Tracking the literature on strategic alliances in the biotechnology industry: Insights from a bibliometric approach over the last 30 years

Carvajal-Camperos, Marisol; Almodóvar, Paloma Article Tracking the literature on strategic alliances in the biotechnology industry: Insights from a bibliometric approach over the last 30 years European Journal of Management and Business Economics (EJM&BE) Provided in Cooperation with: European Academy of Management and Business Economics (AEDEM), Vigo (Pontevedra) Suggested Citation: Carvajal-Camperos, Marisol; Almodóvar, Paloma (2025) : Tracking the literature on strategic alliances in the biotechnology industry: Insights from a bibliometric approach over the last 30 years, European Journal of Management and Business Economics (EJM&BE), ISSN 2444-8451, Emerald, Leeds, Vol. 34, Iss. 2, pp. 245-262, https://doi.org/10.1108/EJMBE-07-2022-0215 This Version is available at: https://hdl.handle.net/10419/325596 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Tracking the literature on strategic alliances in the biotechnology industry: insights from a bibliometric approach over the last 30 years Marisol Carvajal-Camperos Department of Business Economics, Universidad Rey Juan Carlos –Campus de Vicalvaro, Madrid, Spain, and Paloma Almod ovar Department of Business Organisation, Complutense University of Madrid, Madrid, Spain Abstract Purpose –The purpose of this study is to identify papers that have produced the most significant impact on research on strategic alliances in the biotechnology industry. The authors attempt to illustrate the thematic evolution of its intellectual structure through 616 papers published between 1992 and 2021. Design/methodology/approach –The present research methodology relies on three distinct techniques, implemented using SciMat software: (1) bibliometric techniques, (2) scientific map analysis and (3) content analysis of research documents from the Web of Science (WoS). In this manner, the authors analyse the intellectual structure of the field of strategic alliances in the biotechnology industry, tracking its evolution over a period of three decades. Findings –The study emphasises the relevance of “innovation”as a key theme and identifies several potential areas for future research, which could serve as a foundation for further investigations. Originality/value –This studyrepresents a novel contributionto the literature as it is the first to use the SciMat tool to analyse strategic alliances in the biotechnology industry. This research reveals that while strategic alliances have been assessed extensively across various industries, some topics, such as the types and formation of alliances, have not been specifically studied in the biotechnology industry. These areas as well as the barriers and variables influencing the formation of alliances offer promising avenues for future research in this field. Keywords Strategic alliance, Biotechnology industry, Bibliometric analysis, Scientific mapping, Co-word analysis Paper type Research paper 1. Introduction A strategic alliance is characterised as a fluid mode of external expansion that entails two or more entities operating with legal and economic autonomy and without any financial obligations or hierarchical links. The underlying goal of such a partnership is to fulfil mutual objectives that may be difficult to achieve independently, such as enhancing competitive advantage, generating value and fostering synergies, by means of a time-limited contractual arrangement that governs the involvement of the participants (Carvajal-Camperos et al., 2021, Strategic alliances in the biotechnology industry 245 © Marisol Carvajal-Camperos and Paloma Almod ovar. Published in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http:// creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2444-8494.htm Received 11 July 2022 Revised 17 March 2023 Accepted 7 May 2023 European Journal of Management and Business Economics Vol. 34 No. 2, 2025 pp. 245-262 Emerald Publishing Limited e-ISSN: 2444-8494 p-ISSN: 2444-8451 DOI 10.1108/EJMBE-07-2022-0215 p. 303). In the biotechnology industry, strategic alliances are particularly important as they provide companies with access to new technologies that would otherwise be challenging to acquire due to the high costs and high risk of knowledge appropriation associated with this industry. Strategic alliances are instrumental in driving scientific, technological and innovative developments, which in turn foster sustainable economic growth, ultimately resulting in long-term success (Bengoa et al., 2021). The biotechnology industry is academically relevant due to its pivotal position in contributing to the national gross domestic product and generating job opportunities on a global scale. The substantial tax revenues generated by this industry further emphasise its importance. Recent events, such as the COVID-19 pandemic, have highlighted the critical role the biotechnology sector plays in safeguarding human lives. Notably, biotechnology accounts for seven out of 10 drugs currently in development worldwide. Additionally, this industry plays a key role in facilitating the transition to sustainable practices, such as the adoption of eco-friendly agricultural methods and the promotion of a bioeconomy. These efforts have contributed to an 18.4% reduction in the Environmental Impact Quotient and the conservation of water resources in crops (AseBio, 2021). The biotechnology industry is primarily composed of smalland medium-sized enterprises, except for pharmaceutical corporations (Leu, 2022). This sector is responsible for generating numerous ground-breaking innovations globally, which have propelled market growth. The biotechnology sector is also making significant contributions to the attainment of goal 17 of the 2030 Agenda for Sustainable Development (Agenda 2030, 2020). As Carvajal-Camperos et al. (2021, p. 304) stated, strategic alliances have become fundamental to companies’competitive strategies as such partnerships facilitate the achievement of objectives that would otherwise be challenging to accomplish. Alliances present an opportunity for companies, particularly those in the biotechnology industry, to improve their capabilities in innovation, learning and training; to enhance their agility in responding to market demands; to optimise their efficiency and to distribute investment risks with their partnering companies (Lange and Wagner, 2021). However, there is a high risk of opportunism when partners want to leave the alliance, especially in research and development and innovation (R&D&i) activities (Palomeras and Wehrheim, 2021; Robinson and Stuart, 2007;Spieth et al., 2021). According to Chord aet al. (2007, p. 109), biotechnology is considered an emerging sector of advanced technology and is currently in the initial stages of its advancement, with no apparent limits to its potential growth. The authors also highlighted that biomedicine was the fastest-growing area of the sector as it was closely linked to the pharmaceutical industry. Thus, the utilisation of scientific discoveries and advancements for both commercial and public purposes is contingent upon entrepreneurs’initiatives to establish novel technologyoriented enterprises (Segers, 2015;Subramanian et al., 2018). In the biotechnology industry, strategic alliances face two primary hurdles. The first is the partners’ability to acquire current knowledge that is associated with intellectual property, and the second is funding (both public and private) to secure the necessary resources to undertake research projects and develop new products and processes that meet the needs of customers in an increasingly competitive and global market (Gilding et al., 2020). Researchers are interested in numerous areas of study in the field of strategic alliances in the biotechnology sector, but many topics remain to be developed; therefore, objective criteria must be used to evaluate the research that has been conducted to date, analyse its evolution and trends and identify gaps in the literature. To achieve these goals, we use bibliometric techniques: first, citation analysis to demonstrate the potential of the topic of strategic alliances; second, scientific mapping to study the evolution of this area of research and third, content analysis both to deepen the themes developed in the literature and to shed light on the topics that should be studied further. We then analyse the scientific output with SciMAT software (Casc onKatchadourian et al., 2020;Cobo Mart ın, 2012;Cobo et al., 2015;Montero et al., 2018). EJMBE 34,2 246 The literature review showsthat the scientific field of strategic allianceshas experienced vast growth over the years, surpassing even some established scientific areas; however, this growth has not occurred in the biotechnology sector. Our research has two main objectives. The first is to analyse the evolution of strategic alliances in the biotechnology industry from 1992 to 2021 and to illustrate the intellectual structure of this discipline by identifying the key themes and theories that have attracted the attention of the research community. The second is to identify areas that must still be developed, and we hence define some avenues for future research. Based on the literature review carried out in this research, our study is one of the first to apply bibliometric techniques to strategic alliances in the biotechnology industry over the last 30 years using SciMAT software. Prior to this study, there was a dearth of scholarly works that examined the intellectual framework and theoretical foundations of this area. This gap in research underscores the importance of the present work, which enhances the existing body of knowledge on strategic alliances in the biotechnology industry in particular. The remainder of the paper is structured as follows: Section 1 offers an introduction to the topic at hand. Section 2 outlines the methodology employed in the research. Section 3 presents the findings generated through the use of SciMAT, and it details the primary issues, theories and research gaps concerning strategic alliances within the biotechnology industry. Section 4 offers an integrative analysis of the scientific mapping results. Finally, Section 5 provides conclusive remarks on the research conducted, along with its limitations. 2. Research design and methodology The extant literature offers various approaches to conducting a systematic literature review. Some scholars advocate for a traditional (manual) theoretical review, in which the researcher uses their expertise to select relevant documents and conduct a content analysis to derive insights about the state of knowledge in the field (Carvajal-Camperos et al., 2021;Gilal et al., 2019;Paul and Rosado-Serrano, 2019). Other scholars employ a meta-analysis approach that combines statistical results from multiple studies to identify patterns that may not be apparent from individual studies alone (Garc ıa Cruz and Ram ırez Correa, 2004;Gomes et al., 2016;Knoll and Matthes, 2017;Mariano et al., 2012). Bibliometric analyses that systematically examine bibliographic data, such as publication and citation counts, are also utilised to gain insights into various aspects of scholarly communication (Albort-Morant and RibeiroSoriano, 2016;Chatterjee and Sahasranamam, 2018;Dabi cet al., 2020;Fakhar et al., 2020). Moreover, some researchers undertake in-depth hybrid analyses that combine bibliometric and content analysis (de Diego and Almod ovar, 2021;Rodr ıguez-Ruiz et al., 2019). For the present study, we selected a hybrid approach to analyse the conceptual structure of strategic alliances in the biotechnology industry and to identify future research directions through the use of bibliometrics and content analysis. In line with other literature reviews, we selected scholarly papers published in the main databases of the Web of Science (WoS) platform as our unit of analysis. The WoS provides access to a vast array of peer-reviewed bibliographic databases, thus ensuring the high quality of the published papers (Bouckenooghe et al., 2021;Fernandes et al., 2022;Ferreira et al., 2022;Klarin and Suseno, 2021;Xu et al., 2021;Zhang et al., 2021). We specifically focused on the Science Citation Index Expanded and Social Sciences Citation Index databases, which are related to our research topic. We based our research on Cobo et al. (2011) methodology, which involved using SciMAT (Cobo et al., 2012;Cobo et al., 2012;Cobo et al., 2015). This tool is based on the h-index and coword analysis (Callon et al., 1983;Chen et al., 2016;G alvez, 2018;Leung et al., 2017). This methodology allowed us to visualise the behavioural subdomains of a specific area of research over a range of time. Specifically, we used the SciMAT workflow (Cobo et al., 2012, p. 70), which includes four stages that are developed sequentially, as indicated in Figure 1. Strategic alliances in the biotechnology industry 247 Topic detection was based on the analysis of bibliometric performance indicators through published papers and citations received (Brand~ ao, 2019;de Diego and Almod ovar, 2021; Egghe and Rousseau, 2020;Hu et al., 2020). In the WoS search, we used the terminology identified by Carvajal-Camperos et al. (2021, p. 293) regarding the concept of “strategic alliance”(e.g. “alliance”,“cooperation”,“coalition”, “joint venture”,“joint action”and “bilateral agreement”, along with derivations thereof). In addition, the search was limited to biotechnology industry publications and refined by “management”or “business”categories. Finally, we only included published academic papers and reviews because they are considered “certified knowledge”(Fernandez-Alles and Ramos-Rodr ıguez, 2009;Ramos-Rodr ıguez and Ru ız-Navarro, 2004). These conditions ensured a high level of data quality (Fernandez-Alles and Ramos-Rodr ıguez, 2009;RamosRodr ıguez and Ru ız-Navarro, 2004). The data were obtained from the WoS on 1 June 2021. The search yielded 772 articles, and we screened abstracts and manuscripts to discard papers that did not meet the required criteria. A total of 616 papers met the search equation. Figure 2 shows the evolution of these publications and their citations on strategic alliances in the biotechnology industry by year. This allowed us to study the maturity of the topic over a period of almost 30 years. The present study involved exporting 616 articles to the SciMAT software for further analysis. To ensure the accuracy and reliability of the data, we comprehensively refined the set of keywords. This involved consolidating keywords that conveyed similar meanings (e.g. singular and plural forms) and eliminating terms with vague or overly general connotations that failed to contribute substantively to the analysis (such as “research”or “study”). The initial set of 1,946 keywords was subsequently reduced to 992 through a Figure 1. Workflow of the steps of the methodology Figure 2. Evolution of publications and citations in WoS on strategic alliances in the biotechnology industry EJMBE 34,2 248 process of manual filtering. The temporal scope of the study spanned 30 years, from 1 January 1992 to 1 June 2021. Following the approach of Cobo et al. (2011), we divided the 30-year time frame into five consecutive periods, each spanning 6 years. The distribution of the number of documents per period is presented in Figure 3. Once we created the periods, we analysed the data following the methodology outlined by Cobo Mart ın (2011, p. 152). At the end of the 10 steps, we analysed the scientific maps showing two types of visualisations: longitudinal and by period. In the longitudinal visualisation, we present the evolution maps, which identify the development of the themes over the periods, as well as their continuity or disappearance. In the period visualisation, we show detailed information from the results obtained in each period and present strategic diagram. Figure 4 illustrates the implications arising from the distribution of topics across the four quadrants. 22 71 157 197 162 0 50 100 150 200 250 Period 1 (1992-1997) Period 2 (1998-2003) Period 3 (2004-2009) Period 4 (2010-2015) Period 5 (2016-2021) Documents Source(s): Authors’ own elaboration Quadrant 2 (Q2): High density, but low centrality. Themes are highly specialised but are peripheral themes. These themes are well developed internally but play a marginal role in the development of the scientific field. Quadrant 1 (Q1): High centrality and density. These are the driving themes. Here are the developing themes that are important for the construction of the scientific field. Quadrant 3 (Q3): Low density and centrality. These are the topics that disappear. They are topics of low scientific interest, with an upward or downward trend. Quadrant 4 (Q4): Low density, but high centrality. These are cross-cutting and general themes. They are stable themes but are not very developed. They are important for the development of the scientific field. Source(s): Authors’ own elaboration Figure 3. Number of documents per period Figure 4. Strategic diagram of research themes Strategic alliances in the biotechnology industry 249 3. Analysis of the scientific map of strategic alliances in the biotechnology industry Figure 5 displays the thematic evolution of the research field over the five periods. In the first period, research on strategic alliances focuses on a single topic, namely “innovation”in the biotechnology industry. As time progresses, research on “innovation”is maintained, and “transaction costs”theory (TCT) and different approaches to the resource-based view (RBV; “dynamic capabilities”and “capabilities”) come into play. In addition, the specific case of “networks”,“technological discontinuities”and new topics related to corporate finance (“investments”and “public offerings”) are addressed. In the third and fourth periods, there is a large proliferation of different topics, with 12 and 13 topics of high relevance, respectively. In the fifth period, there is a reduction in the number of topics analysed, indicating that academics focus their research on 10 specific sub-areas. A noteworthy observation pertains to the enduring prominence of the “innovation”theme, which has garnered sustained research attention over time, with varied facets of this theme being investigated directly or indirectly. The biotechnology industry poses unique challenges, as companies operating within this sphere require rapid and secure knowledge generation despite typically being comprised of small or medium-sized entities with limited resources, negotiating skills and project management experience and possessing a limited or scarce client portfolio. Consequently, extant literature has focused considerably on examining the benefits and drawbacks of collaborative R&D&i processes involving external organisations. For a specific analysis of the themes developed in each of the periods, it is necessary to design strategic diagrams. Figure 5. Evolution of the longitudinal map of strategic alliances in the biotechnology industry EJMBE 34,2 250 3.1 Strategy diagram analysis: period 1 (1992–1997) The strategic diagram presented in Figure 6 pertains to Period 1 and provides an overview of the single cluster that characterised this period, along with its corresponding performance indicators, including centrality/density values, h-index and the number of citations. Notably, this diagram highlights the fact that strategic alliances within the biotechnology industry revolve around a singular theme, namely “innovation”. This driving theme is characterised by a centrality value of 1, a density value of 31.18, an h-index of 12 and 5,832 citations. This finding suggests that innovation is a central and highly valued aspect of strategic alliances within the biotechnology industry during this period. 3.2 Strategy diagram analysis: period 2 (1998–2003) In Period 2 (1998–2003), the strategic diagram indicates that strategic alliances in the biotechnology industry revolve around six research themes. Figure 7 shows the strategic diagram for the period and the groupings with their performance indicators. The cluster “innovation,”located in the Q1 quadrant, has the following indicators: a centrality of 128.89, a density of 37.86, an h-index indicator of 41 and 19,161 citations. From this, it can be inferred that innovation is a driving theme, of great interest to researchers and important for the structure of strategic alliances in the biotechnology industry. The density of 37.86 is not the highest in Period 2, but it is the cluster of greatest interest to researchers. Two other themes are important to researchers. The first is the “networks”cluster, located in quadrant Q4, with a centrality of 76.87 and a density of 11.55. These figures suggest that the topic under consideration holds significant relevance for advancing the scientific domain, given its consistent, yet underdeveloped nature. The second theme is the “transaction costs” cluster, with a centrality of 65.63 and a density of 34.15. This cluster lies at the intersection of quadrants Q1 and Q4, denoting a theoretical framework that is progressively employed to elucidate diverse facets of strategic alliances. Whilst the “innovation”cluster demonstrates the greatest impact, the “transaction costs” and “networks”clusters are in close proximity. This is reflected in the individual citation counts of 19,161, 16,559 and 5,832, respectively, which far exceed the combined total of the Source(s): Authors’ own elaboration Density Centrality Number of documents = 13 H-index = 12 Number of documents cited = 5,832 Centrality = 1 Density = 31.8 Innovation Figure 6. Strategic diagram with performance indicators. Period 1 (1992–1997) Strategic alliances in the biotechnology industry 251 remaining three themes, amounting to a mere 4,553 citations. These three themes encompass the “investment”,“technology discontinuities”and “initial public offerings”clusters, yet their respective indicators indicate that they remain underdeveloped. For example, the cluster “initial public offerings”, located in quadrant Q3, has a density of 5.51 and a centrality of 28.33; this means that it is underdeveloped and has not evolved much. The last two clusters, “investment”and “technological discontinuities”, present less relevant indicators. 3.3 Strategy diagram analysis: period 3 (2004–2009) In Period 3 (2004–2009), the strategy diagram indicates that partnerships in the biotechnology industry centre on 12 research themes. Figure 8 displays the strategic diagram, featuring distinct clusters and their corresponding performance indicators. The “innovation”cluster remains in the first quadrant, with a centrality of 197.98 and a density of 35.51, an h-index of 63 and 16,122 citations, which are the highest values of the period. In the same quadrant Q1, we observe the clusters “governance”, with a centrality of 59.67 and a density of 24.44, and “dynamic capabilities”, with a centrality of 53.67 and a density of 24.06. These values indicate that the above-mentioned themes are in full development and are of great interest to researchers. Quadrant Q4 features other topics of interest: “biotechnology industry”,“competition”and “technology”, with centrality scores of 75.97, 46.88 and 25.72 and density values of 13.93, 7.1 and 3.94, respectively. These topics, although relevant for the advancement of the scientific field, are underdeveloped. In quadrant Q2, the clusters “R&D alliances”,“success”and “mergers and acquisitions”are of marginal importance for the development of the scientific field. From the position of the cluster “exploitation”in quadrant Q3, we deduce that this is a disappearing theme, as it has both low density and low centrality. The cluster “technology transfer”is located between quadrants Q2 and Q3, with a low centrality of 2.31 and a density of 15.87, which could indicate that it is an under-researched topic and could disappear because it is of little interest to researchers. Finally, the “patents”cluster, located between quadrants Q3 and Q4 and with a centrality of 19.65 and a density of 12.1, is growing and may develop in the future. Source(s): Authors’ own elaboration Density Centrality Number of documents = 35 H-index = 29 Number of documents cited = 16,559 Centrality = 65.63 Density = 34.15 Number of documents = 13 H-index = 12 Number of documents cited = 5,832 Centrality = 76.87 Density = 11.55 Number of documents = 56 H-index = 41 Number of documents cited = 19,161 Centrality = 128.89 Density = 37.86 Number of documents = 4 H-index = 4 Number of documents cited = 855 Centrality = 45.76 Density = 62.96 Number of documents = 3 H-index = 3 Number of documents cited = 678 Centrality = 0 Density = 125 Number of documents = 6 H-index = 6 Number of documents cited = 3,020 Centrality = 5.51 Density = 28.33 Innovation Transaction-cost Technological discontinuities Investment Networks Initial-public-offerings Figure 7. 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