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Statistics show that selecting the best investment program based on both cost and effectiveness can avoid financial losses. However, investment evaluation of a power transformer is full of uncertainty as it is hard to obtain accurate and useful cost-effectiveness results. Therefore, the purpose of the paper is to establish an investment evaluation model.

The cost-effectiveness evaluation model in this study used grey correlation analysis for the power transformer selection based on life cycle cost (LCC). Indices of cost and effectiveness factors were chosen to form a three-level index system including quantitative and qualitative factors. Evidential reasoning was applied to quantify the qualitative indexes. Grey correlation analysis was applied to select the best investment program.

The results from this study show that the proposed approach is effective and offers a new approach to evaluating transformer investment.

The model was applied to an investing decision-making problem of the transformer in a new substation in Wuhan, China.

It is very important to select the best transformer program in the candidate investment programs because the investment program decides almost 70 percent of the LCC of the power transformer.

This study aims to explore the extent to which Chinese multinational enterprises (MNEs) rely on supranational institution – the Belt and Road Initiative (BRI) – versus host country institutional quality to navigate their foreign location choice.

This study uses a conditional logit regression model using a sample of 1,302 greenfield investments by Chinese MNEs in 54 BRI participating countries during the period 2011–2018.

The results indicate that as a supranational institution, the BRI serves as a substitution mechanism to address the deficiencies in institutional quality in BRI participating countries, thereby attracting Chinese MNEs to invest in those countries. In addition, the BRI’s substitution effect on host country institutional quality is more pronounced for large MNEs, MNEs in the manufacturing industry and MNEs in inland regions.

This study expands the understanding of the BRI as a supranational institution for MNEs from emerging markets and reveals its substitution effect on the host country institutional quality. Furthermore, it highlights that MNEs with diverse characteristics gain varying degrees of benefits from the BRI.

The purpose of this paper is, from a resource accumulation and resource allocation perspective, to examine the variant effects of government subsidies among firms with varying levels of market power and to test how industry competition moderates the relationship between market power and allocative efficiency of government subsidies.

This study explores the relationship between government subsidies and firm performance from a resource-based view. The authors study the moderating role of market power and three-way interaction between subsidy, market power and industry competition on firm performance. The authors test their hypotheses using a sample of Chinese A-share manufacturing firms from 2006–2019. The authors apply firm-level panel data regressions and conduct a series of robustness tests. The marginal effect of market power and industry competition is explored via three-way moderator effect models.

This study finds that government subsidies are negatively related to firm performance. Market power, on average, strengthens the negative effect of government subsidies on performance, but such a reinforcement effect is neutralized when industry competition is intense. Government subsidies are least efficiently used when firms have market power and industry competition is low. In addition, the authors use different forms of firm performance and a various of robustness tests to verify their assumptions.

This paper contributes to the literature as follows. First, the authors look into subsidy–performance problem from the perspective of the resource-based view and contribute to explaining and mitigating the divergence of current findings on the subsidy–performance relationship. Second, the authors introduce market power and industry competition as moderators to study how resource allocative efficiency affects the subsidy–performance relationship. Third, the authors propose that managerial incentives have played an important role in the allocation of government subsidies, which enriches management practices.

The aim of the study is to explore how two dimensions of interpersonal relationships (i.e. size and range of relationships) affect supplier-initiating risk management behaviours (SIRMB) and supply-side resilience. Further, the study aims to explore the moderating role of dependence asymmetry.

Nine hypotheses are tested based on a moderated mediation analysis of survey data from 247 manufacturing firms in China. The data are validated using a subset of 57 attentive secondary respondents and archival data.

SIRMB positively relates to supply-side resilience. Further, SIRMB mediates the positive relationship between range and supply-side resilience, and this relationship is stronger at lower levels of dependence asymmetry. Yet, although dependence asymmetry positively moderates the relationship between range and SIRMB, it negatively moderates the relationship between size and SIRMB. We did not, however, find evidence that size has a conditional indirect effect on supply-side resilience through SIRMB.

Managers in buying firms can incentivise SIRMB to enhance supply-side resilience by developing a diverse rather than a large set of interpersonal relationships with a supplier. This might include allocating particular employees with a wide range of contacts within a supplier to that relationship, while it may be necessary to adopt different networking strategies for different supplier relationships. Firms in a highly asymmetrical relationship may seek to raise supplier expectations about the necessity to initiate risk management behaviour or look to change the dynamic of the relationship by managing contracts for fairness.

New knowledge on SIRMB as a mediating variable underpinning the relationship between interpersonal relationships and supply-side resilience is provided; and empirical evidence on the opposing moderation effect of dependence asymmetry is presented.

This paper aims to investigate how supply chain risks can be identified in both collaborative and adversarial buyer–supplier relationships (BSRs).

This research includes a multiple-case study involving ten Chinese manufacturers with two informants per organisation. Data have been interpreted from a multi-level social capital perspective (i.e. from both an individual and organisational level), supplemented by signalling theory.

Buyers use different risk identification strategies or apply the same strategy in different ways according to the BSR type. The impact of organisational social capital on risk identification is contingent upon the degree to which individual social capital is deployed in a way that benefits an individual’s own agenda versus that of the organisation. Signalling theory generally complements social capital theory and helps further understand how buyers can identify risks, especially in adversarial BSRs, e.g. by using indirect signals from suppliers or other supply chain actors to “read between the lines” and anticipate risks.

Data collection is focussed on China and is from the buyer side only. Future research could explore other contexts and include the supplier perspective.

The types of relationships that are developed by buyers with their supply chain partners at an organisational and an individual level have implications for risk exposure and how risks can be identified. The multi-level analysis highlights how strategies such as employee rotation and retention can be deployed to support risk identification.

Much of the extant literature on supply chain risk management is focussed on risk mitigation, whereas risk identification is under-represented. A unique case-based insight is provided into risk identification in different types of BSRs by using a multi-level social capital approach complemented by signalling theory.

Prior studies have largely overlooked the potentially negative consequences of a buyer’s relational capital (RC) with a supplier for supply-side resilience, assuming a positive linear relationship between the constructs. Meanwhile, the focus of research has been at an organisational level without incorporating the role of boundary spanning individuals at the interface between buyer and supplier. Drawing on social capital and boundary spanning theory, the purpose of this paper is to: re-examine the relationship between RC and supply-side resilience, challenging the linear assumption; and investigate how both the strength and diversity of a boundary spanner’s ties moderate this relationship.

Survey data are collected from 248 firms and validated using a subset of 57 attentive secondary respondents and archival data. The latent moderated structural equation method is applied to analyse the data.

An inverted U-shaped relationship between RC and supply-side resilience is identified. Tie strength in particular has a positive moderating effect on the relationship. More specifically, the downward RC–supply-side resilience relationship flips into an upward curvilinear relationship when boundary spanning individuals develop stronger ties with supplier personnel.

A deeper insight into the RC–supply-side resilience relationship is provided. Findings are based on Chinese manufacturing firms and cross-sectional data meaning further research is needed to determine their generalisability.

In evaluating how to enhance supply-side resilience, buying firms must decide whether the associated collaborative benefits of developing RC outweigh the potential costs. Managers also need to be concerned with the impact of developing RC between organisations and enhancing the tie strength of individuals simultaneously.

The paper goes beyond the linear relationship between RC and supply-side resilience. Incorporating the moderating role of boundary spanners identifies a novel phenomenon whereby the RC–resilience relationship flips from an inverted to a U-shaped curve.

In this paper, we aim to answer two questions. First, whether firms manipulate reported earnings via pension assumptions when facing mandatory contributions. Second, whether firms alter their earnings management behavior when the Financial Accounting Standard Board (FASB) mandates disclosure of pension asset composition and a description of investment strategy under SFAS 132R.

Our basic approach is to run linear regressions of firm-year assumed returns on the log of pension sensitivity measures, controlling for current and lagged actual returns from pension assets, fiscal year dummies and industry dummies. The larger the pension sensitivity ratios, the stronger the effects from inflated ERRs on reported earnings. We confirm the early results that the regression slopes are positive and highly significant. We construct an indicator variable DMC to capture the mandatory contributions firms face and another indicator variable D132R to capture the effect of SFAS 132R. DMC takes the value of one for fiscal years during which an acquisition takes place and zero otherwise. D132R takes the value of one for fiscal years after December 15, 2003 and zero otherwise.

Our sample covers the period from June 1992 to December 2017. Our key results are as follows. The estimated coefficient (t-statistic) on DMC is 0.308 (6.87). Firms facing mandatory contributions tend to set ERRs at an average 0.308% higher. The estimated coefficient (t-statistic) on D132R is −2.190 (−13.70). The new disclosure requirement under SFAS 132R constrains all firms to set ERRs at an average 2.190% lower. The estimate (t-statistic) on the interactive term DMA×D132R is −0.237 (−3.29). When mandatory contributions happen during the post-SFAS 132R period, firms tend to set ERRs at 0.237% lower than they would do otherwise in the pre-SFAS 132R period.

When firms face mandatory contributions, typically firm experience negative stock market returns. We examine whether managers manage earnings to mitigate such negative impact. We find that firms inflate assumed returns on pension assets to boost their reported earnings when facing mandatory contributions. We also find that managers alter earnings management behavior, in the case of mandatory contributions, following the introduction of new pension disclosure standards under SFAS 132R that become effective on December 15, 2003. Under the new SFAS 132R requirement, firms need to disclose asset allocation and describe investment strategies. This imposes restrictions on managers' discretion in making ERR assumptions, since now the composition of pension assets is a key determinant of the assumed expected rate of return on pension assets. Firms need to justify their ERRs with their asset allocations.

This paper analyzes county-level firm births across the United States using a spatial count model that permits spatial dependence, cross-correlation among different industry types, and over-dispersion commonly found in empirical count data. Results confirm the presence of spatial autocorrelation (which can arise from agglomeration effects and missing variables), industry-specific over-dispersion, and positive, significant cross-correlations. After controlling for existing-firm counts in 2008 (as an exposure term), parameter estimates and inference suggest that a younger work force and/or clientele (as quantified using each county’s median-age values) is associated with more firm births (in 2009). Higher population densities is associated with more new basic-sector firms, while reducing retail-firm starts. The modeling framework demonstrated here can be adopted for a variety of settings, harnessing very local, detailed data to evaluate the effectiveness of investments and policies, in terms of generating business establishments and promoting economic gains.

This study aims to propose a general and efficient adaptive strategy with local mesh refinement for two-dimensional (2D) finite element (FE) analysis based on the element energy projection (EEP) technique.

In view of the inflexibility of the existing global dimension-by-dimension (D-by-D) recovery method via EEP technique, in which displacements are recovered through element strips, an improved element D-by-D recovery strategy was proposed, which enables the EEP recovery of super-convergent displacements to be implemented mostly on a single element. Accordingly, a posteriori error estimate in maximum norm was established and an EEP-based adaptive FE strategy of h-version with local mesh refinement was developed.

Representative numerical examples, including stress concentration and singularity problems, were analyzed; the results of which show that the adaptively generated meshes reasonably reflect the local difficulties inherent in the physical problems and the proposed adaptive analysis can produce FE displacement solutions satisfying the user-specified tolerances in maximum norm with an almost optimal adaptive convergence rate.

The proposed element D-by-D recovery method is a more efficient and flexible displacement recovery method, which is implemented mostly on a single element. The EEP-based adaptive FE analysis can produce displacement solutions satisfying the specified tolerances in maximum norm with an almost optimal convergence rate and thus can be expected to apply to other 2D problems.