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Emergence of Cloud Computing: How Does the Growth of the Cloud Market Affect the Growth of Small to Medium Sized Enterprises Josh Abraham April 5, 202 2 Advisor: Dr. Michelle Phillips University of Florida
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1 1 Introduction The introduction of technology can have massive gains for economic growth and production. In the 90s the advent of the internet revolutionized the way firms conducted business, leading to an influx of new jobs, markets, and significant GDP gro wth [1] . And similarly, Cloud Computing looks to do the same. Th e concept, in simple terms, is about delivering computing services over the internet (termed "the cloud") in order to provide Information Technology (IT) infrastructure or software as a flexible, scalable solution. However, cloud computing is not a uniform service, but provides a wide array of choices tailored to the provider's clients. There are three main types of cloud: public cloud, private cloud, and hybrid cloud i . And there are several types of services that can be offered ii . However, this paper will focus on Infrastructure as a Service ( IaaS ) , as it is the most basic category of cloud computing most typically used [2] . When buying IaaS from a cloud provider, the provider rents out IT infrastructure l ike servers, virtual machines (VMs) iii , storage, networks, or operating systems, on a pay as you go basis [2] . The advantage of cloud computing is substantial for both household consumers and companies. It allows consumers to access all their data on demand from any device, but notably, allows firms to rent computing power and storage while paying on demand. This l eads to the implication that cloud computing servers can provide huge cost savings by circumventing the upfront cost of building and maintaining personal IT infrastructure. But still retain high performance, speed, reliability, and security due to the econ omies of scale inherent with cloud providers for their datacenters. This in turn means those costs of production and barriers to entry are reduced allowing for the entrance of many small to medium sized enterprises (SMEs) that would have otherwise been u nable to [3] .
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2 Moreover, cloud computing benefits also encompass greener technology, reducing energy consumption and carbon footprint, claimed up to 90%. In addition, it boasts quick deployment, quality control, insights, and business continuity [4] . However, the adoption of cloud computing by SMEs is not so easy and has several challenges or limitations. Concerns over data portability, security, and lack of trust for client privacy all come up as barriers to adoption [5] . Additionally , laws and regulations over data are not standardized and can differ across regions or countries, which is especially concerning if a cloud s ervice provider is international to the firm . And critically, a stable internet connection with a wide enough bandwidth is necessary for any use of cloud services. Which could be questionable in more unstable or developing region s [6] and could make the us e of cloud difficult in rural areas where there is less access [7] . So , the adoption of cloud computing, specifically by SME's, can vary, and it is not necessarily guaranteed. If cloud computing is successful in lowering the barriers to entry, there would be an evident growth in the number of SMEs as cloud service providers grow larger. By running a regression against measures of cloud computing versus general econom ic and business growth measures, it can be determined which parameters are important. Therefore, the claim is that cloud services help create new firms. Thus, t his paper seeks to answer the question : does the growth of cloud computing providers and their commercial IaaS services have a significant impact on the growth of SMEs? 2 Sample The sample for this paper consists of 19 U.S. industries defined by the North American Industry Classification System (NAICS) codes over a five year period from 2015 2019 annually , for a
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3 total of 95 observations . The firms chosen were from the Statis tics of U.S. Businesses (SUSB) survey conducted by the U.S. Census Bureau , a nd only consider ed firms that are small to medium sized enterprises (SMEs) [8] The paper compare s it against a sample of four of the largest IaaS cloud service providers to represent the cloud computing field , namely Amazon (Amazon Web Service (AWS)), Google (Google Cloud), Microsoft (Microsoft Azure), and IBM (IBM cloud). These four companies are American corporations that dominate the industry , compromising over 50% of the market share, year over year in the measured time frame [9] . 3 Dependent Variable Percent Change of Small to Medium Sized Enterprises (per industry, per year) The dependent variable measured is the percent change of firms in each defined industry, measured annually, calculated from the total number firms . And it only consists of those firms that meet the qualification of Small to Medium Sized Enterprises (SMEs) . Typically, these are firms that are non subsidiary and independent that employ fewer than a certain limit. However, this number varies across countries, for example , the frequent upper limit in the European Union designating a n SME is 250 employees [10] . In the United States, the Small Business Administration (SBA) is responsible for defining the size standards and considers both number of employees and financial assets like balance sheet as a measure, leading to variation across industries [11] . But commonly , the broader industries can be differentiated by employee count, with most large companies reaching that status once they are over 500 employees. So , this paper will consider a broader definition of SMEs as any firm that has less than 500 employees [10] . The data for the number of SMEs per industry per year is pulled from the Statistics of U.S. Businesses (SUSB) survey by the U.S. Census [8] . T he total count of SMEs per industry per year
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4 will be measured but this paper will calculate and regress on the percent change over the past year to get an intuitive understanding of how the count changes when other parameters vary over the time frame . 4 Independent Variables The independent variables are split between parameters represent ing cloud computing providers versus variables for general economic and business growth. The intention is to investigate whether the growth of the representative cloud providers has an addi t ional impact on the growth of SME firms versus overall growth. Year A variable for the year is important to account for any discrepancies that happen over time. Any variations that might influence other independent variables or lead to differences will be captured by the year indicator , compared against the year 2015 . Industry ( Dummy Variable) It is important to consider what industry a firm belongs to in order to assess the importance of cloud computing. By definition, cloud is an internet service , so industries that rely more on data storage and internet capabilities have more interaction than industries where that might not be the case. They would be more responsive to firm increase s during economic expansion and resistant to decrease s during contraction. A hypothesis then is that industry does influence SME growth because of the increased usage of cloud services. This data is pulled from the SUSB survey by the US Census [8] and will be coded as a dummy variable for each industry as 0 or 1, compared against the NAICS 81 sector : Other Services (Except Public Administration) . See A ppendix Table A1 for NAICS ID and description .
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5 Herfindal Herschman Index (HHI 50 ) ( percent , per industry ) Th is variable captures the relative market competition of firms in relation to an industry based off their size and subsequent revenue . Used as a measure of market competition iv , the higher an HHI score, the more concentrated a market is, ranging from perfectly competitive to monopoly v . The HHI is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers [12] : , where s i is the firm market share, up to n number of firms. For this paper, Since the industry sector is so general, an HHI will be calculated using the largest 50 firms, represented as HHI 50 (or HHI_50). The hypothesis is th at a higher HHI score suggests a more difficult industry to enter, leading to lower SME growth. This data is pulled from a 2017 US Census survey [13] . Due to constraints with the data, any missing or suppressed values w ere substituted by an HHI 4 score, calculated using the industries major players on IBISWorld [13] . S ince market competition remain ed similar over the short time period, the same values will be used per year . Employment ( per industry ) and Employment Change (%) The employment variable measures the number of current workers per industry in a given year. It gives a general sense of the economy's labor force and is important for a firm's labor supply . As the industry expands or contracts the number of employees should change correspondingly , so this paper will also consider the percent change in employees per industry per year . And if cloud affects businesses the way it claim s then there should be an in flux in the numbers of employees in an industry , leading to more supply of labor to pull from to create new businesses . Therefore, the hypothesis is that as employment increases, the number of SMEs increases . This data is pulled from the SUSB survey by the US Census [8] .
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6 Payroll Per Capita ( per industry, per year, in thousands of USD) Payroll per capita is the measure of the average salary of each employee per industry. It is calculated by dividing the total annual payroll amount by the total number of employees in that industry. This variable accounts for any differences in pay between industries and affects a firm's costs of production as a wage input. Although a higher salary means that firms are paying more in production costs, reducing their growth, it can also be considered that it would incentivize new firms to launch in order to capture the revenue that allows firms to pay such higher wages. The hypothesis is that payroll per capita could positively or negatively affect the number of SMEs. This data is pulled from the SUSB survey by the US Census [8] . P roducer Price Index (PPI) (per industr y, per year, base=2012) Th e producer price index (PPI) measures the average change over time in the selling prices received by domestic producers for their output [12] . It acts as an indicator for how much inflation firms face, and its inclusion is necess ary as an overall determinant for economic and business growth, by affecting the purchasing power of producers. A higher PPI could lead to higher production costs and less revenue, disincentivizing SMEs from entering the market. A hypothesis then is that h igher Producer Price Index, inflation, would decrease the number of SMEs due to increase d costs. This data is pulled from the U.S. Bureau of Labor Statistics [12] . One thing to note, however, is that some industries do not have a direct PPI, so a proxy data series was substituted, i.e., for NAICS 11: Agriculture, the PPI For Farm Product Warehousing and Storage was used.
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7 Industry Gross Domestic Output ( per industry, per year , in billions of USD, base=2012) This variable is an industry by industry breakdown of gross domestic product , showing each sector's contribution to the U.S. economy. The GDP is used to measure the overall strength of each industry and is important in determining what economic state the firms are experiencing and whether firms would want to enter the market. The hypothesis is that a higher industry GDP would lead to more SMEs per year because of the general growth in economy. This data is pulled from the U.S. Bureau of Economic Analysis [16] . Annual Interest Rate (percent, per year) Interest rate is the amount that a lender charges to a borrower as a percen tage of the principal loan. It is essentially a charge to the borrower for the use of assets and acts as a "cost of money". The inclusion of interest rates is significant because higher rates make borrowing the same amount of money more expensive, thus red ucing the desire and ability to receive loans that can be invested into new SMEs. A hypothesis then is that higher interest rates lead to a decrease in the percent change of new SMEs due to a reduction of loans. This data is pulled from the FRED by the St. Louis Fed [12] . Labor Force (millions) La bor force is a n economy wide measure of the US 's working population , that includes all people aged 16 and older who are classified as either employed or unemployed. The use of this variable is a measure of overall population growth and workforce. As the working population increases, the relative number of income earning persons and available labor incr eases, which could support the growth of new businesses in industries. Thus , as the labor force increases , the number of SME's increases. This data is pulled from the Bureau of Labor Statistics [18] .
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8 Cloud Variable: IaaS Service Cost to Firm ( annual price per hour , in USD ) This variable is the actual cost to firms looking to rent IaaS servers and applications. In theory , the different pricing models and subscription plans allow for any firm to tailor the service to their IT needs and upscale or downgrade accordingly. However due to each firm's different requirements , determining a fixed service cost that is applicable to all firms can be difficult. The services are measured as price per hour (USD) and vary depending on the number of virtual cores, memory, and /or disk storage options chosen . So, in order to make a uniform estimation, a median price is calculated for each company and averaged for the four companies per year to repres ent a medium to small scale implementation of servers. The justification is that most SMEs entering the market would not go for the largest infrastructure, simply because they do not have the capital or need . Instead, they would opt for a plan that was most cost effective for a begin ner integration. If cost is significant, then a hypothesis is that decreased costs lead to an increase in SMEs due to reduced factors of production. This data is taken from a dataset by the RedMonk industry analyst firm for the years 2014, 2016 2019 [15] . The dataset for the year 2015 was unavailable so the year 2014 will be substituted. Other cloud variables were also considered to measure the impact of cloud service providers such as market capitalization and provider revenue. Market capitalization would have been a measurement of overall company growth through stock price to reflect market and consumer sentiment and adoption to mainstream. R evenue would have reflected the inc reased usage of cloud services in the market. However, the true growth would have been difficult to discern from the information provided by annual financial reports since IaaS service s are not cleanly segmented within the sheets . Additionally, capitalization and revenue data do not accurately align a s a parameter that affects the growth of SMEs, whereas IaaS Cost acts as a cost of
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9 production for firms. They are also highly correlated as seen in the correlation matrix in Table A2 (see Appendix), and therefore produced the same regression results. As such, only IaaS will be considered. 5 Summary Statistics Table 1 : Summary Statistics N MEAN MEDIAN ST. DEV . MIN. MAX. SME Count 95 316 , 577 .7 244 , 09 8 266 , 591 5 , 698 30 , 074 , 787 SME % Change 95 0. 774 0.919 2. 013 7.942 5.114 HHI 50 (%) 95 50 .506 2 3.1 59.44 3 239 Employment 95 3 , 181 , 686 1 , 909 , 993 2 , 670 , 120 110 , 457 9 , 153 , 196 Employment Change (%) 95 0. 933 1.160 2.864 17.515 8. 551 Payroll per capita ($, thousands) 95 53.21 2 48.384 19.808 17.414 88.879 PPI 95 106.784 106.1 11.775 67. 965 153.218 Industry GDP ($, billions) 95 1 , 542.178 1 , 157.2 1 , 332.038 285.7 6 , 112.8 Interest Rates (%) 95 1.726 1.63 0.78 0.77 2.75 Labor Force ( millions) 95 160.45 160.32 2.239 157 .13 163.54 IaaS Cost ($, per hour) 95 0.264 0.261 0. 0515 0.209 0.348 6 Regression and Results From the correlation matrix Table A2 (see Appendix) , Provider Revenue and Provider Capitalization are strongly correlated with each other at a 0.98 9 level and I aaS Cost is correlated with them at a 0.76 to 0.78 strength. Moreover, Interest Rates and Labor Force are highly correlated with each and with the cloud variables at over .90 strength. And since al l three are proxies for cloud adoption, a single regression will be run against IaaS Cost , to capture the effects of cloud on SMEs since it acts as a cost of production faced by potential firms and determine if it is effective. T o mitigate effects from the interest rates and labor force correlation, a regression will be run without them ( seen in Regression (4) in T able 2) .
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10 Table 2: Regression Results Dependent Variable: Percent Change of SMEs Variable Regression (1) IaaS Service Cost Regression ( 2 ) IaaS Service Cost without Labor Force or Interest Rates (Intercept) 3 , 666 * (0.075) 551 (0. 107 ) HHI_50 (%) 0.0065 (0.579) 0.00 37 (0. 763 ) Employment 1.172 × 10 6 (0.225) 1. 192 × 10 6 (0.2 40 ) Employment Change (%) 0.253 *** (3.32 × 10 8 ) 0.2 5 7 *** ( 5.68 × 10 8 ) Payroll per capita ($, thousands) 0.0656 (0.411) 0.0 6 6 (0. 433 ) PPI 0.0284 (0.357) 0.0 142 (0. 657 ) Industry GDP ($, billions) 0.0029 (0.206) 0. 0031 (0. 198 ) Interest rates (%) 2.296 ** * (0.00 4 ) Labor Force (millions) 0.2916 (0.591) IaaS Cost ($, per hour) 5.148 * (0.059) 1.620 ( 0.511) N 95 95 R 2 adjusted 0. 8372 0.821 Note: Coefficients displayed for Cloud and Economy variables, excluding year and industry dummies *, **, *** denote 90%, 95%, and 99% confidence intervals; p values shown in parentheses Excluded year dummy: 2015; Excluded NAICS dummy: ID 81 Other Services (Except Public Administration) Table 2 highlights the regression output, in particular, for the general economic and cloud related variables, while Table A3 (see Appendix) includes the results for all the year and industry
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11 dummies. When evaluating the results, most of the coefficients are similar or near identical across the two regressions, wit h both of them having an adjusted R 2 value of over 0.82 each. When looking at the industry dummies in Table A3, the majority are insignificant except for two industries (44 45: Retail Trade; 62: Health Care) who were significant at a 95% confidence interv al and two (61: Education Services, 71: Arts, Entertainment, Recreation) who were significant at a 90% confidence interval. However, Retail Trade and Health Care had varying levels of negative coefficients while the other two had varying positive coeffici ents. For example, in Regression (1), Retail Trade would decrease SMEs by 5.473 percentage points whereas Educational Services would increase SMEs by 5.224 percentage points , relative to the omitted industry, 81: Other Services (Except Public Administratio n) . This variation could be attributed to the type of industry that is being regressed on : Retail Trade and Health Care are large pre existing businesses that require a lot of infrastructure and capital to start, and is labor intensive to continue, whereas Education Services and Arts, Entertainment, Recreation industries simply do not require as much upfront costs (such as smaller, "mom and pop", or local firms). Or potentially deal with regulations and permits intrinsic in their respective industry. Looki ng back at Table 2, two of the economic variables had strong significance across the first regression and the cloud adoption variable had a slight significance. The Employment Percent Change variable was statistically significant at the 99% confidence inte rval through both regressions, stating that a 1 percentage point increase in industry employment is associated with a bout a 0.2 5 percentage point increase in SMEs. Unsurprisingly, this result supports the original hypothesis as employment rises, the number of SMEs rise. However, this could still be a case of reverse causation. Rather than the number of employees influencing the total number of SMEs it
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12 is likely due to newly created and established firms requiring more employees as they expand. Further investigation might benefit from the use of a more discrete instrumental variable. T he second statistically significant variable is Interest Rate, at a 99% confidence interval, suggesting that for every percentage point increase in interest, the number of SMEs will grow by 2.296% points for regression (1 ) . This is surprising since it contradicts the hypothesis that higher interest rates cause decreased SME growth. This could possibly be explained by a lag between changes in interest rates and subsequent market response s . The positive coefficients might be representing interest rate values from previous years when they were lower and promoted economic gr owth . Finally, when looking at the cloud variable, it is weakly statistically significant at 90% confidence interval for regression (1) but not for regression (4). This points to the fact that, although it might be significant in tandem with some highly correlated variables, like interest rates, removing the economic variable leads the cloud variable to become insignificant. Suggesting that the signi ficance was likely due to the other variable or the interaction between them. But the IaaS Cost variable did align with the expected hypothesis strongly, suggesting that there is a decrease of 5.148 SMEs for every dollar increase . 7 Conclusion Although the c loud level variables were significant for regressions (1) through (3), removing the highly correlated economic variables led to them becoming insignificant, pointing to other conflicting problems. Further investigation would suggest that a different result might be possible if the cloud variables were able to vary by industry, however that data was unavailable.
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13 Nevertheless, the findings suggest that interest rates and labor force change are the main drivers for SME growth. Which points t o utilizing the underlying, fundamental , macro level economic variables to influence the growth of businesses and suggest policy change or regulation r ather than expecting a single industry or breakthrough to revolutionize growth. Appendix Table A1: Industry NAICS ID and Description NAICS ID NAICS Industry Description 11 Agriculture, Forestry, Fishing and Hunting 21 Mining, Quarrying, and Oil and Gas Extraction 22 Utilities 23 Construction 31 33 Manufacturing 42 Wholesale Trade 44 45 Retail Trade 48 49 Transportation and Warehousing 51 Information 52 Finance and Insurance 53 Real Estate and Rental and Leasing 54 Professional, Scientific, and Technical Services 55 Management of Companies and Enterprises 56 Administrative and Support and Waste Management and Remediation Services 61 Educational Services 62 Health Care and Social Assistance 71 Arts, Entertainment, and Recreation 72 Accommodation and Food Services 81 Other Services (except Public Administration) Excluded NAICS dummy: ID 81 Other Services (Except Public Administration)
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14 Table A2: Correlation Matrix HHI_50 Employment Employment_PC Payroll_per_capita HHI_50 1.000 Employment 0.533 1.000 Employment_PC 0.033 0.155 1.000 Payroll_per_capita 0.439 0.495 0.178 1.000 PPI 0.322 0.189 0.251 0.316 Industry_GDP 0.147 0.308 0.055 0.102 Labor_Force 0.032 0.019 0.039 0.103 Interest_rates 0.035 0.019 0.013 0.105 IaaS_Cost 0.032 0.012 0.005 0.081 Provider_Revenue 0.034 0.019 0.027 0.105 Provider_Cap 0.035 0.019 0.022 0.105 PPI Industry_GDP Labor_Force Interest_rates HHI_50 Employment Employment_PC Payroll_per_capita PPI 1.000 Industry_GDP 0.126 1.000 Labor_Force 0.253 0.038 1.000 Interest_rates 0.263 0.038 0.976 1.000 IaaS_Cost 0.216 0.028 0.663 0.740 Provider_Revenue 0.260 0.038 0.980 0.978 Provider_Cap 0.263 0.038 0.983 0.997 IaaS_Cost Provider_Revenue Provider_Cap HHI_50 Employment Employment_PC Payroll_per_capita PPI Industry_GDP Labor_Force Interest_rates IaaS_Cost 1.000 Provider_Revenue 0.789 1.000 Provider_Cap 0.762 0.989 1.000
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15 Table A3 : Full Regression Results Dependent Variable : Number of SMEs Variable Regression (1) IaaS Service Cost Regression (2) Provider Revenue Regression (3) Provider Capital Regression (4) IaaS Service Cost without Labor Force or Interest Rates (Intercept) 3 , 666 * (0.075) 1 , 861 (0. 440 ) 4 , 159 * * (0.046) 1 , 683 * (0.053) Year 1.845 * (0.082) 0.883 (0.447) 2.103 (0.051) 0.838 (0.052) NAICS_11 4.590 (0.293) 0.470 (0.725) 0.470 (0.725) 5.340 (0.240) NAICS_21 3.541 (0.517) 7.652 (0.079) 7.652 (0.079) 2.104 (0.710) NAICS_22 1.466 (0.801) 2.875 (0.535) 2.875 (0.535) 3.071 (0.610) NAICS_23 3.887 (0.169) 2.883 (0.287) 2.883 (0.287) 3.392 (0.248) NAICS_31_33 19.02 (0.133) 20.473 (0.106) 20.473 (0.106) 18.450 (0.160) NAICS_42 6.011 (0.139) 7.394 (0.060) 7.394 (0.060) 5.156 (0.221) NAICS_44_45 5.473 * * (0.046) 4.947 * * (0.068) 4.947 * * (0.068) 5.420 * * (0.057) NAICS_48_49 1.827 (0.636) 1.672 (0.521) 1.672 (0.521) 2.517 (0. 531) NAICS_51 2.073 (0.766) 6.025 (0.333) 6.025 (0.333) 0.504 (0.944) NAICS_52 6.014 (0.369) 8.906 (0.159) 8.906 (0.159) 4.498 (0.517) NAICS_53 2.348 (0.748) 6.975 (0.268) 6.975 (0.268) 1.411 (0.853) NAICS_54 7.904 (0.116) 6.991 (0.160) 6.991 (0.160) 6.987 (0.180)
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16 NAICS_55 0.034 (0.995) 3.924 (0.328) 3.924 (0.328) 1.523 (0.774) NAICS_56 0.052 (0.975) 1.187 (0.384) 1.187 (0.384) 0.263 (0. 878) NAICS_61 5.224 * (0.076) 1.812 * (0.035) 1.812 * (0.035) 5.596 * (0.068) NAICS_62 10.92 * * (0.042) 6.418 * * (0.097) 6.418 * * (0.097) 11.075 * * (0.048) NAICS_71 5.901 ' (0.058) 2.327 ' (0.015) 2.327 ' (0.015) 6.308 ' (0.051) NAICS_72 4.178 (0.237) 0.206 (0.878) 0.206 (0.878) 4.807 (0.191) HHI_50 (%) 0.0065 (0.579) 0.0065 (0.579) 0.0065 (0.579) 0.0037 (0.763) Employment 1.172 × 10 6 (0.225) 1.172 × 10 6 (0.225) 1.172 × 10 6 (0.225) 1.192 × 10 6 (0.240) Employment Change (%) 0.253 *** (3.32 × 10 8 ) 0.253 *** (3.32 × 10 8 ) 0.253 *** (3.32 × 10 8 ) 0.257 *** (5.68 × 10 8 ) Payroll per capita ($, thousands) 0.0656 (0.411) 0.0656 (0.411) 0.0656 (0.411) 0.066 (0.433) PPI 0.0284 (0.357) 0.0284 (0.357) 0.0284 (0.357) 0.0142 (0.657) Industry GDP ($, billions) 0.0029 (0.206) 0.0029 (0.20 7 ) 0.0029 (0.20 7 ) 0.0031 (0.198) Interest rates (%) 2.296 ** * (0.00 4 ) 1.829 ** (0.011) 4.668 *** (0.007) Labor Force (millions) 0.2916 (0.591) 0.2589 (0.635) 0.8288 (0.148) IaaS Cost ($, per hour) 5.148 * (0.059) 1.620 (0.511) Provider Revenue ($, billions) 0.0494 * (0.0592) Provider Cap ($, billions) 0.0178 * (0.059) N 95 95 95 95 R 2 adjusted 0. 8372 0. 8384 0.9997 0.821 *, **, *** denote 90%, 95%, and 99% confidence intervals; p values shown in parentheses Excluded NAICS dummy: ID 81 Other Services (Except Public Administration)
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17 8 References [1] J. Manyika and C. Roxburgh, "The Great Transformer: The impact of the Internet on economic growth and prosperity," McKinsey Global Institute, 2011. [2] Microsoft Azure, "What is cloud computing? A beginner's guide," Microsoft, [Online]. Available: https://azure.microsoft.com/en us/resources/cloud computing dictionary/what is cloud computing/#benefits. [3] E. Bayrack, J. P. Conley and S. Wilkie, "The Economics of Cloud Computing," Vanderbilt University, Nashville, 20 11. [4] Oracle, "The Top 10 Benefits of Cloud Computing," [Online]. Available: https://www.oracle.com/cloud/what is cloud computing/top 10 benefits cloud computing/. [5] E. O. Yeboah Boateng and K. A. Essandoh, "Factors Influencing the Adoption of Clou d Computing by Small and Medium Enterprises in Developing Economies," International Journal of Emerging Science and Engineering (IJESE), vol. 2, no. 4, pp. 13 20, 2014. [6] The World Bank , "Connecting for Inclusion: Broadband Access for All," The World Bank Group, [Online]. Available: https://www.worldbank.org/en/topic/digitaldevelopment/brief/connecting for inclusion broadband access for all. [7] C. Preimesberger, "IT Science Case Study: Providing Cloud Services to Rural America," eWeek, 10 July 2018. [Online]. Available: https://www.eweek.com/innovation/it science case study providing cloud services to rural america/. [8] United States Census Bureau, "Statistics of U.S. Businesses (SUSB)," [Online]. Available: https://www.census.gov/programs su rveys/susb.html. [9] Turbonomic, "Global Use of Cloud Providers by Organizations 2022, by Vendor," Statista, 22 July 2022. [Online]. Available: https://www.statista.com/statistics/1224552/organization use cloud provider global/. [10] Organisation for E conomic Co operation and Development (OECD), "Small and Medium Sized Enterprises (SMEs)," OECD, 2 December 2005. [Online]. Available: https://stats.oecd.org/glossary/detail.asp?ID=3123. [11] A. W. Hait, "The Majority of U.S. Businesses Have Fewer Than Fi ve Employees," United States Census Bureau, 19 January 2021. [Online]. Available: https://www.census.gov/library/stories/2021/01/what is a small business.html.
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18 [12] Federal Reserve Economic Data (FRED), "Interest rates, Discount Rates for United States," [Online]. Available: https://fred.stlouisfed.org/series/INTDSRUSM193N#0. [13] J. Fernando, "Market Capitalization: How Is It Calculated and What Does It Tell Investors?," Investopedia, 10 August 2022. [Online]. Available: https://www.investopedi a.com/terms/m/marketcapitalization.asp. [14] Macrotrends, "Stock Research," Macrotrends LLC, [Online]. Available: https://www.macrotrends.net/stocks/research. [15] R. Stephens, "IaaS Pricing Patterns and Trends 2019," RedMonk, 1 August 2019. [Online]. Available: https://redmonk.com/rstephens/2019/08/01/iaas pricing patterns and trends 2019/. [16] Amazon, "Annual reports, proxies and shareholder letters," Amazon, [Online]. Available: https://ir.aboutamazon.com/annual reports proxies and share holder letters/default.aspx. [17] A. Hayes, "Dotcom Bubble," Investopedia, 25 June 2019. [Online]. Available: https://www.investopedia.com/terms/d/dotcom bubble.asp. [18] Center for Disease Control and Prevention, "CDC Musuem COVID 19 Timeline," U.S. D epartment of Health & Human Services, 16 August 2022. [Online]. Available: https://www.cdc.gov/museum/timeline/covid19.html. [19] S&P Dow Jones Indices, "S&P 500 The Gauge of the Market Economy," [Online]. Available: https://www.spglobal.com/spdji/en/doc uments/additional material/sp 500 brochure.pdf. [20] S&P Dow Jones Indices, "S&P 500," S&P Global, [Online]. Available: https://www.spglobal.com/spdji/en/indices/equity/sp 500/#overview. [21] Division of Consumer Prices and Price Indexes, "Consumer Price Index," U.S. Bureau of Labor Statistics, [Online]. Available: https://www.bls.gov/cpi/. [22] Federal Reserve Economic Data (FRED), "Consumer Price Index: Total All Items: Wage Earners for the United States," Federal Reserve Bank of St. Lo uis, [Online]. Available: https://fred.stlouisfed.org/series/CPALWE01USQ661N#0.
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19 i Public cloud refers to servers owned by the cloud service provider and all hardware, software, and storage is provided over the internet and managed by the service provider. Private cloud refers to cloud resources exclusively used by a single firm and maintained on a private network. Hybrid cloud refers to a combination of public and private cloud that allows for data and application to move between the two server types. ii Software as a Service (SaaS), Platform as a Service (PaaS), serverless computing, and Infrastructure as a Service (IaaS). iii iii Virtual Machines are software that allow for the emulation of multipl e computer systems on the same machine. They provide the functionality of a physical computer and allows for the implementation of specialized hardware and / or software. iv HHI is considered a better measure of market competition (over concentration ratio which takes the sum of each firm's market share) because it gives more weight to larger firms due to the squaring function v Typically, HHI is measured as decimal or percentage, ranging from 0 to 1.0 or 0 to 10,000 points, respectively. An HHI score below 100 is a highly competitive industry. An HHI below 1 , 500 is an unconcentrated industry. An HHI between 1 , 500 to 2 , 500 is moderately concentrated. An HHI above 2,500 indicates a high concentration in firm market share.
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!-- Emergence of Cloud Computing: How Does the Growth Market Affect Small to Medium Sized Enterprises ( Book ) --
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mods:abstract displayLabel Abstract lang en The introduction of technology can have massive gains for economic growth and production, and similar to the revolution by the internet, cloud computing looks to do the same. It delivers computing services over the internet (termed "the cloud") in order to provide IT infrastructure or software as a flexible, scalable solution, with advantages that are substantial for both household consumers and companies. This paper seeks to answer the question, does the growth of cloud computing providers and their commercial IaaS services have a significant impact on the growth of small-to-medium sized enterprises (SMEs)? A linear regression was run on a sample of 19 US industries over five years compared against an aggregated sample of four cloud providers (Amazon, Google, Microsoft, IBM) that measures the percentage change of SMEs. Variables measured covered both economic and cloud-specific parameters, which included industry, Herfindal-Herschman Index, employment, payroll per capita, producer price index, industry GDP, annual interest rate, labor market, and IaaS service cost. The results found that some industries were significant likely due to attributes specific to those industries, like high costs and pre-existing infrastructure. And while the cloud variable, IaaS service cost, was significant in conjunction with economic variables, employment change and interest rates, removing those variables led to insignificance, suggesting that the result was likely due to the affects or interaction between strong economic variables. Further investigation into cloud variables that vary by industry might provide differing results, but the findings suggest that interest rates and labor force change are the main drivers for SME growth. Which points to utilizing the underlying, fundamental, macro-level economic variables to influence the growth of businesses and suggest policy change or regulation.
mods:accessCondition Copyright Josh Abraham. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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mods:note Awarded Bachelor of Arts, summa cum laude, on April 29, 2022. Major: Economics
College or School: Liberal Arts and Sciences
Advisor: Michelle A. Phillips. Advisor Department or School: Economics.
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mods:caption 2023
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mods:topic Undergraduate Honors Thesis/Project
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mods:title Emergence of Cloud Computing: How Does the Growth of the Cloud Market Affect the Growth of Small-to-Medium Sized Enterprises
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