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Business incubators are advantageous to new venture legitimacy because they provide rich access to entrepreneurial resources, and their incubation networks can offer endorsement to incubatees. However, empirical evidence on this topic is limited, and the existing literature relies predominantly on the Western context. Given that not all developing country incubators have resourceful and reputable external entrepreneurial networks as in the industrialized countries, and that new ventures need to build legitimacy along cognitive and socio-political dimensions that require different actions to influence different stakeholders, this study investigates empirically how business incubators facilitate their incubatees to build legitimacy in a context where resource and reputation conditions are weak. The purpose of this paper is to clarify how business incubators perform legitimacy-building roles effectively.

A multiple case study of business incubators in Chongqing, a second-tier Chinese city, is presented. Using grounded theory, this paper draws its findings from a synthesis of interviews and secondary data of seven incubators and their ten incubatees.

The legitimacy-building role of business incubators is performed well in this research context. Evidence is presented that incubators play different roles in building different dimensions of incubatees’ legitimacy. Government-associated incubators play a salient role in building incubatees’ socio-political legitimacy whilst non-government related incubators shape their incubatees’ cognitive legitimacy.

This study contributes to the business incubators literature by revealing how incubators perform the legitimacy-building role when their resource endorsement is weak. The results suggest that incubators need to strengthen their ties with external stakeholders and that new ventures need to take key stakeholders into consideration when they select incubators to enter.

In large-scale group decision-making (LSGDM) situations, existing TODIM group decision-making methods often fail to account for the influence of social network relationships and the bounded rationality of decision-makers (DMs). To address this issue, a new TODIM-based group decision-making method is proposed that considers the current trust relationships among DMs in a large-scale trust relationship network.

This method consists of two main stages. In the first stage, the large-scale group is partitioned into several sub-clusters based on trust relationships among DMs. The dominance degree matrix of each sub-cluster is then aggregated into the large-scale group dominance degree. In the second stage, after aggregating the large-scale group dominance degree, the consensus index is calculated to identify any inconsistent sub-clusters. Feedback adjustments are made based on trust relationships until a consensus is reached. The TODIM method is then applied to calculate the corresponding ranking results. Finally, an illustrative example is applied to show the feasibility of the proposed model.

The proposed method is practical and effective which is verified by the real case study. By taking into account the trust relationships among DMs in the core process of LSGDM, it indeed has an impact on the decision outcomes. We also specifically address this issue in Chapter Five. The proposed method fully incorporates the bounded rationality of DMs, namely their tendency to accept the opinions of trusted experts, which aligns more with their psychology. The two-stage consensus model proposed in this paper effectively addresses the limitations of traditional assessment-based methods.

This study establishes a two-stage consensus model based on trust relationships among DMs, which can assist DMs in better understanding trust issues in complex decision-making, enhancing the accuracy and efficiency of decisions, and providing more scientific decision support for organizations such as businesses and governments.

The regulatory landscape surrounding international construction projects presents significant challenges, and contractors are still struggling to pay a painful price for their performance in the project. While existing research has identified various causes of contractor compliance, the intricate interplay of these factors and their impact on compliance remain largely elusive. The motivation-opportunity-ability (MOA) framework may hold the key to determining what factors can foster induced contractor compliance in international projects.

This study collected 124 valid data samples from practitioners involved in large-scale international contracting projects through expert interviews and questionnaire surveys. Fuzzy-set qualitative comparative analysis (fsQCA) was employed to analyze the diverse combinations of contractor compliance factors.

The study identifies seven key factors that contribute to compliance behavior among international construction contractors: economic motivation, social motivation, normative motivation, legal completeness, deterrent sanctions, organizational learning and compliance management ability. The interplay of these factors promotes compliance in the following ways: When international construction contractors are influenced by both social and normative motivations, they exhibit a higher level of compliance. In situations where regulatory systems are relatively weak, the ability to manage compliance becomes the primary driver of compliance behavior for businesses. A comprehensive legal framework creates a conducive environment for contractors to improve their compliance through organizational learning.

The findings offer guidance for international construction contractors in enhancing compliance by considering factors such as motivations, legal frameworks, organizational learning and compliance management. This can lead to improved risk management and performance in international projects.

This research enhances fair and ethical practices in international construction by identifying compliance drivers, fostering positive social impact, mitigating negative consequences and empowering local communities. It informs legal and regulatory reform, encourages improved business practices and contributes to knowledge advancement in the field. Overall, the findings have the potential to positively impact the social fabric of international construction projects.

This study has made an important contribution to the field of compliance theory by integrating theories from multiple disciplinary domains and constructing a new theoretical framework from the perspectives of motivation, opportunity and capability. By elucidating how these factors interact and influence compliance behavior among international construction contractors, this research aids in understanding the complex dynamics of contractor compliance behavior and provides theoretical reference for compliance governance within the construction industry.

This paper examines how the level of low-carbon awareness (LCA) affects the remanufacturing strategy in a supply chain with an original equipment manufacturer (OEM) and an independent remanufacturer (IR) competing with each other.

Game theory and operations optimization.

The studies analytically characterize the threshold levels of the LCA in response to which the OEM and the IR will change their remanufacturing strategies from no remanufacturing to partial remanufacturing and then to full remanufacturing. In addition, the studies reveal that as compared with the OEM, the IR has more flexibility in terms of the market entry to remanufacturing with the level of LCA increasing. With the extended studies, it is exhibited that the above findings are robust to a good extent.

It can provide decision support for remanufacturing enterprises.

The purpose of this study is to examine the impact of Confucianism on auditor changes by highlighting the role of the cultural embeddedness mechanism in audit contracts from the perspective of credit governance.

Using a unique sample of Chinese A-share listed firms from 2008 to 2018, this study uses logit regression as the baseline methodology while controlling for macro-level factors and firm-level characteristics, as well as industry and year fixed effects. This study also conducts different mediation/channel analyses, endogeneity tests (using two-stage least squares and difference-in-differences techniques) and robustness checks.

The findings show that the embeddedness of Confucianism in a corporation reduces auditor changes. Furthermore, the channel analyses (using moral self-discipline, social trust, professional ethics and the quality of accounting information as four potential channels) reveal that Confucianism can improve moral credit and consolidate the cultural foundation of credit governance. Specifically, the stronger the embeddedness of Confucianism, the more stable the auditing contract. Finally, Confucianism in formal and informal systems can be mutually substituted.

There is limited research on how culture affects auditing contracts. This study offers new contributions and extends the literature on the connection between cultural embeddedness and contract stability. Confucianism has the potential to strengthen the efficiency of credit governance and maintain the stability of contracts. This study offers a thoughtful orientation toward duly using Confucianism vis-à-vis credit governance.

In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.

A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed.

Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.

The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

The purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com.

The current credit valuation systems are classified into the forward-looking mechanism, which judges the borrowers’ credit levels based on their uploaded information, and the backward-looking mechanism, which judges the borrowers’ credit levels based on their historical repayment performance. Probit models and Tobit models are used to examine the effectiveness of credit evaluation mechanisms.

The results show that only the “hard” information reflecting borrowers’ credit ability can explain the default risk on the platform under the forward-looking credit evaluation mechanism. The backward-looking credit evaluation mechanism (BCEM) based on the repeated borrowings produces both promise-enhancing and “fishing” incentives and thus fails to explain the default risk, and weakens the effectiveness of forward-looking credit indicators in explaining the default risk because it encourages borrowers to invest in forging forward-looking credit indicators. Additional information such as the interest rate and the repayment periods reveals borrowers’ credit and thus can also be used as a predictor of borrowers’ default risk.

The findings suggest that current ex ante screening based on the information collected from the borrowers or repeated borrowings is inadequate to control the default risk in P2P lending markets and thus needs be improved. Ex post monitoring and sharing on defaulter’s information should be strengthened to increase the default cost and thus to deter potential bad borrowers.

To the authors’ knowledge, this is the first paper classifying the credit evaluation system in online P2P lending market into the forward-looking type and the backward-looking type, which is important since they provide different incentives to borrowers. The paper also investigates and provides evidence on the promise-enhancing and “fishing” incentives of BCEMs.

The purpose of this study is to investigate the factors that influence customers’ green consumption intention by integrating social cognitive theory (SCT) and the cognitive affective conative (CAC) framework.

Survey questionnaire was employed to collect data. Then, this study adopts artificial neural network (ANN) to check the robustness of partial least squares-structural equation modelling (PLS-SEM) empirical results.

The findings confirm that social media marketing and collectivism are potent external stimuli to promote green consumption intention. Significant variables identified in the PLS-SEM analysis were used for ANN models, demonstrating the robustness of the PLS-SEM findings.

The primary theoretical contribution lies in the application of SCT theory and the CAC framework in the context of green consumption, an area that has been relatively underexplored in previous studies. Additionally, the study provides managerial implications for marketers by emphasising the significance of social media marketing and collectivism in influencing consumers’ cognition and affect.

As one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.

Four polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.

The experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.

This research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.