citing set C (Fig. 1 B). To correct these potential biases, we propose Eqs. ( 4 ) and ( 5 ) as weighted measures:

where $ y_{m} $ is the reference count of the m -th paper in citing set C , $ z_{n} $ is the citation count of the n -th paper in reference set R , and L is an arbitrary positive number. Appendix 2 further explains the derivation of the weighted measures.

Use of citations for bibliometric measures

Our proposed operationalisation strategy for originality is based on citation links between scientific papers. Although citation network has been widely used for science studies and research evaluations (Garfield 1955 ; Hicks et al. 2015 ; Martin and Irvine 1983 ; Uzzi et al. 2013 ), a few potential limitations are worth noting. In particular, though one paper citing another paper is supposed to indicate an intellectual connection between them (Garfield 1955 ; Small 1978 ), citations may embody different information. For example, citing papers may be considerably influenced by citing papers but may cite them only casually; citing papers may be built on cited papers but may disprove them (Bornmann and Daniel 2008 ; De Bellis 2009 ); and citations may be generated for social and political motivations rather than for intellectual reasons (De Bellis 2009 ; Gilbert 1977 ).

One important complication pertains to field differences. For example, some disciplines (e.g., mathematics) have less citing papers or shorter reference lists (Moed et al. 1985 ); some disciplines (e.g., social sciences) tend to cite older papers than others (e.g., natural sciences) (Price 1986 ); and the citation accumulation process is slower in some fields (e.g., social sciences and mathematics) than others (e.g., medical and chemistry) (Glänzel and Schoepflin 1995 ). As later discussed, these differences might call for adjustment in the scope of the citing set and the weighting, even though the generic framework applies to all fields.

For assessing the criterion validity of our originality measure for individual papers, we conducted a questionnaire survey of the authors of these papers to enquire into self-assessed originality. We selected a sample of active scientists who earned their PhD degrees in the field of life sciences in 1996–2011 in Japan for the following reasons. First, we need articles published several years ago (but not too recently) to compute our originality measure based on their forward citations. Second, we focus on papers from PhD dissertation projects, which are usually the first research project that scientists engage in and could help their recollection. We also expect that the respondents’ desirability bias in evaluating originality could be mitigated since dissertation projects are usually decided by supervisors rather than respondents themselves. Finally, we focus on a single field of life sciences to rule out the heterogeneity across different scientific disciplines. Footnote 1

We randomly chose 573 scientists who meet the following conditions: (1) the information of PhD degree is publicly available through online dissertation databases, (2) the PhD dissertation projects were in the field of life sciences according to the funding information, and (3) the scientists remain in academic careers as of 2018. We mailed a survey to the scientists and collected 268 responses (response rate = 47%). As 22 respondents had no papers during the PhD period, we used remaining 246 scientists as the main sample.

Self-assessed measures of originality

The respondents of the survey were asked to evaluate their own dissertation projects in two dimensions of originality: theoretical and methodological (Dirk 1999 ). Each dimension is measured in a three-point scale—0: not original (all or most of the theories/methods had already been reported in prior literature, or the project did not aim at the originality in the dimension), (1) somewhat original (part of the theories/methods had already been reported in prior literature, and (2) original (the theories/methods had not been reported in prior literature).

Computing proposed originality measures

We selected 564 papers that the respondents published as the first or second author in the year of their graduation or 1–2 year before. We exclude papers published after graduation because they can be either from PhD dissertation or from postdoc research, which may confound our analysis. From Web of Science (WoS), we obtained the bibliometric information of the focal papers, their references, and subsequent citing papers up to 2018. We then identified all citation links between the references and citing papers.

In computing the originality measures, the scope of citing set C can be arbitrarily chosen. For example, it may include all existing citing papers to date or may be a single citing paper. Since the choice of citing sets can influence the quality of the measurement as well as the computational cost, we prepare two series of citing sets and assess their validities. The first series is based on the publication year of citing papers. The citing set C ( t ) includes citing papers published within t years after the publication of the focal paper ( t = 1, 2, …). For example, C (3) includes citing papers published in the same year as the focal paper or 1–3 years after that. Note that the size of C ( t ) can differ between focal papers when they have different forward citation counts. The second series of citing sets control for this variation. The citing set C [ s ] consists of the first s citing papers ( s = 1, 2,…). In order to control for the timing of the publication of citing papers, we include citing papers published only within three years after the focal paper. Footnote 2

The second weighted measure (Eq. 5 ) uses the forward citation count of each cited paper ( z n ) in reference set R . Here, the time-window of the forward citation can be also arbitrarily chosen. In this validation exercise, we use the one-year period after the publication of the focal paper as the citation time-window.

As above discussed, citation accumulation process differs across scientific fields. Thus, the optimal choice of the citing set (parameters t and s ) as well as the citation time-window for the forward citation to the reference set should be identified in respective fields.

Predicting future citations

We also examine whether the proposed measure can predict future citation impact for assessing the construct validity of our proposed originality measure (Babbie 2012 ). Specifically, we use a dummy variable (Top10) as the dependent variable, coded 1 if a paper is among the top 10% highly-cited as of 2018 in the same cohort of papers with the same publication year and in the same field, and 0 otherwise.

Correlation with self-assessed originality

First, we establish the criterion-related validity of the proposed originality measure (Babbie 2012 ). From randomly selected 246 scientists, we obtained the information of self-assessed originality of their past papers by a questionnaire survey. For potential multi-dimensionality of the originality concept, we measured two dimensions of originality: theoretical and methodological (see Appendix 3 for the distribution of the measures) (Dirk 1999 ). Then, we computed the proposed originality measures for 564 journal articles published by the survey respondents Footnote 3 to analyse the correlations with the survey measures.

Because the scope of citing set C can influence the quality of the measurement, we assess the validity of a series of citing sets. We first calculate the originality scores using citing papers published within the first t years after the focal paper ( t = 1,…, 6). For each pair between the two survey measures (theory and method) and the three bibliometric measures (base and two weighted measures) with different citing sets, Fig. 2 A shows the correlation coefficients. We observe that our proposed originality measures have significantly positive correlations with the survey measure of theoretical originality (for example, r base = 0.130, p < 0.05; r weighted1 = 0.136, p < 0.001; r weighted2 = 0.142, p < 0.001 at t = 1) but not with the methodological originality ( p > 0.1). Provided that the previous literature found that life scientists tended to perceive theoretical newness, but not methodological newness, as relevant for originality (Dirk 1999 ), our proposed measures appear to capture the relevant dimension of originality. As to the size of the citing sets, the result indicates that the correlation with self-assessed theoretical originality is significant by using the citing papers only in the first year. A slight increase of the correlation coefficients is observed by using a larger citing sets ( t = 3), but too large citing sets do not improve the correlation. This result suggests using citing papers only in the first or a few years for assessing originality, especially considering the computational cost for large citing sets. Comparing the base and weighted measures, the three originality measures indicate similar levels of positive correlation at t = 1. While the base measure shows rather stable correlations over time, weighted measure 1 has an increase in the first few years and weighted measure 2 has a decrease after the third year. Figure 3 a illustrates the joint distribution of the self-assessed measure and the originality measures based on the first-year citing papers.

Correlation between the proposed originality measures and self-assessed originality. Note: † p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001. A The sample size ranges from 461 to 547. B The sample size ranges from 354 to 540. Since our respondents can have multiple papers during their PhD study, we introduced a weight (the reciprocal of the paper count) into the computation of correlation coefficients. See Online Supplement for the correlation analyses

Joint distribution of self-assessed originality and proposed originality measures

Next, we alternatively focus on the first s citing papers to calculate our originality measures ( s = 1, …, 6). Figure 2 b confirms significant correlations with theoretical originality (for example, r base = 0.093, p < 0.1; r weighted1 = 0.169, p < 0.001; r weighted2 = 0.130, p < 0.05 at s = 2) but not with methodological originality ( p > 0.1), though the correlation with the base measure becomes mostly insignificant. The result also suggests that only the first few citing papers contribute to the positive correlation with theoretical originality and that including more citing papers does not improve the correlation. Figure 3 b shows the joint distribution of the self-assessed measure and the originality measure.

For the respondents who have multiple papers during their PhD study, we also took the mean of the originality measures for each respondent, and analysed the correlation at the scientist level instead of the paper level. This approach tends to present higher correlation coefficients (See Online Supplement), probably because taking the mean mitigates potential volatility of the originality measures at the paper level.

These results imply that the originality measures can be calculated with a small number of citing papers published shortly after the focal paper. That is, reliable measurement is feasible without needing to wait for a long time and with limited computational cost, lending practical utility to the proposed measures.

Prediction of future citation

We next test whether the proposed measures can predict the citation impact in the future. We compute the originality measures based on the first-year citing papers as the independent variable and predict whether the focal paper becomes among the top 10% highly-cited using citation count up to 2018. The regression model is specified as

As the dependent variable is binary, we use a logit regression model, and f is the logistic function. As a scientist can have multiple papers in the sample, we control for random errors at the scientist level ( μ ). Finally, we control for the log number of references ( N ) and citations in the first year ( M ). The model prediction is based on the maximum likelihood estimation. Here we focus on focal papers before 2008 so that the time window for accumulating citations is at least 10 years.

Table 1 A presents the result of the analyses, finding significantly positive coefficients: b base = 9.316, p < 0.01 (Model 1); b weighted1 = 13.167, p < 0.1 (Model 2); b weighted2 = 35.700, p < 0.05 (Model 3). Figure 4 a graphically illustrates the result, suggesting that papers with higher originality scores are significantly more likely to be highly cited in the future. Noticeably, the citation count in the first year ( M ) significantly correlates with both the dependent variable and the originality measures, which can confound the analysis. Thus, we test the predicting power of the originality measures with a fixed number of citing papers, by using the sub-sample of papers that have at least s citations in the first three year ( s = 2, 3, and 4). Table 1 B summarises the results, suggesting that the originality measures computed only with a few citing papers can reasonably predict future citations. Figure 4 b graphically presents the result, suggesting that focal papers with higher originality scores are significantly more likely to be highly cited. Because we expect a positive association between originality and future citations, this finding demonstrates the construct validity of our originality measure (Babbie 2012 ).

Prediction of citation rank. Note: The probability of a focal paper falling within top 10 percentile is predicted on the basis of regression models (Table 1 ). To facilitate interpretation, the horizontal axis takes the percentile of the originality measures. Error bars indicate one standard error

Characterisation of originality measures

We further investigate the behaviour of the proposed originality measures. Specifically, we examine the following ideas. First, since the above analyses suggest that citing papers in later time horizon have little added value for measuring originality, we test to what extent remote citing papers are relevant for measuring originality. Second, the proposed measures are positively correlated with the citation count and the reference count of the focal article, so we aim to confirm that the proposed measures do capture the originality of focal papers even after these confounding factors are controlled. Third, since any bibliometric indicator can be biased by contextual factors, we test particularly whether the publication year and the subfields of focal papers influence the measures.

To these ends, we compute a series of originality measures (Orig) based on citing papers in t -th year following the publication year of the focal paper ( t = 1, …, 10). Here, the citing set includes papers in the specific one year (but not up to that year). We regress this series of originality scores on the self-assessed originality measure, as well as other confounding factors (Table 2 ). The regression model is specified as

where $ \varvec{D}^{T} $ is a row vector of time dummies with the t -th entry $ d_{t} = 1 $ and other entries = 0; OrigTheory is the self-assessed originality measure; N is the number of references; M is the number of citing papers in the citing set; $ \varvec{Field}^{T} $ is a row vector of field dummies; $ \varvec{PubYear}^{T} $ is a row vector of publication-year dummies; $ \mu $ is the random error at the scientist level; and $ \nu $ is the random error at the paper level. The model prediction is based on the maximum likelihood estimation.

First, the result finds that the time dummies ( d 1 , …, d 10 ) have significantly positive coefficients ( p < 0.001) and their magnitude increases over time. This implies that the proposed originality measure increases over time when we include citing papers that is timewise more distant from the focal paper. This is probably because citing papers generally deviate from the focal study over time, and this implies that using long time windows for assessing originality could cause errors rather than to add information.

Second, the models include the interaction terms between the time dummies and the self-assessed originality measure ( OrigTheory ) to evaluate the temporal dynamics in the correlation between the self-assessed originality and the proposed originality measures. Each interaction term is concerned with to what extent the citing papers in the particular year capture the originality of the focal paper. Consistent with the above analyses, the result shows that the coefficients of the interaction terms are significant only in the first few years (base: t = 1 (Model 1), weighted 1: t = 1, 3, 5 (Model 2), weighted 2: t = 1 (Model 3)). This suggests that citing papers in later time horizon do not provide additional information about originality.

Third, as expected, both citation count ( M ) and the number of references ( N ) are positively correlated with the originality measures. Even after controlling for them, the proposed measures are significantly positively correlated with the self-assessed originality measure. Thus, the proposed measures seem to capture the true originality.

Fourth, the models include series of dummy variables for publication years ( PubYear ) and scientific subfields, and the result finds their negligible effects on the proposed measures except for the base measure (Model 1). Thus, at least within the scope of our sample, the contextual difference of the proposed measures seems limited.

The results highlight several features of the proposed measure. First, it presents a significant correlation with scientists’ self-assessment of originality. In particular, the proposed measure is correlated with theoretical newness (but not with methodological newness), which has previously been found as the main source of originality in life sciences (Dirk 1999 ). Because of multi-dimensionality of originality (Guetzkow et al. 2004 ), it is crucial to understand what aspect of originality is captured by any bibliometric indicator. Second, the operationalisation of our proposed measure is consistent with the betweenness centrality in a directed network (Borgatti and Everett 2006 ; Freeman 1979 ). Betweenness centrality has already been actively used in bibliometric studies, but prior studies have rarely analysed directed citation network to identify the flow of knowledge. Third, our measure builds not only on references but also on forward citations and therefore can be manipulated to a lesser extent by authors. Because of this advantage, our measure provides a robust tool for studying science as well as for research evaluation and science decision-making. Fourth, our proposed measure requires a smaller computational cost, especially when compared with the previous novelty measures that require information about the whole universe of papers. Our results suggest that the proposed measure can be computed with limited scope of citation network and without needing to wait for a long time after publication. This adds to the practical utility of our proposed measure. Fifth, our proposed measure helps predict future citation impact. Original discoveries are supposed to be the source of scientific progress, and the result shows that papers with a higher level of originality are also more likely to be highly cited in the long run. This adds to the validity of the proposed measure. Sixth, this study characterises the detailed behaviour of the measure, including its temporal dynamics and contextual contingencies. The result offers guidance for using the measures in future research.

Our approach has a few limitations and further research is needed. First, although we assume that citation links embody the flow of knowledge, citations can be made for various reasons (Bornmann and Daniel 2008 ; Martin and Irvine 1983 ; Wang 2014 ), which challenges the validity of our measure. Second, the proposed measure can be computed only if a citation is made. In addition certain types of original discoveries may be recognised only long after their publication (e.g., sleeping beauties) (Van Raan 2004 ), which cannot be captured by our measure based on short-time citations. Third, there are important differences between disciplines in citation behaviour, but our validation is limited to the field of life sciences. The field is known to have the fastest citation accumulation process compared with other disciplines (Wang 2013 ), which allows us to compute the originality measure in a short time window, but other fields might need longer citation time windows. Future research should identify the optional citing sets for different fields. Fourth, as self-reported originality measures can be biased, the proposed measure could be further validated by alternative approaches such as the use of scientific awards (e.g., Nobel prize) and a text analysis to detect languages associated with originality.

In conclusion, this study proposes a new bibliometric measure of originality. Although originality is a core value in science (Dasgupta and David 1994 ; Merton 1973 ; Stephan 1996 ; Storer 1966 ), measuring originality in a large scale has been a formidable challenge. Our proposed measure builds on the network betweenness centrality concept (Borgatti and Everett 2006 ; Freeman 1979 ) and demonstrates several favourable features as discussed above. We expect that the proposed measure offers an effective tool not only for scholarly research on science but also for practices in research evaluation and various science decision-makings.

Note that our validity exercise is made only in the field of life sciences. Since the citation cycle can differ between fields, future research should assess the validity of our measure in other disciplines.

This time-window is chosen because our evaluation of C ( t ) suggests that citations only in the first few years are informative (see the next section). The measure is not calculated if a focal paper received fewer than s citing papers within 3 years.

See Online Supplement for the distribution of the measures.

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Open access funding provided by Lund University. This study was financially supported by the Japan Society for the Promotion of Science (16K01235).

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We claim that the proposed measure in Eq. ( 3 ) is equivalent to the normalised betweenness centrality of X in the directed network, in which the focal paper X , papers in citing set C , and papers in reference set R are the nodes, and citation links between them are directional edges. The normalised betweenness centrality of node X is defined as

where $ \sigma_{nm} \left( X \right) $ is the number of paths from node n to node m that are shortest and go through node X , $ \sigma_{nm} $ is the number of paths from node n to node m that are shortest, and S is a normalisation factor: the number of all possible paths among all nodes in the network (Freeman 1979 ).

In the given network, the shortest path between n and m is either a direct citation link from m to n or an indirect citation link through X . In either case, the shortest path is unique. Thus, $ \sigma_{nm} = 1 \forall n,m $ . If m cites n (i.e., $ x_{nm} = 1 $ ), n and m are directly linked, and thus, $ \sigma_{nm} \left( X \right) = 0 $ . If m does not cite n (i.e., $ x_{nm} = 0 $ ), then n and m are linked only through X , and thus, $ \sigma_{nm} \left( X \right) = 1 $ . Thus, $ \sigma_{nm} \left( X \right) = 1 - x_{nm} $ .

In directed networks, the number of possible paths is given as $ S = (l_{I} - 1)(l_{O} - 1) $ , where $ l_{I} $ is the number of nodes with incoming links and $ l_{O} $ is the number of nodes with outgoing links (White and Borgatti 1994 ). In the given network, $ l_{I} = M + 1 $ and $ l_{O} = N + 1 $ . Therefore,

To address potential biases in the originality measure in Eq. ( 3 ), we propose weighted measures in Eqs. ( 4 ) and ( 5 ) that control for the reference count of each paper in citing set C and the citation count of each paper in reference set R .

Suppose that papers choose their references from the universe of all existing papers with different probabilities (Barabasi and Albert 1999 ). Each paper in the universe has different visibility or the likelihood of being cited, which we denote by $ f( \cdot ) $ —a non-decreasing function of the citation count of the paper. Focus on the citations between the n th reference in R and the m -th citing paper in C . Suppose that the m -th citing paper chooses one reference from the universe of papers. The probability of the n -th reference being cited is given by $ f\left( {z_{n} } \right) $ . Since the m -th citing paper has $ y_{m} $ references, the probability of the n -th reference being one of the references is given by $ 1 - \left( {1 - f\left( {z_{n} } \right)} \right)^{{y_{m} }} \cong y_{m} f\left( {z_{n} } \right) $ , where the approximation holds because $ f\left( {z_{n} } \right) \ll 1 $ . Summing this up across all combinations of references and citing papers, the expected number of citations between R and C, $ E\left( {\mathop \sum \limits_{m = 1}^{M} \mathop \sum \limits_{n = 1}^{N} x_{nm} } \right) $ , is given by $ \mathop \sum \limits_{m = 1}^{M} y_{m} \mathop \sum \limits_{n = 1}^{N} f\left( {z_{n} } \right) $ . In Eq. ( 3 ), we use $ MN $ to normalise $ \mathop \sum \limits_{m = 1}^{M} \mathop \sum \limits_{n = 1}^{N} x_{nm} $ . An alternative normalisation factor is the expected value of $ \mathop \sum \limits_{m = 1}^{M} \mathop \sum \limits_{n = 1}^{N} x_{nm} $ . Hence,

In particular, we employ two forms of $ f( \cdot ) $ . For simplicity, we first assume that $ f( \cdot ) $ is a constant function: $ f\left( {z_{n} } \right) = 1/L $ , which gives the weighted measure in Eq. ( 4 ). Here, $ L $ is an adjusting factor such that the summation of $ f( \cdot ) $ across the universe of papers equals 1, or the number of the papers in the universe. Second, following the prior literature (Barabasi and Albert 1999 ), we assume that $ f( \cdot ) $ is proportionate to the citation count of each paper: $ f\left( {z_{n} } \right) = z_{n} /L $ , where L is the total number of forward citations that exist in the paper universe. This gives the second weighted measure in Eq. ( 5 ). Though L is unknown, we assume that L is constant across our sample papers. To facilitate interpretation, we set L such that the minimum value of originality scores is zero.

Description of self-assessed originality measures. Note: A N = 236. B N = 234.

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Shibayama, S., Wang, J. Measuring originality in science. Scientometrics 122 , 409–427 (2020). https://doi.org/10.1007/s11192-019-03263-0

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What does originality in research mean a student’s perspective, mandy edwards phd student, university of south wales cardiff, uk.

Aim To provide a student’s perspective of what it means to be original when undertaking a PhD.

Background A review of the literature related to the concept of originality in doctoral research highlights the subjective nature of the concept in academia. Although there is much literature that explores the issues concerning examiners’ views of originality, there is little on students’ perspectives.

Review methods A snowballing technique was used, where a recent article was read, and the references cited were then explored. Given the time constraints, the author recognises that the literature review was not as extensive as a systematic literature review.

Discussion It is important for students to be clear about what is required to achieve a PhD. However, the vagaries associated with the formal assessment of the doctoral thesis and subsequent performance at viva can cause considerable uncertainty and anxiety for students.

Conclusion Originality in the PhD is a subjective concept and is not the only consideration for examiners. Of comparable importance is the assessment of the student’s ability to demonstrate independence of thought and increasing maturity so they can become independent researchers.

Implications for research/practice This article expresses a different perspective on what is meant when undertaking a PhD in terms of originality in the doctoral thesis. It is intended to help guide and reassure current and potential PhD students.

Nurse Researcher . 21, 6, 8-11. doi: 10.7748/nr.21.6.8.e1254

This article has been subject to double blind peer review

None declared

Received: 10 June 2013

Accepted: 29 August 2013

Student perspectives - originality - PhD - doctoral research

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How to review a paper for originality?

Originality with timeliness is among the most important criteria for a paper to get published.

Many a times when we are reviewing a paper, we are faced with dilemma as to how much of it is original, how much of it is based on another paper, and how much of it is just repetition in a slightly different context. Also, at times papers cater to interdisciplinary and cross-area and it may seem that the idea is original.

What preparation does the reviewer need to have in order to review a paper justly?
How do you judge the originality of a paper?
publications
peer-review

4 One tip which you may find useful: I have found that often, the closest existing work to the reviewed manuscript is the authors' previous work. So by reading the authors' previous work you can see whether they have made a large advancement or a small increment. – Bitwise Commented Aug 6, 2013 at 15:32

I've reviewed more than 50 papers, and my preparation to review them has varied widely. At least a few of them have drawn heavily on papers I've written. Generally, these are extending my results. Typically, I ask myself: What does this paper add? Is it something I would have thought of easily or not? If yes, why didn't I include it in my paper?

In contrast, I've also reviewed some papers that I was not well prepared for. They were in my general field, but in a subarea that I hadn't worked in. These papers took a lot more work. ( Up to 10x as long for me to review as a paper in an area where I'm very familiar.) For papers in an area that's new to me, I often have to look up (and skim) at least a few of the references from the introduction to judge whether the paper is original. Originality has at least two very different levels . The easier one to achieve is: (1) this result is new , and doesn't follow simply from anything previous. The much harder one is, (2) this technique is fundamentally new and produces interesting results. If the results are interesting, most journals are happy to publish articles of type (1). Articles of type (2) are much rarer, and can potentially open entire new fields.

Preparation : you need to either know the subarea well or be willing to read a lot to learn about it.
Originality : What papers does it draw on? How likely is it that a reader of those would be able to write this paper? Answering this question may require you to read a lot of background.

Why would you agree to review papers in an area new to you? – user13107 Commented Aug 7, 2013 at 1:33
First, let me clarify that it was usually a subarea that I had not done research in, although I had often heard a lecture on something related before. Typically I said yes because I either wanted to learn more about that area or the work of that specific author, or because I wanted to get experience refereeing for that particular journal (similar to the reasons I would opt to teach a new class in an area I didn't know that well yet). – Dan C Commented Aug 7, 2013 at 13:50

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Research skills for HDR students

Overview and planning
Theses including publications

Originality

Structuring your thesis
Literature reviews
Writing up results
Interpreting results

Originality is one of the most important criteria for a successful thesis. Your thesis should be a significant addition to the accumulated knowledge within your discipline, which implies it must offer something new.

While your thesis will be a big step for you, in most cases, your contribution will be a relatively small step for your field of study as a whole. Therefore, it’s important not to over-emphasise the need for originality. There are many ways you can incorporate originality without your thesis becoming too ambitious or unmanageable.

‘New’ elements can arise when you:

generate new data
apply new methods to existing data
create new interpretations of existing data
provide additional support for existing theories, models or interpretations
modify existing theories, models or interpretations
critique or disprove existing theories, models or interpretations
discover new information
provide new solutions to problems
analyse phenomena in new ways
devise new investigative methods
sample new populations.

By carrying out a thorough review of the literature , particularly of the most up-to-date sources, you’ll discover the gaps or limitations in the current knowledge. These will guide you to the best areas for original research.

Your supervisor will be in the best position to advise you on whether you have enough new ideas. There will also be opportunities within your faculty for you to explain and explore your ideas with other postgraduate research students.

This material was developed by the Learning Hub (Academic Language and Learning), which offers workshops, face-to-face consultations and resources to support your learning. Find out more about how they can help you develop your communication, research and study skills .

See our handout on Writing a thesis proposal (pdf, 341KB) .

Accessibility

Research Process

Introduction
Topic Selection

Originality

Information Needs
Evaluating Sources
Getting Sources
Getting Organized
Understanding Your Sources
Putting it into Words
Bringing it Together

One way to think about originality in research is to think about how many times someone else has analyzed information. These can be described as primary, secondary, and tertiary information, and often instructors want you to use primary sources so they can see what you have to say about the information you find. The gallery provides information about and examples of primary, secondary, and tertiary resources:

First-hand accounts or documents related to the topic from people who had a direct connection with it, such as speeches, diaries, letters, interviews, photographs, original research, and dataset. They can be found in archives, historic newspaper databases, scholarly journal articles, image databases, and data resources.

Example: An original handwritten poem by Frederick Douglass, held in the Xavier archives.

Resources that add some interpretation, analysis, or provide context for primary sources. Books, articles, and documentaries that are about a topic are most likely secondary sources. They can be found in print or electronic books, database articles that are not based on original research, and documentaries.

Example: The "About this Collection" description and contextualization of the poem "Liberty" by Frederick Douglass

Resources used to find primary or secondary sources or give general information rather than analysis. It can be found in reference materials such as encyclopedias, dictionaries, background databases and some websites.

Example: Frederick Douglass page on allpoetry.com, which shares basic facts of his life and the text of the poem.

More about Original Research

As you participate in the scholarly conversation, it is important to understand what kinds of original research are being done by academics and how that fits with the research you are doing. Most original research is done as either qualitative or quantitative:

Characteristics of Qualitative and Quantitative Research
Qualitative	Quantitative

From Research to Publication: The Life of a Journal Article

1. Conduct Original Research

Original research is done when the researchers are responsible for the entire process of coming up with a hypothesis, a means for testing the hypothesis, defending their hypothesis based on prior research, and gathering and analyzing the data, and explaining their findings. This is often done by scientists, doctors, college professors, or people working within a field of study.

Researchers must present their findings in a very thorough manner so that other researchers could replicate their work and reach the same conclusions. Their writing must follow specific style rules and writing conventions that match the preference of the publication, or journal , where they will submit their work.

3. Submit to Editor

The journal editor's initial job it is it make sure that the submission matches the subject matter of the journal and is written according to the style rules for their publication. The editor then identifies other people who are experts on the same content the article is about, or peers , and sends the article for them to read.

4. Peer Review

The peer group of experts receives the article and reviews the content to ensure that the science being used to do the research is reasonable, the data was conducted accurately, analyzed in a way that is free of errors, and the authors have reached a conclusion that is supported by their research. They then respond to the editor letting them know whether they feel the article is ready for publication.

5. Editor Decisions

The editor reviews the feedback of the peer group and decides if the research article should be accepted for publication, rejected, or sent back to the authors for revisions based on peer feedback.

6. Published

If the editor accepts the article for publication, it is now available to be published in that journal. Depending on the frequency, method, and business model of the publication, it can take a year or more for the article to become available for others to read.

<< Previous: Information Sources
Next: Information Needs >>

How to... Write an article abstract

An abstract is a succinct summary of a larger piece of work that aims to persuade readers to read the full document – essentially, it acts as a shop window, enticing people to step inside.

Typically, abstracts are written to accompany a journal research article or book serial chapter, but you are also likely to be asked for an abstract when applying to write a paper for a conference. In this guide you will find tips to help you prepare for both. They include specific guidelines on how the abstract should be written and presented, including a maximum word count.

Choose a category for the paper that best describes it

Tips for writing abstracts for conference papers.

Typically, your abstract is the first element of your published work that potential readers see. It provides the ideal channel to convince them that your work is worth their time. For example, editors will use it to help them decide whether to send your submission out for peer review, and reviewers will refer to it when deciding whether to accept that review invite.

Unless you’ve published your work open access, the title and abstract are the only parts of an article that are freely available to readers The reader will decide whether the rest of your article is interesting to them while they are reading your abstract. And, the more researchers who read your work, the more chance you have it will be cited in further research.

With so much at stake, it’s well worth taking the time to craft a strong and compelling abstract.

How to write a structured abstract

Let’s start with a few essential points to remember when writing your abstract. You should:

Report the essential facts contained within the document.
Avoid exaggerating or including material that doesn’t feature in the main text.
Avoid abbreviations that are only explained in the main text. Your abstract should be able to stand alone.
Remember not to place excessive emphasis on the previous literature – this is a summary of your work.

Many authors recommend waiting until the rest of your paper or chapter is complete before writing your abstract. Whenever you decide to write it, your abstract should be a succinct statement that gives the reader context. Most journal author guidelines set a maximum of 250 words, including keywords and article classification.

The following points should always be featured

Purpose: This is where you explain ‘why’ you undertook this study. If you are presenting new or novel research, explain the problem that you have solved. If you are building upon previous research, briefly explain why you felt it was important to do so. This is your opportunity to let readers know why you chose to study this topic or problem and its relevance. Let them know what your key argument or main finding is.
Study design/methodology/approach: This is ‘how’ you did it. Let readers know exactly what you did to reach your results. For example, did you undertake interviews? Did you carry out an experiment in the lab? What tools, methods, protocols or datasets did you use?
Findings: Here you can explain ‘what’ you found during your study, whether it answers the problem you set out to explore, and whether your hypothesis was confirmed. You need to be very clear and direct and give exact figures, rather than generalise. It’s important not to exaggerate or create an expectation that your paper won’t fulfil.
Originality/value: This is your opportunity to make a clear and succinct case for the value of your results. It’s a good idea to ask colleagues whether your analysis is balanced and fair and again, it’s important not to exaggerate. You can also reflect on what future research steps could be.

The following three items should be included, if relevant to your paper or required by the journal you are submitting to:

Research limitations/implications
Practical implications
Social implications

Follow the chronology of the paper, using headlines as guidelines if necessary. Make sure there is a consistent flow of information.

The language should be active rather than passive, e.g., “we carried out an analysis,” rather than “an analysis was carried out.” It’s also important to use relevant keywords and technical language to help potential readers find your paper. What are keywords? These are the words or phrases a researcher might use when searching for a paper on this topic.

You can find out more in our Make your research easy to find with SEO guide .

This may be:

Research paper
Technical paper
Conceptual paper
Literature review
General review

Make sure to edit, review and peer review to find and correct any grammatical, spelling or typographical errors. You also want to ensure that there is consistency between the information in your abstract and paper.

This is slightly different to writing a general abstract and in this scenario the abstract is likely to be written before the paper has been prepared.

A few tips:

Clarify in your own mind the purpose of the paper.
Look at the themes of the conference and keep them in mind as you write.
Ask yourself the following: What approach am I using? – Is it a review, description or supporting a hypothesis? What are my findings? Do the findings support the initial hypothesis? What is the significance of my findings?
Quite often, the submission procedure will dictate the format and number of words your abstract should follow – make sure not to exceed the word limit.
Choose your keywords carefully, ensuring the key themes of the conference are referenced.

Writing simply

Investing a little time in ensuring your manuscript or case study is easy to follow can really help readers absorb your key messages.

Structure your journal submission

This guide explains the building blocks that are used to construct a journal article and why getting them right can boost your chances of publishing success.

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How to demonstrate the value of your research

A tool to help you master the four Cs: citations, communication, coverage, and collaboration.

Carsten Lund Pedersen

Credit: sorbetto/Getty

20 March 2020

sorbetto/Getty

Successfully publishing a paper in a peer-reviewed journal is only part of the journey – once your research is in the public sphere, you’re expected to demonstrate its value .

This goes beyond simply explaining your work to a broad audience. Future career and funding opportunities depend on you being able to show why it’s valuable, and to whom.

Scientists are rarely taught how to promote their research. Studies have found that 93% of humanities research and 45% of social sciences research remains uncited within five years of publication. There is also a gender gap in academic self-promotion , with studies finding that male researchers are more likely to promote their research.

Science that has no life beyond a published paper leads to many lost opportunities. With this in mind, I’ve created a tool that can help you exhibit the value of your research using “the four Cs”: citations, communication, coverage, and collaboration:

The first thing to consider is who your primary target audience is, whether it’s scholars and scientists or practitioners and policy-makers. Next, you need to define the value of your research through facts and figures or experiences and engagement.

Put together, four different value demonstration opportunities emerge.

1. Citations: If your primary audience is scholars and scientists, and value is best demonstrated through facts and figures, the value demonstration mode is citations. While researchers are well-aware of these metrics, they can be overlooked when demonstrating the value of your research.

Google Scholar is a great tool for this, as it aggregates and disseminates citations information and displays it under individual researcher profiles.

2. Communication: If the primary audience is scholars and scientists, and value is best demonstrated through experiences and engagement, the value demonstration mode is communication.

Here, the researcher demonstrates value in live settings, for example, at a conference. By engaging with other academics in an interactive manner, you can demonstrate the value of your research and your knowledge in the field.

3. Coverage: If your primary audience is practitioners and policy-makers, and value is best demonstrated through facts and figures, the value demonstration mode is coverage.

In this scenario, the researcher demonstrates value to broader societal stakeholders. The proficiency of this can be gauged through measures such as the “ Twi-Li index ”, which is a proxy to assess scientists’ relative social media impact, and the Altmetric Attention Score , which helps to identify how much attention a research output is receiving online.

You can use these measures to demonstrate to societal stakeholders that your research is having an impact beyond the walls of academia.

4. Collaboration: If your primary audience is practitioners and policy-makers, and value is best demonstrated through experiences and engagement, the value demonstration mode is collaboration.

Researchers can collaborate with societal actors in different ways, such as consulting with decision-makers or engaging in committees. By doing so, you are ensuring that stakeholders can experience first-hand the value of your research, as it is demonstrated in the context of their specific project or problem.

How can you use the four Cs?

First, when your paper is published, you can plan relevant activities in each of the four Cs. For instance, how many citations does it have? What’s its Altmetric Attention Score? Which conferences or seminars has it been presented in? Do you intend to collaborate with any practitioners or policy-makers?

Second, what do you want to achieve with your value demonstration? Is it tenure or new funding opportunities? Who is your most important target audience? Your objectives will determine which of the four Cs are most important to you.

Third, which activities are you going to prioritize, and when will you perform them? While it’s beneficial to demonstrate value immediately upon publication, it may also be useful to demonstrate it further down the track, if you can see that the topic gains traction and is becoming more timely.

While you might find it uncomfortable or outside your field of expertise, demonstrating the value of your research is essential. As the saying goes, “If you’ve got it, flaunt it.”

Carsten Lund Pedersen is an assistant professor in the Department of Marketing at the Copenhagen Business School, Denmark.

The Social Value of Hurricane Forecasts

What is the impact and value of hurricane forecasts? We study this question using newly-collected forecast data for major US hurricanes since 2005. We find higher wind speed forecasts increase pre-landfall protective spending, but erroneous under-forecasts increase post-landfall damage and rebuilding expenditures. Our main contribution is a new theoretically-grounded approach for estimating the marginal value of forecast improvements. We find that the average annual improvement reduced total per-hurricane costs, inclusive of unobserved protective spending, by $700,000 per county. Improvements since 2007 reduced costs by 19%, averaging $5 billion per hurricane. This exceeds the annual budget for all federal weather forecasting.

Funding for this project was provided by Grant NA20OAR4320472 from the National Oceanic and Atmospheric Administration. This manuscript benefited from discussions by Jeff Shrader and Manuel Linsenmeier, and comments from Christopher Costello, Gabriel Lade, Derek Lemoine, Cynthia Lin-Lawell, Antony Millner, Christopher Parmeter, Christopher Timmins, and Jinhua Zhao, as well as from feedback by seminar participants at Cornell University, St. John’s University, the University of Miami, the Colorado Environmental Economics Workshop, the Kansas City Fed, the Northeast Environmental Workshop, the Occasional Workshop, the Seminar Series of the National Oceanic and Atmospheric Administration, and the Summer Conference of the Association of Environmental and Resource Economists. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship — as well as online conference reports , video lectures , and interviews .

15th Annual Feldstein Lecture, Mario Draghi, "The Next Flight of the Bumblebee: The Path to Common Fiscal Policy in the Eurozone cover slide

Investing in the new era of value-based care

Value-based care has evolved into a healthcare landscape of its own, with a wide range of organizations contributing to systematic changes that improve quality of care and outcomes while better controlling costs. Providers specializing in value-based care have become attractive to investors because of the distinctive quality of care that they can provide and the investable opportunity they present, with a diversity of risk levels and business models. By building on a decade of increasing value-based payment adoption—combined with enhanced value-based capabilities across payers, providers, employers, and other healthcare stakeholders—continued traction in the value-based care market could lead to a valuation of $1 trillion in enterprise value for payers, providers, and investors. 1 Assumes approximately 160 million lives in value-based care models, accounting for $1.6 trillion to $1.7 trillion in medical spending, with medical cost savings ranging from 3–20 percent based on level of risk, of which 50 percent is realized as profit margin with a 12-fold to 15-fold valuation multiple applied.

Value-based care is emerging as a distinct healthcare landscape

Stakeholders in the healthcare community define value-based care differently. The Health Care Payment Learning and Action Network (LAN) includes performance, reporting, and even infrastructure in its first step of value-based care, while others note that these models fall short of delivering value (in quality or affordability) because they don’t remedy the problems of fee-for-service healthcare. 2 “Why ‘pay for performance’ hasn’t worked,” Center for Healthcare Quality & Payment Reform, accessed December 2022; David Raths, “Current, ex-MedPAC chairs ask: Is value-based care juice worth the squeeze?,” Healthcare Innovation, October 1, 2020.

In this article, we take a more expansive definition of the value-based care landscape and include all care models that align provider incentives to quality or care cost-reduction. Though we recognize that improvements in care quality will vary considerably across models, based on our experience working with a wide range of providers, we assume savings ranges from a low of 3 percent in more limited quality-based models to a high of 20 percent in high-touch primary care groups taking fully capitated risk on Medicare Advantage members.

Value-based care investment quadrupled during the pandemic

Private capital inflows to value-based care companies increased more than fourfold from 2019 to 2021, while new hospital construction—a proxy for investment in legacy-care delivery models—held flat. While these are distinct forms of investment—with private equity seeking returns on enterprise value and construction debt funding seeking safer opportunities for more modest returns—it’s noteworthy that private-capital inflows in value-based care assets rose from 6 percent of the capital investment in hospitals to nearly 30 percent within two years, as illustrated in Exhibit 1. 3 PitchBook private equity and venture capital transaction data, accessed in spring 2022; McKinsey value-based care expertise.

The future potential of value-based care

Given the momentum we see behind value-based care investment, it’s worth examining recent trends to understand the ways in which this landscape could potentially evolve. In imagining the value-based care landscape five years from now, the following scenarios seem possible—and not at all mutually exclusive:

Scenario 1: Value-based care growth will continue to accelerate. Growth in valued-based care has accelerated from creating approximately $500 billion in enterprise value today and may be on track to reach $1 trillion as the landscape matures (see Exhibit 2 and sidebar, “Our approach to estimating this $1 trillion opportunity”). Based on our research, this would likely be driven by a rising number of lives in all value-based care arrangements of 10–15 percent, with growth rates for lives in full or partially capitated contracts well above that (potentially 20–30 percent). Improved medical-cost-management performance from providers in value-based contracts—becoming more critical in the face of potential increases in medical-cost inflation 4 Addie Fleron, Aneesh Krishna, and Shubham Singhal, “ The gathering storm: The transformative impact of inflation on the healthcare sector ,” McKinsey, September 19, 2022. —could further support enterprise value creation, and the cumulative impact of these tailwinds may suggest positive downstream effects on patient health outcomes as well. In fact, some of the largest value-based care performance reviews have found that they correspond to improved outcomes, increased preventative care, and improved patient satisfaction. 5 Value-based care report: Physician progress and patient outcomes based on calendar year 2020 data , Humana, 2021; “Physicians provide higher quality care under set monthly payments instead of being paid per service, UnitedHealth Group study shows,” UnitedHealth Group, August 11, 2020.
Scenario 2: A handful of national platforms could take the lead, with sharp competition among them. Platforms could include integrated primary care, managed-services organizations (MSOs), and specialty-based care. While vertical integration may accelerate, these platforms may not necessarily be “walled garden” silos: a degree of collaborative interoperability will likely be necessary, potentially enabled by platforms specializing in a variety of patient populations.
Scenario 3: Distinctive operational capabilities could become prerequisites for successful value-based care providers. Distinctive operational, clinical, and analytical capabilities could increasingly become prerequisites for successful value-based care providers. These capabilities could range from new technology to the prediction of membership changes and points in between.
Scenario 4: Specialists may begin to adopt value-based care. Specialists appear to accelerate adoption of value-based care models as part of increasingly effective and scalable value-based care platforms. These models are already emerging in specialties like nephrology and oncology.

Growth in valued-based care has accelerated from creating approximately $500 billion in enterprise value today and may be on track to reach $1 trillion as the landscape matures.

Scenario 1: Value-based care growth will continue to accelerate

In our experience, adoption of value-based care has accelerated in recent years, and this trend could continue in the coming years as payers, employers, and the government embrace these models. 6 The McKinsey value-based care market model includes insights from more than 50 expert interviews, published third-party data (for example, payer value-based care reporting, payer financial filings), and publicly available data from government sources (Centers for Medicare & Medicaid Services, California Department of Managed Healthcare). For example, last year the Center for Medicare and Medicaid Innovation issued an ambitious goal to shift 100 percent of Medicare beneficiaries into an accountable-care relationship by 2030, 7 Driving health system transformation: A strategy for the CMS Innovation Center’s second decade , Centers for Medicare & Medicaid Services, October 2021. which we recently analyzed. 8 Zahy Abou-Atme, Stephanie Carlton, and Isaac Swaiman, “ Looking ahead to the next decade of accountability for care delivery ,” McKinsey, November 9, 2022.

Our approach to estimating this $1 trillion opportunity

To arrive at the $1 trillion enterprise value estimate, consider the following:

Approximately 160 million total lives are in value-based care. According to McKinsey analysis, this represents an aggregated and triangulated view that draws on payer financial statements, publications, and press releases; Centers for Medicare & Medicaid Services data for Medicare and Medicaid; state regulatory agency publications; and extended discussions with internal and external healthcare leaders.
There is a total medical spend for these lives at approximately $1.6–1.7 trillion, based on national spending levels. 1 Per member, per year spend calculations are from Centers for Medicare & Medicaid Services and commercial claims data sets (namely Truven).
There is 3–20 percent savings of medical spend, varying across lines of business and value-based payment models, our analysis found.
There is a valuation of 12-fold to 15-fold on earnings before interest, taxes, depreciation, and amortization (EBITDA) applied to a 50 percent assumed margin on the generated savings, assuming the other 50 percent is required operational expenses for the provider to deliver the incremental services and preventative care necessary to realize these aggregate savings, according to our analysis. Review of public research and industry perspectives 2 Sarah Pringle, “Skin in the game: OMERS readies sale of Forefront Dermatology,” PE Hub , June 30, 2021; Claire Rychlewski, “How much is your doctor worth? Investors are trying to decide,” Forbes , January 10, 2020. suggests that valuations can vary widely based on secular and asset-specific factors but are often 12-fold to 15-fold EBITDA for at-scale physician platforms. We therefore assume this range in this analysis.

Ultimately, our research suggests that the number of patients treated by physicians within the value-based care landscape could roughly double in the next five years, growing approximately 15 percent per annum.

Increased physician appetite for value-based models lies at the heart of this acceleration, but within the national community of one million licensed (if not necessarily working) physicians, 9 Katie Arnhart et al., “FSMB census of licensed physicians in the United States, 2020,” Journal of Medical Regulation , July 2021, Volume 107, Issue 2. value-based care adoption remains uneven. Not all primary care providers find value-based models readily accessible, and in our experience, pockets of the market (notably those at institutions that focus on quaternary care rather than primary care) lag behind in adoption. Such physicians, particularly those affiliated with more academically oriented institutions, may require more peer-reviewed research (lacking today) before altering their practice models. 10 Yomi Ajao and Andrew M. Snyder, “Making value-based care more attractive to AMCs,” Academic Health Focus , The Governance Institute, August 2021; Meg Bryant, “Academic medical centers face headwinds in shift to value-based care, Moody’s says,” Healthcare Dive, April 1, 2019. Nevertheless, some recent data suggest that the number of patients aligned with a primary care provider in a value-based care arrangement is increasing—and the associated outcomes are better than those in fee-for-service arrangements. 11 Value-based care report , 2021.

These successes could power further growth, as physicians taking note of improved outcomes and other benefits become more interested in adopting value-based models. Growth could become disproportionately driven by the adoption of meaningful risk (full and partial cap) as these models mature. Our research suggests that the upward trend in the number of people receiving care in value-based models should continue across lines of business (Exhibit 3). This is one of the primary factors powering the growth in enterprise value associated with the value-based care landscape, potentially leading to a $1 trillion cumulative valuation.

Scenario 2: A handful of national platforms could take the lead, with sharp competition among them

A look at mature markets across the country may shed some light on where the risk-bearing provider market is heading. In Southern California, where health maintenance organization (HMO) approaches using independent physician associations and employed risk-bearing providers have been around for two decades, a consolidation of lives over the past five years has been driven by acquisitions, attractive offers to physicians, and member behaviors (Exhibit 4). Southern California may be unique in its value-based care adoption, but as more emergent markets in Florida and elsewhere catch up, their providers have displayed a similar acquisition strategy. 12 “Cano Health acquires University Health Care for $600 million and increases 2021 adjusted EBITDA guidance to over $100 Million,” Cano Health, June 14, 2021; “Cano Health acquires Doctor’s Medical Center for $300 million and updates guidance for 2021 and 2022,” Cano Health, July 7, 2021; “Oak Street Health acquires virtual specialty care provider RubiconMD,” Oak Street Health, October 21, 2021.

Based on data from Definitive Healthcare and the California Department of Managed Health Care, we estimate that 90 percent of Southern California’s commercial and Medicare lives are in value-based contracts, as well as nearly 50 percent of its Medicaid lives, making this one of the more mature markets nationally.

In the next five years, mature markets such as Florida and California will likely see increased competition among provider groups to further improve performance via more operationally and clinically complex levers. Successful providers will likely establish a strong presence with payers looking to delegate their growing memberships.

We have taken an expansive definition of value-based care in this article and included pay-for-quality, pay-for-performance, and similar models. Our experience suggests that private investment has focused on assets that take material financial risk on medical-cost management. This typically includes different types of physician groups, MSOs, independent physician associations, or other care delivery models, but has largely excluded hospitals and health systems in primarily pay-for-performance or pay-for-quality models. Through that lens, we observe investor interest primarily concentrated in three types:

Risk-bearing primary care groups enter value-based care contracts with payers with an aim to take over the accountable care within capitated payments, either on professional and physician services or on a member’s entire cost of care. In our experience, these providers often offer a higher-touch care model for a smaller patient panel than is typically seen in fee-for-service primary care. They spend more time with a smaller panel of patients than their fee-for-service peers, and they focus extensively on preventive care, condition management, and addressing patients’ social determinants of health. The past two to three years have seen a rise of at-scale risk-bearing groups with high valuations. They offer a proven investment rationale for sponsors—recent corrections in public valuations notwithstanding—with clear levers for growth, operational improvement, and multiple exit opportunities.
Value-based care MSOs have developed a compelling value proposition for independent primary- and specialty-care groups by facilitating the transition to risk through a combination of off-the-shelf tools and accompanying wraparound services, including payer contracting and practice transformation support. Successful MSOs can gain rapid scale when entering a new market, aggregating physicians and payer membership and quickly standing up risk-bearing entities or accountable-care organizations to take collective risk.
Risk-bearing specialty groups, while currently less prevalent than their primary care counterparts, are increasingly carving out medical-cost risk in value-based models tied to their specific procedures and conditions. Adoption varies considerably across specialties: orthopedics and nephrology were among the earliest adopters, and traction is emerging in cardiology (more on nephrology below). These groups can ultimately participate in a wide range of risk models, from episodic bundles to specialist subcapitation models that offer an analogue for global or population-level risk.

Scenario 3: Distinctive operational capabilities could become prerequisites for successful value-based care providers

As the market for value-based care providers has matured, public markets have driven market capitalization down substantially relative to the S&P 500 index, but with better results for those companies that have proven the ability to at least break even. Exhibit 5 shows trends over time.

Scrutiny may rise as investors become increasingly discerning about providers’ operational sophistication; providers that realize material savings will likely have clear and comprehensive clinical pathways that cover their members’ needs and a well-disciplined clinical staff immersed in a common approach to care delivery supported by analytical insights. Training clinicians in these models often takes time, which can influence the balance between the growth and operational performance of value-based care organizations. Further, the operational foresight necessary to weather a pandemic or other force majeures is expected to become increasingly important.

That said, market watchers might reasonably propose an array of factors that make this analysis imperfect—rebounding utilization in the third year of the COVID-19 pandemic, market volatility from interest rate changes and attendant investor speculation, and public market skepticism of special-purpose acquisition company valuations chief among them. The divergence in enterprise valuations may create consolidation opportunities that accelerate the emergence of the national platforms relevant to investors, as detailed above.

With a variety of value-based care platforms, dormant value may be achieved from foundational “blocking and tackling” in analytics applications. In our view, predictive and truly advanced analytics, including artificial intelligence and machine learning, 13 Solveigh Hieronimus, Jonathan Jenkins, and Angela Spatharou, “ Transforming healthcare with AI: The impact on the workforce and organizations ,” McKinsey, March 10, 2020. hold substantial promise, but they may not be prerequisites for success in medical-cost management. This reflects both the complexity of the data and the enormity of the analytics challenge—past efforts to predict utilization (particularly emergency department and hospital inpatient utilization) have yielded few actionable insights. But there may be other opportunities for the application of value-additive advanced analytics 14 Ankur Agrawal, Karl Kellner, Jay Krishnan, and Prashanth Reddy, “ How healthcare services and technology companies can boost productivity with data and analytics ,” McKinsey, January 29, 2021. in predicting membership changes; providers may succeed in identifying drivers of patient churn and apply these to their own data on a forward-looking basis, developing mitigating interventions accordingly. 15 Scott Dresden et al., “Predicting avoidable emergency department visits using the NHAMCS dataset.” AMIA Annual Symposium Proceedings Archive , May 23, 2022.

The path to value creation is likely to rest on analytics, standardized clinical practices and operational workflows, and a package of member and physician services designed to reduce medical costs by avoiding unnecessary (or unnecessarily high-cost) practices. From our experience working with value-based care providers, mature markets may be entering a transition in which the low-hanging fruit in operational and clinical performance improvement has largely been picked, as evidenced by the publicly reported performance of provider groups (Exhibit 6). 16 “Investor & Analyst Day Presentation,” Cano Health, March 4, 2021; Marlow Hernandez, “Redefining primary care to transform healthcare,” Cano Health, 2022 Investor Day presentation, June 7, 2022; “J.P. Morgan 2022 Virtual Healthcare Conference,” CareMax, January 13, 2022. This next wave of impact requires material capability building; many providers have already begun investing.

Scenario 4: Specialists may begin to adopt value-based care

Value-based care models have grown more intermittently among specialists than they have among primary care providers in recent years. 17 “ How providers can best confront the reality of value-based care ,” McKinsey, April 17, 2019. Across specialties, there has been a fundamental shift away from a predominantly utilization-management approach to specialty spend to one that aims to use analytics, care coordination, provider integration, and patient engagement to address avoidable spend more holistically. Two main models seem to be emerging:

The subcapitation model has been focused on specialties with high value at stake, predictable condition incidence, and clear value-creation levers under specialist control (for example, oncology care pathway choice, initiation of dialysis). In these models, specialty-specific spend is delegated to the risk-bearing entity, usually a benefit-management/care-management platform or a provider network. Either the payer or a primary care risk group can delegate this spend. Oncology, for example, has seen increased penetration of these models, 18 “COA letter to CMS and CMMI requesting extension of OCM,” Community Oncology Alliance, November 15, 2021; “Investor Presentation,” Oncology Institute of Hope and Innovation, November 2022. especially in markets where the presence of primary care risk delegation is high, with the risk-bearers generating medical cost savings mainly through the close management of specialty drug spend and the redirection of infusion to the highest-value clinically appropriate site of care.
Episode-based model adoption is higher among specialties with a higher prevalence of expensive, clearly defined episodes. Orthopedics, with its high-cost, highly “episodic” joint-replacement procedures, is perhaps the most notable example, 19 CMS Comprehensive Care for Joint Replacement Model: Performance year 4 evaluation report - Fourth annual report , Lewin Group, September 2021. but there is growing adoption in women’s health (for end-to-end maternity journeys), cardiology, and oncology.

Nephrology has seen the most accelerated adoption of value-based care models in recent years, supported by Centers for Medicare & Medicaid Services programs and rules (for example, coverage of end-stage renal disease [ESRD], launch of Kidney Care Choices), but this has occurred through structures that more closely resemble those of primary care. In emerging nephrology models, risk-bearers assume the risks for the total cost of care (versus specialty-spend only) for members with chronic kidney disease or ESRD. 20 Gaurav Jain and Daniel E. Weiner, “Value-based care in nephrology: The Kidney Care Choices model and other reforms,” Kidney360 , October 2021, Volume 2, Issue 10. Current reimbursement rates, cost-savings potential, and multiyear ownership of the patient journey make the model economically and operationally viable for nephrology. These value-based models are in relatively early stages of development, but we observe that nephrology providers adopting them report substantial reductions in hospital admissions, readmissions, and dialysis crashes, as well as widespread adoption of in-home dialysis, both improving outcomes and reducing the cost of care delivery. There are other specialties (for example, oncology and some segments of cardiology) for which the economics could be similarly feasible.

Overall, diverse risk-sharing models continue to grow in specialty care. Exhibit 7 lists some of our expectations by specialty. Episodic and condition-based capitation models should thrive as they continue to propel improved medical cost performance, as should specialty subcapitation models. Enabling and accelerating this trend, specialty provider MSOs are developing (or integrating with) specialty benefit-management solutions to take on more population-level risk. Investors could capture this value by acquiring practices, MSOs, or both. In each scenario, strong secular growth tailwinds across most geographies may bolster the investment thesis.

Investors may continue to look to value-based care for strong growth. With double-digit growth in the penetration of value-based care models, value-based care could continue to present a strong investment thesis—the “$1 trillion prize” in enterprise value that McKinsey described almost ten years ago. 21 Tom Latkovic, “ Claiming the $1 trillion prize in US health care ,” McKinsey, September 1, 2013.

These models hint at the possibility that by incentivizing improved patient outcomes and healthcare equity, value-based care players across the value chain (and the sponsors who back them) could continue to make gains. Competition will likely require operational effectiveness and differentiation, but whatever the approach may be, value-based care is a reality 22 “ How providers can best confront the reality of value-based care ,” McKinsey, April 17, 2019. with potential benefits for everyone from patients to clinicians to investors.

Zahy Abou-Atme is a partner in McKinsey’s New York office, Rob Alterman is an associate partner in the Philadelphia office, Gunjan Khanna is a senior partner in the Pittsburgh office, and Edward Levine, MD , is a senior partner in the Bay Area office.

The authors wish to thank Vamsee Gurram, Amit Kunte, Isaac Swaiman, and others for their contributions to this article.

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What does originality in research mean? A student's perspective
Aim: To provide a student's perspective of what it means to be original when undertaking a PhD. Background: A review of the literature related to the concept of originality in doctoral research highlights the subjective nature of the concept in academia. Although there is much literature that explores the issues concerning examiners' views of originality, there is little on students' perspectives.
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We propose to measure the originality of an individual scientific papers based on its cited papers (i.e., references) and citing papers (i.e. follow-on research). We draw on subsequent papers that cite the focal paper to evaluate whether the authors of these subsequent citing papers perceive the focal paper as an original source of knowledge ...
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As a starting point, we can take the three dimensions distinguished by Polanyi (1962): plausibility, originality, and scientific value. 5 In this view, good research is based on evidence and is scientifically sound (plausibility), it provides new knowledge (originality), and it has importance for other research (scientific value).
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This journal makes it a policy to not accept papers with high similarity indexes, even when it is not plagiarism, as a scientific paper should be original in all aspects of conducting the research and preparing the manuscript for publication in the literature. Therefore, all potential authors should keep the electronic CrossCheck tool in the ...
The Concept of Originality in Academic Research of Engineering
The definition of research appears to be a controversial subject in the academic community. It is almost becoming apparent that research remains a key element in master's and doctoral degrees. Originality is, to varying degrees, the primary concern for the genuineness of research, but there is a difference of opinion on the concept of originality. In practice, a subjective analysis of ...
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A classic postgraduate research project presents aims/hypotheses of a particular study, and then demonstrates arguments that clearly addresses these aims/hypotheses. Postgraduate research projects must demonstrate a degree of originality and analysis. This can cause anxiety as it is difficult to determine what constitutes original work.
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demonstrating critically how and in what way their. research makes a meaningful contribution to the. body of knowledge. Conclusion The concept of originality in the. PhD is complex and multi ...
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Originality in research mean what you are doing is from your own perspective although you may draw arguments from other research work to back up your arguments. -Not copying the writing styles of ...
What does originality in research mean? A student's perspective
Aim To provide a student's perspective of what it means to be original when undertaking a PhD.. Background A review of the literature related to the concept of originality in doctoral research highlights the subjective nature of the concept in academia. Although there is much literature that explores the issues concerning examiners' views of originality, there is little on students ...
Beyond originality in scientific research: Considering relations among
For the purpose of this paper, originality can be defined as a feature of a research project that is realized when it respects the values of academic integrity, on the one hand; and when it is capable of providing a scientific contribution with a transformational nature "not necessarily radical" from the existing paradigm, on the other ...
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Finally, discussing originality of scientific products leads to the conclusion that originality can appear in one or more of the following stuffs: 1 - Originality of topic: Originality of the topic may be one of the most apparent characteristic in research projects. It does not mean that the subject of any research project is absolutely novel.
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The study proposed a new method for assessing the originality of a paper based on the semantic network of its content. Compared with previous methods, our method developed a new way of knowledge combination to assess the originality of a paper, considering the paper's content and structure (i.e. research topics, research methodology and results).
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Tips for writing abstracts for conference papers. This is slightly different to writing a general abstract and in this scenario the abstract is likely to be written before the paper has been prepared. A few tips: Clarify in your own mind the purpose of the paper. Look at the themes of the conference and keep them in mind as you write.
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In a business context, innovation is the ability to conceive, develop, deliver, and scale new products, services, processes, and business models for customers. Successful innovation delivers net new growth that is substantial. As McKinsey senior partner Laura Furstenthal notes in an episode of the Inside the Strategy Room podcast, "However ...
What does originality in research mean? A student's perspective
Conclusion: Originality in the PhD is a subjective concept and is not the only consideration for examiners. Of comparable importance is the assessment of the student's ability to demonstrate ...
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Together Against Trafficking in Human Beings
Facts about trafficking in human beings. 37% of the victims of trafficking in the EU are EU citizens, and a significant number of them are trafficked within their own country. However, non-EU victims have increased in recent years and they now outnumber victims with an EU citizenship. The majority of victims in the EU are women and girls who ...

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Computing proposed originality measures

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Characterisation of originality measures

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