Search results

The purpose of this study is to examine the moderating role of the characteristics of the chief executive officer (CEO) on the association between CEO power and corporate social responsibility (CSR) performance.

This paper conducts multiple regression analyses to empirically test the proposed hypotheses based on a sample of US-based publicly held companies. The sample period extends from 2000 to 2018. Firm-level CSR ratings are obtained from the Kinder, Lydenberg and Domini (KLD) database (currently known as MSCI ESG STATS). Financial data and CEO data are retrieved from Compustat and ExecuComp databases, respectively. Additional test and robustness analysis are performed.

This paper shows that firms with more powerful CEOs are less likely to engage in CSR activities. The negative association between CEO power and CSR is found to be exacerbated by CEOs who are younger, more competent and overconfident; however, this negative association is mitigated by CEOs who are female. This paper also finds that gender plays a more important role among CEO characteristics. Collectively, the findings highlight the potential opportunities to better understand the role of various CEO characteristics that jointly affect CSR.

First, this is the first study providing a comprehensive empirical analysis of how various CEO characteristics jointly affect CSR. Prior studies that focus on standalone CEO characteristics offer an incomplete picture of the relation between a single CEO characteristic and a firm's CSR performance. The current study thus extends the research field by examining the association between seemingly unrelated CEO characteristics and CSR performance. The results also highlight that gender is the critical factor moderating the relationship between CEO power and CSR performance when it is compared with CEO age, ability and overconfidence. Second, the authors add to the literature on employee selection by showing that female CEOs mitigate the negative effect of managerial power on CSR performance. Although the currently available empirical research in management control systems focuses on ex-post analyses of moral hazard mitigation for incumbent employees, both the economics and management literature acknowledge ex ante evidence suggesting that employee selection is even more important. Our findings may provide insight into the selection of CEOs.

The purpose of this paper is to investigate the effects of political connections on the association between firms' business strategy and their tax aggressiveness in an emerging economy such as China.

The authors study a large sample of Chinese public firms from 2011 to 2017 using a panel regression model. In addition, a change analysis, an instrument variable test and alternative measures/samples are implemented as robustness tests.

Firms adopting innovative business strategy are more tax aggressive overall. However, innovative firms with political connections are less tax aggressive compared to those without political connections.

This paper contributes to the understanding of firms' tax behaviors in an emerging economy setting. It suggests that there are costs associated with political connections, such as foregone tax saving opportunities, which are understudies in the prior literature.

This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline.

Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms.

Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles.

To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

This paper aims to examine whether and how two firm-level factors jointly moderate the relation between corporate social responsibility (CSR) activities and firm performance: (1) the “alignment” between a firm's CSR activities and risk preferences and (2) performance measurement systems (PMS).

Using survey responses from top managers of private Italian companies and matching archival data on the financial performance of these companies, the authors show that the positive effect of CSR activities on firm performance is contingent upon CSR–risk alignment, which creates competitive advantages, and the extent to which the firm's PMS are supportive of its strategic initiatives.

The findings suggest that to extract economic benefits from CSR activities, firms must align CSR activities with their risk preferences and rely on PMS to overcome the causal ambiguity between CSR activities and competitive advantage.

Overall, this study contributes to both the CSR–firm performance and consequences of PMS literature and holds significant practical implications.

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality involve labor-intensive processes and rely on the expertise of connoisseurs proficient in identifying taste profiles and key quality factors. In this research, we introduce an innovative and efficient approach centered on the analysis of volatile organic compounds (VOCs) signals using an electronic nose, thereby empowering nonexperts to accurately assess wine quality.

To devise an optimal algorithm for this purpose, we conducted four computational experiments, culminating in the development of a specialized deep learning network. This network seamlessly integrates 1D-convolutional and long-short-term memory layers, tailor-made for the intricate task at hand. Rigorous validation ensued, employing a leave-one-out cross-validation methodology to scrutinize the efficacy of our design.

The outcomes of these e-demonstrates were subjected to meticulous evaluation and analysis, which unequivocally demonstrate that our proposed architecture consistently attains promising recognition accuracies, ranging impressively from 87.8% to an astonishing 99.41%. All this is achieved within a remarkably brief timeframe of a mere 4 seconds. These compelling findings have far-reaching implications, promising to revolutionize the assessment and tracking of wine quality, ultimately affording substantial benefits to the wine industry and all its stakeholders, with a particular focus on the critical aspect of VOCs signal analysis.

This research has not been published anywhere else.

On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety. The connected and autonomous vehicles (CAVs), with their capabilities of real-time communication and precise motion control, hold a great potential to facilitate ramp merging operation through enhanced coordination strategies. This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field.

The review comprehensively covers 44 papers recently published in leading transportation journals. Based on the application context, control strategies are categorized into three categories: merging into sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed traffic flows and merging into multilane freeways.

Relevant literature is reviewed regarding the required technologies, control decision level, applied methods and impacts on traffic performance. More importantly, the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review, which facilitates further advancement in this research topic.

Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades, devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps. Despite the significant progress made, an up-to-date review covering these latest developments is missing to the authors’ best knowledge. This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs, focusing on the latest developments in this field. Based on the review, the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.