Social Networks and Open Innovation: Business Academic Productivity

Journal of Open Innovation: Technology, Market, and Complexity Article Social Networks and Open Innovation: Business Academic Productivity Eva M. Sánchez-Teba * , Mercedes Rodríguez-Fernández and Ana I. Gaspar-González   Citation: Sánchez-Teba, E.M.; Rodríguez-Fernández, M.; Gaspar-González, A.I. Social Networks and Open Innovation: Business Academic Productivity. J. Open Innov. Technol. Mark. Complex. 2021,7, 158. https://doi.org/ 10.3390/joitmc7020158 Received: 7 May 2021 Accepted: 9 June 2021 Published: 15 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Economics and Business Administration, University of Málaga, 29071 Málaga, Spain; [email protected] (M.R.-F.); [email protected] (A.I.G.-G.) *Correspondence: [email protected] Abstract: Is there any type of relationship between the academic productivity of business researchers and their social networking activity? What does this mean in terms of open innovation? With these objectives, in this paper we have focused on the Technology Acceptance Model and the concept of performativity, filling the gap that exists in the current scientific literature. At the empirical level, we carried out a review of 211 articles from the Web of Science (SSCI), obtaining a total set of 12,939 data points. Our statistical model has showed a clear symbiotic relationship between productivity in Google Scholar and presence in ResearchGate. Furthermore, researchers with a greater presence on LinkedIn or Twitter have low Google Scholar or Web of Science h-indices. We concluded that there is currently a dissociation between academic and professional online networks, something that does not help the applicability of research in business and society, the enduring aim of any search for knowledge. Information Science can play an important role in helping to bridge the gap between academia and the real world. Furthermore, in order to contribute to enhancing the role of universities in open innovation practices, it is essential to design and implement new tools such as online communities that stimulate interaction and facilitate network effects. Keywords: online social networks; open innovation; LinkedIn; Twitter; h-index; ResearchGate; business researchers 1. Introduction Information technologies, specifically online social networks (OSNs), are a focus of interest at the international level, and not only concerning individuals but also organizations [1]. On this subject, the emergence of OSNs such as Facebook, LinkedIn, and Twitter has caused a global phenomenon with a strong economic and social impact [ 2 ], and OSNs are recognized as important tools that allow large groups of users to be freely united and exchange knowledge, experiences, and perceptions [3]. In this regard, given the wide popularity of these networks, the study of user attitudes and behaviour in the use of these sites is fundamental to the understanding and development of these not-so-new technologies [ 4 ]. While some educators promote social media skills, more emphasis is still placed on the perceived dangers and abuses of the media in practice [5]. For their part, Kietzmann et al. [ 6 ] explain how consumers have traditionally used the Internet to read, watch, or buy products and services but now increase their use of platforms to create, share, and discuss content [7]. Given the high level of social network utilization in users’ daily lives, these networks are analysed in many different disciplines, providing benefits for researchers and professionals who wish to collect and analyse social network data [8]. Further on the issue, the literature on social networks contributes to expanding the current knowledge concerning how motivations and emotions are coordinated to increase user satisfaction while identifying specific patterns for which these factors are important and greatly influence user satisfaction [ 9 ]. The review of these studies shows us the J. Open Innov. Technol. Mark. Complex. 2021,7, 158. https://doi.org/10.3390/joitmc7020158 https://www.mdpi.com/journal/joitmc J. Open Innov. Technol. Mark. Complex. 2021,7, 158 2 of 17 broad benefits of OSNs and academic social networks (ASNs) in everyday life and their importance as working tools for researchers [10]. However, an analysis of the relationship between the academic productivity of researchers and their activity in ASNs and OSNs has not yet been carried out, and the influence it can have on open innovation processes between universities and companies. This research aims to understand the degree of connection that may exist between academic productivity and activity in the main academic networks, such as ResearchGate, and professional networks, such as LinkedIn or Twitter. Thus, our study attempts to address the first research question: Is there a relationship between academic activity and presence or activity in social networks? Furthermore, it should be noted that the notion of open innovation is becoming more and more widespread both in academia and among policy makers [11]. It was defined by Chesbrough [ 12 ] as the possibility for firms to leverage external ideas and technologies. At the same time, they share internal ideas and technologies on which they do not take full advantage with external actors. This definition directly associates the concept of open innovation with the business. Lichtenthaler is the most prolific author in the study of the impact of open innovation on the business world. His research is related to outbound open innovation with firm performance [ 13 ], firm absorptive capacity and performance related to open innovation [ 14 ], the importance of external knowledge acquisition in relation to technology [ 15 ], and the transformation of innovation processes in new business environments [ 16 ], among others. The contribution of universities in this regard plays an increasingly prominent role in open innovation models [ 17 ]. In fact, Link el al. [ 18 ] and Johnson et al. [ 19 ] highlight the important role played by social networks in the exchange of knowledge between university and business. These same ideas are shared by [ 20 ] in their research on the main factors affecting knowledge transfer in an open innovation context. Among their conclusions, they highlight that social networks are the most important factor affecting the exchange of ideas and methods from the university to the business. After this introduction, the following section sets out the conceptual and theoretical framework, as well as the identification of key study concepts. The hypotheses are set out in the third section, as well as the methodology used, the model variables and the approach followed in sections four, five, and six. The seventh section addresses the results obtained, while the eighth section contains the discussion to finally establish the conclusions, limitations, and future lines of research. 2. Conceptual and Theoretical Framework of the Research Question The scenario in which we move presents a common denominator that appears in most organizations today and is the fact that workers use different social technologies to communicate while sharing knowledge with each other [21]. Social networks have become a standardized and global technological means of communication with great social, organizational, and even academic impact [ 22 ]. Therefore, this techno-social phenomenon [ 2 ] that has taken place since the mid-twentieth century has become the normal life of millions of users, becoming integrated into most organizations and environments, where workers value their presence and use and organizations realize their potential value [23]. There is a plurality of definitions of OSNs as working environments have become increasingly connected through these networks in different ways. Kaplan and Haenlein [ 3 ] (p. 61) define them as “a group of Internet applications that are based on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of user-generated content.” They are also referred to as online communities between people with common interests, activities, backgrounds, and/or friends [24]. There are authors who have already introduced in the definition the idea of the creation of limited groups that are managed according to the interests of each one, such as Boyd and Ellison [ 25 ] (p. 211): “social networking sites are like web-based services J. Open Innov. Technol. Mark. Complex. 2021,7, 158 3 of 17 that allow people to build a public or semi-public profile within a limited system and then articulate a list of other users with whom they share a connection, and try to see and browse their list of connections and those made by others within the system.” However, researchers such as Donath and Boyd [ 26 ], Ellison et al. [ 27 ], and Forest and Wood [ 28 ] have analysed the challenges that can arise when people use social networks widely. These challenges take the form of fear of losing the power of knowledge [ 29 ] or lack of confidence [30]. The basis for the study of OSNs is found in the technology acceptance model (TAM), an information systems theory that models how users come to accept and use a technology [31–34] . Hence, authors such as Rauniar et al. [ 4 ] and Verma et al. [ 35 ] have focused their work on the validation of this model to obtain key variables that provide more information on user behaviour in big data or OSNs. TAM has also been proposed as one of the 20 models that Aboelmaged and Mouakket [ 36 ] have extracted from their bibliometric analysis on emerging models and theories that shape the adoption of big data analytics adoption. TAM has evolved to become the key model in understanding the predictors of human behavior toward potential acceptance or rejection of the technology. The strength of the model is confirmed by numerous studies emphasizing its broad applicability to various technologies [37]. The increase in the use and popularity of social networking sites makes academics and professionals want to understand the behaviour of people using these applications [38–40] . Hence, the purpose of this research is to deepen a part of that behaviour through knowledge of the degree of connection between academic productivity and presence in professional social networks such as LinkedIn and Twitter by researchers. In this regard, it is useful to introduce two concepts related to our research. These are the concept of productivity and the concept of the WOS h-index. The concept of productivity, in general terms, is understood as the relationship between the results or outputs obtained in the work process and the inputs or supplies used to obtain those results [ 41 ]. Applied to the present case, the productivity of academics is measured by their h-index. The h-index, introduced by Jorge Hirsch in August 2005, indicates that a scientist has an h-index if h of his Np papers have at least h citations each and the other papers (Np-h) do not have more than h citations each. This aroused great interest in computer circles, and within a short time, the h-index was applied to compare scientists [42]. The Web of Science h-index represents an indicator of productivity used for the elaboration of academic rankings of universities in the world [ 43 ]. The aim is to verify whether there is a relationship between the Web of Science h-index and the activity of academic business researchers in social networks [44]. These online networks are social spaces where interactions can be both personal and professional, and therefore academic. In these spaces, users can create a personal network with multiple connections to friends, acquaintances, and professional colleagues. They provide interactive and easy-to-use communication, while allowing people to follow the lives of their friends and family, discover useful information, and engage in business transactions [ 45 ]. Hongjie et al. [ 46 ] recently studied how measures to contain pandemics such as COVID-19 can be communicated. Open innovation emphasises the interaction and participation of diverse social actors as a central element of innovative processes; the use of these networks is essential [ 17 ]. Innovation takes place through interaction in the framework of knowledge networks [47] and does not depend on the isolated performance of specific actors, but on the quantity and quality of the relationships that link these actors in networks of knowledge exchange and creation [48,49]. Every second, an average of 6000 tweets are produced, which means that, over the course of a year, approximately 200 billion tweets will be generated [ 50 ]. This figure shows us the capacity of communication and interaction that this platform can have, not only on a social level but also on a professional level. Twitter is not only an online social network J. Open Innov. Technol. Mark. Complex. 2021,7, 158 4 of 17 but also an increasingly important means of communication [ 51 ] with different types of users, some of them very influential [52]. Academic researchers recognize the value of studying Twitter data to gain a better understanding of its users, uses, and impacts on society and culture from different perspectives [25,53,54]. LinkedIn, as a broad professional network covering all professions, also provides researchers with access to the entirety of all users’ careers, which is embodied in a site where professional relationships can also be built academically and where members self-select the groups to which they want to belong and can initiate specific international relationships not otherwise possible [ 55 ]. Meanwhile, ResearchGate is reserved only for researchers seeking to promote their work and connect with users working in the same area or field of research. ResearchGate is one of the most important academic social networks. It provides a wide range of bibliometric indicators, including ResearchGate Score as a point system combining publication downloads, citations, etc. Thelwall and Kousha [ 56 ] found that ResearchGate usage data indicate esteem or influence within the scientific community. Researchers increasingly use the various online profiles provided by ASNs to show their experience and academic and scientific achievements [57–60]. This connectivity at a global level has led authors such as Jarrahi and Sawyer [ 61 ] to consider the performativity of LinkedIn and Twitter, understanding this performativity according to Austin [ 62 ] as the capacity that some expressions or practices carried out in OSNs may have to become actions and therefore transform reality or the environment. Science and Technology studies have completed and enriched the concept of performativity, highlighting that the meaning and effectiveness of scientific statements cannot be dissociated from the socio-technical aspects [ 63 ]. The realist and constructivist perspective of the concept is provided by Latour [ 64 ], since he exposes on the one hand that it is not possible to make anything exist, and on the other hand constructivist because for any entity there are thousands of ways to exist. Hacking [ 65 ] perfectly captured this double dimension of scientific practices. The performativity of technology is based on the implementation of particular social practices for each of these social technologies. In this line, the performativity of Twitter provides access to an interorganizational network of social contacts, while in the case of LinkedIn, this performativity related to the practices of locating experts is the opportunity to expand the capabilities of people to find specialists in different topics and the opportunity to connect with people who can provide valuable solutions to work-related problems from a different perspective [61]. ASNs are being used by an increasing number of early career researchers to build their international profile and connect with researchers around the world [ 66 ]. ResearchGate scores are an effective indicator of reputation [ 67 ] that provides information on academic productivity, and as Yu et al. [ 68 ] point out, the ResearchGate score has shown potential as an alternative performance indicator for academic institutions and could be an “effective indicator to measure the performance of an individual researcher.” At this point, the first research question we raise is: Is there any relationship between the academic productivity of researchers in business and their activity in social networks, whether academic or professional? Fraser et al. [ 69 ] found in this same line that accounting practitioners’ perception of academia and use of academic research is very low, and due to a disconnect from real-world practice, academic business researchers and business schools will become increasingly vulnerable to adverse research funding decisions in the future [ 70 ]. Johnson and Orr [ 71 ] included academic leaders and business stakeholders in their study on academic researchers considering the influence of the Framework for Research Excellence. Van Dalen [ 72 ] detected a clear division among economists regarding the “publish-or-perish” principle. Veletsianos and Kimmons [ 73 ] argue that academic research is being done in a networked and participatory way and point out, together with other authors [ 74 , 75 ], that J. Open Innov. Technol. Mark. Complex. 2021,7, 158 5 of 17 metrics supported by altmetrics technology, indices based on activity in social media environments [ 76 ], are proposed in an attempt to more fully capture the influence of academic work [77]. We should specify that, for our study, we consider all types of social networks as OSNs, whether they are ASNs (e.g., ResearchGate or Publons), professional OSNs (LinkedIn and Twitter), purely social OSNs (Facebook) or mixed OSNs with various uses (e.g., Instagram). ASNs, in particular, can provide academic impact measures that can help to ascertain the relationship between academia and professional visibility [78]. 3. Hypotheses, Method, and Research Model 3.1. Hypotheses The relationship between academia and professional visibility, understood as the appearance and activity in ASNs (ResearchGate and Publons) and professional OSNs (LinkedIn and Twitter), is investigated. Based on the study carried out and previous information, we postulate the following: Hypothesis 1 (H1). There is a relationship between academic productivity measured through the h-index and activity in OSNs. Two sub-hypotheses of hypothesis 1 underlie taking into account the applied h-index. Hypothesis 1.1 (H1.1). There is a relationship between academic productivity according to Google Scholar’s h-index and activity in OSNs. We seek to relate the Google Scholar h-index through citation counts that provide the rankings of an individual researcher’s faculty in relation to others in their field [ 79 ] with the presence that the authors maintain in social networks. Hypothesis 1.2 (H1.2). There is a relationship between academic productivity according to the Web of Science h-index and activity in OSNs. 3.2. Research Method The research methodology employed is explanatory, using the technique of multiple regression analysis based on the study of the relationship between variables measured on a quantitative scale. On 18 April 2020, this research was initiated by conducting a search of the main collection of the Web of Science, limiting SSCI between the years 1900 and 2020 to “all types of documents” in the theme “LinkedIn,” resulting in 616 documents (of which 132 are Open Access). Refining the search by document type to “items only” yielded 411 items, and then refining it again by business* manage* company* gave a result of 211 items. A total of 588 authors or coauthors were counted. It should be noted that the maximum number of authors per article found was 23. Of the 588 authors, we finally focused on the search for data from 521, as some of them participated in more than one article [80]. Another search was also performed by adding the term LinkedIn to Twitter, which extended the results to 1111 articles. We decided to focus on LinkedIn only due to the professional nature of the network, although in the empirical part of our work, we have included the presence of researchers on Twitter as an additional tool that provides us with more data on the professional profile of the academic. LinkedIn, as they define themselves, have as their vision “to create economic opportunities for every member of the global labor market through the continuous development of the world’s first economic chart” and have as their mission “to connect professionals from all over the world to help them be more productive and achieve all their career goals” [ 81 ], while Twitter is a generalist communication tool, defining themselves as “advocates for free expression and protecting the health of the public conversation around the world” [82]. J. Open Innov. Technol. Mark. Complex. 2021,7, 158 6 of 17 The basic research objective, as stated above, is to see the degree of connection between academia and professional visibility, understood today as the appearance and level of activity in the main professional networks on the Internet: LinkedIn and Twitter. For academic activity, we used two productivity measurement indices: WOS h-index and Google Scholar h-index. For the activity in OSNs, we focused on LinkedIn and Twitter; in addition, we collected the activity in ASN ResearchGate and presence in Publons. As intermediate instruments to find academics, when it is difficult to locate them through the previously discussed networks, we will use Facebook, YouTube or Instagram. The 211 articles resulting from our selection written by 521 research authors on the subject of “business” have given rise to two types of analysis items, the article item and the author item. From the item article, we extracted the following data: Keywords, Theories used in the study, Type of analysis (Quantitative/qualitative, Theoretical/empirical), Conclusions, Indexation in Journal Citation Reports (Quartile), Category, and Observations. From the item author, we extracted the following data: Name, Gender, ORCID, Contact Academic, University, Country, h-index WOS, h-index Google Scholar, Profile in Publons, ResearchGate presence/number of items, ResearchGate Score, RG Photo, RG Number of Followers, RG Number of Followed People, LinkedIn Presence, LinkedIn Number of Contacts, LinkedIn Photo, Twitter professional profile, Tw Number of Tweets, Tw Number of followers, Tw Number of followed people, and Observations. In our work, we used two data sets. First was data obtained from 211 articles filtered from the Web of Science (SSCI) with the criteria that have been detailed in the methodology, analysing the information from 7 records extracted from each of these articles. This resulted in a total of 1477 data points in this first set. Second, each of the authors who signed the articles was analysed, with a total of 521 authors, of which a total of 22 records were collected from each of them, ranging from affiliation to different indicators of academic productivity, as well as other indicators of each of the social networks analysed in the articles, such as ResearchGate, LinkedIn, and Twitter. This results in a total of 11,462 data points in this second set. All of this information has brought the size of the total data set of our research to 12,939 data points. Therefore, the sample size in our study, based on previous research, is considered appropriate [83]. In summary, the methodology is compiled in Table 1: Table 1. Methodology. Research Start day 18 April 2020 Objective: To find out the degree of connection between academia and professional visibility on the internet in professional networks: LinkedIn and Twitter. Method: Search in Web of Science main collection, limiting SSCI between the years 1900 and 2020 to all types of documents, on the topic LinkedIn. Result after refining: 211 articles with 521 authors; 12,939 total records. Indices used: WOS h-index and Google Scholar h-index. Source: Own elaboration. 3.3. Model Variables The variables used in the proposed models (Table 2), both dependent (h-index of Web of Science and h-index of Google Scholar) and independent (the rest of them), are presented and defined below: J. Open Innov. Technol. Mark. Complex. 2021,7, 158 7 of 17 Table 2. Variables used in the study. Dependent Variables h-index Web of Science Quantitative value h-index Google Scholar Quantitative evaluation Independent Variables JCR (0, 1, 2, 3, 4) ORCID (0, 1) AC (0, 1) PUB (0, 1) RGPres (0, 1) RGS Quantitative value RGPh (0, 1) RGFWers Quantitative value RGFWing Quantitative value LKPres (0, 1) LKNc Quantitative value LKPh (0, 1) TWPres (0, 1) TWFWers Quantitative value TWFWing Quantitative value Source: Own elaboration. H-index of Web of Science (hindexWOS): the index that the Web of Science gives for each author. H-index of Google Scholar (hindexGoogleScholar): the index that Google Scholar offers for each author. JCR: indexing of the Journal in the Journal Citation Reports list on the Web of Science and, if applicable, the quartile in which it is located. It takes the values (0, 1, 2, 3, 4), 0 in the case of non-indexation or 1 to 4 depending on the quartile in which the journal is positioned at the time of the search [84]. ORCID: Open Research and Contributor ID is a unique identifier whose main purpose is to provide researchers with a persistent and unambiguous author code that clearly distinguishes their scientific production and avoids confusion linked to scientific authorship and the existence of similar names [ 85 ]. It takes the values (0, 1), 0 if no ORCID and 1 if yes. Academic contact (AC): e-mail of the university or affiliation centre that the author puts either in the article or in social or professional networks to be contacted. It takes the values (0, 1) in the case of inexistence or existence. Publons (PUB): presence on your website [ 86 ]. On this platform, part of Web of Science, the author follows the impact of his publications, as well as his reviews of other works. It takes the values (0, 1) in the case of inexistence and existence of a profile. ResearchGate (RG): purely academic social network. They define themselves as “the professional network for scientists and researchers. Over 17 million members from all over the world use it to share, discover, and discuss research. We’re guided by our mission to connect the world of science and make research open to all.” In our study, RG will be represented by a composite index. To calculate it, a factorial analysis has been done as a method of validation of the same [ 87 ] with the idea of looking for the minimum number of dimensions capable of explaining the maximum of information contained in the data. The variables taken into account to carry out the factorial analysis were: RGPresence: takes the value 0 when there is no presence in this network. It takes a numerical value equal to or greater than 1 when the author has put contributions in this network depending on the number of items he has updated. RGScore (RGS): takes the value 0 when it has no mark in this network, and a positive value otherwise. RGPhoto (RGPh): takes the values (0, 1) depending on the existence or absence of a personal photo. RGFollowers (RGFWers): quantitative value in the interval of 0 to the maximum number of followers in the network. J. Open Innov. Technol. Mark. Complex. 2021,7, 158 8 of 17 RGFollowing (RGFWing): quantitative value in the interval of 0 to the maximum number of followed academics. To determine the degree of presence of the authors in social networks, we used various indicators derived from their appearance on LinkedIn and Twitter. A social network presence metric that jointly integrates both networks has not been found in the academic literature. All of the authors use as metrics those thrown by each social network [ 88 ]. In the case of LinkedIn, presence is measured through the existence of an account, the number of contacts, and the existence of a photo. Regarding Twitter, the data we extracted as indicators of presence in this network are the existence of a professional Twitter profile and the numbers of Tweets, Followers, and People Followed. Karampela et al. [ 89 ] use as a scale of measurement of social presence the variables of Ou et al. [ 90 ] adapted to their study (There is a sense of human contact on supplier X’s social media. There is a sense of personalness on supplier X’s social media. There is human warmth on supplier X’s social media). In our case, we created a composite indicator taking into account the five variables referred to ResearchGate, which is adjusted, although the variables explain only 59.24% of the index created. The most irregular items are photo included, with a coefficient extracted from the factor analysis of 0.418 and number of people followed, with a coefficient extracted from the factor analysis of 0.370, so these variables are not advisable to measure the constructed index. The remaining variables (RGPres, RGS, and RGFWers) have an acceptable coefficient because they are closer to one. We perform a second factorial analysis for this index by eliminating these two items. The result is a much tighter model with a Bartlett’s significance of 0.000 and a total variance explained of 80%, where the variables that compose its RGPres, RGS, and RGFWers have a coefficient extracted from the second factorial analysis between 0.760 and 0.817. LinkedIn (LK): They define themselves as the world’s largest professional network with over 645+ million users in over 200 countries and territories whose vision it is to create economic opportunities for every member of the global labour market through the continued development of the world’s first economic chart and whose mission it is to connect professionals around the world to help them be more productive and achieve all their career goals [81]. LinkedIn is a composite indicator obtained through factorial analysis. The variables that have been taken into account in its creation are: LKPresence (LKPres): takes the values (0, 1) based on if the profile exists in this network. LKNumberofContacts (LKNc): if yes, number of contacts in the network. LKPhoto: LKPh: takes the values (0, 1) for the existence or absence of a photo in this network. The variable LKNc has a very low value, very close to 0 (coefficient extracted from the factorial analysis of 0.014), so it must be extracted from the composition of the index. We carry out a second factorial analysis for this index, eliminating this item. The result is a very tight model with a Bartlett’s significance of 0.000 and a total explained variance of 85%, where both variables that compose it, LKPres and LKPh, have a coefficient extracted from the second factorial analysis of 0.850. Twitter (TW): professional network specialized in communication. On their website, they state as their “philanthropic mission to spread and expand the power of Twitter and the talent of our employees through direct civic interaction, staff volunteerism, charitable contributions, in-kind donations, and using the Twitter service in a positive way” [82]. Twitter is a composite indicator also obtained through factorial analysis. The variables that have been taken into account for its formation are: TWPresence (TWPres): takes the values (0, 1) depending on the presence of the researcher in this network. TWFWers: quantitative value indicating the number of followers in the network. TWFWing: quantitative value indicating the number of people followed by the researcher in the network. J. Open Innov. Technol. Mark. Complex. 2021,7, 158 9 of 17 The result is a poorly adjusted model with a very low value of the item TWPres (coefficient extracted from the factor analysis of 0.072), so it must be removed from the composition of the indicator. When the factorial analysis is performed again, the result is that the TWFWers and TWFWing variables that now make up the indicator explain 70% of it, being well adjusted with a Bartlett significance of 0.000 and a coefficient extracted from the second factorial analysis of 0.997. To test our hypothesis, we proposed two models, one with each of the dependent variables, the h-index of Google Scholar (h_indexGS) and the h-index of Web of Science, as follows: h_indexGS =α+β1JCR +β2ORCID +β3AC +β4PUB +β5RG +β6LK +β7TW (Model 1) h_indexWOS =α+β1JCR +β2ORCID +β3AC +β4PUB +β5RG +β6LK +β7TW (Model 2) It must be taken into account when evaluating the results of both models that Google Scholar has more flexibility and is more generic in nature because it is freely accessible and each author designs his or her own academic profile, while Web of Science has restricted access and follows strict control of references to indexed documents and the author does not have access to his or her profile (it is the system itself that includes them), resulting in a certain index [91]. It should be noted that the variables RG, LK, and TW have been included in the model as composite indices resulting from the grouping of each of the individual reference variables. 4. Analysis The statistical analysis of this study was carried out using the statistical package SPSS (Statistical Package for the Social Sciences), version 25. The multiple linear regression procedure was used (with a probability for the input F of p= 0.05 and the output p= 0.10) to analyse both the relationship between academic productivity indicators (Google Scholar’s h-index and WOS h-index) and different independent variables related to the academic and professional world. It was previously analysed if they had outliers (abnormally extreme scores) because they could bias the regression coefficients [92], which went undetected. Following the usual protocol Cohen [ 93 ] to avoid problems of multicollinearity, focused scores were used. Interaction analyses were carried out using the procedure of [ 94 ]. Two multiple regressions were performed for each of our dependent variables (Google Scholar h-index and WOS h-index). Model 1: Regression for the Google Scholar h-Index dependent variable The determination coefficient indicates that the constructed regression model explains 58% of the variance of the Google Scholar h-index (adjusted R 2 = 0.586). Cohen [ 93 ] argues that a magnitude of R2greater than 0.26 indicates a large effect size (Table 3). Table 3. Google Scholar h-index Regression Statistics (Model 1). Model 1 R R2R2Adjusted Standard Error of the Estimation 1 0.765 0.586 0.580 9.135 Source: SPSS. The validation of the predictive model was performed with ANOVA (Table 4), which indicates whether the variance explained by the regression is significantly different and greater than the unexplained variance. The model was found to be statistically significant (F = 103.06; p= 0.000), thus improving the prediction of the Google Scholar h-index and assuming a real effect of the predictor variables on the dependent variable. For the regression model coefficients, t-scores indicate that the variables taken into account provide significance to the prediction model and that the values obtained can be generalized to the population. In this study, the dependent variables ORCID (t= 1710; J. Open Innov. Technol. Mark. Complex. 2021,7, 158 16 of 17 56. Thelwall, M.; Kousha, K. Academia.edu: Social network or Academic Network? J. Assoc. Inf. Sci. Technol. 2014 ,65, 721–731. [CrossRef] 57. Bukvova, H. Scientists online: A framework for the analysis of Internet profiles. First Monday 2011,16. [CrossRef] 58. Stewart, C. Alternative frames of participation: The east-timor newsgroup. Int. J. Electron. Bus. 2008,6, 631–650. [CrossRef] 59. Attfield, S.; Blandford, A.; Makri, S. Social and interactional practices for disseminating current awareness information in an organisational setting. Inf. Process. Manag. 2010,46, 632–645. [CrossRef] 60. Collins, K.; Shiffman, D.; Rock, J. How Are Scientists Using Social Media in the Workplace? PLoS ONE 2016 ,11, e0162680. [CrossRef] 61. Jarrahi, M.H.; Sawyer, S. Social Technologies, Informal Knowledge Practices, and the Enterprise. J. Organ. Comput. Electron. Commer. 2013,23, 110–137. [CrossRef] 62. Austin, J.L. How to Do Things with Words: The Williams James Lectures Delivered at Harvard University in 1955; J. O. Ursom: New York, NY, USA; Clarendon Press: Oxford, UK, 1962. 63. Callon, M. Elaborating the notion of performativity. Le Libellio d’AEGIS 2009,5, 18–29. 64. Latour, B. The promises of constructivism. In Chasing Technoscience: Matrix for Materiality; Indiana University Press: Bloomington, IN, USA, 2003; pp. 27–46. 65. Hacking, I.; Hacking, J. Representing and Intervening: Introductory Topics in the Philosophy of Natural Science; Cambridge University Press: Cambridge, UK, 1983. 66. Mason, S. Adoption and usage of Academic Social Networks: A Japan case study. Scientometrics 2020 ,122, 1751–1767. [CrossRef] 67. Nicholas, D.; Clark, D.; Herman, E. ResearchGate: Reputation uncovered. Learn. Publ. 2016,29, 173–182. [CrossRef] 68. Yu, M.-C.; Wu, Y.-C.J.; Alhalabi, W.; Kao, H.-Y.; Wu, W.-H. ResearchGate: An effective altmetric indicator for active researchers? Comput. Hum. Behav. 2016,55, 1001–1006. [CrossRef] 69. Fraser, K.; Deng, X.; Bruno, F.; Rashid, T.A. Should academic research be relevant and useful to practitioners? The contrasting difference between three applied disciplines. Stud. High. Educ. 2020,45, 129–144. [CrossRef] 70. Woolcott, G.; Keast, R.; Pickernell, D. Deep impact: Re-conceptualising university research impact using human cultural accumulation theory. Stud. High. Educ. 2020,45, 1197–1216. [CrossRef] 71. Johnson, S.; Orr, K. What is business school research for? Academic and stakeholder perspectives, politics and relationality. Stud. High. Educ. 2020,45, 557–578. [CrossRef] 72. Van Dalen, H.P. How the publish-or-perish principle divides a science: The case of economists. Scientometrics 2021 ,126, 1675–1694. [CrossRef] 73. Veletsianos, G.; Kimmons, R. Scholars and faculty members’ lived experiences in online social networks. Internet High Educ. 2013 , 16, 43–50. [CrossRef] 74. Piwowar, H.; Priem, J. The power of altmetrics on a CV. Bull. Am. Soc. Inf. Sci. Technol. 2013,39, 10–13. [CrossRef] 75. Wildgaard, L.; Schneider, J.W.; Larsen, B. A review of the characteristics of 108 author-level bibliometric indicators. Scientometrics 2014,101, 125–158. [CrossRef] 76. Priem, J.; Costello, K.L. How and why scholars cite on Twitter. In 73rd Annual Meeting of the American Society for Information Science and Technology; Willey: Pittsburgh, PA, USA, 2010; pp. 1–4. 77. Thelwall, M.; Haustein, S.; Larivière, V.; Sugimoto, C. Do Altmetrics Work? Twitter and Ten Other Social Web Services. PLoS ONE 2013,8, e64841. [CrossRef] 78. Erdt, M.; Aung, H.H.; Aw, A.S.; Rapple, C.; Theng, Y.-L. Analysing researchers’ outreach efforts and the association with publication metrics: A case study of Kudos. PLoS ONE 2017,12, e0183217. [CrossRef] 79. Kim, H.J.; Grofman, B. The Political Science 400: With Citation Counts by Cohort, Gender, and Subfield. PS Political Sci. Politics 2019,52, 296–311. [CrossRef] 80. Liu, J.S.; Ho, M.H.-C.; Lu, L.Y.Y. Recent Themes in Social Networking Service Research. PLoS ONE 2017 ,12, e0170293. [CrossRef] 81. LinkedIn. 2020. Available online: https://about.linkedin.com/es-es (accessed on 3 June 2020). 82. Twitter. 2020. Available online: https://about.twitter.com/es/advocacy.html (accessed on 3 June 2020). 83. Gan, C.; Li, H. Understanding the effects of gratifications on the continuance intention to use WeChat in China: A perspective on uses and gratifications. Comput. Hum. Behav. 2018,78, 306–315. [CrossRef] 84. ClarivatesAnalitics. Web of Science 2020. Available online: https://apps.webofknowledge.com/UA_GeneralSearch_input.do? product=UA&search_mode=GeneralSearch&SID=F6PP7ivmnpHQoTdFSg6&preferencesSaved (accessed on 3 June 2020). 85. ORCID. 2020. Available online: https://orcid.org/content/initiative?locale_v3=es (accessed on 3 June 2020). 86. Publons. 2020. Available online: https://publons.com/about/home/ (accessed on 3 June 2020). 87. Nunnally, J.C. Psychometric Theory; Tata McGraw-Hill Education: New York, NY, USA, 1994. 88. Demailly, Z.; Brulard, G.; Selim, J.; Compère, V.; Besnier, E.; Clavier, T. Gender differences in professional social media use among anaesthesia researchers. Br. J. Anaesth. 2020,124, e178–e184. [CrossRef] 89. Karampela, M.; Lacka, E.; McLean, G. Just be there. Eur. J. Mark. 2020,54, 1281–1303. [CrossRef] 90. Ou, C.X.; Pavlaou, P.A.; Davison, R.M. Swift Guanxi in Online Marketplaces: The Role of Computer-Mediated Communication Technologies. MIS Q. 2014,38, 209–224. [CrossRef] 91. Bar-Ilan, J. Which h-index?—A comparison of WoS, Scopus and Google Scholar. Scientometrics 2007,74, 257–271. [CrossRef] J. Open Innov. Technol. Mark. Complex. 2021,7, 158 17 of 17 92. Vallejo, P.M. Correlación y Regresión, Simple y Múltiple. Universidad Pontificia Comillas, Madrid. 2012. Available online: http://upav-biblioteca.org/site/wp-content/uploads/2017/10/11-Introduccion-a-la-regresion.pdf (accessed on 3 June 2020). 93. Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Academic Press: New York, NY, USA, 1988. 94. Aiken, L.S.; West, S.G.; Reno, R.R. Multiple Regression: Testing and Interpreting Interactions; Sage: Thousand Oaks, CA, USA, 1991. 95. Menard, S. Applied Logistic Regression Analysis; Sage: Thousand Oaks, CA, USA, 2002. 96. González, E.L. Tratamiento de la Colinealidad en Regresión Múltiple. Psicothema 1998,10, 491–507. 97. Durbin, J.; Watson, G.S. Testing for serial correlation in least squares regression. III. Biometrika 1971,58, 1–19. [CrossRef] 98. Raunio, M.; Nordling, N.; Kautonen, M.; Räsänen, P. Open Innovation Platforms as a Knowledge Triangle Policy Tool–Evidence from Finland. Φорсaˇ um 2018,12. [CrossRef] 99. Vélez-Rolón, A.M.; Méndez-Pinzón, M.; Acevedo, O.L. Open Innovation Community for University–Industry Knowledge Transfer: A Colombian Case. J. Open Innov. Technol. Mark. Complex. 2020,6, 181. [CrossRef] 100. Jonsson, L.; Baraldi, E.; Larsson, L.-E.; Forsberg, P.; Severinsson, K. Targeting Academic Engagement in Open Innovation: Tools, Effects and Challenges for University Management. J. Knowl. Econ. 2015,6, 522–550. [CrossRef] 101. Striukova, L.; Rayna, T. University-industry knowledge exchange: An exploratory study of Open Innovation in UK universities. Eur. J. Innov. Manag. 2015,18, 471–492. [CrossRef] 102. Oganisjana, K. Promotion of university students’ collaborative skills in open innovation environment. J. Open Innov. Technol. Mark. Complex. 2015,1, 18. [CrossRef] 103. Moretti, F. “Open” Lab? Studying the Implementation of Open Innovation Practices in a University Laboratory. Int. J. Innov. Technol. Manag. 2019,16, 1950012. [CrossRef] 104. Hess, M. A Nomad faculty: English professors negotiate self-representation in university Web space. Comput. Compos. 2002 ,19, 171–189. [CrossRef] 105. Ewins, R. Who are You? Weblogs and Academic Identity. E-Learn. Digit. Media 2005,2, 368–377. [CrossRef] 106. Ferguson, R.; Clough, G.; Hosein, A. 17th Association for Learning Technology Conference. In Shifting Themes, Shifting Roles: The Development of Research Blogs; AISeL: Nottingham, UK, 2010; pp. 111–117. 107. Johannsson, M.; Wen, A.; Kraetzig, B.; Cohen, D.; Liu, D.; Liu, H.; Palencia, H.; Wagner, H.; Stotesbury, I.; Jaworski, J.; et al. Space and Open Innovation: Potential, limitations and conditions of success. Acta Astronaut. 2015,115, 173–184. [CrossRef]