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The purpose of this paper is to empirically investigate the impact of equity market valuation and government intervention on the research and development (R&D) investments of listed companies in China and their relationship.

Using a manually collected R&D database in the period 2007–2015, this paper constructs a sample of 6,595 firm–year observations and applies the methods of pooled OLS regressions to examine the effects of market valuation and government intervention on corporate R&D expenditures.

This paper finds that market valuation enhances corporate R&D investments, but there is no evidence that government intervention may significantly affect the R&D investments. Government intervention also decreases the sensitivity of corporate R&D investment to stock price, which implies that government intervention weakens the promotion of market mechanism to corporate R&D investment. Furthermore, these effects are stronger in the non-state-owned firms and the non-regulated industries.

This study suggests that the functional borders of markets and government should be reasonably defined and markets play a decisive role in resource allocation to improve corporate innovation and national innovation.

This paper provides a micro view of the relationship between market and government at the stage of transitional economy in China as well as directions for further research on the relationship between stock prices and corporate investments.

The purpose of this paper is to study the capital structure of firms when controlling shareholders decide on the level of debt financing in an environment with poor legal protection.

Theoretically this paper uses a dynamic model to analyze how the controlling shareholder expropriates the firm's benefit through debt financing. Empirically this paper uses a sample of Chinese publicly listed firms from 2004 to 2007, through the method of OLS and panel data, to verify the theoretical predictions.

Theoretically this paper finds that firms with controlling shareholders will take excess debt financing in an environment of controlled interest rate and poor legal protection to minority shareholders. Government intervention exacerbates while controlling shareholder's cash flow rights constrains excess debt financing. The empirical results conclude that the improvement of the legal environment, limiting government intervention, and raising controlling shareholder's cash flow rights will effectively reduce excess debt level, as well as long‐term debt ratio.

First, this paper provides a theoretical model to explain the mechanism of how the ownership structure, legal environment and government intervention interact to impact debt financing. This result also provides a theory to explain the “paradox” in a transitional economy that better legal protection lowers debt level and long‐term debt ratio. Second, this paper provides further evidence on controlling shareholder's expropriation to minority shareholder through debt financing.

The purpose of this paper is to find a sustainable facility location solution for a closed-loop distribution network in the uncertain environment created by of high levels of product returns from online retailing coupled with growing pressure to reduce carbon emissions.

A case study approach attempts to optimize the distribution centre (DC) location decision for single and double hub scenarios. A hybrid approach combining centre of gravity and mixed integer programming is established for the un-capacitated multiple allocation facility location problem. Empirical data from a major national UK retail distributor network is used to validate the model.

The paper develops a contemporary model that can take into account multiple factors (e.g. operational and transportation costs and supply chain (SC) risks) while improving performance on environmental sustainability.

Based on varying product return rates, SC managers can decide whether to choose a single or a double hub solution to meet their needs. The study recommends a two hub facility location approach to mitigate emergent SC risks and disruptions.

A two-stage hybrid approach outlines a unique technique to generate candidate locations under twenty-first century conditions for new DCs.

The existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.

To achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.

Experiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.

The proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.