ORIGINAL ARTICLE

A bibliometrics data analysis of management science

Yuxiu Zhao1& Yanbao Liu1

Received: 14 October 2019 /Accepted: 5 February 2020# Springer Nature Switzerland AG 2020

AbstractAt present, the journal-Management Science(MS) is constantly developing into one of the top international journals in the fieldsof Management and Operations Research &Management Science. Motivated by a quite good understanding of the developmentof this journal, this paper carries out this research work on the application of bibliometric methods and techniques to systemat-ically study the standard data of the publications included in the Web of Science from 1999 to 2018. To be specific, theknowledge structure and knowledge characteristic of its development are grasped. The former analyzes the distribution of annualpublications, the characteristics of most productive and influential countries, institutions and authors and the latter discussed theresearch foundation, research topics and research hotspots. Innovatively, the terms of “Product Life Cycle”, “Red Sea”, “BlueSea” in the Marketing Management and the time-line map are introduced the analyses on research topics, which is different fromother scholars. The results show that both knowledge structure and knowledge characteristic of MS present certain regularities,especially the evolutions of annual publications and researches topics have gone three main stages. To some extent, this paper canhelp scholars to better understand the journal, and provide some ideas for future scientific research works with higher contribu-tions. In addition, this paper also provides a perspective for researches on journals in combination with bibliometrics and dataanalysis.

Keywords Management science . Research frontier . Knowledge structure . Knowledge characteristics . Bibliometrics

1 Introduction

With the ongoing rise of the world economy, the socialproductivity has been greatly developed. The term “man-agement science” or “the science of management” hasgradually been focused by the public and scholars (Hopp2008). Increasingly, “Management Science” is developedinto an influential social science. It is acknowledged thatManagement Science is an interdisciplinary subject whichapplies scientific methods to address social problems andmake management decisions (Ernest 1957). Since theWorld War II, the frontier researches in this subject havemade great achievements in solving management problems

in military, economy, engineering and society, such as theallocation of scarce resources, production design and de-velopment, supply chain management, supply chain fi-nance, digital marketing and consumer behavior, etc.Meanwhile, with the development of this discipline, theinternational journal-Management Science (MS) has grad-ually been developed into one of the top internationaljournals in the field of Management and OperationsResearch & Management Science, which plays a promi-nent role (Table 1) in leading the development of discipline(Cachon 2009). Moreover, founded in 1954, MS is an ac-ademic journal of the Institute for Operations Research andthe Management Sciences. Its articles cover 10 main topicssuch as business strategy, decision analysis, supply chainmanagement, social network, product development, opti-mization and modeling, etc. It is listed as “the top 20journals valued by business school deans and academicprogram directors” by Business Week (https://www.i n f o rms . o rg / Pub l i c a t i o n s / INFORMS- Jou rn a l s /Management-Science). In addition, Thomson ISI identifiesit in both Social Science Citation Index (SSCI) and ScienceCitation Index (SCI). ABS delegates it as four-star journal,

* Yuxiu Zhaozhaoyvxiu@shu.edu.cn

Yanbao Liuandy_liu@shu.edu.cn

1 School of Management, Shanghai University, Shanghai 200444,China

Journal of Data, Information and Managementhttps://doi.org/10.1007/s42488-020-00024-0

ranking 1st in the field and it has been included in UTD24since 1990.

Nowadays, an increasing number of scholars in the field ofmanagement science are full of curiosity about the main fac-tors to promote the high-quality development of MS, such asthe macro level of most influential countries, institutions, au-thors and their cooperations with each other, and the microlevel of research foundations, research topics, research trendsand research hotspots. Based on the bibliometrics, the datamining of these main factors can be carried out from the per-spectives of knowledge structure and knowledgecharacteristics.

In 1934, Otlet put forward the concept of bibliometrics (Otlet1934), aiming at studying various types of knowledge carriersthrough mathematical and statistical methods, and analyzing thedevelopment thread and evolution law of knowledge. It is thebasic form of scientometrics and lays a foundation for itsdevelopment. In recent years, with the vigorous developmentof computer technology and the continuous improvement ofdatabase technology, many concepts and ideas in bibliometricshave been realized and become more and more specialized.Many scholars have devoted themselves to the bibliometricresearches of journals in order to explore the research trendsand hotspots of various disciplines. For example, Cancino et al.(2017) explored the research trend of Computers & IndustrialEngineering from the perspectives of impacts and researchtopics. Li et al. (2019) applied the bibliometric methods to ex-plore the distribution of annual publications, most productiveauthors, national distribution, high-cited literatures, co-citationnetworks, etc., and revealed the development trend of Entropy.

On the other hand, the software of CiteSpace developed byChen Chaomei in 2004 (Chen 2006) enriched the visual con-tents of the network landscapes, provided great help for

researches on bibliometrics, and gradually gained attentionsand recognitions from researchers. For example, Yu et al.(2017) not only used the bibliometric methods to get the rep-resentative countries, institutions and authors in InformationSciences, but also analyzed the publication trend of thisJournal by visualizing the co-citation network withCitespace. Tang et al. (2018) used the Citespace to depict thenetwork prospect of research topics and research hotspots inSustainability. Obviously, the bibliometric methods can alsobe conducted to analyze journals in the field of managementscience. In this respect, Laengle et al. (2017) analyzed thepublications and cooperation network of the EuropeanJournal of Operational Research to discuss its performance.Merigo and Yang (2017) analyzed the most influentialjournals, the top-200 most cited articles and the most produc-tive authors to identify relevant researches and latest trends inthis field.

From the above, it can be seen that the research method ofbibliometrics and the research technique of CiteSpace havebeen widely used in the studies on the knowledge structureand characteristics of international journals. However, there isno article onthe application of bibliometric method and tech-nique studying MS which enjoys a high reputation in the fieldof management science. This paper carries out this researchwork in order to grasp the knowledge structure and character-istic of its development, so as to present a more comprehen-sive and detailed understanding of the journal and future fron-tier researches for scholars in this area. To ensure the integrityand reliability of data and information, this paper takes thecore database of Web of Science (WoS) developed byThomson Scientific as the data source and gets the full recordsof 3125 published documents from 1999 to 2018, including3012 articles, 20 reviews, 46 editorials and 47 other texts.

Table 1 The impact analyses of some journals in the field of Operations Research & Management Science from 1999 to 2018

Journal TP TC TC/TP

IF5 h II CHL EF AIS

Management Science 3125 15,4150 49.33 4.927 178 0.762 16.2 0.042 3.686

Journal of Operations Management 817 66,246 81.08 7.485 140 1.118 10.5 0.006 2.265

Omega-International Journal of Management Science 1519 51,317 33.78 5.525 101 1.274 8.3 0.010 1.495

International Journal of Operations & Production Management 1427 47,198 33.07 4.371 94 0.671 11.9 0.003 0.683

Transportation Science 872 27,185 31.18 4.581 84 0.649 12.6 0.006 1.830

Operations Research 1924 53,816 27.97 3.047 98 0.297 19.2 0.014 2.061

European Journal of Operational Research 11,645 307,380 26.40 3.968 184 0.817 9.7 0.050 1.183

Mathematical Programming 1936 51,090 26.39 2.931 90 0.762 14.3 0.020 2.494

Production and Operations Management 1402 32,285 23.03 2.921 81 0.379 8.1 0.009 1.209

M&SOM-Manufacturing & Service Operations Management 529 10,071 19.04 2.867 49 0.381 9.2 0.006 1.929

Abbreviations: TP, Total publications; TC, Total citations; TC/TP, Total citations per publication; IF5, 5-year impact factor, the ratio between the numberof citations and the number of publications in the last 5 years; h, h-index, indicating that there are h articles cited more than h times; II, Immediacy Index,the index obtained by diving the number of citations in a certain year by the total number of articles published in the same year; CHL, Cited Half-life, thetime that the citations from the current year forward account for 50% of the total citations; EF, Eigen Factor, a index to evaluate the citations by journalswith high academic influence; AIS, Article Influence Score, the supplement of EF, excludes the influences of total number of publications

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After this brief introduction as Section 1, Section 2 presentsthe knowledge structure of MS. To be specific, it includes thedistribution of annual publications, the characteristics of themost productive and influential countries, institutions and au-thors in the year 1999–2018. In particular, this section gives abrief analysis of the related documents issued by China.Section 3 shows the knowledge characteristics of MS, whichcontain the research foundation, research topics and researchhotspots. However, it should be noted that this section is con-ducted in a different way from other scholars, especially theterms of “Product Life Cycle”, “Red Sea” and “Blue Sea” areintroduced in this study. Finally, Section 4 summarizes themain conclusions in this research.

2 Knowledge structure of managementscience

The total amount of publications (TP) and citations (TC) canintuitively reflect the evolution of journal. Figure 1 shows thedistribution of annual publications and quotations in MS dur-ing its recent 20-year period, and the non-linear function mod-el is used to fit the trend of publications. In general, the growthof publication presents the form of cubic function (R2 = 0.91).With 2013 as the demarcation point, the growth was slow inthe early stage and gradually increased in the later period. It isexpected that the number of annual publications will almostreach 500 in 2021. On the contrary, the amount of quotationsis basically declining, which is highly negatively correlatedwith the publication year. It is in line with the law ofbibliometrics, but there are also special points. For instance,the publications in 2006 have been cited cumulatively morethan 14,400 times.

In order to analyze the publications more microscopically,the knowledge innovation and cooperation of the most pro-ductive and influential countries, institutions and authors areexplored respectively to provide some references for futureresearches and research collaborations.

2.1 Most productive and influential countries

Totally, there are 51 countries all over the world having con-tributed toMS. Here, the top-15 of them are presented in termsof TP, TC, citations per paper (TP/TC), h-index and citationthresholds in Table 2. As far as the total publications are con-cerned, North American countries are the most eye-catching,with the USA and Canada in the top-2. European countries arealso more prominent, with eight countries including the UK,Germany, France and Netherlands in the list. However, Asiaand Oceania are relatively backward. With the exception ofP.R. China and Singapore, other countries have published lessthan 100 papers.

In addition to the total publications, other indicators reflectthe contributions of academic researches and the degree beingaccepted by other scholars. It should be mentioned that the USranked first in all indicators, with an h-index of 173, threetimes higher than that of Canada. And 143 articles were citedmore than 200 times, larger than the sum of other countries.As for other countries, the h-index of France is 52, and 14articles have been cited more than 200 times, which is betterthanmany other countries with a large volume of publications.The publications in Spain are cited 53.91 times per paper,which is the only country except the USA that exceeds theaverage value (49.33). Although China (excluding Taiwan) isthe most frequently cited country in Asia, its performances interms of citations per paper, h-index and high citations areunsatisfactory, indicating that most of the research results havenot received wide attention.

There is a phenomenon that more scholars are inclined tocooperate in research works. With the deepening of scientificresearches, this awareness of research collaboration is increas-ing, and the cooperations among countries are strengtheningday by day. Figure 2 displays the cooperation network of top-15 productive countries by CiteSpace V in which a node rep-resents a country. The colors of its annual rings from deep toshallow indicate the years from far to near and the thickness ofannual rings represents the amount of annual publications.The thickness of links denotes the cooperation between two

Fig. 1 The evolution ofdevelopment of MS from 1999 to2018

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different countries is strong or weak. The thicker the link is,the closer cooperation the two countries build. The colors ofthe links represent the time of the first cooperation. Besides,the purple aperture at the outermost layer of a node representsthat the node plays an pivotal role in the whole cooperationnetwork.

According to Fig. 2, the USA is the largest node with aclear purple aperture, which occupies its central position in thenational cooperation network and its strong cooperative rela-tionships with other countries. In addition, Canada and Franceare also the countries with purple diaphragms. However,France has thicker dark rings and thinner light rings, whichindicates that the amount of publications is high in the earlier

period with the trend of decreasing in recent years. Therefore,it is assumed that its central role in the cooperation networkmay be weakened in the future. The purple aperture of Chinais not obvious, which is because that its cooperation relation-ships with other countries (not including the USA, Canadaand Singapore) are relatively weak and it does not play adominant role in the research collaborations between Chinaand the USA, Canada or Singapore.

2.2 Most productive and influential institutions

According to the statistical analysis by the software ofHistCite, 1142 institutions have published papers in MS intotal. Table 3 depicts the top-15 institutions. In addition tothe fact that the total number of papers published by theUSA is much higher than that by other countries, its institu-tional performance is also terrific. For example, except the10th-INSEAD and the 14th-Univ Toronto, institutions withmore than 100 articles are all American universities. It showsthe high scientific research capacity of USA in the field ofmanagement science. To be specific, on the top of the list isthe University of Pennsylvania (Univ Penn), the world’s lead-ing research institution, which has almost the same volume ofpublications as China. At the same time, the citations perpaper are 78.16, and the h-index and the citation thresholdsrank the first in the world. These conclusions illustrate that itsrelative researches have attracted much attention. In addition,its Wharton School is the birthplace of Master of BusinessAdministration education and has a profound influence onthe field of management science. Also it is worthy to mentionthe MIT and the University of Maryland. Although ranked

Table 2 The top-15 most pro-ductive and influential countriesof MS from 1999 to 2018

Rank Country TP TC TC/TP

h >200 >100 >50 >20 >10

1 USA 2426 132,037 54.43 173 143 327 676 1267 1648

2 Canada 248 8020 32.34 51 4 20 52 104 140

3 P.R. China 222 4739 21.35 39 1 9 26 73 109

4 UK 214 4754 22.21 39 4 14 31 71 109

5 Germany 149 3021 20.28 36 1 3 26 57 77

6 France 143 6342 44.35 52 14 31 55 104 130

7 Singapore 123 4638 37.71 33 3 7 21 53 81

8 Netherlands 133 5002 37.61 36 3 11 28 56 73

9 Australia 56 2424 43.29 22 3 8 16 23 33

10 Spain 56 3019 53.91 29 5 8 15 35 46

11 Switzerland 53 1193 22.51 16 1 1 8 15 28

12 Israel 50 1292 25.84 20 2 13 20 27

13 Italy 49 1381 28.18 20 3 5 7 20 28

14 Denmark 41 1188 28.98 18 3 8 18 23

15 South Korea 38 1479 38.92 19 2 3 9 27

Abbreviations: TP, Total publications; TC, Total citations; TC/TP, Total citations per publication; h, h-index;>200, >100, >50, >20, >10, Number of papers with more than 200, 100, 50, 20, and 10 citations

Fig. 2 The cooperation network of top-15most productive and influentialcountries in MS from 1999 to 2018

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only 5th in terms of publications, the citations per article ofMIT are 82.32 with ranked 2nd. In terms of the h-index andthe highly citations, it is only second to the University ofPennsylvania. Ranked 13th, the University of Maryland iscited 123.32 times per article, which is far more than otherinstitutions. It is also excellent in high citations with 10 articlescited more than 200 times.

Especially, it can be seen that the performance of Chineseinstitutions is not satisfactory. In order to further explore thedifferences among Chinese institutions, Fig. 3 describes theirprovincial distributions, with obvious regional differences.Hong Kong is the backbone of the Chinese institutions thatpublish papers. Among them, the Hong Kong University ofScience and Technology has published 70 articles, ranked17th in the world. Its 7 articles have been cited more than100 times, which is far superior to other institutions inChina. In addition, about 20 articles were respectively pub-lished by the Chinese University of Hong Kong, the HongKong Polytechnic University and the City University ofHong Kong. However, the difference between Hong Kongand the mainland is significant in the field of managementscience. In the mainland, the most papers have published byBeijing and Shanghai while are significantly less than that inHong Kong. For instance, the Cheung Kong Graduate Schoolof Business, Tsinghua University and Peking University inBeijing published more than 10 articles respectively, of whichthe Cheung Kong Graduate School of Business has publishedup to 16. The college is an independent scientific researchinstitute focusing on providing the education of business ad-ministration, and has achieved remarkable results in both

theory and practice. Furthermore, Shanghai JiaotongUniversity in Shanghai has published 13 articles, and the num-ber of citations per paper is 27.08, which is only second toHong Kong University of Science and Technology and HongKong Polytechnic University in China. In addition, FudanUniversity and Shanghai University of Finance andEconomics have published 13 and 7 papers respectively.Except that, the number of publications in other institutionsis quite fewer.

Furthermore, Fig. 4 depicts that Chinese institutions have ex-perienced the process of international cooperation in publicationson the frontier. In the early stage, the cooperation between theuniversities from Hong Kong and foreign institutions was themain one. Subsequently, Shanghai Jiaotong University in themainland began to cooperate with Duke University. After that,the famous institutions in Beijing and Shanghai graduallystrengthened the cooperations with the universities from HongKong and other countries. At the same time, Hong KongUniversity of Science and Technology, Chinese University ofHong Kong, City University of Hong Kong, NationalUniversity of Singapore, University of Toronto, FudanUniversity and Cheung Kong Graduate School of Business haveplayed central roles in the international research collaboration ofChinese institutions. In particular, the universities in Hong Kongwere to some extent links between the mainland and foreigninstitutions. In recent years, besides the National University ofSingapore and University of Toronto, the frontier cooperationsbetween Columbia University, Washington University or theUniversity ofMichigan in theUSA andChinese institutions havealso deepened.

Table 3 The top-15 most productive and influential institutions of MS from 1999 to 2018

Rank Institution Country TP TC TC/TP h >200 >100 >50 >20 >10

1 Univ Penn USA 220 17,195 78.16 68 18 48 97 150 174

2 Columbia Univ USA 151 10,573 70.02 52 13 24 55 95 123

3 Duke Univ USA 143 6777 47.39 46 5 18 43 86 108

4 Stanford Univ USA 132 7119 53.93 45 6 18 40 77 88

5 MIT USA 131 10,784 82.52 55 16 27 62 81 95

6 Carnegie Mellon Univ USA 129 9081 70.40 42 12 25 39 72 94

7 Univ Michigan USA 125 5645 45.16 39 8 15 31 59 82

8 Harvard Univ USA 124 8446 68.11 45 10 26 39 86 103

9 New York Univ USA 124 7941 64.04 45 11 20 38 78 94

10 INSEAD France 120 8642 72.02 46 12 26 43 77 90

11 Northwestern Univ USA 111 7895 71.13 44 9 20 39 60 82

12 Univ Calif Berkeley USA 103 3505 34.03 34 2 8 24 51 67

13 Univ Maryland USA 98 12,085 123.32 41 10 24 35 65 73

14 Univ Toronto Canada 81 2104 25.98 25 5 13 30 45

15 Georgia Inst Technol USA 76 5624 74.00 38 8 18 28 48 56

The rank is based on the principle of alphabetical order if the number of one’s publications is the same as the other

Abbreviations: TP, Total publications; TC, Total citations; TC/TP, Total citations per publication; h, h-index; >200, >100, >50, >20, >10, Number ofpapers with more than 200, 100, 50, 20, and 10 citations

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2.3 Most productive and influential authors

According to the Price Law on the core author group (M =

0.749 *ffiffiffiffi

Np

, N represents the number of publications issuedby the most productive author) (de Solla Price 1986), the coreauthors of MS are those who have published 4 articles andmore. As Fig. 5 shows, the largest collaboration group in-cludes Cachon GP, Terwiesch C of the University ofPennsylvania and Ho TH, Tang CS of the University ofCalifornia. The author group with Mandelbaum A of IsraelInstitute of Technology as the core is next. To be specific,Table 4 lists the top-15 productive authors. It is shown thatexcept Netessine S, the other authors are from American uni-versities, among which the University of Pennsylvania is themost noticed. Scholars who are ranked the 1st, 3rd and 9th areall from this university. Cachon GP, ranked 1st, has published

22 papers on the topics of operational strategy, pricing, supplychain management and sustainability, with an average quota-tion of 205.73 times. In terms of citation thresholds, his 8articles have been cited more than 200 times, which is quiterare in the field of management science. Netessine S, the 2ndfrom the world-famous institution-INSEAD, has published 19articles on the topic of global technology and innovation. Inaddition, Shane S (ranked 5th) of Case Western ReserveUniversity also performed exceptionally well with the cita-tions per paper of 160.36, focusing on the leadership andinnovation management.

In contrast, Ha Albert Y and Yu Man of Hong KongUniversity of Science and Technology are the only two coreauthors of MS in China (Table 5). It is well-known that HaAlbert Y is a distinguished professor of this university withconsiderable influences, who specializes in some research

Fig. 3 The provincial distributionof Chinese institutions. Note:percentage = the number ofpublications issued by allinstitutions in a province/ the totalnumber of China

Fig. 4 The cooperation networkbetween Chinese productiveinstitutions and foreigninstitutions

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subfields containing stochastic models of production and in-ventory control, supply chain management and operation in-centives, with 81.25 citations per paper. Yu Man is an associ-ate professor of the university and researches on the pricingand revenue management. Except Ha Albert Y and Yu Man,there are only 12 scholars who have published 3 articles inMS(Table 5). Among them, the professor Zhang RQ of the HongKong University of Science and Technology devoted himselfto the researches of operation management and statistical pro-cess control with the highest citations per paper (113.33) inChina. Tong Shilu of the Chinese University of Hong Kongmainly studied on operation management, with an averagecitation of 79.33 times. In the mainland, Chen Yuxin ofShanghai New York University published 3 papers by the firstauthor, covering data-driven marketing, internet marketing,retailing, pricing strategies, etc. Jingbing of Cheung KongGraduate School of Business published 3 articles by the thirdauthor, including product differentiation, product line design,pricing and other fields. And Wu Xiaole of Fudan Universitymainly studied the supply chain management, risk manage-ment and purchasing strategy.

Moreover, three main author collaboration groups havealso been formed in the process of international scientific re-search collaboration in China. Among them, with Yu Man asthe core, the cooperation group which mainly includesKapuscinski R and Ahn HS ofthe University of Michiganhas been formed. So far, the first two authors still cooperateon the high-quality researches. In the early stage, Jia Jianminof Chinese University of Hong Kong cooperated closely withFischer GWof Duke University and Luce MFof University ofPennsylvania. Recently, Lu Yi of Tsinghua University, GongJie and Zhong Songfa of Singapore National University have

also formed good cooperative relationships. However, the co-operation among Jia Jianmin, Fischer GW and Luce MF wasrelatively early. Therefore, more attention should be paid tothe frontier researches of Yu Man and Lu Yi in the future.

3 Knowledge characteristics of managementscience

Along with the analyses above, this work witnesses theknowledge structure of MS. In order to reveal the knowledgefoundation, identify research topics, track research trends andexplore research hotspots of MS, this section carries out aseries of analyses on knowledge characteristics by the tech-niques including the timeline map of co-citation network, co-occurrence network, clusters and bursts of nominal terms.

3.1 Knowledge foundation: Time-line mapof co-citation network

The co-citation network, mainly analyses the references ofdocuments aiming at identifying the sub-fields on which re-searches are based. At the same time, the time-line graphicform of co-citation visualized by CiteSpace V can reveal theproduction process of knowledge (Chen et al. 2014; Kim andChen 2015). In addition, articles with strong connectionstrength in the co-citation network have a certain degree ofcorrelation. Thus, co-citation network can be regarded as oneof the ways to explore related research works for researchers.

In this work, the sample data refers to 76,050 references,with an average of 24.34 references per article. For the sake ofgenerality, the time slice is set for 1 year to extract 50

Fig. 5 The core author group ofMS

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references with the highest quotations per year so as to opti-mize the results of clusters. Finally, a total of 2298 papers and5861 links form a time-line map (Fig. 6), in which the twodocuments connected by a line refer to those referenced by thesame article in MS. Thus, it is noticed that on average eacharticle appears in the references of the same article with theother 2.6 articles. Especially, the top-5 most cited papers byMS include (Kahneman and Tversky 1979; Tversky andKahneman 1992; Fama and French 1993; Fischbacher 2007;

Cohen and Levinthal 1990), in which Kahneman and Tverskyproposed a descriptive model of decision making under riskand developed an alternative model of prospect theory(Kahneman and Tversky 1979), which has been cited 153times. Besides, they further expounded the theory in severalrespects (Tversky and Kahneman 1992), which has been cited93 times. Fama and French comprehensively discussed fivecommon risk factors in the returns on stocks and bonds (Famaand French 1993), which has been cited 86 times. Fischbacher

Table 5 The Chinese scholars with 3 or more articles in MS from 1999 to 2018

Author Institution TP TC TC/TP

Year AR

1st 2nd 3rd

Ha AY HK Univ Sci Technol 4 325 81.25 2003, 2008, 2011, 2016 3 1 0

Yu Man HK Univ Sci Technol 4 50 12.50 2015, 2015, 2016, 2018 3 0 1

Chen Yuxin New York Univ Shanghai 3 29 9.67 2008, 2013, 2017 3 0 0

Dasgupta S HK Univ Sci Technol 3 58 19.33 2010, 2013, 2016 2 2 1

Guo Liang Chinese Univ of HK 3 21 7.00 2009, 2015, 2016 0 0 3

Hong Jeff HK Univ Sci Technol 3 79 26.33 2009, 2009, 2012 1 1 1

Hui Kailung HK Univ Sci Technol 3 27 9.00 2008, 2011, 2015 0 3 0

Jing Bing Cheung Kong Grad Sch Business 3 23 7.67 2011, 2016, 2017 0 0 3

Liu Liming HK Univ Sci Technol 3 87 29.00 2003, 2004, 2011 1 2 0

Tong Shilu Chinese Univ Of HK 3 238 79.33 2008, 2011, 2017 2 2 1

Wu Xiaole Fudan Univ 3 43 14.33 2014, 2014, 2018 1 1 1

Zhang Hongtao HK Univ Sci Technol 3 214 71.33 2001, 2008, 2011 0 1 2

Zhang Rachel Q HK Univ Sci Technol 3 340 113.33 2004, 2005, 2007 0 2 1

Abbreviations: TP, Total publications; TC, Total citations; TC/TP, total citations per publication; Year, The year of publication; AR, Author Ranking

Table 4 The top-15 most productive and influential authors of MS from 1999 to 2018

Rank Author Institution Country TP TC TC/TP h >200 >100 >50 >20 ater10

1 Cachon GP Univ Penn USA 22 4526 205.73 20 8 12 18 21 21

2 Netessine S INSEAD France 19 908 47.79 15 1 7 14 16

3 Terwiesch C Univ Penn USA 19 1387 73.00 15 1 4 13 14 16

4 Ho TH Univ Calif Berkeley USA 16 518 32.38 9 1 6 7 9

5 Shane S Case Western Reserve Univ USA 14 2245 160.36 13 3 6 10 12 13

6 Katok E Univ Texas at Dallas USA 13 540 41.54 9 1 1 4 6 9

7 Van Mieghem JA Northwestern Univ USA 13 664 51.08 10 2 6 8 11

8 Arya A Ohio State Univ USA 12 153 12.75 7 1 3 5

9 Hitt LM Univ Penn USA 12 615 51.25 10 1 6 9 10

10 Agarwal R Univ Maryland USA 11 631 57.36 8 3 5 7 8

11 Ghose A NYU USA 11 809 73.55 9 1 3 5 8 9

12 Kouvelis P Washington Univ in St. Louis USA 11 420 38.18 10 1 2 8 10

13 Krishnan R Carnegie Mellon Univ USA 11 267 24.27 10 8 10

14 Taylor TA Univ Calif Berkele USA 11 970 88.18 11 5 5 11

15 Telang R Carnegie Mellon Univ USA 11 281 25.55 7 2 5 7

“Country” refers to the location of “Institution”

Abbreviations: TP, Total publications; TC, Total citations; TC/TP, Total citations per publication; h, h-index; >200, >100, >50, >20, >10, Number ofpapers with more than 200, 100, 50, 20, and 10 citations

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elaborated the design principles, characteristics and limita-tions of the economic experiment software-z-Tree(Fischbacher 2007), which has been cited 78 times. Cohenand Levinthal systematically reveled the absorptive capacityof a firm (Cohen and Levinthal 1990), which has been cited 72times.

More importantly, Fig. 6 shows the evolution of its mainsub-fields of concern, which are represented by different clus-ters. To be explained, the names of clusters are indexed bykeywords in papers using logarithmic likelihood algorithm(LLR). Clusters are arranged one by one from big to small,and the active years of clusters are presented from left to right,reflecting the length of time that different sub-fields were

followed. In addition, the orange node is the paper whosequotations suddenly increase in the corresponding year andthe three documents displayed under each cluster are the mostfrequently cited papers in the whole cluster.

In order to elaborate the sub-fields of concern more pre-cisely, this subsection describes them from both horizontaland longitudinal dimensions. Horizontally, the active durationof each cluster (#) varies with its length. In terms of content,prospect theory (#9) lasted for the longest time interval, basi-cally until 2013, but it can be seen that the whole researches ofMS have hardly combined it with other sub-fields in the pasttwo decades. Secondly, the researches on patent (#0) and sup-ply chain management (#1) before 2007 were highly

Fig. 6 The co-citation network of MS by the form of time-line map

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concerned, and the former was innovated with open sourcesoftware (#20), and the latter was innovated with strategicconsumer behavior (#10) and lead-time management (#31).However, the researches on the unilateral content of opensource software were only made from 1999 to 2005 and thelead-time management was more shorter, while the researcheson strategic consumers have attracted much attention and theywere still paid more attention lately. In addition, recent studieson high-tech industrial clusters (#6), capital structure (#8),healthcare (#13) were also concerned. At the same time, inthe methodology, more attention was paid to literature reviews(#8) before 2004, but then to the experiment-based researches(#2), such as behavioral operations (#14), resource allocationprocesses (#18), etc.

Longitudinally, there were fewer research sub-fields andknowledge clusters in the early years, and the new contentswere relatively narrow. Until 1998, a large number of sub-fields emerged and the contents which could be followed byMS increased substantially. By comparing the size of sub-fields, it can be seen that MS focused more on classical issues,such as auctions problem (#3), user-generated content (#4)and so on. At the same time, in terms of the integration ofdifferent contents, it preferred to combine the sub-fields withthe other sub-fields derived from its own contents, such as the

integration of supply chain management and lead-time man-agement, or to adopt the cutting-edge methods and ideas insolving problems, such as the researches on group decisionscooperation (#21), portfolio choice (#22) and dual sourcing(#32) based on economic experiments, or the combinationamong similar sub-fields, such as user-generated content andbehavior operations (#14). Otherwise, the method of combin-ing different sub-fields to produce new knowledge is condu-cive to innovation and in recent years, the publications ofcross-integration of sub-fields in the field of management sci-ence have gradually increased, but it should be rationally con-sidered that the value of researches with cross-integration ac-cording to the series of the analyses of the co-citation network.

3.2 Research topics: Co-occurrence and clusteranalysis of nominal terms

On the basis of the knowledge foundation discussed in theabove, this subsection explores the research topics of MS bythe co-occurrence network and cluster analysis of nominalterms, and analyses the research status of each topic in detail,which can provide references for future explorations and fron-tier researches. As mentioned above, co-occurrence and clus-ter analysis of nominal terms aim at grasping the research

Fig. 7 The distribution of research topics in MS from 1999 to 2018

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centers and identifying research topics, while the form oftime-line map deduces the development contexts of the topics.Nominal terms are extracted from the title, abstract and key-words of the article, which are the high generalizations of thearticle. Therefore, detailed analyses of the research topics re-veal the current “Blue Sea” and “Red Sea” themes, and sum-marizes the future research trends.

More concretely, co-occurrence analysis is to count thenumber of co-occurrences of a pair of terms in 3125 litera-tures, and then to determine the relationships between termsby the number of co-occurrence to form various clusters. Inthis paper, 1058 terms with more than two co-occurrencetimes are detected and 3772 links are formed. Finally, 21 clus-ters are obtained in Fig. 7. It can be seen that there are closeconnections among different research topics, which is similarto Fig. 6. In order to reflect the cluster results more compre-hensively, three famous algorithms with their own character-istics, term frequency-inverse document frequency (TF*IDF),logarithmic likelihood ratio (LLR) and mutual information(MI) are used to name clusters (Table 6). Among them,TF*IDF emphasizes the mainstream of researches, while the

latter two algorithms emphasize the characteristics of re-searches. At the same time, Table 6 lists the specific clustersinformation includingthe number of terms in clusters, the ho-mogeneity of terms and the average year of terms proposed. Inparticular, the closer the “homogeneity” index approaches 1,the higher the clusters’ reliability is. Generally, the result isreasonable when it is more than 0.5. As a result, the clusterresults in Table 6 are reasonable and acceptable. “Averageyear” refers to the average value of the years of the articlesin which terms appear and this indicator is used to judge thenew or old degree of topics.

Specially, in this subsection, the theory of “Product LifeCycle” in the Marketing Management (Acimovic et al.2018) is introduced to vividly describe the development pro-cess of research topics in MS. According to Table 6, the re-search tracks of MS can be roughly divided into three stages.The first period (1999–2004) is called the introduction stage,in which research topics were few and the research contentswere limited. Mainly, just-in-time manufacturing (#20), themost classical research topic in this period, was a frontierresearch derived from the idealogy of JIT in Japan. The

Table 6 The information of research topics in MS from 1999 to 2018

ID Number Silhouette Mean Year ofPublications

Label(TF*IDF) Label(LLR) Label(MI)

0 114 0.528 2005 supply chain management supply chain management behavioral experiments

1 112 0.560 2009 game theory strategic consumerbehavior

choice models

2 104 0.630 2011 behavioral operations multistage game anchoring

3 103 0.665 2007 dynamic programming retailing; many-severquenes

bayesian bandit

4 88 0.701 2013 organizational studies fund performance division of innovative

5 68 0.801 2007 prospect theory prospect theory incentive in r&d

6 60 0.782 2013 experiment; influence natural experiment capicity-reference levels

7 59 0.756 2010 competition; game theory public debt capital market line

8 56 0.836 2006 knowledge transfer management science information technology and firmperformance

9 55 0.774 2009 completion; supply chainmanagement

stable coalitions hosting; customer engagement

10 50 0.780 2010 contingency theory firm performance input incertainty

11 47 0.812 2005 behavior; organization tardiness supply chain sourcing and design

12 45 0.775 2007 service systems service systems regulatory changes

13 45 0.850 2006 new product development new product development decentralized problem solving

14 38 0.797 2012 experiments low-cost flights non dominated points

15 32 0.798 2014 corporate governance choice overload online open collaboration

16 28 0.845 2006 entrepreneurship entrepreneurship opportunities search

17 24 0.933 2005 yield management yield management repairable systems

18 20 0.876 2014 statistical analysis propensity scorematching

optimal product line and priceselection

19 20 0.961 2012 service operations distribution builder incomplete markets

20 6 0.992 1999 empirical study Japan; just-in-timemanufacturing

profits; non dominated points

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second period (2005–2007), the growth stage, is that a largenumber of research sub-fields have emerged and researcheshave sprang up rapidly, such as supply chain management(#0), supply chain sourcing and design (#11), yield manage-ment (#17), etc. The third period (2008-) is the mature stage,in which previous researches have matured and became thebasis of the discipline of Management Science. However, thesignificant theoretical research results are no longer as out-break as in the growth stage while more attentions have beenpaid to the innovation of substantive contents and the qualityof innovation, such as choice overload (#15), propensity scorematching (#18), fund performance (#4), natural experiments(#6), low-cost flights (#14) and distribution builder (#19).Furthermore, with the continuous evolution of discipline, thecurrent situations of each research topic at different stages arecovered in the following analysis.

JIT production control strategy (#20) in the introductionperiod is no longer the mainstream research topic, but the ideaof JIT is applied to solving problems in other areas, such as thecorporate cash hoarding problem (Gao 2018). In terms ofresearch topics in the growth period including supply chainmanagement (#0), strategic consumer behavior (#1), many-server queues dynamic programming (#3), prospect theory(#5), knowledge transfer in information technology and firmperformance (#8), stable coalitions (#9), supply chain sourc-ing and design (#11), service systems (#12), new product de-velopment (#13), entrepreneurship (#16) and yield manage-ment (#17), they are called “Red Sea” of research topics. Onthe whole, these contents appeared early, so the researchestaking them as the main contents are less in number at present,but on the basis of which many new contents have beenevolved, innovated and created.

To be specific, there are many studies on behavioral eco-nomics (Ozer et al. 2011), supplier alliances (Granot and Sosic2005), dual-source procurement strategy (Xin and Goldberg2018), flexible capacity in a make-to-order environment (Bishet al. 2005) in the field of supply chain management (#0). Theresearches of strategic consumer behavior (#1) give priority tothe pricing strategy, focusing on the impacts of channel coor-dination mechanism on product pricing (Taylor 2001), theapplication of strategic consumer behavior in dynamic pricingand revenue management (Yin et al. 2009; Vulcano et al.2002), the structural estimation of the model containing stra-tegic consumer behavior under stochastic dynamic game (Liet al. 2014) and the price discrimination based on consumerbehaviors (Chen et al. 2005). The theory of many-serverqueues dynamic programming (#3) is applied to the problemswith the property of multi-service counters, such as the powergeneration expansion planning of a power system (Lohmannand Rebennack 2017), etc. At the same time, Gittins index isintegrated with dynamic programming to realize the innova-tion (Kim and Lim 2016). In addition, under the theory ofoptimal control of dynamic programming (#3), yield

management (#17) mainly focuses on product strategy includ-ing R&D portfolio under budget constraints (Schlapp et al.2015), strategic alignment of knowledge-intensive enterprises(Wu 2015), and the allocated production capacity (Cui andZhang 2018). In the context of the St. Petersburg Paradox,the prospect theory (#5) is mainly combined with the dailylow price strategy in retail (Ozer and Zheng 2016) and theindividual preferences in the group decision making underrisk aversion (Murphy and ten Brincke 2018). In the enterpriseinformation technology, knowledge transfer (#8) focuses onthe knowledge transfer in offshore interactions and offshoreoutsourcing services in the context of mass customization(Berry and Kaul 2015), while the core contents of offshoreinteraction and outsourcing are mainly carried out in the re-searches of new product development (#13). With the evolu-tion of knowledge, stable coalitions (#9) focus not only oncooperation and long-term stability in assembly systems(Granot and Yin 2008), but also on strategic supplier allianceunder order default risks (Huang et al. 2016). Supply chainsourcing and design (#11) is based on the design of procure-ment and production distribution systems, centering on thebilateral relationship between supplier and buyer from theperspectives of supply risk and supply value (Yang andBabich 2015). At the same time, there are also some studiesthat consider the pricing mechanism and negotiation of thewhole supply chain sourcing only from the perspective ofone-sided, such as supplier-led (Li 2013). The researches inthe field of service system (#12) still mainly focus on stochas-tic model and queuing theory, but now more empirical re-searches are made (Maglaras et al. 2018). The entrepreneur-ship (#16) explores the self-employment of entrepreneurs andgroup differences in self-employment from the biological per-spective (Nicolaou et al. 2018). In the meanwhile, it combinesthe agglomeration economic effect in economics and leader-ship to evaluate the role of leadership in advantage agglomer-ation (Cheyre et al. 2015). Of course, academics are still com-mitted to the issues of the relationship between gender diver-sity in management executives and leadership.

In the mature stage of MS, it is not only the evolution oforiginal themes, but also the emergences of new research con-tents and methods, which are regarded as the “Blue Sea”themes. Among them, the former include the choice overload(#15), fund performance (#4), distribution builder (#19), low-cost flights (#14). The latter are represented by propensityscore matching (#18) and natural experiment method (#6).Specifically, choice overload (#15) mainly studies the searchbehavior of consumers in the market. It is a situation whenconsumers decide to buy a product, they will search for mul-tiple evaluation schemes in the market, and then there will bechoice overload scenario (Ke et al. 2016). In addition, GuoLiang, a Chinese scholar, pointed out that the choice overloadis also generated as the context deliberation leads to the estab-lishment of preference (Guo 2016). It should be noted that

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choice overload is a phenomenon, and the final result is dif-ferent for some products or events. Therefore, it is needed formanagers to apply rational scientific thinking to maximize theultimate benefit and make customers buy products as many aspossible. In view of the above, its research prospects are close-ly related to the role of open online reviews and its improve-ment strategies with choice overload as the background.

Fund performance (#4) has been studied extensively sincethe year 2000, and now it is turned to empirical analysis, suchas the impacts of investors’ overconfidence on corporatedecision-making (Adebambo and Yan 2018), merger and ac-quisition (Qian and Zhu 2018), the returns on investment ofliquidity hedge funds (Jame 2018), venture fund investmentby limited partners under financing incentives (Chakrabortyand Ewens 2018), decoupling of mutual funds between inves-tors and stocks (Ferreira et al. 2016). With the acceleration ofglobalization, the demand for financial market has increasedin China. In the future, the performance of funds and stockswith China’s national conditions and with China’s financialmarket should be investigated. Distribution builder (#19) isrelated to fund performance (#4) to a certain extent. It is notonly a measure tool for risk preference of venture capitalists,but also a method of customizing investment plans (Vreckoand Langer 2013), which is applied to individualized invest-ment products. Similarly, it can be studied in conjunctioncombined with the venture capital market of China.

The idea of low-cost flights (#14) originates fromSouthwest Airlines, which is seldom studied by MS.Classical studies abstract it as a theoretical model to discussthe reasons why low-cost flights strategy has been so success-ful (Serio et al. 2018). For Chinese scholars, since this idea isless practiced in China, the feasibility and utility of this modein China can be researched from a theoretical perspective inthe future.

Propensity score matching (#18) is a statistical method. In1980s, it was applied to the studies of differences in auditquality between well-known and non-well-known auditinginstitutions (DeFond et al. 2017) and the impacts of qualitymanagement systems-ISO9001 on the sales performance oforganizations and the wages of employees (Levine andToffel 2010), and the impacts of gender diversity of executiveson corporate performance (Amore et al. 2014). In particular,compared with leadership, the researches which apply thepropensity score matchimg to gender diversity of executiveshave achieved the innovation in methodology. Now this meth-od is extended to the fields of medicine and economics, andwill be widely used in the field of management science in thefuture. Natural experiment (#6) is another research method,mainly through controlling, creating certain conditions andobserving the behavior changes of the subjects to analyzethe essence, such as the relationship between social networkand online news consumption (Sismeiro and Mahnood 2018),the comparison of multi-dimensional and single-dimensional

evaluation of consumer online rating system (Chen et al.2018a), the comparison of service efficiency between dedicat-ed queue and shared queue in a supermarket (Wang andZhong 2018). The applications of natural experiment in vari-ous kinds of research emerge endlessly. Thus, the prospects liein the innovation of the method application on the currentpractical problems studied by scholars.

In recent years, scholars in China have published papers inMS mainly based on the analysis of website electronic flowdata (Chen and Yao 2017), the role of information intermedi-ary played by analysts in company disclosure (Huang et al.2018), capacity sharing under supply and demand mismatch(Guo and Wu 2018), daily transaction such as the businessgrowth rate of Groupon based on experimental analysis (Liet al. 2018), the complex algorithm for solving the quadraticMarkov process (Chen et al. 2018b), the difference of produc-tion efficiency caused by behavior price discrimination (Jing2017), the value dependence of consumers on marketing strat-egy (Yu et al. 2015), the impacts of corporate insolvency onloan spreads of main suppliers (Houston et al. 2016), the re-lationship between the level of information uncertainty andthe stock returns (Mao and Wei 2016), etc. Above all, it canbe seen that some of the researches are consistent with themainstream researches, such as the studies of consumer be-havior, the analysis of Groupon by the method of experimen-tal analysis and the theoretical study of the classical Markovprocess. In addition, there is a capacity sharing problem thatdoes not match supply and demand under the conditions ofChina. As for Chinese scholars, with the increasing number ofpapers published in MS, the research contents are more globaland based on the empirical evidences.

3.3 Research hotspots: Bursts analysis of nominalterms

Based on the research topics, this paper further extracts burstterms by applying the software of CiteSpace V to explore theshort-term research hotspots in the future. It should be ex-plained that burst terms are those which are suddenlyresearched extensively in a certain period. According to theanalysis, results show that a total of 105 burst terms appearedfor the first time in the year 1999, which indicates that theseterms are more classical, but also reflects the lack of perti-nence and guiding significance. At the same time, due to thecontinuous advancement of scientific researches, the role ofburst will be gradually weaken with the lapse of time and there-transformation of research sub-fields. In this paper, after thesteps of words removal and label for 105 burst terms, the keyterms that still gain large attentions in 2018 are listed inTable 7.

In Table 7, “Begin” is the time when the number of termsbegan to increase sharply. “Strength” is the intensity of burst.The larger the value is, the more the number of terms

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appeared. Including many key terms related to finance, suchas corporate finance, credit rating, capital structure, financialmarket and stock returns, these indicate that the research focusof finance has gradually turned to the financial investment be-havior of micro-subjects, which belongs to the category of virtualeconomy industry. At the same time, the emergences of behav-ioral economics, micro-economic behavior, behavior and behav-ioral decision making show that future researches on other eco-nomic strategies of micro-subjects are also relatively concentrat-ed besides investment behavior. The term “disclosure” reflectswhethermicro-entities will make information transparent to otherrelevant economic entities, including limited partners whenmak-ing economic decisions. In addition, “healthcare” is the onlyresearch object related to the real economy. Based on the previ-ous analysis, this paper argues that in future researches,“healthcare” will be still more related to customer services, andexplore the efficient management in the medical system.Highlighting the strong stength of “cross section” and “fieldexperiment” involves the methods used in empirical researches.Thus, synchronized innovation inmethods and contents will helpthis research to become a hot spot.

Furthermore, according to the analysis of the researchhotspots in MS, it can be seen that the future research hotspotsbasically originate from the early research themes of “RedSea”, while the “Blue Sea” do not appeared in the researchhotspots exceptfor research methods, mainly due to the factthat the research contents under the relevant research topicshave not been accumulated to a certain extent, and the distri-bution of research contents is relatively scattered. Therefore,academics should suit the time to track the frontier researchesin the “Red Sea”, pay more attention to the innovation worksunder the theme of “blue sea”, link up with theoretical re-searches on high value and strong applicability, and strive tobrighten up in the long term academic contention and ideo-logical collision.

4 Conclusions

Motivated by a quite good understanding of the development ofinternational frontier journal-MS in the past two decades, thispaper proposes the standard data from Web of Science in theyear 1999–2018 to make a detailed study on the knowledgestructure and knowledge characteristics of it. Specifically, fromthe perspective of knowledge structure, this paper explores thetrend of annual publications, the characteristics of the most pro-ductive and influential countries, institutions and authors. In par-ticular, this paper also gives a brief analysis of the related docu-ments issued by the third major productive country-China.Moreover, from the perspective of knowledge characteristics, thispaper explores the research foundation, research topics and re-search hotspots of MS in detail by the techniques of the time-linemap of co-citation network, co-occurrence network, cluster andburst analysis of nominal terms. Based on the above, some mainconclusions are drawn.

The growth of publications in MS presents the form of cubicfunction with 2013 as the demarcation point. It is expected thatthe number of the annual publications will almost reach 500 in2021. The United States is the first contributor of publicationsand plays a central role in the international cooperation network.France has the second highest number of highly cited articles,and the citations per paper are almost equal to those of the UnitedStates. In terms of institutions, those with more than 100 articlesare all American universities except for INSEAD, such as themost influential-University of Pennsylvania, the highly cited ex-cept for the Univ Penn-MIT, the highest citations per paper-theUniversity of Maryland. In addition, almost all of the top-15authors are from American universities, including the mostproductive-Cachon GP (Univ Penn), the highest citations perpaper except for Cachon GP-Shane S (Case Western ReserveUniv). Besides, it is worth mentioning that Cachon GP is in thelargest core author collaboration group. China is ranked 3rd in

Table 7 The research hotspots in MS from 1999 to 2018

)8102-9991(noitaruDnigeBhtgnertSsmreT

11020206.3ecnanfietaroproc21028661.6noitcesssorc21022710.4erachtlaeh

behaviorial economics 4.5513 201331026278.3tekramecnanfi

microeconomic behavior 3.5120 201341029329.5erutcurtslatipac41022715.5tnemirepxedlefi41025195.5nruterskcots

behavior and behavioral decision making 3.7182 201451024804.4erusolcsid61028033.3gnitartiderc

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the total publications, but the number is less than 1/10 of theUnited States, and publications are mainly issued by the well-known institutions in the HK, Beijing and Shanghai. The coreauthors are only represented by Ha Albert Y and Yu man fromtheHKUniv Sci Technol. In recent years, China has deepened itsinternational cooperation in frontier researches, and has workedwith Singapore, Columbia, Washington, Michigan Universityand University of Toronto to form three main core author collab-oration groups, including Yu man from Hong Kong Universityof Science and Technology, LuYi in the TsinghuaUniversity andJia Jianmin in Chinese University Hong Kong.

By analyzing the co-citation network of documents, co-occurrence network, cluster and burst of nominal terms, theresearch trajectory of MS has roughly gone through threedevelopment stages in research topics which introduces thetheory of “Product Life Cycle” in the MarketingManagement (Fig. 8). The relatively mature research topicsin the growth stage are the “Red Sea”, which include sup-ply chain management, strategic consumer behavior,many-server queues dynamic programming, prospect the-ory, knowledge transfer in information technology andfirm performance, stable coalitions, supply chain sourcingand design, service systems, new product development,entrepreneurship and yield management, etc. For scholars,it is necessary to not only understand and trace their devel-opment contexts, evolutions and research works of otherscholars, but also make higher innovation and applicationin future researches. Therefore, the audiences of such re-searches are scholars with rich theoretical research back-grounds and experience in relevant subfields. By contrast,the research topics in mature period are called the “BlueSea”, such as choice overload, fund performance, distribution

builder, low-cost flights, propensity score matching and natu-ral experiment method. Aiming at making use of innovativemethods, closely integrating practical problems, andexplaining management phenomenons, these topics are moresuitable for senior scholars in empirical researches, and havebetter future development prospects. In addition, corporatefinance, financial market, capital structure, stock returns, cred-it ranking, behavioral economics, microeconomic investmentbehavior, other economic behavioral decision making andhealthcare are still hot topics in the future. Meanwhile, theanalysis methods of cross section and field experiment are stillpopular.

To some extent, the research results of this paper canhelp scholars in the field of Management Science to betterunderstand the frontier researches, make them have aclearer understanding of the knowledge structure and char-acteristics of MS, and provide some ideas for future sci-entific research works with higher contributions. Besides,this paper provides a perspective for journal research incombination with bibliometrics and data analysis. In thefuture, data mining can be considered and applied for suchresearches to predict the research trends more accuratelyand quickly.

Acknowledgements This research was funded by the National NaturalScience Foundation of China under grant number 71872110. The authorsthank the anonymous reviewers for their helpful suggestions, which led tosubstantial improvements of the paper.

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflict ofinterest.

Fig. 8 The co-occurrence of research track and main themes in MS from 1999 to 2018

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