
In the past two decades, research on the relationship between corporate social performance (CSP) and corporate financial performance (CFP) has seen considerable growth; however, evidence from Turkey remains scarce, and the results are not uniform. To address this lack, this study investigates the impact of CSP on CFP from the perspective of stakeholder theory. Following the investigation of 47 publicly listed companies from the BIST Corporate Governance Index (XKURY) in the period 2014–2018. The results demonstrate that CSP positively affects CFP in both the short and long term. This study addresses the lack of Turkish experience, and the results indicate that CSP is an intangible resource in corporate strategy that can improve the competitive power of Turkish enterprises. Furthermore, the study emphasizes the positive role of CSP in short-term and long-term CFP in the Turkish context from the stakeholder perspective. The results have implications for Turkish policymakers regarding the rational use of corporate social responsibility (CSR) to promote economic development and insights for Turkish enterprises in terms of gaining stakeholders' trust and improving investors' valuation through the strategic use of CSR to achieve long-term, sustainable development of enterprise competitiveness and finance.
Citation: Hakan KURT, Xuhui Peng. Does corporate social performance lead to better financial performance? Evidence from Turkey[J]. Green Finance, 2021, 3(4): 464-482. doi: 10.3934/GF.2021021
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In the past two decades, research on the relationship between corporate social performance (CSP) and corporate financial performance (CFP) has seen considerable growth; however, evidence from Turkey remains scarce, and the results are not uniform. To address this lack, this study investigates the impact of CSP on CFP from the perspective of stakeholder theory. Following the investigation of 47 publicly listed companies from the BIST Corporate Governance Index (XKURY) in the period 2014–2018. The results demonstrate that CSP positively affects CFP in both the short and long term. This study addresses the lack of Turkish experience, and the results indicate that CSP is an intangible resource in corporate strategy that can improve the competitive power of Turkish enterprises. Furthermore, the study emphasizes the positive role of CSP in short-term and long-term CFP in the Turkish context from the stakeholder perspective. The results have implications for Turkish policymakers regarding the rational use of corporate social responsibility (CSR) to promote economic development and insights for Turkish enterprises in terms of gaining stakeholders' trust and improving investors' valuation through the strategic use of CSR to achieve long-term, sustainable development of enterprise competitiveness and finance.
In the last few decades, more and more scholars have studied fractional-order neural networks (FONNs) due to their powerful functions [1,2]. FONNs have been accurately applied to image processing, biological systems and so on [3,4,5,6] because of their powerful computing power and information storage capabilities. In the past, scholars have studied many types of neural network (NN) models. For example, bidirectional associative memory neural networks (BAMNNs) [7], recurrent NNs [8], Hopfield NNs [9], Cohen-Grossberg NNs [10], and fuzzy neural networks (FNNs) [11]. With the development of fuzzy mathematics, Yang et al. proposed fuzzy logic (fuzzy OR and fuzzy AND) into cellular NNs and established FNNs. Moreover, when dealing with some practical problems, it is inevitable to encounter approximation, uncertainty, and fuzziness. Fuzzy logic is considered to be a promising tool to deal with these phenomena. Therefore, the dynamical behaviors of FNNs have attracted extensive research and obtained abundant achievements [12,13,14,15].
The CVNNs are extended from real-valued NNs (RVNNs). In CVNNs, the relevant variables in CVNNs belong to the complex field. In addition, CVNNs can also address issues that can not be addressed by RVNNs, such as machine learning [16] and filtering [17]. In recent years, the dynamic behavior of complex valued neural networks has been a hot topic of research for scholars. In [16], Nitta derived some results of an analysis on the decision boundaries of CVNNs. In [17], the author studied an extension of the RBFN for complex-valued signals. In [18], Li et al. obtained some synchronization results of CVNNs with time delay. Overall, CVNNs outperform real-valued neural networks in terms of performance, as they can directly process two-dimensional data.
Time delays are inescapable in neural systems due to the limited propagation velocity between different neurons. Time delay has been extensively studied by previous researchers, such as general time delay [19], leakage delays [20], proportional delays [21], discrete delays [22], time-varying delays [23], and distributed delays [24]. In addition, the size and length of axonal connections between neurons in NNs can cause time delays, so scholars have introduced distributed delays in NNs. For example, Si et al. [25] considered a fractional NN with distributed and discrete time delays. It not only embodies the heredity and memory characteristics of neural networks but also reflects the unevenness of delay in the process of information transmission due to the addition of distributed time delays. Therefore, more and more scholars have added distributed delays to the NNs and have made some new discoveries [26,27]. On the other hand, due to the presence of external perturbations in the model, the actual values of the parameters in the NNs cannot be acquired, which may lead to parameter uncertainties. Parameter uncertainty has also affected the performance of NNs. Consequently, scholars are also closely studying the NNs model of parameter uncertainty [28,29].
The synchronization control of dynamical systems has always been the main aspect of dynamical behavior analysis, and the synchronization of NNs has become a research hotspot. In recent years, scholars have studied some important synchronization behaviors, such as complete synchronization (CS) [30], global asymptotic synchronization [31], quasi synchronization [32], finite time synchronization [33], projective synchronization (PS) [34], and Mittag-Leffler synchronization (MLS) [35]. In various synchronizations, PS is one of the most interesting, being characterized by the fact that the drive-response systems can reach synchronization according to a scaling factor. Meanwhile, the CS can be regarded as a PS with a scale factor of 1. Although PS has its advantages, MLS also has its unique features. Unlike CS, MLS can achieve synchronization at a faster speed. As a result, some scholars have combined the projective synchronization and ML synchronization to study MLPS [36,37]. However, there are few papers on MLPS of FOFNNs. Based on the above discussion, the innovative points of this article are as follows:
∙ According to the FNNs model, parameter uncertainty and distributed delays are added, and the influence of time-varying delay, distributed delay, and uncertainty on the global MLPS of FOFCVNNs is further considered in this paper.
∙ The algebraic criterion for MLPS was obtained by applying the complex valued direct method. Direct methods and algebraic inequalities greatly reduce computational complexity.
∙ The design of nonlinear hybrid controllers and adaptive hybrid controllers greatly reduces control costs.
Notations: In this article, C refers to the set of complex numbers, where O=OR+iOI∈C and OR,OI∈R, i represents imaginary units, Cn can describe a set of n-dimensional complex vectors, ¯Oψ refers to the conjugate of Oψ. |Oψ|=√Oψ¯Oψ indicating the module of Oψ. For O=(O1,O2,…,On)T∈Cn,||O||=(n∑ψ=1|Oψ|2)12 denotes the norm of O.
In this section, we provide the definitions, lemmas, assumptions, and model details required for this article.
Definition 2.1. [38] The Caputo fractional derivative with 0<Υ<1 of function O(t) is defined as
ct0DαtO(t)=1Γ(1−α)∫tt0O′(s)(t−s)αds. |
Definition 2.2. [38] The one-parameter ML function is described as
EΥ(p)=∞∑δ=0pδΓ(δΥ+1). |
Lemma 2.1. [39] Make Qψ,Oψ∈C(w,ψ=1,2,…,n,), then the fuzzy operators in the system satisfy
|n⋀ψ=1μwψfψ(Qψ)−n⋀ψ=1μwψfψ(Oψ)|≤n∑ψ=1|μwψ||fψ(Qψ)−fψ(Oψ)|,|n⋁ψ=1υwψfψ(Qψ)−n⋁ψ=1υwψfψ(Oψ)|≤n∑ψ=1|υwψ||fψ(Qψ)−fψ(Oψ)|. |
Lemma 2.2. [40] If the function ϑ(t)∈C is differentiable, then it has
Ct0Dαt(¯ϑ(t)ϑ(t))≤ϑ(t)Ct0Dαtϑ(t)+(Ct0Dαt¯ϑ(t))ϑ(t). |
Lemma 2.3. [41] Let ˆξ1,ˆξ2∈C, then the following condition:
¯ˆξ1ˆξ2+¯ˆξ2ˆξ1≤χ¯ˆξ1ˆξ1+1χ¯ˆξ2ˆξ2 |
holds for any positive constant χ>0.
Lemma 2.4. [42] If the function ς(t) is nondecreasing and differentiable on [t0,∞], the following inequality holds:
ct0Dαt(ς(t)−ȷ)2≤2(ς(t)−ȷ)ct0Dαt(ς(t)), 0<α<1, |
where constant ȷ is arbitrary.
Lemma 2.5. [43] Suppose that function ℏ(t) is continuous and satisfies
ct0Dαtˆℏ(t)≤−γˆℏ(t), |
where 0<α<1, γ∈R, the below inequality can be obtained:
ˆℏ(t)≤ˆℏ(t0)Eα[−γ(t−t0)α]. |
Next, a FOFCVNNS model with distributed and time-varying delays with uncertain coefficients is considered as the driving system:
Ct0DαtOw(t)=−awOw(t)+n∑ψ=1(mwψ+Δmwψ(t))fψ(Oψ(t−τ(t)))+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(Oψ(t))dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(Oψ(t))dt+Iw(t), | (2.1) |
where 0<α<1; O(t)=((O1(t),...,On(t))⊤; Ow(t),w=1,2,...,n is the state vector; aw∈R denotes the self-feedback coefficient; mwψ∈C stands for a feedback template element; △mwψ∈C refers to uncertain parameters; μwψ∈C and υwψ∈C are the elements of fuzzy feedback MIN and MAX templates; ⋁ and ⋀ indicate that the fuzzy OR and fuzzy AND operations; τ(t) indicates delay; Iw(t) denotes external input; and fψ(⋅) is the neuron activation function.
Correspondingly, we define the response system as follows:
Ct0DαtQw(t)=−awQw(t)+n∑ψ=1(mwψ+Δmwψ(t))fψ(Qψ(t−τ(t)))+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(Qψ(t))dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(Qψ(t))dt+Iw(t)+Uw(t), | (2.2) |
where Uw(t)∈C stands for controller. In the following formula, Ct0Dαt is abbreviated as Dα.
Defining the error as kw(t)=Qw(t)−SOw(t), where S is the projective coefficient. Therefore, we have
Dαkw(t)=−awkw(t)+n∑ψ=1(mwψ+Δmwψ(t))~fψkψ(t−τ(t))+n∑ψ=1(mwψ+Δmwψ(t))fψ(SOψ(t−τ(t)))−n∑ψ=1S(mwψ+Δmwψ(t))fψ(Oψ(t−τ(t)))+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ~kψ(t)dt+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(SOψ(t))dt−Sn⋀ψ=1μwψ∫+∞0bwψ(t)fψ(Oψ(t))dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ~kψ(t)dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(SOψ(t))dt−Sn⋁ψ=1υwψ∫+∞0cwp(t)fψ(Oψ(t))dt+(1−S)Iw(t)+Uw(t), | (2.3) |
where fψ~kψ(t)=fψ(Qψ(t))−fψ(SOψ(t)).
Assumption 2.1. ∀O,Q∈C and ∀Lϖ>0, fϖ(⋅) satisfy
|fϖ(O)−fϖ(Q)|≤Lϖ|O−Q|. |
Assumption 2.2. The nonnegative functions bwψ(t) and cwψ(t) are continuous, and satisfy
(i)∫+∞0bwψ(t)dt=1,(ii)∫+∞0cwψ(t)dt=1. |
Assumption 2.3. The parameter perturbations △mwψ(t) bounded, then it has
|△mwψ(t)|≤Mwψ, |
where Mwψ is all positive constants.
Assumption 2.4. Based on the assumption for fuzzy OR and AND operations, the following inequalities hold:
(i)[n⋀ψ=1μwψ∫+∞0bwψ(t)(fψ(Oψ)−fψ(Qψ))dt][n⋀ψ=1¯μwψ∫+∞0bwψ(t)(fψ(Oψ)−fψ(Qψ))dt]≤n∑ψ=1δψ|μwψ|2(fψ(Oψ)−fψ(Qψ))(¯fψ(Oψ)−¯fψ(Qψ)),(ii)[n⋁ψ=1υwψ∫+∞0cwψ(t)(fψ(Oψ)−fψ(Qψ))dt][n⋁ψ=1¯υwψ∫+∞0cwψ(t)(fψ(Oψ−fψ(Qψ))dt]≤n∑ψ=1ηψ|υwψ|2(fψ(Oψ)−fψ(Qψ))(¯fψ(Oψ)−¯fψ(Qψ)), |
where δψ and ηψ are positive numbers.
Definition 2.3. The constant vector O∗=(O∗1,…,O∗n)T∈Cn and satisfy
0=−awO∗ψ+n∑ψ=1(mwψ+△mwψ(t))fψ(O∗ψ)+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(O∗ψ)dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(O∗ψ)dt+Iw(t), |
whereupon, O∗ is called the equilibrium point of (2.1).
Definition 2.4. System (2.1) achieves ML stability if and only if O∗=(O∗1,…,O∗n)T∈Cn is the equilibrium point, and satisfies the following condition:
||O(t)||≤[Ξ(O(t0))Eα(−ρ(t−t0)α)]η, |
where 0<α<1,ρ, η>0,Ξ(0)=0.
Definition 2.5. If FOFCVNNS (2.1) and (2.2) meet the following equation:
limt→∞‖Qw(t)−SOw(t)‖=0, |
so we can say it has achieved projective synchronization, where S∈R is projective coefficient.
In this part, we mainly investigate three theorems. According to the Banach contraction mapping principle, we can deduce the result of Theorem 3.1. Next, we construct a linear hybrid controller and an adaptive hybrid controller, and establish two MLPS criteria.
Theorem 3.1. B is a Banach space. Let ||O||1=n∑ψ=1|Oψ|. Under Assumptions 2.1–2.4, if the following equality holds:
ρ=n∑w=1|mwψ|Lψ+|Mwψ|Lψ+|μwψ|Lψ+|υwψ|Lψaψ<1,w,ψ=1,2,3,...,n, | (3.1) |
then FOFCVNNS (2.1) has a unique equilibrium point with O∗=(O∗1,…,O∗n)T∈Cn.
Proof. Let O=(~O1,~O2,…,~On)⊤=(a1O1,a2O2,…,anOn)⊤∈Rn, and construct a mapping φ:B→B, φ(x)=(φ1(O),φ2(O),…,φn(O))⊤ and
φψ(O)=n∑ψ=1(mwψ+Δmwψ(t))fψ(~Oψaψ)+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(~Oψaψ)dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(~Oψaψ)dt+Iw,w,ψ=1,2,…,n. | (3.2) |
For two different points α=(α1,…,αn)⊤,β=(β1,…,βn)⊤, it has
|φψ(α)−φψ(β)|≤|n∑ψ=1(mwψ+Δmwψ(t))fψ(αψaψ)−n∑ψ=1(mwψ+Δmwψ(t))fψ(βψaψ)|+|n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(αψaψ)dt−n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(βψaψ)dt|+|n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(αψaψ)dt−n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(βψaψ)dt|≤n∑ψ=1|mwψ+Δmwψ(t)||fj(αψaψ)−fψ(βψaψ)|+|n⋀ψ=1μwψfψ(αψaψ)−n⋀ψ=1μwψfψ(βψaψ)|+|n⋁ψ=1υwψfp(αψaψ)−n⋁ψ=1υwψfψ(βψaψ)|. | (3.3) |
According to Assumption 2.1, we get
|φψ(α)−φψ(β)|≤n∑ψ=1|mwψ+Δmwψ(t)|Lψaψ|αψ−βψ|+n∑ψ=1|μwψ|Lψaψ|αψ−βψ|+n∑ψ=1|υwψ|Lψaψ|αψ−βψ|. | (3.4) |
Next, we have
||φ(α)−φ(β)||1=n∑ψ=1|φψ(α)−φψ(β)|≤n∑ψ=1n∑ψ=1|mwψ+Δmwψ(t)|Lψaψ|αψ−βψ|+n∑w=1n∑ψ=1|μwψ|Lψaψ|αψ−βψ|+n∑w=1n∑ψ=1|υwψ|Lψaψ|αψ−βψ|≤n∑ψ=1(n∑ψ=1|mwψ+Δmwψ(t)|Lψaψ+n∑ψ=1|μwψ|Lψaψ+n∑w=1|υwψ|Lψaψ)|αψ−βψ|≤(n∑w=1|mwψ+Δmwψ(t)|Lψaψ+n∑w=1|μwψ|Lψaψ+n∑w=1|υwψ|Lψaψ)n∑ψ=1|αψ−βψ|. | (3.5) |
From Assumption 2.3, then it has
||φ(α)−φ(β)||1≤(n∑w=1|mwψ|Lψ+|Mwψ|Lψ+|μwψ|Lp+|υwψ|Lψaψ)||α−β||1. | (3.6) |
Finally, we obtain
||φ(α)−φ(β)||1≤ρ||α−β||1, | (3.7) |
where φ is obviously a contraction mapping. From Eq (3.2), there must exist a unique fixed point ~O∗∈Cn, such that φ(~O∗)=~O∗.
~Oψ∗=n∑ψ=1(mwψ+Δmwψ(t))fψ(~O∗ψaψ)+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(~O∗ψaψ)dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(~O∗ψaψ)dt+Iw. | (3.8) |
Let O∗ψ=~O∗ψaψ, we have
0=−awO∗ψ+n∑ψ=1(mwψ+Δmwψ(t))fψ(O∗ψ)+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(O∗ψ)dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(O∗ψ)dt+Iw. | (3.9) |
Consequently, Theorem 3.1 holds.
Under the error system (2.3), we design a suitable hybrid controller
uw(t)=u1w(t)+u2w(t). | (3.10) |
u1w(t)={−πkw(t)+λkw(t)¯kw(t−τ(t))¯kw(t),kw(t)≠0,0,kw(t)=0. | (3.11) |
u2w(t)=−n∑ψ=1(mwψ+Δmwψ(t))fψ(SOψ(t−τ(t)))+n∑ψ=1S(mwψ+Δmwψ(t))fψ(Oψ(t−τ(t)))−n⋀ψ=1μwψ∫+∞0bwψ(t)fψ(SOψ(t))dt+Sn⋀ψ=1μwψ∫+∞0bwψ(t)fψ(Oψ(t))dt−n⋁ψ=1υwψ∫+∞0cwψ(t)fψ(SOψ(t))dt+Sn⋁ψ=1υwψ∫+∞0cwψ(t)fψ(Oψ(t))dt−(1−h)Iψ. | (3.12) |
By substituting (3.10)–(3.12) into (2.3), it yields
Dαkw(t)=−awkw(t)+n∑ψ=1(mwψ+Δmwψ(t))~fψkψ(t−τ(t))+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ~kψ(t)dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ~kψ(t)dt−πkw(t)+λkw(t)¯kw(t−τ(t))¯kw(t). | (3.13) |
Theorem 3.2. Based on the controller (3.10) and Assumptions 2.1–2.4, systems (2.1) and (2.2) can achieve MLPS if the following formula holds:
ϖ1=min1≤w≤n{2aw−λ+2π−4n−n∑ψ=1δw|μψw|2L2w−n∑ψ=1ηw|υψw|2L2w}>0,ϖ2=max1≤w≤n{λ+n∑ψ=1(|mψw|2+|Mψw|2)L2w},ϖ1−ϖ2Ω1>0,Ω1>1. | (3.14) |
Proof. Picking the Lyapunov function as
v1(t)=n∑w=1kw(t)¯kw(t). | (3.15) |
By application about derivative v1(t), we derive
Dαv1(t)=n∑w=1Dαkw(t)¯kw(t). | (3.16) |
Following Lemma 2.2, it has
Dαv1(t)≤n∑w=1kw(t)Dα¯kw(t)+n∑w=1¯kw(t)Dαkw(t). | (3.17) |
Substituting Eq (3.13) to (3.17), we can get
Dαv1(t)≤n∑w=1kw(t){−aw¯kw(t)+n∑ψ=1¯(mwψ+△mwψ(t))¯~fψkψ(t−τ(t))+n⋀ψ=1¯μwψ∫+∞0bwψ(t)fψ~kψ(t)dt+n⋁ψ=1¯υwψ∫+∞0cwψ(t)fψ~kψ(t)dt−π¯kw(t)+λ¯kw(t)¯kw(t−τ(t))kw(t)}+n∑w=1¯kw(t){−awkw(t)+n∑ψ=1(mwp+Δmwψ(t))~fψkψ(t−τ(t))+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ~kψ(t)dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ~kψ(t)dt−πkw(t)+λkw(t)¯kw(t−τ(t))¯kw(t)}. | (3.18) |
Combining Lemma 2.3, one gets
n∑w=1kw(t)λ¯kw(t)¯kw(t−τ(t))kw(t)+n∑w=1¯kw(t)λkw(t)¯kw(t−τ(t))¯kw(t)≤λn∑w=1(kw(t)¯kw(t)+kw(t−τ(t))¯kw(t−τ(t))). | (3.19) |
Based on (3.19), we derive
n∑w=1kw(t)(−aw¯kw(t)−π¯kw(t)+λ¯kw(t)¯kw(t−τ(t))kw(t))+n∑w=1¯kw(t)(−awkw(t)−πkw(t)+λkw(t)¯kw(t−τ(t))¯kw(t))≤−n∑w=1[2aw−λ+2π]kw(t)¯kw(t)+λn∑w=1kw(t−τ(t))¯kw(t−τ(t)). | (3.20) |
According to Assumptions 2.1 and 2.3, Lemma 2.3, we can get
n∑w=1n∑ψ=1[kw(t)¯mwψ¯~fψkψ(t−τ(t))+kw(t)¯△mwψ(t)¯~fψkψ(t−τ(t))+¯kw(t)mwψ~fψkψ(t−τ(t))+¯kw(t)△mwψ(t)~fψkψ(t−τ(t))]≤n∑w=1n∑ψ=1[2kw(t)¯kw(t)+|mwψ|2~fψkψ(t−τ(t))¯~fψkψ(t−τ(t))+|△mwψ(t)|2~fψkψ(t−τ(t))¯~fψkψ(t−τ(t))]≤n∑w=1n∑ψ=12kw(t)¯kw(t)+n∑w=1n∑ψ=1|mwψ|2L2ψkψ(t−τ(t))¯kψ(t−τ(t))+n∑w=1n∑ψ=1|Mwψ|2L2ψkψ(t−τ(t))¯kψ(t−τ(t))≤n∑w=1n∑ψ=12kw(t)¯kw(t)+n∑w=1n∑ψ=1(|mwψ|2+|Mwψ|2)L2ψkψ(t−τ(t))¯kψ(t−τ(t)). | (3.21) |
Based on Lemma 2.3, one has
n∑w=1[kw(t)n⋀ψ=1¯μwψ∫+∞0bwψ(t)fψ~kψ(t)dt+¯kw(t)n⋀ψ=1μwψ∫+∞0bwψ(t)fψ~kψ(t)dt]≤n∑w=1[kw(t)¯kw(t)+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ~kψ(t)dtn⋀ψ=1¯μwψ∫+∞0bwψ(t)fψ~kψ(t)dt]. | (3.22) |
Combining Assumption 2.4 and Lemma 2.1, one obtains
n∑w=1n⋀ψ=1μwψ∫+∞0bwψ(t)fψ~kψ(t)dtn⋀ψ=1¯μwψ∫+∞0bwψ(t)fψ~kψ(t)dt≤n∑w=1n∑ψ=1δψ|μwψ|2L2ψkψ(t)¯kψ(t). | (3.23) |
Substituting (3.23) into (3.22), it has
n∑w=1[kw(t)n⋀ψ=1¯μwψ∫+∞0bwψ(t)fψ~kψ(t)dt+¯kw(t)n⋀ψ=1μwψ∫+∞0bwψ(t)fψ~kψ(t)dt]≤n∑w=1kw(t)¯kw(t)+n∑w=1n∑ψ=1δψ|μwψ|2L2ψkψ(t)¯kψ(t). | (3.24) |
Thus,
n∑w=1[kw(t)n⋁ψ=1¯υwψ∫+∞0cwψ(t)fψ~kψ(t)dt+¯kw(t)n⋁ψ=1υwψ∫+∞0cwψ(t)fψ~kψ(t)dt]≤n∑w=1kw(t)¯kw(t)+n∑ψ=1n∑ψ=1ηψ|υwψ|2L2ψkψ(t)¯kψ(t). | (3.25) |
Substituting (3.20)–(3.25) into (3.18), we get
Dαv1(t)≤−n∑w=1[2aw−λ+2π]kw(t)¯kw(t)+λn∑w=1kw(t−τ(t))¯kw(t−τ(t))+n∑w=1n∑ψ=12kw(t)¯kw(t)+n∑w=1n∑ψ=1(|mwψ|2+|Mwψ|2)L2ψkψ(t−τ(t))¯kψ(t−τ(t))+n∑w=1kw(t)¯kw(t)+n∑w=1n∑ψ=1δψ|μwψ|2L2ψkψ(t)¯kψ(t)+n∑w=1n∑ψ=1kw(t)¯kw(t)+n∑w=1n∑ψ=1ηψ|υwψ|2L2ψkψ(t)¯kψ(t)≤−n∑w=1[2aw−λ+2π−4n−n∑ψ=1δw|μψw|2L2w−n∑ψ=1ηw|υψw|2L2w]kw(t)¯kw(t)+n∑w=1[λ+n∑ψ=1(|mψw|2+|Mψw|2)L2w]kw(t−τ(t))¯kw(t−τ(t)). | (3.26) |
Applying the fractional Razumikhin theorem, the inequality (3.26) is as follows:
Dαv1(t)≤−(ϖ1−ϖ2Ω1)v1(t)=−ϖ3v1(t). | (3.27) |
Apparently, from Lemma 2.5, it has
v1(t)≤v(0)Eα[−(ϖ1−ϖ2Ω1)tα], | (3.28) |
and
v1(t)=n∑w=1kw(t)¯kw(t)=||k(t)||2≤v(0)Eα[−(ϖ1−ϖ2Ω1)tα],||k(t)||≤[v(0)Eα(−(ϖ1−ϖ2Ω1)tα)]12. | (3.29) |
Moreover,
limt→∞||k(t)||=0. | (3.30) |
From Definition 2.4, system (2.1) is Mittag-Leffler stable. From Definition 2.5 and limt→∞||k(t)||=0, the derive-response systems (2.1) and (2.2) can reach MLPS. Therefore, Theorem 3.2 holds.
Unlike controller (3.11), we redesign an adaptive hybrid controller as follows:
uw(t)=u2w(t)+u3w(t), | (3.31) |
u3w(t)=−ςw(t)kw(t),Dαςw(t)=Iwkw(t)¯kw(t)−p(ςw(t)−ς∗w). | (3.32) |
Taking (3.12) and (3.32) into (3.31), then
Dαkw(t)=−awkw(t)+n∑ψ=1(mwψ+Δmwψ(t))~fψkψ(t−τ(t))+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ~kψ(t)dt+n⋁ψ=1υwψ∫+∞0cwψ(t)fψ~kψ(t)dt−ςw(t)kw(t). | (3.33) |
Theorem 3.3. Assume that Assumptions 2.1–2.4 hold and under the controller (3.31), if the positive constants Υ1, Υ2, Ω1, and Ω2 such that
Υ1=min1≤w≤n[2n∑w=1aw−4n2−n∑w=1n∑ψ=1δw|μwψ|2L2w−n∑w=1n∑ψ=1ηw|υwψ|2L2w+n∑w=1(ςw(t)+ς∗w)],Υ2=max1≤w≤nn∑w=1n∑ψ=1(|mwψ|2+|Mwψ|2)L2w,Ω1−Υ2Ω2>0,Ω2>1, | (3.34) |
we can get systems (2.1) and (2.2) to achieve MLPS.
Proof. Taking into account the Lyapunov function
v2(t)=n∑w=1kw(t)¯kw(t)⏟v21(t)+n∑w=11Iw(ςw(t)−ς∗w)2⏟v22(t). | (3.35) |
By utilizing Lemmas 2.2 and 2.4, then it has
Dαv2(t)≤n∑w=1kw(t)Dα¯kw(t)+n∑w=1¯kw(t)Dαkw(t)⏟R1+n∑w=12Iw(ςw(t)−ς∗w)Dαςw(t)⏟R2. | (3.36) |
Substituting (3.32) into (3.36), one has
R2=2n∑w=1(ςw(t)−ς∗w)kw(t)¯kw(t)−n∑w=12pIw(ςw(t)−ς∗w)2. | (3.37) |
Substituting (3.33) into R1 yields
R1=n∑w=1kw(t){−aw¯kw(t)+n∑ψ=1¯(mwψ+△mwψ(t))¯~fψkψ(t−τ(t))+n⋀ψ=1¯μwψ∫+∞0bwψ(t)fψ~kψ(t)dt+n⋁ψ=1¯νwψ∫+∞0cwψ(t)fψ~kψ(t)dt−ςw(t)¯kw(t)}+n∑w=1¯kw(t){−awkw(t)+n∑ψ=1(mwψ+Δmwψ(t))~fψkψ(t−τ(t))+n⋀ψ=1μwψ∫+∞0bwψ(t)fψ~kψ(t)dt+n⋁ψ=1νwψ∫+∞0cwψ(t)fψ~kψ(t)dt−ςw(t)kw(t)}. | (3.38) |
According to the inequality (3.20), it has
n∑w=1kw(t){−aw¯kw(t)−ςw(t)¯kw(t)}+n∑w=1¯kw(t){−awkw(t)−ςw(t)kw(t)}≤−2n∑w=1[aw+ςw(t)]kw(t)¯kw(t). | (3.39) |
Substituting (3.39) and (3.21)–(3.25) into (3.38), we have
R1≤−n∑w=1[2(aw+ςw(t))−4n−n∑ψ=1δw|μψw|2L2w−n∑ψ=1ηw|υψw|2L2w]kw(t)¯kw(t)+n∑w=1n∑ψ=1(|mψw|2+|Mψw|2)L2wkw(t−τ(t))¯kw(t−τ(t)). | (3.40) |
Substituting (3.37) and (3.40) into (3.36), we finally get
Dαv2(t)≤−n∑w=1[2(aw+ς∗w)−4n−n∑ψ=1δw|μψw|2L2w−n∑ψ=1ηw|υψw|2L2w]kw(t)¯kw(t)+n∑w=1n∑ψ=1(|mψw|2+|Mψw|2)L2wkw(t−τ(t))¯kw(t−τ(t))−n∑w=12pIw(ςw(t)−ς∗w)2. | (3.41) |
By applying fractional-order Razumikhin theorem, the following formula holds:
Dαv2(t)≤−(Υ1−Υ2Ω2)v21(t)−2pv22(t). | (3.42) |
Let Ξ=min(Υ1−Υ2Ω2,2p), then
Dαv2(t)≤−Ξv21(t)−Ξv22(t)≤−Ξv2(t). | (3.43) |
According to Lemma 2.5, one has
v2(t)≤v2(0)Eα(−Ξtα). | (3.44) |
From Eq (3.35), we can deduce
v21(t)≤(v21(0)+v22(0))Eα[−(Υ1−Υ2Ω2)tα], | (3.45) |
and
||k(t)||2=n∑w=1kw(t)¯kw(t)=v21(t)≤v2(t)≤v2(0)Eα(−Ξtα). | (3.46) |
Therefore, it has
||k(t)||≤[v2(0)Eα(−Ξtα)]12, | (3.47) |
and
limt→∞||k(t)||=0. | (3.48) |
Obviously, from Definition 2.4, system (2.1) is Mittag-Leffler stable. From Definition 2.5 and limt→∞||k(t)||=0, systems (2.1) and (2.2) can reach MLPS.
Remark 3.1. Unlike adaptive controllers [12], linear feedback control [16], and hybrid controller [34], this article constructs two different types of controllers: nonlinear hybrid controller and adaptive hybrid controller. The hybrid controller has high flexibility, strong scalability, strong anti-interference ability, and good real-time performance. At the same time, hybrid adaptive controllers not only have the good performance of hybrid controllers but also the advantages of adaptive controllers. Adaptive controllers reduce control costs, greatly shorten synchronization time, and can achieve stable tracking accuracy. Different from the research on MLPS in literature [33,34,35], this paper adopts a complex valued fuzzy neural network model, while fully considering the impact of delay and uncertainty on actual situations. At the same time, it is worth mentioning that in terms of methods, we use complex-value direct method, appropriate inequality techniques, and hybrid control techniques, which greatly reduce the complexity of calculations.
In this section, we use the MATLAB toolbox to simulate theorem results.
Example 4.1. Study the following two-dimensional complex-valued FOFCVNNS:
Ct0DαtOw(t)=−awOw(t)+2∑ψ=1(mwψ+Δmwψ(t))fψ(Oψ(t−τ(t)))+2⋀ψ=1μwψ∫+∞0bwψ(t)fψ(Oψ(t))dt+2⋁ψ=1υwψ∫+∞0cwψ(t)fψ(Oψ(t))dt+Iw(t), | (4.1) |
where Ow(t)=ORw(t)+iOIw(t)∈C, ORw(t),OIw(t)∈R, τ1(t)=τ2(t)=|tan(t)| I1(t)=I2(t)=0, fp(Op(t))=tanh(ORp(t))+itanh(OIp(t)).
A=a1=a2=1,B=(mwψ+△mwψ(t))2×2=(−2.5+0.3i2.6−1.9i−2.3−1.2i2.8+1.7i)+(−0.4cost−0.6sint−0.5sint−0.3cost),C=(μwp)2×2=(−2.8+1.3i2.5−1.2i−2.2−1.1i2.5+1.6i),D=(υwψ)2×2=(−2.9+0.7i2.8−1.2i−2.1−1.9i2.9+1.9i). |
The response system is
Ct0DαtQw(t)=−awQw(t)+2∑ψ=1(mwψ+Δmwψ(t))fψ(Qψ(t−τ(t)))+2⋀ψ=1μwψ∫+∞0bwψ(t)fψ(Qψ(t))dt+2⋁ψ=1υwψ∫+∞0cwψ(t)fψ(Qψ(t))dt+Iw(t)+Uw(t), | (4.2) |
where Qw(t)=QRw(t)+iQIw(t)∈C. The initial values of (4.1) and (4.2) are
x1(0)=1.1−0.2i, x2(0)=1.3−0.4i,y1(0)=1.3−0.3i, y2(0)=1.5−0.1i. |
The phase portraits of system (4.1) are shown in Figure 1. The nonlinear hybrid controller is designed as (3.10), and picking α=0.95, S=0.55, δ1=δ2=1.1, η1=η2=1.3, L1=L2=0.11, λ=0.5. By calculation, we get ϖ1=4.47>0, ϖ2=0.71. Taking Ω1=1.5, then ϖ1−ϖ2Ω1>0. This also confirms that the images drawn using the MATLAB toolbox conform to the theoretical results of Theorem 3.2. Figures 2 and 3 show the state trajectory of kw(t) and ||kw(t)|| without the controller (3.10). Figures 4 and 5 show state trajectories and error norms with the controller (3.10), respectively.
Example 4.2. Taking α=0.95, S=0.97, k1=k2=5.1, ς1=ς2=0.2, ς∗1=5, ς∗2=8, δ1=δ2=1.1, η1=η2=1.5, L1=L2=0.1. The remaining parameters follow the ones mentioned earlier. According to calculation, Υ1=12.49, Υ2=7.36. Let Ω2=1.1, then we have Ω1−Υ2Ω2>0. Figure 6 depicts the state trajectory diagram of kw(t) with adaptive controller (3.31). Figure 7 describes the error norm ||kw(t)|| with controller (3.31). According to Figure 8, it is easy to see that the control parameters ςw(t) are constant.
Example 4.3. Consider the following data:
A=a1=a2=1,B=(mwψ+△mwψ(t))2×2=(−1.6+0.5i1.8−1.6i−1.6−1.2i2.1+1.7i)+(−0.4cost−0.6sint−0.5sint−0.3cost),C=(μwp)2×2=(−1.8+1.3i1.5−1.1i−1.8−1.1i1.8+1.6i),D=(υwψ)2×2=(−1.9+0.7i1.9−1.4i−1.7−1.3i2.1+1.8i). |
Let the initial value be
x1(0)=3.2−1.2i, x2(0)=3.0−1.4i,y1(0)=3.1−1.3i, y2(0)=3.3−1.1i. |
Picking α=0.88, S=0.9, δ1=δ2=1.2, η1=η2=1.5, L1=L2=0.1, λ=0.5, Ω1=2. After calculation, ϖ1=6.62>0, ϖ2=2.28, and ϖ1−ϖ2Ω1>0. Similar to Example 4.1, Figures 9 and 10 show the state trajectory of kw(t) and ||kw(t)|| without the controller (3.10). Figures 11 and 12 show state trajectories and error norms with the controller (3.10), respectively.
Example 4.4. Taking α=0.88, S=0.9, k1=k2=2.2, ς1=ς2=0.2, ς∗1=4, ς∗2=9, δ1=δ2=1.1, η1=η2=1.5, L1=L2=0.1, Ω2=2. The other parameters are the same as Example 4.3, where Υ1=22.29, Υ2=10.67, and Ω1−Υ2Ω2>0. Thus, the conditions of Theorem 3.3 are satisfied. Figure 13 depicts the state trajectory diagram of kw(t) with adaptive controller (3.31). Figure 14 describes the error norm ||kw(t)|| with controller (3.31). Control parameters ςw(t) are described in Figure 15.
In this paper, we studied MLPS issues of delayed FOFCVNNs. First, according to the principle of contraction and projection, a sufficient criterion for the existence and uniqueness of the equilibrium point of FOFCVNNs is obtained. Second, based on the basic theory of fractional calculus, inequality analysis techniques, Lyapunov function method, and fractional Razunikhin theorem, the MLPS criterion of FOFCVNNs is derived. Finally, we run four simulation experiments to verify the theoretical results. At present, we have fully considered the delay and parameter uncertainty of neural networks and used continuous control methods in the synchronization process. However, regarding fractional calculus, there is a remarkable difference between continuous-time systems and discrete-time systems [14]. Therefore, in future work, we can consider converting the continuous time system proposed in this paper into discrete time system and further research discrete-time MLPS. Alternatively, we can consider studying finite-time MLPS based on the MLPS presented in this paper.
Yang Xu: Writing–original draft; Zhouping Yin: Supervision, Writing–review; Yuanzhi Wang: Software; Qi Liu: Writing–review; Anwarud Din: Methodology. All authors have read and approved the final version of the manuscript for publication.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
The authors declare no conflicts of interest.
[1] |
Adegbite E, Guney Y, Kwabi F, et al. (2019) Financial and corporate social performance in the UK listed firms: The relevance of non-linearity and lag effects. Rev Quant Finance Account 52: 105–158. doi: 10.1007/s11156-018-0705-x
![]() |
[2] |
Akben-Selcuk E (2019) Corporate social responsibility and financial performance: The moderating role of ownership concentration in Turkey. Sustainability 11: 3643. doi: 10.3390/su11133643
![]() |
[3] |
Albitar K, Hussainey K, Kolade N, et al. (2020) ESG disclosure and firm performance before and after IR. Int J Account Info Manage 28: 429–444. doi: 10.1108/IJAIM-09-2019-0108
![]() |
[4] | Arsoy AP, Arabacı Ö, Çiftçioğlu A (2012) Corporate social responsibility and financial performance relationship: The case of Turkey. Muhasebe ve Finansman Dergisi 53: 159–176. |
[5] |
Awaysheh A, Heron RA, Perry T, et al. (2020) On the relation between corporate social responsibility and financial performance. Strat Manage J 41: 965–987. doi: 10.1002/smj.3122
![]() |
[6] |
Bai G, Elyasiani E (2013) Bank stability and managerial compensation. J Bank Financ 37: 799–813. doi: 10.1016/j.jbankfin.2012.10.026
![]() |
[7] |
Barauskaite G, Streimikiene D (2021) Corporate social responsibility and financial performance of companies: The puzzle of concepts, definitions and assessment methods. Corp Soc Responsib Environ Manage 28: 278–287. doi: 10.1002/csr.2048
![]() |
[8] |
Barnett ML, Salomon RM (2012) Does it pay to be really good? Addressing the shape of the relationship between social and financial performance. Strategic Manage J 33: 1304–1320. doi: 10.1002/smj.1980
![]() |
[9] |
Ben Lahouel B, Gaies B, Ben Zaied Y, et al. (2019) Accounting for endogeneity and the dynamics of corporate social - corporate financial performance relationship. J Clean Prod 230: 352–364. doi: 10.1016/j.jclepro.2019.04.377
![]() |
[10] |
Bènabou R, Tirole J (2010) Individual and corporate social responsibility. Economica 77: 1–19. doi: 10.1111/j.1468-0335.2009.00843.x
![]() |
[11] | Bragdon JH, Marlin, JA (1972) Is pollution profitable? Risk Manag 19: 9–18. |
[12] |
Carroll AB, Shabana KM (2010) The business case for corporate social responsibility; a review of concepts, research and practice. Int J Manage Rev 12: 85–105. doi: 10.1111/j.1468-2370.2009.00275.x
![]() |
[13] |
Chatterji AK, Durand R, Levine DI, et al. (2016) Do ratings of firms converge? Implications for managers, investors and strategy researchers. Strategic Manage J 37: 1597–1614. doi: 10.1002/smj.2407
![]() |
[14] | Chen J, Liu J, Qin J (2019) Corporate social responsibility and capacity selection. Transform Bus Econ 18: 530–545. |
[15] |
Chen L, Feldmann A, Tang O (2015) The relationship between disclosures of social performance and financial performance: evidence from GRI reports in manufacturing industry. Int J Prod Econ 170: 375–710. doi: 10.1016/j.ijpe.2015.11.005
![]() |
[16] |
Chung KH, Pruitt SW (1994) A simple approximation of Tobin's q. Financ Manage 23: 70–74. doi: 10.2307/3665623
![]() |
[17] |
Clemens B, Douglas TJ (2006) Does coercion drive firms to adopt 'voluntary' green initiatives? Relationships among coercion, superior firm resources, and voluntary green initiatives. J Bus Res 59: 483–491. doi: 10.1016/j.jbusres.2005.09.016
![]() |
[18] |
De Villiers C, Van Staden CJ (2006). Can less Environmental Disclosure have a Legitimising Effect? Evidence from Africa. Account Org Soc 31: 763–781. doi: 10.1016/j.aos.2006.03.001
![]() |
[19] | Deegan C (2006) Financial Accounting Theory. Sydney: McGraw-Hill Irwin. |
[20] |
Dobers P, Minna H (2009) Corporate social responsibility and developing countries. Corp Soc Responsib Environ Manage 16: 237–249. doi: 10.1002/csr.212
![]() |
[21] |
Driscoll JC, Kraay AC (1998). Consistent covariance matrix estimation with spatially dependent panel data. Rev Econ Stat 80: 549–560. doi: 10.1162/003465398557825
![]() |
[22] |
Eccles RG, Ioannou I, Serafeim G (2014) The Impact of Corporate Sustainability on Organizational Processes and Performance. Manage Sci 60: 2835–2857. doi: 10.1287/mnsc.2014.1984
![]() |
[23] |
Fernandez-Feijoo B, Romero S, Ruiz S (2014) Effect of stakeholders' pressure on transparency of sustainability reports within the GRI framework. J Bus Ethics 122: 53–63. doi: 10.1007/s10551-013-1748-5
![]() |
[24] | Freeman RE (1984) Strategic management: a stakeholder approach. Boston: Pitman. |
[25] | Freeman RE (2014) Stakeholder theory of the modern corporation. In Hoffman WM, Frederick RE, Schwartz MS (Eds.) Business Ethics: Readings and Cases in Corporate Morality, Boston: Wiley-Blackwell. |
[26] | Friedman M (1970) The social responsibility of business is to increase its profits. The New York Times. Available from: https://www.nytimes.com/1970/09/13/archives/a-friedman-doctrine-the-social-responsibility-of-business-is-to.html. |
[27] | Galant A, Cadez S (2017) Corporate social responsibility and financial performance relationship: a review of measurement approaches. Econ Res-Ekon istraž 30: 676–693. |
[28] |
Garcia AS, Mendes-Da-Silva W, Orsato RJ (2017) Sensitive industries produce better ESG performance: Evidence from emerging markets. J Clean Prod 150: 135–147. doi: 10.1016/j.jclepro.2017.02.180
![]() |
[29] |
Greening DW, Turban DB (2000) Corporate social performance as a competitive advantage in attracting a quality workforce. Bus Soc 39: 254–280. doi: 10.1177/000765030003900302
![]() |
[30] | Gujarati DN (1995) Basic Econometrics, 3rd edn. International: McGraw-Hill. |
[31] | Hatfield GB, Cheng LTW, Davidson WN (1994) The determination of optimal capital structure: The effect of firm and industry debt ratios on market value. J Financ Strateg Decis 7: 1–14. |
[32] |
Hillman AJ, Keim GD (2001) Shareholder value, stakeholder management, and social issues: what's the bottom line? Strateg Manage J 22: 125–139. doi: 10.1002/1097-0266(200101)22:2<125::AID-SMJ150>3.0.CO;2-H
![]() |
[33] |
Hoechle D (2007) Robust standard errors for panel regressions with cross-sectional dependence. Stata J 7: 281–312. doi: 10.1177/1536867X0700700301
![]() |
[34] |
Jamali D, Neville B (2011) Convergence versus divergence of CSR in developing countries: An embedded multilayered institutional lens. J Bus Ethics 102: 599–621. doi: 10.1007/s10551-011-0830-0
![]() |
[35] |
Jo H, Na H (2012) Does csr reduce firm risk? evidence from controversial industry sectors. J Bus Ethics 110: 441–456. doi: 10.1007/s10551-012-1492-2
![]() |
[36] |
Junaid M, Xue Y, Syed MW, et al. (2020) Corporate governance mechanism and performance of insurers in Pakistan. Green Finance 2: 243–262. doi: 10.3934/GF.2020014
![]() |
[37] |
Lahouel BB, Zaied YB, Song Y, et al. (2021) Corporate social performance and financial performance relationship: A data envelopment analysis approach without explicit input. Financ Res Lett 39: 101656. doi: 10.1016/j.frl.2020.101656
![]() |
[38] |
Li Z, Liao G, Albitar K (2020) Does corporate environmental responsibility engagement affect firm value? The mediating role of corporate innovation. Bus Strateg Environ 29: 1045–1055. doi: 10.1002/bse.2416
![]() |
[39] |
Liao G, Hou P, Shen X, et al. (2021) The impact of economic policy uncertainty on stock returns: The role of corporate environmental responsibility engagement. Int J Financ Econ 26: 1–7. doi: 10.1002/ijfe.2020
![]() |
[40] |
López MV, Garcia A, Rodriguez L (2007) Sustainable development and corporate performance: a study based on the Dow Jones Sustainability Index. J Bus Ethics 75: 285–300. doi: 10.1007/s10551-006-9253-8
![]() |
[41] |
Luo X, Bhattacharya CB (2009). The debate over doing good: corporate social performance, strategic marketing levers, and firm-idiosyncratic risk. J Mark 73: 198–213. doi: 10.1509/jmkg.73.6.198
![]() |
[42] |
Makni R, Francoeur C, Bellavance F (2009) Causality between corporate social performance and financial performance: evidence from canadian firms. J Bus Ethics 89: 409–422. doi: 10.1007/s10551-008-0007-7
![]() |
[43] |
Marti CP, Rovira-Val MR, Drescher LGJ (2015) Are firms that contribute to sustainable development better financially? Corp Soc Resp Environ Manage 22: 305–319. doi: 10.1002/csr.1347
![]() |
[44] |
Martínez-Ferrero J, Frías-Aceituno JV (2015) Relationship between sustainable development and financial performance: international empirical research. Bus Strateg Environ 24: 20–39. doi: 10.1002/bse.1803
![]() |
[45] | Menezes G (2019) Impact of CSR Spending on firm's financial performance. Int J Adv Res Ideas Innov Tech 5: 613–617. |
[46] |
Moore G (2001) Corporate social and financial performance: an investigation in the U.K. supermarket industry. J Bus Ethics 34: 299–315. doi: 10.1023/A:1012537016969
![]() |
[47] | Moskowitz M (1972) Choosing socially responsible stocks. Bus Soc Rev 1: 71–75. |
[48] | Nyeadi JD, Ibrahim M, Sare YA (2018) Corporate social responsibility and financial performance nexus: Empirical evidence from South African listed firms. J Glob Resp 9: 301–328. |
[49] |
Oba B, Ozsoy Z, Atakan S (2010) Power in the boardroom: A study on Turkish family owned and listed companies. Corp Gov 10: 603–616. doi: 10.1108/14720701011085571
![]() |
[50] |
O'Donovan G (2002) Environmental disclosures in the annual report: Extending the applicability and predictive power of legitimacy theory. Account Audit Account J 15: 344–371. doi: 10.1108/09513570210435870
![]() |
[51] |
Odriozola MD, Baraibar-Diez E (2017) Is Corporate Reputation Associated with Quality of CSR Reporting? Evidence from Spain. Corp Soc Resp Environ Manage 24: 121–132. doi: 10.1002/csr.1399
![]() |
[52] | Özçelik F, Avcı Öztürk B, Gürsakal S (2014) Investigating the relationship between corporate social responsibility and financial performance in Turkey. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi 28: 189–203. |
[53] |
Ozdora-Aksak E, Atakan-Duman S (2016) Gaining legitimacy through CSR: An analysis of Turkey's 30 largest corporations. Bus Ethics Eur Rev 25: 238–257. doi: 10.1111/beer.12114
![]() |
[54] |
Preston LE, O'Bannon DP (1997) The corporate social-financial performance relationship: a typology and analysis. Bus Soc 36: 419–429. doi: 10.1177/000765039703600406
![]() |
[55] | Resmi SI, Begum NN, Hassan M (2018) Impact of CSR on firm's financial performance:A study on some selected agribusiness industries of Bangladesh. Am J Econ Financ Manage 4: 74–85. |
[56] | Saygili E, Arslan S, Birkan AO (2021) ESG practices and corporate financial performance: Evidence from Borsa Istanbul. Borsa Istanb Rev. |
[57] |
Schreck P (2011) Reviewing the business case for corporate social responsibility: new evidence and analysis. J Bus Ethics 103: 167–188. doi: 10.1007/s10551-011-0867-0
![]() |
[58] |
Selcuk EA, Kiymaz H (2017) Corporate social responsibility and firm performance: Evidence from an emerging market. Account Financ Res 6: 42–51. doi: 10.5430/afr.v6n4p42
![]() |
[59] |
Servaes H, Tamayo A (2013) The impact of corporate social responsibility on firm value: The role of customer awareness. Manage Sci 59: 1045–1061. doi: 10.1287/mnsc.1120.1630
![]() |
[60] |
Shahzad AM, Sharfman MP (2017) Corporate social performance and financial performance: sample-selection sssues. Bus Soc 56: 889–918. doi: 10.1177/0007650315590399
![]() |
[61] |
Şimşek H, Öztürk G (2021) Evaluation of the relationship between environmental accounting and business performance: the case of Istanbul province. Green Finance 3: 46–58. doi: 10.3934/GF.2021004
![]() |
[62] |
Surroca J, Tribó JA, Waddock S (2010) Corporate responsibility and financial performance: the role of intangible resources. Strateg Manage J 31: 463–490. doi: 10.1002/smj.820
![]() |
[63] |
Syed MW, Li JZ, Junaid M, et al. (2020) Relationship between human resource management practices, relationship commitment and sustainable performance. Green Finance 2: 227–242. doi: 10.3934/GF.2020013
![]() |
[64] | Ting IWK, Azizan NA, Bhaskaran RK, et al. (2020) Corporate social performance and firm performance: Comparative study among developed and emerging market firms. Sustainability 12: 1–21. |
[65] |
Utomo MN, Rahayu S, Kaujan K, et al. (2020) Environmental performance, environmental disclosure, and firm value: empirical study of non-financial companies at Indonesia Stock Exchange. Green Finance 2: 100–113. doi: 10.3934/GF.2020006
![]() |
[66] |
Wagner M (2010) The role of corporate sustainability performance for economic performance: A firm-level analysis of moderation effffects. Ecol Econ 69: 1553–1560. doi: 10.1016/j.ecolecon.2010.02.017
![]() |
[67] | Zakari M (2017) The relationship between corporate social responsibility and profitability: The case of Dangote cement Plc. J Financ Account 5: 171–176. |
![]() |
![]() |