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Review

Bibliometric Method for Manufacturing Servitization: A Review and Future Research Directions

College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(14), 8743; https://doi.org/10.3390/su14148743
Submission received: 8 June 2022 / Revised: 8 July 2022 / Accepted: 13 July 2022 / Published: 18 July 2022
(This article belongs to the Special Issue Advances in Manufacturing Sustainability in the Industry 4.0 Era)

Abstract

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To gain sustainable development, it is a trend that manufacturing companies are change the value chain from manufacturing-centric to service-centric. Therefore, the capability of the manufacturing service is as significant as the production ability of enterprises, which reflects the supply chain management (SCM), flexible production, production efficiency, and other indicators of the enterprises. It is the first paper to discuss the sustainability of service-oriented manufacturing using bibliometric analysis. It derives a detailed review and future outlook on the development of manufacturing servitization, indicating the research directions for future development, and provides a valuable reference for researchers in related directions. The bibliometric analysis discusses countries or regions, research areas, authors, keywords, institutions, and journals based on the literature data from the Web of Science (WoS). The results show that research on manufacturing services has gradually received attention since its inception and has become popular since 2008. The papers published from 2008 to 2021 account for 77.62%. The USA is the most studied country on this topic, followed by China and the UK. The International Journal of Production Research regarding the most quantity of articles, and Beihang University is the most influential institution in this field. The largest amount of articles published in the area of “business and economics”, amounting to 1565 articles. In recent years, the main research areas included “Industry 4.0”, “cloud manufacturing (CMfg)”, “Internet of Things (IoT)”, “big data” and “services innovation”. Finally, “digital and intelligent manufacturing” and “product-service systems” are potential research directions for the future.

1. Introduction

According to a literature survey, academic research on the servitization of manufacturing began in the 1980s. At that time, Vandermerwe and Rada [1] introduced “manufacturing servitization”. They defined it as increasing the value of core products, and business managers must add a more complete “product-service bundle” by considering customer needs as a whole. Manufacturers must focus on customer-centric services, support, self-service, and knowledge. This view resonated with practitioners, prompting product-oriented companies to develop service growth strategies. In 2009, Baines et al. [2] described the servitization of manufacturing as “service-oriented innovation in organizational capabilities and processes”. They believed that product service systems (PSS) can bring more value to customers. Therefore, product-service innovation and PSS have also become a research area of manufacturing servitization.
Manufacturing servitization has been studied in various aspects. Opresnik and Taisch [3] proposed to combine big data and servitization to create a new basis for decision-making in enterprises. This approach can reduce costs, improve efficiency, increase business opportunities, and increase revenues. It becomes a unique and sustainable model for enterprises. Kohtamäki et al. [4] analyzed the correlation between digitalization and enterprise efficiency. They suggested that manufacturing companies should actively digitize to serve production and customer needs better. From a value chain perspective, Rymaszewska et al. [5] indicated adding the IoT to the manufacturing production processes by acquiring data and using it for the serviceability and profitability of the enterprise. Coreynen et al. [6] investigated how digital methods can extend the servitization function of manufacturing through four cases. Liu [7] et al. developed an optimization model of task scheduling to achieve load balancing of distributed resources and efficient utilization of manufacturing resources in a cloud manufacturing model. Kohtamäki et al. [8] analyzed the cases of four companies to illustrate the difference between a product and a service solution as a product, which helps to explain the development model of the servitization process in manufacturing.
The COVID-19 pandemic has profoundly affected and changed the global and regional economic activity, and manufacturing [9,10], which has accelerated the transformation of manufacturing. With the development of “Industry 4.0”, the service level has become the standard for measuring the manufacturing capability of a country, and various production support modes have derived from it. Therefore, the study of the servitization of manufacturing is necessary. Currently, there are many studies related to the servitization of manufacturing, but all of them are in different directions. For the sustainability of servitization of manufacturing, it is necessary to analyze the literature over these 30 years to review the development and make recommendations for the future.
In contrast to the above literature, a bibliometric perspective is applied to this paper. Rousseau [11] mentioned that Otlet first introduced bibliometry in 1934 in Traité de Documentation. It was defined as measuring indicators of various aspects of a publication. In 1969, Pritchard [12] defined bibliometric as “the application on books and other spread media of mathematic and statistic methods”. With the development of statistics and information science, bibliometric techniques have evolved into a sophisticated method for analyzing data trends. Due to the remarkable intuitiveness and objectivity of bibliometric, it has been used extensively in various disciplinary directions, such as tourism management [13], manufacturing [7], smart cities [14], medicine [15], social psychology [16], ecological sustainability [17], economics [18], neuroscience [19], environmental pollution [20] and chemosphere [21]. It is the first paper to discuss the sustainability of service-oriented manufacturing using bibliometric analysis. It provides a detailed review and outlook on the development of servitization of manufacturing, indicates the research directions for future development, and provides a useful reference for researchers in related directions.
Manufacturing servitization has become a hot topic in various areas, such as science, sociology, economics, and management. The field of manufacturing servitization is still in its infancy. The purpose of this paper is to review the development of manufacturing servitization and to find a potential direction. This paper used a bibliometric method to quantitatively and qualitatively analyze the research areas, journals, countries, keywords, institutions, authors, trends, and citations of manufacturing servitization. Then, the research directions for the future development of manufacturing servitization are analyzed. Finally, it provided valuable suggestions for researchers in manufacturing servitization-related from three aspects: research direction, cooperation, and application development direction. It will benefit industry and academia for further research on manufacturing servitization.
The rest of this paper is organized as follows. Section 2 describes the data source, search strategy, and analysis methods. Section 3 presents the analysis results and discusses these results in detail. Implications for future research are given in Section 4. The conclusions are presented in Section 5.

2. Material and Methodology

Figure 1 shows the flow chart of this paper’s bibliometric analysis method. The process of bibliometric analysis can be summarized as four steps: the search query, data screening, data analysis, and data visualization.

2.1. Literature Source

In this work, bibliometric analyses were implemented based on the WoS database, which enabled the retrieval of literature on the servitization of manufacturing. A multidisciplinary document database is established by the WoS, which covers more than 12,000 authoritative and high-impact academic journals [22,23], including those on natural science, social sciences, arts and humanities [24,25,26] etc., and is widely regarded as an essential tool for accessing global academic information [27].
The literature retrieval time was May 2022. Since the academic research on manufacturing servitization began in the late 1980s, the publication years for the search ranged from 1990 to 2021.

2.2. Search Strategy

The documents were retrieved through the advanced search of the WoS core collection database with the following search terms: manufacturing servitization, manufacturing service, servitization of manufacturing, and service of manufacturing. According to the WoS search formula setting method, the search formula was: TS = (“manufacturing NEAR/2 servi*” OR “servitization of manufacturing”), and the publication date were from 1990 to 2021. As the Figure 1 shows, the original data retrieved were filtered, and only articles and review articles types were retained, such as book reviews, letters, news items, editorial materials, etc., were excluded. Then duplicate documents were removed. Literature whose content was entirely irrelevant to the topic of manufacturing servitization was also removed manually. Finally, a total of 3767 papers were collected.

2.3. Analysis Methods

Some bibliometric indicators were analyzed to evaluate the research trends and milestones of manufacturing servitization. The total records and citations of all documents retrieved are downloaded from WoS and input to Endnote and CiteSpace. Endnote is an internationally renowned software tool for managing and citing references.
The CiteSpace is a data mining and visualization platform that provides burst detection to detect changes in hot research trends across generations and help analyze the rise or decline of a topic or keyword.
Besides, some other metrics, including impact factor (IF), h-index, were analyzed using InCites, a WoS-based tool that allows for data analysis and normalized processing of organization names.
In this paper, the bibliometric techniques were used to analyze the research subject through quantitative and qualitative analysis of data [24,28,29]. The document data searched from the WoS are sent to InCites for data analysis and storage. The indicators, including the number of articles published, citation frequency, h-index, and IF, were demonstrated in table form to illustrate the features of servitization in manufacturing from various perspectives. Python is used to process the Microsoft Excel data. Line charts can intuitively display the trend of global contribution [30,31]. The analysis of the bubble chart was used to reveal the development trends of manufacturing servitization research fields, journals, keywords, and authors, and the cross-relationship chart visually showed the cooperation between countries or regions, research fields, authors, and institutions, etc. The hot topic analysis of CiteSpace provided the latest information on research interests and perspectives.

3. Results and Discussions

3.1. The Global Research Status

The literature data includes 3767 documents, with 3614 articles and 153 reviews, covering research on the servitization of manufacturing in 107 countries or regions. An average of 118 papers are published each year. According to Figure 2, the amount of publications on the topic of servitization of manufacturing shows a slow and steady growth trend from 1990 to 2021. The earliest article on the servitization of manufacturing was published in 1990, with Goldhar [32] as the first author, from the Illinois Institute of Technology in the USA, who studied the opportunities and challenges of “the automation of custom manufacturing” in future through the development of computer-integrated manufacture (CIM) technology.
Table 1 illustrates the 20 countries with an enormous quantity of publications on manufacturing servitization. The USA ranks first with 930 publications, followed by China with 760 and the UK with 501. It is noticeable that although the amount of publications in the Netherlands, Finland, and Switzerland is relatively small, the average impact factor per publication of the articles published in these three countries is high, with the Netherlands at 6.61 and its average citations per publication at 46.08. It indicates that the level of European countries in this research area of manufacturing servitization is high. The number of publications in Northern Europe is small, but those papers have a strong influence.
As Figure 3 shows, the USA, Germany, and Canada are the research countries that published the most documents on manufacturing servitization. From 1990 to 1999, a few papers were published by a small number of countries each year. It can be inferred that manufacturing servitization was still an unpopular research direction at that time or that the research results were insignificant. After 2000, the number of papers in this direction ushered in explosive growth. Although China, Italy, Sweden, France, and Finland started quite late on the servitization of manufacturing, their steady increase in the number of their published documents since 2000 has contributed significantly to this field of research.
As shown in the Figure 4, this cross-correlation chart illustrates the collaboration across the 20 countries. The connecting lines represent their correlation, with thicker lines indicating a greater correlation between them. The size of the yellow circles is positively correlated with the number of papers published in that country. The USA has the highest number of published papers and is the dominant country of cooperation among 107 countries or regions. In addition, countries such as Finland, Sweden, and Switzerland have close collaboration with each other, indicating that the Nordic region also pays more attention to the servitization of manufacturing. Recently, Chinese researchers have closely cooperated with Taiwan, Japan, Canada, Australia, and other countries or regions, demonstrating a successful development.

3.2. The Main Research Fileds

The applicability of “manufacturing servitization” can be reflected in the relevant field. The 3767 papers retrieved from WoS cover 183 research areas, indicating that the area of manufacturing servitization has been widely studied. Table 2 lists the top 20 research areas with the most publications in manufacturing servitization, where “business and economics” ranks the list with 1565 papers, accounting for 41.54%, and its average citations per publication (ACPP) is 36.63. It is followed by “engineering”, with a total of 1262 related studies, accounting for 33.50%, then “operations research & management science” (564, 14.97%), “computer science” (550, 14.60%), “environmental science and ecology” (366, 9.72%). Although the published articles “Robotics” and “automation & control systems” are only 48 and 135, respectively, their ACPP is 40.15 and 41.31.
Figure 5 shows the development trend of manufacturing servitization. The earliest paper on the servitization of manufacturing was published in “computer science”, “business and economics”, “engineering”, “operations research and management science”, and “computer science” were the study areas for fewer than 15 papers per year before 2000. From 2001 to 2010, the number rose and began to surge in 2010. In 2020, the number of publications of “business and economics” boosted to 134. The “environmental sciences and ecology”, “science and technology”, and “material science” were the research areas for fewer papers before 2000, but after 2000, they began to increase at a stable speed. In the past five years, the main research direction of manufacturing servitization has been “business and economics” and “engineering”, which are the top two areas with the most publications. “computer science” and “environmental sciences and ecology” ranked fourth and fifth, respectively, and have also emerged as significant research areas for the servitization of manufacturing.
Figure 6 indicates that the documents belonging to “engineering” have close links to other research areas in the top 20, except “public administration”, “public, environmental & occupational health”, “mathematics”, and “urban studies”, and “social sciences”. It was followed by “business and economics”, which extensively links with 15 other research fields. The strong cross-correlation among the five top areas “business and economics”, “engineering”, “operations research and management science”, “computer science”, and “environmental sciences & ecology” indicates that they appear together with high frequency. Besides the strong cross-correlations of the above study areas, the weak crossover of “automation & control systems”, “social science”, “geography”, and “international relations” also needs to be considered, representing the potential research relevance among these research fields.

3.3. The Leading Journals

It is significant for researchers who study manufacturing servitization to know which journals relate to it. From 1990 to 2021, 3767 articles were published in 2536 journals on the servitization of manufacturing. As Table 3 shows, the International Journal of Production Research has the most comprehensive documents of manufacturing service information (80, 2.12%), and its ACPP is 29.33. This is succeeded by the International Journal of Production Economics (78, 2.07), Sustainability (77, 2.04), the International Journal of Advanced Manufacturing Technology (77, 2.04), etc. The top 10 journals collectively published 16.46% of the total literature, while the rest of each journal was less than 1%. The International Journal of Operations & Production Management has the largest ACPP with 62.98, followed by the International Journal of Production Economics (62.09), Industrial Marketing Management (55.82) and Small Business Economics (44.45), and the average citations of publications are over 44. In terms of the IF of journals, the International Journal of Cleaner Production ranks first with 7.597, and it is followed by Computers in Industry (7.247), and International Journal of Production Economics (7.079) and Small Business Economics (7.005).
As Figure 7 shows, the most published journal is the International Journal of Production Economics, which focuses on SCM, sustainability, the IoT, etc. Sustainable supply management: An empirical study, published by Ageron et al. [33] in 2012, has been cited 314 times in terms of supply chain management. In an article published in 2011, Blome et al. [34] proposed the frameworks and methods to help companies deal with supply chain risks in the production crisis. Saccani et al. [35] explored the options for after-sales and supply chain configurations by studying seven manufacturing companies. In this area of service operation management, Gunasekaran et al. [36] published an article in 2012 that was cited 110 times; it studied the development of operation management and developed a framework for a new operations management strategy to improve the internal competitiveness of enterprises.
They were followed by the International Journal of Production Research. The second most cited literature was published in 2018 by Moeuf et al. [37] with 328 citations which concluded that small and medium-scale corporations do not have enough resources to implement Industry 4.0 but only on cloud computing (CC) and the IoT. Second, Ardolino et al. [38] examined how the IoT, CC, and predictive analytics (PA) can facilitate the servitization of manufacturing through developing digital technologies in a business case. Gunasekaran and Yusuf [39] presented agile manufacturing and pointed out that the feature of the servitization process based on agile manufacturing is an integration of product design, production, marketing, and support services between the whole customer and supplier. Theorin et al. [40] proposed using a line information system architecture model that can help enterprises build the Industrial Internet of Things to make decisions based on factory data.
Figure 8 shows the top 20 most productive publishers. Elsevier is the publisher of the most articles, with 985 documents (26.15%), followed by Taylor and Francis with 510 papers (11.54%) and Springer with 388 documents (10.30%). Emerald Group Publishing ranks fourth with 289 publications (7.67%), followed by Wiley (269, 7.14%) and MDPI (135, 3.58%).

3.4. Analysis of Keywords

The 6112 keywords were analyzed to reveal the research hotspots and trends in manufacturing servitization. The results demonstrated extensive research interest related to manufacturing services, and the most used 20 keywords are shown in Figure 9. In this paper, “services” is the most frequently used keyword, mainly service marketing, service resource combination strategies, and product-service systems. Since 1999, it has continuously increased for over 20 years and has been used 262 times. Among them, 63 articles with more than one hundred citations each. In 1999, Meyer [41] presented introduced the concept of product services and elaborated on these ideas through a comprehensive analysis of different industry cases. In 2008, Tao et al. [42] proposed that in distributed manufacturing systems, especially in manufacturing grid systems, tasks that require multiple service resources to be invoked in a specific order can be accomplished by gathering only one service resource. For product service systems (PSS), a paper by Tukker [43] reviewed the major literature about PSS, delving into the framework for building PSS and the key factors and types of businesses for which they are implemented. In 2011, Gao et al. [44] proposed that service and physical products be integrated into the PSS to provide customers with comprehensive solutions, and the characteristics and evolution of various product-service systems are discussed. The theories about the organizational complexity–innovation relationship were explained in greater detail in a paper published by Damanpour [45].
Another top keyword, “cloud manufacturing”, first appeared in 2011 and has been used 174 times over the past ten years. As of 2021, 6 articles with “cloud manufacturing” as the keyword have been cited more than 100 times. The paper published by Tao et al. [46] describes the architecture and key technologies through several classical service models based on cloud manufacturing and analyzes its advantages and potential challenges. An article published by Wu et al. [47] in 2013 has 372 citations with an ACPP of 41.33. In the same year, the literature was published by Wang et al. [48] in 2013, which was cited 214 times, with an ACPP of 23.78.
“Manufacturing” (131 times), as the earliest keyword, was also the focus of research, especially in the last nine years (from 2012 to 2021), and the citations of 8 documents have exceeded 100 times. Tao et al. [49] proposed a big data-driven service model for manufacturing and illustrated the application architecture and methods of the digital twin (DT), which enables the management of the entire product lifecycle. It has 759 citations, with an ACPP of 253. In 2017, Rymaszewska et al. [5] presented to add the IoT to the organization of a company’s production process and to expand its service chain through big data to obtain better benefits which was cited 180 times with an ACPP of 45.
The keyword “innovation” first appeared in 2001, and the number of papers increased steadily after 2005. The research content included service innovation, product innovation, enterprise internal organizational structure innovation, and 25 articles with no less than 100 citations. In a paper published by Low et al. [50] in 2001, a solution was introduced to generate an innovative service environment through the Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ) model and provided a solution centered on service ecology. In 2009, Van et al. [51] proposed that the most critical challenge of open innovation is related to enterprises’ internal organization and corporate culture, which was 1026 citations, with an ACPP of 78.92.
“Productivity” (ranked fifth, 65 times) was also among the earliest keywords used. Arnold et al. [52] investigated the relationship between service intensity inputs and manufacturing productivity and showed a positive correlation, which provides a theoretical basis for continuing to accelerate the servitization of manufacturing. As a mainstay and primary market of the manufacturing industry, “China” (ranked sixth, 60 times) has been widely studied, and Wang [53] explored the potential mechanism between Western relationship marketing and Chinese relationship. “Industry 4.0” (ranked seventh, 51 times) is one of the significant study hotspots in recent years.
The articles with “supply chain management” (ranked tenth, 36 times) as the keyword received the highest number of citations at 323. Frohlich and Westbrook [54] studied the relationship between supply chain integration strategies supporting the internet and manufacturing and service performance. Olhager [55] proposed a supply chain model about the order penetration point, and the factors considered by manufacturing enterprises, such as customer service, manufacturing efficiency, and inventory cost, were taken as factors affecting the model. Pettit [56] proposed a novel supply chain assessment tool that has been validated on several manufacturing industries’ supply chain management capabilities worldwide, which is used to evaluate the degree of supply chain stability.
It is worth mentioning that the two keywords “Industry 4.0” (ranked seventh, 51 times) and “Internet of Things” (ranked ninth, 41 times) first appeared in this paper published by Yue et al. [57] in 2015. They proposed a new model of industrial network-based information systems. It analyzed the development trend of information and communication technology and explored how to effectively improved service capability under its application. In 2014, Tao et al. [58] established a production model architecture that integrates cloud computing, IoT, and cloud manufacturing analyzes its application methods, and concludes that it has the advantage of optimizing resource allocation. It has received an enormous amount of citations with 459. Followed by Tao et al. [59], studied the correlation between cloud manufacturing and IoT and proposed an architectural model based on IoT fused cloud manufacturing, which was cited 422 times.
As Figure 10 shows, “services” are associated with almost all other keywords, especially “product-service systems (PSS)” and “service innovation”. “Cloud manufacturing” was linked to most other keywords, except “structural change” and “supply chain management”. “Service composition” had a strong relationship with “cloud manufacturing”. Huang et al. [60] proposed a new approach for solving combinatorial optimization problems in CC services and demonstrated its superiority through simulation and comparison with other classical algorithms. “Manufacturing industries” and “service industries” also are highly relevant. Schmenner [61] reviewed the development of servitization and analyzed the relationship between manufacturing and service industries in different stages of history and the reasons for their formation. Not only “cloud manufacturing”, “CC” and “IoT” and smart manufacturing also have a strong correlation. Tao and Qi [62] proposed using information technology to assist the manufacturing, accelerating the development of manufacturing services under smart manufacturing.

3.5. The Leading Institutions

Table 4 shows the 20 institutions with the most publications in manufacturing servitization from 1990 to 2021. The most productive institution was Beihang University, China, with 71 published papers accounting for 1.88% of the total publications; the University of Vaasa, Finland, and the University of California System, the USA, tied for second place with 47 articles, 1.25%; and the University of London and the University of Cambridge, two UK institutions, tied for third place with 44 articles, 1.17%. Concerning ACPP, the State University System of Florida, the USA, ranked highest at 68.19, followed by Beihang University, China (66.93), Linkoping University, Sweden (65.58), and the University of Cambridge, the UK (62.98); except for these four institutions, all other ACPP were below 60.
The top 20 institutions are from 5 countries, of which seven belong to China, accounting for 35%, indicating that China has gradually become a dominant country related to the research of manufacturing services. The USA and the UK also have obvious advantages, with five institutions each (25%), significantly contributing to the servitization of manufacturing literature.

3.6. The Leading Authors

Table 5 shows the 20 authors with the most publications in manufacturing servitization. Tao, Fei, from Beihang University, China, was the most productive author with 37 articles (0.98%), followed by Zhang, Lin, Beihang University, China (32, 0.85%) and Parida, Vinit, Lulea University of Technology, Sweden (24, 0.64%). Regarding the ACPP, Tao, Fei also has the highest ACPP with 106.19. He was then followed by Baines, Tim, Aston University, UK (100.09) and Gebauer, Heiko, Swiss Federal Institute of Aquatic Science & Technology (EAWAG), Swizerland (91.20).
It is worth noting that the first two authors and the sixth author have close collaboration relationships because they published 12 articles [58,63,64,65,66,67,68,69,70,71,72,73] together, and all three come from Beihang University, China. Zhang, Wenyu and Zhang, Shuai, both from Zhejiang University of Finance and Economics in China, are coauthors of 15 articles [74,75,76,77,78,79,80,81,82,83,84,85,86,87,88] on the servitization of manufacturing. Notably, Parida, Vinit not only holds a position at the Lulea University of Technology, Sweden, but he has also worked as a visiting professor at the University of Vaasa, Finland. Therefore, he cooperated deeply with Kohtamaki, Marko, a professor at the University of Vaasa, Finland; they jointly published ten articles [4,89,90,91,92,93,94,95,96,97] on the servitization of manufacturing. In addition, Kohtamaki, Marko, University of Vaasa, Finland, Parida, Vinit, Lulea University of Technology, Sweden, and Gebauer, Heiko, Swiss Federal Institute of Aquatic Science & Technology, Switzerland, have a close cooperative relationship with each other.
As Figure 11 shows, Gebauer Heiko published four articles [98,99,100,101] in 2007, which provided relevant insights on marketing strategies and service improvements to help manufacturing companies shift to servitization.
In 2008, Tao published his first paper on manufacturing servitization. Since 2012, Tao began cooperating with Zhang and continuously output beneficial views for manufacturing servitization. Three authors, Cheng, Zhang, and Zhang, published a steady number of articles per year and were the most important contributors to the servitization of manufacturing from 2012 to 2021. In addition, the annual publications of other authors have been relatively stable since 2014.

3.7. The Most Cited Publications

The indicator of the quantity of cited is used to measure the value of this work. From 1990 to 2021, the citations of 247 documents exceeded 100 times (6.56%), the citations of 294 articles were 50–99 (7.80%), and the citations of 561 papers were 25–49 (14.89%). Table 6 shows the 20 literature with the most citations in manufacturing services from 1990 to 2021. The first literature appeared in 2000, and the latest one in 2018. Three of them were published by the same author, Fei Tao, and two of them were published in the IEEE Transactions on Industrial Informatics. The most cited article was published by Combs et al. [102] in 2006. The literature presents a meta-analysis methodology to systematically assess the impact of superior workability on manufacturing and service organizations.
The second most cited paper was published by Van de Vrande et al. [51] in 2009, with 1024 citations. In this work, the management challenges faced by small and medium scale corporations in the process of open innovation are discussed
Additionally, in 2009, the article published by Hertwich and Peters was cited 967 times and ranked third. The study quantified greenhouse gas (GHG) in 73 countries and 14 regions from the perspective of carbon emissions, analyzed the effects of eight categories, including manufactured products, services, and trade, and provided some compiled statistics and insights into the global carbon cycle that will help in future green manufacturing research.

4. Implication for Future Research

As shown in Figure 12, the concept of the servitization of manufacturing was proposed and put into practice 30 years ago, and the number of publications from 2000 to 2010 steadily increased. Since 2010, an increasing quantity of scholars have been attracted to studying this field, and the research results of manufacturing servitization have been continuously enriched. In the era of Industry 4.0, servitization-based manufacturing has become interdisciplinary with the potential for development in multiple directions. This work is considered the first article to discuss the sustainability of manufacturing around servitization using bibliometric analysis. It also makes the following suggestions for the future development of servitization in manufacturing regarding research areas, collaboration, and future application trends.
First, future directions for the exploration of manufacturing servitization are suggested based on the bibliometric analysis.
Secondly, specific suggestions for cooperative development among different disciplines are proposed based on its current status as an interdisciplinary discipline.
Finally, the research directions for future applications of manufacturing servitization are analyzed.

4.1. A Research Areas Perspective

As shown in Table 2, business and economics are the main research fields of manufacturing servitization. Business and economics are constantly seeking to maximize profits. The manufacturing servitization is a good method to integrate service as the last component of goods into the whole life cycle of products to improve enterprise benefits. Therefore, manufacturing servitization can be explored more widely and deeply from different perspectives, such as, complaint management [114], cloud computing [115], lean production [116], value streams [117], service business models [118], balanced growth models [119], and operations management (OM) [120].
The intersections between different fields bring many research opportunities for the development of manufacturing services; in addition to business and economics, engineering, operations research and management science, computer science and environmental sciences, and ecology, which also have strong cross-relationships. There are also essential references between disciplines with weak cross-relationships, such as robotics, physics, information science and library science, social sciences, urban studies, and other research fields. As the Figure 5 shown, the strong intersecting relationships represent hot research areas, while weak intersecting areas represent potential study fields.

4.2. A Cooperative Perspective

Currently, the field of manufacturing servitization is flourishing, and many disciplines are excelling in manufacturing service, such as the combination of Automation control systems and lean production, which has produced the smart factory, and the IoT combined with simulation technology, which has produced the digital twin. In the era of Industry 4.0, there are many other examples where the combination of different disciplines has been well developed. Based on the previous analysis, researchers in the field of the IoT and big data can collaborate and continue exploring this direction’s potential. Researchers in other related directions, such as CMfg, CC, and DT, can collaborate to facilitate the development of these directions. Researchers in different countries or regions and different directions can also actively collaborate to advance the areas of manufacturing servitization.
The analysis results show that the USA, China, the UK, Germany, and Italy are the countries that contribute the most to the literature on manufacturing servitization. They have the highest number of publications and cooperate closely with each other and with other countries. Iran, Singapore, Turkey, and Japan are the countries that have published a few papers and have relatively weak cooperative relationships with other countries. The three Nordic countries, Sweden, the Netherlands, and Finland have a very close cooperative relationship. Beihang University has the most publications and established a close cooperative network, which continues to produce high-quality papers every year. Table 4 and Table 5 and Figure 11 point out the institutions and authors with the highest number of publications in the direction of manufacturing servitization. It provides suggestions for researchers from different institutions and research directions to collaborate to achieve better development in manufacturing servitization.

4.3. The Future Application Trends Perspective

The research hotspots in manufacturing servitization have changed over time. The results in Section 3.4 and Section 3.7 show that manufacturing and productivity are enduring research directions, while Industry 4.0 and sustainability are emerging hotspots that have attracted increasing attention from scholars. Services, cloud manufacturing, and innovation, including servitization, digital, and business models, are the prevalent topics and have attracted scholarly interest in the past few years.
Figure 13 shows the top 20 topics with the most vigorous citation bursts, which are used to visually show the drop or rise of detected citation topics or keywords over time.The cyan line represents the period from 1990 to 2021. The red line represents the period when the keyword was used most frequently. The length of the red line represents how long the keyword has been popular. The keyword corresponding to the red line on the far right is the most studied now. Similar to the results given in Section 3.4, value cocreation, business model, and production service system are high-frequency phrases in the last three years that play a key role in manufacturing services. On the other hand, the internet, big data, and cyber-physical systems are constantly being proposed and discussed, including digital twins (Tao et al. [49], 2018), smart factories (Wang et al. [105], 2016) and Industry 4.0 (Frank et al. [111], 2019). Recently, with the Internet of Things, cloud services, big data analytics, and other topics becoming hotspots, the future development trend of manufacturing servitization will prosper in applications of digitization and informatization.
Germany’s “Industry 4.0”, the United States’ “Industrial Internet”, and “Made in China 2035” all take manufacturing servitization as the strategic core and strive to improve the value chain of enterprises through manufacturing servitization. With the development of artificial intelligence, cloud manufacturing, big data, IoT, and intelligent manufacturing will be popular research directions for manufacturing services in the coming period. An increasing number of enterprises are considering combining emerging advanced manufacturing technologies (such as CC, IoT, virtualization, and advanced computer technology) with current “informatization technology” manufacturing modes to provide better customer service. In addition, service, service marketing, service resource portfolio strategy, and product-service system innovation will always be essential directions of manufacturing service research. Through the limited resources of service to maximize the provision of the best service to customers, the use of effective service operates to obtain adequate customer information for enterprises. Enterprises are also concerned about supply chain management and total quality management. In the process of manufacturing servitization, outstanding product quality is used to gain the trust of customers. The SCM is used to evaluate the flexibility of a enterprise’s supply approach in the face of complex customer needs and reduce supply chain risks. The multi-objective mixed-integer linear programming models and meta-heuristic algorithms have also shown promising results in solving the SCM problem [121,122], which also reflects the current status of multidisciplinary integration of manufacturing servitization.
To show the development of manufacturing servitization research hotspots over time, some important information is organized in Figure 14. In 2002, Cano et al. [123] researched the relationship between market positioning and firm performance in a global context through a meta-analysis and analyzed some reasons for the continuous shift from manufacturing to service.
In 2004, Lan [124] proposed a networked manufacturing service system structure to develop a Web-based information service system that can help manufacturing create a collaborative production environment to solve production scheduling planning problems.
In 2005, Gebauer et al. [125] proposed that the main reason for servitization was to achieve better financial performance and that the complete servitization management of manufacturing enterprises required an interdisciplinary theory combining service management with a behavioral approach.
In 2008, Neely and Andy [126] believed that manufacturing servitization was creating service value through the innovation in a firm’s product life cycle, from selling products to providing PSS or a more complete market package.
In 2009, Baines et al. [2] introduced lean thinking into PSS operation management under the new information technology environment, aiming to reduce waste and waiting and improve reliability and service level.
Wu et al. [47] introduced cloud manufacturing and its closely related fields and proposed that the service function of cloud manufacturing for the industry drives the innovation of manufacturing production models. In 2015, Opresnik and Taisch [3] put forward a two-stage model of “data generation-data application” to analyze the process of manufacturing services and value creation under big data application: by collecting and analyzing customers’ data, they can perceive changes in customers‘ behaviors, and then provide customers with new services and create new revenue streams.
In 2016, Huxtable and Schaefer [127] proposed that with the support of big data technology, enterprises can design novel service modes, including condition monitoring, preventive diagnosis, data sales, advanced pricing model, big data consulting, big data outsourcing and so on to expand the product-service system. In 2017, Lu et al. [128] presented a sustainable scheduling problem for welding shops from balancing economic and environmental development. It solved it using a novel multi-objective algorithm with an energy-saving strategy, providing a new approach to green manufacturing. In 2018, Lim et al. [129] have shown that by collecting customer behavior data (such as operation records) and product status data (such as operation parameters), enterprises can obtain helpful information (such as product operating status) by processing these data, thus realizing fault warnings and ensuring the smooth operation of products.
In 2019, Tao et al. [130] believed that the main technical issue in achieving smart manufacturing is how to share data efficiently in real-time. He reviewed the development of the digital twin and CPS in several aspects and pointed out their role in transforming the way manufacturing is produced. In 2020, Sholihah et al. [131] proposed a methodology to help manufacturing enterprises analyze their development status and select the appropriate competencies to develop service-oriented strategies, and validate the applicability of the methods through practical applications in enterprises. In 2021, Yu et al. [132] analyzed a sample of dozens of countries and manufacturing companies worldwide and concluded that servitization could increase its advantages and take over critical competitive capabilities internationally. In the same year, Liang et al. [133] introduced a novel deep reinforcement learning approach to solve some combinatorial optimization problems of services in cloud manufacturing and demonstrated through comparative experiments that the method has a good performance both in terms of effectiveness and generalization capability. In 2022, Zhang [134] analyzed the impact of manufacturing services on industrial productivity worldwide, concluded that manufacturing services are significantly and positively correlated with production capacity, and pointed out that the rearrangement of industry chains such as advanced information technology and business services is an important direction to accelerate the future manufacturing services. Caiado et al. [135] introduced a framework in terms of SCM to establish a new paradigm for the sustainable growth of manufacturing servitization. Other contributors [136,137,138] in the field of manufacturing servitization are shown in Figure 14.
The above analysis indicated that the servitization of manufacturing is a process of continuous integration with other disciplines. Therefore, this article also encourages the collaboration of researchers from different directions to promote the progress of manufacturing servitization. According to the analysis, the paper can draw the following conclusions on the future development application of manufacturing servitization.
Firstly, manufacturing servitization is receiving more and more attention and has become one of the most researched fields. The development of SCM, PSS, and DT will revolutionize the manufacturing industry’s industrial model and management system. They have great growth potential, and it is worthwhile for researchers to continue to explore this field. Second, the future development of the DT cannot be separated from the support of technologies such as the CC, CMfg, and CPS, so these directions also have an essential impact on the development of manufacturing servitization. On the other hand, digital twin, cloud manufacturing, CPS, and IoT require the analysis of the collected raw data, which involves the support of big data science such as data mining and data analysis. Finally, the maximum utilization of resources will generate production scheduling problems, and meta-heuristics and deep reinforcement learning approaches have reasonable solutions for this type of NP-hard problem.

5. Conclusions and Limitations

Based on the WoS database, 3767 relevant articles and reviews published from 1990–2021 were retrieved and obtained. Bibliometric methods were used to qualitatively and quantitatively study the development characteristics and trends of manufacturing servitization, analyze the development background of manufacturing servitization in terms of global contributions, countries or regions, leading journals, authors, keywords, and research fields, and look forward to potential future research directions. It is a high reference value for researchers in the servitization of manufacturing and other related directions. And it can help them to choose their future research directions and collaborating institutions or personnel.
The results show that the research on manufacturing servitization started in 1991, and the number of documents increased steadily from 1990 to 2011, rapidly increased after 2011, and peaked in 2020. Bibliometric analysis showed that more than 77.62% of the papers were published between 2008 and 2021. The USA has the largest number of documents on manufacturing servitization, with 930 articles published, followed by China (760) and the UK (501), and they have contributed significantly to the research on manufacturing servitization. Regarding the amount of collaboration, the USA and China are the most proactive in cooperation with other countries or regions, particularly with the UK, Germany, Italy, South Korea, and Canada. Beihang University is the most published institution globally, contributing 71 articles.
Regarding the journals, the International Journal of Production Research and International Journal of Production Economics are the two journals with the most literature, publishing 80 and 78 papers in the servitization of manufacturing, respectively. The impact factors of both journals are above 6, which is significant to the literature references and publications.
Concerning research areas, business and economics are the most extensive study directions, publishing 1565 articles in this field. Engineering (1262) ranked second, followed by operations research and management science (564). According to keyword analysis, “service” is the most used keyword since it was first proposed in 1999, 262 times. It is followed by “cloud manufacturing” and “manufacturing”. The keywords such as “service innovation”, “IoT”, “big data”, and “Industry 4.0” suggest that manufacturing servitization is widely studied in different aspects. “Digital and intelligent manufacturing”, “cyber-physical system”, and “product-service system” are still hot research directions at present and in the foreseeable future. Finally, suggestions are given from three aspects: research area, partnership, and future application development. It is also concluded that collaborative research on big data, CC, CMfg, SCM, IoT, PSS, DT, and other disciplines will effectively promote the development of manufacturing servitization in the future.
However, it is essential to point out that this paper still has limitations. First, data indicators such as h-index, IF, etc., must be updated over time. Secondly, this analysis method can only conclude and make suggestions for future directions but cannot explain the underlying reasons behind the phenomena. In addition, besides the WoS, other databases such as the Scopus may also contain publications with the theme of servitization of manufacturing. The literature may be missed because the data sources are not comprehensive enough.
Future work should add different databases to expand the data sources and be more timely and in-depth in analyzing the underlying reasons.

Author Contributions

Conceptualization, Y.C. and J.Y.; methodology, B.W. and Z.W.; draft preparation, Z.W.; writing-review and editing, W.Y.; Supervision, Z.P.; project administration, J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Zhejiang Provincial Natural Science Foundation of China, grant number LGG22G010002 and LQ21E050014, National Natural Science Foundation of China, grant number 52005447 and 71871203.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available on request from the authors.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Vandermerwe, S.; Rada, J. Servitization of business: Adding value by adding services. Eur. Manag. J. 1988, 6, 314–324. [Google Scholar] [CrossRef]
  2. Baines, T.S.; Lightfoot, H.W.; Benedettini, O.; Kay, J.M. The servitization of manufacturing: A review of literature and reflection on future challenges. J. Manuf. Technol. Manag. 2009, 20, 547–567. [Google Scholar] [CrossRef] [Green Version]
  3. Opresnik, D.; Taisch, M. The value of big data in servitization. Int. J. Prod. Econ. 2015, 165, 174–184. [Google Scholar] [CrossRef]
  4. Kohtamäki, M.; Parida, V.; Patel, P.C.; Gebauer, H. The relationship between digitalization and servitization: The role of servitization in capturing the financial potential of digitalization. Technol. Forecast. Soc. Chang. 2020, 151, 119804. [Google Scholar] [CrossRef]
  5. Rymaszewska, A.; Helo, P.; Gunasekaran, A. IoT powered servitization of manufacturing–an exploratory case study. Int. J. Prod. Econ. 2017, 192, 92–105. [Google Scholar] [CrossRef]
  6. Coreynen, W.; Matthyssens, P.; Van Bockhaven, W. Boosting servitization through digitization: Pathways and dynamic resource configurations for manufacturers. Ind. Mark. Manag. 2017, 60, 42–53. [Google Scholar] [CrossRef]
  7. Liu, Y.; Xu, X.; Zhang, L.; Wang, L.; Zhong, R.Y. Workload-based multi-task scheduling in cloud manufacturing. Robot. Comput. Integr. Manuf. 2017, 45, 3–20. [Google Scholar] [CrossRef]
  8. Kohtamäki, M.; Einola, S.; Rabetino, R. Exploring servitization through the paradox lens: Coping practices in servitization. Int. J. Prod. Econ. 2020, 226, 107619. [Google Scholar] [CrossRef]
  9. Wang, Q.; Zhang, F. What does the China’s economic recovery after COVID-19 pandemic mean for the economic growth and energy consumption of other countries? J. Clean. Prod. 2021, 295, 126265. [Google Scholar] [CrossRef]
  10. Wang, Q.; Su, M. A preliminary assessment of the impact of COVID-19 on environment–A case study of China. Sci. Total Environ. 2020, 728, 138915. [Google Scholar] [CrossRef]
  11. Rousseau, R. Forgotten founder of bibliometrics. Nature 2014, 510, 218. [Google Scholar] [CrossRef]
  12. Pritchard, A. Statistical bibliography or bibliometrics. J. Doc. 1969, 25, 348–349. [Google Scholar]
  13. Ali, F.; Park, E.O.; Kwon, J.; Chae, B.K. 30 Years of contemporary hospitality management: Uncovering the bibliometrics and topical trends. Int. J. Contemp. Hosp. Manag. 2019, 31, 2641–2665. [Google Scholar] [CrossRef]
  14. Mora, L.; Deakin, M.; Reid, A. Combining co-citation clustering and text-based analysis to reveal the main development paths of smart cities. Technol. Forecast. Soc. Chang. 2019, 142, 56–69. [Google Scholar] [CrossRef]
  15. Sugimoto, C.R.; Ahn, Y.Y.; Smith, E.; Macaluso, B.; Larivière, V. Factors affecting sex-related reporting in medical research: A cross-disciplinary bibliometric analysis. Lancet 2019, 393, 550–559. [Google Scholar] [CrossRef] [Green Version]
  16. Ellemers, N.; Toorn, J.; Paunov, Y.; Leeuwen, T.V. The Psychology of Morality: A Review and Analysis of Empirical Studies Published From 1940 Through 2017. Pers. Soc. Psychol. Rev. 2019, 23, 332–366. [Google Scholar] [CrossRef] [Green Version]
  17. Sharifi, A. Urban sustainability assessment: An overview and bibliometric analysis. Ecol. Indic. 2020, 121, 107102. [Google Scholar] [CrossRef]
  18. Ferasso, M.; Beliaeva, T.; Kraus, S.; Clauss, T.; Ribeiro-Soriano, D. Circular economy business models: The state of research and avenues ahead. Bus. Strategy Environ. 2020, 29, 3006–3024. [Google Scholar] [CrossRef]
  19. Tooley, U.A.; Bassett, D.S.; Mackey, A.P. Environmental influences on the pace of brain development. Nat. Rev. Neurosci. 2021, 22, 372–384. [Google Scholar] [CrossRef]
  20. Mao, G.; Hu, H.; Liu, X.; Crittenden, J.; Huang, N. A bibliometric analysis of industrial wastewater treatments from 1998 to 2019. Environ. Pollut. 2021, 275, 115785. [Google Scholar] [CrossRef]
  21. Ji, B.; Zhao, Y.; Vymazal, J.; Mander, Ü.; Lust, R.; Tang, C. Mapping the field of constructed wetland-microbial fuel cell: A review and bibliometric analysis. Chemosphere 2021, 262, 128366. [Google Scholar] [CrossRef]
  22. Franceschini, F.; Maisano, D.A. Analysis of the Hirsch index’s operational properties. Eur. J. Oper. Res. 2010, 203, 494–504. [Google Scholar] [CrossRef] [Green Version]
  23. Geng, S.; Wang, Y.; Zuo, J.; Zhou, Z.; Du, H.; Mao, G. Building life cycle assessment research: A review by bibliometric analysis. Renew. Sust. Energ. Rev. 2017, 76, 176–184. [Google Scholar] [CrossRef]
  24. Hirsch, J.E. An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics 2010, 85, 741–754. [Google Scholar] [CrossRef] [Green Version]
  25. Zyoud, S.H.; Fuchs-Hanusch, D. A bibliometric-based survey on AHP and TOPSIS techniques. Expert Syst. Appl. 2017, 78, 158–181. [Google Scholar] [CrossRef]
  26. Hirsch, J.E. An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. USA 2005, 102, 16569–16572. [Google Scholar] [CrossRef] [Green Version]
  27. Meho, L.I.; Yang, K. Impact of data sources on citation counts and rankings of LIS faculty: Web of Science versus Scopus and Google Scholar. J. Am. Soc. Inf. Sci. Tec. 2007, 58, 2105–2125. [Google Scholar] [CrossRef]
  28. Egghe, L. An improvement of the h-index: The g-index. ISSI Newsl. 2006, 2, 8–9. [Google Scholar]
  29. Meho, L.I.; Rogers, Y. Citation counting, citation ranking, and h-index of human-computer interaction researchers: A comparison of Scopus and Web of Science. J. Am. Soc. Inf. Sci. Tec. 2008, 59, 1711–1726. [Google Scholar] [CrossRef] [Green Version]
  30. Wang, L.; Zhao, L.; Mao, G.; Zuo, J.; Du, H. Way to accomplish low carbon development transformation: A bibliometric analysis during 1995–2014. Renew. Sust. Energ. Rev. 2017, 68, 57–69. [Google Scholar] [CrossRef]
  31. Tan, J.; Fu, H.Z.; Ho, Y.S. A bibliometric analysis of research on proteomics in Science Citation Index Expanded. Scientometrics 2014, 98, 1473–1490. [Google Scholar] [CrossRef]
  32. Goldhar, J.D.; Jelinek, M. Manufacturing as a Service Business: CIM in the 21st Century. Comput. Ind. 1990, 14, 225–245. [Google Scholar] [CrossRef]
  33. Ageron, B.; Gunasekaran, A.; Spalanzani, A. Sustainable supply management: An empirical study. Int. J. Prod. Econ. 2012, 140, 168–182. [Google Scholar] [CrossRef]
  34. Blome, C.; Schoenherr, T. Supply chain risk management in financial crises—A multiple case-study approach. Int. J. Prod. Econ. 2011, 134, 43–57. [Google Scholar] [CrossRef]
  35. Saccani, N.; Johansson, P.; Perona, M. Configuring the after-sales service supply chain: A multiple case study. Int. J. Prod. Econ. 2007, 110, 52–69. [Google Scholar] [CrossRef]
  36. Gunasekaran, A.; Ngai, E.W. The future of operations management: An outlook and analysis. Int. J. Prod. Econ. 2012, 135, 687–701. [Google Scholar] [CrossRef]
  37. Moeuf, A.; Pellerin, R.; Lamouri, S.; Tamayo-Giraldo, S.; Barbaray, R. The industrial management of SMEs in the era of Industry 4.0. Int. J. Prod. Res. 2018, 56, 1118–1136. [Google Scholar] [CrossRef] [Green Version]
  38. Ardolino, M.; Rapaccini, M.; Saccani, N.; Gaiardelli, P.; Crespi, G.; Ruggeri, C. The role of digital technologies for the service transformation of industrial companies. Int. J. Prod. Res. 2018, 56, 2116–2132. [Google Scholar] [CrossRef]
  39. Gunasekaran, A.; Yusuf, Y.Y. Agile manufacturing: A taxonomy of strategic and technological imperatives. Int. J. Prod. Res. 2002, 40, 1357–1385. [Google Scholar] [CrossRef]
  40. Theorin, A.; Bengtsson, K.; Provost, J.; Lieder, M.; Johnsson, C.; Lundholm, T.; Lennartson, B. An event-driven manufacturing information system architecture for Industry 4.0. Int. J. Prod. Res. 2016, 55, 1297–1311. [Google Scholar] [CrossRef]
  41. Meyer, M.H. The strategic integration of markets and competencies. Int. J. Technol. Manag. 1999, 17, 677–695. [Google Scholar] [CrossRef]
  42. Tao, F.; Zhao, D.; Hu, Y.; Zhou, Z. Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans. Industr. Inform. 2008, 4, 315–327. [Google Scholar] [CrossRef]
  43. Tukker, A. Product services for a resource-efficient and circular economy—A review. J. Clean. Prod. 2015, 97, 76–91. [Google Scholar] [CrossRef]
  44. Gao, J.; Yao, Y.; Zhu, V.C.; Sun, L.; Lin, L. Service-oriented manufacturing: A new product pattern and manufacturing paradigm. J. Intell. Manuf. 2011, 22, 435–446. [Google Scholar] [CrossRef]
  45. Damanpour, F. Organizational complexity and innovation: Developing and testing multiple contingency models. Manag. Sci. 1996, 42, 693–716. [Google Scholar] [CrossRef]
  46. Tao, F.; Zhang, L.; Venkatesh, V.; Luo, Y.; Cheng, Y. Cloud manufacturing: A computing and service-oriented manufacturing model. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 2011, 225, 1969–1976. [Google Scholar] [CrossRef]
  47. Wu, D.; Greer, M.J.; Rosen, D.W.; Schaefer, D. Cloud manufacturing: Strategic vision and state-of-the-art. J. Manuf. Syst. 2013, 32, 564–579. [Google Scholar] [CrossRef] [Green Version]
  48. Wang, X.V.; Xu, X.W. An interoperable solution for cloud manufacturing. Robot. Comput. Integr. Manuf. 2013, 29, 232–247. [Google Scholar] [CrossRef]
  49. Tao, F.; Cheng, J.; Qi, Q.; Zhang, M.; Zhang, H.; Sui, F. Digital twin-driven product design, manufacturing and service with big data. J. Adv. Manuf. Technol. 2018, 94, 3563–3576. [Google Scholar] [CrossRef]
  50. Low, M.K.; Lamvik, T.; Walsh, K.; Myklebust, O. Manufacturing a green service: Engaging the TRIZ model of innovation. IEEE Trans. Electron. Packag. Manuf. 2001, 24, 10–17. [Google Scholar]
  51. Van de Vrande, V.; De Jong, J.P.; Vanhaverbeke, W.; De Rochemont, M. Open innovation in SMEs: Trends, motives and management challenges. Technovation 2009, 29, 423–437. [Google Scholar] [CrossRef] [Green Version]
  52. Arnold, J.M.; Javorcik, B.S.; Mattoo, A. Does services liberalization benefit manufacturing firms? Evidence from the Czech Republic. J. Int. Econ. 2011, 85, 136–146. [Google Scholar] [CrossRef]
  53. Wang, C. Guanxi vs. relationship marketing: Exploring underlying differences. Ind. Mark. Manag. 2007, 36, 81–86. [Google Scholar] [CrossRef]
  54. Frohlich, M.T.; Westbrook, R. Demand chain management in manufacturing and services: Web-based integration, drivers and performance. J. Oper. Manag. 2002, 20, 729–745. [Google Scholar] [CrossRef]
  55. Olhager, J. Strategic positioning of the order penetration point. Int. J. Prod. Econ. 2003, 85, 319–329. [Google Scholar] [CrossRef]
  56. Pettit, T.J.; Croxton, K.L.; Fiksel, J. Ensuring supply chain resilience: Development and implementation of an assessment tool. J. Bus. Logist. 2013, 34, 46–76. [Google Scholar] [CrossRef]
  57. Yue, X.; Cai, H.; Yan, H.; Zou, C.; Zhou, K. Cloud-assisted industrial cyber-physical systems: An insight. Microprocess. Microsyst. 2015, 39, 1262–1270. [Google Scholar] [CrossRef]
  58. Tao, F.; Cheng, Y.; Da Xu, L.; Zhang, L.; Li, B.H. CCIoT-CMfg: Cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans. Industr. Inform. 2014, 10, 1435–1442. [Google Scholar]
  59. Tao, F.; Zuo, Y.; Da Xu, L.; Zhang, L. IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans. Industr. Inform. 2014, 10, 1547–1557. [Google Scholar]
  60. Huang, B.; Li, C.; Tao, F. A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system. Enterp. Inf. Syst. 2014, 8, 445–463. [Google Scholar] [CrossRef]
  61. Schmenner, R.W. Manufacturing, service, and their integration: Some history and theory. Int. J. Oper. Prod. Manag. 2009, 29, 431–443. [Google Scholar] [CrossRef]
  62. Tao, F.; Qi, Q. New IT driven service-oriented smart manufacturing: Framework and characteristics. IEEE Trans. Syst. Man Cybern. Syst. 2017, 49, 81–91. [Google Scholar] [CrossRef]
  63. Cheng, Y.; Zhang, Y.; Lv, L.; Liu, J.; Tao, F.; Zhang, L. Analysis of cloud service transaction in cloud manufacturing. In Proceedings of the IEEE 10th International Conference on Industrial Informatics, Beijing, China, 13 September 2012; pp. 320–325. [Google Scholar]
  64. Tao, F.; Cheng, Y.; Zhang, L.; Zhao, D. Utility modelling, equilibrium, and coordination of resource service transaction in service-oriented manufacturing system. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 2012, 226, 1099–1117. [Google Scholar] [CrossRef]
  65. Tao, F.; Guo, H.; Zhang, L.; Cheng, Y. Modelling of combinable relationship-based composition service network and the theoretical proof of its scale-free characteristics. Enterp. Inf. Syst. 2012, 6, 373–404. [Google Scholar] [CrossRef]
  66. Cheng, Y.; Tao, F.; Liu, Y.; Zhao, D.; Zhang, L.; Xu, L. Energy-aware resource service scheduling based on utility evaluation in cloud manufacturing system. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 2013, 227, 1901–1915. [Google Scholar] [CrossRef]
  67. Laili, Y.; Tao, F.; Zhang, L.; Cheng, Y.; Luo, Y.; Sarker, B.R. A ranking chaos algorithm for dual scheduling of cloud service and computing resource in private cloud. Comput. Ind. 2013, 64, 448–463. [Google Scholar] [CrossRef]
  68. Zhang, L.; Luo, Y.; Tao, F.; Li, B.H.; Ren, L.; Zhang, X.; Guo, H.; Cheng, Y.; Hu, A.; Liu, Y. Cloud manufacturing: A new manufacturing paradigm. Enterp. Inf. Syst. 2014, 8, 167–187. [Google Scholar] [CrossRef]
  69. Cheng, Y.; Tao, F.; Zhang, L.; Zhao, D. Dynamic supply-demand matching for manufacturing resource services in service-oriented manufacturing systems: A hypernetwork-based solution framework. In Proceedings of the ASME 2015 International Manufacturing Science and Engineering Conference, Charlotte, NC, USA, 8–12 June 2015; Volume 56833, p. V002T04A017. [Google Scholar]
  70. Cheng, Y.; Tao, F.; Zhang, L.; Zuo, Y. Supply-demand matching of manufacturing service in service-oriented manufacturing systems. Comput. Integr. Manuf. Syst. 2015, 21, 1930–1940. [Google Scholar]
  71. Cheng, Y.; Zhao, D.; Tao, F.; Zhang, L.; Liu, Y. Complex networks based manufacturing service and task management in cloud environment. In Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015, Auckland, New Zealand, 15–17 June 2015; pp. 242–247. [Google Scholar]
  72. Tao, F.; Zhang, L.; Liu, Y.; Cheng, Y.; Wang, L.; Xu, X. Manufacturing service management in cloud manufacturing: Overview and future research directions. J. Manuf. Sci. Eng. 2015, 137. [Google Scholar] [CrossRef]
  73. Cheng, Y.; Tao, F.; Zhao, D.; Zhang, L. Modeling of manufacturing service supply–demand matching hypernetwork in service-oriented manufacturing systems. Robot. Comput. Integr. Manuf. 2017, 45, 59–72. [Google Scholar] [CrossRef]
  74. Zhang, W.; Zhang, S.; Cai, M.; Liu, Y. A reputation-based peer-to-peer architecture for semantic service discovery in distributed manufacturing environments. Concurr. Eng. Res. Appl. 2012, 20, 237–253. [Google Scholar] [CrossRef]
  75. Zhang, W.; Zhang, S.; Qi, F.; Cai, M. Self-Organized P2P Approach to Manufacturing Service Discovery for Cross-Enterprise Collaboration. IEEE Trans. Syst. Man Cybern. Syst. 2014, 44, 263–276. [Google Scholar] [CrossRef]
  76. Zhang, W.; Guo, S.; Zhang, S. Personalized manufacturing service recommendation using semantics-based collaborative filtering. Concurr. Eng. Res. Appl. 2015, 23, 166–179. [Google Scholar] [CrossRef]
  77. Zhang, W.; Zhang, S.; Cai, M.; Jian, W. An agent-based peer-to-peer architecture for semantic discovery of manufacturing services across virtual enterprises. Enterp. Inf. Syst. 2015, 9, 233–256. [Google Scholar] [CrossRef]
  78. Zhang, W.; Yang, Y.; Zhang, S.; Yu, D.; Xu, Y. A new manufacturing service selection and composition method using improved flower pollination algorithm. Math. Probl. Eng. 2016, 2016, 7343794. [Google Scholar] [CrossRef] [Green Version]
  79. Wang, Y.; Dai, Z.; Zhang, W.; Zhang, S.; Xu, Y.; Chen, Q. Urgent task-aware cloud manufacturing service composition using two-stage biogeography-based optimisation. Int. J. Comput. Integr. Manuf. 2018, 31, 1034–1047. [Google Scholar] [CrossRef]
  80. Zhang, S.; Xu, S.; Zhang, W.; Yu, D.; Chen, K. A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization. Neurocomputing 2018, 272, 439–452. [Google Scholar] [CrossRef]
  81. Zhang, W.; Yang, Y.; Zhang, S.; Yu, D.; Chen, Y. A new three-dimensional manufacturing service composition method under various structures using improved Flower Pollination Algorithm. Enterp. Inf. Syst. 2018, 12, 620–637. [Google Scholar] [CrossRef]
  82. Zhang, W.; Yang, Y.; Zhang, S.; Yu, D.; Li, Y. Correlation-aware manufacturing service composition model using an extended flower pollination algorithm. Int. J. Prod. Res. 2017, 56, 4676–4691. [Google Scholar] [CrossRef]
  83. Xiao, J.; Zhang, W.; Zhang, S.; Zhuang, X. Game theory–based multi-task scheduling in cloud manufacturing using an extended biogeography-based optimization algorithm. Concurr. Eng. Res. Appl. 2019, 27, 314–330. [Google Scholar] [CrossRef]
  84. Zhang, S.; Xu, S.; Huang, X.; Zhang, W.; Chen, M. Networked correlation-aware manufacturing service supply chain optimization using an extended artificial bee colony algorithm. Appl. Soft Comput. 2019, 76, 121–139. [Google Scholar] [CrossRef]
  85. Zhang, S.; Xu, Y.; Zhang, W.; Yu, D. A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm. J. Intell. Manuf. 2017, 30, 2069–2083. [Google Scholar] [CrossRef]
  86. Zhang, S.; Yang, W.; Zhang, W.; Chen, M. A collaborative service group-based fuzzy QoS-aware manufacturing service composition using an extended flower pollination algorithm. Nonlinear. Dyn. 2019, 95, 3091–3114. [Google Scholar] [CrossRef]
  87. Zhang, W.; Ding, J.; Wang, Y.; Zhang, S.; Zhuang, X. Energy-efficient bi-objective manufacturing scheduling with intermediate buffers using a three-stage genetic algorithm. J. Intel. Fuzzy. Syst. 2020, 39, 289–304. [Google Scholar] [CrossRef]
  88. Zhang, S.; Xu, Y.; Zhang, W. Multitask-oriented manufacturing service composition in an uncertain environment using a hyper-heuristic algorithm. J. Manuf. Syst. 2021, 60, 138–151. [Google Scholar] [CrossRef]
  89. Parida, V.; Sjödin, D.R.; Wincent, J.; Kohtamäki, M. Mastering the transition to product-service provision: Insights into business models, learning activities, and capabilities. Res. Technol. Manage. 2014, 57, 44–52. [Google Scholar]
  90. Kohtamaki, M.; Hakala, H.; Partanen, J.; Parida, V.; Wincent, J. The performance impact of industrial services and service orientation on manufacturing companies. J. Serv. Theory Pract. 2015, 25, 463–485. [Google Scholar] [CrossRef]
  91. Sjödin, D.R.; Parida, V.; Kohtamäki, M. Capability configurations for advanced service offerings in manufacturing firms: Using fuzzy set qualitative comparative analysis. J. Bus. Res. 2016, 69, 5330–5335. [Google Scholar] [CrossRef]
  92. Kohtamäki, M.; Parida, V.; Oghazi, P.; Gebauer, H.; Baines, T. Digital servitization business models in ecosystems: A theory of the firm. J. Bus. Res. 2019, 104, 380–392. [Google Scholar] [CrossRef]
  93. Sjödin, D.; Parida, V.; Kohtamäki, M. Relational governance strategies for advanced service provision: Multiple paths to superior financial performance in servitization. J. Bus. Res. 2019, 101, 906–915. [Google Scholar] [CrossRef]
  94. Sjödin, D.; Parida, V.; Kohtamäki, M.; Wincent, J. An agile co-creation process for digital servitization: A micro-service innovation approach. J. Bus. Res. 2020, 112, 478–491. [Google Scholar] [CrossRef]
  95. Khanra, S.; Dhir, A.; Parida, V.; Kohtamäki, M. Servitization research: A review and bibliometric analysis of past achievements and future promises. J. Bus. Res. 2021, 131, 151–166. [Google Scholar] [CrossRef]
  96. Kohtamäki, M.; Rabetino, R.; Einola, S.; Parida, V.; Patel, P. Unfolding the digital servitization path from products to product-service-software systems: Practicing change through intentional narratives. J. Bus. Res. 2021, 137, 379–392. [Google Scholar] [CrossRef]
  97. Korkeamki, L.; Kohtamäki, M.; Parida, V. Worth the risk? The profit impact of outcome-based service offerings for manufacturing firms. J. Bus. Res. 2021, 131, 92–102. [Google Scholar] [CrossRef]
  98. Gebauer, H. An investigation of antecedents for the development of customer support services in manufacturing companies. J. Bus.-Bus. Mark. 2007, 14, 59–96. [Google Scholar] [CrossRef]
  99. Gebauer, H.; Fleisch, E. An investigation of the relationship between behavioral processes, motivation, investments in the service business and service revenue. Ind. Mark. Manag. 2007, 36, 337–348. [Google Scholar] [CrossRef]
  100. Gebauer, H.; Fleisch, E. Managing sustainable service improvements in manufacturing companies. Kybernetes 2007, 36, 583–595. [Google Scholar] [CrossRef]
  101. Gebauer, H.; Wang, C.; Beckenbauer, B.; Krempl, R. Business-to-business marketing as a key factor for increasing service revenue in China. J. Bus. Ind. Mark. 2007, 22, 126–137. [Google Scholar] [CrossRef]
  102. Combs, J.; Liu, Y.; Hall, A.; Ketchen, D. How much do high-performance work practices matter? A meta-analysis of their effects on organizational performance. Pers. Psychol. 2006, 59, 501–528. [Google Scholar] [CrossRef]
  103. Hertwich, E.G.; Peters, G.P. Carbon footprint of nations: A global, trade-linked analysis. Environ. Sci. Technol. 2009, 43, 6414–6420. [Google Scholar] [CrossRef] [Green Version]
  104. Desimone, J.M. Practical Approaches to Green Solvents. Science 2002, 297, 799–803. [Google Scholar] [CrossRef]
  105. Wang, S.; Wan, J.; Li, D.; Zhang, C. Implementing smart factory of industrie 4.0: An outlook. Int. J. Distrib. Sens. Netw. 2016, 12, 3159805. [Google Scholar] [CrossRef] [Green Version]
  106. Grant, A.M.; Parker, S.K. 7 Redesigning Work Design Theories: The Rise of Relational and Proactive Perspectives. Acad. Manag. Ann. 2009, 3, 317–375. [Google Scholar] [CrossRef]
  107. Holweg, M. The genealogy of lean production. J. Oper. Manag. 2007, 25, 420–437. [Google Scholar] [CrossRef]
  108. Westhead, P.; Wright, M.; Ucbasaran, D. The internationalization of new and small firms: A resource-based view. J. Bus. Ventur. 2001, 16, 333–358. [Google Scholar] [CrossRef]
  109. Colombo, M.G.; Grilli, L. Founders’ human capital and the growth of new technology-based firms: A competence-based view. Res. Policy 2005, 34, 795–816. [Google Scholar] [CrossRef]
  110. Rose, J.L. A baseline and vision of ultrasonic guided wave inspection potential. J. Press. Vessel Technol. 2002, 124, 273–282. [Google Scholar] [CrossRef]
  111. Frank, A.G.; Dalenogare, L.S.; Ayala, N.F. Industry 4.0 technologies: Implementation patterns in manufacturing companies. Int. J. Prod. Econ. 2019, 210, 15–26. [Google Scholar] [CrossRef]
  112. Siddique, R.; Khatib, J.; Kaur, I. Use of recycled plastic in concrete: A review. Waste Manag. 2008, 28, 1835–1852. [Google Scholar] [CrossRef]
  113. Boyer, R. Is a finance-led growth regime a viable alternative to Fordism? A preliminary analysis. Econ. Soc. 2000, 29, 111–145. [Google Scholar] [CrossRef]
  114. Homburg, C.; Fürst, A. How organizational complaint handling drives customer loyalty: An analysis of the mechanistic and the organic approach. J. Mark. 2005, 69, 95–114. [Google Scholar] [CrossRef]
  115. Oliveira, T.; Thomas, M.; Espadanal, M. Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Inf. Manag. 2014, 51, 497–510. [Google Scholar] [CrossRef]
  116. Riezebos, J.; Klingenberg, W.; Hicks, C. Lean production and information technology: Connection or contradiction? Comput. Ind. 2009, 60, 237–247. [Google Scholar] [CrossRef]
  117. Davies, A. Moving base into high-value integrated solutions: A value stream approach. Ind. Corp. Chang. 2004, 13, 727–756. [Google Scholar] [CrossRef]
  118. Kastalli, I.V.; Van Looy, B. Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance. J. Oper. Manag. 2013, 31, 169–180. [Google Scholar] [CrossRef] [Green Version]
  119. Kongsamut, P.; Rebelo, S.; Xie, D. Beyond balanced growth. Rev. Econ. Stud. 2001, 68, 869–882. [Google Scholar] [CrossRef]
  120. Bendoly, E.; Donohue, K.; Schultz, K.L. Behavior in operations management: Assessing recent findings and revisiting old assumptions. J. Oper. Manag. 2006, 24, 737–752. [Google Scholar] [CrossRef]
  121. Rajak, S.; Vimal, K.; Arumugam, S.; Parthiban, J.; Sivaraman, S.K.; Kandasamy, J.; Duque, A.A. Multi-objective mixed-integer linear optimization model for sustainable closed-loop supply chain network: A case study on remanufacturing steering column. Environ. Dev. Sustain. 2022, 24, 6481–6507. [Google Scholar] [CrossRef]
  122. Rajak, S.; Parthiban, P.; Dhanalakshmi, R. Selection of transportation channels in closed-loop supply chain using meta-heuristic algorithm. Int. J. Inf. Syst. Supply Chain Manage. 2018, 11, 64–86. [Google Scholar] [CrossRef]
  123. Cano, C.R.; Carrillat, F.A.; Jaramillo, F. A meta-analysis of the relationship between market orientation and business performance: Evidence from five continents. Int. J. Res. Mark. 2004, 21, 179–200. [Google Scholar] [CrossRef]
  124. Lan, H.; Ding, Y.; Hong, J.; Huang, H.; Lu, B. A web-based manufacturing service system for rapid product development. Comput. Ind. 2004, 54, 51–67. [Google Scholar] [CrossRef]
  125. Gebauer, H.; Fleisch, E.; Friedli, T. Overcoming the service paradox in manufacturing companies. Eur. Manag. J. 2005, 23, 14–26. [Google Scholar] [CrossRef]
  126. Neely, A. Exploring the financial consequences of the servitization of manufacturing. Oper. Manag. Res. 2008, 1, 103–118. [Google Scholar] [CrossRef] [Green Version]
  127. Huxtable, J.; Schaefer, D. On Servitization of the Manufacturing Industry in the UK. Procedia Cirp 2016, 52, 46–51. [Google Scholar] [CrossRef] [Green Version]
  128. Lu, C.; Li, X.; Gao, L.; Liao, W.; Yi, J. An effective multi-objective discrete virus optimization algorithm for flexible job-shop scheduling problem with controllable processing times. Comput. Ind. Eng. 2017, 104, 156–174. [Google Scholar] [CrossRef]
  129. Lim, C.; Kim, K.H.; Kim, M.J.; Heo, J.Y.; Kim, K.J.; Maglio, P.P. From data to value: A nine-factor framework for data-based value creation in information-intensive services. Int. J. Inf. Manag. 2018, 39, 121–135. [Google Scholar] [CrossRef]
  130. Tao, F.; Qi, Q.; Wang, L.; Nee, A. Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering 2019, 5, 9. [Google Scholar] [CrossRef]
  131. Sholihah, M.; Maezono, T.; Mitake, Y.; Shimomura, Y. Formulating Service-Oriented Strategies for Servitization of Manufacturing Companies. Sustainability 2020, 12, 9657. [Google Scholar] [CrossRef]
  132. Yu, C.; Tang, D.; Tenkorang, A.P.; Bethel, B.J. The Impact of the Opening of Producer Services on the International Competitiveness of Manufacturing Industry. Sustainability 2021, 13, 11224. [Google Scholar] [CrossRef]
  133. Liang, H.; Wen, X.; Liu, Y.; Zhang, H.; Wang, L. Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning. Robot. Comput. Integr. Manuf. 2021, 67, 101991. [Google Scholar] [CrossRef]
  134. Zhang, J. Impact of Manufacturing Servitization on Factor Productivity of Industrial Sector Using Global Value Chain. Sustainability 2022, 14, 5354. [Google Scholar] [CrossRef]
  135. Caiado, R.G.G.; Scavarda, L.F.; Azevedo, B.D.; Nascimento, D.L.d.M.; Quelhas, O.L.G. Challenges and Benefits of Sustainable Industry 4.0 for Operations and Supply Chain Management—A Framework Headed toward the 2030 Agenda. Sustainability 2022, 14, 830. [Google Scholar] [CrossRef]
  136. Szalavetz, A. `Tertiarization’ of Manufacturing Industry in the New Economy-Experiences in Hungarian Companies; IWE Working Papers; Institute for World Economics-Centre for Economic and Regional Studies-Hungarian Academy of Sciences: Budapest, Hungary, 2003. [Google Scholar]
  137. Temouri, Y.; Driffield, N.L.; Higón, D.A. The futures of offshoring FDI in high-tech sectors. Futures 2010, 42, 960–970. [Google Scholar] [CrossRef] [Green Version]
  138. Kowalkowski, C.; Kindström, D.; Alejandro, T.B.; Brege, S.; Biggemann, S. Service infusion as agile incrementalism in action. J. Bus. Res. 2012, 65, 765–772. [Google Scholar] [CrossRef] [Green Version]
Figure 1. The flow chart of literature search and bibliometric analysis methodology.
Figure 1. The flow chart of literature search and bibliometric analysis methodology.
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Figure 2. The quantity of annual literature in manufacturing servitization research.
Figure 2. The quantity of annual literature in manufacturing servitization research.
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Figure 3. The bubble chart of the top 20 productive countries/regions by year.
Figure 3. The bubble chart of the top 20 productive countries/regions by year.
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Figure 4. The cross-relationship chart of the top 20 countries/regions.
Figure 4. The cross-relationship chart of the top 20 countries/regions.
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Figure 5. The bubble chart of the top 20 research fileds by year.
Figure 5. The bubble chart of the top 20 research fileds by year.
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Figure 6. The cross-relationship chart of the top 20 research areas.
Figure 6. The cross-relationship chart of the top 20 research areas.
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Figure 7. The bubble chart of the top 20 journals by year.
Figure 7. The bubble chart of the top 20 journals by year.
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Figure 8. The bubble chart of the top 20 productive publishers by year.
Figure 8. The bubble chart of the top 20 productive publishers by year.
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Figure 9. The bubble chart of the top 20 keywords.
Figure 9. The bubble chart of the top 20 keywords.
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Figure 10. The cross-relationship chart of the top 20 keywords.
Figure 10. The cross-relationship chart of the top 20 keywords.
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Figure 11. The bubble chart of the top 20 productive authors by year.
Figure 11. The bubble chart of the top 20 productive authors by year.
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Figure 12. The quantity of publications in manufacturing servitization by year.
Figure 12. The quantity of publications in manufacturing servitization by year.
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Figure 13. The top 20 keywords with the strongest citation bursts.
Figure 13. The top 20 keywords with the strongest citation bursts.
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Figure 14. The articles about the field of manufacturing servitization [2,3,47,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138].
Figure 14. The articles about the field of manufacturing servitization [2,3,47,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138].
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Table 1. The top 20 places with the most publications in manufacturing servitization research.
Table 1. The top 20 places with the most publications in manufacturing servitization research.
RankCountry/RegionTP 1TC 2ACPP 3IF 4
1USA93034,14236.714.88
2China Mainland76016,07121.154.62
3UK50120,34940.625.13
4Italy198531926.864.48
5Spain196436222.724.45
6Germany184590632.104.73
7Sweden148647043.725.68
8Canada136371627.324.65
9Taiwan134244718.264.07
10Australia122315825.894.56
11South Korea11611089.553.91
12France112537848.025.08
13Netherlands111511546.086.61
14Finland110431939.266.21
15India92267329.054.35
16Switzerland73408255.925.08
17Japan6493914.673.88
18Turkey6276812.393.82
19Singapore60179429.904.88
20Iran5996416.344.03
TP 1: total publications; TC 2: total citations; ACPP 3: average citations per publication; IF 4: average impact factor per publication.
Table 2. The 20 research fields with the most publications during 1990–2021.
Table 2. The 20 research fields with the most publications during 1990–2021.
RankWoS Research AreaTPTPR (%) 1TCACPP
1Business & Economics156541.5457,32936.63
2Engineering126233.5039,43331.25
3Operations Research & Management Science56414.9722,45339.81
4Computer Science55014.601618129.42
5Environmental Sciences & Ecology3669.7210,19627.86
6Science & Technology2125.63490523.14
7Materials Science1674.43535932.09
8Automation & Control Systems1353.58557741.31
9Public Administration1183.13322827.36
10Geography1032.73310130.11
11Mathematics892.36159217.89
12Telecommunications772.04163821.27
13Public, Environmental & Occupational Health701.86172324.61
14Development Studies651.73113517.46
15International Relations651.7389913.83
16Urban Studies601.59184030.67
17Social Sciences551.46136624.84
18Physics531.4177114.55
19Information Science & Library Science501.33167133.42
20Robotics481.27192740.15
TPR (%) 1: the percentage of articles of areas in total articles.
Table 3. The 20 journals with most publications in manufacturing servitization.
Table 3. The 20 journals with most publications in manufacturing servitization.
RankJournalTPTPR (%)TCACPPIF
1International Journal of Production Research802.12234629.336.091
2International Journal of Production Economics782.07484362.097.079
3Sustainability772.046458.382.355
4International Journal of Advanced Manufacturing Technology772.04276135.862.406
5International Journal of Computer Integrated Manufacturing621.65120119.372.795
6Journal of Business Research541.43223541.406.74
7Journal of Cleaner Production541.43225041.677.597
8International Journal of Operations & Production Management491.30308662.985.937
9Industrial Marketing Management451.19251255.824.95
10Computers & Industrial Engineering441.17101823.144.728
11Robotics and Computer-integrated Manufacturing360.96155743.254.753
12IEEE Access340.9038811.412.454
13Journal of Intelligent Manufacturing310.8273223.615.107
14Regional Studies310.8278525.314.033
15Total Quality Management & Business Excellence300.8062620.853.223
16Computers in Industry300.80122240.737.247
17Service Industries Journal270.7253619.855.275
18M&SOM-Manufacturing & Service Operations Management260.6947218.146.32
19Research Policy260.6939915.334.725
20Small Business Economics260.69115644.457.005
Table 4. The 20 institutions with most publications in manufacturing servitization.
Table 4. The 20 institutions with most publications in manufacturing servitization.
RankInstitutionsTPTPR (%)TCACPPH-IndexCountry/Region
1Beihang University711.88475266.9329China Mainland
2University of Vaasa471.25197041.9126Finland
3University of California System471.25161634.3818USA
4University of London441.17166537.8419UK
5University of Cambridge441.17277162.9824UK
6Lulea University of Technology431.14158536.8623Sweden
7University of Birmingham421.11129530.8319UK
8Xi’an Jiaotong University411.0987521.3416China Mainland
9Zhejiang University401.0666316.5814China Mainland
10Shanghai Jiao Tong University391.04138935.6214China Mainland
11Linkoping University381.01249265.5825Sweden
12State University System of Florida370.98252368.1916USA
13Hong Kong Polytechnic University350.93122935.1116Hong Kong
14Tsinghua University340.9078923.2113China Mainland
15Chinese Academy of Sciences330.88145744.1516China Mainland
16University of Manchester320.85182757.0920UK
17University System of Georgia310.82172255.5515USA
18University of Michigan System300.80141247.0719USA
19Aston University290.77157354.2419UK
20University of Michigan280.74128846.0018USA
Table 5. The 20 authors with the most publications in manufacturing servitization.
Table 5. The 20 authors with the most publications in manufacturing servitization.
RankAuthorTPTPR (%)TCACPPH-indexInstitution, Country/Region
1Tao, Fei370.983929106.1922Beihang University, China Mainland
2Zhang, Lin320.85267383.5321Beihang University, China Mainland
3Parida, Vinit240.6488536.8616Lulea University of Technology, Sweden
4Gebauer, Heiko210.56191591.2010Swiss Federal Institute of Aquatic
Science & Technology (EAWAG), Swizerland
5Kohtamaki, Marko200.5383641.8114University of Vaasa, Finland
6Cheng, Ying150.40116077.3313Beihang University, China Mainland
7Zhang, Wenyu150.4016811.208Zhejiang University of
Finance & Economics, China Mainland
8Zhang, Shuai150.4016811.208Zhejiang University of
Finance & Economics, China Mainland
9Vendrell-Herrero, Ferran140.3755739.8010University of Birmingham, UK
10Baines, Tim130.351301100.0911Aston University, UK
11Zhang, Yingfeng130.3545234.799Northwestern Polytechnical
University, China Mainland
12Bustinza, Oscar F.130.3577659.7312University of Granada, Spain
13Yao, Xifan120.3247539.588South China University of
Technology, China Mainland
14Wang, Lihui120.32106088.369Royal Institute of Technology, Sweden
15Huang, George Q.110.2954449.429University of Hong Kong, Hong Kong
16Zhou, Zude110.2926824.407Wuhan University of Technology, China Mainland
17Jiang, Pingyu110.2922120.097Xi’an Jiaotong University, China Mainland
18Kowalkowski, Christian100.2790390.309Linkoping University, Sweden
19Xu, Xun100.2777577.5010University of Auckland, New Zealand
20Jiang, Zhibin100.2710710.736Shanghai Jiao Tong University, China Mainland
Table 6. The top 20 most cited publications related to manufacturing service research.
Table 6. The top 20 most cited publications related to manufacturing service research.
RankAuthorJournalTCTCY 1Year
1Combs et al. [102]Pers. Psychol.111874.532006
2Van de Vrande et al. [51]Technovation102468.272009
3Hertwich and Peters [103]Environ. Sci. Technol.96764.472009
4Desimone [104]Science82955.272002
5Tukker [43]J. Clean. Prod.76851.202015
6Tao et al. [49]J. Adv. Manuf. Technol.75650.402018
7Wang et al. [105]Int. J. Distrib. Sens. Netw.73448.932016
8Grant and Parker [106]Acad. Manag. Ann.61240.802009
9Holweg [107]J. Oper. Manag.58739.132007
10Westhead et al. [108]J. Bus. Ventur.57938.602001
11Colombo and Grilli [109]Res. Policy56037.332005
12Rose [110]J. Press. Vess.-T. ASME53335.532002
13Frank et al. [111]Int. J. Prod. Econ.50533.672019
14Siddique et al. [112]Waste Manag47231.472008
15Boyer [113]Econ. Soc.46631.072000
16Tao et al. [58]IEEE Trans. Ind. Inform.45930.602014
17Homburg andFurst [114]J. Mark.44829.872005
18Oliveira et al. [115]Inf. Manage.43529.002014
19Ageron et al. [33]Int. J. Prod. Econ.42928.602012
20Tao et al. [59]IEEE Trans. Ind. Inform.42128.072014
1 TCY: total citations per year.
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Chen, Y.; Wu, Z.; Yi, W.; Wang, B.; Yao, J.; Pei, Z.; Chen, J. Bibliometric Method for Manufacturing Servitization: A Review and Future Research Directions. Sustainability 2022, 14, 8743. https://doi.org/10.3390/su14148743

AMA Style

Chen Y, Wu Z, Yi W, Wang B, Yao J, Pei Z, Chen J. Bibliometric Method for Manufacturing Servitization: A Review and Future Research Directions. Sustainability. 2022; 14(14):8743. https://doi.org/10.3390/su14148743

Chicago/Turabian Style

Chen, Yong, Zhengjie Wu, Wenchao Yi, Bingjia Wang, Jianhua Yao, Zhi Pei, and Jiaoliao Chen. 2022. "Bibliometric Method for Manufacturing Servitization: A Review and Future Research Directions" Sustainability 14, no. 14: 8743. https://doi.org/10.3390/su14148743

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