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This study aims to find the response by stock market against the announcements of quarterly earnings is empirically tested by exploiting event study methodology. Efficient market hypothesis (EMH) on Saudi stock exchange is also tried on.

The market model is applied to help gauge the expected returns and to illustrate abnormal returns around the event date.

The results established that Saudi Stock Market does not bear semi-strong form of EMH. How efficient is the Saudi market is also reflected through evidence of significant abnormal returns and post-earnings announcement drift around earning announcements dates.

The authors have not used analysts’ forecast as the expected earnings which are the limitation. As mentioned earlier, the authors used the quarterly earnings of the previous year as a proxy and that proxy could have been replaced by analysts’ forecast. Another limitation is that the trading volume in the event window is not considered.

The behavior of Saudi capital market is of much concern, and the study of this with a perspective of EMH is the significance of this paper.

All stakeholders closely watch earnings announcements and its share price movement around the announcement date. Recently, Saudi Arabia has opened its doors to foreign investors, and big foreign investors are going to enter into Saudi capital market, and after their entry, the behavior of market could be different. In the authors’ opinion, this is the right time to study the efficiency of Saudi market before the entry of foreign investors.

This study is based on the gap created by EMH of Saudi market using event methodology, observed in the existing literature, and it will be a contribution to literature.

This research addresses the diverse characteristics of existing railway steel bridges in China, including variations in construction age, design standards, structural types, manufacturing processes, materials and service conditions. It also focuses on prominent defects and challenges related to heavy transportation conditions, particularly low live haul reserves and severe fatigue problems.

The study encompasses three key aspects: (1) Adaptability assessment: It begins with assessing the suitability of existing railway steel bridges for heavy-haul operations through comprehensive analyses, experiments and engineering applications. (2) Strengthening: To combat frequent crack defects in the vertical stiffener end structure of girder webs, fatigue performance tests and reinforcement scheme experiments were conducted. These experiments included the development of a hot-spot stress S-N curve for this structure, validating the effectiveness of methods like crack stop holes, ultrasonic hammering and flange angle steel. (3) Service life extension: Research on the cruciform welded joint structure (non-fusion transfer type) focused on fatigue performance over the long life cycle. This led to the establishment of a fatigue S-N curve, enhancing Chinese design codes.

The research achieved several significant outcomes: (1) Successful implementation of strengthening and retrofitting measures on a 64-m single-span double-track railway steel truss girder on an existing heavy-duty line. (2) Post-reinforcement, a substantial 26% to 32% reduction in live haul stress on bridge members was achieved. (3) The strengthening and retrofitting efforts met design expectations, enabling the bridge to accommodate vehicles with a 30-ton axle haul on the railway line.

This research systematically tackles challenges and defects associated with Chinese existing railway steel bridges, providing valuable insights into adaptability assessment, strengthening techniques and service life extension methods. Furthermore, the development of fatigue S-N curves and the successful implementation of bridge enhancements have practical implications for improving the resilience and operational capacity of railway steel bridges in China.

The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood Model serves as the theoretical framework for understanding the cognitive processing involved in consumers' responses to these advertising appeals and product combinations.

This paper aims to investigate the impact of advertising appeals on consumers' intentions to purchase organic food. We explored the interaction between advertising appeals (egoistic vs altruistic) and product types (virtue vs vice) and purchase intention. The goal is to provide insights that can enhance the advertising effectiveness of organic food manufacturers and retailers.

The analysis reveals significant effects on consumers' purchase intentions based on the matching of advertising appeals with product types. Specifically, when egoistic appeals align with virtuous products, there is an improvement in consumers' purchase intentions. When altruistic appeals match vice products, a positive impact on purchase intention is observed. The results suggest that the matching of advertising appeals with product types enhances processing fluency, contributing to increased purchase intention.

This research contributes to the field by providing nuanced insights into the interplay between advertising appeals and product types within the context of organic food. The findings highlight the importance of considering the synergy between egoistic appeals and virtuous products, as well as altruistic appeals and vice products. This understanding can be strategically employed by organic food manufacturers and retailers to optimize their advertising strategies, thereby improving their overall effectiveness in influencing consumers' purchase intentions.

This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

Confronting the unveiled sophisticated structural and physical characteristics of permanent magnets, notably the samarium–cobalt (Sm-Co) alloy, This work aims to introduce a simulation scheme that can link physics-based micromagnetics on the nanostructures and magnetostatic homogenization on the mesoscale polycrystalline structures.

The simulation scheme is arranged in a multiscale fashion. The magnetization behaviors on the nanostructures examined with various orientations are surrogated as the micromagnetic-informed hysterons. The hysteresis behavior of the mesoscale polycrystalline structures with micromagnetic-informed hysterons is then evaluated by computational magnetostatic homogenization.

The micromagnetic-informed hysterons can emulate the magnetization reversal of the parameterized Sm-Co nanostructures as the local hysteresis behavior on the mesostructures. The simulation results of the mesoscale polycrystal demonstrate that the demagnetization process starts from the grain with the largest orientation angle (a) and then propagates to the surrounding grains.

The presented scheme depicts the demand for integrating data-driven methods, as the parameters of the surrogate hysteron intrinsically depend on the nanostructure and its orientation. Further hysteron parameters that help the surrogate hysteron emulate the micromagnetic-simulated magnetization reversal should be examined.

This work provides a novel multiscale scheme for simulating the polycrystalline permanent magnets’ hysteresis while recapitulating the nanoscale mechanisms, such as the nucleation of domains, and domain wall migration and pinning. This scheme can be further extended to simulate the part-level hysteresis considering the mesoscale features.

The paper examines the ‘Intention to Receive the COVID-19 Vaccines’ or IRV from three perspectives: the health belief model, behavioural economics, and institutional quality.

This study provides quantitative analysis by applying Chi-squared test of contingencies, paired sample t-tests, exploratory factor analysis, and multiple linear regression (stepwise method) on the data collected from 591 respondents mainly from Malaysia.

The results show that Perceived Benefits, Perceived Barriers, Perceived Susceptibility, Herding, and Institutional Quality play roles as predictors of IRV. Perceived Benefits play the most crucial role among the predictors and Perceived Barriers is the least important predictor. People have the herding mentality after being exposed to information encouraging such behaviour.

This study reveals that the respondents changed their behaviour in different circumstances when exposed to information that incorporates the effect of herding. Herding mentality, the effectiveness of government authorities, and regulatory quality have become important factors in enriching public health policies and the effectiveness of interventions.

Competition among higher education institutions has pushed universities to expand their competitive advantages. Based on the assumption that the core functions of universities are academic, understanding the teaching–learning process with the help of student evaluation of teaching (SET) would seem to be a logical solution in increasing competitiveness. The paper aims to discuss these issues.

The current paper presents a narrative literature review examining how SETs work within the concept of service marketing, focusing specifically on the search, experience, and credence qualities of the provider. A review of the various factors that affect the collection of SETs is also included.

Relevant findings show the influence of students’ prior expectations on SET ratings. Therefore, teachers are advised to establish a psychological contract with the students at the start of the semester. Such an agreement should be negotiated, setting out the potential benefits of undertaking the course and a clear definition of acceptable performance within the class. Moreover, connections should be made between courses and subjects in order to provide an overall view of the entire program together with future career pathways.

Given the complex factors affecting SETs and the antecedents involved, there appears to be no single perfect tool to adequately reflect what is happening in the classroom. As different SETs may be needed for different courses and subjects, options such as faculty self-evaluation and peer-evaluation might be considered to augment current SETs.

This paper aims to examine a two-way interaction between social influencers’ number of followers (micro vs meso) and argument quality (weak vs strong) on consumers’ self-reported and brain responses to advertising posts on Instagram. Further, drawing upon source credibility theory and contemporary theories of persuasion, the Instagram users’ perceptions of the influencer’s credibility are predicted to mediate the hypothesized effects.

Through an online (N = 192) and a lab study (N = 112), the authors examined Instagram users’ responses to an advertising post from Instagram influencers in terms of perceived source credibility and electronic word-of-mouth intention, using validated multi-item scales from existing literatures and electroencephalogram (EEG) measures. The hypotheses were tested with a 2 (type of influencer: micro vs meso) × 2 (argument quality: weak vs strong) between-subject design using mediated moderated linear regression analysis.

The results highlight that meso-influencers are perceived as a credible source of information only when their product-related post provides strong argument quality. Moreover, this process involves an increase in users’ cognitive work (measured with EEG), with possible implications on marketing communication strategies and online message design.

The limitations of the work can serve as ideas for future research. First, this study did not account for the influencer’s relevance and resonance. Second, the authors studied consumer responses to online communication produced by Instagram influencers within a single product category. Another important product type distinction that requires further attention is between hedonic and utilitarian products. Finally, the two studies only used positive review content. Further research should study how consumers evaluate the source credibility of a micro- vs meso-influencer when they are exposed to negative reviews containing weak vs strong arguments.

The results suggest that marketers should carefully consider Instagram influencers based on the trade-offs between credibility and reach. Specifically, micro-influencers are perceived as more credible sources of information than meso-influencers, which means that they have greater potential to affect Instagram users’ behavior. Moreover, the results suggest that meso-influencers should leverage argument quality to enhance their credibility and draw greater positive outcomes for the products and brands they endorse.

To the best of the authors’ knowledge, this study is the first to investigate how the interaction between the type of social media influencer and the argument quality affects consumers’ self-reported and brain responses to advertising posts on Instagram. Moreover, using neuroscience, this study aims to shed light on the neurophysiological processes that drive consumer responses to product-related communication posted by different influencer types.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

This study aims to introduce the achievements and benefits of applying wheel/rail-force–based maintenance interval extension of the C80 series wagon in China.

Chinese wagons' existing maintenance strategy had left a certain safety margin for the characteristics of widely running range, unstable service environment and submission to transportation organization requirements. To reduce maintenance costs, China railway (CR) has attempted to extend the maintenance interval since 2020. The maintenance cycle of C80 series heavy haul wagons is extended by three months (no stable routing) or 50,000 km (regular routing). However, in the meantime, the alarming rate of the running state, a key index to reflect the severe degree of hunting stability, by the train performance detection system (TPDS) for the C80 series heavy haul wagons has increased significantly.

The present paper addresses a big data statistical way to evaluate the risk of allowing the C80 series heavy haul wagons to remain in operation longer than stipulated by the maintenance interval initial set. Through the maintenance and wayside-detector data, which is divided into three stages, the extension period (three months), the current maintenance period and the previous maintenance period, this method reveals the alarming rate of hunting was correlated with maintenance interval. The maintainability of wagons will be achieved by utilizing wagon performance degradation modeling with the state of the wheelset and the often-contact side bearing. This paper also proposes a statistical model to return to the average safety level of the previous maintenance period's baseline through correct alarming thresholds for unplanned corrective maintenance.

The paper proposes an approach to reduce safety risk due to maintenance interval extension by effective maintenance program. The results are expected to help the railway company make the optimal solution to balance safety and the economy.