Author Collaboration in Ten Years of IPS²: A Bibliometric Analysis

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11th CIRP Conference on Industrial Product-Service Systems 11th CIRP Conference on Industrial Product-Service Systems

Author Collaboration in Ten Years of IPS²: A Bibliometric Analysis Author Collaboration in Ten Years of IPS²: Bibliometric Analysis 28th CIRP Design Conference, May 2018, A Nantes, France Sebastian Knop*, Robin Merchel, Jens Poeppelbuss

Sebastian Knop*, Robin Merchel, Jensand Poeppelbuss A new methodology to analyze the functional physical architecture of Ruhr-Universität Bochum, Industrial Sales and Service Engineering, Universitätsstr. 150, 44801 Bochum, Germany Ruhr-Universität Bochum, Industrial Sales and Service Engineering, Universitätsstr. 150, family 44801 Bochum,identification Germany existing products for an assembly oriented product * Corresponding author. Tel.: +49-234-32-26403; fax: +49-234-32-14280. E-mail address: [email protected]

* Corresponding author. Tel.: +49-234-32-26403; fax: +49-234-32-14280. E-mail address: [email protected]

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

Abstract École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France Abstract This paper investigates author collaboration at the Conference on Industrial Product-Service Systems (IPS²). Previous work showed that there is *This Corresponding author. Tel.: +33 3collaboration 87 37authors 54 30; E-mail address: [email protected] only a looseinvestigates collaboration between from countries in the field of Product-Service aims to there extend paper author at thedifferent Conference on Industrial Product-Service SystemsSystem (IPS²).research. Previous This workstudy showed that is and refine these findings bybetween also taking the authors’ disciplines and affiliations account. We analyze 694 articles This written by aaims totaltoofextend 1,131 only a loose collaboration authors from different countries in the fieldinto of Product-Service System research. study authors using both bibliometric a machine learning collaboration thatwritten illustrate researcher and refine these findings by also analysis taking theand authors’ disciplines andtechnique. affiliationsWe intoidentify account. We analyze patterns 694 articles by how a total of 1,131 communities within their country, on countrywide or regional Furthermore, the authors’ alsohow influence their authors usingcollaborate both bibliometric analysis and aeither machine learning technique. Welevel. identify collaboration patterns disciplines that illustrate researcher Abstract tendency to collaborate authors other disciplines. We conclude that alevel. sharedFurthermore, cultural background, language, and also discipline promote communities collaboratewith within their from country, either on countrywide or regional the authors’ disciplines influence their the collaboration of authors the IPS² tendency to collaborate withfrom authors fromcommunity. other disciplines. We conclude that a shared cultural background, language, and discipline promote In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of the collaboration of authors from the IPS² community. agile andThe reconfigurable production systems B.V. emerged to cope with various products and product families. To design and optimize production © Authors. Published Published by Elsevier © 2019 2019 as The Authors. by Elsevier B.V. matches, product analysis methods are needed. Indeed, most of the known methods aim to systems well as to choose the optimal product Peer-review under responsibility ofthe thescientific scientific committee the 11th CIRP Conference on Industrial Product-Service Systems. © 2019 The under Authors. Published by Elsevier B.V. Peer-review committee of of the 11th CIRP Conference Industrial Product-Service analyze a product orresponsibility one product of family on the physical level. Different product families,onhowever, may differ largelySystems in terms of the number and Peer-review under responsibility of the scientific committee of the 11th CIRP Conference on Industrial Product-Service Systems. nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production Keywords: Author Collaboration; Industrial Product-Service Systems; Bibliometric Analysis; Machine Learning; Random Forests system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster Keywords: Author Collaboration; Industrial Product-Service Systems; Bibliometric Analysis; Machine Learning; Random Forests these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a1.functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) between is the output whichand depicts the Introduction emphasize the interdependencies product service similarity between product families by providing design support to both,shares, production system planners and product designers. illustrative as the well as between different lifecycle phases of 1. Introduction emphasize interdependencies between productAnand service example a nail-clipper is used toonexplain the proposed methodology. Anindustrial industrialascase study onbetween two product families of steering columns of The ofCIRP Conference Industrial Product-Service PSS, in as particular. Concepts similar to PSS are also shares, well different lifecycle phases of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. Systems place foron the eleventh this year. It is discussed usingin particular. other terms, including servitization [4], The (IPS²) CIRP takes Conference Industrialtime Product-Service industrial PSS, Concepts similar to PSS are also © 2017 The Authors. Published by Elsevier B.V. the prime conference on the design and management of Systems (IPS²) takes place for the eleventh time this year. It is discussed using other terms, including servitization [4], extended products [5], customer solutions [6], as well as smart Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

Product-Service Systemson(PSS) covers economic and the prime conference the and design andboth management of extended products customer solutions [6], as well as smart service systems [7][5], more recently. environmental aspects. Over the past ten years, a remarkable Product-Service Systems (PSS) and covers both economic and service systems [7] more recently. So far, there have been only few bibliometric analyses that Keywords: Assembly; Design method; Family identification stock of articlesaspects. (in sumOver 694) the written large set of authors environmental past by tena years, a remarkable So examined far, there have been only few bibliometric analyses that have author collaboration on the aforementioned (1,131) developed that is worth taking closer stock ofhas articles (in sum 694) written by a alarge setlook. of authors have examined on the hardly aforementioned research themes author [8–13].collaboration These prior papers focus on For even a longer that period of time, wea closer have witnessed a (1,131) has developed is worth taking look. research These hardlyresearcher focus on the PSS themes concept[8–13]. and do not prior look papers at specific 1.steady Introduction of product range and manufactured and/or of academic in PSS thata For increase even a longer periodand of business time, weinterest have witnessed thethePSS concept and docharacteristics not look at specific researcher communities like the IPS² conference participants. With this assembled this In conference this context, theparticipants main challenge in steady across increasethe of academic businessofinterest in PSS like that communities likesystem. the IPS² participants. Withas this goes traditionalandborders disciplines study, we inspecifically analyze the IPS² an Due across to the development inof the domain like of modelling analysis is now cope with single engineering, management, andborders information systems [1]. goes the fast traditional disciplines study, research we and specifically analyze the only IPS² asand an active community that not meets on to aparticipants regular basis communication and anof ongoing trend of digitization and products, a limited productdiscussion range or existing productbasis families, Looking at themanagement, origins this and stream of research, Goedkoep et engineering, information systems [1]. active research community that meets a regular and that propels the academic on on PSS. digitalization, enterprises are important but also to bethe able analyze and to compare define al. [2] defined PSS as set facing of products and Looking at themanufacturing origins of “a thismarketable stream of research, Goedkoep et thatThe propels academic discussion on PSS.products purpose oftothis paper is to illustrate the statustoquo of challenges in today’s environments: aproducts continuing new product families. can that the classical services capable of jointly a user’s in 1999. al. [2] defined PSS asmarket “a fulfilling marketable set ofneed” and The purpose of thisItatpaper isobserved to illustrate statusexisting quo of author collaboration thebeIPS² conference series and to tendency towards times and product are regrouped in function of clients orcollected features. They specifically toproduct the potential economic and services capable reduction of pointed jointly of fulfilling adevelopment user’s need” in 1999. author families collaboration at the IPS² conference series and to explore the underlying reasons for this. We shortened productoflifecycles. In there an provided increasing However, assembly product areWe hardly to ecologic effects PSS. In 2010, et al.iseconomic [3] They specifically pointed to addition, theMeier potential anda explore the underlying reasons for collected information on alloriented authors that families havethis. published in find. the demand customization, at Meier theansame time a global On the product family level, products differ mainly in two dedicated characterization of industrial PSS,a ecologicofeffects of PSS. Inbeing 2010, et al. [3] in provided information on all authors that havedata published in the proceedings until 2018. We used this set to identify competition allofover an the world. Thisservice trend, main characteristics: (i) the number components andidentify (ii) conceptualizing itcompetitors as an “integrated product and dedicated with characterization industrial PSS, proceedings until 2018. used ofthis data setbased to author clusters through a We bibliometric analysis on the which is that inducing development from macro micro type of components (e.g. amechanical, electrical, electronical). offering delivers in industrial applications”. They conceptualizing it the asvalues an “integrated product andto service author clusters through bibliometric analysis basednetwork on the Louvain method, which is a method from social markets, results in diminished sizes due to augmenting Classical methodologies single products offering that delivers values in lot industrial applications”. They Louvain method, which isconsidering a methodmainly from social network product varieties (high-volume to low-volume production) [1]. or solitary, already existing product families analyze the 2212-8271 © 2019 Theaugmenting Authors. Published by Elsevier To cope with this variety as wellB.V. as to be able to product structure on a physical level (components level) which Peer-review the scientific committee the 11th CIRP Conference Product-Service 2212-8271 possible ©under 2019responsibility The optimization Authors. of Published by Elsevier B.V. identify potentials in ofthe existing causeson Industrial difficulties regardingSystems. an efficient definition and doi:10.1016/j.procir.2017.04.009 Peer-review under responsibility of the scientific committee of the 11th CIRP Conference on Industrial Product-Service Systems.families. Addressing this production system, it is important to have a precise knowledge comparison of different product doi:10.1016/j.procir.2017.04.009

2212-8271©©2017 2019The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-reviewunder underresponsibility responsibility scientific committee of the CIRP Conference on 2018. Industrial Product-Service Systems. Peer-review of of thethe scientific committee of the 28th11th CIRP Design Conference 10.1016/j.procir.2019.03.092

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analysis. The identified clusters reflect different kinds of collaboration by authors that have jointly written publications for the IPS² conferences. Using a machine-learning algorithm (i.e., Random Forests), we further examined whether there are shared characteristics (e.g., shared institution or location of authors) among the members of a cluster. As a result, this study reveals patterns of author collaboration within the IPS² research community.

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sample. We then had to correct many of the authors’ names due to mistakes (sometimes there was confusion regarding the given and family name of an author) or special characters in the names, which led to false multiple occurrences of an author in the sample. After correcting, 1,131 unique authors remained.

2. Related Works Bibliometric analyses in the engineering discipline are scarce and they have largely ignored the field of PSS. Only Oliveira et al. [8] give an overview of the PSS field including an investigation on author collaboration. They identify several geographically dispersed and disconnected co-authorship networks. Other relevant bibliometric analyses [9–13] focus either on keyword and/or co-citation networks (i.e., which keywords of an article are typically connected; and which articles are often cited together). Rabetino et al. [9] analyze the co-citation network within the servitization literature. As they investigate servitization, the field of PSS is only one subcommunity in their study. They also do not analyze author collaboration based on authors’ characteristics. The same holds true for the other bibliometric analyses on servitization [10,11], as well as those on PSS design process methodologies [12] and the circular economy [13]. All bibliometric studies give recommendable overviews on their particular area of investigation. Particularly worth mentioning from a methodological perspective is the study by MartínPeña et al. [10], who also perform a principal component analysis to validate their results. Studies in other research fields apart from PSS suggest that characteristics like the author’s affiliation, main research discipline, and whether the author is an academic or practitioner also have an influence on the collaboration [14– 17]. Hence, we include these characteristics into our analysis of author collaboration at the IPS² conference proceedings. 3. Methods Our research approach for this study comprised the following steps (Fig. 1). First, we collected all articles published in the IPS² conference proceedings with the corresponding author information. Then, we split these data into two data sets: One set mapping the collaboration between authors and the other set containing the characteristics of all authors. With the first data set, we revealed author clusters by applying a bibliometric analysis technique. Afterwards, we linked the identified author clusters to the information on authors and ran a machine learning algorithm. This algorithm built a model, which best predicts the membership to a cluster by considering the characteristics of authors. 3.1. Data Collection We collected the meta information of all articles published in the IPS² proceedings from 2009 until 2018. Hence, 694 articles (we excluded five editorials) built the basis of our

Fig. 1. Research Approach.

Then, we gathered more information on the authors in our sample. These included the authors’ affiliation, their main research discipline, and if they are academics or practitioners (there are 104 practitioners in the sample). For determining the affiliation, we considered the information given in the first published article (i.e., the chair/department, university or company, and country of every author). We then identified the research discipline by investigating the website of every particular chair/department. However, in case we were unable to clearly determine the author’s discipline (e.g., at interdisciplinary chairs), we left out the information. This was because a result like ‘interdisciplinary chairs tend to collaborate with other interdisciplinary chairs’ (or vice versa) would be difficult to interpret, since collaboration between interdisciplinary chairs could be both—collaboration between authors from the same or different disciplines. Table 1. Overview of Authors. Discipline

N

Geographic Location

N

Economics

23

Asia

183

Engineering

673

Australia

7

Information Science

29

Europe

744

Management

93

North America

24

Psychology

11

South America

73

N/A

302

Sum

1,131

1,131

3.2. Identification of Author Clusters Patterns For analyzing author collaboration patterns, we first constructed an undirected network, in which nodes represent all authors that published in the IPS². It is an undirected network since there is no in-and-out concept existing behind author collaboration. We assume that the co-occurrence of authors is equal to the collaboration of these authors. Each time a relationship exists between two nodes, an edge

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represents this relationship by linking the nodes. Thus, edges in this network represent collaboration between authors. For identifying clusters within this network, we applied the Louvain method. Clusters divide a network into groups of nodes where the connections within a cluster are dense, while they are sparse between different clusters [18]. The Louvain method is an algorithm for detecting clusters in networks based on a heuristic to maximize modularity [19]. Modularity is a metric that quantifies the quality of an assignment of nodes to clusters by evaluating how much more densely connected the nodes within a cluster are compared to how connected they would be in a suitably defined random network on average [20]. The Louvain method consists of the repeated application of two steps, but a separate cluster is assigned to each node in advance [18]. In the first step, it is decided for each node whether a change into the cluster of the neighboring node results in an improvement of modularity. This node then changes to the cluster of the neighbor node with which the strongest improvement is possible. These two steps are repeated until no further improvement in modularity is possible. The Louvain method achieves excellent modularity values compared to other network clustering algorithms [21,22], but typically in less time, so it enables the study of much larger networks. 3.3. Identification of Author Collaboration Before running the Random Forests algorithm, we had to adjust the data by appending the author’s cluster membership (from the previous step) to the characteristics of the author, what resulted in 556 independent variables (this is the number of all characteristics identified) in the data set. For identifying underlying patterns (e.g., shared location or discipline) in the author clusters, we chose the Random Forests algorithm [23]. This algorithm is a supervised machine learning technique that is based on classification trees. In this study, classification trees predict the author’s cluster by taking his/her characteristics into account [24]. They repeatedly split the initial data set into smaller and more homogeneous data sets based on different values for each characteristic [25]. For example, the decision tree would consider splitting the data set into separate data sets for each university if many authors of the same university also belonged to the same cluster. The Random Forests algorithm then elaborates the classification trees approach by generating a variety of trees which use bootstrap samples and a randomly selected subset of characteristics [23]. By using only a small fraction of the characteristics, Random Forests handle the problem of overfitting [26]. Only two parameters (the number of characteristics each tree is built on and the number of trees) have to be specified, whereupon the classification of new data points is predicted based on the majority vote of the tree ensemble [27]. Moreover, as the Random Forests algorithm draws a number of bootstrap samples from the initial data set, it can thus identify the classification error of the data which is not included in the bootstrap sample, the so-called out-of-bag error [27]. This measure is also an indicator to which extent the collaboration can be predicted by using the authors’ characteristics. It is possible to maximize the prediction

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accuracy by tuning the parameter of randomly selected characteristics per tree. We tuned these parameters to find an optimal solution for our data set. Furthermore, the Random Forests algorithm is able to estimate the importance of each characteristic used for the classification results [27]. If the removal of a characteristic results in a decrease of accuracy, this will indicate a high importance of this specific characteristic: Authors with this characteristic are very likely to collaborate, but unlikely to collaborate with authors from other disciplines, countries and universities. We used the R package igraph [28] to analyze the obtained data. Moreover, we implemented the Random Forests algorithm by using the R package caret [29] and randomForest [27]. 4. Results The data suggest that there is a strong preference for collaboration within the IPS². On average, one author contributed 1.63 articles and an article has 3.36 authors. The author collaboration network consists of 123 disjoint components (i.e., there are 123 separate groups of authors that did not collaborate with the other groups). Hence, an average component contains 9.20 authors, who are connected either directly or indirectly. Considering that an article has an average of 3.36 authors, it is evident that the network is highly connected—especially compared to author networks of other research fields [14,30]. The largest component consists of 575 interconnected authors, whereas the next largest component has 29 authors. Hence, there is one large community and several partly very small communities with 29 and less authors. In general, the distribution of authors in components can be regarded as uneven, what the Gini coefficient value of 0.683 confirms. Table 2 gives an overview on the network metrics. Connectance refers to the number of observed edges divided by the number of possible edges (i.e., a Connectance of 1 means that every author would have collaborated with every other author). Table 2. Network Metrics. Metric

Value

Number of Nodes

1,131

Number of Edges

3,471

Number of Components

123

Proportion of Largest Component

0.507

Connectance

0.005

Running the Louvain algorithm revealed 182 clusters in the overall network. As Fig. 2 suggests, there seems to be a correlation between the location of an author and the tendency to form a collaboration group, whereas international collaboration is rather scarce. For example, nearly all authors located in the United Kingdom form a cluster indicated by the beige-colored nodes (each color represents a cluster in the figure) that are concentrated in the United Kingdom. The same holds true for France. The majority of authors in France (67 of 71 authors) belong to the same, purple-colored cluster. Authors from these countries seem to collaborate

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countrywide. In other countries (e.g., Germany and Brazil) however, the author collaborations do not seem to rely on country solely. Nevertheless, it is also visible that there are some finer geographical patterns. Clusters identified by the Louvain algorithm are mostly located in the area of a country, what might be rooted in the same university or employer of an author group.

Fig. 3. Top 20 Important Variables (above red line) and Disciplines Variables (below red line) of the Random Forests Model.

In order to investigate whether the tendency to collaborate between authors is mainly driven by the geographical location or by the discipline of an author, we executed the Random Forests algorithm. Initially, we set the number of variables randomly selected to 289 and the number of trees to 500

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according to the methodological guidance as given above (see section 3.3 and [26,27,31]). The final resulting model had an accuracy of 0.736 (Standard Deviation: 0.0267 with a p-value of less than 0.000). That means that the (non-)affiliation of an author to a cluster was predicted correctly in 73.6% of all cases. This rather high accuracy supports our assumption of collaboration between authors with shared characteristics. For assessing our model generally, we use weighted kappa [32]. The weighted kappa value of the model is 0.730, thus the model can be regarded as substantial according to Fleiss and Cohen [33]. As the Random Forests analysis confirms, there are patterns in the collaboration of authors in the IPS² conference proceedings (Fig. 3). As regards author country, we observe that authors from specific countries tend to collaborate more strongly with authors from the same country. This is mainly observable for European countries (e.g., France, Sweden, United Kingdom, Italy, and Denmark) and Asian countries (e.g., Japan and South Korea). Especially in Japan, France, the United Kingdom, Germany and Sweden we find clusters of their own as regards author country (i.e., clusters that show no single connection to another country). These countries possess a high variable importance in the Random Forest model (except for Germany; see Fig. 3). In Denmark and Korea, there is only one single cluster each. As regards author institutions, authors from some universities (e.g., Ruhr-Universität Bochum, TU Berlin, or RWTH Aachen) tend to collaborate with authors from the same institution. There are even examples of chairs/departments that tend to only collaborate within themselves, like the Institute of Work Science (IAW), the Chair of IT in Mechanical Engineering (ITM), both being part of the Ruhr-Universität Bochum, and the Institute of Production Science (wbk) as a part of Karlsruhe Institute of Technology (KIT). Additionally, authors who are rooted in the engineering discipline tend to collaborate with other engineering researchers, as can be seen by the green bars below the red line in the Random Forest model chart (Fig. 3). Finally, the data do not suggest any significant patterns regarding the collaboration of practitioners and academics. Hence, it cannot be stated that practitioners tend to collaborate with other practitioners or academics.

Fig. 2. Locations of Authors in the Largest Component and their Clusters.

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5. Discussion Our analyses show that there are specific patterns in the collaboration of authors publishing in the IPS² conference proceedings. The resulting Random Forest model provides a high accuracy. In this case, a high accuracy corresponds with a high precision when classifying authors in clusters by their characteristics. That shows that there is a tendency of collaboration between authors with similar attributes within the IPS² community. However, this also means that both interdisciplinary and international teams of authors are actually scarce within the IPS² research community. This is surprising at first sight, since the PSS research stream is considered interdisciplinary and the IPS² an international conference. As regards the research disciplines, our observations point to the roots of the conference in the International Academy for Production Engineering (CIRP) and, hence, in the engineering discipline. Thus, it is not surprising that the authors from engineering tend to collaborate with one another (Fig. 3). However, authors from other disciplines (although there are comparatively few) tend to collaborate with other disciplines more strongly. This might result from believing that collaboration with other disciplines would help for tying into the IPS² research community. In their bibliometric study, Rabetino et al. [9] have also pointed to a “lack of interdisciplinarity” in servitization research, which “is a fragmented multidisciplinary domain composed of three sharply bounded communities that draw on different disciplines, concepts, methodologies and terminologies.” In order to strengthen the interdisciplinary discussion at future IPS² conferences, we see a fruitful avenue in inviting submissions from non-engineering disciplines more strongly. It could also be considered to stronger collaborate with practitioners, since these collaborations might create new insights for both academics and practitioners. These can yield additional perspectives on Industrial PSS and, thus, provide the starting point for new author collaborations. As regards the international author collaboration, our observations largely confirm the results by Oliveira et al. [8] as well as from other research fields [14]. Concerning the PSS field in specific, Oliveira et al. [8] also identified several geographically dispersed and disconnected co-authorship groups. Reasons for the strong tendency towards collaborations within the same country are likely to be the geographical proximity, shared language, and similar cultural background. These reasons make it easier for researchers to collaborate, since they work in a familiar environment. Especially shared language and similar cultural background appear to be key supporting variables for creating author collaborations, even apart from country borders. In Fig. 4, the light green and the dark blue cluster spread among both Sweden and Finland, two Nordic countries. The light blue cluster in Fig. 3 not only unites different Romance-speaking countries across continents such as Mexico, Brazil, Portugal and Italy, but also the both Greek-speaking countries Greece and (Southern) Cyprus. Interestingly, the USA and the United Kingdom do not belong to the same cluster. For nonEuropean countries, there is almost no international

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collaboration with other non-European countries except for few collaborations between Japan, Korea and China. The reason for this might be that nine of the ten IPS² conferences were situated in Europe—except for Tokyo, which may have promoted the East Asian collaboration.

Fig. 4. Author Clusters in Europe.

As we also included the author affiliations in our study, we can even state that the tendency of authors working together with those having similar characteristics even goes down to the university and chair/department level. Especially in Germany, the universities and chairs/departments tend to collaborate within their own affiliations. For instance, all affiliations in Fig. 3 (except for the Luleå tekniska universitet) are located in Germany. That shows that geographical proximity is also a main driver of collaboration in Germany, but on a more fine-grained level. There is no such thing as a German cluster but several clusters in Germany, mostly at distinct universities. Some particular characteristics might possibly explain these differences between Germany and other European countries. Germany is a decentralized federal state that consists of many different economical and demographical centers. This could also be a reason why there are several clusters in Italy either. Nevertheless, it is Italy on a country-level that shows high variable importance (see Fig. 3). Hence, there is more collaboration between different geographical regions in Italy. More centralized countries such as the United Kingdom and France mainly consist of only one great cluster (beige and purple, respectively in Fig. 4) which is why the respective country variables appear among the top of the list in Fig. 3. Oliveira et al. [8] already indicated that the lack of international author collaboration might be “undesirable for the progress of the PSS research field.” Looking at the IPS² community, we therefore see a fruitful avenue in hosting the conference in places beyond Europe more frequently (e.g., in Asia like this year, but also in the Americas and Africa). This will help connect geographically dispersed researchers and spark new international author collaborations.

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6. Conclusions This study set out to identify patterns of author collaboration in the IPS² conference series. By analyzing 694 articles written by 1,131 authors, this study revealed shared characteristics of authors that influence the tendency to collaborate. The results show that there are several clusters within the IPS² community and that specific shared characteristics, such as a geographically near location or the same researcher’s discipline (in the case of engineering), promote the collaboration among authors. More precisely, we see that British and French researchers tend to collaborate within their countries, whereas German researchers even tend to restrict their collaboration to their own university. The Nordic countries, the Romance-speaking world and East Asia, however, form international research clusters. Researchers from related fields such as management or psychology do not form clusters of their own, but tend to collaborate with engineers when publishing at the IPS² conference. Engineering researchers tend to collaborate with other authors from their discipline. Generally, although the overall IPS² author network appears to be well-connected, interdisciplinary and international teams of authors are scarce. This study focused on works published in previous IPS² conference proceedings only. Moreover, it is limited by the absence of potential further author characteristics that might also influence the tendency to collaborate between authors. For example, the home country (of authors, not affiliations), gender or age would be further characteristics that have been shown to influence collaborations in other research fields [14,15,34]. To develop a full picture of the author collaboration in the IPS² research community (or even the PSS field as well as the engineering discipline in general), additional studies will be needed that investigate these patterns more deeply. From a methodological perspective, this study has gone beyond prior bibliometric analyses. We followed a novel technique for refining bibliometric analyses by combining the Random Forests and Louvain method to calculate a prognostic model of author clusters formed. This approach can be used in further research, when including further author characteristics or when examining other research streams. References [1] Beuren FH, Gomes Ferreira MG, Cauchick Miguel PA. Product-service systems: a literature review on integrated products and services. J Clean Prod 2013;47:222–31. [2] Goedkoop MJ, van Halen CJG, te Riele HRM, Rommens PJM. Product Service systems, Ecological and Economic Basics. PricewaterhouseCoopers N.V. / Pi!MC; Storrm C.S; PRé consultants; 1999. [3] Meier H, Roy R, Seliger G. Industrial Product-Service Systems—IPS2. CIRP Ann 2010;59:607–27. [4] Vandermerwe S, Rada J. Servitization of business: Adding value by adding services. Eur Manag J 1988;6:314–24. [5] Thoben K-D, Eschenbächer J, Jagdev H. Extended Products: Evolving Traditional Product Concepts. 7th Int. Conf. Concurr. Enterprising, Bremen: 2001, p. 429–39. [6] Tuli KR, Kohli AK, Bharadwaj SG. Rethinking Customer Solutions: From Product Bundles to Relational Processes. J Mark 2007;71:1–17.

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