Innovation and Revenue: Deep Diving into the Temporal Rank-shifts of Fortune 500 Companies
IInnovation and Revenue: Deep Diving into theTemporal Rank-shifts of Fortune 500 Companies
Mayank Singh
Dept. of Computer Science and Engg.IIT Gandhinagar, [email protected]
Arindam Pal
Data61, CSIROSydney, NSW, [email protected]
Lipika Dey
TCS Research and InnovationNew Delhi, [email protected]
Animesh Mukherjee
Dept. of Computer Science and Engg.IIT Kharagpur, [email protected]
ABSTRACT
Research and innovation is an important agenda for any companyto remain competitive in the market. The relationship betweeninnovation and revenue is a key metric for companies to decideon the amount to be invested for future research. Two importantparameters to evaluate innovation are the quantity and quality ofscientific papers and patents. Our work studies the relationshipbetween innovation and patenting activities for several Fortune500 companies over a period of time. We perform a comprehensivestudy of the patent citation dataset available in the Reed TechnologyIndex collected from the US Patent Office. We observe several inter-esting relations between parameters like the number of (i) patentapplications, (ii) patent grants, (iii) patent citations and Fortune500 ranks of companies. We also study the trends of these param-eters varying over the years and derive causal explanations forthese with qualitative and intuitive reasoning. To facilitate repro-ducible research, we make all the processed patent dataset publiclyavailable. CCS CONCEPTS • Social and professional topics → Patents ; •
Information sys-tems → Data mining.
KEYWORDS
Patent Citations, Fortune 500 Companies, Innovation, Revenue
Patent articles contain important research results that are valuableto the industry, academia, business, and policy-making organiza-tions. Patent technology produce novel and industrially usable https://github.com/mayank4490/Innovation-and-revenue. This research was done,when the second author was working at TCS Research and InnovationPermission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from [email protected]. CoDS COMAD 2020, January 5–7, 2020, Hyderabad, India © 2020 Association for Computing Machinery.ACM ISBN 978-1-4503-7738-6/20/01...$15.00https://doi.org/10.1145/3371158.3371199 products which enhance industry’s competitive edge. Therefore,industry giants spend extensively in research activities for retainingand increasing their competitive advantages in the correspondingtechnology groups. Multiple previous works have shown that R&Doutcomes are important assets for any industry giant [8, 12]. WorldIntellectual Property Organization (WIPO) reports that nearly 90–95% of the world’s R&D outcomes are covered in patent publications.Only the remaining 5–10% are included in the scientific literaturesin the form of essays and publications [15]. Therefore, it is cru-cial to analyze patent information to understand industrial trendsand compare research growths of several industries in the sametechnology group.In this work, we study the effect of research outcomes in theform of patents on revenue generation of top US industry giants.In contrast to previous works, we correlate patenting activitieswith the Fortune 500 ranks of companies , instead of total revenuevalues. The Fortune 500 ( F Limitations of the existing works : The existing works have sev-eral limitations. First, most of these have studied patents grantedbefore 1990s, so they do not consider recent data. Second, exist-ing systems do not consider inter-industry research competition.Third, most of these studies do not take into account the age andfield expertise of the companies. Fourth, existing systems utilizecrude revenue values that should not be directly compared owingto year-wise dollar inflation rates, global economic trends, etc.
Our contributions : We address some of the above limitations inthis work by introducing temporal buckets that group togethercompanies based on their foundation year and present a thoroughcorrelation study between patenting behavior and performance.Towards this objective, we make the following contributions. • We downloaded a massive patent dataset consisting of morethan 2.6 million full text patent articles with nearly 93 millionpatent citations from the Reed Technology Index. Considering ranks instead of revenue values helps to avoid yearwise dollar inflationrates, global economic trends, etc. a r X i v : . [ c s . D L ] A p r oDS COMAD 2020, January 5–7, 2020, Hyderabad, India Singh et al. • We invested extensive manual effort in extraction, cleaning,indexing, and other related preprocessing steps. • We conducted rigorous empirical study on this manuallycurated dataset by first dividing it into buckets based onthe company foundation year. Subsequently, we conductextensive experiments to identify the correlations betweenthe patenting dynamics of companies and their F
500 ranks. • As a next step, we deep dive further and identify tempo-ral rank-shifts of the companies and show that they alsocorrelate well with company R&D activities. • Finally, we further identify that inter-industry citations rep-resenting competition could lead to decay/rise in the overallgrowth of the companies.
A considerable amount of literature has been published to better un-derstand several parameters like patent citations, number of patentapplications, number of patent grants. The first serious discussionand analysis of patent data emerged during the 1990s [16]. Forbetter organization, we broadly divide the related work into foursubparts:
General patent analysis : Derek De Solla Price [6] first showedexistence of high positive correlation between scientific output(measured in terms of number of research articles) coming froma country with its gross domestic product (GDP). Two decadeslater, Narin et al. [16] presented evidences of similarity betweenliterature bibliometrics and patent bibliometrics. They showed thatthe number of granted patents from a country correlates positivelywith its GDP. James Bessen [4] found that patents issued to smallpatentees are much less valuable than those issued to large corpora-tions. Sampat et al. [17] found that citations to research papers aresignificantly related to the probability that a patent is licensed, butnot to revenues conditional upon licensing. Daim et al. [5] forecastfor three emerging technology areas namely, fuel cell, food safetyand optical storage technologies by utilizing of bibliometrics andpatent analysis.
Patents and society : Many studies have discussed the potentialbenefits and negative effects of patenting research on society [7].Spławiński [18] has presented an overview of patenting ethics. Theypresent real examples to show how certain patenting activities suchas broad claims, poor disclosures, etc., badly affect social benefitsand delay technological advancement. However, recent trends inpatenting activities have led to more openness and existence offierce competition has resulted in overall technological advance-ment and an inherent benefit to the society. Tesla, in 2014, madeopen all of its electric vehicle technology patents to acceleratethe advent of sustainable transport and to support open sourcemovement [1].
Industry R&D and market value : Bronwyn H. Hall conductedseveral interesting studies to understand market value and patent-ing output [8–12]. They showed that market value of the manufac-turing corporations are strongly related to their knowledge assetsand patenting activities beautifully capture this information [8]. Intheir later work [9], they studied patent and citations between 1963–1995 and claimed that extra citation per patent boosts market valueby 3%. They showed that for European corporations also, a firm’sTobin’s q , defined as the ratio of market value to the replacement Table 1: General statistics of the compiled patent dataset.
Patent count 2,608,782Grant year range 2005–2017Application year range 1965–2016Number of citations 93,938,858Number of patent citations 75,118,567Number of non-patent citations 18,820,291 value of firm’s physical assets, is positively and significantly asso-ciated with R&D and patent stocks [12]. In their survey work [10],they observed that private returns to R&D are strongly positiveand higher than those for ordinary capital. Otto et al. [19] studiedrelationship between innovation and the market value of UK firmsin a seven year period (1989–1995). Hsu et al. [13] showed thatindustries that are more dependent on external finance and thatare more high-tech intensive exhibit a disproportionately higherinnovation level in countries with better developed equity markets.
Relating F revenue and patenting activities : Not much isknown about the correlation between Fortune 500 ranks and patent-ing activities. Wang et al. [21] combined social network analysiswith the patent co-citation network of Fortune 500 companies toevaluate technology level of an enterprise and also identified theircore technical competitive power. In their later work [20], they iden-tified several technology groups based on the co-citation networks.They also studied relationship between leading companies and tech-nology groups. Zhu et al. [22] proposed several diverse measuresfor characterizing the financial performance of the Fortune 500companies. Strikingly, they found that only about 3% companieswere operating on the best-practice frontier.Unlike most of the previous studies, this paper is different inthree aspects – (i) we present a correlation study between temporalranks (instead of crude revenue values) of F
500 companies withpatenting outputs, (ii) we meticulously overcome the bias of ex-perience/age by introducing three temporal buckets, and (iii) asopposed to previous works, we present empirical evidences thatinter-company in-citations correlate well with rise/fall in ranks.
We compile two datasets for the current study.
Patent dataset : We construct a structured patent dataset by crawl-ing full text articles indexed in Reed Technology Index [3]. Thecompiled patent dataset consists of patent metadata, such as theunique patent identifier (assigned after the patent granting process),the application year, the grant year, the patent title, the applicants’name, the company name, etc., along with patent bibliography, suchas the patent citations, the non-patent citations including scientificcitations, urls, blogs, white papers, etc. Table 1 presents the detaileddescription of the compiled dataset. The processed dataset is avail-able at: https://github.com/mayank4490/Innovation-and-revenue.
F500 dataset : We compile the Fortune 500 rank lists publishedbetween 2005–2017. The major challenge in the compilation processwas to normalize the company names present in different lists.Figure 2 shows the decay in the number of common companies asnewer yearly rank lists are taken into consideration. Overall, wefind 201 companies that are present across all the rank lists. nnovation and Revenue: Deep Diving into theTemporal Rank-shifts of Fortune 500 Companies CoDS COMAD 2020, January 5–7, 2020, Hyderabad, India C o mm o n c o m p a n i e s Figure 2: Decay in the number of common companies withthe inclusion of the newer rank lists over the years.
In this section, we detail the construction procedure of the 50-year temporal buckets . Filtering : Out of the 201 companies in F
500 dataset, only 72 com-panies have at least 100 patents granted between 2005–2017. Wealso discard few very old (before 1850) and new (after 2000) compa-nies to remove “corner” cases. Overall, we find 68 companies thatsatisfies all the above criterion. The rest of the paper presents allthe experiments on these 68 companies.
Bucketing : We divide these 68 companies into three 50-year tempo-ral buckets to perform the subsequent experiments. The first bucket( bucket I ) consists of companies founded between 1851–1900. The bucket II and bucket III consists of companies founded between1901–1950 and 1951–2000 respectively. The proposed bucketingscheme eliminates the normalization efforts needed to accommo-date the company age. Buckets I, II and III consists of 16, 29 and 23companies respectively (see Table 3). Bucket I mostly consists ofconsumer product companies, while bucket III mostly comprisesinformation technology companies. Bucket II consists of a mix-ture of several groups including consumer product, informationtechnology and automobiles.
Name normalization : We find huge variations in the companynames within the patent metadata. These variations exist due toindustry organization hierarchy such as different geographic loca-tions of the research labs, several technology teams, collaborations,etc. We also find huge number of variations resulting from spellingerrors, acronyms, etc. Table 4 shows one representative example.We manually normalize the different names of all the 68 compa-nies that we experiment on. AT&T has the maximum number ofunnormalized variations (total 95).
We employ standard Pearson’s correlation coefficient metric [14] forcomputing the correlation between companies’ patenting activityparameters (e.g., grant count, application count, citation count,etc.) and the respective F
500 ranks. The next four experiments arecategorized into two sets as follows:(1) Correlating current patenting activities with next five year F
500 ranks. The two experiments are described in Section 5.2and Section 5.4.(2) Correlating current F
500 ranks with the next five year patent-ing activities. The two experiments are described in Sec-tion 5.3 and Section 5.5.
F500 ranks
In this experiment, we measure the correlation between the ‘current’patent grant count of a company with its next five year (denoted by δ ) F
500 ranks. For the 68 companies in our list, we draw the ‘current’grant count from seven different years (2005–2011), call these as‘start’ years and estimate the correlation of each of these start yearwith the F
500 ranks of the next five years. Figure 5 illustrates theaverage correlation over the seven different start years. Each bucketshows a different temporal characteristics.
Key observations : We observe higher correlation values for bucketI compared to the buckets II and III. Bucket I companies’ futurerevenue is therefore heavily dependent on the current patentingvolume. For all the three buckets, an overall positive correlation in-dicates that companies with higher patenting volume tend to garnerhigher revenues. A further interesting point is that for bucket I com-panies, the effect of current patenting volume is more pronouncedon the revenue garnered in the later years demonstrated by theoverall increase in the correlation value. However, the correlationseems to remain stable for the two other buckets.
More experiments : We take a step further, reporting in Figure 6,the correlation values for each of the seven start years separately.Similar to our earlier observations, for each start year, we find ahigher correlation for bucket I as compared to the buckets II and III.Bucket I shows that the correlation is above 0.8 for majority (4 outof 7) of the start years at δ =
5. This leads us to claim that for thebucket I companies, the patenting volume affects the F
500 ranksmore sharply in the long run.In Figure 6, bucket II shows an interesting trend. Initial startyears exhibit a higher correlation as compared to subsequent startyears. Interestingly, for the last two start years (2010 and 2011)the correlation is quite low for all the different δ . This leads us toconclude that the dependence of company revenue on the patentingvolume is on a steady decline for the bucket II companies.Lastly, bucket III shows same trends for every individual valueof δ . The two key observations here are: (i) correlation remainsinvariant for different δ values, and (ii) as opposed to bucket II, ini-tial start years exhibit a significantly low correlation as comparedto subsequent start years. Therefore, for this bucket, as time pro-gresses, there is a steady rise in the influence of patenting volumeon the future F
500 ranks.
F500 ranks on patenting
In this section, we perform the reverse experiment. We correlate the‘current’ F
500 rank of the companies with their respective patentapplication counts in the next five years (denoted by δ ). Once again, . . . . . . C o rr e l a t i o n Bucket I Bucket II Bucket III
Figure 5: (Best viewed in color) Average correlation betweenpatent grant count drawn from seven different start yearsand F ranks of the next five years, δ = { , , , , } . oDS COMAD 2020, January 5–7, 2020, Hyderabad, India Singh et al. Table 3: Buckets I, II and III consist of companies founded between 1851–1900, 1901–1950 and 1951–2000 respectively.
Bucket I Bucket II Bucket IIICompany name Foundation year Company name Foundation year Company name Foundation year
Corning 1851 Archer Daniels Midland 1902 Comcast 1963General Mills 1856 Ford Motor 1903 Nike 1964Kimberly Clark 1872 Harley Davidson 1903 Applied Materials 1967Conocophillips 1875 Rockwell Automation 1903 Quest Diagnostics 1967Ball 1880 Honeywell International 1906 Intel 1968PPG Industries 1883 Kellogg 1906 First Data 1971Avon Products 1886 Xerox 1906 Microsoft 1975Johnson & Johnson 1886 Baker Hughes 1907 Oracle 1977Bristol Myers Squibb 1887 General Motors 1908 Micron Technology 1978Abbott Laboratories 1888 IBM 1911 Boston Scientific 1979Emerson Electric 1890 Whirlpool 1911 AT&T 1983General Electric 1892 Illinois Tool Works 1912 Cisco Systems 1984International Paper 1898 Lear 1917 Qualcomm 1985Pepsico 1898 Parker Hannifin 1917 Staples 1986General Dynamics 1899 Cummins 1919 Capital One Financial 1988Weyerhaeuser 1900 Halliburton 1919 Agco 1990Eastman Chemical 1920 Time Warner 1990Raytheon 1922 Amazon 1994Ecolab 1923 Northrop Grumman 1994Textron 1923 Lockheed Martin 1995Caterpillar 1925 Autoliv 1997Masco 1929 Monsanto 2000Texas Instruments 1930 Verizon Communications 2000Baxter International 1931Morgan Stanley 1935Hewlett Packard 1939Nucor 1940Mattel 1945Stryker 1946
Table 4: Representative company showing respective unnormalized instances occurring in the patent dataset.
Normalized name Unnormalized variationsStryker stryker development llc, stryker biotech, stryker canadian management, stryker combo l l c, stryker coropration, strykerendoscopy, stryker ireland, stryker endo, stryker european holdings i llc, stryker france, stryker gi services c v, strykerstryker gi, stryker, stryker instruments stryker leibinger gmbh co kg, stryker leibinger gmbh co kg, stryker nv operations,stryker ortho pedics, stryker orthopaedics, safe orthopaedics, stryker puerto rico, stryker trauma ag, stryker trauma gmbh,stryker truama s a, stryker trauma sa, stryker trauma s a, stryker spine, styker spine for the 68 companies in our list, we draw the ‘current’ F
500 ranksfrom seven different years (2005–2011), call these as ‘start’ yearsand estimate the correlation of each of these start year with therespective patent application counts in the next five years. Figure 7illustrates this correlation by averaging over seven different startyears (2005–2011). Similar to Figure 5, here also, each bucket showsa different temporal characteristic.
Key observations : We observe higher correlation values for bucketI compared to buckets II and III. For bucket I companies, currentrevenue seems to strongly drive future patenting volume. Com-panies with better ranks tend to produce higher overall researchoutput and vice-versa. In Figure 8, we present correlation for eachstart year separately. All the three buckets exhibit a low correlationduring the “global recession” period (2007–2009) [2].
F500 ranks
This experiment is similar to the one outlined in Section 5.2 exceptthat the grant count is replaced by the overall incoming citations toall the patents produced by the company. Interestingly, we observevery similar trends as noted in Section 5.2 (figure not shown). Weobserve higher correlation values for bucket I compared to thebuckets II and III. Bucket I companies’ future revenue is thereforeheavily dependent on the current incoming citation volume.
F500 ranks on incoming citations
This experiment is a similar to the one discussed in Section 5.3except that here the next five year application count is replaced bythe next five year incoming citations to all the patents producedby the company. The results exhibit very similar trends as those inSection 5.3 (figure not shown). We observe higher correlation valuesfor bucket I compared to bucket II and III. For bucket I companies,current revenue strongly drive future incoming citation volume. nnovation and Revenue: Deep Diving into theTemporal Rank-shifts of Fortune 500 Companies CoDS COMAD 2020, January 5–7, 2020, Hyderabad, India . . . C o rr e l a t i o n Bucket I . . . C o rr e l a t i o n Bucket II . . . C o rr e l a t i o n Bucket III
Figure 6: (Best viewed in color) Correlation between patentgrant count and future F rank at five consecutive years, δ = { , , , , } , for seven different start years, 2005–2017. . . C o rr e l a t i o n Bucket I Bucket II Bucket III
Figure 7: (Best viewed in color) Correlation between F rank and future patent application count averaged overseven different start years for five consecutive years, δ = { , , , , } . In this section, we attempt to explain the overall observations thatwe made in the last four sections. In particular, for each bucket, westudy the incoming citations from the other two buckets between2005–2017. Figure 9 shows the yearwise proportion of inter-bucketincoming citations. As can be noted, bucket I receives marginalnumber of incoming citations from both bucket II and III. Bucket IIreceives less incoming citations from bucket I but a considerablylarge number of incoming citations from bucket III. Bucket III, onthe other hand, receives less incoming citations from bucket I anda moderate volume of incoming citations from bucket II. A crucialpoint to stress here is that the volume of incoming citations frombucket III to bucket II is much larger compared to the other direction(i.e., bucket II to bucket III). We term this as a form of knowledgestealing , i.e, bucket III is able to ‘steal’ many more novel ideas frombucket II and build up on them than the other way round. BucketI is self sustained, witnesses least competition, neither cites norreceives high volume of incoming citations from the rest of the twobuckets. Note that Figure 9 do not show proportion of self-citationsand citations coming from the rest of the companies not consideredin this study. A natural analogy is that the bucket I companies . . . C o rr e l a t i o n Bucket I . . . C o rr e l a t i o n Bucket II . . . C o rr e l a t i o n Bucket III
Figure 8: (Best viewed in color) Correlation between F rank and future application count in the five consecutiveyears, δ = { , , , , } , for seven different start years, 2005–2011. behave like ‘cocoons’, the bucket II companies behave like ‘larva’and the bucket III companies behave like ‘butterflies’. I n c o m i n g c i t a t i o n s ( % ) II IIII I I IIIII II I IIIII III
Figure 9: Proportion of incoming citations.
We, next, select two representative companies from each of thethree buckets and list the top 10 highly citing companies (see Ta-ble 10). Once again, it is apparent that a considerable fraction ofincoming citations to bucket II companies arrive from bucket III. Incontrast, bucket I companies have most of the incoming citationscoming from the same bucket itself.
In this section, we consider a 13-year time period to understandthe shifts in the F
500 rank profiles. 68 companies are classified intofour broad categories based on their rank-shift profiles :(1) MonInc : Rank of a company is becoming better monoton-ically over (lower and lower) time. The revenue for thesecompanies is therefore on a rise as time progresses (hencethe name
MonInc ). At least 80% of the consecutive year ranksdifferences are positive. We perform 5-year moving average to make the rank-shift profiles smooth. oDS COMAD 2020, January 5–7, 2020, Hyderabad, India Singh et al.
Year F o r t un e r a n k (a) AmazonQualcomm
Year (b)
MascoNorthrop Grumman
Year (c)
Pepsico Whirlpool
Year (d)
WeyerhaeuserBaker hughes
Figure 11: Two representative companies from each of the four categories. a)
MonInc , b)
MonDec , c)
Stable , d)
Others . Note thatwhen the ranks are decreasing, the companies are actually rising towards the top (and vice-versa).Table 10: Inter-company incoming citations: Two represen-tative companies from each bucket showing top 10 citingcompanies. Values in parenthesis indicate percentage of in-coming citations from each citing company.
Bucket I: the cocoon
Johnson & Johnson Pepsico
Johnson & Johnson (14.3) The Coca-Cola (27.5)Abbott Laboratories (11.0) Pepsico (17.9)Novartis (10.7) Meadwestvaco (8.3)Brien Holden Vision Inst. (6.9) Concentrate Mfg. (2.5)Pixeloptics (2.6) Kimberly Clark (1.7)Coopervision Int. (2.3) Food Equipment Tech. (1.7)Google (2.3) Crestovo (1.7)Mcneil (2.1) Givaudan (1.7)The Procter & Gamble (1.6) Bunn-o-matic (1.7)E-vision (1.5) Starbucks (1.7)
Bucket II: the larva
IBM Hewlett Packard
IBM (23.5) IBM (8.5)Microsoft (6.2) Hewlett Packard (6.5)Google (2.2) Semiconductor Energy Lab. (5.7)Apple (1.9) Microsoft (4.5)Oracle (1.7) Google (2.0)Taiwan Semiconductor (1.4) Qualcomm (1.9)Intel (1.4) Apple (1.7)Tela Innovation (1.4) Intel (1.6)Micron Technology (1.3) Canon (1.4)Hewlett Packard (1.1) Samsung (1.1)
Bucket III: the butterfly
Intel Microsoft
Intel (14.3) Microsoft (20.1)IBM (9.0) IBM (6.1)Taiwan Semiconductor (3.7) Apple (4.7)Microsoft (3.1) Google (4.0)Micron Technology (2.5) Oracle (1.4)Qualcomm (2.2) Amazon (1.4)Samsung (1.7) AT&T (1.2)Apple (1.5) Qualcomm (1.0)United Microelectronics (1.5) Samsung (0.9)Google (1.2) SAP (0.9) (2)
MonDec : Rank of a company is worsening monotonically(higher and higher) over time. The revenue for these compa-nies is therefore going down as time progresses (hence thename
MonDec ). At least 80% of the consecutive year ranksdifferences are negative.(3)
Stable : Rank of a company remains stable over the years.This classification is carried out in two steps; first, we com-pute 13-year average ( mean ) of ranks. Next, we select com-panies having at least 80% year ranks between mean ± mean ± stdev , mean ± ∗ stdev , etc. The one we have chosen is able toproduce the most clear separation from the other categories. (4) Others : Remaining companies are kept in this category. Thiscategory includes companies with rank profiles having mul-tiple crests and troughs over time.
Key observations : Overall, we find 14, 5, 23 and 26 companies in
MonInc , MonDec , Stable and
Others categories respectively. Figure 11shows the rank profiles of two representative companies from eachcategory. Table 12 groups together companies from different buck-ets in each category. It utilizes a color scheme to represent eachbucket – green color for bucket I, red color for bucket II and bluecolor for bucket III. Interestingly, majority of the items in
MonInc are from bucket III, i.e., the ‘butterfly’ companies. This observationonce again reinforces our earlier claim that these companies areimproving upon their ranks by drawing knowledge (i.e., ‘stealing’)from bucket II companies and effectively building newer and moreinnovative ideas on them. In contrast,
MonDec consists of majorityof bucket II, i.e. the ‘larva’ companies. This indicates that the bucketII companies are not able to draw and build up on the knowledgegenerated in the other buckets to improve upon their ranks. Thismight be a potential sign of such companies ‘drying up’ in the nearfuture.
Table 12: (Best viewed in color) List of companies in eachrank-shift profile category. Green, red and blue color repre-sent buckets I, II and III respectively.
MonInc MonDec Stable
Corning Bristol Myers Squibb Kimberly ClarkGeneral Mills Texas Instruments Johnson JohnsonEcolab Masco Emerson electricStryker Morgan Stanley General ElectricAmazon Northrop Grumman PepsicoComcast General dynamicsCapital One Financial Archer Daniels MidlandMicron Technology Ford MotorAutoliv Honeywell InternationalQualcomm XeroxMonsanto General MotorsCummins IBMOracle Hewlett PackardNike WhirlpoolNucorCaterpillarIntelAT&TMicrosoftCisco SystemsStaplesVerizon CommunicationsLockheed Martin nnovation and Revenue: Deep Diving into theTemporal Rank-shifts of Fortune 500 Companies CoDS COMAD 2020, January 5–7, 2020, Hyderabad, India
We conduct the first plausible correlation study between researchoutput with the Fortune 500 ranks. An interesting future directionwould be to automatically predict future revenue of companiesbased on the correlations established here. oDS COMAD 2020, January 5–7, 2020, Hyderabad, India Singh et al.
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