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Prior studies have confirmed market segmentation as an important shackle to China's macroeconomy upgrade, but the systematic analysis of microenterprise upgrade remains inadequate. This paper aims to investigate the nonlinear impact of market segmentation on microenterprise upgrade.

The price method was applied to calculate China's provincial market segmentation, including commodity, capital, labor and energy. The generalized method of moments was employed to examine the nonlinear impact of market segmentation on the upgrading of manufacturing enterprises based on microenterprise data from 2003 to 2019.

First, China’s heterogeneous market segmentations have been significantly reduced. Macroeconomics and policies are critical factors in market integration. Second, there is an inverted U-shaped relationship between China’s total market segmentation and the upgrading of manufacturing enterprises. Third, the relationship between the commodity market segmentation, labor market segmentation, energy market segmentation and manufacturing enterprises' upgrade is shown as an inverted U-shaped. Nevertheless, the relationship between the capital market segmentation and upgrading of manufacturing enterprises exhibits a U-shape.

The impact of market segmentation on the upgrading of manufacturing enterprises in China performs nonlinearly. An inverted U-shaped relationship exists between market segmentation in commodity, labor and energy and the upgrading of manufacturing enterprises, while a U-shaped relationship prevails between capital market segmentation and the upgrading of manufacturing enterprises.

Previous studies have argued that trust and commitment can create value in cooperative relationships. However, this study observed that, in practice, trust and commitment alone may not ensure value creation in asymmetric relationships. Accordingly, this study aims to investigate the mediating role of specific assets in the effects of trust and commitment on value creation in asymmetric buyer–seller relationships.

Contract manufacturers (CMs) in Asia were sampled to validate the argument proposed by this study. Most Taiwanese CMs are partnered with international brands (original equipment manufacturers [OEMs]) that have stronger bargaining power. This cooperative relationship is characteristically asymmetric. A questionnaire method was applied, and structural equation modeling was performed to verify the proposed hypotheses.

Specific asset investment (SAI) was a crucial mediator that explained the effects of trust and commitment on the relationship value of an asymmetric cooperative relationship. Past studies have claimed that power asymmetry results in an unequal distribution of benefits. Nevertheless, regarding the relationship between CMs and OEMs, the study revealed that relationship value could still be increased once the congruent goals have been achieved by both parties. This finding contradicts past theoretical predictions.

Characteristically asymmetric CMs–OEMs (seller–buyer) relationships cannot be maintained merely through trust and commitment, particularly in the context of power and resource imbalances in which the stronger party often possesses a wider selection of prospective partners. The results of this study suggested that the CM should unilaterally invest in specific assets conducive to a cooperative relationship as an expression of faith in the relationship with the stronger firm, thereby creating opportunities for value cocreation.

The analysis of the relevance of relationship quality in the context of asymmetric cooperative relationships confirmed the mediating influences of SAI on ensuring value creation and the maintenance of the relationships. Relationship value could still be created despite the highly asymmetry power relationship. The CMs’ SAI is the key mechanism for this achievement.

Based on the error analysis, the authors proposed a new kind of high accuracy boundary element method (BEM) (HABEM), and for the large-scale problems, the fast algorithm, such as adaptive cross approximation (ACA) with generalized minimal residual (GMRES) is introduced to develop the high performance BEM (HPBEM). It is found that for slender beams, the stress analysis using iterative solver GMRES will difficult to converge. For the analysis of slender beams and thin structures, to enhance the efficiency of GMRES solver becomes a key problem in the development of the HPBEM. The purpose of this paper is study on the preconditioning method to solve this convergence problem, and it is started from the 2D BE analysis of slender beams.

The conventional sparse approximate inverse (SAI) based on adjacent nodes is modified to that based on adjacent nodes along the boundary line. In addition, the authors proposed a dual node variable merging (DNVM) preprocessing for slender thin-plate beams. As benchmark problems, the pure bending of thin-plate beam and the local stress analysis (LSA) of real thin-plate cantilever beam are applied to verify the effect of these two preconditioning method.

For the LSA of real thin-plate cantilever beams, as GMRES (m) without preconditioning applied, it is difficult to converge provided the length to height ratio greater than 50. Even with the preconditioner SAI or DNVM, it is also difficult to obtain the converged results. For the slender real beams, the iteration of GMRES (m) with SAI or DNVM stopped at wrong deformation state, and the computation failed. By changing zero initial solution to the analytical displacement solution of conventional beam theory, GMRES (m) with SAI or DNVM will not be stopped at wrong deformation state, but the stress error is still difficult to converge. However, by GMRES (m) combined with both SAI and DNVM preconditioning, the computation efficiency enhanced significantly.

This paper presents two preconditioners: DNVM and a modified SAI based on adjacent nodes along the boundary line of slender thin-plate beam. In the LSA, by using GMRES (m) combined with both DNVM and SAI, the computation efficiency enhanced significantly. It provides a reference for the further development of the 3D HPBEM in the LSA of real beam, plate and shell structures.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

The impact of a mixture of positive and negative media coverage on long-run hotel survival remains unknown. This paper aims to investigate how the mixed positive and negative media coverage, namely, inconsistent media coverage, influences long-run hotel survival.

A yearly panel data set covering 792 news-reported hotels in Guangdong province of China, over the period 2010–2020, is analyzed using an inconsistency analysis framework consisting of text mining and survival analysis. The estimates of exponential models on the same observations and Cox estimates on alternative observations are used for robustness checks.

The inconsistency calculation method proposed here can measure the controversy degree well. There exists a U-shaped relationship between inconsistency of media coverage and hotel longevity, and hotel survival is significantly reduced only when the degree of inconsistency is within the range of 17.8%–53.6%. The U-shaped relationship is moderated by negative hotel image and by online media coverage on hotel operation strategy topics.

This study provides suggestions for hotel managers to use media coverage inconsistency to increase long-run hotel survival in the digital era.

To the best of the authors’ knowledge, this paper is one of the first to investigate long-run hotel survival factors from the perspective of media coverage inconsistency. It also proposes a method to calculate the degree of media coverage controversy, which helps to quantify the relationship between the degree of inconsistency and hotel survival.

The circular economy (CE) represents an industry-wide transition from linear to circular processes. There has been a proliferation of literature on CE in the last decade. However, the existing studies on the adaption of CE in small and medium-sized enterprises (SMEs) are scarce. This study aims to develop a research agenda and the way forward for future researchers focusing on the adoption of CE.

This article analyses the CE concepts through a Systematic Literature Review (SLR). Coding and content analysis are performed to generate emergent themes with the help of “Atlas.ti” software.

The authors uncovered the contemporary significance of adopting CE and the state-of-the-art literature on CE. The study's findings fall into four broad themes: Technical know-how, resource and process optimization, reverse practices and technology and innovation. Ten thought-provoking questions were identified in the four themes that researchers can explore further in embracing CE to achieve sustainability in SMEs.

The study has highlighted the importance of CE adoption and CE's benefits to stakeholders across all three dimensions, i.e. social, economic and ecological. Practitioners can use the agenda in four themes to strengthen the practitioners' existing practices in SMEs to promote CE.

The study's uniqueness is the supply of current knowledge from diverse literature and practical consequences for SMEs. This study opens new lines of inquiry to adopt CE in SMEs, streamlining the existing literature into four themes to focus future research.

This study aims to extend the theory of planned behaviour (TPB) model to explain youth’s recycling behavioural intentions in India. Perceived moral obligation (PMO) to perform such pro-environmental activities is incorporated in the TPB model. The study also aims to validate the extended version of TPB models with direct and indirect relationships and identify the best competing model among original TPB, extended TPB model “Model A” (moral obligation is an explanatory variable to recycling behaviour) and extended TPB model “Model B” (moral obligation as an explanatory variable to attitude, perceived behavioural control [PBC] and recycling behavioural intentions; and responsive variable to subjective norms) to predict Indian youth’s waste recycling behaviour.

The descriptive study adopted a hypo-deductive research design to test the proposed extended TPB models. The study used a survey research design with a structured questionnaire. A sample of 782 youth with a mean age of 18 was obtained to perform the correlational analysis. The scale validity and reliability are measured using structural equation modelling and identified the robust model with higher explanatory power using the Chi-square difference (Δχ²) statistic test.

Results show that the extended TPB model “Model B” has a better fit and explanatory power than competing models to predict the waste recycling behaviour of youth. Further findings substantiate that PMO has a higher indirect effect on recycling intention. Model B supports the utility of moral obligation and its association with youth’s higher waste recycling intention and actual recycling behaviour.

The study considers solid waste recycling in general, and therefore future research should test the proposed model specific to other household wastes like water recycling. Furthermore, future studies can experiment with the model with additional variables like perceived relative benefits, social benefits, self-efficacy and education level of the respondents. In a strict sense, the research concern the respondents and the generalisation to a broader population should be made with caution. Hence, further studies in various geographical areas with larger sample sizes would allow the generalizability of the results.

This research provides insights into PMO and its influence on recycling intention. Promoting waste recycling behaviour through campaigns, social pressure and accepting the phenomena of “significant others” will encourage better waste recycling behavioural purposes. Indian households who are highly concerned and obliged towards environmental protection would develop favourable attitudes and subjective norms towards waste recycling.

The study proved the effect of subjective norms on intentions. This contention explains that recycling mostly happens within the house and is mostly not witnessed by society and friends. Therefore, adopting waste recycling behaviour is not socially acceptable as they are not fully aware of its benefits. Policymakers should create awareness to develop environmental-friendly behaviour through recycling solid waste and develop exclusive campaigns to sensitise the negative impact on the environment.

The study’s originality is to test the extended TPB model “Model B”, with PMOs as an additional key variable, which has higher explanatory power to predict the youth’s waste recycling behavioural intentions in the Indian context. PMO found a positive and significant effect on attitude, PBC and recycling behavioural intentions. The higher indirect result of PMO on behavioural purposes through TPB variables indicates the importance of personal moral obligation in pro-environmental behaviour.

Being an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.

The literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.

The sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.

Identifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.

For the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.

To explore income inequality in urban China, this paper investigates disparities between- and within-urban locals and rural migrants from 2002 to 2013, using three waves of the China Household Income Project (CHIP) data. While the existing literature concentrates on the wage disparity between these two groups, our results show that the Gini among the migrants increased by 17.86% between 2007 and 2013 and that among the locals increased by 15.54% from 2002 to 2007. The urban–migrant average income gap decreased during the whole period mainly due to higher growth in migrants’ average income. Estimates based on Mincerian earnings functions for both groups reveal the significant role of the education, occupation and type of contract in determining the within-group inequality. In addition, using a recentred influence function (RIF), we observe that short-term and other types of contracts, duration of the job, in-system ownership, marriage and skill have inequality-enhancing effects for migrants. The variation of skills has a larger impact on the income disparity among migrants than on that among urban locals. The RIF-based Blinder–Oaxaca decomposition of the mean difference of incomes shows that labour market discrimination between the two groups is not significant; however, both pure explained and unexplained differences are significant when applying the RIF decomposition to the variance of the logarithms of incomes. While the type of contract significantly reduces the pure explained difference between migrants and urban locals, occupation has a positive impact on this difference between these two groups. The heterogenity analysis shows that the factors influencing incomes in these two groups are different. We recommend labour market intervention to reduce unreasonable occupational and sectoral disparities, especially in the net inflow provinces, to mitigate urban inequality in China effectively.

In New Zealand, building information modelling (BIM) prevalence is still in its early stages and faces many challenges. This research aims to develop a BIM adoption framework to determine the key factors affecting the success of a BIM project.

Both primary and secondary data were employed in this research, including 21 semi-structured interviews and industry guidelines from the three most well-known global building excellence models (BEMs). The data were analysed through content analysis due to its recognised benefits as a transparent and reliable approach.

Leadership, clients and other stakeholders, strategic planning, people, resources, process and results were identified as seven main categories along with 39 indicators in the BIM adoption framework. Based on the interviewees' perspectives, leadership is considered the most significant category, impacting all of the remaining categories.

Using the developed framework will enhance comprehension of BIM, offering directives for those embracing BIM. This will aid construction stakeholders in being better equipped for BIM projects. Having a skilled BIM manager can lead to the success of construction projects.

This research contributed to the existing body of knowledge by providing the categories with specific factors that assist BIM practitioners in assessing their BIM performance for further BIM practice improvement.