Search results

Academic research has intensively analyzed the relationship between market concentration or market power and banking stability but provides ambiguous results, which are summarized under the concentration-stability/fragility view. We provide empirical evidence that the mixed results are due to the difficulty of identifying reliable variables to measure concentration and market power.

Using data from 3,943 banks operating in the European Union (EU)-15 between 2013 and 2020, we employ linear regression models on panel data. Banking market concentration is measured by the Herfindahl–Hirschman Index (HHI), and market power is estimated by the product-specific Lerner Indices for the loan and deposit market, respectively.

Our analysis reveals a significantly stability-decreasing impact of market concentration (HHI) and a significantly stability-increasing effect of market power (Lerner Indices). In addition, we provide evidence for a weak (or even absent) empirical relationship between the (non)structural measures, challenging the validity of the structure-conduct-performance (SCP) paradigm. Our baseline findings remain robust, especially when controlling for a likely reverse causality.

Our results suggest that the HHI may reflect other factors beyond market power that influence banking stability. Thus, banking supervisors and competition authorities should investigate market concentration and market power simultaneously while considering their joint impact on banking stability.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

This study aims to guide firms in emerging markets on whether corporate social responsibility (CSR) engagement facilitates their access to debt with the moderation of asset structure and firm performance. Considering the moderating effect analysis, this study explores the substitutive or complementary effect of these two contingencies on CSR-oriented firms in accessing debt financing.

Drawing on data collected for 16 emerging markets between 2008 and 2019, this study runs country–industry–year fixed-effects regression.

This study finds that CSR performance and reporting facilitate access to debt in emerging markets. However, CSR performance does not have an inverted U-shaped influence on firms’ access to debt financing. The moderation analysis of this study shows that asset tangibility has a negative moderating effect on the link between CSR engagements (i.e. both CSR performance and reporting) and access to debt, confirming a substitutive relationship between asset tangibility and CSR engagements in accessing debt. In contrast, firm performance is positively moderating the nexus between CSR engagement proxies and access to debt, which confirms a complementary type of relationship between firm performance and CSR engagements in accessing debt.

The empirical evidence of this study implies that creditors critically consider CSR engagements of firms in the loan-granting decision process. Similarly, the inverted U-shaped relationship between CSR and access to debt implies that there is an optimal level of CSR engagement creditors might consider in their decision. Likewise, the moderating effects analysis highlights that asset tangibility and firm performance are two conditions under which CSR performance and reporting are linked to access to debt.

Emerging countries are a different set of countries than developed ones; they have high growth rates and hence need financing, have a weaker institutional environment and have weaker stakeholder power. These particularities motivated the authors to conduct a separate study focusing on CSR and debt financing links drawing on a wide range of emerging countries. Thus, this study adds to the ongoing debate by examining the conditions under which CSR-oriented firms can access debt financing in emerging economies.

This study examined how economic freedom and its related components, such as open markets, regulatory efficiency, rule of law and the size of government, affect bank risk behavior, focusing on the Japanese context.

The study employs a two-step GMM framework on the annual data of Japanese banks ranging from 2005 to 2020 to empirically test the hypotheses. Furthermore, we also use the ordinary least square method to ensure the robustness of our mainline findings.

The finding suggests that economic freedom increases the banks' risk-taking, thus making them fragile. The results also highlight that out of the four main subcomponents of economic freedom, regulatory efficiency and government size increase bank risk-taking, while the rule of law and open markets decrease banks' risk-taking. Additionally, we examine how the banks' specific characteristics affect the results by creating a subsample based on capitalization and liquidity ratios. Overall, the results are consistent with the baseline findings. Moreover, the results are robust to alternative proxy measures of risk.

The study's findings have several implications for regulators and policymakers. The results suggest that regulators and policymakers should reconsider their strategies for economic freedom to ensure that they promote stability in the banking system and reduce banks' risk-taking inclinations.

Although previous studies have examined the impact of economic freedom on bank stability and risk-taking, this study is the first to do so in the Japanese context, contributing to the literature by providing new insights and empirical evidence.

Corporate site visits increase labor investment efficiency.

Our empirical model for the baseline analysis follows those of Jung et al. (2014) and Ghaly et al. (2020).

We show that corporate site visits are associated with significantly higher labor investment efficiency; more specifically, site visits reduce both over-hiring and under-hiring of employees. The effect of site visits on labor investment efficiency is more pronounced for firms with higher labor adjustment costs, greater financial constraints, weaker corporate governance and lower financial reporting quality. We also find that site visits mitigate labor cost stickiness.

First, while the literature has suggested how the presence of institutional investors and analysts may affect labor investment decisions, we focus on institutional investors and analysts’ activities and interactions with firm executives. We provide direct evidence that institutional investors and analysts may use corporate site visits to improve labor investment efficiency. Second, our study adds to a line of recent studies on how corporate site visits reduce information asymmetry and agency conflicts. We show that corporate site visits allow institutional investors and analysts to influence labor investment efficiency. We also provide new evidence that corporate site visits reduce labor cost stickiness.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

This paper aims to reflect on the outcomes of a community-based video-conferencing intervention for depression, predating the COVID-19 pandemic. The study investigates the potential implications of its findings for enhancing adherence to digital mental health interventions. The primary objective is to present considerations for researchers aimed at minimising the intention-behaviour gap frequently encountered in digital mental health interventions.

A randomised control feasibility trial design was used to implement a telehealth model adapted from an established face-to-face community-based intervention for individuals clinically diagnosed with depression. In total, 60 participants were initially recruited in association with a local mental health charity offering traditional talking-based therapies with only eight opting to continue through all phases of the project. Modifications aligning with technological advancements were introduced.

However, the study faced challenges, with low uptake observed after an initial surge in recruitment interest. The behaviour-intention gap highlighted technology as a barrier to service accessibility, exacerbated by participant age. Furthermore, the clinical diagnosis of depression, characterised by low mood and reduced interest in activities, emerged as a potential influencing factor.

The limitations of the research include its pre-pandemic execution, during a nascent stage of technological mental health interventions when participants were less familiar with online developments.

Despite these limitations, this study's reflections offer valuable insights for researchers aiming to design and implement telehealth services. Addressing the intention-behaviour gap necessitates a nuanced understanding of participant demographics, diagnosis and technological familiarity.

The study's relevance extends to post-pandemic society, urging researchers to reassess assumptions about technology availability to ensure engagement. This paper contributes to the mental health research landscape by raising awareness of critical considerations in the design and implementation of digital mental health interventions.

Reflections from a pre-pandemic intervention in line with the developments of a post-pandemic society will allow for research to consider that because the technology is available does not necessarily result in engagement.

This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities.

The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework.

The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field.

The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors.

This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs.

This paper does not discuss social implications

This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

The purpose of this study is to examine the impact of firm environmental, social and governance (ESG) rating scores on market perception and stock behavior from 2017 to 2021 while controlling for COVID-19 severity score.

The authors used panel regression models with robust standard errors based on cross-country and cross-industry sample of 1,324 ESG firms from 25 emerging countries across four regions. Four separate regression analyses are used. Hausman test is used to determine whether fixed-effect (FE) or random-effect approaches should be used in regression models. Lagrange multiplier test is used to test for time FEs, and F-test for individual effects to choose between pooled ordinary least squares model and FE. Two-unit root tests are conducted to check stationarity. Heteroskedasticity and serial correlation were controlled through a robust covariance matrix estimation.

The authors provide evidence that the stakeholder theory persists in emerging countries. Overall, the results suggest that firms’ stock behavior is positively associated with the level of environmental and social performance in the region. However, the results do not provide empirical evidence to support the link between ESG performance and stock market perception proxied by the price-to-sales ratio. The results suggest that Refinitiv and Bloomberg ESG rating scores have a positive impact on stock performance in emerging markets, albeit the Bloomberg rating score is insignificant.

Favorable impact of environmental and social performance on stock performance suggests that policymakers should take initiatives to raise awareness toward investments in ESG projects. Evidence shows that ESG stock performance in emerging markets does not insulate firms from the COVID-19 severity. Furthermore, this study highlights the inconsistency in calculating the ESG ratings, therefore, a more standardized approach is recommended to support investors seeking sustainable investments.

The findings have social implications for investors with proenvironmental preferences and nonpecuniary motives for ethical investments. Asset fund managers should develop ESG investment strategies to promote investor preferences that are linked to the proenvironmental and prosocial attitudes by increasing their investments in stocks of firms that behave ethically and support the environment. Furthermore, the findings show that investors pay a price for ethical and socially responsible investments as they are evaluating the environmental and social activities, hence, the firm ESG profile influences equity valuation and risk assessment.

The study extends the literature and provides evidence from the unique setting of emerging markets by analyzing the relationship between ESG rating scores and the COVID-19 severity scores on one hand, and stock behavior and market perception on the other.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.