The current COVID-19 pandemic crisis is very different from an economic crisis such as the global financial crisis of 2008, which represented an external shock that mainly affected market dynamics. Industry and economic disruptions occurred as a consequence of the recession that ensued rather than as a consequence of the crisis itself (Mandel & Veetil, 2020). In contrast, the COVID-19 pandemic has had immediate effects on people's physical health and the entire socioeconomic system (Dahlke et al., 2021; De Vito & Gomez, 2020; Zhang, Hu & Ji, 2020). Crises pose enormous pressure on technology, user practices, application, infrastructure, industry structure, policy and techno-scientific knowledge (Geels, 2002).

Clinical care and education are areas that have been relatively unaffected by the disruptive changes that have previously transformed other sectors such as banking, retail and manufacturing. The COVID-19 pandemic seems to be the catalyst for disruption in clinical care and educational systems at an unprecedented pace (Dzau, Yoediono, ElLaissi & Cho, 2013; Woolliscroft, 2020). The health crisis generated the need to develop new therapies and vaccines (Verma & Gustafsson, 2020); hence, investors are prioritizing investment in biopharmaceutical companies that have innovated during the COVID-19 crisis (Piñeiro-Chousa, López-Cabarcos, Quiñoá-Piñeiro & Pérez-Pico, 2022). Preliminary COVID-19–related research already indicates that innovative start-ups pivot and aim to exploit emerging entrepreneurial opportunities (Kuckertz et al., 2020; Manolova, Brush, Edelman & Elam, 2020).

Other recent changes include transitions to innovations related to information technologies e.g. cloud, Internet of Things, social media communication and open innovations. The concept of open innovation as a distributed process based on managed knowledge flows across organizational boundaries has been extended not only to businesses, but also to the entire online environment (Chesbrough & Bogers, 2014; Secundo, Toma, Schiuma & Passiante, 2018). Thus, we are talking about innovation communities (Shaikh & Levina, 2019; West & Lakhani, 2008), user innovation activities (Ogink & Dong, 2019; Piller & West, 2014), open source software development (Nagle, 2019; Von Krogh, Haefliger, Spaeth & Wallin, 2012), and open licensing (Gorbatyuk, Van Overwalle & van Zimmeren, 2016; Wu, Little & Low, 2016).

At the World Economic Forum Annual Meeting in 2018, universities were exhorted to embrace their role to drive innovation and catalyze economic development through four main paths: (1) foster entrepreneurship and create a culture of innovative thinking; (2) encourage collaboration with private companies, foundations and other research-intensive institutions; (3) promote diversity and inclusion to ensure that economic gains are shared across the economy; and (4) ensure that there is an ethical nexus between technology and society to make sure that technology will benefit humanity (Jahanian, 2018).

Along these lines, Boh, De-Haan and Strom (2016) identified six stages for bringing to market early technology at universities: (1) idea generation; (2) commercialization decision; (3) prototype generation and establishment of commercial and technical viability; (4) founding team formation; (5) strategy and commercialization process determination, and (6) fundraising to sustain activities, with the aim of convincing investors that the new technology has commercial and technical viability. In addition, they identified university programs and practices that enhance entrepreneurial efforts to commercialize university technologies such as mentoring, accelerators and incubators, and developing competitive business plans (Boh et al., 2016).

Researchers have pointed out that universities have increasingly become ambidextrous organizations reconciling research and business missions. In order to manage this ambidexterity, technology transfer offices (TTOs) or similar entities have been established in many universities (Huyghe, Knockaert, Wright & Piva, 2014). Indeed, academic spinoffs can represent an opportunity to commercialize knowledge developed within the university as well as provide compensatory self-employment opportunities by allowing skilled individuals to exploit their advanced knowledge in a given field (Czarnitzki, Doherr, Hussinger, Schliessler & Toole, 2016; Roach & Sauermann, 2010).

Internal entities such as TTOs or their equivalents can foster academic innovation and entrepreneurship using different strategies for spinning-out companies (Clarysse, Wright, Lockett, Van de Velde & Vohora, 2005). In general, academics who are familiar with technology transfer initiatives are more likely to get involved in entrepreneurial activities as they learn the business norms and skills required to be successful in commercializing research. Generally, TTOs also organize pre-seed capital to be invested in potential spin-offs. In addition, they offer incubation services, which make it possible for academics to set up new businesses while staying on campus (Bercovitz & Feldman, 2008; Civera, Meoli & Vismara, 2020; Clarysse & Bruneel, 2007).

Typically, an innovation has been defined as the initial introduction of a new product or process whose design departs radically from past practice. Innovation is becoming even more important to organizational growth and a way to improve competitive advantages for nations. The variety of products keeps growing, and the organizational settings as well as the external conditions keep changing. Therefore, not surprisingly, there are various frameworks to categorize innovations based on different aspects such as difussion, impact, usage, type, etc. (Bogers et al., 2017; Coccia, 2006; Shenhar, Dvir & Shulman, 1995). The typology proposed by Dahlke et al. (2021) that distinguishes between the needs of the users of the innovation and the needs of the innovators can be used to categorize RDI efforts at universities.( Dahlke et al., 2021; Max-Neef, Elizalde & Hopenhayn, 1989).

In order to identify the scope of the RDI efforts at universities that occurred as a result of the pandemic and to elucidate the patterns of collaboration and RDI capabilities of CEE universities we posited the following research questions: What is the scope of RDI topics in the overall university communications during the pandemic?; What are specific RDI activities that took place at selected universities during the pandemic?; and how can these pandemic related RDI activities be classified or categorized?

This study is based on the analysis of communications posted on the official Facebook pages of 30 universities located in CEE countries. Facebook was chosen because it is a popular social media platform, and higher education institutions are increasingly using it for official communications with the student body (Bachmann, 2020; Fähnrich, Vogelgesang & Scharkow, 2020; Metag & Schäfer, 2019). In addition, the frequency, timeliness and completeness of communication on this platform is higher than on other communication university platforms (Zachos, Paraskevopoulou-Kollia & Anagnostopoulos, 2018). Furthermore, there is evidence that Facebook can be used to disseminate information about technology and innovations as part of a cross- or multimedia- communication strategy (Wirtz-Brückner, Jakobs, Kowalewski, Kluge & Ziefle, 2015).

Based on the analysis of Facebook communications, which were extracted in their original language. Content analysis allows us to make inferences by objectively and systematically identifying specified characteristics of messages. Our process consisted of the following steps: sample selection, definition of terms to be extracted in the languages spoken at the selected universities, category construction, creation of codes, data collection, coding, inter-coder reliability determination, and data analysis (Krippendorff, 2018).

The first research step focused on identifying the overall scope of RDI topics in university communications during the pandemic. Once the RDI items were identified, an in-depth analysis was conducted to get a more accurate qualitative picture of the RDI activities. The second step focused on identifying the type of R&D efforts, and the third step focused on identifying the innovation efforts. The level of involvement of cooperating actors in RDI activities was also assessed. The research design including the research scope are provided in Fig. 1.

Fig. 1.

Research scope and design.

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The research sample consists of 30 top universities based in eight CEE countries: Austria, Croatia, Czech Republic, Hungary, Poland, Serbia, Slovakia and Slovenia. These countries are not only part of a geographic cluster, but they also share a common past history as part of the Austro-Hungarian Empire before its dissolution. Furthermore, all countries except Austria continued a shared socioeconomic and political path as satellite countries of the former Soviet Union. In 1991, the Czech Republic, Hungary, Poland and Slovakia created the Visegrád Group as a cultural and political alliance, which allowed for similar business conditions and socioeconomic development of the group (Bednáriková & Stehlíková, 2012; Mura, Ključnikov, Tvaronavičienė & Androniceanu, 2017). Therefore, Austria serves as a benchmark country in terms of assessing RDI efforts and scope.

Besides the regional and historical consistency of the sample countries, the selection of higher education institutions is based on the Times Higher Education (“THE”) ranking and the knowledge transfer score. The Academic Ranking of World Universities (ARWU) and the QS World University Rankings are also well known. The selected sample universities have similar rankings across these surveys (Altbach & Hazelkorn, 2018; Baty, 2013; Lim, 2021). Since the “THE” rankings are solely based on research or research-related indicators we decided to base our selection on their metrics. The knowledge transfer score reflects the level of university innovation activities in terms of their ability to help industry with innovations, inventions and consultancy. The indicator also tries to capture how much research income universities earn from industry by scaling it against the number of employed academics (TimesHigherEducation, 2021). The knowledge transfer score of the sample ranges between 33. 5 to 51.7 for CEE post-communist countries, while for Austria it ranges from 57.8 to 86.9. The sample is comprised of thirteen technical universities, twelve universities with a general focus (which include engineering, technical sciences, physical sciences, social sciences, business and economics), and five medical schools. Clearly, Austrian universities are ranked higher and have higher knowledge transfer scores than the rest of the universities in the sample. Table 1 provides a list of the selected universities, the number of pandemic oriented communications, the “THE” overall ranking, and the knowledge transfer score.

All the posts published from January 1, 2020 to June 30, 2021 on the official Facebook pages of the selected universities were collected in their original language. Initially, 16,693 posts were obtained, however, after eliminating the non-communicative posts (e.g. changes of status, university logos or timelines) 16,627 posts remained. From this pool, 1892 posts included the keywords “COVID”, “korona”, “corona”, “vírus”, “virus”, and “wirus”. The number of posts comprising the relevant keywords are shown in Table 2.

The 1892 posts that included the keywords were analyzed to identify RDI and non-RDI efforts related to COVID-19 at each university. These RDI efforts were categorized and specific efforts that included business cooperation were further studied as examples of cases of RDI-business collaborative efforts.

The assessment of inter-rater reliability (IRR), also called inter-rater agreement provides a way of quantifying the degree of agreement between two or more coders who make independent ratings about the features of a set of subjects. IRR analysis aims to determine how much of the variance in the observed scores is due to variance in the true scores after the variance due to measurement error between coders has been removed (Hallgren, 2012). Several coding-related considerations were decided a priori. Then, a subset of 200 randomly selected posts was used for the IRR analysis (e.g. fully crossed design). Then the IRR for the subset of subjects was used to generalize to the full sample (Hayes & Krippendorff, 2007; Krippendorff, 2018; Putka, Le, McCloy & Diaz, 2008). The overall IRR was 82%, which indicates strong reliability. The scores related to individual categories are displayed in Table 3.

Topic modeling is one of the most powerful techniques for text and data mining, latent data discovery, and for finding relationships among data and text documents. Latent Dirichlet Allocation (LDA) is one of the most popular topic modeling methods (Jelodar et al., 2019). For robustness purposes we used LDA to see any relevant information from the analysis. The topics identified by LDA matched the topics found through coding.

One fourth (21%) of the pandemic oriented posts were about RDI activities. As can be seen in Table 4, the share of RDI communications varied significantly among universities and countries. Austria had the highest share (7.2%) of RDI communication followed by the Czech Republic (4.2%). For the rest of the countries the percentage share of RDI communications varied from 1.9% for Poland to 1.2% for Slovakia. In terms of RDI communications at the university level, the Medical University of Innsbruck and the Czech Technical University in Prague had much higher levels than the rest.

As expected, medical research efforts were focused on the development of treatments and epidemiology. Logically, medical universities such as the Medical University of Vienna, Semmelweis University, and universities with a medical school (CoUn, USze, UPéc, MeUW) were more active in this respect. This type of research tends to have longer lead-times because potential new treatments or medications need to go through the clinical trial process which can take a long time. The time-frame of this study was relatively short; therefore, it did not always capture the end products of these research efforts.

In most countries, the research efforts were usually carried out independently within the university, although Austrian universities exhibited higher levels of inter-institutional and industry collaboration than the rest. Only the Polish WUST reported that an anti-virus drug development research was conducted in collaboration with U.S. research groups. Data from this study was published without a patent application for easier availability. This is evidence of the solidarity that emerged among researchers during the pandemic. Besides medical oriented research, these institutions also conducted research in sociology, economics and environmental fields. All identified research and development topics are summarized in Table 5.

Table 6 provides a list with a brief description of the innovations identified. Most of the innovations were focused on designing medical equipment and protective gear for healthcare professionals. Prototypes were often shared with open access platforms, e.g. freely accessible files for 3D printing.

At the beginning of the pandemic, in all countries except Austria, technological innovations focused on patenting and certifying protective medical devices, due to shortages of such devices in those countries. As the pandemic progressed, efforts in all the countries shifted to develop testing capabilities and on preventive processes in hospitals such as measuring the body temperature of newcomers and decontamination of the environment. Later the focus shifted to information systems to monitor various activities of citizens, such as data capture for contact tracing, infection risk assessment and compliance with state-imposed measures. For these innovations, universities often worked with regional and state administration entities (e.g. Hygiene Station in the Czech Republic), or already established cooperation with businesses such as the collaboration between VSB-TUO and T-Mobile in the Czech Republic.

Regarding instruction and knowledge dissemination, communications included a wide range of measures to combat the pandemic such as how to behave in public transportation systems, emergency sterilization of respirators or e-learning courses for doctors and specialists. Many virtual conferences, seminars and lectures were also organized. Several innovation hackathons took place at the different institutions.

Finally, many universities hosted events where they provided free services or supplies, donation drives and crowdsourcing events. Several competitions and campaigns were also set up to combat the pandemic. For example, as part of a student online hackathon “Hack the Crisis”, the Smart Triage web application was created. In a similar event, WUST students created a “StopFakeNews” campaign against misinformation about COVID-19, and MeUW joined the international organization “Fight the Fakes” to raise awareness of drugs, as information emerged about counterfeit COVID-19 therapies, vaccines and drugs. Universities also participated in anti-COVID-19 programs at national (Slovak National Technology Transfer Competition) or European levels (EUvsVirus hackathon).

Based on the data analysis, we modified the innovation typology proposed by Dahlke et al. (2021) in order to incorporate research and development efforts as well. For the R&D efforts we identified three clusters as shown in Fig. 2. In the medicine cluster, the sub-themes were pharmaceutical research, treatments for COVID-19 and epidemiological studies. The two other clusters are comprised by studies in the socioeconomic and environmental fields.

Fig. 2.

RDI clusters, sub-themes and domains.

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Regarding innovation efforts, we identified four overarching clusters and nine distinct domains of innovations that cover a wide range of innovations from medical equipment to apps for vaccination registration, as shown in Fig. 2. Cluster one is Adaptations and encompasses the two domains of Medical Equipment and Protectives. Cluster two is Digital Innovations and comprises the domains of Mobile and Web applications, Diagnostics and Monitoring, and High Technology. Cluster three is Online Platforms and branches off into the domains of Virtual Learning and Information sharing. Cluster four is Solidarity with the domains Open Innovation and Pro-bono/Donations.

This study has also shown that cooperation between universities and with external entities such as government institutes and private companies is crucial for RDI. The level of commercialization of the innovated products is also highly dependent on such cooperation. While the identified research was mainly the effort of individual universities, cooperation with other universities was not the norm in most countries except Austria. However, research or innovations with potential commercialization increased with the number of partners involved. Research needs were usually formulated by hospitals or medical staff, then solutions to the problems were mostly formulated by the individual universities or research centers. The commercialization of a product usually occurred with the participation of a university spin-off or a cooperating external company. Examples of successful cooperation include several research projects in Austria, the connection of Slovak firms into the consortium “IT firms help to Slovakia” under the auspices of the TUKE or the connection of technologies, industry and Nano technologies in the international platform Synergy Interreg Central Europe (for researchers, Ph.D. students, students and companies) coordinated by TUL. In addition, there were more specific collaborations such as the agreement between WUST and the Japanese company Peptide for the distribution of chemical compounds. Another case of cooperation was the association of five Polish universities in the fight against COVID-19. Several examples of cooperation between universities and businesses that produced innovations are described in more detail in Table 7.

It is worth mentioning that new research organizational units were established at several universities in Hungary, Poland and Croatia. A new virology laboratory was founded at the UPéc; and the WUST in cooperation with the Croatian University of Kragujevac established a laboratory for research into the reduction of virus penetration into the human body. MeUW has initiated the establishment of an anti-COVID-19 laboratory using molecular methods to examine medical specimens. The Center for Infectious Animal Diseases was established at the CULS with the aim of monitoring the risks associated with the spread of selected infectious diseases in animal populations.

The selected universities located in Croatia, the Czech Republic, Hungary, Poland, Serbia, Slovakia and Slovenia clearly lag behind Austrian universities in terms of the level of inter-institutional and business cooperation as reflected by their knowledge transfer scores. On the other hand, universities from CEE post-communist countries had a higher tendency to communicate appeals to the public about participating in donation drives (e.g. plasma, blood) as well as the development of more tools for national screening and monitoring of the pandemic. Nevertheless, the pandemic seems to have spurred a wave of new collaborations in these countries, that hopefully will be sustained beyond the pandemic. The increased inter-institutional and business collaboration is a positive trend that should be encouraged to continue.

Finally, in all countries, the pace of RDI efforts was much higher in the first three months of the pandemic (March, April and May of 2020) despite the increases in the number of COVID-19 infections and deaths. No RDI efforts were communicated during the summer time when most universities are off.

Although it is not possible to draw precise conclusions about the innovation potential based solely on the analysis of the universities’ social media communication, certain differences between countries and universities are relatively obvious. The highest level of research development collaboration was observed in medical schools and universities with medical schools. Austrian medical schools exhibited the highest levels of inter-institutional research collaboration. The highest potential in terms of the quantity of emerging innovations and in terms of the degree of their completion was observed in technical universities, with Czech and Polish universities leading in this aspect. Lower innovation potentials were observed at Austrian, Croatian, Hungarian, Serbian, Slovak and Slovenian universities. At the same time, there are also visible differences among the universities. Technical universities had higher outputs and primarily became the innovative leaders in technological innovation. Similarly, medical schools and universities with well-established medical schools and science schools also scaled up their research and development projects.

The leader among the sample universities in terms of the highest number of research collaborations were the Austrian medical schools (MUWi, MUGr and MUIn). In terms of finalized, patented, certified and commercialized innovations, the leader was the CTU in Prague.