Search results

This research aims to comprehensively investigate when and how cognitive conflict benefits team innovation in cross-functional project teams (CFPTs), by exploring the moderating role of knowledge leadership and dual mediation mechanisms of elaboration of task-related information/knowledge and affective conflict.

All hypotheses have been empirically tested by using structural equation model to analyze the quantitative data from a questionnaire survey covering 73 CFPTs in China.

Results indicate that knowledge leadership positively moderates the relationship between cognitive conflict and CFPT innovation. This moderating effect is directly or indirectly revealed by the dual mediating roles of task-related information/knowledge elaboration and affective conflict, which are two processes manifesting whether cognitive conflict can or cannot be incorporated into team innovation.

Despite the external validity of results limited by convenient sampling method, the findings offer implications for promoting CFPT innovation. This can be achieved by developing competent knowledge leadership into team sensegiver, dissent reconciler and facilitator to accentuate benefits of cognitive conflict in information/knowledge elaboration and attenuate the likelihood of escalating to affective conflict.

This study advances the understanding of why cognitive conflict has an equivocal effect on team innovation in the context of CFPT by originally revealing how leaders’ role in information/knowledge management acts as a contingency and suggesting the dual mediating mechanisms that reflect the contingent impact. Project-based teams or organizations, characterized by cognitive clashes, can enhance innovation performance by shaping the meaningfulness of information/knowledge activities triggered by cognitive conflict.

The purpose of this paper is to provide a theoretical platform for studying the characteristic of random carrier frequency (RCF) modulation and analyze the related parameters in terms of electromagnetic interference (EMI) suppressing.

In this paper, the expression of the amplitude of switching voltage power spectra under the RCF mode is presented. According to the expression, the effectiveness of related parameters on EMI mitigation is discussed. Theoretical predictions are confirmed with a closed‐loop boost converter which power is 7.5 W and nominal switching frequency is 200 KHz. Finally, special attention has been paid to output voltage ripple.

Under RCF scheme, with increasing of random degree, the spectrum of switching voltage is dispersed, so the power spectrum density amplitude of switching voltage falls, and then the level of EMI is lowered. This theory shows that the voltage power spectrum density attains maximum when d=0.5, so in the spread spectrum modulation, the duty cycle of 0.5 should be avoided in terms of conducted EMI mitigation.

The analytical expression of switching voltage power spectra under the RCF mode is presented, providing a theoretical platform for the related research. The selection of duty cycle has effect on EMI level is put forward for the first time. Voltage ripple is discussed in close‐loop circuit.

The purpose of this paper is to suggest the solution of time-fractional Fornberg–Whitham and time-fractional Fokker–Planck equations by using a novel approach.

First, some basic properties of fractional derivatives are defined to construct a novel approach. Second, modified Laplace homotopy perturbation method (HPM) is constructed which yields to a direct approach. Third, two numerical examples are presented to show the accuracy of this derived method and graphically results showed that this method is very effective. Finally, convergence of HPM is proved strictly with detail.

It is not necessary to consider any type of assumptions and hypothesis for the development of this approach. Thus, the suggested method becomes very simple and a better approach for the solution of time-fractional differential equations.

Although many analytical methods for the solution of fractional partial differential equations are presented in the literature. This novel approach demonstrates that the proposed approach can be applied directly without any kind of assumptions.

This study aims to examine the effects of pathos in sustainable brand stories featuring masculinity on brand masculinity and men’s sustainable brand attitude using Aristotle’s rhetoric theory.

Three independent online experiments (N = 398; N = 216; N = 247) were conducted to observe how participants responded to a sustainable brand story. Data collected through a post-experimental survey were used to test the proposed model. Research hypotheses were inspected using SPSS.

The authors reveal brand masculinity is influenced by varying degrees of pathos: participants who read stories with all three pathos elements (metaphor, humor and empathy) demonstrated the highest level of perceived brand masculinity. Male consumers showed more positive attitudes toward masculine sustainable brand stories than feminine ones. The authors also identify the moderating effect of consumer generation: Gen Z (versus Gen Y) consumers demonstrated stronger character identification with hybrid masculinity (versus hegemonic masculinity) sustainable brand stories, resulting in more favorable sustainable brand attitudes.

The study provides a new angle for exploring the relationship between gendered sustainable brand stories and sustainable brand attitudes. It is the first (to the authors’ knowledge) that links Aristotle’s rhetoric theory to brand gender research, and it empirically demonstrates how male consumers from different generational cohorts respond to different masculinity strategies used by sustainable brands.

This paper aims to examine the information content of changes in fair values of investment property reported under international accounting standards (IAS) 40 and International Financial Reporting Standards (IFRS) 13 to debtholders. This study further examines the effect of fair value hierarchy inputs, valuer types and the quality of fair value measurement-related disclosure on the information usefulness of changes in fair value.

This paper performs a panel regression on the cost of debt capital and changes in fair value of investment properties, and fair value measurement features using data covering periods 2007–2015 from Australian real estate companies.

The findings suggest that changes in fair value of investment property are informative about the real estate firm’s future cash flow to debtholders. Also, the findings show that the use of unobservable inputs in an active market (Level 3 inputs) and Level 2 has no different impacts on the cost of debts. Also, this paper documents that employing the directors solely in valuation may lead to a higher cost of debts. Furthermore, this paper reports that an extensive fair value disclosure appears no additional value in the debt decision.

Collectively, the findings indicate that although the use of unobservable inputs is common in the real estate sector, information on the changes of the fair value of investment properties are informative to debtholders. The findings have important implications for accounting standard setters to consider revisiting the IAS 40 and IFRS 13 on whether the independent valuation should be required and whether the extensive disclosure requirement is worthwhile.

Grey target decision-making serves as a pivotal analytical tool for addressing dynamic multi-attribute group decision-making amidst uncertain information. However, the setting of bull's eye is frequently subjective, and each stage is considered independent of the others. Interference effects between each stage can easily influence one another. To address these challenges effectively, this paper employs quantum probability theory to construct quantum-like Bayesian networks, addressing interference effects in dynamic multi-attribute group decision-making.

Firstly, the bull's eye matrix of the scheme stage is derived based on the principle of group negotiation and maximum satisfaction deviation. Secondly, a nonlinear programming model for stage weight is constructed by using an improved Orness measure constraint to determine the stage weight. Finally, the quantum-like Bayesian network is constructed to explore the interference effect between stages. In this process, the decision of each stage is regarded as a wave function which occurs synchronously, with mutual interference impacting the aggregate result. Finally, the effectiveness and rationality of the model are verified through a public health emergency.

The research shows that there are interference effects between each stage. Both the dynamic grey target group decision model and the dynamic multi-attribute group decision model based on quantum-like Bayesian network proposed in this paper are scientific and effective. They enhance the flexibility and stability of actual decision-making and provide significant practical value.

To address issues like stage interference effects, subjective bull's eye settings and the absence of participative behavior in decision-making groups, this paper develops a grey target decision model grounded in group negotiation and maximum satisfaction deviation. Furthermore, by integrating the quantum-like Bayesian network model, this paper offers a novel perspective for addressing information fusion and subjective cognitive biases during decision-making.

Despite better accessibility and flexibility, peer-to-peer (P2P) lending has suffered from excessive credit risks, which may cause significant losses to the lenders and even lead to the collapse of P2P platforms. The purpose of this research is to construct a hybrid predictive framework that integrates classification, feature selection, and data balance algorithms to cope with the high-dimensional and imbalanced nature of P2P credit data.

An improved synthetic minority over-sampling technique (IMSMOTE) is developed to incorporate the randomness and probability into the traditional synthetic minority over-sampling technique (SMOTE) to enhance the quality of synthetic samples and the controllability of synthetic processes. IMSMOTE is then implemented along with the grey relational clustering (GRC) and the support vector machine (SVM) to facilitate a comprehensive assessment of the P2P credit risks. To enhance the associativity and functionality of the algorithm, a dynamic selection approach is integrated with GRC and then fed in the SVM's process of parameter adaptive adjustment to select the optimal critical value. A quantitative model is constructed to recognize key criteria via multidimensional representativeness.

A series of experiments based on real-world P2P data from Prosper Funding LLC demonstrates that our proposed model outperforms other existing approaches. It is also confirmed that the grey-based GRC approach with dynamic selection succeeds in reducing data dimensions, selecting a critical value, identifying key criteria, and IMSMOTE can efficiently handle the imbalanced data.

The grey-based machine-learning framework proposed in this work can be practically implemented by P2P platforms in predicting the borrowers' credit risks. The dynamic selection approach makes the first attempt in the literature to select a critical value and indicate key criteria in a dynamic, visual and quantitative manner.

Members in a supply chain account for corporate social responsibility (CSR) in different ways. This paper considers a socially responsible supply chain in which the manufacturer innovates in a sustainable product while the retailer exhibits CSR concerns. This paper aims to investigate how socially responsible behavior, namely, sustainable innovations or CSR concerns, affects the pure profit, environmental impact and social welfare, in such a socially responsible supply chain.

This paper first constructs an integrated case as a benchmark and then develops a Manufacturer-Stackelberg game in a decentralized scenario. The pure profit, environmental impact and social welfare are confirmed and analyzed in centralized and decentralized cases. Moreover, two unique coordinating contracts, i.e. wholesale price discount contract and revenue-sharing contract, are used in this socially responsible supply chain.

Analytical analysis shows that, under certain conditions, the optimal CSR strategies hold for maximizing pure channel profit, minimizing environmental impact and maximizing social welfare. Whether the performance in a centralized case outnumbers that in a decentralized case depends on the CSR concerns level and environment-friendly degree of the product. In addition, it is found that a wholesale price discount contract is better for the retailer whereas a revenue-sharing contract is better for the manufacturer in pure profit to improve coordinating efficiency.

These results can offer managerial implications to the socially responsible supply chain in terms of pricing decisions, CSR strategies and sustainability innovations. Specifically, under certain conditions, placing more CSR concerns level increases pure channel profit and the social welfare. A balance between the pure profit and the social welfare is hereby achieved for the two socially responsible individuals by designing a proper contract.

To the best of the authors’ knowledge, this paper is among the first studies so far to combine the CSR concerns strategy and sustainability innovation into a socially responsible supply chain.