Review of Diffusion Research Abstract · 2016-12-10 · service organizations (Greenhalgh et al.,...

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Review of Diffusion Research

Pranpreya Sriwannawit* and Ulf Sandström Department of Industrial Economics and Management, KTH Royal Institute of Technology

Lindstedtsvägen 30 Stockholm 10044 Sweden *corresponding author [email protected] Tel +4687908735

Abstract

Despite the fact that diffusion research has existed for more than one century, there is no quantitative review study that covers this subject in a broad and general context. This article reviews diffusion research by providing an extensive bibliometric and clustering analysis. We identify research trails and explain the characteristics of diffusion research using new methods. We contribute a methodology for the use of advanced mapping and clustering techniques in order to describe the research areas. This method produces a fairly good overview of diffusion research and can be applied to any knowledge field to replace or complement the traditional literature review. Keywords: adoption; bibliometric; cluster; labeling; publication analysis; quantitative; research front; technology transfer JEL codes: O30; O33

1 Introduction The term “diffuse” means “to (cause something to) spread in many directions” (Cambridge Dictionaries Online, 2013). Diffusion research often refers to the diffusion of innovations. In this article, we do not only consider innovation in the diffusion process, but also include innovation as both tangible and intangible objects. For example, tangible matters cover technology and product innovation while intangible aspects encompass knowledge, norm, culture and idea. Diffusion is a social process where objects are communicated among members in the system (cf. Rogers, 2003). Members can be individuals, companies or other organizations. Thus, diffusion research in this article refers to the research that empirically studies the spread of both tangible and intangible objects. The origin of diffusion research can be traced back to the beginning of the 20th century in Gabriel Tarde’s (1903) “The laws of imitation” (see e.g. Katz, 1999; Rogers, 2003; Wejnert, 2002). His book stated that invention and imitation were the keys of cultural change. Tarde was a well-known French scholar who worked in various fields such as law, criminology, social psychology, sociology and statistics. Later on, his concepts became the basis of diffusion research (Katz, 1999). However, it almost took half a century before this field of knowledge advanced with the contribution of Bryce Ryan and Neal C. Gross, both from Iowa State University, who studied the diffusion of hybrid seed corn from a rural economic perspective (Ryan et al., 1943; Wejnert, 2002). The framework was further popularized by Everett M. Rogers, also from Iowa State University, in his book on the diffusion of innovation, first published in 1962 (Rogers, 1962) and later on in four more editions (Rogers, 1983; Rogers, 1995; Rogers, 2003; Rogers et al., 1971). Rogers’ work is an extensive collection of empirical cases and has been cited and used worldwide. Several studies indicate

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that innovation has a positive impact on the development of regions (Griliches, 1960) and countries (Bruland, 1998). In addition, Fagerberg and Verspagen (2002) indicated that innovation has been increasingly of importance for economic growth. At the same time, diffusion and imitation have also become more demanding Without diffusion, innovation would have little impact in both social and economic terms (Hall, 2005). Therefore, we strongly believe that this research area deserves more attention. Since diffusion research has existed for more than one century, there are several review studies on this subject. However, those studies are focused on specific subjects – such as service organizations (Greenhalgh et al., 2004) and modeling (Meade et al., 2006; Peres et al., 2010) – or they have been conducted qualitatively (Wejnert, 2002). The primary objective of this study is to review diffusion research by providing an extensive bibliometric and clustering analysis of this research arena in the widest perspective. Such a study is lacking and has never been conducted before. The general idea is to identify research trails or clusters that are relevant within the general area of diffusion. Also there is a secondary research aim to contribute a method using advanced mapping techniques (White et al., 1997) in order to describe patterns of research areas. Our aim is to provide an overview of a specific research area as a complement to the traditional literature review. The next section explains data sources that are used for the analysis and research methodology. In the third section, we provide the bibliometric and cluster analysis where we discuss the bibliometric map together with the characteristics and relations of the clusters. This section provides a deep and thorough understanding on the diffusion research. The article ends with the overall character of diffusion research, challenges and conclusions.

2 Methodology Bibliometrics is, in general, the application of mathematics and statistics to quantitatively analyze publications. It is also a research area with implications for research policy and innovation policy. It enables researchers to gain understanding of the development and formation of knowledge through scholar publishing (Pritchard, 1969). The rationale of using bibliometrics is that the increase of knowledge drives scientific progress. Even though publications are not the only factor affecting progress, it is certainly a very crucial element in knowledge exchange within the scientific community (van Raan, 2004). Citations, references to prior work that are used by colleagues in the community, can be considered as a reliable measure of the impact of a new knowledge contribution (Brody, 2013; van Raan, 1998; van Raan, 2004). The methodological steps can be separated to two main parts: data selection (section 2.1) and analytical procedure (section 2.2). Due to the complexity of our methodology, we provide the summary of major steps (section 2.3).

2.1 Data Selection The data were retrieved from a large pool of research articles indexed by Thomson Reuters in Web of Science (WoS) based on Social Sciences Citation Index (SSCI) database during August 2012-August 2013. The timespan was limited to 2002-2011 and the document type was limited to articles only.

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The search terms were decided after several rounds of trials and continuous consults with academic fellows. This is our first control of data selection. During the trials, we preliminary reviewed and removed the terms that we considered as irrelevant or less relevant. Five projects were created based on five topics connected to the diffusion framework in a broad sense. In each project, the articles were then clustered based on bibliographical coupling i.e. shared references. Cluster techniques were employed based on shared cited references or citation analysis (Boyack et al., 2010). A new clustering algorithm called the Leuven Method (LM) has proven to produce fairly good results on big data sets (Blondel et al., 2008). We apply that method as it includes an automatic procedure to decide the number of clusters. Consequently, our approach is based on the principle that publications sharing the same references constitute research communities based on a common language and a common world-view. Klavans and Boyack (2010) referred to that as a “paradigm”. After clusters were identified, we ran a labeling procedure based on Natural Language Processing (NLP) (Frantzi et al., 1998), which created stemmed noun phrases with stop words removed (Porter, 1980). Table 1 shows the search criteria of each project, the number of publications and the number of clusters. Table 1 Project topics and their search criteria.

Project Search term Number of publications

Number of initial clusters

Number of remaining clusters

Diffusion diffus* 13,465 48 17 Adoption adopt* 41,712 67 4

Innovation innov* 30,367 46 16

Transfer tech* transfer* policy* transfer* 5,912 26 12

Others

dissemination word of mouth

technology acceptance absorptive capacity north-south trade

7,987 42 9

The term “diffus*” was used as the main search term to capture a core of publications that fall within diffusion research. Closely related to diffusion is “adoption” which puts the focus on the end recipient of diffusion process. Diffusion and adoption are often used together (see e.g. Katz, 1999; Strang et al., 1998; Wejnert, 2002). Both terms are highly related but have different meanings. Diffusion refers to all related processes when an innovation is adopted or rejected by individuals or firms in a society over time. On the contrary, adoption has a focus on the decision making whether to adopt or reject an innovation (Hall, 2005; Rogers, 2003). Diffusion falls within the innovation framework. Thus, at an initial stage, we used the search term “innov*” in order to include any type of relevant research. Furthermore, we include a project on technology transfer and policy transfer because they are very closely related to diffusion. These concepts are also used to refer to the transfer of knowledge from university (Siegel et al., 2003) or government (Eisenberg, 1996) to industry or society (Bozeman, 2000). It is also about the commercialization of new technology (Albert et al., 2004). The last project included wider related knowledge fields. It is worth noting that there are publications that belong to more than one project in table 1. By creating five projects, this functions as the second control of our data selection so that we do not miss out relevant publications. The overlap between the five different clusters is surprisingly low; not more than about 15 % can be considered as redundant information between clusters.

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From these five preliminary projects, the total number of publications retrieved was 99,443 publications. From experience we have learned that it is possible to judge whether a cluster has potential to be included based on the top 20 most frequent noun phrases. If there is a potential, the top cited articles in each cluster were reviewed. Based on own expertise in diffusion research, which we consider sufficient yet limited, we distinguished between relevant and irrelevant clusters. The relevant ones were kept while the irrelevant ones were removed. Next, all publications from the five projects were combined into one single project. At this stage, there were 7,134 publications left, and a clustering procedure was applied once again. Following this last clustering, very small clusters that had less than 100 publications were either removed or combined with larger clusters using the actual mapping and distances as the guiding principle (see section 3). Finally, this left us with 13 clusters and a final count of 6,811 publications. These publications constitute the data set for our review study. During the data selection process, review articles were omitted as they contain many different research directions and normally a huge number of references, which could create difficulties during the clustering procedure based on references. Instead, some of the most influential review articles were manually included in a parallel process in order to enhance the understanding of clusters and research areas. The search for review articles was limited to 2000-2012 and only the most cited papers per year were chosen. Search terms were “diffus*”, “adopt*” and “transfer*”. WoS categorize publications as review articles based on the number of references. It is worth mentioning that there are less review articles from 2008 onwards which could indicate that the area has become more complex or that there is a diminishing interest in summarizing research into reviews.

2.2 Analytical Procedure After clusters were formed, a bibliographic map was created to visualize diffusion research. The mapping technique – bibliographic coupling – takes into account the cited references or similarity between articles due to shared references. The map was created using the OpenOrd layout algorithm (Martin et al., 2011). This map can provide an overview of how clusters relate to each other. There are several methods that can be used in order to identify cluster characteristics. We start with the twenty most frequent noun phrases. They are the basic descriptors of the clusters and are built on information in titles, keywords and abstracts (approximately 200 words). Reviewing the full article of top cited publications provided us with a more comprehensive view. By doing this, we were able to identify detailed characteristics of each cluster. The targets that we reviewed are listed in table 2, which explains the terms that we use in our article and the definition of the terms. Table 2 Terms and their definition

Object Term Definition

Author Most cited scholar Author whose publication is cited the most by the publications in the cluster. The author is not necessarily in the cluster.

Publication Most cited reference Publication which is cited the most by the publications in the cluster. The publication is not necessarily in the cluster.

Author Most cited author Author whose publication is cited the most by the publications in the cluster. The author is in the cluster.

Publication Most cited publication Publication which is cited the most by the publications in the cluster. The publication is in the cluster.

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The terms “author” and “publication” refer to those appearing within the cluster. They represent the so-called “research front”. Research front refers to the papers that move the front in one or other direction. Within the same cluster, there are publications that have higher impact, i.e. more cited than others. Those high impact publications are influential in forming the knowledge front within each respective cluster. Thus, the most cited author and the most cited publication are important pieces of information. In addition, the terms “reference” and “scholar” are not necessarily within the cluster. They are the “knowledge base” of the cluster. Knowledge base refers to the knowledge upon which the research fronts are built. Each cluster is based on the advancement of former knowledge. Thus, the knowledge base can partly be identified according to the most cited scholar and the most cited reference. It should be noted that author names indicated in most cited scholars and most cited authors under section 3.2 are only taken from the first author. For this procedure, we use age normalized citations in accordance with the contemporary h-index proposed as an alternative to the original h-index (Harzing, 2010; Sidiropoulos et al., 2007). To increase our understanding of each cluster, we also employed the Latent Dirichlet Allocation (LDA) method. LDA is a technique for generating a collection of recurring words resulting in the formation of “topics” (Blei et al., 2003). Based on all the publications in thirteen clusters, we used LDA in order to yield 50 topics presented as a number of ten unigrams. Methodogically, the use of this method can be considered as a triangulation of our cluster description based on bibliographical coupling; it complements the clustering based on references with a text-based method (similar to what is usually called hybrid methods). The result of LDA indicates the probability for the distribution of topics over clusters. The probability equals to 1 when all topics are equally distributed over clusters. Higher values indicate that the cluster is focused on a specific topic. For this, we applied the relative specialization index method proposed by Balassa (1965) and selected contributing terms that can describe the clusters.

2.3 Summary of Methodological Process

We are, of course, aware that our judgments to some extent could be perceived as actions of subjective minds. Therefore, an important part is the transparency of the used methods. Our judgments were conducted with self-subjectivity awareness during data selection, interpretation and analysis. We are also aware that each stage may be performed and judged differently which may yield different results. However, every step of our study was motivated based on concrete rationales and the method should be apt for replication. The methodological process is summarized in figure 1.

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Figure 1 Methodological process summarized

3 Bibliometric and Cluster Analysis

In total, there are thirteen clusters, each containing at least 100 publications and up to 1,500 publications. The basic details of each cluster are summarized in table 3. Cluster numbers and noun phrases were automatically generated by a dedicated bibliometric tool (BMX developed by Sanskript). Cluster names were chosen after a thorough review of clusters and terms that were considered as candidates to represent the clusters. Additionally, the LDA topics and approximate geographical location of the clusters in the map are presented in table 3. It should also be noted that in this section we are not aiming for an extensive grasp of all research trails. We realize that each research front has several connected frameworks. However, this study is only interested in diffusion related aspects. Thus, our focus is limited to the extent to which those lines of research are connected to a diffusion framework.

Data selection

• Retrieve data from WoS • Search with trial terms • Decide the final search terms based on 5 topics • Result in 5 projects with 99,443 publications • Remove irrelevant clusters • Combine remaining publications into 1 project and recluster • Result: 13 clusters with 6,811 publications

Analytical procedure

• Bibliographic mapping • Most frequent noun phrases • High impact author and publications • LDA • Manual review of clusters • Assign cluster names • Describe cluster characteristics and relations

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In order to analyze the patterns of diffusion research, we start our attempts with a visualization of publications as nodes in a map. Figure 2 shows a map based on a clustering procedure, i.e. bibliographic coupling, and includes cluster numbers. The dots represent the publications (nodes). It should be noted that not all cluster numbers are visible. In the map, we use an edge cut at 0.6 which means that only the strongest connection between the nodes are shown. Apparently, there is a specific pattern of dense and sparse areas in the map. Dense areas are agglomerations, clusters with many shared references (research communities). Where there are sparse areas, we can search for a phenomena called “structural holes” (Burt, 2004). A point of departure is that information within clusters tends to be rather homogeneous and redundant. Usually, production of non-redundant information is dependent on contacts in and between different clusters. Growing relations between two separate clusters implicates the existence of non-redundant information, which is also the actual definition of a structural hole. Thus, a network that bridges structural holes will provide a benefit that to some degree is additive, rather than overlapping.

Figure 2 Bibliographic coupling map

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The map can be separated into the northern and southern hemispheres. Within the same hemisphere, there are small structural holes with some bridges. The following discussion provides detailed analysis of each cluster. Some highest impact authors and publications are listed together with the most frequent journals, in descending order, to equip the readers with a rough grasp of the clusters. These are followed by descriptive explanations to provide a clearer understanding.

3.1 Southern Hemisphere To begin with, the location of cluster 3 can be considered as a center of the overall map. Taking a closer look, it is also the center node of the southern hemisphere together with cluster 0. Cluster 3 contains ground research about diffusion in various areas, which implicates that there are many connections (edges in the map) to other clusters. Cluster 0 concerns the acceptance of new technology, which can be considered as basic diffusion research. Thus, to a large extent, these two clusters form the knowledge base for other clusters at the south side. They are connected to three smaller nodes: cluster 30 to the east, cluster 81 to the west and cluster 22 to the southeast. The smallest cluster 12 does not have a (shown) node but is dispersed in the south side of the southern hemisphere. It is weakly connected to several nodes in this hemisphere focusing on the adopters’ behavior in various models of diffusion theory. Consequently, the main theme of the southern hemisphere is diffusion of technologies and different variants of the diffusion models. More discussion on the clusters in the southern hemisphere is as follows. Diffusion: Cluster 3 Most cited scholars: EM Rogers; RE Glasgow; LW Green; BF Oldenburg Most cited references: Rogers (1995); Rogers (2003); Rogers (1983) Most cited authors: RE Glasgow; DM Berwick; E Lichtenstein Most cited publication: Berwick (2003); Glasgow, Lichtenstein and Marcus (2003) Most frequent journals: Energy Policy; Telecommunications Policy; Information Society Diffusion is the largest cluster in the southern hemisphere and consists mainly of general research about diffusion in several social science disciplines. The research addresses issues in the diffusion process such as how diffusion takes place for certain market/product, how to overcome the chasm, and what the barriers are etc. Taking a closer look among top cited publications, many of them use empirical evidence from health related issues; they are about promoting better healthcare and preventing diseases. In fact, it is not only in diffusion research that healthcare sector is dominant; but it is also within other academic disciplines because a large part of the database consists of health and/or medical journals. It is a fast moving research arena that receives high attention. However, other publications, although not among the top cited ones, contain a large variety of diffusion research. Three of the five most cited references are ground research (Rogers, 1995) (Ryan et al., 1943) (Granovetter, 1973). Influential scholars are Everett M. Rogers, Russel E. Glasgow, Lawrence W Green and Brian F. Oldenburg. Rogers’ work is a compilation of research on the diffusion in a broad sense (Rogers, 1995). On the other hand, Glasgow, Green and Oldenburg are health researchers. Russel E. Glasgow, Donald M. Berwick and Edward Lichtenstein are the highly cited author; they are also health researchers. A publication on the transfer of healthcare research to practice (Glasgow et al., 2003) comes second as the highly cited publication. The first most cited publication is about the application of diffusion research and theory on the healthcare

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sector (Berwick, 2003). This cluster has a highly interdisciplinary character, which makes it a subject of interest for review studies. Examples of review research on diffusion related topics are re-conceptualizing managerial practice diffusion using rhetorical theory (Green, 2004), distinguishing between innovation generating and adopting organizations (Damanpour et al., 2006) and understanding international diffusion of technologies (Keller, 2004). Technology Acceptance: Cluster 0 Most cited scholars: V Venkatesh; FD Davis, MG Morris; GB Davis Most cited references: Davis (1989); Davis, Bagozzi and Warshaw (1989); Venkatesh and

Davis (2000) Most cited authors: V Venkatesh; FD Davis; A Bandura; R Agarwal; I Ajzen; EM

Rogers Most cited publication: Venkatesh, Morris, Davis and Davis (2003) Most frequent journals: Information&Management; Computers in Human Behavior;

Journal of Computer Information Systems This cluster concerns the issues about how adopters decide to accept and utilize technologies which depend on some certain factors that affect their decision making. In this cluster, Viswanat Venkatesh and Fred D. Davis are the dominant authors; both are the often-cited scholars and authors. They collaborate with two other most cited scholars – Michael G Morris and Gordon B Davis. All four of them conduct research in information science. Their works on user’s view and user acceptance of information technology are the highest impact references (Davis, 1989) (Davis et al., 1989) (Venkatesh et al., 2000). Their study on the user acceptance of information technology (Venkatesh et al., 2003) is the most influential publication in the cluster. Both Venkatesh and Davis’ research focus on the information technology area, thus it is natural to find many articles that study the acceptance model in this industry. However, there are many publications that are based on empirical materials from other areas and the concept can still be applied to other contexts such as organizational innovation (Frambach et al., 2002) and psychology (Lakin et al., 2003). Unlike a unified discipline of influential scholars, other well-cited authors come from more diverse disciplines i.e. Albert Bandura (psychology), Rajshree Agarwal (entrepreneurship), Icek Ajzen (psychology) and Everett M. Rogers (sociology). Research in this cluster is seen from the consumer's side rather than the producer’s side of the diffusion process. Publications in this cluster belong more to psychology and computer science disciplines. Other review studies also confirm that technology acceptance model has been applied to various fields and proved to be a robust and valid model (King et al., 2006; Venkatesh et al., 2008). However, the model itself can still be improved and extended to integrate other variables that are related to human and social change processes and also to the innovation adoption model (Legris et al., 2003). Marketing Model: Cluster 30 Most cited scholars: V Mahajan; FM Bass; C Van den Bulte; EM Rogers Most cited references: Bass (1969); Mahajan, Muller and Bass (1990) Most cited authors: DR Lehmann; D Godes; D Mayzlin; W Elmaghraby Most cited publication: Elmaghraby and Keskinocak (2003); Godes and Mayzlin (2004);

Gupta, Lehmann and Stuart (2004) Most frequent journals: Technological Forecasting and Social Change; Marketing Science;

International Journal of Research in Marketing

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The cluster puts its focus on possible models in marketing research about the diffusion of new products throughout their life cycle. The effect of word of mouth and market predictions or forcasting are also dominant in this cluster. The influential scholars are Vijay Mahajan (business), Frank M. Bass (marketing), Christophe Van den Bulte (marketing) and Everett M. Rogers (sociology). The high impact reference is the Bass model (Bass, 1969) which has become widely cited in management science. Bass model is a growth model of new products based on imitative and innovative behavior. The second most cited reference is the study which reexamines the Bass model after 20 years of development (Mahajan et al., 1990). The empirical materials behind Bass model are consumer variables. However, the authors in this cluster extend their research beyond the classic Bass model but are still within marketing discipline. The well-cited authors respectively are Donald R. Lehmann, David Godes, Dina Mayzlin and Wedad Elmaghraby. All of them are scholars in marketing research except Elmaghraby who is a scholar in management science. The frequently used publications are about dynamic pricing (Elmaghraby et al., 2003), the study of word-of-mouth via online conversation (Godes et al., 2004) and valuing firms through valuing customers (Gupta et al., 2004). The publications are strongly in marketing discipline and weakly in management discipline. In addition, some review studies attempt to provide clearer and more systematic knowledge on modeling (Geroski, 2000; Wejnert, 2002) while others provide critical reviews and propose how the models can be improved and extended (Meade et al., 2006; Peres et al., 2010). These studies implicate that marketing model is still an evolving subject and its potential for future research still abounds, despite the fact that it has existed for more than 40 years (Bass, 1969; Peres et al., 2010). Policy: Cluster 81 Most cited scholars: FS Berry; M Mintrom; JL Walker Most cited references: Walker (1969); Berry and Berry (1990) Most cited authors: C Volden; MK McLendon; CR Shipan Most cited publication: Shipan and Volden (2006); Gillman (2002); Bressman (2005);

Volden (2006) Most frequent journals: Policy Studies Journal; Publius-The journal of Federalism; Social

Science Quarterly The policy cluster addresses the diffusion of policies in various contexts and at different levels. Mainly the research covers general policy planning at federal/state (see e.g. Shipan et al., 2006) and national (see e.g. Gillman, 2002) level. The influential scholars are Frances Stokes Berry (public administration and political science), Michael Mintrom (public administration) and Jack L. Walker (political science) respectively. The highest cited reference is from Jack L. Walker and his work on the differences in the diffusion of innovation process among various states in the United States. In short, he attempted to explain why the rate of adoption of innovations varied across states. This is shown to be related to policies implemented at the state level. His empirics are based on innovation scores from 88 different programs. This set of programs covers a big variety of innovation such as advertising, infrastructure, law and education (Walker, 1969). Since Walker’s work is based on innovation in a broad sense, his article is used as a ground for several publications in this cluster which cover various fields. For example, the three most cited publications are about antismoking policies (Shipan et al., 2006), political parties and federal courts (Gillman, 2002) and law studies (Bressman, 2005). Policy scholars – Craig Volden, Michael K. McLendon and Charles R. Shipan – are the frequently mentioned authors. Most publications and authors belong to the discipline of political science. Additionally, a recent review study on

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international policy diffusion reveals that this topic still possesses large and several research gaps not yet answered. All stances – theoretical, methodological and empirical – can still be considerably explored. The topics on internationalization of politics and policies together with the globalization impact on national economic policies are also growing among political economics and comparative public policy studies (Meseguer et al., 2009). E-Business: Cluster 22 Most cited scholars: K Zhu; EM Rogers Most cited references: Iacovou, Benbasat and Dexter (1995); Swanson (1994) Most cited authors: K Zhu; KL Kraemer Most cited publication: Zhu and Kraemer (2005) Most frequent journals: Information & Management; European Journal of Information

Systems; Technovation E-business or electronic business discusses the diffusion of information within the information system and marketing disciplines. E-business is an emerging topic with many publications from recent years. It has received attention in the research community because it involves the shift towards the use of information and communication technologies to support business activities. Kevin Zhu and Everett M. Rogers are the most influential scholars. The high impact references are about information systems in organizations (Iacovou et al., 1995) (Swanson, 1994). Kevin Zhu and Kenneth L. Kraemer – experts in the management of information technology – are the dominant authors in this cluster. Not only are they the top cited authors, four of the top cited publications are also their work on information technology and e-business. The highest cited publication is titled “Post-adoption variations in usage and value of e-business by organizations: Cross-country evidence from the retail industry” (Zhu et al., 2005). Cognitive Behavior: Cluster 12 Most cited scholars: RE Goldsmith; PA Dabholkar; EM Rogers Most cited references: Midgley and Dowling (1978); Hirschman (1980) Most cited authors: MJ Bitner; SW Brown; ML Meuter; AL Ostrom Most cited publication: Meuter, Bitner, Ostrom and Brown (2005); Arnold and Reynolds

(2003) Most frequent journals: Psychology & Marketing; Journal of the Academy of Marketing

Science; European Journal of Marketing Cognitive behavior as a cluster concerns consumer behavior and consumer reactions on different products and different approaches. It also addresses how adopters make a decision to accept a new product or a new technology. This cluster has a focus on the consumer or adopter’s side instead of source and process. The most influential scholars are Ronald E. Goldsmith (marketing), Pratibha A. Dabholkar (marketing) and Everett M. Rogers (sociology). The high impact reference focuses on the relationship between innovativeness and adoption (Midgley et al., 1978). The frequently cited publication is a collaboration of the four most cited authors – Mary Jo Bitner, Stephen W. Brown, Matthew L. Meuter and Amy L. Ostrom. All of them are marketing scholars. Their publication is titled “Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies” (Meuter et al., 2005). Authors in this cluster belong mainly to the marketing discipline. The publications belong mainly to marketing, often with a focus on psychology and business aspects.

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3.2 Northern Hemisphere In the northern hemisphere, clusters 66, 61 and 14 form three connected nodes. These three clusters share a focus on knowledge and innovation. A small cluster 50, which is about knowledge with a particular focus on the catching up and technological capability building perspectives, is dispersed in this hemisphere connecting these three nodes. Overall, the main theme of the northern hemisphere is diffusion of innovation and knowledge. More discussion on the clusters in this hemisphere is as follows. Geography of Innovation: Cluster 66 Most cited scholars: ME Porter; A Amin; P Cooke; P Maskell Most cited references: Audretsch and Feldman (1996); Storper (1997); Krugman (1991) Most cited authors: H Bathelt; A Malmberg; P Maskell Most cited publication: Bathelt, Malmberg and Maskell (2004); Martin and Sunley (2003);

Boschma (2005); Gertler (2003) Most frequent journals: European Planning Studies; Regional Studies; Research Policy Geography of innovation is the dominant cluster with publications focusing on innovation and knowledge from an economic and geographical point of view i.e. clusters (see e.g. Bathelt et al., 2004), regions (see e.g. Coe et al., 2004) and proximity (see e.g. Boschma, 2005). Geography of innovation research addresses the geographic dimension of the innovation process and is connected to the economics of place and distance. The far most cited scholar is Michael E. Porter, a business economist with an expertise in competitiveness. The most influential reference is titled “R&D spillovers and the geography of innovation and production” (Audretsch et al., 1996). Economic geographers – Harald Bathelt, Anders Malmberg and Peter Maskell – are the well-cited authors. Their work about clusters and knowledge is a widely cited publication (Bathelt et al., 2004). Moreover, publications on the conceptual aspect of clusters (Martin et al., 2003), the impact of proximity on innovation (Boschma, 2005) and the economic geography of tacit knowledge (Gertler, 2003) received considerable citation scores from other publications within the cluster. Discipline-wise, publications derive mainly from economic geography and economics, which is evident from the discipline of several authors and publications. Subject-wise, this is one of the most focused clusters. Some publications are related to regional innovation system (RIS). It is widely used as an analytical framework for generating innovation policy based on empirical support to promote diffusion across geographical areas. Despite a specific focus on regions, review articles reveal that there is still a research gap and conceptual confusion. For example, the analysis is often based on success cases and high technology areas (Todtling et al., 2005). Spillover: Cluster 61 Most cited scholars: Z Griliches; AB Bernard; AB Jaffe Most cited references: Coe and Helpman (1995); Cohen and Levinthal (1989); Eaton and

Kortum (1999) Most cited authors: S Redding; W Keller; R Griffith Most cited publication: Keller (2002); Cohen, Goto, Nagata, Nelson and Walsh (2002);

Griffith, Redding and Van Reenen (2004) Most frequent journals: Research Policy; World Development; Applied Economics Spillover is the exchange or spread of activities from one area to another that has an effect on those not directly involved. In economics, spillover is a positive effect resulting from activities such as R&D and commercialization impacting on third parties. Most publications

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in this cluster are focused on subjects as technology (see e.g. Keller, 2002) and knowledge (see e.g. Cohen et al., 2002) spillover in an international level. The most influential scholar is an economist Zvi Griliches. The most influential references, in respective order, are about the model of international spillovers (Coe et al., 1995), the reasons for firms to invest in R&D activities (Cohen et al., 1989) and the model of the diffusion on new technologies across countries (Eaton et al., 1999). The frequently cited authors in this cluster are Stephen Redding, Wolfgang Keller and Rachel Griffith. All of them are also economists. Keller’s work titled “Geographic localization of international technology diffusion” (Keller, 2002) is the high impact publication in the cluster. Moreover, the studies about the reasons or incentives to perform R&D and patents (Cohen et al., 2002) (Griffith et al., 2004) – which link back to the second most cited reference (Cohen et al., 1989) – are also the high impact publications. Similarly with cluster geography of innovation, articles in this cluster belong to economic geography. A recurring topic in this cluster is foreign direct investment (FDI) which concerns the effect of FDI on firm and country levels. However, it has been shown in a review analysis that FDI spillover research renders mixed and inconsistent outcomes. It depends on several factors and often those factors have undetermined effects (Crespo et al., 2006). Knowledge Management: Cluster 14 Most cited scholars: I Nonaka; L Argote; WM Cohen; G Szulanski; MT Hansen; JS

Brown; B Kogut Most cited references: Cohen and Levinthal (1990); Szulanski (1996); Nonaka and

Takeuchi (1995); Nonaka (1994); Kogut and Zander (1992); Brown and Duguid (1991)

Most cited authors: B McEvily; PR Carlile; L Argote Most cited publication: Reagans and McEvily (2003); Argote, McEvily and Reagans (2003) Most frequent journals: International Journal of Technology Management; Technovation;

Journal of knowledge management Knowledge management research concerns the process of managing an organization’s flow of information. The topic is relevant as it addresses how information, which is abundant and often overflowing, can be handled so that it can be used in more efficient ways. This cluster discusses the topic on the management of knowledge with more of an interest in the transfer (see e.g. Reagans et al., 2003) and sharing (see e.g. Cummings, 2004) of knowledge. The most cited scholars are Ikujiro Nonaka (business management), Linda Argot (organizational behavior and theory), Wesley M. Cohen (business economics), Gabriel Szulanski (strategy), Morten T Hansen (entrepreneurship), John S Brown (organizational studies) and Bruce Kogut (business and management). The most cited references are about absorptive capacity (Cohen et al., 1990), internal knowledge transfers in firms (Szulanski, 1996), the creation of knowledge in organizations (Nonaka, 1994; Nonaka et al., 1995), firm’s knowledge (Kogut et al., 1992) and learning in organizations (Brown et al., 1991). The influential authors are Bill McEvily (strategic management), Paul R. Carlile (information systems) and Linda Argote. The influential publications are titled “Network structure and knowledge transfer: The effects of cohesion and range” (Reagans et al., 2003) and “Managing knowledge in organizations: An integrative framework and review of emerging themes” (Argote et al., 2003). Several publications and authors in this cluster belong to the broader disciplines of management and business economics. There is a similarity of this cluster with cluster spillover. However, the unit of analysis in cluster spillover is on country or regional level while the unit of analysis in cluster knowledge management is at the organizational level. Nonetheless, knowledge management is not a unitary body of literature. There are several review studies attempting to

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describe various streams that belong to this large framework e.g. knowledge management in product innovation (Corso et al., 2001), application of information technologies to manage knowledge (Alavi et al., 2001), international knowledge transfer (Werner, 2002), R&D knowledge transfer (Cummings et al., 2003) and knowledge networks (Phelps et al., 2012). Technological Capability: Cluster 50 Most cited scholars: L Kim; D Ernst; S Lall; M Hobday; M Bell; I Nonaka; RR Nelson;

WM Cohen Most cited references: Kim (1997); Cohen and Levinthal (1990); Nonaka and Takeuchi

(1995) Most cited authors: D Ernst; L Kim; PN Figueiredo; Y Zhou; M Hobday; N

Carbonara; J Howells Most cited publication: Ernst and Kim (2002); Zhou and Xin (2003); Howells, James and

Malik (2003) Most frequent journals: International Journal of Technology Management; Research

Policy; Journal of the Asia Pacific Economy Technological capability refers to the embedded resources, which equip firms with the abilities to manage and generate technological change. Such resources can be acquired through a learning process (Figueiredo, 2003). This cluster focuses on catching up and building of technological capability. There are many publications about emerging economies (see e.g. Figueiredo, 2003; Zhou et al., 2003). The most influential scholar is Linsu Kim, a researcher working with technology/innovation management. Kim’s book on technological capability building in Korea (Kim, 1997) is the most influential reference. Kim is the second most cited author. The top cited author is Dieter Ernst, an innovation economist, who is also the second most cited scholar. Both Ernst and Kim collaborate on a paper titled “Global production networks, knowledge diffusion, and local capability formation” (Ernst et al., 2002) which is the top cited publication in the cluster. Similarly with clusters geography of innovation and spillover, articles in this cluster belong to economic geography discipline with a certain degree of development studies.

3.3 Miscellaneous There are three small clusters that do not form distinct nodes but spread in the map: clusters 54, 57 and 58. Cluster 57 spreads in the central part of the map not relating to any particular cluster. Clusters 54 and 58 have some relations with the larger clusters 61 and 30. Cluster 54 spreads in the central east side between the northern and southern hemispheres as a bridge connecting cluster 61 with 30. This is because cluster 30 is not only about marketing. In order for marketing research to be complete, finance (cluster 54) must also be considered. Spillover (cluster 61) is the consequence of both marketing (cluster 30) and financing (cluster 54). On the other hand, cluster 58 spreads towards the east and connects to the nodes of cluster 61 and 30. Unlike cluster 54, cluster 58 does not bridge these two larger clusters because cluster 61 and 30 address IPR topic (cluster 58) in different perspectives. IPR is an attribute of different activities but it is not in the movement of spillover (cluster 61) and marketing (cluster 30) research lines. The connection between cluster 61 and 58 may be because there are competitors when spillover takes place making IPR an important aspect. This connection is also confirmed by the shared LDA topic term “spillovers”. With regards to the connection between cluster 30 and 58, it can be explained that IPR is relevant for marketing activities

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because there are several legal aspects involved. More discussion on clusters 54, 57 and 58 is as follows. Financing Innovation: Cluster 54 Most cited scholars: P Stoneman; E Mansfield; S Klepper Most cited references: Kiefer (1988); Rose and Joskow (1990) Most cited authors: M Puri; T Hellmann; WJ Henisz; R Agarwal; BL Bayus Most cited publication: Hellmann and Puri (2002); Henisz (2002); Agarwal and Bayus

(2002) Most frequent journals: Research Policy; Journal of Banking & Finance; International

Journal of Industrial Organization Publications in cluster financing innovation are held together by an interest for innovations from an economic disciplinary perspective: investing in production and technologies related to firms and technology transfer mainly during the start-up phase. Most cited scholars are economists such as Paul Stoneman, Edwin Mansfield and Steven Klepper. The influential references are “Economic duration data and hazard functions” (Kiefer, 1988) and “The diffusion of new technologies: Evidence from the electric utility industry” (Rose et al., 1990), respectively. The frequently cited publications are based on empirical evidences about the impact of venture capital on start-up firms (Hellmann et al., 2002), determinants for infrastructure investment (Henisz, 2002) and market takeoff of product innovations (Agarwal et al., 2002). Their authors are also high impact authors in the cluster. Network Effect: Cluster 57 Most cited scholars: ML Katz; N Gandal; J Farrell Most cited references: Katz and Shapiro (1985); Gandal (1994); Farrell and Saloner

(1985) Most cited authors: A Goolsbee; A Bonaccorsi; C Rossi Most cited publication: Goolsbee and Klenow (2002); Bonaccorsi and Rossi (2003);

Bailey, Li, Mao and Zhong (2003) Most frequent journals: Telecommunications Policy; Information Economics and Policy;

Energy Policy Cluster network effect contains publications about the positive effect of network externalities as a result of other adopters i.e. the more users, the more valuable the products become. Several noun phrases in this cluster are about telecommunication because telephone is a classic example of positive network effect: the more people using telephones, the more valuable they are for their adopters. Many publications in the cluster base their analysis on telecommunication and information technologies. Michael L. Katz – an economist specialized in intellectual property and network – is the top cited scholar. His work titled “Network externalities, competition, and compatibility” (Katz et al., 1985) is the top cited reference. Austan Goolsbee, Andrea Bonaccorsi and Cristina Rossi – researchers in economics and management – are the high impact authors. Their publications (Goolsbee et al., 2002) (Bonaccorsi et al., 2003) are highly cited. This cluster contains publications within the disciplines of economic business and management. IPR: Cluster 58 Most cited scholars: AB Jaffe; KE Maskus; E Mansfield; P Almeida; Z Griliches; GM

Grossman; E Helpman Most cited references: Jaffe, Trajtenberg and Henderson (1993); Almeida and Kogut

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(1999); Helpman (1993) Most cited authors: J Alcacer; ELC Lai; M Gittelman; F Murray Most cited publication: Alcacer and Gittelman (2006); Murray and Stern (2007);

Grossman and Lai (2004) Most frequent journals: Research Policy; Journal of International Economics; Rand

Journal of Economics Cluster IPR or intellectual property right addresses legal aspects to protect intellectual properties such as patent and knowledge. There are studies on spillover and a small fraction of publications are about piracy. The influential scholars are economists Adam B. Jaffe and Keith E. Maskus. The top cited reference is about knowledge spillovers observed through patent citations (Jaffe et al., 1993) and knowledge and mobility (Almeida et al., 1999). The influential authors in descending order are Juan Alcacer (business), Edwin L.-C. Lai (economics), Michelle Gittelman (management and business) and Fiona Murray (management). The highly cited publications in the cluster are from these authors in this order about measuring knowledge flows though patent citations (Alcacer et al., 2006), the effect of IPR on knowledge flow (Murray et al., 2007) and international intellectual property protection (Grossman et al., 2004). Most publications emanate from economics and law. Some publications (see e.g. Chen et al., 2005; Grossman et al., 2004) and authors (e.g. Maskus and Lai) in this cluster have a focus in emerging economies because IPR is an ongoing issue in those countries. In addition to the presentation of each cluster above, we present the journals containing most publications in our data set in table 4. This information is useful for scholars who are interested in learning or publishing diffusion related research. There is an interesting aspect regarding the movement of research fronts. There are two obvious movements: cluster 3/0 in the southern hemisphere and cluster 66/61 in the northern hemisphere. With regards to the movement between cluster 3 and 0, it is obviously two different sides of the diffusion coin. While cluster 3 focuses on the producer’s perspectives, cluster 0 moves towards technology acceptance with a focus on the users’ points of view. The movement between cluster 66 and 61 may be because the researches were initially theoretically based on spillover (cluster 61) and then move towards putting more emphasis on geographic and regional dimensions (cluster 66). The discussion above shows that LDA topics confirm and complement other cluster characteristics. There is only one common topic shared by more than one cluster i.e. topic “spillovers” shared by cluster 61 and 58. The rest of the LDA topics are not shared by any other clusters. This means that each cluster possesses its own topics and attributes. Clusters with fewer topics have more precise characteristics and are more focused than those with several topics.

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Table 4 Important journals in diffusion research (sorted in a decreasing order of number of publications)

4 Final Remarks

The dominant disciplines in diffusion research are business economics, innovation studies and business management. Everett M. Rogers (sociology) is the most influential scholar in all clusters combined. Ten other prominent scholars are Michael E. Porter (business economics), Ikujiro Nonaka (business management), Philip Cooke (economic geography), Wesley M. Cohen (business economics), Adam B. Jaffe (economics), David B. Audretsch (economics), Zvi Griliches (economics), Fred D. Davis (information systems), Richard R. Nelson (evolutionary economics) and Viswanat Venkatesh (information systems). Most of them are economists. Even though, diffusion research nowadays is highly interdisciplinary in character, this research stream has been built largely upon various branches of economics. However, the application of diffusion research span across various fields. We attempted to describe the characteristics of various different research streams or clusters within the broad framework of diffusion research. Thus overlapping of disciplines is natural such as the clusters 66/61/14 in the northern hemisphere. Regarding the most influential reference of all clusters combined, Rogers’ extensive books (in various editions) on the diffusion of innovation (see e.g. Rogers, 2003) rank number one. His books also appear among the top cited references in several clusters. They penetrate through various arenas assumedly due to the extensive collection of a large variety of empirical work

Journal Number of Publications

European Planning Studies 158

Research Policy 133

Technological Forecasting and Social Change 131

International Journal of Technology Management 112

Regional Studies 108

Technovation 98

Energy Policy 79

Telecommunications Policy 67

Information and Management 67

Annals of Regional Science 56

Environment and Planning 55

World Development 48

Management Science 48

African Journal of Business Management 47

Journal of Economic Geography 45

Small Business Economics 42

European Journal of Information Systems 41

International Journal of Information Management 39

Industrial Management and Data Systems 39

Urban Studies 38

Entrepreneurship and Regional Development 38

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giving support to a proposed general theory of diffusion. Five other most cited references are Cohen and Levinthal’s work on absorptive capacity (1990); Nonaka and Takeuchi’s book about knowledge creation in companies (1995); Jaffe, Trajtenberg and Henderson’s article about knowledge spillovers (1993); Bass’s infamous model (1969); and Davis’s analysis on user acceptance (1989). All of them are based, at the varying extent, on empirical findings. Diffusion research relies heavily on empirical studies. High impact publications (frequently cited) rely on empirical data supporting and changing theories in modest ways. Even though those empirical data are derived from specific areas, it seems as diffusion theories can be applied to multiple contexts. This can be seen from the following clusters. The top cited references in cluster 3 are from Rogers (1995) and Ryan and Gross (1943); both have very strong empirical support. In cluster 0, the dominant authors – Venkatesh and Davis – based their analysis on concrete evidence from information technology (see e.g. 2000). The Bass model (1969) in cluster 30 is based on consumer variables. In cluster 81, Walker’s studies use the adoption rate of various types of innovation (1969). The main challenges that we have encountered in this study are (1) the delineation of the research area and (2) the difficulty in describing cluster characteristics and research trails only from publication abstracts. We solved these challenges by (1) developing a search term strategy with several rounds of trials and consulting other academic fellows and (2) implementing bibliometric algorithms and performing a manual check of full publications and authors. Manual check on review articles reveal that diffusion framework, despite its long existence, are still debatable. Research contributions in all stances – conceptual, methodological and empirical – can contribute to the advancement, better understanding, more unified view and wider applicability of diffusion research. In conjunction with the research aims, our contributions are two-fold. First of all, we explain the characteristics of diffusion research in the most extensive, objective and general way as has never been conducted before. This contributes to the advancement of diffusion framework and provides better understanding of this research arena. Our second contribution is of methodological character. We present a strategy of labeling and cluster techniques, which was proved to provide a good overview of not only diffusion research but can also be applied to any knowledge field. This method complements traditional literature review as it allows the researchers to work with large amount of publications. It also reduces subjectivity and bias because the review is not based on favorite theories or authors but on concrete statistical aspects. Acknowledgments We are grateful to the participants of INDEK Working Paper Seminar in Stockholm, 2013. In particular, the opponents – Anders Broström and Pernilla Ulfvengren – together with the editors – Kristina Nyström and Charlotte Holgersson – have given useful and constructive comments. Moreover, the participants of IAMOT conference in Porto Alegre, 2013 provided valuable inputs. We also thank Staffan Jacobsson for his insightful recommendations. Lastly, Staffan Laestadius has provided advices through out the entire study.

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