- Open Access
Mapping hotspots and emerging trends of business model innovation under networking in Internet of Things
© The Author(s). 2018
- Received: 8 February 2018
- Accepted: 18 April 2018
- Published: 2 May 2018
Networking in Internet of Things (IoT) has had an immeasurable impact on the existing business models. In this context, exploring the hotspots and trends of business model innovation has become particularly necessary. For the topic literature over the past 20 years retrieved from Science Citation Index and Social Sciences Citation Index databases, scientometrics with information visualization technology was used to carry on the knowledge mapping with the following indicator: the co-cited reference networks, reference bursts, keyword bursts, and keyword co-occurrence networks. The results show (1) “e-commerce,” “open source,” “performance,” “entrepreneurship,” etc. are the main hotpots, and “value creation,” “open innovation,” “small business,” “networks,” etc. are the new hotpots; (2) the trends of hotspots transited from early “information technology” to later “self-service,” “mass customization,” and “biotechnology” and to present “cloud manufacturing,” “telemedicine,” “climate change,” and “sustainable development,”. etc.; (3) “intelligent robot,” “3D printing,” and the methodology of business mode innovation may be the future hotspots. This is the first paper visualizing the hotspots and emerging trends of business model innovation specially through scientometrics from a global perspective.
- Business model innovation
- Internet of things
The IoT is a large network which realizes machine-to-machine communication that integrates the current devices, such as RFID devices, sensors, and other equipment and services. IoT extends the form of interaction between people to the interaction between people and things as well as things and things and then establishes a new ecological environment [1, 2]. Actually, the IoT is a kind of special result of networking. Networking has had an immeasurable impact on the existing business models. The networking of social networks and the networking of information are promoting the transformation of traditional business models [3, 4]. Networked social network has changed the organizational structure and operation strategy of traditional business models [5–9]. Information networking, especially the wireless communication network, has changed the information flow and decision-making mechanism of traditional business models [10–12]. In addition, in view of the growing value of the network [13, 14], enterprises have seen the way to promote the development of the enterprise by optimizing the value network. Therefore, networking in IoT also has the characteristics that affect the existing business models. In this context, exploring the hotspots and trends of business model innovation has become particularly meaningful for the development of IoT and related industries.
Drucker Peter, the father of modern management, argues that “Today’s competition between enterprises is not the competition between products, but the competition between business models” . Under the high attention of all circles of society, the relevant researches about business model innovation have rapidly developed. Some scholars studied dynamic problems of the business model innovation. For example, Afuah and L. Tucci  thought business model is a system composed of three factors: the different component parts, the connection relations between parts, and the dynamic mechanism of system. Osterwalder  believed that business model innovation can be driven not only by the supply chain but also by the demand chain in the value system. Zeng and Song  summed up the driving forces of business model innovation, namely the marketization of new technology, market environment pressure, and the market opportunity to promote development. There are some other scholars studying the paths of business model innovation. For example, Mitchell and Coles  proposed three necessary conditions for business model innovation. Osterwalder et al.  put forward three steps of business model innovation. In addition, some scholars focus on the business model innovation method. These methods include SWOT analysis, the long tail theory, business model analysis model, value chain analysis, value stream analysis, value net analysis, activity-based management, process management and analysis tools, business model elements figure, reference method, correlation analysis method, method of key factors, value innovation, and Theory of Inventive Problem Solving [21, 22]. What is more, a few scholars have carried out the research on the trend of business model innovation. Zhao  studied the discipline, country, and knowledge base of business model innovation literatures and used keywords to analyze the development trend of business model innovation. This research provides a useful reference for this paper. However, after summarizing the previous research, we find that it is still lack of systematic research on “hotspots and their evolution trends of business model innovation,” which is just the focus of the business model innovation, especially in IoT. Therefore, we will visualize business model innovation in this paper to reveal the research hotspot and development trend of business model innovation in different historical periods. The paper can boost the development of business model innovation theory and offer guidance and reference for the enterprises’ business model innovation.
The remainder of the paper is organized as follows: in Section 2, the authors describe the research tools and research methods and explain the data sources and structure. The hotspots and trends of business model innovation of core dataset are analyzed, and the comprehensive analysis based on extended dataset are explored in Section 3. Conclusions and discussion about future work are presented in Section 4.
2.1 Scientometric analysis by information visualization technology
In recent years, with the rapid growth of the scientific literature and the development of network technology, the large scale document processing software and data visualization technology can be used to deal with massive scientific literature data. With the help of information visualization technology, scholars can more easily observe, browse and understand the information, and find the rules and patterns hidden in the data. Information visualization technology is the theory, methods, and techniques using computer graphics and image processing technology to convert data into graphics or images displayed on the screen . Among them, the CiteSpace series application software based on Java platform developed by Professor Chaomei Chen from Dexrel University in the USA is a new generation of information visualization technology, which is suitable for multiple, dynamic, and complex network analysis and has become a new method of scientific metrology . CiteSpace can reveal a citation network map about the evolution of a knowledge field. The basic knowledge of node literature and the co-citation cluster characterization can be marked automatically on the map. In CiteSpace, a field of scientific knowledge map can be reflected through a variety of different types of network. It is designed to synthesize and visualize a time series of individual networks extracted from each year’s publications. The resultant network can be divided into clusters. The size of a cluster is decided by the number of its members, which reflect the degree of hotspots. Clusters with the late average publication year can reflect the emerging trends in a certain field . For literature co-citation network and keyword co-occurrence network, clustering represents the aggregation of similar network nodes including cited literature and keywords. The size of node represents the cited frequency of literature or the occurrence frequency of keywords. The color line represents the time of first co-citation literature or co-occurrence keyword. According to the cluster size and the average time of publication, we can judge a hot topic in the field and its time trends. In addition, the burst detection based on Kleinberg’s algorithm on the cited literature and keywords can show the hotspots and their evolution process of a field during different time period. A burst begins usually in a short period of time like a single year, but it can also last for multiple years, which provides the evidence that the publication evidently has attracted an extraordinary degree of attention from its scientific community. The cited literature cited unusually frequently, and the occurring keywords with the abnormal prominent frequency are called burst literature and burst keywords individually [27, 28].
In addition, VOSviewer owns text mining functionality that can be used to construct and visualize bibliometric networks of important terms extracted from a body of scientific literature presented in various different ways. Through its co-occurrence keyword map, research hotspots and trends in a certain field can be more clearly showed [29, 30].
2.2 Data collection
In order to obtain more comprehensive literature data of basic science and social science research, the databases Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) from Web of Science are used as the data retrieval sources. The advanced search mode is used, and the search structure is set to TS= (“business model” OR “business mode” OR “commercial model” OR “commercial mode”) AND TS= (innovate OR innovati*). It is because that the earliest literature on business model innovation is 1997 in SCI and SSCI databases that the time range used to collect data is set to 1997–2016 in this paper. In addition, in order to improve the quality of analysis, we only select two types of literature, Reviews and Articles, as the data source, and then refined the retrieval results. The retrieved data is divided into the core dataset and the extended dataset. Among them, the dataset collected directly by subject search is called the core dataset, and the extended dataset is a larger dataset which is obtained after clicking the citation report of Web of Science. The retrieval results are that the core dataset includes 602 results and the extended dataset includes 4544 result (the search time is December 30, 2016). In this study, we carry out the related research with the core data set firstly and then conduct further research with the complete dataset integrating the two datasets.
3.1 Hotspots and trends based on co-cited references of the core dataset
TOP 10 clusters of core dataset
Aver. year of publications
Business model innovation
Open source software
Business model challenge
Business model challenge
Sensing business model
Service business model innovation
Technology management issue
In order to analyze the research topics of hotspot clusters deeply, “creating value” and “business model challenge” are selected for detailed analysis.
3.1.1 (1) The biggest hotspot—“creating value”
Clustering #0 is named “creating value” by the TFIDF algorithm and “business model innovation” by the LLR algorithm. The cluster contains the same literature though the extracted themes are different. So there is no influence on the analysis of the literature. It is because business model innovation can be reflected more deeply by creating value that we choose “creating value” as the cluster label. As the largest cluster, #0 cluster contains 127 papers. The average year of publications is 2007, showing that the research results are the leading edge, and the silhouette score 0.773 indicates it has a better clustering quality. The content of the literature is related to the value creation or business model innovation in this cluster. Among them, there are some scholars studying the relationship between value creation and business model. For example, Svejenova et al.  put forward the personal business model, believing that the creation, acquisition, and sharing value can affect the development of business model directly. Wirtz et al.  analyzed the four types of Internet business model and Web 2.0 trends, presenting the main features of business model are creation and acquisition value, and integrating Web 2.0 into the business model can create better value; there are scholars distinguishing the business model from the perspective of value creation. For example, Sanchez and Ricart  identified the isolated business models and the interactive business models from the perspective of value creation after analyzing the data of multiple cases and the strategy of the low-income market. They think the isolated business models use existing opportunities to enter new markets and the interactive business models need to integrate internal resources to create opportunities, and some scholars describe the importance of creating value in the business model. On the basis of theoretical construction and case study, Wu et al.  found that the proper value proposition can attract more customers by business model, which is conducive to the better operation of business model, and other scholars study how to carry out the business model innovation with creating value. Yunus et al.  proposed that business model innovation can be done through the new value propositions.
3.1.2 (2) The emerging hotspot—“business model challenge”
Business model challenge is the fifth cluster (#4), including 52 articles. The silhouette score is 0.914, which indicates that the quality of cluster is high. The average publication year 2011 is the latest at the top 10 clustering, which shows the cluster topic is cutting-edge. The literature in cluster study mainly the challenges and obstacles faced by the business models in different fields. Based on a large number of case studies, Karakaya et al.  explored the business model challenges faced by a solar company in a small German town, combining with the local situation. After analyzing the evolution and obstacles of the four different types of business models during the period of 2006–2010 in electric vehicle enterprises, Bohnsack et al.  put forward that the environmental protection technologies are helpful to create economic value with business models, and the existing electric vehicles will be gradually turned to multipurpose vehicles with the passage of time. Christensen et al.  believed that the business model innovation of the renewable energy system needs the support of creating value, value capture, and technology innovation in the theoretical level, and there are still some challenges in terms of policy support and promotion in practice. Ernkvist  put up the continuous introduction of technology innovation, and the new business model can gradually enhance the status of electronic options trading in the financial sector. The main challenges in the process are the cumbersome and limit of regulatory process. Hermann et al.  developed a conceptual framework to demonstrate the application of the business model of product service systems in the maritime industry and researched the business model challenges of ballast water treatment system with the case of the Danish Maritime Industry.
3.2 Hotpots and trends based on burst literature of core dataset
Top 8 literatures with the strongest burst in the same period (1997–2016)
AMIT R, 2001, STRATEGIC MANAGE J, V22, P493
CHESBROUGH H, 2002, IND CORP CHANGE, V11, P529
MANGEMATIN V, 2003, RES POLICY, V32, P621
MAGRETTA J, 2002, HARVARD BUS REV, V80, P86
CHESBROUGH H, 2006, OPEN BUSINESS MODELS, V, P
MORRIS M, 2005, J BUS RES, V58, P726
CHESBROUGH H, 2010, LONG RANGE PLANN, V43, P354
TEECE DJ, 2010, LONG RANGE PLANN, V43, P172
The first paper  was published on Strategic Management Journal in 2001. The paper burst from 2005 to 2009, which is the earliest burst literature. The authors Amit R and Christoph Z developed a value creation sources model, thinking that the value creation potential of electronic business enterprise are determined by the efficiency, the complementarity, the locking, and novelty through comparing with the business models of 59 e-commerce enterprises from the USA and Europe. Obviously, the value creation of e-business enterprises is one of the hotspots in the study of business model innovation. The second paper  bursts from 2006 to 2010. Its bursting strength reached 15.4612, ranked in the top second so as to be an extremely important literature in the research field of business model innovation. The paper was published in Industrial & Corporate Change journals in 2002. Chesbrough H and Rosenbloom RS believed that the potential value of technology can be mined through business model. Taking Xerox Co as an example, they improved the business model of this company through analyzing the potential value of technology in different business model. The third paper  identified two different business models that are relevant to the research firms and the companies focusing on niche markets and defined the development trajectory and the essence of each business model by analyzing 60 French Biotechnology SME. The fourth paper published by Magretta  elaborated the concept and connotation of business model, believing that a good business model is a vital part of business success, and the business model can improve the business strategy. The fifth literature  is a monograph published in 2006 by Harvard Business School entitled “Open Business How Thrive to in the New Innovation Landscape.” The author believed that the modern business leaders must open their horizons and fully share the external intellectual property rights by adopting “open and innovative” business model. The author also proposed a diagnostic method for business model, put forward the concept of “innovation intermediary” for the first time, and pointed out the road map linking innovation and intellectual property protection. The sixth paper was published by Morris et al. . The burst intensity of the paper reached 10.7677. Six constituent elements of business model were proposed, and the application of the three kinds of business models was listed in this paper. The paper was published earlier, but 6 years later in 2011, it just began to burst, which reflects this paper has been recognized and valued by more scholars in recent years. Without doubt, it is a very important hot literature in the field of business model innovation. Both the seventh paper  and the eighth paper  were published in 2010 and burst till now, which make them be the most emerging research hotspots. Article 7 is a new research result of Chesbrough H after the above literatures 2 and 5. The author pointed out that the competitiveness of enterprises could not be fully promoted by increasing investment and exploring new technology rather than conducting business model innovation. The author also believed the main obstacles to the current business model innovation are the conflict between the existing assets and business models. Article 8 bursts from 2013, and its burst strengthens up to 16.6056 in a short period of 3 years, which has make it become the one bursting fastest and concerned highest. The paper entitled “Business models, business strategy, and innovation” was published on Long Range Planning in 2010. The author DJ Teece designed a specific business model by using the value creation mechanism, thinking the nature of the business model is the way to provide value to customers and to guide customers to pay the value. This research has become a classic enterprise theory which links the business model with the economic theory of business strategy, innovation management, and so on.
3.3 Hotpots and trends based on keyword co-occurrence network of the core data
3.4 Hotspots and trends based on co-cited references of extended dataset
TOP 10 clusters of extended dataset
Aver. year of publications
Local solar company
Upper echelon perspective
Global village v
3.4.1 (1) The most popular hotspot—“open source”
The five most cited papers in the “open source” cluster
Chesbrough H (2006)
Laursen K (2006)
Hair JF (2006)
Bonaccorsi A (2006)
Chesbrough H (2006)
3.4.2 (2) The most cutting-edge hotspot - “small business”
The five most cited papers in the “small business” cluster
Hotho S (2011)
Huang KH (2011)
Goktan AB (2011)
Lee SM (2012)
3.5 Hotspots and trends based on burst keywords of extended dataset
In order to explore the hotspots and trends of business model innovation for the topic literature over the past 20 years retrieved from SCI and SSCI database, scientometrics with information visualization technology was used to carry on the knowledge mapping with the following indicator: the co-cited references networks, reference bursts, keyword bursts, and keyword co-occurrence networks. The conclusions are as follows.
Firstly, “value creation,” “disintegration,” “e-commerce,” “open source software,” “small companies,” “performance,” “entrepreneurship,” “alliance,” “strategic innovation,” “market orientation,” and “capabilities,” etc. are the main hotpots. “Business model challenge,” “service products architecture,” “open innovation,” “product innovation,” “small business,” “renewable energy,” “networks,” etc. are the new hotpots. Secondly, the trend of hotspots transited from “information technology” at the end of twentieth century, “internet,” “e-commerce,” “self-service,” “mass customization,” and “biotechnology” at the beginning of twenty-first century to present “cloud manufacturing,” “cloud computer,” “corporate social responsibility,” “telemedicine,” “climate change,” and “sustainable development,” etc. In addition, the paper [44, 50, 51], etc. plays an important role in the business model innovation research. In recent years, with the emergence of big data, cloud computing, IoT, and other emerging technologies, business model innovation in combination with these trends have been some new hotspots. What will be the hotspot in the next stage? Perhaps the business model innovation of “intelligent robot,” “3D printing,” or “virtual reality (VR)”is placed in the front of academia and industry. Besides, there is a lack of research on the business mode innovation of networking and IoT. The above research results have shown that research on business mode innovation of networking and IoT is very scarce. Therefore, the research on business model innovation under networking in IoT will be an important hot area in the future.
In order to improve the quality of data, we selected articles and reviews as the research object, which may omit some important research results. Besides, top 100 and top 400 were identified as the analysis indicators, which may not reflect the effect of time factor. In the follow-up study, data sources and data screening criteria will be optimized so as to improve the quality of research continuously.
We gratefully acknowledge Miss Gaixia Li from the University of Science and Technology Liaoning for polishing the language of the translation,
The study is supported by the National Natural Science Foundation of China (grant 71572031) and the National Natural Science Foundation of China (grant 71372121).
Availability of data and materials
The data were collected from the Web of science database.
YJ is the main writer of this paper. He proposed the main idea and carried on data extraction and analysis of the main parts. The parameters used in the paper are determined by SJ. The research on burst words were analyzed by SJ, too. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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