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In this paper, the impact of stock-based compensation and further the joint effects of stock-based compensation and investor sentiment on pension discount rate choice is examined.

The hypotheses is tested using fixed effects models and instrumental variable analysis where pension discount rate is the dependent variable, and stock-based compensation and investor sentiment are our variables of interest.

It was found that pension discount rate is negatively associated with managers' stock-based compensation. Further analysis indicates that managers with larger stock-based compensation tend to adjust down their pension discount rates in higher (smaller) degree, responding to high (low) investor sentiment.

The findings provide important insights into how managers use pension discount rates to engage in earnings management. Understanding these relationships has implications for interpreting pension numbers reported in the financial statements and designing pension accounting rules that minimize the possibility that managers take advantage of the complexity associated with pension accounting to influence the reported earnings and executive compensation. Moreover, the findings suggest the need for increased attention from boards of directors, auditors and regulators to reported pension liabilities and service costs, especially for firms paying higher proportion of stock-based compensation to managers and during periods of high investor sentiment.

The findings contribute to the extant literature by identifying the joint impacts of stock-based compensation and investor sentiment as incentives for pension discount rate manipulation. The empirical results of this study also have important implications for corporate governance and regulation.

Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station.

This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train.

A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow.

This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.

The purpose of this paper is to present a novel tactile sensor and a visual-tactile recognition framework to reduce the uncertainty of the visual recognition of transparent objects.

A multitask learning model is used to recognize intuitive appearance attributes except texture in the visual mode. Tactile mode adopts a novel vision-based tactile sensor via the level-regional feature extraction network (LRFE-Net) recognition framework to acquire high-resolution texture information and temperature information. Finally, the attribute results of the two modes are integrated based on integration rules.

The recognition accuracy of attributes, such as style, handle, transparency and temperature, is near 100%, and the texture recognition accuracy is 98.75%. The experimental results demonstrate that the proposed framework with a vision-based tactile sensor can improve attribute recognition.

Transparency and visual differences make the texture of transparent glass hard to recognize. Vision-based tactile sensors can improve the texture recognition effect and acquire additional attributes. Integrating visual and tactile information is beneficial to acquiring complete attribute features.

The purpose of this paper is to examine how investor sentiment, proxied by Michigan consumer confidence index, affects the choice of defined benefit pension plan discount rates.

The authors use multivariate analysis to test our hypotheses. The dependent variable is defined pension plan discount rate and the testing variables are investor sentiment and a dummy variable representing underfunded status.

The authors find a negative and significant relation between investor sentiment and pension plan discount rate. During high (low) sentiment periods, pension discount rate tends to be adjusted downward (upward) discretionarily. Further analysis indicates the relationship between pension discount rate and investor sentiment is more pronounced for firms with underfunded pension plans. The results can be explained by limited attention effects, capital budgeting strategy and earning smoothing.

The empirical results of this study have important implications for corporate governance and regulation. Specifically, the results suggest the need for increased attention from boards of directors, auditors and regulators to reported pension liabilities, especially during periods of high investor sentiment when pension plan sponsors are more likely to adjust down pension discount rate and accordingly to increase pension liabilities.

The paper contributes to the extant literature by identifying investor sentiment as a new incentive of pension discount rate manipulation. The empirical results of this study also have important implications for corporate governance and regulation.

This paper aims to examine whether firms retaining industry-specialist auditors receive better price and non-price terms for bank loans.

Based on a sample of companies retaining big N auditors during the 2000-2010 period, this paper constructed six proxies for auditor industry expertise and tested three major loan terms: loan spreads, number of general and financial covenants and requirements for collateral.

It was found that companies retaining industry-specialist auditors receive lower interest rates and fewer covenants. Banks are also less likely to demand secured collateral. These findings are supported by several sensitivity tests.

The findings suggest that auditor industry expertise provides incremental value to creditors and that bank loan cost is one economic benefit for companies hiring specialist auditors.

To the best of the authors’ knowledge, this study is the first to investigate the impact of auditor industry expertise on the cost of private debts.

The purpose of this paper is to investigate the factors that affect handicraft export from Vietnam to trading partners in the period 2007–2017, and how those factors influence the export of handicraft products of Vietnam.

The research uses the approach of gravity model based on panel data to evaluate the export of Vietnamese handicraft to 50 main trading partners, covering the period from 2007 to 2017.

The estimated results reveal that Vietnam’s GDP, importer’s GDP, trading partner’s population, Vietnam’s inflation, the economic distance between Vietnam and importer, the openness of Vietnam, importing country’s common language and the issue that both Vietnam and importer are member of APEC are the main factors affecting Vietnamese handicraft export.

This study also has some limitations. It is limited in the data, as some other areas in the world have not been observed and included in the research. In the future, a study with large-scale data of space and time should be conducted, which will certainly give a universal result and fewer errors. However, this paper, in our opinion, provides a significant result and may help the government and policy makers to undertake appropriate measures to improve and promote the export of Vietnamese handicrafts to the world markets.

The research describes the current situation, and it studies factors influencing Vietnam’s handicraft export using the qualitative analysis. The result should be useful for the policy maker and enterprises to promote export activities of Vietnamese handicrafts to international markets.

Handicraft export of Vietnam plays an important part in retaining the culture value and social development as well as encouraging sustainable development for the rural poor within the country.

The past research related to Vietnamese handicraft export almost analyzed the situation to promote export handicrafts. This research is based on the study of factors affecting trade and the gravity model to elaborate and supplement the factors that affect the export of handicraft in accordance with the actual conditions of Vietnam.

This paper aims to refine the mechanisms affecting the two-way technology spillover and carbon transfer interactions between supply chain enterprises, and to guide their reduction of carbon emissions.

This study formulates a supplier-led Stackelberg game model to explore the effects of the interactions between two-way technology spillover effects and carbon transfers in decentralized and centralized decision-making scenarios. The optimized Shapley value is introduced to coordinate across the supply chain and determine the overall profits lost in the decentralized scenario.

Emission reductions by the low-carbon manufacturer are negatively correlated with the carbon transfers. Vertical technology spillovers promote carbon reduction, whereas horizontal technology spillovers inhibit it. The vertical technology spillovers amplify the negative effects of the carbon transfers, whereas the horizontal technology spillovers alleviate these negative effects. When the vertical technology spillover effect is strong or the horizontal technology spillover effect is weak in the centralized scenario, the carbon reduction is negatively correlated with the carbon transfers. Conversely, when the vertical technology spillover effect is weak or the horizontal technology spillover effect is strong, the enterprise’s carbon reduction is positively correlated with the carbon transfers. An optimized Shapley value can coordinate the supply chain.

This study examines the effects of carbon transfers on enterprises from a micro-perspective and distinguishes between vertical and horizontal technology spillovers to explore how carbon transfers and different types of technology spillovers affect enterprises’ decisions to reduce carbon emissions.

Although primarily treated as two distinct research streams, strategic alliances and mergers and acquisitions together occupy much of the strategic management discourse. Alliances, in many cases, end in acquisitions as firms use alliances as intermediate strategic options to eventually acquire a partner. As the discipline of strategy matures and the frequency and the volume of inter-firm cooperation continue to rise, it is imperative to integrate these two research streams for a holistic understanding of the theory of the firm. The purpose of this conceptual piece is threefold. First, we review the extant studies that combine these two governance modes: alliance and acquisitions. Second, drawing on the dominant strategic management theories, we highlight how prior inter-firm alliances inform future acquisitions in terms of (a) pre-combination decisions, (b) post-deal integration processes, (c) alternatives and strategies, and (d) performance outcomes. Finally, in view of the emerging trends and evocative gaps, we offer a conceptual road map to encourage future theoretical development and empirical research.

Purpose: The insurance business is confronted with coordination difficulties that necessitate a high level of mobility, flexibility, and the capacity to analyse heterogeneous, location-dependent data from different sources and qualities. Recent innovations in emerging technologies have given the insurance industry new organisational options. When coupled with data analytics, crowdsourcing in the insurance industry facilitates solving complex issues with the wisdom of crowds. The notion of incorporating crowdsourcing and big data into the mainstream activities of insurance management is developed in this article, as are the ramifications and gains of collective intelligence achieved by Crowdsourcing and the added value of crowdsourcing insurance activities.

Design/methodology/approach: This chapter is a conceptual work that builds on relevant literature.

Findings: This chapter analyses what insurance industry managers should consider when coordinating crowdsourced activities and how they may benefit from collective intelligence combined with data analytics in terms of efficient and real-time response management for the insurance industry. Furthermore, it is demonstrated how they may use crowdsourcing to exploit information and benefit from invoking additional resources and eliminating the institutional voids present in the industry.

Practical implications: Exemplary applications that take advantage of crowdsourcing and data analytics would help the insurance sector respond flexibly, efficiently, and effectively in real time.

Originality/value: This chapter offers new collaborative ways to enhance the decision-making of insurance industry managers. The relevance of overcoming institutional voids is expanded, and repercussions from the given framework are suggested using data analytics.

The purpose of this paper is to explore the potential of standard quantum-based particle swarm optimization (QPSO) methods for solving electromagnetic inverse problems.

A modified QPSO algorithm is designed.

The modified QPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic inverse problems. More specially, the experimental results as reported on different case studies demonstrate that the proposed method can find better final optimal solution at an early stage of the iterating process (uses less iterations) as compared to other tested optimal algorithms.

The modifications include the design of a new position updating formula, the introduction of a new mutation strategy and a dynamic control parameter to intensify the convergence speed of the algorithm.