Quantitative metrics for assessing innovativeness are increasingly diverse and continually refined; however, a consensus on the definition of academic innovation has yet to be reached. To bridge the gap between incomplete conceptualization and effective operationalization, a reproducible approach for concept analysis is utilized to identify the antecedent, attributes, and consequences of academic innovation, thereby facilitating a comprehensive understanding. The results indicate that academic innovation originates from a new combination of explicit/tacit knowledge, is characterized by novelty, value, contextuality and cumulativeness, and leads to the creation and diffusion of knowledge, as well as the enhancement or transformation of existing paradigms. Our definition of academic innovation is further differentiated from commonly-confused terms to clarify its boundaries, providing a theoretical foundation for reliable measurement.
Innovation is fundamental to the progress and dynamism of academic research. Evaluating the
innovation of academic papers in a comprehensive, objective, and reasonable manner is crucial
from both management and policy perspectives [
However, to the best of our knowledge, there is no consensus about what academic innovation actually means, resulting in a lack of proper conceptualization to guide accurate and complete operationalization. Moreover, innovation and its related terms, such as novelty and breakthrough, are sometimes employed interchangeably in a single paper, which potentially causes ambiguity in the argumentation or impedes the valid dissemination and application of indicators. Targeting the above problems, we adhere to the standards of Concept Analysis (CA) for conceptual clarification of academic innovation in a rigorous and reproducible way. The objective of this study can be further broken down into two aspects:
Previous studies primarily conceptualize innovation from three perspectives: as a process [
In terms of methodology, current definitions of innovation are principally derived through
inductive reasoning based on empirical cases or by reconstructing existing concepts. Through an
inductive analysis of extensive co-citation patterns, Uzzi [12] argued that high-impact innovation
was grounded in balancing atypical combination with conventional knowledge. In contrast,
concept reconstruction entails gathering, reconciling, and reorganizing prior definitions into a
cohesive one. For instance, Quintane et al. [
This study employs Rodgers’ concept analysis [15], a methodological framework originally developed in nursing scholarship and increasingly applied within Library and Information Science (LIS) research in recent years [16]-[17][18][19]. This inductive approach facilitates the exploration and development of concepts in a given context, offering deeper insights rather than seeking definitive conclusions. Following the systematic framework of CA, we first select an appropriate “realm” for collecting literature related to innovation. After assembling and screening the search results, we examine each application of academic innovation at a detailed “line-by-line” level to extract key phrases and group semantically-similar ones into separate themes. Subsequently, these themes are categorized as either antecedents, attributes, or consequences of academic innovation.
To guarantee a comprehensive analysis, two rounds of literature retrieval are conducted. Firstly, we confine our search scope to core journals and conference proceedings in the LIS field. The topic “academic innovation” is used to retrieve records and references of the included articles are backward-tracked. Given that economic research paid earlier attention to building innovation theory and establishing its foundational concepts [20], a second round of literature retrieval is performed in the Web of Science and Scopus databases without restrictions on research areas, during which search terms are iteratively refined and supplemented to avoid omitting potential articles.
A total of 4,797 records are identified through the literature search, with 3876 retained after removing duplicates. Articles are included in our analysis if they meet the following criteria: 1) published in a peer-reviewed journal or conference; 2) written in English; 3) discuss the concept of “innovation” or “academic innovation”. The process of data collection and concept analysis is illustrated in Figure 1.
As emphasized by Kuhn [21], advances in science entail challenging, revising, expanding, or recombining elements of current knowledge. These knowledge elements essentially refer to explicit knowledge that can be clearly represented, systematically codified and easily disseminated [22], which mainly involve concepts, theories, questions, methods, facts, models, and findings. However, knowledge itself can exist in both explicit and tacit forms. Tacit knowledge is deeply embedded in individuals and difficult to formalize or articulate [23], such as intuition or experiences. If we consider knowledge in its broader sense, the antecedent of academic innovation can be extended to a new combination of knowledge, which aligns with the concept of “recombinant search as the source of novelty,” as advocated by Schumpeter [24] and Fleming [25].
The new combinations of knowledge can be divided into homogeneous and heterogeneous types, corresponding to the Cha-Cha-Cha theory [26]. Specifically, new combinations within explicit knowledge are associated with the “Charge” category, where the focus is on solving clear problems using known knowledge in new ways. New combinations between explicit and tacit knowledge fit into the “Challenge” category, involving deliberate integration to resolve inconsistencies or explain anomalies. Combinations within tacit knowledge (that finally transform into original explicit knowledge) fall under the “Chance” category, as they often result from serendipitous discoveries made by scientists with a “prepared mind.” Analogous to technological innovation, academic innovation can be viewed as a problem-solving process in some cases [27]. Following this perspective, Luo et al. [28] calculated the semantic similarity of question-method combinations to measure the innovation of publications.
Novelty and Value: Novelty is a fundamental and essential feature of any innovation [
Contextuality and Cumulativeness: Apart from novelty and value, academic innovation
inherently exhibits both contextual dependency and cumulative progression. The absorption and
dissemination of scientific knowledge vary with specific temporal and spatial contexts, domain
characteristics, and societal needs [34]. This difference means that academic innovation does not
occur in isolation but engages in complex interactions with various surrounding factors [35],
ultimately causing it to take diverse forms across periods and fields. For example, innovation
research has evolved from being driven by economic traditions to a stage where management
theories gain prominence and ultimately take the lead [36]. Besides, the innovation process is
argued to be continuously cumulative in both temporal and spatial dimensions [
From the perspective of its impact on established paradigms, academic innovation either leads to enhancement or a complete transformation [21], [39]. The transition from the overthrow of an old paradigm to the emergence of a new one can be further divided into two pathways. The first involves disrupting the existing paradigm and reshaping it into a new paradigm [40]-[41][42]. For example, the shift from classical mechanics to quantum mechanics entailed breaking down the previous framework of understanding, incorporating quantum concepts while retaining certain elements of classical physics where applicable. The second involves creating a completely new paradigm that is incompatible with the existing one and eventually replaces it [43]. For instance, the development of the heliocentric model by Copernicus supplanted the geocentric model, introducing a radically new way of understanding planetary motion that was entirely distinct from the earlier view.
A considerable amount of research on innovation-driven economy has empirically confirmed the knowledge spillover effect of universities’ innovative outcomes on local firms’ innovation [44][45]. From the perspective of academic research, these findings imply that the consequence of innovation can be the creation of new explicit knowledge, the diffusion and dissemination of existing knowledge, or a combination of both. Such knowledge creation and diffusion is triggered by decisions on which (explicit/tacit) knowledge to recombine (i.e., the antecedent of academic innovation) [27]. Note that flows of knowledge can take place within or across organizational, disciplinary, or national boundaries, and eventually form a scientific innovation network. Drawing on this viewpoint, several studies construct collaboration or citation networks to explore the inter-community knowledge diffusion and subsequently evaluate academic innovation [46]-[47][48].
Based on the discussions above, our concept analysis of academic innovation yields the following definition: academic innovation originates from a new combination of (explicit/tacit) knowledge, which initiates flows of knowledge and leads to the creation and diffusion of knowledge. Simultaneously, it contributes to either the enhancement of existing paradigms or the emergence of a new one. This creative process exhibits a cumulative nature but varies with specific context, emphasizing both novelty and value, regardless of how they may manifest.
As seen in Figure 2, our proposed definition provides multiple dimensions for understanding academic innovation (covering the antecedent, attributes, and consequences), making it possible to compare innovation with its overlapping concepts in groups.
Originality, novelty versus Innovation: A new combination of knowledge serves as the prerequisite for all three concepts, whereas originality and novelty tend to prioritize the “new” aspect without imposing a strict requirement for value [49]-[50]. Originality is defined as the extent to which a scientific discovery contributes unique knowledge that is absent in prior studies [51]. It embodies the advancement of taking the first step into an unexplored area (from zero to one) [52], with tacit knowledge as a core component in the combination process. From a resultsoriented standpoint, original outcomes are unexpected and surprising [53], which can spark pioneering ideas to stimulate further innovation. By comparison, novelty may also arise through an unusual combination of pre-existing explicit knowledge [25] without necessarily delving into unknown territories or obtaining surprising findings.
Disruption, breakthrough versus innovation: Innovation is the broadest concept among them, the consequences of which encompass incremental improvements and radical transformations [54]. Disruption and breakthrough are two distinct types of innovation, both bringing about changes to scientific paradigms [55]-[56][57]. In contrast to incremental innovation, disruption refers to innovative research that destabilizes established knowledge [58] and renders previous knowledge obsolete [59]. Breakthroughs, on the other hand, are high-value, high-quality innovations that overcome significant obstacles and provide foundational knowledge for future developments [60], whose impact can even be observed in a short time [61]. It is worth noting that breakthroughs are not exclusively associated with radical paradigm shifts; they may originate from prior incremental innovations and can be competence-enhancing as well [62].
This paper adopts concept analysis to define academic innovation in a heuristic and inductive manner. Innovation-related studies in the LIS realm are compared and synthesized, supplemented by literature from other fields on broader concepts of close association. On this basis, we elaborate the connotation of academic innovation from three perspectives: antecedents (i.e., a new combination of explicit/tacit knowledge), attributes (i.e., novelty and value, contextuality and cumulativeness), as well as consequences (i.e., the creation and diffusion of knowledge, and the enhancement or transformation of existing paradigms). Moreover, our definition of academic innovation is further distinguished from commonly-confused terms to clarify its boundaries, providing a valuable reference for the construction and refinement of quantitative indicators.
Our preliminary exploration seeks to understand the meaning of innovation in an academic context with a reproducible method. In fact, conceptualization and operationalization create a dynamic relationship where each step continuously shapes the other to ensure both theoretical clarity and practical measurability. In the future, we will systematically review existing metrics for measuring innovation and its sub-dimensions, so as to expound on the linkages between the definitions that are used and the indicators that are created.
This paper is supported by the National Social Science Fund of China (Grant No. 21&ZD329). The paper is presented at the second Workshop on “Innovation Measurement for Scientific Communication (IMSC) in the Era of Big Data” at 2024 ACM/IEEE Joint Conference on Digital Libraries (JCDL).
[12] B. Uzzi, S. Mukherjee, M. Stringer, B. Jones, Atypical combinations and scientific impact,
Science 342 (2013) 468-472. doi:10.1126/science.1240474. [13] R. M. Henderson, K. B. Clark, Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms, Administrative Science Quarterly 35 (1990) 9-30. doi:10.2307/2393549. [14] L. Wu, D. Wang, J. A. Evans, Large teams develop and small teams disrupt science and technology, Nature 566 (2019) 378-382. doi:10.1038/s41586-019-0941-9. [15] B. L. Rodgers, Concepts, analysis and the development of nursing knowledge: the evolutionary cycle, Journal of Advanced Nursing 14 (1989) 330-335. doi:10.1111/j.1365-2648.1989.tb03420.x. [16] R. A. Fleming-May, Concept analysis for library and information science: Exploring usage,
Library & Information Science Research 36 (2014) 203-210. doi:10.1016/j.lisr.2014.05.001. [17] L. Yu, Y. Liu, Information experience as an object of LIS research: a definition based on concept analysis, Journal of Documentation 78 (2022) 1487-1508. doi:10.1108/jd-10-2021-0195. [18] R. Fleming-May, Scholarly communication: a concept analysis, Journal of Documentation 79 (2023) 1182-1208. doi:10.1108/jd-09-2022-0197. [19] M. A. Belabbes, I. Ruthven, Y. Moshfeghi, D. Rasmussen Pennington, Information overload: a concept analysis, Journal of Documentation 79 (2023) 144-159. doi:10.1108/jd-06-2021-0118. [20] J. A. Schumpeter, R. Swedberg, The Theory of Economic Development, Routledge, 2021. [21] T. S. Kuhn, I. Hacking, The Structure of Scientific Revolutions, 50th anniversary ed., University of Chicago Press, 2012. [22] Z. Dienes, J. Perner, A theory of implicit and explicit knowledge, Behavioral and Brain
Sciences 22 (1999) 735-808. doi:10.1017/S0140525X99002186. [23] V. Ambrosini, C. Bowman, Tacit knowledge: Some suggestions for operationalization, Journal of Management Studies 38 (2001) 811-829. doi:10.1111/1467-6486.00260. [24] J. Schumpeter, Business Cycles, McGraw-Hill Book Company, New York, 1939. [25] L. Fleming, Recombinant uncertainty in technological search, Management Science 47 (2001) 117-132. doi:10.1287/mnsc.47.1.117.10671. [26] D. E. Koshland Jr, The cha-cha-cha theory of scientific discovery, Science 317 (2007) 761-762.
doi:10.1126/science.1147166. [27] X. Liu, S. Jiang, H. Chen, C. A. Larson, M. C. Roco, Modeling knowledge diffusion in scientific innovation networks: an institutional comparison between China and US with illustration for nanotechnology, Scientometrics 105 (2015) 1953-1984. doi:10.1007/s11192-015-1761-9. [28] Z. Luo, W. Lu, J. He, Y. Wang, Combination of research questions and methods: A new measurement of scientific novelty, Journal of Informetrics 16 (2022) 101282. doi:10.1016/j.joi.2022.101282. [29] L. Zuo, G. J. Fisher, Z. Yang, Organizational learning and technological innovation: the distinct dimensions of novelty and meaningfulness that impact firm performance, Journal of the Academy of Marketing Science 47 (2019) 1166-1183. doi:10.1007/s11747-019-00633-1. [30] K. Urabe, Innovation and the Japanese Management System, in: Innovation and Management
International Comparison, Walter de Gruyter, 1988. [31] B. P. Costanzo, L. E. Sánchez, Innovation in impact assessment theory and practice: How is it captured in the literature?, Environmental Impact Assessment Review 79 (2019) 106289. doi:10.1016/j.eiar.2019.106289. [32] I. Stiller, A. van Witteloostuijn, B. Cambré, Do current radical innovation measures actually measure radical drug innovation?, Scientometrics 126 (2021) 1049-1078. doi:10.1007/ s11192020-03778-x. [33] D. M. Marchiori, S. Popadiuk, E. W. Mainardes, R. G. Rodrigues, Innovativeness: a bibliometric vision of the conceptual and intellectual structures and the past and future research directions, Scientometrics 126 (2021) 55-92. doi:10.1007/s11192-020-03753-6. [34] H. W. Park, J. Kang, Patterns of scientific and technological knowledge flows based on scientific papers and patents, Scientometrics 81 (2009) 811-820. doi:10.1007/s11192-008-2224-3. 12 [35] M. Uriona-Maldonado, R. N. M. dos Santos, G. Varvakis, State of the art on the Systems of Innovation research: a bibliometrics study up to 2009, Scientometrics 91 (2012) 977-996. doi:10. 1007/s11192-012-0653-5. [36] S. Muhammad, Thinking inside the box? Intellectual structure of the knowledge base of innovation research (1988–2008), Strategic Management Journal 34.1 (2013) 62-93. doi:10. 1002/smj.2002. [37] B. P. Cozzarin, Performance measures for the socio-economic impact of government spending on R&D, Scientometrics 68 (2006) 41-71. doi:10.1007/s11192-006-0083-3. [38] W. M. Cohen, D. A. Levinthal, Absorptive capacity: A new perspective on learning and innovation, Administrative Science Quarterly 35 (1990) 128-152. doi:10.4324/978008051 7889-9. [39] G. Dosi, Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change, Research Policy 11 (1982) 147-162. doi:10.1016/0048-7333(82)90016-6. [40] C. J. Wang, L. Yan, H. Cui, Unpacking the essential tension of knowledge recombination: Analyzing the impact of knowledge spanning on citation impact and disruptive innovation, Journal of Informetrics 17 (2023) 101451. doi:10.1016/j.joi.2023.101451. [41] D. Li, J. Lin, W. Cui, Y. Qian, The trade-off between knowledge exploration and exploitation in technological innovation, Journal of Knowledge Management 22 (2018) 781-801. doi:10.1108/jkm-09-2016-0401. [42] H. Wang, L. J. Zheng, J. Z. Zhang, A. Kumar, P. R. Srivastava, Unpacking complementarity in innovation ecosystems: A configurational analysis of knowledge transfer for achieving breakthrough innovation, Technological Forecasting and Social Change 198 (2024) 122974. doi:10.1016/j.techfore.2023.122974. [43] D. Shapere, The structure of scientific revolutions, The Philosophical Review 73 (1964) 383394. doi:10.2307/2183664. [44] A. K. Agrawal, University‐to‐industry knowledge transfer: Literature review and unanswered questions, International Journal of Management Reviews 3 (2001) 285-302. doi:10.1111/14682370.00069. [45] S. Qiu, X. Liu, T. Gao, Do emerging countries prefer local knowledge or distant knowledge? Spillover effect of university collaborations on local firms, Research Policy 46 (2017) 1299-1311. doi:10.1016/j.respol.2017.06.001. [46] J. J. Winnink, R. J. W. Tijssen, A. F. J. Van Raan, Searching for new breakthroughs in science: How effective are computerised detection algorithms?, Technological Forecasting and Social Change 146 (2019) 673-686. doi:10.1016/j.techfore.2018.05.018. [47] C. Min, Y. Bu, J. Sun, Predicting scientific breakthroughs based on knowledge structure variations, Technological Forecasting and Social Change 164 (2021) 120502. doi:10.1016/j.techfore.2020.120502. [48] Y. Wang, L. Fan, L. Wu, A validation test of the Uzzi et al. novelty measure of innovation and applications to collaboration patterns between institutions, Scientometrics 129 (2024) 43794394. doi:10.1007/s11192-024-05071-7. [49] S. Shibayama, J. Wang, Measuring originality in science, Scientometrics 122 (2020) 409-427.
doi:10.1007/s11192-019-03263-0. [50] M. Fontana, M. Iori, F. Montobbio, R. Sinatra, New and atypical combinations: An assessment of novelty and interdisciplinarity, Research Policy 49 (2020) 104063. doi:10.1016/ j.respol.2020.104063. [51] J. Hou, D. Wang, J. Li, A new method for measuring the originality of academic articles based on knowledge units in semantic networks, Journal of Informetrics 16 (2022) 101306. doi:10.1016/j.joi.2022.101306. [52] A. Zeng, Z. Shen, J. Zhou, J. Wu, Y. Fan, Y. Wang, H. E. Stanley, The science of science: From the perspective of complex systems, Physics Reports 714 (2017) 1-73. doi:10.1016/j.physrep. 2017.10.001. [53] T. Heinze, Creative accomplishments in science: Definition, theoretical considerations, examples from science history, and bibliometric findings, Scientometrics 95 (2013) 927-940. doi:10.1007/s11192-012-0848-9. [54] R. D. Dewar, J. E. Dutton, The adoption of radical and incremental innovations: An empirical analysis, Management Science 32 (1986) 1422-1433. doi:10.1287/mnsc.32.11.1422. [55] L. Leydesdorff, L. Bornmann, Disruption indices and their calculation using web-of-science data: Indicators of historical developments or evolutionary dynamics?, Journal of Informetrics 15 (2021) 101219. doi:10.2139/ssrn.3777071. [56] S. Fortunato, C. T. Bergstrom, K. Börner, J. A. Evans, D. Helbing, S. Milojević, A. M. Petersen,
Science of science, Science 359 (2018) eaao0185. doi:10.1126/science.aao0185. [57] C. Min, Y. Bu, J. Sun, Predicting scientific breakthroughs based on knowledge structure variations, Technological Forecasting and Social Change 164 (2021) 120502. doi:10.1016/j.techfore.2020.120502. [58] D. Lyu, K. Gong, X. Ruan, Y. Cheng, J. Li, Does research collaboration influence the "disruption" of articles? Evidence from neurosciences, Scientometrics 126 (2021) 287-303. doi:10.1007/s11192-020-03757-2. [59] C. Leibel, L. Bornmann, What do we know about the disruption index in scientometrics? An overview of the literature, Scientometrics 129 (2024) 601-639. doi:10.1007/s11192-023-04873-5. [60] A. Phene, K. Fladmoe‐Lindquist, L. Marsh, Breakthrough innovations in the US biotechnology industry: the effects of technological space and geographic origin, Strategic Management Journal 27 (2006) 369-388. doi:10.1002/smj.522. [61] J. W. Schneider, R. Costas, Identifying potential "breakthrough" publications using refined citation analyses: Three related explorative approaches, Journal of the Association for Information Science and Technology 68 (2017) 709-723. doi:10.1002/asi.23695. [62] A. A. Datta, S. Srivastava, (Re)conceptualizing technological breakthrough innovation: A systematic review of the literature and proposed framework, Technological Forecasting and Social Change 194 (2023) 122740. doi:10.1016/j.techfore.2023.122740.