
The adoption of Big Data Analysis (BDA) has become popular among firms since it creates evidence for decision-making by managers. However, the adoption of BDA continues to be poor among small and medium enterprises (SMEs). Therefore, this study adopted the Technology-Organization-Environment (TOE) framework to identify the drivers of readiness to adopt BDA among SMEs. Chi-square automatic interaction detection (CHAID), Bayesian network, neural network, and C5.0 algorithms of data mining were utilized to analyze data collected from 240 Vietnamese managers of SMEs. The evaluation model identified the C5.0 algorithm as the best model, with accurate results for the prediction of factors influencing the readiness to adopt BDA among SMEs. The findings revealed management support, data quality, firm size, data security and cost to be the fundamental factors influencing BDA adoption readiness. Moreover, the results identified the service sector as having a higher level of readiness toward the adoption of BDA compared to the manufacturing sector. The findings are imperative for the enhancement of the decision-making process and advancement of comprehension of the determinants of BDA adoption among SMEs by researchers, managers, providers and policymakers.
Citation: Nguyen Thi Giang, Shu-Yi Liaw. An application of data mining algorithms for predicting factors affecting Big Data Analysis adoption readiness in SMEs[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 8621-8647. doi: 10.3934/mbe.2022400
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The adoption of Big Data Analysis (BDA) has become popular among firms since it creates evidence for decision-making by managers. However, the adoption of BDA continues to be poor among small and medium enterprises (SMEs). Therefore, this study adopted the Technology-Organization-Environment (TOE) framework to identify the drivers of readiness to adopt BDA among SMEs. Chi-square automatic interaction detection (CHAID), Bayesian network, neural network, and C5.0 algorithms of data mining were utilized to analyze data collected from 240 Vietnamese managers of SMEs. The evaluation model identified the C5.0 algorithm as the best model, with accurate results for the prediction of factors influencing the readiness to adopt BDA among SMEs. The findings revealed management support, data quality, firm size, data security and cost to be the fundamental factors influencing BDA adoption readiness. Moreover, the results identified the service sector as having a higher level of readiness toward the adoption of BDA compared to the manufacturing sector. The findings are imperative for the enhancement of the decision-making process and advancement of comprehension of the determinants of BDA adoption among SMEs by researchers, managers, providers and policymakers.
In the last two decades, the topic in the study of fractional calculus theory has attracted significant attention from researchers. The strong interest stems not only from the important application of the theory, but also from the consideration of its mathematical nature. Indeed, many phenomena arising from scientific fields, including biology, physics, chemistry, financial economics, control theory, materials, medicine, and anomalous diffusion, are precisely described by fractional differential equations [1,2,3]. As an important topic for the theory of fractional differential equations, the existence results of fractional boundary value problems (BVPs) have been investigated comprehensively by scholars [4,5,6,7].
On the other hand, the theory of differential equations on graphs originated from Lumer's research work in the framework of ramification spaces in the 1980s [8]. Differential equations on graphs appear in various fields, including chemical engineering, biology, physics, and ecology [9,10,11,12]. For this reason, many scholars study mathematical models described by fractional BVPs on graphs.
In 2014 [10], Graef et al. investigated the existence of solutions for fractional BVPs on a star graph, which is composed of three nodes and two edges, that is G=V∪E with V={γ0,γ1,γ2} and E={→γ1γ0,→γ2γ0}, where γ0 represents the junction node, →γiγ0 is the edge connecting γi and γ0 with length li=|→γiγ0|,i=1,2. On each edge →γiγ0,i=1,2, the authors considered the fractional BVPs in a local coordinate system with γi as origin on x∈(0,li), given by
{−Dα0+ui=mi(x)fi(x,ui),0<x<li,i=1,2.u1(0)=u2(0)=0,u1(l1)=u2(l2),Dβ0+u1(l1)+Dβ0+u2(l2)=0, | (1.1) |
where Dα0+,Dβ0+ are Riemann-Liouville fractional derivative operators, 1<α≤2,0<β<α,mi∈C[0,li],i=1,2 with mi(x)≢0 on [0,li] and fi∈C([0,li]×R,R),i=1,2. By using Schauder fixed point theorem and Banach contraction mapping theorem, the existence and uniqueness of solutions of BVP (1.1) are obtained.
Later in 2019 [11], Mehandiratta et al. extended the results of Graef et al. on a general star graph (see Figure 1), which is a graph consisting of k+1 nodes and k edges, that is, the authors considered a graph G=V∪E,V={v0,v1,⋯,vk},E={ei=→viv0,i=1,2,⋯,k}, where v0 is the junction node, →viv0 represents the edge connecting vi and v0 with length li=|→viv0|,i=1,2,⋯,k. The author investigated the following fractional BVPs on the star graph G given by
{CDα0,xui(x)=fi(x,ui(x),CDβ0,xui(x)),0<x<li,i=1,2,⋯,k,ui(0)=0,i=1,2,⋯,k,ui(li)=uj(lj),i,j=1,2,⋯,k,i≠j,∑ki=1u′i(li)=0,i=1,2,⋯,k, | (1.2) |
where CDα0,x,CDβ0,x are Caputo fractional derivative, 1<α≤2,0<β≤α−1,fi,i=1,2,⋯,k are continuous functions on [0,li]×R×R. The existence and uniqueness results for BVP (1.2) are established using Schaefer's fixed point theorem and Banach contraction mapping theorem.
Based on the two studies mentioned above, the subject of fractional BVPs on graphs has received significant research attention, and various interesting results have been recently established [12,13,14,15,16,17,18,19]. For example, in [12], Zhang and Liu discussed BVPs of fractional differential equations on a star graph with n+1 nodes and n edges. The existence and uniqueness of solutions are established using Schaefer's fixed point theorem and Banach contraction mapping principle. Etemad and Rezapour in [13] studied the BVPs of fractional differential equations on ethane graph. The existence results of solutions were obtained using Schaefer's fixed point theorem and Krasnoselskii's fixed point theorem. In [14], Baleanu et al. investigated the existence of solutions for BVPs of fractional differential equations on the glucose graphs. In [15], Ali et al. studied the existence of solutions of BVPs for fractional differential equations on the cyclohexane graphs using the fixed point theory. In [16], Mehandiratta et al. considered a nonlinear fractional BVPs on a particular metric graph. They proved the existence and uniqueness of solutions using Krasnoselskii's fixed point theorem and Banach contraction principle.
It is well known that Langevin first formulated the Langevin equation in 1908. Langevin equation is an important tool for describing the evolution of physical phenomena in fluctuating environments [20]. However, people have realized that the traditional integer Langevin equation cannot accurately describe dynamic systems for complex phenomena. Therefore, one way to overcome this disadvantage is to use fractional derivative instead of integer derivative [21]. This gives rise to the fractional Langevin equation. Studies of BVPs on fractional Langevin equations have increased in recent years, and new research is constantly emerging [22,23,24,25]. For example, in [22], Fazli et al. studied the anti-periodic BVPs of fractional Langevin equation and obtained the existence and uniqueness solutions using the coupled fixed point theorem for mixed monotone mappings. In [23], Matar et al. established the existence, uniqueness and stability of solutions for the coupled Caputo-Hadamard fractional Langevin equation with the help of the fixed point theorem. In [24], Salem et al. considered the fractional Langevin equation with three-point boundary value conditions and obtained the existence of solutions by using Krasnoselskii's fixed point theorem and Leray-Schauder nonlinear alternative theorem.
From the literature review, no result is concerned with fractional Langevin equations on graphs. To fill this knowledge gap, this study aims to establish the existence and uniqueness results for fractional Langevin equations on a star graph subject to mixed boundary conditions. Precisely, we investigate the following problems:
{CDα0,x(D+λi)yi(x)=gi(x,yi(x),CDγ0,xyi(x)),0<x<ρi,i=1,2,⋯,k,yi(0)=0,i=1,2,⋯,k,yi(ρi)=yj(ρj),i,j=1,2,⋯,k,i≠j,k∑i=1y′i(ρi)=0,i=1,2,⋯,k, | (1.3) |
where 0<α<1,0<γ<α,λi∈R+,i=1,2,⋯,k,CDα0,x,CDγ0,x are Caputo fractional derivative, D is the ordinary derivative, gi∈C([0,ρi]×R2,R),i=1,2,⋯,k. The star graph has k+1 nodes and k edges, that is G=V∪E,V={v0,v1,⋯,vk},E={ei=→viv0,i=1,2,⋯,k}, where v0 is the junction node, ei=→viv0 represents the edge connecting vi and v0 with length ρi=|→viv0|,i=1,2,⋯,k. We consider a local coordinate system with vi as origin and x∈(0,ρi) as the coordinate. The existence and uniqueness of the solution of BVP (1.3) are discussed using Schaefer's fixed point theorem and Banach contraction mapping principle.
The rest of paper is organized as follows: In Section 2, we recall some basic definitions of fractional calculus and present an auxiliary lemma (Lemma 2.6), which transforms the problem (1.3) to BVP (2.1). In Section 3, we study the existence and uniqueness results of BVP (2.1) by using Schaefer's fixed point theorem and Banach contraction principle, respectively. Finally, two illustrative examples are discussed at the end of this paper.
In this section, we recall some definitions of fractional calculus and provide preliminary results which we will use in the rest of the paper.
Definition 2.1 [1]. The Riemann-Liouville fractional integral of order α>0 for a function f∈C(a,b) is defined by
Iαa+f(t)=1Γ(α)∫ta(t−s)α−1f(s)ds,a<t<b. |
Definition 2.2 [1]. The Caputo fractional derivative of order α>0 for a function f∈Cn(a,b) is presented by
CDαa,tf(t)=1Γ(n−α)∫ta(t−s)n−α−1f(n)(s)ds,a<t<b, |
where n=[α]+1.
Lemma 2.1 [1]. Let α>0. Suppose that u∈ACn[0,1]. Then
Iα0+CDα0,tu(t)=u(t)+c0+c1t+c2t2+⋯+cntn−1, |
where ci∈R,i=1,2,⋯,n,n=[α]+1.
Lemma 2.2 [26]. Let α>0,n∈N, and D=d/dx. Suppose that (Dnx)(t) and (CDα+na,tx)(t) are exist. Then
(CDαa,tDnx)(t)=(CDα+na,tx)(t). |
Lemma 2.3 [1]. If β>0,γ>β−1,t>0, then
CDβ0,ttγ=Γ(γ+1)Γ(γ+1−β)tγ−β. |
Theorem 2.4 [27]. (Scheafer's fixed point theorem) Let X be a Banach space. Assume that T:X→X is a completely continuous operator and the set Ω={x∈X,x=μTx,μ∈(0,1)} is bounded. Then T has a fixed point in X.
Lemma 2.5 [11]. Suppose that y is a function defined on [0,ρ] such that CDα0,xy exists on [0,ρ] with α>0 and let x∈[0,ρ],t=x/ρ∈[0,1],y(t)=y(ρt). Then
CDα0,xy(x)=ρ−α(CDα0,ty(t)). |
Lemma 2.6 Suppose that y be a function defined on [0,ρ] such that CDα0,xy exists on [0,ρ] with α∈(n−1,n) and let x∈[0,ρ],t=x/ρ∈[0,1],y(t)=y(ρt). Then
CDα0,x(D+λ)y(x)=ρ−α−1CDα0,t(D+λρ)y(t). |
Proof. By using the Definition 2.2 and Lemma 2.2, we can obtain
CDα0,x(D+λ)y(x)=CDα+10,xy(x)+λCDα0,xy(x)=1Γ(n−α)∫x0(x−s)n−α−1y(n+1)(s)ds+λΓ(n−α)∫x0(x−s)n−α−1y(n)(s)ds=1Γ(n−α)∫ρt0(ρt−s)n−α−1y(n+1)(s)ds+λΓ(n−α)∫ρt0(ρt−s)n−α−1y(n)(s)ds(x=ρt)=ρn−αΓ(n−α)∫t0(t−ˆs)n−α−1y(n+1)(ρˆs)dˆs+λρn−αΓ(n−α)∫t0(t−ˆs)n−α−1y(n)(ρˆs)dˆs(ˆs=s/ρ)=ρ−α−1Γ(n−α)∫t0(t−ˆs)n−α−1y(n+1)(ˆs)dˆs+λρ−αΓ(n−α)∫t0(t−ˆs)n−α−1y(n)(ˆs)dˆs(y(n)(t)=ρny(n)(ρt))=ρ−α−1CDα+10,ty(t)+λρ−αCDα0,ty(t)=ρ−α−1CDα0,t(D+λρ)y(t), |
This completes the proof of Lemma 2.6.
By a direct calculation with help of Lemmas 2.5 and 2.6, BVP (1.3) can be transformed into a BVP defined on [0, 1] given by
{CDα0,t(D+λiρi)yi(t)=ρα+1igi(t,yi(t),ρ−γiCDγ0,tyi(t)),t∈(0,1),yi(0)=0,i=1,2,⋯,k,yi(1)=yj(1),i,j=1,2,⋯,k,i≠j,k∑i=1ρ−1iy′i(1)=0,i=1,2,⋯,k, | (2.1) |
where yi(t)=yi(ρit),gi(t,u,v)=gi(ρit,u,v),i=1,2,⋯,k.
In this section, we investigate the existence and uniqueness results of problem (2.1). To this end, we consider the space Y={y:y∈C[0,1],CDγ0,ty∈C[0,1]}, endowed with the norm
||y||Y=||y||+||CDγ0,ty||, |
where ||y||=maxt∈[0,1]|y(t)|,||CDγ0,ty||=maxt∈[0,1]|CDγ0,ty(t)|. Then (Y,||⋅||Y) is a Banach space, and the product space (Yk,||⋅||Yk) equipped with the norm
||(y1,y2,⋯,yk)||Yk=∑ki=1||yi||Y,(y1,y2,⋯,yk)∈Yk |
is also a Banach space, where Yk=k⏞Y×Y×⋯×Y.
Lemma 3.1 Let hi∈C[0,1],i=1,2,⋯,k. Then the BVP of fractional Langevin equations
{CDα0,t(D+λiρi)yi(t)=hi(t),t∈(0,1),α∈(0,1),i=1,2,⋯,k,yi(0)=0,i=1,2,⋯,k,yi(1)=yj(1),i,j=1,2,⋯,k,i≠j,k∑i=1ρ−1iy′i(1)=0,i=1,2,⋯,k, | (3.1) |
is equivalent to the integral equations
yi(t)=−λiρi∫t0yi(s)ds+Iα+10+hi(t)+tk∑j=1ℓj(λjρjyj(1)−Iα0+hj(t)|t=1)+tk∑j=1,j≠iℓj(−λjρj∫10yj(s)ds+λiρi∫10yi(s)ds+Iα+10+hj(t)|t=1−Iα+10+hi(t)|t=1), |
where ℓj:=ρ−1j∑kj=1ρ−1j,i,j=1,2,⋯,k.
Proof. Applying the operator Iα0+ on both sides of Eq (3.1) and combining with the Lemma 2.1, we obtain
(D+λiρi)yi(t)=Iα0+hi(t)+ci1, |
where ci1∈R,i=1,2,⋯,k. The above equation can be rewritten as
y′i(t)=−λiρiyi(t)+Iα0+hi(t)+ci1. | (3.2) |
Integrating both sides of Eq (3.2) from 0 to t, we get
yi(t)=−λiρi∫t0yi(s)ds+Iα+10+hi(t)+ci1t+yi(0). |
By conditions yi(0)=0,i=1,2,⋯,k, we conclude
yi(t)=−λiρi∫t0yi(s)ds+Iα+10+hi(t)+ci1t. | (3.3) |
Applying the conditions k∑i=1ρ−1iy′i(1)=0 and yi(1)=yj(1),i,j=1,2,⋯,k,i≠j in Eqs (3.2) and (3.3), respectively, we find
k∑i=1ρ−1i(−λiρiyi(1)+Iα0+hi(t)|t=1+ci1)=0, |
and
−λiρi∫10yi(s)ds+Iα+10+hi(t)|t=1+ci1=−λjρj∫10yj(s)ds+Iα+10+hj(t)|t=1+cj1,i,j=1,2,⋯,k,i≠j. |
Combining the above two equations, we get
k∑j=1ρ−1j(−λjρjyj(1)+Iα0+hj(t)|t=1)+ρ−1ici1=−k∑j=1,j≠iρ−1jci1+k∑j=1,j≠iρ−1j(−λjρj∫10yj(s)ds+Iα+10+hj(t)|t=1+λiρi∫10yi(s)ds−Iα+10+hi(t)|t=1). |
This yields
k∑j=1ρ−1jci1=−k∑j=1ρ−1j(−λjρjyj(1)+Iα0+hj(t)|t=1)+k∑j=1,j≠iρ−1j(−λjρj∫10yj(s)ds+λiρi∫10yi(s)ds+Iα+10+hj(t)|t=1−Iα+10+hi(t)|t=1), |
from which we deduce that
ci1=k∑j=1,j≠iℓj(−λjρj∫10yj(s)ds+λiρi∫10yi(s)ds+Iα+10+hj(t)|t=1−Iα+10+hi(t)|t=1)−k∑j=1ℓj(−λjρjyj(1)+Iα0+hj(t)|t=1),i=1,2,⋯,k. |
Substituting ci1(i=1,2,⋯,k) into the Eq (3.3), we get the desired result. The converse of the lemma is calculated directly. The proof is completed.
In view of Lemma 3.1, we define the operator T:Yk→Yk by
T(y1,y2,⋯,yk)(t):=(T1(y1,y2,⋯,yk)(t),T2(y1,y2,⋯,yk)(t),⋯,Tk(y1,y2,⋯,yk)(t)), |
for t∈[0,1] and yi∈Y,i=1,2,⋯,k, where
Ti(y1,y2,⋯,yk)(t)=−λiρi∫t0yi(s)ds+ρα+1iΓ(α+1)∫t0(t−s)αgi(s,yi(s),ρ−γiCDγ0,syi(s))ds+tk∑j=1ℓj(λjρjyj(1)−ρα+1jΓ(α)∫10(1−s)α−1gj(s,yj(s),ρ−γjCDγ0,syj(s))ds)+tk∑j=1,j≠iℓj(−λjρj∫10yj(s)ds+ρα+1jΓ(α+1)∫10(1−s)αgj(s,yj(s),ρ−γjCDγ0,syj(s))ds)+tk∑j=1,j≠iℓj(−ρα+1iΓ(α+1)∫10(1−s)αgi(s,yi(s),ρ−γiCDγ0,syi(s))ds+λiρi∫10yi(s)ds). | (3.4) |
In the following part, for convenience of presentation, we denote the notations:
M1=1Γ(α+2)+1Γ(α+1)+1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2),M2=2Γ(α+2)+1Γ(α+1)+1Γ(α−γ+2)+1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2). |
Theorem 3.1 Assume that
(H1) The functions gi:[0,1]×R2→R,(i=1,2,⋯,k) are continuous and there exist functions ai(t)∈C([0,1],[0,+∞)), i=1,2,⋯,k, such that
|gi(t,u,v)−gi(t,u1,v1)|≤ai(t)(|u−u1|+|v−v1|), |
for all t∈[0,1] and (u,v),(u1,v1)∈R2. Then the BVP (2.1) has a unique solution on [0, 1], provided that
k∑i=1Pi(k∑i=1Ai)+k∑i=1Qi<1, |
where
Pi=M1k∑j=1,j≠i(ρα+1j+ρα−γ+1j)+M2(ρα+1i+ρα−γ+1i),Qi=3λiρi+3λiρiΓ(2−γ)+k∑j=1,j≠i(2λjρj+2λjρjΓ(2−γ)),Ai=maxt∈[0,1]|ai(t)|. |
Proof. Applying the Banach contraction mapping principle, we have to prove that T is a contractive mapping. To prove this, we let y=(y1,y2,⋯,yk),ˉy=(ˉy1,ˉy2,⋯,ˉyk)∈Yk,t∈[0,1]. By Eq (3.4), we have
|Tiy(t)−Tiˉy(t)|≤ρα+1iΓ(α+1)∫t0(t−s)α|gi(s,yi(s),ρ−γiCDγ0,syi(s))−gi(s,ˉyi(s),ρ−γiCDγ0,sˉyi(s))|ds+λiρi∫t0|yi(s)−ˉyi(s)|ds+tk∑j=1ℓj(λjρj|yj(1)−ˉyj(1)|)+tk∑j=1ℓjρα+1jΓ(α)∫10(1−s)α−1|gj(s,yj(s),ρ−γjCDγ0,syj(s))ds−gj(s,ˉyj(s),ρ−γjCDγ0,sˉyj(s))|ds+tk∑j=1,j≠iℓj(λjρj∫10|yj(s)−ˉyj(s)|ds)+tk∑j=1,j≠iλiρiℓj∫10|yi(s)−ˉyi(s)|ds+tk∑j=1,j≠iℓjρα+1jΓ(α+1)∫10(1−s)α|gj(s,yj(s),ρ−γjCDγ0,syj(s))−gj(s,ˉyj(s),ρ−γjCDγ0,sˉyj(s))|ds+tk∑j=1,j≠iℓjρα+1iΓ(α+1)∫10(1−s)α|gi(s,yi(s),ρ−γiCDγ0,syi(s))−gi(s,ˉyi(s),ρ−γiCDγ0,sˉyi(s))|ds. |
By using the assumption (H1) and t∈[0,1],ℓj∈(0,1),j=1,2,⋯,k, we deduce
|Tiy(t)−Tiˉy(t)|≤2ρα+1iΓ(α+2)Ai||yi−ˉyi||+2ρα−γ+1iΓ(α+2)Ai||CDγ0,tyi−CDγ0,tˉyi||+2λiρi||yi−ˉyi||+k∑j=1λjρj||yj−ˉyj||+k∑j=1,j≠iλjρj||yj−ˉyj||+k∑j=1ρα+1jAjΓ(α+1)||yj−ˉyj||+k∑j=1ρα−γ+1jAjΓ(α+1)||CDγ0,tyj−CDγ0,tˉyj||+k∑j=1,j≠iρα+1jAjΓ(α+2)||yj−ˉyj||+k∑j=1,j≠iρα−γ+1jAjΓ(α+2)||CDγ0,tyj−CDγ0,tˉyj||≤2AiΓ(α+2)(ρα+1i+ρα−γ+1i)(||yi−ˉyi||+||CDγ0,tyi−CDγ0,tˉyi||)+3λiρi||yi−ˉyi||+k∑j=1,j≠i2λjρj||yj−ˉyj||+k∑j=1AjΓ(α+1)(ρα+1j+ρα−γ+1j)(||yj−ˉyj||+||CDγ0,tyj−CDγ0,tˉyj||)+k∑j=1,j≠iAjΓ(α+2)(ρα+1j+ρα−γ+1j)(||yj−ˉyj||+||CDγ0,tyj−CDγ0,tˉyj||). |
Then for any y,ˉy∈Yk, we obtain
||Tiy−Tiˉy||≤(2Γ(α+2)+1Γ(α+1))(ρα+1i+ρα−γ+1i)Ai||yi−ˉyi||Y+3λiρi||yi−ˉyi||Y+k∑j=1,j≠i(1Γ(α+2)+1Γ(α+1))(ρα+1j+ρα−γ+1j)Aj||yj−ˉyj||Y+k∑j=1,j≠i2λjρj||yj−ˉyj||Y. | (3.5) |
On the other hand, by using Lemma 2.3, we have
|CDγ0,tTiy(t)−CDγ0,tTiˉy(t)|≤ρα+1iΓ(α−γ+1)∫t0(t−s)α−γ|gi(s,yi(s),ρ−γiCDγ0,syi(s))−gi(s,ˉyi(s),ρ−γiCDγ0,sˉyi(s))|ds+λiρiΓ(1−γ)∫t0(t−s)−γ|yi(s)−ˉyi(s)|ds+t1−γΓ(2−γ)k∑j=1ℓjλjρj|yj(1)−ˉyj(1)|+t1−γΓ(2−γ)Γ(α)k∑j=1ℓjρα+1j∫10(1−s)α−1|gj(s,yj(s),ρ−γjCDγ0,syj(s))ds−gj(s,ˉyj(s),ρ−γjCDγ0,sˉyj(s))|ds |
+t1−γΓ(2−γ)k∑j=1,j≠iℓjλjρj∫10|yj(s)−ˉyj(s)|ds+t1−γΓ(2−γ)k∑j=1,j≠iλiρiℓj∫10|yi(s)−ˉyi(s)|ds+t1−γΓ(2−γ)Γ(α+1)k∑j=1,j≠iℓjρα+1j∫10(1−s)α|gj(s,yj(s),ρ−γjCDγ0,syj(s))−gj(s,ˉyj(s),ρ−γjCDγ0,sˉyj(s))|ds+t1−γΓ(2−γ)Γ(α+1)k∑j=1,j≠iρα+1iℓj∫10(1−s)α|gi(s,yi(s),ρ−γiCDγ0,syi(s))−gi(s,ˉyi(s),ρ−γiCDγ0,sˉyi(s))|ds. |
In a similar manner, we deduce
|CDγ0,tTiy(t)−CDγ0,tTiˉy(t)|≤ρα+1iAiΓ(α−γ+2)||yi−ˉyi||+ρα−γ+1iAiΓ(α−γ+2)||CDγ0,tyi−CDγ0,tˉyi||+2λiρiΓ(2−γ)||yi−ˉyi||+1Γ(2−γ)k∑j=1λjρj||yj−ˉyj||+1Γ(2−γ)Γ(α+1)k∑j=1ρα+1jAj||yj−ˉyj||+1Γ(2−γ)Γ(α+1)k∑j=1ρα−γ+1jAj||CDγ0,tyj−CDγ0,tˉyj||+1Γ(2−γ)k∑j=1,j≠iλjρj||yj−ˉyj||+1Γ(2−γ)Γ(α+2)k∑j=1,j≠iρα+1jAj||yj−ˉyj||+1Γ(2−γ)Γ(α+2)k∑j=1,j≠iρα−γ+1jAj||CDγ0,tyj−CDγ0,tˉyj||+ρα+1iAiΓ(2−γ)Γ(α+2)||yi−ˉyi||+ρα−γ+1iAiΓ(2−γ)Γ(α+2)||CDγ0,tyi−CDγ0,tˉyi||≤1Γ(α−γ+2)(ρα+1i+ρα−γ+1i)Ai(||yi−ˉyi||+||CDγ0,tyi−CDγ0,tˉyi||)+3λiρiΓ(2−γ)||yi−ˉyi||+2Γ(2−γ)k∑j=1,j≠iλjρj||yj−ˉyj||+1Γ(2−γ)Γ(α+1)k∑j=1(ρα+1j+ρα−γ+1j)Aj(||yj−ˉyj||+||CDγ0,tyj−CDγ0,tˉyj||)+1Γ(2−γ)Γ(α+2)k∑j=1,j≠i(ρα+1j+ρα−γ+1j)Aj(||yj−ˉyj||+||CDγ0,tyj−CDγ0,tˉyj||)+1Γ(2−γ)Γ(α+2)(ρα+1i+ρα−γ+1i)Ai(||yi−ˉyi||+||CDγ0,tyi−CDγ0,tˉyi||). |
This implies that, for any y,ˉy∈Yk,
||CDγ0,tTiy(t)−CDγ0,tTiˉy(t)||≤(1Γ(α−γ+2)+1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))(ρα+1i+ρα−γ+1i)Ai||yi−ˉyi||Y |
+(1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))k∑j=1,j≠i(ρα+1j+ρα−γ+1j)Aj||yj−ˉyj||Y+3λiρiΓ(2−γ)||yi−ˉyi||Y+k∑j=1,j≠i2λjρjΓ(2−γ)||yj−ˉyj||Y. | (3.6) |
By a direct calculation with help of (3.5) and (3.6), we get
||Tiy−Tiˉy||+||CDγ0,tTiy−CDγ0,tTiˉy||≤M2(ρα+1i+ρα−γ+1i)Ai||yi−ˉyi||Y+(3λiρi+3λiρiΓ(2−γ))||yi−ˉyi||Y+M1k∑j=1,j≠i(ρα+1j+ρα−γ+1j)Aj||yj−ˉyj||Y+k∑j=1,j≠i(2λjρj+2λjρjΓ(2−γ))||yj−ˉyj||Y. |
From this it follows that
||Tiy−Tiˉy||Y≤(M2(ρα+1i+ρα−γ+1i)+M1k∑j=1,j≠i(ρα+1j+ρα−γ+1j))(k∑i=1Ai)k∑j=1||yj−ˉyj||Y+(3λiρi+3λiρiΓ(2−γ)+k∑j=1,j≠i(2λjρj+2λjρjΓ(2−γ)))k∑j=1||yj−ˉyj||Y=(Pi(k∑i=1Ai)+Qi)k∑j=1||yj−ˉyj||Y. |
As a consequence, we obtain
||Ty−Tˉy||Yk=k∑i=1||Tiy−Tiˉy||Y≤(k∑i=1Pi(k∑i=1Ai)+k∑i=1Qi)||y−ˉy||Yk. |
It follows from the condition k∑i=1Pi(k∑i=1Ai)+k∑i=1Qi<1 that T is a contractive mapping. Hence, T has a unique fixed point on Yk, that is, BVP (2.1) has a unique solution. Therefore, we obtain the conclusion of the theorem.
Theorem 3.2 Assume that
(H2) The functions gi:[0,1]×R2→R,(i=1,2,⋯,k) are continuous and there exist functions pi(t),qi(t),ri(t)∈C([0,1],[0,+∞)),(i=1,2,⋯,k) such that
|gi(t,u,v)|≤pi(t)+qi(t)|u(t)|+ri(t)|v(t)|, |
for all t∈[0,1],u,v∈R. Then the BVP (2.1) admits at least one solution in Y provided that
k∑i=1θi<1, |
where
θi=Δi(q∗i+ρ−γir∗i)+ϖi+k∑j=1,j≠i(˜Δj(q∗j+ρ−γjr∗j)+˜ϖj), |
and
p∗i=maxt∈[0,1]|pi(t)|,q∗i=maxt∈[0,1]|qi(t)|,r∗i=maxt∈[0,1]|ri(t)|,Δi=M2ρα+1i,ϖi=3λiρi+3λiρiΓ(2−γ),˜ϖj=2λjρj+2λjρjΓ(2−γ),˜Δj=M1ρα+1j. |
Proof. We divide the proof into two steps.
Step 1. We need to verify that the operator T is a completely continuous. In fact, since the functions gi(i=1,2,⋯,k) are continuous, we can easily prove that the operators Ti(i=1,2,⋯k) are continuous, and thus T is continuous. Next, we have to show that T is compact. To see this, we define the bounded subset Λ={yi∈Y,||yi||Y≤εi} on Y, then for any y=(y1,y2,⋯,yk)∈Λ, by (H2), we find that
|Tiy(t)|≤λiρi∫t0|yi(s)|ds+ρα+1iΓ(α+1)∫t0(t−s)α|gi(s,yi(s),ρ−γiCDγ0,syi(s))|ds+k∑j=1ℓj(λjρj|yj(1)|+ρα+1jΓ(α)∫10(1−s)α−1|gj(s,yj(s),ρ−γjCDγ0,syj(s))|ds)+k∑j=1,j≠iℓj(λjρj∫10|yj(s)|ds+ρα+1jΓ(α+1)∫10(1−s)α|gj(s,yj(s),ρ−γjCDγ0,syj(s))|ds)+k∑j=1,j≠iℓj(ρα+1iΓ(α+1)∫10(1−s)α|gi(s,yi(s),ρ−γiCDγ0,syi(s))|ds+λiρi∫10|yi(s)|ds)≤λiρi||yi||+ρα+1iΓ(α+2)(p∗i+q∗i||yi||+ρ−γir∗i||CDγ0,tyi||)+k∑j=1λjρj||yj||+k∑j=1ρα+1jΓ(α+1)(p∗j+q∗j||yj||+ρ−γjr∗j||CDγ0,tyj||)+k∑j=1,j≠iλjρj||yj||+k∑j=1,j≠iρα+1jΓ(α+2)(p∗j+q∗j||yj||+ρ−γjr∗j||CDγ0,tyj||)+ρα+1iΓ(α+2)(p∗i+q∗i||yi||+ρ−γir∗i||CDγ0,tyi||)+λiρi||yi||≤λiρi||yi||Y+ρα+1iΓ(α+2)(p∗i+(q∗i+ρ−γir∗i)||yi||Y)+k∑j=1λjρj||yj||Y+k∑j=1ρα+1jΓ(α+1)(p∗j+(q∗j+ρ−γjr∗j)||yj||Y)+k∑j=1,j≠iλjρj||yj||Y+k∑j=1,j≠iρα+1jΓ(α+2)(p∗j+(q∗j+ρ−γjr∗j)||yj||Y) |
+ρα+1iΓ(α+2)(p∗i+(q∗i+ρ−γir∗i)||yi||Y)+λiρi||yi||Y. |
From which we can deduce that
||Tiy||≤3λiρi||yi||Y+(2Γ(α+2)+1Γ(α+1))ρα+1i(p∗i+(q∗i+ρ−γir∗i)||yi||Y)+k∑j=1,j≠i2λjρj||yj||Y+k∑j=1,j≠i(1Γ(α+1)+1Γ(α+2))ρα+1j(p∗j+(q∗j+ρ−γjr∗j)||yj||Y)≤(3λiρi+(2Γ(α+2)+1Γ(α+1))ρα+1i(q∗i+ρ−γir∗i))||yi||Y+k∑j=1,j≠i(2λjρj+(1Γ(α+1)+1Γ(α+2))ρα+1j(q∗j+ρ−γjr∗j))||yj||Y+(2Γ(α+2)+1Γ(α+1))ρα+1ip∗i+k∑j=1,j≠i(1Γ(α+1)+1Γ(α+2))ρα+1jp∗j. | (3.7) |
On the other hand, by Lemma 2.3 and (H2), we also can get the estimate
|CDγ0,tTiy(t)|≤λiρiΓ(1−γ)∫t0(t−s)−γ|yi(s)|ds+ρα+1iΓ(α+1−γ)∫t0(t−s)α−γ|gi(s,yi(s),ρ−γiCDγ0,syi(s))|ds+t1−γΓ(2−γ)k∑j=1ℓj(λjρj|yj(1)|+ρα+1jΓ(α)∫10(1−s)α−1|gj(s,yj(s),ρ−γjCDγ0,syj(s))|ds)+t1−γΓ(2−γ)k∑j=1,j≠iℓj(λjρj∫10|yj(s)|ds+ρα+1jΓ(α+1)∫10(1−s)α|gj(s,yj(s),ρ−γjCDγ0,syj(s))|ds)+t1−γΓ(2−γ)k∑j=1,j≠iℓj(ρα+1iΓ(α+1)∫10(1−s)α|gi(s,yi(s),ρ−γiCDγ0,syi(s))|ds+λiρi∫10|yi(s)|ds)≤λiρiΓ(2−γ)||yi||+ρα+1iΓ(α−γ+2)(p∗i+q∗i||yi||+ρ−γir∗i||CDγ0,tyi||)+1Γ(2−γ)k∑j=1λjρj||yj||+1Γ(2−γ)Γ(α+1)k∑j=1ρα+1j(p∗j+q∗j||yj||+ρ−γjr∗j||CDγ0,tyj||)+1Γ(2−γ)k∑j=1,j≠iλjρj||yj||+1Γ(2−γ)Γ(α+2)k∑j=1,j≠iρα+1j(p∗j+q∗j||yj||+ρ−γjr∗j||CDγ0,tyj||)+ρα+1iΓ(2−γ)Γ(α+2)(p∗i+q∗i||yi||+ρ−γir∗i||CDγ0,tyi||)+λiρiΓ(2−γ)||yi||. |
In a similar manner, we deduce
||CDγ0,tTiy||≤(1Γ(α−γ+2)+1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1i(q∗i+ρ−γir∗i)||yi||Y |
+k∑j=1,j≠i(2λjρjΓ(2−γ)+(1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1j(q∗j+ρ−γjr∗j))||yj||Y+k∑j=1,j≠i(1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1jp∗j+3λiρiΓ(2−γ)||yi||Y+(1Γ(α−γ+2)+1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1ip∗i. | (3.8) |
From (3.7) and (3.8), we get that
||Tiy||+||CDγ0,tTiy||≤M2ρα+1i(q∗i+ρ−γir∗i)||yi||Y+(3λiρi+3λiρiΓ(2−γ))||yi||Y+k∑j=1,j≠i(2λjρj+2λjρjΓ(2−γ))||yj||Y+k∑j=1,j≠iM1ρα+1j(q∗j+ρ−γjr∗j)||yj||Y+M2ρα+1ip∗i+k∑j=1,j≠iM1ρα+1jp∗j=Δi(q∗i+ρ−γir∗i)||yi||Y+ϖi||yi||Y+k∑j=1,j≠i˜Δj(q∗j+ρ−γjr∗j)||yj||Y+k∑j=1,j≠i˜ϖj||yj||Y+Δip∗i+k∑j=1,j≠i˜Δjp∗j≤(Δi(q∗i+ρ−γir∗i)+ϖi)||yi||Y+k∑j=1,j≠i(˜Δj(q∗j+ρ−γjr∗j)+˜ϖj)||yj||Y+Δip∗i+k∑j=1,j≠i˜Δjp∗j≤[(Δi(q∗i+ρ−γir∗i)+ϖi)+k∑j=1,j≠i(˜Δj(q∗j+ρ−γjr∗j)+˜ϖj)]k∑j=1||yj||Y+Ni=θik∑j=1εj+Ni, |
where
Ni=Δip∗i+k∑j=1,j≠i˜Δjp∗j,i=1,2,⋯k. | (3.9) |
Form this it follows that
||Ty||Yk=k∑i=1||Tiy||Y≤k∑i=1θi(k∑j=1εj)+k∑i=1Ni. |
Hence, the operator T is uniformly bounded on Λ.
Now, We will show that the operator T is equicontinuous on Λ. Indeed, for y=(y1,y2,⋯,yk)∈Λ,t1,t2∈[0,1],t1<t2, we have
|Tiy(t2)−Tiy(t1)|≤ρα+1iΓ(α+1)(∫t10((t2−s)α−(t1−s)α)ds+∫t2t1(t2−s)αds)(p∗i+q∗i||yi||+ρ−γir∗i||CDγ0,tyi||) |
+(t2−t1)k∑j=1(λjρj||yj||+ρα+1jΓ(α+1)(p∗j+q∗j||yj||+ρ−γjr∗j||CDγ0,tyj||))+(t2−t1)k∑j=1,j≠i(λjρj||yj||+ρα+1jΓ(α+2)(p∗j+q∗j||yj||+ρ−γjr∗j||CDγ0,tyj||))+(t2−t1)(ρα+1iΓ(α+2)(p∗i+q∗i||yi||+ρ−γir∗i||CDγ0,tyi||)+λiρi||yi||)+λiρi||yi||(t2−t1)≤λiρiεi(t2−t1)+ρα+1i(p∗i+(q∗i+r∗iρ−γi)εi)Γ(α+2)(tα+12−tα+11)+(t2−t1)k∑j=1(λjρjεj+ρα+1j(p∗j+(q∗j+r∗jρ−γj)εj)Γ(α+1))+(t2−t1)k∑j=1,j≠i(λjρjεj+ρα+1j(p∗j+(q∗j+r∗jρ−γj)εj)Γ(α+2))+(t2−t1)(ρα+1i(p∗i+(q∗i+r∗iρ−γi)εi)Γ(α+2)+λiρiεi), | (3.10) |
and
|CDγ0,tTiy(t2)−CDγ0,tTiy(t1)|≤λiρiΓ(1−γ)||yi||(∫t10((t1−s)−γ−(t2−s)−γ)ds+∫t2t1(t2−s)−γds)+ρα+1i(p∗i+q∗i||yi||+ρ−γir∗i||CDγ0,tyi||)Γ(α−γ+1)(∫t10((t2−s)α−γ−(t1−s)α−γ)ds+∫t2t1(t2−s)α−γds)+(t1−γ2−t1−γ1)Γ(2−γ)k∑j=1λjρj||yj||+(t1−γ2−t1−γ1)Γ(2−γ)Γ(α+1)k∑j=1ρα+1j(p∗j+q∗j||yj||+ρ−γjr∗j||CDγ0,tyj||)+(t1−γ2−t1−γ1)Γ(2−γ)k∑j=1,j≠iλjρj||yj||+(t1−γ2−t1−γ1)Γ(2−γ)Γ(α+2)k∑j=1,j≠iρα+1j(p∗j+q∗j||yj||+ρ−γjr∗j||CDγ0,tyj||)+(t1−γ2−t1−γ1)ρα+1iΓ(2−γ)Γ(α+2)(p∗i+q∗i||yi||+ρ−γir∗i||CDγ0,tyi||)+(t1−γ2−t1−γ1)λiρi||yi||Γ(2−γ)≤λiρiεiΓ(2−γ)(t1−γ1−t1−γ2+2(t2−t1)1−γ)+ρα+1i(p∗i+(q∗i+ρ−γir∗i)εi)Γ(α−γ+2)(tα−γ+12−tα−γ+11)+(t1−γ2−t1−γ1)Γ(2−γ)k∑j=1λjρjεj+(t1−γ2−t1−γ1)Γ(2−γ)Γ(α+1)k∑j=1ρα+1j(p∗j+(q∗j+ρ−γjr∗j)εj)+(t1−γ2−t1−γ1)Γ(2−γ)k∑j=1,j≠iλjρjεj+(t1−γ2−t1−γ1)Γ(2−γ)Γ(α+2)k∑j=1,j≠iρα+1j(p∗j+(q∗j+ρ−γjr∗j)εj)+(t1−γ2−t1−γ1)ρα+1iΓ(2−γ)Γ(α+2)(p∗i+(q∗i+ρ−γir∗i)εi)+(t1−γ2−t1−γ1)λiρiεiΓ(2−γ). | (3.11) |
Form (3.10) and (3.11), we get
||Tiy(t2)−Tiy(t1)||Y≤(3λiρiεi+(ρα+1iΓ(α+1)+ρα+1iΓ(α+2))(p∗i+(q∗i+ρ−βir∗i)εi))(t2−t1)+ρα+1iΓ(α+2)(p∗i+(q∗i+ρ−γir∗i)εi)(tα+12−tα+11)+2λiρiεiΓ(2−γ)(t1−γ2−t1−γ1)+((ρα+1iΓ(2−γ)Γ(α+2)+ρα+1iΓ(2−γ)Γ(α+1))(p∗i+(q∗i+ρ−γir∗i)εi))(t1−γ2−t1−γ1)+λiρiεiΓ(2−γ)(t1−γ1−t1−γ2+2(t2−t1)1−γ)+ρα+1iΓ(α−γ+2)(p∗i+(q∗i+ρ−γir∗i)εi)(tα−γ+12−tα−γ+11)+k∑j=1,j≠i(2λjρjεjΓ(2−γ)+(ρα+1jΓ(2−γ)Γ(α+1)+ρα+1jΓ(2−γ)Γ(α+2))(p∗j+(q∗j+ρ−γjr∗j)εj))(t1−γ2−t1−γ1)+k∑j=1,j≠i(2λjρjεj+(ρα+1jΓ(α+2)+ρα+1jΓ(α+1))(p∗j+(q∗j+ρ−γjr∗j)εj))(t2−t1), |
which implies ||Tiy(t2)−Tiy(t1)||Y→0 as t2→t1, and so ||Ty(t2)−Ty(t1)||Yk→0 as t2→t1. Therefore, the operator T is equicontinuous on Λ. According to Arzelá-Ascoli theorem that T is completely continuous.
Step 2. By applying Scheafer's fixed point theorem, we now prove that T has fixed point in Y. To this aim, we define Ω={(y1,y2,⋯,yk)∈Yk:(y1,y2,⋯,yk)=μT(y1,y2,⋯,yk),μ∈(0,1)} and show that Ω is bounded. In fact, for (y1,y2,⋯,yk)∈Ω, then (y1,y2,⋯,yk)=μT(y1,y2,⋯,yk), that is, for t∈[0,1], we have yi(t)=μTi(y1,y2,⋯,yk),i=1,2,⋯,k. Similarly in the proof of (3.7), by assumption (H2), we deduce
|yi(t)|≤μ[(3λiρi+(2Γ(α+2)+1Γ(α+1))ρα+1i(q∗i+ρ−γir∗i))||yi||Y+k∑j=1,j≠i(2λjρj+(1Γ(α+1)+1Γ(α+2))ρα+1j(q∗j+ρ−γjr∗j))||yj||Y+(2Γ(α+2)+1Γ(α+1))ρα+1ip∗i+k∑j=1,j≠i(1Γ(α+1)+1Γ(α+2))ρα+1jp∗j], |
from which we obtain
||yi||≤(3λiρi+(2Γ(α+2)+1Γ(α+1))ρα+1i(q∗i+ρ−γir∗i))||yi||Y+k∑j=1,j≠i(2λjρj+(1Γ(α+1)+1Γ(α+2))ρα+1j(q∗j+ρ−γjr∗j))||yj||Y+(2Γ(α+2)+1Γ(α+1))ρα+1ip∗i+k∑j=1,j≠i(1Γ(α+1)+1Γ(α+2))ρα+1jp∗j. | (3.12) |
In a similar manner of deduce (3.8), by assumption (H2), we also can obtain the estimate
|CDγ0,tyi(t)|≤μ[(1Γ(α−γ+2)+1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1i(q∗i+ρ−γir∗i)||yi||Y+k∑j=1,j≠i(2λjρjΓ(2−γ)+(1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1j(q∗j+ρ−γjr∗j))||yj||Y+k∑j=1,j≠i(1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1jp∗j+3λiρiΓ(2−γ)||yi||Y+(1Γ(α−γ+2)+1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1ip∗i]. |
Then for t∈[0,1], we get
||CDγ0,tyi||≤(1Γ(α−γ+2)+1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1i(q∗i+ρ−γir∗i)||yi||Y+k∑j=1,j≠i(2λjρjΓ(2−γ)+(1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1j(q∗j+ρ−γjr∗j))||yj||Y+k∑j=1,j≠i(1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1jp∗j+3λiρiΓ(2−γ)||yi||Y+(1Γ(α−γ+2)+1Γ(2−γ)Γ(α+1)+1Γ(2−γ)Γ(α+2))ρα+1ip∗i. |
Combining this with (3.12) gives
||yi||+||CDγ0,tyi||≤M2ρα+1i(q∗i+ρ−γir∗i)||yi||Y+(3λiρi+3λiρiΓ(2−γ))||yi||Y+k∑j=1,j≠i(2λjρj+2λjρjΓ(2−γ))||yj||Y+k∑j=1,j≠iM1ρα+1j(q∗j+ρ−γjr∗j)||yj||Y+M2ρα+1ip∗i+k∑j=1,j≠iM1ρα+1jp∗j≤θik∑j=1||yj||Y+Ni, |
where Ni is defined as in (3.9), from which we deduce that
||y||Yk=k∑i=1||yi||Y≤k∑i=1θi||y||Yk+k∑i=1Ni. |
It follows from k∑i=1θi<1 that Ω is bounded. By Theorem 2.4, the operator T has at least one fixed point, that is, the BVP (2.1) has at least one solution.
Example 4.1 Consider the BVP (1.3) with k=3,α=12,γ=13,λ1=λ2=λ3=ρ1=110,ρ2=120,ρ3=130, and
{g1(x,u,v)=cosx+1(x+2)2(sinu+v),(x,u,v)∈[0,ρ1]×R×R,g2(x,u,v)=1√x+12(x2+4)2(|u|+|v|),(x,u,v)∈[0,ρ2]×R×R,g3(x,u,v)=1+x2+13(x+3)3(u1+u+v),(x,u,v)∈[0,ρ3]×R×R. |
In view of Lemma 2.6, we get the equivalent system
{CD1/20,t(D+1100)y1(t)=(110)3/2[cost+1(t+2)2(siny1(t)+(110)−1/3CD1/30,ty1(t))],CD1/20,t(D+1200)y2(t)=(120)3/2[1√t+12(t2+4)2(|y2(t)|+(120)−1/3|CD1/30,ty2(t)|)],CD1/20,t(D+1300)y3(t)=(130)3/2[1+t2+13(t+3)3(y3(t)1+y3(t)+(130)−1/3CD1/30,ty3(t))],y1(0)=y2(0)=y3(0)=0,y1(1)=y2(1)=y3(1),(1/10)−1y′1(1)+(1/20)−1y′2(1)+(1/30)−1y′3(1)=0. | (4.1) |
From (4.1), for t∈[0,1],u,v,u1,v1∈R, we can conclude that
|g1(t,u,v)−g1(t,u1,v1)|≤1(t+2)2(|u−u1|+|v−v1|),|g2(t,u,v)−g2(t,u1,v1)|≤12(t2+4)2(|u−u1|+|v−v1|),|g3(t,u,v)−g3(t,u1,v1)|≤13(t+3)3(|u−u1|+|v−v1|). |
So, we get
a1(t)=1(t+2)2,a2(t)=12(t2+4)2,a3(t)=13(t+3)3. |
By simple calculation, we obtain
A1=maxt∈[0,1]|a1(t)|=14,A2=maxt∈[0,1]|a2(t)|=132,A3=maxt∈[0,1]|a3(t)|=181. |
P1≐0.8256,P2≐0.7281,P3≐0.7183,Q1≐0.0983,Q2≐0.0878,Q3≐0.0843. |
Then
(3∑i=1Pi)(3∑i=1Ai)+3∑i=1Qi≐0.9374<1. |
From Theorem 3.1 that the BVP (4.1) has a unique solution.
Example 4.2 Consider the BVP (1.3) with k=3,α=12,γ=13,λ1=λ2=λ3=120,ρ2=15,ρ1=ρ3=110, and
{g1(x,u,v)=x10+12(x+3)3u+153√10(x+2)2v,(x,u,v)∈[0,ρ1]×R×R,g2(x,u,v)=sinx+13(x+2)2u+x203√5v,(x,u,v)∈[0,ρ2]×R×R,g3(x,u,v)=2x+1(x+5)2u+1123√10(x+2)v,(x,u,v)∈[0,ρ3]×R×R. |
Then by Lemma 2.6, we obtain the equivalent system
{CD1/20,t(D+1200)y1(t)=(110)3/2[t10+siny1(t)2(t+3)3+CD1/1330,ty1(t)53√10(t+2)2],CD1/20,t(D+1100)y2(t)=(15)3/2[sint+y2(t)3(t+2)2+t203√5CD1/1330,ty2(t)],CD1/20,t(D+1200)y3(t)=(110)3/2[2t+y3(t)(t+5)2+D1/1330,ty3(t)123√10(t+2)],y1(0)=y2(0)=y3(0),y1(1)=y2(1)=y3(1),(1/10)−1y′1(1)+(1/5)−1y′2(1)+(1/10)−1y′3(1)=0. | (4.2) |
Then
q1(t)=12(t+3)3,r1(t)=15(t+2)2,q2(t)=13(t+2)2,r2(t)=t20,q3(t)=1(t+5)2,r3(t)=112(t+2). |
For t∈[0,1], we have q∗1=154,q∗2=112,q∗3=125,r∗1=r∗2=120,r∗3=124. By calculation, we get
Δ1≐0.1783,˜Δ1≐0.1253,ϖ1≐0.0316,˜ϖ1≐0.0211,Δ2≐0.4043,˜Δ2≐0.3544,ϖ2≐0.0632,˜ϖ2≐0.0421,Δ3≐0.1783,˜Δ3≐0.1253,ϖ3≐0.0316,˜ϖ3≐0.0211, |
So,
θ1=Δ1(q∗1+ρ−γ1r∗1)+ϖ1+(˜Δ2(q∗2+ρ−γ2r∗2)+˜ϖ2)+(˜Δ3(q∗3+ρ−γ3r∗3)+˜ϖ3)≐0.1934,θ2=Δ2(q∗2+ρ−γ2r∗2)+ϖ2+(˜Δ1(q∗1+ρ−γ1r∗1)+˜ϖ1)+(˜Δ3(q∗3+ρ−γ3r∗3)+˜ϖ3)≐0.2057,θ3=Δ3(q∗3+ρ−γ3r∗3)+ϖ3+(˜Δ1(q∗1+ρ−γ1r∗1)+˜ϖ1)+(˜Δ2(q∗2+ρ−γ2r∗2)+˜ϖ2)≐0.1935. |
Thus,
θ1+θ2+θ3≐0.5926<1. |
According to Theorem 3.2, the BVP (4.2) has at least one solution.
This paper considers the fractional Langevin equations on a star graph of the form (1.3). By using Lemma 2.6, the problem (1.3) is transformed into an equivalent system of fractional Langevin equations supplemented with mixed boundary conditions defined on [0,1], that is, problem (2.1). Making use of the fixed point theorems (Schauder's fixed point theorem, Banach's contraction mapping principle), sufficient criteria for the existence and uniqueness results are derived. Finally, we present two examples to illustrate the validity of the obtained results. As a possible extension of this paper, we will study the higher-order fractional Langevin-type equations on star graphs in the future, such as
CDα0,x(D2+λi)yi(x)=gi(x,yi(x),CDβ0,xyi(x)),0<x<li,i=1,2,⋯,k, |
supplemented with the boundary conditions
{y′i(0)=yi(0)=0,i=1,2,⋯,k,y′i(li)=y′j(lj),i,j=1,2,⋯,k,i≠j,∑ki=1y″ |
and
\left\{ \begin{gathered} {{\mathfrak{y}'}_i}(0) = {\mathfrak{y}_i}(1) = 0, \;\;i = 1, 2, \cdots , k, \hfill \\ {{\mathfrak{y}''}_i}({l_i}) = {{\mathfrak{y}''}_j}({l_j}), \;\;i, j = 1, 2, \cdots , k, i \ne j, \hfill \\ \sum\nolimits_{i = 1}^k {{{\mathfrak{y}''}_i}({l_i}) = 0, \;i = 1, 2, \cdots , k, } \hfill \\ \end{gathered} \right. |
where 0 < \alpha < 1, \; 0 < \beta < \alpha, \; {\lambda _i} \in \mathbb{R}^+, \; i = 1, 2, \cdots, k, \; {}^CD_{0, x}^\alpha, {}^CD_{0, x}^\beta are Caputo fractional derivative, D^2 is the ordinary second-order derivative, {{\mathfrak{g}_i}} \in C([0, {l_i}] \times \mathbb{R}^2, \mathbb{R}), \; i = 1, 2, \cdots, k. The star graph has k+1 nodes and k edges, that is G = V\cup E, V{\rm{ = }}\{ {v_0}, {v_1}, \cdots, {v_k}\}, E = \{ {e_i} = \overrightarrow {{v_i}{v_0}}, i = 1, 2, \cdots, k\}, where {v_0} is the junction node, {e_i} = \overrightarrow {{v_i}{v_0}} represents the edge connecting {v_i} and {v_0} with length {l_i} = \left| {\overrightarrow {{v_i}{v_0}} } \right|, i = 1, 2, \cdots, k.
The authors wish to express their sincere appreciation to the editor and the anonymous referees for their valuable comments and suggestions. This research is supported by the National Natural Science Foundation of China (11601007) and the Key Program of University Natural Science Research Fund of Anhui Province (KJ2020A0291).
The authors declare that they have no competing interests.
[1] |
I. Yaqoob, I. A. T. Hashem, A. Gani, S. Mokhtar, E. Ahmed, N. B. Anuar, et al., Big data: from beginning to future, Int. J. Inf. Manage., 36 (2016), 1231-1247. https://doi.org/10.1016/j.ijinfomgt.2016.07.009 doi: 10.1016/j.ijinfomgt.2016.07.009
![]() |
[2] |
S. S. Alrumiah, M. Hadwan, Implementing big data analytics in e-commerce: Vendor and customer view, IEEE Access, 9 (2021), 37281-37286. https://doi.org/10.1109/ACCESS.2021.3063615 doi: 10.1109/ACCESS.2021.3063615
![]() |
[3] |
T. M. Le, S. Y. Liaw, Effects of pros and cons of applying big data analytics to consumers' responses in an e-commerce context, Sustainability, 9 (2017), 1-19. https://doi.org/10.3390/su9050798 doi: 10.3390/su9050798
![]() |
[4] |
M. Janssen, H. van der Voort, A. Wahyudi, Factors influencing big data decision-making quality, J. Bus. Res., 70 (2017), 338-345. https://doi.org/10.1016/j.jbusres.2016.08.007 doi: 10.1016/j.jbusres.2016.08.007
![]() |
[5] |
G. Wang, A. Gunasekaran, E. W. T. Ngai, T. Papadopoulos, Big data analytics in logistics and supply chain management: Certain investigations for research and applications, Int. J. Prod. Econ., 176 (2016), 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014 doi: 10.1016/j.ijpe.2016.03.014
![]() |
[6] |
S. Tiwari, H. M. Wee, Y. Daryanto, Big data analytics in supply chain management between 2010 and 2016: Insights to industries, Comput. Ind. Eng., 115 (2018), 319-330. https://doi.org/10.1016/j.cie.2017.11.017 doi: 10.1016/j.cie.2017.11.017
![]() |
[7] |
S. Akter, S. F. Wamba, A. Gunasekaran, R. Dubey, S. J. Childe, How to improve firm performance using big data analytics capability and business strategy alignment?, Int. J. Prod. Econ., 182 (2016), 113-131. https://doi.org/10.1016/j.ijpe.2016.08.018 doi: 10.1016/j.ijpe.2016.08.018
![]() |
[8] |
P. Mikalef, M. Boura, G. Lekakos, J. Krogstie, Big data analytics and firm performance: Findings from a mixed-method approach, J. Bus. Res., 98 (2019), 261-276. https://doi.org/10.1016/j.jbusres.2019.01.044 doi: 10.1016/j.jbusres.2019.01.044
![]() |
[9] |
P. Maroufkhani, M. L. Tseng, M. Iranmanesh, W. K. W. Ismail, H. Khalid, Big data analytics adoption: Determinants and performances among small to medium-sized enterprises, Int. J. Inf. Manage., 54 (2020), 1-15. https://doi.org/10.1016/j.ijinfomgt.2020.102190 doi: 10.1016/j.ijinfomgt.2020.102190
![]() |
[10] |
Z. Xu, G. L. Frankwick, E. Ramirez, Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective, J. Bus. Res., 69 (2016), 1562-1566. https://doi.org/10.1016/j.jbusres.2015.10.017 doi: 10.1016/j.jbusres.2015.10.017
![]() |
[11] |
S. Mandal, An examination of the importance of big data analytics in supply chain agility development, Manag. Res. Rev., 41 (2018), 1201-1219. https://doi.org/10.1108/MRR-11-2017-0400 doi: 10.1108/MRR-11-2017-0400
![]() |
[12] |
L. Wang, M. Yang, Z. H. Pathan, S. Salam, K. Shahzad, J. Zeng, Analysis of influencing ffactors of Big Data Adoption in Chinese enterprises using DANP technique, Sustainability, 10 (2018), 1-16. https://doi.org/10.3390/su10113956 doi: 10.3390/su10113956
![]() |
[13] | B. Marr, Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results, Wiley, 2016. https://doi.org/10.1002/9781119278825 |
[14] |
A. Alharthi, V. Krotov, M. Bowman, Addressing barriers to big data, Bus. Horiz., 60 (2017), 285-292. https://doi.org/10.1016/j.bushor.2017.01.002 doi: 10.1016/j.bushor.2017.01.002
![]() |
[15] |
P. Tabesh, E. Mousavidin, S. Hasani, Implementing big data strategies: A managerial perspective, Bus. Horiz., 62 (2019), 347-358. https://doi.org/10.1016/j.bushor.2019.02.001 doi: 10.1016/j.bushor.2019.02.001
![]() |
[16] |
S. Venkatraman, R. Venkatraman, Big data security challenges and strategies, AIMS Math., 4 (2019), 860-879. https://doi.org/10.3934/math.2019.3.860 doi: 10.3934/math.2019.3.860
![]() |
[17] |
S. Coleman, R. Göb, G. Manco, A. Pievatolo, X. Tort-Martorell, M. S. Reis, How can SMEs benefit from big data? Challenges and a path forward, Qual. Reliab. Eng. Int., 32 (2016), 2151-2164. https://doi.org/10.1002/qre.2008 doi: 10.1002/qre.2008
![]() |
[18] |
C. O'Connor, S. Kelly, Facilitating knowledge management through filtered big data: SME competitiveness in an agri-food sector, J. Knowl. Manag., 21 (2017), 156-179. https://doi.org/10.1108/JKM-08-2016-0357 doi: 10.1108/JKM-08-2016-0357
![]() |
[19] |
P. Del Vecchio, A. Di Minin, A. M. Petruzzelli, U. Panniello, S. Pirri, Big data for open innovation in SMEs and large corporations: Trends, opportunities, and challenges, Creat. Innov. Manag., 27 (2018), 6-22. https://doi.org/10.1111/caim.12224 doi: 10.1111/caim.12224
![]() |
[20] | W. Noonpakdee, A. Phothichai, T. Khunkornsiri, Big data implementation for small and medium enterprises, in 2018 27th Wireless and Optical Communication Conference (WOCC), Hualien, Taiwan, (2018), 1-5. https://doi.org/10.1109/WOCC.2018.8372725 |
[21] |
M. H. Chuah, R. Thurusamry, Challenges of big data adoption in Malaysia SMEs based on Lessig's modalities: A systematic review, Cogent. Bus. Manage., 8 (2021), 81-91. https://doi.org/10.1080/23311975.2021.1968191 doi: 10.1080/23311975.2021.1968191
![]() |
[22] |
S. K. Mangla, R. Raut, V. S. Narwane, Z. Zhang, P. Priyadarshinee, Mediating effect of big data analytics on project performance of small and medium enterprises, J. Enterp. Inf. Manag., 34 (2020), 168-198. https://doi.org/10.1108/JEIM-12-2019-0394 doi: 10.1108/JEIM-12-2019-0394
![]() |
[23] |
J. H. Park, Y. B. Kim, Factors activating big data adoption by Korean firms, J. Comput. Inf. Syst., 61 (2021), 285-293. https://doi.org/10.1080/08874417.2019.1631133 doi: 10.1080/08874417.2019.1631133
![]() |
[24] | L. G. Tornatzky, M. Fleischer, The Processes of Technological Innovation, Lexington Books, Massachusetts, 1990. |
[25] |
D. Grant, B. Yeo, A global perspective on tech investment, financing, and ICT on manufacturing and service industry performance, Int. J. Inf. Manag., 43 (2018), 130-145. https://doi.org/10.1016/j.ijinfomgt.2018.06.007 doi: 10.1016/j.ijinfomgt.2018.06.007
![]() |
[26] |
R. Y. Zhong, S. T. Newman, G. Q. Huang, S. Lan, Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives, Comput. Ind. Eng., 101 (2016), 572-591. https://doi.org/10.1016/j.cie.2016.07.013 doi: 10.1016/j.cie.2016.07.013
![]() |
[27] |
M. K. Saggi, S. Jain, A survey towards an integration of big data analytics to big insights for value-creation, Inf. Proc. Manag., 54 (2018), 758-790. https://doi.org/10.1016/j.ipm.2018.01.010 doi: 10.1016/j.ipm.2018.01.010
![]() |
[28] |
S. F. Wamba, A. Gunasekaran, S. Akter, S. J. f. Ren, R. Dubey, S. J. Childe, Big data analytics and firm performance: Effects of dynamic capabilities, J. Bus. Res., 70 (2017), 356-365. https://doi.org/10.1016/j.jbusres.2016.08.009 doi: 10.1016/j.jbusres.2016.08.009
![]() |
[29] |
S. Dash, S. K. Shakyawar, M. Sharma, S. Kaushik, Big data in healthcare: management, analysis and future prospects, J. Big Data, 6 (2019), 1-25. https://doi.org/10.1186/s40537-019-0217-0 doi: 10.1186/s40537-019-0217-0
![]() |
[30] |
E. Yadegaridehkordi, M. Nilashi, L. Shuib, M. Hairul Nizam Bin Md Nasir, S. Asadi, S. Samad, et al., The impact of big data on firm performance in hotel industry, Electron. Commer. Res. Appl., 40 (2020), 1-32. https://doi.org/10.1016/j.elerap.2019.100921 doi: 10.1016/j.elerap.2019.100921
![]() |
[31] |
R. Dubey, A. Gunasekaran, S. J. Childe, Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility, Manag. Decis., 57 (2019), 2092-2112. https://doi.org/10.1108/MD-01-2018-0119 doi: 10.1108/MD-01-2018-0119
![]() |
[32] |
S. Wang, H. Wang, Big data for small and medium-sized enterprises (SME): a knowledge management model, J. Knowl. Manag., 24 (2020), 881-897. https://doi.org/10.1108/JKM-02-2020-0081 doi: 10.1108/JKM-02-2020-0081
![]() |
[33] |
N. Mahdi, R. Javaneh, S. Mahmoud, The impact of big data adoption on SMEs performance, Res. Sq., 9 (2020), 1-12. https://doi.org/10.21203/rs.3.rs-66047/v1 doi: 10.21203/rs.3.rs-66047/v1
![]() |
[34] |
A. Lutfi, A. Alsyouf, M. A. Almaiah, M. Alrawad, A. A. K. Abdo, A. L. Al-Khasawneh, et al., Factors influencing the adoption of big data analytics in the digital transformation era: Case study of Jordanian SMEs, Sustainability, 14 (2022), 1-17. https://doi.org/10.3390/su14031802 doi: 10.3390/su14031802
![]() |
[35] |
S. Sun, C. G. Cegielski, L. Jia, D. J. Hall, Understanding the factors affecting the organizational adoption of big data, J. Comput. Inf. Syst., 58 (2016), 193-203. https://doi.org/10.1080/08874417.2016.1222891 doi: 10.1080/08874417.2016.1222891
![]() |
[36] |
P. Maroufkhani, R. Wagner, W. K. Wan Ismail, M. B. Baroto, M. Nourani, Big data analytics and firm performance: A systematic review, Information, 10 (2019), 1-21. https://doi.org/10.3390/info10070226 doi: 10.3390/info10070226
![]() |
[37] |
M. I. Baig, L. Shuib, E. Yadegaridehkordi, Big data adoption: State of the art and research challenges, Inf. Process. Manag., 56 (2019), 1-18. https://doi.org/10.1016/j.ipm.2019.102095 doi: 10.1016/j.ipm.2019.102095
![]() |
[38] |
S. Gupta, H. W. Kim, Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities, Eur. J. Oper. Res., 190 (2008), 818-833. https://doi.org/10.1016/j.ejor.2007.05.054 doi: 10.1016/j.ejor.2007.05.054
![]() |
[39] |
P. F. Hsu, S. Ray, Y. Y. Li-Hsieh, Examining cloud computing adoption intention, pricing mechanism, and deployment model, Int. J. Inf. Manage., 34 (2014), 474-488. https://doi.org/10.1016/j.ijinfomgt.2014.04.006 doi: 10.1016/j.ijinfomgt.2014.04.006
![]() |
[40] | J. H. Park, M. K. Kim, J. H. Paik, The factors of technology, organization and environment influencing the adoption and usage of big data in korean firms, in 26th European Regional Conference of the Interational Telecommunications Society, Madrid, Spain, 24-27 June, 3 (2015), 121-129. |
[41] |
Y. Lai, H. Sun, J. Ren, Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management, Int. J. Logis. Manag., 29 (2018), 676-703. https://doi.org/10.1108/IJLM-06-2017-0153 doi: 10.1108/IJLM-06-2017-0153
![]() |
[42] |
K. K. Kapoor, Y. K. Dwivedi, M. D. Williams, Empirical examination of the role of three sets of innovation attributes for determining adoption of IRCTC mobile ticketing service, Inf. Sys. Manag., 32 (2015), 153-173. https://doi.org/10.1080/10580530.2015.1018776 doi: 10.1080/10580530.2015.1018776
![]() |
[43] |
E. M. Rogers, Lessons for guidelines from the diffusion of innovations, Jt. Comm. J. Qual. Improv., 21 (1995), 324-328. https://doi.org/10.1016/S1070-3241(16)30155-9 doi: 10.1016/S1070-3241(16)30155-9
![]() |
[44] |
M. Ghobakhloo, D. Arias-Aranda, J. Benitez-Amado, Adoption of e-commerce applications in SMEs, Industrial Manag. Data Syst., 111 (2011), 1238-1269. https://doi.org/10.1108/02635571111170785 doi: 10.1108/02635571111170785
![]() |
[45] |
N. Kshetri, Big data's impact on privacy, security and consumer welfare, Telecommun. Policy, 38 (2014), 1134-1145. https://doi.org/10.1016/j.telpol.2014.10.002 doi: 10.1016/j.telpol.2014.10.002
![]() |
[46] |
A. A. Jahanshahi, A. Brem, Sustainability in SMEs: Top management teams behavioral integration as source of innovativeness, Sustainability, 9 (2017), 1-16. https://doi.org/10.3390/su9101899 doi: 10.3390/su9101899
![]() |
[47] |
S. Shamim, J. Zeng, S. M. Shariq, Z. Khan, Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view, Inf. Manag., 56 (2019), 1-16. https://doi.org/10.1016/j.im.2018.12.003 doi: 10.1016/j.im.2018.12.003
![]() |
[48] |
H. Gangwar, Understanding the determinants of big data adoption in India: An analysis of the manufacturing and services sectors, Inf. Resour. Manag. J., 31 (2018), 1-22. https://doi.org/10.4018/IRMJ.2018100101 doi: 10.4018/IRMJ.2018100101
![]() |
[49] |
W. Xu, P. Ou, W. Fan, Antecedents of ERP assimilation and its impact on ERP value: A TOE-based model and empirical test, Inf. Syst. Front., 19 (2017), 13-30. https://doi.org/10.1007/s10796-015-9583-0 doi: 10.1007/s10796-015-9583-0
![]() |
[50] |
C. Low, Y. Chen, M. Wu, Understanding the determinants of cloud computing adoption, Ind. Mana. Data Syst., 111 (2011), 1006-1023. https://doi.org/10.1108/02635571111161262 doi: 10.1108/02635571111161262
![]() |
[51] |
J. W. Lian, D. C. Yen, Y. T. Wang, An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital, Int. J. Inf. Manag., 34 (2014), 28-36. https://doi.org/10.1016/j.ijinfomgt.2013.09.004 doi: 10.1016/j.ijinfomgt.2013.09.004
![]() |
[52] |
K. Zhu, K. L. Kraemer, S. Xu, J. Dedrick, Information technology payoff in e-business environments: an international perspective on value creation of e-business in the financial services industry, J. Manag. Inf. Syst., 21 (2004), 17-54. https://doi.org/10.1080/07421222.2004.11045797 doi: 10.1080/07421222.2004.11045797
![]() |
[53] |
T. H. Kwon, J. H. Kwak, K. Kim, A study on the establishment of policies for the activation of a big data industry and prioritization of policies: Lessons from Korea, Technol. Forecast. Soc. Change, 96 (2015), 144-152. https://doi.org/10.1016/j.techfore.2015.03.017 doi: 10.1016/j.techfore.2015.03.017
![]() |
[54] |
J. I. Rojas-Méndez, A. Parasuraman, N. Papadopoulos, Demographics, attitudes, and technology readiness, Mark. Intell. Plan., 35 (2017), 18-39. https://doi.org/10.1108/MIP-08-2015-0163 doi: 10.1108/MIP-08-2015-0163
![]() |
[55] |
A. Parasuraman, C. L. Colby, An updated and streamlined technology readiness index: TRI 2.0, J. Serv. Res., 18 (2014), 59-74. https://doi.org/10.1177/1094670514539730 doi: 10.1177/1094670514539730
![]() |
[56] | T. Wendler, S. Gröttrup, Data Mining with Spss Modeler: Theory, Exercises, and Solutions, 1st edition, Springer Cham, Switzerland, 2016. https://doi.org/10.1007/978-3-319-28709-6 |
[57] | X. S. Yang, Introduction to Algorithms for Data Mining and Machine Learning, Elsevier Inc, 2019. https://doi.org/10.1016/C2018-0-02034-4 |
[58] | SPSS, IBM SPSS Decision Tree 2, SPSS: Chicago, IL, USA, 2012. |
[59] | P. Cortez, A. M. G. Silva, Using data mining to predict secondary school student performance, in Proceedings of 5th Annual Future Business Technology Conference, Porto, (2008), 5-12. |
[60] |
E. Yukselturk, S. Ozekes, Y. K. Türel, Predicting dropout student: An application of data mining methods in an online education program, Eur. J. Open, Distance E-learn., 17 (2014), 118-133. https://doi.org/10.2478/eurodl-2014-0008 doi: 10.2478/eurodl-2014-0008
![]() |
[61] |
C. M. Zhao, J. Luan, Data mining: Going beyond traditional statistics, New Dir. Institutional Res., 131 (2006), 7-16. https://doi.org/10.1002/ir.184 doi: 10.1002/ir.184
![]() |
[62] |
K. D. Brouthers, L. E. Brouthers, Why service and manufacturing entry mode choices differ: The influence of transaction cost factors, risk and trust*, J. Manag. Stud., 40 (2003), 1179-1204. https://doi.org/10.1111/1467-6486.00376 doi: 10.1111/1467-6486.00376
![]() |
[63] |
K. Ferdows, A. De Meyer, Lasting improvements in manufacturing performance: In search of a new theory, J. Oper. Manag., 9 (1990), 168-184. https://doi.org/10.1016/0272-6963(90)90094-T doi: 10.1016/0272-6963(90)90094-T
![]() |
[64] |
M. Ghasemaghaei, The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage, Int. J. Inf. Manag., 50 (2020), 395-404. https://doi.org/10.1016/j.ijinfomgt.2018.12.011 doi: 10.1016/j.ijinfomgt.2018.12.011
![]() |
[65] |
K. K. Y. Kuan, P. Y. K. Chau, A perception-based model for EDI adoption in small businesses using a technology-organization-environment framework, Inf. Manag., 38 (2001), 507-521. https://doi.org/10.1016/S0378-7206(01)00073-8 doi: 10.1016/S0378-7206(01)00073-8
![]() |
[66] |
G. Premkumar, M. Roberts, Adoption of new information technologies in rural small business, OMEGA-Int. J. Manag. Sci., 27 (1999), 467-484. https://doi.org/10.1016/S0305-0483(98)00071-1 doi: 10.1016/S0305-0483(98)00071-1
![]() |
[67] |
E. Yadegaridehkordi, M. Hourmand, M. Nilashi, L. Shuib, A. Ahani, O. Ibrahim, Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach, Technol. Forecasting Social Change, 137 (2018), 199-210. https://doi.org/10.1016/j.techfore.2018.07.043 doi: 10.1016/j.techfore.2018.07.043
![]() |
[68] |
M. Nasrollahi, J. Ramezani, A Model to evaluate the organizational readiness for big data adoption, Int. J. Comput. Communi. Cont., 15 (2020), 1-11. https://doi.org/10.15837/ijccc.2020.3.3874 doi: 10.15837/ijccc.2020.3.3874
![]() |
[69] | J. F. Hair, Multivariate Data Analysis: A Global Perspective, Pearson Education: Upper Saddle River, NJ, USA; London, UK, 2010. |
[70] | J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, Multivariate Data Analysis, Peason, 2014. |
[71] | B. M. Byrne, Structural Equation Modeling with Amos: Basic Concepts, Applications, and Programming, 3rd edition, Routledge: Abingdon, UK, 2016. https://doi.org/10.4324/9781315757421 |
[72] |
C. Fornell, D. F. Larcker, Evaluating structural equation models with unobservable variables and measurement error, J. Mar. Res., 18 (1981), 39-50. https://doi.org/10.1177/002224378101800104 doi: 10.1177/002224378101800104
![]() |
[73] |
G. V. Kass, An exploratory technique for investigating large quantities of categorical data, J. R. Stat. Soc., 29 (1980), 119-127. https://doi.org/10.2307/2986296 doi: 10.2307/2986296
![]() |
[74] | J. Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann Inc.: San Mateo, CA, USA, 1988. https://doi.org/10.1016/B978-0-08-051489-5.50008-4 |
[75] |
J. R. Quinlan, Induction of decision trees, Mach. Learn., 1 (1986), 81-106. https://doi.org/10.1007/BF00116251 doi: 10.1007/BF00116251
![]() |
[76] |
D. Delen, C. Kuzey, A. Uyar, Measuring firm performance using financial ratios: A decision tree approach, Expert Syst. Appl., 40 (2013), 3970-3983. https://doi.org/10.1016/j.eswa.2013.01.012 doi: 10.1016/j.eswa.2013.01.012
![]() |
[77] |
M. Taamneh, Investigating the role of socio-economic factors in comprehension of traffic signs using decision tree algorithm, J. Saf. Res., 66 (2018), 121-129. https://doi.org/10.1016/j.jsr.2018.06.002 doi: 10.1016/j.jsr.2018.06.002
![]() |
[78] |
Z. Chen, M. Yang, Y. Wen, S. Jiang, W. Liu, H. Huang, Prediction of atherosclerosis using machine learning based on operations research, Math. Biosci. Eng., 19 (2022), 4892-4910. https://doi.org/10.3934/mbe.2022229 doi: 10.3934/mbe.2022229
![]() |
[79] |
K. A. Tavakoli, R. Rabieyan, M. M. Besharati, A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers, J. Safety Res., 51 (2014), 93-98. https://doi.org/10.1016/j.jsr.2014.09.004 doi: 10.1016/j.jsr.2014.09.004
![]() |
[80] |
D. Delen, G. Walker, A. Kadam, Predicting breast cancer survivability: a comparison of three data mining methods, Artif. intell. med., 34 (2005), 113-127. https://doi.org/10.1016/j.artmed.2004.07.002 doi: 10.1016/j.artmed.2004.07.002
![]() |
[81] |
R. Sann, P. C. Lai, S. Y. Liaw, C. T. Chen, Predicting online complaining behavior in the hospitality industry: Application of big data analytics to online reviews, Sustainability, 14 (2022), 1-22. https://doi.org/10.3390/su14031800 doi: 10.3390/su14031800
![]() |
[82] |
A. Asiaei, N. Z. A. Rahim, A multifaceted framework for adoption of cloud computing in Malaysian SMEs, J. Sci. Technol. Policy Manag., 10 (2019), 708-750. https://doi.org/10.1108/JSTPM-05-2018-0053 doi: 10.1108/JSTPM-05-2018-0053
![]() |
[83] |
O. Sohaib, M. Naderpour, W. Hussain, L. Martinez, Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method, Comput. Ind. Eng., 132 (2019), 47-58. https://doi.org/10.1016/j.cie.2019.04.020 doi: 10.1016/j.cie.2019.04.020
![]() |
[84] |
Y. Alshamaila, S. Papagiannidis, F. Li, Cloud computing adoption by SMEs in the north east of England, J. Enterp. Inf. Manag., 26 (2013), 250-275. https://doi.org/10.1108/17410391311325225 doi: 10.1108/17410391311325225
![]() |
[85] |
Z. Yang, J. Sun, Y. Zhang, Y. Wang, Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model, Comput. Human. Behav., 45 (2015), 254-264. https://doi.org/10.1016/j.chb.2014.12.022 doi: 10.1016/j.chb.2014.12.022
![]() |
[86] |
M. A. Moktadir, S. M. Ali, S. K. Paul, N. Shukla, Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh, Comput. Ind. Eng., 128 (2019), 1063-1075. https://doi.org/10.1016/j.cie.2018.04.013 doi: 10.1016/j.cie.2018.04.013
![]() |
[87] |
M. Badri, A. Al Rashedi, G. Yang, J. Mohaidat, A. Al Hammadi, Technology readiness of school teachers: An empirical study of measurement and segmentation, J. Inf. Technol. Educ.: Res., 13 (2014), 257-275.https://doi.org/10.28945/2082 doi: 10.28945/2082
![]() |
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