Search results

In recent years, negative spokesperson incidents have raised significant concerns in academia and industry. While several studies have addressed celebrity endorser scandals, comprehensive analyses of current knowledge are lacking. Therefore, this study systematically reviewed the related literature to better understand trends and suggest future research directions for advancing this field.

This study employs the theory–context–characteristics–methodology (TCCM) framework to examine 76 articles on celebrity endorser scandals.

Utilizing the TCCM framework, this study presents a comprehensive research framework, revealing that (1) the celebrity endorser scandal effect primarily includes associative learning, attribution of responsibility, and moral reasoning; (2) entertainment celebrities and athletes have received significant research attention; (3) both individual- and relationship-level characteristics serve as crucial moderators, with focal brand and related brand being the primary outcome variables. Additionally, this study outlines enterprise response strategies, encompassing the reformation of existing spokesperson relationships and the establishment of future spokesperson connections; and (4) quantitative approaches dominate the field.

This study integrates and expands existing research on celebrity endorser scandals while proposing future research opportunities to advance the field.

Research on the effect of executive equity incentives is equivocal. Based on agency theory, some scholars take the convergence of interest logic to highlight the benefits of executive equity incentives. In contrast, others adopt the entrenchment logic to emphasize the increased agency costs. This study attempts to reconcile the debate on executive equity incentives and integrates the opposing views to unveil how executive equity incentives impact corporate social responsibility (CSR) performance.

Using the panel dataset of Chinese A-share listed firms from 2006 to 2022, this study integrates the convergence of interest and entrenchment logic to examine how executive equity incentives affect CSR performance.

We find that the relationship between executive equity incentives and CSR performance follows an inverted U-shaped form. According to the convergence of interest logic, executive equity incentives reduce agency costs when allocating resources to engage in CSR activities and enable firms to increase their CSR investments, ultimately realizing increased CSR performance. After a threshold, however, the accumulation of extensive equity incentives causes the entrenchment effect, resulting in declined CSR performance. Our empirical results also shed new light on its contingent perspective – the inverted U-shaped relationship is attenuated when firms’ stock liquidity is high.

This study attempts to reconcile the debate on executive equity incentives and integrates the opposing views to unveil the inverted U-shaped relationship between executive equity incentives and CSR performance. Our study opens promising avenues for further research on corporate governance and CSR strategies.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

The purpose of this paper is to investigate the relationship between CEOs' inside debt holdings (pension benefits and deferred compensation) and the operating leverage of the firms they manage, with the aim to examine whether CEO incentives play a role in corporate risk-taking.

The authors investigate the relation between CEO inside debt holdings (CIDH) (pension benefits and deferred compensation) and the operating leverage (DOL) of the firms they manage. Using a sample of 11,145 US firm-year observations over the period 2006–2017, the authors find a strong negative association between CIDH and DOL. Additional analyses reveal that the relationship between CIDH and DOL is more pronounced in firms with heightened agency issues, powerful CEOs and for CEOs with stronger professional networks. The results are robust to various sensitivity and endogeneity tests.

The authors find strong evidence confirming the expected negative association between CEO inside debt and DOL suggesting that firms with higher inside debt tend to maintain lower levels of operating leverage. These findings continue to hold with the alternative measure for the inside debt and operating leverage, and across a range of tests designed to rule out the possibility that the primary findings are in any way driven by potential endogeneity. In addition, the findings demonstrate that the presence of manager-shareholder agency conflicts can strengthen the inside debt–DOL relationship suggesting the strong role of inside debt in reducing firm risk.

Findings in this paper have implications for design of compensation structures so that corporate boards can establish incentives as a tool for risk management. A limitation of this study is that it is focused on one market, i.e. US listed companies, so the findings may not be applicable on a global scale.

To the best of the authors’ knowledge, this is the first study that links firm-level management of operating leverage through design of CEO inside debt incentives (two obvious choices for risk-reduction at the CEOs’ disposal include reducing financial risk through reduction of firm leverage and reducing operating risk through reduction of operating leverage). While use of firm leverage as an instrument of choice has been explored in the past, use of operating leverage to achieve risk reduction when CEO possess high inside holding, has received very little attention.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

Drawing upon resource dependence theory, this study aims to examine how a firm’s information technology (IT) capabilities (i.e. IT integration and IT reconfiguration) influence its responses to disruptions – bridging with a current supplier and buffering with an alternative supplier. We further examine how such relationships are moderated by the firm–supplier relative dependence (i.e. firm dependence advantage and supplier dependence advantage).

Based on data from 141 match-paired surveys of firms in China, we test our model.

Our study finds that IT integration positively influences bridging and IT reconfiguration positively influences buffering. Furthermore, our findings indicate that the positive impact of IT integration on bridging is negatively influenced by the firm’s dependence (FD) advantage but positively moderated by the supplier’s dependence advantage. By contrast, the positive impact of IT reconfiguration on buffering is negatively influenced by the FD advantage.

Our study provides a more nuanced insight into the effects of IT capabilities on disruption responses and a better understanding of the buyer–supplier dependence boundary conditions under which these effects vary.

The study aims to examine how different types of green brand ritual (GBR) influence customer engagement behavior and the mediation mechanisms and boundary conditions of the positive and negative pathways.

The study conducts two online experiments to collect data from a total of 940 consumers in China. Hypotheses are tested by independent samples t-test, two-way ANOVA and Hayes' PROCESS model.

Different kinds of GBR have different effects on customer engagement behavior. Internal GBR is more likely to play a positive role by inciting connectedness to nature. External GBR is more likely to play a negative role by inciting psychological resistance. This dual effect is especially pronounced for warm brands rather than competent brands.

The study pioneers the brand ritual into the field of interactive marketing and enriches its dual effect research. Additionally, the study figures out whether the category of brand ritual can trigger negative effect.

Inappropriate brand rituals are worse than no rituals at all. The results provide guidance for green companies to design effective brand rituals to strengthen the connection with consumers. Green brands should describe brand rituals in vivid detail and consciously lead consumers to immerse themselves in them.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether gamification and personalization as environmental stimuli to learners’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs) and, in turn, their learning outcomes in MOOCs.

Sample data for this study were collected from learners who had experience in taking gamified MOOCs provided by the MOOCs platform launched by a well-known university in Taiwan, and 331 usable questionnaires were analyzed using structural equation modeling.

This study demonstrated that learners’ perceived gamification and personalization in MOOCs positively influenced their cognitive LE and emotional LE elicited by MOOCs, which jointly explained their LP in MOOCs and, in turn, enhanced their learning outcomes. The results support all proposed hypotheses and the research model, respectively, explaining 82.3% and 65.1% of the variance in learners’ LP in MOOCs and learning outcomes.

This study uses the S-O-R model as a theoretical base to construct learners’ learning outcomes in MOOCs as a series of the psychological process, which is influenced by gamification and personalization. Noteworthily, while the S-O-R model has been extensively used in prior studies, there is a dearth of evidence on the antecedents of learners’ learning outcomes in the context of MOOCs, which is very scarce in the S-O-R view. Hence, this study enriches the research for MOOCs adoption and learning outcomes into an invaluable context.

Light emitting diode (LED) has been the best resource for commercial and industrial lighting applications. However, thermal management in high power LEDs is a major challenge in which the thermal resistance (R_th) and rise in junction temperature (T_J) are critical parameters. The purpose of this work is to evaluate the R_th and T_j of the LED attached with the modified heat transfer area of the heatsink to improve thermal management.

This paper deals with the design of metal substrate for heatsink applications where the surface area of the heatsink is modified. Numerical simulation on heat distribution proved the influence of the design aspects and surface area of heatsink.

T_J was low for outward step design when compared to flat heatsink design (ΔT ∼ 38°C) because of increase in surface area from 1,550 mm² (flat) to 3,076 mm² (outward step). On comparison with inward step geometry, the T_J value was low for outward step configuration (ΔT_J ∼ 6.6°C), which is because of efficient heat transfer mechanism with outward step design. The observed results showed that outward step design performs well for LED testing by reducing both R_th and T_J for different driving currents.

This work is authors’ own design and also has the originality for the targeted application. To the best of the authors’ knowledge, the proposed design has not been tried before in the electronic or LED applications.

Insufficient productivity development in the global and Finnish infrastructure sectors indicates that there are challenges in genuinely achieving the goals of resource efficiency and digitalization. This study adapts the approach of capability maturity model integration (CMMI) for examining the capabilities for productivity development that reveal the enablers of improving productivity in the infrastructure sector.

Civil engineering in Finland was selected as the study area, and a qualitative research approach was adopted. A novel maturity model was constructed deductively through a three-step analytical process. Previous research literature was adapted to form a framework with maturity levels and key process areas (KPAs). KPA attributes and their maturity criteria were formed through a thematic analysis of interview data from 12 semi-structured group interviews. Finally, validation and refinement of the model were performed with an expert panel.

This paper provides a novel maturity model for examining and enhancing the infrastructure sector’s maturity in productivity development. The model brings into discussion the current business logics, relevance of lifecycle-thinking, binding targets and outcomes of limited activities in the surrounding infrastructure system.

This paper provides a new approach for pursuing productivity development in the infrastructure sector by constructing a maturity model that adapts the concepts of CMMI and change management. The model and findings benefit all actors in the sector and provide an understanding of the required elements and means to achieve a more sustainable built environment and effective operations.