Research ArticleAnalysis of Financing Risk and Innovation MotivationMechanism of Financial Service Industry Based onInternet of Things

Luya Li and Hongxun Li

School of Economies and Management Beijing Forestry University Haidian 100083 Beijing China

Correspondence should be addressed to Hongxun Li lihongxunbjfueducn

Received 16 January 2021 Revised 7 February 2021 Accepted 1 April 2021 Published 13 April 2021

Academic Editor Wei Wang

Copyright copy 2021 Luya Li and Hongxun Li is is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

It is of practical significance to introduce the Internet of ings technology into the financial service industry and find the drivingfactors and mechanisms of financial innovation to accelerate the promotion of financial innovation is article starts from theperspective of banks and other supply chain financial institutions takes mainstream trading products in the commodity tradingmarket as the research object uses the LA-VAR model and fully considers the market price fluctuations and liquidity factors ofsupply chain financial inventory products It analyzes the theoretical basis of the continuous innovation of rural financialproducts On the basis of analyzing the basic characteristics and types of rural financial product innovation we explore theconnotation of sustainable innovation of rural financial products clarify the evaluation criteria and lay a theoretical foundationfor continuous dynamic evaluation Based on technical innovation evaluation theoretical models such as Schumpeterrsquos innovationmodel technical specifications-technological trackmodel and NR relationship model market we analyze the innovation elementsof rural financial products from the external and internal aspects of innovation and discuss the relationship between the factorse interactionmechanism of rural financial products has established a dynamicmechanismmodel for the continuous innovationof rural financial products A fuzzy comprehensive evaluation was made on the continuous innovation power of financial serviceindustry products in a certain area Using a combination of remote surveys and on-site visits a questionnaire survey wasconducted on financial service industry institutions in a certain regionrsquos financial system Each survey object was required toconduct 120times1067 index comparisons and use the data after processing the arithmetic average Matlab carries out the objectiveprocessing of programminge results show that the LA-VARmodel with liquidity indicators can measure the liquidity risk welland more comprehensively evaluate the risk of the inventory pledge financing model According to the index weights determinedby AHP the development of the financial service industry will be promoted in a targeted manner from the internal construction offinancial institutions and the optimization of the external innovation environment

1 Introduction

At present the development of the Internet of ings in thefinancial services industry is in its infancy in the world andhas a certain technology industry and application foun-dation showing a good development trend e matureInternet of ings technology can realize the connection ofall items to the network thus facilitating peoplersquos identifi-cation andmanagement control of items [1ndash3] In the supplychain management of production and circulation

enterprises visual tracking of products in the productionlink storage link transportation link and sales link of theenterprise is realizede financial services industry uses thisvisual tracking product as the basic target to carry out fi-nancial innovation create new financial products servicesand sales channels and provide diversified financingmethods and channels thereby occupying new market areasand improving overall competitiveness [4]

As the Internet of ings technology continues to ma-ture it is being promoted and used at home and abroad and

HindawiComplexityVolume 2021 Article ID 5523290 9 pageshttpsdoiorg10115520215523290

the financial industry is no exception [5 6] By using sensorsinfrared and other devices on financial payment terminalsthe integration of the Internet of ings and the financialtransaction network can be realized Since the current self-service terminals in the financial industry are large in sizepower-consuming of single function difficult to monitorand insufficiently safe such as ATMs self-service inquirymachines and online banking experience machines it isnecessary to accelerate the application of Internet of ingstechnology in the financial industry [7] e security pre-cautions of all business areas of the bank are classified ashigh-risk levels [8] According to the requirements ofChinarsquos banking security practice the bankrsquos businessoutlets treasury and office areas must implement com-prehensive security precautions On the other hand similarto other industries these business areas of banks have somesecurity risks that are difficult to prevent with traditionalsecurity technology [9] For example there is no effectiveoverall solution for the security of self-service banking Forinstance the financial industry has always hoped to solvethem as soon as possible For example the escort storageand opening of cash boxes are very sensitive and the per-sonnel involved are all highly stressed If each cash box canbe accurately positioned authorized to open and authorizedto transfer the safety of the cash box can be ensured [10ndash12]e former is provided by banks and other financial insti-tutions with short-term financing while the latter is solvedinternally by the supply chain mainly focusing on theoptimization of accounts receivable inventory and fixedasset income [13 14] e researchers discussed the evo-lution of financial supply chain management to supply chainfinance from the process of the development of the realeconomy and defined the connotation of supply chain fi-nance as a support [15] At the same time they pointed outthat the development of this business has realized the de-velopment of banks and other financial institutions [16ndash18]By constructing a tripartite game model among banks fi-nancing SMEs and logistics companies relevant scholarshave compared and analyzed the different impacts broughtabout by the participation of logistics companies andwithout the participation of logistics companies [19 20]

First we perform a comparison with the value of riskwhen single-variety supply chain financial inventory isused as pledge and explain the empirical results rea-sonably e calculation results of the model show thatthe continuous innovation power of financial serviceindustry products in a certain region is average

Second the study confirmed that as one of the sixcentral provinces and three northeastern provinces tocarry out innovation pilot projects under the leader-ship of the provincial party committee and the pro-vincial government various financial institutions haveintensified innovation in the financial service industryand achieved certain results However compared withthe eastern coastal areas the overall level of the fi-nancial industry in a certain area is still relativelybackward the development of the financial serviceindustry is obviously lagging behind and the

continuous innovation of financial service products is ageneral reality

Under the premise of ensuring the rationality and sta-bility of the research data this paper uses the LA-VARmodel to quantify the liquidity risk and market risk of thesupply chain financial inventory portfolio composed of rebarand palm oil Specifically the technical contributions of thisarticle can be summarized as follows

e rest of this article is organized as follows Section 2discusses related theories and technologies Section 3 ana-lyzes the financing risk of financial inventory portfoliopledge in the financial service industry supply chain Section4 conducts an empirical analysis of the dynamic mechanismof continuous innovation of financial products Section 5summarizes the full text

2 Related Theories and Technologies

21 Supply Chain Financial Service Industry Model In theentire process of the supply chain cash flow gaps have beengenerated in prepayments supply chain financial invento-ries accounts receivable and other links By classifying theselinks three business models of supply chain finance haveemerged confirmation warehouse financing warehouse fi-nancing and accounts receivable financing

211 Confirmation Warehouse Financing For core enter-prises confirming warehouse financing firstly enhancesdealersrsquo sales capabilities solves the problem of productbacklog expands the market share of products and obtainsgreater commercial profits Secondly it locks in saleschannels in the fierce market competition and obtains thecompetitive advantage of the industrial chain irdly thereis no need to finance from banks which not only reduces thecost of funds but also reduces the occupation of accountsreceivable and guarantees collection For small- and me-dium-sized enterprises there is no need to worry about thedifficulty of purchasing goods due to shortage of funds ebank provides financing facilities for them and obtainspreferential prices from upstream core enterprise suppliersProducts with large gender differences can also be ordered inoff-season and sold in peak seasons to obtain higher profitsFor banks through the business of confirming warehousesthey not only obtain abundant service fees and possible billdiscount fees but also master the power to withdraw goods

212 Financing Warehouse Model e business process isshown in Figure 1 e financing model of Rongtongcangtakes small- and medium-sized enterprises as the mainservice objects based on mobile commodity warehousingand uses third-party logistics companies as a comprehensiveservice platform that connects small- and medium-sizedenterprises and financial institutions to make the movableproperty pledge loan business for small- and medium-sizedenterprises feasible e core enterprises in the supply chainare powerful and large-scale enterprises that can help up-stream and downstream SMEs solve financing guarantee

2 Complexity

difficulties through guarantees or promises of repurchasee financing mode of financing can ensure that SMEs havea stable source of supply or sales channels and establish goodcooperative relations with core enterprises With goodwarehousing logistics and evaluation conditions Rong-tongcang not only assists small- and medium-sized enter-prises to obtain financing support with the pledge ofmovable properties stored in Rongtongcang but also helpsbanks as pledgers solve the problems of pledge valuationsupervision and auction In short through the financingand warehouse model the movable properties of small- andmedium-sized enterprises that banks were not willing toaccept in the past are transformed into pledged movableproperties that they are willing to accept thus serving as anew bridge between small- and medium-sized enterprisesand banks for financing

213 Accounts Receivable Financing Model Specificallyaccounts receivable financing refers to the conditionaltransfer of accounts receivable formed by credit sales by thedemander of funds to a special financing institution whichwill provide enterprises with financing debt recovery andsales account management so that companies that use creditsales as the main method can obtain the necessary funds andstrengthen the turnover of funds Generally accounts re-ceivable financing uses accounts receivable vouchers as thesubject matter pledge or transfer and the term does notexceed the age of the accounts receivable e main entitiesare small- and medium-sized enterprises (debt enterprises)core enterprises (debt enterprises) and commercial banksAccording to the different sources of repayment the fi-nancing model of accounts receivable is divided into pledgeof accounts receivable and transfer of accounts receivablee first source of repayment for pledge of accounts re-ceivable is the sales income of creditor companies and thesecond source of repayment is the accounts receivable paidby the debtor company and the first source of repayment forthe transfer of accounts receivable is directly from the debtorcompany For the accounts receivable paid to the bank thesecond source of repayment is the sales revenue of the

creditor enterprise In addition the financing of accountsreceivable can also be introduced as a third-party guaranteeby logistics companies as shown in Figure 2

rough accounts receivable financing small- andmedium-sized enterprises not only obtain funds withoutincreasing their own liabilities but also improve the com-panyrsquos asset-liability structure When the sales volume in-creases the company can directly convert a large number ofsales invoices into funds with higher flexibility and the costis relatively low At the same time accounts receivable alsohave the characteristics of short financing time and highefficiency e active financing of accounts receivable canalso strengthen internal management and make decision-making more scientific

22 Supply Chain Financial Risks As a financial innovationthe risks faced by supply chain finance include credit riskfinancial risk operational risk warehouse receipt pledgerisk and information transmission risk erefore how toeffectively identify and prevent these risks is the key to thesuccess of supply chain finance

221 Different Risk Assessment Objects Under the tradi-tional credit model the bank pays attention to the staticfinancial data of a single enterprise which is the possibility ofdefault caused by the enterprise itself But in the context ofsupply chain risk is affected not only by the enterprisersquos ownrisk factors but also by the supply chain [21] ereforeunder the supply chain financing model banks should notonly pay attention to the risk factors of SMEs themselves butalso examine the overall operational performance of thesupply chain in which the SMEs are located so as to morecomprehensively systematically and objectively reflect thecomprehensive credit status of SMEs in the supply chain

222 Different Degree of Risk Aggregation Traditionalcredit risk is the risk faced by a single enterprise itself supplychain financial risk is distributed across the entire supplychain centered on the core enterprise [22] Once a member

Pledge storage Valuation Credit

guarantee

Where tosupervise

Financing mode

Logistics Services

Bank credit

Chattel pledgeIssue goods

SMEs

Payment of

funds

Core business

Repurchaseagreement

Commercial bankAsset

valuationLogisticscompany

Based on mobile

commoditystorage

Use third-partylogistics

companies aslinks

Stability ofpledge

Tradingpartners

Supply chainstatus

Figure 1 Schematic diagram of financial services industry financing and warehouse model

Complexity 3

of the supply chain has a financing problem the impact willspread to the entire supply chain

In view of the stability of the model this paper choosesprincipal component analysis as the scoring method Sincethe model has no assumptions about the distribution ofvariables it is not required to assume that the indicators havea multivariate normal distribution e specific evaluationprinciples are as follows

① Select a sample to score the indicators and scoringstandards in the above list and each indicator isdenoted by b List the initial data matrix

X xab1113864 1113865 (1)

② Standardize the initial data the standardized for-mula is

Zij Xij minus Xj

11138681113868111386811138681113868

11138681113868111386811138681113868

Sj

(2)

e standardized matrix is obtained afterstandardization

Z zab1113864 1113865 (3)

③ Calculate the correlation coefficient between everytwo indicators in the standardized matrix andobtain the correlation coefficient matrix R

R Z middot Zprimea minus 1

(4)

④ Select the first m principal components with avariance contribution rate of more than 60 andset the selected i-th principal component as Pi

P1 007Clowast1 minus 0057C

lowast2 + 0017C

lowast3 minus 0021C

lowast4 minus 0056C

lowast5

+ 0087Clowast6 + 0011C

lowast7

(5)

P2 minus0043Clowast1 minus 0051C

lowast2 minus 0014C

lowast3 minus 0023C

lowast4 + 0051C

lowast5

minus 0054Clowast6 minus 0014C

lowast7

(6)

P3 0034Clowast1 minus 0037C

lowast2 + 0022C

lowast3 minus 0031C

lowast4 minus 0086C

lowast5

minus 0086Clowast6 + 0013C

lowast7

(7)

P4 0066Clowast1 + 0052C

lowast2 minus 0016C

lowast3 + 0025C

lowast4 minus 0055C

lowast5

minus 0092Clowast6 + 0013C

lowast7

(8)

23 Financial Service Industry Innovation in the Internet ofings Environment e Internet of ings provides theconditions for ldquovisual trackingrdquo Manufacturing companiesmainly apply the Internet of ings to the ldquosupply chainmanagementrdquo of the enterprise Financial products inno-vative products in this area are called ldquosupply chain

Cloud platform

Applicationlayer

Networklayer

Perceptionlayer

Smart life

Smart officeSmart

communication Smartconnection

Internet

Networkmanagement Nodes

Gateway

Server

Communicationunit

Sensor Smart device

Core business

Repurchaseagreement

GuaranteeCommercial bank

Issuegoods

SMEs Accountsreceivable

Fundpayment Logistics

Services

Assetvaluation

Logisticscompany

Figure 2 Schematic diagram of accounts receivable financing model under the Internet of ings environment

4 Complexity

financerdquo From the perspective of financial institutionsproviding services supply chain finance refers to financialinstitutions and other third-party service institutions thatcomprehensively grant credit to the entire supply chain ofthe industry

e visual tracking provided by the Internet of ingsfor financial service industry innovation can solve the abovethree problems e process of optimizing supply chainfinancing is shown in Figure 3 e Internet of ings canrealize that the product information flow of enterprises inthe production and circulation fields is completely flowed tocommercial banks and commercial banks can have acomprehensive understanding and timely monitoring ofenterprises which saves the time for banks to conduct creditinvestigations on enterprises again and speeds up loansWith the speed of approval the capital flow of commercialbanks to the production and circulation fields is fast and safeWith complete information commercial banks can studynew financial tools and new business forms realize financialtool innovation and extend loans to more types of supplychain financial inventory demanders that can also servemore core enterprises at the same time commercial banksuse the Internet of ings to fully grasp the production andoperation of enterprises after loans so as to facilitate timelydetection and identification of risks risk control andmanagement and improve the efficiency of managementafter loans It can also rely on the construction of the Internetof ings electronic technology platform to identify cus-tomers more quickly provide more accurate services and atthe same time broaden its electronic sales promotionchannels to achieve innovation in transaction methods

3 The Financial Service Industry Supply ChainFinancial Inventory Portfolio PledgeFinancing Risk Analysis

31 Risk Analysis of Financial Inventory Pledge in a Single-Variety Supply Chain

311 Basic Analysis of Financial Inventory Pledges in aSingle-Variety Supply Chain As shown in Figure 4 the priceof rebar futures and palm oil futures fluctuated sharply in 48months e price trend of rebar and palm oil shows anoscillating trend During the observation period the averagevalue of the rebar future contractrsquos median price was2700 yuanton the highest price was 5000 yuanton andthe lowest price was 2500 yuanton e median price ofpalm oil future contracts is 2800 yuanton the highest priceis 3650 yuanton and the lowest price is 2500 yuanton Sucha high level of volatility indicates that there is a great po-tential risk in the use of rebar and palm oil for supply chainfinancial inventory pledge financing

Because the data for CARCH model fitting must be astationary series as otherwise it will cause false regressionmaking the experimental results meaningless it is necessaryto test the stationarity of the time series of future prices toensure that there is no autocorrelation relationship betweenthe data and time and it is a stationary series At present themost commonly used method of sequence stationarity test is

the ADF unit root test which performs unit root test on thetime series of price changes of rebar and palm oil

e unit root test result of the price change series showsthat the t statistic value of the price change series of rebar isminus1901992 and the t statistic value of the price change seriesof palm oil is minus2198863 e result of the statistics obtainedis significantly less than the three confidence levelserefore the null hypothesis can be rejected and the pricechange sequence of rebar and palm oil does not have a unitroot and is stable e data can be used for the next GARCHfamily model fitting

312 GARCH Model Fitting of Single-Variety Supply ChainFinancial Inventory Pledge After determining that the pricechange sequences of the two collaterals are stationary thenext step is to start the fitting estimation of the GARCHfamily model First you check whether the price changesequence of rebar and palm oil follows a normal distributionas shown in Figure 5 It can be seen from the figure that theassumption that the price change sequence obeys a normaldistribution can be rejected

e average value of the price change series of palm oil isminus0000144 the standard deviation is 0009328 the skewnessis 0450833 and the skewness is greater than 0 indicatingthat the price series distribution of palm oil has a long righttail phenomenon

e GARCH model is constructed for the price changesequence of the two collaterals e GARCH family modelsmainly include the GARCH model the T-GARCH modeland the E-GARCH model is paper studies the con-struction of low-level GARCH (1 1) T-GARCH (1 1) andE-GARCH (1 1) models e GARCH fitting parameterdiagram of the rebar price change sequence and the GARCHfitting parameter diagram of the palm oil price change se-quence are shown in Figures 6 and 7 respectively

It can be seen from Figure 8 that under the confidence of005 the p values of the test results are all less than thecritical value of the F statistic indicating that the residualsequence after the GARCH model fitting has eliminated theARCH effect and the model can be used to predict the futureestimates of changes in volatility

32 Research on Liquidity Risk of Supply Chain FinancialInventory Portfolio Pledge It can be seen from Figure 9 thatthe distribution of supply chain financial inventory portfolioprice changes has long tails on both sides e kurtosis is291 indicating that the price change sequence has thecharacteristics of sharp peaks and thick tails

ree GARCHmodels are fitted to the sequence of supplychain financial inventory portfolio price changes and theresults are shown in Figure 10 It can be seen that p corre-sponding to the c value in the E-GARCH (1 1) andT-GARCH (1 1) models is greater than 005 and accordingto the AIC criterion it is decided to choose the GARCH (1 1)model

It can be seen from Figure 11 that the p values of thethree distributions all approach 0 indicating that theGARCH family model fits well e specific model to be

Complexity 5

selected requires an optimal comparison e parameters aand b respectively indicate the influence of external in-formation on the fluctuation of the price change sequenceand the length of time that the fluctuation of the initial pricechange sequence affects future price sequence fluctuationse larger the value the greater the impact Combined withthe principle of the smallest value of AIC and SC the resultshows that the t distribution is most in line with thecharacteristics of the supply chain financial inventoryportfolio price change sequence

Internet of things masters

business operations

Relying on IoT platform

construction

Identify customers faster

Broaden its electronicsales promotion channels

Realize transactioninnovation

Financial servicesindustry Commercial bank

Cash flowIntegration of logistics information

flow and capital flow

Improve productionefficiency

Improve capitalefficiency

Informationflow

Supply chainfinance

Sales Warehouse Transport

Improve cash flow

Reduce related capitalcosts

Reduce inventory to optimizeworking capital management

Extension of accountspayable turnover days

Corebusiness

Financing guarantee

for upstream anddownstream SMEs

Cash flow

Informationflow

Productionarea

Logistics

Circulation field

Figure 3 Financial commercial bank service process

RebarPalm oil

2500

3000

3500

4000

4500

5000

Pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 4 Trends of rebar median price and palm oil median price

0 5 10 15 20 25 30 35 40 45 50Timemonth

Palm oilRebar

30

60

90

120

150

180

Pric

e

Figure 5 Normal distribution test of rebar and palm oil pricechange series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash6Fitted value

p

c

a

b

AIC

SC

Figure 6 GARCH fitting parameter diagram of rebar price changesequence

6 Complexity

4 Empirical Analysis of the DynamicMechanism of Financial ProductsrsquoSustainable Innovation

41 Sample Selection and Data Processing

411 Sample Size Under a certain sampling method thelarger the sample size the higher the estimation accuracy

but considering the actual situation it is impossible toconduct a study without capacity

Because the survey uses the existing PBC mail net-work it is basically not limited by fees the survey isconducted between subordinates managing depart-ments and managed departments and questionnairessuch as ldquono answerrdquo and ldquoinvalidrdquo basically do not existIt is carried out in high-quality population the overallsample is very similar extreme differences basically donot exist and the degree of sample heterogeneity can beignored

412 Data Processing e questionnaire was sent to thecentral branch of the Peoplersquos Bank of China through theLotus system of the Peoplersquos Bank of China Each branch wasrequired to complete 77 questionnaires A regional branchoffice itself also completed 16 questionnaires and the cor-responding financial service institutions completed 15questionnaires e CVR inspection data is shown inFigure 12

0

30

60

90

120

Fina

ncia

l inv

ento

ry

port

folio

pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 9 Normal distribution test of supply chain financial in-ventory portfolio price change rate series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 10 GARCH model fitting parameter diagram of supplychain financial inventory portfolio price change sequence

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Normal distributionT distributionGED distribution

Figure 11 e result of the hypothetical parameter results of thesequence distribution of supply chain financial inventory portfolioprice changes

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 7 GARCH fitting parameter diagram of palm oil pricechange series

F-statistic ObslowastR-squared

ProbF (1 844) ProbChi-Square (1)

RebarPalm oil

0

05

1

15

Fitti

ng re

sidua

l

Figure 8 ARCH LM test results of the volatility fitting residuals ofthe rebar and palm oil price change series

Complexity 7

42 Fuzzy Comprehensive Evaluation of ContinuousInnovation Power

421 CVR Determination Index e content validity ofeach index is tested based on the test data of CVR and thetest results are shown in Figure 13 e CVR test values inthe figure are all greater than 05 that is it is believed that themeasured index can well represent the number of people inthe scope of the measurement object exceeding 75 of thetotal number of people erefore the basic clear indicatorscan more accurately reflect the content of the continuousinnovation power of financial service industry products in acertain area and can be used as indicators for evaluating thecontinuous innovation power of financial service industryproducts

422 AHP Determines the Weight Analytic hierarchyprocess (AHP) is a method to deal with economic man-agement and technical issues with complex factors It de-composes a complex problem into multiple components andfurther decomposes these factors according to the domi-nance relationship and arranges them according to the targetlevel the criterion level and the index level to form amultiobjective multilevel model

e analytic hierarchy process is essentially a way ofdecision-making thinking It decomposes complex decision-making problems into different components according tothe nature of the problem and the general goal required toachieve and then integrates the judgment of people to de-termine the overall order of the relative importance of thefactors in decision-making e analytic hierarchy processembodies the basic way of decision-making thinkingnamely decomposition judgment and synthesis e basicidea is to transform the overall judgment of the weights ofmultiple factors that make up a complex problem into aldquopairwise comparisonrdquo of these factors and then to judge theoverall weight of these elements and finally establish theweight of each factor RI value is shown in Figure 14

Using the basic principles of AHP the data obtained by120times1067 comparisons of indicators according to Sattyrsquosscale are written in matrix form and the eigenvalues and

eigenvectors are calculated by Matlab and the eigenvectorscorresponding to the λmax eigenvalues are normalizedFinally the normalized vectors at all levels are integrated todetermine the final weight of each subindicator

5 Conclusion

e risk value of a supply chain financial inventory com-bination using rebar and palm oil under all set pledge pe-riods or confidence levels is less than the corresponding riskvalue of a single variety of supply chain financial inventorypledge of rebar or palm oil is shows that in the supplychain financial inventory pledge financing business thesupply chain financial inventory portfolio composed ofsupply chain financial inventory products that are negativelycorrelated with market price fluctuations is used as thepledge which can effectively lower banks and other supplychain financial institutions e calculation results of thepledge rate model established based on LA-VAR show thatwhen the supply chain financial inventory combinationconstructed by rebar and palm oil is used as pledge thepledge rate is inversely proportional to the pledge periode longer the pledge time the lower pledge rate of theinventory portfolio but the higher the pledge rate when thecorresponding single species is used as pledge under anycircumstances is means that small- and medium-sizedenterprises can use a combination of supply chain financialinventory pledge financing strategies with a negative rela-tionship compared to only using a single-variety supplychain financial inventory pledge and can obtain more fundsunder the same overall value of the pledge e risks borneby banks and other financial institutions have not increased

06

07

08

09

1

CVR

test

valu

e

10 15 20 25 305Index number

Figure 13 CVR test value

46

5

28

2lt 1

18

Very goodBetterIt is good

Not goodPoorVery bad

Figure 12 Proportion of CVR inspection data

0608

112141618

222

RI10 15 20 25 30 35 40 45 50 55 605

Order n

Figure 14 Average consistency index

8 Complexity

According to the basic principles of fuzzy mathematics theevaluation index is determined according to KPI⟶CVRwhich is used for index content validity test⟶AHP de-termining index weight e 1067 pieces of data obtainedfrom the provincersquos survey and the 120times1067 evaluationindex pairwise comparison data are substituted into themodel

Data Availability

Data sharing is not applicable to this article as no datasetswere generated or analyzed during the current study

Conflicts of Interest

e authors declare that there are no conflicts of interest

Authorsrsquo Contributions

All authors have approved the publication of themanuscript

References

[1] W Viriyasitavat L D Xu Z Bi and V PungpapongldquoBlockchain and internet of things for modern businessprocess in digital economy-the state of the artrdquo IEEETransactions on Computational Social Systems vol 6 no 6pp 1420ndash1432 2019

[2] B Li and Y Li ldquoInternet of things drives supply chain in-novation a research frameworkrdquo International Journal ofOrganizational Innovation vol 9 no 3 pp 71ndash92 2017

[3] S Cockcroft and M Russell ldquoBig data opportunities for ac-counting and finance practice and researchrdquo Australian Ac-counting Review vol 28 no 3 pp 323ndash333 2018

[4] D Knezevic ldquoImpact of blockchain technology platform inchanging the financial sector and other industriesrdquo Mon-tenegrin Journal of Economics vol 14 no 1 pp 109ndash120 2018

[5] J H Nord A Koohang and J Paliszkiewicz ldquoe internet ofthings review and theoretical frameworkrdquo Expert Systemswith Applications vol 133 pp 97ndash108 2019

[6] K Leong and A Sung ldquoFinTech (financial technology) whatis it and how to use technologies to create business value infintech wayrdquo International Journal of Innovation Manage-ment and Technology vol 9 no 2 pp 74ndash78 2018

[7] M Papert and A Pflaum ldquoDevelopment of an ecosystemmodel for the realization of internet of things (IoT) services insupply chain managementrdquo Electronic Markets vol 27 no 2pp 175ndash189 2017

[8] P A Pavlou ldquoInternet of things-will humans be replaced oraugmentedrdquoGfKMarketing Intelligence Review vol 10 no 2pp 42ndash47 2018

[9] A S Wilner ldquoCybersecurity and its discontents artificialintelligence the internet of things and digital misinforma-tionrdquo International Journal Canadarsquos Journal of Global PolicyAnalysis vol 73 no 2 pp 308ndash316 2018

[10] R K R Kummitha and N Crutzen ldquoSmart cities and thecitizen-driven internet of things a qualitative inquiry into anemerging smart cityrdquo Technological Forecasting and SocialChange vol 140 pp 44ndash53 2019

[11] T Tang and A T-K Ho ldquoA path-dependence perspective onthe adoption of internet of things evidence from earlyadopters of smart and connected sensors in the United Statesrdquo

Government Information Quarterly vol 36 no 2 pp 321ndash332 2019

[12] C K M Lee S Z Zhang and K K H Ng ldquoDevelopment ofan industrial internet of things suite for smart factory towardsre-industrializationrdquoAdvances inManufacturing vol 5 no 4pp 335ndash343 2017

[13] I Salami ldquoTerrorism financing with virtual currencies canregulatory technology solutions combat thisrdquo Studies inConflict amp Terrorism vol 41 no 12 pp 968ndash989 2018

[14] L Abualigah and A Diabat ldquoA comprehensive survey of theGrasshopper optimization algorithm results variants andapplicationsrdquo Neural Computing and Applications vol 32pp 1ndash24 2020

[15] E Manavalan and K Jayakrishna ldquoA review of internet ofthings (IoT) embedded sustainable supply chain for industry40 requirementsrdquo Computers amp Industrial Engineeringvol 127 pp 925ndash953 2019

[16] H Wang C Guo and S Cheng ldquoLoC-a new financial loanmanagement system based on smart contractsrdquo FutureGeneration Computer Systems vol 100 pp 648ndash655 2019

[17] S H Lim D J Kim Y Hur and K Park ldquoAn empirical studyof the impacts of perceived security and knowledge oncontinuous intention to use mobile Fintech payment ser-vicesrdquo International Journal of Human-Computer Interactionvol 35 no 10 pp 886ndash898 2019

[18] P K Ozili ldquoImpact of digital finance on financial inclusionand stabilityrdquo Borsa Istanbul Review vol 18 no 4pp 329ndash340 2018

[19] M Yao H Di X Zheng and X Xu ldquoImpact of paymenttechnology innovations on the traditional financial industry afocus on Chinardquo Technological Forecasting and Social Changevol 135 pp 199ndash207 2018

[20] Z Tan Q Tan andM Rong ldquoAnalysis on the financing statusof PV industry in China and the ways of improvementrdquoRenewable and Sustainable Energy Reviews vol 93 pp 409ndash420 2018

[21] Z Mani and I Chouk ldquoConsumer resistance to innovation inservices challenges and barriers in the internet of things erardquoJournal of Product Innovation Management vol 35 no 5pp 780ndash807 2018

[22] L Abualigah ldquoGroup search optimizer a nature-inspiredmeta-heuristic optimization algorithm with its results vari-ants and applicationsrdquo Neural Computing and Applicationsvol 33 pp 1ndash24 2020

Complexity 9

difficulties through guarantees or promises of repurchasee financing mode of financing can ensure that SMEs havea stable source of supply or sales channels and establish goodcooperative relations with core enterprises With goodwarehousing logistics and evaluation conditions Rong-tongcang not only assists small- and medium-sized enter-prises to obtain financing support with the pledge ofmovable properties stored in Rongtongcang but also helpsbanks as pledgers solve the problems of pledge valuationsupervision and auction In short through the financingand warehouse model the movable properties of small- andmedium-sized enterprises that banks were not willing toaccept in the past are transformed into pledged movableproperties that they are willing to accept thus serving as anew bridge between small- and medium-sized enterprisesand banks for financing

213 Accounts Receivable Financing Model Specificallyaccounts receivable financing refers to the conditionaltransfer of accounts receivable formed by credit sales by thedemander of funds to a special financing institution whichwill provide enterprises with financing debt recovery andsales account management so that companies that use creditsales as the main method can obtain the necessary funds andstrengthen the turnover of funds Generally accounts re-ceivable financing uses accounts receivable vouchers as thesubject matter pledge or transfer and the term does notexceed the age of the accounts receivable e main entitiesare small- and medium-sized enterprises (debt enterprises)core enterprises (debt enterprises) and commercial banksAccording to the different sources of repayment the fi-nancing model of accounts receivable is divided into pledgeof accounts receivable and transfer of accounts receivablee first source of repayment for pledge of accounts re-ceivable is the sales income of creditor companies and thesecond source of repayment is the accounts receivable paidby the debtor company and the first source of repayment forthe transfer of accounts receivable is directly from the debtorcompany For the accounts receivable paid to the bank thesecond source of repayment is the sales revenue of the

creditor enterprise In addition the financing of accountsreceivable can also be introduced as a third-party guaranteeby logistics companies as shown in Figure 2

rough accounts receivable financing small- andmedium-sized enterprises not only obtain funds withoutincreasing their own liabilities but also improve the com-panyrsquos asset-liability structure When the sales volume in-creases the company can directly convert a large number ofsales invoices into funds with higher flexibility and the costis relatively low At the same time accounts receivable alsohave the characteristics of short financing time and highefficiency e active financing of accounts receivable canalso strengthen internal management and make decision-making more scientific

22 Supply Chain Financial Risks As a financial innovationthe risks faced by supply chain finance include credit riskfinancial risk operational risk warehouse receipt pledgerisk and information transmission risk erefore how toeffectively identify and prevent these risks is the key to thesuccess of supply chain finance

221 Different Risk Assessment Objects Under the tradi-tional credit model the bank pays attention to the staticfinancial data of a single enterprise which is the possibility ofdefault caused by the enterprise itself But in the context ofsupply chain risk is affected not only by the enterprisersquos ownrisk factors but also by the supply chain [21] ereforeunder the supply chain financing model banks should notonly pay attention to the risk factors of SMEs themselves butalso examine the overall operational performance of thesupply chain in which the SMEs are located so as to morecomprehensively systematically and objectively reflect thecomprehensive credit status of SMEs in the supply chain

222 Different Degree of Risk Aggregation Traditionalcredit risk is the risk faced by a single enterprise itself supplychain financial risk is distributed across the entire supplychain centered on the core enterprise [22] Once a member

Pledge storage Valuation Credit

guarantee

Where tosupervise

Financing mode

Logistics Services

Bank credit

Chattel pledgeIssue goods

SMEs

Payment of

funds

Core business

Repurchaseagreement

Commercial bankAsset

valuationLogisticscompany

Based on mobile

commoditystorage

Use third-partylogistics

companies aslinks

Stability ofpledge

Tradingpartners

Supply chainstatus

Figure 1 Schematic diagram of financial services industry financing and warehouse model

Complexity 3

of the supply chain has a financing problem the impact willspread to the entire supply chain

In view of the stability of the model this paper choosesprincipal component analysis as the scoring method Sincethe model has no assumptions about the distribution ofvariables it is not required to assume that the indicators havea multivariate normal distribution e specific evaluationprinciples are as follows

① Select a sample to score the indicators and scoringstandards in the above list and each indicator isdenoted by b List the initial data matrix

X xab1113864 1113865 (1)

② Standardize the initial data the standardized for-mula is

Zij Xij minus Xj

11138681113868111386811138681113868

11138681113868111386811138681113868

Sj

(2)

e standardized matrix is obtained afterstandardization

Z zab1113864 1113865 (3)

③ Calculate the correlation coefficient between everytwo indicators in the standardized matrix andobtain the correlation coefficient matrix R

R Z middot Zprimea minus 1

(4)

④ Select the first m principal components with avariance contribution rate of more than 60 andset the selected i-th principal component as Pi

P1 007Clowast1 minus 0057C

lowast2 + 0017C

lowast3 minus 0021C

lowast4 minus 0056C

lowast5

+ 0087Clowast6 + 0011C

lowast7

(5)

P2 minus0043Clowast1 minus 0051C

lowast2 minus 0014C

lowast3 minus 0023C

lowast4 + 0051C

lowast5

minus 0054Clowast6 minus 0014C

lowast7

(6)

P3 0034Clowast1 minus 0037C

lowast2 + 0022C

lowast3 minus 0031C

lowast4 minus 0086C

lowast5

minus 0086Clowast6 + 0013C

lowast7

(7)

P4 0066Clowast1 + 0052C

lowast2 minus 0016C

lowast3 + 0025C

lowast4 minus 0055C

lowast5

minus 0092Clowast6 + 0013C

lowast7

(8)

23 Financial Service Industry Innovation in the Internet ofings Environment e Internet of ings provides theconditions for ldquovisual trackingrdquo Manufacturing companiesmainly apply the Internet of ings to the ldquosupply chainmanagementrdquo of the enterprise Financial products inno-vative products in this area are called ldquosupply chain

Cloud platform

Applicationlayer

Networklayer

Perceptionlayer

Smart life

Smart officeSmart

communication Smartconnection

Internet

Networkmanagement Nodes

Gateway

Server

Communicationunit

Sensor Smart device

Core business

Repurchaseagreement

GuaranteeCommercial bank

Issuegoods

SMEs Accountsreceivable

Fundpayment Logistics

Services

Assetvaluation

Logisticscompany

Figure 2 Schematic diagram of accounts receivable financing model under the Internet of ings environment

4 Complexity

financerdquo From the perspective of financial institutionsproviding services supply chain finance refers to financialinstitutions and other third-party service institutions thatcomprehensively grant credit to the entire supply chain ofthe industry

e visual tracking provided by the Internet of ingsfor financial service industry innovation can solve the abovethree problems e process of optimizing supply chainfinancing is shown in Figure 3 e Internet of ings canrealize that the product information flow of enterprises inthe production and circulation fields is completely flowed tocommercial banks and commercial banks can have acomprehensive understanding and timely monitoring ofenterprises which saves the time for banks to conduct creditinvestigations on enterprises again and speeds up loansWith the speed of approval the capital flow of commercialbanks to the production and circulation fields is fast and safeWith complete information commercial banks can studynew financial tools and new business forms realize financialtool innovation and extend loans to more types of supplychain financial inventory demanders that can also servemore core enterprises at the same time commercial banksuse the Internet of ings to fully grasp the production andoperation of enterprises after loans so as to facilitate timelydetection and identification of risks risk control andmanagement and improve the efficiency of managementafter loans It can also rely on the construction of the Internetof ings electronic technology platform to identify cus-tomers more quickly provide more accurate services and atthe same time broaden its electronic sales promotionchannels to achieve innovation in transaction methods

3 The Financial Service Industry Supply ChainFinancial Inventory Portfolio PledgeFinancing Risk Analysis

31 Risk Analysis of Financial Inventory Pledge in a Single-Variety Supply Chain

311 Basic Analysis of Financial Inventory Pledges in aSingle-Variety Supply Chain As shown in Figure 4 the priceof rebar futures and palm oil futures fluctuated sharply in 48months e price trend of rebar and palm oil shows anoscillating trend During the observation period the averagevalue of the rebar future contractrsquos median price was2700 yuanton the highest price was 5000 yuanton andthe lowest price was 2500 yuanton e median price ofpalm oil future contracts is 2800 yuanton the highest priceis 3650 yuanton and the lowest price is 2500 yuanton Sucha high level of volatility indicates that there is a great po-tential risk in the use of rebar and palm oil for supply chainfinancial inventory pledge financing

Because the data for CARCH model fitting must be astationary series as otherwise it will cause false regressionmaking the experimental results meaningless it is necessaryto test the stationarity of the time series of future prices toensure that there is no autocorrelation relationship betweenthe data and time and it is a stationary series At present themost commonly used method of sequence stationarity test is

the ADF unit root test which performs unit root test on thetime series of price changes of rebar and palm oil

e unit root test result of the price change series showsthat the t statistic value of the price change series of rebar isminus1901992 and the t statistic value of the price change seriesof palm oil is minus2198863 e result of the statistics obtainedis significantly less than the three confidence levelserefore the null hypothesis can be rejected and the pricechange sequence of rebar and palm oil does not have a unitroot and is stable e data can be used for the next GARCHfamily model fitting

312 GARCH Model Fitting of Single-Variety Supply ChainFinancial Inventory Pledge After determining that the pricechange sequences of the two collaterals are stationary thenext step is to start the fitting estimation of the GARCHfamily model First you check whether the price changesequence of rebar and palm oil follows a normal distributionas shown in Figure 5 It can be seen from the figure that theassumption that the price change sequence obeys a normaldistribution can be rejected

e average value of the price change series of palm oil isminus0000144 the standard deviation is 0009328 the skewnessis 0450833 and the skewness is greater than 0 indicatingthat the price series distribution of palm oil has a long righttail phenomenon

e GARCH model is constructed for the price changesequence of the two collaterals e GARCH family modelsmainly include the GARCH model the T-GARCH modeland the E-GARCH model is paper studies the con-struction of low-level GARCH (1 1) T-GARCH (1 1) andE-GARCH (1 1) models e GARCH fitting parameterdiagram of the rebar price change sequence and the GARCHfitting parameter diagram of the palm oil price change se-quence are shown in Figures 6 and 7 respectively

It can be seen from Figure 8 that under the confidence of005 the p values of the test results are all less than thecritical value of the F statistic indicating that the residualsequence after the GARCH model fitting has eliminated theARCH effect and the model can be used to predict the futureestimates of changes in volatility

32 Research on Liquidity Risk of Supply Chain FinancialInventory Portfolio Pledge It can be seen from Figure 9 thatthe distribution of supply chain financial inventory portfolioprice changes has long tails on both sides e kurtosis is291 indicating that the price change sequence has thecharacteristics of sharp peaks and thick tails

ree GARCHmodels are fitted to the sequence of supplychain financial inventory portfolio price changes and theresults are shown in Figure 10 It can be seen that p corre-sponding to the c value in the E-GARCH (1 1) andT-GARCH (1 1) models is greater than 005 and accordingto the AIC criterion it is decided to choose the GARCH (1 1)model

It can be seen from Figure 11 that the p values of thethree distributions all approach 0 indicating that theGARCH family model fits well e specific model to be

Complexity 5

selected requires an optimal comparison e parameters aand b respectively indicate the influence of external in-formation on the fluctuation of the price change sequenceand the length of time that the fluctuation of the initial pricechange sequence affects future price sequence fluctuationse larger the value the greater the impact Combined withthe principle of the smallest value of AIC and SC the resultshows that the t distribution is most in line with thecharacteristics of the supply chain financial inventoryportfolio price change sequence

Internet of things masters

business operations

Relying on IoT platform

construction

Identify customers faster

Broaden its electronicsales promotion channels

Realize transactioninnovation

Financial servicesindustry Commercial bank

Cash flowIntegration of logistics information

flow and capital flow

Improve productionefficiency

Improve capitalefficiency

Informationflow

Supply chainfinance

Sales Warehouse Transport

Improve cash flow

Reduce related capitalcosts

Reduce inventory to optimizeworking capital management

Extension of accountspayable turnover days

Corebusiness

Financing guarantee

for upstream anddownstream SMEs

Cash flow

Informationflow

Productionarea

Logistics

Circulation field

Figure 3 Financial commercial bank service process

RebarPalm oil

2500

3000

3500

4000

4500

5000

Pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 4 Trends of rebar median price and palm oil median price

0 5 10 15 20 25 30 35 40 45 50Timemonth

Palm oilRebar

30

60

90

120

150

180

Pric

e

Figure 5 Normal distribution test of rebar and palm oil pricechange series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash6Fitted value

p

c

a

b

AIC

SC

Figure 6 GARCH fitting parameter diagram of rebar price changesequence

6 Complexity

4 Empirical Analysis of the DynamicMechanism of Financial ProductsrsquoSustainable Innovation

41 Sample Selection and Data Processing

411 Sample Size Under a certain sampling method thelarger the sample size the higher the estimation accuracy

but considering the actual situation it is impossible toconduct a study without capacity

Because the survey uses the existing PBC mail net-work it is basically not limited by fees the survey isconducted between subordinates managing depart-ments and managed departments and questionnairessuch as ldquono answerrdquo and ldquoinvalidrdquo basically do not existIt is carried out in high-quality population the overallsample is very similar extreme differences basically donot exist and the degree of sample heterogeneity can beignored

412 Data Processing e questionnaire was sent to thecentral branch of the Peoplersquos Bank of China through theLotus system of the Peoplersquos Bank of China Each branch wasrequired to complete 77 questionnaires A regional branchoffice itself also completed 16 questionnaires and the cor-responding financial service institutions completed 15questionnaires e CVR inspection data is shown inFigure 12

0

30

60

90

120

Fina

ncia

l inv

ento

ry

port

folio

pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 9 Normal distribution test of supply chain financial in-ventory portfolio price change rate series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 10 GARCH model fitting parameter diagram of supplychain financial inventory portfolio price change sequence

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Normal distributionT distributionGED distribution

Figure 11 e result of the hypothetical parameter results of thesequence distribution of supply chain financial inventory portfolioprice changes

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 7 GARCH fitting parameter diagram of palm oil pricechange series

F-statistic ObslowastR-squared

ProbF (1 844) ProbChi-Square (1)

RebarPalm oil

0

05

1

15

Fitti

ng re

sidua

l

Figure 8 ARCH LM test results of the volatility fitting residuals ofthe rebar and palm oil price change series

Complexity 7

42 Fuzzy Comprehensive Evaluation of ContinuousInnovation Power

421 CVR Determination Index e content validity ofeach index is tested based on the test data of CVR and thetest results are shown in Figure 13 e CVR test values inthe figure are all greater than 05 that is it is believed that themeasured index can well represent the number of people inthe scope of the measurement object exceeding 75 of thetotal number of people erefore the basic clear indicatorscan more accurately reflect the content of the continuousinnovation power of financial service industry products in acertain area and can be used as indicators for evaluating thecontinuous innovation power of financial service industryproducts

422 AHP Determines the Weight Analytic hierarchyprocess (AHP) is a method to deal with economic man-agement and technical issues with complex factors It de-composes a complex problem into multiple components andfurther decomposes these factors according to the domi-nance relationship and arranges them according to the targetlevel the criterion level and the index level to form amultiobjective multilevel model

e analytic hierarchy process is essentially a way ofdecision-making thinking It decomposes complex decision-making problems into different components according tothe nature of the problem and the general goal required toachieve and then integrates the judgment of people to de-termine the overall order of the relative importance of thefactors in decision-making e analytic hierarchy processembodies the basic way of decision-making thinkingnamely decomposition judgment and synthesis e basicidea is to transform the overall judgment of the weights ofmultiple factors that make up a complex problem into aldquopairwise comparisonrdquo of these factors and then to judge theoverall weight of these elements and finally establish theweight of each factor RI value is shown in Figure 14

Using the basic principles of AHP the data obtained by120times1067 comparisons of indicators according to Sattyrsquosscale are written in matrix form and the eigenvalues and

eigenvectors are calculated by Matlab and the eigenvectorscorresponding to the λmax eigenvalues are normalizedFinally the normalized vectors at all levels are integrated todetermine the final weight of each subindicator

5 Conclusion

e risk value of a supply chain financial inventory com-bination using rebar and palm oil under all set pledge pe-riods or confidence levels is less than the corresponding riskvalue of a single variety of supply chain financial inventorypledge of rebar or palm oil is shows that in the supplychain financial inventory pledge financing business thesupply chain financial inventory portfolio composed ofsupply chain financial inventory products that are negativelycorrelated with market price fluctuations is used as thepledge which can effectively lower banks and other supplychain financial institutions e calculation results of thepledge rate model established based on LA-VAR show thatwhen the supply chain financial inventory combinationconstructed by rebar and palm oil is used as pledge thepledge rate is inversely proportional to the pledge periode longer the pledge time the lower pledge rate of theinventory portfolio but the higher the pledge rate when thecorresponding single species is used as pledge under anycircumstances is means that small- and medium-sizedenterprises can use a combination of supply chain financialinventory pledge financing strategies with a negative rela-tionship compared to only using a single-variety supplychain financial inventory pledge and can obtain more fundsunder the same overall value of the pledge e risks borneby banks and other financial institutions have not increased

06

07

08

09

1

CVR

test

valu

e

10 15 20 25 305Index number

Figure 13 CVR test value

46

5

28

2lt 1

18

Very goodBetterIt is good

Not goodPoorVery bad

Figure 12 Proportion of CVR inspection data

0608

112141618

222

RI10 15 20 25 30 35 40 45 50 55 605

Order n

Figure 14 Average consistency index

8 Complexity

According to the basic principles of fuzzy mathematics theevaluation index is determined according to KPI⟶CVRwhich is used for index content validity test⟶AHP de-termining index weight e 1067 pieces of data obtainedfrom the provincersquos survey and the 120times1067 evaluationindex pairwise comparison data are substituted into themodel

Data Availability

Data sharing is not applicable to this article as no datasetswere generated or analyzed during the current study

Conflicts of Interest

e authors declare that there are no conflicts of interest

Authorsrsquo Contributions

All authors have approved the publication of themanuscript

References

[1] W Viriyasitavat L D Xu Z Bi and V PungpapongldquoBlockchain and internet of things for modern businessprocess in digital economy-the state of the artrdquo IEEETransactions on Computational Social Systems vol 6 no 6pp 1420ndash1432 2019

[2] B Li and Y Li ldquoInternet of things drives supply chain in-novation a research frameworkrdquo International Journal ofOrganizational Innovation vol 9 no 3 pp 71ndash92 2017

[3] S Cockcroft and M Russell ldquoBig data opportunities for ac-counting and finance practice and researchrdquo Australian Ac-counting Review vol 28 no 3 pp 323ndash333 2018

[4] D Knezevic ldquoImpact of blockchain technology platform inchanging the financial sector and other industriesrdquo Mon-tenegrin Journal of Economics vol 14 no 1 pp 109ndash120 2018

[5] J H Nord A Koohang and J Paliszkiewicz ldquoe internet ofthings review and theoretical frameworkrdquo Expert Systemswith Applications vol 133 pp 97ndash108 2019

[6] K Leong and A Sung ldquoFinTech (financial technology) whatis it and how to use technologies to create business value infintech wayrdquo International Journal of Innovation Manage-ment and Technology vol 9 no 2 pp 74ndash78 2018

[7] M Papert and A Pflaum ldquoDevelopment of an ecosystemmodel for the realization of internet of things (IoT) services insupply chain managementrdquo Electronic Markets vol 27 no 2pp 175ndash189 2017

[8] P A Pavlou ldquoInternet of things-will humans be replaced oraugmentedrdquoGfKMarketing Intelligence Review vol 10 no 2pp 42ndash47 2018

[9] A S Wilner ldquoCybersecurity and its discontents artificialintelligence the internet of things and digital misinforma-tionrdquo International Journal Canadarsquos Journal of Global PolicyAnalysis vol 73 no 2 pp 308ndash316 2018

[10] R K R Kummitha and N Crutzen ldquoSmart cities and thecitizen-driven internet of things a qualitative inquiry into anemerging smart cityrdquo Technological Forecasting and SocialChange vol 140 pp 44ndash53 2019

[11] T Tang and A T-K Ho ldquoA path-dependence perspective onthe adoption of internet of things evidence from earlyadopters of smart and connected sensors in the United Statesrdquo

Government Information Quarterly vol 36 no 2 pp 321ndash332 2019

[12] C K M Lee S Z Zhang and K K H Ng ldquoDevelopment ofan industrial internet of things suite for smart factory towardsre-industrializationrdquoAdvances inManufacturing vol 5 no 4pp 335ndash343 2017

[13] I Salami ldquoTerrorism financing with virtual currencies canregulatory technology solutions combat thisrdquo Studies inConflict amp Terrorism vol 41 no 12 pp 968ndash989 2018

[14] L Abualigah and A Diabat ldquoA comprehensive survey of theGrasshopper optimization algorithm results variants andapplicationsrdquo Neural Computing and Applications vol 32pp 1ndash24 2020

[15] E Manavalan and K Jayakrishna ldquoA review of internet ofthings (IoT) embedded sustainable supply chain for industry40 requirementsrdquo Computers amp Industrial Engineeringvol 127 pp 925ndash953 2019

[16] H Wang C Guo and S Cheng ldquoLoC-a new financial loanmanagement system based on smart contractsrdquo FutureGeneration Computer Systems vol 100 pp 648ndash655 2019

[17] S H Lim D J Kim Y Hur and K Park ldquoAn empirical studyof the impacts of perceived security and knowledge oncontinuous intention to use mobile Fintech payment ser-vicesrdquo International Journal of Human-Computer Interactionvol 35 no 10 pp 886ndash898 2019

[18] P K Ozili ldquoImpact of digital finance on financial inclusionand stabilityrdquo Borsa Istanbul Review vol 18 no 4pp 329ndash340 2018

[19] M Yao H Di X Zheng and X Xu ldquoImpact of paymenttechnology innovations on the traditional financial industry afocus on Chinardquo Technological Forecasting and Social Changevol 135 pp 199ndash207 2018

[20] Z Tan Q Tan andM Rong ldquoAnalysis on the financing statusof PV industry in China and the ways of improvementrdquoRenewable and Sustainable Energy Reviews vol 93 pp 409ndash420 2018

[21] Z Mani and I Chouk ldquoConsumer resistance to innovation inservices challenges and barriers in the internet of things erardquoJournal of Product Innovation Management vol 35 no 5pp 780ndash807 2018

[22] L Abualigah ldquoGroup search optimizer a nature-inspiredmeta-heuristic optimization algorithm with its results vari-ants and applicationsrdquo Neural Computing and Applicationsvol 33 pp 1ndash24 2020

Complexity 9

of the supply chain has a financing problem the impact willspread to the entire supply chain

In view of the stability of the model this paper choosesprincipal component analysis as the scoring method Sincethe model has no assumptions about the distribution ofvariables it is not required to assume that the indicators havea multivariate normal distribution e specific evaluationprinciples are as follows

① Select a sample to score the indicators and scoringstandards in the above list and each indicator isdenoted by b List the initial data matrix

X xab1113864 1113865 (1)

② Standardize the initial data the standardized for-mula is

Zij Xij minus Xj

11138681113868111386811138681113868

11138681113868111386811138681113868

Sj

(2)

e standardized matrix is obtained afterstandardization

Z zab1113864 1113865 (3)

③ Calculate the correlation coefficient between everytwo indicators in the standardized matrix andobtain the correlation coefficient matrix R

R Z middot Zprimea minus 1

(4)

④ Select the first m principal components with avariance contribution rate of more than 60 andset the selected i-th principal component as Pi

P1 007Clowast1 minus 0057C

lowast2 + 0017C

lowast3 minus 0021C

lowast4 minus 0056C

lowast5

+ 0087Clowast6 + 0011C

lowast7

(5)

P2 minus0043Clowast1 minus 0051C

lowast2 minus 0014C

lowast3 minus 0023C

lowast4 + 0051C

lowast5

minus 0054Clowast6 minus 0014C

lowast7

(6)

P3 0034Clowast1 minus 0037C

lowast2 + 0022C

lowast3 minus 0031C

lowast4 minus 0086C

lowast5

minus 0086Clowast6 + 0013C

lowast7

(7)

P4 0066Clowast1 + 0052C

lowast2 minus 0016C

lowast3 + 0025C

lowast4 minus 0055C

lowast5

minus 0092Clowast6 + 0013C

lowast7

(8)

23 Financial Service Industry Innovation in the Internet ofings Environment e Internet of ings provides theconditions for ldquovisual trackingrdquo Manufacturing companiesmainly apply the Internet of ings to the ldquosupply chainmanagementrdquo of the enterprise Financial products inno-vative products in this area are called ldquosupply chain

Cloud platform

Applicationlayer

Networklayer

Perceptionlayer

Smart life

Smart officeSmart

communication Smartconnection

Internet

Networkmanagement Nodes

Gateway

Server

Communicationunit

Sensor Smart device

Core business

Repurchaseagreement

GuaranteeCommercial bank

Issuegoods

SMEs Accountsreceivable

Fundpayment Logistics

Services

Assetvaluation

Logisticscompany

Figure 2 Schematic diagram of accounts receivable financing model under the Internet of ings environment

4 Complexity

financerdquo From the perspective of financial institutionsproviding services supply chain finance refers to financialinstitutions and other third-party service institutions thatcomprehensively grant credit to the entire supply chain ofthe industry

e visual tracking provided by the Internet of ingsfor financial service industry innovation can solve the abovethree problems e process of optimizing supply chainfinancing is shown in Figure 3 e Internet of ings canrealize that the product information flow of enterprises inthe production and circulation fields is completely flowed tocommercial banks and commercial banks can have acomprehensive understanding and timely monitoring ofenterprises which saves the time for banks to conduct creditinvestigations on enterprises again and speeds up loansWith the speed of approval the capital flow of commercialbanks to the production and circulation fields is fast and safeWith complete information commercial banks can studynew financial tools and new business forms realize financialtool innovation and extend loans to more types of supplychain financial inventory demanders that can also servemore core enterprises at the same time commercial banksuse the Internet of ings to fully grasp the production andoperation of enterprises after loans so as to facilitate timelydetection and identification of risks risk control andmanagement and improve the efficiency of managementafter loans It can also rely on the construction of the Internetof ings electronic technology platform to identify cus-tomers more quickly provide more accurate services and atthe same time broaden its electronic sales promotionchannels to achieve innovation in transaction methods

3 The Financial Service Industry Supply ChainFinancial Inventory Portfolio PledgeFinancing Risk Analysis

31 Risk Analysis of Financial Inventory Pledge in a Single-Variety Supply Chain

311 Basic Analysis of Financial Inventory Pledges in aSingle-Variety Supply Chain As shown in Figure 4 the priceof rebar futures and palm oil futures fluctuated sharply in 48months e price trend of rebar and palm oil shows anoscillating trend During the observation period the averagevalue of the rebar future contractrsquos median price was2700 yuanton the highest price was 5000 yuanton andthe lowest price was 2500 yuanton e median price ofpalm oil future contracts is 2800 yuanton the highest priceis 3650 yuanton and the lowest price is 2500 yuanton Sucha high level of volatility indicates that there is a great po-tential risk in the use of rebar and palm oil for supply chainfinancial inventory pledge financing

Because the data for CARCH model fitting must be astationary series as otherwise it will cause false regressionmaking the experimental results meaningless it is necessaryto test the stationarity of the time series of future prices toensure that there is no autocorrelation relationship betweenthe data and time and it is a stationary series At present themost commonly used method of sequence stationarity test is

the ADF unit root test which performs unit root test on thetime series of price changes of rebar and palm oil

e unit root test result of the price change series showsthat the t statistic value of the price change series of rebar isminus1901992 and the t statistic value of the price change seriesof palm oil is minus2198863 e result of the statistics obtainedis significantly less than the three confidence levelserefore the null hypothesis can be rejected and the pricechange sequence of rebar and palm oil does not have a unitroot and is stable e data can be used for the next GARCHfamily model fitting

312 GARCH Model Fitting of Single-Variety Supply ChainFinancial Inventory Pledge After determining that the pricechange sequences of the two collaterals are stationary thenext step is to start the fitting estimation of the GARCHfamily model First you check whether the price changesequence of rebar and palm oil follows a normal distributionas shown in Figure 5 It can be seen from the figure that theassumption that the price change sequence obeys a normaldistribution can be rejected

e average value of the price change series of palm oil isminus0000144 the standard deviation is 0009328 the skewnessis 0450833 and the skewness is greater than 0 indicatingthat the price series distribution of palm oil has a long righttail phenomenon

e GARCH model is constructed for the price changesequence of the two collaterals e GARCH family modelsmainly include the GARCH model the T-GARCH modeland the E-GARCH model is paper studies the con-struction of low-level GARCH (1 1) T-GARCH (1 1) andE-GARCH (1 1) models e GARCH fitting parameterdiagram of the rebar price change sequence and the GARCHfitting parameter diagram of the palm oil price change se-quence are shown in Figures 6 and 7 respectively

It can be seen from Figure 8 that under the confidence of005 the p values of the test results are all less than thecritical value of the F statistic indicating that the residualsequence after the GARCH model fitting has eliminated theARCH effect and the model can be used to predict the futureestimates of changes in volatility

32 Research on Liquidity Risk of Supply Chain FinancialInventory Portfolio Pledge It can be seen from Figure 9 thatthe distribution of supply chain financial inventory portfolioprice changes has long tails on both sides e kurtosis is291 indicating that the price change sequence has thecharacteristics of sharp peaks and thick tails

ree GARCHmodels are fitted to the sequence of supplychain financial inventory portfolio price changes and theresults are shown in Figure 10 It can be seen that p corre-sponding to the c value in the E-GARCH (1 1) andT-GARCH (1 1) models is greater than 005 and accordingto the AIC criterion it is decided to choose the GARCH (1 1)model

It can be seen from Figure 11 that the p values of thethree distributions all approach 0 indicating that theGARCH family model fits well e specific model to be

Complexity 5

selected requires an optimal comparison e parameters aand b respectively indicate the influence of external in-formation on the fluctuation of the price change sequenceand the length of time that the fluctuation of the initial pricechange sequence affects future price sequence fluctuationse larger the value the greater the impact Combined withthe principle of the smallest value of AIC and SC the resultshows that the t distribution is most in line with thecharacteristics of the supply chain financial inventoryportfolio price change sequence

Internet of things masters

business operations

Relying on IoT platform

construction

Identify customers faster

Broaden its electronicsales promotion channels

Realize transactioninnovation

Financial servicesindustry Commercial bank

Cash flowIntegration of logistics information

flow and capital flow

Improve productionefficiency

Improve capitalefficiency

Informationflow

Supply chainfinance

Sales Warehouse Transport

Improve cash flow

Reduce related capitalcosts

Reduce inventory to optimizeworking capital management

Extension of accountspayable turnover days

Corebusiness

Financing guarantee

for upstream anddownstream SMEs

Cash flow

Informationflow

Productionarea

Logistics

Circulation field

Figure 3 Financial commercial bank service process

RebarPalm oil

2500

3000

3500

4000

4500

5000

Pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 4 Trends of rebar median price and palm oil median price

0 5 10 15 20 25 30 35 40 45 50Timemonth

Palm oilRebar

30

60

90

120

150

180

Pric

e

Figure 5 Normal distribution test of rebar and palm oil pricechange series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash6Fitted value

p

c

a

b

AIC

SC

Figure 6 GARCH fitting parameter diagram of rebar price changesequence

6 Complexity

4 Empirical Analysis of the DynamicMechanism of Financial ProductsrsquoSustainable Innovation

41 Sample Selection and Data Processing

411 Sample Size Under a certain sampling method thelarger the sample size the higher the estimation accuracy

but considering the actual situation it is impossible toconduct a study without capacity

Because the survey uses the existing PBC mail net-work it is basically not limited by fees the survey isconducted between subordinates managing depart-ments and managed departments and questionnairessuch as ldquono answerrdquo and ldquoinvalidrdquo basically do not existIt is carried out in high-quality population the overallsample is very similar extreme differences basically donot exist and the degree of sample heterogeneity can beignored

412 Data Processing e questionnaire was sent to thecentral branch of the Peoplersquos Bank of China through theLotus system of the Peoplersquos Bank of China Each branch wasrequired to complete 77 questionnaires A regional branchoffice itself also completed 16 questionnaires and the cor-responding financial service institutions completed 15questionnaires e CVR inspection data is shown inFigure 12

0

30

60

90

120

Fina

ncia

l inv

ento

ry

port

folio

pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 9 Normal distribution test of supply chain financial in-ventory portfolio price change rate series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 10 GARCH model fitting parameter diagram of supplychain financial inventory portfolio price change sequence

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Normal distributionT distributionGED distribution

Figure 11 e result of the hypothetical parameter results of thesequence distribution of supply chain financial inventory portfolioprice changes

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 7 GARCH fitting parameter diagram of palm oil pricechange series

F-statistic ObslowastR-squared

ProbF (1 844) ProbChi-Square (1)

RebarPalm oil

0

05

1

15

Fitti

ng re

sidua

l

Figure 8 ARCH LM test results of the volatility fitting residuals ofthe rebar and palm oil price change series

Complexity 7

42 Fuzzy Comprehensive Evaluation of ContinuousInnovation Power

421 CVR Determination Index e content validity ofeach index is tested based on the test data of CVR and thetest results are shown in Figure 13 e CVR test values inthe figure are all greater than 05 that is it is believed that themeasured index can well represent the number of people inthe scope of the measurement object exceeding 75 of thetotal number of people erefore the basic clear indicatorscan more accurately reflect the content of the continuousinnovation power of financial service industry products in acertain area and can be used as indicators for evaluating thecontinuous innovation power of financial service industryproducts

422 AHP Determines the Weight Analytic hierarchyprocess (AHP) is a method to deal with economic man-agement and technical issues with complex factors It de-composes a complex problem into multiple components andfurther decomposes these factors according to the domi-nance relationship and arranges them according to the targetlevel the criterion level and the index level to form amultiobjective multilevel model

e analytic hierarchy process is essentially a way ofdecision-making thinking It decomposes complex decision-making problems into different components according tothe nature of the problem and the general goal required toachieve and then integrates the judgment of people to de-termine the overall order of the relative importance of thefactors in decision-making e analytic hierarchy processembodies the basic way of decision-making thinkingnamely decomposition judgment and synthesis e basicidea is to transform the overall judgment of the weights ofmultiple factors that make up a complex problem into aldquopairwise comparisonrdquo of these factors and then to judge theoverall weight of these elements and finally establish theweight of each factor RI value is shown in Figure 14

Using the basic principles of AHP the data obtained by120times1067 comparisons of indicators according to Sattyrsquosscale are written in matrix form and the eigenvalues and

eigenvectors are calculated by Matlab and the eigenvectorscorresponding to the λmax eigenvalues are normalizedFinally the normalized vectors at all levels are integrated todetermine the final weight of each subindicator

5 Conclusion

e risk value of a supply chain financial inventory com-bination using rebar and palm oil under all set pledge pe-riods or confidence levels is less than the corresponding riskvalue of a single variety of supply chain financial inventorypledge of rebar or palm oil is shows that in the supplychain financial inventory pledge financing business thesupply chain financial inventory portfolio composed ofsupply chain financial inventory products that are negativelycorrelated with market price fluctuations is used as thepledge which can effectively lower banks and other supplychain financial institutions e calculation results of thepledge rate model established based on LA-VAR show thatwhen the supply chain financial inventory combinationconstructed by rebar and palm oil is used as pledge thepledge rate is inversely proportional to the pledge periode longer the pledge time the lower pledge rate of theinventory portfolio but the higher the pledge rate when thecorresponding single species is used as pledge under anycircumstances is means that small- and medium-sizedenterprises can use a combination of supply chain financialinventory pledge financing strategies with a negative rela-tionship compared to only using a single-variety supplychain financial inventory pledge and can obtain more fundsunder the same overall value of the pledge e risks borneby banks and other financial institutions have not increased

06

07

08

09

1

CVR

test

valu

e

10 15 20 25 305Index number

Figure 13 CVR test value

46

5

28

2lt 1

18

Very goodBetterIt is good

Not goodPoorVery bad

Figure 12 Proportion of CVR inspection data

0608

112141618

222

RI10 15 20 25 30 35 40 45 50 55 605

Order n

Figure 14 Average consistency index

8 Complexity

According to the basic principles of fuzzy mathematics theevaluation index is determined according to KPI⟶CVRwhich is used for index content validity test⟶AHP de-termining index weight e 1067 pieces of data obtainedfrom the provincersquos survey and the 120times1067 evaluationindex pairwise comparison data are substituted into themodel

Data Availability

Data sharing is not applicable to this article as no datasetswere generated or analyzed during the current study

Conflicts of Interest

e authors declare that there are no conflicts of interest

Authorsrsquo Contributions

All authors have approved the publication of themanuscript

References

[1] W Viriyasitavat L D Xu Z Bi and V PungpapongldquoBlockchain and internet of things for modern businessprocess in digital economy-the state of the artrdquo IEEETransactions on Computational Social Systems vol 6 no 6pp 1420ndash1432 2019

[2] B Li and Y Li ldquoInternet of things drives supply chain in-novation a research frameworkrdquo International Journal ofOrganizational Innovation vol 9 no 3 pp 71ndash92 2017

[3] S Cockcroft and M Russell ldquoBig data opportunities for ac-counting and finance practice and researchrdquo Australian Ac-counting Review vol 28 no 3 pp 323ndash333 2018

[4] D Knezevic ldquoImpact of blockchain technology platform inchanging the financial sector and other industriesrdquo Mon-tenegrin Journal of Economics vol 14 no 1 pp 109ndash120 2018

[5] J H Nord A Koohang and J Paliszkiewicz ldquoe internet ofthings review and theoretical frameworkrdquo Expert Systemswith Applications vol 133 pp 97ndash108 2019

[6] K Leong and A Sung ldquoFinTech (financial technology) whatis it and how to use technologies to create business value infintech wayrdquo International Journal of Innovation Manage-ment and Technology vol 9 no 2 pp 74ndash78 2018

[7] M Papert and A Pflaum ldquoDevelopment of an ecosystemmodel for the realization of internet of things (IoT) services insupply chain managementrdquo Electronic Markets vol 27 no 2pp 175ndash189 2017

[8] P A Pavlou ldquoInternet of things-will humans be replaced oraugmentedrdquoGfKMarketing Intelligence Review vol 10 no 2pp 42ndash47 2018

[9] A S Wilner ldquoCybersecurity and its discontents artificialintelligence the internet of things and digital misinforma-tionrdquo International Journal Canadarsquos Journal of Global PolicyAnalysis vol 73 no 2 pp 308ndash316 2018

[10] R K R Kummitha and N Crutzen ldquoSmart cities and thecitizen-driven internet of things a qualitative inquiry into anemerging smart cityrdquo Technological Forecasting and SocialChange vol 140 pp 44ndash53 2019

[11] T Tang and A T-K Ho ldquoA path-dependence perspective onthe adoption of internet of things evidence from earlyadopters of smart and connected sensors in the United Statesrdquo

Government Information Quarterly vol 36 no 2 pp 321ndash332 2019

[12] C K M Lee S Z Zhang and K K H Ng ldquoDevelopment ofan industrial internet of things suite for smart factory towardsre-industrializationrdquoAdvances inManufacturing vol 5 no 4pp 335ndash343 2017

[13] I Salami ldquoTerrorism financing with virtual currencies canregulatory technology solutions combat thisrdquo Studies inConflict amp Terrorism vol 41 no 12 pp 968ndash989 2018

[14] L Abualigah and A Diabat ldquoA comprehensive survey of theGrasshopper optimization algorithm results variants andapplicationsrdquo Neural Computing and Applications vol 32pp 1ndash24 2020

[15] E Manavalan and K Jayakrishna ldquoA review of internet ofthings (IoT) embedded sustainable supply chain for industry40 requirementsrdquo Computers amp Industrial Engineeringvol 127 pp 925ndash953 2019

[16] H Wang C Guo and S Cheng ldquoLoC-a new financial loanmanagement system based on smart contractsrdquo FutureGeneration Computer Systems vol 100 pp 648ndash655 2019

[17] S H Lim D J Kim Y Hur and K Park ldquoAn empirical studyof the impacts of perceived security and knowledge oncontinuous intention to use mobile Fintech payment ser-vicesrdquo International Journal of Human-Computer Interactionvol 35 no 10 pp 886ndash898 2019

[18] P K Ozili ldquoImpact of digital finance on financial inclusionand stabilityrdquo Borsa Istanbul Review vol 18 no 4pp 329ndash340 2018

[19] M Yao H Di X Zheng and X Xu ldquoImpact of paymenttechnology innovations on the traditional financial industry afocus on Chinardquo Technological Forecasting and Social Changevol 135 pp 199ndash207 2018

[20] Z Tan Q Tan andM Rong ldquoAnalysis on the financing statusof PV industry in China and the ways of improvementrdquoRenewable and Sustainable Energy Reviews vol 93 pp 409ndash420 2018

[21] Z Mani and I Chouk ldquoConsumer resistance to innovation inservices challenges and barriers in the internet of things erardquoJournal of Product Innovation Management vol 35 no 5pp 780ndash807 2018

[22] L Abualigah ldquoGroup search optimizer a nature-inspiredmeta-heuristic optimization algorithm with its results vari-ants and applicationsrdquo Neural Computing and Applicationsvol 33 pp 1ndash24 2020

Complexity 9

financerdquo From the perspective of financial institutionsproviding services supply chain finance refers to financialinstitutions and other third-party service institutions thatcomprehensively grant credit to the entire supply chain ofthe industry

e visual tracking provided by the Internet of ingsfor financial service industry innovation can solve the abovethree problems e process of optimizing supply chainfinancing is shown in Figure 3 e Internet of ings canrealize that the product information flow of enterprises inthe production and circulation fields is completely flowed tocommercial banks and commercial banks can have acomprehensive understanding and timely monitoring ofenterprises which saves the time for banks to conduct creditinvestigations on enterprises again and speeds up loansWith the speed of approval the capital flow of commercialbanks to the production and circulation fields is fast and safeWith complete information commercial banks can studynew financial tools and new business forms realize financialtool innovation and extend loans to more types of supplychain financial inventory demanders that can also servemore core enterprises at the same time commercial banksuse the Internet of ings to fully grasp the production andoperation of enterprises after loans so as to facilitate timelydetection and identification of risks risk control andmanagement and improve the efficiency of managementafter loans It can also rely on the construction of the Internetof ings electronic technology platform to identify cus-tomers more quickly provide more accurate services and atthe same time broaden its electronic sales promotionchannels to achieve innovation in transaction methods

3 The Financial Service Industry Supply ChainFinancial Inventory Portfolio PledgeFinancing Risk Analysis

31 Risk Analysis of Financial Inventory Pledge in a Single-Variety Supply Chain

311 Basic Analysis of Financial Inventory Pledges in aSingle-Variety Supply Chain As shown in Figure 4 the priceof rebar futures and palm oil futures fluctuated sharply in 48months e price trend of rebar and palm oil shows anoscillating trend During the observation period the averagevalue of the rebar future contractrsquos median price was2700 yuanton the highest price was 5000 yuanton andthe lowest price was 2500 yuanton e median price ofpalm oil future contracts is 2800 yuanton the highest priceis 3650 yuanton and the lowest price is 2500 yuanton Sucha high level of volatility indicates that there is a great po-tential risk in the use of rebar and palm oil for supply chainfinancial inventory pledge financing

Because the data for CARCH model fitting must be astationary series as otherwise it will cause false regressionmaking the experimental results meaningless it is necessaryto test the stationarity of the time series of future prices toensure that there is no autocorrelation relationship betweenthe data and time and it is a stationary series At present themost commonly used method of sequence stationarity test is

the ADF unit root test which performs unit root test on thetime series of price changes of rebar and palm oil

e unit root test result of the price change series showsthat the t statistic value of the price change series of rebar isminus1901992 and the t statistic value of the price change seriesof palm oil is minus2198863 e result of the statistics obtainedis significantly less than the three confidence levelserefore the null hypothesis can be rejected and the pricechange sequence of rebar and palm oil does not have a unitroot and is stable e data can be used for the next GARCHfamily model fitting

312 GARCH Model Fitting of Single-Variety Supply ChainFinancial Inventory Pledge After determining that the pricechange sequences of the two collaterals are stationary thenext step is to start the fitting estimation of the GARCHfamily model First you check whether the price changesequence of rebar and palm oil follows a normal distributionas shown in Figure 5 It can be seen from the figure that theassumption that the price change sequence obeys a normaldistribution can be rejected

e average value of the price change series of palm oil isminus0000144 the standard deviation is 0009328 the skewnessis 0450833 and the skewness is greater than 0 indicatingthat the price series distribution of palm oil has a long righttail phenomenon

e GARCH model is constructed for the price changesequence of the two collaterals e GARCH family modelsmainly include the GARCH model the T-GARCH modeland the E-GARCH model is paper studies the con-struction of low-level GARCH (1 1) T-GARCH (1 1) andE-GARCH (1 1) models e GARCH fitting parameterdiagram of the rebar price change sequence and the GARCHfitting parameter diagram of the palm oil price change se-quence are shown in Figures 6 and 7 respectively

It can be seen from Figure 8 that under the confidence of005 the p values of the test results are all less than thecritical value of the F statistic indicating that the residualsequence after the GARCH model fitting has eliminated theARCH effect and the model can be used to predict the futureestimates of changes in volatility

32 Research on Liquidity Risk of Supply Chain FinancialInventory Portfolio Pledge It can be seen from Figure 9 thatthe distribution of supply chain financial inventory portfolioprice changes has long tails on both sides e kurtosis is291 indicating that the price change sequence has thecharacteristics of sharp peaks and thick tails

ree GARCHmodels are fitted to the sequence of supplychain financial inventory portfolio price changes and theresults are shown in Figure 10 It can be seen that p corre-sponding to the c value in the E-GARCH (1 1) andT-GARCH (1 1) models is greater than 005 and accordingto the AIC criterion it is decided to choose the GARCH (1 1)model

It can be seen from Figure 11 that the p values of thethree distributions all approach 0 indicating that theGARCH family model fits well e specific model to be

Complexity 5

selected requires an optimal comparison e parameters aand b respectively indicate the influence of external in-formation on the fluctuation of the price change sequenceand the length of time that the fluctuation of the initial pricechange sequence affects future price sequence fluctuationse larger the value the greater the impact Combined withthe principle of the smallest value of AIC and SC the resultshows that the t distribution is most in line with thecharacteristics of the supply chain financial inventoryportfolio price change sequence

Internet of things masters

business operations

Relying on IoT platform

construction

Identify customers faster

Broaden its electronicsales promotion channels

Realize transactioninnovation

Financial servicesindustry Commercial bank

Cash flowIntegration of logistics information

flow and capital flow

Improve productionefficiency

Improve capitalefficiency

Informationflow

Supply chainfinance

Sales Warehouse Transport

Improve cash flow

Reduce related capitalcosts

Reduce inventory to optimizeworking capital management

Extension of accountspayable turnover days

Corebusiness

Financing guarantee

for upstream anddownstream SMEs

Cash flow

Informationflow

Productionarea

Logistics

Circulation field

Figure 3 Financial commercial bank service process

RebarPalm oil

2500

3000

3500

4000

4500

5000

Pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 4 Trends of rebar median price and palm oil median price

0 5 10 15 20 25 30 35 40 45 50Timemonth

Palm oilRebar

30

60

90

120

150

180

Pric

e

Figure 5 Normal distribution test of rebar and palm oil pricechange series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash6Fitted value

p

c

a

b

AIC

SC

Figure 6 GARCH fitting parameter diagram of rebar price changesequence

6 Complexity

4 Empirical Analysis of the DynamicMechanism of Financial ProductsrsquoSustainable Innovation

41 Sample Selection and Data Processing

411 Sample Size Under a certain sampling method thelarger the sample size the higher the estimation accuracy

but considering the actual situation it is impossible toconduct a study without capacity

Because the survey uses the existing PBC mail net-work it is basically not limited by fees the survey isconducted between subordinates managing depart-ments and managed departments and questionnairessuch as ldquono answerrdquo and ldquoinvalidrdquo basically do not existIt is carried out in high-quality population the overallsample is very similar extreme differences basically donot exist and the degree of sample heterogeneity can beignored

412 Data Processing e questionnaire was sent to thecentral branch of the Peoplersquos Bank of China through theLotus system of the Peoplersquos Bank of China Each branch wasrequired to complete 77 questionnaires A regional branchoffice itself also completed 16 questionnaires and the cor-responding financial service institutions completed 15questionnaires e CVR inspection data is shown inFigure 12

0

30

60

90

120

Fina

ncia

l inv

ento

ry

port

folio

pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 9 Normal distribution test of supply chain financial in-ventory portfolio price change rate series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 10 GARCH model fitting parameter diagram of supplychain financial inventory portfolio price change sequence

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Normal distributionT distributionGED distribution

Figure 11 e result of the hypothetical parameter results of thesequence distribution of supply chain financial inventory portfolioprice changes

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 7 GARCH fitting parameter diagram of palm oil pricechange series

F-statistic ObslowastR-squared

ProbF (1 844) ProbChi-Square (1)

RebarPalm oil

0

05

1

15

Fitti

ng re

sidua

l

Figure 8 ARCH LM test results of the volatility fitting residuals ofthe rebar and palm oil price change series

Complexity 7

42 Fuzzy Comprehensive Evaluation of ContinuousInnovation Power

421 CVR Determination Index e content validity ofeach index is tested based on the test data of CVR and thetest results are shown in Figure 13 e CVR test values inthe figure are all greater than 05 that is it is believed that themeasured index can well represent the number of people inthe scope of the measurement object exceeding 75 of thetotal number of people erefore the basic clear indicatorscan more accurately reflect the content of the continuousinnovation power of financial service industry products in acertain area and can be used as indicators for evaluating thecontinuous innovation power of financial service industryproducts

422 AHP Determines the Weight Analytic hierarchyprocess (AHP) is a method to deal with economic man-agement and technical issues with complex factors It de-composes a complex problem into multiple components andfurther decomposes these factors according to the domi-nance relationship and arranges them according to the targetlevel the criterion level and the index level to form amultiobjective multilevel model

e analytic hierarchy process is essentially a way ofdecision-making thinking It decomposes complex decision-making problems into different components according tothe nature of the problem and the general goal required toachieve and then integrates the judgment of people to de-termine the overall order of the relative importance of thefactors in decision-making e analytic hierarchy processembodies the basic way of decision-making thinkingnamely decomposition judgment and synthesis e basicidea is to transform the overall judgment of the weights ofmultiple factors that make up a complex problem into aldquopairwise comparisonrdquo of these factors and then to judge theoverall weight of these elements and finally establish theweight of each factor RI value is shown in Figure 14

Using the basic principles of AHP the data obtained by120times1067 comparisons of indicators according to Sattyrsquosscale are written in matrix form and the eigenvalues and

eigenvectors are calculated by Matlab and the eigenvectorscorresponding to the λmax eigenvalues are normalizedFinally the normalized vectors at all levels are integrated todetermine the final weight of each subindicator

5 Conclusion

e risk value of a supply chain financial inventory com-bination using rebar and palm oil under all set pledge pe-riods or confidence levels is less than the corresponding riskvalue of a single variety of supply chain financial inventorypledge of rebar or palm oil is shows that in the supplychain financial inventory pledge financing business thesupply chain financial inventory portfolio composed ofsupply chain financial inventory products that are negativelycorrelated with market price fluctuations is used as thepledge which can effectively lower banks and other supplychain financial institutions e calculation results of thepledge rate model established based on LA-VAR show thatwhen the supply chain financial inventory combinationconstructed by rebar and palm oil is used as pledge thepledge rate is inversely proportional to the pledge periode longer the pledge time the lower pledge rate of theinventory portfolio but the higher the pledge rate when thecorresponding single species is used as pledge under anycircumstances is means that small- and medium-sizedenterprises can use a combination of supply chain financialinventory pledge financing strategies with a negative rela-tionship compared to only using a single-variety supplychain financial inventory pledge and can obtain more fundsunder the same overall value of the pledge e risks borneby banks and other financial institutions have not increased

06

07

08

09

1

CVR

test

valu

e

10 15 20 25 305Index number

Figure 13 CVR test value

46

5

28

2lt 1

18

Very goodBetterIt is good

Not goodPoorVery bad

Figure 12 Proportion of CVR inspection data

0608

112141618

222

RI10 15 20 25 30 35 40 45 50 55 605

Order n

Figure 14 Average consistency index

8 Complexity

According to the basic principles of fuzzy mathematics theevaluation index is determined according to KPI⟶CVRwhich is used for index content validity test⟶AHP de-termining index weight e 1067 pieces of data obtainedfrom the provincersquos survey and the 120times1067 evaluationindex pairwise comparison data are substituted into themodel

Data Availability

Data sharing is not applicable to this article as no datasetswere generated or analyzed during the current study

Conflicts of Interest

e authors declare that there are no conflicts of interest

Authorsrsquo Contributions

All authors have approved the publication of themanuscript

References

[1] W Viriyasitavat L D Xu Z Bi and V PungpapongldquoBlockchain and internet of things for modern businessprocess in digital economy-the state of the artrdquo IEEETransactions on Computational Social Systems vol 6 no 6pp 1420ndash1432 2019

[2] B Li and Y Li ldquoInternet of things drives supply chain in-novation a research frameworkrdquo International Journal ofOrganizational Innovation vol 9 no 3 pp 71ndash92 2017

[3] S Cockcroft and M Russell ldquoBig data opportunities for ac-counting and finance practice and researchrdquo Australian Ac-counting Review vol 28 no 3 pp 323ndash333 2018

[4] D Knezevic ldquoImpact of blockchain technology platform inchanging the financial sector and other industriesrdquo Mon-tenegrin Journal of Economics vol 14 no 1 pp 109ndash120 2018

[5] J H Nord A Koohang and J Paliszkiewicz ldquoe internet ofthings review and theoretical frameworkrdquo Expert Systemswith Applications vol 133 pp 97ndash108 2019

[6] K Leong and A Sung ldquoFinTech (financial technology) whatis it and how to use technologies to create business value infintech wayrdquo International Journal of Innovation Manage-ment and Technology vol 9 no 2 pp 74ndash78 2018

[7] M Papert and A Pflaum ldquoDevelopment of an ecosystemmodel for the realization of internet of things (IoT) services insupply chain managementrdquo Electronic Markets vol 27 no 2pp 175ndash189 2017

[8] P A Pavlou ldquoInternet of things-will humans be replaced oraugmentedrdquoGfKMarketing Intelligence Review vol 10 no 2pp 42ndash47 2018

[9] A S Wilner ldquoCybersecurity and its discontents artificialintelligence the internet of things and digital misinforma-tionrdquo International Journal Canadarsquos Journal of Global PolicyAnalysis vol 73 no 2 pp 308ndash316 2018

[10] R K R Kummitha and N Crutzen ldquoSmart cities and thecitizen-driven internet of things a qualitative inquiry into anemerging smart cityrdquo Technological Forecasting and SocialChange vol 140 pp 44ndash53 2019

[11] T Tang and A T-K Ho ldquoA path-dependence perspective onthe adoption of internet of things evidence from earlyadopters of smart and connected sensors in the United Statesrdquo

Government Information Quarterly vol 36 no 2 pp 321ndash332 2019

[12] C K M Lee S Z Zhang and K K H Ng ldquoDevelopment ofan industrial internet of things suite for smart factory towardsre-industrializationrdquoAdvances inManufacturing vol 5 no 4pp 335ndash343 2017

[13] I Salami ldquoTerrorism financing with virtual currencies canregulatory technology solutions combat thisrdquo Studies inConflict amp Terrorism vol 41 no 12 pp 968ndash989 2018

[14] L Abualigah and A Diabat ldquoA comprehensive survey of theGrasshopper optimization algorithm results variants andapplicationsrdquo Neural Computing and Applications vol 32pp 1ndash24 2020

[15] E Manavalan and K Jayakrishna ldquoA review of internet ofthings (IoT) embedded sustainable supply chain for industry40 requirementsrdquo Computers amp Industrial Engineeringvol 127 pp 925ndash953 2019

[16] H Wang C Guo and S Cheng ldquoLoC-a new financial loanmanagement system based on smart contractsrdquo FutureGeneration Computer Systems vol 100 pp 648ndash655 2019

[17] S H Lim D J Kim Y Hur and K Park ldquoAn empirical studyof the impacts of perceived security and knowledge oncontinuous intention to use mobile Fintech payment ser-vicesrdquo International Journal of Human-Computer Interactionvol 35 no 10 pp 886ndash898 2019

[18] P K Ozili ldquoImpact of digital finance on financial inclusionand stabilityrdquo Borsa Istanbul Review vol 18 no 4pp 329ndash340 2018

[19] M Yao H Di X Zheng and X Xu ldquoImpact of paymenttechnology innovations on the traditional financial industry afocus on Chinardquo Technological Forecasting and Social Changevol 135 pp 199ndash207 2018

[20] Z Tan Q Tan andM Rong ldquoAnalysis on the financing statusof PV industry in China and the ways of improvementrdquoRenewable and Sustainable Energy Reviews vol 93 pp 409ndash420 2018

[21] Z Mani and I Chouk ldquoConsumer resistance to innovation inservices challenges and barriers in the internet of things erardquoJournal of Product Innovation Management vol 35 no 5pp 780ndash807 2018

[22] L Abualigah ldquoGroup search optimizer a nature-inspiredmeta-heuristic optimization algorithm with its results vari-ants and applicationsrdquo Neural Computing and Applicationsvol 33 pp 1ndash24 2020

Complexity 9

selected requires an optimal comparison e parameters aand b respectively indicate the influence of external in-formation on the fluctuation of the price change sequenceand the length of time that the fluctuation of the initial pricechange sequence affects future price sequence fluctuationse larger the value the greater the impact Combined withthe principle of the smallest value of AIC and SC the resultshows that the t distribution is most in line with thecharacteristics of the supply chain financial inventoryportfolio price change sequence

Internet of things masters

business operations

Relying on IoT platform

construction

Identify customers faster

Broaden its electronicsales promotion channels

Realize transactioninnovation

Financial servicesindustry Commercial bank

Cash flowIntegration of logistics information

flow and capital flow

Improve productionefficiency

Improve capitalefficiency

Informationflow

Supply chainfinance

Sales Warehouse Transport

Improve cash flow

Reduce related capitalcosts

Reduce inventory to optimizeworking capital management

Extension of accountspayable turnover days

Corebusiness

Financing guarantee

for upstream anddownstream SMEs

Cash flow

Informationflow

Productionarea

Logistics

Circulation field

Figure 3 Financial commercial bank service process

RebarPalm oil

2500

3000

3500

4000

4500

5000

Pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 4 Trends of rebar median price and palm oil median price

0 5 10 15 20 25 30 35 40 45 50Timemonth

Palm oilRebar

30

60

90

120

150

180

Pric

e

Figure 5 Normal distribution test of rebar and palm oil pricechange series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash6Fitted value

p

c

a

b

AIC

SC

Figure 6 GARCH fitting parameter diagram of rebar price changesequence

6 Complexity

4 Empirical Analysis of the DynamicMechanism of Financial ProductsrsquoSustainable Innovation

41 Sample Selection and Data Processing

411 Sample Size Under a certain sampling method thelarger the sample size the higher the estimation accuracy

but considering the actual situation it is impossible toconduct a study without capacity

Because the survey uses the existing PBC mail net-work it is basically not limited by fees the survey isconducted between subordinates managing depart-ments and managed departments and questionnairessuch as ldquono answerrdquo and ldquoinvalidrdquo basically do not existIt is carried out in high-quality population the overallsample is very similar extreme differences basically donot exist and the degree of sample heterogeneity can beignored

412 Data Processing e questionnaire was sent to thecentral branch of the Peoplersquos Bank of China through theLotus system of the Peoplersquos Bank of China Each branch wasrequired to complete 77 questionnaires A regional branchoffice itself also completed 16 questionnaires and the cor-responding financial service institutions completed 15questionnaires e CVR inspection data is shown inFigure 12

0

30

60

90

120

Fina

ncia

l inv

ento

ry

port

folio

pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 9 Normal distribution test of supply chain financial in-ventory portfolio price change rate series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 10 GARCH model fitting parameter diagram of supplychain financial inventory portfolio price change sequence

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Normal distributionT distributionGED distribution

Figure 11 e result of the hypothetical parameter results of thesequence distribution of supply chain financial inventory portfolioprice changes

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 7 GARCH fitting parameter diagram of palm oil pricechange series

F-statistic ObslowastR-squared

ProbF (1 844) ProbChi-Square (1)

RebarPalm oil

0

05

1

15

Fitti

ng re

sidua

l

Figure 8 ARCH LM test results of the volatility fitting residuals ofthe rebar and palm oil price change series

Complexity 7

42 Fuzzy Comprehensive Evaluation of ContinuousInnovation Power

421 CVR Determination Index e content validity ofeach index is tested based on the test data of CVR and thetest results are shown in Figure 13 e CVR test values inthe figure are all greater than 05 that is it is believed that themeasured index can well represent the number of people inthe scope of the measurement object exceeding 75 of thetotal number of people erefore the basic clear indicatorscan more accurately reflect the content of the continuousinnovation power of financial service industry products in acertain area and can be used as indicators for evaluating thecontinuous innovation power of financial service industryproducts

422 AHP Determines the Weight Analytic hierarchyprocess (AHP) is a method to deal with economic man-agement and technical issues with complex factors It de-composes a complex problem into multiple components andfurther decomposes these factors according to the domi-nance relationship and arranges them according to the targetlevel the criterion level and the index level to form amultiobjective multilevel model

e analytic hierarchy process is essentially a way ofdecision-making thinking It decomposes complex decision-making problems into different components according tothe nature of the problem and the general goal required toachieve and then integrates the judgment of people to de-termine the overall order of the relative importance of thefactors in decision-making e analytic hierarchy processembodies the basic way of decision-making thinkingnamely decomposition judgment and synthesis e basicidea is to transform the overall judgment of the weights ofmultiple factors that make up a complex problem into aldquopairwise comparisonrdquo of these factors and then to judge theoverall weight of these elements and finally establish theweight of each factor RI value is shown in Figure 14

Using the basic principles of AHP the data obtained by120times1067 comparisons of indicators according to Sattyrsquosscale are written in matrix form and the eigenvalues and

eigenvectors are calculated by Matlab and the eigenvectorscorresponding to the λmax eigenvalues are normalizedFinally the normalized vectors at all levels are integrated todetermine the final weight of each subindicator

5 Conclusion

e risk value of a supply chain financial inventory com-bination using rebar and palm oil under all set pledge pe-riods or confidence levels is less than the corresponding riskvalue of a single variety of supply chain financial inventorypledge of rebar or palm oil is shows that in the supplychain financial inventory pledge financing business thesupply chain financial inventory portfolio composed ofsupply chain financial inventory products that are negativelycorrelated with market price fluctuations is used as thepledge which can effectively lower banks and other supplychain financial institutions e calculation results of thepledge rate model established based on LA-VAR show thatwhen the supply chain financial inventory combinationconstructed by rebar and palm oil is used as pledge thepledge rate is inversely proportional to the pledge periode longer the pledge time the lower pledge rate of theinventory portfolio but the higher the pledge rate when thecorresponding single species is used as pledge under anycircumstances is means that small- and medium-sizedenterprises can use a combination of supply chain financialinventory pledge financing strategies with a negative rela-tionship compared to only using a single-variety supplychain financial inventory pledge and can obtain more fundsunder the same overall value of the pledge e risks borneby banks and other financial institutions have not increased

06

07

08

09

1

CVR

test

valu

e

10 15 20 25 305Index number

Figure 13 CVR test value

46

5

28

2lt 1

18

Very goodBetterIt is good

Not goodPoorVery bad

Figure 12 Proportion of CVR inspection data

0608

112141618

222

RI10 15 20 25 30 35 40 45 50 55 605

Order n

Figure 14 Average consistency index

8 Complexity

According to the basic principles of fuzzy mathematics theevaluation index is determined according to KPI⟶CVRwhich is used for index content validity test⟶AHP de-termining index weight e 1067 pieces of data obtainedfrom the provincersquos survey and the 120times1067 evaluationindex pairwise comparison data are substituted into themodel

Data Availability

Data sharing is not applicable to this article as no datasetswere generated or analyzed during the current study

Conflicts of Interest

e authors declare that there are no conflicts of interest

Authorsrsquo Contributions

All authors have approved the publication of themanuscript

References

[1] W Viriyasitavat L D Xu Z Bi and V PungpapongldquoBlockchain and internet of things for modern businessprocess in digital economy-the state of the artrdquo IEEETransactions on Computational Social Systems vol 6 no 6pp 1420ndash1432 2019

[2] B Li and Y Li ldquoInternet of things drives supply chain in-novation a research frameworkrdquo International Journal ofOrganizational Innovation vol 9 no 3 pp 71ndash92 2017

[3] S Cockcroft and M Russell ldquoBig data opportunities for ac-counting and finance practice and researchrdquo Australian Ac-counting Review vol 28 no 3 pp 323ndash333 2018

[4] D Knezevic ldquoImpact of blockchain technology platform inchanging the financial sector and other industriesrdquo Mon-tenegrin Journal of Economics vol 14 no 1 pp 109ndash120 2018

[5] J H Nord A Koohang and J Paliszkiewicz ldquoe internet ofthings review and theoretical frameworkrdquo Expert Systemswith Applications vol 133 pp 97ndash108 2019

[6] K Leong and A Sung ldquoFinTech (financial technology) whatis it and how to use technologies to create business value infintech wayrdquo International Journal of Innovation Manage-ment and Technology vol 9 no 2 pp 74ndash78 2018

[7] M Papert and A Pflaum ldquoDevelopment of an ecosystemmodel for the realization of internet of things (IoT) services insupply chain managementrdquo Electronic Markets vol 27 no 2pp 175ndash189 2017

[8] P A Pavlou ldquoInternet of things-will humans be replaced oraugmentedrdquoGfKMarketing Intelligence Review vol 10 no 2pp 42ndash47 2018

[9] A S Wilner ldquoCybersecurity and its discontents artificialintelligence the internet of things and digital misinforma-tionrdquo International Journal Canadarsquos Journal of Global PolicyAnalysis vol 73 no 2 pp 308ndash316 2018

[10] R K R Kummitha and N Crutzen ldquoSmart cities and thecitizen-driven internet of things a qualitative inquiry into anemerging smart cityrdquo Technological Forecasting and SocialChange vol 140 pp 44ndash53 2019

[11] T Tang and A T-K Ho ldquoA path-dependence perspective onthe adoption of internet of things evidence from earlyadopters of smart and connected sensors in the United Statesrdquo

Government Information Quarterly vol 36 no 2 pp 321ndash332 2019

[12] C K M Lee S Z Zhang and K K H Ng ldquoDevelopment ofan industrial internet of things suite for smart factory towardsre-industrializationrdquoAdvances inManufacturing vol 5 no 4pp 335ndash343 2017

[13] I Salami ldquoTerrorism financing with virtual currencies canregulatory technology solutions combat thisrdquo Studies inConflict amp Terrorism vol 41 no 12 pp 968ndash989 2018

[14] L Abualigah and A Diabat ldquoA comprehensive survey of theGrasshopper optimization algorithm results variants andapplicationsrdquo Neural Computing and Applications vol 32pp 1ndash24 2020

[15] E Manavalan and K Jayakrishna ldquoA review of internet ofthings (IoT) embedded sustainable supply chain for industry40 requirementsrdquo Computers amp Industrial Engineeringvol 127 pp 925ndash953 2019

[16] H Wang C Guo and S Cheng ldquoLoC-a new financial loanmanagement system based on smart contractsrdquo FutureGeneration Computer Systems vol 100 pp 648ndash655 2019

[17] S H Lim D J Kim Y Hur and K Park ldquoAn empirical studyof the impacts of perceived security and knowledge oncontinuous intention to use mobile Fintech payment ser-vicesrdquo International Journal of Human-Computer Interactionvol 35 no 10 pp 886ndash898 2019

[18] P K Ozili ldquoImpact of digital finance on financial inclusionand stabilityrdquo Borsa Istanbul Review vol 18 no 4pp 329ndash340 2018

[19] M Yao H Di X Zheng and X Xu ldquoImpact of paymenttechnology innovations on the traditional financial industry afocus on Chinardquo Technological Forecasting and Social Changevol 135 pp 199ndash207 2018

[20] Z Tan Q Tan andM Rong ldquoAnalysis on the financing statusof PV industry in China and the ways of improvementrdquoRenewable and Sustainable Energy Reviews vol 93 pp 409ndash420 2018

[21] Z Mani and I Chouk ldquoConsumer resistance to innovation inservices challenges and barriers in the internet of things erardquoJournal of Product Innovation Management vol 35 no 5pp 780ndash807 2018

[22] L Abualigah ldquoGroup search optimizer a nature-inspiredmeta-heuristic optimization algorithm with its results vari-ants and applicationsrdquo Neural Computing and Applicationsvol 33 pp 1ndash24 2020

Complexity 9

4 Empirical Analysis of the DynamicMechanism of Financial ProductsrsquoSustainable Innovation

41 Sample Selection and Data Processing

411 Sample Size Under a certain sampling method thelarger the sample size the higher the estimation accuracy

but considering the actual situation it is impossible toconduct a study without capacity

Because the survey uses the existing PBC mail net-work it is basically not limited by fees the survey isconducted between subordinates managing depart-ments and managed departments and questionnairessuch as ldquono answerrdquo and ldquoinvalidrdquo basically do not existIt is carried out in high-quality population the overallsample is very similar extreme differences basically donot exist and the degree of sample heterogeneity can beignored

412 Data Processing e questionnaire was sent to thecentral branch of the Peoplersquos Bank of China through theLotus system of the Peoplersquos Bank of China Each branch wasrequired to complete 77 questionnaires A regional branchoffice itself also completed 16 questionnaires and the cor-responding financial service institutions completed 15questionnaires e CVR inspection data is shown inFigure 12

0

30

60

90

120

Fina

ncia

l inv

ento

ry

port

folio

pric

e

5 10 15 20 25 30 35 40 450Timemonth

Figure 9 Normal distribution test of supply chain financial in-ventory portfolio price change rate series

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 10 GARCH model fitting parameter diagram of supplychain financial inventory portfolio price change sequence

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Normal distributionT distributionGED distribution

Figure 11 e result of the hypothetical parameter results of thesequence distribution of supply chain financial inventory portfolioprice changes

GARCH (1 1)E-GARCH (1 1)T-GARCH (1 1)

p

c

a

b

AIC

SC

ndash6 ndash5 ndash4 ndash3 ndash2 ndash1 0 1ndash7Fitted value

Figure 7 GARCH fitting parameter diagram of palm oil pricechange series

F-statistic ObslowastR-squared

ProbF (1 844) ProbChi-Square (1)

RebarPalm oil

0

05

1

15

Fitti

ng re

sidua

l

Figure 8 ARCH LM test results of the volatility fitting residuals ofthe rebar and palm oil price change series

Complexity 7

42 Fuzzy Comprehensive Evaluation of ContinuousInnovation Power

421 CVR Determination Index e content validity ofeach index is tested based on the test data of CVR and thetest results are shown in Figure 13 e CVR test values inthe figure are all greater than 05 that is it is believed that themeasured index can well represent the number of people inthe scope of the measurement object exceeding 75 of thetotal number of people erefore the basic clear indicatorscan more accurately reflect the content of the continuousinnovation power of financial service industry products in acertain area and can be used as indicators for evaluating thecontinuous innovation power of financial service industryproducts

422 AHP Determines the Weight Analytic hierarchyprocess (AHP) is a method to deal with economic man-agement and technical issues with complex factors It de-composes a complex problem into multiple components andfurther decomposes these factors according to the domi-nance relationship and arranges them according to the targetlevel the criterion level and the index level to form amultiobjective multilevel model

e analytic hierarchy process is essentially a way ofdecision-making thinking It decomposes complex decision-making problems into different components according tothe nature of the problem and the general goal required toachieve and then integrates the judgment of people to de-termine the overall order of the relative importance of thefactors in decision-making e analytic hierarchy processembodies the basic way of decision-making thinkingnamely decomposition judgment and synthesis e basicidea is to transform the overall judgment of the weights ofmultiple factors that make up a complex problem into aldquopairwise comparisonrdquo of these factors and then to judge theoverall weight of these elements and finally establish theweight of each factor RI value is shown in Figure 14

Using the basic principles of AHP the data obtained by120times1067 comparisons of indicators according to Sattyrsquosscale are written in matrix form and the eigenvalues and

eigenvectors are calculated by Matlab and the eigenvectorscorresponding to the λmax eigenvalues are normalizedFinally the normalized vectors at all levels are integrated todetermine the final weight of each subindicator

5 Conclusion

e risk value of a supply chain financial inventory com-bination using rebar and palm oil under all set pledge pe-riods or confidence levels is less than the corresponding riskvalue of a single variety of supply chain financial inventorypledge of rebar or palm oil is shows that in the supplychain financial inventory pledge financing business thesupply chain financial inventory portfolio composed ofsupply chain financial inventory products that are negativelycorrelated with market price fluctuations is used as thepledge which can effectively lower banks and other supplychain financial institutions e calculation results of thepledge rate model established based on LA-VAR show thatwhen the supply chain financial inventory combinationconstructed by rebar and palm oil is used as pledge thepledge rate is inversely proportional to the pledge periode longer the pledge time the lower pledge rate of theinventory portfolio but the higher the pledge rate when thecorresponding single species is used as pledge under anycircumstances is means that small- and medium-sizedenterprises can use a combination of supply chain financialinventory pledge financing strategies with a negative rela-tionship compared to only using a single-variety supplychain financial inventory pledge and can obtain more fundsunder the same overall value of the pledge e risks borneby banks and other financial institutions have not increased

06

07

08

09

1

CVR

test

valu

e

10 15 20 25 305Index number

Figure 13 CVR test value

46

5

28

2lt 1

18

Very goodBetterIt is good

Not goodPoorVery bad

Figure 12 Proportion of CVR inspection data

0608

112141618

222

RI10 15 20 25 30 35 40 45 50 55 605

Order n

Figure 14 Average consistency index

8 Complexity

According to the basic principles of fuzzy mathematics theevaluation index is determined according to KPI⟶CVRwhich is used for index content validity test⟶AHP de-termining index weight e 1067 pieces of data obtainedfrom the provincersquos survey and the 120times1067 evaluationindex pairwise comparison data are substituted into themodel

Data Availability

Data sharing is not applicable to this article as no datasetswere generated or analyzed during the current study

Conflicts of Interest

e authors declare that there are no conflicts of interest

Authorsrsquo Contributions

All authors have approved the publication of themanuscript

References

[1] W Viriyasitavat L D Xu Z Bi and V PungpapongldquoBlockchain and internet of things for modern businessprocess in digital economy-the state of the artrdquo IEEETransactions on Computational Social Systems vol 6 no 6pp 1420ndash1432 2019

[2] B Li and Y Li ldquoInternet of things drives supply chain in-novation a research frameworkrdquo International Journal ofOrganizational Innovation vol 9 no 3 pp 71ndash92 2017

[3] S Cockcroft and M Russell ldquoBig data opportunities for ac-counting and finance practice and researchrdquo Australian Ac-counting Review vol 28 no 3 pp 323ndash333 2018

[4] D Knezevic ldquoImpact of blockchain technology platform inchanging the financial sector and other industriesrdquo Mon-tenegrin Journal of Economics vol 14 no 1 pp 109ndash120 2018

[5] J H Nord A Koohang and J Paliszkiewicz ldquoe internet ofthings review and theoretical frameworkrdquo Expert Systemswith Applications vol 133 pp 97ndash108 2019

[6] K Leong and A Sung ldquoFinTech (financial technology) whatis it and how to use technologies to create business value infintech wayrdquo International Journal of Innovation Manage-ment and Technology vol 9 no 2 pp 74ndash78 2018

[7] M Papert and A Pflaum ldquoDevelopment of an ecosystemmodel for the realization of internet of things (IoT) services insupply chain managementrdquo Electronic Markets vol 27 no 2pp 175ndash189 2017

[8] P A Pavlou ldquoInternet of things-will humans be replaced oraugmentedrdquoGfKMarketing Intelligence Review vol 10 no 2pp 42ndash47 2018

[9] A S Wilner ldquoCybersecurity and its discontents artificialintelligence the internet of things and digital misinforma-tionrdquo International Journal Canadarsquos Journal of Global PolicyAnalysis vol 73 no 2 pp 308ndash316 2018

[10] R K R Kummitha and N Crutzen ldquoSmart cities and thecitizen-driven internet of things a qualitative inquiry into anemerging smart cityrdquo Technological Forecasting and SocialChange vol 140 pp 44ndash53 2019

[11] T Tang and A T-K Ho ldquoA path-dependence perspective onthe adoption of internet of things evidence from earlyadopters of smart and connected sensors in the United Statesrdquo

Government Information Quarterly vol 36 no 2 pp 321ndash332 2019

[12] C K M Lee S Z Zhang and K K H Ng ldquoDevelopment ofan industrial internet of things suite for smart factory towardsre-industrializationrdquoAdvances inManufacturing vol 5 no 4pp 335ndash343 2017

[13] I Salami ldquoTerrorism financing with virtual currencies canregulatory technology solutions combat thisrdquo Studies inConflict amp Terrorism vol 41 no 12 pp 968ndash989 2018

[14] L Abualigah and A Diabat ldquoA comprehensive survey of theGrasshopper optimization algorithm results variants andapplicationsrdquo Neural Computing and Applications vol 32pp 1ndash24 2020

[15] E Manavalan and K Jayakrishna ldquoA review of internet ofthings (IoT) embedded sustainable supply chain for industry40 requirementsrdquo Computers amp Industrial Engineeringvol 127 pp 925ndash953 2019

[16] H Wang C Guo and S Cheng ldquoLoC-a new financial loanmanagement system based on smart contractsrdquo FutureGeneration Computer Systems vol 100 pp 648ndash655 2019

[17] S H Lim D J Kim Y Hur and K Park ldquoAn empirical studyof the impacts of perceived security and knowledge oncontinuous intention to use mobile Fintech payment ser-vicesrdquo International Journal of Human-Computer Interactionvol 35 no 10 pp 886ndash898 2019

[18] P K Ozili ldquoImpact of digital finance on financial inclusionand stabilityrdquo Borsa Istanbul Review vol 18 no 4pp 329ndash340 2018

[19] M Yao H Di X Zheng and X Xu ldquoImpact of paymenttechnology innovations on the traditional financial industry afocus on Chinardquo Technological Forecasting and Social Changevol 135 pp 199ndash207 2018

[20] Z Tan Q Tan andM Rong ldquoAnalysis on the financing statusof PV industry in China and the ways of improvementrdquoRenewable and Sustainable Energy Reviews vol 93 pp 409ndash420 2018

[21] Z Mani and I Chouk ldquoConsumer resistance to innovation inservices challenges and barriers in the internet of things erardquoJournal of Product Innovation Management vol 35 no 5pp 780ndash807 2018

[22] L Abualigah ldquoGroup search optimizer a nature-inspiredmeta-heuristic optimization algorithm with its results vari-ants and applicationsrdquo Neural Computing and Applicationsvol 33 pp 1ndash24 2020

Complexity 9

42 Fuzzy Comprehensive Evaluation of ContinuousInnovation Power

421 CVR Determination Index e content validity ofeach index is tested based on the test data of CVR and thetest results are shown in Figure 13 e CVR test values inthe figure are all greater than 05 that is it is believed that themeasured index can well represent the number of people inthe scope of the measurement object exceeding 75 of thetotal number of people erefore the basic clear indicatorscan more accurately reflect the content of the continuousinnovation power of financial service industry products in acertain area and can be used as indicators for evaluating thecontinuous innovation power of financial service industryproducts

422 AHP Determines the Weight Analytic hierarchyprocess (AHP) is a method to deal with economic man-agement and technical issues with complex factors It de-composes a complex problem into multiple components andfurther decomposes these factors according to the domi-nance relationship and arranges them according to the targetlevel the criterion level and the index level to form amultiobjective multilevel model

e analytic hierarchy process is essentially a way ofdecision-making thinking It decomposes complex decision-making problems into different components according tothe nature of the problem and the general goal required toachieve and then integrates the judgment of people to de-termine the overall order of the relative importance of thefactors in decision-making e analytic hierarchy processembodies the basic way of decision-making thinkingnamely decomposition judgment and synthesis e basicidea is to transform the overall judgment of the weights ofmultiple factors that make up a complex problem into aldquopairwise comparisonrdquo of these factors and then to judge theoverall weight of these elements and finally establish theweight of each factor RI value is shown in Figure 14

Using the basic principles of AHP the data obtained by120times1067 comparisons of indicators according to Sattyrsquosscale are written in matrix form and the eigenvalues and

eigenvectors are calculated by Matlab and the eigenvectorscorresponding to the λmax eigenvalues are normalizedFinally the normalized vectors at all levels are integrated todetermine the final weight of each subindicator

5 Conclusion

e risk value of a supply chain financial inventory com-bination using rebar and palm oil under all set pledge pe-riods or confidence levels is less than the corresponding riskvalue of a single variety of supply chain financial inventorypledge of rebar or palm oil is shows that in the supplychain financial inventory pledge financing business thesupply chain financial inventory portfolio composed ofsupply chain financial inventory products that are negativelycorrelated with market price fluctuations is used as thepledge which can effectively lower banks and other supplychain financial institutions e calculation results of thepledge rate model established based on LA-VAR show thatwhen the supply chain financial inventory combinationconstructed by rebar and palm oil is used as pledge thepledge rate is inversely proportional to the pledge periode longer the pledge time the lower pledge rate of theinventory portfolio but the higher the pledge rate when thecorresponding single species is used as pledge under anycircumstances is means that small- and medium-sizedenterprises can use a combination of supply chain financialinventory pledge financing strategies with a negative rela-tionship compared to only using a single-variety supplychain financial inventory pledge and can obtain more fundsunder the same overall value of the pledge e risks borneby banks and other financial institutions have not increased

06

07

08

09

1

CVR

test

valu

e

10 15 20 25 305Index number

Figure 13 CVR test value

46

5

28

2lt 1

18

Very goodBetterIt is good

Not goodPoorVery bad

Figure 12 Proportion of CVR inspection data

0608

112141618

222

RI10 15 20 25 30 35 40 45 50 55 605

Order n

Figure 14 Average consistency index

8 Complexity

According to the basic principles of fuzzy mathematics theevaluation index is determined according to KPI⟶CVRwhich is used for index content validity test⟶AHP de-termining index weight e 1067 pieces of data obtainedfrom the provincersquos survey and the 120times1067 evaluationindex pairwise comparison data are substituted into themodel

Data Availability

Data sharing is not applicable to this article as no datasetswere generated or analyzed during the current study

Conflicts of Interest

e authors declare that there are no conflicts of interest

Authorsrsquo Contributions

All authors have approved the publication of themanuscript

References

[1] W Viriyasitavat L D Xu Z Bi and V PungpapongldquoBlockchain and internet of things for modern businessprocess in digital economy-the state of the artrdquo IEEETransactions on Computational Social Systems vol 6 no 6pp 1420ndash1432 2019

[2] B Li and Y Li ldquoInternet of things drives supply chain in-novation a research frameworkrdquo International Journal ofOrganizational Innovation vol 9 no 3 pp 71ndash92 2017

[3] S Cockcroft and M Russell ldquoBig data opportunities for ac-counting and finance practice and researchrdquo Australian Ac-counting Review vol 28 no 3 pp 323ndash333 2018

[4] D Knezevic ldquoImpact of blockchain technology platform inchanging the financial sector and other industriesrdquo Mon-tenegrin Journal of Economics vol 14 no 1 pp 109ndash120 2018

[5] J H Nord A Koohang and J Paliszkiewicz ldquoe internet ofthings review and theoretical frameworkrdquo Expert Systemswith Applications vol 133 pp 97ndash108 2019

[6] K Leong and A Sung ldquoFinTech (financial technology) whatis it and how to use technologies to create business value infintech wayrdquo International Journal of Innovation Manage-ment and Technology vol 9 no 2 pp 74ndash78 2018

[7] M Papert and A Pflaum ldquoDevelopment of an ecosystemmodel for the realization of internet of things (IoT) services insupply chain managementrdquo Electronic Markets vol 27 no 2pp 175ndash189 2017

[8] P A Pavlou ldquoInternet of things-will humans be replaced oraugmentedrdquoGfKMarketing Intelligence Review vol 10 no 2pp 42ndash47 2018

[9] A S Wilner ldquoCybersecurity and its discontents artificialintelligence the internet of things and digital misinforma-tionrdquo International Journal Canadarsquos Journal of Global PolicyAnalysis vol 73 no 2 pp 308ndash316 2018

[10] R K R Kummitha and N Crutzen ldquoSmart cities and thecitizen-driven internet of things a qualitative inquiry into anemerging smart cityrdquo Technological Forecasting and SocialChange vol 140 pp 44ndash53 2019

[11] T Tang and A T-K Ho ldquoA path-dependence perspective onthe adoption of internet of things evidence from earlyadopters of smart and connected sensors in the United Statesrdquo

Government Information Quarterly vol 36 no 2 pp 321ndash332 2019

[12] C K M Lee S Z Zhang and K K H Ng ldquoDevelopment ofan industrial internet of things suite for smart factory towardsre-industrializationrdquoAdvances inManufacturing vol 5 no 4pp 335ndash343 2017

[13] I Salami ldquoTerrorism financing with virtual currencies canregulatory technology solutions combat thisrdquo Studies inConflict amp Terrorism vol 41 no 12 pp 968ndash989 2018

[14] L Abualigah and A Diabat ldquoA comprehensive survey of theGrasshopper optimization algorithm results variants andapplicationsrdquo Neural Computing and Applications vol 32pp 1ndash24 2020

[15] E Manavalan and K Jayakrishna ldquoA review of internet ofthings (IoT) embedded sustainable supply chain for industry40 requirementsrdquo Computers amp Industrial Engineeringvol 127 pp 925ndash953 2019

[16] H Wang C Guo and S Cheng ldquoLoC-a new financial loanmanagement system based on smart contractsrdquo FutureGeneration Computer Systems vol 100 pp 648ndash655 2019

[17] S H Lim D J Kim Y Hur and K Park ldquoAn empirical studyof the impacts of perceived security and knowledge oncontinuous intention to use mobile Fintech payment ser-vicesrdquo International Journal of Human-Computer Interactionvol 35 no 10 pp 886ndash898 2019

[18] P K Ozili ldquoImpact of digital finance on financial inclusionand stabilityrdquo Borsa Istanbul Review vol 18 no 4pp 329ndash340 2018

[19] M Yao H Di X Zheng and X Xu ldquoImpact of paymenttechnology innovations on the traditional financial industry afocus on Chinardquo Technological Forecasting and Social Changevol 135 pp 199ndash207 2018

[20] Z Tan Q Tan andM Rong ldquoAnalysis on the financing statusof PV industry in China and the ways of improvementrdquoRenewable and Sustainable Energy Reviews vol 93 pp 409ndash420 2018

[21] Z Mani and I Chouk ldquoConsumer resistance to innovation inservices challenges and barriers in the internet of things erardquoJournal of Product Innovation Management vol 35 no 5pp 780ndash807 2018

[22] L Abualigah ldquoGroup search optimizer a nature-inspiredmeta-heuristic optimization algorithm with its results vari-ants and applicationsrdquo Neural Computing and Applicationsvol 33 pp 1ndash24 2020

Complexity 9

According to the basic principles of fuzzy mathematics theevaluation index is determined according to KPI⟶CVRwhich is used for index content validity test⟶AHP de-termining index weight e 1067 pieces of data obtainedfrom the provincersquos survey and the 120times1067 evaluationindex pairwise comparison data are substituted into themodel

Data Availability

Data sharing is not applicable to this article as no datasetswere generated or analyzed during the current study

Conflicts of Interest

e authors declare that there are no conflicts of interest

Authorsrsquo Contributions

All authors have approved the publication of themanuscript

References

[1] W Viriyasitavat L D Xu Z Bi and V PungpapongldquoBlockchain and internet of things for modern businessprocess in digital economy-the state of the artrdquo IEEETransactions on Computational Social Systems vol 6 no 6pp 1420ndash1432 2019

[2] B Li and Y Li ldquoInternet of things drives supply chain in-novation a research frameworkrdquo International Journal ofOrganizational Innovation vol 9 no 3 pp 71ndash92 2017

[3] S Cockcroft and M Russell ldquoBig data opportunities for ac-counting and finance practice and researchrdquo Australian Ac-counting Review vol 28 no 3 pp 323ndash333 2018

[4] D Knezevic ldquoImpact of blockchain technology platform inchanging the financial sector and other industriesrdquo Mon-tenegrin Journal of Economics vol 14 no 1 pp 109ndash120 2018

[5] J H Nord A Koohang and J Paliszkiewicz ldquoe internet ofthings review and theoretical frameworkrdquo Expert Systemswith Applications vol 133 pp 97ndash108 2019

[6] K Leong and A Sung ldquoFinTech (financial technology) whatis it and how to use technologies to create business value infintech wayrdquo International Journal of Innovation Manage-ment and Technology vol 9 no 2 pp 74ndash78 2018

[7] M Papert and A Pflaum ldquoDevelopment of an ecosystemmodel for the realization of internet of things (IoT) services insupply chain managementrdquo Electronic Markets vol 27 no 2pp 175ndash189 2017

[8] P A Pavlou ldquoInternet of things-will humans be replaced oraugmentedrdquoGfKMarketing Intelligence Review vol 10 no 2pp 42ndash47 2018

[9] A S Wilner ldquoCybersecurity and its discontents artificialintelligence the internet of things and digital misinforma-tionrdquo International Journal Canadarsquos Journal of Global PolicyAnalysis vol 73 no 2 pp 308ndash316 2018

[10] R K R Kummitha and N Crutzen ldquoSmart cities and thecitizen-driven internet of things a qualitative inquiry into anemerging smart cityrdquo Technological Forecasting and SocialChange vol 140 pp 44ndash53 2019

[11] T Tang and A T-K Ho ldquoA path-dependence perspective onthe adoption of internet of things evidence from earlyadopters of smart and connected sensors in the United Statesrdquo

Government Information Quarterly vol 36 no 2 pp 321ndash332 2019

[12] C K M Lee S Z Zhang and K K H Ng ldquoDevelopment ofan industrial internet of things suite for smart factory towardsre-industrializationrdquoAdvances inManufacturing vol 5 no 4pp 335ndash343 2017

[13] I Salami ldquoTerrorism financing with virtual currencies canregulatory technology solutions combat thisrdquo Studies inConflict amp Terrorism vol 41 no 12 pp 968ndash989 2018

[14] L Abualigah and A Diabat ldquoA comprehensive survey of theGrasshopper optimization algorithm results variants andapplicationsrdquo Neural Computing and Applications vol 32pp 1ndash24 2020

[15] E Manavalan and K Jayakrishna ldquoA review of internet ofthings (IoT) embedded sustainable supply chain for industry40 requirementsrdquo Computers amp Industrial Engineeringvol 127 pp 925ndash953 2019

[16] H Wang C Guo and S Cheng ldquoLoC-a new financial loanmanagement system based on smart contractsrdquo FutureGeneration Computer Systems vol 100 pp 648ndash655 2019

[17] S H Lim D J Kim Y Hur and K Park ldquoAn empirical studyof the impacts of perceived security and knowledge oncontinuous intention to use mobile Fintech payment ser-vicesrdquo International Journal of Human-Computer Interactionvol 35 no 10 pp 886ndash898 2019

[18] P K Ozili ldquoImpact of digital finance on financial inclusionand stabilityrdquo Borsa Istanbul Review vol 18 no 4pp 329ndash340 2018

[19] M Yao H Di X Zheng and X Xu ldquoImpact of paymenttechnology innovations on the traditional financial industry afocus on Chinardquo Technological Forecasting and Social Changevol 135 pp 199ndash207 2018

[20] Z Tan Q Tan andM Rong ldquoAnalysis on the financing statusof PV industry in China and the ways of improvementrdquoRenewable and Sustainable Energy Reviews vol 93 pp 409ndash420 2018

[21] Z Mani and I Chouk ldquoConsumer resistance to innovation inservices challenges and barriers in the internet of things erardquoJournal of Product Innovation Management vol 35 no 5pp 780ndash807 2018

[22] L Abualigah ldquoGroup search optimizer a nature-inspiredmeta-heuristic optimization algorithm with its results vari-ants and applicationsrdquo Neural Computing and Applicationsvol 33 pp 1ndash24 2020

Complexity 9